Genomic and Personalized Medicine, Two-Vol Set, Volume 1-2

Genomic and Personalized Medicine

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Genomic and Personalized Medicine Volume 1 Edited by Huntington F. Willard, Ph.D. Director Duke Institute for Genome Sciences & Policy Nanaline H. Duke Professor of Genome Sciences Howard Hughes Medical Institute Professor Duke University Durham, North Carolina 27708

and Geoffrey S. Ginsburg, M.D., Ph.D. Center Director, Center for Genomic Medicine Duke Institute for Genome Sciences & Policy Professor of Medicine Duke University Durham, North Carolina 27708

AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD • PARIS SAN DIEGO • SAN FRANCISCO • SYDNEY • TOKYO

Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London NW1 7BY, UK First edition 2009 Copyright © 2009 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-369420-1 (set) ISBN: 978-0-12-370888-5 (vol. 1) ISBN: 978-0-12-370889-2 (vol. 2) For information on all Academic Press publications visit our web site at elsevierdirect.com Typeset by Charon Tec Ltd., A Macmillan Company. (www.macmillansolutions.com) Printed and bound in China 09 10 11 12 13 10 9 8 7 6 5 4 3 2 1

Contents in Brief Foreword

xxv

Preface

xxvii

Acknowledgements

xxix

Abbreviations

xxxi

Advisory Board

xxxix

Contributors

xli

PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine 2. Concepts of Population Genomics 3. Genomic Approaches to Complex Disease 4. Human Health and Disease: Interaction Between the Genome and the Environment 5. Epigenomics and its Implications for Medicine 6. Systems Biology and the Emergence of Systems Medicine Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism 8. Genome-Wide Association Studies and Genotyping Technologies 9. Copy Number Variation and Human Health 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine 11. DNA Methylation Analysis: Providing New Insight into Human Disease 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine 13. DNA Microarrays in Biological Discovery and Patient Care 14. Proteomics: The Deciphering of the Functional Genome

3

4 22 33 47 60 74

87 88 101 108 120 131 143 157 173

15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics

180 193

Section 3 Informatic and Computational Platforms for Genomic Medicine 205 17. Bioinformatic and Computational Analysis for Genomic Medicine 206 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine 226 19. Electronic Medical Records in Genomic Medicine Practice and Research 233 20. Clinical Decision Support in Genomic and Personalized Medicine 242 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics 252

PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice 23. Principles of Study Design 24. Biobanking in the Post-Genome Era 25. Application of Biomarkers in Human Population Studies 26. Validation of Candidate Protein Biomarkers 27. Pharmacogenetics and Pharmacogenomics 28. The Role of Genomics and Genetics in Drug Discovery and Development 29. Role of Pharmacogenomics in Drug Development 30. Clinical Implementation of Translational Genomics 31. Translating Innovation in Diagnostics: Challenges and Opportunities 32. The Role of Genomics in Enabling Prospective Health Care

261 262 275 284 299 308 321 335 343 357 367 378

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Contents in Brief

Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations 34. Educational Strategies in Genomic Medicine 35. Federal Regulation of Genomic Medicine 36. Economic Issues and Genomic Medicine 37. Public–Private Interactions in Genomic Medicine: Research and Development

387 388 401 414 424 434

Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 40. Principles of Human Genome Epidemiology 461 41. Genomics and Population Screening: Example of Newborn Screening 470 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention 481 Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine 44. PET Imaging in Genomic Medicine 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges 46. Fluorescence Imaging: Overview and Applications in Biomedical Research 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers 48. Viral Chip Technology in Genomic Medicine 49. Vaccines Against Infectious Diseases: A BiotechnologyDriven Evolution 50. Cancer Vaccines: Some Basic Considerations 51. Biosensors for the Genomic Age 52. Stem Cells 53. Gene Therapy

493 494 500 512 524 532 538 562 573 590 599 610

PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 623 54. The Genomics of Hypertension 624 55. Lipoprotein Disorders 634 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis 652 57. Genomics of Myocardial Infarction 665 58. Acute Coronary Syndromes 680

59. Heart Failure in the Era of Genomic Medicine 60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine 62. Genetics and Genomics of Arrhythmias 63. Hemostasis and Thrombosis 64. Peripheral Arterial Disease 65. Genomics of Congenital Heart Disease 66. Genomics of Perioperative and Procedural Medicine

692

Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment 68. Immune Cells and the Tumor Microenvironment 69. Lymphomas 70. Genomics in Leukemias 71. Genomics of Lung Cancer 72. Breast Cancer and Genomic Medicine 73. Colorectal Cancer 74. Prostate Cancer 75. Molecular Biology of Ovarian Cancer 76. Pancreatic Neoplasms 77. The Multiple Endocrine Neoplasia Syndromes 78. Genomics of Head and Neck Cancer 79. Genomic Medicine, Brain Tumors and Gliomas 80. Molecular Therapeutics of Melanoma 81. Emerging Concepts in Metastasis 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer

807 808 818 830 844 856 869 879 898 913 921 931 945 956 967 977

705 716 729 755 773 781 794

990

Section 10 Inflammatory Disease Genomic Medicine 1009 83. Environmental Exposures and the Emerging Field of Environmental Genomics 1010 84. Molecular Basis of Rheumatoid Arthritis 1017 85. “Omics” in the Study of Multiple Sclerosis 1032 86. Inflammatory Bowel Disease 1040 87. Glomerular Disorders 1056 88. Spondyloarthropathies 1067 89. Asthma Genomics 1084 90. Genomic Aspects of Chronic Obstructive Pulmonary Disease 1098 91. Genomic Determinants of Interstitial Lung Disease 1110 92. Peptic Ulcer Disease 1122 93. Cirrhosis in the Era of Genomic Medicine 1138 94. Systemic Sclerosis 1155 Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity 96. Diabetes 97. Metabolic Syndrome 98. Nutrition and Diet in the Era of Genomics

1169 1170 1187 1194 1204

Contents in Brief

Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia 100. Parkinson’s Disease: Genomic Perspectives 101. Epilepsy Predisposition and Pharmacogenetics 102. Ophthalmology 103. Genomic Basis of Neuromuscular Disorders 104. Psychiatric Disorders 105. Genomics and Depression 106. Bipolar Disorder in the Era of Genomic Psychiatry Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens

1221 1222 1233 1243 1256 1265 1282 1289 1299

1313 1314

108. 109. 110. 111. 112.

Genomic Medicine and Aids Viral Genomics and Antiviral Drugs Host Genomics and Bacterial Infections Sepsis and the Genomic Revolution Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

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1324 1340 1347 1362 1375

Glossary

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Index

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Table of Contents Foreword

xxv

Preface

xxvii

Acknowledgements

xxix

Abbreviations

xxxi

Advisory Board

xxxix

Contributors

xli

PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Huntington F. Willard Introduction The Human Genome Variation in the Human Genome Expression of the Human Genome Genes, Genomes and Disease From Genome to Personalized Medicine Conclusion References Recommended Resources 2. Concepts of Population Genomics Mike E.Weale and David B. Goldstein Introduction Important Concepts in Population Genomics Human Population Genomics Application of Population Genomics to Genomic Medicine Conclusions References Recommended Resources 3. Genomic Approaches to Complex Disease Desmond J. Smith and Aldons J. Lusis Introduction Identifying Common and Rare Genomic Variations in the Population Relating DNA Variation to Phenotypes Integration of “Omic” Technologies with Genetics

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4 4 6 9 11 13 15 18 18 21 22 22 22 26 28 29 30 32 33 33 33 36 40

Conclusions and Prospects Acknowledgements References Recommended Resources 4. Human Health and Disease: Interaction Between the Genome and the Environment Kenneth Olden Introduction Importance of the Environment The Environmental Genome Project Problematic Nature of Gene–environment Interaction Studies Polymorphism and Disease Susceptibility: Case–control Studies Epigenetics and the Environment Conclusion Acknowledgements References 5. Epigenomics and its Implications for Medicine Moshe Szyf Introduction DNA Methylation Patterns Chromatin Modification DNA Methylation and Chromatin States Co-operatively Determine the State of Activity of Genes Epigenetics and Human Disease Conclusions Acknowledgements References 6. Systems Biology and the Emergence of Systems Medicine Nathan D. Price, Lucas B. Edelman, Inyoul Lee, Hyuntae Yoo, Daehee Hwang, George Carlson, David J. Galas, James R. Heath and Leroy Hood Introduction Systems Science in Biology And Medicine Multi-parameter Blood-bourne Biomarkers Emerging in vivo and in vitro Technologies Computational and Mathematical Challenges in Systems Medicine Conclusions and Perspectives References Recommended Resources

43 43 43 46

47 47 48 50 52 53 56 57 57 57 60 60 61 63

65 67 69 70 70 74

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Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism Samuel Levy and Yu-Hui Rogers Introduction DNA Sequencing Other Methodologies for Polymorphism Detection Future Directions Acknowledgements References 8. Genome-Wide Association Studies and Genotyping Technologies Kevin V. Shianna Introduction Principles of Genome-wide Association Studies Platform Overview Conclusion References 9. Copy Number Variation and Human Health Charles Lee, Courtney Hyland, Arthur S. Lee, Shona Hislop and Chunhwa Ihm Introduction Basic Principles of CNVs Detecting CNVs in a Genome-wide Manner Association of CNVs to Disease and Disease Susceptibility Implications of CNVs Conclusions Acknowledgements References Recommended Resources 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine Anthony Antonellis and Eric D. Green Introduction Performing Comparative Sequence Analysis: Resources and Methods Comparative Sequence Analysis and Human Genetic Disease CSA and the Future of Human Genetics and Genomic Medicine References Recommended Resources

87 88 88 89 95 96 97 97

101 101 101 103 106 106 108

108 108 112 114 116 118 118 118 119

120 120 121 124 128 128 130

11. DNA Methylation Analysis: Providing New Insight into Human Disease 131 Susan Cottrell,Theo deVos, Juergen Distler, Carolina Haefliger, Ralf Lesche, Achim Plum and Matthias Schuster Introduction Technology to Assess DNA Methylation

131 132

Clinical Impact of DNA Methylation Analysis Conclusion References Recommended Resources 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine Michelle M. Kittleson, Rafael Irizarry, Bettina Heidecker and Joshua M. Hare Introduction Gene Expression Technology Gene Discovery Molecular Signature Analysis Gene Discovery Versus Molecular Signature Analysis Current Issues in Gene Expression Analysis Alternative Technologies for Analysis of the Transcriptome Conclusion Acknowledgements References Recommended Resources 13. DNA Microarrays in Biological Discovery and Patient Care Andrew J.Yee and Sridhar Ramaswamy Introduction Microarray Technology Data Analysis Applications Limitations and Challenges Future Directions Conclusions References Recommended Resources

136 139 139 142

143

143 143 146 148 151 151 153 153 154 154 156 157 157 157 160 161 165 165 168 168 172

14. Proteomics: The Deciphering of the Functional Genome 173 Li-Rong Yu, Nicolas A. Stewart and Timothy D.Veenstra Introduction 173 Gel-based and Solution-based Proteomics 174 Mass Spectrometry 175 Bioinformatics 176 Impact of Proteomics on Understanding Diseases 178 Conclusions 178 Acknowledgements 179 References 179 Recommended Resources 179 15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 180 Christopher B. Newgard, Robert D. Stevens, Brett R.Wenner, Shawn C. Burgess, Olga Ilkayeva, Michael J. Muehlbauer, A. Dean Sherry and James R. Bain Introduction 180 Current Metabolomics Platforms: Basic Tools and General Features 181

Table of Contents

Comparison of NMR and MS Technologies for Unbiased Metabolic Profiling MS Methods for Targeted Metabolic Profiling Examples of NMR-based Metabolic Profiling in Disease Research Examples of Targeted MS-based Metabolic Profiling for Understanding of Disease Mechanisms Integration of Metabolic Profiling with Other “Omics” Technologies Future Directions References 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics Bjorn T. Gjertsen and James B. Lorens Introduction RNA Interference Gene Function Analysis: An Overview Chemical Genomics Gene Function Studies Conclusions Acknowledgements References Recommended Resources Section 3 Informatic and Computational Platforms for Genomic Medicine 17. Bioinformatic and Computational Analysis for Genomic Medicine Atul J. Butte Introduction Vignettes: How Specific Bioinformatics Methods Can Change the Practice Ofmedicine Analytic Methods Where Data for Studies May be Found Bioinformatics Vocabularies and Ontologies Freely Available Bioinformatics Tools New Questions for Genomic Medicine Acknowledgements References Recommend Resources 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine A. Jamie Cuticchia Introduction Databases for Genomic Medicine Conclusion References Recommended Resources 19. Electronic Medical Records in Genomic Medicine Practice and Research Glenn S. Gerhard, Robert D. Langer, David J. Carey and Walter F. Stewart

182 183 184 184 189 189 190

193 193 194 194 197 199 200 201 201 203

205 206 206 207 213 214 215 216 220 221 221 225 226 226 227 230 231 232

233

Introduction EMRs and Genomic Medicine Clinical Practice EMRs and Genomic Medicine Research Conclusion Acknowledgements References Recommended Resources 20. Clinical Decision Support in Genomic and Personalized Medicine Kensaku Kawamoto and David F. Lobach Introduction CDS Background: History, Examples, Evidence of Effectiveness, and Desirable Attributes Potential Uses of CDS to Support Genomic and Personalized Medicine Limited Deployability: The Potential Achilles’ Heel of CDS Systems for Genomic Medicine Challenges to Widespread Deployment of Effective CDS Systems Conclusions Disclosures References Recommended Resources 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics Mark S. Boguski Introduction Characteristics of Consumer Searches for Health Information What and Where Are Consumers Searching? Personalized Genomics for Consumers Summary and Conclusions References Wikipedia References

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252 252 252 253 255 256 256 257

PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice Geoffrey S. Ginsburg Introduction A Roadmap for Translation Where Can Genomics Have Impact in the Continuum of Health and Disease? The Genomics “Gold Rush”

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The Personal Genome: Precious Code or Fool’s Gold? “Grand Challenges” in Translation of Genomics to Human Health Translational Genomics: Enabling Competencies How Are We Going to Do This? Developing Environments That Foster Translational Genomics to Health Applications References 23. Principles of Study Design Peter Grass Introduction Principles of Experimental Design Design Issues in Genomic Medicine References Recommended Resources 24. Biobanking in the Post-Genome Era Theresa Puifun Chow, Chia Kee Seng, Per Hall and Edison T. Liu Introduction The Biobanking Evolution The Past Imperfect Resources The Evolving Face of Biobanking Existing Models: Biobanking in Europe and the USA Singapore’s National Biobank and National Aspirations In Biomedical Research The Future of National Biobanks References Recommended Resources 25. Application of Biomarkers in Human Population Studies Stefano Bonassi and Monica Neri Introduction Biomarkers in Medicine Biomarkers of Exposure Biomarkers of Early Disease Risk Biomarkers of Genetic Susceptibility to Disease Conclusions References 26. Validation of Candidate Protein Biomarkers Ingibjörg Hilmarsdóttir and Nader Rifai Introduction Optimization of the Candidate Protein Research Assay Analytical Evaluation Reference Intervals Pre-analytical Variation Clinical Evaluation Indicators of Diagnostic Accuracy And Predictability Diagnostic Research Studies Design of Diagnostic Studies

266 266 267

270 272 275 275 276 281 283 283 284

284 285 285 285 286 288 294 296 297

299 299 299 302 303 304 306 306 308 308 308 309 311 311 312 313 316 316

Transferability Of Test Performance Assay Transfer to Diagnostic Company Regulatory Requirements References Recommended Resources 27. Pharmacogenetics and Pharmacogenomics Iris Grossman and David B. Goldstein Introduction Pharmacogenetic Studies: From Concept to Practice Marker Selection – Strategy and Application From Bench to Bedside: Integration of Pharmacogenetic Testing into Clinical Practice Examples of PGx Tests: Promising New Developments and Marketed Products Future Developments Required for the Field to Fully Meet its Expectations References Recommended Resources 28. The Role of Genomics and Genetics in Drug Discovery and Development Robert I.Tepper and Ronenn Roubenoff Introduction The Drug Discovery Process Genomics in Target Discovery Genomic Approaches to Drug Identification Pharmacogenomics and Drug Development Pharmacodynamic Markers and their Role in Drug Discovery and Development Toxicogenomics Genetics and Genomics in Clinical Trial Design Genomics in Drug Approval and Regulation Conclusion References Recommended Resources 29. Role of Pharmacogenomics in Drug Development Colin F. Spraggs, Beena T. Koshy, Mark R. Edbrooke and Allen D. Roses Introduction Drug Development Critical Path Drug Development Economics Methods for Identification of Genetic Classifiers Pharmacogenomics in the Drug Development Pipeline Efficacy Pharmacogenetics – here and Now Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles Investigation and Management of Safety in Clinical Trials Other Genomic Methods: RNA Interference to Direct Drug Usage “No Samples, No Science” Conclusions Acknowledgements References

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335 335 335 336 337 337 338 340 340 340 341 341 342 343

343 344 344 344 346 348 349 350 352 352 354 355 355

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30. Clinical Implementation of Translational Genomics Wendy K. Chung Introduction Genetic Stratification Will Allow Medical Care to be Individualized After a Diagnosis is Made Population-based Germline Genomic Screening Newborn Screening Pharmacogenetics Somatic Genomic Variation Novel Sources of Genomic Variation Laboratory Standards to Ensure Analytic Validity Clinical Validation and Clinical Utility Cost Reimbursement Who Will Provide Genomic Medical Care? Genomic Literacy Ethical, Legal, and Social Issues Conclusions Acknowledgements References Recommended Resources 31. Translating Innovation in Diagnostics: Challenges and Opportunities Matthew P. Brown, Myla Lai-Goldman and Paul R. Billings Introduction Novel Diagnostics Conclusions: Translational Challenges for Innovative Diagnostics References 32. The Role of Genomics in Enabling Prospective Health Care Ralph Snyderman Introduction Predictive Models Predictive Factors Risk Assessment for Breast Cancer Pharmacogenomics Conclusion Acknowledgements References Recommended Resources Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations Susanne B. Haga Introduction Genome Research after the Human Genome Project Policy Issues in Large-scale Genetics and Genomics Research

357 357 357 358 359 360 360 361 361 362 362 363 363 364 364 365 365 365 366

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367 368 374 375

378 378 380 380 382 383 383 383 384 385

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Integrating Genomic Medicine Applications in Healthcare Conclusion References

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392 396 397

34. Educational Strategies in Genomic Medicine Charles J. Epstein Introduction Genetic and Genomic Literacy of the Public and Makers of Public Policy Education of the Providers of Health Care Conclusion References

401

35. Federal Regulation of Genomic Medicine Janet Woodcock Introduction Regulation of Genomic Tests Pharmacogenomics in Drug Development And Clinical Medicine: the Role of Regulation Fda Efforts to Advance Genomic Product Development Conclusions References Recommended Resources

414

36. Economic Issues and Genomic Medicine David L.Veenstra, Louis P. Garrison and Scott D. Ramsey Introduction Economic Evaluation and Cost-effectiveness Analysis Evaluating Genomic Technologies Economic Incentives and the Future of Genomic Medicine Establishing Value-based Reimbursement For Genomic Technologies Conclusions References 37. Public–Private Interactions in Genomic Medicine: Research and Development Subhashini Chandrasekharan, Noah C. Perin, Ilse R.Wiechers and Robert Cook-Deegan Introduction Landscape of Private Sector Genomics Future Trends Acknowledgements References

401 402 404 411 411

414 416 418 421 422 422 423 424 424 424 426 428 430 431 432 434

434 435 441 442 442

387 388 388 389 391

Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 Alison Stewart and Ron Zimmern Introduction 446 The Emergence of Public Health Genomics 446 The Definition of Public Health Genomics 447 Key Concepts in Public Health Genomics 447

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The “Enterprise” of Public Health Genomics Core Activities in Public Health Genomics Moving Public Health Genomics Forward: Leadership And Networks Conclusion References Recommended Resources

448 449 451 452 452 453

39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 Muin J. Khoury and Marta Gwinn Introduction 454 The Continuum from Genetics to Genomics in Health Practice 454 The Role of Public Health in the Translation of Human Genome Discoveries into Health Applications 455 The Focus on Disease Prevention and Health Promotion 457 The Population Perspective: Crucial Role of Public Health Sciences 457 The Role of Knowledge Integration Across Disciplines 458 The Role of Health Services Research and Population Health Assessment, Assurance, and Evaluation 458 Conclusion 458 References 459 40. Principles of Human Genome Epidemiology Marta Gwinn and Muin J. Khoury Introduction Human Genome Epidemiology Epidemiologic Study Designs Epidemiologic Measures Of Disease Frequency, Association, and Risk Measurement and Bias Gene–environment Interaction Probability and Personalized Medicine Building the Evidence Base Conclusion References Recommended Resources 41. Genomics and Population Screening: Example of Newborn Screening John D.Thompson and Michael Glass Introduction Components of the NBS System Screening Technology: Simple Ideas, Complex Realities Variability Among NBS Programs Influence of Genetics and -omic Technologies on NBS Acknowledgements

461 461 461 462 463 464 465 466 466 467 467 469

470 470 471 471 476 476 477

References Recommended Resources 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention Maren T. Scheuner and Paula W.Yoon Introduction Clinical Approach Conclusion Acknowledgements References Recommended Resources Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine Ralph Weissleder Introduction Molecular Imaging and Cancer Detection Molecular Imaging to Determine Treatment Efficacy Molecular Imaging and Drug Development Near-term Needs and Opportunities Acknowledgement References 44. PET Imaging in Genomic Medicine Vikas Kundra and Osama Mawlawi Introduction Physics Imaging Agents and Methods in Analysis of Biological Samples Conclusion References Recommended Resources 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges Dmitri Artemov Introduction Basics of MRI Contrast MR Contrast Agents for Molecular Imaging Applications Molecular Imaging Applications of MRI Conclusions Acknowledgements References 46. Fluorescence Imaging: Overview and Applications in Biomedical Research Vasilis Ntziachristos Introduction Imaging Technology

477 479

481 481 481 488 488 488 492

493 494 494 494 496 497 497 498 498 500 500 500 503 510 510 511

512 512 513 515 517 520 521 521

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Fluorescence Applications in Genomic Medicine References 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers Ahmad R. Hariri Introduction Conceptual Basis of Imaging Genetics Basic Principles of Imaging Genetics Imaging Genetics and the Neurobiology of the 5-httlpr Future Directions References 48. Viral Chip Technology in Genomic Medicine Zeno Földes-Papp Introduction Role of Viruses in Human Infectious Disease Microfabrication Nanofabrication Are there Additional Alternatives to Diagnostic Microarrays? Conclusions Acknowledgements References Recommended Resources 49. Vaccines Against Infectious Diseases: A Biotechnology-Driven Evolution Vega Masignani, Hervé Tettelin and Rino Rappuoli Introduction The Genomic Era: From Microbial Genome to Vaccine Development Impact of Whole Genome Analyses In Vivo Gene Expression: Ivet And Stm Microarray Expression Technology Proteomics From Microbial to Human Genome Sequencing: Genomic Medicine Metagenomics: Deciphering Host–microbe Interactions Conclusions References 50. Cancer Vaccines: Some Basic Considerations Hans-Georg Rammensee, Harpreet Singh-Jasuja, Niels Emmerich and Steve Pascolo Introduction Immune Suppression by Tumors and by Regulatory T-cells The Ideal Therapeuticcancer Vaccine Molecularly Undefinedcancer Vaccines Peptides

530 530

532 532 533 534 535 536 536 538 538 538 541 552 553 554 554 554 560

562 562 563 564 566 567 568

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Proteins and Carbohydrates Nucleic Acids: Plasmid DNA and Messenger RNA Viral and Bacterial Vectors Adjuvants, Formulations, and Route of Application Immunomonitoring Conclusions References 51. Biosensors for the Genomic Age Meghan B. O’Donoghue, Lin Wang,Yan Chen, Gang Yao and Weihong Tan Introduction Biosensors for Detection of Oligonucleotides for the Detection of Disease Nucleic Acid as Tools for Biosensing Outlook References 52. Stem Cells Rikkert L. Snoeckx, Kris Van Den Bogaert and Catherine M.Verfaillie Introduction Types of Stem Cells: Embryonic and Adult Stem Cells How to Define The Molecular Signature of Stem Cells Future Directions to Identify the Global Integrated Regulatory Network Future Directions in Stemcell Therapies Conclusion References 53. Gene Therapy James M.Wilson and Nelson A.Wivel Introduction Gene Delivery Vehicles Gene Therapy Clinical Trials Conclusion References

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590 591 592 596 597 599

599 599 600 601 604 606 606 610 610 611 614 617 617

568 569 570 570 573

573 574 576 576 577

PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 54. The Genomics of Hypertension Chana Yagil and Yoram Yagil Introduction Predisposition Screening Diagnosis

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624 625 627 627

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Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources 55. Lipoprotein Disorders Sekar Kathiresan and Daniel J. Rader Introduction Overview of Lipoprotein Metabolism Plasma Lipid and Lipoprotein Levels and Atherosclerotic Cardiovascular Disease Inherited Basis for Blood Lipid Traits Screening for Lipid Disorders Genetics of Ldl-c Genetics of Hdl-c Genetics of Triglycerides Genetic Lipid Disorders Without Current Proven Molecular Etiology Influence of Lipid-modulating Mutations on Risk of Atherosclerotic Cardiovascular Disease Future Directions in Genetics And Genomics of Lipoproteins Pharmacogenetics of Lipid-modulating Therapies Implications of Genomics of Lipoprotein Metabolism For The Development of Novel Therapies Clinical Recommendations for Genetic Testing for Lipid Disorders Acknowledgements References Recommended Resources 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis Nageswara R. Madamanchi, Aleksandr E.Vendrov and Marschall S. Runge Introduction Sources of ROS in vascular cells Vascular Dysfunction and Atherosclerosis ROS-induced inflammatory gene expression in vascular cells Association of ROS modulators with atherosclerosis ROS signaling in atherosclerotic risk factors ROS-regulated signaling pathways Regulation of transcription factors by ROS ROS signaling in advanced atherosclerosis Polymorphisms in ROS production genes and atherosclerosis Inhibitors of ROS signaling and vascular disease Conclusion Acknowledgements References

628 630 631 631 631 632 632 633 634 634 634 636 636 637 637 639 642 643 643 644 646 646 647 648 648 651 652

652 652 653 653 654 655 655 656 658 658 658 659 660 660

57. Genomics of Myocardial Infarction Carlos A. Hubbard and Eric J.Topol Introduction Predisposition Screening Strategies Diagnosis of Acute MI Prognostic Implications of MI Pharmacogenomics of MI Novel and Emerging Therapies Conclusion References Recommended Resources

665

58. Acute Coronary Syndromes L. Kristin Newby Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources

680

59. Heart Failure in the Era of Genomic Medicine Ivor J. Benjamin and Jeetendra Patel Introduction Predisposition (Genetic and Non-genetic) Screening Pathophysiology Diagnosis Prognosis Pharmacogenomics Monitoring Novel Therapeutics and Future Directions Conclusions and Recommendations Acknowledgements References

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60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection Michael Pham, Mario C. Deng, Jay Wohlgemuth and Thomas Quertermous Introduction Cardiac Allotransplantation as a Definitive Therapy for End-stage Heart Failure The Problem of Allograft Rejection Immunosuppression Strategies to Prevent Rejection Current Strategies for Monitoring Transplant Rejection

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The Cargo Clinical Study Development of a Gene Expression Signature For Cardiac Transplant Rejection Pathways Monitored by the Gep (Allomap™) Test Variability of the Biopsy Gold Standard and Relationship to the Gep (Allomap™) Score Discordance Between Biopsy Grade and Molecular Score Effect of Time Post-transplantation on Performance of the Gep Test Relationship of Gep Score to Corticosteroid Dose Relationship of Gep Scores to Cytomegalovirus Infection Prediction of Future Acr by Molecular Score Clinical Use of the Allomap™ Test Future Directions and Ongoing Research With Gep Testing Further Application of Genomic Science to Transplant Rejection References 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine J. Martijn Bos, Steve R. Ommen and Michael J. Ackerman Introduction Definitions, Clinical Presentation, and Diagnosis Molecular Genetics of HCM Screening And Treatment for HCM Conclusions References 62. Genetics and Genomics of Arrhythmias Jeffrey A.Towbin and Matte Vatta Introduction Specific Cardiac Arrhythmias Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias Complex Forms of Lqts Short Qt Interval Syndrome Familial Vt/cpvt Primary Conduction Abnormalities References 63. Hemostasis and Thrombosis Richard C. Becker and Felicita Andreotti Introduction Genetics of Coagulation Human Hemostatic Variability Genotype–phenotype Influences Gene-environment Influences on Hemostasis Circulating Cellular and Protein Influences on Hemostasis And Thrombosis

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Race-related Influences On Hemostasis and Thrombosis Linkage Studies in Thrombosis Association Studies in Thrombosis Heritability And Thrombosis: Existing Complexities A Personalized Approach to Hemostasis and Thrombosis Patient Screening: A Traditional Paradigm Patient Screening: A Comprehensive And Population-based Approach Prognostic Considerations Emerging Platform for Hemostasis and Thrombosis Research References

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760 762 762 763 763 764 767 768 768 769

64. Peripheral Arterial Disease 773 Ayotunde O. Dokun and Brian H. Annex Introduction 773 Epidemiology and Risk Factors for PAD 773 Clinical Manifestations of PAD 773 Therapeutic Strategies for PAD 774 Ic And Cli Are Distinct Clinical Outcomes of PAD 775 Genetic Background as a Risk Factor for PAD 775 Gene Polymorphisms Contributing to Atherosclerosis and PAD 775 Polymorphisms in Pro-atherothrombotic Genes and PAD 776 Genetic Locus Conferring Susceptibility to PAD 776 Identification of Novel Gene Polymorphisms Involved in PAD 776 Identification of a Quantitative Trait in a Preclinical Model of PAD 777 Refining a QTL Using Haplotype Analysis 777 Identification of Candidate Genes 777 Future Potential Use of Genomic Methodologies in PAD 777 Acknowledgements 778 References 778 65. Genomics of Congenital Heart Disease Jessie H. Conta and Roger E. Breitbart Introduction CHD Gene Discovery by Conventional Genetics Genomic Strategies for CHD Gene Discovery Cytogenetic and Molecular Genetic testing Medical Evaluation and Counseling Recommendations Conclusion Acknowledgements References Recommended Resources

781 781 781 786 787 788 789 790 790 793

66. Genomics of Perioperative and Procedural Medicine 794 Simon C. Body, Mihai V. Podgoreanu and Debra A. Schwinn Introduction 794 Why Perioperative Insults are not Equivalent to Chronic Ambulatory Disease 795

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Perioperative Atrial Fibrillation Perioperative Venous and Arterial Thrombosis Perioperative Stroke and Neurocognitive Dysfunction Hemorrhage and Cardiac Surgery Dynamic Genomic Markers of Perioperative Outcomes Conclusion References

796 796 798 799 799 800 800

Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment Robert L. Strausberg Introduction Acquired Functions of Cancer Cells Chromosomal Aberrations and Cancer Cancer Genes and their Functions Inherited Predisposition Cellular Progression Toward Cancer Through Somatic Changes From Genome to the Clinic Comprehensive Sequencing of the Kinome Expanding the Search Multiple Molecular Mechanisms for Oncogene Activation Microarrays and Cancer Genomics Environmental Cancer Genomics Cancer Genomic Databases Expedite Progress Mouse Models of Cancer Future Directions References

807 808

68. Immune Cells and the Tumor Microenvironment David S. Hsu, Michael Morse,Timothy Clay, Gayathri Devi and H. Kim Lyerly Introduction Immune Cells of the Tumor Microenvironment Examples of Tissue or Gene Microarrays used to Study Tumors Studies of Genomic Immune Stimulation within the Microenvironment Genomic Analysis of Tumor Microenvironment in Immunotherapy Studies Proteomics of Immune Cells and the Tumor Microenvironment Conclusion References

818

69. Lymphomas Lisa Rimsza Introduction Diffuse Large B-cell Lymphoma Primary Mediastinal Large B-cell Lymphoma Hodgkin Lymphoma Follicular Lymphoma Mantle Cell Lymphoma

808 808 808 809 809 810 811 811 812 812 813 813 813 814 814 815

818 819 822 823 824 825 825 826 830 830 833 835 835 836 838

Burkitt Lymphoma Miscellaneous Lymphomas Clinical Applications of Molecular Assays in Lymphoma References

839 839 840 840

70. Genomics in Leukemias Lars Bullinger, Hartmut Dohner and Jonathan R. Pollack Introduction Genomics in Leukemias-insights into Leukemia Biology Genomics in Leukemias – Evaluation of Drug Effects Genomics in Leukemias – Clinical Outcome Prediction Conclusions Acknowledgments References Recommended Resources

844

71. Genomics of Lung Cancer Hasmeena Kathuria, Avrum Spira and Jerome Brody Introduction Early Diagnosis/screening of Lung Cancer Classification And Prognosis Pathogenesis And Treatment of Lung Cancer Conclusion References Recommended Resource

856

72. Breast Cancer and Genomic Medicine Erich S. Huang and Andrew T. Huang Introduction The Promise Genetic Bases Molecular Bases Prognosis and Prediction Molecular Markers Genomic Insights Netherlands Cancer Institute Study Duke-taipei Study Nsabp Study Pathway Prediction The Reality of Clinical Genomics References 73. Colorectal Cancer G.L.Wiesner,T.P. Slavin and J.S. Barnholtz-Sloan Introduction Genomic Model of CRC Predisposition for CRC Risk Assessment, Evaluation, and Genetic Testing Screening and Surveillance Prognosis and Treatment

844 846 847 849 851 852 852 855

856 857 859 862 865 866 868 869 869 869 871 871 872 872 873 873 875 875 876 876 877 879 879 880 882 887 889 889

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Pharmacogenetics/genomics of Chemoprevention and Chemotherapy Novel and Emerging Therapeutics Conclusion References 74. Prostate Cancer Phillip G. Febbo and Philip W. Kantoff Introduction Genetic Predisposition and Alterations in Prostate Cancer Prostate Cancer Detection Genomic Changes Associated with Prostate Cancer Behavior Genomic Changes associated with HormoneRefractory Prostate Cancer Future Prospects of Genomics in Prostate Cancer Care References 75. Molecular Biology of Ovarian Cancer Tanja Pejovic, Matthew L. Anderson and Kunle Odunsi Introduction Inherited Ovarian Cancer Syndromes Options for Screening and Prevention Genomic Instability and Ovarian Cancer Fanconi/anemia Pathway Somatic Mutations in Ovarian Cancer Oncogenes and Growth Factors Tumor Suppressor Genes Epigenetics in Ovarian Carcinogenesis Ovarian Cancer Metastases Angiogenesis Summary References 76. Pancreatic Neoplasms Asif Khalid and Kevin McGrath Introduction Predisposition (Genetic and Non-Genetic) Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapies Conclusion References

890 892 892 893 898 898 898 901 904 906 907 908 913 913 913 914 914 914 915 916 917 917 917 918 919 919 921 921 922 922 923 926 927 927 927 927 928

77. The Multiple Endocrine Neoplasia Syndromes Y. Nancy You,Vipul Lakhani and Samuel A.Wells

931

Introduction The Multiple Endocrine Neoplasia Syndromes

931 932

Conclusion Acknowledgements References Recommended Resources

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941 941 942 944

78. Genomics of Head and Neck Cancer Giovana R.Thomas and Yelizaveta Shnayder Introduction Head and Neck Squamous Cell Carcinoma Conclusion References Recommended Resources

945

79. Genomic Medicine, Brain Tumors and Gliomas Sean E. Lawler and E. Antonio Chiocca Introduction Predisposition Screening Diagnosis and Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References

956

80. Molecular Therapeutics of Melanoma Jiaqi Shi,Yonmei Feng, Robert S. Krouse, Stanely Leong and Mark A. Nelson Introduction Diagnosis Genetics of Melanoma Pharmacogenomics Novel and Emerging Therapeutics Conclusions References 81. Emerging Concepts in Metastasis Nigel P.S. Crawford and Kent W. Hunter Introduction Tools to Investigate the Mechanisms of Metastasis Assessement of Prognosis and New Treatments for Metastasis: the Role of New Technologies Conclusion References Recommended Resources 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer Jeffrey S. Ross Introduction Targeted Therapies for Cancer The Ideal Target The First Diagnostic-therapeutic Combination in Cancer Therapy: Hormonal Therapy for Breast Cancer

945 945 952 953 955

956 957 958 958 961 962 962 963 964 967

967 967 968 969 969 973 973 977 977 979 986 987 988 989 990 990 990 990

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Diagnostic-therapeutic Combinations for Leukemia and Lymphoma 994 Her-2 Positive Breast Cancer and Trastuzumab 994 (Herceptin®) Other Targeted Anticancer Therapies Using Antibodies 995 Selected Targeted Anticancer Therapies Using Small Molecules 999 Pharmacogenomics 1002 Conclusion 1003 References 1003 Section 10 Inflammatory Disease Genomic Medicine 83. Environmental Exposures and the Emerging Field of Environmental Genomics David A. Schwartz Introduction Importance of Environmental Exposures in Human Health Importance of Environmental Exposures in Studying Disease Processes Comparative Environmental Genomics Exposure Assessment in the Gene-environment Paradigm Challenges and Future of Environmental Genomics Conclusion References

1009 1010 1010 1010 1012 1012 1013 1015 1015 1015

84. Molecular Basis of Rheumatoid Arthritis Robert M. Plenge and Michael E.Weinblatt Introduction Clinical Features Predisposition Screening Diagnosis, Prognosis, and Monitoring Pharmacogenomics Conclusions References

1017

85. “Omics” in the Study of Multiple Sclerosis Francisco J. Quintana and Howard L.Weiner Introduction Genomics in MS Transcriptomics in MS Immunomics in MS Proteomics in MS Conclusion References

1032

86. Inflammatory Bowel Disease Ad A. van Bodegraven and Cisca Wijmenga

1040

Introduction Predisposition (Genetic and Non-genetic)

1017 1018 1018 1024 1024 1024 1026 1026

1032 1032 1034 1035 1036 1037 1037

1040 1041

Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References 87. Glomerular Disorders Tadashi Yamamoto, Hidehiko Fujinaka and Visith Thongboonkerd Introduction Techniques for Detection, Quantification, and Profiling of mRNA Expression in the Kidney mRNA Expression Profiles of Glomerular Disorders Genome Variations in Glomerular Disorders Genetics of Congenital Glomerular Disorders Genomic Medicine for Glomerular Disorders Conclusions References 88. Spondyloarthropathies Dirk Elewaut, Filip De Keyser, Filip Van den Bosch, Dieter Deforce and Herman Mielants Introduction Characteristics of SPA Role of Bowel Inflammation Histopathology of Synovitis in SPA Gut and Synovium Transcriptomes Proteome Analysis Novel and Emerging Therapeutics and Biomarkers Conclusions References 89. Asthma Genomics Scott T. Weiss, Benjamin A. Raby and Juan C. Celedón Introduction Asthma: Basic Pathobiology Predisposition (Genetic and Non-genetic) to Asthma Genome-wide Linkage Analyses of Asthma and its Intermediate Phenotypes Candidate-gene Association Studies of Asthma Genome-wide Association Studies of Asthma Asthma Genomics Screening Diagnosis Prognosis Pharmacogenetics Monitoring Novel and Emerging Therapeutics Conclusions Acknowledgements References Recommended Resources

1043 1044 1045 1047 1047 1048 1049 1050 1056

1056 1056 1059 1060 1062 1062 1064 1064 1067

1067 1067 1070 1072 1074 1075 1075 1077 1078 1084 1084 1084 1085 1085 1087 1088 1088 1088 1091 1091 1091 1092 1093 1093 1093 1093 1097

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90. Genomic Aspects of Chronic Obstructive Pulmonary Disease Peter J. Barnes Introduction Predisposition Pathophysiology Cellular and Molecular Mechanisms Diagnosis and Screening Prognosis Management New Treatments Conclusions References 91. Genomic Determinants of Interstitial Lung Disease P.W. Noble and M.P. Steele Introduction Genetic Determinants of Dpld in Mouse Strains Genetic Determinants of Sarcoidosis Surfactant Proteins and Dpld Genetic Determinants of Pulmonary Fibrosis Identified in Rare Inherited Disorders Genetic Determinants of Fip Conclusion References 92. Peptic Ulcer Disease J. Holton Introduction Clinical and Physiological Aspects of Pud Pathophysiology of Ulcer Formation The Helicobacter Genome Human Polymorphism and Pud Genomics in the Management of Disease Future Developments in the use of Genomic Techniques in Relation to Pud Conclusions Acknowledgements References Recommended Resources 93. Cirrhosis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Liver Structure Fibrosis and Cirrhosis Diagnosis of Cirrhosis Treatment of Cirrhosis Genetics of Cirrhosis The Liver Transcriptome The Liver Proteome Development of Liver Fibrosis Transcriptome Analysis of Liver Disease Proteomic Studies of Liver Disease

1098 1098 1098 1099 1101 1104 1105 1105 1107 1107 1108 1110 1110 1111 1111 1114 1115 1115 1117 1118 1122 1122 1123 1124 1125 1130 1131 1132 1132 1133 1133 1137 1138 1138 1138 1140 1140 1141 1142 1143 1143 1144 1146 1148

Proteomics in Other Liver Disease Future Impact of Genomics Studies Conclusion References 94. Systemic Sclerosis Ulf Müller-Ladner Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring and Genomic Factors Therapeutic Strategies Novel and Emerging Therapeutics Conclusions Acknowledgements References

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1150 1151 1151 1151 1155 1155 1155 1158 1159 1159 1159 1159 1162 1164 1165 1165 1165

Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity J. Alfredo Martínez Introduction Obesity: Causes and Genetic Predisposition Search of Genes Involved in Obesity Diagnosis and Characterization of Genes Associated with Obesity Screening and Diagnosis Prognosis and Gene Based-treatments Novel and Emerging Therapeutics Nutrigenomics, Pharmacogenomics and Gene Therapy Conclusions Acknowledgements References

1169 1170

96. Diabetes Maggie Ng and Nancy J. Cox Introduction GWAS in Type 2 Diabetes Future Research in Type 2 Diabetes Genetics GWAS in Type 1 Diabetes Future Studies in Type 1 Diabetes Clinical Utility of Genetic Research in Diabetes Conclusion References

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97. Metabolic Syndrome Rebecca L. Pollex and Robert A. Hegele Introduction Diagnosis: Definition of the MetS Phenotype Pathophysiology of MetS in Brief Genetics of MetS

1170 1170 1172 1175 1179 1180 1182 1183 1183 1184

1187 1188 1190 1191 1192 1192 1192 1192 1194 1194 1194 1196 1196

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Conclusion Acknowledgements References Recommended Resources 98. Nutrition and Diet in the Era of Genomics Jose M. Ordovas and Dolores Corella Introduction Methodological Issues Gene–Nutrient Interactions Path Forward Conclusions Acknowledgements References Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia Robert L. Nussbaum Introduction Incidence of Dementia Primary Dementias Clinical Approach to the Dementias Future Prospects for Genomic Medicine in the Dementias Conclusion References Recommended Resources 100. Parkinson’s Disease: Genomic Perspectives Shushant Jain and Andrew B. Singleton Introduction Clinical Characteristics of PD Genetics of PD Genetics of Sporadic PD Conclusion References Recommended Resources

1200 1200 1200 1202 1204 1204 1205 1206 1214 1214 1215 1215

1221 1222 1222 1223 1224 1229 1229 1230 1230 1232 1233 1233 1233 1235 1238 1241 1241 1242

101. Epilepsy Predisposition and Pharmacogenetics Nicole M.Walley and David B. Goldstein Introduction Mendelian Epilepsies Common Epilepsies Future Program of Work References

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102. Ophthalmology Janey L.Wiggs Introduction Extraocular Muscles Cornea Lens

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1243 1243 1248 1251 1252

1256 1257 1257 1259

Iris Trabecular Meshwork Optic Nerve Retina Genetic Testing for Ocular Disorders Summary References

1259 1259 1260 1260 1261 1261 1261

103. Genomic Basis of Neuromuscular Disorders Erynn S. Gordon and Eric P. Hoffman Introduction Motor Neuron Disease Disorders of the Neuromuscular Junction Disorders of the Muscle Predisposition Screening Diagnosis Prognosis Monitoring Current, Novel, and Emerging Therapies Advances in Genomics and Proteomics Conclusion References Recommended Resources

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104. Psychiatric Disorders Stephan Züchner and Ranga Krishnan Introduction Great Prospect, But are We There Yet? Classification Reconsidered How Complex Can it Be? The Value of Rare Genetic Variation Converging Methods Personalized Medicine Conclusion References 105. Genomics and Depression Brigitta Bondy Introduction Diagnosis, Prevalence and Course of Depression Pathophysiological Mechanisms Pharmacogenomics of Antidepressants Current Concepts Future Aspects References 106. Bipolar Disorder in the Era of Genomic Psychiatry Ayman H. Fanous, Frank Middleton, Carlos N. Pato and Michele T. Pato Introduction Diagnosis Predisposition Pharmacogenetics

1265 1265 1266 1268 1268 1273 1274 1274 1275 1276 1277 1279 1279 1281 1282 1282 1282 1283 1283 1284 1284 1285 1286 1287 1289 1289 1289 1290 1293 1294 1296 1296 1299

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The Role of New Technologies in Elucidating the Genetics of BPD 1305 Conclusion 1308 References 1308 Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens M. Frances Shannon Introduction Genetic Susceptibility to Pathogens Exploring the Host Response Through Expression Profiling Genetical Genomics and Systems Biology: the New Frontiers Application to Clinical Practice Acknowledgement References Recommended Resources 108. Genomic Medicine and AIDS Thomas Hirtzig,Yves Lévy and Jean-François Zagury Introduction Context of HIV and AIDS Predisposition: Susceptibility to HIV-1 Infection Diagnosis Prognosis Monitoring Pharmacogenomics Novel and Emerging Therapeutics Conclusion References 109. Viral Genomics and Antiviral Drug Roberto Patarca Introduction Viral Genomics and the Antiviral Drug Revolution Era Conclusion References

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110. Host Genomics and Bacterial Infections Melissa D. Johnson and Mihai Netea Introduction Genomics and the Study of Bacterial Infections Host Genomics and Gram-positive, Gram-negative and Mycobacterial Infections Future Directions Conclusions References

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111. Sepsis and the Genomic Revolution Christopher W.Woods, Robert J. Feezor and Stephen F. Kingsmore Introduction Genetic Polymorphisms Associated with Sepsis Molecular Signatures and Sepsis Therapeutics Conclusion References

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112. Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Virology of Hepatitis Viruses Acquisition and Predisposition to Viral Hepatitis Screening and Diagnosis of Viral Hepatitis Pathogenesis of Viral Hepatitis Therapeutics and Pharmacogenomics Future Impact of Genomics Studies Conclusion References

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1375 1375 1375 1377 1378 1380 1384 1386 1386 1386

Glossary

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Foreword In the health care field today, there are high expectations for a paradigm shift in care delivery over the coming years. According to this view, we have the opportunity to achieve fundamental change and improvement in the delivery of care, with better health outcomes across the board, led by advances in genomics and molecular medicine. The prospect is exciting: a continuum of health maintenance and medical care that is truly tailored to the individual, based on his or her individual biology. We could know our own genetic profiles from birth. Prevention could be more individually charted, based on individual genetic factors. Disease could be detected and treated earlier through molecular diagnostics. Health care dollars might be spent to much better effect. And our lives could be healthier, longer. The stepping stones to such a future are now widely known. Building on the work of the Human Genome Project and its offspring, medical care can be expected to acquire powerful new tools, especially in diagnosis, that could render care much more effective by making it more precise, more individually targeted, and more predictive. We should be able to prescribe drugs more safely because genetic or other characteristics would help clinicians identify which patients would respond well to a given therapy. As diseases come to be understood at a new level, we should be better able to achieve the right diagnosis and the right treatment for each person, without the trial-and-error process that has long characterized medical treatment. As biomarkers are identified, we should be able to intervene in disease at much earlier stages. And when we are able to know our personal genomic profiles, we should be able to better pinpoint our individual health susceptibilities. Our physicians could give more individualized prevention advice – and perhaps it will even come to pass that we will be more motivated to follow it. I am a believer in this view of the possible future – and I am confident it is not just wishful thinking. The pace of discovery in the genomic field today is unprecedented. Furthermore, enough successful applications of this paradigm already exist to give us reason to look toward a new era of effectiveness in medical care, with new information and new tools for both the clinician and the consumer. At the same time, we must be realistic in our assessment of this future, especially the extent of the efforts that will be needed to achieve it. However desirable the idea of this “paradigm shift” may be, the realization of such a shift rests on a foundation that is still very much under construction.

The shift will require development of new capacities that will enable us to differentiate among the needs of individual patients. In turn, these new capacities will depend on data to be derived, analyzed, and employed on a new scale. Such information demands will surely need to be supported by sophisticated electronic data networks that are yet to be created, informatics tools that are yet to be invented, and clinical decision-support systems that are yet to be devised or adopted. Finally, at the bedrock, the trust and understanding of the medical community and of society at large must be won even as the edifice is being created. This is the work of a generation. It is work that spans professions, economic sectors, and even nations. It is driven by science – but it makes new demands for the rapid translation of scientific discovery into clinical practice and improved health outcomes. One part of the work before us is continued discovery. Phenomenal achievements have been made in mapping the human genome, and a rush of findings is occurring today in understanding associations between genomic factors and health. Yet vast areas still remain to be explored. That work is underway on a global scale. Another part of our work might be called the engineering. This includes the development of interoperable health information technology, with all the implications of that goal: development of technical and clinical standards, adoption of health IT across the health sector, and security of personal health information. In the long term, it should also include the use of health IT to enable us to make faster progress in medical research – and then to feed back what we’ve learned into clinical practice, using IT decision-support tools that are physician-, nurse-, and consumer-friendly. Another element of our task is less defined but equally challenging: the collaboration and cooperation needed to bring this vision to reality. Personalized medicine means care that is information-based at a new level. Gathering that information and using it successfully will transcend many disciplines and make new demands on a health system that is often characterized as “fractured.” It may seem ironic that delivery of individualized care depends on standardization, partnerships, and networks. But these kinds of collaboration are among the most important element of the work that lies ahead. Finally, in today’s health care environment, the success of new products, services, and models of care will depend more than ever on the value that is delivered. At its core, personalized medicine is about care that can achieve new levels in predicting, preventing, and detecting disease. Medical effectiveness of this

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kind should translate into cost effectiveness as well. Achieving and demonstrating high value expectations will be challenging, but this discipline will serve the success of personalized medicine over the long term. The paradigm shift of personalized medicine depends on an extensive foundation of scientific knowledge, professional leadership, health information technology, and a spirit of cooperation, collaboration, and dialogue. The many perspectives offered by the

contributors to this text help us appreciate the breadth of these issues, challenges, debates, and opportunities that lie ahead.

Michael O. Leavitt Secretary Department of Health and Human Services Washington, DC

Preface It seems just yesterday that we were first getting used to the notion of introducing the seemingly freshly uncovered concepts of genetics into the practice of medicine. And yet, with the completion of the Human Genome Project and the rapid development and application of new advances in our ability to understand and query the human genome and its gene set, it is time already to anticipate and outline the early stages of what must be called a transformation of medicine. We are beginning to see the first signs of a fundamental shift in how we behold human physiology and pathology, how we view the concept of what is “normal,” how we consider individuals and their prospects for lifelong health, and how we design healthcare systems that are equally adaptable to the demands of population-wide epidemics and the opportunities for personalized care that utilizes genome-based information to consider individual susceptibility to disease and therapeutic options. Genome-based data, information, knowledge, and eventually wisdom will make possible the kind of healthcare that has been dreamed of since the advent of disease-based medicine early in the 20th century. A system of healthcare that harnesses the might of the genome and its derivatives, along with imaging, clinical and environmental information, will empower physicians and other healthcare providers to do what they have always aspired to do – make medical care as individualized as possible. But this newfound information and knowledge will also allow each of us as consumers of healthcare to take more control of our futures and to develop a more strategic and a prospective approach to health. We stand at the dawn of a profound change in science and medicine’s predictive nature and in our understanding of the biological underpinnings of health and disease. Even in this early light, we can see the outlines of a coming ability to: ●

●

●

● ●

predict individual susceptibility to disease, based on genetic, genomic and other factors; provide more useful tools and individualized programs for disease prevention, based on knowledge of one’s susceptibility; detect the onset of disease earlier and before it is clinically evident, based on newly discovered biological markers that arise from changes at the molecular level; preempt disease progression, as a result of early detection; target medicines and their dose more precisely and safely to each patient, on the basis of a deep understanding of disease

mechanism and the role that genetic and genomic factors play in the individual response to drugs. This revolution in genomic and personalized medicine was anticipated nearly three decades ago by Nobel laureate Paul Berg, who stated so presciently: Just as our present knowledge and practice of medicine relies on a sophisticated knowledge of human anatomy, physiology, and biochemistry, so will dealing with disease in the future demand a detailed understanding of the molecular anatomy, physiology, and biochemistry of the human genome. . . . We shall need a more detailed knowledge of how human genes are organized and how they function and are regulated. We shall also have to have physicians who are as conversant with the molecular anatomy and physiology of chromosomes and genes as the cardiac surgeon is with the structure and workings of the heart.

That time has come. This book is intended to lay out the foundations of this new science, to outline the early opportunities for the practice of medicine to incorporate genome-based analysis into healthcare, and to anticipate the many conditions to which genomic and personalized medicine will apply in the years ahead. The chapters in these volumes are designed to be read either sequentially – introducing the scientific underpinnings of this revolution, exploring aspects of translational medicine and genomics that will be critical for bringing about this revolution, and presenting practical aspects of the first applications of genomic and personalized medicine in the context of specific medical conditions – or one-at-a-time for those interested in particular disorders or approaches. These volumes also describe a field in its infancy, with many challenges for society at large, in addition to those associated with healthcare systems strife with inefficiencies and heterogeneity in their ability to deliver the basics of healthcare. There are “grand challenges” for the visionary science and the clinical care highlighted in these pages. Such challenges include the potential for these innovations to exaggerate existing health disparities, information technology systems that have been described as a “tower of Babel,” an unprepared healthcare work force, and economic incentives that are inadequately aligned for the various stakeholders to fully embrace genomic and personalized medicine. Nonetheless, we are optimistic that the appropriate delivery models and economic incentives will be developed

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in a trustworthy framework that will be embraced by societies around the globe. As an indicator of the importance of personalized medicine, international communities are working together on strategies to overcome these obstacles. In the United States, the Department of Health and Human Services has played a leadership role in this area and a plan was published in 2007 (Personalized Healthcare: Opportunities, Pathways, Resources; see http://www. hhs.gov/myhealthcare/). Other governments (in the United Kingdom, Iceland, Estonia, Luxembourg, and Singapore – to name just a few) have funded initiatives that will secure their place in developing genome-based knowledge and its translation into day-to-day patient care. A collective and global approach to what might be arguably one of the most complex scientific and clinical undertakings in the history of healthcare is undoubtedly what is required. Our international collective of contributors to this work reflects the early adopters and members of a global community of physicians, scientists, and policy makers who will make this happen.

Our intended audience is broad, ranging from medical students (and even the intrepid undergraduate eager to explore this new era of personalized and prospective medicine) to residents and fellows to practitioners in any of the healthcare professions – physicians in any of the medical specialties, surgeons, nurses, genetic (and genomic) counselors, and laboratory directors – and, finally, to members of the genomic and personalized medicine research communities who will, we trust, help write future editions of this text. In times of transformation, we are all students. We hope that this book will help usher in this new era of genomic and personalized medicine and will provide a useful and thorough introduction to the science and practice of this new approach to human health.

Huntington F. Willard, Ph.D. Geoffrey S. Ginsburg, M.D., Ph.D.

Terminology Throughout this book, the terms “genetics” and “genomics” are used repeatedly, both as nouns and in their adjectival forms. Although these terms seem similar, they in fact describe quite distinct (though frequently overlapping) approaches in biology and in medicine. Here, we provide operational definitions to distinguish the various terms and the subfields of medicine to which they contribute. The field of genetics is the scientific study of heredity and of the genes that provide the physical, biological, and conceptual bases for heredity and inheritance. To say that something – a trait, a disease, a code or information – is “genetic” refers to its basis in genes and in DNA. Heredity refers to the familial phenomenon whereby traits (including clinical traits) are transmitted from generation to generation, due to the transmission of genes from parent to child. Genomics is the scientific study of a genome, the complete DNA sequence, containing the entire genetic information of a gamete, an individual, a population or a species. The word “genome” was first used as an analogy with the earlier term “chromosome,” referring to the physical entities (visible under the microscope) that carry genes from one cell to its daughter cells or from one generation to the next. Over the past two decades, “genomics” has given birth to a series of other “-omics” that refer to the comprehensive study of the full complement of, for example, proteins (hence, proteomics), transcripts (transcriptomics), or metabolites (metabolomics). The essential feature of the “-omes” is that they refer to the complete collection of genes, proteins, transcripts, or metabolites, not just to the study of individual entities. Medical genetics is the application of genetics to medicine and is one of the 24 medical specialties recognized by The American Board of Medical Specialties, the preeminent medical organization overseeing physician certification in the United States. Genetic medicine is a term sometimes used to refer to the application of genetic principles to the practice of medicine and thus overlaps medical genetics. Both medical genetics and genetic

medicine approach clinical care largely through consideration of individual genes and their effects on patients and their families. Genomic medicine, by contrast, refers to the use of large-scale genomic information and to consideration of the full extent of an individual’s genome, proteome, transcriptome, or metabolome in the practice of medicine and medical decision-making. The principles and approaches of genomic medicine are relevant well beyond the traditional purview of individual medical specialties and include, as examples, gene expression profiling to characterize tumors or to define prognosis in cancer, genotyping variants in the set of genes involved in drug metabolism or action to determine an individual’s correct therapeutic dosage, scanning the entire genome for millions of variants that influence one’s susceptibility to disease, or analyzing multiple protein biomarkers to monitor therapy and to provide predictive information in presymptomatic individuals. Finally, personalized medicine refers to a rapidly advancing field of healthcare that is informed by each person’s unique clinical, genetic, genomic, and environmental information. The goals of personalized medicine are to take advantage of a molecular understanding of disease to optimize preventive healthcare strategies and drug therapies while people are still well or at the earliest stages of disease. Because these factors are different for every person, the nature of disease, its onset, its course, and how it might respond to drug or other interventions are as individual as the people who have them. For personalized medicine to be used by healthcare providers and their patients, these findings must be translated into precision diagnostic tests and targeted therapies. Since the overarching goal is to optimize medical care and outcomes for each individual, treatments, medication types and dosages, and/or prevention strategies may differ from person to person – resulting in unprecedented customization of patient care. The principles underlying genomic and personalized medicine and their applications to the practice of clinical medicine are presented throughout the chapters that comprise this volume.

Acknowledgements We wish to express our appreciation and gratitude to our many colleagues, especially in the Duke Institute for Genome Sciences & Policy, who have shared their knowledge and ideas about genomic and personalized medicine and who, by doing so, inspired this project. We particularly thank our first editor at Academic Press/Elsevier, Luna Han, who encouraged us to develop the concept of a text on genomic and personalized medicine. We are also grateful to Sally Cheney, Kirsten Funk, and Christine Minihane, our Senior Editors at Academic Press/ Elsevier; to Rogue Shindler, our Developmental Editor; and to Ganesan Murugesan, our Production Project Manager, for their patience, advice, and professionalism in all stages of the project.

We acknowledge our Advisory Board for their suggestions and support and especially thank the nearly 300 authors of the 112 chapters that comprise these volumes. Needless to say, without their efforts, this project could never have come to fruition. We also thank Secretary Mike Leavitt for providing a Foreword for this book, as well as for his enthusiastic support of the concept of genomic and personalized medicine. It gives us pleasure to give special thanks to Kathy Hay and to Lynne Skinner, whose tireless efforts kept us on track and saw this project through to completion. Lastly, we thank our families for their patience and understanding for the many hours we spent creating this, the inaugural edition of Genomic and Personalized Medicine.

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Abbreviations   2D-DIGE 2D-PAGE

Positron Neutrino Two-dimensional difference gel electrophoresis Two-dimensional polyacrylamide gel electrophoresis 3-MT 3-Methoxytyramine 5-HT 5-Hydroxytriptamine or serotonin 5-HTT 5-HT transporter 5-HTTLPR 5-HTT gene-linked polymorphic region 6-MMP 6-Methylmercaptopurine 6-MP 6-Mercaptopurine 6-MTIMP 6-Methyl-thioinosinemonophosphate 6-TGN 6-Thioguanine nucleosides 6-TIMP 6-Thioinosinemonophosphate 18 18 F-FLT F-labeled 3-deoxy-3-fluorothymidine 18 18 F-FMAU F-1abeled-2-deoxy-2-fluoro--d-arabinofuranosyl thymine AAU Acute anterior uveitis ABCA1 Adenosine triphosphate-binding cassette protein A1 ABCB1 Adenosine triphosphate-binding cassette, sub-family B ABI Ankle brachial index ACC American College of Cardiology ACE Angiotensin converting enzyme aCGH Array comparative genomic hybridization ACR Acute cellular rejection AD Alzheimer’s disease AD Aortic dilation ADH Autosomal dominant hypercholesterolemia ADHD Attention deficit hyperactivity disorder ADIGE Autosomal dominant idiopathic generalized epilepsy

ADJME ADME ADNFLE ADP ADR AED Ago2 AHA AIDS AIMs AJCC ALL ALOX5AP AMACR AML Amol AMR AOO AOTF Apo(a) ApoE APOE AR AR ARACNe ARE ARH ARJP ARM

Autosomal dominant juvenile myoclonic epilepsy Absorption, distribution, metabolism, and elimination Autosomal dominant nocturnal frontal lobe epilepsy Adenosine 5-diphosphate Adverse drug reaction Antiepileptic drug Argonaute 2 mRNA endonuclease American Heart Association Acquired immune deficiency syndrome Ancestry informative markers American Joint Committee on Cancer Acute lymphoblastic leukemia Arachidonate 5-lipooxygenase-activating protein Alpha methylacyl CoA reductase Acute myeloid leukemia Attomole Antibody mediated rejection Age of onset Acousto-optic tunable filters Apolipoprotein(a) Apolipoprotein E Apolipoprotein E (gene name) Androgen receptor Autosomal recessive Algorithm for the Reconstruction of Accurate Cellular Networks Androgen response element Autosomal recessive hypercholesterolemia Autosomal recessive juvenile Parkinsonism Armadillo

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ARVC AS ASCA ASCO ASCVD ASD ASO ASPE ATM ATP ATP ATR AZT B1-AR BAC BBB BCAA Bcl BCL-2 Bcl-Abl BDNF BER BEST BH BLAST BLAT BMC BMI BMP-7 BOLD BPDE BPH BRCA BRCA1 BRCA2 BRD2 BSML CA CAD CADASIL CAE CALGB CAP CARD CARGO CAV CBD CBP CBZ CCD

Abbreviations

Arrhythmogenic right ventricular cardiomyopathy Ankylosing spondylitis Anti-Saccharomyces cerevisiae antibodies American Society for Clinical Oncology Atherosclerotic cardiovascular disease Atrial septal defect Anti-sense oligonucleotides Allele-specific primer extension Ataxia telangiectasia mutated Adenosine triphosphate Adult Treatment Panel Ataxia-telangiectasia and RAD3-related Azidothymidine 1-adrenergic receptor Bacterial artificial chromosome Blood–brain barrier Branched-chain amino acids B-cell lymphoma protein B-cell lymphoma 2 Breakpoint cluster region – Abelson kinase fusion protein Brain-derived neurotrophic factor Base excision repair -Blocker Evaluation of Survival Trial Bcl-2 homology domain Basic local alignment and search tool BLAST-like alignment tool Bone marrow cell Body mass index Bone morphogenic protein-7 Blood oxygen level-dependent Benzo (a) pyrene diol epoxide Benign prostatic hypertrophy Breast cancer gene Breast cancer 1, early onset, protein Breast cancer type 2 susceptibility protein Bromodomain containing 2 (gene) Bioinformatic sequence markup language Capsid (viral protein) Coronary artery disease Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy Childhood absence epilepsy Cancer and leukemia group B College of American Pathologists Caspase recruitment domain (gene) Cardiac Allograft Rejection Gene Expression Observational study Cardiac allograft vasculopathy Cortical basal degeneration CREB-binding protein Carbamazepine Charge coupled device

CCL CCND1 CCP CD CD CDAI CDC CDDP cDNA CDS CED-4 CETP CF CFTR CGAP CGH CGHa CGP CHD CHD CHF CHMP CHRNA4 CIBEX CIC CICR CKD CLI CLIA CML CMT CMV CNS CNV COPA COR COX COX-2 CPOE CRC CREB CRF CRP CS CSA CSF CSF1 CT CTC CTGF CTL CTLA

Chemokine (C-C motif) ligand Cyclin D1, a proto-oncogene Citrullinated cyclic peptide Cluster of differentiation Crohn’s disease Crohn’s disease activity index Centers for Disease Control Cisplatin (cis-diamminedichloroplatinum Complementary deoxyribonucleic acid Clinical decision support Cell death abnormality gene 4 Cholesteryl ester transfer protein Cystic fibrosis Cystic fibrosis transmembrane conductance regulator Cancer Genome Anatomy Project Comparative genomic hybridization Comparative genomic hybridization array Cancer Genome Project Congenital heart disease Coronary heart disease Congestive heart failure Committee for Medicinal Products for Human Use Cholinergic receptor, nicotinic, alpha 4 (gene) Center for Information Biology Gene Expression Citrate/isocitrate carrier Calcium-induced calcium release Chronic kidney disease Critical limb ischemia Clinical Laboratory Improvement Amendments Chronic myelogenous leukemia Charcot-Marie-Tooth disease Cytomegalovirus or human herpes virus 5 Central nervous system Copy number variant Cancer outlier profile analysis C-terminal of ROC domain Cyclooxygenase Cyclooxygenase-2 Computerized provider order entry Colorectal cancer Cyclic AMP response binding protein Circulating recombined form C-reactive protein Cell signaling factor Comparative sequence analysis Cerebrospinal fluid Colony stimulating factor 1 Computed tomography Circulating tumor cells Connective tissue growth factor Cytotoxic T lymphocytes Connective tissue late antigen

Abbreviations

CTSA CX32 CX37 CYP CYP2C19 CYP2D6

Clinical and Translational Science Awards Connexin-32 Connexin-37 Cytochrome P450 Cytochrome P450 2C19 Cytochrome P450 2D6

D2R DAI DBS dbSNP DCM DGGE DGI DHF DHLPC

Dopamine-2 receptor Disease Activity Score (for Ulcerative Colitis) Deep brain stimulation NCBI SNP database Dilated cardiomyopathy Denaturing gradient gel electrophoresis Diabetes Genetics/Initiative Diastolic heart failure Denaturing high performance liquid chromatography Diffuse Lewy body disease Diffuse Lewy body disease Diagonalized linear discriminant analysis Disc large homologue (gene) Drug-metabolizing enzymes Deoxyribonucleic acid Deoxyribonuclease DNA methyltransferases Dihydroxyphenylacetic acid Double stranded Double-stranded ribonucleic acid Direct-to-consumer Duodenal ulcer Dizygotic

DLB DLBD DLDA DLG DMEs DNA DNase DNMTs DOPAC ds dsRNA DTC DU DZ EAE EASS EBI EBV ECL ECM EDG-1 EGAPP EGF EGFR EGP EHR EIA EL ELISA ELMO1 EM EMB EMBL-EBI EMEA ENCODE EOAD

Experimental autoimmune encephalomyelitis East Asia specific sequence European Bioinformatics Institute Epstein–Barr virus Eenterochromaffin-like cells Extracellular matrix Endothelial differentiation gene-1 Evaluation of Genomic Applications in Practice and Prevention Epidermal growth factor Epidermal growth factor receptor Environmental Genome Project Electronic health record Enzyme immunoassay Endothelial lipase Enzyme-linked immunosorbent assay Engulfment and cell motility 1 Extracellular matrix protein Endomyocardial biopsy European Molecular Biology Laboratory– European Bioinformatics Institute European Medicines Evaluation Agency Encyclopedia of DNA Elements Early-onset Alzheimer’s disease

EPIYA eQTL ERK ERNA ESI ESI-MS/MS ESR ESRD EST FA FACS FANCA FANCD2 FAP FCCS FCHL fCJD FCS FCS FD FDA FDB FDG FFPE FGF FGF-2 FH FHTG FISH FKBP FL FLT-3 Fmol fMRI FMT FNA FOV FRET FSGS FTD FT-ICR-MS FUSION GABBR1 GABRG2 GAIN GAPDH GBM GBS GCKR GC-MS

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Single letter codes for amino acids Expression quantitative trait loci Extracellular signal-regulated kinase Equilibrium radionucleotide angiography Electrospray ionization Electrospray ionization tandem mass spectrometry Erythrocyte sedimentation rate End-stage renal disease Expressed sequence tag Fanconi anemia Fluorescent-activated cell sorting Fanconi anemia proteins A Fanconi anemia protein D2 Familial adenomatous polyposis Two-color fluorescence cross-correlation spectroscopy Familial combined hyperlipidemia Familial Creutzfeldt–Jakob disease Familial hyperchylomicronemia syndrome Fluorescence correlation spectroscopy Familial dysbetalipoproteinemia Food and Drug Administration (USA) Familial defective apoB-100 18 F-labeled 2-deoxy-d-glucose Formalin-fixed, paraffin-embedded Fibroblast growth factor Fibroblast growth factor-2 Familial hypercholesterolemia Familial hypertriglyceridemia Fluorescence in situ hybridization FK506-binding protein Follicular lymphoma FMS-like tyrosine kinase-3 Femtomole Functional MRI Fluorescence molecular tomography Fine needle aspirate Field of view Fluorescence resonance energy transfer Focal segmental glomerulosclerosis Frontotemporal dementia Fourier transform ion cyclotron mass spectrometry Finland–United States Investigation of NIDDM genetics GABA B receptor 1 (gene) GABA A receptor, gamma 2 (gene) Genetic Association Information Network Glyceraldehyde-3-phosphate dehydrogenase Glomerular basement membrane Guillain–Barre Syndrome Glucokinase regulatory protein Gas chromatography–mass spectrometry

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GCSF GDNF GEFS GEO GEP GFAP GFP GLEPP1 GLP GM-CSF GO GPI GR GSEA GSIS GSTT GU GWAS GZM H HAART HAV HBV HCC HCM HCV HDAC HDL HDM2 HDV HeLa HEPS HER2 HEV HF HFPEF HGF HGPRT HHS HHV-6 HITSP HIV HKMT HL HL7 HLA/HLA HLI HMT HNPCC HNSCC

Abbreviations

Granulocyte colony-stimulating factor Glial cell line-derived neurotrophic factor Generalized epilepsy with febrile seizures plus Gene Expression Omnibus Gene expression profiling Glial fibrillary acidic protein Green fluorescent protein Glomerular epithelial protein 1 Good laboratory practice Granulocye macrophage–colony stimulating factor Gene ontology Globus pallidus interna Glucocorticoid receptor gene Gene set enrichment analysis Glucose-stimulated insulin secretion Glutathione S-transferase Gastric ulcer Genome-wide association study Granzyme Heterozygosity Highly active anti-retroviral therapy Hepatitis A Hepatitis B Hepatocellular carcinoma Hypertrophic cardiomyopathy Hepatitis C Histone deacetylase High-density lipoproteins Human double minute 2 Hepatitis D cervical cancer cell line, also the first cancer cell line established Highly exposed persistently seronegative individuals Human epidermal growth factor receptor 2 Hepatitis E Heart failure Heart failure with preserved ejection fraction Hepatocyte growth factor Hypoxanthine phosphoribosyl transferase Health and Human Services (U.S.) Human herpes virus 6 Healthcare Information Technology Standards Panel Human immunodeficiency virus Histone lysine methyltransferases Hepatic lipase Health Level 7 Human leukocyte antigen/(gene) Hind limb ischemia Histone methyltransferase Hereditary nonpolyposis colorectal cancer Head and neck squamous cell carcinoma

HP-1 HPV HPV-16 HR HRPC HS HSC HSP HSV-1 HSV1-TK HSV-2 hTERT HTLV-I HTLV-III HTS HVJ

Heterochromatin-associated protein-1 Human papilloma virus Human papilloma virus-16 Homologous recombination Hormone refractory prostate cancer Hyper-spectral Hepatic stellate cell Heat shock protein Human herpes simplex virus type 1 Herpes simplex virus type 1-thymidine kinase Human herpes simplex virus type 2 Human telomerase reverse transcriptase Human T-cell leukemia/lymphoma virus type I Human T-cells lymphotropic virus type 3 High-throughput screening Hemagglutinating virus of Japan

IBD IBDQ IC IC ICAM ICAT ICD ICD ICDc

Inflammatory bowel disease Inflammatory bowel disease questionnaire Intermittent claudication Interstitial cystitis Intracellular adhesion molecules Isotope-coded affinity tags Implantable cardioverter defibrillator International Classification of Diseases Cytosolic, NADP-dependent isocitrate dehydrogenase Immature dendritic cells Idiopathic dilated cardiomyopathy Insulin-dependent diabetes mellitus Intermediate-density lipoproteins Indolemaine 2,3-dioxygenase Interferon Interferon-beta Interferon-gamma Immunoglobulin Immunoglobulin-A Insulin-like growth factor-I Immunohistochemistry Integrating the Healthcare Enterprise Inhibitor of B-like (gene) Interleukin Interleukin (4/13) Interleukin-1 beta (gene) Interleukin-1 receptor antagonist Interleukin-6 Interleukin-10 Invasive Monitoring Attenuation through Gene Expression study Immature myeloid cells Integrase (viral protein) Institute of Medicine (U.S.) Insertion sequence Institute for Systems Biology Intreferon stimulated gene

iDC IDCM IDDM IDL IDO IFN IFN- IFN- Ig IgA IGF-I IHC IHE IKBL IL IL (4/13) IL-1 IL-1ra IL-6 IL-10 IMAGE iMC IN IOM IS ISB ISG

Abbreviations

ISHLT IT IVDMIA IVVM JCA JHDM1

International Society for Heart and Lung Transplantation Information technology In vitro diagnostic multivariate index assay In vivo videomicroscopy

JME

Juvenile chronic arthritis JmjC domain-containing histone demethylase-1 Juvenile myoclonic epilepsy

Kb KEGG

Kilobase Kyoto Encyclopedia of Genes and Genomes

LARGO

Lung Allograft Rejection Gene expression Observational study Lymphadenopathy-associated virus Lewy body Lecithin cholesterol acyltransferase Laser capture microdissection Liquid chromatography–mass spectrometry Liquid chromatography/mass spectrometry/ mass spectrometry Liquid crystal tunable filters Linkage disequilibrium Low-density lipoproteins Levodopa Late-onset Alzheimer’s disease Logarithm of the odds Loss of heterozygosity Logical Observation Identifiers, Names, and Codes Line of response Lipoprotein(a) Lysophosphatidic acid Lipoprotein lipase Lipopolysaccharide Leucine-rich repeat Leucine rich repeat kinase 2 Lysine-specific demethylase-1 Lymphotoxin alpha Leukotriene A4 hydrolase Leukotriene B4 Long-term non-progression Left ventricle Left ventricular assist device Left ventricular end-diastolic pressure Left ventricular ejection fraction Left ventricular hypertrophy Left ventricular outflow tract (obstruction)

LAV LB LCAT LCM LC-MS LC-MS/MS LCTF LD LDL l-DOPA LOAD LOD LOH LOINC LOR Lp(a) LPA LPL LPS LRR LRRK2 LSD1 LTA LTA4H LTB4 LTNP LV LVAD LVEDP LVEF LVH LVOT(O) MA mAb MADH6 MAGE-ML MAGUK

Matrix (viral protein) Monoclonal antibodies Mothers against decapentaplegic homolog 6 MicroArray and Gene Expression Markup Language Membrane-associated guanylate kinase

MALDI MALDI-TOF MAM MAO MAOA MAP MAPK MAPK MAQC Mb MBD MCD MCD MCP MCP-1 MCS M-CSF MDM2 MDP MDR MedDRA MEF2A Megsin MEN MeSH MeV MGED MGMT MHC MI MIAME MIM miR-192 miRNA MITF MLEM MLPA MMP MMP-3 MMR MPSS MPTP mQTL MR MRI mRNA mRSS

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Matrix-assisted laser desorption and ionization Matrix-assisted laser desorption ionization time-of-flight Metastasis-averse microenvironment Monoamine oxidase Monoamine oxidate A Mitogen-activated protein Microtubule-associated protein kinase Mitogen-activated protein kinase Microarray quality control Megabase Methylated DNA binding domain protein Malonyl CoA decarboxylase Minimal change disease Monocyte/macrophage chemoattractant protein Monocyte chemoattractant protein 1 Multi-species conserved sequence Macrophage colony-stimulating factor Mouse double minute 2 Muramyl dipeptide Multidrug resistance (gene) Medical Dictionary for Regulatory Activities Terminology Myocyte-enhancing factor 2A Mesangial cell-specific gene with homology to serpin Multiple endocrine neoplasm Medical Subject Headings Multiexperiment viewer Microarray Gene Expression Data society O6-Methylguanine-DNA methyltransferase Major histocompatability complex Myocardial infarction Minimal Information About a Microarray Experiment Metastasis-inclined microenvironment MicroRNA-192 Micro-ribonucleic acid Microphthalmia transcription factor Maximum likelihood expectation maximization Multiplex ligation-dependent probe amplification Matrix metalloproteinase Matrix metalloproteinase-3 Mismatch repair Massively parallel signature sequencing 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine Metabolite quantitative trait loci Magnetic resonance Magnetic resonance imaging Messenger ribonucleic acid Modified Rodnan Skin Score

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MS MS MS MS/MS MSA MTDNA MTHFR mTOR MTP MTX MudPIT MVP MZ NAAT NAD(P)H

Abbreviations

Mass spectrometry Multiple sclerosis Multispectral Tandem mass spectrometry Multiple system atrophy Mitochondrial DNA Methylenetetrahydrofolate reductase (gene) Mammalian target of rapamycin Microsomal triglyceride transfer protein Methotrexate Multidimensional protein identification technology Mitral valve prolapse Monozygotic

NRTI NSAID nt NUD NYHA

Nucleic acid amplification testing Nicotinamide adenine dinucleotide phosphate reduced form Non-alcoholic fatty liver disease Non-alcoholic steatohepatitis N-Acetyltransferase 2 Normal appearing white matter Nucleocapsid (viral protein) Neural cell adhesion molecule National Center for Biotechnology Information New chemical entity National Cholesterol Education Program Refers to a set of 60 cancer cell lines curated at the NCI (National Cancer Institute) Non-coding RNA NEgative regulatory Factor (viral protein) Nucleotide excision repair Neurofibromatosis1/2 National Human Genome Research Institute Non-homologous recombination National Institute of Environmental Health Sciences (NIH) National Institutes of Health Natural killer Nuclear magnetic resonance Non-nucleosidic reverse transcriptase inhibitor Nucleotide oligomerization domain (gene) Negative predictive value Nicotinamide adenine dinucleotide phosphate oxidoreductase Nucleosidic reverse transcriptase inhibitor Non-steroidal anti-inflammatory drug Nucleotides Non-ulcer dyspepsia New York Heart Association

OBO OCTN OHCA OMIM OMP

Open Biomedical Ontologies Organic cation transporter (gene) Out of hospital cardiac arrest Online Mendelian Inheritance in Man Outer membrane protein

NAFLD NASH NAT2 NAWM NC N-CAM NCBI NCE NCEP NCI60 ncRNA NEF NER NF1/2 NHGRI NHR NIEHS NIH NK NMR NNRTI NOD NPV NQO1

OmpC opn OPN OR ORESTES ORF OSEM

Outer membrane porin C of E. coli Osteopontin (gene) Osteopontin (protein) odds ratio Open reading frame expressed sequence tags Open reading frame Ordered subset expectation maximization

p53BP1 PAD PAGE PAI PAM pANCA

p53 Binding protein 1 Peripheral arterial disease Polyacrylamide gel electrophoresis Pathogenicity island Prediction analysis of microarrays Perinuclear antineutrophil cytoplasmatic antibodies Peripheral arterial occlusive disease Papanicolaou Human parvovirus B-19 Primary biliary cirrhosis Peripheral blood lymphocytes Peripheral blood mononuclear cell Pyruvate carboxylase Principal components analysis Proliferating cell nuclear antigen Polymerase chain reaction Proprotein convertase subtilisin/kexin type 9 Procarbazine/CCNU/vincristine Parkinson disease Pharmacodynamic Patent ductus arteriosus Personal digital assistant Parkinson disease with dementia Platelet-derived growth factor Platelet-derived growth factor receptor Pyruvate dehydrogenase Pyrrolidine dithiocarbamate prodynorphin (gene) Positron emission tomography Pulse field gel electrophoresis Prostaglandin E2 Pharmacogenetics and pharmacogenomics Phenytoin Protease inhibitor Phosphotidylinositol triphosphate kinase Prostatic intraepithelial neoplasia Pharmacokinetics Phospholipid transfer protein Polony Multiplex Analysis of Gene Expression Peripheral blood mononuclear cells Pro-myelocytic leukemia retinoic acid receptor oncoprotein Photomultiplier tube Paraoxonase Percutaneous peripheral intervention Positive predictive value

PAOD1 Pap Parvo B19 PBC PBL PBMC PC PCA PCNA PCR PCSK9 PCV PD PD PDA PDA PDD PDGF PDGFR PDH PDTC PDYN PET PFGE PGE2 PGx PHT PI PI3K PIN PK PLTP PMAGE PMBC PML-RAR PMT PON1 PPI PPV

Abbreviations

PR PrEC PRIDE PRNP PS PSA PsA PSC PSP PTEN PTM PUD PZ

Protease (viral) Prostate epithelial cells Proteomics Identifications Database Prion protein (gene) Pulmonary stenosis Prostate-specific antigen Psoriatic arthritis Primary sclerosing cholangitis Progressive supranuclear palsy Phosphatase and tensin homolog Pre-trans-splicing molecule Peptic ulcer disease Plasticity zone

QPCR qRT qRT-PCR

Quantitative PCR Quantitative real-time PCR Quantitative reverse-transcriptase polymerase chain reaction Quantitative trait locus Quadrupole time-of-flight-mass spectrometry Quantitative real-time polymerase chain reaction Quality value

QTL Q-TOF-MS QT-PCR QV R&D RA RAAS RAPD-PCR RB RB RCM REPAIR-AMI

RET REV RFLP RISC RNA RNAi ROC ROCO ROS RP RPA RPLC RRMS RSV RT RT-PCR RT-PCR

Research and Development Rheumatoid arthritis Renin-angiotensin-aldosterone system Random amplified polymorphic deoxyribonucleic acid–polymerase chain reaction Retinoblastoma Retinoblastoma (gene) Restrictive cardiomyopathy Reinfusion of enriched progenitor cells and infarct remodeling in acute myocardial infarction Ret proto-oncogene REgulator of Virion protein expression (viral protein) Restriction fragment length polymorphism RNA interference silencing complex Ribonucleic acid Ribonucleic acid interference RAS in complex proteins ROC (RAS of complex proteins)/COR (C-terminal of ROC) Reactive oxygen species Rapid progression Ribonuclease protection assay Reversed-phase liquid chromatography Relapsing-remitting MS Respiratory syncytial virus Reverse transcriptase (viral protein) Real-time polymerase chain reaction Reverse transcription – polymerase chain reaction

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RV RVHF

Right ventricle Right ventricular heart failure

SACGHS

Secretary’s Advisory Committee on Genetics, Health and Society (U.S.) Serial Analysis of Gene Expression Scoring algorithm for spectral analysis S-Adenosyl-l-methionine (AdoMet) Significance analysis of microarrays Systolic anterior motion of mitral valve Shrimp alkaline phosphatase/exonuclease I Severe acute respiratory syndrome Single-base extension Sudden cardiac death Severe combined immunodeficiency Voltage-gated sodium channel, type 1, alpha subunit (gene) Selective capture of transcribed sequences Strong cation exchange Standard deviation Standards development organization Shared epitope of HLA (human leukocyte antigen) genes Spongiform encephalopathies System for Evidence-Based Advice through Simultaneous Transaction with an Intelligent Agent across a Network Sinusoidal endothelial cell Surface-enhanced laser desorption and ionization Surface-enhanced laser desorption ionization time-of-flight Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry Serological proteome analysis Systolic heart failure Short hairpin ribonucleic acid Smad-interacting protein 1 Small interfering ribonucleic acid Simian immunodeficiency virus Stevens–Johnson syndrome/toxic epidermal necrolysis Solute carrier family 12 member 3 Systemic lupus erythematosus Sentinel lymph node Stanford Microarray Database Substantia nigra Systematized Nomenclature of Medicine, Clinical Terms Single nucleotide polymorphism Substantia nigra pars compacta Substantia nigra pars reticulata SRY-box containing gene 10 Spondyloarthropathy

SAGE SALSA SAM SAM SAM SAP/Exo I SARS SBE SCD SCID SCN1A SCOTS SCX SD SDO SE SE SEBASTIAN

SEC SELDI SELDI-TOF SELDI-TOF-MS SERPA SHF shRNA SIP1 siRNA SIV SJS/TEN SLC12A3 SLE SLN SMD SN SNOMED CT SNP SNpc SNR SOX10 SpA

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SPARC SPECT SPMS SPR SPSM-FCS

SR-BI ss SSc SSCP SSRP SSTR2 STAT STEMI STK11 gene STN SU SVAS SVR SWOG TAM TAT TBB TCF7L2 TCR TDM TF TFAP2B TGF TGF- TH Th1 Th2 TIGR TIMP TIMP-1 TK TLE TLR TLS TM TNF

Abbreviations

Secreted protein, acidic, and rich in cysteine, osteonectin Single photon emission computed tomography Secondary progressive MS Surface plasmon resonance Solution (single)-Phase Single-Molecule Fluorescence auto- and two color crossCorrelation Spectroscopy Scavenger receptor, class BI Single-stranded Systemic sclerosis Single strand conformation polymorphism Simple sequence repeat polymorphism Somatostatin receptor type 2 Signal transducers and activators of transcription ST segment Elevation Myocardial Infarction Serine/threonine kinase 11 Subthalmic nucleus Surface (viral protein) Supravalvar aortic stenosis Sustained viral response Southwest Oncology Group Tumor associated macrophages TransActivator of Transcription (viral protein) Transcriptomic-based biomarkers Transcription factor 7-like 2 T-cell receptor Therapeutic drug monitoring Transcription factor Transcription factor activating protein 2 Transforming growth factor Transforming growth factor beta Tyrosine hydroxylase T-helper cell, type 1 T-helper cell, type 2 The Institute for Genomic Research Tissue inhibitor of metalloproteinase Tissue inhibitor of metalloproteinase-1 Tyrosine kinase Temporal lobe epilepsy Toll-like receptor Trans-lesion DNA synthesis Trans-Membrane (viral protein) Tumor necrosis factor

TNF- TNF- TNM TOF TOF TPMT/TPMT Treg TSA TSG TSP

Tumor necrosis factor-alpha Tumor necrosis factor-beta Tumor-node-metastasis stage Tetralogy of Fallot Time-of-flight Thiopurine methyl transferase/(gene) Regulatory T-cells Trichostatin A Tumor suppressor gene Thrombospondin

UADT UC UCSC UMLS UNAIDS US US USF1 USpA UTR

Upper aerodigestive tract Ulcerative colitis University of California at Santa Cruz Unified Medical Language System United Nation mission on AIDS Ultrasound United States of America Upstream transcription factor 1 Undifferentiated spondyloarthropathy Untranslated region (three prime or five prime)

V-ATPase V-CAM VEGF VF VIF VLDL VPR VPU VS VT VZV

Vacuolar adenosine triphosphatase Vascular cell adhesion molecule Vascular endothelial growth factor Ventricular fibrillation Virion infectivity factor Very low-density lipoproteins Viral protein R Viral protein U Ventricular septal defect Ventricular tachycardia Varicella zoster virus

WGA WGAS WGS WHO AIDS

Whole-genome amplification Whole Genome Association Studies Whole genome shotgun World Health Organization mission on AIDS World Health Organization West Nile virus Western-specific sequence Welcome Trust Case Control Consortium

WHO WNV WSS WTCCC XMRV

Xenotropic murine leukemia virus-related virus

ZDV ZFP

Zidovudine Zinc finger protein

Advisory Board Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043

Raju Kucherlapati Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Pasteur, Ste 250, Boston, MA 02115

Robert Cook-Deegan Center for Genome Ethics, Law & Policy, Duke Institute for Genome Sciences and Policy, Box 90141, Durham, NC 27708

Elizabeth G. Nabel National Heart, Lung and Blood Institute, 31 Center Drive MSC 2486, Building 31, Room 5A52, Bethesda, MD 20892

Kay E. Davies Department of Human Anatomy and Genetics, Oxford University, South Parks Road, Oxford, QX1 3QX, UK

Robert L. Strausberg Human Genomic Medicine, J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850

Brian Druker Department of Medicine, Oregon Health & Sciences University, 3181 S.W. Sam Jackson Park Road, Portland, Oregon 97239

Robert I. Tepper Third Rock Ventures, LLC, 29 Newbury St., Boston, MA 02116

Victor Dzau Office of the Chancellor, Duke University Health System, Box 3701, Durham, NC 27710

Janet A. Warrington External RNA Controls Consortium, Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051

Eric Green National Institutes of Health, National Human Genome Research Institute, 50 South Drive, Room 5222, Bethesda, MD 20892-8002

Ralph Weissleder Molecular Imaging Research Center, Massachusetts General Hospital, 149 13th Street, Room 5406, Charlestown MA 02129

Muin J. Khoury Office of Genomics & Disease Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333

Janet Woodcock Food and Drug Administration, 5600 Fishers Lane, Parklawn Buildling, Rm 14-71, Rockville, MD 20857

Mary-Claire King University of Washington, 1705 Northeast Pacific Street, Box 357720, Seattle, WA 98195-7720

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Contributors Michael J. Ackerman Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Matthew L. Anderson Departments of Obstetrics and Gynecology and Pathology, Baylor College of Medicine, Houston, Texas, USA. Felicita Andreotti Institute of Cardiology, Catholic University Medical School, Rome-Italy. Brian H. Annex Division of Cardiovascular Medicine and Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA. Anthony Antonellis Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. Dmitri Artemov Department of Radiology – MR Research, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. James R. Bain Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA. Peter J. Barnes National Heart and Lung Institute, Section of Airway Disease, London, SW3 6LY, UK. J.S. Barnholtz-Sloan Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. Richard C. Becker Department of Medicine – Cardiovascular Thrombosis Center, Duke University Medical Center, Durham, NC 27705, USA.

Mark Boguski Harvard Medical School, Center for Biomedical Informatics, 10 Shattuck St., Boston, MA 02115 Stefano Bonassi Unit of Molecular Epidemiology, National Cancer Research Institute, Genova, Italy. Brigitta Bondy Section Psychiatric Genetics and Neurochemistry, Psychiatric Hospital of University Munich, Munich, Germany. J. Martijn Bos Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Roger E. Breitbart Department of Cardiology, Children’s Hospital Boston, Boston, MA 02115, USA. Jerome Brody Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA. Matthew P. Brown USA.

Omics Consulting LLC, Clayton, CA,

Lars Bullinger Department of Internal Medicine III, University of Ulm, Ulm, Germany. Shawn C. Burgess Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Atul J. Butte Department of Medicine and Department of Pediatrics, Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94305, USA. David J. Carey Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. George Carlson McLaughlin Research Institute, Great Falls, MT, USA

Ivor J. Benjamin Division of Cardiology, Center for Cardiovascular Translational Biomedicine, University of Utah Health Sciences Center, Salt Lake City, UT, USA.

Juan C. Celedón Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA.

Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043

Subhashini Chandrasekharan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA.

Simon C. Body Department of Anesthesiology, Perioperative & Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Wing C. (John) Chang Department of Pathology, Center for Lymphoma and Leukemia Research, University of Nebraska Medical Center, Omaha, NE, USA. xli

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Contributors

Yan Chen Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Antonio Chiocca Department of Neurological Surgery, Dardinger Laboratory for Neuro-oncology and Neurosciences, The Ohio State University Medical Center and Comprehensive Cancer Center, Columbus, OH 43210, USA. Theresa Puifun Chow Agency for Science, Technology and Research, Singapore Tissue Network, Singapore. Wendy K. Chung Department of Pediatrics, Division of Molecular Genetics, Columbia University, New York, NY 10032, USA. Robert Cook-Deegan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA. Dolores Corella Nutrition and Genomics Laboratory, Jean Mayer–U.S. Department of Agriculture, Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA; Genetic and Molecular Epidemiology Unit, School of Medicine, University of Valencia, Valencia, Spain. Susan Cottrell Amgen, Inc., Seattle, WA 98119, USA. Nancy J. Cox Departments of Medicine and Human Genetics, University of Chicago, Chicago, IL 60637, USA. Nigel P.S. Crawford CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. A. Jamie Cuticchia Bioinformatics Group, Duke Comprehensive Cancer Center, Durham, NC 27708, USA. Dieter Deforce Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium. Mario C. Deng

Columbia University, New York, NY, USA.

Gayathri Devi Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA.

Dirk Elewaut Department of University Hospital, Ghent, Belgium.

Rheumatology, Ghent

Charles J. Epstein Department of Pediatrics and Institute for Human Genetics, University of California, San Francisco, CA 94143, USA. Ayman H. Fanous Washington VA Medical Center, Washington, DC, 20422, USA. Phillip G. Febbo Institute for Genome Sciences and Policy, Department of Medicine – Oncology, Duke University Medical Center, Durham, NC 27710, USA. Robert J. Feezor Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, FL. Yonmei Feng Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, AZ, USA. Zeno Földes-Papp ISS, National Center of Fluorescence, Champaign, Illinois 61822, USA. Hidehiko Fujinaka Institute for Clinical Research, Niigata National Hospital, Niigata 951-8585, Japan. David J. Galas Institute for Systems Biology, Seattle, WA, USA; Battelle Memorial Institute, Columbus, OH, USA. Louis, P. Garrison Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Glenn S. Gerhard Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Geoffrey S. Ginsburg Center for Genomic Medicine, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Bjorn T. Gjertsen Institute of Medicine, Hematology, Haukeland University Hospital, University of Bergen, Bergen, Norway. Michael Glass Newborn Screening Program, Washington State Department of Health, Shoreline,WA 98155, USA.

Juergen Distler Epigenomics, AG, Berlin, Germany.

David B. Goldstein Center for Population Genomics and Pharmacogenetics, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.

Harmut Dohner Department of Internal Medicine III, University of Ulm, Germany.

Erynn S. Gordon Division of Pediatric Genetics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Ayotunde O. Dokun Metabolism and Nutrition.

Endocrinology

Tucker Gosnell Massachussetts General Hospital Cancer Center, Boston, MA, USA.

Mark R. Edbrooke Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA.

Peter Grass Biomarker Development, Novartis Pharma AG, Basel, Switzerland.

Lucas B. Edelman Department of Bioengineering and Institute for Genomic Biology, University of Illinois, Urbana-Champaign.

Eric D. Green Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesa, MD 20892, USA.

Theo deVos

Epigenomics, Inc, Seattle, WA, USA.

Division

of

Contributors

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Iris Grossman Pharmacogenetics, Research and Development, GlaxoSmithKline, Research Triangle Park, NC 27709, USA.

Chunhwa Ihm Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Marta Gwinn National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.

Olga Ilkayeva Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

Carolina Haefliger

Rafael Irizarry Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.

Epigenomics, AG, Berlin, Germany.

Susanne B. Haga Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Per Hall Department of Medical Epidemiology Biostatistics, Karolinska Institute, Stockholm, Sweden.

and

Joshua M. Hare Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami FL 33136, USA. Ahmad Hariri Developmental Imaging Genetics Program, University of Pittsburgh, Pittsburgh, PA 15213, USA. James R. Heath Department of Chemistry, California Institute of Technology, Los Angels, CA, USA.

Shushant Jain Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Reta Lila Weston Institute of Neurological Studies, University College London W1T 4JF, UK; Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK. Melissa D. Johnson Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27710, USA. Philip W. Kantoff Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA, USA.

Robert A. Hegele Robarts Research Institute, Blackburn Genetics Laboratory, London, ONT, Canada.

Sekar Kathiresan Massachusetts General Hospital, Cardiovascular Disease Prevention Center, Boston, MA, 02114, USA.

Bettina Heidecker Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami, FL, USA.

Hasmeena Kathuria Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.

Shona Hislop Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA. Eric P. Hoffman Research Center for Genomic Medicine, Children’s National Medical Center, Washington, DC 20010, USA. John Holton Centre for Infectious Diseases and International Health, Windeyer Institute of Medical Sciences, Royal Free and University College London Medical School, London, W1T 4JF, UK. Leroy Hood The Institute for Systems Biology, Seattle, WA 98103-8904, USA. Andrew T. Huang Koo Foundation Sun Yat-Sen Cancer Center, Taiwan. Erich S. Huang Duke University Medical Center, Durham, NC, USA. Carlos A. Hubbard Southern Medical Group, Tallahassee, FL 32308, USA. Kent Hunter CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. Courtney Hyland Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Kensaku Kawamoto Division of Clinical Informatics, Duke University Medical Center, Durham, NC 27710, USA. Filip De Keyser Department of Rheumatology, Ghent University Hospital, Ghent, Belgium. Asif Khalid Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA. Muin Khoury National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Stephen F. Kingsmore Resources.

National Center for Genome

Michelle M. Kittleson Division of Cardiology, UCLA School of Medicine, Los Angeles, CA, USA. Beena T. Koshy Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA. Ranga Krishnan Department of Psychiatry and Behavioral Science, Duke University Medical Center, Durham, NC 27710, USA. Robert S. Krouse Southern Arizona Veterans Affairs Health Care System, Tucson, AZ, USA. Vikas Kundra Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

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Contributors

Myla Lai-Goldman Laboratory Corporation of America, Holdings, Burlington, NC, USA.

Herman Mielants Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.

Vipul Lakhani Division of Endocrinology, Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.

Michael J. Muehlbauer Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

Robert Langer Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Sean E. Lawler Department of Neurological Surgery, The Ohio State University Medical Center, Wiseman Hall Columbus, OH 43210, USA. Inyoul Lee

Institute for Systems Biology, Seattle, WA, USA.

Ulf Müller-Ladner Department of Internal Medicine and Rheumatology, Justus-Liebig University Giessen, Bad Nauheim, Germany. Mark A. Nelson Department of Pathology, University of Airzona, Tucson, AZ 85724, USA.

Charles Lee Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Monica Neri Unit of Molecular Epidemiology, National Cancer Research Institute, Genoa, Italy.

Arthur S. Lee Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Mihai Netea Department of Medicine, University Medical Center St. Radboud, Nijmegen, The Netherlands.

Stanely Leong Department of Surgery, UCSF Medical Center at Mount Zion, University of California, San Francisco, CA, USA.

L. Kristin Newby Department of Medicine – Cardiology, Duke University Medical Center, Durham, NC 27710, USA.

Ralf Lesche Epigenomics, AG, Berlin, Germany. Samuel Levy J. Craig Venter Institute, Rockville, MD 20850, USA. Edison T. Liu Agency for Science, Technology and Research, Singapore Tissue Network, Genome Institute of Singapore, Singapore. David F. Lobach Division of Endocrinology and Metabolism, Duke University Medical Center, Durham, NC 27710, USA. James B. Lorens Department of Biomedicine, University of Bergen, Bergen, Norway. Aldons J. Lusis Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA 900951679, USA. H. Kim Lyerly Duke Comprehensive Cancer Center, Duke University Medical Center, Durham, NC 27705, USA. Nageswara R. Madamanchi Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. J. Alfredo Martinez Department of Physiology and Nutrition, University of Navarra, Pamplona, Spain.

Christopher B. Newgard Duke Independence Park Facility, Duke University Medical Center, Durham, NC 27704, USA. Maggie Ng Departments of Medicine and Human Genetics, Chicago, IL 60637, USA. Paul W. Noble Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Box 3171, Durham, NC 27710, USA. Vasilis Ntziachristos Technical University of Munich, Helmholtz Center Munich, Munich, Germany. Robert L. Nussbaum Division of Medical Genetics, Department of Medicine & Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, San Francisco, CA 94143 Meghan B. O’Donoghue Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Kunle Odunsi Departments of Gynecologic Oncology and Immunology, Roswell Park Cancer Institute, Buffalo, NY, USA.

Kevin McGrath Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Kenneth Olden Laboratory of Molecular Carcinogenesis, NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC 27709-2233, USA.

John McHutchison Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA.

Steve R. Ommen Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA.

Frank Middleton Upstate Medical University, Washington VA Medical Center, Washington, DC, USA.

Jose M. Ordovas Nutrition and Genomics Laboratory, Tufts University, Boston, MA 02111-1524, USA.

Vega Masignani

Novartis Vaccines, Siena, Italy.

Contributors

Roberto Patarca E. M. Papper Lab of Clinical Immunology and Molecular Biology, University of Miami School of Medicine, Sunny Isles Beach, FL 33160, USA. Jeetendra Patel Division of Cardiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. K. Patel Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA. Carlos N. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.

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xlv

Sridhar Ramaswamy Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Hans-Georg Rammensee Interfakultares Institut fur Zellbiologie, Abt. Immunologie, D-72076 Tubingen, Germany. Scott D. Ramsey Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Rino Rappuoli Novartis Vaccines, Siena, Italy. Nader Rifai Laboratory Medicine, Children’s Hospital Boston, Boston, MA 02115, USA. Lisa Rimsza Department of Pathology, University of Arizona, Tucson, AZ 85724-5043, USA.

Michele T. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.

Yu-Hui Rogers J. Craig Venter Institute, Rockville, MD 20851, USA.

Tanja Pejovic Department of Obstetrics and Gynecology, Oregon Health Sciences University, Portland, OR 97239, USA.

Allen D. Roses Deane Drug Discovery Institute, Duke University Medical Center, One Science Drive, Suite 342, Durham, NC 27708.

Noah C. Perin Tularik, Inc., South San Francisco, CA 94080, USA. Michael Pham Division of Cardiovascular Stanford University, Stanford, CA 94305, USA.

Medicine,

Robert M. Plenge Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA. Achim Plum Epigenomics, AG, Berlin, Germany. Mihai V. Podgoreanu Department of Anesthesiology, Duke University Medical Center, Durham, NC 27710, USA. Jonathan R. Pollack University, CA, USA.

Department of Pathology, Stanford

Rebecca L. Pollex Robarts Research Institute, London, ONT, Canada. Nathan D. Price Department of Chemical and Biomolecular Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois, UrbanaChampaign, IL USA. Thomas Quertermous Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA. Francisco J. Quintana Center for Neurologic Diseases, Harvard Medical School, Boston, USA. Benjamin A. Raby Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA. Daniel J. Rader University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6160, USA.

Jeffrey S. Ross Department of Pathology and Laboratory Medicine, Albany Medical College MC-80, Albany, NY 12208, USA. Ronenn Roubenoff Biogen Idec, Inc.

Immunology

Medical

Research,

Marschall S. Runge Department of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA. Maren T. Scheuner 90407-2138, USA.

Rand Corporation, Santa Monica, CA

Matthias Schuster Epigenomics, AG, Berlin, Germany. David A. Schwartz Center for Genetics and Therapeutics, National Jewish Medical and Research Center, 1400 Jackson Street, Denver, CO 80206 Debra A. Schwinn Department University of Washington, Seattle, USA.

of

Anesthesiology,

Chia Kee Seng Centre for Molecular Epidemilogy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Nicholas A. Shackel Department of Gastroenterology, Royal Prince Alfred Hospital, Sydney, Australia. M. Frances Shannon Division of Molecular Bioscience, John Curtin School of Medical Research, Australian National University, Canberra ACT 2601 Australia. A. Dean Sherry Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas,TX 75235, USA. Jiaqi Shi Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, Arizona, USA.

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Contributors

Kevin Shianna Duke Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710, USA. Yelizaveta Shnayder Department of Otolaryngology – Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, KS, USA. Andrew B. Singleton Laboratory of Neurogenetics, Molecular Genetics Unit, NIH, National Institute on Aging, Bethesda, MD 20892, USA. T.P. Slavin Center for Human Genetics, University Hospitals Case Medical Center; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Pediatrics, University Hospitals Case Medical Center, Cleveland, Ohio, USA. Desmond J. Smith Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095-1735, USA. Rikkert L. Snoeckx Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.

Hervé Tettelin

Novartis Vaccines, Siena, Italy.

Giovana R. Thomas Department of Otolaryngology – Head & Neck Surgery, University of Miami School of Medicine, Miami, FL 33136, USA. John D. Thompson Newborn Screening Program,Washington State Department of Health, Shoreline,WA 98155, USA. Visith Thongboonkerd Medical Molecular Biology Unit, Office for Research and Development, Siriraj Hospital, Mahidol University, Bangkoknoi, Bangkok 10700, Thailand. Eric J. Topol Scripps Clinic Division of Cardiovascular Disease, La Jolla, CA 92037, USA. Jeffrey A. Towbin Pediatric Cardiology, Baylor College of Medicine, Houston, TX 77030, USA. Ad A. van Bodegraven Department of Gastroenterology, VU University Medical Centre, Amsterdam, The Netherlands. Kris Van Den Bogaert Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.

3059,

Filip Van den Bosch Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.

Avrum Spira Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.

Matte Vatta Departments of Pediatrics (Cardiology), Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA.

Colin F. Spraggs Pharmacogenetics, Durham, NC 27709, USA.

GlaxoSmithKline,

Timothy D. Veenstra SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.

Robert D. Stevens Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

David L. Veenstra Department of Pharmacy, University of Washington, Seattle, WA 98195, USA.

Ralph Snyderman Duke Durham, NC 27710, USA.

University,

DUMC

Nicolas A. Stewart SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA. Alison Stewart Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK. F. Stewart Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Robert L. Strausberg J. Craig Venter Institute, Rockville, MD 20850, USA.

Aleksandr E. Vendrov Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. Catherine M. Verfaillie Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium. Nicole M. Walley Center for Population Genomics and Pharmacogenetics, Duke University Medical Center, Durham, NC 27710, USA. Ling Wang Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.

Moshe Szyf Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec H3G 1Y6, Canada.

Mike Weale Statistical Genetics Unit, Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London SE1 9RT, UK.

Weihong Tan Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.

Michael E. Weinblatt Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Robert I. Tepper Third Rock Ventures, LLC, Boston, MA 02116, USA.

Howard L. Weiner Center for Neurologic Diseases, Harvard Medical School, Boston, MA 02115, USA.

Contributors

Scott T. Weiss Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Ralph Weissleder Center for Systems Massachusetts General Hospital, Boston, MA.

Biology,

Samuel A. Wells Department of General Surgery, Duke University Medical Center, Durham, NC 27705, USA. Brett R. Wenner Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Ilse R. Wiechers Massachusetts General/McLean Hospital, Adult Psychiatry Residency Program, Boston, MA 02114, USA. Georgia L. Wiesner School of Medicine – Department of Genetics, Case Western Reserve University, Cleveland, Ohio 44106-4955, USA. Janey L. Wiggs Department of Opthalmology, Harvard Medical School and the Massachusetts Eye and Ear Infirmary, Boston, MA 02114, USA. Cisca Wijmenga Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.

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Christopher W. Woods Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27705, USA. Paula W. Woon National Center for Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Chana Yagil Department of Nephrology and Hypertension and Laboratory for Molecular Medicine, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Yoram Yagil Department of Nephrology and Hypertension, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Tadashi Yamamoto Department of Structural Pathology, Institute of Nephrology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan. Gang Yao Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Andrew J. Yee Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA 02115, USA. Hyuntae Yoo Institute for Systems Biology, Seattle, WA, USA.

Huntington F. Willard Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.

Y. Nancy You Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.

James M. Wilson Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.

Li-Rong Yu SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.

Nelson A. Wivel Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.

Jean-François Zagury Conservatoire National des Arts et Metiers, Chaire de Bioinformatique, 292, rue Saint-Martin, 75003 Paris.

Jay Wohlgemuth Via Pharmaceuticals, San Francisco, CA, USA.

Ron Zimmern Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK.

Janet Woodcock MD 20857, USA.

Stephan Züchner University of Miami School of Medicine, Miami, FL 33136.

Food and Drug Administration, Rockville,

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PART

One

Genomic Approaches to Biology and Medicine

1

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Section

Principles of Human Genomics

1. 2. 3. 4. 5. 6.

1

Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Concepts of Population Genomics Genomic Approaches to Complex Disease Human Health and Disease: Interaction Between the Genome and the Environment Epigenomics and Its Implications for Medicine Systems Biology and the Emergence of Systems Medicine

CHAPTER

1 Organization, Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Huntington F. Willard

INTRODUCTION The genetic variation that can influence health and disease has been a central, if not widely practiced, principle of medicine for over a hundred years, since the prescient observations of the British physician Sir Archibald Garrod established the concept of “chemical individuality” over a century ago (Garrod, 1902). What has limited broad application of this principle until now has been the generally presumed rarity or special nature of clinical circumstances or conditions to which genetic variation was relevant – rare disorders such as Garrod’s alkaptonuria, inherited conditions limited to specific populations such as sickle cell anemia, or specialized situations such as the role of ABO incompatibility in blood transfusion. Now, however, with the availability of a “reference sequence” of the human genome, with emerging appreciation of the extent of genome variation among different individuals, and with a growing understanding of the role of common, not just rare, variation in disease, we are poised to begin to exploit the impact of that variation on human health on a broad scale, in the context of genomic and personalized medicine. Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 4

Variation in the human genome has long been the cornerstone of the field of human genetics (see Box 1.1), and its study led to the establishment of the medical specialty of medical genetics (Nussbaum et al., 2007). A crucial set of connections joining Mendel’s principles of heredity, Garrod’s concept of chemical individuality, the practice of medicine and the sequence of the genome came with Pauling’s discovery of the molecular basis of sickle cell anemia and the direct correspondence between an individual’s genetic make-up and the type of hemoglobins present in that individual’s red cells (Pauling et al., 1949). The general nature and frequency of gene variants in the human genome became apparent with the classic work in the 1960s on the incidence of polymorphic protein variants in populations of healthy individuals (Harris and Hopkinson, 1972; Lewontin, 1967; reviewed in Harris, 1980). Calculations based on those protein polymorphism data – now extended in a robust and comprehensive manner with the analysis of variation on a genome scale – lead to the inescapable conclusion that virtually every individual should be found to have his or her own unique constitution of gene products, the implications Copyright © 2009, Elsevier Inc. All rights reserved.

Introduction

BOX 1.1

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5

Genetics and Genomics

Throughout this and the many other chapters in this volume, the terms “genetics” and “genomics” are used repeatedly, both as nouns and in their adjectival forms. While these terms seem similar, they in fact describe quite distinct (though frequently overlapping) approaches in biology and in medicine. Having said that, there are inconsistencies in the way the terms are used, even by those who work in the field. To some, genetics is a subfield of genomics; to others, genomics is a subfield of genetics. Arguably, depending on the perspective one has in mind, both may be right! Here, we provide operational definitions to distinguish the various terms and the subfields of medicine to which they contribute. The field of genetics is the scientific study of heredity and of the genes that provide the physical, biological and conceptual bases for heredity and inheritance. To say that something – a trait, a disease, a code or information – is “genetic” refers to its basis in genes and in DNA. Heredity refers to the familial phenomenon whereby traits (including clinical traits) are transmitted from generation to generation, due to the transmission of genes from parent to child. A disease that is said to be inherited or hereditary is certainly genetic; however, not all genetic diseases are hereditary (witness cancer, which is always a genetic disease, but is only occasionally an inherited disease). Genomics is the scientific study of a genome or genomes. A genome is the complete DNA sequence, containing the entire genetic information of a gamete, an individual, a population or a species. As such, it is a subfield of genetics when describing an approach taken to study genes. The word “genome” originated as an analogy with the earlier term “chromosome,” referring to the physical entities (visible under the microscope) that carry genes from one cell to its daughter cells or from one generation to the next. “Genomics” gave birth to a series of other “-omics” that refer to the comprehensive study of the full complement of genome products – for example, proteins (hence, proteomics), transcripts (transcriptomics) or metabolites (metabolomics). The essential feature of the “omes” is that they refer to the complete collection of genes or their derivative proteins, transcripts or metabolites, not just to the study of individual entities. While formally the field of genomics refers to the study of genomes (and hence, DNA) only, it sometimes takes on the broader meaning of referring to any large-scale approach; the less specific term “genome sciences” is also sometimes used to refer to all of the “-omics” to connote global and comprehensive approaches to the study of biology and medicine. By analogy with genetics and genomics, epigenetics and epigenomics refer to the study of factors that affect gene (or, more globally, genome) function, but without an accompanying change in genes or the genome. As presented later in this chapter and others, some typical epigenetic factors involve changes in DNA methylation or modifications to chromatin that change genome structure and hence influence gene expression even in the absence of changes in the DNA sequence. The epigenome is the comprehensive set of epigenetic changes in a given individual, tissue, tumor or population.

of which provide a conceptual foundation for what today we call “personalized medicine” as a new-age rediscovery of Garrod’s “chemical individuality.” Thus, with the availability of the human genome sequence (International Human Genome Sequencing Consortium, 2001, 2004; Venter et al., 2001) and determination of

It is the paired combination of the genome and the epigenome that best characterize and determine one’s phenotype. Medical genetics is the application of genetics to medicine with a particular emphasis on inherited disease. Medical genetics is a broad and varied field, encompassing many different subfields, including clinical genetics, biochemical genetics, cytogenetics, molecular genetics, the genetics of common diseases and genetic counseling. Medical genetics is one of 24 medical specialties recognized by The American Board of Medical Specialties, the preeminent medical organization overseeing physician certification in the United States. As of 2007, there were approximately 2300 board-certified medical geneticists in the United States. Genetic medicine is a term sometimes used to refer to the application of genetic principles to the practice of medicine and thus overlaps medical genetics. However, genetic medicine is somewhat broader, as it is not limited to the specialty of Medical Genetics but is relevant to health professionals in many, if not all, specialties and subspecialties. Both medical genetics and genetic medicine approach clinical care largely through consideration of individual genes and their effects on patients and their families. By contrast, genomic medicine refers to the use of large-scale genomic information and to consideration of the full extent of an individual’s genome, proteome, transcriptome, metabolome and/or epigenome in the practice of medicine and medical decision-making. The principles and approaches of genomic medicine are relevant well beyond the traditional purview of medical genetics and include, as examples, gene expression profiling to characterize tumors or to define prognosis in cancer, genotyping variants in the set of genes involved in drug metabolism or action to determine an individual’s correct therapeutic dosage, scanning the entire genome for millions of variants that influence one’s susceptibility to disease, or analyzing multiple protein biomarkers to monitor therapy and to provide predictive information in presymptomatic individuals. Finally, personalized medicine refers to a rapidly advancing field of health care that is informed by each person’s unique clinical, genetic, genomic and environmental information. The goals of personalized medicine are to take advantage of a molecular understanding of disease to optimize preventive health care strategies and drug therapies while people are still well or at the earliest stages of disease. Because these factors are different for every person, the nature of disease, its onset, its course, and how it might respond to drug or other interventions are as individual as the people who have them. In order for personalized medicine to be used by health care providers and their patients, these findings must be translated into precision diagnostic tests and targeted therapies. Since the overarching goal is to optimize medical care and outcomes for each individual, treatments, medication types and dosages, and/or prevention strategies may differ from person to person – resulting in unprecedented customization of patient care. The principles underlying genomic and personalized medicine and their applications to the practice of clinical medicine are presented throughout the chapters that comprise this volume.

the extent of human genome variation, both within and among populations (International HapMap Consortium, 2003, 2007) and within individual genomes (Levy et al., 2007; Wheeler et al., 2008), awareness of widespread human variation can begin to be applied generally to the exploration of common human disease.

6

CHAPTER 1

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

In this chapter, the organization, variation and expression of the human genome is presented as a foundation for the many chapters to follow on human genomics, on genome technology and informatics, on approaches in translational genomics and, finally, on the principles of genomic and personalized medicine as applied to specific diseases.

THE HUMAN GENOME The typical human genome consists of approximately 3 billion (3  109) bp of DNA, divided among the 24 types of nuclear chromosomes (22 autosomes, plus the sex chromosomes, X and Y) and the much smaller mitochondrial chromosome (Tables 1.1, 1.2). The genome can be represented and evaluated in different ways, with different levels of resolution and degrees of sensitivity, depending on the clinical need (Figure 1.1). Individual chromosomes can best be studied at metaphase in dividing cells, and karyotyping of patient chromosomes has been a valuable and routine clinical laboratory procedure for decades (Trask, 2002); various staining or hybridizationbased analytical techniques have the ability to detect chromosome abnormalities ranging from an extra or missing whole chromosome (aneuploidy), to translocations or rearrangements involving just a portion of a chromosome(s), to deletions or duplications involving as little as perhaps a megabase (106 bp; Mb) of DNA. More recent technologies involving overlapping sets (called “tiling paths”) of isolated segments of the genome arrayed on microscope slides have provided vastly improved resolution and precision capable of evaluating in a rapid and comprehensive way the proper dosage (and in some cases the organization) of the corresponding DNA segments within an individual’s genome (Figure 1.1b) (see Chapter 9). The ultimate resolution, of course, comes from direct sequence analysis, and a number of new technologies have reduced the cost and improved the throughput of sequencing individual genomes, facilitating comparisons with the reference human genome sequence and enabling medical resequencing of patient samples (see later section in this chapter) to search for novel variants or mutations that might be of clinical importance (Bentley, 2006) (Figure 1.1c, d) (see Chapter 7). Genes in the Human Genome While the human genome contains a currently estimated 20,000–25,000 genes (Clamp et al., 2007; International Human Genome Sequencing Consortium, 2004), the coding segments of those genes comprise less than 2% of the genome; as represented in Figure 1.1c, most of the genome, therefore, consists of DNA that lies between genes, far from genes or in vast areas spanning several Mb that appear to contain no genes at all (“gene deserts”). A caveat for this statement is that the process of gene identification and genome annotation remains very much a work-in-progress; despite the apparent robustness of recent estimates (Clamp et al., 2007), it is conceivable that

TABLE 1.1

Characteristics of the human genomea

Length of the human genome (basepairs)

3,253,037,807

Number of known protein-coding genes

21,541

b

Average gene density (genes/Mb)

6.6

Number of non-coding RNA genes

4421

Number of SNPsb

13,022,900

a b

From Ensembl v. 48 (accessed February 2008) Mb  megabasepairs; SNP  single nucleotide polymorphism

TABLE 1.2 Chromosome

Variation among human chromosomesa Mb

Genes/ Mb

miRNA genes

1

247.25

2153

8.7

68

2

242.95

1315

5.4

60

3

199.50

1105

5.5

57

4

191.27

786

4.1

42

5

180.86

894

4.9

46

6

170.90

1109

6.5

36

7

158.82

1008

6.3

43

8

146.27

743

5.1

38

9

140.27

904

6.4

40

10

135.37

819

6.1

35

11

134.45

1368

10.2

37

12

132.35

1069

8.1

43

13

114.14

356

3.1

23

14

106.37

662

6.2

62

15

100.34

634

6.3

21

16

88.83

902

10.2

20

17

78.77

1217

15.5

40

18

76.12

289

3.8

15

19

63.81

1427

22.4

82

20

62.44

603

9.7

28

21

46.94

283

6.0

10

22

49.69

508

10.2

18

X

154.91

874

5.6

97

Y

57.77

80

1.4

3

22

–

–

Mitochondrial a

Proteincoding genes

From Ensembl v. 48

0.016

The Human Genome

(a)

(d)

1

2

3

6

7

8

13

14

15

19

20

4

9

21

22

5

10

11

12

16

17

18

X

Y

(b)

0Mb

1Mb

2Mb

3Mb

■

7

>chr1:2040588,2043588 actgcaacctccacctcctgggttcaagtgattctgctgcctcagcctcctgagtagctg ggattacaggtgcccaccaccatgcccaactattttttgtatttttagtagaggcagggt ttcaccatattgaccaggctggtatcgaattcctggcctcaagtgatctgtctgccttgg cctcccaaagtgctggg[t/a]ttacaggcatgagccactgtgcctggcctaattattct tctttccttattgttagtttgtgctattattttatcagtctttgtgctgttattatcatg cctgtaaattctacgtgtatttcagacccacaaaccaagtgttgtcttagacagtggtcc ttcagatttacccccaggttacccttctagtcttcctgcaggacggcgcttacatggaga ccagcttccttctgcctgaagtagtccctttagtattcctttcagcacagacttgtaatt aattctttttatttcttttcttttcttttttttttttttgagatggatttttgctcttgt tgcccaggctggagtgcagtggtgtgattttggctcactgcagcctccacctcccaggtt caagcgattctcctggctcagcctcctgaggagctaggattgcaggtgtgcgccaccacg cccagttgttttttgtttgtgtgggaaatgtctttggcattctttctggagggtgttctc cactctgtgtggagttctaggcaggtagggggtttcccccaacaggtttttgtgttggct tggagtgtt[t/g]gtcatttctgtggtgagggcgccttccagcctcactgccacccctg gaaggcaacatctcttttctctgactcctgttaaaagtgttttcatcacaacagcagcct tgtgaaggacagaggaatcgagaatttctcctaattgagattggtagagcttcttgaatc agggacatgatagcttttgtctcttttggaaaatatcagcccttgacttttcgttttttt ttttttttttttttttttttttgagtctcgctcttgttgcccaggctggagtgcaatggc gcgatctcgactcactgcaatctccacctccccggttcaagtgattctcctgcctcagcg tcccgagtagctgggattacaggcacttgccaccatgaccggctaattttttttgcattt ataggagagacagggtttcaccatgttgaccaggctggtctggaactcctgatcatacat ccaccttggcctcccaaagtgctgggattacaggtgtgagccaccgtgcccggccagccc ttggcttttcaaatagcatcctgttctctctcccctgggacccccacacttcacacctgt gtgtctaatgtgctcttttttctgggtttcttctgcgttggttttttcccgctttgtgct tcaatgtggatttttttctactgttatctcttatttcacccaatctactcttaaatctac cctttaaattattaatttcagtcacttcattttttacttttagaatttccatttgattct ttttttttttttttttgcccaggatggcaatggcacgctctcggctcactgcaacctccg cctcccaggttcaagcaatattcctgccccagcctcccaagcagctgggattacagggtc acactaccacgccccactaatttttatgtttttattagagacggggttttgccatgttgg ccaggctggtctcgaactcctgaccttgggtgatccg[c/t]ttgcctcagcctcccaaa gtgttgggattacaggcgtgagccactgcgcctggcatcgtagttctctcttctggggtg ggaatgtctattctgtgtccttctcacgtgcaaaatactgtcattacatcccaatggccc cagaacccttaactcctcccagtgtggcgggggcagtcttgtctgaacaaggcatggggg agcctggaggcccattcctcctgaggccaagt[t/a]cctccctggctgtgggccagcat aagcgaacaaggcgtgtacttccggaatgctatggactgagtgtgtgtctccccagaatc catatgttgaagccctaaccctccagtgtgatggtgtttggagacgaagcctttgacagg tagttagagtcatggcggtagttagttagagtcatggcggtagttagttagggtcacggt ggtagttaggatcatggtggtacttaaggtcatggcagtagttagggttatatcagtagt tagggctatggctgtagttagggtgatggtggtagttaaggtcacagcagtaattagggt catggtggtggttagggtcacagtggtagttagggtcacggtggtggttagggtcgtggt ggtggttagggtcacggtggtggttagggtcacggtggtagttagggtcacggcggtact tagggtcacggcggtggttagggtcacggcggtggttagggtcacggtggtggttagggt cacggcggtggttagggtcacggtggtggttagggtcgtggtagttaggttcatggtggt ggttagggtcgtggtggttagggtcacggtggtggttagggtcacggtggtagttagggt cacggctgtagttagcgtcatggtggtggttagggtcacggcggtggttagggtcacggt ggtggttagggtcacggcggtggttagggtcacggtggtggttagggtcgtggtagttag gttcatggtggtggttagggtcgtggtggttagggtcacggtggtagttagggtcgtggt ggttagggtcatggtggtggttagggtcacggtggtggttagggtcgtggtggttagggt cgtggtggttagggtcgtggtggttagggttgtggtggttagggtggtggtggttagggt cgtggcggtggttagggtcgtggcggtggttagggttgtggtggttagggtcacggtggt ggttagggtcacggtgg…

4Mb

5Mb

6Mb

Chromosome 6

(c)

p25.2

Figure 1.1 Four views of the human genome. (a) Karyotype of a normal male donor, HuRef, whose genome was the first individual diploid genome to be sequenced (Levy et al., 2007). The 24 types of human chromosome are shown after conventional G-banding – 22 pairs of autosomes and the two sex chromosomes, X and Y. (b) An array of genomic segments, showing 244,000 genomic elements hybridized to DNA from HuRef. (c) Schematic representation of the content of 6 Mb from the short arm of chromosome 6, including the location of various genes and other features from the HuRef genome. (d) DNA sequence from the genome of James D. Watson, showing 3000 bp from chromosome 1. Watson’s sequence is heterozygous at four positions, three (in yellow) that are known polymorphisms in various populations and one (in red) that is a novel variant. Figures in (a) and (b) were provided courtesy of Steve Scherer, Hospital for Sick Children, Toronto, Canada. (c) is part of a large poster representing the complete diploid HuRef genome (Levy et al., 2007).

there are some genes, including clinically relevant genes, that are currently undetected or that display characteristics that we do not currently recognize as being associated with genes. A maximum of 5% of the genome consists of DNA that has been quite

well conserved through evolution, one indication of an important function. These and other considerations have led to the estimate that at most 20% of the genome is of functional importance (Pheasant and Mattick, 2007). Nonetheless, the statement

8

CHAPTER 1

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

that the vast majority of the genome consists of spans of DNA that are non-genic, of no obvious function, and of uncertain clinical relevance remains true. In addition to being relatively sparse in the genome, genes are distributed quite non-randomly along the different human chromosomes. Some chromosomes are relatively gene-rich, while others are quite gene-poor, ranging from a high of 22 genes/Mb to a low of 3 genes/Mb (excluding the Y chromosome and the mitochrondrial chromosome) (Table 1.2). And even within a chromosome, genes tend to cluster in certain regions or in particular bands, a point of clear clinical significance when evaluating genome integrity, dosage or arrangement in different patient samples. Coding and Non-Coding Genes There are a number of different types of gene in the human genome. Most genes are protein-coding and are transcribed into messenger RNAs (mRNAs) that are ultimately translated into their respective proteins; their products comprise the list of enzymes, structural proteins, receptors and regulatory proteins that are found in various human tissues and cell types. However, there are additional genes whose functional product appears to be the RNA itself. These so-called non-coding RNAs (ncRNAs) have a range of functions in the cell, and some do not as yet have any identified function. But the genes whose transcripts make up the collection of ncRNAs represent about a sixth of all identified human genes (Table 1.1). Some of the types of ncRNA play largely generic roles in cellular infrastructure, including transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) involved in translation of mRNAs on ribosomes, spliceosomal RNAs involved in control of RNA splicing, and small nucleolar RNAs (snoRNAs) involved in modifying rRNAs (Griffiths-Jones, 2007; Mattick and Makunini, 2006). Other ncRNAs play roles in gene regulation, for example in epigenetic gene silencing (Ogawa and Lee, 2002). A class of small RNAs of growing importance are the microRNAs, ncRNAs of only 22 bases in length that suppress translation of target genes by binding to their respective mRNAs and thus regulate protein production from the target transcript(s) (Filipowicz et al., 2008). Some 255 microRNA genes were identified in the human genome initially (Lim et al., 2003), although the total number of such genes is now thought to be closer to 1000 (Bentwich, 2005; Griffiths-Jones, 2007) (Table 1.2). Some are evolutionarily conserved, while others appear to be of quite recent origin during primate evolution, thus underscoring the difficulty of determining the precise number and identity of human genes (Clamp et al., 2007). MicroRNAs have been shown to downregulate hundreds of mRNAs each, with different combinations of target RNAs in different tissues (Lim et al., 2005); combined, the microRNAs are thus predicted to control the activity of as many as 30% of all protein-coding genes in the genome (Filipowicz et al., 2008). While this is a fast-moving area of genome biology, several microRNAs have already been implicated in various human diseases, including cancer,

developmental disorders and heart disease (Chang and Mendell, 2007; van Rooij et al., 2008). Genome Composition and Landscape As observed earlier, the distribution of genes in the genome is non-random, both within and between chromosomes. This in part is a reflection of the distribution of different types of DNA sequence, as the genome is partitioned into domains spanning hundreds of kilobasepairs to megabases, reflecting large-scale variation in the GC content of DNA. These so-called “isochores” have been known for decades and, at a very gross level, mimic the pattern of light- and dark-staining bands that one observes in metaphase chromosomes (e.g., Figure 1.1a, c) (Eyre-Walker and Hurst, 2001). While the driving force behind the evolution of isochores is not clear, they influence the GC content of genes contained within them (and, by virtue of the genetic code, therefore, the amino acid composition of the encoded proteins), the patterns of mutation and polymorphism detected, and the nature of various families of repeated DNA that reside there. Further – and most strikingly – different isochore domains contain clusters of genes that are highly or weakly expressed in a coordinated manner in different tissues (Caron et al., 2001; Gierman et al., 2007; Hurst et al., 2004). Thus, isochores reflect both the functional as well as structural organization of the genome. (See later section on “Expression of the Human Genome” for further discussion.) Repetitive DNA Overall, only about half of the total linear length of the genome consists of so-called single-copy or unique DNA, whose sequence is represented only once or at most a few times (International Human Genome Sequencing Consortium, 2001, 2004; Venter et al., 2001). The rest of the genome consists of several classes of repetitive DNA and includes DNA whose sequence is repeated, either perfectly or with some variation, hundreds to millions of times in the genome. Several different categories of repetitive DNA are recognized. Clustered repeated sequences constitute an estimated 10–15% of the genome and consist of arrays of various short repeats organized tandemly in a head-to-tail fashion. Such arrays can stretch several Mb or more in length and constitute up to several percent of the DNA content of individual human chromosomes; a notable outlier in this respect is the male-specific Y chromosome, of which more than half consists of such repeated DNA families (Skaletsky et al., 2003). Other tandem repeat families are based on somewhat longer basic repeats. For example, the alpha-satellite family of DNA is composed of tandem arrays of different copies of an 171 bp unit, found at the centromere of each human chromosome, which is critical for proper segregation of chromosomes during cell division (Rudd and Willard, 2004; Schueler and Sullivan, 2006). Another highly significant family of repeats is found at the very ends of chromosomes, the telomeres. While the repeats at the functional telomeres consist of relatively short stretches of perfect (TTAGGG)n repeats, different subtelomeric regions (just proximal to the telomere repeats) share patterns of homology with other subtelomeres around the genome that

Variation in the Human Genome

create clinically relevant hotspots of interchromosomal recombination (Linardopoulou et al., 2005; Riethman et al., 2005). Other major types of repetitive DNA in the genome consist of related sequences that are dispersed throughout the genome rather than localized. Among the best-studied dispersed repetitive elements are short, interspersed nuclear elements (SINEs). The most prominent family of these contains repeats that are about 300 bp in length and are recognizably related to each other although not identical in DNA sequence. In total, members of this family make up at least 10% of human DNA, although they make up a much higher percentage of the DNA in some isochores. A second major dispersed, repetitive DNA family is called the LINE (where the L stands for long) family, whose members range in size up to 6 kp in length and account for about 20% of the genome. Families of repeats dispersed throughout the genome are clearly of medical importance. Both SINE and LINE sequences have been implicated as the cause of mutations in genetic disease. At least a few copies of these families generate copies of themselves that can integrate elsewhere in the genome, occasionally causing insertional inactivation of a medically important gene. The frequency of such events causing genetic disease in humans is largely unknown, but they have been suggested to account for as many as 1 in 500 mutations (Deininger et al., 2003; Kazazian and Moran, 1998). In addition, aberrant recombination events between different LINE or SINE repeats can also be a cause of mutation in some genetic diseases. Segmental Duplications An important subclass of repetitive DNA, distinct from the large families just mentioned, includes blocks of different sequences (hence, not defining a particular family of sequences) that are present in multiple copies, often with extraordinarily high sequence conservation, in many different locations around the genome. Duplications involving substantial segments of a chromosome, called segmental duplications, account for at least 5% of the genome (Bailey and Eichler, 2006). When the duplicated regions contain genes, genomic rearrangements can result in the deletion of the region (and the genes) between the copies and thus give rise to disease (Conrad and Antonarakis, 2007). In addition, rearrangements between duplicated segments are a source of significant variation between individuals in the number of copies of these DNA sequences (Sharp et al., 2005), as will be discussed in the next section.

With completion of the reference human genome sequence, attention turned to the discovery and cataloging of variation in that sequence among different individuals (including both healthy individuals and those with various diseases) and among different populations. It has been estimated that there are some 10–15 million common sequence variants that are of sufficient frequency (minor allele frequency 5%) in one or more

9

populations to be considered polymorphic in our species. In addition, there are countless very rare variants, many of which probably exist in only a single or a few individuals. In fact, given the number of individuals in our species, essentially each and every base pair in the human genome is expected to vary in someone somewhere around the globe. It is for this reason that the original genome sequence is considered a “reference” sequence, derived as a consensus of the limited number of individual genomes whose sequencing was part of the Human Genome Project, but actually identical to no individual’s genome. Types of Variation Early estimates were that any two randomly selected individuals have sequences that are 99.9% identical or, put another way, that an individual genome would be heterozygous at approximately 3–5 million positions, with different bases (i.e., a T or a G) at the maternally and paternally inherited copies of that particular sequence position. The majority of these differences involve simply a single unit in the DNA code and are referred to as single nucleotide polymorphisms (SNPs) (Table 1.1) (see Chapter 7). The remaining variation consists of insertions or deletions (in/dels) of (usually) short sequence stretches, variation in the number of copies of repeated elements or inversions in the order of sequences at a particular locus in the genome (Figure 1.2). The total amount of in/del variation is more than originally anticipated and approaches 0.5%, not 0.1%, between any two randomly selected individuals (Levy et al., 2007). Any and all of these types of variation can influence disease and thus must be accounted for in any attempt to understand the contribution of genetics to human health (Table 1.3).

A

B

C

D

Reference A

B

C

C

D

Segmental duplication – Biallelic CNV (C)2 A

B

C

C

C

D

Multiallelic copy number variant (C)0-n A

B

C

D

D

D

D

C

D

C

D

C

D

Complex CNV (D)4(CD)3 A

VARIATION IN THE HUMAN GENOME

■

C

B

D

Inversion (CB) Chromosome

Figure 1.2 Schematic representation of different types of structural polymorphism in the human genome, leading to deletions, duplications, inversions and CNV changes relative to the reference arrangement. From Estivill and Armengol (2007), with permission.

10

CHAPTER 1

TABLE 1.3

■

Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

Common variation in the human genome

Type of variation

Size range (approx.)a

Effect(s) in biology and medicine

Single nucleotide polymorphisms (SNPs)

1 bp

Non-synonymous → functional change in encoded protein? Others → potential regulatory variants? Most → no effect? (“neutral”)

Copy number variants (CNVs)

10 kb to 1 Mb

Insertion/deletion polymorphisms (in/dels)

1 bp to 1 Mb

Inversions

Few bp to 100 kb

Segmental duplications

10 kb to 1 Mb

a

Gene dosage variation → functional consequences? Most → no effect or uncertain effect

In coding sequence: frameshift mutation? → functional change Most → uncertain effects ? break in gene sequence ? long-range effect on gene expression ? indirect effects on reproductive fitness Most → no effect? (“neutral”)

Hotspots for recombination → polymorphism (CNVs)

Abbreviations: bp  basepair; kb  kilobasepair; Mb  megabasepair

While the overall estimate of SNP heterozygosity is approximately 1 in 1500 bp, there is much more variation in non-coding sequences than in the coding segments of genes, reflecting strong selective pressure during evolution against certain types of change in gene sequences. The combination of particular alleles along chromosomes is also non-random, with particular combinations (haplotypes) being more prevalent over short distances, due to the relative inefficiency of meiotic recombination to separate alleles at sites that are physically close together (International HapMap Consortium, 2007; Nussbaum et al., 2007). The resulting patterns of linkage disequilibrium are relevant for designing strategies to examine genetic variation genome-wide, both as a practical matter (i.e., reducing the number of SNPs that need to be tested to reveal the underlying patterns of variation) and for evaluating the potential functional importance of any particular SNP allele (see Chapters 2, 8 and 27). Copy Number Variation Over the past few years, a number of important studies have identified a previously unanticipated prevalence of structural variants in the genome, which collectively account for more variation in genome sequence than do SNPs (e.g., Levy et al., 2007; Redon et al., 2006; Sebat et al., 2004; Tuzun et al., 2005). The most common type of structural variation involves changes in the local copy number of sequences (including genes) in the genome, and these are generally referred to as copy number variants (CNVs) (Figure 1.2) (see Chapter 9). A number of different technology platforms are now being used to detect CNVs, including arrays and direct genome sequencing (Korbel et al., 2007; Levy et al., 2007; Wong et al., 2007). As many such CNVs encompass genes (including microRNA genes; Wong et al., 2007) and as a significant number of

novel CNVs are uncovered with every new population studied, a dedicated effort is underway to cataloged CNVs in the human genome worldwide and to associate these with clinical phenotype (Feuk et al., 2006; Scherer et al., 2007; Sharp et al., 2006). While most variation of this type is inherited, some CNVs occur de novo or even in somatic cells; in these cases, an individual will have different repeat lengths than do either of his or her parents. Array-based methods (see Chapter 9) have rapidly gained acceptance for evaluating the association of both inherited and de novo CNVs with mental retardation and other developmental disorders (de Vries et al., 2005; Friedman et al., 2006; Lee et al., 2007;Weiss et al., 2008). It is of considerable ongoing interest to evaluate the role of CNVs and other structural variants including deletions (Conrad et al., 2006) and inversions (Korbel et al., 2007; Stefansson et al., 2005; Tuzun et al., 2005) in the etiology of more common, complex diseases or traits of adulthood, including neurological and psychiatric conditions as well as pharmacogenetic traits (Beckmann et al., 2007; Buckland, 2003). Variation in a Single Genome The most extensive current inventory of the amount and type of variation to be expected in any given genome comes from the direct analysis of the diploid genome sequence of a single male individual, HuRef (Levy et al., 2007). Over 4 million variants were described, spanning some 12.3 Mb of DNA. About 20 Mb of “new” sequence was determined that was not previously available as part of the human reference sequence, reflecting in part the still unfinished nature of the human genome sequence and in part the particular patterns of inserted or deleted sequences that distinguish different genomes. Several hundred thousand in/dels were also found in this single genome. In addition, several hundred CNVs were detected, which overlapped at

Expression of the Human Genome

least 95 well-annotated genes. While most of these variants are identical to those found in other individuals in the population, others are likely to be what are termed “private” mutations, specific to HuRef and his family. In the HuRef genome, at least 850 genes known to be involved in inherited disease contained at least one heterozygous variant, and over 300 of them contained at least one nonsynonymous SNP (i.e., a SNP that, by virtue of the genetic code, is predicted to change the encoded amino acid). Of course, additional genes may also impact disease, and, overall, more than 4000 genes in the HuRef genome contained one or more non-synonymous SNP. Thus, at least 17% and perhaps as many as 44% of the genes in the HuRef genome were heterozygous and could encode proteins that differ in their amino acid sequence and/or are produced in different amounts (Levy et al., 2007). These estimates underscore the impact of gene and genome variation on human biology and on medicine. They also provide remarkable validation of the original estimates of Harris and Lewontin decades ago of the proportion of genes that are heterozygous in any given individual (Harris, 1980; Lewontin, 1967). Table 1.3 and Figure 1.2 capture the general types of and characteristics of the most common variation in the human genome and in human genes. However, it is clear that we are still in a mode of discovery, as relatively few genomes or populations have been assessed to date; no doubt millions of additional SNPs remain to be uncovered, as well as many additional in/ dels, inversions and CNVs, a portion of which will be expected to involve genes and other sequences of direct relevance to medicine. The issue of “what is normal?” – an essential concept in clinical medicine – remains very much an open question when it comes to the human genome (Shianna and Willard, 2006). Variation in Populations Most of the heterozygosity in the human genome is believed to be due to variants with a minor allele frequency of at least 1%. Taking advantage of major technological developments that have greatly increased the throughput of genotyping on a genome-wide scale, several large-scale projects have validated these estimates by gathering genotypic information on millions of SNPs worldwide (Hinds et al., 2005; International HapMap Consortium, 2003, 2007). Most of the studies to date, however, have been restricted to a small number of populations of Northern European, African and Asian origin used for SNP detection. From these and a large number of earlier studies that examined more populations but for many fewer variants, it has been concluded that some 85–90% of the variation found in our species is shared among different population groups; a relative minority of variants, therefore, are specific to or highly enriched/depleted in genomes from a particular population. It is possible to use population-specific variants to obtain information on the geographic origin of a genome or of particular segments within a genome. Given the many millions of SNPs now available, there are at least hundreds of thousands of

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11

SNPs that are informative for such studies (so-called “ancestry informative markers,” AIMs) (Kittles and Weiss, 2003; Paschou et al., 2007; Tian et al., 2008). This had led to two related, but distinct applications of such markers. First is the use of admixture mapping, tracing the location of particular SNPs associated with disease in populations of patients whose genomes are a mixture from at least two original populations, for example, African-Americans or Latinos (Price et al., 2007; Smith et al., 2004). Such an approach has already been used to map genes associated with several phenotypes whose frequency differs markedly between different population groups, including prostate cancer (Freedman et al., 2006), hypertension (Deo et al, 2007), skin pigmentation (McEvoy et al., 2006) and white blood cell count (Nalls et al., 2008). The second use of AIMs is for ancestry testing unrelated to disease studies (Kittles and Weiss, 2003; Shriver and Kittles, 2004). While the motivations behind such testing and the potential uses (and, some fear, abuses) of biogeographic information are varied, the commercial availability and interpretation of genetic ancestry testing is controversial (Bolnick et al., 2007). Nonetheless, the availability of such information as an intentional or unwitting by-product of wide-scale genome analysis is inevitable, and both consumers/patients and health professionals need to be aware of this as genetic variation is explored in the context of individual genomes (see Chapter 33).

EXPRESSION OF THE HUMAN GENOME A key question in exploring the origins, structure and function of the human genome is to understand how proper expression of our 20,000–25,000 genes is determined, how it can be influenced by either genetic variation or by environmental exposures or inputs, and by what mechanisms such alterations in gene expression can lead to pathology evident in the practice of clinical medicine. The control of gene activity – in development, in different tissues, during the cell cycle, and during the lifetime of an individual both in sickness and in health – is determined by a complex interplay of genetic and epigenetic features. By “genetic” features, we here refer to those found in the genome sequence (see Box 1.1), which plays a role, of course, in determining the identity of each gene, its particular form (alleles), its level of expression (regulatory elements such as promoters, enhances, splice sites, etc.), and its particular genomic landscape (domains, isochores). By “epigenetic” features, here we mean packaging of the DNA into chromatin, in which it is complexed with a variety of histones as well as innumerable non-histone proteins that influence the accessibility and activity of genes and other genomic sequences. The structure of chromatin – unlike the genome sequence itself – is highly dynamic and underlies the control of gene expression that shapes in a profound way both cellular and organismal function (Felsenfeld and Groudine, 2003).

12

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

Genomic and Epigenomic Aspects of Gene Expression Identifying the genomic sequences and features that direct spatial and temporal aspects of gene expression remains a formidable challenge in genome annotation. While several decades of work in molecular biology have defined critical regulatory elements such as promoters and enhancers for many individual genes, it is a tall order to perform such studies on a genomewide scale. At the genetic level, such studies have depended on identifying highly conserved elements, whose demonstrated sequence conservation over, in some cases, hundreds of millions of years provides prima facie evidence of their importance (Bejerano et al., 2004; Margulies et al., 2003) (see Chapter 10). Several such ultraconserved elements have been validated in vitro as gene enhancers, providing some confidence that others identified in this way will also play a role in regulating gene expression (Pennacchio et al., 2006). However, by definition, such approaches will overlook regulatory elements that are not well conserved at the sequence level and/or that are newly evolved on the human lineage. A complementary epigenetic approach has been to explore the characteristics of chromatin that are associated with active or repressed genes as a step towards identifying the transcriptional regulatory code for the human genome (Barrera and Ren, 2006; Bernstein et al., 2007). Such studies, largely employing the method of chromatin immunoprecipitation followed by array or sequence analysis (Hawkins and Ren, 2006), have uncovered predictive chromatin “signatures” for promoters and enhancers in the human genome (Heintzman et al., 2007; Kim et al., 2005). These analyses are part of a broad effort to explore epigenetic patterns in chromatin genome-wide to better understand control of gene expression in different tissues or disease states (ENCODE Project Consortium, 2007; Brena et al., 2006). Increasing evidence points to a role for epigenetic changes in human disease in response to environmental influences (Feinberg, 2007). The dynamic nature of epigenetic regulation – changes in DNA methylation or in histone modification over time, between different tissues, or in various disease states – allows for what has been called ”phenotypic plasticity,” relevant both to the origins and potential treatment of disease (Feinberg, 2007) (see Chapters 5 and 11). That monozygotic twins (who share identical genomes, but are frequently discordant with respect to clinical phenotypes) show epigenetic differences in DNA methylation demonstrates the potential for epigenetic effects to extend or modify information contained in the genome and thus to underlie at least some phenotypic differences (Fraga et al., 2005).

Genetic Control of Gene Expression Levels It has been appreciated for decades that there is high variability in gene expression levels among individuals. A number of groups have begun to examine this variation as a complex quantitative trait, under genetic control (Cheung and Spielman, 2002). Several thousand loci exhibiting such variation have now been studied, and the factor(s) controlling the variation mapped around the genome (Cheung et al., 2005; Morley et al., 2004; Stranger et al., 2007a, b). A significant proportion of such effects map to the genes themselves, a result consistent with local sequence variation influencing the expression of such genes; many such local regulatory variants map to the promoter region of the gene or to the 3 untranslated region (Cheung et al., 2005). Most of the mapped variation could be accounted for by SNP variation, but nearly 20% was due to CNVs (Stranger et al., 2007a, b), underscoring the functional importance of this type of genome variation. It is likely (though unproven in specific instances) that the discovered regulatory variants will correlate with the patterns of epigenetic modifications described earlier (Heintzman et al., 2007; Kim et al., 2005) and with the finding of widespread allelic imbalance around the genome (Serre et al., 2008). Other determinants of gene expression variation map not to the variable locus itself, but to another locus elsewhere in the genome. This implies a network of regulatory interactions among different gene products in the cell, a finding that lends itself to a systems biology approach to begin to better define such networks (see Chapter 6). Some of the differences in gene expression also vary among populations. Where examined, the phenotypes are attributable to common genetic variants that are more common in particular populations than in others (Spielman et al., 2007; Stranger et al., 2007a, b). These findings, therefore, may be relevant to complex genetic diseases whose prevalence differs among populations.

Allelic Imbalance Other than well-known examples of monoallelic gene expression where only one of the two copies of a gene in a diploid cell is expressed (such as genomic imprinting and X chromosome inactivation; Nussbaum et al., 2007), it is commonly assumed that the vast majority of autosomal genes in the genome are expressed

The Genome in Three-Dimensional Space In contrast to the impression one gets when viewing the genome as a linear string of sequence (e.g., Figure 1.1c,d), the genome adopts a highly ordered arrangement within the three-dimensional space of the nucleus (Figure 1.3) (Bolzer et al., 2005; Lanctot et al., 2007). This three-dimensional structure is highly predictive of the transcriptome map (Goetze et al., 2007) and

from both homologs at comparable levels. Recent evidence, however, using expressed SNPs to distinguish transcripts from the two alleles, has demonstrated widespread differential allelic expression for up to 20% of genes in the genome (Serre et al., 2008). This phenomenon, whose epigenetic or genetic basis is unknown, may be related to similar variability observed among X-linked genes in heterozygous females (Carrel and Willard, 2005). Further complexity is suggested by the finding of monoallelic gene expression, random with respect to parental origin, for approximately 5% of genes tested (Gimelbrant et al., 2007). This is an area of ongoing study and further implicates interactions between the genome and epigenetic control mechanisms as a determinant of gene expression in the human genome.

Genes, Genomes and Disease

(a)

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13

(b)

1

2

6

13

19

3

7

14

20

8

4

9

10

15

5

11

16

21

22

12

17

18

X

Y

Figure 1.3 Organization of the human genome in metaphase and interphase. (a) Individual metaphase chromosomes from a normal male donor, HuRef. Chromosomes are colored differently after hybridization to chromosome-specific DNA. From Levy et al. (2007), with permission. (b) Arrangement of chromosomes in an interphase nucleus. Each chromosome maintains its own territory, with minimal mixing. From Bolzer et al. (2005), with permission.

reflects the megabase-sized domains evident from examining coordinated patterns of gene expression at the chromosome level (Gierman et al., 2007). The biophysical and/or genomic properties that facilitate or specify the orderly and dynamic packaging of each chromosome during each cell cycle without reducing the genome to a tangled mess within the nucleus remain unknown and are the subject of much investigation in genome biology. Overall, then, one should view the gene expression phenotype as the sum of several different (but interrelated) effects, including gene sequence, regulatory sequences and their epigenetic packaging, organization of the genome into domains and isochores, programmed interactions between different parts of the genome, and three-dimensional packaging in the nucleus (Fraser and Bickmore, 2007). All must coordinate in an efficient and hierarchical fashion, and disruption of any one – due either to genetic change or to disease-related processes – would be expected to alter the overall cellular phenotype.

GENES, GENOMES AND DISEASE In the context of genomic and personalized medicine, a key question is to what extent variation in the sequence and/or expression of one’s genome influences the likelihood of disease onset, determines or signals the natural history of disease, and/or provides clues relevant to the management of disease. Variation in one’s constitutional genome can have a number of different direct or indirect effects on gene expression (Table 1.3), thus contributing to the likelihood of disease. It is not, however, just the human genome whose variation is relevant to an individual’s state of health; there are thousands of microorganisms,

both symbiotic and pathogenic, whose genomes are also relevant to human phenotypes, and sequence determination of their genomes is providing new insights and approaches for the diagnosis, study and treatment of infectious disease (see Box 1.2). Contemporary frameworks for considering the impact of variation in the human genome on disease build on decades of success in establishing the role of individual, typically rare, mutations as causal determinants of now more than 2000 simple Mendelian diseases (Online Mendelian Inheritance in Man, 2008). As a result of that success, much of which paralleled the development of technologies in the early stages of the Human Genome Project (Peltonen and McKusick, 2001), attention has now turned to the genes presumed to underlie susceptibility to common complex diseases, which are the subject of many of the subsequent chapters in this book. There are two distinct, but non-exclusive models for thinking about human genetic variation and disease (Altshuler and Clark, 2005; Fearnhead et al., 2005; Florez et al., 2003; Pritchard, 2001). One – the “common allele, common disease” hypothesis – posits that variation common in the population accounts for the relatively higher or lower risk that some individuals (and their families) have for a particular condition. Under this model, the collection of 10–15 million common SNPs, CNVs and other variants in the genome underlies the range of susceptibility that one finds in the general population, modulated by the particular and often variable environmental inputs and factors that are present in that population and that may, in fact, shift or even obscure the relative impact of inherited factors. An alternative model – the rare variant hypothesis – argues that genetic susceptibility to disease is due to the accumulated risk conferred by multiple rare variants in an individual’s genome,

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

BOX 1.2 The Genomes Within The human genome is not the only genome relevant to the practice of medicine. Both in states of health and disease, our own genome is vastly outnumbered by the genomes of a host of microorganisms, many living peacefully and continuously on various body surfaces, especially throughout the gastrointestinal tract, others wreaking havoc as adventitious viral, bacterial or fungal pathogens. The genomes of thousands of microorganisms have been determined and are being utilized to provide rapid diagnostic tests in clinical settings, to predict antibiotic or antifungal efficacy, to identify the source of airborne, water or soil contaminants, to monitor hospital or community environments, and to better understand the contribution of microbial ecosystems and various environmental exposures to diverse human phenotypes. The human colon contains more than 400 bacterial species comprising some 1013 to 1014 microorganisms. Each adult’s gut provides a unique environment – the microbiome – whose origins and impact on human disease are just being explored. The microbiotic gene set is significantly different from that of the human genome and thus has the capacity to alter the metabolic profile of different individuals or different populations, with clinically meaningful effects on drug metabolism, toxicity and efficacy (Gill et al., 2006; Li et al., 2008; Palmer et al., 2007). The applications to microbiomes of approaches in genomics (as well as proteomics and transcriptomics)

variants therefore not likely to be captured by study of the common variants identified by studies to date. These two hypotheses suggest different approaches that will likely be informative for delineating the genetic contribution to disease, both for designing research studies and for eventual clinical surveillance. It is worth emphasizing that these two hypotheses are not mutually exclusive and are each likely to be correct in some cases; indeed, there is evidence supporting each for different diseases. Genome-wide Association Studies The “common allele, common disease” hypothesis has been explored with notable success in a number of conditions, utilizing large cohorts of well-phenotyped patients and high throughput methods to genotype up to 500,000 or a million SNPs in the genome (Topol et al., 2007) (see Chapter 8). These genome-wide association studies report the statistical association of one or more variants in a narrow genomic region (which may or may not contain an annotated gene) with the presence or absence of the clinical condition. The reported SNPs define immediately accessible risk factors for that condition, at least in the population(s) under study, and can provide novel insights into the biology of the disease. It should be stressed, however, that in most instances, causality of the reported SNP(s) and the increased risk has not been proved; it may be that the actual causal variant is not the SNP itself, but is a currently undetected variant that lies in linkage disequilibrium with the SNP. In the most favorable cases, the associated SNP may be a non-synonymous variant, leading to a pathological amino acid change in the relevant gene (e.g., Thorleifsson et al., 2007), or

are revolutionizing clinical diagnostics, for example to identify unknown viral infections (Delwart, 2007; Long et al., 2004; Wang et al., 2003) or to diagnose antibiotic-resistance infections such as methicillin-resistant Staphylococcus aureus (MRSA) (Francois et al., 2007). The field of metagenomics explores this heterogeneous ecosystem by comprehensive sequence analysis of the collected genomes from biological specimens (such as stool, urine, sputum, water sources and air), followed by both taxonomic and bioinformatic analysis to deconvolute the many genomes contained in such specimens and to define the different organisms, their genes and genome variants. This approach is particularly informative for characterizing organisms that cannot be cultured in standard microbiology labs. A number of diseases have been associated with large-scale imbalances in the gut microbiome, including Crohn’s disease, ulcerative colitis, antibioticresistant diarrhea and obesity (Frank and Pace, 2008; Ley et al., 2006; Turnbaugh et al., 2006). Undoubtedly, the states of health and disease are determined in part by the balance of genomes both within us and external to us. The full complement of genomic information from both of these sources of genomes will provide insights into defining the states of health and disease and the basis for unsurpassed precision in both the prevention of disease and its treatment.

may be a variant in an RNA splice site, leading to a clinically meaningful change in the production of the gene’s transcript(s) (e.g., Heinzen et al., 2007). But in most instances, the functional impact of the associated SNP is obscure, notwithstanding very clear genetic evidence of a role of genome variation in susceptibility to the particular condition. As emphasized throughout this chapter, SNPs are but one of several types of genome variation that can influence gene expression and/or disease (Table 1.3). CNVs have also been associated with some disorders (reviewed in Estivill and Armengol, 2007), and it will require integrated genomic, genetic and functional studies to elucidate the precise basis for the role(s) of genome variation in different diseases. Medical Resequencing in Search of Rare Variants While genome-wide association studies certainly establish that common alleles do indeed underlie susceptibility to common disease in some instances, they do not allow one to conclude that this is always the case. Indeed, in most (but not all) cases to date, the common SNPs found to be associated with disease only explain a small fraction of the total genetic variation, implicating an as yet undiscovered (and presumably rarer) basis for most genetic variation underlying a given condition (Iles, 2008). In other words, while a positive statistical association is sufficient to conclude that a particular variant does indeed contribute to disease in the population under study, such a finding is insufficient to say anything declarative about the cause of or likelihood of disease in a particular case. This conclusion is, of course, highly relevant to the prospects of genomic and personalized medicine.

From Genome to Personalized Medicine

An alternative or complementary approach to genomewide genotyping of common variants is to resequence specific genes in a cohort of affected individuals, in an effort to uncover rare variants responsible for (or at least statistically associated with) the disease in question. To date, most efforts have focused on one or several genes that were believed to be strong candidates for the phenotype under study; the notable exception thus far is the whole-genome resequencing of the HuRef genome and correlation of novel variants detected in that study with his family and personal medical histories (Levy et al., 2007). Rare variants, including non-synonymous variants, in relevant candidate genes have been detected at a statistically significant higher frequency in the genomes of patients with colorectal adenomas (Fearnhead et al., 2004), with low plasma high-density lipoprotein cholesterol (HDL-C) (Cohen et al., 2004), and with triglyceride levels in the lowest quartile (Romeo et al., 2007). These successes point to a strategy of resequencing relevant genes in individuals at the extremes of the population distribution for measurable traits (Topol and Frazer, 2007), in which the only limiting parameters are the cost of sequencing and the quality of the phenotypic or quantitative data. In a proof-of-principle for this case-control approach, a recent study sequenced coding exons and splice junctions of 58 genes in nearly 400 obese and lean individuals, at the 95th or 10th percentile of body mass index (Ahituv et al., 2007). Of the 1000 variants detected, most were rare variants, including over 270 non-synonymous mutations, many of which were found only in the obese cohort and thus become strong functional candidates for a role in obesity. Searching for Somatic Mutations While genome-wide association studies are restricted to inherited variation, medical resequencing studies can target either inherited or somatic variants. In cancer especially, it is of interest to use medical resequencing to search for somatic mutations in tumor tissue in order to identify genes potentially relevant to cancer progression (Greenman et al., 2007; Sjoblom et al., 2006). Two important points emerge from such studies. First, the genes implicated by virtue of discovering rare somatic variants in multiple cases of a particular cancer tend to be different from those identified in previous genetic studies as inherited risk factors. This provides novel insights into the biology of human cancer and suggests candidates for further exploring mechanisms of tumorigenesis or metastasis or for developing therapeutic approaches. Second, however, the large number of mutations uncovered by this approach introduces the need for caution, as many will be “bystander” or “passenger” mutations around the genome that reflect only the increased mutation rate associated with cancer, not genes involved directly in cancer.

FROM GENOME TO PERSONALIZED MEDICINE Of all the promises of the current scientific and social revolution stemming from advances in our understanding of the human

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genome and its variation, genomic and personalized medicine may be the most eagerly awaited. The prospect of examining an individual’s entire genome (or at least a significant fraction of it) in order to make individualized risk predictions and treatment decisions is an attractive, albeit challenging, one (Bentley, 2004; Willard et al., 2005). Having access to the reference human genome sequence has been transformational for the fields of human genetics and genome biology, but by itself is an insufficient prerequisite for genomic medicine. As discussed in subsequent chapters, equally important are the various complementary technologies to reliably capture information on individual genomes, their epigenetic modification, and their derivatives the transcriptome, proteome and metabolome for health and disease status (Table 1.4). Each of these technologies provides information that, in combination with clinical data and evaluation of environmental triggers, will in time contribute to assessment of individual risks and guide clinical management and decision-making (Figure 1.4). Critical enablers of this new approach have been innovations in laboratory technology (to address biologically and medically relevant questions on a scale and with a throughput hardly imaginable just a few decades ago), paired with equally transforming developments in informatics and information systems to handle the onslaught of genomic data (West et al., 2006) (see Chapter 17). Personal Genomics At the heart of the genomic approach to personalized medicine will be information from individual genomes, a fast-moving area of technological development that is spawning a social and information revolution among consumers. Dramatic improvements in sequencing technology (Bentley, 2006) have reduced the cost and time of resequencing projects to a level that invites conjecture about the long awaited “$1000 genome” (Dalton, 2006; Service, 2006; Wolinsky, 2007). The much-ballyhooed release of the genome sequences of Craig Venter (“HuRef ”) (Levy et al., 2007) and James Watson has stirred up concerns about “celebrity genomics” (Check, 2007); however, while the Venter and Watson sequences may have been the first, they will not be alone for long, as numerous additional genomes are already in various sequencing pipelines (Table 1.5). What remains unsettled for now is what degree of genome surveillance will be most useful, either for research or for clinical practice, a topic that is returned to frequently in subsequent chapters in this volume. While whole-genome sequencing is increasingly possible, it is unlikely to provide more information about established disease associations than would highdensity, genome-wide SNP genotyping. Targeted resequencing of, for example, exons and known regulatory regions would allow detection of rare variants in portions of the genome most relevant to disease at a fraction of the cost of whole-genome sequencing (Hodges et al., 2007). The availability of associated clinical data is variable among the studies announced to date, but there exists, at least among some participants, a strong sense of “health-information altruism” to contribute to the much needed large-scale correlation

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TABLE 1.4

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

Personalized signatures of health or disease

Dataset

“omic” approach

Technology platform or approach

Chapter number(s)

Human genome sequence

Genomics

Single nucleotide polymorphisms (SNPs); Other genome variants

Chapters 7–9

Gene expression profiles

Transcriptomics

Microarrays of 20,000 gene transcripts

Chapters 12, 13

Protein abundance

Proteomics

Protein arrays of specific protein products

Chapter 14

Metabolites

Metabolomics

Analysis of hundreds to thousands of metabolites

Chapter 15

Chromatin

Epigenomics

Array- or sequence-based assessment of chromatin modification

Chapters 5, 11

Gene networks, interactions

Systems biology

Large-scale interactions among genes or proteins

Chapter 6

Microbiome

Metagenomics

Analysis of viral, fungal and bacterial populations in human specimens

Chapter 48

Genomic and clinical data

Informatics

Integrated databases of “omic” data and electronic health records

Chapters 17–21

Individual A

Individual B

• Predisposition to disease

Genome sequence Somatic mutation Epigenetic changes Personal environment

Transcriptome

Proteome

• • • •

Diagnosis Natural history Treatment options Prognosis

Phenotype

Figure 1.4

The promise of personal genomics in the era of genomic and personalized medicine.

of genotype and phenotype (Kohane and Altman, 2005). Notwithstanding individual’s willingness to make genome sequence data (much less medical information) available moreor-less publicly, substantial concerns have been raised about privacy, since a surprisingly (to some) small number of SNPs or other genome variants are sufficient to allow identification of individuals (Lin et al., 2004; McGuire and Gibbs, 2006).

A Consumer Revolution While the genome revolution has without doubt been driven by technological improvements and by an explosion in the availability of genome data, the push for incorporation of genome information into clinical practice may come as much or more from consumers as from professionals. A half dozen or more companies are already offering genome-wide SNP profiles to

From Genome to Personalized Medicine

TABLE 1.5

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17

Individual sequenced human genomes (as of March 2008)

Individuals

Nature of genome

Method

Year completed/ anticipated

References

“RP11”a

Partial haploid

Sanger sequencing

2003

International Human Genome Sequencing Consortium, (2001); Osoegawa et al. (2001)

J. Craig Venter

Complete diploid

Sanger sequencing

2007

Levy et al. (2007)

James D. Watson

Complete diploid

454 pyrosequencing

2007

Wheeler et al. (2008)

YanHuang researcher

Complete diploid

Illumina sequencing by synthesis

2007

Qiu and Hayden (2008); Xin (2007)

YanHuang paying customer

Complete diploid

Illumina sequencing by synthesis

2008

Qiu and Hayden (2008)

Yoruba HapMap sample

Complete diploid

Illumina sequencing by synthesis

2008

Knome customers

Complete diploid

Various next-generation technologies

2008 and beyond

Personal Genome Project; 10 individuals

“Exomes” (coding exons only)

Polony sequencing

2008

Venter institute; 10–50 genomes

Complete diploid

Various next-generation technologies

2008

ClinSeq 1000

Exons of 200–400 cardiovascular genes

To be determined

2009 or beyond

http://www.genome. gov/25521304

YanHuang 99

Complete diploid

Various next-generation technologies

2010

Xin (2007)

GATC 100

Complete diploid

Various next-generation technologies

2010

http://www.gatc-biotech.com/ en/discover_gatc/research/ Human_Genome_Sequencing_ Service.php

1000 Genomes Project

Variousb

Various next-generation technologies

2010

www.1000genomes.org; http:// www.genome.gov/26524516; Hayden (2008); Kaiser (2008)

Personal Genome – Project; 500–100,000

Exomes

Polony sequencing

?

http://www. personalgeneomes.org

http://www.personalgenomes. org; Church (2006)

Source: Courtesy of Misha Angrist, Duke Institute for Genome Sciences & Policy a An anonymous male from Buffalo, New York, whose genome was overrepresented in the publicly funded Human Genome Project b 6 people fully sequenced at 20x coverage; 180 at 2x; and exons of 1000 genes in 1000 people

the public, some with associated risk estimates for relevant clinical conditions (see Chapter 21). At least a few companies will also sequence individual genomes for a cost that, while high currently, is not out of reach for some individuals. Some in the medical community have called such genome scans premature and ill advised (Hunter et al., 2008), and similar concerns have been raised for more traditional and less comprehensive commercial tests targeted to specific genes or to specific conditions (Janssens et al., 2008). Hesitation on the part of health

professionals aside, however, it seems clear that at least some consumers, whatever their motivation, will choose to test their genomes, regardless of whether any of the findings are (yet) considered actionable by their physicians. This creates both the need and an opportunity for health professionals to stay abreast of the rapidly developing scientific foundations of genomic medicine, to place in context the dynamic and still poorly understood interplays of the genome with the environment and to communicate effectively the complexities of overall genetic risk to their patients.

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

CONCLUSION In this chapter, we have described the organization, variation and expression of the human genome as a foundation for the many chapters to follow on principles of human genomics, on new technologies in genomics and informatics, on approaches in translational genomics and, finally, on the first applications of genomic and personalized medicine to specific diseases. What is now referred to as the “Genome Revolution” has its roots in a technological revolution over the past two decades that has transformed biology and human genetics as they transition from discovery-based analog sciences to digital systems based on comprehensive information from the genome and its derivatives. Advances in genome technology and the resulting

explosion in knowledge and information stemming from the Human Genome Project are now playing a major and increasingly transformational role in our understanding of human health and in the delivery of health care. While the challenges both for the health care profession and for society are significant, the development of comprehensive, cost-efficient and high-throughput technologies, combined with powerful tools in biomedical and clinical informatics for analyzing and storing vast amounts of data, will enable the growing field of genomic and personalized medicine. As the chapters to follow illustrate, the genome sciences – encompassing large-scale analyses of individual genomes, epigenomes, transcriptomes, proteomes and metabolomes – will drive profound changes in clinical medicine.

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CHAPTER 1

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Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine

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2 Concepts of Population Genomics Mike E. Weale and David B. Goldstein

INTRODUCTION Over the past decade, population genetics – the study of processes affecting the evolution, distribution and correlation of genetic variants within populations – has truly “gone genomic” (Goldstein and Weale 2001; Jorde et al., 2001). The Human Genome Project provided a blueprint for the “average” human genome. Subsequent projects, and in particular the arrival of affordable high-density genome-wide genotyping, has expanded this picture to provide detailed information on the genomes of many thousands of individuals from different parts of the world, focusing on the genetic differences that distinguish us all. Population genetics has thus been transformed into population genomics. This revolution has resulted in fundamental advances in our understanding of the evolution of the human genome and the history of human populations. But this information is not just of academic interest: it has had a direct bearing on how medical genomic research is conducted today. Indeed, this has been the primary motivation for collecting much of this information in the first place. As medical genomics has moved from the description of simple Mendelian traits to the investigation of complex multifactorial traits, so population genomics has assumed pivotal importance. We need population genomic theory in order to know how to look for variants that affect these traits, how to understand their genealogical history and their geographic distribution, and how to use this information effectively in diagnosis Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 22

and treatment (Box 2.1). Understanding Mendelian traits generally requires only an understanding of variation within families; understanding complex traits requires an understanding of the population within which the complex trait occurs. The first section of this chapter reviews basic concepts in population genomics. The second section reviews what we know about human population genomics, while the third section describes the application of this to medical genomics.

IMPORTANT CONCEPTS IN POPULATION GENOMICS Population genomics is the study, on a genomic scale, of the processes affecting the evolution, distribution and correlation of genetic variants within populations. Population genomics has little interest in the parts of the genome that are the same among all individuals and focuses instead on the genomic differences between us. Roughly 0.1% of our DNA varies appreciably among individuals at the single nucleotide level, and considerably more, we have recently learned (Redon et al., 2006), varies at the larger-scale structural level. The raw engine for variation is mutation (see previous Chapter 1). Once created by a mutation event, two forces – chance (modified by population structure) and selection (only relevant if the variant has an effect on viability or reproductive success) – act on the new variant as it passes from one generation to the next. Many are soon lost from Copyright © 2009, Elsevier Inc. All rights reserved.

Important Concepts in Population Genomics

BOX 2.1

Population Genomics in Action

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Generation t-6 Generation t-5

While many aspects of population genomics can appear technical and somewhat distant from both application and real-world problems and interests in biology and medicine, in fact population genomics is transforming fields as diverse as genetic anthropology and medical genomics. Among the more surprising insights won with our increasing knowledge of the pattern of human genetic variation are a string of observations about relationships among certain populations. Patterns of Y chromosome variation have been used to support the claims of Jewish ancestry of a Bantu-speaking population in South Africa and for a long-term patrilineal pattern of inheritance among Jewish men that consider themselves to be “priests” or Cohanim in Hebrew (see Goldstein, 2008). More recently, Joel Hirschhorn, David Reich and their colleagues have identified a genome-wide signature of Jewish ancestry (Price et al., 2007), using PCA. Similar methodologies have also been developed by Reich and others to study the detailed genomic history of populations that have mixed ancestry. For example, looking at the whole genomes of African Americans with prostate cancer, Reich and his colleagues were able to determine which part of each person’s genome came originally from Africa and which from Europe with a high degree of statistical accuracy (Haiman et al., 2007). Most of the genome reflected the overall proportion of European ancestry in African Americans, but a section of chromosome 8 (within band 8q24) stood out strikingly. African Americans with prostate cancer had a much higher chance of having that region of the genome be of African ancestry compared to the rest of the genome. This result provides overwhelming evidence that there is a major risk factor for prostate cancer in this part of the genome and that this risk factor is likely to contribute to the observed differences in risk of prostate cancer between African Americans and Americans of European ancestry.

the gene pool, but some rise in frequency and may, eventually, come to replace the original ancestral allele. This is how evolution works, and population genomics provides the theory that describes it (Graur and Li, 1999; Hartl and Clark, 2007). Here we briefly review the important population genomic concepts of genealogy, the standard neutral model, population structure and linkage disequilibrium. Our aim is to provide enough information to understand the medically related issues discussed later. For a more thorough coverage of population genetic theory, the reader is referred to one of many standard texts (Gillespie, 2004; Halliburton, 2003; Hartl and Clark, 2007; Hedrick, 2004). Genealogy Imagine you have taken a sample of n individuals and typed them for one single nucleotide polymorphism (SNP) – perhaps a coding polymorphism in a medically relevant gene. The 2n allele copies (two for each diploid individual) are connected by an underlying genealogy, a tree that describes the ancestor– descendent relationships linking all the copies in that population. A simple illustration of how a genealogy might emerge in a population of only four individuals is shown in Figure 2.1. Here the sample size is just n  2 (thus four allele copies, sampled in

Generation t-4

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Figure 2.1 A simple example of a genealogy. Here the population in any one generation consists of just 4 individuals (represented by boxes) and 8 allele copies (represented as circles within the boxes). The genealogy connecting a sample of 2 individuals (and 4 allele copies) is shown. A mutation event in Generation t-3 (indicated by a red exclamation mark) has led to two allele types, coded green and blue.

Generation t) and the population from which they are sampled is fixed in each generation at N  4 individuals. For simplicity, we assume discrete non-overlapping generations, so that the set of parents for individuals in Generation t comes exclusively from the previous generation, Generation t-1. The total set of 2N allele copies in any one generation makes up the gene pool. Consider the two allele copies possessed by Individual 1 in Generation t, one inherited from her mother and the other from her father. A separate lineage of ancestor–descendent transmissions can be traced back in time for both copies (note the same thing can be done for any two allele copies sampled from the gene pool, regardless of whether they happen to belong to the same individual or not). Provided that N is finite, eventually one will reach a previous generation where the two lineages coalesce to a common ancestral copy (in Generation t-6 in the example in Figure 2.1). Note that coalescence is inevitable, given enough time, since in each generation there is a non-zero probability that coalescence will occur. However, the timing of coalescence is typically subject to much statistical uncertainty and, in human populations, is usually much further back in time than the six generations depicted here. Superimposed on the genealogical tree are mutation events, here exemplified by a single event in Generation t-3 that results in two distinct allele types (coded as different colors). This overtly genealogical approach to population genomics, focused on an observed sample of allele copies in the present day and tracing a tree backwards in time from these, has given rise to much new work in population genomics over the past 25 years, collectively known as coalescent theory (Donnelly and Tavaré, 1995; Hein et al., 2005; Kingman, 1982; Nordborg, 2001; Rosenberg and Nordborg, 2002; Wakeley, 2007). This contrasts with a more traditional approach that focuses on the entire population and typically arrives at solutions for tracing changes in allele frequencies forward in time. Perhaps the key computational and

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conceptual simplification afforded by coalescent approaches is that they are able to ignore alleles (and the individuals carrying them) that do not contribute in any way to the genetic diversity that is sampled in the current generation. Hence in this example three individuals are irrelevant in Generation t-6, two in Generation t-5, on so on. Note that while these individuals have not contributed to this sample for this locus, they may have contributed to other loci elsewhere in the genome. Thus, for example, the genealogy for a sample of men typed for their mitochondrial DNA is entirely separate from the genealogy for the same sample of men typed for their Y chromosome, and indeed in this case the individuals lying at the root of these genealogies (corresponding to so-called “mitochondrial Eve” and “Y-chromosome Adam”, provided a large enough sample is taken) are thought to have lived at entirely separate times. Both coalescent theory and the more traditional “forward” approach that models all members of a population have their uses, but we have concentrated here on the coalescent approach because it often helps one’s conceptual understanding of population genomics problems if one thinks in terms of the underlying genealogy. The Standard Neutral Model The standard neutral model (or Fisher-Wright model) provides a convenient null case against which other more complex models can be compared. The two basic assumptions are that (1) mating is random, so any allele copy in Generation t has the same probability of descending from any individual in Generation t-1; and (2) reproductive success is random, so a second allele copy picked from Generation t has the same probabilities of descent as the first one. Under these conditions, the probability that two allele copies in Generation t coalesce in Generation t-1 is 1/(2N). This is simply the probability of picking the same parent again (and the same allele copy within this parent) for the second copy, having picked a parent at random for the first allele copy. Much elegant theory flows from this simple rule, and we give two examples here to give a flavor of this. The first is the derivation of the famous Hardy-Weinberg proportions for genotype frequencies based on allele frequencies. Since the two allele copies possessed by an individual represent random samples from the gene pool under the standard neutral model, if Allele Type i has a population frequency of pi then the probability of seeing two Type i copies in the same individual (i.e., a homozygous ii genotype) is pi2 (due to the multiplicative law governing independent statistical events). Expected frequencies for other homozygous genotypes are similarly derived, and the expected frequency of heterozygotes H, known as the heterozygosity, is what is left over after taking away all the other homozym gotes. Thus H  1  ∑ i1 pi2 , where m is the total number of allele types. Note that in the simple case of just two allele types, these relationships reduce to the more familiar p2, q2 and 2pq for the frequencies of the two homozygotes and the heterozygote respectively, where q  1  p. The second example involves the expected value of H under mutation-drift equilibrium, in a model in which mutations

occur with probability  to any one allele copy in any one generation, and in which all mutations lead to a new distinguishable allele type (a reasonable assumption for longer DNA sequences where each new point mutation is likely to occur in a different nucleotide). Consider two allele copies chosen at random in Generation t. Three things can happen to their respective lineages in Generation t-1: (1) they coalesce, with probability 1/(2N); (2) a mutation occurs in one or other ancestor, with probability 2  since there are two ancestors; or (3) nothing interesting happens – just two separate ancestors with no mutation events. If Event (3) happens, one simply asks the same question for Generation t-2, with the probabilities of Events (1), (2) and (3) unchanged. Thus the question of heterozygosity boils down to whether (2) happens before (1) in the genealogy connecting the two allele copies, which in turn depends only on the relative probabilities of (2) versus (1). Thus H  2/(2  1/(2N))  4N/(1  4N). The same relationship can also be derived, somewhat more laboriously, using more traditional forward-in-time population genetics modeling (see, e.g., Crow, 1986). The relationship predicts that heterozygosity (and hence the amount of genetic variation) at neutral loci should be greater in larger populations and with high-mutation rates, and this pattern is indeed generally seen in both human and non-human populations (Graur and Li, 1999). Population Structure The concept of a completely randomly mating population is patently false for humans. Instead, individuals are more likely to have children with some individuals rather than others. One obvious constraint is geographic distance – you are more likely to marry (or otherwise pair with) someone next door than someone on the other side of the world. National boundaries are important barriers to gene flow (the movement of allele copies from one subpopulation to another), but even within national boundaries marriages are more likely to take place within socioeconomic, cultural and ethnic groups than between them (a phenomenon known as endogamy). For humans, however, none of these barriers is absolute; instead there are simply varying probabilities of having children with anyone else. In population genomics, this complexity is often simplified by compartmentalizing people into distinct groups and then by thinking about the different rates of exchange of allele copies between and within these groups. A useful metric that is often used to describe the degree of population differentiation between these groups is the FST statistic. This can be derived in many ways, and, when these result in slightly different answers, there are some differences of opinion on which derivation is best (Balding, 2003; Excoffier, 2001; Nei, 1987). However, a commonly applied derivation of FST is based on contrasting the amounts of genetic variance attributable to within- and between-population differences. For various reasons (Excoffier, 2001; Nei, 1987), the heterozygosity H that we defined earlier is an excellent measure of the genetic variance of a population. Intuitively, one can see that H approaches zero as genetic variability is reduced (i.e., the population becomes dominated by a single allele type) and conversely approaches unity as genetic

Important Concepts in Population Genomics

variability is increased (i.e., the population becomes dominated by many different allele types, such that the probability of picking two of the same type from the gene pool becomes vanishingly small). One interpretation of FST, then, is that it represents the proportion of total heterozygosity, HT (obtained by pooling all populations together), that is attributable to between-population differences. Since it’s easier to calculate the average withinpopulation contribution (simply the mean heterozygosity H calculated over all within-population H values), and since the between- and within-population contributions together sum to HT, the definition of FST is then (H T  H )/H T ). For most of the genome, FST values range from a maximum of 15% for intercontinental differences, to typically 1–3% for international differences within a continent, to less than 0.1% for differences within countries in Europe (Cavalli-Sforza et al., 1994; Steffens et al., 2006). Genetic markers with high FST are used for a number of purposes in genomic medicine, including ancestry testing and tracing the likely origin(s) of all or portions of one’s genome (Box 2.1). Linkage Disequilibrium Linkage Disequilibrium, or LD, is a measure of the degree of nonrandom association between allelic forms at two different sites (or loci) in the genome. Two factors influence this: mutation and recombination. Since the chances of recombination increase the further apart two loci are, LD tends to be highest between loci that lie very close to each other in the genome. However, there is a lot of variability around this general rule, partly because the rate of recombination varies throughout the human genome (Myers et al., 2005) and partly because chance factors in the placement of mutations in the underlying genealogy also play a role. These points are illustrated in Figure 2.2. In the absence of recombination (i.e., if the loci are very close to each other), one still sees variation in the degree of LD between two loci. If the polymorphism-generating mutations happen to occur along the same branch of the genealogy (Figure 2.2a), then only two out of the possible four 2-locus haplotypes are formed, and in this case knowledge of the allele type at one locus allows us to perfectly predict the allele type at the other. But if instead the mutations occur at widely separated points on the genealogical tree (Figure 2.2b), then knowledge of the allele type at one locus is only partially predictive of the allele type at the other. This breakdown of association, due simply to mutation, may or may not be of interest to the population geneticist depending on the application. If one is interested in the prediction of allele type at one locus depending on the allele type at the other, then mutation-led breakdown of LD is of interest and a useful LD metric in this case is r 2, which is the amount of variation at locus A that is explained by knowledge of allele states at locus B. If locus A and B are binary SNPs, then r 2 can be found simply by converting the allelic states into 0/1 variables, then calculating the square of the standard Pearson correlation coefficient between them. If instead one is interested only in the supposed amount of recombination between locus A and B, then a metric that

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attempts to factor out the mutation-led breakdown of association is preferable. One such metric is D, which for SNP loci takes its maximum value of 1 if and only if one observes just three out of the possible four haplotypes in one’s sample (for (a) Generation t-6 Generation t-5 Generation t-4

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Figure 2.2 Example genealogies involving 2 linked loci. (a) Both mutations happen on the same genealogical branch, leading to just two haplotypes – green  red and blue  yellow. Thus knowledge of the allele type at one locus provides perfect prediction of the allele type at the other. LD metrics: r2  1, D  1. (b) The two mutations occur separately on the genealogy tree, leading to three haplotypes and imperfect prediction. LD metrics: r2  0.33, D  1. (c) A recombination event in Generation t-2 (indicated by a red X) has created a recombinant haplotype with contributions from two separate lineages, leading to four different haplotypes and further reducing LD (to zero in the toy example, but this is not a general rule). LD metrics: r2  0, D  0.

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reviews of LD metrics see Devlin and Risch, 1995; Hudson, 2001; Nordborg and Tavaré, 2002; Mueller, 2004). Looking at Figure 2.2b, one should note that wherever one places the two mutations one can never obtain more than three haplotypes in the eventual sample, so D is always 1. Assuming no further mutations, the only way to obtain all four haplotypes in the eventual sample is via one or more recombination events between the two loci, and this is illustrated in Figure 2.2c. Note that recombination events alter the underlying genealogy, which is no longer a bifurcating tree but rather a more complicated web (technically, a “graph”). Linkage disequilibrium is both a boon and a bane for medical genomics, and this is discussed in more detail later.

HUMAN POPULATION GENOMICS We start by describing important sources of genomic data that have emerged recently. Then, we describe what these have told us about the history and evolution of human populations and about how human populations are structured today. Important Sources of Data The HapMap dataset (http://www.hapmap.org/) today constitutes the most important source of information for population genomics (The International HapMap Consortium, 2005, 2007). Instigated in 2002 with funding from the US NIH and other international sources, the project set out to describe and type common SNP variation, evenly spread throughout the human genome, in a collection of 270 individuals sampled from four populations spanning three major continents of origin – Africa, Asia and Europe. This was accomplished in two main phases – an initial Phase I completed in early 2005, featuring around 1.3 million common SNPs at an average density of 0.4 SNPs per kilobase (The International HapMap Consortium, 2005), and a subsequent Phase II completed in late 2005, featuring around 3.1 million common SNPs at an average density of 1 SNP per kilobase (The International HapMap Consortium, 2007). In addition, 10 separate 500 kb regions of the genome (the HapMap-ENCODE regions) were targeted for exhaustive discovery and typing of all but the rarest SNPs, in order to provide a valuable reference source to compare the projected performance of the rest of the HapMap data in representing all common SNP variation. It is estimated that there are some 10 million common SNPs in the human genome (Carlson et al., 2005). HapMap Phase II typed only one third of these; however, comparisons using the HapMap-ENCODE regions show that, thanks to linkage disequilibrium, these 3.1 million SNPs represent or “tag” the other 7 million to a high degree of accuracy (The International HapMap Consortium, 2007). The HapMap project, along with earlier SNP discovery projects such as The SNP Consortium (The International SNP Map Working Group, 2001), played an important role in the development of high-density genome-wide SNP panels by greatly increasing our knowledge of the location of SNPs in the human genome. In addition, the HapMap project provided the

LD data that allowed tag-based SNP panels to be designed (see Chapter 8). Genome-wide SNP panels are now being applied to population samples from around the world, providing valuable extensions of the information available from HapMap in two important ways. Firstly, new datasets have increased the sample sizes for certain populations to thousands of individuals; see, for example, the iControl database (US individuals, http:// www.illumina.com/pages.ilmn?ID231), the WTCCC database (UK individuals, http://www.wtccc.org.uk/) and the dbGaP database (http://www.ncbi.nlm.nih.gov/sites/entrez?dbgap). Secondly, new datasets have expanded the set of populations beyond the four studied for HapMap; see, for example, the Stanford University HGDP-CEPH database (51 distinct worldwide population samples, http://www.cephb.fr/hgdp-cephdb/). A key element in all these datasets is their free availability to the scientific community, reflecting a sea-change in attitudes to data access that has beneficially affected almost all genomic research since the Human Genome Project (see also The GAIN Collaborative Research Group, 2007). Currently, most databases characterize just the SNPs in the human genome. However, much interest is now also focused on Copy Number Variation (CNVs), and this is likely to widen soon to other types of structural variation such as inversions (see previous Chapter 1). The indications are that SNPs have a fair, but not perfect, ability to tag CNVs (Locke, 2006; Redon et al., 2006), and thus the development of genomic databases specifically targeting structural variation remains a priority. A Brief History of Homo sapiens The new genomic data have broadly confirmed the picture of our genetic history that had emerged from previous genetic studies and are also consistent with what we know from paleoanthropological data. This picture is of a relatively small population of ancestral humans living in East Africa between 60–100 thousand years ago, spreading out-of-Africa to central Asia and thence to Europe in one direction and South and East Asia and the Americas in the other, replacing other extant human populations as they went (Cavalli-Sforza et al., 1994; Garrigan and Hammer, 2006; Goldstein and Chikhi, 2002; Jobling et al., 2003). This picture explains why (1) compared to those of other primate species, our genomes are remarkably low in genetic diversity within populations and in differentiation between populations; (2) this diversity steadily decreases in populations further removed from Africa (Prugnolle et al., 2005; Ramachandran et al., 2005); (3) non-African diversity tends to be a subset of African diversity or, to put it in genealogical terms, most gene trees have their root in Africa; and (4) LD in the human genome is high in non-African populations. Not enough time has passed since our recent, small origins to have allowed mutations and recombinations to break down the haplotypic patterns present in the genomes that emerged from Africa. There is still room, and indeed eagerness, for debate regarding this picture. Debate continues over the degree to which there was interbreeding with extant populations either before or after the out-of-Africa expansion (Garrigan and Hammer, 2006;

Human Population Genomics

AA C EA M SA

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2007; Rosenberg et al., 2005; Serre and Pääbo, 2004). Thus, the genome of any one single individual is not composed of a contribution from one single discrete ancestral group, nor even of a series of contributions from different discrete ancestral groups. Just as there are no discrete groups today, so there were none in the past. For these reasons, a more useful paradigm for human population genomic structure is to see it as comprised of a series of quantitative axes, each one reflecting one aspect of a multidimensional structure. We illustrate some of these points in Figure 2.3. The figure displays the principal aspects of the population genomic structure of a sample of approximately 500 individuals collected in North Carolina, USA. Each individual was typed using the Illumina HumanHap 550 k genome-wide SNP panel (see Chapter 8), and these data were then subjected to Principal Components Analysis (PCA), a statistical method that ranks and dissects the correlation structure of multivariate datasets (Patterson et al., 2006; Price et al., 2006). Among the population groups contained within the cohort, some of the allele frequencies change in correlated ways that distinguish between population groups. Thus, in a simple and extreme case, you might have 10,000 SNPs that are all high frequency in group A and low frequency in group B; these would have high FST values, as described earlier.These SNPs would contribute to a single axis (e.g., PC1 in Figure 2.3), and the position of an individual along that axis would be dependent on which of

PC 2 0.00

Population Genomic Structure and Race The new genomics has allowed us, at last, to separate the population genetic concept of biogeographical shared ancestry from the socio-cultural concept of race. This is because the quantity of genetic data that one can routinely collect on a single individual is enough to provide all one needs to draw up a remarkably accurate picture of the population structure of a sample of individuals. In the past, with less genetic data available, other information such as the individual’s own self-declared racial identity was needed to shore up uncertainties in the underlying population genomic structure, and much debate ensued over the effectiveness of using one to predict the other (Barnholtz-Sloan et al., 2005; Burnett et al., 2006; Liu et al., 2006b; Tang et al., 2005; Wilson et al., 2001). Now that debate is largely irrelevant – we can get all the information we need regarding patterns of ancestral relatedness from the genetic data alone. The fact that confusion still exists over conflating population genomic structure with sociocultural race stems from many reasons, but the research community is at least partly to blame. Recent surveys suggest that the term “race” continues to be used ambiguously both in the scientific literature (Sankar et al., 2007; Shanawani et al., 2006) and in dealings with the press (Condit, 2007; Lynch and Condit, 2006). Genetically, there are no such things as human “races”, in the original strict sense of discrete unequivocal groupings. Because of our recent shared roots and our considerable mobility, we are only marginally more similar to a person sharing common biogeographic ancestry and living locally than we are to a person with different roots living on the other side of the world. Leaving aside sex-linked loci, which one must treat separately, average FST never rises much above 15% even for intercontinental comparisons (Cavalli-Sforza et al., 1994), so at least 85% of all genetic differences are accounted for by “within population” variance. There are no absolute barriers to gene flow between any two parts of the world, and, while partial barriers exist, the location and extent of these are difficult to define unequivocally (Barbujani and Belle, 2006; Handley et al.,

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Harding and McVean, 2004; Liu et al., 2006a; Ray et al., 2005), in particular regarding whether there was interbreeding with Neanderthals in Europe (Currat and Excoffier, 2004; Trinkaus, 2007). This debate revolves not only around the interpretation of genetic data, but also around morphological data such as cranial features, again with contrasting conclusions (Manica et al., 2007; Trinkaus, 2005). Likewise there is debate over the mode of the out-of-Africa expansion, for example over the use of coastal routes as preferred routes for expansion (Amos and Manica, 2006; Macaulay et al., 2005) and over the number and extent of population bottlenecks that may have occurred along the way (Liu et al., 2006a; Ramachandran et al., 2005). While our knowledge of genetic variation in the present day is excellent, the use of this to make inferences about the past is not an exact science. It may be that ancient DNA techniques will help to resolve these issues, by providing more direct information on the population genetics of these ancient populations (Caramelli et al., 2007; Noonan et al., 2006;Vernesi et al., 2004).

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Figure 2.3 Population structure in a sample collected in North Carolina, USA, revealed by Principal Components Analysis applied to genome-wide SNP data (Duke Genetics of Memory Cohort typed on Illumina HumanHap 550 panel). Scores for the first two principal component axes are plotted here. Self-declared racial identity labels are: AA  African/AfricanAmerican; C  Caucasian; EA  East Asian; M  Hispanic/ Mixed; SA  South Asian. See text for further details. Figure based on data provided by D. Goldstein and A. Need.

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the two groups that individual came from. Different axes would reflect other correlations in the data, involving, for example, a different subset of SNPs that also distinguish different population groups (e.g., PC2 in Figure 2.3). In this way, PCA uses the correlation patterns in the data to reduce the dimensionality of the dataset. Formal methods exist to determine how much information is reflected in each of the axes. In real datasets, the correlation patterns are much less extreme and simple than the group A and group B case described above, but the idea is the same. The first point the figure illustrates is that, even though PCA is based solely on genotype information and is thus blind to non-genetic information, there is a remarkably good correspondence with self-declared racial identity. Most individuals belonging to three population subgroups (Caucasian in red, South Asian in turquoise, and East Asian in black) are clearly identified and separately from each other by analysis of their genotypes (Figure 2.3). However, a second point, and just as important as the first, is that there is additional population structure that is not picked up by the information on racial identity. For example, there is a large spread in the African American group, reflecting individual differences in the degree of European admixture in their genomes. Thirdly, there is more than one axis along which co-ancestry relationships can be defined, and in fact the PCA reveals many more significant axes for this dataset than the two used to plot Figure 2.3. This underlines the multidimensional nature of human population genomic structure. Finally, the degree of resolution in elucidating such structure, as evidenced by the sharpness in the clustering of some of the groups in Figure 2.3, is something altogether new, and is a largely unforeseen benefit of the step-increase in genetic data afforded by the latest genome-wide SNP chips.

APPLICATION OF POPULATION GENOMICS TO GENOMIC MEDICINE Here we show how the concepts we have discussed so far can be applied to genomic medicine. The first two sections deal with the important role that LD and population structure have in the search for medically relevant causal variants, while the last deals with the insights beginning to emerge on the common population genomic patterns describing these variants (Box 2.1). LD in the Search for Medically Relevant Variants Today the hunt for medically relevant variants is taking place in large population-based samples, using genotyping panels that scan the entire genome. This creates both challenges and benefits, and both require an understanding of population genomics. In essence, the reason why association studies are so much more powerful than linkage studies at localizing a causal genetic variant is that linkage studies only make use of recombination events taking place over one or two generations in the pedigree under study, whereas association studies make use of the much larger number of recombination events that have taken place over the entire genealogy linking the individuals in a sample at a given

locus. Thus the breakdown in association with a nearby marker due to LD happens over a much shorter distance than the breakdown in linkage due to observed recombination events within pedigrees. The cost is that you need to cover the genome with a much greater density of markers. Using a simple theoretical model, Kruglyak estimated that this breakdown might happen over a 3 kb distance (Kruglyak, 1999). We now know that breakdown of LD is both region-specific (i.e., varies between different parts of the genome) and population-specific. The former is due at least in part to variation in the rate of recombination throughout the genome as a reflection of genome organization (Myers et al., 2005), while the latter is due to differences in the demographic histories of different populations. For example, levels of LD in European populations are higher than those in African populations and higher than the level estimated by Kruglyak (1999). This is presumed to be due to one or more population bottlenecks that have occurred since the out-of-Africa expansion (bottlenecks generate higher LD by shortening the average genealogical tree linking a sample of individuals). In a practical sense, this means that fewer SNPs are needed to cover the genome in Europeans than in Africans. Still greater efficiency can be achieved, however, by leveraging what we know about the specific patterns of LD to pick out a special set of SNPs that still largely represents all the common variation in a given genomic region – a procedure known as tagging (de Bakker et al., 2005; Weale et al., 2003). This idea is now applicable to the entire genome, with the availability of genomewide genotyping arrays containing hundreds of thousands of SNPs. Thus either through simply the sheer density of SNPs (e.g., Affymetrix platforms) or through explicit use of tagging methods to guide the choice of SNPs in the first place (e.g., Illumina platforms), these modern genome-wide platforms are able to represent all common variation in the human genome to an extremely high level of accuracy (see Chapter 8). Recent studies have shown that the ability to port tagging SNP sets inferred from one population to another related one (e.g., from the same continent) is generally good (Conrad et al., 2006; de Bakker et al., 2006). Since LD varies in extent among different populations, this presents the possibility of conducting different studies at different stages in different populations in order to maximize efficiency. Two types of special populations have been used to capitalize on larger levels of LD in the initial scan for causal variants. One of these is the admixed population, one in which individuals are descendents of a recent period of admixture between two previously separated ancestral populations. The best-studied example of this is the African American population, as on average their genome is comprised of an 80:20 mixture of West African and European DNA, although there is quite a high degree of variation among different individuals who self-identify as African American in terms of the proportion of the genome that is of West African or European origin (Parra et al., 1998). The other type is the isolated population, one in which the individuals descend from a small number of founders and have, through endogamy, remained relatively free from subsequent admixture (Service et al., 2006). One well-studied example is the Dutch “GRIP” population (Liu et al., 2007). Despite their differences

Conclusions

in origin, both admixed and isolated populations have genomes with elevated levels of LD, and this can be used to positive effect in the early stages of the search for new causal variants (Angius et al., 2008; Seldin, 2007; Smith and O’Brien, 2005). Ultimately, though, the very same LD that allows us to economize in the number of SNPs needed to perform effective genome-wide association studies also acts as a barrier, limiting one’s ability to localize a causal variant based on association alone. Instead, a signal pointing to causal involvement of locus A could equally be due to the actual influence of locus B, if locus B is in high LD with locus A. In such cases, other types of information (e.g., based on the location of the respective polymorphisms and their predicted functional influence) are needed to tease apart the causal relationships. Correcting for Population Structure We have already noted that human populations have a complex structure (also known as stratification) that neither conforms to the notion of distinct races nor to that of one single randomly mating gene pool but rather falls somewhere in between. In practice, what this means is that even within a carefully designed study that attempts to match cases and controls for biogeographical shared ancestry, there may be hidden structure within the study. This creates a problem if cases and controls are nonrandomly distributed with respect to this structure. To illustrate this, consider again the structure revealed in Figure 2.3. Imagine a case-control study conducted within the African American group, in which cases are more likely to come from the bottom right and controls from the top left of the cluster. This sets up a situation in which SNPs that differentiate strongly between these two sides would also differentiate cases and controls, resulting in many false signals of association. Luckily, we can use the fact that population structure affects all SNPs in the genome to affect a correction. Older genomic control methods applied a uniform correction to all SNPs (Devlin et al., 2004; Devlin and Roeder, 1999). Newer methods allow a SNP-specific correction to be applied, tailored to the observed degree to which the SNP in question is seen to be affected by population structure (some SNPs vary more simply by evolutionary chance (Balding and Nichols, 1995), others because of presumed selective gradients (Campbell et al., 2005)). Of these newer methods, the EIGENSTRAT method of Price et al. (2006) is particularly convenient and flexible because it (a) makes use of PCA to efficiently dissect the population structure; (b) uses as many co-ancestry related principal component axes as are deemed necessary to effect the correction (guided by theory developed in Patterson et al., 2006); and (c) automatically recognizes, since principal component axes are quantitative, that human population structure is continuous rather than discrete. Understanding the History and Population Distribution of Variants In this final section, we anticipate the forthcoming flood of data on causal variants that will be discovered as a result of genome-wide association studies on common complex diseases

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(Frayling, 2007). These will soon allow the resolution of questions that have been debated for decades regarding the population genomic properties of causal variation relative to other variation in the human genome (Mitchell-Olds et al., 2007). An early example of this is a meta-analysis by Ioannidis et al. (2004) on the question of whether causal variants tend to exert consistent effects in different parts of the world – in other words, does possession of an AA genotype for locus Q always increase your risk of a given disease relative to genotype aa, or is it different for people living in Africa versus people living in Asia? Their study suggested that the general rule was the former and that differences in population risk were more due to differences in allele frequencies among populations than to differences in the effect of possessing a given genotype. The take-home message is that a new causal variant discovered in Population A because it is at high frequency can still lead to new therapies of benefit to people living in Population B, where the causal variant is rare, because the underlying metabolic pathway being affected by that causal variant is the same. As one example of this, a new class of antiretroviral drugs – the HIV entry inhibitors – was developed following the discovery of the importance of the CCR5-32 polymorphism in progression to HIV-AIDS, and these drugs appear to be of universal benefit despite the fact that the CCR5-32 mutation is almost wholly absent in subSaharan Africa (Esté and Telenti, 2007). Looking ahead, large-scale resequencing technologies will help us to build up a picture of the “allelic architecture” of causal variants for common complex diseases (Wang et al., 2005). A null hypothesis would be that the allele frequency distributions of causal variants are no different from those observed for neutral genetic variants in the human genome. Much opinion has been offered on the number of variants open to detection by association studies (which are only powered to detect common variants) (Blangero, 2004; Pritchard, 2001; Pritchard and Cox, 2002; Reich and Lander, 2001; Smith and Lusis, 2002). One way in which this debate has been couched is over the degree to which causal variants have been under selection, and whether this has been large enough to alter their allele frequency and LD properties.Various methods have been proposed recently to look for signals of selection on a genome-wide scale (Kelley et al., 2006; McVean and Spencer, 2006; Sabeti et al., 2006; Sabeti et al., 2007; Voight et al., 2006; Thornton et al., 2007), but so far, while individual examples exist of what appear to be strong signals of selection around genes affecting Mendelian traits (e.g., the lactase gene and adult lactose intolerance (Bersaglieri et al., 2004; Burger et al., 2007)), a systematic treatment of this question for variants affecting common complex disorders awaits further data.

CONCLUSIONS In this chapter we have given what we hope is a concise and accessible account of the main concepts of population genomics and their importance for medical genomics. The field has been

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transformed over the past 5 or so years by the move away from a single “Human Genome” to the diverse human genomes of many thousands of individuals. The new data have allowed us to quantify with unprecedented accuracy the patterns in different human populations of allele frequency and of linkage disequilibrium throughout the genome. This has allowed us to estimate the variable rates of recombination and mutation that apply to

different part of the genome and to reaffirm the remarkably recent and unified origins of our species in Africa. Most importantly, it has provided us with the necessary tools to carry out association-based genome-wide scans to look for novel causal variant of common complex disease, and the necessary framework for interpreting these in the translation of this knowledge into new effective therapies.

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Mueller, J.C. (2004). Linkage disequilibrium for different scales and applications. Brief Bioinform 5, 355–364. Myers, S., Bottolo, L., Freeman, C., McVean, G. and Donnelly, P. (2005). A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321–324. Nei, M. (1987). Molecular Evolutionary Genetics. Columbia University Press, New York. Noonan, J.P., Coop, G., Kudaravalli, S., Smith, D., Krause, J., Alessi, J., Chen, F., Platt, D., Pääbo, S., Pritchard, J.K. et al. (2006). Sequencing and analysis of Neanderthal genomic DNA. Science 314, 1113–1118. Nordborg, M. (2001). Coalescent theory. In Handbook of Statistical Genetics (D.J. Balding, M. Bishop and C. Cannings, eds), Wiley, Chichester, UK, p. 309. Nordborg, M. and Tavaré, S. (2002). Linkage disequilibrium: What history has to tell us. Trends Genet 18, 83–90. Parra, E.J., Marcini, A., Akey, J., Martinson, J., Batzer, M.A., Cooper, R., Forrester, T., Allison, D.B., Deka, R., Ferrell, R.E. et al. (1998). Estimating African American admixture proportions by use of population-specific alleles. Am J. Hum Genet 63, 1839–1851. Patterson, N., Price, A.L. and Reich, D. (2006). Population structure and eigenanalysis. PLoS Genet 2, e190. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N. A. and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904–909. Price, A.L., Butler, J., Patterson, N., Capelli, C., Pascali, V.L., Scarnicci, F., Ruiz-Linares, A., Groop, L., Saetta, A.A., Korkolopoulou, P. et al. (2007). Discerning the ancestry of European Americans in genetic association studies. PLoS Genet. doi:10.1371/journal.pgen.0030236.eor. Pritchard, J.K. (2001). Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet 69, 124–137. Pritchard, J.K. and Cox, N.J. (2002). The allelic architecture of human disease genes: Common disease-common variant ... or not?. Hum Mol Genet 11, 2417–2423. Prugnolle, F., Manica, A. and Balloux, F. (2005). Geography predicts neutral genetic diversity of human populations. Curr Biol 15, R159–R160. Ramachandran, S., Deshpande, O., Roseman, C.C., Rosenberg, N.A., Feldman, M.W. and Cavalli-Sforza, L.L. (2005). Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc Nat Acad Sci USA 102, 15942–15947. Ray, N., Currat, M., Berthier, P. and Excoffier, L. (2005). Recovering the geographic origin of early modern humans by realistic and spatially explicit simulations. Genome Res 15, 1161–1167. Redon, R., Ishikawa, S., Fitch, K.R., Feuk, L., Perry, G.H., Andrews, T.D., Fiegler, H., Shapero, M.H., Carson, A.R., Chen, W. et al. (2006). Global variation in copy number in the human genome. Nature 444, 445–454. Reich, D.E. and Lander, E.S. (2001). On the allelic spectrum of human disease. Trends Genet 17, 502–510. Rosenberg, N.A., Mahajan, S., Ramachandran, S., Zhao, C., Pritchard, J.K. and Feldman, M.W. (2005). Clines, clusters, and the effect of study design on the inference of human population structure. PLoS Genet 6, e70. Rosenberg, N.A. and Nordborg, M. (2002). Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nat Rev Genet 3, 380–390.

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Sabeti, P.C., Schaffner, S.F., Fry, B., Lohmueller, J., Varilly, P., Shamovsky, O., Palma, A., Mikkelsen, T.S., Altshuler, D. and Lander, E.S. (2006). Positive natural selection in the human lineage. Science 312, 1614–1620. Sabeti, P.C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E.H., McCarroll, S.A., Gaudet, R. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918. Sankar, P., Cho, M.K. and Mountain, J. (2007). Race and ethnicity in genetic research. Am J Med Genet A 143A, 961–970. Seldin, M.F. (2007). Admixture mapping as a tool in gene discovery. Curr Opin Genet Dev 17, 177–181. Serre, D. and Pääbo, S. (2004). Evidence for gradients of human genetic diversity within and among continents. Genome Res 14, 1679–1685. Service, S., DeYoung, J., Karayiorgou, M., Roos, J.L., Pretorious, H., Bedoya, G., Ospina, J., Ruiz-Linares, A., Macedo, A., Palha, J.A. et al. (2006). Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet 38, 556–560. Shanawani, H., Dame, L., Schwartz, D.A. and Cook-Deegan, R. (2006). Non-reporting and inconsistent reporting of race and ethnicity in articles that claim associations among genotype, outcome, and race or ethnicity. J Med Ethics 32, 724–728. Smith, D.J. and Lusis, A.J. (2002). The allelic structure of common disease. Hum Mol Genet 11, 2455–2461. Smith, M.W. and O’Brien, S.J. (2005). Mapping by admixture linkage disequilibrium: Advances, limitations and guidelines. Nat Rev Genet 6, 623–632. Steffens, M., Lamina, C., Illig, T., Bettecken, T., Vogler, R., Entz, P., Suk, E.-K., Toliat, M.R., Klopp, N., Caliebe, A. et al. (2006). SNPbased analysis of genetic substructure in the German population. Hum Hered 62, 20–29. Tang, H., Quertermous,T., Rodriguez, B., Kardia, S.L., Zhu, X., Brown, A., Pankow, J.S., Province, M.A., Hunt, S.C., Boerwinkle, E. et al. (2005). Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. Am J Hum Genet 76, 268–275.

The GAIN Collaborative Research Group (2007). New models of collaboration in genomewide association studies: The Genetic Association Information Network. Nat Genet 39, 1045–1051. The International HapMap Consortium (2005). A haplotype map of the human genome. Nature 437, 1299–1320. The International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–862. The International SNP Map Working Group (2001). A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409, 928–933. Thornton, K.R., Jensen, J.D., Becquet, C. and Andolfatto, P. (2007). Progress and prospects in mapping recent selection in the genome. Heredity 98, 340–348. Trinkaus, E. (2005). Early modern humans. Annu Rev Anthropol 34, 207–230. Trinkaus, E. (2007). European early modern humans and the fate of the Neandertals. Proc Nat Acad Sci USA 104, 7367–7372. Vernesi, C., Caramelli, D., Dupanloup, I., Bertorelle, G., Lari, M., Cappellini, E., Moggi-Cecchi, J., Chiarelli, B., Castrì, L., Casoli, A. et al. (2004). The Etruscans: A population-genetic study. Am J Hum Genet 74, 694–704. Voight, B.F., Kudaravalli, S., Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. PLoS Biol 4, e72. Wakeley, J. (2007). Coalescent Theory: An Introduction. Roberts & Co. Wang, W.Y., Barratt, B.J., Clayton, D.G. and Todd, J.A. (2005). Genomewide association studies: Theoretical and practical concerns. Nat Rev Genet 6, 109–118. Weale, M.E., Depondt, C., Macdonald, S.J., Smith, A., Lai, P.S., Shorvon, S.D., Wood, N.W. and Goldstein, D.B. (2003). Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: Implications for linkage-disequilibrium gene mapping. Am J Hum Genet 73, 551–565. Wilson, J.F., Weale, M.E., Smith, A.C., Gratrix, F., Fletcher, B., Thomas, M.G., Bradman, N. and Goldstein, D.B. (2001). Population genetic structure of variable drug response. Nat Genet 29, 265–269.

RECOMMENDED RESOURCES The seminal work by Cavalli-Sforza et al. (1994) is still a standard reference on the use of genetics to infer aspects of human history and demography. A useful, more up-to-date work with an emphasis on human evolution is the textbook by Jobling et al. (2003).

The HapMap website (http://www.hapmap.org/tutorials.html.en) has some nice tutorial material on the rationale and use of this important population genomic resource.

CHAPTER

3 Genomic Approaches to Complex Disease Desmond J. Smith and Aldons J. Lusis

INTRODUCTION Most of the socioeconomic burden of disease in the industrialized nations is due to complex disorders, in which multiple genes interact with the environment to produce the final disease phenotype (Smith and Lusis, 2002). Typical examples include atherosclerosis, diabetes, cancer, multiple sclerosis, autism, alcoholism, and drug abuse (Diabetes Genetics Initiative et al., 2007; Easton et al., 2007; Moffatt et al., 2007; Samani et al., 2007; Scott et al., 2007; Steinthorsdottir et al., 2007; Wellcome Trust Case Control Consortium, 2007). An understanding of complex disease involves: (1) identifying the genetic variations, whether common or rare, that contribute to the disease; (2) determining how the variations influence function and which pathways they perturb; and (3) determining how the variations interact with each other and with the environment to contribute to the disease. Even if the total genetic contribution to a complex trait is substantial, typically each gene contributes only a small amount to the final phenotype, making detection of the responsible genes an arduous process (Wellcome Trust Case Control Consortium, 2007). Further complicating such analyses are allelic heterogeneity and locus heterogeneity, in which a disease can be due to different alleles of the same gene or to alleles in different genes, respectively. Genetic interactions can also occur, in which multiple genes enhance each others’ effects on a disease (synergy) or

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

block each others’ effects (epistasis). In addition, there is much variation of the environment in human studies, further contributing to “noise” in an analysis (Smith and Lusis, 2002). The advent of genomics, in which the whole genome is the unit of study rather than individual genes, is revolutionizing the study of complex disease (Olson, 2003) and is rapidly helping establish genomic medicine as a new and exciting field. This chapter focuses on the use of genomic technologies to understand complex disease and is organized as follows: First, we discuss methods for the identification of DNA variations, both common and rare (Figure 3.1). Second, we discuss linkage and association methods for relating DNA variation to phenotypes. And lastly, we discuss how the “omic” technologies can help identify the underlying genes and organize them into pathways and networks (Figure 3.2).

IDENTIFYING COMMON AND RARE GENOMIC VARIATIONS IN THE POPULATION Varieties of Polymorphism Mapping genes became practicable in humans with the realization that polymorphic DNA sequences could be used directly as genetic markers (Botstein et al., 1980). Initially, single nucleotide polymorphisms (SNPs), assayed as restriction fragment length polymorphisms (RFLPs) on gels, were employed as markers in

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ELUCIDATING COMPLEX DISEASE Phenotype of interest Identify variants Linkage or association Mixed or isolated populations Common Allele Detection Technology (e.g., TaqMan, MS, Affymetrix, Illumina) Rare Allele Discovery Technology (e.g., 454 Life science, polony sequencing, Solexa, MPSS) Gene/allele discovery interaction variants

Proteomics

Systems biology

Genetical genomics

Fxns/pathways Animal models

Molecular subtyping

Transcript profiling

Figure 3.1 Elucidating complex disease. This flow chart parallels the discussion in the text. Genetic dissection of a disease phenotype of interest requires identification of appropriate variants for mapping. These variants will usually be common and chosen from publicly available databases. The strategy for gene identification will require decisions as to whether linkage or association should be used, which type of population should be employed, and the nature of the allele detection technology. Some phenotypes may require the use of recent approaches to rare allele identification. Once a gene responsible for a phenotype has been identified, the methods of systems biology can be used to understand the relation of the causative allele to disease.

combination with Southern blotting. However, this approach is laborious. The strategy became much more feasible with the discovery of microsatellite repeats (Weber and May, 1989). These densely spaced markers (roughly one per every 2 kb) are polymorphic because of differences in lengths of short oligonucleotide repeats. They have the advantage of being easily scored using the polymerase chain reaction (PCR) and gel electrophoresis and also have multiple alleles, which greatly improves their informativeness compared to biallelic markers such as SNPs. The use of microsatellites dominated mammalian genetics until the last several years, at which time the utility of SNPs reemerged with the invention of medium- and high-throughput approaches for analyzing these digital allelotypes. The SNPs also have the advantage that their average spacing is about one every 300 bp in the human genome, denser than microsatellites. SNP Genotyping Technologies A large number of SNP genotyping technologies have emerged over the last few years (Table 3.1). One of the first rapid methods for SNP genotyping, TaqMan technology, uses real-time PCR (Gibson et al., 1996; Heid et al., 1996; Livak, 1999). The SNPs are distinguished by reporter oligonucleotides containing a fluorescent nucleotide at the 5 end and a quenching nucleotide at the 3 end. If the oligonucleotide is a perfect match to a PCR product flanking

the SNP, the Taq polymerase used for the PCR liberates the 5 fluor through the 5 to 3 exonuclease activity of the polymerase. The liberated fluor-substituted nucleotide is no longer inhibited by proximity to the quenching fluor and fluoresces brightly. This fluorescence increases with each cycle of the PCR and is measured in each cycle that is in real time. However, if the oligonucleotide does not bind because of the presence of an SNP, there is no increase in fluorescence. Because a PCR assay has to be set up for each SNP, the method is only of moderate throughput. Another moderate- to high-throughput technology for genotyping biallelic markers employs matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Cantor, 2005; Griffin and Smith, 2000). Primer extension reactions are performed resulting in differing product lengths depending on the allele present for a particular SNP. The mass of the reaction products are then quantitated using the MALDI-TOF MS, permitting evaluation of which SNP is present. Throughput can be enhanced by multiplexing different assays in one sample. A dramatic increase in throughput came with the invention of photolithographically synthesized arrays of oligonucleotides for genotyping (Kreiner and Buck, 2005; Lipshutz et al., 1995). Affymetrix arrays consist of very high densities of 25-mers matching major or minor alleles of known SNPs in the human genome. Photolithographic activation of chemical groups is used to synthesize the oligonucleotides in situ. The photolithography permits a dramatic degree of spatial miniaturization with very dense packing of oligonucleotides and high-throughput analysis of SNPs. The presence or absence of a particular SNP in the target genome of interest is revealed by the presence or absence of a corresponding hybridization signal on the array. The arrays are spatially encoded, meaning that the position of an oligonucleotide on the array maps to the SNP it assays. The current feature size on these arrays permits up to 500,000 SNPs to be analyzed. Perlegen, a company that employs Affymetrix technology, has used custom-designed photolithographic arrays to query 1.5  106 SNPs (Hinds et al., 2005). Illumina arrays use sequence-encoding rather than spatialencoding for high-throughput genotyping. Beads with assay oligonucleotides are used to interrogate individual SNPs. A fluorescent extension reaction requiring correct hybridization of the assay oligonucleotide to genomic DNA detects which SNP is present (Shen et al., 2005).The small size of the beads means that hundreds of thousands of SNPs can be analyzed in parallel. Fluorescence readouts from each bead are captured using bundles of optical fibers. In addition to the assay oligonucleotide, the beads also contain decoding oligonucleotides that reveal which SNP the bead is interrogating. The decoding oligonucleotides are revealed using a series of fluorescently labeled probes. This bead-based method has been used to date to analyze up to a million SNPs in one assay. Sequencing Technologies The above genotyping technologies are high throughput but only detect already known SNPs. Most of these SNPs have been identified as a result of resequencing projects in the public domain (Wheeler et al., 2006) and represent reasonably common

Identifying Common and Rare Genomic Variations in the Population

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OMIC SPACE

PC 2 Phenome PC 1

Metabolome

Proteome

Transcriptome

Genome

Figure 3.2 Omic space. Multiple partially orthogonal domains, where various sorts of biological networks, experimental findings, and model-based predictions are represented and integrated (Left). Tools for collection of genome-wide data relevant to omic space are being developed. The phenome (global phenotyping) represents much of classical biology and medicine, supplemented with exciting advances in fields such as molecular imaging (MRS, PET). Repositories for such data are in the early stages. One example is the Mouse Phenome database (http://www.jax.org/phenome). Mass spectrometry (MS) and NMR have been used for high-throughput analysis of the metabolome and for non-invasive diagnosis and screening using non-supervised algorithms such as PCA. An example of PCA used to differentiate disease and non-diseased samples is shown to the right, where PC1 and PC2 represent the first and second principal components, and red and blue points represent metabolites differentiating diseased and non-diseased samples, respectively. The proteome is studied using a number of technologies, including MS. The interactome (study of interactions between proteins) has been investigated using whole-genome yeast two-hybrid screens, literature-based analysis of pathways (e.g., gene ontology, http:// www.geneontology.org/), and co-affinity immunopurification. Protein products of genes mutated in inherited genetic disorders tend to interact with proteins involved in similar disorders (Right). Green, proteins involved in developmental disorders; blue, proteins involved in endocrine disorders. Whole-genome expression arrays have been used for transcriptome analysis, while high-density SNP chips, high-throughput sequencing, and HapMap sequences have been used at the genome level. Some databases, such as the “GenomePhenome Superbrain” (http://www.omicspace.riken.jp/), are designed to integrate data from the various omic domains. The elements (nodes) of each stage appear to have a scale-free topology (right, shown linked to transcriptome). Certain elements (hubs) have many connections while most have relatively few (see also Chapters 1 and 6).

alleles. However, the extent to which complex diseases are due to common or uncommon polymorphisms is unclear (see Common Disease/Common Variant Hypothesis below) (Smith and Lusis, 2002).

In the long term, the evaluation of rare SNPs in common complex disorders will require new sequencing technologies to affordably resequence multiple human genomes. This quest is known as “the $1000 genome” (Shendure et al., 2004). While

36

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TABLE 3.1

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Genomic Approaches to Complex Disease

Genotyping and sequencing technologies

Technology

Applications

Organization

TaqMan

Custom SNPs, 102–103 assays

Applied Biosystems (https://www2. appliedbiosystems. com/)

Mass spectrometry Custom SNPs, 102–103 assays

Sequenom (http: //www.sequenom. com)

Photolithographic arrays

Predesigned SNPs, Affymetrix (http:// 105–106 assays www.affymetrix.com) Perlegen (http:// www.perlegen.com/)

Illumina arrays

Predesigned SNPs, Illumina (http:// 105–106 assays www.illumina.com)

Pyrosequencing

SNP discovery

454 Life Sciences (http://www.454. com)

Solexa sequencing arrays

SNP discovery

Illumina (http:// www.illumina.com)

Massively parallel signature sequencing

SNP discovery

Illumina (http:// www.illumina.com)

Single molecule DNA sequencing

SNP discovery

Helicos Biosciences (http://helicosbio. com)

certainly inspiring, it will likely be many years before this goal is achieved. Nevertheless, recent advances in rapid sequencing may improve our discovery of rare and private alleles (Table 3.1) (see Chapter 7). All of the developments increase speed by avoiding laborious molecular cloning steps in Escherichia coli. In one method developed by 454 Life Sciences, individual genomic DNA molecules are cloned on beads (Margulies et al., 2005). The beads are isolated from one another using oil/water emulsions to create spherical lipid bilayer vesicles about the size of a microorganism. Polymerase-mediated amplification of the DNA molecules on the beads can then occur without cross-contamination (emulsion PCR). Sequence information from each bead is then obtained using pyrosequencing. In this method, release of a pyrophosphate during incorporation of a nucleotide in an extension reaction is detected by light emission from a luciferase reaction. The small size of the beads allows large numbers of sequences to be obtained in parallel in picoliter reactors. Acquisition of the pyrosequencing output uses fiber optics. The throughput is about 25 Mb in an afternoon, although the average sequence reads are less than 100 bp. Polony sequencing is another approach using emulsion PCR, in which the individual beads are immobilized in a thin film of polyacrylamide after DNA amplification (Shendure et al.,

2005). The term “polony” is an abbreviation of “polymerase colony”. The DNA molecules represent self-ligated molecules which permit the acquisition of mate-paired sequence tags, that is, sequences from the two ends of the ligated molecule. Fluorescent sequencing by ligation in combination with a light microscope allows sequence tags of about 13 bp to be obtained from each end of the self-ligated molecule, a total of 26 bp. This method appears to be particularly useful for detecting translocations and inversions. Another approach, used by Solexa, employs PCR amplification of individual DNA molecules (Bennett et al., 2005). The individual PCR products remain localized to a small region of a glass slide because of the presence of capture oligonucleotides. The amplification results in “colonies” of different individual DNA molecule at a high spatial density on the glass slide. Fluorescent dideoxy sequencing reactions are then employed to interrogate each of the colonies in parallel, giving high-throughput sequences. Another bead-based sequencing method is massively parallel signature sequencing, MPSS (Brenner et al., 2000). A population of DNA molecules is ligated en masse to a highly complex mixture of identifying oligonucleotides. Amplification by PCR follows. Purification of multiple copies of a single class of DNA occurs when individual identifying oligonucleotides are captured by their complement on a bead. Sequence from each bead is then obtained by sequential hybridization reactions. The method gives sequence ladders of about 20 bp. A fourth method does not employ any type of DNA amplification, but rather uses fluorescent dideoxy sequencing of single DNA molecules (Braslavsky et al., 2003). The data capture uses a light microscope and the potential degree of parallelism is extremely high. Sequence reads of up to 5 bp have been obtained. At present, there is tremendous activity in developing cheaper, faster, and more accurate sequencing methods. Whether the next generation of sequencing tools uses one of the strategies described above or some completely unexpected technology, it seems clear that the contribution of rare and private polymorphisms to common genetic disease will become better understood in the coming years.

RELATING DNA VARIATION TO PHENOTYPES Candidate Gene Approach In the early days of complex trait genetics, the mid-1980s, the candidate gene approach was widely used. This approach evaluates whether common variations of biochemically defined candidate genes are associated with a particular disease by evaluating the frequency of various alleles in case versus control populations (Table 3.2). One of the first important examples was the gene for apolipoprotein E (APOE) which was found to exhibit three common alleles (E2, E3, and E4). The E2 form was originally found to be strongly associated with a relatively rare

Relating DNA Variation to Phenotypes

TABLE 3.2

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37

Examples of sequence variants implicated in complex disease

Disease phenotype

Gene/sequence varianta

Identification strategy

Technology

References

Hyperlipidemia (type III)

ApoE2

Association/candidate gene

Isoelectric focusing

Wardell et al., 1982

Alzheimer’s disease

ApoE4

Association/candidate gene

PCR/restriction isotyping

Strittmatter et al., 1993

Diabetes mellitus (type II)

Calpain 10

Linkage/association

Sequencing, PCR-RFLP

Horikawa et al., 2000

Crohn’s disease

NOD2

Linkage/LD, Linkage, TDT

Microsatellites/ sequencing, allelespecific PCR

Hugot et al., 2001; Ogura et al., 2001

Familial combined hyperlipidemia

USF1

Linkage/isolated population/association

Sequencing

Pajukanta et al., 2004

Myocardial infarct risk

Lymphotoxin alpha

Association/LD

Sequencing/PCR isotyping

Ozaki et al., 2002

Macular degeneration

Complement factor H

Association

Affymetrix arrays

Klein et al., 2005

Low HDL cholesterol

ABCA1, APOA1, LCAT

Association

Sequencing

Cohen et al., 2004

Low LDL cholesterol

NPC1L1

Association

Sequencing/TaqMan

Cohen et al., 2006

Obesity

SLC6A14

Linkage/LD/isolated population/association

Microsatellites/ sequencing/MS

Suviolahti et al., 2003

Diabetes mellitus (type II)

TCF7L2

Linkage/isolated population/association

Microsatellites

Grant et al., 2006

Myocardial infarct risk

LTA4H

Isolated population/association

Sequencing

Helgadottir et al., 2006

Schizophrenia

NRG1

Linkage/isolated population/TDT

Fluorescence polarization

Stefansson et al., 2002

Crohn’s disease

5q31 cytokine gene cluster

Linkage/LD

Microsatellites/ sequencing

Rioux et al., 2001

Breast cancer

FGFR2, TNRC9/ LOC643714, MAP3K1, rs13281615 (8q), LSP1

Association

Affymetrix arrays/TaqMan/MS

Easton et al., 2007

Diabetes mellitus (type II)

TCF7L2, CDKAL1

Association

Illumina

Steinthorsdottir et al., 2007

Diabetes mellitus (type II)

9p21.3 (CDKN2A/ CDKN2B), IGF2BP2, CDKAL1, HHEX, SLC30A8

Association

Affymetrix

Diabetes Genetics Initiative et al., 2007; Zeggini et al., 2007

Diabetes mellitus (type II)

9p21.3 (CDKN2A/ CDKN2B), IGF2BP2, CDKAL1, HHEX, SLC30A8, TCF7L2, FTO PPARG, KCNJ11

Association/isolated population

Illumina

Scott et al., 2007

Myocardial infarct

9p21.3 (CDKN2A/ CDKN2B), MTHFD1L, PSRC1, MIA3, SMAD3, LPL

Association

Affymetrix

Samani et al., 2007

(continued )

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TABLE 3.2

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Genomic Approaches to Complex Disease

(Continued)

Disease phenotype

Gene/sequence varianta

Identification strategy

Technology

References

Asthma

ORMDL3

Association

Illumina

Moffatt et al., 2007

Bipolar disease

16p12, KCNC2, GABRB1, GRM7, SYN3

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

Myocardial infarct

9p21.3 (CDKN2A/ CDKN2B), MTHFD1L, ADAMTS17

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

Crohn’s disease

NOD2, 5q31 cytokine gene cluster, IL23R, ATG16L1, 10q21, 5p13.1, IRGM, NKX2-3, PTPN2

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

Rheumatoid arthritis

HLA-DRB1, PTPN22, IL2RA, IL2RB, TNFAIP2, GZMB, PRKCQ, KAZALD1, 12q24

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

Diabetes mellitus (type I)

MHC, CTLA-4, PTPN22, IL2RA, IFIH1, 12q13, 12q24, 16p13, PTPN2, IL2 receptor

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

Diabetes mellitus (type II)

PPARG, KCNJ11, TCF7L2, FTO, CDKAL1, HHEX,

Association

Affymetrix

Wellcome Trust Case Control Consortium, 2007

a

Where the responsible gene is unclear, the relevant chromosome region is given.

hyperlipidemia (Type III) and with low cholesterol levels in the population (Wardell et al., 1982). Later, the E4 allele was found to be strikingly associated with Alzheimer’s disease (Strittmatter et al., 1993). The candidate gene approach continues to be employed extensively, as thousands of association studies are reported each year. However, the strategy has some important problems. First, of course, the gene has to first be linked to the disease through biochemical studies or mechanistic hypotheses. Second, association tests are prone to artifacts due to population stratification and ethnic admixture. One way to overcome this latter problem is to perform family-based association testing such as the transmission disequilibrium test (TDT) (Spielman and Ewens, 1998). This test evaluates the role of a polymorphism in a disorder by assessing its frequency in affected versus non-affected siblings. The idea is that, since the siblings are raised in the same family, most of the differences in susceptibility will be due to genetics rather than to the environment. Linkage In the early 1990s, linkage analysis employing microsatellite markers became a common approach to map loci for complex diseases. In this approach, families with multiple affected members are phenotyped and genotyped. Regions of the genome relevant to the disease can then be detected using allele association or parametric approaches. One advantage of this approach is that a simplified

set of genes may be responsible for the disorder within a particular family compared to the population as a whole. However, this advantage melts away when multiple families are required, as is nearly always the case to obtain adequate statistical power. Another substantial disadvantage of linkage analysis in humans is the cost and difficulty of identifying multiple multigenerational families with the disorder of interest. In addition, mapping power in this design is limited and the responsible genes are frequently poorly localized to regions covering many megabases. A common paradigm since the late 1990s has been to carry out linkage analysis followed by targeted association testing of candidate genes in promising regions or to test for linkage disequilibrium (LD) with genetic markers in the region. Linkage disequilibrium is a situation in which neighboring alleles are linked more often than expected by chance, either because of low recombination rates between the markers or because of expansion of a founder population in which the alleles were originally linked. The first successful example of the twostep strategy was the identification of the calpain 10 gene as a causal gene in type 2 diabetes in a large set of Mexican families, although this finding remains somewhat controversial (Horikawa et al., 2000). A very clear subsequent success was the identification of the NOD2 gene as an important contributor to Crohn’s disease (Hugot et al., 2001; Ogura et al., 2001). Another example is the identification of the USF1 gene as a cause of familial combined hyperlipidemia (Pajukanta et al., 2004).

Relating DNA Variation to Phenotypes

Genome-wide Association Although the combination of linkage and association has been successful in several instances, only a handful of genes have been identified to date. However, with the advent of cheaper methods for the detection of polymorphisms, genome-wide association studies have become feasible (Hirschhorn, 2005; Hirschhorn and Daly, 2005) (see Chapter 8). Thus, association, the strategy first used for genetic dissection of human traits, has come back to center stage. The first example was a study of heart disease by Ozaki et al. (2002), who typed thousands of SNPs in thousands of individuals in Japan (Ozaki et al., 2002). The several genes exhibiting strong evidence of association with heart disease were then typed in a second set of individuals, and one gene, encoding lymphotoxin-, was found to exhibit a consistent association. Another very striking success was the identification of complement factor H in age-related macular degeneration (Klein et al., 2005). In that study, a relatively small group of individuals (96 cases, 50 controls) were typed for 116,204 SNPs, and a common non-synonymous SNP was found to be strongly associated with the disease even after a conservative Bonferroni correction for multiple comparisons. Such whole-genome association studies are rapidly becoming the major source of information on complex disorders in humans as the available technologies become more widely used and are allowing interrogation of 105–106 SNPs. Very recent successful examples include the identification of loci for breast cancer (Easton et al., 2007), type II diabetes (Diabetes Genetics Initiative et al., 2007; Scott et al., 2007; Steinthorsdottir et al., 2007; Zeggini et al., 2007), coronary artery disease (Samani et al., 2007), asthma (Moffatt et al., 2007), and a large study which identified loci contributing to bipolar disorder, coronary artery disease, Crohn’s disease, rheumatoid arthritis, and type 1 and type 2 diabetes (Wellcome Trust Case Control Consortium, 2007). Most gratifying is the high degree of agreement between different studies for identified loci for the same phenotype. Also interesting was the fact that large majority of the loci exerted small effects on the final disease risk (see Chapter 8). Copy Number Variation Another recent realization emerging from the use of microarray genotyping is the prevalence of copy number variants (CNVs) which constitute about 10% of the human genome (e.g., Sebat et al., 2004) (see Chapter 9). The CNVs are type of polymorphism in which segments of DNA with an average length of 500 kb show variation in copy number either due to deletion or duplication. This source of variation is beginning to be explored with respect to common complex disorders, one recent example being autism (Sebat et al., 2007). Common Disease/Common Variant Hypothesis The success of modern genotyping technologies in analyzing common complex disorders depends partly upon the correctness of the common disease/common variant hypothesis. This hypothesis states that common genetic diseases are due

■

39

to common polymorphisms. On the face of it, this hypothesis seems plausible, and there is evidence that it is at least partially true for many common complex diseases (Hirschhorn, 2005). However, it now appears that a significant fraction of common disease will be due to many different rare mutations. In fact, this was observed in a subset of the NOD2 mutations underlying Crohn’s disease (Hugot et al., 2001). More recently, studies of common variations in plasma lipoprotein levels, involving sequencing of candidate genes in many individuals, have clearly demonstrated the importance of rare alleles (Cohen et al., 2004, 2006). Thus, efficient sequencing approaches are likely to be important in understanding at least some forms of complex traits. Although there have been tremendous recent advances in genotyping speed, the throughput is still insufficient to interrogate all possible polymorphisms over the entire genome. In addition, even modern genotyping technologies will not easily identify rearrangements such as inversions, which may be more common than previously thought (Tuzun et al., 2005). Estimates based on mutation rates suggest that nearly every nucleotide in the genome is altered in at least one individual on the planet (see Chapter 1). Isolated Populations As described in a previous section, one of the major stumbling blocks in human genetics is allelic heterogeneity. One potentially helpful approach in analyzing complex human genetic traits is to use isolated populations. These populations are thought to have an extremely small number of founder individuals, often estimated as being in the hundreds. This bottleneck means that a much smaller number of genes and alleles cause the disease phenotype, simplifying the genetic heterogeneity of many complex diseases. Useful isolated populations have been identified in the Hutterites of rural North America, as well as populations in Finland and Iceland. Some theoretical studies suggest that for common complex disorders the advantages of using an isolated population may be blunted compared to that for rare alleles (Hirschhorn and Daly, 2005). This is because common disease alleles will be proportionally represented even in a relatively small founding population. Nevertheless, the promise of isolated populations has been fulfilled in at least a few examples. These include the identification of genes involved in familial combined hyperlipidemia (Pajukanta et al., 2004) and obesity (Suviolahti et al., 2003) in Finland and genes involved in type 2 diabetes (Grant et al., 2006), myocardial infarction (Helgadottir et al., 2006), and schizophrenia in Iceland (Stefansson et al., 2002). HapMap Project Human genetic analysis is currently limited by both genotyping power and ascertainment of sufficiently large study populations for analysis. Strategies are therefore needed to maximize the information obtained from these precious datasets. It was observed that contiguous regions of the mammalian genome tend to remain linked together on the chromosome in which

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Genomic Approaches to Complex Disease

they reside (Rioux et al., 2001). These regions in LD are called haplotype blocks and their existence led to the realization that one SNP, a so-called “tag SNP”, could in principle effectively “stand in” for a larger number of adjacent SNPs. It was conjectured that using carefully chosen tag SNPs could extend the power of present genotyping technologies, thus improving coverage of the genome (see Chapter 2). These observations led to the international HapMap project, an endeavor to comprehensively identify haplotype blocks in the human genome. The main problem in decoding haplotypes is that genotype information represents the admixture of two different linked haplotypes from the parental chromosomes. The admixed “messages” have to be deconvoluted to provide haplotype information. Two distinct approaches have been taken. In the publicly funded approach, genotyping of 269 individuals from four different ethnic human groups was used to identify blocks of SNP alleles that tend appear together more often that would be expected by chance (Altshuler et al., 2005). The other approach taken by the company Perlegen used purification of individual chromosomes by somatic cell genetics, so that the haplotypes could be read out directly (Hinds et al., 2005; Patil et al., 2001). In both approaches, blocks were found to extend for varying lengths in the genome, ranging from 1 kb to 100 kb. Despite this variation, the data should allow more efficient design of genotyping assays. In addition, the uncovered haplotype data will likely be useful in understanding the history and structure of human populations throughout the world. Quantitative Trait Locus Mapping in Model Organisms Animal models, particularly strains of mice and rats, exhibit common variations in traits that are relevant to most common diseases. The mapping of loci contributing to such traits using quantitative trait locus (QTL) analysis is straightforward (Flint and Mott, 2001). At present, thousands of loci for hundreds of complex traits have been mapped, but, apart from positional candidates, only a handful of genes have been identified. The QTL studies in mice suffer from some of the same problems as linkage in humans: the loci identified are usually very large and the underlying genes usually explain only a small fraction of the variance of the trait. Flint et al. (2005) provide an excellent summary of the status of QTL mapping and of new strategies. As discussed below, genome-wide “omics” technologies are expected to greatly facilitate disease gene identification in studies with animal models.

INTEGRATION OF “OMIC” TECHNOLOGIES WITH GENETICS Systems Biology High-throughput assays for DNA sequence variation, transcript levels, protein levels, protein interactions, and metabolites, in combination with data integration methods, are beginning to

transform studies of complex disease and of biology in general. This area of research has become broadly known as systems biology, the scientific discipline that seeks to quantify all of the elements of a biological system and assess their interactions (Hood et al., 2004). An important concept is that the interaction of the parts of a system creates properties or functions that would not be expected from looking at the individual components on their own (Table 3.3) (see Chapter 6). Transcriptomic Analysis The “omic” technology that has had the largest impact on complex disease is the measurement of transcript levels using highdensity microarrays.The original arrays consisted of spotted cDNA capture probes, but the more recent technologies, including Affymetrix and Illumina, utilize synthetic oligonucleotides as capture probes (discussed in Pravenec et al., 2003) (see Chapter 13). In any segregating population, there will be hundreds or thousands of genetic variations that affect gene expression. It seems clear that these variations in expression will make important contributions to complex disease. With the development of microarray technology, it is now possible to quantify transcript levels globally and to integrate such data with information relating to disease. One approach has been to map a critical genomic region for a disease using affected and unaffected individuals and then pinpoint the responsible gene by identifying differentially expressed genes in the critical region. For example, in the rat animal model, a combination of congenic mapping and microarray analysis identified the fatty acid translocase Cd36 gene as a cause of insulin resistance and hypertriglyceridemia (Aitman et al., 1999). In the case of complex, heterogeneous disorders, no one gene would necessarily be expected to exhibit altered expression in cases versus controls, but certain pathways might be perturbed. For example, Mootha and colleagues developed an analytic strategy, termed “Gene Set Enrichment Analysis,” to detect modest but coordinate changes in the expression of functionally related genes (Mootha et al., 2003). They applied the method to muscle biopsies from diabetic versus control individuals. Although no single gene was significantly associated with diabetes, the mitochondrial and oxidative phosphorylation pathways were significantly enriched in genes that were modestly perturbed in diseased individuals. Genetical Genomics A particularly powerful way of integrating expression and clinical data is to apply genetics, an approach that has been called “genetical genomics” (Jansen and Nap, 2001). Thus, if transcript levels are measured in segregating populations, the loci controlling transcript levels can be mapped using the same approaches that have been used to map loci for complex clinical traits. The loci controlling transcript abundance have been termed “expression QTLs,” or eQTLs (Schadt et al., 2003). When an eQTL encompasses the physical location of the gene encoding that transcript, it is likely that the causative genetic variation resides

Integration of “Omic” Technologies with Genetics

TABLE 3.3

■

41

Systems biology and genomic medicine

Disease phenotype

Systems biology approach

Insights

Technology

References

Insulin resistance/ hypertriglyceridemia

Transcript analysis

Cd3 gene causative

Microarrays

Aitman et al., 1999

Diabetes mellitus (type II)

Gene set enrichment analysis

Mitochondrial/oxidative phosphorylation plays a role

Microarrays

Mootha et al., 2003

Obesity

Genetical genomics

Molecular subtyping can better define complex phenotypes

Microarrays

Schadt et al., 2003

Hypertension

Genetical genomics

Correlation of biological and expression phenotypes

Microarrays

Hubner et al., 2005

Obesity, diabetes

Genetical genomics

Correlation of biological and expression phenotypes

Microarrays

Lan et al., 2006

Embryonic viability

Interactome mapping

Hub genes more likely to be essential for survival

Yeast 2 hybrid, co-affinity purification, literature curation

Gandhi et al., 2006

Inherited disease

Interactome mapping

Gene products from similar disorders tend to interact

Yeast 2 hybrid, co-affinity purification, literature curation

Gandhi et al., 2006

Cancer

Interactome mapping

Oncogenes can be related to known signal transduction pathways

Yeast 2 hybrid, co-affinity purification, genetic crosses in C. elegans

Tewari et al., 2004

within the gene itself (i.e., the transcript is likely to be regulated in cis). Conversely, when an eQTL does not encompass the physical location of the gene, the transcript must be regulated in trans (i.e., under the control of a different gene). Thousands of such eQTL have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees (Cheung et al., 2005; Doss et al., 2005; Ghazalpour et al., 2005; Hubner et al., 2005; Schadt et al., 2003). One of the initial surprises in these studies has been the extent of variation at the level of transcript levels. In recent studies in mice, where experiments are greatly simplified compared to human studies, a large F2 intercross between two common laboratory strains of mice revealed 6676 significant eQTLs (p  5  105) in liver, over a thousand of which were cis-acting eQTLs (Wang et al., 2006). Similarly, large numbers of common variations are likely to exist in human populations. In the near future, one can envision a genome-wide database relating common DNA variation with common transcript level variations (in other words, a map, such as the HapMap of all common variations in transcript levels). Obviously, such a map would help in prioritizing candidate gene for loci contributing to complex diseases (i.e., genes with cis-eQTLs at the

locus of interest would constitute a restricted set of candidates). Also, it might be expected that transcript levels of the causative genes will be significantly correlated with clinical trait values, so determining Pearson or Spearman correlation coefficients between these may be helpful. Genetical genomic data could also be used in a converse manner to exclude potential candidate genes (Lan et al., 2006). There are two important caveats in considering the relationship of an eQTL and a clinical trait. First, gene expression patterns are cell- and tissue-specific, and certain inaccessible tissues may be required to understand a complex trait (although it has been observed that cis-eQTLs usually tend to affect gene expression in all tissues in which that gene is expressed). The second is that the regulation of biologic processes occurs via control of gene expression in many, but certainly not all, situations. Molecular Subtyping Another use of genomic and other “omic” technologies is to define distinct molecular subtypes for a clinical phenotype. This has been particularly useful in the case of cancers (Bucca et al., 2004), but it should be applicable to most complex disorders. For example, in one study examining liver expression array data

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Genomic Approaches to Complex Disease

in an intercross, distinct subgroups of mice were identified with respect to body fat (Schadt et al., 2003). The array data were consistent with the idea that the classically defined trait of fat pad mass actually consists of at least two molecularly distinct subgroups. Analyzing these two subgroups separately gave much greater sensitivity in detecting loci for obesity. While such an approach has yet to be employed in human complex traits, there is a strong interest in using high-dimensional data methods, such as microarrays and mass spectrometry, to identify biomarkers for disease (Bailey and Ulrich, 2004). It is conceivable that some of these investigations may detect different subclasses of disease that would usually be lumped into one group using classical methods. Separate genetic analysis of these subclasses may well give greater sensitivity to detect the genes responsible for complex human conditions. A particularly powerful application of genetical genomics is to identify genes that are causally related to clinical traits. For example, in a recent study of a cross between two strains of mice differing in adiposity, Schadt and colleagues used liver expression array data to identify genes likely to be causally involved in visceral fat levels (Schadt et al., 2005). It was reasoned that genes controlling adiposity would likely also control expression of genes determining fat levels. However, correlation of gene expression levels with adiposity could represent an effect rather than a cause of obesity. Using mathematical conditioning, it was possible to distinguish between these possibilities. Some of the top candidates were then examined using transgenic mice either overexpressing or underexpressing the causal genes, and it was observed that a large fraction of those genes did indeed influence adiposity. Proteomics The above approaches were made possible by the high-throughput analysis of the transcriptome. The analysis of the full proteome has not yet been achieved, but rapid progress is being made (Domon and Aebersold, 2006). It is clear that the number of distinct proteins in mammals will certainly greatly exceed the number of genes, and proteomes will exhibit large and dynamic complexity. While proteomic and systems approaches have had only a limited role in understanding complex disease to date, this situation is bound to change as the depth and reliability of the databases continues to improve. Interactome The connections between gene products can be mapped using genome-scale surveys of protein-binding partners. The universe of protein–protein interactions is known as the interactome. Two widely used technologies for charting this space are yeast twohybrid screening and co-affinity purification combined with detection by MS (Cusick et al., 2005). Yeast (Gavin et al., 2002; Ho et al., 2002; Ito et al., 2001; Uetz et al., 2000), worms (Li et al., 2004), flies (Formstecher et al., 2005; Giot et al., 2003; Stanyon et al., 2004), and mammalian cells (Rual et al., 2005) have been investigated using these methods. For example, a recent study used yeast two-hybrid screening to examine all pairwise interactions

among 8100 human genes (38% of all genes) and detected 2800 interactions (Rual et al., 2005). A major disadvantage of high-throughput protein interaction surveys is their limited reliability. False-positive and false-negative results are currently estimated at 40–50% for both yeast two-hybrid and MS methods (de Silva and Stumpf, 2005; Rual et al., 2005). Another strategy to map genetic networks employs curation of protein interactions from the literature (Gandhi et al., 2006; Reguly et al., 2006). These datasets are thought to have higher reliability since many interactions have been verified by independent methods from more than one group. The Human Protein Reference Database (HPRD) contains 8800 proteins (42% of all genes) and 25,000 interactions (Gandhi et al., 2006). Nevertheless, curation databases suffer from selection bias and do not randomly sample all protein interactions. Furthermore, comparison of yeast two-hybrid data and literature-derived interactions suggests that the literature-curated data can also suffer from variable quality (Rual et al., 2005). The networks derived from interaction mapping are often scale-free. Such networks show a power law distribution relating the probability of interactions, P(k), of a node on the network with k other nodes: P(k)  k, where  = 2 (Carter et al., 2004). A feature of scale-free networks is the presence of a few highly connected nodes or hubs, representing genes that are highly connected to many other genes. This network class is robust to random disconnection of individual nodes, but is vulnerable to disruption of highly connected hubs. Correspondingly, genes in hubs are found significantly more likely to be essential for survival (Gandhi et al., 2006). Both scale-free and classical random graphs display “small world” behavior, in which moving between any two nodes on the network takes far fewer steps (edges) than naively expected. Interactome Insights Some interesting insights related to disease have emerged from analysis of these networks. For example, the HPRD revealed that protein products of genes mutated in inherited genetic disorders tend to interact with proteins involved in similar disorders. This observation suggested that certain gene subnetworks are involved in particular diseases and can help guide the search for new genetic culprits. In another example, it was recently shown that combining yeast two-hybrid data, co-affinity purification data, and genetic experiments in worms gave a much sharper outline of the TGF- signaling pathway than using any one dataset alone (Tewari et al., 2004). A total of nine new TGF- signaling related genes were found, of which seven were conserved in humans. One of these had functional homology to the human SNO/SKI oncoprotein. Metabolomics No less promising than proteomics in its relation to early diagnosis and treatment of disease is metabolomics (Claudino et al., 2007) (see Chapter 15). It is estimated that there are approximately 3,000 metabolites, compared with 20,000 genes. The

References

main analytic tool for the metabolome is proton nuclear magnetic resonance spectroscopy (1H NMR). Another useful approach is MS, which has greater processing cost per sample but is thought to have more sensitivity. Metabolomic approaches have been used successfully for non-invasive diagnosis of a number of disorders, including breast cancer, hepatitis, ovarian cysts, and ovarian cancer (Claudino et al., 2007). Many of these studies used unsupervised analytic approaches such as principal components analysis (PCA) to distinguish diseased and nondiseased samples (Griffin, 2006) (Figure 3.2).

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to fruition with the first complete genome sequences of individual members of many species. With the goal of the $1000 genome on the horizon, it is probable that relatively soon we will have complete genome sequences for many individuals within one species. Recent genomic advances in transcript profiling, proteomics, metabolomics, and molecular imaging suggest that comprehensive knowledge in orthogonal descriptive directions is also possible (Figure 3.2). This is systems biology. The challenge now is to integrate these diverse modes of knowledge to a better understanding of complex genetics in both health and disease and make the field of genomic medicine a practical reality.

CONCLUSIONS AND PROSPECTS In his famous textbook Molecular Biology of the Gene (Watson, 1965), James Watson pointed out that the genome is not of infinite size (Olson, 2003). He therefore argued (within physical limits) that in principle we could know everything about the molecular description of a cell or organism. This vision has come

ACKNOWLEDGEMENTS Supported by NIH R01 NS050148, MH71779, NS043562 (D.J. Smith) and NIH HL30568, HL30568, HL28481, DK071673 (A.J. Lusis).

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RECOMMENDED RESOURCES 454 Life Sciences www.454.com Affymetrix www.affymetrix.com Applied Biosystems https://www2.appliedbiosystems.com/ Gene ontology – the gene ontology classification, http://www.geneontology.org Genome-phenome superbrain http://omicspace.riken.jp HapMap http://www.hapmap.org/ Helicos Biosciences http://helicosbio.com Illumina www.illumina.com

Jackson laboratory – a repository for genetic and genomic information on the mouse, http://www.jax.org National Center for Biotechnology Information – repository for SNP information, comprehensive literature database and much more, www.ncbi.nlm.nih.gov Perlegen http://www.perlegen.com/ Sequenom www.sequenom.com UCSC Genome Browser – available genome sequences, http:// genome.ucsc.edu/

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4 Human Health and Disease: Interaction Between the Genome and the Environment Kenneth Olden

INTRODUCTION The importance of inheritance versus the environment in the origin of various diseases has been debated for more than a century. The phrase “nature versus nurture” was coined by Francis Galton (a 19th-century British scientist) to distinguish between characteristics one is born with and those that are acquired as a result of one’s surroundings. Early on, humans looked to the external environment to explain morbidity and mortality as they were cognizant of their exposure to natural agents that were poisonous. Because of investments in environmental health research, we now know that many of the chemical, physical, and biological agents in the environment not only cause acute illness and death, but also increase risk for various chronic diseases such as cancer, asthma, hypertension, stroke, Alzheimer’s, Parkinson’s, infections, and various developmental disorders. However, the era of dominance of external factors as the primary contributor to human disease gradually faded with the development and growth of Mendelian genetics (Dobzhansky, 1941) and the more recent field of genomics (Trask, 1999). In fact, the geocentric view of disease has dominated our concept of the origin of disease for the past 50 years. However, with the completion of the sequencing of the human genome (Lander et al., 2001; Venter et al., 2001), it now seems apparent that complex human Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

phenotypes cannot be explained based solely on the action of genes or their protein products. Genes account for only a small part of the phenotype, and the environment has a spectacular impact. That is, the onset and clinical course of most chronic diseases are influenced by complex interactions between genes and the environment (Olden, 2007). The concept of gene–gene and gene–environment interactions is not new. Garrod (1902) was one of the first to suggest that the effects of genes could be modified by the environment. He suggested that individual differences in genetics could play a role in variation in response to drugs, and that the effect of genotype could be modified by the diet. Wright (1932) also emphasized the existence of a functional relationship between various biological endpoints and networks of genes and environmental factors in his studies of mutation, selection, and breeding, and Dobzhansky and Levene (1955) reported the existence of genotype–environment interaction with respect to viability of some chromosomes in Drosophila. Whereas concerns about gene–environment interactions have had a long history (reviewed by Haldane, 1946), the importance of understanding these interactions as a means to prevent complex diseases emerged only over the past 15 years (Olden, 1993; Olden and Guthrie, 2000; Olden et al., 2001; Schwartz and Collins, 2007). The geocentric view of disease was virtually unchallenged until the leadership of Copyright © 2009, Elsevier Inc. All rights reserved. 47

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the environmental health community began to emphasize gene– environment interaction as a top research priority in 1992 (Olden, 1993; Washington FAX, January 21, 1992). A few years later, the initiation of the Environmental Genome Project (EGP) (discussed later in this chapter) represented the first large-scale effort to discover susceptibility alleles likely to be important in gene–gene or gene–environment interactions (Brown and Hartwell, 1998; Guengerich, 1998; Kaiser, 1997; Olden and Wilson, 2000). Prior to the initiation of the EGP, most of the momentum for research on gene–environment interactions was occurring in the field of pharmacogenetics to identify genetic variants that influence drug efficacy. As a consequence, many published studies have linked specific polymorphisms with specific drug responses (Frye, 2004; Hulla et al., 1999; Nebert et al., 1996). Studies have shown that interindividual pharmacokinetic variation in rates of drug elimination can vary dramatically (Vessel, 1991). Multinational twin studies were conducted on the kinetics of several drugs to compare relative contribution of genetics and environmental factors with respect to interindividual variations. Results were remarkably similar for the various populations, and pharmacokinetic variation virtually disappeared within monozygotic twins, but was preserved within most dizygotic twins; meaning that variation in drug metabolism is primarily under genetic control. Altering the environment of the monozygotic twin had no effect on kinetics. If reported pharmacogenetic differences of 10- to 200-fold can be extrapolated directly to the risk of human diseases, one could conclude that an individual can be 10- to 200-fold more sensitive to a given drug or environmental chemical because of differences in expression and activity of metabolic enzymes. Recent studies have shown a similar association between specific diseases and various genetic polymorphisms. Some examples of both types of genotype–phenotype association studies will be described later. However, even when all the highly penetrant alleles have been discovered and all the gene–gene and gene–environment interactions are understood, it may still be difficult to relate genotype to phenotype because of the additional layers of complexity associated with post-translational processing and protein–protein interactions. Kouhry and Wagner (1995) suggested that there are at least six types of gene– environment interactions. In the first situation, neither the environment nor the genotype has any effect alone, but can interact to increase risk. In the second situation, the genotype has no effect in the absence of exposure, but can exacerbate the effects of the latter. The third situation is the converse of the second; that is, the environment is without effect alone, but can enhance the effect of the genotype. In the fourth situation, both the environmental exposure and the genotype increase risk for disease, but the combination is interactive or synergistic. The fifth and sixth situations represent cases in which gene–environment interactions are protective. Phenylketonuria is an example of disorder that is caused by both the susceptibility genotype and exposure to phenylalanine in the diet. In contrast, lung cancer can be caused by cigarette smoking even if the susceptibility genotype is not present, and acute hemolytic anemia can be caused by the susceptibility

genotype alone even in the absence of fava bean digestion. Individuals with the susceptibility genotype for a1-antitrypsin deficiency have a high risk for development of emphysema, but the risk is greatly increased by cigarette smoking. The model of gene–environment interaction that has been most studied relates to metabolic susceptibility genes and chemical carcinogenesis. Polymorphisms in carcinogen-metabolizing enzymes (to be discussed later) are relatively common in the population and can play a major role in the development of the common cancers. The phrase gene–environment interaction means that the direction and magnitude of the effect that a genetic variant has on the phenotype can vary as the environment changes; that is, genetic risk is modifiable in an environment-specific manner or that the phenotypic effect of a mutation or environmental exposure depends on the genetic background. Whether a particular variant allele is expressed, the degree to which it is expressed, and when it is expressed can be influenced by the environment or by other genes. However, since the genotype evolves or changes very slowly over hundreds of years, one can assume that most of the increase in disease burden in industrialized nations is the result of a variable environment interacting with a relatively constant genetic substrate. Similar to gene–environment interactions, variants in multiple genes can contribute to the expression of phenotype. For example, multiple genetic loci influence the rate of progression of HIV infection to full blown AIDS (Dean, 2003). The nonsense allele, V641, in the CCR2 gene and heterozygosity for the cytokine receptor/HIV-1 co-receptor CCR5- 32 allele is reported to be associated with the slower progression of AIDS (Dean et al., 1996; Smith et al., 1997); in contrast, a haplotype in the promoter region of CCR5 is associated with the more rapid progression of HIV infection to AIDS (Martin et al., 1998). Also, progression of HIV-infected patients with more heterologous HLA Class I genes is considerably slower (Carrington et al., 1999).

IMPORTANCE OF THE ENVIRONMENT Over the past 50 years, the prevalence of chronic diseases has reached epidemic proportions in industrialized countries, and various governments have expended enormous resources to identify genetic causes. Nevertheless, the expectation that simple direct paths would be found from genes to disease has not been realized. Several explanations have been put forward to explain failure, but gene–gene and gene environment interactions are among the most likely. The merging view is that exposure to environmental toxicants has conspired with genetics to promote the development of these disorders. It is now well established that alterations in high penetrant genes explain only a small fraction of complex diseases, and such genes represent only a small fraction of variations relative to the more common polymorphisms that have a less disruptive effect on protein function. However, it is much less obvious what portion of the disease burden is attributable to interactions among low penetrant

Importance of the Environment

genes versus interactions of low penetrant genes with the environment. Nevertheless, a prominent role for the environment is supported by findings of geographic differences in incidence of disease, by variations in trends over time and by studies of disease patterns in immigrant populations (Doll and Peto, 1981). Also, population-based, twin-cohort studies, the “gold standard” for distinguishing between the contribution of genetics and the environment, suggest that the environment plays a prominent role in the development of human disease. In many cases, concordance among monozygotic twins is very low. For example, such recent studies show that no more than one-third of the cancer burden can be attributed to the action of genes alone (Lichtenstein et al., 2000; Verkasala et al., 1999), only 15% of Parkinson’s disease (Tanner et al.,1999), and about a third of autoimmune diseases (Powell et al., 1999). In a much earlier landmark study, Doll and Peto (1981) estimated that about one-third of all cancers are caused by genes alone based on demographic distribution of the disease in the United States. And, a recent study (Khot et al., 2003) reported that 90% of individuals with severe heart disease have at least one or more of the four classic risk factors captured in the current definition of the environment (Olden et al., 2001). Because of these and other studies, it is now generally accepted that a single risk factor is rarely responsible for the development of most chronic diseases (see Chapter 3). Rather, diseases commonly arise from complex interactions involving genes, environmental factors, behavior, and stochastic events associated with DNA replication and endogenous metabolism, as depicted in the schematic model shown in Figure 4.1. Age and stage of development are also important factors in disease development, as they influence the timing of gene expression and susceptibility to environmental toxicants. Many people with susceptibility genes never actually develop the disease, even for diseases caused by highly penetrant alleles (e.g., BRCA1 and BRCA2 breast cancer genes) (Futreal et al., 1994). There is always a subpopulation of carriers of susceptibility genes who do not progress to the ultimate disease state. Whether one can account for the discordance between genotype and phenotype by lack of exposure to unknown environmental risk factors or to yet undiscovered genomic variations or gene–environment interactions

Intrinsic Genetic Susceptibility

Environmental Exposures

Human Health/Disease

Behavior/Age/Stage of Development

Figure 4.1 Schematic model of interactive factors involved in human health and disease.

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is not known. However, existing evidence for a prominent role for the environment in the development of human disease is so compelling that Rothman et al. (2001) concluded that “the epidemiologic evidence accumulated to date indicates that environmental exposures, broadly defined to include lifestyle factors, are responsible for most cancers.” Others have concluded that the new data now being reported “blow away” the myth that “bad genes” are responsible for the majority of human morbidity and mortality (Olden, 2004a, b). That is, individual life history may be just as important as genetic background in predicting and understanding the development of complex traits. Genetics Loads the Gun, but the Environment Pulls the Trigger Is the relationship between genes and the environment analogous to that of a loaded gun and its trigger, as previously proposed? Olden and Wilson (2000) argued that the inheritance of disease susceptibility alleles creates the potential for adverse health outcomes, but that the adverse health outcome may never be realized provided the carrier can avoid environmental exposures capable of interacting with the susceptibility gene(s) to trigger disease development. If this analogy fully captures the relationship between environmental susceptibility alleles and specific environmental exposures, this can be good news because amelioration of environmental hazards could lead to a dramatic reduction in the incidence of chronic diseases in human populations. From an evolutionary point of view, one could argue that genes and gene variants that were adaptively selected in the past because they endowed the human species with survival or reproductive advantage, now have the potential to increase disease risk because of their incompatibility with the environment. The change in the environment, resulting from human activity and technology innovation, has outpaced evolution of the human genome to the extent that there is now a nature–nurture mismatch. For years, environmental health advocates have had to deal with the question of why more bodies are not piling up on the streets if industrial chemicals, such as benzene, vinyl chloride, and dioxins, are so harmful. Why can some people withstand exposure to nerve gases such as sarin, and organophosphate pesticides such as malathion and parathion? Why do fewer than 25% of smokers develop lung or bladder cancer? How do some individuals who work in the petrochemical industry survive years of exposure to benzene and never develop leukemia? Human population studies to assess gene–environment interactions are beginning to provide answers to these questions. For example, about 25% of Asians and 10% of Caucasians have a protective form of the gene that codes for paraoxonase (PON1, to be discussed later), an enzyme that detoxifies organophosphates, such as sarin and the above-mentioned insecticides, ten times faster than the more common form of the enzyme (Costa and Furlong, 2002). Also, common polymorphisms in genes for the cytochrome P450 enzymes determine how one metabolizes carcinogens (i.e., arylamine) in cigarette smoke or benzene in the environment of petrochemical workers (Lower et al., 1979; Smith et al., 2002). Such studies are beginning to

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explain why a given polymorphism can be highly beneficial in one environment and virtually lethal in another (Boguski, 1999). Furthermore, they suggest that risk reduction can be achieved by re-engineering of genes or by modification of the environment. Now that the Human Genome Project (Lander et al., 2001; Venter et al., 2001) and the haplotype mapping (HapMap) project (Altshuler et al., 2005) have been completed, the major challenge facing biomedical researchers is to elucidate the interactive relationship between genes and between genes and the environment. While descriptive studies by toxicologists and epidemiologists have shed some light on the possible genetic and environmental risk factors, the actual causes and mechanisms of disease development remain poorly understood. Investments in the aforementioned sequencing and mapping projects led to the development of powerful new tools to conduct large-scale, population-based studies necessary to untangle complex interactions between genes and environmental factors. However, the design and conduct of such studies will require the development of even better tools and fundamental restructuring of the biomedical research enterprise to put greater, although not exclusive, emphasis on interdisciplinary research teams. Promise of New Technologies for Unraveling Complex Interactions Between the Human Genome and the Environment The Nobel Prize laureate Sydney Brenner has written that “progress in science depends on new technologies, new discoveries and new ideas, probably in that order” (Brenner, 2002). In no discipline is this more evident than in environmental health research. Environmental health regulation has long been constrained by limitations in our ability to determine the risk presented by specific xenobiotic agents. Integration of genomics and toxicology to develop a new field of toxicogenomics may revolutionize risk assessment and environmental health regulation (Lovett, 2000). Over the past 50 years, advances in molecular biology have dramatically improved our understanding of human biology. Such innovations include the discovery of the DNA double helix (Watson and Crick, 1953), development of recombinant DNA technology (Watson et al., 1983), the polymerase chain reaction (Chamberlin, 1982), siRNA technology (Baulcombe, 2002), gene and protein microarray technology (Brown and Botstein, 1999; Schena et al., 1995), and DNA sequencing methodology (Lander et al., 2001). For example, an important insight gained from gene sequencing studies is that the human genome consists of a large number of polymorphic sites, some of which are associated with altered biological function or phenotype (see Chapter 1). The finding of a large number of variant forms of genes suggested that genetic polymorphisms may play a significant role in susceptibility to disease or toxic exposure. While descriptive studies by toxicologists, molecular geneticists, and epidemiologists, working independently, have shed some light on this topic, the actual role of gene variation in susceptibility to disease development remains poorly understood.

THE ENVIRONMENTAL GENOME PROJECT Although we are beginning to understand how mutations in single genes effect phenotype, we have very limited knowledge of how variations in different genes influence each other and the sensitivity of biological mechanisms to environmental perturbation. But, with the completion of the reference sequence of the human genome, the field of genomics shifted from gene discovery to the study of gene function and elucidation of the genetic basis for differences in susceptibility to common diseases. Although reference is made to the human genome, the concept of a single genome is misleading. Whereas variation in the human genome is not greater than 0.1% among individuals in the population, this represents very significant variation considering that the human genome is composed of approximately three billion nucleotide base pairs. Genetic linkage analysis has been a powerful approach in the discovery of high penetrance allelic variants (e.g., BRCA1/2). However, this approach has not yet been particularly useful in the identification of low penetrance susceptibility alleles associated with the development of complex diseases such as cancer, cardiovascular disease, asthma, diabetes, Alzheimer’s and Parkinson’s disease because the relative low risk associated with a specific allele may be influenced by unknown environmental factors. Of the many types of variations in the human genome, single nucleotide polymorphisms (SNPs) are the most common (Collins et al., 1999). Estimates are that there is on average one SNP per 1300 base pairs, four to five per gene-coding region, or approximately 11 million in total in the human genome (Kruglyak and Nickerson, 2001). The EGP was formally initiated by the US National Institute of Environmental Health Sciences (NIEHS) in 1997 to investigate the role of genetic polymorphisms in susceptibility to environmentally induced disease (Brown and Hartwell, 1998; Kaiser, 1997, 2003). In this regard, NIEHS was way out front in recognizing the importance of genetic variation for understanding human disease. Following their lead, other large-scale public and private initiatives have been developed to identify the millions of SNPs in the human genome. Most SNPs can be expected to be neutral with respect to alteration of environmental sensitivity. To examine the extent of diversity in the human genome, the EGP was organized into five research areas: (1) SNP discovery; (2) functional analysis; (3) disease association studies; (4) development and refinement of technologies to improve sequencing, functional analysis, and population-based studies; and (5) development of policies to address possible ethical, social, and legal issues associated with genetic susceptibility studies. The 544 candidate genes included in the initial survey (see http://www.genome. utah.edu/genesnps and http://www.niehs.nih.gov/research/ supported/programs/egp/) had already been characterized with respect to function and were known to have a significant probability of playing a role in susceptibility to specific environmental exposures. The resequencing analysis of individual DNA samples was based on samples from 90 individuals, representative of the

The Environmental Genome Project

diversity in the US population. This design would allow for the detection of variation that occurs at a frequency of one percent or greater. As of September 2007, approximately 619 environmental response genes have been resequenced with the discovery of 88,016 SNPs. All of the SNPs entered into the database have been validated by multiple sequence analysis and include genotype frequency information. This highly useful SNP database is publicly available (http://www.egp.gs.washington.edu/welcome.html). Decades of studies by environmental health researchers, demonstrating tremendous variation in individual response to environmental exposures, provided the intellectual foundation for the massive resequencing effort undertaking in the EGP. Presumably, natural selection, resulting from exposures to various environmental insults, has operated over the course of human history to promote the evolution of sophisticated metabolic pathways to suppress phenotypic variation or buffer against toxic injury. Buffering is a fundamental aspect of biology, and there are likely to be multiple mechanisms involved, including genetic redundancy, feedback regulation, and cooperative biochemical interactions (Wagner et al., 1997; Gibson and Wagner, 2000; Hartman et al., 2001). Buffering was first inferred from the observation that organisms can acquire a wide range of phenotypes when subjected to varying genetic or environmental stressors (Scheiner et al., 1991). Collectively, the various buffering pathways involved in protecting the organism against environmental stressors have been referred to as the “environmental response machinery” (Olden and Wilson, 2000). According to this model, all human genes, including those that encode protein components of the environmental response machinery, are subject to genetic variability (mutations or polymorphisms) that can alter the metabolic efficiency of protective pathways. Some alleles can strengthen protection and others can reduce it. Another result emerging from the SNP discovery effort is that the variants tend to associate with one another in a nonrandom manner known as linkage disequilibrium (Carlson et al., 2004); this pattern of association is called a haplotype. On average, the EGP candidate genes are organized into 25 haplotypes (Wilson and Olden, 2004). The current thinking is that the generation of haplotype and linkage disequilibrium data will be extremely useful in large-scale genotyping studies to investigate the association between specific genes and disease. Such studies are now underway in several laboratories (see Chapter 8). However, the goal of assigning specific genotypes to phenotypes with respect to environmental sensitivity has proven to be a daunting assignment. For functional analysis of the SNPs discovered in the EGP, both in vitro biochemical and animal model studies have been initiated. For example, Furlong and his colleagues (Costa and Furlong, 2002) investigated the functional consequences of polymorphisms in paraoxonase (PON1) genes. PON1 is a highdensity lipoprotein associated with the plasma membrane that metabolizes oxidized lipids, environmental xenobiotics (e.g., organophosphate insecticides mentioned above) and some pharmaceutical agents (e.g., statins). Studies by Furlong and colleagues suggest that PON1 may play a significant role in the development of cardiovascular disease independent of environmental exposures (Costa and Furlong, 2002). However, their

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studies have shown that the activity of PON1 is significantly altered by genetic variation and that these alterations influence sensitivity to the organophosphate insecticides chlorpyrifos and diazinon. For example, one of the two SNPs in the coding region significantly increased the substrate-specific activity of the enzyme and provides much greater protection against chlorpyrifos oxon than the wild-type PON1. However, the Q192R allele did not correlate with carotid artery disease in a case– control study (Jarvik et al., 2003). Whereas individual SNPs in the promoter region had no effect on enzyme activity, combinations of three of the common haplotypes of the five SNPs in this region of the gene did alter the plasma activity of PON1. While these studies do suggest that polymorphisms in PON1 may play a role in susceptibility to organophosphate toxicity, the exact involvement and possible interactions of the various SNPs in the PON1 gene are yet to be determined. In another project in the EGP, Martyn Smith and colleagues (Smith et al., 2001) identified a gene that codes for NAD(P)H: quinone receptor oxidoreductase1 (NQO1) and demonstrated its potential involvement in the etiology of leukemia. This enzyme plays an important role in protection against oxidative damage from exposure to quinones. And they found that the common C609T polymorphism resulted in an amino acid substitution with a complete loss of enzyme activity in homozygotes. The C609T variant was associated with a 2.5-fold increase in risk for leukemia in case–control studies (Krajinovic et al., 2002; Smith et al., 2001, 2002). In contrast, several polymorphisms in genes involved in folate metabolism reduced the risk of leukemia by two- to threefold in heterozygotes and 3- to 10-fold in homozygotes (Skibola et al., 1999, 2002). Folate is a source of precursors for DNA synthesis, repair and methylation; and dietary folate protects against leukemia (Thompson et al., 2001). Also, there is evidence for a role of gene–environment interactions between polymorphic variants of thymidylate synthetase and methylenetetrahydrofolate reductase (Skibola et al., 2002). Finally, for functional analysis of the various polymorphisms discovered in the EGP, NIEHS developed mouse genome centers in 2000. The mouse is the most widely used mammalian model system for the study of the impact of the environment on human health. The mouse is particularly useful in studies of gene–environment interactions because most human genes have counterparts in the mouse genome. Furthermore, extensive toxicological and pathological databases and molecular genetic techniques for construction of gene-knockout and knock-in strains have been developed using the mouse as a model. The mouse model also offers the advantage of using strain-specific differences in response to environmental exposure to investigate how the genetic background influences susceptibility. First, the mouse genome center resource was used to create genetic variation map databases of laboratory mouse strains for use in studies of gene–environment interactions. This was achieved by using knockout and knock-in technologies to generate polymorphic variations in candidate genes similar to those discovered in the EGP. Exposing such animals to putative environmental risk factors is the most obvious and direct way

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to investigate the relationship between genetic background and susceptibility to environmental exposures. Second, since various mouse strains are known to exhibit significant variation in susceptibility to carcinogens and other environmental toxicants, efforts were undertaken to discover the haplotype maps of the 15 most commonly used mouse strains to identify genetic sequences that underlie differences in response (Olden 2004a; Pearson 2004). Once susceptibility haplotypes have been identified, one can track down the genes involved by crossing a susceptible strain with a resistant strain and identifying the chunks of the genome that are associated with the susceptibility. As the biological and genetic databases expand, the need for such mouse crosses might no longer be necessary.

PROBLEMATIC NATURE OF GENE– ENVIRONMENT INTERACTION STUDIES While there is considerable interest and enthusiasm for research on gene–environment interaction, there is also apprehension and uncertainty about the feasibility of such studies and practical questions about how to conduct them. Current studies underway in different parts of the world are problematic because they do not employ a standard definition of the environment nor standard metrics to assess exposure. But, even more problematic is the fact that methods to accurately measure exposure and to statistically model multiple interactive components that make modest contributions to the phenotype are not yet available. To alter the current state of affairs with respect to technology and database needs will require investments in the following areas of research: (i) identification of disease risk factors; (ii) elucidation of genetic differences and similarities between human and mouse; (iii) development of better tools and statistical models to assess exposure and interactions of multiple components; (iv) development of high-throughput, low-cost and more informative strategies to assess toxicity of drugs and environmental xenobiotics, and (v) elucidation of mechanisms and metabolic pathways influenced by gene–environment interactions. Furthermore, it has become clear that addressing the role of gene–environment interactions in the etiology of complex diseases will require the development of a robust framework to account for “the environment” (see Chapter 83). In spite of current understanding of the multifactorial nature of the etiologic mechanisms of chronic diseases, biomedical researchers still tend to focus on limited or circumscribed components of disease. Epidemiologists tend to focus on exposures; geneticists target susceptibility genes; cell/molecular biologists tend to explore mechanisms; and social scientists study behavior. While the various disciplinary approaches have led to new insights into disease causation, they are unlikely to be able to provide a coherent explanation for disease causation. Given this reality,

the environmental health community moved swiftly to take advantage of new databases and tools, derived from the human genome project, to identify and characterize all the environmental susceptibility alleles in the human genome and all the functional molecules (e.g., RNA, protein, carbohydrates, lipids, and metabolites) encoded by human and mouse genomes (Kaiser, 1997; Lovett, 2000; Olden, 2004a, b; Waters et al., 2003). With approximately 25,000 genes, hundreds of thousands of protein species, and numerous metabolites, the task of identifying and characterizing them with respect to function is an ambitious undertaking, one requiring improvement, standardization, and validation of existing methods to achieve reproducibility, increased sensitivity and specificity, and high throughput. Such studies will also require large-scale, multi-institutional collaboration and interdisciplinary expertise to amass and analyze such large databases. Until the latter part of the 19th century, infectious diseases were the chief causes of death; and their development was characterized by relatively short latency periods between exposure and the onset of illness. The relative ease in identifying the causative agents led to the development of effective prevention measures in the United States and other industrialized nations. Unfortunately, our capacity to prevent infectious diseases tracked with the emergence of chronic diseases, which have latency periods of 10–20 years. The increased prevalence of chronic diseases, the long latency period, and the more insidious nature of their causes necessitated development of new approaches to study their etiology. Epidemiologists responded by developing association studies to analyze the statistical relationship between two or more variables (e.g., genotype, exposure, and disease), based on two fundamental assumptions: first, that human disease does not occur at random; and second, that human disease has causal factors that can be identified by studying different populations. This disciplinary approach has been remarkably successful in the discovery of major risk factors for many common diseases, but has been less successful in discovering risks resulting from gene–gene or gene–environment interactions. Gene–environment interactions have been identified by taking advantage of existing models that take into account various ways that genetic effects can be modified by environmental exposures (Hunter, 2005). Epidemiologists have typically used one of three study designs: retrospective case–control, prospective cohort, and family-based. Family-based association design estimates association between genes and phenotypes based on the assumed Mendelian transmission of alleles from parent to offspring (Kraft and Hunter, 2005). In this study design, it is often difficult to obtain genetic data on parents for late-onset diseases, and exposure data is subject to recall bias as it is collected retrospectively. A further limitation of this approach is that the penetrance of the gene has to be sufficiently strong to cause familial aggregation. However, family-based studies allow one to assess the influence of genes over several generations; if the penetrance of the gene changes over time, this might represent the effect of the environment and lifestyle. In the case–control design, data on exposure is obtained after diagnosis of the disease, so errors in

Polymorphism and Disease Susceptibility: Case–Control Studies

estimating risk from gene–environment interactions can result from exposure recall or from bias in selection of the controls. Even if appropriate controls are found, incentive for participation in the study is often low; so, obtaining high participation is challenging. Also, it is difficult to estimate the effect of the disease on biomarkers used in the study. However, the case–control design is popular among epidemiologists because cases and controls can be enrolled in a short period of time, which leads to reduced cost and delay in reporting out the results. Finally, in the populationbased cohort design, information on exposure is collected prospectively, and tissue for DNA and baseline biomarker analysis is collected before the disease develops. The advantage of this study design is that assessment of exposure is more reliable and it does not suffer from selection bias. Unfortunately, such studies are very costly and time consuming and lack sufficient number of subjects to evaluate risks associated with gene–gene or gene–environment interactions. However, one can increase the power of populationbased cohort studies by pooling data from several studies. Because of the cost and time involved, most of the previous studies of gene–environment interactions have used the case– control design, which relied on family history to infer genetic predisposition and questionnaires to assess environmental and lifestyle exposures. In future studies of gene–environment interactions, it is likely that data from case–control and population-based cohort studies will be combined, and that genotype and exposure will be analyzed with greater precision using more direct and quantitative technologies. For example, the human genome and the various polymorphism discovery projects have opened up a vast new terrain to search for gene–gene or gene–environment interactions. Once these discovery efforts have evolved to the point that genetically susceptible populations have been identified, targeting such populations would increase the magnitude of relative risk, the significance of the observed associations, and the likelihood of discovering important gene–gene or gene– environment interactions. But, because of cost, rigorously designed case–control studies will remain the only option for assessing gene–environment interactions for rare diseases. Exploration of this new vast genomic landscape presents major challenges with respect to design and analysis of meaningful studies, and translation of the findings into public health and clinical practice. In addition to the challenges associated with population studies, the study of gene–environment interactions requires accurate information on exposure to eliminate a major source of uncertainty and the most serious problem in environmental health risk assessment. Not only is it difficult to measure exposure accurately, it is often prohibitively expensive for use in studies of large populations needed to test for association between genes and disease to correlate genetic variation with risk for disease.The current practice of estimating exposure using indirect surrogates, such as toxic release and production inventories and environmental monitoring, is inadequate and limits our understanding of dose–response relationships. Current methods available for measuring exposure include the use of environmental sensors, geographical information systems, biological sensors, direct analysis of body fluids, and more recently, toxicogenomics. Unfortunately,

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all of these exposure assessment tools, with the possible exception of toxicogenomics, have serious limitations. The biological response to environmental exposure is so complex and involves so many interactive factors that the utilization of a combination of exposure assessment tools, capable of integrating all the interactive components, will be required in prospective epidemiologic studies. In my opinion, the development of the field of toxicogenomics will provide powerful and relatively inexpensive tools to identify biomarkers and to relate exposure and biological events during disease progression. In addition to impediments imposed by sample size and exposure assessment tools, population-based studies are fraught with other challenges related to the need for better statistical models. To define complex exposure-disease relationships and interactions between genes and environmental factors in the development of human disease, a comprehensive view of exposure is needed. The achievement of this goal will require improved sophisticated modeling capabilities to explain how components of the disease process respond when perturbed by environmental factors. Even in the simplest case of four combinations of genotype and exposure, where one is comparing risk of carrier versus noncarrier in exposed and nonexposed situations, there are several models for describing interactions between genetic susceptibility and environmental exposures. Of course, modeling of interactions gets more complex as the categories of environmental exposures and the number of variant alleles involved increase (Hunter, 2005). Also, new statistical methods may be needed to extract meaning from large datasets and to integrate information from many sources. Finally, the most critical technological challenge is how to relate exposure-disease association studies to pathways and mechanisms. To address this issue, NIEHS developed the National Center for Toxicogenomics (Lovett, 2000) to promote the development of technologies to monitor gene, protein, and metabolite expression in cells and tissues under various environmental conditions. To understand how genes and environmental factors interact to perturb biological pathways to cause injury or disease, scientists will need tools with the capacity to monitor the global expression of thousands of genes, proteins, and metabolites simultaneously. The generation of such data in multiple species can be used to identify conserved and functionally significant genes and pathways involved in gene–environment interactions.

POLYMORPHISM AND DISEASE SUSCEPTIBILITY: CASE–CONTROL STUDIES An article by Lower et al. (1979) transformed population health research and provided the foundation for the massive sequencing efforts to identify genetic variations involved in modulating human response to drugs and other environmental xenobiotics. This landmark investigation also demonstrated the power of hypothesis-driven, population-based studies for assessing disease risk associated with specific interactions between genes and the

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environment. Furthermore, this pioneering study validated the use of epidemiological tools to investigate the biological significance of human genetic variants in disease risk. Such studies indicate that functional polymorphisms in genes that code for DNA repair or metabolism of environmental xenobiotics can modify the effects of environmental exposures. Gene variants can influence the balance of metabolic intermediates produced by altering the structure, function, and level of enzymes or the catalytic efficiency of the biotransformation process. Several large-scale projects are underway to build databases and to develop tools and infrastructure to investigate gene–gene and gene–environment interactions, and some results are already being reported. An encyclopedic review of the various studies is beyond scope of the present chapter, but a few examples will be presented as illustrative. Bladder Cancer and N-acetyltransferase 2 (NAT2) Polymorphism The seminal study by Lower and colleagues (1979) was prompted by their desire to elucidate the relationship between N-acetyltransferase phenotype and susceptibility to the development of bladder cancer from exposure to arylamines, as a result of exposure to cigarette smoke or chemical dyes. Earlier studies had suggested that (i) a significant portion of bladder cancer can be attributed to cigarette smoking and occupation exposures; (ii) the distribution of N-acetyltransferase activity in the liver is highly variable among individuals, and (iii) individuals with low enzyme activity (the so-called “slow acetylator phenotype”) were most susceptible to the development of bladder cancer (Case and Hasker, 1954; Cole, 1974; Hueper, 1969; Hueper et al., 1938). These observations led Lower et al. (1979) to propose that the slow acetylator phenotype would be over-represented in a population of bladder cancer patients occupationally exposed to arylamine dyes. Their hypothesis was confirmed in a cohort of bladder cancer patients who worked in the dye industry in Denmark. Bladder cancer has been described as a classic illustration of the principle of dose–effect modification of an environmental exposure by genetic polymorphisms (Kelada et al., 2003). Exposure to aromatic amines can cause bladder cancer depending on one’s NAT2 genotype/phenotype. Smokers who are slow acetylators have a higher risk of developing bladder cancer compared to smokers who are rapid acetylators. Marcus et al. (2000) calculated an odds ratio of 1.3–1.7 for smokers who are rapid acetylators, confirming that there is an interaction between NAT2 and smoking. Using nonsmoking rapid acetylators as the reference with estimates of the prevalence of smoking, these investigators were able to demonstrate that the populationattributable risk of the gene–environment interaction was 35% for slow acetylators who had ever smoked and 13% for rapid acetylators who had ever smoked. In laboratory studies designed to investigate plausible mechanisms to explain the results, Hein et al. (1993) and Mattano et al. (1989) demonstrated that NAT2 enzyme can bioactivate procarcinogenic aromatic amines in the bladder. Following N-oxidation of aromatic amines by cytochrome P4501A2 in the liver, O-acetylation of the resulting hydroxylamine by NAT2 can produce unstable acetoxy esters or

proximate carcinogens. The acetoxy ester intermediates can be N-acetylated or N-oxidized to yield N-hydroxy-N-acetyl aromatic amines that can bind to DNA to create point mutations or adducts. Myeloperoxidase Polymorphism and Lung Cancer Myeloperoxidase is a phase I enzyme found in the lysosomes of polymorphonuclear leukocytes and monocytes. It converts lipophilic carcinogens into hydrophilic forms. Exposure to tobacco smoke stimulates the recruitment of neutrophils into lung tissue with local release of myeloperoxidase. This enzyme converts 7,8-diol benzo(a)pyrene to the highly reactive and carcinogenic form called benzo(a)pyrene 7,8-diol epoxide, which can form DNA adducts in lung tissue. Myeloperoxidase can also convert other procarcinogens (e.g., polycyclic aromatic hydrocarbons and aromatic amines) in cigarette smoke to free radicals or reactive oxygen species. A common G to A transition at position 463 of the promoter region of the myeloperoxidase gene, which leads to loss of a SP1 transcription factor binding site in an Alu hormone responsive element has been shown to reduce myeloperoxidase mRNA expression (Kiyohara et al., 2005). Because transcriptional activity is decreased in individuals with the variant A allele, less enzyme is available to convert benzo(a)pyrene intermediate to the highly carcinogenic benzo(a)pyrene 7,8-diol epoxide species. The frequency of the A allele varies between Caucasian (23.4%) and Asian (14.4%) populations. As the myeloperoxidase G-463A variant allele is related to low metabolic activation, individuals with at least one variant allele may be associated with decreased risk of lung cancer. London et al. (1997) reported that Caucasian individuals with the A/A genotype were associated with a significant reduction in risk for lung cancer. Although the biological effects of each genotype (i.e., G/G, G/A and A/A) have not been clarified, lung cancer among the three genotypes decreases from G to A. Similar results have been obtained for Japanese and Hawaiian populations (LeMarchand et al., 2000). The association between myeloperoxidase genotype and lung cancer risk was modified by duration of smoking. In heavy smokers versus light smokers, the combined G/A and A/A genotypes had an odds ratio for squamous cell carcinoma of 6.22 for heavy smokers and 1.72 for light smokers, and for never smokers 1.14. Other studies showed that myeloperoxidase G/A genotype interacted with the glutathione S-transferase T1 (GSTT1) locus to significantly decrease lung cancer among males but not females (Cajas-Salizar et al., 2003; Feyler et al., 2002). NAD(P)H Quinine Oxidoreductose 1 Polymorphism and Lung Cancer Polymorphism in the gene that codes for NAD(P) quinine oxidoreductase 1 (NQO1) is also a lung cancer susceptibility gene that interacts with carcinogens in cigarette smoke (Kiyohara et al., 2005). NQO1 has a dual function in that it can both activate and detoxify carcinogenic quinoid compounds, such as benzo(a)pyrene. The catalytic reduction of benzo(a)pyrene prevents the formation of cancer-causing quinone-DNA adducts. The combined variant genotypes, Pro-Ser and Ser-Ser were

Polymorphism and Disease Susceptibility: Case–Control Studies

significantly associated with decreased risk of lung cancer in Mexican Americans (Wiencke et al., 1997). Similar results were obtained in three Japanese studies (cited in review article by Kiyohara et al., 2005); however, these genotypes were only weakly associated with lung cancer in Chinese, Hawaiians, Caucasians, and African-Americans. In terms of gene–environment interactions, there was an excess small cell lung cancer risk associated with the presence of the Ser allele in heavy smokers, where the odds ratio for the Ser-Ser and Pro-Ser genotypes combined was 12.5; in light smokers the odds ratio was 0.90. In contrast, the frequency of the NQO1 genotypes did not differ significantly between smokers and nonsmokers. The NQO1 Pro187Ser polymorphism was also involved in gene–gene interactions. For example, the combined NQO1 Pro-Pro and GSTT1 null genotype showed a significant association with lung adenocarcinoma risk, with an odds ratio of 4.61 (Sunaga et al., 2002). The odds ratio was calculated using the NQO1 Ser-Ser and GSTT1 nonnull genotypes combined as the reference. Although the risk of developing lung cancer conferred by the various susceptibility alleles is small, lung cancer is such a common malignancy that this small increase in risk translates to a large number of lung cancer cases in the population and is clearly important from a public health point of view. These results also show that lung cancer cannot be explained by variation in a single allele, but rather by multiple polymorphisms within genes involved in the same or different pathways. Discovery of other functional variants may shed some light on the development of this important malignancy, as the burden of this disease surely develops from complex interactions between multiple genetic and environmental factors over time. That is, the effect of polymorphisms may be best represented by their haplotypes. Fibrinogen Polymorphism and Coronary Heart Disease Fibrinogen is an acute phase protein and its plasma level is responsive to trauma, serum cholesterol, hormones, and infections. Increase in genetically determined levels of this glycoprotein is associated with risk of cardiovascular disease. Several epidemiologic studies have shown that gene–environment interactions involving fibrinogen polymorphisms play a role in coronary heart disease. The glycoprotein consists of three different polypeptide chains (a, b, and c) linked by disulfide bonds (Vischetti et al., 2002). Studies show that b-chain synthesis limits the rate of production of mature fibrinogen (Yu et al., 1984). Many polymorphisms have been discovered in the b-chain, two of which show a strong correlation with plasma concentration of the mature protein. The BclI and the G/A-455 polymorphisms have been selected for analysis of gene–environment interaction. Such studies have shown that moderate physical activity decreases the level of fibrinogen; however, these effects are evident only in carriers of the B2 allele of the BclI b-chain polymorphism. Montgomery et al. (1996) have shown that there is an acute rise in fibrinogen levels in response to exercise, and that this response is strongly influenced by the G/A-455 polymorphism in the b-chain gene; in fact, a gradient of increasing

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acute fibrinogen response was seen across the G/G, G/A, and A/A genotypes, respectively. Zito et al. (1999) reported that infection with Helicobacter pylori, the pathogen responsible for most peptic ulcers and gastritis, resulted in a fourfold increase in risk of myocardial infarction; the response to infection was higher in carriers of the BclI polymorphism, with carriers of the B2 allele showing higher fibrinogen levels compared to the B1 homogygous subjects. Carriers of the B2 allele have increased risk for myocardial infarction and have the capacity to amplify the effect of seropositivity for Helicobacter. So patients with both seropositivity for Helicobacter and carriers of the B2 genotype have an additional increased risk. In another study, some subjects with high dietary fat intake developed abnormal high-density lipoprotein cholesterol concentrations, and some did not, depending on their genotype or polymorphisms in the hepatic lipase gene promoter (Ordovas and Corella, 2002; Ordovas 2004). Also, tobacco smokers developed coronary heart disease, or did not, depending on their lipoprotein lipase genotype (Talmud et al., 2000) and their apolipoprotein E4 genotype (Humphries et al., 2001). These results show that genetic polymorphisms in the b-chain gene do not cause myocardial infarction, but influence risk for the disease by rendering individuals more susceptible to environmental risk factors. Mental Disorders and Monoamine Oxidase A Polymorphism A functional polymorphism in the promoter region of the MAOA gene encoding the neurotransmitter metabolizing enzyme monoamine oxidase moderates the effects of child maltreatment on the cycle of violence (Moffitt et al., 2005). Their results show that maltreated children, with the polymorphism in the promoter region, produced low levels of monoamine oxidase A and were more prone to develop conduct disorders, to demonstrate antisocial personality, and to commit crimes as an adult than maltreated children with high-activity levels of monoamine oxidase A. In a related study, Caspi et al. (2003) demonstrated that the serotonin transporter (5-HTT ) gene moderated the effects of stressful life events on depression. Individuals with one or two copies of the 5-HTT polymorphic allele exhibited more depressive symptoms, diagnosable depression, and suicidal tendencies following stressful life events than individuals who were homozygous for the wild-type allele. Asthma and Gene Polymorphisms Asthma is a complex multifactorial disorder with variable phenotypes attributed to interaction of multiple genes with multiple environmental factors (see Chapter 89). The interplay between genes and the environment is best illustrated by the hygiene hypothesis to explain the increase in asthma prevalence. The fundamental tenet of this hypothesis is that the emergence and severity of the disease depends on the interaction between genetic background and environmental exposure. According to the hygiene hypothesis, children in industrialized nations have decreased incidence of bacterial infections, hence reduced exposure to endotoxins. In fact, this is supported by several studies

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which show that exposure to increased endotoxin levels in the domestic environment is associated with decreased occurrence of asthma (for recent review see Kleeberger and Peden, 2005). The risk of developing asthma is influenced by the Western lifestyle, including diet, air pollution, reduced risk of infections, indoor air, and stress (reviewed by Arruda et al., 2005). The length and type of exposure, as well as the time when it occurs, can either promote or suppress the inflammatory process and the subsequent course of the disease. CD14 is an innate immunity gene that has been studied extensively with respect to its genetics, function, and distribution in various populations because of its important role in the development of asthma and other allergic reactions. The CD14 gene product is a component of the endotoxin-recognition receptor complex and is critical for the development of the optimal responses to low concentrations of the environmental stimulant via the Toll-like receptors (TLR4 and TLR2). By some unknown mechanism, both the membrane-bound and soluble forms of CD14 enhance the affinity of the interaction between environmental allergens and the TLRs. In 1999, Baldini et al., as part of the Tucson Children Respiratory Study, demonstrated an association between a SNP in the CD14 promoter (CD14*C-159T), increased serum levels of soluble CD14, and decreased total serum IgE. The discovery of an association between this polymorphism in the CD14 promoter and a reduction in serum IgE levels provided important support for the view that innate immunity genes are key determinants in the development of asthma. Several studies have shown that pro-oxidant environmental exposures are associated with increased incidence and exacerbation of asthma and other lung diseases. For example, a large study conducted in Southern California showed that in utero exposure to tobacco smoke was associated with several indicators of lung morbidity, including increased incidence of asthma diagnosis, increased need for emergency room use for airways symptoms, and increased occurrence of bronchitis (reviewed by Kleeberger and Peden, 2005). Numerous candidate genes have been shown to be associated with asthma and asthma-associated phenotypes, such as elevated IgE levels, bronchial hyperactivity, and SNPs within specific cytokines, IgE regulating enzymes, and phase I and phase II xenobiotic metabolism enzymes. However, only a few genes conferring significant risk have been discovered, and the functional significance of the various gene variants is largely unknown. A glutathione S-transferase M1 (GSTM1) null allele (M1*0), which results in no protein expression and no glutathione antioxidant protection, was shown to be strongly associated with decreased lung growth and function in a cohort of school children in Southern California due to increased oxidant stress (Gilliland et al., 2002a, b, c). These investigators also examined the relationship between the GSTM1 null genotype, the prevalence of asthma, and the in utero exposure to tobacco smoke (Gilliland et al., 2002a, b, c). Children with the GSTM1 null genotype and who were born to mothers who smoked during pregnancy showed increased prevalence of early onset asthma, persistent asthma, wheezing with exercise, and increased need for medical intervention for asthma. Children with the functional GSTM1 genotype did not have asthmatic symptoms; nor did the children

with GSTM1 null genotype if they were born to nonsmoking mothers. Studies have also shown that children with the GSTP1 Val 105/Val 105 genotype had fewer school absences related to asthma, which suggests that this antioxidant protective gene may influence asthma severity (Gilliland et al., 2002a, b, c). Both GSTM1 and GSTP1 appear to modulate the effect of exposure to particulates in diesel exhaust as measured by IgE response following a nasal challenge; both IgE and histamine levels were increased. In a study of the effects of diesel exhaust on response to ragweed challenge, subjects were stratified on the basis of GSTM1 and GSTP1 genotypes and were allowed to inhale diesel exhaust particles followed by ragweed allergen. Subjects with either the GSTM1 null or GSTP1 Ile 105 genotype exhibited a stronger response than did subjects with other GSTM1 and GSTP1 genotypes (Bastain et al., 2003; Gilliland et al., 2004). Studies of the risk for asthma from lifetime exposure to ozone show that GST proteins are important determinants of asthma severity (David et al., 2003). In studies of the combined genotypes of NQO1 Pro187Ser and GSTM1 null or functional sequence, the presence of the Pro-Pro allele of NQO1 significantly reduced asthma risk in subjects homozygous for the GST1 deletion. The studies on lung function and gene variation cited above are representative examples of published and ongoing studies; for a more comprehensive survey, the reader is referred to the excellent review by Kleeberger and Peden (2005) and to other chapters in this book.

EPIGENETICS AND THE ENVIRONMENT A discussion of gene–environment interactions could not be concluded without at least a brief mention of epigenetics, as it represents yet another mechanism by which the environment can influence phenotype or disease development (see Chapter 5). Cells of multicellular organisms are genetically the same, yet structurally and functionally diverse owing to the differential expression of genes. Many of these differences in gene expression arise during development, and most are stable throughout the life of the organism. However, it is important to note that epigenetic changes can occur in the adult. Such stable alterations are referred to as “epigenetic” in that they do not involve mutations in the DNA itself. Most of the research on epigenetic mechanisms has focused on DNA methylation during early development and gene silencing. But studies are now focusing on epigenetic regulation of gene expression response to environmental exposures as a possible pathway for disease development. For example, one could imagine that inactivation of proteins that buffer the cell against environmental stress could lead to disease by allowing the expression of previously suppressed genetic variants. Much attention has been devoted to diet as it represents an abundant source of methyl group donors. Also, diet is known to be a particularly significant determinant in the timing of disease expression. For example, dietary supplements such as folate or vitamins that influence the activity of methyl-transfer enzymes can modulate the rate of disease development and can exert

References

marked effects on the incidence of colon cancer (for review, see Van den Veyver, 2002). Reduced amounts of folate have been associated with genomic instability, neural tube defects, and genomic hypomethylation. In addition, a diet deficient in methylcontaining components has been shown to induce liver cancer. The possibility that classical genetic mechanisms may not be adequate to explain the development of complex diseases looms large on the horizon. Normal physiological responses to certain environmental stimuli may be mediated by epigenetic mechanisms, so progress is likely to require a more intense research effort to fully understand how humans and other organisms respond to environmental cues. For example, epigenetic changes might explain the phenotypic discordance often observed among monozygotic twins, differences in age of onset of diseases, fluctuation in diseases and sporadic cases of disease. Also, since epigenetic-mediated changes in gene expression can occur at a much faster rate than DNA mutation, it represents a more adaptable mechanism for organisms to respond to rapidly changing environmental conditions.

CONCLUSION Although most of the visible environmental problems of the 1950s and 1960s have been ameliorated, massive quantities of toxic agents are still polluting our environment. Whether current levels of exposure to these agents are contributing to the high or increasing incidence of cancer, Parkinson’s and Alzheimer’s disease, asthma, autism, learning disabilities, diabetes, or other complex disorders is a matter of considerable concern. Finding answers to these questions has been a slow and difficult process. The traditional methodologies available to

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medical researchers have not been adequate to elucidate the intricate gene–environment interactions involved in the development of complex diseases. But, thanks to the rare confluence of technology breakthroughs and the rethinking and redirection of the environmental health sciences over the past decade, the link between the environment and human health and disease can now be investigated with more rigor and specificity. For example, the sequencing of the human genome and the development and application of high throughput technologies to monitor the expression of genes and proteins in response to specific environmental exposures has created an unparalleled opportunity to study gene–environment interactions. By using a combination of new “-omic” technologies (genomics, proteomics, and metabolomics), in combination with population studies and better exposure data, one can achieve an integrative view of gene–environment interaction at the level of the whole organism. So, perhaps the most important development in healthcare in the 21st century will be the incorporation of knowledge of gene–environment interactions into public health and the practice of medicine. Many of the expected breakthroughs from use of these technologies will almost definitely lead to new opportunities to prevent, diagnose, and treat human diseases.

ACKNOWLEDGEMENTS I am grateful to my colleague, Dr Samuel Wilson, for his intellectual input and leadership in developing many of the concepts and research initiatives on gene–environment interactions discussed in this chapter. I am also grateful to Ms Betty Mills for editorial and technical assistance.

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5 Epigenomics and Its Implications for Medicine Moshe Szyf

INTRODUCTION The genome holds the entire information required to encode all the proteins expressed in an organism. However, expression of this information is programmed so that only a fraction of the potential repertoire of proteins is expressed in a given cell type. This programming is defined by the epigenome. The epigenome controls gene expression programs by defining the accessibility of transcription machineries, as well as other nuclear machineries, to genes (Razin, 1998). Programming of the epigenome occurs during embryonic development, whereby it establishes the diversity of gene expression patterns amongst organs, tissues, and cell types (Razin, 1998). These programs are stable and maintained through life. The epigenetic machinery is responsive to signaling pathways, which could be activated by different triggers. It has become clear recently that epigenetic programming could potentially change through life, and not just during embryogenesis, in response to environmental exposures such as nutrition, drugs, and social interactions (Meaney and Szyf, 2005). The epigenome thus mediates the effect of the environment on our genes. These effects could result in novel gene expression programs triggering new physiological, behavioral, or pathological states. Improper epigenetic programming might lead to consequences that could be similar to the effects of genetic mutations or deletions. For example, aberrant epigenetic programming could lead to activation of a gene that is normally silenced or

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to inactivation of a gene that is normally active. An example is the aberrant silencing of tumor-suppressor genes in cancer, which could be mediated by either an inactivating mutation or by epigenetic silencing through DNA methylation (Baylin et al., 2001). Epigenetic differences amongst individuals can result in interindividual phenotypic differences with respect to behavior, physiology, and pathology and response to drugs similar to genetic polymorphisms. However, the extent to which epigenetic interindividual differences play a role in generating interindividual phenotypic differences is still unknown. In contrast to the genome, which is identical in different cell types and through life, the epigenome is dynamic and varies from cell type to cell type and from time point to time point in life. This diversity allows the epigenome to regulate the assortment of gene expression programs in a multicellular organism. The epigenome consists of chromatin and its modifications, as well as a covalent modification of cytosine rings found at the dinucleotide sequence CpG (CG) (Razin, 1998). The basic building block of chromatin is the nucleosome, which is formed of an octamer of histone proteins containing an H3-H4 tetramer flanked on either side with a H2A-H2B dimer (Finch et al., 1977). The N-terminal tails of these histones are extensively modified by methylation (Jenuwein, 2001), phosphorylation, acetylation (Wade et al., 1997), and ubiquitination (Shilatifard, 2006). Different histone variants also play a regulatory role (Henikoff et al., 2004). The pattern of histone modifications and

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DNA Methylation Patterns

variants creates a “histone code” that delineates the parts of the genome expressed at a given point in time in a given cell type (Jenuwein and Allis, 2001). Specific transcription factors and transcription repressors recruit histone-modifying enzymes to specific genes and thus define the profile of histone modification around genes (Jenuwein and Allis, 2001). The best-investigated examples are histone acetyltransferases (HAT), which acetylate histone H3 and H4 tails at the lysine-9 (K9) residue, and histone deacetylases (HDAC), which deacetylate histone tails (Kuo and Allis, 1998). Histone acetylation is believed to be a predominant signal for an active chromatin configuration. Deacetylated histones signal inactive chromatin, chromatin associated with inactive genes. Many repressors and repressor complexes recruit HDACs to genes, thus causing their inactivation. Histone tail acetylation is believed to enhance the accessibility of a gene to the transcription machinery whereas deacetylated tails are tightly interacting with DNA and limit its accessibility to transcription factors (Kuo and Allis, 1998). Histone methylation at K9 of H3-histone signals inactivity and is determined by the recruitment of histone methyltransferases (HMTs) such as SUV3–9 to genes (Lachner et al., 2001). The heterochromatin-associated protein HP-1, which binds methylated histones and precipitates an inactive chromatin structure (Lachner et al., 2001), recognizes methylated histones. Recently described histone demethylases remove the methylation mark, causing either activation or repression of gene expression (Shi et al., 2004;Tsukada et al., 2006). Chromatin remodeling complexes, which are ATP dependent, alter the position of nucleosomes around the transcription initiation site and define its accessibility to the transcription machinery (Varga-Weisz and Becker, 2006). It is becoming clear now that there is an interrelationship between chromatin modification and chromatin remodeling. For example, the presence of BRG1 (the catalytic subunit of SWI/ SNF-related chromatin remodeling complexes) is required for histone acetylation and regulation of -globin expression during development (Bultman et al., 2005). The targeting of chromatin modifications and remodeling to discrete genetic loci is established by specific trans-acting factors, which might be responsive to cellular as well as extracellular signaling. Chromatin is dynamic and plastic and could respond by altered recruitment of the different histone modification enzymes in response to a variety of signals. In addition to chromatin, which is associated with DNA, the DNA molecule itself is chemically modified by methyl residues at the 5ⴕ position of the cytosine rings in the sequence CG in vertebrates (Razin, 1998) (see Chapter 11). What distinguishes DNA methylation in vertebrate genomes is the fact that not all CGs are methylated in any given cell type (Razin, 1998). Distinct CGs are methylated in different cell types, generating cell type– specific patterns of methylation. Thus, the DNA methylation pattern confers upon a genome itself its cell type identity (Razin, 1998). Since DNA methylation is part of the chemical structure of the DNA itself, it is more stable than other epigenetic marks and thus has extremely important diagnostic potential (Beck et al., 1999). It was originally believed that the DNA methylation

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pattern is established during development and is then maintained faithfully through life by the maintenance DNA methyltransferase (DNMT) (Razin and Riggs, 1980). The DNA methylation reaction was believed to be irreversible; thus the only way methyl residues could be lost was through replication in the absence of DNMT by passive demethylation (Razin and Riggs, 1980). Recent data support the idea that similar to chromatin modification DNA methylation is also potentially reversible even in post-mitotic tissues (Weaver et al., 2004a, b). A hallmark of DNA methylation patterns is the correlation between chromatin and the DNA methylation pattern. Active chromatin is usually associated with unmethylated DNA while inactive chromatin is associated with methylated DNA (Razin, 1998). Current data suggest a bilateral relation between chromatin structure and DNA methylation. This interrelationship of chromatin modification state and DNA methylation has important implications for understanding of how the epigenome is generated and of its utility as a diagnostic and as a therapeutic target. The gene carries the basic unit of information, but this information has an impact only once it is properly programmed by the epigenome. Thus, the epigenome is a prime target for intervention in human diseases and other conditions that involve altered gene function. Genetic mutations and polymorphisms are irreversible. In difference from the genome, the epigenome is reversible. One could potentially alter epigenetic patterns by either targeting pharmacologically the different chromatin modifications and DNA methylation enzymes or affecting the environmental exposures and the signaling pathways mediating these effects. Epigenetic profiles have therefore important practical implications in prophylaxis, diagnosis, and therapeutics. A true realization of the potential of epigenetics will require a thorough understanding of the mechanisms leading to the formation of specific DNA methylation patterns and a comprehensive mapping of dynamic epigenomes with relation to different environmental exposures. This chapter will review the basic machineries involved in shaping the epigenome and the potential implications of epigenetics on understanding, diagnosing, treating, and preventing human pathologies.

DNA METHYLATION PATTERNS The DNA Methylation Pattern Is Cell Type Specific and Correlates with Chromatin Structure Modification of nucleotides in DNA by addition of a methyl group is widespread but not ubiquitous in biology. Both 5-methylcytosine and 6-methyladenine methylation are found in bacteria and are encoded by sequence-specific DNMTs. The methylated sequence distinguishes one strain from the other and serves for self versus non-self demarcation, protecting bacteria from invading DNA arriving from a different host. For this restriction modification system to function properly, it requires complete methylation of the host DNA in a unique 4–8 base sequence (Bestor, 1990; McClelland et al., 1994).

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DNA methyltransferase (DNMT)

HO HO

S-Adenosylmethionine (SAM)

—

CH N

N

N

CH2SCH2CH2CH(NH2) 

—

O— —C

N

—

CH N

COOH

CH — CH3

COOH

—

—

O— —C

C

—



—

CH2SCH2CH2CH(NH2)

—

— N

—

N

CH

N

—

C

CH3

NH2

NH2

—

N

—

N

NH2

—

—

NH2

HO HO

S-Adenosylhomocystein (SAH)

Figure 5.1 The DNA methylation reaction. The DNMTs catalyze the transfer of a methyl group from the methyl donor S-adenosyl methionine onto the 5 in the cytosine ring in DNA.

DNA methylation is not found in all organisms. Certain yeast strains, including Saccharomyces cerevisiae, are devoid of DNA methylation. Since these organisms are eukaryotes with extensive epigenetic control in spite of lack of methylation, it suggests that the primary component of epigenetic control is chromatin, which is highly developed in yeast, and not DNA methylation, which is absent in yeast. Similarly, it was long believed that nematodes and insects are devoid of DNA methylation. However, recent data suggest that Drosophila flies do contain minute amounts of 5-methylcytosine, which is relatively abundant during early development (Lyko et al., 2000). In contrast to bacterial methylation systems, the primary methylated sequence in vertebrates is composed of only two bases, the dinucleotide sequence CG. In plants, CNG (where N is either A, G, or T) sequences are also methylated in addition to CGs (Razin and Riggs, 1980). Another feature that distinguishes bacterial from vertebrate methylation is the fact that only a fraction (80%) of the methylatable CG population is methylated in vertebrate DNA. Different CG sites are methylated in different tissues, creating a pattern of methylation that is gene and tissue specific (Razin and Riggs, 1980). In some cases, such as parentally imprinted genes (Swain et al., 1987) or genes residing on the inactive X chromosome (Mohandas et al., 1981), the two alleles of the same gene are differentially methylated in the same cell. DNA methylation is the only component of the covalent DNA structure that shows cell-, parent of origin- and allelespecific identity. An identical sequence could be either methylated or nonmethylated in different cell types or in different alleles in the same cell type. The DNA methylation pattern is not copied by the DNA replication machinery, but by independent enzymatic machinery the DNMTs (Razin and Cedar, 1977) (Figure 5.1). DNA methylation patterns in vertebrates are distinguished by their tight correlation with chromatin structure. Active regions of the chromatin, which enable gene expression, are associated with hypomethylated DNA whereas hypermethylated DNA is packaged in inactive chromatin (Razin and Cedar, 1977). It is generally accepted that DNA methylation plays an important role in regulating gene expression. Establishing and Maintaining DNA Methylation Patterns Vertebrates establish their DNA methylation patterns during development and these patterns are then maintained in each

cell type during life. Nevertheless, differences in environmental exposure result in differences in DNA methylation patterns that emerge later in life (Weaver et al., 2004a, b), even in monozygotic twins (Fraga et al., 2005). DNA methylation patterns might be affected by changes in the methyl content of diets, maternal behavior (Weaver et al., 2005), and exposures to certain drugs. Understanding the epigenetic drift during life and its environmental causes is now attracting considerable attention and could potentially develop into an important field in medicine. DNA Methyltransferases The methylation of DNA occurs immediately after replication by a transfer of a methyl moiety from the donor S-adenosyl-lmethionine (SAM) (AdoMet) in a reaction catalyzed by DNMTs (Figure 5.1). DNA methylation is only one of the methylation reactions occurring in the cell that uses AdoMet as a cofactor. The N-terminus of most cytosine 5-methyltransferases defines their unique features and regulatory functions which are responsible for specific targeting of DNMTs to their site of action. In mammalian DNMT1, the N-terminus contains a number of functional domains that integrate DNMT activity with chromatin-modifying enzymes such as histone deacetylase 1 (HDAC1) (Fuks et al., 2000) and HDAC2 (Robertson et al., 2000) and coordinate DNMT activity with the replication fork and DNA replication (Milutinovic et al., 2003). This obviously has therapeutic implications, since it might be possible to target these functions independently of DNA methylation and steer clear of the adverse effects associated with loss of DNA methylation (for a review see Szyf [2001]). De Novo and Maintenance Methyltransferase Activities It is critical to accurately replicate the methylation pattern during cell division. Razin and Riggs proposed more than two decades ago that the maintenance DNMT prefers a hemimethylated substrate (Razin and Riggs, 1980). Since hemimethylated sites are generated during DNA replication when a nascent unmethylated C is synthesized across a methylated C in the template parental strand, the DNMT accurately copies the methylation pattern of the template strand (Figure 5.2). Some DNMTs distinguish hemimethylated from nonmethylated sites. In support of the Razin and Riggs model, three distinct phylogenic DNMTs have been identified in mammals.

Chromatin Modification

De novo DNMT (DNMT3a, 3b, and DNMT1?)

Demethylase

DNA replication

Maintenance DNMT (DNMT1)

Figure 5.2 DNA DNMT and demethylases sculpt the methylation pattern. De novo DNMTs introduce new methyl groups into CG sites, which are unmethylated on both strands of DNA. Demethylases remove methyl groups from DNA. Maintenance DNMT copies the DNA methylation pattern from the parental strand into the nascent strand of DNA during DNA replication. Open circles indicate an unmethylated CG site in DNA; red circles indicate a methylated CG site.

DNMT1 shows preference for hemimethylated DNA in vitro, which is consistent with its role as a maintenance DNMT, whereas DNMT3a and DNMT3b methylate unmethylated and methylated DNA at an equal rate, which is consistent with a de novo DNMT role (Okano et al., 1998). Two additional DNMT homologs have been found: DNMT2, whose substrate and methylation activity is unclear (Vilain et al., 1998), and DNMT3L, which belongs to the DNMT3 family of DNMTs by virtue of its sequence and is essential for the establishment of maternal genomic imprints but lacks key methyltransferase motifs, and is possibly a regulator of methylation rather than an enzyme that methylates DNA (Bourc’his et al., 2001). Knockout mouse data indicate that DNMT1 is responsible for a majority of DNA methylation marks in the mouse genome (Li et al., 1992), whereas DNMT3a and DNMT3b are responsible for some but not all de novo methylation during development (Okano et al., 1999). It has recently been demonstrated that an additional factor is required for targeting DNMT1 to newly replicating hemimethylated DNA, the protein UHRF1 (ubiquitin-like, containing PHD and RING finger domains 1), also known as NP95 in mouse and ICBP90 in human (Bostick et al., 2007). Several lines of evidence indicate that DNMTs are targeted to specific sequences by sequence-specific factors, which recognize specific sequences of DNA. For example, the HMT EZH2 or the oncoprotein PML-RAR target DNMTs to specific sequences in DNA (Di Croce et al., 2002;Vire et al., 2006). Thus, in addition to template-directed DNA methylation in replicating DNA, specific factors are responsible for specific methylation in specific genes. Is DNA Methylation a Reversible Reaction? The most controversial issue in the DNA methylation field is the question of whether the DNA methylation reaction is reversible. A passive process through replication in the absence of DNMT

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would result in loss of methylation. This could serve as a mechanism for both site specific and global demethylation (see Figure 5.2 for a scheme of the different DNA methylation reactions). There is a long list of data from both cell culture and early mouse development supporting the hypothesis that active methylation occurs in embryonal and somatic cells. There are now convincing examples of active, replication-independent DNA demethylation during development as well as in somatic tissues. Active demethylation was reported for the myosin gene in differentiating myoblast cells (Lucarelli et al., 2001), the INTERLEUKIN-2 gene upon T-cell activation (Bruniquel and Schwartz, 2003), the INTERFERON- gene upon antigen exposure of memory CD8 T cells (Kersh et al., 2006), and the glucocorticoid receptor gene promoter in adult rat brains upon treatment with the HDAC inhibitor trichostatin A (TSA) (Weaver et al., 2004a, b). Enzymatic Activities Responsible for Demethylation The characteristics of the enzymes responsible for active demethylation are controversial. One proposal has been that a G/T mismatch repair glycosylase also functions as a 5-methylcytosine DNA glycosylase, recognizing methylcytosines and cleaving the bond between the sugar and the base. The abasic site is then repaired and replaced with a nonmethylated cytosine resulting in demethylation (Jost, 1993). An additional protein with a similar activity was recently identified, the methylated DNA binding domain protein 4 (MBD4) (Zhu et al., 2000). Another report has proposed that MBD2 has demethylase activity. MBD2b (a shorter isoform of MBD2) was shown to directly remove the methyl group from methylated cytosine in methylated CGs (Bhattacharya et al., 1999). This enzyme was therefore proposed to reverse the DNA methylation reaction. However, other groups disputed this finding (Ng et al., 1999). Understanding the mechanisms responsible for demethylation is one of the most important questions in the field.

CHROMATIN MODIFICATION Modifications of Histones with Implications for Gene Expression Programming: The Histone Code The basic unit of the chromatin is the nucleosome, which is a dimer of two identical complexes, each consisting of four histone proteins: H2A, H2B, H3, and H4 (Pruss et al., 1995). The N-terminal tails of histones H3 and H4 are highly charged and are tightly associated with DNA. Chromatin had been viewed in the past as a static entity, which packaged DNA in a condensed form and maintained its integrity. However, a vast body of literature has established that histones undergo diverse covalent modifications, which include acetylation, methylation, phosphorylation, ubiquitination, and sumoylation (Strahl and Allis, 2000). Histone acetylation of H3 and H4 histone N-terminal tails is believed to be a predominant signal for active chromatin by enhancing the accessibility of the transcription machinery. Histone methylation at the K9 of histone H3 is repressive and results in exclusion of acetylation at the same residue, which is

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required for activation of gene expression. H3-histone K4 methylation is associated, on the other hand, with gene activity and transcription elongation (Santos-Rosa et al., 2002). These modifications are believed to form a “histone code” that regulates chromatin function by affecting the structural dynamics of the nucleosome (Jenuwein and Allis, 2001). Proteins That Interpret the Histone Code Histone modifications regulate chromatin function by either altering the accessibility of DNA to different trans-acting factors or recruiting specific proteins that recognize a single or conformational set of modifications (Strahl and Allis, 2000). Examples of proteins whose interaction with DNA is defined by different histone modifications include bromodomain-containing proteins, which interact with acetylated histones (Filetici et al., 2001), and chromodomain-containing proteins, which interact with methylated lysines (Lachner and Jenuwein, 2002). A classic example is the HP-1, which recognizes histone H3 methylated at K9 and is believed to be involved in gene silencing in heterochromatin (Lachner and Jenuwein, 2002). Different histone variants also play regulatory roles (Henikoff et al., 2004; Pusarla and Bhargava, 2005; Sarma and Reinberg, 2005; Zhang et al., 2005). Remodeling of chromatin is also critical for gene function and involves ATPasecontaining complexes such as SWI/SNF, which propels energydependent nucleosomal movement required for opening up of chromatin around transcription initiation regions (Gibbons, 2005; Muchardt and Yaniv, 1999;Wolffe and Hayes, 1999). The State of Modification of Histones Is a Dynamic Balance of Modifying and Demodifying Enzymatic Activities Histone acetylation is catalyzed by HATs, which transfer an acetyl group from the cofactor acetyl CoA onto the -position on lysine, and reversed by HDAC (Kuo and Allis, 1998). Two phylogenetic superfamilies of HAT are known to date: the GCN5 N-acetyltransferases related family GNAT and the MYST family (Verdone et al., 2005). PCAF is a mammalian transcriptional coactivator, which was discovered based on its homology to GCN5-HAT. PCAF acts as a transcriptional coactivator in many processes and is known to interact with the CREB binding protein (CBP) and its homolog p300. P300 and CBP are ubiquitously expressed transcription coactivators. The MYST superfamily includes the human acetyltransferases MOZ and Tip60 (Borrow et al., 1996). Tip60 is involved in histone acetylation mostly on histone H4 in response to DNA damage and transcriptional activation of genes involved in repair. MOZ is involved in acute myeloid leukemia as suggested by the fusion of MOZ and CBP found in these leukemias, which results in mistargeting of the fused protein causing aberrant activation of gene expression (Borrow et al., 1996). There are four distinct phylogenetic classes of HDACs known to date (see Holbert and Marmorstein [2005] for a review). Class 1 includes HDACs 1–3, 8; class 2 includes the human HDACs 4–7, 9, 10; class 3 includes the sirtuins; and class

4 includes recently identified HDAC 11. Class 1 and 2 HDACs were shown to be involved in malignancies in humans and are the target of the HDAC inhibitors TSA and SAHA. Alterations in class 3 HDACs or sirtuins were shown to be associated with age-associated diseases such as type II diabetes, obesity, and neurodegenerative disorders. The mechanism of action of class 1 and 2 HDACs seems to involve a mode of catalysis in which the bound zinc ion mediates the nucleophilic attack of a water molecule on the acetylated lysine substrate whereas the class 3 HDACs require the presence of the oxidized form of the cofactor nicotinamide adenine dinucleotide (NAD+) (Holbert and Marmorstein, 2005). Methylation marks on histones could either activate or silence gene expression as discussed above. Arginine and lysine HMTs catalyze the transfer of a methyl group from the methyl donor SAM to either arginines or lysines. In some cases, a single lysine can be mono-, di-, or tri-methylated, with different functional consequences for each of the three forms. Mammalian homologs of Suv3–9 that methylate lysine-9 on the tail of H3-histones were the first HKMT (histone lysine methyltransferases) to be identified (Rea et al., 2000). SET-containing HKMT methylates Lys-4, -9, -27, or -36 of histone H3 and lys-20 of histone H4. There are two distinct families of lysine methyltransferases SET which contain a SET domain of approximately 130 amino acids and Dot1p (Peters and Schubeler, 2005). All arginine methyltransferases are members of the PRMT family (for a review, see Cheng et al. [2005]). PRMT1 and PRMT4/CARM1 methylate histones H3, H4, and H2B. Although it was long believed that histone methylation is irreversible, histone demethylases have been identified that can remove either repressive or activating histone methylation marks (Metzger et al., 2005; Shi et al., 2004). Two classes of histone demethylases were recently discovered: FAD-dependent amine monooxygenases, which demethylate histones via an oxidation reaction releasing formaldehyde as the leaving group; and JmjC domain demethylases, which also release formadelhyde but require -ketoglutarate and Fe(II). Lysine-specific demethylase-1 (LSD1) is a FAD-dependent enzyme and demethylates monoand di-methyl H3 K4 (for a review see Holbert and Marmorstein [2005]). Since K4 methylation marks histones associated with active genes, it was anticipated that demethylation of this mark would have a repressive effect on gene expression. In agreement with this prediction, LSD1 was found in a complex with the CoREST complex, which represses neuronal genes in nonneuronal cells (Lee et al., 2005). A representative of the second class of histone demethylases, JHDM1 (JmjC domain-containing histone demethylase-1), which specifically demethylates histone H3 at lysine 36 (H3-K36), was recently discovered (Tsukadaet al., 2006). H3-K36 is probably involved in promoting transcription elongation and thus maintenance of a transcriptional active state (Peters and Schubeler, 2005). Since other methylation sites are known to exist in histones, it is anticipated that more demethylases with different specificities will be discovered in the coming years. In summary, the histone modification state is defined by a dynamic equilibrium of modifying and demodifying enzymes.

DNA Methylation and Chromatin States Cooperatively Determine the State of Activity of Genes

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Transcription factor

X CH3

Ac

Transcription factor

Ac

CH3

Ac

Ac Sin3A

HDCA

MECP2

CH3

H3

CH3

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SUV39 HP1

Figure 5.3 DNA methylation silences genes by two mechanisms. The first mechanism is direct (upper right). A methyl group in the recognition element for a transcription factor inhibits the binding of the transcription factor to the promoter resulting in inhibition of transcription. The second mechanism (bottom right) involves recruitment of MBDs which in turn recruit co-repressors (Sin3A), HDAC, HMTs (Suv39), and HP-1, a protein that binds K9 methylated H3-histones. This results in formation of an inactive chromatin structure and silencing of transcription.

The direction of the equilibrium is responsive to cellular signaling pathways which activate transcriptional activators or repressors. Transcription factors recruit HATs and K4-HMTs, while transcriptional repressors recruit HDACs, K9-HMTs, and K4-histone demethylases to suppress gene expression. The relative balance of activators and repressors thus defines the state of modification of a gene. This dependence on targeting of histone-modifying enzymes by sequence-specific factors explains the gene selectivity of the histone modification state (Figures 5.3, 5.4 and 5.5).

A second mechanism involves the recruitment of methyl-domain binding proteins such as MeCP2 to methylated genes (Nan et al., 1997). All these proteins share a methyl-binding domain, which recognizes methylated CG dinucleotides in regulatory regions of genes. MBD3 does not bind methylated DNA, but is probably recruited to methylated genes through its interactions with MBD2 (Zhang et al., 1999). MeCP2 recruits a repressive multiprotein complex, which contains the co-repressor Sin3A and histonemodifying enzymes such as HDAC1 and the HMT Suv3–9 (Fuks et al., 2003). Thus, methylation of DNA can lead to chromatin modification, which creates conditions for gene silencing.

DNA METHYLATION AND CHROMATIN STATES COOPERATIVELY DETERMINE THE STATE OF ACTIVITY OF GENES

DNMTs Form Protein–Protein Interactions with Chromatin-Modifying Enzymes There is a bilateral relation between chromatin and DNA methylation. As much as DNA methylation targets chromatinmodifying enzymes to genes, chromatin-modifying enzymes recruit DNA methylating enzymes (Figures 5.4 and 5.5). The mechanisms by which chromatin directs DNA methylation is via protein–protein interaction, through recruitment of DNMTs to specific genes by chromatin-modifying enzymes. A growing list of histone-modifying enzymes has been shown to interact with DNMT1, such as HDAC1 and HDAC2, the HMTs SUV3–9 and EZH2, a member of the multiprotein Polycomb complex PRC2, which methylates H3-histone at the

DNA Methylation Leads to Chromatin Inactivation DNA methylation can mark gene for silencing by two mechanisms (Figure 5.3). First, DNA methylation in a recognition element of a transcription factor such as MYC (Prendergast and Ziff, 1991) interferes with its binding. By blocking the binding of these transcription factors, recruitment of acetyltransferases is inhibited, probably resulting in a hypoacetylated chromatin structure, which is inaccessible to the transcription machinery.

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K27 residue (Fuks et al., 2000, 2003; Rountree et al., 2000;Vire et al., 2006). DNMT3a was recently also shown to interact with EZH2, which targets the DNA methylation-histone modification multiprotein complexes to specific sequences in DNA (Vire et al., 2006). The targeting of DNMTs is required not merely for initiating de novo methylation but also for the safeguarding of this pattern. Trans-acting repressors also target both histonemodifying enzymes and DNMTs to specific cis-acting signals. For example, the promyelocytic leukemia fusion protein PMLRAR engages HDACs and DNMTs to its target binding sequences and produces de novo DNA methylation of adjacent genes (Di Croce et al., 2002). There are also documented interactions between proteins, which read the DNA methylation and histone methylation marks and either histone or DNA modifying enzymes. The MBD MeCP2 interacts with the HMT SUV3–9 (Fuks et al., 2003), and in plants it was shown that Chromomethylase3 (CMT3), a plant CNG-specific DNMT, interacts with an Arabidopsis homolog of HP1, a protein that binds histone H3 methylated at Lysine-9 (Jackson et al., 2002). In summary, DNA methylation and histone modification are dynamically interrelated through protein–protein interactions between DNMT and MBDs, on the one hand, and histone-modifying enzymes and modified-histone binding proteins, on the other (see Figures 5.4 and 5.5 for a scheme). Chromatin Activation Leads to DNA Demethylation The relationship between DNA methylation and chromatin is bilateral in both directions of the DNA methylation equilibrium. Pharmacological histone acetylation using HDAC inhibitors leads to replication-independent demethylation in human cell lines (Cervoni and Szyf, 2001; Szyf et al., 1985) as well as in Neurospora crassa (Selker, 1998). TSA induces demethylation in non-replicating cells in the hippocampus of adult rats, illustrating the reversibility of the DNA methylation pattern even in somatic tissues (Weaver et al., 2004a, b). The relationship between histone acetylation and DNA demethylation could explain gene-targeted

demethylation (Figure 5.4). Transcription factors target HATs to specific genes, causing gene-specific acetylation, and thus facilitate their demethylation. For example, the intronic kappa chain enhancer and the transcription factor NF-kappaB are required for B-cell–specific demethylation of the kappa gene (Lichtenstein et al., 1994). The maize Suppressor-mutator (Spm) transposon demethylation is mediated by the transposon-encoded transcriptional activator TnpA protein (Bruniquel and Schwartz, 2003). The mechanism by which histone acetylation induces demethylation is still unknown. Increase in histone acetylation through triggering of cellular signaling pathways by environmental signals is a possible route for environmental signals to induce changes in the DNA methylation pattern. The partial overlap between HDAC inhibitors and DNA methylation inhibitors has to be considered when one uses these agents either therapeutically or for research purposes (see Figure 5.5 for a summary of the relationship between DNA and chromatin modifications and gene expression).

DNMTs Are Involved in Cellular Functions Independent of Their DNMT Activity Recent data suggest that DNMT proteins encode functional domains that enable them to interact with other cellular machineries, and thus DNMTs might exert their biological effects by methylation-independent mechanisms. DNMT1 was shown to have a number of distinct functional domains: a fork-targeting domain that targets it to the replication fork (Leonhardt et al., 1992), a stretch of amino acids that bind the replication fork protein PCNA (proliferating cell nuclear antigen) (Chuang et al., 1997), and domains that mediate the binding of DNMT1 to HDAC1 and 2 as well as the tumor-suppressor gene retinoblastoma (Rb) protein (Robertson et al., 2000). DNMT1 inhibitors were shown to induce expression of tumor-suppressor genes in a methylation-independent manner (Milutinovic et al., 2004). This possibility must also be considered when interpreting data from DNA methylation inhibition experiments.

DNMT Transcription repressor

HAT

dem

eth

CH3 CH3

CH3

Ac

TSA

Ac

Ac Ac Ac

HDAC

ylas

e

Transcription factor

HAT binding

CH3

CH3

Ac

Ac

X

demethylase

Figure 5.4 Gene activation by histone acetylation and DNA demethylation. An inactive gene is methylated and is associated with HDAC, DNMT, and hypoacetylated histones. Upon either pharmacological inhibition of HDACs with TSA or recruitment of HATs to the gene, the histones are acetylated. The “open” chromatin facilitates interaction of demethylases with the gene and DNA demethylation.

Epigenetics and Human Disease

MBD2AS SAM

demethylase

Ac

Ac

K9

K9 M

TR+ HDAC

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INHAT M

DNMT

HAT

TSA

K9-

K9M

M

X

X

TA+ Histone dMTase?

Histone MTase K9-M

demethylase

K9-M M

X

DNMT

MG98 5-azaC

M

X

Figure 5.5 Epigenetic cycle. A gene could be found in different epigenetic states that are a balance of modification and demodification reactions. Methylation of DNA and chromatin modifications are interrelated. Ac, histone acetylation; horizontal arrow indicates transcription; empty circle, CG site in DNA; M in circle indicates methylated CG site; K9-M, methyl-K9-H3-histone; TR, transcriptional repressor; histone MTase, histone methyltransferase; HAT, histone acetyltransferase; INHAT, inhibitors of histone acetyltransferase; histone dMTase, histone demethylase; TA, transcription activator; MBD2 AS, antisense oligonucleotide inhibitor of MBD2 a putative demethylase; SAM, S-adenosylmethionine; MG98, antisense oligonucleotide inhibitor of DNMT; 5-azaC, a nucleoside inhibitor of DNA methylation.

EPIGENETICS AND HUMAN DISEASE Aberrant DNA Methylation and Chromatin Modification States in Cancer Aberrant epigenetic markings could have the same effect as genetic mutations, and thus they are candidates to be involved in any human disease caused by aberrant gene expression (for recent reviews see Feinberg [2007] and Gosden and Feinberg [2007]). Since epigenetic marks are potentially responsive to extracellular signals, they are candidates to mediate gene–environment interactions. Epigenetic changes are especially attractive candidates to mediate age-related disease, such as cancer, schizophrenia, type 2 diabetes, and asthma. Although the involvement of epigenetic changes in most disease is at this point in time still speculative, there are extensive data implicating DNA methylation and chromatin modification changes in cancer. The alterations in DNA methylation in cancer include a global loss in methylation concurrently with regional hypermethylation of specific genes (Feinberg and Vogelstein, 1983; Feinberg et al., 1988; Lu et al., 1983). Cancer progression involves concurrent silencing of genes such

as tumor-suppressor genes that block cancer growth, as well as activation of genes that promote metastasis such as certain proteases; the changes in DNA methylation appear to correspond with these changes in gene expression programming. Loss of methylation was observed in repetitive elements such as LINE1 (Chalitchagorn et al., 2004), in genes that promote the cancer state such as oncogenes (Feinberg and Vogelstein, 1983), as well as in pro-metastatic genes such as S100P (Sugimoto et al., 2004), S100A4 (Rosty et al., 2002), and uPA (Guo et al., 2002). Loss of methylation is associated with increased invasiveness in vitro (Figure 5.6). The regional hypermethylation observed in cancer is associated with changes in chromatin state and, as a consequence, with gene silencing. There is an increasing list of genes affected by hypermethylation that includes genes encoding tumor suppressors such as p16, RB, and BRCA1; adhesion molecules such as E CADHERIN; repair enzymes such as MGMT; and inhibitors of metastasis such as the different TIMPs (for review on tumorsuppressor gene methylation see Baylin et al. [2001]). The assortment of hypermethylated genes varies in different cancer types and at different cancer stages, creating a methylation profile that is unique for each cancer cell type. These methylation

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DNMT

DNA methylation

Demethylation

M M Tumor-suppressor genes

Metastasis genes

X Figure 5.6 DNA methylation changes in cancer. In cancer, tumor-suppressor genes are methylated and silenced, and metastatic genes are unmethylated and activated.

profiles are gaining increased importance in cancer diagnosis (Novik et al., 2002). In addition to changes in DNA methylation, the abundance of components of the DNA methylation machinery is altered in cancer. DNMT1, for example, was shown to be upregulated in many cancers by different oncogenic signaling pathways (Szyf, 2001). It is unclear whether the upregulation of DNMT1 is responsible for the hypermethylation of tumor-suppressor genes or whether aberrant targeting of DNNMT1 to specific genes by specific trans-acting factors such as the fusion protein PML-RAR causes gene-specific hypermethylation (Di Croce et al., 2002). DNMT1 might be promoting tumorigenesis by DNA methylationindependent mechanisms through protein–protein interactions with critical cellular regulatory proteins such as Rb, HDAC1, and PCNA as discussed above (Chuang et al., 1997; Milutinovic et al., 2004; Robertson et al., 2000). DNA Methylation and Late-Onset Human Disease Although cancer is the most well-established case for epigenetically driven human disease, there is some evidence suggesting involvement of epigenetic processes in other diseases. It stands to reason that epigenetic drift would lead to altered gene expression programming, which would lead to a pathological state. Good candidates are autoimmune diseases and inflammatory states such as asthma. It is by now well established that alteration in DNA methylation plays a role in the autoimmune disease Systemic Lupus Erythematosus (SLE) (Richardson, 2002). Drugs known to induce lupus such as procainamide and hydralazine induce also demethylation (Cornacchia et al., 1988; Deng et al., 2003; Scheinbart et al., 1991) and the demethylating agent 5-azaC induces lupus (Quddus et al., 1993). The genome of T cells from SLE patients is demethylated in comparison with T cells from control subjects (Balada et al., 2007). It is proposed that demethylation activates genes whose expression confers upon T cells self-reactivity. Interestingly, MBD2, which was implicated

in demethylation, is upregulated in T cells from Lupus patients (Balada et al., 2007). The involvement of DNA methylation in asthma was proposed (Kaminsky et al., 2006), and promoters of genes that encode cytokines involved in asthma were shown to be regulated by DNA methylation (Santangelo et al., 2002), but there is no evidence yet for aberrant DNA methylation in asthma. Similarly, it has been speculated that DNA methylation disregulation is involved in cardiovascular diseases (Corwin, 2004). There are some anecdotal data indicating involvement of DNA methylation in cardiovascular pathologies. Global hypermethylation was noted in leukocytes from chronic kidney disease, and hypermethylation was shown to associate with inflammation and increased mortality (Stenvinkel et al., 2007). There is some evidence for epigenetic dysregulation of estrogen receptor beta in atherosclerosis and vascular aging (Kim et al., 2007), and global hypomethylation was noted in atherosclerotic lesions (Hiltunen et al., 2002). These data justify further studies into the possible epigenetic basis of cardiovascular disease. If indeed epigenetic aberrations are found to be involved in cardiovascular diseases, this might result in new diagnostic, prophylactic, and therapeutic approaches. DNA methylation has also been proposed to be involved in insulin resistance and type 2 diabetes (Devaskar and Thamotharan, 2007). It is known that nutritional restriction during gestation is associated with development of insulin resistance and type 2 diabetes later in life (Devaskar and Thamotharan, 2007; Simmons, 2007). Uteroplacental insufficiency also causes global changes in methylation and histone acetylation in brain and liver in rodents (Ke et al., 2006; MacLennan et al., 2004). Although these studies did not establish a causal relationship between hypomethylation and type 2 diabetes, they are suggestive. Epigenetic mechanisms could provide a molecular link between early exposures and late onset of type 2 diabetes. The advent of techniques to perform whole-epigenome mapping will allow us to directly test the attractive hypothesis that epigenetic aberrations driven by different environmental exposures cause epigenetic aberrations that can lead to lateonset diseases such as type 2 diabetes or cardiovascular disease. There is emerging evidence that aberrations in DNA methylation are involved in mental health. Mutations in the MBD MECP2 lead to Rett syndrome (Amir et al., 1999; Wan et al., 1999), one of the most common causes of mental retardation in females. There are some data indicating aberrant methylation in late-onset mental pathologies, although it is unclear whether these changes in DNA methylation originated during embryogenesis or later in life as a response to an environmental exposure. The gene encoding REELIN, a protein involved in neuronal development and synaptogenesis, which is implicated in long-term memory, was found to be methylated in brains of schizophrenia patients. The methylation of REELIN was correlated with its reduced expression and increased DNMT1 expression in GABAergic neurons in the prefrontal cortex (Chen et al., 2002; Costa et al., 2002, 2003; Grayson et al., 2005; Veldic et al., 2007). Our preliminary data demonstrate hypermethylation of the GLUCOCORTICOID RECEPTOR exon 1f promoter and

Conclusions

its reduced expression in hippocampi of subjects who committed suicide, relative to age-matched control subjects (McGowan et al., unpublished data). The analysis of epigenetic aberration in mental disease is an active area of research, which is bound to produce exciting data in the coming years. Therapeutic Implications of Aberrant DNA Methylation Patterns in Cancer Epigenetic states, in contrast to genetic mutations, are potentially reversible by pharmacological agents (Figure 5.5). Thus, this field carries much promise for future novel therapeutics and prophylactics. A number of clinical trials are underway with catalytic inhibitors of DNMT1, antisense inhibitors of DNMT1, and HDAC inhibitors in different cancers, with the goal of activating silenced tumor-suppressor genes (Goffin and Eisenhauer, 2002; Szyf, 2003). It is anticipated that, as our understanding of other histone modifications is improved, inhibitors of HKMT and DNA demethylases will be used in cancer therapy as well. An important objective is to develop isotypic specific inhibitors, which would target the isotypes relevant to cancer and limit adverse effects. A critical issue here is to activate tumor-suppressor genes without activating pro-metastatic genes (Figure 5.6) and to identify the specific chromatin modification and DNA modification enzymes critical for tumor-suppressor genes. DNMT1 might promote cancer through DNA methylationindependent mechanisms in addition to a methylationdependent process (Szyf, 2001). Demethylation of DNA could trigger the induction of pro-metastatic genes as discussed above; it might be therefore ill advised to use catalytic inhibitors of DNMT1. A preferred approach is to target the methylationindependent activities of DNMT1 specifically. While the main focus in the field is on activating tumor-suppressor genes using DNA methylation and HDAC inhibitors, we suggest that it might be worthwhile to target the opposite process hypomethylation. Inhibitors of the DNA demethylation machinery might be utilized to silence pro-metastatic genes (Pakneshan et al., 2004). An important issue that needs to be determined in future studies is whether demethylation inhibitors would cause silencing of tumor-suppressor genes through hypermethylation, which could contraindicate their use. DNA methylation profiles are promising to turn into central diagnostic tools. We now know that not all tumors are methylated at the same sites. Each tumor type bears a “methylation profile,” a unique combination of methylated CGs, which has important predictive and diagnostic potential (Adorjan et al., 2002). The development of whole-genome high-density oligonucleotide arrays will allow in the future to map in detail the DNA methylation and chromatin changes associated with disease and behavioral abnormalities. These methylation profiles might offer an explanation for interindividual phenotypic diversity. Identifying target genes whose methylation and chromatin is changed in correlation with altered phenotypes will facilitate unraveling of the biological mechanisms behind these phenotypic differences.

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The second area that carries great promise is the utilization of DNA methylation and epigenetic profiling in prophylactics. We need to map the response of DNA methylation to different environmental exposures such as social environment, food, and drugs. Epigenetics is a candidate to serve as the main mechanism used by the environment to stably reprogram genes. Understanding the relationship between the environment and disease-state–specific methylation profiles will allow charting prophylactic strategies to avoid deleterious exposure and to promote positive environmental interventions. One of the interesting pending questions in this respect is the issue of methyl-supplemented diets. Folate-fortified diets have been in place for some time. Folate-enriched diets might be helpful in protection from the global hypomethylation which is associated with cancer (Poirier, 2002). However, it is also possible that they might cause hypermethylation and silence critical genes such as tumor-suppressor genes. It is important to map the relationship between methyl supplementation and changes in DNA methylation profiles. Understanding how the environment utilizes epigenetics to sculpt the genome is perhaps one of the greatest challenges in biology, which would have a broad impact in medicine.

CONCLUSIONS Epigenetic mechanisms control gene expression programs and serve as an interface between the dynamic environment and the genome. Many of the proteins, which lay down the DNA methylation pattern as well as histone remodeling complexes and modifying enzymes, have been identified. Moreover, recent discoveries of chromatin demodification enzymes suggest that epigenetic processes are reversible and that the state of chromatin, as well as DNA methylation, is an equilibrium between modification and demodification processes. There is a bilateral relation between chromatin structure and demethylation, which is in part determined by protein–protein interactions between DNA methylation enzymes and chromatin modification enzymes. The establishment of new epigenetic patterns requires the targeting of DNA methylation and chromatin modification enzymes to specific genes, while maintenance of this pattern requires their continuous presence on these genes. Epigenetic proteins are targeted to specific sequences through interaction with specific trans-acting activators or repressors, which recognize specific cis-acting signals next to genes. Future experiments are required to understand the cellular signaling pathways leading to activation of these targeting factors and to determine whether they link physiological, environmental, or pathological signals with chromatin modification states and the DNA methylation pattern. One of the great challenges in the field is to define the mechanisms linking environment and the sculpting of the genes by epigenetic modifications. Epigenetic aberrations act similar to genetic mutations to precipitate pathological states. In cancer this is now well established. It stands to reason that similar processes are involved in other diseases such as mental disorders, cardiovascular disorders, and metabolic disorders. The main challenge now is to unravel

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the epigenetic code and identify the epigenetic profiles associated with human disease. Recent advances in whole-genome mapping using high-density oligonucleotide arrays and the use of 5-methylcytosine–specific antibodies, as well as chromatin immunoprecipitation (ChIP) with histone-modification–specific antibodies, will allow for mapping in unbiased way the state of methylation and chromatin modification of entire genomes and for identifying methylation profiles that are associated with disease. Since epigenomes vary from cell type to cell type and at different time points in life, this mapping effort is bound to be an enormous task that requires development of high-throughput methodologies. The whole-epigenome mapping efforts would comprehensively test for the first time the hypothesis that interindividual epigenetic differences are the basis for the well-known interindividual differences in appearance, behavior, and physiology. One provocative hypothesis that needs to be addressed by whole-epigenome mapping is that some of the environmentally-driven epigenetic changes are passed through the germline, thus explaining welldocumented trans-generational environmental effects. Since the epigenome is dynamic, it is potentially reversible. A further understanding of the DNA methylation and histone

modification machineries will allow us to determine whether we could intercede either pharmacologically or by behavioral and nutritional interventions to reverse deleterious epigenetic programming. We ought to link the main cellular signaling pathways, specific DNA methylation and chromatin modification enzymes, and DNA methylation profiles in order to design intelligent interventions that will have main positive effects in the absence of adverse outcomes. Epigenetics has made important headways and has advanced dramatically in the last few years; however, many methodological and conceptual challenges remain. If the promise of this field would be realized, it would revolutionize our understanding of biology, medical diagnosis, and therapeutics.

ACKNOWLEDGEMENTS The work from the author’s laboratory discussed in this chapter was funded by the National Cancer Institute of Canada, the Canadian Institute of Health Research, and the Canadian Institute of Advanced Research.

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6 Systems Biology and the Emergence of Systems Medicine Nathan D. Price, Lucas B. Edelman, Inyoul Lee, Hyuntae Yoo, Daehee Hwang, George Carlson, David J. Galas, James R. Heath and Leroy Hood

INTRODUCTION A new approach to biology, which we call systems biology, has emerged over the past 10 years or so – an approach that looks at biology as an informational science, studies biological systems as a whole, and recognizes that biological information in living systems is captured, transmitted, modulated, and integrated by biological networks that pass this information to molecular machines for execution. This approach differs from early “systems approaches to biology” in that it attempts both a bottomup approach (from large molecular datasets) and a top-down approach (from computational modeling and simulations) where there is an attempt to trace complex observations of phenotype back to the digital core encoded in the genome. New measurement and visualization technologies, together with powerful computational and modeling tools, have transformed systems biology by making it possible for the first time to execute the five uniquely defining features of contemporary systems biology: 1. measurements of the various types of biological information that are global or comprehensive to the greatest extent possible (e.g., measure the digital code of all genes or, for example, the concentrations of all mRNAs, all proteins, all metabolites, etc.); Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 74

2. the different levels of information (DNA, RNA, protein, etc.) must be integrated to understand or capture how the environment has modified the basic digital information of the genome at each level of the biological information hierarchy (DNA, RNA, proteins, interactions, biological networks, cells, individuals and ecologies) and thereby to induce biological responses; 3. all biological systems (e.g., networks) must be studied dynamically as they capture, transmit, integrate and utilize the biological information necessary for the execution the two most fundamental responses of living organisms – development or physiological responses to the organism’s environment; 4. all measurements must be quantitatively determined to the greatest extent possible; and 5. the global and dynamic data from the variety of information hierarchies must be integrated and modeled. These models essentially create hypotheses about biological functions and mechanisms of disease that can then be tested experimentally by systems perturbations – the enumeration of new datasets, their integration, modifications of models, in an iterative manner, until the working models reflect the reality of the experimental data. Once the models accurately fit the biological systems measurements, they can then be used to predict the results of perturbing the system in new ways (e.g., for Copyright © 2009, Elsevier Inc. All rights reserved.

Systems Science in Biology and Medicine

designing treatment strategies or for identifying likely underlying causes for disease). The information revolution in biology will enable systems medicine to emerge in the next 10 years. Key to this transformation will be harnessing computationally the vast amount of biological information becoming available through rapid advancement in measurement, visualization and computational technologies. Two fundamental types of biological information, the digital information of the genome and interacting information from the environment, are integrated together to specify the five fundamental mechanisms of life – evolution, development, physiological responses, aging and the onset and progression of disease (Hood et al., 2004). Advances in high-throughput DNA sequencing will enable each person’s genome to be sequenced rapidly and at a reasonable cost, providing digital information for each person and making possible the prediction of increasingly accurate probabilistic health futures. Also, the advance of highthroughput measurement technologies will enable the assessment of dynamic environmental information emerging from the integration of genome and environmental information from each individual, as reflected, for example, by the changing levels of proteins in the blood – thus providing a real time (current) health assessment of the individual. These technologies will generate tremendously large, dynamical datasets about health states and hence about the states of relevant biological networks of the individual patients. This detailed information will arise through the use of perturbation experiments coupled with global experimental technologies, and progress in systems biology research is increasingly elucidating the functions and structures of these networks. A systems approach to medicine argues that disease arises from disease-perturbed biological networks and that the dynamically changing, altered patterns of gene expression that are controlled by these perturbed networks give rise to the disease manifestations. Here, we present a systems view of biology and disease together with a discussion of some recent advances in state-of-the-art in vitro and in vivo diagnostics technologies, and we suggest how, as these technologies mature, they will move us towards a future of predictive, personalized, preventive, and participatory medicine (P4 medicine) (Hood et al., 2004).

SYSTEMS SCIENCE IN BIOLOGY AND MEDICINE Systems Biology How do systems approaches to biology and medicine lead to a revolutionary view of medicine? We present this systems view in some detail, because an understanding of P4 medicine and its implications for society are predicated upon understanding these systems principles. Two primary domains of biological information lend themselves readily to such systems-level analysis: the static, digital information of the genome, and the dynamic information arising from environmental interactions with the subcellular, cellular, and tissue levels of organization (Hood et al.,

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2004). Digital genome information encodes two types of biological networks – protein interactions and gene regulatory networks. Protein networks transmit and use biological information for development, physiology and metabolism. Gene regulatory networks – transcription factors, their regulatory binding sites and the small RNAs that regulate networks of other transcription factors and other RNAs interacting with one another – receive information from signal-transduction networks, integrate and modulate it, and convey the processed information to networks of genes or molecular machines that execute developmental and physiological functions. In biological systems, these two types of networks are closely integrated. The organization of these networks can be inferred from various types of measurements including, for example, global measurements of dynamically changing levels of mRNAs and proteins during developmental and physiological responses, as well as large-scale measurements of protein–protein and protein–DNA interactions. There are multiple hierarchical levels of organization and information (for example, DNA, RNA and protein networks, cellular and metabolic networks, and organization and responses of organ systems). To understand biological systems, information must be gathered from as many information levels as possible and integrated into models that generate hypotheses about how biology works. Let us now consider the logic of systems approaches to medicine. Systems Medicine The central premise of systems medicine is that diseaseperturbed networks alter patterns of expression in genes and proteins – and that these altered patterns encode the dynamic pathophysiology of the disease and necessarily results in altered molecular fingerprints that can be detected clinically. Through advances in measurement technologies and computational analysis tools for tissue and blood analyses, we will be able to read these signals to make a multitude of diagnoses to distinguish health from disease and to determine the nature of any pathology. Multi-parameter analyses of biological information will be key to tracking these altered patterns in disease-perturbed networks. Diagnostic methods of the past have been pauciparameter in nature – usually measuring just a single parameter relevant to a specific disease state (e.g., PSA levels to assess prostate health), and so our ability to track health and disease has been limited. Current and emerging technologies are creating a transition into a new era of predictive, preventative, and personalized medicine. A causal disease perturbation could be the result of specific, disease-causing DNA mutations, pathogenic organisms, or other pathological environmental factors such as toxins. Molecular fingerprints of pathological processes can take on many molecular forms, including the analyses of proteins (Wang et al., 2005a, b), DNA (Papadopoulou et al., 2006), RNA (Scherzer et al., 2007), and metabolites (Solanky et al., 2003), as well as informative, post-translational modifications to these molecules such as protein phosphorylation. Signals related to health and disease can be found in multiple sites. For instance, many bodily fluids such as the blood, urine, saliva, cerebral spinal fluid and so forth can be sampled to

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identify evidence of perturbed molecular fingerprints reflecting the altered expression patterns of genes and proteins in diseaseperturbed biological networks. Of these, the blood is likely the most information rich organ (or fluid) in that it bathes all of the tissues in the body, and it is easily accessible for diagnostic procedures. In addition to the biomolecules secreted into the blood from cells and tissues throughout the body, the transcriptomes and proteomes of cells circulating in the blood (e.g., white blood cells) are also potentially an abundant source of biomedically important information (Buttner et al., 2007). Thus, the amount of information available in the blood about health and disease is enormous if we learn to read and interpret the molecular signals. As an example of an environmental disease perturbation, we have studied the dynamic onset of the infectious prion disease in several strains of mice at the level of mRNA in the brain (the affected organ) and showed that a series of interlocking protein networks that surround the prion protein are significantly perturbed across the 150-day span from disease initiation to death. Figure 6.1 shows differential networks that were derived from comparisons of mRNA-expression patterns in normal and diseased brains at each of three time points after infection. They involve 67 proteins in the prion replication and accumulation network. The initial network changes occur well before the clinical signs of the disease can be detected and predict later widespread histopathological events. These dynamically changing, disease-perturbed networks lead to two important conclusions. First, some significant network nodal points change before the related clinical or histological changes are evident. Therefore, labeled biomarkers that are specific for the changing nodes or the biological processes they regulate could be used for in vivo imaging diagnostics even before symptoms arise, as has already been shown in patients (Golub et al., 1999; Quackenbush, 2006; Ramaswamy et al., 2001;Wang et al., 2005a, b). Alternatively, if some of these altered nodes encode secreted proteins, they could provide readily accessible in vitro diagnostic blood markers for early disease detection. Second, many of the sub-networks of proteins that change during the onset of disease affect changes in phenotypic traits that are consistent with the pathology of the disease. About 900 perturbed mRNAs appear to encode the core prion-disease response, and, during the progression of the priondisease process, several hundred undergo significant gene expression changes well before the clinical signs of prion disease. Many of these potential early disease “sentinels” are predicted to be secreted into the blood and therefore represent potential protein biomarkers for early disease diagnosis through blood protein analysis. One interesting new diagnostic method evaluates relative expression reversals in protein concentrations or gene expression levels. This procedure eliminates the need for data normalization or the establishment of population-wide thresholds. This approach has been successfully used to identify robust and accurate classifiers for prostate cancer (Xu et al., 2005), sarcomas (Price et al., 2007), and a variety of other cancers (Tan et al., 2005), as well as to predict treatment outcomes in breast

cancer (Ma et al., 2004), demonstrating the efficacy of even relatively low-order systems analysis in medicine. Another key issue that needs to be addressed is how to best decouple two primary dimensions of disease information: namely, disease stratification (e.g., which type of prostate cancer is present) and disease progression (e.g., stage of development of a particular prostate cancer stratification in time). Molecular signature changes can indicate the presence of different diseases (stratification) as well as their stage (progression). Thus, a key challenge in future will be to develop analysis tools that will enable us to differentiate the location of the physiologic state in regards to both of these critical clinical dimensions.

MULTI-PARAMETER BLOOD-BOURNE BIOMARKERS In principle, it is clear how blood samples containing secreted molecules from all of the tissues in the body can be used as a window into health or disease states (Anderson and Anderson, 2002; Fujii et al., 2004; Hood et al., 2004; Lathrop et al., 2003; Lee et al., 2006). In practice, however, the task of identifying markers of disease states in the vast array of secreted proteins can seem daunting. There are millions of different proteins present in the blood and they are expressed at levels that probably differ by 12 orders of magnitude (1012). Thus, there are indeed very significant challenges associated with defining relevant biomarkers in the plasma proteome, as is evidenced by the paucity of blood protein markers found thus far, despite significant efforts (Wilson, 2006). This fact highlights the need to develop wellfounded systems approaches to diagnostics in order to hasten the identification of such markers and to harness the tremendous information potential of the blood. While the potential information available to diagnose heath and disease is enormous, it is also true that the challenges of separating signal from background noise (biological or measurement noise) are also very significant. Sources of noise include error in measurements, polymorphisms in the population, environmental variations, stochastic variations, as well as signal dilution through mixing and other processes of molecule transport. Thus, enhancing signal while reducing noise will be a primary theme of research in predictive medicine for the foreseeable future. An important strategy for dealing with noise arising from genetic polymorphisms and health histories in the population are through dynamic, subtractive analyses carried out in the individual patient. In other words, measurements taken from individual patients at different time points (longitudinal data) can be used to perform subtractive analyses where only the differences observed are considered. Thus, each patient becomes their own control, which eliminates many of the sources of noise. Eventually, it will be important for each patient to have biannual blood analyses taken so that changes can be observed relative to the background of what is normal in each individual rather than relative to the population at large. Such databases of measurements will be essential to enabling personalized and predictive

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medicine. Using each patient as their own control is one of the essential features of the emerging personalized medicine. Organ-Specific Blood Protein Fingerprints We discuss the organ-specific blood protein fingerprint approach to diagnostics because it lies at the very heart of the predictive medicine that will emerge over the next 10 years. Our feeling is that this will be one of the central data gathering strategies for predictive and personalized medicine and, accordingly, we will discuss it in detail. The idea is that disease arises from dynamically changing disease-perturbed networks, that the diseased organs will secrete proteins into the blood, and that if these proteins are encoded by disease-perturbed networks their levels of expression in the blood will be altered in a manner that reflects the specific nature of the disease. Indeed, this idea is the basis for the very broad range of blood biomarker studies that are being carried out by many scientific centers (Anderson and Anderson, 2002; Fujii et al., 2004; Hood et al., 2004; Lathrop et al., 2003; Lee et al., 2006). The major difficulty with this simple view is that if you identify multiple blood proteins whose changes are specific for a particular disease state (as compared against normal controls) and then examine the same blood markers for that disease in bloods drawn from individuals, with say 10 other pathologies, these marker proteins can change in unpredictable ways since multiple organs control the expression of most blood proteins and these organs respond differently to various environmental signals. The important point is that if a marker protein synthesized in five organs changes in the blood, we cannot be certain which organ(s) is the origin of the change. While that marker may sample a biological network relevant to disease diagnosis, since its origin is not clear its use as a disease biomarker may raise more questions than it answers. The solution to this dilemma is clear – employ organspecific blood protein biomarkers whose changes must therefore reflect changes only in the organ itself. If enough of these organspecific blood proteins are sampled, they will represent a survey of many different biological networks in the organ of interest and will provide sufficient diagnostic information for any disease. Eventually, correlation of these organ-specific biomarkers with more general biomarkers may prove to be the best long-term strategy for developing diagnostic fingerprints. We have several lines of preliminary evidence that suggest this organ-specific blood protein approach will be effective. In prostate cancer, for example, there are disease-mediated altered patterns of mRNA and protein expression in the prostate. Some of these genes are expressed primarily in the prostate (organspecific products) and some of these organ-specific proteins are secreted into the blood, where they collectively constitute a protein molecular fingerprint comprised of say 100 or more proteins whose relative concentration levels probably report the status of the biological networks in the prostate gland. We have demonstrated that changes in the blood concentrations of several of these prostate-specific blood proteins reflect the various stages of prostate cancer and, as discussed above, various brainspecific blood proteins also reflect the progression of prion disease in mice. We propose that the distinct expression levels of the

individual proteins in each fingerprint represent a multiparameter (and therefore potentially information-rich) diagnostic indicator reflecting the dynamic behavior of, for example, the disease-perturbed networks from which they arise. The analysis of 50 or so organ-specific proteins should allow us to both stratify diseases in the organ as well as determine their stage of progression. We have identified tens to hundreds of organ-specific transcripts in each of the 40 or so organs that we have examined in mouse and human. We can envision a time over the next 5–10 years when 50 or so organ-specific blood proteins will be identified for each of the 50 or so major organs and tissues in humans – so that computational analyses of the relative concentrations of the protein components in these organ-specific fingerprints will enable blood to become the primary window into health and disease. When we analyze data from these blood indicators, we also may be in a position to identify the dynamically changing disease-perturbed network(s). The analysis of these dynamic networks will allow us to study in detail the pathophysiology of the disease response and hence be in a position to think of new approaches to therapy and prevention. To generate the ability to assess simultaneously all 50 organ blood protein fingerprints in patients, we ultimately need to develop the microfluidic or nanotechnology tools for making perhaps 2500 rapid protein measurements (e.g., 50 proteins from each of 50 human organs) from a droplet of blood. Nanotechnology is necessary because only this severe miniaturization can allow the necessary thousands of measurements from a single drop of blood. In order to reach this stage we will also have to create the appropriate computational tools to capture, store, analyze, integrate, model, and visualize the information arising from this approach.

EMERGING IN VIVO AND IN VITRO TECHNOLOGIES In vitro Measurement Technologies P4 medicine will require that in vitro measurements of thousands of blood proteins be executed rapidly, automatically and inexpensively on small blood samples. This will require miniaturization, parallelization, integration and automation of tissue and blood purification and measurement technologies. In short, it will require making in vitro measurements inexpensively – perhaps for a penny or less per protein measured. This need for inexpensive in vitro measurements is driving the development of integrated microfluidics and nanotechnologies for in vitro diagnostics. We further argue that the complex changes in these protein levels must be analyzed computationally to search for the patterns that correlate with particular diseases – and indeed to stratify each disease as well as determine its stage of progression. To meet the expanding requirements of systems medicine, in vitro measurements of cells, proteins, mRNAs, etc., whether for fundamental biological studies or for pathological analysis,

Emerging In vivo and In vitro Technologies

must not just be inexpensive, but must also be sensitive, quantitative, rapid, and executed on very small quantities of tissues, cells, serum, etc. We are working towards developing chip platforms that can take a few microliters (a finger prick) of blood, separate the plasma from the serum, and then measure on the order of 100 or more proteins and/or mRNAs quantitatively, with high sensitivity and specificity, and in a few minutes time. We can envision a time 5–10 years hence when small hand-held devices will be able to make these 2500 measurements from a fraction of a droplet of blood, send them via wireless to a server for analysis and consequently inform the patient and physician as to the status of the patient. While many fundamental scientific challenges remain to be solved before this goal is achieved, none of those challenges appears insurmountable at this point. One will also be able to use the blood cells as powerful diagnostic markers – either of infectious diseases or of genetic diseases. Microfluidic cell-sorting technologies for being able to sort blood cells into their 10 or so individual types for analysis are now available. Even more important is the emergence of single-cell analytic tools where DNA, mRNAs or small RNAs, proteins and even metabolites can be analyzed rapidly from individual cells. The cells can also be perturbed with appropriate environmental stimuli to identify defects. It is possible that an appropriate analysis of immune cells (both innate and adaptive) from the blood will reveal important information about past antigenic history of the individual as well as current state of immunological responsiveness. Similarly, analysis of rare blood cells such as circulating cancer cells may also be utilized to guide therapies. Another type of in vitro measurement will be a determination of the complete genome sequence of individuals with a nanotechnology approach to sequencing single strands of DNA on a massively parallel basis (billions of DNA strands simultaneously). A device like this will emerge over the next 5–10 years and will allow millions, if not billions, of individual human genome sequences to be determined rapidly, inexpensively and in a massively parallel manner. The 2500 organ-specific blood marker measurements and the complete individual human genome sequences will be the heart of the predictive and personalized medicine that will emerge over the next 10 years or so. While transcriptomic approaches have proven useful for identifying informative molecular signatures for a number of diseases (Quackenbush, 2006), high-throughput proteomic characterizations have lagged behind. The reason for this difference is clearly the more difficult challenge of measuring proteins compared to mRNA transcripts. Emerging proteomics technologies hold the promise of greatly improving our ability to make detailed disease assessments using protein-based molecular signatures. One key advantage of protein signatures relative to gene expression is that the proteins found in the blood and other accessible bodily fluids exhibit slower degradation rates than their mRNA counterparts – and of course proteins are the direct agents mediating the disease process itself. Thus, protein concentration signatures represent an important class of molecular signature for disease diagnosis.

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The Imaging of Proteomics and Blood Protein Biomarkers The blood is an ideal organ for identifying biomarkers because it interacts with virtually every organ and major tissue type in the body, and each secretes proteins into the blood. Moreover, the blood is easily accessible for diagnostic studies – and presumably the analysis of changes in protein levels or protein modifications that could serve as surrogates or reflections of health and disease. As noted earlier, the blood is an enormously complex organ; it contains hundreds of thousands to millions of proteins whose concentrations range over 12 orders of magnitude. Since there is for proteins no equivalent to the polymerase chain reaction (PCR) amplification procedure, only the more abundant proteins can be seen by conventional protein analysis studies. It should also be pointed out that many different features of proteins ultimately must be characterized or quantified: identification, quantification, chemical modifications, alternative mRNA splicing products, cellular localizations, three-dimensional structures (and their dynamics), as well as their functions. We here are only concerned with the quantification and identification of proteins. There are two general approaches to the analysis of protein mixtures. In one case protein-capture agents such as antibodies are used – and with appropriate controls these approaches can quantify the levels of proteins. These techniques include Western blot analyses, ELISA assays, surface plasmon resonance and protein chips. A second approach is to use mass spectrometry. Since proteins have large masses that are difficult to analyze accurately by mass spectrometry, most proteins are analyzed after converting them into tryptic peptides (or other proteolytic fragments) whose masses are far smaller and hence more easily analyzed. Because blood is such a complex mixture, proteins of interest are often enriched by some type of fractional procedure (charge, hydrophobility, size and/or the presence of modifications such as glycosylation or phosphorylation), before (or after) their conversion into peptides and analysis by mass spectrometry. Isotopically labeled and synthesized peptides may be used to identify and quantify (relative or absolute) the same peptides from unknown samples (and hence one obtains a proxy for the quantification of their corresponding proteins) very effectively by mass spectrometry. We discuss below examples of some of these techniques. Antibody Microarrays Using Surface Plasmon Resonance Imaging (SPRI) Protein chip methods hold great potential for broad quantitative screens of proteins, and a variety of techniques have been developed based on antibody binding (Haab et al., 2001; Olle et al., 2005; Song et al., 2007).Various types of antibody arrays have been used for biomarker discovery and protein profiling of serum from patients with prostate, lung, pancreas, and bladder cancer (Gao et al., 2005;Miller et al., 2003;Orchekowski et al., 2005;Sanchez-Carbayo et al., 2006). One emerging approach with tremendous promise is SPRI (Hu et al., 2007; Huber and Mueller, 2006; Koga et al., 2006), which enables real-time, label-free measurement of protein expression by large numbers of different antibodies. SPR-based chips have a detection sensitivity of 10–100 times less than ELISA

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(Hu et al., 2007), but have a spatial resolution down to approximately 4 m (Lyon et al., 1998). It is thus possible to print up to 800 unique antibodies on Lumera Nanocapture Gold™ microarray slides and monitor the abundance of the target proteins in real time (Hu et al., 2007), even in complex samples such as blood, because the same slide can be regenerated for reuse many times in 10 minutes or less (Z. Hu, C. Lausted, unpublished observations). We are in the process of automating the sample introduction procedures using microfluidic chips, which means that SPRI has the capacity necessary to screen rapidly through tens to hundreds of patient samples. Thus, this approach holds tremendous promise to be able to screen through large numbers of proteins, including secreted proteins and cell-surface markers and not only measure their presence, but also abundance and the dynamics of their binding. The limitation of this technique is its dependence on the affinity and specificity of the antibodies it employs for detection – cross-reactivities in complex protein mixtures (such as blood) can pose significant problems. DNA-Encoded Antibody Libraries (DEAL) One recently developed technique that offers great potential for detailed analyses is DEAL. The primary advantage of DEAL is that it uses a single, robust chemistry – that of DNA hybridization – to spatially localize and detect proteins, mRNAs, and cells, all in a multiplexed fashion. Antibodies are typically too fragile to survive the fabrication procedures associated with assembling robust microfluidics chips, but DNA oligomers are significantly more robust. DEAL thus enables the detection of panels of protein biomarkers within a microfluidics environment and from very small quantities of biological material (100 nanoliters or so) (Yang et al., 2006). This amount of plasma can be readily separated from whole blood on-chip, thus allowing for the measurement of serum biomarkers from a fingerprick of whole blood. In addition, within the environment of flowing microfluidics, the rate-limiting step in performing a surface immunoassay is the kinetics of the binding of the analyte to the surface-bound capture agent (Zimmermann et al., 2005). Thus, DEAL-based immunoassays can be executed very rapidly. The versatility of DEAL also enables multiplexed cell sorting and localization, followed by few- or single-cell measurements of protein, RNA, and other biomolecules (Bailey et al., 2007). DEAL can be engineered into a highly sensitive and very rapid measurement technique, with a reported detection limit of 10 f M for the protein IL-2, 150 times more sensitive than the corresponding commercial ELISA assay. This sensitivity can be applied to the isolation of rare cells based on combinations of cell-surface markers, enabling the isolation and addressing of individual cancer stem cells. DEAL can also be used to make single-cell measurements of secreted proteins from each of these isolated single cells. Thus, DEAL offers superb sensitivity and the ability to perform spatially multiplexed detection for characterization of rare cell types, such as circulating cancer cells or cancer stem cells. These advantages still face the inherent limitations of antibodies, so the development of new approaches to generating

protein-capture agents is a critical part of future development of comprehensive blood diagnostics. Mass Spectrometry-based Techniques Isotopic Tagging for Relative and Absolute Quantification (iTRAQ) of Proteins Analyzed by Mass Spectrometry Stable isotope labeling enables the quantitative analysis of protein concentrations through mass spectrometry (MS). One stateof-the-art technique for quantitative MS is iTRAQ (Ross et al., 2004), which uses stable isotope labeling of proteolytic peptides. This technique modifies primary amino acid groups of peptides by linking a mass balance group, and a reporter group by forming an amide bond. When MS/MS is used for analysis with iTRAQ-tagged peptides, the mass balancing carbonyl moiety is released as a neutral fragment generating 4–8 distinct sets of peptides whose relative abundances can be determined quantitatively. Because eight different iTRAQ reagents are currently available, comparative analysis of a set of two to eight samples is feasible within a single MS/MS run (Hu et al., 2007). The, iTRAQ technology represents the state-of-the-art in quantitative proteomics and represents a promising technology for using proteomics to differentiate key differences in protein networks. Glycopeptide Capture – Front-End Enrichment of Blood Proteins Containing Sugar Residues Mass spectrometry-based methods will allow for the identification of proteins spanning approximately three orders of magnitude in concentration from a given sample. Therefore, methods that can select specified fractions of the proteome are important for simplifying the sample sufficiently to identify the proteins of interest. One recently developed approach is the shotgun glycopeptides capture approach (Sun et al., 2007). This approach selects for N-linked or O-linked glycosylated peptides by selectively coupling these peptides to beads – allowing the uncoupled peptides to be washed away – then the glycosylated peptides are be released and analyzed by mass spectrometry. Both secreted and cell-surface proteins are enriched for glycopeptides compared with their nuclear and cytoplasmic counterparts. Thus, this approach can be used to, for example, identify candidates for unique cell-surface markers to make identifications of clinically relevant cellular subpopulations. In Vivo Molecular Diagnostics As it relates to personalized and predictive medicine, in vivo molecular diagnostics will also require the development of a diverse library of molecular imaging probes. These modular tools can be used to: 1. identify the specific location of disease-perturbed networks in patients; 2 link in vivo molecular measurements in diseased tissue in patients to in vitro measurements; 3. rapidly assess the efficacy of personalized therapeutics; and 4. validate that a drug is hitting its target and inducing the desired pharmacological outcome.

Conclusions and Perspectives

Although there are many in vivo imaging modalities, perhaps the best current method from the point of view of personalized and predictive medicine in patients is positron emission tomography (PET) molecular imaging (Czernin and Phelps, 2002). For PET, trace quantities of radiolabeled molecular probes are injected into the patient. As the probes circulate through the body and its various organ systems, they interact with target proteins to provide imaging assays for, as examples, the rate of metabolic processes, the concentration of receptors in signal transduction, enzyme activity, DNA-replication rates, hormone status, pharmacokinetics, and pharmacodynamics.

COMPUTATIONAL AND MATHEMATICAL CHALLENGES IN SYSTEMS MEDICINE Molecular Signature Classifiers Computational methods are needed to reduce the high degree of data dimensionality associated with global datasets to identify molecular signatures that can be used for disease diagnosis. Despite notable and significant challenges that remain (Dupuy and Simon, 2007; Simon, 2005), computational analyses to identify molecular signatures from global gene expression datasets that can be used for diagnosis and treatment selection (Quackenbush, 2006) is an area of research that has shown significant promise. These studies typically involve the collection of samples from two or more classes (e.g., cancer versus normal, or responsive versus non-responsive to treatment) and the use of a set of data on which to train the classifier and another set on which to test. In the absence of a true test set, re-sampling methods (such as cross-validation) are generally used to estimate likely performance of the classifier on future data. The ability to generate an accurate classifier is a function of factors such as: (1) the size of the training set relative to the number of features, (2) the computational method used, and (3) the inherent distinctness of the selected phenotypes. Typically, the number of samples is far less than the number of transcripts, and consequently over-fitting is a significant problem. This leads to the need for computational methods that avoid over-fitting when selecting a classifier. A variety of methods have been applied to disease diagnoses, including approaches based on support vector machines (Furey et al., 2000; Ramaswamy et al., 2001) and relative expression reversals (Geman et al., 2004; Price et al., 2007; Tan et al., 2005; Xu et al., 2005), among many others. Application of these methods has perhaps been applied most extensively to the study of cancer, leading to the discovery of molecular classifiers of varying degrees of accuracy to identify prognostic signatures for breast cancer (Adler and Chang, 2006; Buyse et al., 2006; Dai et al., 2005; Foekens et al., 2006; Glinsky et al., 2004a, b; Goncalves et al., 2006; Ivshina et al., 2006; Liu et al., 2007; Ma et al., 2004; Nuyten et al., 2006; Park et al., 2007; Pawitan et al., 2005; Thomassen et al., 2007; van ‘t Veer et al., 2002; van de Vijver et al.,

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2002; Wang et al., 2005a, b; Weigelt et al., 2005), ovarian cancer (De Cecco et al., 2004; Smith, 2002; Spentzos et al., 2004), colon cancer (Barrier et al., 2006; Giacomini et al., 2005), prostate cancer (Dhanasekaran et al., 2001; Glinsky et al., 2004a, b; Halvorsen et al., 2005; Lin et al., 2005; Luo et al., 2002; Singh et al., 2002; Xu et al., 2005), and brain cancer (Fuller et al., 2005; Kim et al., 2002), among others. Biological Networks and the Interactome Among the most promising opportunities presented by systems biology is the integrated assessment of multiple biological parameters to magnify our understanding of complex developmental or pathological states. The interaction among all individual biomolecules in complex regulatory, signal transduction, and feedback networks present in eukaryotic organisms forms the theoretical framework for this higher-order analysis and is referred to as the interactome. These dynamic associations include protein–protein interactions, transcriptional regulation, and post-transcriptional gene silencing by short-interfering RNA and microRNA (Ruvkun, 2001). Multiple high-throughput methods have been developed to characterize the interaction between proteins, including the yeast-two-hybrid system (Ito et al., 2001) and surface plasmon resonance (Usui-Aoki et al., 2005). These methods are to determining interactome interactions what microarray studies are to determining relative mRNA concentrations.

CONCLUSIONS AND PERSPECTIVES Nanotechnology and microfluidic sensing chips with integrated microfluidic delivery devices will be developed over the next 5–10 years so that we reach a point at which thousands of proteins and/or RNAs can be analyzed quantitatively from only a fraction of a droplet of blood. This will allow physicians to assess the health/disease status of virtually every organ in the body at 6-month intervals and will become the first critical element of predictive medicine. We also predict that nanotechnology approaches to the sequencing of single DNA strands will over the next 5–10 years lead to instrumentation that can rapidly, cheaply and simply sequence individual human genomes. This will be a second foundation of predictive medicine – assessing the variability in individual human genomes to generate probabilistic future health histories for each individual human, and integrating it with diagnostics. Similarly, in vivo imaging agents will permit the functional visualization of informational molecules and drugs in humans, which will provide powerful and informative in vivo diagnostics approaches. These measurement technologies, together with both new computational approaches to extract information from these data and the systems view of medicine, will lead to three important revolutions in medicine. First, over the next 10–20 years, a medicine – P4 medicine – that is predictive, personalized, preventive and participatory will emerge. Obtaining individual human genome sequences will lead to individual predictive health histories, whereas biannual blood measurements of thousands of proteins from the molecular

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fingerprints of human organs will give us a real-time assessment of individual health. Predictive medicine will result in a personalized medicine that focuses on the illnesses of an individual and eventually their wellness, preventing disease rather than treating it. The systems approach to disease will in time permit the identification of the key disease-perturbed subnetworks and the identification of important nodal points, which, if perturbed by drugs, could make the network behave in a more normal fashion or at least delete the more deleterious effects of the disease-perturbed network. This capability will provide a new approach for drug-target discovery, and a powerful and rapid new approach for developing drugs. Predictive, personalized and preventive medicine, if appropriately orchestrated with patient-oriented interpretations, will also enable patients to understand more deeply and actively participate in personal choices about illness and wellness. Participatory medicine will necessitate the development of powerful new approaches to handling enormous amounts of personal information in a secure manner, and to a new form of medical education of individual patients as well as their physicians. Thus, over the next 5–20 years, medicine will become predictive, personalized, preventive and participatory (P4 medicine). Another very exciting idea is that, in the long-term, systems analysis of blood offers the potential to open up a new avenue for studying human biology – when we learn to read and interpret the information inherent in secreted organ-specific protein patterns. The key patterns in these secreted proteins represent different network perturbations, and hence, different diseases. When secreted proteins enter the blood, they can provide a novel means to study biology in higher organisms and to identify drug targets through linking blood measurements to perturbations in underlying biological networks. Importantly, learning to study perturbations in underlying networks through secreted protein patterns (to whatever resolution is possible) will provide access to studying biological networks in vivo that are not amenable to direct experimentation. Developing the capacity to identify in vivo network perturbations through secreted protein measurements in the blood will open up a new avenue to drug

target identification and will provide a novel means to discover the perturbed sub-networks. Since taking blood is relatively non-invasive, it has less potential than biopsies or other invasive techniques of greatly distorting the system being studied. Thus, this approach has the potential to strongly complement existing approaches to study human biology. The effect of drugs, toxicity, human development and even aging may all be amenable to study through the blood if we can learn to read and interpret the signals in the proteins, which has the potential to be very significant in human biology. Second, as the sensitivity of measurements increases (both in vitro and in vivo), we will achieve a digitalization of biology and medicine – that is, the ability to extract relevant information content from single individuals, single cells and ultimately single molecules – with its attendant economies of scale. Just as Moore’s law led in time to the widespread digitalization of information technologies and communications, the exponentially increasing ability to extract quantized biological information from individual cells and molecules will transform biology and medicine in ways that we can only begin to imagine. Finally, all of these changes – a systems approach to medicine with its focus on disease prevention and more efficient drug discovery, the introduction of increasingly inexpensive nanotechnology based diagnostics and in vivo measurement technologies, the highly accurate and specific molecular characterization of the systems biology of disease, and the digitalization of medicine – will start to reduce the inexorably increasing costs of health care. This can, in principle, enable us to afford to provide for the more than 45 million people in the United States who currently lack health insurance, to reduce the crushing costs of healthcare to society, and to export our digital predictive and preventive medical approach to the developing world. Just as the mobile phone has become a fundamental communication mode in developing countries and has changed the lives of much of the world’s population, so digital medicine of the 21st century can bring to the world’s citizens a global and strongly improved state of human health and healthcare. In our view P4 medicine will become the foundational framework for global medicine.

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Ma, X.J., Wang, Z., Ryan, P.D., Isakoff, S.J., Barmettler, A., Fuller, A., Muir, B., Mohapatra, G., Salunga, R., Tuggle, J.T. et al. (2004). A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5, 607–616. Miller, J.C., Zhou, H., Kwekel, J., Cavallo, R., Burke, J., Butler, E.B., Teh, B.S. and Haab, B.B. (2003). Antibody microarray profiling of human prostate cancer sera: Antibody screening and identification of potential biomarkers. Proteomics 3, 56–63. Mulquiney, P.J. and Kuchel, P.W. (2003). Modelling Metabolism with Mathematica Detailed Examples Including Erythrocyte Metabolism. CRC Press, Boca Raton, Fla. Nuyten, D.S., Kreike, B., Hart, A.A., Chi, J.T., Sneddon, J.B.,Wessels, L.F., Peterse, H.J., Bartelink, H., Brown, P.O., Chang, H.Y. et al. (2006). Predicting a local recurrence after breast-conserving therapy by gene expression profiling. Breast Cancer Res 8, R62. Olle, E.W., Sreekumar, A., Warner, R.L., McClintock, S.D., Chinnaiyan, A.M., Bleavins, M.R., Anderson, T.D. and Johnson, K. J. (2005). Development of an internally controlled antibody microarray. Mol Cell Proteomics 4, 1664–1672. Orchekowski, R., Hamelinck, D., Li, L., Gliwa, E., vanBrocklin, M., Marrero, J.A.,Vande Woude, G.F., Feng, Z., Brand, R. and Haab, B.B. (2005). Antibody microarray profiling reveals individual and combined serum proteins associated with pancreatic cancer. Cancer Res 65, 11193–11202. Palsson, B. (2004). Two-dimensional annotation of genomes. Nat Biotechnol 22, 1218–1219. Papadopoulou, E., Davilas, E., Sotiriou, V., Georgakopoulos, E., Georgakopoulou, S., Koliopanos, A., Aggelakis, F., Dardoufas, K., Agnanti, N.J., Karydas, I. et al. (2006). Cell-free DNA and RNA in plasma as a new molecular marker for prostate and breast cancer. Ann N Y Acad Sci 1075, 235–243. Park, E.S., Lee, J.S., Woo, H.G., Zhan, F., Shih, J.H., Shaughnessy, J.D. and Frederic Mushinski, J. (2007). Heterologous tissue culture expression signature predicts human breast cancer prognosis. PLoS ONE 2, e145. Pawitan,Y., Bjohle, J., Amler, L., Borg, A.L., Egyhazi, S., Hall, P., Han, X., Holmberg, L., Huang, F., Klaar, S. et al. (2005). Gene expression profiling spares early breast cancer patients from adjuvant therapy: Derived and validated in two population-based cohorts. Breast Cancer Res 7, R953–R964. Price, N.D. and Shmulevich, I. (2007). Biochemical and statistical network models for systems biology. Curr Opin Biotechnol 18, 365–370. Price, N.D., Reed, J.L. and Palsson, B.O. (2004). Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nat Rev Microbiol 2, 886–897. Price, N.D., Schellenberger, J. and Palsson, B.O. (2004). Uniform sampling of steady-state flux spaces: Means to design experiments and to interpret enzymopathies. Biophys J 87, 2172–2186. Price, N.D.,Trent, J., El-Naggar, A.K., Cogdell, D.,Taylor, E., Hunt, K.K., Pollock, R.E., Hood, L., Shmulevich, I. and Zhang, W. (2007). Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas. Proc Natl Acad Sci USA 104, 3414–3419. Quackenbush, J. (2006). Microarray analysis and tumor classification. N Engl J Med 354, 2463–2472. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E. and Mesirov, J.P. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98, 15149–15154.

Ross, P.L., Huang, Y.N., Marchese, J.N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S. et al. (2004). Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3, 1154–1169. Ruvkun, G. (2001). Molecular biology. Glimpses of a tiny RNA world. Science 294, 797–799. Sanchez-Carbayo, M., Socci, N.D., Lozano, J.J., Haab, B.B. and CordonCardo, C. (2006). Profiling bladder cancer using targeted antibody arrays. Am J Pathol 168, 93–103. Scherzer, C.R., Eklund, A.C., Morse, L.J., Liao, Z., Locascio, J.J., Fefer, D., Schwarzschild, M.A., Schlossmacher, M.G., Hauser, M.A., Vance, J.M. et al. (2007). Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc Natl Acad Sci USA 104, 955–960. Simon, R. (2005). Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol 23, 7332–7341. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P. et al. (2002). Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209. Smith, D.I. (2002). Transcriptional profiling develops molecular signatures for ovarian tumors. Cytometry 47, 60–62. Solanky, K.S., Bailey, N.J., Beckwith-Hall, B.M., Davis, A., Bingham, S., Holmes, E., Nicholson, J.K. and Cassidy, A. (2003). Application of biofluid 1 H nuclear magnetic resonance-based metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal Biochem 323, 197–204. Song, S., Li, B., Wang, L., Wu, H., Hu, J., Li, M. and Fan, C. (2007). A cancer protein microarray platform using antibody fragments and its clinical applications. Mol Biosyst 3, 151–158. Spentzos, D., Levine, D.A., Ramoni, M.F., Joseph, M., Gu, X., Boyd, J., Libermann, T.A. and Cannistra, S.A. (2004). Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol 22, 4700–4710. Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A. and Tyers, M. (2006). BioGRID: A general repository for interaction datasets. Nucleic Acids Res 34, D535–D539. Sun, B., Ranish, J.A., Utleg, A.G.,White, J.T.,Yan, X., Lin, B. and Hood, L. (2007). Shotgun glycopeptide capture approach coupled with mass spectrometry for comprehensive glycoproteomics. Mol Cell Proteomics 6, 141–149. Tan, A.C., Naiman, D.Q., Xu, L., Winslow, R.L. and Geman, D. (2005). Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21, 3896–3904. Thiele, I., Price, N.D.,Vo, T.D. and Palsson, B.O. (2005). Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J Biol Chem 280, 11683–11695. Thomassen, M.,Tan, Q., Eiriksdottir, F., Bak, M., Cold, S. and Kruse,T.A. (2007). Prediction of metastasis from low-malignant breast cancer by gene expression profiling. Int J Cancer 120, 1070–1075. Usui-Aoki, K., Shimada, K., Nagano, M., Kawai, M. and Koga, H. (2005). A novel approach to protein expression profiling using antibody microarrays combined with surface plasmon resonance technology. Proteomics 5, 2396–2401. van ‘t Veer, L.J., Dai, H., van de Vijver, M.J., He,Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T. et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536.

Recommended Resources

van de Vijver, M.J., He, Y.D., van’t Veer, L.J., Dai, H., Hart, A.A., Voskuil, D.W., Schreiber, G.J., Peterse, J.L., Roberts, C., Marton, M.J. et al. (2002). A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347, 1999–2009. Wang, X.,Yu, J., Sreekumar, A., Varambally, S., Shen, R., Giacherio, D., Mehra, R., Montie, J.E., Pienta, K.J., Sanda, M.G. et al. (2005). Autoantibody signatures in prostate cancer. N Engl J Med 353, 1224–1235. Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J. et al. (2005). Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679. Weigelt, B., Hu, Z., He, X., Livasy, C., Carey, L.A., Ewend, M.G., Glas, A.M., Perou, C.M. and Van’t Veer, L.J. (2005). Molecular

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portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res 65, 9155–9158. Wilson, J.F. (2006). The rocky road to useful cancer biomarkers. Ann Intern Med 144, 945–948. Xu, L., Tan, A.C., Naiman, D.Q., Geman, D. and Winslow, R.L. (2005). Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 21, 3905–3911. Yang, S., Undar, A. and Zahn, J.D. (2006). A microfluidic device for continuous, real time blood plasma separation. Lab Chip 6, 871–880. Zimmermann, M., Delamarche, E., Wolf, M. and Hunziker, P. (2005). Modeling and optimization of high-sensitivity, low-volume microfluidic-based surface immunoassays. Biomed Microdevices 7, 99–110.

RECOMMENDED RESOURCES Website

Article

www.systemsbiology.org Contains overviews of systems biology discipline, particularly in “Systems Biology in Depth” section of the website.

Hood, L., Heath, J.R., Phelps, M.E. and Lin, B. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640–643.

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Section

Technology Platforms for Genomic Medicine

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7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism 8. Genome-Wide Association Studies and Genotyping Technologies 9. Copy Number Variation and Human Health 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine 11. DNA Methylation Analysis: Providing New Insight into Human Disease 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine 13. DNA Microarrays in Biological Discovery and Patient Care 14. Proteomics: The Deciphering of the Functional Genome 15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics

CHAPTER

7 DNA Sequencing for the Detection of Human Genome Variation and Polymorphism Samuel Levy and Yu-Hui Rogers

INTRODUCTION The discovery and characterization of DNA polymorphisms in human populations is an important step toward understanding the contribution of genome sequence variants to predisposition, onset and progression of disease phenotypes. By way of example, molecular epidemiology studies have been able to establish the importance of the wide range of DNA variants that cause monogenic disorders such as cystic fibrosis, where over 1000 mutations in the CFTR gene have been identified and implicated in the disease phenotype (Rowntree and Harris, 2003). The impact of the mutational landscape of the CFTR gene on biological function is complex, and emerging evidence implicates additional modifier genes contributing to the range of disease phenotypes observed (Davies et al., 2005). The situation is likely to be compounded further when identifying and characterizing the contribution of multiple genetic loci to phenotypes found in polygenic diseases such as coronary heart disease, diabetes and hypertension (see Chapter 3). While our understanding of how polymorphisms can contribute either singly or collectively to either disease onset or progression is still actively being developed, so too are the technologies that enable their detection and ascertainment in human populations.

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Human DNA polymorphism detection has been greatly empowered in the last decade by the production of a high quality, mostly finished, DNA sequence of the human genome. The production of this sequence, and the fact that several distinct DNA sources were employed, enabled the direct identification of 2.1 million single nucleotide polymorphisms (SNPs) (Lander et al., 2001; Venter et al., 2001) to supplement those already found in the public variant databases (dbSNP). The discovery of these SNPs in a small number of humans set the stage for an ambitious experiment, the HapMap project, to define common haplotypes through the use of 4 million SNP types in 269 distinct humans (Consortium, 2005). It is anticipated that these datasets will enable the identification of a set of informative SNPs with which disease association studies can be accomplished with improved success. Indeed, early successes highlighting the utility of the HapMap data in identifying alleles implicated in complex diseases such as asthma (Laitinen et al., 2004), age related macular degeneration (Klein et al., 2005) and type II diabetes (Sladek et al., 2007), amongst others, have clearly set the stage for more studies of this kind (see Chapter 8). Essentially, one potential study design would likely involve the use of SNPs as markers for a disease in an affected DNA population compared to a control DNA population. Subsequently, any marker statistically associated with

Copyright © 2009, Elsevier Inc. All rights reserved.

DNA Sequencing

the disease group would inform the genomic loci requiring further sequence analysis to detect the causative variant. There are currently over 10 million SNPs and other polymorphisms such as insertion/deletion variants (indels) in public databases that potentially provide a marker set for efficient disease-gene association studies. It is important to note that even this large variant set still might not represent the variants causative of diseases. This is likely due to the fact that significant polymorphism discovery efforts to date have been performed in a random fashion across the genomes of only a limited number of individuals. Therefore, we still need to discover genetic variation in regions of functional DNA sequence in the genomes of individuals with disease, in order to provide an accurate understanding of disease etiology. In this chapter, we outline the sequencing protocols and the attendant computational approaches employed for the discovery and initial characterization of DNA variants, with an emphasis on the most commonly used Sanger-based sequencing (Sanger et al., 1977) methodologies established in our laboratories. These polymorphisms range from the substitution of single bases (i.e., SNPs) to the insertion or deletion of nucleotides in size ranges from 1 basepair (bp) to greater than 20,000 bp. Recent reports on the unexpected number and scope of larger scale copy number variants or structural variants ranging from 1–3 million bases (Mb) in size (Redon et al., 2006; Sebat et al., 2004) will not be addressed here since current methods used for their analysis involve either genotyping or microarray-based comparative genome hybridization, as presented elsewhere (see Chapter 9). We also highlight, where possible, how the application of these methods and advances has impacted our understanding of human biology. Several recent reviews addressed the methodologies and development of advanced sequencing methods and recent commercial platforms that exploit their use (Fan et al., 2006; Shendure et al., 2004). These advances in genome sequencing for variant detection are of particular importance as we have begun to see genome sequences and complete variants sets of individual humans, including as of this writing James D. Watson by 454 Life Sciences (Wheeler et al., 2008) and J. Craig Venter (Sanger-based sequencing (Levy et al., 2007)). The discussion in this chapter will focus primarily on mature approaches for use in high-throughput experimental environments, as well as on the types of experimental results that can and have been generated. It will be evident that comprehensive experimental designs and economies of scale are possible when applying these techniques in a production environment.

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and genomic variants therein (Osoegawa et al., 2001). Other sequencing projects have employed expression sequence tag (EST) or cDNA sequencing of mRNA libraries to characterize variants primarily at the transcript, and by inference, at the protein level (Strausberg et al., 2003). All these approaches benefit from the random sampling of DNA regions by selecting bacterial colonies containing DNA inserts, DNA sequence determination using capillary-based electrophoresis of incorporated dye terminator nucleotide bases and subsequent sequence alignment of reads to construct reliable assemblies of genomic or mRNA molecules. Other approaches to DNA sequence elucidation that will be covered in this section involve the targeted amplification of defined genomic loci typically for study in large populations, the use of microfabricated high-throughput bead or surfacebased sequencing devices and high-density oligonucleotide arrays. A comparison of these distinct methodologies is provided in Table 7.1. Whole Genome Shotgun Sequencing Polymorphism discovery on a genome-wide level can be accomplished by applying the Sanger sequencing technique (Sanger et al., 1977) and the whole genome shotgun (WGS) strategy (Fleischmann et al., 1995; Venter et al., 2001; Venter et al., 1996) on a small pool of DNA samples. Owing to its high accuracy, versatility and ability to generate complete DNA sequence information, the Sanger sequencing method has been considered the definitive approach for DNA variant discovery. The WGS methodology relies upon generating a random sampling of genomic regions at a sufficiently deep coverage level to produce enough sequence data that can then be assembled by sequence alignment methods. First, multiple copies of the genome are randomly shredded into pieces of approximately 2–40 kilobasepairs (Kb) in size and subsequently cloned into a vector or plasmid. These constructs are then replicated in bacteria and sequenced from both ends to produce pairs of linked sequences, termed “mate pairs”, representing 500–800 bp at the end of each insert. These sequence reads are then assembled computationally to generate a set of contiguous high-quality regions with potential information regarding any associated sequence gaps of a known size. Currently, the Sanger-based WGS sequencing process, excluding the library construction step, is typically fully supported by equipment automation, as outlined in Figure 7.1, and managed as a production “pipeline” with varying degrees of integration between different steps employing a laboratory information management system.

DNA SEQUENCING DNA sequencing methods applied to either single or multiple DNA samples provide the definitive approach to discovering polymorphisms. These polymorphisms can be detected at the genomic and/or the mRNA level depending on the source of the material employed. For example, genomic DNA subcloned into BACs or in small to medium insert size bacterial libraries were created to produce a finished human genome sequence

Library Construction Genomic DNA is randomly sheared via nebulization to produce fragments with a distribution of approximately 2–40 Kb. The DNA fragments are ligated to adapters after end-polishing and size selection. After several rounds of purification, the fragments are inserted into plasmid vectors. The resulting library is electroporated into E. coli cells.

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TABLE 7.1

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DNA Sequencing for the Detection of Human Genome Variation and Polymorphism

Comparison of distinct approaches in DNA sequence acquisition and the detection of variants Sanger based WGS

Targeted PCR

Sequence by synthesis

Sequence by hybridization

Current platforms

ABI 3730 xl

ABI 3730 xl

454, Illumina/Solexa, ABI SOLiD (strictly hybridization-ligation chemistry)

Affymetrix, Perlegen, Nimblegen

Read length (bp)

500–800

500–800

25–200

60–100

Detection SNP/Indel

Y/Y

Y/Y (up to amplicon size)

Y/Y

Y/Y (primarily deletions in sample)

Unbiased polymorphism detection

Y

Limited to targeted regions

Y

Limited to targeted regions

Biased polymorphism detection

N

Y

Y

Y

Construction of longrange haplotypes

Y

Limited to targeted regions

Uncertain

Limited to targeted regions

Current cost per sample  $2 million

N

Uncertain

Y

Y

WGS Library

Figure 7.1

Clone picking and growth

Template preparation

Sequencing reaction set up and clean-up

Electrophoresis and sequence detection

Whole Genome Shotgun sequencing process flow.

Colony Plating and Picking The E. coli cells containing the plasmid vectors with inserted DNA fragments are then spread onto agar plates and incubated to form E. coli colonies. The colonies are picked into liquid media and incubated for further growth to generate sufficient numbers of plasmid template copies. Subsequently, these template copies are isolated and purified from the host cells. Template Preparation The alkaline lysis plasmid isolation approach (Sambrook et al., 1989) is the most widely utilized method for plasmid template isolation due to its robustness. Bacterial cells are lysed, cell debris removed by centrifugation and plasmid DNA recovered from the cleared lysate by isopropanol precipitation. Sequencing Reactions For DNA sequence determination, the most widely used technique is the Sanger dideoxy sequencing method (Sanger et al., 1977). It mimics the natural process of DNA replication in vivo with the presence of target DNA molecules, universal M13 sequencing primers, polymerase, deoxynucleotides (dNTPs) as well as chain terminating dideoxynucleotides which interrupt chain elongation upon incorporation generating DNA

fragments of different lengths. Several improvements have been made to the original method to greatly enhance its throughput, robustness and detection sensitivity. These include the development of multicolor fluorescent detection (Smith et al., 1986), fluorescently tagged chain-terminators (Prober et al., 1987), a cycle sequencing protocol (McCombie et al., 1992) and fluorescence resonance energy transfer (FRET) dyes (Ju et al., 1995; Lee et al., 1997). Currently, most sequencing reactions are carried out using FRET dye terminators, modified Taq polymerase and a cycle sequencing protocol. Sequence Detection The technology for the electrophoresis-based separation of Sanger sequenced DNA fragments and the detection of individual bases have advanced substantially in the past two decades. As a result, the throughput, detection sensitivity and costs of sequencing, as well as the accuracy and quality of the data generated by these sequencers, have improved significantly. The invention of multicolor fluorescent sequencing in 1986 (Smith et al., 1986) replaced laborious and hazardous radioisotope labeling techniques (Sanger et al., 1982) with automated signal detection, high-speed computational recording and signal processing. In the mid-1990s, the successful integration of the capillary-based

DNA Sequencing

T

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reads

Figure 7.2 Display and detection of a single nucleotide polymorphism in a human genome assembly of a single DNA donor. Sequence reads overlap and can be assembled to form the consensus sequence of the contig where 5 of the 12 reads show a T variation at a C consensus base (variant position is highlighted with an arrow). T  type of sequence read, either assembled contig or individual read, I  unique identifier, R  co-ordinates of alignment, O  strand orientation that aligns.

electrophoresis and multicolor fluorescent detection (Bashkin et al., 1996a; Bashkin et al., 1996b; Behr et al., 1999) further enabled the creation of the true high-throughput sequencing machines. Today, state-of-the-art capillary-based sequencers have the ability to read over 1 million bp of sequence per 24-hour period with long read lengths (an average of 800 bp per read) and an average quality value (QV) of greater than 30 (99.9% accuracy). Whole Genome Assembly The whole genome assembly process utilizes several aspects of experimental design to ensure that unambiguous construction of long contiguous sequence can be generated. These include the generation of sequence reads from each end of clone inserts using universal primers pairs to ensure that a majority of inserts will have their ends sequenced. This enables both the generation of contiguous sequence and the potential for ordering and orienting larger sequence segments essentially created by sequence alignment of the individual reads whose mate pair relationship is known. A variety of genome assembler software packages have been designed with this basic rationale at their core (Celera Assembly (Myers et al., 2000), Phusion (Mullikin and Ning, 2003)). The Atlas assembler uses as input both BAC-based clone sequences and reads generated via the WGS strategy (Havlak et al., 2004), an approach that was used to assemble the rat genome (Gibbs et al., 2004). There are two basic steps to assembly: (i) the creation of unique regions of contiguous (contigs) assemblies from the sequence overlaps between reads and (ii) the sequential end-toend organization of contigs by employing the mate pair information contained in the reads constituting each contig.The successful application of this strategy was a significant challenge for the sequencing and assembly of human DNA since greater than 45%

of the genome sequence is repetitive in nature (Lander et al., 2001; Venter et al., 2001). The repetitive nature of the human and other mammalian genomes thus confounds the ability to determine accurate read sequence overlaps and the placement of contigs into the correct order and orientation. Our solution is to employ clone insert libraries of a size larger than the corresponding repeat regions, typically either fosmid or BAC libraries (40 kb and 100 kb respectively), thus enabling the spanning of repeats by assembling adjacent unique sequence (Venter et al., 2001). The selection of a range of insert sizes libraries is typically built into the experimental design when sequencing a new genome. Variant Detection Variants can be reliably detected from assembled sequence reads from either a single or multiple DNA donors in the region of unique genome sequence assembly. The example in Figure 7.2 show the reads assembled from a single human identifying a known SNP found in a coding region of the APOE gene and, as expected, displays a close to equiprobable occurrence of both allelic forms (five reads with the T allele and seven reads with C allele). This approach has been employed with success to identify 974,400 heterozygous variants in a single canine individual (Kirkness et al., 2003). The inclusion of random shotgun sequence reads from distinct DNA donors is another approach to employing genome assembly and read placement to identify polymorphic site. This approach was employed using five DNA donors in the initial shotgun sequence assembly of the human genome (Venter et al., 2001). An important factor in using a WGS assembly approach to detecting polymorphic sites is that enough sequence coverage from a single donor should be available. In this manner it is possible to provide a good quality assembly with associated consensus sequence and associated allelic variants. This means that

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additional donors can be included either post-assembly or as part of the assembly process if the species in question does not possess extensive interindividual polymorphism. Recent work attempting to detect both SNP and indels in an individual human sample suggests that detection of heterozygous variants, with 99% probability using a Sanger chemistry-based WGS approach, necessitates a variant being reported by 20 reads or more (Levy et al., 2007). Another challenge to polymorphism detection during genome assembly is the occurrence of many small heterozygous indel polymorphisms in human DNA, typically between 1–20 bp length (Ball et al., 2005). These indel events result in the occurrence of long and short alleles, requiring a methodology to accurately separate the reads from each allele, thereby providing an accurate representation of the indel. Subsequently, a practical decision needs to be made regarding which form will be represented in the consensus sequence. Typically this can be the “major” allele even though in a single donor this will be present effectively at equal probability. Lastly, a strategy needs to be implemented to best catalog the occurrence and type of the other allele or alleles, in the case of sequencing multiple DNA donor samples. An example of the detection and display of an indel from a genome assembly of a single DNA donor can be seen in Figure 7.3. The figure displays the consensus sequence (the contig sequence) containing the short, major, allele since gaps are included in the alignment, whilst the long form (inclusion of ATTCT) is found in two of the eight assembled reads. We recently applied a modified sequence assembly algorithm to detect heterozygous indels variants from the diploid genome sequence of an individual human (Levy et al., 2007). Using this approach we detected over 345,000 heterozygous

indels events of lengths 1–571 bp with at least 200 indels in protein-coding regions of genes. PCR Amplicon Re-Sequencing The goal of directed sequencing or re-sequencing is to use the existing sequence data from a particular genomic locus or loci to determine sequence variations of these same loci in different DNA samples. This provides the nature and the frequency of DNA polymorphisms and can be used to assess whether these variants could be implicated in, for example, either disease onset or progression. PCR re-sequencing methodology is widely accepted as the gold standard (Kwok and Chen, 2003) for the discovery of polymorphisms (both SNPs and indels) in targeted regions since it provides the most complete information including the genotype, the location and sequence context.This approach has been applied to understand sequence variation in populations (Crawford et al., 2004) and for the identification of variants and haplotypes that could potentially explain disease phenotypes in cancer (Cox et al., 2005). It combines the powerful PCR technique (Mullis et al., 1986) with the informative Sanger sequencing method (Sanger et al., 1977). Rapid advancement of the technologies and dramatic reduction in costs for direct sequencing of PCR products, as well as the completion of the reference human genomes in 2001 (Lander et al., 2001;Venter et al., 2001) has enabled the systematic large-scale discovery of polymorphisms in targeted regions. The approach generally involves PCR primer design, the amplification of the targeted region from whole genomic DNA using PCR, followed by Sanger sequencing and the analysis of the targeted sequences (Figure 7.4). T

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O Consensus

reads

2 reads with 5 bp insertion ATTCT

Figure 7.3 Display and detection of a 5 bp insertion/deletion polymorphism in a human genome assembly of a single DNA donor. The consensus sequence displays the “short” allelic form whilst the “long” form (ATTCT) is found in 2 of the 8 sequence assembled reads. T  type of sequence read, either assemblyed contig or individual read, I  unique identifer, R  co-ordinates of alignment, O  strand orientation that aligns. “”  gap inserted into read to permit correct alignment.

PCR primer design

Figure 7.4

PCR reaction and clean-up

PCR directed sequencing process flow.

Sequencing reaction and clean-up

Electrophoresis and trace analysis

DNA Sequencing

Primer Design The goal of directed sequencing is to provide reliable amplification of a single genomic region, and this is readily achieved via the design of unique pairs of oligomers for priming of the PCR. The resulting amplicon encompassing the targeted region can vary in size; however reproducible and robust assays range from 350 bp to 1000 bp in length per amplicon. In order to provide coverage of a larger genomic locus with this amplicon size, it becomes necessary to tile PCR products with some overlap (typically 100 bp) between amplicons. The primer design process attempts to generate oligomers for products with similar melting temperatures whilst avoiding primer-dimerization forming interfering secondary structures. Other factors to permit specific amplification would be the GC content of the region, the proximity of repeat regions, the uniqueness of the oligomer sequences in the genome and the absence of known DNA variants in the primer binding sites. These considerations can be embodied in a computational pipeline as illustrated in Figure 7.5. The sequence context, in this case percent GC quantity, are considered before identifying a candidate genomic region for primer design. In a high-throughput directed sequencing pipeline, it is frequently desirable to employ few, or preferable one, PCR protocol, and in our experience GC content allows a distinction between at least two desired PCR conditions (high and low GC content). Once the PRIMER3 software (Rozen and Skaletsky, 2000) generates candidate primer pairs for a desired target region, these are further

Gene/region of interest

Pre-Compute/Load GC content

Generate 1st tile

Decrease tile size change 5 tile position Fail Generate next tile position

Fail

Primer3

Critique primer pair for genome specificity. Check for: Homopolymers Known SNP/indels Repeat regions Pass Order primers

Figure 7.5 Primer design pipeline for PCR based directed sequencing.

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assessed for their product uniqueness (low and high copy repeat regions) and to ensure that resulting amplicons contains only one type of complex sequence, i.e. homopolymer or known indel polymorphism. The last requirement is important since sequence detection is performed using capillary-based detection methods and chromatograms potentially contain a mixture of alternate alleles at any one mobility position. This renders the process of allelic definition potentially difficult in the case of multiple indel events in a single amplicon. PCR Set-Up and Clean-Up As described in the primer design section, due to the variable GC content within the genome, application of different PCR primer design criteria, as well as different PCR protocols might be necessary when targeting different regions. In our laboratory, we have two fully validated high-throughput amplification protocols (Rand et al., 2005), one for regions of normal GC content and another for amplicons with melting temperatures (Tm) 82°C. AmpliTaq Gold® DNA polymerase (Applied Biosystems) is used to perform hot-start PCR to minimize the formation of primer–dimers and spurious amplicons and ensure efficient, consistent and specific amplification results in a highthroughput environment. A Shrimp Alkaline Phosphatase/Exonuclease I (SAP/Exo I) mix is used to digest excess dNTPs and amplification primers. (While SAP is commonly used to remove 5 phosphate groups from DNA, it also degrades dNTPs.) The SAP/Exo I method provides an effective, single tube approach to PCR clean-up that is easily automated and scalable. Sequencing Reaction Set-Up and Clean-Up In order to simplify the downstream sequencing process, the PCR primers are frequently tagged with M13 forward and reverse sequences on their 5 ends. The resulting amplicons can then be sequenced using universal M13 sequencing primer sets and a standard sequencing protocol (see previous section). Reaction products can be purified with sodium acetate and ethanol prior to analysis on the sequencers. Electrophoresis and Trace Analysis The state-of-the-art technology for the electrophoresis-based separation of Sanger sequenced DNA fragments and the detection of individual bases can be accomplished by a combination of trace filtering, trace separation and mixed base calling. Electropherogram peaks are described as “mixed” in that a primary peak is detected with a secondary but significantly present sub-peak due to two distinct and different allelic forms contributing to the locus, We have developed a trace analysis pipeline that applies digital signal filters to reduce the occurrence of secondary, typically noisy, sequence peaks (Figure 7.6), thus enabling the accurate distinction of a single position with mixed peaks. This approach has enabled a substantial improvement of common artifacts observed in diploid genomic regions sequenced after PCR amplification that include dye-blob, PCR slippage

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Unfiltered trace

Filtered trace

Mixed base Figure 7.6 Sequence chromatograms of a locus revealing a SNP before and after digital signal filtering were applied to remove PCR slippage artifacts due to the presence of proximal (not seen) homopolymeric DNA.

and primer synthesis length errors, all of which confound mixed peak detection. After generating either a raw (unfiltered) or digital signal filtered chromatogram, it is then possible to identify mixed peaks that vary significantly in height, area and width to characterize departures from the reference sequence. This general approach can be performed with many different software tools in the public domain (Polyphred, Mutation Surveyor, novoSNP and SNPdetector, InSNP), the most extensively tested of which is Polyphred. In the most recent version of this software, detailed parameterization was performed using sequence reads from PCR-amplified diploid samples and a statistical framework was developed to report evidence of genotypes being called (Stephens et al., 2006). Heterozygous indel polymorphisms result in significant changes in the chromatograms from diploid DNA samples where typically traces contain mixed peaks from the point of the insertion/deletion onwards. One can develop computational methods that essentially separate the traces to enable the definition of the long and short alleles (Bhangale et al., 2005; Bhangale et al., 2006). Polyphred, InSNP (Manaster et al., 2005) and Mutation Surveyor are all capable of detecting heterozygous indels, but as of this writing an unbiased detailed comparison of performance has not yet been attempted. Sequencing by Synthesis Recently implemented sequencing by synthesis technologies can employ PCR (Mullis et al., 1986) to achieve clonal amplification of single DNA molecules. In addition, the sequencing by synthesis principle in a massively parallel format has been developed by Rothberg and colleagues (Margulies et al., 2005) and by Church and colleagues (Shendure et al., 2005). In each of these new technologies, different chemistries are applied to interrogate, tag and detect the sequence bases. For example, utilizing nanotechnology and pyrosequencing chemistry (Nyren et al., 1993), coupled with an enzymatic luminometric inorganic pyrophosphate detection technique, Rothberg and colleagues at 454 Life Sciences recently developed a novel and highly parallel system capable of sequencing approximately 100 million bp in a 7-hour period by sampling over 400,000 sequence clones. They were able to illustrate this technological

advance by sequencing and assembling the Mycoplasma genitalium and Streptococcus pneumoniae genomes (0.6 and 2.1 Mb respectively in size). Compared to the current state-of-the-art Sanger DNA sequencing and capillary-based electrophoresis platform, their system generates “raw” data with approximately 100 times higher throughput. However, compared to the Sanger/electrophoresis-based sequencing approach, it still has several limitations, such as the short sequence read lengths which, averaging 200–250 bases per read, are only a quarter of Sanger read lengths. Another problem is that the accuracy of base calling on individual reads is low especially for genomic regions containing homopolymers. This leads to the increased likelihood of spurious polymorphism detection. In addition, the 454 sequencing methodology currently does not incorporate the use of mate pairs that come from large insert libraries (10 kb), which for the successful assembly of repeat rich genomes, like those of most eukaryotes, are important in order to span large repeat regions. Nonetheless, this technology has been employed to generate some sequence data from DNA extracted from fossil remains (Green et al., 2006) and has been employed most recently to generate sixfold coverage of an individual genome (Wheeler et al., 2008). Some of these problems can be circumvented by adopting a hybrid sequencing approach, that is one that employs, in addition to the sequencing-by-synthesis approach (454), a small number of large insert clones (typically fosmid libraries) sequenced by Sanger technology to provide order and orientation in the final genome assembly.While this approach has been successfully employed for a number of bacterial genomes (Goldberg et al., 2006), establishing the most efficient mix of the sequence-by-synthesis contribution and fosmid libraries will be the challenge for the completion of larger mammalian genomes. Each of the sequencing-by-synthesis approaches mentioned above share similar advantages as well as limitations. However, they do show great promise for the discovery of polymorphisms in a rapid, cost effective manner. The massively parallel sequencing-by-synthesis approach enabled by the 454 picoliter plate technology can also be utilized for targeted polymorphism discovery. In this manner, PCR amplicons that contain the targeted genomic regions are extensively sampled since it is possible to generate currently up to 400,000 clonal amplifications of the PCR product per experiment. This

Other Methodologies for Polymorphism Detection

approach has potentially great value for the discovery and detection of DNA variations present in low frequency in the samples. This is especially applicable for mutation detection in tumor tissue, in which potentially only a few cells carry activating mutations and the surrounding cell population contributes to the “contamination” of the tumor DNA. The lowest detection level thus far obtained has been for alleles present in the sample as low as 9% for indel mutations in the EGFR gene in lung adenocarcinoma specimens (Thomas et al., 2006) and at a similar frequency for non-synonymous point mutations in the FGFR1 gene from glioblastoma samples (Strausberg et al., personal communication). In both of these cases the corresponding PCRamplified, Sanger sequence chromatograms lack the necessary secondary peak signal to detect a variant above the background noise level, where the limit of detection of a secondary allele is thought to be between 15–25%. Sequencing by Hybridization The sequencing by hybridization on microarray approach has also become a widely utilized tool for SNP detection and analysis (Kukita et al., 2005; Patil et al., 2001; Maraganore et al., 2005; Kozal, 1996; Cronin et al., 1996; Chee et al., 1996; Hacia et al., 1996; Kapranov et al., 2002) due to the technological advances that make the manufacturing of high-density DNA arrays possible The genomic DNA is generally prepared by PCR to amplify the regions of interest or by using a whole genome amplification methodology (Dean et al., 2002) and tagged with fluorescent dyes for detection prior to hybridizing to the oligonucleotide array. This approach allows the detection of sequence present in a test sample via direct hybridization and was able to detect the unique (non-repeat) contiguous sequence from human DNA chromosome 21 conserved with mouse and dog (Frazer et al., 2001) and the haplotype block structure of chromosome 21 in 24 human DNA samples (Patil et al., 2001). In both cases, this was accomplished by creating a tiling array where each position was interrogated for all possible bases using a set of eight distinct oligonucleotide probes comprising the position in question and 12 bp flanking to provide for binding specificity. The interspecies hybridization experiments revealed additional putative exonic region of current genes and identified other regions of significant sequence conservation that could represent other forms of functional sequence. The human population study revealed that 80% of the DNA samples could be described by three common haplotypes. Such knowledge of chromosomal block structure and inheritance patterns enables the choice of a smaller set of variants with which one can perform disease-gene association studies (see Chapter 8). The sequencing-by-hybrization approach clearly necessitates that all the sequence being tested is present on the microarray. While this may be a limitation for discovering novel sequence in some genomes, even for the multiple, yet to be sequenced, human genomes, the methodology is well suited and has been extensively employed for the genotyping of known SNPs in disease conditions and deletions in arrayed sequence. Recent whole genome association studies

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employing large-scale SNP assays performed on microarrays have identified genes as either biomarkers or therapeutic targets in Parkinson’s disease (200,000 loci) (Maraganore et al., 2005) and copy number variations and deletions in mental retardation (100,000 loci) (Friedman et al., 2006).

OTHER METHODOLOGIES FOR POLYMORPHISM DETECTION Many other methodologies have been developed for genomewide discovery and characterization of DNA polymorphisms. Although each of the methodologies has their own unique capability in detecting various types of polymorphisms, they tend to have limited utility and are often employed at a lower scale than the approaches described above. Restriction Fragment Length Polymorphism One of the most widely utilized techniques is Restriction Fragment Length Polymorphisms (RFLP). RFLPs are polymorphisms in the lengths of particular restriction fragments. It was one of the first marker systems developed and has been used in creating genetic maps of chromosomes (Botstein et al., 1980; Rothschild et al., 1993), tracking changes occurring in the cancer genome (Jenkins et al., 2002), as well as serving as a starting points for chromosome walks (Bakker et al., 1985). The technology is based on using restriction endonucleases that cleave DNA at highly specific sites; therefore mutations or polymorphisms change the enzymatic activity at precise genomic loci. Thus by measuring the differences in length of the cleaved DNA strands (accomplished by electrophoresis separation and probing with cloned fragments of the genome), it is possible to monitor the presence of genetic variations. While reliable, this method’s detection power is limited to the small number of the bases around the restriction sites and can only detect the presence and not the identities of the sequences variations. In the simplest and most common type of RFLP, the polymorphism results from a single nucleotide difference that provides a recognition site for a restriction enzyme in one allelic form and not the other. A polymorphism of this type, once identified and characterized by sequencing, can be detected readily among a population of individuals in a targeted fashion by first amplifying the region around the polymorphic site from each sample, digesting the amplified material with the appropriate restriction enzyme and distinguishing the undigested PCR product from the smaller digested fragments by gel electrophoresis (Saiki et al., 1985). This PCR-based protocol provides results rapidly and is a significant improvement over the Southern blot alternative which requires blotting, probe labeling, hybridization and autoradiography. Simple Sequence Repeat Polymorphisms Simple Sequence Repeat Polymorphisms (SSRPs), generally referred to as “microsatellite” markers, are tandem repeats units of short sequences of nucleotides (generally 2–6 units in

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length). The detection of these markers can be readily accomplished by probing the genomic DNA libraries with synthetic oligonucleotides followed by confirmation sequencing of the positive clones with the Sanger technique. PCR primers can be designed to amplify the markers once the sequences flanking the SSRP markers are known. The variations in the length of PCR products can then be separated by electrophoresis and detected (Cornall et al., 1991; Love et al., 1990). This method is relatively easy to automate at a low cost for large scale genotyping. It can be very informative for fingerprinting applications; however, its utility is obviously limited to the detection of length polymorphisms. Chromatography or Electrophoresis Separation Methods for Conformation Polymorphism Detection The natural intra- or inter-molecular structural distortions (conformational changes) of the DNA double helix induced by mispairing of the bases could result in detectable or measurable physical properties changes that can be used to detect the presence and the approximate locations of polymorphisms. These detectable or measurable physical property changes include melting temperature and mobility shift. These methods generally utilize separation techniques such as electrophoresis or chromatography to resolve conformational differences among DNA samples. For example, the Denaturing Gradient Gel Electrophoresis (Myers et al., 1987; Burmeister et al., 1991) method takes advantage of the fact that the denaturing of double stranded DNA is highly dependent on its sequence. One base mispairing could induce conformational changes and alter the melting temperature of a DNA molecule, thereby suggesting the presence of an allelic variant in a particular locus. The DNA molecules with different melting temperatures show distinguishable motility shifts under denaturing conditions for electrophoresis. Other methods utilizing these properties include Denaturing High-Performance Liquid Chromatography (Huber et al., 2001; Underhill et al., 1997), Single Strand Conformation Polymorphism (Nataraj et al., 1999; Sheffield et al., 1993) and Heteroduplex Analysis (Nataraj et al., 1999; Thomas et al., 2001). The main drawback of these methods is that they can only be used to detect the presence and not the identities of the polymorphisms. Other Enzymatic and Chemical Cleavage Methods Cleavage techniques also exploit the conformational changes of the DNA double helix caused by mispairing of the bases. Several approaches utilizing the different chemical and enzymatic properties of the perfectly matched homoduplexes and the mismatched heteroduplexes for targeted polymorphism detection in specific regions have been developed. These methods include chemical cleavage of mismatched DNA (Curiel et al., 1990; Lambrinakos et al., 1999; Tabone et al., 2006), ribonuclease cleavage of mismatched DNA (Myers et al., 1985), cleavase fragment length polymorphism analysis (Nomura et al., 2001; Uehara et al., 1999), T4 Endonuclease VII cleavage of heteroduplex DNA (Babon et al., 1999), mismatch repair detection (Faham et al., 2005),

UNG-Mediated T-Sequencing (Hawkins and Hoffman, 1997), and RNA-mediated fingerprinting with MALDI MS Detection (Krebs et al., 2003; Stanssens et al., 2004). The detection sensitivity and accuracy of these techniques highly depend on the specificity and efficiency of the particular enzyme activity or chemical used. Ideally, little or no cleavage would be seen in a perfectly matched DNA fragment and a near complete digestion or modification of all distortions of the helix generated by base mismatches would be accomplished. However, none of the techniques mentioned above are able to detect both the matched and mismatched DNA states equally well. The utility and the choice of a technique becomes a balance between ease of use, sensitivity and specificity. Some of these methods can be applied directly to extracted genomic DNA if the quantity of the DNA is not an issue. However, PCR is commonly used to select the targeted genomic region and generate enough DNA material for the assays. The separation and detection of the digested or modified DNA fragments is generally accomplished through electrophoretic techniques.

FUTURE DIRECTIONS The generation of a first draft of the human genome sequence permitted the global identification of small scale variation such as SNPs and heterozygous indels. However, it also set the stage for detection of large scale genome-to-genome comparisons (Istrail et al., 2004). These and other such studies (Khaja et al., 2006) have identified large-scale difference (1–3 Mb ranges) between human individuals that have been confirmed in larger populations (Redon et al., 2006). Recent studies have also implicated such large scale polymorphism (copy number variants) in developmental verbal dyspraxia (Feuk et al., 2006) and Crohn’s disease (Fellermann et al., 2006), to name but a few. The complete human genome sequence has also fueled the development of newer sequencing technologies that employ short sequence reads but generate large quantities of sequence data at increasingly lower cost without the need of elaborate production facilities. These approaches that involved the interrogation of single DNA molecules via clonal amplication (454, Solexa, ABI-Agencourt) will permit de novo sequencing of distinct human individuals. However de novo human genome assembly is currently not attainable due to the short read length, the current absence of well developed methodologies for generating large insert clone mate sequence and computational approaches that account for both the error profile and the input characteristics of reads generated. These new sequencing platforms do permit the generation of sequence data from defined genomic regions of multiple individuals via PCR and other methods, to allow the targeted amplification or separation of genomic loci (Thomas et al., 2005;Thomas et al., 2006). The goal for sequencing in the next decade will be to achieve de novo assembly of multiple human genomes, since the mapping of newly generated reads to an existing human genome reference constrain new sequence data to an existing, likely unique, genome structure. Therefore establishing de novo assembly approaches in the analysis of future genomes will create

References

an unbiased understanding of human chromosomal structure and the underlying individual genotypes that define each person uniquely in the context of growing phenotypic information. Lastly, any human DNA variation will need to be interpreted in the context of the functional sequence that it potentially modulates. This appears to be an easier problem to address when considering a SNP in a protein-coding domain (Livingston et al., 2004; Ng and Henikoff, 2006), and computational prediction methods can rank which putative functional changes are to be tested experimentally. These prediction methods are less defined for polymorphisms larger than 1 bp (2–100 bp) that occur distal to known protein-coding domains, and clearly much of the impact on human disease may be modulated by these variants (Ball et al., 2005; Chuzhanova et al., 2003). More detailed recent analyses of 1% of the human genome sequence revealed that many transcriptional signals and cues reside in non-coding DNA and a more significant fraction of the human genome is functional than thought previously

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(Birney et al., 2007). This indicates that a large amount of DNA variation that has been identified in non-coding DNA (Levy et al., 2007) may elicit a variety of phenotypic effects, whose elucidation will be a rich source for future research. Much of the human genetic and genomic research since the completion of the human genome sequence has led us to conclude that much still remains to be discovered in primary sequence space as a predecessor to our understanding of basic functional changes in cells and tissues. Such knowledge is crucial so that we can bring genomic data to bear in a useful manner to the medical context.

ACKNOWLEDGEMENTS The authors would like to thank Drs Robert Strausberg, Jiaqi Huang and Pauline Ng for their suggestions and critical review of the manuscript.

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CHAPTER

8 Genome-Wide Association Studies and Genotyping Technologies Kevin V. Shianna

INTRODUCTION Since the completion of the International HapMap Project (International HapMap Consortium, 2005; The International HapMap Consortium, 2003), a wealth of genetic information within the human genome has been uncovered. Over 12 million single nucleotide polymorphisms (SNPs) have been identified (Wheeler et al., 2007). This dense map of common genetic variation has revolutionized the field of human genetics. Genomewide linkage scans can now be performed using efficient and cost-effective high-throughput SNP genotyping technologies, while at the same time achieving a fine map at a density not previously possible when performing earlier-generation genome scans based on microsatellite polymorphisms. The SNP map, along with recent technological gains, has also made it possible to comprehensively study complex diseases and traits by allowing for the genotyping of hundreds of thousands of SNPs for many samples. Also, with the identification of a specific type of SNP, called tagging SNPs, almost complete genomic coverage of common variation can be attained. This chapter will provide an introduction to genome-wide association studies, followed by an overview of two commonly used genome-wide genotyping platforms, the Illumina Infinium Assay and the Affymetrix GeneChip. The methods and concepts discussed here complement those described elsewhere in this book.

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

PRINCIPLES OF GENOME-WIDE ASSOCIATION STUDIES Before performing a genome-wide association study, it is essential to begin with a fully developed plan.The key issues to consider are the kind of study to perform, the required sample size, the importance of a well-defined phenotype, the need to correct for population stratification and the technologies available for genome-wide genotyping. This section will provide an introduction to the basic experimental design for a genome-wide association study. Type of Study First, the study should be focused on a key question based on a specific phenotype or disease. As an example, a study could be established to identify genetic variants associated with epilepsy. The key would be to collect a large cohort of individuals that have a definitive and well-defined diagnosis of epilepsy (cases) and compare these individuals to a group that does not have epilepsy (controls). In the past it was always necessary to collect matched controls, but with an abundance of control samples containing data from the various genome-wide array-based studies, it is now possible to use control genotyping data from the available databases. One example is the database made available by Illumina (iControlDB) where individual users have deposited genotyping data for control samples from previously processed samples. It has been demonstrated that the use of general control

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TABLE 8.1

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Genome-Wide Association Studies and Genotyping Technologies

Examples of successful genome-wide association studies

Disease

Chip type

Gene/locus

HIV

Illumina HumanHap 550

HLA-C, HCP5/HLA-B*5701

486

(Fellay et al., 2007)

Type 2 Diabetes

Illumina HumanHap 300

TCF7L2, SLC30A8, HHEX, FTO, PPARG and KCNJ11

1161/1174

(Scott et al., 2007)

Affymetrix Human Mapping 500

CDKN2A, CDKN2B, IGF2BP2, CDKAL1

1464/1467

(Saxena et al., 2007)

Illumina HumanHap 300

TCF7L2, AHI1-LOC441171 region

500/497

(Salonen et al., 2007)

Illumina HumanHap 300

TCF7L2, IDE–KIF11–HHEX and EXT2–ALX4

1363

(Sladek et al., 2007)

Illumina HumanHap 550

KIAA0350

563/1146

(Hakonarson et al., 2007)

Affymetrix Human Mapping 500

Chromosome regions 18q22, 12q24, 12q13, 16p13 and 18p11

4000/5000

( Todd et al., 2007)

Affymetrix Human Mapping 100

IFIH1

2029/1775

(Smyth et al., 2006)

Restless leg syndrome

Affymetrix Human Mapping 500

MEIS1, BTBD9

401/1644

( Winkelmann et al., 2007)

Colorectal cancer

Illumina HumanHap 550

8q24.21 locus

930/960

( Tomlinson et al., 2007)

Affymetrix Human Mapping 100

8q24 locus

1257/1336

(Zanke et al., 2007)

Illumina HumanHap 300

TCF2

1501/11290

(Gudmundsson et al., 2007)

Illumina HumanHap 300

8q24

1453/3064

(Gudmundsson et al., 2007)

Illumina HumanHap 300

8q24

1172/1157

( Yeager et al., 2007)

Illumina HumanHap 300

Chromosome region 4q25 near PITX2

550/4476

(Gudbjartsson et al., 2007)

Type 1 Diabetes

Prostate cancer

Atrial fibrillation

Sample size (cases/controls)

Reference

Celiac disease

Illumina HumanHap 300

IL2, IL21

778/1422

(van Heel et al., 2007)

Crohn’s disease

Illumina HumanHap 300

IL23R

2877/1345

( Tremelling et al., 2007)

Illumina HumanHap 300

IL23R

567/571

(Duerr et al., 2006)

Macular degeneration

Affymetrix Human Mapping 100

CFH

96/50

(Klein et al., 2005)

Asthma

Illumina HumanHap 300

ORMDL3

994/1243

(Moffatt et al., 2007)

samples across studies is feasible (Wellcome Trust Case Control Consortium, 2007).This greatly decreases the cost to run a project and potentially allows for the inclusion of more cases (if available). A study could also be arranged to look at a specific continuous phenotype or quantitative trait within a group of individuals. Using the previous example, one could study how individuals with epilepsy responded to a specific drug or series of drugs. Sample Size The sample sizes required for a genome-wide association study are dependent on several factors. The first is the effect size of an unknown genetic variable. Of course, this is impossible to know in advance; however, power calculations can be performed to determine how many samples would be required to identify a variant with a specific effect size. These calculations can serve as a guide when deciding how many samples to include in the study. Purcell and colleagues (2003) have created a web-based genetic power calculator (http://pngu.mgh.harvard.edu/~purcell/gpc/) that can be used to determine power of a study. Another factor

that is important when determining sample size is the type of study: case/control or a continuous phenotype. The current view for case/control studies is that large sample sizes are needed (1,000 of each), but this is not always true. A few studies have identified associated variants with only 150–200 samples (Table 8.1). Again, it is impossible to know the effect size of an unknown variant so it is best to include a large number of samples when designing a study. For a continuous phenotype, studies have had success while using fewer samples (Table 8.1). Importance of Phenotype The most important aspect when designing a genome-wide association study is the quality and discrete nature of the phenotype. It is essential that the studied phenotype is evaluated carefully for accuracy and is well defined across all samples. The inclusion of incorrectly phenotyped samples in a study could lead to a decrease in the statistical power to identify associated variants. This could consist of control samples that are in fact cases, but have yet to be identified with the specific phenotype/diagnosis or a misdiagnosis

Platform Overview

of an individual within the cases. For some complex diseases, the phenotypic characteristics overlap with other closely related diseases. This can make it difficult when choosing which samples should be included in the study and, because of genetic heterogeneity, will decrease the power of the study. Population Stratification When analyzing the data from genome-wide association studies, it is essential to evaluate and account for population stratification between cases and controls. Cryptic population structure can generate false positive and/or negative associations. This can happen if allele frequencies are different in any unknown subgroups. One solution to correct for this difference in ancestry is described by Price and colleagues (2006). Left uncorrected, population stratification can lead directly to false associations.

Address

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Probe

Bead

Figure 8.1 An example of a bead on the Illumina Infinium BeadChip. A schematic of a single bead, containing a 75nucleotide oligomer consisting of an address and a specific probe sequence, as described in the text.

Genomic DNA A T Extended nucleotides emit signal

Bead 1

Genomic DNA

Genotyping Technologies, Tagging SNPs and Genomic Coverage Another necessity for genome-wide association studies is to have a method to genotype hundreds of thousands of genetic variants around the genome. Fortunately, there are now standard products that allow for screening of very large numbers of SNPs. Depending on the chosen platform, the SNPs on the arrays have been selected based either on even spacing or on the basis of certain SNPs being able to represent multiple SNPs.The SNPs in the second approach are termed “tagging SNPs” and greatly increase the genomic coverage that can be obtained compared to SNPs that are evenly spaced across the genome (Barrett and Cardon, 2006; Pe’er et al., 2006). Reflecting now the well-established patterns of linkage disequilibrium (Hinds et al., 2005; International HapMap Consortium, 2005), these tagging SNPs work by acting as proxies for other nearby associated SNPs, making it possible to genotype only the tagging SNP and yet capture the content of other associated SNPs in the region. There are currently commercially available arrays (SNP chips) that contain close to 1 million tagging SNPs, representing most of the common variation in the human genome. Products where the SNPs have not been specifically chosen based on the close association with other SNPs will have much less genomic coverage, because the SNPs have not been optimally chosen for the ability to tag. For example, Illumina’s HumanHap300 array, containing around 300,000 tagging SNPs, has essentially the same statistical genomic coverage as Affymetrix’s Mapping 500 K Array Set, which contains 500,000 evenly spaced SNPs (Barrett and Cardon, 2006; Pe’er et al., 2006). Further background information on genome-wide association studies and tagging SNPs has been elegantly presented elsewhere (Hirschhorn and Daly, 2005).

PLATFORM OVERVIEW Illumina Infinium Assay The Illumina genotyping technology is based on 3 silicon beads that have hundreds of thousands of DNA oligomers attached to

A

(a)

G

Bead 2

Genomic DNA A T*

(b)

Bead

G

Figure 8.2 Infinium I and II assay. (a) A graphical representation of the Infinium I assay. This assay uses an ASPE and requires two bead types to detect a single SNP. (b) A depiction of an SBE reaction as used for the Infinium II assay. Only one bead type is required to detect a single SNP. The diagram shows a single dideoxy base (T) being added to the 50-base oligomer on the bead.

the surface (Shen et al., 2005). For Illumina’s Infinium wholegenome genotyping BeadChip, the attached oligomers consist of a stretch of 75 nucleotides (Gunderson et al., 2005; Steemers et al., 2006) (Figure 8.1). The first 25 nucleotides (the address) serve as a barcode to identify the location of specific beads on the array, and the next 50 bases (the probe) are complementary to a genomic region containing a specific SNP (or copy number variant). On average, there is an 18-fold redundancy of each bead type on the BeadChip. The placement of the beads on the BeadChip is a random process, thus requiring a decoding step. The decoding involves multiple hybridizations to the 25 oligomer address. This not only identifies the location of each bead, but performs a functional quality control for each bead on the BeadChip. There are two Infinium chemistries, type I and II (Figure 8.2). The Infinium I assay uses an allele-specific primer extension (ASPE) reaction in which fluorescently labeled nucleotides are inserted at the 3 end of the primer. With this assay it takes two bead types to determine the genotype at a single SNP. The Infinium II assay involves a single-base extension (SBE) reaction that occurs on a single bead type for each SNP. Most of Illumina’s off-the-shelf BeadChips use the Infinium II assay.

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Genome-Wide Association Studies and Genotyping Technologies

rs2381194

Restriction site

Restriction site

6000

Intensity (B)

Enzyme digestion 4000

2000

Adapter ligation 0

(a)

0

2000

4000 6000 Intensity (A)

8000

10000

PCR

rs2381194 1.20 1

Fragment and label

Norm R

0.80 0.60 0.40 0.20 0 0.20 (b)

26 0

227 0.20

0.40 0.60 Norm Theta

696 0.80

1

Figure 8.3 Raw and normalized data for SNP genotyping, using the Illumina platform.(a) Raw data from genotype analysis of one SNP (rs2381194) from 949 individuals. Each dot corresponds to a single individual. Blue, homozygote for one allele (n  696); Purple, heterozygous (n  227); Red, homozygote for other allele (n  26). (b) Same SNP and individuals as in (a), but after normalization using Illumina’s BeadStudio software.

The steps for the Infinium II assay are as follows: (1) 750 ng of intact genomic DNA is whole-genome amplified (WGA); (2) the amplified product is fragmented; (3) the fragmented DNA is hybridized to a BeadChip; (4) an SBE reaction is performed directly on the chip with the insertion of a labeled (2,4-dinitrophenol or biotin) dideoxy base; (5) the inserted base is stained with a fluorescently labeled antibody and (6) fluorescence on the beads on the BeadChip is detected by scanning with the Illumina BeadStation. An average of the raw intensity per bead type is then imported into the Illumina BeadStudio software, where the data can be efficiently managed and individual SNPs can be automatically or manually called. The quality of the raw and normalized genotype calls can be visually confirmed within this software (Figure 8.3).

Figure 8.4 Overview of DNA processing steps for the AffymetrixGeneChip. The diagram demonstrates the steps implemented to reduce the genomic complexity allowing for efficient allele-specific hybridization.

Affymetrix GeneChip The Affymetrix genotyping GeneChip is manufactured using the same principles as for the expression GeneChip, using a technology called photolithography. This has been described in great detail (Dalma-Weiszhausz et al., 2006; Matsuzaki et al., 2004), including elsewhere in this book. However, the genotyping GeneChip contains additional probes for each interrogated SNP, to allow for differentiation of true signal over noise (DalmaWeiszhausz et al., 2006). The steps required to process a DNA sample in preparation for hybridization to the GeneChip lead to a reduction in genome complexity allowing for allele-specific hybridization (Figure 8.4). The following is the procedure for processing a sample: (1) perform separate genomic DNA (250 ng) digestions using the NspI and StyI restriction enzymes; (2) ligate specific adapters containing a single universal primer binding site to the digested DNA; (3) pool the DNA fragments from the separate digestions and amplify using a single universal PCR primer specific to the primer site within the adaptor; (4) fragment the amplified products; (5) end label the DNA; (6) hybridize to a GeneChip and (7) scan the chip. The raw data can then be converted into genotype calls by using the appropriate algorithms (from Affymetrix).

Platform Overview

Platform Comparison Numerous laboratories worldwide have successfully used both the Illumina and Affymetrix platforms to identify genetic variants involved with complex disease (Table 8.1). This section will describe potential benefits of each platform, paying particular attention to (1) differences in the basic design of the chips; (2) statistical tagging power (genomic coverage); (3) ability to detect copy number variation (CNV); (4) processing issues and (5) overall quality of the final raw genotyping data. The length of the probes to detect an SNP during hybridization constitutes one of the major differences between the platforms. The probe length on the Affymetrix GeneChip is 25 nucleotides, while on the Illumina BeadChip the probes contain 50 nucleotides. This difference in length for hybridization provides for higher specificity for probes on the Illumina BeadChip. The other main difference is the content of the probe for each SNP. For Affymetrix, each SNP is represented by a series of probes consisting of mismatches and perfect homology, allowing for differentiation between signal and noise. The Illumina BeadChips do not require specific mismatch probes for every SNP because of the increased length of the probes for hybridization, but contain control beads that detect the overall level of background noise. The probe length constitutes the major difference between the two platforms and is most likely directly linked to the differences in quality between the platforms. Most of the SNPs on the Illumina Infinium BeadChips were selected as tagging SNPs, resulting in excellent genomic coverage for most populations (based on HapMap populations) (International HapMap Consortium, 2005). On the other hand, the Affymetrix GeneChips are based on SNPs that are evenly spaced throughout the genome. The positive aspect of even spacing is better physical coverage for detecting CNVs (Redon et al., 2006). One disadvantage of this approach is less statistical power to capture the known common variation when compared to arrays that contain tagging SNPs. A multimarker or haplotype-tagging approach may gain increased power/coverage, but the downstream statistical analyses become more difficult. Multiple groups (Barrett and Cardon, 2006; Magi et al., 2007; Pe’er et al., 2006) have performed statistical comparisons of the Illumina HumanHap300 and the HumanHap550 BeadChips with the Affymetrix Mapping 500 K Set. From these analyses it is evident that the Illumina array with only around 300,000 tagging SNPs has essentially the same statistical genomic coverage as the Affymetrix Mapping 500 K Set, while the Illumina HumanHap550 array is statistically superior for genome-wide association studies. Overall, both platforms provide good quality results (Table 8.1). However, a comparison of the SNP conversion rate (percentage of SNPs that work) between the two technologies reveals a significant difference. In our hands, for example, when using the Illumina pre-defined cluster, an average sample call rate of 99.6% is achieved, with an average reproducibility of over 99.99%. With the Affymetrix platform, the average sample call rate is variable and depends greatly on the chosen stringency within the calling algorithm, but a typical call rate would be in the range of 95–98% (personal communications). As discussed above, the increased SNP conversion rate with the Illumina BeadChip is most likely related

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to the size of the probe (50 nucleotides) which increases the specificity during the hybridization step. Recently, there has been a push to detect CNVs throughout the human genome by using genome-wide genotyping chips (Komura et al., 2006; Redon et al., 2006) (see Chapter 9). Using these SNP-based arrays is an efficient method to detect large deletions or duplications. The Affymetrix GeneChip with its even spacing of SNPs has great genomic coverage to detect large CNVs. Most of the BeadChips from Illumina have specific SNPs or non-polymorphic probes added in regions where there are known CNVs. In general, both arrays are capable of detecting large CNVs. However, with the current SNP densities, the detection of small CNVs is difficult with the use of SNP-based arrays. Raw Data Quality Control Genetic association studies using genome-wide genotyping chips produce very large amounts of data. A typical project with 2,000 samples, each containing over 500,000 SNPs per sample, will result in over one billion data points. With this much data, the probability for false positives becomes much greater. A few reasons that can attribute to this are samples being prepared using different purification methods and the processing of the chips in different labs. These slight variations can lead to minor differences in intensity, thus making it more difficult to accurately call the genotypes. To prevent false positives from clouding the results from the statistical analyses, it is of utmost importance to begin with a clean or curated data set. This is accomplished by establishing a high threshold of sample and SNP quality. The number of SNPs that are obtained for each sample (the “call rate”) and the raw fluorescent intensity are the main variables that can be analyzed to determine whether a sample should be included or excluded. If any sample’s call rate is much lower (1–2%) than the average call rate for the project, the sample should be excluded. A 1% difference can represent as many as 5,000 fewer SNPs being called in a sample when compared with the average number of SNP calls in a population of samples. There are many variables that can be used to assess SNP quality. Metrics such as call frequency (number of samples that are being called for an SNP), cluster separation (degree of split between the heterozygote grouping and homozygotes, e.g. Figure 8.3), extreme deviation from Hardy–Weinberg proportions and overall raw signal intensity should be evaluated for all SNPs. A very aggressive approach to maintaining a clean data set is to adhere to a rigorous level of acceptable SNP call frequency. We have established a high threshold for SNP inclusion and have coined the phrase the “1% rule”; we delete any SNP that has greater than 1% of the samples not being called or called ambiguously. This rule was established from personal experiences when attempting to extract as much genotype data as possible. After performing statistical analyses, we noticed a large number of false positives due entirely to poor genotyping calls. For the 1% rule to work effectively, each new experiment needs to be reclustered using its own raw data. With the 1% rule, the use of predetermined cluster files will not work, because slight variations from experiment to experiment can result in slight shifts of intensity and cluster separation (i.e., separation of heterozygote

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and homozygote clusters), resulting in incomplete calling of truly callable samples for a large number of SNPs. When applying the 1% rule, it is a balance between including a greater number of samples or a greater number of SNPs. Attempting to keep good but lower quality samples can result in the deletion of many SNPs that fail to meet the 1% rule standard. By deleting a few extra samples across the entire data set, it is possible to keep many of the SNPs that would have violated the 1% rule. In practice, in our experience, a typical project will have between 1% and 3% samples deleted (not all because of 1% rule) across all SNPs and between 1% and 2% of SNPs deleted because of violating the 1% rule. We have successfully used this approach for multiple studies (Fellay et al., 2007). After completing the first pass of statistical tests, the SNPs with the lowest 100–200 p-values should be manually checked for genotyping data quality. Visualization of the raw/normalized genotype call can increase one’s confidence in any potentially associated SNP with a low p-value. Inspecting the data is especially important if a data set has not been stringently curated under the 1% rule. For a given SNP, if only 95% of samples are called, over 50 samples from a sample set of 1,000 samples will not have a genotype call. Across a very large data set (over 500,000 SNPs), this loosened threshold can result in many false positives solely because of the increased opportunity for an imbalance of the case/control ratio in the uncalled samples. Sample Collection, Processing and Throughput It is now feasible for institutional core facilities or small- to medium-sized labs to run hundreds of samples per week using genome-wide genotyping chips. For each platform described here, there are off-the-shelf kits allowing for easy processing of many chips/samples at once with minimal variation across experiments. Also, with the implementation of automation developed specifically for each assay, sample throughput and tracking have all been simplified.

Genome-wide genotyping chips require less than 1 g of DNA, a very low amount considering the amount of data generated from this sample. In general, high-quality genomic DNA should be used. DNA of lesser quality or WGA DNA can be used, but will result in a lower sample call rate. The Affymetrix platform is more amenable to the use of WGA DNA, because of the lack of an initial WGA step as in the Illumina Infinium assay. However, WGA DNA will work for the Infinium platform, although lower call rates will be achieved (personal communication). Formalin-fixed, paraffin-embedded (FFPE) tissue samples represent a rich and abundant source of well-annotated material, but DNA isolated from FFPE samples using currently available methods is not amenable to genome-wide genotyping assays. The main reason for this is that the interaction of the formalin with the DNA prevents efficient enzymatic amplification of the DNA. Creating methods to successfully perform whole-genome genotyping on FFPE samples will be of great importance. This will benefit current projects by allowing for the potential increase in sample sizes. Also, precious samples obtained only via FFPE tissues could then be included in genome-wide association studies.

CONCLUSIONS This chapter has provided a basic overview of genome-wide association studies and the current technologies used for genome-wide genotyping. Since the completion of the International HapMap Project in October 2005 (International HapMap Consortium, 2005), there have been great technological advances that now allow for the genotyping of over one million SNPs within many individual samples in a matter of days. The ability to achieve genomewide SNP coverage has lead to the identification of multiple variants associated with disease (Table 8.1). These sorts of studies will continue in the coming years until it becomes feasible to simply and cost-effectively sequence the entire genome of individuals.

REFERENCES Barrett, J.C. and Cardon, L.R. (2006). Evaluating coverage of genomewide association studies. Nat Genet 38, 659–662. Dalma-Weiszhausz, D.D.,Warrington, J.,Tanimoto, E.Y. and Miyada, C.G. (2006). The affymetrix GeneChip platform: an overview. Methods Enzymol 410, 3–28. Duerr, R.H., Taylor, K.D., Brant, S.R., Rioux, J.D., Silverberg, M.S., Daly, M.J., Steinhart, A.H., Abraham, C., Regueiro, M., Griffiths, A., Dassopoulos, T., Bitton, A., Yang, H., Targan, S., Datta, L.W., Kistner, E.O., Schumm, L.P., Lee, A.T., Gregersen, P.K., Barmada, M.M., Rotter, J.I., Nicolae, D.L. and Cho, J.H. (2006). A genomewide association study identifies IL23R as an inflammatory bowel disease gene. Science 314, 1461–1463. Fellay, J., Shianna, K.V., Ge, D., Colombo, S., Ledergerber, B., Weale, M., Zhang, K., Gumbs, C., Castagna, A., Cossarizza, A. et al. (2007). A Whole-Genome Association Study of Major Determinants for Host Control of HIV-1. Science 317, 944–947. Gudbjartsson, D.F., Arnar, D.O., Helgadottir, A., Gretarsdottir, S., Holm, H., Sigurdsson, A., Jonasdottir, A., Baker, A., Thorleifsson, G., Kristjansson, K. et al. (2007). Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature 448, 353–357.

Gudmundsson, J., Sulem, P., Manolescu, A., Amundadottir, L.T., Gudbjartsson, D., Helgason, A., Rafnar, T., Bergthorsson, J.T., Agnarsson, B.A., Baker, A. et al. (2007). Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 39, 631–637. Gudmundsson, J., Sulem, P., Steinthorsdottir, V., Bergthorsson, J.T., Thorleifsson, G., Manolescu, A., Rafnar, T., Gudbjartsson, D., Agnarsson, B.A., Baker, A. et al. (2007). Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 39, 977–983. Gunderson, K.L., Steemers, F.J., Lee, G., Mendoza, L.G. and Chee, M.S. (2005). A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 37, 549–554. Hakonarson, H., Grant, S.F., Bradfield, J.P., Marchand, L., Kim, C.E., Glessner, J.T., Grabs, R., Casalunovo, T., Taback, S.P., Frackelton, E.C. et al. (2007). A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature 448, 591–594. Hinds, D.A., Stuve, L.L., Nilsen, G.B., Halperin, E., Eskin, E., Ballinger, D.G., Frazer, K.A. and Cox, D.R. (2005). Whole-genome

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CHAPTER

9 Copy Number Variation and Human Health Charles Lee, Courtney Hyland, Arthur S. Lee, Shona Hislop and Chunhwa Ihm

INTRODUCTION Early cytogenetic studies recognized that microscopically visible aberrations such as duplications or deletions of entire chromosomes (aneuploidy) were associated with specific congenital developmental disorders. For example, an extra copy of chromosome 21 (i.e., trisomy 21) has long been established as being correlative with the mongoloid phenotype that was first recognized in 1866 by John Langdon Down (Down 1866; Lejeune et al., 1959). However, recent development of high-resolution assays, capable of detecting small segmental genetic alterations in a genome-wide fashion, have led to the detection of widespread copy number variation (CNV) among the genomes of healthy individuals (Iafrate et al., 2004; Sebat et al., 2004). This finding has now sparked intense efforts to identify and characterize the extent of this type of genetic variation in human populations and to understand its impact on human health.

BASIC PRINCIPLES OF CNVS CNVs have been operationally defined as genomic gains and losses of 1 kb or larger (Freeman et al., 2006; Feuk et al., 2006). This definition helps to differentiate it from other forms of

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polymorphism and/or repeated DNAs in the human genome (see Chapter 1), which include indels, microsatellites, minisatellites, simple repeats (e.g., dinucleotide repeats, trinucleotide repeats, etc.), telomeric and centromeric repetitive DNAs, and most interspersed repetitive elements (although LINES and other long interspersed repeats have repeat elements greater than 1 kb in size). In addition, since CNVs are considered subchromosomal imbalances, they are differentiated from whole chromosomal aneuploidies, such as trisomy 21 or monosomy X. When CNV data are available for many individuals within or across different human populations, CNVs can be categorized into biallelic or multiallelic states (Figure 9.1). Biallelic CNVs have only two alleles and thus produce three different genotypes. For example, a CNV that exists solely in the form of a 1-copy allele or a 2-copy allele (Figure 9.1a, right) can produce diploid copy numbers of 2 (1 allele1 allele), 3 (1 allele2 allele), or 4 (2 allele2 allele) (Figure 9.1a, left). CNVs with greater than two alleles are considered multiallelic and result in more than three different genotypes (Figure 9.1b and Figure 9.2). Heritable CNVs are thought to arise from germline genomic rearrangements (or in some cases, possibly very early somatic events).The genomic rearrangements (mutational events) that are thought to cause CNVs can be broadly categorized as arising via one of two mechanisms: (i) Non-allelic homologous recombination (NAHR) – where homologous recombination

Copyright © 2009, Elsevier Inc. All rights reserved.

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Alleles

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(b) Multi-allelic CNV

Figure 9.1 Examples of bi-allelic and multi-allelic CNVs. a) An example of a bi-allelic CNV that has a 1-copy allele and a 2-copy allele. The reference individual has two 1-copy alleles but 50% of individuals in this population have a total of three copies of this gene per cell. All bi-allelic CNVs have three genotypes per diploid cell, and in this case, copy numbers of 2, 3, and 4 per diploid cell. b) An example of a multi-allelic CNV that has a 0, 1, 2 and 3 copy alleles, resulting in six genotypes in this population. Only the allelic combinations for the three most common genotypes are shown.

Figure 9.2 The amylase gene is considered a multiallelic CNV with diploid copy number estimates ranging from 2 to 15 among humans (Perry et al., 2007). Here, a two-color fiber-FISH experiment of the amylase gene (each copy of the gene is represented by a redgreen combination) shows three copies on a DNA fiber from a single chromosome. It is interesting to note here that the second amylase gene copy is inverted relative to the others.

occurs between highly identical sequences in the genome, such as segmental duplications or related interspersed repetitive elements (Figure 9.3) and (ii) non-homologous end joining (NHEJ) – a repair mechanism whereby double-strand breaks

that occur in the genome are ligated together with the assistance of specific protein complexes that form at the sites of the double-strand breaks (Figure 9.4). The rate for NHEJ is likely influenced significantly by environmental factors and localized DNA

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(gene a and b deleted)

(gene a and b duplicated)

Segmental duplication – allele 1 Segmental duplication – allele 2 Gene a Gene b

Figure 9.3 Non-allelic homologous recombination (NAHR) is a mechanism for generating CNVs where recombination between non-allelic repeats with 90% sequence homology (indicated by black- and gray-colored DNA segments). Intervening DNA sequences are deleted and duplicated on different chromatids.

conformations, but in general has been estimated to occur at a rate of less than 107 per generation, similar to the 108 per generation estimated mutation rate of single nucleotide polymorphisms (SNPs) (Conrad and Hurles, 2007). NAHR events are believed to occur more frequently, with estimates of up to 104 per locus per gamete per generation (Shaffer and Lupski 2000). Since NAHR events lead to duplication and deletion of DNA sequences that lie between highly identical sequences in the genome, this mutational event tends to be associated with larger CNVs (Redon et al., 2006). CNVs are scattered throughout the human genome (Figure 9.5) with at least 6% of a chromosome’s total DNA content being potentially copy number variable (Redon et al., 2006). Taken together, current estimates are that more than 500 Mb (or 18.8%) of the reference human genome is copy number variable (Scherer et al., 2007). In fact, one recent study suggested that structural

genomic variation (i.e., non-SNP variation that is predominantly in the form of CNVs) account for as much as 22% of all genetic variable events found in a given individual and total 74% of all variant bases (Levy et al., 2007). Many of the earlier CNV discovery projects relied on array comparative genomic hybridization (aCGH) technologies that utilized large-insert clones (on the order of 120kb–150kb each) as individuals probles and therefore yielded CNV data with ill-defined boundaries. This has likely led to a size over-estimation for many currently identified CNVs. Documented CNVs are being cataloged and collated in several databases. For example, CNV data from healthy individuals can be found in the Database of Genomic Variants (http:// projects.tcag.ca/variation) and the human paralogy database (http://humanparalogy.gs.washington.edu/structuralvariation), while CNVs from patients affected with a neurodevelopmental disorder are being collated in databases such as DECIPHER

Basic Principles of CNVs

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Figure 9.4 Non-homologous end joining (NHEJ) can result in a genomic rearrangement that immediately or eventually results in the gain or loss of genetic material after multiple double-strand DNA breaks occur and are repaired. (a) A balanced rearrangement has occurred and there is no immediate net gain or loss of genomic material within the cell, but it will result in gains or losses of DNA in subsequent generations depending on which chromosome is inherited. (b) In an unbalanced rearrangement, genomic material can be lost immediately (e.g., in this case, the acentric fragment containing one copy of genes b and c will be lost in subsequent cell divisions).

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Figure 9.5 Genomic distribution of CNVs identified in one study (Redon et al., 2006). No large stretches of the genome appear to be exempt from CNVs and the proportion of any given chromosome that is susceptible to CNVs conservatively varies from 6% to 19%.

(http://www.sanger.ac.uk/PostGenomic/decipher/), the Chromosome Abnormality Database (http://www.ukcad. org.uk/cocoon/ukcad/) and the European Cytogenetics Association Register of Unbalanced Chromosome Aberrations (http://www.ecaruca.net). Most CNVs found in healthy individuals are biased away from genes and reside within intergenic regions (i.e., DNA sequences between genes) (Conrad et al., 2006; Nguyen et al., 2006). Nevertheless, the remaining CNVs have been shown to overlap some 3,000 RefSeq genes and 300 genes implicated in genetic disease (as listed in the Online Mendelian Inheritance in Man database, http://www.ncbi.nlm.nih.gov/sites/ entrez?dbOMIM). Ontology analyses of the genes thought to be copy number variable demonstrate a substantial number that can be classified as “environmental sensor/interaction” genes (Tuzun et al., 2005). These are genes that are involved in sensory perception, neurophysiological processes, drug detoxification, immunity and inflammation, as well as cell surface integrity and cell surface antigens. Clearly, such genes are not critical for early development but rather are involved in our perception and interaction with external stimuli, helping us to adapt to our ever-changing environment. The functional impact of certain CNVs can be relatively straightforward. For example, reduced copy number of a given gene can often be correlated with reduced expression levels, while additional copies of a gene could lead to increased expression levels of the CNV gene (McCarroll et al., 2006). CNVs that involve parts of a gene could result in fusion gene products or aberrant proteins with addition or loss of specific protein domains. Some CNVs in intergenic regions could overlap regulatory elements that affect the expression of genes as far away as 4 Mb (Stranger et al., 2007), and correlations of CNVs with transcriptional levels do not necessarily have to be positive. For example, deletion of a repressor element may cause upregulation of an associated gene.

DETECTING CNVS IN A GENOME-WIDE MANNER Array-Based Comparative Genomic Hybridization There are different genome-wide methods for detecting CNVs. By far, the most widely used method has been array-based comparative genomic hybridization (aCGH). This technology was first introduced as “matrix-CGH” (Solinas-Toldo et al., 1997) and later referred to as “array CGH” (Pinkel et al., 1998). In aCGH, the “test” genome being interrogated is labeled with one type of fluorescent molecule (e.g., Cy5), and a “normal” or “reference” control genome is labeled with another type of fluorescent molecule (e.g., Cy3) (Figure 9.6). The labeled DNAs are combined, denatured and hybridized to an array of DNA fragments or oligonucleotides on a microscope slide, with each DNA fragment or oligonucleotide representing a unique part of the human genome. The labeled DNAs are then allowed to hybridize to their complementary DNA sequences on the array, in a stoichiometric fashion, such that – by measuring the fluorescence ratio of the two fluorescent dyes at each spot on the array – one can infer the relative copy number of that particular DNA sequence in the genome being tested, with respect to the reference genome (Figure 9.6). There have been many advances in aCGH technology over the past 5 years. Originally, aCGH platforms contained several hundred or even up to a thousand, large-insert DNA clones (e.g., BAC clones that have an average insert size of about 120–150 kb), which recapitulated the human genome with a clone per 3 Mb. More recently, the trend has been to manufacture arrays that use smaller DNA sequences as hybridization targets but with increasing number of targets on an array. On such arrays, targets can be oligonucleotides of 45–75 bases in length that have been designed to have similar annealing temperatures (isothermic), based primarily on the length of the oligonucleotide

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Gain

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Figure 9.6 (a) A schematic of an aCGH assay where a test genome (labeled with Cy5, denoted in green) is cohybridized with a reference genome (labeled with Cy3, denoted in red). The DNA probes are mixed and allowed to hybridize to its complementary sequences on the array in a stoichiometric fashion. Fluorescence intensities of the spots on the microarray (each containing a specific DNA sequence) are measured and DNA sequences occurring in greater copy number in the test than in the reference will result in more green fluorescence for those spots on the microarray. A lower copy number of the same DNA sequences will result in more red fluorescence. (b) Typically the log2 of the fluorescence ratios for each DNA segment on the array is then plotted from one end of a chromosome to the other. A gain is indicated in green and a loss in red. DNA segments having no significant change in DNA copy number in the test sample (with respect to the reference DNA) is indicated in yellow.

and its GC base pair content. Two companies that produce such arrays are NimbleGen Systems, Inc. (www.nimblegen.com) and Agilent, Inc. (www.agilent.com/chem/goCGH). NimbleGen uses a programmable mirror array to synthesize 385,000 oligonucleotides (more recently 2.1 million targets) directly on a glass surface using photolithography. Agilent, on the other hand, uses ink-jet technology to synthesize 244,000 oligonucleotides (more recently 1.1 million targets), on a spot-by-spot basis. When using oligonucleotide-based arrays, it is important to note that their primary disadvantage is that each oligonucleotide probe tends to have lower signal-to-noise ratios than a large-insert

genomic clone target. This results in more experimental “noise” for oligonucleotide-based aCGH assays. Indeed, the typical standard deviation of log2 ratios for an oligonucleotide-based array is approximately 0.25–0.3, five times the standard deviation of log2 ratios obtained for BAC-based arrays. However, the inclusion of hundreds of thousands to millions of targets on a given oligonucleotide-based platform provides an assay with an increased resolution. The “effective resolution” of one of these platforms depends largely on the minimum number of consecutive probes needed to confidently call a CNV, which in turn is a function of how well the target sequences were chosen to accurately and consistently

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respond to copy number changes. Hence, a particular platform that has 500,000 targets and requires only three consecutive probes to make a confident CNV call actually has a higher effective resolution than an array platform with one million targets, but requiring 10 consecutive probes to make a CNV call, assuming that both platforms distribute targets evenly throughout the genome. Genotyping Arrays High-throughput array technologies for identifying SNPs can also be used to identify CNVs (see Chapter 8). In general, these arrays contain short targets (20–30 base oligonucleotides) that make them ideal for detecting single base alterations, but less ideal for identifying CNVs (especially when compared to long oligonucleotide-based arrays). For genotyping arrays, only a single labeled DNA source (“test sample”) is hybridized, and the signal intensities obtained at each hybridized target appear to have a linear relationship with respect to copy numbers of that particular DNA sequence in the test genome. For example, if a given DNA sequence has four copies in test genome “X” and only two copies in test genome “Y,” the signal intensity obtained for that DNA sequence when test genome “X” is hybridized would be twice that of when test genome “Y” is hybridized. In general, genotyping platforms can detect larger CNVs (especially when there is a higher level of copy number change) or smaller CNVs (that are detectable by numerous targets on the array platform). Affymetrix (www.affymetrix.com) SNP arrays contain targets that are ~25 bases long and the predecessor to the Affymetrix 500K array was recently used to identify CNVs in 270 HapMap individuals (Redon et al., 2006). Illumina Inc. (www.illumina.com) has designed a genotyping platform that uses 50 base oligonucleotides attached to indexed beads on a glass slide. Labeled test DNAs are hybridized to this slide, followed by primer extension and then immunofluorescence detection. Peiffer et al. (2006) recently showed that the ability of Illumina genotyping platforms have also been used to detect CNVs in both constitutional and tumor samples. Clearly, there would be significant benefits to having platforms that are capable of accurately determining both SNP and CNV genotypes, including reduced reagent costs and expenditure of minimal amounts of DNA. However, the fluorescence intensity data obtained from these genotyping platforms typically have more noise when trying to obtain copy number information than do long oligonucleotide-based arrays. Hence, both Affymetrix and Illumina are now designing “next generation” arrays that incorporate thousands of non-polymorphic probes (i.e., probes that do not target known SNPs) that fall within known CNV regions. It is thought that a CNV can be confidently detected if enough targets are strategically chosen for a given CNV region and included in the array. In other words, one probe may not be able to reliably detect a single copy loss in a CNV region but cumulative data from one hundred targets in the same CNV region may result in a consistent and confident CNV call. Whole-Genome Sequence Comparisons CNVs can also be detected via whole-genome sequence comparison analyses. The main advantage of this method for

identifying CNVs is that the acquisition of fine-scale genomic architecture of CNVs (i.e., accurate CNV sizes and breakpoint information). A major disadvantage of this approach is the limited number of individuals for which whole-genome sequence data are currently available. The first human whole-genome sequence was made available from the Human Genome Project. This reference human genome is actually a compilation of DNA sequences from over ten different individuals with ~67% of the DNA sequences originating from the RPCI-11 DNA library, derived from a single individual male. Tuzun et al. (2005) aligned end-sequence data from thousands of fosmid clones from the G248 DNA library (derived from a single North American female) and compared these with the human reference genome sequence. Taking advantage of the tight size restriction of fosmid clones, they were able to identify genomic gains/losses in the reference genome, when pairs of end-sequences aligned with intervening spacing significantly greater or less than the expected 40 kb. In this manner, 241 CNVs were identified in one of these two healthy and presumably normal individuals. More recently, sequencing technologies have advanced to a point where complete human genome sequences can be obtained more efficiently and cost-effectively than previously possible. For example, using Solexa or 454 DNA sequencing approaches, an individual’s complete genome sequence can now be obtained within months and at costs of under $250,000. Undoubt-edly, these advances will be used to comprehensively identify and define CNVs at a DNA sequence resolution, provided that a minimal fold-coverage of the person’s genome is achieved. Recent studies that have obtained complete genome sequences for different individuals have reported thousands of CNVs in a given individual, encompassing hundreds of millions of bases of DNA (Korbel et al., 2007; Levy et al., 2007). A major advantage of utilizing DNA sequence comparison strategies is the ability to identify balanced chromosomal rearrangements that cannot be detected by aCGH-based methods. For example, Tuzun et al. (2005) found evidence for 56 inversion breakpoints in their comparative analysis of the two genomes of two individuals, and Korbel et al. (2007) found 132 inversion breakpoints when comparing the genomes of two different individuals.

ASSOCIATION OF CNVS WITH DISEASE AND DISEASE SUSCEPTIBILITY Genomic imbalances, including CNVs, can contribute to human diseases in at least two ways. First, certain genomic imbalances appear to directly cause neurodevelopmental diseases, that can occur at birth or even later on in life. These genomic imbalances (referred by some as “pathogenic” CNVs) are usually de novo in nature and recent estimates have associated specific genomic imbalances with as many as 50 such genetic syndromes (http://www.sanger.ac.uk/PostGenomics/decipher/). Most of the remaining known genomic imbalances – sometimes referred

Association of CNVs to Disease and Disease Susceptibility

to as “benign” CNVs because of their identification in healthy individuals – actually have more subtle consequences on human health. Fcgr3 Variation in Glomerulonephritis Glomerulonephritis is a major contributor to human kidney failure. Fcgr3 is a gene that encodes for a receptor found on the surfaces of macrophages and that has low-affinity binding properties to immunoglobulin G. The copy number of Fcgr3 can vary in humans and among rat strains from 0 to 4 per diploid cell. Individuals with fewer copies of this gene, due to deletions of paralogous Fcgr3 genes (which appear to have a negative regulatory effect on the “full length” Fcgr3 gene/gene products), demonstrate increased macrophage activity and an autoimmune response (Aitman et al., 2006; Fanciulli et al., 2007). DEFB4 CNV in Crohn Disease Human -defensins are a family of genes predominantly secreted from leukocytes and epithelial tissues. -defensins are small proteins (15–20 residues) that function in antimicrobial defense by penetrating a microbe’s cell membrane and cause microbial death in a manner similar to that of antibiotics. In the presence

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of interleukin 1-alpha (IL-1), which is secreted by macrophages and other immunologically relevant cell types at the site of tissue inflammation, the expression levels of the -defensin gene, DEFB4, increases (O’Neil et al., 1999), to protect the tissue from further microbial invasion. Therefore, individuals with a lower copy number of this -defensin have decreased immunity against microbes and increased susceptibility to Crohn disease (Inflammatory bowel disease 1) (Fellermann et al., 2006; Naser et al., 2004). UGT2B17 Variation in Prostate Cancer Park and colleagues (2006) found that CNV of the UGT2B17 gene (Murata et al., 2003; Redon et al., 2006; Wilson et al., 2004) confers differential susceptibility to prostate cancer (Figure 9.7). Testosterone is normally processed into dihydrotestosterone (DHT) and other androgens within human cells. In the presence of functioning UGT2B17 gene product, excess DHT molecules are converted into water-soluble glucuronic acid which can be subsequently eliminated from the cell. When UGT2B17 is deleted, this process becomes less efficient and results in greater amounts of DHT within the cells. Increased intracellular DHT concentrations lead to increased interactions with androgen

Dissolution / expulsion

DHT Testosterone Glucuronic acid Androgen receptor (a) Normal copy number of UGT2B17 gene

DHT Testosterone Androgen receptor Deleted UGT2B17 (b) Two copy deletion of UGT2B17 gene

Figure 9.7 Potential mechanism of the UGT2B17 CNV in prostate cancer pathogenesis. (a) Testosterone is normally processed into DHT and other androgens. When there are two copies of UGT2B17, the gene product converts excess molecules of DHT into the water-soluble glucuronic acid and is subsequently eliminated from the cell. (b) When there is a homozygous deletion of UGT2B17, the DHT-sequestration pathway no longer exists, increasing endogenous interaction with androgen receptors (AR) and leading to elevated cell proliferation, which in some cases can result in the development of prostate cancer (Park et al., 2006).

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receptors that cause elevated cell proliferation. When this occurs in the prostate cells, it can result in prostatic tumorigenesis. Interestingly, while UGT2B17 null deletions significantly increases prostate cancer susceptibility as a whole, stratified analysis revealed that this correlation to prostate cancer susceptibility is only significant in the Caucasian populations studied, but not among African-Americans. The CNV of Complement Component C4 in Systemic Lupus Erythematosus (SLE) Although a link between the complement component C4 (and its isotypes, C4A and C4B) and SLE has been previously reported (Hauptmann et al., 1974a,b), a recent study suggests that the gene’s variable copy number actually serves as a significant risk factor for the disease (Yang et al., 2007). The complement system is comprised of over 20 proteins/protein fragments that normally circulate in the blood but, when activated, cause a biochemical cascade that clears pathogens from the human body, often by forming new transmembrane channels in the pathogen and causing osmotic lysis of the target cell. The median copy number of complement component C4 is 4 but can range from 0 to 5 copies among humans (Yang et al., 2007). Low copy numbers of this gene are correlated with increased risk for SLE (Fanciulli et al., 2007;Yang et al., 2007). CCL3L1 CNVs in HIV/AIDS A number of studies on HIV/AIDS have focused on the gene copy number and protein levels of CCL3L1 and CCR5 (Gonzalez et al., 2001, 2005) in diseased and healthy individuals. CCR5 is a major co-receptor for the HIV-1 virus as well as the CCL3L1 chemokine. It was recently found that the combination of low CCL3L1 gene copy number, which is populationspecific, and high protein levels of CCR5 showed the greatest increase in susceptibility to AIDS (Gonzalez et al., 2005). It is speculated that increased CCR5 protein levels lead to greater number of CD4CCR5 receptor complexes on the surfaces of T cells and decreased copy number of CCL3L1 leads to decreased expression (and protein levels) of this chemokine, leaving more CD4CCR5 receptor complexes available for binding with the HIV-1 virus.

IMPLICATIONS OF CNVS Disease Association Studies SNPs have become powerful markers for identifying important disease loci in genetic association studies. However, recent analysis of the complete DNA sequence from a single human individual has shown that CNVs (and other structural genomic variants including small balanced chromosomal rearrangements) account for some 22% of all genetic variation events in the individual and 74% of the total DNA sequence variation, when compared to the human reference genome (Levy et al., 2007). Although it is premature to predict the relative contribution of

CNVs to the etiology of common, complex diseases (compared to SNPs), it is clear that CNVs represent a substantial component of human genetic variation that should not be ignored in future disease association studies. Indeed, the presence of CNVs in the human genome has actually reduced the power of certain SNPs. For example, SNPs that lie within CNV regions are difficult to genotype, result in Hardy–Weinberg equilibrium distortions, and are often erroneously calculated as having reduced linkage disequilibrium to nearby causative genomic regions. Moreover, CNVs are far more complex in nature than SNPs and demand appreciation of several factors in order to use this form of genetic variation appropriately in disease association studies. First, the absolute copy number (rather than relative copy number obtained from aCGH-based experiments) needs to be established for each CNV. Second, the exact genomic location of duplicated CNVs should also be considered. For example, the presence of a third copy of a gene may have dramatically different phenotypic effects depending on where the third copy occurs in the genome. Finally, the precise boundaries (at the DNA sequence level) of each CNV should also be known, as should the specific allelic state of a CNV (e.g., two copies of a gene could be distributed as one copy per chromosome or both copies on a single chromosome). Without this level of information, the power of CNVs in genetic association studies diminishes. Indeed, of the several thousand CNVs that have been identified to date, only a small percent have actually been annotated and genotyped to this precision. Some efforts have been made to determine if CNVs are in linkage disequilibrium with nearby SNPs. If so, this would allow for specific CNV alleles to be assayed indirectly with a subset of well-characterized SNPs (i.e., “tagging” SNPs) (see Chapter 2). Initial observations suggested that a subset of CNVs did appear to be ancestral in nature and therefore taggable by specific SNPs (McCarroll et al., 2006). Larger CNVs, especially those that are in segmental, duplication-rich areas of the genome, appear to be less taggable by known SNPs (Locke et al., 2006). Part of the reason for this may be that the mutation rate for such CNVs is higher than the mutation rates for nearby SNPs (recall that NAHR mutations rates are estimated to be substantially higher than that of SNPs). Overall, this suggests that, for association studies, a substantial number of CNVs may need to be genotyped directly. Pharmacogenetics Since many CNV genes are involved in metabolism and drug detoxification, it has been speculated that CNVs may also make significant contributions to future pharmacogenomic studies (Ouahchi et al., 2006). For example, the CYP2D6 genes code for enzymes that are involved in the metabolism of more than 30 medications that include antiarrhythmics, antihypertensives, beta-blockers, etc. CYP2D6 CNVs have been identified and shown to result in gene products with differential metabolism efficiency (Rotger et al., 2007). Similarly, CNVs of genes involved in metabolism may explain some cases of interindividual variation in drug toxicity. McCarroll et al. (2005) and Conrad et al. (2006) found more than 120 CNV genes that were

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14p13 14p12

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RP11-557015

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Figure 9.8 Example of a de novo genomic imbalance that is likely causative of the clinical phenotype of a patient seen in the clinical genetic clinic. (a) A chromosome 14 profile from a 1 Mb resolution aCGH assay that has been run in dye swap. Deviation of the red line to the right of the expected 1:1 ratio and the blue line to the left of the expected 1:1 ratio is indicative of a genomic loss in the patient DNA. Here, nine BAC clones (each clone represented by a dot on the array profile) are within the deleted region, suggesting a 9–10 Mb sized deletion. The position of BAC clones used in FISH confirmation studies are indicated in red and green. (b) A metaphase spread from the patient after a two-color FISH confirmation assay using a clone within the deletion interval (RP11-557O15) and a control clone outside of the deletion interval (RP11-98N22). The absence of the green signal on one of the chromosome 14s (indicated with a white arrowhead) confirms the aCGH findings. Parental FISH studies were normal (not shown), indicating that this chromosomal aberration was a de novo event.

homozygously deleted. Individuals harboring such homozygous deletions presumably have low toxicity tolerance to medications that depend heavily on the homozygously deleted CNV gene product for proper metabolism. Rapid and accurate identification of these individuals should be made a priority in pharmacogenomic research (see also Chapter 27). Clinical Cytogenetic Diagnostics Array CGH-based techniques are now being more widely used in the clinical cytogenetic diagnostic arena to identify smaller genomic imbalances that may be associated with neurodevelopmental disorders. Indeed, it has been estimated that aCGHbased assays are now detecting apparently pathogenic genomic imbalances in as much as 20% of cases that have had normal results from chromosome-banded karyotyping tests (Figure 9.8). However, the recent recognition of widespread CNVs among the genomes of healthy individuals has made interpretation of aCGH-based assays more difficult [for a recent review on this, see Lee et al. (2007)]. Specifically, all CNVs detected in an

aCGH clinical test need to be assessed for their potential to contribute to the clinical presentation. One of the primary means for assessing pathogenicity of a CNV is by determining whether or not the CNV is de novo. Indeed, 2–3% of children are born with a major birth defect that is sporadic in nature, and the genetic alteration causing these birth defects is similarly assumed to be sporadic in nature. This strategy for determining the pathogenicity of a genomic imbalance in “constitutional” genetic testing is not new and has been applied by cytogeneticists for decades (albeit at the chromosomal banding analysis level). Therefore, for genomic imbalances detected by aCGH assays, it must be determined if the same genomic imbalance is observed in either of the biological parents, and close attention must be paid to the absolute copy number of the CNV (i.e., not just the relative copy number that is obtained from aCGH experiments), breakpoint information when available and genomic distribution of the CNV DNA segment. Similarly, determining whether a given CNV is present in affected (or unaffected) relatives can also be highly informative.

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Another factor to consider is whether the CNV has been found in other unrelated but affected individuals with similar clinical phenotypes (using CNV databases such as DECIPHER) or among healthy individuals (using CNV databases such as the Database of Genomic Variants). This information is only as useful as the accuracy of the CNV entries in these databases (i.e., minimal false positives and precise definition of the architecture of each CNV entry). Moreover, the gene content within a given CNV will also dictate the clinical interpretation of that CNV. Finally, the overall size of the CNV can be a minor indicator of its pathogenicity, with the caveat that “benign” CNVs, as large as 10 Mb in size in gene-poor regions of the genome, have been documented (Hansson et al., 2007). Accurate clinical interpretation of CNVs will improve as our knowledge of the structure and function of the human genome increases. Clearly, CNVs cannot be considered as isolated genomic aberrations and eventually should be interpreted in the context of all genomic variants, including other CNVs as well as SNPs. For example, the same deletion CNV in a healthy individual may uncover a recessive SNP mutation on the other allele in an unrelated individual – leading to a drastically different phenotype. Since we are at a stage in clinical cytogenetic diagnostics where the technology and the data being obtained are beyond our understanding of human genome function, caution continues to be advised in the application of genome-wide aCGH assays in prenatal diagnostics. Instead, targeted aCGH platforms that primarily utilize probes with well-defined associations with genetic disorders have been used for prenatal diagnoses.

CONCLUSIONS It was previously touted that the genomes of healthy individuals were 99.9% similar (Lander et al., 2001). Over the last 3 years, however, it has become obvious that there is more genetic variation than had been appreciated and that a substantial portion is in the form of structural genomic variation, including genomic imbalances of DNA segments greater than 1 kb in length (CNVs). As a result, CNVs are now requiring special consideration in many aspects of genomic medicine, including disease association studies, pharmacogenomics and clinical cytogenetic diagnostics. While our understanding of the locations, structures and frequencies of CNVs remains rudimentary, it is anticipated that there will be rapid growth in CNV knowledge over the next few years. This growth will be essential for accurately integrating this type of genetic variation with SNPs (and other forms of genetic alterations) for each human being. New findings will lead researchers and clinicians toward a greater appreciation of the genetic factors that make each person so unique, which is essential to the practice of genomic and personalized medicine.

ACKNOWLEDGEMENTS The authors would like to express their gratitude to George Perry for providing the image for Figure 9.2 and Marc Listewnik for his assistance in generating Figure 9.8.

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Gonzalez, E., Dhanda, R., Bamshad, M., Mummidi, S., Geevarghese, R., Catano, G., Anderson, S.A. et al. (2001). Global survey of genetic variation in CCR5, RANTES, and MIP-1alpha: Impact on the epidemiology of the HIV-1 pandemic. Proc Natl Acad Sci USA, 5199–5204. Gonzalez, E., Kulkarni, H., Bolivar, H., Mangano, A., Sanchez, R., Catano, G. et al. (2005).The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 307, 1434–1440. Hansson, K., Szuhai, K., Knijnenburg, J., van Haeringen, A. and de Pater, J. (2007). Interstitial deletion of 6q without phenotypic effect. Am J Med Genet A 143, 1354–1357. Hauptmann, G., Grosshans, E. and Heid, E. (1974a). Lupus erythematosus syndrome and complete deficiency of the fourth component of complement. Boll Ist Sieroter Milan 53(Suppl), 228. Hauptmann, G., Grosshans, E., Heid, E., Mayer, S. and Basset, A. (1974b). Acute lupus erythematosus with total absence of the C4 fraction of complement. Nouv Presse Med 3, 881–882. Iafrate, A.J., Feuk, L., Rivera, M.N., Listewnik, M.L., Donahoe, P.K., Qi,Y. et al. (2004). Detection of large-scale variation in the human genome. Nat Genet 36, 949–951. Korbel, J.O., Urban, A.E., Affourtit, J.P., Godwin, B., Grubert, F., Simons, J.F. et al. (2007). Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426.

Recommended Resources

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J. et al. (2001). Initial sequencing and analysis of the human genome. Nature 409, 860–921. Lee, C., Iafrate, A.J. and Brothman, A.R. (2007). Copy number variations and clinical cytogenetic diagnosis of constitutional disorders. Nat Genet 39, S48–S54. Lejeune, J., Gautier, M. and Turpin, R. (1959). Etude des chromosomes somatiques de neuf enfants mongoliens. C R Hebd Seances Acad Sci 248, 1721–1722. Levy, S., Sutton, G., Ng, P.C., Feuk, L., Halpern, A.L., Walenz, B.P. et al. (2007). The diploid genome sequence of an individual human. PLoS Biol 5, e254. Locke, D.P., Sharp, A.J., McCarroll, S.A., McGrath, S.D., Newman, T.L., Cheng, Z. et al. (2006). Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome. Am J Hum Genet 79, 275–290. McCarroll, S.A., Hadnott, T.N., Perry, G.H., Sabeti, P.C., Zody, M.C., Barrett, J.C. et al. (2006). Common deletion polymorphisms in the human genome. Nat Genet 38, 86–92. Murata, M., Warren, E.H. and Riddell, S.R. (2003). A human minor histocompatibility antigen resulting from differential expression due to a gene deletion. J Exp Med 197, 1279–1289. Naser, S.A., Ghobrial, G., Romero, C. and Valentine, J.F. (2004). Culture of Mycobacterium avium subspecies paratuberculosis from the blood of patients with Crohn’s disease. Lancet 364, 1039–1044. Nguyen, D.Q., Webber, C. and Ponting, C. (2006). Bias of selection on human copy-number variants. PLoS Genet 2, e20. O’Neil, D.A., Porter, E.M., Elewaut, D., Anderson, G.M., Eckmann, L., Ganz, T. and Kagnoff , M.F. (1999). Expression and regulation of the human beta-defensins hBD-1 and hBD-2 in intestinal epithelium. J Immunol 163, 6718–6724. Ouahchi, K., Lindeman, N. and Lee, C. (2006). Copy number variants and pharmacogenomics. Pharmacogenomics 7, 25–29. Park, J., Chen, L., Ratnashinge, L., Sellers, T.A., Tanner, J.P., Lee, J.H. et al. (2006). Deletion polymorphism of UDP-glucuronosyltransferase 2B17 and risk of prostate cancer in African American and Caucasian men. Cancer Epidemiol Biomarkers Prev 15, 1473–1478. Perry, G.H., Dominy, N.J., Claw, K.G., Lee, A.S., Fiegler, H., Redon, R., Werner, J.,Villanea, F.A., Mountain, J.L., Misra, R., Carter, N.P., Lee, C. and Stone, A.C. (2007). Diet and the evolution of human amylase gene copy number variation. Nat Genet 39, 1256–1260. Peiffer, D.A., Le, J.M., Steemers, F.J., Chang, W., Jenniges, T. and Garcia, F.K. (2006). High-resolution genomic profiling of

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chromosomal aberrations using Infinium whole-genome genotyping. Genome Res 16, 1136–1148. Pinkel, D., Segraves, R., Sudar, D., Clark, S., Poole, I., Kowbel, D. et al. (1998). High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 20, 207–211. Redon, R., Ishikawa, S., Fitch, K.R., Feuk, L., Perry, G.H., Andrews,T.D. et al. (2006). Global variation in copy number in the human genome. Nature 444, 444–454. Rotger, M., Saumoy, M., Zhang, K., Flepp, M., Sahli, R., Decosterd, L. et al. (2007). The Swiss HIV Cohort Study. Partial deletion of CYP2B6 owing to unequal crossover with CYP2B7. Pharmacogenet Genomics 17, 885–890. Scherer, S.W., Lee, C., Birney, E., Altshuler, D.M., Eichler, E.E., Carter, N.P. et al. (2007). Challenges and standards in integrating surveys of structural variation. Nat Genet 39, S7–S15. Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., Lundin, P., Maner, S. et al. (2004). Large-scale copy number polymorphism in the human genome. Science 305, 525–528. Shaffer, L.G. and Lupski, J.R. (2000). Molecular mechanisms for constitutional chromosomal rearrangements in humans. Annu Rev Genet 34, 297–329. Solinas-Toldo, S., Lampel, S., Stilgenbauer, S., Nickolenko, J., Benner, A., Dohner, H. et al. (1997). Matrix-based comparative genomic hybridization: Biochips to screen for genomic imbalances. Genes Chromosomes Cancer 20, 399–407. Stranger, B.E., Forrest, M.S., Dunning, M., Ingle, C.E., Beazley, C., Thorne, N. et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853. Tuzun, E., Sharp, A.J., Bailey, J.A., Kaul, R., Morrison, V.A., Pertz, L.M. et al. (2005). Fine-scale structural variation of the human genome. Nat Genet 37, 727–732. Wilson, W., III, Pardo-Manuel de Villena, F., Lyn-Cook, B.D., Chatterjee, P.K., Bell, T.A., Detwiler, D.A. et al. (2004). Characterization of a common deletion polymorphism of the UGT2B17 gene linked to UGT2B15. Genomics 84, 707–714. Yang, Y., Chung, E.K., Wu, Y.L., Savelli, S.L., Nagaraja, H.N., Zhou, B. et al. (2007). Gene copy-number variation and associated polymorphisms of complement component c4 in human systemic lupus erythematosus (SLE): Low copy number is a risk factor for and high copy number is a protective factor against SLE susceptibility in European Americans. Am J Hum Genet 80, 1037–1054.

RECOMMENDED RESOURCES Affymetrix, Inc. (http://www.affymetrix.com) Agilent, Inc. (www.agilent.com/chem/goCGH) Chromosome Abnormality Database (http://www.ukcad.org. uk/cocoon/ukcad/) Database of Genomic Variants (http://projects.tcag.ca/variation) DECIPHER (http://www.sanger.ac.uk/PostGenomic/decipher/) Ensembl Genome Browser (http://www.ensembl.org/index.html) European Cytogenetics Association Register of Unbalanced Chromosome Aberrations (http://www.ecaruca.net)

Human Paralogy Database (http://humanparalogy.gs.washington.edu/ structuralvariation) Human Segmental Duplication Database (http://projects.tcag.ca/ humandup/) Illumina Inc. (www.illumina.com) NimbleGen Systems, Inc. (www.nimblegen.com) UCSC Genome Browser (http://www.genome.ucsc.edu/)

CHAPTER

10 Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine Anthony Antonellis and Eric D. Green

INTRODUCTION In 2001, the Human Genome Project completed a draft sequence of the human genome (International Human Genome Sequencing Consortium, 2001). Since then, the human genome sequence has been refined (International Human Genome Sequencing Consortium, 2004) and attention has turned to generating the genome sequences of a number of biomedically relevant vertebrate species, including mouse (Mouse Genome Sequencing Consortium, 2002), rat (Rat Genome Sequencing Project Consortium, 2004), dog (Lindblad-Toh et al., 2005), chicken (International Chicken Genome Sequencing Consortium, 2004), chimpanzee (Chimpanzee Sequencing and Analysis Consortium, 2005), and others (www.intlgenome.org). A major rationale for elucidating these additional sequences is that their comparison with the human genome sequence would reveal important details about the functional landscape of all human chromosomes, which in turn would empower studies aiming to understand the genetic basis of human disease (Collins et al., 2003). In reality, our current understanding of the functional sequence elements residing within the 3 billion base pairs that constitute the human genome is relatively nascent. Needed are approaches that can identify such functional elements – including both sequences that encode protein (coding sequences) and those that function in other ways (non-coding Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 120

functional sequences). Importantly these functional sequences may harbor disease-associated mutations. Comparative sequence analysis (CSA) involves crossanalyzing related nucleotide or protein sequences, so as to identify similarities and differences among them. Here, we limit our discussion to one type of CSA – that involving the comparison of genomic sequences from different species. Such interspecies CSA can be used to identify genomic sequences that are highly conserved among species. When applied to the analysis of sequences from distantly related species, conserved sequences can be identified that reflect genomic regions that have not changed substantially over millions of years of evolutionary time; such sequences represent strong candidates for being functionally important. For example, protein-coding sequences are typically highly conserved among vertebrates, even between organisms as distantly related as human and fish (Aparicio et al., 2002). Similarly, many non-coding functional sequences, such as those that regulate the expression of genes, also tend to be highly conserved (e.g., Loots et al., 2000; Prabhakar et al., 2006a). In addition to identifying similarities, CSA can be used to identify differences between species. Indeed, comparing genomic sequences among closely related species can reveal sequence changes that are specific to certain lineages (e.g., human-specific sequences). Importantly, such analyses can provide insight into the evolution

Performing Comparative Sequence Analysis: Resources and Methods

of human-specific traits (e.g., Pollard et al., 2006; Prabhakar et al., 2006b). Because of its ability to provide clues about functional sequences in the genome, CSA can facilitate the identification and characterization of genes associated with human disease. Specifically, CSA can benefit such studies in several ways. First, CSA can be used to identify and prioritize the genomic regions that are most likely to harbor disease-causing mutations. For example, it is estimated that roughly 5% of the human genome sequence is functionally important (Lindblad-Toh et al., 2005; Mouse Genome Sequencing Consortium, 2002; Rat Genome Sequencing Project Consortium, 2004), but the precise nucleotides constituting that 5% are, at present, not known; CSA can be used to help hone in on that small fraction of functional sequence, prioritizing it for more detailed study in patients with genetic disease. Second, CSA can be used to characterize disease-associated sequence variants once they are identified, helping to classify them as disease-causing mutations versus innocent polymorphisms. For example, demonstrating that a variant occurs at a highly conserved nucleotide (or amino-acid residue) provides support for its relevance in the disease. Finally, CSA can aid in the design of functional analyses aiming to characterize a gene of interest in another species. For example, elucidating the function of a newly discovered disease gene often requires studying the functional consequences of mutating that gene in a model organism, such as yeast, worm, fruit fly, zebrafish, or mouse. Such studies allow rapid, in vivo analyses of gene function and mutational consequences that are not possible in patient populations. In this setting, CSA can help to identify the presence or absence of the corresponding gene (i.e., the orthologous gene) in another species as well as the conservation of affected nucleotides or amino-acid residues; if present, that gene can then be studied in greater detail using that species as a model system. These applications of CSA have implications for the practice of genomic medicine. Specifically, CSA can directly aid the identification and characterization of sequence variants, both common and rare, that are responsible for disease onset (or severity) and differential response to therapeutics. In the long run, such information will be valuable for providing patients with accurate diagnoses and prognoses, and for directing a course for proper care. In this chapter, we describe how to access and utilize resources for performing CSA, demonstrate how such studies can help identify and characterize disease-associated mutations, and discuss the role of this approach in genomic medicine.

PERFORMING COMPARATIVE SEQUENCE ANALYSIS: RESOURCES AND METHODS Step 1: Species Selection and Sequence Acquisition CSA generally involves a series of sequential steps, with the typical ones illustrated in Figure 10.1. Different resources and methodologies are utilized in each of these steps. The first step

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involves acquiring genomic sequences from species of interest (Figure 10.1, Step 1), with the choice of species often dictated by the biological question(s) being asked. For example, the study of the basic genomic elements that are relevant for vertebrate development requires the acquisition of sequences from distantly related vertebrate species (Plessy et al., 2005). In contrast, examination of mammalian- or primate-specific genomic features requires sequences from discrete lineages (Prabhakar et al., 2006a). Often, the initial questions are quite broad and not associated with a priori expectations, in which case it is prudent to collect all available (i.e., already generated) genomic sequence data for subsequent CSA. Once the species have been selected, the relevant sequences for a genomic region of interest must be collected. Genomic sequences can be identified and obtained from several public databases and Web sites. These include the National Center for Biotechnology Information (NCBI; this is the home of GenBank), the University of California at Santa Cruz (UCSC) Genome Browser (Kent et al., 2002), and Ensembl (Stalker et al., 2004) (Table 10.1). Each of these sites has different routines for identifying sequences of interest. For example, genomic sequences can be obtained from the UCSC Genome Browser by selecting a species and performing a query using a gene name, marker, or chromosome band; this site is particularly useful because its graphical interface is tailored for CSA. In a similar fashion, protein sequences can be collected (e.g., for determining the conservation of an amino-acid residue implicated in a human genetic disease). In this case, a text query using the name or symbol of the human protein may be performed using either the HomoloGene or protein databases at NCBI (Table 10.1); this will yield a list of related sequences from various species that can be easily downloaded. A critical aspect of sequence acquisition is ensuring that appropriate sequences are actually obtained. Genomic regions that originate from a common ancestor are considered homologous, and these subdivide based on their origins. Homologous regions generated by genome-duplication events in a given species are considered paralogs. Homologous regions generated by speciation are considered orthologs. For the applications described here, CSA involves the study of orthologous sequences – that is, those from corresponding genomic regions among a set of species. There are two general approaches to ensure that orthologous sequences are obtained. First, alignment tools such as BLAST (Basic Local Alignment Search Tool; Altschul et al., 1990) and BLAT (BLAST-Like Alignment Tool; Kent, 2002) (Table 10.1) can be used to search nucleotide databases, requiring that a “reciprocal best hit” is obtained for a pair of sequences. For example, a human sequence is used to query the dog genome sequence; the most closely related dog sequence is then used to query the human genome sequence. If the most closely related sequence identified in this second query is the same as the starting human sequence, then it can be assumed that those human and dog sequences are orthologous. Second, the content and order of genes within related sequences from two species can be compared, with strong similarity providing support that the sequences are orthologous.

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Step 1. Acquire Sequence Non-Primate Mammals Non-Mammals Primates

Pig

Human

Cat

Rat

Dog

Mouse

Chicken

Chimp Fugu Cow

Zebrafish

Step 2. Align Sequences ~30 kb

Human Conservation Chimp Mouse Rat Dog Cow Chicken Zebrafish Fugu

Step 3. Identify and Localize Conserved Regions 1

2

3

4

Exons Conserved Regions Coding

Step 4a. Computational Analysis Predict Transcription Factor-Binding Sites

Non-Coding

Step 4b. Experimental Analysis In Vitro

In Vivo

Expression

LacZ Expression

Conserved Predicted Binding Sites Search for Common Motifs

Patient Screening Mutation in Conserved Region

T G C Y C A C Common Motifs

Figure 10.1

Figure Caption on Page 123.

Performing Comparative Sequence Analysis: Resources and Methods

Repetitive elements are a broad class of DNA sequences that occur more than once in a genome. Two types of these sequences that form a substantial fraction of most vertebrate genomes are tandemly repeated repetitive elements and interspersed repetitive

TABLE 10.1 Bioinformatic resources for comparative sequence analysis Sequence Databases and Sequence-Analysis Tools National Center for Biotechnology Information

www.ncbi.nlm.gov

Ensembl Genome Browser

www.ensembl.org

University of California at Santa Cruz (UCSC) Genome Browser

genome.ucsc.edu

RepeatMasker

www.repeatmasker.org

Sequence-Alignment Tools BLAST

www.ncbi.nlm.nih. gov/BLAST

BLAT

genome.ucsc. edu/cgi-bin/hgBlat

ClustalW

www.ebi.ac.uk/clustalw

PipMaker/MultiPipMaker

pipmaker.bx.psu. edu/pipmaker

VISTA

genome.lbl.gov/vista

Programs for Conservation and Motif Detection WebMCS

zoo.nhgri.nih.gov/mcs

ExactPlus

research.nhgri.nih. gov/projects/exactplus

TRANSFAC

www.gene-regulation. com/pub/databases.html

MEME

meme.sdsc.edu/meme

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elements (see Chapter 1). When analyzing and comparing genomic sequences, it is important to consider whether or not to include repetitive elements. Including repetitive sequences can lead to the spurious alignment of non-orthologous sequences; excluding such sequences can result in completely missing functional elements residing therein, and, indeed, repetitive elements can harbor functional sequences (Kamal et al., 2006; Nishihara et al., 2006). For most initial CSA studies, it is reasonable to “mask” (or remove) repetitive sequences, and this can be accomplished in one of two ways. First, masking repetitive elements can be performed while downloading the sequence using tools available at the UCSC Genome Browser and Ensembl (Table 10.1). Alternatively, this step can be accomplished by subjecting already obtained sequence to processing by the program RepeatMasker (Table 10.1). Step 2: Generating Multi-Sequence Alignments The next step in CSA is to generate multi-sequence alignments (Figure 10.1, Step 2), a process in which the orthologous genomic sequences from different species are mapped (or organized) relative to one another. Various computational tools for aligning sequences have been developed and refined in recent years (Table 10.1). There are two general types of alignment methods: those that produce local alignments and those that produce global alignments. Local-alignment methods [e.g., PipMaker and MultiPipMaker (Schwartz et al., 2000; Schwartz et al., 2003)] generate alignments based on similarity of subregions within the sequence, placing more importance on local sequence identity and less on the long-range pattern of alignment across the entire sequence. As such, these methods can produce alignments of orthologous regions that are inverted or otherwise shuffled between species. In contrast, global-alignment methods [e.g., ClustalW (Thompson et al., 1994) and VISTA (Frazer et al., 2004)] generate alignments based on the similarity over the entire length of the sequence, placing more importance on maintaining the relative long-range organization of the genomic region among species and less on local sequence identity. It is generally accepted that local alignments are more appropriate for analyzing genomic regions where the long-range organization may not be well-conserved among species, while

Figure 10.1 Utilization of comparative sequence data. A generic set of steps for using comparative sequence data in human genetic studies are represented. In Step 1, sequences of a genomic region of interest are acquired from relevant (often highly diverged) species. Next (Step 2), these sequences are aligned relative to one another, revealing regions that are identical between a given species and the human reference sequence (indicated by vertical lines within the “track” for each species at the UCSC Genome Browser). Analysis of these sequences allows the overall level of conservation to be established across the region. Levels of conservation above a defined threshold can be used to designate “conserved regions” (Step 3), and these typically reflect both coding and non-coding sequences. Note the hypothetical model for a four-exon gene (in Step 3 and at the top of Step 2). In Step 4, various analyses can be performed to characterize the conserved regions in greater detail. Computational analyses (Step 4a) can include searching for predicted sites of transcription factor binding (based on the sequences of known transcription factor-binding sites) within the conserved regions as well as searching for common motifs (arrow in the upper panel indicates the transcription start site of a gene of interest). Experimental analyses (Step 4b) can include in vitro assays to assess if any or all of a conserved region influences expression of a reporter gene, in vivo testing to see if a conserved region can drive the expression of a marker gene (e.g., LacZ), and screening patients of interest for mutations in a conserved region.

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global alignments are more appropriate for analyzing genomic regions where the long-range organization is likely to be wellconserved (Frazer et al., 2003). For aligning nucleotide sequences, PipMaker generates alignments between two orthologous sequences, while MultiPipMaker and VISTA generate alignments of up to 20 and 100 sequences, respectively (Table 10.1). For example, to identify sequences conserved in human, mouse, and rat across a genomic region of interest, an investigator would typically submit the human sequence (referred to as the “reference sequence”) to the MultiPipMaker web server followed by the orthologous sequences from mouse and rat. The program then produces a text-based multi-sequence alignment as well as a graphical representation of conservation levels across the region. Importantly, all of the output is indexed relative to the reference sequence, thereby organizing and facilitating downstream analyses. As an aside, multi-sequence protein alignments are typically generated using CLUSTALW (Table 10.1). Briefly, CLUSTALW analyzes a set of text-formatted protein sequences and produces an alignment of the amino-acid sequences. Individual regions of interest can then be examined for conservation (e.g., Figure 10.2A, left panel). Step 3: Identifying Conserved Sequences In contrast to examining relatively simple protein-sequence alignments, analyzing a multi-sequence alignment of a genomic region of interest typically involves scanning across many kilobase pairs of sequence with highly variable levels of conservation. As a result, identifying the most highly conserved regions cannot be accomplished by manual inspection of the alignment. Rather, additional computational analysis is required; indeed, some of the available alignment programs perform this step as well. For example, PipMaker and VISTA will identify the subset of regions with a defined level of conservation between two sequences in a pair-wise fashion (e.g., 70% identity over at least 100 bp). Other stand-alone programs [e.g., WebMCS (Margulies et al., 2003) and ExactPlus (Antonellis et al., 2006); Table 10.1] analyze multi-sequence alignments (e.g., generated by tools such as MultiPipMaker), and identify regions that are highly conserved across sequences from multiple species. It is important to then localize such conserved regions relative to other annotated genomic landmarks (e.g., genes); for example, classifying conserved regions into coding versus non-coding is particularly important (see Figure 10.1, Step 3). The UCSC Genome Browser and Ensembl (Table 10.1) both allow the results of such conservation analyses to be uploaded, thereby facilitating the direct assimilation of user-produced data with all other available annotations. Above, we have emphasized performing CSA in a customized, step-wise fashion. An alternative is to use the results of genome-wide CSA studies that are available on most of the genome browsers. For example, the UCSC Genome Browser provides various graphical representations of sequence conservation across the entire human genome (e.g., Figure 10.1, Step 2), often computed using multiple different methods; further,

such analyses are frequently updated and refined. The data about conserved regions of interest can be readily downloaded from such sites and studied in greater detail. One caution is that while such pre-computed analyses can be rapidly and easily accessed, the genome-wide nature of these CSA studies is associated with inherent limitations, and thus more focused and customized studies of a region of interest typically yield more informative results. Step 4: Computational and Experimental Analyses of Conserved Sequences Once sequence conservation is assessed for a genomic region of interest, computational and experimental analyses are typically performed to further investigate the role of specific sequences. In the case of conserved coding regions, the amino-acid sequence of the encoded protein can be deduced in various species. Mutation screening might reveal a human disease-associated sequence variant that changes an amino acid, and such a change can then be analyzed within the context of knowing the conservation level of that particular residue (e.g., Figure 10.2A, left panel). In addition, computational predictions can be made about mutation-associated structural changes in the encoded protein, and assays can be developed to test the function of the mutant protein. The analysis of conserved non-coding sequences is less straightforward (Figure 10.1, Steps 4a and 4b). This is because we know very little about the “language” of functional noncoding sequences, especially compared to our understanding of how coding sequences encode information about proteins. As a result, the biological relevance of newly encountered disease-associated variants in non-coding sequence is almost never immediately obvious (e.g., compared to a variant that clearly changes an amino-acid residue in the case of coding sequence). As a first step, conserved non-coding sequences can be analyzed for the presence of transcription factor-binding sites using the TRANSFAC database (Matys et al., 2006) and/or commonly occurring sequence motifs using programs such as MEME (Figure 10.1, Step 4a and Table 10.1) (Bailey and Elkan, 1994). Such analyses can help identify sequences involved in transcriptional regulation. In parallel to computational studies, conserved non-coding sequences can also be tested for regulatory activity using relevant in vitro or in vivo assays. Such studies can provide evidence for function that is directly associated with sequence conservation, thereby helping to prioritize further analyses. Finally, conserved regions can be screened for disease-associated mutations in relevant patients; it is then important to assess the extent of conservation at the position of any disease-associated variant that is encountered (e.g., Figure 10.3B, middle panel).

COMPARATIVE SEQUENCE ANALYSIS AND HUMAN GENETIC DISEASE CSA can play a valuable role in establishing the relevance of disease-associated sequence variants. Specifically, human genetic studies often lead to the identification of multiple nucleotide

Comparative Sequence Analysis and Human Genetic Disease

A. CFTR Protein-Coding Mutations Conservation of CFTR G551 Human Chimp Cow Pig Dog Mouse Rat Chicken Frog Salmon Dogfish

TLSGGQRAR TLSGGQRAR TLSGGQRAR TLSGGQRAR TLSGGQRAR TLSGGQRAR TLSGGQRAR ILSGGQRAR TLSGGQRAR TLSGGQRAR TLSGGQRAR

Function of CFTR G551

Cl TMD

*

NBD

* G551

B. CFTR Splice-Site Mutations 6211 G to T 6212 T to G 6213 A to G

6213 A to G

CFTR Intron 4

6211 G to T

AAG GTA

AAGAAG GTAATACTT... AAGAAG GTAATACTT... AAGAAG GTAATACTT... AAAAAG GTAATACTT... AAAAAG GTAATACTT... AAGAAG GTAATGCTT... AAGAAG GTAATACTT... AAGAAG GTAATACTT... AAGAAG GTAATACTT... AAGAAG GTAACTGTT... AAGAAG GTAAACAGA... AAGAAG GTAAGGGGA...

Wt CFTR mRNA

CFTR Exon 4 Human Chimp Cow Pig Sheep Dog Hedgehog Fruit Bat Mouse Chicken Zebrafish Tetraodon

Figure 10.2 Conserved sequences are important for protein function and mRNA processing. Mutations in coding and non-coding regions of the CFTR gene can cause cystic fibrosis. (A) The position of a coding mutation (G551D) is indicated in a multi-species protein-sequence alignment (left panel, arrow). The importance of this amino-acid residue in CFTR function is underscored by its conservation in species ranging from human to fish. The G551D change is shown relative to a schematic model of the CFTR protein within a plasma membrane (right panel). The G551 residue resides within a nucleotide-binding domain (NBD) that is critical for activation of the chloride channel, and the G551D mutation severely impairs this process [(Anderson and Welsh, 1992; Logan et al., 1994); note that the two green areas represent transmembrane domains (TMD)]. (B) The positions of three non-coding CFTR mutations are shown, with at least two (621+1 G to T and 621+3 A to G) resulting in improper splicing and deletion of exon 4 in the mature CFTR transcript (Hull et al., 1993; Tzetis et al., 2001). Inset: illustration of improper CFTR splicing caused by two mutations, as detected by RT-PCR. The importance of these nucleotides in CFTR mRNA processing is again underscored by the striking conservation of this noncoding sequence in species ranging from human to fish (indicated in red).

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differences that correlate with the inheritance of a disease. Sorting out which of these, if any, is responsible for the disease can be challenging. CSA can establish the extent of evolutionary conservation at the genomic position of each disease-associated variant, with evidence of high conservation providing support that the variant is functionally relevant to the disease. Also note that this process can occur in a slightly reversed order, in which CSA is first used to identify conserved regions of interest, and these in turn are examined closely in patients to search for diseaseassociated sequence variants (see below). In this section, we provide illustrative examples where CSA has been used to implicate specific mutations in human genetic diseases. Conserved Sequences Important for Protein Function CSA is often used as a validation step to help implicate a protein-coding sequence variant with a genetic disease. Specifically, the presence of a disease-associated amino-acid variant at a residue that is highly conserved among widely diverged species is supportive evidence that the mutation plays a role in the disease. Cystic fibrosis (CF) is an autosomal recessive disease characterized by the production of abnormally viscous mucus, which frequently obstructs the pulmonary airways and leads to chronic, life-threatening respiratory infections. Loss-of-function mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR), which encodes a chloride channel, cause CF (Kerem et al., 1989; Riordan et al., 1989; Rommens et al., 1989). One of the most common missense mutations found in patients with CF is G551D (genet.sickkids.on.ca/cftr). Examination of the affected amino-acid residue by CSA reveals that G551, as well as its adjacent amino acids, are highly conserved among species ranging from human to fish (Figure 10.2A, left panel). This indicates that G551 is likely to be important for CFTR function. Indeed, G551 resides within the nucleotide-binding domain (NBD; Figure 10.2A, right panel), which is critical for proper function of the encoded chloride channel (Anderson and Welsh, 1992). Furthermore, the G551D mutation appears to cause abnormal channel activity (Logan et al., 1994). Conserved Sequences Important for mRNA Processing Identifying disease-associated mutations in protein-coding sequences is relatively straightforward. However, screening a gene or genomic region implicated in a disease does not always reveal mutations in coding sequence, making it necessary to search for mutations in the surrounding non-coding sequence. A common strategy is to then analyze conserved non-coding nucleotides – especially those at positions with a known or expected role in gene function. Proper splicing of heteronuclear RNA to form mature messenger RNA (mRNA) is essential for proper protein translation. The splicing process is carried out by a complex set of interactions between intron sequences and the nuclear spliceosome

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A. CX32 Promoter Mutations

B. RET Enhancer Mutation Conserved Disease-Associated SNP in RET Intron 1

SOX10-Binding Sites

RET Intron 1 CX32 Exon 1 SNPs

CATTGTGCA CAGTGTGCA CATTCTGCA

Mutation Analysis 528 T to G 526 G to C

Experimental Analysis Wt Promoter

526 G to C

35% of Wt

528 T to G

50% of Wt Expression

RET3 C to T

Conserved Regions

MCS9.7

Sequence Conservation of RET+3 C Allele RET+3 C to T Human Chimp Baboon Cow Pig Cat Dog Rat Mouse

GGGGGCC-AGTGACCCTTACACGGTCATCCACAGGCCACTTGG GGGGGCC-AGTGACCCTTACACGGTCATCCACAGGCCACTTGG GGGGGCC-AGTGACCCTTACACGGTCATCCATAGGCCACTTGG GGGAGCCTGGTGACCTGCACACAGTCATCAGCAGGCCACTTGG GCGGACTTTGTGACCCTCACACGGTCATCATCAGGCCACTTGG GTGGGCCTGGTGACCCCCACACAGTCATCAGCAGGCCACTTGG GCGGGCCTGGTGACCCACACACAGTCATCAGCAGGCCACTTGG AGGGGCCTGGTCACTCACACGCACTCATCCACAGGCCACTTGG AGAGGTCTGGTCACTCACACACGCTCATCCCCAGGCCACTTGG Predicted Retinoic Acid Response Elements

Conservation Analysis 528 T to G 526 G to C Human Mouse Rat Rabbit Dog Opossum Fugu

CATTGTGCA CATTGTACA CATTGTACA TATTGTGCA CATTGTGCA TATTGTGTA CATTGTATG

Experimental Analysis of RET+3 Alleles Promoter Only MCS9.7 / RET3 C Allele 6.3-fold change MCS9.7 / RET3 T Allele 0

10 20 30 Expression

40

Figure 10.3 Conserved sequences are important for gene expression.Two examples of non-coding, disease-causing mutations in regulatory elements are schematically illustrated. (A) Mutations in the CX32 promoter (arrow indicates transcription start site) are associated with one form of Charcot-Marie-Tooth disease. Specifically, mutations in a SOX10-binding site (indicated in red) have been identified in patients with Charcot-Marie-Tooth disease. Experimental analysis of the wild-type (Wt) and mutant (526 G to C and  528 T to G) promoters, involving in vitro expression of a reporter gene, demonstrates mutation-associated decreases in promoter activity (based on Houlden et al. 2004). Conservation analysis reveals that the mutated nucleotides are conserved in vertebrates (highlighted in red). (B) Within intron 1 of the RET gene, a specific single-nucleotide polymorphism (SNP) – RET+3 C to T – is highly associated with Hirschsprung disease in a large subset of patients. That SNP resides within a conserved region – MCS+9.7 (grey box in upper panel). The RET+3 C allele is conserved among mammals (middle panel; highlighted in red), with that nucleotide position residing between two predicted transcription factor-binding sites involved in the response to retinoic acid (grey shading). Experimental analysis, which involved testing the MCS+9.7 segment for enhancer activity, revealed that the disease-associated RET+3 T allele shows greatly reduced expression compared to the wild-type RET+3 C allele. These studies demonstrate that a mutation in a RET-associated enhancer is responsible for Hirschsprung disease in some patients (based on Emison et al. 2005).

machinery. Specifically, three intron sequences are required for splicing to occur: (1) a donor site at the 5-end of the intron beginning with the sequence GT(A/G)ATG; (2) an acceptor site at the 3-end of the intron ending with the sequence CAG; and (3) an internal branch site with the sequence CT(A/G)A(C/T). Indeed, the identification of disease-causing mutations in these sequences underscores their importance in gene function. In a similar fashion to the analysis of amino-acid residues, the functional importance of nucleotides involved in mRNA processing can be established by CSA. In addition to missense mutations, CF can be caused by splice-site mutations in the CFTR gene. For example, three

mutations in the CFTR intron 4 donor splice site (621+1 G to T, 621+2 T to G, and 621+3 A to G) have been identified in patients with CF (genet.sickkids.on.ca/cftr), and these likely lead to the deletion of exon 4 in mature CFTR transcripts (Figure 10.2B, inset) (Hull et al., 1993; Tzetis et al., 2001). Comparing the genomic sequence of this region in highly diverged species reveals complete conservation of the 621+1 G, 621+2 T, and 621+3 A nucleotides (Figure 10.2B). Interestingly, while the donor-site consensus sequence GT(A/G)ATG appears to allow for an A to G nucleotide change at position 621+3, the G allele appears to cause CF and the A allele is conserved to fish (Figure 10.2B). Thus, CSA supports a pathogenic role for all three of

Comparative Sequence Analysis and Human Genetic Disease

these mutations and illustrates the importance of donor-site nucleotides for proper mRNA processing. Conserved Sequences Important for Promoter Function Transcriptional regulation is mainly controlled by trans-acting proteins (e.g., transcription factors) and cis-acting sequences (e.g., promoters, enhancers, and repressors). Sequence variants affecting either of these components can alter levels of transcription and lead to disease. Other non-coding sequences typically screened for diseaseassociated mutations are those corresponding to putative cis-acting transcriptional regulatory elements. This frequently begins by analyzing the region thought to harbor a gene’s promoter; specifically, the segment immediately upstream (roughly 500–2000 bases) of a gene’s known or predicted transcription start site (Figure 10.3A, arrow). Charcot-Marie-Tooth (CMT) disease consists of a heterogeneous group of inherited peripheral neuropathies that are generally characterized by muscle weakness and wasting in the distal extremities. One form of CMT, CMTX1, is associated with peripheral nerve demyelination and is caused by mutations in the connexin 32 gene (CX32) (Ionasescu et al., 1994). CX32 encodes a gap-junction protein expressed in Schwann cells that facilitates the radial transport of material through the concentric layers of the myelin sheath. One transcription factor involved in CX32 expression is SRY-related HMG-box 10 (SOX10), which binds to two sequences in the CX32 promoter (Bondurand et al., 2001). Interestingly, two mutations (526 G to C and 528 T to G) have been identified in these SOX10binding sites in patients with CMTX1 (Figure 10.3A, top) (Houlden et al., 2004). To establish the functional consequences of these disease-associated mutations, the promoter activities of fragments bearing each allele were tested. Specifically, CX32promoter fragments bearing the wild-type sequence, 526 G to C, or 528 T to G were cloned upstream of a luciferase reporter gene; in turn, each construct was transfected into cultured cells. There is a notable decrease in reporter-gene expression with 526 G to C and 528 T to G compared to the wild-type promoter sequence (Figure 10.3A, middle) (Houlden et al., 2004). Finally, CSA studies reveal that the wild-type nucleotides (526 G and 528 T) are conserved across a diverse array of vertebrates. Thus, CSA adds to a body of evidence supporting a pathogenic role for two CX32-promoter mutations in CMT disease. Conserved Sequences Important for the Function of Distal Regulatory Elements In the above examples, CSA was used to help implicate identified amino-acid and nucleotide variants with a human genetic disease. These cases all involved regions within genes that are relatively “obvious” to search for such mutations (i.e., coding, splice-site, and promoter sequences). A current obstacle in human genetics is finding disease-causing variants in other types of non-coding functional sequences, which are more difficult to

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identify (i.e., less “obvious”). For example, distal transcriptional regulatory elements can reside within large introns or many kilobase pairs away from their associated gene. Yet, such sequences are believed to harbor mutations that cause human genetic disease. CSA can play a major role in identifying such elements in the first place, and these in turn can be screened for mutations (Figure 10.1, Step 4b). Hirschsprung disease is a genetically heterogeneous developmental disorder of the enteric nervous system, which is characterized by the absence of enteric ganglia in the distal colon. To date, eight loci have been implicated in the disease, with the majority of mutations identified residing in the RET gene, which encodes the ret proto-oncogene (Edery et al., 1994). However, mutations in these genes can only be identified in less than 30% of patients with Hirschsprung disease, indicating that there are other disease-associated genes or novel alleles of already identified genes. A common Hirschsprung disease-associated haplotype was identified involving RET intron 1, with no associated RET coding mutation (Carrasquillo et al., 2002). This haplotype contains multiple single-nucleotide polymorphisms (SNPs; red triangles in Figure 10.3B, top panel) that are associated with the disease, rendering it difficult to determine which one, if any, is pathogenic. CSA was used to identify highly conserved regions in and around RET intron 1 (Emison et al., 2005). One such region (MCS+9.7) overlapped one of the disease-associated SNPs (RET+3; Figure 10.3B, top panel). Importantly, the T allele of this SNP is over-transmitted to affected individuals, and the C allele is highly conserved among mammals (Figure 10.3B, middle panel). Analysis of the MCS+9.7 sequence for transcription factor-binding sites revealed two predicted, highly conserved retinoic acid-binding sites that flank the RET+3 SNP (Figure 10.3B, middle panel). Retinoic acid is known to regulate RET expression in certain tissues (Batourina et al., 2001; Shoba et al., 2002), suggesting that these sites are biologically relevant. Finally, experimental analysis of each RET+3 allele revealed enhancer activity with the C allele and significantly lower activity, with the T allele (Figure 10.3B, bottom panel), suggesting that this genomic region may function as an enhancer to regulate RET expression (Emison et al., 2005). In this example, CSA played a central role in identifying candidate functional non-coding sequences that were then screened for disease-associated variants. Without the evidence of sequence conservation, it would have been far more difficult to ultimately establish that this region functions as an enhancer or that one of the variants residing therein is disease-causing. The Utility of CSA in the Study of Complex Genetic Diseases A key promise of the Human Genome Project was to enhance the study of complex genetic diseases. This could involve using the generated human genomic sequence for identifying genetic variants in human populations. In turn, these variants can be studied in genome-wide association studies to identify risk alleles for

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common genetic diseases (see Chapter 8), including, for example, type II diabetes (e.g., Scott et al., 2007 and Zeggini et al., 2007). A major challenge with such studies is that it typically is unclear whether a given identified variant is conferring disease risk, or if it is just closely linked to the true risk variant. CSA can be used to evaluate the degree of evolutionary conservation of a given region harboring identified variants. Those variants of particular interest (e.g., perhaps because they reside in a highly conserved region) can then be tested in experimental assays (see above for examples) to study their functional consequences. Another important consideration for interpreting the results of CSA in studying complex genetic diseases is the expected molecular pathology of the disease in question. If a loss of gene function is expected in disease pathogenesis, then CSA can be quite informative since conservation will often imply function. Multiple examples of this are provided above. Alternatively, if a gain of gene function is expected in disease pathogenesis (e.g., certain neurodegenerative diseases and some forms of cancer), then CSA may be less informative. On the one hand, a gainof-function mutation may mutate a highly conserved nucleotide or amino acid to generate higher levels of a gene product (e.g., an increase in affinity between a promoter and a transcription factor) or a gene product with a higher activity (e.g., an increase in affinity for a binding partner); here, CSA can provide information for implicating a disease-causing variant. On the other hand, a gain-of-function mutation may change a lessconserved nucleotide or amino acid to one that creates a novel or toxic function of the encoded protein; here, CSA would not help to support this variant as a probable disease-causing mutation.

CSA AND THE FUTURE OF HUMAN GENETICS AND GENOMIC MEDICINE The availability of sequences of the human genome and that of multiple other evolutionarily diverse species is greatly advancing the study of human genetic diseases. In this chapter, we have focused on how CSA can facilitate the identification and study of disease-causing mutations. The identification of genes defective in diseases that are inherited in a Mendelian fashion is now (almost) a routine process. Aided by CSA, investigators can now screen a more-complete catalog of conserved protein-coding and non-coding functional elements for mutations in such disease-associated genes. In a similar fashion, these same conserved sequences can be carefully scrutinized for the presence of mutations

causing more-common, but genetically complex, diseases (see section “The Utility of CSA in the Study of Complex Genetic Diseases” for a discussion of this issue). A related utility of CSA will be in more-completely characterizing the functional landscape of the human genome. Specifically, by establishing which sequences have been conserved over various evolutionary time frames (e.g., between human and mouse or between human and fish) and by linking those sequences to specific biological functions, an enhanced view of human genome function should emerge. Such a comprehensive examination of sequence conservation and sequence function is the primary goal of the ENCODE (ENCyclopedia Of DNA Elements) Project (www.genome.gov/ENCODE) (ENCODE Project Consortium, 2007). For this Project, a consortium of investigators is performing myriad computational (including CSA) and experimental studies (some similar to the ones described above) to catalog all functional sequences in the human genome, initially focusing on a targeted set of regions that comprise 1% of the genome. This last application of CSA has important implications for genomic medicine. It is predicted that the human genome harbors 3 to 5 million sites of SNPs that differ between any two individuals (Altshuler and Clark, 2005). While many of these SNPs have no functional consequence, a subset of them reside in functional regions of the genome and are functionally important. These latter SNPs may contribute to disease onset (as described above), modify the phenotypes caused by mutations at other loci (Nadeau, 2001), or modulate a patient’s response to medication (Goldstein et al., 2003). CSA can aid the identification of SNPs that are most likely to have functional consequences. For example, a subset of patients may experience adverse side effects when treated with a given drug. These side effects may be caused by an allele of a SNP within an enhancer that increases the expression of a gene in the pathway that metabolizes that drug. Through the use of CSA and other studies, this SNP can be identified and a test developed that determines which patients are most likely to experience the adverse side effects upon treatment with that drug (see Chapter 27). In summary, CSA continues to hold enormous potential as a tool for research in genome function, human genetics, and genomic medicine. One of the remaining challenges is to establish which species are most relevant for each biological question (Figure 10.1, Step 1). In the coming years, the continued collection and analysis of genomic sequences from multiple species should make this less of an issue, thereby enhancing the utility of CSA and its applications.

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Riordan, J.R., Rommens, J.M., Kerem, B., Alon, N., Rozmahel, R., Grzelczak, Z., Zielenski, J., Lok, S., Plavsic, N., Chou, J.L. et al. (1989). Identification of the cystic fibrosis gene: Cloning and characterization of complementary DNA. Science 245, 1066–1073. Rommens, J.M., Iannuzzi, M.C., Kerem, B., Drumm, M.L., Melmer, G., Dean, M., Rozmahel, R., Cole, J.L., Kennedy, D., Hidaka, N. et al. (1989). Identification of the cystic fibrosis gene: Chromosome walking and jumping. Science 245, 1059–1065. Schwartz, S., Zhang, Z., Frazer, K.A., Smit, A., Riemer, C., Bouck, J., Gibbs, R., Hardison, R. and Miller, W. (2000). PipMaker – a web server for aligning two genomic DNA sequences. Genome Res 10, 577–586. Schwartz, S., Elnitski, L., Li, M., Weirauch, M., Riemer, C., Smit, A., Green, E.D., Hardison, R.C. and Miller, W. (2003). MultiPipMaker and supporting tools: Alignments and analysis of multiple genomic DNA sequences. Nucleic Acids Res 31, 3518–3524. Scott, L.J., Mohlke, K.L., Bonnycastle, L.L., Willer, C.J., Li, Y., Duren, W.L., Erdos, M.R., Stringham, H.M., Chines, P.S., Jackson, A.U., Prokunina-Olsson, L. et al. (2007). A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345.

Shoba, T., Dheen, S.T. and Tay, S.S. (2002). Retinoic acid influences Phox2 expression of cardiac ganglionic cells in the developing rat heart. Neurosci Lett 321, 41–44. Stalker, J., Gibbins, B., Meidl, P., Smith, J., Spooner, W., Hotz, H.R. and Cox, A.V. (2004). The Ensembl Web site: Mechanics of a genome browser. Genome Res 14, 951–955. Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994). CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22, 4673–4680. Tzetis, M., Efthymiadou, A., Doudounakis, S. and Kanavakis, E. (2001). Qualitative and quantitative analysis of mRNA associated with four putative splicing mutations (621+3A→G, 2751+2T→A, 296+1G→C, 1717-9T→C-D565G) and one nonsense mutation (E822X) in the CFTR gene. Hum Genet 109, 592–601. Zeggini, E., Weedon, M.N., Lindgren, C.M., Frayling, T.M., Elliott, K.S., Lango, H., Timpson, N.J., Perry, J.R., Rayner, N.W., Freathy, R.M. et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341.

RECOMMENDED RESOURCES Websites http://www.intlgenome.org Provides an overview of major genomesequencing projects as well as links to species-specific databases and other useful web resources. http://www.genome.gov/ENCODE Describes the rationale and specific components of the ENCODE (ENCyclopedia Of DNA Elements) Project. Also provides direct links to the generated data sets.

Articles Dermitzakis, E.T., Reymond, A. and Antonarakis, S.E. (2005). Conserved non-genic sequences – an unexpected feature of mammalian genomes. Nat Rev Genet 6, 151–157. This review gives an introduction to conserved non-coding sequences, and discusses their potential roles in genome biology.

Kleinjan, D.A. and van Heyningen, V. (2005). Long-range control of gene expression: Emerging mechanisms and disruption in disease. Am J Hum Genet 76, 8–32. This review discusses long-range transcriptional regulatory elements, and describes examples where disruption of such elements cause human disease. Miller, W., Makova, K.D., Nekrutenko, A. and Hardison, R.C. (2004). Comparative genomics. Annu Rev Genomics Hum Genet 5, 15–56. Nardone, J., Lee, D.U., Ansel, K.M. and Rao A. (2004). Bioinformatics for the “bench biologist”: How to find regulatory regions in genomic DNA. Nat Immunol 5, 768–774. These two reviews provide an excellent overview of comparative sequence analysis, and discuss a number of issues to consider when pursuing such studies.

CHAPTER

11 DNA Methylation Analysis: Providing New Insight into Human Disease Susan Cottrell, Theo deVos, Juergen Distler, Carolina Haefliger, Ralf Lesche, Achim Plum and Matthias Schuster

INTRODUCTION The human genome contains four bases – guanine, adenine, thymine, and cytosine; but the cytosines can be either methylated or unmethylated at the fifth carbon position in the pyrimidine ring (Figure 11.1a). In general, cytosines can only be methylated when they are in the context of a CpG dinucleotide, or in other words, a cytosine immediately followed by a guanine. These CpG dinucleotides are under-represented in the genome, occurring at only about 20% of the frequency expected assuming random distribution. In some regions, called CpG islands, the frequency of CpG dinucleotides is only 65% of the expected frequency (Takai and Jones, 2002). These stretches of 500–1000 base pairs typically overlap the promoter or exon 1 region of genes, and there are estimated to be 30,000 CpG islands in the human genome (Lander et al., 2001). CpGs in islands are predominantly unmethylated, while CpGs outside of islands are typically methylated. The methylation status of a CpG island is normally correlated with the chromatin structure and expression levels of nearby genes (Stirzaker et al., 2004). CpG islands associated with actively transcribed genes are typically unmethylated. When a CpG island Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

is methylated, methyl-CpG-binding domain (MBD) proteins recognize the methylated CpG and recruit the necessary factors for chromatin condensation and gene inactivation. This DNA methylation state is maintained during cell division by a family of enzymes called DNA methyltransferases (DNMTs). DNMT1 is thought to be responsible for maintaining methylation patterns through cell divisions, and DNMT3b is the primary enzyme involved in de novo methylation (Lopatina et al., 2002). Mutations in one of the DNMT genes, DNMT3B, are responsible for the rare genetic disorder ICF syndrome, which is characterized by severe immunodeficiency and DNA hypomethylation (Hansen et al., 1999). Mice lacking any of the DNMTs die either in utero or soon after birth (Okano et al., 1999). Although most CpG islands in the human genome are typically unmethylated in healthy cells, methylation of some islands plays a role in several normal physiological processes. In females, CpG islands on one of the X chromosomes become hypermethylated in the process of chromosomal inactivation (Hellman and Chess, 2007; Mohandas et al., 1981). On autosomes, methylation is also important during development for inactivation of maternally- or paternally-derived alleles of certain genes in a process called genomic imprinting. For instance, a region 2 kb upstream Copyright © 2009, Elsevier Inc. All rights reserved. 131

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NH2

—

—

NH2

C

C

N C — O—

N Cytosine

C

N

C

C — O—

CH3

(c)

C

CH3 —

—

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genomic DNA

5-TCGCTGG-3 3-AGCGACC-5 —

132

CH3

C bisulfite treatment

N

5-methyl-cytosine

(b)

bisulfiteconverted DNA Methylation-specific restriction digestion

5-TCGUTGG-3

bis I strand

3-AGCGAUU-5

bis II strand

PCR

bisulfite-converted PCR amplified DNA Amplification

5-TCGTTGG-3 3-AGCAACC-5

“G-rich” strand “C-rich” strand

3-TCGCTAA-5 5-AGCGATT-3

Figure 11.1 Detection of methylated cytosines. (a) Cytosine and 5-methyl-cytosine. (b) Cleavage by some restriction enzymes is blocked by methylation of cytosines in the recognition site. After digestion, various methods can be used to detect the intact methylated DNA, including locus-specific PCR amplification. (c) Genomic DNA is shown at the top with methylated (red) and unmethylated (green) cytosines. The bisulfite treatment converts unmethylated cytosines into uracils. The resulting strands are no longer complementary, and each strand can be amplified by conversion-specific PCR primers.

of the H19 gene for a non-coding RNA is paternally methylated, leading to repressed IGF2 expression (Bartolomei et al., 1993). During the process of carcinogenesis, two important changes in the methylation pattern of the genome occur. First, sporadic CpGs (outside of CpG islands) become hypomethylated (Ehrlich et al., 1982). This global hypomethylation has been linked to genomic instability, possibly due to mobilization of repetitive elements (Chen et al., 1998; Feinberg, 2004). Meanwhile, some CpG islands, particularly near tumor-suppressor genes, can become hypermethylated during carcinogenesis (Herman and Baylin, 2003; Jones and Baylin, 2002). The hypermethylation is functionally similar to a deletion or mutation and can serve as one method of gene inactivation in Knudson’s classic two-hit hypothesis (Jones and Laird, 1999). Different combinations of aberrantly methylated genes are found in nearly all types of cancer (Costello et al., 2000).

TECHNOLOGY TO ASSESS DNA METHYLATION Cytosine methylation has minimal impact on both thermodynamics and kinetics of common DNA polymerases and

can, therefore, not be directly detected by any amplification or hybridization-based methods. Instead, there are three basic principles that can be employed to distinguish differentially methylated DNA sequences: (i) methylation-specific restriction enzymes (Figure 11.1b), (ii) methylation-specific antibodies and proteins, and (iii) methylation-specific bisulfite modification (Figure 11.1c). All existing methods for discovering and evaluating DNA methylation markers are based on one or more of these principles. Methylation-sensitive restriction enzymes, which typically digest unmethylated but not methylated DNA, have been in use for many years (Bird and Southern, 1978). The recognition sites contain one or more CpG sites. Sometimes cleavage is blocked completely by methylation of the CpG site, but more often the rate of cleavage is affected. Therefore, these methods must be optimized for complete and specific digestion. More recently, restriction enzymes have been introduced that specifically digest methylated DNA (Krueger et al., 1995). Methods based on restriction enzymes are limited to CpGs that are present within a restriction site. The second major category of DNA methylation methodology relies on naturally occurring proteins whose activity requires methylation detection. Methylation-specific antibodies

Technology to Assess DNA Methylation

have been generated that specifically bind to DNA containing methylated cytosines (Gebhard et al., 2006). In addition, methylbinding proteins, which play an integral part in bridging DNA methylation information with cellular function, have been successfully used to separate differentially methylated DNA fractions (Cross et al., 1994). Methylation analysis methods based on bisulfite conversion of sample DNA rely on the unique property of inorganic bisulfite to transform unmethylated cytosine to uracil (Figure 11.1) without affecting methylated cytosines (Frommer et al., 1992). Thus, the bisulfite reaction converts the epigenetic methylation information into sequence information, which can be analyzed by common molecular biology methods, most notably PCR. By converting most of the cytosines to uracils, bisulfite conversion of DNA has the effect of reducing the complexity of the genome, thereby making primer design and multiplexing more challenging. In addition, the chemical reaction can cause DNA damage if sub-optimal conditions are used (Grunau et al., 2001). Despite these caveats, bisulfite-based methods are extremely powerful for sensitive and quantitative analysis of DNA methylation at one or many CpG sites. Methods for Methylation Marker Discovery The human genome contains tens of thousands of CpG islands associated with individual genes and their activity. The number of individual methylation positions that could be aberrantly methylated in diseased states is several orders of magnitude higher. Several genome-wide discovery methods to identify differentially methylated regions associated with the specific diagnostic question have been developed. Many of these methods are based on digestion of sample DNA with a methylationsensitive restriction enzyme. The individual discovery techniques differ in their methods for detection of these restriction cuts, but they include techniques such as linker ligation combined with subtractive hybridization (MCA; Toyota et al., 1999), arbitrarily primed PCR (AP-PCR; Liang et al., 2002), array hybridization (Differential methylation hybridization, DMH; Huang et al., 1999), and two-dimensional gel electrophoresis (RLGS; Costello et al., 2002; Hatada et al., 1991). These methylation marker discovery methods use methylation-sensitive restriction enzymes to differentiate two classes of samples, such as a DNA from samples of responders to a drug and DNA from non-responders. For a thorough analysis, the entire marker discovery process often involves multiple comparisons. Furthermore, the throughput is low enough that only a few samples can reasonably be processed. Using pooled DNA from multiple patients reduces the likelihood of identifying polymorphic differences associated with individual patients, and it allows for the enrichment of methylation differences associated with disease, which is essential for identification of markers with high prevalence for screening applications. One approach that is becoming more commonly used is array-based discovery of aberrant methylation (Fukasawa et al., 2006; Hayashi et al., 2007;Taylor et al., 2007). In DMH, originally used for the identification of epigenetic alterations in breast and

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ovarian cancer (Huang et al., 1999; Yan et al., 2001), genomic DNA is digested by a methylation-insensitive restriction enzyme and then linkers are ligated to the fragments. These products are then digested with a methylation-sensitive restriction enzyme and finally amplified by PCR via the linker sequence. Amplificates are only generated from methylated templates that are not digested by the methylation-sensitive restriction enzymes. The PCR products can be labeled with fluorescent dyes and hybridized to a CpG-island microarray. A recent paper describes the combination of a modified and optimized pre-chip DMH workflow and a custom-made oligonucleotide array representing approximately 50,000 regions of the human genome (Lewin et al., 2007). The advantages of DMH are its reproducibility, speed, and throughput (allowing the option of analyzing individual samples instead of pools). The readout of the microarray not only identifies the differentially methylated sequence, but also provides semi-quantitative methylation values. Recently, the Human Epigenome Project, a large-scale effort to identify, catalog, and interpret genome-wide DNA methylation patterns of all human genes in all major tissues, has been initiated by The Wellcome Trust Sanger Institute (Cambridge, UK), the Centre National de Génotypage (Paris, France), and Epigenomics AG (Berlin, Germany) (Table 11.1). Based on public versions of the sequence of the human genome established by the Human Genome Project, the Human Epigenome Project will systematically uncover the epigenetic information layer that is still hidden to a large extent. A second Human Epigenome Project was recently proposed by an international group of 40 scientists (Esteller, 2006; Jones and Martienssen, 2005) (Table 11.1). Methods for Analyzing Markers in Tissue Samples Methylation analysis in tissues is often not limited by the quantity of sample material available, but the quality of the material, particularly formalin-fixed and paraffin-embedded samples, can be an issue (Schuster, 2004). The isolated DNA is usually damaged to some extent depending on the fixation procedure. In addition, the proportion of the tissue type with aberrant methylation (i.e., the percent tumor) can vary from sample to sample. Laser capture microdissection (LCM), which is used to isolate the tissue type of interest, helps overcome this issue. There are very different levels at which DNA methylation can be assessed (Figure 11.2) (Laird, 2003), ranging from genomewide to a particular locus or CpG. The “methylation content” of a cell population corresponds to the overall proportion of methylated cytosines within the entire genome. In contrast, the “methylation level” designates the percentage of methylated DNA strands at one genomic locus, while the “methylation pattern” describes the individual methylation status of a specific set of CpG positions on one DNA strand within a region of interest. Tissue testing is best performed using marker information contained in the “methylation level”. In principle, the methylation level of a DNA sample can be measured in a straightforward manner after bisulfite treatment because the methylation ratio at the CpG site of interest is

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TABLE 11.1

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Multicenter efforts for large-scale epigenetic analysis Goals

Human Epigenome Project

●

●

International Alliance for the Human Epigenome and Disease (AHEAD)

●

Encyclopedia of DNA Elements (ENCODE)

●

●

Approach

Institutions

References

“To identify, catalogue and interpret genomewide DNA methylation patterns of all human genes in all major tissues” Identify “Methylation Variable Positions”

Bisulfite DNA sequencing

Wellcome Trust Sanger Institute; Epigenomics, AG; The Centre National de Genotypage

Eckhardt et al., 2006 http://www.epigenome. org/

A comprehensive catalog of potential chromatin alterations Describe “reference epigenomes”

To be determined; likely to include a variety of methods to assess DNA methylation and histone modifications

To be determined

Jones and Martienssen 2005

Identify all functional elements in the human genome

Chip-on-chip assays for DNA methylation and chromatin modifications; comparative genomics; DNase I hypersensitivity site mapping

Welcome Trust Sanger Institute; Stanford University; University of Virginia; Affymetrix; Municipal Institute of Medical Research; Ludwig Institute for Cancer Research; Yale University; University of Washington; others

ENCODE project consortium, 2007 http://www.genome. gov/10005107

(a) Methylation content

(b) Methylation level

(c) Methylation pattern

Figure 11.2 Methods for assessment of DNA methylation. The “methylation content” of a cell population corresponds to the overall proportion of methylated cytosines within the entire genome (a), the “methylation level” designates the percentage of methylated DNA strands at a one genomic locus (b), and the “methylation pattern” describes the individual methylation status of a specific set of CpG positions on one DNA strand within a region of interest (c).Reproduced with permission of Macmillan Magazines Ltd from Nature Reviews Cancer 3(4), April 2003, 253–266. Copyright (2003) Macmillan Magazines Ltd.

translated into an allele ratio. However, in most cases not enough DNA will be available for direct analysis. The bisulfite-treated DNA can be amplified without disturbing this allele ratio using conventional PCR with the primers designed to avoid CpG

sites. After amplification, the product can be analyzed using any of a number of analysis methods typically used for the quantification of allele ratios. In MS-SNuPE (Methylation-Specific Single Nucleotide Primer Extension), extension primers

Technology to Assess DNA Methylation

designed to hybridize just upstream of a CpG site are extended by one nucleotide in a methylation-specific manner. Detection and quantification based on HPLC (El-Maarri et al., 2002) and incorporation of radioactivity have been described (Gonzalgo et al., 1997). Another example is pyrosequencing, in which the sequential incorporation of additional bases into the extension primer is translated into a light flash of the same intensity (Colella et al., 2003). Alternatively, hybridization of methylationspecific probes to amplified bisulfite-treated DNA has been combined with different readout systems, including microarrays (Adorjan et al., 2003) and real-time PCR (Quantitative MethyLight; Eads et al., 2000). All of the methods mentioned measure relative levels of methylation, but can be used to quantify DNA methylation levels when they are appropriately calibrated on mixtures of methylated and unmethylated DNA templates. The methods described in the previous paragraph use an unbiased amplification of bisulfite-treated DNA for measuring methylation levels. Methylation in tissues can also be estimated using various forms of methylation-specific amplification. The most commonly used method in methylation research is methylation-specific PCR (MSP) or the real-time version, MethyLight (Eads et al., 2000). These methods use primers designed for a certain methylation pattern of bisulfite-converted DNA. MethyLight technology is a combination of methylation-specific primers and a methylation-specific real-time PCR probe (Figure 11.3). Since these methods only amplify and detect the methylated portion of the DNA sample, they can only provide qualitative methylation pattern information. In combination with an estimation of total DNA amount, which can be measured for instance by a control real-time PCR assay, methylation-specific assays can be used to estimate relative methylation pattern levels (Ogino et al., 2006a). For tissue-based analyses, a quantitative methylation assay is likely to be important, because in many cases the required information will depend not on the presence or absence of methylation but on the level of methylation. Responders to a certain drug, for instance, might have fewer inactivated copies of a gene than non-responders. In some instances, the methylation difference could be quite subtle, and it is not clear which of the methods will provide the level of precision required. Methods for Analyzing Markers in Disease Screening Samples The samples of choice for early detection or screening are body fluids that can be obtained by non-invasive procedures. Serum and plasma are optimal targets for methylation marker analyses, but investigators have also analyzed methylation in urine (Hoque et al., 2005), sputum, bronchial lavage, ductal lavage, ejaculate, and other “remote samples” (reviewed in Laird, 2003). In healthy individuals, only small amounts of circulating DNA are observed in plasma and serum, whereas high amounts have been described in patients with various conditions. The main sources of circulating DNA are thought to be apoptotic or necrotic cell death (Holdenrieder et al., 2001). DNA is released from degrading cells after nuclease cleavage of the chromatin between the nucleosomes. Therefore, the tumor- or disease-derived DNA fragments

(a)

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light F

R

+ ++ + + + + + +

(b)

light

(c) F

R

+++ + ++ +++ ++++ +

(d)

Figure 11.3 Sensitive real-time PCR assays for methylation detection MethyLight assays (a, b) have two methylationspecific primers that bind only to the methylated (a) but not the unmethylated (b) versions of their binding sites. If both primer binding sites are methylated, PCR products are formed and will be detected by methylation-specific real-time PCR probes (a). In contrast, HM assays (c, d) use primers that amplify all methylation states. The methylation specificity of the priming is provided by the blocker oligonucleotide, which prevents the primer from binding to unmethylated DNA (d). Therefore, only methylated DNA is amplified and will be detected by a methylation-specific fluorescent probe (c). Filled circles indicate methylated CpGs, and open circles indicate unmethylated CpGs.

can be as short as the smallest nucleosomal fragment, approximately 180 base pairs (Cottrell and Laird, 2003; Jahr et al., 2001). In contrast to tissue-based applications, methylation markers for screening must fulfill additional biological requirements. Even if the majority of free DNA in blood is derived from cancer cells, a significant amount of DNA from other cell types, particularly leukocytes, will be present. Therefore, the marker must be methylated in a high percentage of tumor cells and relatively unmethylated in leukocytes and other tissues contributing background DNA. Qualitative markers, which are methylated only in tumor-derived DNA, are well suited for screening applications. The clinical sensitivity (proportion of diseased people testing positive) and specificity (proportion of healthy individuals testing negative) of the marker is not only dependent on its biological properties, but also on the analytical performance of the assay. The challenge for the final diagnostic assay is a high analytical sensitivity (detecting small amounts of methylated tumor DNA) at a maximum specificity (no detection of unmethylated DNA). In practice, methylation assays suitable for screening must be capable of detecting a few copies of the methylated marker DNA in a background of 1–10 thousand-fold excess unmethylated DNA. In addition, the DNA extraction method must be optimized for high recovery yield, particularly of short DNA fragments found in plasma or serum. Bisulfite treatment is known to cause damage to the DNA, but milder treatment

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conditions can be used without risk of incomplete conversion of the DNA (Berlin et al., 2005). MethyLight may be an assay that is suitable for detection of disease in remote samples. In contrast to MSP, MethyLight minimize false-positive results caused by possible mis-priming of the methylation-specific primer, as the methylation status of the amplificates are analyzed on a second level by the methylation-specific fluorescent probe. MethyLight has been successfully used for various applications, but primers and probes must be carefully designed in order to ensure sensitive and specific detection. HeavyMethyl (HM) technology is another way to achieve methylation-specific priming, and, like MSP, can be combined with a probe for real-time detection (Cottrell et al., 2004). HM assays combine methylation-non-specific primers and a nonextendable 3-modified blocker oligonucleotide. The blocker binds to unmethylated DNA, preventing the primers from accessing the priming site (Figure 11.3). The blocker does not bind to the methylated version, allowing the primers to access the priming sites. A methylation-specific fluorescent probe is used to ensure specificity and allow for methylation quantification. HM assays typically have a limit of detection of 25–50 pg of methylated DNA and can detect this methylated DNA in a background of 50 ng of unmethylated DNA. Although HM design requires more effort compared to a MethyLight assay, the HM assay has advantages. Unmethylated template that is amplified unintentionally will be targeted by the blocker in the next cycle. Therefore, the analytical specificity of HM assays is very high.

CLINICAL IMPACT OF DNA METHYLATION ANALYSIS Cancer has been viewed as an accumulation of chromosomal aberrations and therefore been called a “genetic disease”. However, it has become clear over the last two decades that epigenetic changes play a crucial role in carcinogenesis. Research on DNA methylation in cancer has been expanding rapidly, and for this reason, in the following sections, we will rely on examples from oncology to demonstrate how methylation profiles can be applied for basic disease research, early detection, disease stratification, treatment response prediction, and drug target identification. While attention has focused on methylation in carcinogenesis, a similar groundswell of research is emerging on methylation in other diseases, especially autoimmune and cardiovascular conditions (see Table 11.2). For example, aberrantly hypomethylated DNA in circulation in systemic lupus erythematosus might induce an immune response due to its similarity to unmethylated microbial DNAs (Januchowski et al., 2004). Further contributions of defective methylation to lupus pathogenesis could be due to transcription of endogenous retroviruses (Okada et al., 2002) or the increase in the expression of certain genes related to autoreactivity (Richardson, 2002). In cardiovascular disease, DNA methylation is associated with atherosclerosis pathogenesis in two ways: First, the decrease in essential factors (e.g., folate and vitamin D)

for the synthesis of S-adenosyl methionine (the main methyl group donor in the methylation reactions) leads to global DNA hypomethylation and coexisting hyperhomocystinemia (Zaina et al., 2005). Second, Lund et al. (2004) provided evidence that methylation profiles are early markers of the disease in a mouse model. Taken together, these data reinforce the notion that methylation profiles can be highly valuable to study the pathogenesis of a variety of conditions (Table 11.2). Impact of Methylation Research on Basic Disease Understanding Researchers have spent many years studying the role of permanent alterations in DNA sequence, such as mutations, deletions, and insertions, in carcinogenesis. Cancer has long been thought of as a disease that arises after accumulation of mutational events in growth control genes. In recent years, RNA expression profiling has been used to examine the functional consequences of these sequence alterations, as outlined in other chapters in this book. There is now overwhelming evidence that epigenetic modification can also control RNA and protein levels. As the methylation analysis toolbox expands, we are gaining more insight into how alterations in these patterns of methylation influence disease. As an example, methylation analysis has provided a new molecular understanding of colon cancer. Colon cancer develops in a well-described pathway from normal epithelium, to aberrant crypts, to adenomas, then to adenocarcinoma, each with specific histological features and molecular alterations. The first alterations to be studied were sequences changes: for instance, APC mutations have been implicated in the formation of adenomas (Powell et al., 1992) and TP53 mutations have been implicated in the transition from adenoma to adenocarcinoma (Baker et al., 1990). More recently, the role of aberrant methylation events of genes in these same pathways has been elucidated. For example, a hereditary form of colon cancer is commonly caused by mutations in the MLH1 gene, but this same gene can induce sporadic forms of the disease when it is aberrantly methylated (Cunningham et al., 1998). Some colon cancers are characterized by microsatellite instability (MSI), and the majority of these tumors have methylated MLH1 alleles. Furthermore, research on methylation in colon cancer has established that tumors can not only be divided based on microsatellite stability status, but also on their CpG island methylator phenotype (CIMP) status (Toyota et al., 1999). Colon tumors, particularly those with MSI, have a bimodal distribution based on the methylation status of multiple genes. Those tumors with multiple methylated genes (CIMP+) are more likely to have BRAF mutations and wildtype KRAS (Ogino et al., 2006a, b). Clearly, a complete understanding of the pathogenesis of colon cancer requires knowledge of both genetic and epigenetic events. Methylation Markers for Early Detection and Diagnosis Early detection is an effective success factor in the fight against any type of cancer. Aberrant DNA methylation patterns show

TABLE 11.2

Examples of DNA methylation in human disease

Disease/Disorder

Phenotype

Mechanism

Genes involved

Reference

Cancer

Tumorigenesis; Increased cell division; metastasis

Hypermethylation

Tumor-suppressor genes

Hypomethylation Loss of imprinting

Global Insulin-like grow factor 2 (IGF2), GTP-binding RAS-like 3 (DIRAS3), mesoderm-specific transcript homolog (MEST) Mut L Homolog 1 (MLH1)

Jones and Baylin, 2002; Herman and Baylin, 2003 Ehrlich et al., 1982 Feinberg, 2007

Heritable germline hypermethylation

Hitchins et al., 2007

Embryonal tumors; omphalocele; macroglossia (large tongue); gigantism

Disruption of an imprinting locus

11p15 (IGF2, H19 ncRNA, cyclindependent kinase inhibitor 1C (CDKN1C), pleckstrin homologylike domain, family A, member 2 (PHLDA2), solute carrier family 22, member 1 (SLC22A1), potassium voltage-gated channel (KCNQ1))

Weksberg et al., 2003

Prader–Willi syndrome

Developmental delay; obesity; genital hypoplasia; distinct facial features

Disruption of an imprinting locus

15q11-13 (small nuclear ribonucleoprotein polypeptide N (SNRPN), ncRNAs)

Nicholls and Knepper, 2001

Angelman syndrome

Developmental delay; ataxia; microcephaly; distinct facial features

Disruption of an imprinting locus

15q11-13 (Ubiquitin E3 ligase (UBE3A))

Nicholls and Knepper, 2001

Pseudohypoparathyroidism

Hypoparathyroidism without sensitivity to parathyroid hormone

Disruption of an imprinting locus

20q13 (GNAS complex locus (GNAS1))

Bastepe et al., 2001

Immunodeficiency/centromeric instability/facial anomalies (ICF syndrome)

Immunodeficiency; chromosomal breakage; facial defects

Mutations

DNA Methyltransferase 3b (DNMT3b)

Hansen et al., 1999

Rett syndrome

Motor abnormalities; ataxia; handwringing; poor verbal skills

Mutations

Methyl-CpG-binding protein 2 (MeCP2)

Amir et al., 1999

Systemic Lupus Erythematosus

Autoimmune disorder; inflammation; tissue damage

Hypomethylation

global, perforin (PRF1), CD70, integrin, alpha L (ITGAL)

Januchowski et al., 2004

Atherosclerosis

Thickening and hardening of arterial walls

Hypermethylation

Estrogen receptor, Monocarboxylate Transporter 3 (SLC16A3)

Kim et al., 2007; Zhu et al., 2005

Schizophrenia

Hallucinations; delusions

Hypomethylation

Global

Shimabukuro et al., 2006

Hypermethylation

Reelin (RELN)

Grayson et al., 2005

Hypermethylation

Homeobox A10 (HOXA10)

Wu et al., 2005

Fragile X syndrome

Mental retardation; autism; large ears; macroorchidism

Hypermethylation of an expanded trinucleotide repeat

Fragile X mental retardation 1 (FMR1)

Oberle et al., 1991

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Growth of endometrial cells outside of the uterus

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Endometriosis

Clinical Impact of DNA Methylation Analysis

Beckwith–Wiedemann syndrome

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promise as biomarkers for early detection of cancer for several reasons: Tumor-specific methylation changes occur early in tumorigenesis, appear to be stable, provide an amplifiable signal, and can be assayed with high analytical sensitivity. DNA methylation patterns characteristic of tumor cells have been found for many types of cancer in several types of body fluids (Laird, 2003). DNA methylation changes occur frequently in colon cancer and can be present early in malignant transformation (Grady, 2005). Feasibility studies have shown that tumor-derived methylated DNA markers can be detected in serum, plasma, and stool (reviewed in Laird, 2003). These studies analyzed a limited number of patient samples, and larger clinical studies will be necessary to assess the quality of these markers for a screening approach. Prostate-specific antigen (PSA) based screening has resulted in detection of many cases of prostate cancer, but, due to the low specificity of this assay, its usefulness for prostate cancer screening is no longer certain. DNA methylation markers may improve screening and diagnosis of prostate cancer considerably in the future. GSTP1, encoding glutathione S-transferase P1, is the bestcharacterized methylation marker for prostate cancer. Methylation of GSTP1 in prostate cancer has been consistently reported with a frequency of about 70–90% (reviewed in Henrique and Jeronimo, 2004). Interestingly, GSTP1 methylation is also reported in some high-grade prostatic intraepithelial neoplasia (PIN) lesions, a precursor to prostate cancer, indicating that GSTP1 hypermethylation is an early event in prostate cancer development (Henrique et al., 2006). Several groups have demonstrated the feasibility of using GSTP1 methylation and other methylation markers to detect prostate cancer in bodily fluids. For example, Hoque et al. (2005) analyzed a panel of nine genes in urine sediments from 52 cancer patients and 91 controls using MethyLight assays. A panel containing GSTP1 and three other genes (CDKN2A, ARF, and MGMT) detected the prostate cancer cases with 87% sensitivity and 100% specificity. Recently, Lofton-Day et al. (2006) conducted a study to identify and validate DNA methylation-based markers for colorectal cancer using plasma samples. At a set specificity of 95%, the lead methylation marker (Septin 9) showed in several studies sensitivity values of 51, 65, and 50%, respectively. In total, approximately 2000 samples were tested, including over 600 plasma samples from colorectal cancer patients, 600 with related non-malignant diseases, and over 600 normal controls from an age-matched, colonoscopy-verified healthy population. Importantly, early-stage cancers were identified with the same sensitivity as later stage cancers (Lofton-Day et al., 2006). The test was able to detect colorectal cancers regardless of their location, addressing a critical medical need and shortfall of the existing fecal occult blood tests. Once a methylation-based screening test is developed, its use can potentially be extended to monitoring for disease recurrence after treatment, similar to the role of PSA for prostate cancer.

aggressiveness of tumors and the responsiveness to certain drug regimens, respectively. For example, prognostic markers for breast cancer are required to determine which patients can be spared from aggressive therapy due to very good prognosis. Recently, several reports have been published exploring the prognostic relevance of DNA methylation at the promoter of the PLAU gene, which codes for the matrix remodeling enzyme urokinase-type plasminogen activator (uPA) (Guo et al., 2002; Pakneshan et al., 2004; Xing and Rabbani, 1999). Interestingly, PLAU-promoter DNA methylation was negatively associated with tumor grade; grade 1 tumors were frequently methylated and grade 3 tumors were unmethylated. These findings are consistent with results from large studies that have demonstrated that protein expression in serum of uPA and its cell surface receptor uPAR are important determinants of distant spread of breast cancer (Look et al., 2002). Shinozaki et al. (2005) found that in a panel of 151 primary breast tumors, hypermethylation of the CDH1 gene was significantly associated with lymphovascular invasion, infiltrating ductal histology, and lack of estrogen receptor expression. On the other hand, RASSF1A and RARB hypermethylation were significantly more common in estrogen receptor-positive and human epidermal growth factor receptor 2-positive tumors, respectively. Other genes that have been associated with prognosis in breast cancer and which appear to be regulated via DNA methylation are CDH3 (Paredes et al., 2005), the tuberous sclerosis (TSC) genes TSC1 and TSC2 (Jiang et al., 2005), and LATS1/LATS2, tumor-suppressor genes that have been implicated in the regulation of the cell cycle (Takahashi et al., 2005). We have recently identified a methylation marker, PITX2, which predicts outcome in hormone receptor-positive, node-negative breast cancer patients treated with tamoxifen monotherapy (Maier et al., 2004, 2007). The marker was originally identified and validated in two independent cohorts of patients treated with adjuvant tamoxifen monotherapy. The findings were then validated in an independent study analyzing paraffin-embedded tumors of 422 node-negative patients treated with tamoxifen only after surgery. In the group with low PITX2 methylation, 94% of the patients were metastasis-free after 10 years, compared to only 84% in the group with high PITX2 methylation. In a multivariate model, PITX2 methylation added significant information to conventional factors such as tumor size, grade, and age (Harbeck et al., 2005). Analysis of PITX2 methylation in a large cohort of patients not receiving adjuvant therapy confirmed that PITX2 methylation is a prognostic marker associated with tumor aggressiveness (Martens et al., 2005). These results provide substantial evidence that PITX2 DNA methylation is suitable for routine clinical use in order to predict outcome in node-negative, tamoxifen-treated patients, and to identify low-risk patients who can be spared the burden of additional cytotoxic therapy.

Methylation Markers for Disease Prognosis Molecular classification holds great promise for assessment of optimal treatment options. DNA methylation markers have been reported as both prognostic and predictive markers, indicating the

Methylation Markers for Treatment Response Prediction A number of studies have provided evidence that specific methylation changes are associated with responses to a variety of cancer

References

therapeutic agents currently used in the clinics (reviewed in Maier et al., 2005). These alterations in methylation patterns could serve as predictive markers of drug response and be used by clinicians and patients to support treatment selection. Furthermore, methylation markers could be developed together with a novel drug during clinical trials in order to better select the patients who are likely to respond. The inclusion of predictive markers in clinical trials is becoming increasingly important as more targeted therapies are being tested. Many anti-cancer therapies disturb DNA integrity and thus interfere with DNA synthesis and successful cellular replication, ultimately inducing cell death. Tumor response to DNA damaging agents is closely linked to expression of several DNA-repair enzymes, many of which are regulated by methylation. Resistance can result from either a gain of methylation event or loss of methylation event. Methylation of MGMT, which codes for a DNArepair enzyme, is associated with response to alkylating agents. In this case, tumor cells rely on the excess MGMT product to specifically repair the damage that occurred during therapy with the alkylating agent (Ludlum, 1990). Esteller et al. (2000) investigated the methylation status of the MGMT gene in 47 glioma samples from patients treated with the chloroethylating agent carmustine. The MGMT promoter region was methylated in 19 (40%) of the samples, 12 (63%) of which were from patients who had a response to carmustine. Of the 28 tumors with unmethylated MGMT, only 1 patient (4%) responded to the drug (p  0.001, univariate analysis). Paz and coworkers (2004) found similar results in glioma patients. Balana and colleagues (2003) demonstrated that detection of these treatment response markers might be possible in serum for disease where affected tissue is unavailable. MLH1 is a mismatch repair enzyme that is activated in response to DNA damage. However, MLH1 expression is not only required to repair the damage, but also seems to be linked to apoptotic signaling: Its activation induces processes leading to programmed cell death. Therefore, in contrast to MGMT, methylation of the MLH1 promoter is associated with resistance to DNA damaging agents, such as temozolomide, dacarbazine, and

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cisplatin. Expression of MLH1 increases the effect of the drugs, presumably by acting as a sensor detecting DNA damage caused by the drug, and by activating processes that eventually lead to apoptosis of the cell (Agarwal and Kaye, 2003; Brown et al., 1997; Karran and Bignami, 1994). Other drug classes for which methylation markers were shown to be associated with tumor response in patients or sensitivity of preclinical models are taxanes (CHFR, TRAG3, RASSF1, BRCA1), platinum compounds (ABCB1, FANCF, RASSF1), retinoids (RARB, RBP1), and anti-hormonal therapies (ESR1, ESR2, AR) (Maier et al., 2005).

CONCLUSIONS With methylation-specific restriction enzymes, methylationbinding proteins, and bisulfite conversion, researchers have many technical options for detecting and measuring methylation information in the human genome. While MSP and bisulfite sequencing have been the workhorses in basic research on methylation, new developments in real-time PCR and arraybased analysis are expanding the repertoire. Techniques such as MethyLight have the level of analytical performance required for clinical application, allowing basic research on aberrant methylation in disease to be translated into sensitive and specific diagnostic tests that will be extremely valuable to clinicians and patients. The growing knowledge of aberrant methylation has already been integrated into models of carcinogenesis, and a complete understanding of disease initiation and progression must incorporate epigenetic analysis as well as traditional genetic analysis. With the development and validation of biomarkers based on aberrant methylation events, the emergence of methylation markers for clinical oncology is anticipated in short order. The wealth of information provided by methylation analysis in oncology and the fundamental importance of methylation in gene expression are certain to drive the expansion of methylation analysis into autoimmune, cardiovascular, and other diseases as well.

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Bird, A.P. and Southern, E.M. (1978). Use of restriction enzymes to study eukaryotic DNA methlation: I, The methylation pattern in ribosomal DNA from Xenopus laevis. J Mol Biol 118, 27–47. Brown, R., Hirst, G., Gallagher, W., McIlwrath, A., Margison, G., van der Zee, A. and Anthoney, D. (1997). hMLH1 expression and cellular responses of ovarian tumour cells to treatment with cytotoxic anticancer agents. Oncogene 15, 45–52. Chen, R., Pettersson, U., Beard, C., Jackson-Grusby, L. and Jaenisch, R. (1998). DNA hypomethylation leads to elevated mutation rates. Nature 395, 89–93. Colella, S., Shen, L., Baggerly, K.A., Issa, J.-P.J. and Krahe, R. (2003). Sensitive and quantitative universal Pyrosequencing methylation analysis of CpG sites. BioTechniques 35, 146–150. Costello, J.F., Fruhwald, M.C., Smiraglia, D.J., Rush, L.J., Robertson, G.P., Gao, X.,Wright, F.A., Feramisco, J.D., Peltomaki, P., Lang, J.C. et al. (2000). Aberrant CpG-island methylation has nonrandom and tumour-type-specific patterns. Nat Genet 24, 132–138. Costello, J.F., Smiraglia, D.J. and Plass, C. (2002). Restriction landmark genome scanning. Methods 27, 144–149. Cottrell, S. and Laird, P. (2003). Sensitive detection of DNA methylation. Ann N Y Acad Sci 983, 120–130. Cottrell, S., Distler, J., Goodman, N., Mooney, S., Kluth, A., Olek, A., Schwope, I., Tetzner, R., Ziebarth, H. and Berlin, K. (2004). A realtime PCR assay for DNA-methylation using methylation-specific blockers. Nucleic Acids Res 32, e10. Cross, S.H., Charlton, J.A., Nan, X. and Bird, A.P. (1994). Purification of CpG islands using a methylated DNA binding column. Nat Genet 6, 236–244. Cunningham, J.M., Christensen, E.R.,Tester, D.J., Kim, C.Y., Roche, P.C., Burgart, L.J. and Thibodeau, S.N. (1998). Hypermethylation of the hMLH1 promoter in colon cancer with microsatellite instability. Cancer Res 58, 3455–3460. Eads, C., Danenberg, K., Kawakami, K., Saltz, L., Blake, C., Shibata, D., Danenberg, P. and Laird, P. (2000). MethyLight: A high-throughput assay to measure DNA methylation. Nucleic Acids Res 28, E32. Eckhardt, F., Lewin, J., Cortese, R., Rakyan,V.K., Attwood, J., Burger, M., Burton, J., Cox, T.V., Davies, R., Down, T.A. et al. (2006). DNA methylation profiling of human chromosomes 6, 20, and 22. Nat Genet 38, 1378–1385. Ehrlich, M., Gama-Sosa, M.A., Huang, L.H., Midgett, R.M., Kuo, K.C., McCune, R.A. and Gehrke, C. (1982). Amount and distribution of 5-methylcytosine in human DNA from different types of tissues of cells. Nucleic Acids Res 10, 2709–2721. El-Maarri, O., Herbiniaux, U., Walter, J. and Oldenburg, J. (2002). A rapid, quantitative, non-radioactive bisulfite-SNuPE- IP RP HPLC assay for methylation analysis at specific CpG sites. Nucleic Acids Res 30, e25. ENCODE Project Consortium (2007). Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816. Esteller, M. (2006). The necessity of a human epigenome project. Carcinogenesis 27, 1121–1125. Esteller, M., Garcia-Foncillas, J., Andion, E., Goodman, S., Hidalgo, O., Vanaclocha,V., Baylin, S. and Herman, J. (2000). Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med 343, 1350–1354. Feinberg, A.P. (2004). The epigenetics of cancer etiology. Semin Cancer Biol 14, 427–432. Feinberg, A.P. (2007). Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440.

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RECOMMENDED RESOURCES Websites

Journal

http://www.epigenome.org – The website for the Human Epigenome Project. http://www.microarrays.ca – This is the website for the microarray center at the University Health Network in Toronto. They describe a spotted array containing over 12,000 CpG island clones. http://epigenome-noe.net/ – The website for the Epigenome Network of Excellence, a consortium of 25 European research groups. http://www.dnamethsoc.com/ – Website for the Epigenetics Society.

Laird, P.W. (2003). The power and promise of DNA methylation markers. Nat Rev Cancer 3(4), 253–266. This paper provides a comprehensive review of methylation detection and measurement methods as well as a review of the literature applying these methods to clinical samples.

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12 Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine Michelle M. Kittleson, Rafael Irizarry, Bettina Heidecker and Joshua M. Hare

INTRODUCTION Transcriptomics is defined as the genome-wide study of mRNA expression levels. With this promising technology, it is now possible to assess the expression of tens of thousands of gene transcripts simultaneously, providing a resolution and precision of phenotypic characterization of the transcriptome not previously possible. Because the state of the transcriptome in a given diseased tissue may contain a highly accurate representation of key biological phenomena, patterns of gene expression have potential to provide insights into disease mechanisms and also to identify markers useful for diagnostic, prognostic, and therapeutic purposes. This chapter will provide an overview of this powerful new technology, with a discussion of the statistical methods and applications of transcriptomics as well as current issues in this field. We discuss microarrays that are based on genomic information as well as specialized microarrays designed to detect micro-RNAs.

GENE EXPRESSION TECHNOLOGY Types of Microarrays The study of the transcriptome can be accomplished with the use of microarrays. Microarrays rely on the principle of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

complementary hybridization of nucleotide sequences and have taken advantage of robotic technology to create platforms as small as a few inches wide, containing tens of thousands of DNA sequences for analysis. There are two commonly used types of gene expression microarrays. The first type, traditionally known as cDNA microarrays, utilizes relatively long multimers of probe cDNAs (500–5000 bases). Such microarrays are made primarily in individual institutions and are often organ- or disease-specific, examples include the CardioChip from Brigham and Women’s Hospital (Barrans et al., 2001) and the LymphoChip from Stanford University (Alizadeh et al., 1999). Usually, one or a few long probes are used to assay the expression of a given gene. They often use two-colors to measure the identity and intensity of gene expression (i.e., red and green), so that on an individual microarray, the gene expression of two samples is compared or individual samples are compared to a reference sample. Thus, the two samples are tagged with distinct labels that fluoresce at a distinct and non-overlapping wavelength and the relative, as opposed to absolute, level of expression is obtained (Figure 12.1) (Albelda and Sheppard, 2000). The second type of microarray is known as an oligonucleotide microarray (Figure 12.2). These microarrays use smaller DNA probes (20–80 bases) and have a number of advantages: (i) they are commercially produced by companies such as Affymetrix, Copyright © 2009, Elsevier Inc. All rights reserved. 143

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(a) RNA isolation Sample A

Sample B

(e) Imaging Sample A  B Sample B  A

Sample A  B

(b) cDNA generation (c) Labeling of probe Reverse Transcriptase Fluorescent tags



(d) Hybridization to array

Figure 12.1 The use of two-color cDNA microarrays. First, the messenger RNA (mRNA) is isolated from two separate samples (a). The mRNA from each sample is treated with reverse transcriptase (b) and labeled with a distinct fluorescent tag (c). The two pools of labeled RNA are hybridized to the microarray, containing a full set of thousands or tens of thousands of DNA sequences, (d). The microarray is scanned and the color of each spot is determined (e). In this example, genes expressed only in Sample A would be red, genes expressed only in Sample B would be green and those genes expressed equally in both samples would be yellow. This allows genes that are specifically expressed in response to the specific treatment or disease to be determined. (Reproduced with permission from Albelda, S.M. and Sheppard, D. Functional genomics and expression profiling: Be there to be square. Am J Respir Cell Mol Biol 2000: 23, 265–269).

Agilent, Amersham, or AME Bioscience, and thus the quality control is standardized and the cost of production is minimized; (ii) they typically contain an unbiased genome-wide rather than organ system-specific set of probes; and (iii) they contain more gene transcripts (almost 50,000 on the newest Affymetrix U133 Plus 2.0 GeneChip Array, for example). These are one-color microarrays, so that individual chips are used to study individual samples. In addition, multiple short probes are used to assay the expression of a given gene. Thus, a given gene may be represented by multiple probes spanning the length of the gene, such that the absolute gene expression is determined by incorporating the level of hybridization intensity of all the probes that make up a given gene. Both cDNA and oligonucleotide microarrays consist of a solid support, usually a glass slide or a nylon membrane, onto which DNA sequences are attached. For a microarray experiment, RNA from a sample is extracted, labeled, and, after

hybridization, complementary sequences remain bound to the array. Expressed genes are identified by the position of the bound probes on the microarray, and their abundance is determined by the intensity of the measured signal. Thus, an experiment with a single microarray can provide information on the expression of thousands of genes simultaneously: the transcriptome of a given tissue sample can thus be determined. The two most popular microarrays can be divided into Affymetrix GeneChips, a one channel, multi-probe, high-density oligonucleotide microarray, and two-color, one-probe per gene cDNA microarrays.There is no company that dominates the market for the latter so we will refer to these here as two-color arrays. Quality Control After microarray hybridization, the next essential step in microarray analysis is assurance of adequate array quality. All arrays

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Figure 12.2 (a) Schematic relating the oligonucleotide microarray platform, probe sequences, and scanned, hybridized image. The microarray is a solid support upon which nucleic acid probes corresponding to known gene transcripts are attached to specific locations. (b) The many steps involved in microarray analysis. Total RNA is isolated from tissue samples and used to synthesize doublestranded cDNA, which is then used as a template to make biotin-labeled cRNA. Fragmented, biotin-labeled cRNA is hybridized to a microarray, containing nucleic acid probes attached to the solid support. After washing, the microarray is scanned. By monitoring the amount of label associated with each probe location, it is possible to infer the abundance of each mRNA species represented. (Reproduced with permission from www.affymetrix.com).

should be subject to quality control. Array manufacturers typically provide software for quality control assessments. Typically these include tools for visual inspection of the array images for the presence of artifacts, and assessment of background levels (Figure 12.3). The academic community has produced quality control methodology as well for the Affymetrix (Bolstad et al., 2005) and two-color platforms (Ritchie et al., 2006). Only

arrays in which all parameters are within acceptable levels should be subject to further analysis. Normalization The final step in microarray preparation is conversion of feature-level data into gene expression levels (see also Chapter 13). Because genes on a microarray are represented by multiple

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TABLE 12.1 Two major applications of microarray studies – Gene discovery and molecular signature analysis Gene discovery

Molecular signature analysis

Goal of analysis

Elucidate novel genetic pathways and therapeutic targets

Refine diagnosis and treatment

What is identified

Differentially expressed genes between disease states

A pattern of genes that characterizes a clinical parameter: diagnosis, prognosis, or response to therapy

Goal of validation

Confirm levels of gene expression

Assess the predictive accuracy of the molecular signature

Validation strategy

Quantitative PCR, Northern blotting, RNase protection assays

Apply the molecular signature to independent samples

(b)

Figure 12.3 Scanned images of hybridized Affymetrix microarrays. (a) A proper scanned image, with uniform intensity and no image artifacts. (b) An image with artifacts. The four, irregular dark spots at the center of the scanned image suggest irregularities of hybridization caused by variation in technique. This could affect the evaluation of gene expression and a new sample should be processed.

features, the hybridization intensities must be summarized to determine the overall expression or relative expression levels. In the case of two-color platforms, red and green foregrounds and background measurements are summarized into one value that quantifies relative expression between the two samples. In the Affymetrix platforms, the various features representing a gene need to be summarized into one value that quantifies expression. Background correction and normalization are issues that are common to all platforms. Background and signal adjustment corrects for background noise, adjusts for cross hybridization, and ensures that all values fall on the proper scale. Normalization eliminates systematic differences between arrays, either using a reference array or the combined information from all the arrays. The background-corrected, normalized, and converted probe summaries of gene expression can then be used for gene-level microarray analysis.

Major Applications of Microarray Analysis The challenge in microarray experiments is in the experimental design and statistical analysis, where the number of variables assessed is orders of magnitude higher than the number of individuals studied. There is concern of the dangers of data-mining, where lists of thousands of genes are generated without an understanding of the technical and statistical pitfalls of microarray analysis. To avoid this, it is essential to understand the distinct applications of this technology. Because the state of the transcriptome in a given diseased tissue may contain a highly accurate representation of key biological phenomena, patterns of gene expression have potential to provide insights into disease mechanisms and also to identify markers useful for diagnostic, prognostic, and therapeutic purposes. Thus, the two distinct major applications of this powerful technology are gene discovery and molecular signature analysis, and these two applications have different goals, statistical methods, and validation strategies (Table 12.1).

GENE DISCOVERY Overview Gene discovery involves the identification of differentially expressed genes between two or more states, for example disease

Gene Discovery

and normal tissue. The goal of gene discovery is to elucidate novel genetic pathways or cause-specific therapies. Experimental Design Given that gene discovery involves the comparison of gene expression between two or more disease states, samples must be chosen that belong to different groups: for example, normal versus disease, with and without treatment, or before and after an intervention. However, three major issues in experimental design bear further consideration: the utility of randomization, replication, and pooling. 1. Randomization is not widely used in biomedical laboratory experiments, but offers significant advantages here. One of the most striking aspects of oligonucleotide microarray experiments is the extent to which the experimental conditions can affect the resulting expression data. While normalization methods can reduce the impact of some of these differences, randomization is the best way to deal with them. In this manner, the samples can be grouped so that any unique chance features are shared equally by the two groups and thus are not potentially confounded with group differences. For example, all normal samples should not be analyzed on one day and all diseased samples on the next day. Similar considerations can apply whenever differences will occur across other factors known to contribute extraneous variability, such as chip batch, operator, reagents, scanners, and so on. Arrange those varying factors that can be controlled in a manner similar to that just described, and randomize across the remainder (Bolstad et al., 2004). 2. Another major issue in experimental design is whether replication should be performed. Here, it is essential to make the distinction between technical replicates and biological replicates. Technical replicates involve multiple measurements from the same tissue sample, whereas biological replicates involve multiple measurements from the same disease state. Clearly biological replication leads to data that are better for reaching conclusions that might apply more generally to the disease state, and technical replication leads to data better for reaching conclusions about that particular sample. In most cases, biological replicates are more relevant to the aims of an experiment than technical replicates. Chips are expensive, and biological replication will allow better use of limited resources getting more data at the level exhibiting higher variation, across subjects, say, than at the level exhibiting lower variation, within subjects (Bolstad et al., 2004). 3. A third major experimental design issue in gene discovery analyses is whether or not to pool samples. One perspective is that pooling provides a form of “biological averaging” and should make it possible to get more precise results with fewer chips than hybridizing RNA from individual subjects to separate chips. While this may be true, there is no compelling evidence for this in the literature, and pooling could mislead without being apparent. Suppose that one subject contributing to the pool is very different from the

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remainder. Depending on the tissue under study, pooling this subject’s RNA with that of the others gives it the ability to exert a large influence on the measured expression values for many genes, whereas running it on a separate chip leaves open the possibility of identifying this subject as an outlier. Furthermore, averaging can be performed at the statistical instead of biological level. This provides the same variance reduction property without losing the ability to detect outliers and assess variance. Thus, if pooling is envisaged in an experiment that could be carried out at the same cost without pooling, it is better not to pool (Kendziorski et al., 2005). Frequently, pooling is seen to be necessary to get sufficient mRNA from the tissue in question, in which case the possible drawbacks must inevitably be accepted or at least weighed against the possible drawbacks of the alternative, which is usually amplification (Bolstad et al., 2004). Statistical Methods Many statistical methods have been used to identify differentially expressed genes. The first attempts simply looked at fold-changes, and fold-changes 2 were initially considered meaningful. However, investigators soon realized that the significance of twofold change depends on both gene and sample. For this reason, it is now considered essential to compare expression between two or more groups by taking into account not only the magnitude of the difference between groups, but also the variability of expression between groups. The naïve solution to summarizing the relationship within and across-group variances is the well-known t-test/F-test and their respective p-values. However, there are two major problems with this approach. First, for small samples (fewer than 5 subjects), the t-and F-statistics tend to favor genes that, by chance, have small within-group variances. The second problem is that of multiple comparisons. When performing thousands of tests at once, the customary definition of a p-value no longer holds. For example, if we are measuring expression for 10,000 genes, all of which have no true across-group difference, we expect the 100 genes to reach p-values equal or smaller than 0.01. Various solutions have been given to both these problems. Empirical Bayes methods and Stein estimators have been proposed to improve estimates of within-group variation (Cui et al., 2005; Kendziorski et al., 2003; Lonnstedt and Speed, 2002; Smyth, 2004), and these methods greatly improve the results obtained with the t-test in small samples. Solutions to the multiple comparison problem have also been proposed (Dudoit et al., 2003; Storey, 2002). A popular piece of software that combines all these ideas is the Significance Analysis of Microarrays (SAM) (Tusher et al., 2001). SAM identifies genes with statistically significant changes in expression by identifying a set of gene-specific statistics (similar to the t-test, thus taking into account both magnitude of change and variability of expression) and a corresponding false discovery rate (similar to a p-value adjusted for multiple comparisons; see Figure 12.4). These ad hoc procedures provide similar results to the more rigorous procedures cited above.

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(a)

d(i ) 

Observed relative difference d(i )

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efficiency, as well as posttranslational protein modification and interactions, will all affect the measured protein abundance.

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10

Overview In contrast to transcriptome analysis for gene discovery, the goal of molecular signature analysis is to identify a pattern of gene expression that is associated with a clinical parameter, such as etiology, prognosis, or response to therapy, thus potentially providing diagnostic or prognostic precision greater than that available from standard clinical information (Table 12.1).

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Figure 12.4 Depiction of Significance Analysis of Microarrays method of identifying differentially expressed genes. (a) SAM uses a modified t-test statistic to determine the observed and expected relative differences in gene expression. To calculate the observed relative difference in gene expression, d(i), the difference in average gene expression between the two groups is divided by the standard deviation of repeated measures of gene expression, s(i) and a constant, so to minimize the impact of genes with low levels of expression. The expected relative difference is the average of the relative difference of all possible permutations of the samples. (b) The observed relative difference in gene expression is then plotted against the expected relative difference, and this scatterplot is used to determine which genes are differentially expressed. Differentially expressed genes are those displaced from the line of unity by a distance greater than the chosen threshold, and the threshold is chosen to minimize the false discovery rate. (Reproduced with permission from Tusher, V.G., Tibshirani, R. and Chu,G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc Nat Acad Sci USA 98, 5116–5121).

Validation Once differentially expressed genes are identified, the transcript abundance of selected genes of interest are confirmed by a complementary method, such as quantitative PCR, Northern blotting, or RNase protection assays (Cook and Rosenzweig, 2002b). Less commonly, levels of the corresponding protein have also been measured, with less agreement between transcript and protein abundance. However, this is not surprising, since differences in mRNA localization, processing, stability, translation

Experimental Design Many features already discussed in the section on “Gene Discovery” apply as well to the design of experiments focused on molecular signature analysis, including the importance of randomization of experimental conditions and the utility of biological, rather than technical, replicates. While pooling samples is not ideal in microarray analyses focused on gene discovery, it is clearly impossible in studies focused on molecular signature analysis since the identity of individual samples is the goal. Thus, pooling of samples should never be performed in a study focused on molecular signature analysis. In molecular signature analysis, samples are first divided into groups based on a clinically relevant parameter, such as disease etiology, prognosis, or response to therapy. Then a molecular signature is created by choosing genes whose expression is solidly associated with the parameter in question, and by weighting genes based on their individual predictive strengths. Statistical Methods From a mathematical standpoint, molecular signature analysis is nothing other than what the computer science community denotes “machine learning” and the statistics community denotes “classification.” There are dozens, if not hundreds, of methods that have been used successfully in real-world applications such as voice recognition, zip code readers, and fraud detection. Relatively simple methods that are successful in other fields appear to work well in the context of microarrays. However, all these methods have been developed for cases where the number of predictors is smaller than the number of outcomes. With microarrays, this is rarely the case, given that there are more than 10,000 predictors. Nevertheless, if the number of genes included as predictors can be reduced to, say, less than 50 and chosen according to evidence in favor of differential expression, then existing methods work rather well (Dudoit et al., 2002). In fact, the simplest methods, including K-nearest methods and diagonalized linear discriminant analysis (DLDA), work better than more complicated ones. Prediction Analysis of Microarrays (PAM) is one approach, similar to DLDA, that is designed specifically for molecular signature analysis (Tibshirani et al., 2002).

Molecular Signature Analysis

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Figure 12.5 Depiction of Prediction Analysis of Microarray’s method of nearest shrunken centroids. Samples are first divided into classes based on a pre-defined parameter. In this case, there are four groups from a dataset containing samples of four different types of small round blue cell tumors of childhood: Burkitt lymphoma (BL), Ewing sarcoma (EWS), neuroblastoma (NB), and rhabdomyosarcoma (RMS). The gray bars represent the standardized class centroid, the average expression of each gene in a given class divided by the within-class standard deviation (thus genes with stable expression have a greater contribution to the class centroid). The red bars represent the shrunken centroid, “de-noised” versions of centroids after subtraction of a given threshold determined by crossvalidation. After shrinkage, a small number of genes remain and these act as prototypes for each class. Independent samples are classified based on their squared distance from each prototypic class shrunken centroid. (Reproduced with permission from Tibshirani et al. 2002. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Nat Acad Sci USA 99, 6567–6572).

PAM uses the method of nearest shrunken centroids to identify and validate the smallest set of genes whose expression is associated with a predefined class (Figure 12.5). A standardized class centroid is a composition of mean values of expression of all individual genes in a given class divided by the within-class standard deviation (SD). Standardization allows greater weight to be given to genes whose expression is stable within a class.

PAM creates shrunken centroids by shrinking the class centroids towards the overall centroid by a threshold amount. This overall centroid is calculated by dividing the average expression of each gene in all classes by the pooled within-class SD. Thus, the shrunken centroids are “de-noised” versions of centroids that act as prototypes for each class. The threshold is chosen by 10-fold cross-validation. In this process, gene expression prediction profiles

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Overall CV plot 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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Figure 12.6 Sample output from Prediction Analysis of Microarrays (PAM) software. (a) Cross-validation plot listing the error rates in classification from 10-fold cross-validation of the training set. The x-axis is the threshold and number of genes in the molecular signatures, and the y-axis is the error rate. Here, the error rate in the training set is minimized by between 52 and 545 genes. Thus, a profile with 52 genes will be chosen as the molecular signature in order to minimize both the error rate and the number of genes in the profile. (b) The output using this 52-gene signature on the test set to determine if samples belong to class 1 or class 2. PAM provides the predicted class as well as the probability of belonging to class 1 or class 2. The sample is assigned to the class with the greater probability.

with different numbers of genes (i.e., from different thresholds) are fit on the basis of 90% of the samples and then tested on the remaining 10%. This process is executed multiple times, and the output is the error rate using different thresholds. The prediction profile is chosen as the smallest list of genes that can be used to predict the classification of an individual sample with the minimal error rate (Figure 12.6). To then classify independent samples, PAM computes each test sample’s squared distance from each of the class centroids.The predicted class is the one whose centroid is closest to the expression profile of the test sample. In contrast to SAM and other methods of identifying differentially expressed genes, PAM focuses mainly on the stability of gene expression and on the smallest number of genes required to create a molecular signature. Validation While the goal of gene discovery is to identify differentially expressed genes that offer insight into novel genetic pathways or cause-specific therapies, the goal of molecular signature analysis is to identify a pattern of genes that differentiates between clinical entities with a precision not possible based on standard clinical information. Thus, the identity of the mRNA transcripts in the signature or whether they are translated into protein may or may not have immediately discernable bearing on the utility of

the pattern. Therefore, the validation strategy is also unique: testing the accuracy of the identified molecular signature in samples distinct from those used to create the signature: an independent test set of samples (Table 12.1). However, because most laboratories have access to only a limited number of samples, withholding a substantial proportion of the samples from the training set for the sake of creating a validation set may considerably reduce the performance of the molecular signature. Cross-validation procedures use the data more efficiently. A small number of specimens are withheld, and most of the specimens are used to build a signature. The signature is used to predict class membership for the withheld specimens. This process is iterated, leaving out a new set of specimens at each step, until all specimens have been classified. In leave-one-out cross-validation, for example, each specimen is excluded from the training set one at a time and then classified on the basis of the predictor built from the data for all of the other specimens. The leave-one-out crossvalidation procedure provides a nearly unbiased estimate of the true error rate of the classification procedure (Simon et al., 2003a). However, when cross-validation (instead of an independent dataset) is used, all three steps of creating a molecular signature must undergo cross-validation, including: (i) selection of informative genes, (ii) computation of weights for selected informative

Current Issues in Gene Expression Analysis

genes, and (iii) creation of a prediction rule. Failure to crossvalidate all steps may lead to substantial bias in the estimated error rate and is a major pitfall in studies focused on molecular signature analysis (Simon et al., 2003b). Applications in Cancer Research In neoplastic disease, molecular signature analysis can determine prognosis and response to therapy. In breast cancer, a molecular signature was identified that predicted disease outcome in young patients with breast cancer better than standard clinical and histological criteria: a poor prognosis signature was associated with a fivefold increased risk of distant metastases in 5 years, a difference that would justify early intensive adjuvant chemotherapy (van de Vijver et al., 2002). In large B-cell lymphoma, a molecular signature predicted survival better than standard clinical methods (Rosenwald et al., 2002). Similar results have been obtained for acute myeloid leukemia (Bullinger et al., 2004; Valk et al., 2004), chronic lymphocytic leukemia (Calin et al., 2005), prostate cancer (Dhanasekaran et al., 2001; Lapointe et al., 2004), CNS tumors (Pomeroy et al., 2002), and non-small cell lung cancer (Chen et al., 2007). After its successful application in cancer, molecular signature analysis has been expanded to other fields in medicine, for example, congenital diseases (Eshaque and Dixon, 2006) enabling even preimplantation diagnosis (Salvado et al., 2004), and cardiovascular medicine. Applications in Cardiovascular Disease In cardiovascular disease, molecular signature analysis is in its earliest stages, but shows promise. In a proof-of-concept study, a diagnostic TBB was developed, which is able to distinguish between the two major forms of cardiomyopathy, ischemic and nonischemic (Kittleson et al., 2004). Several studies, investigating transcriptomic changes during left ventricular assist device (LVAD) placement followed, suggesting possible TBBs of recovery, which might provide mechanistic insight and suggest potential therapeutic targets (Hall et al., 2004; Margulies et al., 2005c). However, those studies also raised an important issue in microarray analysis, namely the difficulty to distinguish transcriptomic changes that were causative for a certain event from those that could result from a bystander effect (Depre et al., 1998; Margulies et al., 2005b). A way to reduce such confounding variables, is to obtain tissue at very early disease-stages, for example, by obtaining biopsies or blood samples (Heidecker and Hare, 2007b). Clinical TBBs derived from peripheral blood mononuclear cells (PBMC) are commercially available as diagnostic microarray chips to detect early rejection in cardiac transplant recipients (Deng et al., 2006b). Further, the use of TBBs was suggested as a screening tool for cardiovascular patients, who might require cardiac catheterization and stent implantation (Seo et al., 2004). Function of MicroRNAs in Cardiology Recent transcriptomic studies have discovered important regulatory functions of microRNAs (miRNAs) in cardiac remodeling

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and heart failure (van et al., 2007b). MiRNAs are non-translated small single-stranded RNA molecules of 21–23 nucleotides in length. These small RNA species (Jackson and Standart, 2007b) either degrade bound mRNA or directly inhibit translation of mRNA by pairing with their target mRNAs in a sequence specific manner. Thus, miRNAs conduct negative regulatory function on gene expression (Jackson and Standart, 2007a). MiRNA of an intron of the MHC gene has been shown to be responsible for stress-induced cardiomyocyte growth and gene expression (van et al., 2007a). These findings point out the significant impact of gene–environment interactions on the phenotype of a cell or tissue and emphasize the value of comprehensive highthroughput technologies such as transcriptome profiling (Birney et al., 2007).

GENE DISCOVERY VERSUS MOLECULAR SIGNATURE ANALYSIS One important distinction in molecular signature analysis is the need first to identify a set of genes whose expression characterizes a pre-defined group of patients (with a clinically relevant distinction such as etiology, prognosis, or response to therapy), and then to test the predictive accuracy of this profile prospectively in an independent set of patients with varying phenotypes (Cook and Rosenzweig, 2002a). Another important distinction is that molecular signature analysis is based upon a pattern of gene expression rather than the identity of specific genes (Simon et al., 2003c) (Table 12.1). The prediction algorithm is able to compare an unknown sample and determine how closely it resembles one pattern versus the other; the absolute expression of an individual gene carries relatively small weight compared to the overall signature. Thus, validation cannot solely involve the confirmation of gene expression or gene product levels via a complementary technique such as quantitative PCR or immunofluorescence. Nevertheless, such validation can prove useful to address a different issue: whether the molecular signature offers utility independent of the microarray platform used to create it. This is important if diseasespecific platforms are developed.

CURRENT ISSUES IN GENE EXPRESSION ANALYSIS There are a number of issues in transcriptomic analysis that are being addressed by workers in the field (Table 12.2). Information Management and Reproducibility in Microarray Experiments Given the rapid growth of microarray research and the vast amount of information that can be gleaned from a single experiment, there are many challenges in designing studies and interpreting the results. To this end, the Microarray Gene Expression Data society (MGED) has developed the Minimum Information

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TABLE 12.2 Current unresolved issues in transcriptomic analysis Current issues

Solutions

Study design, interpretation of results, reproducibility ●

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Guidelines for experimental design, sample preparation, hybridization, normalization of array data, array design

MIAME

Data sharing

Standards Public Databases: National center for biotechnology information (NCBI); Gene expression omnibus (GEO); European bioinformatics institute (EMBLEBI) arrayexpress repository; Center for information biology gene expression (CIBEX); Cardiogenomics

Sample source ●

Relevance of investigated sample for later clinical application

Sample collection at early stage of disease, preferred tissue source: biopsies

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Possible surrogates for diseased tissue or organ

PBMC

Power and sample size ●

Conventional power analysis not applicable (magnitude of change and error standard deviation not available)

Large sample size (in particular in clinical research), integrate biological knowledge with statistical analysis

About a Microarray Experiment (MIAME) standards that are needed to enable the interpretation of the results of the experiment unambiguously and potentially to reproduce the experiment (Brazma et al., 2001a). MIAME includes details of the experimental design, sample preparation, hybridization procedures, normalization algorithms, and array design. The ultimate goal is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. Such public repositories exist, including the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO); the European Bioinformatics Institute (EMBL-EBI) ArrayExpress repository; and the Center for Information Biology Gene Expression CIBEX) database. There is also a cardiology-specific repository, the Cardiogenomics Web site (www.cardiogenomics.com), a National Heart Lung and Blood Institute-sponsored Program for Genomic Applications. Many

journals currently require that submissions comply with the MIAME standard, but not all require that a complete dataset be submitted to one of three databases prior to manuscript submission. Furthermore, the public repositories do not require data being submitted in raw as well as normalized fashion, which would be essential for future studies. We believe that mandatory submission of raw microarray data files to public repositories, by promoting collaboration among scientists, could be essential for maximizing the utility of microarray research. The Source of Tissue for Analysis To date, microarray analyses in oncology have used mainly tumor tissue from biopsies. However, other pathological conditions cannot rely on such a ready source of tissue access. For example, microarray studies in cardiovascular disease have mainly utilized discarded myocardial tissue obtained at the time of cardiac transplantation, and there are limitations to this approach. The tissue is obtained from patients late in the disease course, and the conclusions, therefore, may not be applicable to patients at an earlier stage of disease. Thus, in the future, microarray analyses in cardiomyopathy will ideally focus on endomyocardial biopsy tissue obtained from patients at earlier stages of disease. Although endomyocardial biopsy is a safe procedure, future research will focus on PBMC as possible surrogate of diseased tissue, given easy accessibility by venipuncture (Heidecker and Hare, 2007a; Liew, 2005). In the cancer literature, molecular signatures derived from peripheral blood leukocytes offer comparable predictive accuracy to those from solid tumor samples in classifying subjects by cancer type and type of therapy (DePrimo et al., 2003; Twine et al., 2003). This may also be feasible in cardiovascular disease, as peripheral blood molecular signatures correlated with biopsyproven allograft rejection in cardiac transplant recipients (Deng et al., 2006a; Horwitz et al., 2004), cardiac genes in circulating blood were differentially expressed in patients with coronary artery disease relative to controls (Ma and Liew, 2003), and TBBs for familial combined hyperlipidemia were discovered in lymphocytes (Morello et al., 2004). Power and Sample Size in Microarray Experiments Clearly, molecular signature analysis is still in its earliest stages in cardiomyopathy, and there are still many unanswered questions. For example, there is limited knowledge on the sample sizes required in microarray experiments. The largest microarray study in cardiomyopathy to date has involved 199 patient samples (Margulies et al., 2005a), and in the oncology literature, each study has employed fewer than 300 patients. However, this should be considered a strength of these analyses: a succession of smaller studies, performed quickly and with the use of improving technology, will outperform larger studies locked into outdated approaches (Liu and Karuturi, 2004). The greater question is, however, whether traditional power calculations have any role in microarray experiments. A conventional power analysis is of limited value because the necessary variables are usually all unknown: we will only rarely be able

Conclusions

to nominate in advance the gene, the magnitude of change () of interest, or the magnitude of the measurement error SD. We also need to know the SD for all genes, and these are not the same. Nevertheless, it might still be argued that we should be able to determine the power we have with a given sample to detect all genes whose log fold change is some value or greater, and whose measurement SD is no more than a given (conservative) value, even if we will not know in advance which genes these are. However, there is a much greater obstacle to making use of conventional power analyses. In microarray studies, or other similar “-omic” studies, we are not measuring just one gene’s expression; in a typical experiment, we measure gene expression in 20,000 genes. Not only do we have to specify the number of genes we expect to be differentially expressed, but we need to decide on loss functions for false positives and negatives. In the typical situation where power calculations are used, we either get it right, commit one Type I (false positive) error, or one type II (false negative) error. Keep in mind that power is the probability of not committing a type II error when the null hypothesis is false. This definition does not apply to microarray applications where we report lists of genes hoping that a small percentage of the list are false positives and the list is long enough so that the number of false negatives is small. There is no easy solution to these problems, but current experiments rely on a few principles. First, one should aim to get as many case and control samples as possible, bearing in mind the important requirement of homogeneity. Second, the quality of the statistical analysis should improve, since the sample size cannot be increased beyond a certain point. A “smarter analysis” is needed to overcome the limitations of modest sample size. In addition, we need to depart from the conventional “context-free” search for differentially expressed genes that get embodied in power calculations and their multiple testing analogs, and more fully integrate biological knowledge with statistical analysis. However, even with these approaches, multiple testing issues will remain, so we have to extend that theory to apply to our stronger analysis. In brief, we are still in the early days of the statistical analysis of microarray data. Much traditional thinking must be extended and strengthened (Bolstad et al., 2004).

ALTERNATIVE TECHNOLOGIES FOR ANALYSIS OF THE TRANSCRIPTOME While, as presented here, microarray analysis is the most widely used approach for evaluating the transcriptome, there are several additional technologies that have been developed and have been used in particular settings. Serial Analysis of Gene Expression (SAGE) SAGE is a method of quantification of “tags”, rather than direct measurement of gene expression (Lash et al., 2000a). After isolation of mRNA, a small part of sequence is extracted from

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each transcript at a defined position. All extracted pieces get linked together for subsequent cloning in a bacterial vector. Tags contain a nucleotide sequence of 9–11 bases in length and are representative for a unique gene transcript (Lash et al., 2000b). Tags are directly 3-adjacent to the 3-most restriction site for a particular restriction enzyme. Whereas the most widely used restriction enzyme is NlaIII, others such as Sau3A have also been used (Lash et al., 2000c). Several pitfalls with this technique warrant consideration. Because of the short length of tags, genes might share common base sequences, resulting in ambiguous tag-to-gene assignment. On the other hand, one gene can have more than one tag, because of polymorphism in a population or alternate termination in an individual (Lash et al., 2000d). Finally, by providing a digital output, fidelity of this method is reduced (Lash et al., 2000e). However, results from SAGE analysis have been shown to correlate highly with microarray data (Ishii et al., 2000), and results can be shared in public databases (http://www.ncbi.nlm.nih.gov/projects/SAGE). An important advantage of SAGE compared to microarrays is that the mRNA sequence does not have to be known in advance, so new polymorphisms can be discovered. Polony Multiplex Analysis of Gene Expression (PMAGE) PMAGE has been recently introduced as an extremely sensitive technique, which detects mRNA even in low abundance as small as one transcript per three cells (Kim et al., 2007a). Each mRNA is converted to cDNA, which gets cleaved and amplified onto beads carrying primers (Kim et al., 2007b). Each polony (for “PCR colony”) bead gets cross-linked to an aminosylated glass slide, from which fluorescent signal intensity can be measured via microscopy-based imaging (Kim et al., 2007c). Massively Parallel Signature Signaling (MPSS) MPSS was developed primarily to reduce time and cost of DNA sequencing as compared to conventional techniques (i.e., Sanger Sequencing (Sanger et al., 1977) and fluorescence-based electrophoresis technologies (Prober et al., 1987)). MPSS enables sequencing of as much as 25 million bases in a four-hour period, a rate that is about 100-times faster than current stateof-the art conventional sequencing. DNA is fragmented and bound to beads that are encased in droplets containing all reactants necessary to amplify the DNA. After amplification to about 10 million copies, DNA carrying beads are loaded into picolitre reactor wells and sequenced via luciferin reaction (Rogers and Venter, 2005). The same principles have been used for quantification of gene transcripts (Jongeneel et al., 2003).

CONCLUSIONS In conclusion, transcriptomics with microarray or other technologies offers a powerful means of phenotypic resolution not possible with standard clinical or histological criteria. The

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promising applications of gene expression analysis include gene discovery and molecular signature analysis.These applications could elucidate novel genetic pathways, provide insight into causespecific therapies, and provide information on prognosis and response to therapy to individualize patient management in a variety of diseases. Although there are unresolved issues in this field, including power and sample size requirements, there is still

much promise, and we can expect further advances in refining the design and statistical methods of this technology in the future.

ACKNOWLEDGEMENTS This work was supported by NIH grant HL-65455 to JHM.

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RECOMMENDED RESOURCES Books Pevsner J. (2003). Bioinformatics and Functional Genomics. John Wiley & Sons, Inc. Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A., Dudoit, S. (2005). Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer ScienceBusiness Media, Inc.

Public Online Databases Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/ MGED society: http://www.mged.org/Workgroups/MIAME/miame.html European Bioinformatics Institute: http://www.ebi.ac.uk/ Swiss Institute of Bioinformatics: http://www.isb-sib.ch/ DBGET Database: http://www.genome.jp/dbget/dbget.links.html Weizmann Institute of Science, Gene Cards: http://www.genecards.org/ index.shtml

Kyoto Encyclopedia of Genes and Genomes: http://www.genome. jp/kegg/

Download Sites for Free Software Bioconductor: open source software: http://www.bioconductor.org/ Significance Analysis of Microarrays: http://www-stat.stanford.edu/tibs/ SAM/ Prediction Analysis of Microarrays: http://www-stat.stanford.edu/tibs/ PAM/ The R Project for Statistical Computing: http://www.r-project.org/ DNA chip Analyzer (dChip): http://biosun1.harvard.edu/complab/dchip/ install.htm Gene Pattern: http://www.broad.mit.edu/cancer/software/genepattern/

CHAPTER

13 DNA Microarrays in Biological Discovery and Patient Care Andrew J. Yee and Sridhar Ramaswamy

INTRODUCTION While “expression profiling” is now synonymous with DNA microarray technology, expression profiling dates back to the 1970s with techniques such as northern blotting for measuring RNA expression (Alwine et al., 1977). However, only one gene at a time could be queried with these modalities. Later developments enabled analysis of multiple genes (Liang and Pardee, 2003), including differential hybridization (Sargent, 1987), subtractive hybridization (Zimmermann et al., 1980), differential display (Liang and Pardee, 1992), and serial analysis of gene expression (SAGE) (Velculescu et al., 1995). However, these approaches required a significant amount of starting RNA and investment in labor and technical expertise, and were difficult to scale up. DNA microarrays revolutionized the approach to gene expression profiling. Compared to prior methods, DNA microarrays were both dramatically high throughput and less cumbersome. It should be noted that the concept behind the microarray format was not new, as microarrays were originally developed as a technique for large-scale DNA mapping and sequencing (Hoheisel, 2006). However, changing the support surface from a porous membrane to a solid surface, such as glass, afforded significant improvements (Schena et al., 1998) by increasing reaction kinetics (Southern et al., 1999) and reducing background noise (Cheung et al., 1999).

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

As an illustration of approaches to define and develop applications of the transcriptome measured in biological samples (as reviewed in Chapter 12), here we present specific issues related to the use of DNA microarrays, using applications especially in the context of cancer to illustrate the uses and challenges of gene expression profiling in genomic and personalized medicine.

MICROARRAY TECHNOLOGY Microarray Platforms Although the technology for DNA microarrays continues to evolve, the commonly used arrays can be divided into two main categories, as described in detail in Chapter 12: (i) oligonucleotide and (ii) spotted or complementary DNA (cDNA) microarrays (see Figure 13.1). Over the years, commercial oligonucleotide platforms have gained in popularity over “homemade” spotted cDNA microarrays as costs have fallen considerably, to the point where commercial microarrays are the de facto platform. At the time of this writing, the dominant platforms are produced by Affymetrix and Agilent. For additional details, the reader is referred to a review by Hardiman (2004) and also to reviews collected in a series of Nature Genetics supplements (Bowtell, 1999; Cheung

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(a) M1 X X X X

M2 Shine light

M1 X X

X X Couple

X X A A

Repeat

Microarray (b)

Touch surface

Move pins

Repeat

Microarray

Figure 13.1 Comparison of microarray technologies. (a) Photolithography (as used by Affymetrix): a glass wafer modified with photolabile protecting groups (X) is selectively activated for DNA synthesis by shining light through a photomask (M1). The wafer is then flooded with a photoprotected DNA base (A–X), resulting in spatially defined coupling on the chip surface. A second photomask (M2) is used to deprotect defined regions of the wafer. Repeated deprotection and coupling cycles result in a highly dense microarray. (b) Spotted arrays: the gene of interest is loaded into a spotting pin by capillary action, and a small volume is transferred to a solid surface by physical contact between the pin and the solid substrate. After the first spotting cycle, the pin is washed and a second sample is loaded and deposited to an adjacent location. Robotic control systems and multiplexed printheads allow automated microarray fabrication. Adapted from Schena et al. (1998). (Reprinted with permission from Elsevier).

et al., 1999; Churchill, 2002; Duggan et al., 1999; Holloway et al., 2002; Lipshutz et al., 1999; Southern et al., 1999). Oligonucleotide Arrays Oligonucleotide microarrays are created either by in situ synthesis or deposition of presynthesized oligonucleotides ranging in size from 25- to 60-mers. Oligonucleotides can be synthesized directly in situ using photolithography techniques adapted from the microelectronics industry. The Affymetrix GeneChip platform is the primary example of this method, and because of its popularity, merits further discussion. Emerging from research carried out in the early 1990s (Fodor et al., 1991), this technology combines combinatorial, solid phase DNA synthetic chemistry with the benefits of photolithography. Successive photolithographic masks are used to select regions of the chip surface for exposure to light. Exposure to light deprotects photolabile groups on the nascent oligonucleotides undergoing synthesis. The light-mediated deprotection then allows these specified regions to couple with activated nucleoside monomers using standard DNA phosphoramidite synthetic chemistry. Each cycle thus extends the oligonucleotide by one base (see Figure 13.1). Over the past 15 years, the feature size of each oligonucleotide probe or feature has been reduced 10-fold, resulting in a 1.6 cm2 chip with 1.3 million unique features (multiple features are used to measure the expression of a particular gene). The detection sensitivity of these microarrays has been estimated at

1 in 300,000 RNAs (Lockhart et al., 1996). A practical limitation is the efficiency of each synthesis step – about 90–95%. This places a ceiling on in situ synthesis of an oligonucleotide to 25-mers. To control for non-specific binding, the probes on the Affymetrix chip are designed in pairs. One sequence is the exact complement of the target transcript, and the other paired sequence differs from the complement typically by one base pair near the middle of the probe. Difference in signal between the two probes can be used to control for non-specific binding and background contributions (Pease et al., 1994); 22 probes are routinely used for each expression measurement. Compared to cDNA arrays (see below), oligonucleotide arrays offer greater specificity and can distinguish single nucleotide polymorphisms (SNPs) and splice variants (Guo et al., 1994); the same technology is used in arrays for measuring DNA copy number and SNP profiling. The current generation of Affymetrix microarrays can measure the expression of over 47,000 transcripts. Hybridization is detected by using a confocal laser scanning microscope to image the intensity of one color, fluorescently labeled samples. First, experimental mRNA is enzymatically amplified (see below for details of RNA amplification) and then labeled with biotin through the partial substitution of UTP and CTP with biotin-11-UTP and biotin-11-CTP (see Chapter 12). The labeled mRNA is then hybridized to the microarray and detected by the binding of a fluorescent compound (streptavidin– phycoerythrin) to the biotin-labeled samples. Because of the

Microarray Technology

robustness and consistency of the manufacturing process, the single-color readout provides an absolute quantitation of mRNA populations (Ishii et al., 2000). In addition, experiments can be compared with samples hybridized to different Affymetrix arrays. Oligonucleotide microarrays may also be fabricated by using ink-jet printing technology. Nucleotide monomers are printed onto the chip and coupled using phosphoramidite chemistry (Hughes et al., 2001). Agilent has adopted this technology to create a 60-mer oligonucleotide array. Compared to the 25-mer photolithographic chip, a 60-mer array might offer increased sensitivity per probe. In contrast to the Affymetrix platform, there is only one probe per gene in this platform. The longer probes are also more tolerant of mismatches, improving analysis of polymorphic regions. Spotted, Complementary DNA Microarrays The first DNA microarrays were developed at Stanford University and based on cDNA samples spotted by a robotic arrayer at defined locations onto glass slides (Schena et al., 1995). These cDNA samples were generally PCR products generated from cDNA libraries or clone collections. The major benefit of spotted arrays is that they can be constructed in-house and tailored to the user’s needs. The features are generally 100–300 m in size and spaced at about the same distance. About 30,000 cDNAs can be spotted onto the surface of a microscope slide (Schulze and Downward, 2001). In the initial report, the sensitivity was estimated at 1 in 50,000 RNAs starting with 5 g of total RNA (Schena et al., 1995). While spotted arrays offer customizability, they are better suited for smaller-scale experiments, since from a practical perspective managing large clone libraries and creating high-quality arrays consistently can be arduous. In the past, an advantage of spotted arrays was that a priori knowledge of the sequence being spotted was not necessary. However, this is less relevant now that complete sequence information for many experimental organisms and humans is readily accessible. Gene expression using spotted microarrays is determined as a ratio of two samples rather than as an absolute measurement, owing to technical limitations and variability in spot size and probe concentration. Generally, cDNAs derived from experimental and reference RNA samples are labeled with deoxyribonucleotides coupled to different fluorophores (e.g., with Cy3 or Cy5) during first-strand cDNA synthesis. The two different populations are then competitively hybridized to the same array and imaged. While this method accurately compares expression levels between samples, comparing data from different platforms can be difficult, often requiring complex normalization algorithms (Dudley et al., 2002). Sample Preparation Tissue Sampling For biological applications, tissue sampling is an important variable. For example, tumors are heterogeneous mixtures of different cell types, including malignant cells with varying degrees of

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differentiation, stromal elements, blood vessels, and inflammatory cells. Profiling of this whole sample may dilute out the “signal” profile of cancer cells with extraneous expression “noise” from surrounding tissues. Newer techniques such as laser capture microdissection (LCM) address the challenges of tissue heterogeneity by offering an approach for isolating homogeneous, individual cell populations (Emmert-Buck et al., 1996). While there are other approaches, such as fluorescence-activated cell sorting (FACS) and manual microdissection using micromanipulators (Emmert-Buck et al., 1994), LCM is faster, more versatile, and can be applied to solid tumors directly without the need for disaggregation into a cell suspension. Sgroi et al. (1999) first showed that it was feasible to use LCM to isolate subpopulations of breast cancer cells for expression profiling. Coupled with LCM has been the evolution of methodologies for purifying adequate amounts of high-quality RNA for expression profiling (see below). However, a theoretical limitation of focusing only on individual tissue components such as cancer cells relates to the growing appreciation that interactions with the tumor microenvironment (e.g., with stroma, endothelial, and immune cells) play a critical role in tumor progression. Sampling of “non” cancer cell components thus may be necessary for a profile to adequately reflect in vivo biology. An additional consideration is that the actual surgical manipulation itself may affect the expression profiling. Lin et al. (2006) compared expression profiles of prostate cancer from biopsies obtained in situ to biopsies obtained from prostates that were surgically removed. They found that 1.5% of measurable genes were altered as a result of the surgery, including transcripts for acute phase response proteins and regulators of cell proliferation. RNA Isolation Traditionally, microarray experiments have required roughly 50–200 μg of total RNA (Duggan et al., 1999). However, these quantities are not always easily obtainable, especially with techniques such as LCM. Protocols therefore have been developed for amplifying RNA from much smaller quantities, perhaps even down to the level of a single cell. For example, a single cell contains an estimated 0.1 pg of mRNA or 10 pg of total RNA, requiring an expansion of 108–109 fold in order to yield adequate amounts for profiling. Two commonly used amplification techniques include in vivo transcription and PCR amplification. In vivo transcription is a linear amplification approach, first described by Van Gelder et al. (1990). Messenger RNA is converted to double stranded cDNA using an oligo(dT) primer attached to a T7 promoter. T7 polymerase is then used to transcribe complementary copies of the cDNA population. One round of amplification can amplify the starting amount of mRNA by 1000-fold. A second or a third round of amplification can further increase the yield (Baugh et al., 2001). A second approach takes advantage of PCR amplification. PCRbased approaches have several advantages over linear amplification, including speed, cost, and theoretically unlimited degree of amplification. While it has been assumed that exponential amplification may lead to a biased representation of the starting

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sample, results from one group suggest that this may not be as problematic as previously suggested (Iscove et al., 2002). Standardization of Data Critical to the success of data analysis is standardization of microarray data and annotation of the genes that comprise the microarrays. In 1999, the Microarray Gene Expression Database (MGED) group was created to establish guidelines for the format of microarray data. This led to the development of the Minimal Information About a Microarray Experiment (MIAME) standard (Brazma et al., 2001; Stoeckert et al., 2002). Most major scientific publications require compliance with MIAME standards and submission of data to public repositories. An additional key factor is the functional annotation of the genes being studied. Biological “knowledge” is intricate, hierarchical, and interrelated. Ontologies are a formalized, standardized means of coding biological knowledge in which concepts are systematically described by both their meaning and their relationship to each other (Bard and Rhee, 2004). Adequate ontologies can thus serve as a common anchor point for systematic integration of data from various microarray sources. An example of commonly used ontology is one devised by the Gene Ontology Consortium (Harris et al., 2004).

DATA ANALYSIS Gene expression studies pose many challenges for data organization, storage, and analysis (see Chapter 17). In concert with the development of microarray technology has been the evolution of sophisticated statistical methods for analyzing and interpreting highly dense microarray information (Ermolaeva et al., 1998; Quackenbush, 2001). These data can be one’s own experimental data or existing data from other investigators’ experiments, made available through public databases. Microarray data may be so rich in information that important findings may not be recognized initially, and only better appreciated in retrospect when re-analyzed or combined with newer data. Data analysis methodology is discussed further in the section “Informatic and Computational Platforms for Genomic Medicine” in Part I of this book and has been reviewed also by Allison et al. (2006). Unsupervised Learning The computational analysis of gene expression data has largely centered on two approaches: unsupervised and supervised learning (see Figure 13.2). Unsupervised methods include hierarchical and k-means clustering (Gaasterland and Bekiranov, 2000). The goal of clustering is to organize genes and samples clusters based on relative similarity (D’Haeseleer, 2005; Eisen et al., 1998). A hierarchy of clusters can be created, which may be visualized as tree diagrams or dendrograms. Unsupervised approaches have the advantage of being unbiased and allow for the identification of structure in a complex dataset without making any a priori assumptions. In fact, many of the initial microarray studies on cancer samples utilized unsupervised classification, for example,

lymphoma (Alizadeh et al., 2000) and breast cancer (Perou et al., 2000). However, because many different relationships are possible in a complex dataset, the predominant structure uncovered by clustering may not be clearly related to the most clinically or biologically interesting aspect of a dataset. Supervised Learning In contrast, supervised learning starts off with a known class distinction and uses this information upfront to select genes whose expression is best associated with this class distinction in a “training dataset” (Golub et al., 1999). This model is then applied to an independent “test” dataset to validate the accuracy of selected gene expression features in classification. Numerous supervised learning algorithms have been applied to gene expression datasets, such as k-nearest neighbors, neural networks, or support vector machines (Brown et al., 2000). The accuracy of supervised learning approaches heavily depends on the quality of the initial training data. Mining Several tools have been developed for distilling higher-level information from microarray data in order to better appreciate the “forest” among the innumerable “trees” of expression data. Two notable examples include modular analysis and Cancer Outlier Profile Analysis (COPA). Modular analysis examines the coordinated behavior of a set of genes in order to detect significant changes in modules of genes, rather than the individual genes themselves. This offers the ability to extract more “signal” from a given microarray experiment. Indeed, groups of gene expression may be more biologically interpretable and statistically robust than deciphering lists of individual genes. Mootha et al. (2003) used this approach to study changes in gene expression in the muscle of patients with type 2 diabetes. Their group introduced the gene set enrichment analysis (GSEA) and used the Kolmogorov– Smirnov test statistic, a type of non-parametric test statistic that does not make assumptions about population distribution. While the decrease in expression for individual genes was modest, around 20%, it was consistently decreased across a set of genes involved in oxidative phosphorylation, accounting for 89% of 106 genes. Thus, significant changes could be seen when looking at a group of genes, even when the expression of individual genes was not significantly different. GSEA has emerged as a useful tool for identifying biological processes from microarray data and has been refined into a robust method (Subramanian et al., 2005). Similarly, Segal et al. (2004) created a cancer-focused compendium of expression profiles from 26 studies. They were able to extract 456 modules and found that some modules were unique to a particular type of tumor whereas other modules were shared across a range of conditions, raising the possibility that these modules reflect common tumor progression mechanisms. For example, in acute lymphoblastic leukemia (ALL), a growthinhibitor module was specifically repressed. On the other hand, a bone osteoblastic module was found across a range of tumor types, suggesting a common mechanism for bone metastasis.

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Supervised learning

Unsupervised learning Dataset Known classes Class A Class B

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Figure 13.2 Unsupervised versus supervised learning. Unsupervised learning: multiple tumor samples are clustered into groups based on overall similarity of their gene expression profiles. This approach is useful for discovering previously unappreciated relationships. Supervised learning: multiple tumor samples from different known classes are used to train a model capable of classifying unknown samples. This model is then applied to a test set for class label assignment. Adapted from Ramaswamy and Golub (2002). (Reprinted with permission from the American Society of Clinical Oncology).

The COPA method focuses on genes characterized by marked overexpression (Tomlins et al., 2005). This strategy is used in order to efficiently process large amounts of data. COPA was applied to the Oncomine database, a compilation of 132 gene expression datasets representing 10,486 microarray experiments (Rhodes et al., 2004). This approach was instrumental for discovering two transcription factors with outlier profiles, ERG and ETV1, and subsequently shed light on recurrent gene fusions of the 5 untranslated region of TMPRS22 to ERG or ETV1 in prostate cancer tissues. This study was notable for being the first study to identify recurrent gene re-arrangements in an epithelial cancer and suggested a novel mechanism for prostate cancer pathogenesis.

APPLICATIONS DNA microarrays provide a panoramic, quantitative overview of a sample’s expression output. The power of this is obvious, as biological processes generally result from the coordinated interaction of multiple genes.

Research with microarrays in humans can be divided into four categories: 1. 2. 3. 4.

normal tissue taxonomy; disease diagnosis and classification; disease prognostication; and dissection of biological mechanisms.

Since the lion’s share of research to date has been in cancer biology, in part due to the ready availability of tissue samples procured during routine diagnostic procedures, the following examples will draw largely from this field’s literature. Normal Tissue Taxonomy Microarrays provide a new way of approaching the same fundamental question posed by Linnaeus in the 18th century: how is A different from and similar to B? An interesting example of previously unknown molecular heterogeneity was illustrated with human fibroblasts (Chang et al., 2002). Here, the authors demonstrated that the major factor responsible for differences in gene expression was the site of origin of the fibroblast. This

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suggests that a detailed positional memory is encoded in a fibroblast’s expression profile, and that different fibroblast subsets might be functionally distinct. Prior to the advent of microarray technology, this geographic variation was not appreciated. In addition to topographic location, the effect of aging on fibroblasts can also be measured and classified by expression profiling (Ly et al., 2000). In this study, using an Affymetrix oligonucleotide microarray, the expression profiles of fibroblasts isolated from young, middle-age, and old-age individuals and individuals with progeria were compared. The authors found consistent differences in the expression of genes between these groups in two functional classes: genes involved in cell cycle progression and genes involved in maintenance and remodeling of the extracellular matrix. Disease Diagnosis and Classification There have been a multitude of studies using expression profiling for disease diagnosis and classification, most notably in cancer. One seminal study used oligonucleotide microarrays to examine the expression of 6817 human genes in 72 patients with acute leukemia (Golub et al., 1999). Using unsupervised learning, gene expression was able to independently cluster leukemia samples into the known subsets of acute myelogenous leukemia (AML) and ALL (see Figure 13.3). Then, using supervised learning, gene sets that were differentially expressed in AML and ALL were used to correctly classify a group of unknown samples into the correct categories. Significantly, many markers that were both known, such as myeloperoxidase and terminal transferase, and unknown were useful for making this distinction. Although the difference between AML and ALL is often not difficult using modern histopathology, this study provided the first evidence that tumor expression profiles can be used for cancer classification. Tumor cell lines can also be classified molecularly through expression profiling (Ross et al., 2000). The National Cancer Institute’s Developmental Therapeutics Program bank of 60 cancer cell lines (NCI60) was profiled using a spotted cDNA array of 9703 cDNAs. The gene expression patterns observed in these cancer cell lines corresponded to the tissue of origin, which is remarkable since these cell lines have been selected for survival in the host and later in tissue culture. This illustrates a theme that the tissue of origin plays a major role in determining the expression profile. Similarly, a molecular taxonomy of tumor samples from 14 different tumor classes was created using a supervised learning algorithm (Ramaswamy et al., 2001). Some of these samples were from metastatic sites, and interestingly, the majority of them could be categorized from the classification information derived from the primary site, again demonstrating the importance of the tissue of origin in disease. Breast cancer has been intensively profiled. Perou et al. (2000) reported a molecular classification system based on the expression patterns of 65 breast adenocarcinoma specimens from 42 individuals using a spotted cDNA array. Using an unsupervised method, these breast tumors were categorized into four different subtypes based on their patterns of gene expression.

One clinically important subtype was already known (Erb-B2 or Her2/neu overexpressing cancers), and three others were previously unknown: estrogen receptor-positive/luminal-like cancers, basal-like cancers, and normal breast. In addition to this classification system, the authors were able to show the importance of cellular lineage in determining the expression profile. This was illustrated by examining the variation in expression between primary tumors that were biopsied before and after a course of chemotherapy. They also looked at the profiles of two primary lymph node metastasis pairs. The authors found that the paired samples in the same patient were significantly more related to each other than to tumors from other patients, despite intervening chemotherapy or metastatic evolution. These initial observations were subsequently validated in a larger dataset (Sorlie et al., 2001, 2003). Importantly, these molecularly defined subtypes were clinically relevant, as patients with basal-like tumors had a significantly worse prognosis compared to patients with other breast cancer subtypes. Outside of the realm of cancer, cDNA microarray technology has proved useful for exploring a broad range of diseases such as inflammatory diseases like rheumatoid arthritis and Crohn’s disease (Heller et al., 1997), schizophrenia (Mirnics et al., 2000), multiple sclerosis (Lock et al., 2002), and differentiating between ischemic and non-ischemic cardiomyopathy (Kittleson et al., 2004). Microarrays have also been helpful for re-examining basic questions such as aging, as discussed previously (Ly et al., 2000). Disease Prognosis A challenge in medicine and especially in oncology is prediction– forecasting which patients will and will not benefit from therapy, particularly when the therapy, such as chemotherapy, has significant risks. DNA microarrays offer the possibility of radically transforming medicine from a traditional one-size-fits-all approach to individualized care. Intuitively, more information, when rich in content and appropriately analyzed, should lead to better prediction models. However, this has not always been borne out with various histopathologic tumor markers (e.g., breast and colon cancer), despite much initial excitement when these markers first became available (Bast et al., 2001). In contrast, there have been several microarray experiments involving different types of cancers that have found that the expression signature can serve as a robust biomarker, better than currently used criteria, for predicting prognosis. A common approach is to identify an expression profile that can subclassify a tumor type and then find correlations between this subclassification and prognosis. The first such application was described by Alizadeh et al. (2000) in diffuse large B cell lymphoma (DLBCL). These investigators constructed the Lymphochip, a specialized spotted cDNA microarray enriched for genes preferentially expressed in lymphoid cells and for genes with known roles in immunology or cancer. Using this microarray, hierarchical clustering revealed two molecularly distinct forms of DLBCL: germinal center B-like DLBCL and activated B-like DLBCL. These subtypes were not

Applications

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AML C-myb (U22376) Proteasome iota (X59417) MB-1 (U05259) Cyclin D3 (M92287) Myosin light chain (M31211) RbAp48 (X74262) SNF2 (D26156) HkrT-1 (S50223) E2A (M31523) Inducible protein (L47738) Dynein light chain (U32944) Topoisomerase II β (Z15115) IRF2 (X15949) TFIIEβ (X63469) Acyl-Coenzyme A dehydrogenase (M91432) SNF2 (U29175) (Ca2)-ATPase (Z69881) SRP9 (U20998) MCM3 (D38073) Deoxyhypusine synthase (U26266) Op 18 (M31303) Rabaptin-5 (Y08612) Heterochromatin protein p25 (U35451) IL-7 receptor (M29696) Adenosine deaminase (M13792) Fumarylacetoacetate (M55150) Zyxin (X95735) LTC4 synthase (U50136) LYN (M16038) HoxA9 (U82759) CD33 (M23197) Adipsin (M84526) Leptin receptor (Y12670) Cystatin C (M27891) Proteoglycan 1 (X17042) IL-8 precursor (Y00787) Azurocidin (M96326) p62 (U46751) CyP3 (M80254) MCL1 (L08246) ATPase (M62762) IL-8 (M28130) Cathepsin D (M63138) Lectin (M57710) MAD-3 (M69043) CD11c (M81695) Ebp72 (X85116) Lysozyme (M19045) Properdin (M83652) Catalase (X04085)

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Figure 13.3 Gene expression profiling in acute leukemia. The 50 genes most highly correlated with the distinction between ALL and AML are shown. Each row corresponds to a gene, and each column corresponds to expression levels in different samples. Expression levels greater than the mean are shaded in red, and those below the mean are shaded in blue. The scale indicates SDs above or below the mean. The top panel shows genes highly expressed in ALL; the bottom panel shows genes more highly expressed in AML. Adapted from Golub et al. (1999). (Reprinted with permission from AAAS).

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Figure 13.4 Kaplan–Meier analysis of the probability that patients would remain free of distant metastases among 151 patients with lymph node-negative breast cancer by gene expression profiling (a) and by St Gallen clinical criteria (Goldhirsch et al., 2001) (b). Adapted from Van de Vijver et al. (2002). (Copyright © 2002, Massachusetts Medical Society. All rights reserved).

appreciated using traditional histopathology and immunohistochemical techniques, yet nevertheless have clinical significance as patients with germinal center B-like DLBCL had significantly better overall survival when treated with standard chemotherapy. Indeed, as observed with other initial studies in cancer classification, cell lineage was an important determinant of the molecular phenotype. Ultimately, it remains to be seen whether or not molecular phenotyping will supplant traditional diagnostic tools, and this remains an ongoing area of research. Similar predictive power has been exemplified in microarray studies of breast cancer. Traditional factors for predicting a patient’s outcome, such as tumor size, lymph node status, and estrogen receptor status, are imperfect. Van’t Veer et al. (2002) used an oligonucleotide microarray to analyze primary tumors from 117 patients with early-stage breast cancer and no lymph node involvement, who received minimal treatment after surgery, to identify a 70-gene expression signature that strongly predicted metastasis. In a subsequent validation study on 295 patients (which included the original cohort of 117 patients), this signature outperformed all currently used clinical predictors and more accurately identified patients at risk for distant metastasis (van de Vijver et al., 2002) (see Figure 13.4). For example, the profile could assign more patients into a low-risk group for metastasis (40%) compared to traditional clinical criteria (15%). These initial findings were subsequently confirmed in an independent group of patients from five European centers (Buyse et al., 2006). Taken together, these results suggested that primary tumor gene expression profiles can be used to molecularly stratify cancer patients according to risk of progression and treatment outcome.

Molecular Biology Expression profiling has also been a new approach for discovering novel roles for both individual genes and biological processes. In addition, expression profiling also reveals the power of performing experiments in silico and mining data from previous experiments to integrate existing information with new insights. For example, Clark et al. (2000) used expression profiling to identify RhoC, a small GTPase, as a potential mediator of melanoma metastasis. Using a mouse model to select for metastatic variants of the human melanoma cell line A375, the investigators were able to compare the expression profiles of the parental cell line and the metastasis. RhoC was one of several genes, which was overexpressed in the metastatic sublines. Functional studies with RhoC confirmed its role in this model system. Lamb et al. (2003) also used integrative methods to describe a novel mechanism for the action of cyclin D1, a cell cycle regulator implicated in cancer progression. After first developing an expression signature for cyclin D1 action through retroviral transduction and gene expression profiling, they then used this profile to mine a compendium of human tumor gene expression data to demonstrate the in vivo relevance of this signature, subsequently leading to the identification of C/EBPb as novel downstream effector of cyclin D1. Cellular Biology Expression profiling has also been able to uncover intriguing similarities between cancer and certain physiological processes such as wound repair and the response to hypoxia. Leveraging on the similarities between wound healing and cancer (Dvorak,

Future Directions

1986), Chang et al. (2004) derived an expression signature of the fibroblast response to serum in tissue culture, noting that serum is encountered in vivo at sites of tissue injury. Using this signature, they identified corresponding expression signatures in a variety of cancers that were similar to active wounds. Furthermore, the molecular features of wound healing also predicted increased risk of metastasis and death in several cancers such as breast, lung, and stomach cancer. The authors validated their findings with microarray data from the Dutch cohort of breast cancer patients (Chang et al., 2005; van de Vijver et al., 2002). Overall survival and distant metastasis-free survival were markedly diminished in patients whose tumors expressed the wound-response signature compared to tumors that did not express this signature. In addition to the serum response, using a similar strategy, the gene expression response to hypoxia in cell culture was also identified in several human cancers, particularly breast and ovarian cancer (Chi et al., 2006). Interestingly, this phenomenon was independent of the wound-response signature. Signatures identified in vitro, that are associated with activation of specific oncogenic pathways, have also been used to identify similar patterns of pathway dysregulation in human tumors (Bild et al., 2006). Interestingly, these signatures might be clinically relevant, as they appear to correlate with survival and treatment outcome in patients with solid tumors. Expression profiling has also yielded provocative insights into the biology of cancer metastasis (Ramaswamy et al., 2003). By comparing expression profiles of various primary and metastatic tumors from a wide variety of sites, the authors discovered a 17-gene-expression signature that identified primary cancers with a greater likelihood of metastasis. Interestingly, many of these “metastasis signature” genes were also genes commonly expressed by cancer-associated fibroblasts and other stromal cells. These findings suggested for the first time that generic molecular determinants of metastasis might exist, and that these determinants might arise very early in tumorigenesis. This observation suggests a model for the biology of metastasis that contrasts with traditional dogma that molecular changes leading to metastasis arise only later in the natural history of a tumor.

LIMITATIONS AND CHALLENGES While microarray technology continues to mature, as with any technology, expression profiles must be interpreted in the context of broader biological knowledge. For example, one assumption often made is that gene expression correlates well with protein quantity and therefore protein activity. This view does not take into account additional variables such as mRNA stability, protein degradation, or post-translational modifications that ultimately determine protein activity. Another supposition is that mRNA expression levels correlate well with biological activity. In contrast, small differences in expression may have a dramatic biological effect. For example, the expression level of transcription factors is often quite low even in tissues where these factors play active roles. Finally, expression profiles are not fixed; they

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may vary dramatically in different physiological contexts (King and Sinha, 2001). Validation with Non-microarray Technologies Gene expression data generated from microarray experiments can be credentialed using other methods of expression measurement such as northern blotting (Taniguchi et al., 2001), real-time PCR (Dallas et al., 2005), or other transcriptomic approaches described in Chapter 12. These studies and others demonstrate that genes found to be expressed with current generation microarrays can be consistently verified. Microarray data can also be verified at the protein expression level. A particularly useful high-throughput technology for validation using immunohistochemistry involves tissue microarrays (Hans et al., 2004). Reproducibility of Microarray Data An ongoing concern since the inception of the DNA microarray has been its technical reproducibility (Marshall, 2004). For a general review of these initial concerns, the interested reader is referred to a review by Chuaqui et al. (2002). While initial studies raised concerns about the reproducibility of microarray data (e.g., Tan et al. [2003]), subsequent studies have found that reproducibility is better than initially described (Bammler et al., 2005; Irizarry et al., 2005; Larkin et al., 2005). Recently, the MicroArray Quality Control (MAQC) project involved 137 participants from 51 academic, government, and commercial institutions to assess the performance of seven microarray platforms of two commercially available RNA samples (Shi et al., 2006). Reassuringly, the MAQC project showed high intra-platform consistency across test sites as well as a high level of interplatform concordance in terms of identifying differentially expressed genes in a common set of biological samples.

FUTURE DIRECTIONS Newer Platform Technologies Alternatives to conventional microarray platforms are emerging. One notable example is a bead-based platform developed by Illumina, the BeadChip. This uses randomly assembled arrays of 50-mer coated 3-m beads in order to create an extremely dense platform that is denser than spotted arrays or photolithographically made arrays (Gunderson et al., 2004). Whereas in conventional arrays, the sequence and location of each probe is known beforehand, the identity (and the corresponding probe sequence) of the beads is decoded after deposition. A DNA-based decoding algorithm is used to identify each bead. Compared to conventional platforms, the main advantage of this platform is lower per-sample cost. A different type of platform departing from conventional technology is the CombiMatrix platform. This platform synthesizes oligonucleotides in situ on a semiconductor chip using routine phosphoramidite chemistry under electrochemical control

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(Liu et al., 2006). Each feature on the chip is digitally addressed and controlled. When activated, an electrochemical reaction generating acid occurs, ultimately leading to oligonucleotide synthesis in situ. The main advantage of this technology is flexibility. Custom microarrays can be created on demand with less labor, whereas technologies with “fixed” content such as the Affymetrix chip require a significant investment of resources to create a new chip (e.g., creation of a new photolithographic mask). Formalin-Fixed, Paraffin-Embedded Samples Traditionally, obtaining RNA from human tissues for expression profiling has required that specimens be snap-frozen in liquid nitrogen within a half hour of resection and stored at 80°C or colder to minimize RNA degradation. Samples need to be processed quickly because changes in some mRNA species have been noted even a few minutes after manipulation and devascularization of a tissue (Huang et al., 2001). However, clinical specimens are generally fixed in formalin and embedded in paraffin for routine histopathologic analysis, which causes significant RNA degradation. Only a fraction of the RNA from fixed specimens, roughly 3%, is intact enough for cDNA synthesis, posing a major barrier to obtaining expression profiles from clinical specimens (Godfrey et al., 2000; Masuda et al., 1999). A method to maneuver around this degradation obstacle is the cDNA-mediated, annealing, selection, extension, and ligation (DASL) assay. This assay is a technology that combines the advantages of microarray-based gene expression profiling with multiplexed quantitative PCR (Fan et al., 2004). An upstream and downstream pair of oligonucleotides is designed to amplify a cDNA of interest. Each pair of oligonucleotides incorporates a universal primer sequence for PCR as well as a specific address sequence that hybridizes to a capture sequence on an array. With this methodology, cancer-specific expression profiles were generated with as little as 50 ng of total RNA from formalin-fixed tissues stored over a decade (Bibikova et al., 2004). Approaches such as the DASL assay thus have the potential to capitalize on the vast supply of archived, paraffin-embedded samples from large clinical trials or population-based cohort studies to advance expression profiling efforts. Functional Genomics A goal of functional genomics is to assign biological meaning to genes on a genome-wide level (Steinmetz and Davis, 2004) (see Chapter 16). As illustrated by the examples described earlier, expression profiling has proved useful for assigning functional groups to genes in model organisms such as yeast (Hughes et al., 2000) and for uncovering a role for metastasis with RhoC (Clark et al., 2000). Expression profiling has also been used to decipher regulatory networks of transcription by discovering common sequence motifs in upstream regions of genes that have similar expression profiles (Pilpel et al., 2001). These studies, though, have been mainly been done in yeast and await application in more complex systems such as humans. A new opportunity in functional genomics is to integrate expression profiling with RNA interference (RNAi) technologies

now that genome-wide RNA interference libraries are coming to fruition (Moffat et al., 2006). One can imagine using RNAi libraries to systematically knock down individual genes and the comparing the resulting expression phenotypes. One can then look for similarities and differences between expression profiles as a starting point for inferring the function of the genes that have been ablated by RNAi. Profiling the Transcriptome Expression profiling has also been used to explore the transcriptome (see also Chapter 12). The transcriptome represents the complete set of all the transcribed elements of the genome in contrast to the collection of transcripts that are only translated into proteins.There is a burgeoning interest in the function of the noncoding part of the transcriptome as the number of actual genes transcribed into proteins has been substantially revised downward and increasing numbers of non-coding transcripts have been discovered. Key to their detection has been tiling microarrays, specifically those developed by Affymetrix with photolithographic technology. Rather than only using probes for known exons, tiling arrays probe across the entire genome in an unbiased fashion. Cheng et al. (2005) used a 25-mer oligonucleotide-based array spaced every 5 bp to examine ten human chromosomes. They discovered that interestingly, of all the transcribed sequences, 19% were polyadenylated, 44% were not polyadenylated, and 37% were both polyadenylated and not polyadenylated. Additionally, about 10% of the genome was transcribed as polyadenylated transcripts, compared to 1–2% of the genome corresponding to exons. These findings suggest that the expressed genome may be orders of magnitude larger than previously appreciated. Part of the transcriptome also includes small (roughly 22 nucleotide) non-coding microRNAs (miRNAs). These miRNAs play an important role in regulating gene expression by either degrading mRNA or inhibiting its translation (Bartel, 2004). In cancer, for example, Lu et al. (2005) used a new bead-based profiling system to quantify miRNA expression because of the short size of the miRNAs.This used oligonucleotide capture probes that were coupled to beads, which in turn were differentially impregnated with variable degrees of a fluorescent mixture. Flow cytometry was then used to quantitate the beads and therefore the associated miRNA. Expression profiling of the miRNA population of various malignant and normal tissues revealed an unusually large degree of diversity of miRNA expression across cancers. Furthermore, miRNA signatures could be used to classify poorly differentiated cancers, which have traditionally been difficult to classify using standard mRNA-based profiling strategies. The interested reader is referred to a recent review by Calin and Croce (2006).

Clinical Translation Expression Profiling as a Biomarker The ability of expression signatures of tumor samples to predict prognosis has immediate potential clinical translation. A key lesson from these early clinical microarray studies is that appropriate sample size and careful experimental design are necessary

Future Directions

for identifying signatures that can serve as robust biomarkers (Michiels et al., 2005; Ntzani and Ioannidis, 2003; Simon et al., 2003). However, reassuringly, a recent study of prognostic breast cancer signatures showed that four out of five reported gene signatures had significant concordance in predicting breast cancer outcome, indicating that these signatures were tracking similar biological behaviors (Fan et al., 2006), pointing to the evolving sophistication of clinically oriented profiling efforts. The clinical application of expression profiles is most mature in breast cancer, owing to the extensive microarray data already available, and has recently been reviewed (Sotiriou and Piccart, 2007). The 70-gene-expression signature originally formulated by Van’t Veer et al. (2002) has been commercialized into the MammaPrint assay for clinical use. This assay serves as the basis of the MINDACT (Microarray In Node negative Disease may Avoid ChemoTherapy) clinical trial, which aims to determine whether the expression signature can be used for making clinical decisions (Novak, 2006). It will test the question of whether or not patients with tumors that have high risk features by clinical criteria – individuals who are routinely recommended adjuvant chemotherapy – but with a low-risk molecular prognosis as determined by MammaPrint, can defer chemotherapy without affecting metastasis-free survival. In other words, it will test if the molecular prognosis generated by MammaPrint can outperform clinical criteria. An alternative to using existing signatures is to adapt the expression profile into a RT-PCR-based assay. Compared to DNA microarray technology, RT-PCR has the potential to be less technically challenging, more cost-effective, and more easily integrated into routine clinical practice. Genomic Health International has developed the Oncotype assay which is currently available for clinical use (Paik et al., 2004). The Oncotype assay is an RTPCR assay based on a 21-gene signature (including five control genes) derived from 250 candidate genes selected from the published microarray literature. Rather than using fresh frozen tissues, the researchers were able to exploit archived, paraffinembedded tissues using RT-PCR as their profiling method (Cronin et al., 2004). They systematically quantified the expression of these 250 candidate genes across three historical groups of breast cancer patients. From this data, they derived a gene expression-based recurrence score for predicting breast cancer recurrence that was more powerful than standard histopathologic criteria. The reproducibility of the score was robust and was subsequently validated in a large, population-based study (Habel et al., 2006). Furthermore, Oncotype had the potential to identify patients who may be able to defer adjuvant chemotherapy treatment. Patients with low and intermediate recurrence score tumors did not appear to derive a significant benefit from chemotherapy (Paik et al., 2006). An advantage of using this RT-PCR-based approach is that it can be applied to tumor samples that have been collected and processed in routine fashion; no special handling (e.g., freezing tissues in liquid nitrogen) is required. Other examples of adapting expression profiling to an RT-PCR assay include a two-gene breast cancer signature (Ma et al., 2003) and a six-gene signature predicting survival in DLBCL (Lossos et al., 2004).

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In the future, one of the challenges for translational research will be the integration of expression profiling with routine histopathology in clinical practice. As can be seen with the 21gene breast cancer signature, expression profiling in this application offers the advantage of reproducibility and minimizes human error and variability with pathological interpretation. However, cost continues to be a major limitation to widespread adoption of profiling technologies. An ongoing prospective study, similar in concept to MINDACT, is the TAILORx (Trial Assigning Individualized Options for Treatment) trial in North America, which will help answer the question of how to integrate microarray technology (i.e., Oncotype DX) into clinical practice for patients with breast cancer. TAILORx is examining whether chemotherapy is required for the intermediate-risk group defined by Oncotype’s recurrence score (Paik, 2007). Drug Development DNA microarrays have also facilitated drug target discovery and prediction of drug response. For general reviews, the reader is referred to a recent one by Stoughton and Friend (2005). Using the NCI60 cancer cell line panel, Scherf et al. (2000) constructed a database that correlated gene expression and drug activity patterns. They were able to find relationships between gene expression and drugs such as 5-fluorouracil and l-asparaginase. Gunther et al. (2003) employed a similar strategy with primary human neuronal precursor cells and various psychotropic drugs. Potti et al. (2006) have expanded on this approach by using the expression profiles of sensitive and resistant cancer cell lines for six different chemotherapy drugs to predict response to neoadjuvant chemotherapy for breast cancer. Stegmaier et al. (2004) have developed a high-throughput approach to drug discovery using expression profiling: gene expression-based high-throughput screening (GE-HTS). These authors were interested in compounds that could terminally differentiate AML cells. To discover these compounds, they used 5-gene-expression signature of the differentiated neutrophil and leukemic states as a surrogate. With an RT-PCR-based readout of these five genes, they then screened a small molecule library to identify drugs that could induce differentiation of leukemic blasts and discovered an epidermal growth factor receptor kinase inhibitor with this property (Stegmaier et al., 2005). Remarkably, GE-HTS discovered this potential therapeutic without prior detailed biological knowledge. A related approach to screening small molecules has been implemented through The Connectivity Map, an in silico tool recently developed at the Broad Institute that uses expression profiling to systematically discover functional connections among diseases, genetic perturbations, and drug actions (Lamb et al., 2006). Lamb et al. first created a reference collection of gene expression profiles from cultured human cancer cells treated with small molecules. They then developed pattern-matching software to mine these data. The Connectivity Map has been used to perform in silico discoveries of rapamycin as a new approach for treating glucocorticoid-resistant ALL (Wei et al., 2006) and the inhibition of HSP90 as a novel mechanism for inhibition of androgen receptor signaling in prostate cancer (Hieronymus et al., 2006).

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CONCLUSIONS Over the past decade, expression profiling has matured from a revolutionary new technology to a routine, fundamental component of scientific inquiry. Its ability to globally quantitate the expression of thousands of genes at once has provided a new lens for classifying diseases, predicting outcomes, and discovering new drug targets. Commensurate with the technical achievements

have been advances in statistical analysis and interpretation of the microarray data being generated. In the future, one can imagine additional insights through integration of expression profiling with other “–omic” approaches. Ultimately, the real benefits of DNA microarrays will be realized only through translation into better treatment strategies and therapeutics, and the results of these efforts are eagerly anticipated.

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RECOMMENDED RESOURCES Resources for expression profiling experiments Web site

URL

Resources

National Human Genome Research Institute

http://research.nhgri.nih.gov/ microarray/main.html

Protocols, web links

Broad Institute, Cancer Genomics Group

http://www.broad.mit.edu/cancer/

Gene expression analysis software, web links

Stanford Genomics

http://genome-www.stanford.edu/

Software, web links, cDNA microarray protocols

Jackson Laboratory, Statistical Genomics Group

http://www.jax. org/staff/Churchill/labsite/

Advice about the design of microarray experiments

Microarray Gene Expression Data Society (MGED)

http://www.mged.org/ Workgroups/MIAME/miame_ checklist.html

Minimal requirements for the publication of microarray data

Gene Expression Omnibus database

http://www.ncbi.nlm.nih.gov/geo/

Repository of gene expression data

Array Express database

http://www.ebi.ac.uk/arrayexpress/

Repository of gene expression data

Affymetrix

http://www.affymetrix.com

Commercial website with useful resources

This listing is adapted from Ebert and Golub (2004).

CHAPTER

14 Proteomics: The Deciphering of the Functional Genome Li-Rong Yu, Nicolas A. Stewart and Timothy D. Veenstra

INTRODUCTION The continuing development of novel analytical technologies and instrumentation has brought a dramatic new dimension to how biological research can be pursued. This new dimension has enabled discovery-driven research to be conducted at par with hypothesis-driven research. In a hypothesis-driven approach, the understanding of a particular biological entity – whether it be a cell, gene, or protein – is used to develop studies that are designed to answer a specific question about a single gene, transcript, or protein, for example. Hypothesis-driven studies utilize technologies that focus on one specific entity per experiment. Discoverydriven studies, however, are designed around broad questions and focus on global characteristics of a cell or organism. These studies utilize advanced technologies that are able to gather information on thousands of different genes, transcripts, proteins, and metabolites in a relatively rapid and comprehensive fashion. While these globally directed studies have seemingly been thrust upon the scientific community overnight, in reality it has taken decades of technological and instrumental development to bring this field of science to fruition. The first giant step in discovery-driven science is the completion of the Human Genome Project. This monumental effort could only have been accomplished with the many developments in cloning, amplification, and high-throughput gene sequencing (Yager et al., 1991). The analysis of thousands of gene transcripts using mRNA arrays has been made possible by the ability to synthesize mRNA probes

and coupling them to solid surfaces (Gilham, 1970). Shortly after the development of these genome – and transcriptome-scanning technologies, many scientists shifted their focus onto proteins. Proteomics – the analysis of the entire protein complement of a cell, tissue, or organism under a specific, defined set of conditions – in its present state has also been dependent on decades of technological and instrumental developments. These developments have included advances in mass spectrometry (MS) technology, protein fractionation techniques, and bioinformatics, to name a few. One of the difficult challenges in proteomics is the uncertainty of its complexity. Let us assume that as many as 15,000 genes are expressed within a given human cell at any single time. Let us then assume that these genes give rise, on average, to four different mRNA transcripts each, or 60,000 different transcripts in total. This estimate is very conservative given the presence of splice variants, single nucleotide polymorphisms, etc. that can arise from expressed genes. Now consider all of the events that can happen to a single transcript during and after translation into a mature protein. The protein can be processed from a preprotein into a mature protein.The most common event that happens to change a protein is post-translational modification. There are over 300 known modifications, however; some of the more common are (in no particular order) phosphorylation, glycosylation, methylation, and acetylation. A single protein that may have four potential phosphorylation sites will, in itself, give rise to 16 different isoforms ranging from all the sites phosphorylated to none of them being phosphorylated. If each of the 60,000

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possible protein products contained only four possible sites of modification, these would give rise to as many as 960,000 different proteins. Obviously, these numbers are extremely conservative and suggest that the human proteome is likely to contain well over 1,000,000 distinct species. This number is well beyond the current capacity of state-of-the-art technologies, resulting in a consistent undersampling of the proteome regardless of the analysis method chosen. It is this uncertainty in the overall measurement that results in the question that hovers around many global proteomic studies: “Was the protein absent in the sample or did we simply fail to detect it?”

Isoelectric point

Isoelectric point

(a)

(b) Molecular mass

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In-gel enzymatic digestion

GEL-BASED AND SOLUTION-BASED PROTEOMICS Proteomics relies on three basic technological cornerstones: a method to fractionate complex protein or peptide mixtures, MS to acquire the data necessary to identify individual proteins, and bioinformatics to analyze and assemble the MS data. While the MS and bioinformatic components are somewhat similar in most applications, there are two very distinct methods to separate out complex protein samples in proteomics. The mere mention of the word “proteomics” conjures up an image of stained protein spots that have been fractionated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). This separation technology, which was developed over 30 years ago, is still the most common separation technique used in proteomics today (O’Farrell, 1975). Advances have been made in this technology that enables thousands of proteins to be resolved in a single gel. While the primary role of 2D-PAGE is to separate complex mixtures of proteins, it also allows a means to compare the relative abundances of proteins from different proteomes. In a typical 2D-PAGE-based study, the proteomes from two distinct cell populations (e.g., control versus treated cells) are extracted and fractionated on separate gels (Figure 14.1) (Van den Bergh and Arckens, 2005). Since most proteins are colorless, the separated proteins need to be visualized (Westermeier and Marouga, 2005). This visualization is routinely accomplished using colorimetric stains, such as coomassie blue or silver stain. The resulting images are typically quite complex; therefore the spots observed on both gels are aligned so that the relative staining intensity of individual proteins can be compared between gels. Protein spots that are stained more intensely on one gel compared to the other are excised from the gel. The protein within the gel is then enzymatically digested by introducing trypsin into the gel piece. The resultant tryptic peptides are extracted from the gel and analyzed by MS or tandem MS (MS/MS) to acquire the raw data necessary to identify the protein(s) within the gel spot. This identification is accomplished using software designed to compare this type of data against databases containing large amounts of genomic or protein sequence information. While 2D-PAGE fractionation has been the subject of many different criticisms over the years, it still remains a cornerstone technology in proteomics (Van den Bergh and Arckens, 2005). Many of

Protein identification by peptide mapping or tandem MS m/z

Search database

Generate list of differentially abundant proteins

Figure 14.1 Quantitative proteomics using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In this method, comparative proteome samples are separated on distinct 2DPAGE gels. After staining, protein spots that are more abundant on one gel compared to the other are excised from the gel. The protein(s) within the gel is then subjected to in-gel tryptic digestion and the resultant peptides are extracted and analyzed by mass spectrometry (MS). The MS data are then searched against the appropriate database to identify the protein(s) with the gel spot.

the criticisms, such as its inability to resolve membrane proteins and gel-to-gel irreproducibility have been addressed through the development of better reagents, equipment, and gel alignment software. For instance, over the past several of years, 2D-DIGE reagents have been developed that allow comparative proteomes to be labeled with different fluorophores (i.e., Cy2, Cy3, and Cy5) (Hoorn et al., 2006). This differential labeling allows for the relative abundance of proteins from different proteomes to be compared by their combination prior to their run on a single gel, obviating the separation irreproducibility that can be observed between gels. No single chromatographic or electrophoretic procedure, including 2D-PAGE, can completely resolve a mixture as complex as that of a proteome. As a complement to 2D-PAGE methods,

Mass Spectrometry

Proteome A (ICAT-13C0)

175

Proteome B (ICAT-13C9)

Pool proteins

Digest with trypsin

Enrich ICAT-peptides

Cleave biotin tags

light

heavy 9.03 Da

% Intensity

solution-based chromatographic techniques have been developed that permit direct on-line MS analysis of proteomes (Issaq et al., 2005). Analogous to 2D-PAGE, these solution-based methods typically use at least two different fractionation schemes to separate peptides/proteins prior to their entering the mass spectrometer. While many different combinations have been proposed, the most effective and popular has been the use of strong cation exchange (SCX) followed by reversed-phase high-performance liquid chromatography (RPLC). John Yates III first demonstrated the effectiveness of combining this multidimensional separation with MS (Washburn et al., 2001).This method, termed “MudPIT” (multidimensional protein identification technology), has shown the capability of identifying thousands of proteins per proteome study. In this scheme, the proteins extracted from a cell lysate are first digested into peptides. There are two major advantages in working with peptides instead of proteins. Peptides are more soluble and easier to separate than intact proteins, especially hydrophobic and membrane proteins, and peptides are more amenable to identification by MS than are proteins (as described in more detail later). The challenge with this strategy is that the number of species that have to be resolved has dramatically increased. The digestate is initially separated by SCX and fractions collected off this column are then loaded onto a RP column that is coupled directly on-line with the mass spectrometer. The mass spectrometer can then dynamically select and acquire the data necessary to identify peptides as they elute from the RP column. A limitation of MudPIT when compared to 2D-PAGE is in its inability to directly measure the relative abundance of proteins in different proteome samples. To meet this need, many different approaches have been developed, all of which use the data provided by the mass spectrometer for quantitation (Ong and Mann, 2005). Many of the methods rely on the use of differential stable-isotope labeling (Julka and Regnier, 2005). Analogous to DIGE, which uses differently fluorescent molecules with identical electrophoretic properties, stable-isotope labeling of proteomes uses chemically identical reagents that differ in their isotope content (i.e., mass). For example, the most popular labeling method termed isotope-coded affinity tags (ICAT) utilizes thiol-modifying reagents that differ in their carbon-13 (13C) content (Gygi et al., 1999). In this procedure, one proteome sample is labeled with the “light” ICAT reagent while a comparative sample is labeled with the “heavy” ICAT reagent that contains nine 13C atoms in place of nine 12C atoms found in the “light” version (Figure 14.2). The label is attached to the proteins after they have been separately extracted from the samples to be compared. Once the two samples are differentially modified, they can be combined. The proteins are digested into peptides and the modified peptides are extracted by virtue of a biotin tag that is present at the termini of the ICAT reagents. This enriched peptide mixture is finally analyzed using MudPIT and the relative abundance and identity of the peptides are determined from measurements provided by the mass spectrometer. While ICAT illustrates a post-extraction/chemical modification method, stable-isotope labeling can also be performed at the metabolic level by culturing cells in growth media that is highly enriched in a

■

NCSETQYESK ICAT

m/z MS quantitation

m/z MS/MS identification

Figure 14.2 Quantitative proteomics using isotope-coded affinity tags (ICAT). In this method, comparative proteome samples are labeled with chemically identical reagents that differ only by their carbon isotope content (i.e., 12C9 for the light reagent and 13C9 for the heavy reagent). After the proteins are modified, the proteome samples are combined and digested into tryptic peptides. The ICAT-modified peptides are extracted using avidin chromatography by virtue of the biotin moiety on the terminus of the ICAT reagents. After removal of the biotin portion, these ICAT-peptides are analyzed by reversed-phase liquid chromatography coupled directly on-line with a mass spectrometer. The mass spectrometer is operated in a data-dependent tandem mass spectrometry (MS/MS) mode, enabling the relative quantitation of the peptide in the two samples to be measured in the MS mode as well as its identity be discerned from the data acquired by MS/MS.

particular heavy isotope (e.g., 15N in place of 14N) or a “heavy” amino acid (e.g., 13C6-lysine) (Ong et al., 2003). A list of various sample preparation used in proteomics today, along with their advantages and disadvantages is provided in Table 14.1.

MASS SPECTROMETRY While high-resolution separations are critical for complex protein mixture analysis, MS is inarguably the technology that

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T A B L E 1 4 . 1 List of advantages and disadvantages of popular sample preparation methods used in proteomics Method

Advantages

2D-PAGE

●

●

2D-DIGE

●

●

MudPIT

● ● ●

ICAT

●

Disadvantages

Staining provides relative quantitation Only sequence proteins of interest

●

Can compare multiple proteomes on single gel Reproducible separation

●

Highly sensitive Broad proteome coverage High throughput

●

Reduces complexity prior to MS analysis

●

● ●

●

●

● ●

iTRAQ

●

Allows 4 samples to be simultaneously compared

● ●

Laborious Limited sensitivity Irreproducibility a factor Laborious Requires fluorescent scanner

Limited individual protein coverage Unable to target specific proteins Lacks inherent ability for quantitative comparison Low throughput Limited to a binary comparison Unable to target specific proteins Low throughput Unable to target specific proteins

has driven the proteomics revolution. Although, it has been around for approximately a century, MS has gained increased prominence with the development of techniques to rapidly identify proteins. There are many different reasons that have propelled the MS to its prominent position within the field of proteomics. Among these are the sensitivity afforded using MS, allowing proteins and peptides present in the low femtomole (fmol, 1015 mol) to high attomole (amol, 1018 mol) range to be successfully identified. The mass measurement accuracy available using current MS technology, which can routinely be in the range of 1–5 ppm, also increases the confidence in the identification provided by the bioinformatic search of the raw data. While these and other technology specifications have been important, the development of MS/MS (McLafferty, 1981) and the coupling of on-line protein/peptide separations with MS (Henion, 1978) have been the key determinants that have enabled proteomics. Tandem MS allows tryptic peptides obtained from an enzymatic digestion of a complex proteome mixture, to be fragmented in such a way that sufficient sequence information can be obtained for its unambiguously identification. While proteins are typically identified through MS/MS identification of

their peptide surrogates, methods to characterize intact proteins by MS/MS are becoming more and more popular. The coupling of on-line separations, most commonly RPLC, has enabled complex mixtures to be fractionation prior to MS analysis so that thousands of peptides can be identified within a few hours, as shown in Figure 14.3 (Washburn et al., 2001). The throughput by which proteins in complex mixtures are identified by LC-MS is unparalleled using any other technology: a critical parameter for discovery science in which large amounts of data are collected in order to find the important factors. There are many different types of commercially available mass spectrometers, and it can be confusing as to which type is most suitable for a specific application. For instance, ion-trap mass spectrometers equipped with electrospray ionization (ESI) sources are the “work-horse” instruments for the characterization of complex proteomes when solution-based separations are used. Time of flight (TOF) instruments are popular for identifying proteins separated using 2D-PAGE, primarily because of their speed and mass accuracy. The choice of what instrument to be utilized needs to be weighed against the type of research that is being focused on. How is MS able to identify so many peptides in a complex mixture? A schematic of the process is shown in Figure 14.3. The mass spectrometer is operated in what is referred to as a “data-dependent” mode of operation. In this mode, the instrument takes a series of “snapshots” of peptides eluting from a LC column into the mass analyzer. This scan is referred to as the MS scan. In between these snapshots, a series of MS/MS scans of peptides that are isolated within the instrument for CID are acquired. The instrument selects peptides for MS/MS analysis based on their intensity in the preceding MS scan. A datadependent experiment can perform 10 MS/MS scans between every MS scan. The mass spectrometer continues this cycle of MS scan followed by ten MS/MS scans throughout the entire LC/MS/MS experiment. Depending on the length of the LC separation, upwards of 7000 MS/MS sequencing attempts are made on peptides eluting from the column into the mass spectrometer. Bioinformatic analysis will result in 10–25% (i.e., 700–1750) of these sequencing attempts resulting in successful peptide identifications. A list of popular mass analyzers used in proteomics and their performance characteristics is given in Table 14.2.

BIOINFORMATICS Probably no area of proteomics evolves faster than bioinformatics. Proteomics is critically dependent on bioinformatics to process the raw mass spectral data into protein data. While routinely used by every proteomic laboratory, the most critical software programs are those that take peptide mapping and/or tandem MS results and determine the protein or peptide sequence that most closely matches the experimental data. The two most popular software packages for matching experimental MS data to peptide/protein sequences are MASCOT

Bioinformatics

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1789.8007 Automatically selected for tandem MS  7000/hr

1789.8007

100

100 1750

1821.8724

1790

Relative abundance

Relative abundance

80 1810

60 40 1404.6632 20

1203.6808

0 1000

1400

1912.8071 2442.0809 2829.2381 2328.9829 1800

2200

2600

60 40 20 0

3000

MS/MS spectrum

80

400

800

1200 m/z

1600

2000

m/z

Relative abundance

100 80 60 40 Peptide identifications

20 0 20

40

60

80 Time (min)

100

120

140

Protein identifications

Figure 14.3 Principles of data-dependent tandem mass spectrometry (MS/MS). In data-dependent MS/MS, the mass spectrometer conducts an MS scan to record peptide masses that can be detected during a reversed-phase liquid chromatography (RPLC)-MS analysis of a complex proteome sample. The mass spectrometer isolates the peptide producing the most intense signal (i.e., m/z 1789.8007) and subjects it to collisional induced dissociation (CID). The resultant fragment ions are detected by the mass spectrometer and are subsequently used to identify the peptide in a bioinformatic analysis of the data. The mass spectrometer then isolates the next most intense peptide and collects its MS/MS spectrum. Linear ion-trap mass spectrometers may sequentially select on the order of 10 peptides for CID prior to recording the next MS scan. In the following MS scan, the instrument looks for new peptides that may have eluted from the LC column and selects these for CID and possible identification.

TABLE 14.2 Performance characteristics of various mass analyzers commonly used in proteomic research Analyzer

Sensitivity

Resolution

Mass accuracy

Ion-trap

Good

Low

Low

Linear ion-trap

Excellent

Low

Low

Triplequadrupole

Good

Good

Good

TOF

Good

High

High

FTICR

Excellent

High

High

(Lubec et al., 2005) and SEQUEST (Yates et al., 1995). As with most software algorithms, they both do what can also be done manually but at a much faster speed. For instance, SEQUEST is capable of transforming thousands of raw tandem MS files into potential peptide identifications within minutes, whereas manually assigning a single raw file would take on the order of hours. These software algorithms are also capable of identifying modified peptides when the mass added to the peptide as a result of some covalent modification (e.g., phosphorylation) is known. While not in widespread distribution, software packages, such as Scoring ALgorithm for Spectral Analysis (SALSA) developed by Daniel Leibler, provides a means of identifying unanticipated modifications, by identifying spectra that display characteristic

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product ions, neutral or charged losses of groups of signals that are indicative of a particular modification (Hansen et al., 2001). Taken together, these varieties of software packages have been critical to the enabling of data analysis on the scale needed for discovery-driven proteomic studies. Throughout this chapter, the theme has been to use proteomic technologies to measure the relative abundances of proteins in different samples. Software has been developed to do this both at the gel-staining level and the mass spectral data level (i.e., when stable-isotope labeling quantitation is employed). Where the major lack has been is how this relative abundance data is presented. It is still typically displayed as lists of proteins with their relative abundance between two samples being given as a fold-difference. Unfortunately, with the ability to generate abundance data for thousands of proteins, these lists become an “unsolvable puzzle” when trying to identify major effects on a cell’s proteome as a result of a specific treatment. Recently, software packages such as Ingenuity™ have been developed to distill abundance data into annotated protein pathways enabling the probability that any specific signaling pathway has been affected can be statistically measured (www.ingenuity.com/products/ pathways_analysis.html). Software packages such as these also allow transcriptomic and proteomic data to be directly compared at both the single biomolecule and pathway levels.

IMPACT OF PROTEOMICS ON UNDERSTANDING DISEASES It is impossible to find a common characteristic that threads every disease together. One prevalent characteristic, primarily in the area of cancer, is that the earlier it is detected the better the chance of survival. To detect cancer at earlier stages is going to require novel biomarkers that possess high sensitivity and specificity. This need has driven much of the proteomic research over the past 3–5 years. Much of the search for novel biomarkers falls within discovery-driven research, and therefore MS has played a major role. The aim, in most studies, is to leverage the ability of MS to identify proteins in biological samples from patients with specific cancers and compare that to lists of proteins identified in the same sample types obtained from healthy-matched controls. Proteins that are found to be more prevalent in samples from disease-afflicted patients are considered as potential biomarkers. While on the surface the premise sounds simple enough, in reality there are several daunting challenges, both technological and physiological. Even with the speed at which MS is capable of identifying proteins, it still undersamples any complex proteome mixture. When such entities as post-translational modifications and splice variants are taken into account, it is impossible to know exactly the number of distinct proteins within a cell or biofluid proteome. Therefore, when a protein is detected in a diseased sample but not in a matched control, there is always that uncertainty as to whether the protein is not really present or it just was not detected in the present experiment. As is

commonly quoted in science, “the absence of evidence is not the evidence of absence”. On the physiological side, the fact that the proteome of any cell or biofluid is dynamic results in a constantly changing background that occurs both between different patients and within the same patient. For instance, the serum proteome of an individual is different before and after lunch. Compound this with the genetic and lifestyle variability that is observed between different patients and it is easy to grasp how difficult it is to confidently identify a disease-specific change in a protein in this shifting matrix. Regardless, the promise of technologies such as MS that can scan proteomes at levels never before possible propels investigators to search for new diagnostic markers. While the success in discovering new markers has been limited, they do however exist. For instance, a biomarker for the bladder disorder interstitial cystitis (IC) was found using a combination of protein fractionation and MS techniques (Keay et al., 2004). Previous to this report, no useful diagnostic marker for this disorder existed and patients were typically diagnosed by the exclusion of other maladies. This study resulted in the discovery of a novel nineresidue sialoglycopeptide that was shown to be present only in the supernatant of cultured bladder epithelial cells obtained from patients with IC and not in healthy controls or patients with bacterial cystitis. One of the more intriguing MS-based proteomic techniques to identify signatures of diseases in blood involves the detection of peptides that signify exo- and endo-protease activities (Villanueva et al., 2006). In this study, low molecular weight peptides were extracted from serum and analyzed using matrixassisted laser desorption ionization (MALDI) TOF MS. The study showed that a subset of the serum peptides were able to provide a signature allowing patients with three types of solid tumors to be classified separately from controls without cancer. Sixty-one of the signature peptides were identified and shown to fall into several tight clusters (Villanueva et al., 2006). Most of the identified peptides were generated by cancertype specific exopeptidase activities that enabled highly accurate classification of a set of prostate cancer samples used to validate the markers. While this method does not identify a novel diagnostic or therapeutic protein marker, it does use MS to reveal a cancerspecific activity found within serum of cancer-affected patients.

CONCLUSIONS While proteomics technology has advanced greatly in the past decade and allows scientists to characterize proteomes to an extent never before possible or imagined, this capability in itself brings a whole new set of challenges. The initial challenge is how to glean important clinical information from massive datasets that are amounts of data “contaminated” with a vast background of other less important proteins. While MS provides the ability to conduct comparative analyses of biological samples for the discovery of novel diagnostic and therapeutic biomarkers, it is unable to indicate which of the differentially abundant proteins may be

Recommended Resources

disease-specific. Many times these experiments represent proverbial “needle-in-the-haystack” searches with the additional complication that no information about the “needle” is known. While many potential “needles” can be found, the next area of development is to determine those that should graduate to validation phase. Since validation of any prospective biomarkers is expensive both in terms of time and money, continuing developments in optimal methods to both acquire and analyze proteomics data will be critical.

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ACKNOWLEDGEMENTS This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organization imply endorsement by the United States Government.

REFERENCES Gilham, P.T. (1970). RNA sequence analysis. Annu Rev Biochem 39, 227–250. Gygi, S.P., Rist, B., Gerber, S.A.,Turecek, F., Gelb, M.H. and Aebersold, R. (1999). Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17, 994–999. Hansen, B.T., Jones, J.A., Mason, D.E. and Liebler, D.C. (2001). SALSA: A pattern recognition algorithm to detect electrophile-adducted peptides by automated evaluation of CID spectra in LC-MS-MS analyses. Anal Chem 73, 1676–1683. Henion, J.D. (1978). Drug analysis by continuously monitored liquid chromatography/mass spectrometry with a quadrupole mass spectrometer. Anal Chem 50, 1687–1693. Hoorn, E.J., Hoffert, J.D. and Knepper, M.A. (2006). The application of DIGE-based proteomics to renal physiology. Nephron Physiol 104, 61–72. Issaq, H.J., Chan, K.C., Janini, G.M., Conrads,T.P. and Veenstra,T.D. (2005). Multidimensional separation of peptides for effective proteomic analysis. J Chromatogr B Analyt Technol Biomed Life Sci 817, 35–47. Julka, S. and Regnier, F.E. (2005). Recent advancements in differential proteomics based on stable isotope coding. Brief Funct Genomic Proteomic 4, 158–177. Keay, S.K., Szekely, Z., Conrads, T.P., Veenstra, T.D., Barchi, J.J., Jr., Zhang, C.O., Koch, K.R. and Michejda, C.J. (2004). An antiproliferative factor from interstitial cystitis patients is a frizzled 8 proteinrelated sialoglycopeptide. Proc Natl Acad Sci USA 101, 11803–11808. Lubec, G., Afjehi-Sadat, L., Yang, J.W. and John, J.P. (2005). Searching for hypothetical proteins: Theory and practice based upon original data and literature. Prog Neurobiol 77, 90–127.

McLafferty, F.W. (1981). Tandem mass spectrometry. Science 214, 280–287. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250, 4007–4021. Ong, S.E. and Mann, M. (2005). Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1, 252–262. Ong, S.E., Foster, L.J. and Mann, M. (2003). Mass spectrometric-based approaches in quantitative proteomics. Methods 29, 124–130. Van den Bergh, G. and Arckens, L. (2005). Recent advances in 2D electrophoresis: An array of possibilities. Expet Rev Proteomics 2, 243–252. Villanueva, J., Shaffer, D.R., Philip, J., Chaparro, C.A., ErdjumentBromage, H., Olshen, A.B., Fleisher, M., Lilja, H., Brogi, E., Boyd, J. et al. (2006). Differential exoprotease activities confer tumorspecific serum peptidome patterns. J Clin Invest 116, 271–284. Washburn, M.P., Wolters, D. and Yates, J.R. III (2001). Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19, 242–247. Westermeier, R. and Marouga, R. (2005). Protein detection methods in proteomics research. Biosci Rep 25, 19–32. Yager, T.D., Nickerson, D.A. and Hood, L.E. (1991). The Human Genome Project: Creating an infrastructure for biology and medicine. Trends Biochem Sci 16, 454–458. Yates, J.R., Eng, J.K., McCormack, A.L. and Schieltz, D. (1995). Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal Chem 67, 1426–1436.

RECOMMENDED RESOURCES Books Proteomics for Biological Discovery by Timothy D. Veenstra and John R. Yates Introduction to Proteomics:Tools for the New Biology by Daniel C. Liebler The Proteomics Protocols Handbook by John M. Walker Proteomics in Cancer Research by Daniel C. Liebler Proteins and Proteomics: A Laboratory Manual by Richard Simpson

Journals Molecular and Cellular Proteomics (mcponline.org) Journal of Proteome Research (jpr.org)

Proteomics (proteomics.org) Proteomics – Clinical Applications

Websites http://expasy.org http://www.hupo.org http://www.eupa.org http://www.asms.org

CHAPTER

15 Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms Christopher B. Newgard, Robert D. Stevens, Brett R. Wenner, Shawn C. Burgess, Olga Ilkayeva, Michael J. Muehlbauer, A. Dean Sherry and James R. Bain

INTRODUC TION Despite decades of research, the molecular etiology of chronic and indolent diseases and conditions such as diabetes, obesity, cancer, and cardiovascular disease remains obscure, in part because they represent slowly developing phenotypes caused by the interplay of a complex mixture of genetic and environmental effectors. For similar reasons, it has been very difficult to detect and diagnose these diseases at a stage that allows sufficient time for effective intervention. Comprehensive measurement of intermediary metabolites and changes in metabolic activity (metabolic flux) could lead to improved detection and understanding of these diseases. As will be discussed in this chapter, instrumentation for measurement of large groups of intermediary metabolites as well as computational methods for analyzing such data are emerging. This surge in development of “metabolomics” technologies confers a number of potential advantages to scientists interested in disease research. First, it is estimated that human beings contain approximately 5000 discrete small molecule metabolites, far

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 180

less than the estimate of 25,000 genes and 100,000 proteins. Thus, metabolomics may ultimately be the most tractable of the “omics” sciences. Second, metabolomics measures changes in metabolic or chemical milieu that are downstream of genomic and proteomic alterations, therefore potentially providing the most integrated picture of biological status. Third, identification of metabolic fingerprints for specific diseases may have particular practical utility for development of therapies, because metabolic changes immediately suggest enzymatic drug targets. And fourth, metabolomics is likely to be a powerful and precise tool for discerning mechanisms of action and possible toxicological effects of drug therapies. The purpose of this chapter is to describe current methodologies for metabolic profiling and flux analysis and to provide selected examples of insights into biological and disease mechanisms gained from these approaches. Studies involving integration of metabolomics with genomic and transcriptomic profiling methods are just beginning to emerge, and early examples of this exciting area will also be highlighted. It is important

Copyright © 2009, Elsevier Inc. All rights reserved.

Current Metabolomics Platforms: Basic Tools and General Features

to emphasize from the outset that metabolic profiling tools are in a rapid phase of development, such that our description of currently useful methods will likely represent a “snapshot in time” that will evolve rapidly over the next several years.

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100 P

L A %

CURRENT METABOLOMICS PLATFORMS: BASIC TOOLS AND GENERAL FEATURES As described in other chapters in this volume, genome-wide analysis of genetic variation (“genomics”) and surveys of changes in mRNA abundance by microarray technologies (“transcriptomics”) are examples of robust and relatively mature technologies for investigation of mechanisms underlying biological variability. For example, mRNA profiling has developed to a point that microarray cores have become common in both the academic and private sectors, with many thousands of RNA profiling experiments performed on a yearly basis. This is not yet the case for laboratories that focus on comprehensive metabolic analysis. One reason for this is the complexity inherent in attempting to measure small molecule metabolites (intermediary metabolites as discussed in this chapter are defined as nonpeptidic and with a molecular weight of 3000) in a quantitatively rigorous fashion. Underlying issues include the variable lability of metabolites (e.g., amino acids are often quite stable, whereas organic acid or lipid species can be quite labile), the chemical diversity of different metabolite classes, problems encountered in efficient extraction of metabolites from different biological matrices (e.g., tissues, blood, urine, etc.), and the wide dynamic concentration range of metabolite species (ranging from sub-nanomolar to milimolar). Thus, it is not surprising that there is currently no single technology for measurement of all of the metabolites in the “metabolome”. This means that the small number of existing laboratories that engage in comprehensive metabolic analysis have usually been constructed on an “ad hoc” basis, often driven by the direct technical experience and/or scientific goals and philosophies of the founding scientists. This leads to varying choices in instrument platforms, and places an even greater burden on individual laboratories for rigor in quantitative analysis, since standardized methods do not yet exist for the majority of analytes. The two major instrument platforms for measuring metabolite levels in biological samples are nuclear magnetic resonance (NMR) and mass spectrometry (MS) (Dettmer et al., 2007; German et al., 2005; Hollywood et al., 2006; Lindon et al., 2007; Watson, 2006). Both technologies can also be used for metabolic flux analysis following provision of stable isotope-labeled metabolic fuels (e.g., U-13C glucose) to cells and organisms, as discussed later. In general, when applying these technologies to assays of metabolite levels, research groups in the field tend to fall into two camps that adopt either “unbiased”/“top-down” approaches or “targeted”/”bottom-up” methods as their core technologies. Unbiased profiling involves use of either NMR

F

A

F

L VP GG

V

S S

M

Y Y

M H

E D D

E

0 140

160

180

200 m/z

220

240

260

Figure 15.1 Tandem mass spectrum of butyl esters of acidic and neutral amino acids in serum. Data were obtained using a neutral loss of 102 Da scan function. The figure illustrates the use of internal standards for “targeted” quantitative profiling of metabolites. Each amino acid in serum (shown by black letters) is measured against its own stable-isotope-labeled internal standard (shown in red letters). G, glycine, A, alanine, S, serine, P, proline, V, valine, L, leucine, M, methionine, H, histidine, F, phenylalanine, Y, tyrosine, D, aspartate, E, glutamate.

or MS for measurement of as many metabolites as possible in a biological specimen simultaneously, regardless of the chemical class of the metabolites. When applied to unbiased profiling, both NMR and MS have advantages and limitations, as discussed below, and neither technology can presently be used for surveying all of the metabolites in a sample in a quantitative fashion. In contrast, targeted profiling focuses on quantification of discrete clusters of chemically related metabolites (“modules”) using various combinations of chromatographic separations technologies and MS instruments that are most compatible with the analyte class. For example, electrospray ionization (ESI) and tandem mass spectrometry (MS/MS) are used for quantitative measurements of multiple amino acids and urea cycle intermediates or acylcarnitines (byproducts of mitochondrial oxidation of fatty acids, amino acids and glucose) of varying chain length and degree of saturation, whereas gas chromatography-MS (GCMS) is used routinely for measurement of multiple species of free fatty acids and organic acids. For each separate module, accurate quantification of the analytes is facilitated by addition of a cocktail of stable isotope labeled standards to the biological sample prior to the extraction and derivatization steps that may be necessary for the particular MS approach (see below and example shown in Figure 15.1). Ideally, each unknown analyte will be paired with its labeled cognate (usually an M+3 heavy isotope, e.g., methionine

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TABLE 15.1

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Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms

Summary of mass spectrometry (MS) methods used for metabolic profiling

Technique

Mass Analyzer

Reference

Metabolites

GC/MS

Quadrupole

Fiehn et al. (2000) Roessner et al. (2001) Gullberg et al. (2004) Pope et al. (2007) Denkert et al. (2006)

Fatty acids, alcohols, sterols, aliphatic and aromatic acids, amino acids, sugars, sugar alcohols, phosphates, polyamines

An et al. (2004) Haqq et al. (2005) Allen et al. (2003) Scholz et al. (2004) Oikawa et al. (2006)

Acylcarnitines, amino acids, coenzyme-As,

Bajad et al. (2006) Sabatine et al. (2005) Han and Gross (2003) Murphy et al. (2005) Lafaye et al. (2003) Lafaye et al. (2004) Plumb et al. (2005) Lenz et al. (2007) Rainville et al. (2007)

Phoshpholipids, sphingolipids, eicosanoids, amino acids, sulfoconjugates, carboxylic acids, amines, glucuronides, glycosides, glutathione conjugates

Sato et al. (2004) Williams et al. (2007) McNally et al. (2006) McNally et al. (2007) Soga et al. (2006) Soga et al. (2006)

Amino acids, sugar nucleotides, carboxylic acids, sugar phosphates, amines

TOF

FIA or Infusion

QqQ TOF Q-TOF FT-ICR

LC/MS

QqQ

QIT TOF Q-TOF CE/MS

Quadrupole QqQ-IT TOF Q-TOF

Direct Ionization

MALDI-TOF MALDI-TOF/TOF DESI-QIT

Yu et al. (2006) Fraser et al. (2007) Sun et al. (2007) Cooks et al. (2006) Pan et al. (2007)

Amino acids, fatty acids, carotenoids, nucleosides, carboxylic acids

Abbreviations: GC, Gas Chromatography; MS, Mass Spectrometry; LC, Liquid Chromatography; CE, Capillary Electrophoresis; TOF, Time of Flight; FT-ICR, Fourier Transform Ion Cyclotron Resonance;Q, Quadrupole; QqQ Triple Quadrupole; QIT Quadrupole Ion Trap; MALDI, Matrix Assisted Laser Desorption Ionization; DESI, Desorption Electrospray Ionization.

with methyl-D3 methionine, pyruvate with 13C3-pyruvate, etc.) to control for differences in analyte loss during chemical extraction and/or derivatization of individual analyte species and to compensate for ionization suppression effects. In practice, the limited range of stable isotope-labeled metabolites available from commercial suppliers and their relatively high cost constitute a current impediment to more broadscale development and deployment of these technologies. To overcome this problem in the future, it may be necessary for metabolomics core laboratories to import chemical synthesis capabilities for production of comprehensive libraries of internal standards.

Table 15.1 provides a summary of various MS-based metabolic profiling technologies that are currently used for measurement of different metabolite classes.

COMPARISON OF NMR AND MS TECHNOLOGIES FOR UNBIASED METABOLIC PROFILING Nuclear magnetic resonance spectroscopy is theoretically an excellent tool for unbiased metabolic profiling of all small

MS Methods for Targeted Metabolic Profiling

molecule metabolites, since the method is based on detection of any molecules that contain carbon or hydrogen (German et al., 2005; Lindon et al., 2007). Moreover, the method is non-destructive, in that analyses can be conducted directly in bodily fluids, cells, and even in intact tissues without the need for chemical extraction or derivatization of the individual analytes. These advantages are off-set by significant technical challenges, including poor sensitivity (it is generally not possible to detect metabolites at sub-micromolar levels with NMR) and the difficulty of deconvoluting spectra of complex metabolite mixtures such as typically found in biological matrices like plasma, urine, or tissue extracts. When NMR is applied to such samples, all metabolites within the detection limits of the technology resolve as hundreds to thousands of proton magnetic resonance peaks. The complexity of these data and the absence of comprehensive libraries of NMR spectra make it very difficult to assign the different resonances to specific metabolites. Thus, although NMR spectra are rich with information, the full harvesting of these datasets is not yet possible. Instead, peaks within NMR datasets are often treated as independent statistical variables, and the data analyzed by methods such as principal components analysis (PCA) to identify clusters of resonances that characterize different biological or disease states. Mass spectrometry has the immediate advantage of much higher sensitivity compared to NMR, and the most advanced MS platforms such as Fourier transform ion cyclotron mass spectrometers (FT-ICR-MS) have the capacity to detect metabolites at concentrations as low as the femtomole range (Dettmer et al., 2007). Moreover, modern MS platforms have very high-mass resolution (100,000), can resolve literally thousands of individual small molecules without the need for chromatography, and can achieve mass accuracies of 1 ppm. Seemingly, current MS platforms could therefore provide a means of circumventing problems encountered when using NMR methods for unbiased metabolic profiling of complex biological matrices, as described earlier. In practice, however, MS techniques usually require a sample extraction step that can cause metabolite losses. Moreover, different kinds of MS instruments use varying methods for the generation and detection of ions, and in concert, different kinds of metabolites have discrete ionization properties and are therefore variably resolved and detected in instruments that rely on mass and charge for analyte separation (Dettmer et al., 2007). Thus, while several current MS instrument platforms can be used for resolution and detection of large numbers of metabolites (e.g., high-performance liquid chromatography-MS, Q-TOF-MS, and FT-ICR-MS), the variable lability, solubility, recovery, and ionization of the different analyte species, coupled with the lack of broad-coverage spectral libraries makes these unbiased methods inherently nonquantitative and useful mainly for detecting gross changes in spectral peaks in samples of different biological origin. Similar to NMR, masses in these complex MS spectra are often not assigned to a specific analyte, and the data are instead analyzed by PCA or other multi-factorial statistical tools. However, an

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advantage of MS-based unbiased profiling methods is that when specific peaks or clusters of peaks consistently differentiate biological states (e.g., a metastatic tumor from a benign tumor), their detection by high-resolution MS platforms provides an immediate clue for analyte identification by providing an accurate mass estimate of the analyte. These mass estimates can be used to query databases such as KEGG, HMP, and ChemSpider for chemical formulae that match the estimated mass, thereby usually providing a strong starting point for identifying the analyte, and ultimately facilitating a return to more mechanistically oriented studies. The number of possible formulae is also significantly reduced by high-mass accuracy; for example, the number of possibilities for a mass 500 at a mass accuracy 5 ppm with an elemental palate CHNOS is 23. A mass accuracy of 2 ppm would reduce the number to 10 and an accuracy of 1 ppm would yield only 4 possibilities.

MS METHODS FOR TARGETED METABOLIC PROFILING For scientists with core interests in biological mechanisms rather than biomarkers, knowledge about the identity of metabolites being surveyed and their exact concentration greatly enhances the value of the datasets. This means that some metabolic profiling laboratories are built in a bottom-up fashion, with emphasis on targeted and quantitative MS-based analyses. Although these approaches tend to utilize simpler tools such as ESI-MS/MS and GC-MS, significant challenges must still be overcome in building rigorous and fully vetted analysis modules for groups of metabolites. For example, GC-MS, when coupled with the use of stable isotope standards, can provide sufficient chromatographic resolution and analyte-specific detection to allow quantification of groups of metabolites in a class (e.g., fatty acids of different change length and degree of saturation or organic acids/TCA cycle intermediates). However, because the chromatography is performed in the gas phase at high-temperature, analytes must be volatile and have sufficient thermal stability to survive the analysis. To help stabilize the analytes under study, reactive carboxyl, sulfhydryl, amine, or hydroxyl groups are derivatized by akylation, oximation, acylation, or silylation (Dettmer et al., 2007; German et al., 2005). These methods, while effective, add complexity beyond that already introduced by sample extraction to the analytical procedures, and can result in batchto-batch variation if not properly controlled. Similarly, analysis of acylcarnitines or amino acids/urea cycle intermediates by ESI-MS/MS requires different derivatization steps for each module – for example, treatment with acidic methanol for acylcarnitines and n-butanol for amino acids/urea cycle intermediates (Haqq et al., 2005). Nevertheless, with care, these modules can provide quantitative data for highly informative clusters of analytes (e.g., fatty acids, fatty acylCoAs, and acylcarnitines of different chain length and degree of saturation, amino acids/urea

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cycle intermediates, and organic acids), with coefficients of variation in replicate assays in the range of 10%.

EXAMPLES OF NMR-BASED METABOLIC PROFILING IN DISEASE RESEARCH The most common application of 1H-NMR-based metabolic profiling in mammals and humans to date has been for evaluation of toxic effects of xenobiotic drugs. Work in this area has been reviewed by others (Lindon et al., 2007), so we focus instead on newer applications in the realm of disease research. Coronary artery disease (CAD) is the leading cause of death in the United States and, in concert with the epidemic of obesity and diabetes, is rapidly becoming the leading cause of death in many developing countries. The genetic predilection of CAD is well established; family history has repeatedly been shown to be a robust, independent risk factor for CAD. Many of the commonly accepted risk factors for CAD are metabolic, such as lipid abnormalities and diabetes, making this disease a particularly logical target for comprehensive metabolic profiling technologies. Indeed, 1H NMR was recently reported to detect metabolic profiles that predicted the presence and severity of CAD (Brindle et al., 2002). The study involved comparison of a relatively small group of patients with triple vessel coronary heart disease (n  36) to angiographically normal subjects (n  30). Partial least squares-discriminant analysis was used to identify peaks in the major lipid regions of the spectra that appeared to provide separation between the groups. The specific lipid species involved were not identified by this analysis, although it was suggested that choline-containing metabolites were particularly diagnostic. However, a subsequent study using very similar techniques demonstrated that the predictive value of the NMR-based metabolic profiles was weak when other factors such as gender and use of medical interventions such as statins were taken into account (Kirschenlohr et al., 2006). This second group of authors demonstrated that the 1H NMR technique could identify male versus female subjects with 100% accuracy, but was much less able to identify statin users or subjects with CVD, despite expectations of substantial changes in lipid profile in the former group. This study makes it clear that 1H NMR is currently not a substitute for the more invasive procedure of angiography in detection of CVD. An intriguing and more promising recent application of 1 H NMR-based metabolic analysis has been to study the influence of intestinal bacteria (microbiota) on pathophysiologic outcomes of high-fat (HF) feeding (Dumas et al., 2006). This involved feeding of a HF diet to a mouse strain (129S6) known to be susceptible to HF-induced hepatic steatosis and insulin resistance, and for comparison, to a strain (BALBc) with relative resistance to these effects of diet. Metabolic profiling of plasma and urine samples revealed low circulating levels of plasma phosphatidylcholine (PChol) and high levels of methylamines

in urine in the 129S6 strain. Since the conversion of choline to methylamines is accomplished by gut microbiota rather than mammalian enzymes (Zeisel et al., 1983), and choline deficient diets are associated with hepatic steatosis (Buchman et al., 1995), the authors propose that the increased propensity of the 129S6 strain for hepatic steatosis and insulin resistance could be due to a reduced PC pool necessary for the assembly of VLDL particles, leading to deposition of triglycerides in liver. As with its application to diagnosis of CVD, the value of NMR for detecting the contributions of bacterial metabolism to whole animal metabolic profiles and disease development remains to be confirmed or refuted by an independent study. However, in this instance, other studies suggest that this could indeed be an important pathway. Thus, treatment of germ-free mice with microbiota from the cecum of conventionally raised animals produced an increase in body fat content and appearance of insulin resistance within 14 days of transfer (Backhed et al. 2004). In addition, more recent studies have revealed that genetically obese rodents or obese humans have changes in relative abundance of two main intestinal bacterial groups, the Bacteroidetes and the Firmicutes, relative to lean controls, and that the “obese microbiome” has an increased capacity to harvest energy from the diet (Turnbaugh et al., 2006). The mechanism by which the obese microbiome influences peripheral metabolism of the host seems to involve changes in circulating levels of fasting-induced adipose factor (FIAF), a circulating lipoprotein lipase inhibitor, as well as changes in activity of the important metabolic regulatory proteins PGC-1 and 5 AMPactivated kinase (AMPK) in skeletal muscle and liver (Backhed et al., 2007). An intriguing area for future study is to attempt to define metabolic profiles for specific gastrointestinal bacteria such that their relative contributions to changes in overall metabolic profile can be monitored and fully understood in the context of fuel homeostasis in the host.

EXAMPLES OF TARGETED MSBASED METABOLIC PROFILING FOR UNDERSTANDING OF DISEASE MECHANISMS Development of Tools The past decade has seen a rapid increase in the use of MS-based analysis platforms for metabolic profiling. Many of the early applications of MS technologies were in the area of plant biology (Fiehn et al., 2000) or for detecting inborn errors of metabolism in newborn children (Frazier et al., 2006; Shekhawat et al., 2005). Application of tandem mass spectrometry (MS/MS) to newborn screening has allowed detection of more than 40 different genetic diseases of lipid and amino acid metabolism (see Chapter 41). In newborn screening, less emphasis is placed on absolute quantification of multiple analytes in a module, since the screen only requires detection of changes

Examples of Targeted MS-Based Metabolic Profling for Understanding of Disease Mechanisms

in a single or discrete cluster of analytes with respect to established laboratory norms. For example, a defect in HMG-CoA lyase results in large and specific increases in C5-OH-carnitine and methylglutaryl-carnitine species detected by MS/MS, whereas defects in long-chain L-3-hydroxyacyl CoA dehydrogenase (LCHAD) or mitochondrial trifunctional protein (MTP) are associated with increases in C16-OH and C18OH-carnitine metabolites (Shekhawat et al., 2005). Similarly, defects in amino acid catabolizing enzymes such as phenylalaine hydroxylase to cause phenylketonuria, or the branchedchain -ketoacid dehydrogenase complex to cause Maple Syrup urine disease are readily detected by dramatic increases in phenylalanine or branched-chain amino acid (BCCA) levels, respectively. In more recent years, many of the MS-based targeted metabolic profiling techniques developed for diagnosing inborn errors of metabolism have been adopted and refined for studies of metabolic regulatory mechanisms, disease detection, and mechanisms of disease pathogenesis. Selective examples, meant to be illustrative rather than comprehensive, are provided. One approach has been to combine several targeted MSbased assay modules as an assemblage of tools that can report on several critical metabolic pathways (An et al., 2004; Haqq et al., 2005; Koves et al., 2008). Using a combination of GC/MS and MS/MS, we are currently able to perform quantitative analysis of approximately 150 metabolites in six groups: 1. free fatty acids of varying chain length and degrees of saturation; 2. total fatty acids (free+esterified); 3. acylcarnitines (representing products of mitochondrial fatty acid, amino acid, and glucose oxidation); 4. acyl CoAs of varying chain length and degree of saturation; 5. organic acids (TCA cycle intermediates and related metabolites), and 6. amino acids, including urea cycle intermediates. Although the total number of analytes measured with these tools is small relative to estimates of 5000 total metabolites in the “metabolome”, they are nevertheless highly useful for understanding changes in metabolic fuel selection under different physiologic and pathophysiologic circumstances. Moreover, expansion of the platform to include a broader range of analytes of interest in disease pathogenesis would appear possible in the near term, via adoption of published methods for analysis of sphingolipids (including ceramides), phospholipids, and prostaglandins and their metabolites (eicosanoids) (Dettmer et al., 2007; Han and Gross, 2005; Merrill et al., 2005; Watson, 2006). However, the reader should appreciate that development of new modules is not a trivial or inexpensive undertaking, as it requires acquisition or synthesis of stable isotope-labeled internal standards to cover most if not all of the individual analytes in the module, development of extraction procedures that are efficient for multiple analytes in a class, and demonstration of quantitative reproducibility of the method. The advantage gained is that such tools can be

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applied to understanding of metabolic regulatory mechanisms in isolated cells, animal models of disease, and human disease states, as summarized in the following specific examples. Metabolic Profiling for Studies of Metabolic Regulatory and Signaling Mechanisms in Cultured Cells An example of integration of NMR- and MS-based tools for understanding of metabolic regulation and signaling mechanisms comes from recent work on the process of glucose-stimulated insulin secretion (GSIS) in pancreatic islet -cells (Boucher et al., 2004; Jensen et al., 2006; Joseph et al., 2006; Joseph et al., 2007; Lu et al., 2002; Ronnebaum et al., 2006; Ronnebaum et al., 2008). Stimulation of islet -cells with glucose causes increases in insulin secretion within seconds to minutes, and this response is mediated by signals that are generated by -cell glucose metabolism. A commonly accepted idea is that increases in the rate of glucose metabolism in -cells leads to increases in ATP:ADP ratio, which causes inhibition of ATP-sensitive K channels (KATP channels), membrane depolarization, and activation of voltage-gated calcium channels. The resulting influx of extracellular Ca2 is then thought to stimulate exocytosis of insulin containing secretory granules. However, this model clearly does not provide a complete description of signals that regulate GSIS, since pharmacologic or molecular inhibition of KATP-channel activity does not abrograte glucose regulation of insulin secretion (Henquin et al., 2003; Nenquin et al., 2004). To investigate non-KATP-channel related signals for GSIS, a set of insulinoma (INS-1)-derived cell lines with varying capacities for GSIS were developed (Hohmeier et al., 2000). Application of 13C NMR-based isotopomer analysis and MS-based profiling of intermediary metabolites led to the discovery of a critical link between pyruvate carboxylase (PC)mediated pyruvate exchange with TCA cycle intermediates (“pyruvate cycling”) and GSIS, and demonstration that these pathways are dysregulated in lipid-cultured and dysfunctional -cells (Boucher et al., 2004; Jensen et al., 2006; Joseph et al., 2006; Joseph et al., 2007; Lu et al., 2002; Ronnebaum et al., 2006). More recent studies have focused on identification of the specific pyruvate cycling pathways that may be involved in generation of signals for insulin secretion. One important pathway appears to involve export of citrate and/or isocitrate from the mitochondria via the citrate/isocitrate carrier (CIC), and subsequent conversion of isocitrate to -ketoglutarate (-KG) by the cytosolic NAPD-dependent isoform of isocitrate dehydrogenase (ICDc) (Joseph et al., 2006; Ronnebaum et al., 2006). siRNA-mediated suppression of CIC or ICDc cause substantial impairment of GSIS in both insulinoma cell lines and primary rat islets. These studies suggest that a metabolic byproduct of pyruvate/isocitrate cycling may be an important amplifying signal for control of GSIS. Possible mediators that are under investigation include NADPH, -KG or its metabolites, or GTP generated by the succinyl CoA dehydrogenase reaction, all of which are direct or downstream products of the ICDc reaction.

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[U-13C] glucose

pyruvate PDH PC acetyl-CoA

PC  ME  1  TCA

TCA αKG

glutamate C2

Figure 15.2 Use of 13C NMR for measurement of metabolic flux in cells. To measure flux of pyruvate into the TCA cycle via the oxidative enzyme pyruvate dehydrogenase (PDH) versus the anaplerotic enzyme pyruvate carboxylase (PC), [U-13C] glucose is administered to cells in culture. After 3 hours of incubation, cells are extracted and the NMR spectra of different carbons of glutamate are obtained. If all of the 13C-labeled glucose is converted to pyruvate to enter the TCA cycle through PDH, the spectrum of carbon 2 of glutamate will appear as shown at the bottom right of the figure. In contrast, if the labeled pyruvate enters the TCA cycle both through PDH and PC in equal proportions, the glutamate C2 spectrum will appear as shown at the top right of the figure. Arrows show significant difference in peak height in the two spectra. The change in the spectra under these two scenarios occurs because pyruvate that enters the TCA cycle via PC can engage in pyruvate recycling pathways that results in dilution of 13C (black circles) with natural abundance 12 C (gray circles), thus changing the pool of glutatmate mass isotopomers. Spectra of other glutamate carbons are obtained in the same fashion, and the aggregate data are deconvoluted to derive estimates of flux through PDH versus PC (Boucher et al., 2004; Lu et al., 2002; Sherry et al., 2004).

In the foregoing studies, measurement of pyruvate cycling flux was accomplished by incubation of -cells in low (3 mM) or high (12 mM) concentrations of U-13C glucose for several hours, followed by extraction of cells and analysis of the glutamate spectrum by NMR (see e.g., Figure 15.2). Specific resonances for each of the carbons of glutamate are affected by the population of mass isotopomers (glutamate with varying mixtures of 13C and 12C at each of the 5 carbons of the molecule), and this information can be used to calculate flux through the oxidative (PDH) and anaplerotic (PC) entry points of the tricarboxylic acid (TCA) cycle (Boucher et al., 2004; Lu et al., 2002; Sherry et al., 2004). When coupled to measurements of oxygen consumption (respiration), these rates can be expressed in

absolute rather than relative terms (Boucher et al., 2004; Sherry et al., 2004). These methods revealed that the capacity for GSIS in variously glucose responsive INS-1-derived cell lines was tightly correlated with PC-catalyzed pyruvate cycling activity, but not PDH-catalyzed glucose oxidation. To gain deeper understanding of the specific pyruvate cycling pathway involved in GSIS, these NMR-based methods of flux anlaysis were combined with MS/MS and GC/MS methods for profiling of acylcarnitines and organic acids, respectively. These experiments revealed that siRNA-mediated suppression of PC activity has little influence on pyruvate cycling rates or GSIS due to a compensatory rise in C2-acylcarnitine, which reflects the acetyl CoA pool; acetyl CoA is an allosteric activator of PC, which offsets the decrease in PC protein. These studies therefore uncovered a compensatory mechanism for preservation of pyruvate cycling activity and GSIS even when PC levels are reduced (Jensen et al., 2006). In another study, organic acid profiling was used to show that suppression of CIC caused a fall in cytosolic citrate levels, as would be expected if a major pathway for export of citrate from the mitochondria to the cytosol is blocked (Joseph et al., 2006). Finally, suppression of ICDc caused a decrease in pyruvate cycling flux, as measured by NMR, coupled with a rise in lactate levels measured by GC/MS, suggesting that in the face of suppressed pyruvate cycling activity, pyruvate is converted to lactate by default (Ronnebaum et al., 2006). These examples illustrate two important application of MS-based metabolic profiling in cellular research: (1) use in validation of specific genetic engineering or pharmacologic manipulations of cellular systems; (2) integration with flux analysis to provide a complete picture of changes in metabolic pathways under varying experimental conditions. Targeted MS-Based Metabolic Profiling Applied to Animal Models of Disease Targeted MS-based methods have also been used to gain understanding of metabolic control mechanisms in animal models of disease. Lipid infusion or the ingestion of a HF diet results in insulin resistance and eventual development of diabetes, but the mechanism by which this occurs has not been elucidated. In rats fed a HF diet, whole-animal, as well as muscle and liver insulin resistance are ameliorated in response to hepatic overexpression of a gene that affects lipid partitioning, malonyl-CoA decarboxylase (MCD) (An et al., 2004). Metabolic profiling of 37 acylcarnitine species by tandem mass spectrometry (MS/MS) revealed a decrease in the concentration of a lipid-derived metabolite, -OH-butyrylcarnitine (HB) in muscle of MCD-overexpressing animals, that was likely due to a change in intramuscular ketone metabolism. These findings spurred further investigation that led to a new model of diet-induced insulin resistance in muscle, involving accumulation of mitochondrially derived byproducts of lipid oxidation rather than diversion of lipid metabolites into cytosolic biosynthetic pathways (Muoio and Newgard, 2008). Supporting this idea are studies showing that chronic exposure

Examples of Targeted MS-Based Metabolic Profling for Understanding of Disease Mechanisms

of muscle to elevated lipids results in an increase rather than a decrease in expression of genes in the fatty acid -oxidative pathway (Koves et al., 2005). Importantly, this lipid-induced upregulation of the enzymatic machinery for -oxidation of fatty acids in muscle is not coordinated with upregulation of downstream metabolic pathways such as the TCA cycle and electron transport chain. This results in incomplete metabolism of fatty acids in the -oxidation pathway, accumulation of lipid-derived metabolites in the mitochondria, and a decrease in the levels of TCA cycle intermediates, as revealed by quantitative MS/MS and GC/MS analysis (Koves et al., 2005; Koves et al., 2008) (Figure 15.3). These abnormalities are reversed by exercise intervention in mice fed a HF diet, in association with increased TCA cycle activity and restoration of insulin sensitivity and glucose tolerance (Koves et al., 2005). Also supporting the model, knockout mice lacking MCD, which promotes fatty acid -oxidation, had markedly lower acylcarnitine levels in muscle and were protected against diet-induced insulin resistance, despite high levels of long-chain acyl CoAs (Koves et al., 2008). Conversely, transgenic mice with muscle-specific overexpression of PPAR, a nuclear receptor that activates -oxidative genes, developed both local and systemic glucose intolerance (Finck et al., 2005, Figure 15.3). The point to emphasize in the context of the current chapter is that the initial insights that led to this new model of muscle insulin resistance came mainly from metabolic profiling of liver and muscle tissue samples. These initial findings were then used to generate hypotheses that were tested by genetic engineering, nutritional modulation, and physical activity studies, illustrating that in addition to its traditional role as a disease screening tool, metabolic profiling can be used as a discovery tool for metabolic control mechanisms and disease pathogenesis. Application of MS-Based Metabolic Profiling for Understanding of Human Disease Pathogenesis Targeted MS-based metabolic profiling has also been increasingly applied to studies of human diseases and conditions. Specific examples of applications to the areas of obesity and cardiovascular disease (CVD) are discussed here, but these tools are also being applied to diverse areas such as cancer (Fan et al., 2006) and mental disorders (Kaddurah-Daouk et al., 2007). It has been appreciated for many years that obesity is associated with impaired metabolic regulation, including development of insulin resistance in muscle, liver, and adipose tissue, as well as loss of mass and function of pancreatic islet -cells (Muoio and Newgard, 2008). However, the specific mechanisms that link overnutrition and obesity to global metabolic dysfunction remain incompletely defined. We recently completed a metabolic analysis of 74 obese (median BMI of 36.6 kg/m2) and 67 lean (median BMI of 23.2 kg/m2) human subjects using the MS-based metabolic, hormonal and physiologic profiling tools described earlier (Haqq et al., 2005; Newgard et al., 2008). PCA was used to detect a metabolic signature comprised of changes

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in amino acid, acylcarnitine, and organic acid metabolites that suggests that pathways of BCAA catabolism are overloaded in obese humans. To test the significance of this for development of obesity-related disease, we fed normal rats on HF, high-fat with supplemented BCAA (HF/BCAA) or standard chow (SC) diets. Despite a low rate of body weight gain equivalent to the SC group, HF/BCAA rats were equally insulin resistant as HF rats, demonstrating a contribution of BCAA to development of insulin resistance independent of body weight. However, the effects of BCAA on body weight and insulin sensitivity seem to require the presence of fat, since animals fed on SC diet with supplemented BCAA gain weight normally and have normal insulin sensitivity. These findings demonstrate a synergistic interaction of dietary protein and fat in the development of insulin resistance, and provide a demonstration of how broad-based metabolic profiling tools can identify unanticipated contributions of dietary and metabolic factors to disease development. MS-based metabolic profiling has also been applied to cardiovascular disease. In one study, liquid chromatography/triple quadrupole high-sensitivity MS analysis was applied to plasma samples of 18 subjects with exercise-inducible ischemia compared to 18 normal controls (Sabatine et al., 2005). Blood samples were taken immediately before and immediately after exercise, and levels of 173 known and several more minor intermediary metabolites were measured. A number of metabolites were found to be discordant between the two groups, including lactate, byproducts of AMP metabolism, metabolites of the citric acid cycle, and several of the unknowns. Using the six most discordantly regulated metabolites, a metabolic ischemia risk score was created and found to be statistically correlated to the probability of ischemia. However, it should be noted that the number of subjects was quite small in this study, necessitating follow up studies that either confirm or refute these interesting initial findings. In another recent study, MS/MS and GC/MS-based profiling of fatty acids, acylcarnitines, and amino acids was performed on plasma samples from 117 subjects from eight multiplex families with familial early-onset CVD (Shah et al., 2008). Using SOLAR and adjusting for variables such as diabetes, hypertension, dyslipidemia, body-mass-index (BMI), age and sex, high heritabilities were found for several amino acids (arginine, glutamate, alanine, ornithine, valine, leucine/isoleucine, h2 0.50–0.85, P  0.00004  7.2  1015), free fatty acids (arachidonic, linoleic, h2 0.57–0.59, P  0.0004  0.00005), and acylcarnitines (h2 0.54–0.86, P  0.002  0.0000002). Moreover, PCA was used to identify several metabolite clusters, with several of these factors found to be highly heritable. Interestingly, GENECARD families showed two distinct metabolite profiles, tracking with their clinical characteristics and reflective of factors identified from PCA, suggesting different genetic backgrounds and consequent variation in control of key metabolic pathways that converge on CVD. However, once again, the utility of these profiles for predicting CVD in other high-risk families or in the general population will require further study.

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(a)

C20, C18:2, C18:1, C16 Fatty Acids

37 acylcarnitine metabolites

Intermediates (C3-C14) CATs CATs C2 (acetylcarnitine)

βOX

CATs

TCA

CoA

AJD11-3 dm031303a24 1 (1.694) Sm (Mn, 20.75) 100

IS

6

218

417

C2

Mitochondria

Acylcarnitines %

C4OH

305

C18:1

262 249

440 418

235

276

414

290

221 246 260 274

Tissue or serum extracts

438 441 456 462

306

430

0 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 m/z

Muscle acylcarnitines (nmol/mg protein)

(b)

75

3.0

2.5 MCD (Active) 2.0

MCDmut (Inactive)

1.5

* 1.0

0.5

C2 C3 C4 C5:1 C5 *C4-OH C6 C5-OH C4-DC C8:1 C8 C5-DC C6-DC C10:3 C10:2 C10:1 C10 C8-DC C12 C12-OH C10-DC C14:2 C14:1 C14 C14-OH C12-DC C16 C16-OH C18:2 C18:1 C18 C18:1-OH C18-OH C20

0

Figure 15.3 Use of tandem mass spectrometry to profile acylcarnitines in an animal model of insulin resistance. (a). Acylcarnitines provide a profile of products of mitochondrial fatty acid oxidation. As long-chain fatty acyl CoAs enter the mitochondria through carnitine palmitoyltransferase I (CPT1), they are oxidized by successive removal of two carbon acetyl CoA units to generate CoA esters ranging from 2 to 20 carbons in length and with varying degrees of sauration. These CoA intermediates are converted to carnitine esters, which are readily extracted from tissues or measured in plasma. The extracts are processed and analyzed by ESI-MS/MS to produce spectra such as that shown at the right of the panel. (b). Acylcarnitines in muscle samples taken from high-fat fed, insulinresistant rats (black bars) or high-fat fed rats rendered insulin sensitive by hepatic expression of malonyl CoA decarboxylase (MCD) (white bars). Note the dramatic decrease in concentration of one particular acylcarnitine, -hydroxybutyryl carnitine (labeled C4-OH). Data are from An et al., 2004.

References

INTEGRATION OF METABOLIC PROFILING WITH OTHER “OMICS” TECHNOLOGIES Genetic linkage and association studies have the power to establish a causal link between gene loci and physiological traits (see Chapter 8). These studies can make novel connections between biological processes that would not otherwise be predictable based on current knowledge. Numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been identified through gene mapping and positional cloning. While it has become relatively straightforward to map a phenotype to a broad-genomic region, identification of the individual gene(s) and variants responsible for the phenotype remains difficult. Moreover, traditional QTL mapping is based on association with physiological phenotype, but often does not reveal the molecular pathways leading to that phenotype. One way to overcome these limitations is to broadly expand the types of phenotypes analyzed in genetic screens. For example, with microarray technology, one can measure the abundance of virtually all mRNAs in a segregating sample. Importantly, mRNA abundance shows sufficient heritability in outbred populations or experimental crosses to allow mapping of gene loci that control gene expression, termed expression QTL (eQTL) (Schadt et al., 2005). When eQTL co-localize with a physiological QTL, one can hypothesize a shared regulator and offer a potential pathway leading to the physiological trait. The pathway between a QTL and a physiological trait often involves changes in the steady-state levels of metabolic intermediates, in addition to changes in mRNA abundance. Recent studies in plants (Wentzell et al., 2007) and in animal models of disease (Ferrara et al., 2008) have demonstrated that metabolite profiles show sufficient heritability to allow mapping of metabolic quantitative trait loci (mQTL), thereby facilitating integration of metabolic profiles with genomic and transcriptomic analyses. For example, one recent study mapped expression QTLs controlling glucosinolate content in Arabidopsis Bay–Sha recombinant lines, and found a strong correlation between loci controlling enzymes involved in glucosinolate synthesis and the levels of metabolites within this particular pathway (Wentzell et al., 2007). Similarly, analysis of mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetessusceptible BTBR leptinob/ob mouse strains was used to show that

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liver metabolites map to distinct genetic regions, thereby demonstrating that tissue metabolite profiles are heritable (Ferrara et al., 2008). This study also integrated liver microarray and metabolic profiling data to construct putative regulatory networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this multi-disciplinary approach, a regulatory network that links gene expression and metabolic changes in the context of glutamate metabolism was identified, and the validity of the approach was validated by experiments showing that genes in the network respond to changes in glutamine and glutamate availability. Although further development of these methods will be required, they already appear to have the potential to unearth regulatory networks that are altered to contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity, diabetes, and cardiovascular disease.

FUTURE DIRECTIONS In the post-sequence era, biologists and translational investigators alike have gained a new appreciation for metabolic analysis as a critical tool for assessing the physiologic and pathophysiologic impact of genetic variation. The current growth in instrument and methods development in the field of metabolomics is built on the foundation of decades of analytical biochemistry. The major difference between then and now is that the current emphasis is on methods that allow simultaneous measurement of multiple analytes in a biological sample, whereas earlier work was often focused on one or a small number of metabolites per assay. Despite these advances, no single profiling method currently allows simultaneous analysis of all of the metabolites in the metabolome. Ultimate achievement of this goal will require continued intensive development of deeper libraries of chemical standards, instrument platforms with broad-sensitivity range and high-mass accuracy, and likely, integration of MS and NMR methods to gain full analyte coverage. These advances must be coupled to continued development of computational methods for analysis of complex datasets and their integration with equally complex genomic, transcriptomic, and proteomic profiles. The examples provided in this chapter of new scientific insights gained by application of current tools suggest a broad horizon and provide strong encouragement for further technology development in this area.

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An, J., Muoio, D.M., Shiota, M., Fujimoto, Y., Cline, G.W., Shulman, G.I., Stevens, R., Millington, D. and Newgard, C.B. (2004). Hepatic expression of malonyl CoA decarboxylase reverses muscle, liver, and whole-animal insulin resistance. Nat Med 10, 268–274.

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CHAPTER

16 Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics Bjorn T. Gjertsen and James B. Lorens

INTRODUCTION Geneticists have for decades mutagenized cells and model organisms to induce phenotypic changes essential for our understanding of gene function (Bier 2005; Peters et al., 2003). Mutations engendering these cell behavioral changes have been mapped and the cognate gene products identified. Pharmaceutical scientists have for decades conducted diversity screens to identify chemical entities that affect biological function (Landro et al., 2000). Large libraries of small molecule compounds that cover a diverse chemical space have been interrogated in high-throughput screening schemes. Compounds that inhibit critical protein functions have been revealed. In recent years molecular biologists have combined these principles, developing different types of dominant genetic effectors (“perturbagens”) that affect gene function at multiple levels (Bredel and Jacoby, 2004; Kawasumi and Nghiem, 2007; Knight and Shokat, 2007; Rognan, 2007). This conflation of high-throughput chemical diversity screening and trans-dominant genetics is particularly evident in the recent advent of RNA interference (RNAi) and chemical genomic approaches with promising clinical translation. For example, the Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

identification of tumor-specific gene functional-dependence for oncogenic growth led to the development of imatinib mesylate, a small molecule inhibitor of the Bcr-Abl fusion protein in chronic myelogeous leukemia (Hunter, 2007; Sherbenou and Druker, 2007). This type of synthetic lethality via small molecule or modulation of gene function through RNAi is a promising strategy for future therapeutic development (Kaelin, 2005;Westbrook et al., 2005). Gene function analysis needs to be considered in conjunction with currently available technology. Recent technological development allows us to elucidate gene function more extensively, particularly in mammalian systems.Two recent approaches – RNAi and chemical genomics – provide new opportunities to elucidate the role of specific genes in complex biological systems. RNAi has rapidly evolved from a curious genetic phenomenon to the most widely used gene function analysis tool in less than a decade (Fire et al., 1998; Mello and Conte, 2004). Chemical genomics has established the use of small molecule libraries and high-throughput screening, a domain previously unique to the pharmaceutical industry, to delineate gene functions in diverse biological systems (Knight and Shokat, 2007). Copyright © 2009, Elsevier Inc. All rights reserved. 193

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Genetic effectors*

Effector type

Delivery

Library

Effects

Protein Full-length Truncated

Vector

cDNA fragmented cDNA

Gain Gain/Loss

Fusion

cDNA

Gain/Loss

Protein domains

cDNA

Gain/Loss

Random oligo

Loss (Gain)

Random oligo

Loss (Gain)

Oligo/split intein

Loss (Gain)

cDNA

Loss

Ribozyme

random oligo

Loss

shRNA

fragmented cDNA/oligo

Loss Loss

siRNA

oligo

Peptide Free

Vector, internalization motif

Scaffolded (GFP, protease, scFv, singlechain antibody) Cyclic peptide RNA Antisense

Vector, transfection

* Dominant genetic effectors or “perturbagens” are trans-acting molecules that alter gene function by interfering with a specific gene product the different stages of gene function (e.g., gene expression, protein-product function). Genetic effectors can be derived from protein, peptide or RNA and exert either gain- or loss-of-function effects on gene function. Libraries of genetic effectors are generally based on expressed (mRNA/cDNA), specific or random oligonucleotide sequences. These can be delivered directly to cells by transfection or expressed by gene vectors.

GENE FUNCTION ANALYSIS: AN OVERVIEW The assignment of function to a gene product entails an interruption of the genotype to phenotype flow. This can be accomplished at every stage: DNA mutation, mRNA destruction or inhibition of protein function. Classical genetic approaches involve cis-changes in DNA sequence or structure (insertion, deletions) that are generally technically challenging in mammalian cells (e.g., specific gene knockout). Trans-dominant genetics offers an alternative approach (Gudkov et al., 1994; Xu et al., 2001). Dominant genetic effectors or “perturbagens” are trans-acting molecules that alter gene function by interfering with a specific gene product at different levels (gene expression, protein-product function). Perturbagens exhibit different modes of action and include dominant negative proteins, siRNA, antisense RNA, ribozymes, inhibitory linear or cyclic

peptides, single chain antibodies or chemical inhibitors (Table 16.1) (Lorens et al., 2001). These dominant inhibitory molecules can be taken up by target cells or expressed by various vectors. Each effector type has distinct advantages and optimal uses. Hence, the assignment of gene function invariably enlists a combination of approaches. Recent efforts in gene functional analysis have focused on chemical genetic approaches based on the availability of many new classes of small chemical inhibitors of protein function, and RNAi. RNAi is a sequence-specific post-transcriptional gene-silencing mechanism used by endogenous small regulatory RNAs. Experimentally induced gene silencing has been used extensively for gene functionalization experiments.

RNA INTERFERENCE RNAi is initiated by the presence of double-stranded RNA (dsRNA) in the cytosol (Figure 16.1). The dsRNA is recognized by the RNAse III endonuclease Dicer complex and cleaved into characteristic short, interfering RNAs (siRNA), each 21–23 nucleotides with a core 19 base-paired region, 2 nucleotide 3OH-overhangs and 5-P-ends. Subsequent gene-silencing effects are triggered by siRNA. One of the siRNA strands (guide strand), identified by a less stable 5-duplex region, is preferentially assimilated by an Argonaute (Ago) family protein into the RNA Interference Silencing Complex (RISC). The other (passenger strand) is rejected and destroyed. The guide strand endows the RISC complex with sequence specificity, by hybridizing to cognate mRNA. The RISC:siRNA:mRNA complex localizes to processing bodies (P-bodies), cytoplasmic sites of cellular RNA degradation and storage. The RISC interaction with mRNA via guide strand hybridization leads to one of two outcomes, dictated by the stability of the hybridization and the presence of specific proteins in the RISC complex. Degradation of the mRNA requires a perfect match between the short RNA and the mRNA. Perfect base pairing promotes endonucleolytic cleavage in middle of guide:mRNA duplex (10 nucleotides from the guide strand 5end) by Ago2/RISC complexes. The products of RISC cleavage are degraded by bulk RNA degradation enzymes. In contrast, imperfect hybridization, characteristic of microRNA, promotes translational repression by RISC complexes. The suppression of translation is tolerant of significant mismatches outside of a short “seed” region (6 bp) at the 5 end of the guide RNA. The relative rates of cleavage versus translation inhibition may promote this difference, with more stable complexes kinetically favorable for mRNA degradation. Design of siRNA The design of effective siRNA is based on empirical criteria determined by statistical surveys of siRNA collections and improved understanding of the biochemistry of the RNAi process (Elbashir et al., 2001; Hsieh et al., 2004). A key consideration is that the antisense sequence be utilized as the guide strand. Sequence motifs in the siRNA, mirrors of the cognate mRNA

RNA interference

Dicer

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195

siRNA

PAZ

Exportin shRNA

PAZ

m7 Target mRNA

Assembly of RISC

Argonaute

Guide strand AAAA

PIWI

Cleavage of passenger strand

Cleavage of mRNA m7

mRNA degradation AAAA

Figure 16.1 The basic approach to RNA interference. RNAi pathways are guided by small RNAs that include siRNA and miRNAs. This figure indicates how retroviral RNAi works through formation of a shRNA exported from the nucleus to the Dicer complex in the cytoplasm. The double-stranded hairpin RNA is cleaved by the Dicer enzyme complex into siRNA. These siRNAs are incorporated into Ago and the RISC. After cleavage of the RNA passenger strand the siRNA guide strand recognizes target sites to direct mRNA cleavage, carried out by the catalytic domain of Ago. RNAi therapeutics developed to harness the siRNA pathway typically involves the delivery of synthetic siRNA into the cell cytoplasm. The miRNA pathway begins with endogenously encoded primary microRNA transcripts (pri-miRNAs), or retrovirally transduced that encode for similar transcripts as indicted in this figure. For more details see de Fougerolles et al., 2007.

secondary structure, influence overall silencing efficiency and may reflect accessibility constraints. Important determinant siRNA features include overall hybridization energy and H-bonding patterns, asymmetric stability at the 5 ends (e.g., AU rich 5 end of guide strand), low duplex stability adjacent to the cleavage site (positions 9–14) and preferred nucleotides at specific positions (e.g., U at position 1 and 17; A at the cleavage site, position 10). RNAi specificity is achieved by limiting siRNA complementarity to non-target transcripts. The rule of thumb is that a given siRNA sequence should have at least three mismatches to non-target transcripts to abrogate silencing, with the central duplex region surrounding the cleavage site (position 9–12) (Birmingham et al., 2006) being particularly sensitive to mismatches. However, recent studies demonstrate that much of the off-target effects are engendered by the seed region, a hexamer stretch at position 2–8 of the guide strand that mediates translational repression by microRNA (miRNA) in 3UTR of mRNA. The presence of more than one seed region sequence in an mRNA 3UTR significantly increases the probability of gene silencing. An in silico analysis of published siRNA sequences concluded that the majority has a significant risk of non-specific gene knock-down (Snove and Holen, 2004). Chemical modification of seed region bases in (e.g., 2-O-methyl

ribosyl substitution at position 2) synthetic siRNA oligonucleotides can reduce this miRNA-like silencing (Jackson et al., 2006). Hence, as off-target silencing is largely sequence specific, RNAi-based results must be validated by at least two independent siRNA sequences derived from the target transcript, phenotypic “rescue” by co-expression of a siRNA-resistant cDNA version of the target gene or by corroborating evidence from other techniques (Cullen, 2006; Echeverri et al., 2006). Delivery of RNAi Effectors Several different methods can be used to deliver the active siRNA into the cell, depending on the organism of interest. In the nematode Caenorhabditis elegans, simply feeding the worm with bacteria that are expressing dsRNA encoding a target gene is sufficient to trigger long-lasting knock-down, as the long dsRNA is converted into active siRNAs intracellularly by Dicer (Fire et al., 1998). In mammalian cells, long dsRNA activates the antiviral interferon pathway leading to non-specific suppression of gene expression (de Veer et al., 2005). siRNAs or short (less than 30 bp) dsRNA precursors of siRNAs can be produced in vitro by chemical synthesis or enzymatic cleavage of longer RNAs and then introduced into cells by transfection. The transfection of

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siRNA pools appears to enhance silencing specificity as the concentration of each individual siRNA is low, reducing the magnitude of off-target effects for any particular sequence. Alternatively, the siRNA can be produced within the cell by expression from a vector. These vector-based systems generally use RNA polymerase III promoter cassettes to produce a short primary transcript that folds into a hairpin structure (shRNA) and utilize the endogenous miRNA processing pathway: shRNA is cleaved within the nucleus by the microprocessor complex and exported to the cytoplasm where Dicer converts these into siRNA (Brummelkamp et al., 2002, Chang et al., 2006). Recently, miRNA-based expression scaffolds were developed to allow shRNA expression from more widely used RNA polymerase II promoter systems. These RNAi triggers are designed to be perfectly matched to the target mRNA and can result in potent and sustained transcript degradation. A number of conditional expression systems, mainly based on tetracyclinemediated gene regulation principles, have also been developed, allowing regulated gene silencing. RNAi Loss-of-Function Library Screens Forward genetic screens are conducted in mammalian cells using libraries of dominant genetic effectors introduced into cells to produce a population of individually “mutagenized” cells where

RNAi library

RNAi Classical genetics

Forward (Hypothesis-generating) Phenotype of interest Reverse (Hypothesis-based)

Gene (or protein) of interest

Chemical genetics Library of small molecules

Targeted small molecules

Figure 16.2 Chemical genomics in medicine. Random DNA mutagenesis is a classical genetics approach to create phenotypes that mimic disease in a cell or organism. Random mutagenesis may be replaced by library of interfering RNA, and targeted mutagenesis may be replaced by specific interfering RNA sequences that target the mRNA of interest. In chemical genomics a small molecule compound is used to achieve the same phenotype, or to correct a defined disease-related phenotype. After Kawasumi and Nghiem, 2007.

specific cellular functions are interrupted by the dominantly acting protein, peptide, RNA or organic chemical (Figure 16.2). Individual cells in which the genetic effector has caused a particular phenotypic response can then be selected and the genetic effector responsible identified. By defining the interactions of these effectors, potential targets for the development of therapeutic agents can be identified. Several different groups have produced collections of chemically synthesized siRNAs targeting all predicted human genes. Typically 3–5 different siRNAs are designed for each gene, with the expectation that at least half will knock-down gene expression more than 70%. For use in screens, these siRNAs are transfected into cells, usually as siRNA-lipid complexes, or by pre-spotting the siRNAs onto cell culture surfaces (“reverse transfection”) (Wheeler et al., 2005). These libraries have certain limitations: the approach is limited to transfectable cell types, and the transfected reagents produce only transient RNAi. Alternatively, retroviral vector shRNA libraries can be used. The distinct advantage of this system is the stable integration of viral vectors into host cell chromosomes and subsequent long-term expression of the shRNA. In a recent example, MacKeigan and colleagues conducted synthetic lethal screens to identify kinases and phosphatases that regulate apoptosis and chemoresistance (MacKeigan et al., 2005). HeLa cells were transfected with siRNAs derived from 650 human kinase and 222 phosphatase genes. Remarkably, siRNAs for 73 kinases (11%) and 70 phosphatases (32%) induced greater than twofold increases in apoptosis level relative to controls. These 143 “survival kinases and phosphatases” hence all contribute to HeLa cell survival, emphasizing the potential for novel synthetic interactions in transformed cells. Moffat and colleagues recently reported the development of a large arrayed lentiviral RNAi library (TRC1) and highcontent screen for mitosis (Moffat et al., 2006). A selected subset of the TRC1 library (4903 shRNA constructs) was used to target 1028 human genes (mostly kinases/phosphatases) with single, distinct shRNA-expressing lentiviruses in each well. Automated fluorescence microscopy and image analysis was used to screen for effects on mitosis. Over 100 genes met the screening criteria and were verified to affect mitotic progression using standard microscopy. Brummelkamp and colleagues used a combined chemical genetic and RNAi library screen to define genes required for p53-dependent cell cycle arrest (Brummelkamp et al., 2006). Treatment of breast carcinoma cells with nutlin-3, a small molecule inhibitor of the p53-negative regulator MDM2 (Vassilev et al., 2004), results in upregulation of p53 protein levels and cell cycle arrest. Cells were transduced with a retroviral shRNA library targeting  8000 genes (3 shRNA per gene) and divided into two populations, one that was treated with nutlin-3 and one receiving vehicle. shRNA sequences were isolated by PCR from colonies arising in the respective cell populations and compared by microarray (barcode) analysis. A small number of sequences were specifically enriched in the treated cell population, including the positive control p53 targeting sequence, and

Chemical Genomics

the p53 binding protein, p53BP1, that are required for p53 activation in nutlin-3 treated cells (Brummelkamp et al., 2006). In Vivo Use of siRNA The notion of using siRNAs as small molecule inhibitors of any gene is a particularly exciting prospect for in vivo gene analysis, drug target validation and novel therapeutic modalities. Significant obstacles remain, however, regarding efficient in vivo delivery of highly charged dsRNA. Unmodified siRNAs are rapidly degraded by serum RNAses and do not easily traverse cellular membranes. Chemical modification of specific bases can provide resistance to RNAse but can also compromise base pairing. Hence these modification strategies must balance stability and potency, such as distributed 2-O-methyl modifications. Systemically administered siRNAs are conjugated to targeting groups (e.g., proteins, cholesterol), encapsulated by lipids or carried by nanoparticles to improve cellular uptake. Alternatively various viral vectors can be used to express shRNA in target cells. Indeed, siRNA-based drugs are already in early clinical development. Off-Target Effects of siRNA Unintended gene regulation by siRNA can be induced by the delivery method and activation of innate cellular immunity. Offtarget effects may also be caused by base pair complementarities that alter dozens to hundreds of genes up to fourfold. Off-target effects are present in both in synthetic produced RNA duplexes and in siRNA generated in intact cells by vector-based expression of hairpin RNAs (Kettner-Buhrow et al., 2006). An in silico analysis of published siRNA concluded that the majority of reported siRNA have a risk of non-specific gene knock-down (Snove and Holen, 2004), but suggested that most of these redundancies can be removed in future siRNA design. Birmingham and colleagues have analyzed off-target genes by expression profiling in human cells, and have isolated the off-targeting to the 3 untranslated region (Birmingham et al., 2006). Modification of the siRNA, a 2-O-methyl ribosyl substitution at position 2 in the guide strand, is suggested to decrease the silencing of partially complementary transcripts (Jackson et al., 2006). Successful use of siRNA in the study of gene function should therefore carefully consider both the bioinformatic rules used for target gene complimentary analysis, as well as the chemical synthesis design.

CHEMICAL GENOMICS In recent years the increasing availability of large sets of bioactive small molecule compounds and the ability to systematically interrogate their effects on specific protein targets or cellular functions has engendered the field of chemical genomics (Kawasumi and Nghiem, 2007; Knight and Shokat, 2007). Based on the tenet of chemical genetics, that small molecules can be used to alter gene function and thus mimic mutant alleles, chemical genomics endeavors to discover and characterize small

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molecules that modulate every gene. This is based either on the ability to design de novo a specific inhibitor of a given protein or the ability to survey a large diverse set of compounds for this activity. This latter approach is analogous to creating random mutations in DNA and necessarily requires significant followup to identify the compound target (“mutation”) (Hoyne and Goodnow, 2006; Inamdar, 2001). Hence most chemical genomic screens have focused on a specific target protein. While the generation of mutations in DNA is straightforward, the chemical synthesis of diverse structures is much more difficult. The advent of combinatorial chemistry has significantly improved our ability to generate very large collections of molecules (Dolle et al., 2005; Huwe, 2006). Natural compounds and other “privileged” bioactive scaffolds that affect known proteins or biological functions can form the basis for more targeted libraries (Ortholand and Ganesan, 2004). Hence, a practical description of chemical genomics must include tools that allow scientists to rapidly characterize a large pool of small molecules against target proteins. This implies an inversion of the drug discovery process, where the conventional industry strategy up to now has been to find drugs for targets, instead of finding targets for drugs. Chemical genomics is thus a modern conflation of well establish pharmacology principles, advances in genomics technology and effective chemical synthesis methodologies (Figure 16.2). Chemical Genomic Examples Combinatorial chemistry has made it possible to generate large libraries of small molecules in a short time, while automated high-throughput screening technologies facilitate the rapid interrogation of these compounds. Much of this work, conducted by the pharmacological industry, has been targeted to the discovery of novel chemical entities that modulate important signaling enzymes and cell death regulators (Roland and Dolle, 2004). Synthesis on beads allows transfer of minute amounts of a library of compounds to cell-based screens (MacBeath, 2001). We describe three different examples of chemical biology that illustrate the potential of this approach. FK506 The first example is FK506 (rapamycin, sirolimus), a natural macrolide antibiotic from the bacterium Streptomyces hygroscopicus, discovered as an antifungal agent from natural compound screening in the 1970s. During the 1990s, it was approved as an immunosuppressant for use in kidney transplant. Through study of the FK506 binding protein, a new class of proteins, the immunophilins, were discovered (Schreiber, 1991). The immunophilins appear to be essential in regulation of T-cell functions, predominantly through inhibition of the mTOR (mammalian target of rapamycin) signaling pathway. The result is attenuated T-cell related immunoreactions and avoidance of graft-versushost reactions and rejection of transplanted organs like kidneys. The mTOR pathway is also involved in cell growth of various cancers, and FK506 has been such a successful inhibitor of

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proliferation that has lead to clinical cancer trials of this and similar compounds (Sabatini, 2006). This exemplifies the strength of screening natural sources for bioactive compounds, and illustrates how these compounds may result in discovery of new protein classes and cellular pathways. Alzheimer’s Disease New diagnostic imaging modalities, in particular the positron emission tomography combined with computer tomography, allow functional imaging of tissues or organs based on highly specific molecular probes (see Chapter 44). Alzheimer’s disease is a devastating neurological degenerative disease characterized by progressive loss of cognitive function. Only clinical examination together with imprecise imaging examinations have been used to determine the diagnosis, while post-mortem histopathological examination has revealed its disease-related protein structural features with beta-amyloid senile plaques and neurofibrillary tangles. These pathological protein structures are related to neural cell death, but therapy development is seriously hampered by lack of precise in vivo disease monitoring in humans. Small molecules have been developed that bind to senile plaques and neurofibrillary tangles (Kepe et al., 2006). The development of these molecular imaging probes has been relatively slow, based on decades of empirical use of dyes binding to these structures. More extensive use of chemical libraries, in combination with animal models, may lead to even more precise diagnostics. Bcl-2 Binding Compounds The Bcl-2 (B-cell lymphoma protein) was discovered in nonHodgkin lymphoma, where a recurrent chromosomal translocation had placed the Bcl-2 gene under control of the strong immunoglobulin heavy chain promoter (Adams and Cory, 2001). The actual mechanism of action of Bcl-2 first was elucidated when it was realized that Bcl-2 is homologous to the CED-4 gene in the worm Caenorhabditis elegans, a gene that is central in the worm’s genetic programs for cell death regulation. The function of Bcl-2 was explored by worm and mouse genetics, and the structure of this novel class of proteins mapped with high resolution. Through structure-guided design, small molecules have been designed that bind to a deep hydrophobic binding pocket in Bcl-2 (Bruncko et al., 2007). One such compound was developed based on a high-throughput NMR-screen of a chemical library in search for a lead compound that bound to the hydrophobic BH3-binding groove of Bcl-XL. Further structure analysis by NMR was used to avoid binding to serum albumin, as well as enhance the binding to Bcl-2, Bcl-XL and Bcl-w. One of the final drug compounds, ABT-737, bound to Bcl-2 in the sub-nanomolar range, illustrating the power of NMR-based high-throughput screening with parallel synthesis and structure-based design. Preclinical studies are promising for this new class of drugs that block the anticell death action of Bcl-2 (Fesik, 2005).

TABLE 16.2

Phenotypic screens*

Cellular function

Target phenotype

Read-out

Cell death

Survival

Growth/colony formation

Proliferation

Cell cycle arrest

Various dyes (e.g., tetrazolium salt) DNA staining Cell tracker fluorescence (flow cytometry)

Protein localization

Mislocalization

GFP-fusion Immunofluorescence

Protein modification

Proteasome activity Kinase activity

GFP-PEST

Promoter activation

Reporter genes (luciferase)

Transcription

Phospho-specific antibodies (microscopy, flow cytometry)

Flow cytometry surface marker *Phenotypic screens are designed to address a specific cellular function. General cellular functions (cell proliferation, cell death) involve multiple gene products, while more specific functions (protein localization, enzyme activity) involve fewer genes. Phenotypic screens can be focused on specific signaling events using gene expression reporters. Many detection methods can be used including enzymatic assays, microscopy and flow cytometry.

Phenotypic Screening Phenotypic screening can take advantage of forward and reverse genetics to identify a tentative disease target. Forward genetics is based on random modulation of the phenotype and then identification of the gene. Reverse genetics is based on a specific gene modification and then examination for a phenotype (Figure 16.2). An in vitro phenotype screen can comprise cells that are treated with a library of small molecules or siRNA and examined for activation or inhibition of the process of interest. High-throughput screening of defined compound libraries and cell responses that is detected by automated microscopy is a powerful approach when combined with a carefully designed strategy for verification. Such assays can be used to screen for compounds that alter simple intracellular translocations of labeled proteins or more complex cellular mechanisms like phagocytosis, phagosome maturation and bacterial invasion into human cells (Steinberg et al., 2007). Importantly, cell-based phenotypic screens present the target proteins in their native configuration, often a confounding challenge for screens based on purified proteins (Table 16.2). Combination of drug target gene expression signatures and high-throughput gene expression analysis has been used to determine new compounds that modulate cancer phenotypes,

Gene Function Studies

here exemplified by inhibition of androgen-receptor signaling (Hieronymus et al., 2006). The cellular mRNA from a defined set of genes were bar coded and amplified through PCR, and quantified by two color flow cytometer after hybridization to uniquely colored polystyrene bead. This gene expression analysis classified the two androgen-receptor signaling inhibiting compounds, celastrol and gedunin, as HSP90-inhibitors (Hieronymus et al., 2006). Most high-throughput screening approaches utilize relatively straightforward readouts. In contrast, high-content screening seeks to capture multiple parameters simultaneously. This “high resolution” examination of cell responses can reveal unique characteristics of drug action, though at the expense of throughput. Hence, smaller focused libraries are generally used. In a search for novel cancer cell apoptosis-inducing agents from cyanobacteria, Herfindal and colleagues carefully compared nuclear morphologies in treated normal human hepatocytes (Herfindal et al., 2005). The result was identification of specific hepatototoxic strains of algae not involving the known hepatotoxins microcystins and nodularin. A similar approach using primary blood platelets and neural derived cell lines has identified specific neural and platelet targeting toxins (Selheim et al., 2005). Structure elucidation of the active natural compound is necessary to fully understand the mechanisms involved (Antonopoulou et al., 2002) and provide a basis for future drug development. Parallel Chemical and Genome-wide RNAi Screens Parallel screening for siRNA and small molecule inhibitors is a particularly fruitful combination to map constituents of a cellular function (Figure 16.2). Further siRNA hits provide an effective starting point to reveal the targets of small molecule hits. In a study focused on cytokinesis, the final step of cell division, by searching for siRNAs and small molecules that generate binuclear cells (Eggert et al., 2004). Comparative analysis of phenotypes revealed distinct categories of drug action, providing a basis for further target validation.

GENE FUNCTION STUDIES Therapeutic development invariably involves experiments in animal models. Gene function analysis in the intact organism is a critical component of validation. Genetically tractable organisms like fly, worm and zebrafish allow the researcher to study gene function within short periods of time. Mice have been used for decades, more recently in large institutional set ups, for example to dissect the genetic basis for human neurological disorders (Oliver and Davies, 2005). But due to obvious differences, many gene functions of importance in animals may be irrelevant or absent in humans and vice versa. This is exemplified by cancer genetics where the phenotype of certain oncogenes in mouse does not mimic the disease spectra in humans with a similar genetic defect (Rangarajan and Weinberg, 2003).

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Clinical Trials and Epidemiology Experimental early phase human clinical trials allow a chemical genetic elucidation of gene function. Many patients are treated with one specific drug during the disease course. These compounds have defined target and off-target effects, and gene function can be studied in sentinel cells or tissues, like peripheral leukocytes, or the diseased tissues. There is increasing use of gene expression studies in patients who undergo therapy. One example is examination of gene expression analysis within hours after intensive chemotherapy against acute myeloid leukemia (Anensen et al., 2006). In this study peripheral blood cells, in these samples representing more than 95% pure cancer cells, were harvested from 2 h to 6 h after start of chemotherapy. As early as 2 h after start of chemotherapy, the p53 protein was activated, and the 100 most expressed genes were dominated by known p53 target genes. The tumor suppressor p53 is mutated in more than 40% of all cancers, but in leukemia the gene mutation frequency is below 10%. However, mutated p53 in AML is associated with highly chemoresistant disease. Together, this indicates that normal p53 activation is of paramount importance in the first intensive chemotherapy in acute myeloid leukemia, and that other therapeutic strategies should probably be searched for in cases were p53 is mutated. In some countries or regions there are extensive health registries, including general health status and careful registration of diseases like cancer, cardiovascular disease and multiple sclerosis. These comprehensive registries take advantage of unique personal identifiers and limited population mobility. As a result of governmental financed insurance plans, corresponding detailed information of prescribed drugs is available in populationbased registries (Andersen, 2006). Impacting most fields of modern medicine, epidemiological studies have been performed by linking different registries. Increased risk for miscarriage in pregnant users of non-steroidal anti-inflammatory drugs have been identified by combining data from the prescription registry, birth registry and hospital discharge registry (Nielsen et al., 2001). Protective association of non-steroidal anti-inflammatory drugs against risk for colon, rectal, stomach and ovarian cancer has been described (Sørensen et al., 2003). Finally, registries were used to identify increased mortality of myocardial infarction in patients on cyclooxygenase-2 inhibitors (Gislason et al., 2006). The molecular cause of these associations will in the future be addressed by linkage of genetics and proteomics data from population-based biobanks with the health registries (Pukkala et al., 2007; Stoltenberg, 2005). Combination of drug prescription registries, health registries and biobanks could facilitate retrospective genomic screens for function of drugs and drug combination. A European registry and biobank with more than 600,000 twin pairs and a corresponding biobank may be such a unique source for retrospective chemical genomics in humans (Muilu et al., 2007). Genomics combined with epidemiological data may be an important future tool to determine relationships between genes and drugs that are inaccessible in animal models or require large number of individuals

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(a)

Diseases

Genes

Drugs

Genome-wide mRNA-expression profiles N

N

(b)

N

N O P HO OH

Na   P OH

O

OH

Unannotated chemical

cmap hypothesis

Disease state

cmap hypothesis

Experimental verification

N3

O

O

O

O

N

N

N

N

N

N

O O

OH

New inhibitors

(c)

Experimental verification

Clinical evaluation

Figure 16.3 A universal functional bioassay forms the Connectivity Map tool. (a) Based on the assumption that all transcripts are known, and simultaneous measurement of these transcript are possible through standardized gene expression array data collection, it may be possible to portrait all induced and organic biological conditions in a common analytic space. Similarities can thereby be determined between these induced and biological conditions, and allow the foundation for Connectivity Maps. (b) An uncharacterized molecule is identified as an active compound in a particular cellular pathway through the Connectivity Map. (c) A particular disease state is coupled with perturbations in certain molecular pathways, and suggests a small molecule as experimental therapeutic. Both (b) and (c) illustrates that the Connectivity Map is a tool for the experimental researcher, where in vitro and in vivo experimental verification of the proposed in silico molecular connection is needed. Modified from Lamb (2007).

for detection. Hypotheses generated in genetic epidemiology may then be tested experimentally by chemical genomics.

CONCLUSIONS New developments in the understanding of RNAi will allow design of siRNA molecules with more potent and specific mRNA targeting characteristics for all genes. The growing access to small

compound collections, liquid handling robotics, miniaturization of assay formats and streamlined readout detection methods will undoubtedly lead to both less time-consuming, labor-intensive and expensive siRNA and chemical genomic screening. As recently proposed, connectivity mapping based on geneexpression signatures may be able to successfully connect disease-related pathways and bioactive small molecules (Lamb et al., 2006). Lamb and colleagues propose a large-scale Connectivity Map project similar to the Human Genome Project, where an

References

open resource can be used for investigators to find connections between small molecule action, physiological processes and disease (Figure 16.3). Several proof-of-principle reports have emerged, for example, identifying novel compounds for established pathways or suggest known drugs and pathways to resolve known drugresistance causing mechanism (Hieronymus et al., 2006; Wei et al., 2006). Connectivity Map technology, even with its limitations (Lamb et al., 2006, for review see Lamb, 2007), may be a useful tool for the experimentalist to focus his or her interest. We have entered the post-genome sequence era. But without having the key to the cipher of gene function, the outstanding effort in genome sequencing will remain just an impressive

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code, without helping us to resolve the enigmas of our most common diseases and making genomic medicine a reality for the patients.

ACKNOWLEDGEMENTS This work was supported by the Norwegian Research Council Functional Genomics Program (FUGE) grant numbers 151859 and 183675. We thank Lars Helgeland Emmet McCormack, David Micklem, Maja Mujic and Line Wergeland for fruitful discussions and for providing illustrations for Figure 16.3.

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A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298. Muilu, J., Peltonen, L. and Litton, J.E. (2007). The federated database – a basis for biobank-based post-genome studies, integrating phenome and genome data from 600,000 twin pairs in Europe. Eur J Hum Genet 15(7), 718–723. Nielsen, G.L., Sørensen, H.T., Larsen, H. and Pedersen, L. (2001). Risk of adverse birth outcome and miscarriage in pregnant users of non-steroidal anti-inflammatory drugs: Population based observational study and case-control study. BMJ 322(7281), 266–270. Oliver, P.L., Davies, K.E. (2005). Analysis of human neurological disorders using mutagenesis in the mouse. Clin Sci 108, 385–397. London. Ortholand, J.Y. and Ganesan, A. (2004). Natural products and combinatorial chemistry: Back to the future. Curr Opin Chem Biol 8(3), 271–280. Peters, J.L., Cnudde, F. and Gerats, T. (2003). Forward genetics and mapbased cloning approaches. Trends Plant Sci 8(10), 484–491. Pukkala, E., Andersen, A., Berglund, G., Gislefoss, R., Gudnason, V., Hallmans, G., Jellum, E., Jousilahti, P., Knekt, P., Koskela, P. et al. (2007). Nordic biological specimen banks as basis for studies of cancer causes and control – more than 2 million sample donors, 25 million person years and 100,000 prospective cancers. Acta Oncol 46(3), 286–307. Rangarajan, A. and Weinberg, R.A. (2003). Opinion: Comparative biology of mouse versus human cells: Modelling human cancer in mice. Nat Rev Cancer 3, 952–959. Rognan, D. (2007). Chemogenomic approaches to rational drug design. Br J Pharmacol 152, 38–52. Roland, E. and Dolle, J. (2004). Comprehensive Survey of Combinatorial Library Synthesis: 2003. Comb Chem 6, 623–679. Sabatini, D.M. (2006). mTOR and cancer: insights into a complex relationship. Nat Rev Cancer 6, 729–734. Schreiber, S.L. (1991). Chemistry and biology of the immunophilins and their immunosuppressive ligands. Science 251, 283–287. Selheim, F., Herfindal, L., Martins, R., Vasconcelos, V. and Doskeland, S. O. (2005). Neuro-apoptogenic and blood platelet targeting toxins in benthic marine cyanobacteria from the Portuguese coast. Aquat Toxicol 74, 294–306. Sherbenou, D.W. and Druker, B.J. (2007). Applying the discovery of the Philadelphia chromosome. J Clin Invest 117, 2067–2074. Snove, O, Jr and Holen, T. (2004). Many commonly used siRNAs risk off-target activity. Biochem Biophys Res Commun 319, 256–263. Steinberg, B.E., Scott, C.C. and Grinstein, S. (2007). High-throughput assays of phagocytosis, phagosome maturation, and bacterial invasion. Am J Physiol Cell Physiol 292, C945–C952. Stoltenberg, C. (2005). Merging genetics and epidemiology: what is in it for public health?. Scand J Public Health 33, 1–3. Sørensen, H.T., Friis, S., Nørgård, B., Mellemkjaer, L., Blot, W.J., McLaughlin, J.K., Ekbom, A. and Baron, J.A. (2003). Risk of cancer in a large cohort of nonaspirin NSAID users: A populationbased study. Br J Cancer 88(11), 1687–1692. Vassilev, L.T., Vu, B.T., Graves, B., Carvajal, D., Podlaski, F., Filipovic, Z., Kong, N., Kammlott, U., Lukacs, C., and Klein, C., et al. (2004). In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–848. de Veer, M.J., Sledz, C.A. and Williams, B.R. (2005). Detection of foreign RNA: Implications for RNAi. Immunol Cell Biol 83, 224–228. Westbrook, T.F., Stegmeier, F. and Elledge, S.J. (2005). Dissecting cancer pathways and vulnerabilities with RNAi. Cold Spring Harb Symp Quant Biol 70, 435–444.

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Section

Informatic and Computational Platforms for Genomic Medicine

17. 18. 19. 20. 21.

3

Bioinformatic and Computational Analysis for Genomic Medicine Fundamentals and History of Informatics for Genomic and Personalized Medicine Electronic Medical Records in Genomic Medicine Practice and Research Clinical Decision Support in Genomic and Personalized Medicine Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics

CHAPTER

17 Bioinformatic and Computational Analysis for Genomic Medicine Atul J. Butte

INTRODUCTION The past 10 years have seen development and deployment of a variety of measurement tools in molecular science that enable the large-scale parallel quantitative assessment of molecular state. The premier example of such measurement tools is the RNA expression detection microarray, which provides quantitative measurements of expression of over 40,000 unique RNAs within cells (Chee et al., 1996; DeRisi et al., 1996). Yet, as illustrated in previous chapters in this volume, microarrays represent just one of at least 30 or so measurement or experimental modalities available to investigators in molecular biology and genomics, including metabolite quantification (Edwards et al., 2001), DNA polymorphism measurements (Johnson et al., 2001), protein quantification (Espina et al., 2003; Ghaemmaghami et al., 2003; Gygi et al., 1999; Liotta et al., 2003), protein activity (Hestvik et al., 2003), protein interactions with small molecules (Jessani et al., 2002) and DNA (Lee et al., 2002; Odom et al., 2004; Ren et al., 2000;Wang et al., 2001; Weinmann et al., 2002), and many others. Though these molecular technologies are commonly described as being “high-throughput” or “genome-wide” in nature, we will use the term high-bandwidth to describe them in this chapter. An analogy explains our reasoning: high-bandwidth is to a supermarket, which serves a large variety of different foods irrespective of speed, while high-throughput is to a fast-food restaurant, serving a few types of

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 206

foods quickly. Genome-wide technologies are biased towards genes, while we are covering measurements of many other types besides genes. The service role for bioinformatics in research using these new tools is undeniable. Each of these high-bandwidth modalities yields a sizeable amount of raw data, so distilling these raw data and filtering out measurement noise through the proper use of bioinformatics methods is crucial. Bioinformatics clearly plays a role in the storage, retrieval, and sharing of these measurements from local and international repositories and in relating these measurements to clinical outcomes. However, what we will argue in this chapter is that the role for bioinformatics in genomic and personalized medicine is well beyond that of providing a service and lies more broadly in enabling new and interesting questions to be asked in biomedical, translational, and clinical research. In this chapter, we will present several examples that illustrate how the use of specific bioinformatics methods has enabled a revolution of discovery in medical diagnostics, prognostics, therapeutics, anatomy, and nosology. We will then review a few specific analytic methods and standardized vocabularies useful in studying and identifying high-bandwidth measurements for genomic and personalized medicine. After covering a few freely available software tools valuable for conducting analyzes, we will end with some thoughts about where the future discoveries in genomic medicine, enabled by bioinformatics, may come from.

Copyright © 2009, Elsevier Inc. All rights reserved.

Vignettes: How Specific Bioinformatics Methods can Change the Practice of Medicine

VIGNETTES: HOW SPECIFIC BIOINFORMATICS METHODS CAN CHANGE THE PRACTICE OF MEDICINE While many high-bandwidth measurement technologies have been used to enable basic discoveries in the life sciences, certainly the most intriguing and provocative use of these measurement modalities has been in enabling novel types of medical discoveries. Here, we will present case examples of how these discoveries were enabled by bioinformatics techniques and have demonstrably altered our abilities in four distinct areas of medicine: diagnosis, therapeutics, histopathology, and nosology (Table 17.1). For these vignettes, we will particularly focus on the use of microarrays, as this is currently the most commonly used highbandwidth measurement modality. Diagnosis Diagnosis is a process in which a physician, when presented with a patient holding a chief complaint, gathers information through questioning, history taking, physical examination, and testing, compares the results of those queries with patterns established for known diseases, and assigns the disease with the highest likelihood. High-bandwidth testing in molecular biology has impacted every step of the diagnostic process. For example, those being evaluated for a disease may not yet even have a complaint, as DNA

TABLE 17.1

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polymorphisms are discovered and used to predict future disease (Kohane et al., 2006). Medical history taking becomes problematic when considering the genetic context of a patient, which is increasingly known; for example, many infants now have one or more genes sequenced at birth as part of state-mandated newborn screening (Khoury et al., 2003). History taking has potential to become even more convoluted as more adults have genes (and even whole genomes) sequenced. The bioinformatics challenge in storing, providing, and teaching patients and parents how to use this type of medical information, while protecting against others finding this information, has only just started to be addressed (Adida and Kohane, 2006; 2004; Centers for Disease Control and Prevention, 2004; Henneman et al., 2006; Roche and Annas, 2001, 2006). The nature of the physical examination itself has changed. Positive and negative physical findings (e.g., acanthosis nigricans, or darkening of the skin of the neck) are traditionally considered as probes into the process of disease (e.g., type 2 diabetes mellitus). With the democratization of genomics throughout all fields of medicine, physical findings can now be viewed collectively as phenotype and as a probe into the inner workings of the genome (Freimer and Sabatti, 2003). Two different diseases with similar sets of physical findings may indeed appear quite similar when viewed from the perspective of genes and gene products (Brunner and van Driel, 2004). But certainly the greatest impact on diagnosis from highbandwidth molecular testing has been felt on our ability to redefine our patterns of disease using these new measurements. One

Bioinformatics techniques have demonstrably altered our abilities in four distinct areas of medicine

Area of the practice of medicine Diagnosis Chief complaint Medical history Medical record keeping Physical examination Differential diagnosis Disease sub-type Therapeutics Finding novel effects for existing drugs Side effects Finding novel therapies for untreatable conditions Histopathology Metastasis of unknown origin Pathological mechanism of action of disease Nosology Finding differences between diseases Finding similarities between diseases Linking cellular models to human diseases

Example of bioinformatics altering practice Predicting future disease from polymorphisms before complaint Genetic screening at birth should become part of a patient’s life-long electronic medical record Patient-owned Internet-based genomic records Findings collectively viewed as phenotype and matched against patterns Molecular measurements can be compared against case-examples Hierarchical clustering to determine novel subtypes from molecular measurements Linking drug effects to molecular measurements Predicting drug adverse effect from pre-clinical molecular measurements Matching molecular profiles of disease with molecular profiles of drug effect Matching molecular measurements from metastasis with profiles of known primary cancers Identifying differentially expressed genes and linking back to known biological pathways Principal components of variance of disease sample microarrays Common signatures across microarray measurements of disease Comparing cellular and disease microarray measurements

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of the earliest examples of this impact was in the work of Golub and colleagues published in 1999 (Golub et al., 1999). Acute lymphomas were first divided into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) in the 1960s based on histochemical testing, and more recently by antibody-based testing against cell-surface molecules, but there was no single established test to make this diagnosis. Golub was the first to show that gene expression microarrays could be used to measure gene

expression levels from samples from acute lymphomas, and using a bioinformatics technique known as supervised machine learning, trained a prediction system to find and use those genes that uniquely distinguished ALL from AML (Figure 17.1). He then used this predictor to accurately classify additional new leukemia cases. Many others have used alternative computational and bioinformatics techniques to build predictors since this landmark study, while many additional diagnostic dilemmas have similarly

ALL

AML C-myb (U22376) Proteasome iota (X59417) MB-1 (U05259) Cyclin D3 (M92287) Myosin light chain (M31211) RbAp48 (X74262) SNF2 (D26156) HkrT-1 (S50223) E2A (M31523) Inducible protein (L47738) Dynein light chain (U32944) Topoisomerase II β (Z15115) IRF2 (X15949) TFIIEβ (X63469) Acyl-Coenzyme A dehydrogenase (M91432) SNF2 (U29175) (Ca2)-ATPase (Z69881) SRP9 (U20998) MCM3 (D38073) Deoxyhypusine synthase (U26266) Op 18 (M31303) Rabaptin-5 (Y08612) Heterochromatin protein p25 (U35451) IL-7 receptor (M29696) Adenosine deaminase (M13792) Fumarylacetoacetate (M55150) Zyxin (X95735) LTC4 synthase (U50136) LYN (M16038) HoxA9 (U82759) CD33 (M23197) Adipsin (M84526) Leptin receptor (Y12670) Cystatin C (M27891) Proteoglycan 1 (X17042) IL-8 precursor (Y00787) Azurocidin (M96326) p62 (U46751) CyP3 (M80254) MCL1 (L08246) ATPase (M62762) IL-8 (M28130) Cathepsin D (M63138) Lectin (M57710) MAD-3 (M69043) CD11c (M81695) Ebp72 (X85116) Lysozyme (M19045) Properdin (M83652) Catalase (X04085)

3

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low

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Figure 17.1 Todd Golub and colleagues measured genome-wide expression levels in samples from patients with ALL and AML. The top 50 genes distinguishing ALL and AML are displayed, with genes elevated in expression shown in pink, and genes decreased in expression shown in blue. From Golub et al., 1999 Science 286:531. Reprinted by permission from AAAS.

Vignettes: How Specific Bioinformatics Methods can Change tjhe Practice of Medicine

Therapeutics Bioinformatics, coupled with the exponential growth in data collected using high-bandwidth measurement modalities, has the potential to revolutionize the way patients are treated with pharmaceuticals. Much has already been written on how highbandwidth measurements have generally altered the process of drug discovery (Debouck and Goodfellow, 1999). Here, we will cover specific case examples on how the conceptualization of pharmacology can be radically changed through development and use of biomedical informatics methods. In 2000, Butte and colleagues focused on generating hypotheses of functional relationships between pairs of genes and pharmaceuticals, with the hope that specific genes could be found that altered a cancer’s ability to respond to specific drugs. To do this, they started with two databases, both related to the NCI60, a standardized set of 60 human cancer cell lines used by the National Cancer Institute Developmental Therapeutics Program (Weinstein et al., 1997). One database contained the baseline microarray measurements of the expression level of 6701 genes measured in these cell lines. The other database contained a set of drug susceptibility measurements for the same cell lines, across

209

(a)

spi-1PU.1 CD86B7-2 RAD50 CD21 Gertminal center kinase

Casein kinase l, 2 Diacylglycerol kinase delta Arachidonate 5-lipoxygenase CD22 JNK3 Myosin-IC KCNN3 Ca activated K channel PI3-kinase p110 catalytic, isoform WIPWASP interacting protein JAW1 APS adapter protein Protocadherin 43 Terminal deoxynucleotide transferase Focal adhesion kinase BCL-7A BCL-6 FMR2 A-myb CD10 OGG18-oxyguanine DNA glycosylase LMO2 CD38 CD27 Ick IRS-1 RDC-1 ABR OP-1 RGS13 PKC delta MEK1 SIAH-2 IL-4 receptor alpha chain APRPMA-responsive peptide GADD34 IL-10 receptor beta chain c-myc NIK ser/thr kinase BCL-2 MAPKK5 kinase PBEFpre-B enhancing factor TNF alpha receptor II Cyclin D2 Deoxycytidylate deaminase IRF-4 CD44 FLIPFLICE-like inhibitory protein SLAPsrc-like adapter protein DRIL1Dead ringer-like 1 Trk3Neurotrophic tyr kinase receptor IL-16 SP100 nuclear body protein LYSP100 K channel, shaker-related, member 3 ID2 NET tyrosine kinase IL-2 receptor beta chain

(b)

All patients

1.0

GC B-like Probability

been addressed using gene expression microarrays, including melanoma (Bittner et al., 2000) and prostate cancer (Singh et al., 2002). Other types of high-bandwidth modalities have been used to assist in other diagnostic questions, including proteomics (Petricoin et al., 2002; Robinson et al., 2002) and metabolomics (Clayton et al., 2006) (see also Chapters 14 and 15). While enabling physicians to make more efficient and accurate decisions between diseases itself is remarkable, the potential for high-bandwidth molecular testing in diagnosis is even greater. Beyond just improving on older tests for established diagnostic dilemmas, high-bandwidth studies have also been used to redefine and even divide diagnoses into novel sub-types. The earliest example of this was in the work of Alizadeh and colleagues in 2000 (Alizadeh et al., 2000). Alizadeh used gene expression microarrays to measure gene expression in samples from patients diagnosed with diffuse large B-cell lymphoma, a cancer of B-lymphocytes. This type of cancer was previously divided into low-, intermediate-, and high-grade categories based on growth pattern and immunohistochemistry. After applying a bioinformatics technique called hierarchical clustering (described later in this chapter) to the microarray data, Alizadeh discovered that the patient samples of B-cell lymphoma could be divided into an equal split of two subtypes (Figure 17.2). Most importantly, patients with these two different sub-types of lymphoma retrospectively demonstrated significant differences in survival by Kaplan–Meier analysis. This was a landmark study because it showed how high-bandwidth molecular measurements could provide a next level of resolution to resolve sub-types of disease not otherwise discernable by physicians. This type of study has been replicated for other types of cancers, including lung cancer (Bhattacharjee et al., 2001) and breast cancer (Sorlie et al., 2001), has been replicated using other types of measurements, including proteomics (Welsh et al., 2003), and is starting to enter clinical trials (Potti et al., 2006).

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19 patients, 6 deaths 0.5 Activated B-like 21 patients, 16 deaths P  0.01

0.0 0

2

4 6 8 10 Overall survival (years)

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Figure 17.2 (a) Ash Alizadeh and colleagues measured genome-wide expression levels in samples from patients with diffuse large B-cell lymphoma and found samples clustered into two sub-types based on expression patterns. (b) Patients in these two sub-types have a significant difference in survival (Alizadeh et al., 2000). Reprinted by permission from Macmillan Publishers Ltd: Nature 403:503, copyright 2000.

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nearly 5000 anti-cancer agents. They then used a database technique known as join to combine these two databases through the cell lines in common. After joining the two tables, they searched for baseline RNA expression levels in the cell lines that correlated with the inhibition of growth of these same cell lines by thousands of anti-cancer agents. They found only one network containing an association between a gene expression and a measure of anti-cancer agent susceptibility (Figure 17.3). The association suggested that increased expression of lymphocyte cytosolic protein-1 (LCP1) is associated with increased susceptibility to the anti-cancer agent NSC 624044, a thiazolidine carboxylic acid derivative. Though a specific role for LCP1 in tumorogenicity had been postulated and though other thiazolidine carboxylic acid derivatives were known to inhibit tumor cell growth, there was no known relationship between this specific anti-cancer agent and gene in the biomedical literature. The significance of this work was that drug efficacy measurements could be joined to microarray measurements and potentially other types of

genomic measurements, enabling the discovery of hidden associations between drugs and genes. Fliri and colleagues joined databases in a similar manner in 2005, but instead of joining using cell lines, they used the drugs themselves (Fliri et al., 2005). Specifically, they took a database of known side-effects of drugs and joined it to a database of ligand binding assay results as measured in the presence of those same drugs. With this method, they were able to show that drugs with similar side-effect profiles actually had similar ligandbinding assays. The significance of this finding is that pre-clinical studies, such as ligand-binding assays and others, could be used to predict clinical side-effects, which, when severe enough, can lead to withdrawal of a drug from the market. Lamb and colleagues in 2006 built a database from microarrays measured across the genome after the application of 164 different small molecules to a breast cancer cell line (Lamb et al., 2006). They then queried this database of drug responses using responses measured from other experiments for which a detailed

BioDn-3 AFFX-M27830_5 D32129_f AFFX-M27830_5 BioDn-3 AFFX-M27830_5 HG3597-HT3800_f M14199 P624044 J02923

U43901_rna1

P169517

HG2743-HT2845

P685105 P637436

M83216 L18877_f HG2743-HT2846

L18920_f

U03735_f

Figure 17.3 Relevance networks calculated by joining drug activity data with gene microarray measurements. White rectangles indicate genes, and green rectangles indicate anti-cancer agents. The only discovered network involving both genes and drugs is shown in the middle. The activity of four anti-cancer agents against multiple cancer cell lines correlates with the expression pattern of a single gene across those same cancer cell lines. J02923 indicates the gene LCP1, while drug identifiers correspond to National Cancer Institute NSC identifiers (Butte et al., 2000).

Vignettes: How Specific Bioinformatics Methods can Change the Practice of Medicine

mechanism had not been known, using a measure of similarity known as a Kolmogorov-Smirnov statistic (Figure 17.4). For example, when queried with the gene expression differences between dexamethasone-treatable and dexamethasone-resistant ALL, they found that the genome-wide difference between these two types of leukemia most closely matched that of the genome-wide response to the drug sirolimus. The significance of this approach is that existing drugs could be associated with unexplained diseaserelated processes, and might even be used to treat those diseases. But most importantly, this work highlights the importance of building structured searchable repositories of experimental data. Histopathology Histopathology is the study of diseased tissue by sectioning, staining, and multi-resolution microscopy. Given high-bandwidth measurements from samples of potentially diseased tissue, bioinformatics methods can be applied to determine whether such samples do quantitatively resemble a disease (van de Rijn and Gilks, 2004). For example, Ramaswamy and colleagues tackled the problem of finding the original source of a cancer, given just a metastatic sample (Ramaswamy et al., 2001). They used a method called support vector machines, each of which was designed to recognize one specific type of cancer (Figure 17.5). This work was significant in that it was one of the first to show how multiple types of cancers could be distinguished using high-bandwidth measurements, and that unknown samples could be accurately assigned a primary diagnosis. Similar work has been published using alternative bioinformatics methods (Su et al., 2001). Fibroblasts are cells in the connective tissue that create much of the extracellular matrix between tissues and proliferate during wound healing. Though fibroblasts from different parts of the body may have similar properties and resemble each

Biological state of interest (signature)

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other under a microscope, Chang and colleagues demonstrated that when fibroblasts are considered by high-bandwidth measurements of gene expression, significant differences between fibroblasts are seen depending on where in the body they were originally obtained. In other words, cells that appear exactly the same by microscope appear quite differently by microarray, consistent with their original source (Chang et al., 2002). Microarrays have also been used to uncover histopathological findings that might have otherwise been missed. Sarwal and colleagues used microarrays to study the process of acute rejection after kidney transplantation (Sarwal et al., 2003). After hierarchically clustering their samples, they noticed a set of B-cell specific genes that corresponded to one sub-type of acute rejection. This was particularly interesting because B-cells had not previously been thought to be involved in acute rejection. Staining for a B-cell specific cell surface protein showed large aggregations of B-cells in samples of acute rejection. The significance of this finding was the realization that while histochemistry can offer a broad survey of the cellular makeup in a sample, these cells will still remain invisible unless illuminated with the right stain. The proper stain can be found using gene expression microarrays coupled with the right bioinformatics techniques, especially the prior annotation of genes as being specific for a certain cell type. Nosology In the mid 1700s, Carl Linnaeus promoted the binomial nomenclature to classify living things into a hierarchy, or taxonomy. Though we still organize species into taxonomies based on anatomic and physiological similarities, our modern-day use of DNA sequencing and genome sequence has enabled the reorganization of the position of species within established taxonomical trees.

Reference database (profiles)

Connections

positive up

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output

down negative strong positive

weak positive

211

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Figure 17.4 A reference database of high-bandwidth molecular measurements can be made after the application of various drugs, then queried with an unknown biological response, yielding those drugs sharing a similar response. From Lamb et al., 2006, Science 313:1929. Reprinted by permission from AAAS.

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This is because DNA sequence similarity analysis provides a quantitative measurement of how different two species are (Baldauf et al., 2000). Linnaeus was also a co-founder of systematic nosology, or the classification of disease, for which he is not as well known (Linnâe and Schrèoder, 1763). Genomic data and bioinformatics methods have now advanced to the point that we can begin to modernize the classification of disease itself, similar to how DNA sequencing has modernized taxonomy. Crescenzi and Giuliani were one of the first to demonstrate this principle in 2001 (Crescenzi and Giuliani, 2001). They gathered samples representative of 60 cancer cell lines and used a statistical method known as principal components analysis to find combinations of genes that capture the significance variance across cancers. They then showed how analyzes using just the top five principal components could reproduce the findings from the entire dataset. In other words, Crescenzi and Giuliani were able to show that when measured using high-bandwidth measurements,

the 60 varied types of cancers differed in only a few ways that could be modeled. Instead of modeling the significant differences between cancers, Rhodes and colleagues searched for commonalities in 2004 (Rhodes et al., 2004). After collecting 40 published microarray datasets totaling more than 3700 samples of cancer, Rhodes determined a genome-wide signature representative of neoplastic transformation. As expected, most of the genes in this cancer signature were known to participate in the cell cycle, the biological process involved in cell replication. A similar study by Bild and colleagues found commonly deregulated biological pathways across cancers that correspond with worsening survival (Bild et al., 2006). This type of work is significant because it suggests that a quantitative classification of cancers, or one based on actual measurements, might be more useful than the traditional anatomic or even histopathological classification of cancer (Figure 17.6).

SAMPLES BR BL CNS CO

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BR PR LU CO LY BL ML UT LE RE PA OV ME CNS

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Figure 17.5 Over 200 samples of 14 different types of tumors were clustered to indicate similarities in tumor types and then classified to find genes uniquely implicating each type of tumor (Ramaswamy et al., 2001).

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Figure 17.6 Cells made to over-express particular oncogenes were measured using gene expression microarrays, and a “signature” or pattern of gene expression changes for each oncogene was then computed and used to probe samples of cancer (Bild et al., 2006). Reprinted by permission from Macmillan Publishers Ltd: Nature 439:353, copyright 2006.

Analytic Methods

ANALYTIC METHODS The field of computer science involved in the search for patterns and the induction of rules from data is called machine learning. Current methodologies to analyze high-bandwidth measurements can broadly be divided into two categories: supervised machine learning approaches, or analysis to determine genes or proteins that fit a predetermined pattern; and unsupervised machine learning approaches, or analysis to characterize of the components of a dataset, without the a priori input or knowledge of a right answer (Butte, 2002). Supervised machine learning methods are commonly used to find those individual or sets of measured elements with measurements that (1) are significantly different between defined groups of samples, and (2) accurately predict a characteristic of the sample. An example of an application of a supervised machine learning method is the vignette described above in which Golub and colleagues found genes that distinguish ALL and AML. There are many published supervised methods that find measured elements that accurately predict characteristics of samples, such as distinguishing one type of disease from another, or a malignant disease from a benign one (Dudoit et al., 2002). There are methods that find individual measured elements, such as the nearest-neighbor approach (Golub et al., 1999), and those that find sets of multiple elements, such as decision trees (Quinlan, 1992), neural networks (Rumelhart et al., 1986), and support vector machines (Brown et al., 2000; Chow et al., 2001; Furey et al., 2000). Unsupervised machine learning methods are used to find internal structure or relationships within a dataset, instead of trying to determine how best to predict a “correct” answer. Two of the example vignettes provided above – in which Crescenzi determined those genes that best explained the differences between cancers and in which Alizadeh found sub-types of Bcell lymphoma – are good examples of the use of unsupervised machine learning methods. Within unsupervised learning, there are three classes of techniques, including (1) measured element discovery, or finding elements (e.g., genes) with interesting properties without specifically searching for a specific predefined pattern, such as by principal component analysis (Alter et al., 2000; Fiehn et al., 2000; Hilsenbeck et al., 1999; Raychaudhuri et al., 2000; Wen et al., 1998); (2) cluster discovery, or finding groups of measured features or samples with similar patterns, such as by nearestneighbor clustering (Ben-Dor et al., 2000; Golub et al., 1999), self-organizing maps (Tamayo et al., 1999;Toronen et al., 1999), k-means clustering, and one and two-dimensional hierarchical clustering (Eisen et al., 1998; Ross et al., 2000); and (3) network discovery, or finding graphs representing associations between measured elements, such as by using Boolean networks (Liang et al., 1998; Szallasi and Liang, 1998; Wuensche, 1998), Bayesian networks (Friedman et al., 2000), and relevance networks (Butte and Kohane, 1999; Butte and Kohane, 2000; Butte et al., 2000). Several recent articles have reviewed many of the available methods (Butte, 2002; Quackenbush, 2004, 2006). Here we will

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consider two of the most commonly used supervised and unsupervised methods used in those publications with the greatest impact for medicine. Case-Control Studies The majority of experiments performed using high-bandwidth measurements still typically use only a handful of cases in two or three conditions, and the experimenter typically has the goal of finding those measured elements (e.g., genes) that are significantly different between these groups. Significance has and continues to be evaluated in a multitude of different methods, including parametric (Tusher et al., 2001), non-parametric (Butte et al., 2001), Bayesian (Baldi and Long, 2001), and many others. Analysis of variance has been used for measurements from samples with multiple characteristics (Pavlidis and Noble, 2001). Signal processing (Butte et al., 2002), trend detection (Tseng et al., 2005), Bayesian (Ramoni et al., 2002), non-parametric (Reis et al., 2001), and many other methods have been used for measurements from samples across time (Simon et al., 2005). The challenge in using any of these methods across the set of measured elements (e.g., genes) is compensating for the high number of measurements made. For example, even a standard t-test could be used on every gene measured in two sets of microarrays from two conditions. The null hypothesis for a particular gene would be that the gene expression level is not significantly different across the two conditions, and a p-value can be calculated to assess the ability to reject that null hypothesis for each gene. But in such a case, it would be incorrect to use the “standard” threshold of p  0.05 to determine those genes that are significantly different. Traditionally, such a threshold indicates the likelihood of calling a truly negative finding positive, otherwise known as the false positive rate or a type 1 error in statistics. For a single test, a 5% false positive rate may not be unreasonable, but when multiplied across 40,000 transcripts, such a false positive rate would be expected to yield 2000 falsely positive genes, too many to validate. With high-bandwidth measurements, one is typically more interested in the false discovery rate, rather than the false positive rate. The false discovery rate is the expected proportion of accepted type 1 errors within a list of tested hypotheses, and controls for the number of tests being made. A false discovery rate can actually be calculated for every measured element. In essence, such a rate would indicate the likelihood of false discoveries if that element were considered positive. In practice, this is called the q-value for that element (Storey and Tibshirani, 2003). Those elements with low p-values also have low q-values. Though q-values and false discovery rates are gaining in popularity, it is important to note that there have been many other proposed methods to compensate for multiple-hypothesis testing (Dudoit et al., 2003b). Sub-Type Discovery The discovery of disease sub-types using high-bandwidth measurements has been the prime motivator for personalized disease

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therapeutics, where a sample of disease from an individual could be potentially measured and the most appropriate set of therapies potentially prescribed. Typically, the discovery of sub-types involves a two-step process. First, an unsupervised machine learning method is applied yielding subsets of samples (e.g., patients) with similar measurements. Many choices are available in this step, including the specific clustering method used (e.g., hierarchical clustering (Eisen et al., 1998), self-organizing maps (Tamayo et al., 1999), or others), the mathematical definition of similarity used (e.g., Pearson correlation coefficient, Spearman rank correlation, Euclidean distance, or others), and the threshold similarity at which clusters are demarcated. While this first step will yield potential sub-types of disease, the utility of these sub-types still needs to be determined. Typically, utility is defined using a marker of clinical outcome, such as survival, disease-free survival, or response duration. The two or more sub-types of disease identified in the first step are then tested statistically against the marker, using a Kaplan–Meier estimator or Cox Proportional Hazards Model. The most important point to note with these types of analyzes is the censored nature of the samples. A particular patient that is lost to follow-up can only “contribute” his or her survival up to the length of follow-up. Recent methodological work has provided numerous new methods that combine both of these steps, enabling the single-step discovery of sets of measured elements (e.g., genes) that best discriminate for censored survival or

other outcomes (Bair and Tibshirani, 2004; Nguyen and Rocke, 2002; Park et al., 2002).

WHERE DATA FOR STUDIES MAY BE FOUND Corresponding with the successful application of highbandwidth molecular measurement modalities, the amount of data in international repositories has grown exponentially, because top-tier journals require the public availability of such data and because of increased calls for transparency of raw data in publications (Nature, 2002; Ball et al., 2004; Perou, 2001). For example, the Gene Expression Omnibus (GEO) is an international repository for gene expression data, developed and maintained by the National Library of Medicine (Wheeler et al., 2004). As of this writing, GEO holds 103,000 samples (i.e., microarrays) from over 4300 experiments involving 189 species, across over 2100 types of microarrays, with 2.6 billion individual gene measurements (Figure 17.7). More impressively, GEO has been gaining data at 300% per year. GEO is not the only international repository for microarray data. ArrayExpress is a similar database of gene expression measurements supported by the European Bioinformatics Institute (Parkinson et al., 2005). As of this writing, ArrayExpress contains 42,424 samples from 1437 experiments. The Stanford Microarray Database (SMD) was the first international repository

Figure 17.7 Screen shot of the home page for the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO).

Bioinformatics Vocabularies and Ontologies

for microarray data (Ball et al., 2005). As of this writing, SMD holds 61,573 samples involving 37 species, with a total of 1.84 billion individual gene measurements. It is not clear how much overlap there is in experiments with GEO, but it is likely that over 150,000 unique samples are available for study. Recently, international repositories for proteomic experimental datasets have been instituted (Prince et al., 2004). PeptideAtlas, supported by the Institute for Systems Biology (ISB), is an example of one such repository (Desiere et al., 2006; Deutsch et al., 2005). As of this writing, PeptideAtlas holds the raw and processed mass spectra for 76 experiments. The Proteomics Identifications Database (PRIDE), supported by EBI, currently holds data from over 1655 experiments (Jones et al., 2006). Though GEO, ArrayExpress, SMD, and PeptideAtlas are already incredible resources for gene expression measurements, these measurements are poorly indexed. Even for microarray data stored in standardized formats like MIAME and MAGE-ML (Brazma et al., 2001; Spellman et al., 2002), the contextual annotations needed to determine relevance of a dataset are unfortunately still represented by unstructured narrative text. Determining the phenotypes, diseases, and environmental contexts studied by these experiments is no longer a tractable manual process. Most repository websites provide only a free-text based search facility for experimental annotations. A case example demonstrates the significance of the problem. At the time of this writing, searching on the NCBI GEO website yields 27 datasets annotated with the term “breast cancer.” However, a search for “breast tumor” yields only 8 datasets. A search for the plural, “breast tumors,” yields 9 datasets, while a search for “breast carcinoma” yields only 6 datasets. Breast cancer is one of the most important cancers affecting women, but searching for existing microarray datasets relevant to this important disease yields arbitrary results and is fraught with false negatives. The basic problem is that data in these repositories are not consistently annotated with terms from a standardized vocabulary. While some initial work has been done in re-annotating these datasets using a standardized vocabulary (Butte and Kohane, 2006), investigators interested in finding relevant data will have to try multiple terms to comprehensively find data. Another caveat is that much of the most relevant clinical data associated with high-bandwidth measurements may not be stored in international repositories. For example, a dataset of microarray measurements on patients with varying lengths of survival after cancer diagnosis may be available in GEO, but the survival times may not be. Such clinical data may be available on an investigator’s laboratory website or in an associated publication, but is often not publicly available at all.

BIOINFORMATICS VOCABULARIES AND ONTOLOGIES A cell biologist interested in finding clinical samples relevant to a disease being studied in the lab will not find those samples unless she can “speak the same language” as the pathologists

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storing and indexing the samples. A physician interested in learning more about genes that are significantly different in his experiments will not be able to learn more about the pathways implicated by those genes, unless he can “speak the same language” as those geneticists providing annotative information for those genes. In the application of bioinformatics to genomic and personalized medicine, it is often the case that shifting focus like this crucially depends on the use of a standardized vocabulary. The formal modeling and representation of concepts of knowledge, the terms and other attributes used to describe these concepts, and the relationships of these concepts, is called a ontology. The importance of using proper ontologies in biomedical research has been described in recent publications (Blake, 2004; Soldatova and King, 2005). Here, we will describe some of the more commonly used ontologies used in the research of genomic medicine (Table 17.2). Clinical vocabularies The largest disease ontology (or nosology) in use today is the Systemized Nomenclature of Medicine-Clinical Term (SNOMED-CT) ontology. SNOMED-CT has a lineage spanning 75 years and is used by pathologists world-wide (Chute, 2000). SNOMED-CT has over 340,000 biomedical concepts in 18 hierarchies with 1.3 million relationships that classify diseases based on syndromic and pathophysiological mechanisms (Cho et al., 1998). For example, relations in SNOMED-CT assert that clear cell carcinoma of the kidney is a malignant tumor of the kidney (i.e., a type of tumor of a particular organ) and separately is also

TABLE 17.2 Some of the ontologies and vocabularies in use in bioinformatics Ontology and vocabulary

Use

Systemized Nomenclature of Medicine-Clinical Term (SNOMED-CT)

Pathological based classification of disease

International Classification of Diseases (ICD) with Clinical Modification

Diagnosis codes for billing, public health, and epidemiology

Gene Ontology (GO)

Molecular functions, biological processes, and cellular components of proteins

Open Biomedical Ontologies (OBO)

Includes Gene Ontology and many other ontologies describing cell types and anatomy

InterDom

Protein–protein interactions

InterPro

Protein domains

Unified Medical Language System (UMLS)

Unified compendium of 140 biomedical vocabularies

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a malignant neoplasm of the retroperitoneum (i.e., a type of tumor in a particular anatomical location). Another disease classification more familiar to physicians is the International Classification of Diseases (ICD) with Clinical Modification, a nosology with origins in the 1850s. ICD-9-CM has nearly 7000 codes, while ICD-10-CM has over 14,000 codes to describe causes of morbidity and mortality (World Health Organization, 2005). Both ICD-9-CM and ICD-10-CM are in use by various health care providers, the US Federal Government, and the World Health Organization. Though most health care institutions in the United States use ICD-9, ICD-10 was recently approved for use by the US Congress. To continue the example above, relations in ICD-9-CM assert that a malignant neoplasm of the kidney is a malignant neoplasm of the genitourinary organs. Importantly, however, the limited resolution in ICD-9-CM does not permit the specific diagnosis of clear cell carcinoma of the kidney. There are several important differences between the SNOMED-CT and the ICD nosologies. As shown above, SNOMED-CT has greater expressiveness than ICD to represent diseases. SNOMED provides a rich set of relationships between medical concepts. ICD provides only a hierarchical relationship between concepts. Despite their differences, it is important to note that both SNOMED-CT and ICD are viewed as administrative nosologies, typically used for billing, reporting, electronic medical records, and decision support systems. However, important sources of stored tissues in medical institutions, such as frozen tissue banks, may only be accessible through SNOMED or ICD codes, and these coding systems commonly serve as an index into these repositories. Genomic Ontologies The Gene Ontology (GO) is a taxonomy that is used to describe the normal molecular function of proteins, the cellular components in which proteins operate, and the larger biological processes in which they participate (Ashburner et al., 2000). At the time of this writing, GO currently holds 7470 molecular functions, 1823 cellular components, and 12,250 biological processes. Over 186,000 genes across a variety of species have been assigned to one or more GO categories, but this essentially translates to one-third of human genes having some coded function. The easiest way to find the GO classification for a gene is to execute a query using Entrez Gene. For example, a query for the gene coding for the insulin receptor (INSR) yields 13 molecular functions (e.g., phosphoinositide 3-kinase binding), 7 biological processes (e.g., carbohydrate metabolism), and 2 cellular components. It is important to note that an actual publication detailing the evidence for each annotation is available for many GO annotations, and these publications can be easily referenced. It is also important to note that the GO annotations for genes can change quickly, as both the annotations for genes and GO itself continue to be “works in progress.” The GO is actually one of 61 ontologies in the set known as Open Biomedical Ontologies (OBO) (Mungall, 2004). These ontologies are open, in the sense that they can be freely downloaded and used without constraints. Other ontologies in OBO include ontologies to describe the gross anatomy of flies,

worms, and mice, cell types, and functional portions of proteins, as well as ontologies to describe cancers and other concepts related to humans. Other vocabularies used for gene and protein annotation include InterDom (Ng et al., 2003), InterPro (Mulder et al., 2003), and PRINTS (Attwood et al., 2003) for protein domains, and other gene identifiers include the stable NCBI Gene identifier (Wheeler et al., 2006) and the unstable NCBI UniGene identifier (Wheeler et al., 2000). Unified Medical Language System The Unified Medical Language System (UMLS) is the largest available compendium of biomedical vocabularies, containing 140 biomedical vocabularies with over 1.2 million concepts and 41 million relations between concepts (Bodenreider, 2004; Butte and Kohane, 2006). UMLS already unifies vocabularies used extensively in molecular biology and genomics, such as the Medical Subject Headings (MeSH), NCBI Taxonomy, and the GO, with medical vocabularies including the International Classification of Diseases and SNOMED Clinical Terms (Ashburner et al., 2000; ICD, 2003; Wheeler et al., 2004). The UMLS is a unified vocabulary, which means that concepts listed in multiple vocabularies are brought together. For example, the concept of fever, which holds the UMLS concept unique identifier of C0015967, is represented in 78 component vocabularies, such as D005334 in MeSH, 386661006 and 50177009 in SNOMED-CT, 780.6 in ICD-9-CM, 10016558 in the Medical Dictionary for Regulatory Activities Terminology (MedDRA), X25 in Perioperative Nursing DataSet, GO:0001660 in GO, and U001776 in the Library of Congress Subject Headings. Each of these individual codes represents the same concept, and thus have been unified to a single biomedical concept in UMLS. Because of its ability to span nearly every other relevant vocabulary, UMLS best serves as a bridging vocabulary, providing terms commonly used by both physicians as well as molecular biologists. Given the support for UMLS and its component vocabularies shown by the National Library of Medicine and the US Department of Health and Human Services, it is likely that terms and concepts from UMLS will constitute important portions of the future electronic health records of patients in the United States (see also Chapter 19). As samples from patients continue to be valuable for study using high-bandwidth measurements, tapping into the medical records associated with these samples will only grow in importance and UMLS can help enable these secondary research uses of medical records.

FREELY AVAILABLE BIOINFORMATICS TOOLS There is an incredible preponderance of software tools freely available for research in bioinformatics. As an example, as of this writing, over 300 articles are published each year specifically describing bioinformatics software tools in the journals BMC Bioinformatics, Bioinformatics, Nucleic Acids Research, and Genome Biology. The vast majority of these are freely available for

Freely Available Bioinformatics Tools

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Figure 17.8 Screen shot of the MeV (Saeed et al., 2006). Reprinted from Methods in Enzymology, Volume 411, Saeed, A.I., Bhagabati, N.K., Braisted, J.C., Liang, W., Sharov, V., Howe, E.A., Li, J., “TM4 microarray software suite”, 134–93, copyright 2006, with permission from Elsevier.

researchers. Describing all of the available free tools is clearly intractable, though a recent book and paper serve as nice collections of most of the commonly used tools (Dudoit et al., 2003a; Parmigiani, 2003). Here instead, we will arbitrarily focus on a few important tools useful in the study of genomic and personalized medicine, dividing our set into two groups: those useful in the analysis of high-bandwidth data and those useful in the interpretation of results. Analytic Tools The Multiexperiment Viewer (MeV) is an easy-to-use analytic tool initially developed at the Institute for Genomic Research and currently maintained at the Dana Farber Cancer Institute (Saeed et al., 2006). It specifically supports 17 tools for the analysis of gene expression microarrays, and presents these tools using easy-touse on screen buttons (Figure 17.8). It runs under the Macintosh, Windows, and Linux operating systems, and the programming source code is available and can be modified by bioinformatics programmers. At the time of this writing, MeV has been cited by nearly 200 publications and has been extensively documented. GenePattern is a sophisticated analytic tool developed and maintained by the Broad Institute (Reich et al., 2006). GenePattern supports the analysis of multiple types of high-bandwidth measurements, including gene expression microarrays, proteomics, and single nucleotide polymorphisms. Each analytic and visualization tool is contained within a module, which can be chained together to represent a reproducible pipeline for analysis (Figure 17.9).

GenePattern runs under the Macintosh, Windows, and Linux operating systems. GenePattern is well-documented and supported, and as of this writing, approximately 30 papers have cited its use. GenePattern clearly excels in its advanced features. One particularly useful feature of GenePattern is that it offers a modular architecture allowing users to plug in new analytic tools or visualization methods as they become available. These tools can be created to interface with other bioinformatics analytic tools, including Matlab and R and Bioconductor (described below). GenePattern can distribute the work required for an analysis onto a parallel computer cluster, which, if available, can speed the time required to complete an analysis. In addition, GenePattern supports the output of measurements into the standardized formats necessary for submission to international repositories, a step that is increasingly required before a top-tier journal accepts a manuscript. At the opposite extreme of user-friendliness lies the statistical computing and graphics environment known as R (R Development Core Team, 2004). Based on the S statistical computing environment developed by Bell Labs in the late 1970s, R is the most flexible of the tools listed here. R is almost essentially “command line” driven, in that commands to execute statistical and visualization tools are typed and interpreted serially, but most users write scripts and sophisticated programs that can be run to generate the appropriate output and figures. There is essentially no user-interface (i.e., menus and windows) that can be used to perform analyzes; getting up-to-speed with R involves climbing a steep learning curve.

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Analytical methodology

Pipeline representation and results

1 Heat map

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Marker genes

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Figure 17.9 The conceptual analytic “pipeline” needed to analyze high-bandwidth molecular data is represented on the left, while its computational equivalent is represented on the right. Using GenePattern, these steps can be coded and documented, yielding reproducible research results (Reich et al., 2006). Reprinted by permission from Macmillan Publishers Ltd, Nature Genetics 38, 500, copyright 2006.

Despite the severe user-interface handicaps, the most important advantage of R is its community of users. Because of its designed similarity to S, users of R can benefit from decades of developed analytic tools (some statistical tool plug-ins for R and S available on the Internet date back to the late 1980s). The most important set of plug-in tools for research in genomic medicine are those contained in the Bioconductor package, which offers tools for reading microarray data, processing raw data files, common supervised and unsupervised machine learning methods, and visualization methods (Gentleman et al., 2004). More than 50 books have been written that explain both commonly-used and cutting-edge statistical techniques using R (and S), including a few specifically for bioinformatics and Bioconductor (Gentleman et al., 2004; Parmigiani, 2003). R runs

under the Macintosh, Windows, and Linux operating systems and its source code is available. Intepretation After several analyses, it quickly becomes obvious that the rate-limiting step in experiments involving high-bandwidth measurements is neither the handling of the biological samples nor the actual statistical or numerical work, but instead the post-analytic work in determining what the results actually mean. First, detailed names and knowledge might not yet exist for genes and proteins that have been found to be significantly involved in an experiment, even though these elements may have already been measured for many years. This complicates the interpretation of results. The official gene name, predicted

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TCA cycle glycolysis fatty-acid degradation triacylglycerol

view same data set on hundreds of MAPPs

lipoprotein lipase glycerol

fatty acid

glycerol kinase

fatty acid CoA ligase

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acyl-CoA

glycerol-3-PO4 dehydrogenase

carnitine acetyltransferase carnitine palmitoyltransferase

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triosephosphate isomerase

acyl-CoA dehydrogenases: short chain medium chain long chain very long chain

glyceraldehyde-3-phosphate

see glycolysis MAPP

legend upregulated, P  0.05 downregulated, P  0.05 no criteria met not found

see TCA cycle MAPP

acyl-CoA (n2)

acetyl-CoA

thiolase

trans-Δ2-enoyl-CoA

3-ketoacyl-CoA

enoyl-CoA hydratase

3-OH-acyl-CoA dehydrogenase

click on genes for annotation, data and hyperlinks

3-OH-acyl-CoA

Figure 17.10 GenMAPP has the ability to color-code proteins participating in biological pathways based on measurements (Dahlquist et al., 2002). Reprinted by permission from Macmillan Publishers Ltd, Nature Genetics 31, 19, copyright 2002.

protein domains, or GO classification may become available for a gene or protein as early as tomorrow, or as late as years from now. Operationally, this means that one is never done analyzing a set of microarray data. One has to develop the infrastructure to constantly reinvestigate genes and proteins from analyzes performed in the past. It may be next week, for example, that new knowledge about a protein that was significantly implicated in an analysis performed three months ago gets published, finally leading to a novel and important finding. The challenges in determining the proper analytic methods to use are usually only a short-term difficulty, and typically after the “functional genomics pipeline” has been established, the rate-limiting step shifts to the post-analytic challenges (Kohane et al., 2002). In the future, truly demonstrating a “return on investment” from high-bandwidth molecular measurements depends on taking findings past the measurement stage and integrating them with the rest of the research pipeline, including interpretation and validation. The list of elements resulting from an analysis should not be viewed as an end in itself; its real value only increases as that list moves through biological validation, ranging from the numerical verification of measurement levels with alternative techniques, to the ascertainment of the meaning

of the results, such as finding common mechanisms or biological pathways involving the genes or proteins. While tools that link measurement elements back to known biological pathways are still in their infancy, some have been shown to be quite useful. GenMAPP is a software tool that can visualize of highbandwidth molecular measurements using pre-drawn diagrams illustrating biological pathways (Dahlquist et al., 2002). Though GenMAPP comes with nearly a thousand useful biological pathways across numerous species, GenMAPP also comes with an editor enabling a biologist or bioinformatician to draw any new pathway (Figure 17.10). Because of this, the GenMAPP user base has developed into a large community of users who share pathways. GenMAPP runs only under the Windows operating system. At the time of this writing, GenMAPP has been cited by nearly 600 publications and has been extensively documented. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is an extensive web-based database of biological pathways and functions under development for more than a decade (Kanehisa, 1997). Similar to GenMAPP, KEGG offers tools that can color-code diagrams of biological pathways based on quantitative measurements, instead using a web-based interface (Figure 17.11). KEGG offers more than 42,000 biological pathways for nearly 500 species.

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INSULIN SIGNALING PATHWAY

Flotillin APS

CAP

CrkII

Cb1

GRF2

Exo70

GLUT4 vesicle

TC10

Glucose homeostasis

Translocation to PM

Glucose uptake

GLUT4

CIP4/2

Glucose p

p p AMPK aPKC

SKIP

GK PGC-1 p

p

py

DNA

ps

IRS

PI3K

PDK1/2

p

p

Akt

GSK-3β

PIP3

py

p Phosphatidyl inositol signaling system

Glycolysis/ Gluconeogenesis

FBP

FOXO1

IKK

p py

Glycolysis G6PC

JNK

INSR

PYK

DNA

PTP1B

INS

Lipogenesis Fatty acid biosynthesis (path 1)

PFK

SREBP-1c

p

SOCS

ACC FAS

SHIP

p

PEPCK

Glycogen GYS

Glycogenesis

p p

PP1

PHK

p

Starch and sucrose metabolism

PYG

TSC2 TSC1

p

PKA

PDE3

p

Antilipolysis

HSL

cAMP Rheb

LAR

p

p

Antiapoptosis

BAD

p mTOR

MAPK signaling pathway

p

p p GRB2

SOS

S6

Raptor

p SHC

p

p70S6K

Lipid homeostasis

Apoptosis

Ras

Raf

4EBP1 p

MEK1/2

Protein synthesis

eIF4E p

p MNK

ERK1/2 p

ElK1 DNA

Proliferation, differentiation

Figure 17.11 The insulin signaling pathway, as represented in KEGG as of this writing. A click on any protein yields additional data about that protein.

Beyond biological pathways, KEGG also provides knowledge on ligands, metabolites, small-molecule drugs, and diseases. At the time of this writing, KEGG has been cited by nearly 1000 publications. Other free web-based resources available to study genes by known function and protein domains include Entrez Gene and GeneCards (Safran et al., 2002;Wheeler et al., 2006).

NEW QUESTIONS FOR GENOMIC MEDICINE While this chapter has demonstrated the utility of bioinformatics and computational biology for answering specific questions for genomic medicine, it is clear that future revolutionary discoveries are going to come from those who can handle, manage, and exploit the exponential growth in measurement methods and data. Even as newer high-bandwidth systems become available for measuring proteins, lipids, and protein-interactions, it is clear that significant discoveries can be enabled by putting measurement-systems and data together, an area of research informally termed integrative biology. Here, we will describe some significant studies that involved the integration of large-scale measurements across modalities. In one integration study, Schadt and colleagues started with gene expression differences in liver between two inbred strains of mice. They used the most significantly different genes as traits

which were then mapped as quantitative trait loci using genetics. They first highlighted genes with known polymorphisms that affected their transcript levels, then showed loci associated with fat pad mass (and thus relevant to obesity) that would otherwise have been insignificant had the expression data not been considered (Schadt et al., 2003a, b). In other words, their association of genes with obesity required both microarray and genetic tools. Mootha and colleagues integrated four publicly available expression datasets with linkage data and proteins identified from mitochondria to ascertain the gene and mutation responsible for Leigh syndrome, French-Canadian type (Mootha et al., 2003). Stoll and colleagues integrated 125 phenotypes with linkage data from rats to determine candidate genes potentially involved in cardiovascular function (Stoll et al., 2001). Chiang and colleagues took genes known to be associated with Bardet-Biedl syndrome and found a set of species in which these genes were conserved and other species in which these genes did not exist. Using this comparative genomic information, Chiang was able to predict and discover a novel gene with a mutation for Bardet-Biedl syndrome (Chiang et al., 2004). English collected 49 genome-scale experiments all related to the study of obesity, but of varying types, including gene expression microarrays, proteomics, RNA interference, and genetic scans. She evaluated the sensitivity and specificity of each against a gold-standard list of known obesity-associated genes. While the sensitivity and specificity of each independent experiment was

References

poor, she found that a simple integrative model could statistically significantly outperform each of the independent experiments in rediscovering known obesity-associated genes. Moreover, she found that the likelihood of discovering a novel obesity-associated gene increased when pairs of genome-scale experiments were considered, instead of individual experiments. This work showed that integrating experiments performed better than single experiments at finding complex disease-associated gene variants, and that it is now possible to predict how likely a gene is to have a variant associated with a complex condition (English and Butte, 2007). These examples are given to demonstrate that exploration into the integration of multiple high-bandwidth modalities has started. Instead of focusing on the cell, or the genotype ranging from one locus to the entire genome, or on any single measurement modality, integrative biology allows one to think holistically and horizontally. A disease such as diabetes can lead to myocardial infarction, nephropathy, and neuropathy; to study diabetes in the era of genomic and personalized medicine would require reasoning from a disease to all its various complications to the genome and

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back, across many vantage points and cellular models that bridge the genome sequence, gene expression, proteomics, metabolomics, and phenotype. As we have shown in this chapter, there is an enormous supply of prior data that can be quite potent, if it can be brought to bear on the right problems, with the right tools. Though it has yet to be formally proven, the future study of complex diseases is highly likely to benefit from integrative biology and bioinformatics.

ACKNOWLEDGEMENTS The work was supported by grants from the Lucile Packard Foundation for Children’s Health, National Library of Medicine (K22 LM008261), National Institute of General Medical Sciences (R01 GM079719), Howard Hughes Medical Institute, and the Pharmaceutical Research and Manufacturers of America Foundation. The author is a scientific advisor to Genstruct, Inc., a computational systems biology company.

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(2002). Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 3, RESEARCH0046. Stoll, M., Cowley, A.W, Jr., Tonellato, P.J., Greene, A.S., Kaldunski, M.L., Roman, R.J., Dumas, P., Schork, N.J., Wang, Z. and Jacob, H.J. (2001). A genomic-systems biology map for cardiovascular function. Science 294, 1723–1726. Storey, J.D. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100, 9440–9445. Su, A.I., Welsh, J.B., Sapinoso, L.M., Kern, S.G., Dimitrov, P., Lapp, H., Schultz, P.G., Powell, S.M., Moskaluk, C.A., Frierson, H.F, Jr. et al. (2001). Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 61, 7388–7393. Szallasi, Z. and Liang, S. (1998). Modeling the normal and neoplastic cell cycle with “realistic Boolean genetic networks”: Their application for understanding carcinogenesis and assessing therapeutic strategies. Pac Symp Biocomput, 66–76. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S. and Golub, T.R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96, 2907–2912. Toronen, P., Kolehmainen, M., Wong, G. and Castren, E. (1999). Analysis of gene expression data using self-organizing maps. FEBS Lett 451, 142–146. Tseng, Y.H., Butte, A.J., Kokkotou, E., Yechoor, V.K., Taniguchi, C.M., Kriauciunas, K.M., Cypess, A.M., Niinobe, M., Yoshikawa, K., Patti, M.E. et al. (2005). Prediction of preadipocyte differentiation by gene expression reveals role of insulin receptor substrates and necdin. Nat Cell Biol 7, 601–611. Tusher, V.G., Tibshirani, R. and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98, 5116–5121. van de Rijn, M. and Gilks, C.B. (2004). Applications of microarrays to histopathology. Histopathology 44, 97–108. Wang, L., Wu, Q., Qiu, P., Mirza, A., McGuirk, M., Kirschmeier, P., Greene, J.R., Wang, Y., Pickett, C.B. and Liu, S. (2001). Analyses of p53 target genes in the human genome by bioinformatic and microarray approaches. J Biol Chem 276, 43604–43610. Weinmann, A.S., Yan, P.S., Oberley, M.J., Huang, T.H. and Farnham, P.J. (2002). Isolating human transcription factor targets by coupling chromatin immunoprecipitation and CpG island microarray analysis. Genes Dev 16, 235–244. Weinstein, J.N., Myers, T.G., O’Connor, P.M., Friend, S.H., Fornace, A.J, Jr., Kohn, K.W., Fojo,T., Bates, S.E., Rubinstein, L.V.,Anderson, N.L. et al. (1997). An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349. Welsh, J.B., Sapinoso, L.M., Kern, S.G., Brown, D.A., Liu, T., Bauskin, A.R.,Ward, R.L., Hawkins, N.J., Quinn, D.I., Russell, P.J. et al. (2003). Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc Natl Acad Sci USA 100, 3410–3415. Wen, X., Fuhrman, S., Michaels, G.S., Carr, D.B., Smith, S., Barker, J.L. and Somogyi, R. (1998). Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci USA 95, 334–339. Wheeler, D.L., Chappey, C., Lash, A.E., Leipe, D.D., Madden, T.L., Schuler, G.D., Tatusova, T.A. and Rapp, B.A. (2000). Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 28, 10–14.

Recommended Resources

Wheeler, D.L., Church, D.M., Edgar, R., Federhen, S., Helmberg, W., Madden, T.L., Pontius, J.U., Schuler, G.D., Schriml, L.M., Sequeira, E. et al. (2004). Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res 32(Database issue), D35–D40. Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R.,

Websites http://www.ebi.ac.uk/arrayexpress/ http://www.bioconductor.org/ http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?dbgene http://www.ncbi.nlm.nih.gov/geo/ http://www.broad.mit.edu/cancer/software/genepattern/ http://www.genmapp.org/ http://www.who.int/classifications/icd/en/ http://www.genome.jp/kegg/ http://www.tm4.org/mev.html http://obo.sourceforge.net/ http://www.peptideatlas.org/ http://www.ebi.ac.uk/pride/ http://www.r-project.org/ http://genome-www5.stanford.edu/ http://umlsks.nlm.nih.gov/

Books Brown, T.A. (2006). Genomes 3, 3rd edition. Garland Science Publisher, New York.

Review articles Butte, A. (2002). The use and analysis of microarray data. Nat Rev Drug Discov 1, 951–960. Quackenbush, J. (2006). Microarray analysis and tumor classification. N Engl J Med 354, 2463–2472. Dudoit, S., Gentleman, R.C. and Quackenbush, J. (2003). Open source software for the analysis of microarray data. Biotechniques Suppl, 45–51.

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Federhen, S. et al. (2006). Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 34, D173–D180. World Health Organization (2005). International Statistical Classification of Diseases and Health Related Problems.Tenth Revision/Ed, Geneva. Wuensche, A. (1998). Genomic regulation modeled as a network with basins of attraction. Pac Symp Biocomput, 89–102.

RECOMMENDED RESOURCES ArrayExpress Bioconductor Entrez Gene Gene Expression Omnibus GenePattern GenMAPP International Classification of Diseases Kyoto Encyclopedia of Genes and Genomes MultiExperiment Viewer Open Biomedical Ontologies PeptideAtlas Proteomics Identifications Database R Stanford Microarray Database Unified Medical Language System

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18 Fundamentals and History of Informatics for Genomic and Personalized Medicine A. Jamie Cuticchia

INTRODUCTION Bioinformatics is the collection, classification, storage, and analysis of biochemical and biological information using computers especially as applied to molecular genetics and genomics. Bioinformatics arose out of necessity as part of the infrastructure and analytic needs of genome research (see below). Medical informatics refers to computer applications in medicine and health care and historically has focused on clinical data and patient records and on medical terminology (Shortliffe et al., 2003). This information is increasingly being stored and manipulated through electronic health records (see Chapter 19). Genomic and personalized medicine is leading to a unification of these disciplines, and an intersection of the two is the main topic of the current chapter. The success of the Human Genome Project (1990–2003) rested largely on the ability to collect, manage and share gene mapping and sequencing information (Collins and Galas, 1993; Watson, 1990; Watson and Cook-Deegan, 1991). As early as 1988, researchers recognized the need for the collection and sharing of molecular data. In that year, the US Congress created the National Center for Biotechnology Information (NCBI) as a division of the National Library of Medicine (NLM) (Benson et al., 1990;Wheeler et al., 2007). NCBI’s mission includes “creating automated systems for storing and analyzing knowledge Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 226

about molecular biology, biochemistry, and genetics.” The history of modern biological databases began with the creation of GenBank in 1985 (Burks et al., 1985). The goal of GenBank was to collect and distribute nucleotide sequence information and provide accession numbers, which are unique identifiers for each sequence record. Prior to GenBank, journals published articles describing nucleotide sequence data, as well as the data itself in printed form, but the latter became infeasible as sequencing strategies and technologies advanced and the data began to grow exponentially (www.ncbi.nlm.nih.gov/Genbank/index.html). Similarly, physical and genetic mapping data were described and reported through printed journal articles. Periodically, compendia of such data were published as a product of scientific conferences where geneticists gathered together to integrate the data and build increasingly comprehensive genetic maps. The publications that resulted from the Human Gene Mapping Conferences lead to the development of an online resource for genetic maps, and online access began to be provided by the Genome Database (GDB) (Pearson, 1991) in 1990. Genetic and phenotypic data for genes involved in human disease were also originally compiled in printed form in a series of books by Victor McKusick entitled Mendelian Inheritance in Man (MIM) spanning 12 editions, the final of which was published a decade ago (McKusick, 1998). MIM was also provided Copyright © 2009, Elsevier Inc. All rights reserved.

Databases for Genomic Medicine

(and still is) as Online Mendelian Inheritance in Man or OMIM (www.ncbi.nlm.nih.gov/sites/entrez?db=OMIM). The development and evolution of various physical markers for genes is beyond the scope of this chapter. Single nucleotide polymorphisms (SNPs) are variations that occur in DNA sequences from different members of a population (see Chapter 7). Most of these variants are “silent”, that is, have no known phenotypic effects but are useful as signposts to regions of the genome in which they occur. Currently, there are more than 31,000,000 SNPs in the human population with the information stored in the dbSNP repository at NCBI (Wheeler et al., 2006). The database records in dbSNP are relatively simple: they contain the SNP itself in the context of its flanking sequence as well as chromosome localization, frequency, and association with genes. The large number of SNPs and the ease of detection make it feasible to survey human populations for genetic variation (Germer et al., 2000; Lavebratt and Sengul, 2006; Norton et al., 2002; Tahira et al., 2003). Thus, the HapMap project was created whereby large geographically diverse populations were surveyed across millions of SNPs (International HapMap Consortium, 2003; Couzin, 2002; Thorisson et al., 2005). Sets of nearby SNPs on the same chromosome that are inherited as a unit are called haplotypes, and a haplotype map or HapMap of the human genome now contains in excess of 3.3 million genotypes derived from 270 individuals and serves as an indispensable tool for researchers (Thorisson et al., 2005). One goal of the HapMap project was to provide a blueprint of human variation with associated genetic markers in order to facilitate efficient genotyping of patient populations, ultimately for the use in gene-disease association studies. By providing data that can be used for study design, researchers can chose to use commercially available genotyping chips or employ technologies that focus on specific regions of the genome. Two major companies providing assays are Affymetrix and Illumina. Gene chips containing 500,000 SNP markers are commercially available from Affymetrix, while Illumina allows researchers to chose which markers are to be used in a study from their database of over 1,000,000 validated markers (see Chapter 8). The discovery of millions of SNPs, a survey of human variation across those SNPs, and the low cost of SNP detection in patient populations is a major step forward for personalized medicine. For example, the HapMap is used to discover and study the relationships between different genotypes and the way patients respond to particular drugs, a field of study known as pharmacogenomics (Deloukas and Bentley, 2004) (see Chapter 27). Thus, the foundation has been laid for examining patient genetic and phenotypic variation as a first step toward personalized medicine (Andrawiss, 2005).

DATABASES FOR GENOMIC MEDICINE In the past few years there has been increasing interest in the scientific community for the creation of personalized medicine databases. The US National Institutes of Health (NIH) established

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ClinicalTrials.gov as a resource providing information on federally and privately supported clinical trials (Gillen et al., 2004; Zarin and Keselman, 2007). This is the first major effort to attempt to provide a central resource cataloging ongoing clinical research. The National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) established the Central Repositories Program in 2003 with the goal of providing a catalog for data available from completed clinical studies, as well as reporting the availability of DNA and biospecimens from both completed and ongoing research. The NIDDK Data Repository provides web access to general information on studies and acts as a catalog of data (Cuticchia et al., 2006). Since the data in this repository include patient data, it is not possible to provide such data freely on the web. Rather, researchers interested in using data from a single study (or multiple studies for a meta-analysis) work with the NIDDK Data Repository staff to determine the availability and suitability of the data. If the researchers wish to use the data, they must submit a formal request along with the approval of their institutional review board (IRB) for the handling of patient data. The documents are then reviewed by the NIDDK, and, if data access is approved, the data are provided on CD-ROM. In 2001, PharmGKB: the Pharmacogenetics Knowledge Base was publicly released.The PharmGKB mission includes the development, implementation, dissemination of a public genotype– phenotype resource focused on pharmacogenomics (Hewett et al., 2002). PharmGKB is publicly available and supported by the NIH and is part of the NIH Pharmacogenetics Research Network (PGRN). Although it has been supported for nearly 6 years, the amount of data available through the resource is small. As of September 2007, PharmGKB has information on 608 variant genes, 523 drugs, and 123 phenotypes. In order to create an extensive resource, the issues of appropriate levels of funding and incentives to submit data must be addressed. It has been estimated that an initial investment of $50–100 million over 5–10 years would be needed to meet this goal (Gurwitz et al., 2006). The Genome Medicine Database of Japan (GeMDBJ) represents an initial attempt to warehouse data for the Millennium Genome Project (MGP) (Tsujimoto, 2001). In particular, the database contains polymorphism information, expression profiles, and limited clinical and demographic information for Alzheimer’s disease, diabetes, hypertension, and gastric cancer. While rudimentary in its search capabilities and presenting little more than a collection of files, it is nevertheless a result of a growing interest for the development of molecular medicine databases. Moreover, the Marshfield Clinic has undertaken database development as part of its Personalized Medicine Research Project (McCarty et al., 2005). They have created a database of genotypic and phenotypic information of over 18,000 participants and have produced analytical tools to support the project. This database is an example of the population-based study databases that must be created under the NIH Genome-Wide Association Studies (GWAS) Project. NIH has designated NCBI as the group for the development of databases to support GWAS. The Database of Genotype

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and Phenotype (dbGAP), already has data from studies on macular degeneration and Parkinson’s diseases. The database collects and disseminates data from medical sequencing, and molecular diagnostic assays in addition to genotype–phenotype correlations. Database funding, unfortunately, is one of the areas most easily decreased or eliminated during times of limited funding. However, even if funding could be readily and steadily obtained, the issue of incentives to submit data is a much more difficult challenge to meet. Even though the success of the HGP was largely based on the ability of numerous research groups to work together in the sharing of data, the ability to get groups to submit data publicly during the initial stages was difficult (Cuticchia et al., 1993). This difficulty arose because the laboratories participating in the HGP were hesitant to divert resources away from the research process in order to perform data submission. Even when HGP funding required proof of data release, some research groups interpreted that as being met by merely placing files (sometimes with no documentation) on the Internet. It was only when “peer-pressure,” combined with a recognition of the overall value to all research groups, that data submission became commonplace. For pharmacogenomics data, a great deal of which is obtained through privately funded research, the incentives for public database submission are not immediately apparent. Some have argued that financial incentives such as tax exemptions for the cost of depositing data or increasing the US patent lives for drugs when their clinical trial data are publicly accessible are needed (Gurwitz et al., 2006). It remains to be seen the manner in which public databases receive both data and funding; however, it is likely that such a resource will eventually be developed. Knowledge and Data Management As an example relevant to genomic and personalized medicine, the key to pharmacogenomic research is the ability to accurately collect and interrelate data. While there are numerous components used to determine the genotype at a particular locus, the data that would be used for a gene association study at its simplest form is shown in Table 18.1. The data can be broken down into just four columns corresponding to: Run_ID, Patient_ID, Marker_ID, and Genotype (A, B, or AB) The fields Patient_ID and Marker_ID are references to other datasets, which might include patient records or associated genes or SNPs. From a database perspective, those fields are “foreign keys,” meaning that the information contained in those fields can be used to directly link to other data. The data could be stored together along with additional data in the same database or could be used as links to external databases. In the case of linkage to other databases, diligence must be given to ensure that the identifier used to link the data remains static. As an example, if the Marker_ID field were used to link to dbSNP, then the field must be populated with RefSNP identifier (i.e., rs#), as in Table 18.1, of each marker in order to unambiguously link the two data records. Nearly all publicly accessible biological databases provide

TABLE 18.1

Sample patient genotypes

Run ID

Patient ID

Marker ID

Genotypea

101

1

rs28771969

A

102

2

rs28771969

A

103

3

rs28771969

B

104

4

rs28771969

AB

105

5

rs28771969

A

201

6

rs28771969

AB

202

7

rs28771969

B

203

8

rs28771969

A

204

9

rs28771969

B

205

10

rs28771969

AB

a

These labels represent the presence of alleles at the particular locus. The patient may be homozygous for either allele A or B, or may be heterozygous having one copy of each (AB).

unchanging (and usually purposely ambiguous) identifiers, accession numbers, for their records. If it is unclear which field the database is using for its accession number, then it is important to contact the database host. The field Patient_ID would similarly be linked to data records relevant to that patient. However, the implementation of this link would depend on the manner in which clinical data are made available. Most hospitals and many private medical practices have continued to migrate from the collection of medical information on paper toward its collection electronically (Ford et al., 2006; Geiger et al., 1995; Miller and West, 2007; Welch et al., 2007). Even where paper is still used for the initial recording of medical information, it is likely that the data will later be input into a computer. For clinical trials, the data that are to be collected are predetermined before the start of the trial. Case report forms are created that are used by the health care providers for the collection of necessary data. In many cases, due to the environment in which the data is to be collected, the data collection form is completed on paper. However, with the increase in computer access within clinics, it is becoming easier for the health care provider to complete electronic versions of the form using laptops, tablet computers, or PDA’s. Regardless of the mode of collection, ultimately the medical information needed for the clinical trial is stored electronically. It is incumbent upon both the database creator and user to make certain that the data collected is stored in a manner compatible with IRB and HIPAA standards (Erlen, 2005; Fain, 2003; Harrelson and Falletta, 2007). In some cases, the data may need to be imported directly from electronic medical records. In those cases it will be necessary to work with the administrators of the medical records in order to determine the manner of access. It is quite unlikely, nor is it recommended, that links will be made

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ensures that fields on different patient report forms that collect the same data type are related, but also facilitates the linking of clinical trial data across multiple trials (Murphy et al., 2003). There are two problems that must be avoided in relating data: (1) linking data that should not be linked and (2) failing to link identical data. As an example:

Electronic patient records EPR Honest broker EPR_ID  RD_ID

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Research database RD

Figure 18.1 Honest broker association between two databases. The “honest broker” is an independent database with the sole purpose of storing the association of identifiers for linked data stored in two or more databases. Thus, related data from two or more databases can only be retrieved when sufficient access privilege is granted.

directly to the electronic health records. Rather, it is preferable to have an extract of the data prepared by the medical records administrator, which may be directly loaded into the research database or stored as its own database. Those data are then linked by Patient_ID to the research database. The advantage to storing this information separately is that the medical records administrators can create their own database using database tools and protocols compatible to the electronic medical records. They also would not be burdened with the need to understand the research database. Moreover, if the data are stored in a separate database, changes can be made to the structure (or even a full replacement) of the research database without interfering with the medical records. Accessing medical records through an intermediate database also allows for the use of an “honest broker” approach to the data management (Boyd et al., 2007). In some cases patient data that are stored in the intermediate database may need to be accessed by users with different data privileges (Figure 18.1). Additionally, there may be a requirement that no medical records can be directly linked to a research database. By using an honest broker, the patient record identifier from both databases are linked by another database. Thus, there is no way (other than through the honest broker) for someone to link the patient information with the genotype data. Data Modeling While the storage and modeling of genotype data are simple, the modeling of patient data is not! For example, in a recent investigation of a very well thought out clinical trial, there were no less than 10 different places, names, and values in which the data value referred to patient death. The problem of relating identical fields across studies becomes even more difficult if a common naming scheme is not employed (see Chapter 17). The use of a common naming convention, a “controlled vocabulary” in the creation of clinical trial databases, not only

Data Record A: IV

Data Record B: 4

If the records referred to Drug Number in A, and Drug Number in B, then the records should be linked since Record A is the Roman Numeral representation of Record B. Additionally, if the records referred to manner of drug administration in A (intravenous), and manner of drug administration (where 4  intravenous) they should be linked. However, if Record A referred to manner of drug administration (intravenous) and Record B referred to Drug Number (Drug 4) then the records should not be linked. Thus, two pieces of data, which to a computer would be a character data type and a numeric data type, can in fact represent the same values. In databases that are properly designed with meaningful field names, it should be relatively simple to make the appropriate connections. However, enough ambiguity will exist in field names that it is always recommended to go back to that patient report forms and their explanation in order to make associations. A controlled vocabulary can be defined as a collection of consistent terms used to refer to identical objects, that is to say that they have been enumerated explicitly. There are numerous examples such as the Systemized Nomenclature of Medicine (SNOMED) (Kudla and Rallins, 1998) and the Clinical Data Interchange Standards Consortium (CDISC) (Kuchinke et al., 2006) for clinical data, and the Gene Ontology (also known as the GO Ontology, 2006; Camon et al., 2003) and NCI thesaurus (Sioutos et al., 2007) for more basic biological data. Both SNOMED and CDISC curate a list of medical terminology in such a way as to unambiguously connect terms which refer to the same “entity.” As an example, the entity is defined as “a person who is participating in a clinical study.” In one instance “Patient” is used to refer to that entity, and in another instance “Subject” is used. The GO Ontology is an attempt to create a hierarchical system for describing biology by breaking genes into functions and biological process (as an example). Together, these tools provide different means and terminology to map common concepts to a unique identifier. To a computer: ● ● ● ● ●

Myocardial infarction MI Infarction of heart Myocardial infarct Heart attack

all appear as different objects. By using a tool such as those available for SNOMED, one could either use a single definitive term replacing all synonyms or create a linking table that would

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CHAPTER 18

TABLE 18.2 Patient ID 1

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Fundamentals and History of Informatics for Genomic and Personalized Medicine

Simplified sample clinical report forms Patient name (Last)a Smith

2

Jones

3

Treatment

Response

External Link to Patient Records Run_ID

X

Yes

X

Yes

Williams

Placebo

No

4

Douglas

X

Yes

5

Baker

Placebo

No

6

Clark

X

No

7

Carey

Placebo

No

8

Sanders

Placebo

No

9

Hawkins

X

Yes

10

Thomas

Placebo

No

Patient_ID

Patient_ID

Patient_Name Table Link

Genotype

Treatment

Marker_ID

Response

External Link to dbSNP

a

Figure 18.2 Data representation. Run_ID and Patient_ID are the primary keys for each of the two tables. Here Patient_ID is also a foreign key for the first table which is used to link those records with matching records in the second table. Patient_ID and Marker_ID also serve as foreign keys to external databases.

Fictional

associate all the terms with the same object. By doing the latter, one could create a query such as “Show me all patients who had a MI since 2001” and the query would retrieve all records with the synonyms matched to MI. The use of controlled vocabulary can also be related to ontologies. Ontologies are ‘specifications of a relational vocabulary’ (Bard, 2007; Bittner and Donnelly, 2007; Pinciroli and Pisanelli, 2006; Soldatova and King, 2005). In other words, once one has created a set of common terms, the terms can be networked together. The use of ontologies would allow researchers to perform queries such as “Show me all patients having adverse events involving the heart since 2001.” Through the ontological network, this would retrieve MI and other adverse events (e.g., cardiac arrest). Software Development As an example, a database to support a very simple clinical trial is outlined. The trial will test the effect of Drug X on headaches. Five patients with headaches are given Drug X, while five patients receive a placebo. Additionally the patients are genotyped at a locus rs28771969 to determine whether variation in the gene has an effect on response. The information received from the Genotyping Center is shown the Table 18.1, while the data collected from the clinical report forms are listed in Table 18.2. The database would look something like the representation shown in Figure 18.2. Points to note are: 1. The Marker_ID field is used as an external identifier to link to other genetic databases. 2. The Patient_ID field is an internal identifier set by the research study and can be used to link to medical records. This information would be covered as Private Health Information (PHI) and will require appropriate IRB and HIPAA treatment.

3. X and Placebo are both terms that refer to same class of treatment. While this in an overly simplistic example, it demonstrates the key points regarding database construction for genomic and personalized medicine: 1. It provides for unambiguous linking of genotypes and patients. 2. It provides for linking to electronic medical records without the need to extract information directly from the medical records database. 3. It uses the term “Treatment” to refer to drug given; where treatment is a general term that can be mapped to other studies. 4. It uses keys to link together database tables and external databases.

CONCLUSION As the field of genomic and personalized medicine evolves, the need for the effective collection and dissemination of data will become increasingly important. While data initially are being collected for genotype–phenotype association studies, ultimately such research will lead to the use of genomic information in the health care process. For this reason, good database standards must be created in the research phase and used as models for the establishment of databases used for diagnostics. No new information technologies need to be invented in order to meet the needs of genomic medicine. Rather, researchers and informaticians need to concentrate on the use of “best-of-breed” information technology in the building of databases. By using good information technology practices, the needed databases should easily be constructed and once completed, issues regarding data collection and the incentives needed to populate the databases can then be addressed by the community.

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Hewett, M., Oliver, D.E., Rubin, D.L., Easton, K.L., Stuart, J.M., Altman, R.B. and Klein, T.E. (2002). PharmGKB: The Pharmacogenetics Knowledge Base. Nucleic Acids Res 30, 163–165. International HapMap Consortium (2003). The International HapMap Project. Nature 426, 789–796. Kuchinke, W., Wiegelmann, S., Verplancke, P. and Ohmann, C. (2006). Extended cooperation in clinical studies through exchange of CDISC metadata between different study software solutions. Methods Inf Med 45, 441–446. Kudla, K.M. and Rallins, M.C. (1998). SNOMED: A controlled vocabulary for computer-based patient records. J AHIMA 69, 40–44. quiz 45–46. Lavebratt, C. and Sengul, S. (2006). Single nucleotide polymorphism (SNP) allele frequency estimation in DNA pools using Pyrosequencing. Nat Protoc 1, 2573–2582. McCarty, C., Wilke, R., Giampietro, P., Wesbrook, S. and Caldwell, M. (2005). Marshfield Clinic Personalized Medicine Research Project (PMRP): Design, methods and recruitment for a large populationbased biobank. Personalized Medicine 2, 49–79. McKusick,V.A. (1998). Mendelian Inheritance in Man; a Catalog of Human Genes and Genetic Disorders. The Johns Hopkins University Press, Baltimore, MD. ISBN 0-8018-5742-2 Miller, R.H. and West, C.E. (2007). The value of electronic health records in community health centers: Policy implications. Health Aff (Millwood) 26, 206–214. Murphy, L.S., Reinsch, S., Najm, W.I., Dickerson, V.M., Seffinger, M.A., Adams, A. and Mishra, S.I. (2003). Searching biomedical databases on complementary medicine: The use of controlled vocabulary among authors, indexers and investigators. BMC Complement Altern Med 3, 3. Norton, N., Williams, N.M., Williams, H.J., Spurlock, G., Kirov, G., Morris, D.W., Hoogendoorn, B., Owen, M.J. and O’Donovan, M.C. (2002). Universal, robust, highly quantitative SNP allele frequency measurement in DNA pools. Hum Genet 110, 471–478. Pearson, P.L. (1991). The genome data base (GDB) – a human gene mapping repository. Nucleic Acids Res 19(Suppl), 2237–2239. Pinciroli, F. and Pisanelli, D.M. (2006). The unexpected high practical value of medical ontologies. Comput Biol Med 36, 669–673. Shortliffe, E.H., Perreault, L.E., Wiederhold, G. and Fagan, L.M. (2003). Medical Informatics: Computer Applications in Health Care and Biomedicine. Springer, New York. Sioutos, N., de Coronado, S., Haber, M.W., Hartel, F.W., Shaiu, W.L. and Wright, L.W. (2007). NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information. J Biomed Inform 40, 30–43. Soldatova, L.N. and King, R.D. (2005). Are the current ontologies in biology good ontologies?. Nat Biotechnol 23, 1095–1098. Tahira, T., Suzuki, A., Kukita, Y. and Hayashi, K. (2003). SNP detection and allele frequency determination by SSCP. Methods Mol Biol 212, 37–46. Thorisson, G.A., Smith, A.V., Krishnan, L. and Stein, L.D. (2005). The International HapMap Project Web site. Genome Res 15, 1592–1593. Tsujimoto, G. (2001). [“Millennium Project” of MHLW]. Nippon Rinsho 59, 1884–1888. Watson, J.D. (1990). The human genome project: Past, present, and future. Science 248, 44–49.

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Watson, J.D. and Cook-Deegan, R.M. (1991). Origins of the Human Genome Project. FASEB J 5, 8–11. Welch, W.P., Bazarko, D., Ritten, K., Burgess, Y., Harmon, R. and Sandy, L.G. (2007). Electronic health records in four community physician practices: Impact on quality and cost of care. J Am Med Inform Assoc 14, 320–328. Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R., Federhen, S. et al. (2006). Database resources of the National

Center for Biotechnology Information. Nucleic Acids Res 34, D173–D180. Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R., Federhen, S. et al. (2007). Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 35, D5–D12. Zarin, D.A. and Keselman, A. (2007). Registering a clinical trial in ClinicalTrials.gov. Chest 131, 909–912.

RECOMMENDED RESOURCES Human Genome Project www.genome.gov NCBI www.ncbi.nlm.nih.gov GenBank www.ncbi.nlm.nih.gov/Genbank/ Human Genome Organisation (HUGO) www.hugo-international.org GDB Human Genome Database www.gdb.org Online Mendelian Inheritance in Man www.ncbi.nlm.nih.gov/omim/ dbSNP www.ncbi.nlm.nih.gov/projects/SNP/

HAPMAP Project www.hapmap.org Clinical Trials Registry www.clinicaltrials.gov NIDDK Repository www.niddkrepository.org PharmGKB www.pharmgkb.org Genomic Medicine Database of Japan gemdbj.nibio.go.jp Marshfield Clinic Personalized Medicine Research Project www.marshfieldclinic.org/chg/pages/default.aspx?page=chg_pers_med_res_prj

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19 Electronic Medical Records in Genomic Medicine Practice and Research Glenn S. Gerhard, Robert D. Langer, David J. Carey and Walter F. Stewart

INTRODUCTION The implementation of electronic medical records (EMRs) is expected to substantially improve the quality and efficiency of health care and provide an important vehicle to advance patientcentered personalized care, the sine quo non of genomic medicine. In the United States, the use of EMRs in care delivery is expanding rapidly, especially among large integrated health delivery systems. The amount of clinically relevant genomic data and the number of resources devoted to research on genomic medicine are increasing in parallel. However, relatively few publications have yet addressed the use of EMRs in genomic medicine, although the need for such integration has been clearly established (Martin-Sanchez et al., 2004). In this chapter, the role of EMRs in the practice of genomic medicine and their use for genomic research will be considered. The need for digital approaches to storing, processing, and using information is driven by the growing density of genomic data, whether derived from gene expression profiling, single nucleotide polymorphism (SNP) genotyping, or DNA sequencing, and from the pace of discovery. Moreover, the complexity of clinical decision-making will change commensurately as genetic data relevant to diagnosis and therapeutic interventions increases. Historically, the disciplines of medical (or clinical) informatics and biological informatics (or bioinformatics) have both evolved to address the needs of genomic medicine

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

practice and research (Maojo and Kulikowski, 2003). Differences between the two communities have resulted in an attempt to integrate the approaches (Altman, 2000), with some proposing that health care organizations should be more proactive in collaborating with other public and private sector organizations in the development of an EMR containing both clinical and genomic data (Groen et al., 2005). These are laudable goals, but most of the work published thus far has come from those who have focused on the manipulation and analysis of genomic data for the clinical setting. However, the major factors driving the development of EMRs, such as quality measures, efficiency, and reimbursement, are not directly related to genomic medicine. This chapter is written from the perspective of those involved in the use of EMRs in large integrated delivery systems that make use of data and populations for research. While the US health system is fragmented, the large health systems that are adopting EMRs are becoming increasingly integrated, especially in adopting and implementing practice standards (Orlova et al., 2005). A significant window of opportunity thus exists to incorporate various aspects of genomic medicine into the development of these emerging systems. The data relevant to genomic medicine are highly diverse and applicable to essentially all medical specialties. As a result, multiple approaches and solutions will likely need to be developed for the myriad of needs in genomic medicine research and practice (Stein, 2003; Sujansky, 2001).

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EMRs AND GENOMIC MEDICINE CLINICAL PRACTICE The EMR is a longitudinal electronic record of patient health information generated by encounters in any care delivery setting, encompassing patient demographics, progress notes, problem lists, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The EMR substitutes for traditional paper-based “charts”. The EMR has the ability to generate a complete record of a clinical patient encounter, as well as supporting other care-related activities directly or indirectly, such as evidence-based decision support, quality management, and outcomes reporting. It may also be part of a solution to addressing a decades old problem of decreasing the long lag time that exists before evidence-based medical knowledge is routinely used in clinical care (Stewart et al., 2007). This may be of tremendous importance for the rapid progress being made in genomic medicine. In this section, the integration and use of genomics data in EMRs for the clinical practice of genomic medicine is discussed. Example of an EMR The Geisinger Clinic, a large integrated health system in northeastern and central Pennsylvania, initiated installation of an EMR (i.e., EpicCare) in 1996, which was completed (i.e., completely paperless operations) in all community practice sites and specialty clinics by 2001. The EMR contains patient information from a variety of sources that is routinely integrated into a common database and includes age, sex, height, weight, and other demographics; lifestyle data (e.g., smoking, alcohol, etc.); clinical measures (e.g., BP, pulmonary function, cardiac information); digital imaging (MRI, CT, X-ray); all orders (i.e., labs, prescriptions, imaging, procedures) that require at least one clinical indication (i.e., ICD-9 code); clinical notes, which are increasingly created using smart-sets, or structured protocols; and laboratory measures (including results of genetic and molecular tests). Patients have on-line access to their EMR in the EpiCare system through the My Geisinger Internet portal. With My Geisinger patients can securely access their EMR, schedule visits, and email their doctor. Such web-based communications represent future opportunities for genomic medicine research. To date, the Geisinger clinic EMR database contains information on more than 2.2 million patients. Integration of Genomic Medicine Data into the EMR EMRs make use of relational database structures and utilities to access and display data to facilitate medical care and clinical decision-making. Although most observers and users consider the EMR to be a single application, in reality there are multiple data feeds from disparate sources that comprise the EMR. Some data sources, for example laboratory information systems, are usually integrated with the main EMR application, while others, such as radiology, histopathology, and molecular data are incompletely integrated or are resident in entirely separate databases. Genomic data currently used for clinical practice are

often generated by hospital, reference, or specialty laboratories that produce a wide variety of reporting formats, data types, and nomenclature (Ogino et al., 2007). Integration of these data into the EMR thus requires the ability to assemble the different data sources into an accessible format. Customized systems are sometimes developed in-house using on-site information technology expertise that are motivated by institution- and/or laboratoryspecific needs and allow for flexibility and control over how the software evolves. However, the tendency is to invest significant resources into these in-house solutions, which sometimes underestimate the effort needed and fail to meet the intended goals. The choice of integration architecture and format for data representation are thus significant decisions and have a number of important issues associated with them (Haas et al., 2001). The disparate sources that generate genomic data often report text-based data that are distributed through laboratory information systems into the EMR. These text results are commonly discrete, that is genotype A or genotype B, rather than continuous, although quantitative results are also produced, such as the number of trinucleotide repeats in the gene (FMR1) responsible for many cases of fragile X syndrome (Murray et al., 1997). Genomics results may also be reported in unique formats developed by diagnostic companies in niche markets. The disparate nature of the data and data sources further complicates the growing trend of integrating genomics data with other laboratory and/or clinically relevant data to generate a comprehensive clinical report. For example, cytogenetic and molecular remissions are recommended in reporting standards for acute myelogenous leukemia (Cheson et al., 2003). With the tendency of health care organizations towards the delivery of care in a service line structure and/or disease-specific care model (e.g., heart hospitals, neuroscience centers, etc.), more comprehensive and integrated reporting of genomic medicine results will likely be needed. This will apply to both genomic and other types of data-intensive results, such as proteomic and metabalomic data (Table 19.1). Currently most genomic data generated for clinical care are in the form of a discrete result. However, as the cost of full DNA sequencing continues to decline (Bennett et al., 2005), it is likely that discrete and highly relevant-specific results will be provided along with a large amount of DNA sequence data that has no known clinical significance (Mitchell and Mitchell, 2007). Ultimately, complete genome sequencing may become the most cost effective means of identifying sequence variants (Hutchison, 2007). It would not be unexpected in the future if DNA sequence determination became the standard of care for every patient, even though most of the sequence data may not be relevant to clinical decisions. Prior to that, many smaller iterations of DNA sequence data will be determined, for example, comprehensive mutation detection for BRCA1 and BRCA2 (Myriad Genetics, Salt Lake City, Utah, “BRAC Analysis for BRCA1 and BRCA2, Breast and Ovarian Cancer, Comprehensive Analysis”). In this analysis, full sequence determination in both forward and reverse directions of approximately 5400 base pairs comprising 22 coding exons and approximately 750 adjacent base pairs in the non-coding intervening sequences (introns) of the BRCA1

EMRs and Genomic Medicine Clinical Practice

T A B L E 1 9 . 1 Examples of genomic and other related data types potentially relevant for inclusion in the EMR ● ● ● ● ● ● ● ●

DNA sequence data Single nucleotide polymorphism (i.e., SNP) genotypes Multiple nucleotide polymorphism (e.g., insertion, deletion, repeat) genotypes Cell and/or tissue-specific microarray gene expression profiles Proteomic (e.g., mass spectroscopy) profiles Glycomic (i.e., carbohydrate) structural data Lipomic (i.e., lipid) profiles Metabolic phenotypes

gene is made. For BRCA2, full sequence determination in both forward and reverse directions of approximately 10,200 base pairs comprising 26 coding exons and approximately 900 adjacent base pairs in the non-coding intervening sequence (intron) is performed. Similar sequence analysis is offered for several other gene or genes that are associated with specific inherited cancer syndromes. Such testing generates an enormous amount of extra information that is not currently clinically relevant. However, as new discoveries are eventually translated into practice, ready access to relevant DNA data on patients may become necessary. How to maintain such dense sequencing data is thus an important decision (Mitchell and Mitchell, 2007). For example, placing every base sequenced into the patient’s medical record may clutter the record with predominantly clinically insignificant data. Alternatively, only bases that differ from reference data may need to be tracked. Reference data may change, which may alter the interpretation of patient data. The American College of Medical Genetics (ACMG) has previously recommended a minimum amount of information for inclusion in DNA sequencing reports (ACMG, 2000). Important considerations include reporting of all of the bases sequenced, as well as linked sequence reference data. Such recommendations are likely to evolve as more clinical experience is acquired with sequence data. Family History Data An integral component of genomic medicine is the application of family history to clinical care. Interest in collecting family history data as a routine part of care delivery is growing, as knowledge advances in linking family history of disease to patient risk. Many common diseases, such as coronary heart disease, diabetes, several types of cancer, osteoporosis, and asthma, are known to occur more frequently in some families than in others; thus the identification of at-risk individuals is important for high quality medical care. Family history data are also necessary for research aimed at identifying environmental and/or genetic factors that may underlie these common diseases (Hopper et al., (2005)). However, the acquisition of such data by most physicians is inadequate, in part because tools do not exist for efficient, reliable, and valid collection and analysis of such data (Guttmacher et al., 2004). Structured family history data are almost universally absent in current EMRs (see Chapter 42).

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The need for the development of better family history tools has been highlighted by projects at the Centers for Disease Control (CDC) and by the US Surgeon General’s Family History Initiative (Wolpert, 2005). However, these efforts have not directly addressed the integration of tools into the real-world scenario of busy physicians and a multiplicity of health record systems, and do not provide an adequate breadth of data capture necessary for research. Various paper family history tools are available, as are various computer-based tools for drawing pedigrees (Trager et al., 2007). Several EMRs incorporate free text documentation of family history, while others allow for the development of family history templates and support checklists for taking and documenting family history. However, currently available tools are inadequate for many situations, especially primary care (Rich et al., 2004). The need for new tools is therefore apparent, and several characteristics of the ideal family history tool have been suggested, including patient-completed (e.g., paper, desktop, telephone, or web input), adapted to patient age, gender, ethnicity, common conditions, elicits-patient specific concerns, brief, understandable, easy to use, compatible with multiple clinical applications (e.g., paper, EMR, personal digital assistant), contains clinical decision support, and prioritizes based on clinical significance (Rich et al., 2004). No such electronic family history tools have yet been developed, despite the availability of suitable technologies. EMRs, Genomic Medicine, and Clinical Decision Support Genomic medicine is accelerating the growth of medical knowledge. In contrast, the means by which knowledge is translated into clinical practice have not evolved to keep up with the accelerating growth. Translation of knowledge into practice relies on a century-old tradition, the direct education of clinicians, an approach that was useful when knowledge was relatively limited. Today, it is not possible for most physicians to stay abreast of medical knowledge, especially genomics, and provide state-of-the-art care. The nuances in clinical decision-making in genomic medicine already render many care scenarios complex. Access to an EMR and the ongoing codification of medical knowledge (i.e., Clinical Practice Guidelines or CPGs) will be essential to addressing this growing translation gap. CPGs greatly facilitate, but are not sufficient for translating knowledge to practice. Almost 2000 active CPGs exist in the US National Guideline Clearinghouse (Kozma, 2006) and an individual CPG may encompass dozens to hundreds of clinical recommendations. These recommendations will rapidly expand in the era of genomic medicine, and thus codification of knowledge will be essential to increasing its access. EMRs offer a platform to translate codified knowledge into real-time actionable processes. Genomic data will need to be accessed with other patient data located in disparate locations within the EMR and evaluated in relation to a rule set. Real-time actionable recommendations will need to be created and be supported by an integrated and intuitive visual display of information, such as a set of orders or recommendations for the physician or other care provider.

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An example of clinical decision-making using an EMR potentially relevant to genomic medicine is a process with a specific focus on cardiovascular risk management in primary care being developed at the Geisinger Health System. The process involves a decision support rules “engine”, external to the EMR, in which patient data such as labs, questionnaire data for phenotyping, and other clinical measures (Figure 19.1) are extracted in real time and evaluated in relation to clinical rules. The process can be used to generate a span of outputs including orders, clinical notes, and medication lists, etc. (Figure 19.2). The current model

involves rules for smoking cessation, control of LDL, control of blood pressure, and management of other cardiovascular risk factors. Together, the combination of rules for different risk factors translates into more than 100,000 possible ordersets. The process itself demonstrates that it is technically feasible to develop and implement a comprehensive set of rules for state-of-the-art management of a clinical condition. Genetic variants reported to predispose to coronary heart disease (Samani et al., 2007) may impact this process and could greatly increase the number of ordersets.

EMRs AND GENOMIC MEDICINE RESEARCH

Figure 19.1 Screenshot of EMR (Epic Systems, Corporation, Verona, WI). The software uses a tab-based design for access to medical record information including encounters, clinic notes, lab and imaging results, orders, medications, and procedures. These data can be extracted and used to automatically generate orders or recommendations, such as laboratory tests or imaging procedures for health maintenance or disease management.

Figure 19.2 Screenshot of EMR (Epic Systems, Corporation, Verona, WI). Examples of output from problem list, immunizations, allergies, medications, and health maintenance fields.

The EMR offers an exciting opportunity for genomic medicine research, both as a tool and as a data resource. It also represents an economically efficient means of obtaining phenotypic data and biosamples for generating genotypic data. Initial large-scale efforts to use genomic and clinical information for research, for example, deCode Genetics in Iceland, required the conversion of medical information from paper records into an electronic format (Hoffman, 2007). Even current large-scale efforts, such as the UK BioBank (Ollier et al., 2005), are based upon a mix of paper and electronic sources. The EMR thus represents a potentially large increase in efficiency for obtaining phenotypic data, and as described below, can also be an extremely efficient tool for patient recruitment and biosample acquisition. EMRs and Genome-Wide Association Studies The continued acceleration in genotyping technologies has enabled a dramatic increase in the use of genome-wide association studies (GWAS) to identify genetic variants underlying a variety of clinical phenotypes. However, the very large sample sizes and the logistics of collecting phenotype data and biological samples often require multi-center studies with coordinating centers and are very costly and time consuming to complete. Moreover, multiple replication studies are essential to verify initial GWAS findings. The long time period and substantial costs in initiating population-based studies limits the scope of such approaches. As an alternative, biorepositories are being developed to provide the resources necessary for GWAS. In Iceland (Hakonarson et al., 2003), the United Kingdom (Ollier et al., 2005), and elsewhere (Austin et al., 2003a), efforts are being made to link biobanking activities to national health records. In the United States, however, there are challenges with devising a national biobanking strategy (Austin et al., 2003b; Hsieh, 2004; McCarty et al., 2007; Sanner and Frazier, 2007). The US population is very heterogeneous, highly mobile, and health care is highly fragmented. Information on patients is highly dispersed and not in an easily accessible form. Hence, GWAS and related validation studies are largely conducted in conventional single project formats (i.e., de novo data collection), where there is a long lead-time between data acquisition and analysis.

EMRs and Genomic Medicine Research

In the United States, integrated delivery systems that use EMRs may be critical to addressing logistical challenges to conducting large-scale GWAS studies. While health care in the United States is highly fragmented, approximately 10% of care is provided by integrated delivery systems. Nationwide, these systems are undergoing a quiet revolution through the introduction of EMRs. Efforts to standardize data elements for research purposes and data sharing are being pursued by the larger systems (Hornbrook et al., 2005). There may be substantial opportunities to accelerate genomics research in the United States by leveraging the growing number of systems that are both adopting EMRs and creating biorepositories. However, there are many unanswered questions regarding whether such resources can be effectively used for genomics research. At the individual system level, there are questions regarding the quality, specificity, and completeness of data for phenotyping. In addition, little is known about system-level challenges that influence the quality and completeness of data captured during clinical encounters (e.g., impacts upon workflow, technical, and business priorities). Moreover, in some systems, even if quality data are available, there may be substantial technical, ethical, or policy barriers that limit access or use. Finally, GWAS and follow-up replication studies will require collaboration of multiple systems to achieve sample sizes that are sufficiently large, but there are enormous data quality, technical, and policy challenges to pooling data across institutions. Using the EMR to Access Populations for GWAS Case-control studies and family based studies have been the two primary designs for GWAS, but the economic efficiencies of the case-control format conducted within health systems are significant. Patient populations and inherent selection bias also impact GWAS, which may vary substantially depending on the source (e.g., primary care, specialty care, outpatient, inpatient). Primary care populations tend to be similar to general population samples with the exception that younger individuals (especially males) and individuals from lower middle-income strata (i.e., not eligible for Medicaid and no access to health care benefits through work) are under-represented. Selection bias inherent to specialty care samples depends on the extent to which there are competing providers in the same market and whether a provider has local, regional, national, or international reach. Selection bias increases directly in relation to market reach. These potential biases will be reflected in clinical data available in EMRs, depending upon where and when the system was installed. Genetic heterogeneity in a population may also arise due to population structure and recent admixture and may confound the results of genetic association studies in unrelated individuals, leading to a potential excess of both false positive and false negative results (Ziv and Burchard, 2003). The degree of confounding is dependent in part on how genetically different the subgroups are within the population. For example, among many contemporary populations, recent admixture between two or more genetically distinct subgroups has added to the genetic complexity. In the United States, this complexity is particularly

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pertinent for admixed populations such as Latino- and AfricanAmericans (Parra et al., 1998). Within indigenous continental population groups, European subpopulations appear to be the most similar to each other (Bowcock et al., 1994; Mountain and Cavalli-Sforza, 1997). The ability of an EMR to distinguish among ethnicities within its patient base may impact the usability of the data for GWAS. The Geisinger MyCode Biorepository The creation of biorepositories that are closely linked with EMRs may be an extremely efficient approach to genomic medicine research, especially for GWAS that require DNA samples from large populations with corresponding robust phenotypic data. The challenges to establishing such biorepositories involve complex institutional, legal, and social issues that also must be addressed. However, the EMR opens new opportunities to address these concerns, such as patients’ safety, rights, informed consent, and privacy. The Geisinger Clinic MyCode biorepository project is perhaps the first large-scale biobanking initiative built around an EMR. In this project, the EMR is used as a tool for identifying and recruiting patients, obtaining blood samples, and retrieving phenotype data. The primary goal of the MyCode project is to bank blood/serum/DNA samples from patients on a large-scale coupled with access to their EMR data. MyCode was initiated in 2007 to leverage the resources of the Geisinger Health System, an integrated delivery system offering healthcare services to residents of 31 of Pennsylvania’s 67 counties, with a significant presence in central and northeastern Pennsylvania between Pittsburgh and Philadelphia. Census data indicate that in most counties, the out-migration rate is less than 1% per year, thus providing an extremely attractive population for longitudinal studies. The population within the Geisinger Clinic catchment area is relatively homogeneous. Based on available historical, demographic, and self-reported ethnicity data, the Geisinger population is almost entirely Caucasian of mixed European ancestry, with only small contributions from other continental population groups, such as African, Asian, or Latino. The relatively lower degree of genetic variation among European subpopulations, and the length of time for intermingling of European subgroups in Pennsylvania, also serves to minimize population stratification. Such a population straddles genetically homogeneous populations such as Iceland or Finland with strong founder effects, and admixed populations, now present in many urban areas. The MyCode patient recruitment process (Figure 19.3) begins with a search of the EMR for patients scheduled to visit an outpatient or designated specialty clinic in which a consenter will be stationed. Eligible patients are identified through a daily EMR report, while consent status (e.g., consent/did not consent/ not interested) is verified in the patient’s EMR to prevent unnecessary requests for participation at future appointments. Patients are invited to participate in MyCode through direct interaction with a project consenter. After consent, a standing order for the additional MyCode blood draw is placed in

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Use of EMR in MyCode Biorepository Project

Obtain Clinic Schedule from EMR

Generate Eligibility List from EMR

Patient Consent

Questionnaire Data into EMR

Blood Draw Order Placed in EMR

Frozen Storage

Blood Samples Processed

Blood Drawn

EMR Blood Draw Order Read by Phlebotomy

Patient to Phlebotomy

Figure 19.3 Use of the EMR in the MyCode Biorepository Project. The EMR functions as a tool at multiple points in the patient recruitment and consenting processes, and for acquisition of blood samples. The EMR also serves as the repository for phenotype data.

the EMR such that it is appended to the new order for blood work ordered by a physician at a future visit. With this strategy, blood is not specifically drawn for research which has resulted in high patient participation rates, as has been found in other studies (Sanner and Frazier, 2007). The sample collection process is highly efficient because it leverages the EMR (i.e., maintaining a standing order with an automatic protocol to activate the order), the laboratory infrastructure (e.g., standard protocols for phlebotomy, data systems, transportation of blood samples to a central location, etc.), and a passive low cost means of obtaining samples. Blood samples are transported from clinics across the Geisinger coverage region using the existing Geisinger Lab courier service to a central research laboratory that processes the samples for the MyCode biorepository. The EMR serves as the electronic enabler of the entire MyCode process. The approach to creating the MyCode biorepository mitigates several methodological challenges with GWAS, including possible genotype errors, phenotype misclassification, and selection bias. While genotyping platforms are robust, DNA quality can affect genotyping results. Frozen blood and/or DNA are collected using the same protocol and processed using the same methodology. Errors in labeling patient samples are minimal, since an integrated clinical lab quality protocol is used for tracking samples from the point of phlebotomy to freezer storage. Phenotype misclassification is minimized through the use of careful case and control definition. Selection bias is minimized through the selection of cases and controls from the same population base. Example of EMR Data Extraction The EMR is a powerful and convenient source of phenotypic data for GWAS and genomic medicine research. A reference

dataset has been created with a record for each patient in the Geisinger EMR population that includes demographics, physical measurements, personal medical history, health behaviors, laboratory tests, surgical and medical procedures, and medications prescribed. The dataset was developed to reflect the top 80% of elements most likely to be used for standard research purposes. Core data were extracted from the EMR using imbedded routines (known as Clarity), provided by the software vendor. Clarity tables can be manipulated using standard queries in SQL applications. The Clarity tables containing the specific elements in the data dictionary for a particular domain (e.g., physical measurements, or endocrine-related lab) are identified, and those fields are extracted into a text file. Typically, all instances of an element are pulled (e.g., the dataset contains all blood pressure measurements, or all glucose lab for a patient), not just the most recent or the first. The text file is read into SAS/STAT software (SAS Institute Inc., Cary, NC) and mapped to pre-defined fields. The resulting files are documented, cleaned and stored on a dedicated directory controlled by the biostatistics team. Analysis files are created by merging the appropriate data and applying any algorithms necessary for derived values. As an example, this extraction of core data from the EMR can be used to obtain body mass index (BMI) data on over 270,000 adults (Table 19.2). Analyses were limited to adult patients 20 years of age and older. The mean and median BMIs were 28.4 and 27.2, respectively. Slightly over 30% of patients had a BMI 25 while 7.8% had a BMI of 40+. Patients with a BMI of 40+ were three times more likely to be female and more likely to be between 40 and 59 years of age (i.e., with 46.2% versus 29.6%) compared to those with a BMI 25. The relatively smaller proportion above age 60 and the gender bias may suggest a survival effect for this subgroup.

EMRs and Genomic Medicine Research

TABLE 19.2

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Percent of adult patients by BMI and specific phenotypes

Phenotype

BMI Category ≤25 n ⴝ 80,861

>25, <30 n ⴝ 85,626

>30, <40 n ⴝ 83,298

40ⴙ n ⴝ 21,187

Male

31.2

46.2

42.2

27.9

Female

68.8

53.8

57.8

72.1

Age 40

45.4

28.2

25.1

30.2

Age 40–59

29.6

36.5

40.7

46.2

Age 60+

25.0

35.3

34.2

23.6

Diagnosis of HTN

39.5

59.9

72.2

78.5

Diagnosis of LoHDL

18.8

28.2

39.8

47.8

7.8

12.8

21.1

32.1

13.3

23.4

34.4

42.5

Diagnosis of HTN and T2D

6.2

11.1

19.0

29.0

Diagnosis of LoHDL and T2D

5.3

9.0

15.3

23.6

Diagnosis of HTN, LoHDL, and T2D

4.9

8.3

14.4

22.2

35.4

20.2

11.0

6.1

Diagnosis of T2D Diagnosis of HTN and LoHDL

No Diagnosis of HTN, LoHDL, or T2D

The prevalence of Type II diabetes (T2D), hypertension (HTN), and low HDL levels (LoHDL), and combinations of these, for each BMI subgroup was also determined. A patient was defined as having T2D or HTN if the condition was on the EMR Problem List (i.e., ICD-9 linked searchable fields) or if they had two encounters within 24 months where the condition(s) were listed as a reason for visit. HTN was also designated if there was any record of diastolic blood pressure of 90 mm Hg or more, or a systolic blood pressure of 140 mm Hg or more, or if the patient was taking an antihypertensive medication. T2D was designated if any of the following conditions were true: a random blood sugar of 200 or more, a hemoglobin A1C of 7% or higher, or if there was a prescription for insulin or an oral hypoglycemic medication. LoHDL was designated if the patient was male and had an HDL 35 or was female with an HDL 45 mg/dl. In general, prevalence of HTN, LoHDL, and T2D increase substantially in relation to BMI category. A diagnosis of all three conditions was found for 22.2% of those with a BMI of 40+, more than four times higher than the prevalence among those with normal BMI. In contrast, fully 35.4% of individuals with a BMI 25 had one of these conditions (as opposed to only 6.1% of patients with a BMI of 40+). Data Warehouses In addition to EMRs, data warehouses, which refer to the assemblage of diverse data sources into a single point of reference accessible using a generalized global architecture (Louie et al., 2007), are an instrument for genomic medicine research. Data warehouses allow for optimizing performance and maintaining local control and privacy aspects of data handling, all of which

are important aspects for clinical genetics databases (Birch and Friedman, 2004), biobanks, or clinical trials databases. Data warehouses are built to be reliable and relatively robust, and to have fast response times for queries. Performance is a key issue with such databases (Haas et al., 2001), since the volume of data to be built into a data warehouse can provide some formidable challenges for initial construction as well as continued maintenance. Another significant challenge is to develop global data structures that allow for data diversity. Nuances of the specific data sources of data may be lost or homogenized, or analysis structures may become quite complex. At the Geisinger Clinic, a data warehouse project in collaboration with IBM was initiated in 2006, the Clinical Decision Information System (CDIS) project. The goal of this project is to convert all data relevant to clinical practice into an enterprise data warehouse (EDW), to be completed in late 2008. In its first iteration, the EDW will incorporate clinical, financial, administrative, operational, claims, and patient survey data. A total of 40 different source systems including laboratory, pathology, pharmacy, scheduling, billing, cost accounting, claims (inpatient, specialty, pharmacy, and ambulatory), questionnaires, operating room, radiology, and patient safety and care management will feed data to the CDIS, complemented by hundreds of look up tables for input databases (e.g., ICD-9, CPT, etc.). In anticipation of its growing importance, a research module for genomics data is also in development. Data Federations and Multi-System Studies Unlike a data warehouse, a database federation does not incorporate data into a single source but leaves it housed locally. Constituent databases therefore remain under local control but

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are distributed across a network. Such federations are easy to update since each constituent is responsible for its own updates. However, performance can be limited by the query load capacities of individual constituent members of the federation. A data federation has been initiated by the HMO Research Network (HMORN). The network is a consortium of 15 research centers, each affiliated with a non-profit integrated health care delivery system, all of which have or are developing ambulatory care EMR systems. The HMORN is managed by a board of directors, comprised of directors of the respective research centers. In addition to the development of best practices for research administration for multi-site collaborations, the HMORN has initiated efforts to establish a Virtual Data Warehouse (VDW), to simplify data sharing among network participants. Although formally a data federation, the VDW is “virtual” because data are only stored locally within each research center, not in a central warehouse. Linkages across data systems are established through a set of files created to map relevant data elements within each health systems databases to standardized variable definitions, names, and codes. These common VDW standards allow investigators and data analysts at one site to develop SAS programming code to be used by participating

sites and run against a local health system database with minimal site-specific effort. Such VDWs may ultimately play a role in the conduct of GWAS, since phenotypic information from very large sample populations may be obtained. Corresponding biorepository efforts, such as the Geisinger MyCode project, will facilitate the use of VDWs for genomic medicine research.

CONCLUSION The use of the EMR for either genomic medicine practice or research is in its early stages. To fully exploit the power and potential of EMRs for genomic and personalized medicine, efforts such as those described in this chapter will need to be expanded. The continued implementation of EMRs throughout the United States, especially in large health systems, sets the stage for these efforts.

ACKNOWLEDGEMENTS The authors wish to thank the Geisinger Clinic and the Pennsylvania State Department of Community and Economic Development for support of the MyCode project.

REFERENCES ACMB Laboratory Practice Committee Working Group (2000). ACMG recommendations for Standards for interpretation of Sequence Variations. Genet Med 2(5), 302–303. Altman, R.B. (2000). The interactions between clinical informatics and bioinformatics: A case study. J Am Med Informat Assoc 7(5), 439–443. Austin, M.A., Harding, S. et al. (2003a). Genebanks: A comparison of eight proposed international genetic databases. Community Genet 6(1), 37–45. Austin, M.A., Harding, S.E. et al. (2003b). Monitoring ethical, legal, and social issues in developing population genetic databases. Genet Med 5(6), 451–457. Bennett, S.T., Barnes, C. et al. (2005). Toward the 1,000 dollars human genome. Pharmacogenomics 6(4), 373–382. Birch, P. and Friedman, J.M. (2004). Utility and limitations of genetic disease databases in clinical genetics research: A neurofibromatosis 1 database example. Am J Med Genet 125(1), 42–49. Bowcock, A.M., Ruiz-Linares, A. et al. (1994). High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368(6470), 455–457. Cheson, B.D., Bennett, J.M. et al. (2003). Revised Recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol 21(24), 4642–4649. Groen, P., Wine, M. et al. (2005). Genomic Information Systems and Electronic Health Records (EHR). Virtual Medical Worlds Monthly. (October). Guttmacher, A.E., Collins, F.S. et al. (2004). The family history––more important than ever. N Engl J Med 351(22), 2333–2336. Haas, L.M., Schwarz, P.M. et al. (2001). DiscoveryLink: A system for integrated access to life sciences data sources. IBM Syst J 40(2). Hakonarson, H., Gulcher, J.R. et al. (2003). deCODE genetics, Inc. Pharmacogenomics 4(2), 209–215.

Hoffman, M.A. (2007). The genome-enabled electronic medical record. J Biomed Informat 40(1), 44–46. Hopper, J.L., Bishop, D.T. et al. (2005). Population-based family studies in genetic epidemiology. Lancet 366(9494), 1397–1406. Hornbrook, M.C., Hart, G. et al. (2005). Building a virtual cancer research organization. J Natl Cancer Inst (35), 12–25. Hsieh, A. (2004). A nation’s genes for a cure to cancer: Evolving ethical, social and legal issues regarding population genetic databases. Columbia J Law Soc Probl 37(3), 359–411. Hutchison, C.A., III (2007). DNA sequencing: Bench to bedside and beyond. Nucleic Acids Res 35: 6227–6237. Kozma, C.M. (2006). The National Guideline Clearinghouse. Manag Care Interface 19(5). 43, 51. Louie, B., Mork, P. et al. (2007). Data integration and genomic medicine. J Biomed informat 40(1), 5–16. Maojo, V. and Kulikowski, C.A. (2003). Bioinformatics and medical informatics: Collaborations on the road to genomic medicine?. J Am Med Informat Assoc 10(6), 515–522. Martin-Sanchez, F., Iakovidis, I. et al. (2004). Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J Biomedl Informat 37(1), 30–42. McCarty, C.A., Nair, A. et al. (2007). Informed consent and subject motivation to participate in a large, population-based genomics study: The Marshfield Clinic Personalized Medicine Research Project. Community Genet 10(1), 2–9. Mitchell, D.R. and Mitchell, J.A. (2007). Status of clinical gene sequencing data reporting and associated risks for information loss. J Biomed Informat 40(1), 47–54. Mountain, J.L. and Cavalli-Sforza, L.L. (1997). Multilocus genotypes, a tree of individuals, and human evolutionary history. Am J Human Genet 61(3), 705–718. Murray, J., Cuckle, H. et al. (1997). Screening for fragile X syndrome. Health Technol Assess 1(4), 1–71, i-iv.

Recommended Resources

Ogino, S., Gulley, M.L. et al. (2007). Standard mutation nomenclature in molecular diagnostics: Practical and educational challenges. J Mol Diagn 9(1), 1–6. Ollier, W., Sprosen, T. et al. (2005). UK Biobank: From concept to reality. Pharmacogenomics 6(6), 639–646. Orlova, A.O., Dunnagan, M. et al. (2005). Electronic health recordpublic health (EHR-PH) system prototype for interoperability in 21st century healthcare systems. AMIA Annu Symp Proc, 575–579. Parra, E.J., Marcini, A. et al. (1998). Estimating African American admixture proportions by use of population-specific alleles. Am J Hum Genet 63(6), 1839–1851. Rich, E.C., Burke, W. et al. (2004). Reconsidering the family history in primary care. J Gen Intern Med 19(3), 273–280. Samani, N.J., Erdmann, J. et al. (2007). Genomewide association analysis of coronary artery disease. N Engl J Med 357(5), 443–453.

RECOMMENDED RESOURCES James M. Walker (Editor), Eric J. Bieber (Editor), Frank Richards (Editor), Implementing an Electronic Health Record System (Health Informatics) Publisher: Springer 2004. Implementing an Electronic Health Record System addresses the range of issues and opportunities that implementing an electronic health records system (EHR) poses for any size of medical organizationfrom the small one-man operation to a large healthcare system. The book is divided into sections on preparation, support, implementation and a summary and prospects section, enabling the clinician to define the framework necessary to implement and evaluate a clinically effective EHR system. With the increasing involvement of clinicians in the day-to-day running of the practice, interest is now focused on EHR as a key area for improving clinical efficiency. This book uniquely provides the guidance a clinical team needs to plan and execute an effective EHR system within any clinical setting. URL: http://www.hmoresearchnetwork.org/

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Sanner, J.E. and Frazier, L. (2007). Factors that influence characteristics of genetic biobanks. J Nurs Scholarsh 39(1), 25–29. Stein, L.D. (2003). Integrating biological databases. Nat Rev Genet 4(5), 337–345. Stewart, W.F., Shah, N.R. et al. (2007). Bridging the inferential gap: The electronic health record and clinical evidence. Health Aff (Millwood) 26(2), w181–w191. Sujansky, W. (2001). Heterogeneous database integration in biomedicine. J Biomed Informat 34(4), 285–298. Trager, E.H., Khanna, R. et al. (2007). Madeline 2.0 PDE: A new program for local and web-based pedigree drawing. Bioinformatics 23(14), 1854–1856. Wolpert, C.M. (2005). Surgeon General launches new public health campaign: The family history initiative. JAAPA 18(1), 20–22. Ziv, E. and Burchard, E.G. (2003). Human population structure and genetic association studies. Pharmacogenomics 4(4), 431–441.

CHAPTER

20 Clinical Decision Support in Genomic and Personalized Medicine Kensaku Kawamoto and David F. Lobach

INTRODUCTION Genomic medicine, in which individuals’ health is optimized through the use of genomic patient data (Willard et al., 2005), is a critical component of personalized medicine, in which individuals’ health is optimized through the use of all available clinical, molecular, and genetic patient data (Abrahams et al., 2005). Current genomic medicine interventions include the use of a monoclonal antibody to the HER2 receptor to treat HER2-positive breast cancers (Piccart-Gebhart et al., 2005); the use of a gene expression assay to guide breast cancer therapy (Paik et al., 2006); and the genotyping of individuals’ HIV infections to guide antiretroviral therapy (Blum et al., 2005). The potential benefits of genomic medicine include disease prevention, earlier disease detection, more effective disease treatment, and reduced overall health care costs (The Personalized Medicine Coalition, 2006). Given this enormous promise, the Secretary of the US Department of Health and Human Services (HHS) in 2007 identified personalized medicine as one of the top priorities of the Department (United States Department of Health and Human Services Press Office, 2007). This chapter has been written with a particular focus on the US health care system. However, many of the issues and challenges discussed in this chapter are also applicable to the health care systems of other industrialized nations. Despite the tremendous promise of personalized medicine, past experience would predict that new genomic interventions, like any new medical intervention, will remain significantly Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 242

under-utilized for some time without the concurrent introduction of supportive technologies. Patients in the United States only receive about half of recommended care relative to established care standards (McGlynn et al., 2003), and many medical interventions remain significantly under-utilized decades after their efficacy was established in landmark clinical trials (National Committee for Quality Assurance, 2006). Moreover, genomic interventions may face even greater barriers to clinical adoption compared to more traditional medical interventions, due to such factors as limited clinician familiarity with genomics and the volume and complexity of the data and patient management algorithms that need to be considered. In recognizing this challenge, the Secretary of HHS announced in March 2007 that supporting the achievement of personalized medicine through health information technology (IT) is a top priority of his department (United States Department of Health and Human Services Press Office, 2007). As noted by the HHS Secretary, “Personalized health care will combine the basic scientific breakthroughs of the human genome with computer-age ability to exchange and manage data…. Increasingly it will give us the ability to deliver the right treatment to the right patient at the right time – every time” (United States Department of Health and Human Services Press Office, 2007). An important component of HHS’s vision for the role of IT in personalized health care is the use of computer systems to provide clinical decision support (CDS), which entails the delivery of pertinent knowledge and/or person-specific Copyright © 2009, Elsevier Inc. All rights reserved.

CDS Background: History, Examples, Evidence of Effectiveness, and Desirable Attributes

information to clinicians, patients and other health care stakeholders to enhance health and health care (Osheroff et al., 2006; United States Department of Health and Human Services, 2007b). In its draft March 2007 report entitled Realizing the Promise of Pharmacogenomics: Opportunities and Challenges, the HHS Secretary’s Advisory Committee on Genetics, Health, and Society identifies the need for CDS tools as an important challenge to achieving the promise of personalized medicine with regard to pharmacotherapy (United States Department of Health and Human Services Secretary’s Advisory Committee on Genetics, 2007). Given the need for CDS to fulfill the full potential of genomic and personalized medicine, this chapter explores the role that CDS could play in ensuring the consistent and equitable use of genomic technologies to personalize individuals’ health care. In beginning this exploration of the role of CDS in personalized medicine, relevant background information is provided on CDS in general.

CDS BACKGROUND: HISTORY, EXAMPLES, EVIDENCE OF EFFECTIVENESS, AND DESIRABLE ATTRIBUTES Brief History of CDS Research on the use of computers to support clinical decisionmaking began soon after computers became more widely available with the advent of minicomputers in the 1960s and microcomputers in the 1970s. These early CDS systems included a system for providing advice on the management of acid–base disorders (Bleich, 1969); a system for guiding antimicrobial therapy (Shortliffe et al., 1975); and a system that assisted with diagnosing a patient’s medical problem given the patient’s clinical findings (Pople et al., 1975). The first randomized controlled trial demonstrating the ability of a CDS system to improve patient care was reported in the mid-1970s (McDonald, 1976), and since then, numerous randomized controlled trials have demonstrated the ability of CDS systems to significantly improve clinical care for both acute and chronic medical conditions (Kawamoto et al., 2005). Despite mounting evidence of the ability of CDS systems to improve care quality and enhance patient safety, most health care decisions in the United States and elsewhere continue to be made without the assistance of CDS (Osheroff et al., 2006). The only major exception to the limited use of CDS has been with regard to the automated screening of drug–drug interactions and drug-allergy contraindications during the medication ordering process (Osheroff et al., 2006). Many factors have contributed to the limited use of CDS systems in the United States, including a reimbursement model that has traditionally failed to reward the provision of higher quality care (Petersen et al., 2006) and a frequent belief among clinicians that the use of decision aids would reduce the art of medicine to a “cookbook” approach to patient care (Cabana et al., 1999). Another factor restricting the use of CDS systems has been the limited adoption of core health IT

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systems that facilitate the provision of CDS, such as electronic health record (EHR) systems that support the secure storage and efficient use of electronic health information regarding individuals (Jha et al., 2006; Institute of Medicine, 2003). Furthermore, a particularly important barrier to the widespread adoption of CDS systems has been the limited portability of most existing CDS systems and their underlying knowledge bases (Osheroff et al., 2006). This critical barrier to widespread CDS adoption will be the focus of later sections of this chapter. CDS Examples To gain insights into how CDS could support genomic and personalized medicine in the future, it is important to understand how CDS has been applied in the past. Table 20.1 provides an overview of traditional ways in which CDS has been leveraged to support clinical decision-making. In addition, Figures 20.1 and 20.2 provide screenshots from a clinical information system at Duke University Health System that provides a prototypical CDS function – the provision of reminders to a clinician regarding a patient’s overdue health maintenance needs and disease management needs (Lobach et al., 2007b). Also, Figure 20.3 provides a sample patienttailored care reminder letter that is sent to Medicaid beneficiaries residing in North Carolina regarding overdue disease management services. As discussed later in this chapter, these types of CDS interventions could be modified so that the care recommendations provided are optimized using relevant data regarding a patient’s genome and its downstream products. Evidence of CDS Effectiveness A large number of clinical trials have been conducted in both inpatient and outpatient settings to assess the impact of CDS interventions on health care delivery. Approximately twothirds of such trials have resulted in significant improvements in clinical practice (Kawamoto et al., 2005; Garg et al., 2005). Moreover, when CDS interventions possessing four critical attributes were evaluated in randomized controlled trials, 94% of the trials reported significant improvements in clinical practice (see next section for a description of the four critical attributes) (Kawamoto et al., 2005). Thus, CDS represents one of the most promising approaches available for improving care quality and ensuring patient safety. The positive impacts of CDS can include improved disease management, more appropriate pharmacotherapy, and improved adherence to recommended care standards (Garg et al., 2005). For example, when patient-specific care recommendations based on National Cholesterol Education Program (NCEP) treatment guidelines were placed on the charts of patients attending an academic cardiology clinic, patients in the intervention group achieved 93.8% overall compliance with the NCEP guidelines, as compared to 35.2% overall compliance among the control patients (p  0.001) (Stamos et al., 2001). As another example of CDS effectiveness, a time series study conducted at Brigham and Women’s Hospital in Boston identified significant benefits from the provision of CDS within a computerized provider order entry

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TABLE 20.1

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Clinical Decision Support in Genomic Medicine

Traditional CDS types and examples

CDS type

Example

Care reminders to clinicians

When a clinician sees a patient in a clinic, he is provided with a printed encounter form that notes the patient appears to be overdue for a Pap test, a pneumococcal vaccine, and several laboratory tests for monitoring the patient’s diabetes.

Care reminders to patients

A clinic uses its clinical and/or billing systems to identify patients who appear to be overdue for mammograms and sends out care reminder letters.

Alerts

The clinician of a hospitalized patient is paged regarding a critical electrolyte imbalance detected from a recently processed serum chemistry panel. When ordering a cephalosporin antibiotic using a computerized provider order entry (CPOE) system, a clinician is alerted that the patient’s allergy to penicillin may result in an allergic reaction to the cephalosporin.

Order sets

When placing initial admission orders in a CPOE system for a patient with an acute ischemic stroke, the clinician is able to order the hospital’s standardized interventions for stroke patients by selecting the “acute ischemic stroke” order set.

Interactive consultation

A clinician enters a patient’s clinical findings into a web-based tool, which in turn returns a differential diagnosis given the patient’s findings. A clinician uses a web-based tool to obtain guidance on the optimal dose of a drug with a narrow therapeutic window (e.g., warfarin).

Figure 20.1 Sample care reminder module for health maintenance within a clinical information system.

(CPOE) system, which allows a clinician to directly enter medical orders using a computer (Ash et al., 2004). In this study, a CDSenabled CPOE system reduced the incidence of serious medication errors by 86%, with increasing benefits resulting from the incorporation of more advanced CDS capabilities into the system (p  0.0003) (Bates et al., 1999). Desirable CDS Attributes As with any complex intervention, CDS must be provided in an appropriate manner in order to have the desired impact on clinical care. In order to help determine the attributes of CDS systems that are most important to system effectiveness, we recently

Figure 20.2 Sample care reminder module for chronic disease management within a clinical information system.

conducted a systematic review of the CDS literature to (a) identify CDS attributes that were considered to be potentially important to CDS effectiveness by at least three sources and to (b) conduct a meta-analysis to determine whether the presence or absence of these attributes could explain the results of randomized controlled trials of CDS interventions (Kawamoto et al., 2005). The meta-analysis included 70 randomized controlled trials of computer-generated and manually-generated CDS

Potential uses of CDS to Support Genomic and Personalized Medicine

Durham Community Health Network Lincoln Community Health Center – Duke University Medical Center – Durham Country Department of Social Services Durham Country Health Department – Durham Pediatrics – Regional Pediatric Associates – Central Family Practice August 9, 2005 To the parents of We are sending you this letter to address your child’s health care needs. Based on our records, it appears your child may be due for the following services:

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hand, a disease management module that is integrated with a specific electronic health record (EHR) system may have desirable attributes with regard to system effectiveness but may be much more difficult to deploy in a setting that uses a different EHR system or no EHR system at all. To a great extent, the central challenge in designing, developing, and deploying CDS interventions for genomic and personalized medicine will lie in creating systems that are effective yet still widely deployable.

Diabetes services that may be due: Hemoglobin A1c test: This test is recommended every 6 months for patients with diabetes. Cholesterol test: This test is recommended every 12 months for patients with diabetes. Urine protein test: This test is recommended every 12 months for patients with diabetes. Please call our office at (919) 477-2202 to schedule an appointment, so that the doctor can check to see if your child is in need of these services. Also, please bring this letter with you to the appointment and show it to the doctor. We look forward to seeing you soon! Sincerely,

Your Care Team Your Care Team Regional Pediatric Associates A Member of the Durham Community Health Network

Figure 20.3

Sample patient care reminder letter.

interventions. This analysis revealed that four features of CDS systems were significantly associated with these systems’ ability to significantly improve clinical practice: 1. automatic provision of CDS as part of clinician workflow, 2. provision of recommendations rather than just assessments, 3. provision of CDS at the time and location of decisionmaking, and 4. computer-based generation of the CDS. In particular, the automatic provision of CDS as a part of the routine clinician workflow was tightly correlated with study outcomes (p  0.00001). Additionally, CDS systems lacking one or more of these four features failed to improve clinical practice in over half of the studies (n  39), whereas CDS systems with all four features significantly improved clinical practice in 94% of the randomized controlled trials (n  32). Thus, CDS systems should be designed to incorporate these features whenever feasible and appropriate. Beyond features desirable for CDS effectiveness, there are attributes that are desirable for ease of CDS maintenance and deployment. These attributes include centralized knowledge management; the ability to re-use knowledge created for one CDS application for a different CDS application; adherence to relevant health IT standards; low cost of initial deployment and ongoing maintenance; and the ability to easily re-deploy the CDS capability in various clinical settings with heterogeneous IT infrastructures (Kawamoto, 2006). An important insight is that a trade-off often arises between making a CDS system effective and making a CDS system easy to maintain and deploy. For example, a standalone Web application or a personal digital assistant (PDA) tool may be easy to deploy widely, but such systems require clinicians to proactively access the tool and therefore cannot provide CDS automatically as a part of routine clinician workflow. On the other

POTENTIAL USES OF CDS TO SUPPORT GENOMIC AND PERSONALIZED MEDICINE The vast size and enormous complexity that can be associated with genomic data will undoubtedly pose new challenges to existing approaches to delivering CDS. At the same time, the purpose of genomic and personalized medicine – the use of available data to optimize an individual’s health – is fully aligned with the way in which CDS has been leveraged for many decades. Thus, the use of CDS for genomic and personalized medicine should be seen as a natural extension of how CDS has been traditionally used, rather than as a completely novel development. Indeed, from the perspective of clinicians and patients, CDS for “traditional medicine” and “genomic medicine” should be delivered seamlessly and in concert. For example, consider the case of a patient with hypertension for whom pharmacotherapy is about to be initiated. In practice, CDS can facilitate the optimal selection of antihypertensive therapy whether a particular drug class is selected for the patient because the patient has a genetic profile predicting a favorable response to the drug class or whether a particular drug class is selected because the patient has a clinical condition favoring a particular drug class (e.g., type I or type II diabetes mellitus, which is considered a compelling indication for the use of an angiotensin converting enzyme inhibitor or an angiotensin II receptor blocker as the antihypertensive therapy of choice). Regardless of whether the rationale for a clinical recommendation is due to genomic data, non-genomic data, or a combination of the two, a clinician would expect to receive the CDS in a similar manner. Thus, CDS for genomic and personalized medicine should be provided alongside the CDS provided using traditional medical data. CDS supporting genomic and personalized medicine could be delivered in a variety of ways and in a variety of settings. Essentially, CDS on genomic medicine could be provided in any situation in which a clinician, patient, or other health care stakeholder needs to make a clinical decision that could be optimized through the use of genomic data. Table 20.2 provides examples of how traditional CDS mechanisms could be adapted to support genomic medicine. Given the current prominence of personalized cancer management and pharmacogenomics in clinical genomics, the examples in the table place particular emphasis on these aspects of genomic and personalized medicine.

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TABLE 20.2

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Traditional CDS types and potential applications to genomic and personalized medicine

CDS type

Example application to genomic and personalized medicine

Care reminders to clinicians

When a clinician sees a patient in a clinic, she is provided with a printed encounter form that notes the patient appears to be overdue for a mammogram and a pneumococcal vaccine. The Pap test is recommended more frequently for this patient compared to the general population because the patient has a family history of cervical cancer.

Care reminders to patients

A clinic identifies patients who appear to be overdue for mammograms and sends out care reminder letters. A patient’s family history, BRCA1/2 status, and other genetic predictors are considered when determining the screening frequency for the patient.

Alerts

When ordering a thiazide diuretic for a patient with hypertension using a CPOE system, a clinician is alerted that the patient’s genetic profile is associated with significant adverse reactions to the drug class. An angiotensin converting enzyme inhibitor is recommended as the optimal therapy given the patient’s genetic profile. When an oncologist orders a chemotherapy regimen for a patient with lung cancer using a CPOE system, the system alerts the clinician that the patient should be placed on a more aggressive regimen given the poor prognosis associated with the gene expression profile of the patient’s tumor.

Order sets

When placing initial admission orders in a CPOE system for a patient with an acute ischemic stroke, the clinician is able to use the hospital’s standardized order set for patients with the condition. The CPOE system automatically adjusts the recommended orders based on the patient’s genetic profile, which was on file from a previous visit. For example, drug doses are adjusted based on the patient’s age, gender, serum creatinine level, and genotype with respect to drug metabolizing enzymes.

Interactive consultation

An oncologist uses a web-based tool provided by the National Cancer Institute to identify whether any genomic tests should be ordered to determine the best therapeutic regimen for a patient with cancer.

LIMITED DEPLOYABILITY: THE POTENTIAL ACHILLES’ HEEL OF CDS SYSTEMS FOR GENOMIC MEDICINE Given the rapid advances in the scientific community’s understanding of how our health is impacted by our genes and their downstream products, it will likely only be a matter of time until there is a large body of knowledge on how the management of various conditions can be optimized through the use of genomic data. This knowledge will bring with it an increasing need for CDS to support the use of genomic data in clinical practice. Currently, there are only a small number of reports in the literature describing the use of a CDS tool to support decision-making based on genetic data, and these tools focus almost exclusively on the prediction of cancer risk based on family history (Bianchi et al., 2007; Emery et al., 2000; Wilson et al., 2006). In the near future, however, we anticipate that there will be an increasing number of reports regarding CDS tools for genomic medicine and the impact of these tools as determined through clinical trials. As has been the case with CDS systems in support of traditional medical interventions, we anticipate that high-quality clinical trials will soon demonstrate that significant clinical benefits

can be achieved through the use of CDS systems to support genomic medicine. For example, we anticipate clinical trials will demonstrate that the use of CDS systems can improve clinicians’ appropriate use and interpretation of various genomic tests, including tests that characterize patients’ drug metabolizing enzymes (see Chapter 27) and tests that measure gene expression levels in tumor specimens (see Chapter 13). Such trials will be an important step toward fulfilling the promise of genomic and personalized medicine, as they will provide valuable insights into how information regarding genomic interventions can be effectively delivered to clinicians and patients. However, a critical question that must be asked of such trials is whether the CDS intervention of interest can be widely replicated in other settings for operational use. As discussed earlier in this chapter, an important limitation of many effective CDS systems is that they are difficult to deploy in settings that differ from the original deployment setting. Unless this issue is explicitly considered and addressed, it is likely that many CDS interventions for genomic medicine will also have limited impact due to the difficulty of re-deploying the interventions in other settings. Thus, the remainder of this chapter will be devoted to exploring challenges to the widespread deployment of effective CDS systems and potential approaches to overcoming these challenges for genomic and personalized medicine.

Challenges to Widespread Deployment of Effective CDS Systems

CHALLENGES TO WIDESPREAD DEPLOYMENT OF EFFECTIVE CDS SYSTEMS The widespread deployment of effective CDS systems is hindered by several important challenges. This section describes four of the most important challenges that exist in the United States: 1. the limited adoption of EHR systems; 2. significant variability in how clinical and genomic data are represented in the various health IT systems that are used across the country; 3. the high cost of developing and maintaining medical knowledge that has been encoded in a machine-executable format suitable for CDS; and 4. the limited reusability and portability of the knowledge in many CDS systems. Challenge # 1: Limited adoption of EHR systems. EHR systems facilitate the delivery of CDS by capturing the coded patient data required for CDS and by providing a natural user interface for making CDS available to clinicians. Thus, the limited adoption of EHR systems in the United States hinders efforts at widely providing CDS. Currently, it is estimated that only about 25% of US physicians use EHR systems in the outpatient setting (Jha et al., 2006). Challenge # 2: Significant variability in how patient data are represented in health IT systems. In the United States, a large variety of commercial and locally developed health IT systems are in use. These systems can vary widely in how they represent both clinical and genomic patient data. For example, health IT systems oftentimes use idiosyncratic codes and terminologies to identify medical concepts (e.g., diagnoses, medications, and laboratory tests). Moreover, the manner in which relevant clinical information is modeled (i.e., represented) can differ in important ways between systems. For example, one EHR system may record the results of a Pap test as a narrative text, while another system may record the results using both a narrative text and structured data (e.g., whether the result was abnormal and the nature of the abnormality). As another example, different EHR systems may store the results of genetic tests as structured data or as narrative text (Scheuner, 2007). This variability makes it difficult to take a CDS module that was originally designed to work with one EHR system and re-leverage that module in a different EHR system. Challenge # 3: High cost of developing and maintaining machineexecutable medical knowledge. In order to support CDS, medical knowledge must be encoded in a machine-executable format that can be used to interpret structured patient data and generate patient-specific care recommendations. This knowledge encoding process can become quite expensive due to a number of factors, including the need for highly skilled and expensive personnel who are knowledgeable in both clinical medicine and information technology; the need to precisely define concepts that are oftentimes left ambiguous in care guidelines intended

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for human consumption; and the need to rigorously test the knowledge to ensure the accuracy of the encoded algorithms. The cost of encoding the knowledge goes up with the complexity of the underlying clinical algorithm, and as such, the complex algorithms associated with genomic medicine will require significant resources to encode in a format suitable for use in CDS systems. Challenge # 4: Limited reusability and portability of knowledge in many CDS systems. Given the high cost of knowledge engineering, it is critical that machine-executable medical knowledge created for one CDS application be re-used across institutions and across different types of CDS applications. However, in many cases, machine-executable medical knowledge that is used to drive a CDS application at one institution cannot be easily re-used at a different institution or in a different type of CDS application. Common reasons for the difficulty of re-using executable medical knowledge include the embedding of the knowledge within a specific CDS system’s application code; a dependence of the knowledge on how patient data are represented and stored in a specific clinical information system; and the lack of a widely accepted standard formalism for representing machine-executable medical knowledge.This difficulty of re-using machine-executable medical knowledge is a major factor in the limited availability of CDS capabilities in clinical practice. Therefore, it is important to work toward CDS knowledge resources that can be decoupled from the applications that they support and, thus, can be ported to other environments. Strategies for Addressing Challenges to Widespread Deployment of CDS Systems Table 20.3 outlines strategies for addressing the challenges to the widespread deployment of CDS systems that were just described. Many of these strategies are part of a standards-based software architecture that we have proposed for enabling CDS on a national scale and for fulfilling the strategic objectives of the Roadmap for National Action on CDS commissioned by the US Department of Health and Human Services’ Office of the National Coordinator for Health IT (Kawamoto and Lobach, 2007). Strategies for Addressing Limited Adoption of EHR Systems With regard to the limited adoption of EHR systems, an important strategy for addressing this challenge is encouraging and facilitating the adoption of EHR systems. In the United States, both the government and the private sector have been taking steps to encourage the adoption of EHR systems, due in no small part to the anticipated benefits of CDS delivered in the context of EHR systems. For example, the federal government has eased regulations that had previously forbidden hospitals from financially supporting the acquisition of EHR systems by referring physicians (Egan, 2006), and health insurers have financially supported the adoption of EHR systems among physician practices (Havenstein, 2007). Despite these efforts, however, the

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T A B L E 2 0 . 3 Strategies for addressing challenges to widespread deployment of effective CDS systems Challenge Limited adoption of EHR systems

Strategy for addressing challenge Increase adoption of EHR systems Design CDS systems that do not require EHR systems

Significant variability in how patient data are represented in health IT systems

Standardize the representation of clinical and genomic data in IT systems Allow relevant clinical and genomic data to be located and retrieved using a standard software interface Encourage adoption of relevant standards

High cost of developing and maintaining machineexecutable medical knowledge

Create and manage machine-executable medical knowledge in a centralized manner

Limited re-use and portability of CDS knowledge

Allow machine-executable medical knowledge to be accessed using a standard software interface

reality remains that a large majority of physicians in the United States continue to practice without an EHR system. Given this reality, another strategy to address the limited adoption of EHR systems is to design and deploy CDS systems that do not depend on the availability of an EHR system. In particular, all public and private health insurance plans in the United States require a standard set of data from health care providers for the purposes of reimbursement. These claims data include standardized data on a patient’s diagnoses, prescription medications that were dispensed, and the procedures that were conducted (including the identity of laboratory tests, but not test results). While claims data collected for the purposes of reimbursement are not as detailed and accurate as clinical data collected for the purposes of patient care in an EHR system, claims data can be used to drive a number of effective CDS interventions, even in clinical settings that make minimal or no use of health IT systems (Javitt et al., 2005). With regard to genomic medicine, claims data could be combined with genomic test results to deliver CDS on a large scale, as claims data are typically managed by a relatively small number of large health insurers, and as a significant portion of the laboratory test market is controlled by a small number of large commercial laboratories (Fuhrmann, 2006). For example, if a major insurer could establish a data sharing agreement with the major clinical laboratories servicing its covered individuals, then the insurer would be capable of identifying patients for whom genetic tests are potentially indicated or for whom the results of genetic tests

indicate that a certain course of therapy should be taken. These genomic care recommendations could then be communicated to clinicians and/or patients to guide their decision-making. Strategies for Addressing Variability in Patient Data Representation in Health IT Systems The CDS scenario just described, as well as any widely replicable model for CDS, relies on the ability to consistently access patient data in a standardized format. Thus, an important strategy for addressing the significant variability that exists with regard to patient data is to standardize how clinical and genomic data are represented and communicated across various health IT systems. Standardization of such data include (i) standardization of the information models used to represent concepts important to clinical care (e.g., problems, procedures, test results, medications, DNA sequences, and gene expression assay results); (ii) standardization of the terminologies used to identify concepts in the information models; and (iii) standardization of how such data are communicated between systems (e.g., between an EHR system and a CDS system). Relevant standards have been developed by standards development organizations (SDOs) for all of these aspects of standardization (Hammond, 2005). In particular, standards in all three categories have been developed by Health Level 7 (HL7), a not-for-profit, volunteer-driven SDO which is the most prominent SDO in the health care sector internationally. HL7 standards include standards for the representation of traditional clinical data as well as clinically relevant genomic data (Shabo, 2006). These standards make use of standard clinical terminologies, including the World Health Organization’s International Classification of Diseases (ICD) for medical conditions; the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) for a variety of clinical concepts; the Logical Observation Identifiers, Names, and Codes (LOINC) for laboratory tests; RxNorm for medications; the microarray and gene expression markup language (MAGE-ML) for gene expression data; and the bioinformatic sequence markup language (BSML) for sequencing data (Hammond, 2005; Shabo, 2006). Furthermore, an HL7 specification known as the HL7 Retrieve, Locate, and Update Service standard provides a common approach for locating and retrieving patient data – including genomic data – using a standard software interface (Health Level 7, 2006b). As noted above, many of the standards needed for interoperable, standards-based exchange of clinical and genomic data do exist. Thus, the problem with data heterogeneity in the current health care industry lies less with the lack of appropriate standards and more with the lack of widespread and effective use of the available standards (Hammond, 2005). In recognition of this problem, there are currently significant efforts underway to specify how existing standards should be used to meet important clinical needs (e.g., the communication of laboratory data) and to encourage the use of existing standards in the specified manner (e.g., through certification). An initiative prominent in this regard is Integrating the Healthcare Enterprise (IHE), which was initiated in 1998 as a joint effort between the Radiological Society of North America and the Healthcare Information and Management Systems Society (Siegel and Channin, 2001).

References

Another prominent organization in this space is the Healthcare Information Technology Standards Panel (HITSP), which is a public-private effort initiated in 2005 by the US Department of Health and Human Services to identify and harmonize data and technical standards for health care (United States Department of Health and Human Services, 2007a). Strategies for Addressing High Cost of Developing and Maintaining Machine-Executable Medical Knowledge This challenge can be addressed by centralizing the knowledge authoring and management process so that the translation of medical knowledge into a machine-executable format suitable for CDS occurs once rather than being duplicated unnecessarily across a large number of institutions. For example, leading academic institutions, professional medical associations, or governmental agencies could create and maintain machine-executable medical knowledge on behalf of a large group of health care organizations. Then, as the second step, these centrally managed knowledge resources could be made accessible to health care organizations and vendors of health IT systems through a standard mechanism. Strategies for such standardized sharing of CDS knowledge are discussed next. Strategies for Addressing the Limited Reusability and Portability of Knowledge in CDS Systems One approach for CDS knowledge sharing that has been attempted in the past involves standardizing how machineexecutable medical knowledge is represented. While a formalism for knowledge representation known as the Arden Syntax (Pryor and Hripcsak, 1993) has been adopted as an HL7 standard, the use of this approach has been limited by the fact that it has been difficult to obtain industry-wide consensus on a single best way to represent machine-executable medical knowledge. An alternate approach to accommodating the reuse and portability of machine-executable medical knowledge is to expose the decision-making capabilities of the knowledge through a standard software interface. This approach does not place a restriction on how the knowledge is represented, as what is shared is not the knowledge itself but the decisionmaking capabilities of that knowledge. In this approach, knowledge resources are hosted by a CDS software service which takes patient data as the input and returns patient-specific inferences as the output. For example, a CDS service may take in as input a patient’s cytochrome P450 genotype and drug name and return the patient’s P450 metabolism status for the specified drug. Using this service-based approach to CDS, our research group has leveraged a centrally managed knowledge base to implement a number of operational CDS applications, including an enterprise-wide disease management system within the

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Duke University Health System (Figures 20.1 and 20.2) (Lobach et al., 2007b) and a population health management system for North Carolina Medicaid beneficiaries that provides alerts to care providers, feedback reports to clinics, and care reminder letters to patients (Figure 20.3) (Lobach et al., 2007a). The Web-based CDS service at the core of these systems is known as SEBASTIAN (Kawamoto and Lobach, 2005), and it is the basis of an HL7 standard for CDS services known as the HL7 Decision Support Service standard (Health Level 7, 2006a). Together with a centralized approach to knowledge management, the HL7 Decision Support Service standard could allow genomic medicine algorithms to be widely leveraged by various CDS applications in a plug-and-play manner.

CONCLUSIONS Genomic and personalized medicine has the potential to transform the delivery of health care, making clinical medicine increasingly a science rather than an art. Effective CDS systems will be needed to make genomic and personalized medicine a routine aspect of clinical care, but a deliberate and concerted effort will be required to ensure that CDS interventions for genomic medicine can be deployed in a widespread manner. In particular, efforts at enabling the widespread practice of genomic medicine through CDS should (i) encourage the adoption of EHR systems while minimizing the IT infrastructure required for a CDS system to be used; (ii) be standards-based; (iii) support the centralized management of machine-executable genomic medicine algorithms; and (iv) enable these algorithms to be leveraged by a large number of health care organizations through a standard software interface. While significant challenges do exist in the road to a health care system in which genomic and personalized medicine is routinely practiced through the support of CDS, we believe the strategies outlined in this chapter will provide a solid foundation for tackling these challenges in the years to come.

DISCLOSURES The authors are seeking to establish a commercial implementation of the HL7 Decision Support Service described in this chapter using the SEBASTIAN CDS technology. The authors hold intellectual property rights to SEBASTIAN (patent pending). The authors and Duke University may benefit financially if products utilizing the SEBASTIAN technology are commercially successful. With regard to the recommendations made in this chapter, the SEBASTIAN system represents just one of potentially many approaches for instantiating an HL7 Decision Support Service.

REFERENCES Abrahams, E., Ginsburg, G.S. and Silver, M. (2005). The personalized medicine coalition: Goals and strategies. Am J Pharmacogenomics 5, 345–355.

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Kawamoto, K. and Lobach, D.F. (2005). Design, implementation, use, and preliminary evaluation of SEBASTIAN, a standardsbased Web service for clinical decision support. Proc AMIA Symp, 380–384. Kawamoto, K. and Lobach, D.F. (2007). Proposal for fulfilling strategic objectives of the US roadmap for national on decision support through a service-oriented architecture leveraging HL7 services. J Am Med Inform Assoc 14, 146–155. Kawamoto, K., Houlihan, C.A., Balas, E.A. and Lobach, D.F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ 330, 765–768. Lobach, D.F., Kawamoto, K., Anstrom, K.J., Kooy, K.R., Eisenstein, E.L., Silvey, G.M., Willis, J.M., Johnson, F. and Simo, J. (2007a). Proactive population health management in the context of a regional health information exchange using standards-based decision support. Proc AMIA Symp 473–477. Lobach, D.F., Kawamoto, K., Anstrom, K.J., Russel, M.L., Woods, P. and Smith, D. (2007b). Development, deployment and usability of a point-of-care decision support system for chronic disease management using the recently-approved HL7 Decision Support Service standard. Medinfo 861–865. McDonald, C.J. (1976). Protocol-based computer reminders, the quality of care and the non-perfectability of man. N Engl J Med 295, 1351–1355. McGlynn, E.A., Asch, S.M., Adams, J., Keesey, J., Hicks, J., DeCristofaro, A. and Kerr, E.A. (2003). The quality of health care delivered to adults in the United States. N Engl J Med 348, 2635–2645. National Committee for Quality Assurance. (2006). The state of health care quality 2006. http://www.ncqa.org/tabid/136/Default.aspx. Osheroff, J.A., Teich, J.M., Middleton, B., Steen, E.B., Wright, A., and Detmer, D.E. (2006). A roadmap for national action on clinical decision support. American Medical Informatics Association, Bethesda, MD. http://www.amia.org/inside/initiatives/cds/ cdsroadmap.pdf. Paik, S., Tang, G., Shak, S., Kim, C., Baker, J., Kim, W., Cronin, M., Baehner, F.L., Watson, D., Bryant, J. et al. (2006). Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24, 3726–3734. Petersen, L.A., Woodard, L.D., Urech, T., Daw, C. and Sookanan, S. (2006). Does pay-for-performance improve the quality of health care?. Ann Intern Med 145, 265–272. Piccart-Gebhart, M.J., Procter, M., Leyland-Jones, B., Goldhirsch, A., Untch, M., Smith, I., Gianni, L., Baselga, J., Bell, R., Jackisch, C. et al. (2005). Trastuzumab after adjuvant chemotherapy in HER2positive breast cancer. N Engl J Med 353, 1659–1672. Pople, H.E., Myers, J.D. and Miller, R.A. (1975). DIALOG: A model of diagnostic logic for internal medicine. Proceedings of the Fourth International Joint Conference on Artificial Intelligence., 848–855. Pryor, T.A. and Hripcsak, G. (1993). The Arden syntax for medical logic modules. Int J Clin Monit Comput 10, 215–224. Scheuner, M.T. (2007). Family history and genetic tests in electronic health records. http://www.hhs.gov/healthit/ahic/materials/03_ 07/phc/scheuner.ppt. Shabo, A. (2006). Clinical genomics data standards for pharmacogenetics and pharmacogenomics. Pharmacogenomics 7, 247–253. Shortliffe, E.H., Davis, R., Axline, S.G., Buchanan, B.G., Green, C.C. and Cohen, S.N. (1975). Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res 8, 303–320.

Recommended Resources

Siegel, E.L. and Channin, D.S. (2001). Integrating the Healthcare Enterprise: A primer. Part 1. Introduction. Radiographics 21, 1339–1341. Stamos, T.D., Shaltoni, H., Girard, S.A., Parrillo, J.E. and Calvin, J.E. (2001). Effectiveness of chart prompts to improve physician compliance with the National Cholesterol Education Program guidelines. Am J Cardiol 88, 1420–1423. The Personalized Medicine Coalition. (2006). The case for personalized medicine. http://www.personalizedmedicinecoalition.org/communications/TheCaseforPersonalizedMedicine_11_13.pdf. United States Department of Health and Human Services. (2007a). Data and technical standards: Health Information Technology Standards Panel (HITSP). http://www.hhs.gov/healthit/standards/activities/. United States Department of Health and Human Services. (2007b). Goals of personalized healthcare initiative. http://www.hhs.gov/ myhealthcare/goals/index.html. United States Department of Health and Human Services Press Office. (2007). HHS Secretary Leavitt announces steps toward a future

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of “personalized health care”. http://www.hhs.gov/news/press/ 2007pres/20070323a.html. United States Department of Health and Human Services Secretary’s Advisory Committee on Genetics, Health and Society. (2007). Realizing the promise of pharmacogenomics: opportunities and challenges (draft report). http://www4.od.nih.gov/oba/ SACGHS/SACGHS_PGx_PCdraft.pdf. Willard, H.F., Angrist, M. and Ginsburg, G.S. (2005). Genomic medicine: Genetic variation and its impact on the future of health care. Philos Trans R Soc Lond B Biol Sci 360, 1543–1550. Wilson, B.J., Torrance, N., Mollison, J., Watson, M.S., Douglas, A., Miedzybrodzka, Z., Gordon, R., Wordsworth, S., Campbell, M., Haites, N. et al. (2006). Cluster randomized trial of a multifaceted primary care decision-support intervention for inherited breast cancer risk. Fam Pract 23, 537–544.

RECOMMENDED RESOURCES Health Level 7. (2007). Technical committees and special interest groups. http://www.hl7.org/Special/committees. This Web page provides links to committees within the HL7 standards development organization, which is playing an active role in standardization efforts related to genomic medicine and the use of CDS to support this approach to health care. Committees of particular interest may include the HL7 Clinical Genomics Special Interest Group and the HL7 CDS Technical Committee. Each committee’s Web site includes a link to register for the committee’s list service. Kawamoto, K. and Lobach, D.F. (2007). Proposal for fulfilling strategic objectives of the US roadmap for national action on decision support through a service-oriented architecture leveraging

HL7 services. J Am Med Inform Assoc 14, 146–155. This article outlines a standards-based approach to CDS that could form the basis of a national strategy for enabling the widespread deployment of effective CDS systems. The recommendations made in this chapter draw from the approach proposed in this article. Greenes, R.A. (2007). Clinical Decision Support: the Road Ahead. Elsevier, Inc., USA.This book provides a comprehensive overview of CDS, including lessons learned from past implementations and state-ofthe-art approaches to managing CDS systems and their underlying knowledge bases. This book is recommended for the reader who wishes to obtain an in-depth understanding of CDS from a single authoritative source.

CHAPTER

21 Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics Mark S. Boguski

INTRODUCTION In an age of personalized medicine, nothing represents the zeitgeist more than individual consumers using the Internet and World Wide Web to seek medical and health information. According to surveys by the Jupiter organization and Harris Interactive, 71% of people who use the Internet also used it to seek health information in 2007, and this percentage, which represents an estimated 160 million people in the United States, had increased by 37% since 2005 (Anonymous, 2007b; Levy, 2007). Consumers perform health information search (HIS) and retrieval (HIR) for themselves as well as for friends and family. Studies have shown that most of these consumers do not later discuss the information with a health-care provider and that, for many people, the Internet may be the primary or even sole source of health information. (Fox and Fallows, 2003). Seventy percent of people who obtain health information online say that it has influenced a decision about their treatment. Clearly, it is important for health care professionals to understand how their patients find health information and the pitfalls associated with this activity. Indeed, given the challenges that consumers face in obtaining quality health care information from Internet sources, health-care providers will increasingly be in a position where they have to act as reviewers of information Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 252

and as educators of patients who bring this information to their attention.

CHARACTERISTICS OF CONSUMER SEARCHES FOR HEALTH INFORMATION A 2007 study by the Pew Internet & American Life Project (Fox, 2006) showed that the top 10 reasons American adults search online for health information are: ● ● ● ● ● ● ● ● ● ●

a specific disease or medical condition; a particular medical treatment or procedure; diet, nutrition and supplements; exercise or fitness; prescription and non-prescription drugs; a particular doctor or hospital; alternative medicine or treatments; health insurance; mental health issues and environmental health hazards.

Zeng and Tse (Zeng and Tse, 2006) reported that, in performing searches, consumers use query terms that consist of Copyright © 2009, Mark S. Boguski All rights reserved.

What and Where are Consumers Searching?

“every-day language, technical terms (with or without knowledge of the underlying concepts) and various explanatory models, all influenced by psychosocial and cultural variations...” According to Lorence and Spink (Lorence and Spink, 2004), lay terminology is only partly effective in retrieving useful health information and often produces irrelevant or misleading information because, as reported by Zeng et al. (Zeng et al., 2006), “… the terms and concepts used by consumers often do not accurately reflect their information needs and therefore do not constitute effective queries.” Problems with such consumer health vocabularies may occur at various levels (Zeng et al., 2002): ● ●

●

Lexical (form-level) mismatches (e.g., misspellings) Semantic misunderstanding (e.g., incomplete or misinterpretation of abbreviations or acronyms, over-generalization, redundancy) Misleading mental models, conceptual models that consumers employ either incorrectly and/or in a manner different than health care professionals.

Form-level (lexical) mismatches need no further explanation, as examples are easy to imagine. In the case of semantic mismatches, searches with general search engines may yield a large amount of irrelevant information. Consider the abbreviation “MS”, which a consumer might use to mean multiple sclerosis but could also be interpreted to mean Microsoft, mass spectrometry or the state of Mississippi (Romacker et al., 2006). In another example, “CHF” would be interpreted as congestive heart failure by a physician whereas an international banker would interpret CHF as a unit of currency, the Swiss franc. Other groups (Tse and Soergel, 2003) have similarly characterized problems with consumer health vocabularies on multiple levels, namely: ●

●

●

Shared forms/different concepts (e.g., the phrase “the results of the [diagnostic] test were negative” could mean “unfavorable” in the mind of a patient but is usually a positive step forward in the mind of a physician.) Different forms/shared concepts (e.g., “blood cancer” is a lay term that a physician might refer to as a “hematologic malignancy.” Likewise when a physician refers to “metastatic disease”, the patient would say “the cancer has spread.”) Different forms/different concepts (e.g., “Miracle cure” rather than a “statistically unlikely” or “idiopathic” remission of a disease, emergence from coma, etc.).

The most challenging HIS/HIR problems of all undoubtedly arise at the conceptual level because it is intimately tied to an individual’s level of health literacy (McCray, 2005). Conceptlevel mismatches might include notions outside the framework of mainstream medicine such as homeopathy, acupuncture and other alternative medicine concepts. Another example is when a patient may focus on a body part (anatomical location) or symptom when a clinician might view the same problem in terms of pathophysiology or from a sub-specialty perspective. Even mainstream medical concepts are subject to significant cultural influences (Payer, 1996).

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There have been a number of attempts and exploratory studies to map lay terminology to controlled vocabularies in a number of formal coding systems and comprehensive collections of medical terms such as CTP, SNOMED, MeSH, ICD-9 and the UMLS Metathesaurus (Shortliffe et al., 2003). Furthermore, ontologies have been employed to perform semantic expansion on queries that may be too general or simplistic (see (Spasic et al., 2005) and below). The idea is to take queries consisting of consumer health terms and to translate or reformulate them into professional terms and qualifiers with the aim of improving query meaning, precision and recall. So far such experiments have met with only minor or mixed success (Plovnick and Zeng, 2004; Zeng et al., 2006). There is also the challenge of “back translating” from professional to consumer language the results of a search using a reverse medical dictionary. (See also Chapter 17.) Two additional reasons for suboptimal performance of consumer HIS/HIR is short query length and low complexity. Consumer HIS/HIR queries tend to be very short and too general to be effective. Indeed one study found that 63% of consumer queries consisted of only a single word and only 10% contained more than two words (Zeng et al., 2002). Also, consumers are generally unaware of stop words that add little or nothing to search performance. Thus, limited knowledge of both medical vocabulary and query string search principles contributes to the construction of simplistic, ineffective queries. Another important aspect is the inability or unwillingness to construct complex queries (such as Boolean combinations of terms) even when such “advanced search” options are provided via simple check boxes. Users seem to prefer (or at least have become accustomed to) search and retrieval as an iterative process during which their goals are refined, focused or revised again and again (Fredin and Prabu, 1998) until either a satisfactory answer is obtained or they abandon the effort altogether due to frustration or fatigue. A precise, unambiguous and well-formed query would ideally produce the answer(s) one is looking for in the first iteration. This ideal assumes that the information sources containing the information exist and are accessible.

WHAT AND WHERE ARE CONSUMERS SEARCHING? Equally important to how searches are formulated by consumers are the sources of the information that are being searched. As an editorial in the British Medical Journal put it: “…the internet has vastly increased the availability of information, but often what it offers is untailored, incomplete, irrelevant, and plain wrong” (Jones, 2003) although actual cases of harm have been difficult to document (Crocco et al., 2002). Online health information is problematic as it encompasses everything from evidencebased, peer-reviewed content in professional journals to advertiser-sponsored content and personal testimonials (Table 21.1). A consumer’s online HIS/HIR experience may even be greatly influenced by non-substantive characteristics of a site such as an attractive and professional-looking appearance even though the quality of the underlying information is unknown.

254

CHAPTER 21

TABLE 21.1

■

Online Health Information Retrieval

Comparisons of Selected Online Health Information Sites Libraries, Publishers

e-Magazines

Search portals

Social Networks

Content

Curated, Aggregated

Commissioned, Aggregated

Distributed

User-generated

Audience

Healthcare Professionals

General public

General public

General public

Stage

Pre-Internet

Dot-com era

Beta

Pre-alpha or alpha

Revenue

Gov’t-sponsored, Subscription, Advertising

Gov’t-sponsored, Advertising, Services portal

Advertising

Advertising Services?

Examples

National Library of Medicine http://www.nlm.nih.gov/ Reed-Elsevier http://www.elsevier.com/wps/ find/homepage.cws_home Macmillan/Nature http://us.macmillan.com/ splash/publishers/nature.html Thompson http://www.thompson. com/public/library. jsp?catHEALTHCARE

National Institutes of Health http://health.nih.gov/ Healthline http://www.healthline.com/ WebMD http://www.webmd.com/ default.htm RevolutionHealth http://www. revolutionhealth.com/

Healia http://www. healia.com/healia/ Mamma http://www. mammahealth.com/ Medstory http://www. medstory.com/ Kosmix http://www. kosmix.com/health

TauMed http://www. taumed.com/ Trusera http://www.trusera. com/corp

Sixty-six percent of consumer HIS/HIR begin with a general search engine such as Google or Yahoo! (Fox, 2006), and the rest will start with one of a number of emerging or established specialty sites that are surveyed in Table 21.1. These sites fall into four general categories (online libraries, e-magazines, search and social networking) that largely reflect their historical origins and/ or stages of development. Although a complete description of the origins and development of these sites is beyond the scope of this chapter, one pioneering organization deserves special mention. Online medical libraries date back to mid-1960s, when the US National Library of Medicine (NLM) experimented with teletypewriter machines and satellite communications (Schoolman and Lindberg, 1988). Searches at this time were not interactive but rather conducted by “batch processing.” In the early 1970s, interactive searches of NLM’s MEDLINE became possible through dedicated remote terminals in 10 regional and 14 large academic medical libraries (Atlas, 2000). During this era, users of these systems were trained librarians who learned specific query commands and retrieval protocols. It was not until after the advent of personal computers that online search of the medical literature became accessible to consumers. In 1986 a computer application named “Grateful Med,” that ran on IBM-compatible DOS computers was produced by NLM, was distributed at a cost of $9.95 through the National Technical Information Service of the US Department of Commerce (Schoolman and Lindberg, 1988). Using Grateful Med and a 300 bits-per-second modem, users could use dial-up telephone lines to NLM computers and receive literature citations. The current descendents of these pioneering efforts are PubMed (Wheeler et al., 2007) for the professional literature and MedlinePlus (Miller et al., 2004) for consumer information. The Dot-com era from 1995 to 2001 (2007) was characterized by experimentation with new sites and services, including

commercial entities supported by sponsored advertising. Compared with online “libraries,” which maintained content and user interfaces more oriented toward health care professions than consumers, Dot-com era sites introduced magazine-type articles and layouts focused on non-professional users, enabled by the development of web browsers such as NCSA Mosaic (later named Netscape Navigator (2007)). Somewhat prior to this, sites providing comprehensive (“horizontal”) Internet search were mainly being used by academic researchers and computer professionals (e.g., (Boguski and Ouellette, 1995)). But gradually, at first, search engines of the time (e.g., AltaVista (2007)) began to index content that expanded into the consumer realm. The ascendance of horizontal Internet search by companies like Yahoo! (2007) and Google (2007) has recently led to the emergence of several startup companies applying a similar approach to deep vertical (specialized) health care content on the web (Table 21.1). The latest experimentation with consumer-oriented, medically-oriented websites involves social-networking, following the example of more horizontal (although demographically differentiated) socialnetworking communities and sites (e.g., MySpace (2007) and Facebook (2007)). In July 14, 2007 article in the Wall Street Journal, Borzo reported running a variety of searches on several vertical health sites and noted that many features were not self-evident and required repeated experimentation to uncover. Borzo’s overall conclusion from this limited, non-scientific study was that conducting a useful search requires the consumer to run multiple queries on several sites and to then compare the results and reach their own consensus on the adequacy and usefulness of the retrieved information. These observations are consistent with more formal research (Fox, 2006). Borzo did not examine, however, the underlying sourcing and quality of the information.When one examines

Personalized Genomics for Consumers

TABLE 21.2 Search Sites

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255

Survey* of Content of Selected “Vertical” Health Information

Site

Primary content

Healia www.healia.com/healia/

www.pubmed.gov/ www.clinicaltrials.gov/

Mamma www.mammahealth.com/

www.emedicinehealth.com/ www.healthatoz.com/ www.mayoclinic.com/ www.medem.com/ www.medicinenet.net/ medlineplus.gov/ www.nhsdirect.nhs.uk/ www.webmd.com/

Medstory www.medstory.com/

http://www.breastcancer.org/ http://online.wsj.com/public/us

Kosmix www.kosmix.com/health

www.nlm.nih.gov www.americanheart.org www.cdc.gov www.mayoclinic.com health.nih.gov www.cancer.org www.revolutionhealth.com www.womentowomen.com www.4women.gov www.healthline.com

*

Survey date was November 16, 2007

this aspect, a perplexing assortment of both public and propriety information from both consumer-oriented sources as well as sources designed for medical professionals is revealed (Table 21.2). Only a tiny fraction of Internet health information sites publish any sourcing and date-stamped information or other information quality indicators (Anonymous, 2007). Published guidelines for quality assurance and quality control are rare and consumers are left to evaluate the quality of the information based on whether or not they consider the site to be a trusted “brand.” Given this situation, and the difficulty non-professionals have in constructing effective queries (see previous section), the best advice on Internet HIS/HIR that one can give to the consumer at the present time is to validate their findings with a health care professional. The US National Library of Medicine provides information for consumers to finding health information and includes a “Guide to Healthy Web Surfing” at their MedlinePlus site (Table 21.1).

PERSONALIZED GENOMICS FOR CONSUMERS Personalized genomics involving DNA polymorphism scans (e.g., see (Weber, 2006)) is currently in a tumultuous gestational

stage that will ultimately lead to a new way to teach and practice medicine (Childs et al., 2005) (see Chapter 1). Consider the following examples: ●

●

●

●

●

A father uses commodity DNA sequencing technologies and publicly-available medical databases to investigate the elusive cause of his daughter’s genetic illness (Maher, 2007). A controversial scientist publishes his autobiography which is partially based on the complete sequence of his own genome (Venter, 2007). A Harvard geneticist launches a Personal Genome Project (Table 21.3) to encourage medical altruism and self-knowledge, makes his own biological material available for study and encourages others to volunteer to do likewise (Church, 2005). Several start-up companies (one of which is backed by Google) form to provide genotyping services as direct-toconsumer businesses (Winslow, 2007) and Table 21.3. The cover story of WIRED magazine asserts that “a new $1000 DNA test can tell how you’ll live – and die” (Goetz, 2007).

The ethical, legal and social issues arising in this milieu certainly signify a brave new world well beyond the scope of this chapter. But what are the medical issues? Kohane and colleagues (Kohane et al., 2006) consider the situation “a threat to genomic

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CHAPTER 21

TABLE 21.3

■

Online Health Information Retrieval

Selected Sites of Interest for Personal Genomics*

Site

Services Offered

Cost

“a forum for those searching for explanations and the help of the interested community of geneticists, patients, physicians, scientists and family members”

Not applicable

“focuses on the practical issues of recruiting and informing volunteers… a test bed for personalized medicine and new ways of interfacing with the research subjects”

Not applicable

“personal insight into ancestry, genealogy, and inherited traits”

Not available

Navigenics www.navigenics.com

“a personalized genetic analysis that, combined with relevant health and wellness information, enables a far more personalized health strategy for each individual”

$2500

deCODE genetics www.decode.com

“Subscribers … can take their genome and examine it in the context of the literature”

$985 (Introductory, promotional price)

Not-for-Profit www.MyDaughtersDNA.org

Personal Genome Project www.pgen.us Commercial Businesses 23andMe Personal Genome Service www.23andme.com

*Information current as of November 16, 2007.

medicine” because the volume and complexity of genotypes and their statistical, phenotypic associations will undoubtedly lead to a plethora of incidental, “abnormal” findings that will be pursued at great cost but little benefit by the patients and their physicians. A person’s genotype will become an “incidentalome” – analogous to the incidentalomas (Mirilas and Skandalakis, 2002) recognized by a previous generation of physicians but unbelievably more complex. Kohane and colleagues call for several key actions including the creation of information systems for “estimating and explaining the risks associated with various incidental genomic findings” (Kohane et al., 2006). They envisioned such systems as being used by medical professionals “…in the clinic and at the bedside.” However, it is likely that consumer versions will lead the way as patients “surf the web” trying to understand the implications of their genotypes and manage their personal health accordingly. Dr. Google has office hours 24/7.

SUMMARY AND CONCLUSIONS Major challenges exist in both educating and assisting consumers with HIS/HIR queries, assessing the sources and quality of the information they find, and helping them decide how to act on it. Much more work is needed on the theory and practice of health information search and retrieval by consumers as health care systems become more patient-centric and consumers are expected to make informed choices and exert more control over the management of their personal health. Health care professionals need to become familiar with the challenges consumers face. Health care systems will need to devise a mechanism for joint consumerprovider review of information, and decision support for the development of personalized health care. Personal genomes are an extreme example of the challenges we all face.

REFERENCES Anonymous. (2007a). CDC Wonder DATA2010: Focus Area 11-Health Communication, from http://www.wonder.cdc.gov/data2010/. Anonymous (2007b). Harris Poll Shows Number of “Cyberchondriacs” – Adults Who Have Ever Gone Online for Health Information – Increases to an Estimated 160 Million Nationwide. Harris Interactive. Atlas, M.C. (2000). The rise and fall of the medical mediated searcher. Bull Med Libr Assoc 88(1), 26–35. http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?cmdRetrieve&dbPubMed&doptCitation &list_uids10658961 Boguski, M.S. and Ouellette, B.F.S. (1995). Internet Basics for Biologists. Current Protocols in Molecular Biology. John Wiley & Sons, New York. Childs, B., Wiener, C. and Valle, D. (2005). A science of the individual: Implications for a medical school curriculum. Annu Rev Genomics

Hum Genet 6, 313–330. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?cmdRetrieve&dbPubMed&doptCitation&list_ uids16124864 Church, G.M. (2005). The personal genome project. Mol Syst Biol 1, 0030. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmdRet rieve&dbPubMed&doptCitation&list_uids16729065 Crocco, A.G., Villasis-Keever, M. and Jadad, A.R. (2002). Analysis of cases of harm associated with use of health information on the internet. JAMA 287(21): 2869–2871. http://www.ncbi.nlm.nih. gov/pubmed/12038937?ordinalpos1&itoolEntrezSystem2. PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Fox, S. (2006). Online Health Search 2006. Washington, DC, Pew Internet & American Life Project.

Wikipedia References

Fox, S. and Fallows, D. (2003). Internet Health Resources. Washington, DC, Pew Internet & American Life Project. Fredin, E.S. and Prabu, D. (1998). Browsing and the hypermedia interaction cycle a model of self-efficacy and goal dynamics. J Mass Comm Quarterly 75(1), 35–54. http://www.eric.ed.gov/ERICWebPortal/ custom/portlets/recordDetails/detailmini.jsp?_nfpb  true &_ &ERICExtSearch_SearchValue_0EJ569971&ERICExtSearch_ SearchType_0no&accnoEJ569971 Goetz, T. (2007).Your life decoded. Wired 283, 256–265. Jones, G. (2003). Prescribing and taking medicines. BMJ 327(7419), 819. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmdRetrieve& dbPubMed&doptCitation&list_uids14551062 Kohane, I.S., Masys, D.R. and Altman, R.B. (2006). The incidentalome a threat to genomic medicine. JAMA 296(2), 212–215. http://www. ncbi.nlm.nih.gov/entrez/query.fcgi?cmdRetrieve&dbPubMe d&doptCitation&list_uids16835427 Levy, M. (2007). Assessing consumers demand for health vertical search engines. Jupiter Research Industry Focus Health. Lorence, D.P. and Spink, A. (2004). Semantics and the medical web a review of barriers and breakthroughs in effective healthcare query. Health Info Libr J 21(2), 109–116. http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?cmdRetrieve&dbPubMed&doptCitation &list_uids15191602 Maher, B. (2007). His Daughter’s DNA. Nature 449(7164), 772–776. McCray, A.T. (2005). Promoting health literacy. J Am Med Inform Assoc 12(2), 152–163. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?cmdRetrieve&dbPubMed&doptCitation&list_ uids15561782 Miller, N., Tyler, R.J. and Backus, J.E.B. (2004). MedlinePlus® the National Library of Medicine® brings quality information to health consumers. Library Trends 53(2), 375–388. http://hdl.handle. net/2142/1735 Mirilas, P. and Skandalakis, J.E. (2002). Benign anatomical mistakes incidentaloma. Am Surg 68(11), 1026–1028. http://www.ncbi.nlm. nih.gov/entrez/query.fcgi?cmdRetrieve&dbPubMed&dopt Citation&list_uids12455801 Payer, L. (1996). Medicine and Culture. Holt Paperbacks. Plovnick, R.M. and Zeng, Q.T. (2004). Reformulation of consumer health queries with professional terminology a pilot study. J Med Internet Res 6(3), e27. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?cmdRetrieve&dbPubMed&doptCitation&list_ uids15471753 Romacker, M., Grandjean, N., Parisot, P., Kreim, O., Cronenberger, D., Vachon, T., Peitsch, M.C. (2006). The UltraLink an expert system for contextual hyperlinking in knowledge management. In

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Computer Applications in Pharmaceutical Research and Development (S. Ekins, ed.), Wiley Interscience, 729–753. Schoolman, H.M. and Lindberg, D.A.B. (1988). The information age in concept and practice at the National Library of Medicine. Annals of the American Academy of Political and Social Science 495, 117–126. Shortliffe, E.H., Perreault, L.E., Wiederhold, G. and Fagan, L.M. (2003). Medical Informatics Computer Applications in Health Care and Biomedicine. Springer, New York. Spasic, I., Ananiadou, S., McNaught, J. and Kumar, A. (2005). Text mining and ontologies in biomedicine making sense of raw text. Brief Bioinform 6(3), 239–251. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?cmdRetrieve&dbPubMed&doptCitation&list_ uids16212772 Tse, T. and Soergel, D. (2003). Exploring medical expressions used by consumers and the media an emerging view of consumer health vocabularies. AMIA Annu Symp Proc 674-8. http://www.ncbi.nlm. nih.gov/entrez/query.fcgi?cmdRetrieve&dbPubMed&dopt Citation&list_uids14728258 Venter, J.C. (2007). A Life Decoded: My Genome My Life.Viking, New York. Weber, J.L. (2006). Clinical applications of Genome Polymorphism Scans. Biol Direct 1, 16. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?cmdRetrieve&dbPubMed&doptCitation&list_ uids16756678 Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R., Federhen, S. et al. (2007). Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 35(Database issue), D5–D12. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd Retrieve&dbPubMed&doptCitation&list_uids17170002 Winslow, R. (2007). Is there a heart attack in your future. New York Times. Zeng, Q., Kogan, S., Ash, N., Greenes, R.A. and Boxwala, A.A. (2002). Characteristics of consumer terminology for health information retrieval. Methods Inf Med 41(4), 289–298. http://www.ncbi.nlm. nih.gov/entrez/query.fcgi?cmdRetrieve&dbPubMed&dopt Citation&list_uids12425240 Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim, E., Ngo, L. and Dibble, E. (2006). Assisting consumer health information retrieval with query recommendations. J Am Med Inform Assoc 13(1), 80–90. http:// www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmdRetrieve&db PubMed&doptCitation&list_uids16221944 Zeng, Q.T. and Tse, T. (2006). Exploring and developing consumer health vocabularies. J Am Med Inform Assoc 13(1), 24–29. http:// www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmdRetrieve&db PubMed&doptCitation&list_uids16221948

WIKIPEDIA REFERENCES (2007). AltaVista. Wikipedia. (2007). Dot-com bubble. Wikipedia. (2007). Facebook. Wikipedia. (2007). Google. Wikipedia.

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(2007). Mosaic (web browser). Wikipedia. (2007). MySpace. Wikipedia. (2007).Yahoo! Wikipedia.

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Section

Enabling Strategies in the Translation of Genomics into Medicine

22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.

4

Translational Genomics: From Discovery to Clinical Practice Principles of Study Design Biobanking in the Post-Genome Era Application of Biomarkers in Human Population Studies Validation of Candidate Protein Biomarkers Pharmacogenetics and Pharmacogenomics The Role of Genomics and Genetics in Drug Discovery and Development Role of Pharmacogenomics in Drug Development Clinical Implementation of Translational Genomics Translating Innovation in Diagnostics: Challenges and Opportunities The Role of Genomics in Enabling Prospective Health Care

CHAPTER

22 Translational Genomics: From Discovery to Clinical Practice Geoffrey S. Ginsburg I am convinced that within five years every college-educated person in America is going to have a [whole genome] profile ... you cannot afford not having this. deCODE chief executive Kari Stefansson, PhD (Wohlsen, 2008)

INTRODUCTION The use of genomic technologies to improve medical decisionmaking so as to achieve the goal of personalized medicine is poised to transform health care (Ginsburg and McCarthy, 2001; Ginsburg et al., 2005; Snyderman and Williams, 2003; Willard et al., 2005). New knowledge from the study of genomes and their by-products now has permitted the development of predictors of disease predisposition (“who is at risk”), prognosis (“who to treat”) and therapeutic response (“how to treat”) in individual patients and thus provides an opportunity to employ these predictors in present day practice. Many of these opportunities are highlighted in Chapters 54–112, in which an astounding number of genome-based discoveries are highlighted with the potential to fundamentally alter how medicine is practiced in cardiovascular disease, oncology, metabolic disease, neuropsychiatric disorders, infectious disease and diseases with a basic inflammatory component. The rate of genome-based discovery is stunning. However, despite the vast amount of genomic information now in the Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 262

scientific literature, the translation of basic scientific findings from the genome and its derivative products, RNA and protein, to daily use in medical practice has been slow (Graham et al., 2006; Sung et al., 2003). It now takes an average of 17 years for 14% of scientific discoveries to enter day-to-day practice (Westfall et al., 2007). For genomic discoveries, the path is undoubtedly more complex. The research paradigm for translational genomics is inconsistent and nonsystematic; multiple, and in some cases non-linear, pathways to translation of genomics to clinical medicine exist. For example, diagnostic companies have traditionally “cherrypicked” a narrow repertoire of new genomics-based molecular diagnostics from academic laboratories and the literature. The pharmaceutical industry has developed “targeted therapies” such as trastuzamab, imatinib and erlotinib that use molecular diagnostic tests – some of which are based on genetic variation (Lynch et al., 2004) – to identify individual patients who are likely to benefit from the drug. The lack of a clear system of regulatory oversight of genetic and genomic tests has led to gaps and ambiguity in the pathways of translation, an area that Copyright © 2009, Elsevier Inc. All rights reserved.

Where Can Genomics Have Impact in the Continuum of Health and Disease?

the US Secretary’s Advisory Committee on Genetics, Health and Society has recently addressed (SACGHS, 2007; see also Chapters 31 and 33). Personal genomics and direct-to-consumer avenues are being opened to bring genomics directly to the public, thus circumventing the traditional health care delivery and regulatory channels (Feero et al., 2008; Hunter et al., 2008; Janssens et al., 2008; McGuire et al., 2007). These issues speak to the need for a comprehensive agenda for translational research to move human genome discoveries into health practice. This chapter will focus on paradigms for translational genomics to human health as well as the various opportunities, challenges, and strategies that will enable the effective translation of human genome information to clinically relevant actions and outcomes.

A ROADMAP FOR TRANSLATION Khoury and colleagues (2007) have developed a framework for the acceleration of human genome discoveries into health care and disease prevention. This is a logical first step in guiding genomic innovation to health care and more widespread use. The framework (Table 22.1) contemplates four phases of translational research from discovery to public health impact. T1 research seeks to move a basic genome-based discovery into a

T A B L E 2 2 . 1 The continuum of translation research in genomic medicine: Types of research Translation research phase

Notation

Types of research

T1

Discovery to candidate health application

Phases I and II clinical trials; observational studies

T2

Health application to evidence-based practice guidelines

Phase III clinical trials; observational studies; evidence synthesis and guidelines development

T3

Practice guidelines to health practice

Dissemination research; implementation research; diffusion research; Phase IV clinical trials

T4

Practice to population health impact

Outcomes research (included many disciplines); population monitoring of morbidity, mortality, benefits and risk

Adapted from Khoury et al. (2007).

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candidate health application (e.g., the association of a gene mutation with a health outcome). This phase of research starts after gene discovery and provides the early evidence that genomic information might support predictive testing, screening, diagnostic, prognostic or pharmacogenomic testing. T1 research will usually take advantage of observational studies or clinical trials where the correlation of the genomic information with health characteristics or outcome can first be made. T2 research assesses the value of a genomic application for health practice, leading to the development of evidence-based guidelines (e.g., prospective or retrospective assessment of the predictive value of the mutation test for the health outcome). This phase of research usually begins after the analytical validity of the test has been established and progresses to establish clinical utility, or impact on clinical decision-making. T3 research examines the pathways for delivery, dissemination, and diffusion of genomic testing into practice (e.g., defining the barriers to the use of the test in relevant populations). Public–private partnerships will usually be required to facilitate T3 research, and this research phase will also involve policy makers, regulatory bodies, professional organizations and insurers. Finally, T4 research assesses the impact on human health (e.g., outcomes studies across a broad and diverse population). This area of research is about the results of the implementation, as opposed to the implementation research itself (T3). T1 research is illustrated in many of the clinical chapters in this book that highlight genomic discoveries with potential disease relevance. The molecular signatures described by Dave et al. (2006), Bullinger and Valk (2005) and Potti et al. (2006) to stratify and number of cancer phenotypes based on their molecular signature are illustrative. Examples of T2 research are found in Marcom et al. (2008) and Potti et al. (2006), in which a genomic-guided clinical trial design is described that uses a microarray-based molecular profile to assign patients to the various treatment arms in the trial. The clinical outcomes in the trial are dependent on the genomic signatures and the evidence for the genomic application to humans will be determined by these prospective studies. Examples of T3 research are described in Khoury et al. (2007) as surveys of physicians, internists and oncologists on the knowledge of genetic testing for susceptibility for breast and ovarian cancers. This information would provide support for the basis of strategies to enhance the dissemination of innovative genomic tools and information. T4 research is ongoing in areas such as BRCA1 testing for hereditary breast and ovarian cancers or for HNPCC testing in colon cancer (see Chapters 72 and 73). In these early cases, the value of such testing is being assessed on a population level in terms of survival, quality of life and economic value. We are clearly in the early days of T2–T4 research.

WHERE CAN GENOMICS HAVE IMPACT IN THE CONTINUUM OF HEALTH AND DISEASE? DNA-based approaches (SNP detection, copy number variation and sequencing) (see Chapters 7–9) can now be used to

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Disease burden

quantify one’s predisposition, susceptibility, and risk for complex diseases. These measures can be made during health and even at birth or at any time point over a person’s lifetime, as these “stable” genomic measures do not change from the time of conception. Individual SNPs or multi-SNP panels are emerging from genome-wide approaches that might be used as part of health risk assessment (see e.g.: Bare et al., 2007; Zheng et al., 2008). DNA variation may also provide information about the possibility of being relatively protected from disease development (Cox et al., 2007), as well as one’s sensitivity or resistance to certain medications (Eichelbaum et al., 2006) and ability to metabolize nutrients in our diets (Ordovas and Corella, 2004). All of this can and probably should be done early in life, such that a strategy to maintain health can be formulated well in advance of the development of potentially detrimental lifestyle habits and exposures. The “dynamic” components of the genome (gene-, protein- and metabolite expression; see Chapter 1) that are responsive to environmental stimuli, lifestyles, diets and pathogens are rapidly improving capabilities to predict and intervene at an individual level. Transcriptional profiles, protein expression and levels of metabolites combined with dynamic imaging modalities should provide more precise ways to screen individuals who are at high risk for developing a disease for its earliest molecular manifestations while the disease is subclinical (Seo and Ginsburg, 2005).This same type of information may also provide a definitive diagnosis and a molecular classification of a disease state that foretells prognosis. For example, today a her2/neu-positive breast cancer ascribes that patient to a more aggressive form of the disease and directs care to a much different course than a her2/neu negative cancer

Baseline risk Initiating events

(see Chapter 72). Similarly the selection of drugs can be guided both by the patients underlying genetic make up and by the molecular architecture of the disease in the individual (see Chapters 27, 28 and 30). The emerging picture is one of applied genomics throughout an individual’s lifetime, from health through death, that enables assessment of disease predisposition, screening, diagnosis, prognosis, therapeutic selection with precision that until now has been unachievable (Figure 22.1). “Strategic health planning” (see Chapter 32 and Snyderman and Yoediono, 2006) and disease prevention should be possible and will allow a shift in the current paradigm of care from the time when disease is manifest (rightward in Figure 22.1) to proactive personalized predictive care at a more cost-effective time in the disease life cycle (leftward in Figure 22.1).

THE GENOMICS “GOLD RUSH” While genetic testing for Mendelian disorders such as cystic fibrosis, Huntington’s disease, familial breast cancer and phenylketonuria among others have been widely available prior to the genomic era, the genetic basis for complex disease remains unclear. From 1980 through 2002, fewer than 10 genes were associated with complex diseases in contrast to more than 90 genes that were associated with Mendelian disorders (Glazier et al., 2002). Today we are witnessing a “genomics gold rush” afforded by genome-wide association studies using high-density genotyping technologies that allow for assaying 500,000–1,000,000 SNPs per individual at relatively low cost

Earliest molecular detection

Typical Earliest current clinical detection intervention

Time Baseline risk Sources of New Stable Genomics: Biomarkers: SNPs

Preclinical progression

Disease initiation and progression

Dynamic Genomics: Gene expression

Haplotypes

Proteomics

Gene sequencing

Metabolomics MolecularImaging

Figure 22.1 Use of genomic markers predict, prognose, diagnose, treat and monitor health and disease (adapted from Snyderman and Yoediono, 2006).

The Genomics “Gold Rush”

(Topol et al., 2007) (see Chapter 8). The results in the past year have been no less than astounding (Topol et al., 2007), with genetic loci being identified for many complex diseases on almost a weekly basis (Table 22.2). T A B L E 2 2 . 2 Selected genome-wide association study findings for complex disease susceptibility: 2007–2008 Disease

Gene or loci (references)

Cancer Prostate cancer

TCF2 (Gudmundsson et al., 2007a)

Prostate cancer

8q24 (Gudmundsson et al., 2007b)

Multiple solid tumors

CASP8 (Bethke et al., 2008; Cox et al., 2007)

Colon

11q23, 8q24, 18q21 ( Tenesa et al., 2008)

Breast cancer

FGFR2, TNCR9, MPA3K1, LSP (Easton et al., 2007; Hunter et al., 2007)

Metabolic disease Obesity

FTO (Frayling et al., 2007)

Diabetes, Type I

IL2RA (Lowe et al., 2007)

Diabetes, Type II

TCF7L2 (Saxena et al., 2007; Sladek et al., 2007)

Diabetes, Type II

WFS1 (Sandhu et al., 2007)

Diabetes, Type II

CDKAL1 (Steinthorsdottir et al., 2007)

Cardiovascular disease Myocardial infarction, CAD

9p21 (Helgadotti et al., 2007; McPherson et al., 2007)

Atrial fibrillation

4q25 (Gudbjartsson et al., 2007)

Coronary artery disease

6q25, 2q36 (Samani et al., 2007)

Inflammation/infectious disease Rheumatoid arthritis

TRAF1-C5, STAT-4 (Plenge et al., 2007; Remmers et al., 2007)

Multiple sclerosis

IL7Ra; IL2Ra (Gregory et al., 2007)

Crohn’s disease

IRGM (Parkes et al., 2007)

HIV host control

HLA-B*5701 (Fellay et al., 2007)

Celiac disease

IL-2, IL-21 ( Van Heel et al., 2007)

Asthma (childhood)

ORMDL3 (Moffatt et al., 2007)

CNS disease Bipolar disorder

16p12 (Sklar et al., 2007)

Glaucoma

LOXL1 (Ma et al., 2007)

Age-related macular degeneration

C3 ( Yates et al., 2007)

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Thus far, notwithstanding significant challenges (see Chapter 8), genome-wide association studies have produced robust and reproducible findings. For example, three genomewide studies for coronary artery disease have been published using three different populations and have all identified a locus at 9p21 (Helgadotti et al., 2007; McPherson et al., 2007). Type 2 diabetes has been extensively studied and independent genomewide scans have identified several loci for diabetes susceptibility: CDKN2A/CDKN2B, CDKAL1 and IGSF2BP2 as well as confirming TCF7L2, PPARG and KCNJ11 that had been previously identified via other methods (Saxena et al., 2007; Scott et al., 2007; Sladek et al., 2007; Steinthorsdottir et al., 2007; Zeggini et al., 2007, 2008). A susceptibility gene for obesity, FTO, was identified by the same groups studying type 2 diabetes (Zeggini et al., 2007). Genes for Crohn’s disease, rheumatoid arthritis, adult macular degeneration and prostate cancer have all been identified in the past year using genome-wide approaches (Gudmundsson et al., 2007a, b; Klein et al., 2005; Plenge et al., 2007; Rioux et al., 2007). Perhaps the most important and powerful result of these studies has been their unbiased approach and their resulting insights into novel disease genes and molecular pathways that contribute mechanistically to these complex diseases, their etiologies and the basis for their progression and clinical manifestations. As a result of the above types of studies, large numbers of additional susceptibility markers are sure to emerge in the coming years. Whether these markers will be applied to clinical practice remains uncertain; their clinical utility must first be demonstrated – that is, the impact on health outcomes of the clinical use of these genetic markers. These findings raise a series of questions that must be evaluated; for example, do these genetic markers add additional estimates of disease risk compared to current clinical models? If combined, do multiple SNPs, each with only a small predictive value, used in concert with one another provide enough power to be clinically significant? What actionable options are there for patients with these results and how do these change outcomes compared to the current standard of care? The recent study by Zheng and colleagues (2008), who examined the association between prostate cancer and five SNPs that map to the three 8q24 loci, to 17q12, and to 17q24.3, is emblematic of these issues. When four or five high-risk genotypes were present, they were associated with a composite risk ratio for prostate cancer of 4.47. When family history was added to the model, the risk ratio was an astounding 9.46. So what are these results telling us? They certainly point to the multifactorial underpinnings of prostate cancer. But as was pointed out in the accompanying editorial to this report (Gelmann, 2008): “What we cannot yet derive from these studies is an easily applicable test that will assist in the identification of men who are at high risk for prostate cancer. Most men with a family history of prostate cancer are aware that screening is recommended by some groups as early as the age of 40 years. Genetic tests are unlikely to alter the screening behavior of men who are already under surveillance for prostate cancer by the monitoring of prostatespecific antigen.” So even in cases of very high risk for common

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complex disease on the basis of genetic prediction, the clinical applications are at best ambiguous.

THE PERSONAL GENOME: PRECIOUS CODE OR FOOL’S GOLD? The same advances in SNP technology that are providing insightful gene association data also are allowing a glimpse variation in our personal genomes and at low cost. Direct-to-consumer initiatives to make variation in one’s genome available in the form of SNPs at specific loci for $1000–$2500 have been recently announced by three companies (Navigenics, 23andMe and deCODEme). These initiatives use the 500K–1000K SNP chip or similar technologies (see Chapter 8). They report information back to their customers on 20–30 areas of the genome that have been identified as disease risk susceptibility loci and some offer additional information such as one’s ancestry (see Chapter 1). This may be a “disruptive technology” in health care delivery with the provision of health and disease risk information to consumers without physician intervention and guidance. It will not be long before a patient will bring a report of a whole genome to a physician’s office and ask for guidance. What will the physicians of today, armed with a paucity of genomic training, tell them? Whether our genomes are “SNP’d” or sequenced, our genomic information will be available in a short period of time. There is a clear and important research agenda that needs to be developed in concert with these technological breakthroughs that allows health providers and the public to understand the information and more so to believe that is accurate, informative and actionable (Feero et al., 2008; Hunter et al., 2008; McGuire et al., 2007). With the vast amount of information contained in the human genome sequence, the stakes are high for patients, physicians and the public to ensure the proper reading, interpretation and communication of the information are carried out.

“GRAND CHALLENGES” IN TRANSLATION OF GENOMICS TO HUMAN HEALTH Despite the clear advances in technology to bring genomic information closer to physicians, patients and the public, looming ever closer are issues that are outside of the sphere of the scientists that have been involved in the discovery and early translational activities. In the United States, the Institute of Medicine has convened a Roundtable on Translating Genomics to Health (IOM, 2007), the Centers for Disease Control has independently developed a pilot project on the Evaluation of Genomic Applications in Practice and Prevention (EGAPP, 2004) and the Genomic and Personalized Medicine Act of 2007 (Personalized Medicine, 2007) was introduced in Congress to also address similar issues.

Recently Scheuner and colleagues (2008) carried out an extensive meta-analysis of studies using genomics toward clinical application in chronic disease. This study aimed to understand the current state of translation focusing on the following questions: “what are the outcomes of genomic medicine? What is the current level of consumer understanding about genomic medicine and what information do consumers need before they seek services? How is genomic medicine best delivered? What are the challenges and barriers to integrating genomic medicine into clinical practice?” Using a total of 68 articles in their analysis, the authors synthesized information on the delivery of genomic medicine for common adult-onset conditions (Scheuner et al., 2008). The major findings of the study were not surprising in terms of the genome policy issues that have been previously summarized (Haga and Willard, 2006): ●

●

●

●

Education – The primary care workforce feels woefully unprepared to integrate genomics into regular practice. Consumers are also unclear about genetics and genetic testing for common diseases. Privacy – Consumers are worried about the possible adverse consequences of genetic testing, particularly the privacy issues and discrimination against receiving employment and health insurance. Evidence – There needs to be outcome data for genetic testing and chronic disease to assess whether patients who receive the test do better clinically. Cost – Cost uncertainty (both in terms of delivery and reimbursement) is an important issue to many of the stakeholders of genomic issues.

Health Professional and Public Education Education of health professionals and the public will be essential to advancing the use of genomics into health care (see Chapter 34 and Frueh et al., 2005). With all of the rapid advancements in genomics research and technologies, it will be challenging to keep health professionals informed about the benefits, risks and limitations of new tools as they become available. In addition, the public and health care workforce will need to understand the appropriate applications of genomic tools – including their benefits, risks and limitations, how they may improve clinical management, inherited versus acquired genomic variations (e.g., implications for family members) and privacy and confidentiality. Although several surveys have documented the below average physician knowledge of genetics (Metcalfe et al., 2002; Wilkins-Haug et al., 2000), none has assessed knowledge of the newer field of genomics. But several papers have been published recognizing the importance of pharmacogenetics (Frueh and Gurwitz, 2004; Gurwitz et al., 2003), and steps are underway to develop more educational materials in this area. Privacy Fears There has been ongoing debate about the uniqueness of genetic information and whether it warrants special protections beyond

Translational Genomics: Enabling Competencies

those in place for standard medical information (see Chapter 33 and Haga and Willard, 2006). In the United States, fear of discrimination by employers and health insurers is the main concern, whereas in the United Kingdom, use of genetic information by life insurers is the major concern (Apse et al., 2004; Hall et al., 2005). Despite the outcome of these debates, the attention paid to genetics by the popular press and public has raised genetic information to a different level compared to other medical information. The potential for genetic discrimination has been a major concern for researchers, health professionals, patients and the public. In order for genomic biomarkers to be integrated into routine clinical practice, associated fears with this type of testing must be put to rest. While the majority of states in the United States have enacted legislation to protect against genetic discrimination by employers and health insurers, national protections are only now being addressed at the federal level. Building the Evidence for Clinical Utility Perhaps the most important factor hindering the appropriate integration of genomics into clinical practice is the lack of evidence for its clinical utility (i.e., evidence that use of a genomic technology improves health outcomes) (see Chapters 30 and 31). Evidence generation needs to be more practicable and practical. There is also a need for greater collaboration among stakeholder groups and for innovation in both study design and analysis methods. Clinical outcome studies are needed that demonstrate the clinical utility of genomic interventions that are linked to specific, actionable clinical recommendations or practice guidelines. Public–private partnerships are likely to be required to generate the evidence base for genomic medicine. These collaborations are desirable because no single stakeholder group is likely to have sufficient resources or expertise to conduct the necessary studies. Cost Issues As with any new innovation, genomic testing must be demonstrated to be clinically useful and cost-effective and of value (see Chapter 36). But because genomic technologies inherently involve diagnostic or prognostic testing, in addition to the complexities of incomplete gene penetrance and multiple gene and environmental interactions, their assessment can be more challenging. In addition, perhaps more than in any other area of medicine, questions have arisen in regard to the economic incentives to develop these technologies. Formal health economics frameworks can be used to gain insights into these issues and provide guidance for research and development investment, technology appraisal and policy development (Carlson et al., 2005; Flowers and Veenstra, 2004; Stallings et al., 2006). It is important to examine the drivers of cost-effectiveness of genomic technologies and to consider approaches that include value-based reimbursement for genomic testing technologies. A particularly challenging area is pharmacogenomics, in which the economic incentives for developing diagnostics linked to therapeutics in the pharmaceutical industry are unclear. An integrated business model is needed that will be favorable for the effective delivery of genomic information to patients and clinicians.

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TRANSLATIONAL GENOMICS: ENABLING COMPETENCIES The pharmaceutical industry has a clearly defined and decisiongated series of steps to move a potential therapeutic from discovery to the clinic. Translational genomics may also benefit from defining the processes, the key competencies, and capabilities that support the directional movement of genomics from discovery to human health applications (Khoury et al., 2007). Figure 22.2 is a potential (and admittedly simplified) schema for moving genomic discoveries from bench to bedside from the perspective of needed and enabling capabilities and competencies. Academic centers, industry and government research groups right benefit from developing organizing principles to move a potential genomic discovery into practice through a variety of stages similar to what the pharmaceutical industry has done for drug development candidates. Below and in the ensuing chapters, we will consider these capabilities in greater detail. Centralized Biobanking and Biorepositories Biobanks – centralized, institutional repositories for biological samples – are among the most important enabling resources in support of genomic medicine (Ginsburg et al., 2008). The study of complex diseases has been greatly facilitated by the availability of well-annotated DNA, blood and tissue specimens for high-throughput genomic analyses. Scale and scope characterize biobanks today from previous tissue repositories in Departments of Pathology or held by individual investigators. Today biobanks manage hundreds of thousands to millions of samples and require robotics and advanced informatics tools. Key capabilities for efficient and effective biobanking include centralized sample and data management systems and clearly define standards for handling of samples and their associated data. Equally important is the ability of biobanks to provide support for longitudinal studies enabling sample and data capturing in the entire disease cycle from pre-disease onset, to disease diagnosis, and clinical outcomes. In Chapter 24, Chow and Liu explores several existing and developing biobanking models and highlight developing trends in biobanking organization and management. The Singapore experience is particularly instructive in that the establishment of a national biobank was coupled with simultaneous development of the entire biomedical research infrastructure at an accelerated pace. Key considerations involve professional acceptance, custodianship, security and access. Data privacy for subjects is of paramount importance and applications of informatics technologies may provide the requisite security. Once properly implemented, biobanks may not only accelerate medical research but also contribute to public health. Genomic “Profiling” There are many sources of genomic data (see Chapters 7–16) that can now be applied to an integrated view of genomic epidemiology. A competency in genomic profiling technologies is clearly

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Surgical specimens Animal models

Biomarkers

Drug development and pharmacogenomics

Novel targets

Registries

Biorepository

– Sample management – Data management – Standards – Centralization

Profiling

– SNPs/genotyping – Transcript profiling – Proteomics – Metabolomics

Biomarker discovery and development

Informatics

– Data acquisition and storage – Computational biology – Data standards – Novel methods – Statistical modeling

– Model systems – Human samples – Animal physiology – Clinical discovery – Phase 0 studies

Genomebased clinical trials

– Test platform development – Public–Private Partnerships – Translational diagnostics

Population studies Outcomes research

Implementation in health care systems

– Cost-effectiveness – Clinical decision support – Outcomes measures – Diagnostic capabilities

Technology infrastructure and development

Figure 22.2 Enabling capabilities and competencies to translation genomic-based discoveries into health care applications. Based on Willard et al., 2005.

needed to optimize the value of clinical samples and data to develop novel predictive models of disease states and outcomes. The information provided by today’s clinical and biochemical markers of disease falls well short of an adequately describing disease complexity and heterogeneity (West et al., 2006). The advent of the full complement of technologies spawned by the Human Genome Project capable of interrogating complex diseases such as cancer, cardiovascular disease, obesity and diabetes mellitus provides opportunities for acquiring quantitative data that can match the complexity of the disease. Genomic technologies can now potentially capture data that do provide this complexity, identifying discrete subsets of disease that have not been previously recognized. Profiles and patterns that identify new subclasses of tumors, such as the distinction between acute myeloid leukemia and acute myeloid leukemia (Bullinger and Valk, 2005), or Burkitt’s lymphoma from diffuse B cell lymphomas (Dave et al., 2006), without prior knowledge of the classes. More recently several genomic signatures that go beyond disease classification have been discovered and validated that predict prognosis and response to therapy for many solid tumors and hematologic malignancies (Potti et al., 2006; Staudt, 2003). Not far behind, although lacking the throughput of RNA expression analyses, are whole-proteome analyses of tissue homogenates or serum using surface-enhanced laser desorption ionization–timeof-flight or liquid chromatograph/mass spectrometry/mass spectrometer technologies (Caprioli, 2005; see Chapter 14). The capability to discern structure in this data – in the form of patterns of gene, protein, or metabolite expression that provide snapshots of gene activity in a cell or tissue sample at a given instant of time that can then be used to describe a phenotype – is

transforming biology from an observational molecular science to a data-intensive quantitative genomic science. The dimension and complexity of such data provide opportunities for uncovering patterns and trends that can distinguish subtle phenotypes in ways that traditional methods cannot (West et al., 2006). In an integrated view of genomic epidemiology, all of these sources of data will be used to develop signatures and models that classify disease and predict outcomes (Figure 22.3). To fully realize the clinical potential of genome-scale information requires a dramatic shift in the way complex, large-scale data are captures, viewed, analyzed and used. Data Acquisition, Standards and Bioinformatics All of the exciting medical, biological and technological advances described in this book are limited by requirements for intensive data acquisition, storage and computational capacity. Individuals with skills in this arena are in critically short supply. Informatics is a fundamental competency for the era of genomic medicine. Unless the nomenclature of the current “Tower of Babel” is improved, we will miss the opportunity for applying our newfound knowledge, because these measurements will not be compatible when attempts are made to aggregate them. Currently, most data collection efforts are relatively isolated and many different groups are working on controlled vocabularies and data standards (see Chapters 17 and 18 and Hanauer et al., 2007). In addition to nomenclature standards that allow observations to be compared across observers, the methods for exchanging data must be standardized (see Chapter 19 and Klein, 2002). Health Level Seven has essentially solved this problem for common laboratory measurements that can be measured as a

Translational Genomics: Enabling Competencies

Gene expression profiles Clinical data Treatments Family history Demographics Environmental

Signatures Models

Genomic data SNPs Genome-scale sequence Metabolomic data Proteomic data

Imaging

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applied in medical research. In Chapter 25, Bonassi and colleagues address the basic issues concerning the use of genomic biomarkers in human population studies, and how they allow a better insight into early stages of the disease. In other chapters the use of biomarkers in clinical trials or as enabling diagnostics is also covered.

???

Predictions: Risk Individualized prognosis and diagnosis Drug response Environmental response

Figure 22.3 Integration of clinical, genomic, imaging, and other data to develop signatures and models to predict disease and response to drugs and environmental stimuli.

number or coded output. Unfortunately, such standards are only now emerging for clinical data, and considerable variation exists in the purposes, stated or perceived, for this effort, including better individual patient care, quality measurement, research and improved administrative efficiency and billing. An effective and unifying cross-platform system will be needed that will allow collaborations among industry, academia, the NIH and other partners (see below). There are fundamental “bioinformatics blocks” in translation of genomics that must be overcome (see Chapters 17–21). First, there is an enormous amount of data at the level of fundamental biological measurements. Second, at the level of the patient in the intact health care system, a major challenge exists in integrating disparate electronic health records. Third, there are further challenges in integrating research, clinical and demographic/societal data into a common format. And lastly, there must be ways to link the clinical and molecular information, so that analyses that yield both cross-sectional and longitudinal patterns relevant to disease sub-classification can be carried out in a reproducible fashion. Biomarker Discovery and Development The discovery and development of physiologic and disease biomarkers has provided a valuable set of tools to assess disease outcomes (see Chapters 25 and 26 and Downing, 2000; Goodsaid et al., 2008). Biomarker research has also greatly improved our knowledge and understanding of mechanisms of etiology and disease pathogenesis. This will require access to robust model systems, human samples and exquisitely curated phenotypic data. Indeed, a genomic signature that characterizes a disease subset also provides a wealth of biological information through the members of genes, gene transcripts or proteins that constitute such a signature, either as markers of disease or as target for potential therapeutics. This approach has been particularly well developed in cancer research, although the driving concept of the gene–environment interaction has been widely

Genome-Based Clinical Trials Fundamental to defining the clinical validity and clinical utility of genomic biomarkers is the genomics-based clinical trial (see e.g., Marcom et al., 2008). In this setting, genetic and genomic biomarkers are fundamental to the trial design and the selection of patients for the trial (inclusion criteria) or for assignment to a specific experimental arm of the study. The basic principles of experimental design can also be applied to genomic studies. The task is to design a study in as economical a way as possible and simultaneously to optimize the information content of the experimental data. Clinical studies that serve to enrich a trial with a particular genomic biomarker or to test that a particular genomic subset of patients respond to therapy are required to advance translational genomics. Often these studies will both “validate” the biomarker – showing that it indeed identifies the relevant group of patients – and at the same time show its clinical utility – that the patient group selected by the biomarker indeed responds differently to the intervention in the trial. Key to enabling genome-based clinical trials is the interface between discovery genomics with the platforms for developing a genomic assay that is ready for clinical use. In many cases, this will require key public–private partnerships to facilitate the transition from discovery research to human applications. As discussed in Chapter 23 by Grass, there are several open issues in the design of genomic studies that need further investigation, such as selection bias at the patient recruitment level, tissue specimen sampling procedures, assays, and time point selection; in addition lack of calibration, poor reproducibility of molecular signatures across different studies and technical variation between different chip technologies also are in need of greater study as these novel study designs are developed. Integration of Genomics into Drug Discovery and Development Currently, the pharmaceutical industry is facing challenges in terms of the low number of new chemical entities (NCEs) that are approved each year, despite soaring research and development (R&D) costs. A main cause for low pharmaceutical productivity is pipeline attrition due to failure of NCEs to demonstrate efficacy or the emergence of safety issues during or after development. In parallel to genomic-guided clinical trials, pharmaceutical studies that use biomarkers for trial enrichment or for pharmacogenomics will be another pathway for genomics to reach the clinic in the form or diagnostic/therapeutic combinations. In Chapters 28 and 29, the practice of genomics and related technologies in transforming drug discovery and development is discussed in detail. Genomics has provided sophisticated and novel new approaches for target identification, target

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validation, the drug screening process and the understanding of the mechanism of action of targets and drugs (Stoughton and Friend, 2005). In drug development, the process by which new drugs are tested in model organisms and in humans, genomics has provided a means to understand heterogeneous disease populations at the gene level. This has enabled the selection of subjects for clinical trials with more homogeneous disease characteristics, thereby enabling a much better assessment of drug activity. In addition, the understanding of drug metabolism on a genetic level has offered a means to provide drugs with a better safety profile for certain patient populations. Pharmacogenetics and Pharmacogenomics The impact of the genome on our ability to predict drug response is one of the most promising and fertile areas of genomic and personalized medicine (see Chapters 27 and 29). Pharmacogenetics is the study of genetic variation that ultimately gives rise to the variable responses in individuals to any given drug treatment. More recently, pharmacogenetics has provided an explanation as to why certain individuals do not respond, or respond differently to a given drug treatment. Pharmacogenomics utilizes genomic technology to understand the effects of all relevant genes on the behavior of a drug or conversely the effect of a drug on gene expression. Pharmacogenomics, like pharmacogenetics, has rapidly embraced genomic technologies to identify molecular patterns of response, drug disposition and drug targets, yielding molecular biomarkers of drug response both of which have great potential to positively affect the area of medicine. Grossman and Goldstein (Chapter 27) outline the scope of study designs for pharmacogenomics studies, depending on specific features related to the study population, underlying disease, phenotypes of interest (i.e., drug efficacy, safety and dosing) and drug compound being tested. Considerations related to the realization of a valid genetic effect into an acceptable diagnostic kit are reviewed, providing analytical tools objectively evaluating the validity, utility and cost-effectiveness, as well as ethical properties, associated with a specific drug application. Translational Diagnostics Translational diagnostics is defined in Chapter 31 as “the subfield of translational medicine concerned with diagnostic methods and information.” Many of the translational issues affecting genomics as whole also affect this area of innovation. These include communication and education of researchers, institutional support for translational research activities, the regulatory environment, cost and reimbursement issues, the need for bioinformatics and complex data handling capabilities, intellectual property and licensing protection and the willingness to embrace new paradigms for diagnostic and therapeutic strategies (Horig and Pullman, 2004; Horig et al., 2005; Humes, 2005; Marincola, 2003; Sonntag, 2005). Integration of Genomic Testing into Clinical Practice A final hurdle that genome-based medicine faces is the integration of testing into day-to-day clinical practice. Despite the

optimism that genomic testing might bring to improving the practice of medicine, there are barriers that must be overcome for their seamless integration into clinical practice. The incorporation of genetics and genomics into patient management guidelines has largely failed to occur thus far, perhaps because researchers, diagnostic firms and the regulatory authorities are still seeking to establish methodologies by which to judge their effectiveness because practicing clinicians and guideline writers are still working to understand how such new tests fit into current models of care and risk assessment, and because payers are just beginning to foresee new pressures to cover the additional costs. Indeed, population studies and outcome studies will be critical for this final phase of clinical uptake, implementation into guidelines and into reimbursement strategies. To address some of these issues, a framework has been proposed to assist in genetic testing evaluation, consisting of six areas (Table 22.3): technical and operational excellence, diagnostic capability, impact on diagnostics and/or prognostics, impact on therapeutic strategy, cost-effectiveness and health outcomes (Douglas and Ginsburg, 2008). It is clear that the physician community faces many of the same issues the payer and diagnostics groups do as well. Therefore, it makes sense to consider developing the partnerships that bring the stakeholders together to jointly address these translational obstacles.

HOW ARE WE GOING TO DO THIS? DEVELOPING ENVIRONMENTS THAT FOSTER TRANSLATIONAL GENOMICS TO HEALTH APPLICATIONS It is clear that academicians and industry scientists are on convergent paths to personalized medicine using genomic technologies and information. Specialized centers for genomic and personalized medicine in academic health systems can be instrumental in integrating, facilitating and catalyzing the needs of both the academic and industry stakeholders by providing: ●

●

●

●

●

●

Access to patients, patient data, and molecular and biological data that drive the development and exploration of genomic information and its link to clinical outcomes. The scientific foundation for novel biomarker discovery for both disease and drug response based on mechanism. An environment for innovation that fuels the development of novel translational strategies. A vehicle for aligning the efficiency and quality metrics of patient care with the goals of personalized medicine. The infrastructure for the types of public–private partnerships required for executing genomic assay-guided clinical trials, and, finally, A place to engage in a dialog and research on the key issues challenging the translation of genomics into personalized health care (Haga and Willard, 2006): education, facilitating clinical trials, information systems and integration into practice.

How Are We Going to Do This? Developing Environments that Foster Translational Genomics to Health Applications

TABLE 22.3

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271

Framework for evaluating genetic tests*

Technical capability and operational excellence

●

Diagnostic capability

●

●

●

Impact on diagnostics and/or prognostics

● ●

Impact on therapeutic strategy

● ● ●

Cost-effectiveness

● ●

Health outcomes

● ● ●

development of analytic standards computational capabilities to acquire, store and analyze large datasets define the clinical situation(s) that may be informed by genomic data validation of genomic associations or predictors in distinct and separately ascertained cohorts development of clinical decision support platforms and “just-in–time” solutions in the practice environment linkage to authoritative knowledge sources evaluate genomic testing in the context of physicians’ confidence in decision-making develop educational strategies with test implementation assess physician behavior assessment of system wide costs resultant health care utilization and outcomes assess patient, provider, payer and government perspectives incorporation into guidelines driven by the evidence base carry out prospective randomized trials of usual care versus genomics guided care development of an educational strategy aimed at the patient

*Modified from Douglas and Ginsburg (2008).

Public private partnerships

Diagnostic Companies

Specialized centers for genomic and personalized medicine

Personalized medicine

Dx tools Genome scientists Clinicians Clinical researchers Statisticians Informaticians Assay developers Policy analysts

Rx/Dx

Academic medical centers

Health systems Genomic discoveries

Clinical implementation

Drug Companies

Figure 22.4 A framework for an organization to translate genomics to personalized medicine through public–private partnership and academic health centers.

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To bring about the transformation in health care promised by the genome sequence and its derivatives will require assembling diverse stakeholders focused on the application and translation of genomics with a goal of improving the health of individuals and driving efficiency in health care. Specialized centers housing basic genome science laboratories, clinical researchers, informaticians, clinicians, health policy makers and in partnership with industry (pharmaceutical and diagnostic companies), and with health systems that will enable the scientific output of the genome to cross the chasm between bench and bedside. Just a few years ago, academic medical centers alone were the primary mechanism to foster translational research and facilitate collaboration between researchers and clinicians. In the United States, the key ingredients for this effort are in place through the NIH Roadmap and the Clinical and Translational Science Awards (NIH, 2007). This NIH initiative will eventually fund up to 60 academic health centers and their associated clinical practice partners, with significant infrastructure capability in most of the areas above. These large centers are being bolstered by multiple grants and contracts that are intended to provide

avenues for innovative approaches to scientific problems ranging from biomedical informatics to community-based research networks. Similar programs are under way in Canada, the United Kingdom, Singapore, Germany and other countries. In particular, it will be important for governmental and foundational funding agencies to anticipate the critical imperative for interdisciplinary work (Figure 22.4). Academic health systems will need to consciously steer faculty activities in this direction to reap the benefits of the enormous societal investment in the biomedical enterprise that has made this clinical scientific revolution possible. Industry will need to engage earlier in the discovery process and assist in guiding the research agenda. Regulatory agencies and payers will need to prepare for this fundamental shift in the basis for assessing diagnostic and therapeutic technologies and their reimbursement. Effective planning for this coming shift in the way human disease and its prevention and treatment are viewed through the genomic lens, particularly the systems aspects of creating interdisciplinary hubs for the development of translational genomics, will result not only in an advanced clinical scientific discipline, but improved health for people worldwide.

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23 Principles of Study Design Peter Grass

INTRODUCTION The general procedure in scientific research is to formulate hypotheses and then to verify them directly or indirectly by their consequences. This verification necessitates the collection of observations, and the design of the experiment is essentially the pattern of the observations to be collected. A formal condition for a hypothesis is that it must be formulated in such a way that verification or lack of it may be achieved by direct observation with an experimental procedure, or that deductions made from the hypothesis lead to predictions that may be verified. The purpose of the theory of the design of experiments is to ensure that the experimenter obtains data relevant to his hypothesis in as efficient a way as possible; it strives for optimization in terms of time, resources, information content, and degree of relevance. There are more and less efficient ways of using resources. A measure of accuracy is obtainable, and it is necessary as in all phases of practical life to consider the costs and time consumption of obtaining a particular accuracy, whether it is worth this cost, and at which stage the costs of obtaining increased accuracy are too great (Kempthorne, 1952). In the case of clinical and animal studies, the principles of optimal study design should also consider ethical reasons. During the planning phase of an experimental study it is good practice to prepare a written draft of the proposal for the experiment that includes a statement of the objectives, the description of the experiment covering such matters as the experimental treatment, the sample size, and the experimental material and finally an outline of the method of analysis of the Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

data. Since experimental design and statistical analysis of the data are very much interrelated, it is recommended to involve the statistician or seek his/her advice at the time of planning of the study (Cochran and Cox, 1962). The statement of the objectives may have the form of a hypothesis to be tested or an effect to be estimated. Alternatively, the study may be of exploratory nature with the objectives of generating a hypothesis rather than testing a hypothesis. In any case, the formulation of the objectives should be clear, specific, and devoid of ambiguity. Objectives should not be too ambitious and if necessary, they should be classified into main and secondary objectives because not all objectives can be met with the same level of accuracy with a given study design. There should also be a statement about the area over which generalizations are to be made, or the populations about which inferences will be made. Genomic medicine encompasses a wide range of study designs that are known from preclinical and clinical research and other life sciences. First, there are studies with an exploratory intention in order to generate hypotheses about, for instance, the mode of action of a drug or the biological pathway being impaired by a specific disease. These exploratory studies are followed by confirmatory studies in order to provide evidence for or reject these hypotheses and to obtain some confidence. For instance, a genomic biomarker signature of patients responding or non-responding to an investigational compound could be derived from blood samples collected from patients participating in a classical clinical trial. Usually the sample size of such trials is limited and the patient population is restricted by stringent inclusion and Copyright © 2009, Elsevier Inc. All rights reserved. 275

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exclusion criteria. The design of the study is sub-optimal for the identification of biomarkers because biomarker identification is not the main objective of the trial. The level of confidence in such a genomic signature would be low due to these limitations. Consequently, it is necessary to validate the biomarker signature in one or more subsequent studies and to obtain reliable information about its predictive performance in terms of selectivity, specificity, and generalizability. This approach is well established and principally not different from the development of other diagnostic tools, such as diagnostic imaging techniques or laboratory tests. There is, however, one fundamental difference between genomic studies and classical studies. Genomic, proteomic, and metabolomic studies provide usually multiple endpoints, which can be up to 50,000 in case of microarrays or more than 100 different multiplexed assays, in contrast to a single endpoint of a classical study. Multiple endpoints need to be taken into account when planning a study because the sample size of the study has to be adjusted accordingly. In the following, the fundamental design concepts will be introduced before measures to increase precision and accuracy of experimental studies will be presented including randomization, blocking, and replication with reference to sample size calculation. There are many further measures that may help to optimize the study design such as blinding, appropriate choice of control groups, and deviation from baseline measurements. Finally, possible biases that are typical for genomic and proteomic studies will be presented.

PRINCIPLES OF EXPERIMENTAL DESIGN Design Concepts Several statistical design concepts have to be explained before we can apply the principles of experimental design to genomic medicine studies. These design concepts are very generic and hold true for any experimental study, be it a typical clinical trial or a clinical genomic study. The first design concept refers to the observation of an effect that usually involves a measurement process. We all know that each measurement process is inherently imperfect, and that the measured value usually deviates from the true value by some amount called the experimental or measurement error. This type of error may be due to technical variations such as temperature fluctuations, small inadvertent impurities of the reagents needed for an assay, or small differences in the manual handling of the instrument. Small inaccuracies of the treatment conditions such as small variations in dosing may also contribute to the experimental error. Any optimization of the measurement process leads towards a minimization of the measurement error and ultimately to a higher precision of the measurement result. Obvious measures to improve the precision include repeated measurement of the same sample, standardization of the measurement process, and automation.

Second, there is the biological variation of the parameter of interest. We know clinical parameters may vary between populations, for instance, between Caucasians and Blacks, and between individuals within a given population; for instance, male versus female. Even within a subject a parameter may vary over time due to circadian rhythms, according to their health conditions, demographic particularity of subjects or other nutritional and environmental factors. Normal ranges of laboratory parameters have been defined within which the measurement values may vary under normal physiological conditions. Generally, biological variation cannot be minimized by a more precise measurement, but it can be reduced by specifying it for more homogeneous groups; for example, for males and females separately and within each gender for different age groups. Measurement error as well as biological variation are known to be random and both are very often normally distributed; that is, small positive and negative-random variations around an average value are more frequent than large variations which yields the well-known bell-shaped Gaussian distribution curve. In addition to random variation, there may be systematic deviation of the measurement value from the true value which is called bias. Such systematic deviations may be due to faults in the measurement process. For instance, incorrect calibration of an instrument would yield concentrations of a laboratory parameter being measured systematically either too high or too low, which is an example of measurement bias. A systematic error may also be introduced at the level of recruitment when patients or subjects are not representative of the entire population. For instance, only young healthy males are included in a study with the objective to assess the concentrations of a given parameter, but conclusions about the parameter distribution of the entire population will be made. This is a typical example of selection bias. Ultimately, the goal of any experimental study is to draw general conclusions about the behavior of a parameter of interest in an entire collective. Therefore, another important item refers to the design concepts of population and random sample. A population encompasses the entirety of subjects under a given experimental condition. An experimental study, however, will never be conducted in the whole population, but rather in a small sub-population, referred to as a sample, which would include a limited number of N subjects only. It is assumed that the sample is a valid representation of the entire population if the sample elements are drawn randomly from the entire population and the sample size N is sufficiently large. Within a population, the parameter of interest has a given distribution which may be characterized by its population mean () and standard deviation ( ) in the case of a normal distribution. Mean and standard deviation as calculated from the sample data is only an estimation of the true population parameters where the precision of the estimate is influenced by the sample size. The individual elements of a sample that undergo a predetermined treatment or belong to a certain condition are called experimental units (e.g., patients, subjects, animals) while repeated measurements of a parameter in the same experimental unit are called observational units; for instance, several readings of blood pressure in the same patient obtained at the same time under the same treatment condition.

Principles of Experimental Design

Further design concepts in the context of genomic studies include experimental conditions or treatments where conditions may refer to a disease status of a patient and treatment may refer to different medications. The investigation of the influence of the different conditions or treatments on a target parameter is usually the main objectives of a study. A main objective of experimental design is to determine which conditions or treatments and which levels should be chosen in a systematic way. Random allocation of the experimental units to the different conditions or treatments is mandatory. The choice of an appropriate control group is very important and affects the outcome of the study. Obviously, it makes a difference whether you take a placebo treatment or the reference treatment as a control condition. In contrast, blocking factors are conditions or treatments that are expected to influence the measurement systematically but are of no particular interest, so called nuisance variables. Typical examples of nuisance variables in genomic studies are ethnicity and gender of the patients, or the centers of a multicenter trial, but also operators of the laboratory where the mRNA extraction is performed. Confounding means the “mixing” of sources of variation in data so that their effects cannot be distinguished from each other. The sources of variation can be factors introduced by the experimenter or unknown variables introduced by the patient or the environment (Wooding, 1994). A general rule (Box et al., 2005) says “Block what you can, randomize what you cannot.” One of the most prominent objectives of experimental design is to identify possible nuisance variables and account for them by using them as blocking factors. Measures to Increase Precision and Accuracy Accuracy and precision are two characteristics that determine the quality of an experimental study. Precision describes how well the measurement can be reproduced and is usually reported as an average replication error. It can be determined by performing repeated measurements of the same experimental unit. Accuracy describes how close to a true population value a measurement lies. In many situations the true population value is unknown; this is in particular true for microarray experiments due to the absence of calibration samples. In experiments, however, where calibration samples of known concentrations are used, the accuracy can be estimated (Moreau et al., 2003). Bias effects accuracy and if the bias is large, a measurement may be of high precision but of low accuracy. Basically, there are three ways to increase the precision and accuracy of experimental studies: (a) refinement of experimental techniques; (b) careful selection of subjects with additional information about them that can account for a part of variation or by grouping them according to certain characteristics; and (c) enlargement of the size of experiment by employing more replicates. Fundamental techniques applied in statistical design (Kerr, 2003) include randomization, blocking, and replication. Randomization A random sample is one in which each individual of a population to be sampled has an equal chance of being selected. This

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ensures that there is no bias; that is, on the average, the estimates of the population parameters will be accurate (Bolton and Bon, 2004). Furthermore, we assume that individuals being randomly sampled from a population are valid representatives of this population. Randomization encompasses both random selection from a population and random assignment to a treatment. Another reason for randomization is that statistical theory is based on the idea of random sampling. In a study with random allocation, the differences between treatment groups behave like the differences between random samples from a single population. We know how random samples are expected to behave and can compare the observations with what we would expect if the treatments were equally effective (Altman and Bland, 1999). However, randomization does not eliminate the possibility that treatment and control groups will differ according to some unknown factors that affect outcome (confounding variables). We can expect the random assignment of treatments to ensure some rough balancing of those factors. It is important to recognize that because randomization relies on the averaging of sampling variation, in studies with small numbers of subjects it may not effectively reduce bias (Sica, 2006). Blocking Blocking is another measure to increase precision and to reduce bias. In practice, subjects of similar characteristics are grouped together in blocks and randomly assigned to treatments. In a multi-center trial, for example, patients would be blocked by center, and within each block they would be randomly assigned to the different treatments. Other common blocking variables include gender, age groups, and ethnicity. In crossover trials, where each patient represents a block, each patient receives each of the two or more treatments in random order. Such a randomized block design is advantageous when the level of responses are very different between patients, but the within-patient variability is relatively small. If the subjects are properly randomized within the blocks, data analysis techniques, such as analysis of variance, are able to separate the variability due to the different blocking factors resulting in a decreased experimental error. Replication In analogy to biological and technical variation we can specify biological and technical replication. Biological replication is directly linked to the number of experimental units (e.g., subjects or patients) included in the study. Technical replication refers to repeated measurements of a particular parameter derived from one and the same experimental unit under the same experimental condition. Multiple observations made on material from the same experimental unit should not be confused with biological replications. Whatever the source of the experimental error, replication of the experiment by increasing the sample size steadily decreases the error associated with the difference between the average results for two treatments/conditions. Precautions have to be taken to ensure that one treatment/condition is no more

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δ

Number of Subjects

1200

Populations μσ males 15.5  2.0 females 13.5  2.5

900

T A B L E 2 3 . 1 Results of a Monte Carlo Simulation: Mean, standard deviation and coefficient of variations (CV) of the differences between the 5000 sample means drawn from a male and female Sample Differences (n ⴝ 5000)

600

N 300

0 10

12

14

16

18

Hemoglobin (g/dL)

Figure 23.1 Frequency distribution of hemoglobin concentrations (g/dL) in healthy female and male subjects.

likely to be favored in any replicate than another, so that the errors affecting any treatment tend to cancel out as the number of replications is increased (Cochran and Cox, 1962). Monte Carlo Simulation The effect of replication on the precision of an experimental study can be demonstrated by a Monte Carlo simulation. Assuming that the true distribution of the hemoglobin concentrations in male and female healthy subjects is known by measuring hemoglobin in thousands of representatives in both populations, the results of this investigation are depicted in Figure 23.1. The hemoglobin concentrations are normally distributed within both populations with population means and standard deviations of 13.5  2.5 g/dL in females and 15.5  2.0 g/dL in males. Hence, the true difference () between the two population means is exactly 2 g/dL, and the standard deviations of both populations are relatively large compared to the difference between the means as indicated by the ratio ⁄  1.125. The objective of the Monte Carlo simulation is to provide an estimate of the difference between the two populations based on samples being drawn randomly from these populations and to demonstrate how the sample size influences the reliability or confidence of the estimated parameter. To do so, we draw small samples of N individuals from both populations and we assume that the samples are valid representatives of the entire populations. Based on the statistical characteristics of the samples we want to draw conclusions that hold true for the entire populations. Intuitively, we know that the individuals of each sample should be drawn randomly from the entire populations. Let us generate two samples by drawing randomly 3 individuals from a female population and 3 individuals from a male population. We calculate the mean x f , N 3 of the female sample and xm, N 3 of the male sample, and then the difference between the two sample means  N 3  x f , N 3  xm, N 3 .

Mean

Stdev

CV (%)

3

2.05

1.86

91

6

2.01

1.29

64

12

2.03

0.91

45

24

1.99

0.65

33

48

2.00

0.46

23

96

2.00

0.33

16

192

2.00

0.23

12

The mean converges to the true difference (  2.0) between the two populations and simultaneously, the CV decreases from 91% for N  3 down to 12% for N  192.

Let us repeat this procedure for n  5000 times and thereafter, calculate the mean and standard deviation of all n  5000 differences among the sample means.  N 3 

SN 3 

n

1 n

∑  i , N 3

and

i1

∑ (i,N 3  N 3 )2 i

n 1

For N  3 per group the average difference and standard deviation is 2.05  1.86 g/dL. We can see that the average difference is close to the true difference, but the estimate is not very precise as indicated by the standard deviation; the coefficient of variation amounts to 91% (see Table 23.1). We continue the Monte Carlo simulation by increasing the sample size from N  3 to N  6, 12, 24, 48, 96, and 192 and perform the same procedure as described above. Figure 23.2 displays the results of the simulation study; the mean differences between the average sample hemoglobin concentrations in males and females as a function of sample size are quite accurately close to the true value of 2 g/dL. The standard deviation of the difference is indicated by the error bars, and the trumpetlike shape of the envelope clearly demonstrates how the estimation error decreases with increasing sample size. We realize that the estimate of the hemoglobin difference between male and female becomes more and more precise with increasing sample size. With a sample size of N  192 the coefficient of variation has decreased to a level of 12% (see Table 23.1). If the variability of the populations is increased and/or the true difference  between the populations is decreased a higher sample size is needed in order to obtain the same level of precision. Figure 23.2 displays the result of the simulation with

Principles of Experimental Design

Hemoglobin Difference (g/dL)

4

Difference of Sample Means (g/dL) between Males and Females from 5000 Monte Carlo Simulation per Sample Size Means  Stdev

3

2

1

0 0

48

96 144 192 Sample Size per Group

240

288

Figure 23.2 Mean differences (solid diamonds) between mean hemoglobin concentrations in males and females as a function of sample size which was successively increased (N  3, 6, 12, 24, 48, 96, and 192) with ⁄   1.125. The standard deviation of each difference is indicated by the error bars. The trumpet-like shape of the envelope clearly demonstrates how the error bars decrease with increasing sample size. In consequence, the precision of the estimate of the hemoglobin difference between male and female increases. Dotted lines are the results of a simulation study with increased variability and/or decreased difference with

⁄   1.625.

the ratio ⁄  1.625; the estimation errors as indicated by the dashed lines are wider for a given sample size compared to the previous simulation with a ratio of ⁄  1.125. In summary, the Monte Carlo simulation study showed us how the estimate of a population mean becomes more and more precise with increasing sample size N. In addition, we have noticed that – with a given sample size N – the difference  between the two populations and the variability , in particular the ratio / play a crucial role in the estimation of the population parameters. Large ratios due to higher variability and/or smaller differences yield less reliable estimates and vice versa. Sample Size Calculation The reliability of the estimate of the difference between the two conditions can be related to the probability of committing an error. When comparing two samples obtained from two conditions, there are principally two different types of error that may occur and that we want to control. A type I error () is defined as the risk of concluding that conditions differ when, in fact, they are the same. The  level is usually set at 5%. A type II error () is the risk of erroneously concluding that the conditions are not significantly different when, in fact, a difference of a given size  or greater exists. Commonly chosen values of  are between 5% and 20%. The type II error is directly linked to the power of test (1-) which implies the probability of being able

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to demonstrate a statistically significant difference between the samples if a true difference of the estimated size  actually exists in a larger population. In the planning phase of an experimental study we can use the Greek Quadruple (, , , and ) in order to calculate the sample size N of the experiment needed to detect a difference of size  with given a standard deviation . The difference  should be of “practical significance” and should make sense in the context of the experimental setting. The standard deviation of the parameter of interest is usually taken from already existing data; that is, from a pilot study or from data published in literature or by an educated guess. It is the ratio / that impacts the sample size at a given - and -level. Intuitively, we know that a greater sample size is needed when we want to detect a smaller difference , or when the variability is greater. If we want to have more confidence in our results by minimizing type I and type II error we would increase the sample size too. There are formulas to calculate sample size that are specific for the different experimental designs. It is beyond the scope of this article to provide explicit formulas for sample size calculations under the different experimental designs and it is recommended to get advice from a statistician or use an appropriate computer program. In the context of genomic studies it is important to note that classical sample size calculations are only valid for studies with a single endpoint. Genetic, genomic, proteomic, and metabolomic studies, however, are characteristic for their multiple endpoints which need to be accounted for in sample size calculations, for instance by choosing false discovery rate (Benjamini and Hochberg, 1995) or adjusting the Type I error for multiple comparisons. In genomic studies, the estimation of the standard deviation is aggravated by the facts that the number of genes with a reliable signal and the associated standard deviation are different for each of the thousands of features on a chip, that they deviate from tissue to tissue, and that they differ between chip technologies (Lee et al., 2000). Further Measures to Optimize Study Design Accuracy as well as precision can strongly be influenced by how the different treatments or conditions are allocated to the experimental units. Well-known designs of clinical trials include parallel group design and cross-over design. Many other design concepts are employed for the purpose of bias elimination and reduction of the experimental error; for instance, blinding techniques, inclusion of positive control groups, repeated measurements, and others. The objective is always to reduce the heterogeneity of the experimental units by careful selection of patients by specifying inclusion and exclusion criteria and check of patient’s eligibility. It must be pointed out, however, that patients fulfilling the eligibility criteria for homogeneous groups are usually not representatives of all those with the disease or all those for whom the therapy is intended; that is, the target patient population. The art of good experimental design is to find the right balance between taking into consideration as many sources of variation as possible on one hand and the simplicity and practicability

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of an experimental study on the other. Without doubt, more complex study designs have more restrictions and they lack flexibility; they are difficult to implement and they are bias-prone. For instance, the number of patients participating in cross-over studies is usually smaller in comparison to parallel studies, but the impact of patients dropping out from the study is much higher. Another important aspect is related to the symmetry of study designs. Although most data analysis methods are able to cope (to a limited extent) with unbalanced data and missing values, a certain symmetry should be strived for; for example, equal number of patients per treatment group, equal number of visits per patient, balanced order of administration, and equal number of replicates per patient. Symmetry of design increases the power of the analysis and – even more important – makes the conduct of the study less error prone and facilitates the quality control of the data. Optimal study design is also concerned with maximization of the information content of the study data. In a placebo controlled trial, the relative deviation of a clinical parameter under a given treatment condition from its baseline value is more informative than comparing the mean parameter of a placebo treated patient group with that of a different group of actively treated patients. Note that deviation from baseline in the absence of a placebo control group may be due to changes in environmental conditions during the intervening time period; therefore, the inclusion of a placebo group is strongly recommended. Classical pharmacokinetic/pharmacodynamic studies are very often designed as placebo-controlled cross-over studies. Their objective is to assess the time course of the drug concentration in blood and tissue, and simultaneously to measure the pharmacological effects that might be elicited by that compound or placebo in the same patient. Indisputably, such related concentration-effect time profiles provide more information than a single reading of the effect at a predetermined visit. However, in many situations the design is too complex for a clinical routine situation, and another caveat is a possible carry-over effect if the wash-out phase between treatments is not long enough. The majority of studies are designed as comparative trials, and the appropriate choice of the control treatment or condition is crucial. Several types of comparisons are possible: between a patient group being treated with an active compound and another patient group being treated with placebo; or between a patient group being treated with a developmental compound and another patient group being treated with the current clinical gold standard as a positive control. Even the simultaneous inclusion of placebo treatment, positive control and negative control may make sense in certain situations. Not only the choice of the control treatment is important but also the choice of the control population should not be neglected. It makes a difference whether the control group consists of age-matched healthy volunteers or age-matched co-morbid patients. Blinding, sometimes called masking, is another measure to help avoid bias. Human behavior is influenced by what we know and what we believe. In research there is a particular risk of expectation influencing findings, most obviously when there is some subjectivity in the assessment, leading to biased results.

Blinding is used to try to eliminate such bias. Blinding is particularly important when the response criteria are subjective; a typical example is the assessment of pain. Double-blind usually refers to keeping patients, those involved with their management such as nurses and study monitors, and those collecting and analyzing clinical data unaware of the assigned treatment, so that they should not be influenced by that knowledge (Day and Altman, 2000). It is important that the treatments look similar: same taste and smell, same color, same number of capsules to be taken, same route of administration which is called the double dummy method. Blinding in the assessment of the performance of a diagnostic test is crucial: those who do the assessment must be blinded; also when testing the reproducibility of a diagnostic test the observer must be unaware of the previous measurements. If not blinded, clinical trials show larger treatment effects and diagnostic test performance is overestimated. Possible Bias in Genomic Studies Two kinds of bias may be of particular importance for genomic studies. The first kind refers to the selection of the experimental units, that is which subjects or patients are to be recruited to participate in a study, and the second kind of bias may be introduced at the measurement level. Again, the process of patient selection should ensure that they are representative for the target population. Inclusion and exclusion criteria should not be too stringent because they may limit the generalization of the study findings. Selection bias in biomarker development are manifold and they usually lead to an overestimation of the sensitivity of the assay. Selection bias examples include the exclusion of patients with mild disease who are usually difficult to diagnose. The limitation of the patient population to only those who are volunteering to participate in the study might introduce the bias that health-conscious patients are over-represented. Patients dropping out from a study may be different from patients who remain in the study; consequently, the exclusion of those patients from the analysis will introduce a selection bias. The choice of a control group may also introduce bias. It makes a difference whether you choose young, disease-free subjects or non-diseased, but age-matched patients as a control group to be compared with a diseased group (Sica, 2006;Whiting et al., 2004). Observational biases may be caused by differences in methods in which information is collected and data are obtained or due to the fact that patients may under- or over-report their medical history or personal habits if they are aware of their diagnosis. Bias due to different methods being applied to different experimental groups occurs in clinical studies quite frequently. For instance, a reference test is only applied in case of a positive study test result. The consequence is that more sick subjects than healthy subjects undergo the reference test. Patients with negativetest results and those with benign-appearing lesions do not typically undergo an invasive reference test, for instance a kidney biopsy. On the other hand, bias may be introduced because patients with positive test results undergo more intensive follow-up investigations and therefore, missing data are present nonrandomly for disease-free subjects. Another issue is represented by

Design Issues in Genomic Medicine

the fact that the reference standard against which a new genomic biomarker is to be compared is not 100% accurate or a surrogate reference test is used instead of the clinical endpoint; for instance, coronary angiography for the diagnosis of coronary artery disease. Between-group differences in reporting may happen because patients with positive diagnostic results may better recall or even exaggerate their presentation symptoms. When an unblinded investigator is the interviewer and also involved in the interpretation of the results he/she may inquire details of medical history more rigorously in the case of positive test results. There are several sources of variation that are specific to genomic and proteomic experiments. The process of tissue sampling should be randomized across conditions in order to ensure uniform handling of the tissue specimen. In a toxicology study, for instance, it makes a difference when vehicle treated animals are all sacrificed first, followed by the low, medium and high dose group animals in a sequential way. Due to a considerable time differences between last dosing and tissue harvest the tissue exposure to the drug may be different, and in consequence, mRNA response may be changed due to this systematic error in sample collection. Another sample collection bias refers to the time of food intake in relation to specimen collection which should be blocked for, or if blocking is impossible, should be randomized. The sample collection scheme should also take circadian and seasonal changes into account (Rudic et al., 2005) and the stress exposure of the patients and animals should be balanced across treatments. Brain is a complex organ, with a heterogeneous distribution of distinct subpopulations of cells, intricate signaling and regulatory circuits, and exquisite lifelong sensitivity to environmental variation. These factors result in high levels of interindividual variability of gene expression (Karssen et al., 2006). Due to a difficult sampling situation in pathology possible tissue contaminations may occur; for instance, rat duodenum samples are often contaminated by pancreas tissue. The tissue sample volume is related to the amount of mRNA that can be extracted and should therefore be balanced across conditions. In clinical trials, co-medication can considerably influence the gene expression pattern of the patients. The way specimens are stored may also impact the outcome of the experiment; nuisance variables include storage duration and temperature, and whether tissue samples are conserved in paraffin blocks or as snap frozen samples. Another source of variation is how specimens are harvested from heterogeneous tissues such as brain or kidney. The mRNA extraction protocol should be standardized and the samples should be randomized across operators. Batch effects may be introduced by age and impurities of reagents. It is a well-known fact that microarray chips vary between production batches and it is strongly recommended to use one batch of chips for one study. If this is not possible the samples should be blocked by batches. It is good experimental practice to record temperature, experimenter, and date/time of hybridization (Li et al., 2002; Zakharkin et al., 2005). Systematic variation can be due to the use of different scanners or scanner settings, or different photo-multipliers and gains. Different spot-finding software and different grid alignments may also introduce bias (Coombes et al., 2002).

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Needless to say, the entire technical process should be as standardized as possible and tissue sample processing should be as homogeneous as possible. If for any reason this is not possible, a proper blocking or randomization of the sample processing should be taken into consideration. Obviously, technical variation of the measurement process can be controlled to a certain extent (Tinker et al., 2003).

DESIGN ISSUES IN GENOMIC MEDICINE The basic concepts of experimental design are also applicable to genomic medicine studies; randomization, blocking, and replication are the most important measures to improve the accuracy and precision of the experimental outcome. Typical sources of random as well as systematic variation known from classical preclinical and clinical studies could possibly be identified also in genomic studies. There are, however, additional sources of bias that are directly related to the new technologies, and this requires the extension of the design concepts also to the tissue sampling and measurement level. From an experimental design point of view there are some fundamental issues that are not yet completely solved and need further investigation. Genomic and proteomic studies produce information of unparalleled wealth.Without doubt is the high sensitivity of these technologies in combination with the high number of features – genes or proteins – being interrogated simultaneously a great achievement for genome-wide screening purposes. The downside of this is that nuisance variables which are negligible in the clinical routine may gain importance on the molecular level; factors such as co-medication or co-diseases or personal habits like cigarette smoking may interfere with the molecular signature of a disease or a drug treatment under investigation. When designing a genomic study one should keep in mind that gene expression is a highly dynamic phenomenon that develops over time and may be subjected to feed-back and feedforward mechanisms (Yang and Speed, 2002). Therefore, the time point when the tissue specimen is collected in relation to a stimulus such as the administration of a drug is important. For instance, the experimental design of a toxicology study needs to take into consideration that immediate response genes are differentially expressed within minutes after injection of a toxic compound and may be back to normal levels after a few hours followed by mid term response genes that may react hours later. Late response genes mainly including residual persistent effects or compensatory repair mechanisms may react even days later. The gene signature is dependent on the time point of sampling. Another open issue represents the lack of microarray validation in a form as we know it from other bioanalytical assays, although promising attempts in this direction are made (Van Bakel and Holstege, 2004). Microarray technologies, whether single- or dual-channel microarray designs, always produce relative gene expression values. At the moment it is impossible to obtain an absolute measure of the concentration or the amount

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of a specific mRNA segment. Validated assays would employ calibration samples for the calculation of a calibration curve and to determine upper and lower limit of quantification and detections, respectively. Quality control samples of known content could be randomly distributed among the unknown samples in order to provide in-process quality control. As long as we do not know the true expression values of the genes it is extremely difficult to identify measurement bias that is introduced at the mRNA extraction and hybridization level. Real-time PCR confirmation will always be limited to a subset of genes and cannot replace the validation of an entire microarray (Wang et al., 2006). Furthermore, it cannot provide information about the selectivity and precision of the microarray itself. From an experimental design point of view, dualchannel microarrays do not really provide advantages over single-channel microarrays; quite the reverse is true, due to possible dye effects special designs of the hybridization need to be used which include dye swapping and dye balancing in order to take these systematic errors into account (Churchill, 2002; Kerr and Churchill, 2001a, b;Wit and McClure, 2004). A related open issue is the poor reproducibility of genomic results across studies. Even the same tissue specimen being analyzed by different laboratories may produce different genomic signatures even when using the same microarray technology. The reason is only partly due to variation in mRNA extraction protocols, hybridization process and scanning. In particular in the field of biomarker research it had to be realized that many peerreviewed studies publishing genomic biomarker signatures based on statistically significant differences are not reproducible, and the conclusions drawn are overly optimistic (Halloran et al., 2006). Validation studies need to be conducted by several completely independent teams with appropriate sample size. Small sample sizes might actually hinder the identification of truly important genes (Ioannidis, 2005). In contrast to the current standard, thousands of samples are needed to generate a robust gene/ protein signature for predicting outcome of disease (Ein-Dor et al., 2006). If the identification and validation of gene signatures is the objective the approach of sample size calculation needs rigorous reconsideration. In the context of personalized medicine we can expect more and more clinical trials that will include genomic or genetic markers for population enrichment. Genomic biomarkers will be developed for the identification of patients likely to respond to treatment with a developmental compound. The availability of such response biomarkers will greatly influence the design of clinical phase III trials and will have a major impact on the sample size and statistical power. Furthermore, enrichment of clinical studies by selecting patients who are more likely to exhibit an effect fits with the growing appreciation that patients who seem similar may differ in their risk and likelihood of response to treatment (Temple, 2005). If responders represent only a small fraction of the patients of a particular disease, it may be difficult, if not impossible, to find a treatment effect in an unselected-non-enriched-population.

Let us assume a valid biomarker of response to a developmental compound exists before commencing a clinical phase III trial – which is, admittedly, rarely the case-, then patients with positive test result will be the only ones being included in the study. The response biomarker must be available and, in conjunction with the compound, has clinical utility. Under these circumstances, the approval will be limited to responders, only. However, if the test is commercially not available or is not validated safety assessments in both-test positive and test negativepatient groups is needed (White et al., 2006). An interesting adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients was proposed by Freidlin and Simon (Freidlin and Simon, 2005; Simon, 2005). According to this proposal a phase III trial is designed as usual, that is randomization of patients to new drug and control treatment, but dividing the study into first part involving N1 patients and second part involving N2 patients. After completion of the first part of the study, an interim data analysis is conducted based on the available data. The aim of this analysis is to identify a genomic or genetic marker of response. Then, the study will be completed by entering all planned patients, responder as well as non-responder. The final analysis is conducted in a two step approach. First, the new drug is compared to the control treatment including all patients and ignoring the responder classification. If the treatment effect on the primary pre-specified endpoint is significant at the 0.04 level then effectiveness for the eligible population as a whole can be claimed. However, if the overall test is not significant at the 0.04 level, a single subset analysis will be performed by evaluating the new drug in the responder patients accrued in the second part of the study relative to control treatment. If the treatment effect is significant at the 0.01 level, effectiveness for responder population can be claimed. In this situation, the diagnostic test to identify responders to treatment needs to be validated and made available. A critical requirement of this design is the a priori planning of the data analysis. Obviously, this adaptive design does not reduce the sample size; it requires even slightly more patients for a given statistical power since the overall significance level is at 0.04 rather than 0.05. However, the design has some advantages that might compensate for the slightly higher sample size. Safety assessment, for instance, is performed in the whole study population as in any classical trial. In addition, the design provides an opportunity to demonstrate a significant treatment effect even in a small fraction of responders. Based on their simulation results (Freidlin and Simon, 2005) highest statistical power is obtained with a N1/N2 ratio slightly less than or equal to one provided that sensitivity and specificity of the response biomarker is in the range of 0.9–1.0. We are in the beginning of trying to incorporate pharmacogenomics and pharmacogenetics into clinical trials and there is very limited practical experience yet. Certainly, it will not always be possible to find genetic associations that would relate to drug response, and if an association is found, it may be difficult to replicate it.

Recommended Resources

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REFERENCES Altman, D.G. and Bland, J.M. (1999). Treatment allocation in controlled trials: Why randomize. BMJ 318, 1209. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false alarm discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc B57, 298–300. Bolton, S. and Bon, C. (2004). Pharmaceutical Statistics. Practical and Clinical Applications, Fourth Edition. Marcel Dekker, New York. Box, G.E.P., Hunter, J.S. and Hunter, W.G. (2005). Statistics for Experimenters. Design, Innovation, and Discovery, 2nd Edition. John Wiley & Sons, Hoboken, NJ. Churchill, G.A. (2002). Fundamentals of experimental design for cDNA microarrays. Nat Genet 32, 490–495. Cochran, W.G. and Cox, G.M. (1962). Experimental Design. John Wiley & Sons, New York. Coombes, K.R., Highsmith, W.E., Krogmann, T.A., Baggerly, K.A., Stivers, D.N. and Abruzzo, L.V. (2002). Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays. J Comp Biol 9, 655–669. Day, S.J. and Altman, D.G. (2000). Blinding in clinical trials and other studies. BMJ 321, 504. Ein-Dor, L., Zuk, O. and Domany, E. (2006). Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. PNAS 103, 5923–5928. Freidlin, B. and Simon, R. (2005). Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res 11, 7872–7878. Halloran, P.F., Reeve, J. and Kaplan, B. (2006). Lies, damn lies, and statistics: The perils of the p value. Am J Transplant 6, 10–11. Ioannidis, J.P.A. (2005). Microarray and molecular research: Noisy discovery. Lancet 365, 454–455. Karssen, A.M., Li, J.Z., Her, S., Patel, P.D., Meng, F., Evans, S.J., Vawter, M.P., Tomita, H., Choudary, P.V. and Bunney, W.E. (2006). Application of microarray technology in primate behavioral neuroscience research. Methods 38, 227–234. Kempthorne, O. (1952). The Design and Analysis of Experiments. Robert E. Krieger, Malabar. Kerr, M.K. (2003). Design considerations for efficient and effective microarray studies. Biometrics 59, 822–828. Kerr, M.K. and Churchill, G.A. (2001a). Experimental design for gene expression microarrays. Biostatistics 2, 183–201. Kerr, M.K. and Churchill, G.A. (2001b). Statistical design and the analysis of gene expression microarray data. Genet Res 77, 123–128. Lee, M.L.T., Kuo, F.C., Whitmore, G.A. and Sklar, J. (2000). Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci 97, 9834–9839.

Li, X., Gu, W., M, S. and Balink, D. (2002). DNA Microarrays: Their use and misuse. Microcirculation 9, 13–22. Moreau, Y., Aerts, S., De Moor, B., De Strooper, B. and Dabrowski, M. (2003). Comparison and meta-analysis of microarray data: From the bench to the computer desk. TrendGenet 19, 570–577. Rudic, R.D., McNamara, P., Reilly, D., Grosser,T., Curtis,A.M., Price,T.S., Panda, S., Hogenesch, J.B. and FitzGerald, G.A. (2005). Bioinformatic analysis of circadian gene oscillation in mouse aorta. Circulation 112, 2716–2724. Sica, G.T. (2006). Bias in research studies. Radiology 238, 780–789. Simon, R. (2005). A roadmap for developing and validating genomic classifiers for treatment selection. www.fda.gov/cder/genomics/ presentations_20051006/051007_15_Simon.pdf Temple, R. (2005). Use og genomic biomarkers in a regulatory environment. DIA Genomics Biomarker Conference, October 7, 2005. www.fda.gov/cder/genomics/presentations_20051006/051007_ 10_Temple.pdf Tinker, N.A., Robert, L.S., Butler, G. and Harris, L.J. (2003). Data pre-processing issues in microarray analysis. In A Practical Approach to Microarray Data Analysis (D.P. Berrar, W. Dubitzky and M. Granzow, eds), Kluwer Academic Publishers, Norwell, pp. 47–64. Van Bakel, H. and Holstege, F.C.P. (2004). In control: Systematic assessment of microarray performance. EMBO Rep 5, 964–969. Wang,Y., Barbacioru, C., Hyland, F., Xiao,W., Hunkapiller, K.L., Blake, J., Chan, F., Gonzales, C., Zhang, L. and Samaha, R. (2006). Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays. BMC Genomics 7, 59. White, T.J., Clark, A.G. and Broder, S. (2006). Genome-base biomarkers for adverse drug effects, patient enrichment and prediction of drug response, and their incorporation into clinical trial design. Person Med 3, 177–185. Whiting, P., Rutjes, A.W.S., Reitsma, J.B., Glas, A.S. and Bossuyt, P.M.M. (2004). Sources of variation and bias in studies of diagnostic accuracy. Ann Intern Med 140, 189–202. Wit, E. and McClure, J. (2004). Statistics for Microarrays. John Wiley & Sons, Chichester. Wooding,W.M. (1994). Planning Pharmaceutical ClinicalTrials.John Wiley & Sons, New York. Yang, Y.H. and Speed, T. (2002). Design issues for cDNA microarray experiments. Nat Rev Genet 3, 579−588. Zakharkin, S.O., Kim, K., Mehta, T., Chen, L., Barnes, S., Scheirer, K.E., Parrish, R.S., Allison, D.B. and Page, G.P. (2005). Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics 6, 214.

RECOMMENDED RESOURCES Statistics for Experimenters. Design, Innovation, and Discovery by G.E.P. Box, J.S. Hunter, and W.G. Hunter (2005) is a new edition of their classical work on experimental design. It is an excellent presentation of the concepts of experimental design with many illustrative examples from different areas of experimentation. W.M. Wooding’s (1994) book is a valuable source of information for the planning of clinical trials.

G.T. Sica’s (2006) about bias in research studies is recommended because it is a comprehensive presentation of all possible biases that may occur in the context of genomic and biomarker studies. Kerr and Churchill (2001a, b) are recommendable for planning dualchannel experiments.

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24 Biobanking in the Post-Genome Era Theresa Puifun Chow, Chia Kee Seng, Per Hall and Edison T. Liu

INTRODUCTION The common definition of a Biobank is a collection of specimens of human bodily substances linked, or linkable, with personal data and information on their donors “that are intended for medical research, set up with a broad purpose, accessible to a variety of research groups, contain genetic data and are of substantial size” (Knoppers and Newton, 2001). In addition, a Biobank could also be described as the system for the efficient collection of information associated with the biological materials, such as lifestyle factors, therapy, etc. Previously, collections in tissue banks were mainly that of remnants from surgical and clinical processes to characterize physiological, morphological, and histological abnormalities. The collections were mostly ad hoc, clinic- or hospital-based. The processes and procedures vary widely, and many tissue banks are under-funded. Biobanks are perhaps best viewed as part of the continuum of tissue repositories. Starting with small investigator sponsored tissue banks, to institutional tissue repositories, to national repositories or biobanks, the difference is one of scale. Rapid advances in genomics-based technologies have radically changed the tissue banking practices in the last decade. Moreover, studies require large numbers to achieve statistical power and the management of thousands to tens of thousands of samples per study raises challenges based on the scale of the operations. Thus, larger biobanks with dedicated infrastructures

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have superseded the investigator-operated freezer sample storage. Such large sample numbers requires automation for laboratory processes with informatics frameworks to manage sample processes, inventories, and associated data. Biobanks are fundamentally systems for information management. A prerequisite for a management system is standardized procedures and ontology (common language) for all levels of information. Quality control and information security systems with data encryption and access control are an essential part of this enterprise. Above this all, transparent governance in the management and distribution of these tissue resources are needed to protect donor privacy and confidentiality and to ensure good utilization. This also means that an open but controlled access to these tissue resources be made available to the larger scientific community. This data-intensive repository of individuals followed over time is also often thought of as a large-scale longitudinal cohort study. The distinction between such longitudinal studies and a biobank is becoming progressively blurred. However, a biobank is tissue centric whereas a standard longitudinal cohort study can be primarily based on epidemiological data. The greatest scientific power, of course, is derived when the two are combined. A review of all current biobanking practices is beyond the scope of this chapter. Instead, we wish to illustrate principles common to biobanking and discuss hands-on issues facing the contemporary biobank, specifically describing our experience in Singapore as an instructive model. The accelerated nature of development in Singapore provides a unique perspective on the Copyright © 2009, Elsevier Inc. All rights reserved.

The Evolving Face of Biobanking

interconnections between tissue repositories, the health and legal systems, and social culture.

THE BIOBANKING EVOLUTION Biospecimen collection has evolved from that of supporting basic discovery type of work where minimal clinical annotation is required, to the translational phase, where validation and clinical development is pursued. The challenge today is to create prospective, longitudinal studies with repeated measurements and blood samplings that allow studies of early markers of disease based on proteomics, analysis of treatment prediction, leading to individualized medicine, and most likely molecular phenotypes substituting the prevailing 20th century disease classification. Such a prospective, longitudinal cohort study allows comparison of large numbers of people who develop a disease with those who do not and a comparison with those that survive the disease to those who do not. Comparisons need to be based on knowledge of genetic alterations, high-quality information on nongenetic factors, and the interaction between the two of them. The advantage of a prospective longitudinal cohort study, sometimes (misleadingly) also called a Biobank, are numerous. An inherent strength of a prospective cohort study is the ability to efficiently investigate multiple endpoints. If cancer is used as an example, it is possible to study not only the risk factors for cancer but issues related to cancer survivorship such as local recurrences, distant metastases, second cancers, and quality of life issues. The prospective design adds to the flexibility of the study although a large cohort of individuals is needed to produce reliable results for a range of effects both overall and in small but important subgroups. Prospective cohort studies with longitudinal data collection also have the invaluable benefit of allowing studies of a host of exposures, information about which may be updated over time. Selection bias and population stratification are problems in association studies. Many of these problems could be reduced, but not avoided, by adopting a prospective, populationbased cohort design when setting up a biobank. The quality of a prospective cohort study builds on the prerequisite of rigorous follow-up mechanisms that could trace patients over long periods of time. The final translation into clinical practice lies not only in validation, but in actual proof of clinical utility based on long-term follow up of patient survival status found in public health registries like disease and death registries. Record linkage for research purposes is necessary to complete the full cycle of research data to convince policy makers and health care providers the value of new targeted therapies. However, as registries contain personal identifiers, special precautions are needed to be in place to ensure participant’s confidentiality and to encourage public participation. Since public health registries are government initiatives, mainly for epidemiological reasons, a biobank that has the capability to access public registry’s data will be a highly valuable resource for researchers’ effort in advancing genomics medicine. An advantage of a prospectively collected biobank is the possibility to generate results that are better generalized relative to

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those derived from clinical trials and case-control studies because of the reduction in potential selection biases. These types of studies at most times address narrow ranges of patients regarding age, exposure, outcome, performance status, etc. By contrast, studies designed to test a particular hypothesis, such as case-control studies, have advantages but can only consider an a priori hypothesis and are thus somewhat inflexible. Some methodological concerns have to be taken into consideration when establishing a longitudinal cohort study.The responsibility, work, and expense of creating a cohort study may be best taken on by an organization set up for this specific purpose. Individual research groups will be given access data on scientific merits. The approach is in the interests of the public, the scientific community, and the funding agencies because a much wider range of research groups can be given access to information. This is one of the fundamental applications of a national biobank.

THE PAST IMPERFECT RESOURCES Two separate reports relating to a survey of tissue banking of 147 institutes in six European countries conducted from 1999 to 2001 (Hirtzlin et al., 2003) and another, a National Cancer Institute (NCI) USA commissioned report by the Rand Corporation of the existing tissue repositories in the United States of America (Eiseman et al., 2000), both highlighted the heterogeneity of tissue banks in size and purpose and the lack of standard practices across the tissue banking arena. Despite the NCI’s investments in biorepositories of $50 million per year involving 125 programs and projects that included biospecimen collection of 4 million human biospecimens in FY2003 alone, the major obstacle has been the lack of biorepository and data standards to enable the mounting of large scale studies from existing pools. The existing state of variation in consenting methods, accession policies, management principles, and operational processes affect sample quality and therefore interpretability, processes that do not provide useful biomolecules for analysis, the lack of common standard operating procedures (SOPs) or quality assurance/quality control (QA/QC) measures in sample handling and processing, the lack of a common database to facilitate common access to inventories and clinical data on available biospecimens present significant hurdles to mounting large scale or cross-comparison studies of statistical value.

THE EVOLVING FACE OF BIOBANKING Since 2003, a series of international biobank meetings and invitational workshops brought together repository managers, researchers, biotechnology executives, epidemiologists, information technology (IT) experts, funders, patient advocates, ethicists, regulatory bodies, and policy makers to actively engage in discussions toward understanding of the existing state of biobanks. Outputs of the meetings were presented in several reports between 2004 and 2005 on biobanking summits (Baig and

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Doyle, 2005; Louie and Reeve, 2005; Melnikova et al., 2005; Zimmerman et al., 2004) and the best practice guidelines were produced by the International Society of Biological and Environmental Repositories/ISBER (ISBER, 2005), Organization for Economic Co-operation and Development/ OECD (OECD, 2004) and the NCI, USA (NCI, 2006). Parallel to this development, large private or public biobanking initiatives have been established (Figure 24.1). These include collections focusing mainly on DNA samples by a single institutional entity as in deCode Genetics Inc. to broader collections that include serum/plasma for proteomics, and from single collection agencies to the establishment of large networked biobanks Position Papers & Best Practice Guidelines 1993–2002 2003 National Biospecimen Blueprint (NBN) USA 2004

2005 ISBER Best Practices 2006

2007

Biobanks Initiatives & International Harmonization 1993

NCTR Rand Report

IDC Report, Summit I,II OECD Guidance for Operation of Biological Resource IDC Report, Summit III,IV First Generation Guidelines for NCI supported Biorepositories (USA)

UK Biobank (1999) EGP (2001) KI Biobank 2003

EPIC (1993) HUNT-2 (1995) DeCode Genetics, NMCC (1999) ISBER (2000) Singapore Tissue Network (2002) Biobank Japan

2004 2005

2006 OnCore UK

2007

P3G Consortium CaBIGTM Prostate SPORE NBN Pilot SCCS Marble Arch Group UK Biobank Launch Taiwan Biobank CCB (UK) GAIN WAGHP Life Gene CARTaGENE

Figure 24.1 The biobanking evolution. Examples of key position papers on principles and best practice guidelines on biobanking and parallel development of world-wide biobank initiatives from the1990s. Biobank Japan; Cancer Bioinformatics Grid (caBIGTM); CARTaGENE; Confederation of Cancer Biobanks, UK (CCB); deCode Genetics, Inc.; Estonia Genome Project (EGP); European Prospective Investigation into Cancer Nutrition (EPIC); Genetic Association Information Network (GAIN); HUNT-2, Norway; IDC Reports; International Society for Biological Environmental Repositories (ISBER); Karolinska (KI) Biobank; Life Gene; Marble Arch Working Group (MAWG); National Biospecimen Network Blueprint (NBN); NCI First – Generation guidelines for NCI-supported repositories; National Cancer Tissue Resource (NCTR); Norwegian Mother and Child Cohort; oNCORE UK; Organization for Economic Co-operation and Development (OECD); Prostate Specialized Program of Research Excellence (SPORE) NBN Pilot; Public Population Project in Genomics (P3G); RAND Report on case studies of human tissue repositories: Best Practices; STN; Taiwan Biobank; Western Australian Genome Health Project (WAGH) (see References).

as proposed by the National Biospecimen Network Blueprint (USA), and piloted by the NCI’s Prostate Specialized Program of Research Excellence National Biospecimen Network Pilot (Friede et al., 2003; Prostate SPORE NBN Pilot) which is comprised of 11 centers in the United States linked through a federated informatics system, OnCore UK (oNCORE UK) for cancer clinical trials, Biobank Japan (The Biobank Japan project) for pharmacogenomics research (300,000 participants), and the UK Biobank (UK Biobank) for prospective cohort studies (500,000 targeted participants). Moreover, the range includes private biobank collections and large national biobanks with responsibility for national scale projects as in Singapore Tissue Network (STN) and Karolinska (KI) Biobank. The size of biobanks is increasing, and the diversity of biosamples collected is broadened for “omics” research. There is a trend toward collecting more extensive and higher quality clinical data and open source biobanking where IT tools are being developed for sharing encourages collaboration as seen in the US NCI Cancer Biomedical Informatics Grid (caBIGTM) and the p3G Consortium effort toward sharing and harmonization. Active alliances like the Confederation of Cancer Biobanks (CCB) in the United Kingdoms have been initiated. For example, the Public Population Project (p3G Consortium) has been established to facilitate international information exchange. Exemplifying this trend, megascale prospective cohort studies are being planned or have begun such as the Western Australian Genome Health Project (WAGHP), Life Gene (Sweden), Singapore Consortium of Cohort Studies (SCCS) and CARTaGENE (CARTaGene project). Other examples of large biobank initiatives include the European Prospective Investigation into Cancer Nutrition (EPIC), Estonia Genome Project (EGP), HUNT-2 (Norway) and the Norwegian Mother and Child Cohort (NMCC). The principles of clinical trial design with stringent requirements for clinical data is followed for the pre-clinical and/or laboratory research, but the challenges lie with the infrastructure requirements for establishing and maintaining biobanks of this size and scope, and in the distribution of large numbers of samples with consistent quality for various downstream analysis.

EXISTING MODELS: BIOBANKING IN EUROPE AND THE USA The following examples have been chosen for the level of organization and progress. The UK Biobank The UK Biobank is one of the number of prospective populationbased studies underway and aims at collection of baseline blood samples and data from 500,000 individuals aged 45–69 years with a follow up of at least 10 years. The project is supported by the Medical Research Council, Wellcome Trust, Department of Health, the Scottish Executives, and the Northwest Regional Development Agency with combined funding of £61 million for the 5 year collection phase. The objective is to create a research

Existing Models: Biobanking in Europe and the USA

resource for future investigations on the effects of genetic, environmental, and lifestyle factors on major diseases. A baseline questionnaire with a limited number of physical measurements is taken, and a 50 ml blood sample is taken and used for baseline clinical analysis and archiving. Subjects will be followed through general practitioners (GP) records and through linkage to public records for mortality, hospital admission, and cancer statistics. Issues such as consent, confidentiality, and security of the data are guided by an Ethics and Governance Framework overseen by an independent council in accordance with the UK Data Protection Act 1998. It is estimated that there would be 750 participants per day, generating more than 3750 tubes of blood and 750 tubes of urine per day. The large number of participants and samples requires specific IT systems to handle the recruitment, collection, and sample processing logistics. The UK Biobank has engaged Thermo Nautilus Laboratory Information System for sample hierarchy tracking through system of barcodes, and special automated setups with high throughput blood fractionator system (RTS Vision System – RTS Life Science UK). Sample storage is split into 80°C automated (and long-term archival in liquid nitrogen tanks). The 80°C storage is housed in a 20°C environment where samples are retrieved with robotic arms. Although hugely expensive, with the cost £4 million (for a capacity of 9 million cryovials), the automated retrieval of samples by automated robotic arms is the only solution for preventing repeated freeze thawings and is the state-of-the-art solution for preserving sample integrity for proteomics studies. Biobanks, like cohort studies, estimate the number of individuals needed to be recruited based on incidence rates for the diseases of interest. Therefore the more rare the disease, the larger the biobank collection that is required to make any observation statistically robust. Even with the large number of participants, it is estimated that the UK Biobank is just large enough to be of value as a stand alone research structure since for less common diseases like stomach cancer, it has been estimated that it would take 29 years to achieve 2500 cases. Thus, there is a call for cooperation amongst international biobanks to achieve 2–3 million recruits to have sufficient statistical power for research on rare disease within a reasonable time frame (Burton, 2006; Manolio et al., 2006). The proposed harmonization of the UK Biobank and other national cohort study into “The Last Cohort” (Potter, 2004) is one such call for coordination amongst national biobanks. The Karolinska Institute Biobank The Karolinska Institute Biobank (Sweden) is a national, noncommercial tissue resource that links biological samples and the large databases containing phenotype and genotype information of the individual donors. This information includes lifestyle, psychosocial, and clinical data in addition to molecular data, all of which require the development of appropriate database models and ontologies. An important task therefore has been to define, structure, and standardize collection of different kinds of information. Arising from these efforts is the Biobank Information Management System (BIMS), which is responsible for the linkage of the biological samples and databases containing information

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Other Biobanks

DBs

Lab robot LIMS

Freezer

BIMS

Web interface

Figure 24.2 The Karolinska BIMS. The information management system designed to link biological samples information from the LIMS to databases (DBs) containing information on the individual sample donors.

on the individual sample donors following de-identification (Figure 24.2; Ölund et al., 2007). The KI Biobank also seeks to take advantage of communication tools such as the internet, and mobile phones for quick and cost-efficient assembly of patient data, and follow-up information. A key feature of the KI Biobank is in the ownership of the tissue and information. Due to the legal considerations, the owner of the data and at the same time the responsible authority is the KI Biobank, not the individual principal investigators. Nearly all Swedish patients use the tax-funded National Health Care system and therefore any material generated through this system shall not be restricted in use, but will be accessible solely based on scientific merits of a suggested project. The Prostate Specialized Program Of Research Excellence National Biospecimen Network (Prostate SPORE NBN) Pilot The Prostate SPORE NBN Pilot plans to establish a common biospecimen coordination system and informatics infrastructure/data-sharing for multiple Specialized Programs of Research Excellence funded by the US NCI SPOREs located at 11 different institutions. A key innovation is the National Biospecimen Network Blueprint (Friede et al., 2003) which spells out key concept that calls for a national “best practices” based tissue distribution of high-quality biospecimens and linked data to support and reduce variability through the use of standardized approaches for collecting, processing, storing, annotating, and distributing biospecimens. Initially, the program will support an Inter-SPORE Prostate Biomarker Study (IPBS) for biomarker validation, but biospecimens and the data collected will ultimately be available to qualified non-SPORE researchers. The caBIGTM is a voluntary network or grid connecting individuals and institutions to enable the sharing of data

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TABLE 24.1 ● ● ● ●

● ● ●

●

●

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Key features of a centralized biobank

Mostly publicly funded initiatives of national and/or international scale. Complexity of multiple stakeholders requires implementation time. High set up cost. Complex technical requirements due to diversity of projects for example ability to process different sample types, molecular processes and clinical data. Central committees for policy, accession, and ethical oversight decisions. Quality management and operational efficiency. Standardization/harmonization for operating procedures (SOPs), collection workflows, clinical annotations, and IT frameworks. Robust information technology infrastructure for high volume sample tracking, processing, inventory, withdrawal, and automation management. Data privacy and confidentiality protection.

and tools. The Prostate SPORE NBN Pilot will interact with caBIGTM by using plug-in applications provided by caBIGTM and will share application development with caBIGTM. This pilot project adopts a federated model to address interoperability instead of requiring a single centralized database or a single standard system for all centers (Table 24.1). All personally identifiable information will be removed, and data will be submitted using common data elements. This is becoming the standard organizing principle for consortial databases. The resources and data moved to the interoperability layer will be organized and tracked in four modules: a searchable Web catalog; protocol tracking and biospecimen tracking modules, and a common results bank. Besides the issue of scalability, the information technology intensive approach is needed for multi-site collections separated by significant geographical distances.

SINGAPORE’S NATIONAL BIOBANK AND NATIONAL ASPIRATIONS IN BIOMEDICAL RESEARCH Singapore’s Biomedical Sciences Initiative In the year 2000, the government of Singapore announced the launch of a Biomedical Sciences Initiative to develop biomedical sciences as the country’s fourth pillar of economic growth, alongside the already successful electronics, engineering, and chemical manufacturing industries. An associated goal was to transition Singapore from a manufacturing-based nation to one focused on knowledge creation. The first phase of development (2000–2005) concentrated on establishing a firm foundation of basic biomedical research in Singapore and was supported by a S$4 billion fund. Five research institutes were established with the core public research capabilities in the areas of bioprocessing; chemical synthesis; genomics and proteomics; molecular

and cell biology; bioengineering and nanotechnology; and computational biology. The “extramural” research performed at the hospitals and the National University Medical School was bolstered by a significant increase in research funds as well. The initiative used a “systems approach” to create a total infrastructure to support biomedical industry. The components for success is based on high-upfront investment from the government to build infrastructure, fund research, develop human capital, attract foreign investments through tax incentives, foster local collaborations, and aggressive protection of intellectual property rights. The added advantage is Singapore’s flexibility as a small but well organized country willing to modify plans when needed. The vision was bold as it was risky. It was during this phase that Singapore’s national tissue repository, the STN was established. In 2006, with the success of Phase I of this initiative, the Government increased the commitments in Phase 2 (2006–2010) to S$12 billion over the next 5 years of which S$1.4 billion was earmarked to support translational and clinical research. Singapore’s Bioethics Governance National tissue repositories and biobanks function appropriately only in an environment with ethical rules and transparency of decision-making. In medical research, the absence of a moral framework in its governance places a nation on a dangerous slippery slope to unacceptable behavior. Governmental and academic leaders of this Biomedical Sciences Initiative recognized this danger very early in their planning. Biomedical research has social implications in its conduct and will generate outcomes that often challenge the ethical and legal framework of the country. So, also in 2000, the Singapore Cabinet appointed a high level Bioethics Advisory Committee (BAC) to openly deliberate and develop guidelines for the ethical, legal, and social issues for biomedical research in Singapore. The BAC guidelines are developed through a process of public consultation and its reports are then presented to the Steering Committee on Life Sciences, a panel established to oversee the “holistic” development of biomedical sciences in Singapore engaging the ministries of education, health, and industry and trade (Figure 24.3). The establishment of the BAC was perhaps one of the most important foundation pieces in the success of Singapore’s Tissue Network and national biobank. Without this national and governmental consensus, the STN’s work would inevitably diverge from national sensibilities. Among the reports already released, several are pertinent to the function of this national tissue repository, specifically surrounding the use of human tissues and human subjects research: Human Tissue Research (BAC, 2002), Research Involving Human Subjects: Guidelines for institutional ethics/review boards (IRBs) (BAC, 2004), Personal Information in Biomedical Research (BAC, 2007) and Genetic Testing and Genetic Research (BAC, 2005). Some of their deliberations already have had a significant impact in providing the necessary clarity for researchers to move forward. Of note, is the stand of the BAC that affirms the importance of biomedical research in addition to the primacy of patient rights. Specifically, it encouraged organizational flexibility and the use of information technology in resolving issues surrounding patient confidentiality.

Singapore’s National Biobank and National Aspirations in Biomedical Research

BAC’s recommendations process Consultations

Public

Religious & Community Groups

Deliberations

Recommendations

Examine current practices in major jurisdictions BAC

SCLS IAC

Consider responses from public and consultation parties

Professional Groups includes medical, scientific and legal

Public

International experts

Figure 24.3 The BAC of Singapore and its recommendation process. Appointed by the Cabinet and reporting to the Life Science Ministerial Committee (LSMC) with the expert advice of an international advisory committee (IAC), the BAC examines current ethical, legal and social issues arising from research on human biology and behavior , consults, deliberates and makes recommendations to the ministries of education, health, industry, and trade.

As an example, because Singapore is a small country that has a limited pool of clinical research expertise, IRBs within the hospitals had difficulty in continuously retaining high-quality reviewers. A solution devised by one of the hospital groups after the BAC recommendations was to establish four “Domain Specific” IRBs that covered all six hospitals within the health care group (Domain Specific IRB, 2004). Each application is triaged into one of the four broad but related disease subgroups thus maximizing the utilization of expertise within scientific domains for the review process. This innovation resulted in improved review times and review quality. Moreover, this arrangement has the potential of facilitating multi-institutional human subjects investigations pertinent to a national biobank. Singapore’s National Biobank: The STN The establishment of a publicly funded national tissue repository was considered a strategic step for the fledgling Singapore’s biomedical science initiatives. The STN was formed in the year 2002 as a joint initiative between the Ministry of Health (MOH), the Genome Institute of Singapore (GIS) and the Biomedical Research Council (BMRC) of the Agency of Science Technology and Research (A*STAR), then the key funding agency for public research in Singapore. The concept was that for Singapore to succeed in biomedicine, a robust and ethical tissue repository platform is needed to be available to laboratory and clinical investigators. Historically, like in many countries, tissue repositories were managed by small groups of academicians

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supported by hospitals or universities commonly with meager funds. The planners and advisors of the Biomedical Sciences Initiative recognized that a larger, more professional unit was necessary to support the research demands and to enhance the quality of tissue repositories within the Republic. Moreover, as a small compact nation, Singapore can centralize many of the biobanking operations given the simplicity of sample logistics (e.g., accession and distribution) that would be challenging in a geographically larger country. Thus, both the need and the advantage of such a national tissue repository in Singapore were evident. In the initial planning for the STN, several key issues were identified: expertise/infrastructure, governance, flexibility, and patient confidentiality/data security. Expertise It was recognized that, in Singapore, there is little experience and expertise in starting and maintaining a high capacity national tissue repository at the beginning. Though several overseas companies offered to provide a complete service for the nation, the decision is to build the indigenous capabilities rather than outsource the operation. However, to accelerate the development, outside help was sought. A private genomics company specializing in tissue repositories, Genomics Collaborative Inc. (GCI) (Boston MA, now part of SeraLife Inc.) was contracted to train personnel and to provide SOPs for the start-up phase of the STN. As a commercial provider for tissues with annotated data, the GCI had developed one of biobank’s first Laboratory Information Management System (LIMS) to manage and track samples from “cradle-to-grave” and implemented stringent ethical policies and good QC processes for tissues and data. The GCI was ranked highly in a study on Human Tissue Repositories in the USA conducted by the Rand Corporation (Eiseman et al., 2003). Within 18 months, once the establishment of the STN was secure, the transition into independent operations went smoothly while additional designs to execute sample processing and improvement for more sophisticated tracking systems through LIMS continued. Though a major goal of the STN was to develop a national biobank for a large longitudinal cohort study, the reality was that smaller principal investigators (PIs) led projects that would constitute the majority of activity in Singapore for the foreseeable future. To this end, we built a technical infrastructure with capabilities to: (a) support a variety of projects (Figure 24.4), (b) expand capacities as needed, (c) provide quality sample management, and (d) foster collaborations, eliminating unnecessary duplications and wastage. The STN’s infrastructure is characteristic of a post-genome biobank and is distinguished from earlier tissue repositories by the engagement of robotics and information technology to manage large sample numbers, to link phenotype data with sample inventories, and to safeguard privacy and confidentiality. Tracking Samples Through a LIMS The core of any national biobank is its sample tracking and tissue archiving capabilities. All samples within the STN are tracked by a system of barcoding in the LIMS from kit building

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National Pool

Public

Diabetes cohort

Private Cancer specific groups (BCC, POG)

Multi-ethnic cohort S’pore Prospective Study S’pore Malay Eye Study

Twin

Tissues Blood DNA/RNA Buccal samples/saliva Immortalized cell lines Urine

Myocardial Infraction Study

Identifiers COLLECTION SITE

Tardive Dyskinesia

STN

Dengue Schizophrenia

Stroke Atrial fibrillation

Figure 24.4 STN collections. The collections of the STN constitute a national pool for researchers in the areas of cancer including unscheduled banking of a majority of cancer types to more specific cancer tissue like basal cell carcinoma (BCC) and leukemias from the pediatric oncology group (POG), cohort studies (diabetic and multi-ethnic cohorts), several disease specific projects and twin studies.

to distribution, eliminating transcript errors. Both the KI Biobank and the STN uses a system of coding and barcoding to track samples, and samples are identified with sample numbers and not personal identifiers. The LIMS connects workflows and processes, and captures all sample related data allowing instant querying of sample status from receiving through processing to archiving, aliquoting and distribution. LIMS, originally invented for QC purposes, has been widely adapted for the managing and manufacturing purposes and has recently been applied to biobanking as a solution to organized sample tracking. There are only a few commercial vendors offering LIMS software and many biobanks have resorted to customized solutions. However, the commercial solutions as offered by Thermo-Fisher, used both by the STN and the UK Biobank, and LabVantage, used by KI Biobank, represents easily configurable solutions to accommodate changes in workflows for a variety of sample types. Different from “home grown” customized solutions, they offer a multitude of benefits, including standardization on a single solution across multiple laboratories and adhering to more stringent regulatory requirements and validation (Perry and Reeve, 2004) (Figure 24.5). Work Flow Conceptually, the biospecimen collections encompass two major formats that must be handled differently: Unscheduled

Phenotype data

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Hepatitis B • • • • • •

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Barcoded kits Sample log in Assign workflow

Work lists LIMS

Robots

Process Test & Storage Distribute QA Approval result QC entry 20°C, 80 °C, liquid nitrogen, security alarm, back-up site

Figure 24.5 Specimen and clinical data workflows at the STN. The STN collection work flow: Barcoded kits are sent to collection sites. Personal identifiers are sent with sample barcodes to the Data Protection Office that maintains link. Coded samples are processed with SOP with QC and QA processes. Workflows are assigned by the LIMS and sample updates are uploaded into the STN phenotypic database (DB) for display to researchers.

collections – defined as tissue collected in the course of standard medical care with no defined research hypotheses and collected outside a specific project to answer a scientific question (as in tumor collections present in the hospitals); and scheduled collections – defined as tissues collected as part of a prospective study or program (as in a cohort study or a clinical trial) in which a specific scientific question or goal is addressed. Motivations and incentives are also different. Unscheduled collections are fundamentally based on the altruism of clinicians who agree to participate in tissue collections that are above and beyond their standard delivery of care and their institutions who believe that clinical research is ultimately important to patient care. Scheduled collections already represent the aspirations of a funded cadre of investigators studying a specific disease or scientific question. Of the two, the unscheduled collections proved to be the most challenging to establish. Whereas unscheduled collections of tumor work adequately at the level of a single institution driven by an internal “champion”, the results have been equivocal on a national scale. Globally, with some exceptions, broad-based nationally mandated unscheduled collections of fresh human tumors for academic research have not worked well. In the United States, tissue services such as Cooperative Human Tissue Network or CHTN (Cooperative Human Tissue

Singapore’s National Biobank and National Aspirations in Biomedical Research

Network) that collect fresh tissues without an underlying scientific question function solely as a service entity and are limited to a network of dedicated hospitals. Moreover, the CHTN collects tissue prospectively when a project is negotiated by the administrators of the service with the requesting PI. Therefore, in effect, the CHTN uses a scheduled collection strategy. The difference in Singapore is that most of the cancer surgery in the public sector was performed in two large hospitals approximately 35 minutes apart by car: Singapore General Hospital and the National University Hospital. This compact configuration simplifies the organization of a national tissue repository. Through a negotiated agreement, the STN funded the base infrastructure (freezers and full time employees/FTEs for data-manager, phlebotomist, and consent nurse) in each hospital for a predetermined number of tumors. To encourage institutional buy-in, a 50/50 rule is applied, where, whenever feasible, half of each processed samples is returned to the contributing hospital’s tissue repository for distribution within the institution. The other half is banked as a national resource for future research use. The STN provides logistics and barcoded kits, and temperature controlled containers for transport. For the national resource, sample processing and storage, aliquoting and distribution is provided by the STN. Access to the banked tissue is determined by the STN Steering committee through a standardized process and review (see below) and requires approval by an ethics review or IRB and proof of funding support for the research. The scheduled collection has worked well in support of investigator driven clinical and epidemiologic studies. A principal investigator with a funded population study will approach the STN and request support for tissue collection, most commonly peripheral blood isolation and DNA preparation and storage. Upon receipt of IRB approval, the STN steering committee will review and approve the support of a study. The STN will bear the costs of accession, processing, sample storage, and distribution. Though originally, scheduled collections also followed the 50% rule (i.e., 50% of the samples “belong” to the principal investigator, and 50% belongs to the national repository), we are moving to one where only a predetermined access policy and process is required. Because of the success of this scheduled collection strategy, we are exploring the conversion of unscheduled tumor collections to a scheduled approach. This means that instead of random collections of tumor tissues, consortia of investigators and clinicians interested in a specific disease, like lung cancer, would propose to collect lung cancer tissues with clinical data and follow-up as part of a “registry” of lung cancers. The cancers will be stored at the STN. Fifty per cent of the tissues will be used by the consortium to perform molecular studies previously approved, the other 50% will be archived for potential future collaborations. In this manner, we believe that the impact of the tissues will be significantly augmented since they will be accompanied by both clinical information and molecular/genomic data (Table 24.2). The Governance The STN is run by an executive director and managing director and reports to a Steering Committee. Like many biobanks, the

TABLE 24.2

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Scheduled versus unscheduled collections

Scheduled collections ● ● ●

●

Associated with specific research objectives for example disease specific collections in case-control studies. Study protocol defining collection period, target donor size, and demographics. Detailed clinical data and analysis with follow up on treatment outcomes according to study protocol and research objectives. Informed consent required.

Unscheduled collections ●

● ● ●

Regular collections without specific pre-designed research objectives for example broad-based tumor collection in hospital. Study protocols are submitted in future applications after biospecimens have been banked. Less specific clinical data collected as research objectives are not defined. Broad/general consent generally practiced with stipulations for further review and approval from Institutional Review Boards or appropriate Ethics Committees for future studies.

STN structures its central governance around participation from all respective stakeholders but is unique in that because the STN functions as the national tissue repository, we have diverse responsibilities and a range of stake holders. Thus, the oversight body or Steering Committee consists of policy and decision-makers representing executive members of the funding agency (BMRC), the MOH, research institutes, medical academic centers, universities, hospitals, IRB, the Economic Development Board (EDB), and the BAC. Because of the high level involvement, decisions are made at a level that can have far reaching implications. The Steering Committee votes on the approval of national scale projects based on internal and external reviews, subjected to approval from relevant ethics review boards, and the availability of peer-reviewed research funding. Emphasis is placed on ensuring focus and harmonization to maximize resources critical for a small country like Singapore (Figure 24.6). Confidentiality Protection: Data Privacy Issues in Biobanks A fully-functional national tissue repository/biobank collects and centralizes tissues and data from patients and normal research subjects. These are usually collected within the context of a doctor–patient or a researcher–study subject relationship. The collection of such data and tissue are subjected to approval and review by ethical review committees and or prevailing legislations/regulations of governmental authorities. Informed consent is the acceptable standard applied to such collections. To maximize the value of the collected data and tissue, some form of (electronic) record linkages is also included in the consent. Ultimately, the aim of a biobank is beyond being a data management and storage center for individual researchers.

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EDB

MOH

BAC Clinical experts

BMRC Steering Committee

Director

Collaborators

IRB Scientific advisory board

Research grant approval

IRB approval

Withdrawal

Withdrawal

Secretariat Management

Operations

QA

IT

Admin.

Facilities Safety

Phenotypic DB

LIMS

Lab QC

Figure 24.6 STN governance and infrastructure. Governance of STN is through a Steering Committee comprising members from the Ministry of Health (MOH), the Biomedical Research Council (BMRC), Collaborators (hospitals, collection sites), IEB Chairs, STN Director, clinical experts, BAC and executive members of the Economic Development Board (EDB). Key departments under the management include Laboratories (Lab), QA, QC, Informatics supporting STN’s phenotypic database (phenotypic DB) and LIMS and Administration (Admin).

To maximize its utility, the information collected should be a national resource for the research community for future research. To this end, the data and tissues need to be used for purposes and biomarker investigations beyond what was proscribed in the original informed consent. Technically, each patient/respondent would have to give reconsent for every new hypothesis to be tested on the cohort. Obviously, this would be impracticable for studies of the magnitude of a national cohort study. More recently attitudes toward reconsent requirements have changed both in Europe which favors broad consent and in the United States of America where the definition of non-identifiable samples have been expanded to include those that have been previously considered coded. Ethicists have argued in favor of using general consent after IRB approval together with the right to “opt out” as the preferred method (Elger and Caplan, 2006). But broad consents still have legal and ethical issues that are in debate (Hansson et al., 2006). For example, medical data collected within the context of a doctor–patient relationship will be made available to non-doctors. Furthermore, the initial consent between doctor and patient or researcher and study subject is legally seen as a contractual agreement. Further transfer of data/tissue to researchers downstream is then viewed as a new agreement. In this case,

disconcertingly, the later researchers would have no legal obligations to the original patient/respondent. Though the solutions to these issues will be dependent on the existing legal and ethical framework in the community contributing to the biobank, the technical solution involves some form of de-identification. The challenge is often not in the IT arena at the ground level but in the policy framework that needs to be drawn up at the macro-level. Unfortunately, such macrolevel policies are often hindered by a lack of understanding of technical issues and/or the imagined fears of privacy invasion. Given adequate technical safeguards and a strong data protection legal framework, the future benefit of science to humanity should outweigh the potential threat of personal privacy violation. In Singapore, the BAC has given national consensus statements that are now being crafted into legislative protections. Important in these regulations is the acknowledgement that scientific research and progress is important to our society, and that information technologies should be used help safeguard the privacy of this information. Conceptually, though we deemed that a national tissue repository should be centralized for efficiency, quality, and economy, the accompanying data repositories of clinical and epidemiologic information should be distanced from the tissue collection and should be constructed as a distributed but linked collection of databases. This is because of the recognition that information in these databases must be updated and managed by the local investigators. Moreover, distributed but federated databases pose less of a security risk than a single massive database of national proportions. The key, however, is that these databases should be linked. For security, a stringent de-identification process needs to be adopted. Unlike aggregate data, data from individuals with the potential for research use can be divided into two groups: ● ●

Personal identity data Research data

Personal identity data consist of data items that singly or in combination could potentially identify a specific individual. It is accepted that name and unique personal identification number (e.g., passport numbers, social security numbers, and other form of national registration numbers) are considered personal identity data. Some would extend the list to ethnicity, date and place of birth, and gender. In rare situations, simple combinations like date of birth (DOB) and diagnosis of an extremely rare condition may potentially reveal the identity of an individual. However, it is not necessary to invest vast resources to build a system that claims to be “fail-safe” for such cases. The performance of such “fail-safe” systems is cumbersome and tends to dissuade users. Prudent judgment is needed to balance efficiency and privacy. De-identification is a process where personal identity data are separated from the research data. There have been many different terminologies for the concept of de-identification: annonymized data, pseudo-annonymized data, partially de-identified

Singapore’s National Biobank and National Aspirations in Biomedical Research

data, etc. Conceptually, it will be easier to have three levels of de-identification: 1. Completely identifiable data: Personal identity data and research data are stored as single electronic or paper records. The data items may be coded or reversibly encrypted for security reasons. The personal identity and research data are physically linked after the items are decoded and de-encrypted. 2. Completely de-identified data: Personal identity and research data are de-linked in such a way that it is impossible to identify an individual from the research data. This is the same as anonymization. 3. Reversible de-identified data: Personal identity data are separated from the research data but linked via a newly created unique identifier. Each record in the research data is identified by this unique identifier which does not carry any personal identity information. For simplicity, this unique identifier could be termed a “private unique identification number” (PUIN). The personal identity data are linked to PUIN which serves as a bridge between the two datasets. To maximize the value of biobanks and cohort studies, the data should be managed as reversible de-identified data. The datasets and samples sent to researchers should be completely de-identified. However, the data managers of the biobank and cohorts should have some form of capability for reversing the de-identification as new data on the same individual may be obtained subsequent to the initial sample collection. A system that allows for active and passive follow-up of recruited subjects to a biobank and cohort study will need to meet three main criteria: 1. A trusted third party (TTP)/Data Protection Framework (DPF) Office that holds the link between PUIN and personal identity data. 2. A mechanism whereby the ground operation is partitioned such that no one is able to have all three sets of information: PUIN, personal identity data, and research data. 3. A mechanism of record linkage with external agencies such that they do not need to release completely identifiable data. Recruitment of subjects for a cohort study could be in the community or a clinical setting. Field workers will therefore have access to personal identity data and research data. Therefore, they should not have access to the PUIN. Furthermore, the personal identity data and the research data should be separated early, if possible, just after collection. The personal identity data should be sent to the DPF Office with the STN kit number where a PUIN is generated (if the subject has not been previously recruited) or the existing PUIN is retrieved. The “deidentified” phenotype and clinical data and tissue is sent to the biobank with the STN kit number. The PUIN is then sent by the DPF Office to the biobank with the STN kit number where the PUIN is now used to identify the subject. In this way, longitudinal data of the same subject could be constructed without revealing the actual identity of the subject.

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The DPF Office can also serve as a portal for record linkages protecting external agencies from releasing completely identifiable data. The biobank will provide a listing of PUIN of cases that require external record linkage (e.g., follow-up information like death) to the DPF Office. The DPF Office creates a temporary listing consisting of the PUIN, personal identity data, and a DPF number. The DPF number is a unique number used only once (NONCE: number used only once) for the purpose of this linkage exercise. The DPF Office sends to the external agency a listing consisting of personal identity data linked to the DPF number. At the same time, the DPF Office sends back to the biobank a listing of PUIN linked to the DPF number. Once the external agency has retrieved the followup data, it sends back the follow-up data linked with the DPF number, without the personal identity data. The biobank could then attach the follow-up data with its original tissue and data using the PUIN–DPF number listing. Various other combinations and levels of sophistication could be added to this system to balance efficiency and confidentiality (Figure 24.7a, b). In Singapore, this structure is being implemented on a national scale to also assist with similar needs amongst government agencies. For example, the Ministry of Home Affairs holds the birth and death registries, but the MOH manages the disease registries. Though it would seem obvious that the union of the two datasets is an important linkage for research, the technical reality based on organizational history is that Ministerial databanks are rightfully highly protected both physically and by regulation. The structuring of an official Record Linkage Office (RLO) to administer a national TTP policy has been an important step in resolving this issue of citizen data security (Figure 24.8). Lessons Learned from the Singapore Experience In the 4 years since its inception, some lessons can be drawn from the STN experience. Once the decision to develop national tissue and data repositories was made, by far, the major challenges were the social and political issues. The greatest hurdle was in obtaining the buy-in of individual participants for contributing to a “national cause”. Fortunately, the problem was restricted only to the unscheduled collection of tumor tissues. Several solutions were devised: the “50/50” policy where the samples are divided between the STN and the submitting hospital, support from the STN to the local hospitals for personnel and reagents for the establishment of the satellite procurement stations, and the relatively low to no cost for processing and archiving samples. Patience, flexibility, and coordination were needed to overcome obstacles. When large scale projects engaging multiple institutions faced long institutional ethics review process, the establishment of Domain Specific IRBs helped to expedite the review process. When the issue of data-security and confidentiality became a concern, we embarked on a 2-year process of consensus building and problem solving with governmental and academic parties and emerged with the DPF and the establishment of an RLO.

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Data Form Name, DOB, Ethic, etc

DPF Office NRIC-PUIN New PUIN for new subjects Old PUIN for existing subjects Personal data-Sample no. Name, NRIC, Gender, Ethnicity, DOB, address, date of collection, Sample no. (S/N)

Remote site keys in personal data form and scans all bar codes

STN scans in bar codes

Biomedical Record Linkage Office (RLO): National TTP

NUSDPF

NTUDPF

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NTU

DPF

A*STAR

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SCCS

DPF Office Personal data form PUIN Sample bar codes SAMPLE COLLECTION SITE Tissue sample form Clinical data form Personal data form NO PUIN

No Clinical data form No Tissue data form

STN Tissue data form Clinical data form PUIN NO personal data form

Figure 24.7 STN–DPF operations concept (a) The process of reversible de-identification where remote collection site keys in personal data like name, National Registry Identification Card (NRIC) number, gender, ethnicity, and date of birth (DOB) which are sent to the DPF Office. DPF Office returns a “PUIN” alongside the sample barcode number to STN. The PUIN is the substitute number for subject identity for future processes and linkage is kept at the DPF Office. (b) Segregation of information for increased security and privacy.

We also found that when the national funding agencies are aligned in strategic vision, incentives can be structured to ensure the success of programs. The STN have collaborated with a funding agency, the Singapore Cancer Syndicate to develop disease specific tissue and data repositories (Table 24.3).

Figure 24.8 Biomedical record linkage through TTP. Conceptual approach for record linkage through a TTP record linkage office (RLO). Individual institutes, hospitals, and tissue banks, for example, National University of Singapore (NUS), Nanyang Technology University (NTU), STN, Singapore Consortium of Cohort Studies (SCCS) and from the National Healthcare Group (NHG), SingHealth group each have their own DPF Office that communicates via a common RLO for external record linkage, for example follow-up information from hospital clusters (NHG, SingHealth) and from Ministry of Health disease registry data, and Ministry of Home Affairs’ birth and death data.

THE FUTURE OF NATIONAL BIOBANKS An emerging debate is on how large should be the scale of future biobank efforts. The dilemma in study design was recently highlighted by Willett and colleagues (Willett et al., 2007) who discussed the merits of merging existing cohorts versus a proposed single national cohort of several hundred thousands of Americans. They noted that since 1986, there are over 10 large scale cohorts that totaled 1.39 million donors with 845,000 stored biological samples, many with detailed annotation that could serve as the basis for a national biobank. Collins and Manolio (Collins and Manolio, 2007) responded that such a consortium of cohort studies is “necessary but not sufficient” due to the suboptimal aspects of the merging approach including issues of lack of standardization, and the failure to include new tools for genotypic and phenotypic measurements. In Singapore, we believe that both concepts have merit, but adhere to the Flaubert’s dictum that “perfection is the enemy of the good.” So, rather than to structure a national biobank scaled to several hundred thousand participants, we have begun our biobanking efforts by merging existing cohort studies funded

The Future of National Biobanks

T A B L E 2 4 . 3 Lessons learned from the Singapore experience for establishing a national biobank ● ● ●

● ● ●

●

●

Attention to social and political issues through process of consensus building Buy-ins for contributions to ‘national cause’ Clear incentives for driving participation, for example o equitable sharing between contributors and national bank, o manpower support o low to cost free sample processing, archiving and distribution o other non-research project needs Alignment with national funding agencies’ strategic vision to maximize coordination for large scale national projects Need for mandatory national data protection and legal framework Need for national guidelines for ethics governance for example Singapore’s Bioethics Advisory Committee to establish dialogue with public with recommendations for government policy-makers on specific issues for example consent requirements for research Need for pragmatic approaches for example establishment of Domain Specific Institutional Review Board to streamline ethics approval process Understanding concept of biobanking as long-term infrastructure building rather than project-based initiatives

by the government. This initiative is called the SCCS. The goal however, is to expand into a true national biobank/longitudinal cohort study when the requisite regulatory (such as the national TTP systems), social, and structural capabilities are mature. This transitional strategy is attractive because it is scalable and modular and can engage ongoing studies as well as new ones. For this strategy to work however, standardization and harmonization are keys. Luckily, global efforts are afoot in establishing best standard practices that includes not only sample quality and integrity (as practiced in Good Laboratory Practices), but in the more stringent Quality Management System (QMS) as seen in the adoption of internationally recognized quality standards like the ISO 9001 and the Current Good Manufacturing Practices (cGMP) (ISBER and NCI Best Practice Guidelines). This strong push toward standardization across institutional centers for critical data, but allowing individual variation within local centers, permits important harmonization and standardization as well as experimental flexibility. Progressively, there has been a focus on data access from cohort studies to make the genotype–phenotype data from large population studies available for the larger scientific community to analyze. The movement has been advanced in a number of association and linkage studies. The Genetics Association Information Network (Foundation for the National Institutes of Health, GAIN, 2007): a genotype–phenotype database for all of NIH supported genome-wide association studies, funded by Pfizer Inc. (New York City, USA), Affymetrix Inc (Santa Clara CA, USA), the Foundation of NIH (FNIH, USA), Perlegen

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Sciences Inc. (Mountain View CA, USA), Abbott (Abbott Park IL, USA) and the Biomarkers Consortium, a public–private biomedical research partnership seeks to genotype approximately 18,000 cases in 6 diseases (psoriasis, schizophrenia, diabetic nephropathy, bipolar disease, depression, and attention deficit hyperactivity disorder. This genotype information will be coupled with extensive phenotype information which will be made available to the general research community through an access plan. Similarly, the Autism Genetic Resource Exchange (AGRE, 2007) is a DNA repository and family registry, housing a database of genotypic and phenotypic information on over 800 families that is available to the scientific community. Another similar project is the Wellcome Trust Case Control Consortium (WTCCC, 2005). Launched in September 2005, with the support of €9 million from Wellcome Trust, the WTCCC is a large collaborative network of geneticists, clinicians, statistical geneticists, and technologists across the United Kingdom. Phase I of the study involves the analysis of 675,000 SNP markers in each of eight common diseases (tuberculosis, coronary heart disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, Crohn’s disease, bipolar disorder, and hypertension) in 1000 cases, followed by confirmation in Phase II where 5–10% of selected markers will be followed in 1000 new cases for each disease. Control samples will be from a set of 3000 common nationally-ascertained controls from the 1958 British Birth Cohort (~1500) and blood donors recruited by the three national UK Blood Services (~1500). A total of 17,000 samples will be typed with the 500 K Affymetrix chips. It is anticipated that the data from biobank initiatives will ultimately be released in a similar manner. If so, then these changes mirror those that have occurred in high-energy physics or cosmology where massive data collection is the responsibility of a national resource, and the data – which is accessed in an open fashion – is analyzed by the wider PI-based community. Unlike physics, however, the data have personalized information about people whose privacy might be compromised. For this reason, we believe the greatest challenges will be in the arena of information security. If solved, then biobanks not only have great scientific potential, but also represent a significant national resource. That such biobank initiatives can be used to help structure the health care of a nation is seen in the collaboration between the company deCode Genetics Inc. and the government of Iceland. The information from this national catchment provides a genetic understanding of disease origins within a homogeneous population and can be used for devising public health strategies. Though it is not likely that such an exclusive relationship can be or should be structured in larger countries, less commercial and exclusive arrangements will be as beneficial, but will require significant investment from government (e.g., UK Biobank initiative). In such large scale longitudinal studies, each subject volunteer functions as a health sentinel for the country. When genetics are added to this equation, then the possibility of defining genetic risk for specific diseases in a nation is real. For example, the prevalence of known at-risk alleles of diabetes, breast cancer, susceptibility to infectious diseases can be inferred from the frequency of the at-risk alleles in a population.

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The allele frequencies in specific populations of genes associated with adverse drug reactions are beginning to be used by pharmaceutical companies in the marketing plans for drugs with idiosyncratic reactions. Therefore, biobanks can potentially serve

as an important resource for national health planning. With both molecular and information technologies advancing at such remarkable speeds, it is likely that biobanks will play an even more significant role in the future.

REFERENCES Autism Genetic Resource Exchange (2007). AGRE program description. http://www.agre.org/program/descr.cfm?do=program Baig, S. and Doyle, A. (2005). From Biobanks to Biomarkers: Translating the potential of human population genetics research to improve the quality of health of the EU citizen. Proceedings of a conference held at the Wellcome Trust Conference Centre, Hinxton, Cambridge, 20–22 September. Bioethics Advisory Committee, Singapore (2002). Human tissue research http://www.bioethics-singapore.org/resources/reports2.html Bioethics Advisory Committee, Singapore (2004). Research involving human subjects: Guidelines for Institutional Review Boards (IRBs). http://www.bioethicssingapore.org/resources/reports3.html Bioethics Advisory Committee, Singapore (2005). Genetic testing and genetic research. http://www.bioethics-singapore.org/resources/ pdf/GT%20Report.pdf Bioethics Advisory Committee, Singapore (2007). Personal information in biomedical research. http://www.bioethics-singapore.org/ resources/reports5.html Burton, P. (2006). UK Biobank: how big is “big”? http://www.p3 gobservatory.org/download/ICHG.Aug.2006.ppt#1 Cancer Biomedical Informatics Grid (caBIGTM) http://cabig.cancer.gov CARTaGENE project. http://www.cartagene.qc.ca Collins, F. and Manolio, T. (2007). Merging and emerging cohorts necessary but not sufficient. Nature 445, 259. Confederation of Cancer Biobanks, UK. http://www.oncoreuk.org/ pages/about_confederation.html Cooperative Human Tissue Network (CHTN). http://www-chtn.ims. nci.nih.gov/ deCODE genetics Inc. http://www.decode.com Domain Specific IRB (2004). http://www.research.nhg.com.sg/ Eiseman, E., Bloom, G., Brower, J., Clancy, N. and Stuart, O. (2003). Case Studies of Existing Human Tissue Repositories Best Practices for a Biospecimen Resource for the Genomic and Proteomic Era. RAND Corporation, Santa Monica, California, USA. http://www. biospecimens.cancer.gov/nbn/rand.asp Elger, B. and Caplan, A. (2006). Consent and anonymization in research involving biobanks. EMBO Rep 7, 661–666. Estonia Genome Project. http://www.geenivaramu.ee/ European Prospective Investigation into Cancer Nutrition (EPIC). http://www.iarc.fr/epic Foundation for the National Institutes of Health (2007). Genetic Association Information Network. http://www.fnih.org/GAIN2/ home_new.shtml Friede, A., Grossman, R., Hunt, R., Li, R.M., Stern, S. and Andrew, S. (eds) (2003). National Biospecimen Network Blueprint. Constella Group Inc, Durham, NC.http://www.biospecimens.cancer.gov/ nbn/blueprint.asp Hansson, M., Dillner, J., Bartram, C., Carlson, J. and Helgesson, G. (2006). Should donors be allowed to give broad consent to future biobank research?. Lancet Oncol 7, 266–269. Hirtzlin, I., Dubreuil, C., Preaubert, N., Duchier, J., Jansen, B., Simon, J., Lobato, P., Perez-Lezaun, A., Visser, B., Williams, GD.,

Cambon-Thomsen, A. EURPOGENBANK Consortium et al. (2003). An empirical survey on biobanking of human genetic material and data in six EU countries. Eur J Hum Genet 11, 475–488. HUNT-2 Biobank. http://www.hunt.ntnu.no/index.php?side=english International Society for Biological Environmental Repositories (ISBER) (2005). Best practices for repositories I: Collection, storage, and retrieval of human biological materials for research. Cell Preserv Technol 3, 5–47. Karolinska Biobank. http://www.meb.ki.se Knoppers, B., and Newton, J. (2001). Creation of Population Biobanks: Design and Conduct. The Public Population Project in Genomics (P3G) publication. http://www.p3gconsortium.org Knox, K. and Ratcliff, C. (2002). A Strategic Framework for Establishing a National Cancer Tissue Resource for Cancer Biology and Treatment Development. National Translational Cancer Research Network Coordinating Centre, UK. Life Gene. http://www.lifegene.se/ Louie, A. and Reeve, B. (2005). Biobanks Strategies to Address NextGeneration Challenges for Biobanking. IDC, Framingham, Massachusetts. USA. Manolio, T., Bailey-Wilson, J. and Collins, F. (2006). Genes, environment and the value of prospective cohort studies. Nat Rev Genet 7, 812–820. Marble Arch Working Group. http://www.oncoreuk.org/pages/ MarbleArchWorkingGroup.html Melnikova, I., Reeve, B. and Swenson, M. (2005). Biobanks Collaborating for Cures. IDC, Framingham, Massachusetts. USA. http://www03.ibm.com/industries/healthcare/doc/content/bin/Biobanks.pdf National Cancer Institute, USA (2006). First-Generation Guidelines for NCI-Supported Biorepositories. http://www.biospecimens.cancer.gov/biorepositories/guidelines_full_formatted.asp Norwegian Mother and Child Cohort. http://www.fhi.no/eway/ default.aspx? Ölund, G., Lindqvist, P. and Litton, J-E. (2007). BIMS An information management system for biobanking in the 21st century. IBM Sys J 46, 171–182. oNCORE UK. http://www.oncoreuk.org/ Organization for Economic Co-operation and Development (2004). Guidance for the Operation of Biological Research Centres (BRCs): Certification and Quality Criteria for BRCs. Perry, R. and Reeve, B. (2004). Standardizing on LIMS: TCO and ROI for the Multilab Setting. IDC, Framingham, Massachusetts. http://www.thermo.com/www.thermo.com/com/cda/resources/ resources_detail/1,2166,111652,00.htm Potter, J. (2004). Toward the last cohort. Cancer Epidemiol Biomarkers Prev 6, 895–897. Public Population Project in Genomics. http://www.p3gconsortium.org/ Public Population Project in Genomics (2005). The Open Biobank Project. http://www.p3gobservatory.org/openBiobank.do Singapore Tissue Network. http://www.stn.org.sg Taiwan Biobank. http://www.ibms.sinica.edu.tw/biobank/biobank.htm The Biobank Japan project. http://www.biobankjp.org/

Recommended Resources

The Prostate Specialized Program of Research Excellence National Biospecimen Network (Prostate SPORE NBN) Pilot. http://www. prostatenbnpilot.nci.nih.gov/http://prostatenbnpilot.nci.nih.gov/ The UK Data Protection Act (1998). The Data Protection Act. http:// www.dh.gov.uk/PolicyAndGuidance/OrganisationPolicy/ RecordsManagement/DataProtectionAct1998Ar ticle/fs/ en?CONTENT_ID=4000489&chk=VrXoGe The Wellcome Trust Case Control Consortium. http://www.wtccc.org. uk/info/050928.shtml

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UK Biobank. http://www.ukbiobank.ac.uk/ Western Australian Genome Health Project. http://www.genepi.org. au/waghp Willett, W., Blot, W., Colditz, G., Folsom, A., Henderson, B. and Stampfer, M. (2007). . Nature 445, 257–258. Zimmerman, Z., Swenson, M. and Brock, R. (2004). Biobanks Accelerating Molecular Medicine. Challenges Facing the Global Biobanking Community. IDC, Framingham, Massachusetts. USA.

RECOMMENDED RESOURCES Biobank Websites CARTaGENE project: http://www.cartagene.qc.ca.The CARTaGENE project will map genetic variation in a large reference population of Quebec that integrates and exploits the advantages of the existing Canadian health care and legal system, that is, universal health care, public health care, public health expertise, unique medical identifier numbers, comprehensive health and environmental record linkage with a genealogical/demographic database, extensive privacy and access legislation and a heterogeneous modern population. Cooperative Human Tissue Network (CHTN): http://www-chtn.ims. nci.nih.gov/. The CHTN is supported by the NCI to provide biomedical researchers with access to human tissues. Six member institutions coordinate the collection and distribution of tissues across the US and Canada in six regional divisions. The CHTN specializes in the prospective procurement, preservation and distribution of human tissues for research. Danubian Biobank Consortium: http://www.danubianbiobank.de. Danubian Biobank foundation for public utility in molecular medicine of aging disorders that networks and expands all biobanking activities and the core scientific competences of the Danubian universities, teaching hospitals and rehabilitation clinics between Ulm and Budapest and of associated collaborating regional centers in the field of non-cancer aging disorders. GenomeEUTwin: http://www.genomeutwin.org/. This project will apply and develop new molecular and statistical strategies to analyze unique European twin and other population cohorts to define and characterize the genetic, environmental and lifestyle components in the background of health problems like obesity, migraine, coronary heart disease, and stroke, representing major health care problems worldwide. Karolinska Biobank: http://www.nitordesign.se/biobank/about.php. A national biobank for collection, handling, and storage of human biological material has been established at Karolinska Institutet-KI Biobank. Singapore Tissue Network: http://www.stn.org.sg. The national tissue bank of Singapore. The STN is a non-profit tissue and DNA bank set up as a joint initiative between the Ministry of Health, the Genome Institute of Singapore and the Biomedical Research Council (BMRC) of the Agency of Science Technology and Research (A*STAR) supporting Singapore’s Biomedical Initiatives. UK Biobank: http://www.ukbiobank.ac.uk. UK Biobank is a longterm project aimed at building a comprehensive resource for medical researchers to gather information on the health and lifestyle of 500,000 volunteers aged between 40 and 69. Over the next 20–30 years UK Biobank will allow fully approved researchers to use these

resources to study the progression of illnesses such as cancer, heart disease, diabetes and Alzheimer’s disease. Western Australian Genome Health Project (WAGHP): http://www. genepi.org.au/waghp. The WAGHP aims to capture biospecimens on all consenting members of the living population of WA (approximately 2 million people) and leverage on a number of internationally unique, population-based datasets.

International Harmonization Websites Biobank Central: http://www.biobankcentral.org. A website sponsored by IBM, Affymetrix, Bioaccelerate, and Invitrogen that provides a working group venue, patient and public education programs, and a forum for international collaboration and harmonization of best practices. Confederation of Cancer Biobanks: http://www.info.cancerresearchuk. org/news/newsarchive/2006/october/17771016. Consortium of UK new tissue banks to standardize cancer samples established in October 2006. International Society for Environmental and Biological Repositories (ISBER): http://www.isber.org. The primary goal of the International Society for Biological and Environmental Repositories, ISBER, is to provide information and guidance on the safe and effective management of specimen collections. HealthNex: http://www.healthnex.typepad.com. The mission of this blog is to drive discussion and collaboration on some of the key elements necessary for a global, interconnected health care ecosystem: electronic health records, a new information infrastructure for improving the quality and efficiency of care, and a platform for transforming treatment by marrying personal genomics with care. HuGENet: http://www.hugenet.org.uk/. Human Genome Epidemiology Network, or HuGENet™ is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. The International Agency for Research on Cancer: http://www.euro. who.int/HEN/Resources/IARC/20030723_1. The IARC is part of the World Health Organization. IARC’s mission is to coordinate and conduct research on the causes of human cancer, the mechanisms of carcinogenesis, and to develop scientific strategies for cancer control. The Agency is involved in both epidemiological and laboratory research and disseminates scientific information through publications, meetings, courses, and fellowships. The International Society for Environmental and Biological Repositories (ISBER): http://www.isber.org. The primary goal of the ISBER is to provide information and guidance on the safe and effective management of specimen collections.

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Public Population Project in Genomics (p3G Consortium): http://p3gconsortium.org. The p3G Consortium is a not-for-profit international consortium to promote collaboration between researchers in the field of population genomics. It provides the research community with resources, tools and know-how to facilitate data management for improved methods of knowledge transfer and sharing. STROBE (STrengthening the Reporting of OBservational studies in Epidemiology): http://www.strobe-statement.org. International, collaborative initiative of epidemiologists, methodologists, statisticians, researchers and editors involved in the conduct and dissemination of observational studies, with the common aim of strengthening the reporting of observational studies in epidemiology. TuBaFrost: www.tubafrost.org. TuBaFrost is an infrastructure for a European Tumor Tissue Bank. It was developed in a 3-year Fifth Framework Program (FP5, 1998–2002) of the European Commission involving OECI members as participants as well as the EORTC and a Dutch law firm.

Australia, Canada, Council for International Organizations of Medical Sciences (CIOMS), France, Great Britain, Helsinki Declaration, Nuremberg Code, and the Belmont Report.

Legislation on Biobanks Estonia: Human Genes Research Act. Estonia/Government, Estonia, 2000, http://www.legaltext.ee/text/en/X50010.htm. Iceland: Ministry of Health and Social Security, Iceland, 2000, Government regulation on a Health Sector Database, http://www. bibliojuridica.org/libros/5/2292/54.pdf. Latvia: Human Genome Research Law, Latvia/Government, Latvia, 2002, http://bmc.biomed.lu.lv/gene/print/Human%20Genome% 20Research%20Law,%20Latvia.doc4. United Kingdom: Human Tissue Act 2004,United Kingdom/ Government, 2004, http://www.opsi.gov.uk/acts/acts2004/ 20040030.htm.

Legislation Relating to Biobanks Biobanking Tools Website caBIG TM (Cancer Biomedical Informatics Grid): http://www.cabig. cancer.gov/index.asp.caBIG™ is a voluntary network of infrastructure launched by the NCI in February 2004 to create a virtual community that shares resources with tools, and ideas that enables the collection, analysis, and sharing of data and knowledge along the entire research pathway from laboratory bench to patient bedside. Public Population Project in Genomics (p3G Consortium): http:// www.p3gconsortium.org. The p3G Consortium is a not-forprofit international consortium to promote collaboration between researchers in the field of population genomics. It provides the research community with resources, tools and know-how to facilitate data management for improved methods of knowledge transfer and sharing.

Policies Websites HumGen: http://www.humgen.umontreal.ca/int/. An international database on the legal, ethical and social aspects of human genetics, developed as a collaboration between academia, government and industry by the Centre de recherché en droit public at the University of Montreal. PHUGU Genetics Policy Database: http://:www.phgu.org.uk/policydb/index.html. A searchable web-based database of literature on policy development for genetics in health services and health care.

IRB Review Guidance http://www.hhs.gov/ohrp/humansubjects/guidance/reposit.htm, useful website with national and international guidelines including

Australia: Australian Law Reform Commission, Australia, 2003, Essentially Yours: The Protection of Human Genetic Information in Australia, http://www.austlii.edu.au/au/other/alrc/publications/ reports/96/. Canada: Network of Applied Genetic Medicine, Canada, 2003, Statement of Principles on the Ethical Conduct of Human Genetic Research Involving Populations, http://www.rmga.qc.ca/ en/index.htm. France: Opinion no 77 National Consultative Ethics Committee for Health and Life Sciences (CCNE), France, 2003, http://www. ccne-ethique.fr/english/start.htm. Germany: German National Ethics Council, Germany, 2004, Guiding principles of the legal position in Germany for biobanks used in medical research, http://www.ethikrat.org/_english/publications/ Opinion_Biobanks-for-research.pdf. Norway: Norway/Government, Norway, 2003, http://www.ec. europa.eu/research/biosociety/pdf/norwegian_act_biobanks.pdf. Scotland: Generation Scotland – Legal and Ethical Aspects, http:// www.129.215.140.49/gs/Documents/GSlawandethicsSep03.pdf. Singapore: Bioethics Advisory Committee, Singapore Bioethics Advisory Committee (BAC), 2002, Human Tissue Research, Research Involving Human Subjects: Guidelines for IRBs, 2004, http://www.bioethics-singapore.org8. Sweden: Sweden/Government, Sweden, 2003, http://www.sweden.gov. se/content/1/c6/02/31/26/f69e36fd.pdf. United Kingdom: UK Biobank Ethics and Governance Framework, 2006, http://www.ukbiobank.ac.uk/docs/EGF_Version2_July%2 006%20most%20uptodate.pdf.

CHAPTER

25 Application of Biomarkers in Human Population Studies Stefano Bonassi and Monica Neri

INTRODUCTION The introduction of biomarkers in human population studies is an effective strategy to gain knowledge about the occurrence of disease, to improve the understanding of etiology and pathogenesis, and to measure the effort to control the disease outcome. More specifically, under a gene–environment model of disease causation, biomarkers are used to measure exposures to causative agents that may induce or prevent the disease, to find out those genetic or acquired factors that may change the individual probability of disease, to predict the outcome of a disease through early damages, and to reduce the time interval between exposure to risk factors and identification of potentially harmful effects (Toniolo et al., 1997; IARC Workshop report, 1997; Bonassi and Au, 2002; Ugolini et al., 2007). The multidisciplinary nature of biomarkers is reflected in the difficulty to find a single definition that applies to the many fields in which biomarkers can be used. From a very general viewpoint, a biomarker is a measure of a chemical, cellular, molecular, immunologic, genetic, or physiologic signal, biologic event, or biologic state, measured in biologic material, although there are other definitions more oriented to disease diagnosis and treatment; that is, a biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic response to a therapeutic intervention. In addition to assessing pharmacologic response, biomarkers can be applied as a

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

diagnostic tool, for staging disease, to predict and monitor clinical response to any intervention. The potential applications of biomarkers in biomedical research are numerous. They represent the most suitable alternative to traditional endpoints used in clinical research and in public health. This growing interest has been refueled by the increased accessibility to high-throughput techniques that can impact in many instances disease prevention – detecting diseases at early stages or identifying subgroups of susceptible individuals – and disease diagnosis and treatment-anticipating clinical diagnosis or surrogating hard endpoints commonly used in clinical trials. The use of biomarkers in human studies has been essentially directed in two directions, that is, to implement the traditional epidemiologic approach with new endpoints and to improve and accelerate clinical trials and drug development.

BIOMARKERS IN MEDICINE In a clinical and pharmacologic setting, biomarkers can provide a basis for selecting lead compounds for subsequent clinical evaluation. Furthermore, they can provide information to guide dosing and to minimize interindividual variation in response and in toxicity. Finally, some biomarkers can be employed as substitutes for clinical endpoints in clinical trials, under the assumption that they can predict clinical benefits (Downing, 2000).

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Before discussing some examples of the use of biomarkers in drug development and clinical trials, two major concepts must be properly described: surrogate endpoint and biomarker validation. A surrogate endpoint is a laboratory measurement or a physical sign used in a clinical trial as a substitute for an endpoint directly measuring how a patient feels, functions, or survives. For example, changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint (Piantadosi, 1997). The use of a “surrogate” variable in lieu of the true endpoint (usually survival or irreversible morbidity) allows one to evaluate the outcome more rapidly, less expensively, and/or less invasively. The use of a biomarker as surrogate endpoint requires validation, that is a demonstration of accuracy (correlation of the measure with the clinical endpoint) and precision (the reproducibility of the measure) (Downing, 2000). These properties are the basis for any biomarker validation; however, surrogate biomarker validation requires a more rigorous standard of evidence that links drug exposure to desired clinical drug effect (Prentice, 1989; Fleming and DeMets, 1996). The surrogate should be a correlate of the true clinical outcome and “fully capture the net effect of the treatment on the clinical outcome.” Validation of a surrogate endpoint requires not only that the surrogate correlates with the clinical endpoint, but also that changes in the clinical endpoint be reflected by changes in the surrogate (Racusen, 1999). Interestingly, there are a number of surrogate markers that are currently accepted by regulatory authorities such as blood pressure, serum cholesterol, and tumor response, that do not fully meet the standard for surrogate biomarkers, and that may be sensitive only to selected candidate drug mechanism of action in phase 3 evaluation. There are many potential valuable applications of biomarkers in medicine. These applications include: (1) diagnosis and differential diagnosis, (2) screening of potential new therapies either in vitro or in vivo, (3) measuring severity, progression of disease, and responses to therapy, (4) predicting prognosis, and (5) measuring toxicity. Some of these possible applications will be described with selected examples from nephrology, neurology, and cardiology. Renal biopsy is considered the gold standard for evaluation of renal allograph dysfunction in renal transplant recipients. Biopsies are examined for evidence of T cell and antibodymediated rejection according to standard criteria such as the Banff or the CCTT classification score (Racusen et al., 1999). These scores are in essence diagnostic markers, useful to discriminate immune-based rejection from other causes, to predict the probability of response to immuno-suppressive therapies, and to predict graft survival (Lachenbruch et al., 2004). Most markers used in renal transplantation can be considered predictive of renal allograph rejection, since they explain part of the variability of the endpoint. On the other hand, a surrogate biomarker is a substitute of the clinical endpoint, and changes in the outcome must be reflected in the surrogate. A few examples of candidate surrogates from different fields are reported in Table 25.1. A number of potential surrogates for renal allograph rejection have been proposed, especially for long-term graft survival,

TABLE 25.1

Examples of surrogate endpoints

Disease

Endpoint

Surrogate endpoint

Cancer

Mortality

Tumor size reduction CEA Level (Colon cancer) PSA Level (Prostate)

Disease progression Disease progression HIV infection

AIDS Disease progression

CD4count Plasma viremia

Cardiovascular disease

Hemorrhagic stroke

Blood pressure Myocardial infarction Cholesterol level

Glaucoma

Vision loss

Intraocular pressure

Adapted from Lachenbruch et al., 2004.

including interstitial fibrosis at 6–12 months, chronic allograft arteriopathy at 3–12 months, CADI at 12 months, etc. (Lachenbruch et al., 2004). However, none of them has been fully validated given the difficulty of planning and running longterm follow-up studies. Among these biomarker a great interest is focused on molecular markers, especially on the quantitative mRNA levels of immune effector molecules, like perforin, granzyme B, and cytokines (IL-10, IL-15, IL-7, IFN-). More recently microarray gene expression analysis has been proposed to identify those genes that most likely discriminate rejection sub-types. Among most exciting findings was the heterogeneous behavior of biopsies from acute rejection, with expression profiles resembling those of drug toxicity or chronic allograft nephropathy (Sarwal et al., 2003). Other expression arrays identified a set of genes predicting the development of chronic allograft pathology 6 months later (Scherer et al., 2003). Many examples of the use of biomarkers in clinical studies comes from neurology, especially in major diseases such as multiple sclerosis (MS), stroke, and neurodegenerative diseases including Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). The use of these biomarkers relies on the understanding of the pathological basis for the disease under study at the molecular genetic, biochemical, or anatomical level. For example, the cytosine–adenosine–guanosine (CAG) repeat length in HD may contribute as a diagnostic test in symptomatic individuals (MacMillan et al., 1995), or as a prognostic test for presymptomatic individuals at risk for HD. Nevertheless, this marker can hardly be used as a surrogate clinical endpoint in a clinical trial, because it would be unlikely to change with a therapeutic intervention and can be abnormal in individuals

Biomarkers in Medicine

with no clinical signs of disease (presymptomatic HD) (Feigin, 2004). Biochemical markers used in clinical practice and clinical trials include measuring CSF oligoclonal bands to confirm the diagnosis of MS, and functional imaging of dopaminergic neurons in PD with dopamine transporter ligands, measures of dopamine metabolism (fluorodopa). Anatomical biomarkers include magnetic resonance imaging (MRI) of lesion burden or cerebral atrophy in MS, and confirmation of the clinical diagnosis of stroke with computed tomography (CT) or MRI. A major controversy in the field concerned the choice of best endpoints in clinical trials of neurodegenerative disorders aimed at slowing disease progression or delaying disease onset. The choice of clinical endpoints is quite controversial, and conditions like “no-change in clinical status,” or a rate of worsening slower than expected may be considered as a successful result. However, while clinical scales are commonly used, they may be really misleading in trials testing symptomatic therapies, as demonstrated by the misinterpretation of results from the DATATOP study (DATATOP, 1989; Feigin, 2004). The diagnosis and treatment of atherosclerosis is a classic example for the use of biomarkers, not only to identify at risk subjects, but also for the understanding of the molecular basis of disease pathogenesis. Although most clinical events associated with thrombosis are caused by plaque rupture or erosion, most plaques that rupture do not cause symptoms. Therefore, clinical trials measuring only clinical events do not detect the majority of asymptomatic plaque ruptures and have poor sensitivity to detect therapeutic effects on plaque stabilization and thrombosis. A major contribution to the understanding of etiology and pathogenesis of this disease comes from the Atherosclerosis Risk in Communities (ARIC) study, which included approximately 16,000 men and women between the ages of 45 and 64 years, and evaluated the incidence of coronary heart disease (CHD) events (fatal or nonfatal myocardial infarction and surgical revascularization procedures), and carotid artery atherosclerosis (assessed by B-mode ultrasonography). This study allowed the definition of a set of biomarkers that may impact clinical practice and pharmaceutical research. In particular the investigators found that a high level of intercellular adhesion molecule1 (ICAM-1) increased the risk for a coronary event 5.5-fold, after adjusting for an impressive list of potential and actual confounders. They also found that individuals with either elevated ICAM-1 or E-selectin had a higher risk for carotid artery atherosclerosis (Hwang et al., 1997). These results were confirmed in the Physicians’ Health Study, which suggested that a low-grade chronic inflammation may contribute to the development of atherothrombotic disease (Ridker et al., 1998). The progressive evolution of high-throughput techniques and their rapid application to clinical practice has changed the perspectives for the use of biomarkers in clinical trials as well as in the drug development process. Efforts have been dedicated to the identification of gene expression profiles, for the purpose of defining susceptible subjects, and allowing early detection of disease. These tools have allowed us to categorize otherwise

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indistinguishable disease stages and histotypes, and the correlation of molecular features with the likelihood of treatment response or with prognosis. However, this technique has not been properly put to the test in prospective clinical trials. An example of these difficulties comes from oncology. The MINDACT (Microarray In Node-negative Disease may Avoid ChemoTherapy) trial was designed to provide definitive evidence on the clinical relevance of a 70-gene prognosis signature, comparing its performance with that of traditional prognostic indicators for assigning adjuvant chemotherapy to patients with node-negative breast cancer. The MINDACT investigators recently discussed the intrinsic difficulties in designing trials using gene expression profiling, reporting the major challenges inherent logistics, implementation, and interpretation of the results (Bogaerts et al., 2006). The effector role of proteins has been exploited by the recent availability of arrays for studying the proteome. The use of proteomics is in its infancy in clinical trials; however, the evolution of proteomic techniques may be of importance for the drug development process. While the complexity of structure and functions in the proteome represents a significant challenge, the pharmaceutical industry and its biotech and academic partners are expending a tremendous amount of resources to decipher and utilize proteomic data to make drug development more efficient and successful. In addition to using proteomic tools to identify an array of proteins that are modified in the disease state, a significant focus of proteomic activities in drug discovery has turned to the identification of biomarkers in easily accessible biological fluids. The development and validation of a biomarker aids not only in the understanding of the disease process and its progression, but also are critical as a monitoring tool in later stages of drug development. Good examples of such markers are the serum and urine biomarkers used to identify arthritis. Several biomarkers from synovial fluid, blood and urine have been used to identify and study the stages of osteoarthritis (OA) (e.g., urinary collagen fragments; Downs et al., 2001). A group of three markers best distinguished OA patients: one inflammatory marker and markers of both cartilage anabolism and catabolism. So, in this example, biomarkers that are directly indicative of the disease process (skeletal metabolism) are readily measured in body fluids and can be used throughout the drug development process (Walgren and Thompson, 2004). The authors emphasized that often more than one biomarker is necessary or preferable. A single marker may not readily track disease progression in each patient, especially if the marker is not directly linked to the molecular basis of the disease. The role of biomarkers in epidemiologic, non-clinical studies is similar to the examples discussed so far. Nevertheless, major differences exist, such as the reduced emphasis on the individual level and the focus on early stages of disease, which have a lower association with the outcome, but have a stronger potential for prevention. The effort to disentangle the complex interaction existing between the various steps of pathogenesis and the corresponding biomarker started during early 1980’s, when the National

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Research Council and other researchers proposed the classical picture of the black box and described the evolution of the detailed continuum of events eventually leading to a clinical diagnosis of disease (Perera and Weinstein, 1982; National Research Council, 1987). This figure has been represented in many versions and is now an icon of the molecular epidemiology approach. A comprehensive version of the figure describing the hypothesized continuum of events in the pathway leading to cancer is reported here (Figure 25.1). Exposure to environmental risk factors

Phase I, II reactions

Target dose

DNA damage promotion

DNA repair

Micronucleus

Normal cell

Metabolically activated cell

DNA adducts bearing cell

Markers evaluating biologically effective dose DNA adducts Protein adducts Tests evaluating internal dose Metabolites presence in body fluids or tissues Susceptibility markers Phase I, II activities

DNA damaged cell Early biological effect markers Cytogenetic markers UDS, DPXL, pADPRP Alkaline elution Comet assay

Mutated cell Early biological response markers Oncogenes TSGs Oncoproteins HPRT, HLA-A, Hb, GPA mutations

Figure 25.1 Intermediate endpoints used as biomarkers in molecular epidemiology as related to carcinogenesis process (Izzotti et al., 1997).

According to the classification proposed in those early papers and basically still adopted, biomarkers are divided depending upon their position in the disease natural history. The predominant model for the pathogenesis of complex diseases such as cancer is based on the interaction between environmental exposure to carcinogens and the individual genetic profile. This interaction, occurring at molecular level, determines early biological effects, which may progress to a clinical diagnosis of disease (Figure 25.2). Biomarkers are then classified as biomarkers of exposure, early effect (health risk), and susceptibility.

ENVIRONMENT

GENES

Biomarkers of exposure

Biomarkers of susceptibility

Biomarkers of early effect DISEASE

Figure 25.2 Human diseases are caused by some combination of environmental and genetic factors.

BIOMARKERS OF EXPOSURE An important piece of the information to gather for a molecular epidemiology study is the documentation of exposure to causal factors. Traditional epidemiology studies collect this information via questionnaire and historical information such as employment records and monitoring data. The use of biomarkers provides a new way to document the presence and the intensity of genotoxic exposures, no longer based on the amount of the agent in the environment, but on the dose reaching the target (DNA for genotoxic compounds): an individually tailored exposure measurement. Among the biomarkers currently used for this purpose, adducts are particularly useful for identifying chemicals that form covalent bonds with biological macromolecules (e.g., protein and DNA adducts). These biomarkers have become very popular because the presence of specific DNA adducts are associated with the development of disease (Vineis and Perera, 2000; Perera, 2000). Carcinogenic and mutagenic agents contained in most common carcinogens such as tobacco smoke and air pollution induce the formation of aromatic DNA adducts, which have been indicated to play an important role in the etiology of lung cancer (Peluso et al., 2005). However, tobacco smoke is also a major source of oxidative stress and may induce the production of endogenous DNA lesions, through oxidative DNA damage and lipid peroxidation of cell membrane (Panda et al., 2000). There are many features that should be considered when choosing a biomarker to measure exposure to carcinogenic agents in human populations. One of the most important is flexibility. The following example shows the possible application of the same biomarker in the same population to measure two major sources of DNA damage. A small population of subjects undergoing bronchoscopy for diagnostic purposes was included in a transitional study, aimed at evaluating the extent of DNA adduction due to exposure to tobacco smoke in target and surrogate tissues (Peluso et al., 2004). Samples of peripheral blood lymphocytes (PBL), nasal mucosa, and bronchial mucosa were collected during bronchoscopy in 55 subjects, both smokers and non-smokers, who gave their consent to participate to the study. The level of DNA adducts measured by 32P labeling assay in nasal mucosa and in PBL were correlated with those in bronchial mucosa (p  0.01 and p  0.05 respectively). DNA adducts in smokers were significantly increased in both nasal mucosa and PBL, with a significant dose-response linear trend (p  0.05). Many compounds among those present in tobacco smoke form DNA adducts, and their measurement is an excellent index of exposure to DNA-damaging carcinogens in humans. The formation of carcinogen-DNA adducts is an initial event in carcinogenesis (Peluso et al., 2005; Tang et al., 2001) and could be efficiently used to assess the oncogenic risk in exposed individuals. Besides the direct adduction of macromolecules to DNA, oxidative damage has been recognized to play a major role in early stages of carcinogenesis. Among oxidative pathways, most studies are those involving malondialdehyde (MDA). MDA is

Biomarkers of Early Disease Risk

(a)

(b)

U1

U2

U2 U1

U3 OR

U3 OR

Figure 25.3 Profiles of MDA–DNA adducts (U1–U3 adduct spots) in (a) bronchi and in (b) a standard MDA sample (from Peluso et al., 2006).

a natural product of membrane lipid peroxidation, formed also during prostaglandin biosynthesis via cyclooxygenase, capable to interact with DNA, forming exocyclic DNA adducts (Marnett, 2000). MDA-DNA adducts may be also induced by direct DNA oxidation, through the production of base propenal intermediates. MDA-DNA adducts have been shown to be premutagenic in mammalian cells and to induce frameshift and base pair substitution mutations (Benamira et al., 1995). Using the same material collected during the first study, the levels of MDA-DNA adducts were measured in the bronchial specimen of subjects undergoing a bronchoscopic examination for the clinical suspicion of lung cancer (Munnia et al., 2006). MDA-DNA adducts were higher in smokers than in never-smokers (Frequency Ratio (FR)  1.51, 95% Confidence Interval (CI)  1.01–2.26). MDA-DNA adducts were also increased in lung cancer cases with respect to matched controls, but only in the smoker subgroup (FR)  1.70; 95% CI (95% CI  1.16–2.51) (see Figure 25.3). In both studies the effect of exposure to tobacco smoke was evaluated in the context of the genetic profile of the study subjects, using a set of candidate genes directly involved in the metabolism of carcinogenic compounds or in the resistance to the oxidative damage. In the first study the frequency of bulky DNA adducts was not modified by the functionality of CYP1A1, GSTM1 and GSTT1 genes, while in the study on MDA a cyclin D1 (CCDN1) G870A polymorphism, previously associated with an impaired cell-cycle regulation and an accumulation of DNA damage, determined an increased frequency of MDA adducts in the heterozygotes FR  1.51 (95% CI 1.04–2.20) and in homozygotes (FR  1.45; 95% CI 1.02–2.07). Recent changes in the availability of high-throughput techniques have allowed the experimental use of expression arrays to highlight those genes that may constitute a fingerprint of a specific exposure. Workers exposed to benzene (Smith et al., 2005), and children exposed to air pollution (van Leeuwen et al., 2006) were among the first human populations to be screened with this assay, whose results are still to be validated for this purpose.

BIOMARKERS OF EARLY DISEASE RISK Biomarkers from this class represent a large number of biological events, including altered mRNA expression, chromosome damage,

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cytotoxicity, somatic cell mutations, and immunologic alterations. These biomarkers, besides describing the early interaction of genotoxic agents with the human body, can be used as outcome measures in epidemiologic research, and can potentially allow risk factors for disease to be identified early in the exposuredisease process and allow for more timely interventions. This is particularly useful for diseases with long latency such as cancer. Biomarkers of early effect can be used at the population level, where research results may be incorporated into policy-making decisions and, potentially, at the individual level, where biomarkers may be employed for screening purposes and ultimately in clinical practice (Schulte and Rothman, 1998). Among this class, biomarkers measuring chromosome stability are among the most extensively used and best validated in population studies (Bonassi et al., 2005). Extensive literature exists on the use of chromosome aberrations (CA) to measure exposure to ionizing radiation and to genotoxic chemicals. More recently the causal role played by CA in the pathogenesis of cancer have been confirmed by epidemiological evidence, and this finding has opened a number of perspectives for the planning of cancer prevention policies (Bonassi et al. 2004; Hagmar et al., 2004). A drawback of traditional cytogenetic assays is the laborintensive laboratory procedure. Therefore, several molecular techniques have been developed to enhance the assay. The most significant improvement is the use of the fluorescencein-situ-hybridization procedure (FISH) to detect chromosomespecific aberrations. Chromosome-specific probes that are labeled with fluorescent dyes are used to hybridize to chromosomes for the determination of chromosome rearrangements and chromosome region-specific breaks. This approach, besides the significant reduction of the labor time, increased the specificity of the association exposure-damage, allowing the evaluation of specific chromosome damage and rearrangement (Swiger and Tucker, 1996). A classic example of this enhanced specificity are the studies on the effect of benzene exposure on selected chromosomes (Zhang et al, 2002) or studies of specific oncogene activation (Scaruffi et al., 2004). The most promising assay in the class of biomarkers of early biological effect seems to be the micronucleus (MN) assay. This assay has proved to be much simpler, quicker and cheaper than CA, and recently the capability of MN frequency in lymphocytes of healthy subjects to predict their risk of cancer has been published with parallel results as for CA (Bonassi et al., 2007). Moreover, its recently described multi-endpoint nature is extremely promising for a more extended use of the assay in exposed human populations. The cytokinesis-block micronucleus (CBMN) assay was originally developed as an ideal system for measuring MN; however, it can also be used to measure nucleoplasmic bridges (NPBs), nuclear buds (NBUDs), cell death (necrosis or apoptosis) and nuclear division rate. Current evidence suggests that (a) NPBs originate from dicentric chromosomes in which the centromeres have been pulled to the opposite poles of the cell at anaphase and are therefore indicative of DNA mis-repair, chromosome rearrangement

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CYTOTOXIC GENOTOXIC AGENT

G0

G1, S, G2

APOPTOSIS

NECROSIS CHROM. BREAKAGE DICENTRIC GENE and CHROM. AMPLIFICATION LOSS

Figure 25.4 The cytokinesis-block micronucleus (CBMN) assay has evolved into a “cytome” method for measuring comprehensively chromosomal instability phenotype and altered cellular viability (from Fenech et al., 2003).

or telomere end-fusions, (b) NPBs may break to form MN, (c) the nuclear budding process is the mechanism by which cells remove amplified and/or excess DNA and is therefore a marker of gene amplification and/or altered gene dosage, (d) cell-cycle checkpoint defects result in MN formation and (e) hypomethylation of DNA, nutritionally induced or by inhibition of DNA methyl transferase, can lead to MN formation either via chromosome loss or chromosome breakage. In this comprehensive mode, the CBMN assay measures all cells including necrotic and apoptotic cells as well as number of nuclei per cell to provide a measure of cytotoxicity and mitotic activity. The CBMN assay has in fact evolved into a “cytome” method for measuring comprehensively chromosomal instability phenotype and altered cellular viability caused by genetic defects and/or nutritional deficiencies and/or exogenous genotoxins (Figure 25.4).This great availability of endpoints, that allows measuring a number of various damages in the same (simple) assay, opens up an exciting future for the use of this methodology in emerging fields such as nutrigenomics, toxicogenomics, and their combinations (Fenech, 2006)

BIOMARKERS OF GENETIC SUSCEPTIBILITY TO DISEASE The role of genes in the etiology of cancer have been studied since long ago by family-based studies, and this approach led to the mapping of several cancer-related genes. However, these genes, which are characterized by high penetrance, high absolute, and relative risk, have a low impact on the total burden of cancer (Toniolo et al. 1997; IARC, 1997). Molecular epidemiology

in genetic research has greatly impacted the development of etiologic models based on the gene–environment interaction and their application to population studies. In the field of cancer research the most studied polymorphisms are those involving genes active in various steps of chemical metabolism of xenobiotics and in DNA repair. Biomarkers of susceptibility were first studied at phenotypic level, by quantifying enzyme proteins and functions or by administering test substances and looking for their metabolites in urine. These kinds of methods are still important in pharmacokinetic and pharmacodynamic studies; however, the development of molecular biology allowed the study of polymorphisms at genetic level by PCR, PCR/RFLP or PCR/SSCP in a molecular epidemiologic framework. The availability of these techniques lead to the identification of disease risks associated to genetic variation as small as single nucleotide polymorphisms (SNPs). In some cases the SNPs may have a direct causal significance, while sometimes they simply tag a region of the genome that is associated with the risk of disease. Despite the definition of polymorphisms as common genetic variation, from the epidemiological viewpoint these events are rather rare, and as a consequence most epidemiologic studies involving these biological markers are generally inconclusive. This common limitation has called attention to the need to summarize the results of individual studies, while waiting for the completion of larger studies, designed to answer questions that have been raised by preliminary studies. Recent papers have examined methodological issues related to meta-analyses and pooled analysis of biomarker studies (Taioli and Bonassi, 2002). Pooled analysis seems to provide a relevant improvement over meta-analysis in molecular epidemiology studies, although more research on methodology is needed. The need of larger databases of data on biomarkers has contributed to the start of various collaborative studies, aimed at providing results not available from the analysis of single studies. Among these large projects, GSEC and HuGE have contributed significant results to the literature on genetic susceptibility (Hung et al., 2005; Raimondi et al., 2005). One of the major advantages of these collaborative studies is the potential to investigate small population subgroups for which single studies could not reach enough statistical power. For example a recent paper published by the GSEC consortium investigated the association between metabolic gene polymorphisms and lung cancer risk in non-smokers (Raimondi et al., 2005). Since genetic factors may play an important role in lung cancer development at low dose of carcinogen exposure, non-smokers are a good model to study genetic susceptibility and its interaction with environmental factors. The authors evaluated the role of the metabolic gene polymorphisms CYP1A1MspI, CYP1A1lle462Val, GSTM1, and GSTT1 in non-smoker lung cancer patients. Non-smokers (defined as subjects who never smoked on a regular basis) were selected from the raw data of 21 case-control studies for a total of 668 cases and 2479 controls. A significant association between lung cancer and CYP1A1lle462Val polymorphism was observed in Caucasians (adjusted OR  2.04, 95% CI 1.17–3.54). GSTT1 deletion variants appear to be a risk

Biomarkers of Genetic Susceptibility to Disease

factor for lung cancer in Caucasian non-smokers only when the analysis was restricted to studies including healthy controls (adjusted OR  1.66, 95% CI 1.12–2.46). A protective effect on lung cancer was observed with the combination of CYP1A1 wild type, GSTM1 null, and GSTT1 non-null genotypes. Interestingly, pooled analyses of data allows one to evaluate the impact of genetic polymorphisms not only in major pathways, such as metabolism or DNA repair, but also in pathways that are much less common. A good example is a recent paper from the HuGE consortium that evaluated the role of genetic polymorphism in a DNA repair pathway in modulating cancer risk (Hung et al., 2005). In this case a meta-analysis of associations was conducted between genes in the base excision repair pathway and cancer risk, focusing on three key genes: 8-oxoguanine DNA glycosylase (OGG1), apurinic/apyrimidinic endonuclease (APE1/APEX1), and X-ray repair cross-complementing group 1 (XRCC1). Using a large number of cases and controls, they found increased lung cancer risk among subjects carrying the OGG1 Cys/Cys genotype (OR  1.24, 95% CI: 1.01, 1.53), and a protective effect of the XRCC1 194Trp allele for tobacco-related cancers (OR  0.86, 95% CI: 0.77, 0.95). The XRCC1 399Gln/399Gln genotype was associated with increased risk of tobacco-related cancers among light smokers (OR  1.38, 95% CI: 0.99, 1.94), but decreased risk among heavy smokers (OR  0.71, 95% CI: 0.51, 0.99), suggesting effect modification by tobacco smoking. Even with large datasets gathered by international pooling efforts, many association studies yields-negative results. Possible explanations for the lack of association are both methodological, that is, low-study power to demonstrate small effects and too few cases to investigate disease heterogeneity (e.g., by tumor histology), and substantive; that is the existence of multiple pathways that can compensate for each other. Moreover, the possibility of gene–gene or SNP–SNP interactions, or the possibility of linkage disequilibrium between polymorphisms are rarely considered. Further investigations of the haplotype effect of a gene and the study of multiple polymorphisms in different genes within the same pathway and different pathways are needed (Manuguerra et al., 2007). Recommendations for future studies include pooling of individual data to facilitate evaluation of multigenic effects and detailed analysis of effect modification by environmental exposure. One of the major problems in studying the influence of genetic polymorphisms is difficulty collecting large enough study populations. To make association studies more suitable, the use of cancer risk biomarkers (cytogenetic, DNA adducts, and others) instead of cancer incidence has been used (reviewed in Au et al., 2004). Recently a pooled re-analysis of data from seven laboratories that performed biomonitoring studies using the in vivo cytokinesis-block MN assay was performed to evaluate the influence of genetic polymorphisms in GSTM1 and GSTT1 genes on MN frequencies in human PBL (Kirsch-Volders et al., 2006). A total of 301 unexposed individuals and 343 workers exposed to known or suspected genotoxic substances were analysed by Poisson regression. The results of the pooled analysis

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indicate that the GSTM1 and GSTT1 null genotypes are associated with reduced MN frequency in the studied populations. In both exposed and total populations, the GSTT1 null genotype seems to have a protective effect (p  .016). The function of many genotypes is modified in some subgroups; for instance, the protective effect of the GSTT1 null genotype is reversed with increased age (above 40 years). This study confirmed the efficiency of pooled studies, which by increasing the statistical power, can assess the involvement of genetic variants on genome stability and resolve discrepancies among individual studies. Recently new techniques such as DNA microarray analysis allowed the detection of multiple SNPs in thousands of genes at once, testing many subjects at reasonable cost. The vast amount of data that is made available by these assays has required the development of new techniques for statistical analysis. The overwhelming amount of information and the cost of microchips has directed researchers involved in population studies to develop alternative array-based techniques, more specific and more adequate for large population surveys. A suitable technique for these purpose is the arrayed-primer extension (APEX), which allows the simultaneous analysis of a large number of specific cancer-related SNPs potentially relevant to the tumor etiology and pathogenesis. An example of this approach is a case-control study on 90 cases of malignant pleural mesothelioma (MPM) and 349 controls, which were extensively genotyped to evaluate the extent of the gene–environment interaction between these genes and asbestos exposure (Landi et al., 2007). APEX consists of a sequencing reaction primed by an oligonucleotide anchored with its 5 end to a glass slide and terminating just one nucleotide before the polymorphic site. A DNA polymerase extends the oligonucleotide by adding one fluorescently labeled dideoxy-nucleotide (ddNTP) complementary to the variant base. Reading the incorporated fluorescence identifies the base in the target sequence. Since both sense and antisense strands are sequenced, two probes were designed for each polymorphism. Five-prime (C-12) aminolinker oligonucleotides are synthesized and spotted onto silanized slides. Genomic DNA is amplified to enrich the fragments carrying the SNPs by using specific primer pairs. Then, PCR products are pooled, purified, concentrated, and fragmented. See (Landi et al., 2003; Pastinen et al., 2000) for details. The microarrays used in this case-control study were designed to genotype 300 SNPs occurring on selected highly polymorphic genes. To allow a proper evaluation of the overall dataset the statistical analysis was conducted in by addressing separate pathways. The first was the oxidative pathway, that has been extensively reported as to play a major role in the asbestos-mediated carcinogenesis of MPM. An increased risk of MPM was found in subjects bearing a GSTM1 null allele (OR  1.69, 95% CI  1.04–2.74; p  0.034), and in those with the Ala/Val and Ala/Ala genotypes at codon 16 within MnSOD (OR  3.07 in the recessive model, 95% CI  1.55 – 6.05; p  0.001). A stronger effect of MnSOD was observed among patients without a clear exposure to asbestos fibers. No effect was found for other GSTs genes. MPM cases with a MnSOD Val/Val genotype

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showed a significantly better survival (Hazard Ratio  0.47; 95% CI 0.25–0.89). These findings contribute substantial evidence to the hypothesis that oxidative stress and cellular anti-reactive oxygen species systems are involved in the pathogenesis and in the natural history of MPM (Landi et al., 2007).

CONCLUSIONS The introduction of biomarkers in the design of clinical and epidemiological studies is a recent achievement, but in the past few decades a number of discoveries have been made using these new tools (Perera, 2000). Among the challenges to be addressed in the future, two seem more urgent: the validation of biomarkers of disease risk, and the introduction of validated

biomarkers into disease prevention strategies, and eventually in clinical practice. The best example for this latter application is the use of total cholesterol to evaluate the risk of CHD. This model was validated many years ago in one of the first example of population cohort studies, and is still the most popular risk biomarker for the prevention of the CHD (Durrington et al., 1999). The second challenge is the adaptation and the validation of high-throughput techniques for the use in population studies. This effort, although close to the success, suffers from a number of limitations in the reliability of the technique and in the analysis of data that require a bigger final effort of validation. In summary, addressing these priorities will generate a group of biomarkers with a great potential to change our approach to the determination of the disease risk, which will offer a new perspective for strategies of disease prevention and treatment.

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26 Validation of Candidate Protein Biomarkers Ingibjörg Hilmarsdóttir and Nader Rifai

INTRODUCTION The completion of the Human Genome Project is one of the most remarkable achievements in biology and medicine. It offers the promise of insights into the molecular basis of pathologies and possible means for diagnosis, treatment, and prevention of disease. However, information about the genotype is often insufficient for the understanding of a pathological process. After the transcription of genetic materials to mRNA and the subsequent translation to proteins, the latter undergo various structural modifications; the human genome consists of only 30,000 genes while over a million proteins are estimated to exist. Proteins therefore represent the phenotype and are functionally closer to the disease process than genes. Proteomics technology has evolved over the past decade to identify protein biomarkers that are useful in diagnosing, staging, and screening disease, and monitoring treatment. The process of proteomic biomarker discovery has, however, proven to be uncertain and more difficult than initially anticipated. Of more than 100 protein markers used clinically today, only 2 have been approved by the FDA since 1998: B-type natriuretic peptide (BNP) for the diagnosis of congestive heart failure and cancer antigen (CA) 19-9 for monitoring disease activity in pancreatic cancer. Mass spectrometry has been the preferred approach for candidate protein discovery, and plasma, considered to be the most comprehensive human proteome, is the preferred medium.

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 308

Human plasma candidates are present in a vast range of concentrations, from 1 pg/ml for interleukin-6 (IL-6) to 10  109 pg/ml for albumin. Although only low abundance proteins are targeted in candidate protein discovery, several hundred candidate proteins may be identified at the early stage, with the false-positive rate expected to be quite high. Qualification and verification studies in well-characterized populations and larger numbers of patients than initially used in the discovery phase will considerably reduce the number of proteins to 5–20. With current methods, candidate proteins will most likely be evaluated individually through the laborious, complex, and costly processes of research assay optimization, analytical and clinical validation, before the commercialization of the clinical assay. This chapter addresses these processes with the hope that familiarity with their strengths and limitations will aid the proteomics researchers in their quest for introducing novel clinical biomarkers. This chapter is in part based on an article by Rifai, N., Gillette, M.A. and Carr, S.A. (2006).

OPTIMIZATION OF THE CANDIDATE PROTEIN RESEARCH ASSAY After a candidate protein is identified, suitable antibodies must be developed to enable its quantification. Because the candidate

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Analytical Evaluation

protein is assumed to have a plasma or serum concentration of only picograms to nanograms per milliliter, sensitive immunotechniques such as Radio Immuno Assay (RIA) or Enzyme Linked Immuno Sorbant Assay (ELISA), rather than turbidimetry or nephelometry, should be employed. These techniques are more sensitive than the more sophisticated non-immuno-based technologies such as liquid chromatography/tandem mass spectrometry and are readily available in clinical laboratory research and development settings (Vitzthum et al., 2005). Because the use of isotopes is discouraged in the clinical laboratory, ELISA offers a desirable alternative to RIA. As indicated earlier, IL-6 is one of the least abundant proteins in plasma that can be reliably measured by ELISA at concentrations as low as 0.15 pg/ml (coefficient of variation [CV] of 5%, internal data), with alkaline phosphatase or peroxidase as a label. Two antibodies, one for capture and another for detection, that recognize distinct epitopes of the protein are needed to form the ELISA sandwich reaction. Either monoclonal or polyclonal antibodies can be used and can be generated with a synthesized, recombinant, or isolated and purified protein. Before antibodies are used in an immunoassay, their specificity must be established by Western blot and immunostaining or other suitable techniques. Development and optimization of an ELISA must be undertaken with great care because ELISA performance is affected by a wide variety of variables, including the avidity and concentration of the capture and detection antibodies, incubation time and temperature, sample volume and dilution, pH and composition of diluent, enzyme and substrate type, and detector quality (Wild, 2005). Although general guidelines are recommended for choosing the appropriate antibody concentrations, sample dilutions, and incubation temperature and time, the optimal conditions can be determined only empirically. If the desired sensitivity cannot be achieved with an enzymatic colorimetric reaction, other alternatives such as fluorescent (fluorescein, europium chelate) or chemiluminescent (acridinium ester, isoluminol) tags may be used (Price and Newman, 1996). If the candidate protein is not present in healthy subjects, the standard curve can be constructed by adding different concentrations of the purified protein to a pool of normal plasma. Otherwise, other matrices such as bovine serum albumin (5 g/dl) can be used. A parallel between matrix-based and non–matrix-based calibrators must be established before the latter is deemed acceptable (Blirup-Jensen et al., 2001; Dati and Brand, 2000; Johnson et al., 2003). Finally, the appropriate curve-fitting model (linear, semi-log, log/log, or 4 parameter logistic) for calculation should be determined (Price and Newman, 1996). The analytical performance of an assay can be assessed only after the assay is completely optimized. Multiplexing technology, which is designed to simultaneously evaluate several novel proteins, may become a viable option in the future. Although this technique is currently available (e.g., Luminex), the simultaneous optimization of several protein assays is seldom achieved; for example, an ideal buffer for one protein, rather than being ideal when used for another protein, is likely to result in an analytically suboptimal assay that could jeopardize the clinical findings (Liu et al., 2005).

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ANALYTICAL EVALUATION Before a newly developed assay is evaluated for clinical utility, its analytical performance must be carefully examined. For established measurands, the performance characteristic limits are specified according to outcome studies, clinical requirements, published professional recommendations (e.g., National Cholesterol Education Program performance recommendations for lipid measurements), proficiency testing findings (external quality assessment), or goals set by regulatory agencies (e.g., Clinical Laboratory Improvement Amendments [CLIA]) (Fraser and Petersen, 1999;Vitzthum et al., 2005). For novel measurands, performance specifications may not be available, but an analytical performance comparable to that of an established similar assay system should be expected. In addition, a common practice for establishment of analytical performance goals takes into consideration the biological variation both within (CVI) and between subjects (CVG) and stipulates that imprecision should be 0.5  CVI and bias 0.25  (CVI2CVG2)1/2. The main aspects of the analytical evaluation are presented below (Fraser and Petersen, 1999). Indicators of Accuracy Trueness and accuracy are similar but not identical concepts for assessing assay performance. Trueness is the closeness of agreement between the average measurand value of different samples and the true concentration value, whereas accuracy is the closeness of the agreement between the value of a measurement and the true concentration of the measurand in that sample (Dybkaer, 1997; ISO, 1994). Trueness reflects bias, a measure of systematic error, and accuracy reflects uncertainty, which comprises both random and systematic errors. Trueness can be assessed by the use of unbiased regression analysis (Deming) (Linnet, 1993) and residuals plots (Bland–Altman) (Bland and Altman, 1986) to compare the values obtained by the newly developed method with those determined by a reference method (as illustrated in Figure 26.1), or by measuring reference materials, with values assigned by a reference method or proficiency testing materials. For novel analytes for which reference methods and materials do not exist, recovery can be used as an alternative. In this procedure, various concentrations of the purified protein are added to a set of plasma or serum samples, and the recovery is determined by comparing the measured with the added amounts. Ideally, a recovery of 100% should be seen. Because antibody specificity is initially established by Western blotting and immunostaining, no significant cross-reactivity with other proteins is expected. It is imperative, however, to demonstrate that the newly developed assay is not affected by endogenous or exogenous interferences (NCCLS, 2002). Because in ELISA the wells are washed prior to the addition of the detection reagents, this technique is not affected by endogenous interferents such as hemoglobin, bilirubin, or triglycerides, a problem that often occurs with turbidimetric or nephelometric immunoassays. The presence of heterophilic, or anti-animal, antibodies in the patient sample, however, may lead to values that are falsely increased or

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3 Tina-quant CRP-N High Sensitivity CRP (mg/l)

Tina-quant CRP method (mg/l)

(a)

9

6

3

2 1 0 1 2 3

0 0

3 6 9 N High Sensitivity CRP method (mg/l)

12

(b)

0

2

4 6 8 Mean concentration (mg/l)

10

Figure 26.1 Comparison of results from the N hsCRP and Tina-quant methods. (a) Deming regression analysis. The dashed line represents the line of identity and the solid line represents the Deming regression line. Slope  0.958 (95% confidence interval, 0.954–0.962); intercept  0.280 (95% confidence interval, 0.268–0.292); Sy/x  0.264, r  0.992. (b) Bland–Altman plot. The solid line is the zero line. The thick dashed line indicates the mean difference (0.19 mg/l) and the thin dashed lines indicate the limits of agreement (0.36 to 0.74 mg/l) (Reprinted with permission from Lolekha et al., 2005).

decreased, ultimately resulting in disease misdiagnosis or patient mismanagement (Boscato and Stuart, 1988). For example, when human–mouse antibodies (HAMA) are present in the human sample being tested with an human choriogonadotropin (hCG) assay that employs two mouse antibodies, a false-positive result will occur if the HAMA recognize both the capture and the detection antibodies and form complexes indistinguishable from the hCG–antibody complex. A false-negative result will occur if the HAMA recognize only one antibody and make it inaccessible to the other antibodies, thus preventing complex formation. This effect can be minimized by the addition of either non-immune serum or IgG from the species used to raise the antibodies to the immunoassay reagents (Kricka, 1999). Indicators of Precision Repeatability and reproducibility are indicators of precision used to quantitatively express the closeness of agreement among results of measurements performed under stipulated conditions (Dybkaer, 1997; ISO, 1993). Repeatability refers to measurements performed under the same conditions and reproducibility to measurements performed under different conditions. Imprecision, which reflects random error and is expressed as a standard of deviation (SD) or a CV%, is assessed by the use of pools with different measurand concentrations (low, normal, high) over a defined period of time. Ideally the matrix of the pools should be identical to, or an appropriate simulation of, the matrix of the patient samples (Miller and Kaufman, 1993). The range of measurand values used in the series of pools to be tested should reflect, most importantly, the clinically relevant range, and also the span of the standard curve. For example, when Creactive protein (CRP) concentrations are high (100 mg/l), traditional CRP assays are suitable for detecting and monitoring

active infection and inflammation but inadequate for assessing risk of cardiovascular disease. The latter task requires high reproducibility at the lower concentration range (3 mg/l). Although some of these assays may be able to measure CRP at these concentrations, the CV% is often unacceptable (30–50%). High sensitivity CRP (hsCRP) methods can achieve such determinations with reproducibility of CV 5% and therefore are recommended for cardiovascular disease risk prediction. One recommended method for assessing precision calls for two replicates per specimen per run and two runs per day for 20 days (NCCLS, 1999). This method permits the estimation of within-run, within-day, between-run, between-day, and total precision. In general, assays are less imprecise at higher concentrations of the measurand than at lower concentrations, because assay designs generally result in direct proportionality between quantity of measurand and signal generated. Indicators of Analytical Measurement Range Linearity and limits of detection and quantitation must be assessed to determine the range of values of the measurand over which measurements can be performed with acceptable precision and accuracy (Currie, 1995; ISO, 2000; NCCLS, 2003). Linearity refers to the range of measurand values over which there exists a constant relationship between observed and expected values. Linearity is usually assessed by making serial dilutions of a sample containing a large quantity of measurand, using a diluent with a matrix similar to that of the undiluted specimen. Using a visual (graphic) and/or statistical assessment, this procedure will reveal whether the measured concentrations decline as expected, in proportion to the amount of dilution (Linnet, 2005). Furthermore, linearity assessment will determine the highest measurable value within the specified conditions.

Pre-Analytical Variation

The limit of detection is defined as the lowest value that significantly exceeds the measured value obtained with a blank sample (in this case a plasma or serum sample that does not contain the protein of interest). The limit of detection is calculated as the mean value of repeated blank sample measurements plus 2 or 3 SD. Although this approach is practical and widely used in clinical laboratories, it has recently been challenged by the introduction of a more complicated scheme by International Standard Organization (ISO) (ISO, 2000). The limit of quantitation is the lowest reported quantitative patient value within the established acceptable total error of the assay (Currie, 1995). For example, if the acceptable total error (bias  2 CV) for an ELISA method was set a priori at 30% (10% bias and 10% imprecision), then the limit of quantitation is the lowest value measured that conforms to these specifications. Ideally, these performance criteria should be based on the clinical needs of the test. Often however, at least initially, they are established arbitrarily.

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Approaches used to develop reference intervals for biochemical markers vary according to the biomarker in question. Some markers, such as myoglobin, are usually present in apparently healthy individuals and increase in concentration in the disease state. In contrast, troponin is not normally present in circulation, but only values above the 99th percentile of the reference population are associated with cardiomyocyte injury (Apple et al., 2002). For certain analytes such as cholesterol, the developed cutoff values for risk assessment are not based on a reference population but on clinical evidence associating these values with increased risk of cardiovascular disease ( JAMA, 2001).

PRE-ANALYTICAL VARIATION Characterization and control of pre-analytical variation is essential for correct analyte measurement. Pre-analytical variation is influenced by both physiologic and non-physiologic variables (Figure 26.2) and can be minimized by standardizing sample collection procedures and making adjustments based on the limitations of the assay in various physiologic and pathologic conditions.

REFERENCE INTERVALS The frequency distribution of the candidate protein in the target population must be examined in apparently healthy individuals to establish reference intervals to which patient results can be compared (Solberg, 1987; Solberg and PetitClerc, 1988). The examined population must be similar to that in which the test would be used, and at least 120 subjects should be included in the sample group (Harris and Boyd, 1995; Solberg and PetitClerc, 1988). In the event of statistically significant differences in the range of values related to differences in age, sex, race, or physiological states (e.g., puberty, menopause), the reference intervals must be stratified into appropriate subgroups and a larger sample size must be used (Harris and Boyd, 1990). For example, alkaline phosphatase activity changes differently with age among males and females in the first two decades of life, thus necessitating the use of sex- and age-specific cutoff values for clinical interpretation. In contrast, studies have demonstrated that age, sex, and ethnicity do not significantly affect the population distribution of hsCRP, indicating the appropriateness of using a single set of cutoff values for cardiovascular disease risk prediction in the entire population. If the distribution of the protein of interest is gaussian, then parametric analysis is used and the reference intervals are presented as mean  2SD (Harris and Boyd, 1995; Solberg, 1983). However, if the distribution is skewed, then non-parametric analysis is needed and the reference intervals may be presented as the lower 2.5th to the upper 97.5th percentiles. An alternative approach is to log transform the data to approximate the gaussian distribution, identify the 2.5th and the 97.5th percentiles, and then transform back the estimates of the upper and lower limits of the range to the original measurement scale for use as the reference intervals (Harris and Boyd, 1995; Solberg, 1983). Other statistical methods for estimating reference intervals, such as bootstrap methods, have also been used (Harris and Boyd, 1995).

Patient Status and Specimen Collection and Handling An important determinant for specimen collection is whether the sample needed for the measurement must be collected while the person undergoing the procedure is in a fasting state, because fasting versus non-fasting status can affect measurements both physiologically and analytically. An example is interference by postprandial hypertriglyceridemia, which is not a significant issue for ELISA, since unlike other techniques such as nephelometry and turbidimetry is unaffected by the presence of chylomicrons. Another consideration is time of collection, because many analytes, such as IL-6, exhibit significant circadian rhythm fluctuations and therefore, samples for their measurement must be collected at a set time (e.g., in the morning) (Gudewill et al., 1992). The diurnal variation of the candidate protein can be determined by measurement in samples collected from individuals over a 24-hour period (Meier-Ewert et al., 2001). During the sample collection process, it is important to note that factors such as the length of time of tourniquet application and the posture of the patient (e.g., supine or seated position) may affect the analyte concentration by up to 15% (Tan et al., 1973). Serum and plasma (anticoagulated with heparin, citrate, or EDTA) samples are widely used in the clinical laboratory. Although the choice of the appropriate type of specimen for the candidate protein should be empirically determined, one should be aware of certain caveats. EDTA and citrate cause osmotic shifting of fluid from cells to plasma, leading to value reductions of up to 10% compared to serum (Cloey et al., 1990). If tubes are not completely filled with blood during collection, larger effects can be expected, especially with citrate. EDTA-containing plasma is not suitable when the detection enzyme used requires a cation (Ca, Mg) to be active or when the protein itself contains a cation. Pregnancy-associated plasma protein

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Variables known to affect test results

Pre-analytic variables

Analytic and post-analytic variables

Non-physiologic

Physiologic

Patient preparation

Race

Time of collection

Age

Method of collection

Sex

Sample type and quality

Season

Transportation Sample treatment and stability

Laboratory methodology Sensitivity Precision Antigen excess Matrix effects

Lifestyle (exercise, smoking, obesity, alcohol, antiinflammatory drugs, hormone therapy, etc.)

Calibration and curve fitting Method correlation Interpretive report

Other (pregnancy, altitude)

Figure 26.2

Variables known to affect test results.

A (PAPP-A), for example, contains 16 zinc atoms (Apple et al., 2005), and chelation of the zinc by EDTA changes the configuration of the protein and reduces its immunoreactivity, leading to falsely reduced results. Heparin may mask the antigenic epitope and prevent appropriate binding with the antibody, as also occurs with PAPP-A (Apple et al., 2005). If the protein of interest is partially or mainly derived from platelets (e.g., plasminogen activator inhibitor-1, monocyte chemoattractant protein-1, or CD40L), platelet-poor plasma is required to prevent the results from being falsely increased. Finally, length of sample storage time, short- or long-term, and storage conditions are of paramount importance because the clinical utility of the candidate protein will likely be assessed using stored samples from an appropriate clinical trial. For example, CRP is a very stable protein that can be stored up to 20 years at 20°C, whereas tumor necrosis factor  (TNF) is a labile protein that requires sample collection on ice and storage at 70°C or in liquid nitrogen (Ledue and Rifai, 2003). Because of the instability of TNF, researchers have used TNF receptors I and II, which are stable, as surrogates for TNF. In some cases, such as for atheranol, ascorbic acid and butylated hydroxytoluene are added immediately after plasma separation to stabilize the analyte. Such steps, however, may hinder the utility of the marker. Physiologic Considerations Within- and between-subject variation must be considered when characterizing the clinical utility of a new analyte. Biological variability differs greatly among analytes (42% for triglyceride and 6.5% for cholesterol) and is usually higher than the analytical variability (imprecision of 1–2% for triglyceride and cholesterol) (Cooper et al., 1995).Total error, which encompasses

both biological and analytical variability, is used to determine the number of measurements needed to make a diagnosis. For example, for both cholesterol and hsCRP, the mean of two independent measurements made several weeks apart is used to classify individuals into categories of risk for cardiovascular disease (Ledue and Rifai, 2003). Data from reference interval studies enable determination of whether age, sex, ethnicity, or race significantly affect protein concentrations. As indicated earlier, this information is important in developing interpretive criteria and determining whether single or multiple cutoff values specific for the various subgroups need to be established (Rifai and Ridker, 2003). Furthermore, data from the clinical evaluation of the protein assay can be used to determine whether lifestyle factors (smoking, alcohol intake, diet, exercise, and obesity), ancillary pathological conditions, or certain drugs influence protein concentrations. Studies specifically designed to address these issues may have to be conducted in the future.

CLINICAL EVALUATION As indicated earlier, diagnostic markers are used for the diagnosis, staging, screening, and prediction of a disease, for the prediction and monitoring of treatment response and measure of treatment compliance (Vitzthum et al., 2005). The clinical evaluation of the candidate protein’s potential for one or more of these indications starts when the analytical evaluation is complete. The clinical evaluation is done through a series of carefully planned diagnostic studies involving patients with and without the target condition. This section will focus on the indicators of

Indicators of Diagnostic Accuracy and Predictability

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T A B L E 2 6 . 1 Diagnostic performance of BNP testing for the diagnosis of congestive heart failure in dyspneic patients in the emergency room (adapted from Maisel et al. [2002]) Diagnostic test results (BNP  100 pg/ml)

Disease status (CHF)

Positive

Negative

Total

Positive

673 TP

71 FN

744 TP  FN

Negative

FP 198

TN 644

FP  TN 842

TP  FP 871

FN  TN 715

TP  FP  FN  TN 1586

Total

Sensitivity  TP/TP  FN  673/744  90% Specificity  TN/TN  FP  644/842  76% LRpos  sensitivity/(1  specificity)  0.9/(1  0.76)  3.75 LRneg  (1  sensitivity)/specificity  (1  0.9)/0.76  0.13 Predictive value of a positive test  TP/TP  FP  673/871  77% Predictive value of a negative test  TN/TN  FN  644/715  90% Pre-test probability of having CHF (prevalence)  TP  FN/TP  FN  TN  FP  673  71/673  71  644  198  47% Pre-test odds of having CHF  prevalence/(1  prevalence)  0.47/(10.47)  0.89 Post-test odds of having CHF  pre-test odds  LR pos  0.89  3.75  3.34 Probability  odds ratio/(odds ratio  1) Post-test probability of having CHF  post-test odds/(post-test odds  1)  3.34/(3.34  1)  77% Abbreviations: BNP, B-type natriuretic peptide; TP, true-positive; FN, false-negative; FP, false-positive; TN, true-negative; LRpos, likelihood ratio for a positive test result; LRneg, likelihood ratio for a negative test result; CHF, congestive heart failure.

diagnostic accuracy and predictability, diagnostic research studies, importance of design quality, and issues regarding the transferability of diagnostic test performance. For simplification, the term “disease” will be used for the target condition under study.

INDICATORS OF DIAGNOSTIC ACCURACY AND PREDICTABILITY The diagnostic performance of a test involves its diagnostic accuracy and predictability. These are commonly measured with a handful of basic methods; some of which have been elaborated further for complex situations and are then best performed by the use of statistical computer programs. Table 26.1 shows the calculation of diagnostic performance of BNP testing for the diagnosis of congestive heart failure. Diagnostic accuracy estimates how accurately the test discriminates between individuals who have the disease and those who don’t. Diagnostic accuracy is measured by calculating the test’s sensitivity and specificity, which can be further examined through the likelihood ratio and receiver operating characteristic (ROC) curve. Sensitivity is the proportion of the diseased that are correctly identified by the test while specificity is the proportion of the nondiseased that are correctly identified. When test results in the two groups do not overlap, the test achieves perfect discrimination. In most instances this is not the case though; therefore, a trade-off between sensitivity and specificity is inevitable (Figure 26.3).

Sensitivity and specificity characteristics of a test are not absolute; they depend on several phenomena. Firstly, sensitivity and specificity are affected by the distribution of results in the diseased (which usually correlates with the spectrum of disease) and the non-diseased groups, respectively, and the distribution varies between populations. Therefore, the diagnostic accuracy characteristics observed for a given population are not necessarily applicable to other populations (Moons et al., 1997). The example in Table 26.2 shows how the spectrum of left ventricular systolic dysfunction affects the sensitivity of BNP testing for the diagnosis of congestive heart failure. Likewise, the specificity is affected by the spectrum of values in the non-diseased group. For example, BNP values are higher in individuals with impaired renal function (McCullough et al., 2003), but free of left ventricular systolic dysfunction, than in healthy ones. If the non-diseased group (i.e., free of left ventricular systolic dysfunction) includes such patients, the curve for the distribution of BNP values will be shifted to the right on the x-axis (see Figure 26.3). A larger proportion of the group would thus fall into the false-positive category, and the specificity of the test would be reduced compared to that seen in a healthy group. Secondly, the reference standard (“gold standard”) against which the test is measured should provide a clear and binary – present or absent – definition of the disease of interest. It can be simple or composite, consisting of laboratory tests and/or clinical data. In rare occasions, the true disease state is represented by a continuous scale of values, which requires the choice of cutoff values

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in order to obtain a binary standard (Obuchowski et al., 2004).The reference standard for a particular disease may vary across studies; such variation could lead to different diagnostic accuracy. Ideally, the reference standard should identify correctly the true disease condition of all study subjects; however, this is not always the case. Thirdly, the study test itself requires a definition of positive and negative test results. In the case of continuous scale results, this definition is often unknown in the early phases of the clinical evaluation and is determined after evaluation of different cutoff values against the reference standard. An example of how diagnostic performance varies with the cutoff value is shown in Table 26.3. More complex situations in which the disease and/ or the test results are non-binary can be analyzed with the use of multiple and logistic-regression models (Altman, 1991).

Frequency in population

Non-diseased Threshold Diseased TN

TP

FN

Negative

FP

Test value

Positive

Figure 26.3 Hypothetical distribution of test results in a population of diseased and non-diseased. The location of the threshold for a positive test (cutoff value), as well as the location and shape of the curves affect the diagnostic performance of the test.

Sensitivity and specificity are calculated from two different groups, that is, the diseased and the non-diseased, respectively, and are therefore not dependent on prevalence; the same is true for their derivatives, the likelihood ratio and the ROC curve. Likelihood ratio for a positive test result (LRpos) is the ratio of the proportion of the diseased that are correctly identified by the test (true-positives) to the proportion of the non-diseased that are wrongly identified (false-positives). Inversely, the likelihood ratio for a negative test result (LRneg) is the proportion of the diseased that are missed by the test (false-negatives) to the proportion of the non-diseased that are correctly identified (true-negatives). As likelihood ratios combine information about a test’s sensitivity and specificity, they can be used to calculate post-test probability of a disease from its pre-test probability. This is done by the use of Bayes’ theorem according to which the post-test odds  pre-test odds  LR (Altman, 1991). The pre-test probability represents the prevalence of a disease in the population that is subjected to the test. The post-test probability represents the chances of having a disease after test results have been taken into account. The LRpos and LRneg provide an estimate of how much a positive or negative test result will increase or decrease, respectively, the pre-test odds of having a disease. While high LRs increase the certainty about a positive test being truly positive, test results for each subject still need to be interpreted in light of the pre-test odds of having the disease. A positive test result in a low prevalence population is more likely to be falsely positive than the same result in a high prevalence population (Altman, 1991). The example in Table 26.1 shows that a cutoff value of 100 pg/ml for BNP for the diagnosis of congestive heart failure had an LRpos of 3.75, which means that this result is almost four times as likely to occur in patients with the disease as in

TABLE 26.2 A study that examined the usefulness of BNP for screening for LVSD reveals how the spectrum of the target condition affects sensitivity of the test % (95% confidence interval) Severity of LVSD

Sensitivity

Specificity

PPV

NPV

LRpos

LRneg

LVEF  50%

14 (4–25)

95 (94–96)

7 (2–12)

98 (97–98)

2.94

0.9

LVEF  40%

40 (10–70)

95 (94–96)

5 (0.2–9)

96 (99–100)

8.08

0.63

The table shows the diagnostic performance for women (n  42) at a cutoff value of 50 pg/ml ( Vasan et al., 2002). The “LVEF  50%” and “LVEF  40%” groups differ with regard to spectrum of disease; the former includes mild, moderate, and severe cases, whereas the latter includes only moderate and severe ones. The curve for the distribution of BNP values for the group including mild cases is shifted to the left on the x-axis (see Figure 26.1), compared to the other group, and therefore the chosen cutoff value increases the fraction of false-negatives and detects fewer cases of impaired LVEF (sensitivity of 14% versus 40%). Abbreviations: BNP, B-type natriuretic peptide; LVSD, left ventricular systolic dysfunction; LVEF, left ventricular ejection fraction.

T A B L E 2 6 . 3 A study of BNP testing for the diagnosis of CHF in dyspneic patients in the emergency room (n  1586) reveals how diagnostic performance characteristics vary with different cutoff values for the test (Maisel et al., 2002) % (95% confidence interval) BNP pg/ml

Sensitivity

Specificity

PPV

NPV

LRpos

LRneg

50

97 (96–98)

62 (59–66)

71 (68–74)

96 (94–97)

2.55

0.05

150

85 (82–88)

83 (80–85)

83 (80–85)

85 (83–88)

5.00

0.18

Indicators of Diagnostic Accuracy and Predictability

persons without it. In this case a positive test result increases the probability of having congestive heart failure from 47% to 77%. In contrast, an LRneg of 0.13 decreases the probability of having congestive heart failure from 47% to 10% in those patients having a negative test. This example involved a likelihood ratio for only one test. When two or more independent tests are used, their likelihood ratios can be multiplied to assess the combined value of the tests in increasing the diagnostic certainty. Although the likelihood ratio incorporates information about sensitivity and specificity, it does not show the actual percentages and should therefore not be used alone to assess or compare test performance (Zweig and Campbell, 1993). This was demonstrated in the study cited in Figure 26.4 in which the LRpos for a clinical diagnosis of congestive heart failure was 11.5, whereas for BNP it was 3.4. The sensitivity and specificity were 49% and 96% for the former, respectively, and 90% and 73% for the latter (McCullough et al., 2002). An additional interesting feature of the likelihood ratio, besides estimating post-test odds, is that unlike sensitivity and

1.0 Combined BNP

0.9 0.8

E.D. Probability

Sensitivity

0.7 0.6

Area Under ROC Curve 0.86 (0.84–0.88) E.D. Probability 0.90 (0.88–0.91) BNP 0.93 (0.92–0.94) Combined

0.5 0.4 0.3 0.2 0.1 0.0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1-Specificity

Figure 26.4 A study of BNP testing in dyspneic patients in the emergency room (n  1586) examined whether BNP added to clinical judgement in the diagnosis of congestive heart failure. The graph showing the ROC curves for clinical diagnosis (E.D. Probability), BNP testing, and the combination of both demonstrates how the area under the curve can be used to compare diagnostic accuracy for two or more tests and test combinations. When the criterion of BNP values at 100 pg/ml was added to a high clinical suspicion of congestive heart failure (80  100% probability, as estimated by the emergency department physicians), the area under the curve increased from 0.86 to 0.93 and the proportion of correctly diagnosed cases increased to 81.5% from 74% (TP  TN/TP  FN  FP  TN) (McCullough et al., 2002).

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specificity calculations, which require binary test results, the LR can be obtained for multicategory test results (Deeks and Altman, 2004). The ROC curve provides a graphic index of the accuracy of quantitative tests. The majority of tests in the clinical laboratory yield a numerical value on a continuous scale, and, in order to differentiate between the diseased and non-diseased in the population, an appropriate cutoff value needs to be chosen. A convenient way to examine the potential cutoff values is to plot their performance on a graph to obtain the ROC curve. The curve shows the fraction of true-positive results (sensitivity) over the fraction of false false-positive results (1–specificity) for the entire range of values and thus depicts the overlap between the distribution of test results in the diseased and non-diseased groups. When there is no overlap, and thus perfect discrimination between the two groups, the curve passes through the upper left corner, and the area under the ROC curve (AUROC) is 1. In case of a complete overlap, that is, the true-positive fraction equals the false-positive fraction, the plot generates a 45° diagonal line from the lower left corner to the upper right corner, and the AUROC is 0.5. Most often the curve falls between these two extremes. The larger the AUROC the better, although it should always be appreciated along with the curve, since the area by itself does not convey information about the curve’s shape. The main advantage of the ROC curve is to enable: (i) visualization of the performance of potential cutoff values in the same graph, although the actual values are not directly visible; (ii) comparison of the curves generated from two or more tests, as demonstrated in Figure 26.4; (iii) measure of accuracy by calculating the AUROC, and (iv) discriminant analysis and logistic regression that allow the incorporation of other tests or covariates to obtain the optimal cutoff value and improve the diagnostic approach (Obuchowski et al., 2004; Zweig and Campbell, 1993). Predictive values show the ability of the test to predict the presence or absence of disease for a given test result. The positive predictive value is the proportion of patients with positive test result who have the disease, and the negative predictive value is the proportion of patients with negative test who do not have the disease. The predictive values of a test vary with the prevalence of the disease in the population examined. As prevalence increases the positive predictive value increases and the negative predictive value decreases, and vice versa. To demonstrate how predictive values change with prevalence of the condition under study, let’s imagine that the number of patients with congestive heart failure in Table 26.1 were 1000 instead of 744, and the prevalence therefore 63% instead of 47%. Given that sensitivity and specificity remain unchanged, the new positive predictive value would increase to 86% from 77% and the negative predictive value would decrease to 82% from 90%. As mentioned before, the Bayes’ theorem allows one to calculate the post-test probability (i.e., positive predictive values) for any prevalence of disease, by using the prevalence and the likelihood ratio derived from previous studies. A nomogram that enables an easy appreciation of post-test probability was created from Bayes’ theorem (Deeks and Altman, 2004; Fagan, 1975).

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The use of Bayes’ theorem is limited, however, by the influence of test result distribution in the diseased and non-diseased groups on diagnostic accuracy and therefore likelihood ratio, and this should be borne in mind when post-test probabilities are calculated using likelihood ratios from studies on other populations.

following both patient groups with regard to post-test diagnostic and therapeutic interventions and the subsequent effect on health outcome (Sackett and Haynes, 2002).

DESIGN OF DIAGNOSTIC STUDIES DIAGNOSTIC RESEARCH STUDIES In order to establish its clinical utility, the candidate protein needs to be evaluated in sequential phases that emphazise different performance characteristics of the test and therefore require different study populations (Sackett and Haynes, 2002). The exploratory phase (Phase I) usually involves patients with confirmed disease and people known not to have the disease. The question here is: are test results different for these two groups? The ROC curve is often used to examine the discriminative potential of the test. If the AUROC is equal or less than 0.5 the test is not useful as it does not distinguish the diseased from the non-diseased. Further studies are not indicated in these cases. In contrast, satisfactory discrimination is suggestive of a useful test, and Phase II studies will subsequently determine its diagnostic accuracy (Obuchowski et al., 2004; Sackett and Haynes, 2002). The challenge phase (Phase II) often involves similar patient groups as Phase I, but here the question is: do test results predict disease or absence of disease? This may be addressed by assessing different cutoff values for the calculation of sensitivity and specificity. Hence, the diagnostic accuracy of the test is determined for this well-defined study population. At this stage, it is still not known how the test will perform in the population that will ultimately benefit from it, that is, those suspected of having the disease. This is addressed in Phase III studies (Obuchowski et al., 2004; Sackett and Haynes, 2002). The choice of cutoff values is based on the most appropriate sensitivity/specificity combination with regard to the purpose of the test, the prevalence in the target population, cost and the potential harm and benefit associated with incorrect and correct disease classification, respectively (Zweig and Campbell, 1993). The advanced phase (Phase III) involves patients who are suspected of having the disease. The question here is: what are the diagnostic accuracy and predictive values in the target population for whom the test is intended in routine clinical practice? Whereas earlier research phases are conducted on well-defined and controlled study groups, Phase III involves patients that may vary greatly across different health care and geographical settings. It follows that test performance may vary accordingly and performance characteristics may not be transferable between settings. Diagnostic tests should be validated in independent studies before they are adopted in clinical practice (Obuchowski et al., 2004; Sackett and Haynes, 2002) Outcome phase (Phase IV) involves patients who undergo the study test and a comparable group of patients who do not. The question here is: does the test influence positively the ultimate health outcome of tested patients? The answer is obtained by

It is the responsibility of those who plan diagnostic studies to strive toward the highest quality in order to maximize the health benefit for the individual patient and the population, and minimize costs related to inefficient diagnostic testing and misuse of resources in poor research. A well-designed study can be analyzed in a number of ways, but no conclusion can be derived from a poor one. The major components of a study design include the patient group, reference standard, interpretation of test and standard with regard to blinding, data collection, and analysis. Any of these can lead to biased study results, usually toward an overly optimistic performance of the test. Meta-analyses of the effect of potential bias factors on the estimates of diagnostic accuracy showed that a study population consisting of severe cases and healthy control (as opposed to a cohort population), non-consecutive inclusion of subjects, retrospective data collection, different reference standards for different study test results, non-blinding of test interpretation, incorporation of test results into the reference standard, and post hoc definition of cutoff value for positivity tend to overestimate the diagnostic odds ratio (Lijmer et al., 1999; Rutjes et al., 2006). Rutjes et al. concluded that “studies of the same test can produce different estimates of diagnostic accuracy depending on choices in design” (Rutjes et al., 2006). Furthermore, it is increasingly recognized that advanced phase studies need to reflect a realistic clinical setting. The diagnostic process is a multifactorial one that includes patient history, physical examination, and diagnostic tests that vary in complexity, invasiveness, performance, and cost. The design of diagnostic studies should be adapted to real life in the clinical setting, where the choice of a particular test is usually guided by a suspicion of a disease, based on previous examination results. Diagnostic tests may have excellent diagnostic accuracy without being clinically helpful. More convenient or cheaper diagnostic processes may perform equally well or the nature of the disease may not justify the inconvenience and cost of a particular test. The real contribution of a test can be assessed by the use of multivariable analyses. Discerning mutual dependencies between different examination results permit to establish whether the test is redundant or has an independent predictive value for the diagnosis of the disease (Moons et al., 2004). This was done in a study of BNP testing for the diagnosis of congestive heart disease in dyspneic patients in the emergency room (n  1586; see Table 26.3). A multiple logistic-regression analyses including clinical, radiological, and laboratory data revealed that the test was not only the strongest independent indicator of congestive heart disease, but also added significant additional information when it was entered after other clinical indicators (Maisel et al., 2002).

Assay Transfer to a Diagnostic Company

TRANSFERABILITY OF TEST PERFORMANCE When a test shows a promising diagnostic performance in a known population, it is tempting to adopt the test for use in other populations, on the sole basis of its performance characteristics. This simple approach has pitfalls though, because performance characteristics are not “fixed.” The diagnostic accuracy, measured by sensitivity, specificity, LR and the ROC curve, and predictive values of a test may vary across patient populations (Moons et al., 2004; Sackett and Haynes, 2002). The transferability of a test depends on several criteria that affect diagnostic accuracy and predictive values (Irwig et al., 2002). In order to obtain equivalent diagnostic performance in a new clinical setting, all of the following characteristics need to be comparable to the study setting: test characteristics, including calibration (see “Indicators of Accuracy”) and manufacturer, disease definition, and the population to be tested. Populations may differ with regard to distribution of test results in the diseased and non-diseased groups (Irwig et al., 2002), thus giving rise to different diagnostic accuracy and predictive values. Table 26.4 demonstrates how diagnostic performance can vary between two populations. Whenever test characteristics and disease definition in the new setting differ from those used in reported studies it is wise to validate the test with regard to diagnostic accuracy before its implementation. The question to be asked is whether this is the appropriate test for the setting. Furthermore, as predictive values are dependent on prevalence, they need to be recalculated in the new population. As explained above, the predictive values can be estimated in a new setting by the use of the Bayes’ theorem.

ASSAY TRANSFER TO A DIAGNOSTIC COMPANY Once evidence for the clinical usefulness of a novel marker has been obtained, firms producing in vitro diagnostics (IVD) will determine whether the marker is worth pursuing

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commercially. Many technical, medical, financial, and legal factors are considered in making the decision (Vitzthum et al., 2005). Intellectual property issues relating to the protein, the antibodies used, and the intended clinical use may limit the availability of the marker and hinder commercial pursuit. For example, Roche Diagnostics has exclusivity for troponin T, which is measured in clinical laboratories only on the Elecsys platform. As a result, fewer than 300 labs in the United States are using this assay compared to more than 2500 labs using troponin I, measured on platforms from eight different manufacturers (CAP Survey, 2005). Dade Behring has the exclusive license for marketing hsCRP for the prediction of cardiovascular disease. Other manufacturers of hsCRP reagents (30 worldwide) must obtain a sublicense from Dade Behring if their product is intended to be used in risk assessment of heart disease. These legal matters tend to complicate the process and increase the cost of introducing new tests to the market, potentially limiting wide availability. The developed assay must be suitable for modern clinical laboratories and meet basic requirements (e.g., robustness, reagent stability, ease of use, acceptable turnaround time, adaptability to automated immunoassay systems, and low cost). Most major IVD manufacturers have immunoassay instrument platforms with non-isotopic detection systems that enable the transfer of a research ELISA to an automated clinical laboratory test format. The manufacturer then must evaluate the optimized assay in the context of the new platform and protocol, analytically and clinically, to satisfy regulatory agencies. It is important to note that often multiple generations of a particular assay must be developed as the perceived clinical utility of the marker evolves and/or the technology advances. For example, a third-generation troponin T assay is currently in clinical use, with a fourth generation under evaluation. The sensitivity and the performance of the assay improved over the years, thus enabling the detection of minor cardiomyocyte injury and permitting the clinician to use the assay as a diagnostic and a prognostic tool in patients with acute coronary syndromes. Although improvements in assays are useful to clinicians and beneficial to patients, they are quite costly to IVD

TABLE 26.4 Two studies evaluated the use of BNP for the diagnosis of CHF in dyspneic patients in the emergency room, using the same commercialized test and cutoff value % (95% confidence interval) Studies

Sensitivity

Specificity

PPV

NPV

LRpos

LRneg

Area under ROC curve

Study 2001 (Dao et al., 2001)

94 (89–97)

94 (89–97)

92 (85–96)

96 (91–98)

15.7

0.06

0.98

Study 2002 (Maisel et al., 2002)

90 (88–92)

76 (73–79)

79 (76–81)

89 (87–91)

3.75

0.13

0.91

The studies differed with regard to sex distribution of the patients (95% versus 56% male) and perhaps race (unknown versus 45% African Americans), and number of study sites and teams evaluating true disease status (1 versus 7). Although the test was shown to be useful in both studies, the impressive diagnostic performance reported in the earlier study was not repeated in the second one.

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manufacturers, requiring development, evaluation, and validation of the assay, and often do not lead to a significant increase in the marketability of the test. These issues can only make IVD manufacturers more cautious in pursuing novel markers.

REGULATORY REQUIREMENTS In many countries, for an IVD device to enter the market, it must conform to a set of rules and regulations such as those of the FDA in the United States (FDA, 2005, 2006), the Pharmaceutical and Medical Safety Bureau in Japan (www. mhlw.go.pj/english/org/policy/p13-4.html), and the European Directive in the European Union (Dati, 2003). Regulations are somewhat similar among these agencies, and here we discuss the FDA process as an example. According to the Medical Device Amendments of 1976 (Greenberg, 1976), an IVD device to be commercialized in the US market must undergo one of two primary regulatory processes stipulated by the FDA; 510(k) clearance or Pre-Market Approval (PMA). The 510(k) process is used when the new test measures an existing FDA-classified analyte (Class I or II) for which there exists a commercially available predicate test method that has been cleared by the FDA or was in commercial distribution prior to May 28, 1976 (pre-amendment devices [2005]). The submitted information required for the new test includes its intended use and classification, data comparing its performance to the predicate device, and characterization of its analytical capability (e.g., precision, linearity, specificity, and accuracy compared to the predicate device, determined via patient correlation studies). This process requires 100 days for FDA review time and User Fees of $3800 (www.fda.gov/oc/

mdufma/coversheet.html). The PMA process is used when the test is classified as Class III (the riskiest devices [e.g., cancer diagnostics]), or the clinical utility of the marker or the technology of the measurement are novel and no predicate device can be identified (2006). For these tests the required information includes the same data required for 510(k) as well as clinical outcomes data that present independent clinical criteria establishing that the concentration of the marker is related to disease status. This process requires 180–360 days to review, and often requires an FDA panel meeting and User Fees of ~$240,000 (www.fda. gov/oc/mdufma/coversheet.html). The FDA recognizes that some newly discovered analytes that have no obvious predicate devices do not have safety concerns that automatically trigger a Class III designation. To address these situations, in 1997 the agency created a new hybrid classification termed “de novo” or “risk-based” (FDA, 1998). This process allows a new analyte to be regulated as in a 510(k) but requires the demonstration of clinical effectiveness. Although the de novo process provides a viable third option for novel marker registration, the regulatory route is not always obvious and is determined ultimately by the FDA. BNP is an example of a novel marker that was cleared by the FDA using the de novo route. This process requires 150–180 days for FDA’s review and clearance, and User Fees of $3800. Therefore, in the absence of a predicate device and depending on the intended use and clinical utility, either a PMA or a de novo process will be required before a new protein of interest becomes commercially available. Because of the nature of protein measurement, new proteins are classified in the United States according to CLIA in the “high complexity testing” category and subjected to all analytical performance requirements for that class (www.phppo.cdc.gov/clia/regs/).

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RECOMMENDED RESOURCES http://www.fda.gov/oc/mdufma/coversheet.html http://www.mhlw.go.pj/english/org/policy/p13-4.html http://www.phppo.cdc.gov/clia/regs

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CHAPTER

27 Pharmacogenetics and Pharmacogenomics Iris Grossman and David B. Goldstein

INTRODUCTION Western medicine has been extremely successful in producing mass pharmaceutical solutions by following the “one-drug-fitsall” paradigm. Indeed, the majority of individuals plotted on a Gaussian distribution show reasonable efficacy and safety profiles, justifying the overall validity of this approach. However, alarming reports are amassing on the frequency and gravity of adverse drug responses, as well as inadequate efficacy, clearly signaling the need to shift from “blockbuster” to more individualized treatment models. The objective of pharmacogenetic research is to identify a genetic marker, or a set of genetic markers, that serve as prognostics for the fashion in which a given person is likely to respond to a given medicine. The appropriate formulation, dose, and regimen are presumed to be predicted, at least partially, by genetic determinants, thus serving as diagnostics that can be tested prior to initiation of drug treatment. In this fashion, treatment would be allocated only, or mainly, to individuals who are expected to benefit from it, thus reducing dramatically the suffering and costs associated with adverse drug reactions (ADRs) and inadequate efficacy. As summarized by Wilkinson, “most major drugs are effective in only 25–60% of patients, and more than two million cases of ADRs occur annually in the US, including 100,000 deaths” (Wilkinson, 2005). It should be noted that two terms exist in the scientific literature, pharmacogenetics and pharmacogenomics, each with its own

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

connotation and a range of alternative and poorly defined distinctions. Here, we use the term pharmacogenetics (PGx), which has historical priority, according to its broadest meaning – relating heritable variation to inter-individual variation in drug response (Goldstein et al., 2003). It should be mentioned that the term pharmacogenomics is often used to describe the study of differential gene-expression profiles in tissues of interest and their relation to drug-response phenotypes. While such expression signatures may be affected by underlying genetic variation, non-inherited environmental determinants play an important role as well. We therefore focus here solely on inherited sequence variation (i.e., single nucleotide polymorphisms, SNPs), insertions–deletions, copy number variations, microsatellites, Alu repeats, and so on) and its association with treatment response end-points. A comprehensive review of geneexpression signatures, their association with response to chemotherapy, and the search for underlying genetic determinants is provided in Nevins and Potti (2007). The complexity of genetic relationships and its interplay with drug-response phenotypes has been recently acknowledged by the US Food and Drug Administration (FDA) in a rather crude and controversial decision to stratify patients’ populations by race in the case of severe heart failure adjunct treatment with BiDil (Meadows, 2005). Although little beneficial effect has been registered in the general population, BiDil treatment showed a dramatic reduction in death and hospitalization in African

Copyright © 2009, Elsevier Inc. All rights reserved. 321

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Americans. Indisputably, this stratification criterion is simplistic, biased, and ethically provocative, but it illustrates the concept of inherent differences between individuals in the general population and serves as a bridging step from indiscriminant allocation of therapeutics to genetic-based stratification into risk/benefit subgroups. This example also illustrates the fact that the popular description of PGx as aspiring to “individually tailor” therapy is an exaggeration. The field is not centered around each individual specifically, but rather seeks to increase homogeneity among patients to an acceptable efficacy and safety level. In other words, PGx aspires to generate an individualized guide to proper therapeutic option among predictable segments of the patient population (Roses, 2004). The discipline of PGx is closely tied to human genetics and genomics as a whole, but focuses on specific phenotypes of interest. While disease predisposition research in the realm of non-Mendelian traits is faced with extremely complex etiology, dictated by interplay between various pathways in the human body, it is assumed that pharmacogenetic phenotypes are largely governed by a limited number of molecules (an oligogenic structure), most of which interact with the drug as pharmacokinetic or pharmacodynamic entities. Although genetic factors associated with underlying disease etiology may impact treatment response features, these effects are hypothesized to have a more modest effects in comparison to genetic variations in molecules that come in physical contact with the drug upon its administration. Moreover, the phenotypic changes that are induced by drugs are clearly and reproducibly caused by treatment exposure, which renders them less difficult to relate to genotype in comparison to studies of other complex traits. Thus, PGx research is equipped with the enormous advantage of being more TABLE 27.1

readily and fruitfully realized in clinical practice. In addition, the (potential) direct application of pharmacogenetic findings in the daily management of disease burden prompts this field to be attractive, not only to academic circles but also to the pharmaceutical industry, patients, health care providers, regulators, and health systems as a whole. Any treatment for which inter-individual variability is significantly larger than intra-individual diversity indicates an underlying genetic effect associated with response variables. In order to maximize the potential benefit from PGx research, it is helpful to focus and prioritize the analyzed therapies as follows (Table 27.1): ●

●

Chronic disease states: in these cases disease burden on both the patients and health systems translates into compromised patients’ quality of life, continuous suffering, low medication adherence rates, polypharmacy, and ultimately considerable care management costs. Therefore, management of chronic diseases comprises an attractive aim for optimization of available treatments and development of new therapeutic solutions (e.g., hypertension, diabetes, asthma, and so on). Narrow therapeutic index drugs (i.e., those with little difference between toxic and therapeutic doses) require close and constant dosage adjustment procedures to achieve therapeutic levels and minimize toxicity (usually by repeated measurements of therapeutic drug monitoring (TDM) protocols). Utilization of predictive genetic tests that may identify individuals at risk – either prior to treatment initiation or during the initial dose adjustment period – will save patients’ lives, while diminishing substantially the costs of care management (e.g., warfarin, 6-mercaptopurine, and so on).

Circumstances defining high priority targets for pharmacogenetic investigation

Circumstances The underlying condition being treated is chronic, incurable, and/or recurrent Current clinical practice of the underlying condition follows a “trial and error” paradigm

Rationale

Epilepsy ( Tate et al., 2005) Multiple sclerosis (Grossman et al., 2007a) Arrest the progression of the underlying disease, reduce patients’ suffering, and decrease disease management burden on the health system

Asthma ( Weiss et al., 2006) HIV ( Telenti and Goldstein, 2006) Hypertension (Arnett et al., 2006) Warfarin (Aquilante et al., 2006) TPMP (van den Akker-van Marle et al., 2006)

The studied drug has a narrow therapeutic index The studied drug is associated with severe, lifethreatening side-effects

Examples

Save patients’ lives and decrease hospitalization costs associated with ADRs

Herceptin (Krejsa et al., 2006)

The predictive value and clinical utility of the studied drug are high

Develop accurate and useful clinical diagnostic tools

Abacavir ( Warren et al., 2006) TPMT (van den Akker-van Marle et al., 2006)

Rescue drugs post Phase II development

Market drugs to patient subpopulations that are predicted to benefit from the treatment, protecting potential non- or adverse responders

GW320659 (Spraggs et al., 2005)

Pharmacogenetic Studies: From Concept to Practice

●

●

Disease states for which current practice follows a “trialand-error” paradigm due to absence of predictive markers of efficacious and safe treatment regimens often require long adjustment periods, thus resulting in months or even years of incrementing doses and drug switching until a successful course of treatment is identified. Predictive genetic tests may assist in reducing this elongated adjustment period, resulting in increased compliance rates, and reduction of side effects, disease management burden and costs. As an integral part of the drug research and development process, rescuing formulations efficacious in subgroups of the patient populations, but failing to exhibit overall benefit in the all-inclusive cohort.

Noteworthy is the fact that most of the current examples in the PGx literature, including those discussed here, investigate marketed drugs retrospectively, when the drug and its clinical responses are available to be studied by academic investigators. A comprehensive review of PGx research along the drug development process with examples from discovery through phase III trials is presented elsewhere (see Chapter 29 and Roses et al., 2006).

The Ultimate Goal: Definition of the Key Research Question A PGx study aims at identifying genetic determinants associated with drug–response features. The latter focus mainly on treatment efficacy, dosing, and safety. The definition of each of these elements is not trivial and is tightly dependent upon the specific parameters of a given dataset: the underlying disease and relevant clinical follow-up measurements dictate the available tests and clinical information that is not only relevant, but also obtainable, especially with respect to retrospective data acquisition. For example, in an investigation of the response to multiple sclerosis, treatment efficacy may be determined by clinical observations evaluated by the Extended Disability Status Scale (Kurtzke, 1983) (EDSS), reduction in relapse rate, severity of relapses, and/or MRI findings estimating T1-weighted hypointense lesions and central nervous system (CNS) atrophy measures (Grossman et al., 2007a). Even a phenotype as “simple” as dosing may be regarded by various experts in a host of different ways. For instance, dosing phenotypes relevant to treatment with anti-psychotics for the treatment of schizophrenia are often assessed by “maintenance dose,” which may reflect halving the drug dose that was proven effective during the acute phase (Gaszner and Makkos, 2004) or a titration regimen starting from minimum doses determined experimentally up to a recommended target dose on average (Perry and Hradek, 2004). When considering phenotypes of treatment safety recombinant protein therapeutics (including supplements to, or blockers of, endogenous proteins, immunomodulatory molecules and small, highly selective, targeted binding proteins), as opposed to

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small-molecule drugs, introduce an additional dimension requiring consideration of their potential immunogenicity (Krejsa et al., 2006). Whatever the case may be, the phenotypes to be tested must be determined prior and blinded to acquisition of results and are required to be precise, consistent, obtainable in the current investigation, reproducible and clinically relevant. Tailor-Design of Your Study by Available Resources and Specific Goals Researching the genetic determinants underlying a phenotype of interest is a task largely pre-defined by the specific characteristics of the studied phenomena. The investigator has no control over attributes such as phenotype penetrance; frequency of the (unknown) causative polymorphism(s); number of involved and modifier genes; genotype relative risk; phenocopy and genocopy effects; linkage disequilibrium extent between the typed markers and the causative polymorphism(s); and so on. Notwithstanding, an array of tools are available at the disposal of the pharmacogenetic researcher aimed to better the chances of identifying genotype–phenotype associations by sophisticated tailor fitting each individual project (Figure 27.1): ●

PHARMACOGENETIC STUDIES: FROM CONCEPT TO PRACTICE

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●

● ●

collection of a large enough sample size as reflected by power estimations (Chapman et al., 2003; Kelly et al., 2005; Singer et al., 2005); distinct and precise, yet simple and broad, phenotype definitions; skilled marker selection (as detailed later); and appropriate choice of study design.

Classic Case–Control Study Design The classic most “simplistic” study design, which is still considered the basic and fundamental association test pursued in genetic literature, is the case-control study. Originally, this analysis was used for testing significant differences in exposure without attempting to quantify the risk associated with exposure: Statistically, do more lung cancer patients have a history of smoking than controls? Rather than by how many times does smoking increases the risk of lung cancer? Case-control studies contain only group-level information and may determine the association between potential cause and effect on an individual basis. In pharmacogenetic investigations, cases and controls do not refer to health states or existence of drug exposure (affected versus unaffected, treated versus untreated), but rather to subpopulations within a source population of affected and treated individuals: positive responders versus non-responders, or patients manifesting ADRs versus safely treated individuals. Investigation of quantitative traits (as opposed to qualitative traits) is traditionally pursued by linear regression methods assuming that the Y-axis variable is a continuous measurement (e.g., optimized drug doses prescribed to schizophrenia patients [Grossman et al., 2007b]). In order to avoid bias due to confounding effects inherent in the study populations while employing qualitative, as well as quantitative, phenotype definitions, and so as to allow follow-up replication analysis within the total or the remaining trialed population (Figure 27.2), it is

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Study population Study population: Affected

Study population: Healthy Clinical trial blinded to genotype

Genotype

Genotype AA

Study population: All

AA Aa

Aa aa

aa Placebo

Drug dose 1

Drug dose 2

Clinical challenge

aa Aa AA

Responder

Nonresponder

Select equisize subgroups defined by “aa” carriers

Continuous phenotype

Training set  all individuals in the current study

Statistical association analysis for unrelated/related individuals

Replicate in an independent population

Validation Training set  5% extremities of phenotype distributions in the current study

Replicate in 10/25/45% remaining individuals

Figure 27.1 Pharmacogenetic study design alternatives in a simplified case, genotyping a single nucleotide polymorphism in a study population. In research settings pharmacogenetic study populations may either be healthy or affected subjects. A priori genotyping may be used as inclusion criteria and in several models may be used to reduce study population, when equisize groups of genotype carriers (aa, aA, and AA) are randomized to treatment and subsequent analysis. In clinical trial settings, individuals are randomized to treatment groups (active drug, parallel dose comparison arms, gold standard comparator drug, and/or placebo) blinded to genotype. Only once trial is completed and efficacy and safety end-points are clinically analyzed does statistical analysis take into account genetic underlying effects. Similar approach can be utilized in adaptive trial design for interim analysis. It is expected that some genetic associations will reach statistical significance at random and thus it is vital to validate any association results in either the replication set within the current cohort, or in an independent population.

becoming acceptable to employ case–control designs in which cases are chosen as patients exhibiting the lowest 5% of a phenotype distribution, while controls represent the 95th percentile of this distribution. Contrasting these two subgroups in the initial analysis enables focusing on the most extreme representations of the phenotype of interest with a lenient significance level, accentuating the roles that causative factors (genetic and environmental) play in exhibition of these phenotype states, and expecting most of the findings to reflect false-positive associations. In the second stage, designed to replicate and validate the obtained results, individuals represented by the remainder of the distribution space are split into cases and controls. Analysis can be performed at this level by using a stringent Bonferroni-corrected significance level as a “replication-based analysis” or by combining

test statistics from both stages and applying significance level corrected only for the number of markers tested, titled “jointanalysis” (Skol et al., 2006). The above-described alternatives of one-stage versus two-stage association study designs are summarized in Figure 27.2. Familial Study Design Familial and twin genetic association studies allow refined elucidation of genetic associations while inherently correcting for environmental effects and risk factors (Endrenyi et al., 1976; O’Reilly et al., 1994). The task of collecting appropriate related cohorts suffering from the same conditions and exposed to the same pharmacological agents is often not trivial (although has been demonstrated to be helpful in the case of nicotine

Marker Selection – Strategy and Application

Phenotype Continuous

Dichotomous

H 5

ADRs

No ADRs

Cases

Controls

95

%

e.g., biomarker blood levels 5%

5%

Stage I

Cases

Controls Stage II Replicationbased analysis

Joint analysis Combining test statistics from both stages at significance level of α /M

5

Cases

50

95%

Controls

Significance level of α/(πmarkers  M )

Figure 27.2 One-stage versus two-stage case–control genetic association analyses. The phenotype of interest in pharmacogenetic studies can be categorized as either continuous or dichotomous. When occurrence of adverse drug reactions is analyzed, the natural statistical model calls for a case-control design. However, efficacy end-points tend to display a continuous distribution, such as measurements of biomarker blood levels. A two-stage design would split the study population into (Stage I) a case–control analysis contrasting the 5% extremities of the Normalized distribution; thereafter (Stage II) would employ a replication-based analysis of the remaining 90% of the distribution with adjusted requirements for significance level. A joint analysis will then ensue, combining test statistics from both stages at a corrected significance level (Skol et al., 2006). ADR, adverse drug reaction; , study false-positive rate (type I error rate); M, number of genotyped markers; markers, the proportion of markers found to be associated in Stage I and followed up in Stage II.

metabolism [Swan et al., 2004] and alcohol exposure [Heath and Martin, 1994]). However, implementation of healthy volunteer challenge studies (see later) within related cohorts may be highly effective (Kendler et al., 1988). Healthy Volunteer Challenge Design Pharmacogenetic investigations, as opposed to disease predisposition genetic analyses, bear the extraordinary advantage of inducing the phenotype of interest in any given individual – including in healthy volunteers. In other words, in order to study the effects of drug intake in a genetic context, one can either investigate affected individuals prescribed the drug in question

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for the therapeutic purpose of controlling their condition (the traditional approach) or administer the drug to healthy consenting individuals specifically for the purpose of PGx research. This unique option allows not only the rapid recruitment of study population to be screened for genetic variation but also enables specific tailoring of the study design to address PGx questions, rather than clinical trial end-points. In practice, researchers assess the impact of pre-selected polymorphisms on the PGx phenotypes (e.g., efficacy, safety, dosing, and pharmacokinetic properties) of the studied drug, by prospective clinical analysis of healthy volunteers chosen conditioned on their genotype profile. In this manner, Kirchheiner et al. (2004) identified a total of 25 healthy males carrying one of three genotypes of interest (CYP2D6 ultra, extensive, and poor metabolizers) out of more than 1000 healthy volunteers in a PGx study of Mitrazapine and clinically challenged this miniature group of interest for clinical measurements. This cohort size matched power estimates for detecting at least a 1.5-fold effect on drug clearance differences between subgroups. Similar designs have been published for a variety of pharmaceutical applications and bear a great potential for simplification of PGx research in agents with favorable safety profile. Placebo and Multiple Dosing Cohort Comparisons PGx association studies in general, and those of medications for psychiatric and immunological diseases in particular, are characterized by a poor signal-to-noise ratio: approximately one-third of the patients enrolled in efficacy trials may respond to placebo treatment. The placebo “response” in randomized clinical trials includes such statistical artifacts as regression to the mean, drift in measurement of the response over time, and bias of expectations by both patients and evaluators, as well as real effects such as spontaneous recovery, a tendency to seek treatment outside the study, and the response to additional attention and concern involved with participating in clinical trials (Singer et al., 2005). It has been shown that placebo “response” strongly affects statistical power of association studies and that employing a placebotreated control group in the PGx analysis enables elucidation of specific drug-induced genetic effects, differentiating these from confounding effects related to underlying disease progression and severity (Grossman et al., 2007a). Similarly, employment of trial arms administering different drug doses may shed light on the genetic underlying mechanisms and their magnitude (Risner et al., 2006).

MARKER SELECTION – STRATEGY AND APPLICATION Candidate Genes Strategy Versus Whole-Genome Scans A comprehensive search for genetic influences on drug response would involve examining all genetic differences in a large number of affected individuals and controls. This will eventually

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(and probably not too far ahead) be achievable by complete genome re-sequencing. However, current practice is limited to a systematical test of common genetic variants (those with 2–5% minor allele frequency [MAF] and higher). These variants explain most of the genetic diversity in human populations and may be sampled taking advantage of the genetic property termed linkage disequilibrium (LD) (see also Chapter 2). LD reflects shared ancestry of contemporary chromosomes, measuring the degree of non-random co-occurrence of two (or more) polymorphic sites along a stretch of genomic sequence. Polymorphisms showing a high degree of LD may be considered redundant, eliminating the necessity to type all SNPs in this correlated group. It suffices to genotype a representative SNP and virtually represent the entire variation captured by the whole group, given the LD threshold imposed on the model. Traditionally, studies of common traits have been studied by two different strategies: family-based linkage studies across the entire genome and population-based association studies of individual candidate genes (usually only a handful). Although there have been notable successes, progress has been slow due to the inherent limitations of both methods: linkage analysis has low power except when a single locus explains a substantial fraction of disease, and association studies of one or a few candidate genes examine only a small fraction of the “universe” of sequence variation in each patient. Progress in technology and the development of sophisticated statistical tools have paved the way toward the application of Whole Genome Association Studies (WGAS) in a population-based cohort (see Chapter 8). This progress makes it possible to study the full scope of possible genetic determinants involved with phenotypes of drug response in a hypothesis-free fashion (i.e., unbiased approach). However, the quest for the Holy Grail, that is, the identification of causal polymorphisms underlining the phenotype of interest, remains a key unanswered question even when interrogating the entire genome. While many approaches have been suggested in the literature as to how to decipher between true associations and falsepositive results, no consensus is likely to be established in the next few years (for recommendations on replication design in large-scale association studies see the NCI-NHGRI Working Group on Replication in Association Studies [2007]). Expert genetic, biological, and pharmaceutical skills will be needed to establish a prioritization scheme by which result interpretation will reflect not only statistical plausibility but also scientific rationale. Independent comparative studies will eventually give rise to the most successful strategies that should be pursued. Hence, the strategy of choice for years to come will progressively rely on a single universal tagging set that will be used in all whole-genome association studies. This would not only ensure comparability for replication efforts within complex traits but also allow direct comparison of the role of the same variants as risk factors for different conditions. Led by this vision, The National Center for Biotechnology Information (NCBI) is currently managing the submission, storage, and access for clinical phenotype measures and associated whole-genome genotype data for several different programs, making this data as widely

available to the research community as possible while protecting the privacy of the participants as defined by consent agreements for individual projects. Application of whole-genome scans for PGx phenotypes is currently being undertaken by various academic and pharmaceutical researchers. One pioneering example depicts a WGAS investigating drug-induced liver injury ADRs and represents the incorporation of PGx in pharmaceutical R&D (Kindmark et al., 2007). Further examples are discussed in Chapter 28. Pharmacokinetics- Versus Pharmacodynamics-Based Gene Selection Once administered, a drug goes through a series of interactions in the body that are responsible not only for the therapeutic effect conveyed by this agent, but also for side effects and bystander alterations to other compounds processed in conjunction. As a whole, all interactions related to modifications acted upon the drug by the host body are termed “drug pharmacokinetics” and include all aspects of drug absorption, distribution, metabolism, and elimination (ADME). The most prominently studied molecule group within the sphere of pharmacokinetics has indisputably been drug-metabolizing enzymes (DMEs). These enzymes exist in every eukaryotic cell and in most, if not all, prokaryotes and are thought to have evolved in humans to neutralize xenotoxins and/or to control concentrations of signaling molecules in endogenous pathways (Nebert and Dieter, 2000). The fact that modern drugs have been introduced to the in vivo environment very recently in evolution uniquely excludes selection forces from having impacted the functional genetic properties of DMEs, thus potentially permitting whopping effects on drug-response phenotypes (Nebert and Dieter, 2000). In the early years of PGx research and up until very recently, genes encoding pharmacokinetic properties of drugs have been the focus of research (for a comprehensive table of drug–drug interactions based on cytochrome P450 enzymatic activity see [Flockhart, 2007]). This has been due to the scientific expectation of marked clinical effects underlined by diminished or absent capacity of the body to obliterate drugs or transform prodrugs into active compounds, but also due to the pragmatic constraint posed by considerable gaps in our understanding of, and technological methods for, investigating genes encoding drug pharmacodynamic features. Pharmacodynamics relates to any modifications acted upon the host body by the drug, that is, drug targets (receptors or enzymes) and their downstream signal pathways. With the advent of the Human Genome Project, the HapMap project (see below) and incredible progress in genotyping techniques and costs, it is currently feasible to interrogate known functional, as well functionally as-yet-unknown, genetic variants in pharmacodynamic candidate genes. It is now becoming increasingly evident that polymorphisms within genes involved in the pharmacodynamic properties of drugs may in fact be more important and clinically relevant than variants of ADME genes for non-Mendelian drug-response phenotypes. Examples supporting this notion include reports on the relative contribution of variants in the VKORC1 and CYP2C9 genes to warfarin

Marker Selection – Strategy and Application

response (Aquilante et al., 2006); SCN1A and CYP2C9 to phenytoin response (Tate et al., 2005); and 5-HTR2A and CYP2D6 to paroxetine response (Murphy et al., 2003). Pathway-Based Analyses and Search for Epistatic Associations Advances in haplotype tagging strategies have made it possible to represent variation in entire pathways relevant to drug response economically. The focus in pathway-based PGx investigation is further facilitated by the accumulation of knowledge regarding pathways relevant to drug response (e.g. PharmGKB Pathways). Although promising, pathway-based analyses face considerable challenges. Most fundamentally, the huge number of tests conducted when analyzing large SNP datasets, multiple phenotype definitions, and application of a diverse spectrum of parametric and non-parametric statistical methodologies pose considerable barriers on power, statistical design, and interpretation. Increasing the population sample size, applying permutation-based multiple correction methods, and seeking independent replication are obvious solutions dealing with these difficulties. A further complication is that all components of all pathways that a drug acts on are rarely understood completely (Need et al., 2005). As PGx investigations rapidly progress toward a pathwaycentered approach, considering dozens and hundreds of genes in each instance, or assigning prior probability scores of wholegenome scans founded on the biological processes involved with a given drug’s PGx, it is progressively evident that research should embrace methods that take into account possible interactions between genetic variants along a pathway of interest. It is very likely that, for instance, genetic-based conformational changes brought forth in one molecule along a drug-related pathway will require complementary structural, quantitative, or functional changes in a downstream (or upstream) molecule to produce a detectable sum total effect on phenotype measurements. This genetic interaction is termed “epistasis” and it is yet unclear how best, computationally and statistically, to surmount the complexity arising from the combinatorial magnitude of all the possible interactions involved within a pathway. Moreover, the high dimensionality of the resulting analysis overwhelms traditional analysis methods, a problem exponentially prohibitive for large candidate gene studies and whole-genome investigations. The Multifactor Dimensionality Reduction (MDR) approach (Wilke et al., 2005) has been suggested as one potential solution for this problem, although sophisticated analytical approaches capable of modeling high order interactions and sizable datasets will have to be developed in order to handle genome-wide and large-scale candidate gene studies. Gene-expression and proteomic profiling may in the future provide additional knowledge and complementary diagnostics associated with pathways relevant to drug response, although at this time it is difficult to differentiate incidental differences in expression levels from those that are causally relevant to drug response (Need et al., 2005). In conclusion, thanks to technological and computational advances, it is no longer justifiable to look at the effects of single genes in isolation, but rather

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incorporate the milieu of potential effects more comprehensively describing the sum total of a studied phenotype. HapMap – A Public Resource for Marker Selection The International HapMap Project was launched in October 2002 to create a public, genome-wide database of common human sequence variation, providing information needed as a guide to genetic studies of clinical phenotypes (The International HapMap Project, 2003). Four different ethnic populations were systematically genotyped for each SNP variant as follows: (i) 90 individuals (30 parent–offspring trios) from Yoruba, Ibadan, Nigeria; (ii) 90 individuals (30 trios) from Utah, USA (the Centre d’Etude du Polymorphisme Humain collection); (iii) 45 non-related Han Chinese from Beijing, China; and (iv) 44 nonrelated Japanese from Tokyo, Japan. The HapMap project set out to describe patterns of association between typed polymorphisms (LD) in the above-described four population samples in order to identify a minimum set of SNPs (“tagging SNPs”) sufficient to represent all common variants in the human genome. A publicly available software for tagging has been incorporated into the HapMap website (description and a more detailed version are available in de Bakker et al. [2005]), enabling any given user to select a desirable set of tag SNPs based on a variety of criteria (such as population ethnicity, force inclusion of SNPs with known functional effects or which have previously been described in the literature, and so on). The chief criterion for tagging capacity employed is the r2 threshold. This is a study-independent measure of SNP utility that has become a leading standard for evaluating performance of marker sets. It represents the correlation coefficient between any observed marker and a putative causal allele, reflecting the expected drop in non-centrality of an association test statistic under specified conditions (Pe’er et al., 2006). It is customary to enforce a minimum pair-wise r2 threshold of 0.7–0.8. Analyses increasingly attest to the remarkable performance of tagging (Conrad et al., 2006; de Bakker et al., 2006), indicating that the HapMap samples provide an appropriate resource for selecting globally useful tags (although African populations remain partially unrepresented by tagging approaches due to their high diversity). Thanks to the fact that all phases of the HapMap database have been publicly available ever since the projects’ inception, both commercial companies and academic laboratories routinely implement HapMap data in their drug research and development programs. In the realm of pharmacogenetics, for instance, elucidation of differential response to, and dosing of, warfarin anti-coagulation treatment as governed by VKORC1 was performed using the HapMap data (Rieder et al., 2005). Another example, specifically designed to examine the capacity of HapMap tools to discover and validate PGx phenotypes, was recently published (Jones et al., 2007). In this study, HapMap SNPs in the thiopurine methyltransferase (TPMT) gene region (tags) were tested for association with cell line absolute levels of TPMT activity. It was expected that a variant of biochemically and clinically validated relevance to thiopurine plasma levels and induced toxicity will rank as most significantly associated in all analyses. Indeed, this SNP and others in high LD with it tested positively, out of a total of 66 HapMap

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SNPs in the TPMT gene region. However, an agnostic genomewide approach to assess genomic predictors of TPMT phenotype in this dataset ranked TPMT haplotypes at 97th place out of the 17,542 genes tested. The latter result demonstrates that wholegenome tagging approaches are useful screening tools for the highest few percent of associated genes; nonetheless, ultimately biology, biochemistry, and clinical expertise are required to decipher true associations from false-positives. Validation of Association Results Any results detected in “first stage” genetic analyses require further validation. This is usually accomplished by two lines of investigation: functional analysis and replication studies. Functional studies are aimed at revealing direct causality between the reported genetic variant and measurable functional modifications elicited in the encoded molecule by employing animal models (e.g., knockout mice) or in vitro investigations. Replication studies are performed in independent patient populations mimicking as closely as possible the study design settings and testing only the top priority fraction of SNP associations detected in the first stage. Replication analysis serves not only for confirmation of true association between a genetic marker and a phenotype of interest, but it is also a means for better assessing the true magnitude of the detected effect, as first publications tend to report inflated effects due to the phenomenon termed “the winner’s curse” (Ioannidis et al., 2001). Prospective pharmacogenetic analyses compromise an important step toward the validation of genetic markers as predictors of clinical management in “real-life” settings (Grossman, 2007). Although few such studies have been conducted thus far, it is becoming generally accepted that prospective analyses comprise necessary and integral evidence of the potential utility of genetic tests in routine health care management. Initiatives already prospectively monitoring patients for PGx outcomes include several studies aimed at analysis of cost-effectiveness of HLA-B*5701 genotyping in abacavir-treated HIV-positive patients prone to suffer from hypersensitivity reaction (Hughes et al., 2004, 2007; Rauch et al., 2006; Zucman et al., 2007); a genotype-guided clinical cancer trial for management of rectal cancer by irinotecan therapy based on analysis of a polymorphic tandem repeat in the 5-untranslated region of the TYMS enhancer region, resulting in 2–9 copies of a 28-bp repeated sequence (McLeod et al., 2005); and genotype-guided dosing analyses of warfarin therapy (Hillman et al., 2005).

FROM BENCH TO BEDSIDE: INTEGRATION OF PHARMACOGENETIC TESTING INTO CLINICAL PRACTICE Analytic Assessment of Pharmacogenetic Tests and Their Utility in Clinical Practice Demonstration of unambiguous association between genotype(s) and drug-response features in controlled retrospective datasets,

albeit promising, does not guarantee the usefulness of routine genetic testing for the clinical decision-making process in the daily clinical environment. A whole host of factors need to be further assessed in order to predict and determine the costeffectiveness and practical utility of a pharmacogenetic diagnostic test. This evaluation should follow recommendations such as those devised by the Centers for Disease Control (CDC, 2006), including four components: Analytic Validity; Clinical Validity; Clinical Utility; and Ethical, Legal, and Social implications. Each of these components focuses on a different aspect of test performance and applicability in real-life settings, starting at the laboratory performing the test, through clinical studies proving its benefits, to more subjective evaluation of managed care burden associated with introduction of the test to routine practice and reimbursement frameworks. Each of these components can be empirically measured to reflect high validity and clear utility standards: 1. Analytic validity of a genetic test defines its ability to accurately and reliably measure the genotype of interest. Empirically, analytic validity includes analytic sensitivity (or the analytic detection rate, number of polymorphism carriers detected by the test divided by the total number of polymorphism carriers in the population); analytic specificity (number of normal genotype carriers detected by the test divided by the total number of individuals who do not carry the polymorphism); laboratory quality control (including measurements such as inter- and intra-assay variability, repeatability, and so on); and assay robustness (measuring how resistant the assay is to changes in pre-analytic and analytic variables, such as inter-laboratory variability, DNA extraction methodology, and so on). 2. Clinical validity of a genetic test defines its ability to detect or predict the associated phenotype. Empirically, clinical validity includes clinical sensitivity (or the clinical detection rate, the proportion of individuals who have a well-defined clinical phenotype and whose test values are positive); clinical specificity (the proportion of individuals who do not have the well-defined clinical phenotype and whose test results are negative); prevalence of the specific phenotype (the proportion of individuals in the selected setting who have, or who will develop, the phenotype); positive predictive value (the proportion of individuals with a positive test result who have, or will develop, an unwanted response when the drug is administered, PPV); negative predictive value (the proportion of individuals with a negative test result who will not have, or will not develop, an unwanted response when the drug is administered, NPV); penetrance (the frequency, under given environmental conditions, with which a specific phenotype is expressed by those individuals with a positive test result); and modifiers (genetic or environmental). 3. Clinical utility of a genetic test defines the elements that need to be considered when evaluating the risks and benefits

Examples of PGx Tests: Promising New Developments and Marketed Products

associated with its introduction into routine practice. Empirically, clinical utility includes administration settings recommendations (such as optimal time line for administration of the test, either prior to treatment initiation, during the first phase of dosing adjustments, or else); availability and effectiveness of counter-interventions; quality assurance (procedures in place for controlling pre-analytic, analytic, and post-analytic factors that could influence the risks and benefits of testing); pilot trials (assessing the performance of testing under real-world conditions, where inclusion criteria allow for individuals to experience co-morbidities, take concomitant medications, and so on); health risks (adverse consequences of testing or interventions in individuals with either positive or negative test results); economic evaluation (financial costs and benefits of testing); facilities (assess the capacity of existing resources to manage all aspects of the service); education (quality and availability of informational materials and expertise for all aspects of a screening service); and monitoring (assess a program’s ability to maintain surveillance over its activities and make adjustments). 4. Ethical, legal, and social implications surrounding a genetic test represent the safeguards and impediments that should be considered in the context of all the other components. FDA Regulation Policy and Its Impact on PGx Testing Application in Real-Life Health Care The FDA has recognized the importance of PGx and encourages its use in drug development. Recognizing that the clinical component of the overall cost of successful new drug development is ~58% of total costs (DiMasi et al., 2003) and that ~50% of Phase III trials fail, the FDA is promoting identification of PGx biomarkers at early drug development stages that would predict which drugs are likely to either fail or succeed in Phase III trials. This approach is reflected in the FDA’s 2004 white paper (FDA, 2004a), which identifies PGx as a key opportunity for the Critical Path. In addition, the FDA believes that there is value in applying long-established PGx testing to older, marketed drugs in the post-marketing period to improve their risk/benefit ratio by optimizing or individualizing dosing (Lesko and Woodcock, 2004). While multiple enthusiastic statements supporting the incorporation of PGx analysis to both new drug development and monitoring of marketed drugs have been made by FDA officials (Frueh et al., 2005; Lesko and Woodcock, 2002, 2004), there has not been a proactive regulatory action enforcing genomic data submission. In fact, ever since the publication of Guidance for Industry: Pharmacogenomic Data Submissions in 2004 (FDA, 2004b), relatively few drug labels have been updated to include recommendation for PGx testing (Table 27.2). The current policy refrains from enforcement of submissions, while promoting Voluntary Genomic Data Submission (VGDS). It is remarkable to note that despite the non-compulsory nature of this guiding principle, VGDS submissions are rapidly amassing and were filed for over 30 different compounds in the first year, exhibiting a growth trend going forward (Frueh, 2006).

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EXAMPLES OF PGx TESTS: PROMISING NEW DEVELOPMENTS AND MARKETED PRODUCTS Warfarin Perhaps the best example of a successful pharmacogenetic association, for which the clinical relevance is clear, features management of treatment with the oral anti-coagulant warfarin, Warfarin, a derivative of coumarin, is prescribed for the longterm treatment and prevention of thromboembolic events, with more than 21 million prescriptions annually in the United States alone (Rieder et al., 2005). However, due to the drug’s narrow therapeutic index, a variety of complications are associated with its treatment, even after dose adjustment according to age, sex, weight, disease state, diet, and concomitant medications. Investigation of pharmacokinetic and pharmacodynamic drug properties indicated the additive involvement of two genes in determination of warfarin maintenance dose. The first gene to be identified encodes CYP2C9, which is responsible for most of the metabolic clearance (~80%) of the more pharmacologically potent S-enantiomer of warfarin (Rettie et al., 1992). Both CYP2C9*2 and *3 cause a reduction in S-warfarin clearance, with 10-fold variation seen from the genotype linked with the highest (CYP2C9*1/*1) to lowest (CYP2C9*3/*3) activity. Numerous studies have associated these genotypes with initial dose sensitivity, delayed stabilization of maintenance dose, delays in hospital discharge, and increased bleeding complications (Gardiner and Begg, 2006). However, it is estimated that CYP2C9 variants account for only 10–20% of the total variation in warfarin dose, with additional genetic and environmental factors playing larger roles in dose determination. The second gene identified as a predictor of dosing encodes the vitamin K epoxide reductase complex protein 1 (VKORC1), targeted by warfarin. Consideration of the VKORC1 genotype or haplotype, together with the CYP2C9 genotype, and factors such as age and body size are estimated to account for 35–60% of the variability in warfarin dosing requirements. Despite the fact that these data have been reproduced by multiple independent groups, it remains to be tested in large, prospective, clinical studies whether initial dose may be tailored to patients by CYP2C9 and VKORC1 genotyping, coupled with known clinical variables. While these studies are currently under way, the clinical pharmacology advisory panel to the FDA acknowledged (November 2005) the importance and potential for genotyping of CYP2C9 and VKORC1 during the early phase of warfarin therapy, and the drug label was amended accordingly in August 2007. A month later, the FDA cleared for marketing the Nanosphere Verigene Warfarin Metabolism Nucleic Acid Test to aid physicians manage warfarin therapy. Trastuzumab Trastuzumab therapy for the treatment of breast cancer (involving a monoclonal antibody specifically targeting HER2/neu

TABLE 27.2

FDA valid pharmacogenetic biomarkers – associated dosage guidelines in drug labels and availability of approved genetic tests (Summer, 2007)

330

Gene (variant)

Gene function/ significance

Dosage guidelines

Label section

PGx test?

FDA-approved test available?

Voriconazole [6]

CYP2C19 (poor metabolizers)

Major metabolizer

1. Contraindicated concomitantly with CYP450 inducers; 2. Increased maintenance dose regimen outlined for Phenytoin; 3. No adjustmentsa for oral contraceptives inhibiting CYP2C19 or HIV protease inhibitors that inhibit CYP3A4; 4. Reduce Cyclosporine and Omeprazole by half; 5. Reduce Tacrolimus to one third

Clinical pharmacology; Drug Interactions;

No

AmpliChip/Roche

Celecoxib [1]

CYP2C9 (poor metabolizers)

Major metabolizer

No

Clinical pharmacology; Drug interactions;

No

No

Atomoxetine [17]

CYP2D6 (poor metabolizers)

Major metabolizer

Specific regimen suggested when strong CYP2D6 inhibitor co-administered

Clinical pharmacology; Drug interactions; CYP2D6 metabolism; General dosing information

No

AmpliChip®/Roche®

Capecitabine [2]

DPD (deficiency)

Rate-limiting metabolizer

Contraindicated

Contraindications; Clinical pharmacology; Precautions

No

No

Rusburicase [3]

G6PD (deficiency)

Severe hemolysis caused by hydrogen peroxide by-product

Contraindicated

Boxed warning; Contraindications; Warnings

Recommended

No

Rifampin, isoniazid, and pyrazinamide [1]

NAT (slow acetylator)

Isoniazid major metabolizer

No

Clinical pharmacology; Adverse reactions; Drug interactions

No

No

Azathioprine [2]

TPMT (*2, *3A and *3C)

Metabolizer

Recommended altered regimen, but not specified

Warnings; Lab tests; Dosage and administrations; Drug interactions

Recommended

No

Irinotecan

UGT1A1 (*28)

Conjugation of active metabolite

Consider reduction of dose in homozygotes

Clinical pharmacology; Warnings; Dosage and administration

No

Invader assay/Third Wave

■

Most adjustments relate to CYP3A4 drug–drug interactions, although it is a minor metabolizer of Voriconazole.

Pharmacogenetics and Pharmacogenomics

This table is adopted from Grossman (2007). Details in this table are derived from package inserts of the listed drugs and the table of valid genomic biomarkers in the context of approved drug labels http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm. Note – A drug label change was introduced in August 2007 VKORC1 (in addition to CYP2C9) genotyping in patients requiring warfarin therapy. An accompanying genetic test (Nanosphere Verigene Warfarin Metabolism Nucleic Acid Test) was FDA-approved in late September 2007. a

CHAPTER 27

Drug [# drugs with similar label references]

Future Developments Required for the Field to Fully Meet its Expectations

over-expressing tumors) is not only an example of a protein therapeutic for which an obligatory biomarker assay has been issued, but it is also an example of the utilization of PGx research for drug rescue: trastuzumab is marketed solely for the subset of patients (~10–25%) who over-express HER2/neu, providing care to eligible patients and returning the investment to the developing manufacturer. Interestingly enough, although in practice the obligatory biomarker assay has been set to measure protein over-expression by immunohistochemistry, studies have shown stronger association when patient subsets were determined by fluorescence in situ hybridization, reflecting gene copy number (Krejsa et al., 2006). 6-Mercaptopurine In 1953, the drug 6-mercaptopurine (6-MP) was marketed in the United States for the treatment of leukemia. Despite great expectations in the medical world, about 20 years ago fatal toxicity was discovered in 0.3% of treated children. A similar scenario was repeated for azathioprine marketed in 1968. It was later discovered that polymorphisms within the TPMT gene underlie the large inter-individual differences in the enzyme’s activity, leading to a high risk of thiopurine-induced toxicity in homozygotes for the defective alleles, and inadequate therapeutic efficacy in patients with high activity TPMT. Tests for TPMT activity (genotype, enzyme activity, and metabolite screening) are available in the United States and throughout Europe; however, clinical implementation of these tests is very low. This is contrary to expectations based on cost-effectiveness analysis of TPMT testing in children with acute lymphocytic leukemia (ALL) showing high savings per life-year (Zika et al., 2006). Current clinical practice for the management of leukemia via thiopurine medications dictates careful monitoring of white blood cell counts and clinical outcomes in a fashion that negates the necessity of incorporating the genetic test for prevention of serious adverse events. It is expected, however, that as genetic tests become generally accepted for a variety of conditions, it will become progressively acceptable both by physicians and health systems, as well as by the general public, to use TPMT genetic tests as prognostic tools. AmpliChip The first microarray-based gene-chip, approved both in the United States and EU, was released to the market by F. Hoffmann-La Roche Ltd (Switzerland) in 2003 as the AmpliChip CYP450®. The product was designed to identify key genetic polymorphisms in two CYP450 genes, CYP2D6 and CYP2C19, the products of which are cumulatively responsible for much of the first pass metabolism of many pharmaceutical compounds. As highlighted by insurance companies who refuse to cover the costs ($600–$1300), the test has not been clearly demonstrated to convey clinical utility and sufficient cost-effectiveness (Grossman et al., 2007b; Thakur et al., 2007) and is deemed “experimental, investigational or unproven.” The regulatory agencies cleared this test based solely on analytical performance and validity information, but indicated its utility

■

331

specifically for clinical application. Thus clinicians, as well as patients, were misled to expect firm impact on clinical decisionmaking guidelines. Until unambiguous evidence proves the clinical use of this and other genetic tests, caution is advised in their interpretation and application in health care management.

FUTURE DEVELOPMENTS REQUIRED FOR THE FIELD TO FULLY MEET ITS EXPECTATIONS Table 27.3 summarizes steps still needed for PGx testing to materialize into practical tools used in clinical decisionmaking. First, in order to capture the entirety of processes involved in, and affected by, any given drug, both in vivo and in interaction with environmental factors, open communications between researchers, practitioners, regulatory agencies, and pharmaceutical industries must be established. Launching PGx educational programs within the academic curriculum will facilitate acceptance of genetic testing both by health care providers and by the general public. Next, facilitated by the latter, large prospective studies must be conducted in order to truly evaluate the utility of genetic tests for specific indications in “real-life” clinical setting.Tailoring the research to specifically target PGx-based end-points, rather than adopting and artificially manipulating retrospective measurements from treatment efficacy clinical trials, will assist in elucidating “true” PGx effects. In this fashion, employing placebo, control, and multiple dosing treatment groups may act as means for detection of specific drug-induced genetic mechanisms. As research progresses and technology develops the need for investigation of gene–gene and gene–environment effects in a computationally reasonable manner will become more and more pressing. Such lines of investigation will require large, often combined, datasets, necessitating the standardization of phenotype definitions by the research community. Lastly, incorporation of genetic testing in clinical practice will only become possible if regulatory and funding agencies acknowledge its potential and promote its development by changing their policy. PGx is a field uniquely attractive to both academic medical centers and the pharmaceutical and biotechnological industries. As such, governmental agencies, such as the US FDA and the EMA, regulate the development, marketing, and clinical applications associated with maturation of research advances. In order for PGx to wholly fulfill its potential and expectations a structured framework of incentives, priorities, and policies must be devised that would promote allocation of resources by each of the concerned parties. A potential solution to improve surveillance might be sampling and privacyprotected DNA banking from the first 250,000 patients treated with a newly marketed drug, or a similar risk-management system coordinated with regulatory agencies (Roses, 2004). The most pronounced deficiency identified in PGx research relates to the need to improve drug safety and efficacy profiles of generic, off-patent drugs. In this aspect it is important to mention

332

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TABLE 27.3

■

Pharmacogenetics and Pharmacogenomics

Steps required for PGx to fully meet its potential as “the right drug for the right patient”

Future challenges ● ● ● ● ● ● ● ● ●

Integrative approach: Cross-talk between geneticists, statisticians, pharmacologists, molecular biologists, physicians, regulatory agencies, and pharmaceutical companies. Education programs should be incorporated into the academic curriculum. Prospective studies have to be conducted to evaluate the potential predictive power and clinical utility of PGx testing in clinical settings. Possibly employ placebo and control treatment groups in analysis to elucidate specific drug-induced genetic effects. Focus on gene–gene interactions and the net genetic effect. Integrate environmental effects and gene – environment interactions into the statistical models. Standardize unequivocal and reproducible phenotype definitions. Genetics will get more and more acceptable as a tool for clinical practice as policy will catch up with scientific developments. Surveillance of ADRs in the post-marketing phase through risk-management systems coordinated by both manufacturers and regulatory agencies. Establishment of incentive structure for the industry to improve drug safety and efficacy beyond the terms of current patent protection.

that withdrawn medications lose their patent protection, and thus commercially driven pharmaceutical companies lose interest in rescuing these formulations by conducting safety PGx experiments. To date, it has yet to be demonstrated whether PGx would be cost-effective in resurrecting failed marketed

drugs (Shah, 2006), but thorough investigations have yet to be conducted. Only controlled incentives, calling for prolongation of patent protection terms and similar motivations, will drive the pharmaceutical industry to invest the considerable costs and resources required.

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RECOMMENDED RESOURCES Journals

Websites

Ioannidis, J.P., Ntzani, E.E., Trikalinos, T.A., and Contopoulos-Ioannidis, D.G. (2001). Replication validity of genetic association studies. Nat Genet 29, 306–309. NCI-NHGRI Working Group on Replication in Association Studies (2007). Replicating genotype-phenotype associations, Nature 447, 655–660. Roses, A.D. (2004). Pharmacogenetics and drug development: The path to safer and more effective drugs. Nat Rev Genet 5, 645–656.

http://www.hapmap.org/International Hap Map Project web site http://www.cdc.gov/genomics/gtesting/ACCE.htm ACCE: A CDCsponsored project for evaluation of genetic testing http://www.fda.gov/cber/gdlns/pharmdtasub.htm US Food and Drug Administration site for pharmacogenetic data submissions http://www.pharmgkb.org/search/pathway/pathway.jsp PharmGKB Pathways

CHAPTER

28 The Role of Genomics and Genetics in Drug Discovery and Development Robert I. Tepper and Ronenn Roubenoff

INTRODUCTION The elucidation of the structure and the increasing knowledge of the function of human genes and their interactions are transforming the process by which novel therapeutic agents are identified. Prior to 1990, the collection of therapeutic agents commonly used in medicine targeted less than 400 gene products (Overington et al., 2006). These therapeutic agents, mostly low molecular weight (e.g., 800 d) chemical entities, were typically identified based on the pharmacological activity of natural product extracts or synthesized chemical molecules screened in complex in vitro or in vivo assays. The identification of the target proteins for these drugs typically came after the identification of a pharmacological effect and involved years of complex research involving biochemical purification and characterization. Using this labor-intensive approach, most of the gene products of the human genome associated with important physiological and pathophysiological states could not be identified. The genomic revolution of the 1990s allowed for the rapid identification of the vast majority of human genes and thereby provided a means to identify most human gene products. The potential set of targets for which drugs could be identified therefore increased dramatically. In addition, many new approaches were conceived that allowed for the functional correlation of genes with each other into functional “pathways” (Boehm et al., 2007). The association of individual genes, or

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

pathways, with disease states has led to a dramatic advance in our understanding of the genetic deregulation that characterizes human disease. This fundamental molecular understanding of disease has provided many novel targets for drug discovery. In addition, the subclassification of common diseases on the bases of the genetic pathways that are altered has led to a more precise way of identifying which patients are likely to benefit from a particular therapeutic agent. The drug discovery process itself has also taken advantage of new methodologies for screening drugs, using “whole-genome” approaches (Kramer and Cohen, 2004). These advances in drug discovery and development resulting from genomics are described in more detail in this chapter.

THE DRUG DISCOVERY PROCESS The steps involved in the discovery and development of novel drugs are depicted in Figure 28.1. The time period from the start of novel target identification to the entry of a new drug into the marketplace is a lengthy one, typically over 12 years. Moreover, the average cost for this process is very high and is increasing. Current estimates for the research and development cost to deliver one successful drug into the market is in excess of one billion dollars, when one includes in the average cost of drugs that have failed during the discovery and development process (Munos, 2006). Approximately 10–12 drug candidates are generated and tested

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The drug discovery and development process Target identification and validation

Chemical screening

Lead identification and optimization

Discovery 3–5 years Preclinical animal studies

Clinical trials

Regulatory approval and marketing

Development 5–7 years

Figure 28.1 Basic steps in the drug discovery and development process.

for one successful drug to be developed. Taken together, the time interval, cost, and 90% failure rate make the pharmaceutical process an extremely risky endeavor, particularly for drug candidates that are “first in class.” The development of genomic and associated proteomic strategies has provided a wealth of opportunities for the development of completely novel drugs. While the prospects for major breakthroughs in our understanding of disease pathophysiology remain great, genomic strategies in their first decade of incorporation into the drug discovery process have not resulted in an increase in productivity (decreased cost and/or time) (Betz, 2005; Brown and Superti-Furga, 2003). This is in part due to the need to create the requisite informatics processes and the biological material (e.g., patient tissues for DNA, RNA, and protein analyses) and correlative clinical information to make genomic information optimally useful. In addition, experience with genomics techniques is rapidly increasing, and this is also expected to impact positively on the productivity of processes that utilize genomic information.

GENOMICS IN TARGET DISCOVERY Despite the relatively nascent use of genomics in drug discovery, there are already many aspects of the process that have been improved through the use of genomic information. Target discovery, the process by which novel drug targets (i.e., the gene product, typically a protein to which the drug is directed) are identified and biologically validated, has perhaps been the most mature focus of genomics applications to date. Extensive analyses of the human genome have identified many of the 20,000 protein gene products in man (International Human Genome Sequencing Consortium, 2004). This information has allowed scientists to comprehensively identify major classes of proteins in the human genome. Take for example the case of protein kinases, of which there are about 1000 representatives in the human genome (Kostich et al., 2002). Kinases are “druggable,” meaning that there are well-established methods for developing chemical

modulators of these targets (most often inhibitors) bearing drug-like properties. They are also known to play important roles in human physiology, for example, by regulating the process of intracellular signal transduction which controls fundamental cellular processes including cell division, programmed cell death (apoptosis), and cellular differentiation in various cell types (Kostich et al., 2002). An estimated seventy or more novel kinase targets have entered the drug discovery process over the past 10 years, and the associated chemical antagonists generated have been studied in a number of pathophysiologic states, including cancer, various inflammatory states, and metabolic disorders. A list of major “druggable” protein classes that have been extensively expanded and studied using genomic approaches are provided by Zheng et al. (2006), with an estimate of the number of drug targets in 2006 being over 1500, compared to less than 500 in 1996. Some examples of drug candidates that have been developed as a result of genetic or genomic screening include the Janus kinase and p38 mitogen-activated protein (MAP) kinase inhibitors for inflammation; sunitinib, a multiple receptor tyrosine kinase inhibitor approved for renal cell carcinoma; and gefitinib, an epidermal growth factor receptor tyrosine kinase inhibitor approved for lung cancer. Another fundamental genomics technology used extensively in the target validation process is that of expression profiling. Expression profiling refers to the determination of the transcriptional activity of multiple genes (often the entire human genome) in one or more tissue or cell types. Expression profiling studies have been used extensively to determine the differences in gene expression between normal and diseased tissues (Golub et al., 1999; Hedenfalk et al., 2001; Khan et al., 2001); for example, the comparison of normal lung tissue with lung cancer of various types or the comparison of normal and inflamed organs. In this way, genes that are candidates for involvement in pathologic states may be identified. Further biological studies may then be performed to determine whether any of these candidate genes correlated with disease activity may be causally involved in the disease process. If so validated, these gene products, if chemically tractable, may be novel targets for drug discovery. Comparative genomics has also played an important role in drug discovery (Youngman et al., 2001). Comparative genomics refers to the identification of the DNA sequence variability in gene orthologs. Orthologs are genes in different species that evolved from a common ancestral gene by speciation. Normally, orthologs retain the same function in the course of evolution. Identification of orthologs is critical for reliable prediction of gene function, as invariant regions of orthologous genes are typically essential for normal gene function. By identifying orthologs from a number of species, drug discovery scientists often obtain valuable information on how to inhibit the function of a given gene product. Moreover, identification and testing of multiple orthologs also ensures that chemical inhibitors that are being developed against one species (e.g., humans) are active in others. As the process of drug efficacy and safety testing makes extensive use of non-human organisms (e.g., rodents and monkeys), ortholog testing has been an important advance

Pharmacogenomics and Drug Development

Identify/Validate Identify Lead Gene Targets Compounds

Target Identification

Target Validation

Lead Development

Eliminate Targets Genomics

Improve Efficacy and Safety

Preclinical

Validate Leads

Clinical

Target Patients

Market

Select Patients Genomic Medicine

Figure 28.2 Genetic and genomic methods have impacted nearly every phase of drug development.

in the generation of new drugs. The ability to rapidly determine sequence variation in a target gene from one human to another has also been a valuable tool in the drug discovery process. In this way, one can test whether any sequence variation is present in the target gene within the population and at what frequency and whether or not the variation affects the ability of the drug to act on the target. Sequence variation in disease-associated genes has also shed light on the susceptibility of individuals to a given disease (e.g., see Duerr et al., 2006).

GENOMIC APPROACHES TO DRUG IDENTIFICATION The advent of genetic and genomic capability has modified how drug candidates are discovered and developed (Figure 28.2). The process of high-throughput screening (HTS), by which thousands of chemical compounds are rapidly assayed for a biological effect using automated methods, has become a routine part of modern drug discovery (Lang et al., 2006; Sams-Dodd, 2005). Compounds that score positive in high-throughput screening assays (commonly known in the drug discovery vernacular as “hits”) are then followed up with secondary assays that validate the specificity of the primary screen. Validated hits are then prioritized based on their chemical tractability and further refined using medicinal chemistry to generate lead compounds (or simply “leads”). An iterative process known as lead optimization then follows, consisting of repeated cycles of chemical modifications and biological assays, the latter to define the lead molecule’s potency against the target and a number of pharmacological properties including predicted solubility, distribution, and metabolism. While the drug screening, hit validation, and “hit to lead” processes can be performed quite efficiently (often less than 6 months for all three), it is not unusual for the lead optimization process to require more than a year of effort on the part of 15–20 biologists and chemists. Genomics has already contributed quite extensively to the drug screening and identification process. Completely novel

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screening methodologies that rely on “whole-genome” screening approaches have been designed. An example of this is RNAi-based screens (Chen et al., 2007). RNAi, or RNA interference, is a biological process of gene-expression regulation in which double-stranded RNA inhibits the expression of individual genes with complementary nucleotide sequences. Using this approach, genes can be inactivated, one at a time, and the phenotypic effect can be studied in a cell-based screen. If one is interested, for example, in understanding how the effects of a cytotoxic anti-cancer drug can be augmented, one can devise a high-throughput RNAi screen in which individual genes are inactivated in a cell in the presence of the drug (Whitehurst et al., 2007). In individual assay wells where cytotoxicity has been augmented, one can hypothesize that inactivation of the specific gene product complements the anti-cancer effect of the drug. Conversely, inhibition of the drug’s cytotoxicity would suggest that inhibition of the specific gene product confers a degree of resistance of the drug’s cytotoxic effect on the cancer cell. In this way, novel targets for anti-cancer agents and/or an understanding of cancer drug resistance can be studied. Innovative databases and knowledge management tools are also being devised to facilitate the link between drug action, functional genomic information and disease biology. Representative of this class of tools is the “Connectivity Map” devised by Justin Lamb, Todd Golub, and colleagues (Lamb et al., 2006). The connectivity map is a reference collection of geneexpression profiles from cultured human cells treated with bioactive small molecules, together with software that allows for pattern-matching. This map has demonstrated utility in finding connections among small molecules sharing a common mechanism of action, as well as chemicals and biological processes. As such, it serves as an important tool to link diseases, known drugs, and drug candidates. Genomic signatures from patient specimens (e.g., gene-expression patterns and DNA polymorphisms), for example in cancer, have also been studied as a guide in selecting specific chemotherapeutic agents (Dressman et al., 2007; Potti et al., 2006a, b), demonstrating the relevance of novel genomics approaches to clinical medicine.

PHARMACOGENOMICS AND DRUG DEVELOPMENT There is a growing concern in the pharmaceutical industry that the “Blockbuster Model” of doing business is coming to an end. There are many reasons for this, which are beyond the scope of this discussion. However, one important reason is that the notion of a single drug being adequate for all patients with a disease is no longer tenable in the post-genomic era, when it is possible to identify genetic markers of response or non-response to a drug even before it is given. The reluctance of drug companies to pursue this line of research in the hopes that a drug will be given to everyone even if it only works in a small subgroup of patients was dealt a severe blow by the experience of Astra Zeneca with gefitinib, a first-in-class tyrosine kinase inhibitor

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for non–small-cell lung cancer. The drug was approved by Food and Drug Administration (FDA) with an approximately 10% response rate on the basis of two Phase 2 studies and an ongoing Phase 3 study. At the time of approval, in May 2003, the sponsor had not been able to discover a pharmacogenomic marker that would identify responders. However, such a biomarker was actually published only a year later, on May 20, 2004, by an academic group (Lynch et al., 2004). Within months, two academic labs were providing research-grade tests to clinicians, and a validated, commercial test was available shortly thereafter. As a result, gefitinib sales declined precipitously because physicians could identify the 10% or so of patients who were likely to respond before prescribing the drug. Subsequently, Phase 3 data showed no survival benefit for patients treated with gefitinib of and the drug was abandoned in favor of erlotinib (Tarceva, Genentech/ OSIP), a more efficacious follow-on medication. In contrast to this unhappy experience, there is a growing list of drugs for which pharmacogenomic biomarkers play a key role in development, approval, and subsequent use (see Table 28.1). However, failures in the development of genomically based drugs are still achingly common, although the success stories are often triumphs of science and drug development, along with a modicum of luck. The first great success, which remains in many ways the hallmark, is trastuzumab (Herceptin, Genentech), a drug that has become a mainstay of breast cancer therapy. Trastuzumab is indicated for the treatment of breast cancer that overexpresses a cell surface protein called HER2/ neu, which happens in about 25% of patients. If this protein is present, the disease is more likely to metastasize, and trastuzumab has been shown to prolong survival significantly. However, in the absence of the protein, which is the case in the majority of patients, there would be no benefit to treatment, and the patient would only incur the expense and potential side effects, including congestive heart failure. Therefore, the availability of a diagnostic test for HER2/neu is as important as the drug itself in both drug development and therapeutics. In 1998, trastuzumab was the first drug to be approved jointly with a paired diagnostic test, based on immunohistochemistry. An improved assay, using fluorescence in situ hybridization (FISH) was approved in 2001. The new assay was a major improvement because it was more precise and accurate than the original, which misclassified as many as 1/4 of patients and limited clinician’s enthusiasm for the drug. After the new assay appeared, trastuzumab went on to become a blockbuster therapeutic. The two examples cited above are both based on pharmacogenomic variability in the drug target, which in turn alters the pharmacodynamics of the drugs. However, this is not the only type of pharmacogenomics that can be useful in drug development. Variability in genes encoding drug-metabolizing enzymes can markedly alter the pharmacokinetics of drugs, as can variations in drug transporters. Some examples of how genetic variations in both target genes and metabolic/transporter genes can affect drug action are shown in Table 28.2 (see Chapter 27). Variation in metabolism of purine analogs such as azathioprine (Imuran) and mercaptopurine (6-MP) has been

T A B L E 2 8 . 1 Examples of drugs developed based on genetic or genomic information Drug

Year approved

Target

Biomarker

Imatinib

2001

Bcr-abl mutation

Philadelphia chromosome

Trastuzumab

1998

HER2/neu mutation

FISHa assay

Mirostipen

Not approved; withdrawn by sponsor in 2002

Myeloid progenitor inhibitory factor-1

NA

MLN518

In development

FLT-3 tyrosine kinase

FLT-3 mutation by PCRa

Cetuximab

2004

Epidermal growth factor receptor

EGFR expression

Gefitinib

2003–2005b

Erlotinib

2005

Panitumumab

2006

EGFR mutations EGFR mutations K-RAS wild type by PCR

a

FISH, fluorescence in situ hybridization; PCR, polymerase chain reaction. b Withdrawn after failing to demonstrate survival benefit in phase 3 studies.

demonstrated by Weinshilboum and colleagues (Weinshilboum, 2003). These drugs are metabolized by thiopurine methyltransferase (TPMT). People who are homozygous for loss of function variants in this gene – about 1% of Caucasians – are at risk for bone marrow aplasia if given standard doses of these drugs for treatment of cancer or autoimmune disease. In addition, about 10% of patients who are heterozygous for loss of TPMT function are at risk for bone marrow toxicity. Conversely, activity of TPMT can interfere with the success of chemotherapy for leukemia with these purine analogs, and testing of genetic variants has now become commonplace and is recommended in the drug label for 6-MP (Lennard et al., 1990).

PHARMACODYNAMIC MARKERS AND THEIR ROLE IN DRUG DISCOVERY AND DEVELOPMENT It is virtually impossible to develop a successful drug without a robust pharmacodynamic (PD) marker. In early stages of

Pharmacodynamic Markers and their Role in Drug Discovery and Development

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TABLE 28.2 Examples of medications that are affected by pharmacogenetic variation in drug target or metabolism or both (from Roden et al., 2006) Drug

Variable clinical effect

Genes with associated variants

Possible mechanism

Azathioprine and Mercaptopurine

Bone marrow aplasia Reduced therapeutic effect at standard doses

TPMT

Hypofunctional alleles Wild-type alleles

Some antidepressants and -blockers

Increased side-effect risk Decreased efficacy

CYP2D6

Hypofunctional alleles Gene duplication

Omeprazole

Helicobacter pylori cure rate

CYP2C19

Hypofunctional alleles

Irinotecan

Neutropenia

UGT1A1

Decreased expression due to regulatory polymorphism

HIV protease inhibitors

Central nervous system levels

MDR1

Altered P-glycoprotein function

-blockers

Blood pressure lowering and heart rate slowing

ADRB1

Altered receptor function or number

Inhaled 2-agonists

Bronchodilation

ADRB2

Altered receptor function or number

Diuretics

Blood pressure lowering

ADD1

Altered cytoskeletal function by adducin variants

Warfarin

Anticoagulation

VKORC1 CYO2C9

Variant haplotypes in regulatory regions leading to variable expression Coding region variants causing reduced S-warfarin clearance

Abacavir

Immunological reactions

HLA variants

Altered immunologic responses

QT-prolonging antiarrhythmics

Drug-induced arrhythmia

Ion-channel genes

Exposure of subclinical reduction in repolarizing currents by drugs

General anesthetics

Malignant hyperthermia

RYR1

Anesthetic-induced increased release of sarcoplasmic reticulum calcium by mutant channels

Inhaled steroids

Bronchodilation

CRHR1

Unknown

HMG-CoA reductase inhibitors (statins)

Low-density lipoprotein cholesterol lowering

HMGCR

Altered HMG-CoA reductase activity

ADD1, the gene encoding-adducin; ADRB1, gene encoding the 1-adrenergic receptor; ADRB2, the gene encoding the 2-adrenergic receptor; CRHR1, the gene encoding corticotrophin-releasing hormone receptor-1; CY2C19, the gene encoding the 2C19 cytochrome P450 isoform; CYP2C9, the gene encoding the 2C9 cytochrome P450 isoform; CYP2D6, the gene encoding the 2D6 cytochrome P450 isoform; HMG-CoA, 3 hydroxy-3-methylglutaryl coenzyme A; HMGCR, the gene encoding HMG-CoA reductase; MDR1, the gene encoding P-glycoprotein; RYR1, the gene encoding the skeletal muscle calcium-release channel; TPMT, the gene encoding thiopurine methyltransferase; UGT1A1, the gene encoding uridine diphosphate glycosyltransferease 1 family, polypeptide A1; VKORC1, the gene encoding vitamin K epoxide reductase complex, subunit 1.

pre-clinical development, the biomarker is needed to select among competing drug candidates, usually using rodents to compare the pharmacokinetic (PK), PD, and toxicological profiles of leading candidate molecules to select the best ones to take forward into more extensive (and expensive) evaluation. Once the drug enters human trials, a PD marker is crucial for understanding dose selection. If a drug fails to elicit the desired

clinical response, is this because there was not enough drug, or because the drug did not reach its target, or because the target itself is not an important cause of the disease? Without having both PK and PD information, this question cannot be answered. Instead, the program must rely on toxic effects to determine the highest possible dose, and then reduce the dose sufficiently to achieve tolerability and hope that there will still be efficacy.

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Such an approach can work, but it is obviously suboptimal, more hazardous, and carries a higher risk of ultimately failing in latestage trials. Thus, the development and validation of a robust PD marker is an essential step in any drug development program. Genetic information and genomic methods are playing an increasingly prominent role in the discovery and validation of PD assays. Understanding of the pathway in which a drug target operates is crucial to developing potential PD assays against it. It has become commonplace to use transcriptional profiling to measure changes in expression of genes up and downstream from the drug’s target in order to understand which points in the pathway are most sensitive measures of the drug’s effects. From this rapid and rather non-specific method, more specific assays can be planned. Methods used for PD assays include flow cytometry, Western blotting, plate-based immunoassays (ELISAs), single- and multiplexed sandwich immunoassays, and immunohistochemistry. In the final assay, the ability to create a Good Laboratory Practice (GLP) acceptable test becomes an important consideration. However, for interim use and internal decision-making, GLP assays are generally not required. For example, anti-tumor drugs that affect molecular chaperones such as heat shock protein (HSP)-90 are known to affect the conformation, stability, and function of various “client” proteins, including ERBB2, AKT, and ERK. Thus, drugs that inhibit HSP-90 are expected to cause declines in these proteins, as well as an increase in HSP-70 (Powers and Workman, 2007). Working from this knowledge, it is possible to measure declines in ERBB2 or phosphorylated ERK in circulating white blood cells as PD markers of exposure to inhibitors of HSP-90. These assays, although derived from genetic and genomic understanding of the target pathway, can be converted to proteomic assays and used in GLP-compliant fashion to facilitate regulatory acceptance of the results.

TOXICOGENOMICS Toxicogenomics is the use of genetic and genomic assays to perform tests of the safety of a drug or drug candidate. The goal is to identify toxic compounds earlier in the discovery process than is currently the norm. Traditionally, toxicological evaluation has been done in laboratory animals, beginning with rodents and rabbits. Because these studies are laborious and expensive, they do not lend themselves to high throughput and are generally performed only after a lead compound has been identified. As a result, toxicity profiles of molecules are not generated until relatively late in drug discovery, and such findings can stop a molecule’s development only after a high initial expenditure. With new genetic and genomic toxicology, the goal is to develop high-throughput toxicity profiling that can be applied earlier in the drug discovery process to more compounds, effectively weeding out potential toxins early and at a lower price. Several companies, such as Iconix Biosciences and GenoLogic, have developed platforms for gene-expression toxicity studies. Not only can these methods be used to do experiments faster, but they can also give additional

insight into the pathogenesis of the toxicity, since expression profiling can give clues to the biology far more comprehensively than traditional histology or clinical testing can. Once the cause of the toxicity is understood, pharmacologists can develop screening tests for identifying these problems in back-up molecules and do this even earlier in the drug development process.

GENETICS AND GENOMICS IN CLINICAL TRIAL DESIGN It has now become feasible to obtain DNA results within a few days of drawing a sample, so that clinical trials can use genetic results to either screen potential subjects or stratify them. The most commonly used example is testing for Her2/neu mutations (using immunohistochemistry or in situ hybridization on clinical biopsy samples) before treatment for breast cancer. Sometimes, tight linkage between a genetic haplotype and serum protein marker allows the latter to be used as an index of the former without necessarily needing to do the genetic test. This is the case in rheumatoid arthritis, where anti-citrullinated cyclic peptide (CCP) can be used as an index for the presence of the human leukocyte antigen (HLA) shared epitope (SE). Recently, the Committee for Medicinal Products for Human Use (CHMP) recommended approval of panatimumab, an EGFR inhibitor, for colorectal cancer, only in patients with the unmutated KRAS allele (see www.emea.europa.eu/pdfs/human/ opinion/40511307en.pdf dated September 20, 2007). In the near future, it is likely that gene-expression profiling (using whole blood RNA profiling, for example) will also be feasible in “real time”, could be used to identify patients in whom targeted pathways are activated, and should thus be more amenable to pharmacological intervention.

GENOMICS IN DRUG APPROVAL AND REGULATION Drug development is a regulated process, and success is measured by receiving regulatory approval to manufacture and sell the resulting product. Because health care is so expensive, and new medications are an appreciable and growing part of that cost, regulators are very interested in any information that clarifies the risk-benefit and cost-benefit of a new drug. Thus, both the FDA in the United States and the European Medicines Evaluation Agency (EMEA) have been very involved in supporting and even demanding pharmacogenomic and genetic analyses. Both groups have developed guidelines for submitting genomic and genetic data, and FDA has offered a “safe harbor” mechanism under which such data can be submitted without risk to the critical path for drug approval. In 2008, FDA is expected to follow up with new guidance for use of biomarkers in early drug development, again with genomic and genetic testing being prominently involved.

References

CONCLUSION The comprehensive nature of genomic technologies and the progress to date underscore their importance to drug discovery and development in the future. Genomics has already had a major impact on the identification of novel targets for drug discovery, the development of novel drug-screening methodologies, the link between disease pathways and pharmacological effects, and the selection of drug treatment options for individual patients. The vast amount of data served up by the genomics revolution has required a “lag phase” of growth, as tools and biological

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and computational resources and reagents have been developed to complement the genomic information. However, it is now likely that critical mass has been reached and that the benefits of genomic and genetic methods will become evident at an increasing pace. The wealth of information emerging in the last several years implies that we are emerging from the lag phase into a log phase of growth in the application of genomics to important disease-relevant biological inquiries, including novel drug discovery. The investment in genomics over the past two decades is providing a wealth of important information which will be essential in providing therapeutic solutions for major human diseases.

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Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N. et al. (2006). The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935. Lang, P.,Yeow, K., Nichols, A. and Scheer, A. (2006). Cellular imaging in drug discovery. Nat Rev Drug Discov 5, 343–356. Lennard, L., Lilleyman, J., Van Loon, J. and Weinshilboum, R. (1990). Genetic variation in response to 6-mercaptopurine for childhood acute lymphoblastic leukaemia. Lancet 336, 225–229. Lynch, T., Bell, D., Sordella, R. et al. (2004). Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350, 2129–2139. Munos, B. (2006). Can open-source R&D reinvigorate drug research?. Nat Rev Drug Discov 5, 723–729. Overington, J.P., Al-Lazikani, B. and Hopkins, A.L. (2006). How many drug targets are there?. Nat Rev Drug Discov 5, 993–996. Potti, A., Dressman, H.K., Bild, A., Riedel, R.F., Chan, G., Sayer, R., Cragun, J., Cottrill, H., Kelley, M.J., Petersen, R. et al. (2006a). Genomic signatures to guide the use of chemotherapeutics. Nat Med 12, 1294–1300. Potti, A., Mukherjee, S., Petersen, R., Dressman, H.K., Bild, A., Koontz, J., Kratzke, R., Watson, M.A., Kelley, M., Ginsburg, G.S. et al. (2006b). A genomic strategy to refine prognosis in early-stage non-smallcell lung cancer. N Engl J Med 355, 570–580. Powers, M. and Workman, P. (2007). Inhibitors of the heat shock protein response: Biology and pharmacology. FEBS Lett 581, 3758–3769. Roden, D.M., Altman, R.B., Benowitz, N.L., Flockhart, D.A., Giacomini, K.M., Johnson, J.A., Krauss, R.M., McLeod, H.L., Ratain, M.J., Relling, M.V. et al. (2006). Pharmacogenomics: Challenges and opportunities. Ann Intern Med 145, 749–757. Sams-Dodd, F. (2005). Target-based drug discovery: Is something wrong?. Drug Discov Today 10, 139–147. Weinshilboum, R. (2003). Inheritance and drug response. N Engl J Med 348, 529–537. Whitehurst, A.W., Bodemann, B.O., Cardenas, J., Ferguson, D., Girard, L., Peyton, M., Minna, J.D., Michnoff, C., Hao, W., Roth, M.G. et al. (2007). Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature 446, 815–819. Youngman, P., Tepper, R. and Moore, J. (2001). Genomics technologies and strategies for identification of novel anti-infective agents. Curr Clin Top Infect Dis 21, 366–390.

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(2007). Rebuilding Big Pharma’s business model. http://www.trinity. edu/sbachrac/drugdesign/Drug%20Costs%20Articles/rebuilding_big_pharma.pdf.

RECOMMENDED RESOURCES European Medicines Evaluation Agency http://www.emea.europa.eu/ Food and Drug Administration Guidelines Page http://www.fda. gov/cder/guidance/#clinical%20medicine The Personalized Medicine Coalition http://www.personalizedmedicinecoalition.org/

US Centers for Disease Control Population Genomics Homepage http://www.cdc.gov/genomics/ US National Institutes of Health Animal Model Genomics Homepage http://www.nih.gov/science/models/rat/ US Clinical Trials Listing http://www.clinicaltrials.gov/

CHAPTER

29 Role of Pharmacogenomics in Drug Development Colin F. Spraggs, Beena T. Koshy, Mark R. Edbrooke and Allen D. Roses

INTRODUCTION Fast paced developments in biomedical science continue to increase understanding of the molecular complexities of biological pathways and offer new possibilities for the prevention, treatment and cure of common, serious diseases. These opportunities are accompanied by the challenges of applying these new discoveries to produce affordable, safe and efficacious new therapies for the combat of disease. In this chapter, we review current application of genomic strategies in drug development that are having an impact across the drug development continuum and discuss how these genetic approaches re-define clinical trial design to incorporate non-traditional end points and transform pharmaceutical research and development productivity. Pharmacogenomics describes genomic/genetic investigations of drug response using relationships between DNA and other genomic (RNA) analytes and drug response (Phillips and Van Bebber, 2005; Weinshilboum and Wang, 2004). In this chapter, we use the term pharmacogenetics to also describe specifically the relationship between inherited genetic (DNA) variation and drug response (see also Chapter 27). The past decade has witnessed a decline in pharmaceutical productivity, where despite year after year increases in research and development expenditure, there has been a decline in the submission and approval of new, innovative drugs to treat disease and benefit patients. Recent estimates place successful pharmaceutical Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

launch rates at 10% overall of those drugs entering clinical development, with reasons for failure ascribed to adverse drug reactions (10%), lack of efficacy (30%) and unfavorable pharmacokinetic profile and/or metabolic properties (39%) (Towse, 2003). The highest failure rates are observed during Phases II (65%) and III (40%) which correspond to the most expensive phases for clinical development cost (current estimates $200 M). Poor productivity has been attributed to the pharmaceutical industry’s fascination with blockbuster drugs; rather, the rising development costs are a consequence of pipeline attrition due to the termination of drugs during clinical development through failure to achieve safety and efficacy target profiles. This is a shift from the early 1990s, when pipeline attrition was in the early phase of drug development (mostly Phase I) chiefly to poor pharmacokinetics and bioavailability. Efficacy failures are in part due to poor predictive animal models for some diseases and also to the inability to measure clinical endpoints that are relevant to treating the disease, rather than endpoints that reporting the physiology or pharmacodynamics of drug treatment (Littman and Williams, 2005). For example in Chronic Obstructive Pulmonary Disease (COPD), a chronic progressive disease of lung inflammation and damage coupled with deterioration of lung function, forced expiratory volume in one second (FEV1) is an established clinical endpoint for the evaluation of new chemical entities (NCEs) in COPD. However, studies have demonstrated that patients with equivalent FEV1 values Copyright © 2009, Elsevier Inc. All rights reserved. 343

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have different lung pathologies (emphysema severity) determined by high resolution CT scanning and different responsiveness to administration of the inhaled corticosteroid beclomethasone (Gelb et al., 1996). This suggests that FEV1 does not correlate with more sensitive disease markers and may obscure the efficacy of NCEs in COPD clinical trials, indicating that more sensitive, disease relevant measurements are required to document response phenotypes. Adverse drug reactions (ADRs) also contribute to attrition, occurring either during drug development or after approval and launch. A number of successfully launched products including Rezulin (troglitazone, Sankyo, GSK, Pfizer) Lotronex (alosetron, GSK), Baycol (cerivastatin, Bayer), Tysabri (natalizumab, Elan, Biogen) and Vioxx (rofecoxib, Merck) have been withdrawn from the market following serious side effects. Due to their low incidence, these side effects were not observed during extensive clinical development but required access to post-marketing patient populations to become manifest. Review data suggest that 51% of medicines can cause serious ADRs and result in 5% of all hospital admissions and 700,000 injuries or deaths per year (Einarson, 1993; Lazarou et al., 1998). Furthermore, of 499 drug approvals since 1980, 21% have required post-launch changes in dose, of which 79% have been safety-related (Cross et al., 2002).

DRUG DEVELOPMENT CRITICAL PATH Safety remains a prime concern for regulatory authorities. Recognizing this concern and the productivity decline in the provision of new medicines, regulatory authorities have produced white paper discussion documents: “Innovation-Stagnation: Challenge and Opportunity on the critical path to new medicines” from the FDA (FDA, 2004) and Roadmap to 2010 from the EMEA (2004) which outline opportunities to support the development of new medical products. Pharmacogenomics is identified as a key opportunity in these strategy documents. To facilitate incorporation of pharmacogenomic approaches, FDA has also developed regulatory and non-regulatory guidance (FDA, 2005a) to support formal and non-formal review of genomic data through Voluntary Genomic Data Submissions (VGDS) and established the Interdisciplinary Pharmacogenomic Review Group (IPRG). Similar activities are being conducted in Europe through the EMEA Pharmacogenomics Briefing Meetings and VGDS discussions are held jointly by the United States and EU Regulators. Regulatory authorities are establishing an environment conducive to the integration and application of genomic technologies in support of NCE development. Regulatory encouragement of the use genomic technology is validated by recently FDA approved integration of clinically relevant pharmacogenomic data into product labels (Table 29.1). This information and usage guidance incorporating pharmacogenomics has been incorporated into some product prescribing information. At this stage, there are few examples of clinical decision making that utilizes pharmacogenomics. Nevertheless,

pharmacogenomic investigation is occurring more rapidly upon the introduction of new medicines and examples where pharmacogenomic publications precede the registration and approval of new medicines, as shown in Figure 29.1 (Krejsa et al., 2006). This trend suggests pharmacogenomics will become and extensively applied tool to inform clinical decision-making and match the right medicine to the right patient.

DRUG DEVELOPMENT ECONOMICS The typical cost of research and development for a NCE is $880–900 million and an average of 8 years to develop (Rawlins, 2004). The costs of product development and marketing have more than doubled during the last decade and high attrition rates in expensive pipeline development and subsequent promotion, make failures expensive to fund. Long term, these factors will have healthcare impacts of a failing return on pharmaceutical investment and the delivery of fewer new medicines to patients. The current drug development paradigm is not able to keep pace with the opportunities of basic science innovation and changes are required to deliver affordable, novel and effective drug therapies that will improve outcomes in common serious diseases. The goals are clear: decrease development costs, focus on the patient and work with the regulatory authorities in managing expectation around co-development of genomic based drugs and diagnostic tests. The application of genomics for drug development will lead to enhanced benefit/risk predictions, dose selection and exposure–response understanding, as well as more accurate prediction of efficacy in early clinical development and improved management of the risk profiles of potential medicines. Genomics will reduce attrition and increase drug development productivity. The high potential of pharmacogenomics to improve medicine approval and usage and healthcare is exemplified in many reports, including this one; however, studies using established economics-based resource allocation frameworks have shown that corresponding data to measure the impact of pharmacogenomics on clinical practice and health outcomes has not been collected (Phillips and Van Bebber, 2005) and there is incomplete documentation of the associations of metabolism, drug response and clinical outcome for many marketed drugs (Evans and Relling, 2004). These shortcomings limit currently the measurement of the value of pharmacogenomic interventions in healthcare.

METHODS FOR IDENTIFICATION OF GENETIC CLASSIFIERS Pharmacogenomics (PGx) studies at any phase in drug development involve the generation of genetic classifiers or markers. These genetic classifiers are used to predict pharmacokinetic, efficacy and/or safety variability, depending on the questions being addressed in the PGx study. Genetic classifiers are generated through one of two approaches, a candidate gene approach

Methods for Identification of Genetic Classifiers

TABLE 29.1

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Integration of pharmacogenomic data into product labels

Gene

Drug

Indication

Details

Comment

HER2

Herceptin (trastuzumab)

Metastatic breast cancer

Clinical benefit limited to patients with tumor HER2 overexpression (20–30% of breast tumors). Label requires that patients are tested for HER2 expression in tumor prior to receiving herceptin treatment.

Approved September 1998

CYP2D6

Strattera (atomoxetine)

Attention Deficit Hyperactivity Disorder (ADHD)

Correlation of CYP2D6 poor metabolizer genotypes with increased drug levels, adverse event incidence and efficacy.

Change to existing label, January 2003

TPMT

Thiopurines

Acute lymphoblastic leukemia

TPMT is the enzyme that inactivates 6-mercatopurine (6MP), the prodrug for the thioguanine nucleotide (TGN) active metabolites. TPMT deficiency results in increased 6MP availability and increased TGN formation, which may lead to increased incidence of Grade IV neutropenia and thrombocytopenia in affected patients. Three variant alleles, TPMT *2, 3A and 3C account for 95% of all dysfunctional alleles and have a high concordance with reduced TPMT enzyme activity (Schaeffeler et al., 2004; Yates et al., 1997). Furthermore, review of patients with documented 6MP toxicity demonstrated that TPMT genotyping could identify patients at risk and enable integration of TPMT genotyping into the clinical management of 6-MP therapy (Evans et al., 2001).

Change to existing label, July 2003

EGFR

Erbitux (cetuximab)

Metastatic colorectal cancer

Clinical benefit limited to patients with tumors shown to be positive for Epidermal Growth Factor Receptor (EGFR) expression. Immunohistochemical evidence of tumor EGFR positivity is required before treatment.

Approved February 2004

UGT1A1

Camptosar (irinotecan)

Colorectal cancer

Irinotecan is a prodrug that is converted by esterases to its active metabolite SN-38. Increased SN-38 levels are associated with increased efficacy and side effects incidence (Grade III-IV diarrhea and/or neutropenia). The predominant route of SN-38 elimination is glucuronidation by the UDP glucuronyltransferase, UGT1A1and patients with the highest ratios of SN-38 parent /SN-38 glucuronide have been shown to have the highest incidence of side effects (Gupta et al., 1994). Clinical studies have shown association of common UGT1A1 polymorphisms with the risk of irinotecan induced diarrhea and/or neutropaenia. A prospective study confirmed association of the UGT1A1*28 variant with these side effects, where all patients suffering neutropenia had at least one UGT1A1*28 allele (Innocenti et al., 2004). Camptosar label changed to include information that patients with reduced UGT1A1 activity (determined by UGT1A1*28 genotyping) have increased risk of neutropenia. Heterozygous patients may also have increased risk. A reduced initial dose should be considered. FDA have approved a UGT1A1 genotyping test (Third Wave Technologies, Invader assay) for the detection of *1(TA6 repeat) and *28 (TA7 repeat) alleles of the UGT1A1 gene as an aid in the identification of patients with greater risk for decreased UGT enzyme activity (FDA, 2005b).

Change to existing label, November 2004

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20 Somatropin

Years to first pharmacogenetic report

18 16 14

β-agonist IFNα

12 Fluoxetine 10

IFNβ

8

Irinotecan

6

Alteplase Rituximab

4

Anakinra

2

Cetuximab. Gefitinib

Pravastatin Trastuzumab

0 1980

1985

1990

1995 Approval date

2000 Erlotinib. 2005 Panitumumab*

Figure 29.1 Pharmacogenomic investigation is occurring more rapidly upon the introduction of new medicines (from Kresja et al., 2006).*Pantiumumab was approved by the EMEA in December, 2007. IFN: interferon.

or through a whole genome scan (WGS) approach. In the candidate gene approach, a set of genes is derived based on the hypothesis in question. The candidate gene lists are generated ad hoc based on the question being addressed to include drug target and mechanism pathway genes. In addition, a comprehensive panel of genes may be developed to interrogate issues that are fairly common in drug development such as variable pharmacokinetics. A straightforward approach is to use a set of tagging SNPs (tSNPs) that will capture the preponderance of common genetic variation (5%) throughout each selected gene (Carlson et al., 2004). In addition, functional SNPs for each selected are added based on a literature survey. Following the application of these candidate gene SNP panels, simple analyses largely focused on individual variations are carried out to generate the genetic classifier. To address questions of PK variability that occur usually in Phase I studies, a standardized, systematic approach has been introduced in GSK clinical development programs. Absorption, distribution, metabolism and excretion (ADME) genes play a central role in drug pharmacokinetics and pharmacodynamics (PK-PD). For most compounds in phase I of drug development, factors contributing to the differences in pharmacokinetic phenotypes are not fully understood. A pilot study to examine the utility of a comprehensive panel of ADME genetic markers has been initiated in early Phase studies in the GSK drug development pipeline. This provides an opportunity to gain additional human genetic insight into the pharmacokinetics of NCEs. The panel consists of 134 ADME enzymes, transporters and other genes involved in drug ADME. From these selected genes, approximately 1500 polymorphisms have been identified

for the marker panel, which includes all known functional polymorphisms that have described ADME phenotypes and includes selected tagging SNPs (tSNPs) that capture all known common variation in the ADME genes. Application of an ADME panel to assets where variability in exposure is outside of the accepted range (similar to bioequivalence parameters) will build knowledge and support dose selection rationale, particularly where PK/exposure is influenced by multiple genes with ADME pathway redundancy. With ever improving technology, WGS are becoming feasible for pharmacogenetic studies. These WGS provide a hypotheses free approach and are carried out using the commercially available chips targeting either 100,000 SNPs or 500,000 SNPs or even 1000,000 SNPs coverage of the genome. Although these WGS are becoming more tractable, they are still a number of hurdles that need to be overcome, including problems with high throughput analysis of such a large number of markers, multiple testing and marker–marker interaction assessment and new methodologies are being developed to address these challenges (Bowman and Delrieu, 2005; Delrieu and Bowman, 2006; Ritchie and Motsinger, 2005) (see Chapter 8).

PHARMACOGENOMICS IN THE DRUG DEVELOPMENT PIPELINE Figure 29.2 summarizes the opportunities for pharmacogenomics applications to better understand drug performance and improve productivity in the drug development pipeline. This can occur across all of the phases of drug development. Preclinical

Pharmacogenomics in the Drug Development Pipeline

Pharmacogenomic applications in the drug discovery and development “pipeline” Responder population identification

Target selection pathway analysis

Risk management post marketing surveillance

Early risk discharge (tolerability)

Target lead

Candidate

Predictive toxicology

FTIH

PK/dosing

POC Phase 2

Phase 3

Regulatory submission

Launch and LCM

Responders: stratify/enrich

Figure 29.2 Opportunities for application of pharmacogenomics during the drug discovery and development process. PK: pharmacokinetics. FTIH: first time in humans. POC: proof of concept. LCM: lifecycle management.

opportunities include application of genomics (genetic and gene expression profiling) to enhance target selection, through disease understanding and susceptibility gene screening (Roses et al., 2005) and to predict potential toxicity of chemotypes. Pharmacokinetic profiling in human volunteers conducted in Phase I studies and subsequently in Phase II studies in disease patients, provides early information of drug exposure that may influence subsequent assessments of safety and efficacy and dose selection (see “Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles”). Phase II studies are conducted in patients and are concerned with demonstration of drug efficacy and building a database of drug safety in the relevant patient population. Efficacy Proof of Concept (POC) studies, usually conducted in Phase II, are key decision making clinical trials, where based on achievement of the target product profile (predefined efficacy level with acceptable tolerability and side effect profiles), the NCE is progressed to larger, more expensive pivotal Phase III studies, aimed to demonstrate the safety and efficacy consistent with a registerable product. In addition to demonstrating an acceptable safety profile, the decision to progress NCEs to Phase III pivotal studies are based on three trial outcomes: (i) molecules demonstrating robust efficacy, which are progressed, (ii) molecules with no discernable efficacy, which are not progressed, (iii) molecules where the efficacy target is not achieved in the study population, but with a segment of the treated study population showing efficacy. Without the ability to classify and differentiate the responding subjects and so enable further molecule evaluation in a subset of clinical patients, the outcome of the progression decision in (i) is the same as for outcome (ii); the NCE is not progressed. Recent examples using pharmacogenetics to address outcome (iii) and also to identify efficacy response hypotheses for evaluation in subsequent development studies have been

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generated in the obesity area (Roses, 2004; Spraggs et al., 2005) and also for rosiglitazone in Alzheimer’s Disease (AD) (Risner et al., 2006), as described below (see “Efficacy Pharmacogenetics – Here and Now”). Phase III trials provide the clinical evidence of safety and efficacy determined in sufficiently large numbers of appropriate patients to enable submission for regulatory approval of the drug for use as therapy. In addition to providing increased sample size for pharmacogenetic analyses through larger patient numbers, Phase III studies also enable the testing of genetic hypotheses that have been determined from earlier studies. Prospective Phase III study designs may accommodate pharmacogenetic considerations through genotype based stratification or enrichment of patient subgroups (see “Efficacy Pharmacogenetics – Here and Now”). Drug exposure to large numbers of patients for extended periods occurs in many Phase III trials, and low incidence safety signals may become manifest in sufficient numbers to enable retrospective pharmacogenetic investigation, leading to additional understanding and possible management of safety events (see “Investigation and Management of Safety in Clinical Trials” and “No Samples, No Science”). As described earlier (see “Challenge and Opportunity”), serious, but very low incidence side effects may not be manifest until after drug approval and launch and extensive exposure in the disease population. Opportunities exist to use pharmacogenetics to manage risk as a component of post-marketing surveillance strategies, but such approaches are currently in their infancy (EFPIA, 2005) and will likely require changes in regulatory and healthcare practices to enable their fuller application. Clinical study designs that incorporate pharmacogenetic objectives and methods will lead to faster and improved implementation of pharmacogenetic markers into clinical development and practice. Generally, the stages of pharmacogenetic marker evaluation and validation parallel the clinical development process. Pharmacogenetic marker hypotheses may be generated from preclinical data (e.g., disease susceptibility genetic data and metabolic profiling using in vitro recombinant ADME gene cell systems) or early clinical data (e.g., Phase I pharmacokinetic data or Phase IIa efficacy data). Marker hypothesis generation may also be achieved by retrospective patient stratification by candidate genotypes or alleles in “all comer” (not-genetically randomized) Phase I and II studies (see CYP2C19 genotype on incidence of Tykerb induced rash and diarrhea, “Investigation and Management of Safety in Clinical Trials”). Subsequent marker hypothesis testing and validation can be conducted in follow-on studies. Hypothesis testing can be achieved through different study designs, including prospective genotype or allele enrichment of study patient population in Phase III studies (see rosiglitazone and APOE 4 allele status for efficacy response in AD, “Efficacy Pharmacogenetics – Here and Now”), or by prospective comparison of genetic marker impact on clinical outcome in studies following successful launch which attempt to further support effective clinical management of serious side effects (see abacavir and comparison of HLA B*5701 marker

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status determination, or not, on hypersensitivity reaction (HSR) incidence, “Investigation and Management of Safety in Clinical Trials”). As shown later in this chapter, marker evaluation and even validation can be achieved within the established clinical development timeframe.

EFFICACY PHARMACOGENETICS – HERE AND NOW Pharmacogenetics/pharmacogenomics seeks to associate genetic/ genomic variations with differences in the response to drugs in terms of efficacy and safety and by classifying trial subjects and patients using genetic/genomic variation in order to guide drug development decision-making and improve therapeutic outcome for patients. There is a need to generate pharmacogenetic classifiers that are accurate, sensitive, specific and, above all, clinically meaningful for utility in clinical trials and subsequently in healthcare. The focus of efficacy pharmacogenetics is on reducing attrition in drug development. Where the means to classify patients using genetic variation is available, pharmacogenetics can be used to enrich the study population with responders and not enroll those subsets genetically predicted to be poor or inadequate responders. Increased success by selectively enrolling genetically predicted responders in efficacy studies will lead to more successful drug launches, less attrition and, with less drug development failures to pay for, a more successful pharmaceutical pipeline. As illustrated below, this paradigm is happening now. Alzheimer’s Disease is a debilitating disease of the elderly. There are estimated to be more than 30 million sufferers worldwide and with demographic changes leading to an increased aging population, estimates indicate that the number of AD sufferers could triple by 2050. Current pharmacological treatment of AD is based on enhancing cholinergic neurotransmission through use of acetylcholinesterase inhibitors (ACHEi). Clinical evidence shows that ACHEis are not fully effective with estimates of between 20 and 60% of AD patients showing little or no response. The burden of disease places a strong requirement for novel, safe and effective therapies for AD. Based on clinical imaging data linking decline in cerebral glucose utilization with progression in AD (Reiman et al., 1996) and positive cognitive effects in a small pilot study in AD patients (Watson et al., 2005), the insulin sensitizing peroxisome proliferator-activated receptor gamma (PPAR) agonist rosiglitazone has been evaluated in a Phase II, 24 week, placebo controlled, dose-ranging (2, 4 and 8 mg/day) efficacy POC study in mild-to-moderate AD patients (Risner et al., 2006). Based on the previously described robust association of apolipoprotein E4 (APOE4) allele with increased risk and earlier onset of AD (Corder et al., 1993) and the association of APOE4 allele carriage with reduced cerebral glucose utilization in younger, unaffected subjects (Reiman et al., 1996), a prospective study analysis decision was made to genotype consenting patients in this study for common variants in the APOE gene. The pharmacogenetic study population was stratified by APOE4

allele carriage and efficacy outcomes were compared between the two groups (APOE4 positive, 44% of study subjects and APOE4 negative, 56% of study subjects). Following 24 weeks of treatment, rosiglitazone had no statistically significant treatment effect on cognition endpoints when analyzed in the “all-comer” (Intent to Treat) study population (Figure 29.3a). However, a significant interaction between APOE4 allele status and cognition endpoint (ADAS-Cog, p  0.014) was observed. Exploratory analysis showed a nominally significant improvement in cognition endpoints at 24 weeks observed at the highest dose (8 mg) in the subjects who were APOE4 negative (56% of study population) (Figure 29.3b). Furthermore, the corresponding treatment in the APOE4 positive subgroup (44% of the study population) failed to demonstrate cognitive improvement and also showed cognitive decline at the lowest dose of rosiglitazone (2 mg). No conclusions can be drawn about clinical management of patients with AD from this study, but it provided the means to identify a genetically defined population, as a function of APOE4 allele status, who demonstrated a clinically relevant beneficial efficacy response in this novel potential therapy for mild-to-moderate AD. Further Phase III trials that are powered to confirm and distinguish APOE allele specific response to rosiglitazone in AD are now underway. The Phase III program consists of three large studies (approximately 1400 patients recruited per study) evaluating rosiglitazone as monotherapy (one study) and as adjunctive combination with ACHEi’s (two studies). Patients will be recruited globally from North America, South America, Europe, India and Asia. These studies will compare efficacy response in two equally sized, prospectively stratified groups based on APOE 4 allele status ( 4 positive: one or two alleles, 4 negative: no alleles). During eligibility screening, individual patient’s APOE genotype and 4 allele status will be determined from a buccal swab sample which is sent to a central genotyping laboratory, and 4 allele status is used, along with other clinical criteria, to randomize the patient into the study. Inclusion of prospective APOE genotyping has been accommodated in these large Phase III studies with minimal projected impact on enrollment and timelines and modest cost. To our knowledge, this is the first example in a major drug program where a molecule has been progressed based on the ability to pharmacogenetically predict and define (classify) the subset of patients that demonstrate adequate efficacy. This real case study demonstrates the potential for pharmacogenomics to impact attrition in drug development. The means to genetically select the responder subset has converted a drug that would have failed at efficacy POC to a progression opportunity and so provide a potential, novel therapy in a disease of high unmet need and the means to significantly impact attrition in the drug development pipeline. By reducing attrition and increasing drug development success, the economics of drug development are impacted beneficially: there is less failure to pay for and so development costs are reduced and funds can be made available to invest in other drug opportunities. In addition, determination of responder/non-responder subsets in disease populations

Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles

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349

2.5 2 1.5

DRUG EXPOSURE PHARMACOGENETICS TO TUNE EFFICACY AND SAFETY PROFILES

Clinical improvement

1 0.5 0 0.5

Clinical decline

1 1.5

Baseline

Week 8

RSG 8 mg

2.5 2 1.5 1 0.5 0 0.5 1 1.5 2 2.5 Baseline

RSG 4 mg

Week 8

Week 16 RSG 2 mg

Week 16

8 mg E4

4 mg E4

2 mg E4

8 mg E4

4 mg E4

2 mg E4

Week 24 Placebo

Week 24 Pbo E4 Pbo E4

Figure 29.3 Efficacy of Rosiglitazone in a genetically defined population with mild-to-moderate alzheimer’s Disease. Reproduced from Risner et al., 2006. (a) Effects of rosiglitazone on ADAS-Cog scores in the intent to treat (ITT) population. A positive number indicates worsening and a negative number indicates improvement. Change from baseline in ADAS-Cog total scores in the ITT population showed that treatment differences between rosiglitazone and placebo for ADAS-Cog total scores did not reach statistical significance at Week 24 LOCF. (b) ADAS-Cog scores in APOE E4-negative versus APOE E4-positive cohorts. Analysis of interaction between APOE carriage status and ADAS-Cog change from baseline to Week 24 was significant (P  0.014). Subsequent exploratory testing revealed that APOE E4-negative patients, after 24 weeks, showed an improvement that was statistically significant at the highest rosiglitazone dose (8 mg) compared to placebo (P  0.024, not adjusted for multiplicity). APOE E4-positive patients do not show an improvement in cognition but rather showed a decline at rosiglitazone 2 mg (P  0.012; not adjusted for multiplicity).

enables more focus on novel mechanisms for the provision of effective therapy for the “non-responder” populations. Using this paradigm, more drug programs are likely to be successful and the patient and healthcare providers are supported by the provision of affordable, more effective drugs to treat serious common diseases.

Extensive pharmacogenetic data are available to characterize the effects of genes responsible for the ADME of drugs (Evans and McLeod, 2003; Weinshilboum, 2003). Where exposure varies based on genotype, this can be corrected for routinely by genotype based dose selection, leading to reductions in the overall variability in efficacy and safety and a better understanding of drug performance during development. Based on modeling of CYP2D6 drug metabolism from a range of pharmacokinetic examples, it has been estimated that dose adjustments based on CYP2D6 genotype to achieve equivalent systemic drug exposure can be achieved within a fourfold range in dose option within the treated population (Kirchheiner et al., 2005). Assuming the standard indicated dose is 100 units, dose ranging would vary as follows: poor Metabolizers (PM, 7% Caucasian population) 60 units, Intermediate Metabolizers (IM, 40% Caucasian population) 80 units, Extensive Metabolizers (EM, 50% Caucasian population) 160 units and Ultra-Rapid Metabolizers (UM, 24% Caucasian population) 240 units. This simulation shows that the standard “global” dose for a CYP2D6 influenced drug overcompensates for the few individuals that are PM (7%). As a consequence, a majority of the Caucasian population (EM 50% and UM 3%) actually receive a lower dose than indicated by their empirical exposure, that is they are actually receiving a suboptimal dose for efficacy, based on the population risk of overexposure in PM individuals. Further analysis by Kirchheiner and colleagues shows a number of drug examples for various metabolizing enzymes (CYP2D6, CYP2C19, CYP2C9, TPMT and NAT2) where, based on DME influenced exposure, genotype based dose adjustment (sometimes 10× across the population) could be made to ensure the same level of exposure across the study population (Kirchheiner et al., 2005). These simulations have been applied and CYP 2D6 and CYP 2C19 genotype based recommendations have been proposed for a range of marketed antidepressants (Kirchheiner et al., 2001). Managing variability is a key factor in the understanding of drug performance in early studies. Covariate analysis of standard factors (age, gender, weight, disease severity, etc.) is routinely applied to explain variability in pharmacokinetic-pharmacodynamic modeling. Modeling and adjustment for dosage based on genotype will reduce overall study variability and so help identify other factors influencing efficacy and safety variability and support risk management and clearer evaluation of drug performance. An example of this is the identification of common polymorphisms, not only in CYP2C9, but also in VKORC1 (vitamin K epoxide reductase C1, efficacy pathway) that are associated with warfarin response variability (Wadelius et al., 2005). This work shows that the VKORC1 SNPs have a greater impact (30%) on dose than CYP2C9 SNPs (12%) and when combined with patient clinical characteristics (including bodyweight) that explain 60% of variability and provide the basis for

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a planned prospective clinical evaluation of a dosing algorithm to support the rapid and safe attainment of anticoagulant efficacy in patients (www.warfarindosing.com). Preclinical in vitro data in hepatic microsomes characterized CYP2D6 as a major route of metabolism for the GSK investigational compound “A.”Variability in CYP2D6 enzyme activity has the potential to affect drug exposure levels and response (safety and efficacy) when administered alone and in drug combination therapies where the combination drugs are also CYP2D6 ligands. In a group of three small Phase I studies, comprising 35 subjects (volunteers and patients) treated with “A,” four subjects were characterized a priori as pharmacokinetic outliers based on review of pharmacokinetic phenotypic data. Subsequent genotypic analysis of CYP2D6 variation, analyzing nine polymorphisms associated with null/reduced enzyme function (Daly et al., 1996), in DNA collected from study subjects during their participation in the clinical trials and prior to pharmacokinetic analysis. This revealed that all four predefined outlier subjects had CYP2D6 Poor Metabolizer genotypes: two subjects were homozygous for the previously reported *4 null function genotype (G1934A, Caucasian minor allele frequency (MAF) 19%), two subjects had complex genotypes with multiple loss of function alleles, one was heterozygous for *4, *6 (delT1795, null function, Caucasian MAF 1%) and *10 (C188T, reduced function, Caucasian MAF 22%) alleles and one was heterozygous for *3 (delA2637, null function, Caucasian MAF 2%) and *9 (delAGA, reduced function, Caucasian MAF 2%) alleles. Subsequent Phase I studies for other analogs of “A” will prospectively include a cohort of CYP2D6 Poor Metabolizer subjects recruited based on genotype, to enable evaluation of the potential impact of increased exposure through impaired metabolism by CYP2D6 and inclusion of genotype as a covariate in pharmacokinetic modeling. The GSK drug Tykerb/Tyyerb (lapatinib) is approved in more than 20 countries for the treatment of patients with advanced or metastatic breast cancer whose tumours overexpress HER2/ErbB2 and who have received prior therapy including an anthracycline, a taxane and trastuzumab and is being developed for the treatment of breast cancer and other solid tumors (Nelson and Dolder, 2006). Preclinical in vitro studies showed that Tykerb was metabolized predominantly by CYP3A4 and CYP3A5 genes and to a lesser extent, CYP2C19 and that it interacted with the transporters MDR1 (ABCB1) and BCRP (ABCG2). During early phase studies in volunteers and patients, interindividual variation of Tykerb plasma concentrations was observed. In addition, 15% of study subjects experienced rash and diarrhea side effects. A retrospective pharmacogenetic study was undertaken using the above five genes evaluated in DNA samples (107 Caucasians, comprising 34 patients and 73 healthy volunteers) collected prospectively during eight clinical trials (Zaks et al., 2006). Single point (genotypic and allelic) association analyses were performed on 246 single nucleotide polymorphisms located within 10 Kb of the five genes. Whilst there was no evidence for association of side effects for ABCG2, ABCB1, CYP3A4 or CYP3A5, 22 SNPs in CYP2C19 showed significant association with incidence of rash and six of these

SNPs also showed significant association with incidence of diarrhea. Notably, three out of three subjects (two healthy volunteers, one patient) homozygous for the CYP2C19 *2 allele experienced both rash and diarrhea. The ability to investigate associations in the integrated setting of human subjects in clinical trials has complemented and extended the preclinical, in vitro data and provide a hypothesis that CYP2C19 metabolism is likely more important in the development of Tykerb induced side effects than other drug metabolizing genes. No conclusions can be drawn about the clinical management of Tykerb treated patients from this exploratory study, however, subject to further clinical evolution, identification of patients without this allele may allow clinicians to treat the cancer more aggressively with higher doses and less risk of side effects. Further work is ongoing in additional, follow up clinical trials to confirm these exploratory findings and determine the magnitude and clinical significance of these associations and their potential application in the management of side effects during chronic Tykerb treatment. These examples demonstrate the feasibility and provision of decision making utility of pharmacogenetics in early studies with small subject numbers, in contrast to previous perspectives (Cardon et al., 2000). Genotype-based dose selections will support the regulatory imperative to make more informed decisions around dose selection for therapy. Of course, application of genotype-based dose selection into therapy would require a sea change in clinical practice and pharmaceutical provision, not the least of which requiring more than one tablet strength and provision of therapy on a mg/kg basis, as is done in animals, children and cancer patients receiving chemotherapy. The benefits of such change would likely be more molecules making it into healthcare and better management of efficacy and safety profiles for patients.

INVESTIGATION AND MANAGEMENT OF SAFETY IN CLINICAL TRIALS In addition, Phase II studies often provide adequate population exposure to demonstrate common safety and tolerability issues. Genetic classifiers have been used successfully to explain and reduce risk associated with potential safety findings in Phase II studies and so enable the NCE to progress to further clinical studies. The previously reported pharmacogenetic studies on the coronary artery restenosis inhibitor, Tranilast (Danoff et al., 2004; Xu et al., 2004) demonstrated the utility of markers at UGT1A1 to characterize and explain the hyperbilirubinemia observed in patients treated with Tranilast as the benign and reversible condition Gilbert’s syndrome. These markers have subsequently been used successfully to discharge potential elevated bilirubin risk in two further GSK investigational drugs during Phase II (Figure 29.4, data for one of these compounds). Whilst these results are exploratory, generated from studies with small subject numbers and not balanced for genotypes, they provide confidence and hypothesis generation for integration and evaluation in follow up studies on these compounds. Similar data generated by

Investigation and Management of Safety in Clinical Trials

2.2

2.2

2.0

2.0

1.8

1.8

1.6 1.4 1.2 1.0 0.8 0.6

A,A A,G G,G

0.4 0.2

351

Treatment Group (N  244)

Treatment Phase Bilirubin

Treatment Phase Bilirubin

Placebo Treatment Group (N  168)

■

1.6 1.4 1.2 1.0 0.8 0.6

A,A A,G G,G

0.4 0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

Pre-Treatment Phase Bilirubin

Pre-Treatment Phase Bilirubin

Adjusted Means of Standardised Bilirubin (on-treatment phase) 1 0.95 0.9 Adjusted Mean

0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 G,G

A,G

A,A

Genotype Marker

Placebo

Drug

Figure 29.4 Elevated bilirubin in a Phase II drug trial explained by UGT1A1 polymorphism associated with Gilbert’s Syndrome. (a) Pre- and post-treatment bilirubin values for placebo and GSK investigational drug treatment for individual subjects coded by UGT1A1 RS887829 genotype. Data taken from a Phase II clinical study with a GSK investigational drug “B.” Plasma bilirubin levels were measured at baseline and during the study using standard clinical chemistry methods. Plots show individual patient bilirubin normalized values measured at pre-treatment (x axis) and during treatment (y axis) for placebo study arm (left panel, n  168) and GSK investigational drug treatment arm (right panel, n  244). Values that occur in the upper left quadrant relate to individuals with increased bilirubin values above the standard normal level following treatment. Patients were genotyped using a UGT1A1 SNP (RS887829), which has been shown to be in absolute linkage disequilibrium with the reported 7/7 TA repeat polymorphism. A significant association between RS887829 genotype and bilirubin levels was observed (p  0.001). Furthermore, comparison of placebo and treatment panels in above figure (individual data points coded according to RS887829 genotype) illustrates a higher proportion of patients receiving GSK investigational drug had the RS887829 A, A genotype than placebo, suggestive of a pre-existing condition of Gilbert’s Syndrome, more than an effect of the investigational drug. (b) No difference in bilirubin levels between placebo or GSK investigational drug “B” treatment within UGT1A1 RS887829 genotypes. Using standardized bilirubin values as a dependent variable, analysis of covariance showed no interaction with treatment (p  0.423), but significant interactions were observed for pre-treatment bilirubin (p  0.001) and UGT1A1 RS887829 genotype (p  0.001). Furthermore, as shown in the figure above, treatment by genotype interactions were not significant (p  0.137).

CHAPTER 29

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Role of Pharmacogenomics in Drug Development

Bristol Myers Squibb has resulted in inclusion of UGT1A1 *28 genotype information in a product information sheet to provide context for hyperbilirubinemia observed in some patients treated with HIV antiretroviral Atazanavir (Bristol Myers Squibb). Abacavir is a cornerstone of highly effective combination therapy for the management of HIV infection. Whilst efficacious, approximately 5% of abacavir-treated patients experience a hypersensitivity reaction (HSR) that may be life-threatening. Genetic classifiers of some of the hypersensitivity risk have been identified, including SNPs in the HLA B*57 region (Hetherington et al., 2002; Mallal et al., 2002) and confirmed in some, but not all subsequent studies comprising different ethnic populations (Hughes et al., 2004). Whilst clinical awareness and early signs monitoring of hypersensitivity risk provide good clinical management and adequate maintenance of the benefit/ risk profile for abacavir, these data enabled the opportunity to evaluate the potential for genetic classifiers to improve the management of hypersensitivity risk and reduce incidence to below 5%. A study was conducted by GSK to prospectively investigate the relevant genetic markers by comparing clinical outcome (incidence of hypersensitivity) in groups randomized to standard care (all patients included) and standard care plus prescribing based on genotype (HLA B*5701 marker positive patients excluded) (Mallal et al., 2008). Prospective HLA B*5701 screening reduced the incidence of clinically diagnosed HSR and eliminated HSR confirmed by skin patch testing. Furthermore, when ascertainment of HSR cases is supplemented with skin patch testing, the association between HLA B*5701 and HSR appears to be generalizable to other ethnic populations (Saag et al., 2008).

OTHER GENOMIC METHODS: RNA INTERFERENCE TO DIRECT DRUG USAGE Response classifiers are not limited to genetic variation. Tykerb (Lapatinib) is being developed for the treatment of tumors that are regulated through ERbB2 and EGFR tyrosine kinase receptor pathways, with breast cancer as the major indication for registration (Nelson and Dolder, 2006). The presence of estrogen receptor (ER), progesterone receptor (PR), and bcl–2 in tumor samples is associated with a lack of biological and clinical response after only 10–14 days of treatment. Furthermore, the presence of ER/PR has been shown to be a negative predictor of response to Tykerb in two large phase II breast cancer studies. Immunohistochemistry of breast tumors from patients who became resistant to Tykerb showed a switch from ER /PR to ER+/PR. Therefore, the role of ER alpha in the development of resistance to Tykerb was determined in a target-selective manner using RNA interference (RNAi) (see Chapter 16). The results of this study were considered to have high clinical interest since combinations of anti-ER along with ErbB inhibitors (like Tykerb) might delay or prevent the onset of resistance to Tykerb. RNAi was used to determine the role of ER signaling

in the development of Tykerb resistance in ErbB2-overexpressing breast cancer cell lines (Xia et al., 2006). Specifically, this technology was used to investigate whether molecular knockdown of ER gene expression could convert Tykerb-resistant BT474 cells to become Tykerb-sensitive. Tykerb-resistant ErbB2overexpressing BT474 cell lines showed a conversion from ER to ER, and therefore reflected the situation seen in the clinic. RNAi effector reagents (siRNAs) were generated in Tykerbsensitive breast carcinoma cells, and then tested in Tykerb-resistant cells. It was found that knockdown of ER lead to increased cell death of Tykerb-resistant BT474 breast carcinoma cells. More profoundly, ER knockdown was found to lead to a reversing of the resistance of these cells to Tykerb treatment (Figure 29.5). These results have made a significant impact on the selection of patients in ongoing and planned phase II/III registration trials for Tykerb. The clinical protocol for the inflammatory breast cancer phase II trial has been amended, where ER/PR expressors will now be excluded. If tumor biopsies convert to ER+/PR+ then these patients will be offered a combination therapy involving Tykerb and an anti-ER therapy.

“NO SAMPLES, NO SCIENCE” A key pre-requisite to successful pharmacogenomic study is the prospective collection of appropriate samples and data, even in situations where the study phenotype(s) are not yet known. During the conduct of clinical trials it is necessary to collect all relevant data to enable full phenotype description and determination of covariates (gender, body weight, disease severity,

Gated Cells (%)

352

100 80 60 40 20 0 Cells

500nM lapatinib

siPool siPool500nM lapatinib

ER

ER500n Mlapatinib

Treatment Sub2N

G0/G1

S

G2/M

4N

Figure 29.5 siRNA knockdown of ER in Tykerb-resistant BT474 cells restores Tykerb efficacy. Anti-tumor effects were measured as increased cell apoptosis. Columns show percent of cells residing in five stages of the cell cycle (color coded as per legend). Cell cycle analysis was conducted using FACS analysis and cell apoptosis was measured as the percent of cells in the sub2N phase. In resistant BT474 cells, Tykerb (500 nM lapatinib) had little effect on cell apoptosis rate (sub2N column 2), whilst selective knockdown of ER by siRNA increased apoptosis to 60% (sub2N column 5). Furthermore, combination of ER siRNA plus Tykerb (50 M lapatinib) increased apotosis further (sub2N column 6). These data are representative of three independent experiments.

“No Samples, No Science”

concomitant medications, etc.). Indeed, it is simpler and less costly to collect all relevant data at this time than attempt its collection once the clinical trial has completed. Retrospective studies of clinical data suffer from challenges of collecting accurate data from patients and subjects no longer participating in trial. These include ascertainment bias limited access to the patient, limited recall by the patients and limited verification of data. Collection of samples with appropriate informed consent during clinical studies provides context for the sample collection and is logistically more efficient than retrospective follow up. In addition, successful (maximal) prospective sample collection reduces potential misrepresentation of the study population and sample bias that may occur retrospectively. The strategy to collect and store DNA from clinical trials has enabled pharmacogenetic study of clinical endpoints when new biological information emerges, even after the study has completed, as described below. The PPAR agonist farglitazar has been evaluated for efficacy and safety in a large Phase III program (10 studies) in Type 2 Diabetes patients, but was discontinued for Type 2 Diabetes due to dose-related fluid retention safety concerns in this population. Collection and storage of DNA samples during the studies enabled subsequent pharmacogenetic investigation of PPAR induced fluid retention and edema side effects some time after the studies had been completed. The impetus to conduct this investigation was the emergence of new data implicating epithelial sodium channel pathways in the mechanism of PPAR induced fluid retention and edema (Hong et al., 2003). This work led to selection of a candidate panel of 25 genes based on involvement in epithelial sodium transport. DNA samples were collected from 647 of 1106 patients participating in the Phase III studies (Spraggs et al., 2007). Four hundred and sixty six evaluable, Caucasian subjects were available for analysis and were divided into three groups based on study

TABLE 29.2

■

353

treatment: farglitazar alone (n  329), farglitazar plus glyburide (n  72) and farglitazar plus insulin, (n  140), with incidences of edema adverse events in each study group of 38%, 46% and 31%. Case-control analysis of edema reported as an adverse event in farglitazar treated subjects was conducted. Consistent and significant associations were observed for three polymorphisms in SCNN1B (p  0.05 to 0.0005). These associations were observed for farglitazar combination treatment groups (plus glyburide or insulin), but not in the group receiving farglitazar monotherapy. Additional sequencing of SCNN1B in 207 Caucasian subjects receiving combination therapy identified additional polymorphisms that were also significantly associated with edema (p  0.0005, Table 29.2). These studies have provided clinical pharmacogenetic evidence in distinct study groups to support a pivotal role for epithelial sodium channel regulation in PPAR induced edema and provide insight into mechanisms and possible management of this side effect using the selective sodium channel diuretic, amiloride in preference to loop diuretics, such as furosemide and hydrochlothiazide. This data has provided hypotheses for the further evaluation and possible management of edema side effects in future PPAR agonist studies in a range of disease indications. This work was only possible due to the collect and storage of DNA samples and data for subsequent investigation, after the clinical trials had completed. The pharmacogenomic model employed at GSK has established default, consented DNA collection and extraction in all Phase I, II, III and IV trials, with selective collection of other tissues (plasma, serum, urine, etc.) and phenotypes for biomarker studies, including imaging. Identification of pharmacogenomic opportunities (efficacy and safety) are identified through interactions of clinical project teams with genetics research physicians and scientists to support clinical development decision making.

SCNN1B variants associate with farglitazar induced fluid retention and edema in Type 2 diabetic patients Farglitazar /glyburide (33 cases, 39 controls)

Farglitazar /insulin (44 cases, 96 controls)

Genotypic Chi Sq p

Genotypic Chi Sq p

Allelic Chi Sq p

Global ID

Position relative to ATG

Gene region

MAF

Allelic Chi Sq p

rs2887481

–5724

5 flank

0.342

0.0136

0.0175

ss46565647

–3817

5 flank

0.032

0.00086

0.00096

ss46565649

–3771

5 flank

0.035

0.0008

0.0009

ss46565622

–786

5 flank

0.034

0.0006

0.0007

ss46565623

–454

5 flank

0.034

0.0006

0.0007

rs250563

65640

Exon 5

0.065

0.02821

0.3695

rs889299

68275

Intron 5

0.222

0.0029

0.0005

rs2303157

69608

Intron 7

0.226

0.00003

0.00051

SNPs are presented in genomic order, 5 to coding region; Chi square p values shown where p  0.05 and Hardy Weinberg Equilibrium p value;  0.01, where cell is blank, p  0.05; MAF: minor allele frequency

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Pre-clinical

Rosiglitazone and APOE4 for efficacy in AD

Tykerb and CYP2C19 for rash and diarrhoea AEs

Phase I

APOE as AD progression gene

2C19 and other genes implicated in vitro

Phase II

APOE allele stratification for efficacy

Retrospective stratification for AEs by candidate genes

Abacavir and HLA B*5701 for HSR AE

Phase III

Post-launch and Phase IV

APOE allele enrichment for efficacy

Prospective stratification for AEs by 2C19

Retrospective stratification for HSR by candidate genes

Prospective evaluation of HLA B*57 on HSR incidence

Figure 29.6 Stages of pharmacogenetic marker validation during clinical development programs is illustrated using the examples of (1) Rosiglitazone and efficacy response classification by APOE ε4 allele status (see “Efficacy Pharmacogenetics – Here and Now”), (2) Tykerb and identification of patients at increased risk of rash and diarrhea adverse events by CYP 2C19 genotype (see “Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles”) and (3) Abacavir and identification of patients at increased risk of hypersensitivity reaction (HSR) adverse events by HLA B*5701 marker status (see “Investigation and Management of Safety in Clinical Trials”). Hypothesis marker generating studies are shown in orange and hypothesis marker testing studies are shown in green.

As shown in Figure 29.6, the availability of prospectively collected DNA (via appropriate informed consent) in all clinical studies for pharmacogenetic purposes provides the means to investigate genetic associations with drug response in early studies, generate marker hypotheses for drug safety and efficacy and test and/or validate marker hypotheses in subsequent studies.

CONCLUSIONS Clearly, after a decade of small retrospective studies, pharmacogenetics has come of age and is poised to translate genomics research into gains both in reduction of pipeline attrition and better clinical outcomes. In this review, through use of illustrative examples, we have demonstrated the potential of genomic technologies to influence clinical decision making and reduce attrition in the drug development pipeline. The capability to classify drug response (safety and efficacy) by genomic means enables enrichment

of clinical trials with appropriate subject and patient subsets. Current examples demonstrate the opportunity to manage variability in systemic drug exposure, reduce risk around possible safety concerns and convert drugs that are efficacy failures in the whole population into potential therapies in patient subsets where there is high unmet need. Pharmacogenetics will not just be an additional exploratory part in the drug development process but will become integrated as a clear objective in clinical trials. This becomes imperative as clinical trials access geographically more extensive populations and proof of efficacy studies are spread across different ethnic groups, where information pertaining to the causal variant will provide greater precision than ethnic categorizations (Tate and Goldstein, 2004). Multidisciplinary genomic approaches including genetics, transcriptomics and proteomics and incorporation of non-traditional endpoints such as imaging into clinical trials will improve understanding of the heterogeneity of response to drugs and refine clinical trials. In conjunction with better validated preclinical targets using tools such

References

as transcriptional profiling that predict toxicities used in early preclinical studies, this increases the ability to discover and develop new drugs for common important diseases with a higher probability of efficacy and a lower risk of safety concerns. Improving pharmaceutical productivity in this manner will benefit patients, healthcare providers and payers.

ACKNOWLEDGEMENTS

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GlaxoSmithKline who have generated the data that is illustrated in the examples used in this paper. The authors particularly appreciate the contributions of Anthony Akkari, Cathy Burrows, Kirstie Davies, Olivier Delrieu, Suzanne Edwards, Priti Hegde, David Hosford, Louise Hosking, Arlene Hughes, Intisar Husain, Linda McCarthy, Mike Mosteller, Marc Risner, Ann Saunders, Ganesh Sathe, Neil Spector, Liling Warren and Wenle Xia. We thank the patients and physicians who contributed to the provision of DNA samples and clinical data for these studies.

The authors acknowledge the contributions of many colleagues in Genetics Research and Worldwide Development at

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Schaeffeler, E., Fischer, C., Brockmeier, D., Wernet, D., Moerike, K., Eichelbaum, M., Zanger, U.M. and Schwab, M. (2004). Comprehensive analysis of thiopurine S-methyltransferase phenotype-genotype correlation in a large population of GermanCaucasians and identification of novel TPMT variants. Pharmacogenetics 14, 407–417. Spraggs, C.F., Pillai, S.G., Dow, D., Douglas, C., McCarthy, L., Manasco, P.K.,Stubbins, M. and Roses,A.D. (2005). Pharmacogenetics and obesity: Common gene variants influence weight loss response of the norepinephrine/dopamine transporter inhibitor GW320659 in obese subjects. Pharmacogenet Genomics 15, 883–889. Spraggs, C., McCarthy, A., McCarthy, L., Hong, G., Hughes, A., Lin, X., Sathe, G., Smart, D., Traini, C., Van Horn, S., Warren, L., and Mosteller, M. (2007). Genetic variants in the epithelial sodium channel associate with oedema in Type 2 Diabetic patients receiving the PPARγ agonist Farglitazar. Pharmacogenet Genomics 17, 1065–1076. Tate, S.K. and Goldstein, D.B. (2004). Will tomorrow’s medicines work for everyone?. Nat Genet 36, S34–42. Towse, A. (2003). CMR workshop on regulating personalized medicine. CMR International Institute for Regulatory Science. Accessed at http://www.cmr.org/institute/pdf/RD39.pdf. Wadelius, M., Chen, L.Y., Downes, K., Ghori, J., Hunt, S., Eriksson, N., Wallerman, O., Melhus, H., Wadelius, C. and Bentley, D. et al. (2005). Common VKORC1 and GGCX polymorphisms associated with warfarin dose. Pharmacogenomics J 5, 262–270. Watson, G.S., Cholerton, B.A., Reger, M.A., Baker, L.D., Plymate, S.R., Asthana, S., Fishel, M.A., Kulstad, J.J., Green, P.S. and Cook, D.G. et al. (2005). Preserved cognition in patients with early Alzheimer disease and amnestic mild cognitive impairment during treatment with rosiglitazone: A preliminary study. Am J Geriatr Psychiatry 13, 950–958. Weinshilboum, R. (2003). Inheritance and drug response. N Engl J Med 348, 529–537. Weinshilboum, R. and Wang, L. (2004). Pharmacogenomics: Bench to bedside. Nat Rev Drug Discov 3, 739–748. Xia, W., Bacus, S., Hegde, P., Husain, I., Strum, J., Liu, L., Paulazzo, G., Lyass, L., Trusk, P. and Hill, J. et al. (2006). A model of acquired autoresistance to a potent ErbB2 tyrosine kinase inhibitor and a therapeutic strategy to prevent its onset in breast cancer. Proc Natl Acad Sci U S A 103, 7795–7800. Xu, C.F., Lewis, K.F.,Yeo, A.J., McCarthy, L.C., Maguire, M.F.,Anwar, Z., Danoff, T.M., Roses, A.D. and Purvis, I.J. (2004). Identification of a pharmacogenetic effect by linkage disequilibrium mapping. Pharmacogenomics J 4, 374–378. Yates, C.R., Krynetski, E.Y., Loennechen, T., Fessing, M.Y., Tai, H.L., Pui, C.H., Relling, M.V. and Evans, W.E. (1997). Molecular diagnosis of thiopurine S-methyltransferase deficiency: Genetic basis for azathioprine and mercaptopurine intolerance. Ann Intern Med 126, 608–614. Zaks T, Akkari A, Briley L, et al., (2006). Role of pharmacogenetic studies in early clinical development: Phase I studies with Lapatinib (Tykerb). ASCO Annual Meeting 2–6 June 2006.

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30 Clinical Implementation of Translational Genomics Wendy K. Chung

INTRODUCTION In the 55 years since the structure of DNA was described by Watson and Crick, there have been remarkable advances in our understanding of the genetic basis for human variation and disease. We have now defined the genetic basis for over 2130 monogenic human disorders, and clinical genetic tests are available for over 1410 disorders (http://www.genetests.org/). Despite the remarkable advances in our scientific understanding of genetics and genomics, the impact of such information on medical care for common conditions has to this point been modest. With the complete sequence of the human genome and many other model organisms as well as detailed characterization of human haplotype structure, we have now begun elucidating the complex genetic basis for common diseases such as macular degeneration, diabetes, obesity, inflammatory bowel disease, and breast cancer using genome-wide association studies. In some cases the same allele will increase risk for one disease and protect against another. The ApoE4 allele is associated with a protective effect for age-related macular degeneration yet susceptibility to Alzheimer disease and hyperlipidemia. As additional scientific discoveries unfold, we will be challenged to clinically integrate this new information into routine patient care to improve health and quality of life in a cost-effective, socially acceptable manner. Early adopters of this new genetic information will provide invaluable experience to guide future implementation strategies.

Genomic and Personalized Medicine, 2-vol set by Willard & Glinsburg

In this chapter, we will review the current and projected future use of genetics and genomics in clinical medicine and define the steps to successful integration of genomic medicine to improve the quality of health care. A schematic of the elements necessary to develop clinical genomic medicine is provided in Figure 30.1.

GENETIC STRATIFICATION WILL ALLOW MEDICAL CARE TO BE INDIVIDUALIZED AFTER A DIAGNOSIS IS MADE How will germline genomic variation be clinically utilized? There are emerging data that the genetic stratification of phenotypically similar diseases has important therapeutic implications. Genetic characterization following an initial diagnosis may clarify prognosis and response to therapy. Three examples to illustrate this point. 1.

Genetic testing may provide information about future risk. Women with breast cancer due to mutations in Breast Cancer 1/Breast Cancer 2 (BRCA1/BRCA2) have an increased risk for a second primary cancer, usually breast or ovarian cancer, for which increased surveillance, chemoprevention, or prophylactic surgery are recommended (Verhoog et al., 1998). However, women with breast cancer without BRCA1/BRCA2 mutations are not at increased risk for ovarian cancer (Kauff et al.,

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Development of Genomic Medical Infrastructure • Improved genetic literacy of health care professionals • Increased numbers of genomic providers • Increased third-party coverage of genomic tests • Revisions of CPT billing codes for adequate reimbursement • Electronic medical records to efficiently integrate pharmacogenomic and other test results • Creation of laboratory standards for test performance and reporting results

Types of Genomic Tests

Pre-clinical Scientific Requirements • Demonstration of genomic basis for disease • Validation of clinical utility, including outcome data with intervention • Determination of spectrum of genomic variants and frequency by ethnicity • Development of sensitive, specific, reliable less expensive testing

• Population-based screening • Pre-symptomatic risk stratification • Defining molecular basis of diagnosed condition • Prognostic information • Molecularly based management • Pharmacogenetics • Expression profiling

Monitoring of Established Testing • Assessment of impact of innovations on • Health care cost • Morbidity, mortality • Quality of life • Assessment of unintended consequences • Assessment of public acceptance • Ongoing quality control and quality assurance of laboratories

Development of Ethical, Legal, Societal Infrastructure • Passage of Genetic Non-Discrimination Legislation • Limitation of exclusive licenses for genes • Improved genetic literacy of public • Privacy of sensitive genomic information

Figure 30.1 Development of clinical genomic medicine. The requirements to develop a health care system efficiently and effectively integrating genomic information are shown and include pre-clinical scientific data, improvements in medical infrastructure, changes in laws, policy, and public opinion, and monitoring of testing and its impact once established.

2005). Determining which women with breast cancer are at increased risk of ovarian cancer and should have prophylactic oophorectomies to reduce the risk of future ovarian cancer can be clarified with BRCA1 and BRCA2 testing. 2. Genetic testing may provide information to optimize therapy. Long QT syndrome (L\QTS) is an inherited predisposition to cardiac arrhythmias characterized by a prolonged QT interval on an electrocardiogram that can result in syncope and sudden cardiac death. There are currently nine different genetically identified causes of LQTS, all affecting cardiac ion channel conductance that are clinically difficult to distinguish. The three most common forms of LQTSs (LQT1, LQT2, and LQT3) have specific triggers for arrhythmias they can be avoided such as exerciseinduced tachycardia in LQT1 and auditory stimuli during sleep in LQT2 (Schwartz et al., 2001). Specific molecularly based pharmacological intervention is also now available. For example, patients with LQT3, due to mutations in the cardiac sodium channel gene SCN5A that inappropriately activate and open the sodium channel, respond to sodium channel blockers such as flecainide (Moss et al., 2005). In contrast, beta-blockers are routinely prescribed for LQTS1 and LQTS2. Therefore, identifying the molecular subtype

of LQTS allows for selection of the most appropriate medication and avoidance of specific triggers. 3. Genetic testing may provide prognostic information to guide management. An autosomal dominantly inherited form of diabetes called maturity onset diabetes of the young (MODY) is genetically heterogeneous and caused by mutations in six different genes. Prognosis, including diabetic complications, for patients with mutations in Glucokinase (MODY2) is much better than other forms of MODY. Molecular diagnosis can now provide reassurance that tight glycemic control is unnecessary to prevent diabetic complications in MODY2 patients who will remain only mildly hyperglycemic (Codner et al., 2006).

POPULATION-BASED GERMLINE GENOMIC SCREENING Rather than waiting until a diagnosis is made, genetic and genomic variation can be used to prevent disease. Populationbased genomic screening could provide the opportunity to improve health outcomes by disease prevention and increased surveillance to facilitate early diagnosis. It would allow population

Newborn Screening

stratification to identify individuals at increased risk who are most likely to benefit from preventive medications or interventions for which population-based therapy would not be appropriate based on cost or side effects of treatment. Data to support an individual’s susceptibility to disease could also provide the necessary motivation to increase compliance with recommended health behaviors such as exercise and weight control for individuals at increased risk for diabetes and smoking cessation for lung cancer. Screening individuals at risk for adverse outcomes to specific environmental exposures would allow susceptible individuals to modify their work or home environment to minimize exposure. Screening individuals for adverse pharmacological reactions would provide a rational basis for drug selection and avoid harmful side effects. Such a strategy would ultimately increase the total number of drugs available for clinical use and decrease the cost of drug development by identifying and eliminating drug exposure to the small number of patients who would have adversely responded to the medication. Although there is great promise for population-based screening, it is important to ensure that the natural history and clinical utility (see section on clinical utility) have been adequately defined before introduction into clinical care. As an example, consider the case of hereditary hemachromatosis. Two common mutations with a single gene (HFE) account for the majority of autosomal recessively inherited hereditary hemachromatosis in Europeans (Jazwinska et al., 1996). Furthermore, because the complications of hereditary hemachromatosis are completely preventable by reducing iron stores through phlebotomy (which is inexpensive), presymptomatic screening for hereditary hemachromatosis could be clinically useful. With a frequency of 1 in 400 Caucasians carrying one or both of two common mutations in HFE, large-scale population-based screening is technically feasible. As many experts considered introducing hereditary hemachromatosis screening on a population-wide basis, it became increasingly apparent that the natural history of mutation carriers was incompletely understood and that the disease penetrance is less than 1% (Beutler et al., 2002). Although population-based screening for hereditary hemachromatosis still has clinical utility, the implications of a positive genetic test are less significant than had once been anticipated, dramatically altering the cost-benefit ratio. Criteria to use in considering further tests for population-based screening include the disease prevalence, disease penetrance, availability of sensitive, specific, and cost-effective screening tests which may depend upon the number of genes involved and the mutation spectrum, availability of acceptable means of prevention or early detection, and the infrastructure available to perform the screening and follow up the results with confirmatory testing.

NEWBORN SCREENING Newborn screening is the largest population-based public health screening program currently in practice in the United States and throughout the world. Recent technological advances including tandem mass spectrometry and molecular genetic diagnostics

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have allowed many states in the United States to cost-effectively increase the number of disorders for which newborns are screened well beyond the classically screened diseases such as phenylketonuria, hypothyroidism, and galactosemia. Over 30 disorders are now screened in many US states. Most of recent expansion in newborn screening is for inborn errors of metabolism detected by metabolomic characterization of a dried spot of blood from a newborn heel stick. Additional molecular genetic diagnostic platforms have recently been added by many states as a second-tier screen to decrease the number of false-positives from metabolomic characterization. Two-tiered screening provides a complementary method to confirm inherited diseases with a small number of well-characterized mutations such as in medium-chain acyl dehydrogenase deficiency (MCAD), cystic fibrosis, and congenital adrenal hyperplasia. The use of tandem mass spectrometry and molecular genetic testing have now opened the possibility to screen for a vast number of inherited conditions. At the request of the General Accounting Office and Health Resources and Services Administration, the American College of Medical Genetics recently re-examined the criteria for determining which disorders to include in newborn screening (http://mchb.hrsa.gov/screening/). Unlike universally accepted screening for disorders such as phenylketonuria for which early detection provides the opportunity for complete prevention of permanent disabilities, some of the newly added disorders allow for early diagnosis and treatment initiation but do not provide a cure or effectively prevent all the long-term disabilities. There is now an ongoing, evolving dialogue about what disorders should be included in newborn screening and whether the criteria proposed by Wilson and Jungner (1968) must be satisfied. Cystic fibrosis is currently screened in newborns in 28 states. Krabbe disease, treatable only by bone marrow transplantation, is now performed in New York. Many other common genetic conditions such a spinal muscular atrophy, Fragile X, and muscular dystrophy have been considered even though only supportive care is currently available for these conditions. What treatment is efficacious enough to warrant population-based screening is debated. Clearly an important factor limiting screening for many conditions is availability of technology to allow for inexpensive, sensitive, and specific screening. Multiplexed DNA microarrays to screen for inherited disease susceptibility and infectious agents in newborns have been suggested (Green and Pass, 2005) and are being piloted. In general, pediatricians have endorsed the expansion of screening in high-risk infants (Acharya et al., 2005) since it leads to reduced time to diagnosis and initiation of treatment, decreased expense and invasiveness of diagnostic testing, and allows for informed reproductive planning for the family. However, as the number of disorders screened increases, it is equally necessary to improve test parameters to increase specificity and reduce the number of false-positive tests to spare the parents of unaffected children the anxiety of a potential diagnosis that will never be made (Waisbren et al., 2003). Not all genetic testing is appropriate for newborns. Genetic testing should be defined and limited to conditions for which there will be medical implications for minors,

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and no genetic testing should be performed for adult onset conditions for which there is no intervention during childhood.

PHARMACOGENETICS Screening for variation in drug metabolism and adverse side effects (pharmacogenetics) could allow for safer and more efficacious use of drugs. There is heritable variation in drug metabolism that alters the levels of active drugs and metabolites, thereby producing differential therapeutic effect and toxicity in different patients on the same dose of medication due to differences in drug levels. Pharmacogenetic variation is common within the population and affects the majority of patients taking medications. With genetic characterization of the genes involved in drug metabolism, it should be possible to more accurately predict the dosing of medication required to remain in the therapeutic window and circumvent the current practice of trial and error resulting in harmful side effects and delaying time to effective treatment (see Chapters 27 and 29). There is now a clinically available diagnostic panel to begin to approach pharmacogenetics. The amplichip CYP450 offers the ability to genotype 29 polymorphisms, mutations, deletions, and duplications for CYP2D6 and 2 polymorphisms in CYP2C19, two genes responsible for metabolism of approximately 25% of drugs, including tricyclic antidepressants, selective serotonergic reuptake inhibitors, beta-blockers, antipsychotics, benzodiazepines, and proton pump inhibitors. The information from the 31 assays is integrated to produce a simple interpretation quantifying a patient’s predicted metabolism to be poor, intermediate, extensive, or ultra-rapid. For some medications, package inserts now contain dosing information specific to the predicted metabolizing phenotype. Although clinical testing on the amplichip CYP450 has been available for over 2 years, clinicians have been slow to utilize the test in part due to insufficient outcome data to clearly guide use in clinical practice. Within the field of oncology, several commonly used medications are known to have common polymorphisms in genes that metabolize the drugs that can produce toxic side effects. 5Fluorouracil, irinotecan, thiopurines, and methotrexate have common mutations/polymorphisms in the drug-metabolizing genes dihydropyrimidine dehydrogenase (Gonzalez and FernandezSalguero, 1995), UDP-glycosyltransferase I (Iyer et al., 1998), thiopurine methyltransferase (Tai et al., 1996), and 5, 10-methylenetetrahydrofolate reductase (Ulrich et al., 2001), respectively. Mutations in these genes are associated with toxicity that can be easily screened at any time prior to administration of the medication. It is simple to imagine that each patient will have a pharmacogenetic profile for drug-metabolizing genes contained within their electronic medical record that will automatically warn prescribers against use of certain medications or suggest altered dosing. However, implementing the data systems necessary to integrate this information efficiently into molecular genetic diagnostic laboratories, pharmacies, hospitals, and physicians’ offices will not be trivial until electronic medical records are firmly established.

Using pharmacogenetics to rationally select the appropriate medication at the correct dose to prevent and treat disease should be economically efficient with less money spent on ineffective drugs or drug doses, fewer adverse outcomes and complications, and increased number of medications that can safely be made available to properly screened patients. As beneficial as pharmacogenetics appears to be, polymorphisms in the same genes such as CYP2D6 responsible for drug metabolism may also indicate increased susceptibility to gastrointestinal, lung, and liver cancer about which patients may not wish to be themselves aware or have in their medical record (Clapper, 2000). Other polymorphisms in ALDH2 and CYP2A6, two genes responsible for ethanol and nicotine metabolism, could be misinterpreted as socially stigmatizing markers for addiction (Agarwal, 1997). Therefore, careful consideration should be given to the genes included within pharmacogenetic profiles and clarify the intended and possible unintended use of the results.

SOMATIC GENOMIC VARIATION In addition to testing for heritable germline genetic characteristic, we will need to characterize somatic variation over the lifetime of an individual, especially in characterizing oncological specimens. Cancer treatment is likely to be rapidly redefined with genomic characterization of tumors using a combination of comparative genomic hybridization, quantification of gene amplifications/deletions, identification of acquired genetic mutations, and gene-expression profiling. These methods are already clinically utilized and used on a small scale. Molecularly targeted medications with fewer side effects will be more frequently developed as we rationally develop drugs based upon specific molecular targets. Examples already include the tyrosine kinase inhibitor imatinib mesylate (Gleevec) for chronic myelogenous leukemia and gastrointestinal stromal tumors that over express the tyrosine kinases, trastuzumab (Herceptin) for HER2 over expressing breast cancer, and gefitinib for activating mutations in Epidermal Growth Factor Receptor for non-small cell lung cancer (Lynch et al., 2004). These medications offer the possibility of increased efficacy and rely critically on accurate molecular tumor characterization to identify the subset of patients who will likely respond to these expensive therapies. Genomic Health Inc. offers another form of genomic tumor characterization, a limited expression profiling array (Oncotype Dx) of 21 genes for estrogen receptor positive, node negative, stage one or two breast cancer. The expression profile quantifies the likelihood of breast cancer recurrence in women with newly diagnosed, early stage breast cancer and assesses the benefit of chemotherapy with a numerical scoring system ranging from 1 to 100. The ability of this assay to analyze gene expression on paraffin embedded tissue offers an advantage over the requirement of freshly frozen tissue since new procedures for tissue handling and storage need not be developed to utilize this technology. The ability to simply, numerically quantify and integrate complex genomic information facilitates physician and patient communication and understanding to allow patients

Laboratory Standards to Ensure Analytic Validity

and physicians to make more rational decisions about therapy. Although not always covered by third-party payers, some patients are willing to personally bear the cost of testing to provide additional reassurance that chemotherapy will not benefit them and will allow them to avoid chemotherapy associated morbidity without increasing risk of recurrence. Third-party payer coverage will increase as clinical utility is demonstrated, patients and physicians requests for testing increases, and as diagnostic laboratories perform sufficient volumes of tests to negotiate carve out contracts with major health insurers and CMS to cover testing. There are currently multiple oncology clinical trials in progress to determine the precise algorithms by which expression profiles will be weighted and analyzed and to determine the clinical utility for prognosis and treatment efficacy (Buckhaults, 2006). Similar expression profiles from blood samples are currently being used to predict rejection for cardiac transplant recipients and obviate the need for an invasive cardiac biopsy if the expression profile indicates a low probability of rejection (Deng et al., 2006). It is likely that clinically relevant diagnostic tools will soon be available for multiple tumor types to assist with oncological management and multiple solid organ transplants to predict rejection.

NOVEL SOURCES OF GENOMIC VARIATION In addition to the nuclear germline and somatic mutations resulting from alteration in one or a small number of nucleotides, there are additional sources of genomic variation, the clinical significance of which is not yet fully appreciated. Human genomic architecture and variation are now being characterized. It is likely that previously undetected variation in the copy number of genes will underlie a significant fraction of birth defects, mental retardation, and autism (de Vries et al., 2005; Schoumans et al., 2005; Sebat el al., 2007). It is also possible that copy number variation increases susceptibility to common psychiatric, neurological, and medical conditions (Sebat et al., 2004). Somatic copy number variation has long been associated with cancer and amplification or deletion of specific genes that may provide additional prognostic and therapeutic information (Pinkel and Albertson, 2005). Bacterial artificial chromosome and oligonucleotide arrays are currently being developed and are available on a research basis to provide a molecular karyotype with resolution of 10–100 kb and are recently clinically available with a resolution of 100–500 kb. It is likely that such oligonucleotide arrays will replace standard karyotypes as the first means of analyzing chromosomes for an overall assessment of genomic balance. However, to maximize its clinical utility, it will be necessary to characterize normal copy number variation and demonstrate clinically relevant associations of copy number variations/alterations with disease. As it has become less expensive to genomically characterize gene copy number on many of the same platforms used to genotype single nucleotide polymorphisms, tests of association with common diseases will provide the data necessary to judge clinical validity and utility.

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In addition to nuclear genetic variation, genetic variation also exists within the mitochondria which can vary from cell to cell and over the course of a lifetime due to mitochondrial heteroplasmy. Mitochips are currently available on a research and clinical basis to sequence the complete mitochondrial genome. As correlations between mitochondrial mutations are associated with diseases associated with degeneration and advancing age, it may become important to include methods to detect and quantify mitochondrial variation in blood as well as other tissues over the course of a patient’s lifetime.

LABORATORY STANDARDS TO ENSURE ANALYTIC VALIDITY As new methodologies and diagnostic tests such as those described above are developed, it will be necessary for diagnostic laboratories to maintain rigorous standards of quality control and quality assurance. New diagnostic assays will be developed in Clinical Laboratory Improvement Amendment (CLIA) certified laboratories. Some assays will use reagents developed and utilized only by that laboratory (“laboratory developed tests”) as has traditionally been done in molecular genetic diagnostic laboratories for rare and ultra-rare disorders. As molecular genetic testing is applied to more common disorders, it is more likely that analytespecific reagents (ASRs) will be commercially prepared and packaged for wider distribution to a large number of laboratories. It will be necessary for novel diagnostic platforms to be FDA-approved before the platforms and ASRs can be widely distributed to commercial diagnostic laboratories. The rigorous review provided by the FDA for products such as the AmpliChip CYP450 provide assurance of the analytical performance (sensitivity, specificity, and reproducibility) of the diagnostic assay and interpretability of results by clinicians. Increasingly, review and oversight of novel diagnostic tests will be necessary to assure analytical quality; however, regulation by the FDA if not expeditious could impede the efficient transfer of diagnostic methods into clinical practice. Each laboratory will need to rigorously validate the analytical performance of new assays including splitting of samples within and between laboratories to compare results between analytical methods. Standards and guidelines for genetic testing have been developed and are regularly revised by the American College of Medical Genetics (http://www.acmg. net/resources/s-g/s-g-yes-no.asp). Regular proficiency testing including the use of common samples between laboratories will ensure ongoing test quality. It is anticipated that as new ASRs are developed, the level of technical expertise necessary to perform the assays and the cost of running the assays should decrease simultaneously as throughput increases with automation and multiplex assays. It is likely that in the future the majority of molecular genetic testing will move from the many small academic boutique laboratories currently performing most testing to large commercial laboratories and/or hospitals with larger capacity and potentially one day could be performed as point of care testing.

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Communicating test results clearly and concisely is as important as the laboratory’s analytical performance. Test reports should present sufficient detail about test methodologies to allow for comparison of methods between laboratories and allow the physician to determine the likelihood of a false-negative based upon the mutations included in the assay. As the scientific data and methods are likely to change over time, these reports will require periodic revision, especially with regard to clinical utility. Results should be succinctly reported in a final summary statement including sensitivity and specificity of the test, the possible implications of a positive and negative test result, and reference to or inclusion of necessary supporting data documenting clinical utility. Ideally, Gene Clinics (http://www.geneclinics.org/) or a similarly organized web-based format would provide periodically revised summaries of the genetic tests and their clinical utility. As data from these tests accumulate, de-identified centralized databases defined by gene or test should be maintained with phenotypic information and outcomes to allow for ongoing data collection and analysis to constantly improve test utility and interpretation, especially that of variants of unknown clinical significance.

CLINICAL VALIDATION AND CLINICAL UTILITY Genomic testing is expected to simplify diagnostic work ups, provide prognostic information, improve and refine clinical management, identify individuals at increased risk, and decrease adverse outcomes. However, before this can be implemented, genomic testing will require clinical validation and demonstration of clinical utility before acceptance into health care. The data for such validation may initially come from studies of patients with the most severe disease manifestations. However, it will be important to analyze population-based samples as well to accurately determine association between genetic susceptibility and disease in an unbiased manner. Studies in multiple ethnic groups will allow characterization of allele frequencies in various populations. It is likely that large clinical trials will provide much of these data necessary for validation since many trials now routinely integrate genetic and genomic information to stratify response to therapy and adverse outcomes by genotype. Integration of genetic data into existing and developing clinical data collection systems such as birth registries, vital statistics, cancer registries, and biorepositories linked to electronic medical records will facilitate testing and validation of preliminary genetic associations and facilitate rapid, independent confirmation of results before clinical introduction. Clinical utility is the ability in specific clinical circumstances of a genomic test to assist in clinical decision-making and improve health outcomes. Whenever possible, a genetic/genomic test should predict a defined clinical outcome with high positive and negative predictive value. Ideally, prospective, randomized, blinded controlled clinical trials genetically stratified prior to treatment would provide prospective data to validate clinical utility. To effectively evaluate the cost-effectiveness of a genetic

test, the population frequency of the at-risk genotype, the agerelated penetrance, morbidity and mortality of the disease, and the effects of interacting modifiable risk factors on genotypic expression must all be accurately known. Recommendations for clinical introduction of genetic testing should only be made when the diagnostic methodologies are reliable, patients have access to the clinical services necessary to make informed decisions and interpret the results of genetic testing, and when it becomes clinically apparent how to utilize the results of genetic testing. Once clinical testing is offered, it is also important to maintain ongoing data collection after testing is introduced to define any additional, unintended consequences. As examples, we can compare the clinical utility of genetic testing for hereditary breast/ovarian cancer conferred by BRCA1 and BRCA2 and the risk for venous thromboembolism due to Factor V Leiden. The lifetime risks of breast cancer for carriers with BRCA1 or BRCA2 mutations are approximately 65% and 45%, respectively (Antoniou et al., 2003). Mutations carriers can pursue increased surveillance for breast cancer, risk reducing prophylactic mastectomy, or chemoprevention with tamoxifen. Arguments can even be made that with a mutation frequency of 2.5% in the Ashkenazi population (Hartge et al., 1999), population-based screening for population-specific founder mutations in adult women should be considered. The clinical utility of BRCA1/BRCA2 testing is derived in large part from the high penetrance of the mutations. On the other hand, thrombophilia susceptibility conferred by Factor V Leiden is the most commonly requested molecular genetic test, although the risk conferred is much more modest and ranges from two- to eightfold (Rosendaal, 1999). It has been proposed that women should be tested for the Factor V Leiden mutation prior to initiation of oral contraceptives due to the 30-fold increased risk of venous thrombosis for mutation carriers on oral contraceptives (Vandenbroucke et al., 1994). However, although the relative risk of thrombosis is significantly increased, the absolute risk is only 28/10,000 person years and the mortality is low in young women. Failure to use oral contraceptives by the numerous Factor V Leiden mutations carriers could have other unforeseen implications including unwanted pregnancies. Thus, no professional consensus has yet emerged about the utility of testing.

COST A major impediment to integration of genetic/genomic information into health care has been the high cost of molecular genetic testing and the unwillingness of many third-party payers to cover this expense, many citing that it remains experimental. The cost of molecular genetic testing is determined largely by the costs of instrumentation, reagents, personnel, licensing fees, and professional liability insurance. As testing becomes increasingly automated and multiplexed using high-throughput assays on miniaturized scales, the cost of reagents and personnel will be reduced significantly. A goal of the National Human Genome Research Institute is to provide individual genome sequence for

Who will Provide Genomic Medical Care?

$1000 (Collins et al., 2003). Clearly if the technology can drive the cost of testing down to this extent, a major financial barrier to testing will be removed. Licensing fees have significantly increased the cost of testing for a limited number of conditions such as hereditary breast and ovarian cancer (BRCA1 and BRCA2) to $3120 (http://www.myriadtests.com/) and LQTS to $5400 (http://www.pgxhealth.com/genetictests/familion/ index.cfm). The genetic/genomic community is opposed to exclusive licenses on genes and is mixed in opinion on gene patenting, but it remains to be seen how much the gene patents and intellectual property regarding clinical utility of specific diagnostic testing will increase the average cost of testing. In addition to the absolute cost of testing, the extent to which these costs are borne by third-party payers differs widely by laboratory, test, and payer. In the 12 years since BRCA1/ BRCA2 genetic testing became clinically available, the number of individuals tested has increased dramatically, in large part as insurance companies, Medicare, and recently Medicaid began to cover the cost of testing for patients meeting medical eligibility. While many patients might elect to pursue genetic testing for health maintenance and disease prevention, third-party payers are reluctant to expose themselves to this financial burden until the utility is proven. The majority of molecular genetic testing currently performed and paid for by third-party payers surrounds reproductive issues such as carrier screening for cystic fibrosis, recommended by the American College of Obstetrics and Gynecology for consideration in all women contemplating conception. Standard of clinical care guidelines endorsed by major professional medical organization are influential in getting coverage for new tests. Without third-party payer coverage for genomic testing, genomic testing will be utilized only by a small, motivated segment of society who can afford to self-pay. A significant proportion of the financial benefit to genomic health care is attributable to preventive medicine and the opportunity to prevent chronic disease and cancer. Regional markets in which there are single, dominant insurers or organizations that are self-insured are more likely to initially support genomic health maintenance since they will see the financial benefits within a single organization. Markets with large numbers of competing insurers are reluctant to absorb large financial costs for which they will not personally see a return as consumers change between insurance carriers with regularity. However, as the cost incurred for genomic testing decreases and the utility becomes more apparent and as consumers demand this benefit, it is likely that additional major health insurance providers will add these services to both remain competitive and as a long-term cost-saving measure.

REIMBURSEMENT Many diagnostic tests, especially those that are highly multiplexed, have been slow to be developed and made clinically available because the testing laboratories cannot charge third-party payers enough to cover their operating expenses. As new technologies for molecular genetic testing are developed, it will be necessary

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to revise and develop new Current Procedural Terminology (CPT) codes that accurately reflect the complexity of the testing. For many multiplex assays composed of hundreds to thousands of simultaneous assays, it will be necessary to raise the current ceiling for the maximum number of probes, hybridizations, and amplicons to reflect the complexity and cost of the new test methodologies. Once this ceiling is raised, laboratories may find it financially feasible to expand their testing platforms and test menus.

WHO WILL PROVIDE GENOMIC MEDICAL CARE? Genetic medicine is currently provided in large part by boardcertified medical geneticists and genetic counselors (see Box 1.1 in Chapter 1). Given the projected rate of growth and increasing specialization of genetic and genomic medicine and small number of 80 MD and/or PhD graduates per year in medical genetics (Korf et al., 2005), it will be necessary for all health care providers to integrate genomic information to some extent into their provision of health care. Genomics may follow other diagnostic tests such as pathology, microbiology, and diagnostic imaging with specialized physicians performing and interpreting the tests, while practitioners become largely responsible for determining which patients require testing and how to utilize the test results in heath care. Medical geneticists will continue to play important roles in research, clinical development of the field, education, laboratory medicine, and possibly treatment, in addition to providing direct clinical care for patients with disorders with which they have clinical expertise. Genetic counselors will continue to play vital roles in patient education and counseling. It will be important to recognize the role of genetic counselors by granting formal licensure and persuading insurers to reimburse for codes for genetic counseling to allow for reasonable reimbursement for genetic counselors’ services. Genetic counselors may be increasingly employed by diagnostic laboratories to offer counseling to patients referred by their physicians for testing. The demand for genetic counselors will surpass supply and will necessitate expansion of training programs that currently graduate only 160 new genetic counselors annually. In the future, there may be a specialized track for genomic counselors who will provide patient education and laboratory support not for rare monogenic disorders, but for common, polygenic conditions. Other health care professionals and/or multimedia educational programs will also need to assist with patient education. Additional responsibility will be placed upon the patient to actively gather and share medical information with their family through the use of interactive, computer-based family history tools. However, the majority of medical professionals outside genetics are currently ill equipped to provide genomic medical care. When surveyed, 65% of obstetricians and gynecologists who routinely utilize genetics in their practice did not consider themselves sufficiently educated in genetics. They rarely referred patients for genetic counseling and 86% did

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not obtain informed consent for genetic testing (Wilkins-Haug et al., 2000). Genomic education on a massive, unprecedented scale will be necessary to implement genomic medicine (see Chapters 22 and 34).

GENOMIC LITERACY Integration of genomic information into health care will require at least rudimentary understanding of genetics and genomics by health care providers and health care consumers. Increasingly, there will be medicolegal implications for physicians if they do not recognize specific heritable risks based upon the patient’s personal or family history (Deftos, 1998). Additional time must be dedicated to genetics and genomics within medical training, beyond the average 29 hours currently allocated in 4 years of medical school (Friedman et al., 1998). Medical educators themselves will need to keep abreast of advances within genomics and rapidly revise curricula. Because genomic medicine is so rapidly changing, it will be necessary to also teach the standards by which genetic tests should be evaluated for scientific validity and clinical utility so practitioners will be equipped to judge new data as they emerge. As genomics is integrated into medical, dental, nursing, and allied professional education, a new generation of health care providers will hopefully be capable of rapidly assimilating genomic information into the life cycle of their patients. The younger generation of health care providers is already anxious to adopt this new technology in their practices. Perhaps, they even place more confidence in this new scientific field than is yet warranted. A greater challenge remains for health care professionals educated prior to the expansion of genomics – perhaps graduating from medical school only 5 years ago. For previously trained health care professionals, it will be necessary to learn general underlying principles of genetics and genomics as well as specific tools relevant to their own area of clinical expertise. The National Coalition for Health Professional Education in Genetics (NCHPEG) has produced a set of core competencies in genetics essential for all health care professionals (http://www. nchpeg.org/eduresources/core/Corecomps2005.pdf). NCHPEG is currently developing a core curriculum for genetic education (http://www.nchpeg.org/eduresources/core/coreprinciples. pdf) and has produced a CD ROM “Genetics and Common Disorders: Implications for Primary Care and Public Health Providers” that should assist in educating health care professionals currently in practice. Geneticists will play critical roles as genomic medical educators and discern when information is clinically relevant for various subspecialty areas with clinical care recommendations supported by major medical professional organizations. Genomic educators will need to continuously update medical professionals with new discoveries and applications in simple terms that emphasize clinical utility. It will remain a scientific challenge to define clinical situations in which complex genomic information can be distilled into an appropriately simple interpretation that has unambiguous clinical implications that impact clinical outcomes.

Genomic literacy in the public must simultaneously improve and remove the mysticism and misconceptions some patients may have regarding hereditary information. We must enlighten those who believe genetics is deterministic who would otherwise fail to modify health behaviors, believing instead in an inevitable, unalterable fate. There is a wide and growing divide between patients utilizing genetic and genomic information in their health care, driven in part by education and socioeconomic status. Younger patients tend to utilize genetic information more aggressively and seek genetic information both out of curiosity and to make more informed life and reproductive decisions. Younger generations have also become accustomed to advances in assisted reproductive technology and increasingly push the ethical and social boundaries to use such genetic and reproductive technology for such trivial genetic factors as elective sex selection. Older patients tend to be less educated about emerging genomic technologies, and often the reason they cite for seeking genetic testing is to provide information for their children or grandchildren rather than for themselves. As the public becomes more genetically literate, there will likely be a push from diagnostic laboratories to market genetic testing directly to consumers. This has already been done with BRCA1/BRCA2 testing and is done with nutriceuticals. While such advertising campaigns may be effective in increasing awareness and forcing health care professionals to learn new information about genetic testing, there will also be the danger that inappropriate tests will be ordered and potentially misused by patients and physicians who are not fully informed.

ETHICAL, LEGAL, AND SOCIAL ISSUES Disparities in genetic literacy are correlated with education which is itself correlated with race. Race independently further complicates the scientific interpretation of genomic data as minority populations are initially generally less well studied than Caucasians. Insufficient information about normal genetic diversity in various ethnic groups complicates and delays development and accurate interpretation of clinical diagnostic tests across in all populations. For instance, missense variants of unknown clinical significance in BRCA2 are clinically reported more commonly in minority populations due in part to deficiencies in our knowledge of the spectrum of normal variation in non-Caucasian populations. These ambiguous results can lead to increased anxiety and possibly unnecessary prophylactic surgeries when misinterpreted by patients (and physicians) who may be educationally ill equipped to comprehend complex and evolving test results. Within the United States, minority populations have historically been concerned about discrimination. It is important to present educational information about genomics in a culturally sensitive way, ideally by members within each community. Access to genomic information and treatment should be equitable. Currently there are great disparities based upon geography, inequities in coverage of genetic testing by insurance companies, Medicare, and Medicaid and cultural and

References

language barriers with 93% of genetic counselors being Caucasian (Farmer and Chittams, 2000). Patients have great concerns about the misuse of their genetic information. Although the Americans with Disabilities Act provides some reassurance against discrimination in the workplace and the Health Insurance Portability and Accountability Act (HIPAA) protects against eligibility discrimination for group health care for individuals with genetic susceptibility to disease, patients continue to feel vulnerable. Failure by Congress previously to pass the Genetic Information Nondiscrimination Health Act left many consumers with concern that genetic information could be used to discriminate against them for individual health insurance plans, life insurance, long-term disability, longterm care insurance, admission to educational institutions, or the military, jobs, loans, or social status. The Genetic Information Nondiscrimination Health Act has passed the House and the Senate. It should provide Americans with additional reassurance about genetic discrimination. Concerns about genetic discrimination are now even discussed on the sports page as Eddy Curry, a professional basketball player, refused to have genetic testing for hypertrophic cardiomyopathy because of concerns that he would be unable to play professional basketball if he tested positive. Although there have been no documented successful cases of health insurance discrimination on the basis of genetic predisposition, because more subtle forms of discrimination can be difficult to document, some patients still prefer to have genetic testing performed anonymously. Legislation ensuring genetic privacy will be necessary before many consumers will confidently embrace and utilize genomic medicine.

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CONCLUSIONS There will come a time in the future when we will be able to determine, in real time at different points in our lives, our susceptibility to disease and specific steps we can take to intervene with individualized recommendations about lifestyle and dietary changes, pharmacological intervention, and increased surveillance to improve health outcomes and decrease adverse outcomes. It is important for all stakeholders to have realistic expectations about the time required to implement these changes. Clinical validation and utility should be rigorously demonstrated prior to clinical implementation. Genomic literacy among health care providers, insurers, and patients must increase, requiring a massive educational effort. Costs of testing must decrease dramatically and coverage for the cost of testing must be borne by third-party payers.This will not happen as quickly as some might naively expect, and fostering unrealistic public expectations will surely lead to disappointment. However, with advances in scientific knowledge and verification of clinical applications of human genetics, genomics, metabolomics, proteomics, and molecular imaging, it should be possible to improve health outcomes at decreased cost.We must be mindful to ensure equal access to genomic medicine and utilize these medical advances rationally and cost-effectively without patient’s fear of discrimination.

ACKNOWLEDGEMENTS Roberto Almazan and Josue Martinez provided assistance with manuscript preparation.

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Deftos, L.J. (1998). The evolving duty to disclose the presence of genetic disease to relatives. Acad Med 73, 962–968. Deng, M.C., Eisen, H.J., Mehra, M.R., Billingham, M., Marboe, C.C., Berry, G., Kobashigawa, J., Johnson, F.L., Starling, R.C., Murali, S. et al. (2006). Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant 6(1), 150–160. de Vries, B.B., Pfundt, R., Leisink, M., Koolen, D.A., Vissers, L.E., Janssen, I.M., Reijmersdal, S., Nillesen,W.M., Huys, E.H., Leeuw, N. et al. (2005). Diagnostic genome profiling in mental retardation. Am J Hum Genet 77, 606–616. Farmer, J. and Chittams, J. (2000). Professional status survey 2000. Perspect Genet Couns 22, S1–S12. Friedman, J.M., Blitzer, M., Elsas, L.J., 2nd., Francke, U. and Willard, H.F. (1998). Clinical objectives in medical genetics for undergraduate medical students. Association of Professors of Human Genetics, Clinical Objectives Task Force. Genet Med 1, 54–55. Gonzalez, F.J. and Fernandez-Salguero, P. (1995). Diagnostic analysis, clinical importance and molecular basis of dihydropyrimidine dehydrogenase deficiency. Trends Pharmacol Sci 16, 325–327. Green, N.S. and Pass, K.A. (2005). Neonatal screening by DNA microarray: Spots and chips. Nat Rev Genet 6, 147–151. Hartge, P., Struewing, J.P., Wacholder, S., Brody, L.C. and Tucker, M.A. (1999). The prevalence of common BRCA1 and BRCA2 mutations among Ashkenazi Jews. Am J Hum Genet 64, 963–970.

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Iyer, L., King, C.D.,Whitington, P.F., Green, M.D., Roy, S.K.,Tephly,T.R., Coffman, B.L. and Ratain, M.J. (1998). Genetic predisposition to the metabolism of irinotecan (CPT-11). Role of uridine diphosphate glucuronosyltransferase isoform 1A1 in the glucuronidation of its active metabolite (SN-38) in human liver microsomes. J Clin Invest 101, 847–854. Jazwinska, E.C., Cullen, L.M., Busfield, F., Pyper, W.R., Webb, S.I., Powell, L.W., Morris, C.P. and Walsh, T.P. (1996). Haemochromatosis and HLA-H. Nat Genet 14, 249–251. Kauff, N.D., Mitra, N., Robson, M.E., Hurley, K.E., Chuai, S., Goldfrank, D.,Wadsworth, E., Lee, J., Cigler,T., Borgen, P.I. et al. (2005). Risk of ovarian cancer in BRCA1 and BRCA2 mutation-negative hereditary breast cancer families. J Natl Cancer Inst 97, 1382–1384. Korf , B.R., Feldman, G. and Wiesner, G.L. (2005). Report of Banbury Summit meeting on training of physicians in medical genetics, October 20–22, 2004. Genet Med 7, 433–438. Lynch,T.J., Bell, D.W., Sordella, R., Gurubhagavatula, S., Okimoto, R.A., Brannigan, B.W., Harris, P.L., Haserlat, S.M., Supko, J.G., Haluska, F.G. et al. (2004). Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350, 2129–2139. Moss, A.J.,Windle, J.R., Hall,W.J., Zareba,W., Robinson, J.L., McNitt, S., Severski, P., Rosero, S., Daubert, J.P., Qi, M. et al. (2005). Safety and efficacy of flecainide in subjects with Long QT-3 syndrome (DeltaKPQ mutation): A randomized, double-blind, placebo-controlled clinical trial. Ann Noninvasive Electrocardiol 10, 59–66. Pinkel, D. and Albertson, D.G. (2005). Comparative genomic hybridization. Annu Rev Genomics Hum Genet 6, 331–354. Rosendaal, F.R. (1999). Venous thrombosis: A multicausal disease. Lancet 353, 1167–1173. Schoumans, J., Ruivenkamp, C., Holmberg, E., Kyllerman, M., Anderlid, B.M. and Nordenskjold, M. (2005). Detection of chromosomal imbalances in children with idiopathic mental retardation by array based comparative genomic hybridisation (array-CGH). J Med Genet 42, 699–705. Schwartz, P.J., Priori, S.G., Spazzolini, C., Moss, A.J., Vincent, G. M., Napolitano, C., Denjoy, I., Guicheney, P., Breithardt, G., Keating, M.T. et al. (2001). Genotype-phenotype correlation in

the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias. Circulation 103, 89–95. Sebat, J., Lakshmi, B., Malhortra, D.,Troge, J., Lese-Martin, C., Walsh, T., Yamrom, B., Yoon, S., Krasnitz, A., Kendall, J. et al. (2007). Strong association of de novo copy number mutations with autism. Science 316, 445–449. Sebat, J., Lakshmi, B.,Troge, J.,Alexander, J.,Young, J., Lundin, P., Maner, S., Massa, H.,Walker, M., Chi, M. et al. (2004). Large-scale copy number polymorphism in the human genome. Science 305, 525–528. Tai, H.L., Krynetski, E.Y., Yates, C.R., Loennechen, T., Fessing, M.Y., Krynetskaia, N.F. and Evans, W.E. (1996). Thiopurine S-methyltransferase deficiency: two nucleotide transitions define the most prevalent mutant allele associated with loss of catalytic activity in Caucasians. Am J Hum Genet 58, 694–702. Ulrich, C.M., Yasui, Y., Storb, R., Schubert, M.M., Wagner, J.L., Bigler, J., Ariail, K.S., Keener, C.L., Li, S., Liu, H. et al. (2001). Pharmacogenetics of methotrexate: Toxicity among marrow transplantation patients varies with the methylenetetrahydrofolate reductase C677T polymorphism. Blood 98, 231–234. Vandenbroucke, J.P., Koster, T., Briet, E., Reitsma, P.H., Bertina, R.M. and Rosendaal, F.R. (1994). Increased risk of venous thrombosis in oral-contraceptive users who are carriers of factor V Leiden mutation. Lancet 344, 1453–1457. Verhoog, L.C., Brekelmans, C.T., Seynaeve, C., van den Bosch, L.M., Dahmen, G., van Geel, A.N.,Tilanus-Linthorst, M.M., Bartels, C.C., Wagner, A., van den Ouweland, A. et al. (1998). Survival and tumour characteristics of breast-cancer patients with germline mutations of BRCA1. Lancet 351, 316–321. Waisbren, S.E.,Albers, S.,Amato, S.,Ampola, M.,Brewster,T.G.,Demmer, L., Eaton, R.B., Greenstein, R., Korson, M., Larson, C. et al. (2003). Effect of expanded newborn screening for biochemical genetic disorders on child outcomes and parental stress. JAMA 290, 2564–2572. Wilkins-Haug, L., Erickson, K., Hill, L., Power, M., Holzman, G.B. and Schulkin, J. (2000). Obstetrician-gynecologists’ opinions and attitudes on the role of genetics in women’s health. J Womens Health Gend Based Med 9, 873–879. Wilson, J.M. and Jungner,Y.G. (1968). Principles and practice of screening for disease.World Health Organization, Geneva.

RECOMMENDED RESOURCES http://www.genetests.org http://www.geneclinics.org/ http://mchb.hrsa.gov/screening

http://www.acmg.net/resources/s-g/s-g-yes-no.asp http://www.nchpeg.org/eduresources/core/Corecomps2005.pdf http://www.nchpeg.org/eduresources/core/coreprinciples.pdf

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31 Translating Innovation in Diagnostics: Challenges and Opportunities Matthew P. Brown, Myla Lai-Goldman and Paul R. Billings

INTRODUCTION The last decade has seen rapid advances in diagnostic tools and novel therapies as a result of basic research in fields as diverse as genomics, drug discovery, information science, imaging, and nanotechnologies (Mankoff et al., 2004; Reece, 2006). These areas of advancing knowledge are changing the practice of modern medicine. However, their uptake in the health care setting is often slow and haphazard (Graham et al., 2006; Kerner, 2006). It is clear that new knowledge and clever technologies are not enough to improve patient care. Basic discoveries must be linked to insights surrounding the development, delivery, deployment, and application of new information or techniques in health care for translation to successfully occur. The study of translating knowledge in health care or “translational medicine” has been characterized in many ways, but one definition is that efforts to find cures for affected individuals are complemented by the pursuit to understand human diseases and their complexities (Mankoff et al., 2004). Within this idealized framework, advances in basic research are properly tested in the clinical setting, then knowledge gained is both fed back to basic researchers and efficiently translated into new therapeutic strategies to treat, cure or prevent disease (Marincola, 2003; Sonntag, 2005). However, gaps in translation and the provision of new knowledge in the health care setting (so-called “translational gaps”) do occur. They can adversely impact the quality of medical care, decrease patient safety, and escalate costs. An example Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

of an important topic in translational medicine is the estimated 30–45% of all patients who do not receive medical care according to the best scientific evidence. In fact, it has been reported that 20–25% of medical care currently provided is either unnecessary or potentially harmful (Graham et al., 2006). Efforts to close these translational gaps have focused in large part on the perceived obstacles and challenges in translating basic research into viable diagnostic and therapeutic strategies. These include: communication and education between researchers from diverse fields, institutional support for translational research activities, regulatory hurdles, the high cost of developing diagnostics and novel therapeutics, the need for bioinformatics and complex data handling capabilities, adapting new technologies to high-throughput automation, intellectual property and licensing protection, and the willingness to move away from standard therapies and embrace new paradigms for diagnostic and therapeutic strategies (Horig and Pullman, 2004; Horig et al., 2005; Humes, 2005; Marincola, 2003; Reece, 2006;Tsongalis, 2005). There is a need to recognize the key role of technological innovation in closing translational gaps in medicine as compared to incremental improvements with current practice. However, significant health gains can only occur when the fidelity with which medical advances are delivered is optimized. These two processes can sometimes be seen as competing (Woolf and Johnson, 2005), but in actuality represent two factors necessary for medical progress. There may be no area where this is more clearly demonstrated than in translational diagnostics, a field Copyright © 2009, Elsevier Inc. All rights reserved. 367

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driven by innovation (Ratner, 2006). The implementation of genomics and other innovative diagnostics for personalized medicine requires the translation of a vast array of technologies and therapeutic strategies from basic science to clinical application. Delivery of these diagnostics to patients requires physician and patient education, as well as evidence-based demonstration of efficacy for healthcare uptake, payment, and regulatory approval. How have some of these challenges been addressed? Just a few years ago, academic medical centers were the primary mechanism to foster translational research and facilitate collaboration between researchers and clinicians (Puderbaugh, 2006). Multidisciplinary educational programs were funded primarily by private institutions, such as the Doris Duke Charitable Foundation (Mankoff et al., 2004). In the United States, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) then began initiatives to identify, finance, and develop translational research programs. The NIH Roadmap for Medical Research aimed to identify and support research beyond the scope of any single NIH component (NIH, 2006). Roadmap initiatives sought to improve the development and availability of modern scientific tools and information resources, foster novel methods of research collaboration, and enhance the nation’s clinical research enterprise. The NIH funded the first 12 Clinical and Translational Science Awards (CTSAs) in 2006 for academic centers conducting projects on translational research (NCRR, 2006); additional centers are being named in subsequent years. The FDA plays a key regulatory role in medical practice and has promoted translational medicine. The FDA’s Critical Path Initiative, subtitled “Stagnation to Innovation,” identified key areas for development: better evaluation tools – Biomarkers and Disease Models; streamlining clinical trials; harnessing bioinformatics; moving manufacturing into the 21st century; and products to address urgent public health needs and at-risk populations (FDA, 2006a). In early 2006, the FDA released The Critical Path Opportunities List, which outlined 76 science projects to bridge the translational research gap (FDA, 2006b, c). Of note, the first 33 items on the FDA’s Opportunities List were in diagnostic-related areas, reflecting the importance of diagnostics in driving innovation in medical practice (Ratner, 2006). Translational diagnostics is the subfield of translational medicine concerned with diagnostic methods and information. Given the importance of diagnostics, it is crucial to look at translational issues affecting this area of innovation with the intent of closing the gap between basic research and the medical system. As the role of diagnostics continues to evolve, examining translational diagnostics from the view of the largest providers of labbased tests, the national reference laboratories, will be important. Reference labs translate everything from “home brew” technologies (also called laboratory-developed tests) licensed directly from academic institutions, to FDA-approved kits purchased from manufacturers, and sophisticated in vitro diagnostic multivariate index assays (IVDMIAs) used to generate patient specific risk data (FDA, 2006d). They provide complex data analysis of laboratory tests, educate both physicians and patients, and in this and other ways spearhead the uptake of new diagnostic technologies.

With this in mind, this chapter will focus on translational diagnostics in the context of genomic and personalized medicine and on the practical lessons learned from bringing innovative diagnostics to market at larger reference laboratories. The large national labs in the United States – including Quest Diagnostics, Laboratory Corporation of America Holdings, Sonic Healthcare, Genzyme, Mayo Clinic, Associated Regional University Pathology (ARUP), and others – provide about 50% of all lab testing delivered in this country and complete several million assays a day in support of patient care. Understanding translational diagnostics as practiced by these national reference laboratories should shed light on both challenges and successes in bridging the translational diagnostics gap from bench to bedside. The potential for translational diagnostics in cancer, infectious disease, and other health care sectors will also be discussed.

NOVEL DIAGNOSTICS The goal of translational diagnostics is to provide improved diagnostic procedures leading to more efficacious therapies and improved medical outcomes. Early laboratory medicine consisted primarily of the direct examination of tissue or the analysis of simple analytes in blood. Molecular biology techniques such as positional DNA cloning, DNA sequencing and nucleic acid hybridization assays led to early molecular diagnostics. Southern blot hybridization assays were used to detect gene deletions in Duchenne muscular dystrophy, repeat expansions in fragile X syndrome, and gene rearrangements diagnostic for B- and T-cell lymphoma (Tsongalis and Silverman, 2006). The first molecular diagnostic products to reach the market included tests for the detection of viral RNA or DNA, genetic tests for single gene diseases, and tests to determine risk for developing certain cancers, such as breast or colon cancer. Many of the new diagnostic technologies entering the clinical laboratory are part of an evolution from nucleic acid amplification technologies for use in amplifying, identifying, and sequencing target single gene sequences towards a systems biology approach which looks at the expression of multiple genes in response to a variety of stimuli and conditions such as infectious disease, drug therapy, and cancer (Billings and Brown, 2004). Systems biology is often used as a synonym for functional genomics, a description of the genomic and epigenomic influences on a trait and interactions with environmental variants (see Chapter 6). New Diagnostic Technologies To understand translational diagnostics and the gaps that currently exist in the movement of knowledge, it is necessary to understand what technological innovations and potential applications are coming forth (Table 31.1). Microarrays Microarray technology typically involves tethering numerous probes to a solid substrate. For DNA microarrays, the probes usually consist of small oligonucleotides or complementary

Novel Diagnostics

TABLE 31.1

Diagnostic applications and technologies

Application

Example

Technology

Pathogen quantity

HIV viral load

QPCR

Pathogen detection

Bordetella pertussis

Real-time PCR

Emerging infectious disease detection

SARS

Protein microarray

Drug selection

HER2

IHC/FISH

Disease screening

HPV Testing

Hybrid capture

Predisposition

BRCA

Gene sequencing

Prognosis

Oncotype Dx

Expression array

Therapeutic response monitoring

HIV-1 genotyping

PCR

Prediction

Huntington’s disease genotyping

PCR

Pharmacogenomics companion diagnostic

Irinotecan selection UGT1A1 genotyping

PCR

Pharmacogenomics drug selection/ dosage

Warfarin Test CYP2D9/VKORC1 genotyping

PCR

HIV, Human immunodeficiency virus; QPCR, quantitative polymerase chain reaction; SARS, Severe acute Respiratory syndrome; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; HPV, human papillomavirus.

DNAs (cDNAs). DNA targets, commonly in the form of fluorescently labeled cDNA or genomic DNA fragments, are then hybridized to the probe. Detection of a fluorescent signal from directly labeled nucleic acid or protein samples is the most common microarray detection method (Tefferi et al., 2002). DNA microarray technology provides data on DNA sequence variation (mutations and polymorphisms) and gene expression levels (see Chapters 8, 9, and 13). Gene expression profiles can identify upregulated and downregulated genes, which are then targets for novel therapeutics. cDNA arrays have also been used to classify pathological subgroups of specific disorders. When polymorphisms are identified, genomic DNA targets are probed with oligonucleotides which define allelic differences. Recent advances in microarray technology involve the use of liquid-phase or microparticle-based arrays. One example would be bead-based multiplexing, which allows multiple analytes to be assayed in the same well. Molecular diagnostic applications of microarray technology include cancer diagnosis, typing, and prognosis; infectious disease identification, biodefense applications, and pharmacogenomics (Petrik, 2006).

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Another application of array technology is array comparative genome hybridization. This is a powerful technique that detects high-level amplification and homozygous deletions in small genomic regions. Array technology has been used to detect highresolution copy number changes in breast, renal, and bladder cancer (Gabriele et al., 2006; Petrik, 2006). Using the genomic clone sequence data, this technique can lead quickly to the identification and cloning of genes associated with disease for use in diagnostics and targeted therapeutics. Array technology promises to revolutionize the field of medical cytogenetics by providing molecular karyotyping without the need to culture cells or stain chromosomes for visualization (see Chapter 9). Proteomics Proteomics seeks to understand the structure, function, and expression of all proteins encoded by a given genome (see Chapter 14).The human genome project identified approximately 25,000 human genes. However, it is estimated that the number of protein products is 10–30 times higher due to alternative splicing and post-translational modification of gene products (Petrik, 2006). Proteomics also encompasses protein expression profiles as a function of age, state, and environment. The basic technique of proteomics is the separation of proteins from a biological sample by various separation technologies (2-D PAGE, liquid chromatography, affinity capture) followed by detection of proteins peptide fragments by technologies such as peptide fingerprinting by mass spectrometry, protein arrays, or antibody arrays (Rice et al., 2006). Proteomic analysis based on mass spectrometry may be better suited for the identification of useful protein biomarkers, while technologies such as automated chip technology using protein arrays may have higher clinical diagnostic utility. Protein arrays use protein probes that in solution retain the ability to interact specifically with other proteins or molecules. Protein arrays can be used to identify protein–protein interactions, enzyme–substrate interactions, and antibody–antigen interactions. Glycomics Glycosylation is a critically important post-translational modification of cellular proteins. Glycan moieties are involved in a wide variety of intracellular, cell–cell, and cell–matrix recognition events. Glycomics is the study of the glycans produced by humans and their role in protein function in health and disease (Morrelle and Michalski, 2005). Early technologies used separation methodologies and mass spectrometry analyses that were similar to proteomic methods. New approaches are being developed for clinical applications and include such technologies as microcapillary chromatography, lectin affinity chromatography, and carbohydrate microarray and mass spectrometry (Miyamoto, 2006). Glycan analysis has contributed to drug discovery, clinical assays, and basic research into the underlying biology of cancer. However, its greatest promise will be to enhance genomic and proteomic analysis of clinical samples to determine the effects of the genetic, environmental, lifestyle, and nutritional state of patients on their health status.

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Epigenetics Epigenetics involves the study of heritable changes in gene function that occur without a change in DNA sequence (see Chapter 1). Epigenetic mechanisms include DNA methylation, histone acetylation, and RNA interference (see Chapters 5 and 11). Some neuropsychiatric and rheumatologic diseases and cancers may affect both the genotype and epigenotype. Small interfering RNAs (siRNAs) can induce specific post-transcriptional gene silencing in mammalian systems and hold great promise as therapeutic agents. Diagnostic assays will have to be developed to analyze the effectiveness of these new therapies. Cancer cells are known to undergo changes in 5-methylcytosine distribution including hypomethylation and hypermethylation of promoter CpG islands, which are associated with tumor-suppressor genes (Esteller, 2002). An integrated analysis of the epigenetic state of cancer cells can involve DNA methylation, transcription factor binding analysis, and histone acetylation analysis. Treatments being evaluated include histone deacetylase inhibitors and DNA methyltransferase inhibitors. DNA methylation patterns are being developed as diagnostic biomarkers for detection and risk assessment (Laird, 2005) (see Chapter 11). Biological Imaging Biological imaging has begun to play an increasingly important role in the early detection and staging of disease (see Chapter 43). Recent advances in imaging include multislice computer tomography (CT) and whole-body magnetic resonance imaging (MRI) to provide earlier detection and more accurate determination of the extent of disease (Schwaiger and Peschel, 2006). Tracer techniques in combination with positron emission tomography (PET) have aided in discriminating between normal and tumor tissues. Such molecular imaging technologies seek to visualize biological processes and molecular binding sites. New advances such as PET-CT provide qualitative and quantitative assessment of tumor tissue. Taken together with other molecular diagnostic methods, biological imaging should aid in disease detection, be predictive of therapeutic efficacy, and provide markers for disease treatments. Nanotechnology The near future of nanobiosensing envisions a merger of molecular diagnostic technology with nanotechnology to produce nanobiosensors capable of detecting biological processes at the molecular level with continuous operation in real time (see Chapter 51 and Demidov, 2004). Molecular beacons comprised of both nucleic acid probes and self-reporting optical transducers should be capable of providing feedback for monitoring therapeutic intervention or for periodic checkup. Nanoparticlebased molecular detection is being used currently to develop multiplexed molecular recognition arrays and label free ways to quantify specific binding events (Ming-Cheng Cheng et al., 2006). Quantum dot nanocrystals provides a nearly unlimited range of sharply defined color indicators that can be tagged to biomolecules of interest and provide long-lived, sensitive probes. Nanodevices using microfluidic technologies may produce useful cancer, infectious diseases and other tests. These technologies

will aid the continuing miniaturization of molecular diagnostic platforms; a necessary evolution if molecular diagnostics are to be expanded to point of care use and for system biology approaches that require the simultaneous detection of specific biological processes associated with disease. Applications Infectious Disease Factors affecting disease progression and therapy management in infectious disease associated with viral, bacterial, fungal, and parasitic pathogens include rapid detection and identification, quantifying pathogen burden, and pathogen genotyping (NIH, 2002). As with the Human Immunodeficiency Virus (HIV), quantifying the viral load of a wide variety of viral pathogens is needed to monitor both the progression of the disease and the response to therapy. Pathogen genotyping is also essential in order to identify variants resistant to therapy, and to differentiate closely-related species or viral types. Currently, the gold standard for pathogen detection is growth of the pathogen in culture. This process is time-consuming and in many cases non-informative. Diagnostic methods for viral detection consist primarily of immunological identification of antibodies produced in response to viral infection. Molecular diagnostics utilizing nucleic acid amplification testing (NAAT) have been used for viral load monitoring and genotyping for HIV and screening for HIV1, hepatitis B and C (HBV, HCV), and West Nile virus (WNV) (Paxton, 2004). In HIV and HCV there is a window between infection and detection of antibody production in which immunological methods fail to detect viral infection. This is a very important issue for blood safety and for the identification of acute infections which may account for a high percentage of new clinical cases. NAAT testing has been implemented for blood screening and has been estimated to prevent about five HIV-1 and 56 HCV infections per year. Another advantage of NAAT testing lies with the flexibility of the platform in the face of emerging infectious disease threats. When WNV became a public health concern in 2002, these platforms were converted for the detection and monitoring of WNV within 9 months. With the incorporation of DNA microarray technologies, rapid pathogen detection and genotyping may be accomplished simultaneously. Recently, a protein microarray for Coronaviruses, including the severe acute respiratory syndrome (SARS) virus, has been tested and found to distinguish between SARS and other Corona viruses (Zhu et al., 2006). Low-density microarray tests are being researched for use in subtyping influenza A and avian type H5N1. These types of arrays may soon have clinical utility for monitoring influenza worldwide, developing vaccines, and eventually for point of care applications (Mehlmann et al., 2007). This last application is very important, as determining the presence of influenza on solely clinical grounds is very difficult due to poor specificity and sensitivity of clinical findings, the other pathogens that cause similar symptoms, and the influenza subtypes that cause different symptoms. For example, up to 70% of patients with influenza symptoms are not infected with influenza virus, and up to 30% of those are due to Coronaviruses

Novel Diagnostics

(Montalto, 2003; Brown, 2006). CombiMatrix has developed a DNA microarray to detect and type influenza strains and Tm Bioscience Corp. (now Luminex) has developed a major human respiratory virus array. Molecular diagnostics can be utilized for sexually transmitted diseases such as Chlamydia trachomitis and Nisseria gonorrhea, as well as other bacterial pathogens such as Legionella pneumophilia (Legionnaire’s disease), Borrelia burgdoreri (Lyme disease), Mycobacterium tuberculosis, and Bordetella pertussis and B. parapertussis (whooping cough). Despite effective vaccination programs, Bordetella pertussis remains an important and highly contagious disease due to the fact that newborns do not appear to gain passive maternal protection and vaccine-induced immunity is far less complete than expected. A real-time PCR assay that accurately detects and differentiates the DNA from both agents of pertussis syndromes has been developed (Vincart et al., 2007). This real-time PCR assay provides results within 24 h of sample receipt and provides clinicians with more timely and accurate information than that provided by culture, direct fluorescence antibody, serological testing, or even conventional PCR methods. Additional applications include tissue product testing, bioterrorism monitoring, and characterization of host–pathogen interactions in the search for additional diagnostic biomarkers. Oncology Standard diagnostic procedures for human tumors use a combination of histopathology, special stains, immunohistology, radiology, and clinical data. Diagnostics derived in this manner provide data on tissue origin, tumor type, stage, and grade, along with information on completeness of surgical tumor removal (Dietel and Sers, 2006). However, these tests are neither sensitive nor specific enough to differentiate patients who will respond to treatment from those who will not. The application of high-throughput DNA array technologies, which provide gene expression profiles, has revolutionized tumor diagnostics and led to the identification of novel tumor subgroups for otherwise indistinguishable tumors (Dietel and Sers, 2006). Researchers have also identified genes the expression of which can be used to predict the metastatic potential of breast carcinomas. In the future, technologies that take a systems approach will be needed to identify patients who will benefit from novel therapies. These methods must be capable of detecting multiple oncogenic pathways in tumors both before and during therapy and they must be adaptable to routine clinical diagnostic testing. Possible methods include: tissue microarrays; forward-phase protein microarrays, capable of simultaneously detecting multiple protein interactions with a single sample; and reverse-phase protein microarrays, capable of probing multiple analytes with specific antibodies (Gulmann et al., 2006). These testing modalities may be applied to primary or metastatic tumor biopsies, or to ‘blood biopsies’ of rare circulating tumor cells. Pharmacogenomics Understanding the role of human genetic variation in disease susceptibility and in the selection and efficacy of therapeutics is

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an essential component of optimizing care. Pharmacogenomics looks at the impact of patient genetic variation on response to therapeutics (see Chapter 27). Pharmacogenomics also influences the delivery and cost of healthcare (Phillips et al., 2004). Sensitivity, toxicity, and resistance to medications at standard doses are prime examples of the effect of genotypic heterogeneity within the patient population. Well known, clinically relevant examples include differential responses to warfarin, codeine, thiopurine antileukemic drugs, and succinylcholine. Genetic differences in receptor structure and affinity, drug metabolism, and drug transport systems are now known to play an important role in both differential responses to drug therapy and adverse drug reactions (ADRs). a) CYP450

Adverse drug reactions result in 6.7% of hospitalizations and 0.32% of mortalities (Montgomery and Louie, 2001). The genes most associated with ADR encode receptors, metabolic enzymes, and metabolite transport proteins, the same genes have been implicated in environmental toxin susceptibility and cancer predisposition. Drug efficacy is directly related to the binding of the drug molecule to cell surface receptors. For example, it has been demonstrated that patients expressing high levels of beta-adrenergic receptors are more responsive to beta-agonists and antagonists. Conversely, those expressing low levels of this receptor require higher-drug levels to achieve a comparable pharmacological effect. Once inside the cell, the drug is metabolized by a number of enzymes catalyzing alterations in the molecular structure of the therapeutic drug. Genetic variation in the genes encoding metabolic enzymes leads to differences in enzyme activity that can be characterized as extensive (standard dosage), intermediate (slower than extensive, altered dosage), poor (enzyme deficiency, do not treat with some drugs), and ultrarapid (break down drugs faster than extensive, no effect or reduced effectiveness from drug therapy). One class of metabolic enzymes is the cytochrome P450 superfamily (CYP) that comprises more than 40 isozymes. These enzymes metabolize a large number of drugs, small molecules, mutagens, and carcinogens by modifying parent molecule functional groups. Polymorphisms in the gene CYP2D6 can result in a homozygous recessive inactive genotype that cannot convert codeine into the active metabolite morphine (Rabinowitz and Poljak, 2003). In 2005, the FDAapproved Roche Diagnostic’s AmpliChip CYP450 test, a P450 molecular array which detects mutations in both the CYP2D6 and CYP2C19 genes, which contribute to the metabolism of about a quarter of all prescribed drugs. The data provided by the AmpliChip aids physicians in determining drug selection and dosage. An example of the potential impact of CYP450 typing is illustrated by the drug warfarin which is metabolized by enzymes encoded by the gene CYP2C9. Polymorphisms in this gene influence warfarin dosing requirements and warfarin-associated bleeding (Wittkowsky, 2002). It has been demonstrated that the maintenance warfarin dose can be estimated from demographic, clinical, and pharmacogenetic factors (Gage et al., 2004). A second gene, VKORC1, has been associated with

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the average weekly warfarin dose required to maintain patients at their desired anticoagulation target. With more information it is expected that predictive models for warfarin dosing can be developed and help the more than one million patients which each year take warfarin at appropriate dosage to avoid thromboembolic events (Li et al., 2006). Twelve percent of those users currently experience major bleeding episodes and death results in as many as 2%. b) HLA Genotyping

Abacavir is a nucleoside analog that is a potent inhibitor of the HIV reverse transcriptase enzyme. However, between 4–8% of patients develop hypersensitive reactions which can result in life threatening hypotension. The possession of human leukocyte antigen (HLA) B*5701 is a risk factor for abacavir hypersensitivity. Use of HLA-B*5701 genotype screening has been shown to effectively reduce the incidence of abacavir hypersensitivity through the exclusion of patients positive for this allele from abacavir combination treatment (Mallal et al., 2008). Adoption of this pharmacogenomic test was impacted by drug safety issues and the demonstration of its cost-effectiveness (Hughes et al., 2004). Additional associations between specific HLA genotypes and life threatening ADRs include Allopurinol and HLA-B*5801 (Hung et al., 2005), and carbamazeprine and HLA-B*1502 (Dainichi et al., 2007). In December, 2007, the FDA released guidelines calling for genetic testing of the HLAB*1502 genotype prior to starting carbamazeprine therapy for patients with Asian ancestry (FDA, 2007e). c) Cancer Pharmacogenomics

Cancer pharmacogenomics impacts patient safety and therapeutic efficacy and can be used to select treatment choice based on tumor genomics and patient genotype. Methodologies such as molecular cytogenetics, somatic mutation detection, and gene expression profiling have been used to examine genotypic differences in cancer tissue and in patient response to therapy. Chronic myelogenous leukemia (CML) is one of the most common forms of leukemia. Most cases of CML result from a chromosome abnormality whereby DNA from chromosome 9, which contains most of the proto-oncogene c-abl, is translocated onto chromosome 22 within the breakpoint cluster region (BCR) gene (Billings and Brown, 2004). This results in a gene fusion constitutive for expression of a protein with tyrosine kinase activity. This activity affects intracellular signaling pathways that result in unregulated cell proliferation. Molecular diagnosis of CML utilizes quantitative PCR. Fluorescence in situ hybridization (FISH) can also be used to visualize the translocated chromosomes. The oral drug imatinib (Gleevec) was specifically designed as a selective inhibitor of the BCR-ABL tyrosine kinase and has demonstrated therapeutic superiority over conventional drug therapy. Molecular diagnostic testing can be used to monitor responsiveness to Gleevec or the development of therapeutic resistance. Hereditary nonpolyposis colorectal cancer (HNPCC) is the most common hereditary cause of colon cancer, accounting for about 2–5% of all colon cancer cases (see Chapter 73).

It is caused by mutations in any of at least five DNA mismatch repair genes, and DNA tests are available for the most common genes. The HNPCC syndrome predisposes a person to developing colon cancer at a young age. Presymptomatic and predispositional testing in families have been conducted. Since approximately 90% of tumors from HNPCC patients show microsatellite instability, testing for this phenomenon alone can be a good guide to the necessity for further molecular characterization. Gene sequencing can be used to identify the precise mutation. Mutational data, along with DNA microsatellite instability testing results, can be used to identify first-degree relatives with HNPCC. Once HNPCC has been implicated from clinical data, immunohistochemistry testing can be conducted on tumor tissue for confirmation of diagnosis. In this way, members of families with HNPCC have a means to know if they need aggressive monitoring and treatment or not. Thiopurines, thioguanine, and mercaptopurine are commonly used anticancer therapeutics. The thiopurine methyltransferase (TPMT) enzyme catalyses the methylation of thiopurines. The TPMT gene is polymorphic, and one in 300 patients is deficient in enzyme activity. At standard doses, this can lead to toxic accumulation of thiopurines, which can be fatal. Three mutations account for the majority of mutant alleles, and genetic testing is available. Children with leukemia who receive these medications are routinely screened for these deficiency genes (Billings and Brown, 2004). In late 2005, the FDA-approved Third Wave Technologies’ Invader UGT1A1 molecular assay, which is used to identify patients who could experience adverse reactions to Camptosar (irinotecan), a colorectal chemotherapy drug (Grebow, 2005). The test detects mutations in the UGT1A1 gene, which produces a metabolizing enzyme active on the therapeutic. This was one of the first pharmacogonomic tests to be FDA approved for use as a companion diagnostic to a specific drug. Finally, genetic testing to predict future disease risk based on an inherited germline mutation is also available. The BRCA1/2 mutations are associated with a higher risk of breast and ovarian cancer. Approximately 60–80% of women with BRCA1/2 will develop breast cancer. BRCA testing, the BRAC Analysis test, is expensive and provides information with limited therapeutic response. Tests such as these can experience initial resistance among insurers to provide reimbursement (Phillips et al., 2004). Adoption of Pharmacogenomic Testing What are the factors that influence the uptake of pharmacogenomic diagnostic testing? Clinical validity and utility need to be demonstrated and communicated to physicians and patients. Rapid, reliable, inexpensive, and easily interpretable molecular tests will increase the clinical use of these diagnostic tests (Shah, 2004). Clinical relevance must also be demonstrated. This may limit the use of this technology to applications where there exists choice of treatments. In addition, due to the screening nature of these tests, cost-effectiveness must be demonstrated (Flowers and Veenstra, 2004). However, determining cost-effectiveness of genetic technology will not be simple, a fact that has led to some opposition to their large scale uptake in healthcare

Novel Diagnostics

(Rogowski, 2007). Additional obstacles to the deployment of pharmacogenomic testing include obtaining reasonable reimbursement for testing, and the education of physicians and patients as to the value of tests and interpretation of the results. Examples of Translational Diagnostics Investigation of specific examples of translational diagnostics can reveal crucial issues and challenges faced as new methods enter health care. HIV Viral Load Testing Prior to the 1990s, therapeutics that truly impacted the course of viral diseases were not available. The symptoms could be affected, but there was no antibiotic equivalent for treating viral diseases. However, by the mid-1990s, pharmaceutical companies had a number of antiviral compounds; some specifically tailored to HIV, well into clinical trials and needed an accurate biomarker to analyze effectiveness of the new therapies. This convergence of therapeutics and diagnostics would lead to the birth of the theranostic, a diagnostic test linked to the application of specific therapies (Warner, 2004). In 1996, Roche Diagnostics began the Roche Amplicor® Access Program to provide two free baseline HIV viral load tests to anyone in the United States with HIV (James, 1996). Reference laboratories helped execute this program, which began the successful uptake of HIV viral load testing in the marketplace. As with traditional diagnostics, molecular diagnostic tests needed to demonstrate utility and performance before their adoption into practice. First, there must be an unmet clinical need, which was clearly the case with HIV. Secondly, with the development of antiviral therapies, HIV viral load testing provided information that resulted in a therapeutic action, and also provided the means to continually monitor the efficacy of the treatment. Without a therapeutic, the test would have provided information that would not have informed the treatment of HIV. Additional factors sped the uptake of HIV viral load testing. Initial testing involved a relatively small group of highly focused clinicians. This made it much simpler to educate physicians in the use of the viral load test for the management of HIV. Highly motivated patients fueled demand for the test. The viral load test had a significant role in early clinical trials of antiviral therapeutics and became an objective measurement tool, even before any protease inhibitors were approved for physician use. Publications on the antiviral therapeutics included the HIV viral load results. Thought leaders who participated in the trials became early adopters of the test, even before the test was FDA approved. The viral load test was put into a kit format and became FDA approved at approximately the same time as the first protease inhibitors became available. Recommendations for viral load testing became part of standard guidelines for treating HIV infection (Chesebro and Everett, 1998). The theranostic became tied to the use of the drug. Roche’s Amplicor Access Program led to rapid test utilization. As volumes increased, manufacturers developed automation. HIV management requires relatively frequent monitoring, as often as four times per year. Therefore, the test was not a “one off ” test as in constitutional

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genetic testing. HIV viral load testing also obtained its own CPT code, facilitating reimbursement. Additionally, the kit manufacturer, the national reference labs, patients, and doctors all worked to ensure that the test was reimbursed. These factors helped make HIV viral load testing cost effective and a foundation of HIV prevention efforts. The combination of viral load testing and new therapeutics transformed HIV from a uniformly fatal disease to a chronic treatable disease. Therefore, HIV has become a disease with an “actionable” result. Recent Centers for Disease Control (CDC) recommendations now call for general population screening for HIV status. This example of translating a diagnostic into patient care was successful because so many of the hallmarks of successful diagnostic tests were in place. There was an unmet clinical need, an actionable result was obtained from testing, the test was ultimately cost effective, high-throughput analysis became available, and reimbursement issues were rapidly resolved. Breast Cancer, HER2 Receptor Assay, and Herceptin Similar to the convergence of HIV therapeutics and the need for HIV viral load assays, the development of the anti-cancer therapeutic Herceptin required a clinical laboratory test to determine Her2 receptor status (Tsongalis and Silverman, 2006). Amplification of the human epidermal growth factor receptor 2 gene (HER2) in primary breast cancer carcinomas had been shown to correlate to poor clinical prognosis for breast cancer patients (Ferretti et al., 2007). The Her2/neu oncogene is active in 25–37% of breast cancers, and overexpression of the HER2 protein on cell surfaces was found to stimulate uncontrolled tumor growth. Herceptin (trastuzumab) is a recombinant DNA-derived monoclonal antibody that binds with high affinity and specificity to the extracellular domain of the HER2 receptor and inhibits the proliferation of HER2 overexpressing tumor cells. Herceptin alone was associated with an objective response in 15% of extensively pretreated patients with metastatic breast cancer overexpressing HER2, and 26% of previously untreated patients (McKeage and Perry, 2002). Cardiac dysfunction occurred in 13% of patients receiving Herceptin and paclitaxel and in 4.7% of patients receiving only Herceptin. Given the high cost of Herceptin therapy, the significant side effects of Herceptin treatment for some patients, and the targeted nature of the therapeutic, it was necessary to restrict therapy to patients expected to respond to treatment. In 1998, the FDA approved both Herceptin and the HerCepTest for the treatment of metastatic breast cancer. HER2 protein is detected primarily by IHC or FISH methods. HercepTest and PathVysion, a FISH assay for HER2 amplification, are now used to select patients for Herceptin therapy. Uptake of diagnostic testing for HER2 receptor status was greatly accelerated due to safety issues associated with the therapy. Additionally, the assay did not require new tissue samples as in many cases archived samples in paraffin blocks were already being sent to reference labs for estrogen and progesterone analysis, and physicians could merely check another box on the test order sheet. Randomized clinical trials have recently demonstrated significant differences in survival when comparing chemotherapy to chemotherapy plus

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Herceptin in women with HER2 overexpressing breast cancer in either the metastatic or adjuvant setting (Ferretti et al., 2007). Thus, what was once a prognosticator of poor outcome for the patient has become a predictive marker of response to therapy. Unfortunately, a lack of concordance among detection techniques, different scoring systems used to determine HER2 status, and a lack of lab standardization and quality based on test experience have led to significant variation in HER2 testing (Nelson, 2000). Recent CAP/ASCO guidelines, that include a testing algorithm, QA requirements, lab evaluations, and a scoring system, should help address these issues (CAP/ASCO, 2007). Cervical Cancer, HPV, and Gardisil Human papillomavirus (HPV) is the major cause of cervical cancer, a disease that kills more than 200,000 women worldwide each year (Gottlieb, 2002). In the United States more than 6 million new cases are reported annually and at least 20 million people in this country are already infected (NIAID, 2006). Approximately 40 of the 100 types of HPV virus can be sexually transmitted, but most rarely cause symptoms or disease. Types 6 and 11 are low-cancer risk types that cause genital warts. Lowand high-risk types can cause the growth of abnormal cells which can be detected when a Pap test is done during a gynecologic exam. The Bethesda system divides the most common clearly abnormal Pap results into either: low-grade squamous intraepithelial lesions (LSILs) which are mild cell changes associated with HPV; high-grade squamous intraepithelial lesions (HSILs) which are precancerous cell changes which should be treated by a physician; and cancer. Clinicians managing a notcompletely-normal Pap result, known as ASCUS (atypical squamous cells of undetermined significance), may benefit from the additional information provided by HPV testing. In 2000, the FDA-approved Digene Corporation’s hc2 High-risk HPV DNA test for use in women with abnormal Pap test results. In 2003, the FDA expanded the use of HPV testing in conjunction with the Pap test for routine screening. The test uses hybrid capture technology to directly detect HPV virus DNA and the current system allows high-throughput testing. A woman fitting the appropriate clinical criteria with both a negative Pap and negative HPV DNA test has less than a one in one thousand chance of developing cervical cancer. In 2006, the FDA-approved Merck and Co’s Gardisil, a recombinant vaccine which is designed to prevent the majority of HPV-related clinical diseases, those caused by HPV 6, 11, 16, and 18. HPV types 16 and 18 account for approximately 70% of cases of cervical cancer, while HPV 6 and 11 cause approximately 90% of genital wart cases. In clinical studies, Gardasil prevented 100% of HPV 16- and 18-related cervical cancer in women not previously exposed to the relevant HPV types. These studies were conducted on 21,000 women ages 16–26 and the vaccine was nearly 100% effective in preventing precancerous lesions and genital warts. Gardisil was evaluated and approved in 6 months under the FDA’s priority review process, a process for products with potential to provide significant health benefits (FDA, 2006f ). On June 28, 2006 the CDC’s Advisory Committee on Immunization Practices (ACIP) recommended that all females

between the ages of 11–26 receive the HPV vaccine as part of routine primary care. HPV diagnostic tests expanded in utility from a test limited to abnormal Pap test results, to a diagnostic test that in conjunction with the Pap test is now standard of care for routine screening for women 30 years of age or older. The move to a liquid Pap test from previously common smear methods removed the need for additional sampling for HPV testing and the development of an effective vaccine bolstered the public awareness of HPV. Technological innovations reducing sampling barriers and improving therapeutic options along with other factors aided the rapid uptake of HPV testing.

CONCLUSIONS: TRANSLATIONAL CHALLENGES FOR INNOVATIVE DIAGNOSTICS Diagnostic tests are being developed at a rapid rate, and the technologies of diagnostic testing are expanding. However, not all diagnostic tests find their way to application in health care. For some, clinical utility may not be demonstrated due to the lack of an actionable result, cost-effectiveness, high-throughput methods, or adequate reimbursement. For example, early asymptomatic glaucoma testing was widely abandoned because early detection did not affect the outcome. Other tests may not achieve the analytical performance characteristics required of a good diagnostic. Assay sensitivity, specificity, and predictive value must be high (Gaeta, 2005). Reproducible precision and a clinically reportable range must be demonstrated (Irwig, et al., 2002). For example, before microarray technology can be translated widely to diagnostic applications, problems with sensitivity and reproducibility will have to solved (Petrik, 2006). A rigorous evaluation of diagnostic tests prior to their introduction into clinical practice is the goal of the standards for the reporting of diagnostic accuracy (STARD) initiative (Bossuyt et al., 2003) and evaluation of genomic applications in practice and prevention (EGAPP), a CDC project to establish an evidence-based process for assessing applications of genomic technology (CDC and National Office of Public Health Genomics, 2008). Ultimately, the role of diagnostics in clinical care must be balanced with other tools available to the physician, such as patient history and physical exam (Halkin et al., 1997). Additionally, technological advancements must produce an increase in efficacy of treatment in excess of that produced by improving the delivery of older treatments (Woolf and Johnson, 2005). The future of innovative diagnostics holds great promise for providing rapid identification of disease susceptibility and status, for monitoring disease progress and therapeutic efficacy, for reducing ADRs and speeding appropriate therapy selection, for the development of personalized medicine, and for optimizing disease prevention and cures. To see this promise unfold, a number of challenges must be overcome. Diagnostic testing must address an unmet medical need, lead to an actionable event, and demonstrate its clinical utility. These diagnostics must also demonstrate high specificity, sensitivity, and predictive value.

References

T A B L E 3 1 . 2 Features of successfully translated diagnostic tests and technologies ● ● ● ● ● ● ● ● ● ● ● ● ●

Addresses an unmet medical need and leads to an actionable result Promotes patient safety and/or stratifies patient risk Highly sensitive and specific, with significant positive or negative predictive value Monitors disease progress or treatment efficacy Speeds clinical trials of innovative therapeutic treatments Clinical validity and utility confirmed in multiple peer reviewed publications Demonstrable cost-effectiveness leading to CPT coding and reimbursement Amenable to automation and easy clinical sample acquisition Elicits patient advocacy and thought leader support Included in guidelines developed by professional societies or government agencies Easy technical adoption by high volume reference labs Favorable reviews by technology assessment groups Obtains FDA clearance or approval, if applicable

For a novel diagnostic to be ultimately translated effectively, additional obstacles must also be overcome. The diagnostic test must prove functional in the health care setting. For example, do new sample types need to be procured, or can the diagnostic test replace or add on to existing assays while retaining current sampling procedures? Will the clinical value of the test be clearly demonstrated to thought leaders, early adopters, patient advocacy groups, and other stakeholder; required for rapid uptake of the diagnostic? The role of professional society guidelines, CMS coverage, technology review by the insurance industry, and regulators must be considered. Will reimbursement issues delay the uptake and access of the test to a large sector of the patient population? Are there patient safety issues, within specific treatments, that warrant rapid clinical uptake? What are the training requirements? How does the clinical lab select the appropriate technology from a pool of technical approaches to the diagnostic problem? What is the best way to deliver the diagnostic to healthcare providers and users? How should a new diagnostic test best be used to optimize the healthcare benefit, as a stand alone diagnostic, or linked to a treatment protocol (a companion diagnostic or theranostics)? Finally, will new accurate and reliable diagnostic methods make the biases and inaccuracies that have characterized the practice of clinical medicine and patient reports a thing of the past? And, if they do, what will be lost and what will be gained?

TABLE 31.3 ● ● ● ● ● ● ● ●

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Improving diagnostic translation

Document health value of diagnostic innovation Enhance support for translational research Identify gaps and key role of technological innovation in their reduction Speed development of innovation and delivery ease Increase multidisciplinary communication and training Reduce regulatory hurdles Enhance clinical trials for new test validity, utility and health economic outcomes Improve communication of diagnostic test functionality to thought leaders, patient advocacy groups, early adopters, regulatory agencies, and payers Support test standardization, quality assurance standards, and laboratory self-evaluations Improve awareness of the lessons learned from previously translated diagnostics

These are crucial concerns and challenges that must be addressed for virtually any new diagnostic to reach its full potential to improve health care. Closing the translational gaps highlighted by these issues and others requires research, its own knowledge translation, financial support, and an awareness of the lessons learned from previously translated diagnostics (Table 31.2). Even though diagnostics influence 60–70% of healthcare decision-making, they make up less than 5% of hospital costs and 1.6% of Medicare costs (Nordhoff, 2005). In the next decade, new diagnostics are predicted to have an even greater impact on healthcare decision making by providing improved disease prediction and prognostication, and therapy guidance. Studies have found a 30–50% reduction in direct hospital and outpatient charges when changes in patient health status are accurately monitored.Yet, diagnostic tests recommended as standard of care are underused 51% of the time (Olsen, 2006). Low compliance with diagnostics-based quality measures for diabetes, cardiovascular disease, colorectal cancer, and breast cancer can be linked to 34,000 avoidable deaths and $899 million in avoidable healthcare costs (Olsen, 2006). Efficient translation of new knowledge and technology is critical so that the promise of innovative diagnostics and personalized medicine can be realized. Improving the translation of innovative diagnostics will require not only an understanding of specific lessons learned from previous translations, but also an understanding of the conceptual framework within which diagnostic translations occur (Table 31.3).

REFERENCES Billings, P.R. and Brown, M.P. (2004). The future of clinical laboratory genomics. MLO, 8–15. Bossuyt, P., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziuo, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D. and de Vet, H.C.W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. BMJ 326, 41–44.

Brown, I.H. (2006). Advances in molecular diagnostics for avian influenza. Dev Biol (Basel) 124, 93–97. CAP/ASCO. (2007). HER2 and You: Guidelines provided by CAP and ASCO: http://www.cap.org/apps/docs/diseases/cancer/ AboutHer2Testing.pdf. CDC, National Office of Public Health Genomics. (2008). (http:// www.cdc.gov/ genomics/gtesting/EGAPP/about.htm).

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32 The Role of Genomics in Enabling Prospective Health Care Ralph Snyderman

INTRODUCTION In the early 1900s, the emerging sciences of physiology, chemistry, immunology, microbiology, and radiology began to be introduced into the practice of medicine, which at that time, considered disease to be due to imbalances of bodily humors and transmitted through climacteric miasmas. For the first time in history, the application of science to medicine laid a solid foundation for understanding the pathophysiology of disease (Bynum, 2002; Snyderman, 2004; Snyderman and Williams, 2003). In particular, microbiology, through the identification of “causative” agents for numerous infectious diseases, had a profound impact on concepts concerning the cause and potential cure of illnesses. Given the increasing number of diseases determined to have specific “causes” along with the new found ability of chemistry to synthesize therapeutic “magic bullets,” early 20th Century medicine logically pursued a scientific, reductionist approach to health care which continues today: that is, identify a disease’s root cause and eliminate it. This approach has enabled medicine to reverse some diseases, prolong life, and at times, effect wondrous cures. The reductionist approach to medicine, however, is limited in that the evolution of virtually all diseases is more complicated than a single root cause. The development of disease is based upon one’s genetic susceptibilities, complex interactions between initiating factors, and health status. In the aggregate, these can lead to disease progression, or not (Figure 32.1). For example,

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while the tubercle bacillus is the causative agent of tuberculosis, the clinical manifestation of exposure, as a consequence of host susceptibility, can range from transient subclinical infection to rapidly fatal miliary disease. Even a well-defined genetic disease such as sickle cell anemia, known to be due to a single mutation, can have manifestation ranging from death in adolescence to far milder forms compatible with long-term survival. By focusing primarily on the reductionist model of disease development, our health care system has, unfortunately, not focused on the prevention or treatment of complex chronic diseases. At present, individuals with any of five chronic conditions account for roughly two-thirds of all health care expenses (Figure 32.2). A more rational approach to health care is now possible. New emerging sciences, notably genomics, can transform medical practice now just as new applications of science and know-how transformed medicine 100 years ago (Figure 32.3). Genomic and related expanding fields of research can impact health care in numerous ways (Figure 32.4), but most profound may be their ability to predict risk, track disease progression and anticipate clinical events, thereby enabling truly personalized, predictive, preventative care. To do this, the strengths of today’s diseaseoriented reductionist approach must be integrated with an approach that focuses on prevention and minimization of disease with an emphasis on long-term strategies. This personalized, predictive, and preventative approach to health care is known as “prospective health care” (Snyderman and

Copyright © 2009, Elsevier Inc. All rights reserved.

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Reductionism: Single factor (20th Century) Causative factor

Disease

Emergence: Multiple factors (21st Century) Baseline risk Preclinical progression

Disease initiation

Disease progression

Irreversible damage

Environmental factors

Figure 32.1 Concept of disease: reductionism versus emergence. The reductionist approach considers disease to be a consequence of a pathogenic factor; that is microbe. More accurately, disease development depends on the host’s susceptibility to pathogenic factors and exposure to them (Snyderman and Yoediono, 2006).

Total US health care spending 2002: $1.5 trillion $1.6T

$1.5T

$1.2T

All other care

$0.8T

$0.4T

Treating chronic disease

Leading chronic diseases

Population affected

National bill

Diabetes

16 million

$44–$98B

Cancer

15 million

$37–$107B

Congestive heart failure

5 million

$20–$40B

Respiratory disease

30 million

$24–$36B

Hypertension

50 million

$23–$33B

US healthcare spending

Initiating events

Earliest molecular detection

Earliest clinical detection

Typical current intervention Cost

Disease burden

Baseline risk

1/reversibility

Figure 32.2 Consequences of current approaches to health care. While little is spent on prevention, nearly three-quarters of health care expenditures fund the treatment of late-stage chronic disease. (Source: American Heart Association, American Cancer Society, American Lung Association, National Institute of Diabetes and Digestive and Kidney Disease.)

Time

Figure 32.3 Disease progression. Diseases develop over time, with pathology increasing, reversibility decreasing and cost of care increasing (Snyderman and Yoediono, 2006).

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Impact

PREDICTIVE MODELS • Genomics

• Disease risk prediction

• Proteomics

• Quantification disease burden

• Metabolomics

• Tracking of pathogenses

• Medical technologies

• Disease event prediction

• Informatics

• Therapeutic evaluation

• Systems Biology

Figure 32.4 Impact of new technologies on prospective health care. Last century’s science enabled reactive responses to disease while this century’s science enables prospective approaches as well.

Traditional medical evaluation and record • Chief complaint • History of illness • Past medical history • Family history • Social history • Physical exam • Diagnostic tests • Assessment and plan

Prospective evaluation and record

At the core of prospective health care are personalized health risk assessment and predictive modeling. A predictive model uses statistical algorithms to identify factors that predict events (Anderson et al., 1991). The predictive model can be used to forecast events based on factors or biomarkers, which are most likely to correlate with or be causative of the future clinical event. The development of a predictive model uses various methods, such as logistical regression or neural network, to differentiate predictive factors from other variables which are not as useful for anticipating the clinical outcome of interest. The predictive model can then be applied to a current situation to determine the risk and timing of an event occurring. The better the model, the better the accuracy in predicting the occurrence and timing of the event. The details involved in developing a predictive model, as well as other related components such as individualized patient databases and risk model libraries, are beyond the scope of this chapter, but a simplified version is shown in Figure 32.6.

• Health risk assessment • Current health status • Disease burden and pathogenesis tracking • Clinical event prediction • Therapeutic plans and evaluation

Figure 32.5 Current medical record versus prospective approach to health care. Physicians currently evaluate patients using a disease-focused approach. A more effective approach would include strategic health planning as indicated by the prospective approaches shown on the right (Snyderman and Yoediono, 2006).

Williams, 2003; Snyderman and Yoediono, 2006;West et al., 2006; Williams et al., 2003). Key features of prospective health care include personalized health risk assessment, tracking of pathogeneses, prediction of clinical events, and strategic planning to mitigate risks and evaluate therapeutic benefits. With a prospective health care model, individuals will be provided with personalized health plans which promote strategic health planning by incorporating individualized risk assessments for diseases, disease tracking, clinical event predictions and therapeutic planning (Figure 32.5). Genomic research will play an integral role in providing these capabilities given that the molecular processes underlying disease susceptibility, progression, and therapeutic response differ among individuals and can be measured, in part, through genomic analyses. Technological advances allowing the measurements at the DNA, mRNA, protein, and metabolic levels are already beginning to enable inferences regarding health risks or clinical outcomes (Snyderman and Langheier, 2006).

PREDICTIVE FACTORS The predictive factors most commonly used for disease-related risk assessments encompass clinical, demographic, family history, and laboratory data. These types of data can provide insight into the likelihood of an individual developing a condition or event and are relatively cost-effective and collected routinely. However, generally they are limited in terms of being able to accurately predict when disease will occur. Furthermore, many false-positives and false-negatives are associated with these types of data, which are often epidemiologic in nature (i.e., Framingham Study) (Brindle et al., 2003; The International HapMap Consortium, 2003). Predictive factors, which serve as more accurate markers of disease susceptibility, likely will come in the form of biomarkers which are directly related to the cause of the clinical event. A biomarker is a characteristic (i.e., expressed gene, protein or metabolite) that is objectively measured and evaluated as an indicator of biological or pathogenic processes or pharmacological responses to therapeutic interventions. Genomic research will play an integral role in identifying predictive biomarkers which, in combination with other types of predictive factors, will enable more accurate risk analyses for baseline risk assessment, disease tracking, clinical event predictions and response to therapeutics. Biomarkers can be thought of as belonging to two general categories: (1) stable biomarkers that are inherited and/or change rarely over a lifetime, and (2) dynamic biomarkers which express ongoing biological activities.The latter include gene expression, protein expression and the measurement of metabolic factors. Measurement of genomic factors that lie in the causal pathway of a disease or a therapeutic response, or factors such as single-nucleotide polymorphisms (SNPs) that are highly associated with causal genes, will serve as better predictors of adverse outcomes than much of the demographic data now being collected. Stable DNA gene predictors will enhance baseline clinical risk assessment and primary prevention, while dynamic mRNA, protein and metabolic factors will reflect ongoing biological or pathological processes.

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Out of sample populations

Clinical data cohort Data Clinical

Genetic

Proteomic

Other Prediction models Statistical validation

Baseline risk Disease progression Event prediction Decision support

y  f (x1, x2,…xn) Predictive model

Patient’s data

Predictive modeler

Additional data New biomarkers

Patient

Physician

Figure 32.6 Methodology for creating clinical risk predictive models. Relevant clinical data and/or biomarkers are statistically analyzed to create validated risk models for particular clinical conditions or events (Snyderman and Yoediono, 2006).

Assess risk

Refine assessment

Initiating events

Earliest molecular detection

Earliest clinical detection

Monitor progression Predict events Inform therapeutics Typical current intervention Cost

Disease burden

Baseline risk

Predict/diagnose

1/reversibility

Decision support tools:

Time Baseline risk Sources of new biomarkers:

Figure 32.7

Stable genomics: • Single-nucleotide • Polymorphisms (SNPs) • Haplotype mapping • Gene sequencing

Preclinical progression Dynamic genomics: • Gene expression • Proteomics • Metabolomics • Molecular imaging • Clinical risk models

Disease initiation and progression Therapeutic decision support

Contributions of new technologies to disease prediction and prediction of clinical events (Snyderman and Yoediono, 2006).

Analysis of these will enhance refined risk assessments to track disease progression, predict events, and guide therapeutic choices (Figure 32.7). The advantage of genotypic data for baseline prediction is that they can be collected at birth or any time during one’s life

and, theoretically, needs to be collected only once. Baseline risk assessments using demographic data or static genomic information will likely have lower specificity (a higher number of false positives) than molecular measures that are dynamic and change as a function of time. Nonetheless, as more information regarding

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inherited risks is gathered, baseline risk assessment will become increasingly useful. For secondary prevention (for example, predicting heart attack in an individual with diabetes), stable genomic data may be less valuable. In this case, relevant dynamic indicators will provide a more powerful predictor of the disease event. Over time, the cost of genotyping should plummet and the value of baseline risk analysis should increase markedly. Moreover, the identification of clinically valuable dynamic biomarkers is an area of fertile research and will enhance accurate ongoing risk assessment timing of disease events and prediction of therapeutic outcomes. Identifying accurate predictors for baseline risk assessment will be facilitated greatly by clinical research and the HapMap project and the Genetic Association Information Network (GAIN) initiative, a public/private partnership between the NIH, foundation of NIH and industry. The GAIN study will fund $20 million of research to identify SNPs associated with common diseases. The International HapMap Consortium is characterizing common patterns of DNA sequence variation and the extent of linkage disequilibrium in the human genome. This will facilitate the characterization of genotypes and identification of key SNPs related to chronic disease; traditional and advanced association algorithms will allow the analysis of the HapMap (International HapMap Project, http://www.hapmap.org)(Niu, 2004; Liu et al., 2004; Evans et al., 2004). Online Mendelian Inheritance in Man (OMIM), a database of disease risk genes, is already revealing an increasing number of stable genomic factors that should be useful in risk assessment (Online Mendelian Inheritance in Man, http:// www.ncbi.nlm.nih.gov/entrez/query.fcgi?dbOMIM). The role of an individual’s gene variants in altering their metabolism and response to drugs is becoming important in drug development and in certain areas of clinical practice, particularly oncology (Lee et al., 2005). For individuals whose genes, SNPs, family history, or clinical information identify them as high risk for a particular disease, surveillance will be needed to track possible disease progression and, when relevant, to evaluate therapeutic support. Such tracking will likely include the measurement of dynamic factors, including gene-expression, proteomics, and metabolomic assessments. Analyses to track baseline risk and disease development will hopefully be incorporated into personalized health plans in the future (Langheier and Snyderman, 2004). For example, children with a family history of type 1 diabetes could have a baseline risk assessment that evaluates various predictive SNPs. Those determined to be at high risk could undergo a surveillance protocol, tracking their levels of biomarkers associated with actual disease development and progression. (Barker et al., 2004; Eisenbarth, 2004). As effective preventative therapies are identified, these analyses could guide appropriate intervention before -cells are destroyed. Initial applications of genomic technologies are being applied to predict outcomes in defined clinical conditions. For example, gene-expression microarrays and proteomic techniques show promise for identifying the aggressiveness of cancer, allowing the creation of predictive models for likely survival time with and

without treatment (Anderson and LaBaer, 2005; Berchuck et al., 2005; Pittman et al., 2004; Rich et al., 2005). Moreover, gene expression in circulating mononuclear cells is being used to predict organ rejection in patients with heart transplants, obviating the need for myocardial biopsy in some conditions (Deng et al., 2006). Many gene-expression tests are being developed to gauge appropriate chemo-therapeutic regimens for a given patient’s cancer (Bild et al., 2006).

RISK ASSESSMENT FOR BREAST CANCER Breast cancer provides a useful example for how genomic research and predictive models can improve clinical care. For baseline risk measurement, a tool was developed in 1989 to estimate the likelihood that a woman at a given age with defined risk factors will develop breast cancer over a specified time (Gail et al., 1989). This Gail breast cancer model aids physicians in developing a personalized strategy for screening and treatment. The model was constructed from case-control data of the Breast Cancer Detection Demonstration Project and included age at menarche, age at first live birth, number of previous biopsies, and number of first-degree relatives with breast cancer as indicators. Newer and improved predictive models include more robust family history (i.e., the Claus model) (Santen et al., 2007) and causal disease genotypes such as BRCA1 or BRCA2 (for example, in the BRCAPRO model) (Euhus, 2001) as predictors. In contrast to the Gail model, which uses logistic regression, the Claus model uses genetic modeling to determine age-specific breast cancer development probabilities from family history. BRCAPRO employs a Bayesian model, focused on BRCA1 and BRCA2 to determine the risk of breast cancer. Accurate prediction of breast cancer will require continued research. While the incorporation of BRCA1 and BRCA2 disease alleles as predictors facilitates the risk assessment of cancer, these alleles account for only a small proportion (5%) of breast cancer in the United States. Other genes of interest will certainly be found as breast cancer is a feature of many other syndromes with known genetic mutations, such as, the Li-Fraumeni syndrome (caused by a germline p53 mutation), the Cowden syndrome (a PTEN mutation), and the Peutz-Jegher syndrome (an STK11 mutation) (Garber and Offit, 2005; Nagy et al., 2004). Other genotypes associated with increased risk of breast cancer are located in several genes, including BRCATA on 11q, BRCA3 on 13q21, RB1CC1 on 8q11, BWSCRIA on 11p15.5, and BRIP1 on 17q22 (OMIM #114480 Breast cancer, http://www.ncbi.nlm.nih.gov/ entrez/dispomim.cgi?id114480). Further research may enable baseline analysis of breast cancer risk to be used for primary care screening (Nelson et al., 2005). A validated “SNP chip” to test for the presence of disease genotypes for multiple alleles should help improve the quality of the baseline risk assessments in the broader population (Listgarten et al., 2004). When they become cost-effective, early screening of a broader range of relevant genotypes could be incorporated into

Acknowledgements

personal health plans. Although no high-throughput genotyping tool is currently available for breast-cancer onset prediction, Genomic Health, Inc. has commercialized its “Oncotype Dx” 21-gene predictor of breast cancer recurrence (Paik et al., 2004), and Veridex, LLC has published research on its gene-expression tests, reporting improvements in the accuracy of predicting cancer prognosis (Wang et al., 2005). These enhancements are based on molecular tumor analysis; the Oncotype Dx test has already been used to enhance “Adjuvant Online!,” a predictive model for cancer recurrence and survival (Surveillance epidemiology and end results, http://seer.cancer.gov; Adjuvant! http://www.adjuvantonline.com). Gene-expression strategies are also being made to customize cancer chemotherapy. By analyzing expressed genes in breast cancer specimens, the operative pathogenic pathways are being identified and specific chemotherapies chosen to block them (Bild et al., 2006). These applications indicate that clinicalgenomic predictive models may soon have broad utility in clinical practice.

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major side effect of inducing peripheral neuropathy. Genetic studies have shown that isoniazid-related peripheral neuropathy occurs among individuals with specific alleles for a polymorphic gene for N-acetyltransferase 2, a liver enzyme. Persons with these alleles acetylate isoniazid slowly, which in turn increases their risk for peripheral neuropathy as well as liver damage. Individuals found to have these alleles based on a pharmacogenetic profile, would most likely not receive isoniazid. A “genotyping” microarray chip for the cytochrome P450 (CYP) metabolism genes CYP2D6 and CYP2C19 might effectively aid physicians in assessing genetic susceptibilities to toxicity from a wide range of drugs (http://circ. ahajournals.org/cgi/content/full/101/14/1749; http://www.aafp. org/afp/ 20070801/391.html). Polymorphisms detected in these two enzymes can affect the rate at which an individual metabolizes up to 25% of drugs on the market, including those used to treat cardiovascular disease, high blood pressure, depression, and Attention Deficit Hyperactivity Disorder (ADHD) (http://us. diagnostics.roche.com/press_room/2003/062503.htm Roche Diagnostics – press release; http://www.devicelink.com/ivdt/ archive/03/04/002.html).

PHARMACOGENOMICS Pharmacogenomics determines how genetic inheritance affects an individual’s response to drugs.This field holds the promise that the use of drugs might be adapted to reach individual’s genetic makeup (http://www.ornl.gov/sci/techresources/Human_Genome/ medicine/pharma.shtml). In addition to improving the accuracy of risk assessments related to disease, biomarkers will play a role in prospective health care by enabling individualized therapeutic approaches. Just as there are differences among individuals in regards to disease susceptibility and progression, there are differences among individuals in terms of how they respond to therapeutics. Drug responsiveness is impacted by the interaction among many factors underlying those determined through genomics. From a genetic perspective, individual variations can influence how a drug is absorbed, distributed, metabolized and excreted, which in turn impact how an individual responds to a particular drug. Pharmacogenomics determines how individual genetic differences impact one’s response to a medication (see Chapter 27). Biomarkers associated with drug response could be utilized to develop individualized pharmacogenetic profiles, which would determine an individual’s likelihood of responding to a drug as well as adverse reactions for which an individual could be at high risk. For example, isoniazid, a drug used to treat tuberculosis, has a

CONCLUSION Genomic research is critical to enabling personalized, predictive, and preventative medicine through the identification and validation of predictive biomarkers. Prospective approaches to health care require individualized baseline risk assessments, disease tracking, clinical event prediction and analysis of an individual’s therapeutic response. The application of genomic risk predictive technologies to health care provide a far more detailed understanding of health and its evolution toward disease. Importantly, it will support the ability to predict, and thereby prevent, clinical events through appropriate and personalized interventions. Highly accurate risk assessment is an important component of a shift of disease-based medicine to a more rationale approach to prospective health care.

ACKNOWLEDGEMENTS The author would like to acknowledge the outstanding editorial assistance of Dr Ziggy Yoediono and Ms Cindy Mitchell.

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models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clin Cancer Res 10, 2725–2737. Nagy, R., Sweet, K. and Eng, C. (2004). Highly penetrant hereditary cancer syndromes. Oncogene 23, 6445–6470. Nelson, H.D., Huffman, L.H., Fu, R. and Harris, E.L. (2005). U.S. Preventive Services Task Force: Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: Systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 143, 362–379. Niu, T. (2004). Algorithms for inferring haplotypes. Genet Epidemiol 27, 334–347. Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F.L., Walker, M.G., Watson, D., Park, T. et al. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-Negative breast cancer. N Engl J Med 351, 2817–2826. Pittman, J., Huang, E., Dressman, H., Horng, C.F., Cheng, S.H., Tsou, M.H., Chen, C.M., Bild, A., Iversen, E.S., Huang, A.T. et al. (2004). Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Natl Acad Sci USA 101, 8431–8436. Rich, J.N., Hans, C., Jones, B., Iversen, E.S., McLendon, R.E., Rasheed, B.K., Dobra, A., Dressman, H.K., Bigner, D.D., Nevins, J.R. et al. (2005). Gene expression profiling and genetic markers in glioblastoma survival. Cancer Res 65, 4051–4058. Santen, R., Boyd, N., Chlebowski, R., Cummings, S., Cuzick, J., Dowsett, M., Easton, D., Forbes, J., Key, T., Hankinson, S. et al. (2007). Critical assessment of new risk factors for breast cancer: Considerations for development of an improved risk prediction model. Endocr Relat Cancer 14(2), 169–187. Snyderman, R. (2004). AAP Presidential address: The AAP and the transformation of medicine. J. Clin Invest 114, 1169–1173. Snyderman, R. and Langheier, J. (2006). Prospective health care: The second transformation of medicine. Genome Biol 7, 104–127. Snyderman, R. and Williams, R.S. (2003). Prospective medicine: The next health care transformation. Acad Med 78(11), 1079–1084. Snyderman, R. and Yoediono, Z. (2006). Prospective care: A personalized, preventative approach to medicine. Pharmocogenomics 7(1), 5–9. The International HapMap Consortium (2003). The International HapMap Project. Nature 426, 789–796. Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J. et al. (2005). Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679. West, M., Ginsburg, G., Huang, A. and Nevins, J. (2006). Embracing the complexity of genomic data for personalized medicine. Genome Res 16, 559–566. Williams, R.S., Willard, H.F. and Snyderman, R. (2003). Personalized health planning. Science 300, 549.

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RECOMMENDED RESOURCES Adjuvant! [http://www.adjuvantonline.com] Human Genome Project Information [http://www.ornl.gov/sci/ techresources/Human_Genome/medicine/pharma.shtml] International HapMap Project [http://www.hapmap.org] Medical Device Link [http://www.devicelink.com/ivdt/archive/ 03/04/002.html] Online Mendelian Inheritance in Man [http://www.ncbi.nlm.nih.gov/ entrez/query.fcgi?dbOMIM]

Online Mendelian Inheritance in Man #114480 Breast cancer [http:// www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id114480] Roche Diagnostics – press release [http://www.us.diagnostics.roche. com/press_room/2003/062503.htm] Surveillance epidemiology and end results [http://www.seer.cancer.gov]

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Section

Policy Challenges in Genomic and Personalized Medicine

33. 34. 35. 36. 37.

5

From Sequence to Genomic Medicine: Genome Policy Considerations Educational Strategies in Genomic Medicine Federal Regulation of Genomic Medicine Economic Issues and Genomic Medicine Public–Private Interactions in Genomic Medicine: Research and Development

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33 From Sequence to Genomic Medicine: Genome Policy Considerations Susanne B. Haga

INTRODUCTION The success of the Human Genome Project will be measured largely with respect to advancements in biology and the realization of a new era of medicine based on the use of genomic data to predict, prevent, diagnose, and treat disease. However, the scientific challenge of sequencing the human genome will likely pale in comparison to the efforts required to translate genome sciences into personalized medicine. As we begin to apply the data and technologies arising from the Human Genome Project and subsequent projects including the HapMap Project (International HapMap Consortium, 2005) and the ENCODE Project (The ENCODE Project Consortium, 2007), the initial discovery, validation, and the development of a clinical test or therapy will be influenced by a range of science, health, and public policies. Although many of the policy issues are not unique to those faced by other new medical innovations, the genome revolution raises issues that span both the traditional science and health policy arenas (see Figure 33.1), some warranting resolutions specific to genomic medicine. This chapter will provide an overview of the major policy issues pertaining to the research, development, and translation of genomic medicine applications, including: research allocation and prioritization; the use and analyses of race in genome Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 388

Research issues

Acceptance and implementation issues

Economic issues

Legal issues

Education issues

Figure 33.1 The spectrum and inter-connectedness of genome policy issues, based on (Haga and Willard, 2006).

studies; the ethical issues linked to large-scale genome efforts such as biobanks, oversight, coverage, and reimbursement of new genomics applications; privacy and discrimination; and enhancing public and professional awareness. An in-depth analysis of several of the issues introduced here can be found in other chapters in this volume (see Chapters 34–37). Copyright © 2009, Elsevier Inc. All rights reserved.

Genome Research After the Human Genome Project

GENOME RESEARCH AFTER THE HUMAN GENOME PROJECT The completion of the Human Genome Project marked the beginning of a new era, rather than the end of an era (Collins, 2003). Although a substantial investment in basic research, it is unlikely that anyone could have foreseen the widespread impact of not only the ultimate product – a reference sequence of the human genome – but also the parallel technology development that enabled the early completion of the sequence of the human genome and the genomes of countless other species. Its impact on the biotechnology sector and other fields as diverse as evolutionary biology, agriculture, biodefense, and medicine is still in the early stages but its scope has been staggering already. Research Allocation and Prioritization In 2003, after the completion of the Human Genome Project, a new vision for genomics research was created (Collins et al., 2003). The next 5 years of genomic research would concentrate on three major areas – society, health, and biology. Across these three areas, six crosscutting research tracts were identified, including technology development and ethical, legal, and social issues. This differs markedly from past strategic plans that focused predominantly on mapping and sequencing of the human genome and model organisms (see Table 33.1; Collins and Galas, 1993; Collins et al., 1998, 2003). Despite the huge investment in basic biomedical research and the excitement generated by the Human Genome Project, concerns have been raised that the investment has not resulted in the anticipated development of new products, as the number of submissions and approval of new drugs and diagnostics has been on the decline (FDA, 2004). Because of this and political pressures to demonstrate return on investment from the doubling of the US NIH budget, the emphasis over the past several years has shifted toward translational and clinical science (Zerhouni, 2005). Recognition of the importance of translational research has led to changes in science policy such as the development of the NIH Roadmap (Zerhouni, 2003) and emphasis on the development of new tools and applications (FDA, 2006a). Translational genomics research will be an important part in each of these new efforts (see Chapter 22). Over the past decade, Europe has also increased its supports for both basic and translational genomics research. In 2001, the European Commission allocated €100 million for an initiative called Genome Research for Human Health. Starting in 2002, the European Community Framework Program for Research, Technological Development and Demonstration (otherwise known as FP6) identified genomics as one of its seven major thematic areas (European Commission, 2002). The research budget for life sciences, genomics, and biotechnology was more than €2 billion. The program aimed to promote European genomics research through three major strategies: establishing a robust infrastructure; fostering interdisciplinary research with other fields such as physics, chemistry, mathematics, and

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T A B L E 3 3 . 1 Strategic goals for genomics research (1993–2008) in the United States (Collins and Galas, 1993; Collins et al., 1998, 2003) 5-Year period

Major goals

1993–1998

• Genetic map • Physical map • DNA sequencing technologies • Gene identification • Technology development • Model organisms • Informatics • Ethical, legal, social implications • Training • Technology transfer • Outreach

1998–2003

• Human DNA sequence • Sequencing technology • Human genome sequence variation • Technology for functional genomics • Comparative genomics • Ethical, legal, social implications • Bioinformatics and computational biology • Training

2003–2008

• Three thematic areas: Genomics to Biology, Genomics to Health, Genomics to Society • Six cross-cutting elements: Resources, technology development, computational biology, training, ethical, legal and social implications, and education

computer science; and integrating genomics into medicine and biotechnology. In 2007, FP7 was revised with the primary goal of supporting Europe’s global competitiveness. Genomics and biotechnology continue to have a prominent role in the development of new applications in medicine and food and agriculture. For the first time, investigator-driven projects will be funded in addition to the continued support of network initiatives (European Commission, 2002). The field of genome sciences has already taken the next step forward toward applying this new knowledge and capability to medically relevant initiatives. For example, the NIH Chemical Genomics project involves the development of a public repository of small organic molecules to study cellular pathways in health and disease and hasten the identification of new drug targets and drug development (NIH Chemical Genomics Center, 2005). The Medical Sequencing Project involves the sequencing of well-phenotyped patients with autosomal and X-linked Mendelian diseases as well as common diseases to identify the underlying genetic cause of disease (National Human Genome Research Institute, 2007).

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With respect to diagnostic screening and testing, the emphasis on translational research has provided opportunities to gather data about the clinical utility of new tools. Demonstrating clinical validity and utility is critical to the development of clinical guidelines and reimbursement policies that will support the appropriate use of these tests. For example, the National Cancer Institute is supporting a long-term prospective study called The Trial Assigning IndividuaLized Options for Treatment (Rx), or TAILORx (National Cancer Institute, 2006). This study will explore whether an expression profile of 16 genes associated with risk of recurrence for women with early stage breast cancer is useful in identifying those women who will most likely benefit from chemotherapy in addition to radiation and hormonal therapy. The study will follow 10,000 women for a period of 10 years, with follow-up of up to 20 years after initial therapies. While clinical trials represent the gold standard of evidence, these types of studies will not be feasible for every new genome application given the cost and time required, and other study approaches will need to be utilized. The continued support across translational genome research will be critical to the development of safe and effective applications in genomic medicine. Race and Genomics Population genetics research has spurred debate regarding the validity and use of racial categories as a variable in biological research. In particular, genomics research has provided insight into the genetic diversity (or lack thereof) of the human population, giving rise to questions about the use of race as a variable in biomedical research and whether genomics could provide improved measurements of variation in place of the concept of race (Burchard et al., 2003; Cooper et al., 2003). While a major conclusion of the Human Genome Project was the high degree of sequence identity between individuals, subsequent research has focused on the small differences between individuals and populations, particularly with respect to disease (Foster, 2005). For instance, the 3-year US-led HapMap project generated a map of one million common genetic variants through analyses of more than 200 genomes of individuals from four different populations (International HapMap Consortium, 2005). Similar to the HapMap project, the Mexican Genome Project, started in 2005, sought to determine the genetic variation of the heterogeneous mestizo population in Mexico (Mothelet and Herrera, 2005). Mestizo refers to individuals with mixed ancestry of European (primarily Spaniards) and Indian. The goal of the project was to determine whether any genetic differences between populations may be correlated to known health differences. Preliminary analysis of data from the project concluded that mestizo ancestry is a mixture of 35 ethnic groups (Wall, 2007). In general, 65% of Mexican ancestry can be traced to indigenous populations and 35% is due to non-indigenous groups (African, Asian, and European). Not surprisingly, regional differences were also found as numerous indigenous populations existed for some time prior to the arrival of the Europeans. The medical implications of these data remain to be determined. However, it is anticipated that the data will benefit other Latin

American countries as well and will aid in the development of safe and effective drugs for these populations which are not typically studied in clinical trials conducted by United States and European drug makers (Mothelet and Herrera, 2005). For genomic medicine, the main question is whether race can be used as a surrogate for biological variance to aid prevention, diagnosis and treatment of disease. Studies have detected not only differences in the prevalence of genetic variants (Hall, 1999; Monaghan et al., 1996), but also in the risks associated with the same genetic variant (Helgadottir et al., 2006), implying a role for additional genetic or environmental factors. While studies have shown that individuals from populations around the world can be clustered genetically into six groups (Rosenberg et al., 2002), other studies have demonstrated a range of mixed ancestry in African-Americans, Hispanics, and Mexicans (Parra et al., 1998; Shriver et al., 2003; Sinha et al., 2006). Our new understanding of human variation and ancestry has yet to impact federal policies regarding the use of race in biomedical research and clinical trials. Current policies such as the National Institutes of Health Revitalization Act (1993), the US Food and Drug Administration (FDA) (1998, 2005a), and the European Medicines Agency (1998, 2005) require that study participants be identified by race and/or ethnicity to ensure a diverse study population and to allow subset analysis. In light of recent findings, however, the application of race and ethnicity categories to biomedical research and the requirement to subset and analyze clinical trials data seem to be outdated and inadequate measures of differences in treatment outcome or response (Haga and Venter, 2003). Although self-identified race has been shown to correspond well with genetic clustering (Tang et al., 2005), it appears to be more problematic clinically since it has not always been shown to be a consistent and reliable measure (Hahn et al., 1996; Johnson, 1974; Rankin and Bhopal, 1999). Moreover, the context in which the self-identification is made may influence an individual’s decision, resulting in variable responses depending on who is asking the question and how the information might be used (Senior and Bhopal, 1994). In using race as an analytical variable, it is important to consider the two major constructs of race: the use of race as a social measure to detect differences of potentially important factors such as access to healthcare, environmental exposures, and lifestyle; or the use of race as a biological variable to identify genetic (and therefore, biological) differences between populations underlying differences in disease prevalence, severity, and outcome. While there is little disagreement that genetic epidemiology studies would benefit from more diverse sample populations (Ioannidis et al., 2004; Tang, 2006), which may be achieved through using race as a surrogate, it is still unclear what the significance (social and/or biological) of a positive association is between a given phenotype and race. Thus, difficulty in defining the impact of different factors makes race a poor surrogate in general, and direct measurement of relevant factors (e.g., diet, environment) may reduce some of the challenges arising from the use of such a broad undefined variable such as race. Genetics and genomics present an alternative measure of human variation for which race

Policy Issues in Large-Scale Genetics and Genomics Research

has been used as a surrogate that will lead to more definitive and quantifiable data points (Shields et al., 2005). Another major issue is the relationship between genetic and genomic data and health disparities. Differences in disease prevalence and outcome between groups, typically characterized by race, have been well-documented (Institute of Medicine, 2003). If individual differences in disease predisposition, prognosis or response to drugs may be accounted for by genetic variation, could group health differences also be accounted for by genetic variation between populations? Despite the small role that genetics is believed to play regarding disease prevalence and outcomes of common diseases, there is an inherent danger of extrapolating differences detected in individuals from one group to an entire race, potentially resulting in unvalidated support of race as a biological category (Wiegmann 2006). Indeed, the temptation to attribute health disparities to genetic causes may distract from other types of social and biomedical research that may lead to greater reduction in these observed differences (Sankar et al., 2004). Furthermore, race-based medicines such as BiDil, a drug approved by the FDA for the treatment of heart failure in self-identified black patients, have the added potential of reifying the biological concept of race and hinder efforts to reduce disparities (Haga and Ginsburg, 2006; Kahn, 2005).

POLICY ISSUES IN LARGE-SCALE GENETICS AND GENOMICS RESEARCH The shift to study the genetic etiology of common, complex diseases and the development of high-throughput technologies such as microarray analysis and new sequencing techniques at increasingly cheaper costs have enabled the expansion of study populations in genomic studies. Whereas genetic studies traditionally focused on small groups, particularly families, genomics studies are characterized by the collection of genetic data on hundreds or thousands of individuals, healthy and affected. The demand for DNA samples from individuals of various phenotypes has led to the creation of local and national biobanks or biorepositories worldwide. Given the enormity of national biobanks, several policy issues arise, particularly with respect to research policy. Among the many issues that have been debated about biobanks are the scientific need and merit of a national biobank, the cost and feasibility of successfully establishing and operating a biobank, the required infrastructure, accessibility to samples and data, informed consent, intellectual property (IP), privacy and confidentiality of data, and disclosure of research results to participants. While current policies may be applicable to some of these issues as they are not unique to biobanks, new policies may be required to address some of these issues such as data-sharing and data disclosure and to help reassure the public. Biobanking Several years prior to the completion of the Human Genome Project, a number of countries contemplated setting up a

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national research resource or “biobank” that would contain both clinical specimens and data for the purposes of large-scale genetic epidemiology studies. Samples and data would be collected from 60,000 to one million individuals and stored for five to 30 years (Godard et al., 2004). Given that a national biobank depends on the public’s support and participation, engaging the public is critical to its success (Haga and Willard, 2006; Haga and Beskow, 2008).Two approaches have been used to communicate with the public about national biobanks – a communication or partnership approach (Godard et al., 2004). In Iceland, a brief public consultation took place in the form of radio and television programs, town hall meetings, and public surveys. In 1998, the Act on a Health Sector Database was passed, authorizing the Ministry of Health and Social Security in Iceland to license a private firm (deCODE Genetics, Inc.) to establish and maintain the Health Sector Database, which would store the health records of Iceland’s 270,000 citizens. Strong criticism from researchers and ethicists worldwide quickly followed, particularly with respect to the issue of “presumed consent.” In response, an option to decline was made available and about 10% of the Icelandic population chose to opt out. In contrast, the United Kingdom embarked on a massive public 3-year consultation campaign about its proposed national databank. The national diallog included town hall meetings, focus groups, interactive workshops, and published reports. The exchange of information helped inform policy-making to ensure that public concerns were addressed while at the same time raised public awareness about the project. In addition, public comments were requested on procedural and governance documents. Other countries such as Japan, Canada, Estonia, United States, Tonga, and Latvia are considering or have already established national biobanks. Their approach to public consultation has fallen in between Iceland’s communication approach and the UK’s partnership approach. The different approaches utilized are reflective of different cultures, attitudes towards scientific research, history, and government and healthcare systems. Data Disclosure Research results can be disclosed in a variety of formats including individual or aggregate reports. Aggregate reports would be the only way to inform participants about study outcomes if the samples are collected anonymously or pooled. However, aggregate reports may dilute the value of results to individuals because participants will be left with uncertainty regarding the significance of the findings for themselves specifically (e.g., whether they were found to be at high risk versus low risk) (Shalowitz and Miller, 2005). A number of studies have examined the attitudes and responses of research participants with respect to access to clinical research results, though not specifically to results from genetic studies. In general, studies have found that disclosure of research results to participants, even those of a serious nature, do not result in negative psychological impacts in the majority of research participants (Bunin et al., 1996; Schulz et al., 2003; Snowdon et al., 1998; Shalowitz and Miller, 2008). Participants indicated they

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would want access to research results (Wendler and Pentz, 2007), even if upsetting (Schulz et al., 2003) or if not considered clinically valid or useful (Wendler and Emanuel, 2002). Despite current evidence that research participants would like the option of requesting research results even if of little clinical utility, the practice does appear to be common. An analysis of consent forms in the Children’s Oncology Group found that only two of 202 studies offered the option to participants to receive research results and only 10 of 202 studies offered to provide new information after the study was completed (Fernandez et al., 2003a). Although almost 80% of clinical investigators surveyed agreed that clinical trial results should be offered to research participants, about 60% of investigators only offered results less than 20% of the time (Partridge et al., 2004). Moreover, more than 60% did not believe that the disclosure of research results would have a negative impact on participants (Partridge et al., 2004). The major reasons given for not returning results include potential harm of research participants and time constraints and cost related to development of research summaries or individual reports and re-contacting of participants (Fernandez et al., 2003b; Rigby and Fernandez, 2005). While several policies on data disclosure have been developed, few exist at the federal level. Of the guidelines specific to genetic studies, the importance of clinical relevance and utility is emphasized. For example, the NHLBI guidelines identified the availability of a clinical intervention (treatment or preventative) as a key criterion if results were to be returned to study participants (Bookman et al., 2006). It has been suggested that investigators have an ethical responsibility to offer participants access to research results (MacNeil and Fernandez, 2006). Providing an option to disclose research data shows respect to the research participant (Shalowitz and Miller, 2005). Those investigators who choose not to provide access to research data should have to justify this decision (Shalowitz and Miller, 2005). Given concerns about the potential harms of unvalidated or inconclusive data, it has been suggested that the option for access to research results should be provided but not necessarily encouraged and that the uncertain nature of the results may be emphasized (Shalowitz and Miller, 2005). Because of these potential risks, it has also been suggested that a second consent should be obtained prior to disclosure of research results (Fernandez et al., 2003c, d). Data-sharing Publicly accessible genomic databases employ a range of data access policies. Initially, the leaders of the Human Genome Project determined that all data generated from the project should be placed in a public repository and accessible to all interested users (The Wellcome Trust, 1996, 2003). Rapid deposit of sequence data was required from each of the sequencing centers. This policy decision was influenced by concerns that access to basic data would be impeded if traditional methods of scientific data disclosure were followed. For new publicly funded, large-scale international collaborations, similar policies may need to be developed in advance to address issues of datasharing, intellectual property and use of specimens (if applicable)

(Chokshi et al., 1999). The 2006 NIH proposal specifying policies on data-sharing for NIH-supported or conducted genomewide association studies is a step in this direction (National Institutes of Health, 2006a). Several studies have examined data-sharing and datawithholding practices of US biomedical scientists and, in particular, geneticists. According to the most recent study, geneticists were more likely to engage in data-withholding practices than other life scientists (Blumenthal et al., 2006). Among academic geneticists, at least one-third of requests for additional information, data, or materials and 10% of post-publication requests were denied (Campbell et al., 2002). Twenty-eight percent stated that data-withholding impacted their ability to confirm research findings (Campbell et al., 2002). Reasons for withholding included concerns about additional time and effort, protection of junior investigators, and protection of data intended for future publications (Campbell et al., 2002). Delay in publication may be caused by factors such as IP or desire to maintain a competitive advantage (Blumenthal et al., 1997). These three surveys combined have assessed data-sharing practices from the 1980’s until today and are among the few sources of information about these scientific practices in genetics or, by extension, genomics. Little research has been conducted to assess public attitudes regarding sharing of biomedical research data and specifically genomic research data. The privacy of genomic information, even if de-identified or stored anonymously, is tenuous, since as few as 30 single nucleotide polymorphisms (SNP) may uniquely identify an individual (Lin et al., 2004). Therefore, participants may need to be informed about the potential risks due to datasharing practices.

INTEGRATING GENOMIC MEDICINE APPLICATIONS IN HEALTHCARE While human genetics has focused traditionally on testing for Mendelian and rare diseases through established techniques such as karyotyping, fluorescence in situ hybridization, and restriction fragment length polymorphism analysis, genomic medicine builds on new technologies such as genome-wide analysis of SNP and copy number variation, metabolomics and whole genome sequencing. These new technologies have led to testing for a broader range of diseases including common, complex conditions such as Type II diabetes. However, the expanded scope of clinical genomic applications necessitates consideration of the adequacy of current policies to ensure the safe, effective, and appropriate use of the genomic applications. In particular, changes may be need to be made to the regulatory system, education of health professionals involved in the provision of these new applications, and patient protections. Oversight In the United States, it is difficult to determine the actual number of traditional genetic tests performed annually given the

Integrating Genomic Medicine Applications in Healthcare

privatized health care system; the only published survey data estimated that over 175,000 tests were performed in 1996 (McGovern et al., 1999). In contrast, the Clinical Molecular Genetics Society conducts annual audits of the UK National Health Service laboratories. In 2005–2006, 78,600 postnatal genetic tests were performed, almost 20% more than in 2004–2005 (Clinical Molecular Genetics Society, 2004–2005, 2005–2006). More than 1500 prenatal genetic tests were also performed, representing a 12% increase from the previous year (Clinical Molecular Genetics Society, 2004–2005, 2005–2006). Of particular interest, the number of predictive and confirmatory tests almost doubled over the prior year. About 85 conditions were tested for overall, although the majority of tests were for familial cancers such as breast cancer (BRCA1/2) and colorectal cancer (HNPCC and FAP). In the European Union, it is estimated that more than 700,000 tests are performed annually (Ibarreta et al., 2003). In the United States, the Clinical Laboratory Improvement Amendments (CLIA) program administered by the Centers for Medicare and Medicaid oversees laboratory operations including quality control, quality assurance and analytical test validity. Laboratories are subject to inspection every two years by a state CLIA inspector or an accredited organization such as the College of American Pathologists. In 2006, 198,000 laboratories were registered by the CLIA program. Tests that are packaged and marketed as kits are considered medical devices and require clearance or approval by the FDA. However, as the majority of genetic tests are offered as a clinical laboratory service, otherwise known as a “home-brew” test, they are not currently subject to FDA regulations. In addition to analytical validity, FDA also reviews the clinical validity of tests. Over the past decade, two federal committees have explored the adequacy of current regulatory oversight mechanisms of genetic tests. In 1997, a NIH-Department of Energy Task Force indicated that an assessment of current review processes of the validity and utility of genetic tests may be warranted, but stopped short of providing specific suggestions (Holtzman and Watson, 1997). In 2000, the Secretary’s Advisory Committee on Genetic Testing recommended that the FDA regulate all genetic tests and that oversight of genetic testing laboratories under the CLIA be strengthened (Secretary’s Advisory Committee on Genetic Testing, 2000). To date, these recommendations have not been implemented, although the creation of a genetic testing specialty under CLIA has been proposed and debated (Centers for Disease Control and Prevention, 2000). Several guidance documents have been issued by FDA, resulting in a gradual increase in oversight of new genomics applications. In 2005, a guidance document was issued encouraging sponsors to voluntarily submit pharmacogenomics data to help prepare reviewers on the type and complexity of genomics-based data (FDA, 2005b). Two relevant draft guidances were also released in 2005 – a draft guidance on nucleic acid-based in vitro diagnostic devices for detection of microbial pathogens was released (FDA, 2005f) and another on the co-development process of drug-device

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combination products as evident from their concept paper (FDA, 2005e). In addition, other guidance documents have been developed focusing on drug metabolizing enzymes genotyping system (FDA, 2005c), multiplex technology (FDA, 2005d), analyte specific reagents (FDA, 2006b), algorithm-based genetic tests (FDA, 2006c), genetic toxicology studies (FDA, 2006d), and pharmacogenetic tests (FDA, 2007). Direct-to-Consumer Marketing Genetic services and applications are often promoted directly to consumers, similar to drugs and other healthcare services. However, for many of these services and products, the involvement of a health care professional is not required, and consumers can purchase goods directly from the laboratory or manufacturer (an “over-the-counter” test). Laboratory services include testing for paternity, lifestyle, health, and ancestry. Products include DNA-tailored cosmetics and nutritional supplements. Consumers who purchase these services may decline to share the results with their health practitioner due to fear of genetic discrimination. Direct-to-consumer (DTC) provision of genetic services and products, particularly those that are not health-related, represent an emergent industry. However, at this early stage of research and development, the complex genetic etiology and role of environmental factors for many phenotypes and traits are not clearly understood. Therefore, many of the early tests promoted directly to consumers likely lack robust validity and utility. As these tests are not subject to FDA oversight, the evaluation of the validity of genetic tests and products may be challenging for some consumers. Consumers may be vulnerable to ambiguous and exaggerated claims. In addition, the complex nature of genetic information related to health may warrant consultation with professionals trained in genetics or other specialist to ensure appropriate test interpretation and intervention. General advertising of genetic tests accessible only through a physician has also increased. For example, Myriad Genetics launched an advertising campaign in two cities in 2002 for the BRCA1/2 breast cancer tests. A study conducted the following year to assess the impact of the campaign on women’s awareness of the tests found that consumers were substantially more likely to have heard of the test and seen an advertisement (Jacobellis et al., 2004). However, despite the increased awareness, the perceived knowledge and interest in testing did not differ between the pilot and control cities. Physician knowledge of breast cancer testing did not differ between the pilot and control cities, although physicians reported ordering a significantly higher number of tests in the pilot cities (Myers et al., 2006). DTC genetic services and products have raised concerns among a number of groups. In 2002, the UK Health and Science Ministers requested an investigation by the Human Genetics Commission into DTC genetic testing. The commission identified two potential harms from DTC genetic testing: the impact of inaccurate or misinterpreted test results on consumer health and the absence of an adequate informed consent process (UK Human Genetics Commission, 2003). As a result, the

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Commission recommended that stricter controls be put in place for DTC genetic tests and that predictive genetic tests should be restricted from DTC sale. In the United States, the American College of Medical Genetics (2004 and 2008) recommends “a knowledgeable healthcare professional should be involved in the process of ordering and interpreting a genetic test” to prevent potential harms such as test misinterpretation and inappropriate follow-up. In 2006, an investigation conducted by the General Accounting Office concluded that DTC nutrigenetic tests were medically unproven and ambiguous (Government Accountability Office, 2006). To educate consumers about home genetic test kits, the Federal Trade Commission, the Centers for Disease Control and Prevention, and the Food and Drug Administration developed a fact sheet (Federal Trade Commission, 2006). And, in 2007, the American Medical Association’s House of Delegates passed Resolution 522, which requested further study of the practice of DTC advertising of genetic tests and, in particular, at the existing oversight of this field (American Medical Association, 2007).

Access/Reimbursement Coverage of genomic applications is intimately linked to uptake of a new test and integration into clinical practice. The demonstration of the validity and utility of a genomic application as well as cost-effectiveness is essential to securing reimbursement. More than 150 economic analyses of genetic services have been conducted, many for adult conditions such as cancer (Carlson et al., 2005; Phillips and Van Bebber, 2004). While many tests have been shown to be cost-effective, others still have questionable utility despite demonstrated clinical validity. For example, a commercially available molecular profiling test known as Allomap has been shown to be cost-effective to identify heart transplant patients at risk for rejection compared to endomyocardial biopsy to detect rejection, mainly due to the costsavings gained from not having to perform a biopsy (Evans et al., 2005). But several tests developed to guide therapy decisions are considered by some to be of questionable value (Matchar et al., 2006). The increased involvement of FDA review of genomic tests and more focused translational research has encouraged the coverage of these applications. For example, in 2005, the Blue Cross Blue Shield Association’s Technology Evaluation Center (TEC) assessed four gene expression profiles for use in the management of breast cancer. Based on the available evidence at the time, the report concluded that none of the profiles met the TEC criteria (Blue Cross Blue Shield Association Technology Evaluation Center, 2005). The TEC criteria include approval by federal regulatory bodies and evidence demonstrating an improved outcome and comparable benefits to existing alternative treatments or applications. However, in 2007, a second review of the Oncotype DX test (one of the four tests evaluated in 2005) concluded that the test now met the TEC criteria and was considered to be useful specifically regarding adjuvant

chemotherapy for women with estrogen receptor-positive, node-negative, tamoxifen-treated breast cancer (Blue Cross Blue Shield Association Technology Evaluation Center, 2007). In addition to evaluating both clinical validity and utility, pharmacogenetic testing policies must also consider coverage of the drugs identified as being most likely to perform well or least likely to cause adverse side effects. For tests that are co-developed with drugs, the cost of the testing and drug could be determined by a single policy decision. But in the event that a test indicates a different drug, payors may be forced to revise their coverage policies for drugs that are not listed on their formularies. Health Professional Education As the applications of genetic and genomic information have expanded beyond the traditional specialties of medical genetics, pediatrics, and obstetrics into other fields such as oncology, cardiology, neurology, and psychiatry, the education of health professionals needs to evolve as well. While a number of surveys have documented the variable level of physician knowledge of genetics (Metcalfe et al., 2002), none has assessed knowledge of the newer field of genomics. The uptake and use of new applications will be stalled until health professionals gain some understanding about the appropriate use of these tests and the interpretation and application of test results. As a result, the continuum of health education from graduate or professional school to continuing education should aim to increase awareness and understanding of these new tools and therapies across virtually all medical specialties (Challen et al., 2005; Gurwitz et al., 2003). In addition, it will be important for health professionals to understand other issues related to the use of these technologies such as implications for family members and privacy and confidentiality issues. Re-defining Roles of Health Professionals Due to the expected widespread use of genomic applications, some re-organization of health professional roles may be required, given the limited number of genetics specialists and new applications such as pharmacogenetic testing (see Table 33.2) (Guttmacher et al., 2001). For example, the pharmacist may play a much greater role in the integration of pharmacogenetic testing by helping to ensure that the drug and dosage are safe based on the patient’s genotype (Clemerson et al., 2006). Monitoring pharmacogenetic information to assure appropriate drug dosing would appear to be a natural extension of the role of pharmacists. To fulfill this expanded role, pharmacists would need greater access to patient medical records (Clemerson et al., 2006), although physicians and patients have expressed concerns regarding patient confidentiality if full access were permitted (Porteous et al., 2003). In contrast, a board-certified genetics professional (physician or genetic counselor) may have a revised, lesser or even non-existent role in the provision of genomic testing such as

Integrating Genomic Medicine Applications in Healthcare

T A B L E 3 3 . 2 Changing roles of health professionals in genomic medicine Health professional

New or revised role

Primary care Physician

Increased focus on family-history assessment, identification of appropriate patients for testing, discussion of risks and benefits of testing, interpretation of test results, revision of medical management based on test results, focus on preventative strategies

Nurse

Enhanced family-history taking and counseling skills; enhanced education in corresponding areas of genetics (e.g., cancer genetics for oncology nurses); awareness of ethical, social, and legal issues associated with testing

Genetic or Genomic Specialist (Genetic Counselor or Physician)

Consultation reserved for complex genetic cases; interpretation of wholegenome data, including sequence; increased role in professional education development

Pharmacist

Increased understanding of genetic factors in drug safety and efficacy; enhanced education of genetic etiology of drug response; consideration of pharmacogenetic test results prior to drug treatment; increased collaboration with prescribing physician; increased patient education with respect to pharmacogenetic testing; added consideration of pharmacogenetic test result to determine appropriate dose adjustment or drug selection

a pharmacogenetic profile as these tests become routine. The need for a genetics specialist may be determined by several factors including complexity in determining appropriate candidates for testing and interpretation of test results, the extent of the informed consent process, reimbursement policies, familial implications and the risks and benefits of the test. However, given the relatively small size of the genetics workforce, any new requirements for their services may not be feasible. Consultation by a board-certified genetics specialist may need to be reserved for only the most complicated cases. To increase the number of physicians with training in genetics or genomics, one possibility may be to increase the number of dual board-certified specialties with medical genetics. For example, an individual dual-certified in medical genetics and pharmacology would have expertise in genetics and pharmacology and

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be able to advise the treating physician about the best choice of treatment based on the patient’s pharmacogenetic profile and other clinical information. However, the training requirements to fulfill the medical genetics certification should be substantially revised to provide direct knowledge relevant to the individual’s primary profession with greater focus on test appropriateness, interpretation and test utility. In addition, genetics certification should not lengthen the overall training program. Pilot development studies have found that new roles associated with genetic testing were mostly fulfilled by non-genetics specialists, in particular nurses (Bennett et al., 2007). For example, the cancer family nurse specialist is trained to identify patients with a family history of cancer that would place them at increased risk and indicate testing (Bennett et al., 2007). Indeed, genetic competencies have been developed and training programs have been accordingly revised to meet these new needs and to enable nurses to adequately provide genetics services and guidance (Jenkins and Calzone, 2007; Lewis et al., 2006). Studies have demonstrated the equivalence of care provided by nurses trained in genetics compared to board-certified genetic counselors (Torrance et al., 2006). Several groups have developed professional genetic competencies including the National Coalition for Health Professional Education in Genetics (2005), and the National Health Service’s National Genetics Education and Development Centre and the Skills for Health Service (2007) have developed general competences for non-genetics practitioners. Privacy and Confidentiality The issue of medical privacy is a major concern in the United States. In 2005, the National Consumer Health Privacy Survey reported that 66% of Americans were very or somewhat concerned about medical privacy (California Health Care Foundation, 2005). Due to concerns that employers or other groups may have access to a medical record, 11% of survey respondents indicated paying for a cancer test or procedure out-of-pocket. The predictive nature and often deterministic views associated with genetic information have likely contributed to the heightened concerns linked to testing. In addition, as the number of health professionals with access to a patient’s genomic test results increases, the ability to safeguard the information increases becomes more challenging. Genetic Discrimination Genetic discrimination involves the inappropriate use of genetic information, primarily with respect to health insurance and employment, but including other areas such as life insurance, long-term care insurance, adoption services, egg and sperm banks or educational admissions programs. While the fear of genetic discrimination has existed prior to the start of the Human Genome Project, the solutions have been slow to come by and, at best, provide a patchwork of protections in the United States and around the world. For example, about two-thirds of states have enacted legislation to prohibit or limit the use of

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genetic information for risk selection and risk classification for health insurance purposes. Passage of national legislation would provide universal coverage against health insurance and employment discrimination on the basis of genetic information for all. But despite all of the interest and support shown by federal lawmakers including the past two US presidents, national legislation has only just been enacted. Unbelievably, it has taken longer to pass genetic anti-discrimination legislation than it actually took to sequence the human genome. Since the first bill on genetic discrimination in health insurance was introduced in the United States in 1995, at least 20 other bills have been introduced, but only one has passed providing partial protection against genetic discrimination for the group insurance market. In 2007, the Genetic Information Nondiscrimination Act was re-introduced to “prohibit discrimination on the basis of genetic information with respect to health insurance and employment.” In contrast to previous years, it swiftly moved through the three House committees who have jurisdiction and passed by a floor vote of 420 to 3, thus moving further along than in any previous Congress. The bill was also passed by the Senate and signed into law by President Bush in May 2008. Despite universal condemnation against genetic discrimination (UNESCO, 1997, 2003), the development and implementation of national protections has also been slow outside the United States. For example, although “genetic features” is included in the European Union’s Charter of Fundamental Rights’ section on non-discrimination, countries have had variable success in providing national protections (European Parliament, 2000). Austria, Belgium, France, the Netherlands, Luxembourg, Greece, and Italy have enacted legislation prohibiting access of genetic information without consent. Various initiatives have been undertaken in the United Kingdom, Australia, and Canada to protect against genetic discrimination by life insurers and employers. Similar to the United States, a patchwork of provincial legislative protections in Canada exist. A recent review of the Canadian Human Right Act by the Department of Justice recommended that the definition of disability include genetic predispositions (Department of Justice, Canada, 2005). Australia has been particularly active in investigating the protections of genetic information. In 2000, the Attorney-General of Australia and the Minister for Health and Aged Care called for an inquiry into genetic discrimination. An extensive public consultation was undertaken including widespread dissemination of two consultation papers (Australian Law Reform Commission, 2001, 2002), 15 public forums around the country, and 185 meetings with key stakeholders and interested parties. In 2003, a final report was released containing 144 recommendations directed at local, regional, and national governments as well as statutory authorities (Australian Law Reform Commission, 2003). And in the United Kingdom, a moratorium is in effect banning the use of genetic testing information for insurance underwriting purposes until 2014 (Association of British Insurers, 2008).

Intellectual Property Intellectual property (IP) of genes and genetic material has been a major point of controversy since the early years of the Human Genome Project. Nearly 20% of the human genome is under US patents, comprising about 4400 genes (Jensen and Murray, 2005). More than 60% of these patents are owned by private entities (Jensen and Murray, 2005). In addition, almost a dozen complete genomes have been patented or are pending patent approval (O’Malley et al., 2005). A constellation of issues pertaining to ownership has garnered significant attention and concern from various stakeholders. From the single laboratory to national advisory bodies to international organizations, gene patenting has become a controversial topic, particularly with respect to the limitation of access to gene-related inventions and benefit-sharing (World Health Organization, 2005; Canadian Biotechnology Advisory Committee, 2006). The patenting of genomes has significantly broadened the scope of IP, and its impact remains to be seen. Although there has been a significant amount of debate about the patenting of genes, concern also exists about the licensing practices of patent holders, as exemplified by BRCA1/BRCA2 and Canavan’s disease. To address some of these concerns, the NIH has also taken steps to promote open access to its large-genomic datasets and encourage fair licensing practices through IP user agreements and guidance documents (National Institutes of Health, 2005). In addition, an amendment to a Request for Applications for genome sequencing centers requires applicants to develop an “IP Management Plan” to ensure wide availability of sequencing data generated by awardees (National Institutes of Health, 2006b).

CONCLUSION While advances stemming from the genome sciences are certain to significantly impact the practice of medicine and our understanding of human development, biology, biochemistry, and physiology, the safe and appropriate use of this new information and its associated technologies will warrant new policies and an improved level of understanding by patients, consumers, and providers. New policies should not obstruct or prevent scientific advancements from moving forward, but rather should aim to facilitate the expansion of the field in a manner that addresses concerns about the direction, pace, and application of the science. In addition, while not explicitly discussed, the ethical aspects of genome research and medical applications will substantially guide and influence policy-making related to the development and use of these new tools. Given the wide range of stakeholders involved in the translation of genomic medicine applications from bench to bedside, it will be critical to gather their perspectives as new policies are being considered and developed.

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34 Educational Strategies in Genomic Medicine Charles J. Epstein

INTRODUCTION Education for what? Education for whom? What is there about genomic medicine that requires us to consider matters of education? The answers to these questions reside, of course, in what we mean by genomic medicine (Epstein, 2006), and for the purposes of introducing this article I shall cite two definitions: 1. the use of industrialized methods of data acquisition and analysis to improve medical care, including prognostics, diagnostics, preventive intervention, therapeutic selection, and individualized treatment based on the complex interaction between inherited and acquired elements of human variation (Harvard-MIT Division of Health Sciences and Technology, 2004), and 2. an approach that will build on the comprehensive nature of the genome sciences … As a clinical paradigm, genomic medicine will provide global, comprehensive, and multidimensional treatment and management strategies based on the science now emerging from the study of genomes (Willard, 2004) … Having access to the entire human sequence is a necessary but insufficient prerequisite for genomic medicine. What is equally important is having the technology at hand to reliably visualize individual genomes (and their derivatives, the transcriptome, proteome and metabolome) for health information that, in combination with clinical data, can Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

contribute to assessment of individual risks and guide clinical management and decision-making. [This makes] the prospect for developing truly individualized care … even more real (Willard et al., 2005). These two definitions are quite broad. They certainly encompass the “medicine” side of “genomic medicine,” but there is also emphasis in the first definition on “industrialized methods of data acquisition and analysis” and in the second on “global, comprehensive, and multidimensional … strategies” and “the technology to reliably visualize individual genomes.” It is clear that, as the table of contents of this handbook would suggest, what is really new in genomic medicine is the application of genomic technology on a large scale to virtually every problem of medicine. We might call this the process of genomic medicine. However, these definitions also make it clear that an, if not the principal, intent of genomic medicine is what has been variously described as preventive intervention, individualized treatment, prospective medicine (Snyderman and Williams, 2003) or personalized medicine (Ginsburg et al., 2005) and medicines – the last referring, of course, to pharmacogenetics (Royal Society, 2005). Weston and Hood (2004) combined all of these together as the 3 P’s: predictive, preventative, and personalized medicine. Since the term genomic medicine certainly has considerably wider connotations, it might be more accurate to refer to the 3 P’s as applied genomic medicine, and this is what the following discussion will be concerned with. Copyright © 2009, Elsevier Inc. All rights reserved. 401

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Key to all of this, of course, will be risk assessment based on testing, and there are indeed many forms of testing already in use and being developed – biochemical, genetic, genomic, proteomic, metabolomic, and pharmacogenetic (or pharmacogenomic). From the point of view of public perception, testing is where things are headed when we talk about genomic medicine. Indeed, there appears to be pervasive belief in both scientific and public circles that genetic testing or profiling is going to be the cornerstone of much, if not all of what genomic medicine – in fact, all of medicine – holds for the future (Epstein, 2004). [Genetic testing, risk assessment, and profiling already are and will, to a much greater extent in the future, be based on information gathered by genomic approaches and will utilize genomic technologies. However, for the sake of convenience and in conformity with current usage, I shall use the terms “genetic testing,” “genetic risk assessment,” and “genetic profiling” throughout.] There has been an extensive debate swirling around this notion of the centrality of genetic testing in the future of medicine, which I shall not repeat here, but there a few important points that are worth noting: ●

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In most instances, genetic risk assessment will attain sufficient predictive power to be of use if – and only if – analyses of many genetic loci are combined with evaluations of non-genetic life-style and environmental factors (Janssens et al., 2006). As a result, complicated algorithms, the nature of which may not, for proprietary reasons, always be revealed, will often be required to compute the risks (Food and Drug Administration, 2006). The results of such risk assessments will not be absolutely definitive. The information will be probabilistic, and we shall always be dealing with estimates of risk, not predictions of certain outcomes. Even though risk assessment or profiling can ultimately be done does not mean that it actually will or, indeed, should be done. This will ultimately be determined by whether genetic testing really offers more than currently available forms of risk assessment in terms of predictive power and securing compliance – in other words, by whether it will have greater clinical utility and would enhance a person’s motivation to comply with recommendations for therapy and changes in life-style. [For a discussion of clinical utility and its definitions, see Burke et al., 2006; Grosse and Khoury, 2006; Scheuner and Rotter, 2006.] My assumption is that genetic profiling and risk assessment will ultimately be done, and that the pressures from both consumers and the developers of tests – and perhaps from the medical profession as well – will lead to its being done quite extensively. The announcement of a $10 million prize to the first group that can “successfully map 100 human genomes in 10 days … to usher in a new era of personalized preventative medicine” (X Prize Foundation, 2006) bears dramatic testimony to this, as does the NIH Request for Applications for the $1000 genome (National Institutes of Health, 2004).

The definitions and issues that have just been outlined describe the what in the first question – education for what?

More specifically, the what is the ability for the whom to understand and participate in the process of genetic risk assessment and those things related to it, and the whom consists of both the providers and the recipients of genetic information and of the recommendations that are based on it, as well as those who make and implement the public policies that ultimately determine how genomic medicine will be practiced. The educational needs of all of these groups were extensively discussed in Assessing Genetic Risks: Implications for Health and Social Policy, the report of the Institute of Medicine Committee on Assessing Genetic Risks (Andrews et al., 1994). Remarkably, the issues raised in that report are as valid today as they were then. The only real difference is that we are much farther along the pathway of genetic testing and risk assessment, all of which was predicted in the report, than we were when the report was written.

GENETIC AND GENOMIC LITERACY OF THE PUBLIC AND MAKERS OF PUBLIC POLICY The central thesis of the Institute of Medicine report with regard to public education in genetics is the need to develop a genetically literate public (Andrews et al., 1994, p. 185 et seq.). In this context, the term public encompasses both the policy makers and the consumers or recipients of genetic testing and risk assessment, as well as the public at large, and genetically literate translates to being able to understand basic biological research and the personal and health implications of genetics and to participate effectively in the public debate of relevant public policy issues. Of particular importance with regard to genomic medicine, the report placed emphasis on attaining an understanding of the limits of genetics so as not to fall into the trap of genetic determinism – the belief that genetics is completely predictive. Another formulation of the reasons for improving education in basic genetics suggests that the goals are to allow individuals to understand general genetic concepts, applications, and social and ethical issues and to become informed users of genetic and genomic technologies and their applications (Haga, 2006). Both of these formulations are certainly reasonable for the public at large, consumers and policy makers alike, but it is the last goal in each formulation that is particularly germane to the consumers of the genetic testing and risk assessment applications of genomic medicine. The probabilistic nature of risk assessment and pharmacogenetic testing, whether for the chance of developing diabetes or coronary artery disease or for benefiting from a particular drug, will require a level of genetic and genomic understanding that has not heretofore been required on the part of the general population of patients and consumers of medical services. It is true, of course, that genetic counseling, as it is presently being carried out for many conditions, does involve risk assessment based on genetic testing and requires the presentation and handling of probabilistic information. The best

Genetic and Genomic Literacy of the Public and Makers of Public Policy

example of this in current medical practice is risk assessment for breast and ovarian cancer based on the testing for mutations in BRCA1 and BRCA2. However, there are two major differences between this type of risk assessment and the more widespread risk assessment for common (complex) conditions and pharmacogenetic testing that will constitute genomic medicine. One is that cancer risk assessment is directed at a targeted and restricted population – those already suspected to be at risk because of prior medical or family history. This makes it possible for a significant educational component to be provided by a genetic counselor or other highly trained (in genetics and genomics) health professional – a luxury that may not be possible for the population at large. Therefore, as will be discussed below, the burden of communicating and interpreting the results of risk assessments will likely fall to individuals less expert in genetics and genomics and with less time to spend with individual patients. It is this fact that makes it essential that the members of the general public have a basic knowledge and appreciation of genetic and genomic principles if they are to participate intelligently in the risk assessment process. And, as has already been noted, one principle that it is essential for the public to understand is that probabilities are not certainties. I have used the term “probabilistic” several times in the preceding paragraphs, and a few amplifying comments are warranted. The result of a presymptomatic genetic test or set of tests performed before the onset of disease will be certain only when the penetrance of the condition(s) that are being tested for is complete (100%) within a specific time frame. In all other situations, risks are presented as probabilities. These probabilities may be expressed quantitatively, usually as a percentage (e.g., if a particular mutation of BRCA1 is present, the risk of developing breast cancer in the next X years is Y %) or qualitatively (from very low to very high). While the quantitative expression of probabilities is more precise, not all individuals are able to fully comprehend them. Sometimes, especially when the risks are relatively low, the probabilities may be inverted and given in terms of not developing disease (e.g., the odds are 10 to 1 that breast cancer will not occur). Sometimes risks are given in relative rather than absolute terms (e.g., the risk of developing disease Z is Y times greater than that of the general population), an approach that is frequently lacking because the “general population” risks are often not stated. However, what is most important is that the interpretation of risks can be greatly influenced by precisely how the risks are formulated and presented and by the preconceptions of the recipients (Tversky and Kahneman, 1981). These factors will determine the actions that will ultimately be taken. For this reason, there needs to be sufficient understanding on both sides of how risks, the probabilistic information to which I have been referring, should be presented and interpreted, and this will require education of all concerned. Returning to the rationale for improved public genetic and genomic literacy, one other thing that can be added is the increase in direct-to-consumer advertising and promotion of genetic tests (Wolfberg, 2006). Just at the time that this chapter was being written, a new home gene test for schizophrenia

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was being touted (SureGene, 2006), and the Federal Trade Commission felt it necessary to issue a warning against at-home genetic testing in general (Federal Trade Commission, 2006). Clearly, an educated public will be essential if the potential problems likely to accrue from unbridled genetic testing and risk assessment are to be prevented. The Institute of Medicine report (Andrews et al., 1994) lamented the relatively low level of genetic literacy in the public, and the same still appears to the case today (Haga, 2006). Since it is likely that the solution to this problem ultimately resides in the education of children, when their flexibility and scientific interest are at a maximum (Andrews et al., 1994), the 2005 report of the National Assessment of Educational Progress with regard to science in American schools is particularly sobering (Grigg et al., 2006). Since 1996, there has been an increase in the average science score (as well as in the life science subscore, which would include genetics) in grade 4, no change in grade 8, and a decline in grade 12. In grade 12, nearly half (46%) of students scored below the basic level. [An example of a basic question is to identify the relationship between genes and enzymes.] How to go about increasing the amount of genetics and genomics in the curricula of students from pre-school through college and the quality of genetics and genomics instruction at all levels is beyond the scope of this chapter, but the subject was dealt with extensively in the Institute of Medicine report (Andrews et al., 1994, p. 189 et seq.), Two summary recommendations are worth citing: Variation and kinship in the context of the environment should be the fundamental concepts of genetics/genomics education for the public [note the emphasis is on genetics/genomics and the environment, not just genetics/genomics alone], and genetics/genomics education should include the ethical, legal, and social issues stemming from science and technology. To these two recommendations may be added a third, that the concept of using family health histories is a way to motivate students to explore genetics in a way that is personal and has direct meaning for them. Generating a personal family health history by individuals as the first step in learning about potential health risks has recently attained prominence with the launching of the US Surgeon General’s Family History Initiative (United States Department of Health and Human Services, 2006). Professionally obtained family histories have already been recognized as a comprehensive risk assessment method for the common diseases of adulthood (Scheuner et al., 1997). Useful teaching resources in genetics and genomics were recently reviewed by Haga (2006), who cites numerous online resources for genetic and genomic education. However, it will take more than on-line resources to accomplish the goal. What will ultimately be required is a systematic integration of genetics and genomics into all levels of education, both vertically (through the years) and horizontally (across subject areas) (Haga, 2006), and this will have to be done by the educational establishment. Although it will ultimately be the educators who will have to accomplish the task, human geneticists and genetic counselors and their professional societies (such as the American Society of Human Genetics, the American College of Medical

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Genetics, and the National Society of Genetic Counselors) can and should serve as advocates and catalysts for change and as sources of expertise in genetics and genomics and related areas. To this end, the American Society of Human Genetics has established a Mentor Network to work with teachers to create in-depth and personal learning opportunities for students, has created a database of education standards for all grades between kindergarten and 12th grade, and commissioned a review of the genetics content in introductory biology courses for nonscience majors (Hott et al., 2002). The consumers of genetic services and the makers of public policy have been lumped together in this discussion of genetic literacy, on the assumption that they all need to become genetically literate. However, more than just the basic elements of genetic and genomic literacy will certainly be required for those who make policy and those who advise them. And, when I speak of policy makers, I am referring to an extremely broad range of individuals who will need to be educated about genomics and genomic medicine when they are students in universities and professional schools and particularly when they have taken up their professional careers. Included in this group, at the United States federal level, are members of Congress and their staffs and executives and members of the Department of Health and Human Services and many of its constituent organizations (including but not limited to the Food and Drug Administration, the Centers for Medicare and Medicaid Services, the Agency for Health Care Research and Quality, the Centers for Disease Control and Prevention, the Health Resources and Services Administration, and the National Institutes of Health with its many Institutes). Also at the federal level are the White House Office of Science and Technology Policy and the Domestic Policy Council. To this list can be added the parallel group of individuals operating at the state government level, along with officials of insurance companies, health maintenance organizations, and hospital centers. Individuals from all of these groups and organizations may (many certainly will) directly or indirectly influence how genetic testing and genomic medicine will be practiced, financed, and regulated. Many approaches, ranging from lectures and seminars to courses in schools of law, government, and public policy, will be required to educate these people.

EDUCATION OF THE PROVIDERS OF HEALTH CARE As genetically and genomically literate as the consumers of genomic medicine may become, the burden for conducting and interpreting testing will fall, of course, on the providers of health care – a diverse group of professionals involved in the delivery of preventive and therapeutic medical services to the public. These would include, in particular, primary care physicians, physician specialists, medical geneticists, genetic counselors and nurses, disease-specific providers and counselors, the public health community, pharmacists, and other allied health professionals.

The choice of educational strategies to bring health care providers into the world of genomic medicine will depend, of course, on what the various types of professionals will be doing. Once again, this subject was discussed extensively in the 1994 Institute of Medicine report. The basic premise of the discussion was the following: Historically, genetic tests have been administered and interpreted by highly trained health professionals working in academic health settings, usually with a strong genetics research and service record. In the future, however, genetic tests will become available for a growing variety of monogenic and complex diseases and for susceptibility to more common disorders such as breast, colon, and other cancers. Testing on such a broad scale will necessarily move us beyond the models of service delivery and professional roles that have characterized genetic testing and screening in the past. Increasingly, genetic tests will be offered and interpreted within the context of the mainstream of medicine in primary care practice including pediatrics, obstetrics, internal medicine, and family practice in a variety of individual and group practice settings. This exciting and challenging prospect for the future involves a large pool of potential personnel for genetic testing, screening, education, and counseling, but will they be prepared to play this role? (Andrews et al., 1994, p. 202 et seq.)

Others have echoed the idea that primary care will be the principal locus for genetic testing. We have to anticipate that every health care provider is going to become a genetic counselor in the next 10 years or they’re not going to be doing a good job (Jones and Collins, 2003). Clearly, the day-to-day genetic health providers of the future will be the primary care clinicians (Caskey, 2001).

Explicit or implicit in these statements is the assumption that there will not be enough genetic professionals to handle the burden that wide-scale genetic testing and risk assessment would entail and that primary care practitioners will be have to assume the responsibility. In a sense, this is a negative reason for doing so. However, a Genetics White Paper published by the British National Health Service presents the argument in positive terms: GPs, practice nurses and the primary care practitioner will all be able to help their patients benefit from the new genetic knowledge and its applications. They already understand the long term, psychosocial aspects of illness. They work with individuals in the context of their families over time. They are adept at identifying health problems and making appropriate referrals. They co-ordinate the care of the affected patient. And, they are at the forefront of health promotion and prevention. [Therefore,] the roles for primary care in genetics [will include] managing patients’ concerns and expectations, identifying genetic conditions, assessing risk, managing risk, screening, [and] testing … (Department of Health, 2003).

Education of the Providers of Health Care

There is certainly no question that the supply of trained and certified medical geneticists and genetic counselors is very limited. At the latest tally, which covers through the 2007 certifying examinations, there were, in the United States, 1253 certified physician clinical geneticists, 900 certified PhD geneticists, most working as laboratory directors, and 2512 certified genetic counselors (American Board of Genetic Counseling, 2008; American Board of Medical Genetics, 2008). Since these figures date back as far as the first certifying examinations in 1982, it is clear that the number of geneticists and counselors actually in practice in substantially less than the figures cited. Furthermore, whereas the number of individuals seeking certification in genetic counseling has been increasing over the past decade, the number of new physician clinical geneticists has been declining. Therefore, it is fair to say that there is no way that genetic professionals could handle all of the genetic testing and risk assessment likely to come in the future, even if they wanted to. So, who will do what, and how should they be trained to do it? In a broad assessment of the roles of primary care physicians, specialists, and medical geneticists, Korf (2002a) divided medical disorders in which genetics is involved into three not mutually exclusive three groups – single gene or chromosomal, major gene multifactorial (such as breast cancer), and complex multifactorial (otherwise referred to as common or common complex). The first group is principally within the realm of medical genetics as now practiced, with primary care and specialist physicians being responsible for referral and general management and the medical geneticists and counselors for diagnosis, counseling, and, for the inborn errors of metabolism at least, treatment. In fact, it has been suggested by some that this is just where the focus of medical genetics should remain (Guttmacher et al., 2001; Khoury, 2003). The second and particularly the third groups fall within the purview of genomic medicine, as I have defined it here. For these, the medical geneticist has been assigned a more advisory role with regard to test interpretation and counseling, with primary care and specialist physicians arranging for genetic testing to guide prevention, treatment, and, if necessary, diagnosis. Given their present small numbers, reality dictates that medical geneticists, and genetic counselors for that matter, would certainly not be in a position to become the principal providers of genetic testing and risk assessment for the greater population (Epstein, 2006). Nevertheless, medical geneticists and counselors still possess, albeit with different areas of expertise, knowledge of genetics and genomics and human disease that should be brought to bear on the provision of these services. This can be accomplished through their playing a role in the education of other physicians and health providers (Caskey, 2001), but it also involves serving as designated referral sources for problems that may be too complex for those without substantial knowledge of genetics and genomics to handle alone. The summary of a roundtable discussion of the future of genomic medicine states that: The different responsibilities of the primary care physician, genetic counselor, and medical geneticist need to be examined and perhaps redefined. Currently, there are only

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2,000 genetic counselors in the United States. If genetic counselors are to play a central role as genomic medicine is integrated into clinical practice, there is a need for more genetic counselors. At the same time, other health care providers will need to learn more genomics so they can better inform and treat their patients. The standard practice of care for doctors with regard to genomic medicine needs to be defined (NHGRI Policy Roundtable, 2005).

Although what this statement says is true, it unfortunately gives short shrift to medical geneticists. Given the breadth of their knowledge and expertise, medical geneticists should certainly also have a central role in genomic medicine. Medical geneticists know genetics and how testing is done, are comfortable with family histories and probabilities and with counseling and decision-making, and are already doing genetic testing and risk assessment (Epstein, 2004). A primary care physician has been defined as a generalist physician who provides definitive care to the undifferentiated patient at the point of first contact and takes continuing responsibility for providing the patient’s care (American Academy of Family Physicians, 2006a). In the context of the various statements cited above, family practice medicine and general internal medicine would be regarded as the principal primary care specialties, as would general pediatrics for infants and children. For some women, obstetricians and gynecologists also serve as primary care physicians. Primary Care Physicians However the precise balance is struck between primary care physicians and other potential participants in the provision of genetic testing and risk assessment (or, as previously mentioned, applied genomic medicine), it is clear that all groups will have to become knowledgeable about genetics and genomics and their applications. Although the formal educational process for all physicians begins in medical school, medical students should be expected to be no less genetically and genomically literate than what has been proposed for the public at large, and, if possible, more so. It would certainly be helpful if they could master the principles of human genetics and genomics during their premedical education, but this may not be absolutely necessary. However, what will be essential is that the principles and skills that are necessary to understand the role of genetics and genomics in all of medicine are taught both in formal courses and during the various clinical clerkships. The only hope of having primary care physicians who will be able to practice genomic medicine in the future is to begin training them in medical school now. The Medical School Curriculum At the present time, the teaching of genetics and genomics in medical schools is quite variable in terms of when it is taught, how much time is allocated, and how it is taught, and there is certainly no consistent curriculum (see, e.g., the listings in the online Health Sciences Library: Genetics Curriculum Resource, 2006). A medical school core curriculum in genetics, described

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TABLE 34.1 What medical students should appreciate about genetics and genomics prior to graduation from medical school (Genetics Education MSOP Expert Panel, 2004) ●

the potential for genetics to contribute to the development of new approaches to prevention, diagnosis, and treatment of disease

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the potential for genetics to expand understanding of the basic pathophysiology of all human disease

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the possibility of using a genetic approach to provide personalized health care with a much greater focus on prevention

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current limitations in the existing knowledge base

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that the principles for use of genetic information in decision-making are largely the same as for other areas of medicine

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the rapidity of the advancing front of knowledge

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that genetic information may have implications not only for an individual patient, but also for a family, and in some cases an entire community

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the potentially disconcerting nature of genetic information particularly as it relates to interpretation of predictive tests

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the need to reduce public fear and misinformation about genetics information

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the diversity in public understanding of genetic information and evaluation of information sources

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the need for continued learning and receptivity to advances in knowledge and changes in medical practice

as a work in progress, was developed by the Association of Professors of Human and Medical Genetics and the American Society of Human Genetics (APHMG/ASHG). With a total of 74 requirements presented in terms of essential general medical competencies and specific knowledge, skills, and behaviors, this curriculum covers all of the traditional aspects of medical genetics, and, as might be expected, has a heavy focus on mendelian genetics and monogenic and chromosomal disorders (Association of Professors of Human and Medical Genetics and the American Society of Human Genetics, 2001; see also Korf, 2002b). However, in a more recent formulation of curricular objectives published under the aegis of the Association of American Medical Colleges (Genetics Education MSOP Expert Panel, 2004), which appears to be based on the APHMG/ASHG report, the balance, at least in terms of a set of attitudes (Table 34.1) has shifted quite considerably in the direction of genomic medicine (both overall and applied). The knowledge base, skill set, and set of educational strategies that follow are generally consonant with these attitudes. Of particular note are an understanding of multifactorial inheritance and the role of genetic factors in common disease, concepts of genetic linkage and association, the role of genetics in determining response of individual to environmental factors or pharmacological agents, and the role of genetics in modification of risk for common multifactorial disorders. However, what is not explicitly stated, but definitely should be, is an in depth knowledge, summarized in Table 34.2, of how the whole process of genetic testing and risk assessment, both disease-related and pharmacogenetic, will actually work, at least as far as it can now be visualized. I have outlined these items in detail because I am concerned that in real practice the process of genetic testing and risk assessment could well become an automated operation. A source of DNA would go into a genotyping

robot, the output of which would be sent to a computer from which, after the genotype was combined with personal information, again computer generated, would issue a printout of probabilities and recommendations – essentially a “black box” operation (Epstein, 2004). It can be argued that it will not be necessary for the physician to know everything that goes on inside the black box, just as we do not need to understand how a combustion engine works in order to drive a car, and I would agree. Nevertheless, if a physician is to be able to explain intelligently the basis on which risk assessment is performed and how its outcome is to be interpreted, he or she really needs to understand what goes into the process. Residency Training Although training in genetics and genomic medicine must be an essential component of the medical school curriculum, it is only during the years of residency and specialty training that physicians develop the attitudes and skills that will determine how well they will integrate genomic medicine into their practice of medicine. The Association of American Medical Colleges report (Genetics Education MSOP Expert Panel, 2004) mentioned earlier contains a table entitled “What do physicians have to know, and when do they need to know it?” The table contains a list, part of which is summarized in Table 34.3, of what it is predicted that physicians will need to know when in practice 10 years later (i.e., the year 2014). This projection is based on current knowledge, but it could of course be quite different when the 10 years actually passes and things are possible that we cannot even imagine at present. Unfortunately, acquiring the practice skills and attitudes to make it possible for physicians to know these things in the future is, at the present time, more easily said than done. There are

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TABLE 34.2

407

What needs to be understood to understand genetic testing and risk assessment

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how susceptibility genes or haplotypes are identified

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how susceptibility genes confer susceptibility

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how susceptibility genes are tested for

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which susceptibility genes should be tested for

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why it will most often be necessary, for most complex traits, to look at several susceptibility genes simultaneously and how algorithms will be developed to do so

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how environmental and family history data will become part of the calculation

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what is the significance of the probabilities or likelihoods that will constitute the output of the calculations

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how these probabilities form the basis for preventative interventions

TABLE 34.3 Panel, 2004)

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What physicians will need to know in 2014 about genomic medicine (from Genetics Education MSOP Expert

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routine use of proteomic screens for very early detection of common cancers

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increasing use of screening for risk for common disorders to achieve risk stratification and implement prevention strategies

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use of panels of molecular tests to stratify common disorders such as asthma or hypertension

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routine molecular characterization of tissues in pathology

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use of panels of tests to achieve precise diagnosis of monogenic and chromosomal disorders inborn errors of metabolism and other disorders

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routine use of pharmacogenetic profiling

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stratification of common disease and selection of specifically targeted therapies

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use of expression arrays to determine treatment strategies

two factors that have conspired together to make this difficult to do. The first is the tremendous competition for the resident’s time, with the demands of learning how to diagnose and treat a multitude of disorders taking precedence over concerns about genetic risk assessment and testing, which, at least at present, are more hypothetical than real. Somewhat ironically, it is to a great extent exactly the same set of conditions – the common disorders of adulthood – that both compete for the residents’ time and are the targets of testing and risk assessment.There is no easy solution to the problem of competition for time in training programs. Therefore, it will be necessary that the requirements for residency training programs and for specialty certification, as specified by the relevant residency review committees and specialty boards, respectively, insure that genomic medicine is appropriately covered during residency training. The second factor interfering with the integration of genomic medicine into residency training is that those who provide the role models for the residents in training – the more senior residents and fellows, as well as the attending faculty – are themselves, for the most part, not well versed in genetics and genomic medicine. However, this will gradually change with

time, as medical students trained in genomic medicine enter the residency programs and as faculty members themselves become more experienced in the use of genetics and genomics in medical practice (see below). Medical geneticists, when they are available, can and should play an important role in the education of both faculty and residents. The Genetics in Primary Care project (Burke et al., 2002) was an early effort to integrate genetics (not genomics) into primary care residencies that was based on training the faculty first, certainly a reasonable approach. It is not yet known how well this approach has worked. Continuing Medical Education For those physicians currently in practice, the only means for entering the world of genomic medicine will be through participation in various forms of postgraduate medical education. The primary care specialty societies, such as the American Academy of Family Physicians, with its 2005 Annual Clinical Focus on Genomics (American Academy of Family Physicians, 2006b), have already begun to make major efforts in this direction. However, even though such efforts are to be encouraged, I am concerned that they will not prove to be a highly effective

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means of education, again because of the competition for the physician’s time and interest and the lack of a substantial background in the principles of genetics and genomics. Medical Specialists and Subspecialists Everything that has been said about the training of primary care physicians applies to specialist physicians as well. However, nonprimary care specialists and subspecialists will need to acquire a more in depth knowledge about those areas of genomic medicine that apply particularly to their areas of practice. It is reasonable that this should occur during their residency and fellowship training, and I anticipate that the necessary instruction will be built into the training programs. Again, specialty societies have recognized that specialists need to be appropriately trained in genetics and genetic testing. For example, the American Society for Clinical Oncology (ASCO), which had earlier developed a curriculum in cancer genetics (American Society of Clinical Oncology, 1997), affirmed the importance of training oncology practitioners to become more knowledgeable with regard to clinical genetics and called upon a broad range of organizations to work in concert to address the important issues related to credentialing of clinical cancer genetics practitioners (American Society of Clinical Oncology, 2003). Although probably not exactly what ASCO had in mind, the statement concerning credentialing raises another possibility for the training of specialists and subspecialists in genomic medicine, and that is the creation of specialty and subspecialty training programs in genetics and genomic medicine (Korf, 2005). Within the American Board of Medical Specialties certifying system, this could take the form of individual or conjoint (between two specialty boards) specialty training programs and certificates. The logical board to partner in conjoint programs would be the American Board of Medical Genetics, and a series of subspecialities such as cancer genetics, cardiovascular genetics, and genetic testing and risk assessment could be envisioned. Medical Geneticists Mention has already been made of the role that might, and I believe should, be played by medical geneticists in the genomic medicine of the future. Medical genetics is the primary specialty recognized by the American Board of Medical Specialties that deals with both the clinical and laboratory aspects of genetics as applied to human health and disease. Clinical medical geneticists complete a 2 year residency program that generally follows 2 or more years in another medical specialty, primary care or otherwise. The current formulation of the training in clinical genetics is summarized in the Program Requirements (Accreditation Council for Graduate Medical Education, 2007), which describe what medical geneticists do and specify how medical genetics training programs are to operate. These descriptions and specifications are, of course, quite broad, and not every medical geneticist is expert in or practices all of the components listed. In fact, whether formally or informally defined, there are areas of specialization within clinical genetics, the three most important

being dysmorphology, metabolic diseases, and prenatal diagnosis. Nevertheless, all medical geneticists are expected to have a general knowledge of all of the areas specified in the program requirements. However, despite allusions to genetic risk assessment and the interpretation of clinical and laboratory genetic tests, there is relatively little in the requirements that addresses the issues most relevant to genomic medicine. As a result, the training presently being provided to clinical genetics residents is, in most instances, not sufficient to allow them to assume a major role in genomic medicine, whether as a provider, consultant, or teacher (Epstein, 2005). What is lacking is greater and more intensive instruction in population genetics and epidemiology (genetic and otherwise), genomics, pharmacogenetics, bioinformatics, the principles of risk assessment, and the genetics of the common adult diseases and complex traits (Epstein, 2006). Therefore, training programs in medical genetics need to be reformulated to incorporate these subjects, and discussions about how this might be done are currently under way in organized medical genetics (Korf, 2005). In addition to training medical geneticists for their future responsibilities, such a refocusing of medical genetics training could also have the effect of attracting more physicians to the field, which is essential if the manpower deficit mentioned earlier is to be alleviated. Genetic Counselors As was noted earlier, there is a general presumption that genetic counselors will be heavily involved in genetic testing and risk assessment in the future. The training and certification of genetic counselors is carried out under the aegis the American Board of Genetic Counseling, and the specified curricular requirements, like those for medical geneticists, are quite broad (American Board of Genetic Counseling, 2006). Of specific relevance to genomic medicine/genetic testing and risk assessment, the curriculum specifies didactic training in the principles of human genetics (including, among a longer list of topics, population and quantitative genetics, the basis of human variation and susceptibility, and family history and pedigree analysis), the applicability of related sciences (including molecular and cancer genetics) to medical genetics, and the principles and practice of clinical/ medical genetics (including the clinical features and natural history of a broad range of genetic diseases, complex common disorders and syndromes of unknown etiology, genetic testing of all types, utilizing risk assessment skills, and the use of genetics literature, databases, and computerized tools). The training that genetic counselors now receive is already well along the way to enabling them to play a major role in applied genomic medicine, as they now do in cancer risk assessment, and it should be relatively easy to modify the curriculum in the future as genetic testing and risk assessment expand. However, what is less clear at the present time is just what the role of genetic counselors will and should be. The traditional model for genetic counseling has been for the counselors to work with and under the supervision of clinical geneticists. But, whereas this model generally applies to conventional forms of counseling, it seems to have become less applicable to genetic risk assessment, as for breast and

Education of the Providers of Health Care

ovarian cancer, and even to prenatal diagnosis, where the supervising physician may be an oncologist or obstetrician who is much less knowledgeable about genetics and genomics than the counselor. And, beyond this, there is a movement within the genetic counseling community to obtain the legal authority through licensure to operate wholly independently of physicians and medical geneticists. A discussion of whether or not this would be desirable is not germane to the purposes of this chapter. Nevertheless, from the educational point of view, whether or not such independence is attained, both the formal and the postgraduate/continuing education of counselors will have to insure that they possess the requisite knowledge and skills to enable them to function effectively. Genetic Nurses In recent years, nurses in the United States with genetic training have begun to aspire to and assume roles very similar to those of genetic counselors. “In a few short years genetic nursing practice has been transformed from a nearly hidden specialty to a recognized specialty practice with formal recognition, publication of scope and standards of practice, and most recently the availability of credentialing for genetic nurses” (Greco and Mahon, 2003). The International Society of Nurses in Genetics (ISONG) defines genetic nurses as licensed professional nurses with special education and training in genetics who perform risk assessment, analyze the genetic contribution to disease risk and discuss the impact of risk on health care management for individuals and families (International Society of Nurses in Genetics, 2006). Similar to what is occurring in genetic counseling, Greco and Anderson (2002) have proposed a transdisciplinary model that is in contrast to the traditional hierarchical model of genetic services, in which medical geneticists oversee genetic counselors and nurses. In the transdisciplinary model, genetic nurses work in parallel with genetic counselors as core team members of genetic service programs for multifactorial (complex) disorders such as cancer, diabetes, Alzheimer disease, cardiac diseases, with medical geneticists and medical and non-medical specialists serving as consultative team members. In addition to performing what are seen as the usual responsibilities of genetic counselors and genetic nurses (which include taking family history, constructing pedigree, risk assessment/calculation, and test results disclosure), advanced practice nurses and clinical nurses certified in genetics are also seen as potentially being able, by virtue of the terms of their licenses, to perform or participate in certain functions, such as ordering tests and prescribing medication, that are usually assigned to medical geneticists. Training programs in genetic nursing have been established at the masters degree level for the training of genetics clinical nurses and advanced practice genetic nurses, sometimes in association with training in other nursing specialties. Essential competencies and curricular guidelines have been established (American Nursing Association, 2001). The Genetic Nursing Credentialing Commission carries out certification, and the requirements for certification encompass both clinical experience and a modest amount (45–50 hours over 3–5 years) of academic or continuing education courses (Genetic Nursing

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Credentialing Commission, 2006a,b). To date, the number of nurses actually certified by the Commission is very small, only 39 as of 2005 (Ormond and Faucett, 2005). However, there are certainly many more who are currently involved in genetics without being certified, and the pool of nurses who could potentially be trained in genetics is quite large. I have described genetic nursing in some detail because it seems likely that nurses, by virtue of the major roles that they already play in specialty practices, will be in a position to assume a much greater role in genomic medicine than they have to this point. They are already involved in cancer risk assessment, and other forms of risk assessment and especially pharmacogenetic profiling will undoubtedly afford attractive opportunities. It has also been suggested, in a British study of cancer risk counseling, that genetic nurses may prove to be more cost effective than clinical geneticists as providers of genetic risk assessment (Torrance et al., 2006). Therefore, it would seem reasonable to expand training opportunities in genetics for nurses and to enhance the content of such training in the area of genomic medicine. Pharmacists In the discussion above, all forms of genetic testing and risk assessment have been lumped together. However, one form of testing and risk assessment that might conceivably be implemented more rapidly than that for common disease susceptibility is pharmacogenetic testing to look for variants that might make a person more or less sensitive to the therapeutic and toxic effects of drugs. There has certainly been enormous press on the subject, and even some of the strongest critics of susceptibility testing have seen promise in this approach (Holtzman and Marteau, 2000). Nevertheless, even if inheritance does influence the effect of a drug, the relatively simple, one-to-one relationship observed for of some drugs will not always be obvious (Weinshilboum, 2003). Given this complexity, multiple genes that simultaneously affect the metabolism and effectiveness of individual drugs will have to be looked at simultaneously (Johnson, 2003), and other factors beyond the genetic ones that also have an influence will have to be taken into account (Food and Drug Administration, 2003, 2006; Nuffield Council on Bioethics, 2003). Therefore, pharmacogenetic (pharmacogenomic) testing may turn out to be more complex than previously thought, and Malinowski (2004) has suggested that primary care physicians and nurses will not be in a position to shoulder the burden alone. His solution to this problem is for pharmacists and non-physician clinicians to assume an expanded role in the health care process. It is certainly not clear how this suggested division of labor will actually occur or that pharmacists should play a role in the decision-making process. Although I personally believe that such decisions should remain within the province of physicians, primary care or otherwise, the American Association of Colleges of Pharmacy suggests otherwise: “The potential is enormous for pharmacogenomics to yield a powerful set of molecular diagnostics that will become routine tools by which pharmacists and physicians select the proper medications and doses for each individual patient … The results of these tests will not be simply a list of

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gene SNPs, but rather a report formatted and interpreted according to the patient’s diagnosis and treatment options … to which the patient’s future physicians and pharmacists would be granted access as needed to make treatment decisions” (Johnson, et al., 2004). However it eventually plays out, it is clear that pharmacists, as well as medical geneticists and genetic counselors, will all have to become knowledgeable about pharmacogenetic testing.To this end, schools of pharmacy are beginning to introduce courses in genetics and genomics, and pharmacogenetics and bioinformatics, but it is likely, even if the pharmacist’s role is only explanatory, that much more extensive exposure will be required than is currently being provided (Latif and McKay, 2005). Laboratory Geneticists Although the principal focus of this chapter is on those who work directly with patients, there is, of course, another side to genetic testing and risk assessment – the actual performance of the tests in laboratories. For all practical purposes, this part of the process is invisible to the patient or client, but very often not to the persons, whether physicians, counselors, or nurses, who are involved in ordering the tests. Therefore, the directors of such laboratories need to be well trained not only in the technical aspects of the tests they are performing, but more broadly is how these tests are being used and interpreted in the risk assessment process. Laboratory geneticists in the United States are certified by either the American Board of Medical Genetics (as clinical molecular geneticists) or the American Board of Pathology (as molecular genetic pathologists). In general, the training required for these certifications is quite germane to genomic medicine, but again it will need to be focused and enhanced as the number and volume of tests increases. Although less than 500 individuals were certified as of 2007 (American Board of Medical Genetics, 2008), a single certified laboratory geneticist can handle testing for a very large number of people, so his/her impact is greater than might first appear. Genetic testing is carried out in both academic and commercial settings, with the balance rapidly shifting to the latter. Furthermore, many directors of testing laboratories are neither TABLE 34.4

board-certified medical geneticists nor molecular pathologists, although many are certified in pathology, which includes both anatomic and clinical (laboratory) pathology. It is in the latter area that exposure to molecular genetics occurs and includes basic molecular techniques and methods, human genetic principles, types of mutations and their detection, and gene structure and function and the application of these techniques and principles to inherited, neoplastic, and infectious diseases and to histocompatibility (American Board of Pathology, 2005). Given the large numbers of areas in which training is required, the exposure to molecular genetics cannot be very intensive. Several other boards, none of which are specifically oriented to genetics and genomics (except for the American Board of Medical Genetics, which is included in the list) are also authorized under the Clinical Laboratory Improvement Amendments (CLIA) to certify laboratory directors of so-called high complexity testing (Centers for Medicare and Medicaid Services, 2005). The import of all of this is that the education of laboratory directors required for their participation in genomic medicine will have to be enhanced and carried on over many fronts. Other Health Professionals In addition to physicians, nurses, genetic counselors, pharmacists, and laboratory geneticists, there are many other health professionals who may be involved directly or indirectly in genomic medicine. These range from dentists and optometrists to psychologists, physician assistants, and physical and occupational therapists. The National Coalition for Health Education in Genetics (NCHPEG), which includes health professional societies, certifying boards, consumer groups, foundations, commercial entities, government agencies, and hospital and health care systems among its membership (NCHPEG, 2006), has prepared a list of core competencies in genetics that are considered essential for all health professionals (NCHPEG, 2005). In addition to several knowledge items, the skills listed in Table 34.4 have been identified. It is clear from this list that health professionals who possess only the skills enumerated would not be in a position to perform genetic risk assessments, but they would certainly be in

Skills considered essential for all health professionals (NCHPEG, 2005)

The ability to: ●

gather genetic family history information, including an appropriate multigenerational family history

●

identify clients who would benefit from genetic services

●

explain basic concepts of probability and disease susceptibility, and the influence of genetic factors in maintenance of health and development of disease

●

seek assistance from and refer to appropriate genetics experts and peer support resources

●

obtain credible, current information about genetics, for self, clients, and colleagues

●

use effectively new information technologies to obtain current information about genetics

●

educate others about client-focused policy issues

●

participate in professional and public education about genetics

References

a position to be knowledgeable about what is involved and who should be referred.

CONCLUSION In presenting the competencies just listed, the NCHPEG committee that prepared them made some observations that bear directly on the issue of exactly who will be doing what that I alluded to earlier in this chapter: Some in the genetics community were concerned that broad adoption of the competencies would result in the transfer of all genetic services to providers not formally trained in genetics, thereby obviating medical genetics and genetic counseling as specialties. Others in the genetics community viewed the competencies as unrealistic and unattainable by non-geneticists. Some non-geneticists concurred and wondered whether the intent of the competencies was to turn all health-care providers into genetic specialists. Those questions became part of the larger debate about the future of genetically based health care, one more variable in the complex mix of factors influencing a health-care system in rapid flux. There is, of course, no final resolution at this time to the questions of who will provide genetic services, in what settings, and under what financial structures. Indeed, there is as yet no clear answer to the question of how genetic services will be defined in the future, and that answer is unlikely to emerge any time soon. It is crucial, however, that all health professionals incorporate genetic and genomic knowledge so they are well informed about the ways in which that information can enhance patient outcomes (my emphasis) (NCHPEG, 2005).

The uncertainties expressed in this statement with regard to how genetic services, which include applied genomic medicine, will be incorporated into the health care system make it difficult to develop any definitive educational strategies for preparing for the future. Nevertheless, it seems reasonable to proceed on the premise that many health care professionals are likely to be involved in genomic medicine, with physicians in primary care and in those specialties that are principally involved in the prevention and treatment of common diseases will being central

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to the process. However, it is also likely that many of the nonmedical aspects of genetic risk assessment may and perhaps should be delegated to genetically trained counselors and nurses, Although their numbers are presently quite small, training programs for these two groups could be expanded relatively quickly. When pharmacogenetic testing is involved, pharmacists, whose numbers are considerably greater, may be in a position to play an important role. What are less certain are the roles beyond the consultative and educational that clinical medical geneticists will play. It is conceivable that they could become directly involved in risk assessment, but again their numbers are small and are unlikely to increase very rapidly. In this chapter, I have discussed the education of the several professional groups just mentioned, and the conclusion is the same for each – more intensive training in genetics and genomics beginning earlier in their careers will be required. The fact that all health care professionals are themselves part of the public at large, for which genetic literacy will be essential, means that this training must begin as early as elementary and high school and continue into their professional training and practice. It is not clear at this time who will actually take responsibility for formulating the curricula in genetics and genomics for the many groups that have been considered in this chapter. Several different professional organizations have already been alluded to along the way, and it is likely that they and many others like them will have a part to play. However, as a practical matter, it would seem that the promulgation of curricula will ultimately have to be done by the organizations that are most directly involved with particular target audiences. Lastly, the discussion of training in genetics and genomics for the public and health care professionals has been cast principally in terms of science and technologies. However, it is likely that many of these individuals will be drawn into discussions of the policy issues that will certainly develop and revolve around genomic medicine. Therefore, consideration of the social, ethical, and legal issues that will form the basis for the making of policy should be included in the educational curricula at all levels. For health professionals, this would best done during the time of their formal professional training. For the public, high school and university would be most appropriate.

REFERENCES Accreditation Council for Graduate Medical Education (2007). Program requirements for graduate medical education in Medical Genetics. http://www.acgme.org/acWebsite/RRC_130/130_prIndex.asp American Academy of Family Physicians (2006a). AAFP Policies: Primary Care: Definitions. http://www.aafp.org/online/en/home/policy/ policies/p/primarycare.html American Academy of Family Physicians (2006b). AAFP Annual Clinical Focus: 2005 Genomics. http://www.aafp.org/online/en/ home/clinical/acf/genomics.html American Board of Genetic Counseling (2006). Specific requirements for accreditation in genetic counseling. http://www.abgc. iamonline.com/view.asp?x1642&mid110

American Board of Genetic Counseling (2008). Certification. http:// www.abgc.net/english/view.asp?x1418 American Board of Medical Genetics (2008). Numbers of certified specialists in genetics. http://www.abmg.org/pages/ resources_certspecial.shtml American Board of Pathology (2005). Description of examinationsanatomic pathology and clinical pathology. http://www.abpath. org/CandBkltDescrAPCP.htm American Nurses Association (2006). Essential nursing competencies and curriculum guidelines for genetics and genomics, established by consensus panel, September 21–22, 2005. http://www.isong. org/resources/genetics_competencies_092206.pdf

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American Society of Clinical Oncology (1997). Resource document for curriculum development in cancer genetics education. J Clin Oncol 15, 2157–2169. American Society of Clinical Oncology (2003). American Society of Clinical Oncology Policy Statement Update: Genetic testing for cancer susceptibility. J Clin Oncol 21, 2397–2406. Andrews, L.B., Fullarton, J.E., Holtzman, N.A. and Motulsky, A.G. (1994). Assessing Genetic Risks. Implications for Health and Social Policy. National Academy Press,Washington, D.C. Association of Professors of Human and Medical Genetics and the American Society of Human Genetics (2001). Medical school core curriculum in genetics. http://www.ashg.org/pages/pubs_curriculum.shtml Burke, W., Acheson, L., Botkin, J., Bridges, K., Davis, A., Evans, J., Frias, J., Hanson, J., Kahn, N., Kahn, R. et al. (2002). Genetics in primary care: A USA faculty development initiative. Comm Genet 5, 138–146. Burke, W., Khoury, M.J., Stewart, A. and Zimmern, R.I. (2006). The path from genome-based research to population health: Development of an international public health genomics network. Genet Med 8, 451–458. Caskey, C.T. (2001). Foreword. The future of medical genetics. In Genetics in the Clinic. Clinical, Ethical, and Social Implications for Primary Care (M.B. Mahowald, V.A. McKusick, A.S. Scheurle and T.J. Aspinwall, eds), Mosby, St. Louis, pp. ix–xi. Centers for Medicare and Medicaid Services (2005). Certification boards for clinical consultants and laboratory directors of high complexity testing. http://www.cms.hhs.gov/CLIA/16_Certification_Boards_ Clinical_Consultants_&_Laboratory_Directors.asp#TopOfPage Department of Health (2003). Our Inheritance, our Future. Realizing the Potential of Genetics in the NHS, London. Epstein, C.J. (2004). Genetic testing: Hope or hype? Am J Med Genet 6, 165–172. Epstein, C.J. (2005). Medical geneticists in the 21st century. Genet Med 7, 375–379. Epstein, C.J. (2006). Medical genetics in the genomic medicine of the 21st century. Am J Hum Genet 79, 434–438. Federal Trade Commission (2006). At-home genetic tests: A healthy dose of skepticism may be the best prescription. http://www.ftc. gov/bcp/edu/pubs/consumer/health/hea02.htm Food and Drug Administration (2003). Guidance for industry. Pharmacogenetic data submissions. Draft guidance. November, 2003. http//www.fda.gov/cder/guidance/index.htm Food and Drug Administration (2006). Draft guidance for industry, clinical laboratories, and FDA Staff on in vitro diagnostic multivariate index assays; Availability. http://www.fda.gov/OHRMS/DOCKETS/ 9 8 f r / c h 0 6 4 1 . p d f w w w. f d a . g ov / O H R M S / D O C K E T S / 98fr/ch0641.pdf Genetic Nursing Credentialing Commission (2006a). Advanced practice nurse in genetics. http://www.geneticnurse.org/advancedpracticeapng.html Genetic Nursing Credentialing Commission (2006b). Genetics clinical nurse. http://www.geneticnurse.org/geneticsnursegcn.html Genetics Education MSOP Expert Panel (2004). Contemporary Issues in Medicine: Genetics Education. Association of American Medical Colleges Medical School Objectives Project,Washington, D.C. Ginsburg, G.S., Donahue, M.P. and Newby, L.K. (2005). Prospects for personalized cardiovascular medicine. The impact of genomics. J Am Coll Cardiol 46, 1615–1627. Greco, K. and Anderson, G. (2002). Redressing policy in cancer genetics: Moving toward transdisciplinary teams. Policy Polit Nurs Pract 3, 129–139.

Greco, K.E. and Mahon, S.M. (2003). Genetics nursing practice enters a new era with credentialing. The Internet JAdv Nurs Pract 5(2). http://www.ispub.com/ostia/index.php?xmlFilePathjournals/ ijanp/vol5n2/genetics.xml Grigg, W., Lausko, M. and Brockway, D. (2006). The Nation’s Report Card: Science 2005. (NCES-2006-466). U.S. Department of Education, National Center for Education Statistics. U.S. Government Printing Office,Washington, D.C. Grosse, S.D. and Khoury, M.J. (2006). What is the clinical utility of genetic testing?. Genet Med 8, 448–450. Guttmacher, A.E., Jenkins, J. and Uhlmann, W.R. (2001). Genomic medicine: Who will practice it? A call to open arms. Am J Med Genet (Semin Med Genet) 106, 216–222. Haga, S.B. (2006). Teaching resources for genetics. Nat Rev Genet 7, 223–229. Harvard-MIT Division of Health Sciences & Technology (2004). Genomic medicine. HST 512/513 https://www.hstdev.mit.edu/ servlet/ControllerServlet?handlerPublicHandler&actionview Course&courseIDHST512&term2004SP Health Sciences Library: Genetics Curriculum Resource (2006). http:// www.library.nymc.edu/informatics/genetics.cfm Holtzman, N.A. and Marteau, T.M. (2000). Will genetics revolutionize medicine?. N Engl J Med 343, 141–144. Hott,A.M.,Huether, C.A.,McInerney, J.D.,Christianson, C.,Fowler, R., Bender, H., Jenkins, J., Wysocki, A., Markle, F.G. and Karp, R. (2002). Genetics content in introductory biology courses for non-science majors: Theory and practice. BioScience 52, 1024–1035. International Society of Nurses in Genetics (2006). What is a genetic nurse? www.isong.org/resources/what_is.pdf Janssens, A.C.J.W., Aulchenko, Y.S., Elefante, S., Borboom, G.J.J.M., Steyerberg, E.W. and van Duijn, C.M. (2006). Predictive testing for complex diseases using multiple genes: Act or fiction?. Genet Med 8, 395–400. Johnson, J.A. (2003). Pharmacogenetics: Potential for individualized drug therapy through genetics. Trends Genet 19, 660–668. Johnson, J.A., Bootman, J.L., Evans, W.E., Hudson, R.A., Knoell, D., Simmons, L., Straubing, R.M. and Meyer, S.M. (2004). Pharmacogenomics: A scientific revolution in pharmaceutical sciences and pharmacy practice. Final report of the 2001/02 Academic Affairs Committee, American Association of Colleges of Pharmacy. http://www.aacp.org/site/page.asp?VID1&CID1039&DID 6100&TrackIDwww.google.com/search?q Jones, S. and Collins, F. (2003). Genomics in medicine: Hype or real promise? Interview by Ed Rabinowitz. Healthplan 44, 20–24. Khoury, M.J. (2003). Genetics and genomics in practice: The continuum from genetic disease to genomic information in health and disease. Genet Med 5, 261–268. Korf , B.R. (2002a). Genetics in medical practice. Genet Med 4(6, Supplement), 10S–14S. Korf , B.R. (2002b). Integration of genetics into clinical teaching in medical school education. Genet Med 4(6, Supplement), 33S–38S. Korf , B.R. (2005). Genetics training in the genomic era. Curr Opin Pediatr 17, 747–750. Latif, D.A. and McKay, A.B. (2005). Pharmacogenetics and pharmacogenomics instruction in colleges and schools of pharmacy in the United States. Am J Pharm Educat 69, 152–156. Malinowski, M.J. (2004). Law, policy, and marketing implications of genetic profiling in drug development. http://www.hofstra.edu/ academics/law/law_faculty_workshop_archives.cfm

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National Institutes of Health (2004). Revolutionary genome sequencing technologies – the $1000 genome. http://www.grants.nih. gov/grants/guide/rfa-files/RFA-HG-05-004.htmlhttp://grants. nih.gov/grants/guide/rfa-files/RFA-HG-05-004.html NCHPEG (2005). Core competencies in genetics essential for all health-care professionals. http://www.nchpeg.org/content.asp? dbsectionbasic&dbid1 NCHPEG (2008). NCHPEG member listing. http://www.nchpeg. org/core/Corecomps2005.pdf NHGRI Policy Roundtable (2005). The future of genomic medicine: Policy implications for research and medicine. http://www. genome.gov/17516574 Nuffield Council on Bioethics (2003). Pharmacogenetics: Ethical issues. London: 2003. Ormond, K. and Faucett, A. (2005). Working group report. http:// www.nsgc.org/client_files/news/SACGHS_Feb05.pdf Royal Society (2005). Personalised medicines: Hopes and realities. http: www.royalsoc.ac.uk/displaypagedoc.asp?id15874 Scheuner, M.T. and Rotter, J.I. (2006). Quantifying the health benefits of genetic tests: A clinical perspective. Genet Med 8, 141–142. Scheuner, M.T., Wang, S.J., Raffel, L.J., Larabell, S.K. and Rotter, J.I. (1997). Family history: A comprehensive genetic risk assessment method for the chronic conditions of adulthood. Am J Med Genet 71, 315–324. Snyderman, R. and Williams, R.S. (2003). Prospective medicine: The next health care transformation. Acad Med 78, 1079–1084. SureGene (2006). AssureGene test for schizophrenia susceptibility. http://suregene.net/AssureGeneTest.aspx

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Torrance, N., Mollison, J., Wordsworth, S., Gray, J., Miedzybrodzka, Z., Haites, N., Grant, A., Campbell, M., Watson, M.S., Clarke, A. and Wilson, B. (2006). Genetic nurse counsellors can be an acceptable and cost-effective alternative to clinical geneticists for breast cancer risk genetic counselling. Evidence from two parallel randomised controlled equivalence trials. Br J Cancer 95, 435–444. Tversky, A. and Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science 211, 453–458. United States Department of Health and Human Services (2006). U.S. Surgeon General’s family history initiative. http://www.hhs. gov/familyhistory/ Weinshilboum, R. (2003). Inheritance and drug response. N Engl J Med 348, 529–537. Weston, A.D. and Hood, L. (2004). Systems biology, proteomics, and the future of heath care: Toward predictive, preventative, and personalized medicine. J Proteome Res 3, 179–196. Willard, H.F. (2004). Message from the director. http://www.genome. duke.edu/genomelife/glarchive/issue9/dirmessage Willard, H.F., Angrist, M. and Ginsburg, G.S. (2005). Genomic medicine: Genetic variation and its impact on the future of health care. Phil Trans Roy Soc B 360, 1543–1550. Wolfberg, A.J. (2006). Genes on the web – direct-to-consumer marketing of genetic testing. N Engl J Med 355, 543–545. X Prize Foundation (2006). Archon Genomics X Prize http://genomics.xprize.org/

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35 Federal Regulation of Genomic Medicine Janet Woodcock

INTRODUCTION Regulation of the medical uses of genomic technologies is an evolving and controversial field. In the ideal case, medical product regulation strikes an appropriate balance between protecting patients and fostering innovation. Such balance can be difficult to achieve during the dynamic evolution of a new area of technology. Currently, the fields of genomic testing and pharmacogenomics are experiencing very active growth and corresponding regulatory and policy interest, and there is ongoing debate about the appropriate level of regulation of these technologies (Hudson, 2006; Javitt and Hudson, 2006). This chapter provides an introduction to both the regulatory framework and the ongoing policy challenges for genomic medicine. Commercial introduction of genetic tests began in the late 1970s; however, the pace of introduction was slow at first and has rapidly accelerated over the last decade. It is estimated that over 1300 genetic tests are currently available in the United States (Gene Tests, www.genetests.org). Most of these are diagnostic tests for genetically based disorders. Recently, new types of in vitro diagnostics (IVDs) – for example, assays evaluating gene expression – are being introduced. Based on the current amount of activity in the field, an explosion of new assays can be anticipated over the next several years. The landscape of genetic test regulation is somewhat confusing. The vast majority of available genetic tests are

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“laboratory-developed tests.” Such assays are not FDA approved; rather, they are developed and conducted within a specific laboratory and are offered as a service by the laboratory. In contrast, all marketed test kits go through an FDA regulatory process of some type. Laboratories that develop an in-house test may submit an application for marketing clearance to the FDA; however, few have chosen to do so. This framework has resulted in a perception of an “uneven playing field” for the two assay categories. All laboratories reporting out human results require Clinical Laboratory Improvement Amendments (CLIA) certification. The CLIA program is administered by the Centers for Medicare and Medicaid Services (CMS). The respective roles of FDA and CMS have not been clear to all, especially newcomers, in the field and have resulted in considerable confusion. Recently FDA issued several “Guidance” documents intended to specifically clarify FDA regulation of newer diagnostic tests, including genomic tests. These will be discussed in more detail below. Researchers interested in developing a specific assay for use in clinical medicine will need a basic understanding of the various regulatory regimes, as described further in this section. Exploratory assay development using human samples – for example, tissues or body fluids – can usually be conducted under the general human subject protection and privacy provisions implemented by academic institutions. Prior to undertaking analytical validation or clinical studies, however, it would be prudent for a developer to determine the potential regulatory

Introduction

status of the test, and the types of studies and regulatory clearances needed to bring the test to market successfully. Further evolution of the current regulatory framework for genomic tests is likely if the new discipline of genomic medicine is as successful as is currently anticipated. In contrast to genetic diagnostic testing, the field of pharmacogenomics is in fairly early development. Genomic technologies have been used extensively in drug discovery for over a decade, but application to drug development is a more recent phenomenon. Pharmacogenetics can be defined as the study of the genetic contributions to drug responses (see Chapter 27). Drug metabolizing enzyme polymorphisms are the best understood examples of pharmacogenetics because correlations between phenotypes and drug concentrations have been well worked out for a number of polymorphisms. Despite decades of study, however, metabolizing enzyme polymorphisms are rarely taken into account in drug development or clinical practice, in part because convenient assays have not been available. Recently, genetic tests for common drug metabolizing enzyme polymorphisms have been introduced and several have been approved by the FDA; however, as of this writing, no drug label contains specific requirements for dose adjustment based on metabolizing enzyme test results. Routine use of such tests awaits demonstration of added value. Pharmaceutical developers generally seek to eliminate, prior to clinical testing, candidate drugs that are subject to polymorphic metabolism or that may be prone to drug– drug interactions based on metabolic factors. Traditional clinical drug development processes have not included individualized dose adjustments, and there are strong commercial and logistical disincentives to incorporating such individualization. The first pharmacogenetically directed, dose-adjusted drug development program will likely involve: ●

●

●

a promising molecule that developers are unable to engineer to avoid an undesirable metabolic pathway (Hudson, 2006); an indication for a very serious or life-threatening illness (Javitt and Hudson, 2006); and an expected narrow therapeutic index (e.g., cancer therapeutics).

Much of the current scientific interest in drug metabolism involves marketed drugs, many of which are metabolized by polymorphic enzymes. Clinical practitioners have long been accustomed to performing empirical dose adjustments based on clinical parameters, for example, INR for warfarin, leukocyte counts for 6-mercaptopurine, or side effects for many drugs. There is considerable skepticism within the clinical and reimbursement communities about the value of up-front genetic testing and dose adjustment. Resolution of these issues will require randomized clinical outcome trials that demonstrate added safety or effectiveness, or improved patient convenience/adherence and/or cost-effectiveness as a result of testing and dose adjustment. From a scientific perspective, it is unquestionable that the many-fold variation in drug exposure often introduced by polymorphic metabolism has important clinical

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consequences in both drug development and in patient care, particularly for drugs with narrow therapeutic indices. However, ingrained practices change slowly, and, in any case, there is a need for evidence-based dosing algorithms for practitioners in instances where dose adjustment is found to be important (see for example www.warfarin-dosing.org). Although drug metabolism is the best understood example, the promise of pharmacogenomics for drug and vaccine development and use is much more expansive. Pharmacogenomic tests are being explored for selecting patient subgroups for treatment (based on probability of response, prognostic category, or disease subtype), identifying patients with a high probability of an adverse response to therapy, and for monitoring response. Additionally, the field of toxicogenomics, that evaluates the genomic responses to toxicants, is expected to contribute much more sensitive toxicity assays and greatly enhanced understanding of the mechanisms of drug toxicity. All these applications are in the early stages of development. The most extensive clinical use occurs in the field of antiretroviral therapy, where HIV drug resistance testing is routinely performed by assaying the viral genome, and therapy is directed by the results. Both FDA-approved and laboratory-developed HIV drug resistance assays are commercially available. With respect to the human genome, the most developed examples occur in cancer, where various genomic assays are employed to provide information about tumor aggressiveness and prognosis, or about likelihood of response to a targeted therapy (e.g., imatinib, trastuzumab). NIH, FDA and the pharmaceutical industry are also pursuing efforts to understand the genetic basis of drug-related adverse events. An historical example of a genetically based drug adverse event is drug-induced hemolysis in people with G6PD deficiency (metabolism based); a non-metabolic, recent example is the abacavir hypersensitivity reaction (Rauch et al., 2006). It is expected that some, but not all, drug-related adverse events will have a genetic contribution. Because of the potential for pharmacogenomics to improve drug safety and effectiveness, the FDA has been vigorously involved in efforts to foster this new field. In November 2003, the Agency established the “voluntary genomic data submission process,” a kind of safe harbor where pharmaceutical developers can share genomic experiments and data with the FDA in a non-regulatory context (www.fda.gov/cder/genomics/GDS. htm). Multiple data submissions have been shared with FDA and this process is expected to help ease the transition of pharmacogenomic technologies into mainstream preclinical and clinical drug development. Nevertheless, numerous policy questions related to the use and regulation of pharmacogenomics in drug development remain to be answered. For example, what regulatory standards should apply during co-development of a pharmacogenomic test and drug to be used in combination? What data and studies are needed to qualify genomic biomarkers for regulatory use? “Regulation of Genomic Tests” explores the regulatory framework for the use of pharmacogenomic technologies in drug development and therapy, and discusses various unresolved policy areas.

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Federal Regulation of Genomic Medicine

REGULATION OF GENOMIC TESTS Researchers intending to translate basic genomic discoveries into new diagnostic or prognostic assays will need to have a basic understanding of the regulatory framework governing such tests. FDA regulations define IVDs products as “those reagents, instruments and systems intended for use in the diagnosis of disease or other conditions, including a determination of the state of health, in order to cure, mitigate or prevent disease or its sequelae.” The regulations go on to say: “These products are devices as defined in Section 201(h) of the Federal Food Drug and Cosmetic Act …” (21 CFR 809.3) Therefore reagents, systems, and assays for human use are considered medical devices under the law and are subject to FDA regulation. However, medical device regulation has considerable flexibility, and this has been exercised by FDA in its oversight of IVDs. Additionally, the FDA Modernization Act passed by Congress in 1997 called upon FDA to use an approach to device regulation that is “least burdensome” on manufacturers while still achieving public health objectives, and this charge is taken into account when implementing regulatory standards. Regulation of In Vitro Diagnostics as Medical Devices Prior to embarking on a development program for a genomic test, researchers and developers should strongly consider consulting with the FDA staff about the development plan. Most genomic assays regulated as medical devices will be overseen within the Office of In Vitro Diagnostic Device Evaluation and Safety (OIVD) in the Center for Devices and Radiological Health (CDRH) at FDA. CDRH offers assistance to researchers, developers, and device manufacturers, who can request a “preIDE” meeting with OIVD to discuss the proposed development plan. Such consultations can save time and prevent wasted efforts. Developers can submit their planned protocols for evaluating the performance of the IVD and reach agreement with FDA staff on the extent and nature of the studies. Contacts can be reached through the CDRH web page (www.fda.gov/cdrh/index.html). FDA has also recently published a relevant draft Guidance entitled “Pharmacogenetic Tests and Genetic Tests for Heritable Markers” that contains advice on product development (www. fda.gov/cdrh/oivd/guidance/1549.html). Regulatory Classification of In Vitro Diagnostics Medical devices are classified into categories, based on risk, that govern the intensity of their regulation. For IVDs, the risk determination is primarily a function of the intended use of the device, based generally on the claims sought by the manufacturer. Many types of IVDs have been formally classified by the FDA in regulations. Class I devices are considered low risk and are subject to the fewest regulatory controls. These so-called “general” controls include, for example, facility registration, requirements for label information and format, manufacturing procedures, and reporting of adverse events. Many (non-genetic) IVDs have been assigned to Class I and are exempted from submitting applications for marketing to the FDA. Class II devices are moderate

risk and require additional controls. Many genomic tests will be considered Class II IVDs. As a relevant example, drug metabolizing enzyme genotyping systems are Class II. In making this determination, FDA published the document “Class II Special Controls Guidance Document: Drug Metabolizing Enzyme Genotyping System,” in 2005 (www.fda.gov/cdrh/oivd/guidance/1551.html). This document explains what data to submit to FDA in a Premarket Notification Submission, also known as a 510(k) (see below) for a diagnostic of this type. By submitting this information, and also complying with the “general” controls, a manufacturer can meet the requirements for marketing a drug metabolizing enzyme genotyping system. Special controls vary with the category of IVD. In some cases, clinical studies may need to be submitted as part of a 510(k) submission for a Class II IVD. Class III IVDs are considered high risk and often require submission of a Premarket Approval Application (PMA). PMAs usually include data from clinical studies. If a device category has not been classified, it is considered Class III. Applications for Marketing In Vitro Diagnostic Tests The Premarket Notification Submission or 510(k)

When submitting a traditional 510(k) application, a manufacturer intends to demonstrate that its IVD is “substantially equivalent” (the legal terminology) to a previously legally marketed IVD, called the “predicate device.” If a predicate IVD exists, clinical data in the 510(k) for a genomic test would include information on the analytic validation of the test, plus comparisons with the predicate IVD performance, using appropriate clinical samples (Figure 35.1). If no predicate exists, the FDA can, when appropriate, use a process know as a de novo 510(k) to establish the special controls needed for a Class II IVD, as discussed for the drug metabolizing enzyme genotyping test systems mentioned above. In these instances, the medical literature or submitted data can be used by FDA to help determine the needed extent of special controls. The de novo 510(k) may contain clinical data consisting of protocol-based testing of clinical samples, may sometimes also include data gathered in prospective clinical trials, and in some cases may include data derived from the clinical literature and/ or information found in recognized clinical practice standards. In February 2007 FDA approved Agendia’s MammaPrint® as a de novo 510(k). This test is intended to evaluate the likelihood of breast cancer recurrence within 5–10 years in patients with Stage I or Stage II disease. It is the first FDA-cleared molecular test using genetic profiling to predict breast cancer prognosis and is indicated as a prognostic marker only. Total FDA review time for this application was 30 days; the entire process (which necessitated the firm submitting a petition for down-classification) took less than 180 days. The special controls guidance document for these types of assays, “Class II Special Controls Expression Profiling Test System for Breast Cancer Prognosis,” has been published (www.fda.gov/cdrh/oivd/guidance/1627.pdf). The clinical data for this application was based on analyses of stored samples from various studies (www.fda. gov/cdrh/reviews/K062694.pdf).

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Application review

IDE Submission/review (If Required)

Investigational phase pre-IDE or IDE meeting as Appropriate

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PMA or 510(k) application

510(k) – 90 Day Review PMA – 180 Day Review

Figure 35.1 In vitro diagnostic development process. The requirement for an Investigational Device Exemption (IDE) filing is based on risk: most clinical studies of IVDs do not need to be conducted under an IDE. Sponsors may request a meeting with FDA at any time during the development process to obtain regulatory guidance on the development program or on how to submit an application: request a “pre-IDE” meeting if no IDE exists, or an “IDE” meeting if an IDE has been filed. The timeframe for FDA review depends on whether a 510(k) or PMA has been filed.

FDA has published extensive guidance on submission of 510(k)s for IVDs, including the documents “Format for Traditional and Abbreviated 510(k)s” (www.fda.gov/cdrh/ode/ guidance/1567.pdf) and “Points to Consider for Collection of Data in Support of in vitro Device Submissions for 510(k) Clearance” (www.fda.gov/cdrh/ode/95.pdf) FDA has also recognized dozens of voluntary standards (most developed by the Clinical Laboratory Standards Institute or CLSI) for use in establishing test performance. Published guidances and FDArecognized standards can be found on the CDRH web page (www.fda.gov/cdrh/index.html). The Premarket Approval Application

The claims attached to certain genomic tests may cause them to be classified into Class III (high risk). An example of such claims could include prognostic information linked to therapeutic decision-making (e.g., more extensive radiation or chemotherapy for cancer). PMA submissions contain clinical data that substantiate these claims, usually derived from clinical trials. Developers often meet with the FDA staff to reach agreement on the design of these trials prior to their initiation. Investigational In Vitro Diagnostics Investigational device exemptions (IDEs) must be submitted to the FDA prior to beginning clinical studies of certain devices. IDEs are rarely needed for IVDs. In clinical studies where the results of the test will not be used in patient management, an IDE is not needed; however, the usual requirements for informed consent and IRB approval remain, where applicable. In those instances where the test results will determine management (e.g., patients are randomized to further interventions based on test results), an IDE should be submitted. OIVD staff may be consulted on the need for an IDE filing. If investigational IVDs are shipped for research testing, for example, analytical validation, they should be labeled “For

Research Use Only. Not for use in diagnostic procedures” (21 CFR 809.10 (c)(2)(i)). If they are shipped for use in comparative testing or as part of a clinical trial, they should be labeled “For Investigational Use Only. The performance characteristics of this product have not been established” (21 CFR 809.10 (c)(2)(ii)). FDA recently issued a document entitled “Guidance on Informed consent for In Vitro Diagnostic Device Studies Using Leftover Human Specimens that are Not Individually Identifiable” that offers advice on the use of de-identified human specimens for testing (www.fda.gov/cdrh/oivd/1588.pdf). Laboratory-Developed Tests As stated above, the vast majority of currently marketed genetic tests are laboratory-developed tests that have not undergone the FDA review process. These tests are either fully developed by the laboratory or utilize a purchased “analyte-specific reagent” (ASR, see below) that is then configured into an assay by the laboratory, which carries out the test as a service. Such laboratory developed tests are not shipped for use outside the originating site. These tests are considered medical devices under the Food, Drug and Cosmetic Act (and the laboratories are considered device manufacturers), but FDA has not required marketing applications (using what is legally termed “enforcement discretion”), considering that the controls provided by the ASR regulations and the certifications for high complexity laboratories under CLIA (see below) to be sufficient. In September 2006 FDA issued a draft guidance document entitled “In Vitro Diagnostic Multivariate Index Assays” explaining that certain complex test systems, including certain genomic test systems, are Class II or Class III medical devices requiring 510(k) submissions or PMAs, respectively (www.fda. gov/cdrh/oivd/1610.pdf). The document defines the difference between in vitro diagnostic multivariate index assays (IVDMIAs) and the types of tests generally performed as laboratorydeveloped tests. This document and the related policy issues around laboratory-developed tests are generating considerable

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discussion and debate, and the outcome of these debates will have consequences for developers of genomic tests. Analyte-Specific Reagents In 1997, FDA defined a group of reagents known as ASRs, and classified them (with some exceptions that are not germane to genomics) as Class I medical devices. This meant that ASRs were subject to “general controls” such as manufacturing and labeling requirements, but did not have to have applications cleared or approved by FDA to be put on the market. FDA accomplished this by publishing three regulations that defined and classified ASRs (21 CFR 864.4020), imposed restrictions on their sale, distribution and use (21 CFR 809.30) and established requirements for ASR labels (21 CFR 809.10(e)). In the regulations, ASRs were defined as “antibodies, both polyclonal and monoclonal, specific receptor proteins, ligands, nucleic acid sequences, and similar reagents which, through specific binding or chemical reactions with substances in a specimen, are intended for use in a diagnostic application for identification and quantification of an individual chemical substance or ligand in biological specimens” (21 CFR 864.4020). Thus, an ASR is a building block for an assay, but is not a test system. Subsequent to publication of these regulations, FDA observed a broadening of the implementation of ASRs beyond the stated parameters. Therefore, in September 2006, FDA issued a draft guidance entitled “Commercially Distributed Analyte Specific Reagents (ASRs): Frequently Asked Questions” (www.fda.gov/cdrh/oivd/guidance/1590.html). An important point in this document, for the purposes of this discussion, was that FDA did not consider “bundled” moieties (e.g., sets of primers), or extensively processed (e.g., arrayed on beads) reagents, or microarrays, to be ASRs. These distinctions are important to the genomic testing community and have elicited extensive comment. Undoubtedly the boundary between ASRs and test systems will continue to be clarified in the coming years. Clinical Laboratory Improvement Amendments Congress passed the CLIA in 1998. This law established quality standards for all laboratory testing to ensure the accuracy, reliability and timeliness of patient test results regardless of where the test is performed. The CLIA program is administered by the CMS (www.cms.hhs.gov/CLIA). The ASR regulations discussed above stipulated that the only clinical laboratories to which ASRs could be sold were those qualified under CLIA to perform high complexity testing (or alternatively, were regulated under the Veteran’s Health Administration Directive 1106) (21CFR 809.30 (a)(2)). Currently, most genetic testing is performed by such laboratories. The oversight under CLIA relates to the quality of performance of laboratory testing. It does not extend to evaluation of the clinical utility of a given assay. There is ongoing controversy about the degree of oversight of genetic testing under CLIA (Hudson et al., 2006); specifically, whether there should be a specific genetic testing specialty area that would incorporate proficiency testing for genetic tests. To summarize the current Federal regulation of genomic testing: most genetic tests are on the market as laboratory-developed

tests. Such laboratories are subject to quality standards under CLIA. This is possible outside of FDA marketing clearance as a result of the ASR regulations and enforcement discretion on the part of FDA. FDA has approved a number of genetic tests, including tests for drug metabolizing enzyme polymorphisms, generally based on information available in the scientific literature. Recently, FDA issued draft guidances pointing out, among other things, that reagents such as micoarrays and sets of primers are not ASRs, and that IVDMIAs are not among the types of assays considered to be appropriate for laboratory-developed tests. These guidances have resulted in ongoing discussion and controversy, and the policy issues they raise have yet to be definitively resolved.

PHARMACOGENOMICS IN DRUG DEVELOPMENT AND CLINICAL MEDICINE: THE ROLE OF REGULATION Researchers interested in translating genomic discoveries into new approaches to drug therapy will need to understand the challenges involved in incorporating these new scientific approaches into traditional drug development and use patterns. While genomic technologies are used extensively and routinely during drug discovery and in early drug development phases (e.g., lead optimization) routine utilization of genomics drops precipitously as candidates enter the animal safety and clinical testing phases. As a result, few investigational drugs now in the clinical pipeline are being studied in concert with pharmacogenomic assays (although most development programs include collection of human samples for potential genetic testing). Likewise, although extensive information exists about the contribution of drug metabolizing enzyme polymorphisms to variability in drug exposure, as of early 2007, no marketed drug label contains an explicit requirement (as opposed to recommendations) for dose modification based on genetic test results. (See “Genomics at FDA” (www.fda.gov/cder/ genomics/genomic_biomarkers_table.htm) for an up-to-date list of pharmacogenomic tests utilized in drug labels.) These facts, when considered in light of the enormous potential for improvement in drug therapy that pharmacogenomics represents, demonstrate that the field is in its nascent stages. Many barriers to rapid uptake of genomics in drug development and clinical medicine exist, beyond the purely scientific and technical challenges. Drug development is an enormously risky endeavor. Only about one in nine drug candidates entering clinical investigation reach the market, and every year promising candidates encounter catastrophic, highly public late-stage failures. Some compounds that reach the market fail to achieve commercial success. Additionally, drug development programs are extremely expensive. Furthermore, the clinical investigation and marketing application stages of development are subject to extensive regulatory scrutiny worldwide. These factors result in very conservative approaches on the part of pharmaceutical developers. Developing an investigational drug using a relatively untested pharmacogenomic-directed approach is perceived

Pharmacogenomics in Drug Development and Clinical Medicine: The Role of Regulation

as adding additional risk. The fact that drug development is a highly competitive activity also restricts the flow of information among developers, so that those who instigate new approaches may not share the successes or failures with others. Under these circumstances, the regulators may be the only group possessing a broad perspective on the state of the field. Commercial considerations are also important in development. Developers normally pursue the broadest possible market for a new intervention. Many pharmacogenomic approaches (e.g., targeting therapy to a specific-identified population), would limit the indicated population. Complexity of use (e.g., dose adjustments, need for pre-testing) is also commercially undesirable and in fact can limit clinical uptake. For these reasons, along with the natural human optimism about the performance of a new discovery, pharmacogenomic tests have often been considered as a salvage approach when one-size-fits all development fails. Concerns about restrictions on markets are slowly being mitigated by the success of targeted therapies but still limit enthusiasm for pharmacogenomic approaches during drug development (Bernstein, 2006). Once a drug is approved by the FDA, a manufacturer has a limited amount of time to recoup its investment before patent and exclusivity protections expire and generic competition occurs (generic copies are permitted for small molecule drugs but not for therapeutics approved as biological products). Therefore there is limited time to explore refinements of an approved therapy using pharmacogenomics. Once generic competition occurs there is no commercial incentive for further development. The promise of pharmacogenomics and the barriers to its adoption led FDA to aggressively promote investigations of the new technology. Senior FDA staff published a 2002 paper calling for progress in the field and pointing out the Agency’s interest (Lesko and Woodcock, 2002). In May 2002, a workshop between FDA and the pharmaceutical industry was held to explore barriers to information sharing (Lesko et al., 2003). At the workshop, industrial sponsors stated reservations about providing genomic information on compounds in development to the Agency because they feared uninformed questions and delays in product development; however, all agreed on the desirability of information sharing. FDA subsequently established a process for sharing data and results in a non-regulatory context, the “Voluntary Genomic Data Submission” (VGDS) (www.fda.gov/cder/genomics/GDS.htm). Subsequently, over thirty submissions of genomic data involving drugs in development, encompassing topics such as animal studies, preventive indications, identification of responsive subgroups, and genetic links to adverse events, have been discussed with FDA scientists. The goals of VGDS process include (1) encouraging the development and sharing of genomic data linked to therapeutic and preventive indications; (2) helping familiarize FDA scientific staff with the emerging uses of genomic information in drug development; (3) developing data standards and analytical methods suitable for regulatory use; and (4) enabling the transition of genomic studies from exploratory research to regulatory submissions. (See www.fda.gov/cder/genomics/presentations/ Webinar.pdf for a detailed presentation of the VGDS process.)

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Subsequent workshops on drug-diagnostic co-development issues and on genomic data submission standards have been held (www.fda.gov/cder/genomics/biomarkers.htm, Hinman et al., 2006). The increased level of dialog between industry and the FDA has mitigated some of the concerns about regulatory barriers. The European Medicines Agency (EMEA) also instituted pharmacogenomics “Briefing Meetings” in 2003 (The European Agency for the Evaluation of Medicinal Products, 2003). In May 2005 FDA and EMEA held their first joint VGDS meeting, and additional joint meetings are occurring. Discussions of regulatory aspects of pharmacogenomics are now being held among regulators and industry worldwide under the auspices of the International Conference on Harmonization (ICH) of the Technical Requirements for Pharmaceuticals. Translation from the laboratory into clinical development is only one of the translational barriers faced by new technologies. Achieving uptake in clinical practice and obtaining reimbursement are also major challenges: the utility and value of new technologies must be explicitly demonstrated. Ironically, the tight regulation of pharmaceuticals will tend to enhance acceptance of FDA-approved pharmacogenomic test-directed drugs. The regulatory standards for drug approval include the scientific demonstration of effectiveness and safety. When a new drug is approved in conjunction with a pharmacogenetic test, the likelihood of use as recommended in the drug label is very high – in part because of confidence in the approval standard, and in part because promotion (which is often extensive for a new therapy) is limited by law to the label uses. Conversely, “retrofitting” a pharmacogenetic test into established practice patterns can be very difficult. Both the clinical and reimbursement communities have made it very clear that, in the majority of cases, randomized outcome trials will be the standard that will govern acceptance – even for dose adjustment with drug metabolizing enzyme assays. From a regulatory standpoint, the current issues in pharmacogenomics applied to drug development and clinical use include the following: development of an investigational drug incorporating a pharmacogenomic test; use of a pharmacogenomic test to enhance the performance of an approved drug; drug safety pharmacogenomics and toxicogenomics; and qualifying genomic biomarkers for regulatory use. These issues are discussed in more detail in the sections below. Development of an Investigational Drug with a Pharmacogenetic Test to Select Patients for Therapy or for Monitoring Use of diagnostics to discriminate among individuals who present with similar symptoms is routine clinical practice. Genomic tests are conceptually no different; however, these are new types of tests, and they will yield novel insights into the sources of human variability that are currently not utilized in decision-making. Pharmacogenomic tests may be used during drug development to identify patients with different disease subsets (that differ by prognosis, response to therapy, etc.), to identify individuals with disease responsive to an particular intervention (i.e., targeted therapies), to stratify individuals to different dose

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regimens, to identify individuals with a high probability of an adverse reaction to treatment, or to monitor treatment response. In all these cases, the use of the diagnostic is intended to enhance drug safety or effectiveness. One major challenge in using pharmacogenomic tests during drug development is that often the assay itself must be developed in parallel. This raises the level of complexity significantly. A full discussion of the issues involved in a drug-diagnostic co-development program is beyond the scope of this chapter. The points below indicate the major areas that must be considered. FDA is planning to issue a guidance document covering this topic in detail (Woodcock, 2005). Regulatory Considerations for Pharmacogenetic-TestDirected Therapy Development The types of data that need to be generated for an investigational drug paired with a pharmacogenomic test depend on the intended label claims for the drug and the test. If use of the drug requires results from the test, demonstration of the safety and effectiveness of the drug-test combination in the definitive trials will be required. If mention of the test in the drug label is strictly informational, a lesser standard of evidence applies. For example, data from retrospective analysis of samples might lead to a suggestion in the label that patients with result x on test y may have a lower probability of response. However, in this scenario, the drug would be indicated for the entire population, absent test y, and would need to be shown effective and safe in the entire population. Most pharmacogenomic information in current drug labels is of this latter type. Exceptions include trastuzumab and imatinib (Bernstein, 2006). Often, developers do not know, at the start of clinical trials, which label scenario they will end up with. In these cases, design of an adaptive program that could support either result may be desirable (see trial designs, below). Drug and Diagnostic Development Considerations Development of the Diagnostic Test

A reliable, reproducible test configuration should be achieved prior to use of the diagnostic in prospective drug trials. Stored samples are frequently used for this purpose, and methods for analytical validation of diagnostics are well worked out. A more significant challenge involves setting cut-off values for the test results. Genomic assays often contain multiple analytes, which are sometimes continuously variable, giving rise to a nearly infinite range of results. Use to direct drug therapy will require achieving unambiguous, often dichotomous (i.e., yes/no) results. These cut-offs often will be constructed via retrospective analysis of clinical samples from the investigational drug trials. This exercise will create an hypothesis: test x will segregate population y into distinct subgroups. This hypothesis can then be verified using additional stored samples or in prospective studies. Depending on the clinical implications of the subgroups, such data might suffice for a stand-alone claim for the diagnostic. However, a claim for use in combination with the drug will require trials demonstrating the clinical utility of the diagnostic as well as drug safety and effectiveness. If the results of such trials

require retrospective analyses that readjust diagnostic cut-offs to achieve significant results, they will be regarded as hypothesis generating rather than confirmatory. Trial Design Considerations for Investigational Drug Combinations

Major design issues arise in this context. These involve ways to integrate the developing information on drug and diagnostic performance when it is unclear whether the diagnostic adds value to the drug therapy (and it is still unclear if the drug has value). At the end of Phase 2 trials, developers may know that the diagnostic can discriminate subgroups but will not have information on how much this impacts performance of the drug. For example, suppose a genomic test appears to identify a patient subset more responsive to the therapy. One approach might be to perform an “enriched” trial, randomizing test-positive patients to investigational drug versus control. This trial answers the question: does the drug work in test-positive patients? It does not yield information on drug performance in test-negative patients (so it does not tell you if the test discriminates anything meaningful), or whether test-positive patients have generally better or worse prognoses. A more elaborate trial might enroll all patients with the condition and then stratify both treatment and control arms by genomic test result. This design can answer many questions but risks answering none definitively because of the multiple comparisons that arise. Simon (2005) and Temple (2005) have proposed various methodological approaches including adaptive designs to address these problems. A number of sequential designs could also be useful. The appropriate design in a given case will be influenced by both mechanistic (how plausible is the test-drug link?) and clinical considerations – for example, how much information on the test-negative subgroup is needed when a claim for effectiveness in the test-positive subgroup is sought? In cases where the mechanistic link is well understood – for example, HIV drug resistance – studying a test-defined subgroup alone is standard practice. In other cases, information on results from the test-negative group may be crucial. Developers should consult with FDA early in such development programs. Dosing Adjustments

Incorporating pharmacogenetically driven dose adjustments into Phase 2 and 3 clinical trials is straightforward. Typically, dose finding is performed in the second phase of drug development. These trials evaluate the dose-response relationships for safety/tolerance and efficacy (which may be defined by a pharmacodynamic marker). “Dose-response” is more properly understood as “concentration response”; and there are excellent pharmacometric methods for analyzing the impacts of variables such as test results. When the effect of a polymorphism is large, the drug developer may then plan to adjust dosing in the efficacy trials for alleles with known effects on concentration. Questions will arise about default dosing for individuals with non-evaluable alleles, as well as how to extrapolate the results to populations with different allele frequencies. For life-threatening diseases, for example some cancer indications, where development programs may incorporate

FDA Efforts to Advance Genomic Product Development

a combined Phase 2–3 development program and may lack pharmacodynamic endpoints, opportunities to understand the impact of variation in drug metabolism are fewer, and evaluation should start as early as possible in the development program. Pharmacogenomic Safety Tests

Drug toxicity is a major issue in development. Beyond toxicity attributable to relative overdosing from variable metabolism, genetically related adverse events can arise from drug target (or related pathway) polymorphisms, immunologic predisposition (e.g., abacavir hypersensitivity), genetically based disease variability, and genetically based organ vulnerabilities (White et al., 2006). Sorting out the mechanistic bases of drug adverse events will be straightforward in some cases and highly complicated in others. The size of most drug development programs will only allow elucidation of the most clear-cut genetic association. Regulatory issues do not complicate such investigations, except in the rare instance where the drug is not safe enough to be approved absent the test. In these cases, sponsors should discuss with FDA the amount of evidence needed to demonstrate that use of the test confers an adequate level of safety. Pharmacogenomic Tests for Approved Drugs It is likely that successful implementation of pharmacogenomic testing would significantly improve therapeutic outcomes of drug therapy. In particular, drug toxicity exacts a severe human and economic toll in the United States, being estimated as the 4th–6th leading cause of death, causing tens of thousands of hospitalizations, and costing billions of dollars (Budnitz et al., 2006; Lazerou et al., 1998). Surveys suggest that drugs with polymorphic metabolism contribute a disproportionate share of harm (Phillips et al., 2001). Nevertheless, as detailed above, there are significant barriers to even initiating the work that needs to be done. Once opportunities are identified out of the basic science, funding for controlled clinical outcome trials needs to be obtained. Pharmaceutical companies may sponsor trials for marketed drugs that are not off-patent. Trials of pharmacogenomic tests for off-patent drugs may require public funding or sponsorship by consortia of consisting of government, health care systems, the pharmaceutical industry, etc. For example, currently a number of groups are considering or initiating trials of pharmacogenetic-directed dosing of warfarin. Issues beyond funding include how to actively involve in consortia diagnostic companies and researchers who have developed tests. FDA has been actively seeking, as an initial step, to incorporate established information on drug metabolizing enzymes into drug labels without stipulating testing and dose adjustment. Examples include 6MP (6-mercaptopurine) (the enzyme TPMT), atomoxitine (Strattera) (CYP2D6) and irinotecan (UGT1A1) (www.fda.gov/cder/genomics/genomic_biomarkers_table.htm) It is possible that in the future, pharmacogeneticdirected dosing could be explored during studies of these agents that are being conducted for other purposes. For a good discussion of the issues involved in developing pharmacogenetic information on a marketed drug (see Ratain, 2006).

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Many rare, serious adverse drug reactions occur across multiple drug classes and may have a genetic association (e.g., Stevens Johnson syndrome). These reactions occur so infrequently, however, that it is likely that international consortia will be required to assemble adequate cases and controls to perform genetic analysis. Other serious drug side effects occur more commonly, within a drug class (e.g., tardive dyskinesia). Public private partnerships between government and industry are being explored as ways to gather cases and carry out the needed analyses. Toxicogenomics When the toxicant is a drug, the areas of toxicogenomics and pharmacogenomics overlap. Many drugs share mechanisms of toxicity with non-pharmaceutical chemicals. Mechanistic understanding of drug toxicity will greatly improve the science of drug safety. The current protocols for preclinical animal toxicology testing during drug development are highly empirical and offer few ways to evaluate the relevance of a given toxic finding to humans. Toxicogenomics may offer methods to discriminate by comparing cellular responses in various animal species and in human cells. In some cases, a toxic finding in an animal species may not be mechanistically relevant in humans. Toxicogenomics is also anticipated to be a source of more sensitive toxicity assays than currently those currently used in traditional animal and human testing. Under the auspices of the C-Path Institute, a nonprofit organization in Tucson, Arizona, a consortium of twelve pharmaceutical companies are pooling, evaluating and validating nontraditional toxicity assays (some of them genomic), on an organ system basis, with the intent of submitting the best performing assays to FDA for regulatory assessment (www. c-path.org/programs/SafePath/tabid/61/Default.aspx). It is expected that toxicogenomic tests will be generally relevant to drug development, rather than linked to a particular drug.

FDA EFFORTS TO ADVANCE GENOMIC PRODUCT DEVELOPMENT Genomic medicine promises to bring new, mechanistic insights into health and disease, to improve drug development, and to move towards more individualized therapeutic interventions that enhance both safety and effectiveness. In recognition of the potential benefits of new sciences, including genomics, in 2004 FDA launched its “Critical Path Initiative,” which is intended to accelerate use of new science in medical product development and regulation (www.fda.gov/oc/initiatives/criticalpath/). This initiative focuses on the use of consortia and public–private partnerships to accomplish the significant translational work needed to fully utilize new technologies in development and in the clinic. In March 2006 the Agency published “The Critical Path Opportunities Report and List,” which identifies biomarker development, modernization of clinical trials, and bioinformatics as top priorities for improvement (www.fnih.org/news/June_Press_2007.shtml). The document also listed 76 separate critical path projects that,

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if accomplished, could significantly improve the evaluation and delivery of medical products to the public. A major focus of the Critical Path Initiative is how to translate existing basic science knowledge into valid new biomarkers that can be used reliably to guide medicine and regulation. Pharmacogenomic tests, like most diagnostics, are biomarkers. The process of establishing the utility of a biomarker for a given use (e.g., selecting patients who should receive, or should not receive, a specific drug) is termed “biomarker qualification.” Until recently, the need for qualification of biomarkers for various uses has been confused with the extremely difficult process of validating a biomarker as a surrogate endpoint for drug approval. As part of the Critical Path Initiative, FDA has been assessing the conceptual framework needed to understand the evidence needed to “qualify” a biomarker for use in various types of regulatory decision-making, and hopes to publish guidance on this topic. The major barrier to adoption of new biomarkers for regulatory use, and in clinical medicine, has been the lack of solid evidence about their value. Developing this evidence usually requires some type of clinical studies. Many of the currently marketed genetic tests have relied on pre-existing evidence derived from epidemiologic studies or clinical investigations of disease syndromes. Such data will rarely be available for genomic tests, and often diagnostic developers will lack the resources to conduct the necessary clinical trials. For this reason, FDA has focused on the creation of consortia that bring together interested stakeholders, enable pooling of resources, and can accept philanthropic contributions. For example, “The Biomaker Consortium,” a public–private partnership among NIH, FDA, PhRMA, BIO, and others, convened at the Foundation for NIH, has as its goal the discovery and qualification of new biomarkers (www.fnih.org/news/June_Press_2007.shtml). It is expected that genomic biomarkers will be prominent among these. Other consortia, either industrial or based at foundations or academic sites, are being formed in order to accomplish other biomarkerrelated work. Such groups can leverage ongoing trials being conducted by one of the partners (e.g., NIH or the pharmaceutical

industry) for other purposes by sponsoring substudies aimed at qualification of biomarkers relevant to the disorder being investigated in the trial. Clearly, the design and conduct of clinical trials, particularly trials during drug development, will need to be modified to incorporate exploratory use of genomic markers. Regulatory acceptance of various Bayesian, adaptive, and sequential designs will be needed. As part of the Critical Path Initiative, FDA plans to issue draft guidance on these topics, and engage in an effort to reach consensus within the scientific community on methodological approaches that are sufficiently robust to use in regulatory decision-making. New, multianalyte genomic tests are creating both opportunities and challenges in bioinformatics. FDA is developing experience in review of the resulting large datasets through its VGDS project. The agency is using the “Array Track” software developed at FDA’s National Center for Toxicologic Research to enable data review and storage. With further experience, the Agency plans to participate in data standards organizations to enable development of standards in this area. FDA is an active participant in the MACQ (microarray quality control) consortium that is aimed at standardizing microarray platforms (see http://www. fda.gov/nctr/science/centers/toxicinformatics/maqc/).

CONCLUSIONS Federal regulation of new technologies is intended to strike an appropriate balance between protecting the public and promoting innovation. This balance can be difficult to achieve during periods of rapid evolution. The FDA is seeking to both stimulate the field of genomics and also institute reasonable, scientifically based regulation. The next decade will bring significant advances in genomics, many new marketed genomic products, and a continuing evolution in the regulatory framework. Collaboration of the biomedical, regulatory, policy and legal communities will be necessary to ensure that the promise of genomic medicine is delivered in an orderly, responsible, timely and effective manner.

REFERENCES Bernstein, K. (2006). Targeted Narrow. BioCentury 14, 1–9. Budnitz, D.S., Pollock, D.A., Weidenbach, K.N., Schroeder, T.J. and Annest, J.L. (2006). National surveillance of emergency department visits for outpatient adverse drug events. JAMA 296, 1858–1866. Hinman, L.M., Huang, S-M., Hackett, J., Koch, W.H., Love, P.Y., Pennello, G., Torres-Cabassa., A. and Webster, C. (2006). The drug diagnostic co-development paper. Commentary from the 3rd FDA-DIA-PWG-PhRMA-BIO pharmacogenomics workshop. Pharmacogenomics J 6, 375–380. Hudson, K.L. (2006). Genetic testing oversight. Science 313, 1853. Hudson, K.L., Murphy, J.A., Kaufman, D.J., Javitt, G.H., Katsanis, S.H. and Scott, J. (2006). Oversight of US genetic testing laboratories. Nat Biotech 24, 1083–1090. Javitt, G.H. and Hudson, K.L. (2006). Federal neglect. Federal regulation of genetic testing. Issues SciTechnol XXII, 59–66.

Lazerou, J., Pomeranz, B. and Corey, P. (1998). Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA 279, 1200–1205. Lesko, L.J., Salerno, R.A., Spear, B.B., Anderson, D.C., Anderson, T., Brazell, C., Collins, J., Dorner, A., Essayan, D., Gomez-Mancilla, B. et al. (2003). Pharmacogenetics and pharmacogenomics in drug development and regulatory decision making: Report of the first FDA-PhRMA-DruSAFE-PWG workshop. J Clin Pharmacol 43, 1–17. Lesko, L.J. and Woodcock, J. (2002). Pharmacogenomic-guided drug development: Regulatory perspective. Pharmacogenomics J 2, 20–24. Phillips, K.A., Veenstra, D.L., Oren, E., Lee, J.K. and Sadee, W. (2001). Potential role of pharmacogenomics in reducing adverse drug reactions. A systematic review. JAMA 286, 2270–2279. Ratain, J.M. (2006). From bedside to bench to bedside to clinical practice: An odyssey with irinotecan. Clin Cancer Res 12, 1658–1660.

Recommended Resources

Rauch, A., Nolan, D., Martin, A. et al. (2006). Prospective genetic screening decreases the incidence of abacavir hypersensitivity reactions in the Western Australia HIV Cohort Study. Clin Infect Dis 43, 99–102. Simon, R. (2005). Developing and validating genomic classifiers. http:// www.fda.gov/cder/genomics/biomarkers.htm Temple, R. (2005). Use of genomic biomarkers in a regulatory environment. http://www.fda.gov/cder/genomics.htm The European Agency for the Evaluation of Medicinal Products, London, 23 January, 2003. (CPMP/4445/03). White, T.J., Clark, A.G. and Broder, S. (2006). Genome-based biomarkers for adverse drug effects, patient enrichment and prediction of drug response, and their incorporation into clinical trial design. Pers Med 3, 177–185.

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Woodcock, J. ( 2005). Importance of genomic biomarker validation in the context of pharmacogenomic initiatives at the FDA. http:// www.fda.gov/cder/genomics/biomarkers.htm 21 CFR 809.10 (c)(2)(i) 21 CFR 809.10 (c)(2)(ii) 21 CFR 809.10(e) 21 CFR 809.30 21 CFR 809.30 (a)(2) 21 CFR 809.3 21 CFR 864.4020

RECOMMENDED RESOURCES See presentations from FDA-DIA-PhRMA-BIO-PWG Workshop on “Application and Validation of Genomic Biomarkers for Use in Drug Development and Regulatory Decision Making”, Bethesda, MD, October 6–9, 2005. http://www.fda.gov/cder/genomics/ biomarkers.htm

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CHAPTER

36 Economic Issues and Genomic Medicine David L. Veenstra, Louis P. Garrison, and Scott D. Ramsey

INTRODUCTION The era of genomic medicine offers significant promise for the development of novel health care technologies that ultimately improve patients’ quality of life and life expectancy. As with any new health care technology, questions arise as to their potential budgetary impact and cost-effectiveness. But because genomic technologies inherently involve diagnostic or prognostic testing, and because of the complexities of incomplete gene penetrance and multiple gene and environmental interactions, their assessment can be more challenging. In addition, perhaps more than in any other area of medicine, questions have arisen in regard to the economic incentives to develop these technologies. Formal health economics frameworks can be used to gain insights into these issues, and provide guidance for resource investment, technology appraisal, and policy development. In this chapter, we provide a brief introduction to the principles of health economics and discuss the unique aspects of the application of cost-effectiveness analysis to genomic medicine. We review drivers of the cost-effectiveness of genomic technologies and assess several recent state-of-the-art examples. We then examine the particular challenges in pharmacogenomics of economic incentives for developing genetic tests linked to therapeutics. Lastly, we propose an approach to help establish value-based reimbursement of genomic testing technologies. The content of this chapter will help readers understand the multidisciplinary

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nature of the factors that will influence the value of genomic medicine, and provides a framework for assessing these factors in a systematic, quantitative fashion.

ECONOMIC EVALUATION AND COST-EFFECTIVENESS ANALYSIS Health Economics and Foundations of Economic Analysis Drawing on methods and concepts from economics, clinical epidemiology, psychology, and the decision sciences, the field of “cost-effectiveness” research has synthesized a set of tools and a theoretical framework for evaluating the complex issues in health care (Garber and Phelps, 1997; Weinstein et al. 1996). At a broad level, effectiveness estimates are generally derived from randomized clinical trials, disease progression estimates from long-term follow-up epidemiological studies, quality of life values from health state preferences studies, and costs from a wide variety of sources including payment schedules (i.e., Medicare) and attributable- and micro-costing studies. These factors, which are often considered either explicitly or implicitly in decisionmaking, can be synthesized using decision analytic techniques to inform formal economic analyses as discussed below (Detsky et al., 1997).

Copyright © 2009, Elsevier Inc. All rights reserved.

Economic Evaluation and Cost-Effectiveness Analysis

Methodological Approaches to Economic Evaluation in Health Care Several specific yet related methodological approaches are used in the economic evaluation of health care technologies: (1) costminimization, (2) cost-consequences analysis, (3) cost-benefit analysis, (4) cost-effectiveness analysis, and (5) cost-utility analysis (Flowers and Veenstra, 2004). These methods vary primarily in the way intervention effectiveness is valued. For example, for cost-minimization analysis, it is assumed there is no difference in effectiveness or side effects. In both cost-effectiveness and cost-consequences analysis, effectiveness is measured in natural, clinical units such as heart attacks or infections avoided. In cost-benefit analysis, a monetary value is assigned to effectiveness (e.g., a heart attack avoided might be “valued” at $100,000). And in cost-utility analysis, effectiveness is measured in qualityadjusted life years (QALYs), which account for improvements in both life expectancy and quality of life. Another characteristic that differentiates these methods is that cost-minimization and cost-consequences analyses do not involve the calculation of a ratio; for cost-minimization, only costs are presented, and in cost-consequences both costs and effectiveness are calculated, but not combined in a ratio. Of note, although “cost-effectiveness analysis” is a specific type of economic evaluation, the term is used generally to refer to all types of economic evaluation in health care. In some cases, cost-effectiveness studies can be based on a single randomized clinical trial. However, because clinical trials are usually in a controlled setting, the costs incurred are not representative of utilization in a real-world setting. The “efficacy” observed in a controlled setting can also be distinguished from the “effectiveness” that could be expected in practice. Finally, the timeframe of clinical trials for chronic conditions are generally not sufficient to evaluate long-term outcomes. Because of these reasons, as mentioned above, modeling techniques such as decision analysis are often used to extrapolate the results from clinical studies using primarily epidemiologic and economic data from other sources. In any economic evaluation, it is important that the technology being evaluated is compared to current medical practice. Weinstein and Stason, in 1977, defined the incremental costeffectiveness ratio (ICER) as ICER  (C2  C1 ) (E2  E1 ) where C2 and E2 are the cost and effectiveness of the new intervention being evaluated, and C1 and E1 are the cost and effectiveness of the standard therapy (Weinstein and Stason, 1977). The costs and effects that are included in the equation depend on the perspective of the analysis. From a societal perspective, indirect costs and effects such as patient time away from work, downstream medical care costs years or decades after the intervention, and the quality of life of the patient and even their family need to be considered. Because of the all-encompassing nature of the societal perspective, it is generally better suited for

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national health care plans. More relevant to health care plans or providers in the United States is the payer perspective, which addresses primarily direct medical care costs incurred by the payer (e.g., drug cost, professional fees, hospital stay). Resource Allocation Decision-Making How can cost-effectiveness information guide health policy decisions? The favored approach, from an academic perspective, is to utilize cost-utility analysis because it allows for comparisons across interventions and diseases, accounts for impact on life expectancy and quality of life, and has theoretical foundations in welfare economics. Medical interventions are considered to be cost-effective when they produce health benefits at a cost comparable to other commonly accepted treatments. A general guide is that interventions that produce one QALY (equivalent to 1 year of perfect health) for under $50,000 are considered costeffective, those between $50,000 and $100,000 per QALY are of intermediate cost-effectiveness, and above $100,000 per QALY generally is not considered cost-effective. The cutoff of $50,000 per QALY was derived loosely from the cost of providing dialysis for a patient for one year – a service paid for by Medicare for any US citizen. However, these criteria are somewhat arbitrary, and can vary across disease and indication-specific therapeutic areas, particularly in the United States (Neumann, 2007). The application of cost-effectiveness analysis has increased dramatically in the past decade as a result of increasing health care costs and the desire to deliver the greatest health value for the money. The formal application of cost-effectiveness analysis to drug coverage decisions has its origins in countries with single-payer health care systems (e.g., government sponsored). Recently, multiple countries and health care systems have begun to adopt requirements for such pharmacoeconomic information. These requirements formalize an otherwise implicit demand for health care technologies that are cost-effective, and will influence, to a certain extent, “go/no-go” decisions in drug research and development. The United Kingdom, Canada, and Australia all have formal requirements in place for cost-effectiveness information and programs in place for evaluating cost-effectiveness data – the National Institute for Health and Clinical Excellence (NICE, United Kingdom), the Canadian Agency for Drugs and Technologies in Health (CADTH, Canada), and the Pharmaceutical Benefits Advisory Committee (PBAC, Australia) (CADTH 2007; NICE 2007; PBAC, 2007), as well as several European countries such as the Netherlands (Drummond et al., 1999). In the United States, cost-effectiveness information is most often used in support of drug formulary listing in managed care settings. Many managed care organizations and pharmacy benefits managers utilize guidelines that require outcomes and economic information for formulary evaluation. In addition, the Academy of Managed Care Pharmacy (AMCP) has adopted guidelines for the submission of information, including outcomes and cost-effectiveness data, to support formulary consideration (Fry et al., 2003; Neumann, 2004; Spooner et al., 2007).

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EVALUATING GENOMIC TECHNOLOGIES Unique Aspects of Genomic Medicine The tools of health economics and cost-effectiveness analysis can be used to assess any health care technology, from immunization strategies, to new drug therapies, to educational programs. Genomic medicine is not so unique (i.e., exceptional) that fundamentally new methods in economic evaluation are needed. However, genomics can add layers of complexity to health care interventions such that particular care should be taken in their evaluation. Disease-Risk Testing The validity of a prognostic genomic test will have a significant impact on its potential cost-effectiveness. The evaluation of validity will be similar to other non-genomic prognostic tests, yet potentially more complex, due to challenges in validating genetic markers with small contributions from multiple genes and environmental interactions. Thus, when undertaking an economic study, care should be taken that the performance of the test has been evaluated by peer review, and perhaps more importantly, reproduced by independent investigators in different patient populations. An issue that will be more unique, to a degree, to genetic testing for disease risk is whether to test family members. The potential costs and clinical and patient outcomes should be considered. If potentially substantial, this strategy should be included in the analysis (Ramsey et al., 2001). Pharmacogenomics Pharmacogenomics tests face a similar issue with validation of a test’s ability to predict drug response and differentiating it from prognostic ability – that is, predicting disease prognosis rather than the effect of treatment on the disease. Furthermore, as discussed later in the chapter, the pairing of pharmacogenomic tests with drugs may lead to uncertainty about which – the test or the drug – provides the incremental value, and this can have implications for pricing or reimbursement decisions. Lastly, it is possible that genetic information obtained for one purpose may also provide information, for example, about disease risk. The potential impact of such ancillary information should be considered in a broad sense; for example, disease-risk information that is available after the test is conducted versus the use of CYP2D6 drug-metabolizing enzyme polymorphism data to help guide therapy for drugs other than the original one for which the test was obtained. Thus, there could be potential benefits but also harms (Henrikson et al., 2007). Cost-effectiveness studies of both disease-risk and pharmacogenomic tests in some cases may benefit from more complex analytical frameworks and simulation methods. However, it generally is not possible, due to statistical power limitations, to identify specific risks for individual patients. Rather, in genetic association studies, patients tend to be categorized into

larger subgroups to improve statistical power. For example, the breast cancer disease recurrence risk score (RS) derived from a 21-gene profile with the Oncotype Dx test could be utilized in a similar fashion to the Framingham models that predict the risk of a cardiovascular event based on non-genetic factors. In general, when more complex modeling approaches are employed, the added value should be explained in addition to justification of data sufficiency. Cost-Effectiveness Framework and Drivers We have previously developed a set of criteria, based on a formal cost-effectiveness framework, for evaluating the potential clinical and economic benefits of genomic tests (Table 36.1) (Flowers and Veenstra, 2004). The key aspects of these criteria are highlighted in the following paragraphs, and a more in-depth evaluation of the epidemiologic considerations in bringing pharmacogenomics to clinical practice is presented. Assessing the incremental cost-effectiveness of a pharmacogenomic strategy involves evaluating factors that are common to the evaluation of all screening strategies, as well as factors that are specific to genetic testing. These include factors associated with the genetic variation of interest, the genetic test, the disease state, and the treatment options. Questions to consider in assessing the cost-effectiveness of a pharmacogenomic treatment strategy include: ● ●

●

● ● ●

●

● ●

What is the frequency of the genetic variation? How closely is the variation linked to a consistent phenotypic drug response? Are there other significant influences on drug response such as diet, disease, or drug interactions? What are the sensitivity and specificity of the genomic test? How prevalent is the disease of interest? What are the characteristic outcomes associated with the disease with and without treatment? How does the pharmacogenomic strategy alter these outcomes? What alternative treatment options are available? How effective are current monitoring strategies for preventing severe adverse drug reactions and predicting drug response?

Pharmacogenomics is more likely to be cost-effective when: (1) the polymorphism under consideration is prevalent in the population and has a high degree of penetrance; (2) genetic testing is highly sensitive and specific, and less costly alternative tests that could be used to individualize therapy are not readily available; (3) the disease state involves outcomes with significant morbidity or mortality without treatment; and (4) the treatment involves significant outcomes and/or costs that can be impacted by genotype-individualized therapy. Case Study: Gene-Expression Profiling and Breast Cancer Treatment Breast cancer is the leading incident cancer among women of all major ethnicities in the United States and is the second highest

Evaluating Genomic Technologies

TABLE 36.1

Gene Test Disease

427

Factors that influence the cost-effectiveness of genomic testing strategies Factors to assess

Features that favor cost-effectiveness

Prevalence Penetrance

●

Sensitivity, specificity, cost

●

●

Variant allele is relatively common Gene penetrance is high

●

High specificity and sensitivity A rapid and relatively inexpensive assay is available

Prevalence

●

High disease prevalence in the population

Outcomes and economic impacts

●

High untreated mortality Significant impact on quality of life High costs of disease management using conventional methods

● ●

Treatment

■

Outcomes and economic impacts

● ● ● ●

Reduction in adverse effects that significantly impact quality of life or survival Significant improvement in quality of life or survival due to differential treatment effects Monitoring of drug response is currently not practiced or difficult No or limited incremental cost of treatment with pharmacogenomic strategy

Adapted from Flowers and Veenstra (2004).

source of cancer mortality. Adjuvant chemotherapy has been shown to increase recurrence-free and overall survival, but also may produce significant toxicity such as alopecia, nausea/vomiting, and myelosuppresion, and may lead to longer-term complications such as permanent ovarian failure in pre-menopausal patients (Shapiro and Recht, 1994, 2001). Current NIH clinical guidelines (Eifel et al., 2001) recommend adjuvant chemotherapy for women with tumors larger than 1 cm or lymph node involvement. Additionally, tumor markers such as HER2 and histologic grade are used for risk assessment (Bast et al., 2001; NCCN, 1999). Despite widespread use, these criteria are imprecise predictors of distant recurrence (Sauter and Simon, 2002). Gene-expression profiling (GEP) utilizing DNA microarrays (van de Vijver et al., 2002) or RT-PCR (Paik et al., 2004) has been proposed as an alternative approach to identify patients for adjuvant chemotherapy, (King and Sinha, 2001) potentially sparing low-risk patients from this treatment. There are two gene-expression profiles currently marketed for clinical use in breast cancer. One of these profiles, MammaPrint® (Agendia), was developed by van’t Veer and colleagues at the Netherlands Cancer Institute (van de Vijver et al., 2002). The other test, Oncotype DxTM, was developed by Genomic Health, Inc. in the United States (Paik et al., 2004). The assay marketed by Agendia utilizes a 70-gene microarray-based profile performed on fresh frozen tissue and is intended for patients younger than 55 years with Stage I invasive breast cancer or Stage II node-negative invasive breast cancer. In contrast, the test marketed by Genomic Health employs a 21-gene profile utilizing RT-PCR for expression analysis on paraffin embedded tissue and is intended for patients with node-negative, estrogen receptor positive (ER) disease. Although gene-expression

profiling has been proposed as an alternative to clinical guidelines to identify appropriate patients for adjuvant chemotherapy, the potential long-term clinical and economic outcomes associated with gene-expression profiling are not clear. In other words, “Do the quality of life benefits and cost savings of avoiding chemotherapy outweigh the potential increased risk of recurrence in women not given chemotherapy and cost of the test?” To address this question, we developed a decision analytic model to evaluate the incremental cost and QALYs of geneexpression profiling using MammaPrint versus NIH clinical guidelines in a hypothetical cohort of pre-menopausal earlystage breast cancer patients (Oestreicher et al., 2005). We assumed patients and doctors would follow test or guideline recommendations, and that chemotherapy response would be similar regardless of risk group, as no data to the contrary were available. Our findings suggested that use of MammaPrint gene-expression profiling could result in an absolute 5% decrease in the proportion of cases of distant recurrence prevented, 0.21 fewer QALYs, but a cost savings of $2800 (Table 36.2). Regardless of the test cutoff used to identify a poor prognosis tumor, the MammaPrint gene-expression assay did not produce equal or greater QALYs than NIH guidelines. These findings are fairly striking, and importantly, what do they imply? First, test performance is very important when a pharmacogenomic test is being used to recommend against treatment that is standard of care. In this case, because of test sensitivity of 84% for MammaPrint versus 98% for NIH criteria, some women who will progress will be categorized as low risk and not recommended for chemotherapy. Secondly, the efficacy of treatment in women categorized as low risk versus high risk can have a significant impact. In other words, prognostic ability

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TABLE 36.2 Performance, costs and outcomes of gene expression profiling versus NIH guidelines

Outcome

Gene Expression Profiling

NIH Guidelines

Difference

Sensitivity

84%

98%

14%

Specificity

51%

5%

46%

Proportion of women treated with chemotherapy

61%

96%

35%

Proportion of distant recurrences prevented

29%

34%

5%

Costs

$29,754

$32,636

$2882

Quality-adjusted life years

9.86

10.08

0.21

Adapted from Oestreicher et al. (2005).

to evaluate disease risk is important, but so is predictive ability for treatment response. In an evaluation of the clinical and economic utility of a test, these two aspects of test performance, disease-risk and treatment response, should be clearly delineated. In some cases, as illustrated here, prognostic (disease-risk) information alone may not be sufficient to justify utilization of a genomic test. The Oncotype Dx assay has been evaluated for its ability to predict chemotherapy outcomes in addition to disease recurrence risk. Paik et al. reported in a retrospective study that women with a low RS are also unlikely to respond to chemotherapy (relative risk, 1.31; 95% CI, 0.46–3.78) (Paik et al., 2006). The impact of this effect was incorporated in a cost-effectiveness analysis conducted by Hornberger and colleagues (2005). Using a similar methodological approach to Oestreicher et al., the author developed a Markov model and forecast overall survival, costs, and cost-effectiveness of using the Oncotype Dx RS in patients classified as having low or high risk of distant recurrence based on National Comprehensive Cancer Network (NCCN) clinical guidelines. In the analysis, 8% of patients were classified as having low risk of distant recurrence by NCCN guidelines, and the RS reclassified 28% of these patients to an intermediate/highrisk group. The remaining 92% of patients were classified at high risk of distant recurrence by NCCN guidelines and the RS reclassified 49% of these to a low-risk group. Overall, use of the Oncotype Dx test was predicted to increase quality-adjusted survival by 0.09 years and reduce overall costs by $2000. The costeffectiveness was most influenced by the propensity to administer chemotherapy based on test results, and by the proportion of patients at low risk as defined by NCCN guidelines. The author concluded that if applied appropriately, the test is predicted to increase quality-adjusted survival and save costs.

These cost-effectiveness studies served several valuable purposes. Most importantly, they clarified that without the ability to predict response to chemotherapy, these gene-expression profiles may not provide justifiable improvements (if any) in overall patient outcomes in a cost-effective manner. Thus, important areas for subsequent validation work have been highlighted. Furthermore, such analysis can help inform reimbursement decisions (Watkins et al., 2007). Thus, for example, with the Oncotype Dx test, assuming the prognostic and particularly the predictive validity of the test hold, the test may offer not only a cost-effective intervention, but potentially a cost-saving one. Another issue that remains to be addressed, preferably in “real-world” (non-controlled) settings, is the concordance of test results with actual treatments received. For example, a recent study indicated that a majority of sampled physicians and patients found the test results useful, but there was an absolute decrease in the proportion of patients choosing chemotherapy of only 2% (Lo et al., 2007). These treatment patterns may change as clinicians and patients gain experience in the use of genomic test results in decision-making. Of note, in an effort to validate these retrospective findings, the first US-based phase III randomized controlled clinical trial – The Trial Assigning IndividuaLized Options for Treatment (Rx), or TAILORx, comparing the efficacy of Oncotype Dx for choosing women receiving adjuvant chemotherapy – was launched on May 23, 2006, to examine whether genes that are frequently associated with risk of recurrence for women with early-stage breast cancer can be used to assign patients to the most appropriate and effective treatment [http://www.cancer. gov/clinicaltrials/digestpage/TAILORx, accessed 11/9/06]. This study will provide valuable prospective validation of previous retrospective studies, as well as the modeled outcomes from cost-effectiveness studies.

ECONOMIC INCENTIVES AND THE FUTURE OF GENOMIC MEDICINE Economic Incentives in Drug and Test Development Eight years have elapsed since the initial sequencing of the human genome, and the number of new genetic tests commonly used in clinical practice is quite small. For example, a recent report from the Royal Society cautions: “Pharmacogenetics is unlikely to revolutionize or personalize medical practice in the immediate future” (The Royal Society, 2005). Robert Califf has argued that achieving this promise will require a major overhaul of the US clinical research enterprise as well as substantial educational efforts (Califf, 2004). In two recently published papers, Garrison and Austin explored the lack of appropriate economic incentives that may in part be contributing to the current pace, in addition to the inherent challenges in genomics research (Garrison and Austin, 2006, 2007). The translation of the basic science of pharmacogenetics

Economic Incentives and the Future of Genomic Medicine

and other “-omics” biomarkers as applied to drug development and clinical care is occurring in a complex legal, regulatory, and reimbursement environment. Understanding and appropriately shaping this environment is vital for encouraging biomarker research and personalized health care. Suppliers of new medical technologies – both therapeutics and diagnostics – face a complex and heavily regulated commercial environment. Furthermore, the manner in which diagnostics are reimbursed in most developed countries may provide limited incentives for the development of new genomics-based tests. Both the proponents of genetic tests and the skeptics sometimes fail to appreciate this complexity. Proponents sometimes argue: “How could this not be a good thing?” We would be able to target drugs to the subgroups who respond most favorably and limit the delivery to those most likely to suffer side effects. Skeptics sometimes argue that no pharmaceutical company would have incentive to restrict its market to a smaller subgroup: “Why would they ever want to develop a test to do this?” Both sides have a valid point to some extent, but neither represents the full complexity of the incentives involved. Garrison and Austin analyzed the incentives in terms of “value creation and capture.” Innovative therapies can create aggregate economic value in at least five alternative ways that are not mutually exclusive. First, they may reduce mortality and morbidity compared to current treatments. Second, they may save on average on the costs of treating side effects or the underlying disease. Third, they may result in greater utilization by those afflicted. Fourth, they may encourage better compliance, providing individual patients with more net benefit. Fifth – and this is less recognized and is important for new diagnostic tests – they can make people better off by reducing uncertainty. The authors developed and analyzed five different scenarios of combining a new genomics-based diagnostic with a therapeutic treatment. They argued that who captures the value created by the combination depends on a number of factors, and that it is the potential for value capture that generates the incentives for genomic test development and marketing. In thinking about incentives in this marketplace, it is important to keep a few key features in mind. First, intellectual property rights are essential to provide a basic incentive for the invention and development for new diagnostic tests and new therapeutics, particularly pharmaceuticals. It is widely known and appreciated that new innovative drugs receive temporary (20-year) patents that limit the ability of competitors to enter since they can’t just copy the innovative compound and would have to develop and fully test a new molecule. It is less appreciated that effective patent life is 8–12 years, as it takes years to perform the trials that regulators review for licensing. A second key feature is that once a product is on the market and has a price, it can be difficult to change that price. Indeed, in some countries, “administered pricing” is used to set the price based on reference products or prices in other countries. The price granted in those situations may only relate in a limited way to the value created, and they may not be allowed to change over time. This is particularly true in Europe, whereas there is some ability to change pharmaceutical prices over time in the United States.

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Diagnostics differ from drugs in that their prices are even more heavily controlled in most markets, as initial price is often pegged to perceived costs of provision rather than additional value created. This can be called “cost-based” pricing and reimbursement as opposed to “value-based” pricing and reimbursement. In effect, suppliers of new genomics-based diagnostics are likely to capture less of the incremental value they create. Given these marketplace realities and constraints, Garrison and Austin argue that the incentives for value creation through a linked genomics-based diagnostic and therapeutic will depend on these factors as well as whether the therapy is already on the market. For their hypothetical exercise, they posited that a new diagnostic is developed in a market where only 20% of patients truly benefit from a treatment, but heretofore they could not be identified. Eliminating the 80% of users who don’t benefit would be a great benefit to payers but would be damaging to the pharmaceutical company if they could not raise price for the 20% of responders. On the other hand, it should clear that if the test were available before the drug was launched, the company might have been able to charge a price nearly five times as high and still capture the same amount of value. In principle, if the innovative diagnostic enters the market after that drug has a price, then the diagnostic manufacturer might be able to capture this value, creating a powerful incentive for developing the diagnostic. However, in a regulated, costbased pricing and reimbursement environment for diagnostics, there will not be this strong incentive. Hence, if the drug manufacturer can’t easily increase price for the 20% who respond, and if the potential diagnostic manufacturer can’t capture the cost savings because of price controls, there will be a limited incentive to develop tests for already marketed products. The authors point out that if the diagnostic and therapeutic could be developed in tandem, even more total economic value would be created due to the additional reduction in uncertainty. The drug manufacturer would be in a better position to capture this value, given that they operate in a relatively value-based pricing environment. However, drug development is a challenging and uncertain scientific enterprise in and of itself, and adding the complexity of companion diagnostic may not be that appealing unless the scientific rationale is already well developed. In addition to the scientific and economic barriers highlighted above, two other factors deserve mention that might be inhibiting the development of pharmacogenetics-based test-drug combinations. One factor is the high cost of the basic research that is needed to validate genetic markers. Financing this remains a question: What should fall to the public sector, how much should the private sector contribute, and what is best done in partnership are under active debate. Second, approval and reimbursement for drugs in the United States and EU require greater levels of evidence than is customary for new diagnostic tests. Some would argue that the lower evidentiary requirements for regulatory approval of tests have discouraged the development of better clinical data and that payers practice “cost-based” reimbursement in part because of this lack of evidence on clinical and economic value. In contrast, both the AMCP Format for

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Formulary Submissions guidelines in the United States and the requirements for submissions to the National Center for Clinical and Health Excellence (NICE) in the United Kingdom request economic models that synthesize the clinical and cost evidence to assess value added of new drugs. Similar standards and mechanisms do not exist for diagnostics, although the discussion is beginning. But asking for more evidence means raising the costs of developing – and particularly validating – new diagnostics. What are the incentives for diagnostic manufacturers if no additional rewards are forthcoming through the reimbursement system? Below we discuss a potential approach for incorporating evidence assessment into reimbursement decisions for genomic tests.

ESTABLISHING VALUE-BASED REIMBURSEMENT FOR GENOMIC TECHNOLOGIES Rationale Laboratory tests are an important yet often overlooked segment of health care and the health economy. For example, it has been estimated that as much as $56 billion was spent on laboratory diagnostic services in 2005 (Blue Cross Blue Shield Association, 2005). More importantly, laboratory tests initiate a cascade of decisions regarding further testing, prevention or treatment-decisions that ultimately determine health outcomes and costs of care. A recent report estimated that although diagnostics account for 1.6% of the Medicare total costs, they influence 60–70% of downstream treatment-decisions (The Lewin Group, 2005). Genomic testing for mutations, polymorphisms, and haplotypes expands the laboratory diagnostics market in many areas, including pregnancy and neonatal testing, predictive testing for disease susceptibility, and pharmacogenomic testing. As noted above, gene-expression profiling of tumors is a burgeoning field that includes both predictive and pharmacogenomic characteristics. Historically, the evidence supporting the validity and clinical utility of laboratory tests has been limited (Feinstein 2002; Reid et al., 1995; Weinstein et al., 2005). The pathway to the marketing of tests does not require the same data as therapeutics, and technology assessments of laboratory tests have not undergone the revolutionary changes in evidence review that has occurred for drugs (Neumann, 2004). Instead, payment for tests is linked to an antiquated coding system, as noted above (Raab and Logue, 2001). Recently, we presented a rationale and outline for re-structuring the way laboratory tests are evaluated and reimbursed (Ramsey et al., 2006). We summarize those concepts below. Process We propose that coverage and reimbursement for laboratory tests, including genomic tests, should move toward an evidenceand value-based approach, using some of the tools that have been adopted for pharmaceutical assessment by many US health care payers.

Developing a Language for Describing Benefit Test manufacturers, laboratory service providers, and health insurance plans will benefit from standardizing the way evidence supporting new laboratory tests is presented. Methodological standards for the evaluation of diagnostic tests have been published (Reid et al., 1995), and a useful framework has been proposed to evaluate diagnostic technologies (Table 36.3) (Fryback and Thornbury, 1991). While many agree about the value of using these domains to evaluate tests, including genomic tests, there is less agreement on how much evidence is necessary for an insurance coverage decision. Considering the domains from Table 36.1 when applied to genomic tests, it is important to evaluate the incremental impact of a test; that is, the improvement that the new test provides overcurrent (non-genomic) strategies (Garrison and Austin, 2006). Since prospective trials directly comparing new laboratory tests with established diagnostic strategies are uncommon (particularly those evaluating their impact on patient outcomes), decisionanalytic modeling techniques can aid evaluations (Buxton et al., 1997). Models help frame questions, provide transparent mechanisms for stating hypotheses about cause and effect, highlight deficiencies in clinical data, and force decision-makers to make explicit judgments about values for data that are used to inform the model. Published standards for models are readily accessible to decision-makers who wish to assess their quality (Weinstein et al., 2003).

TABLE 36.3

Hierarchy of diagnostic evaluation

Level

Characteristic

Description

1

Technical feasibility and optimization

Ability to produce consistent results

2

Diagnostic accuracy

Sensitivity, specificity, positive predictive value, negative predictive value

3

Diagnostic thinking impact

Percentage of times physicians’ estimated probability of a diagnosis changes following the test result

4

Therapeutic choice impact

Proportion of times planned therapeutic strategy changes following test results

5

Patient outcome impact

Percentage of patients who improve with the test versus those who improve without the test

6

Societal impact

Cost-effectiveness

Adapted from Fryback and Thornbury (1991).

Conclusions

A Definition of Value for Genomic Tests We define value for genomic tests as it is defined for other health technologies: the intervention provides an overall benefit to the patient at an acceptable cost; or in other words is costeffective. There are four well-recognized criteria for identifying an intervention cost-effective: (1) Less costly and at least as effective; (2) More effective and more costly, with the added benefit worth the added cost; (3) Less effective and less costly, with the added benefit of the alternative not worth the added cost; and (4) Cost saving with an outcome equal to or better than that of the alternative. Assessing value for tests can be difficult because tests are intermediate steps in the treatment pathway. The advantage of a cost-effectiveness framework is that it allows flexibility, because value for genomic tests can be defined narrowly (e.g., least expensive way to make a diagnosis) or broadly (improves survival at an acceptable added cost). A Format for Dialogue Among Genomic Test Manufacturers, Providers, and Payers When health insurers and test representatives come together to discuss genomic tests, the process should be transparent, and adopting a standard format would help clarify expectations and improve the decision-making process. A useful template for tests has been developed by the AMCP for the evidence-based evaluation of drugs. More than 50 public and private health insurers covering over 100 million lives have adopted the AMCP format (Fry et al., 2003; Neumann, 2004). A template for manufacturer’s reporting of clinical and economic information regarding laboratory tests is shown in Figure 36.1. Since the clinical utility of genomic tests is most often similar to other laboratory tests, the template accommodates differences in evidence that are typically available for new genomic tests. Implementation Some health plans might have a designated organizational unit that evaluates genomic tests, providing a structure for soliciting

1. Product Information

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and reviewing manufacturer’s products. In the case of pharmaceutical products, that unit is the Pharmacy and Therapeutics (P&T) committee. Health plan staff working with P&T committees usually are receptive to receiving information from manufacturers or service laboratories about new products. Indeed, the “unsolicited request” process pioneered by the AMCP was designed to create a structure for dialogue between manufacturers and payers. A similar scheme would improve transparency and the flow of information for new genomic tests. P&T committee members are often not health plan employees. Having such a quasi-independent group evaluate novel laboratory tests may improve the credibility of the decision process in the eyes of manufacturers, clinical laboratories, physicians, and patients. Still, maintaining committees is costly, and may not be justified given the relatively low volume of genomic tests that are introduced annually. One option is to fold the test evaluation process into the existing P&T structure. Alternatively, payers could hire consultants to evaluate select genomic tests and make recommendations regarding coverage and reimbursement. Payers should be timely, in both coverage decisions and setting reimbursement levels, and when decisions are made, they should be supported with a rationale. In cases where requests for coverage are denied, such information allows manufacturers to design studies or collect other data that address concerns regarding the quality or content of the information supporting the product. Genomic tests are developed under a different regulatory structure than pharmaceuticals and have a unique and complex function in medical care. Novel genomic tests often come to market with little information supporting their role in clinical decision-making or evidence regarding their impact on patient outcomes. We posit that patient care and outcomes will improve when there is a structured process for evidence generation and discussion between payers, manufacturers, and marketers. While it is unlikely and perhaps not necessary that the evaluation process for genomic tests will equal what is required for drugs, we can move much further toward a system that supports better gathering and sharing of high-quality evidence. Test manufacturers, clinical laboratories, payers, and clinicians all must play a role in this process.

1.1. Product description. 1.1.1. Place of the product in therapy. 1.1.2. Disease description. The disease description should include the disease and characteristics of the patients in the target population.

2. Supporting Clinical and Economic Information 2.1. Evidence-table spreadsheets of all published and unpublished clinical studies. 2.2. Outcomes studies and economic evaluation supporting data 2.2.1. Evidence-table spreadsheets of all published and unpublished outcomes studies.

3. Cost-effectiveness Modeling Report 3.1. Model overview. We recommend that producers and users of modeling studies subscribe to the sound guidance provided by the ISPOR Good Practice Modeling Principles.

4. Supporting Information 4.1. 4.2.

References contained in dossiers. References for economic models.

Figure 36.1 Evidence and transparency standard to support coverage and reimbursement for diagnostic, therapeutic, and genetic testing (from Ramsey et al., 2006).

CONCLUSIONS The primary economic challenges facing genomic medicine can be surmised as the following: (1) providing evidence of the value of genomics and (2) providing economic rewards/incentives commensurate with the value added. More specifically, as outlined in a recent report developed on the implications of pharmacogenomics for the pharmaceutical and biotechnology industries, there are several key challenges for genomic medicine (Garrison et al., 2007): ●

Regulatory pathways have not yet been optimized to encourage the co-development of diagnostics and therapeutics.

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●

●

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Economic Issues and Genomic Medicine

Current economic incentives – as reflected in our intellectual property and reimbursement systems for diagnostics and drugs – are generally not structured to reward appropriately and consistently innovative value creation for drugs, diagnostics, and pharmacogenomics-based targeted regimens. The integration of pharmacogenomic diagnostic development with pharmaceutical development is difficult because of differences in the underlying business and translational science models of the two sectors. Genomics technologies are perceived to raise ethical, legal, and social issues to such a degree that a special National Institutes of Health program was established to address them; although specific genomic applications may not always involve such issues, this suggests the broad range of stakeholders that will be involved in the public debates. Stakeholder literacy about genomics is limited, and positions on public policy issues are not yet clearly defined.

What can be done to help achieve these goals? We suggest several policy areas for focus (Garrison et al., 2007): ●

●

●

●

●

Pharmaceutical and biotechnology companies will need to add a systematic evaluation of potential pharmacogenomic and other biomarkers as part of their due diligence research and development processes. Substantial federal government support for basic research generally will be critical before translational private research activities are viable. The pharmaceutical and biotechnology industries will be major beneficiaries of this basic research, and should participate actively in the public discussion of priorities. Companies will need to provide a rationale to regulators why they have or have not included genomics or other biomarkers in their clinical trial development programs. The pharmaceutical, biotechnology, and diagnostic industries have not taken a unified or proactive position on

●

appropriate regulatory processes and initiatives, and it may not be possible to reach a consensus. Still, it may be a good time to begin a policy review and discussion. For pharmacogenomics-targeted pharmaceuticals to have greater commercial viability, the pharmaceutical, biotechnology, and diagnostic industries must engage in the public policy debate on national coverage and reimbursement issues for such drugs and tests.

Will cost-effectiveness analysis by itself speed the development of genomic medicine? This is not inherently likely. The fundamental challenges facing genomic medicine at this point are (1) the identification and validation of associations between genetic variation and clinically meaningful outcomes and (2) evaluation of interventions based on genomic information in prospective, comparative trials. However, cost-effectiveness analysis can help guide this process in several important ways: ●

●

●

By identifying genomic technologies that have potential value in early stages of drug and test development, thus aiding research portfolio optimization. By providing a framework for value-based reimbursement of genomic technologies, thus conferring appropriate incentives to the market. By identifying areas where future research would provide significant value to the health care system even once a technology has been developed.

Thus, quantitative economic analyses in combination with scientific research and policy analysis can help optimize the benefits of genomic medicine to society. In summary, genomic medicine offers significant hope for fundamental improvements in health care outcomes. These gains can likely be achieved in a cost-effective manner, but the challenges of providing evidence of and reward for value will prove to be critical in the coming decade.

REFERENCES Bast, R.C., Jr., Ravdin, P. et al. (2001). 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: Clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol 19, 1865–1878. Blue Cross Blue Shield Association. (2005). Medical technology as a driver of healthcare costs: Diagnostic imaging. Retrieved August, 2005, from http://www.bcbs.com/coststudies/reports/Medical_ Tech_Drivr_Rept_10.pdf. Buxton, M.J., Drummond, M.F. et al. (1997). Modelling in economic evaluation: An unavoidable fact of life. Health Econ 6(3), 217–227. CADTH (2007). Canadian Agency for Drugs and Technologies in Health. Califf, R.M. (2004). Defining the balance of risk and benefit in the era of genomics and proteomics. Health Aff (Millwood) 23(1), 77–87.

Detsky, A.S., Naglie, G. et al. (1997). Primer on medical decision analysis: Part 2—Building a tree. Med Decis Making 17(2), 126–135. Drummond, M., Dubois, D. et al. (1999). Current trends in the use of pharmacoeconomics and outcomes research in Europe. Value Health 2(5), 323–332. Eifel, P., Axelson, J.A. et al. (2001). National Institutes of Health Consensus Development Conference Statement: Adjuvant therapy for breast cancer, November 1–3, 2000. J Natl Cancer Inst 93, 979–989. Feinstein, A.R. (2002). Misguided efforts and future challenges for research on diagnostic tests. J Epidemiol Community Health 56(5), 330–332. Flowers, C.R. and Veenstra, D. (2004). The role of cost-effectiveness analysis in the era of pharmacogenomics. Pharmacoeconomics 22(8), 481–493.

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Fry, R., Avey, S. et al. (2003). The Academy of Managed Care Pharmacy Format for Formulary Submissions: An evolving standard–a Foundation for Managed Care Pharmacy Task Force report. Value Health 6(5), 505–521. Fryback, D.G. and Thornbury, J.R. (1991). The efficacy of diagnostic imaging. Med Decis Making 11(2), 88–94. Garber, A.M. and Phelps, C.E. (1997). Economic foundations of costeffectiveness analysis. J Health Econ 16(1), 1–31. Garrison, L.P., Jr. and Austin, M.J.F. (2006). Linking pharmacogeneticsbased diagnostics and drugs for personalized medicine: Scientific and economic challenges. Health Aff (Millwood) 25(5), 1281–1290. Garrison, L.P. and Austin, M.J.F. (2007). The economics of personalized medicine: A model of incentives for value creation and capture. Drug Information journal 41, 501–509. Garrison, L.P., Veenstra, D.L. et al. (2007). Backgrounder on Pharmacogenomics for the Pharmaceutical and Biotechnology Industries: Basic Science, Future Scenarios, Policy Directions, University of Washington, Pharmaceutical Outcomes Research and Policy Program. Henrikson, N.B., Burke, W. et al. (2007). Ancillary risk information and pharmacogenetic tests: Social and policy implications. Pharmacogenomics J 8, 85–89. Hornberger, J., Cosler, L.E. et al. (2005). Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-nodenegative, estrogen-receptor-positive, early-stage breast cancer. Am J Manag Care 11(5), 313–324. King, H.C. and Sinha, A.A. (2001). Gene expression profile analysis by DNA microarrays: Promise and pitfalls. JAMA 286, 2280–2288. Lo, S., Norton, J. et al. (2007). Prospective multi-center study of the impact of the 21-gene Recurrence Score (RS) assay on medical oncologist (MO) and patient (pt) adjuvant breast cancer (BC) treatment selection. Am Soc Clin Oncol. Comment: Chicago NCCN (1999). Update: NCCN practice guidelines for the treatment of breast cancer. National Comprehensive Cancer Network. Oncology (Huntington) 13, 41–66. Neumann, P.J. (2004). Evidence-based and value-based formulary guidelines. Health Aff (Millwood) 23(1), 124–134. Neumann, P. (2007). The Cost-Effectiveness Analysis Registry [Internet], Tufts-New England Medical Center, ICRHPS. NICE (2007). National Institute for Health and Clinical Excellence. Oestreicher, N., Ramsey, S.D. et al. (2005). Gene expression profiling and breast cancer care: What are the potential benefits and policy implications?. Genet Med 7(6), 380–389. Paik, S., Shak, S. et al. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27), 2817–2826. Paik, S., Tang, G. et al. (2006). Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23), 3726–3734.

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PBAC. (2007). Pharmaceutical Benefits Advisory Committee. Retrieved 7/10/07, from http://www.health.gov.au/internet/wcms/ publishing.nsf/Content/health-pbs-general-listing-committee3. htm#esc. Raab, G.G. and Logue, L.J. (2001). Medicare coverage of new clinical diagnostic laboratory tests: The need for coding and payment reforms. Clin Leadersh Manag Rev 15(6), 376–387. Ramsey, S.D., Clarke, L. et al. (2001). Cost-effectiveness of microsatellite instability screening as a method for detecting hereditary nonpolyposis colorectal cancer. Ann Intern Med 135(8 Pt 1), 577–588. Ramsey, S.D., Veenstra, D.L. et al. (2006). Toward evidence-based assessment for coverage and reimbursement of laboratory-based diagnostic and genetic tests. Am J Manag Care 12(4), 197–202. Reid, M.C., Lachs, M.S. et al. (1995). Use of methodological standards in diagnostic test research. Getting better but still not good. JAMA 274(8), 645–651. Sauter, G. and Simon, R. (2002). Predictive molecular pathology. N Engl J Med 347, 1995–1996. Shapiro, C.L. and Recht, A. (1994). Late effects of adjuvant therapy for breast cancer. J Natl Cancer Inst Monogr, 101–112. Shapiro, C.L. and Recht, A. (2001). Side effects of adjuvant treatment of breast cancer. N Engl J Med 344, 1997–2008. Spooner, J.J., Gandhi, P.K. et al. (2007). AMCP Format dossier requests: Manufacturer response and formulary implications for one large health plan. J Manag Care Pharm 13(1), 37–43. The Lewin Group. (2005).The Value of Diagnostics Innovation, Adoption and Diffusion into Health Care. Retrieved August, 2005, from http://www.advamed.org/publicdocs/thevalueofdiagnostics.pdf. The Royal Society (2005). Personalised Medicine: Hopes and Realities.The Royal Society, London. van de Vijver, M.J., He, Y.D. et al. (2002). A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347, 1999–2009. Watkins, J., Bresnahan, B. et al. (2007). Determining the Value of Diagnostic/ Genetic Testing to Inform Health Plan Decision Making: Oncotype DX as a Case Study. International Society of Pharmacoeconomics and Outcomes Research,Washington DC. Weinstein, M.C., O’Brien, B. et al. (2003). Principles of good practice for decision analytic modeling in health-care evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value Health 6(1), 9–17. Weinstein, M.C., Siegel, J.E. et al. (1996). Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA 276(15), 1253–1258. Weinstein, M.C. and Stason, W.B. (1977). Foundations of costeffectiveness analysis for health and medical practices. N Engl J Med 296(13), 716–721. Weinstein, S., Obuchowski, N.A. et al. (2005). Clinical evaluation of diagnostic tests. Am J Roentgenol 184(1), 14–19.

CHAPTER

37 Public–Private Interactions in Genomic Medicine: Research and Development Subhashini Chandrasekharan, Noah C. Perin, Ilse R. Wiechers, and Robert Cook-Deegan

INTRODUCTION Genomics grew out of the publicly funded Human Genome Project (HGP), which began to take shape in 1985 from programs initiated by the US Department of Energy (DOE), National Institutes of Health (NIH), and other non-profit and government partners around the world (Cook-Deegan, 1994a; National Research Council, 1988; US Congress Office of Technology Assessment, 1988). As originally conceived, the HGP was a public works project intended to create informational tools to enhance and expedite science. These tools would reside in the public domain available through scientific publications in open literature and public databases. One goal of the HGP was to create a reference sequence of the human genome, a “Rosetta Stone from which the complexities of gene expression in development can be translated and the genetic mechanisms of disease interpreted” (McKusick and Ruddle, 1987). The HGP also depended on automated DNA sequencing and sample-handling robotics, products of private sector Research and Development (R&D). DNA sequencing instruments made by Applied Biosystems and LKB-Pharmacia, LI-COR, and MJ played a role in creating the human reference sequence. In the late 1980s, instrumentation for DNA sequencing, mapping, and polymerase chain reaction became a growth sector. Applied

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Biosystems (ABI), which introduced instruments to sequence and synthesize both DNA and proteins when it was an independent startup, subsequently became a major profit center within its parent company (initially Perkin-Elmer Cetus, then PE, and later Applera). The 1987 debut of ABI’s DNA “sequenator”, which pioneered four-color fluorescence in each lane of slab-gel electrophoresis, was a major technological landmark. In the early 1990s, more than a half decade after the government and non–profit-funded genome projects began, the first dedicated genomic startup firms began to appear. Most of the private genomics R&D investment that began in 1992 and 1993 was initially in US firms or foreign forms investing in US genomics R&D (Cook-Deegan, 1994b). In 1992 and 1993, new genomics firms such as Human Genome Sciences (HGS), Hyseq, Mercator Genetics, Millennium Pharmaceuticals, Myriad Genetics, and Sequenom were created. Incyte shifted from doing contract research for Genentech and turned its attention to genomics; Collaborative Research changed its name to Genome Therapeutics to reflect its emphasis on genomics. These first stirrings later proliferated to the more than 300 firms reported here, which have a significant aspect of their business plan based on genomics. A feature shared by the many companies that have become known as “genomics” firms is that all or a substantial fraction of

Copyright © 2009, Elsevier Inc. All rights reserved.

Landscape of Private Sector Genomics

their business plans hinge on creating or using data about DNA: for example, using large datasets containing information about DNA sequence, or interpreting such data for practical use (e.g., as a research service, in diagnostics, or as a component in drug discovery). These firms have drawn heavily upon the scientific commons – three decades of molecular biology and human genetics research – to create the subsector of biotechnology that is commercial genomics. Some of the firms have been acquired or merged; others have died; some continue as independent firms. Large and established firms such as Perkin-Elmer, HewlettPackard (through Agilent), Beckman, and others have developed technologies for both public and private genomics R&D laboratories. While US federal health research funding grew faster than funding in other areas of basic science for most of the period 1985–2004 (National Science Foundation, 2005), privately funded health R&D grew even faster, and surpassed public and non-profit funding by the late 1980s (Moses, 2005). This general pattern of private health R&D exceeding government and nonprofit funding was even more sudden and extreme in genomics. A 2000 world survey of genomics research funding found that private firms funded significantly more genomics R&D than the public sector (government and non-profit) (Cook-Deegan et al., 2001). Another distinctive historical feature of genomics is its growth at a time when intellectual property norms shifted to much wider use of patenting in the academic sector. The HGP was conceived in 1985, just four years after the Bayh-Dole and Stephenson-Wydler Acts went into effect in the United States (Rudolph, 1995; US Congress Office of Technology Assessment, 1995). Those laws codified and strengthened incentives for patenting inventions arising from federal funding (Mowery et al., 2004). Many other countries also developed incentives to stimulate technology transfer, and the nascent field of commercial genomics was greatly affected by these policies. Genomic technologies, some of them developed in private R&D laboratories, become applied in “downstream” biotechnology, namely, pharmaceutical, agricultural, or other R&D, either immediately or after a period of gestation in academic R&D. One interesting aspect of genomics has been the degree to which small firms have developed technologies far “upstream,” as tools for science, including academic science. Most genomic technologies are in fact research tools, and genomics as a field quickly grew large enough to sustain a market for DNA synthesis and sequencing machines, sample-handling robotics, microarrays, and other technologies. Academic science is a first home and the “opening market” for these technologies that may later develop into much broader application markets such as diagnostics, agriculture, environmental remediation, or energy production. For example firms such as Lynx, Illumina, Solexa, 454, Affymetrix, and many others have been started to develop technologies being used extensively in academic genomics laboratories. The market for genomic technologies is thus a complex hybrid of public and private R&D laboratories, and many technologies start in private R&D or are moved into private R&D

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very early in their history, with an eye toward development for academic research, such that academic research laboratories, as users, are actually downstream from private R&D. This history gives genomics firms an unusual business model that involves both direct and indirect funding from government and non-profit sources. Federal grants and contracts are an important source of the money that drives this sector, occasionally through direct grants to the firms, but much more consistently and to a larger extent by sale of products and services to federally funded or non-profit laboratories. This model belies the simple pipeline model that generally sees federal and nonprofit basic science producing knowledge that is developed into end products and services through industrial R&D and then sold in private markets.

LANDSCAPE OF PRIVATE SECTOR GENOMICS In this section, we present financial and intellectual property data for private sector genomics firms through 2004. These figures provide a snapshot of privately funded genomics R&D and of some underlying trends in the financial inputs and scientific outputs of this sector. Defining the Genomics Sector What Is Genomics? The word “genome” is an irregular hybrid of the words “gene” and “chromosome,” a term coined in 1920 by Hans Winker decades before DNA was understood to be the repository of Mendel’s “particles of inheritance,” and well before the concept and application of genetic mapping and nucleic acid sequencing had become fully intertwined with efforts to understand development and disease (Lederberg and McCray, 2001). The origin of the term “genomics” can be traced to Tom Roderick, as first cited by McKusick and Ruddle in 1987, in the inaugural editorial for their new journal, Genomics. Genomics is a relative term referring to the study of genomes, coined to contrast with traditional genetics, which studied genes for traits and disorders, often focusing on Mendelian characters. At that time, genomics distinguished large-scale mapping and sequencing efforts from molecular studies of one or a few genes (Kuska, 1998). The term genomics gained popularity and came to describe a rapidly growing field of molecular biology, applying to large-scale, rapid DNA analysis, and intensive use of instruments and new technologies. Lederberg and McCray (2001) noted that, by 2001, “genomics” had acquired a broader meaning, referring to any study that involved the analysis of DNA sequence and even to the study of how genes affected biological mechanisms and phenotypes. This included the original meaning of genomics, but went well beyond it to almost any analysis that incorporated DNA technologies on a grand scale. What Is a Genomics Firm? The definition of a genomics firm is somewhat arbitrary. “Genomics” applies more to an approach than to an industry.

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The “biotechnology industry” is often, if somewhat misleadingly, used to refer to a subset of mainly pharmaceutical and agricultural firms that employ molecular biological methods as their R&D base. In commercial biotechnology, genomics came to refer to biotechnology firms that employed techniques that generated massive amounts of data about DNA of various kinds (e.g., DNA sequence information or chromosomal position data) in all or a substantial part of their work. Genomics was adopted as a “buzz word” to attract private capital, particularly from 1998 through mid-2001. After the financial market bubble burst in 2001, genomics went from being a desirable term to one used with some caution when trying to attract new capital. As a financial term, after 2001 it carried undesirable associations with the bubble it helped to create. Capital markets moved on to new terms associated with newer technologies such as synthetic biology, nanotechnology, interfering RNA, and other “hot” subfields of the

TABLE 37.1

day. It has become increasingly difficult to determine exactly what portion of a firm’s business is related to genomics. R&D allocations by firms on our list range from complete dedication to genomics to only a small, but important and crucial, fraction of R&D funds attributed to genomics. With this in mind, our dataset of genomics firms is a best effort estimation of private R&D in genomics, but should not be viewed as a precise valuation of how much R&D is truly “genomics”. A “genomics” taxonomy emerged from reviewing descriptions of R&D carried out by private firms. The firms were described by themselves, on websites or in annual reports, or by others in the trade press and news websites as “genomics firms.” The categories in the taxonomy are derived from these descriptions (Table 37.1). The categories are not mutually exclusive; each firm can be classified under multiple headings in the taxonomy.

Genomics firms taxonomy

AGRIVET

Agriculture and veterinary genomics

DATABSE

Database creation, subscription, or licensing

DNASEQN

DNA sequencing

DNATEST

DNA testing service, clinical or diagnostic screening service, test kit manufacturing

DRUGDEV

Drug, biologic, and vaccine development

GENEFNL

Gene function and functional genomics; characterization of genes and their products

GENEMAP

Gene mapping; linkage, association studies; SNP discovery, use and analysis

GENEPOP

Genetic epidemiology; population studies

GENETFR

Gene transfer and gene therapy; vectors for gene therapy

GENEXPR

Gene expression analysis; microarray analysis; analysis of siRNAs and other regulation element

IDNTFCN

DNA forensics, DNA identification service

INFRMTX

Bioinformatics for DNA analysis; data mining

INSTRMT

Instruments for DNA analysis

LEGLSVC

Legal services; privacy protection

PHRMGEN

Pharmacogenetics or pharmacogenomics

STANDRD

Setting standards, testing service benchmarks

SUPPLYR

Genomics reagents supplier; microarray manufacturer; service provider

TRSTFND

Trust fund or genomics capital source

SYNBIOL

Synthetic biology

Criteria for deciding if a firm is a “genomics” firm: 1. Analysis of DNA analysis or synthesis a core business (types of analyses classified above). 2. “Genomics” listed on website, annual report, or in news stories as part of business plan. 3. Listed as “genomics” by stock analysts or trade press (subject to correction if determined not to meet criteria 1 or 2 above). Firms may be assigned to one or more categories, and the categories are not mutually exclusive.

Landscape of Private Sector Genomics

Snapshot of the Genomics Sector – Financial and Market Factors The survey database spanned 26 years and included 470 genomics firms from 25 countries. Figure 37.1 shows the firms by designation in 2004 – private (211), public (88), non-profit (2), acquired (90), subsidiary (27), dissolved (23), and other (29). An industry snapshot for 2004 found the total global market capitalization for the 88 publicly traded firms to be around $41 billion, a 53% drop from the 2000 peak value of $88 billion (Figure 37.2). The genomics subsector employed over 25,000 people, invested $3.2 billion in R&D, and generated $6.3 billion in revenue during 2004. The top 15 firms by market value represented almost 70% of the total genomics sector’s value, approximately $28 billion. These top 15 firms spent a combined $2 billion in R&D, generated $4.3 billion in annual revenues, and employed over 15,000 people in 2004. Analysis of the top 15 firms demonstrated broad growth trends, indicating that R&D, capital improvement, and hiring continued to increase both before and

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after the 1998–2001 bubble (Figure 37.3), despite the 2001 peak and post-2001 decline in market capitalization. These trends in the top 15 firms paralleled the consistent growth in total revenues for all publicly traded firms, despite significant fluctuations in market valuation (see later). The number of publicly traded firms grew to 88, reaching this peak in 2002 and subsequently stabilizing in 2003 and 2004. Steady state occurred after 2001, as some firms disappeared as they went out of business, merged, were acquired, while other firms were created (but far fewer per year than 1998–2001) (Figure 37.4). Business Plans and Applications in the Genomics Sector The most common categories of activity and representative examples from both public and private firms for each category $5.0 $4.5

Subsidiary 27 (6%) Acquired 90 (19%) Public 88 (19%)

USD in billions

$4.0 $3.5 $3.0

$2.0 $1.5 $1.0

Dissolved 23 (5%)

$0.5

Non-profit 2 (0.4%)

$0.0

Figure 37.1 Distribution of genomics firms by type in 2004. Genomics firms were classified into the types indicated above based on authors’ analysis of study data.

Total Revenue Total Exp PPE

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Other 29 (6%)

Private 211 (45%)

R&D Exp

$2.5

Years

Figure 37.3 Financial trends of top 15 genomics firms. Top 15 genomics firms by market capitalization based on authors’ analysis of study data are: Applera, Millennium Pharmaceuticals, Invitrogen, OSI Pharmaceuticals, Gen-Probe, Affymetrix, Protein Design Labs, Human Genome Sciences, ZymoGenetics, Abgenix, Incyte, Digene, Exelixis Pharmaceuticals, Lexicon Genetics, Rigel Pharmaceuticals. Total Exp, total expenditures; R&D Exp, research and development expenditures; PPE, plant, property, and equipment.

$100

100

$90 90 $80 80 70

$60

Number of firms

USD in billions

$70

$50 $40 $30

60 50 40

$20

30

$10

20

$0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Years

Figure 37.2 Aggregate market capitalization of all genomics firms. The aggregate market capitalization of all publicly traded genomics firms for the years 1990–2004 in US dollars based on authors’ analysis of study data.

10 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Years

Figure 37.4 Aggregate number of public genomics firms. The aggregate number of publicly traded genomic firms for the years 1990–2004 based on authors’ analysis of study data.

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are illustrated in Table 37.2. One important point to glean from the taxonomy is that the genomics sector comprises firms with varying business plans, resulting in strategic heterogeneity despite similar core technologies. This fact is often glossed over when analysts describe “genomics” as a category, usually referring to early drug discovery using platform technologies, DNA sequencing, or just one or a few categories within the taxonomy. The most common category for both public and private firms was “drug, biologic and vaccine development” (55% of public firms; 33% of privately held firms). As illustrated in Table 37.2, nearly 25% of public firms are in the business of providing DNA analysis instruments (e.g., Affymetrix or Applied Biosystems). Another 20% of public firms supply genomics reagents including microarrays (e.g., Invitrogen) while almost a quarter of public firms provide services including DNA testing, clinical or diagnostic screening and test kit manufacturing (e.g., Gen-Probe). Almost 30% of private firms are involved in “bioinformatics for DNA analysis; data mining” (e.g., DNAStar) and over 20% conduct “gene expression analysis; microarray analysis; or analysis of siRNAs; and other regulation elements” (e.g., Ipsogen). A significant fraction of genomics companies (15%) focus on diagnostic applications and DNA testing services. Even among the early leaders in genomic drug development, there were many business plans and strategies for commercializing genomic discoveries. For example, Millennium sought

TABLE 37.2 firms

Top five categories in the taxonomy of

Public firms

Category DRUGDEV

Percenta 55

Representative firms Millennium, Incyte

INSTRMT

25

Gen-Probe, Affymetrix

SUPPLYR

23

Invitrogen, Affymetrix

DNATEST

22

Gen-Probe, Digene

GENEXPR

15

Exelixis, Diversa

Category

Percenta

Representative firms

DRUGDEV

33

AGY Therapeutics, Xenon

Private firms

INFRMTX

29

Genomatix, DNAStar

GENEXPR

21

Ipsogen, Ambion

DNATEST

15

Gentris, HandyLab

GENEFNL

14

Agilix, Xantos

Five most common categories of public and private firms by number of firms in each category. Firms can be classified by multiple taxonomies based on business function. a Percent of firms conducting research or business under a given category in the taxonomy.

a competitive advantage through genetic linkage analyses, largescale sequencing, and characterization of genes to link genomic information with disease biology in order to develop a targeted, novel pipeline of drug candidates (Millennium Pharmaceuticals, 2003). HGS and Incyte started by sequencing and characterizing full-length human genes, focusing especially on those with characteristics likely to make them promising drug targets (e.g., with sequence motifs predicting cellular transport, membrane or DNA binding potential). Both HGS (Human Genome Sciences, 2007) and Incyte (Incyte) have since augmented this approach by focusing on validation of drug targets, developing diagnostics, licensing-in promising drugs and biologics, and focusing on drug discovery. Celera Genomics initially focused on bioinformatics and producing reference genomic sequences (e.g., fruit fly, mouse, and human), under a business plan premised on selling subscriptions to the Celera database and informational tools. The business plan shifted to include drug discovery and a separate unit, Celera Diagnostics, aiming to develop diagnostics that identify and link genetic variation with disease was formed (Celera Genomics, 2007). Not all genomics companies are startups. Some firms moved into genomics from other lines of business. For instance, in 1993, the general science instrumentation firm Perkin-Elmer acquired the startup firm Applied Biosystems, whose primary business was instrumentation for analysis of DNA and proteins (for both synthesis and sequencing of nucleic acids and peptides). This line of business became the most profitable and fastest-growing component of Perkin-Elmer through the 1990s. After a series of name changes and re-organizations, what emerged was Applera, the parent firm of Applied Biosystems, Celera Genomics, and Celera Diagnostics, becoming one of the largest genomics firms. Output and Intellectual Property The majority of genomics firms were not profitable by the end of 2004; even those considered successful and ranking among the top 15 by market capitalization had an aggregate net income in 2004 of negative $1.2 billion. However, beginning in 2003 net income for the sector began an upward trend (i.e., an aggregate reduction in losses). Although many firms were not yet profitable at the end of 2004, total revenues for the genomics sector continued to climb, and in 2004 it generated approximately $6.3 billion in revenues, with a combined net income of negative $2.5 billion (Figure 37.5). Another measure of output for genomics firms is the number of patents issued. While patent counts are only rough indicators of productivity, and can even be considered crude measures, they are better than the tools available to measure output from other areas of research-intensive innovation, such as software, telecommunications, or computing. The patents associated with genomics and genetics can be identified more readily than for many other fields, providing an analytical tool to study genomic innovation. The many distinctive terms for DNA (and RNA) permit DNA patents to be identified with a relatively high degree of specificity and sensitivity. The claims of US patents that contain one or more terms specific to DNA

Landscape of Private Sector Genomics

(or RNA) can be used to capture intellectual property corresponding roughly to genetics and genomics, permitting analysis of trends. The intellectual property owned by currently active and independent genomics firms was 5859 total US patents and 3682 DNA-based US patents. Table 37.3 illustrates the top firms by total US and DNA patents owned. In 2004, the top 10 firms held nearly 60% of the total US patents and 64% of DNA patents. Among the top 10 DNA patent-holding firms, the percentage of their intellectual property portfolio attributed to DNA patents ranged from 44% to 93%. This ratio of DNA-based patents to total patents was a rough indicator of “genomics” intensity. Global Scope of the Genomics Private Sector The majority of publicly traded and privately held genomics firms in our database are in the United States; 75% (public) and 62% (private), respectively. Canada, Germany, France, United Kingdom, and Japan round out the top six countries, as shown in Table 37.4. These numbers should be interpreted with some caution because of the bias in favor of North America and Western Europe in our data sources. The database missed some firms even in the United States and is most likely missing even more from other countries, particularly outside North America and Europe. The heavy concentration of firm formation in North America and Europe, particularly the United States, reflects the origins of private genomics, which developed concurrently with the publicly funded HGP, based primarily in the regions where the publicly funded science was conducted. These trends also indicate the supportive venture capital environment in the United States and Europe. In recent years, leading private genomics firms from the United States and Europe have increasingly partnered in collaborative international genome sequencing projects focused on both agriculture and human disease, specifically, infectious diseases in the developing world. For example, primary sequencing $8

USD in billions

$6

Total Revenue

$4

$2

$0

Net Income

$2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Years

Figure 37.5 Aggregate net income and total revenue for all genomics firms. Aggregate net income and total revenue of public and private genomics firms for the years 1990–2004 in US dollars based on authors’ analysis of study data.

439

for the Anopheles gambiae genome sequencing project (Holt et al., 2002) was performed by Celera Genomics (Acharya et al., 2004; Hoffman et al., 2002;WHO, 2002). The geographic base of genomics will continue to broaden as firms mature and as more TABLE 37.3 Intellectual property ownership of genomics firmsa Top 10 firms by total US patent countb Name

US patents

Incyte

779

Human Genome Sciences

461

Millennium Pharmaceuticals

457

Applera

380

ZymoGenetics

345

Affymetrix

289

Caliper Life Sciences

206

Gen-Probe

205

Invitrogen

195

Sirna Therapeutics

130

Top 10 firms by DNA patent countc Name

DNA patents

Genomics patent intensity (%)d

Incyte

561

72

Human Genome Sciences

390

85

Millennium Pharmaceuticals

302

66

Affymetrix

247

85

Applera

182

48

Gen-Probe

180

88

ZymoGenetics

151

44

Invitrogen

139

71

Sirna Therapeutics

121

93

94

86

Stratagene a

$4

■

Data on active firms only; excluding genomics firms designated as acquired, subsidiary, dissolved, or other. b Total US patent data obtained through searches for firm name as assignee using Delphion patent database as of February 7, 2006. c DNA patent data obtained through searches for firm name as assignee in DNA Patent Database (http://dnapatents.georgetown. edu) on January 11, 2006. d Percentage of DNA patents as a fraction of total US patents for each firm.

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countries develop the requisite R&D infrastructure and expertise that will stimulate investment (Reineke and Cook-Deegan – in preparation). Several countries, such as Singapore (Genome Institute of Singapore, 2007), Estonia (Estonian Genome Project, 2007), Taiwan (Harris, 2002; National Research Program for Genomic Medicine, 2007), China (Hui, 2000) South Korea, Mexico (Jimenez-Sanchez, 2003; Mothelet and Herrera, 2005; Rabinowicz, 2001), and India (Mudur, 2001), have explicit government programs to support genomics R&D. Iceland and Brazil have seen private institutions invest substantial resources in genomics. In Iceland, much of the investment came from for-profit ventures through deCODE Genetics Inc. (2007) and a few other companies (Hakonarson et al., 2003; Specter, 1999). In Brazil, genomics largely grew from a set of non-profit research projects conducted at public universities and at privately owned Ludwig Institute for Cancer Research in the State of Sao Paolo (Camargo and Simpson, 2003; Simpson, 2001; Simpson and Perez, 1998). A committed investment in genomics R&D has produced strong scientific output from nations such as Brazil (Chandrasekharan and Fric-Shamji – in preparation), Iceland (Grant et al., 2006; Helgadottir et al., 2007), and Singapore (Ruan et al., 2003). India (Jayaraman and Louet, 2004) and China (Qiang, 2004) have been intensively building capacity for genomics R&D and bioinformatics through publicly funded initiatives, both as a tool for economic development, by cultivating a talent pool and attracting foreign investment, and for improving national health. Much like the trend observed in North America and Western Europe, a commercial genomics sector is growing up alongside publicly funded genomics science programs, with the founding of startup companies, some as spin-offs of publicly funded efforts. In contrast to private venture capital in the United States, however, much of the venture capital in India comes from national or state government (Sunder Rajan, 2006). This indicates increasing intellectual property consciousness in these countries. Similar to the history of genomics in the United States, larger biotech/pharmaceutical firms (e.g., firms involved in manufacture of generic drugs and vaccines) are also increasingly adopting or integrating genomic technologies in their R&D. Commercial genomics was an offshoot of the publicly funded HGP and has coalesced into a set of technologies used TABLE 37.4

Top countries with genomics firms

Country

Public

Private

Non-profit

Total

65

130

1

196

Canada

6

17

1

24

Germany

6

15

21

France

0

10

10

United Kingdom

3

6

9

Japan

0

5

5

United States

for diverse purposes in biomedical research and other fields. Genomics has evolved from purely non-profit and government research into a hybrid public–private R&D enterprise. While private sector genomics was practically non-existent in 1990, by 2000, private genomics R&D expenditure exceeded public and non-profit funding. Revenues of genomics firms rose to over $6.3 billion in 2004, $3.2 billion of which was expended on R&D. As indicated by our taxonomy of firm business strategies, most of this investment was for medical applications such as drug, biologics, and vaccine development, as opposed to agriculture and veterinary genomics or other areas. Genomic medicine was thus clearly the dominant end-target for genomics firms. The genomics subsector of biotechnology is not profitable, although the size of net losses diminished from 2002 to 2004. A long period of aggregate net loss has a precedent in biotechnology more generally. It took the better part of two decades for Amgen, Genentech, Biogen, Genzyme, and other “first generation” biotechnology firms to become established as a profitable subsector of the pharmaceutical industry. If this pattern holds true for genomics firms, some may become profitable over the next decade. In fact, several firms are already showing positive net incomes – 15 public firms reported a positive net income in 2004: Affymetrix, Applera, Digene, Discovery Partners International, Eurogentec, Genetix, Gen-Probe, Harvard Bioscience, Insightful, Invitrogen, Maxygen, Molecular Devices, Morphosys, Stratagene, and Tripos (Chandrasekharan et al. – in preparation). Many of the “platform technology” companies, which sell tools used in conducting R&D, can be expected to be profitable before other companies with longer product cycles, such as development of therapeutics, biologics, or vaccines. Private sector genomics R&D interacts with publicly funded genomics research in highly complex ways that do not entirely comport with traditional frameworks of R&D that assume federal and non-profit funding for “basic” research will produce knowledge that is applied downstream by private firms. Academic genomic research now entails the use of “research tools” purchased from private genomics firms, the majority of which were created after the HGP began. This is also reflected by increasing private partnership in large-scale public genomic projects such as the Genetic Association Identification Network (GAIN, 2007), a partnership between government funders, academic centers, and commercial firms including Pfizer, Affymetrix, and Perlegen Sciences. The public–private Encyclopedia of DNA Elements Project is another prominent example (ENCODE Project Consortium, 2004, 2007). Private sector firms are significant contributors to innovation in genomics. One such area of commercial R&D is the development of new technologies for cheaper, faster DNA sequencing, commonly referred to as “Next-Gen Sequencing.” Sequencing technology innovation is being driven by a complex and often collaborative relationship between private and public research efforts. These technologies are likely to have a significant impact on genomic medicine by paving the way for and possibly making whole-genome sequencing of individuals a routine medical procedure.

Future Trends

A number of firms dedicated to Next-Gen sequencing technology have emerged in the past few years and have received significant funding from federal sources including the NHGRI (National Human Genome Research Institute [2007], Genome Technology Program, Prasad S. and Chandrasekharan S. – unpublished data). These companies have received much industry interest and media attention, particularly in the context of the “race for the $1000 genome” (Service, 2006). Companies with more mature sequencing technologies, such as 454 Life Sciences (recently acquired by Roche Life Sciences) and Solexa (acquired by Illumina), have increasingly partnered with academic institutions to support genome sequencing projects for several organisms and, in return, have academic laboratories test and validate their technologies. For example, 454 Life Sciences has entered collaborative research agreements with the Broad Institute of MIT and Harvard for “Ultra-deep Sequencing” of specific genes of interest involved in complex diseases, such as cancer, diabetes, and heart disease. Similarly 454 has collaborated with the Max Planck Institute (Neanderthal Genome Sequencing), Yale University (HIV variants sequencing), and with the Malaysia Genome Resource Center (454 Life Sciences, 2007). Advances in conventional and Next-Gen sequencing have dramatically reduced the cost and time of sequencing and moved the goal of personal genome sequencing within reach, illustrated by the recent sequencing of the diploid genomes of geneticists, James Watson, by a 454 and Baylor College of Medicine partnership (454 Life Sciences, 2007; Wade, 2007) and J. Craig Venter’s sequence compiled by the J Craig Venter Institute (Levy et al., 2007). Individual whole-genome sequencing projects such as The Personal Genome Project (Church, 2005) headed by George Church at Harvard and the ClinSeq project at NHGRI, that aims to sequence 1000 individual genomes (ClinSeq, 2007) are also enabled by Next-Gen sequencing technologies developed in the private sector. These projects presage much more widespread use of these technologies. The ultimate markets may be clinical genetic testing, or industrial applications in pharmaceutical R&D and other areas, but academic research constitutes a market critical to the early history of these new technologies. Similarly, high-throughput genotyping is progressing rapidly, along with bioinformatics databases, analytical algorithms, analytical software, and other tools for creating and analyzing genomic data. Platform technologies for high-throughput SNP genotyping developed by companies such as Illumina and Affymetrix were integral to the International HapMap project and genome-wide association studies. Furthermore, products of such platform technologies, enabling rapid and extensive genotyping with several thousands of SNPs, have greatly expedited research aimed at identifying susceptibility loci for diseases such as macular degeneration (Hirschhorn and Daly, 2005) and several common diseases such as diabetes Type I and II, hypertension, coronary artery disease, bipolar disease rheumatoid arthritis, using genome-wide association approaches, as reported by the Wellcome Trust Case Control Consortium (2007). Foreseeable applications of such research include products that can facilitate early diagnosis, preventive interventions, and more individualized

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therapies. As detailed in other chapters in this book, genomic technologies are expected to make a large impact on pharmacogenomics and pharmacogenetics, where greater understanding of polymorphisms in genes responsible for drug metabolism may be translated into medical practice by using diagnostics to identify these genetic variations and help tailor pharmacotherapy in terms of drug selection and dosage (Need et al., 2005). Large-scale expression profiling, particularly of human tumors, has relied heavily on microarray platform technologies (primarily from Affymetrix and Agilent). This research has yielded a wealth of information, enhancing our understanding of the molecular underpinnings of different cancers. More importantly, the molecular signatures of different tumor types revealed by expression profiling have also allowed for reliable predictions about individuals’ response to chemotherapeutic agents or their risk of tumor recurrence (Potti et al., 2006), suggesting that genomic profiling will be incorporated into clinical decision trees to guide cancer diagnosis and treatment. The impact of genomic technologies in the emergence of actual products that can be integrated into medical practice is illustrated by the introduction of two in vitro diagnostics, Amplichip CYP450 from Roche Molecular Diagnostics and Mammaprint™ developed by Agendia. AmpliChip CYP450 (AmpliChip® CYP450 Test, 2007), which is based on Affymetrix microarray technology, detects polymorphisms and mutations in two CYP450 genes, CYP2D6 and CYP2C19, that are involved in the metabolism of a variety of prescription drugs. Introduced into the US market in 2003 (and 2004 in the EU), AmpliChip was the first FDA-approved pharmacogenetic test, which by allowing the physician to predict the drug metabolism phenotype of the patient, promised to improve patient outcome by increasing drug efficacy and reducing adverse reactions (AmpliChip®). Mammaprint™ was the first commercially available microarray-based cancer in vitro prognostic test. Based on research from the Netherlands Cancer Institute and the Antoni Van Leeuwenhoek Hospital in Amsterdam, the test determines the mRNA expression profile of 70 genes in breast tumor tissue and predicts the risk for breast tumor metastasis and recurrence within 10 years for the patient (Glas et al., 2006). The test, which uses custom microarrays manufactured by Agilent, was commercialized by Netherlands-based Agendia and received FDA approval in February 2007 (Food and Drug Administration, 2007; Mammaprint™, 2007). This was the first FDA-approved “in vitro diagnostic multivariate index assay” (IVDMIA) device and was seen as the first of several expression profiling-based diagnostic and prognostic tests that will enter the market.

FUTURE TRENDS The commercial genomics sector is a key stakeholder in genomic and personalized medicine. It accounts for a significant fraction of genomics research investment, as supported by our data showing high R&D expenditure by genomics firms.

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It promises to be a major player in clinical applications, as illustrated by the strong representation of private sector firms with a primary or significant business interest in genomics in both the Personalized Medicine Coalition (see http://www.personalizedmedicinecoalition.org/) and in the Coalition for 21st Century Medicine in the United States (Coalition for 21st Century Medicine, 2007). While the commercial development of genomic diagnostics appears poised for growth, new drugs, biologics, and vaccines developed from genomic information obtained by whole-genome sequencing and functional genomics approaches are still scarce. The identification of genetic variants associated with common diseases such as hypertension and diabetes may fuel further growth in custom diagnostic tests and testing services. Similarly, data from pharmacogenetic/pharmacogenomic and gene-expression profiling studies should help inform and design “individualized” drug regimens. These new application areas are likely to spur a further increase in the number of companies developing and providing custom pharmacogenetic and prognostic tests. Given the long products cycle for drugs and biologics, the coming decade may also see the first of personalized therapies for common diseases, particularly as advances are made in understanding the functions of disease susceptibility genes identified from genome-wide association studies and the molecular pathways they illuminate. “Personal Genomics” companies that use a direct to consumer model, such as Navigenics and 23andMe Inc., are also likely to proliferate. The market here is individuals who seek information about their own DNA, either through extensive genotyping or whole-genome sequencing of their DNA. This business model avails itself not only of genomic technologies, but depends crucially on the Internet, with its capacious data-transmission and individualized communication features.

Genome sequence analyses of various pathogens, combined with identification of human (and veterinary) host genetic variation that alters susceptibility, will likely result in the development of novel high-throughput tests for diagnosis and management of infectious diseases. Such tests will potentially be used in combination with specific therapeutics (drugs or biologics) or prevention strategies (e.g., vaccines) that are themselves products of functional genomics-based target discovery and drug development. The systematic integration of genomics with other “omics” and computational tools for the analysis and interpretation of information will be an essential next step in realizing the promise of genomic medicine and its implementation in health care. This is therefore likely to translate into a growth of companies offering solutions for genomic information storage, processing and integration for specific applications, and meeting the needs of individuals. Private sector genomics in both the developed and developing world is likely to evolve in unexpected ways to the meet the growing need for high-throughput and automated technologies for basic research and remain an integral player in the emergence of genomic medicine for addressing global health needs in the coming decades.

ACKNOWLEDGEMENTS The authors would like to thank the following students and research assistants at the Center for Genome Ethics, Law & Policy for invaluable help with data gathering and entry: Phebe Ko, Suparna Salil, Whitney Laemmli, Joe Fore, Alessandra Colaianni, Daidree Tofano, Molly Nicholson, Stacy Lavin, Britt Rusert, Marjorie Gurganus, Anupama Kotha, Cindy Wang, and Nancy Wang.

REFERENCES 23andMe Inc. (2007). http://23andme.com, accessed October 29, 2007. 454 Life Sciences (2007). Press release http://www.454.com/newsevents/press-releases.asp, accessed October 29, 2007. Acharya, T. and Daar, A.S. et al. (2004). Strengthening the role of genomics in global health. PLoS Med 1(3), e40. AmpliChip® CYP450 Test. (2007). http://www.amplichip.us/accessed/ September 2007. Camargo, A.A. and Simpson, A.J. (2003). Collaborative research networks work. J Clin Invest 112, 468–471. Celera Genomics (2007). Celera Genomics: Targeted Medicine http:// www.celera.com/celera/targeted_medicine, accessed September 2007. Chandrasekharan, S. and Fric-Shamji, E. Metrics of Genomics and Bioinformatics R&D activity – A case study of Brazil, India and Singapore – in preparation. Chandrasekharan, S., Perin, N.C., Ilse, R., Weichers, I.R. and Cook– Deegan, R.M. The landscape of commercial genomics – in preparation.

Church, G.M. (2005). The personal genome project. Mol Syst Biol 1, 0030. ClinSeq (2007). A Large-Scale Medical Sequencing Clinical Research Pilot Study. http://www.genome.gov/20519355, accessed October 29, 2007. Coalition for 21st Century Medicine (2007). About us. http:// www.twentyfirstcenturymedicine.org/about.html, accessed 10 September 2007. Cook-Deegan, R. (1994a). The Gene Wars: Science, Politics, and The Human Genome.WW Norton, New York. Cook-Deegan, R. (1994b). Survey of Genome Science Corporations, Contract report for the Office of Technology Assessment, US Congress; cited in Tables 1–5, The Human Genome Project and Patenting DNA Sequences, unpublished draft report approved by the Technology Assessment Board http://www.kie.georgetown.edu/ nrcbl/documents/dnapatents/OTAdraft.pdf, accessed 13 September 2007. Cook-Deegan, R., Chan, C. and Johnson, A. (2001). World Survey of Funding for Genomics Research: Final Report to the Global

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Section

Genomic Medicine and Public Health

6

38. What Is Public Health Genomics? 39. Why Do We Need Public Health in the Era of Genomic Medicine? 40. Principles of Human Genome Epidemiology 41. Genomics and Population Screening: Example of Newborn Screening 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention

CHAPTER

38 What Is Public Health Genomics? Alison Stewart and Ron Zimmern

INTRODUCTION In the mid-1990s, as the Human Genome Project gathered pace, some within the profession of public health began to realize that, in time, new knowledge and technologies stemming from this endeavor would have profound implications for the health of populations and the organization and delivery of health care services. These developments would mean that public health could no longer focus exclusively on social and environmental determinants of health, but would need to incorporate relevant knowledge from the molecular and cellular life sciences in the development of public health policies and programs. Public health genomics is a new discipline that brings together genetic and genomic science, genetic epidemiology, and a recognition that scientific and technological advance must go hand in hand with an understanding of its ethical, legal, and social dimensions. It is distinct from “genomic medicine” in that its focus is primarily on populations, health services, and public health programs rather than on individual clinical care. This chapter outlines the development of public health genomics over the last decade, sets out its key concepts, and describes examples of public health genomics programs and activities. New national, regional, and international networks are being established to further the goals of this new field.

THE EMERGENCE OF PUBLIC HEALTH GENOMICS Public health genomics, which began life as “public health genetics,” has its origins in the public health communities of the Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 446

United Kingdom and the United States. In the United States, publication of a seminal paper by Khoury (1996) on the application of genetics in disease prevention was followed by the establishment of a Task Force on this topic by the Centers for Disease Control (CDC) and Prevention in Atlanta. The Task Force’s report (Centers for Disease Control and Prevention, 1997) led to the setting up of the Office of Genetics and Disease Prevention under Khoury’s leadership in 1997. At around the same time, Schools of Public Health at some US universities were beginning to recognize the need to ensure that public health professionals understood the implications of genetics for public health. Multi-disciplinary post-graduate courses, involving input from disciplines including law, medicine, arts and science, pharmacy, nursing and public policy, were developed at the Universities of Michigan and Washington. In the United Kingdom, an expert advisory group reporting to the Department of Health in 1995 made the point that the country’s National Health Service could not ignore the potential implications of the “new genetics” for health services (NHS Central Research and Development Committee, 1995). The first move toward bringing genetics into the realm of public health came in 1997, when Ron Zimmern set up the Public Health Genetics Unit in Cambridge. During the last few years of the 20th century, the principles of public health genetics became established on both sides of the Atlantic. A growing body of literature setting out the intellectual underpinnings of the field was accompanied by the development of programs and activities in areas such as human genome epidemiology, service development, policy analysis, and education and training of health professionals. Copyright © 2009, Elsevier Inc. All rights reserved.

Key Concepts in Public Health Genomics

There was also recognition that the scope of the field was broader than that implied by the word “genetics.” As well as heritable disease, it included knowledge about the structure and composition of the human genome, the properties of genes and their products, and how genes function together during the development and life of the organisms – in other words, all of modern molecular and cell biology. For this reason, there has been a trend toward using the term “genomics” rather than “genetics” (Khoury, 2003). CDC’s Office of Genetics and Disease Prevention became first the Office of Genomics and Disease Prevention and now the National Office of Public Health Genomics, while in 2007 Cambridge’s Public Health Genetics Unit became the Foundation for Genomics and Population Health. Interest in public health genomics has also grown in other countries of Europe, North America, Asia and Australasia. In 2005, at an international workshop held in Bellagio, Italy, agreement was reached to set up an international network that brings together all those involved in furthering the aims of public health genomics (Burke et al., 2006; Stewart, 2006). The Genomebased Research and Population Health International Network (GRaPH Int) was launched in June 2006, with an administrative hub hosted by the Public Health Agency of Canada.

THE DEFINITION OF PUBLIC HEALTH GENOMICS The early practitioners of public health genetics suggested a variety of definitions for their discipline. At the University of Washington Institute of Public Health Genetics it was defined as “The application of advances in human genetics and molecular biotechnology to improve public health and prevent disease,” while in the United Kingdom a modified form of the Acheson definition of public health was adopted, describing public health genetics as “The application of genetics on the art and science of promoting health and preventing disease through the organized efforts of society.” All of the early definitions emphasized that the subject matter of public health genetics was how the health of populations and the practice of public health and clinical medicine were affected by genetic science and technology. They also highlighted the importance of harnessing genetic knowledge for disease prevention; the word “prevention” in this context included not just measures to prevent or delay disease onset (primary prevention), but also, if disease was already present, interventions that enabled early detection and treatment, reduced disability, and delayed progression (secondary and tertiary prevention). The UK definition also made it clear that practitioners of public health genetics needed, as well as a working knowledge of genetic science, an understanding of the diverse views within society about genetics and its consequences, and an ability to work across a range of disciplines and cultures. As the field broadened its outlook beyond the traditional connotations of “genetics” and began to think in terms of genomics, a new definition became necessary. As a result of the 2005 Bellagio workshop, public health genomics is now defined

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as: “The responsible and effective translation of genome-based knowledge and technologies for the benefit of population health” (Burke et al., 2006; Stewart, 2006). The Bellagio definition makes a number of important points. By using the term “genome-based” it indicates that the scope of the knowledge base includes not only genes but also their protein products, the metabolites synthesized by those proteins, and the complex molecular and cellular interactions that make up a biological system. It also includes bioinformatics: the management and analysis of large amounts of biological information using advanced computing techniques. “Technologies” are mentioned explicitly in the definition because biotechnological developments such as drug delivery platforms, highthroughput sequencing technologies, and microarray applications are vital to the clinical exploitation of genomic science. The adjectives “responsible” and “effective” convey two concepts: firstly, that any clinical tests or interventions resulting from genomic research must be fully validated before they are implemented and, second, that this evidence base must include due consideration of their ethical, legal, and social implications (often shortened to the acronym ELSI) (Clayton, 2003; Ojha and Thertullien, 2005). Finally, the Bellagio definition emphasizes that public health genomics focuses on the health and health care of populations. This may at first sight seem to be at odds with the individual or familial nature of genomic information and the prospects for a future era of “personalized medicine.” However, public health genomics recognizes that populations are not genetically homogeneous and that programs and policies incorporating differences in individual susceptibility to disease and response to treatment offer new opportunities that are complementary to the traditional “one size fits all” approach of public health.

KEY CONCEPTS IN PUBLIC HEALTH GENOMICS Genes and Environmental Factors as Determinants of Health Implicit in the various definitions of public health genomics is the recognition that all human characteristics – including susceptibility to disease – result from the combined effects of genes and environment. Figure 38.1 is a conceptual representation of determinants of health, showing the complex array of possible interactions between genomic determinants and the components of the physical and social environment. The relative contributions of genes and environment to disease risk can vary widely. In the case of highly penetrant heritable conditions such as cystic fibrosis or Huntington’s disease, a single genetic mutation may be sufficient to cause disease, but even for these diseases the range and severity of symptoms may vary widely among individuals, partly as a result of environmental factors. In the case of common chronic diseases, where a variety of low-penetrance genomic variants are implicated, the effect of environment is generally much more evident.

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GENETIC

Genetic endowment

PHYSICAL

BIOLOGICAL

POPULATIONS

INDIVIDUALS SOCIAL

POLITICAL

Individual behavior

Natural environment

Structural environment

BEHAVIORAL

Figure 38.1 Determinants of health. The figure makes the point that external factors such as natural environmental and structural determinants are modifiable by interventions at the population level, whereas genetic factors and behavior are essentially individual (although they are, of course, influenced by external factors).

It is a mistake, however, to think of any disease as resulting from a fixed and quantifiable combination of genomic and environmental determinants. For example, phenylketonuria, a condition inherited in a Mendelian fashion, is classified as a genetic (heritable) disease because the environmental determinant (dietary phenylalanine) is ubiquitous and the mutation rare and highly penetrant. However, the disease could also be thought of as 100% environmental, because it is only manifest when phenylalanine is present. We might also characterize the situation by stating that the genomic defect is a necessary factor in the pathogenesis of phenylketonuria but not in itself sufficient. From the representation in Figure 38.1 it is clear that the health status of an individual may be influenced by altering either genomic or environmental factors, or both. In the context of disease prevention, Juengst (1995) distinguished these two types of intervention as “genotypic” and “phenotypic.” Genotypic intervention may be appropriate in some circumstances, such as the use of genetic testing to avoid the birth of a child affected by a highly penetrant genetic disease. However, in the context of common chronic disease, which does not usually develop until some time during adult life, only phenotypic intervention will generally be either feasible or ethically acceptable. Public health genomics aims to prepare the ground for a future era when we have a much more complete understanding of the full range of genomic and environmental determinants of health and – crucially – how they work together. The hope is that this fuller understanding of disease etiology and of the mechanisms of disease at the molecular and cellular levels will enable development of new diagnostic tools, new therapies, and perhaps new preventive options (Guttmacher and Collins, 2005), and that this new era of genomic medicine will offer opportunities to achieve benefits for population health. Enthusiasm about the potential benefits from genomic research must be balanced by realism about the likely time scale of these developments (Davey Smith et al., 2005; Haga et al., 2003). Unraveling the genomic contribution to disease susceptibility is an immensely complex task, and many applications in mainstream

health care are unlikely to materialize for some decades to come. In the meantime, it is a key function of public health genomics to protect patients and health services from the damaging effects of interventions that are premature, not supported by robust evidence or do not fulfill the criterion of public acceptability. Avoiding Genetic Exceptionalism It is important to avoid “hype” about advances in genomics and it is equally important to dispel the notion that genetic or genomic factors have a significance and predictive power beyond that of other determinants of health, a view that has been dubbed “genetic exceptionalism” (Murray, 1997). A related pitfall is genetic reductionism, the tendency to over-simplify the relationship between a genetic factor and a disease or other phenotypic trait (Sankar, 2003). Genetic exceptionalism and reductionism demand special protection for genomic information on the grounds of its predictive power and implications for other family members but this thinking derives from a misguided attempt to extrapolate from the highly penetrant mutations associated with Mendelian disease to the much more weakly predictive variants associated with common disease. Public health genomics recognizes that genes must be included among the determinants of health but their significance should be kept in proportion: they should neither be over-emphasized nor demonized. Genetic exceptionalism can lead to policy responses that are both disproportionate and irrational, in that they place restrictions on uses of DNA test information but leave other physiological or biomarker tests – which may be equally or even more highly predictive – unregulated.

THE “ENTERPRISE” OF PUBLIC HEALTH GENOMICS The Bellagio workshop participants developed a visual representation of the “enterprise” of public health genomics, which is represented by the shaded regions of Figure 38.2; the aim of

Core Activities in Public Health Genomics

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Society Communication and Stakeholder Engagement Knowledge generation Population Sciences

Genome-based Science and Technology

Informing Public Policy Knowledge integration

Strategy

Analysis

within and across disciplines

Action

Improvement in Population Health

Evaluation

Developing and Evaluating Health Services Humanities and Social Sciences Education and Training

Applied and Translational Research

Figure 38.2 Strategy for the effective translation of genome-based knowledge and technologies for the benefit of population health. The shaded areas represent the “enterprise” of public health genomics.

this representation is to convey a consensus understanding of what sorts of activities come under the banner of public health genomics, and how its work is conducted. The starting point for the “enterprise” (but not part of the enterprise itself) is genomics research, shown at the left of Figure 38.2. Basic research is also needed in the population sciences (including, e.g., epidemiology and biostatistics) and in the humanities and social sciences (including law, philosophy, social anthropology, theology, and so on). Basic research can be thought of as the phase of knowledge generation. Public health genomics comes into play with the phase of knowledge integration, both within and across disciplines. The raw material for this process is the output from the research phase. Knowledge integration is defined as the process of selecting, storing, collating, analyzing, integrating, and disseminating information. This includes computer-based approaches to classifying, linking, and retrieving information (essentially, what an informatician would understand by the term “knowledge integration”) but it is also a broader and more qualitative process encompassing selection, critical analysis, and synthesis of concepts and information from many different fields in the arts, humanities, biological, and social sciences. As the means by which information is transformed into knowledge, it is an essential step in making that information usable in the practical world of clinical medicine and public health practice. The knowledge base generated by the knowledge integration function of public health genomics is used to underpin four core sets of activities: communication and stakeholder engagement; informing public policy; developing and evaluating health services; and education and training. Examples of some of these activities will be discussed later in this chapter.

Knowledge integration and the four core sets of activities set out what public health genomics does. How it carries out its work may be described by the cycle of public health action developed by the Institute of Medicine Committee for the Study of the Future of Public Health (1998) and others. This cycle consists of an initial phase of analysis followed by development of a strategy, implementation of that strategy, and evaluation of its outcomes. This is not a once-and-for-all process: successive rounds of the cycle lead to continued refinement of the strategy, both before and after implementation. Research does have a place in the enterprise of public health genomics, as shown at the bottom of the diagram. Distinct from basic academic research, this is applied and translational research with direct applicability to health service implementation; such research can also identify gaps in the knowledge base that need to be filled by further basic research. Finally, Figure 38.2 emphasizes that the whole enterprise of public health genomics – and the phase of basic research – is embedded within a societal context. The views and priorities of society play a part in determining what research is carried out and how it is used and, in turn, the perceived results of those activities shape societal attitudes.

CORE ACTIVITIES IN PUBLIC HEALTH GENOMICS Knowledge Integration Knowledge integration can be thought of as the driving force of public health genomics. A number of initiatives to support knowledge integration are underway at various centers. These activities

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aim to bring knowledge together from different sources across the sciences, arts, and humanities so that a multi-disciplinary resource is available for application to projects in public health genomics. For example, the HumGen database at the Centre de recherche en droit public (CRDP) at the University of Montreal, Canada, is a searchable international database of laws and policies relevant to the application of human genetics. The PolicyDB database curated by the Public Health Genetics Unit in Cambridge also deals with the policy field but differs from HumGen in that it concentrates mainly on the United Kingdom and the EU, and coverage includes policy for scientific research, public health, and health services as well as legal and bioethical documents. A highly developed example of knowledge integration in public health genomics is the Human Genome Epidemiology Network (HuGE Net) initiative (Ioannidis, 2005). HuGE Net was set up by Muin Khoury in 1999 with the aim of assembling and critically analyzing population data on genotype prevalences and gene–disease associations emerging from research in genetic epidemiology. This task is an essential prerequisite to the development of any clinical or public health applications of this information. HuGE Net, discussed in more detail in another chapter of this book, has grown into an international multi-disciplinary initiative comprising around 800 collaborators in 43 countries. HuGE Net’s core activities are information exchange through its website and newsletter, training and technical assistance in systematic review and meta-analysis, methodological development, knowledge base development, and information dissemination. Another international initiative, spearheaded by the CRDP in Montreal, is the Public Population Project in Genomics (P3G) Consortium. The aim of P3G is “to provide the international population genomics community with the resources, tools, and know-how to facilitate data management for improved methods of knowledge transfer and sharing.” P3G members include representatives from the major biobank projects underway across the world. A key feature is the P3G Observatory, a knowledge transfer platform that includes information on the design and status of the various projects and their governance arrangements, to enable comparisons and sharing of best practice.

Service Development and Evaluation This activity includes development of policies, programs, and services – both clinical and preventive – in the health sector; strategic planning; service organization; manpower planning and capacity building; service review and evaluation; and guideline development. The evaluation of population genetic screening programs, either proposed or current, is an example of work in this strand of public health genomics. In the United States, for example, public health leadership was instrumental in securing a rational approach to the proposed introduction of population screening for hereditary hemochromatosis. A multi-agency process produced a consensus statement that the current evidence base did not justify screening and made recommendations for further research (Burke et al., 1997). Further population-based research was funded to establish the penetrance of hemochromatosis-associated mutations and the prevalence of symptomatic hemochromatosis, and a web page is available on the website of the National Office of Public Health Genomics providing education about hereditary hemochromatosis for health care providers and the general public. The public health genomics community has also taken the lead on the evaluation of DNA tests for clinical use, developing both the theoretical foundations for test evaluation and protocols for implementation in practice. For example, CDC’s National Office of Public Health Genomics has convened a project known as EGAPP (Evaluation of Genomic Applications in Practice and Prevention) that aims “to develop a coordinated process for evaluating genetic tests and other genomic applications that are in transition from research to clinical practice.”The EGAPP project draws on experience gained from application of an earlier evaluation protocol, the ACCE framework (standing for analytical validity, clinical validity, clinical utility, and ethical, legal, and social implications; Haddow and Palomaki, 2003). The UK’s Public Health Genomics Foundation also has a strong involvement in genetic test evaluation, working with the UK Genetic Testing Network to develop and apply a protocol for the evaluation of tests funded by the National Health Service (Sanderson et al., 2005).

Communication and Stakeholder Engagement Productive dialogue with the public and other stakeholders, including patient groups, government, and industry, is important to ensure that scientific and clinical developments proceed in harmony with the attitudes and expectations of other sectors of society. Among examples of work in this area, the Public Health Genetics Unit (PHGU) in Cambridge established a public panel to provide an independent perspective on its work and actively involved patients and carers in its service development projects. For example, a project on learning disability employed a mixture of online discussion, telephone interviews, and focus groups to develop a document, Parents as Partners, which provides detailed evidence of the value that parents place on a genetic diagnosis in learning disability, and sets out recommendations for the health service (Gogarty, 2006).

Informing Public Policy The term “policy” refers to a broad range of public policies and programs that have a direct or indirect impact on the application of genomics in health care. Activities may encompass legal, philosophical, and social analysis at an applied level; development of regulatory frameworks; engagement in the policy making process; promoting relevant research; and seeking international comparisons. Current policy issues include legal and regulatory frameworks for genetic testing; the funding of science and the prioritization of relevant research; consent, confidentiality, data protection, and the use of human tissue; attitudes to and relationships with the pharmaceutical and biotechnology industries; and the patenting of genes and genomic technologies. There is considerable academic research activity on these topics. For example, the US National Human Genome Research

Moving Public Health Genomics Forward: Leadership and Networks

Institute “ELSI” program has an annual budget of around $18 million to support research on topics including privacy and fair use of genomic information, DNA banking, ethical conduct of genomic research, genetic discrimination, and the psychosocial impact of genomic testing and technologies. In the United Kingdom, the Economic and Social Research Council has also invested heavily in this field, supporting social science research in genomics through six Genomics Centers that together form the ESRC Genomics Network. Despite the volume of theoretical work that has been done on these issues, however, there has been less attention to finding practical policy solutions; an aim of public health genomics is to fill this gap. The PHGU’s policy team, for example, carried out detailed scrutiny of draft UK legislation governing the storage and use of human tissue samples for purposes including DNA analysis (Liddell and Hall, 2005). Flaws in the draft legislation were identified that could have impeded both research and clinical practice. Working with partner organizations in clinical genetics and biomedical research, PHGU was able to help influence the political process to achieve amendments that at least partly resolved these problems, and was involved in initiatives to produce practical guidance for health professionals needing to comply with the new legislation. PHGU’s mode of working on the Human Tissue Bill highlights another key aspect of public health genomics: the importance of forging working alliances with relevant stakeholder groups from research, clinical medicine, industry, patients, and the general public to achieve a consensus approach that is vital for securing “ownership” of a project and its conclusions. Education and Training As genomics and genomic technologies begin to have an impact on clinical practice, all health professionals will need to become “literate” in the general principles of genomics and its specific applications in their own specialist field. Public health professionals and health service managers will also need a working knowledge of genomics in order to make sound judgments about the planning and evaluation of health services (Burke, 2005). Public health genomics contributes to these goals by promoting programs of genomic literacy for health professionals (as well as generally within society); specific training for public health genomics specialists; and development of educational materials, courses, workshops, and seminars. For example, a set of competencies in genomics for the public health workforce has been developed by the National Office of Public Health Genomics in Atlanta. Competencies are documented for the public health workforce as a whole and for specific groups including leaders/administrators, clinicians, epidemiologists, health educationalists, laboratory staff, and environmental health workers. For public health genomics specialists, the multidisciplinary Masters and PhD programs developed at the Universities of Michigan and Washington offer a solid grounding in the field: the University of Washington Masters course, for example, includes core modules in genetic epidemiology; ethical

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and social issues; pharmacogenetics and toxicogenomics; genetics and the law; public perspectives on ethical issues; introduction to genetic services and bioinformatics; and economic and policy issues for genetic technologies and services. In the United Kingdom, the PHGU led a project to develop a national strategy for genetics education for health professionals; the project’s report (Burton, 2003) was one of the influences that led to the establishment of a government-funded NHS National Genetics Education and Development Centre in Birmingham. A multi-disciplinary post-graduate course in public health genomics is under development at Cranfield University, based on a textbook authored by members of the Public Health Genetics Unit (Stewart et al., 2007).

MOVING PUBLIC HEALTH GENOMICS FORWARD: LEADERSHIP AND NETWORKS Public health genomics is still in an early phase of development. It has yet to reach “critical mass” in any individual country and in many parts of the world does not yet exist. The pioneer groups and organizations are taking the lead and working together to share resources, provide credibility for those wishing to develop public health genomics in their own countries, and establish collaborations in key areas of work. GRaPH Int, the international public health genomics network, aims to fulfill a coordinating role and to facilitate collaboration and sharing of information (Burke et al., 2006). The GRaPH Int website provides a portal to the organizations and resources that are available worldwide to support public health genomics. Multi-disciplinary GRaPH Int working groups are identifying priorities and setting up collaborations in key areas including education and achieving more effective integration between the ELSI field and other parts of the enterprise. Regional and national networks also have a role to play. In Europe, the Public Health Genomics European Network (PHGEN), funded by the EU, supports the development of public health genomics in the countries of the EU, the EU-applicant countries, and the members of the European Free Trade Area. Its activities include developing an inventory of key issues for public health genomics in Europe, identifying areas of similarity and diversity in legal and regulatory regimes for genomic applications, stimulating exchange of information, and identifying key experts in relevant fields. In the United Kingdom, a national society for public health genomics, the Society for Genomics, Policy and Population Health has been set up as a special interest subgroup of the British Society for Human Genetics. Partnerships and collaborations with institutions and organizations that have related goals will also be an important part of the development of public health genomics. Such partnerships will be particularly important in extending the reach of public health genetics into the developing world.

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CONCLUSION The new field of public health genomics faces many challenges, both intellectual and practical. Chief among the intellectual challenges is the need to inspire a change in the traditional mindset within the profession of public health. Public health professionals can no longer ignore the role of genes as important determinants of health, or the recognition that disease susceptibility is a consequence of gene–gene and gene–environment interaction. They must begin to master a broad knowledge base in genetic and genomic science, the population sciences, and the humanities and social sciences (Table 38.1; also see Fineman, 1999). Practical challenges for public health genomics include finding ways of facilitating the integration of genomic science into mainstream medicine; devising and implementing sound methodologies for evaluation of genomic tests and complex molecular biomarkers; achieving a rational organization of molecular and cytogenetic testing services; evaluating current and proposed genetic screening programs; and working with primary care practitioners to clarify the role of genomics in the primary care setting. A further challenge will be to ensure that the developing world does not miss out on the potential benefits of advances in genomics. Heritable diseases such as sickle cell disease and the thalassemias are a major burden in many developing countries but screening, diagnostic, and health care services for these conditions are often rudimentary at best (Weatherall, 2005). Developing countries also stand to derive huge benefit from new vaccines and diagnostics for infectious diseases and from biotechnological approaches to fight malnutrition and environmental degradation (Daar et al., 2002). The approach of public health genomics, emphasizing knowledge integration and a multidisciplinary approach, proceeding from a sound evidence base, promoting coherent public policy development, and ensuring that the necessary infrastructure is in place before interventions

TABLE 38.1 genomics

The knowledge base for public health

Genomic science

Population sciences

Humanities

Basic concepts of molecular, cell, and developmental biology

Epidemiology

Sociology

Biostatistics

Anthropology

Environmental health sciences

Law

Infectious diseases

Ethics

Structure and composition of the human genome Roles of genes in health and disease Gene–gene and gene–environment interactions Mendelian genetics, family histories, and pedigrees Principles of genome epidemiology Genetic testing and screening

Social and behavioral sciences Health Economics

Economics Metaphysics and epistemology Theology Political philosophy

Health services research Management science Information science

Pathogen genomes Genomic technologies Bioinformatics

are implemented, is as relevant to ensuring population benefit in this setting as it is in the developed world. In rising to all these challenges, public health genomics is poised to make an important contribution to the goal of translating the fruits of genomic research into health benefits for all.

REFERENCES Burke, W. (2005). Contributions of public health to genetics education for health professionals. Health Educ Behav 32, 668–675. Burke, W. et al. (1997). Hereditary haemochromatosis: Gene discovery and its implications for population-based screening. JAMA 280, 172–178. Burke, W., Khoury, M.J., Stewart, A. and Zimmern, R.L. (2006). The path from genome-based research to population health: Development of an international collaborative public health genomics initiative. Genet Med 8, 451–458. Burton, H. (2003). Addressing Genetics, Delivering Health. Public Health Genetics Unit, Cambridge. Available at http://www.phgfoundation.org. Centers for Disease Control and Prevention (1997). Translating advances in human genetics into public health action: A strategic plan. Clayton, E.W. (2003). Genomic medicine: Ethical, legal, and social implications of genomic medicine. N Engl J Med 349, 562–569. Daar, A., Thorsteinsdottir, H., Martin, D.K., Smith, A.C., Nast, S. and Singer, P.A. (2002). Top ten biotechnologies for improving health in developing countries. Nat Genet 32, 229–232.

Davey Smith, G.D., Ebrahim, S., Lewis, S., Hansell, A.L., Palmer, L.J. and Burton, P.R. (2005). Genetic epidemiology and public health: Hope, hype, and future prospects. Lancet 366, 1484–1498. EGAPP project. National Office of Public Health Genomics, Centers for Disease Control and Prevention. http://www.cdc.gov/genomics/ gtesting/egapp.htm. Fineman, R. (1999). Qualifications of public health geneticists? Community Genet 2, 113–114. Gogarty, B. (2006). Parents as Partners. Public Health Genetics Unit, Cambridge. Available at http://www.phgfoundation.org. GRaPH Int. http://www.graphint.org. Guttmacher, A.E. and Collins, F.S. (2005). Realizing the promise of genomics in biomedical research. N Engl J Med 347, 1512–1520. Haddow, J.E. and Palomaki, G.E. (2003). ACCE: A model process for evaluating data on emerging genetic tests. In Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information

Recommended Resources

to Improve Health and Prevent Disease (M.J. Khoury, J. Little and W. Burke, eds), Oxford University Press, New York, pp. 217–233. Haga, S.B., Khoury, M.J. and Burke, W. (2003). Genomic profiling to promote a healthy lifestyle: Not ready for prime time? Nat Genet 34, 347–350. Human Genome Epidemiology Network (HuGE Net). http://www. cdc.gov/genomics/hugenet/default.htm. HumGen database. http://www.humgen.umontreal.ca/int/. Ioannidis, J.P. (2005). A road map for efficient and reliable human genome epidemiology. Nat Genet 38, 3–5. Institute of Medicine Committee for the Study of the Future of Public Health (1998). The Future of Public Health. National Academies Press,Washington DC. Juengst, E.T. (1995). ‘Prevention’ and the goals of genetic medicine. Hum Gene Ther 6, 1595–1605. Khoury, M.J. (1996). From genes to public health: Applications of genetics in disease prevention. Am J Public Health 86, 1717–1722. Khoury, M.J. (2003). Genetics and genomics in practice. The continuum from genetic disease to genomic information in health and disease. Genet Med 5, 261–268. Liddell, K. and Hall, A. (2005). Beyond Bristol and Alder Hey: The future regulation of human tissue. Med Law Rev 13, 170–223. Murray, T. (1997). Genetic exceptionalism and ‘future diaries’: Is genetic information different from other medical information? In Genetic Secrets: Protecting Privacy and Confidentiality in the Genetic Era (M.A. Rothstein, eds.),Yale University Press, New Haven, pp. 60–73. National Office of Public Health Genomics, Centers for Disease Control and Prevention. Genomic Competencies for the Public Health Workforce. http://www.cdc.gov/genomics/training/competencies/default.htm. National Office of Public Health Genomics, Centers for Disease Control and Prevention. Hereditary Haemochromatosis: A Public

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Health Perspective. http://www.cdc.gov/genomics/training/perspectives/hemo.htm. NHS Central Research and Development Committee (1995). Genetics and Common Disease. Department of Health, London. NHS National Genetics Education and Development Centre. http:// www.geneticseducation.nhs.uk/. Ojha, R.P. and Thertullien, R. (2005). Health care policy issues as a result of the genomic revolution: Implications for public health. Am J Public Health 95, 385–388. PHG Foundation Policy DB Database. http://www.phgfoundation. org/policydb. Public Health Genomics European Network. http://www.phgen. nrw.de/. Public Population Project in Genomics (P3G). http://www.p3gconsortium.org/. Sanderson, S., Zimmern, R., Kroese, M., Higgins, J., Patch, C. and Emery, J. (2005). How can the evaluation of genetic tests be enhanced? Lessons learned from the ACCE framework and evaluating genetic tests in the United Kingdom. Genet Med 7, 495–500. Sankar, P. (2003). Genetic privacy. Annu Rev Med 54, 393–407. Stewart, A. (2006). Genome-based Research and Population Health. Report of an expert workshop held at the Rockefeller Foundation Study and Conference Centre, Bellagio, Italy, 14–20 April, 2005. Available at http://www.phgfoundation.org. University of Michigan School of Public Health Interdepartmental Concentration in Public Health Genetics. http://www.sph.umich. edu/genetics/. University of Washington Institute of Public Health Genetics, Degree Programs in Public Health Genetics. http://depts.washington. edu/phgen/degreeprograms/DegreeProgs_OV.shtml. Weatherall, D.J. (2005). The global problem of genetic disease. Ann Hum Biol 32, 117–122.

RECOMMENDED RESOURCES Gwinn, M. and Khoury, M.J. (2006). Genomics and public health in the United States: Signposts on the translation highway. Community Genet 9, 21–26. (A recent review of progress in public health genomics in the United States.) Halliday, J.L., Collins, V.R., Aitken, M.A., Richards, M.P. and Olsson, C.A. (2004). Genetics and public health – evolution, or revolution? J Epidemiol Community Health 58, 894–899. (A discussion of the prospects and challenges raised by a convergence between public health and genetics.) Institute of Medicine Committee on Genomics and the Public’s Health in the 21st Century (2005). In Implications of Genomics for Public Health (Hernandez, L., ed.), National Academies Press, Washington DC. (This report summarizes the “state of the art” for public health genomics in the United States, and prioritizes the issues that need to be addressed.) Khoury, M.J., Burke, W. and Thomson, E., eds. (2000). Genetics and Public Health in the 21st Century. Oxford University Press, New York. (An excellent overview of the conceptual framework of public health genetics and its early development during the 1990s.) National Office of Public Health Genomics website. http://www.cdc. gov/genomics. Provides information on NOPHG projects, training

resources, and the GDPInfo database of published literature relating genes, diseases, and environmental exposures. Hosts the HuGE Net website. Users can sign up for a weekly e-mail update of news, event, and publications in public health genomics. PHG Foundation website. http://www.phgfoundation.org. Features information on the PHGU work program, and an online newsletter of genomics policy news, linked to related websites and records in the PolicyDB database. Users can request a monthly e-mail compilation and commentary of genomics policy news. Stewart, A., Brice, P., Burton, H., Pharoah, P., Sanderson, S. and Zimmern, R. (2007). Genetics, Health Care and Public Policy. Cambridge University Press, Cambridge UK. (This textbook sets out the knowledge base for public health genetics and the policy and service issues raised by genomic advances, from a UK perspective.) Zimmern, R. and Cook, C. (2000). Genetics and health. Policy issues for genetic science and their implications for health services. The Nuffield Trust, London. (A useful and particularly broad-ranging discussion of service and policy issues raised by genomics.)

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39 Why Do We Need Public Health in the Era of Genomic Medicine? Muin J. Khoury and Marta Gwinn

INTRODUCTION We live in the “omics” era. New fields with “omics” attached to existing disciplines seem to emerge on a regular basis. For example, we now have pharmacogenomics, nutrigenomics, metabonomics, transcriptomics, proteomics, toxicogenomics, and many others (Khoury, 2003). Although there are currently a few clinical applications that we can ascribe to the genomic technologies, more applications are anticipated in the next decades. In a book devoted to the applications of genomic sciences in clinical practice, one might wonder about the role of public health in the practice of genomic medicine in the 21st century. This section explores the growing intersection of public health with genomic medicine. Chapter 38 outlines the need and emergence of “public health genomics” as multidisciplinary field of science and practice. This chapter describes the growing role of epidemiologic methods and approaches in collecting the type of information needed for genomic medicine to become a reality. Chapter 42 explores the growing importance of using family history in practice in relation to genetics and genomics. Finally, Chapter 41 explores the evolution of newborn screening, a traditional public health function from traditional genetic diseases to the new ear of genomic medicine. In this introductory chapter, we describe the importance of public health-medicine collaboration in the genomics era, namely in four areas for a joint vision: the focus on prevention,

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the importance of population sciences, the importance of evidence-based knowledge synthesis, and the need for health services research and population health outcome monitoring. This collaboration is discussed in detail elsewhere (Khoury et al., submitted). First, we explore the continuum from genetic to genomics in practice and discuss the implications to how public health and medicine can collaborate in the era of genomics.

THE CONTINUUM FROM GENETICS TO GENOMICS IN HEALTH PRACTICE Guttmacher and Collins (2002) viewed “genetics as the study of single genes and their effects” while genomics refers “to the study not just of single genes, but of the functions and interactions of all the genes in the genome.” This definition implies a quantitative difference between the two fields (the study of multiple genes versus one gene) and makes genetics part of genomics. In addition to this quantitative difference, genomicsbased sciences lead to a fundamental qualitative difference in the application of genetics in medicine and public health. Although set as a dichotomy, Table 39.1 shows how genetics and genomics can be viewed on a continuum ranging from the two extremes of a genetic disorder in genetics to the concept of genetic information in genomics. The current practice of medical genetics has focused on rare clinically recognized disorders associated with

The Role of Public Health in the Translation of Human Genome Discoveries into Health Applications

TABLE 39.1

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The continuuma from genetics to genomics in health practice Genetics

Genomics

Focus

Genetic disorders

Genetic information

Types of conditions

Single gene disorders Chromosomal anomalies Diseases with genetic component

All human diseases

Genetic involvement

Mutations with high or incomplete penetrance

Genetic variation at single and multiple loci

Environmental involvement

Variable; many disorders have no known environmental determinants

Strong interaction with chemical, infectious, physical, pharmacologic agents

Medical practice

Genetic services (Genetic counseling, testing, management)

●

Public health practice

Assuring delivery of genetic services to individuals, families and society Population screening (e.g., newborn screening)

Health care Risk stratification using genetic variation/family history ● Pharmacogenomics ● Diagnostic tests ● Prognostic tests Developing health policy and assuring appropriate use of genetic information in population health Population screening

a

From Khoury M.J. (2003). Genetics and genomics could be viewed on a continuum from clinically recognized disorders associated with single gene mutations for which the traditional genetic services model applies, to genetic information expressed at multiple loci which can be used in prediction, diagnosis or treatment of a disease not classified as genetic. Along this continuum could be clinically recognized genetic disorders with low penetrance resulting from interaction with other genes and exposures.

single gene mutations, for which the traditional genetic services model applies with its accompanying medical processes (genetic counseling/testing/management) and public health processes (delivery of genetic services and newborn screening). Until recently, the focus of genetics has been on those conditions that are known to be due to mutations in single genes (e.g., Huntington disease), whole chromosomes (e.g., Down syndrome), or diseases with strong genetic components (e.g., birth defects and developmental disabilities). On the other hand, the practice of genomics in medicine will be centered on the concept of “genetic information” due to variation at one or multiple loci and strong interactions with environmental factors (broadly defined to include diet, drugs, infectious agents, chemicals, physical agents, and behavioral factors). Such information can then be used in diagnosis, treatment and prevention of all diseases, not only genetic disorders.

THE ROLE OF PUBLIC HEALTH IN THE TRANSLATION OF HUMAN GENOME DISCOVERIES INTO HEALTH APPLICATIONS Public health has been traditionally identified with state, federal and local public health agencies; however, recent reports by the Institute of Medicine in the United States and others have adopted a more inclusive view, defining public health professionals

as those working on improving health from a population perspective (IOM, 2003). “Public health professionals” are those employed not only in government but also in health care delivery, academia, community organizations, and the private sector; together, they are actors in the “public health system” (IOM, 2002), which is working to assure the conditions under which a population can be healthy. If we adopt the expanded view of public health as population health, it becomes much easier to consider genomics at the interface of the translational research agenda between medicine and public health. Another reason to adopt an expanded view of population health is that future applications of genomics will occur primarily in the health care delivery system and not in the context of the national or state-mandated screening programs (traditional public health). Unfortunately, the sad history of eugenics will serve as constant reminder of horrifically failed applications for the perceived benefits of population health. Furthermore, the only current model of intersection of genetics and public health, namely newborn screening, should not necessarily serve as a model of future applications and joint partnership between medicine and public health. Undoubtedly, while newborn screening programs make important contributions to health, they continue to raise important ethical questions about informed consent, especially in the light of proposed expanded programs where health benefits are absent or less clear cut than medical emergencies provided by phenylketonuria (PKU) and congenital hypothyroidism. Thus, under the rubric of population health, collaboration between public health and medicine in genomics will emphasize the

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TABLE 39.2

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The continuum of translation research in human genomics: Types of research and BRCA example

Translation research phase

Notation

Types of research

Examples

TI

Discovery to candidate health application

Phases I and II clinical trials Observational studies

Is there an association between BRCA mutations and breast cancer?

T2

Health application to evidence-based practice guidelines

Phase III clinical trials Observational studies Evidence synthesis and guidelines development

What is the positive predictive value of BRCA mutations in at-risk women?

T3

Practice guidelines to health practice

Dissemination research Implementation research Diffusion research Phase IV clinical trials

What proportion of women who meet the family history criteria are tested for BRCA and what are the barriers to testing?

T4

Practice to population health impact

Outcomes research (includes many disciplines) Population monitoring of morbidity, mortality, benefits, and risks

Does BRCA testing in asymptomatic women reduce breast cancer incidence/improve outcomes?

Adapted from Khoury et al. (2007b). See also Chapter 22.

importance of an individual-oriented perspective but also the need for a population-based evaluation strategy and evidencebased health care with access of validated information to all segments of the population. Advances in genomics have also been accompanied by an emerging emphasis on translational research, a movement fueled by the NIH road map initiative (Zerhouni, 2003, 2005) to accelerate the translation from basic science to clinical applications. The translation framework is based primarily on the discovery of new drugs and their accelerated use in human clinical trials, with little or no emphasis on prevention. However, the “bench to bedside” paradigm covers only part of the distance from discovering new knowledge to delivering health benefits at the population level (Green and Seifert, 2005; Horig and Pullman, 2004). In 2003, Claude Lenfant (2003), the retiring director of the National Heart, Lung and Blood Institute, presented the Shattuck lecture titled “Clinical research to clinical practice – lost in translation?” He described many discoveries of curative or preventive interventions that nevertheless do not reach the end of the translation highway and asked, “If we can’t do it with aspirin, how will we do it with DNA?” The “lost in translation” problem is complicated by the increasing costs of health care delivery and persistent inequities in access. Some have called the next stage in translation “translation type II” (Rohrbach et al., 2006), which requires more applied research on the best ways to deliver or disseminate interventions that work in real life settings (delivery research) and to evaluate health outcomes and population impact (outcomes research). In 2007, Westfall et al. (2007) proposed that the evaluation of evidence-based interventions in practice can be called “type 3 translation research.” As shown in Table 39.2 we have also extended (Khoury et al., 2007b) the translation pathway

Focus on prevention

Public health sciences

Discovery

T1/T2/T3/T4

Delivery

Assessment policy assurance evaluation

Knowledge integration

Figure 39.1 A framework for medicine-public health partnership in the genomics era (Adapted from Khoury et al., 2007a, b).

to type 4 translation (T4) research, which seeks to evaluate the “real world” health outcomes of a genomic application in practice. We have reported that most human genomics research between 2001 and 2006, inclusive, was not translational and have estimated that no more than 3% of published research focuses on T2 and beyond. Indeed, evidence-based guidelines and T3 and T4 research currently are rare (Khoury et al., 2007b). As discussed below, an enhanced focus on translation and translation research from T1 through T4 can foster a true partnership between medicine and public health in the genomics era in four major areas (see Figure 39.1).

The Population Perspective: Crucial Role of Public Health Sciences

THE FOCUS ON DISEASE PREVENTION AND HEALTH PROMOTION Advances in genomics will provide new opportunities for disease prevention and health promotion – the main focus of public health, regardless of whether it is delivered at the individual level or through population-wide interventions. Understanding genetic effects and gene–environment interactions in disease processes could lead to the recommendation that certain subgroups avoid defined exposures or receive targeted interventions. Stratification by genotype or family history already provides a means for tailoring screening tests for early disease detection (e.g., colorectal cancer screening in genetically susceptible persons), a paradigm likely to be extended to early detection of other conditions. A review of the public health implications of genomic research related to asthma (UWCGPH, 2004) illustrates the potential opportunities and challenges for translating new knowledge into improved prevention and treatment of a common disease. Strong evidence supports a causal role for both genetic and environmental factors. Genomics research has identified numerous gene loci associated with asthma, and further studies of biological pathways associated with asthma, are likely to yield new approaches to prevention and therapy. The earliest clinical applications will be in pharmacogenomics, using genetic information to optimize therapy and prevent adverse events. The path for translating results of genomic research to population-level interventions will not always lead through genetic testing and knowledge of individual genotypes. For example, a study in Mexico of children with asthma found that supplementation with the antioxidant vitamins C and E improved lung function in children with a common polymorphism of glutathione S-transferase M1 (GSTM1) who are exposed to ozone (Romieu et al., 2004). These findings might suggest a simple intervention – antioxidant vitamin supplementation – for children with asthma who are exposed to ozone. Without genotype-specific analysis, a potentially important population-level intervention could have been overlooked. New gene discoveries are reported daily and have initiated dialog on the value of genetic information with respect to prevention. Consider, for example, the discovery in 2006 that a variant of the TCFT7L2 gene is associated with increased risk of type 2 diabetes (Grant et al., 2006). This finding was noteworthy for several reasons. First, type 2 diabetes is a serious disease and a major public health problem. Family history is an important risk factor but until now, few genetic associations have been identified. The investigators replicated the association with TCF7L2 in three independent populations and presented molecular evidence that the gene product is a high mobility group boxcontaining transcription factor related to blood glucose homeostasis. The gene product may act through regulation of proglucagon gene expression via the Wnt signaling pathway (Grant et al., 2006). The senior investigator commented to the New York Times that a practical consequence of the discovery could be a diagnostic test to identify people who are at increased risk of type 2 diabetes. He speculated that “these people, knowing their

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risk, would be motivated to exercise more and adopt a healthier diet” (Jannssens et al., 2006). However, a relatively simple analysis is sufficient to show that a test for TCF7L2 variants by itself would have very poor predictive value, adding little to the risk information already provided by family history, and would have limited utility for prevention (Jannssens et al., 2006). In particular, the hypothesis that genetic risk information of this kind will motivate behavioral change remains to be tested. Once a genetic variant is discovered, producing a molecular test to detect it is theoretically straightforward. However, the clinical validity of such a test is highly dependent on population characteristics; these include not only prevalence of the genetic variant and the strength of its association with disease, but prevalence of the disease and interactions between this genetic variant and many other risk factors. Clinical utility is even more difficult to establish because it depends on the availability of a specific, effective intervention that adds value to existing practice. Although most gene discoveries for common diseases are not ready for prevention applications, they have, nevertheless, initiated a common interest in medical and public health to develop a common framework for how such discoveries can be evaluated for their potential applications for disease prevention.

THE POPULATION PERSPECTIVE: CRUCIAL ROLE OF PUBLIC HEALTH SCIENCES Gene discovery and characterization is a basic science enterprise. Gene discovery typically occurs in highly selected patient groups and the associations observed in such groups may not be generalizable to the population as a whole. This is where the population perspective is crucial. Indeed, Omenn (2002) described a “golden age” for the public health sciences which include epidemiology, biostatistics, environmental health, health education, behavioral and social sciences, and many other fields. As predicted by Kerr White at the start of the Human Genome Project, “molecular biology, especially once the human genome is mapped, must surely turn increasingly to the study of populations” (White, 1991), seeking the partnership of epidemiologists. In fact, we are seeing a convergence of genetic and epidemiologic methods beginning to take the field of genetic epidemiology beyond gene discovery. As discussed in Chapter 40 human genome epidemiology is a multidisciplinary field that uses systematic application of epidemiologic methods in studies of human genetic variation in association with health and disease in populations. Using the previous example of the TCF7L2 gene and type 2 diabetes gene discovery above, a population perspective would require an evaluation of whether or not a specific intervention or prevention strategy can be developed beyond the universal importance of healthy lifestyle in the prevention of type 2 diabetes. Even if this is the case, additional research will be needed to assess the prevalence of high-risk variants in the populations where testing is considered, a population-based estimate of risk, information about modification of risk by other genetic and environmental factors and behavioral

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and social science research to determine how genetic information can be used, if at all, to promote behavior change (Ames and McBride, 2006). Because of the well known difficulties of implementing behavior change for the prevention of common diseases, it is not clear whether or not adding genetic information such as the diabetes genetic risk would be helpful or detrimental.

THE ROLE OF KNOWLEDGE INTEGRATION ACROSS DISCIPLINES As discussed in Chapter 38 knowledge integration is at the heart of public health genomics. The translation of genomic discoveries from the bench to the bedside is a long and arduous process that requires accumulation and synthesis of knowledge in many fields, including observational epidemiologic studies on gene-disease associations, gene–environment interactions, and clinical trials of efficacy of general and genotype-specific interventions. Because of the proliferation of information on genomics and health (e.g., more than 25,000 articles on gene-disease associations have been published in the past 6 years, Lin et al., 2006), evidence must be integrated systematically before “discovery” can lead to “delivery.” False leads and blind alleys have to be eliminated through an iterative process of evidence-based information synthesis, such as efforts of the Human Genome Epidemiology Network, which promotes systematic reviews of gene-disease associations (CDC, 2007a). An enhanced partnership between medicine and public health will be essential in the knowledge integration process given the transdisciplinary nature of research that leads to genetic discoveries and their potential applications. By its nature, it will require a close collaboration of basic sciences, clinical sciences, public health and social sciences. This process will lead to evidence-based guidelines on appropriate use of genetic information in health practice, such as ones provided by the Cochrane collaboration (Grimshaw et al., 2006), US Preventive Services Task Force (Harris et al., 2001); and the Guide to Community Preventive Services (CDC Community Guide, 2007).These efforts examine on a regular basis what works and what does not work in health services to achieve health impact for individuals, families, and populations. An extension to these efforts in the United States is the CDC-led Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative, which focuses exclusively on the utility of genomic applications and family history in clinical practice and disease prevention (CDC, 2007b).

THE ROLE OF HEALTH SERVICES RESEARCH AND POPULATION HEALTH ASSESSMENT, ASSURANCE, AND EVALUATION Like other challenges in translation, genomics can hit a roadblock if no investments are made in evaluating the best ways to assure

delivery and monitor effectiveness and safety of gene-based interventions, whether they are population screening programs like newborn screening, or early case detection and interventions delivered by clinicians. This step requires a crucial partnership between medicine and public health. Beyond basic research, efficacy research, and development of practice guidelines, ensuring access depends on adoption by health care providers of practice guidelines, and acceptance by consumers. All of these factors contribute to the effectiveness of interventions in the real world. Many of the challenges have been articulated before, including public participation, information systems, workforce training, and funding (Crowley et al., 2004). Models of research-practice partnerships have been proposed to overcome some of the current difficulties, such as the national public–private partnership research enterprise (Crowley et al., 2004). As Zerhouni and others are predicting, the advent of genomics-based healthcare delivery will require research on the best ways to integrate new knowledge into practice, achieving translation for best results from the population perspective (Lenfant, 2003). As a major new research area with real promise (as well as commercial potential), genomic medicine may offer a new opportunity to shine the spotlight on translation because it is grabbing attention, represents a major new research area, has commercial potential, and offers genuine promise – but its applications are readily generalizable to other areas of health. For example, HMO-based research (Mouchawar et al., 2005) and general provider surveys (Myers et al., 2006) were collaboratively conducted to assess the impact of a direct-to-consumer campaign for BRCA1 testing on providers’ attitude, knowledge, behaviors, and practices. Another example is the collaboration between providers, consumers, professional organization and various government agencies to assess and increase the public’s awareness and use of family history as an additional tool for disease prevention and public health (CDC, 2007c; DHHS, 2007). Clearly, such efforts need to be sustained and extended into emerging areas like pharmacogenomics and genetic tests in general (Davis and Khoury, 2006). Among others, these applications should include economic evaluations of gene-based tests and interventions which are increasingly important in the face of ever escalating health care expenditures. Invariably, translation efforts will uncover gaps in our knowledge base on genes and health, and in the processes of health care delivery and population health outcomes. With the current expanding use of genetic information in practice without any real oversight, a medicine-public health partnership will be essential for identifying these gaps, further shaping the research agenda, and keeping practitioners, consumers and policy makers abreast of the best applications of scientific discoveries for population health impact.

CONCLUSION Advances in genomics – especially in relation to common diseases – are leading to increasing interaction and interdependence

References

between the traditional health care delivery system, which focuses on treatment of individuals, and the public health system that focuses on prevention and control in populations. This closer interaction is creating a shared “population health” focus on using genomic advances appropriately and effectively to promote health and prevent disease. Because the field of genomics is still in its infancy, this is a crucial time for medicine and public health to get together to develop a strong partnership with a joint focus on prevention, population sciences, knowledge translation and health

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services research and outcome monitoring. Emerging genomic information is presenting policy makers, practitioners, and researchers with a new opportunity and new urgency to bridge the divide for the benefit of population health. Dr James Marks from the Robert Wood Johnson Foundation commented that “no important health problem will be solved by clinical care alone, or research alone, or by public health alone but rather by all public and private sectors working together” (Marks, 2005). We hope the chapters in this section can begin to explore this enhanced collaboration in the era of genomic medicine.

REFERENCES Ames, S.L. and McBride, C. (2006). Translating genetics, cognitive science, and other basic science research findings into applications for prevention. Eval Health Prof 29, 277–301. Centers for Disease Control and Prevention Community Guide (2007). Evidence-based recommendations for programs and policies to promote population health. Accessed online on March 18, 2007 at: http://www.thecommunityguide.org/ Centers for Disease Control and Prevention (2007a). The Human Genome Epidemiology Network. Accessed online March 19, 2007 at: http://www.cdc.gov/genomics/hugenet/default.htm Centers for Disease Control and Prevention (2007b). Evaluation of Genomic Applications in Practice and Prevention. Accessed online March 19, 2007 at: http://www.cdc.gov/genomics/gtesting/egapp. htm#topics Centers for Disease Control and Prevention (2007c). Family history public health initiative. Accessed online March 19, 2007 at: http:// www.cdc.gov/genomics/activities/famhx.htm Crowley, W.F., Sherwood, L., Salber, P. et al. (2004). Clinical research in the United States at a crossroads: Proposal for a novel publicprivate partnership to establish a national clinical research enterprise. JAMA 291, 1120–1126. Davis, R.L. and Khoury, M.J. (2006). A public health approach to pharmacogenomics and gene-based tests. Am J Pharmacogenomics 7, 331–337. Department of Health and Human Services (2007). US Surgeon General’s family history initiative. Accessed online March 19, 2007 at: http:// www.hhs.gov/familyhistory/ Grant, S.F., Thorleifsson, G., Reynisdottir, I. et al. (2006). Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 38, 320–323. Green, L.A. and Seifert, C.M. (2005). Translation of research into practice: Why we can’t just do it. J Am Board Fam Pract 18, 541–545. Grimshaw, J.M., Santesso, N., Cumpston, M., Mathew, A. and McGowan, J. (2006). Knowledge for knowledge translation: The role of the Cochrane Collaboration. J Contin Educ Health Prof 26, 55–62. Guttmacher, A.E. and Collins, F.S. (2002). Genomic medicine – a primer. N Engl J Med 347, 1512–1520. Harris, R.P., Helfand, M., Woolf , S.H. et al. (2001). Current methods of the US Preventive Services Task Force: A review of the process. Am J Prev Med 3(Suppl), 21–35. Horig, H. and Pullman, W. (2004). From bench to clinic and back: Perspectives on the 1st IQPC translational research conference. J Trans Med 2, 44.

Institute of Medicine (2003). Who Will Keep the Public Healthy? Educating Public Health Professionals for the 21st Century. National Academies Press,Washington, DC. Institute of Medicine (2002). The Future of the Public’s Health in the 21st Century. National Academies Press,Washington, DC. Jannssens, A.C.W., Gwinn, M.L., Valdez, R. et al. (2006). Predictive genetic testing for type 2 diabetes may raise unrealistic expectations. BMJ 333, 509–510. Khoury, M.J. (2003). From genetics to genomics in practice: The continuum from genetic disease to genetic information in health and disease. Genet Med 5, 261–268. Khoury, M.J., Gwinn, M., Burke, W., Bowen, M.S. and Zimmern, R. (2007a). Will genomics widen or heal the schism between medicine and public health. Am J Prev Med 33, 310–317. Khoury, M.J., Gwinn, M., Yoon, P.W., Dowling, N., Moore, C.A. and Bradley, C.A. (2007b). The continuum of translation research in genomic medicine how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?. Genet Med 9, 665–674. Lenfant, C. (2003). Shattuck lecture: Clinical research to clinical practice – lost in translation?. N Engl J Med 349, 868–874. Lin, B., Clyne, M., Walsh, M., Gomez, O., Yu, W., Gwinn, M. and Khoury, M.J. (2006). Tracking the epidemiology of human genes in the literature: The HuGE published literature database. Am J Epidemiol 164, 1–4. Marks, J.S. (2005). Preventive care – the first step. Manag Care 14(Suppl), 10–12. Mouchawar, J., Hensley-Alford, S., Laurion, S. et al. (2005). Impact of direct-to-consumer advertising for hereditary breast cancer testing on genetic services at a managed care organization: A naturallyoccurring experiment. Genet Med 7, 191. Myers, M.M., Chang, M.H., Jorgensen, C. et al. (2006). Genetic testing for susceptibility to breast and ovarian cancer: Evaluating the impact of a direct-to-consumer marketing campaign on physicians’ knowledge and practices. Genet Med 8, 361–370. Omenn, G.S. (2002). The crucial role of the public health sciences in the postgenomic era. Genet Med 4(suppl), 21S–26S. Rohrbach, L.A., Grana, R., Sussman, S. and Valente, T.W. (2006). Type II translation: Transporting prevention interventions from research to real world settings. Eval Health Prof 29, 302–333. Romieu, I., Sienra-Monge, J.J., Ramirez-Aguilar, M. et al. (2004). Genetic polymorphism of GSTM1 and antioxidant supplementation influence lung function in relation to ozone exposure in asthmatic children in Mexico City. Thorax 59, 8–10.

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University of Washington Center for Genomics and Public Health (UWCPGH) (2004). Asthma genomics: Implications for public health. 2004. Accessed online on March 15, 2007 at: http://depts. washington.edu/cgph/workinggroups/Asthma%20Genomics_Im plications%20for%20Public%20Health.pdf Westfall, J.M., Mold, J. and Faqnan, L. (2007). Practice-based research – blue highways on the NIH road map. JAMA 297, 403–406.

White, K.L. (1991). Healing the schism: Epidemiology, medicine, and the public’s health. Springer-Verlag, New York. Zerhouni, E.A. (2003). The NIH Roadmap. Science 302, 63–72. Zerhouni, E.A. (2005). Translational and clinical science: Time for a new vision. New Engl J Med 353, 1621–1623.

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40 Principles of Human Genome Epidemiology Marta Gwinn and Muin J. Khoury

INTRODUCTION Early forecasts for genomic medicine underestimated the challenge of translating advances in basic science into new applications for improving health (Collins, 1999; Hoffman, 1994). The genetic architecture of human health and disease arises from interactions among multiple genes and environmental factors over the course of a lifetime and thus is far more complex than the genome itself. In this context of complex causation, the effects of single factors – including genetic variants – are difficult to detect and interpret (Ioannidis et al., 2006b). Current research approaches are often inconsistent, hindering attempts to synthesize the available evidence. Successful translation now hinges on establishing new norms for integrating the results of biologic and epidemiologic research and for bridging individual and population-level interpretations. In this chapter, we present the perspective of human genome epidemiology, which promotes the use of epidemiologic methods to study the distribution of genetic variants in populations, along with their associations with disease, their interactions with other factors, and their value for informing interventions. Human genome epidemiology is concerned with genetic variation – whether measured at the level of DNA sequence, gene expression (transcriptomics), or protein analysis (proteomics) – and more broadly with “omic” biomarkers based on modern quantitative biology (Thomas, 2006). The principles

of human genome epidemiology include population-based sampling, sound epidemiologic study design and analysis, emphasis on gene–environment interactions, careful attention to potential errors and biases, and a commitment to systematic review and integration of biological information (Little et al., 2003). Human genome epidemiology aims to provide a scientific basis for using genetic information to prevent, diagnose, and treat disease. This chapter provides an overview of human genome epidemiology and its role in translating genomic research results into an evidence base for medical and public health practice.

HUMAN GENOME EPIDEMIOLOGY Genetics and epidemiology share a common approach, using quantitative methods to examine the variation of normal and pathological traits in human populations. Until recently, however, the intersection of genetics and epidemiology was narrowly defined and was focused almost exclusively on discovering genetic “causes” of traits and diseases by studying familial aggregation. Developments in genetic epidemiology and statistical genetics have mostly centered on methods for discovering disease susceptibility genes in families with a clear pattern of inheritance (Burton et al., 2005) (described in other chapters). This approach has identified mutations in more than 10,000 genes associated with Mendelian disorders (e.g., cystic fibrosis), as well as high-penetrance

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genetic variants associated with certain common diseases (e.g., breast cancer) in families with multiple affected members (National Library of Medicine [OMIM]). Typically, the variants identified in such families explain only a small proportion of all cases; for example, BRCA1 and BRCA2 mutations account for only an estimated 1–2% of all breast cancer cases in the general population (McClain et al., 2005). During the past 10 years, the search for genetic factors in common diseases has relied increasingly on genetic association studies. Association studies compare the distribution of one or more genetic variants in persons with or without a disease, either related (e.g., affected child and unaffected parents) or unrelated (Hattersley and McCarthy, 2005). Family-based study designs and methods are reviewed in other chapters as well as in several articles in a valuable series published in The Lancet (Cordell and Clayton, 2005; Palmer and Cardon, 2005;Teare and Barrett, 2005). Association studies that compare the frequency of genetic variants in unrelated persons with or without disease are fundamentally identical to traditional epidemiologic studies. They can be analyzed by using many of the same methods and are subject to the same types of limitations, errors, and biases (Cordell and Clayton, 2005; Hattersley and McCarthy, 2005). The value of genetic association studies for discerning useful gene–disease relationships is still a matter of debate (Davey Smith et al., 2006). Although many such studies aim to detect a novel gene–disease association, many more attempt to replicate previously reported findings. Much of the frustration with association studies stems from their poor track record when it comes to replication. Reasons for this phenomenon include not only the complexity of gene–disease relationships but also methodologic shortcomings in the conduct, analysis, and reporting of the studies themselves (Davey Smith et al., 2006; Ioannidis et al., 2001). In addition to improved research approaches, the field can benefit from more rigorous application of established methods. The essential characteristics of a good association study were recently reviewed by Hattersley and McCarthy (2005). Beyond gene discovery, which is the traditional domain of genetic epidemiology, human genome epidemiology is primarily concerned with characterizing the effects of genetic factors in populations (Khoury et al., 2004a). The main objectives of human genome epidemiology are to measure the prevalence of genetic polymorphisms in well-defined populations, characterize genotype–phenotype associations, investigate gene– environment interactions, and evaluate the clinical validity and utility of genetic tests.

EPIDEMIOLOGIC STUDY DESIGNS Population-based study design is a benchmark in epidemiologic research, but the term is often misapplied. Strictly speaking, a study is population-based only if the subjects are representative of a population defined by geopolitical boundaries and demographic characteristics. In general, the results of a well-conducted

study provide a basis for inference only to the population from which the study sample was drawn. Nevertheless, studies based on a well-defined population subgroup (e.g., defined by demographic, personal, or clinical criteria) may offer useful insights into larger groups that share similar characteristics. Most epidemiologic studies are based on cross-sectional, cohort, or case–control designs. Each design is useful for addressing particular aspects of genetic variation in relation to disease outcomes. Cross-sectional studies ascertain risk factors and disease status at the same time. Cross-sectional studies are relatively quick and inexpensive, and they are often used to suggest avenues for further epidemiologic studies. Such studies can be used to estimate the prevalence of genetic variants (although variants associated with decreased survival may be underrepresented). Crosssectional studies may be unable to distinguish between the effects of genetic factors on disease occurrence or on sequelae and survival. However, because a person’s genotype remains the same throughout life, a cross-sectional study shares characteristics with a retrospective cohort study when genotypes are the only factors analyzed. Cohort studies follow two or more groups of people with different risk factors over time (either prospectively or retrospectively) looking for the development of disease. Although they are useful for studying rare exposures and describing natural history, cohort studies are usually lengthy, expensive, and not well suited to examining rare outcomes. Cohort studies yield a direct estimate of disease incidence or cumulative incidence, which can be interpreted in terms of relative and absolute risk. “Experimental” cohort studies (randomized, controlled trials) are ideal for evaluating the effects of gene–environment interactions or specific interventions, but they are often not feasible; “Mendelian randomization” has been proposed as an alternative approach (see “Gene–Environment Interaction”) (Davey Smith and Ebrahim, 2003). Retrospective cohort studies are attractive because genetic information is invariant and can be measured long after the study has ended; however, they are subject to the usual biases introduced by non-random selection of participants, exposures, or interventions. Although cohort studies are expensive and time-consuming, there is increasing global interest in conducting them under the umbrella of population biobanks (see Chapter 24) to study the effects of genes, environments, and their interactions on various disease outcomes throughout the lifespan (Austin et al., 2003; Collins, 2004). Case–control studies compare groups of persons with disease (cases) and without disease (controls) on the basis of risk factors that are ascertained retrospectively. Case–control studies are useful for studying multiple risk factors and rare outcomes, but not for studying rare risk factors. Case–control studies can generally measure gene–disease associations more quickly, efficiently, and at lower cost than cohort studies. Case–control or case–cohort studies can also be “nested” within existing cohort studies or population-based biobanks, an approach that combines advantages of both case–control (efficiency, cost) and cohort (causal inference, risk estimation) study designs.

Epidemiologic Measures of Disease Frequency, Association and Risk

TABLE 40.1

Characteristics

Measures of disease frequency

Cross-sectional

Persons assessed simultaneously for disease and risk factors

●

Persons with and without risk factors are followed up for disease

●

Case–Control

Persons with and without disease are studied for risk factors

●

●

Epidemiologic methods are based on comparing disease frequency in different groups. Measures of disease frequency in epidemiologic studies include the following:

●

●

Prevalence

Measures of association ● ●

EPIDEMIOLOGIC MEASURES OF DISEASE FREQUENCY, ASSOCIATION, AND RISK

●

463

Characteristics, measures of disease frequency, and measures of association in epidemiologic studies

Study design

Cohort

■

Incidence rate: The number of new (incident) cases of disease occurring during a defined time period, divided by the sum of all person-time at-risk during that period (expressed as cases per unit person-time). Cumulative incidence: The number of new cases of disease occurring during a defined time period, divided by the number of people under observation at the beginning of the time period; this measure corresponds to the risk or probability of developing disease, to the attack rate in traditional epidemiologic studies of infectious diseases, and to penetrance in genetics. Prevalence: The number of persons with disease at a specific point in time, which is a function of incidence and disease duration.

Measures of disease frequency and measures of association for cross-sectional, cohort, and case–control study designs are summarized in Table 40.1. The most basic analysis of crosssectional, cohort, and case–control studies can be represented as a 2  2 table, in which a given risk factor and disease are either present or absent (Table 40.2). Relationships among the elements of this table are the basis for estimating key parameters, including measures of risk (rates and ratios). Human genome epidemiology aims to estimate the contribution of genetic variants to the health of individuals and populations by estimating the associated absolute, relative, and attributable risks. Absolute risk is the probability that persons with a particular characteristic (e.g., a specific genotype) will develop disease. Absolute risk is estimated from cumulative incidence of disease in a well-defined population with the relevant characteristic. Only cohort studies can provide a direct estimate of absolute risk related to environmental exposures (or gene–environment

Incidence Cumulative incidence None

TABLE 40.2

Prevalence difference Relative prevalence (rate ratio)

●

Risk difference Relative risk (risk ratio) Odds ratio

●

Odds ratio

● ●

Measures of association in a 2  2 table Disease

Risk factor

Present

Absent

Total

Present

A

B

AB

Absent

C

D

CD

Total

AC

BD

A  BC  D

E  proportion of persons exposed to risk factor who have disease  A/(A  B). U  proportion of persons unexposed to risk factor who have disease  C/(C  D). Difference measure: E  U  A/(A  B)  C/(C  D) Ratio measures: (Rate ratio, risk ratio, or prevalence ratio): E/U  [A/(A  B)]/[C/(C  D)] Odds ratio: E/(1  E)/U/(1  U)  AD/BC

interactions). Even though genotype is invariant, analyzing cross-sectional data by genotype as a retrospective cohort is problematic; in particular, the age distribution of the population must be taken into account. Measures of relative risk include the rate ratio, risk ratio, and prevalence ratio (Table 40.2). These ratios are calculated in the same way but inference depends on the study design. Cohort studies support direct estimation of relative risk. In case–control studies, the odds of exposure (or genotype) among cases, divided by the odds of exposure (or genotype) among controls, is identical to the odds of disease in cohort studies. Relative risk can be estimated from case–control studies by the odds ratio, which approximates the relative risk as long as the disease is rare or controls are sampled randomly (independent of disease status or genotype) from the source population (Rothman and Greenland, 1998). Two reasons why the odds ratio has become increasingly popular in epidemiologic studies are that not only can it be obtained from cross-sectional, cohort, and case–control studies but also it is amenable to common statistical techniques for multivariate analysis (e.g., logistic regression). Population attributable fraction (a measure of attributable risk) is the overall contribution of a particular risk factor (e.g., genotype)

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to the occurrence of disease in a given population. The term refers to the fraction of cases that would not have occurred within a certain time period had the risk factor been absent. Several formulas for calculating the attributable fraction have been proposed; one common formula, proposed by Miettinen, is: population attributable fraction  f c (R  1)/R where fc is the fraction of cases with the risk factor, and R is the measure of relative risk (Miettinen, 1974). Attributable fraction is not a straightforward concept in the context of complex interactions among genetic and environmental factors that are poorly understood (Vineis and Kriebel, 2006). The epidemiologic concept of attributable fraction needs to be differentiated from the genetic concept of heritability. Heritability, a measure derived from analysis of variance or correlation, is sometimes used to describe the extent to which genotypic variation appears to explain phenotypic variation in a particular population. Heritability estimates are often based on studies of closely related persons (e.g., twins or other siblings), and they are highly dependent on context. They are not useful for determining the genetic or environmental contributions to disease or for estimating attributable fraction (Burton et al., 2005).

MEASUREMENT AND BIAS A well-conducted epidemiologic study first defines the sampling frame or target population from which participants will be selected (see “Epidemiologic Study Designs”). Population-based sampling is relatively straightforward in those few countries providing their people with universal health care and unique personal identifiers. In other areas, geographically defined disease registries offer a starting point (Goodman et al., 2005). The defining characteristic of a population-based case–control study is a set of a priori criteria – applied to the selection of controls as well as cases – that specifies the study’s source population (e.g., by geographic area and ethnicity). Systematic differences between cases and controls on such characteristics can introduce confounding and produce spurious results (Colhoun et al., 2003). Confounding introduced by selecting cases and controls with different genetic backgrounds (e.g., ancestry, race, or ethnicity) is termed population stratification. The potential effects of population stratification have been widely discussed, and several different statistical approaches have been proposed to test or control for it (Marchini et al., 2004). Studies comparing the genotypes of patients in a clinical case series with those of an undefined convenience sample of control subjects are numerous in the published literature, but they provide little basis for inference. An epidemiologic case definition is analogous to the genetic concept of phenotype. The case definition uses explicit, observable criteria to classify cases and non-cases. The case definition may include a time dimension, such as disease stage or diagnosis by a given age. Imprecise case criteria that disregard potentially important phenotypic differences, such as age at onset or key

clinical manifestations, can lead to case heterogeneity and misclassification. On the other hand, very narrow selection criteria limit the sample size available for study. Thus, the investigator faces a trade-off between specificity and power (Hattersley and McCarthy, 2005). If sufficiently detailed clinical data are collected systematically and the sample size is large enough, cases can be further stratified for analysis on the basis of phenotypic subgroup. Candidate genes are often selected for analysis on the basis of information from prior studies of gene function (in humans or animals) or association, although other data may also be available (e.g., from pharmacologic studies) (Hattersley and McCarthy, 2005). Because the molecular pathways underlying human health and disease are not well understood, candidate gene selection is as much art as science. Even within a candidate gene, particular variants (most often single nucleotide polymorphisms, or SNPs) must be selected for typing because determining the entire sequence in large numbers of study participants is still too expensive. Ideally, variants are selected because they are known to alter the structure and function of gene products; however, the functional effects of sequence variation are largely unexplored, especially in non-coding regions (e.g., regulatory elements, splice sites) (Todd, 2006). As increasingly powerful, fast, and inexpensive methods for genotyping become available, researchers are able to scan the entire genome for associations, raising the stakes for strategic SNP selection (Thomas et al., 2005). The International HapMap Project has approached this problem by using linkage disequilibrium to identify a reduced set of “tag” SNPs for capturing variation throughout the genome (International HapMap Consortium, 2005). Several platforms for whole-genome association studies have been evaluated by comparison with data from HapMap (Di et al., 2005; Gunderson et al., 2006). Genome-wide association studies are now demonstrating the potential to identify novel genetic variants associated with common, complex diseases (Manolio et al., 2008). New gene-chip technologies based on HapMap tag SNPs allow cost-efficient scanning of thousands of genetic variants in large numbers of people. For example, the Wellcome Trust Case Control Consortium has analyzed 500,000 genetic variants in approximately 2000 people affected with each of seven common diseases and in 3000 healthy people who served as control subjects (Wellcome Trust Case Control Consortium, 2007). The novel associations that they uncovered had modest effect sizes (i.e., per-allele odds ratios of 1.2–1.5); the sample sizes needed to measure such small effects are in the thousands (Khoury et al., 2007). Published reports of association studies often fail to address the potential effects of genotyping error, which can never be entirely eliminated even in the best laboratories. When random, the resulting misclassification reduces study power; however, systematic genotyping error can also introduce bias. Systematic errors may occur when cases and controls are genotyped in different laboratories or even in different batches in the same laboratory (Davey Smith et al., 2006). In studies of complex diseases, even true associations are likely to be small, and genotyping error can easily produce spurious associations of

Gene–Environment Interaction

similar magnitude (Hattersley and McCarthy, 2005; Ioannidis et al., 2006b). Until more journals require authors of association studies to discuss the performance characteristics of their genotyping assays and quality control measures, genotyping error must be considered as a potential explanation for virtually any reported association (Bogardus et al., 1999; Little et al., 2002). In the absence of a strong rationale for selecting or prioritizing candidate genes and variants, the association study that examines multiple genotypes for association with a disease is similar to an epidemiologic “fishing expedition.” In the setting of multiple comparisons, statistically significant associations are likely due to chance alone, resulting in type 1 error. No clear consensus exists on how to handle this problem. The goal of this type of study is to find true associations, all with a low (usually unknown) prior probability. A proposed approach that aims to optimize the balance between true-positive and false-positive findings is closer in outlook to screening than hypothesis testing (Storey and Tibshirani, 2003). In genetic association studies – whether they are candidate gene analyses or genome-wide association studies – small effects are to be expected (Ioannidis et al., 2006b). With rare exceptions, associations that are consistently replicated have relative risks on the order of 1.1–2.0 (Hattersley and McCarthy, 2005;Todd, 2006). Many association studies are conducted without sufficient power to detect effects of this size. Meta-analysis of published data is one approach that has proved useful, especially when several well-conducted studies have used the same case definition and genotyping methods to study an association in similar populations (GarciaClosas et al., 2005; Ioannidis et al., 2001). Meta-analysis of individual participant data (pooled analysis) requires a much greater commitment of time and resources than traditional meta-analysis of published data; however, it also has substantial advantages, including the ability to standardize definitions of cases and phenotypic subgroups and to achieve better control of confounding (Ioannidis et al., 2002). Joint efforts on the pooled analysis may create a focus for prospective collaboration to update the meta-analysis and conduct other collective analysis projects (see “Building the Evidence Base”) (Ioannidis et al., 2006a). Improving power and allowing examination of between-study heterogeneity are potential benefits of meta-analysis even in the setting of genome-wide association studies (Evangelou et al., 2007).

GENE–ENVIRONMENT INTERACTION As the basic science of public health, epidemiology aims to identify environmental causes of disease that are amenable to intervention. Much research in epidemiologic methods has focused on problems of measurement, analysis, and bias in evaluating the relationships of environmental factors with health outcomes. Nevertheless, because the physiologic and behavioral changes they bring about are dynamic, environmental exposures present far more difficult measurement and analysis challenges than simple genotypes. Case–control studies are particularly problematic

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because exposures are assessed only after disease has developed in cases, often many years after the exposure actually occurred. So far, only a small fraction of published research in human genome epidemiology includes analysis of gene–environment interactions (Lin et al., 2006). Often, the results are presented in formats that do not permit evaluation of the marginal and joint effects of genetic and environmental factors. In the simplest case, this evaluation can be accomplished by using a “two-by-four table.” A simple gene–environment interaction model in the context of a cohort study is described in Table 40.3. Stratified analysis to estimate the risk of disease in persons with only environmental risk factor, with only the genetic factor, or with both can clarify whether gene–environment interactions exist; however, sample size often becomes the limiting factor in such analyses. For example, to detect with 80% power a strong interaction (twofold risk) between a genotype and exposure, each with 10% prevalence in the population, would require 1500 cases and the same number of controls (Hoover, 2007). For less common atrisk genotypes – especially those composed of variants at multiple loci, such as those proposed for “genomic profiling” – the necessary sample size is much larger. The case-only study has been proposed as an exploratory method for identifying interaction in a case series (Gatto et al., 2004; Khoury and Flanders, 1996). Under a multiplicative model of interaction where the genotype and exposure are independent, a departure of the case-only odds ratio from 1.0 indicates the presence of gene–environment interaction (Table 40.3). The case-only study is limited, however, because it cannot evaluate effects of genotype or exposure alone. Interactions observed at the population level do not translate easily into models of pathogenesis within individuals (Vineis

TABLE 40.3 A simple gene–environment interaction model in a cohort study Genotype

Environmental exposure

Disease risk

Relative risk

Absent

Absent

I

1

Absent

Present

IRe

Re

Present

Absent

IRg

Rg

Present

Present

IRge

Rge

I  background risk of disease in persons without genotype or environmental exposure. Rerelative risk in persons with environmental exposure but not genotype. Rgrelative risk in persons with genotype but not environmental exposure. Rgerelative risk in persons with both genotype and environmental exposure. Re, Rg and Rge can also be estimated as odds ratios in a case–control study, where the reference group has neither the genotype nor the exposure. Two common statistical models of interaction are: 1. Additive model, where Rge  Rg  Re  1 and 2. Multiplicative model, where Rge  Rg  Re In a case-only study: ORcase-only  ORge/(ORe  ORg)  1 when there is no multiplicative gene–environment interaction.

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and Kriebel, 2006). However, Davey Smith and others have proposed the concept of “Mendelian randomization” as a way to use biological information to strengthen the evidence for the causal role of environmental factors from observational studies (Clayton and McKeigue, 2001; Davey Smith and Ebrahim, 2003). The basic idea is that if a genetic variant has a well-characterized biological function that imitates or reverses the effect of an environmental risk factor, then that variant should also have a predictable association with disease risk. The assumption that genotype is “randomized” at birth (i.e., independent of environmental exposure) provides a less-biased framework for interpreting observational data on gene–environment interactions. This approach could have important implications for traditional risk-factor epidemiology, in which the seemingly robust findings of case–control studies all too often prove illusory when tested in clinical trials (Davey Smith et al., 2006). At present, the further investigation and application of Mendelian randomization as an epidemiological tool is limited by incomplete understanding of human pathobiology, as well as by inadequacy of the knowledge base on gene–disease association (Davey Smith et al., 2006; Little et al., 2003). Advances in both areas will lead to additional opportunities to test the utility of this approach.

PROBABILITY AND PERSONALIZED MEDICINE Genomics research enjoys considerable public support because of its promise to deliver health benefits, including new therapies tailored to individual genotypes (Personalized Medicine Coalition, 2007). The concept of “personalized medicine” both inspires and frustrates researchers although, ironically, the most extreme optimistic and pessimistic viewpoints tend to stem from the same misconception: that “genomic tests” should be as predictive as the conventional genetic tests used to diagnose hereditary disorders resulting from single, high-penetrance gene variants (Collins and McKusick, 2001; Holtzman and Marteau, 2000). Indeed, this paradigm is out of date even for classic “single gene disorders” such as cystic fibrosis, in which DNA sequencing is revealing increasingly diverse genotype–phenotype relationships (Cutting, 2005). As observed by critics of individualized general preventive medicine, few risk factors for common chronic diseases have sufficient predictive ability to serve as screening tools (Wald et al., 1999); in this respect, common polymorphisms associated with disease susceptibility are unlikely to be different. Most risk estimates useful to individuals will be obtained only by considering the joint effects of many factors, including health history, personal characteristics, environmental exposures, and behaviors. This revelation surely comes as no surprise either to most medical practitioners, who are accustomed to integrating imperfect data from multiple sources to arrive at a clinical judgment, or to health care managers,

who strive to base decisions on evidence-based, predictive models (Bianchi and Alexander, 2006; Sipkoff, 2005). Whether genetic information adds value to prediction models based on clinical characteristics is an empirical question; one approach to evaluating it is by analyzing receiver operating characteristics (ROC) curves (Janssens et al., 2006). One proposed approach to predictive genomic testing is based on assessing the joint effects of multiple genes by “genomic profiling.” Although theoretically feasible, the evidence base for such applications is almost completely lacking (Haga et al., 2003; Khoury et al., 2004b). “Proteomic profiles” based on mass spectrometry have also been proposed as predictive tests, although their validity is even more difficult to establish (Myers et al., 2006). As large-scale genotyping and other sophisticated laboratory methods become increasingly available, technology is no longer the greatest challenge; instead, it is epidemiology (Khoury et al., 2004a). Functional information on gene products and biological pathways cannot substitute for the knowledge gained from genetic association studies (Todd, 2006). Only epidemiology has the capacity to integrate population-level information on intrinsic characteristics, exposures, and their interactions, while acknowledging the essential role of chance. Existing epidemiologic methods are challenged to collect, assimilate, analyze, synthesize, and interpret data on complex relationships among all of these factors in large numbers of people across the lifespan (De Stavola et al., 2006). The results – like those of all epidemiologic studies – will be expressed in terms of probabilities because they are observations of a stochastic process. Researchers, health care providers, and the media will be challenged to explain the results in terms of probabilities, which are often not intuitive (Bianchi and Alexander, 2006). Thus, paradoxical as it may seem, the key to the realization of the vision of personalized medicine is not contained within each person’s genome but derived from a synthesis of population-level research. The Human Genome Project was able to capture most of the information in the genome with a single sequence because all human genomes are 99.9% identical, but developing health applications will depend on understanding variation in the remaining 0.1%. Even this small fraction of the genome contains millions of SNPs, too many to study systematically for association with clinical phenotypes. The International HapMap Project has made a start by studying SNPs throughout the genomes of 269 persons from four ethnic groups (McVean et al., 2005). Clearly, expanding the vision of human genomics research to encompass individual variation and its role in health and disease is a challenge that dwarfs the Human Genome Project in complexity, scale, and scope.

BUILDING THE EVIDENCE BASE The Human Genome Project demonstrated the value of collaboration, standards, and synthesis in a large scientific enterprise. Now these principles can be a guide to building the evidence base on human genome epidemiology. Two major approaches

References

have been proposed: creation of “biobanks” and collaborative efforts to promote research synthesis. These approaches are not mutually exclusive. Prospective studies of large, representative populations have been proposed in many countries, including the United States (Austin et al., 2003; Collins, 2004). Of those currently underway, the largest is the UK Biobank, which aims to collect information on the health and lifestyle of 500,000 volunteers aged between 40 and 69; during 20 or more years of follow-up, this information – along with DNA samples – will be available for scientifically and ethically approved research (UK Biobank, 2006). The UK National Health Service, which provides health care to the entire population, provides the framework for enrolling and following up the biobank cohort. No comparable infrastructure exists in the United States, with its highly decentralized health system and mobile population. Population-based biobanks have the disadvantages of other prospective cohort studies, being slow, expensive, and inefficient for studying rare outcomes; thus, even where they are feasible, complementary approaches are desirable. New consortia, such as the Wellcome Trust Case Control Consortium and the Genetic Association Information Network (GAIN), have been organized to mine genetic association data within previously conducted, large-scale epidemiologic studies (Foundation for the National Institutes of Health, 2006;Wellcome Trust Case Control Consortium, 2006). A self-organized “network of networks” is extending the reach of formal consortia created by funding agencies to focus collaborative efforts on particular diseases, from construction of retrospective cohorts to research synthesis (Ioannidis et al., 2005). Such networks have a critical role in “harmonizing” research approaches, study methods, nomenclature, definitions of phenotype, and data presentation to promote the integration of research results, including meta-analysis and pooled analysis. By mobilizing the combined expertise and resources of researchers in a particular field, such efforts can reduce duplication of effort,

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promote good research practices, and more quickly recognize promising findings and research gaps (Ioannidis et al., 2006a).

CONCLUSION In 1999, Francis Collins presented a memorable “hypothetical case study in 2010,” in which a young man undergoes a battery of DNA-based tests to predict his risk of chronic diseases (Collins, 1999). Since that scenario appeared in print, thousands of studies have examined genetic variants for association with chronic diseases; however, none of those on the hypothetical patient’s test report – or any others studied so far – has been found to predict lung cancer, colorectal cancer, or coronary heart disease well enough to be useful for screening healthy adults (Davey Smith et al., 2006). Nevertheless, several companies now offer “genetic profile” tests to practitioners and the public, along with personalized lifestyle recommendations, such as advice on dietary supplements. Although these tests are not currently regulated by the Food and Drug Administration, their potential to mislead consumers has been reviewed by Congress and further scrutiny is likely (Food and Drug Administration, 2006; United States Government Accountability Office, 2006). All too often, a new gene discovery is followed by a haphazard rush of scientific excitement, media coverage, and commercial interest in developing and marketing genetic tests, without the necessary intervening research (Janssens et al., 2006). Among the numerous reasons for this phenomenon is a profound misunderstanding of the relationship between observation and prediction, cause and effect at the individual and population levels (Vineis and Kriebel, 2006). By attempting to make these issues more explicit and to promote transparency, rigor, and synthesis in epidemiologic research that includes genetics, human genome epidemiology aims to improve the return on public investment in genomics.

REFERENCES Austin, M.A., Harding, S. and McElroy, C. (2003). Genebanks: A comparison of eight proposed international genetic databases. Community Genet 6, 37–45. Bianchi, M.T. and Alexander, B.M. (2006). Evidence based diagnosis: Does the language reflect the theory? BMJ 333, 442–445. Bogardus, S.T., Jr., Concato, J. and Feinstein, A.R. (1999). Clinical epidemiological quality in molecular genetic research: The need for methodological standards. JAMA 281, 1919–1926. Burton, P.R., Tobin, M.D. and Hopper, J.L. (2005). Key concepts in genetic epidemiology. Lancet 366, 941–951. Clayton, D. and McKeigue, P.M. (2001). Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 358, 1358–1360. Colhoun, H.M., McKeigue, P.M. and Davey Smith, G. (2003). Problems of reporting genetic associations with complex outcomes. Lancet 361, 865–872.

Collins, F.S. (1999). Shattuck lecture – medical and societal consequences of the Human Genome Project. N Engl J Med 341, 28–37. Collins, F.S. (2004). The case for a US prospective cohort study of genes and environment. Nature 429, 475–477. Collins, F.S. and McKusick, V.A. (2001). Implications of the Human Genome Project for medical science. JAMA 285, 540–544. Cordell, H.J. and Clayton, D.G. (2005). Genetic association studies. Lancet 366, 1121–1131. Cutting, G.R. (2005). Modifier genetics: Cystic fibrosis. Annu Rev Genomics Hum Genet 6, 237–260. Davey Smith, G. and Ebrahim, S. (2003). ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease?. Int J Epidemiol 32, 1–22. Davey Smith, G., Ebrahim, S., Lewis, S., Hansell, A.L., Palmer, L.J. and Burton, P.R. (2006). Genetic epidemiology and public health: Hope, hype, and future prospects. Lancet 366, 1484–1498.

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De Stavola, B.L., Nitsch, D., dos Santos Silva, I., McCormack, V., Hardy, R., Mann, V., Cole, T.J., Morton, S. and Leon, D.A. (2006). Statistical issues in life course epidemiology. Am J Epidemiol 163, 84–96. Di, X., Matsuzaki, H., Webster, T.A., Hubbell, E., Liu, G., Dong, S., Bartell, D., Huang, J., Chiles, R., Yang, G. et al. (2005). Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. Bioinformatics 21, 1958–1963. Evangelou, E., Maraganore, D.M. and Ioannidis, J.P. (2007). Meta-analysis in genome-wide association datasets: Strategies and application in Parkinson disease. PLoS ONE 7(2), e196. Food and Drug Administration. (2006). Draft Guidance for industry, clinical laboratories, and FDA staff on in vitro diagnostic multivariate index assays; availability [Docket No. 2006D–0347]. http:// www.fda.gov/OHRMS/DOCKETS/98fr/ch0641.pdf. Foundation for the National Institutes of Health (2006). Genetic Association Information Network (GAIN). http://www.fnih.org/ GAIN/GAIN_home.shtml. Garcia-Closas, M., Malats, N., Silverman, D., Dosemeci, M., Kogevinas, M., Hein, D.W., Tardon, A., Serra, C., Carrato, A., Garcia-Closas, R. et al. (2005). NAT2 slow acetylation, GSTM1 null genotype, and risk of bladder cancer: Results from the Spanish Bladder Cancer Study and meta-analyses. Lancet 366, 649–659. Gatto, N.M., Campbell, U.B., Rundle, A.G. and Ahsan, H. (2004). Further development of the case-only design for assessing geneenvironment interaction: Evaluation of and adjustment for bias. Int J Epidemiol 33, 1014–1024. Goodman, M.T., Hernandez, B.Y., Hewitt, S., Lynch, C.F., Cote, T.R., Frierson, H.F., Jr., Moskaluk, C.A., Killeen, J.L., Cozen, W., Key, C.R. et al. (2005). Tissues from population-based cancer registries: A novel approach to increasing research potential. Hum Pathol 36, 812–820. Gunderson, K.L., Kuhn, K.M., Steemers, F.J., Ng, P., Murray, S.S. and Shen, R. (2006). Whole-genome genotyping of haplotype tag single nucleotide polymorphisms. Pharmacogenomics 7, 641–648. Haga, S.B., Khoury, M.J. and Burke, W. (2003). Genomic profiling to promote a healthy lifestyle: Not ready for prime time. Nat Genet 34, 347–350. Hattersley, A.T. and McCarthy, M.I. (2005). What makes a good genetic association study?. Lancet 366, 1315–1323. Hoffman, E.P. (1994). The Evolving Genome Project: Current and future impact. Am J Hum Genet 54, 129–136. Holtzman, N.A. and Marteau, T.M. (2000). Will genetics revolutionize medicine?. N Engl J Med 343, 141–144. Hoover, R. (2007). The evolution of epidemiologic research: From cottage industry to “big science”. Epidemiology 18, 13–17. International HapMap Consortium (2005). A haplotype map of the human genome. Nature 437, 1299–1320. Ioannidis, J.P., Ntzani, E.E., Trikalinos, T.A. and Contopoulos-Ioannidis, D.G. (2001). Replication validity of association studies. Nat Genet 29, 306–309. Ioannidis, J.P., Rosenberg, P.S., Goedert, J.J. and O’Brien, T.R. (2002). International Meta-analysis of HIV Host Genetics. Commentary: Meta-analysis of individual participants’ data in genetic epidemiology. Am J Epidemiol 156, 204–210. Ioannidis, J.P., Bernstein, J., Boffetta, P., Danesh, J., Dolan, S., Hartge, P., Hunter, D., Inskip, P., Jarvelin, M.R., Little, J. et al. (2005). A network of investigator networks in human genome epidemiology. Am J Epidemiol 162, 302–304.

Ioannidis, J.P., Gwinn, M., Little, J., Higgins, J.P., Bernstein, J.L., Boffetta, P., Bondy, M., Bray, M.S., Brenchley, P.E., Buffler, P.A. et al. (2006a). A road map for efficient and reliable human genome epidemiology. Nat Genet 38, 3–5. Ioannidis, J.P., Trikalinos, T.A. and Khoury, M.J. (2006b). Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases. Am J Epidemiol 164, 609–614. Janssens, A.C., Gwinn, M., Valdez, R., Narayan, K.M. and Khoury, M.J. (2006). Predictive genetic testing for type 2 diabetes. BMJ 333, 509–510. Khoury, M.J. and Flanders, W.D. (1996). Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: Casecontrol studies with no controls! Am J Epidemiol 144, 207–213. Khoury, M.J., Millikan, R., Little, J. and Gwinn, M. (2004a). The emergence of epidemiology in the genomics age. Int J Epidemiol 33, 936–944. Khoury, M.J., Yang, Q., Gwinn, M., Little, J. and Flanders, W.D. (2004b). An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions. Genet Med 6, 38–47. Khoury, M.J., Little, J., Gwinn, M. and Ioannidis, J.P. (2007). On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies. Int J Epidemiol 36, 439–445. Lin, B.K., Clyne, M., Walsh, M., Gomez, O., Yu, W., Gwinn, M. and Khoury, M.J. (2006). Tracking the epidemiology of human genes in the literature: The HuGE Published Literature database. Am J Epidemiol 164, 1–4. Little, J., Bradley, L., Bray, M.S., Clyne, M., Dorman, J., Ellsworth, D.L., Hanson, J., Khoury, M.J., Lau, J., O’Brien, T.R. et al. (2002). Reporting, appraising, and integrating data on genotype prevalence and gene-disease associations. Am J Epidemiol 156, 300–310. Little, J., Khoury, M.J., Bradley, L., Clyne, M., Gwinn, M., Lin, B., Lindegren, M.L. and Yoon, P. (2003). The Human Genome Project is complete. How do we develop a handle for the pump? Am J Epidemiol 157, 667–673. Manolio, T.A., Brooks, L.D. and Collins, F.S. (2008). A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118, 1590–1605. Marchini, J., Cardon, L.R., Phillips, M.S. and Donnelly, P. (2004). The effects of human population structure on large genetic association studies. Nat Genet 36, 512–517. McClain, M.R., Palomaki, G.E., Nathanson, K.L. and Haddow, J.E. (2005). Adjusting the estimated proportion of breast cancer cases associated with BRCA1 and BRCA2 mutations: Public health implications. Genet Med 7, 28–33. McVean, G., Spencer, C.C.A. and Chaix, R. (2005). Perspectives on human genetic variation from the HapMap Project. PLoS Genet 1, e54. DOI: 10.1371/journal.pgen.0010054. Miettinen, O.S. (1974). Proportion of disease caused or prevented by a given exposure, trait or intervention. Am J Epidemiol 99, 325–332. Myers, E.R., Havrilesky, L.J., Kulasingam, S.L., Sanders, G.D., Cline, K.E., Gray, R.N., Berchuck, A. and McCrory, D.C. (2006). Genomic tests for ovarian cancer detection and management. Evidence Report/Technology Assessment No. 145. (Prepared by the Duke University Evidence-based Practice Center under Contract No. 290-02-0025.) AHRQ Publication No. 07-E001. Rockville, MD: Agency for Healthcare Research and Quality. http://www.ahrq. gov/downloads/pub/evidence/pdf/genomicovc/genovc.pdf.

Recommended Resources

National Library of Medicine. Online Inheritance in Man (OMIM). http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?dbOMIM. Palmer, L.J. and Cardon, L.R. (2005). Shaking the tree: Mapping complex disease genes with linkage disequilibrium. Lancet 366, 1223–1234. Personalized Medicine Coalition (2007). http://www.personalizedmedicinecoalition.org/. Rothman, K.J. and Greenland, S. (1998). Case-control studies. In Modern Epidemiology 2nd edition (K.J. Rothman, ed.). Lippincott Williams & Wilkins, Philadelphia, pp. 93–114. Sipkoff , M. (2005). Predictive modeling and genomics: Marriage of promise and risk. Manag Care 14, 60 63–64, 66. Storey, J.D. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100, 9440–9445. Teare, M.D. and Barrett, J.H. (2005). Genetic linkage studies. Lancet 366, 1036–1044. Thomas, D.C. (2006). High-volume “-omics” technologies and the future of molecular epidemiology. Epidemiology 17, 490–491. Thomas, D.C., Haile, R.W. and Duggan, D. (2005). Recent developments in genomewide association scans: A workshop summary and review. Am J Hum Genet 77, 337–345.

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Todd, J.A. (2006). Statistical false positive or true disease pathway? Nat Genet 38, 731–733. UK Biobank (2006). UK Biobank: Improving the health of future generations. http://www.ukbiobank.ac.uk/. United States Government Accountability Office. (2006). Testimony before the Special Committee on Aging, U.S. Senate. Nutrigenetic testing: Tests purchased from four web sites mislead consumers. http://www.gao.gov/new.items/d06977t.pdf. Vineis, P. and Kriebel, D. (2006). Causal models in epidemiology: Past inheritance and genetic future. Environ Health 5, 21. Wald, N.J., Hackshaw, A.K. and Frost, C.D. (1999). When can a risk factor be used as a worthwhile screening test? BMJ 319, 1562–1565. Wellcome Trust Case Control Consortium (2006). http://www.wtccc. org.uk/. Wellcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678.

RECOMMENDED RESOURCES GeneTests. http://www.geneclinics.org/. A publicly funded medical genetics information resource developed for physicians, other health care providers, and researchers and available at no cost. Includes expert-authored disease reviews, laboratory directory, and clinic directory. Human Genome Epidemiology Network (HuGENet). http://www. cdc.gov/genomics/hugenet/default.htm. Website for a global collaboration of individuals and organizations committed to the assessment of human genome variation in population health. Includes a searchable database of publications in human genome epidemiology compiled weekly from PubMed. Links provide information on becoming a HuGENet collaborator and connect with other HuGENet hubs in Cambridge (UK), Ottawa (Canada), and Ioannina (Greece). HuGENet Network of Networks. http://www.hugenet.org.uk/ networks/list.html. Directory of participating networks, maintained by the HuGENet UK hub. International HapMap Project. http://www.hapmap.org/. Website for an international partnership of scientists and funding agencies to identify and catalog genetic similarities and differences in human beings, which affect health, disease, and individual responses to medications and environmental factors.

Khoury, M.J., Beaty, T.H. and Cohen, B.H. (1994). Fundamentals of Genetic Epidemiology. Oxford University Press, Oxford. Textbook of genetic epidemiology. Khoury, M.J., Little, J. and Burke, W. (2004). Human Genome Epidemiology. Oxford University Press, Oxford. Overview of human genome epidemiology in 29 chapters by experts, describing methods and case studies. National Institutes of Health. National Human Genome Research Institute. Human Genome Project. http://www.genome.gov/. Website for the international, publicly funded Human Genome Project. Online Mendelian Inheritance in Man (OMIM). http://www.ncbi. nlm.nih.gov/sites/entrez?dbOMIM. Searchable public online database of human genes and diseases. Although the original focus was on Mendelian disorders, it now includes information on genes associated with common, complex diseases. Public Population Project in Genomics (P3G). http://www.p3gconsortium.org/. Website for a not-for-profit, international consortium to promote collaboration among researchers and “harmonization” of data management in the field of population genomics with the aim of improving knowledge transfer and sharing.

CHAPTER

41 Genomics and Population Screening: Example of Newborn Screening John D. Thompson and Michael Glass

INTRODUCTION Newborn screening (NBS) is a population screening program that tests babies to identify those who have treatable congenital disorders and strives to link these babies into the specialty care systems that will provide treatment and long-term support for them and their families. The focus of this chapter will be on NBS using dried blood specimens, usually collected from the newborn’s heel, absorbed onto filter paper and dried before shipping to a screening laboratory. The argument for administering NBS as a population screening program (rather than a screening tool for at-risk populations) is so compelling that offering NBS is mandated by law in every state in the United States and in many other countries.1 Because the disorders are so rare, most affected babies have no

1

Newborn screening was first introduced in the United States in the 1960s. The subsequent widespread implementation of newborn screening in all developed countries and in most developing nations is a testament to the critical role this public health program plays in the lives of newborns. The authors both work for the Washington State Newborn Screening Program and are therefore most familiar with dried blood spot newborn screening practices in the United States. We believe, however, that the issues discussed in this chapter are relevant to programs throughout the world.

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family history which would indicate a need for testing. Severe neurological and physical problems and death may result if the conditions are not treated soon after birth, so NBS conditions are considered to be public health emergencies requiring immediate attention (Grosse et al., 2006). Although a key component, NBS is a small piece of the system in place for newborn health. In the hours after birth, babies are checked for vital signs, measured, weighed, administered Hepatitis B vaccine and Vitamin K shots, given Erythromycin ointment in both eyes and screened for early hearing loss and elevated bilirubin. Dried blood spot NBS, or “the PKU test” as it is often misleadingly called, is just one of many events for infants that occur prior to leaving the hospital or birthing center. As technologies rapidly advance, the number of conditions that could be included in NBS panels continues to grow. Traditionally, disorders in NBS programs meet the following inclusion criteria: 1. Identifying the condition early provides a clear health benefit to the newborn 2. Irreversible harm is done before the condition is identified clinically 3. A sensitive and specific test is available, adapted for highthroughput screening

Screening Technology: Simple Ideas, Complex Realities

4. Treatments and systems of care are available for babies with the condition 5. The benefits justify the costs of screening2 Because the conditions are relatively rare, NBS is often lost in the bustle of everything else going on during post-partum care (Clayton, 2005). For the vast majority of babies, NBS is a poke in the foot and nothing more. But for the few affected individuals, it makes a world of difference.

COMPONENTS OF THE NBS SYSTEM Nurses, midwives and phlebotomists caring for babies in hospitals, birthing centers, clinics and at home are at the front line of NBS. They are charged with the important task of drawing quality specimens with accurate demographic information for every infant and delivering them in a timely fashion to the screening laboratory. These four aspects are imperative to the success of any NBS program. Poor quality, inaccurate or incomplete demographics, babies without screens or slow delivery can significantly delay diagnosis and treatment of an affected baby with catastrophic consequences. For babies with certain disorders (classic galactosemia and salt-wasting congenital adrenal hyperplasia are examples), the delay of an extra day or two could mean the difference between life and death. Testing for the various conditions typically begins the same day the specimens are received by the NBS laboratory and most abnormal results are identified within 1 to 2 days of receipt. Results can be classified as screen negative (normal), screen positive (abnormal) and QNS (quality/quantity not suitable for testing), although terms vary between programs. NBS programs may employ a variety of reporting mechanisms for screen negative results: mail, fax or electronic posting to either the submitter or the primary care provider. Most programs stratify their response for a given screen positive result based on the likelihood of the infant truly having the condition. Extreme values trigger an aggressive follow-up that includes contacting the primary care provider directly with results and recommendations for diagnostic testing. For NBS programs that receive two routine specimens for each child, a mildly abnormal result (often called “borderline”) may require no action beyond monitoring to ensure that a second specimen is received for that baby. Poor quality specimens are unsuitable for testing and necessitate follow-up to ensure that each child has a valid specimen submitted for screening. Short-term follow-up services include efforts to ensure that every child has appropriate testing and proper response based on the results. A key component is the sometimes aggressive follow-up to provide timely diagnostic testing and linkage

2

Several NBS cost analyses have been performed. They have generally supported screening, although their individual assumptions and estimates vary considerably. A review of NBS cost analyses was published recently as an invited commentary by Dr. Scott Grosse, an economist at the Centers for Disease Control and Prevention (Grosse, 2005).

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of affected babies and their families with specialty care providers and treatment facilities. NBS programs may have additional follow-up components that monitor the long-term health and developmental progress of affected individuals and helps provide assistance to families in need. Education plays several critical roles in NBS programs. Hospitals and clinics must understand the NBS system and how to best perform their role. Primary care providers and the families of affected babies need information about these rare conditions tailored to their different levels of understanding complex medical conditions. The general public can learn information about NBS from the various publications distributed by NBS programs and via the internet. Many excellent websites provide current information about NBS for practitioners, parents and the public (please see the recommended resources section at the end of this chapter). Success in NBS programs necessitates a seamless system from birth to specialty care treatment for affected babies. The NBS system is only as good as its weakest link, so constant efforts to strengthen and improve its individual components are critical to overall success. NBS laboratories and follow-up groups are constantly involved in improving the program through quality assurance and quality control measures. Laboratory staff ensure that instruments and testing systems are operating at peak performance on a daily basis. All programs in the United States and many others throughout the world participate in the proficiency testing program run by the Centers for Disease Control and Prevention (CDC) (CDC, 2006). Monitoring specimen quality and transit times and providing regular feedback to hospitals have improved compliance in Washington State (Resler et al., 2005). Testing (especially screening) methods inherently have imperfect sensitivities and specificities. An NBS test, however, needs great sensitivity to identify the affected newborns, and because of the low prevalence of these disorders, it needs exquisite specificity to manage the number of false positive results. For example, consider a hypothetical screening test that has a perfect (100%) sensitivity for a condition with a prevalence of one in 10,000. If the specificity of the screening test were 95%, there would be 500 false positives for every true positive. In reality, the specificities of NBS tests are typically well above 99%, but this example illustrates the importance of recognizing the imperfections of the screening process and the potential magnitude of false positive results, which can lead to misdiagnosis and parental anxiety (Tarini et al., 2006;Waisbren, Albers, Amato et al., 2003). Advances in technology have played a key role in improving the utility of NBS by providing tests that meet these demanding sensitivity and specificity requirements.

SCREENING TECHNOLOGY: SIMPLE IDEAS, COMPLEX REALITIES Technological advances have fostered new methods and also allowed expansion of the number of disorders available for

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screening. Historically, NBS programs have been very responsive to advances in technology. Examples include the quick uptake of the radioimmunoassay technique by NBS programs in the 1970s when a test for primary congenital hypothyroidism was described (Hannon et al., 1993) and the contemporary implementation of tandem mass spectrometry by many NBS programs (Sweetman, 2001). Table 41.1 provides a brief description of technologies commonly used in NBS and examples of the disorders they are used to detect. NBS expansion is driven by both advances in technology and by the desire to screen for certain conditions. Often it is difficult to distinguish the predominant factor in these improvements. However, disorders are not generally added to an NBS panel solely because a robust screening test is available. Candidate disorders must also meet the other inclusion criteria described earlier. Implementing screening for each new condition has presented unique challenges for public health programs. Most of the disorders have complex etiologies that are imperfectly understood at the initiation of screening, but benefit from the experience of early identification and treatment. The following four examples highlight how population-based NBS is much more complex than the simple idea of identifying affected babies and putting them on treatment to avoid negative outcomes. For each section, the impetus for NBS is explained, the screening platform is described, and some of the challenges and implications (including unintended consequences) of screening for the disorder are reviewed.

TABLE 41.1

Phenylketonuria Impetus: Phenylketonuria (PKU) is a metabolic disorder caused by deficiency in the enzyme that converts the amino acid phenylalanine to the amino acid tyrosine. Untreated PKU leads to a build-up of phenylalanine that causes central nervous system damage. In the 1950s, researchers found that people with PKU have improved health if placed on a phenylalanine-restricted diet, but that any neurological damage suffered by the individual is irreversible (Armstrong et al., 1957). The simple idea for NBS was to identify those babies with PKU early in infancy, remove phenylalanine from their diets and avoid mental retardation. Platform: Hospital-based screening for PKU was utilized in some locations during the 1950s, but it was performed by a ferric chloride urine “diaper test” that was unreliable, particularly with younger infants (Guthrie, 1961). In the early 1960s, Dr. Robert Guthrie developed a better method for testing newborns for PKU called the bacterial inhibition assay (BIA). In this test, punches from dried blood spots are placed on agar infused with bacteria spores and a chemical that inhibits their growth. Phenylalanine reverses the inhibition, allowing development of a zone of bacterial growth where phenylalanine has diffused into the agar. The size of the zone is proportional to the concentration of phenylalanine in the blood spot (Aldis et al., 1993; Guthrie et al., 1963). Challenges and Implications: In the beginning years of NBS, administering treatment for babies with PKU was a challenge (Kennedy et al., 1967). There were no comprehensive systems of screening and care for those diagnosed with PKU, so some

Newborn screening technologies

Technology

Brief description

Examplesa

Bacterial inhibition assay

Metabolic product or precursor in baby’s blood inactivates inhibitor resulting in bacterial growth zone proportional to concentration

Phenylketonuria, Maple syrup urine disease

DNA testing

Genotyping is used as a second-tier test to increase the screen’s sensitivity; multiplex platformb

Cystic fibrosis, Hemoglobinopathies

Enzyme assays

Enzyme ability to convert substrate into product is measured

Biotinidase deficiency, Galactosemia

High performance liquid chromatography

Proteins or amino acids are sorted and quantified using pressurized chromatography

Hemoglobinopathies, Phenylketonuria

Immunoassay

Amount of protein or hormone is measured by competitive binding with labeled protein for sites on specific antibodies

Congenital hypothyroidism, Cystic fibrosis

Isoelectric focusing

Proteins are sorted on a pH gradient gel within an electrical field based on their relative charge; multiplex platformb

Hemoglobinopathies

Tandem mass spectrometry

Chemicals characteristic of metabolic disorders are sorted and quantified based on mass and characteristic fragmentation products; multiplex platformb

Isovaleric acidemia, MCAD deficiency

a

This is not a comprehensive listing of disorders detected by the different technologies. Multiplex platform means that this technology can be used to screen for many different conditions or genotypes simultaneously using the same blood sample. b

Screening Technology: Simple Ideas, Complex Realities

babies slipped through the cracks and did not receive treatment. Some babies were given formula completely void of phenylalanine and became malnourished. The lesson learned was that people with PKU need a very small amount of phenylalanine in their diets for normal development. Also, many believed that the restricted diet only needed to be followed during early childhood when the nervous system was rapidly developing. Current recommendations are for patients with PKU to stay on the diet for life (National Institutes of Health, 2001). The variability of expression in people with PKU is not well understood. Some people with PKU must strictly adhere to the dietary regimen to keep their blood phenylalanine at acceptable values, while others do not need to follow the diet as carefully to achieve the same results. This has even been observed in siblings, with presumably the same PKU-causing mutations, who are affected differently by the disorder. A delicate dietary balance must be found for each patient with PKU. Typically, babies with PKU have their phenylalanine levels monitored as they grow to determine the proper level of treatment. NBS for PKU is a good representation of the intricacies involved in treating a metabolic disorder with significant phenotypic variability. The PKU screening story became more complex when women with PKU, identified through NBS, began to have children of their own. Experience revealed that babies born to PKU mothers with high phenylalanine levels during their pregnancies have an increased risk for microcephaly, mental retardation and congenital heart disease even though the babies do not have PKU. This condition was named maternal PKU (National Institutes of Health, 2001). Data from the Maternal PKU Collaborative Study show that there is a negative correlation between maternal phenylalanine levels and both cognitive and behavioral outcomes of their babies, meaning that women with PKU who do not maintain blood phenylalanine levels within recommended limits are more likely to have children with disabilities (Waisbren and Azen et al., 2003). Another complexity that only became apparent through screening experience is that approximately 1% of babies with positive PKU screens have a related disorder called biopterin deficiency. Tetrahydrobiopterin (BH4) is a cofactor for the enzyme that converts phenylalanine to tyrosine. It is impossible to distinguish between the two conditions based solely on NBS results, so part of the diagnostic testing for PKU includes a workup for biopterin deficiency. Complicating matters further, three different enzymes are responsible for BH4 biosynthesis. Treatment and success at managing biopterin deficiency vary based on which enzyme is deficient (Blau et al., 2001; Naylor, 1985). Because both the screening methodology and appropriate treatment have been greatly improved over 40 years of experience, NBS for PKU is widely regarded as a shining example of a population-based screening program. Dr. Guthrie and parent advocates were influential in mandating PKU screening in the beginning, even though the quality of the scientific evidence justifying the creation of screening programs was questioned at the time. However, they were right when they claimed that it would be beneficial to affected newborns. Today, thousands of

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people around the world have been spared the negative effects of untreated PKU. Sickle-Cell Anemia Impetus: Since the 1960s, it has been known that babies and young children with sickle-cell anemia (hemoglobin SS or Hb SS) are particularly susceptible to life-threatening bacterial infections. In 1983, a randomized controlled trial was organized to examine the effect of prophylactic therapy with oral penicillin in children with Hb SS. The early data about the benefits of treatment were so compelling that the investigators stopped the study 8 months early (Gaston et al., 1986). Shortly thereafter, the National Institutes of Health (NIH) held a consensus conference and published its recommendations for universal neonatal screening for Hb SS with prophylactic treatment before 4 months of age (Consensus Conference, 1987). Sickle-carrier screening programs predated NBS for sicklecell disease, which was widely implemented only after a strong public health rationale was proven.3 Infants with Hb SS are susceptible to rapid onset of overwhelming pneumococcal sepsis that in some cases leads to death. These children are also at risk for sudden pooling of blood in the spleen, acute chest syndrome, and stroke, any of which can be life-threatening. Studies showed that parental education about early warning signs of sickling crises, such as enlarged spleen or temperature above 38.3°C (101°F), helped reduce the mortality associated with Hb SS (Vichinsky et al., 1988).The simple idea for NBS was to identify those infants with Hb SS and enroll them into comprehensive care, including prophylactic administration of penicillin and parental education about early warning signs of complications of the disease. This would save lives and improve outcomes for babies with Hb SS. Platform: At the time of the 1987 consensus conference, hemoglobin screening was performed by most laboratories using cellulose acetate and citrate-agar electrophoresis (Garrick et al., 1973). A newer technique called isoelectric focusing (IEF) provided better resolution of hemoglobins and was successfully adapted for mass screening (Kleman et al., 1989). Isoelectric focusing operates upon the principle that hemoglobin molecules migrate to specific positions on a gel pH gradient when placed within an electric field. This position is determined by the composition of amino acids present in the protein. Hemoglobin variants, such as the sickle cell mutation, migrate to a different location on the gel than the normal hemoglobin protein. Hemoglobin molecules of a specimen are separated by IEF and stained for visualization. Technicians make classifications based on where the molecules migrate within the gel. High performance liquid chromatography (HPLC) testing is an alternative method used by some laboratories

3 Sickle-cell carrier screening programs began in the 1970s, and in many instances, screening was required only in black populations. Generally, there were not good protections for confidentiality and adequate counseling was not always a part of these programs. This led to misinterpreted results, stigmatization, and in some cases discrimination. Many people who were carriers of the sickle-cell mutation thought they had sickle-cell disease (Hampton, et al., 1974; Markel, 1997).

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for hemoglobin screening, often as a second-tier test on the original blood specimen for babies with initial abnormal results. Also, a few laboratories employ direct DNA testing as a second-tier test to better refine the screening results. Challenges and Implications: From a genetic standpoint, Hb SS is the most simple of diseases: it is caused by a single nucleotide change in the DNA. This mutation substitutes a valine for glutamic acid at the sixth amino acid residue in the -globin protein and causes the hemoglobin to polymerize under conditions of low oxygen concentration. This makes the red blood cells change from their normal doughnut-like shape to a long, thin sickle shape. Sickled cells have difficulties passing through the body’s capillaries and can cause episodes of severe pain and other complications (Lane, 1996). Despite its genetic simplicity, Hb SS exhibits a wide phenotypic variability. Although everyone with Hb SS has the same single mutation in both copies of the β-globin gene, some suffer greatly from painful sickling episodes that cause repeated, frequent hospitalizations while others experience few consequences from the disorder. Screening for Hb SS using IEF comes with several unintended consequences because of the qualitative nature of the test. Babies (and families) with hemoglobin variants other than the sickle mutation, and both α and β thalassemia are identified. Another unavoidable consequence of using IEF is the identification of unaffected carriers of the sickle and other variant hemoglobins. The majority of hemoglobin variants are benign conditions, but a few of them are clinically significant, alone and in combination with the sickle mutation (e.g., SC disease, SE disease, Sβ thalassemia) and require specialized medical care from a hematologist. Only in rare instances are carriers of the sickle mutation or other variant hemoglobins affected clinically, although studies have shown that this concept can be difficult for families to understand (Hampton et al., 1974). Abnormal hemoglobin detection, including carrier status, is generally reported to the baby’s primary care provider, and genetic counseling services are recommended when a clinically significant mutation is detected (Council of Regional Networks for Genetic Services, 1997). Even though the first NBS programs started screening for hemoglobins in the 1970s, there remains much to learn about the clinical presentation of these disorders. Additionally, access to services for affected individuals continues to be a challenge in some places. Cystic Fibrosis Impetus: Cystic Fibrosis (CF) is a condition that affects the way the body regulates salt transport across cell membranes. It causes thick mucus to build up in the lungs, intestines and other organs, leading to failure to thrive, digestive problems, nutritional deficits and chronic lung problems. The simple idea for NBS was to identify babies with CF and start treatment early to improve clinical outcomes, with the hope that a cure will someday be found. Platform: An immunoassay was developed in 1979 to measure immunoreactive trypsinogen (IRT), a pancreatic protein that is elevated in the first months of life in babies with CF (Crossley

et al., 1979). NBS for CF began shortly thereafter as pilot studies and randomized control trials in parts of Australasia, Europe and the United States (Hammond et al., 1991; Heeley et al., 1982; Wilcken et al., 1983). A baby with an elevated IRT level would need a second NBS specimen collected at 2–4 weeks of age. If the second specimen also had elevated IRT levels, the baby would be referred for a diagnostic work-up (sweat chloride test). Because elevated IRT levels are not specific to babies with CF, the false positive rate on the initial blood spot is very high. The discovery of the cystic fibrosis transmembrane conductance regulator (CFTR) gene in 1989 and advances in genotyping technology have changed the way that most NBS for CF is performed. The trend among NBS programs has been to adopt DNA testing as a second-tier screen (after finding elevated IRT) using a multiple-mutation panel. These panels exist or can be created to test for the most common mutations in a specific population or subpopulations. The cost of this technology has dramatically decreased over the years and the methods have been adapted to mass screening. Challenges and Implications: Because this genotyping is performed from the original blood spots, using DNA testing offers the advantage of finding babies with two identified mutations and establishing a genetic diagnosis of CF without requiring a second blood specimen. This testing, however, also identifies many more babies who have only one mutation detected. For these infants, a diagnostic sweat test is necessary because even the most comprehensive DNA panels only include a fraction of the known CF-causing mutations. Most of these babies with only one mutation detected are simply unaffected carriers, meaning that one gene is normal and the other has a mutation. Another challenge arises from the differing severities of the CF mutations. In a panel of multiple mutations, some alleles may result in a late onset form of CF or may never be clinically significant. It is difficult to predict the effects of certain combinations of alleles. Furthermore, DNA testing results may be difficult for some families to understand. Cystic fibrosis is most prevalent among Caucasians and the majority of research has been done on the alleles most common in this population. The disease, however, is found among all ethnicities. The current DNA panels do a poor job of identifying CF in non-Caucasian babies (Comeau et al., 2004). Implementing direct-DNA testing of newborns for CF mutations has significant implications. One is the identification of unaffected carriers discussed earlier. During a pilot study in Massachusetts, eleven carriers were identified by screening for each true case of CF (Comeau et al., 2004). Because DNA testing has implications for reproductive decision-making for some families, genetic counseling is generally offered to those families that have babies with either one or two CF mutations detected. While programs may feel differently about whether carrier identification is a positive or negative consequence of screening, it is costly for society because of the increase in required diagnostic testing and recommended genetic counseling. New mutations added to NBS panels will improve the screening sensitivity. However, for each mutation added, the

Screening Technology: Simple Ideas, Complex Realities

number of unaffected carriers that will require sweat testing increases dramatically (Comeau et al., 2004). NBS programs using multiple mutation panels must balance the benefits and risks of increasing their detection rate because it comes at the cost of increased carrier identification. Recent data from the state of Colorado (Sontag et al., 2005) show that when a NBS program measures IRT levels on two specimens (one at 24–48 h after birth and another at about 14 days of age), similar sensitivity and negative predictive values can be achieved by screening for persistent IRT elevation in two specimens compared to using DNA with a multiple-mutation panel. This means that both algorithms would identify about the same number of babies with CF and miss about the same number of babies with CF over time (Thompson et al., 2005). These new data have some programs thinking carefully about the pros and cons of using direct DNA testing as a second-tier screen for CF (personal communications). MCAD Deficiency Impetus: Mass spectrometry is a powerful analytic tool to study mixtures of compounds. A group of scientists and metabolic specialists teamed in the 1980s to develop an application of the technology called tandem mass spectrometry (MS/MS) for NBS. They used MS/MS to measure acylcarnitines which accumulate in certain fatty-acid oxidation and amino acid disorders (Millington, 2002; Millington et al., 1990). Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency is the most common and best understood of the fattyacid oxidation disorders. It is caused by a defect in one of the enzymes that participates in the beta oxidation cycle of fat breakdown. Carbohydrates are the only safe energy source for people with MCAD deficiency because toxic intermediate metabolites build up if they are forced to break down fats, for example during fasting or illness (Roe et al., 2001). The case fatality rate for clinically diagnosed MCAD deficiency is estimated to be about 16% (Pollitt et al., 1998). The simple idea for NBS was to identify babies with MCAD deficiency and put them on treatment (avoiding fasting or fasting-like states during illness) to prevent death and disability. Platform: Tandem mass spectrometers are complex instruments that separate the array of molecules in a blood sample. The molecules are first sorted by size in an initial mass spectrometer. The molecules then pass through a collision chamber where they are broken into pieces that are of characteristic size, dependent on their molecular structures. The fragments are then sorted by size in the second mass spectrometer and then quantified. The results of the analysis are plotted on a graph: the x-axis represents the mass of the different molecules, and the height of each peak on the y-axis represents the amount of that molecule present in the sample (Chace et al., 2003). Challenges and Implications:Tandem mass spectrometers can be used to examine a profile of metabolites for an individual, identifying abnormal levels and ratios of specific components in the blood characteristic of metabolic diseases. The MS/MS platform can be used to simultaneously screen for 30 or more disorders

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using one blood specimen. While it is generally accepted that MCAD deficiency meets the traditional criteria for inclusion in NBS programs, some of the other disorders detectable through MS/MS clearly do not. There are yet other conditions that might not meet the criteria because of a very low prevalence. However, these disorders can ride the coattails of MCAD deficiency into NBS panels because the incremental cost of looking for additional disorders is minimal (Green et al., 1999). The MS/MS instruments can either be set to identify specific molecules associated with a particular disorder, or set of disorders, or they can be set to scan a range of many different molecules thus identifying a variety of metabolic conditions. Even if the MS/MS instrument is used narrowly to identify specific conditions, peaks representing masses characteristic of other disorders will occasionally be found during the screening process (Sweetman, 2001). Using the full-scan capacity of MS/MS will identify babies who have abnormal metabolic profiles that may not be specific to any disorder. Additionally, it appears that MS/MS identifies a subpopulation of people who may have remained asymptomatic without treatment. For example, many more cases of MCAD deficiency have been detected by NBS than is predicted by the prevalence of clinically identified populations. And, cases detected through NBS appear to have different genotype frequencies than those identified through clinical presentation (Andresen et al., 2001). In addition to identifying asymptomatic individuals, MS/ MS screening can identify babies with conditions that do not have treatments with proven benefits. This reality could lead parents and physicians to what Fielding et al. call “therapeutic odysseys,” where the doctors use what limited knowledge and experience they have about these rare disorders to give their best guess for treatments (Fielding et al., 2003). However, proponents of expanded screening argue that early identification is preferable to the “diagnostic odysseys” that usually accompany the identification of a rare metabolic disorder that presents clinically. Also, they argue, novel treatments may be discovered that are beneficial. Early diagnoses can also help some couples with reproductive decision-making. NBS programs with MS/MS capacity have either chosen to use this technology narrowly to identify a subset of disorders, or broadly in the full-scan mode to identify all of the detectable conditions. In 2004, a group of experts from the American College of Medical Genetics (ACMG), commissioned by the United States Health Resources and Services Administration (HRSA), recommended universal screening for a core panel of 29 disorders (20 of which are identified by MS/MS). The report suggests that another 25 conditions, largely conditions that are not well understood or do not have effective treatments, fall into a class called “secondary targets” for screening (22 of which are identifiable by MS/MS). The experts recommended that these conditions be reported but gave no guidance about what primary care providers should do with these results (NBS, 2005). Deciding to add these disorders to NBS panels is a contemporary challenge facing NBS programs. The trend in the United States has been to follow these guidelines by expanding

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NBS panels to the “ACMG 29.” Companion commentaries in the medical journal Pediatrics entitled “Newborn Screening Technology: Proceed with Caution” and “We Need Expanded Newborn Screening” offer clear arguments on both sides of the current debate (Botkin et al., 2006; Howell, 2006).

VARIABILITY AMONG NBS PROGRAMS The preceding four examples have highlighted some of the array of challenges that NBS presents. Considering these complexities, it is not surprising that states and countries have differences in their programs, both in the disorders they screen for and in the services they provide (Serving the family from birth to the medical home, 2000;Townes, 1986). Different groups of knowledgeable and well-intentioned individuals have come to different decisions about what to screen for and how to operate NBS programs. Most NBS programs operate at a regional level. In the United States, for example, each state and Washington DC operates its own NBS program. Among them there are variations in the disorders included in their panels, screening technologies used in the laboratory, funding mechanisms supporting the program and services available beyond screening in the laboratory (i.e., follow-up, genetic counseling, systems of care, etc.). Most states utilize one NBS laboratory, usually operating within the state health department. Because it has been suggested that laboratories should have a minimum of 50,000 specimens per year to maintain quality of screening (Guthrie, 1972), some smaller states collaborate by sending blood specimens to a regional laboratory for testing. A few programs contract with private laboratories to perform the screening. Historically, NBS has been a relatively low-profile public health program that has enjoyed a behind-the-scenes role of helping babies. Advocates for child health have long been instrumental in NBS expansion. Recently, parent advocacy groups, as well as non-profit health organizations, and the federal government of the United States have raised public awareness about NBS and pushed for national standards such as the ACMG report (Cunningham, 2002; NBS, 2005; Serving the family from birth to the medical home, 2000). Advocacy organizations such as Save Babies Through Screening, the March of Dimes, and the American Academy of Pediatrics were quick to voice their support of the report. Although a federal advisory committee accepted the recommendations on September 23, 2004, as of November 2006, the Department of Health and Human Services has taken no formal action regarding the ACMG report. The United States federal funding has increased in recent years supporting the development of new technologies and improvement of NBS services, in part to address issues of variability among NBS programs. Other federally funded projects, such as the Human Genome Project, have increased the general public awareness of genetics and brought hopes of cures and therapies for those who suffer from genetic disorders, including the prospect of personalized medicine where therapies are prescribed based on an individual’s genotype.

INFLUENCE OF GENETICS AND -OMIC TECHNOLOGIES ON NBS NBS is often regarded as a genetic screening program because most of the conditions included in NBS panels are monogenic, meaning they are disorders caused by mutations in a single gene. However, the fact that these conditions are genetic is not the reason that they are included in NBS panels (Motulsky, 1997). Although the genotype of the baby is not usually determined by NBS, genomic expression is analyzed. The majority of NBS assays either detect specific biomolecules (proteins, hormones, amino acids, organic acids or acylcarnitines) or measure the activity of certain enzymes. In some cases, affected babies are genotyped as part of the diagnostic work-up or have DNA testing done after diagnosis to improve clinical care. While most of these disorders exhibit a simple inheritance pattern, the physical manifestations of these conditions are complex and highly variable. The genotype/phenotype correlation, or how well an individual’s DNA predicts disease expression, is frequently weak because many of the disorders are affected by environmental influences and/or interactions with other genes. This complexity is compounded in some disorders because of allelic heterogeneity, meaning that more than one disease causing mutation exists in a given gene, as seen in more than 1400 mutations in the CFTR gene that are known to cause cystic fibrosis (Tsui et al., 2005). A related phenomenon is locus heterogeneity, where the same disease phenotype is caused by mutations in more than one gene. For example, defects in any of five different genes can cause congenital adrenal hyperplasia (Donohoue et al., 2001). While most of the conditions in NBS panels are monogenic disorders, direct DNA testing is not routinely employed. Currently, no NBS programs in the United States use direct DNA testing as a primary screening method, although the concept has been proposed (Green et al., 2006). While genotyping is becoming increasingly less costly, primary DNA testing for NBS is problematic because of the large amount of heterogeneity in the relevant genes and the inevitable high rates of carrier detection. Direct DNA analysis is used in some instances, as a second-tier test for specimens with abnormal results on the first screen. The largest use of this type of DNA testing in NBS is using panels of multiple mutations as part of the screen for CF. Second-tier genotyping for certain hemoglobin variants and mutations that cause galactosemia have proven useful for guiding follow-up in Washington State (Neier et al., 2005). For example, a baby with a very low galactose-1-phosphate uridyl transferase (GALT) enzyme activity can be genotyped for mutations that cause both the severe and mild forms of galactosemia. This allows NBS follow-up staff to temper their response based on the genotype: if a mild allele is detected, there is no need for immediate follow-up and diagnostic work-up. Research efforts in NBS are directed at improving current methodologies, designing new technologies and developing tests for “new” disorders not currently included in NBS panels. For example, new methods using MS/MS identify a class of disorders

References

known as lysosomal storage disorders that may become candidates for NBS (Li et al., 2004). Another condition being researched for NBS is type 1 diabetes. International efforts are being made with The Environmental Determinants of Diabetes in the Young (TEDDY) study. Families are recruited to include their newborns in the study. The initial screen uses the residual blood from the mandated NBS program. This blood is tested for certain genotypes that identify those babies with a higher risk of developing type 1 diabetes. The families of these babies are asked to participate in a long-term study on the environmental determinants of this complex childhood disease (Vogt et al., 2003). NBS has benefited modestly from the knowledge of genetics and genotyping technology. However, much of the “low-hanging fruit” of the human genome has already been picked (Risch, 2000). As reviewed in this chapter, even seemingly simple NBS conditions are often difficult to understand and treat. What remains is to tease out the genetics of common and complex conditions such as diabetes, obesity, heart disease, cancer and hypertension (Khoury et al., 2003). Advances in the fields of genomics, proteomics (de Hoog et al., 2004), metabolomics (Rochfort, 2005) and their integration into systems biology research (Weston et al., 2004) herald much promise for the future of medical science, including NBS. This type of integrated approach will be critical to better understand common and complex conditions that are probably influenced by multiple genes and the environment. Alexander and van Dyck outline a vision of the future of NBS in a recent article, calling for equity among states in the screening panels, testing for many more disorders using DNA technology, and improving NBS education and infrastructure (Alexander et al., 2006). They also advocate for “changing the dogma” of NBS, meaning that conditions for which effective treatment does not exist should not be excluded from NBS panels. They argue that this practice is outdated because it does not consider the benefits to families in avoiding diagnostic odysseys and for reproductive decision-making, and the benefits for the

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child of early intervention and participating in research such as clinical trials of new treatments. NBS programs have generated more than 40 years of experience as a population-based genetic screening program. Lessons learned by NBS may be of benefit to policymakers and entrepreneurs as they consider other types of genetic screening programs: 1. In most cases, DNA testing is not an easy answer. Many disorders are influenced by more than one gene. Clinical heterogeneity of disease complicates matters further because a specific mutation may exhibit a spectrum of clinical outcomes, from severe disease to asymptomatic. 2. Experts are needed to interpret results to maximize the benefits of the genetic information (empowered patients and physicians) and to minimize the negative effects (poor decision-making based on misinformation and parental stress). The future holds many exciting advances in science, technology and medicine. These will include techniques to improve the screening and treatment of conditions currently included in NBS programs. Many new disorders will become candidates for NBS. Laboratories will adapt to these changes and learn new methods, but barring some global change in the way public health is viewed, it is likely that future expansion of NBS programs will be guided toward those conditions that meet the traditional inclusion criteria that were listed at the beginning of this chapter. In addition to advances in technology, improvements in some of the more mundane aspects of the NBS system, such as collecting good quality blood spots and decreasing specimen transit time, will surely yield great benefits to babies with NBS conditions.

ACKNOWLEDGEMENTS The authors thank Sheila Weiss and JoLyn Thompson for their helpful comments on draft versions of this chapter.

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Vichinsky, E., Hurst, D., Earles, A., Kleman, K. and Lubin, B. (1988). Newborn screening for sickle cell disease: Effect on mortality. Pediatrics 81(6), 749–755. Vogt, R.F., Meredith, N., Henderson, L.O. and Hannon, W.H. (2003). Newborn screening and type 1 diabetes: Historical perspective and current activities and the CDC division of laboratory sciences. Diabetes Technol Ther 5(6), 1017–1021. Waisbren, S.E., Albers, S., Amato, S., Ampola, M., Brewster, T.G., Demmer, L., Eaton, R.B., Greenstein, R., Korson, M., Larson, C. et al. (2003). Effect of expanded newborn screening for biochemical genetic disorders on child outcomes and parental stress. JAMA 290(19), 2564–2572. Waisbren, S.E. and Azen, C. (2003). Cognitive and behavioral development in maternal phenylketonuria offspring. Pediatrics 112(6 Pt 2), 1544–1547. Weston, A.D. and Hood, L. (2004). Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine. J Proteome Res 3(2), 179–196. Wilcken, B., Brown, A.R., Urwin, R. and Brown, D.A. (1983). Cystic fibrosis screening by dried blood spot trypsin assay: Results in 75,000 newborn infants. J Pediatr 102(3), 383–387.

RECOMMENDED RESOURCES Centers for Disease Control and Prevention (2006). Office of Genomics and Disease Prevention Home Page. http://www.cdc.gov/genomics/. This website “provides information about human genomic discoveries and how they can be used to improve health and prevent disease in populations. It also provides links to CDC wide activities in public health genomics across the lifespan.” Health Resources and Services Administration (2006). Maternal and Child Health Bureau (MCHB) Home Page. http://mchb.hrsa. gov/default.htm. “MCHB seeks a nation where there is equal access for all to quality health care in a supportive, culturally competent, family and community setting.” This website provides information about MCHB programs and grants related to newborn screening. Health Resources and Services Administration (2006). National Newborn Screening and Genetics Resource Center Home Page. http://genes-r-us.uthscsa.edu/.This excellent resource center “provides information and resources in the area of newborn screening and genetics to benefit health professionals, the public health community, consumers and government officials.” Their website includes current information about newborn screening panels and links to each of the program websites in the United States. Levy, H.L. and Albers, S. (2000). Genetic Screening of Newborns. Annu Rev Genomics Hum Genet 1, 139–177. This comprehensive article reviews the genetic conditions widely included in newborn screening programs and covers recent advances in tandem mass spectrometry and DNA testing.

(HuGE) Review focuses on the sickle hemoglobin and other globin gene variants and contains data regarding morbidity and mortality rates and a section that discusses unique interactions with other genes. Centers for Disease Control and Prevention (2001). Using Tandem Mass Spectrometry for Metabolic Disease Screening among Newborns: A Report of a Work Group. MMWR 50(No. RR-3). “In June 2000, the National Newborn Screening and Genetics Resource Center, in collaboration with CDC and the Health Resources and Services Administration, convened a workshop…. This work group report contains proposals for planning, operating, and evaluating tandem mass spectrometry technology in newborn screening and maternal and child health programs.” Centers for Disease Control and Prevention (2004). Newborn Screening for Cystic Fibrosis: Evaluation of Benefits and Risks and Recommendations for State Newborn Screening Programs. MMWR Recomm Rep 53(RR-13), 1–36. “In November 2003, CDC and the Cystic Fibrosis Foundation cosponsored a workshop to review the benefits and risks associated with newborn screening for cystic fibrosis. This report describes new research findings and outlines the recommendations of the workshop.” Centerwall, S.A. and Centerwall, W.R. (2000). The Discovery of Phenylketonuria: The Story of a Young Couple, Two Retarded Children, and a Scientist. Pediatrics 105(1 Pt 1), 89–103. This article is a narrative about Asbjörn Fölling, the Norwegian physician that discovered PKU, and the two children with PKU under his care. It includes a section about the early treatment of PKU.

Disorders/Technologies Covered in Chapter

Advocacy Organizations

Ashley-Koch, A., Yang, Q. and Olney, R.S. (2000). Sickle Hemoglobin (Hbs) Allele and Sickle Cell Disease: A Huge Review. Am J Epidemiol 151(9), 839–845. This Human Genome Epidemiology

Save Babies Through Screening (2006). Save Babies Through Screening Home Page. http://www.savebabies.org/. “Save Babies Through Screening Foundation is a national non-profit public charity

Newborn Screening: General

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run by volunteers. Its mission is to improve the lives of babies by working to prevent disabilities and early death resulting from disorders detectable through newborn screening.” Save Babies Through Screening is a well-organized and active advocacy group composed largely of family members of individuals affected by congenital disorders. Their website offers a wide variety of resources for parents and the lay public including personal stories from families of affected individuals. This organization has been a strong and effective force advocating for expanded screening. March of Dimes Foundation (2006). March of Dimes Foundation Home Page. http://www.marchofdimes.com/. “Our mission is to improve the health of babies by preventing birth defects, premature birth, and infant mortality. We carry out this mission through research, community services, education and advocacy to save babies’ lives.” The March of Dimes has been a vocal advocate for expanded newborn screening.

New Technologies Guttmacher, A.E. and Collins, F.S. (2005). Realizing the Promise of Genomics in Biomedical Research. JAMA 294(11), 1399–1402. This commentary considers the future of genomic research and its relations to health care and society. Jones, P.M. and Bennett, M.J. (2002). The Changing Face of Newborn Screening: Diagnosis of Inborn Errors of Metabolism by Tandem Mass Spectrometry. Clin Chim Acta 324(1–2), 121–128. This article reviews changes in newborn screening for inborn errors of metabolism and discusses tandem mass spectrometry technology. Wilcken, B. (2003). Ethical Issues in Newborn Screening and the Impact of New Technologies. Eur J Pediatr 162 Suppl. 1, S62–66. This article applies the principles of medical ethics (autonomy, beneficence, non-maleficence and justice) to new technologies available for screening newborns.

CHAPTER

42 Family History: A Bridge Between Genomic Medicine and Disease Prevention Maren T. Scheuner and Paula W. Yoon

INTRODUCTION Understanding the genetic basis for disease may substantially improve health care by providing options for disease management and prevention that are targeted to an individual’s genetic make-up. This personalized approach can improve health outcomes and efficiency through earlier and more accurate diagnosis, enhanced prevention efforts, and choice of therapies that optimize response and avoid adverse drug reactions. Providing such personalized information to patients also fits well with the concept of shared decision-making, a communication process between a clinician and patient that recognizes that certain recommendations for disease management and prevention need to be individualized according to a patient’s special circumstances and preferences. Thus, genetic and genomic information have the potential to revolutionize the way health care is delivered – ensuring access to more appropriate and more effective patientcentered care. Family history is an important tool for identifying individuals and families with genetic susceptibility to common chronic diseases such as coronary heart disease, stroke, diabetes and most cancers, as well as the rarer single gene disorders like cystic fibrosis, sickle cell anemia, hereditary forms of breast and colorectal cancer, and hemochromatosis. Generally for rare genetic Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

disorders, family history primarily reflects the genetic risk factors shared by affected family members. Usually these are single gene disorders that follow specific patterns of inheritance and can be recognized on that basis. For the common, chronic diseases that are multifactorial in nature, family history of disease reflects the complex interactions of genetic and non-genetic risk factors (e.g., exposures, behaviors, cultural factors) shared by affected family members. In this chapter we will describe the role of family history in health promotion and disease prevention from both a clinical and public health perspective. Both perspectives emphasize an individualized rather than a one-size-fits-all approach to health care, which has the potential to result in improved health outcomes.

CLINICAL APPROACH Familial Risk Assessment For many common chronic diseases, a positive family history can increase the risk of disease from two to more than ten times those of the general population, and this risk generally increases with an increasing number of affected relatives and earlier ages of disease onset (i.e., occurring about 10–20 years earlier than Copyright © 2009, Elsevier Inc. All rights reserved. 481

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TABLE 42.1

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Risks associated with family history for selected common diseases

Disease

Risk due to family history

References

Coronary heart disease

OR  2.5 (one first-degree relative diagnosed at or after age 60) OR  5.0 (one first-degree relative diagnosed before age 60) OR  5.1 (two or more first-degree relatives at any age of onset) OR  4.6 (one second-degree relative diagnosed before age 60)

Scheuner et al. (2006)

Type 2 diabetes

RR  2.4 (mother) RR  4.0 (maternal and paternal relatives)

Klein et al. (1996) Bjornholt et al. (2000)

Osteoporosis

OR  2.0 (female first-degree relative) RR  2.4 (father)

Keen et al. (1999) Fox et al. (1998)

Breast cancer

RR  2.1 (one first-degree relative) RR  3.9 (three or more first-degree relatives)

Pharoah et al. (1997) Collaborative Group (2001)

Colorectal cancer

OR  1.7 (one first-degree relative) OR  4.9 (two first-degree relatives)

Fuchs et al. (1994) Sandhu et al. (2001)

Prostate cancer

RR  3.2 (one first-degree relative) RR  11.0 (three first-degree relatives)

Cerhan et al. (1999) Steinberg et al. (1990)

OR  odds ratio; RR  relative risk

typical) (Table 42.1). Additional characteristics of high-risk family histories include multifocal or bilateral disease, higher rates of disease recurrence, occurrence of disease in the less often affected sex, and the occurrence of related diagnoses in a pattern suggestive of a single gene disorder. By recognizing the magnitude of risk associated with these familial characteristics, as well as patterns of familial disease suggestive of an inherited susceptibility, stratification of familial risk into different risk groups (e.g., weak, moderate, and strong) is possible (see Box 1) (Hampel et al., 2004; Scheuner et al., 1997). For certain common diseases, quantitative assessment of familial risk can also be performed for specific conditions using mathematical models or published estimates (Amos et al., 1992; Claus et al., 1993, 1994; St John et al., 1993). For example, using tables published by Claus (1993, 1994) an estimate of a woman’s risk for breast cancer in the next 10 years or by age 80 can be BOX 1

determined on the basis of her family history, including age at diagnosis of breast or ovarian cancer in first- and second-degree relatives, and these estimates can be contrasted to the population risk estimates. Familial risk assessment methods can provide relatively good estimates of disease risk when family history is the predominant risk factor, and they can be used as a “screening test” to identify individuals who should be referred to genetic susceptibility models that estimate the probability of a gene mutation associated with an inherited susceptibility (e.g., the BRCAPRO model by Parmigiani et al. (1998) that estimates the probability of a BRCA1 or BRCA2 gene mutation). However, familial risk assessment methods and genetic susceptibility models are not ideal for assessing overall risk for complex multifactorial disease, since they do not consider other risk factors that might influence disease risk, such as clinical history, exposures, or behaviors.

Familial Risk Stratification Strong familial risk is assigned:

Generally, for many common chronic diseases the risks are as follows: Weak familial risk can be assigned if there is: ● ●

No family history of disease Disease in only one second-degree relative from one or both sides of the family Moderate familial risk is assigned if there is:

● ● ● ●

Only one first-degree relative with late-onset disease Only one first-degree relative with late-onset disease and one second-degree related with late-onset disease from the same lineage Only one second-degree relative with early-onset disease and one second-degree relative with late-onset disease from the same lineage Only two second-degree relatives with late-onset disease from the same lineage

●

●

For all other family histories of disease, including having at least one first-degree relative with early-onset disease and combinations of multiple affected family members within and across generations When family histories are suggestive of known single gene disorders

Early age of disease onset refers to disease that occurs about 10– 20 years earlier than typical. Lineage refers to the side of the family where disease is reported. Maternal lineage refers to an affected mother and/or her relatives, and paternal lineage an affected father and/or his relatives. Some individuals may have only siblings or children affected; this can be described as a “nuclear” pattern of lineage. Adapted from Scheuner et al. (1997).

Clinical Approach

Unfortunately, the available absolute disease risk prediction models such as the Gail model for breast cancer (Rockhill et al., 2001) or the Framingham risk equation for coronary heart disease (Wilson et al., 1998) that provide disease risk estimates based primarily on socio-demographic and personal risk factors (e.g., medical conditions, exposures, and habits) consider only limited family history or do not consider family history at all in deriving their risk estimates. These models were developed with data collected from large epidemiologic studies that did not ascertain comprehensive family history. The models work well in predicting risk for most people, but they can underestimate disease risk for people who have a strong familial risk. Ideally, future epidemiologic studies should collect comprehensive socio-demographic, clinical, and family history data so that absolute risk prediction models can be developed that adequately address family history as a risk factor. Until that time, disease risk assessment strategies should include a combination of absolute risk prediction models, familial risk assessment methods, and genetic susceptibility models, when available. The figure provides an illustration of this strategy (Figure 42.1). Family History and Disease Management and Prevention Interventions Knowledge of the familial risk level can guide risk-specific recommendations for disease management and prevention, including referral for genetic evaluation or testing (Table 42.2). Recognizing personal and family history characteristics is crucial for identifying individuals suspected of having rare Mendelian disorders and for determining which patients should be offered testing (Scheuner et al., 2004). Furthermore, available guidelines for genetic testing referral rely heavily on specific family history criteria. Examples include the BRCA1/2 testing guidelines developed by the United States Preventive Services Task Force (2005) and the National Comprehensive Cancer Network (2006). Prevention strategies for people with increased familial risk could include targeted lifestyle changes; screening at earlier ages, more frequently and with more intensive methods than used for average risk individuals; use of chemoprevention; and for those at highest risk, prophylactic procedures, and surgeries. Data are accumulating regarding the effectiveness of these strategies in high-risk individuals (Scheuner et al., 2004). For example, the United States Preventive Services Task Force has found fair to good evidence supporting family history as an important clinical consideration for screening and prevention recommendations for breast cancer, colorectal cancer, lipid disorders, coronary heart disease, and abdominal aortic aneurysm (Wattendorf and Hadley, 2005). Breast cancer screening and prevention strategies appropriate for women with genetic susceptibility to breast cancer provide some of the best examples demonstrating the clinical utility of a family history based approach to prevention (Table 42.3). Family History and Emerging Genomics Technologies With the completion of the human genome sequence in 2003, we can expect rapid progress in the characterization of the genetic basis of rare Mendelian disorders, which number over 6000. But perhaps more importantly from a public health perspective, we will be able to define the genetic basis for the common

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chronic diseases that result from complex interactions between variations in multiple genes and the environment. These genomic discoveries will dramatically increase the number of available genetic tests. Today there are about 1000 clinical genetic tests, and most are for rare Mendelian disorders (GeneTests, 2007). In the next 10 years we can expect the development of new genetic tests – including concurrent testing of multiple genetic markers using microarray technologies (i.e., multiplex testing) – that will be used to help refine diagnoses, improve risk prediction, and target therapies for common, chronic diseases. Will the family history become obsolete and eventually be replaced by modern genomic testing technologies? Many experts are of the opinion that the family history will remain highly relevant for many years to come. Recognizing personal and family history characteristics is crucial for identifying individuals suspected of having rare single gene disorders and for determining which patients should be offered testing (Scheuner et al., 2004), and there is no reason to believe that this strategy will change. For the complex multifactorial common chronic diseases, until we completely understand the role of specific genetic factors and their interaction with each other and the environment in determining health and disease, the family history may still be the best approach to select individuals for whom testing is most appropriate, and it may be most effective to integrate these genetic test results with selected family history and personal risk factors to suggest how best to individualize care (Guttmacher et al., 2004). The Need for Family History Tools Results of a recent national survey show that most Americans believe that knowing family history is very (73%) or somewhat (24%) important to their personal health (Centers for Disease Control and Prevention, 2004). Yet additional studies have shown that having a close family member with disease does not automatically translate to improvements in rates of selfinitiated participation in screening or risk-reducing lifestyles (Kip et al., 2002; West et al., 2003). How can we explain this apparent inconsistency? It may be that consumers do not appreciate how family history relates to their own risk or they may not be aware that despite a familial predisposition there are actions they can take to lower their risk. Further complicating this limitation on the use of family history, the literature suggests that physicians perform poorly with respect to collection and interpretation of family history for risk stratification and recommendation of risk-specific interventions (Acheson et al., 2000; Frezzo et al., 2003; Hayflick et al., 1998; Koscica et al., 2001; Sifri et al., 2002; Sweet et al., 2002). The reluctance of clinicians to examine the role of family history appears to be due to concerns about the amount of time required to collect the information, their ability to interpret such information accurately, and fears that they will not be compensated for the time spent researching family history (Gramling et al., 2004; Rich et al., 2004; Suther and Goodson, 2003). Typically, an absolute risk prediction model would be appropriate for individuals with a weak or moderate familial risk for a common chronic disease, and individuals with a strong familial risk would be referred to high-risk clinics for genetic

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Familial risk assessment Familial risk result

Weak

Moderate

Absolute risk model

Strong

Genetic susceptibility model Probability of mutation

Disease risk Low

High Genetic testing

Average

Increased Familial mutation excluded

Standard preventive interventions

Familial mutation not excluded

Deleterious mutation found

Enhanced preventive interventions

Figure 42.1 Integrating familial risk assessment methods, genetic susceptibility models, and absolute risk prediction models for optimal guidance in disease prevention

TABLE 42.2

Prevention strategies for colorectal cancer according to familial risk level

Familial risk level

Prevention strategy

References

Weak

Beginning at age 50, annual fecal occult blood testing (FOBT) and/or flexible sigmoidoscopy every 5 years, or doublecontrast barium enema every 5–10 years, or colonoscopy every 10 years.

Winawer et al. (1997); Rex et al. (2000); Smith et al. (2002); USPSTF (2002a)

Moderate

Beginning at age 40, annual FOBT and/or flexible sigmoidoscopy every 5 years, or double-contrast barium enema every 5–10 years, or colonoscopy every 10 years.

Winawer et al. (1997); Rex et al. (2000)

Strong

Beginning at age 40 or 10 years before the earliest age at diagnosis in the family, colonoscopy every 3–5 years. Consider daily use of aspirin. If family history is suggestive of hereditary colorectal cancer, consider screening and preventive interventions for associated cancers (e.g., endometrial and ovarian cancer).

Rex et al. (2000); Baron et al. (2003)

evaluation and application of genetic susceptibility models, when available. Individuals with a strong familial risk but a low probability of carrying a mutation (e.g., 10%) would be less likely to pursue testing (or may not be offered testing). However, enhanced preventive strategies may still be appropriate given their increased familial risk; this increased risk might be due to another inherited susceptibility not evaluated by the available genetic susceptibility models or to familial aggregation of a multifactorial form of disease. Individuals with a high probability of a mutation (e.g., 10%) would be most likely to undergo testing. If such individuals decline testing, clinicians should recommend enhanced preventive

interventions given their increased familial risk. Enhanced preventive interventions would also be recommended for individuals who undergo testing and are found to carry a deleterious mutation, as well as for individuals with indeterminate or normal results when there is no prior knowledge of a familial mutation (i.e., familial mutation not excluded). If a deleterious mutation has been previously identified in another family member and is excluded in an at-risk family member, it would be most appropriate to refer these individuals to absolute risk prediction models since their family history is generally no longer a significant risk factor and other socio-demographic and clinical risk factors would predominate.

Clinical Approach

TABLE 42.3

Efficacy

Chemo-prevention

●

●

● ●

Prophylactic surgery

●

●

●

●

● ●

●

●

Lifestyle

485

Screening for and prevention of breast cancer among women with a genetic susceptibility

Intervention

Screening for early detection

■

● ●

●

References

Tamoxifen reduces the occurrence of breast cancer by about 50% in women with an increased risk due in part to family history. Raloxifene is effective in reducing breast cancer incidence in postmenopausal women at risk, especially for women with a family history. Tamoxifen may reduce by 50–75% the risk of contralateral breast cancer in women with BRCA mutations. Tamoxifen may reduce the incidence of breast cancer in women with BRCA2 gene mutations, but not in women with BRCA1 gene mutations.

Fisher et al. (1998)

Prophylactic bilateral mastectomy reduced the incidence of breast cancer by 90% in women with both moderate and high-risk family histories of breast cancer. For women with a personal and family history of breast cancer, contralateral prophylactic mastectomy reduced the incidence of a second primary breast cancer by 94% and 96% for both premenopausal and postmenopausal women, respectively. Bilateral prophylactic mastectomy reduces the risk of breast cancer in women with BRCA mutations by about 95% in women with prior or concurrent bilateral prophylactic oophorectomy (BPO) and by about 90% in women with intact ovaries. Women with a BRCA gene mutation who undergo prophylactic BPO prior to menopause benefit not only from reducing their ovarian cancer risk by 95% but also from reducing their breast cancer risk by about 50%.

Hartmann et al. (1999)

Vogel et al. (2006); Lippman et al. (2006)

Narod et al. (2000) King et al. (2001)

McDonnell et al. (2001)

Meijers-Heijboer et al. (2001); Rebbeck et al. (2004)

Rebbeck et al. (2002, 2005); Kauff et al. (2002); Eisen et al. (2005)

The predictive value of mammography may be increased three-fold if there is a family history of breast cancer. Half of the tumors in BRCA mutation carriers appear in the interval between annual mammograms. Thus, shorter mammogram screening intervals and/or the addition of other imaging techniques might be warranted. In general, beginning at age 25–35, monthly self-examination, clinical examination every 6 months, and annual mammography are suggested for women with BRCA gene mutations. Good evidence shows that MRI has higher sensitivity for detecting breast cancer among women with BRCA mutations than does mammography, clinical breast examination, or ultrasound; use of combination screening with MRI, ultrasound, and mammography has the highest sensitivity of 95%.

Kerlikowske et al. (1993); Brekelmans et al. (2001) Meijers-Heijboer et al. (2001); Brekelmans et al. (2001); Smith et al. (2003); Scheuer et al. (2002); Komenaka et al. (2004); Warner et al. (2004) Burke et al. (1997); Meijers-Heijboer et al. (2001); Vasen et al. (1998); Smith et al. (2003)

Weight loss in early adult life has been associated with a reduced breast cancer risk in BRCA mutation carriers. Breastfeeding for more than 1 year may reduce the risk of breast cancer for women with BRCA1 mutations, but not for women with BRCA2 mutations. Women with BRCA1 mutations who take oral contraceptives have an increased risk for breast cancer; however, oral contraceptives do not appear to increase the risk in BRCA2 carriers.

Kotsopoulos et al. (2005)

Warner et al. (2004); Kriege et al. (2004)

Jernström et al. (2004)

Narod et al. (2002)

(continued)

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TABLE 42.3

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Family History and Disease Prevention

(Continued)

Intervention

Efficacy

Lifestyle (continued)

●

●

●

References

Women with family history of breast cancer may increase their risk for breast cancer by using hormone replacement therapy (HRT); however, the rate of overall mortality may not be increased. The protective effect of BPO on breast cancer risk appears to be sustained in women with BRCA mutations who choose to use short-term postmenopausal HRT. Use of HRT in women with BRCA mutations does not increase the risk of ovarian cancer.

The obstacle posed by the lack of sufficient time or expertise to obtain, organize, and analyze the family history could be overcome in large part through use of family history tools (e.g., stand-alone tools or applications integrated into electronic health record and personal health record systems) that have the capabilities to: (1) collect relevant personal and family health history in a structured format, (2) organize the data in a usable form such as a graphic display following pedigree drawing standards, (3) interpret the familial risk and recognize patterns of familial disease suggestive of inherited susceptibilities (i.e., pedigree analysis), and (4) provide recommendations about interventions that are tailored to the familial risk and personal factors. Such tools have the potential to: (1) aid in identifying individuals with increased risk for common chronic diseases and rare genetic conditions, (2) facilitate discussion about health issues between patients and their health care professionals and among family members, (3) improve management of disease and disease risk factors by patients (i.e., self-management) and their providers, (4) raise awareness of the importance of family as a social unit integral to health and well-being through educational messaging, and (5) increase accuracy and completeness of family health history information, because patients could enter the data in a non-clinical setting at their own pace, affording them more time to access records and consult with family members than they have during an office visit. Accuracy of the family history data is paramount particularly if clinical decisions will depend on the information. Many studies have shown that self-reports of family health history are relatively accurate for many common chronic conditions, such as coronary heart disease, stroke, diabetes, and many forms of cancer. Most sensitivity values for self-reports of a positive family history of these conditions in a first-degree relative range from 70% to 90% and specificity is usually 90% or greater (Bensen et al., 1999; Kee et al., 1993; Hastrup et al., 1985; Murabito et al., 2004; Murff et al., 2004; Silberberg et al., 1998;Watt et al., 2000; Ziogas and Anton-Culver, 2003). Accuracy, however, depends on the type of disease, and it is less for more distant relatives and when historians are older. Therefore, before clinical decisions are based on such information, confirmation of family health histories is advisable.

Steinberg et al. (1991); Sellers et al. (1997)

Rebbeck et al. (1999, 2005)

Kotsopoulos et al. (2006)

Currently such confirmation is handled by health professionals who request and review medical records, pathology reports, and death certificates of family members to verify self-reports. This can be time-consuming and costly. One genetics clinic reported that 3–5.5 h were spent per initial consultation, with over half of this time devoted to activities such as obtaining and verifying family history data (Bernhardt and Pyeritz, 1989). Family History Tools and Electronic Health Records If family history tools were integrated into electronic medical record (EMR) systems, they could offer clinicians the advantages of standardizing the collection and organization of family history data. Electronic clinical decision support could also provide familial risk assessment and pedigree analysis, standardized guidance for referral for genetics consultation and testing, and evidence-based recommendations for interventions specific to the familial risk. Additionally, family history tools integrated within personal health record (PHR) systems used by consumers have the potential to improve self-management of disease and disease risk factors through built-in messaging about health promotion and disease prevention activities tailored to an individual’s familial risk and personal characteristics. Such PHR products could further improve the accuracy of family history reports by capabilities that allow health information (including results of genetic testing) to be transferred directly to family members rather than relying on clinicians to obtain and review records and transmit information. This direct communication among family members would improve familial risk assessment and pedigree analysis. The privacy concerns that are often raised by consumers regarding familial or genetic information could also be addressed if these PHRs offer methods for giving consumers control over (1) who may receive their information, (2) what specific types of information these recipients may get, and (3) how the recipients are limited in further sharing of this information. Family history applications developed for EMR and PHR products must have common data requirements and technical standards to ensure optimal exchange of family history data and ultimately use of family history information for disease management and health promotion activities, including the ordering

Clinical Approach

and interpretation of genetic tests. At present, several different standards development organizations are working to create a framework for representing and exchanging the contents of EHRs (including the family health history). The two most prominent of these are Health Level 7’s Clinical Document Architecture and the American Society of Testing and Materials International Continuity of Care Record. Two in this case is not better than one, as the differing standards and data architectures may prove to be substantial obstacles to efficient data exchange. Fortunately, it appears that both organizations will work collaboratively to define a common standard (Ferranti et al., 2006). Population Approach Although family history is known to be a risk factor for many chronic diseases, its use in preventive medicine and public health has been de-emphasized compared with modifiable risk factors such as smoking and diet. However, recent studies show that a large fraction of the population is likely to have a family history of one or more common diseases. For example, a populationbased study of family history of cardiovascular disease in Utah showed that 72% of early coronary heart disease (diagnosed before age 55 years) in the population occurred in 14% of families and 86% of early stroke occurred in 11% of families (Hunt et al., 2003). In a recent analysis of the National Health and Nutrition Examination Survey 1999–2002, 48% of the study population reported having at least one relative with diabetes (Hariri et al., 2006). And in Michigan, 7% of the population reported having an immediate family member diagnosed with colorectal cancer according to the 2005 Behavioral Risk Factor Survey (Personal communication, Deb Duquette, Michigan Department of Community Health, 2006). These data suggest that implementation of family history based strategies to assess disease risk, influence early disease detection, and encourage lifestyle changes could lead to overall population health benefits. An advantage of family history based approaches to prevention is that they do not focus exclusively on an individual’s risk factors but can work within a framework of biologic and cultural relationships to affect risk factor reduction. Family-Based Risk Assessment and Prevention Modifiable risk factors for common chronic diseases aggregate in families. For example, in an analysis of the Third National Health and Nutrition Examination Survey, adults with a parental history of coronary heart disease were more likely to have multiple risk factors such as smoking, elevated blood pressure, and elevated cholesterol. The odds ratio for four or five risk factors compared with none was 2.9, 95% CI, 1.4–6.3 (Brown et al., 2002). Furthermore, studies have shown that disease risk is substantially increased for individuals having lifestyle risk factors and a family history of disease compared to having only lifestyle risk factors or only family history, suggesting that individuals with increased familial risk have the most to gain from interventions related to traditional, modifiable risk factors (Tavani et al., 2004a, b).

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Therefore, modifying risk factors within a family rather than focusing on an individual ought to be an effective strategy, and this has been shown in a few studies looking at modification of cardiovascular disease risk factors (Knutsen and Knutsen, 1991; Pyke et al., 1997). Lifestyle changes, such as dietary modification, weight control, and smoking cessation are likely to be more effective when delivered to the family than to an individual because family members can influence each other and provide ongoing support to one another. Familial Risk Stratification: A Public Health Screening Tool The added value of family history as a public health screening tool or motivational strategy should be rigorously tested as an adjunct to population-level prevention activities. Comprehensive population-based data are needed to develop family history risk assessment tools and evaluate the validity and utility of this approach. For each disease of interest, data are needed to (1) determine the accuracy and reliability of reporting disease status for each relative (analytic validity), (2) estimate the risk associated with family history and how well it can predict future disease (clinical validity), (3) determine the impact and usefulness of family history screening for disease prevention and early detection (clinical utility), and (4) monitor the ethical, legal, and social implications of assessing family history and conveying disease risk. As with any public health strategy to prevent diseases, there is a series of activities that are involved in developing an effective intervention: (1) assessing the health issue, (2) prioritizing the health issues and related interventions, (3) setting objectives for the intervention, (4) selecting effective interventions, (5) implementing activities, and (6) evaluating the interventions (Brownson et al, 2003). Information obtained from population-based surveys and follow up studies of family history are needed for each of these steps. More specifically, data are needed to do the following: ●

●

●

●

●

●

●

●

●

Assess population characteristics such as gender, race, age, and socio-economic indicators that may affect attitudes, knowledge, and practice regarding family history Establish the prevalence of family history for each disease of interest Assess the association between family history and disease occurrence Validate algorithms and stratification schemes developed for individual familial risk assessment Determine what risk factors modify the effect of family history on disease occurrence Monitor patient and provider practices regarding the collection and use of family history information Examine associations between knowledge of familial risk and preventive behaviors Measure the impact of family history based interventions on reduction of risk behaviors and disease prevention Estimate the cost-effectiveness of family history based screening for early detection and prevention of disease

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There have been many studies that have shown that family history is an independent and often strong risk factor for common chronic diseases (Table 42.1). More recently studies are revealing the potential of family history as a screening tool to identify subclinical disease, for example, coronary artery calcification (Michos et al., 2005; Nasir et al., 2004) and undiagnosed diabetes (Hariri et al., 2006). From a public health perspective, family history may be most useful in settings where individuals don’t have access to more invasive tests such as glucose and cholesterol testing. Family history and other risk factors, such as body mass index (based on weight and height), that can be self-reported could be the basis for a low-cost low-tech screening tool to identify individuals and families at risk for diabetes and cardiovascular diseases (Hariri et al., 2006) and for whom preclinical disease detection strategies might be most appropriate. The use of cancer family history to identify individuals at risk and guide prevention activities is well established in clinical practice but has not been used widely as a population-level screening tool. Family history is a potentially valuable tool for identifying individuals in the population who might benefit from earlier or more intensive cancer screening. In addition, educating people about a familial risk of cancer might motivate them to adhere to screening guidelines. For example, only about 45% of men and 40% of women aged 50 years and older have undergone colon cancer screening (American Cancer Society, 2006). Therefore, any strategies that can improve participation in colorectal cancer screening are welcome. However, there is a paucity of literature describing the impact of familial risk assessment on health-related behaviors (Audrain et al. 2003). As an alternative, decision analysis methods have been used to estimate the impact of family-based screening (Ramsey et al., 2005;Tyagi and Morris, 2003). While results from these simulation studies are promising, further data are needed to determine the effectiveness of this strategy for disease prevention, as well as the likely burden on the medical system. Family History Public Health Initiatives There has been a renewed interest in using family health history for individualized risk assessment and disease prevention, and efforts such as the Surgeon General’s Family History Initiative have begun to raise awareness among the public and health professionals about the value of family history (see

Recommended Resources). In 2004, the US Surgeon General declared Thanksgiving as National Family History Day and encouraged Americans to collect their family members’ health histories and share the information with their health care providers (Guttmacher et al., 2004). A website was created with educational materials as well as a family history data collection tool (http://www.hhs.gov/familyhistory). Several federal, state, and professional organizations are developing new family history tools for data collection and risk assessment (see the National Office of Public Health Genomics website, listed under Recommended Resources) and community-based studies are under way to assess awareness, understanding, and self-efficacy among population subgroups for the purpose of developing more appropriate and effective educational materials and health promotion messages.

CONCLUSION Family history is an important risk factor for both common chronic diseases and rare genetic disorders. Recognizing patterns of familial disease that signify increased risk can help to identify individuals who may have the most to gain from preventive interventions including genetic testing technologies. Tools that aid in the collection, organization, and interpretation of family health information, especially applications for interoperable electronic health record systems, will be crucial to the success of this approach. Such tools can educate users about the important role of the family in defining health risks and prevention options, thereby facilitating discussion about health promotion activities between consumers and their family members and health care providers. Finally, to ensure widespread adoption of family-based risk assessment and prevention, evidence must be generated from studies evaluating the feasibility, validity, and utility of this approach.

ACKNOWLEDGEMENTS No conflict of interest to report. Dr. Scheuner has a patent pending for a familial risk stratification and pedigree analysis method and apparatus (US Patent Application Number: 20060173717, Publication Date: 8/3/06).

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RECOMMENDED RESOURCES National Office of Public Health Genomics, Centers for Disease Control and Prevention. 2006 Family History: Resources and Tools. (http://www.cdc.gov/genomics/public/famhistMain.htm)

Department of Health and Human Services. 2006 US Surgeon General’s Family History Initiative (http://www.hhs.gov/ familyhistory/)

Section

Clinical Technologies Supporting Personalized Medicine

7

50. 51. 52.

Molecular Imaging as a Paradigm for Genomic and Personalized Medicine PET Imaging in Genomic Medicine MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges Fluorescence Imaging: Overview and Applications in Biomedical Research Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers Viral Chip Technology in Genomic Medicine Vaccines Against Infectious Diseases: A Biotechnology-Driven Evolution Cancer Vaccines: Some Basic Considerations Biosensors for the Genomic Age Stem Cells

53.

Gene Therapy

43. 44. 45. 46. 47. 48. 49.

CHAPTER

43 Molecular Imaging as a Paradigm for Genomic and Personalized Medicine Ralph Weissleder

INTRODUCTION Many modern disease treatments require precise positional information. For example in cancer, critical questions are: where is a tumor located, how large is it, is it confined or has it spread to lymph nodes, does it involve any critical anatomical structures that would alter the treatment strategy? These and other questions are being answered, at ever-increasing spatial resolution, through application of traditional anatomical imaging methods such as computed X-ray tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). While these methods still represent the mainstay of clinical imaging, it has become clear that acquisition of molecular and physiological information by nuclear, optical, and magnetic resonance imaging technologies could vastly enhance our ability to treat many diseases more efficiently (Weissleder and Pittet, 2006; Jaffer and Weissleder, 2005; Juweid and Cheson, 2006;Weissleder, 2002). Table 43.1 provides an overview of these different imaging approaches (some of which are described in greater detail in other chapters in this section) and their spatial resolutions. The recent revolution in genomic and proteomic technologies has provided a wealth of new molecular information that may transform the way in which diseases are clinically managed. Molecular imaging is poised to play a central role in this transformation because it will allow integration of molecular and physiological information specific to each patient with

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anatomical information obtained by conventional imaging methods. Depending on the imaging agents used, molecular imaging can already be used experimentally to monitor and quantify in vivo gene/protein expression as well as specific biological processes that determine disease behavior and drug response. The hope is that the information obtained by clinical molecular imaging will one day be used to (i) detect molecular or physiological alterations that signal the presence of disease when it is still at a curable stage, (ii) evaluate and adjust treatment protocols and choices in real time, and (iii) speed up the drug development process. Using cancer as a specific example, this chapter highlights some of the potential roles of molecular imaging. Similar advances are being made in cardiovascular (Table 43.2), inflammatory, autoimmune, neurologic, and other diseases (Jaffer and Weissleder, 2005; Jaffer et al., 2007).

MOLECULAR IMAGING AND CANCER DETECTION Detection of stage 1 cancers carries a 90% 5-year survival rate for the majority of malignancies (Etzioni et al., 2003), while removal of precancerous lesions is curative. Genetic, proteomic, and epidemiological studies largely aim at identifying individuals at high risk but the correct spatial localization of early cancers in a given patient often remains a challenge. Conventional anatomic imaging techniques typically detect cancers when they are

Copyright © 2009, Elsevier Inc. All rights reserved.

TABLE 43.1

Overview of imaging systems

Technique

Resolution

Depth

Time

Imaging agents

Target

Cost

Primary small animal use

Clinical use

MR imaging

10–100 μm

No limit

min– hours

Gadolinium, dysprosium, magnetic particles

A, P, M

$$$

Versatile imaging modality with high soft tissue contrast

Yes

CT imaging

50 μm

No limit

min

Iodine

A, P

$$

Primarily for lung and bone imaging

Yes

Ultrasound imaging

50 μm

mm

min

Microbubbles

A, P

$$

Vascular and interventional imaging

Yes

PET imaging

1–2 mm

No limit

min

F-18, Cu-64, C-11, O-15

P, M

$$$

Versatile imaging modality with many different tracers

Yes

SPECT imaging

1–2 mm

No limit

min

Tc-99 m, In-111 chelates

P, M

$$

Commonly used to image labeled antibodies, peptides, etc.

Yes

Fluorescence reflectance imaging (FRI)

2–3 mm

1 cm

sec–min

(NIR) Photoproteins (GFP) fluorochromes

P, M

$

Rapid screening of molecular events in surface-based disease

Yes

Fluorescence mediated tomography (FMT)

1 mm

10 cm

sec–min

NIR fluorochromes

P, M

$$

Quantitative imaging of targeted or “smart” fluorochrome reporters in deep tumors

In development

Bioluminescence imaging

Several mm

cm

min

Luciferins

M

$$

Gene expression, cell and bacterial tracking

No

Intravital microscopy (confocal, multiphoton)

1 m

400– 800 m

sec–min

Photoproteins (GFP)

P, M

$$$

All of the above at higher resolutions but at limited depths and coverage

Limited development (skin, endoscopy)

Adapted from Weissleder, 2002. The resolution and cost columns refer to high-resolution, small animal imaging systems and are different for clinical imaging systems. Primary area that a given imaging modality interrogates: A: anatomic, P: physiologic, M: molecular targets. Cost of system: $: 100K; $$: 100–300K; $$$: 300K.

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TABLE 43.2 as of 2007

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Molecular Imaging as a Paradigm for Genomic and Personalized Medicine

Representative examples of clinically promising molecular imaging approaches in cardiovascular disease

Agent

Target

Platform

Clinically tested?

Magnetic nanoparticles (iron oxide)

Cellular Inflammation (Macrophage smooth muscle and endothelial cells)

MRI

Yes (40 patients)

18

Atherosclerosis

Glucose tranporter-1, hexokinase

PET

FDA approved

99m

F-fluorodeoxyglucose

Apoptosis/macrophages/intraplaque hemorrhage

SPECT

Yes (24 patients)

99m

Lymphocytes

SPECT

Yes (25 patients)

Prosense

Cysteine protease activity

NIRF

Planned

Paramagnetic nanoparticles

Angiogenesis (integrin vβ3)

MRI

Planned

99m

Platelet GP IIb/IIIa receptor

SPECT

FDA approved

EP-2104R

Fibrin

MRI

Yes (76 patients)

99m

Angiogenesis (integrin vβ3)

SPECT

Yes (10 patients)

Magnetic nanoparticles, 111 Indium-oxine

Stem Cells

MRI, SPECT

Yes (in cancer, 11 patients)

Tc-annexin Tc-interleukin-2

Thrombosis Tc-apcitide

Myocardial infarction Tc-NC100692

From Jaffer and Weissleder, 2005.

centimeter-sized, that is, already consist of 109 cells (including circulating and microscopic metastatic deposits). Molecular imaging is expected to play an important role in this setting by imaging key molecular targets and host responses associated with early cancer development. For example, a number of key oncogenes are activated in lung cancer (e.g., K-ras, EGFR, HER2/neu, 2, Bcl-2), but there are also a myriad of other targets, more ideally suited for imaging (Sweet-Cordero et al., 2005). Some of these targets (e.g., proteases from the cathepsin family) have recently been used for detecting early lung cancer (Grimm et al., 2005). Importantly, the imaging agents were fluorescencebased and can also be used for detection of microscopic foci of epithelial (pre)cancers using endoscopic microscopy (Alencar et al., 2005; Evans and Nishioka, 2005; Marten et al., 2002). This technological advance is important because it allows detection of microscopic lesions elusive to conventional imaging and could one day enable in vivo characterization of tumors without the need for multiple excisional biopsies. Several other imaging technologies play important roles in staging and re-staging. For example, the use of magnetic nanoparticles targeted to macrophages in lymph nodes has been used as a sensitive means to detect nodal metastases in clinically occult disease (Harisinghani and Weissleder, 2004). Because of the exquisite spatial resolution of MRI, mm-sized metastases are detectable in non-enlarged lymph nodes (Harisinghani et al., 2003) beyond the detection threshold of many other imaging techniques. This approach has already been validated for a number of genitourinary malignancies, head and neck, and breast cancer.

Positron Emission Tomography (PET) imaging has emerged as a clinical cornerstone in cancer staging and re-staging for a number of malignancies and remains one of the few FDA approved technologies to date (Juweid and Cheson, 2006). The most frequently used PET agent (90% of all cancer-related scans) is 18F-labeled 2-deoxy-d-glucose (FDG), a glucose analog that is taken up by cells in proportion to their rate of glucose metabolism (Quon and Gambhir, 2005). Glucose metabolism is markedly increased in malignant cells, and FDG-PET imaging has been approved for staging of breast, colorectal, esophageal, head and neck, non-small-cell lung cancers, melanoma, and lymphoma (Guller et al., 2002; Juweid and Cheson, 2006; Quon and Gambhir, 2005).

MOLECULAR IMAGING TO DETERMINE TREATMENT EFFICACY FDG-PET imaging has also emerged as a clinical tool for measuring efficacy of novel anticancer therapies and predicting outcome (responders versus non-responders). The technique is particularly established for lymphoma, gastrointestinal stromal tumors, esophageal, head and neck, ovarian cancer, and approved for breast cancer (Wieder et al., 2004). For example, in one recent study, sequential FDG-PET imaging predicted patient outcome as early as after the first cycle of neoadjuvant chemotherapy and was more accurate than clinical or histopathologic response criteria including changes in tumor marker CA-125

Near-Term Needs and Opportunities

(Avril et al., 2005). Furthermore, FDG-PET imaging is increasingly being used to image other inflammatory diseases and the efficacy of molecularly targeted drugs (Goerres et al., 2005; Radu et al., 2007; Tahara et al., 2006; Tawakol et al., 2006). While FDG-PET imaging has been quite successful clinically, there has been a continued search for other agents that more specifically monitor tumor growth and cell death to follow treatment response more accurately. A wealth of new targets has already emerged from expression profiling and proteomic studies and is expected to give rise to new imaging agents. For example, radiolabeled nucleoside analogs such as thymidine compounds which theoretically are incorporated into DNA and could serve as a marker of cell proliferation have been developed. Clinical trials attesting to the utility of such agents are currently ongoing. Monoclonal antibodies developed against specific antigen targets have also been developed (e.g., CEA, PSMA, her2/neu) but are often problematic because of their relatively slow clearance from blood often leading to high background signals, even up to 1 week after injection of the antibody (Quon and Gambhir, 2005). Efforts are under way to construct engineered antibody fragments more suited for imaging, such as minibodies and diabodies, which show much more rapid blood clearance albeit at the expense of affinity. Other targeted radiolabeled agents include engineered proteins such as annexin-V, nanoparticles targeted to αvβ3 or VCAM-1, peptides or small molecules including dihydrotestosterone, estrogen, kinase inhibitors or active site binders.

MOLECULAR IMAGING AND DRUG DEVELOPMENT The development of new cancer therapeutics is expensive, timeconsuming, and often requires vast numbers of patients – factors that all contribute to the final cost of the therapies once they are approved for clinical use (Kelloff and Sigman, 2005). On average, it takes 10–12 years to take a new drug from discovery to regulatory approval at costs that can exceed $880 million (BIAG, 2006). In addition, many newer drugs (cytotoxic, cyctostatic, and molecular targeted) are often efficacious only in subgroups of patients (Lynch et al., 2004), while others – despite robust scientific rationale and promising preclinical results – have failed to show efficacy in clinical trials (Park et al., 2004). Reducing the number and cost of failed projects (75% attrition rate) would benefit the pharmaceutical industry, the healthcare system, and most importantly the patients. Molecular imaging is widely viewed as one of the most promising tools to improve the efficiency and cost-effectiveness of drug development programs. Imaging-based biomarkers (specific molecular targets or biological cancer processes e.g., HER2/neu, apoptosis) have many potential uses in all phases of the cancer drug development process, from target discovery and validation to the pivotal clinical trials that precede drug approval (Rudin and Weissleder, 2003). Similarly, such biomarkers are being defined for cardiovascular (Jaffer and Weissleder, 2005), rheumatoid arthritis (Izmailova et al., 2007), and other disease processes. Genetic reporter strategies

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involving bioluminescence and fluorescent-tagged proteins have been extremely valuable for the study of cancer biology and preclinical drug evaluation in mouse models (Contag, 2002; Gross and Piwnica-Worms, 2005; Herschman, 2003; Massoud and Gambhir, 2003;Weissleder and Ntziachristos, 2003; Ntziachristos, 2000). In recent examples, these types of imaging approaches have been used to test the anti-tumor efficacy of synthetic epothilone analogs (Wu et al., 2005) or novel TRAIL constructs (Shah et al., 2005). While the reporter gene strategy is not directly translatable clinically, high-resolution mouse imaging with injectable imaging agents can now provide a windows into the effect of drugs on specific targets (Tang et al., 2005; Weissleder and Pittet, 2008; Herschman, 2003; Rudin and Weissleder, 2003;Weissleder, 2002). Molecular imaging can help identify new efficacy endpoints that are more easily monitored and less labor-intensive than currently used endpoints such as histological analyses of tumor biopsies. For example, steady-state imaging of vascular tumor volumes can be obtained in minutes in live mice whereas CD31 microvascular density measurements are much more labor intensive. Furthermore, because molecular imaging is non-invasive, in the preclinical setting it allows for longitudinal studies in a single animal, which can reduce the number of animals required for an experiment without compromising statistical significance. In the clinical setting, molecular imaging endpoints could be used to identify the most appropriate patient populations in which to test new drugs. Imaging of cancer drugs that have been labeled with 11C 18 or F can facilitate clinical pharmacokinetic and pharmacodynamic assessments, dosing and comparative efficacy studies of different lead compounds. In particular, microdosing studies (defined as 1% of the therapeutic dose, which typically has negligible toxicities in patients) have been advocated as a way to quickly obtain data on drug absorption, distribution, metabolism, excretion, and toxicity (Lappin and Garner, 2003; Propper et al., 2003; Saleem et al., 2001).

NEAR-TERM NEEDS AND OPPORTUNITIES What’s needed to catalyze the field of molecular imaging and drive new imaging agents into the clinic at a faster pace? Of utmost importance is the need to discover and validate new biomarkers suited for imaging, particularly those with amplification potential such as internalizing cell surface receptors, enzymes, and abundant non-protein targets (e.g., growth factor receptors). Tighter integration of imaging and therapeutic agent developments and systems level analysis of pathways and networks are expected to significantly speed up the discovery processes. Recent technological developments that may further hasten the biomarker discovery process are “imaging filters” to screen existing databases for targets ideally suited for imaging, newer conjugation chemistries such as “click chemistry” (Kolb and Sharpless, 2003) and use of yeast/phage surface display to identify recombinant antibodies/peptides (Joyce et al.,

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2003). There is also a pressing need for synthesis of new imaging agents. Here, there is an opportunity to apply combinatorial methods, chemical biology, synthetic small molecule compounds, and newer nanomaterials as scaffolds to improve pharmacological behavior (Anderson et al., 2004;Weissleder et al., 2005). The continued development of “smart” imaging reagents whose signal depends on biochemical activities remains a top priority to improve target-to-background ratios in vivo. For example, small molecule prodrugs that change their imaging signal upon target interaction can dramatically boost target-to-background ratios (Meade et al., 2003; Querol et al., 2005). Additional opportunities exist for the development fluorescence-based imaging agents, one of the most important growth areas in molecular imaging. Fluorochromes such as indocyanines are inexpensive, stable, use no radiation, and have been used safely for the last 20 years. Just as in the in vitro setting where they have been key

tools in both genomics and proteomics, fluorochromes can be uniquely converted into sensing agents in vivo (Weissleder et al., 1999). Continuing improvements in instrumentation including high spatial resolution endomicroscopy, near infrared intraoperative reflectance imaging, and fluorescence tomography will also be important. The latter technology is of particular interest because it allows accurate in vivo quantitation of near infrared fluorochrome, important to differentiate target binding from pharmacokinetics (Ntziachristos et al., 2005).

ACKNOWLEDGEMENT Portions of this chapter are based on a previously published article (Weissleder R. (2006). Molecular Imaging in Cancer, Science 312, 1168–1171) and are used here with permission.

REFERENCES Alencar, H., Mahmood, U., Kawano, Y., Hirata, T. and Weissleder, R. (2005). Novel multiwavelength microscopic scanner for mouse imaging. Neoplasia 7, 977–983. Anderson, D.G., Levenberg, S. and Langer, R. (2004). Nanoliter-scale synthesis of arrayed biomaterials and application to human embryonic stem cells. Nat Biotechnol 22, 863–866. Avril, N., Sassen, S., Schmalfeldt, B., Naehrig, J., Rutke, S., Weber, W.A., Werner, M., Graeff, H., Schwaiger, M. and Kuhn, W. (2005). Prediction of response to neoadjuvant chemotherapy by sequential F-18-fluorodeoxyglucose positron emission tomography in patients with advanced-stage ovarian cancer. J Clin Oncol 23, 7445–7453. Contag, P.R. (2002). Whole-animal cellular and molecular imaging to accelerate drug development. Drug Discov Today 7, 555–562. Etzioni, R., Urban, N., Ramsey, S., McIntosh, M., Schwartz, S., Reid, B., Radich, J., Anderson, G. and Hartwell, L. (2003). The case for early detection. Nat Rev Cancer 3, 243–252. Evans, J.A. and Nishioka, N.S. (2005). Endoscopic confocal microscopy. Curr Opin Gastroenterol 21, 578–584. Goerres, G.W. et al. (2006). F-18 FDG whole-body PET for the assessment of disease activity in patients with rheumatoid arthritis. Clin Nucl Med 31, 386–390. Grimm, J., Kirsch, D.G., Windsor, S.D., Kim, C.F., Santiago, P.M., Ntziachristos, V., Jacks, T. and Weissleder, R. (2005). Use of gene expression profiling to direct in vivo molecular imaging of lung cancer. Proc Natl Acad Sci USA 102, 14404–14409. Gross, S. and Piwnica-Worms, D. (2005). Spying on cancer: molecular imaging in vivo with genetically encoded reporters. Cancer Cell 7, 5–15. Guller, U., Nitzsche, E.U., Schirp, U.,Viehl, C.T., Torhorst, J., Moch, H., Langer, I., Marti, W.R., Oertli, D., Harder, F. et al. (2002). Selective axillary surgery in breast cancer patients based on positron emission tomography with 18F-fluoro-2-deoxy-D-glucose: not yet!. Breast Cancer Res Treat 71, 171–173. Harisinghani, M.G. and Weissleder, R. (2004). Sensitive, noninvasive detection of lymph node metastases. PLoS Med 1, e66.

Harisinghani, M.G., Barentsz, J., Hahn, P.F., Deserno,W.M.,Tabatabaei, S., Van Dekaa, C.H., De La Rosette, J. and Weissleder, R. (2003). Noninvasive detection of clinically occult lymph-node metastases in prostate cancer. N Engl J Med 348, 2491–2499. Herschman, H.R. (2003). Molecular imaging: Looking at problems, seeing solutions. Science 302, 605–608. Izmailova, E.S. et al. (2007). Use of molecular imaging to quantify response to IKK-2 inhibitor treatment in murine arthritis. Arthritis Rheum 56, 117–128. Jaffer, F.A. and Weissleder, R. (2005). Molecular imaging in the clinical arena. JAMA 293, 855–862. Jaffer, F.A., Libby, P. and Weissleder, R. (2007). Molecular imaging of cardiovascular disease. Circulation 116(9), 1052–1061. Joyce, J.A., Laakkonen, P., Bernasconi, M., Bergers, G., Ruoslahti, E. and Hanahan, D. (2003). Stage-specific vascular markers revealed by phage display in a mouse model of pancreatic islet tumorigenesis. Cancer Cell 4, 393–403. Juweid, M.E. and Cheson, B.D. (2006). Positron-emission tomography and assessment of cancer therapy. N Engl J Med 354, 496–507. Kelloff , G.J. and Sigman, C.C. (2005). New science-based endpoints to accelerate oncology drug development. Eur J Cancer 41, 491–501. Kolb, H.C. and Sharpless, K.B. (2003). The growing impact of click chemistry on drug discovery. Drug Discov Today 8, 1128–1137. Lappin, G. and Garner, R.C. (2003). Big physics, small doses: The use of AMS and PET in human microdosing of development drugs. Nat Rev Drug Discov 2, 233–240. Lynch,T.J., Bell, D.W., Sordella, R., Gurubhagavatula, S., Okimoto, R.A., Brannigan, B.W., Harris, P.L., Haserlat, S.M., Supko, J.G., Haluska, F.G. et al. (2004). Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-smallcell lung cancer to gefitinib. N Engl J Med 350, 2129–2139. Marten, K., Bremer, C., Khazaie, K., Sameni, M., Sloane, B., Tung, C.H. and Weissleder, R. (2002). Detection of dysplastic intestinal adenomas using enzyme-sensing molecular beacons in mice. Gastroenterology 122, 406–414.

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Massoud, T.F. and Gambhir, S.S. (2003). Molecular imaging in living subjects: Seeing fundamental biological processes in a new light. Genes Dev 17, 545–580. Meade, T.J., Taylor, A.K. and Bull, S.R. (2003). New magnetic resonance contrast agents as biochemical reporters. Curr Opin Neurobiol 13, 597–602. Nadler, E., Eckert, B. and Neumann, P.J. (2006). Do oncologists believe new cancer drugs offer good value?. Oncologist 11, 90–95. Ntziachristos, V., Yodh, A.G., Schnall, M. and Chance, B. (2000). Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement. Proc Natl Acad Sci USA 97, 2767–2772. Ntziachristos, V., Ripoll, J., Wang, L.V. and Weissleder, R. (2005). Looking and listening to light: The evolution of whole-body photonic imaging. Nat Biotechnol 23, 313–320. Park, J.W., Kerbel, R.S., Kelloff, G.J., Barrett, J.C., Chabner, B.A., Parkinson, D.R., Peck, J., Ruddon, R.W., Sigman, C.C. and Slamon, D.J. (2004). Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development. Clin Cancer Res 10, 3885–3896. Propper, D.J., De Bono, J., Saleem, A., Ellard, S., Flanagan, E., Paul, J., Ganesan, T.S., Talbot, D.C., Aboagye, E.O., Price, P. et al. (2003). Use of positron emission tomography in pharmacokinetic studies to investigate therapeutic advantage in a phase I study of 120-hour intravenous infusion XR5000. J Clin Oncol 21, 203–210. Querol, M., Chen, J.W., Weissleder, R. and Bogdanov, A.J. (2005). DTPA-bisamide-based MR sensor agents for peroxidase imaging. Org Lett 7, 1719–1722. Quon, A. and Gambhir, S.S. (2005). FDG-PET and beyond: molecular breast cancer imaging. J Clin Oncol 23, 1664–1673. Radu, C.G., Shu, C.J., Shelly, S.M., Phelps, M.E. and Witte, O.N. (2007). Positron emission tomography with computed tomography imaging of neuroinflammation in experimental autoimmune encephalomyelitis. Proc Natl Acad Sci USA 104, 1937–1942. Rudin, M. and Weissleder, R. (2003). Molecular imaging in drug discovery and development. Nat Rev Drug Discov 2, 123–131. Saleem, A., Harte, R.J., Matthews, J.C., Osman, S., Brady, F., Luthra, S.K., Brown, G.D., Bleehen, N., Connors, T., Jones, T., Price, P.M. and Aboagye, E.O. (2001). Pharmacokinetic evaluation of N-[2-(dim ethylamino)ethyl]acridine-4-carboxamide in patients by positron emission tomography. J Clin Oncol 19, 1421–1429.

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Shah, K., Tung, C.H., Breakefield, X.O. and Weissleder, R. (2005). In vivo imaging of S-TRAIL-mediated tumor regression and apoptosis. Mol Ther 11, 926–931. Sweet-Cordero, A., Mukherjee, S., Subramanian, A., You, H., Roix, J.J., Ladd-Acosta, C., Mesirov, J., Golub, T.R. and Jacks, T. (2005). An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 37, 48–55. Tahara, N. et al. (2006). Simvastatin attenuates plaque inflammation: evaluation by fluorodeoxyglucose positron emission tomography. J Am Coll Cardiol 48, 1825–1831. Tang, Y., Kim, M., Carrasco, D., Kung, A.L., Chin, L. and Weissleder, R. (2005). In vivo assessment of RAS-dependent maintenance of tumor angiogenesis by real-time magnetic resonance imaging. Cancer Res 65, 8324–8330. Tawakol, A., Migrino, R.Q., Bashian, G.G., Bedri, S., Vermylen, D., Cury, R.C., Yates, D., Lamuraglia, G.M., Furie, K., Houser, S. et al. (2006). In vivo 18F-fluorodeoxyglucose positron emission tomography imaging provides a noninvasive measure of carotid plaque inflammation in patients. J Am Coll Cardiol 48, 1818–1824. Weissleder, R. (2002). Scaling down imaging: Molecular mapping of cancer in mice. Nat Rev Cancer 2, 11–18. Weissleder, R. and Ntziachristos, V. (2003). Shedding light onto live molecular targets. Nat Med 9, 123–128. Weissleder, R. and Pittet, M.J. (2008). Imaging in the era of molecular oncology. Nature 452, 580–589. Weissleder, R., Tung, C.H., Mahmood, U. and Bogdanov, A., Jr. (1999). In vivo imaging of tumors with protease-activated near-infrared fluorescent probes. Nat Biotechnol 17, 375–378. Weissleder, R., Kelly, K., Sun, E.Y., Shtatland, T. and Josephson, L. (2005). Cell-specific targeting of nanoparticles by multivalent attachment of small molecules. Nat Biotechnol 23, 1418–1423. Wieder, H.A., Brucher, B.L., Zimmermann, F., Becker, K., Lordick, F., Beer, A., Schwaiger, M., Fink, U., Siewert, J.R., Stein, H.J. et al. (2004). Time course of tumor metabolic activity during chemoradiotherapy of esophageal squamous cell carcinoma and response to treatment. J Clin Oncol 22, 900–908. Wu, K.D., Cho, Y.S., Katz, J., Ponomarev, V., Chen-Kiang, S., Danishefsky, S.J. and Moore, M.A. (2005). Investigation of antitumor effects of synthetic epothilone analogs in human myeloma models in vitro and in vivo. Proc Natl Acad Sci USA 102, 10640–10645.

CHAPTER

44 PET Imaging in Genomic Medicine Vikas Kundra and Osama Mawlawi

INTRODUCTION This chapter describes the principles and techniques of PET imaging with an emphasis on applications for analysis of the genome and its derivative products, RNA and protein. Nuclear medicine, including PET, affords the possibility to image in vivo biologic processes in animals and patients that were heretofore confined to in vitro or ex vivo analysis. DNA synthesis, promoter function/gene expression, mRNA, protein expression and function, and protein–protein interactions may each be evaluated at a whole organism level. With appropriate experimental design, gene expression, for example, can be studied specifically in the organ of interest and compared to expression after an intervention, such as pharmacotherapy or radiation therapy. In the setting of gene therapy, one would be able to quantify the amount of expression, to design appropriate dosing schedules and compare with clinical outcome, and localize expression, to determine if it is appropriately located for therapeutic effect or if it is in an undesirable location that may be resulting in toxicity. Because the imaging agents are noninvasive, serial imaging may be performed. Thus, in biomarker discovery, PET enables the possibility to quantitatively recapitulate in vitro analyses from the level of DNA to protein function in the whole animal over time. Clinically, PET imaging has already proven utility in diagnosing and staging disease and monitoring treatment. Although the illustrations in this chapter utilize PET, other imaging modalities such as gamma camera (planar and/or single

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photon emission computed tomography, SPECT) and lightbased techniques may also be applied to biological questions, in both research and medicine. A primary advantage of nuclear medicine–based techniques is superior tissue penetration when compared to light-based imaging, making nuclear medicine more amenable to clinical translation for percutaneous imaging. Sophisticated tomographic techniques for three-dimensional localization are currently utilized in PET and SPECT imaging. For PET imaging, coincidence imaging improves localization and sensitivity. Higher resolution may be achieved by gamma camera imaging using magnification techniques, which can be important for imaging small parts in patients and small animals. Nuclear medicine relies on radiopharmaceuticals to image targets and/or function. With the appropriate imaging agents and/ or reporter constructs, PET imaging enables imaging of fundamental cellular processes such as nucleotide utilization, transcription, metabolism, amino acid utilization, and protein function.

PHYSICS Basic Principles Before examining specific applications of PET imaging in genomic or personalized medicine, it is important to introduce the basic physics principles of this approach. PET is based on imaging radionuclides that decay by positron emission. The

Copyright © 2009, Elsevier Inc. All rights reserved.

Physics

radionuclides are usually introduced synthetically into a molecule of potential biological relevance and administered to a patient, usually intravenously. Positron emission is one of two decay schemes that characterizes radionuclides that possess an excess number of protons to neutrons in their nucleus. In this regard, the positron emission decay process reduces the number of protons by transforming a proton to a neutron along with the creation and emission of a positron () and a neutrino ( ) from the nucleus (Figure 44.1). A positron is a charged particle that has the same mass but opposite charge to an electron. Upon emission, positrons travel a short distance while losing their kinetic energy through multiple collisions with electrons in surrounding tissues. Eventually, the positron combines with an electron in an annihilation reaction that transforms their combined mass into energy according to E  mc2, resulting in two 511 keV gamma ray photons traveling in opposite directions. In PET imaging, a ring of radiation detectors is placed around the subject to detect the emitted gamma rays. The simultaneous emission and subsequent detection of the paired gamma rays by opposing detectors, called coincidence detection, represents a unique feature of PET imaging for event localization, and is capitalized upon by placing the annihilation event along a line of response (LOR) that connects the two detected gamma rays. In this regard, PET, unlike SPECT imaging, does not require a collimator to help identify the source of activity, hence, can have higher sensitivity.

511 KeV

tro

n

ra

ng

e

e f

ar

si

No

nc

oli

ne

Po

ity

β

511 KeV

Figure 44.1 Positron () decay: transformation of a proton to a neutron with the creation and ejection of a positron and a neutrino from the nucleus. The positron then travels a short distance (positron range) before annihilating with an electron (e) to generate two 511 KeV gamma rays that are emitted at 180°. Positrons that annihilate prior to loosing all of their kinetic energy produce noncollinear gamma rays which result in loss of resolution indicated by f on the figure.

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Two main criteria are tested every time a detector registers an event – energy and timing. The first test assesses the energy of the incident gamma ray. Scintillation detectors made from dense crystalline materials linearly converts gamma ray energy to visible light, which is subsequently converted into an electrical pulse and amplified using a photomultiplier tube (PMT). If the electrical pulse amplitude falls inside the 511 keV preset energy threshold window, it is counted; otherwise, it is discarded. The timing test assesses the difference in the gamma ray detection time between a coincidence detector pair. A valid coincidence event represents detection of two 511 keV photons practically simultaneously (within a few nanoseconds). Others are discarded. Valid events are stored as part of a sinogram; after completing the acquisition, the sinogram is reconstructed into the final PET image. PET Scanner Design In a PET scanner, several rings of detectors are placed adjacent to one another axially. Current clinical scanners have axial extents of 15–25 cm. Animal scanners have maximum axial extents of 2–13 cm. PET scanners acquire data in either twoor three-dimensional (2D or 3D) modes. In the 2D mode, thin septa of lead or tungsten separate each ring, and coincidences are only recorded between detectors within the same ring or adjacent rings (Figure 44.2a). In the 3D mode, the septa are removed, and coincidences are recorded between detectors lying in any ring combination (Figure 44.2b), which increases sensitivity, but also reduces contrast. PET Data Acquisition Modes PET data can be acquired in static, dynamic, gated, or list modes. In static mode, valid coincidence events are accumulated in the sinogram over a defined period of time. Dynamic mode is akin to stacking multiple static mode acquisitions over time and is primarily used to study the distribution of a new radiopharmaceutical over time, as well as for radiation dosimetry studies. Gated mode is primarily used to freeze motion of organs of interest. In this mode, the PET data is acquired into multiple bins during each motion cycle. This process is usually facilitated by external monitoring devices such as EKG or respiratory bellows. In list mode, events are stored in separate memory locations with time marks. The acquired list data can then be rebinned retrospectively, providing flexibility in motion correction and temporal resolution. Furthermore, if the patient moves during any portion of the scan time, that artifactual data can be discarded. PET Data Correction One of the main advantages of PET imaging is its ability to provide accurate quantification of radionuclide concentration. This advantage, however, is largely dependent on the application of numerous corrections to the acquired data including attenuation of the gamma rays (Carroll et al., 1983; Huang et al.,

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(a) 2D

Septa

(b) 3D

Figure 44.2 2D versus 3D PET imaging. (a) In 2D PET, lead or tungsten septa (red lines) are present in the field of view limiting the number of oblique line of response (LOR) that are detected along the axial direction of the scanner. (b) In 3D, the septa are removed allowing oblique LORs to be detected thereby increasing the scanner sensitivity but also increasing the amount of detected scatter events (noise).

1979; Kinahan et al., 1998; Phelps et al., 1975; Xu et al., 1991), random and scatter events (Bailey and Meikle, 1994; Casey and Hoffman, 1986; Cherry and Huang, 1995; Cooke et al., 1983; Hoffman et al., 1979; Ollinger, 1996; Shao et al., 1994), variation between detector responses (Bailey et al., 1996; Hoffman et al., 1989), detector dead time (Badawi and Marsden, 1999; DaubeWitherspoon and Carson, 1991), and decay of radioactivity. Attenuation correction deserves special mention. Attenuation correction accounts for the missing gamma rays absorbed by the patient to more accurately quantify the injected activity. This is commonly performed by acquiring transmission data (by rotating a gamma ray source around the patient) to generate an attenuation correction map to account for the different tissues. Because of their small size and hence low attenuation, small animal PET imaging is often performed without this correction. In hybrid PET–CT scanners, CT data are used for PET attenuation correction. CT-generated attenuation maps have low noise content. CT provides short acquisition time, which decreases patient motion and allows more accurate anatomic localization of the PET signal. Small animal PET–CT scanners have been recently developed. Image Reconstruction Image reconstruction by filtered back projection or iterative reconstruction follows data correction. In filtered back projection,

coincidence detectors acquire a series of LORs to form a profile of detected events versus distance for each angle. Parallel LORs are grouped to form a projection profile for each angle. (The sinogram is a collection of these projections acquired from different angles.) In back projection, each profile is then back projected along its acquired angle into the image grid. The summation has inherent blurring because the exact location of the annihilation event is not known; we only know that the event occurred somewhere along the path of the LOR. Filters attempt to minimize blurring. The choice of filters largely depends on the desired sharpness and noise in the final image. In iterative reconstruction, an initial guess of the activity distribution in the subject is made. Then, projection images are calculated from this guess and compared to the acquired projection data. This process is repeated until a match between the initial guess and the acquired data is found. Stop conditions can be based on residual difference between the measured and calculated projection images or number of iterations. This reconstruction approach is generally known as maximum likelihood expectation maximization (MLEM). The most widely used variant is ordered subset expectation maximization (OSEM) (Hudson and Larkin, 1994), which divides the projection images into several subsets, so that, rather than comparing the whole projection images to one another, subsets are compared, thereby expediting the reconstruction. Factors Affecting PET Image Resolution One of the most striking features of PET images is their characteristic low resolution. PET images cannot reproduce sharp transitions such as edges on CT and MR images. There are several reasons for the low resolution of PET images, including detector size, detector type/design, positron range, non-collinearity of the emitted 511 keV gamma rays, depth of interaction effects, linear and angular sampling, and image reconstruction parameters. All of these factors except for reconstruction parameters are intrinsic to the scanner and set the intrinsic scanner resolution limit. For current clinical scanners, this limit is about 4–7 mm, whereas for animal scanners, the limit is 1–3 mm. Positron range pertains to the fact that the annihilation event that generates the paired 511 keV gamma rays does not occur at the site of positron emission, but at some distance away depending on the kinetic energy of the emitted positron (Figure 44.1). The kinetic energy, hence the distance, is dependent on the radionuclide under investigation (Table 44.1) (Derenzo, 1985). Noncollinearity occurs when annihilation events occur before the positron looses all of its energy, resulting in slightly different than 180° (Cherry et al., 2003). The combined effects of the positron range and the noncollinearity suggest a theoretical spatial resolution limit for PET imaging. In contrast, decay from radionuclides used in gamma camera imaging originates from the location of the radionuclide itself and allows magnification to improve resolution. Due to depth-of-interaction, the resolution of PET images degrades as one moves radially from the center to the edge of

Imaging Agents and Methods in Analysis of Bilogical Samples

TABLE 44.1 Physical properties of positron-emitting radionuclides used in PET Radionuclide

Physical half-life T1/2

Maximum ⴙ energy (MeV)

ⴙ Range (Rrms) in water (mm)

Carbon-11

20.4 min

0.96

0.4

Nitrogen-13

9.96 min

1.2

0.6

Oxygen-15

2.05 min

1.7

0.9

Fluorine-18

1.83 h

0.64

0.2

Copper-62

9.74 min

2.9

1.6

Copper-64

12.7 h

0.58

0.2

Gallium-66

9.49 h

3.8

3.3

Gallium-68

1.14 h

1.9

1.2

Bromine-76

16.1 h

3.7

3.2

Rubidium-82

1.3 min

3.4

2.6

Yttrium-86

14.7 h

1.4

0.7

Iodine-124

4.18 days

1.5

0.8

Adapted from Derenzo, S.E. (1986).

PET scanner B A Patient

D A’ B’

Figure 44.3 Depth of interaction. The two gamma rays generated from the annihilation event (red spot) will be detected in detectors BB’ rather than AA’ due to scanner curvature. The location of the measured LOR (dotted line) will be different from the true LOR (dashed line). Depth of interaction results in a radial loss of spatial resolution indicated by D on the figure. Spatial resolution is optimal at the center of the scanner.

■

503

the field of view (FOV). At the edge of the FOV, a gamma ray has a higher probability of penetrating the first detector and being detected in an adjacent detector. This has greater effect on small animal scanners due to their small bore size (Figure 44.3) and practically suggests placing a subject in the center of the FOV for best resolution (Bartzakos and Thompson, 1991). The chosen reconstruction algorithm and its parameters significantly contribute to image resolution. In iterative reconstruction, as the number of iterations and subsets increase, the resolution improves, but the noise increases (Schoder et al., 2004). High frequency filters result in sharper images with higher resolution, but greater noise. Conversely, low frequency filters result in smooth images, but loss of detail. Clinically, two iterations and 20–30 subsets are commonly used as a trade-off between image sharpness and noise.

IMAGING AGENTS AND METHODS IN ANALYSIS OF BIOLOGICAL SAMPLES Radiopharmaceuticals In general, radiopharmaceuticals for gamma camera imaging consist of three parts, (i) the localizing agent, (ii) a linker, and (iii) a radionuclide. This has proven successful for many clinical applications. In comparison, radionuclides for PET commonly allow direct labeling of the localization agent without an intervening linker. For example, a hydroxyl group may be directly replaced by 18F. Other radionuclides, such as 11C, 15O, 13N, 82Rb, 64Cu, and 68Ga among others, may be more advantageous in particular situations. Many atoms that naturally occur in molecules of interest also have forms that are positron emitters, for example, 11 C, 15O, and 13N. Others, such as 18F, 64Cu, and 68Ga, may be used to label molecules while maintaining specificity and favorable biodistribution. In addition to chemistry, considerations for choosing a radionuclide include availability and half-life. One advantage of 68Ga is that it can be produced from a generator and thus may be more available than radionuclides that need to be produced in a cyclotron. 18F is commonly used because of its chemistry and its favorable 110-min half-life (Table 44.2). Imaging DNA DNA Replication For imaging DNA synthesis, radioactive nucleotide analogs have been used. Focus has centered on thymine analogs to avoid incorporation into RNA. For example, 18F labeled 3-deoxy-3fluorothymidine (18F-FLT) has been used as a marker of DNA replication. As with all drugs, one must be careful that the desired biologic event is the one actually being imaged. 18F-FLT is also retained intracellulary after phosphorylation by thymidine kinase 1 (Rasey et al., 2002); a significant percentage may not be incorporated during DNA synthesis, but may reflect the activity of this enzyme. Other thymidine analogs such as 18F-1-(2-deoxy-2fluoro--D-arabinofuranosyl)thymine (18F-FMAU) are also under investigation.

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TABLE 44.2 proteomics

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PET Imaging in Genomic Medicine

PET applications in genomics and

Phosphodiester

2-O -methyl

Phosphorothioate

Evaluation of ●

promoter function

●

endogenous gene and exogenous gene expression

●

RNA transcripts

●

protein expression

●

protein function

●

protein–protein interaction

●

metabolism

Direct Methods For imaging specific sequences of genomic DNA, direct methods that take advantage of Watson–Crick base pairing have been proposed. Short stretches of DNA, oligonucleotides, produced in the antisense orientation may be used to bind specific stretches of DNA. For in vivo imaging, the oligonucleotide needs to be stable in the blood stream, have limited interaction with nonspecific macromolecules, be taken up by the cell, bind a specific stretch of DNA, and have less efflux from the cell with the complementary DNA compared to the cell without the complementary DNA. The native phosphodiester backbone of DNA is susceptible to degradation by nucleases found in plasma. Therefore, oligonucleotides with modified backbones have been created, such as phosphorothioates, methylphosphonates, and peptide nucleic acids, to enhance stability. Unlike therapy, degradation of the target DNA (e.g., by affecting RNAse H activity) is not required for imaging. Cellular uptake of oligonucleotides appears to be receptor-mediated since it is temperature dependent, appears to be saturable, and can be competed by other oligonucleotides (Iversen et al., 1992; Loke et al., 1989). This is a potential barrier for imaging, because it may limit the number of oligonucleotides that can enter the cell. Fortunately, subcellular fractionation studies indicate that once taken up, oligonucleotides are found where DNA is found, in nuclei and mitochondria (Iversen et al., 1992). For imaging, the oligonucleotides’ analogs have been linked to radiopharmaceuticals for both gamma camera and PET imaging (Figure 44.4). For example, labeling has been successfully performed using 18F. The PET was then used to follow in vivo kinetics and biodistribution of the labeled oligonucleotides (Tavitian et al., 1998). Once created, specificity needs to be demonstrated using controls such as a random sequence, a sequence with one or more base alterations, and imaging cells without the target DNA sequence or with different number of copies of the target sequence. Accumulation of modified oligonucleotides labeled with positron (Kobori et al., 1999) and gamma-emitters have been reported (Cammilleri et al., 1996; Dewanjee et al., 1994; Shi et al., 2000; Urbain et al., 1995). PET

0–5 minutes after injection

60–65 minutes after injection

Figure 44.4 Coronal PET imaging of the biodistribution of different 18F labeled oligonucleotide analogs in nonhuman primates. With phophodiester and 2-O-methyl analogs, uptake is seen primarily in the vasculature, lungs, liver, kidneys, nasal area (and bladder with the phosphodiester) at 5 min and kidneys and bladder at 1 h. With the phosphorothioate analog, uptake is seen primarily in the vasculature, lungs, liver, spleen, nasal area, and kidneys at 5 min and liver, spleen, kidneys, and bladder at 1 h. (Images courtesy of Bertrand Tavitian, Copyright 1998 Bertrand Tavitian/CEA-INSERM U803 modified from Nature Medicine 1998;4:467–471.)

imaging studies targeting specific nucleotide sequences have focused on mRNA instead of DNA itself. For example, 11Clabeled antisense phosphorothioate oligodeoxynucleotide has been used to image the mRNA of the glial fibrillary acidic protein (GFAP) gene. Gliomas expressing GFAP were imaged in rats by antisense, but not sense or 30% mismatch probes (Kobori et al., 1999). Modified oligonucleotides also have the potential to distinguish point mutations. For example, in a preliminary study, a tumor with a point mutation of the K-ras oncogene was distinguished from a tumor with wild-type K-ras using a 68Galabeled phosphorothioate (Roivainen et al., 2004). Indirect Methods Indirect methods for imaging DNA include reporter genes. The constructs may be based on simple designs, incorporating a promoter to drive expression of a gene whose product can

Imaging Agents and Methods in Analysis of Bilogical Samples

be imaged. Based on the central dogma, this scheme provides a built-in amplification system that increases the number of targets that can be imaged, that is, transcription of genomic DNA results in tens to hundreds of mRNA and translation of mRNA produces thousands of copies of the encoded protein. If the protein can be imaged either directly or indirectly, it may thus serve as a reporter of gene expression. A limited number of reporters have been proposed for PET imaging. Examples include receptors such as the somatostatin receptor type 2 (SSTR2) and dopamine-2 receptor (D2R), pumps such as the sodium iodide symporter, and enzymes such as herpes simplex virus type 1-thymidine kinase (HSV1-TK). One advantage that may be afforded by enzymes is that they may amplify signal by their activity. For example, phosphorylation of substrates by thymidine kinase adds negative charges that prevent the substrates from crossing the cell membrane, resulting in accumulation of the substrates within the cell. Considerations in choosing a reporter include strength of eventual signal to noise required, immunogenicity, effect of the reporter on the cell, potential interference with the model/molecule being studied, cost, ease of preparation of the radiopharmaceutical used for imaging, model to be studied, and availability of Food and Drug Administration (FDA)-approved radiopharmaceuticals for eventual translation into the clinic. For example, in many situations it would be desirable to have a human, signaling-deficient receptor, such as that based on SSTR2. It may be necessary to use two different reporters in some instances, such as when evaluating expression from two different promoters. Then, for example, one may image a first reporter followed by the second reporter as long as the two radiopharmaceuticals do not have crossreactivity with the two reporters. Unlike gamma camera imaging, where different energy windows may be used for simultaneous imaging of radionuclides that emit at different energy peaks, positron emissions occur at only one peak, 511 KeV. Thus, imaging of two or more reporters by PET may be separated instead in time. The simple promoter–reporter construct may be used for transcriptional analysis. Activation of the promoter will result in expression of the reporter. After delivery (usually intravenously) of the radioligand/radiosubstrate for the reporter, positron emission is imaged by PET. With such a paradigm, promoter sequence and structure can be analyzed. For example, if one performs in vitro transcription analysis on various promoter mutants and finds a more robust/interesting sequence than the wild type, the new and wild-type mutant can be compared for activity in the appropriate in vivo context. One iteration may be to use three constructs with control, wild type, or mutant promoter sequence driving the same reporter. Expression may be confirmed in vitro, for example, by Western blotting for the reporter or radiopharmaceutical accumulation/binding. Using a variety of techniques, the construct may then be transferred into the organ under study, that is, mutant in one group of mice, wild type in the second group of mice, and control in the third group. Approximately 2 days later, the animals may be injected with the radiopharmaceutical for imaging. Region of interest analysis for

Control

■

505

Hypoxia-inducible promoter–reporter construct

Figure 44.5 In vivo microPET imaging of a hypoxiainducible promoter–reporter construct. Eight repeats of the hypoxia responsive HIF-1 alpha promoter driving expression of herpes simplex virus thymidine kinase-green fluorescent protein fusion reporter gene. Expression of the reporter was visualized by the PET tracer 18F-29-fluoro-29-deoxy-1-b-D-b-arabinofuranosyl-5-ethyluracil, [18F]FEAU, which is a substrate for thymidine kinase that once phosphorylated is entrapped in the cell. Greater expression is seen in the hypoxic center of the larger tumor (bottom right). Two sagittal microPET images per side of wild-type C6 (control, left) and transfected C6 (reporter, right) tumor xenografts of different size are presented. (Images courtesy of Dr J. Gelovani modified from Cancer Research 2004; 64:6101–6108.)

uptake of the radiopharmaceutical may be used to reflect promoter activity. After the appropriate time for clearance/decay of the radiopharmaceutical, the animals may be reinjected with the radiopharmaceutical if serial imaging is needed. In this way, the function of the mutant promoter may be compared to wildtype promoter in vivo, and of particular importance, in the organ of interest. The promoter–reporter paradigm has wide applicability for the study of multiple different genes in vivo. It, however, works best for strong promoters such as CMV. It has been successfully used with other promoters such as the hypoxia-sensitive HIF-1 promoter and the albumin promoter. For example, in larger tumors with hypoxic centers, the amount of HSV1-TK expression driven by the HIF-1 promoter was shown to increase as revealed by PET imaging (Figure 44.5) (Serganova et al., 2004). Promoter– reporter constructs may also be used to create transgenic mice. For example, in vivo PET imaging revealed that the degree of HSV1-TK expression driven by the albumin promoter varied depending on the amount of protein in the diet of albumin promoter–HSV1-TK transgenic mice (Green et al., 2002). Thus, promoter function may be evaluated in vivo. However, many promoters tend to have weak activity. This is particularly problematic for tissue-specific promoters. For these, amplification schemes can be helpful and may be cis or trans. In the trans system, for example, a weak promoter may be

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used to drive the expression of multiple intermediary proteins that drive a stronger promoter. Specificity is maintained by the weak promoter that drives the expression of the intermediary proteins. Amplification occurs by creation of multiple intermediary proteins, use of multiple binding sites for the intermediary proteins, and activation of a second, stronger promoter. One model is the use of a trans activation system based on a GAL4– VP16 fusion protein, expression of which is driven by a weak promoter. The GAL4 portion of the fusion proteins then binds to multiple GAL4 binding sites. This brings the VP16 activation domain portion of the fusion proteins adjacent to the promoter consisting of the GAL4 binding sites and a minimal TATA box to drive expression of a reporter gene. Using a modified HSV1-TK reporter, such an amplification system has been used to image androgen-dependent expression from an androgenresponsive, enhanced prostate-specific antigen promoter, the PSE-BC promoter (Sato et al., 2005). Imaging RNA Direct methods for imaging specific RNAs include modified oligonucleotides as described above. There have only been a few examples of specific RNA sequence imaging in vivo. Limitations for oligonucleotide imaging are described above. A major problem is the relatively few RNA molecules of any one sequence that can bind to RNA, resulting in a relatively small amount of signal for each detection event. An interesting paradigm for increasing signal has been described using luciferase imaging. This indirect method potentially may be translatable to PET imaging. It uses spliceosome-mediated RNA trans-splicing to drive expression of a reporter (Bhaumik et al., 2004). Engineered pre-trans-splicing molecules (PTMs) are embedded with active splicing elements. These are recognized by the cell’s splicing machinery and promote trans-splicing of the PTM encoded exon into the target transcript. The normal cis-splicing within the target pre-mRNA is avoided. The PTM binding domain can be designed to be complementary to intronic sequences in the target of interest in order to confer specificity to the trans-splicing event. Thus, the PTM includes a binding domain that will bind to a specific RNA sequence and a 3 acceptor site for trans-splicing. Proof of principle for this methodology was provided using luciferase. Expression of either the N-terminal or C-terminal part of this protein does not confer enzymatic activity, and thus no signal in the presence of the substrate luciferin. A PTM containing the N-terminal portion of luciferase was constructed with a 5 splice site followed by sequences of human papilloma virus-16 (HPV-16) that contained 3 splice sites. A second construct contained a PTM with a binding site for HPV-16 sequences to confer specificity and to bring the 3 splice site followed by the C-terminus of luciferase closer to 5 splice site. The HPV-16 sequences and 3 splice sites were excluded upon trans-splicing resulting in mRNA containing the full luciferase transcript that was transcribed into the enzyme. Upon addition of luciferin, signal in cells was seen after transfection of

both constructs suggesting splicing to bring both the mRNA N-terminus and C-terminus of luciferase into a new mRNA for expression of the entire protein. The experiment has limitations such as imaging expression of exogenous, not endogenous genes and a schema that is not easily generalizable to all mRNA. It does provide a tool for studying splicing in vivo that may be applicable to PET imaging in the future. It also demonstrates the utility of using reporter technology for amplifying signal. Imaging Proteins and Their Function Labeled amino acids have been used to evaluate their metabolism, for example, in the setting of cancer. For genomics and proteomics, proteins are of greater interest. A simple approach to image proteins is to radiolabel the ligand so that it retains binding to the protein, but not to nontarget proteins. An approach for enzymes is to radiolabel the substrate so that it is altered by the enzyme under study in such a way that it becomes entrapped in the cell. For pumps, again, the substrate may be radiolabeled, but in this case, the retention in the cell will be due to the action of the pump concentrating the radiolabeled substrate in the cell and not necessarily any alteration of the radiolabeled substrate. Retention may be enhanced by binding of the radiolabeled substrate to proteins within the cell. For all of these methods, the ligand/substrate usually must be relatively easy and economical to produce; stable in vivo; usually have minimal nonspecific binding such as to albumin; able to leave the vasculature; bound by the cell, taken up by the cell and/or penetrate the cell, and have a favorable route of excretion in order to produce minimal background in the organ of interest. Most importantly, the rate of efflux of the ligand/substrate in the target tissue must be greater than in nontarget tissue in order to produce a specific signal. Ligands An example of imaging with a radioligand is evaluation of the dopamine receptor D2R. Benzamides are ligands for dopamine receptors. A substituted benzamide, raclopride was labeled with the positron emitter 11C. Binding studies demonstrated that the Kd of 11C-raclopride for binding the D2R was 1.1 nM with relatively less affinity for binding other dopamine receptor subtypes (Farde et al., 1985). In monkeys, uptake of the compound was high in the striatum, where D2 receptors are expected, compared to nonspecific cerebellar binding. Further, the uptake could be competed by haloperidol, which binds D2 receptors particularly as well as other sites. Once a specific agent is created, it can find a variety of uses, including in pharmacology. For example, PET imaging of displacement of 11C-raclopride from the basal ganglia by cocaine has been used to suggest that cocaine modulates the amount of dopamine available for D2R binding (Schlaepfer et al., 1997). PET studies using 11C-raclopride have found a discrepancy in the mean plasma half-life of two antipsychotics, olanzapine and risperidone (24.2 and 10.3 h, respectively) and time to decline to 50% of peak striatal D2R occupancy (75.2 and 66.6 h, respectively). This suggests that

Imaging Agents and Methods in Analysis of Bilogical Samples

kinetics of receptor occupancy, such as that evaluated by PET, may improve dosing schedules of psychotropic medications compared to plasma kinetics (Tauscher et al., 2002). Enzyme Substrates An example of an enzyme substrate for PET imaging is 18Ffluoro-2-deoxyglucose (18F-FDG). It mimics glucose. It enters cells primarily via the glucose transporters (GLUT). Once inside the cell, it serves as a substrate for hexokinase. Phosphorylation of 18F-FDG is a terminal event, since, unlike glucose-6-phosphate, phosphorylated FDG cannot continue along the glycolytic pathway. The negative charge added upon phosphorylation prevents efflux. Thus, phosphorylated 18F-FDG accumulates in the cell. Unlike glucose, FDG does not undergo tubular reabsorption and is more readily excreted through the urine, decreasing background activity. Uptake of 18F-FDG is felt to reflect the metabolic rate of the cell. Although its uptake is felt to primarily reflect hexokinase activity, it is a complex event. For example, 18F-FDG accumulates to a greater degree in many cancers compared with normal tissues and reasons may include increased transport due to overexpression of membrane GLUT transporters (primarily GLUT1) as well as increased rate of glycolysis, for example, due to increased metabolic demand, hypoxia, signaling from oncogenes, increased hexokinase expression (primarily HK-1 and HK-2) and/or activity, and decreased glucose6-phosphatase activity. Increased 18F-FDG can be found in not only cancer, but also in inflammation and infection. Increased 18 F-FDG uptake reflects a functional process, not a specific disease; therefore, has found multiple applications.

(a)

R

507

Currently, 18F-FDG imaging has its greatest impact in oncology for diagnosis and staging and now monitoring of many cancers (Figure 44.6). It has also had a significant impact in cardiology, primarily for assessing ischemia and infarction. One of its major influences in neurology has been in seizure focus imaging. The applications of 18F-FDG PET imaging illustrate the myriad of pathologic events that may be evaluated by developing a radiotracer for a central biologic event, such as metabolism; and at the molecular level, in studying a key enzyme such as hexokinase. Use of 18F-FDG also illustrates that the readout of a molecular event need not be the immediate molecule under study, but may be a downstream event. One of the most successful new targeted drugs is Gleevec. Gleevec inhibits tyrosine kinases containing a kinase insert domain, such as the c-kit receptor. This receptor is overexpressed in gastrointestinal stromal tumors and is thought to be the primary inciting event for its malignant phenotype. At presentation, these tumors and their metastases avidly accumulate 18F-FDG, thus, PET imaging with this tracer aids diagnosis and staging. With Gleevec therapy, the kinase activity of the c-kit receptor is inhibited and the metabolic activity of the tumor is curtailed. Within 48 h after therapy, uptake of 18 F-FDG wanes (Stroobants et al., 2003), far before there is any anatomic change in tumor size. Thus, metabolic change reflects upstream inhibition of tyrosine kinase activity. And, 18F-FDG PET imaging may be used to assess whether Gleevec therapy will be successful. Further, when a tumor mutates so that it no longer is responsive to Gleevec therapy, it again accumulates 18FFDG (Shankar et al., 2005).

(b)

L R

■

(c)

L

R

L

Figure 44.6 Imaging of metabolism/enzymatic activity. PET–CT using 18F-FDG. 18F-FDG mimics glucose and its mechanisms of uptake include glut receptor expression on the plasma membrane and glucose-6 phosphate enzymatic activity, which phosphorylates and thus entraps the radiopharmaceutical inside the cell. Imaging of a 53-year-old man with lymphoma demonstrates increased 18FFDG uptake in the spleen and multiple intra- and extra-thoracic nodes, consistent with multifocal malignancy. Among its applications, 18 F-FDG may be used to stage and assess therapeutic response of lymphoma. (a) CT, (b) PET, (c) PET–CT fusion image.

508

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PET Imaging in Genomic Medicine

■

Pumps An example of a pump that can be imaged is the sodium iodide symporter. This pump drives its substrate into the cell using energy from ATP. Its actions result in greater accumulation of the substrate in the cell compared to outside of the cell. Substrates may include pertechnetate, which contains 99mTc for gamma camera imaging, as well as iodide. The symporter is expressed primarily in the thyroid and its use has proven clinical value for imaging thyroid pathology. Pertechnetate is an example of a substrate that results is accumulation in cells solely due to the action of the pump. On the other hand, in the thyroid, iodide is accumulated into the cell by the sodium iodide symporter; and, it is then entrapped by incorporation into thyroglobulin resulting in organification and may be subsequently used to produce thyroid hormone. In cancer cells, organification may not occur, and then iodide accumulation is primarily via the action of the sodium iodide symporter. Because the more complex process of organification is not required for accumulation of iodide, the sodium iodide symporter has been used as a reporter of gene transfer. Different isotopes of iodine are available, including those that emit primarily gamma rays,  particles, or positrons. 124I emits positrons and has been used primarily for PET-based reporter imaging in small animals. The different isotopes of iodine also illustrate that different isotopes may be used not only for imaging a protein, but for therapy as well.

(a)



Protein–Protein Interactions To study protein–protein interaction in vivo by PET, a modification of the yeast two-hybrid system may be employed (Figure 44.7) (Luker et al., 2002). For example, a bidirectional promoter can be used to express two fusion proteins. To control for the amount of expression, the promoter may be inducible, for example, by tetracycline. This will result in a fixed ratio of expression of the two fusion proteins. If the ratio of expression needs to be varied, the fusion proteins may be expressed under the control of two different promoters. The first fusion protein may consist of the protein of interest 1 fused to a GAL4 binding domain. The second fusion protein may consist of protein of interest 2 fused to VP16. The reporter construct consists of a promoter containing multiple GAL4 binding sites and a minimal TATA box. The first fusion protein localizes to the GAL4 binding sites via the GAL4 portion of the fusion protein. The protein of interest 1 portion of the first fusion protein can then interact with the second fusion protein. When such interaction occurs with the protein of interest 2 portion of the second fusion protein, the VP16 activation domain of the second fusion protein drives expression

(c)

Dox

rtTa Transactivator

Gal4-BD-p53

In addition to emission of gamma rays for gamma camera imaging, 131I emits  particles that can be used for therapy, for example, for Graves’ disease or for thyroid cancers that take up iodide.

VP16-TAg

Tet Promoter

(b) (d) p53

TAg

(e)

VP16

Gal4-BD Gal4

Gal4

Gal4

Gal4

Gal4 promoter

Gal4

TATA TATA box

HSV-TK

GFP

Fusion reporter

Figure 44.7 In vivo PET-based imaging of protein–protein interaction. A two-hybrid system was modified for detecting protein– protein interactions by fluorescence imaging and by in vivo PET imaging. Upon interaction, fusion proteins bring together transactivator and DNA binding domains of a transcription factor to induce transcription of a reporter gene. Two-hybrid systems are applicable to protein interactions that can translocate to the nucleus. (a) Construct of a bidirectional, tetracycline (Tet) inducible promoter for driving equal expression of two interacting proteins such as p53 fused to a Gal4 binding domain and the oncogenic T-antigen (TAg) fused to a VP16 activator domain. As a control, a protein that does not bind p53 such as Coat Protein (CP)-VP16 may replace TAgVP16. (b) Upon promoter induction by the tetracycline analog doxycycline (Dox), the two interacting proteins bring together GAL4 and VP16 to drive expression of a reporter such as Herpes simplex virus-thymidine kinase fused to green fluorescent protein. (c) Fluorescence due to green fluorescent protein (GFP) indicating protein–protein interaction. (d) Photograph of the thorax of a nu/nu mouse with axillary xenograft tumors derived from cells stably transfected with the negative control p53-GAL4 CP-VP16 (arrowhead) or p53-GAL4 TAg-VP16 (arrow). (e) Coronal microPET image of the same mouse 48 h after Doxycycline induction. Uptake of the HSV-TK substrate 18F-FHBG is seen only in the tumor derived from cells transfected with p53-GAL4 TAg-VP16 (arrow), confirming interaction of p53 and TAg. Asterisk denotes excretion of radiotracer into the gallbladder. Intestinal activity (I) from normal hepatobiliary clearance of the radiotracer was observed in the lower portion of the image. (Image courtesy of David Piwnica-Worms and modified from Proceedings of the National Academy of Sciences USA 2002;99:6961–6966.)

Imaging Agents and Methods in Analysis of Bilogical Samples

of the reporter. A radiopharmaceutical can then be administered for PET imaging of the expressed reporter. Protein–protein interaction based on two-hybrid technology has been employed for in vivo PET imaging to demonstrate interaction of p53 and the large T antigen of simian virus-40 (Luker et al., 2002).

(a)

(b)

In vivo image (SPECT and MR) derived % injected dose/g

R  0.87

1.5

1

509

Multimodality Imaging One of the problems with functional imaging is the lack of anatomic detail. As tracers improve, signal will become more and more specific to the region of its origin resulting in a spot on a functional image without anatomic reference. Another problem is that function does not always equal form.The signal from a greater amount of radioactivity may appear larger than a signal from a smaller amount of radioactivity although the shape and size of the underlying object is the same. The apparent size and apparent signal of the object may also be influenced by volume averaging artifacts that may occur when the resolution of the object of interest is less than 2.7 times the resolution of the imaging system (Kessler et al., 1984). Reference to an anatomic image, such as that generated by CT or MR, can be helpful in understanding the underlying anatomy and size of the object of interest in order to improve quantification of the functional signal. Using MR and gamma camera imaging, this has been demonstrated for image-based quantification of gene expression of a somatostatin receptor–based reporter (Figure 44.8) (Yang et al., 2005). Thus, the functional

Prescan procedures

(c)

■

CT component

PET component

Injection of 18 F-FDG

Patient positioning

Couch moved to PET FOV

60–90 min uptake phase

CT scout acquired (confirm/adjust position)

Empty bladder

CT scan acquired

PET emission acquisition

CT reconstruction

PET data correction and image reconstruction

0.5

0 0

0.5 1 Ex vivo tumor tissue derived % injected dose/g

Figure 44.8 Gene expression in tumors can be quantified by combining functional and anatomic imaging. (a) Functional gamma camera–based tomographic (SPECT) imaging of a mouse with tumors expressing greater (arrow) and lesser (arrowhead) amounts of exogenously introduced hemaglutinin A–tagged somatostatin receptor type-2 reporter gene 1 day after intravenous injection of 111In-octreotide. (b) Anatomic T2-weighted MR imaging of the same tumors. Note that the internal morphology of the tumors can be identified (arrow). (c) Reporter gene expression assessed by in vivo image-based biodistribution (% injected dose per gram) correlates with that derived from excised tumors. (a) and (b) are axial images at the level of the shoulders. (Images modified from Radiology 2005;235:950–958.)

Attenuation maps generated

Viewable CT images

Occurs in parallel to PET component

Viewable PET images

Viewable fused PET/CT images

Figure 44.9 Flow chart of 18F-fluoro-deoxy glucose (18FFDG) PET–CT imaging sequence of a patient with cancer. See text for description.

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PET Imaging in Genomic Medicine

signal can be normalized to the size or weight of the object of interest. With new fusion system incorporating functional and anatomic imaging, such as PET–CT (Figure 44.6) or SPECT–CT, anatomic localization of the functional signal is improved. PET– MR and SPECT–MR systems are under development. As with PET imaging, care must be taken to use appropriate protocols for anatomic imaging to maximize signal and anatomic delineation. Fusion systems are available for small animal imaging. PET–CT has already proven valuable in evaluating patients. Sample PET–CT Imaging Protocol Figure 44.9 is a flow chart of an example 18F-FDG PET–CT imaging sequence of a patient with cancer. It may be of assistance in developing patient and/or animal PET studies. Critical parameters include radiopharmaceutical dose, delay (incubation phase), voiding, subject positioning, and duration of PET scanning (to obtain sufficient counts). Intravenous and oral contrast may be used to improve anatomic delineation by CT.

CONCLUSION After finding increased or decreased expression in a gene array or after proteomic profiling (see Chapters 13 and 14), one can potentially image the gene product/protein with an appropriate PET radiopharmaceutical, as outlined in this chapter. Evaluation of the promoter driving the expression may be evaluated using reporter technology. A promoter–reporter construct may be introduced exogenously by gene transfer, including creating transgenic animals. RNA transcripts may be imaged using labeled oligonucleotides and reporter systems. Protein function and protein–protein interactions are approachable. In short, PET imaging has the potential to quantitatively image processes from the level of DNA to the tissue in vivo. Clinically, it has already found significant utility via 18F-FDG for diagnosing and staging disease and monitoring treatment.

REFERENCES Badawi, R.D. and Marsden, P.K. (1999). Self-normalization of emission data in 3D PET. IEEE Trans Nucl Sci 46, 709–712. Bailey, D.L. and Meikle, S.R. (1994). A convolution-subtraction scatter correction method for 3D PET. Phys Med Biol 39, 411–424. Bailey, D.L., Townsend, D.W., Kinahan, P.E., Grootoonk, S. and Jones, T. (1996). An investigation of factors affecting detector and geometric correction in normalization of 3-d pet data. IEEE Trans Nucl Sci 43, 3300–3307. Bartzakos, P. and Thompson, C.J. (1991). A PET detector with depthof-interaction determination. Phys Med Biol 36, 735–748. Bhaumik, S., Walls, Z., Puttaraju, M., Mitchell, L.G. and Gambhir, S.S. (2004). Molecular imaging of gene expression in living subjects by spliceosome-mediated RNA trans-splicing. Proc Natl Acad Sci U S A 101, 8693–8698. Cammilleri, S., Sangrajrang, S., Perdereau, B. et al. (1996). Biodistribution of iodine-125 tyramine transforming growth factor alpha antisense oligonucleotide in athymic mice with a human mammary tumour xenograft following intratumoral injection. Eur J Nucl Med 23, 448–452. Carroll, L., Kertz, P. and Orcutt, G. (1983). The orbiting rod source: Improving performance in PET transmission correction scans. In Emission Computed Tomography: Current Trends (235–247) (P.D. Esser, ed.), Society of Nuclear Medicine, New York, NY. Casey, M.E. and Hoffman, E.J. (1986). Quantitation in positron emission computed tomography: 7. A technique to reduce noise in accidental coincidence measurements and coincidence efficiency calibration. J Comput Assist Tomogr 10, 845–850. Cherry, S.R. and Huang, S.-C. (1995). Effects of scatter on model parameter estimates in 3D PET studies of the human brain. IEEE Trans Nucl Sci 42, 1174–1179. Cherry, S.R., Sorenson, J.A., Phelps, M.E. and Sorenson, J.A. (2003). Physics in nuclear medicine, 3rd ed.. Saunders, Philadelphia, PA. xiii, 523 p. Cooke, B.E., Evans, A.C., Fanthome, E.A., Alarie, R. and Sendyk, A.M. (1983). Performance figures and images from the Therascan

3128 positron emission tomograph. IEEE Trans Nucl Sci NS-31, 640–644. Daube-Witherspoon, M.E. and Carson, R.E. (1991). Unified deadtime correction model for PET. IEEE Trans Med Imaging 10, 267–275. Derenzo, S.E. (1985). Mathematical removal of positron range blurring in high resolution tomography. IEEE Trans Nucl Sci NS-33. Dewanjee, M.K., Ghafouripour, A.K., Kapadvanjwala, M. et al. (1994). Noninvasive imaging of c-myc oncogene messenger RNA with indium-111-antisense probes in a mammary tumor-bearing mouse model. J Nucl Med 35, 1054–1063. Farde, L., Ehrin, E., Eriksson, L. et al. (1985). Substituted benzamides as ligands for visualization of dopamine receptor binding in the human brain by positron emission tomography. Proc Natl Acad Sci U S A 82, 3863–3867. Green, L.A.,Yap, C.S., Nguyen, K. et al. (2002). Indirect monitoring of endogenous gene expression by positron emission tomography (PET) imaging of reporter gene expression in transgenic mice. Mol Imaging Biol 4, 71–81. Hoffman, E.J., Guerrero, T.M., Germano, G., Digby, W.M. and Dahlbom, M. (1989). PET system calibrations and corrections for quantitative and spatially accurate images. IEEE Trans Nucl Sci 36, 1108–1112. Hoffman, E.J., Huang, S.C. and Phelps, M.E. (1979). Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 3, 299–308. Huang, S.C., Hoffman, E.J., Phelps, M.E. and Kuhl, D.E. (1979). Quantitation in positron emission computed tomography: 2. Effects of inaccurate attenuation correction. J Comput Assist Tomogr 3, 804–814. Hudson, H.M. and Larkin, R.S. (1994). Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13, 601–609. Iversen, P.L., Zhu, S., Meyer, A. and Zon, G. (1992). Cellular uptake and subcellular distribution of phosphorothioate oligonucleotides into cultured cells. Antisense Res Dev 2, 211–222.

Recommended Resources

Kessler, R.M., Ellis, J.R., Jr. and Eden, M. (1984). Analysis of emission tomographic scan data: Limitations imposed by resolution and background. J Comput Assist Tomogr 8, 514–522. Kinahan, P.E., Townsend, D.W., Beyer, T. and Sashin, D. (1998). Attenuation correction for a combined 3D PET/CT scanner. Med Phys 25, 2046–2053. Kobori, N., Imahori, Y., Mineura, K., Ueda, S. and Fujii, R. (1999). Visualization of mRNA expression in CNS using 11Clabeled phosphorothioate oligodeoxynucleotide. Neuroreport 10, 2971–2974. Loke, S.L., Stein, C.A., Zhang, X.H. et al. (1989). Characterization of oligonucleotide transport into living cells. Proc Natl Acad Sci U S A 86, 3474–3478. Luker, G.D., Sharma, V., Pica, C.M. et al. (2002). Noninvasive imaging of protein-protein interactions in living animals. Proc Natl Acad Sci U S A 99, 6961–6966. Phelps, M.E., Hoffman, E.J., Mullani, N.A. and Ter-Pogossian, M. M. (1975). Application of annihilation coincidence detection to transaxial reconstruction tomography. J Nucl Med 16, 210–224. Ollinger, J.M. (1996). Model-based scatter correction for fully 3D PET. Phys Med Biol 41, 153–176. Rasey, J.S., Grierson, J.R., Wiens, L.W., Kolb, P.D. and Schwartz, J.L. (2002).Validation of FLT uptake as a measure of thymidine kinase-1 activity in A549 carcinoma cells. J Nucl Med 43, 1210–1217. Roivainen, A.,Tolvanen,T., Salomaki, S. et al. (2004). 68Ga-labeled oligonucleotides for in vivo imaging with PET. J Nucl Med 45, 347–355. Sato, M., Johnson, M., Zhang, L., Gambhir, S.S., Carey, M. and Wu, L. (2005). Functionality of androgen receptor-based gene expression imaging in hormone refractory prostate cancer. Clin Cancer Res 11, 3743–3749. Schlaepfer, T.E., Pearlson, G.D., Wong, D.F., Marenco, S. and Dannals, R. F. (1997). PET study of competition between intravenous cocaine and [11C]raclopride at dopamine receptors in human subjects. Am J Psychiatry 154, 1209–1213. Schoder, H., Erdi,Y.E., Chao, K., Gonen, M., Larson, S.M. and Yeung, H. W. (2004). Clinical implications of different image reconstruction

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parameters for interpretation of whole-body PET studies in cancer patients. J Nucl Med 45, 559–566. Serganova, I., Doubrovin, M.,Vider, J. et al. (2004). Molecular imaging of temporal dynamics and spatial heterogeneity of hypoxia-inducible factor-1 signal transduction activity in tumors in living mice. Cancer Res 64, 6101–6108. Shao, L., Freifelder, R. and Karp, J.S. (1994). Triple energy window scatter correction technique in PET. IEEE Trans Medi Imaging 13, 641–648. Shankar, S., vanSonnenberg, E., Desai, J., Dipiro, P.J.,Van Den Abbeele, A. and Demetri, G.D. (2005). Gastrointestinal stromal tumor: New nodule-within-a-mass pattern of recurrence after partial response to imatinib mesylate. Radiology 235, 892–898. Shi, N., Boado, R.J. and Pardridge, W.M. (2000). Antisense imaging of gene expression in the brain in vivo. Proc Natl Acad Sci U S A 97, 14709–14714. Stroobants, S., Goeminne, J., Seegers, M. et al. (2003). 18FDG-Positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec). Eur J Cancer 39, 2012–2020. Tavitian, B., Terrazzino, S., Kuhnast, B. et al. (1998). In vivo imaging of oligonucleotides with positron emission tomography. Nat Med 4, 467–471. Tauscher, J., Jones, C., Remington, G., Zipursky, R.B. and Kapur, S. (2002). Significant dissociation of brain and plasma kinetics with antipsychotics. Mol Psychiatry 7, 317–321. Urbain, J.L., Shore, S.K., Vekemans, M.C. et al. (1995). Scintigraphic imaging of oncogenes with antisense probes: Does it make sense?.Eur J Nucl Med 22, 499–504. Yang, D., Han, L. and Kundra, V. (2005). Exogenous gene expression in tumors: Noninvasive quantification with functional and anatomic imaging in a mouse model. Radiology 235, 950–958. Xu, E.Z., Mullani, N.A., Gould, K.L. and Anderson, W.L. (1991). A segmented attenuation correction for PET. J Nucl Med 32, 161–165.

RECOMMENDED RESOURCES Bendriem, B. and Townsend, D.W. (1998). The Theory and Practice of 3D PET. Kluwer Academic, Dordrecht, London, xvi, 167 pp. Humm, J.L., Rosenfeld, A. and Del Guerra, A. (2003). From PET detectors to PET scanners. Eur J Nucl Med Mol Imaging 30, 1574–1597. Kelloff, G.J., Krohn, K.A., Larson, S.M., et al. (2005). The progress and promise of molecular imaging probes in oncologic drug development. Clin Cancer Res 11, 7967–7985. Mawlawi. O., Pan, T. and Macapinlac, H.A. (2006). PET/CT imaging techniques, considerations, and artifacts. J Thorac Imaging 21, 99–110. Nutt, R. (2002). 1999 ICP Distinguished Scientist Award. The history of positron emission tomography. Mol Imaging Biol 4, 11–26. Phelps, M.E. (2004). PET: Molecular Imaging and Its Biological Applications. Springer, New York, London, 615 pp. Valk, P.E. (2006). Positron Emission Tomography: Clinical Practice. Springer, London, xiv, 475 pp.

Zanzonico, P. (2004). Positron emission tomography: A review of basic principles, scanner design and performance, and current systems. Sem Nucl Med 34, 87–111.

Websites Badawi, R.D. (1999). Introduction to PET Physics. University of Washington Division of Nuclear Medicine. Retrieved on October 15, 2007 from http://depts.washington.edu/nucmed/IRL/pet_ intro/index.html Phelps, M.E., Gambhir, S.S., Mahoney, D.K. and Markham, J.A. (1993). Let’s Play PET. Regents of the University of California. Retrieved on October 15, 2007 from http://www.uib.no/med/avd/miapr/ arvid/MOD3_2002/Bildedannelse/lets_play_PET.pdf

CHAPTER

45 MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges Dmitri Artemov

INTRODUCTION Advances in molecular biology that include the decoding of the human genome have given a strong impetus to molecular-based and -targeted medicine. Genomic medicine is a novel concept that explores complex interactions within the genome and is focused on the role of multiple genes in human diseases (Guttmacher and Collins, 2002). These recent developments have resulted in an urgent need to measure noninvasively the expression pattern of a specific gene product(s) and to determine the response to molecular interventions, first in preclinical models of human disease that typically involve experimental animals such as mice and rats, but with the ultimate goal of clinical translation. Molecular imaging is a rapidly advancing area of biomedical research that can provide a matching set of tools to address the emerging demands of genomic and molecular medicine applications (Rome et al., 2007) (see Chapter 43). Monitoring of molecular events in vivo requires imaging modalities that permit repetitive in vivo measurement of these events with sufficient sensitivity and spatial resolution. Nuclear imaging techniques such as PET and SPECT are traditionally used for molecular imaging with highly specific probes and provide the highest Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 512

sensitivity of detection that allow detection of tracer concentrations of the probes combined with inherent three-dimensional (3D) imaging capabilities. The main problems of nuclear imaging are relatively low spatial resolution and the exposure of the subject to ionizing radiation. Optical imaging, an alternative technique that is widely used in preclinical models, allows detection of nanomolar concentrations of luminescent and fluorescent probes in vivo. Unfortunately optical imaging is restricted to imaging of relatively superficial tissue due to the strong absorption and scattering of light in living tissue.This also makes implementation of 3D imaging technology quite difficult especially for relatively large objects. Magnetic resonance imaging (MRI) is a unique imaging modality where the image intensity is a complex function of multiple parameters that can be effectively modified by choosing an appropriate imaging pulse sequence and by application of MR contrast agents. As a result MR images provide good delineation of morphological and functional parameters such as blood perfusion (Duyn et al., 2005) and tissue composition and micro-architecture (Mori et al., 2002). This inherently high spatial resolution of MRI is combined with a relatively low sensitivity, and typically concentrations in the millimolar range are required for robust MR detection. In a typical example of Copyright © 2009, Elsevier Inc. All rights reserved.

Basics of MRI Contrast

imaging of cell surface receptors, expressed at a density of one million receptors per cell, the resulting total concentration of the receptors is below 1 μmol/l. Therefore efficient strategies for signal enhancement are required to enable MRI of this important biological parameter. In this chapter, we will consider different approaches to improve MR capability for molecular imaging and will discuss in detail mechanisms of contrast generation such as T1 and T2 relaxation contrast, proton exchange, basic of MR spectroscopic imaging, and potential applications of the methods to clinically relevant problems. The ability to image the expression pattern of relevant genes noninvasively is an ultimate goal of molecular imaging applications in the field of genomic medicine.

BASICS OF MRI CONTRAST Source of the MR Signal MRI and spectroscopy are based on the detection of radiofrequency (RF) signals generated by magnetic nuclear spins precessing in the external magnetic field B0. The resonance frequency 0 of the MR linearly depends on B0 and the gyromagnetic ratio of the nucleus, as 0  B0. The intensity of the MR signal depends on (i) concentration of the nuclear spins and (ii) on the MR frequency or of this particular spin. These values are listed in Table 45.1 for nuclei used in biological MRI studies. Natural abundance shows the concentration of magnetic isotope or the percentage of the total number of nuclei for the given element with magnetic moment that is required to generate MR signal. From Table 45.1 it is apparent that proton (1H) is the most sensitive species and, due to the high content of protons in the form of water and/or fat in living tissues, proton MRI is most often used for in vivo studies. Therefore the discussion here is primarily limited to proton MRI and to various mechanisms, apart from proton concentration, that determine MRI signal intensity in the images. MRI Contrast Mechanisms Water constitutes more than 80% of living organisms and is uniformly distributed in soft tissues. Therefore, unless there are TABLE 45.1 in vivo MRI

Magnetic properties of nuclei used for

Nuclei

␥ [MHz/T]

u0 [MHz] @ B0 ⴝ 3T

Natural abundance (%)

1

H

42.58

128

100

3

He

32.44

97

1.37 E-4

13

10.71

32

1.11

19

40.08

120

100

23

11.27

34

100

31

17.25

52

100

C F Na P

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differences in the concentration, MR images of proton concentration are usually featureless. On the other hand there are several other parameters that affect image intensity in MRI including relaxation, diffusion, flow rate, and water exchange. Relaxation A typical MRI experiment consists of a series of RF pulses applied to the spins of the object (excitation) and detection of the pulse response from the precessing spins (Gillies, 1994). After a single excitation cycle, the longitudinal magnetization (parallel to the external magnetic field B0) is reduced from its equilibrium value M0 to a new transient value Mt due to saturation effects of the RF pulses. If no further RF excitation is applied to the system during time t, then the magnetization M(t) relaxes back from Mt to its equilibrium value M0 according to the Bloch equation: M (t )  M t ⋅ exp(t /T1 )  M 0 ⋅ (1  exp(t /T1 )) T1 is called the longitudinal relaxation time of the spins, and for water in biological systems T1 typically is close to 1 s. Two important cases are usually defined: (i) when starting magnetization is completely saturated (Mt  0), so called “saturation recovery” and (ii) when initial magnetization is inverted (Mt  M0), “inversion recovery.” Similarly, the excited precessing magnetization Mp decays to zero with a characteristic time T2, the transversal relaxation time, that generally is shorter than T1 due to (i) additional dephasing of rotating spins by local inhomogeneities of the magnetic field B0, (ii) diffusion, and (iii) additional relaxation mechanisms that are only effective for transversal relaxation (Slichter, 1990). The general equation for the transversal magnetization after complete excitation is: M p (t )  M p (0)exp(t /T2 ) An important case is complete excitation with Mp(0)  M0. If local fields are refocused by the use of spin-echo (SE) pulse sequence, then we have true T2 decay. However, if the imaging pulse sequence is sensitive to dephasing processes due to the local field inhomogeneities such as gradient-echo (GE) imaging, a much faster decay of Mp is typically characterized by T2* relaxation time. A detailed discussion of T1 and T2 relaxation mechanisms is beyond the scope of this chapter and can be found in (Slichter, 1990). Further in the chapter we will discuss different classes of MR contrast agents and how they can modify inherent T1 and T2 to enhance imaging contrast. Diffusion Diffusion, or more correctly self-diffusion, of water molecules results in random changes of the position of the molecule with

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MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges

time. In an inhomogeneous magnetic field, B0, moving water molecules experience different strengths of the magnetic field in different positions. In this case the transversal magnetization is dephased by these randomly changed local fields at the current position of the molecule and cannot be completely refocused by RF pulses in a SE imaging sequence. For diffusion measurements a pair of variable diffusion weighting gradients is typically incorporated into a standard SE sequence. Signal is measured as a function of the gradient strength and, optionally, orientation. The magnetization decay depends on the diffusion coefficient of water molecules in the direction of the applied gradient. If a sufficient number of different gradient orientations are used then all the components of the diffusion tensor can be measured in each pixel of the MR image (Le Bihan et al., 1991). One example of application of this technique in MRI studies of human brain is determination of brain microfiber orientation that relates to white matter tracks as shown in Figure 45.1 (Mori et al., 2002). Water Exchange A novel contrast mechanism, CEST (chemical exchange saturation transfer), that utilizes proton exchange between bulk water and exchangeable chemical groups such as amides of proteins, imino- and hydroxyl protons (Guivel-Scharen et al., 1998) was proposed as an ultra-sensitive MR marker (McMahon et al., 2006; Snoussi et al., 2003). Briefly, when a prolonged RF pulse is applied to the system at the frequency of water resonance, water, immediately before imaging sequence, the resulting saturation of the water peak results in a very low signal ideally approaching zero for complete saturation. When the frequency of the saturation RF field shifts from the water

resonance, the efficiency of saturation decreases and the intensity of the water signal increases with the shift. However, if there is an exchangeable chemical group, group, then saturation on the resonance frequency of this group will lead to attenuation of the bulk water signal because magnetization of many exchanging water molecules will be saturated while their protons reside on the exchangeable group. Typically the intensity of the water resonance is measured for two saturation RF fields with frequencies equal to   water  , where   group – water, and their difference is a measure of concentration of the exchangeable group. Two important properties of this contrast mechanism is a significant amplification of the signal because a single exchangeable group can affect signals of multiple water molecules and the possibility to express endogenous compounds that will produce a desirable CEST effect (McMahon et al., 2006). MRI Techniques Here we will discuss two most important modes of acquiring MR images that are based on (i) formation of spin-echo (SE) and (ii) gradient-echo (GE) imaging. Detailed description of the methods, as well as more modern MRI techniques that include fast and/or parallel imaging, can be found elsewhere (Brown, 2003). Diagrams of the SE and GE imaging pulse sequences are shown in Figure 45.2.

(a)

Excitation RF pulse

Refocusing RF pulse

Spin echo

Preparation RF t Gr t

Gp,s,d t (b)

Excitation RF pulse

Gradient echo

Preparation RF t Gr t Gp,s t

Figure 45.1 Diffusion Tension Imaging (DTI) uses water diffusion as a probe for white matter anatomy. Colors represent orientation of white matter tracks: red for right–left, green for anterior–posterior, and blue for superior–inferior orientation respectively (picture kindly provided by Dr Mori).

Figure 45.2 Time diagrams of 2D spin-echo (a) and gradientecho (b) MRI pulse sequences. Gr denotes read gradient and Gp,s,d phase encoding, slice selection, and diffusion weighting (solid blocks) gradients respectively.

MR Contrast Agents for Molecular Imaging Applications

The main features of the sequences are T2 and/or self-diffusion contrast for SE sequence that refocuses magnetic field inhomogeneities for stationary spins by echo formation with the refocusing RF pulse, and T2* contrast due to local magnetic fields for GE pulse sequence. The preparation period before the imaging sequence can be used to generate additional contrast. For example, T1 contrast can be generated by saturation or inversion of the initial magnetization and CEST contrast by selective saturation at frequencies of proton-exchangeable chemical groups as discussed above. Three-dimensional MRI sequences are analogous to 2D sequences discussed here and use a second phase-encoding gradient ramp. T2 and T2* contrast produced by shortening of T2 and T2* relaxation times respectively results in a negative contrast, that is, image intensity decreases in these regions. On the other hand, T1 contrast generated by shortening of T1 relaxation time produces positive contrast or brighter images that often are advantageous for image analysis. Recently, novel technologies were developed to generate positive contrast with T2* contrast agents by detecting off-resonance water signals (Cunningham et al., 2005) or use noncompensated local gradients generated by T2* contrast agent (Seppenwoolde et al., 2003).

MR CONTRAST AGENTS FOR MOLECULAR IMAGING APPLICATIONS Paramagnetic MR Contrast Agents The most important class of paramagnetic MR contrast agents is based on the transition metal, gadolinium. Gadolinium (Gd) has unique chemical and physical properties that make it an ideal MR tracer. It has nine coordination sites that form stable biologically inert complexes with linear and cyclic chelates such as DTPA and DOTA (Corot et al., 1998), it has an unpaired electron that has high magnetic moment (almost 700-fold higher than magnetic moment of a proton), inverse electron relaxation time is close to 0 for practically used MR magnetic fields, and correlation time of the complexes can be adjusted to 01, which makes it an efficient T1 contrast agent. Indeed, the strong magnetic moment of Gd fluctuates with a characteristic frequency e  0, and therefore significantly enhances T1 relaxation processes. To quantitatively measure the efficiency of a contrast agent relaxivity parameter, R, is often used. For T1 and T2 relaxation R is defined as:

R1, 2 

1 1  T1, 2 T1, 20 [C ]

,

where T1,20 and T1,2 are corresponding relaxation times before and after addition of the contrast agent and [C] is the concentration of the contrast. Typical values of T1 relaxivity of lowmolecular weight GdDTPA/GdDOTA complexes are in the range 5–10 [s(mM Gd)]1. Usually a chelate group coordinates 8 of the 9 electrons of Gd, and one remaining coordination site

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is used for rapidly exchanging water molecules that rapidly relax due to the fluctuating magnetic moment of the unpaired electron. It was suggested that the relaxivity of the complex could be modulated by blocking this site with a cleavable chemical group as originally proposed by Louie and colleagues (Louie et al., 2000). They designed a “smart” relaxation agent in which the access of water to the first coordination sphere was blocked with a -galactosidase substrate, galactopyranose, which can be removed by enzymatic cleavage. Following the cleavage of the blocking sugar group, the paramagnetic ion can interact directly with water protons to reduce their T1 by the inner sphere (contact) relaxation with a corresponding increase in the MR signal. Analogous approaches can be used to design CA with polypeptide blocking chains to probe protease activity. The major obstacle is intracellular expression of these enzymes therefore the contrast agent should be either microinjected to the cell(s) (Louie et al., 2000) or delivered to the cells by means of certain cellular transport systems such as amphiphilic Tat-type membrane translocation peptides (Bhorade et al., 2000). Another group of paramagnetic contrast agents uses manganese ion (Mn1), a biological analog of calcium, to visualize calcium influx and cell activation in vivo (Pautler et al., 1998) such as neuronal tract tracing (Koretsky et al., 2004; Wadghiri et al., 2004). To generate detectable T1 contrast in MR images (10% T1 change), the required concentration of Gd-based agents with T1 relaxivity of 苲10 [s · mM]1 should be in the range of 苲10 moles and above. Therefore, to detect molecular targets with submicromolar biological concentrations, multiple Gd groups should be attached to a single target molecule. One way to accomplish this is to use macromolecular carriers decorated with a large number of Gd chelate groups. Conjugation chemistry for this type of reaction is well established and typically a free amine group on the surface of the molecule is used to provide conjugation to derivatized Gd chelate complexes. Several imaging platforms were proposed as Gd carriers including proteins (albumin, avidin), dendrimers, poly-l-lysine, liposomes, and nanoemulsions (Artemov et al., 2002; Sipkins et al., 1998; Uzgiris et al., 2004; Winter et al., 2003). Generally, larger carriers provide proportionately larger T1 relaxivity per molecule (i.e., no saturation effects on T1 relaxivity is observed). However, there may be significant problems with the delivery of the large imaging probes to molecular targets that require extravasation of the molecule from blood capillaries and diffusion across tissue interstitium to access the binding site. Delivery issues will be discussed in more detail later in the chapter. Superparamagnetic MR Contrast Agents As was shown before, the large magnetic moment of the contrast agent induces very efficient relaxation, therefore superparamagnetic iron-oxide (SPIO)-based MR contrast agents were introduced, with an iron-oxide core protected by various polymeric coats. The magnetic core is typically composed from magnetite, that is an inverse spinel with formula Fe2+O · Fe23+O3  Fe3O4. Depending on the size, the core consists of several thousands of

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iron atoms and generates very high magnetic moment. Due to the relatively large molecular size of SPIO nanoparticles they have long rotational correlation time, c, and they act as efficient T1 contrast agents only at low magnetic fields where the condition 0  B0  c1 is fulfilled. On the other hand SPIOs generate strong low-frequency local magnetic fields that make them very sensitive T2 and T2* contrast agents with typical T2 relaxivity R2 in the order of 50–100 [s(mM Fe)]1. Because SPIO particles pack large amount of iron the resulting relaxivity of these nanoparticles is several orders of magnitude higher than for Gd compounds (per molecule of the contrast agent). Different preparations of SPIO were investigated for MRI applications including monocrystalline iron-oxide particles, MION, with a core diameter of 4.6 nm and nanoparticles diameter of about 20 nm, ultrasmall SPIO (USPIO) with 3–4 nm core and hydrodynamic diameter 25 nm, cross-linked iron-oxide (CLIO), classical SPIO such as Feridex (Berlex) with 5–6 nm polycrystalline core and dextran coating and 苲35 nm average diameter (Jung et al., 1995). Large SPIO particles with polystyrene coating and d  1 m are produced by Bangs, Inc. and are very useful for in vitro cell labeling. As with Gd-based contrast agent larger particles generally provide higher relaxivity. However, in vivo the delivery of this type of MR contrast agents may be not optimal for targeting tissue epitopes. Indeed, traditional use of SPIO relies on rapid internalization of the particles by macrophages in the liver, lymph nodes, and peripheral circulation. Biodistribution of CLIO particles in rats was studied by Moore and colleagues (Moore et al., 2000), and the results are summarized in Table 45.2, which is adapted from that paper. Nanoparticles have low distribution even in the tumors with presumably leaky vasculature, however, the high relaxivity of the agent enabled visualization of the brain tumor from normal brain (Moore et al., 2000). Delivery of monoclonal antibodies (mAb) and fluorescent nanospheres to Lewis lung carcinoma models in mice was studied by Nakahara et al. (Nakahara et al., 2006). Fluorescent micrographs of distribution of nonspecific fluorescent IgG and fluorescent 50-nm diameter nanospheres obtained 6 h after i.v. injection are shown in Figure 45.3. Extravasated IgG (arrows) had a patchy distribution with only vague association with CD31-stained blood vessels. On the other hand extravasated nanospheres were closely associated with focal regions of tumor vessels. Therefore contrast agents that can

specifically recognize tissue molecular markers and can efficiently diffuse to these markers should have a smaller molecular weight probably close to that of the mAb or about 150 kDa that corresponds to a molecular diameter less than 10 nm. While hyperpermeable tumors may have “hot spots” with increased extravasation of large macromolecules (Monsky et al., 1999), generally lower-molecular weight imaging markers are required for molecular MRI of tissue targets, unless the target is expressed by the vasculature. Interestingly, accumulation of endogenous iron by cells overexpressing iron-binding protein, ferritin can also lead to significant negative T2 contrast detectable in MR images. This method was proposed for molecular imaging of cancer cells transfected with ferritin, that can be used as a reporter gene for successful cell transfection in vivo (Cohen et al., 2005). CEST and PARACEST MR Agents This new class of MR contrast agent is designed to enable selective irradiation of exchangeable protons of these CA with an RF field to induce a drop in the MR signal of bulk water because of transfer of the saturated magnetization from the irradiated protons to water protons by chemical exchange. Originally proposed by the group of Balaban (Guivel-Scharen et al., 1998), CEST contrast was demonstrated for exchangeable amide protons of proteins (Zhou et al., 2003a, b), imino- and hydroxyl protons (Snoussi et al., 2003). A significant increase in the sensitivity of detection can be gained if a polymer with a large number of equivalent exchangeable groups is used. In experiments with polyuridine (2000 uridine units) the sensitivity gain of more than 5000 per imino-proton was demonstrated and the CA could be detected at concentration as low as 5 M (Snoussi et al., 2003). The efficiency of CEST contrast increases as the frequency offset, , between resonance frequency of the exchangeable group and water protons increases. The frequency difference can be significantly increased from several parts per million (ppm) to more than 20 ppm by attaching a paramagnetic shift reagent to the CEST agent. Experiments with a model poly-l-arginine/Tm(HDOTP)4 system lowered the detection limit to 2.8 M in a phantom at 7T magnetic field (Aime et al., 2003). This modification of the method was named PARACEST for paramagnetic enhanced CEST. The major potential advantages

TABLE 45.2 Biodistribution of long-circulating 125I-labeled CLIO nanoparticles in rats with 9L-GFP brain tumor (4 animals) 24 h post-injection (adapted from Moore et al. [2000]) Organ

Blood

Brain

Heart

Lymph nodes

Kidney

Liver

Lung

Muscle

Spleen

Tumor

Injected dose per gram of tissue (%)

0.5  0.2

0.01  0.01

0.2  0.1

25.0  3.2

0.8  0.1

1.9  0.3

0.2  0.1

0.1  0.1

9.8  0.8

0.1  0.1

Molecular Imaging Applications of MRI

of the CEST/PARACEST methods to generate MR contrast are: (i) the ability to “turn” the contrast “on” and “off ” by applying saturating RF field at the resonance frequency of the exchangeable group; (ii) high sensitivity that can be achieved by using polymer probes and/or PARACEST agents with large  shifts and fast exchange rates; (iii) certain types of CEST agents such as poly-amino acids can be expressed in vivo by target cells with an appropriate reporter vector; (iv) it is possible to design PARACEST probes with different  values and image them independently using RF saturation field with the appropriate frequency offset.

MOLECULAR IMAGING APPLICATIONS OF MRI MRI Cell Tracking A typical experiment for MR cell trafficking includes in vitro labeling of cell population with an iron-oxide nanoparticle preparation using standard transfection reagents such as poly-l-lysine (Bulte et al., 2004) and/or electroporation (Walczak et al., 2005). The magnetically labeled cells are administered systemically or via local injection, and T2- or T2*-weighted MRI is used for longitudinal imaging of the cell migration and accumulation to the target site. T2*-weighted GE MRI is sensitive to “long range” disturbances in the magnetic field produced by the concentrated SPIO particles that affect T2* relaxation of distant protons within an area much larger than the size of the nanoparticles. Therefore this technique provides significant amplification of the contrast and largest MR sensitivity (Bulte et al., 1992; Dodd et al., 1999). In several excellent reviews, Bulte et al. discussed in detail MR tracking of magnetically labeled cells (Bulte et al., 2004; Karmarkar et al., 2004; Lee et al., 2004;Walter et al., 2004). SPIO provides very efficient contrast enhancement in MRI, and isolated cells labeled with iron-oxide nanoparticles can be detected at iron concentrations as low as 17 ng per 106 cells or 8.5  104 MION particles/cell (Weissleder et al., 1997). MR detection of a single magnetically labeled T cell was demonstrated

(a)

IgG (iv) CD31

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517

by Dodd et al. (Dodd et al., 1999). Typically for reliable in vivo MR detection, several million SPIO or USPIO particles must be loaded within the cell. Large micron diameter SPIO particles (such as Bangs) provide sufficient T2* contrast to enable detection of a single cell labeled with just a single micron size iron-oxide particle (Shapiro et al., 2004). One potential benefit of this approach is that the label is not diluted by subsequent cell division and daughter cells can be detected for a long time post-implantation. To further improve cell uptake properties CA nanoparticles can be linked to the HIV viral transport Tat peptide that can channel the cargo molecules across the cell plasma membrane (Gammon et al., 2003). Efficient cellular uptake of Tat-CLIO as well as Tat-Macrocyclic gadolinium chelates was recently demonstrated in in vitro and in vivo systems (Bhorade et al., 2000; Prantner et al., 2003). This approach is especially useful for loading of cells with low endocytotic/pinocytotic activity such as lymphocytes. Tat-CLIO nanoparticles were used to load cytotoxic T lymphocytes (CTL) ex vivo and to track their docking to the antigen-expressing tumor xenografts in vivo by T2-weighted MRI (Kircher et al., 2003). Magnetic labeling of transplanted islets with SPIO was used to noninvasively monitor transplantation efficiency and graft survival in small animal models (Evgenov et al., 2006). Briefly, isolated human islets were loaded with dextran-coated SPIO by overnight incubation and implanted in the kidney capsule or hepatically by intraportal infusion. In vivo MRI was performed with a B0  4.7T animal scanner using T2*-weighted SE pulse sequence. Results obtained at different time points after intrahepatic transplantation are shown in Figure 45.4.

MR Receptor Imaging with Targeted Contrast Agents Various mechanisms of contrast generations were explored for MRI of cell surface receptors. Several attempts to directly label mAb with Gd chelate groups did not produce satisfactory results in solid tumors primarily due to the limited number of groups that can be conjugated to the mAb without diminishing its binding affinity and limited extravasation and diffusion of the large molecular complexes in the tumor microenvironment. On

(b)

Microspheres (iv) CD31

Figure 45.3 Confocal micrographs showing the distribution of extravasated nonspecific IgG and 50-nm microspheres at 6 h after injection in RIP-Tag2 tumors (adapted from Nakahara et al. [2006]).

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MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges

Day 3

Day 5

Day 7

SC A S

V P

Figure 45.4 In vivo MRI obtained at different time points after intrahepatic transplantation of labeled human islets. A representative slice (0.5 mm) shows islets scattered throughout the liver. Pancreatic islets appear as hypointense spots on T2*-weighted images (red ovals). A, aorta; P, portal vein; S, stomach; SC, spinal cord; V, caudal vena cava (adapted from Evgenov et al. [2006]).

the other hand, several reports demonstrated successful imaging of v3 integrins that are marker of the tumor neovasculature and are expressed in the angiogenic endothelium of the tumors. High accessibility of this marker enables the use of large blood-pool nanocomplexes such as paramagnetic polymerized liposomes (Sipkins et al., 1998) or Gd-perfluorocarbon nanoparticles (Anderson et al., 2000) that can contain several thousands Gd ions per particle. For MRI of tissue targets, macromolecular carriers such as protein or dendrimer Gd conjugates can be administered independently from the targeting antibodies following a multistep labeling approach (Paganelli et al., 1999). This approach should provide more efficient delivery of the components in comparison to a single large molecular weight targeted imaging agent. This protocol was used for in vivo imaging of HER-2/ neu receptors in a preclinical model of breast cancer using biotinylated primary anti-HER-2/neu mAb and avidin (GdDTPA) conjugate (Artemov et al., 2003). The combination of high receptor expression levels, relatively small molecular sizes of the components, and multiplication effect of multiple Gd ions attached to a single avidin molecule and several avidin-binding biotins per mAb resulted in detectable positive T1 MR contrast in HER-2/neu positive tumors (Artemov et al., 2003). Histological sections of HER-2/neu expressing BT-474 tumor model (Figure 45.5) demonstrated efficient and uniform labeling of the breast cancer cells with fluorescent-avidin (MW  60 kDa) and biotinylated-Herceptin 24 h after systemic administration of this antibody with molecular weight of 150 kDa. It is likely that the use of multistep avidin–biotin labeling system in addition to the specific recognition of biotinylated mAb by avidin-based contrast agent also induces crosslinking that results in a rapid internalization of the HER-2/neu receptors with the attached probe (Zhu et al., 2006). It is also possible that the higher sensitivity of this approach resulted from efficient loading of cells with the internalized contrast agent. Superparamagnetic iron-oxide nanoparticles were also explored as sensitive MR probes for targeted imaging of tumor cell surface receptors. Two alternative strategies for the use of SPIO in molecular MRI were proposed. The first approach

utilizes specific cellular uptake of the SPIO via plasma membrane specific transporters. Iron transport protein, transferrin, conjugated with MION iron-oxide nanoparticles was used to image 9L rat glioma cancer cell overexpressing engineered transferring receptor (ETR) (Weissleder et al., 2000). The ETR cancer cells internalized up to 8  106 of the TF-targeted CA within an hour and MION loaded cancer cells generated strong negative T2* contrast in vivo in gradient-echo MR images obtained at 1.5T 24 h after intravenous injection of 3 mg of TfMION to a nude mouse. No differential contrast was detected in the T1-weighted image, and strong negative contrast was detected in the T2*-weighted image of the ETR tumor. The second approach relies on the use of mAb (or other targeting molecules) conjugated SPIO nanoparticles. In a study by Weissleder et al., human polyclonal IgG were used to target MION to sites of inflammation (Weissleder et al., 1991). A similar approach was used for molecular MRI of apoptosis in EL4 solid tumor models exposed to a chemotherapy; there SPIO particles were conjugated to the C2 domain of the protein synaptotagmin that binds with high affinity to phosphatidylserine residues that translocate to the outer leaflet of the plasma membrane in apoptotic cells (Zhao et al., 2001). Fluorescent CLIO particles conjugated with the targeting peptide were used to image underglycosylated MUC-1 tumor antigen (Moore et al., 2004). Combined T2 MRI and near-infrared fluorescence imaging demonstrated a specific accumulation of the contrast in the tumor that expressed the antigen. One of the potential problems of using SPIO nanoparticles for detection of cellular epitopes is their relatively poor delivery properties. The situation can be more favorable in tumors where hyperpermeable vasculature in combination with long circulation time of the agent results in an efficient extravasation and contrast enhancement. However, the question still remains regarding uniform and reliable delivery of nanosize contrast agents to the specific receptors expressed in tissues other than endothelial lining of blood vessels. Accumulation of endogenous iron in ferritin-overexpressing cells can also be used as a sensitive MR marker as was demonstrated by Cohen et al. (Cohen et al., 2005). C6 cells stably

Molecular Imaging Applications of MRI

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519

R2

TET

TET

Figure 45.5 Histological frozen section of BT-474 tumor xenograft in SCID mouse treated with biotin-Herceptin and avidin-red 24 h after the mAb. Cell nuclei are stained blue with Hoeschst-3334.

expressing a TET-EGFP-HA-ferritin construct enabled the dynamic detection of TET-regulated gene expression by MRI, also validated by fluorescence microscopy and histology. Induction of the gene significantly elevated both in vitro and in vivo MR relaxation rates that were consistent with induced expression of ferritin and increased iron uptake. Figure 45.6 demonstrates the effect of TET-induction on R2 relaxation rate in the transfected tumor. CEST and PARACEST MRI CEST and PARACEST MR contrast mechanisms can be used for the development of a new generation of highly sensitive MRI agents (Woods et al., 2006). Specific CEST agents for MRI of glucose and lactate were also proposed (Aime et al., 2002; Zhang et al., 2001). For molecular MRI two interesting applications of CEST MRI use endogenously expressed markers or transgenic constructs expressed in the target cells. CEST MRI of exchangeable amide protons of endogenous proteins and peptides was used to detect amide proton transfer (APT) in rat brain tumors in vivo (Zhou et al., 2003a, b). Highly efficient CEST agent poly-l-lysine (25 lysine amino acids) was expressed in 9L rat glioma tumors, and MRI detection of the CEST effect in the tumor expressing this reporter gene was demonstrated in vivo (Gilad et al., 2006). MR Spectroscopy MR spectroscopy (MRS) provides information about the chemical environment of the nuclear spin such as chemical bonds, neighboring nuclei, and chemical structure. The extension of MRS to imaging has resulted in the development of spectroscopic MRI [Chemical Shift Imaging (CSI), Brown et al. (1982)] where chemical information is spatially encoded and selective images of

15

s1

25

Figure 45.6 In vivo MRI detection of switchable ferritin expression in C6 tumors. Scalebar  2.5 mm (adapted from Cohen et al. [2005]).

distribution of specific chemical compounds such as metabolites, exogenous substances, drugs, etc. can be obtained. Representative applications of CSI that can provide molecular information with MRI are discussed here. In comparison to contrast-enhanced MRI, where micromolar concentrations of CA can be detected, the low sensitivity of MRS limits the detectable concentration of CSI agents at millimolar range for proton and 19F MRS; even higher concentrations are required for less-sensitive nuclei such as 31P, 23Na, and 13C. Proton CSI of choline is an important tool for monitoring concentrations of this important metabolite that is a marker of malignancy in breast, prostate, brain, and possibly other tumors (Katz-Brull et al., 2002; Kurhanewicz et al., 2002; McKnight, 2004). Fluorine-19 is an attractive nucleus for CSI because of the high sensitivity provided by large magnetic moment of the spin, 100% natural abundance of the isotope, and the absence of any 19F background signals in biological systems in vivo. The feasibility of 19F MRI to track dendritic cells prelabeled with perfluoropolyether particles has been explored (Ahrens et al., 2005; Bulte, 2005). Hyperpolarization of the spins is a novel technique that produces a dramatic increase in MR signal (Kauczor et al., 1998). However, the lifetime of hyperpolarized state is limited by T1 relaxation time of the nucleus (usually in the range of few seconds) and typically first-pass MRI is performed with hyperpolarized 3He or 13C-labeled substrates (Golman et al., 2003, 2006).

520

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TABLE 45.3

MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges

■

Application of molecular MRI/MRS for biomedical research in genomic medicine

Method

Targets

Feasibility for preclinical studies with animal models

Clinical translation potential

Magnetic labeling of exogenous cells in vitro

Somatic/stem cells used for therapeutic transplantation

High, can be used for preclinical testing of novel therapeutic paradigms

Possible applications for stem cell therapy and islet transplantation using magnetically labeled cells

Specific MR contrast agents targeted to cell surface receptors

Cancer or endothelial cells expressing unique endogenous or engineered (reporter) cell surface receptors

High

Low due to potential toxicity of the contrast agent delivered at high dose

Endogenous CEST reporter genes

Cells engineered to express CEST-generating molecules under control of an appropriate promoter

High, can be used as sensitive reporter probes for noninvasive MR imaging

Possible application in transplanted cells as viability/differentiation reporters

MR spectroscopy

Endogenous and exogenous compounds. Later can be used with enriched magnetic isotope concentration (13C) and/or with hyperpolarization

High, used for metabolic studies in animal models as well as in reporter systems

Multiple applications are possible including 31P spectroscopy, 19F MRS of fluorine-containing drugs, 13C MRS of hyperpolarized compounds

Fluorine-19 MRS and CSI were proposed to detect activation of -galactosidase enzyme with a prototype reporter substrate, 4-fluoro-2-nitrophenyl-b-d-galactopyranoside. Enzymatic cleavage of the sugar changes the chemical shift of 19F nucleus on the nitrophenyl ring by 4–8 ppm depending on the pH of the environment. Phosphorus-31 MRS was used for the detection of creatine kinase activity as a model reporter enzyme. The enzyme was expressed in the mouse liver by adenoviral in vivo transduction and conversion of creatine to phosphocreatine was only detected in the liver of mice injected with the creatine kinase viral vector (Auricchio et al., 2001). The low sensitivity of 31P MRS translates to low spatial resolution of 31P CSI of the order of 10 cm3, which is a potential problem for practical applications of this technique. MRI and MRS applications to molecular imaging that are relevant for problems and challenges of genomic medicine are summarized in Table 45.3.

CONCLUSIONS In comparison to nuclear imaging MR, molecular imaging is still in its infancy. The main advantages of MRI are high spatial resolution, the ability to provide functional information such as in perfusion dynamic MR studies with clinically approved small molecular weight contrast agents, and chemical information that can be detected with MRS and/or spectroscopic imaging. While MRI provides excellent morphological resolution, soft tissue contrast, and functional information, its main problem is a relatively low sensitivity that requires some form of signal amplification to detect sparse molecular targets that are often expressed

in micromolar concentrations. We have reviewed several experimental approaches that may provide a framework for the development of novel sophisticated MRI technique that can bring receptor imaging into the realm of radiological testing. One of the important strategies is the development of novel targeted MR paramagnetic and superparamagnetic contrast agents with high specific relaxivity and favorable in vivo pharmacokinetics. These can be used for in vivo imaging of molecular targets and for in vitro labeling of cells for MRI cell tracking studies. Optimization of their relaxation properties, delivery efficiency, in vivo stability, and targeting mechanisms are required to facilitate efficient MRI of molecular targets. Specifically targeting strategies are not discussed here since an excellent comparison of intact mAb and their derivatives such as minibody, diabody, and single-chain fragments can be found in a recent review article (Wu et al., 2005). The development of a waterexchange type MR contrast mechanism (CEST) is also important as these imaging markers provide strong signal amplification and can be expressed endogenously by transfecting the target cell with an appropriate transfection vector. These may be feasible to monitor the efficiency of gene delivery to the cell for gene therapy. Imaging of molecular epitopes expressed on the surface of the target cell would require the use of contrast agents that can efficiently extravasate in the target organ and diffuse through the interstitium to their binding sites. To enable sufficient delivery, these agents should be of a relatively low-molecular weight and still have high MR relaxivity that translates to the high detection sensitivity in micromolar range of concentrations. Multicomponent contrast agents are an interesting alternative as the different subunits can be optimized for efficient

References

delivery, affinity to the target molecule, and high signal amplification. Biotin/streptavidin (or avidin) systems used to link different subunits have been tested extensively for nuclear imaging and therapy. For MRI applications, the required concentrations of the imaging agents are significantly higher in comparison to PET and SPECT, imaging and therefore potential issues of toxicity and immunogenecity have to be addressed accordingly. Clinical applications of molecular MRI are currently limited to in vivo tracking of prelabeled progenitor cells (Bulte et al., 2002). Another relatively straightforward application of molecular MRI is the imaging of vascular targets. These molecules are expressed in the lumen of blood vessels and therefore are accessible to large targeted MR contrast agents that can provide sufficient sensitivity of detection. The development of novel agents for imaging of solid tumor markers on the other hand requires extensive research. Major problems include accumulation of sufficient amount of the agent at the target site to generate MR

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detectable imaging contrast, efficient delivery of macromolecular contrast agents to the target site, and associated toxicity and immune response of the host. The lower magnetic field of clinical MR scanners in comparison to small animal MRI research systems results in the reduced sensitivity of detection and amplifies these problems. Therefore several technical, biological, and methodological issues have to be resolved before moleculartargeted MRI can become a clinically feasible, routinely used, imaging technology.

ACKNOWLEDGEMENTS This publication was supported in part by NIH/NCI grants P50 CA103175 and RO1 CA97310. The author thanks Dr Z.M. Bhujwalla for helpful discussion and critically reading the manuscript.

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Gammon, S.T., Villalobos, V.M., Prior, J.L., Sharma, V. and PiwnicaWorms, D. (2003). Quantitative analysis of permeation peptide complexes labeled with Technetium-99m: Chiral and sequencespecific effects on net cell uptake. Bioconjug Chem 14, 368–376. Gilad, A.A., McMahon, M.T., Walczak, P., Winnard, P.T.J., Raman, V., Bulte, J.W.M. and van Zijl, P.C.M. (2006). Artificial Reporter Gene Providing MRI Contrast In Vivo Based on Chemical Exchange. International Society for Magnetic Resonance in Medicine, Seattle, WA. Gillies, R.J. (1994). NMR in Physiology and Biomedicine. Academic Press, San Diego. Golman, K., Olsson, L.E., Axelsson, O., Mansson, S., Karlsson, M. and Petersson, J.S. (2003). Molecular imaging using hyperpolarized 13C. Br J Radiol 76(Spec No 2), S118–S127. Golman, K., Zandt, R.I., Lerche, M., Pehrson, R. and ArdenkjaerLarsen, J.H. (2006). Metabolic imaging by hyperpolarized 13C magnetic resonance imaging for in vivo tumor diagnosis. Cancer Res 66(22), 10855–10860. Guivel-Scharen, V., Sinnwell, T., Wolff , S.D. and Balaban, R.S. (1998). Detection of proton chemical exchange between metabolites and water in biological tissues. J Magn Reson 133, 36–45. Guttmacher, A.E. and Collins, F.S. (2002). Genomic medicine – Aprimer. New Engl J Med 19, 1512–1520. Jung, C.W. and Jacobs, P. (1995). Physical and chemical properties of superparamagnetic iron oxide MR contrast agents: Ferumoxides, ferumoxtran, ferumoxsil. Magn Reson Imaging 13, 661–674. Karmarkar, P.V., Kraitchman, D.L., Izbudak, I., Hofmann, L.V., Amado, L.C., Fritzges, D.,Young, R., Pittenger, M., Bulte, J.W. and Atalar, E. (2004). MR-trackable intramyocardial injection catheter. Magn Reson Med 51, 1163–1172. Katz-Brull, R., Lavin, P.T. and Lenkinski, R.E. (2002). Clinical utility of proton magnetic resonance spectroscopy in characterizing breast lesions. J Natl Cancer Inst 94, 1197–1203. Kauczor, H., Surkau, R. and Roberts, T. (1998). MRI using hyperpolarized noble gases. Eur Radiol 8, 820–827. Kircher, M.F., Mahmood, U., King, R.S., Weissleder, R. and Josephson, L. (2003). A multimodal nanoparticle for preoperative magnetic resonance imaging and intraoperative optical brain tumor delineation. Cancer Res 63, 8122–8125. Koretsky, A.P. and Silva, A.C. (2004). Manganese-enhanced magnetic resonance imaging (MEMRI). NMR Biomed 17, 527–531. Kurhanewicz, J., Swanson, M.G., Nelson, S.J. and Vigneron, D.B. (2002). Combined magnetic resonance imaging and spectroscopic imaging approach to molecular imaging of prostate cancer. J Magn Reson Imaging 16, 451–463. Le Bihan, D., Turner, R., Moonen, C.T. and Pekar, J. (1991). Imaging of diffusion and microcirculation with gradient sensitization: Design, strategy, and significance. J Magn Reson Imaging 1, 7–28. Lee, I.H., Bulte, J.W., Schweinhardt, P., Douglas, T., Trifunovski, A., Hofstetter, C., Olson, L. and Spenger, C. (2004). In vivo magnetic resonance tracking of olfactory ensheathing glia grafted into the rat spinal cord. Exp Neurol 187, 509–516. Louie, A.Y., Huber, M.M., Ahrens, E.T., Rothbacher, U., Moats, R., Jacobs, R.E., Fraser, S.E. and Meade, T.J. (2000). In vivo visualization of gene expression using magnetic resonance imaging. Nat Biotechnol 18, 321–325. McKnight, T.R. (2004). Proton magnetic resonance spectroscopic evaluation of brain tumor metabolism. Semin Oncol 31, 605–617. McMahon, M.T., Gilad, A.A., Zhou, J., Sun, P.Z., Bulte, J.W. and van Zijl, P.C. (2006). Quantifying exchange rates in chemical exchange

saturation transfer agents using the saturation time and saturation power dependencies of the magnetization transfer effect on the magnetic resonance imaging signal (QUEST and QUESP): Ph calibration for poly-l-lysine and a starburst dendrimer. Magn Reson Med 55, 836–847. Monsky,W.L., Fukumura, D., Gohongi,T., Ancukiewcz, M.,Weich, H.A., Torchilin, V.P., Yuan, F. and Jain, R.K. (1999). Augmentation of transvascular transport of macromolecules and nanoparticles in tumors using vascular endothelial growth factor. Cancer Res 59, 4129–4135. Moore, A., Marecos, E., Bogdanov, A., Jr. and Weissleder, R. (2000). Tumoral distribution of long-circulating dextran-coated iron oxide nanoparticles in a rodent model. Radiology 214, 568–574. Moore, A., Medarova, Z., Potthast, A. and Dai, G. (2004). In vivo targeting of underglycosylated MUC-1 tumor antigen using a multimodal imaging probe. Cancer Res 64, 1821–1827. Mori, S. and van Zijl, P.C. (2002). Fiber tracking: principles and strategies – a technical review. NMR Biomed 15, 468–480. Nakahara, T., Norberg, S.M., Shalinsky, D.R., Hu-Lowe, D.D. and McDonald, D.M. (2006). Effect of inhibition of vascular endothelial growth factor signaling on distribution of extravasated antibodies in tumors. Cancer Res 66, 1434–1445. Paganelli, G., Grana, C., Chinol, M., Cremonesi, M., De Cicco, C., De Braud, F., Robertson, C., Zurrida, S., Casadio, C., Zoboli, S. et al. (1999). Antibody-guided three-step therapy for high grade glioma with yttrium-90 biotin. Eur J Nucl Med 26, 348–357. Pautler, R.G., Silva, A.C. and Koretsky, A.P. (1998). In vivo neuronal tract tracing using manganese-enhanced magnetic resonance imaging. Magn Reson Med 40, 740–748. Prantner, A.M., Sharma,V., Garbow, J.R. and Piwnica-Worms, D. (2003). Synthesis and characterization of a Gd-DOTA-D-permeation peptide for magnetic resonance relaxation enhancement of intracellular targets. Mol Imaging 2, 333–341. Rome, C., Couillaud, F. and Moonen, C.T. (2007). Gene expression and gene therapy imaging. Eur Radiol 17, 305–319. Seppenwoolde, J.H., Viergever, M.A. and Bakker, C.J. (2003). Passive tracking exploiting local signal conservation: The white marker phenomenon. Magn Reson Med 50, 784–790. Shapiro, E.M., Skrtic, S., Sharer, K., Hill, J.M., Dunbar, C.E. and Koretsky, A.P. (2004). MRI detection of single particles for cellular imaging. Proc Natl Acad Sci USA 101, 10901–10906. Sipkins, D.A., Cheresh, D.A., Kazemi, M.R., Nevin, L.M., Bednarski, M.D. and Li, K.C. (1998). Detection of tumor angiogenesis in vivo by alphaVbeta3-targeted magnetic resonance imaging. Nat Med 4, 623–626. Slichter, C.P. (1990). Principles of Magnetic Resonance. Springer-Verlag, Berlin. Snoussi, K., Bulte, J.W., Gueron, M. and Van Zijl, P.C. (2003). Sensitive CEST agents based on nucleic acid imino proton exchange: Detection of poly(rU) and of a dendrimer-poly(rU) model for nucleic acid delivery and pharmacology. Magn Reson Med 49, 998–1005. Uzgiris, E.E., Cline, H., Moasser, B., Grimmond, B., Amaratunga, M., Smith, J.F. and Goddard, G. (2004). Conformation and structure of polymeric contrast agents for medical imaging. Biomacromolecules 5, 54–61. Wadghiri, Y.Z., Blind, J.A., Duan, X., Moreno, C., Yu, X., Joyner, A.L. and Turnbull, D.H. (2004). Manganese-enhanced magnetic resonance imaging (MEMRI) of mouse brain development. NMR Biomed 17, 613–619.

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46 Fluorescence Imaging: Overview and Applications in Biomedical Research Vasilis Ntziachristos

INTRODUCTION Fluorescence methods have widely impacted biotechnology and genomics. From decoding the genome of various species to flow cytometry, high-throughput screening and uses in microscopy, fluorescence can easily qualify as one of the most common modalities in the biomedical laboratory (Giepmans et al., 2006; Herschman, 2003; Tsien, 2005; Weissleder and Ntziachristos, 2003). The high diversity in utilizing fluorescence molecules comes with three key advantages that make fluorescence an attractive tool. First, there is the high contrast that can be imparted with fluorescence approaches. A single molecule, a membrane receptor or several other cellular structures and organelles could go undetected under a camera or a microscope due to resolution and contrast limitations, but they can be identified with high sensitivity when they are tagged with a fluorescent agent. Second, fluorescent dyes come in many colors to simultaneously differentiate several parameters at moderate instrumentation complexity and cost. Finally, the use of non radio-decaying isotopes allows measurements over extended periods of time and enables longitudinal studies offering user-friendliness associated with the use of safe, nonionizing radiation. These fundamental principles that make fluorescence a powerful technique for in vitro assays also make it an attractive modality for in vivo intravital applications (Jain et al., 2002). Fluorescence Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 524

imaging of tissues, however, comes with two major complications. The first is that in vivo tissue staining necessitates elaborate methods for imparting high contrast and differentiate between the targeted moiety and nonspecific background fluorochrome bio-distribution that may be present (Achilefu et al., 2005; Tung, 2004; Tyagi et al., 2000). The second is that in contrast to clear solutions or ultra-thin (5–10 μm) tissue slices, intact tissue scatters light significantly and makes the formation of images challenging (Ntziachristos et al., 2005). Fluorescence imaging of tissues therefore refers to a group of methods that operate under high light-scattering conditions using appropriate in vivo staining methods and aims at resolving or sensing cellular and subcellular function from depths ranging from 10 μm to several centimeters. In this role, in vivo fluorescence imaging plays an essential role toward the study of genomes and proteomes in unperturbed environments in real time. In particular, fluorescence imaging can be pivotal in asserting dynamic interactions in the pathophysiology of multifactorial disease and enables monitoring of disease evolution and response to external factors on the same host organism over time (Brown et al., 2001; Gross and Piwnica-Worms, 2005). This, in contrast to the mainstream of laboratory research, pieces together the time evolution of events by in vitro analysis of multiple samples obtained at different time-points, which may offer inaccurate observations and time-inefficient procedures. Copyright © 2009, Elsevier Inc. All rights reserved.

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Ultimately the same technology can be used for reading disease susceptibility and response from individuals, to study gene– environment interactions and overall lead personalized treatment and health care. Essential tools for achieving these goals include in vivo tissue staining techniques via reporter technology used to report on a specific molecular parameter or function (Shaner et al., 2004). A widely used example is the use of fluorescent proteins as endogenous markers to report on virtually any genomic process in vivo (Giepmans et al., 2006). In parallel, the advent of fluorescence agents, referred to as fluorescent probes (Weissleder and Ntziachristos, 2003), which attain specificity to particular biomarkers, further increase the application potential for fluorescence imaging to be applied beyond the biology bench to preclinical and clinical settings.

IMAGING TECHNOLOGY The following section summarizes the major fluorescence methodologies used for in vivo imaging. Penetration depth is an important distinguishing parameter that can be used to classify different fluorescence in vivo imaging technologies, in particular confocal and multiphoton microscopy, planar fluorescence macroscopic imaging and tomographic macroscopic imaging. These basic classes of fluorescence imaging and representative applications that exemplify its use are summarized in the following sections. Intravital Microscopy Microscopy has seen great advances in the recent years and new technologies continue to emerge at high rates. In vivo tissue microscopy employs methods that account for tissue scattering and

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produce high-resolution (diffraction-limited) images at depths of the order of a few hundred microns. A widely used approach is confocal microscopy. A schematic explaining the principle of operation is shown in Figure 46.1. Light is focused at an intended focal point inside the tissue. Upon incidence on the tissue surface however, this beam will illuminate an extended volume as shown in Figure 46.1a. The exact volume covered depends on the beam shape, the objective’s numerical aperture and the tissue’s optical characteristics. By consequence, all fluorochromes present in this volume are excited and emit fluorescence. If the entire light signal that reaches the microscope’s objective was to be collected, the image would appear blurred. This is because the light collected represents fluorescence activity from the entire volume illuminated and many of these photons have been scattered multiple times by cellular organelles so that they do not retain high-resolution information as to the place of origin. To improve the resolution, confocal microscopy uses a pinhole placed in front of the detector to reject the light emitted or scattered by depths other than the intended depth of the focal point inside the tissue, that is, the “confocal” depth. This is better explained in Figure 46.1b. At the confocal depth, the area excited has the narrower cross-section and therefore only fluorescence from a very confined area reaches the photon detector, most other fluorescence light hits the edges of the pinhole and it is not detected. Therefore resolution is retained at the expense of rejecting a significant part of the light emitted. To account for the low photon yield, light of high intensity is typically used, which however may lead to fluorochrome photobleaching or phototoxicity effects. Regardless of the experimental limitations however, confocal microscopy is highly utilized to reject the effects of tissue scattering and produce high-resolution fluorescence images.

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z Excitation area

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Figure 46.1 Principle of operation for achieving depth (z-axis) resolution in (a and b) confocal and (c) two-photon (or multiphoton) microscopy. Spatial in-plane resolution, that is, the x–y resolution, is achieved by mechanically moving (scanning) the photon beam in relation to the sample imaged.

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While confocal microscopy has been used to imaging fixed tissues (see Figure 46.2), it is increasingly employed for in vivo imaging at tissue depths of up to 200 μm also depending on the optical properties of the tissue investigated. To image tissues other than superficial epithelial structures, several invasive approaches have been developed such as the use of implantable skin window chambers (Jain et al., 2002) or invasive imaging by surgically exposing tissues for gaining access to different tissue types. Correspondingly, intravital imaging has been applied to many applications, for example, in tumor biology and the study of angiogenesis (Jain et al., 2002; Sipkins et al. 2005), pharmacological studies (Norman, 2005) or the microdistribution of fluorescent probes (Bogdanov et al., 2002). Most confocal microscopes employ lens-based noncontact imaging as shown in Figure 46.1. Fiber-based delivery and collection has been also considered, typically by direct contact of an optical fiber probe, consisting of several thousands of fibers tightly packed together, onto tissue. This approach allows access to an increased number of deep-seated organs and has been used endoscopically or through minimally invasive procedures (D’Hallewin et al., 2005). An imaging example obtained in vivo using a flexible endoscopic confocal microscope is shown in Figure 46.3. Another popular technique for rejecting tissue scattering is two- and multi-photon microscopy (Denk et al., 1990). In contrast to confocal detection, no pinhole is used here; instead the method selectively excites fluorochromes within a very small tissue volume at a predetermined location as shown on Figure 46.1c. Therefore, although multiple scattered photons are collected, high resolution is granted by the size of the volume excited, allowing for high sectioning capability. To achieve small volume excitation, the technique makes use of two- or multi-photon absorption phenomena, where the excitation of

a fluorochrome is based on the virtually simultaneous absorption of two or multiple photons with energy that in summation matches the energy required for fluorochrome excitation. Typically, photons that arrive within a femtosecond contribute to multiphoton absorption phenomena. To achieve conditions of virtually simultaneous photon arrival at a given tissue volume, high intensity needs to be delivered locally over short periods of time. This is typically implemented using a high numerical aperture objective and by tightly focusing ultra-short highpower pulses such as the ones produced by a Ti:Saphire laser. The resulting fluorescence detected has a quadratic nonlinear dependence on illumination strength; a twofold increase in illumination intensity results in a fourfold increase in fluorescence. Two- or multi-photon in vivo microscopy of living tissues can penetrate at depths beyond 500 μm inside tissue, which is significant penetration improvement over confocal microscopy (Helmchen and Denk, 2005), and they are gaining in popularity for biomedical in vivo imaging (Germain et al., 2005, Halin et al., 2005). Applications include the study of angiogenesis (Bird et al., 2005; Brown et al., 2001) and metastasis (Voura et al., 2004; Wang et al., 2002), immunology (Germain et al., 2005; Runnels et al., 2006), kidney physiology (Dunn et al., 2002; Molitoris and Sandoval, 2005) and neurological research (Majewska et al., 2006; Misgeld and Kerschensteiner, 2006). An example of twophoton microscopy applied to imaging a mammary adenocarcinoma is shown in Figure 46.4. Macroscopic Imaging In contrast to microscopy, macroscopic fluorescence imaging exchanges resolution for penetration depth and field of view. In macroscopy, the scattered light is not rejected as in

1 integrins/E-cadherin

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Figure 46.2 Collective cell movement from primary melanoma explants: plasticity of cell–cell interaction, 1 integrin function and migration strategies. Multicellular cell group emigrating from a primary melanoma explant after 6 days of culture in a 3D collagen lattice. 1 integrins (green) cluster and provide traction in a subset of cells at the leading edge only, while E-cadherin (red) shows linear staining along cell–cell junctions throughout the cluster. Images represent confocal sections of cells fixed in the process of migration, visualized by confocal microscopy. The arrow indicates the direction of migration. The asterisk shows the region of partial matrix degradation and remodeling at the trailing edge (matrix defect). (Modified from Hegerfeldt, Y., et al. (2002). Cancer Res 62:2125–2130. Courtesy of Professor P. Friedl, University of Wurzburg.)

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Figure 46.4 Multiphoton intravital imaging of a TG1-1 mammary adenocarcinoma growing in a dorsal skinfold chamber of a TIE2-GFP mouse. The chamber places a window on the tumor to allow for direct visualization of tumor biology and function. In this case, GFP expressed by endothelial cells is shown in green while Tetramethlyrhodamine (TMR)-Dectran injected i.v. is shown in red. The image is a maximum intensity projection of ~150–200 μm under the surface, 250 μm across. (Courtesy of Ed Brown, University of Rochester.)

Figure 46.3 Confocal intravital images obtained in vivo with a flexible fiber probe of 650 m. (a) Normal human alveoli: visualization of normal distal lung, with distinct alveolar microarchitecture. The signal shown is tissue auto-fluorescence; no dye was applied in this case. The miniaturized fiber probes can be used in conjunction with a traditional bronchoscope since they are compatible with the working channel of conventional endoscopes. Field of view is 600  500 m and the slice thickness is 20 m obtained from the surface of the tissue. (b) In vivo angiogenesis imaging: visualization of tumoral vessels in a mouse prostate after FITC-Dextran (500 kDa) injection in the tail. The site was accessed through a micro-incision in the skin at the site of the tumor. Field of view is 400  280 m. An optical slice of 20 m is imaged at the surface of the tissue. (The images were obtained by Anne-Carole Duconseille and Olivier Clément, Université Paris V, Paris, France. Images courtesy of Mauna Kea Technologies (Paris, France). The composite image is by reproduction from Ntziachristos [2006].)

microscopy but is the major source of contrast. Images can be formed by direct collection of the light scattered from superficial and deeper tissues but accurate and high performing methods use elaborate theoretical models of photon propagation and inversion techniques to resolve depth and improve on the resolution and the quantification achieved. Fluorescence macroscopy is

separated to planar methods and tomographic methods respectively, as described in the following sections. Planar Imaging Simple “photographic methods” have been used to enable in vivo fluorescence imaging from small animals and tissues. The method operates using light with a defined bandwidth, such as a laser source, laser diode or an appropriately filtered bright lamp. This light source is expanded and emitted toward tissues in order to generate an illumination field. Light incident on the animal surface can propagate for several millimeters in tissues, especially in the far-red and near-infrared (NIR) (630–900 nm) because of the low light attenuation by tissues in this spectral range. The corresponding photon field established in the tissue has a depthdependent strength and can excite tissue fluorochromes in its path. Fluorescence signals generated by this process can similarly propagate to the tissue surface where they are recorded with a highly sensitive charge-coupled device (CCD) camera through appropriate filters. This approach can be applied in the epi-illumination or reflectance mode where illumination and detection are at the same side of the animal as shown in Figure 46.5a. An alternative imaging mode is transillumination, where illumination and detection are performed from opposite sides of the animal, as shown in Figure 46.5b. In both modes it is custom to acquire both a fluorescence image and a second image measured through different filters to obtain a “photograph” of the animal at the excitation

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Figure 46.5

Fluorescence Imaging: Overview and Applications in Biomedical Research

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wavelength. An example of epi-illumination imaging is shown in Figure 46.6. Generally, epi-illumination imaging offers better sensitivity to surface activity whereas transillumination is more sensitive and accurate in detecting deeper-seated activity. Planar imaging comes with significant implementation simplicity and offers high-throughput imaging. However, it also has important shortcomings as it does not correct for signal intensity changes with varying tissue optical properties or with fluorochrome depth (Ntziachristos et al., 2005). Therefore the intensity reported is generally of qualitative nature, unless the depth of the fluorescence activity or the optical properties of tissue are not changing throughout the course of the measurements. In such case measurements can be related to each other in a relative, semiquantitative way. Other limitations include the single projection viewing that does not allow for resolving or localizing activity with depth. Fluorescence Molecular Tomography (FMT) In order to improve imaging performance, it is necessary to account for light intensity variations as a function of depth and tissue optical properties.To achieve this, it is necessary to resolve the position of the fluorescent lesions imaged. Three-dimensional information can be gained by illuminating tissue at different projections and correspondingly collect light around the animal, as shown schematically in Figure 46.7. Ideally, the animal is illuminated using projections over 360° angles but different implementations exist that utilize a limited number of projections, which may compromise the resolution achieved but attains implementation simplicity and improves imaging over planar imaging methods. Similar to other tomographic methods, FMT uses a forward model, that is, a theoretical prediction of the measurements expected assuming a known fluorochrome distribution. For image reconstruction, this forward model is used with the set of measurements collected at multiple projections to retrieve the unknown fluorochrome distribution. The development of appropriate forward problems plays an important role in FMT. Largely based on the theoretical mainframe of theory of photon diffusion (Gibson et al., 2005), in vivo fluorescence tomography needs to account for signal variations due to tissue absorption

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Figure 46.6 Imaging protease activity associated with HT1080 fibrosarcoma formation due to subcutaneous implantation of 106 HT1080 cells 2 weeks prior to imaging. (a) Planar image (photograph) of the animal at the excitation wavelength (672 nm), (b) planar image (photograph) at the emission (fluorescence; 700–750 nm) wavelength showing increased fluorescence activity from the tumor, due to increased cathepsin expression at the propagating tumor border. Cathepsins are tagged with an activatable fluorescence probe, intravenously administered 24 h prior to imaging. (c) Merged images.

Multiprojection illumination

Figure 46.7 Multiprojection imaging used for macroscopic fluorescence tomography. Projections are typically separated by time-sharing, that is, by sequentially switching the light on, in each projection. While projection is schematically shown here as plane propagation, in reality, for each light source turned on, there is a diffusive photon pattern propagating through tissue that covers a large part of the volume imaged.

and scattering heterogeneity (Ntziachristos et al., 2005). This is generally achieved by using measurements at the excitation wavelength to obtain information of the spatially dependent photon attenuation in tissues due to the variation in optical attenuation of the various organs (Figure 46.8).

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Figure 46.8 FMT imaging of spontaneous lung cancer of a mouse model of conditional K-ras oncogene mutation. (a) Three consecutive slices from approximately the center of the animal in the coronal view, showing increased cathepsin activity associated with two spontaneous tumors developed. Contrast is due to fluorescence at 700–740 nm observed as a result of activation of quenched Cy5.5 dye carried on a biocompatible molecular probe with specificity to cathepsins. (b) Corresponding X-ray CT slice. Arrows demonstrate the anatomical presence of tumors, congruent with the fluorescent signals. (c) Co-registration and rendering of the three-dimensional fluorescence activity reconstructed by FMT (red) onto the anatomical X-ray CT volume (green).

Original tomographic systems utilized multiple fibers, brought in contact with tissue, coupled in some instances with matching fluids in order to perform tomographic measurements from animals. Using more elaborate forward problems, recent implementations operate on direct photon collection from CCD cameras and free tissue surfaces at the absence of matching media. Such approaches improve animal handling and offer high quality datasets for improved imaging performance. FMT, in its simplest implementation, needs only constant intensity light. Other approaches utilize ultra-short laser pulses for illumination and time-gated detection, or light of intensity that is modulated in the 100 MHz to 1 GHz range. These methods are appropriate for simultaneously resolving fluorochrome concentration and lifetime. For small animal imaging, resolution of the order to 0.5–1 mm is possible although the computational requirements for achieving this resolution in large volumes remain challenging. There is a diversity of applications for tomographic methods, due to the availability of an increasing number of fluorescent probes and the improved access to a larger number of organs granted by three-dimensional imaging (Weissleder and Ntziachristos, 2003). FMT has been used for example for assessing protease upregulation (Ntziachristos et al., 2002), monitoring apoptotic signatures based on annexin-V fluorescent conjugates (Ntziachristos et al., 2004) or imaging angiogenesis and receptor upregulation (Montet et al., 2005; Patwardhan et al., 2005). Hyper-Spectral and Multi-Spectral Imaging Hyper-spectral (HS) imaging generally refers to imaging methods that capture multiple images (n  10) at different spectral regions. Typically the spectral regions are selected consecutive over a wide spectral window and have a fine spectral step of a

few nanometers. In this way tens to hundreds of images may be used to cover the visible or NIR spectrum for example. In contrast, multi-spectral (MS) imaging typically refers to methods that capture a few and wider spectral regions. Hyper-spectral and multi-spectral methods can be applied in generic fluorescence applications such as plate and array readers, in microscopy applications and in macroscopic tissue imaging. Typically HS is used for determining spectral features in unknown fluorescence signatures. Applications include improved reading of microarrays when contaminating fluorescence signatures may be present (Martinez et al., 2003), in removing unknown auto-fluorescence signatures in planar fluorescence imaging and improve contrast (Mansfield et al., 2005) or in tissue diagnostics based on auto-fluorescence patterns, for example detecting cancer based on changes in auto-fluorescence emission (Demos et al., 2005; Farkas and Becker, 2001; Martin et al., 2006). The related instrumentation utilizes tunable filters, such as liquid crystal tunable filters (LCTF) or more commonly acousto-optic tunable filters (AOTF) to scan over wide spectral regions. Due to the wealth of spectral information, model-based fitting methods or decomposition methods, such as principle component analyses, can be used for data processing and obtaining meaningful answers. Conversely, MS is used when known and spectrally differentiated fluorescence signatures are imaged, for example, in microscopy or tissue imaging of selected fluorescent dyes or proteins. Then each of the spectral windows imaged carries information on the distribution of certain known fluorochromes. In this case, it is assumed that there is either little signal contamination and crosstalk or spectral un-mixing techniques can be otherwise employed. For example, one or few spectral channels can be assumed as background measurements, at spectral regions where the fluorochromes employed are not emitting, and used

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for auto-fluorescence subtraction. While LCTF or AOTF solutions are employed in MS imaging, it is also common to use individual band-pass filters optimized for separating fluorescence light coming from different fluorochromes. Since a smaller number of individual spectral measurements is obtained, there is flexibility in selecting higher performance filters, than the spectral separation available in HS techniques. Better illumination systems can be also used, for example laser-based systems containing multiple excitation lines that are better tuned for each particular fluorochrome used, to achieve optimal signal-to-noise characteristics.

FLUORESCENCE APPLICATIONS IN GENOMIC MEDICINE A variety of targeted probes (NIR fluorochrome attached to affinity ligand) and activatable probes (based on quenching or fluorescence resonance energy transfer, FRET) have recently been developed for in vivo imaging of cellular and subcellular biomarker (Weissleder and Ntziachristos, 2003), which expands the repertoire of fluorescence applications to biomedical research and/or clinical medicine. These probes allow targeting of proteases, cellular receptors and other proteins in vivo. In addition, fluorescent proteins and bioluminescence approaches have revolutionized

modern biology and are used in reporting on molecular function in vivo (Gross and Piwnica-Worms, 2005; Massoud and Gambhir, 2003). A particular recent advance is the availability of red-shifted fluorescent proteins, which can significantly increase the detection sensitivity and penetration depth over the green (GFP), yellow (YFP) and common red proteins, such as DsRed2 (Giepmans et al., 2006;Tsien, 2005). This technology, combined with the imaging approaches presented above, offers a new dimension to the already widespread use of fluorescence methods in genomics, that is, fluorescence in situ hybridization, other genomic hybridization approaches and high-throughput microarray technology, as it raises the possibility of in vivo study of gene function in organisms. While gene reporter technologies (i.e., the use of fluorescent proteins) are more suitable for animal imaging, extrinsically administered fluorescence probes can be used in clinical applications as well. A particular benefit of in vivo imaging is that it does not study a single time point, as microarray studies, but can follow evolving function over time. In vivo imaging can therefore study the evolution and dynamic interactions of targets identified on high-throughput arrays. In this sense, fluorescence presents a highly involved modality for genomic imaging that can link in vitro to in vivo for understanding dynamic interactions and in diagnostic and therapeutic preclinical and clinical applications.

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D’Hallewin, M.A., El Khatib, S., Leroux, A., Bezdetnaya, L. and Guillemin, F. (2005). Endoscopic confocal fluorescence microscopy of normal and tumor bearing rat bladder. J Urol 174, 736–740. Dunn, K.W., Sandoval, R.M., Kelly, K.J., Dagher, P.C., Tanner, G.A., Atkinson, S.J., Bacallao, R.L. and Molitoris, B.A. (2002). Functional studies of the kidney of living animals using multicolor twophoton microscopy. Am J Physiol Cell Physiol 283, C905–C916. Farkas, D.L. and Becker, D. (2001). Applications of spectral imaging: Detection and analysis of human melanoma and its precursors. Pigment Cell Res 14, 2–8. Germain, R.N., Castellino, F., Chieppa, M.C., Egen, J.G., Huang, A.Y.C., Koo, L.Y. and Hai, Q. (2005). An extended vision for dynamic high-resolution intravital immune imaging. Semin Immunol 17, 431–441. Gibson, A.P., Hebden, J.C. and Arridge, S.R. (2005). Recent advances in diffuse optical imaging. Phys Med Biol 50, R1–R43. Giepmans, B.N.G., Adams, S.R., Ellisman, M.H. and Tsien, R.Y. (2006). Review: The fluorescent toolbox for assessing protein location and function. Science 312, 217–224. Gross, S. and Piwnica-Worms, D. (2005). Spying on cancer: Molecular imaging in vivo with genetically encoded reporters. Cancer Cell 7, 5–15. Halin, C., Mora, J.R., Sumen, C. and von Andrian, U.H. (2005). In vivo imaging of lymphocyte trafficking. Ann Rev Cell Dev Biol 21, 581–603.

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47 Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers Ahmad R. Hariri

INTRODUCTION Individual differences in stable and enduring aspects of behavior – traits such as personality and temperament – are important predictors of vulnerability to neuropsychiatric disorders, including depression, anxiety, and addiction. Accordingly, identifying the biological mechanisms that give rise to trait individual differences affords a unique opportunity to develop both predictive markers of disease liability and novel targets for individualized treatment. In the past 5 years, human neuroimaging studies, especially those employing functional MRI (fMRI), which provides an indirect measure of brain activation (see Chapter 43), have begun to reveal the neural substrates of interindividual variability in these and related constructs (Drabant et al., 2006; Pezawas et al., 2005). Moreover, recent studies have established that blood oxygen level-dependent (BOLD) fMRI measures represent temporally stable and reliable indices of brain function (Johnstone et al., 2005; Manuck et al., 2007). Thus, much like their behavioral counterparts, patterns of brain activation represent enduring, trait-like phenomena, which in and of themselves may serve as important markers of liability and pathophysiology.

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As neuroimaging studies continue to illustrate the predictive relationship between regional brain activation and trait-like behaviors (e.g., increased amygdala reactivity predicts core features of anxious temperament (Haas et al., 2007; Most et al., 2006; Ray et al., 2005; Somerville et al., 2004; van Reekum et al., 2007)), an important next step is to systematically identify the underlying mechanisms driving variability in brain circuit function. In this regard, recent neuroimaging studies employing pharmacological challenge paradigms, principally targeting monoamine neurotransmission, have revealed that even subtle alterations in dopaminergic, noradrenergic, and serotonergic signaling can have profound impact on the functional response of brain circuitries supporting affect, personality and temperament (Bigos et al., 2008; Hariri et al., 2002a, b; Harmer et al., 2006; Tessitore et al., 2002; Strange and Dolan, 2004). Similarly, multimodal neuroimaging approaches have provided evidence for directionally specific relationships between key components of monoaminergic signaling cascades, assessed with radiotracer positron emission tomography (PET), and brain function, assessed with BOLD fMRI. Collectively, pharmacological challenge neuroimaging and multimodal PET/ fMRI are revealing how variability in behaviorally relevant brain activation emerges as a function of underlying variability in key

Copyright © 2009, Elsevier Inc. All rights reserved.

Conceptual Basis of Imaging Genetics

brain neurotransmission systems (e.g., increased serotonin signaling predicting increased amygdala reactivity (Fisher et al., 2006)). The next logical step is to identify the sources of interindividual variability in these key neurochemical signaling mechanisms. In the modern genomic era of human genetics, this step is firmly planted in the direction of identifying the relationships between common variation in the genes encoding components of these signaling cascades, their protein products and, subsequently, brain circuit function (Figure 47.1). As sequence variation across individuals represents the ultimate wellspring of variability in emergent neurobiological and related behavioral processes, understanding the relationships between genes, brain and behavior is critical for establishing the etiology and pathophysiology of psychiatric disease. The emerging field of imaging genetics seeks to establish a principled framework for the integration of modern genetics and neuroimaging technologies toward the ultimate goal of identifying truly predictive markers of disease vulnerability (Hariri and Weinberger, 2003a, b) (Figure 47.1). The current chapter provides a brief review of the basic principles of imaging genetics and highlights the vast potential of such an integrated approach by briefly reviewing recent studies of the neural correlates of genetically driven variability in serotonin (5-HT) neurotransmission. The collective results of such studies demonstrate that common sequence variation in the human serotonin transporter gene is associated with downstream alterations in serotonin signaling cascades, resulting in relatively increased serotonin signaling and, eventually, increased amygdala reactivity to environmental threat (Hariri and Holmes, 2006).

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This genetically driven variability in serotonin neurotransmission and threat-related amygdala reactivity likely represent a key mechanism of increased temperamental anxiety and risk for depression, especially in the context of environmental adversity. With increased utilization of such imaging genetics strategies and their continued expansion to include pharmacological and multimodal neuroimaging techniques, many more behaviorally and clinically relevant neurobiological pathways and predictive markers will be illuminated in forthcoming years. Such discoveries, in turn, will directly inform ongoing efforts to establish genomic and personalized medicine.

CONCEPTUAL BASIS OF IMAGING GENETICS Genes have potential impact on all levels of biology. In the context of disease states, particularly behavioral disorders, genes represent the cornerstone of mechanisms that – either directly or in concert with environmental events – ultimately result in disease. Moreover, genetic and genomic analyses offer the potential to identify both at-risk individuals and biological pathways for the development of new treatments. While most human behaviors cannot be explained by genes alone (and certainly much variance in aspects of brain information processing will not be genetically determined directly), it is anticipated that variations in the genome sequence that impact gene function will contribute an appreciable amount of variance to these resultant complex behavioral phenomena. This conclusion is implicit

Neurobiology of Inter-Individual Variability in Behavior

Genes: multiple alleles each of small effect

Cells: subtle molecular alterations

Systems: response bias to environmental cues

Behavior: complex functional interactions and emergent phenomena

Figure 47.1 Schematic of the complex path from the genome to behavior. Imaging genetics allows for the discovery of genetic effects at the level of brain information processing, which represents a proximate biological link to the genome as well as an intermediate of behavior. (Adapted with permission from Hariri et al., 2006.)

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in the results of studies of twins that have revealed heritabilities ranging from 40% to 70% for various aspects of cognition, temperament, and personality (Plomin et al., 1994). In the case of psychiatric illness, genes appear to be the only consistent risk factors that have been identified across populations, and the majority of susceptibility for major psychiatric disorders is accounted for by inheritance (Moldin and Gottesman, 1997) (see Chapter 104). Traditionally, the impact of genetic polymorphisms on human behavior has been examined directly using clinical evaluations, personality questionnaires, and neuropsychological batteries. Genetic epidemiological investigations have directly examined the relationship between specific genetic polymorphisms and behaviors and have reported equivocal results (Malhotra and Goldman, 1999). This is not surprising for at least two reasons. First, there is considerable individual variability in dimensions of observable behavior as well as subjectivity in the assessment of behavior, necessitating very large samples – often exceeding several hundred subjects – to identify even small gene effects (Glatt and Freimer, 2002). Moreover, it is apparent that there are etiological subgroups within any given disease that obscure effects at the broader group level. Second and perhaps most importantly, the effects of genes are not expressed directly at the level of behavior. As discussed in detail below, gene effects on behavior are mediated by their molecular and cellular effects on information processing in the brain. Thus, examining gene effects on the brain represents a critical step in understanding their ultimate contribution to variability in behavior. Since genes are involved directly in the development and function of brain regions subserving specific cognitive and emotional processes, functional polymorphisms in genes may be strongly related to the function of these specific neural systems, in turn, may mediate the genes involvement in behavioral outcomes. This is the underlying assumption of our investigations examining the relation between genes and neural systems – what we initially called “imaging genomics” (Hariri and Weinberger, 2003a, b). More recently, we have described this approach as “imaging genetics” (Neumann et al., 2006), because it is typically utilized to explore variation in specific genes and not the genome broadly. The potential for marked differences at the neurobiological level underscores the need for a direct assay of brain function. Accordingly, imaging genetics within the context of a candidate gene association approach (see Chapter 8) provides an ideal opportunity to further our understanding of biological mechanisms potentially contributing to individual differences in behavior and personality. Moreover, imaging genetics provides a unique tool with which to explore and evaluate the functional impact of brain-relevant genetic polymorphisms with the potential to understand their impact on behavior (Figure 47.1). Of course, the relevance of imaging genetics findings for disease vulnerability will only be established if the variants under study are further associated with disease risk directly or if their impact on brain function is manifest, or even exaggerated, in the diseases of interest.

BASIC PRINCIPLES OF IMAGING GENETICS Selection of Candidate Genes The protocol for imaging genetics typically involves first identifying a meaningful variation in the genomic sequence within a candidate gene. For the variant to be meaningful, it should have an impact at the molecular and cellular level in gene or protein function (i.e., a functional variant), and the distribution of such effects at the level of brain systems involved in specific forms of information processing should be predictable. Consistent with the goal of genetic association studies to identify variation impacting individual differences in behavior and related risk for disease in the general population, candidate polymorphisms in imaging genetics studies should be relatively common. That is, the frequency of the less common or minor allele should ideally be greater than 5%. In light of the considerable costs of neuroimaging in comparison with behavioral studies and the related sample size limitations, the minor allele frequency of candidate polymorphisms in imaging genetics studies should ideally be greater than 30%. In such cases, the relative contributions of all potential genotypes (i.e., homozygotes for both major and minor alleles, as well as heterozygotes) to brain function can be examined using imaging genetics. When frequently occurring genetic variants have been demonstrated to affect specific physiological processes at the molecular and cellular level in distinct brain regions and circuits, neuroimaging can be employed to explore their effects on these substrates in both normal and impaired populations. Short of well-defined functional polymorphisms, candidate genes with identified single nucleotide polymorphisms (SNPs) or other allele variants in coding or promoter regions with likely functional implications (e.g., nonconservative amino acid substitution or missense mutation in a promoter consensus sequence) involving circumscribed neuroanatomical systems would also be attractive substrates for imaging genetics. In fact, recent imaging genetics studies have taken the lead in exploring the functionality of candidate variants by first describing in vivo effects at the level of brain systems (Brown et al., 2005). As such, imaging genetics can provide the initial impetus for further characterization of molecular and functional effects of specific candidate genes in brain systems involved in regulating behavior. In this manner, the contributions of abnormalities in these systems to complex behaviors and emergent phenomena, possibly including psychiatric illnesses, can then be understood from the perspective of their neurobiological origins. Control for Non-genetic Factors The contribution of single genes to the structural and functional integrity of brain systems, while putatively more substantial than that to emergent behavioral phenomena, is still presumably small. Furthermore, typically large effects of age, sex, and IQ as well as environmental factors such as illness, trauma, or substance abuse

Imaging Genetics and the Neurobiology of the 5-HTTLPR

on phenotypic variance can easily obscure these small potential gene effects. Since association studies in imaging genetics, as in any case–control association study, are susceptible to population stratification artifacts (i.e., differing ancestral genetic backgrounds for specific candidate genotypes; see Chapter 2), ethnic matching within genotype groups and genomic controls is also potentially critical (Devlin et al., 2001). Thus, the identification and contribution of genetic variation to specific phenotypes should be limited to studies in which other potential contributing and confounding factors are carefully matched across genotype groups. If the imaging protocol involves performance of a task, as is typical in fMRI, the groups should also be matched for level of performance, or at least any variability in perfor mance should be considered in the analysis and interpretation of the imaging data. This is because task performance and the fMRI BOLD signal are tightly linked, and systematic differences in performance between genotype groups could either obscure a true gene effect or masquerade for one. Task Selection There has been tremendous proliferation of functional neuroimaging studies accompanied by behavioral tasks designed specifically for this experimental setting. Many of these are modified versions of classic behavioral and neuropsychological tests (e.g., the Wisconsin Card Sorting Task (Axelrod, 2002)) designed to tap neural systems critical to particular behaviors. More recent paradigms have emerged that focus on interactions of specific behaviors and disease states as these questions have become newly accessible with noninvasive imaging (e.g., the emotion Stroop, which taps abilities to override emotionally provocative stimuli when performing simple behavioral tasks, and Obsessive Compulsive Disorder (Whalen et al., 2006)). Because of the relatively small effects of single genes, even after having controlled for non-genetic and other confounding variables, imaging tasks must maximize sensitivity and inferential value. As the interpretation of potential gene effects depends on the validity of the information processing paradigm, it is best to select well-characterized paradigms that are effective at engaging circumscribed brain regions and systems, and which produce robust signals in every individual and show variance across individuals. In short, imaging genetics studies are typically not the appropriate venue to design and test new functional tasks, and to do so might undermine their tremendous potential.

IMAGING GENETICS AND THE NEUROBIOLOGY OF THE 5-HTTLPR Converging evidence from rodents and non-human primates, as well as extensive human research, has implicated variability in 5-HT neurotransmission as a key predictor of individual differences in multiple, overlapping behavioral constructs related to trait negative affect (Lucki, 1998). Research employing pharmacologic challenge of the 5-HT system (via specific

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receptor agonism/antagonism or general reuptake blockade) has consistently illustrated that manipulations resulting in relatively increased postsynaptic 5-HT neurotransmission produce potentiated responses in affective neural circuitries, peripheral stress responses, and subjective negative affect (Burghardt et al., 2004; Forster et al., 2006; Harmer et al., 2006; Maier and Watkins, 2005). These and other findings have subsequently spurred intensive efforts to identify genetic polymorphisms in 5-HT subsystems, which ultimately control the regulation of 5-HT neurotransmission as a function of both homeostatic drive and environmental feedback. Such polymorphisms might predict trait negative affect as well as differentiate relative risk for disease (Glatt and Freimer, 2002; Lesch, 2005; Stoltenberg and Burmeister, 2000). The 5-HT transporter (5-HTT), which is responsible for the active clearance of synaptic 5-HT and, thus, regulation of pre- and postsynaptic 5-HT receptor stimulation, is of particular importance in efforts to identify genetic polymorphisms in 5-HT subsystems that impact trait negative affect as well as differentiate relative risk for disease. In 1996, a relatively common functional promoter polymorphism in the human 5-HTT gene (SLC6A4) was linked to relatively increased trait anxiety in healthy adult volunteers (Lesch et al., 1996). The so-called 5-HTTLPR (5-HTT gene-linked polymorphic region) is typically defined by two variable nucleotide tandem repeat elements, a short (S) allele comprised of 14 copies of a 20–23 base pair repeat unit and a long (L) allele comprised of 16 copies (Wendland et al., 2006). While initial in vitro and in vivo assays revealed relatively diminished 5-HTT density associated with the S allele (Heinz et al., 1998;Lesch et al., 1996), recent work has indicated that more complex mechanisms (e.g., regional up- and down-regulation of specific 5-HT receptors) and not altered 5-HTT density may mediate the long-term impact of the 5-HTTLPR on 5-HT neurotransmission (David et al., 2005; Hariri and Holmes, 2006; Lee et al., 2005; Parsey et al., 2006). Regardless of the underlying mechanisms of action, a modest association has been widely reported between the 5-HTTLPR S allele and relatively increased trait negative affect (Munafo et al., 2005; Schinka et al., 2004; Sen et al., 2004). Moreover, the 5-HTTLPR S allele has been associated with increased risk for depression in the context of environmental adversity, a relationship that may be mediated by increased neuroticism, a psychometrically robust index of trait negative affect (Caspi et al., 2003; Munafo et al., 2005). Imaging genetics studies have provided a unique understanding of how the 5-HTTLPR may impact temperamental anxiety and risk for depression. In a landmark study, fMRI revealed that the reactivity of the amygdala, a core brain region responsible for triggering both physiological and behavioral arousal, to threat-related facial expressions was significantly exaggerated in S allele carriers (Hariri et al., 2002b). Since this original study, there have been multiple replications of the association between the S allele and relatively increased amygdala reactivity in both healthy volunteers and patients with mood disorders; a recent meta-analysis revealed that the 5-HTTLPR may

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account for up to 10% of the variability in amygdala reactivity (Munafo et al., 2007). In addition to this consistent effect on amygdala reactivity, the 5-HTTLPR S allele has been further linked with reduced gray matter volumes of and functional coupling between the amygdala and medial prefrontal cortex (Canli et al., 2005; Heinz et al., 2005; Pezawas et al., 2005). As the magnitude of amygdala reactivity (as well as its functional coupling with medial prefrontal cortex) is associated with temperamental anxiety, these imaging genetics findings suggest that the 5-HTTLPR S allele may be associated with increased risk for depression upon exposure to environmental stressors because of its effects on corticolimbic reactivity to potential threat.

FUTURE DIRECTIONS The emerging field of imaging genetics is in its infancy, as the number of genes explored is very few and the strategies for looking at gene effects in brain are relatively simplistic. The studies reviewed above provide compelling evidence that gene effects at the level of amygdala reactivity are more robust (i.e., “penetrant”) than at the level of manifest emotional behaviors. This is consistent with the conclusion that genes related to affect, mood, and temperament are not coding for behavior per se but rather impact on the development and function of neural systems that mediate emotional experience and behavior. Emotional responses are complex and not the result of variation in any single genes; futures studies will emphasize interactions of genes and interactions of genes with the environment. This is likely to add complexity but also a refined resolution to the analyses. Combining existing neuroimaging modalities is another important future direction for imaging genetics. Implementation of multimodal strategies is critical for identifying intermediate

mechanisms mediating the effects of genetic polymorphisms on neural circuit function and related behaviors. The potential of multimodal neuroimaging was recently demonstrated in a study employing both PET and fMRI to identify the impact of 5HT1A autoreceptor regulation of 5-HT release on amygdala reactivity (Fisher et al., 2006). In the study, adult volunteers underwent PET to determine levels of 5-HT1A autoreceptors using a specific radiotracer that binds to 5-HT1A and provides an in vivo index of receptor density. During the same day, all subjects also underwent fMRI, to determine the functional reactivity of the amygdala. Remarkably, the density of 5-HT1A autoreceptors accounted for 30–44% of the variability in amygdala reactivity. Downstream effects on 5-HT1A autoreceptors, notably reduced receptor density, have been hypothesized to mediate neural and behavioral changes associated with the 5-HTTLPR S allele (David et al., 2005; Hariri and Fisher, 2007; Lee et al., 2005). Thus, these findings suggest that 5-HT1A autoreceptor regulation of corticolimbic circuitry represents a key molecular mechanism mediating the effects of the 5-HTTLPR. Although such findings are not yet readily translated to the clinical practice of psychiatry today, they do provide examples of how the integration of advances in genetics and neuroimaging can lead to the eventual application of these technologies in the diagnosis and treatment of psychiatric illness. Individualized treatment strategies based on targeted neurogenetic pathways will very likely contribute to more effective and tolerated intervention platforms. Even more compelling is the potential of these approaches to establish predictive genetic and neurobiological markers of disease risk that will guide strategies for active prevention in the era of genomic and personalized medicine. Continued imaging genetics research at the interface of genes, brain, and behavior holds great promise for further explicating the neurobiological mechanisms through which variability in behavior emerges and affects risk for psychiatric disease in the context of environmental adversity.

REFERENCES Axelrod, B.N. (2002). Are normative data from the 64-card version of the WCST comparable to the full WCST?. Clin Neuropsychol 16(1), 7–11. Bigos, K.L., Pollock, B.G., Aizenstein, H.J., Fisher, P.M., Bies, R.R., Hariri, A.R. (2008). Acute 5-HT reuptake blockade potentiates human amygdala reactivity. Neuropsychopharmacology. [Epub ahead of print] Brown, S.M., Peet, E. et al. (2005). A regulatory variant of the human tryptophan hydroxylase-2 gene biases amygdala reactivity. Mol Psychiatry 10(9), 805. Burghardt, N.S., Sullivan, G.M. et al. (2004). The selective serotonin reuptake inhibitor citalopram increases fear after acute treatment but reduces fear with chronic treatment: A comparison with tianeptine. Biol Psychiatry 55(12), 1171–1178. Canli, T., Omura, K. et al. (2005). Beyond affect: A role for genetic variation of the serotonin transporter in neural activation during a cognitive attention task. Proc Natl Acad Sci USA 102(34), 12224–12229.

Caspi, A., Sugden, K. et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science 301(5631), 386–389. David, S.P., Murthy, N.V. et al. (2005). A functional genetic variation of the serotonin (5-HT) transporter affects 5-HT1A receptor binding in humans. J Neurosci 25(10), 2586–2590. Devlin, B., Roeder, K. et al. (2001). Genomic control, a new approach to genetic-based association studies. Theor Popul Biol 60(3), 155–166. Drabant, E.M., Hariri, A.R. et al. (2006). Catechol O-methyltransferase val158met genotype and neural mechanisms related to affective arousal and regulation. Arch Gen Psychiatry 63(12), 1396–1406. Fisher, P.M., Meltzer, C.C. et al. (2006). Capacity for 5-HT1Amediated autoregulation predicts amygdala reactivity. Nat Neurosci 9(11), 1362–1363. Forster, G.L., Feng, N. et al. (2006). Corticotropin-releasing factor in the dorsal raphe elicits temporally distinct serotonergic responses

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Manuck, S.B., Brown, S.M. et al. (2007). Temporal stability of individual differences in amygdala reactivity. Am J Psychiatry 164(10), 1613–1614. Moldin, S.O. and Gottesman, I.I. (1997). At issue: Genes, experience, and chance in schizophrenia – positioning for the 21st century. Schizophr Bull 23(4), 547–561. Most, S.B., Chun, M.M. et al. (2006). Attentional modulation of the amygdala varies with personality. Neuroimage 31(2), 934–944. Munafo, M.R., Clark, T. et al. (2005). Does measurement instrument moderate the association between the serotonin transporter gene and anxiety-related personality traits? A meta-analysis. Mol Psychiatry 10(4), 415–419. Munafo, M.R., Brown, S.M. et al. (2007). Serotonin transporter (5-HTTLPR) genotype and amygdala activation: A meta-analysis. Biol Psychiatry. Neumann, S.A., Brown, S.M. et al. (2006). Human choline transporter gene variation is associated with corticolimbic reactivity and autonomic-cholinergic function. Biol Psychiatry 60(10), 1155–1162. Parsey, R.V., Hastings, R.S. et al. (2006). Effect of a triallelic functional polymorphism of the serotonin-transporter-linked promoter region on expression of serotonin transporter in the human brain. Am J Psychiatry 163(1), 48–51. Pezawas, L., Meyer-Lindenberg, A. et al. (2005). 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nat Neurosci 8(6), 828–834. Plomin, R., Owen, M.J. et al. (1994). The genetic basis of complex human behaviors. Science 264(5166), 1733–1739. Ray, R.D., Ochsner, K.N. et al. (2005). Individual differences in trait rumination and the neural systems supporting cognitive reappraisal. Cogn Affect Behav Neurosci 5(2), 156–168. Schinka, J.A., Busch, R.M. et al. (2004). A meta-analysis of the association between the serotonin transporter gene polymorphism (5-HTTLPR) and trait anxiety. Mol Psychiatry 9(2), 197–202. Sen, S., Burmeister, M. et al. (2004). Meta-analysis of the association between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety-related personality traits. Am J Med Genet 127B(1), 85–89. Somerville, L.H., Kim, H. et al. (2004). Human amygdala responses during presentation of happy and neutral faces: Correlations with state anxiety. Biol Psychiatry 55(9), 897–903. Strange, B.A. and Dolan, R.J. (2004). Beta-adrenergic modulation of emotional memory-evoked human amygdala and hippocampal responses. Proc Natl Acad Sci USA 101(31), 11454–11458. Stoltenberg, S.F. and Burmeister, M. (2000). Recent progress in psychiatric genetics-some hope but no hype. Hum Mol Genet 9(6), 927–935. Tessitore, A., Hariri, A.R. et al. (2002). Dopamine modulates the response of the human amygdala: A study in Parkinson’s disease. J Neurosci 22(20), 9099–9103. van Reekum, C.M., Urry, H.L. et al. (2007). Individual differences in amygdala and ventromedial prefrontal cortex activity are associated with evaluation speed and psychological well-being. J Cogn Neurosci 19(2), 237–248. Wendland, J.R., Martin, B.J. et al. (2006). Simultaneous genotyping of four functional loci of human SLC6A4, with a reappraisal of 5-HTTLPR and rs25531. Mol Psychiatry 11(3), 224–226. Whalen, P.J., Bush, G. et al. (2006). The emotional counting Stroop: A task for assessing emotional interference during brain imaging. Nat Protoc 1(1), 293–296.

CHAPTER

48 Viral Chip Technology in Genomic Medicine Zeno Földes-Papp

INTRODUCTION The advances discussed in this chapter should be relevant to a broad and heterogeneous readership because of their significance, novelty, or wide applicability of viral chip technology in genomic medicine. These advances are making an outstanding contribution to the development of an important field of research. The state-of-the-field, particularly as it has evolved over the last years, is surveyed. As the field is already too expansive for a single article, I apologize in advance for the many omissions, but hope that this chapter captures the excitement of recent achievements in viral chip technology for genomic medicine. Viruses are a heterogeneous group of infectious agents composed mainly of nucleic acids, either DNA or RNA, packed tightly inside the protein coat. The outer shell mainly protects virus from physical, chemical, or enzymatic damage. Following entry into the cell by specific receptors (Table 48.1), the virus uses the host cellular regulatory system and the host cell’s synthetic machinery to produce enzymes required for its replication, and in many cases the viral genome encodes proteins, which act, for example, as RNA-polymerases, transcriptases that are needed in replication. These components are then assembled and released as viral particles.Viruses also use host cell ribosomes to translate viral proteins. Viruses can only replicate within cells. Thus, viruses are an exception to what is understood as the concept of “life.” Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 538

ROLE OF VIRUSES IN HUMAN INFECTIOUS DISEASE Viruses cause diseases ranging from acute infections (e.g., poliomyelitis) to chronic infections that are relatively benign (e.g., herpes) to life-threatening chronic infections (e.g., AIDS). As shown in Table 48.2 and in the recommended general references, different viruses lead to markedly different diseases, reflecting the diverse processes by which they damage human tissues and cells. Some common viruses are listed with typical examples of diseases caused in humans. The variety of viruses has required potential hosts to develop two crucial features of adaptive immunity. First, the need to recognize a wide range of different DNA and RNA pathogens had driven the development of receptors on B and T cells of equal or greater diversity. Second, the distinct habitants and life cycles have to be countered by a range of different effector mechanisms. Characteristic features of each virus are its mode of transmission, its mechanism of replication, its pathogenesis or the means by which it causes disease, and the immune response it elicits. Elimination of a viral infection generally requires the destruction of virally infected cells by osmotic lysis, which follows natural killer (NK) or cytotoxic T-cell (CTL) activation. Activation of these immune cells involves cytokines secreted by antigen presenting cells such as macrophages and dendritic cells, or Type I cytokine producing CD4 T cells. NK and CTL Copyright © 2009, Elsevier Inc. All rights reserved.

Role of Viruses in Human Infectious Disease

TABLE 48.1 receptors

Representative viruses and viral binding

Virus

Cell surface receptora

Influenza

Glycophorin A

Rhinovirus

Adhesion molecule ICAM-1

Reovirus

-adrenergic hormone receptor

Rabies

Acetylcholine receptor

Vaccinia

Epidermal growth factor receptor

Epstein-Bar

B cell complement receptor

HIV

T cell, macrophage CD4 receptor

a All viruses need to bind to a specific cell surface molecule in order to enter into the cell. The binding can be blocked by antibody, but there are cases in which the antibody helps the virus to get in.

cells differ in killing virus–infected cells mainly at the recognition site. NK and CTL cells active in these processes recognize unique surface features of virally infected cells, which are not present on non-infected cells. NK cells can use several different receptors that signal them to kill, including lectin-like receptors that recognize carbohydrate on self cells, killing inhibitory receptors (KIRs) that recognize “free” major histocompatibility complex (MHC) class I molecules encoded by genes of the human leukocyte antigen (HLA) C region and overrule the actions of the killing activation receptors (KARs). Once recognition has occurred, the cytotoxic mechanisms of the NK and CTL cells are similar. During the initial phase of viral infection and when the viruses bud from the cell, the particles can be targeted by IgM and IgG antibodies, leading to opsonin-mediated phagocytosis and/or neutralization. Phagocytosis and neutralization by antibodies are components of the natural and adaptive immune system in response to viral infection when the virus is disseminated via lymph, blood, or interstitial fluid, rather sequestered within a cell. Immune response to viruses also varies according to the site where the virus penetrates the human organisms and whether the infection is primary or secondary. General Problem of Needing to Identify the Relevant Virus Because viruses are sequestered within host cells and replicate rapidly, viruses produce mutations rapidly. When these mutations occur at the antigenic epitope, previously active immune effector cells may no longer recognize the altered virus. Immunological resistance develops. Many disease states have been associated with immune dysfunction of varying degrees of severity and significance, for example HIV/AIDS, flu, or colds. Antigenic diversity is the result of extensive antigenic variation in the past. Viruses interfere with the proper functioning of the cytokine network. For example, adenoviruses and rotaviruses inhibit

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interferon formation, hepatitis B virus does not induce interferon, and the EBV releases inhibitory cytokines suchas IL-10. Not all virus infections cause disease. The objective of the virus is to survive and be transmitted. Sometimes this may cause pathology; for example, diarrhea helps the rotaviruses (see Table 48.2) to get into water and to infect another host. Some viruses are obliged to destroy cells in order to spread, but many viruses inhabit the human body without causing disease symptoms for a long time, and these could be regarded as the most successful viruses. EBV in the throat and HSV-1 in the ganglia are examples of this. How Were Viruses Identified Prior to the Development of Chip Technology? Diagnosis depends on the detection of antiviral antibody and/ or viral antigen, not on immunological markers. The nature of investigations undertaken should be selected accordingly. For example, immune cell proliferation is often abnormal in viral infections but does not help in identification of the relevant virus. Accurate monitoring of viral load is now performed by quantitative real-time PCR. In general, there are three main and well-established approaches to identify a virus in the human (for overview, see Striebel et al., 2003): (i) Isolation and cultivation techniques These techniques have some disadvantages, in particular long cultivation times from 3 up to 30 days, lack of sensitivity, and the rather limited number of viruses that can be identified in comparison to the broad spectrum of possible viruses that cause infection and/or disease. Identification of isolates from cell cultures occurs by titration with different serum pools. This is additionally hampered by constant evolving of new viral subtypes. Alternatively, type specific detection may directly be performed using monoclonal antibodies, immunofluorescence, or enzyme immunoassay (EIA). (ii) Indirect virus detection by virus-specific antibodies Indirect virus detection methods include serological determination of specific antibodies by immunofluorescence assay, enzyme immunoassay, or by titration. After infection, only a few viruses release antigens in amounts sufficient to be detectable in body fluids by antibody assays (ELISAs). The presence of antibodies indicates immunity of the human organism in most virus infections, whereas re-infections do not show any symptoms. Antibody generation is detectable shortly after disease breaks out (e.g., in patients with viral hepatitis), but not during the highly infectious incubation time. Diagnosis of infections by cytopathogenic viruses, such as influenza or respiratory infections, may be easily performed in late states of these diseases, but are hardly possible during early disease states. While serotyping of viruses is mostly done by conventional immunological methods, many clinical isolates remain unclassifiable due to the limited number of antibodies against virus surface proteins, for example, of enteroviruses. Array-based assays are able to detect several

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TABLE 48.2

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Viral Chip Technology in Genomic Medicine

Some common viruses that cause diseases in humansa

Virus genome

Virus family

Virus Genus with type species

Human disease

DNA(ds)

Herpesvirida

Simplex virus with human herpesvirus 1 and 2 or herpes simplex type 1and 2 (HSV-1, HSV-2)

Cold sores, genital herpes, encephalitis

Varicellovirus with human herpesvirus 3 or varicella-zoster virus (VZV)

Chickenpox, shingles

Cytomegalovirus with human herpesvirus 5 (CMV)

Mononucleosis

Lymphocryptovirus with human herpesvirus 4 or Epstein-Bar virus (EBV)

Mononucleosis, Burkitt’s lymphoma

Roseolovirus with human herpesvirus 6 (HHV-6)

Erythema subitum, roseola infantum

(ds)

Adenoviridae

Mastadenovirus with human adenoviruses types 3 (HAdV-A), 4 (HAdV-E), and 7 (HAdV-B)

Acute respiratory disease (ARD)

(ds)

Poxviridae

Orthopoxvirus with vaccinia virus

Cowpox, vaccinia, smallpox

(ss)

Parvoviridae

Erythrovirus with human parvovirus B-19

Erythema infectiosium, seronegative arthritis, hydrops fetalis

(ds)

Papillomaviridae

Human papilloma virus with human papilloma virus types (strains) 16, 18, and 31

Cervical cancer

(ds)

Hepadnaviridae

Orthohepadnavirus with human hepatitis B virus

Hepatitis B

RNA(ss)

Orthomyxoviridae

Influenza virus A with types H1N1, H2N2, H3N2, and H5N1

Spanish flu, Asian flu, Hong Kong flu, or pandemic threat in 2006-7 flu season

(ss)

Paramyxoviridae

Morbillivirus with measles virus

Measles

Rubulavirus with Mumps virus

Mumps

(ss)

Coronaviridae

Coronavirus with SARS coronavirus

Severe acute respiratory syndrome (SARS)

(ss)

Picornaviridae

Hepatovirus with hepatitis A virus

Hepatitis A

Enterovirus with polioviruses and coxsackieviruses

Poliomyelitis and infectious myocarditis by coxsackievirus B

Rhinovirus with human rhinovirus A

Common cold (acute viral nasopharyngitis)

Rotavirus with rotavirus A, and B

Severe diarrhea among infants and children, or adult diarrhea by rotavirus B

Coltivius with Colorado tick fever virus

Colorado tick fever

(ds)

Reoviridae

(ss)

Togaviridae

Rubivirus with rubella virus

Rubella

(ss)

Flaviviridae

Flavivirus with yellow fever virus, or Dengue virus

Yellow fever, or Dengue hemorrhagic fever found in the tropics

Hepacivirus with hepatitis C virus

Hepatitis C

Arenavirus with lymphocytic choriomenigitis virus

Lymphocytic choriominigitis

Arenavirus with Lassa virus

Lassa fever

(ss)

Arenaviridae

(ss)

Rhabdoviridae

Lyssavirus with rabies virus

Rabies

(ss)

Retroviridae

Deltaretrovirus with human T-lymphotropic virus

T-cell leukaemia and T-cell lymphoma in adults

Lentivirus with human immunodeficiency virus 1 and 2 (HIV-1 and HIV-2)

Acquired immunodeficiency syndrome (AIDS)

a

For general information, see: Rich, R.R., Fleisher, T.A., Shearer, W.T., Schroeder, H.W., Frew, A.J. and Weyand, C.M. (eds.) (2008). Clinical Immunology: Principles and Practice, 3rd edition. Mosby Ltd., New York.

Microfabrication

serotypes with high accuracy. Highly sensitive array-based assay may become a useful alternative in clinical diagnostics. (iii) Direct detection of viral antigen or genomic features Nucleic acid detection methods in virology may be separated into two basic groups: in vitro nucleic acid-amplifying techniques such as PCR including reverse transcriptase (RT)-PCR and non-amplifying techniques. Analysis of virus specific nucleic acids without in vitro amplification may be performed best by hybridization of gene specific probes, followed by radioactive or immunological detection methods (i.e., ELISA, or chemiluminescence). Practical applications of these direct methods are rare, as sensitivities often are too low in comparison to quantitative real-time PCR or RT-PCR by LightCycler or TaqMan. However, PCR and RT-PCR bear increased risks of contamination by non-specific amplification products but the main limitation resides in the difficulty of designing compatible multiplex primer sets for large numbers of viruses to be screened in a single assay, being restricted to a limited number of viral families. By contrast, serial analysis of gene expression and differential display is a powerful high-throughput methodology (ConejeroGoldberg et al., 2005).

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insert. Of course, positive selection vectors facilitated screening, but still we had to do the hard stuff by hand. Today’s DNA chip/ array technology reverses that early approach. Instead of screening an array of unknowns with a defined probe like a synthetic oligonucleotide, PCR product or cloned gene, now a defined probe occupies the chip and then the chip is probed with the unknown sample. Today, identification of disease-relevant genes or proteins often starts with transcription profiling experiments on DNA chips and/or proteomics approaches. The interpretation of the measured data is largely based upon the complete sequences of the human genome and the most important model organisms, respectively. The overall objective of this chapter is twofold. First, the objective is to increase awareness in clinical medicine of the challenges and opportunities presented by DNA microarray technology and the emerging and rapidly changing field of genomic medicine. Second, the aim is to publicize to a broader research community additional challenges associated with the use of microarray technology in medically relevant genome research.

MICROFABRICATION Chip Technology Modern DNA microarray technology started in the mid-1990’s when Pat Brown and his colleagues at Stanford University first reported a high-capacity system in which DNA sequences specific to individual transcripts were immobilized at defined locations in an array format on a solid surface (Brown et al., 1999; DeRisi and Iyer, 1999; DeRisi et al., 1996; Schena et al., 1995), either through mechanical spotting (Lipshutz et al., 1995) or some implementation of in situ synthesis (Fodor et al., 1991). With the original DNA chip technology, microarrays were prepared by high-speed robotic printing of complementary DNAs on glass. Because of the small format and high density of the arrays, hybridization volumes of 2 L could be used that enabled detection of rare transcripts in probe mixtures derived from 2 g of total cellular messenger RNA (Schena et al., 1995). Differential expression measurements were performed by means of simultaneous, two-color fluorescence hybridization in parallel. This microarray-based genome-wide expression analysis of 45 genes first provided an unbiased view of the transcriptional state of a cell population (from Arabidopsis). DNA chips/microarrays are the biotechnology tool of our decade. Pathogen detection, resequencing, comparative genome analysis, and gene expression – they do it all. The amount of data they are generating is overwhelming. The expression levels of thousands of viral genes as well as many mutant variants are documented for numerous states of growth and disease. It is inevitable that DNA chips/microarrays will drive genomic medicine to new horizons. In the early 1990’s, we purified DNA, ligated it, transformed it, and the cloned gene had to be somewhere. We screened thousands of clones by hand with an oligonucleotide just to find one

Probe selection and microarray/chip design are central to the reliability, sensitivity, specificity, and robustness of viral arrays. Because common viral microarray/chip manufacturing technology synthesizes probes with defined sequences, positions, and lengths, array performance can be optimized using data collected from multiple databases, bioinformatics tools, and computer models. Viral oligonucleotide sequences can be designed according to open reading frames sequences obtained from Entrez Genome and Nucleotide Databases (http://www.ncbi.nlm.nih.gov/). The probe selection should be based on the criteria for specificity, sensitivity, and uniformity (Hughes et al., 2001;Wright and Church, 2002). Arrays can be designed with Array Designer 2.02 software (http://www.premierbiosoft.com). Recently, a bioinformatic platform and database were developed with specific probes of all known viral genome sequences to facilitate the design of diagnostic chips (Lin et al., 2006: http://www.bioinfo.csie.ncu.edu.tw/). Some of the key elements of viral chip microfabrication are shown in Figure 48.1 and are common to the production of all microarrays/chips. Probe concentration, probe DNA length, and printing buffer need to be optimized for high quality chip performance depending on the surface chemistry. Slide autofluorescence, spot morphology, reproducibility of arraying, binding efficiency and purity of probes are crucial for the accuracy and reliability of chip data analysis. The manufacturing process ends with a comprehensive series of quality control tests (Földes-Papp et al., 2004). Additionally, a sampling of arrays from every wafer is used to test the batch by running control hybridizations. A quantitative test of hybridization is performed using standardized control probes. For example, the results of 12,900 hybridization reactions on about 150 configured human herpes virus microarrays for the parallel detection of HSV-1 and HSV-2, VZV, EBV,

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Viral Chip Technology in Genomic Medicine

CMV, and HHV-6 showed that the established microarray/chipbased typing procedure was reproducible, virus-specific and sufficiently sensitive with a lower limit of 100 viral copies per mL sample (Földes-Papp et al., 2004, 2005b). A core element of array design, the perfect match/mismatch probe strategy, is also universally applied to the production of viral chips. For each probe designed to be perfectly complementary to a target sequence, a partner probe is generated that is identical except for a single base mismatch in its center. These probe pairs, which are called the perfect match probe and the mismatch probe, allow the quantization and subtraction of signals caused by nonspecific cross-hybridization. The difference in hybridization

signals between the partners, and their intensity ratios, serve as indicators of specific target abundance. The efficiency with which RNA target molecules are captured by, or hybridized to, surface-immobilized oligonucleotides depends upon secondary and tertiary structure of the RNA target strand. To overcome this limitation, RNA is often fragmented to reduce structural effects. The hybridization efficiency of the resulting fragments was determined as a function of fragment length and the amount of RNA captured was evaluated qualitatively by fluorescence intensity normalized to an internal standard (Mehlmann et al., 2005). Optimized conditions for influenza RNA were determined to include a fragmentation time of

Sample preparation

DNA/RNA extraction

AATAGCCTGG Oligo labelling

Primer selection

PCR NH2 NH2 NH2 NH2 NH2

Microarray design Hybridization

Detection

Interpretation

Figure 48.1

Some key elements of viral chip microfabrication (see Striebel et al., 2003).

Immobilization chemistry

Microfabrication

20–30 min at 75°C. These conditions resulted in a maximum concentration of fragments between 38 and 150 nucleotides in length and a maximum in the capture and label efficiency. In the case of viral chips, for example, an online platform and database that provide users with specific probe sequences of all known viral genome sequences was established to facilitate the design of diagnostic chips (Lin et al., 2006). A user can select any number of different viruses and set the experimental conditions such as melting temperature and length of probe. The system then returns the optimal sequences from the database. The experimental design of a microarray determines the confidence that can be assigned to the data. Specificity, sensitivity, reproducibility, and robustness of the experimental data are essential to a successful use of a viral DNA chip (Földes-Papp et al., 2005b). The microarray-chip platform to be chosen has high value for “multiplex” array diagnostics, that is the parallel determination of several parameters from one sample. There are several platform categories available, such as amplified cDNAs, oligonucleotides chemically synthesized from known sequences, and one-color and two-color samples labeled and hybridized to a chip. The selected chip platform has an effect on the complexity and flexibility of the generated data. For example, the analysis of one-color chips is more straightforward than the analysis of two-color chips. Data formats like CEL, CHP, MAS 5, RMA GenePix, or ImaGene affect the signal intensity and quality of data for downstream analysis. In differential gene expression studies, thousands of genes are simultaneously interrogated, and this in turn determines the statistics to be applied (Draghici, 2002) and the use of real and simulated chip data for testing multiple hypotheses (Reiner et al., 2003). The application of z-score statistics to gene ontology terms is described in Doniger et al. (2003). Because microarray experiments produce tremendous amounts of data, data management by databases efficiently organizes and retrieves the raw data. Sharing the data both within the lab and with collaborators requires web-based system access. Several aspects of probe selection and array design are dictated by an array’s intended use. To select probes for viral microarrays/chips, sequence and annotation data from multiple databases are integrated. Custom arrays can be designed for subsets of known genes. Structured Substrates Traditional hybridization assays used nitrocellulose and nylon membranes, and measurements were carried out by autoradiography. Microarrays and other chip assays are based on solid supports, usually glass. Measurements are commonly performed by fluorescence labeling and detection. In contrast to membranebased macroscopic arrays, microarray/chip supports (substrates) are non-porous, and therefore prevent absorption of reagents and samples on the substrate matrix. Non-porosity permits fast removal of organic and fluorescence substances during chip microfabrication and application. Microarray/chip supports allow highly parallel reactions, as well as significant increase in accuracy

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of measured data (Schena, 2001). These advantages maintain high sample concentrations and fast hybridization kinetics. In general, glass substrates are characterized by high reproducibility of the fluorescence signal during hybridization of immobilized oligonucleotides with fluorescence-labeled samples due to low autofluorescence and thus high signal-to-noise ratio. They also show high reaction stability over extended time periods. Borofluorate glass substrates are particularly suitable because of their low alkali content and therefore low autofluorescence. Spotters, Growers, and Grabbers Spotting Probe immobilization can be achieved using spotters for creating the arrays. The spatial resolution of the robotics determines the density of the array. Mechanical pins (contact printing) and ink-jetting usually gives spots of 100 m in diameter. The pin diameter and shape, solution viscosity, and substrate characteristics lead to some variation in spot size, shape, and concentration of solution transferred. A number of companies offer robotic systems for spotting arrays, but only commercially available DNA chips will pack as many spots as possible onto a chip. Modern inkjet-like printers reduce the spot volume into the picoliter range. Efficient covalent immobilization of biological molecules on solid supports is a crucial step during microarray production (Pirrung, 2002). Biomolecules need to be immobilized firmly enough to prevent their replacement during reaction and hybridization steps, but flexibly enough to allow conformational changes and binding of target molecules. An advantage of spotting of prefabricated oligonucleotides onto solid supports (substrates) followed by chemical immobilization is the independence from the length of oligonucleotides to be immobilized. Oligonucleotides of 20–70 nucleotides in lengths are usually aminomodified and covalently bound onto the support. For glutaraldehyde immobilization, aminated slides and 5-aminated oligonucleotides may be used (Pease et al., 1994). Glutaraldehyde-mediated coupling of biomolecules to solid supports is the classical chemical immobilization method. In order to covalently attach oligonucleotides to the chip, preactivated or surface-modified solid supports, homo- or hetero-bifunctional crosslinkers, and modified oligonucleotides are needed. Table 48.3 surveys common immobilization techniques (see also Wittmann 2005). To immobilize modified DNA, glass slides with reactive aldehyde, amino, mercapto, or epoxy groups are commercially available. Another covalent and direct immobilization of DNA on glass with oxidic surface (Jung et al., 2001) requires positioning of DNA droplets on a heated surface with an efficiency of 150–300 fmol/mm2 during the coupling reaction. Lindroos et al. (2001) compared six different commercial slides with respect to fluorescence background, turnover efficiency and signal-to-noise ratio. They found that attachment chemistry affects genotyping accuracy when mini-sequencing is used for genotyping on microarrays. Genotyping results were best when mercaptosilane-coated slides were used to attach disulfide-modified oligonucleotides.

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TABLE 48.3

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Viral Chip Technology in Genomic Medicine

A selection of DNA immobilization techniques on microarrays/chips (see Striebel et al., 2003)

Immobilization

Examples

Non-covalent complexation

Biotin–avidine/streptavidine

Self assembled monolayer

Alkanethioles, mercapto- or epoxysilane-compounds

Inclusion/ crosslinking

NH2-dextrane matrix, aminoethylcellulose, polyacrylamide

Polymerization

Co-polymerization of pyrrole and oligo-nucleotides with pyrrole residue at 5-ends, pyrimidinepyrimidine-dimers, vinylsubstituted nucleotides

Silanization by bifunctional reagents

Aminopropyltriethoxysilane (aminofunction with succinic anhydride convertible into carboxic acid function), 3-mercaptopropyl-triethoxysilane (disulfide bridge/ thioether), glycidoxypropyltriethoxysilane, p-amino-phenyltrimethoxysilane (ATMS)/diazotation chemistry and unmodified oligonucleotides

Covalently by linkerphotoactivated crosslinking

3-Hydroxyfunction, 5phosphate residue, bromcyan, cyanurchloride, carboxylfunction, aldehyde, primary amine, DNA coupling by thymidines, DNA coupling via alkylamines, photolinkage (i.e., psoralen, p-nitrophenyl-3-diazopyruvate)

Diazotized chip surfaces are described for immobilization of unmodified oligonucleotides (Dolan et al., 2001). Using paminophenyltrimethoxysilane (ATMS), diazotization chemistry was developed, and microarrays were fabricated and analyzed. The method produced uniform spots containing equivalent or even larger amounts of DNA than those obtained by commercially available immobilization techniques Oligonucleotides with hairpin stem-loop structure and multiple phosphorothioate moieties in the loop were used to anchor the oligonucleotide to glass slides that are pre-activated with bromoacetamidopropylsilane (Zhao et al., 2001). Consolandi et al. (2002) describe two robust procedures for oligonucleotide microarray preparation based on polymeric coating. Chemical procedures include a glass functionalization step with -aminopropyltriethoxy-silaneAPTES, or poly-L-lysine, or polyacrylic acid-polyacrylamide co-polymer, which is covalently bound to the modified glass. A surface activation step allows attachment of amino-modified oligonucleotides. Results show high loading capacity, good uniformity, ready availability of immobilized DNA to hybridization targets, and stability to thermal cycles. The AR Chip Epoxy obtained from two competitors (3D-LinkTM and Easy Spot) was compared with respect to slide autofluorescence, spacer length and signal-to-noise ratio (Preininger and Sauer, 2003). The two chip surfaces were assayed by hybridizations with the same targets, and under the same hybridization and scanning conditions. When polyamide (PAMAM) dendrimers containing 64 primary amino groups were used as linkers, signal intensities could be increased significantly compared with amino and epoxy silanized surfaces (Benters et al., 2002). It was shown that dendritic PAMAM linker systems reveal high immobilization efficiencies for amino-modified DNAoligomers. This was used to assay the performance of dendrimerbased DNA microarrays for discrimination of SNPs. Aminated glass slides are the most common functionalized supports available. The slides may be used for biomolecule immobilization by reaction of the amino groups with glutaraldehyde,

which in turn is coupled to amino groups of target molecules in a sandwich-like manner. Resulting Schiff bases need to be reduced to secondary amines. This may be performed by sodium cyanoborohydride (Birch-Hirschfeld et al., 2002). The method has disadvantages such as complex storage conditions for chemically fully active glutaraldehyde and low biomolecule binding capacity. In addition, glutaraldehyde coatings often do not meet the criteria for low fluorescence background due to impurities and artifacts acquired during production. Another immobilization chemistry is based on epoxycoated slides. For preparation, activated glass slides are coated with 3-glycido-oxypropyltrimethoxysilane (GOPS), dissolved in toluene. When low-fluorescence glass substrates are used, the resulting microarray supports show greatly reduced fluorescence background, combined with superior DNA immobilization properties (Földes-Papp et al., 2004; Striebel et al., 2003, 2004). A hetero-bifunctional photoreactive cross-linking reagent, 4-nitrophenyl 3-diazopyruvate (DAPpNP), reacts with glass slides bearing amino groups (Földes-Papp et al., 2004; Striebel et al., 2003). After design and spotting of NH2-modified probe oligonucleotides, the DAPpNP-coated glass slides are irradiated at 360 nm.This UV-radiation leads to conversion of diazogroups into reactive ketene groups, which react with amino groups of the DNA (Goodfellow et al., 1989; Harrison et al., 1989; Kalachikov et al., 1992). In aqueous solutions, ketene groups formed by UV-irradiation of diazopyruvic acid are transformed into carboxyl groups, which then may be used to react with 5amino-modified 2-deoxynucleotide oligomers in the presence of carbodiimide (Penchovsky et al., 2000). A further light-driven oligonucleotide immobilization technique applies psoralen-mediated covalent coupling of complementary DNA strands (Kittler and Löber, 1995; Pieles and Englisch, 1989). In a first step, an oligonucleotide of 15 alternating adenine and thymidine nucleotides is synthesized directly on a glass support.The sequence carries an amino group at its 3-end and a psoralen group at its 5-end. Psoralen may be added using

Microfabrication

2-[4-(hydroxymethyl)-4,5,8-trimethylpsoralen]-hexyl-1-O[(2-cyanoethyl)-(N,N-diiso-propyl)]phosphoramidite. A strand, which is complementary to the synthesized support oligonucleotide carrying a stem of five thymidines plus the favored cDNA sequence at its 3-end, is then hybridized to the support. In an irradiation step (2 min at 280 nm), the psoralen function of the support oligonucleotide is bound covalently to the complementary sequence. Microarrays generated that way may be hybridized to single- or double-stranded, Cy3-labeled probe DNAs. Advantages of the psoralen-based technique are: oligonucleotides may be synthesized completely in the standard 3 to 5 direction; densities of immobilized oligonucleotides are optimally suited for subsequent hybridizations, and the light-driven coupling process may be performed in an aqueous environment without impairing bases or other parts of the DNA carrying the cDNA sequence (Földes-Papp et al., 2004). Growing In addition to spotting, oligonucleotide/DNA sequences can be grown directly on the microarray/chip. Photolithography opened the way for in situ fabrication of defined oligonucleotide arrays (Fodor et al., 1991). The oligonucleotide sequence grows on the surface of a glass wafer in a manner similar to conventional solid-phase oligonucleotide synthesis but modified to include a light-sensitive deprotection step. Masks are used to add photospecific bases to selected points on the chip to create a series of oligo-nucleotides with a variety of different sequences. The process, which comprises solid-phase synthesis, photolithography, and affinity labeling, allows the synthesis of up to 250,000 different oligonucleotides (or oligopeptides) per square centimeter on a glass slide by light-directed, spatially addressable chemical synthesis with spot sizes of about 20 m in diameter. Optimal spacer lengths are crucial for hybridization behavior and fluorescence yields. Different spacer compositions to reduce steric interference of immobilized oligonucleotides with the support were studied in order to improve hybridization behavior (Shchepinov et al., 1997). Optimal spacer length was determined to be at least 40 atoms in length, yielding up to 150-fold increase in hybridization. These spacers are composed of a variety of monomeric units that are synthesized using phosphoramidite chemistry, by condensation onto an amino-functionalized polypropylene support (Matson et al., 1994). Steric hindrance during hybridization may also become problematic, if the immobilized oligonucleotides are too close to each other. Surface coverage was varied using a combination of cleavable and stable linkers. Results showed that highest hybridization yields were obtained from surfaces containing approximately 50% of the maximally possible oligonucleotide concentration. High-density oligonucleotide arrays are one of these tools for large-scale hybridization. With their ability to produce global views of genome sequences and activity, they have emerged as a key analytical research tool. A vast amount of data must be collected and analyzed to understand biological functions. Affymetrix Inc. creates breakthrough tools for genomic applications. The unique combination of photolithography and

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combinatorial chemistry eliminates the need for individual laboratories to produce and test their own chips. Using technologies adapted from the semiconductor industry, GeneChip™ manufacturing begins with a 5-inch square quartz wafer. Initially the quartz is washed to ensure uniform hydroxylation across the surface. Because quartz glass is naturally hydroxylated, glass provides a very good substrate for the attachment of chemicals such as linker molecules that are later used to position the probes on the chips. The wafer is placed in a bath of silane that reacts with the hydroxyl groups of the quartz glass surface, and forms a matrix of covalently linked molecules. The distance between these silane molecules determines the probes’ packing density, allowing arrays to hold over 500,000 probe locations, or features, within a mere 1.28 cm2. Linker molecules attached to the silane matrix provide a surface that may be spatially activated by UV light. Probe synthesis occurs in parallel and results in the addition of an A, C, T, or G nucleotide to multiple growing chains simultaneously. To define which oligonucleotide chain will receive a nucleotide in each coupling (growing, propagation step), photolithographic masks, which carry 18–20 square micron windows corresponding to the dimensions of individual features, are placed over the coated wafer. The windows are distributed over the mask based on the desired sequence of each probe. When UV light is shone over the mask in the initial step of oligonucleotide synthesis, the exposed linkers become deprotected and are then available for nucleotide coupling. Critical to this step is the precise alignment of the mask with the wafer. To ensure that this critical step is accurately completed, chrome marks on the wafer and on the mask are perfectly aligned. Once the desired features have been activated, a solution containing a single type of deoxynucleotide with a removable protection groups is flushed over the wafer’s surface. The nucleotide attaches to the activated linkers, initiating the synthesis process. Although the synthesis process is highly efficient, some activated molecules failed to attach the new nucleotide. To prevent these “outliers” from becoming probes with missing nucleotides, a capping step truncates them in each round of oligonucleotide synthesis. In addition, the side chains of the nucleotides are protected to prevent the formation of branched oligonucleotides. In the next round of synthesis, another mask is placed over the wafer to allow the next cycle of deprotection, coupling and capping. The cyclic process is repeated until the probes reach their full length, usually 25 nucleotides. Although each position in the sequence of an oligonucleotide can be occupied by one of four nucleotides resulting in an apparent need for 25  4  100 different masks per wafer, situations are identified when the same mask can be used repeatedly. Once the synthesis is complete, the wafers are deprotected and diced. Disadvantages of this method are oligonucleotide lengths that are limited to approximately 30 nucleotides, as well as an unfavorable oligonucleotide orientation, where the 3-end is bound to the solid support. This is important since hybridization efficiencies depend on orientation and accessibility of the immobilized probe (Southern et al., 1999). A further disadvantage of photolithographic array generation is the accumulation of truncated sequences that

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cannot be removed (fidelity of the synthesis process: FöldesPapp et al., 1994). For optimal performance, for example, of a photolithographically generated microarray, requirements are therefore (Földes-Papp et al., 1994): (i) Minimization of failure sequences during chemical synthesis of oligonucleotides. This may be achieved by keeping side reactions of chemical synthesis low. Side reactions comprise clustering, depurination in the detritylation reaction, substitution of nucleotides at nucleobases, or breaking of internucleotide bonds. (ii) Minimization of homologous sequences. Oligonucleotides become increasingly more similar in their physico-chemical properties with increasing chain lengths. Therefore, sequences of the type N-1, N-2, N-3, and so forth (N stands for the number of nucleotides in the projected probe oligonucleotide/DNA sequence) are to be minimized by optimization of the coupling and capping reactions. (iii) Suppression of incomplete reactions during chemical cycles. Incomplete reactions are due to masking of non-converted ends by degradation of protection groups. (iv) High reproducibility of chemical syntheses within single steps. The chemical criteria “yield” (e.g., trityl yield) and “coupling and capping efficiency” used in practice do not meet the demands of the quantitative characterization of chemical oligonucleotide/DNA/RNA/peptide syntheses. Yields, coupling and capping efficiencies only relate to single-reaction steps, but do not consider the length N of projected probe sequences; therefore, these parameters do not say anything about the frequency distribution of failure (truncated, false, erroneous) sequences, and they do not express the dynamics with multi-cyclic syntheses. If syntheses of different nucleotide lengths are compared regarding the optimization of external and internal synthesis conditions, then a lower yield of achieved full length sequences does not mean poorer synthesis conditions. Synthesis conditions leading to more probe sequences of lengths N are better optimized if fewer false sequences are synthesized (Földes-Papp et al., 1995a, b, 1996, 1997a, b, 1998). From theoretical considerations, exact measures of cyclic multistep syntheses, such as chemical oligonucleotide/DNA/RNA/peptide syntheses, were derived. The studies showed that fractal dimensions are such measures for oligonucleotide/DNA/RNA/peptide syntheses. They are superior to commonly used parameters like trityl yield and coupling efficiencies, and they can be applied to the experimental separation of crude synthesis products by high-resolution ion-exchange HPLC, capillary electrophoresis, and gel electrophoresis. Based on experiments, the dynamics of the syntheses on solid support was modeled by stochastic processes with nucleotide chain length N, propagation/elongation (coupling, oxidation), and termination (capping) probabilities as the basic parameters. The following Eqn. 48.1 provides an exact measure for quantitative assessment and comparison of multi-cyclic syntheses of different target length, such as chemical oligonucleotide/DNA/RNA/ peptide syntheses:

D( N , d0 )  2  N

d0N 1 1 ln 1 N  1  d0 d0

(48.1)

Here, N is the projected length of the probe sequence, and d0 the average (constant) coupling (propagation/elongation) probability that can be expressed, for example, as averaged trityl yield in the case of chemical oligonucleotide/DNA syntheses (Földes-Papp et al., 1995a, b). Oligonucleotide syntheses of different projected probe lengths are equally well optimized regarding their synthesis conditions, if the fractal dimensions D  D(N, d0) are equal; syntheses showed less propagation and accumulation of erroneous sequences with respect to their projected target lengths N, which is formulated by Eqn. 48.1 and experimentally tested and validated (Földes-Papp et al., 1995a, b, 1996, 1997a, b, 1998). Grabbing A grabber is an electronic addressing technology. A chip is flooded with a solution of a probe, and a charge is introduced onto the surface by electrical activation of a row or spot on the chip. The oligonucleotide/DNA probe concentrates close to the charge and is then chemically bonded in place. The chip is washed and another probe solution is introduced. One mm2 of the microarray/chip contains 25 up to 400 wire-bonded electrodes, or even 10,000 sites.

Labeling Strategies and Fluidics Workstations Once the DNA microarray/chip is fabricated, some form of chemistry has to occur between the sample and the array. The simplest approach is direct labeling of the viral target DNA with fluorophores. Common methods employ enzymatic incorporation of fluorescent nucleotides, or PCR amplification with fluorescent primer pairs. RNA probes are usually prepared from cloned DNA by incorporation of fluorescent nucleotides via RNA polymerase. Selection of fluorescence dyes for primer labeling depends on the filter system of the microarray reader and scanner, respectively. As fluorescence dyes are large hydrophobic molecules, care has to be taken to avoid intercalation of these dyes during PCR (Földes-Papp et al., 2001a, b). Suitable dyes that provide a good long-time stability, such as cyanide dyes Cy3 or Cy 5, are commercially available. Cyanide dyes show an intensive fluorescence and are all water-soluble. This property, combined with their good quantum yield, makes them superior to many other fluorescence dyes (Wessendorf and Brelje, 1992). Usually, the dyes are attached to the respective DNA sequences in the form of their N-hydroxy-succinimide (NHS) esters. The attachment requires an amino group at the 3 or 5ends of the DNA, which may be introduced via an amino link. Additionally, the method allows introduction of spacers of different lengths (Földes-Papp et al., 2001a, b). Optimal spacer lengths were obtained using C6-amino link reagents. If solvents and pH values are appropriately adjusted (reactions of some fluorescence dyes require heterogeneous phases), covalent fluorescence dye coupling may be achieved within 1 h. Subsequently, an HPLC purification step is required.

Microfabrication

An alternative to NHS ester coupling is coupling of some fluorescence dyes in the form of their phosphoramidite derivatives. Dye-phosphoramidites are easily attached covalently to DNA 5-ends in computer-assisted automatic oligonucleotide synthesis without any difficulties. After cleavage from the support and chemical removal of protection groups, purification is carried out by preparative Rp18-HPLC. Incorporation of fluorescent dyes, such as Cy3, Cy5, or fluorescein into nucleic acids, can also be performed with their appropriate 2-desoxyuridine-5-triphosphates. These commercially available compounds carry the respective dyes that are attached to spacers at nucleotide 5-positions. With this internal incorporation of the dye-labeled nucleotides, false base pairing cannot be excluded during PCR reactions. In particular, this holds true for multiplex PCR reactions. Additionally, these dyetagged nucleotides need to be added in excess, since they are less well inserted by DNA-polymerases than naturally occurring nucleotides (Földes-Papp et al., 2001a, b). A serious problem of viral diagnostics is the variable, often small quantity of viral particles in patient samples. The amount may vary between one and several thousands of virus copies per milliliter. Signal intensities may be amplified by adding dendrimer building blocks at 5-ends of PCR primers, allowing multiple coupling of fluorescence dyes per primer (Striebel et al., 2004). The review article of Caminade et al. (2006) focuses on the improvements that hyperbranched and perfectly defined dendrimers also called nanomolecules can provide to DNA microarrays. Two main uses of dendrimers for such purpose have been described up to now (Caminade et al., 2006). Either the dendrimer is used as linker between the solid surface and the probe oligonucleotide, or the dendrimer is used as a multilabeled entity linked to the target oligonucleotide. In the first case the dendrimer generally induces a higher loading of probes and an easier hybridization, due to moving away the solid phase. In the second case, the high number of localized labels (generally fluorescent) induces an increased sensitivity, allowing the detection of small quantities of biological entities. Using dendrimer technology for multilabeling of viral DNA, Striebel et al. (2004) incorporated dendrimer building blocks at DNA ends during oligonucleotide synthesis, and then fluorescence dyes coupled to amino groups at dendrimers’ branches via NHS ester chemistry. In this way, two, four, or eight fluorescence dye molecules may be attached covalently to a primer, using “doubler” dendrimer building blocks. By using “trebler” dendrimer building blocks, three or nine fluorescence dyes may be linked to a primer. In oligonucleotide chemistry, branched oligonucleotides are already being used for signal amplification in hybridization assays. Branched oligonucleotides allow detection of a concentration below 100 human CMV copies per milliliter sample (template) in the case of 5-(Cy3)3-dendrimer-labeled CMV DNAs (Striebel et al., 2004). The fluorescence signal was enhanced via the dendrimers up to 30 times compared with the quenched single Cy3-labeled human HSV-1 DNA. The on-chip signal-amplifying effect depended upon the number of branches and the concentration of fluorophore-labeled pathogenic DNAs.

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Treblers were superior to doublers, as trebler-labeled nucleic acids had fluorescence-signal-enhancing effects over a broad range of labeled DNA concentrations exemplified for the quenched single Cy3-labeled HSV-1 and non-quenched single Cy3-labeled CMV DNAs. Fluorescence dye labeling invariably involves hybridization of the sample to the oligonucleotides/DNA present on the array. Because chip-bound DNA is often quite short and varied in sequence, a single stringent hybridization condition that is optimal for every spot on the chip is impossible. However, it is possible to find temperature and salt conditions that gives acceptably strong signals for the desired hybridization products and much weaker signals for mismatches. Unbound viral target DNA is then washed away, and the chip is ready to be scanned. Fluidics workstations are available to automate some or all of the wet working steps. The traditional array detection strategy was autoradiography of radiolabeled samples, but other options are available including electronic signal transduction. With bioelectronic detection, electron transfer reactions from the DNA to the substrate allow fast and probe-cell specific detection systems. The ability of mass spectroscopy to identify and, increasingly, to precisely quantify genes and genome sequences (as well as proteins) from complex samples can be expected to impact broadly on biology and medicine. The systematic analysis of the much larger number of genes expressed in a cell, an explicit goal of functional genomics, is now also rapidly advancing, due mainly to the development of new experimental approaches. Using MALDI-TOF, DNA fragments are analyzed with the SpectroChip™ from Sequenom Inc. A piezoelectric gene sensor microarray for HBV quantification in clinical samples was constructed using crystal units that oscillate independently (Chen et al., 2005a, b). Imaging and Scanning, Data Processing Scanning a fluorescence-labeled DNA array is conceptually quite simple. A light source excites the labeled samples and a detector system measures and records the emitted fluorescence. However, the instrumentation requirements differ according to the precise nature of the array. Most image capture instruments and microarray readers, respectively, use a scanning detector similar to line-scanning detector systems for DNA sequencing instruments. The number of discrete points that the detector can sample across the array and the row-to-row step interval determine the size of the features that the detector can image. It is important that the detector pixels are sufficiently small to gather data from enough significant pixels in each probe cell. Confocal laser-scanning systems for fluorescent microarray biochips should have a spatial resolution of a detector on the order of 1/8 to 1/10 of the diameter of the smallest element in the microarray. While the absolute number of DNA molecules that can be crammed into a small space will probably prevent the field from reaching the 0.2 m features found in semiconductors, 5 m probe cells appear attainable. This would put about four million features on a 1 cm2 chip.

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Image analysis is performed by placing a grid over the array to allow integration of the signal from each probe cell. If the signals from the arrays are strong and fairly consistent, a threshold algorithm is usually adequate, but weak signals demand a computationally intensive approach. The background signal or baseline is the fluorescence from the support matrix. Raw fluorescence intensity data are background subtracted. Hybridization outcomes can be represented as log2 fluorescence intensity ratio of test versus reference samples. To virtually enrich viral specific concordant probes, data filtration can be further filtered by elimination of spots below signal intensity and a predefined absolute log2 ratio; this eliminates outlier data from further analysis due to labeling bias. The processing improves data correlation. Hierarchical clustering and visualization of data can be performed using Gene Cluster and Tree View software (Stanford University, USA). High-density DNA chips with thousands of probe cells need special managing software for microarray probe tracking and data analysis like Affymetrix’s LIMS workstation, BioDiscovery’s Clone Tracker software or GeneSpring, a software package offered by Silicon Genetics. Based on observed microarray hybridization patterns an algorithm called E-Predict is reported for microarray-based viral species identification. E-Predict compares observed hybridization patterns with theoretical energy profiles representing different viral species in a set of clinical samples but its relevance to other metagenomic applications is discussed (Urisman et al., 2005). Viral Genotyping Regardless of how the microarrays and chips, respectively, are made, viral chip technology is superior to conventional PCR methods for the fast, sensitive, specific, and parallelized diagnostics of heterogeneous populations of different viral species and strains (Li et al., 2001; Striebel et al., 2003). Viral genotyping is an approach made possible by the availability of viral gene and genome sequence databases and technical and conceptual advances in chip technology. Interest exists in providing highthroughput platforms and readout technologies that can selectively examine changes in the transcript levels of specific genes in response to multiple treatments. Although conventional microarrays (DeRisi et al., 1996; Schena et al., 1995; Wang et al., 2002) have, in principle, the necessary throughput, they were limited with respect to cost and dynamic range and therefore are not well suited for the task of producing genotyping data for multiple viruses from which accurate reliable information can be calculated. For routine clinical testing, it is not possible to test thousands of genes on a microarray. Therefore, some researchers have called for “less” genes to be put on the array, because most of the other data points are inconsequential (Wooster, 2000) or even detrimental to the usefulness of a set of classifier genes (Draghici et al., 2003). Can array data be used to identify a handful of critical genes that will lead to a more-detailed taxonomy and can this or similar array data be used to predict clinical outcome (Wooster, 2000)? In genotyping applications, one looks for sequence variation that has previously been characterized. Oligonucleotides of known sequence variants of a viral gene or collections of viral

genes are represented in the chip. Genotyping arrays that are designed to examine viral DNA sequences are listed in Table 48.4. The viral chip has the enormous strength that only one chip is needed to analyze a mixture of different viral DNA sequences. In practice, there may be some pitfalls, as exemplified by long-term variability studies of the human herpes simplex virus type 1 (HSV-1) genome. The HSV-1 genome consists of iterative DNA sequences of variable amounts. The HSV-1 genome from a HSV-1 DNA pool has an isomolar genome organization with L and S components. Genome complexity is due to tetra-isomers derived from the rolling circle-like intracellular replication process. In the L-S region, transient variabilities are formed by insertions or deletions of the repetitive a-sequences. The genomic changes are more or less strain-specific. Outside the L-S junction the virus progeny under study showed no or negligible deviations from the parent strain. The viral DNA material of three HSV-1 strains F (Roizman), Mst (Ulm) and AK (Ulm) was sampled after intracellular self-assembled virus particles were formed (Földes-Papp et al., 1997a; Klauck et al., 1995). The DNA replication was carried out in Vero cell infections and the purified viral DNA was digested with BamHI restriction endonuclease (Földes-Papp et al., 1997a). The resulting BamHI-K2-fragments of HSV-1 were extracted from agarose gels by electroelution, ligated into plasmid pBR322 or pAT153 and cloned in E.coli K 12 C600 cells for hybridization in Southern blots. The restriction patterns obtained by endonucleases KpnI, and SalI were analyzed by hybridization with K2fragments of HSV-1 (F) labeled by the random priming method with [32P]-dCTP. Blots were autoradiographed and the bands quantified by scanning densiometry. We found that the nucleotide sequence pattern of the HSV-1 genome in the L-S region can be approximated by distribution profiles of digested components expressed in powers of nucleotide length N of the largest component in the region. The largest components of interest studied had nucleotide length N of 13,500 base pairs obtained with KpnI of HSV-1 strains F, Mst, AK and nucleotide length N of 9730 base pairs obtained with SalI of HSV-1 strains F, Mst, AK. We obtained the fractal dimensions D  1.63 (strain F), D  1.66 (strain Mst), and D  1.66 (strain AK) with restriction endonuclease KpnI. With restriction endonuclease SalI, we found the fractal dimensions D  1.36 (strain F), D  1.36 (strain Mst), and D  1.39 (strain AK). The fractal dimension D is here a measure for producing internal repeats in the L-S region of strains F, Mst, AK during replication at the viral genome level (Földes-Papp et al., 1997a). Here, a lower value of the fractal dimension D indicates a distribution of sequence variability with lower amounts of internal repeats. The fractal dimension D was obtained from the scaling to the largest nucleotide length of interest, N, and is interpreted as typical longterm DNA sequence variability. Viral Sequence Determination At its most complex, viral genotyping using a DNA chip involves complete determination of the nucleotide order via resequencing by hybridization. Viral resequencing discovers

Microfabrication

TABLE 48.4

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Representative examples of viral genotyping studies with microarrays/chips

Viral species and strains

Examples

Herpes viruses including Epstein-Barr virus

Mezzasoma et al. (2002); Duburcq et al. (2004); Conejero-Goldberg et al. (2005)

HSV-1, HSV-2, VZV, EBV, CMV, HHV-6

Földes-Papp et al. (2004, 2005b); Striebel et al. (2004)

VZV

Sergeev et al. (2006)

Influenza viruses

Chen and Evans, (2001); Sengupta et al. (2003); Ivshina et al. (2004); Kessler et al. (2004); Mehlmann et al. (2006); Townsend et al. (2006); Dawson et al. (2007); Dankbar et al. (2007)

Adenovirus

Lin et al. (2004)

SARS

Wang et al. (2003); Wong et al. (2004)

Noro- and astroviruses

Jääskeläinen and Maunula, 2006

Hepatitis B and C viruses

Zhao et al. (2003); Perrin et al. (2003); Duburcq et al. (2004)

Hepatitis B virus

Song et al. (2006)

Drug resistant HBV

Tran et al. (2006); Chen et al. (2005a, b)

Hepatitis C virus

Simmonds (2001); Martell et al. (2004); Daiba et al. (2004)

Human papilloma viruses

An et al. (2003); Cho et al. (2003); Klaassen et al. (2004); Oh et al. (2004); Hoffmann et al. (2004); Park et al. (2004); Delrio-Lafreniere et al. (2004); Wallace et al. (2005); Albrecht et al. (2006); Gheit et al. (2006); Son et al. (2006); Min et al. (2006); Kim et al. (2006); Seo et al. (2006); Lin et al. (2007); Luo et al. (2007)

Polioviruses

Cherkasova et al. (2003)

Orthopoxviruses including variola, monkeypox, cowpox, vaccinia

Laassari et al. (2003); Ryabinin et al. (2006)

Measles virus

Neverov et al. (2006)

Human group A rotaviruses

Chizhikov et al. (2002); Lovmar et al. (2003)

Hantaviruses

Nordstrom et al. (2004)

Flaviviruses

Nordstrom et al. (2005)

HIV

Günthard et al. (1998); Bean and Wilson, (2000); Ghedin et al. (2004); Boriskin et al. (2004)

Drug resistant HIV-1

Cherkasova et al. (2003); Duburcq et al. (2004); Gonzalez et al. (2004)

Baculoviruses

Yamagishi et al. (2003)

novel sequence variations (see Table 48.5). The longer the target DNA, the longer the oligonucleotides must be to eliminate ambiguities. For example, a viral chip was designed after dividing each viral genome into overlapping 70-mer oligonucleotides using sequencing analysis tools BLASTn (Wang et al., 2003; Wong et al., 2004). The 70-mers were ranked by the number of viral genomes to which significant homology were found by alignments. Virus resequencing of the genomes in infected humans was performed with the Affymetrix™ SARS resequencing GeneChips (Sulaiman et al., 2006, 2007). High-density resequencing GeneChips have potential biodefense applications and may be used as an alternate tool for rapid identification of smallpox virus in the future. For sequencing of any viral DNA/RNA, a gene of unknown sequence can be rapidly screened. In this approach, oligonucleotide probes represent all possible combinations of

sequence in a given length. The number of probe “cells” can be calculated by four to the power of the oligonucleotide length, that is 65,536 8-mers, 262,144 9-mers, and so forth. The viral target sequence to be sequenced is broken into small pieces, fluorescently labeled, and hybridized with the immobilized oligonucleotides on the chip. However, the random sequencing procedure is weak for non-random sequences such as direct and inverted repeats. Viral Gene Expression There is a diverse range of experimental objectives and uses for viral gene expression microarray data, which makes the areas of experimental design and data analysis quite broad in scope (see Table 48.6). As such, there are many ways to design expression profiling experiments, as well as many ways to analyze and

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TABLE 48.5 Representative examples of viral resequencing studies with microarrays/chips Viral species and strains

Examples

SARS coronavirus

Rota et al. (2003); Ksiazek et al. (2003); Wong et al. (2004); Sulaiman et al. (2006)

Smallpox viruses (variola virus)

Sulaiman et al. (2007)

Coccolithovirus

Allen et al. (2007)

Human influenza virus

Wang et al. (2006)

TABLE 48.6 Representative examples of viral gene expression studies with microarrays/chips Viral species and strains

Examples

Pan-viral

Wang et al. (2002, 2003)

Human and mammalian retroviruses

Seifarth et al. (2003)

Choristoneura fumiferana nucleopolyhedrovirus

Yang et al. (2007)

Human immunodeficiency virus type 1, Human T cell leukemia virus types 1 and 2, Hepatitis C virus, EpsteinBarr virus, Human herpesvirus 6A and 6B, and Kaposi’s sarcomaassociated herpesvirus

Ghedin et al. (2004)

HSV-1 and HSV-2

Aguilar et al. (2006)

HPV18 (E2 mutants)

Thierry et al. (2004)

mine data functional genomics expression profiling experiments, including transcriptional analysis of normal biological processes of viral and host replication, discovery and validation of viral drug targets, and studies into the mechanism of action and toxicity of pharmaceutical compounds. Chambers et al. (1999) described the first viral DNA microarray for the temporal profiling of viral gene expression human cytomegalovirus, CMV. Basically, viral DNA microarrays/chips that apply a genomic approach from known open reading frames allow analysis of many virally encoded genes at the steady-state level of mRNA in the cell for a single (Kellam, 2001) or multiple pathogen detection (Wang et al., 2002). Single Pathogen Detection Viral RNA expression monitoring studied for a single pathogen identified patterns of immediate early, early, and late gene expression for some herpes viruses (Aguilar et al., 2004; Chambers et al., 1999; Stingley et al., 2000). Lytic expression profiles of viral herpes genes are established following

reactivation from latency (Jenner et al., 2001; Leenman et al., 2004; Paulose-Murphy et al., 2001). The predicted open reading frames of the human CMV and an established protocol for simultaneously measuring the expression of all CMV genes have been studied by Yang et al. (2006). In order to study the global pattern of VZV gene transcription, VZV microarrays using 75base oligomers to 71 VZV ORFs were designed and validated (Kennedy et al., 2005). A human herpes virus chip for six latent EBV genes (EBNA1, EBNA2, EBNA3A, EBNA3C, LMP1, LMP2), and four lytic EBV genes (BZLF1, BXLF2, BKRF2, BZLF2) allowed Bernasconi et al., 2006 to accurately measure EBV gene transcription changes triggered by treatment interventions. The EBV infection rate and the gene expression profile of EBV in tumor biopsies were determined using EBV genome chips (Li et al., 2006). By using Affymetrix™ human gene chip and NetAffx analysis through the Affymetrix™ website, the gene expression profiles of variant core proteins were implicated in HCV replication, pathogenesis, or oncogenesis in the Huh-7 cell line, which is useful for our understanding of HCV variant core protein biological function and its pathogenic mechanism (Dou et al., 2005). Multiple Pathogen Detection A viral detection DNA microarray composed of oligonucleotides corresponding to the most conserved sequences of all known viruses identified the presence of gammaretroviral sequences in cDNA samples that impair function of RNase L, particularly R462Q, from seven of 11 R462Q-homozygous cases, and in one of eight heterozygous and homozygous wild-type cases in human prostata tumors (Urisman et al., 2006). The novel virus, named XMRV, is closely related to xenotropic murine leukemia viruses (MuLVs), but its sequence is clearly distinct from all known members of this group. Attempts to identify differentially expressed transcripts in complex neuropsychiatric disorders, such as schizophrenia, have shown the necessity of screening a large sample set in order to rule out medication effects and normal individual variation when attempting to determine disease association (Conejero-Goldberg et al., 2005;Wang et al., 2002). The genome wide gene expression profile aids in the understanding of genes that may be regulated in a particular pathological condition-like cardiovascular disease and how it pertains to viral and parasitic infections of the heart (Mukherjee et al., 2006). Viral Chips for Several Diagnostic Purposes Recent successes with viral chips illustrate their role as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. Studies focusing on the analysis of viral populations from cells or tissues typically pose challenges owing to the high degree of complexity of a mixture of numerous viral species and the low abundance of many of the viruses, which necessitates highly parallelized analysis. The ability of viral chips to identify thousands of viruses from complex samples can be expected to impact broadly on biology and medicine (Wang et al., 2002). However, the use of viral chips

Microfabrication

for a diagnostic purpose is still limited because there is little agreement as to which portion of the viral genome is specific for diagnostics and allows establishment of intra- and inter-species relationships (Földes-Papp et al., 1997a; Striebel et al., 2003). One application is the “forecast” of the efficacy of drugs for therapy (Chambers et al., 1999; Murphy, 2002). Identification of mutations responsible for resistances of bacterial and viral particles (i.e., mycobacteria, HIV), including the proof of special resistance genes, plays an important role (Günthard et al., 1998; Hamels et al., 2001; Ramaswamy et al., 2003; Rogers and Barker, 2002; Schembri et al., 2002). Studying pathogen–host relations contributes to the understanding of disease pathogenesis and helps develop therapeutic strategies for new drugs (Cummings and Relman, 2000; Haselbeck et al., 2000; Kato-Maeda et al., 2001; Mahony, 2002; Peek et al., 2001). Since phenotype-based testing of resistance with virus isolation and growth on cell cultures is very time and labor consuming, genotype-based testing of resistance represents a meaningful alternative. In hepatitis C, duration and success of an interferon therapy mainly depends upon the HCV genotype (Zein, 2000), apart from other factors such as age, duration of infection and height of the HCV RNA titer before specific therapy starts. So far, there have been very few applications of microarrays/chips for quantitative estimations of viral loads. Preliminary studies are reported for quantification of hepatitis B virus (HBV) DNA in serum (Kawaguchi et al., 2003). Because of the high clinical importance of virus quantification, in particular for estimation of virus replication and therapy monitoring, there is a great potential for chip application. For example, a quarter of the chronically infected people suffer from SNPs in the precore/basal core promoter region of the HBV genome, causing the loss of HBeAg expression. These HBeAg-negative patients often remain asymptomatic and viremic until the alanine aminotransferase level in serum is persistently elevated (Lai et al., 2003). It would therefore be important to develop methods to detect the precore/basal core promoter mutations of the HBV genome at an earlier stage of infection for large-scale clinical and diagnostic analysis. The conventional and common methods for detecting mutations such as single-strand conformation polymorphisms, denaturing gradient gel electrophoresis, direct sequencing and chemical cleavage are not practical for detecting multiple SNPs in high-throughput format. The arrayed primer extension method (APEX), which is based on a hybridization of short single-stranded DNA templates with an array of immobilized oligonucleotide probes on a glass surface followed by a polymerase-assisted incorporation of different fluorescently tagged dideoxynucleotides (Tönisson et al., 2000), enables large-scale SNP detection. All methods of mutant detection so far described can only identify mutants in the serum, and cannot determine the proportion of those mutants. Li et al. (2005) reported the development of a novel technique that can quantify the relative proportion of mutants in serum utilizing gene microarray technology. Clinical applications of microarray technology predominantly refer to collections of virus variants and to differentiation of subtypes. A pan-virus DNA microarray (Virochip) was used to detect

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a human metapneumovirus (hMPV) strain associated with a critical respiratory tract infection in an elderly adult with chronic lymphocytic leukemia (Chiu et al., 2007). Using the Virochip, human parainfluenzavirus 4 (HPIV-4) infection was detected in an immunocompetent adult presenting with a life-threatening acute respiratory illness (Chiu et al., 2006). So far, viral chip technology has no practical applications in clinical diagnostics due to its high costs and insufficient standardization; it so cannot be compared to biological chip experiments. This technology thus only can be a useful supplement to other diagnostic methods (Wang et al., 2002). The main focus of clinical diagnostics with viral DNA chips will be to find answers to a handful of questions about viruses safely, quickly and inexpensively. Clinical diagnostics approaches using viral DNA chip technology simply ask the question whether particular genes, which are indicative of particular viruses, are present in a sample or not. For example, the knowledge-based, low-density “focused” microarray is based on the molecular diagnostics of six human herpesvirus types: HSV-1, HSV-2, VZV, EBV, CMV, HHV-6 (Földes-Papp et al., 2004, 2005b; Striebel et al., 2004). These studies attempted to optimize parameters of chip design, surface chemistry, oligonucleotide probe spotting, sample labeling, and DNA hybridization. The well-designed and tailored chip platform developed utilizes low-fluorescence background coverslips, epoxy surface chemistry, standardized oligonucleotide probe spotting, PCR-labeling with Cy3 of isolated viral DNA, array hybridization, and detection of specific spot fluorescence by an automatic microarray reader. Korimbocus et al. (2005) reported on the simultaneous detection of major CNS pathogens HSV-1, HSV-2, CMV; all serotypes of human enteroviruses, and five flaviviruses (West Nile virus, dengue viruses, and Langat virus) using amplification by PCR and detection of amplified products by a DNA microarray. Consensus primers for the amplification of all members of each genus and sequences that are specific for the identification of each virus species were selected from the sequence alignments of each target gene. A multiplex PCR-based DNA array for EBV, CMV, and Kaposi’s sarcoma-associated herpesvirus (KSHV) may serve as a rapid and reliable diagnostic tool for clinical applications (Fujimuro et al., 2006). The macroarray test of Fitzgibbon and Sagripanti (2006) for capturing generic members of the orthopox- or alphavirus families and a collection of additional oligonucleotides to bind specifically nucleic acids from five individual alphaviruses, including Venezuelan equine encephalitis, or DNA from each of four orthopoxviruses, including variola virus (VAR) is easy to perform, inexpensive, relatively fast, uncomplicated to interpret, and its end point is read visually without the need of additional equipment. This nucleic acid hybridization assay onto nylon membranes in macroarray format can help in detecting or excluding the presence of threat viruses in environmental samples and appears promising for a variety of biodefense applications. A microarray-in-a-tube system, for example, in an eppendorf-tube format, was first suggested by Földes-Papp et al. (2005a, b). It is a solution to complicated handling and expensive microarry equipment. Recently, a microarray-in-a-tube

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system with a 5  5 oligonucleotide “microarray” was developed by Liu et al. (2007), for detecting four respiratory tract viruses (severe acute respiratory syndrome-associated coronavirus, influenza A virus, influenza B virus, and enterovirus) in a specially designed Eppendorf cap with a flat, optically transparent window.

NANOFABRICATION Nanofabrication combines different metals, semiconductors, or carbon in spherical particles as quantum dots and in linear particles as nanowires, nanotubes, or nanorods. Nanofabrication reduces the amount of biological samples and reagents for the detection of viruses. A number of assays of metal nanoparticles for DNA detection and analysis and their basic principle, advantages, disadvantages and detection limits are very recently reviewed (Möller and Fritzsche, 2007). The detection schemes can aptly be divided into optical category such as quantum dots, stripped nanovirus and electrical/electrochemical category such as semiconducting nanowires and carbon nanotubes (Rosi and Mirkin, 2005). However, I distil the salient features of nanofabrication for viral detection. Quantum Dots Quantum dots are also known as nanocrystals or artificial atoms. They consist of clusters of a few hundred to a few thousand atoms. Their diameter is in the nanometer range. Quantum dots (QD) are made from metals such as gold, silver, cobalt, and semiconductor materials such as cadmium sulfide, cadmium selenide, or cadmium telluride (Gerion et al., 2003). QDs are characterized by broad absorption spectra from the ultraviolet to the far infrared, good photostability, and a narrow emission spectrum. These properties could improve the sensitivity of biological detection and imaging by 10- to 100-fold. Conjugated gold nanoparticles with nucleic acids were used as labels for DNA microarrays (Taton et al., 2000). With this method, target nucleic acids at concentrations of 500 femtomolar were detected (Bao et al., 2005; Liang et al., 2005; Park et al., 2002). QDs have also been applied to the detection of viral species (Perez et al., 2003). To detect specific DNA sequences without first performing an amplification step, Wang and colleagues developed an ultrasensitive nanosensor that uses QDs linked to DNA probes (Zhang et al., 2005). The nanosensor can detect less than 50 copies of DNA and has much less background fluorescence than conventional fluorescence resonance energy transfer (FRET) systems. The nanosensor includes two oligonucleotide probes that bind to separate regions of an assayed strand of DNA (target DNA). The reporter probe is labeled with a Cy5 fluorophore, and the capture probe is labeled with biotin. The probes bind the target DNA, and a streptavidin-coated QD binds to the capture probe. This process brings the Cy5 molecule close to the QD for FRET to occur. Because QDs have broad absorption and narrow emission spectra, the QD-based nanosensor system

can be finely tuned so that background fluorescence is negligible. QDs also concentrate the FRET signal by binding many target–probe complexes. To detect FRET signals, the researchers developed a novel confocal fluorescence microscopy platform. The QD–target–probe complexes were continuously flowed through a microcapillary past two detectors that were specific for signals from either FRET donors or acceptors. When the system was tested with a single-stranded target DNA, the fluorescence was observed with both detectors. However, when the target was absent or when a non-complementary DNA strand was used, only donor fluorescence was observed. Wang and colleagues also compared the performance of the QD nanosensor with that of a molecular beacon, which is typically used in FRET assays. The QD nanosensor was about 100 times more sensitive. Finally, the researchers combined the nanosensor with an oligonucleotide ligation assay. This method allowed the discrimination of point mutations in DNA samples from ovarian cancer patients. Stripped Nanowires These can be made of metals such as Au, Pt, Ni, Co, or Cu by chemical vapor deposition or more commonly by electrodeposition. The length of the stripped nanowires depends upon the electrochemical deposition time for each metal band (Nicewarner-Pena et al., 2001). Nanowires consisting of two metals and 13 strips are metallic barcodes in biological assays with up to 4160 distinguishable patterns. Stripped nanowires of three metals yield 8.0  105 different tags (Keating, 2003). On the surface of nanorods, the barcoded nanowire molecular beacon approach with a quenched fluorophore was used to detect DNA binding of human immunodeficiency virus (HIV), hepatitis B (HBV) and hepatitis C (HCV) virus sequences. Semiconducting Nanowires They are building blocks for nanoscale electronics. For example, they are set up for field-effect transistors for the real-time detection of DNA. The hybridized nucleic acid is used as substrate for the deposition of metal and construction of a nanowire. There are several nucleic acid metallization chemistries available. Hybridization between target DNA and the probe bound to the nanowire or a support followed by metallization of nucleic acid changes conductance. The hybridized and metallized nucleic acid is electrocatalytically detected by applying voltage to one of the two electrodes in each test structure and measuring the increased current of a redox indicator or the changes in conductivity or capacity (Jianrong et al., 2004). Semiconductorbased oligonucleotide microarrays were used for rapidly identifying influenza A virus hemagglutinin subtypes 1 through 15 and neuraminidase subtypes 1 through 9 (Lodes et al., 2006). Silicon Nanowires Silicon semiconducting nanowires transport electrons and holes (Cui and Lieber, 2001; Cui et al., 2001). They can be synthesized by chemical vapor deposition, electrochemical size reduction, and

Are There Additional Alternatives to Diagnostic Microarrays?

pulsed laser vaporation. Slicon nanowires with 20 nm gold clusters as catalysts, silane as reactant, and diboran as p-type dopant are label-free, electrochemical biosensors that were used for the detection of herpesvirus type 1 and 2 DNA (Patolsky et al., 2004). With an antibody functionalized nanowire, single influenza A viruses could also be detected. Carbon Nanowire The graphitic surface chemistry and electronic properties depend upon the tube diameter and chirality. Carbon nanowires can be metallic or semiconducting and yield semiconductor– semiconductor and semiconductor–metal junctions. They can be functionalized with nucleic acids, proteins and antibodies (Cai et al., 2003).

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ARE THERE ADDITIONAL ALTERNATIVES TO DIAGNOSTIC MICROARRAYS? For phenotype screening in functional genomics, live-cell chips are powerful new tools. For example, a cell-based microarray on primary and cancer cells based on the localized reverse infection by retroviruses (Carbone et al., 2007). Viral vectors are immobilized on a nanostructured titanium dioxide (ns-TiO2) film obtained by depositing a supersonic beam of titania clusters on a glass substrate. We validated the retroviral cell array by overexpression of Green Fluorescence Protein reporter genes in primary and cancer cells, and by RNA interference of p53 in primary cells by analyzing effects in cell growth.

Stochastic SPSM-FCS experiment

ln {N  P(X 1,   T  C)}  ln {N  P1}  ln C  C ln {P(X  2,   T  C)}  ln {P2}  2  ln C  ln 2  C (Criterion 3) N : Absolute number of molecules measured in the detection volume with N  1 NA:Avogadro‘s number



C : Average frequency number of molecules in the detection volume

of a single molecule

(Criterion 2) : Analytical sensitivity to detect

j2 N . P(L1 ∩ L2)  cxp  2p 4  D t

a single molecule

: Arrival and departure probability of the same single molecule

Measurable map (q, t)

* * * *

(Criterion 1) : Arrival probability

Real function x(q, t )

*

I

 : Detection probability per time unit T

0.3 f 0.2 D : Diffusion coefficient of a single I 0.1 molecule measured 0 j  v : Half axis of the detection volume measured x,y 4 cm: Molar concentration of t : Measurement time

other molecules of the same kind in the bulk

τdiff : Diffusion time

2

Single-Phase Single-Molecule Fluorescence autoand two-color cross-Correlation Spectroscopy

1 0.8 0.6 0.4 t 0 q

0.2 2 4

0

Tm : Meaningful time in which one is able to study just one single molecule in the probe region V

(Criterion 4 ) t  Tm 

τdiff cmNA V  exp {cm NA V}

:

The repertoir of single-molecule spectroscopy in solution is extended to continuous observation of an individual (“selfsame” single) molecule within the measurement time from millisecond range up to hours

Figure 48.2 Synopsis of a new physically grounded technology of fluorescence fluctuation spectroscopy for observing single molecules at longer time scales than currently available. (See also Földes-Papp, 2001c, 2002, 2005, 2006, 2007a, b, c; Földes-Papp et al., 2001, 2005a) In many respects, such methods are an alternative to DNA chip technology (Földes-Papp et al., 2005a, b). They detect the molecular Brownian movement of fluorescent particles in a very tiny volume of the laser focus. Quantitative understanding of molecular interactions at the level of single molecules within single cells is the next step in basic and applied biomedical research for the analysis of the dynamics and localization of molecules in a variety of physiological and pathophysiological processes.

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The technical solutions for high mutation and recombination rate studies are power-low fluidic chips. The state-of-the-art in microfluidics is that different systems at different scales are available; almost any macrosystem can be micro. This novel direction in chemical/biochemical analytics offers the possibility of using nanofluidic functional elements (Mulvaney et al., 2007) and electrodynamic separation in nanochannels (Pal et al., 2005; Schwartz et al., 2004; Ye et al., 2005). However, technologies in the labs are not available in a cheap-and-easy form (Zaytseva et al., 2005). A further trend is single molecule detection. It has the advantages of small volume platform, digital analysis, elimination of processing steps, approach for real-time measurements, and automation of sample preparation. This is definitely good work and is likely the trigger for further investigations on the behavior of single molecule. Current technologies can only measure biological mechanisms as an average of a population of molecules, as only their combined effect can be detected. The simplification ignores the fact that biological macromolecules oscillate between different activity and conformational states. Figure 48.2 summarizes a new physically grounded approach in single-molecule fluorescence fluctuation spectroscopy and imaging that is based upon the meaningful-time concept (Földes-Papp, 2006, 2007a, c). If we want to perform a single-molecule measurement in solution without immobilization or hydrodynamic flow, or within a live cell then we do not want to collect (integrate) data longer than we have to. The minimum time that we need for the measurement with one single molecule is: Tm 

diff c m N A V  exp{c m N A V }

CONCLUSIONS DNA microarrays/chips stand out for their simplicity, comprehensiveness, robustness, data consistency and high-throughput. They can be devided for form’s sake into viral chips and host chips (Piersanti et al., 2004; Livingston et al., 2005), but the majority of applications can inherently be categorized as genotyping, resequencing, or gene expression.Viral microarrays/chips provide a particularly powerful tool to reap these benefits. Their ability to assess the contribution of non-specific signals in a probe-specific manner allows the detection and quantitation of low abundance viral species and strains. In addition, analysis software can be used to adjust the balance between sensitivity and specificity to meet the particular requirements of an application. The microarray/chip experiment begins with well-defined goals, anticipated pitfalls, and minimized costs. It paves the way to the use of all kinds of molecular and clinical information to optimize diagnosis, treatment and health outcomes for individual patients. Today, we have entered into an era where biological and medical research can make inquiries about the entire genome of an organism. Investigators now have a unique opportunity to ask fundamental biological questions at an unexpected scale and depth. Viral microarrays/chips are one of the few technological tools available to take advantage of this explosion of genetic and sequence information. Each breakthrough that either allows a new type of measurement or improves the quality of data expands the range of potential applications of viral chips in genomic medicine.

(48.2)

where Tm is the meaningful time that one can study the selfsame molecule, for example the viral nucleic acid, diff is the measurable diffusion time of the single fluorescent viral nucleic acid molecule at the measurable number of viral molecules N  1 in the confocal probe volume V of about 0.2 fL  2  1016 L and less, cm is the molar concentration of viral nucleic acid molecules of the same kind in the bulk solution (sample), and NA is Avogadro’s number of [mol1].

ACKNOWLEDGEMENTS Zeno Földes-Papp, who is principal investigator, is supported in part by the FWF Austrian Science Fund through his research project P20454-N13. Within this research project, Z.F.-P. has received visiting professorships at ISS Inc., Champaign, IL61822, USA, and at the Department of Molecular Biology and Immunology, Health Science Center, University of North Texas in Dallas-Fort Worth, U.S.A.

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Wright, M.A. and Church, G.M. (2002). An open-source oligomicroarray standard for human and mouse. Nature Biotechnol 20, 1082–1083. Yamagishi, J., Isobe, R., Takebuchi, T. and Bando, H. (2003). DNA microarrays of baculovirus genomes: Differential expression of viral genes in two susceptible insect cell lines. Arch Virol 148, 587–597. Yang, D.H., Barari, M., Arif, B.M. and Krell, P.J. (2007). Development of an oligonucleotide-based DNA microarray for transcriptional analysis of Choristoneura fumiferana nucleopolyhedrovirus (CfMNPV) genes. J Virol Meth 143m(2), 175–185. Yang, S., Ghanny, S., Wang, W., Galante, A., Dunn, W., Liu, F., Soteropoulos, P. and Zhu, H. (2006). Using DNA microarray to study human cytomegalovirus gene expression. J. Virol. Meth 131(2), 202–208. Ye, M.Y., Yin, X.F. and Fang, Z.L. (2005). DNA separation with lowviscosity sieving matrix on microfabricated polycarbonate microfluidic chips. Anal Bioanl Chem 381(4), 820–827. Zhang, C.-Y.,Yeh, H.-C., Kuroki, M.T. and Wang, T.-H. (2005). Singlequantum-dot-based DNA nanosensor. Nat Mater 4(11), 826–831. Zaytseva, N.V., Montagna, R.A. and Baeumner, A.J. (2005). Microfluidic biosensor for the serotype-specific detection of dengue virus RNA. Anal Chem 77(23), 7520–7527. Zein, N.N. (2000). Clinical significance of hepatitis C virus genotypes. Clin Microbiol Rev 13(2), 223–235. Zhao, X., Nampalli, S. and Serino A.J., Kumar,S. (2001). Immobilization of oligonucleotides with multiple anchors to microchips. Nucl Acids Res 29(4), 955–959. Zhao, W., Wan, J.M., Liu, W., Liu, Q.J., Zhang, L., Zhou, Z.X., Liu, X.J. and Zhang, H.R. (2003). Hepatitis gene chip in detecting HBV DNA, HCV RNA in serum and liver tissue samples of hepatitis patients. Hepatobiliary Pancreas Dis Int 2, 234–241.

RECOMMENDED RESOURCES Chiu, C.Y., Alizadeh, A.A., Rouskin, S., Merker, J.D., Yeh, E., Yagi, S., Schnurr, D., Patterson, B.K., Ganem, D. and DeRisi, J.L. (2007). Diagnosis of a critical respiratory illness caused by human metapneumovirus by use of a pan-virus microarray. J Clin Microbiol 45(7), 2340–2343. Földes-Papp, Z. (2007a). “True” single-molecule molecule observations by fluorescence correlation spectroscopy and two-color fluorescence cross-correlation spectroscopy. Exp Mol Pathol 82(2), 147–155. Földes-Papp, Z. (ed.) (2007b). Half special issue exploring the biomedical applications of microscopy and spectroscopy. Exp Mol Pathol 82(2), 103–189. Földes-Papp, Z., Enderlein, J., Widengren, J. and Kinjo, M. (2003). Special Edition. The way down from single genes and proteins to single molecules: Nucleic acid analyses in many-molecule systems (first part of the first two-part special issue). Curr Pharm Biotechnol 4(6), 351–484. Földes-Papp, Z., Enderlein, J., Widengren, J. and Kinjo, M. (2004a). Special Edition. The way down from single genes and proteins to

single molecules: Nucleic acid and protein analyses in many-molecule systems (second part of the first two-part special issue). Curr Pharm Biotechnol 5(1), 1–126. Földes-Papp, Z., Enderlein, J., Widengren, J. and Kinjo, M. (2004). Special Edition. The way down from single genes and proteins to single molecules: Fluorescence correlation spectroscopy (auto- and two-color cross-correlation mode) in single-molecule systems (first part of the second two-part special issue). Curr Pharm Biotechnol 5(2), 135–241. Földes-Papp, Z., Enderlein, J., Widengren, J. and Kinjo, M. (2004c). Special Edition. The way down from single genes and proteins to single molecules: Applying single-molecule analyses (second part of the second two-part special issue). Curr Pharm Biotechnol 5(3), 243–319. Földes-Papp, Z., Egerer, R., Birch-Hirschfeld, E., Striebel, H.-M. and Wutzler, P. (2005b). Human herpes virus detection by DNA microarray technology. Distinguished article entry. In Encyclopedia of Medical Genomics & Proteomics (J. Fuchs and M. Podda, eds), Marcel Dekker, New York.

Recommended Resources

Livingston, A.D., Campbell, C.J., Wagner, E.K. and Ghazal, P. (2005). Biochip sensors for the rapid and sensitive detection of viral disease. Genome Biol 6(6), 112. Rich, R.R., Fleisher, T.A., Shearer, W.T., Schroeder, H.W., Frew, A.J. and Weyand, C.M. (eds) (2008). Clinical Immunology: Principles and Practice, 3rd edition. Mosby Ltd, New York. Schena, M., Shalon, D., Davis, R.W. and Brown, P.O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235), 467–470. Seliger, H. (2007). Introduction: Array technology – an overview. In Microarrays. Synthesis Methods Volume 1 (J.B. Rampal, ed.), 2nd edition.Academic Press. Urisman, A., Fischer, K.F., Chiu, C.Y., Kistler, A.L., Beck, S., Wang, D. and DeRisi, J.L. (2005). E-Predict: A computational strategy for species identification based on observed DNA microarray hybridization patterns. Genome Biol 6(9), R78. Wang, D., Coscoy, L., Zylberberg, M., Avila, P.C., Boushey, H.A., Ganem, D. and DeRisi, J.L. (2002). Microarray-based detection and genotyping of viral pathogens. Proc Natl Acad Sci U S A 99(24), 15687–15692.

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Websites National Center for Biotechnology Information and resource for molecular biology information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information: http://www.ncbi.nlm.nih.gov/5 Software to accelerate research in molecular biology: http://www.premierbiosoft.com DeRisi Lab: E-Predict Download. derisilab.ucsf.edu/epredict/ Stanford microarray database: genome-www5.stanford.edu/ ArrayExpress of EMBL-EBI: http://www.ebi.ac.uk/arrayexpress/ Microscale products and services for Biochemistry and Molecular Biology at the Open Directory Project: http://dmoz.org/dmoz. org/Science/Biology/Biochemistry_and_Molecular_Biology/ Products_and_Services/Micro_Scale/

CHAPTER

49 Vaccines Against Infectious Diseases: A Biotechnology-Driven Evolution Vega Masignani, Hervé Tettelin and Rino Rappuoli

INTRODUCTION Since their introduction into medical practice at the end of the 18th century, the development of vaccines has been strictly dependent on the state of knowledge and technology available at that time. The first available vaccines (smallpox, rabies and anthrax) were in fact the result of an intuitive approach and followed the basic paradigm established by Louis Pasteur in 1881, which included the isolation, inactivation and injection of the causative microorganism. These basic principles have guided the development of live-attenuated vaccines, which have been successful in many cases and allowed for the control or the complete eradication of important viral and bacterial diseases. Live-attenuated vaccines have been and continue to be a milestone for the control of measles, mumps, rubella, varicella and rotavirus, and for the global eradication of polio virus. In some instances, after the eradication of the disease, they have been replaced by killed vaccines to avoid the fact that live-attenuated microorganisms could revert to the virulent form and therefore cause important side effects. When the new technologies of growing viruses in embryonated eggs and in cell cultures became available, development of killed viral vaccines, such those against influenza, polio and thick-borne encephalitis, became possible. The knowledge of the pathogenesis of many microorganisms, the identification of the main virulence factors and characterization of the immune response induced following infection Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 562

have been crucial for the design of second-generation vaccines mainly based on highly purified antigenic components. Classic examples are the diphtheria and tetanus vaccines, where the antigen is represented by the major toxin, chemically detoxified to yield the nontoxic toxoid (Rappuoli, 1990). Other microbial components routinely used as immunogens are the capsular polysaccharides, which have the advantages of being easy to purify in large quantities and safe when injected in animals and humans. Polysaccharide vaccines have been developed against meningococcus A, C, Y and W135 (Jodar et al., 2002), 23 types of pneumococcus (Wuorimaa and Kayhty, 2002), H. influenzae type b (Hib) (Ward and Zangwill, 1999), and S. typhi (Plotkin and Bouveret-Le Cam, 1995); however, while effective in adults, the use of these vaccines is unsatisfactory in infants, where they are unable to mount an immune response. The solution was found by covalently linking the sugar to a carrier protein (conjugation), a technology that represents one of the biggest achievements of rationally applied vaccinology. The first conjugate vaccine was the one against Hib, which has been introduced as a routine immunization in the United States in December 1987 and afterwards in several countries worldwide. After a few years of use, this vaccine eradicated the diseases and the bacterium from all countries where it has been introduced (Peltola, 2000). The success of Hib conjugate vaccine prompted several groups to consider the development of conjugated vaccines against other capsulated bacteria. The first conjugated vaccines Copyright © 2009, Elsevier Inc. All rights reserved.

The Genomic Era: From Microbial Genome to Vaccine Development

against Meningococcus C were licensed in the United Kingdom in 1999–2000 and used as national immunization campaign. In a few months, cases of MenC meningitis almost disappeared in vaccinated population and was reduced even in unvaccinated people, with evidence of herd immunity (Balmer et al., 2002). Tetravalent, conjugated vaccines against serogroup A, C, Y and W135 have been licensed for adolescents in the United States and are in the late phase of development for infants. With the advent of the new technologies of genetic engineering and recombinant DNA, the following step was to produce subunit vaccines based on specific antigens. This new approach has generated two very efficacious recombinant vaccines: the hepatitis B vaccine based on a highly purified envelope protein (Valenzuela et al., 1979), and the acellular vaccine against Bordetella pertussis containing three highly pure proteins. The latter vaccine can be considered as the first example of a new generation of products obtained by a computerdriven strategy for vaccine design. In this case structure-function studies were used to guide site-directed mutagenesis of specific residues within the catalytic subunit of the pertussis toxin molecule (PT) so as to make this protein a valid immunogen devoid of any residual toxic activity (Pizza et al., 1989). The subunit-based conventional approach is extremely time-consuming and not always successful, especially for bacteria that cannot be cultivated in vitro and do not express obvious immunodominant antigens. These obstacles were eventually overcome with the introduction – one decade ago – of genomic technology. Since 1995, when the first complete sequence of a living organism was made available (Fleischmann et al., 1995), 696 bacterial, 53 archaeal and 94 eukaryotic genomes have now been published, and close to 3000 are still in progress (www.genomesonline. org, August 2008). Based on the availability of this extraordinary amount of novel information, new disciplines of molecular biology have emerged, which have revolutionized the field of bacterial pathogenesis and vaccine design. One of the most interesting applications of genome analysis is the Reverse Vaccinology approach, where potential antigens are selected in silico, regardless of their abundance or expression conditions in vivo. In the post-genomic era, pathogen genome sequencing efforts have expanded in order to include multirepresentatives of the same species and this pan-genome approach has shown tremendous potential for making vaccines that once might have been impossible to design. Complementary to computer-based antigen discovery, the growing field of Functional Genomics includes a number of novel technologies, such as DNA microarrays, proteomics, in vivo expression technology (IVET) and signature-tagged mutagenesis (STM), which represent new exciting opportunities for vaccine research. Large-scale sequencing and genomics technologies not only have influenced the study of infectious diseases, but are also affecting the investigation of complex diseases, by opening the way to the new field of Genomic Medicine, one of the fastest growing areas of biomedical research today. The achievement of the complete sequence of the human genome has in fact improved

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our knowledge of the molecular pathophysiology that underlies common diseases, acute and chronic infectious diseases, cancer, cardiovascular diseases, and can now represent the basis for the development of personalized medical treatment. Furthermore, the possibility of measuring genotypes at thousands of different loci will result in the identification of fingerprints, which will help to predict genetic predisposition to illness, and to develop novel methods to detect disease and monitor response to therapies. In this chapter we will discuss the impact of genomics and related genome sciences on vaccine design and clinical medicine.

THE GENOMIC ERA: FROM MICROBIAL GENOME TO VACCINE DEVELOPMENT Genome Sequencing The whole genome shotgun sequencing strategy, first applied a decade ago to decode the genome sequence of Haemophilus influenzae (Fleischmann et al., 1995), is now the routine approach to genome sequencing. It involves mechanically or enzymatically shearing the genomic DNA into fragments of defined size range (e.g., 2–4 kb), building libraries of cloned fragments and sequencing a large number of clones at random. Sequencing is conducted on both strands in separate reactions with primers annealing to the cloning vector on either flank of the targeted DNA insert. Sequencing reactions are generated by the Sanger method that uses di-deoxy-nucleotides to terminate and label synthesized DNA chains at every nucleotide. The random shotgun strategy has proven to be robust and has been successful when applied to genomes with differing characteristics, such as variations in genome size, heterogeneous base compositions, presence of various repeat elements, and multiple chromosomal molecules and plasmids. Nowadays, new sequencing technologies that do not rely on the Sanger method are emerging and are amenable to highthroughput sequencing (Tettelin, 2004). A very promising and increasingly popular novel technology is based on the automation of the pyrosequencing technique. This technology, commonly referred to as “454” technology (www.454.com), was applied to the re-sequencing of a number of species including Mycoplasma genitalium and Streptococcus pneumoniae (Margulies et al., 2005), and holds great promise for sequencing of many additional genomes of species of interest cheaply and rapidly when a complete reference genome is already available. Indeed, a single 5-h run of sequencing on a 454 machine yields the equivalent of ca. 20 Mb of sequence data. In addition, the absence of cloning in 454 will alleviate the bias observed in shotgun clone libraries that is due to the inability to propagate certain DNA fragments in Escherichia coli (e.g., toxicity of foreign promoters or instability of inserts). It is predictable that within the next few years this and other new technologies will revolutionize the ability to sequence hundreds of genomes of species of interest.

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Although very promising, the 454 technology is not yet able to replace in toto the sequencing of whole genomes, mainly because the short sequence reads (80–90 nucleotides) make virtually impossible to assemble de novo new genomes or to solve very repetitive regions. However, hybrid approaches of Sanger and 454 technologies can increase a lot the speed of sequencing (Goldberg et al., 2006).

IMPACT OF WHOLE GENOME ANALYSES The study of whole genome sequences from numerous microbial species has revealed a tremendous amount of information on the physiology and evolution of microbial species and provided novel approaches to the diagnosis and treatment of infectious diseases (Binnewies et al., 2006). One of the major observations is that almost half of the ORFs in each species are of unknown function (Fraser et al., 2000). Elucidating the function of these new genes through functional assays is likely to lead to new biochemical pathways, new virulence determinants, etc. From the pool of genes whose biological function could be determined by in silico analyses, it is possible to reconstruct all the major metabolic pathways that a given organism relies on to live. The correlation of these pathways to the ability of the organism to transport substances across its membrane(s) through detailed analysis of its various transporters provides for hypotheses on modes of survival and niche adaptation. Microbial evolutionary biology has also greatly benefited from whole genome sequences. Analyses suggested for instance that horizontal gene transfer is more frequent than previously expected and revealed how genomes evolve on short timescales (Eisen, 2000). Potential virulence factors and pathogenicity islands can be identified through analysis of protein similarities and secondary structure (amino acid motifs), and nucleotide composition and organization of chromosomal regions. Finally, genome-scale prediction of proteins likely to be exposed on the surface of an organism leads to the identification of novel potential vaccine candidates as described below. Reverse Vaccinology: The MenB Paradigm Reverse vaccinology derives its name from the application of an approach that reverses the steps of classical vaccine candidate discovery (Adu-Bobie et al., 2003; Kelly and Rappuoli, 2005; Masignani et al., 2002; Rappuoli, 2000). Indeed, all potential vaccine candidates are first identified in silico based on the complete genome sequence of the pathogen of interest. The candidates are then expressed and characterized experimentally, including serological tests against the pathogen itself as a final step prior to clinical trials. The first application of reverse vaccinology was aimed at identifying novel vaccine candidates against serogroup B Neisseria meningitidis (Pizza, 2000;Tettelin et al., 2000). N. meningitidis is a Gram-negative bacterium that causes life-threatening invasive infections, meningitis and septicemia, especially in

young infants. While vaccines were available against four of the five pathogenic serogroups of N. meningitidis, none existed against serogroup B. The serogroup B capsular polysaccharides could not be used for vaccine development because their structure is identical to a carbohydrate widely distributed on the surface of human cells, and could therefore induce autoimmunity. To overcome these obstacles, whole genome sequencing of a virulent serogroup B strain was enterprised using the shotgun strategy (Tettelin et al., 2000). The initial step of reverse vaccinology is to identify potential vaccine candidates based on genome sequence data. Bioinformatics enable systematic identification of proteins that are likely to be exposed at the surface of the bacteria. Surface-exposed proteins are predicted based on the combination of several pieces of evidence including proteins known to carry out functions at the surface of the cell; exclusion of proteins known to be cytoplasmic; exclusion of proteins likely to be embedded in the cell’s membrane and inaccessible to antibodies; and amino acid motifs characteristic of targeting to the membrane (signal peptides), anchoring in the lipid bilayer (lipoproteins), anchoring in the outer-membrane of Gramnegative bacteria or the cell wall of Gram-positive bacteria (like streptococci), and interaction with host proteins or structures (e.g., integrin binding domain). Within 18 months after the beginning of sequencing, 600 potential candidates were identified on the basis of these criteria. The selected proteins were expressed in E. coli, purified and used for immunization of mice. Antisera raised against the injected proteins were recovered and assayed for specificity by western blot. Accessibility of the candidate on the surface of pathogen was also tested by flow cytometry or immunoprecipitation using the antisera. Finally, the antisera could be combined in vitro with human complement to assay bacterial killing that correlates with protection in humans (Figure 49.1). Each experimental step of the process reduced the number of potential vaccine candidates to a defined set of proteins that satisfied all the criteria and warranted high probability of success for the development of a vaccine. For N. meningitidis serogroup B, 350 of the 600 candidates surface proteins were successfully expressed and purified as recombinant proteins. Of these, 91 were proved to be surfaceexposed, and 29 were also able to induce complement-mediated bactericidal antibody response in a bactericidal assay (BCA). As one of the major problems with meningococcal antigens is sequence variability among different strains, several good candidates were selected and sequenced across a panel of diverse strains of N. meningitidis representing all serotypes and spanning the phylogeny of the species (Pizza, 2000). The conserved antigens were shown to be positive in BCA using a panel of different MenB strains, demonstrating that they could be able to confer general protection against serogroup B strains of N. meningitidis. These promising vaccine candidates are currently being characterized in clinical trials (Giuliani et al., 2006). The encouraging results obtained for Meningococcus prompted several groups to apply the reverse vaccinology approach to their preferred pathogen, leading to the identification

Impact of Whole Genome Analyses

Outer membrane Periplasmic space Inner membrane

Genome sequencing and analysis

Antigen prediction

Purification

Expression

Immunization

Sera analysis

Vaccine candidates

Figure 49.1

In vivo testing

The reverse vaccinology approach.

of a number of new proteins suitable for vaccine purposes (Ariel et al., 2002; Montigiani et al., 2002; Ross et al., 2001;Wizemann et al., 2001) (Table 49.1). The Pan-Genome Evolution: The Case of Group B Streptococcus The complete genome sequence of a single representative strain of an organism is useful in revealing its core machinery and comparing it to other sequenced species. However, it does not provide information about the diversity encountered across multiple strains of the species and also limits genome-wide screens for vaccine candidates or for antimicrobial targets to a single strain. The advantage of multiple genome analysis in vaccine design is demonstrated by the discovery of universal vaccine candidates against Streptococcus agalactiae. Streptococcus agalactiae or group B Streptococcus (GBS) is the leading cause of bacterial sepsis, pneumonia and meningitis in neonates in the United States and in Europe and an emerging cause of infection in the elderly (Harrison et al., 1998; Tyrrell et al., 2000). Nine distinct capsular serotypes of GBS have been

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described; however, the major disease-causing isolates in Europe and United States belong to only five serotypes: Ia, Ib, II, III and V. The reverse vaccinology approach was applied to serotype V strain 2603V/R of S. agalactiae, however microarray-based comparative genomic hybridizations revealed that S. agalactiae is an extremely diverse species (Tettelin et al., 2000), with most of the diversity restricted to several large genomic islands. This degree of diversity indicated that multiple genomes of this species should be used to enable the identification of broadly protective vaccine candidates. As a consequence, the complete genome sequences of six additional strains of S. agalactiae representing the major disease-causing serotypes of S. agalactiae were generated (Tettelin et al., 2000). Comparative analysis of the newly sequenced genomes, together with two genomes already available in the databases, revealed that a bacterial species can be described by its “pangenome,” which includes a “core genome” containing genes present in all strains, and a “dispensable genome” composed of genes absent from one or more strain and genes that are unique to each strain. Whereas the core genome encodes a wide variety of cellular functions mostly dedicated to housekeeping, genes belonging to the dispensable genome includes functions that enable a subset of strains to adapt to specific conditions, colonize particular niches or resist to certain antibiotics. Maione et al. (2005) have applied the pan-genome concept to GBS vaccine discovery. Bioinformatic algorithms were used to select genes from the two subgenomes that encode putative surface-associated and secreted proteins. Among the identified putative surface-exposed proteins, 396 were core genes and 193 were dispensable genes. Selected potential antigens were purified proteins and tested for protection using an active maternal immunization/neonatal pup challenge model. Four antigens were capable of significantly increasing the survival rate among challenged infant mice. Unexpectedly, only one of these antigens was part of the core genome, while the remaining three protein encoding genes were present in approximately 75% of strains. When the four antigens were given in combination, nearly universal protection was observed, with levels of protection similar to that seen when using capsular carbohydrate-based vaccines (Figure 49.2). Analysis of the S. agalactiae pan-genome reveals that new genes are added to the pan-genome every time a new genome is sequenced. This led to the concept of an open pan-genome indicating that the S. agalactiae species has access to a very large and possibly unlimited number of genes. Similar results were obtained by analyzing the available genome sequences of Streptococcus pyogenes or group A Streptococcus and other bacterial pathogens (Medini et al., 2005). Functional Genomics As a result of the availability of complete genome sequences of several organism, new disciplines of molecular biology have emerged, which offer the unique possibility to discover the biological function of particular genes and to uncover how sets of genes and their products work together in health and disease. Techniques such as IVET, STM, DNA microarrays, and

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T A B L E 4 9 . 1 Examples of bacterial pathogens that have been explored for the identification of vaccine candidates using genome-based approaches Pathogen

Genomic-based approach

References

Neisseria meningitidis B

Reverse vaccinology STM Microarray

Pizza et al. (2000); Grifantini et al. (2002); Sun et al. (2000)

Streptococcus pneumoniae

Reverse vaccinology STM Microarray

Wizemann et al. (2001); Polissi et al. (1998); Hava and Camilli (2002); Orihuela et al. (2004)

Staphylococcus aureus

Genomic peptide libraries IVET STM Serological proteome analysis

Etz et al. (2002); Benton et al. (2004); Mei et al. (1997); Vytvytska et al. (2002)

Porphyromonas gingivalis

Reverse vaccinology

Ross et al. (2001)

Bacillus anthracis

Reverse vaccinology Proteomics

Ariel et al. (2002); Ariel et al. (2003)

Chlamydia pneumoniae

Reverse vaccinology

Montigiani et al. (2002)

Streptococcus agalactiae (Group B streptococcus)

Reverse vaccinology/Pan genome analysis STM Proteomics

Tettelin et al. (2002, 2005); Maione et al. (2005); Jones et al. (2000); Hughes et al. (2002)

Streptococcus pyogenes (Group A streptococcus)

Pan genome analysis Proteomics

Medini et al. (2005); Rodriguez-Ortega et al. (2006)

Vibrio cholerae

IVET and RIVET STM Microarray

Camilli and Mekalanos, (1995); Slauch and Camilli, (2000); Chiang and Mekalanos, (1998); Zhu et al. (2002)

Shigella flexneri

Proteomics

Ying et al. (2005)

Helicobacter pylori

Serological proteome analysis

Baik et al. (2004)

Mycobacterium tuberculosis

STM Microarray

Schnappinger et al. (2003); Camacho et al. (1999)

Yersinia enterocolitica

STM

Darwin (2005)

Pseudomonas aeruginosa

IVET Microarray

Firoved et al. (2003); Wang et al. (1996)

Salmonella typhimurium

IVET STM

Heithoff et al. (1999); Hensel et al. (1995)

Klebsiella pneumoniae

STM

Lawlor et al. (2005)

Plasmodium falciparum

Microarray

Daily et al. (2005)

proteomics have the potential to accelerate the process of identifying important virulence factors as well as novel protective antigens to be exploited as subunit vaccine targets (Figure 49.3).

IN VIVO GENE EXPRESSION: IVET AND STM In contrast with the in silico approach, where antigens are identified through the analysis of the genome, technologies such as IVET and STM are used to determine which are the genes whose expression is specifically induced in vivo and therefore are more likely to be implicated in the infection mechanism. IVET selection is performed by creating a plasmid library in which random fragments of a bacterial chromosome are fused to a promoterless gene that encodes an auxotrophic marker (i.e., the purA gene). The pathogen is then transformed with the library so that mutants are obtained through a single cross-over event between the plasmid and the corresponding region in the

chromosome. When injected in the animal, only those mutants carrying the auxotrophic gene downstream of an in vivo-induced promoter will survive (Mahan et al., 1993). This technology has been successfully applied to the identification of virulence genes in a number of pathogens (Table 49.1), among which Salmonella typhimurium (Heithoff et al., 1999), Pseudomonas aeruginosa (Wang et al., 1996), Vibrio cholerae (Camilli and Mekalanos, 1995) and Staphylococcus aureus (Benton et al., 2004). In the case of S. typhimurium, a specific gene was found to control the expression of several pathogenicity-related factors, including the DNA-adenine methylase gene (dam). Mutants deficient in the dam gene were unable to replicate in the spleen and liver and were shown to be effective as live vaccines against murine typhoid fever (Heithoff et al., 1999). More recently, a new version of this method has been proposed, called RIVET (Recombination-based In Vivo Expression Technology), which allows the detection of genes that are transiently turned on during adaptation to a new environment (Slauch and Camilli, 2000).

Microarray Expression Technology

Multi-strain genome sequencing and analysis

Pan-genome definition and selection of core and dispensable genes

Expression/purification of selected candidates

In vitro assays

In vivo assays

Antigen combination

Figure 49.2 The pan-genome approach applied to the development of vaccine against GBS.

In silico analysis

IVET

DNA

Proteomics Protein mixture

Bacteria Host cells

Random mutants

In vivo testing

2D gel electrophoresis

Spot digestion and MALDI-TOF analysis

STM

Recovery of bacteria Data analysis

567

Another approach in vaccine design, which is also facilitated by genome sequencing, is STM. This procedure was initially developed by David Holden (Hensel et al., 1995) and makes use of extensive collections of mutants generated by insertional mutagenesis in order to identify genes required for in vivo survival. DNA tags are incorporated into the mutagenesis vector to label each mutant with a unique signature tag at the site of insertion. Pools of mutants are then screened through an animal model or cell culture to identify clones in which a mutation impaired the multiplication. Mutants that fail to be recovered after the screen are likely to be attenuated and therefore altered within virulence genes. Proteins identified as being essential for infection are likely to be good vaccine candidates. STM has been applied to a variety of pathogens (Table 49.1) including Mycobacterium tuberculosis (Camacho et al., 1999), Staphylococcus aureus (Mei et al., 1997), S. typhimurium (Hensel et al., 1995), Vibrio cholerae (Chiang and Mekalanos, 1998), Yersinia enterocolitica (Darwin, 2005), Streptococcus pneumoniae (Polissi et al., 1998; Hava and Camilli, 2002) Streptococcus agalactiae (Jones et al., 2000), Klebsiella pneumoniae (Lawlor et al., 2005) and Neisseria meningitidis (Sun et al., 2000). By combining this approach with the complete sequence of two available genomes, Sun and coworkers identified 73 genes of N. meningitidis that were essential for bacteremia in an infant rat model. Sixteen of these are surface-exposed proteins, currently under investigation as potential vaccine candidates. Finally, another method for the identification of in vivo expressed antigens suitable for the development of vaccines makes use of genomic peptide libraries. An interesting example is represented by the work performed on Staphylococcus aureus where peptides were displayed on the surface of E. coli via fusion to one or two outer membrane proteins (LamB and FhuA) and probed with sera selected for high-antibodies titers and opsonic activity. The exhaustive screening of these libraries by magnetic cell sorting determined the profile of antigens, which are expressed in vivo and elicited an immune response in humans. A total of 60 antigenic proteins were identified (Etz et al., 2002).

MICROARRAY EXPRESSION TECHNOLOGY

IVET library Digested DNA clone into vector In vivo induced genes Selection of mutants Pool of fusions transferred and recovery into host of bacteria

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Database search

Comparision

Input Output membrane membrane Attenuated mutants

Figure 49.3 Identification of vaccine candidates using different functional genomics approaches.

DNA microarray technology is based on the use of chips carrying an entire genome, which can be exploited for several applications such as expression profiling, genotyping and DNA sequencing. A particularly useful application of this technology consists in the analysis of the complete set of transcripts of an organism (transcriptome) in response to variable environmental conditions. By analyzing global variations in gene expression occurring during infection, this technology allows the study of infectious diseases and provides a strong contribution to the understanding of how a pathogen modulates its response to the host environment. The transcriptional changes of N. meningitidis were investigated from meningococci incubated in human serum as well as adherent to human epithelial and endothelial cells. The authors discovered a wide range of surface proteins that are induced

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under in vivo conditions and that could represent novel candidates for a protein-based vaccine for meningococcal diseases (Grifantini et al., 2002). Similarly, the expression profile of S. pneumoniae virulence genes was investigated using pneumococci isolated from infected blood and cerebrospinal fluid (Orihuela et al., 2004). Another interesting example of how microarrays can be exploited to understand host–pathogen interactions is represented by the study conducted by Daily and coworkers on the comparative analysis of the transcriptomes derived from in vivo and in vitro samples of P. falciparum. These studies may identify important strategies used by the pathogen for survival in the human host and highlight new candidate antigens for vaccine development that were not previously identified through the use of in vitro cultures (Daily et al., 2005). These and other applications of the microarray technology are reported in Table 49.1.

PROTEOMICS Improvements in protein separation technologies, together with mass spectrometry and genome sequencing have allowed the discovery of the entire set of proteins components (proteome) of a cellular population. The analysis of the proteome, and in particular, the selection of the subgroup of membrane-associated proteins, becomes a valuable and useful tool for antigen discovery (Grandi, 2001). In the proteomic analysis, the protein mixture is first separated into its individual components by separation methods such as 2D gel electrophoresis. Each protein is then digested with specific protease to generate peptide fragments, whose molecular mass can be accurately determined by-mass spectrometry. The comparison between the digestion pattern experimentally obtained and that predicted in silico allows the rapid identification of the related protein. Proteomics, assisted by genomic mining, has been used to identify novel bacterial vaccine candidates for several human pathogens (Table 49.1), including Streptococcus pyogenes (Rodriguez-Ortega et al., 2006), Streptococcus agalactiae (Hughes et al., 2002), Shigella flexneri (Ying et al., 2005) and Bacillus anthracis (Ariel et al., 2003). Furthermore, the combination of proteomics with serological analysis has recently led to the development of a new valuable approach defined as SERPA (SERological Proteome Analysis) for the identification of in vivo immunogens suitable as vaccine candidates (Klade, 2002). Among others, this strategy has been successfully applied to Helicobacter pylori (Baik et al., 2004) and Staphylococcus aureus (Vytvytska et al., 2002).

FROM MICROBIAL TO HUMAN GENOME SEQUENCING: GENOMIC MEDICINE While sequencing a bacterial pathogen was becoming a routine, several groups in the world started to point towards an incredibly

ambitious goal: deciphering the human genome. In 1990, the Human Genome Project (HGP) was officially initiated in the United States under the direction of the National Institutes of Health and the US Department of Energy with a 15-year plan for completing the genome sequence. The draft sequence of the human genome was first published in 2001 (Venter et al., 2001). Three years later, the analysis of the finished sequence appeared in Nature, marking the beginning of a new phase of research (International Human Genome Sequencing Consortium, 2004). Since then, the major goal of scientists involved in this area was trying to apply what has been learned to human health. As Thomas Lewis pointed out in 1944, diagnosis of most human diseases provides only “insecure and temporary conceptions,” and in fact only infectious diseases have a truly mechanismbased definition. The fact that clinicians still rely on phenotypic criteria to define most diseases, may obscure the underlying genetic mechanisms and often mask significant heterogeneity. With the advent of genomics-related disciplines, it is common hope that these old conceptions may eventually be overcome. In this context, Genomic Medicine is defined as the application of the knowledge originating from the HGP in health sciences. That means finding the genes that contribute to development of common conditions such as heart failure, chronic illness, Parkinson’s and Alzheimer’s diseases, diabetes, and cancer. Although most of the above-mentioned conditions are originated by the interplay of multiple genes – together with the environment – yet determining which genes are involved is important because it can help to predict an individual’s risk of developing a particular disease, as well as to guide development and application of specific therapies, and recommendation of particular vaccines. Linkage of genomic information with clinical data will result in predictive biosignatures in order to classify disease on a molecular basis, identify individuals predisposed to illness, and improve diagnostics by developing noninvasive methods to detect disease. Over the past few years, single nucleotide polymorphisms (SNPs) information has been proposed as the new gold standard technique for the identification of loci associated with complex diseases and for pharmacogenomic applications (Lander and Schork, 1994). For example, it is known that single base differences in the APOE gene are associated with Alzheimer’s disease (Martin et al., 2000) and that longer repeat polymorphisms in the IGF1 and/or CYP19 genes are significantly associated with low survival rates among patients with metastatic prostate cancer (Tsuchiya et al., 2006). However, only in a few cases are these predictive biomarkers specifically associated with a particular disease state, therefore it will be important to use the human SNP map to dissect the contributions of individual genes to diseases that have a complex, multigene basis. Another application of SNP variations in genome sequences is the understanding of the way our bodies respond to medical treatment. In this context, the discipline termed pharmacogenomics deals with the genetic basis underlying variable drug response in individual patients. Individual variability

Metagenomics: Deciphering Host–Microbe Interactions

in drug response is in fact influenced by variation in genes that control the absorption, metabolism and excretion of drugs. For example, gene variants have been identified in the cytochrome P450 system that affect drug metabolism, and P450-based genotyping microarrays have been developed to assist clinicians with medication selection and dosing (Roses, 2004). Beside complex diseases, a growing body of evidence also indicates that many severe infections are often associated with inherited host genetic factors. For example, gene mutations leading to C6 complement deficiency have been linked with higher susceptibility to invasive meningococcal meningitis, chemokine receptor polymorphism with susceptibility to HIV, and mutations within the interferon-gamma receptor 1 gene with susceptibility to fatal mycobacterial infections (Kwiatkowski, 2000). As the analysis of the human genome progresses, the number of reported associations between gene polymorphisms and acute and chronic disease susceptibility will grow, thus improving our knowledge on the mechanisms of pathogenesis and helping to design more effective approaches to the treatment and prevention of many important diseases. The field of vaccination could also benefit from the knowledge originated from genomic medicine. Several new technologies are in fact now available to diagnose genetic defects that could lead to the development of a tumor, and predictive oncology is very successful in appraising the risk of a particular tumor on the basis of family history, sex, age and genetic background. Therefore, prophylactic vaccination can be proposed as a new form of cancer prevention for all those patients for which an increased risk of cancer development can be assessed. Indeed, this strategy has already proved effective in preventing the onset of different tumor types in various preclinical models of carcinogenesis (Forni et al., 2000). Similarly, genetic host factors play an important role in controlling disease susceptibility to many intracellular pathogens. For instance, it has recently been shown that patients with particular mutations in genes encoding major type-1 cytokine proteins developed severe infections from otherwise poorly pathogenic species of Mycobacterium and Salmonella. The knowledge of these genes and of the pathways in which they are involved could eventually provide new insight for the design of improved strategies to achieve host resistance by vaccination (Ottenhoff et al., 2005) and to select a subpopulation of patients for which vaccination would be particularly useful.

METAGENOMICS: DECIPHERING HOST–MICROBE INTERACTIONS The human body represents the natural habitat for several million of different microbes (microbiota), and it is easy to predict that this mutualistic relations shall influence both sides heavily. For example, some microbes residing in the gastrointestinal tract synthesize vitamins and participate in the processing of substances otherwise indigestible by our organism, and recent studies have shown that this partnership can influence maturation of

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the immune system (Mazmanian et al., 2006) and modulate the response to epithelial cell injury. With the possibility of sequencing many organisms simultaneously, metagenomic analysis of complex communities can offer the unprecedented opportunity to study how our microbial ecosystem varies in response to changing environments and how it contributes to human health and disease. Several studies have used large-scale random shotgun sequencing strategies using samples from complex microbial communities to investigate host–microbe interactions, either in healthy subjects and in chronically infected patients. For instance, bacterial diversity within the human gastric mucosa of patients colonized by Helicobacter pylori showed that the microbial flora of this compartment is significantly different from that found associated with the mouth and esophagous, thus indicating that a distinct bacterial community has adapted to this acidic compartment, and suggesting a possible role in human health and disease (Bik et al., 2006). A similar metagenomic analysis applied to the human intestine has found that the microbiota at this site is composed of more than 1013 organisms, including many new species that collectively encode 100 times as many genes as the human genome. This huge microbiome contributes important metabolic properties not encoded by the human genome, thus showing that humans are “superorganisms” whose metabolism is a mix of human and microbial attributes (Gill et al., 2006). Although these studies are crucial to the understanding of biological diversity and evolution, more comprehensive investigations are needed to understand how microbial communities influence so far unknown pathologies and participate in the onset of diseases, such as diabetes and Crohn disease, which might be caused by as yet undiscovered infectious agents. Furthermore, the analysis and modulation of gene transcripts in response to changing environments will provide novel diagnostic tools, thus potentially improving the management of infectious diseases. Finally, genome-based strategies have been applied to the study of chronic infections, and more in particular to the understanding of how a pathogen can cohexist with its host during a long-term infection. In a recent work, Smith and coworkers have presented a detailed whole-genome analysis applied to a cystic fibrosis (CF) patient infected by the opportunistic pathogen Pseudomonas aeruginosa. The study compares the genomes of two related isolates collected 8 years apart from the same patient and shows that during this period the bacteria had undergone numerous genetic adaptations, with an overwhelming signal affecting specific genes. This result suggests that CF caused by P. aeruginosa has a mechanism, which is somehow reminiscent of typical cancers, where an accumulation of genetic mutations promote longterm survival and clonal expansion (Smith et al., 2006). As the cost of large-scale DNA sequencing becomes cheaper and cheaper, this whole-genome comparison approach will become increasingly amenable and applicable to several types of chronic infections, also offering novel therapeutic opportunities.

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CONCLUSIONS Since the first microorganism was sequenced over a decade ago, genomic-based disciplines have proliferated, leading to an unprecedented revolution in medical research. In the field of vaccination, the availability of complete genome sequences has created novel opportunities to tackle bacterial pathogens for which vaccines are currently not available or inadequate. In this context, reverse vaccinology has proved as one of the most successful strategies to accelerate the discovery and development of novel vaccine candidates, alleviating the limitations of classical approaches. Subsequently, comparative analysis of several strains of the same pathogen indicated that a global, “pan-genomic” approach can be more appropriate for the study of variable pathogens, thus providing for a more informed selection of vaccine candidates and increasing the chances of obtaining a successful vaccine product. The possibility of sequencing many organisms at a time has also allowed the genetic investigation of microbial communities present in various environmental samples. This technology – called metagenomics – not only enables a survey of the microorganisms present in different compartment of the human body, but also helps to decipher the complex mechanisms of host– microbe interaction and to understand how this interplay contributes to health and disease. In parallel, the availability of the human genome has opened up new opportunities in biomedicine, such as the possibility to understand the association between genetic background and

predisposition to chronic and acute diseases, paving the way to the discovery of novel therapeutic strategies for the treatment of lifethreatening infectious diseases, and of complex diseases for which successful treatments have not yet been discovered. Only when the bases of these syndromes will be clarified from a genetic point of view, we will be able to combat them using the right strategies. Although this area of research undoubtedly holds great promise for future developments in medical research, its application is less simple than it sounds. Linking a gene to a disease can take years, and decoding one person’s entire genome is still an extraordinary expensive enterprise today. Therefore, in order to bring genomic medicine into clinical practice, we still need to pursue major technological improvements, as well as to maintain the motivating forces that promote innovation and that will make the new technologies available to everyone. Efforts to develop new sequencing technologies towards this goal are underway. A race to reduce the cost to sequence a human genome from $30,000,000 to $1,000 started recently. In February 2004, the NIH issued a request for applications to develop revolutionary genome sequencing technologies. Existing companies are currently developing such technologies and some are already on the market, although they are not yet mature enough to generate accurate genome sequence data for complex genomes such as that of human or other mammals (Bennett et al., 2005; Margulies et al., 2005). When the $1,000 human genome is available, personalized genomics will be at our fingertips, together with ethical issues that will need to be addressed carefully.

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Tettelin, H., Masignani, V., Cieslewicz, M.J., Eisen, J.A., Peterson, S., Wessels, M.R., Paulsen, I.T., Nelson, K.E., Margarit, I., Read, T.D. et al. (2002). Complete genome sequence and comparative genomic analysis of an emerging human pathogen, serotype V Streptococcus agalactiae. Proc Natl Acad Sci USA 99, 12391–12396. Tettelin, H., Masignani, V., Cieslewicz, M.J., Donati, C., Medini, D., Ward, N.L., Angiuoli, S.V., Crabtree, J., Jones, A.L., Durkin, A.S. et al. (2005). Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial “pangenome”. Proc Natl Acad Sci USA 102, 13950–13955. Tsuchiya, N., Wang, L., Suzuki, H., Segawa, T., Fukuda, H., Narita, S., Shimbo, M., Kamoto, T., Mitsumori, K., Ichikawa, T. et al. (2006). Impact of IGF-I and CYP19 gene polymorphisms on the survival of patients with metastatic prostate cancer. J Clin Oncol 24(13), 1982–1989. Tyrrell, G.J., Senzilet, L.D., Spika, J.S., Kertesz, D.A., Alagaratnam, M., Lovgren, M. and Talbot, J.A. (2000). Invasive disease due to group B streptococcal infection in adults: Results from a Canadian, population-based, active laboratory surveillance study – 1996. Sentinel Health Unit Surveillance System Site Coordinators. J Infect Dis 182(1), 168–173. Valenzuela, P., Gray, P., Quiroga, M., Zaldivar, J., Goodman, H.M. and Rutter, W.J. (1979). Nucleotide sequence of the gene coding for the major protein of hepatitis B virus surface antigen. Nature 280(5725), 815–819. Venter, J.C.,Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A. et al. (2001). The sequence of the human genome. Science 291(5507), 1304–1351. Vytvytska, O., Nagy, E., Bluggel, M., Meyer, H.E., Kurzbauer, R., Huber, L.A. and Klade, C.S. (2002). Identification of vaccine candidate antigens of Staphylococcus aureus by serological proteome analysis. Proteomics 2(5), 580–590. Wang, J., Mushegian, A., Lory, S. and Jin, S. (1996). Large-scale isolation of candidate virulence genes of Pseudomonas aeruginosa by in vivo selection. Proc Natl Acad Sci USA 93(19), 10434–10439. Ward, J.I. and Zangwill, K.M. (1999). Haemophilus influenzae vaccines. In Vaccines (S.A. Plotkin and W.A. Orenstein, eds), 3rd edition. Saunders, Philadelphia, pp. 183–221. Wizemann,T.M., Heinrichs, J.H., Adamou, J.E., Erwin, A.L., Kunsch, C., Choi, G.H., Barash, S.C., Rosen, C.A., Masure, H.R. and Tuomanen, E. (2001). Use of a whole genome approach to identify vaccine molecules affording protection against Streptococcus pneumoniae infection. Infect Immun 69, 1593–1598. Wuorimaa, T. and Kayhty, H. (2002). Current state of pneumococcal vaccines. Scand. J Immunol 56, 111–129. Ying, T., Wang, H., Li, M., Wang, J., Wang, J., Shi, Z., Feng, E., Liu, X., Su, G., Wei, K. et al. (2005). Immunoproteomics of outer membrane proteins and extracellular proteins of Shigella flexneri 2a 2457T. Proteomics 5(18), 4777–4793. Zhu, J., Miller, M.B., Vance, R.E., Dziejman, M., Bassler, B.L. and Mekalanos, J.J. (2002). Quorum-sensing regulators control virulence gene expression in Vibrio cholerae. Proc Natl Acad Sci USA 99(5), 3129–3134.

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50 Cancer Vaccines: Some Basic Considerations Hans-Georg Rammensee, Harpreet Singh-Jasuja, Niels Emmerich and Steve Pascolo

INTRODUCTION Paul Ehrlich assumed that the immune system might be able to attack cancer. Even way before that, in 1866, the surgeon Wilhelm Busch at Bonn University observed a correlation between occasional infections and tumor regression in cancer patients. Later, he performed the first experimental cancer therapy using transfer of bacteria (fluid from infected wounds of other patients) to a 19-year-old woman suffering from head and neck carcinoma. He observed a transient reduction of the tumor (Busch, 1868). In the late 19th century, William B. Coley made similar observations and deliberately applied killed bacteria as an experimental therapy in cancer patients (Coley, 1991). In the following century, the 20th, the theory of “immunosurveillance” of cancer, assuming the continuous generation of cancer cells that are spontaneously eliminated by the immune system, experienced an up-and-down course. For example, the observation that T-cell deficient nude mice did not come up with a higher tumor incidence than normal mice, appeared to be the end for that theory, until the nature of Natural Killer (NK) cells had been unveiled. Today numerous mouse systems, for example, IFN- receptor deficient mice, as well as observations in patient cohorts, for example, kidney transplant recipients who are on longtime immunosuppression, indicate a higher tumor incidence in immunocompromised individuals (Dunn et al., 2005, 2004). The antigens recognized by Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

tumor-directed immunity had long been elusive, until the first T-cell defined tumor associated and even tumor-specific antigens were identified in cancer patients in 1991, by Thierry Boon and colleagues (van der Bruggen et al., 1991). Based on a large body of mouse experiments in the last 100 years as well as on clinical studies more recently, we now know three major immune mechanisms active against cancer: antibodies, T-cells, and NK cells. Antibodies recognize surface structures on tumor cells, or on neighboring cells such as stroma or neovasculature. Such structures are either unique to the tumor (tumor-specific), for example, due to mutation or aberrant gene expression, or are, in the majority of known cases, tumor-associated. The latter term describes expression or overexpression of a structure (antigen) on tumor cells or cells in the tumor area, whereby the same structure is also expressed on other tissues in lower amounts or is expressed in few other tissues only. A well known example for a highly tumor-associated antigen is the ganglioside GD2, expressed on most melanomas and neuroblastomas, but only on few normal tissues, and targeted in a number of antibody therapy trials (Osenga et al., 2006). Main effector functions of tumor-directed antibodies are ADCC, antibody-dependent cytotoxicity mediated by NK cells, or complement (Batova et al., 1999). In addition, activation of other cells, like granulocytes, leading to phagocytosis and release of soluble mediators might Copyright © 2009, Elsevier Inc. All rights reserved. 573

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contribute to antibody effector function (Curcio et al., 2003). Since some antibodies do not perform well on their effector side, recombinant molecules, for example bispecific antibodies with one arm against the tumor antigen, the other against a molecule on an effector cell, for example, CD3 or CD28 on T-cells, have been constructed (Grosse-Hovest et al., 2003). In the context of vaccine approaches, however, only natural antibody effector functions are relevant. T-cells recognize cell surface structures of a particular kind: fragments of cellular proteins presented by MHC molecules (Rammensee et al., 1993a, b; Stevanovic and Schild, 1999). Any of the 10,000 or so proteins expressed in a cell is represented by one or more peptides on the MHC class I, and sometimes also class II molecules, on the surface. Since tumor cells differ from their normal counterparts in numerous alterations, including mutations and aberrantly expressed gene products, T-cells are, in principle, able to recognize multiple tumor associated or specific peptides on any given tumor cell (as long as it is expressing MHC) (Stevanovic, 2002). In cancer patients, however, it appears that in most cases such antigens do not spontaneously lead to effective T-cell responses, most likely due to appearance of the antigen in the absence of costimulation, or by immune suppression by the tumor, its microenvironment, or by regulatory T-cells. Induction of T-cell response against such tumor antigens by vaccination, therefore, should be a useful measure against the tumor. Natural Killer cells, apart from their function as the effector cells of ADCC, are able to recognize tumor cells with reduced or absent MHC class I expression (Moretta et al., 2006), or expression of stress-induced molecules such as MICA or MICB (Friese et al., 2003; Holdenrieder et al., 2006). NK cells kill their targets via perforin and in addition produce IFN that in turn acts on dendritic cells (DCs) and influences T-cell responses toward Th1 and CTL. NK cells can be activated by cytokine application (Smyth et al., 2004), but not durably induced by a vaccine. For patients with manifest cancers, therapeutic vaccines aim at reducing or removing the tumor load. That the immune system is able to remove preexisting large tumor masses has been multiply shown in mouse models and in a few clinical settings. The most impressive success of T-cell-mediated tumor immunotherapy is the graft-versus-leukemia effect (GvL) following donor lymphocyte infusion in bone marrow or stem cell transplant recipients (Kolb et al., 2004). More recently, Rosenberg et al. showed that infusion of massive amounts of in vitro expanded autologous tumor-infiltrating T-cells (Dudley et al., 2002) or T-cells retrovirally transduced with a T-cell receptor against a single HLA-A2/tumor peptide combination (Morgan et al., 2006) can effectively destroy tumor masses. Passively applied monoclonal antibodies, such as Herceptin or Rituximab, also show the success of immune mechanisms against established tumors (Adams and Weiner, 2005). Plain vaccination, however, had shown no breakthrough so far; above considerations, however, lend promise to its eventual success. Prophylactic vaccines against cancer already exist in the case of virus-induced cancer: vaccines against HPV (Zur Hausen, 2002) developed by two companies especially to prevent cervix

carcinoma, are at their start to widespread use (Lowy and Schiller, 2006); vaccines against Hepatitis A and B, used against infectious disease, also help to prevent cancer. The following review will be limited on therapeutic cancer vaccines. The weight given to the different possibilities is evidently influenced by the interest and expertises of the authors. One of the possibilities, the direct injection of mRNA for vaccine purposes, is treated in greater detail, since this is a rather new approach.

IMMUNE SUPPRESSION BY TUMORS AND BY REGULATORY T-CELLS Regulatory T-cells (Tregs) represent a T-cell population that can functionally suppress an immune response by influencing the activity of other immune effector cells. The existence of Tregs was first established in 1971, when Gershon and Kondo transferred antigen-specific tolerance to antigen-naïve animals by transferring T-cells which had previously been exposed to the specific antigen (Gershon and Kondo, 1971). Several phenotypically distinct Tregs may exist. The object of most intensive recent research are CD4 CD25 Foxp3 T-cells, which also express high levels of glucocorticoid-induced TNFR-related protein (GITR). These Tregs are considered key mediators of peripheral tolerance. Another type of Tregs (IL-10  CCR7), possibly involved in central priming suppression rather than in peripheral effector suppression, was described (Zou, 2005). The following part focuses on CD4 Foxp3 Tregs suppressing the execution of effector functions of T-cells in the periphery. It has been reported that cancer patients have increased numbers of CD4 CD25 Foxp3 Tregs (Hueman et al., 2006; Liyanage et al., 2002;Woo et al., 2001, 2002) as compared to healthy individuals. Observations from preclinical and clinical testing that establish correlations between the depletion/reduction of Tregs and therapeutic effects have been published for several agents already. A more and more detailed understanding of the tumor microenvironment, its influence on the generation of Tregs. The suppressive measures taken by both tumor cells and Tregs on effector T-cells, leads to clinical trials evaluating combinations of therapeutic vaccines with compounds that have strong potential to increase the efficacy of active immunotherapy approaches. Induction of Tregs The induction and differentiation of Tregs is favored by tumor cells releasing TGF, IL-10, CCL22, or PGE2 into the local environment. As mentioned above, patients with breast, ovarian or lung cancer and possibly other solid and hematological malignancies have higher numbers of both peripheral and central Tregs. Dysfunctional myeloid DCs and tumor-conditioned plasmacytoid DCs contribute directly to the induction of Tregs in the tumor. Phenotypically, Tregs constitutively express CTLA-4, secrete TGF and IL-10, and bear the ligand of B7-H1, PD-1, on their surface (Chen, 2004). For a literature overview see Beyer and Schultze, 2006.

Immune Suppression by Tumors and by Regulatory T-cells

Interaction of Tregs with Effector T-Cells The suppression of effector T-cells by Tregs is mediated directly and indirectly. Indirect interaction is caused by reduced levels of immunogenic, T-cell-activating DCs. The direct mechanisms by which Tregs suppress CTL have not been fully understood. Tregs release TGF and IL-10, both of which suppress effector T-cells. Tregs isolated from the tumor of a colon cancer patient depended on the presence of tumor cells for growth but did not lyse the autologous tumor cells. At the same time, the Tregs inhibited autologous effector CTL in a TGF-dependent fashion. No direct cell–cell interaction between the Tregs and the effector CTL was required for suppression (Somasundaram et al., 2002). This view was recently confirmed by in vivo observation of effector CTL-mediated cytotoxic activity and the suppression of such effects by secreted TGF from Tregs (Mempel et al., 2006). In addition to the effects mediated by TGF secretion, Tregs constitutively express CTLA-4, and blocking of CTLA-4 with antibodies leads to improved tumor rejection, especially when anti-CTLA-4 administration is combined with anti-CD25 antibody (mouse) or peptide-based vaccines (currently in late-stage clinical testing). However, it has not been shown that administration of anti-CTLA-4 alone leads to reduced levels of Tregs in humans. Reduction/Depletion of Tregs Depletion of Tregs in man with anti-CD25 monoclonal antibody (mAb) has so far only been therapeutically successful in prevention of transplant rejection, suggesting that the mAb does not discriminate between Tregs and effector T-cells at the doses tested in man. However, in mice depletion of Tregs with antiCD25 antibody led to increased tumor rejection. This effect is synergistically increased when anti-CD25 antibody and antiCTLA-4 antibody are used at the same time (Sutmuller et al., 2001). The neutralization of TGF with antibodies can reverse the Treg-mediated suppression of CTL-mediated tumor cell destruction (Yu et al., 2005). Also, it was shown that depletion of VEGF with bevacizumab improves the maturation of DCs in mice (Gabrilovich et al., 1996; Oyama et al., 1998). In mouse models, it was shown that inhibition of COX2, which results in decreased levels of PGE2, resulted in decreased numbers of Tregs and tumor shrinkage (Sharma et al., 2005). These results are supported by the notion that in vitro exposure of human Tregs to PGE2 induced FOXP3. Tregs and Cancer Immunotherapy Mouse experiments using B16 melanoma demonstrated convincingly that Tregs are the major regulators of concomitant tumor immunity against this weakly immunogenic tumor (Turk et al., 2004). Further evidence was corroborated in a mouse colon carcinoma model where transfer of Tregs abrogated CTLmediated tumor rejection by specifically suppressing cytotoxicity of CTL (Chen et al., 2005). This view is confirmed by a

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striking correlation of tumor Treg content with reduced patient survival, providing evidence linking Treg cells to the pathogenesis of human cancer (Barnett et al., 2005; Curiel et al., 2004). In clinical settings, especially the use of cyclophosphamide for depletion of Tregs led to promising and in some cases impressive results. Following a report by the group of S. Rosenberg (Dudley et al., 2002), who treated stage IV melanoma patients with cyclophosphamide and fludarabine before adoptive transfer of autologous ex vivo-activated effector T-cells, low-dose infusions of cyclophosphamide before administration of tumor vaccines are now used more frequently. Under these circumstances, the evaluation of compounds contributing to a reversal of the balance between Tregs and CTL both in draining lymph nodes and in the tumor appears to be valuable for optimizing the efficacy of therapeutic cancer vaccines. Tumor Microenvironment Besides regulatory T-cells the tumor microenvironment should also be regarded as an important mediator of peripheral tolerance: tumors interact with Tregs as well as with other immune effector cells. Developing tumors increasingly shape the local environment in and around the lesion. This process often leads to a successful escape from antitumor effector mechanisms of the immune system. Commonly referred to as “immune editing,” this process has various important elements that affect the availability of metabolic substrates, the balance between pro- and anti-inflammatory cytokines, the state of professional antigenpresenting cells (APCs), and the proportions between different immune cells (Smyth et al., 2006). It is suggested that before the development of systemic metastasis (including lymphoid metastasis), the antigenic cancer cells (expressing tumor-associated antigens) are embedded in the solid tumor. The stroma of the tumor prevents the efficient release of tumor-associated antigens, which are ignored in the conventional central priming sites – the draining lymph nodes. In the later stages of tumor development, tumor-associated antigens are thought to be efficiently released, which induces the protective immune system to mount an effective response. However, tolerizing mechanisms are already established in the tumor microenvironment by this stage, disabling the functions of professional APCs and effector T-cells. For reviews on tumor microenvironment, see Muller and Scherle, 2006; Yu et al., 2006; Zou, 2005. Effects of the Tumor Microenvironment on Tregs From today’s perspective, tumor cells acquire the ability to synthesize proteins that mimic immune cells, including secreted primary messengers such as VEGF, IL-10, TGF, which influence the behavior of both APCs and T-cells. Tumor cells can also secrete the ligand of CCR4 on Tregs, CCL22. A CCL22 gradient attracts Tregs to the tumor. Other cytokines that can be released by tumor cells and are thought to foster the activity of Tregs at the tumor site are IL-13, CCL2, and CXCL12.

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Effects of the Tumor Microenvironment on Effector T-cells Tumor cells can produce enzymes such as arginase (ARG), indoleamine 2,3 dioxygenase (IDO), cyclooxygenase-2 (COX2) and inducible nitric oxide synthese (iNOS). These enzymes act through direct inhibition of signaling pathways (PGE2 synthesis by COX2, NO-synthesis by iNOS) as well as by depletion of metabolites (tryptophane) required for proliferation and expansion of tumor-infiltrating effector T-cells. Effects of the Tumor Microenvironment on Dendritic Cells Professional APCs, especially DCs, can be turned into nonimmunogenic, dysfunctional cells by the tumor microenvironment. While functional myeloid DCs can induce T-cell mediated antitumor responses, dysfunctional DCs impair the destruction of tumor cells by CTL. The strongest inhibitory effect arresting DCs in an immature state and prohibiting the stimulation of T-cells comes from IL-10 and TGF. Additionally, VEGF, IL-6, M-CSF, iNOS, ARG, IDO, PGE2, COX2, and gangliosides promote the conversion into dysfunctional DCs. Subtypes of suppressive DCs have been described recently, among them IDO DCs, B7-H1 myeloid DCs, and vascular CD11c CD45 DCs.

THE IDEAL THERAPEUTIC CANCER VACCINE It is accepted that all tumor cells differ from their normal counterparts in several if not many alterations, including gene sequence differences occurring by mutation or gene fusion, gene expression differences, and differences in posttranslational modifications. The immune system is in possession of tools to recognize most of such alterations. However, it appears that an effective immune response is not mounted spontaneously in most cancer patients. There are at least two reasons for this failure: 1. T-cells and B cells with specificity for tumor antigen are present but are not activated due to lack of costimulation, or 2. T-cells are suppressed either by measures of the tumor itself, its microenvironment, or by regulatory T-cells (see above). The ideal tumor vaccine, therefore, should contain the antigens relevant for the tumor and in addition an agent which induces costimulation by APCs, that is, an adjuvant (see below) as well as an agent that impairs tumor-mediated immune suppression as well as Treg action. A problem regarding the choice of tumor antigens is their individual distribution – among individual tumor cells within a given tumor, among tumors of the same entity from different individuals, and, of course, among tumors of different tissues. Known tumor antigens or candidates for tumor antigens (Stevanovic, 2002) are derived from virus proteins (HPV, EBV, HCV), oncogene products, tumor suppressor gene products (examples: ras, p53), fusion proteins (bcr/abl), mutations (CDK4), tissue-specific antigens (Tyrosinase), differentiation antigens

(MAGE), overexpression of normal genes, aberrant expression of normal genes (intron, out-of-frame), or products of protein splicing (Hanada et al., 2004;Vigneron et al., 2004;Warren et al., 2006). Ideally, a tumor vaccine should contain all tumor structures distinguishing the tumor from normal cells, including the mutations, the aberrant gene expressions, and the posttranslational modifications (see Figure 50.1). Especially tumor-specific mutations elicit T-cells that are expected to have higher affinity than those against overexpressed antigens due to constraints of self tolerance. Indeed, the spontaneous T-cell response against melanoma is dominated by T-cells specific for point mutations (Lennerz et al., 2005). An ideal tumor vaccine, however, should not contain structures shared between tumor cells and normal cells at the same expression level, in order not only to avoid autoimmunity but also dilution of the antigens by irrelevant ones, or even the induction of more regulatory T-cells by self antigens. For practical reasons, an ideal tumor vaccine should be suitable for all patients at least of a given tumor entity – this criterion, however, contradicts the above requirement of coverage of all tumor-associated antigens due to the individualized genome and proteome of tumors. Thus, the ideal tumor vaccine can never be achieved; however, future developments should allow better approximations to the ideal as compared to the present possibilities. Molecular undefined antigen preparations, such as tumor cell lysates, fulfill the criterion of containing all relevant antigens, if taken from autologous material, but have the disadvantages of containing many self antigens in addition, and of being of limited supply. All molecular defined antigen forms – peptides, proteins, nucleic acids, tumor genes wrapped in viral or bacterial vectors – cannot cover all tumor-associated structures completely. For practical reasons, antigens in forms that require complex and expensive preparation procedures, such as proteins and live vectors, cannot be easily prepared for individual patients or small subgroups of patients, and thus, are limited to antigens shared by a sufficient number of tumors from different individuals.

MOLECULARLY UNDEFINED CANCER VACCINES These include the oldest forms of cancer vaccines. Lysates of native tumor tissue, killed allogeneic tumor cell lines with or without genetic (e.g., inclusion of genes for cytokines and costimulatory molecules) (Frankenberger et al., 2005; Guckel et al., 2005) or other alterations, such as infection with Newcastle disease virus (Karcher et al., 2004), fusion products of tumor cells with B cells or dendritic cells, tumor or tumor cell extracts, for example, heat shock proteins (Pilla et al., 2006; Srivastava, 2006), or nucleic acid libraries from tumor cells fall under this category. The variations within this group are too numerous to be treated in detail here. General advantages of this group are their suitability for individualized approaches and their potential of providing

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Normal tissue Tumor tissue

Gene expression analysis

Gene expression analysis

Differential analysislist of overexpressed genes

25 000 gene sequencing

25 000 gene sequencing

Differential analysislist of tumor-specific mutations

HLA ligandome analysis

HLA ligandome analysis

Differential analysislist of tumorassociated peptides

Design and synthesis of molecularly defined, personalized vaccine consisting of peptides and mRNA/DNA containing all tumor associated/specific structures

Figure 50.1 Designing antigen composition of the ideal tumor vaccine. A sample each of tumor and autologous normal tissue is subjected to comparative gene expression analysis (Weinschenk et al., 2002), sequencing of all expressed genes by techniques presently still in development (Margulies et al., 2005), and comparative HLA ligand analysis including post translational modifications (Dengjel et al., 2005; Lemmel et al., 2004). The resulting list of tumor associated or tumor-specific structures is then used to synthesize the antigens for a personalized vaccine.

costimulation together with antigen delivery, for example, via heat shock proteins, or necrotic cells. Disadvantages are the limited amounts as well as serious difficulties in manufacturing, standardization, logistics and quality control (mainly for material derived directly from autologous tumors), the inclusion of self antigens, which at least dilute the tumor relevant antigens, and the difficulties in the choice of antigen(s) for immunomonitoring. Numerous studies have been reported, some with success. It appears to be possible to get closer to our ideal for a cancer vaccine by using a RNA (Carralot et al., 2005) or DNA library from fresh autologous tumor tissue of the patient depleted by the genes expressed in normal tissue, in an individualized setting.

PEPTIDES The most reduced form of antigens recognized by T-cells are the peptides presented by MHC molecules. Such peptides can be easily produced by a peptide synthesizer, purified by HPLC, and quality-controlled by HPLC and mass spectrometry. This eases GMP production. Identification of such T-cell epitopes is much helped by the knowledge of MHC specificity for peptides, which is available for many, but by far not all of the MHC alleles in the human population (Stevanovic, 2002). For frequent

HLA class I alleles (the human MHC is called HLA), for example, HLA-A*0101, A*0201, HLA-B*0701, and B*4402, prediction of candidate T-cell epitopes within a sequence of interest is easily possible by tools via internet, for example, www.syfpeithi. de. Many of the T-cell epitopes representing tumor-associated or tumor-specific antigens have been identified this way. A very fine database for cancer-related T-cell epitopes, ordered in the groups mutated antigens, shared tumor-specific antigens, differentiation antigens, overexpressed antigens, and potential antigens can be found at www.cancerimmunity.org. Several hundreds of cancer-related HLA presented peptides are known, many of which have already been applied in experimental vaccination, or appear suitable for such use. HLA class II presented cancer-associated peptides, either presented by the tumor cells themselves, or, probably, via crosspresentation by surrounding cells, are being identified now in higher numbers than until recently (Dengjel et al., 2006). Still, however, for most tumor entities, apart from melanoma, where a high number of T-cell epitopes is known, and especially for HLA-A*02 negative patients, not enough peptides have been identified. This leads to a disadvantage of peptides related to their use in cancer vaccines: Peptides have to be matched for the HLA expression of patients. Since the polymorphism of HLA genes is very high, it is difficult now or in the near future to provide a given cancer patient with peptides

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reflecting his or her tumor-associated antigens and presented on all four of the patient’s HLA-A and B molecules. For practicability, however, sets of peptides fitting to a particular HLA molecule, for example, HLA-A*02, and reflecting antigens found to be frequently expressed in a given tumor entity, for example, renal cell carcinoma, can be put together. We have recently evaluated a phase 1 clinical trial (IMA901 developed by immatics biotechnologies) where 28 HLAA*02 patients were immunized i.d. with a set of 10 A*02restricted peptides from antigens frequently overexpressed in renal cell carcinoma, for example c-met, adipophilin, cyclin D1, and muc-1, and one promiscously binding HLA class II peptide. GM-CSF was used as immunological adjuvant. The result of the trial was very favorable, in that toxicity was neglectible, and the T-cell response to multiple tumor-associated antigens correlated significantly with the clinical benefit. Additionally, it was found that regulatory T-cell levels in the periphery inversely correlated with the T-cell response. This highly interesting finding may allow further enhancing of immunological and clinical response by modulating regulatory T-cell levels in further studies. The peptide set used in this clinical trial was defined based on a systematic analysis of the HLA ligandome of primary renal cell cancer samples using a combination of genomics, proteomics (peptidomics would be the more appropriate term) and Tcell immunology. HLA-restricted peptides had been identified directly from the tumor tissue. Thus, the resulting peptides are known to be naturally presented and processed on tumor material, which is a major advantage in comparison to the prediction of candidate T-cell epitopes as described above. In the next step those peptides were selected that were shared among the majority of tumors and overexpressed versus various healthy tissue to define tumor-associated peptides. This was largely based on genomic analysis of various tumor tissues and 30 different types of healthy tissues. Antigens that were associated with relevant tumor functions (metastasis, angiogenesis, cell cycle control etc.) were preferred antigens as their expression was maintained also at advanced tumor stages and in metastases. In the final drug discovery step, immunogenicity of the peptides was defined using a standardized approach based on artificial APCs (Walter et al., 2003), and antigens with high immunogenicity were selected. This approach shows a combination of genomics with other fields such as proteomics, bioinformatics and immunology can be used in a very systematic fashion to identify, select and validate novel T-cell epitopes for cancer immunotherapy. Thus, the above mentioned strategy appears to be highly feasible and useful, and also economic, once a sufficient set of HLA allelespecific peptides is available. Our ideal of a cancer vaccine could be approached in the future if such sets are prepared for a limited number of frequently occuring HLA alleles, for example, HLA-A*01, A*02, A*03, A*24, B*07 and thus combinations of sets adapted to the individual HLA expression of a patient, could be made available to the vast majority of patients (up to 95% with these alleles alone). One step further would be an approach not only matching the HLA alleles of a patient but also the individual antigen

expression of the patient’s tumor. This would be achieved using a combination of gene expression analysis with high-throughput sequencing in an analogous fashion as described above, but on a individualized “per tumor” basis (Rammensee, 2006; Rammensee et al., 2002). In this scenario, a piece of the patient’s tumor and a piece of normal autologous tissue would be analyzed for (1) genes and peptides overexpressed in tumor tissue, and (2) genes mutated in the tumor (see Figure 50.1). Analysis of such data will be rather complex, since it involves the sequencing of 25,000 genes each from the two sources. We have started working in this direction and have initiated a study in which sets of peptides are selected for the renal cell carcinoma of a given patient, considering all HLA alleles expressed by the patient as far as possible, and used for vaccination. We find that this is logistically possible. The next step will be to search for tumor-specific mutations in a selection of about 30 genes prone to mutations, such as p53, and to add peptides representing the mutations found.

PROTEINS AND CARBOHYDRATES Proteins-representing cancer antigens are able to induce antibody, CD4, and CD8T-cell responses. Together with adjuvants and carriers, proteins can induce powerful immune responses, as documented in mice for decades. For human use, however, most reports depend on fusion proteins, for example, with cytokines, or with heat shock proteins, or loading on DCs in order to improve immunogenicity without the necessity to use a strong adjuvant (Cho et al., 2000; Helguera et al., 2006; Santin et al., 2006). Upon injection, proteins will be phagocytosed by APCs, in particular DCs, and presented to T-cells on HLA class II, and in case of DCs, also cross-presented by class I molecules. Recombinant proteins for vaccine purposes are usually produced in bacterial or eukaryotic expression systems. Functionality is not required for induction of T-cell responses; for antibody responses, however, it is of advantage to use proteins with proper glycosylation, so that production in yeast, insect, or mammalian cell systems is preferable. A disadvantage of proteins for vaccines is the rather complex and costly production in GMP quality, as indicated for example by the lasting development of production and purification of recombinant cancer-testis NY-ESO-1 antigen under cGMP conditions, which is also being tried as a secreted protein in yeast in addition to conventional production in E.coli (Murphy et al., 2005; Piatesi et al., 2006). One reason for producing a given protein in two systems is to try to avoid artifactual false positive tests of patient immune response due to reaction against expression-host-specific contaminants. For each protein, the production in cellular systems and especially the purification procedure has to be optimized separately. Thus, protein-based cancer vaccines appear to be useful for cancer antigens that are widely found to be expressed, for example, in most cases of a given cancer entity, or even in several cancer types, and are especially suited for cell surface expressed antigens accessible to antibodies.

Nucleic Acids: Plasmid DNA and Messenger RNA

Particularly for induction of tumor-directed antibody responses, carbohydrate vaccines are being developed as immunotherapy for cancer. (Slovin, 2005; Slovin et al., 2005). Considering the suitability of whole molecule based (protein and/or carbohydrate) cancer vaccines for reaching our ideal, as defined above, on has to state that it will be technically, logistically, and economically never possible to get near to it, especially regarding mutations, due to the above mentioned production difficulties.

NUCLEIC ACIDS: PLASMID DNA AND MESSENGER RNA The simplest genetic vectors coding for one particular protein of interest when introduced in eukaryotic cells are: 1. plasmid DNA (pDNA): bacteria-derived 2 to ca. 15 kb circular double stranded DNA molecules that contain three basic elements: (i) an origin of replication active in bacteria, (ii) a prokaryotic expression cassette (prokaryotic promoter and terminator of transcription) that codes for an antibiotic-resistance protein and (iii) an eukaryotic expression cassette (eukaryotic promoter, start codon in Kozak surrounding, coding sequence adapted to the eukaryotic codon usage, stop codon and terminator of transcription) that codes for the protein of interest; 2. messenger RNA (mRNA): few hundreds up to ca.10 kb single stranded RNA molecules obtained by in vitro transcription of pDNA and that contains three basic elements: a Cap structure (methyl-7-Guanine followed by three phosphate groups) at the 5 end, a coding sequence (start codon in Kozak surrounding, coding sequence adapted to the eukaryotic codon usage, stop codon) and a poly-A tail of at least 30 residues at the 3 end. In the late eighties, Wolff et al. have made the surprising observation that the injection of naked pDNA and mRNA vectors in the mouse skeletal muscle results in a local uptake of the exogenous nucleic acid that can be seen by the expression of the protein of interest (Wolff et al., 1990). Thus, in vivo, foreign nucleic acids can somehow penetrate into the cytosole (mRNA) and nucleus (pDNA) of somatic cells before they are degraded by nucleases. The uptake mechanism is saturable and can be competed away for both pDNA (review by Wolff and Budker, 2005) and mRNA (Probst et al., 2007). It probably involves endosomes and specific receptors. Other sites than the skeletal muscle can be used for in vivo gene delivery using naked nucleic acids: skin, liver, and heart muscle, for example (review by Nishikawa and Hashida, 2002). Moreover, pDNA and mRNA being ligands of Toll Like Receptor (TLR) 9 and TLR7 plus TLR8, respectively, stimulate innate immunity. This way, at the site of injection, DCs which may acquire the nucleic acid or the protein encoded after production by neighboring cells that took up the nucleic acid, get activated, migrate to the lymph node and present to lymphocytes the antigen encoded by the recombinant genetic vector. Through

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this process, injected pDNA (review by Liu and Ulmer, 2005) and mRNA (review by Pascolo, 2004) induce the development of an adaptive immune response (T- and B-lymphocytes) that is specific for the protein encoded by the foreign nucleic acid. This is independent of the MHC haplotype and generates effector as well as memory persisting lymphocytes. A great optimization of this technology consists in the injection of the nucleic acids directly into the lymph nodes instead of intradermal or intramuscular delivery. It was shown that, out of the thousand billions of injected pDNA, only a few thousands are successfully expressed at the site of injection (Ledwith et al., 2000). Due to the quick degradation of RNA by exogenous processive, ubiquitous and stable RNases (Probst et al., 2006), a similar inefficient utilization of the injected formulation is to be expected with naked mRNA. From these data, several methods were developed for the improvement of nucleic acid-based vaccinations: 1. Electroporation (Prud’homme et al., 2006): after the injection of the nucleic acid in the muscle or skin, two electrodes are placed around the site of delivery and an electric pulse is applied. This greatly improves the expression of pDNA (but not mRNA: Pascolo et al. unpublished observation) vectors, which results in a higher immune response as compared to simple injections (Smorlesi et al., 2006). 2. Encapsulation in (or coating on) biodegradable particles or cationic liposomes: Both pDNA (Perrie, 2001) and mRNA (Martinon et al., 1993; Hess et al., 2006) vectors encapsulated in cationic liposomes have been shown to be potent vaccine formulations. Meanwhile, cationic particles which are less toxic than liposomes and more controllable for their size distribution and stability were used in association with pDNA. Usually, the biodegradable anionic polymer poly(lactide co-glycolide, PLG) is used as a particle scaffold. Mixed with cationic chemicals (e.g., cetyltrimethylammonium bromide (CTAB), dimethyl dioctadecyl ammonium bromide (DDA) or 1,2-dioleoyl-1,3-trimethylammoniopropane (DOTAP)) or cationic organic molecules (e.g., protamine or chitosan), PLG generates positively charged homogenous particles with a defined size (hundred microns down to hundred nanometers in diameter depending on the protocol used), zeta potential (surface charge) and stability (Singh et al., 2001). They can be coated with pDNA. Alternatively, pDNA can be introduced during the formation of the particles by solvent evaporation and consequently be entrapped. In all cases, these micro- or nanoparticle formulations of pDNA were shown to be potent and safe vaccines that also can be delivered orally (Jones et al., 1997; Chen et al., 1998; Herrmann et al., 1999), intranasally (Singh et al., 2001) or through other mucosal routes (intrarectal e.g., (Sharpe et al., 2003)). 3. Coating on gold particles (gene-gun): Nucleic acids can be precipitated onto micrometric gold particles that are then dried on the internal surface of a 1 cm long tube of 0.4 cm diameter. The nucleic acid-loaded gold particle containing tube is used as a cartridge. It is placed in the

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cartridge holder of a gun that is connected to pressured Helium. Upon firing, the gas that goes through the cartridge propels the gold particles with high speed. Should the gun be placed few centimetres from the skin, the particles go through the stratum corneum and reach the dermis. At this location, they release the nucleic acids. Some particles directly penetrate the skin-resident APCs such as Langerhans cells or dermis DCs. This allows the expression of the protein of interest within these immune cells and the triggering of an efficient immune response. As opposed to needle-injections, this process is very reproducible from one application to another (only controlled by the pressure of the gas) and painless. 4. Transfection in vitro in APCs: Monocyte-derived dendritic cells (MODCs) are potent antigen-presenting cells that can be derived from peripheral blood in vitro within 1 week of cell culture. These cells can be transfected (optimally using electroporation) with pDNA or mRNA. The former inducing apoptosis in MODCs, mRNA is preferred. It was shown in 1996 (Boczkowski et al., 1996) that mRNAtransfected APCs are potent stimulator of antigen-specific T-lymphocytes in vitro and in vivo (in mice after adoptive transfer). Thus, they can be used as patient-specific vaccine formulations. All these formulations were found to greatly increase the efficacy of nucleic acid-based vaccinations. They allow a considerable reduction in the amount of genetic material that must be applied for triggering an efficient immune response: Although ca. 50 g of nucleic acids are typically used for vaccinating mice with naked mRNA or pDNA, equivalent immunity is found with the application of less than 1 g of nucleic acids delivered by cationic particles, liposomes or gene gun. However, the validation of a batch of naked nucleic acid for vaccination of humans is a routine procedure while the validation (batch-tobatch reproducibility, homogeneity, stability, toxicity, pharmacokinetic, etc.) of a specially formulated nucleic acid is difficult. Design and Optimization of pDNA and mRNA Vectors for Anti-Cancer Vaccines Optimizing Expression For pDNA vectors, a strong promoter must be used in order to achieve in vivo the highest possible expression of the antigen. The ubiquitously expressed early promoter of CMV is frequently used. As an alternative, a promoter specifically active in APCs such as the Fascin promoter, allows a specific expression of the antigen in APCs and prevents the persistence of the transgene (APCs are terminally differentiated cells that do not divide) or the induction of tolerance (nonimmune cells expressing for a long time an antigen may induce immune tolerance) while guaranteeing an optimal stimulation of clonotypic lymphocytes (Sudowe et al., 2006). For mRNA vectors, an optimal translation is depending on an adequate 5 Cap structure and a long poly-A tail. The in vitro production of mRNA uses usually a classical Cap analog

(m7G(5)ppp(5)G) that can be incorporated in two orientations: the methyl guanine at the 5 or the canonical guanine at the 5. Only the first orientation allows recognition by the proteins involved in the initiation of translation. Thus, to enhance the potency of mRNA-based vaccination, the utilization of a modified Cap analog called ARCA-Cap (Stepinski et al., 2001) and which can be incorporated only in the correct orientation, is of interest. Meanwhile, the poly-A tail should be of a minimum length of 30 residues but optimally of more than 120 residues (Holtkamp et al., 2006) in order for the mRNA to be stable in the cytosol and well expressed. For both pDNA and mRNA vectors, the utilization of codon optimized sequences (synthetic genes) instead of the wild-type coding sequences is a general and potent method to optimize nucleic acid vectors for vaccination purposes. Enhancing the Antigenic Properties of the Encoded Protein: Enhancing Antigen Processing One of the great advantage of minimal nucleic acid vectors for vaccination is that the sequence of the antigen of interest can easily be modified for an optimal utilization by the immune system. Standard modifications of the antigen include the addition of sequences that can enhance the production of MHC class I and MHC class II epitopes. The addition of an upstream sequence coding ubiquitin, calreticulin (Anthony et al., 1999) or Herpes Simplex Virus VP22 (Chhabra et al., 2004) was shown to enhance the presentation of antigen-derived MHC class I epitope. Meanwhile, the addition of an upstream sequence coding the N-terminus of the invariant chain (Momburg et al., 1993) or the flanking of the antigen by sequences derived from the lysosomal LAMP proteins (Ruff et al., 1997) was shown to enhance the presentation of antigen-derived MHC class II epitopes. Enhancing the Stimulation of the Immune System Tumor antigens are frequently self proteins that the immune system has learnt not to recognize. Consequently, the antigen expressed by the nucleic acid-vaccine must be in conditions (e.g., inflammation) or configurations (modified compared to the endogenous protein) that allow priming of the immune response that is breaking tolerance. Concerning the conditions, nucleic acid vaccines are frequently applied together with purified or vector-encoded cytokines. In particular, GM-CSF have been found to potentate the relevant type (Th1) of immune response triggered by nucleic acid vaccination (Carralot et al., 2004; Kusakabe et al., 2000). Inflammatory cytokines such as IL-12, IL-6, or IL-17 are also of interest. Aside from that, the natural capacity of pDNA or mRNA vectors to stimulate innate immunity through TLR can be enhanced by adding, respectively, CpG motifs or stabilized sequences (phosphorothioate backbone) (Scheel et al., 2004). Concerning the configuration, the addition of a foreign immunogenic sequence such as the pan-HLA-DR epitope “PADRE” (Alexander et al., 1994) or the utilization of an ortholog sequence (homologous protein from another species:

Nucleic Acids: Plasmid DNA and Messenger RNA

xenogenic vaccination) were shown to enhance the antitumor immune response probably by rendering the vaccine antigen somewhat “foreign” to the immune system. Another possibility is to create a secreted targeted protein from the cellular tumor antigen. To this end an export signal is introduced in front of the antigen (a leader sequence) and a sequence corresponding to a DC-ligand such as CD40L, Flt-3L or CTLA4 is added before or after the antigen (Boyle et al., 1998; Hung et al., 2001; Xiang et al., 2001). This allows the secreted antigen to be taken up by APCs and thereby enhances the efficacy of the nucleic acid vaccine. Clinical Results The capacity of nucleic acid-based vaccines to control tumors in prophylactic or therapeutic settings was clearly proven in animal models (mostly in tumor-prone rodents) (Smorlesi et al., 2006; Spadaro et al., 2005). Both pDNA and mRNA-based immunotherapies appeared as safe and efficacious methods to prevent the growth of a tumor, the appearance of metastasis or to slow down cancer progression. Eventually, the regression of existing tumors can be also obtained in animal models by nucleic-acidbased vaccinations. Consequently, these methods were tested in phase I and II trials in cancer patients as depicted below. Plasmid DNA-Based Anti-Tumor Vaccines In the first published trial, up to 2 mg of a pDNA vector coding Hepatitis B Surface antigen (HBS) and Carcino Embryonic Antigen (CEA) were repetitively injected intramuscularly in patients with colorectal carcinoma (Conry et al., 2002). Although some patients developed a good immune response (antibodies) toward HBS, there was a very limited response against CEA (no antibodies but a lymphoproliferative response) and no clinical response. Similar negative results were obtained using intramuscular or intradermal injections of a pDNA coding gp100 in metastatic melanoma patients (Rosenberg et al., 2003). As an optimized method, the intranodal delivery of up to 800 g every 2 weeks of pDNA coding tyrosinase was tested in stage IV melanoma patients (Tagawa et al., 2003). Eleven out of 26 patients developed Cytotoxic T Lymphocytes (CTLs) against tyrosinase thanks to this treatment that also proved to be safe. Although no regression was observed, the overall survival in the treated population, especially in the immune responders, seemed to be unexpectedly long. In a patient-specific setting, the needleless (Biojector: intramuscular delivery) monthly application of up to 1.8 mg of pDNA coding the idiotype of B-cell lymphoma resulted in T-cell but also B-cell immune response in altogether 7 out of 12 patients (Timmerman et al., 2002). The capacity of cytokines to enhance the nucleic acid-based vaccine efficacy was tested in prostate tumor patients: pDNA coding prostate-specific antigen (PSA) applied intramuscularly and intradermally (up to 0.9 mg per injection, 5 injections) with recombinant IL-2 and GM-CSF given subcutaneously resulted in humoral and cellular anti-PSA immunity in 2 out of 3 patients that received the highest pDNA dose. The 2 responding patients experienced biochemical regression (Pavlenko et al., 2004a, b). Several trials are ongoing (see www.clinicaltrials.com and the review by Shaw and Strong, 2006). Naked, formulated or

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gene-gun delivered pDNA vectors eventually engineered to code for optimized tumor antigens are evaluated as anticancer immunotherapies. The capacity of pDNA vaccines not only to trigger an immune response but most importantly to induce regression or stabilization of tumor diseases in some tumor patients will be eventually demonstrated from phase III studies. Messenger RNA-Based Anti-Tumor Vaccines The method described by the team of E. Gilboa in 1996 (Boczkowski et al., 1996) was the first to be turned into a mRNA-based anticancer immunotherapy trial. Metastatic prostate cancer patients received autologous MODCs transfected with PSA coding mRNA. Specific T-cells were induced in all patients. This successful vaccine eventually had a clinical impact (decrease in the log slope of PSA and transient clearance of circulating tumor cells) in some patients (Heiser et al., 2002). In another trial, MODCs were transfected with the total mRNA from renal tumors before being adoptively transferred to the patient suffering from metastatic renal cell carcinoma. Most treated patients responded to the vaccine as shown by monitoring antitumor T-cells (Su et al., 2003). Superior results were obtained in a third trial where on top of the mRNA vaccine, patients received ONTAK which is a IL-2-toxin fusion that kills CD25 expressing T-cells such as the CD4  /CD25 Tregs (Dannull et al., 2005). To conclude, mRNA-transfected MODCs are safe and efficacious vaccine formulations that may be validated as potent antitumor immunotherapies in adequate phase III trials. As a simplest and broadly applicable alternative, the direct injection of naked or encapsulated mRNA is currently being evaluated in pilot trials in Tübingen. In the first published study, we showed that intradermal injections of mRNA libraries prepared from autologous melanoma metastasis are feasible and safe. In a cohort of 13 evaluable patients, three patients have developed de nove anti-tumor antibodies during the vaccinaton period. T-cell immunomonitoring studies indicate also the induction of a CD8 and/or CD4 Tcell response in some patients (Weide et al., 2008). Regulatory Issues Therapies based on non-replicative mRNA as described here are classified by the authorities (FDA in the United States and Paul Ehrlich Institute in Germany) as not to be gene therapy approaches. Consequently, the toxicology studies that must be performed before a human trial are standard: systemic and local reactogenicity, histopathology and toxicity with acute and chronic applications of ca. 100 times the amount of nucleic acid per kilogram that will be used in humans. These studies should optimally be performed in rodent and non-rodent animals. Since pDNA-based vaccines are on the contrary to mRNAbased vaccines classified as gene therapies, additional preclinical toxicology studies must be performed. Further Directions Anticancer immunotherapies based on nucleic acid-vaccines were validated in numerous preclinical models and demonstrated to be safe and effective. Genetic vaccines in their simplest formulations

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consist in the injection of naked nucleic acids. This method has been validated in animal models as an antitumor immunotherapy regimen. The intradermal, intramuscular, or intra-lymph node injection of naked pDNA was shown to induce immune responses (humoral and/or cellular) in cancer patients during phase I/II trials. The delivery of mRNA through adoptive transfer of transfected MODCs showed similar results. As presented above, the first trials using direct injection of naked mRNA also indicates the induction of anti-tumor immunity by this method. The availability of GMP-certified nucleic acids guaranties a quick transfer from the bench to the clinic. Thus, nucleic acids coding for improved versions of the antigens (higher presentation of epitopes on MHC class I and II molecules or higher immunogenicity thanks to xenogenic settings) eventually in combination with parallel treatments (chemotherapies or immunomodulating drugs) can be tested in clinical trials. Meanwhile, more evolved generic formulations of the nucleic acids (biodegradable particles, gene-gun, liposomes) show higher potencies. The GMP production, preclinical toxicity evaluation and pharmacodynamic studies of these compounds being more challenging than for naked nucleic acids or patient-specific formulations, such products need time before being evaluated in human trials. In mice, in vivo electroporation after intramuscular injection of pDNA is found to be superior to gene-gun delivery (Smorlesi et al., 2006). However, being a cost effective (only 2 g of nucleic acids are delivered per application), reproducible (controlled by gas pressure), safe (exclusive delivery in the dermis that avoids systemic distribution while guarantying the elimination of recombinant nucleic acid through natural skin regeneration) and painless method, gene-gun vaccination holds great promises in the context of antitumor immunotherapies. Optimized nucleic acid structures (efficient promoters for pDNA and Cap  poly-A tail for mRNA) coding immunologically improved tumor antigens (with enhanced MHC presentation and immunogenicity) in adequate formulations (buffers, particles, additional cytokines, or adjuvants) will constitute a second generation of nucleic acid-based vaccines. Used in combination with chemotherapies or immunomodulating regimens dedicated to reduce immunosuppressive mechanisms in cancer patients, these new genetic vaccination interventions should demonstrate higher efficacy than the products used until now. They are expected to be clinically efficient prophylactic or therapeutic nucleic acid based antitumor immunotherapies. Nucleic acid based vaccines are suitable for designing the ideal vaccine, as defined above (see Figure 50.1).

VIRAL AND BACTERIAL VECTORS The principle of this approach is the integration of a sequence of interest, coding for a cancer-associated gene, or a combination of several genes, or a set of truncated genes containing T-cell epitopes, into a virus that is able to infect human cells but is deficient in the capacity to replicate in vivo. A large number of such constructs have been used in preclinical models and also in some clinical studies (Liu et al., 2004). Adenoviruses have been

used with good antigen expression, and clinical trials have been started (Gallo et al., 2005). Viruses causing infectious diseases, such as measles or influenza virus, are being used in attenuated forms. Derivatives of established vaccine viruses, in particular vaccinia virus especially the ankara form (Drexler et al., 2003), have been used successfully to induce immune responses against the inserted antigens (Liu et al., 2004). The ankara form expressing one particular tumor-associated antigen (5T4) has been used successfully in colon cancer patients (Harrop et al., 2006). Other virus constructs used in clinical trials are fowlpox (Morse et al., 2005) canary pox (ALVAC) (Spaner et al., 2006). Results can be improved by adding additional (the virus itself usually contains a TLR ligand or other pathogen-associated patterns) adjuvants (Tormo et al., 2006), and by adding cytokines either as molecules or as additionally encoded genes into the vector (Liu et al., 2004). One particular complication of recombinant viruses is the immunogenicity of proteins of the carrier virus (Drexler et al., 2003; Smith et al., 2005), which may dominate the response against the antigens of interest. It is being tried to overcome this problem by combining application of virus vector with another form of vaccine, for example, plasmid DNA coding for the tumor antigen, in order to focus the immune response to the antigen of desire (Marshall et al., 2005). Preexisting immunity to the carrier virus, for example, vaccinia in older patients previously immunized against small pox, might quickly eliminate the vaccine, or, even worse, might cause a harmful immune response against the vector, as has been the case in an attempt of adenovirus-mediated gene therapy. An additional problem with recombinant viruses, for example, adenovirus is the safety issue (Gallo et al., 2005). Bacterial vectors offer the option of the oral route. Salmonella typhimurium has been engineered to secrete a tumor antigen, NYESO-1, through its type III system. Such recombinant bacteria were given orally to cancer patients, who developed a favorable response (Nishikawa et al., 2006). Listeria monocytogenes is a particular interesting vector for cancer vaccines, because these bacteria escape the endolysosome by virtue of a particular virulence factor, listeriolysin, and continue their existence in the cytosol, where their proteins have direct access to conventional MHC I processing (Paterson and Maciag, 2005). Bacteria-mediated DNA transfer in vaccination has been reviewed recently by (Loessner and Weiss, 2004), and the development of live, attenuated bacterial vectors in (Roland et al., 2005). Bacterial and viral vectors certainly have a great potential and certain advantages (e.g., the possibility of oral delivery) for vaccination against one or a few defined cancer antigens shared by a reasonable patient population. Regarding our ideal of a cancer vaccine, a similar statement as for protein vaccines can be reached: logistically and technically not possible.

ADJUVANTS, FORMULATIONS, AND ROUTE OF APPLICATION As mentioned above, already established tumors in most cases do not induce a spontaneous, effective immune response, most

Immunomonitoring

likely for one or more of three reasons: lack of costimulation, suppression by regulatory T-cells, or suppression by the tumor or its microenvironment (see above). The task of an efficient cancer vaccine, therefore, is to overcome these obstacles, in addition to directing the immune response to the relevant antigens. These tasks can be met by appropriate adjuvants, formulations, and routes of delivery. Since adjuvants in general are treated in detail elsewhere in this book, only special cancer-related aspects are considered below. Antigen Delivery Adjuvants that create a depot enhancing the slow release of antigen and inhibiting early degradation of antigen have long been used in experimental animals and also in a limited numbers of clinical trials. A well known example is incomplete Freund’s Adjuvans; a modification for human use is Montanide ISA-51 (Seppic SA, France), which has been used in numerous trials especially as adjuvant for peptide vaccines. DC Attraction/Activation A large number of adjuvants that stimulate immune cells, ideally substances that attract and activate DCs creating a pro-inflammatory milieu at the vaccination site, are known now. Most of them are molecularly defined. A substance shown to be effective, for example, by enhancing immune responses against peptides applied intradermally, is GM-CSF (DC attraction and indirect DC activation, Molenkamp et al., 2005). GM-CSF has been used in various trials, particularly with peptide vaccines, and has been shown to be safe and effective in adjuvanting immune responses. Numerous ligands of TLRs (direct DC activation) have been identified and characterized in the recent years; several are being developed for adjuvant use, in particular the TLR9 ligand CPG DNA (Appay et al., 2006). Other TLR ligands, or ligands of non-TLR pathogen-associated pattern receptors, are or will be developed in the near future. Coupling an antigen of interest – in form of peptide, protein, or nucleic acid – appears to be a particular interesting way of combining antigen delivery and DC activation. The first report of such an approach – a TLR2 ligand, Pam3Cys, coupled covalently to an extended T-cell epitope induced efficient CTL responses in mice – dates back to 1989 (Deres et al., 1989). Similarly, tumor antigens fused to immunogenic bacterial components, such as the B subunit of Shiga toxin targets DCs to allow MHC class I-restricted presentation of peptides derived from exogenous antigens and elicits CTL (Haicheur et al., 2000). A very common and relatively efficient approach, though not very practical, is the loading of antigens – peptides, proteins, or RNA, for example – onto autologous DCs, as reviewed by Slingluff et al., 2006. Although ligands of TLRs appear to be among the most attractive adjuvants, many recently described TLR ligands are not readily available in GMP quality for clinical trials. Routes of Application The traditional route of application of vaccines against infectious disease is i.m. or s.c. For cancer vaccines, almost all thinkable

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routes have been tried, in particular i.d. injection of peptides, i.m. injection of nucleic acids, s.c. injection of peptide or protein antigens emulsified in oily depots, intranodal injection of peptides or nucleic acids (see above), and oral or otherwise mucosal application of bacterial vectors. The best combinations of antigen formulation, adjuvant, and route of application can only be found empirically using systematic controlled clinical trials. The i.d. route is considered as highly attractive for many different forms of vaccines as the dermis is the home to Langerhans cells and dermal DCs. However, successful immunizations have also been shown with the s.c. route, especially if the antigen was administered together with a depot like Montanide.

IMMUNOMONITORING Judging the success of a prophylactic vaccine against acute infections has traditionally been done by comparing infection rates in vaccines versus a not vaccinated population. As surrogate markers, antibody titers against the vaccine, for example, Hepatitis B, are analyzed. The task of monitoring therapeutic vaccines against cancer is more difficult. Immunomonitoring of vaccinated cancer patients for T and B cell responses against the vaccine gives fast results, but the relation of immune response to clinical benefit is a matter of debate and has been observed only in a very limited number of trials. Antibody as well as T-cell responses against tumor-associated antigens can occur spontaneously, without obvious correlation to cancer progression, and even in healthy individuals. For most cancer vaccines, T-cell responses are desired, and therefore monitored. The techniques of IFN-gamma ELISpot, intracellular cytokine staining, and T-cell specificity analysis by HLA tetramers, the latter two by flow cytometry allowing single cell analysis, have been found to be useful for screening antigen-specific T-cell responses (Keilholz et al., 2006). These techniques can be performed with few blood cells, which are usually available only in limited quantity, typically from a 10– 50 ml blood sample of the patient depending of the number of antigens to be tested. For tetramer analysis and especially for the ELISpot assay, quality assessment and standardization of methods are mandatory in order to achieve serious data. An attempt for such quality assessment has been made by the CIMT organization for ELISpot and for tetramers (www.c-imt.org). Is a T-cell response induced by a vaccine a predictor for improved clinical outcome, that is regression of cancer or at least stable disease? Boon and colleagues, after a very careful in depth analysis of peptide vaccinated melanoma patients, come to the conclusion that this is not the case (Boon et al., 2006). Celis et al., however, reach the opposite conclusion upon peptide vaccination of patients with metastatic melanoma: they report improved clinical outcome in patients demonstrating effective immunization (Markovic et al., 2006), similarly as Banchereu et al. in a long-term follow-up study (Fay et al., 2006). Our own recent study (IMA901) on multi-peptide vaccination of metastatic renal cell carcinoma patients also indicates a significant correlation

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(p  0.02): those patients showing T-cell responses against two or more of the peptides included in the vaccine do significantly better clinically (in terms of tumor stabilization and shrinkage) than those with response to no or one peptide. Monitoring of antibody responses is rarely done, since frequent, patients have serum antibodies against tumor associated but also other self-antigens, a phenomenon being the basis of the SEREX technology, which is used to define antigens potentially being cancer associated (Sahin et al., 1997). A previously unexpected correlation between vaccine success and clinical response was reported by Itoh et al.They find humoral responses to the short HLA class I-restricted peptides (typically nonamers) used for immunization to correlate with overall survival in advanced cancer patients, whereby only peptides were used recognized by pre-existing T-cell responses (Fukuda et al., 2004). Such peptide-specific antibodies themselves are probably without effect against surface antigens expressed on tumor cells, because they most likely do not react against the protein of origin.

CONCLUSIONS In the last few years, therapeutic cancer vaccines have matured beyond the conceptual stages. Encouraging results from randomized late-stage clinical trials support the exciting vision the cancer vaccine approach offers. Two recent examples: Data published by the companies Biomira and Merck KGaA using BLP-25 (a liposomal formulation of a peptide derived from MUC-1) showed prolongation of survival in stage IIIb NSCLC patients from 13.3 to 30.6 months (Butts et al., 2005). The US-based company Dendreon demonstrated statistically significant survival benefits with a vaccine based on DCs transfected with PAP, a prostate cancer-associated antigen. In a randomized phase III trial for metastatic prostate cancer patients, the 3-year survival was 33% versus 15% in the placebo group. Although the FDA recently asked for additional data in order to grant marketing approval for this vaccine, such advantages in late-stage, randomized, controlled trials are providing first proof for the efficacy of cancer vaccines. Mellstedt and colleagues recently reviewed the issue of cancer immunotherapy. (Choudhury et al., 2006). They come to a conclusion which we completely share. They write: “Active, specific immunotherapy for cancer holds the potential of providing an approach for treating cancers, which have not been controlled by conventional therapy,

with very little or no associated toxicity. Despite advances in the understanding of the immunological basis of cancer vaccine therapy as well as technological progress, clinical effectiveness of this therapy has often been frustratingly unpredictable. Hundreds of preclinical and clinical studies have been performed addressing issues related to the generation of a therapeutic immune response against tumors and exploring a diverse array of antigens, immunological adjuvants, and delivery systems for vaccinating patients against cancer. […] The design of clinical trials have not yet been optimized, but meaningful clinical effects have been seen in B-cell malignancies, lung, prostate, colorectal cancer, and melanoma. It is also obvious that patients with limited disease or in the adjuvant settings have benefited most from this targeted therapy approach. It is imperative that future studies focus on exploring the relationship between immune and clinical responses to establish whether immune monitoring could be a reliable surrogate marker for evaluating the clinical efficacy of cancer vaccines.”

The notion that cancer vaccines generally have a low toxicity is widely shared (Slingluff and Speiser, 2005). In addition to these well taken statements, we expect that the near future will show improved success of vaccines which are still quite far away from what we have defined as the ideal vaccine, but which are designed based on practical constraints. In our view, multi-epitope peptidebased vaccines – although limited by the necessity for HLA matching – will be the fastest way to a successful cancer immunotherapy product, because they are technically the simplest to use, once the relevant peptides fitting to a given tumor entity and HLA molecule have been identified and their immunogenicity can be improved by using efficient adjuvants and regiments to reduce Tregs. RNA or DNA-based vaccines, not dependent on HLA typing, similarly have the potential to be used for multi-epitope vaccines in the near future, again, if their immunogenicity can be improved. The more established forms of vaccines, in experimental settings, such as protein conjugates, viral or bacterial vectors, might be very useful for the delivery of single defined widespread antigens, and may also be used as an adjuvant component to peptide or nucleic acid vaccines. Since tumors are genetically instable, and tend to lose their antigens and MHC molecules, especially if under immune attack, successful vaccines will contain multiple antigens, such as our ideal vaccine defined above. Such vaccines should be combined with two additional measures: cytokine or other treatment to activate NK cells that can attack MHC negative tumor cells, and other combination treatment, such as chemotherapy.

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antigen delivery by a Salmonella typhimurium type III secretion system for therapeutic cancer vaccines. J Clin Invest 116, 1946–1954. Nishikawa, M. and Hashida, M. (2002). Nonviral approaches satisfying various requirements for effective in vivo gene therapy. Biol Pharm Bull 25, 275–283. Osenga, K.L., Hank, J.A., Albertini, M.R., Gan, J., Sternberg, A.G., Eickhoff, J., Seeger, R.C., Matthay, K.K., Reynolds, C.P., Twist, C. et al. (2006). A phase I clinical trial of the hu14 18-IL2 (EMD 273063) as a treatment for children with refractory or recurrent neuroblastoma and melanoma: A study of the Children’s Oncology Group. Clin Cancer Res 12, 1750–1759. Oyama, T., Ran, S., Ishida, T., Nadaf, S., Kerr, L., Carbone, D.P. and Gabrilovich, D.I. (1998). Vascular endothelial growth factor affects dendritic cell maturation through the inhibition of nuclear factorkappa B activation in hemopoietic progenitor cells. J Immunol 160, 1224–1232. Pascolo, S. (2004). Messenger RNA-based vaccines. Expert Opin Biol Ther 4, 1285–1294. Paterson, Y. and Maciag, P.C. (2005). Listeria-based vaccines for cancer treatment. Curr Opin Mol Ther 7, 454–460. Pavlenko, M., Roos, A.K., Lundqvist, A., Palmborg, A., Miller, A.M., Ozenci, V., Bergman, B., Egevad, L., Hellstrom, M., Kiessling, R. et al. (2004a). A phase I trial of DNA vaccination with a plasmid expressing prostate-specific antigen in patients with hormonerefractory prostate cancer. Br J Cancer 91, 688–694. Pavlenko, M., Roos, A.K., Lundqvist, A., Palmborg, A., Miller, A.M., Ozenci, V., Bergman, B., Egevad, L., Hellstrom, M., Kiessling, R. et al. (2004b). A phase I trial of DNA vaccination with a plasmid expressing prostate-specific antigen in patients with hormonerefractory prostate cancer. Br J Cancer 91, 688–694. Piatesi, A., Howland, S.W., Rakestraw, J.A., Renner, C., Robson, N., Cebon, J., Maraskovsky, E., Ritter, G., Old, L. and Wittrup, K.D. (2006). Directed evolution for improved secretion of cancer-testis antigen NY-ESO-1 from yeast. Protein Exp Purif 48, 232–242. Pilla, L., Patuzzo, R., Rivoltini, L., Maio, M., Pennacchioli, E., Lamaj, E., Maurichi, A., Massarut, S., Marchiano, A., Santantonio, C. et al. (2006). A phase II trial of vaccination with autologous, tumorderived heat-shock protein peptide complexes Gp96, in combination with GM-CSF and interferon-alpha in metastatic melanoma patients. Cancer Immunol Immunother 55, 958–968. Probst, J., Brechtel, S., Scheel, B., Hoerr, I., Jung, G., Rammensee, H.G. and Pascolo, S. (2006). Characterization of the ribonuclease activity on the skin surface. Genet Vaccines Ther 4, 4. Probst, J., Weide, B., Scheel, B., Pichler, B.J., Hoerr, I., Rammensee, H. G. and Pascolo, S. (2007). Spontaneous cellular uptake of exogenous messenger RNA in vivo is nucleic acid-specific, saturable and ion dependent. Gene Ther 14(15), 1175–1180. Prud’homme, G.J., Glinka, Y., Khan, A.S. and Draghia-Akli, R. (2006). Electroporation-enhanced nonviral gene transfer for the prevention or treatment of immunological, endocrine and neoplastic diseases. Curr Gene Ther 6, 243–273. Rammensee, H.G. (2006). Some considerations on the use of peptides and mRNA for therapeutic vaccination against cancer. Immunol Cell Biol 84, 290–294. Rammensee, H.G., Falk, K. and Rotzschke, O. (1993a). MHC molecules as peptide receptors. Curr Opin Immunol 5, 35–44. Rammensee, H.G., Falk, K. and Rotzschke, O. (1993b). Peptides naturally presented by MHC class I molecules. Annu Rev Immunol 11, 213–244.

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51 Biosensors for the Genomic Age Meghan B. O’Donoghue, Lin Wang, Yan Chen, Gang Yao and Weihong Tan

INTRODUCTION With the completion of the human genome sequencing project and the rise in understanding of the integral relationship between genes and diseases, there is a resulting change in the focus of quantitative studies of genomic information from the gathering and archiving of genomic data to using this analysis for disease prediction, diagnosis, and treatment. One area of research that is especially poised to reap the benefits of this new genetic information is the development of biosensors. A biosensor is a device that detects, records, and transmits information regarding a physiological change or the presence of various chemical or biological materials in the environment. More technically, a biosensor is a probe that integrates a biological component, such as a whole bacterium or a biological product (e.g., an enzyme or antibody) with an electronic component to yield a measurable signal. Biosensors, which come in a large variety of sizes and shapes, are used to monitor changes in environmental conditions. They can detect and measure concentrations of specific biochemicals. Biosensors harness the unique specificity and affinity inherent to biological molecules in order to recognize biologically relevant targets in a variety of settings. This recognition event produces a signal, typically electrochemical or optical, that can be analyzed by a detector. Targets for detection are potentially limitless including small analytes like lactate, antigens for cancer like prostate specific antigen (PSA) (Yu et al., 2004), and possible agents of bioterrorism like Baccilus anthrax and

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E. coli 0157:H7 (Zhao et al., 2004), or single nucleotide polymorphisms (SNPs) related to a specific genetic disorder (Corradini et al., 2004). All of these targets are currently detected in the laboratory by various accurate yet cumbersome and expensive processes such as standard PCR, cell culturing, HPLC, or gel electrophoresis. Biosensors, through clever design, offer the advantage of putting these tests into a small, fast, easy to use format. Some commercially successful biosensors have been taken out of the lab entirely and are used daily by many people to gauge pregnancy or HIV status. The transduction mechanisms in most biosensors are simple and straightforward as seen in Figure 51.1, consisting of a substrate to support the biorecognition element and an external stimulus to determine the presence of analyte molecules or species that specifically interact with the recognition element. This information is transformed into an optical or electrical signal that is detected with a detection unit. By far the most widely used biosensor is the blood glucose detector, crucial to maintaining the health of diabetics. The sensor works by bringing a small sample of blood in contact with the enzyme (glucose oxidase) that catalyses the oxidation of glucose into lactone, releasing hydrogen peroxide (Newman and Setford, 2006). The increase in hydrogen peroxide produces an electrical potential in the electrode that can be measured, creating an accurate readout of blood glucose concentration. A biosensor in essence is the distillation of years of medical research into a product that can give fast, easy to obtain diagnostic

Copyright © 2009, Elsevier Inc. All rights reserved.

Biosensors for Detection of Oligonucleotides for the Detection of Disease

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Figure 51.1

Schematic of a biosensor. Figure 51.2 Common biosensors: (a) Electronic glucose monitor from Precision®; (b) Oraquick® HIV testing dipstick.

information. This chapter will deal with two main topics at the intersection between genomics and biosensing: 1. The current efforts being made to develop biosensors for the detection of oligonucleotides to diagnose disease especially for (a) cancer, (b) virus, and (c) bioterrorism detection. 2. The use of nucleic acids as tools for biosensing through (a) molecular beacons, (b) aptamers, and (c) nanoparticles. Through these two topics we will also examine future trends on the biosensor horizon including the use of nanomaterials, multiplexed biosensors, multivariate analysis, and new biorecognition molecules capable of signal production without need for sample purification.

BIOSENSORS FOR DETECTION OF OLIGONUCLEOTIDES FOR THE DETECTION OF DISEASE While biosensors have the potential for improving disease diagnosis in nearly every field of medicine, there are several main areas that are particularly amenable to the incorporation of biosensors into daily practice, and as such have been the focus of much research and funding. These areas include cancer diagnosis, profiling for genetic disease, viral detection, and early sensing of a bioterror attack. The advent of PCR technology especially single cell PCR, real-time PCR, and reverse-transcriptase PCR has allowed the research community to quickly ascertain the genetic make-up and status of many types of disorders including hereditary diseases such as cystic fibrosis (Lagoe, 2005), the viral burden in HIV-infected individuals ( Jansen et al., 2006), and the genetic profile of malignant tumors (Harris, 2007). After these

advances the next step has been the development of DNA-based biosensors that can detect these changes and abnormalities in a simple, rapid, and accurate fashion. Several common examples can be seen in Figure 51.2. Cancer Biosensors At present the two most important indicators for cancer detection and prognosis are the morphological and histological characteristics of tumors observed through physical examination. These examinations in recent years have been fortified with new molecular tools examining individual proteins and genes associated with these tumors to create a “molecular signature.” These signatures give clinicians a more detailed view of particular cancer types, aiding in diagnosis and treatment. As cancer is a genetic disease caused by mutation or modification of DNA sequences, analysis of key genes responsible for cancer in particular have great potential for diagnostics (Soper et al., 2006). Currently there are a host of biomarkers being tested as biosensors for numerous cancers. These efforts fall into three main classes: (1) monitoring proteins expressed by tumor cells such as HER2 for breast cancer (Rossi et al., 2004), (2) assaying specific genetic point mutations found in a majority of tumor types including mutations in the p53 gene involved in cell cycle control and apoptosis (Hashimoto et al., 2004) and the K-ras gene involved in colorectal cancer (Dell’Atti, 2006), or (3) predicting the hereditary likelihood of contracting cancer based on a genetic profile as is the case with increased ovarian cancer incidence associated with the BRCA1/2 gene mutation (Mitchell et al., 2006).

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Currently there are also strategies being explored to place numerous biomarkers on a single “DNA Chip” detection system, which would allow for simultaneous interrogation of multiple biomarkers simultaneously, thereby greatly increasing the biosensor’s accuracy, speed, and convenience for creating tumor molecular signature. For instance a diagnostic test that creates a tumor profile that scores a patient’s likelihood for recurrence and response to certain chemotherapy options upon receiving a breast cancer diagnosis is currently sold by Genomic Health (Redwood City, CA) under the name Oncotype DX™. Oncotype DX™ may not be recognized as a traditional biosensor, but it can perform all the functions a biosensor does. In this system RNA is collected from histological sections of the tumor and amplified using a reverse-transcriptase PCR, and the PCRproduct is then probed on a microarray with DNA probes for 20 target genes associated with breast cancer outcome. After incubation and washing the microarray is assayed with a fluorescent marker (Cobleigh et al., 2005). The information gleaned from the microarray is fed into a computer algorithm that computes a predictive score of breast cancer outcome. For the patient, the test takes about 2 weeks and costs around $3500. Recently the FDA began regulating what it classes “multivariate index assays” such as Oncotype DX™ that measure multiple genes, proteins, or other pieces of clinical information taken from a patient and then uses a software program to analyze the data (Pollack, 2006). While likely to slow biosensor development, this regulation is indicative of both their growth and success in this area. Viral Biosensors Detection of virus type and load is another area where nucleic acid biosensors have made several inroads in recent years. One of the most successful applications of this technology identifies specific types of viral infection, most notably detection of cancer causing strains of humanpapilloma virus (HPV). About a dozen of the estimated 70 HPV types are termed “high-risk” types because they cause various cancers by incorporating HPV viral sequences into cellular DNA. Several of the highest risk HPV genes are known as oncogenes because they promote malignant transformation and tumor growth (Parkin and Bray, 2006). Until 2003 the main method to detect HPV was a Pap smear where detection of potential cancerous cells was performed by microscopic evaluation of a swab of cells from a woman’s cervix. In 2003 the FDA approved the first DNA biosensor for detection of high-risk HPV infection as a follow-up confirmation of an irregular Pap smear. The product marketed by Digene (Gaithersburg, MD) is a nucleic acid hybridization assay where samples containing the target DNA hybridize with a specific HPV-RNA probe cocktail. The resultant RNA:DNA hybrids are captured onto the surface of a microplate well coated with antibodies specific for RNA:DNA hybrids. They are then imaged with alkaline phosphatase and the light emitted detects the presence or absence of target HPV DNA in the specimen (Digene, 2006). Another instance where nucleic acid biosensors have been used to manage viral disease are the numerous HIV-1 RNA viral load kits on the market today. These systems pioneered the

process used in the Oncogene DX™ system described above. Measurement of blood plasma HIV-1 RNA concentration using nucleic acid-based molecular diagnostic assays is the standard of care prior to starting antiretroviral therapy. Many sensitive and precise viral load assays that incorporate PCR, have been developed to quantify HIV-1 RNA accurately. In these systems viral RNA is isolated, and reverse transcription PCR is performed to yield single-stranded complimentary DNA (cDNA).The cDNA is then amplified exponentially in repeated cycles of heating and cooling by PCR and the result is subsequently probed using ELISA or a microarray to gauge the viral load (Liegler and Grant, 2006). Bioterrorism Detection Finally with a growing awareness of the threat from bioterrorism, there has been a big push in recent years for extremely sensitive biosensors for rapid detection of agents of biowarfare such as B. anthrax. In this line, several rapid automated systems that integrate PCR-based assays have been introduced to detect agents of bioterror because these assays offer the most selective and sensitive detection (Deisingh and Thompson, 2004). There are currently two federally sponsored programs, BioWatch and BioSense, which seek to provide round-the-clock biosensing of air in high-risk places including post offices and government buildings (Burnett et al., 2005). Biosensors for nucleic acid detection are fast becoming integrated into health care and defense. Most of the methods cited above, however, rely on PCR technology, which while rapidly becoming highly automated and in some cases portable (Liao et al., 2005) still place limits on biosensor’s detection time, availability, and cost.

NUCLEIC ACID AS TOOLS FOR BIOSENSING PCR-based approaches have been crucial to the production of ultra-sensitive biomarkers for a host of applications. Their inherent requirements for sample purification and amplification, however, increase the cost, time, and complexity of the analysis – three limitations that are contrary to the biosensor’s aim to be a rapid, simple, portable system for detection. Several new classes of engineered oligonucleotide probes promise to eliminate the need for PCR and thereby greatly improve current biosensor’s performance. These new probes include molecular beacons (MBs), aptamers, and oligonucleotide-conjugated fluorescent nanoparticles (NPs). Molecular Beacon Biosensors Fluorescent biosensors possess high sensitivity and excellent selectivity for the detection of many target molecules. With the recent advent of DNA probe technology, a number of selective fluorescence biosensors which interact with specific DNA sequences in biological species have been identified and used to provide a new type of selective biorecognition information. MBs are one such probe that has provided a variety of exciting opportunities. They

Nucleic Acid as Tools for Biosensing

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Figure 51.3 Molecular beacons (a) MB where F is the fluorophore and Q is the quencher – upon hybridization of the MB with the DNA target a signal is produced. (b) Immobilized MB hybridization kinetics study. Real-time measurements of the hybridization dynamics of immobilized MBs were obtained with target DNA molecules (solid circle) and noncomplementary DNA molecules (hollow circle).

have been applied to detect specific DNA and mRNA sequences as well as for the study of DNA hybridization kinetics. The target DNA concentration detection limit of the MB biosensors is in the picomolar (1012 M) range, and the smallest amount of DNA that has been detected in the temptomolar range (Li et al., 2008). MBs operate on the principle of DNA-base pairing. They are synthetic DNA molecules that conform to a basic “stemloop” or hairpin structure (Figure 51.3). DNA hybridization acts as the basis for target recognition and signal transduction in MB studies. The loop sequence (15–30-mer) is complementary to a target DNA, while the stem is a 5–7-mer sequence complementary to itself so that prior to binding target DNA sequences the structure remains in the closed state, preventing observation of fluorescence from the fluorophore. Upon hybridization to the target molecules, MB undergoes a spontaneous conformational change, which separates the fluorophore from the quencher, restoring fluorescence. Due to the unique structural and thermodynamic properties of MBs, these probes offer several advantages such as high

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sensitivity, excellent selectivity, efficient intrinsic signal transduction, and detection without separation. All these properties have enabled MBs to be widely utilized in biotechnology, chemistry, and biomedical science. They have found a variety of applications such as DNA/RNA detection (Tyagi and Kramer, 1996; Tyagi et al., 1998, 2000; Zhang et al., 2001), living systems investigation (Bratu et al., 2003; Perlette and Tan, 2001; Medley et al., 2005; Santangelo, 2004), enzymatic process monitoring (Tang et al., 2003, 2005; Li et al., 2000) and protein–DNA interactions (Fang et al., 2000a, b; Li et al., 2000; Heyduk and Heyduk, 2002). While MBs have been used broadly in a variety of solutionbased applications, their use on surfaces and interfaces as biosensors is also rapidly expanding (Yao and Tan, 2004; Liu et al., 2005; Steemers et al., 2000). In order to integrate the advantages of using MBs in solutions such as a low background and high selectivity into biosensors and biochips, two issues must be confronted. The first is how to immobilize the MBs with high efficiency and stability, and the second is how to optimize the design of the MBs for use on a surface. Similar to other DNA/ RNA probe immobilization strategies, three bioconjugation methods are employed for most applications, which include biotin–avidin conjugations, thiol–Au linkages, and aldehyde– amine reactions. Corresponding with the various immobilization strategies, different support substrates including glass (Fang et al., 1999;Yao and Tan, 2004), gold (Du et al., 2003), and polyacrylamide (Wang et al., 2005) are used for fixing MBs. All of these strategies provide highly efficient and reliable immobilization, contributing to the reproducibility, sensitivity, and response speed of the MB-based biosensors and bioarrays. Although the performance of MBs is impressive in solutionbased applications, the high backgrounds encountered when MBs are fixed on a surface tremendously cut down MBs’ function. In order to minimize the negative effects of surface immobilization on the MB’s features, several approaches have been developed to address. One way is to add a spacer between the MB sequence and fixing group, so that the MBs will be separated from the fixing group and support surface, thus minimizing potential interactions among the MBs, fixing group, and surface (Horejsh et al., 2005; Li et al., 2001). A similar solution has been developed by extending a poly-T linker at one end of MB. Using these MB probes, a DNA array has seen limited improvement in assay sensitivity (Figure 51.4). MBs have also been settled onto a functionalized hydrophilic gel film such as agarose or polyacrylamide (Wang et al., 2002;Wang et al., 2005) to provide a homogeneous local environment for immobilized MBs. The results of settling MBs on agarose-based gels show a lower background, higher sensitivity, faster response, and better selectivity than MBs fixed on glass. Immobilizing MBs on a gold surface is another solution to decrease background, owing to the high efficient quenching effect of the gold surface to fluorophores in close proximity (Du et al., 2003, 2005). Experiments show that MB fluorescence can be enhanced more than 100-fold after hybridizing with target DNA on the chip (Du et al., 2005). The reporters give the signal when the MB’s conformational switch caused by hybridizing with target nucleic acids occurs.

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(b)

Figure 51.4 Molecular beacon optimization. (a) Schematic of biotin/avadin immobilized MB with different poly-T linker lengths. (b) Fluorescence image of the MB array before and after hybridization with target DNA.

These novel signaling mechanisms give MB applications more flexibility and open up new possibilities for analysis in other applications, including single molecule detection. In this case fluorophores are excited by an evanescent wave produced on an optical fiber surface (Fang et al., 1999; Yao et al., 2003). The fluorescent signal is detected by an ICCD camera that has the capability of detecting single photons. This approach offers a novel means to study single molecule interactions and enables biochemical mapping of surface heterogeneity at the single molecule level. MB hybridization has been monitored after target DNA binding. Single molecule studies such as these provide a solid foundation for the ultra-sensitive detection urgently needed in early diagnosis of cancer and other diseases (Yao and Tan, 2004). MB functionalized beads have also been created for multiple analyte detection.Various MB-coated microspheres are randomly entrapped within an array of wells etched on a 500 μm diameter optical imaging fiber. This array has high throughput capabilities and fast response time allowing accurate analysis of multiple genetic mutations (Steemers et al., 2000). Combined with flow cytometry analysis, a fluid array system using MB functionalized microspheres has been established to detect multiple unlabeled nucleic acid targets (Horejsh et al., 2005). In this approach, a size and color-coded classification strategy is used to differentiate signals from the various MB-coated beads. These assays are expected to provide fast, easy and accurate genetic analysis for disease diagnosis and therapy. Ultra-small optical fiber probes are another class of powerful biosensors because of their ultrasensitivity and spatial resolution (Kopelman et al., 1993;Tan et al., 1992). These renewable MBs fiber probes hold the promise

of detecting the concentration and distribution of specific DNA/RNA targets in living cells, which can provide significant information for biomedicine and cell biology. The molecular beacon has proven to be an excellent probe for homogeneous solutions in biological, biochemical, and clinical studies. MBs can be applied successfully to optical fiber biosensors, microwell biosensors, nanoparticles biosensors, MB array biosensors, and single molecule biosensors. MB biosensors inherit the advantages of MBs, such as high sensitivity, nonlabeled target, detection without separation, and one base match specificity. They have demonstrated a good capability in target detection or monitoring. Aptamer Biosensors Aptamers are another class of engineered nucleic acid that holds tremendous potential for biosensor applications. Aptamers are engineered through an in vitro process called SELEX (systematic evolution of ligands by exponential enrichment) to bind selectively to a target that can be a small molecule, protein, nucleic acid, or even a whole cell (Bunka and Stockley, 2006; Herr et al., 2006). Aptamers can be engineered completely in a test tube, are readily produced by chemical synthesis, possess desirable storage properties, and elicit little or no immunogenicity in therapeutic applications. As aptamer sequences are more stable than proteins such as antibodies (Bunka, 2006), whose function they mimic, under a wide range of conditions they can be used repeatedly without losing their binding capabilities. Compared to antibodies they typically have higher affinity, specificity, and signal-to-noise ratio; additionally they can be selected to bind even very small proteins. Nucleic acid synthetic chemistry also facilitates conjugation of these aptamer sequences to fluorescent dyes, radiolabels, or other biomolecules. Binding to a target molecule induces a conformational change in the aptamer. These changes can be measured and when coupled to fluorescent dyes produce two fluorescent states – bound and unbound, similar to that seen with MBs (Yang et al., 2005). Molecularly engineered aptamer-beacons for rapid and sensitive detection of a biomarker protein take advantage of this property to detect biologically relevant targets such as platelet-derived growth factor (PDGF) (Yang et al., 2005). Labeled with one pyrene at each end, this aptamer switches its fluorescence emission from 400 nm (pyrene monomer) to 485 nm (pyrene excimer) upon binding with PDGF (Figure 51.5). This fluorescence wavelength change from monomer to excimer emission is a result of the aptamer conformation change induced by binding. The aptamer MB is able to effectively detect picomolar PDGF in homogeneous solutions. Because the excimer has a much longer fluorescence lifetime (40 ns) than that of the background (5 ns), time-resolved measurements are able to eliminate the biological background. This allows for quantitative PDGF detection in complex buffer without sample pretreatment. As aptamers other then PDGF exhibit similar conformational changes upon binding, there are many potential targets for aptamer beacons such as human thrombin protein (Paborsky et al., 1993; Hamaguchi et al., 2001; Yang et al., 2005), cocaine (Stojanovic et al., 2001), and HIV1

Nucleic Acid as Tools for Biosensing

PDGF

(b) 1.1 1.0 0.9

Probe response (A.U)

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 BSA

LYS

VEGF

595

allowing for direct hybridization of a complementary sequence of DNA.This complementary DNA can contain a signaling element, such as a fluorophore, which signals only upon binding much like a MB. These systems as well as the aptamer-beacon technology described above can be placed on a multiplexed array for sensitive detection of several targets of interest at the same time. While these aptamer microarrays were first described in 1999 (Brody et al., 1999), they have only recently started to be investigated on a large scale (Collett et al., 2005; Yamamoto-Fujita and Kumar, 2005). Another method for harnessing the distinctive properties of aptamers allows for the rapid, sensitive detection of adenosine and cocaine (Liu and Lu, 2006). In this assay aptamers specific for their target were bound to the surface of aggregated gold NPs. When an aptamer binds to its ligand, a conformation change in the aptamer leads to the disassembly of the aggregated NPs. This disassembly results in a visible color change. By coupling an aptamer’s ability to bind specific targets in a similar manner as monoclonal antibodies with their easy of manufacture and conformational change upon binding, aptamers are slated to become the bioreceptor of choice for the next generation of biosensors.

(a)

0.1

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PDGF-AA PDGF-AB PDGF-BB

Figure 51.5 Use of the aptamer beacon to probe PDGF. (a) PDGF aptamer (red) is end-labeled with pyrene molecules (blue) that are separated from each other because of the open structure of the aptamer. The pyrene molecule has monomer emission peaks at 378 and 398 nm. After binding to PDGF (purple), the aptamer adapts a closed conformation, bringing two pyrene molecules close to each other. Consequently, pyrene excimer (green) forms and green light (485 nm) is emitted after photoexcitation. (b) Responses of the excimer probe (50 nm) to BSA, lysozyme (LYS), growth factor VEGF, PDGF-AA, PDGF-AB, and PDGF-BB.

TAT protein (Yamamoto et al., 2000). Even if an aptamer does not have an obvious target-induced structure change, it can be designed to change its secondary structure as desired upon target binding with rational structure engineering (Nutiu and Li, 2003, 2004; Bayer and Smolke, 2005). This strategy has been well demonstrated by Bayer and Smolke who used the aptamer/ target-binding event to switch the aptamer structure to convert it to a gene-expression regulator (Bayer and Smolke, 2005). Aptamers can also be fixed on solid surfaces. When bound to a target these aptamers alter their conformation, only then

Nanoparticle Biosensors As more and more molecular mechanisms for disease have been uncovered in recent years, clinical diagnosis has depended more and more on the detection and monitoring of individual chemical interactions of smaller and less abundant targets such as individual cells, mRNA, DNA, proteins, and peptides. As these targets exist on a nanoscale, probes with dimensional similarities allowing for integration of nanotechnology and biology have become crucial to the next generation of probes (Niemeyer, 2001; Nicewarner-Pena et al., 2001). Several novel classes of nanoscale probes, such as fluorescent dye-doped silica nanoparticles (NPs) (Tan et al., 2004; Wang et al., 2006), semiconductor crystal quantum dots (QDs) (Medintz et al., 2005; Michalet et al., 2005; Alivisatos 2004) and metallic NPs (Mirkin and Nathaniel, 2005) have already yielded small, bright, and easily functionalized tools for diagnosis and detection. Through careful molecular engineering, these probes have become small (5–80 nm), stable, readily biofunctionalized, and easily detectable. When conjugated to biorecognition molecules like antibodies and oligonucleotides, nanoparticles have found applications ranging from gold NPs detection of attomolar to femtomolar concentrations of PSA in blood samples (Nam et al., 2003) to dye-doped silica NP’s detection of a single pathogenic E. coli O157:H7 bacterium in spiked beef samples as seen in Figure 51.6 (Zhao et al., 2004). In the future, use of NPs may obviate the need for PCR and produce biosensors for multiplex diagnostic applications. Taking these nanoscale probes and integrating them into a system optimized for the production of biosensors are the current goals of much research in this field (Nicewarner-Pena et al., 2001; Niemeyer, 2001). One interesting example of this work involves the use of a system of magnetic NPs and fluorescent NPs for the

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concentration and detection of leukemia cells from patient blood (Herr et al., 2006). In this system 60 nm magnetic NPs and fluorescent silica NPs were conjugated with an aptamer specific for a leukemia cell line. These aptamer conjugated NPs were mixed in whole blood samples spiked with leukemia cells, collected with a magnet, and then imaged, allowing for selective concentration and fluorescent assay of the targeted leukemia cells (Figure 51.7). Although nanoparticles have made some initial advances in biosensors, they have not yet been practically useful except for a few nanoparticle-based biosensors. Most of the nanomaterials-based biosensors are still in a stage of demonstrating the principle. There are still quite a few technical and environmental difficulties before these nanotechnologies can be used effectively in real life. For example, in spite of the efforts in NP surface modification to render them water soluble, chemically stable, and biocompatible in physiological media, more studies

are needed to develop strategies to improve the properties of the NP support matrices and surfaces. This can help reduce nonspecific binding and facilitate the subsequent attachment of biological moieties that will improve the binding kinetics and affinities of the NPs for their target molecules. It is believed that nonspecific binding and NP aggregation are still major issues blocking or slowing progress in realizing the power of nanomaterials for biosensors. Total elimination of nonspecific binding is a difficult or probably impossible task especially when the NPs are used in a biological milieu. This problem demands the scientific community to make a huge effort in designing new strategies to greatly reduce NP background signal due to nonspecific binding in order to detect ultra-trace amounts of analytes using biosensors based on nanomaterials.

OUTLOOK (b)

(a)

100.00 nm

(c)

(d)

1 μm

Figure 51.6 NP for ultra-sensitive detection. (a) Different dyedoped NPs in PBS; (b) SEM image of dye-doped silica-coated NPs: 634 nm. The NPs are uniform and easily produced; (c) SEM image of E. coli 0157:H7 cell incubated with antibody conjugated NPs; (d) fluorescence image of single E. coli 0157:H7 with antibody conjugated NPs. The strong fluorescence signal allows for rapid single detection of bacteria.

(a)

(b)

The biosensor format is viewed as a valuable tool in many bioassays, but possesses numerous limitation due primarily to the single element nature of these sensing platforms. As such, if biosensors are to become valuable tools in the arsenal of the clinician to manage cancer patients, new formats are required. There is a clear need for molecular profiling of multiple targets for diagnosis and prognosis of diseases such as cancer. Once biosensors can be made for multiple biomarker profiling, they should be highly useful for point of care of patients. As we have seen, biosensors have the ability to bring important diagnostic tools to patients in a rapid, cheap, and accessible fashion. As biosensor technology is improved and optimized for the rigors of real-life clinical and diagnostic use, perhaps through the wider application of nanomaterials, MBs, and aptamers, current problems in health care and defense will hopefully be addressed. As such there has been a great push in the last 15 years toward the commercialization (Kissinger, 2005) of these types of products and, while the results have been promising, it seems the optimization of the biosensor development process – from biomarker discovery to pharmacy counter – needs to become more streamlined before biosensors for bacteria, genetic disorders, or cancer profiles become commonplace.

(c)

(d)

Magnet

Figure 51.7 Magnetic and fluorescent NPs with aptamers. (a) incubation of NP-aptamers with normal cells (yellow) and leukemia cells (green); (b) binding of aptamer to target cell; (c) magnetic separation; (d) detection.

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CHAPTER

52 Stem Cells Rikkert L. Snoeckx,1 Kris Van Den Bogaert1 and Catherine M. Verfaillie

INTRODUCTION Put simply, stem cells are self-renewing, unspecialized cells that can give rise to multiple types of specialized cells of the body. The process by which dividing unspecialized cells are equipped to perform a specific function is called differentiation and is fundamental to the development of the mature organism. It is now known that stem cells, in various forms, can be obtained from the embryo and the adult. The self-renewal and multilineage differentiation characteristics of stem cells make these cells uniquely suited for regenerative medicine, tissue repair and gene therapy applications. How and when stem cells derived from any of these sources will be used as therapeutics will depend on the increasing knowledge of their basic properties and developmental pathways. In this respect, the coupling of stem cells with the information learned from the human genome project and the subsequent development of innovative whole-genome methods (RNAi, microRNAs, protein–DNA interactions, etc) will likely have many unanticipated benefits in the future with regard to translational genomics.

TYPES OF STEM CELLS: EMBRYONIC AND ADULT STEM CELLS Through the years, scientists have defined stem cells in many ways but the consensus definition would encompass three main 1

Both authors contributed equally to this work.

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

principles (Weissman, 2000). First, a stem cell must be capable of self-renewal, that is, undergoing symmetric or asymmetric divisions through which the stem cell population is maintained. Second, a single cell must be capable of multilineage differentiation. The third principle is in vivo functional reconstitution of a given tissue. Two broad categories of stem cells can be distinguished: embryonic stem cells (ESCs) and adult stem cells. ESCs are the quintessential pluripotent stem cells and fulfill all of the three principles. Under defined conditions, ESCs can be kept in an undifferentiated pluripotent state and will proliferate indefinitely. In this way they provide a potentially unlimited source of cells. They are able to form all the somatic tissues (i.e., all three lineages, endoderm, ectoderm and mesoderm) as well as the germ cells when injected into a blastocyst (Smith, 2001). One way of demonstrating pluripotency, which is also one of the hurdles that will need to be overcome for application of ESCs in cell-based therapies, is the fact that ESCs form teratomas upon in vivo implantation, that is, tumors that are composed of cells from all three embryonic germ layers (Reubinoff et al., 2000;Wobus et al., 1984). Adult stem cells, derived from various postnatal organs, also fulfill the three stem cell principles. However, the degree of selfrenewal and differentiation potential is far more restricted when compared to ESCs. Over the past 35 years the most extensively studied adult stem cell is without question the hematopoietic stem cell (HSC) (Bhatia et al., 1997; Spangrude et al., 1988). HSCs undergo self-renewal, differentiate into all different blood Copyright © 2009, Elsevier Inc. All rights reserved. 599

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cells at the single cell level, and can functionally repopulate the hematopoietic system of an ablated recipient. Although HSC transplantation carries a potential risk because of immunosuppression of the host, it is being used to treat various diseases, including lysosomal storage disorders, immunodeficiencies, hemoglobinopathies, leukodystrophies, and hematopoietic malignancies (www.marrow.org). More recently, other adult stem cells have been identified. For example, neural stem cells (NSC), present in the subventricular zone in postnatal brain, give rise to neurons, astrocytes, and oligodendrocytes (Gage, 2000). Mesenchymal stem cells (MSCs), which can be isolated from bone marrow as well as adipose tissue, differentiate into fibroblasts, osteoblasts, chondroblasts, adipocytes, and skeletal muscle (Pittenger et al., 1999; Prockop, 1997). Also in the gastrointestinal tract (Potten, 1998), skin and skin appendages (Watt, 1998), liver (also called oval cells) (Alison and Sarraf, 1998) and many other tissues, putative stem cells have been identified. Some stem cells, such as germ stem cells (de Rooij and Grootegoed, 1998), corneal stem cells (Daniels et al., 2001), and angioblasts (Rafii et al., 1994) fulfill the three stem cell principles though they only differentiate into a single mature cell type. Recently, several groups have discovered populations of pluripotent stem cells that reside in the bone marrow. Such cells may have persisted beyond the earliest steps of embryogenesis and can differentiate into cells different than the organ of origin, depending on the milieu (Verfaillie, 2002). Multipotent Adult Progenitor Cells (MAPCs) have been isolated from the bone marrow of postnatal humans, swine, and rodents ( Jiang et al., 2002; Reyes and Verfaillie, 2001; Reyes et al., 2001; Zeng et al., 2006). These cells can be expanded in vitro without senescence and show clonal in vitro differentiation potential to cells of the three germ lineages. When injected into an early blastocyst, some MAPC lines have contributed to most tissues and on transplantation into a non-irradiated host, MAPCs engraft and differentiate to the hematopoietic lineage (Serafini et al., 2007), and in limited fashion to epithelium of liver, lung, and gut. Enhanced engraftment is seen when MAPCs are infused in injured animals ( Jiang et al., 2002). D’Ipolitto et al. described the isolation of Marrow-Isolated Adult Multilineage Inducible (MIAMI) cells (D’Ippolito et al., 2004), which express ESC markers, as well as low levels of lineage-specific markers. Unrestricted Somatic Stem Cells (USSCs), another pluripotent adult stem cell population, are CD45-negative cells derived from human cord blood and possess the in vitro capacity to differentiate into cells with a mesodermal and neuroectodermal phenotype (Kogler et al., 2004). Yoon et al. described a population clonally expanded cells with unlimited self-renewal capacity, termed human Bone Marrow-derived Multipotent Stem Cells (hBMSCs) (Yoon et al., 2005). These cells do not express markers typical for MSCs or HSCs and show triple lineage differentiation capacity in vitro. Despite minor differences in cell surface phenotype and expressed gene profile, MAPCs, MIAMI cells, USSCs, and BMSCs are likely all related cell populations with more or less primitive features. Recently Anjos-Afonso et al. has shown that cells with similar potency as MAPCs and expressing the

ESC-specific transcription factors Oct4 and Nanog, can be isolated based on the expression of SSEA1 from murine bone marrow (Anjos-Afonso and Bonnet, 2007). In contrast to ESCs, MAPCs, BSSCs, USSCs, MIAMI cells, and SSEA1 MSCs do not form teratomas or embryoid bodies (EBs). Guan et al. recently isolated spermatogonial stem cells from testis of adult mice which upon in vitro culture under ESC conditions acquire all characteristics of ESCs, namely EB formation, teratoma formation and the ability to contribute robustly to chimeric animals following injection into the blastocyst including the germline (Guan et al., 2006). Hence these cells, termed maGSCs, are the only postnatal-derived stem cell with all features of ESCs. Table 52.1 shows an overview of different types of stem cells categorized by their potency.

HOW TO DEFINE THE MOLECULAR SIGNATURE OF STEM CELLS In order to exploit the full therapeutic potential of stem cells, it will be required to unravel their molecular repertoire by obtaining insights in genes and genetic pathways that are key players in self-renewal and differentiation processes. Such studies will not only yield a better understanding of normal developmental processes, but will also shed light on the pathogenesis of various diseases, which may form the basis of programs of drug discovery to enhance self-renewal, differentiation, or alleviate pathological conditions. In a first attempt to define a stem cell signature, different stem cell populations were investigated by means of global gene expression profiling (Fortunel et al., 2003; Ivanova et al., 2002; Ramalho-Santos et al., 2002). Three groups analyzed independently three types of mouse cell populations: ESCs derived from mouse embryos, neurospheres containing NSCs derived from postnatal brain tissue, and HSCs derived from bone marrow. Remarkably, they found different sets of genes that were “common” to stem cells. Only one gene (Itga6) was found to be the same between all studies. These results point to the fact that characterization of the expressed gene profile likely is affected by minor changes in techniques, cell purification, and other technical issues. In fact, the number of common “stemness” genes increased substantially when the data were compared using the same algorithms to define differential gene expression. However, as a much higher degree of congruence was seen when gene expression in ESCs (n  332) or NSCs (n  236) was compared between the studies (p  108), methodological differences alone might not fully explain the failure to find common stem cell genes (Ivanova et al., 2002). In general, it has been notoriously difficult to obtain pure populations of stem cells. Furthermore, stem cells may initiate differentiation steps that are currently out of the control of experimental set-ups, when maintained in culture and during development in vivo. Ideally, transcriptome analysis would be done on highly purified stem cells, with minimal culturing, at a defined state of development and in sufficient quantities, such that comparative methods can be performed with sufficient rigor

Future Directions to Identify the Global Integrated Regulatory Network

TABLE 52.1

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Different types of stem cells categorized by their potency Differentiation capacity

Type of stem cell Organ specific

Potency

Extraembryonic

Germ line

Three embryonic germ layers

Totipotent







Zygote





Embryonic stem cell, primordial germ cell, Testis derived maGSC

a

MAPCs, MIAMI cells, USSCs, BMSCs, SSEA1 MSCs

Pluripotent

Multipotent Unipotent

Multiple cell types

One cell type



HSCs, MSCs, NSCs,… 

Corneal epithelial stem cells, germ stem cells, angioblast,…

a

No embryoid bodies and teratomas.

to overcome the variability inherent in the comparison techniques itself. Although no definitive set of genes has been identified that defines all stem cells, several signaling pathways have been described that regulate different types of stem cells. These pathways include the Bmi-1, Notch, Wnt, and Sonic hedgehog pathways (Bhardwaj et al., 2001; Krosl et al., 2003; Lessard and Sauvageau, 2003;Taipale and Beachy, 2001;Varnum-Finney et al., 2000). It should be noted that few of these shared stem-cell regulators were identified by global gene expression analysis. Gene expression analysis will probably be a valuable tool for defining final cell populations. By analyzing the transcription state of a cell in a specific cell phase, different cell types or stem cells in a specific phase can be identified, thereby creating a library of potential markers of each differentiation state of a given tissue. However, it should be noted that the level of transcripts in a given cell does not necessarily correlate with the levels of the corresponding proteins, as the latter is not only regulated at the transcriptional level but also posttranscriptionally (Gygi et al., 1999). As a consequence, this complicates the elucidation of the functional role of expressed genes in stem cell function and validation of any gene profile will be needed to demonstrate the causal relationship between a given transcriptome and the proteome and the fate of a cell. Such an analysis will ultimately lead to the identification of target gene repertoires of all the transcription factors involved in self-renewal and differentiation processes, as well as determination of the expression profiles of these genes, and will form the basis for understanding of how the regulatory networks control cell fate.

FUTURE DIRECTIONS TO IDENTIFY THE GLOBAL INTEGRATED REGULATORY NETWORK Functional Characterization by Gene-Targeting Approaches A first essential step in the valorization of gene expression profiles is to design studies wherein gene function is enhanced or eliminated, and evaluate the effect on target cells. Gain-of-function and loss-of-function studies wherein genes are overexpressed, or knocked out or down in cell-culture models or in animal models is being explored. Until recently the most popular animal model approach was the design of transgenic or knockout mice. However, besides the fact that creating transgenics and knockouts is money and time consuming, embryonic lethality is a major risk that needs to be taken into account when manipulating genes that control stem cells and their earliest differentiated progeny. Therefore investigators are resorting more and more to less expensive and higher throughput animal models to evaluate loss-of-function and gain-of-function phenotypes. Traditionally, invertebrates such as Drosophila melanogaster (fruit fly) and Caenorhabditis elegans (worm) have been the models for high-throughput screens as they are easily used in the lab thanks to their rapid development, they are easy to mutate and manipulate genetically, and maintaining large colonies is inexpensive. Although C. elegans in particular may be useful to evaluate selfrenewal mechanisms of stem cells (Kipreos, 2005), more complex

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aspects of morphogenesis, patterning, and differentiation cannot be studied. Developmental processes are highly conserved from zebrafish (Danio rerio) and frog (Xenopus laevis), to mouse and man and hence make these model organisms suitable to study stem cell differentiation processes. Unlike the mouse, zebrafish embryos are externally fertilized and transparent, allowing easy manipulation and visualization of developmental defects. These attributes are enhanced by the availability of fluorescent transgenic fish and frogs, wherein specific cell types can be easily identified under fluorescence microscopy. The development of morpholino antisense oligonucleotides (MOs) has created the possibility of knocking down single or multiple genes in the early zebrafish or frog embryo and evaluating the effect of loss of gene function in a very high-throughput fashion (Nasevicius and Ekker, 2000). Likewise, injection of cDNA or mRNA molecules in the early embryo allows the fast evaluation of gain-offunction manipulations. Instead of using animals, in vitro characterization of genes in cell lines is relatively fast, less costly and can also provide important clues of gene functions and corresponding pathways. The usefulness of loss-of-function and gain-of-function experiments in cell culture models was clearly demonstrated for Oct4, a key transcription factor responsible for the pluripotency of cells within the inner cell mass of the blastocyst and ESCs (Niwa et al., 2000). ESC identity cannot be maintained when Oct4 is eliminated (loss-of-function). Instead, the cells differentiate into trophectoderm, a lineage not normally made by mouse ESCs. Conversely, when Oct4 is overexpressed (gain-of-function), the ESCs differentiate into cells expressing markers of primitive endoderm and mesoderm. Thus, a critical level of Oct4 expression is required to sustain stem cell self-renewal and pluripotency, as any change in expression induces differentiation. Further demonstration of the usefulness of evaluation of gene function using in vitro cell line models comes from a recent elegant study of Ivanova et al. (Ivanova et al., 2006), wherein an integrated approach of loss- and gain-of-function was used to identify additional key genes that regulate self-renewal of ESCs. By combining within one lentiviral vector in an inducible manner shRNAs against the gene of interest as well as an shRNAnon-responsive cDNA copy of that gene, they downregulated genes that, in a previous microarray analysis of ESCs (Ivanova et al., 2002), were found to be important in their differentiation. They identified seven genes that are required for efficient self-renewal of ESCs in vitro, as loss of expression leads to differentiation, which could be specifically reversed by inducing re-expression of the transcript and protein. Downregulation of each gene induced differentiation of ESCs along specific lineages. This integrated approach of combining microarray data with functional knockdown of important genes is an excellent example of a study that starts to unravel the complexity of selfrenewal pathways. This approach should also be suitable to other systems, such as hematopoietic and other adult stem cells. Although loss-of-function and gain-of function studies may provide an approach to achieve new insights in our understanding of the molecular regulation of “stemness” and stem-cell fate,

the transition of stem cells to differentiated cells is far more complex. The intricate system, where various nuclear machineries interact with one another and bind to DNA regions to create an impressive network of protein–DNA and protein– protein interactions, is made even more complex by the need for a very stringent control in order to ensure normal cell growth and differentiation. Dissecting Protein–DNA Interactions The interaction of many proteins with genomic DNA is required for the expression, replication, and maintenance of the integrity of mammalian genomes. Protein–DNA interactions are mainly established by transcription factors, which in eukaryotes account for 3–5% of all genes. The efficiency of this interaction depends on many different factors. First of all, the necessary transcriptional regulators must be present and expressed to initiate transcription, and the genes must have the corresponding promoter motifs for recognition and induction. Several microarraybased methods of the genome-wide mapping of protein–DNA interactions, including chromatin immunoprecipitation (ChIP) combined with microarray detection and genomic tiling arrays, have contributed considerably to our current knowledge of gene regulatory networks (Ren and Dynlacht, 2004; Robyr et al., 2004; van Steensel and Henikoff, 2003). Another important factor influencing the protein–DNA interaction efficiency is the accessibility of the DNA to regulatory proteins and the transcriptional machinery, which depends mainly on chromatin remodeling. As chromatin consists of a complex of DNA wrapped around proteins (mainly histone proteins), changes in chromatin structure are affected by covalent histone modifications and DNA methylation. Histones are subject to dozens of different modifications, including acetylation, methylation, and phosphorylation of particular amino acid residues ( Jenuwein and Allis, 2001; Peterson and Laniel, 2004). Most DNA in the human genome is found to be heavily methylated chromatin in which deacetylated histones form compact nucleosomes. These chromatin regions are transcriptionally silent. Chromatin in which transcription occurs is characterized by acetylated histones and widely spaced nucleosomes, which permits transcription factors access to the promoter. The acetylation of histones depends mainly on the methylation of histone H3 Lysine 4 (K4) and lysine 27 (K27). K4 methylation positively regulates transcription by recruiting nucleosome remodeling enzymes and histone acetyl transferases, which catalyze the addition of acetyl groups to conserved residues and are correlated with more transcriptionally accessible chromatin. On the contrary, K27 methylation negatively regulates transcription by promoting a compact chromatin structure (Francis et al., 2004; Pray-Grant et al., 2005; Ringrose et al., 2004; Santos-Rosa et al., 2003;Wysocka et al., 2005). The involvement of individual chromatin-remodeling genes in stem cell homeostasis has for example, been documented for several members of the polycomb group (PcG) gene family. PcG proteins form two major complexes, although additional variations and developmental modulations remain to be

Future Directions to Identify the Global Integrated Regulatory Network

fully understood (Kuzmichev et al., 2005). The PRC2 complex, comprising EED, EZH2, and SUZ12 in mammals, initiates gene silencing and catalyzes histone H3 methylation on lysine 27 (H3K27) at target loci (Kirmizis et al., 2004). The more complex and variable PRC1 is then recruited, in part by the presence of H3K27me3, where it helps to maintain transcriptional repression either through chromatin compaction or by interfering with transcription initiation. Notable examples of polycomb genes involved in stem cell homeostasis include Bmi1 (Iwama et al., 2004), Mel18 (Kajiume et al., 2004), Ezh2 (Kamminga et al., 2006). Recently it has been shown that a specific modification pattern, termed “bivalent domains,” consisting of large regions of K27 harboring smaller regions of K4 methylation tend to coincide with transcription factor genes expressed at low levels (Bernstein et al., 2006). It has been proposed by the authors that these bivalent domains function to silence developmental genes in ESCs, while keeping them poised for induction upon initiation of specific developmental pathways, suggesting a novel chromatin-based mechanism for maintaining pluripotency. DNA methylation, the second process involved in chromatin remodeling, involves the methylation of the C5 position of cytosine (5mC) in CpG dinucleotides in DNA. Genome-wide DNA methylation patterns are non-randomly distributed and undergo significant remodeling events during embryogenesis. The DNA of the zygote is substantially methylated. During early development, the genome undergoes global demethylation, and in the epiblast lineage it becomes globally re-methylated de novo after implantation (Jaenisch and Bird, 2003). ESCs correspond to a stage of development after the blastocyst stage. The DNA of ESCs is relatively highly methylated and has high de novo methylation activity (Stewart et al., 1982). For example, the STAT3 binding site in the GFAP gene promoter is highly methylated in ESCs. Demethylation of this site is only programmed when pluripotent cells are committed to a neural lineage that is capable of producing astrocytes (Takizawa et al., 2001). Multiple layers of epigenetic modification seem to regulate key transitions in the temporal development of stem cells and their differentiation, resulting in expression of unique repertoires of transcription factors at each stage of development and in different lineages. A Role for miRNAs in Posttranscriptional Regulation In addition to the involvement of transcription factors and epigenetic marks in this extremely complex regulatory system, posttranscriptional gene regulation seems to fulfill, until recently, an unexpected role in development. MicroRNAs (miRNAs) are 21–25 nucleotides, non-coding RNAs that are expressed in a tissue-specific and developmentally regulated manner and comprise about 1% of the total genes in the animal genome (Bartel, 2004). They are transcribed as long primary transcripts, which are subsequently cleaved into hairpin precursors. These precursors are transported to the cytoplasm where mature miRNAs are generated by the ribonucleases Drosha and Dicer1 in association with a RISC/Argonaute complex. This complex directs miRNAs

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to their targets resulting in posttranscriptional repression either by inhibiting translation or degradation of target mRNAs (Pasquinelli et al., 2005). Given that the mature sequence is short, and exact complementarity is not required for silencing, a large number of different mRNAs can be engaged by a single species of miRNA. There are currently 326 confirmed miRNAs in humans, and computational searches suggest that the total count might approach 1000 (Bentwich et al., 2005; Berezikov et al., 2005). The large number of genes, combined with the regulatory nature of miRNAs, suggests that they are essential regulators of a wide range of cellular processes. Because of the simultaneous repression of many transcripts, miRNAs are uniquely poised to rapidly affect expression patterns and therefore regulate stem cell self-renewal. This role in selfrenewal is suggested by the presence of distinct sets of miRNAs that are specifically expressed in pluripotent ESCs but not in differentiated embryoid bodies or adult tissues (Houbaviy et al., 2003; Suh et al., 2004). Additionally, loss of Dicer1 causes embryonic lethality and loss of stem cell populations (Bernstein et al., 2003; Wienholds et al., 2003). An increasing amount of reports have been published that describe the importance of miRNAs during development and in directing the proper differentiation of cells into various tissues. Examples include miR-273 and the miRNA that is encoded by lys-6, which are involved in patterning the C. elegans nervous system ( Johnston and Hobert, 2003; Chang et al., 2004); miR-430 in Danio rerio brain development (Giraldez et al., 2005); miR-181 in the differentiation of mammalian hematopoietic cells toward the B-cell lineage (Chen et al., 2004); miR-375 in mammalian pancreatic islet-cell development and the regulation of insulin secretion (Poy et al., 2004); miR-143 during mammalian adipocyte differentiation (Esau et al., 2004); miR-196 in mammalian limb patterning (Hornstein et al., 2005); and the miR-1 genes during mammalian heart development (Zhao et al., 2005). Further characterization of novel miRNAs might reveal other gene regulators that coordinate proper organ formation, embryonic patterning and body growth, and might also provide insight into the mechanisms of human diseases such as cancer. Unraveling the Proteome Although protein expression is regulated at the mRNA level (i.e., transcription and splicing), the production and activity of proteins depends on translation, posttranslational modifications (e.g., phosphorylation) and degradation.Thus, to gain insight into pathways activated during proliferation and maintenance, as well as differentiation of stem cells, extensive analysis of the proteome is pivotal. Complementation of microarray studies with protein analyses may provide the missing link between gene transcription and cell behavior. Proteomic approaches have been applied to create maps of expressed proteins for studying the characteristics of stem cells and for discovering stem cell specific molecular markers. Two-dimensional electrophoresis coupled with mass spectrometry has been used to generate proteome maps for hippocampal NSCs (Maurer et al., 2003), mouse ESC lines (Elliott et al., 2004), and MSCs (Feldmann et al., 2005). An approach

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based on mass spectrometry has recently been applied to create a proteomic map of the mESC line (Nagano et al., 2005), which has led to the identification of hundreds of proteins involved in different molecular functions and processes. Proteomic tools are also valuable in studying stem cell differentiation and for elucidating underlying molecular mechanisms. There have been an increasing number of publications comparing differentiated and undifferentiated stem cells including ESCs and neural cells (Wang and Gao, 2005), before and after adipose-derived stem cell differentiation (DeLany et al., 2005), and identification of candidate regulators of ESC differentiation (Puente et al., 2006). This increasing knowledge of the stem cell proteome will undoubtedly be beneficial to the study of its interactome. Besides the DNA–protein interactions and posttranscriptional regulation, networks of protein–protein interactions mediate many cellular responses to environmental stimuli and direct the execution of developmental programs. Each protein typically interacts and reacts with other interaction partners to execute their functions. The selectivity of these interactions determines the developmental potential of the cell and its response to extracellular stimuli.

Currently, the therapeutic use of stem cells is based chiefly on the preparation of cells in small culture vessels. Undifferentiated cells, like ESCs, are treated in culture to nudge them toward a differentiated cell type before implantation. It is assumed that implantation of undifferentiated cells or lineage committed cells in a specific region of the body will provide them with the correct signals that will direct their further maturation into the desired type of cells. Several cell types have already been derived from ESCs, like neural tissue (Reubinoff et al., 2001; Schuldiner et al., 2001; Zhang et al., 2001), insulin secreting cells (Assady et al., 2001), cardiomyocytes (Boheler et al., 2002; He et al., 2003; Kehat et al., 2001), endothelial cells (Levenberg et al., 2002), osteoblasts (Sottile et al., 2003), and hepatocytes (Rambhatla et al., 2003). The differentiation of cells in vitro is a difficult and timeconsuming method that relies on finding factors, like growth factors, hormones, and other signaling molecules that help cells survive and proliferate and/or differentiate. The major obstacles in the therapeutic application of stem cells are insufficient insights in the signaling pathways that control stem cell fate and an inadequate ability to manipulate stem cell proliferation and differentiation. New insights gained from basic stem cell studies in pathways that regulate self-renewal, specification, and final differentiation will be needed to support ultimate therapeutic use of stem cells in vivo.

in the organ in which they reside. Adult stem cells are responsible for tissue repair and renewal, processes that are hoped can be harnessed for therapeutic use. Recent research has suggested that the plasticity of adult stem cell may be broader than initially thought. Under certain conditions, adult stem cells have been reported to cross what were previous thought to be “lineage boundaries” to produce unexpected cells types. The majority of these reports of adult stem cell plasticity have involved bone marrow-derived stem cells. Bone marrow contains two types of stem cells. HSCs give rise to leukocytes, erythrocytes, and thrombocytes. MSCs have the capability to differentiate into osteoblasts, chondrocytes, myocytes. However, various studies have reported that bone marrow-derived cells may also give rise to epithelial cells in the kidney (Szczypka et al., 2005), liver, lung, gastrointestinal tract, skin (Krause et al., 2001), or to cells in the heart (Orlic et al., 2001a, b), the brain (Brazelton et al., 2000; Mezey et al., 2000), and blood vessels (Sata et al., 2002). Many of these studies have reported that injury promotes the incorporation of bone marrow-derived cells into organs. This observation strengthens the argument that the incorporation of bone marrow-derived cells is part of an organ repair process. There is however no classical sound explanation for this developmental plasticity. Five possible mechanisms that may underlie the observation of apparent stem cell plasticity are: (1) presence of multiple tissuespecific stem cells in one tissue, (2) cell fusion, (3) transdifferentiation/transdetermination, (4) de- and re-differentiation, or (5) the persistence of more pluripotent cells. Before proceeding to clinical use of adult marrow stem cell therapy for degenerative diseases in various organs on a large scale, more research on the molecular and cellular processes of stem cell plasticity needs to be performed. In addition, studies aimed at determining how to encourage the replication and differentiation of resident and circulating stem cells could form the basis of future reparative therapies that take advantage of existing adult stem cell differentiation and trafficking mechanisms. Alternatively, preparations of harvested adult stem cells could be used in transplantation-based therapies similar to those proposed for ESCs. An advantage of using adult stem cells is that they could be harvested from the individual being treated to prevent the graft-rejection problems. Adult stem cells are also unlikely to give rise to the uncontrolled growth of inappropriate tissue types that occurs when undifferentiated ES produce tumors (teratomas) following transplantation. However, ex vivo expansion of adult stem cells such as HSCs, NSCs or MSCs is difficult and is associated with premature senescence of the cells, a phenomenon not seen with ESCs. In addition, although stem cells have been identified for many tissues, the existence of stem cells for some tissues remains controversial, including for endocrine pancreas and cardiac muscle.

Adult Stem Cells and Therapeutics As discussed earlier, adult stem cells exist in various tissues. The normal differentiation repertoire of these adult stem cells is limited and they typically give rise to one or more cell types found

miRNAs and Cancer It is believed that only a small fraction of cells in a tumor possesses the ability to sustain the malignant growth, and hence have the stem cell property of self-renewal. These cancer stem cells

FUTURE DIRECTIONS IN STEM CELL THERAPIES

Future Directions in Stem Cell Therapies

are responsible for initiating and maintaining the tumor (Al-Hajj et al., 2004). An important key event in tumorigenesis is the disruption of genes involved in the normal regulation of stemcell self-renewal. In this respect, miRNAs can function as tumor suppressors and oncogenes (Esquela-Kerscher and Slack, 2006). Also, components of the miRNA machinery, like Dicer and the Argonaute proteins, are implicated in tumorigenesis (Karube et al., 2005). Interestingly, the miRNA profiles are surprisingly informative in the classification of tumors, reflecting the developmental lineage and differentiation state of them (Lu et al., 2005). An established library of miRNA signatures or miRNA expression profiles, assigned for each class of tumor, might help both the diagnosis and treatment of cancer. In this way, techniques to overexpress miRNAs that function as tumor suppressors, or downregulate miRNAs that act as oncogenes, could be used to treat specific tumor types. Before this step can be taken, however, important fundamental questions remain to be answered. For example, which mechanisms regulate miRNA gene expression under normal conditions and which go awry during tumorigenesis? Finally, large-scale expression screens that compare miRNA levels in tumors versus normal tissues will be needed to determine whether miRNAs exist that are involved in cancer but not normal stem cell homeostasis. Small Molecules, a New Promising Tool in the Stem Cell Field The translation of genomic information into drug therapies is a major challenge facing pharmaceutical companies. The main limitations are lack of knowledge regarding which gene products are functionally involved in the pathology of a disease and the druggability of the gene products by small molecule compounds. Development of small molecule drugs clearly illustrates the importance of a combinatorial approach of basic research and therapeutics. One approach to generating functional small molecules that control stem cell fate involves the use of cell-based phenotypic- or pathway-specific screens of synthetic chemical product libraries. Cell-based phenotypic assays have historically provided useful chemical tools to modulate and/or study complex cellular processes. For example, MyoD was discovered to be a master transcription factor for skeletal myogenic fate determination, in studies using 5-aza-C, a DNA methylating agent (Chen and Jones, 1990). Cell permeable small molecules have proven useful for inducing the differentiation of various stem cells. Small molecule inhibitors, such as suberoylanilide hydroxamic acid (histone deacetylase inhibitor) (Lyden et al., 2001), geldanamycin (Hsp90 inhibitor) (Maloney and Workman, 2002), imatinib mesylate (Gleevec, kinase inhibitor) (Druker, 2002), and bortezomib (b) (proteasome inhibitor) (Albanell et al., 2002) induce differentiation of various progenitor and transformed cells and are used clinically for the treatment of cancers. Although the value of such small molecule screenings in the stem cell field is just now beginning to be realized, challenges remain and many basic questions still need to

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be answered. Some important issues will be the global effect of these small molecules on cellular processes and to what degree can effects be generalized to other cell types and fates. Also some of the challenges facing the large-scale clinical use of such molecules include difficulties in determining whether in vitro effects of small molecules can be recapitulated in vivo and whether a sufficient degree of selectivity and therapeutic window can be achieved to allow clinical applications. A Combination of Stem Cell and Gene Therapy Another promising therapeutic potential of stem cells lies in the field of gene therapy, where a gene that provides a missing or necessary protein is introduced into an organ for a therapeutic effect.Viral vectors are the preferred system for gene delivery in clinical trials owing to their higher in vivo transduction efficiency compared to non-viral vectors. Nevertheless, viral vectors can trigger immune reactions that curtail long-term gene expression. An additional cause for concern with randomly integrating viral vectors is the phenomenon known as insertional mutagenesis, in which the ectopic chromosomal integration of viral DNA either disrupts the expression of a tumour-suppressor gene or activates an oncogene, leading to the malignant transformation of cells. Non-viral approaches may therefore be a safer alternative to viral vectors. However, until recently they have had limited success owing to the low efficiency of integration into chromosomes that is necessary for long-term therapeutic benefit. Strategies have been developed that take advantage of either transposable elements, such as Sleeping Beauty (SB) (Ivics et al., 1997) or phage integrase systems, such as phiC31 (Groth and Calos, 2004; Groth et al., 2000), to promote non-viral integration of therapeutic genes. The SB system consists of two components: a transposon consisting of the gene of interest flanked by inverted repeats, and a source of transposase. Transposase is an enzyme that allows integration of DNA into genomic DNA. In this way, integration frequencies 100-fold greater than that achieved by random recombination can be seen when using the SB transposon system. However, despite the advantage of the SB delivery system to deliver DNA in a non-viral way, the integrations are still random causing insertional activation of genes, resulting in malignancies. The site-specific integrase from bacteriophage phiC31 catalyzes precise, unidirectional recombination between its 30- and 40-bp attP and attB recognition sites. In mammalian cells, the enzyme also mediates integration of plasmids bearing attB into native sequences that have partial sequence identity with attP, termed pseudo attP sites. It has been shown that the phiC31mediated integration is limited to these pseudo attP sites, only slightly favored genes and did not favor promoter regions, like most viral vectors do (Chalberg et al., 2006). Although the most frequently observed integration sites were found in gene-dense regions, an analysis of the safety of integration sites in terms of proximity to cancer genes suggested minimal cancer risk. It is possible that integration systems derived from phiC31 integrase have great potential utility, as improved protocols to create

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more useful forms of phiC31 with highly selective integration at desirable locations are investigated. The only way to completely avoid insertional mutagenesis is by the directed integration of a transgene into a host genome at “safe genomic sites” – sites within regions of the genome that are not associated with cell proliferation or tumor suppression. Although homologous recombination (HR) offers high sequence specificity, its frequency is generally 0.001% making it currently unsuitable for use in a clinical setting. As ESCs and postnatal stem cells with pluripotent characteristics can be easily maintained and cultured in vitro for several generations without senescence or loss of differentiation potential into several lineages, such stem cells might be an ideal source for gene targeting. In this cell-based delivery strategy, the defective gene in a stem cell derived from the patient would be exchanged with a normal copy of the gene. Such genetically corrected cells would then be multiplied in the laboratory and infused back into the patient. This method is advantageous relative to direct gene transfer because (a) it allows researchers more control over selection of genetically modified cells when they are manipulated outside the body, and (b) investigators can control the level and rate of production of the therapeutic agent in the cells (Kaji and Leiden, 2001). All these features make stem cells a suitable source of cells for ex vivo gene therapy applications.

CONCLUSION The progress made in unraveling the human genome, together with methods to evaluate expression of specific transcripts and epigenetic mechanisms, start to make it possible to obtain insights in the integrated global regulatory networks in different cell types and self-renewal and differentiation processes. It has become clear that the very finely tuned regulation of stem cells depends on the dynamic interplay of chromatin remodeling components, transcription factors, and miRNAs to choreograph stem cell self-renewal and the generation of cell diversity. The study of the transcriptional and posttranscriptional networks together with the unraveling of the proteome and protein interactome will be a major challenge toward a better understanding of the stem cell molecular signature. Until now, the therapeutic application of both embryonic and adult stem cells is still in its infancy, hampered by our incomplete knowledge of their properties. The use of stem cells for cell therapy has, on or about, been demonstrated in dystrophic mice (Sampaolesi et al., 2003), in the treatment of human blood cancer (Barker and Wagner, 2003) and Parkinson’s disease (Lindvall and McKay, 2003). Albeit limited, these examples are very encouraging for future stem cell research, which in the next decades will undoubtedly provide us with clues to elucidate the promising mystery of stem cells.

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53 Gene Therapy James M. Wilson and Nelson A. Wivel

INTRODUCTION Gene therapy has always maintained an intellectual appeal because it has the potential to treat disease at the molecular level. Many of the seminal advances in this field are a direct reflection of the developments in recombinant DNA technology. However, some of the concepts relating to gene therapy were advanced by investigators at a time when molecular genetics was still in its formative stages (Wolff and Lederberg, 1994). Edward Tatum proposed that viruses could be used to introduce genes into cells and the outline of the experimental conditions could be interpreted, in today’s terminology, as ex vivo transduction (Tatum, 1966). Joshua Lederberg thought it would be possible to culture germ cells in vitro, and it would become possible to interchange chromosomes and their segments and to control nucleotide sequences along with recognition, selection, and integration of the desired genes (Lederberg, 1968). Arthur Kornberg predicted that one would ultimately be able to attach a gene to a harmless viral DNA and to use such a virus to deliver the gene to cells (Kornberg, 1971). In the early 1970s, Stanfield Rogers attempted a form of genetic engineering when he administered the Shope rabbit papilloma virus to two research subjects suffering from argininemia.The basis for this approach was tethered to the observations that animals infected with the Shope virus had a decreased level of blood arginine and that scientists working with this virus had reduced arginine levels (Rogers et al., 1973). Although the experiment was unsuccessful, it was defended on the basis that it offered an opportunity to interrupt progressive deterioration in a setting where no other treatments existed.

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There were considerable laboratory advances in the 1980s that led to the development of recombinant viral vectors, chief among them the murine amphotrophic retroviruses (Miller and Rosman, 1989). The first proposed gene therapy trial began its trek through an extended public review process in 1987 and, by 1990, the first protocol was approved (Wivel, 2002). On September 14, 1990, the first research subject, a young girl suffering from the adenosine deaminase form of severe combined immunodeficiency, was treated with an infusion of autologous, gene-corrected peripheral blood T lymphocytes. In postulating the prime uses for gene therapy, the single gene deficiency diseases were identified as the first order target for intervention. The reasons for this are entirely self-evident as these disorders are characterized by a mutated gene that is associated with a consistent symptom complex and oftentimes a lethal outcome. However, as the field has developed over the past 15 years, it has become apparent that gene transfer is an enabling technology and that the majority of experimental protocols involve the study of such common maladies as cancer and cardiovascular disease (OBA Report, 2007). The intrinsic nature of gene therapy research has now become rather well defined. Clearly, it is an iterative process and the phrase, bench-to-bedside-to-bench, is operative in much of the research that has taken place. In part, this is occasioned by the fact that there are inherent deficiencies in any animal model system that one might use for preclinical study; the ultimate test is presented by the research subjects in a human trial. Of the hundreds of trials that have actually occurred, there are repeated

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Gene Delivery Vehicles

instances of proof-of-principle, that is, genes have been successfully transduced into cells, there is a resulting gene product that can be measured, and in some instances, there is a measurable improvement in the disease state (Cavazzana-Calvo et al., 2000). At this juncture there have been some minor successes in the field but the absence of cures should not be interpreted in an entirely negative light. The fields of organ transplantation and monoclonal antibody therapeutics literally took decades to develop, and there is no reason to suppose that this particular biotechnology is exempt from the usual developmental challenges.

GENE DELIVERY VEHICLES Clearly, the essence of gene therapy is the delivery of genes into cells. There is a significant size disparity when such DNA entities are compared to traditional drugs or small molecules. As the research has evolved, two major systems of gene delivery have been developed, viral and non-viral. Each has its defining set of characteristics and there are advantages and disadvantages to both. Two particular features of viruses have made them appealing as vectors; first, there is a pre-existing set of cell surface receptors that facilitate viral entry into the cell, and secondly, viruses have developed the capacity to evade certain defense mechanisms normally posed by the cell and its organelles. Less attractive elements of the viral profile are the immunogenicity and the pathology associated with some wild-type genomes. Production of non-viral vectors is less problematic because it often relates more to chemistry than biology, and production of significant quantities of vector needed for clinical trials is more easily achieved. However, there is a wealth of literature to document that non-viral vectors are less efficient than viral vectors. Retroviral and Lentiviral Vectors The family Retroviridae includes seven genera with its members containing a single-stranded RNA, type-specific and groupspecific structural proteins, and a reverse transcriptase (Francki et al., 1991). Retroviruses are widely distributed as exogenous infectious agents, particularly in mammals and birds. In the rodent systems, endogenous proviruses have infected the germ line and are inherited as Mendelian genes. Such rodent viruses have reasonably simple genomes that consist of two long terminal repeats that are important for cellular integration and that contain promoter elements and a packaging signal. There are three principal structural genes, gag, pol, and env. The envelope glycoproteins of these retroviruses bind to cell surface receptors to facilitate cytoplasmic entry where the viral RNA is reverse transcribed to form a cDNA, the provirus. The amphotrophic form of the mouse Moloney Murine Leukemia virus has been the most popular retroviral vector and was used fairly extensively in the early gene therapy clinical trials. There is a number of appealing characteristics of this vector that account for its popularity. All three major structural genes can be removed to create room for insertion of a therapeutic gene; vector production is achieved by supplying the deleted

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functions in trans with stable cell lines over-expressing helper proteins. With a reduction of 80% in the viral genome, there is a reduced opportunity for recombinational events that would lead to the production of a replication-competent virus. Another aspect of retroviral replication that has appeal for gene transfer is the ability of the virus to integrate into the chromosomes of the host cell, thus opening up the possibility of long-term gene expression. On the negative side of the equation, it is known that most of the retroviruses require dividing cells in order for replication to take place. Furthermore, there is no specific integration site, and with purely random integration there is a real potential for insertional mutagenesis that can predispose toward the development of malignancy. In recent years, considerable attention has been given to the Lentivirus genus in the retrovirus family, and this particular virus is the Human Immunodeficiency Virus (HIV). A principal property of the recombinant vectors derived from HIV is the ability to integrate into non-dividing cells (Naldini et al., 1996). The genomes of lentiviruses are more complex than the mouse retroviruses and contain multiple genes that encode regulatory proteins and pathogenesis factors; in addition there are genes that utilize the cellular nuclear import machinery such as IN, MA, and Vpr with the result that the preintegration complex is targeted to the nucleus. Because of the natural pathogenicity of the virus, the development of replication defective vectors has progressed through three stages. The first generation of these vectors consisted of a packaging construct expressing the HIV accessory genes, but the second generation was a minimal packaging construct with deletion of all the accessory genes. Using a strategy to provide an even safer construct, the third generation HIV vectors use only three of the nine genes of HIV-1 and rely on four separate transcriptional units for the production of transducing particles (Dull et al., 1998). These vectors have deletions in the LTR designed to prevent transcription (selfinactivating or SIN vectors). The envelope glycoproteins of the wild-type retroviruses bind to cell surface receptors in order to enable cell entry. In order to broaden the tropism of the vectors, the virus particles have been pseudotyped by using genes encoding either the amphotrophic envelope of the murine leukemia virus (retrovirus) or the G glycoprotein of vesicular stomatitis virus (Naldini et al., 1996). More recent experiments have resulted in HIV vector constructs that contain a Filovirus (Ebola virus) envelope protein; such vectors possess a specificity for transducing apical surface respiratory epithelium in both in vitro and in vivo model systems (Kobinger et al., 2001). As one evaluates the early history of clinical gene therapy trials, it is apparent that the retroviral vectors were used almost exclusively and that ex vivo transduction was the order of the day. Typically, small cell populations were targeted for transduction and these transduced cells were then expanded before being returned to the research subject. This approach accomplished three things: it compensated for the fact that there were no targeted vectors available for in vivo use, it was safer to have controlled conditions for transduction, and cells dividing in culture

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facilitated the use of retroviral vectors. In making the transition to in vivo transduction, adenoviral vectors were logical candidates for such an exercise. They were capable of transducing terminally differentiated cells that rarely divided and many different cell types were known to possess the requisite receptors that could mediate viral entry. Adenoviral Vectors Adenoviruses are double-stranded DNA viruses that contain a sizeable genome of 36 kb that consists of early and late region genes. The early region of E genes encode proteins that have multiple functions: E1A and E1B activate other viral genes, E2 and E4 affect replication of the viral genome, and E3 modulates the host response to the virus. The late region or L genes encode the viral structural proteins (Francki et al., 1991). The classical characterization of human adenoviruses is defined by serological criteria, and six subgroups (A–F) are based on the capacity of the virus to agglutinate erythrocytes (Rosen et al., 1962). Each of these subgroups has a number of serotypes that cannot be neutralized by antibodies generated to other known serotypes; using such a scheme has led to the identification of at least 51 distinct serotypes (Shenk, 1996). Adenoviruses are known to enter cells by receptor-mediated endocytosis through a variety of different receptors. A number of adenoviruses used as vectors are internalized by interactions with the coxsackie/adenoviral (CAR) receptor (Bergelson et al., 1997). Internalization is mediated by an alpha(v)beta integrin endocytic process (Goldman and Wilson, 1995), and the virus possesses properties that enable it to avoid lysis in the endosomal compartment, thus allowing for translocation to the nucleus where it functions as an episome or extrachromosomal element. In developing vectors using an adenovirus backbone, there have been three generations produced. The first attenuated version of adenovirus involved the deletion of the immediate early genes encoding E1A and E1B (Danthinne and Imperiale, 2000). By eliminating these genes it was assumed that the virus would be replication defective because there would be little or no expression of other viral genes. However, such a premise proved to be faulty because there were endogenous transcription factors such as necrosis factor or IL-6 from the liver with E1-like functions and the potential capacity to overcome the E1 deletions. As a result, several second-generation vectors were created and entailed additional deletions. One vector was constructed by deleting the E1A and E1B genes and introducing a temperature-sensitive mutant in the E2A gene; this mutant replicated in 293 cells at 32°C but not at 39°C (Yang et al., 1994). One of the most commonly used constructs involved deletions in both E1 and E4 genes, a maneuver that increased the margin of safety in that two recombinational events would have to occur in order to yield replication competent viruses (Gao et al., 1996). The third generation of adenovirus vectors involved the deletion of all the viral open reading frames with retention of only a small packaging signal and inverted terminal repeats (Morsy et al., 1998). Because of the extensive loss of viral genes, additional space was created for the insertion of transgenes and large

genes such as dystrophin were candidates for insertion in this type of construct. However, there is a disadvantage to this system in that producer cells require infection with E1-deleted helper virus and such a virus is a potential contaminant in vector production stocks. There are two methods for addressing this issue: one involves using physical separation on cesium chloride density gradients and the other involves the use of Cre-lox recombination technology to reduce the rate of contamination to 1% or less (Lieber et al., 1996). One of the most telling limitations in the use of adenoviral vectors is the host immune response. It is well known that expression of the transgene can rapidly diminish with the development of inflammation at the gene transfer site, and most recipients become refractory to a second administration of vector. Using experimental animals rendered deficient in T-lymphocyte function abrogates the effects of the immune response and suggests that antigen-specific immunity has a major role in preventing longterm gene expression and diminished gene transfer efficiency following a second vector administration (Yang et al., 1996). One can invoke a number of ways to modulate the host immune response but the more realistic approach is to define the situations where adenoviral vectors can be used advantageously. Such vectors can be used for the delivery of gene-based vaccines where the viral backbone contains a transgene encoding a viral structural protein. Many of the gene therapy approaches for cancer treatment involve adoptive immunotherapy which requires only transient gene expression and a fairly rapid immune response. Adeno-Associated Virus Vectors As the field of gene therapy research has developed, there has been a rapidly increasing interest in vectors derived from the adenoassociated virus (AAV). AAV is a member of the Parvoviridae family and is classified under the genus Dependovirus (Francki et al., 1991). It contains a single strand 4.7 kb DNA that can be in the form of either plus or minus strands; the wild-type genome consists of two major open reading frames (ORF). The 5 ORF encodes four replication proteins (Rep) that are responsible for viral replication and integration (Balague et al., 1997; Pereira et al., 1997), and the 3 ORF encodes three overlapping AAV capsid (CAP) proteins, VP1, VP2, and VP3 (Wobus et al., 2000). There are two inverted terminal repeats that consist of 145 bp oriented in a palindromic fashion. There are two quite distinct elements that comprise the AAV life cycle; in the latent phase the virus does not replicate but it is integrated as a stable provirus at a specific site, the q13.4-ter arm of chromosome 19 (Kotin et al., 1990). This particular property contrasts with the retroviruses that integrate in a totally random fashion. In order for the lytic phase to occur, AAV requires the presence of either adenovirus or herpesvirus. As a result of this helper virus rescue, the host cells are lysed and progeny virus is released so that horizontal transmission to neighboring cells can occur. It is well known that adenovirus early region genes such as E1 and E4 are pivotal in triggering AAV replication. Some of the most definitive data on adenovirus enhancement are derived from work on recombinant AAV

Gene Delivery Vehicles

vectors wherein a series of carefully characterized adenovirus E1-deleted mutants were used to determine which gene products were necessary for the helper effect. From this work it was established that only the E4 ORF 6 gene product was essential (Samulski et al., 1999). As was true for the adenoviruses, the initial characterization of this virus group was based on serotyping, using neutralizing assays and complement fixation (Boucher et al., 1970). Six primate AAVs have been isolated, and five were contaminants in adenovirus preparations while one was isolated from a condylomatous wart (Hoggan et al., 1966). All of these initial isolates are serologically distinct except for AAV6 which is apparently a naturally emerged hybrid of AAV1 and AAV2 and not serologically distinguishable from AAV1 (Gao et al., 2004). AAV2 is most serologically prevalent in human populations, but seroepidemiological data suggests that all of these early serotypes are endemic to primates. There are no known sequelae of infection in either humans or primates and this is one of the characteristics that make AAVderived vectors of particular interest in gene therapy. The first isolation of a molecular clone of AAV led to the construction of recombinant vectors that were devoid of all AAV ORF and essentially consisted of the two terminal repeats plus the transgene and its associated elements (Samulski et al., 1983). Using such vectors, relatively long-term gene expression could be demonstrated in such tissues as liver, retina, skeletal muscle, and neurons (Rabinowitz et al., 1998). Of note is the fact that many of the successfully transduced cells were terminally differentiated and rarely divided. Analysis of the AAV genome in transduced cells revealed the presence of both integrated and non-integrated forms (Chen et al., 2001; Duan et al., 1998). In animal model studies, the recipients do not mount T cell responses to AAV-delivered transgene products even though such products may represent foreign epitopes. This is thought to reflect the observation that AAVs do not infect antigen-presenting cells, a property that is just the opposite of what occurs with adenovirus infection (Jooss et al., 1998). More recent studies suggest that in vivo delivered AAV may actually activate a population of regulatory T cells that actively suppress antigen-specific T cells providing an alternative explanation for the paucity/absence of cellular immune responses following AAV gene transfer (Mingozzi et al., 2003). Most of the initial research on AAV vectors involved AAV2, and it revealed that there is considerable longevity of gene expression but a relatively low level of expression. Other problems arise from restricted tissue tropism and delayed onset of gene expression, which has been particularly marked in studies of gene delivery to the retina. As a consequence of these issues, a search for more potent AAV vectors was begun. In an attempt to isolate novel AAV genomes from tissues of latently infected non-human primates (NHP), a series of molecular techniques was employed. Tissues from rhesus monkeys were screened by PCR for the presence of sequences homologous to known AAV serotypes 1–6 as well as AAVs from duck and goose origins. A stretch of AAV sequences spanning 2886–3143 bp of AAV1 and corresponding sequences from the

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aforementioned serotypes was selected as a PCR amplicon (Gao et al., 2002). This DNA segment is about 255 bp in length and both 5 and 3 sequences are highly conserved; however, the middle sequence is variable, unique to each known serotype and designated at the “signature region.” A total of 474 NHP and 259 human tissues were analyzed for the presence of endogenous AAVs and more than 18% of these tissues were positive; 110 non-redundant novel AAVs were isolated and characterized by sequence analysis. Based on phylogeny and functional analyses, the NHP AAVs were organized into clades whose members are closely related (Gao et al., 2003, 2004). It was necessary to confirm that these AAV proviral sequences recovered from tissue DNAs could actually represent rescueable viruses. Tissue DNAs from AAV8 and AAVrh39 were restricted with an unusual endonuclease that did not cut within known AAV genomes; such restricted AAV-containing tissue DNAs were transfected along with E1-deleted infectious molecular clones of type 5 adenovirus DNA into 293 cells. It was possible to rescue, propagate, and purify viruses from these cells; electron microscopy and Western blot analysis confirmed the identity of the viral isolates (Gao et al., unpublished data). In order to determine if these novel new vectors had preferential tissue tropisms and increased vector efficiency, functional screening procedures were carried out. AAVs 8, 9, cy5, rh2, rh8, rh20, rh39, and rh43 transduced liver with efficiencies several logs higher than AAV2. AAV9 was the only vector that outperformed other AAVs in all tissues tested (Gao et al., 2005). These results are most encouraging and suggest that new AAV vectors derived from primates may turn out to be the next generation of delivery vehicles for certain types of gene therapy trials. Naked DNA/Plasmid DNA by Direct Delivery Although some of the first experiments using naked DNA for gene transfer were done at the time of increasing interest in gene therapy, the use of naked DNA for viral gene transfer dates back to the 1960s. When naked DNA was purified from viruses and delivered to mammalian cells either in culture or in intact animals, viral infection occurred (Herriott, 1961). Subsequent developments involved the use of a DNA precipitate created by adding calcium phosphate (Graham and van der Eb, 1973). Further refinements occurred as plasmid expression vectors became a part of recombinant DNA technology. Some of the first work on in vivo gene transfer was done using skeletal muscle as the target organ and several different types of reporter genes (Wolff et al., 1990). Chloramphenicol acetyl transferase activity could be detected at 48 h after injection and luciferase expression could be detected at 60 days. Multiple restriction endonuclease studies failed to reveal evidence of DNA integration. Other factors affecting the level of foreign gene expression included the use of EDTA which damaged myofibrils, preinjection of muscles with large volumes of hypertonic solutions and polymers, and injection techniques that positioned the needle parallel to the long axis of the muscle (Levy et al., 1996; Wolff et al., 1991). Multiple injections apparently did not particularly aid expression nor did electrical stimulation of nerve

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contractions. Expression decreased as the size of the animal increased; there was little difference between mice and rats but there was a significant reduction in rabbits, cats, and monkeys. Plasmid DNA has been delivered into a variety of organs including liver, thyroid, skin, and arteries, but comparative data revealed that the most efficient expression was seen in skeletal muscle. There is a potential side effect of naked DNA administration, and it is the development of an autoimmune disorder. Studies done to create a mouse model for systemic lupus erythematosis have indicated that an immune response to DNA becomes problematic only if the DNA is denatured and complexed with a protein or adjuvant (Gilkeson et al., 1991). Apparently, repetitive intramuscular injections of DNA have failed to produce antinuclear antibodies. There are three principal issues that adversely affect the use of naked DNA: efficiency, stability, and longevity of expression. However, there is little evidence for integration, and this obviates the problem of insertional mutagenesis. Preparations can be standardized and there is the absence of immunogenicity, an often-seen complication that accompanies the use of viral vectors. Liposomes Liposomes represent a class of heterogeneous compounds that can be synthesized as long cylinders of roughly circular vesicles. The basic structures represent a combination of phospholipids with a fatty acid tail and hydrophilic head groups. There are two major classes of such compounds, anionic and cationic liposomes. DNA is encapsulated in the aqueous interior of the vesicle in anionic liposomes; because there is no direct binding to the DNA, this places a limit on the size of the transgene DNA. Cellular uptake occurs through receptor-mediated endocytosis, and following this, the lipid–DNA complex is readily shunted to the endosomal compartment where nucleases can readily hydrolyze the DNA. As a result, it is rare that the transgene DNA reaches the nucleus, and thus integration into host cell chromosomes rarely occurs. Cationic liposomes are made up of lipids mixed in varying molar ratios with cholesterol and dioleylphosphotidylethanolamine, a neutral phospholipid (Scherman et al., 1998). The capacity for transgene DNA is considerable and can range up to 50 kb. Cell entry is similar to that seen with anionic liposomes: there is susceptibility to endosomal shunting and there is no integration into host cell DNA. Although the preparation of liposomes is essentially a chemical process and there is more transgene capacity than is available in several of the viral vectors, the transduction efficiency is relatively poor and gene expression is uniformly short-term. There are some reports indicating that inflammation can occur; it has been seen in the lung in a clinical trial studying cystic fibrosis (Alton et al., 1999). The offending entity has not been characterized but it could be due to unmethylated dinucleotide sequences of bacterial origin that are in the plasmid DNA.

DNA–Protein Complexes This method of gene delivery has much in common with other non-viral vectors. The production process is chemically based and involves combining DNA with protein complexes; there is little immune response and thus the potential for repeat administration. Again, the endosomal shunt poses a major problem in that there is frequent degradation of transgene DNA; in order to counteract this difficulty, DNA has been conjugated to polylysine to form a binary complex that has the potential to resist DNAase digestion. Leupeptin and chloroquine have been utilized to disrupt endocytic trafficking and the latter contributes to endosomal stability by raising its pH (Perales et al., 1994). Since viruses have developed the innate capacity to circumvent the endosomal shunt, adenovirus has been complexed to polylysine DNA to create a ternary complex that improves the efficiency of gene expression. Despite the employment of these various strategies, DNA–protein complexes require approximately 1 million more copies of DNA to achieve a level of gene expression comparable to that obtained with a viral vector.

GENE THERAPY CLINICAL TRIALS A review of the past 15 years of clinical trials reveals a profile that might not have been anticipated at the outset. Of the 842 protocols that have been reviewed by the NIH Recombinant DNA Advisory Committee, 570 or approximately 67% have been directed to the study of cancer, but only 67 or approximately 8% have focused on monogenic deficiency diseases (OBA Report, 2007; see Table 53.1). AIDS has been the subject of 48 protocols and 106 protocols have studied a plethora of diseases ranging from Alzheimer’s disease to Parkinson’s disease to erectile dysfunction. Most of these clinical trials have been Phase I trials but a few have progressed as far as Phase III. The single gene therapy product approved for public use is available only in China; Gendicine is based on an adenovirus serotype 5 vector containing a transgene expressing p53 and is used in treating patients who have squamous cell carcinoma of the head and neck (Wilson, 2005). A similar product is being developed in the United States but it has not yet been approved for clinical use by the FDA. Cancer A review of the cumulative results of the gene therapy trials for cancer has to be evaluated as somewhat disappointing. However, one has to place such data in the proper context. Successful eradication of cancer requires the destruction of all malignant cells, and it is not realistic to assume that a therapeutic gene could be transduced into every tumor cell that exists in a given patient. As a result, investigators have examined strategies that could amplify the transduction of a relatively small number of tumor cells, and clearly, the immune system provides a means of achieving this. Of the 570 cancer protocols, about 386 have utilized approaches that could be classed as adoptive immunotherapy.

Gene Therapy Clinical Trials

TABLE 53.1

Human gene transfer protocolsa

TABLE 53.1

Categories

Total

Therapy

791

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615

(Continued)

Categories

Total

Epilepsy

1

Marking and non-therapeutic

51

Eye Disorders

7

Infectious diseases

48

Erectile Dysfunction

1

44

Intractable Pain

1

Autoimmune Disease

4

67

Salivary Gland Hypofunction

1

Alpha-1-Antitrypsin Deficiency

3

Overactive Bladder Syndrome

1

Chronic Granulomatous Disease

3

Renal

1

Familial Hypercholesterolemia

1

Fanconi Anemia

4

Gaucher Disease

3

Hunter Syndrome

1

Ornithine Transcarbamylase Deficiency

1

Purine Nucleoside Phosphorylase Deficiency

1

SCID

6

Leukocyte Adherence Deficiency

1

Canavan Disease

3

Hemophilia

5

Muscular Dystrophy

3

Amyotrophic Lateral Sclerosis

1

Epidermolysis Bullosa

2

Retinal Disorders

2

Neuronal Ceroid Lipofuscinosis

1

Mucopolysaccharidosis

1

Blood Disorders (non-hemophilia)

1

Human immunodeficiency Virus Other viral diseases Monogenic diseases

4

Cancer

570

Other diseases/disorders

106

Peripheral Artery and Coronary Artery Disease

61

Arterial Restenosis and Heart Failure

7

Arthritis

5

Cubital Tunnel Syndrome

1

Alzheimer’s Disease

2

Ulcer

4

Bone Fracture

1

Peripheral Neuropathy

4

Parkinson’s Disease

4

a

Data from Office of Biotechnology Activities, NIH as of June 8, 2007.

In the earlier phases of these trials, the most popular protocol involved the ex vivo transduction of either autologous or allogeneic tumor cells with cytokine genes such as IL-2, IL-4, IL7, IL-12, and GM-CSF. The intent was to provoke an immune response that would result in the production of cytotoxic T cells specific for the tumor; melanoma was the most popular neoplasm for these studies. Some of the published data revealed a delayed hypersensitivity reaction at the injection site of the tumor, and occasionally there was a slight reduction in tumor volume (Ellem et al., 1997). A number of other approaches have been attempted and these include the use of a pro-drug such as herpes simplex virus thymidine kinase in combination with ganciclovir, the use of a tumor suppressor gene such as p53 to induce apoptosis in tumor cells, the downregulation of oncogenes, and the use of vectordirected cell lysis to take advantage of the selective replication of certain adenoviruses in tumor cells. As one projects the future of cancer gene therapy, it is reasonable to assume that its role will be that of an adjuvant that will hold tumor growth in check and that it will be used in appropriate sequence with chemotherapy and radiation therapy. It offers a potentially significant advantage over standard therapies in that it does not induce immune suppression of the patient. Inborn Errors of Metabolism: Monogenic Defects Twenty single gene deficiency diseases have been targeted in the 67 trials presented for public review. Specific diseases include cystic fibrosis, hemophilia (types A and B), familial hypercholesterolemia, Fanconi anemia, alpha-1-antitrypsin deficiency, Gaucher disease, Hunter syndrome, ornithine transcarbamylase deficiency, Canavan disease, limb girdle muscular dystrophy, neuronal ceroid lipofuscinosis, retinal disorders, amyotrophic lateral sclerosis, junctional epidermolysis bullosa, mucopolysaccharidosis,

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and five types of severe combined immune deficiency (SCID) (adenosine deaminase [ADA] deficiency; chronic granulomatous disease; leukocyte adherence deficiency; purine nucleoside phosphorylase deficiency; and X-linked SCID). In many ways the various types of SCID present an ideal target for an experimental intervention like gene therapy. The common deficiency is a block in T cell differentiation that is associated with direct or indirect impairment of B cell function, thus totally compromising both cellular and humoral immunity. Because of the resulting inter-current infections, death occurs in the first 12–18 months of life. It is significant that the greatest success in clinical gene therapy has occurred with the efficient correction of X-linked SCID and ADA-SCID (Aiuti et al., 2002; Cavazzana-Calvo et al., 2000). As a result of these trials, about 20 patients are still alive and exhibiting evidence of full reconstitution of T lymphocytes. Another interesting facet of these trials is the use of CD34 cells derived from either bone marrow or umbilical cord blood. These target cells are essentially the equivalent of stem cells, and transduction of stem cells has been a goal of gene therapy since the outset. Efficient transduction of these cells remains difficult but their ability for self-renewal is critically important for prolonged gene expression. As a result of the X-linked SCID trial, serious adverse events occurred; they were related to insertional mutagenesis associated with the use of a retrovirus vector derived from the Moloney murine leukemia virus. The target cells were autologous CD34 bone marrow cells. Almost 3 years after gene therapy, 2 of the youngest patients out of 10 exhibited uncontrolled exponential proliferation of clonally-derived mature T cells. Both patients’ clones showed retrovirus vector integration proximal to the LMO2 protooncogene promoter, leading to aberrant transcription and expression of LMO2 (Hacein-Bey-Abina et al., 2003). Approximately 1 year later a third patient developed the same pathology. Several testable hypotheses have been proposed to account for these results. The LMO2 targeting could represent a “physical hotspot” or LMO2 integrants could be selected by the growth advantage conferred on them. Because of the block in T cell differentiation, there are more T lymphocyte precursors among CD34 cells in SCID-X1 patients, thus increasing the number of cells at risk for integration. The fact that the affected patients were the youngest in the study suggests that they may have suffered the “disadvantage” of exceptional proliferative capacity associated with neonatal hematopoiesis; again this could increase the number of target cells at risk (Hacein-Bey-Abina et al., 2003). AIDS AIDS presents a challenge for gene therapy that is analogous to that seen with cancer. It is essentially impossible to transduce every HIV-infected CD4 lymphocyte that exists in a given patient.As a result, there have been two general approaches to developing gene therapy for this disease. One involves the creation of genetically engineered vaccines and the other focuses on the inhibition of HIV replication. Clearly the central problem confronting vaccine

development relates to the extremely frequent, naturally occurring mutations in the envelope genes of the virus. In terms of HIV inhibition, the predominant strategy has involved creating mutations in viral genes essential for replication. Mutations introduced into the Rev gene resulted in the production of a defective protein that functioned as a transdominant inhibitor. When the mutated Rev gene was transduced into CD4 T lymphocytes, the cells became resistant to HIV infection both in vitro and in vivo (Ranga et al., 1998;Woffendin et al., 1994). One of the more innovative approaches involved the participation of 10 pairs of identical twins discordant for HIV infection. In each case, the uninfected twin was the lymphocyte donor. Nineteen separate infusions of transduced CD4 T cells were given to the ten infected twins. Control T cells were transduced with the neo gene and two anti-HIV genes were used in the experimental group, the antisense trans-activation response gene (TAR) element or the trans-dominant Rev gene. Peripheral blood lymphocytes were monitored for 3 years and demonstrated a preferential survival of CD4 lymphocytes containing anti-HIV gene(s) (Morgan et al., 2005). Based on this data it could be postulated that efficient transduction of CD34 cell populations could lead to a repopulating cell subset capable of resisting HIV infection. Cardiovascular Diseases Although it is one of the monogenic deficiency diseases, homozygous familial hypercholesterolemia produces a rapid onset and severe progression of arteriosclerosis. Since these patients lack low-density lipoprotein receptors in the liver parenchyma, they have extremely high levels of serum LDL cholesterol and the negative consequences of cardiovascular disease can appear as early as age 5. One of the earlier gene therapy studies involved the ex vivo transduction of hepatocytes with reinfusion of these transduced cells through the portal vein. Five research subjects participated in this study and two of them exhibited a significant reduction in LDL cholesterol while a third exhibited a measurable reduction (Grossman et al., 1995). As would be expected, the positive results were of limited duration because the transduced hepatocytes were terminally differentiated cells and thus had a finite lifespan. A further confounding factor was presented by the fact that all of the research subjects were on chronic maintenance with 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors that are known to reduce cholesterol. For several years there has been a sustained interest in the use of the gene encoding vascular endothelial growth factor (VEGF). Initial studies were done on patients with arteriosclerosis obliterans whose ischemia put their lower limbs at risk of amputation. Following intramuscular administration of VEGF, newly visible collateral blood vessels could be demonstrated by contrast angiography (Baumgartner et al., 1998). Following this initial work, there have been a number of studies utilizing VEGF to stimulate angiogenesis in patients with severe coronary arteriosclerosis. In one report, 13 patients were enrolled and left ventricular electromechanical mapping was done to evaluate the effects of

References

gene transfer. The results suggested improved perfusion scores calculated from single-photon emission CT-sestamibi myocardial perfusion scans (Vale et al., 2000). All these results were derived from Phase I trials that were not controlled. There are several unresolved issues surrounding this particular research. First, the VEGF gene is being used in adults as an exogenous agent, whereas, under normal circumstances, the gene is expressed in early gestation and then is silenced. Secondly, it has not been determined if the vascular networks stimulated by VEGF are permanent or if they are temporary; only a time study can help resolve this problem. It is possible that repeat administration of VEGF would be required for a more lasting therapeutic effect.

CONCLUSION A current evaluation of gene therapy would reveal a modicum of definite success in a group of rare genetic diseases (SCID) that has been mixed with a series of adverse events, insertional mutagenesis leading to a leukemia-like syndrome. These results would argue for the use of non-integrating vectors where possible. There are data to indicate that AAV vectors can persist for prolonged periods of time (up to 6 years) as stable episomes. A principal limitation of AAV is the transgene capacity; transgenes much larger than 4 kb cannot be used in this delivery system. Since the problems of random integration have been demonstrated rather dramatically, it is important to note that there is now a system that offers the potential for targeted integration. In a study of hereditary tyrosinemia, investigators used the phage C31 integrase which has a 34-bp recognition sequence that is rare in mammalian genomes. As a result, it was demonstrated that only seven different integration sites accounted for greater than 90% of all integration (Held et al., 2005). Although this system has potential utility, some of the transduced hepatocytes displayed an abnormal morphology that could be a phenotype for toxic mutations. The entire thrust of this article has been about gene addition but there is recent evidence to suggest that actual gene repair is possible. It is known that permanent in vivo modification of the human genome is very difficult because of the low frequency of homologous recombination. However, it has been recently shown that a zinc-finger protein could be engineered to recognize a unique chromosomal site and then fused to a

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nuclease domain. The resulting zinc-finger nuclease could initiate a specific double strand break and create specific sequence alterations by stimulating homologous recombination between the chromosome and the extrachromosomal DNA (Urnov et al., 2005). In a study of X-linked SCID, it was possible to repair a c mutation in 5–17% of T lymphocytes in culture without any selection methods. As was the case with the aforementioned targeted integration, delivery of the chimeric nuclease and repair template necessitated the use of a vector. Other approaches to the repair of mutant genes have involved the use of a combination of RNA interference and exon skipping. Mutations in the dystrophin gene create a frameshift or a stop in the mRNA. It was possible to induce persistent exon skipping and remove the mutated exon 23 of dystrophin mRNA in the mdx mouse by administering an AAV vector with a transgene encoding antisense sequences linked to a modified U7 small nuclear RNA. As a result, the treated mice exhibited sustained production of functional dystrophin at physiological levels (Goyenvalle et al., 2004). Recently, there has been a rather intense interest in pharmacogenomics, a discipline that examines the relationships between common variations in the human genome (single nucleotide polymorphisms or SNPs) and response to drugs. Much of the current research is focused on genes that encode metabolic enzymes affecting drug activity as well as defective proteins that lead to increased disease susceptibility. In the case of gene therapy, DNA represents the drug and thus one is considering tailored drug therapy of a different kind. Pharmacogenomics may not have much of a direct bearing on the monogenic deficiency diseases since the gene mutations are already well known and the resulting diseases are most often well characterized. However, one could postulate that the genetic profile of patients with cancer could have some relevance to the response to gene therapy. This area of research is largely unexplored and such applications are not likely to be developed in the near future, given that the general response of a wide variety of tumors to multiple gene transfer approaches has been disappointing. On balance, gene therapy remains a highly experimental technique. As is true for essentially all of genomics research, the developments will be incremental, but from this series of small steps will emerge a technology that is extremely useful to medicine. This common utility may require another 10–15 years of effort but success will ultimately emerge.

REFERENCES Aiuti, A., Slavin, S., Aker, M., Ficara, F., Deola, S., Mostellaro, A., Morecki, S., Andolfi, G., Tabucchi, A., Bordignon, C. et al. (2002). Correction of ADA-SCID by stem cell gene therapy combined with nonablative conditioning. Science 296, 2410–2413. Alton, E., Stern, M., Farley, R., Jaffe, A., Chadwick, S., Phillips, J., Davies, J., Smith, S., Browning, J., Davies, M. et al. (1999). Cationic lipid-mediated CFTR gene transfer to the lungs and nose of patients with cystic fibrosis: A double-blind placebo-controlled trial. Lancet 353, 947–954.

Balague, C., Kalla, M. and Zhang, W. (1997). Adeno-associated virus Rep 78 protein and terminal repeats enhance integration of DNA sequences into the cellular genome. J Virol 71, 3299–3306. Baumgartner, I., Pieczek, A., Manor, O., Blair, R., Kearney, M., Walsh, K. and Isner, J. (1998). Constitutive expression of phVEGF165 after intramuscular gene transfer promotes collateral vessel development in patients with critical limb ischemia. Circulation 97, 1114–1123. Bergelson, J., Cunningham, I., Droguett, G., Kurt-Jones, E., Krithws, A., Hong, J., Horwitz, M., Crowell, R. and Finberg, R. (1997).

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Isolation of a common receptor for coxsackie B viruses and adenoviruses 2 and 5. Science 275, 1320–1323. Boucher, D., Parks, W. and Melnick, J. (1970). A sensitive neutralization test for the adeno-associated satellite viruses. J Immunol 104, 555–559. Cavazzana-Calvo, M., Hacein-Bey, S., de Saint Basile, G., Gross, F., Yvon, E., Nusbaum, P., Selz, F., Hue, C., Certain, S., Fischer, A. et al. (2000). Gene therapy of human severe combined immunodeficiency (SCID)-X1 disease. Science 288, 669–672. Chen, Z.,Yant, S., Cheng-Yi, H., Meuse, L., Shen, S. and Kay, M. (2001). Linear DNAs concatermerize in vivo and result in sustained transgene expression in mouse liver. Mol Ther 3, 403–410. Danthinne, X. and Imperiale, M. (2000). Production of first generation adenovirus vectors: A review. Gene Ther 7, 1707–1714. Duan, D., Sharma, P., Yang, J.,Yu, Y., Dudus, L., Zhang, Y., Fisher, K. and Engelhardt, J. (1998). Circular intermediates of recombinant AAV have defined structural characteristics responsible for long-term episomal persistence in muscle tissue. J Virol 72, 8568–8577. Dull, T., Zufferey, R., Kelly, M., Mandel, R., Nguyen, M., Trono, D. and Naldini, L. (1998). A third-generation lentivirus vector with a conditional packaging system. J Virol 72, 8463–8471. Ellem, K., O’Rourke, M., Johnson, G., Parry, G., Misko, I., Schmidt, C., Parsons, P., Burrows, S., Fell, A., Li, C. et al. (1997). A case report: immune responses and clinical course of the first human use of granulocyte/macrophage-colony-stimulating-factor-transduced autologous melanoma cells for immunotherapy. Cancer Immunol Immunother 44, 10–20. Francki, R., Fauquet, C., Knudson, D. and Brown, F. (1991). Classification and nomenclature of viruses. Archiv Virol 170 (Suppl 2), 140–144, 290-299. Gao, G-P., Yang, Y. and Wilson, J. (1996). Biology of adenovirus vectors with E1 and E4 deletions for liver-directed gene therapy. J Virol 70, 8934–8943. Gao, G-P., Alvira, M., Wang, L., Calcedo, R., Johnston, J. and Wilson, J. (2002). Novel adeno-associated viruses from rhesus monkeys as vectors for human gene therapy. Proc Nat Acad Sci USA 99, 11854–11859. Gao, G-P., Alvira, M., Somanathan, S., Lu, Y., Vandenberghe, L., Rux, J., Calcedo, R., Sanmiguel, J., Abbas, Z. and Wilson, J. (2003). Adeno-associated viruses undergo substantial evolution in primates during natural infections. Proc Nat Acad Sci USA 100, 6081–6086. Gao, G-P.,Vandenberghe, L., Alvira, M., Lu,Y., Calcedo, R. and Wilson, J. (2004). Clades of adeno-associated virus are widely disseminated in human tissues. J Virol 78, 6381–6388. Gao, G-P., Vandenberghe, L. and Wilson, J. (2005). New recombinant serotypes of AAV vectors. Curr Gene Ther 5, 285–297. Gilkeson, G., Pritchard, A. and Pisetsky, D. (1991). Specificity of antiDNA antibodies induced in normal mice by immunization with bacterial DNA. Clin Immunol Immunopath 59, 288–300. Goldman, M. and Wilson, J. (1995). Expression of alpha v beta 5 integrin is necessary for efficient adenovirus-mediated gene transfer in the human airway. J Virol 69, 5941–5958. Goyenvalle, A., Valin, A., Fougerousse, F., Leturcq, F., Kaplan, J-C., Garcia, L. and Danos, O. (2004). Rescue of dystrophic muscle through U7 sn RNA-mediated exon skipping. Science 306, 1796–1799. Graham, F. and van der Eb, A. (1973). A new technique for the assay of infectivity of human adenovirus 5 DNA. Virology 52, 456–467. Grossman, M., Rader, D., Muller, D., Kolansky, D., Kozarsky, K., Clark, B., Stein, E., Lupien, P., Brewer, H., Raper, S. et al. (1995). A pilot

study of ex vivo gene therapy for homozygous familial hypercholesterolemia. Nature Med 1, 1148–1154. Hacein-Bey-Abina, S., Von Kalle, C., Schmidt, M., McCormack, M., Wulffroat, N., Leboulch, P., Lim, A., Osborne, C., Pawliuk, R., Morillon, E. et al. (2003). LM02-associated clonal T cell proliferation in two patients after gene therapy for SCID-X1. Science 302, 415–419. Held, P., Olivares, E.,Aguilar, C., Finegold, M., Calos, M. and Grompe, M. (2005). In vivo correction of murine hereditary tyrosinemia type 1 by C31 integrase-mediated gene delivery. Mol Ther 11, 399–408. Herriott, R. (1961). Infectious nucleic acids, a new dimension in virology. Science 134, 256–260. Hoggan, M., Blacklow, N. and Rowe, W. (1966). Studies of small DNA viruses found in various adenovirus preparations: Physical, biological and immunological characteristics. Proc Nat Acad Sci USA 55, 1467–1474. Jooss, K., Yang, Y., Fisher, K. and Wilson, J. (1998). Transduction of dendritic cells by DNA viral vectors directs the immune responses to transgene products in muscle fibers. J Virol 72, 4212–4223. Kobinger, G., Weiner, D., Yu, Q-C. and Wilson, J. (2001). Filoviruspseudotyped lentiviral vectors can efficiently and stably transducer airway epithelia in vivo. Nat Biotech 19, 225–230. Kornberg, A. (1971). Remarks announcing the in vitro synthesis of DNA. In Genes Dreams and Reality (E. Burnet, ed.), Basic Books, New York, p. 71. Kotin, R., Siniscalco, R., Samulski, R., Zhu, X., Hunter, L., Laughlin, C., McLaughlin, S., Muzyczka, N., Rocchi, M. and Berns, K. (1990). Site-specific integration by adeno-associated virus. Proc Nat Acad Sci USA 87, 2211–2215. Lederberg, J. (1968). Tomorrow’s babies. Proc World Cong Fertil Steril 6, 18–23. Levy, M., Barron, L., Meyer, K. and Szoka, F. (1996). Characterization of plasmid DNA transfer into mouse skeletal muscle: Evaluation of uptake mechanism, expression and secretion of gene products into blood. Gene Ther 3, 201–211. Lieber, A., He, C., Kirillova, I. and Kay, M. (1996). Recombinant adenoviruses with large deletions generated by Cre-mediated excision exhibit different biological properties compared with firstgeneration vectors in vitro and in vivo. J Virol 70, 8944–8960. Miller, A. and Rosman, G. (1989). Improved retroviral vectors for gene transfer and expression. Biotechniques 7, 980–990. Mingozzi, F., Liu, Y.L., Dobrzynski, E., Kaufhold, A., Liu, J.H., Wang, Y., Arruda, V.R., High, K.A. and Herzog, R.W. (2003). Induction of immune tolerance to coagulation factor IX antigen by in vivo hepatic gene transfer. J Clin Invest 111, 1347–1356. Morgan, R., Walker, R., Carter, C., Natarajian, V., Tavel, J., Bechtel, C., Hespin, B., Muul, L., Zheng, Z., Jagannatha, S. et al. (2005). Preferential survival of CD4 T lymphocytes engineered with anti-human immunodeficiency virus (HIV) genes in HIV-infected individuals. Hum Gene Ther 16, 1065–1074. Morsy, M., Gee, M., Motzel, S., Zhao, J., Lin, J., Su, Q., Allen, H., Franklin, L., Parks, R., Graham, F. et al. (1998). An adenoviral vector deleted for all viral coding sequences resuls in enhanced safety and extended expression of a leptin transgene. Proc Nat Acad Sci USA 95, 7866–7871. Naldini, L., Blomer, U., Gallay, P., Ory, D., Mulligan, R., Gage, F., Verma, I. and Trono, D. (1996). In vivo gene delivery and stable transduction of nondividing cells by a lentiviral vector. Science 272, 263–267.

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Vale, P., Losordo, D., Milliken, C., Maysky, M., Esakof, D., Symes, J. and Isner, J. (2000). Left ventricular electromechanical mapping to assess efficacy of phVEGF165 gene transfer for therapeutic angiogenesis in chronic myocardial ischemia. Circulation 102, 965–974. Urnov, F., Miller, J., Lee, Y-L., Beausejour, M., Rock, J., Augustus, S., Jamieson, A., Porteus, M., Gregory, P. and Holmes, M. (2005). Highly efficient endogenous human gene correction using designed zinc-finger nucleases. Nature 435, 646–651. Wilson, J. (2005). Gendicine: The first commercial gene therapy product. Hum Gene Ther 16, 1014. Wivel, N. (2002). Gene therapy: Historical overview and public oversight. In Gene Therapy in Lung Disease (S. Albelda, ed.), Marcel Dekker, New York, pp. 1–27. Wobus, C., Hugle-Dorr, B., Girod, A., Petersen, G., Hallels, M. and Kleinschmidt, J. (2000). Monoclonal antibodies against the adenoassociated virus type 2 (AAV-2) capsid: epitope mapping and identification of capsid domains involved in AAV-2 cell interactions and neutralization of AAV-2 infection. J Virol 74, 9281–9293. Woffendin, C., Yang, Z-Y., Udaykumar, X., Yang, N-S., Sheehy, M. and Nabel, G. (1994). Nonviral and viral delivery of a human immunodeficiency virus protective gene into primary human T cells. Proc Nat Acad Sci USA 91, 11581–11585. Wolff , J. and Lederberg, J. (1994). An early history of gene transfer and therapy. Gene Ther 5, 469–480. Wolff , J., Malone, R., Williams, P., Chong, W., Ascadi, G., Jani, A. and Felgner, P. (1990). Direct gene transfer into mouse muscle in vivo. Science 247, 1465–1468. Wolff , J., Williams, P., Ascadi, G., Jiao, S., Jani, A. and Chong, W. (1991). Conditions affecting direct gene transfer into rodent muscle in vivo. BioTechniques 4, 474–485. Yang, Y., Nunes, F., Berencsi, K., Gonczol, E., Engelhardt, J. and Wilson, J. (1994). Inactivation of E2a in recombinant adenoviruses improves the prospect for gene therapy in cystic fibrosis. Nat Genet 7, 362–369. Yang, Y., Haecker, S., See, Q. and Wilson, J. (1996). Immunology of gene therapy with adenoviral vectors in mouse skeletal muscle. Hum Mol Genet 5, 1703–1712.

Genomic and Personalized Medicine

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Genomic and Personalized Medicine Volume 2 Edited by Huntington F. Willard, Ph.D. Director Duke Institute for Genome Sciences & Policy Nanaline H. Duke Professor of Genome Sciences Howard Hughes Medical Institute Professor Duke University Durham, North Carolina 27708

and Geoffrey S. Ginsburg, M.D., Ph.D. Center Director, Center for Genomic Medicine Duke Institute for Genome Sciences & Policy Professor of Medicine Duke University Durham, North Carolina 27708

AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD • PARIS SAN DIEGO • SAN FRANCISCO • SYDNEY • TOKYO

Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London NW1 7BY, UK First edition 2009 Copyright © 2009 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-369420-1 (set) ISBN: 978-0-12-370888-5 (vol. 1) ISBN: 978-0-12-370889-2 (vol. 2) For information on all Academic Press publications visit our web site at elsevierdirect.com Typeset by Charon Tec Ltd., A Macmillan Company. (www.macmillansolutions.com) Printed and bound in China 09 10 11 12 13 10 9 8 7 6 5 4 3 2 1

Contents in Brief Foreword

xxv

Preface

xxvii

Acknowledgements

xxix

Advisory Board

xxxi

Contributors

xxxiii

PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine 2. Concepts of Population Genomics 3. Genomic Approaches to Complex Disease 4. Human Health and Disease: Interaction Between the Genome and the Environment 5. Epigenomics and its Implications for Medicine 6. Systems Biology and the Emergence of Systems Medicine Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism 8. Genome-Wide Association Studies and Genotyping Technologies 9. Copy Number Variation and Human Health 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine 11. DNA Methylation Analysis: Providing New Insight into Human Disease 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine 13. DNA Microarrays in Biological Discovery and Patient Care 14. Proteomics: The Deciphering of the Functional Genome 15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms

3

16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics Section 3 Informatic and Computational Platforms for Genomic Medicine 17. Bioinformatic and Computational Analysis for Genomic Medicine 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine 19. Electronic Medical Records in Genomic Medicine Practice and Research 20. Clinical Decision Support in Genomic and Personalized Medicine 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics

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205 206 226 233 242

252

4 22 33 47 60 74

87 88 101 108 120 131 143 157 173 180

PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice 23. Principles of Study Design 24. Biobanking in the Post-Genome Era 25. Application of Biomarkers in Human Population Studies 26. Validation of Candidate Protein Biomarkers 27. Pharmacogenetics and Pharmacogenomics 28. The Role of Genomics and Genetics in Drug Discovery and Development 29. Role of Pharmacogenomics in Drug Development 30. Clinical Implementation of Translational Genomics 31. Translating Innovation in Diagnostics: Challenges and Opportunities 32. The Role of Genomics in Enabling Prospective Health Care

261 262 275 284 299 308 321 335 343 357 367 378

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Contents in Brief

Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations 34. Educational Strategies in Genomic Medicine 35. Federal Regulation of Genomic Medicine 36. Economic Issues and Genomic Medicine 37. Public–Private Interactions in Genomic Medicine: Research and Development

387 388 401 414 424 434

Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 40. Principles of Human Genome Epidemiology 461 41. Genomics and Population Screening: Example of Newborn Screening 470 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention 481 Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine 44. PET Imaging in Genomic Medicine 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges 46. Fluorescence Imaging: Overview and Applications in Biomedical Research 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers 48. Viral Chip Technology in Genomic Medicine 49. Vaccines Against Infectious Diseases: A BiotechnologyDriven Evolution 50. Cancer Vaccines: Some Basic Considerations 51. Biosensors for the Genomic Age 52. Stem Cells 53. Gene Therapy

493 494 500 512 524 532 538 562 573 590 599 610

PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 623 54. The Genomics of Hypertension 624 55. Lipoprotein Disorders 634 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis 652 57. Genomics of Myocardial Infarction 665 58. Acute Coronary Syndromes 680

59. Heart Failure in the Era of Genomic Medicine 60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine 62. Genetics and Genomics of Arrhythmias 63. Hemostasis and Thrombosis 64. Peripheral Arterial Disease 65. Genomics of Congenital Heart Disease 66. Genomics of Perioperative and Procedural Medicine

692

Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment 68. Immune Cells and the Tumor Microenvironment 69. Lymphomas 70. Genomics in Leukemias 71. Genomics of Lung Cancer 72. Breast Cancer and Genomic Medicine 73. Colorectal Cancer 74. Prostate Cancer 75. Molecular Biology of Ovarian Cancer 76. Pancreatic Neoplasms 77. The Multiple Endocrine Neoplasia Syndromes 78. Genomics of Head and Neck Cancer 79. Genomic Medicine, Brain Tumors and Gliomas 80. Molecular Therapeutics of Melanoma 81. Emerging Concepts in Metastasis 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer

807 808 818 830 844 856 869 879 898 913 921 931 945 956 967 977

705 716 729 755 773 781 794

990

Section 10 Inflammatory Disease Genomic Medicine 1009 83. Environmental Exposures and the Emerging Field of Environmental Genomics 1010 84. Molecular Basis of Rheumatoid Arthritis 1017 85. “Omics” in the Study of Multiple Sclerosis 1032 86. Inflammatory Bowel Disease 1040 87. Glomerular Disorders 1056 88. Spondyloarthropathies 1067 89. Asthma Genomics 1084 90. Genomic Aspects of Chronic Obstructive Pulmonary Disease 1098 91. Genomic Determinants of Interstitial Lung Disease 1110 92. Peptic Ulcer Disease 1122 93. Cirrhosis in the Era of Genomic Medicine 1138 94. Systemic Sclerosis 1155 Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity 96. Diabetes 97. Metabolic Syndrome 98. Nutrition and Diet in the Era of Genomics

1169 1170 1187 1194 1204

Contents in Brief

Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia 100. Parkinson’s Disease: Genomic Perspectives 101. Epilepsy Predisposition and Pharmacogenetics 102. Ophthalmology 103. Genomic Basis of Neuromuscular Disorders 104. Psychiatric Disorders 105. Genomics and Depression 106. Bipolar Disorder in the Era of Genomic Psychiatry Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens

1221 1222 1233 1243 1256 1265 1282 1289 1299

1313 1314

108. 109. 110. 111. 112.

Genomic Medicine and Aids Viral Genomics and Antiviral Drugs Host Genomics and Bacterial Infections Sepsis and the Genomic Revolution Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

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1324 1340 1347 1362 1375

Glossary

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Index

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Table of Contents Foreword

xxv

Preface

xxvii

Acknowledgements

xxix

Advisory Board

xxxi

Contributors

xxxiii

PART I GENOMIC APPROACHES TO BIOLOGY AND MEDICINE Section 1 Principles of Human Genomics 1. Organization,Variation and Expression of the Human Genome as a Foundation of Genomic and Personalized Medicine Huntington F. Willard Introduction The Human Genome Variation in the Human Genome Expression of the Human Genome Genes, Genomes and Disease From Genome to Personalized Medicine Conclusion References Recommended Resources 2. Concepts of Population Genomics Mike E.Weale and David B. Goldstein Introduction Important Concepts in Population Genomics Human Population Genomics Application of Population Genomics to Genomic Medicine Conclusions References Recommended Resources 3. Genomic Approaches to Complex Disease Desmond J. Smith and Aldons J. Lusis Introduction Identifying Common and Rare Genomic Variations in the Population Relating DNA Variation to Phenotypes Integration of “Omic” Technologies with Genetics Conclusions and Prospects

3

4 4 6 9 11 13 15 18 18 21 22 22 22 26 28 29 30 32 33 33 33 36 40 43

Acknowledgements References Recommended Resources 4. Human Health and Disease: Interaction Between the Genome and the Environment Kenneth Olden Introduction Importance of the Environment The Environmental Genome Project Problematic Nature of Gene–environment Interaction Studies Polymorphism and Disease Susceptibility: Case–control Studies Epigenetics and the Environment Conclusion Acknowledgements References 5. Epigenomics and its Implications for Medicine Moshe Szyf Introduction DNA Methylation Patterns Chromatin Modification DNA Methylation and Chromatin States Co-operatively Determine the State of Activity of Genes Epigenetics and Human Disease Conclusions Acknowledgements References 6. Systems Biology and the Emergence of Systems Medicine Nathan D. Price, Lucas B. Edelman, Inyoul Lee, Hyuntae Yoo, Daehee Hwang, George Carlson, David J. Galas, James R. Heath and Leroy Hood Introduction Systems Science in Biology And Medicine Multi-parameter Blood-bourne Biomarkers Emerging in vivo and in vitro Technologies Computational and Mathematical Challenges in Systems Medicine Conclusions and Perspectives References Recommended Resources

43 43 46

47 47 48 50 52 53 56 57 57 57 60 60 61 63

65 67 69 70 70

74

74 75 76 78 81 81 82 85 ix

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Table of Contents

Section 2 Technology Platforms for Genomic Medicine 7. DNA Sequencing for the Detection of Human Genome Variation and Polymorphism Samuel Levy and Yu-Hui Rogers Introduction DNA Sequencing Other Methodologies for Polymorphism Detection Future Directions Acknowledgements References 8. Genome-Wide Association Studies and Genotyping Technologies Kevin V. Shianna Introduction Principles of Genome-wide Association Studies Platform Overview Conclusion References 9. Copy Number Variation and Human Health Charles Lee, Courtney Hyland, Arthur S. Lee, Shona Hislop and Chunhwa Ihm Introduction Basic Principles of CNVs Detecting CNVs in a Genome-wide Manner Association of CNVs to Disease and Disease Susceptibility Implications of CNVs Conclusions Acknowledgements References Recommended Resources 10. Inter-Species Comparative Sequence Analysis: A Tool for Genomic Medicine Anthony Antonellis and Eric D. Green Introduction Performing Comparative Sequence Analysis: Resources and Methods Comparative Sequence Analysis and Human Genetic Disease CSA and the Future of Human Genetics and Genomic Medicine References Recommended Resources

87 88 88 89 95 96 97 97

101 101 101 103 106 106 108

108 108 112 114 116 118 118 118 119

120 120 121 124 128 128 130

11. DNA Methylation Analysis: Providing New Insight into Human Disease 131 Susan Cottrell,Theo deVos, Juergen Distler, Carolina Haefliger, Ralf Lesche, Achim Plum and Matthias Schuster Introduction Technology to Assess DNA Methylation

131 132

Clinical Impact of DNA Methylation Analysis Conclusion References Recommended Resources 12. Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine Michelle M. Kittleson, Rafael Irizarry, Bettina Heidecker and Joshua M. Hare Introduction Gene Expression Technology Gene Discovery Molecular Signature Analysis Gene Discovery Versus Molecular Signature Analysis Current Issues in Gene Expression Analysis Alternative Technologies for Analysis of the Transcriptome Conclusion Acknowledgements References Recommended Resources 13. DNA Microarrays in Biological Discovery and Patient Care Andrew J.Yee and Sridhar Ramaswamy Introduction Microarray Technology Data Analysis Applications Limitations and Challenges Future Directions Conclusions References Recommended Resources

136 139 139 142

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143 143 146 148 151 151 153 153 154 154 156 157 157 157 160 161 165 165 168 168 172

14. Proteomics: The Deciphering of the Functional Genome 173 Li-Rong Yu, Nicolas A. Stewart and Timothy D.Veenstra Introduction 173 Gel-based and Solution-based Proteomics 174 Mass Spectrometry 175 Bioinformatics 176 Impact of Proteomics on Understanding Diseases 178 Conclusions 178 Acknowledgements 179 References 179 Recommended Resources 179 15. Comprehensive Metabolic Analysis for Understanding of Disease Mechanisms 180 Christopher B. Newgard, Robert D. Stevens, Brett R.Wenner, Shawn C. Burgess, Olga Ilkayeva, Michael J. Muehlbauer, A. Dean Sherry and James R. Bain Introduction 180 Current Metabolomics Platforms: Basic Tools and General Features 181

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Comparison of NMR and MS Technologies for Unbiased Metabolic Profiling MS Methods for Targeted Metabolic Profiling Examples of NMR-based Metabolic Profiling in Disease Research Examples of Targeted MS-based Metabolic Profiling for Understanding of Disease Mechanisms Integration of Metabolic Profiling with Other “Omics” Technologies Future Directions References 16. Comprehensive Analysis of Gene Function: RNA interference and Chemical Genomics Bjorn T. Gjertsen and James B. Lorens Introduction RNA Interference Gene Function Analysis: An Overview Chemical Genomics Gene Function Studies Conclusions Acknowledgements References Recommended Resources Section 3 Informatic and Computational Platforms for Genomic Medicine 17. Bioinformatic and Computational Analysis for Genomic Medicine Atul J. Butte Introduction Vignettes: How Specific Bioinformatics Methods Can Change the Practice Ofmedicine Analytic Methods Where Data for Studies May be Found Bioinformatics Vocabularies and Ontologies Freely Available Bioinformatics Tools New Questions for Genomic Medicine Acknowledgements References Recommend Resources 18. Fundamentals and History of Informatics for Genomic and Personalized Medicine A. Jamie Cuticchia Introduction Databases for Genomic Medicine Conclusion References Recommended Resources 19. Electronic Medical Records in Genomic Medicine Practice and Research Glenn S. Gerhard, Robert D. Langer, David J. Carey and Walter F. Stewart

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Introduction EMRs and Genomic Medicine Clinical Practice EMRs and Genomic Medicine Research Conclusion Acknowledgements References Recommended Resources 20. Clinical Decision Support in Genomic and Personalized Medicine Kensaku Kawamoto and David F. Lobach Introduction CDS Background: History, Examples, Evidence of Effectiveness, and Desirable Attributes Potential Uses of CDS to Support Genomic and Personalized Medicine Limited Deployability: The Potential Achilles’ Heel of CDS Systems for Genomic Medicine Challenges to Widespread Deployment of Effective CDS Systems Conclusions Disclosures References Recommended Resources 21. Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics Mark S. Boguski Introduction Characteristics of Consumer Searches for Health Information What and Where Are Consumers Searching? Personalized Genomics for Consumers Summary and Conclusions References Wikipedia References

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PART II TRANSLATIONAL APPROACHES IN GENOMIC AND PERSONALIZED MEDICINE Section 4 Enabling Strategies in the Translation of Genomics into Medicine 22. Translational Genomics: From Discovery to Clinical Practice Geoffrey S. Ginsburg Introduction A Roadmap for Translation Where Can Genomics Have Impact in the Continuum of Health and Disease? The Genomics “Gold Rush”

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The Personal Genome: Precious Code or Fool’s Gold? “Grand Challenges” in Translation of Genomics to Human Health Translational Genomics: Enabling Competencies How Are We Going to Do This? Developing Environments That Foster Translational Genomics to Health Applications References 23. Principles of Study Design Peter Grass Introduction Principles of Experimental Design Design Issues in Genomic Medicine References Recommended Resources 24. Biobanking in the Post-Genome Era Theresa Puifun Chow, Chia Kee Seng, Per Hall and Edison T. Liu Introduction The Biobanking Evolution The Past Imperfect Resources The Evolving Face of Biobanking Existing Models: Biobanking in Europe and the USA Singapore’s National Biobank and National Aspirations In Biomedical Research The Future of National Biobanks References Recommended Resources 25. Application of Biomarkers in Human Population Studies Stefano Bonassi and Monica Neri Introduction Biomarkers in Medicine Biomarkers of Exposure Biomarkers of Early Disease Risk Biomarkers of Genetic Susceptibility to Disease Conclusions References 26. Validation of Candidate Protein Biomarkers Ingibjörg Hilmarsdóttir and Nader Rifai Introduction Optimization of the Candidate Protein Research Assay Analytical Evaluation Reference Intervals Pre-analytical Variation Clinical Evaluation Indicators of Diagnostic Accuracy And Predictability Diagnostic Research Studies Design of Diagnostic Studies

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Transferability Of Test Performance Assay Transfer to Diagnostic Company Regulatory Requirements References Recommended Resources 27. Pharmacogenetics and Pharmacogenomics Iris Grossman and David B. Goldstein Introduction Pharmacogenetic Studies: From Concept to Practice Marker Selection – Strategy and Application From Bench to Bedside: Integration of Pharmacogenetic Testing into Clinical Practice Examples of PGx Tests: Promising New Developments and Marketed Products Future Developments Required for the Field to Fully Meet its Expectations References Recommended Resources 28. The Role of Genomics and Genetics in Drug Discovery and Development Robert I.Tepper and Ronenn Roubenoff Introduction The Drug Discovery Process Genomics in Target Discovery Genomic Approaches to Drug Identification Pharmacogenomics and Drug Development Pharmacodynamic Markers and their Role in Drug Discovery and Development Toxicogenomics Genetics and Genomics in Clinical Trial Design Genomics in Drug Approval and Regulation Conclusion References Recommended Resources 29. Role of Pharmacogenomics in Drug Development Colin F. Spraggs, Beena T. Koshy, Mark R. Edbrooke and Allen D. Roses Introduction Drug Development Critical Path Drug Development Economics Methods for Identification of Genetic Classifiers Pharmacogenomics in the Drug Development Pipeline Efficacy Pharmacogenetics – here and Now Drug Exposure Pharmacogenetics to Tune Efficacy and Safety Profiles Investigation and Management of Safety in Clinical Trials Other Genomic Methods: RNA Interference to Direct Drug Usage “No Samples, No Science” Conclusions Acknowledgements References

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30. Clinical Implementation of Translational Genomics Wendy K. Chung Introduction Genetic Stratification Will Allow Medical Care to be Individualized After a Diagnosis is Made Population-based Germline Genomic Screening Newborn Screening Pharmacogenetics Somatic Genomic Variation Novel Sources of Genomic Variation Laboratory Standards to Ensure Analytic Validity Clinical Validation and Clinical Utility Cost Reimbursement Who Will Provide Genomic Medical Care? Genomic Literacy Ethical, Legal, and Social Issues Conclusions Acknowledgements References Recommended Resources 31. Translating Innovation in Diagnostics: Challenges and Opportunities Matthew P. Brown, Myla Lai-Goldman and Paul R. Billings Introduction Novel Diagnostics Conclusions: Translational Challenges for Innovative Diagnostics References 32. The Role of Genomics in Enabling Prospective Health Care Ralph Snyderman Introduction Predictive Models Predictive Factors Risk Assessment for Breast Cancer Pharmacogenomics Conclusion Acknowledgements References Recommended Resources Section 5 Policy Challenges in Genomic and Personalized Medicine 33. From Sequence to Genomic Medicine: Genome Policy Considerations Susanne B. Haga Introduction Genome Research after the Human Genome Project Policy Issues in Large-scale Genetics and Genomics Research

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Integrating Genomic Medicine Applications in Healthcare Conclusion References

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34. Educational Strategies in Genomic Medicine Charles J. Epstein Introduction Genetic and Genomic Literacy of the Public and Makers of Public Policy Education of the Providers of Health Care Conclusion References

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35. Federal Regulation of Genomic Medicine Janet Woodcock Introduction Regulation of Genomic Tests Pharmacogenomics in Drug Development And Clinical Medicine: the Role of Regulation Fda Efforts to Advance Genomic Product Development Conclusions References Recommended Resources

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36. Economic Issues and Genomic Medicine David L.Veenstra, Louis P. Garrison and Scott D. Ramsey Introduction Economic Evaluation and Cost-effectiveness Analysis Evaluating Genomic Technologies Economic Incentives and the Future of Genomic Medicine Establishing Value-based Reimbursement For Genomic Technologies Conclusions References

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37. Public–Private Interactions in Genomic Medicine: Research and Development Subhashini Chandrasekharan, Noah C. Perin, Ilse R.Wiechers and Robert Cook-Deegan Introduction Landscape of Private Sector Genomics Future Trends Acknowledgements References

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Section 6 Genomic Medicine and Public Health 445 38. What Is Public Health Genomics? 446 Alison Stewart and Ron Zimmern Introduction 446 The Emergence of Public Health Genomics 446 The Definition of Public Health Genomics 447 Key Concepts in Public Health Genomics 447

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The “Enterprise” of Public Health Genomics Core Activities in Public Health Genomics Moving Public Health Genomics Forward: Leadership And Networks Conclusion References Recommended Resources

448 449 451 452 452 453

39. Why Do We Need Public Health in the Era of Genomic Medicine? 454 Muin J. Khoury and Marta Gwinn Introduction 454 The Continuum from Genetics to Genomics in Health Practice 454 The Role of Public Health in the Translation of Human Genome Discoveries into Health Applications 455 The Focus on Disease Prevention and Health Promotion 457 The Population Perspective: Crucial Role of Public Health Sciences 457 The Role of Knowledge Integration Across Disciplines 458 The Role of Health Services Research and Population Health Assessment, Assurance, and Evaluation 458 Conclusion 458 References 459 40. Principles of Human Genome Epidemiology Marta Gwinn and Muin J. Khoury Introduction Human Genome Epidemiology Epidemiologic Study Designs Epidemiologic Measures Of Disease Frequency, Association, and Risk Measurement and Bias Gene–environment Interaction Probability and Personalized Medicine Building the Evidence Base Conclusion References Recommended Resources 41. Genomics and Population Screening: Example of Newborn Screening John D.Thompson and Michael Glass Introduction Components of the NBS System Screening Technology: Simple Ideas, Complex Realities Variability Among NBS Programs Influence of Genetics and -omic Technologies on NBS Acknowledgements

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References Recommended Resources 42. Family History: A Bridge Between Genomic Medicine and Disease Prevention Maren T. Scheuner and Paula W.Yoon Introduction Clinical Approach Conclusion Acknowledgements References Recommended Resources Section 7 Clinical Technologies Supporting Personalized Medicine 43. Molecular Imaging as a Paradigm for Genomic and Personalized Medicine Ralph Weissleder Introduction Molecular Imaging and Cancer Detection Molecular Imaging to Determine Treatment Efficacy Molecular Imaging and Drug Development Near-term Needs and Opportunities Acknowledgement References 44. PET Imaging in Genomic Medicine Vikas Kundra and Osama Mawlawi Introduction Physics Imaging Agents and Methods in Analysis of Biological Samples Conclusion References Recommended Resources 45. MRI for Molecular Imaging Applications: Overview, Perspectives, and Challenges Dmitri Artemov Introduction Basics of MRI Contrast MR Contrast Agents for Molecular Imaging Applications Molecular Imaging Applications of MRI Conclusions Acknowledgements References 46. Fluorescence Imaging: Overview and Applications in Biomedical Research Vasilis Ntziachristos Introduction Imaging Technology

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Fluorescence Applications in Genomic Medicine References 47. Imaging Genetics: Integration of Neuroimaging and Genetics in the Search for Predictive Markers Ahmad R. Hariri Introduction Conceptual Basis of Imaging Genetics Basic Principles of Imaging Genetics Imaging Genetics and the Neurobiology of the 5-httlpr Future Directions References 48. Viral Chip Technology in Genomic Medicine Zeno Földes-Papp Introduction Role of Viruses in Human Infectious Disease Microfabrication Nanofabrication Are there Additional Alternatives to Diagnostic Microarrays? Conclusions Acknowledgements References Recommended Resources 49. Vaccines Against Infectious Diseases: A Biotechnology-Driven Evolution Vega Masignani, Hervé Tettelin and Rino Rappuoli Introduction The Genomic Era: From Microbial Genome to Vaccine Development Impact of Whole Genome Analyses In Vivo Gene Expression: Ivet And Stm Microarray Expression Technology Proteomics From Microbial to Human Genome Sequencing: Genomic Medicine Metagenomics: Deciphering Host–microbe Interactions Conclusions References 50. Cancer Vaccines: Some Basic Considerations Hans-Georg Rammensee, Harpreet Singh-Jasuja, Niels Emmerich and Steve Pascolo Introduction Immune Suppression by Tumors and by Regulatory T-cells The Ideal Therapeuticcancer Vaccine Molecularly Undefinedcancer Vaccines Peptides

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Proteins and Carbohydrates Nucleic Acids: Plasmid DNA and Messenger RNA Viral and Bacterial Vectors Adjuvants, Formulations, and Route of Application Immunomonitoring Conclusions References 51. Biosensors for the Genomic Age Meghan B. O’Donoghue, Lin Wang,Yan Chen, Gang Yao and Weihong Tan Introduction Biosensors for Detection of Oligonucleotides for the Detection of Disease Nucleic Acid as Tools for Biosensing Outlook References 52. Stem Cells Rikkert L. Snoeckx, Kris Van Den Bogaert and Catherine M.Verfaillie Introduction Types of Stem Cells: Embryonic and Adult Stem Cells How to Define The Molecular Signature of Stem Cells Future Directions to Identify the Global Integrated Regulatory Network Future Directions in Stemcell Therapies Conclusion References 53. Gene Therapy James M.Wilson and Nelson A.Wivel Introduction Gene Delivery Vehicles Gene Therapy Clinical Trials Conclusion References

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PART III DISEASE-BASED GENOMIC AND PERSONALIZED MEDICINE: GENOME DISCOVERIES AND CLINICAL APPLICATIONS Section 8 Cardiovascular Genomic Medicine 54. The Genomics of Hypertension Chana Yagil and Yoram Yagil Introduction Predisposition Screening Diagnosis

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Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources 55. Lipoprotein Disorders Sekar Kathiresan and Daniel J. Rader Introduction Overview of Lipoprotein Metabolism Plasma Lipid and Lipoprotein Levels and Atherosclerotic Cardiovascular Disease Inherited Basis for Blood Lipid Traits Screening for Lipid Disorders Genetics of Ldl-c Genetics of Hdl-c Genetics of Triglycerides Genetic Lipid Disorders Without Current Proven Molecular Etiology Influence of Lipid-modulating Mutations on Risk of Atherosclerotic Cardiovascular Disease Future Directions in Genetics And Genomics of Lipoproteins Pharmacogenetics of Lipid-modulating Therapies Implications of Genomics of Lipoprotein Metabolism For The Development of Novel Therapies Clinical Recommendations for Genetic Testing for Lipid Disorders Acknowledgements References Recommended Resources 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis Nageswara R. Madamanchi, Aleksandr E.Vendrov and Marschall S. Runge Introduction Sources of ROS in vascular cells Vascular Dysfunction and Atherosclerosis ROS-induced inflammatory gene expression in vascular cells Association of ROS modulators with atherosclerosis ROS signaling in atherosclerotic risk factors ROS-regulated signaling pathways Regulation of transcription factors by ROS ROS signaling in advanced atherosclerosis Polymorphisms in ROS production genes and atherosclerosis Inhibitors of ROS signaling and vascular disease Conclusion Acknowledgements References

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57. Genomics of Myocardial Infarction Carlos A. Hubbard and Eric J.Topol Introduction Predisposition Screening Strategies Diagnosis of Acute MI Prognostic Implications of MI Pharmacogenomics of MI Novel and Emerging Therapies Conclusion References Recommended Resources

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58. Acute Coronary Syndromes L. Kristin Newby Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusion Acknowledgements References Recommended Resources

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59. Heart Failure in the Era of Genomic Medicine Ivor J. Benjamin and Jeetendra Patel Introduction Predisposition (Genetic and Non-genetic) Screening Pathophysiology Diagnosis Prognosis Pharmacogenomics Monitoring Novel Therapeutics and Future Directions Conclusions and Recommendations Acknowledgements References

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60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection Michael Pham, Mario C. Deng, Jay Wohlgemuth and Thomas Quertermous Introduction Cardiac Allotransplantation as a Definitive Therapy for End-stage Heart Failure The Problem of Allograft Rejection Immunosuppression Strategies to Prevent Rejection Current Strategies for Monitoring Transplant Rejection

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The Cargo Clinical Study Development of a Gene Expression Signature For Cardiac Transplant Rejection Pathways Monitored by the Gep (Allomap™) Test Variability of the Biopsy Gold Standard and Relationship to the Gep (Allomap™) Score Discordance Between Biopsy Grade and Molecular Score Effect of Time Post-transplantation on Performance of the Gep Test Relationship of Gep Score to Corticosteroid Dose Relationship of Gep Scores to Cytomegalovirus Infection Prediction of Future Acr by Molecular Score Clinical Use of the Allomap™ Test Future Directions and Ongoing Research With Gep Testing Further Application of Genomic Science to Transplant Rejection References 61. Hypertrophic Cardiomyopathy in the Era of Genomic Medicine J. Martijn Bos, Steve R. Ommen and Michael J. Ackerman Introduction Definitions, Clinical Presentation, and Diagnosis Molecular Genetics of HCM Screening And Treatment for HCM Conclusions References 62. Genetics and Genomics of Arrhythmias Jeffrey A.Towbin and Matte Vatta Introduction Specific Cardiac Arrhythmias Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias Complex Forms of Lqts Short Qt Interval Syndrome Familial Vt/cpvt Primary Conduction Abnormalities References 63. Hemostasis and Thrombosis Richard C. Becker and Felicita Andreotti Introduction Genetics of Coagulation Human Hemostatic Variability Genotype–phenotype Influences Gene-environment Influences on Hemostasis Circulating Cellular and Protein Influences on Hemostasis And Thrombosis

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Race-related Influences On Hemostasis and Thrombosis Linkage Studies in Thrombosis Association Studies in Thrombosis Heritability And Thrombosis: Existing Complexities A Personalized Approach to Hemostasis and Thrombosis Patient Screening: A Traditional Paradigm Patient Screening: A Comprehensive And Population-based Approach Prognostic Considerations Emerging Platform for Hemostasis and Thrombosis Research References

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64. Peripheral Arterial Disease 773 Ayotunde O. Dokun and Brian H. Annex Introduction 773 Epidemiology and Risk Factors for PAD 773 Clinical Manifestations of PAD 773 Therapeutic Strategies for PAD 774 Ic And Cli Are Distinct Clinical Outcomes of PAD 775 Genetic Background as a Risk Factor for PAD 775 Gene Polymorphisms Contributing to Atherosclerosis and PAD 775 Polymorphisms in Pro-atherothrombotic Genes and PAD 776 Genetic Locus Conferring Susceptibility to PAD 776 Identification of Novel Gene Polymorphisms Involved in PAD 776 Identification of a Quantitative Trait in a Preclinical Model of PAD 777 Refining a QTL Using Haplotype Analysis 777 Identification of Candidate Genes 777 Future Potential Use of Genomic Methodologies in PAD 777 Acknowledgements 778 References 778 65. Genomics of Congenital Heart Disease Jessie H. Conta and Roger E. Breitbart Introduction CHD Gene Discovery by Conventional Genetics Genomic Strategies for CHD Gene Discovery Cytogenetic and Molecular Genetic testing Medical Evaluation and Counseling Recommendations Conclusion Acknowledgements References Recommended Resources

781 781 781 786 787 788 789 790 790 793

66. Genomics of Perioperative and Procedural Medicine 794 Simon C. Body, Mihai V. Podgoreanu and Debra A. Schwinn Introduction 794 Why Perioperative Insults are not Equivalent to Chronic Ambulatory Disease 795

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Perioperative Atrial Fibrillation Perioperative Venous and Arterial Thrombosis Perioperative Stroke and Neurocognitive Dysfunction Hemorrhage and Cardiac Surgery Dynamic Genomic Markers of Perioperative Outcomes Conclusion References

796 796 798 799 799 800 800

Section 9 Oncology Genomic Medicine 67. Cancer Genes, Genomes, and the Environment Robert L. Strausberg Introduction Acquired Functions of Cancer Cells Chromosomal Aberrations and Cancer Cancer Genes and their Functions Inherited Predisposition Cellular Progression Toward Cancer Through Somatic Changes From Genome to the Clinic Comprehensive Sequencing of the Kinome Expanding the Search Multiple Molecular Mechanisms for Oncogene Activation Microarrays and Cancer Genomics Environmental Cancer Genomics Cancer Genomic Databases Expedite Progress Mouse Models of Cancer Future Directions References

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68. Immune Cells and the Tumor Microenvironment David S. Hsu, Michael Morse,Timothy Clay, Gayathri Devi and H. Kim Lyerly Introduction Immune Cells of the Tumor Microenvironment Examples of Tissue or Gene Microarrays used to Study Tumors Studies of Genomic Immune Stimulation within the Microenvironment Genomic Analysis of Tumor Microenvironment in Immunotherapy Studies Proteomics of Immune Cells and the Tumor Microenvironment Conclusion References

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69. Lymphomas Lisa Rimsza Introduction Diffuse Large B-cell Lymphoma Primary Mediastinal Large B-cell Lymphoma Hodgkin Lymphoma Follicular Lymphoma Mantle Cell Lymphoma

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Burkitt Lymphoma Miscellaneous Lymphomas Clinical Applications of Molecular Assays in Lymphoma References 70. Genomics in Leukemias Lars Bullinger, Hartmut Dohner and Jonathan R. Pollack Introduction Genomics in Leukemias-insights into Leukemia Biology Genomics in Leukemias – Evaluation of Drug Effects Genomics in Leukemias – Clinical Outcome Prediction Conclusions Acknowledgments References Recommended Resources 71. Genomics of Lung Cancer Hasmeena Kathuria, Avrum Spira and Jerome Brody Introduction Early Diagnosis/screening of Lung Cancer Classification And Prognosis Pathogenesis And Treatment of Lung Cancer Conclusion References Recommended Resource 72. Breast Cancer and Genomic Medicine Erich S. Huang and Andrew T. Huang Introduction The Promise Genetic Bases Molecular Bases Prognosis and Prediction Molecular Markers Genomic Insights Netherlands Cancer Institute Study Duke-taipei Study Nsabp Study Pathway Prediction The Reality of Clinical Genomics References 73. Colorectal Cancer G.L.Wiesner,T.P. Slavin and J.S. Barnholtz-Sloan Introduction Genomic Model of CRC Predisposition for CRC Risk Assessment, Evaluation, and Genetic Testing Screening and Surveillance Prognosis and Treatment

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Pharmacogenetics/genomics of Chemoprevention and Chemotherapy Novel and Emerging Therapeutics Conclusion References 74. Prostate Cancer Phillip G. Febbo and Philip W. Kantoff Introduction Genetic Predisposition and Alterations in Prostate Cancer Prostate Cancer Detection Genomic Changes Associated with Prostate Cancer Behavior Genomic Changes associated with HormoneRefractory Prostate Cancer Future Prospects of Genomics in Prostate Cancer Care References 75. Molecular Biology of Ovarian Cancer Tanja Pejovic, Matthew L. Anderson and Kunle Odunsi Introduction Inherited Ovarian Cancer Syndromes Options for Screening and Prevention Genomic Instability and Ovarian Cancer Fanconi/anemia Pathway Somatic Mutations in Ovarian Cancer Oncogenes and Growth Factors Tumor Suppressor Genes Epigenetics in Ovarian Carcinogenesis Ovarian Cancer Metastases Angiogenesis Summary References 76. Pancreatic Neoplasms Asif Khalid and Kevin McGrath Introduction Predisposition (Genetic and Non-Genetic) Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapies Conclusion References

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77. The Multiple Endocrine Neoplasia Syndromes Y. Nancy You,Vipul Lakhani and Samuel A.Wells

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Introduction The Multiple Endocrine Neoplasia Syndromes

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Conclusion Acknowledgements References Recommended Resources

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78. Genomics of Head and Neck Cancer Giovana R.Thomas and Yelizaveta Shnayder Introduction Head and Neck Squamous Cell Carcinoma Conclusion References Recommended Resources

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79. Genomic Medicine, Brain Tumors and Gliomas Sean E. Lawler and E. Antonio Chiocca Introduction Predisposition Screening Diagnosis and Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References

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80. Molecular Therapeutics of Melanoma Jiaqi Shi,Yonmei Feng, Robert S. Krouse, Stanely Leong and Mark A. Nelson Introduction Diagnosis Genetics of Melanoma Pharmacogenomics Novel and Emerging Therapeutics Conclusions References 81. Emerging Concepts in Metastasis Nigel P.S. Crawford and Kent W. Hunter Introduction Tools to Investigate the Mechanisms of Metastasis Assessement of Prognosis and New Treatments for Metastasis: the Role of New Technologies Conclusion References Recommended Resources 82. Diagnostic-Therapeutic Combinations in the Treatment of Cancer Jeffrey S. Ross Introduction Targeted Therapies for Cancer The Ideal Target The First Diagnostic-therapeutic Combination in Cancer Therapy: Hormonal Therapy for Breast Cancer

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Diagnostic-therapeutic Combinations for Leukemia and Lymphoma 994 Her-2 Positive Breast Cancer and Trastuzumab 994 (Herceptin®) Other Targeted Anticancer Therapies Using Antibodies 995 Selected Targeted Anticancer Therapies Using Small Molecules 999 Pharmacogenomics 1002 Conclusion 1003 References 1003 Section 10 Inflammatory Disease Genomic Medicine 83. Environmental Exposures and the Emerging Field of Environmental Genomics David A. Schwartz Introduction Importance of Environmental Exposures in Human Health Importance of Environmental Exposures in Studying Disease Processes Comparative Environmental Genomics Exposure Assessment in the Gene-environment Paradigm Challenges and Future of Environmental Genomics Conclusion References 84. Molecular Basis of Rheumatoid Arthritis Robert M. Plenge and Michael E.Weinblatt Introduction Clinical Features Predisposition Screening Diagnosis, Prognosis, and Monitoring Pharmacogenomics Conclusions References

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85. “Omics” in the Study of Multiple Sclerosis Francisco J. Quintana and Howard L.Weiner Introduction Genomics in MS Transcriptomics in MS Immunomics in MS Proteomics in MS Conclusion References

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86. Inflammatory Bowel Disease Ad A. van Bodegraven and Cisca Wijmenga

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Introduction Predisposition (Genetic and Non-genetic)

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Screening Diagnosis Prognosis Pharmacogenomics Monitoring Novel and Emerging Therapeutics Conclusions References 87. Glomerular Disorders Tadashi Yamamoto, Hidehiko Fujinaka and Visith Thongboonkerd Introduction Techniques for Detection, Quantification, and Profiling of mRNA Expression in the Kidney mRNA Expression Profiles of Glomerular Disorders Genome Variations in Glomerular Disorders Genetics of Congenital Glomerular Disorders Genomic Medicine for Glomerular Disorders Conclusions References 88. Spondyloarthropathies Dirk Elewaut, Filip De Keyser, Filip Van den Bosch, Dieter Deforce and Herman Mielants Introduction Characteristics of SPA Role of Bowel Inflammation Histopathology of Synovitis in SPA Gut and Synovium Transcriptomes Proteome Analysis Novel and Emerging Therapeutics and Biomarkers Conclusions References 89. Asthma Genomics Scott T. Weiss, Benjamin A. Raby and Juan C. Celedón Introduction Asthma: Basic Pathobiology Predisposition (Genetic and Non-genetic) to Asthma Genome-wide Linkage Analyses of Asthma and its Intermediate Phenotypes Candidate-gene Association Studies of Asthma Genome-wide Association Studies of Asthma Asthma Genomics Screening Diagnosis Prognosis Pharmacogenetics Monitoring Novel and Emerging Therapeutics Conclusions Acknowledgements References Recommended Resources

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90. Genomic Aspects of Chronic Obstructive Pulmonary Disease Peter J. Barnes Introduction Predisposition Pathophysiology Cellular and Molecular Mechanisms Diagnosis and Screening Prognosis Management New Treatments Conclusions References 91. Genomic Determinants of Interstitial Lung Disease P.W. Noble and M.P. Steele Introduction Genetic Determinants of Dpld in Mouse Strains Genetic Determinants of Sarcoidosis Surfactant Proteins and Dpld Genetic Determinants of Pulmonary Fibrosis Identified in Rare Inherited Disorders Genetic Determinants of Fip Conclusion References 92. Peptic Ulcer Disease J. Holton Introduction Clinical and Physiological Aspects of Pud Pathophysiology of Ulcer Formation The Helicobacter Genome Human Polymorphism and Pud Genomics in the Management of Disease Future Developments in the use of Genomic Techniques in Relation to Pud Conclusions Acknowledgements References Recommended Resources 93. Cirrhosis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Liver Structure Fibrosis and Cirrhosis Diagnosis of Cirrhosis Treatment of Cirrhosis Genetics of Cirrhosis The Liver Transcriptome The Liver Proteome Development of Liver Fibrosis Transcriptome Analysis of Liver Disease Proteomic Studies of Liver Disease

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Proteomics in Other Liver Disease Future Impact of Genomics Studies Conclusion References 94. Systemic Sclerosis Ulf Müller-Ladner Introduction Predisposition Screening Diagnosis Prognosis Pharmacogenomics Monitoring and Genomic Factors Therapeutic Strategies Novel and Emerging Therapeutics Conclusions Acknowledgements References

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Section 11 Metabolic Disease Genomic Medicine 95. Genomic Medicine of Obesity J. Alfredo Martínez Introduction Obesity: Causes and Genetic Predisposition Search of Genes Involved in Obesity Diagnosis and Characterization of Genes Associated with Obesity Screening and Diagnosis Prognosis and Gene Based-treatments Novel and Emerging Therapeutics Nutrigenomics, Pharmacogenomics and Gene Therapy Conclusions Acknowledgements References

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96. Diabetes Maggie Ng and Nancy J. Cox Introduction GWAS in Type 2 Diabetes Future Research in Type 2 Diabetes Genetics GWAS in Type 1 Diabetes Future Studies in Type 1 Diabetes Clinical Utility of Genetic Research in Diabetes Conclusion References

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97. Metabolic Syndrome Rebecca L. Pollex and Robert A. Hegele Introduction Diagnosis: Definition of the MetS Phenotype Pathophysiology of MetS in Brief Genetics of MetS

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Conclusion Acknowledgements References Recommended Resources 98. Nutrition and Diet in the Era of Genomics Jose M. Ordovas and Dolores Corella Introduction Methodological Issues Gene–Nutrient Interactions Path Forward Conclusions Acknowledgements References Section 12 Neuropsychiatric Disease Genomic Medicine 99. The Genetic Approach to Dementia Robert L. Nussbaum Introduction Incidence of Dementia Primary Dementias Clinical Approach to the Dementias Future Prospects for Genomic Medicine in the Dementias Conclusion References Recommended Resources 100. Parkinson’s Disease: Genomic Perspectives Shushant Jain and Andrew B. Singleton Introduction Clinical Characteristics of PD Genetics of PD Genetics of Sporadic PD Conclusion References Recommended Resources 101. Epilepsy Predisposition and Pharmacogenetics Nicole M.Walley and David B. Goldstein Introduction Mendelian Epilepsies Common Epilepsies Future Program of Work References 102. Ophthalmology Janey L.Wiggs Introduction Extraocular Muscles Cornea Lens

1200 1200 1200 1202 1204 1204 1205 1206 1214 1214 1215 1215

1221 1222 1222 1223 1224 1229 1229 1230 1230 1232 1233 1233 1233 1235 1238 1241 1241 1242 1243 1243 1243 1248 1251 1252 1256 1256 1257 1257 1259

Iris Trabecular Meshwork Optic Nerve Retina Genetic Testing for Ocular Disorders Summary References

1259 1259 1260 1260 1261 1261 1261

103. Genomic Basis of Neuromuscular Disorders Erynn S. Gordon and Eric P. Hoffman Introduction Motor Neuron Disease Disorders of the Neuromuscular Junction Disorders of the Muscle Predisposition Screening Diagnosis Prognosis Monitoring Current, Novel, and Emerging Therapies Advances in Genomics and Proteomics Conclusion References Recommended Resources

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104. Psychiatric Disorders Stephan Züchner and Ranga Krishnan Introduction Great Prospect, But are We There Yet? Classification Reconsidered How Complex Can it Be? The Value of Rare Genetic Variation Converging Methods Personalized Medicine Conclusion References 105. Genomics and Depression Brigitta Bondy Introduction Diagnosis, Prevalence and Course of Depression Pathophysiological Mechanisms Pharmacogenomics of Antidepressants Current Concepts Future Aspects References 106. Bipolar Disorder in the Era of Genomic Psychiatry Ayman H. Fanous, Frank Middleton, Carlos N. Pato and Michele T. Pato Introduction Diagnosis Predisposition Pharmacogenetics

1265 1265 1266 1268 1268 1273 1274 1274 1275 1276 1277 1279 1279 1281 1282 1282 1282 1283 1283 1284 1284 1285 1286 1287 1289 1289 1289 1290 1293 1294 1296 1296 1299

1299 1299 1300 1304

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The Role of New Technologies in Elucidating the Genetics of BPD 1305 Conclusion 1308 References 1308 Section 13 Infectious Disease Genomic Medicine 107. Genomic Approaches to the Host Response to Pathogens M. Frances Shannon Introduction Genetic Susceptibility to Pathogens Exploring the Host Response Through Expression Profiling Genetical Genomics and Systems Biology: the New Frontiers Application to Clinical Practice Acknowledgement References Recommended Resources 108. Genomic Medicine and AIDS Thomas Hirtzig,Yves Lévy and Jean-François Zagury Introduction Context of HIV and AIDS Predisposition: Susceptibility to HIV-1 Infection Diagnosis Prognosis Monitoring Pharmacogenomics Novel and Emerging Therapeutics Conclusion References 109. Viral Genomics and Antiviral Drug Roberto Patarca Introduction Viral Genomics and the Antiviral Drug Revolution Era Conclusion References

1313 1314 1314 1315 1317 1320 1320 1320 1320 1323 1324 1324 1324 1328 1328 1329 1331 1331 1333 1334 1334 1340 1340 1341 1344 1345

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110. Host Genomics and Bacterial Infections Melissa D. Johnson and Mihai Netea Introduction Genomics and the Study of Bacterial Infections Host Genomics and Gram-positive, Gram-negative and Mycobacterial Infections Future Directions Conclusions References

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111. Sepsis and the Genomic Revolution Christopher W.Woods, Robert J. Feezor and Stephen F. Kingsmore Introduction Genetic Polymorphisms Associated with Sepsis Molecular Signatures and Sepsis Therapeutics Conclusion References

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112. Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison Introduction Virology of Hepatitis Viruses Acquisition and Predisposition to Viral Hepatitis Screening and Diagnosis of Viral Hepatitis Pathogenesis of Viral Hepatitis Therapeutics and Pharmacogenomics Future Impact of Genomics Studies Conclusion References

1347 1347 1351 1357 1357 1357

1362 1363 1368 1371 1371 1372

1375 1375 1375 1377 1378 1380 1384 1386 1386 1386

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Foreword In the health care field today, there are high expectations for a paradigm shift in care delivery over the coming years. According to this view, we have the opportunity to achieve fundamental change and improvement in the delivery of care, with better health outcomes across the board, led by advances in genomics and molecular medicine. The prospect is exciting: a continuum of health maintenance and medical care that is truly tailored to the individual, based on his or her individual biology. We could know our own genetic profiles from birth. Prevention could be more individually charted, based on individual genetic factors. Disease could be detected and treated earlier through molecular diagnostics. Health care dollars might be spent to much better effect. And our lives could be healthier, longer. The stepping stones to such a future are now widely known. Building on the work of the Human Genome Project and its offspring, medical care can be expected to acquire powerful new tools, especially in diagnosis, that could render care much more effective by making it more precise, more individually targeted, and more predictive. We should be able to prescribe drugs more safely because genetic or other characteristics would help clinicians identify which patients would respond well to a given therapy. As diseases come to be understood at a new level, we should be better able to achieve the right diagnosis and the right treatment for each person, without the trial-and-error process that has long characterized medical treatment. As biomarkers are identified, we should be able to intervene in disease at much earlier stages. And when we are able to know our personal genomic profiles, we should be able to better pinpoint our individual health susceptibilities. Our physicians could give more individualized prevention advice – and perhaps it will even come to pass that we will be more motivated to follow it. I am a believer in this view of the possible future – and I am confident it is not just wishful thinking. The pace of discovery in the genomic field today is unprecedented. Furthermore, enough successful applications of this paradigm already exist to give us reason to look toward a new era of effectiveness in medical care, with new information and new tools for both the clinician and the consumer. At the same time, we must be realistic in our assessment of this future, especially the extent of the efforts that will be needed to achieve it. However desirable the idea of this “paradigm shift” may be, the realization of such a shift rests on a foundation that is still very much under construction.

The shift will require development of new capacities that will enable us to differentiate among the needs of individual patients. In turn, these new capacities will depend on data to be derived, analyzed, and employed on a new scale. Such information demands will surely need to be supported by sophisticated electronic data networks that are yet to be created, informatics tools that are yet to be invented, and clinical decision-support systems that are yet to be devised or adopted. Finally, at the bedrock, the trust and understanding of the medical community and of society at large must be won even as the edifice is being created. This is the work of a generation. It is work that spans professions, economic sectors, and even nations. It is driven by science – but it makes new demands for the rapid translation of scientific discovery into clinical practice and improved health outcomes. One part of the work before us is continued discovery. Phenomenal achievements have been made in mapping the human genome, and a rush of findings is occurring today in understanding associations between genomic factors and health. Yet vast areas still remain to be explored. That work is underway on a global scale. Another part of our work might be called the engineering. This includes the development of interoperable health information technology, with all the implications of that goal: development of technical and clinical standards, adoption of health IT across the health sector, and security of personal health information. In the long term, it should also include the use of health IT to enable us to make faster progress in medical research – and then to feed back what we’ve learned into clinical practice, using IT decision-support tools that are physician-, nurse-, and consumer-friendly. Another element of our task is less defined but equally challenging: the collaboration and cooperation needed to bring this vision to reality. Personalized medicine means care that is information-based at a new level. Gathering that information and using it successfully will transcend many disciplines and make new demands on a health system that is often characterized as “fractured.” It may seem ironic that delivery of individualized care depends on standardization, partnerships, and networks. But these kinds of collaboration are among the most important element of the work that lies ahead. Finally, in today’s health care environment, the success of new products, services, and models of care will depend more than ever on the value that is delivered. At its core, personalized medicine is about care that can achieve new levels in predicting, preventing, and detecting disease. Medical effectiveness of this

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kind should translate into cost effectiveness as well. Achieving and demonstrating high value expectations will be challenging, but this discipline will serve the success of personalized medicine over the long term. The paradigm shift of personalized medicine depends on an extensive foundation of scientific knowledge, professional leadership, health information technology, and a spirit of cooperation, collaboration, and dialogue. The many perspectives offered by the

contributors to this text help us appreciate the breadth of these issues, challenges, debates, and opportunities that lie ahead.

Michael O. Leavitt Secretary Department of Health and Human Services Washington, DC

Preface It seems just yesterday that we were first getting used to the notion of introducing the seemingly freshly uncovered concepts of genetics into the practice of medicine. And yet, with the completion of the Human Genome Project and the rapid development and application of new advances in our ability to understand and query the human genome and its gene set, it is time already to anticipate and outline the early stages of what must be called a transformation of medicine. We are beginning to see the first signs of a fundamental shift in how we behold human physiology and pathology, how we view the concept of what is “normal,” how we consider individuals and their prospects for lifelong health, and how we design healthcare systems that are equally adaptable to the demands of population-wide epidemics and the opportunities for personalized care that utilizes genome-based information to consider individual susceptibility to disease and therapeutic options. Genome-based data, information, knowledge, and eventually wisdom will make possible the kind of healthcare that has been dreamed of since the advent of disease-based medicine early in the 20th century. A system of healthcare that harnesses the might of the genome and its derivatives, along with imaging, clinical and environmental information, will empower physicians and other healthcare providers to do what they have always aspired to do – make medical care as individualized as possible. But this newfound information and knowledge will also allow each of us as consumers of healthcare to take more control of our futures and to develop a more strategic and a prospective approach to health. We stand at the dawn of a profound change in science and medicine’s predictive nature and in our understanding of the biological underpinnings of health and disease. Even in this early light, we can see the outlines of a coming ability to: ●

●

●

● ●

predict individual susceptibility to disease, based on genetic, genomic and other factors; provide more useful tools and individualized programs for disease prevention, based on knowledge of one’s susceptibility; detect the onset of disease earlier and before it is clinically evident, based on newly discovered biological markers that arise from changes at the molecular level; preempt disease progression, as a result of early detection; target medicines and their dose more precisely and safely to each patient, on the basis of a deep understanding of disease

mechanism and the role that genetic and genomic factors play in the individual response to drugs. This revolution in genomic and personalized medicine was anticipated nearly three decades ago by Nobel laureate Paul Berg, who stated so presciently: Just as our present knowledge and practice of medicine relies on a sophisticated knowledge of human anatomy, physiology, and biochemistry, so will dealing with disease in the future demand a detailed understanding of the molecular anatomy, physiology, and biochemistry of the human genome. . . . We shall need a more detailed knowledge of how human genes are organized and how they function and are regulated. We shall also have to have physicians who are as conversant with the molecular anatomy and physiology of chromosomes and genes as the cardiac surgeon is with the structure and workings of the heart.

That time has come. This book is intended to lay out the foundations of this new science, to outline the early opportunities for the practice of medicine to incorporate genome-based analysis into healthcare, and to anticipate the many conditions to which genomic and personalized medicine will apply in the years ahead. The chapters in these volumes are designed to be read either sequentially – introducing the scientific underpinnings of this revolution, exploring aspects of translational medicine and genomics that will be critical for bringing about this revolution, and presenting practical aspects of the first applications of genomic and personalized medicine in the context of specific medical conditions – or one-at-a-time for those interested in particular disorders or approaches. These volumes also describe a field in its infancy, with many challenges for society at large, in addition to those associated with healthcare systems strife with inefficiencies and heterogeneity in their ability to deliver the basics of healthcare. There are “grand challenges” for the visionary science and the clinical care highlighted in these pages. Such challenges include the potential for these innovations to exaggerate existing health disparities, information technology systems that have been described as a “tower of Babel,” an unprepared healthcare work force, and economic incentives that are inadequately aligned for the various stakeholders to fully embrace genomic and personalized medicine. Nonetheless, we are optimistic that the appropriate delivery models and economic incentives will be developed

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in a trustworthy framework that will be embraced by societies around the globe. As an indicator of the importance of personalized medicine, international communities are working together on strategies to overcome these obstacles. In the United States, the Department of Health and Human Services has played a leadership role in this area and a plan was published in 2007 (Personalized Healthcare: Opportunities, Pathways, Resources; see http://www. hhs.gov/myhealthcare/). Other governments (in the United Kingdom, Iceland, Estonia, Luxembourg, and Singapore – to name just a few) have funded initiatives that will secure their place in developing genome-based knowledge and its translation into day-to-day patient care. A collective and global approach to what might be arguably one of the most complex scientific and clinical undertakings in the history of healthcare is undoubtedly what is required. Our international collective of contributors to this work reflects the early adopters and members of a global community of physicians, scientists, and policy makers who will make this happen.

Our intended audience is broad, ranging from medical students (and even the intrepid undergraduate eager to explore this new era of personalized and prospective medicine) to residents and fellows to practitioners in any of the healthcare professions – physicians in any of the medical specialties, surgeons, nurses, genetic (and genomic) counselors, and laboratory directors – and, finally, to members of the genomic and personalized medicine research communities who will, we trust, help write future editions of this text. In times of transformation, we are all students. We hope that this book will help usher in this new era of genomic and personalized medicine and will provide a useful and thorough introduction to the science and practice of this new approach to human health.

Huntington F. Willard, Ph.D. Geoffrey S. Ginsburg, M.D., Ph.D.

Terminology Throughout this book, the terms “genetics” and “genomics” are used repeatedly, both as nouns and in their adjectival forms. Although these terms seem similar, they in fact describe quite distinct (though frequently overlapping) approaches in biology and in medicine. Here, we provide operational definitions to distinguish the various terms and the subfields of medicine to which they contribute. The field of genetics is the scientific study of heredity and of the genes that provide the physical, biological, and conceptual bases for heredity and inheritance. To say that something – a trait, a disease, a code or information – is “genetic” refers to its basis in genes and in DNA. Heredity refers to the familial phenomenon whereby traits (including clinical traits) are transmitted from generation to generation, due to the transmission of genes from parent to child. Genomics is the scientific study of a genome, the complete DNA sequence, containing the entire genetic information of a gamete, an individual, a population or a species. The word “genome” was first used as an analogy with the earlier term “chromosome,” referring to the physical entities (visible under the microscope) that carry genes from one cell to its daughter cells or from one generation to the next. Over the past two decades, “genomics” has given birth to a series of other “-omics” that refer to the comprehensive study of the full complement of, for example, proteins (hence, proteomics), transcripts (transcriptomics), or metabolites (metabolomics). The essential feature of the “-omes” is that they refer to the complete collection of genes, proteins, transcripts, or metabolites, not just to the study of individual entities. Medical genetics is the application of genetics to medicine and is one of the 24 medical specialties recognized by The American Board of Medical Specialties, the preeminent medical organization overseeing physician certification in the United States. Genetic medicine is a term sometimes used to refer to the application of genetic principles to the practice of medicine and thus overlaps medical genetics. Both medical genetics and genetic

medicine approach clinical care largely through consideration of individual genes and their effects on patients and their families. Genomic medicine, by contrast, refers to the use of large-scale genomic information and to consideration of the full extent of an individual’s genome, proteome, transcriptome, or metabolome in the practice of medicine and medical decision-making. The principles and approaches of genomic medicine are relevant well beyond the traditional purview of individual medical specialties and include, as examples, gene expression profiling to characterize tumors or to define prognosis in cancer, genotyping variants in the set of genes involved in drug metabolism or action to determine an individual’s correct therapeutic dosage, scanning the entire genome for millions of variants that influence one’s susceptibility to disease, or analyzing multiple protein biomarkers to monitor therapy and to provide predictive information in presymptomatic individuals. Finally, personalized medicine refers to a rapidly advancing field of healthcare that is informed by each person’s unique clinical, genetic, genomic, and environmental information. The goals of personalized medicine are to take advantage of a molecular understanding of disease to optimize preventive healthcare strategies and drug therapies while people are still well or at the earliest stages of disease. Because these factors are different for every person, the nature of disease, its onset, its course, and how it might respond to drug or other interventions are as individual as the people who have them. For personalized medicine to be used by healthcare providers and their patients, these findings must be translated into precision diagnostic tests and targeted therapies. Since the overarching goal is to optimize medical care and outcomes for each individual, treatments, medication types and dosages, and/or prevention strategies may differ from person to person – resulting in unprecedented customization of patient care. The principles underlying genomic and personalized medicine and their applications to the practice of clinical medicine are presented throughout the chapters that comprise this volume.

Acknowledgements We wish to express our appreciation and gratitude to our many colleagues, especially in the Duke Institute for Genome Sciences & Policy, who have shared their knowledge and ideas about genomic and personalized medicine and who, by doing so, inspired this project. We particularly thank our first editor at Academic Press/Elsevier, Luna Han, who encouraged us to develop the concept of a text on genomic and personalized medicine. We are also grateful to Sally Cheney, Kirsten Funk, and Christine Minihane, our Senior Editors at Academic Press/ Elsevier; to Rogue Shindler, our Developmental Editor; and to Ganesan Murugesan, our Production Project Manager, for their patience, advice, and professionalism in all stages of the project.

We acknowledge our Advisory Board for their suggestions and support and especially thank the nearly 300 authors of the 112 chapters that comprise these volumes. Needless to say, without their efforts, this project could never have come to fruition. We also thank Secretary Mike Leavitt for providing a Foreword for this book, as well as for his enthusiastic support of the concept of genomic and personalized medicine. It gives us pleasure to give special thanks to Kathy Hay and to Lynne Skinner, whose tireless efforts kept us on track and saw this project through to completion. Lastly, we thank our families for their patience and understanding for the many hours we spent creating this, the inaugural edition of Genomic and Personalized Medicine.

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Advisory Board Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043

Raju Kucherlapati Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Pasteur, Ste 250, Boston, MA 02115

Robert Cook-Deegan Center for Genome Ethics, Law & Policy, Duke Institute for Genome Sciences and Policy, Box 90141, Durham, NC 27708

Elizabeth G. Nabel National Heart, Lung and Blood Institute, 31 Center Drive MSC 2486, Building 31, Room 5A52, Bethesda, MD 20892

Kay E. Davies Department of Human Anatomy and Genetics, Oxford University, South Parks Road, Oxford, QX1 3QX, UK

Robert L. Strausberg Human Genomic Medicine, J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850

Brian Druker Department of Medicine, Oregon Health & Sciences University, 3181 S.W. Sam Jackson Park Road, Portland, Oregon 97239 Victor Dzau Office of the Chancellor, Duke University Health System, Box 3701, Durham, NC 27710 Eric Green National Institutes of Health, National Human Genome Research Institute, 50 South Drive, Room 5222, Bethesda, MD 20892-8002 Muin J. Khoury Office of Genomics & Disease Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333

Robert I. Tepper Third Rock Ventures, LLC, 29 Newbury St., Boston, MA 02116 Janet A. Warrington External RNA Controls Consortium, Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051 Ralph Weissleder Molecular Imaging Research Center, Massachusetts General Hospital, 149 13th Street, Room 5406, Charlestown MA 02129 Janet Woodcock Food and Drug Administration, 5600 Fishers Lane, Parklawn Buildling, Rm 14-71, Rockville, MD 20857

Mary-Claire King University of Washington, 1705 Northeast Pacific Street, Box 357720, Seattle, WA 98195-7720

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Contributors Michael J. Ackerman Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Matthew L. Anderson Departments of Obstetrics and Gynecology and Pathology, Baylor College of Medicine, Houston, Texas, USA. Felicita Andreotti Institute of Cardiology, Catholic University Medical School, Rome-Italy. Brian H. Annex Division of Cardiovascular Medicine and Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA. Anthony Antonellis Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. Dmitri Artemov Department of Radiology – MR Research, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. James R. Bain Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA. Peter J. Barnes National Heart and Lung Institute, Section of Airway Disease, London, SW3 6LY, UK. J.S. Barnholtz-Sloan Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. Richard C. Becker Department of Medicine – Cardiovascular Thrombosis Center, Duke University Medical Center, Durham, NC 27705, USA.

Mark Boguski Harvard Medical School, Center for Biomedical Informatics, 10 Shattuck St., Boston, MA 02115 Stefano Bonassi Unit of Molecular Epidemiology, National Cancer Research Institute, Genova, Italy. Brigitta Bondy Section Psychiatric Genetics and Neurochemistry, Psychiatric Hospital of University Munich, Munich, Germany. J. Martijn Bos Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA. Roger E. Breitbart Department of Cardiology, Children’s Hospital Boston, Boston, MA 02115, USA. Jerome Brody Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA. Matthew P. Brown USA.

Omics Consulting LLC, Clayton, CA,

Lars Bullinger Department of Internal Medicine III, University of Ulm, Ulm, Germany. Shawn C. Burgess Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Atul J. Butte Department of Medicine and Department of Pediatrics, Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94305, USA. David J. Carey Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. George Carlson McLaughlin Research Institute, Great Falls, MT, USA

Ivor J. Benjamin Division of Cardiology, Center for Cardiovascular Translational Biomedicine, University of Utah Health Sciences Center, Salt Lake City, UT, USA.

Juan C. Celedón Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA.

Paul R. Billings President and Chief Executive Officer, CELLPOINT DIAGNOSTICS, 265 N. Whisman Road, Mountain View, CA 94043

Subhashini Chandrasekharan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA.

Simon C. Body Department of Anesthesiology, Perioperative & Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Wing C. (John) Chang Department of Pathology, Center for Lymphoma and Leukemia Research, University of Nebraska Medical Center, Omaha, NE, USA. xxxiii

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Yan Chen Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Antonio Chiocca Department of Neurological Surgery, Dardinger Laboratory for Neuro-oncology and Neurosciences, The Ohio State University Medical Center and Comprehensive Cancer Center, Columbus, OH 43210, USA. Theresa Puifun Chow Agency for Science, Technology and Research, Singapore Tissue Network, Singapore. Wendy K. Chung Department of Pediatrics, Division of Molecular Genetics, Columbia University, New York, NY 10032, USA. Robert Cook-Deegan Center for Genome Ethics, Law and Policy, Duke Institute for Genome Sciences and Policy, Durham, NC 27708, USA. Dolores Corella Nutrition and Genomics Laboratory, Jean Mayer–U.S. Department of Agriculture, Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA; Genetic and Molecular Epidemiology Unit, School of Medicine, University of Valencia, Valencia, Spain. Susan Cottrell Amgen, Inc., Seattle, WA 98119, USA. Nancy J. Cox Departments of Medicine and Human Genetics, University of Chicago, Chicago, IL 60637, USA. Nigel P.S. Crawford CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. A. Jamie Cuticchia Bioinformatics Group, Duke Comprehensive Cancer Center, Durham, NC 27708, USA. Dieter Deforce Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium. Mario C. Deng

Columbia University, New York, NY, USA.

Gayathri Devi Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA.

Dirk Elewaut Department of University Hospital, Ghent, Belgium.

Rheumatology, Ghent

Charles J. Epstein Department of Pediatrics and Institute for Human Genetics, University of California, San Francisco, CA 94143, USA. Ayman H. Fanous Washington VA Medical Center, Washington, DC, 20422, USA. Phillip G. Febbo Institute for Genome Sciences and Policy, Department of Medicine – Oncology, Duke University Medical Center, Durham, NC 27710, USA. Robert J. Feezor Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, FL. Yonmei Feng Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, AZ, USA. Zeno Földes-Papp ISS, National Center of Fluorescence, Champaign, Illinois 61822, USA. Hidehiko Fujinaka Institute for Clinical Research, Niigata National Hospital, Niigata 951-8585, Japan. David J. Galas Institute for Systems Biology, Seattle, WA, USA; Battelle Memorial Institute, Columbus, OH, USA. Louis, P. Garrison Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Glenn S. Gerhard Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Geoffrey S. Ginsburg Center for Genomic Medicine, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Bjorn T. Gjertsen Institute of Medicine, Hematology, Haukeland University Hospital, University of Bergen, Bergen, Norway. Michael Glass Newborn Screening Program, Washington State Department of Health, Shoreline,WA 98155, USA.

Juergen Distler Epigenomics, AG, Berlin, Germany.

David B. Goldstein Center for Population Genomics and Pharmacogenetics, Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.

Harmut Dohner Department of Internal Medicine III, University of Ulm, Germany.

Erynn S. Gordon Division of Pediatric Genetics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Ayotunde O. Dokun Metabolism and Nutrition.

Endocrinology

Tucker Gosnell Massachussetts General Hospital Cancer Center, Boston, MA, USA.

Mark R. Edbrooke Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA.

Peter Grass Biomarker Development, Novartis Pharma AG, Basel, Switzerland.

Lucas B. Edelman Department of Bioengineering and Institute for Genomic Biology, University of Illinois, Urbana-Champaign.

Eric D. Green Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesa, MD 20892, USA.

Theo deVos

Epigenomics, Inc, Seattle, WA, USA.

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Iris Grossman Pharmacogenetics, Research and Development, GlaxoSmithKline, Research Triangle Park, NC 27709, USA.

Chunhwa Ihm Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Marta Gwinn National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.

Olga Ilkayeva Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

Carolina Haefliger

Rafael Irizarry Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.

Epigenomics, AG, Berlin, Germany.

Susanne B. Haga Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA. Per Hall Department of Medical Epidemiology Biostatistics, Karolinska Institute, Stockholm, Sweden.

and

Joshua M. Hare Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami FL 33136, USA. Ahmad Hariri Developmental Imaging Genetics Program, University of Pittsburgh, Pittsburgh, PA 15213, USA. James R. Heath Department of Chemistry, California Institute of Technology, Los Angels, CA, USA.

Shushant Jain Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Reta Lila Weston Institute of Neurological Studies, University College London W1T 4JF, UK; Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK. Melissa D. Johnson Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27710, USA. Philip W. Kantoff Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA, USA.

Robert A. Hegele Robarts Research Institute, Blackburn Genetics Laboratory, London, ONT, Canada.

Sekar Kathiresan Massachusetts General Hospital, Cardiovascular Disease Prevention Center, Boston, MA, 02114, USA.

Bettina Heidecker Division of Cardiology and Interdisciplinary Stem Cell Institute, University of Miami, Miller School of Medicine, Miami, FL, USA.

Hasmeena Kathuria Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.

Shona Hislop Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA. Eric P. Hoffman Research Center for Genomic Medicine, Children’s National Medical Center, Washington, DC 20010, USA. John Holton Centre for Infectious Diseases and International Health, Windeyer Institute of Medical Sciences, Royal Free and University College London Medical School, London, W1T 4JF, UK. Leroy Hood The Institute for Systems Biology, Seattle, WA 98103-8904, USA. Andrew T. Huang Koo Foundation Sun Yat-Sen Cancer Center, Taiwan. Erich S. Huang Duke University Medical Center, Durham, NC, USA. Carlos A. Hubbard Southern Medical Group, Tallahassee, FL 32308, USA. Kent Hunter CCR/NCI/NIH, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892-4264, USA. Courtney Hyland Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Kensaku Kawamoto Division of Clinical Informatics, Duke University Medical Center, Durham, NC 27710, USA. Filip De Keyser Department of Rheumatology, Ghent University Hospital, Ghent, Belgium. Asif Khalid Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA. Muin Khoury National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Stephen F. Kingsmore Resources.

National Center for Genome

Michelle M. Kittleson Division of Cardiology, UCLA School of Medicine, Los Angeles, CA, USA. Beena T. Koshy Pharmacogenetics, GlaxoSmithKline, Durham, NC 27709, USA. Ranga Krishnan Department of Psychiatry and Behavioral Science, Duke University Medical Center, Durham, NC 27710, USA. Robert S. Krouse Southern Arizona Veterans Affairs Health Care System, Tucson, AZ, USA. Vikas Kundra Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

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Myla Lai-Goldman Laboratory Corporation of America, Holdings, Burlington, NC, USA.

Herman Mielants Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.

Vipul Lakhani Division of Endocrinology, Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.

Michael J. Muehlbauer Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

Robert Langer Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Sean E. Lawler Department of Neurological Surgery, The Ohio State University Medical Center, Wiseman Hall Columbus, OH 43210, USA. Inyoul Lee

Institute for Systems Biology, Seattle, WA, USA.

Ulf Müller-Ladner Department of Internal Medicine and Rheumatology, Justus-Liebig University Giessen, Bad Nauheim, Germany. Mark A. Nelson Department of Pathology, University of Airzona, Tucson, AZ 85724, USA.

Charles Lee Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

Monica Neri Unit of Molecular Epidemiology, National Cancer Research Institute, Genoa, Italy.

Arthur S. Lee Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Mihai Netea Department of Medicine, University Medical Center St. Radboud, Nijmegen, The Netherlands.

Stanely Leong Department of Surgery, UCSF Medical Center at Mount Zion, University of California, San Francisco, CA, USA.

L. Kristin Newby Department of Medicine – Cardiology, Duke University Medical Center, Durham, NC 27710, USA.

Ralf Lesche Epigenomics, AG, Berlin, Germany. Samuel Levy J. Craig Venter Institute, Rockville, MD 20850, USA. Edison T. Liu Agency for Science, Technology and Research, Singapore Tissue Network, Genome Institute of Singapore, Singapore. David F. Lobach Division of Endocrinology and Metabolism, Duke University Medical Center, Durham, NC 27710, USA. James B. Lorens Department of Biomedicine, University of Bergen, Bergen, Norway. Aldons J. Lusis Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA 900951679, USA. H. Kim Lyerly Duke Comprehensive Cancer Center, Duke University Medical Center, Durham, NC 27705, USA. Nageswara R. Madamanchi Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. J. Alfredo Martinez Department of Physiology and Nutrition, University of Navarra, Pamplona, Spain.

Christopher B. Newgard Duke Independence Park Facility, Duke University Medical Center, Durham, NC 27704, USA. Maggie Ng Departments of Medicine and Human Genetics, Chicago, IL 60637, USA. Paul W. Noble Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Box 3171, Durham, NC 27710, USA. Vasilis Ntziachristos Technical University of Munich, Helmholtz Center Munich, Munich, Germany. Robert L. Nussbaum Division of Medical Genetics, Department of Medicine & Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, San Francisco, CA 94143 Meghan B. O’Donoghue Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Kunle Odunsi Departments of Gynecologic Oncology and Immunology, Roswell Park Cancer Institute, Buffalo, NY, USA.

Kevin McGrath Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Kenneth Olden Laboratory of Molecular Carcinogenesis, NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC 27709-2233, USA.

John McHutchison Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA.

Steve R. Ommen Mayo Clinic, Windland Smith Rice Sudden Death Genomics Laboratory, Rochester, MN 55905, USA.

Frank Middleton Upstate Medical University, Washington VA Medical Center, Washington, DC, USA.

Jose M. Ordovas Nutrition and Genomics Laboratory, Tufts University, Boston, MA 02111-1524, USA.

Vega Masignani

Novartis Vaccines, Siena, Italy.

Contributors

Roberto Patarca E. M. Papper Lab of Clinical Immunology and Molecular Biology, University of Miami School of Medicine, Sunny Isles Beach, FL 33160, USA. Jeetendra Patel Division of Cardiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. K. Patel Department of Gastroenterology, Duke University Medical Center, Duke University Clinic Research Institute, Durham, NC, USA. Carlos N. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.

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Sridhar Ramaswamy Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Hans-Georg Rammensee Interfakultares Institut fur Zellbiologie, Abt. Immunologie, D-72076 Tubingen, Germany. Scott D. Ramsey Department of Pharmacy, University of Washington, Seattle, WA 98195, USA. Rino Rappuoli Novartis Vaccines, Siena, Italy. Nader Rifai Laboratory Medicine, Children’s Hospital Boston, Boston, MA 02115, USA. Lisa Rimsza Department of Pathology, University of Arizona, Tucson, AZ 85724-5043, USA.

Michele T. Pato Center for Neuropsychiatric Genetics and Department of Psychiatry, Department of Neuroscience and Physiology, Upstate Medical University, Syracuse, NY.

Yu-Hui Rogers J. Craig Venter Institute, Rockville, MD 20851, USA.

Tanja Pejovic Department of Obstetrics and Gynecology, Oregon Health Sciences University, Portland, OR 97239, USA.

Allen D. Roses Deane Drug Discovery Institute, Duke University Medical Center, One Science Drive, Suite 342, Durham, NC 27708.

Noah C. Perin Tularik, Inc., South San Francisco, CA 94080, USA. Michael Pham Division of Cardiovascular Stanford University, Stanford, CA 94305, USA.

Medicine,

Robert M. Plenge Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA. Achim Plum Epigenomics, AG, Berlin, Germany. Mihai V. Podgoreanu Department of Anesthesiology, Duke University Medical Center, Durham, NC 27710, USA. Jonathan R. Pollack University, CA, USA.

Department of Pathology, Stanford

Rebecca L. Pollex Robarts Research Institute, London, ONT, Canada. Nathan D. Price Department of Chemical and Biomolecular Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois, UrbanaChampaign, IL USA. Thomas Quertermous Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA. Francisco J. Quintana Center for Neurologic Diseases, Harvard Medical School, Boston, USA. Benjamin A. Raby Channing Laboratory, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care, Brigham & Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA. Daniel J. Rader University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6160, USA.

Jeffrey S. Ross Department of Pathology and Laboratory Medicine, Albany Medical College MC-80, Albany, NY 12208, USA. Ronenn Roubenoff Biogen Idec, Inc.

Immunology

Medical

Research,

Marschall S. Runge Department of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA. Maren T. Scheuner 90407-2138, USA.

Rand Corporation, Santa Monica, CA

Matthias Schuster Epigenomics, AG, Berlin, Germany. David A. Schwartz Center for Genetics and Therapeutics, National Jewish Medical and Research Center, 1400 Jackson Street, Denver, CO 80206 Debra A. Schwinn Department University of Washington, Seattle, USA.

of

Anesthesiology,

Chia Kee Seng Centre for Molecular Epidemilogy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Nicholas A. Shackel Department of Gastroenterology, Royal Prince Alfred Hospital, Sydney, Australia. M. Frances Shannon Division of Molecular Bioscience, John Curtin School of Medical Research, Australian National University, Canberra ACT 2601 Australia. A. Dean Sherry Departments of Radiology and Internal Medicine, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas,TX 75235, USA. Jiaqi Shi Department of Pathology and the Arizona Cancer Center, University of Arizona, Tucson, Arizona, USA.

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Contributors

Kevin Shianna Duke Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710, USA. Yelizaveta Shnayder Department of Otolaryngology – Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, KS, USA. Andrew B. Singleton Laboratory of Neurogenetics, Molecular Genetics Unit, NIH, National Institute on Aging, Bethesda, MD 20892, USA. T.P. Slavin Center for Human Genetics, University Hospitals Case Medical Center; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine; Department of Pediatrics, University Hospitals Case Medical Center, Cleveland, Ohio, USA. Desmond J. Smith Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095-1735, USA. Rikkert L. Snoeckx Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.

Hervé Tettelin

Novartis Vaccines, Siena, Italy.

Giovana R. Thomas Department of Otolaryngology – Head & Neck Surgery, University of Miami School of Medicine, Miami, FL 33136, USA. John D. Thompson Newborn Screening Program,Washington State Department of Health, Shoreline,WA 98155, USA. Visith Thongboonkerd Medical Molecular Biology Unit, Office for Research and Development, Siriraj Hospital, Mahidol University, Bangkoknoi, Bangkok 10700, Thailand. Eric J. Topol Scripps Clinic Division of Cardiovascular Disease, La Jolla, CA 92037, USA. Jeffrey A. Towbin Pediatric Cardiology, Baylor College of Medicine, Houston, TX 77030, USA. Ad A. van Bodegraven Department of Gastroenterology, VU University Medical Centre, Amsterdam, The Netherlands. Kris Van Den Bogaert Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium.

3059,

Filip Van den Bosch Department of Rheumatology, Ghent University Hospital, Ghent, Belgium.

Avrum Spira Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.

Matte Vatta Departments of Pediatrics (Cardiology), Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA.

Colin F. Spraggs Pharmacogenetics, Durham, NC 27709, USA.

GlaxoSmithKline,

Timothy D. Veenstra SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.

Robert D. Stevens Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA.

David L. Veenstra Department of Pharmacy, University of Washington, Seattle, WA 98195, USA.

Ralph Snyderman Duke Durham, NC 27710, USA.

University,

DUMC

Nicolas A. Stewart SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA. Alison Stewart Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK. F. Stewart Weis Center for Research/Geisinger Clinic, Danville, PA 17822-2607, USA. Robert L. Strausberg J. Craig Venter Institute, Rockville, MD 20850, USA.

Aleksandr E. Vendrov Department of Medicine, Carolina Cardiovascular Biology Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7126, USA. Catherine M. Verfaillie Stem Cell Institute Leuven, Catholic University Leuven, Leuven, Belgium. Nicole M. Walley Center for Population Genomics and Pharmacogenetics, Duke University Medical Center, Durham, NC 27710, USA. Ling Wang Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.

Moshe Szyf Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec H3G 1Y6, Canada.

Mike Weale Statistical Genetics Unit, Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London SE1 9RT, UK.

Weihong Tan Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA.

Michael E. Weinblatt Division of Rheumatology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA.

Robert I. Tepper Third Rock Ventures, LLC, Boston, MA 02116, USA.

Howard L. Weiner Center for Neurologic Diseases, Harvard Medical School, Boston, MA 02115, USA.

Contributors

Scott T. Weiss Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Ralph Weissleder Center for Systems Massachusetts General Hospital, Boston, MA.

Biology,

Samuel A. Wells Department of General Surgery, Duke University Medical Center, Durham, NC 27705, USA. Brett R. Wenner Sarah W. Stedman Nutrition and Metabolism Center, Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27704, USA. Ilse R. Wiechers Massachusetts General/McLean Hospital, Adult Psychiatry Residency Program, Boston, MA 02114, USA. Georgia L. Wiesner School of Medicine – Department of Genetics, Case Western Reserve University, Cleveland, Ohio 44106-4955, USA. Janey L. Wiggs Department of Opthalmology, Harvard Medical School and the Massachusetts Eye and Ear Infirmary, Boston, MA 02114, USA. Cisca Wijmenga Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.

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Christopher W. Woods Department of Medicine – Infectious Diseases, Duke University Medical Center, Durham, NC 27705, USA. Paula W. Woon National Center for Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. Chana Yagil Department of Nephrology and Hypertension and Laboratory for Molecular Medicine, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Yoram Yagil Department of Nephrology and Hypertension, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon, Israel. Tadashi Yamamoto Department of Structural Pathology, Institute of Nephrology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan. Gang Yao Department of Chemistry, Shands Cancer Center and UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA. Andrew J. Yee Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA 02115, USA. Hyuntae Yoo Institute for Systems Biology, Seattle, WA, USA.

Huntington F. Willard Duke Institute for Genome Sciences and Policy, Durham, NC 27710, USA.

Y. Nancy You Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.

James M. Wilson Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.

Li-Rong Yu SAIC Frederick, Inc., National Cancer Institute, Frederick, MD 21702-1201, USA.

Nelson A. Wivel Gene Therapy Program, Translational Research Lab, University of Pennsylvania, Philadelphia, PA 19104-3403, USA.

Jean-François Zagury Conservatoire National des Arts et Metiers, Chaire de Bioinformatique, 292, rue Saint-Martin, 75003 Paris.

Jay Wohlgemuth Via Pharmaceuticals, San Francisco, CA, USA.

Ron Zimmern Strangeways Research Laboratory, Public Health Genetics Unit, Worts Causeway, Cambridge, CB1 8RN, UK.

Janet Woodcock MD 20857, USA.

Stephan Züchner University of Miami School of Medicine, Miami, FL 33136.

Food and Drug Administration, Rockville,

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PART

Three

Disease-Based Genomic and Personalized Medicine: Genome Discoveries and Clinical Applications

621

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Section

Cardiovascular Genomic Medicine

8

54. The Genomics of Hypertension 55. Lipoprotein Disorders 56. Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis 57. Genomics of Myocardial Infarction 58. Acute Coronary Syndromes 59. Heart Failure in the Era of Genomic Medicine 60. Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection 61. 62. 63. 64. 65. 66.

Hypertrophic Cardiomyopathy in the Era of Genomic Medicine Genetics and Genomics of Arrhythmias Hemostasis and Thrombosis Peripheral Arterial Disease Genomics of Congenital Heart Disease Genomics of Perioperative and Procedural Medicine

CHAPTER

54 The Genomics of Hypertension Chana Yagil and Yoram Yagil

INTRODUCTION One of the promises of genomics has been that it would provide a better understanding and improve our ability to treat common diseases that afflict humanity. Hypertension is one of the most common and perhaps more important diseases that carry a high rate of morbidity and mortality. Hypertension, which is defined as systolic blood pressure above 140 mmHg and/or diastolic blood pressure above 90 mmHg, has a worldwide prevalence estimated at over 600 million people and in the United States alone at over 65 million. A wide variety of populations are affected worldwide, ranging over both sexes and all ethnic groups (Kearney et al., 2004). Despite decades of intensive research in human populations and in experimental animal models, the pathophysiology underlying hypertension remains unresolved. In the majority of cases, hypertension is thought to result from an interaction of genes with environmental factors. In only a small minority of cases, genetic dissection of hypertension has uncovered single gene mutations that lead to the development of the so-called “monogenic” forms of the disease (Luft, 2001). The pathophysiology of these rare monogenic forms of hypertension has been elucidated during the past decade, but the pathophysiology underlying the common form of hypertension remains incomplete and elusive, hence the term “essential hypertension.” Our

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 624

current understanding of essential hypertension is limited to the knowledge that a yet undetermined number of “causative” genes encode for a probably larger number of proteins that are involved in a wide variety of pathophysiological pathways that somehow lead together to a rise in blood pressure and hypertension. These causative genes interact with a multitude of environmental factors, not all of which have yet been identified, and an unknown number of susceptibility genes. The susceptibility genes in turn modulate the response of the individual to the environment. Causative genes, environmental factors and susceptibility genes interact in a highly complex network (Figure 54.1), the outcome of which is a rise in blood pressure to abnormal levels that are clinically defined as hypertension. Hypertension thus belongs to the group of common multifactorial complex diseases. The complexity of hypertension is perhaps best illustrated by Guyton’s traditional model of cardiovascular dynamics (Figure 54.2a) in which multiple circuits of physiological mechanisms act in conjunction with one another to cause hypertension (Guyton and Coleman, 1969). The advent of genomics has allowed the translation of each component of Guyton’s model into its underlying genetic make-up. Instead of simplifying the model, however, this allegedly “forward” step only renders the highly complex physiological scheme into an even more complicated micro-circuitry of genes and proteins that interact with

Copyright © 2009, Elsevier Inc. All rights reserved.

Predisposition

Gene 1 /

Gene 2 /

Susceptibility genes Environment

Susceptibility genes BP

Body mass Susceptibility genes

Susceptibility genes

Sex

Gene…n /

Age Susceptibility genes Diet

Figure 54.1 pressure.

The complexity of hypertension. BP, blood

one another in multiple ways that eventually cause hypertension (Figure 54.2b). Unraveling the huge complexity of the pathophysiology underlying the most common form of hypertension, the so called “essential hypertension,” has been a daunting task which still remains far from being completed. The hope is that where traditional physiology has failed at large, genomics along with physiological genomics will provide more detailed insight into the mechanisms underlying essential hypertension, which will in turn facilitate a more rational therapeutic approach to the treatment of the disease, as well as development of new drugs for hypertension. In this chapter, we will focus on how genomics has affected our understanding of the predisposition to hypertension, on our ability to screen, diagnose and determine the prognosis of patients with hypertension, on the applicability of genomics to the monitoring of hypertension and on the present and anticipated future impact of genomics on the treatment of hypertension.

PREDISPOSITION In hypertension, two questions arise with regards to “predisposition.” The first is what factors predispose the individual to develop hypertension. This issue will be discussed henceforth in depth. The second issue, which is of no lesser importance, is what predisposes a hypertensive individual to develop end-organ damage, the clinically significant and often devastating direct result of high blood pressure. End-organ damage, which determines the “clinical outcome” of hypertension, will be discussed in the section on “prognosis.” There is a major difficulty in dealing with the question what predisposes the individual to develop hypertension if one considers that if a subject lives to the age of 90, his/her likelihood to develop hypertension approaches 80–90% (Table 54.1). Most human beings are therefore “naturally” inclined and genetically

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predisposed to develop hypertension sometime in their lifetime, assuming that they live long enough. The incidence and prevalence of hypertension increase with age, and the immediate sequelae of hypertension, cardiovascular, cerebrovascular and renal disease indeed constitute important causes of morbidity and mortality in the elderly. The development of hypertension at an earlier age, however, perhaps “pre-maturely,” is less frequent but more intriguing. The real issue of interest is therefore what confers the susceptibility of the individual to develop hypertension at an early age. The issue of predisposition to develop hypertension, as with other multifactorial diseases, can also be viewed in a different way. One can ask the question what confers resistance, as opposed to susceptibility, of the individual to develop hypertension, resistance which prevents the development of hypertension at an early age as well as at a later age. It is possible, after all, that individuals are physiologically predisposed to hypertension and that blood pressure is maintained at “normal” levels through protective mechanisms, unless these fail. Irrespective of whether sensitivity or resistance prevail, there is generalized agreement that the likelihood that an individual will develop hypertension sometimes along his/her lifetime is dependent on a multitude of factors, most likely a combination of genetic and environmental factors, notably “bad” genes in a “bad” environment (Geller, 2004). Researchers have used genomics in an attempt to dissect the predisposition of the individual to environmental factors that leads to the development of hypertension and identify the culprit genes. A classical example has been the study of the susceptibility to dietary salt that causes hypertension. It has long been recognized that individuals are either salt-sensitive or salt-resistant. Those who are salt-sensitive develop hypertension when salt-intake is increased, and those who are salt-resistant remain normotensive, irrespective of dietary salt-intake. It has also been recognized that what renders individuals salt-sensitive and others salt-resistant is their genetic make-up. Studies in animal models such as the Dahl (Rapp, 1982) and Sabra (Yagil et al., 1996) rat models of salt-sensitivity provide unequivocal experimental proof that the trait of salt-susceptibility that leads to the development of hypertension is genetically inherited. Have decades of research into the genetic basis of saltsusceptibility resulted in the detection of the genes or proteins that account for salt-susceptibility? Have genomics provided the solution to the question which mechanisms account for saltsensitivity? There have been major problems in dissecting the genetic basis of salt-susceptibility directly in human populations where it is next to impossible to separate and isolate the various modulators of hypertension, including environmental factors, and focus on one at a time. It is certainly even more complicated in humans to combine interventional studies with genomic investigation of the underlying genes. Thus, there have been only few genomic investigations in humans that have aimed specifically at detecting susceptibility genes, and much of the investigation had to be carried out in experimental models of the disease that allow not only isolation but also perturbations and alterations of

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(a)

(b)

Figure 54.2 (a) Guyton’s model of cardiovascular dynamics (adapted from Guyton, A.C., and Coleman, T.G. (1967). Long-term regulation of the circulation: interrelationships with body fluid volumes. In: Physical Bases of Circulatory Transport Regulation and Exchange. Saunders, Philadelphia, PA) and (b) after genomic annotation and translation of the model components into individual genes (colored boxes) .

single environmental factors, one at a time. Indeed, investigations of the genetic basis of salt-sensitivity using genomic approaches such as positional cloning in the experimental animal models of salt-sensitivity have yielded a large number of quantitative trait loci (QTLs), at least one on each autosome and on chromosome X (Garrett et al., 2002). A number of these QTLs, which allegedly incorporate within them the salt-sensitivity-related genes, have been confirmed through the use of genetically designed consomic and congenic strains (Cowley, Jr. et al., 2004a, b;Yagil and Yagil, 2003a). A considerable number of genes have been

identified within these QTLs as possible candidate genes for salt-susceptibility. Nonetheless, the investigation aiming to elucidate the genetic basis of salt-sensitivity or resistance is nowhere resolved and is actively ongoing. The search for the genetic basis of salt-susceptibility or the genetic basis for the predisposition to salt-sensitivity, however, is only one of the many directions that need to be investigated, as environmental factors other than dietary salt also predispose individuals to hypertension. What mechanisms predispose individuals to develop hypertension in face of stress? Climate? Obesity? Very little is known about these

Diagnosis

T A B L E 5 4 . 1 Prevalence of hypertension in the United States population during 1999–2000 Age (years)

Males (%)

Females (%)

20–34

11.8

3.1

35–44

19.2

18.6

45–54

36.9

33.4

55–64

50.7

57.9

65–74

68.3

73.4

75

70.7

84.9

Health United States, 2003, NCHS.

other factors, and once again most of the data on susceptibility genes originate from studies in experimental models. There have been attempts, nonetheless, to study genetic predisposition to hypertension directly in humans using a seemingly less sophisticated alternative approach aiming to link the hypertensive phenotype to variations in the composition of the genome by focusing directly on candidate genes and polymorphisms within those genes. It was thought that such association would allow identification of a predisposition of the individual who is not yet hypertensive but who carries the specific genetic variant to develop hypertension at a later age, the predisposition being expressed as “odds ratio.” The list of candidate genes is long and has even merited a website listing 150 such candidate genes for hypertension (http://cmbi.bjmu.edu.cn/genome/candidates/ candidates.html). Genetic polymorphisms within these candidate genes have been identified. Little insight has been gained, however, from attempts to associate these polymorphisms with hypertension, and no clinically useful information has been derived as to the ability to identify those individuals who are more prone to develop hypertension “prematurely.” Possible explanations for this failure to identify clinically useful genetic polymorphisms may be related to the small population size that have been used in many of the studies, the possibility that the “wrong” candidate genes were chosen, or the inability to identify the polymorphisms that actually account for the development of hypertension. The link between genetic polymorphisms of candidate genes and hypertension remains thus elusive at this stage. Nevertheless, it is quite likely that the correct and relevant candidate gene variants will eventually be identified and these will help determine who within a population is predisposed to hypertension. When dealing with predisposition to hypertension, one cannot overlook in this era of globalization the interesting, although emotionally laden, issue of ethnic/racial predisposition to hypertension. There is a disparity in the prevalence of hypertension amongst different racial/ethnic populations worldwide. Such variations in the prevalence of hypertension amongst different populations raises the question of whether there is ethnic/racial

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predisposition to hypertension and what has been the contribution of genomics to resolve the issue. The racial issue in relation to predisposition to disease in general has been difficult to deal with, as it may imply the existence of an inborn genetic flaw (Cooper and Kaufman, 1998). However, arguments regarding the existence or absence of racial or ethnic differences must also consider the large body of human genetic research in rarer Mendelian forms of diseases, some of which have been described as being largely restricted to single racial or ethnic populations. Racial differences may be within genetic subtypes that are particularly sensitive to environmental factors that can raise blood pressure. Race, however, cannot be regarded as an etiological factor per se, but rather as a “risk factor” for hypertension, without inferring causality. In this sense, race, as is age, should be considered a risk factor (O’Donnell and Kannel, 1998).

SCREENING Screening for disease is defined as testing a random healthy population in search for markers or signs of the disease, aiming to detect the disease among unsuspecting populations. The screening for hypertension is currently based entirely on the physical measurement of blood pressure in the individual, using a blood pressure measuring device. As a result of activities of various national and international health promoting societies, large populations are in fact intermittently screened worldwide by random measurements of blood pressure. Yet this screening is only partially effective, mostly because the frequency at which populations are screened is inadequate, and the ability to screen the entire population is severely limited by resources and population compliance and readiness to be screened. What is the place of genomics in the screening for high blood pressure? At present, no genomic tools are available that can replace the actual blood pressure measurement that in itself is simple and straightforward. The screening for hypertension could benefit, nonetheless, if it were performed at a very early age, for example for interventional preventive purposes in infants, or in populations that are identified as being at high risk. If genomics were able to provide tools that effectively determine who within a population is predisposed to develop hypertension, then the screening for the development of hypertension in these populations defined as “high risk” will be more focused and therefore practical and effective. Such genomic screening might become available once those individuals who are prone to develop hypertension, the “susceptible” individuals, are identified by genomic tools that are not available as yet.

DIAGNOSIS The diagnosis of hypertension at present is solely based on the measurement of blood pressure, using a blood pressure cuff, and

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on threshold levels above which subjects are defined as “hypertensive.” Whether hypertension is best defined based on sporadic office measurements using standard blood pressure measuring devices or on 24-h intermittent measurements of blood pressure using an ambulatory blood pressure device (Pickering et al., 2006) is a matter that is still hotly debated and thus yet unresolved. The threshold values for hypertension, whether based on standard or ambulatory blood pressure measurements, are determined in many countries by national guidelines that base their decisions on observations that above a certain level, target organ damage occurs. What is the place of genomics in the process of diagnosing hypertension? Because of the simplicity in the diagnosis of hypertension, genomics have no place in the process of determining whether a subject is hypertensive or not. Genomics may have a place though, at least theoretically and conceptually, in diagnosing what type of hypertension the subject is suffering from. If one can grossly divide hypertension into “primary” or “essential” hypertension, two terms that are used interchangeably, and “secondary” hypertension, then it is theoretically possible that genomic tools might help eventually determine what kind of hypertension the subject has. Unfortunately, no genomic tools are yet available that differentiate between primary and secondary hypertension. This matter is further complicated by the fact that the term “essential hypertension” is used to describe hypertension of unknown or “idiopathic” origin. In fact, essential hypertension is likely to represent several separate clinical entities, the number and nature of which are presently unknown. Once the pathophysiological mechanisms of “essential hypertension” are elucidated, however, it is likely that virtually all cases of hypertension will be classified as “secondary,” and the term “essential” for hypertension will have become obsolete. But until that is achieved, all subjects with hypertension in whom a primary cause of hypertension has not been identified continue to be labeled “essential hypertension” by default, and the need to differentiate between primary and secondary hypertension prevails. Genomics can be of help in identifying within the “secondary” hypertension group specific disease entities that are due to genetic alterations. This screening can be based on the search and identification of known mutations that are known to be associated with disease, or of patterns of gene expression that are typical for the disease. By identifying known specific genetic mutations, genomics can already be useful in the diagnosis of specific genetic variants that cause hypertension, the rarer forms of monogenic hypertension (Lifton et al., 2001). For example, Liddle’s syndrome, a disorder that is associated with hypertension, low plasma renin and aldosterone levels, and hypokalemia, is due to a mutation in the sodium channel gene, specifically to a deletion or missense mutations of a PPPxY motif in the cytoplasmic COOH terminus of either the beta or gamma subunit of the epithelial Na channel (ENaC). Type 2 pseudohypoaldosteronism or Gordon’s syndrome, is another hypertensive syndrome characterized by hypertension, hyperkalemia, normal renal function, and low or low-normal

plasma renin activity and aldosterone concentrations; this clinical entity is due to mutations in WNK kinases 1 and 4 (Wilson et al., 2001). Glucocorticoid-remediable aldosteronism is a disorder in which a chimeric gene formed from the 11-hydroxylase and aldosterone synthase genes (Lifton et al., 1992) results in ACTH stimulating aldosterone synthase, leading to persistent hyperaldosteronism. In congenital adrenal hyperplasia with 11hydroxylase deficiency, 10 different mutations of the CYP11B1 gene have been identified (White et al., 1994), causing steroid 11-hydroxylase deficiency with signs of androgen excess and hypertension. The syndrome of apparent mineralocorticoid excess arises from mutations in the gene encoding the kidney enzyme 11-hydroxysteroid dehydrogenase, allowing normal circulating concentrations of cortisol to activate the mineralocorticoid receptors (Mune and White, 1996). Are there readily available clinically useful means to diagnose these genetic mutations that cause secondary hypertension? Theoretically, a specially designed DNA microarray could provide the answer. Unfortunately, such high-throughput genomic tools are not available yet to the clinician, not even for the known forms of monogenic hypertension.

PROGNOSIS Hypertension constitutes a major risk factor for the development of heart disease and stroke, end-stage renal disease, and peripheral vascular disease and is a chief contributor to adult disability. The prognosis of hypertension is solely and entirely dependent on the development of such target organ damage. Even though the rule in general is that the higher the blood pressure, the greater the likelihood to develop organ damage, clinical observations indicate that not all individuals are similarly susceptible to develop organ damage. Certain individuals develop severe hypertension-related organ damage even when blood pressure levels are not high, and others are highly resistant to end-organ damage despite very high levels of blood pressure. The explanation for this interindividual variability in the susceptibility to develop end-organ damage lies in genetic determinants of target organ damage. In the following sections, we will discuss what is known about genetic determinants of end-organ susceptibility in hypertension. Stroke, the most dramatic expression of cerebrovascular accident (CVA) and one of the most common complications of hypertension, is a complex trait and not as a mere consequence of hypertension. Like other complex diseases, stroke appears to result from an interaction between several genetic and environmental factors. The identification of the genetic determinants of stroke, much like hypertension, is a difficult task in humans, due to the genetic heterogeneity of human populations and the confounding presence of other risk factors. It is nonetheless clear that there is a genetic predisposition to the interaction between stroke and hypertension. Clinical observations have shown that subjects with well-controlled hypertension can develop strokes, whereas others with severe hypertension may not develop stroke.

Prognosis

What renders some individuals sensitive to hypertension-related stroke whereas others resistant is unclear, although individual genetic susceptibility must come into play. The genes that render susceptibility to stroke have been subject of intensive research over the past decade (Rubattu et al., 2004b). Much of the research has been using an animal model of stroke, the strokeprone spontaneously hypertensive rat (Nagaoka et al., 1976; Yamori et al., 1992). Among the genes that these studies have uncovered and that predispose to hypertension-related stroke is the gene encoding atrial natriuretic peptide (ANP) which has been identified in the stroke-prone spontaneously hypertensive rat but also in two different human populations (Rubattu et al., 1999a, b). The gene encoding fibrinogen is another strokerelated gene. Elevated fibrinogen levels have also been suggested as a factor that increases the risk of stroke and carriers of the A allele of the fibrinogen-455G/A polymorphism have increased plasma fibrinogen levels. Hypertensive patients carrying the A allele have been found to have a four-fold increased risk for lacunar infarcts (Martiskainen et al., 2003). The role of the reninangiotensin-aldosterone system (RAAS) genes in predisposing to hypertension-related stroke has been under intensive scrutiny, but the only positive association that has been detected so far is between ischemic stroke and the AT1 receptor C1166/AT1 gene allelic variant (Rubattu et al., 2004a). No other RAAS gene has been identified as contributing to stroke. Hypertensive heart disease is a term applied generally to heart diseases that are caused by direct or indirect effects of elevated blood pressure. Prolonged elevation of blood pressure can lead to a variety of changes in the myocardial structure, coronary vasculature, and conduction system of the heart leading to the development of left ventricular hypertrophy, coronary artery disease, various conduction system diseases, and systolic and diastolic dysfunction of the myocardium (Diamond and Phillips, 2005; Gradman and Alfayoumi, 2006; Prisant, 2005). The clinical manifestations are angina pectoris, myocardial infarction, cardiac arrhythmias (especially atrial fibrillation) and congestive heart failure. Even though hypertension predisposes to hypertensive heart disease, there appears not always to be a direct correlation between the level of blood pressure and the type and severity of damage to the heart. Individual predisposition, based on the genetic make-up, is likely to determine whether an individual with hypertension will develop hypertensive heart disease, the specific subtype of cardiac damage and its severity. What are the genes underlying the individual susceptibility to hypertensiveinduced cardiac disease? Even though there is a vast amount of basic and clinical research that has been carried out on the genes involved in left ventricular hypertrophy, coronary heart disease, diseases of the conduction system and dysfunction of the myocardium, very little if any data are available at this time on the individual genetic susceptibility to develop these complications as result of hypertension, and certainly none is of any clinical usefulness. Stated otherwise, a vast amount of information is available on the genes involved in the various forms of heart disease secondary to hypertension, well beyond the scope of this chapter, but no data are yet available that might allow

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prediction who will and who will not develop these cardiac complications. Hypertensive nephrosclerosis, another important complication of hypertension, is characterized by long-term essential hypertension, minimal proteinuria and progressive renal insufficiency (Luke, 1999). This type of nephrosclerosis accounts for 26% of patients reaching end-stage renal disease (ESRD) each year in the United States. Hypertension is considered the second most common cause of ESRD in white people (24%) and the leading cause of ESRD in black people (33%) (http://www. emedicine.com/med/topic1611.htm). Much like cerebrovascular disease and hypertensive heart disease, nephrosclerosis develops in susceptible individuals with hypertension, at times irrespective of their level of blood pressure (Rostand et al., 1989). Part of the individual susceptibility to develop nephrosclerosis is due to genetic factors. The presence of such genetic factors has been demonstrated in an experimental animal model in which two genetically different yet histocompatible kidneys were chronically and simultaneously exposed to the same blood pressure profile and metabolic environment within the same host. The kidney of the one strain was inherently much more susceptible to hypertension-induced damage than the kidney of the other strain (Churchill et al., 1997). There have been several attempts to identify the genes that underlie the susceptibility to hypertensive nephrosclerosis in both experimental models (Griffin and Bidani, 2004) and in humans (Griffin and Bidani, 2004; Hayden et al., 2003). Linkage studies in the fawn-hooded hypertensive rat followed by construction of congenic strains have suggested that genes influencing susceptibility to hypertension-associated renal failure may exist on rat chromosome 1q (St Lezin et al., 1999). In humans, several genes have been proposed as predisposing to hypertensive nephrosclerosis, including the homozygous 677TT mutation of the MTHFR gene (Koupepidou et al., 2005). A genetic predisposition to hypertensive nephrosclerosis has also been attributed to ethnic\racial groups. For example, in the Multiple Risk Factor Intervention Trial (MRFIT), a significant loss in kidney function was observed in African-American but not in Caucasian subjects despite similar control of blood pressure (Flack et al., 1993). Similarly, the Modification of Diet in Renal Diseases (MDRD) study demonstrated that at equivalent blood pressure, African-Americans had a greater rate of reduction in glomerular filtration rate than Caucasians (Hebert et al., 1997). One of the possible explanations for the discrepant susceptibility of African-Americans and Caucasian subjects has the prevalence of the DD genotype, which is more common in former than in the latter population and which has been associated with a higher prevalence of progressive renal disease (Duru et al., 1994). African-Americans with hypertension also have increased angiotensinogen mutations compared with hypertensive Caucasians (Bloem et al., 1995), but whether these are related to the increased susceptibility to nephrosclerosis remains to be determined. Although some genetic variants have been identified or at least suggested for stroke and nephrosclerosis, the data have been so far derived from studies in select populations and have not

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been validated at large. Their relevance to the general population remains unclear, and their usefulness will have to be validated in future studies in well-defined populations before they can be applied to clinical practice and determination of the prognosis of the hypertensive patient.

PHARMACOGENOMICS The complexity of essential hypertension and the uncertainty as to the underlying pathophysiology render the management of hypertension problematic. Currently, such management is based mostly on our clinical perception of high blood pressure, on our limited knowledge of the pathophysiology of hypertension and on experiments in clinical pharmacology that have successfully achieved a reduction in blood pressure. How does pharmacogenomics apply to hypertension and how can it affect our therapeutic approach to the disease? Based on the achievements of pharmacogenomics in clinical fields other than hypertension, it is clear that this field of genomics has the potential to significantly improve the clinical management of hypertension. Pharmacogenomics can be used to predict the targeted therapeutic response of anti-hypertensive drugs, the hypotensive effect, as well as the occurrence of untoward side effects which may limit patient compliance and use of the drug. Pharmacogenomics can also be useful in the development of new anti-hypertensive drugs. The first two applications of pharmacogenomics will be discussed in this section; the third will be discussed in the section on novel and emerging therapeutics. The blood pressure response of individuals to any single drug is highly variable and clinically very difficult to predict. Ideally, pharmacogenomics could improve our ability to predict the effectiveness of an anti-hypertensive drug in any given patient by correlating a genetic profile with the type of response. Such prediction, however, has been very difficult to come by, as hypertension is in most cases multifactorial and involves a complex interaction between multiple genes and environmental factors. One should also not overlook the potential contribution of gender, ethnic and racial factors to the individuals’ response to anti-hypertensive drugs, which complicates matters even further. Any piece of evidence for a genomic effect may therefore need to be validated in each of the numerous population subtypes before it can be applied to clinical use. This may turn out to be a huge cost-ineffective task, unless a major gene effect is discerned that heavily impacts the anti-hypertensive response of a wide variety of populations. Investigators in the field of pharmacogenomics, nonetheless, have made sincere efforts to try to correlate genetic variability within known candidate genes with therapeutic effects of anti-hypertensive drugs, hoping to utilize this information to improve the clinical response of patients to those drugs (Trotta et al., 2004). Are these efforts truly relevant to the field of pharmacogenomics, or do they pertain to the much older field of pharmacogenetics? There is a fundamental conceptual difference

between the two terms (Yagil and Yagil, 2002). Pharmacogenetics relates to the study of how individual genes affect the way individuals respond to medications, whereas pharmacogenomics refers to how the individual’s genomic composition as a whole affects the way individuals respond to medicines. Pharmacogenetics focus on how a single gene modulates the effect of drugs. Pharmacogenomics deals, on the other hand, with how the genome as a whole modulates the action of drugs, involving multiple genes at a time. The tasks of pharmacogenomics and pharmacogenetics are nonetheless similar. They consist of tailoring drug therapy to the individual by developing specific tests that allow the clinician to optimize the drug regimen. These tests ultimately aim to identify the most suitable patient-specific therapy that can reduce adverse events and improve outcome. The promise of pharmacogenomics has been to use genomic data to achieve these goals. The published literature attests, however, to the achievements of pharmacogenetics, and only little if at all to those of pharmacogenomics (Turner and Schwartz, 2005). The matter is further complicated by the differentiation within the field of pharmacogenetics between pharmacokinetics and pharmacodynamics. Pharmacokinetics deal with mechanisms that affect the level of the drug in blood and ultimately at its target and that are influenced by drug absorption, distribution, excretion and metabolism. Pharmacodynamics deal with mechanisms which determine the interaction of the drug with its target and the subsequent events in the cells, organs and systems (Schwartz and Turner, 2004). Reviewing the achievements of pharmacogenetics in hypertension on the basis of published reports, it appears that the results have so far been limited mostly to the blood pressure response to drugs, and even there only a limited number of genetic polymorphisms of a limited number of candidate genes have so far been successfully associated with the blood pressure response to drugs (Marteau et al., 2005; Turner and Schwartz, 2005). Most of the data relate to genetic polymorphisms within the RAAS. Polymorphisms of the genes that encode for renin, angiotensinogen, angiotensin converting enzyme, angiotensin II receptor type 1, angiotensin II receptor type 2 and aldosterone synthase genes have all been associated with a hypotensive or a lack of hypotensive response to anti-hypertensive drugs including diuretics, beta-blockers, ACE inhibitors, angiotensin II receptor blockers and calcium channel blockers (Turner and Schwartz, 2005). Additional data relate to polymorphisms of genes involved in signal transduction pathways including the G-protein alpha and beta-3 subunits, the alpha-2 adrenergic and beta-1 and 2 adrenoreceptors, endothelin and adducin (Turner and Schwartz, 2005). Of particular interest is the association of the G460W adducin polymorphism and the anti-hypertensive response to thiazides which appears to be valid for some but not all populations (Schelleman et al., 2006). Are these pharmacogenetic data useful in clinical practice? Theoretically, yes, as it should be possible to obtain the genetic profile of each patient and find the best matching hypotensive therapy for the individual patient. The difficulty lies, however, in that most patients whose blood pressure is difficult to manage

Conclusion

require more than one anti-hypertensive drug for blood pressure control, and very often up to three or four drugs. Much of the available data that associate gene polymorphisms with the magnitude of the blood pressure response stem from studies that did not take into account drug interaction. Therefore, a gene polymorphism that has been associated with a lack of hypotensive response during monotherapy may be associated with a much improved response during combination therapy. An additional problem in applying currently available data is that the studies that have reported positive associations have used different combinations and different drug doses in populations of varying ages, sizes and ethnicity, each and all having a potential impact on the resulting data (Marteau et al., 2005). As a result, the clinical usefulness of the currently available pharmacogenetics data with regard to tailoring anti-hypertensive therapy to the patient is at best questionable at this time. One possible exception is the adducin paradigm in which six linkage studies, 18 of 20 association studies, and four of five follow-up studies that measured organ damage in hypertensive patients suggest a potential clinical impact of adducin polymorphism on the management of hypertension (Manunta and Bianchi, 2006). Are patients currently being tested for the adducin polymorphism prior to being prescribed a diuretic? The answer lies in the fact that it is currently much simpler to prescribe the diuretic and wait 1 month to assess the response than to send DNA for adducin genotyping. Should patient genotyping become in the future a routine procedure, it is possible that clinicians might take into consideration the adducin genotype prior to prescribing a thiazide diuretic for hypertension. In addition to predicting the therapeutic response to drugs, an important task of pharmacogenomics would be to predict untoward side effects to drugs. One example is the development of cough when using angiotensin converting enzyme (ACE) inhibitors; another is orthostatic hypotension when using alpha blockers. It is likely that some individuals are more prone to develop such side effects than others, and that genetically mediated factors are involved. The task of pharmacogenomics is to identify the genes involved and specific polymorphisms that are associated with such side effects. Some data are already available, for example with respect to ACEI and cough, which has been associated with the I/D ACE gene polymorphism in some studies (Takahashi et al., 2001) but not in others (Zee et al., 1998).Attempts to associate this cough with polymorphisms of the genes encoding for chymase and the B2-bradykinin receptors have failed (Zee et al., 1998). The clinical implementation of such data, however scarce they may be, has not materialized as yet. The conclusion at this time is that genetic variants of candidate genes are promising tools for individualizing antihypertensive therapy, but that they are currently of little value in the clinical practice of hypertension. It is nonetheless important to be aware of the available data, as they most likely represent early reports of a field that is bound to break out and expand from pharmacogenetics into pharmacogenomics. Further welldisciplined research is required at a global scale to produce

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data that will become useful for the clinical management of hypertension.

MONITORING Hypertension is an overt disease, as opposed to other diseases such as cancer that may be occult. As with the diagnosis of hypertension, monitoring of blood pressure is entirely based on simple and straightforward clinical measurements using a blood pressure cuff. Monitoring of hypertension holds therefore no place for genomics. Yet genomics may hold some promise for the monitoring of hypertension-related end-organ damage which may not become clinically apparent or detectable by simple laboratory measurements in the early stages. Examples are incipient cerebrovascular disease, hypertensive heart disease and nephrosclerosis, which become clinically apparent only in their more advanced stage of development. Should monitoring of hypertension-related damage become available early enough, it is possible that more aggressive anti-hypertensive therapy or other therapeutic means may be applied to prevent progression to overt disease. Theoretically, a genomic profile consistent with the incipient development of end-organ damage could be defined, and hypertensive patients could be monitored for the appearance of such incipient signs of pending hypertensive heart disease, nephrosclerosis or cerebrovascular disease. Such profile however, is a futuristic matter that is certainly not yet available to the clinician.

NOVEL AND EMERGING THERAPEUTICS Genomics could be used as an effective tool in identifying new molecules involved in pathophysiological pathways that are involved in hypertension which would become immediate targets for anti-hypertensive drug development. Such task has probably already been undertaken by the pharmaceutical industry but the results of such endeavors have not been made public nor are they available at this time to the clinician. One exception is the advent of a new anti-hypertensive drug, Rostafuroxin, which mechanism of action is through antogonism of the adducin pathway (Ferrari, 2006). It is quite possible, though, that additional novel drugs, direct products of genomics, are already in the pipeline, but it may be a matter of many years until they become clinically available.

CONCLUSION It is important to realize that our current state of knowledge of the underlying pathophysiology of hypertension is unsatisfactory and incomplete. Despite intensive research efforts over

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two decades and more and allocation of very substantial funds, genomic research per se has yielded so far only two novel genes of direct relevance to hypertension, the adducin (Bianchi et al., 2005) and the ACE2 (Crackower et al., 2002; Yagil and Yagil, 2003b) genes. Genomics have not yet delivered clinical tools that would allow us to improve the clinical management of our patients with hypertension. And although it appears that further major developments are pending, it is unclear if and when they will be translated into clinically useful tools. Genomics continues, nonetheless, to hold much promise in the field of hypertension, as in all other multifactorial complex diseases, and it is anticipated that through genomics and related fields including transcriptomics, proteomics and metabolomics, much of what is still unknown in the field of hypertension will eventually

be unraveled. Once the pathophysiology of hypertension is better understood, it is likely that our ability to apply the art of genomics to predict the development of hypertension, to diagnose the various clinical syndromes that cause hypertension and to identify those individuals who are predisposed to develop end-organ damage and modulate their susceptibility will all be very significantly improved.

ACKNOWLEDGEMENTS Disclosure of conflicts of interests: The authors of this manuscript have no affiliation associated with financial benefit or ownership of a company relating to the contents of this manuscript.

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St Lezin, E., Griffin, K.A., Picken, M., Churchill, M.C., Churchill, P.C., Kurtz, T.W., Liu, W., Wang, N., Kren, V., Zidek, V. et al. (1999). Genetic isolation of a chromosome 1 region affecting susceptibility to hypertension-induced renal damage in the spontaneously hypertensive rat. Hypertension 34, 187–191. Takahashi, T., Yamaguchi, E., Furuya, K. and Kawakami, Y. (2001). The ACE gene polymorphism and cough threshold for capsaicin after cilazapril usage. Respir Med 95, 130–135. Trotta, R., Donati, M.B. and Iacoviello, L. (2004). Trends in pharmacogenomics of drugs acting on hypertension. Pharmacol Res 49, 351–356. Turner, S.T. and Schwartz, G.L. (2005). Gene markers and antihypertensive therapy. Curr Hypertens Rep 7, 21–30. White, P.C., Curnow, K.M. and Pascoe, L. (1994). Disorders of steroid 11 beta-hydroxylase isozymes. Endocr Rev 15, 421–438. Wilson, F.H., Disse-Nicodeme, S., Choate, K.A., Ishikawa, K., NelsonWilliams, C., Desitter, I., Gunel, M., Milford, D.V., Lipkin, G.W., Achard, J.M. et al. (2001). Human hypertension caused by mutations in WNK kinases. Science 293, 1107–1112. Yagil, C., Katni, G., Rubattu, S., Stolpe, C., Kreutz, R., Lindpaintner, K., Ganten, D., Ben-Ishay, D. and Yagil, Y. (1996). Development, genotype and phenotype of a new colony of the Sabra hypertension prone (SBH/y) and resistant (SBN/y) rat model of slat sensitivity and resistance. J Hypertens 14, 1175–1182. Yagil, Y. and Yagil, C. (2002). Insights into pharmacogenomics and its impact upon immunosuppressive therapy. Transpl Immunol 9, 203–209. Yagil,Y. and Yagil, C. (2003a). Congenics in the pathway from quantitative trait loci detection to gene identification: Is that the way to go? J Hypertens 21, 2009–2011. Yagil, Y. and Yagil, C. (2003b). Hypothesis: ACE2 modulates blood pressure in the mammalian organism. Hypertension 41, 871–873. Yamori,Y., Nara,Y., Mizushima, S., Murakami, S., Ikeda, K., Sawamura, M., Nabika, T. and Horie, R. (1992). Gene-environment interaction in hypertension, stroke and atherosclerosis in experimental models and supportive findings from a world-wide cross-sectional epidemiological survey: A WHO-cardiac study. Clin Exp Pharmacol Physiol (Suppl) 20, 43–52. Zee, R.Y.L., Rao, V.S., Paster, R.Z., Sweet, C.S. and Lindpaintner, K. (1998). Three candidate genes and angiotensin-converting enzyme inhibitor-cough related: A pharmacogenetic analysis. Hypertension 31, 925–928.

RECOMMENDED RESOURCES http://cmbi.bjmu.edu.cn/genome/candidates/candidates.html A list of 150 candidate genes for hypertension with relevant genomic data including referral to known polymorphisms within those genes. http://www.emedicine.com/med/topic1611.htm An extensive and updated overview of various important aspects of hypertensive nephrosclerosis.

http://www.sin-italy.org/jnonline/Vol14s4/Fogo/FOGO.htm Another extensive and updated overview of various important aspects of hypertensive nephrosclerosis. http://www.emedicine.com/med/topic3432.htm An extensive and updated overview of various important aspects of hypertensive heart disease.

CHAPTER

55 Lipoprotein Disorders Sekar Kathiresan and Daniel J. Rader

INTRODUCTION Plasma lipoproteins are integral to energy and cholesterol metabolism, but disorders involving lipoprotein metabolism can predispose to atherosclerotic vascular disease (ASCVD). Genetic factors play an important role in influencing lipoprotein metabolism and therefore plasma levels of the major lipoproteins and risk for cardiovascular disease. Molecular characterization of classic Mendelian monogenic lipoprotein disorders has provided major insights into the physiology and regulation of lipoprotein metabolism and new targets for therapeutic drug development. Much attention is now focused on greater elucidation of the genetic factors that influence the complex lipoprotein phenotypes that are much more common and important in influencing cardiovascular risk in the general population. Lipoprotein metabolism is a ripe area for the application of genomic medicine because of frequency with which plasma lipids are measured in clinical practice, the quantitative importance of genetics in determining their levels, the large number of gene products involved in lipoprotein metabolism, and the broad clinical relevance of the field to the most important cause of morbidity and mortality in most of the world.

OVERVIEW OF LIPOPROTEIN METABOLISM Lipoproteins are large macromolecular complexes that transport hydrophobic lipids (primarily triglycerides, cholesterol, and fatsoluble vitamins) through body fluids (plasma, interstitial fluid,

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 634

and lymph) to and from tissues. Lipoproteins play an essential role in the absorption of dietary cholesterol, long-chain fatty acids, and fat-soluble vitamins; the transport of triglycerides, cholesterol, and fat-soluble vitamins from the liver to peripheral tissues; and the transport of cholesterol from peripheral tissues to the liver. Lipoproteins contain a core of hydrophobic lipids (triglycerides and cholesteryl esters) surrounded by hydrophilic lipids (phospholipids, unesterified cholesterol) and proteins that interact with body fluids. The plasma lipoproteins are divided into five major classes based on their relative density: chylomicrons, very-low-density lipoproteins (VLDL), intermediate-density lipoproteins (IDL), low-density lipoproteins (LDL), and highdensity lipoproteins (HDL). Each lipoprotein class comprises a family of particles that vary slightly in density, size, migration during electrophoresis, and protein composition. The density of a lipoprotein is determined by the amount of lipid per particle. HDL is the smallest and most dense lipoprotein, whereas chylomicrons and VLDL are the largest and least dense lipoprotein particles. Most plasma triglyceride is transported in chylomicrons or VLDL and most plasma cholesterol is carried as cholesteryl esters in LDL and HDL. The proteins associated with lipoproteins, called apolipoproteins, are required for the assembly, structure, and function of lipoproteins. Apolipoproteins activate enzymes important in lipoprotein metabolism and act as ligands for cell surface receptors. ApoA-I, which is synthesized in the liver and intestine, is found on virtually all HDL particles. ApoA-II is the second most abundant HDL apolipoprotein and is on approximately two-thirds of all HDL particles. ApoB is the major structural

Copyright © 2009, Elsevier Inc. All rights reserved.

Overview of Lipoprotein Metabolism

TG Chol

B-48

Chylomicron

TG

B-100

MTP

LPL

LDLR

Fatty acids (muscle, adipose)

E

B-48

635

TG apoB

C-II Liver

■

TG Chol

Liver

Chol

C-II LPL

LDLR

VLDL

Intestine

Chylomicron remnant

Figure 55.1 The exogenous pathway of apoB-containing lipoprotein metabolism. This pathway transports exogenous dietary fat from intestine to peripheral tissues and ultimately to the liver.

protein of chylomicrons,VLDL, IDL, and LDL; one molecule of apoB, either apoB-48 (chylomicrons) or apoB-100 (VLDL, IDL, or LDL), is present on each lipoprotein particle. The human liver synthesizes apoB-100 and the intestine makes apoB-48, which is derived from the same gene by mRNA editing. ApoE is present in multiple copies on chylomicrons, VLDL and IDL and plays a critical role in the metabolism and clearance of triglyceride-rich particles. ApoC-I, apoC-II, and apoC-III also participate in the metabolism of triglyceride-rich lipoproteins. The exogenous pathway of lipoprotein metabolism involves the absorption and transport of dietary lipids to appropriate sites within the body (Figure 55.1). Dietary triglycerides are hydrolyzed by lipases within the intestinal lumen and emulsified with bile acids to form micelles. Dietary cholesterol, fatty acids, and fat-soluble vitamins are absorbed in the proximal small intestine. Cholesterol and retinol are esterified (by the addition of a fatty acid) in the enterocyte to form cholesteryl esters and retinyl esters, respectively. Longer-chain fatty acids (12 carbons) are incorporated into triglycerides and packaged with apoB-48, cholesteryl esters, retinyl esters, phospholipids, and cholesterol to form chylomicrons. Nascent chylomicrons are secreted into the intestinal lymph and delivered through the thoracic duct directly to the systemic circulation, where they are extensively processed by peripheral tissues before reaching the liver. The particles encounter lipoprotein lipase (LPL), which is anchored to proteoglycans that decorate the capillary endothelial surfaces of adipose tissue, heart and skeletal muscle. The triglycerides of chylomicrons are hydrolyzed by LPL and free fatty acids (FFAs) are released; apoC-II, which is transferred to circulating chylomicrons from HDL, acts as a cofactor for LPL in this reaction. The released FFAs are taken up by adjacent myocytes or adipocytes and either oxidized to generate energy or re-esterified and stored as triglyceride. Some of the released FFAs bind albumin before entering cells, and are transported to other tissues, especially the liver. The chylomicron particle progressively shrinks in

E HL Chol B-100

B-100 LDL

TG Chol IDL

Figure 55.2 The endogenous pathway of apoB-containing lipoprotein metabolism. This pathway transports endogenous stored fat from liver to peripheral tissues and ultimately back to the liver.

size as the hydrophobic core is hydrolyzed and the hydrophilic lipids (cholesterol and phospholipids) and apolipoproteins on the particle surface are transferred to HDL, creating chylomicron remnants. Chylomicron remnants are rapidly removed from the circulation by the liver through a process that requires apoE as a ligand for receptors in the liver. The endogenous pathway of lipoprotein metabolism refers to the hepatic secretion of apoB-containing lipoproteins and their metabolism (Figure 55.2). VLDL particles resemble chylomicrons in protein composition but contain apoB-100 rather than apoB-48 and have a higher ratio of cholesterol to triglyceride (~1 mg of cholesterol for every 5 mg of triglyceride). The triglycerides of VLDL are derived predominantly from the esterification of long-chain fatty acids in the liver. The packaging of hepatic triglycerides with the other major components of the nascent VLDL particle (apoB-100, cholesteryl esters, phospholipids, and vitamin E) requires the action of the enzyme microsomal triglyceride transfer protein (MTP). After secretion into the plasma, VLDL acquires multiple copies of apoE and apolipoproteins of the C series by transfer from HDL. As with chylomicrons, the triglycerides of VLDL are hydrolyzed by LPL, especially in muscle and adipose tissue. After the VLDL remnants dissociate from LPL, they are referred to as IDL, which contain roughly similar amounts of cholesterol and triglyceride. The liver removes approximately 40–60% of IDL by LDL receptor– mediated endocytosis via binding to apoE. The remainder of IDL is remodeled by hepatic lipase (HL) to form LDL; during this process most of the triglyceride in the particle is hydrolyzed and all apolipoproteins except apoB-100 are transferred to other lipoproteins. The cholesterol in LDL accounts for over half of the plasma cholesterol in most individuals. Approximately 70% of circulating LDL is cleared by LDL receptor–mediated endocytosis in the liver. Lipoprotein(a) [Lp(a)] is a lipoprotein similar to LDL in lipid and protein composition, but contains an additional protein called apolipoprotein(a) [apo(a)]. Apo(a) is synthesized

636

CHAPTER 55

Lipoprotein Disorders

■

Intestine

A-I ABCA1

Kidney

FC

FC

Lipid poor HDL Bile ABCB11

ABCG5/8

LCAT

HL, EL

ABCA1

BA

ABCA1

better substrate for HL, which hydrolyzes the triglycerides and phospholipids to generate smaller HDL particles. A related enzyme called endothelial lipase (EL) hydrolyzes HDL phospholipids, generating smaller HDL particles that are catabolized faster. Remodeling of HDL influences the metabolism, function, and plasma concentrations of HDL.

FC

ABCG1

FC FC

FC

FC

FC

CE

LXR

SR-BI

SR-BI

A-I HDL

Liver LDLR PLTP

B

CETP

Macrophage

CE TG VLDL/LDL

Figure 55.3 HDL metabolism and reverse cholesterol transport. This pathway transports excess cholesterol from the periphery back to the liver for excretion in the bile and feces.

in the liver and is attached to apoB-100 by a disulfide linkage. The major site of clearance of Lp(a) is the liver but the uptake pathway is not known. HDL metabolism is complex (Figure 55.3) (Rader, 2006). Nascent HDL particles are synthesized by the intestine and the liver. Newly secreted apoA-I rapidly acquires phospholipids and unesterified cholesterol from its site of synthesis (intestine or liver) via efflux promoted by the membrane protein ATPbinding cassette protein A1 (ABCA1). This process results in the formation of discoidal HDL particles, which then recruit additional unesterified cholesterol from the periphery. Within the HDL particle, the cholesterol is esterified by lecithin-cholesterol acyltransferase (LCAT), a plasma enzyme associated with HDL, and the more hydrophobic cholesteryl ester moves to the core of the HDL particle. As HDL acquires more cholesteryl ester it becomes spherical, and additional apolipoproteins and lipids are transferred to the particles from the surfaces of chylomicrons and VLDL during lipolysis. HDL cholesterol (HDL-C) is transported to hepatoctyes by both an indirect and a direct pathway. HDL cholesteryl esters can be transferred to apoB-containing lipoproteins in exchange for triglyceride by the cholesteryl ester transfer protein (CETP). The cholesteryl esters are then removed from the circulation by LDL receptor–mediated endocytosis. HDL-C can also be taken up directly by hepatocytes via the scavenger receptor class BI (SR-BI), a cell surface receptor that mediates the selective transfer of lipids to cells. HDL particles undergo extensive remodeling within the plasma compartment by a variety of lipid transfer proteins and lipases. The phospholipid transfer protein (PLTP) has the net effect of transferring phospholipids from other lipoproteins to HDL. After CETP-mediated lipid exchange, the triglyceride-enriched HDL becomes a much

PLASMA LIPID AND LIPOPROTEIN LEVELS AND ATHEROSCLEROTIC CARDIOVASCULAR DISEASE Plasma total cholesterol was firmly established as an independent risk factor for cardiovascular disease in 1961 after researchers at the Framingham Heart Study demonstrated that participants with total cholesterol 245 mg/dl had a threefold increased risk of future coronary heart disease (CHD) compared with participants with a total cholesterol 210 mg/dl (Kannel et al., 1961). Subsequently, it was clarified that the two major lipoproteins carrying cholesterol, LDL and HDL, are associated with opposite influences on risk for cardiovascular disease, with LDL cholesterol (LDL-C) associated with increased risk and HDL-C with decreased risk. In observational studies, every 1 mg/dl increase in LDL-C has been shown to be associated with a 2% increased risk for cardiovascular disease, whereas every 1 mg/dl increase in HDL-C is associated with a 2–3% decreased risk (Gordon and Rifkind, 1989). For both LDL-C and HDL-C, there is a continuous, graded relationship between blood levels and subsequent risk for cardiovascular disease. Thus genetic factors that influence plasma levels of LDL-C and HDL-C are critically important to the risk of developing cardiovascular disease. The independent relationship of triglyceride levels and cardiovascular disease has been a topic of debate over the years, but recently a general consensus has developed that triglycerides are an independent predictor of risk for cardiovascular disease. Thus genetic factors that influence triglycerides are also important determinants of risk for cardiovascular disease.

INHERITED BASIS FOR BLOOD LIPID TRAITS Though lipid levels are affected by many nongenetic factors, interindividual variability in lipids has been shown to have a strong inherited component. In simplest terms, an important role for shared genes is suggested by the fact that the correlation between family members for LDL-C, HDL-C, or triglycerides is considerably greater than that between unrelated individuals. Heritability, or the proportion of total phenotypic variance that is due to genetic variance, has been consistently estimated to be ~50% for the major blood lipid traits. For example, in the Framingham Heart Study, heritability for single time-point measurements of LDL-C, HDL-C, and triglycerides are 0.59, 0.52, and 0.48 (Kathiresan et al., 2007).

Genetics of LDL-C

Broadly, traits that have an inherited basis may display a simple (or Mendelian) pattern of inheritance where variation at a single genetic locus is both necessary and sufficient to cause a phenotype. Alternatively, traits may depend on multiple genetic loci (or multifactorial inheritance). For blood lipid disorders, both patterns of inheritance are operational. Mendelian syndromes where LDL-C, HDL-C, or triglyceride levels are extremely high or low have been successfully studied and a number of specific genes have been isolated (see below). Knowledge derived from these genes has transformed our understanding of lipoprotein biology and treatment of cardiovascular disease (Goldstein and Brown, 2001). However, each of these syndromes is individually rare in the population and cannot explain the overall heritability of blood lipids. Instead, blood lipid variation in the population depends on the additive effects of multiple loci. As initially demonstrated by the British geneticist R.A. Fisher in 1918 and now widely accepted, the additive effects of alleles at multiple loci can lead to a continuous trait that is normally distributed in the population. This is likely to be the case for blood lipid levels. However, several aspects of the underlying genetic architecture for lipid levels are unknown. Important unanswered questions are (1) how many loci affect blood lipid variation? and (2) at each locus, what is the number and frequency of alleles that affect risk? Emerging data from large population-based sequencing and genotyping studies suggests that the number of loci will be large, at least greater than 20. In addition, each locus is likely to harbor a spectrum of alleles rare and common in the population.

SCREENING FOR LIPID DISORDERS Guidelines for the screening and management of lipid disorders have been established by an expert Adult Treatment Panel (ATP) convened by the National Cholesterol Education Program (NCEP) of the National Heart Lung and Blood Institute. The NCEP ATPIII guidelines published in 2001 recommend that all adults over age 20 to have plasma levels of cholesterol, triglyceride, LDL-C, and HDL-C measured after a 12-h overnight fast. In most clinical laboratories, the total cholesterol and triglycerides in the plasma are measured enzymatically and then the cholesterol in the supernatant is measured after precipitation of apoB-containing lipoproteins to determine the HDLC. The LDL-C is estimated using the following equation: LDL-C  Total cholesterol  (Triglycerides/5) – HDL-C. (The VLDL-C is estimated by dividing the plasma triglyceride by 5, reflecting the ratio of cholesterol to triglyceride in VLDL particles.) This formula is reasonably accurate if test results are obtained on fasting plasma and if the triglyceride level does not exceed 300 mg/dl, and by convention cannot be used if the TG are greater than 400 mg/dl. The accurate determination of LDLC levels in patients with triglyceride levels greater than 300 mg/ dl requires application of ultracentrifugal techniques or other direct assays for LDL-C. Further evaluation and treatment is based

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637

primarily on the plasma LDL-C level and the assessment of overall cardiovascular risk. The fact that screening for lipids is so widely performed has resulted in frequent identification of extreme lipid phenotypes due to Mendelian syndromes as well as widely available data for genetic studies in populations. Thus the genetics and genomics of lipids is among the most widely studied of complex genetic traits that are broadly clinically important.

GENETICS OF LDL-C Studies of the genetic basis of substantially elevated or reduced LDL-C levels have provided major insights into the regulation of LDL metabolism and novel targets for therapeutic development. We first review the Mendelian disorders of high and low LDL-C (Table 55.1) and then review the current state of knowledge regarding common polymorphisms and their association with LDL-C levels. Mendelian Disorders Primarily Causing Elevated LDL-C Levels Familial Hypercholesterolemia (FH) Familial hypercholesterolemia (FH) (Rader et al., 2003) is caused by loss-of-function mutations in the LDL receptor. Homozygous FH, caused by mutations in both LDL-receptor alleles, is a rare (approximately 1 in 1 million persons) condition characterized by markedly elevated cholesterol (greater than 500 mg/dl), cutaneous and tendon xanthomas, and accelerated atherosclerosis developing in childhood. Because they work through upregulation of the LDL receptor, statins, cholesterol absorption inhibitors, and bile acid sequestrants have only modest effects in reducing cholesterol. Liver transplantation is effective in decreasing LDL-C levels and gene therapy has been attempted. LDL apheresis is the therapy of choice at this time. Heterozygous FH is one of the most common (approximately 1 in 500 persons) single gene disorders. It is characterized by substantial elevations in LDL-C (usually 200–400 mg/dl), tendon xanthomas, and premature atherosclerotic cardiovascular disease. Treatment usually requires more than one drug, usually a statin plus a cholesterol absorption inhibitor and often a bile acid sequestrant and/or niacin. In some cases, LDL apheresis should be considered. Familial Defective Apolipoprotein B-100 (FDB) Familial defective apoB-100 (FDB) (Tybjaerg-Hansen and Humphries, 1992) is caused by mutations in the receptor binding region of apoB-100, which impairs its binding to the LDL receptor and delays the clearance of LDL. FDB is generally recognized as an autosomal dominant condition and occurs in approximately 1 in 700 persons of European descent. Like heterozygous FH, FDB is associated with elevated LDL-C and normal triglycerides. The most common mutation causing FDB is a substitution of glutamine for arginine at position 3500 in apoB-100; other mutations have also been reported that have a similar effect on apoB

638

CHAPTER 55

TABLE 55.1

■

Lipoprotein Disorders

Mendelian disorders of lipoprotein metabolism

Genetic disorder

Gene

Lipoproteins affected

Clinical findings

Genetic transmission

Familial hypercholesterolemia

(LDLR)

↑ LDL

Tendon xanthomas, CHD

ACD

Familial defective apoB-100

(APOB)

↑ LDL

Tendon xanthomas, CHD

AD

Autosomal recessive hypercholesterolemia

(ARH)

↑ LDL

Tendon xanthomas, CHD

AR

Sitosterolemia

ABCG5 or ABCG8

↑ LDL

Tendon xanthomas, CHD

AR

Autosomal dominant hypercholesterolemia

PCSK9 (gain of function)

↑ LDL

Tendon xanthomas, CHD

AD

Abetalipoproteinemia

MTP

↓ LDL

Fat malabsorption, spinocerebellar degeneration, retinopathy, possible hepatic steatosis

AR

Hypobetalipoproteinemia

(APOB)

↓ LDL

Fat malabsorption, spinocerebellar degeneration, retinopathy, possible hepatic steatosis

ACD

PCSK9 deficiency

PCSK9 (loss of function)

↓ LDL

ACD

APOA-I mutations

APOA1

↓ HDL

AD

Tangier disease

ABCA1

↓ HDL

Hepatosplenomegaly, enlarged orange tonsils, CHD

ACD

LCAT deficiency

LCAT

↓ HDL

Corneal opacification, hemolytic anemia, progressive renal insufficiency

AR

CETP deficiency

CETP

↑ HDL

Lipoprotein lipase deficiency

(LPL)

↑ Chylomicrons

Eruptive xanthomas, hepatosplenomegaly, pancreatitis

AR

Familial apolipoprotein C-II deficiency

(APOC2)

↑ Chylomicrons

Eruptive xanthomas, hepatosplenomegaly

AR

Familial hepatic lipase deficiency

(LIPC )

↑ VLDL remnants

Premature atherosclerosis, pancreatitis

AR

Familial dysbetalipoproteinemia

(APOE )

↑ Chylomicron, and VLDL remnants

Palmar and tuberoeruptive xanthomas, CHD, PVD

AD

LDL cholesterol

HDL cholesterol

AR

Triglycerides

AD, autosomal dominant; ACD, autosomal co-dominant; AR, autosomal recessive.

binding to the LDL receptor. FDB is treated with statins and often additional drugs, similar to the treatment of heterozygous FH. Autosomal Recessive Hypercholesterolemia (ARH) Autosomal recessive hypercholesterolemia (ARH) (Garcia et al., 2001) is caused by mutations in the ARH gene, which produces

a protein that regulates LDL receptor–mediated endocytosis in hepatocytes. ARH is very rare and in some ways the clinical presentation resembles homozygous FH. However, in contrast to FH, the condition is formally recessive and obligate heterozygotes have normal cholesterol levels. Statins and other LDL upregulating therapy sometimes result in partial LDL lowering response.

Genetics of HDL-C

Autosomal Dominant Hypercholesterolemia (ADH) Autosomal dominant hypercholesterolemia (ADH) is caused by apparent gain-of-function mutations in the proprotein convertase subtilisin/kexin type 9 (PCSK9) gene (Abifadel et al., 2003). PCSK9 is secreted by hepatocytes and appears to downregulate the density of functional LDL receptors in hepatocytes by promoting endosomal degradation rather than recycling of the receptor (Horton et al., 2007). Interestingly, loss-of-function mutations in this gene appear to cause low LDL-C levels (see below). The discovery of the molecular basis of ADH ultimately led to the identification of PCSK9 as a novel therapeutic target. Sitosterolemia Sitosterolemia is caused by mutations in one of two members of the adenosine triphosphate (ATP)-binding cassette (ABC) transporter family, ABCG5 and ABCG8 (Berge et al., 2000). These genes are expressed in the intestine and liver where they form a functional complex to limit intestinal absorption and promote biliary excretion of plant- and animalderived neutral sterols. In sitosterolemia, normally the low level of intestinal absorption of plant sterols is markedly increased and biliary excretion of plant sterols is reduced, resulting in increased plasma levels of sitosterol and other plant sterols. Because the hepatic LDL receptor is downregulated, LDLC levels tend to be high in this condition. Patients with sitosterolemia often have tendon xanthomas and are at risk for premature cardiovascular disease. Treatment of sitosterolemia is focused on dietary counseling, cholesterol-absorption inhibitors, and bile acid sequestrants, rather than statins. Mendelian Disorders Primarily Causing Reduced LDL-C Levels Abetalipoproteinemia Abetalipoproteinemia is caused by mutations in the gene encoding microsomal transfer protein (MTP) (Sharp et al., 1993), a protein that transfers lipids to apoB in the ER, forming nascent chylomicrons and VLDL in the intestine and liver, respectively. It is a very rare autosomal recessive disease characterized by extremely low plasma levels of cholesterol and no detectable apoB-containing lipoproteins in plasma (Rader and Brewer, 1993). Abetalipoproteinemia is characterized clinically by fat malabsorption, spinocerebellar degeneration, pigmented retinopathy, and acanthocytosis. Most clinical manifestations of abetalipoproteinemia result from defects in the absorption and transport of fat-soluble vitamins, especially alpha tocopherol (vitamin E) which is dependent on VLDL for efficient transport out of the liver. The discovery of MTP as the basis of abetalipoproteinemia led to the concept of MTP inhibition as a novel therapeutic target for lowering LDL-C levels. Familial Hypobetalipoproteinemia Familial hypobetalipoproteinemia generically refers to low LDLC levels that have a genetic basis. Historically it has been used to refer to low LDL-C due to mutations in apoB (Schonfeld et al.,

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639

2005). There is a range of missense and nonsense mutations in apoB that have been shown to reduce secretion and/or accelerate catabolism of apoB. Individuals heterozygous for these mutations generally have LDL-C levels less than 100 mg/dl. They appear to be protected from the development of ASCVD. There are rare patients with homozygous hypobetalipoproteinemia who have mutations in both apoB alleles and have plasma lipids similar to those in abetalipoproteinemia. PCSK9 Deficiency More recently, loss-of-function mutations in PCSK9 have also been shown to cause low LDL-C levels (Cohen et al., 2005; Kotowski et al., 2006), and therefore PCSK9 is another molecular cause of the generic phenotype of familial hypobetalipoproteinemia. The mechanism is uncertain, but presumably involves reduced PCSK9-mediated targeting of the LDL receptor to degradation pathways, resulting in upregulation of the hepatic LDL receptor and increased catabolism of LDL. This condition, which is more common in people of African descent, provided the opportunity to demonstrate that the effects of lifelong low LDL-C levels are a substantial reduction in CHD with no other adverse consequences (Cohen et al., 2006). This strongly supports the concept that aggressive LDL-C reduction is associated with long-term substantial reduction in cardiovascular risk. Common Gene Variants Segregating in Populations with LDL-C Levels Common gene variants within at least seven loci, APOB, APOE, CILP2/PBX4, HMGCR, LDLR, PCSK9, and SORT1, have been reproducibly related to LDL-C (Benn et al., 2005; Boright et al., 1998; Kathiresan et al., 2008a; Kotowski et al., 2006; Saxena et al., 2007; Sing and Davignon, 1985; Willer et al., 2008). In Table 55.2, we present representative associations of single nucleotide polymorphisms (SNPs) in these genes with LDL-C from a single community-based cohort study in southern Sweden, the Malmo Diet and Cancer Study-Cardiovascular Cohort (Berglund et al., 1993). These variants vary in frequency from 1% at PCSK9 to 48% at APOE. These SNPs explain 0.5–1.7% of the interindividual variability in LDL-C in the population (Kathiresan et al., 2008b). In comparisons between the major and minor allele homozygote classes, the difference in LDL-C ranges from 4 mg/dl at HMGCR to 51 mg/dl at PCSK9.Variants in other genes have been reported to be associated with LDL-C but have been reproduced less frequently.

GENETICS OF HDL-C Studies of the genetic basis of substantially reduced or elevated HDL-C levels have also provided important insights into the regulation of HDL metabolism and novel targets for therapeutic development. We first review the Mendelian disorders of low and high HDL-C (Table 55.1) and then review the current state of knowledge regarding common polymorphisms and their association with HDL-C levels.

■ 640 55Association Lipoprotein Disorders T A B LCHAPTER E 55.2 of common single nucleotide polymorphisms and low-density lipoprotein, high-density

lipoprotein cholesterol or triglycerides in the Malmo Diet and Cancer Study – Cardiovascular Cohorta Gene

SNP type

MAF

M/M

M/m

m/m

LDL cholesterol rs693

APOB

Coding

0.48

AAc 167  38 n  1349

AG 160  37 n  2462

GG 157  38 n  1173

0.9

2  1011

rs4420638

APOE cluster

5 Upstream

0.20

AA 157  37 n  3291

AG 167  38 n  1621

GG 173  38 n  224

1.7

3  1021

rs12654264

HMGCR

Intronic

0.39

AA 158  38 1911

AT 163  38 2405

TT 162  38 764

0.2

0.002

rs688

LDLR

Coding

0.42

CC 160  39 n  1717

CT 161  38 n  2551

TT 163  37 n  860

0.1

0.04

rs11591147

PCSK9

Coding

0.01

GG 161  38 n  4885

GT 146  30 n  114

TT 89  22 n2

0.5

7  107

ABCA1

Intronic

0.13

GG 54  15 n  3818

GA 53  14 n  1163

AA 51  12 n  82

0.2

0.003

rs28927680

APOA5

5 Upstream

0.07

CC 53  14 n  4444

CG 52  14 n  677

GG 46  8 n  28

1.1

7  1014

rs1800775

CETP

5 Upstream

0.49

CC 51  13 n  1397

CA 54  14 n  2456

AA 56  15 n  1245

2.5

2  1029

rs1800588

LIPC

5 Upstream

0.21

CC 53  14 n  3157

CT 54  14 n  1754

TT 57  16 n  247

0.8

4  1010

rs328

LPL

Coding

0.09

CC 53  14 n  4219

CG 56  15 n  863

GG 58  16 n  49

0.9

3  1012

Triglycerides rs693

APOB

Coding

0.48

AA 115  65 n  1120

AG 120  73 n  2385

GG 124  69 n  1303

0.3

7  105

rs28927680

APOA5

5 Upstream

0.07

CC 117  60 n  4481

CG 131  68 n  684

GG 156  84 n  29

0.4

6  106

rs780094

GCKR

Intronic

0.34

CC 117  70 n  2207

CG 123  81 n  2457

GG 127  73 n  639

0.5

2  107

rs328

LPL

Coding

0.09

CC 122  72 n  3996

CG 111  58 n  832

GG 91  34 n  46

0.7

9  109

HDL cholesterol rs3890182

% of variance explained

Pb

SNPS

SNP, single nucleotide polymorphism; MAF, minor allele frequency; M/M, major allele homozygote; M/m, heterozygote; m/m, minor allele homozygote. a Plus–minus values are means  sd. To convert values for cholesterol to millimoles per liter, multiply by 0.02586. To convert values for triglycerides to millimoles per liter, multiply by 0.01129; b Association analyses were conducted using multivariable-adjusted lipid concentration (adjusted for age, age2, sex, and diabetes status) as the phenotype; c Within each cell are the alleles for the SNP on the forward strand of the human genome reference sequence (from National Center for Biotechnology Information Build 35), the mean unadjusted cholesterol value in mg/dl plus minus standard deviation in mg/dl, and the number of individuals of that genotype class.

Genetics of HDL-C

Mendelian Disorders Primarily Causing Reduced HDL-C Levels ApoA-I Deficiency and Structural Mutations A rare cause of extremely low HDL-C is complete deficiency of apoA-I either from apoA-I gene deletion or nonsense mutations which result in virtually absent plasma HDL (Ng et al., 1994; Norum et al., 1982; Schaefer et al., 1982). Most of these cases are associated with premature CHD, consistent with the concept that apoA-I is atheroprotective and supportive of the concept that apoA-I elevation could be a therapeutic strategy. Another relatively rare cause of low HDL-C are missense or nonsense mutations that result in structurally abnormal or truncated apoA-I proteins. The best known of these mutations is apoAIMilano, where a substitution of cysteine for arginine at position 173 (Chiesa and Sirtori, 2003) results in increased turnover of the mutant apoA-IMilano protein, as well of the wild-type apoA-I and a substantial reduction in HDL-C. The low HDL-C levels, however, are not associated with an increased risk of atherosclerosis. Animal studies with intravenous infusion of recombinant apoAIMilano show less atherosclerosis (Chiesa and Sirtori, 2003), and a small trial of the intravenous infusion of apoA-IMilano–phospholipid complexes in humans demonstrated a reduction from baseline in coronary atheroma volume as measured by intravascular ultrasound (Nissen et al., 2003). There have been several other apoA-I structural mutations described that cause low HDL-C (von Eckardstein, 2005) but structural apoA-I mutations are rare, and in the general population apoA-I mutations are not a common source of variation in HDL-C levels. Tangier Disease (ABCA1 Deficiency) Tangier disease is caused by loss-of-function mutations in both alleles encoding the gene ABCA1 (Bodzioch et al., 1999; Brooks-Wilson et al., 1999; Rust et al., 1999). It is characterized by cholesterol accumulation in the reticuloendothelial system causing enlarged orange tonsils, hepatosplenomegaly, intestinal mucosal abnormalities, and peripheral neuropathy, as well as markedly low HDL-C (5 mg/dl) and apoA-I levels (Hobbs and Rader, 1999). The lack of ABCA1 results in markedly impaired efflux of cholesterol and phospholipids from cells to lipid-free apoA-I. The lack of intestinal and hepatic ABCA1 is probably largely responsible for the low HDL due to impaired lipidation of newly secreted apoA-I by these organs. Poorly lipidated apoA-I is then extremely rapidly catabolized. Impaired cholesterol efflux from other tissues, particularly macrophages, results in cholesterol accumulation leading to many of the typical clinical characteristics of this disorder. However, Tangier disease patients do not develop rapidly accelerated atherosclerosis to the extent one might expect based on the cholesterol efflux defect and the extremely low HDL-C levels. Heterozygotes for ABCA1 mutations have reduced HDL-C levels that are intermediate between Tangier disease and normal but have no evidence of cholesterol accumulation in tissues. However, they are at some increased risk for premature CHD. Mutations in ABCA1 have been found to cause low HDL-C levels in some families in which Tangier disease homozygotes are not found

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(Brooks-Wilson et al., 1999; Marcil et al., 1999). Rare private mutations in the ABCA1 gene may even be a cause of low HDL-C levels in the general population (Cohen et al., 2004). As a result of the discovery of the molecular basis of Tangier disease, ABCA1 is now a major target for the development of new therapies intended to upregulate ABCA1 expression (Linsel-Nitschke and Tall, 2005). Lecithin:Cholesterol Acyltransferase (LCAT) Deficiency LCAT deficiency is caused by loss-of-function mutations in both alleles of the LCAT gene (Kuivenhoven et al., 1997). LCAT is the enzyme that esterifies the free cholesterol present on HDL to cholesteryl ester, creating a cholesteryl ester core and resulting in maturation of HDL. In the absence of functional LCAT and cholesterol esterification, mature HDL particles are not formed and nascent HDL particles containing apoA-I and apoA-II are rapidly catabolized (Rader et al., 1994). Two genetic forms of LCAT deficiency have been described, complete deficiency known as classic LCAT deficiency and partial deficiency known as fish-eye disease (Kuivenhoven et al., 1997). In addition to extremely low HDL-C, both types of LCAT deficiency are characterized by corneal opacification, but only individuals with complete LCAT deficiency have low-grade hemolytic anemia and progressive chronic kidney disease leading to end-stage renal disease. Interestingly, neither form of LCAT deficiency is clearly associated with premature coronary disease despite the markedly reduced HDL-C levels (Kuivenhoven et al., 1997), raising questions about the importance of LCAT in protecting against CHD. LCAT heterozygotes have relatively normal HDL-C levels. Nevertheless, promotion of LCAT activity is of therapeutic interest as an HDL-raising approach. Mendelian Disorders Primarily Causing Elevated HDL-C Levels Cholesteryl Ester Transfer Protein (CETP) Deficiency CETP deficiency is caused by loss-of-function mutations in both alleles of the CETP gene (Brown et al., 1989). CETP transfers cholesteryl esters from HDL to apoB-containing lipoproteins in exchange for triglycerides (Rader, 2006). Lack of functional CETP results in markedly elevated HDL-C levels due to lack of HDL remodeling, accumulation of cholesteryl esters in HDL, and slower turnover of apoA-I and apoA-II (Ikewaki et al., 1993). LDL-C levels are also low because of increased catabolism of LDL and apoB with endogenous upregulation of the LDL receptor (Ikewaki et al., 1995). CETP deficiency is extremely rare outside Japan; among the Japanese the most common mutations are a 5 donor splice site intron 14 G to A substitution and a missense mutation in exon 15 (D442G) (Inazu et al., 1990). Heterozygous individuals for CETP deficiency have 60–70% of normal CETP activity and only a modest increase in HDL-C levels, and otherwise normal LDL-C levels. Whether homozygous or heterozygous CETP deficiency is associated with increased, decreased, or unchanged cardiovascular risk remains to be resolved (Rader, 2004). Nevertheless,

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the identification of CETP deficiency led to the concept that CETP is a therapeutic target for inhibition as an HDL-raising strategy (Rader, 2006). The current status of CETP inhibition is unclear due to failure of the CETP inhibitor torcetrapib, which caused increased mortality in a phase III outcomes trial (Tall et al., 2007). Common Gene Variants Segregating in Populations with HDL-C Levels Common gene variants within at least nine loci (ABCA1, ANGPTL4, APOA5, cluster, CETP, GALNT2, LIPC, LIPG, MVK/MMAB and LPL) have been reproducibly found to be associated with HDL-C (Boekholdt et al., 2005; Frikke-Schmidt et al., 2004; Guerra et al., 1997; Kathiresan et al., 2008a; Lai et al., 2004; Rip et al., 2006;Willer et al., 2008). Common genetic variation in the ABCA1 gene is clearly associated with variation in HDL-C levels in the general population and may even be associated with CHD risk (Brousseau et al., 2001; Clee et al., 2001). Common CETP polymorphisms are also associated with HDL-C levels, though the association with CHD risk is less clear (Boekholdt and Thompson, 2003). The most commonly studied SNPs in CETP are the Taq1B polymorphism (associated with increased HDL-C) and the I405V SNP (also associated with modestly increased HDL-C) (Boekholdt and Thompson, 2003). In Table 55.2, we present representative associations of SNPs in these genes with HDL-C from a single communitybased cohort study in southern Sweden, the Malmo Diet and Cancer Study-Cardiovascular Cohort (Berglund et al., 1993). The variants associated with HDL-C vary in frequency from 7% at APOA5 to 49% at CETP (Kathiresan et al., 2008b). These SNPs explain 0.2–2.5% of the interindividual variability in HDL-C in the population. In comparisons between the major and minor allele homozygote classes, the difference in HDLC ranges from 3 mg/dl at ABCA1 for to 7 mg/dl at APOA5. In addition to the five genes in Table 55.2, the nonsynonymous variant in ANGPTL4 (E40K) has been strongly related to HDL-C (Romeo et al., 2007).

GENETICS OF TRIGLYCERIDES Studies of the genetic basis of substantially elevated triglyceride levels have been useful in understanding the regulation of triglyceride metabolism. We first review the Mendelian disorders of high triglycerides (Table 55.1) and then review the current state of knowledge regarding common polymorphisms and their association with triglyceride levels. Mendelian Disorders Influencing Triglyceride Levels Familial Chylomicronemia Syndrome (LPL Deficiency and ApoC-II Deficiency) The familial hyperchylomicronemia syndrome (FCS) is caused by homozygosity for loss-of-function mutations in one of two

genes, LPL and apoC-II (Santamarina-Fojo, 1992). These conditions are virtual phenocopies and are therefore discussed together. FCS is characterized by extreme hypertriglyceridemia (greater than 1000 mg/dl) usually presenting in childhood with acute pancreatitis, eruptive xanthomas, lipemia retinalis, and/or hepatosplenomegaly. Chylomicron triglycerides are hydrolyzed in muscle and adipose capillary beds by LPL with apoC-II acting as a required cofactor, thus loss of function of either protein produces the phenotype of hyperchylomicronemia. Interestingly, despite the markedly elevated triglyceride (and cholesterol) levels, premature atherosclerotic cardiovascular disease is not generally a feature of this disease. This observation contributed to our understanding of the importance of the nature of lipoprotein, not just the lipid, in determining cardiovascular risk. Primary therapy is restriction of total dietary fat. ApoAV Deficiency Genetic deficiency of apoAV due to loss-of-function mutations on both alleles causes late-onset hyperchylomicronemia (Priore Oliva et al., 2005). In addition, heterozygosity for mutations such as Q139X can lead to the same phenotype due to a dominant negative effect (Marcais et al., 2005). ApoAV is a minor apolipoprotein that promotes LPL-mediated hydrolysis of lipoprotein triglycerides. Subjects with apoAV deficiency have a severe lipolysis defect and markedly reduced VLDL catabolism. Familial Dysbetalipoproteinemia (Type III Hyperlipoproteinemia) Familial dysbetalipoproteinemia (FD), or type III hyperlipoproteinemia, is caused by mutations in the gene for apolipoprotein E (apoE) (Mahley et al., 1999). ApoE on chylomicron and VLDL remnants normally mediates their catabolism by binding to receptors in the liver. FD is usually caused by homozygosity for a common variant called apoE2, which differs from the wild-type apoE3 form by a substitution of a cysteine for an arginine at position 158. ApoE2 has impaired binding to lipoprotein receptors such as the LDL receptor, resulting in defective removal of chylomicron and VLDL remnants. About 0.5% of individuals are homozygous for apoE2 but the prevalence of FD is only about 1 in 10,000, indicating that other genetic or environmental factors are required for expression of the phenotype. Because remnant lipoproteins are elevated and contain both triglycerides and cholesterol, plasma levels of both triglycerides and cholesterol are elevated. Palmar xanthomata and tuberoeruptive xanthomata on the elbows, knees, or buttocks are distinctive skin findings in this condition. Importantly, premature atherosclerotic CVD is common in this disorder, an observation that helped to clarify that remnant lipoproteins are highly atherogenic. This disorder is important from a clinical genomics sense, because it is one of the few genetic lipid disorders in which genotyping is clinically indicated for diagnosis; the finding of the apoE2/E2 genotype is diagnostic in the appropriate clinical setting.

Influence of Lipid-modulating Mutations on Risk of Atherosclerotic Cardiovascular Disease

Hepatic Lipase Deficiency Hepatic lipase (HL) deficiency is caused by loss-of-function mutations in both alleles of the LIPC gene (Hegele et al., 1993). HL deficiency is characterized by elevated plasma levels of cholesterol and triglycerides due to the accumulation of circulating lipoprotein remnants as a result of lack of HL activity. HL deficiency is very rare and therefore it is difficult to determine its true relationship to atherosclerotic CVD. Common Gene Variants Segregating in Populations with Triglyceride Levels Common gene variants within at least ten loci, ANGPTL3, ANGPTL4, APOB, APOA5, CILP2/PBX4, GALNT2, GCKR, LPL, MLXIPL, and TRIB1, have been reproducibly related to triglyceride levels (Benn et al., 2005; Kathiresan et al., 2008a; Kooner et al., 2008; Pennacchio et al., 2001; Rip et al., 2006; Saxena et al., 2007; Willer et al., 2008). In Table 55.2, we present representative associations of SNPs in these genes with triglycerides from a single community-based cohort study in southern Sweden, the Malmo Diet and Cancer Study-Cardiovascular Cohort (Berglund et al., 1993). These variants vary in frequency from 7% at APOA5 to 48% at APOB. These SNPs explain 0.3–0.7% of the interindividual variability in triglyceride in the population (Kathiresan et al., 2008b). In comparisons between the major and minor allele homozygote classes, the difference in triglycerides ranges from 9 mg/dl at APOB to 39 mg/dl at APOA5. In addition to these variants, the same 3% nonsynonymous variant in ANGPTL4 noted above with regard to HDL-C has been related to triglyceride level (Romeo et al., 2007).

GENETIC LIPID DISORDERS WITHOUT CURRENT PROVEN MOLECULAR ETIOLOGY Familial Combined Hyperlipidemia (FCHL) Familial combined hyperlipidemia (FCHL) is a dominantly inherited condition characterized by elevated triglycerides, elevated cholesterol, and reduced HDL-C (Grundy et al., 1987). Premature ASCVD is common in patients with FCHL. The metabolic basis of FCHL is thought to be hepatic overproduction of VLDL. It is the most common inherited lipid disorder, with a prevalence of approximately 1 in 200 persons. The genetic basis of FCHL is not well understood, and probably several different genes can cause a similar phenotype (Shoulders et al., 2004; Suviolahti et al., 2006). Genome-wide linkage studies have demonstrated a repeated linkage of FCHL to a locus on chromosome 1q21-q23 (Coon et al., 2000; Pajukanta et al., 1998), and association with SNPs in the gene encoding the upstream transcription factor 1 (USF1) has been repeatedly shown to be associated with the FCHL phenotype (Coon et al., 2005; Pajukanta et al., 2004). Because USF1 is a transcription factor known to regulate the expression of genes involved

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in lipid metabolism (Lee et al., 2006), it is plausible, though unproven, that genetic variation in USF1 expression or function could cause the phenotype of FCHL. However, the mechanisms of this association are unknown, and other genes likely contribute to the phenotype of FCHL. The molecular basis of FCHL is one of the most important questions in the field of the genomics of lipid disorders, and additional discoveries in this area are likely to provide important insights into the pathophysiology of hepatic VLDL overproduction as well as new targets for the development of new therapies for lipid disorders. Familial Hypertriglyceridemia (FHTG) Familial hypertriglyceridemia (FHTG) is an autosomal dominant trait characterized by elevated triglycerides with normal or only modestly increased total cholesterol levels, and LDL-C levels are usually normal. In contrast to FCHL, FHTG is often not associated with increased risk of ASCVD. Both VLDL overproduction and reduced VLDL catabolism have been implicated in causing this phenotype, but the pathophysiology is not well understood. The molecular basis of FHTG has not yet been elucidated. Familial Hypoalphalipoproteinemia Familial hypoalphalipoproteinemia is a dominantly inherited trait characterized by low HDL-C levels, usually in the setting of relatively normal TG levels. Premature CVD is often, but not always, associated with this condition. Accelerated catabolism of apoA-I is thought to be the metabolic basis of this trait (Lewis and Rader, 2005). ABCA1 mutations can cause this phenotype, but this phenotype occurs in families without ABCA1 mutations and is likely caused by mutations in more than one other gene. Familial Hyperalphalipoproteinemia Familial hyperalphalipoproteinemia is a dominantly inherited trait characterized by high HDL-C levels. This trait is usually, but not always, associated with lower risk for CHD and increased longevity. Interestingly, while homozygous CETP deficiency is a cause of hyperalphalipoproteinemia as discussed above, obligate heterozygotes for CETP generally do not have exceptionally elevated HDL-C levels. The molecular basis of this condition is not known, but the discovery of genes that cause this phenotype would increase our understanding of the regulation of HDL metabolism and potentially provide new targets for the development of therapeutics for raising HDL.

INFLUENCE OF LIPIDMODULATING MUTATIONS ON RISK OF ATHEROSCLEROTIC CARDIOVASCULAR DISEASE Mendelian Syndromes and ASCVD With regard to LDL, the recognition of FH was instrumental in proving that elevated LDL-C levels cause accelerated ASCVD

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without a requirement for additional traditional risk factors. This observation strongly supported the concept that reduction in LDL-C would reduce risk of ASCVD, a hypothesis subsequently proven with large CV outcome trials. Other Mendelian syndromes of elevated LDL-C, such as FDB, ARH, and ADH, though less common, have also been associated with early onset ASCVD, consistent with this concept. Mendelian syndromes of low LDL are generally not common enough to allow definitive demonstration of protection against ASCVD (much more difficult to prove than accelerated disease), but studies of patients with hypobetalipoproteinemia due to apoB mutations are consistent with the concept that the risk of CVD is reduced (Schonfeld et al., 2005). Overall, the study of Mendelian disorders of high and low LDL has had a major impact on the understanding of the role of LDL as a risk factor for atherogenesis. Mendelian syndromes of HDL are generally not prevalent enough to allow definitive proof of a relationship to CVD. However, inability to synthesize apoA-I, while rare, has been consistently associated with accelerated ASCVD (Norum et al., 1982; Schaefer et al., 1982). In contrast, Tangier disease, while associated with similarly extremely low HDL-C levels, has not been associated with markedly increased ASCVD (Schaefer et al., 1980). In a similar fashion, LCAT deficiency is also associated with extremely low HDL but is also not associated with increased CVD (Kuivenhoven et al., 1997). Conversely, while CETP deficiency is associated with extremely elevated HDL-C levels, its relationship to CVD still remains uncertain. Finally, the sole Mendelian disorders associated with hyperchylomicronemia, LPL deficiency and apoC-II deficiency, have not been convincingly associated with increased cardiovascular risk, confirming the relative lack of atherogenicity of chylomicrons. Conversely, FD (type III hyperlipoproteinemia) is also associated with elevated triglycerides but in this case due to elevated remnant lipoproteins, and causes substantially increased risk of ASCVD. These Mendelian disorders of elevated triglycerides demonstrate that it is the nature of the lipoprotein that is elevated, not the plasma level of triglycerides per se, that determines the risk of atherosclerosis. Common Mutations in Aggregate Affecting CVD Risk As common SNPs influence blood lipid levels and blood lipids affect risk for cardiovascular disease, a natural question is whether lipid-modulating SNPs influence risk for cardiovascular disease. One of the best examples illustrating this issue is that of PCSK9, with relatively common variants Y142X or C679X causing reduced LDL-C levels and substantially reduced risk of CHD (Cohen et al., 2006). However, for many other common variants that influence LDL-C (e.g., at the APOB and APOE loci), the connection to cardiovascular disease has less compelling evidence. Likewise, common SNPs in CETP and HL that reproducibly influence HDL-C levels have not been definitively associated with cardiovascular outcomes; the same is true for common SNPs that influence plasma triglyceride levels. A key

limiting factor may be that each common SNP only modestly affects the lipid level. This raises the hypothesis that a combination of lipid-modulating SNPs might contribute to risk for cardiovascular disease. With the recent establishment of several reproducible associations for each of the three lipid traits, it is now feasible to test this hypothesis in prospective studies. The identification of a panel of lipid polymorphisms that influence CVD risk could help target preventive therapies or aid in risk prediction at the population level.

FUTURE DIRECTIONS IN GENETICS AND GENOMICS OF LIPOPROTEINS Over the next few years, the widespread application of the genome-wide association study (GWAS) method is expected to lead to substantial progress in defining the inherited basis for blood lipids. The GWAS is defined as an experiment in which a substantial set of common SNPs across the genome (300,000–500,000 SNPs) are simultaneously tested for association with disease or quantitative risk factors for disease (Hirschhorn and Daly, 2005). Blood lipid levels and ASCVD are among the phenotypes being widely studied by this approach and the early results are promising. Table 55.3 summarizes the design features of several GWASs recently completed or in progress for lipid or ASCVD phenotypes. For blood lipids, an early example is the Diabetes Genetics Initiative of Broad, Lund And Novartis. This study was designed to examine type 2 diabetes but 18 other phenotypes including blood lipids were analyzed as secondary traits (Saxena et al., 2007). Patients with type 2 diabetes and controls were genotyped for 500,000 SNPs across the genome and these SNPs were tested for association with blood LDL-C, HDL-C, triglycerides, apoB, apoA-I, and apoA-II. Several findings from this study highlight key issues in defining the genetic basis for lipid levels. First, the largest effect size for a common SNP approached 2% of trait variance explained (e.g., an APOE cluster SNP at 2.1% of LDL-C variance and a CETP SNP at 2.1% for HDL-C variance). As most other common variants will each individually confer a more modest effect size (1% of trait variance explained), large sample sizes will be needed to convincingly demonstrate that a gene variant influences lipid levels. Second, several loci with common alleles affecting lipid levels (APOE, ABCA1, APOA5, CETP, LIPC, and LPL) have also been shown to cause Mendelian syndromes or contain multiple rare alleles. Thus, sequencing all loci with validated common alleles will be required to define the spectrum of common and rare alleles at each locus and the full impact of each locus on trait variation. Third, loci previously unsuspected to play a role in lipid phenotypes may be identified using an unbiased discovery approach such as GWAS. For example, a highly significant association with triglycerides was observed for rs780094 (P  3.7  10–8), explaining 1% of residual variance in triglyceride levels. This single SNP was then tested in 5217 individuals from the Malmö

Future Directions in Genetics and Genomics of Lipoproteins

TABLE 55.3

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Selected genome-wide association studies for atherosclerotic cardiovascular disease and/or lipid phenotypes

Study

Ascertainment scheme

Ancestry

N

Phenotypes

Diabetes Genetics Initiative (Saxena et al., 2007)

Cases with type 2 diabetes, controls free of diabetes from Sweden and Finland

European

2931

LDL-C, HDLC, triglycerides, apolipoproteins

FUSION (Scott et al., 2007)

Cases with type 2 diabetes, controls free of diabetes from Finland

European

2457

LDL-C, HDL-C, triglycerides

SardiNIA (Scuteri et al., 2007)

Family-based sample from 4 towns in Sardinia, Italy

European

4305

LDL-C, HDL-C, triglycerides

Framingham Heart Study (Splansky et al., 2007)

Community-based prospective cohort study from single town in Framingham, USA

European

9000

LDL-C, HDL-C, triglycerides, apolipoproteins, lipoprotein subfractions, others

Atherosclerosis Risk in Communities (ARIC, 1989)

Community-based prospective cohort study, four communities in United States

European, AfricanAmerican

16,000

LDL-C, HDL-C, triglycerides, apolipoproteins, others

Kosrae (Bonnen et al., 2006)

Founder population on island in South Pacific

Asian

3000

LDL-C, HDL-C, triglycerides

Jackson Heart Study ( Wilson et al., 2005)

Community-based prospective cohort study from single town in United States

AfricanAmerican

4000

LDL-C, HDL-C, triglycerides

McPherson et al. (2007)

Hospital-based case collection; incident cases from two communitybased cohort studies, and prevalent coronary artery calcium in a community-based cohort study

European

4277 cases, 20,054 controls

Coronary artery bypass grafting, angioplasty, myocardial infarction, coronary artery calcium

DeCode (Helgadottir et al., 2007)

Population-based registry from Iceland

European

4589 cases, 12,768 controls

Myocardial infarction

WTCCC ( WTCCC, 2007; Samani et al., 2007)

UK-wide ascertainment of cases aged 66 with at least one affected sibling

European

1988 cases, 3004 controls

Myocardial infarction, coronary artery bypass surgery or angioplasty

German MI Family Study (Samani et al., 2007)

Premature MI and at least one firstdegree relative with premature CAD; cases from cardiac rehabilitation programs

European

875 cases, 1644 controls

Myocardial infarction

Myocardial Infarction Genetics Consortium (MIGen)

Premature MI; 3 hospital-based case collections, a community-based ascertainment of cases, and cases from two prospective cohort studies

European

3300 cases,3300 controls

Early-onset myocardial infarction

PennCath/Medstar

Cardiac cath-lab based collection of premature angiographic CAD with or without history of MI; controls older and free of CAD by angiography

European

1000 cases with MI, 1000 cases without MI,1000 controls

Obstructive coronary atherosclerosis on angiography, MI, LDL-C, HDL-C, triglycerides, apolipoproteins, others

Diet and Cancer Study–Cardiovascular Cohort and the association was replicated (P  8.7  10–8). This SNP resides within the gene glucokinase regulatory protein (GCKR), which regulates glucokinase (GCK), the first enzyme in the glycolytic pathway, and has the potential to influence hepatic TG synthesis.

For ASCVD, three separate studies have identified a locus on chromosome 9p21 as reproducibly and highly significantly associated with myocardial infarction or coronary artery disease (Helgadottir et al., 2007; McPherson et al., 2007; Samani et al., 2007). The risk allele at 9p21 is common at 47% frequency in

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controls, and each copy of the risk allele increases risk by 25%. The associated SNPs exist in a 190-kilobase region of strong linkage disequilibrium which contains two cyclin-dependent kinase inhibitors – CDKN2A and CDKN2B. Though the association is robust, the mechanism behind which the associated SNPs confer risk for MI or coronary artery disease remains to be defined. In addition to the 9p21 variant, the study by Samani et al. (2007) highlighted six other loci with either convincing or suggestive evidence for association. From the three initial GWASs for ASCVD, it is clear that the effect sizes of common variants will be less than an odds ratio of 1.4 per copy of the risk allele. Several of the newly discovered loci, including 9p21, do not seem to be acting through established risk factors such as lipids. As additional studies are performed, the number of loci and alleles affecting ASCVD should become clear.

PHARMACOGENETICS OF LIPID-MODULATING THERAPIES Drug therapy to modulate lipids with the goal of preventing or treating atherosclerotic cardiovascular disease is well established clinically and commonly used in practice. The three major classes of drugs used to reduce LDL-C are HMG CoA reductase inhibitors (statins), cholesterol absorption inhibitors (currently ezetimibe is the only member of this class), and bile acid sequestrants. The three major classes of drugs used to treat the HDLTG axis are nicotinic acid (niacin), PPAR agonists (fibrates), and omega 3 fatty acids (“fish oils”). Interindividual variability in response to all of these classes is substantial and undoubtedly influenced by genetic factors. A limited number of studies have investigated the genetic variation underlying response to lipidmodifying drugs. Statins inhibit 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, the rate-limiting step in cholesterol biosynthesis, and in doing so alter cholesterol metabolism in the hepatocyte, resulting in compensatory upregulation of the LDL receptor, leading to increased LDL catabolism. A few studies have investigated candidate genes with regard to variability in LDL-lowering response to statins. In a study involving the treatment of 1536 healthy persons with moderate hypercholesterolemia with pravastatin 40 mg for 24 weeks, 148 SNPs in 10 candidate genes were assessed with regard to LDL-C reduction. Two common SNPs in strong linkage disequilibrium, rs17244841 and rs17238540, in the gene encoding HMG-CoA reductase were significantly associated with reduced LDLC reduction on pravastatin (Chasman et al., 2004). In another study in 1360 individuals on atorvastatin therapy, 43 SNPs in 16 candidate genes were assessed with regard to LDL-C reduction. A significant association with HMG-CoA reductase was not found, but a modest significant association with the apoE2 polymorphism was noted, whereby heterozygotes had 3.5% greater LDL-C reduction (Thompson et al., 2005). Thus the majority of the variability in statin response remains unexplained by the existing candidate gene studies.

Ezetimibe inhibits cholesterol absorption in the small intestine by binding to the enterocyte brush border cholesterol transporter NPC1L1.This reduces cholesterol transport from intestine to liver and results in secondary compensatory upregulation of the LDL receptor, leading to increased LDL catabolism. Hegele and colleagues identified a patient who exhibited extremely poor clinical response to ezetimibe with regard to LDL lowering and sequenced the NPC1L1 gene, finding two rare nonsynonymous polymorphisms in NPC1L1, V55I and I1233N, that were absent in control subjects and suggesting that compound heterozygosity for these mutations may have caused the lack of response to ezetimibe (Wang et al., 2005). In a follow-up study, 101 dyslipidemic subjects were treated with ezetimibe for 12 weeks and common NPC1L1 SNPs and haplotype were assessed for association with LDL-C reduction. Subjects treated with ezetimibe lacking the common NPC1L1 haplotype 1735C25342A-27677T had significantly greater reduction in LDLC (36%) than subjects with at least one copy of this haplotype (24%) (Hegele et al., 2005). Thus genetic variation in NPC1L1 appears to influence the response to ezetimibe treatment. Nicotinic acid activates the G protein-coupled receptor GPR109A on adipocytes, resulting in reduced adipocyte triglyceride lipolysis and FFA release and reduced flux of FFA to the liver (Offermanns, 2006). This is believed to be the primary mechanism of TG-lowering of nicotinic acid; the mechanism of HDL-raising remains uncertain. While polymorphisms in GPR109A have been described (Zellner et al., 2005), no studies of the pharmacogenetics of niacin response have been reported to date. Fibrates are PPAR agonists that activate the transcription of a variety of lipid metabolism genes in multiple tissues, including muscle, adipose, and liver. The triglyceride lowering and modest HDL-raising mechanisms of fibrates are due to regulation of several genes, including LPL, apoA-I, and apoC-III. Only a few pharmacogenetic studies of fibrate lipid responses have been reported to date. In 292 hypertriglyceridemic subjects treated with fenofibrate for 3 months, the TG-lowering response to fenofibrate was reduced in carriers of the P207L mutation in LPL and greater in carriers of the apoE2 polymorphism and the L162V polymorphism in PPAR (Brisson et al., 2002). In 791 hypertriglyceridemic subjects treated with fenofibrate for 3 weeks, carriers of the C56G tagSNP in APOA5 had a greater decrease in TG and increase in HDL-C (Lai et al., 2007). Interestingly, carriers of C56G also had a greater response to fenofibrate after a defined fat load. Thus, genetic variation clearly plays a role in determining fibrate response, though additional work is required.

IMPLICATIONS OF GENOMICS OF LIPOPROTEIN METABOLISM FOR THE DEVELOPMENT OF NOVEL THERAPIES The study of the genetics and genomics of lipoprotein metabolism has led to several novel therapeutic targets. The discovery of the molecular basis of FH being mutations in the LDL receptor

Clinical Recommendations for Genetic Testing for Lipid Disorders

led to the concept that upregulation of the LDL receptor could be a strategy for reducing plasma levels of LDL-C. While statins were developed based on their ability to inhibit HMG-CoA reductase, the rate-limiting step in cholesterol biosynthesis, their physiological effect in the liver is upregulation of the LDL receptor with consequent reduction in LDL-C levels. Despite the success of statins and other LDL-lowering therapies such as cholesterol absorption inhibitors, there remains a need for additional LDL-lowering therapies. The fact that mutations that impair the biosynthesis of apoB cause reduced LDL-C levels supports the concept of targeting apoB therapeutically. While small molecule approaches to apoB inhibition are impractical, an approach using an antisense oligonucleotides (ASO) has been shown to be effective in animal models as well as in humans. The efficacy of an ASO targeted to apoB (apoB-ASO) was demonstrated in hypercholesterolemic mice (Crooke et al., 2005). In a phase I multiple-dose study in subjects with mild dyslipidemia, subcutaneous injection of an apoB-ASO reduced plasma apoB by up to 50% and LDL-C by up to 44% (Kastelein et al., 2006). Alternatively, small-interfering RNA (siRNA) molecules complementary to apoB mRNA also significantly reduced the plasma concentration of apoB-containing lipoproteins in nonhuman primates (Zimmermann et al., 2006). Thus inhibition of apoB expression using systemic administration of either an ASO or siRNA to apoB reduces LDL-C levels comparable to that seen in subjects heterozygous for familial hypobetalipoproteinemia. As discussed above, another Mendelian disorder associated with low LDL-C levels is abetalipoproteinemia. The discovery that genetic deficiency of MTP causes abetalipoproteinemia led to the concept that MTP inhibitors would disruptVLDL assembly and secretion, with consequent reductions in circulating LDL (Burnett and Watts, 2007). Indeed, studies in mouse models have demonstrated that small molecule inhibitors of MTP effectively reduce plasma levels of total and LDL-C (Liao et al., 2003; Spann et al., 2006). Furthermore, an MTP inhibitor was shown to be effective in substantially reducing cholesterol in the Watanabe heritable hyperlipidemic (WHHL) rabbit, a model of homozygous FH (Wetterau et al., 1998). Finally, a small clinical trial in six subjects with homozygous FH definitively demonstrated that MTP inhibition reduces LDL-C levels in humans by reducing hepatic apoB production (Cuchel et al., 2007). After titrating the dose over 4 months, LDL-C was reduced by 51% and apoB by 55% at the highest dose of the MTP inhibitor. ApoB kinetic studies demonstrated a marked reduction in LDL apoB production as the basis for the reduction in LDL-C and apoB. This trial also demonstrated the mechanism-based increase in hepatic steatosis associated with MTP inhibition, which occurred in four of the six subjects. While this clearly requires more study, including longer-term trials, it is possible that high-dose MTP inhibition could be developed as an orphan drug approach for homozygous FH patients and that lower doses of MTP inhibition could potentially have a role in treating patients who are unable to reach LDL-C goal on present therapies.

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The story of PCSK9 provides the best example in the lipoprotein field of how human genetics can identify novel therapeutic targets. As discussed above, it was first discovered that rare nonsynonymous variants of PCSK9 can cause a form of ADH (Abifadel et al., 2003). After studies of overexpression of PCSK9 in mice unexpectedly caused elevated LDL-C levels (Benjannet et al., 2004; Maxwell and Breslow, 2004), loss-of-function mutations in PCSK9 were shown to result in substantially reduced LDL-C levels (Cohen et al., 2005). Importantly, individuals heterozygous for loss-of-function mutations in PCSK9 were subsequently shown to have markedly reduced lifetime risk of CHD (Cohen et al., 2006; Kathiresan et al., 2008c). A compound heterozygote with loss-of-function mutations in both PCSK9 alleles has been reported to have a very low LDL-C but is in good health (Zhao et al., 2006). Thus, inhibition of PCSK9 is an extremely attractive target based on the human genetics. Because PCSK9 is upregulated by statin therapy, the addition of a PCSK9 inhibitor to a statin could result in additive or even synergistic reduction of LDL-C. Investigation of the human genetics of HDL has provided important information regarding potential therapeutic targets. The fact that LCAT deficiency is a cause of severely low HDL supported the concept that LCAT upregulation or stimulation would be a strategy for raising HDL. More importantly, the discovery of ABCA1 as the mutated gene in Tangier disease created immediate interest in ABCA1 as a therapeutic target for upregulation as a strategy to raise HDL-C levels and inhibit or regress atherosclerosis. Perhaps the best example of human genetics identifying a potential therapeutic target for HDL is that of CETP deficiency. As noted above, CETP-deficient subjects have markedly elevated HDL levels, directly identifying CETP as a target for inhibition as an approach to raising HDL. While the first CETP inhibitor to enter phase III, torcetrapib, failed to reduce cardiovascular events, it has the off-target property of raising blood pressure. Thus the status of CETP inhibition as a therapeutic approach remains uncertain. Other molecular causes of high HDL could provide additional new targets for therapeutic development.

CLINICAL RECOMMENDATIONS FOR GENETIC TESTING FOR LIPID DISORDERS The majority of lipid disorders are diagnosed based on the clinical presentation, and genetic testing is not usually clinically indicated. For example, while heterozygous FH has a prevalence of 1 in 500 individuals, the majority of LDL-receptor mutations are (with the exception of a few regions in which a founder effect is present) private and could not be screened for short of sequencing. Furthermore, knowledge of the specific mutation, or even that the gene defect is in the LDL receptor and not, for example, in apoB, has no direct impact on the clinical care of the patient. Therefore, in FH genetic screening is not

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recommended. One exception to this might be in prenatal diagnosis of homozygous FH. If both parents have heterozygous FH, the chance of a homozygous child is one in four. Knowledge of the specific mutations in each parent (which would require sequencing) could permit prenatal testing for these mutations. This same rationale and approach could be applied to other serious lipoprotein disorders, for example, abetalipoproteinemia in which knowledge of the specific mutation(s) through sequencing could be used for prenatal diagnosis. The one major exception in which genetic testing is clinically recommended for diagnosis of lipid disorders is in the definitive diagnosis of FD (also called type III hyperlipoproteinemia). If this diagnosis is suspected on clinical grounds, it can be confirmed by performing apoE genotyping (with a focus on the apoE2 polymorphism). The finding of homozygosity for apoE2/E2 in the appropriate clinical setting is diagnostic for FD, and may help with clinical management decisions and family counseling. Interestingly, clinical apoE genotyping generally includes typing for not only the apoE2 but also the apoE4 polymorphism. This is not associated with FD, but is associated with Alzheimer’s disease, raising the issue of providing unwanted

genetic prognostic information that has no immediate clinical utility and could impact on issues such as insurability if recorded in the medical record. Thus, while clinically appropriate, apoE genotyping should be performed only after careful consideration, and ideally should be limited to typing for the apoE2 polymorphism when being done for diagnosis of FD.

ACKNOWLEDGEMENTS Dr Rader has received funding from the National Heart Lung and Blood Institute, the National Institute for Diabetes and Digestive and Kidney Diseases, the National Center for Research Resources, the American Heart Association, the Burroughs Wellcome Fund, the Doris Duke Charitable Foundation, and GlaxoSmithKline through an industry-academic alliance (Alternative Drug Discovery Initiative, ADDI) with the University of Pennsylvania School of Medicine. Dr Kathiresan has received funding from the National Heart, Lung, and Blood Institute, the Doris Duke Charitable Foundation, the Fannie E. Rippel Foundation, and the Donovan Family Foundation.

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Recommended Resources

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Study: Design, recruitment, and initial examination. Am J Epidemiol 165, 1328–1335. Suviolahti, E., Lilja, H.E. and Pajukanta, P. (2006). Unraveling the complex genetics of familial combined hyperlipidemia. Ann Med 38, 337–351. Tall, A.R., Yvan-Charvet, L. and Wang, N. (2007). The failure of torcetrapib: Was it the molecule or the mechanism? Arterioscler Thromb Vasc Biol 27, 257–260. Thompson, J.F., Man, M., Johnson, K.J., Wood, L.S., Lira, M.E., Lloyd, D.B., Banerjee, P., Milos, P.M., Myrand, S.P., Paulauskis, J. et al. (2005). An association study of 43 SNPs in 16 candidate genes with atorvastatin response. Pharmacogenomics J 5, 352–358. Tybjaerg-Hansen, A. and Humphries, S.E. (1992). Familial defective apolipoprotein B-100: A single mutation that causes hypercholestolemia and premature coronary artery disease. Atherosclerosis 96, 91–107. von Eckardstein, A. (2006). Differential diagnosis of familial high density lipoprotein deficiency syndromes. Atherosclerosis 186, 231–239. Wang, J., Williams, C.M. and Hegele, R.A. (2005). Compound heterozygosity for two non-synonymous polymorphisms in NPC1L1 in a non-responder to ezetimibe. Clin Genet 67, 175–177. Wetterau, J.R., Gregg, R.E., Harrity, T.W., Arbeeny, C., Cap, M., Connolly, F., Chu, C., George, R.J., Gordan, D.A., Jamil, H. et al. (1998). An MTP inhibitor that normalizes atherogenic lipoprotein levels in WHHL rabbits. Science 282, 751–754. Willer, C.J. et al. (2008). Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40, 161–169. Wilson, J.G., Rotimi, C.N., Ekunwe, L., Royal, C.D., Crump, M.E., Wyatt, S.B., Steffes, M.W., Adeyemo, A., Zhou, J., Taylor, H.A., Jr et al. (2005). Study design for genetic analysis in the Jackson Heart Study. Ethn Dis 15, S6-30–S6-37. WTCCC (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678. Zellner, C., Pullinger, C.R., Aouizerat, B.E., Frost, P.H., Kwok, P.Y., Malloy, M.J. and Kane, J.P. (2005). Variations in human HM74 (GPR109B) and HM74A (GPR109A) niacin receptors. Hum Mutat 25, 18–21. Zhao, Z., Tuakli-Wosornu, Y., Lagace, T.A., Kinch, L., Grishin, N.V., Horton, J.D., Cohen, J.C. and Hobbs, H.H. (2006). Molecular characterization of loss-of-function mutations in PCSK9 and identification of a compound heterozygote. Am J Hum Genet 79, 514–523. Zimmermann, T.S., Lee, A.C., Akinc, A., Bramlage, B., Bumcrot, D., Fedoruk, M.N., Harborth, J., Heyes, J.A., Jeffs, L.B., John, M. et al. (2006). RNAi-mediated gene silencing in non-human primates. Nature 441, 111–114.

RECOMMENDED RESOURCES www.hapmap.org: A catalog of human genetic variation in 270 individuals from four different populations. www.pharmgkb.org/: A resource for pharmacogenetics and pharmacogenomics. http://bioinf.itmat.upenn.edu/cvdsnp: A website for a vascular disease 50K SNP array developed by a consortium of the Institute for Translational Medicine and Therapeutics (University of Pennsylvania) and the Broad Institute (MIT/Harvard).

http://www.broad.mit.edu/tools/data.html: A website hosted by the Broad Institute with a rich source of genetic information and resources.

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56 Reactive Oxygen Species Signals Leading to Vascular Dysfunction and Atherosclerosis Nageswara R. Madamanchi, Aleksandr E. Vendrov, and Marschall S. Runge

INTRODUCTION Reactive oxygen species (ROS) are produced by partial reduction of oxygen during respiration in all aerobic cells. ROS are also produced in response to cellular stresses such as heat shock and UV irradiation, environmental pollutants such as ozone and tobacco smoke, and during phagocytosis in host defense. However, recent evidence indicates that vascular cells generate intracellular ROS at low levels in response to humoral mediators such as growth factors and cytokines. Superoxide (O2 ), hydrogen peroxide (H2O2) and nitric oxide (NO ) are the important ROS that play a central role in vascular physiology under normal conditions. For example, low levels of these ROS elicit pulmonary arterial relaxation (Burke and Wolin, 1987), reduce platelet activation and aggregation (Ambrosio et al., 1994) and precondition the heart against ischemic injury (Tritto et al., 1997). Similarly, NO is a key mediator of endothelium-dependent vasodilation (Quyyumi et al., 1995) and an inhibitor of vascular inflammation (Tritto and Ambrosio, 2004). However, overproduction of ROS or deficiency of ROS scavenging under pathophysiologic conditions can damage cellular components including cell membranes, proteins and DNA.

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SOURCES OF ROS IN VASCULAR CELLS Superoxide is generated by the univalent reduction of oxygen by enzyme systems such as membrane-bound NAD(P)H oxidase and xanthine oxidase (XO) (Figure 56.1). NAD(P)H oxidase, the predominant ROS-generating system in inflammatory cells, is a major source of O2  production in vascular cells as well. It catalyzes the reduction of molecular oxygen by transferring an electron from NAD(P)H. XO generates O2  by catalyzing the conversion of hypoxanthine and xanthine to uric acid. XO is expressed in endothelial cells and circulates in plasma. NO is produced in vasculature by the activation of constitutive endothelial nitric oxide synthase (eNOS) as well as by the inducible nitric oxide synthase (iNOS) whose expression is induced in macrophages, fibroblasts and vascular smooth muscle cells (VSMC) either in response to injury or disease (Lloyd-Jones and Bloch, 1996). Reaction of NO with O2  generates ONOO , a potent mediator of LDL oxidation and protein nitration. The effervescent O2  is dismutated enzymically by superoxide dismutase (SOD) to produce H2O2 which is then converted to H2O by catalase or glutathione peroxidase (GPx). H2O2 can react with reduced transition metals to produce highly reactive hydroxyl radical (OH ), or it can be

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Figure 56.1 Sources of ROS in vascular cells. NAD(P)H oxidase, XO (xanthine oxidase), uncoupled NOS (nitric oxide synthase), 12/15-LO (lipoxygenase) and COX (cyclooxygnease) generate superoxide (O2 ). Dysfunctional mitochondrial respiratory chain is another source of O2  generation. SOD isoforms (MnSOD, and CuZnSOD) dismutate O2  to produce H2O2 (hydrogen peroxide) which in turn is converted to H2O by GPx or catalase. Myeloperoxidase generates HOCl (hypochlorous acid) from H2O2 in the presence of Cl–. H2O2 reacts with transition metals (Me) to produce hydroxyl radicals (OH). Nitric oxide (NO) reacts with O2  to produce peroxynitrite (ONOO ).

metabolized by myeloperoxidase (MPO) to form hypochlorous acid (HOCl). Lipoxygenases, cyclooxygenases, and cytochrome P450 isozymes also generate ROS in vasculature (Griendling and Fitzgerald, 2003). In addition, ROS are produced in vascular cells when mitochondrial oxidative phosphorylation is uncoupled under pathological conditions.

VASCULAR DYSFUNCTION AND ATHEROSCLEROSIS The vascular endothelium is an interface between the vessel wall and circulating blood and is a sensor and transducer of signals from the blood into the vessel wall. The healthy endothelium regulates vascular homeostasis by mediating vasodilation, inhibiting leukocyte adhesion and migration, platelet adhesion and migration,VSMC migration and proliferation and via anticoagulant and profibrinolytic effects (Bonetti et al., 2003). Endothelial dysfunction is the initial step in the development of atherosclerosis (Ross, 1999) because it alters vascular homeostasis not only by impairing the aforementioned processes but also by activating inflammatory and immunological reactions.

One of the earliest events in atherosclerotic lesion development is the infiltration of monocyctes and T lymphocytes into the arterial wall and their transformation into fatty streaks. The recruitment of leukocytes is triggered by the chemotactic molecules released from the endothelial cells and VSMC by the inflammatory stimuli (Kunsch and Medford, 1999). The inflammatory stimuli induce the production of ROS which regulate secretion of the adhesion and chemotactic molecules as described below and thus ROS-mediated signaling mechanisms underlie endothelial dysfunction associated with atherosclerosis.

ROS-INDUCED INFLAMMATORY GENE EXPRESSION IN VASCULAR CELLS Circulating levels of inflammatory agonists interleukin-6 (IL-6) and tumor necrosis factor- (TNF-) are strong predictors of cardiovascular events (Cesari et al., 2003). These agonists induce the expression of inflammatory gene products such as cellular adhesion molecules associated with atherosclerosis and ROS integrate the signaling pathways involved in these processes. For example, TNF- induces expression of vascular cell adhesion molecule 1

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(VCAM-1) and intercellular cell adhesion molecule 1 (ICAM-1) in human endothelial cells via ROS-mediated transactivation of NF-B (Chen et al., 2003). Similarly, IL-6-induced ICAM-1 gene expression in endothelial cells is regulated by the activation of signal transducer and activator of transcription 3 (STAT3) in a Rac-dependent manner (Wung et al., 2005). Recently, we identified that CD44, an adhesion molecule that participates in leukocyte adhesion and mediates inflammatory reactions in the vasculature, was significantly induced by thrombin in a redoxsensitive manner (Vendrov et al., 2006). Further support for the role of ROS in the inflammatory gene expression is obtained from the observation that pretreatment of HUVEC with antioxidants significantly reduces the expression of VCAM-1 induced by oxidized LDL (oxLDL) (Cominacini et al., 1997). Consistent with the enhanced expression of cell adhesion molecules by inflammatory agonists, increased levels of soluble ICAM-1 and VCAM-1 were strongly associated with high incidence of coronary artery disease (CAD) (Ridker et al., 1998, Blankenberg et al., 2001). Additional evidence for the importance of these gene products in atherogenesis was obtained from the animal models of atherosclerosis. Soluble ICAM-1 levels increase with age in apolipoprotein E (ApoE)-deficient mice and a significant decrease in atherosclerotic lesions was observed in ApoE-deficient mice that lack ICAM-1 which strongly suggests that soluble ICAM-1 levels correlate with the burden of atherosclerosis (Kitagawa et al., 2002). Similarly, reduced atherosclerosis was observed in mice that are deficient in VCAM-1 expression (Cybulsky et al., 2001). Together, these results suggest that ROS activate the signaling pathways that transduce the signals generated by inflammatory agonists at the cell membrane to the nucleus resulting in proatherogenic gene expression.

ASSOCIATION OF ROS MODULATORS WITH ATHEROSCLEROSIS In view of the large number of sources of ROS generation and scavenging in the cell, and the likelihood of cross talk among the various redox-sensitive pathways, it is difficult to identify a definitive causative role for any one ROS-induced signaling pathway in atherosclerosis. However, work in the past decade has shown that modulation of ROS levels affects atherosclerosis. We have shown recently that ApoE-null mice that were deficient in p47phox, a regulatory component of NAD(P)H oxidase, had less total lesion area than ApoE-null mice (Barry-Lane et al., 2001). Human investigations also support the role of NAD(P)H oxidase-derived ROS in atherosclerosis. Superoxide production and NAD(P)H oxidase subunit expression were higher in coronary arteries of CAD patients compared with those from non-CAD patients (Guzik et al., 2006). Even in a population without clinically overt atherosclerosis, increased NAD(P)H oxidase-derived O2  production in phagocytes was associated with enhanced carotid intima-media thickness (Zalba et al., 2005). Cytokines and growth factors such as platelet-derived growth factor (PDGF), angiotensin II (Ang II) and thrombin

induce ROS generation in vascular cells via the activation of NAD(P)H oxidase (Li et al., 2005, Kreuzer et al., 2003, Griendling et al., 1994, Patterson et al., 1999). The notion that NAD(P)H oxidase-derived ROS signaling leads to atherosclerosis is supported by the observation that coinfusion of an inhibitor of NAD(P)H oxidase subunit (gp91ds-tat peptide) decreases Ang II-induced increase in O2  production and ICAM-1 expression and medial hypertrophy (Liu et al., 2003). Similarly, inhibition of p22phox subunit of VSMC NAD(P)H oxidase attenuated not only thrombin-induced ROS generation, but also p38 MAP kinase activation and monocyte chemoattractant protein-1 (MCP-1) expression (Brandes et al., 2001). These observations suggest that ROS generation via NAD(P)H oxidase activity regulates the expression of genes involved in atherogenesis. Xanthine oxidase-derived ROS were also implicated in the development of atherosclerosis. Activity and protein expression of XO were increased in coronary arteries of CAD patients and XO, as the source of O2 , is second only to NAD(P)H oxidase in these vessels (Spiekermann et al., 2003). Endothelial O2  production in hypercholesterolemic rabbits was inhibited by oxypurinol, an inhibitor of XO (Ohara et al., 1993) and oxypurinol improved vasodilation in hypercholesterolemic and CAD patients (Baldus et al., 2005). The mainly endothelial-derived NO inhibits platelet adhesion and aggregation, VSMC migration and proliferation, and LDL oxidation (Channon et al., 2000). Deficiency of eNOS affects atherosclerosis development as ApoE, eNOSdouble knockout mice developed enhanced atherosclerotic lesions (Kuhlencordt et al., 2001). However, eNOS when uncoupled can also be a source of ROS generation. Several mechanisms, in particular, the deficiency of a cofactor tetrahydrobiopterin (BH4), may underlie the uncoupling of eNOS (Vasquez-Vivar et al., 1998). ROS from NAD(P)H oxidase oxidize BH4 and uncouple eNOS (Landmesser et al., 2003). For instance, overexpression of eNOS in ApoE-null mice lowered NO production and enhanced O2  production leading to augmented atherosclerotic lesion formation, an effect that was overcome by BH4 supplementation (Ozaki et al., 2002). Furthermore, NO regulates the expression of VCAM-1 (Khan et al., 1996) and MCP-1 (Zeiher et al., 1995) and NO donors inhibit the activation and nuclear translocation of NF-B in endothelial cells (Peng et al., 1995). Together, these observations indicate that NO regulates redox-sensitive gene expression that plays a role in atherosclerotic lesion formation. Catalytically active MPO is expressed in atherosclerotic lesions where it is colocalized with cholesterol clefts of lipidrich regions, suggesting that MPO contributes to atherosclerosis (Daugherty et al., 1994). NO is a physiological substrate for MPO (Abu-Soud and Hazen, 2000), which also catalyzes both protein nitration and initiation of lipid peroxidation (Baldus et al., 2001, Zhang et al., 2002). Consistent with this, MPO catalyzed nitration and chlorination of HDL promote atherosclerosis (Zheng et al., 2004, Bergt et al., 2004). These data are further supported by the observations that MPO promotes the formation of 3-chlorotyrosine and 3-nitrotyrosine in plasma HDL of CAD

ROS-Regulated Signaling Pathways

patients (Pennathur et al., 2004) and that MPO is a predictor of cardiac events in patients with chest pain (Brennan et al., 2003). Lipoxygenases (LO) are another important source of ROS production in the vascular wall; these non-heme containing dioxygenases oxidize polyunsaturated fatty acids yielding inflammatory mediators such as prostaglandins, thromboxanes and leukotrienes. 15-LO is expressed in human atherosclerotic lesions (Yla-Herttuala et al., 1990) and oxidizes LDL (Heydeck et al., 2001). Similarly, the presence of 5-LO was reported in atherosclerotic lesions of ApoE(/) and LDLR(/) deficient mice (Mehrabian et al., 2002). Activation of the 12/15-LO pathway stimulates ICAM-1 expression via regulation of NF-B (Bolick et al., 2005). Consistent with these data, deficiency of 5-LO and 12/15-LO causes remarkable inhibition of atherosclerosis in LDLR(/) and ApoE(/) mice, respectively (Mehrabian et al., 2002, Cyrus et al., 1999). These findings suggest that LO contribute to vascular inflammation and atherosclerosis via redox-sensitive gene regulation. Antioxdiant enzymes affect atherosclerosis by modulating ROS levels in vascular cells. Overexpression of human copper/zinc SOD (Cu,ZnSOD) or manganese SOD (MnSOD) inhibits LDL oxidation by endothelial cells (Fang et al., 1998). Cu,ZnSOD was shown to inhibit cytokine-induced VCAM1 and ICAM-1 expression in endothelial cells (Lin et al., 2005). Consistent with these observations, overexpression of Cu,ZnSOD and catalase or catalase in ApoE-null mice reduced atherosclerotic lesions (Yang et al., 2004). Ballinger et al. (2002) reported that deficiency of MnSOD in ApoE-null mice accelerated atherosclerosis at arterial branch points and this was preceded by mitochondrial DNA damage indicating increased ROS generation. In CAD patients activity of endothelium-bound extracellular SOD, which is positively correlated with vasodilation, was reduced (Landmesser et al., 2000). Similarly, GPx-1 activity is absent or decreased in atherosclerotic plaques compared to plaque-free arteries (Lapenna et al., 1998). These data support that antioxidant enzymes protect against atherosclerosis by suppressing divergent redox-sensitive cellular signaling pathways.

ROS SIGNALING IN ATHEROSCLEROTIC RISK FACTORS A large body of evidence supports a role for ROS-induced signaling in various atherosclerotic risk factors giving the notion that oxidative stress is the unifying mechanism of many vascular diseases. Both animal models of hypertension and clinical evidence support the role of renin-angiotensin system in the pathogenesis of hypertension-associated atherosclerosis. Ang II-induced hypertension in rats is mediated in part by O2  (Laursen et al., 1997). AT1-receptor antagonist improved endothelial dysfunction and reduced atherosclerotic plaque formation in rabbits by inhibiting NAD(P)H oxidase-dependent vascular O2  production (Warnholtz et al., 1999). Further evidence for ROS in hypertension is obtained from the report that mice deficient in p47phox have significantly decreased Ang

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II-induced O2  production and hypertension (Landmesser et al., 2002). In addition, Ang II-induced expression of VCAM-1, ICAM-1 and MCP-1 in rat aorta were attenuated by NAD(P)H oxidase inhibitors (Chen et al., 1998, Liu et al., 2003, Tummala et al., 1999). Consistent with these data, angiotensin-coverting enzyme (ACE) inhibitors not only ameliorate vasoconstriction, but also inhibit vascular O2  production and reduce the incidence of myocardial infarction (Munzel and Keaney, 2001). Together, these data suggest that Ang II-induced ROS transducer the signals involved in inflammatory gene expression and may serve as a molecular link between hypertension and atherosclerosis (Kunsch and Medford, 1999). ROS-stimulated signaling is also implicated in cardiovascular dysfunction associated with diabetes characterized by hyperglycemia. Chronic hyperglycemia induces ROS generation in endothelial cells via mitochondrial dysfunction (Evans et al., 2002) or NAD(P)H oxidase activation (Christ et al., 2002). High glucose not only leads to LDL oxidation (Maziere et al., 1995), but also upregulates LOX-1 (lectin-like oxidized LDL-receptor-1; an endothelial receptor for oxLDL) and LOX-1-dependent monocyte adhesion (Li et al., 2003). In addition, monocytederived macrophages of patients with diabetes overexpress LOX-1 (Li et al., 2004). NF-B is activated early in response to high glucose in endothelial cells which suggests that hyperglycemia may regulate redox-sensitive gene expression to cause endothelial injury (Pieper and Riaz-ul-Haq, 1997).

ROS-REGULATED SIGNALING PATHWAYS ROS modulate a variety of biological processes such as cell proliferation, migration and apoptosis by functioning as second messengers in cellular signaling pathways that transduce stimuli at the cell surface to the nucleus to affect changes in gene expression. Modulation of the gene expression by ROS is achieved by inducing the activation of proteins integral to the upstream signaling pathways and the consequent posttranslational modification of transcription factors. Effects of ROS on Calcium levels Ca2 regulates multiple biological processes including gene transcription. ROS modulate Ca2-induced signaling in the vasculature by affecting Ca2 levels in both endothelial cells and VSMC (Lounsbury et al., 2000). Oxidants cause Ca2 influx/ accumulation into the cytoplasm by several mechanisms: (1) from intercellular spaces and from endoplasmic reticulum/ sarcoplasmic reticulum through inositol 1,4,5-triphosphate (InsP3)-gated channels; (2) by inhibiting Ca2 transport out of the cytoplasm through ATPase pumps; and (3) by inhibiting/ reversing Na/Ca2 exchangers (Ermak and Davies, 2002). A transient rise in Ca2 followed by a sustained elevation was observed in endothelial cells treated with hypoxanthinexanthine oxidase (HX-XO) system (Dreher et al., 1995). Similarly, rapid mobilization of Ca2 was observed in VSMC treated with

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H2O2 and this was mediated via phospholipase C gamma activation (Gonzalez-Pacheco et al., 2002). ROS mediate Ang IIinduced increases in cytosolic Ca2 levels, synthesis and activity of calcineurin, a Ca2-calmodulin-regulated cellular phosphatase and the activity of NF-B in human neutrophils (El Bekay et al., 2003). Together, these data suggest that ROS-mediated Ca2 flux causes the activation of signaling pathways that are potentially involved in atherogenesis. Protein tyrosine kinases and protein tyrosine phosphatases ROS activate protein tyrosine kinases (PTKs) by two mechanisms – oxidation of SH groups on PTKs results in autophosphorylation of tyrosine residues and initial activation or by concomitant inhibition of protein tyrosine phosphatases (PTPs) by oxidation leading to the formation of an intramolecular S-S bridge or a sulphenyl-amide bond (Chiarugi, 2005). PDGF-induced tyrosine phosphorylation of ERK as well as DNA synthesis and migration of VSMC were attenuated by blocking H2O2 production which suggests that ROS play a direct role in tyrosine phosphorylation of signaling proteins (Sundaresan et al., 1995). Ang II-induced VSMC hypertrophy may in part be mediated by the transactivation (tyrosine phosphorylation) of the epidermal growth factor receptor (EGFR) (Ushio-Fukai et al., 2001). The EGFR transactivation is dependent on ROS production and mediated by a cytosolic tyrosine kinase, cSrc. H2O2 activates several tyrosine kinases including the EGFR (Frank et al., 2001), JAK2 (Madamanchi et al., 2001a) and PYK2 (Frank et al., 2003) in VSMC. We have shown that thrombin-induced JAK2 activation is dependent on ROS production in VSMC (Madamanchi et al., 2001b, 2005a). Inhibition of NAD(P)H oxidase blocks Ang II-induced ROS production and JAK2 phosphorylation in VSMC, further supporting the role of ROS in PTK activation (Shaw et al., 2003). ROS mediate endothelin-1-induced cardiac fibroblast proliferation by transiently inhibiting Src homology 2-containing tyrosine phosphatase (SHP-2) and inducing EGFR transactivation (Chen et al., 2006). ROS-induced inactivation of PTPPEST was implicated in endothelial migration. Binding of TNF- receptor-associated factor 4 (TRAF4) to p47phox results in ROS production in the lamellae of motile endothelial cells which facilitates focal complex signaling through targeted inactivation of PTP-PEST (Wu et al., 2005). These data suggest that ROS induce signaling cascades involved in vascular dysfunction and atherosclerosis by regulating the mutually antagonistic activities of PTKs and PTPs. Mitogen-activated protein kinases Mitogen-activated protein kinases (MAPKs) are a family of serine/ threonine kinases that play an important role in the regulation of transcription factors. The MAPK family includes three main subgroups – extracellular signal regulated kinases (ERK1/2), c-jun-N-terminal kinases (JNKs/SAPKs) and the p38 MAPKs – that are redox sensitive. TNF--induced NAD(P)H oxidase activation, ERK1/2 and p38 MAPK stimulation and ICAM-1

expression were inhibited in coronary microvascular endothelial cells from p47phox(-/-) mice (Li et al., 2005). Thrombin acts through p38 MAPK and NF-B to induce ICAM-1 expression in endothelial cells (Rahman et al., 2004). Similarly, TNF-induced VCAM-1 (Pietersma et al., 1997) and MCP-1 (Goebeler et al., 1999) expression were regulated by p38 MAPK signaling cascade. Further evidence for redox-sensitive regulation of MAPKs in HUVEC is obtained from the report that direct exposure of these cells to H2O2 causes significant upregulation of p38 MAPK (Huot et al., 1997). ROS-simulated MAPK activation in VSMC is also implicated in vascular dysfunction and atherosclerosis. H2O2 induced activation of p38 MAPK alone or in response to Ang II treatment and Ang II-induced p38 MAPK activation mediated VSMC hypertrophy (Ushio-Fukai et al., 1998). VSMC proliferation induced by lactosylceramide, a glycospingolipid found at high concentrations in atherosclerotic plaques, is mediated via increased ERK1 activity and c-fos expression, consequent to NAD(P)H oxidase activation (Bhunia et al., 1997). Activation of JNK1 in VSMC by arachidonic acid, a membrane lipid peroxidation product, was dependent on Rac-dependent H2O2 production (Madamanchi et al., 1998, Shin et al., 1999). Further, ROS-mediated stimulation of JNK and p38 MAPK play an important role in Ang II-induced differentiation of adventitial fibroblasts into myofibroblasts, a process associated with vascular remodeling (Shen et al., 2006). Together, these reports indicate that ROS-induced MAPK signaling pathways are the molecular link that connect the pathological stimuli to vascular dysfunction and atherosclerosis.

REGULATION OF TRANSCRIPTION FACTORS BY ROS NF-␬B Eukaryotic transcription factor NF-B plays an important role in the regulation of genes involved in atherosclerosis. In unstimulated cells it exists as a transcriptionally inactive dimer in the cytoplasm bound to an inhibitor protein, IB. The critical regulatory step in the activation of NF-B upon agonist stimulation is the rapid phosphorylation of IB on two serine residues (S32 and S36) which results in its ubiquitination and subsequent degradation by the 26S proteasome (Whiteside and Israel, 1997). Activation of NF-B by diverse agonists was attributed to oxidative stress (Bowie and O’Neill, 2000). This hypothesis was based on four lines of evidence: 1) exogenous addition of H2O2 activates NF-B in some cell lines; 2) agonists that increase ROS levels activate NF-B in some cell types; 3) antioxidants inhibit signaling pathways to NF-B activation; and 4) enzymatic modulation of intracellular ROS levels affects agonist-induced NF-B activation. A growing body of literature suggests that NF-B activation by ROS signaling cascades contributes to the initiation of atherosclerosis (Figure 56.2). An important component of oxLDL, 13-hydroperoxyoctadecadienoic acid (13-HPODE), induces VCAM-1 expression in VSMC by stimulating NF-B

gp91/ Nox1/ Nox4 Rac XO

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657

p22phox

Regulation of Transcription Factors by ROS

p47phox

p67phox/ Noxa1

MPO

12/15-LO

ROS

Uncoupled eNOS

IκB p50

p65

Degradation

IκB

c-Jun c-Fos

NF-κB AP-1 p50 p65

c-Jun c-Fos

VCAM-1  ICAM-1  MCP-1  PAI-1  Inflammation

Figure 56.2 In vascular cells excess ROS produced from stimulation of various enzymes or uncoupling of eNOS activate transcription factors AP-1 and NF-B via regulation of redox-sensitive signaling pathways. Upregulation of redox-sensitive transcription factors results in inflammatory gene expression leading to vascular dysfunction and atherosclerosis.

activity (Natarajan et al., 2001). Similarly, Ang II-induced NF-B activation upregulated VCAM-1 in ROS-dependent manner (Costanzo et al., 2003). Further, activated NF-B localizes to intimal layer in coronary arteries of hypercholesterolemic pigs (Wilson et al., 2000). Consistent with these observations, increases in circulating levels of NF-B were associated with unstable angina pectoris (Ritchie, 1998) and were attributed to elevated level of oxLDL (Cominacini et al., 2005). Several mechanisms have been proposed for ROS-dependent activation of NF-B in vascular cells. OxLDL induces the activation of phosphoinositide 3-kinase (PI-3 K)/Akt pathway

in VSMC (Auge et al., 2002). The PI-3K/Akt pathway activates IKK (IkappaB kinase) which in turn transactivates NF-B (Romashkova and Makarov., 1999). OxLDL induces intracellular ROS generation in endothelial cells by binding to LOX-1 and anti-LOX-1 monoclonal antibody not only reduced ROS generation but also attenuated NF-B activation (Cominacini et al., 2000). AP-1 In endothelial cells, Ang II-induced expression of endothelin-1, a potent vasoconstrictor and VSMC growth inducer, is mediated

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by AP-1 in ROS-dependent manner (Hsu et al., 2004). NAD(P)H oxidase-mediated AP-1 activation was also implicated in VSMC proliferation in response to various agonists (Rao et al., 1999) (Figure 56.2). Cytotoxic action of oxysterols in atherogenesis was attributed to VSMC apoptosis induced by increased expression of Nox4 through the activation of JNK/ AP-1 pathway (Pedruzzi et al., 2004). Induction of proatherogenic plasminogen activator inhibitor 1 (PAI-1) in VSMC in response to high glucose and Ang II was mediated by ROSdependent AP-1 activity (Park et al., 2005). Together, these data suggest that redox-sensitive transcription factors integrate the pathological stimuli in the vasculature to alter gene expression that leads to atherosclerosis.

ROS SIGNALING IN ADVANCED ATHEROSCLEROSIS Matrix metalloproteinase (MMP) activation in advanced atherosclerotic lesions may lead to excessive degradation of extracellular matrix and plaque destabilization. MMPs are upregulated by inflammatory cytokines and growth factors secreted by plaque-resident macrophages and VSMC. In VSMC, ROS mediate the activation of MMPs induced by cytokines and growth factors via the stimulation of NF-B (Bond et al., 2001). Similarly, NAD(P)H oxidase regulates MMP-2 secretion in aortic endothelial cells treated with lysophosphatidylcholine (Inoue et al., 2001a). The recent report that Ang II-induced MMP-2 expression and activity were attenuated in p47phox-null VSMC further supports the notion that ROS are involved in agonistinduced MMP activation in vascular cells (Luchtefeld et al., 2005). ROS stimulate MMPs by either directly oxygenating the thiol residue of cysteine switch domain (Fu et al., 2001) or by activating PI3-K/NF-B pathway (Yoon et al., 2002). VSMC apoptosis contributes to the expansion of necrotic core, thinning of fibrous cap, and eventually to plaque rupture (Bauriedel et al., 1999). OxLDL and high glucose induce apoptosis in VSMC and endothelial cells, respectively, through generation of ROS (Hsieh et al., 2001, Du et al., 1999). High glucose-induced apoptosis in HUVEC is mediated by ROSstimulated activation of JNK and caspase-3 (Ho et al., 2000). Together, these data suggest that ROS-induced signaling cascades not only promote atherosclerosis lesion development, but also participate in plaque rupture.

POLYMORPHISMS IN ROS PRODUCTION GENES AND ATHEROSCLEROSIS Genetic and/or functional alterations in cellular regulators of ROS production have been implicated in atherosclerosis (Madamanchi et al., 2006). For example, allelic variants in the promoter and coding region of CYBA, the gene encoding the human p22phox subunit of NAD(P)H oxidase, are associated with atherosclerosis. The C242T polymorphism located

in exon 4 results in the substitution of tyrosine for histidine at residue 72 and results in reduced O2  production in blood vessels (Guzik et al., 2000). Consistent with this observation, CC genotype is associated with hypertension (Moreno et al., 2006) whereas C242T polymorphism is associated with improved coronary endothelial vasodilatory function and atherosclerosis (Schachinger et al., 2001, Inoue et al., 1998). Similarly, G894T (glu298Asp) polymorphism in exon 7 of eNOS gene may lead to decrease in vascular production of NO and significantly increased risk of ischemic heart disease (Casas et al., 2004). Although polymorphisms in ROS production genes appear to correlate with atherosclerosis in human studies, the importance of specific polymorphisms varies from one population to another. For example, in contrast to the Japanese population (Inoue et al., 1998), the C242T polymorphism in the white population is not associated with the presence and extent of coronary artery disease (Gardemann et al., 1999). The consistent lack of correlation of ROS production gene polymorphisms with atherosclerosis could also be due to multifactorial regulation of oxidative stress and individual polymorphisms may exert their effects by interacting with other polymorphisms or environmental factors (Bracken, 2005). In addition, epigenetic factors such as methylation or histone modification can also alter gene expression.

INHIBITORS OF ROS SIGNALING AND VASCULAR DISEASE Evidence linking ROS signaling with CVD made oxidative stress the target of pharmacological intervention. Despite the experimental, basic biological and epidemiological studies supporting the notion that antioxidants protect against atherosclerosis, use of antioxidants for prevention of cardiovascular events produced mixed outcomes in clinical trials (Madamanchi et al., 2005b; Brown and Crowley, 2005). The recently concluded randomized controlled trials further dampened the enthusiasm for antioxidants in controlling cardiovascular events (Lonn et al., 2005, Lee et al., 2005). The main reasons attributed to the ineffectiveness of antioxidants in preventing CVD include absence of a subset of patient population with oxidative phenotype that might benefit from antioxidant therapy and the use of inappropriate type and dose of antioxidants (Madamanchi et al., 2005b). In contrast, successful use of statins, HMG-CoA reductase inhibitors, in the prevention of CVD was partly attributed to their nonlipidmediated, antioxidative properties (Liao, 2005, Sugiyama et al., 2005). Statins inhibit isoprenylation of membrane-associated GTPases such as Rac1, decrease expression of other subunits of NAD(P)H oxidase and inhibit ROS generation (Wassmann et al., 2001, 2002) (Figure 56.3). However, in meta-analysis of clinical trails, nonlipid effects of statins did not impart additional CVD risk reduction beyond that expected from their lipid lowering effects (Robinson et al., 2005). Peroxisome proliferator-activated receptors (PPARs) are ligandactivated transcription factors that belong to nuclear receptor superfamily and synthetic agonists of PPAR and  are used to

Conclusion

Rac p47phox Statins

HMG-CoA

659

p22phox

gp91/ Nox1/ Nox4

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p67phox/ Noxa1

ROS

HMG-CoA reductase

TZD

Rac

io n

Fibrates

pre

ny la t

Mevalonate

PPARγ

Iso

PPARα

Geranylgeranyl pyrophosphate PPARs

RxR

NAD(P)H oxidase subunits

Cu,ZnSOD

Figure 56.3 Statins attenuate ROS generation in vascular cells by decreasing Rac1 activity and membrane translocation as well as expression of other subunits of NAD(P)H oxidase. Peroxisome proliferator-activated receptors (PPARs) act as transcription factors by forming heterodimeric complex with the retinoid X receptor upon ligand stimulation. Activated PPARs suppress agonist-induced ROS generation by inhibiting the expression of NAD(P)H oxidase subunits and induction of Cu,ZnSOD.

treat pathophysiologic alterations in lipid and glucose metabolism, respectively. Emerging evidence suggests that PPAR agonists, like statins, contribute to additional CVD risk reduction by affecting ROS generation and inflammation. It was shown that PPAR and  agonists inhibit basal and agonist-induced ROS generation by suppressing the expression of NAD(P)H oxidase subunits and by inducing Cu,ZnSOD in human endothelial cells (Inoue et al., 2001b; Hwang et al., 2005) (Figure 56.3). In contrast, it was reported that PPAR activation induces ROS generation through activation of NAD(P)H oxidase in macrophages (Teissier et al., 2004). Intriguingly, ROS generated by this activation interact with LDL to produce metabolites that activate PPAR to limit inflammation as evidenced by the repression of iNOS gene transcription. Significant decrease in plasma TNF-, ICAM-1, MCP-1, and PAI-1 levels observed in response to PPAR treatment in nondiabetic obese patients also supports the anti-inflammatory effect of these agonists (Ghanim et al., 2001). Together, these observations suggest that in addition to normalizing the pathologic lipid and glucose levels, regulation

of ROS generation and inflammatory signaling cascades is necessary for the treatment of CVD.

CONCLUSION In conclusion, a large body of evidence strongly suggests that ROS-induced signaling is intricately involved in cardiovascular dysfunction and atherosclerosis. A major obstacle for pharmacological intervention for CVD is the lack of sensitive and specific “oxidative stress marker panel” that can be used clinically to identify the subset of a population that might benefit from antioxidative therapeutics. New genomic tools such as high-density oligonucleotide arrays for genotyping up to 2,500,000 single nucleotide polymorphisms (SNPs), microarray technology for the analysis of changes in gene expression in conjunction with pathway-based approach to elucidate networks of gene interactions will help in establishing a putative oxidative stress marker panel that will enable determining the disease onset, progression

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and therapeutic response. As a corollary, such a stress marker panel might expedite the discovery of new therapies that will target the molecular interactions that underlie atherosclerosis and other diseases.

ACKNOWLEDGEMENTS This work was supported by NIH grant HL57352 and NIH/ NIA grant P01 AG024282 to M.S. Runge.

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57 Genomics of Myocardial Infarction Carlos A. Hubbard and Eric J. Topol

INTRODUCTION Despite advances in screening and treatment, coronary artery disease (CAD) and myocardial infarction (MI) remain the leading causes of death in the world (Bonow et al., 2002). The global economic implications of MI are staggering, with the 2006 estimated cost of CAD at over $142 billion in the United States alone (Thom et al., 2006). The economic consequences are particularly felt in developing countries which account for 80% of the burden of cardiovascular disease and where resources for prevention and access to treatment are limited (Yusuf et al., 2004). Therefore, a better understanding of the molecular mechanisms that impart risk would have a dramatic economic effect by allowing earlier detection of individuals at highest risk and targeting preventive therapies to individuals most likely to benefit. Advanced atherosclerotic disease can lead to the formation of large lesions that can encroach upon the lumen of the coronary vasculature and result in ischemic injury and even infarction when myocardial oxygen demand is high. However, the catastrophic consequences of CAD are generally related to acute MI due to the rupture of vulnerable plaques and subsequent occlusive thrombosis. Although there is a high prevalence of atherosclerotic coronary disease worldwide, only a fraction of patients with this disease progress to having an acute MI. Although a number of risk factors have been shown to contribute to the development of cardiovascular disease, a family history of CAD remains one of the most powerful independent predictors of risk, which suggests a significant genetic component to the disease. Several linkage studies suggest that the genes responsible for plaque rupture and Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

thrombosis may differ from the genes responsible for atherosclerotic disease progression, which may account for this discrepancy.

PREDISPOSITION Genetic Influence on Risk of MI Data from the Swedish Twin Registry (Lichtenstein et al., 2002) have been used in several studies to establish the moderate but significant effect of genetic factors on the relative risk of death due to CAD. Monozygotic twins have a significantly higher relative risk of death from CAD than dizygotic twins even when outcomes are adjusted for the effects of conventional risk factors associated with CAD (Marenberg et al., 1994; Zdravkovic et al., 2004). Even among siblings who are affected by MI the angiographic location of disease is remarkably concordant (Fischer et al., 2005). The molecular events that lead to plaque rupture and acute MI are likely influenced by a host of intricately related genetic factors involved in a variety of cellular processes such as the inflammatory cascade, extracellular matrix regulation, apoptosis and lipid metabolism. Advances in molecular biology have provided us with an array of tools to begin elucidating the genetic factors underlying these diverse mechanisms. A significant number of genes involved in the inflammatory cascade are clustered on the short arm of chromosome 6p21 in the major histocompatibility complex region. A review of the available literature regarding polymorphisms of genes (TNFalpha, lymphotoxin-alpha, HLA-DR, heat shock protein 70-1, hemochromatosis gene and C4) in this region points out the Copyright © 2009, Elsevier Inc. All rights reserved. 665

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variability in the results to date (Porto et al., 2005). In most cases the strong linkage disequilibrium among these genes appears to point to a complex haplotypic pattern of cardiovascular risk rather than a single genetic variant. Atherosclerotic plaque instability is a central concept in the current theories of the process of plaque rupture. The matrix metalloproteinase (MMP) gene family has been implicated in the development of unstable plaques. The proteins MMP-1, 2, 3, 7, 8, 9, 12, 13 and 14 are all expressed in atherosclerotic plaques (Faber et al., 2002). Upregulation of MMP-9 by a functional polymorphisms in the promoter region of the gene was shown to correlate with an increase in angiographically significant CAD (Zhang et al., 1999). Also expressed in plaques are the tissue inhibitors of metalloproteinases (TIMPs), which bind and inhibit the activity of MMPs. Upregulation of TIMP-1 and TIMP-2 expression in human saphenous vein cultures resulted in a reduction of neointimal thickening (George et al., 1998a, b). The balance between MMPs and TIMPs affects overall plaque stability such that derangements in the regulation of this process may lead to plaque rupture and MI. Genome-wide Scans for Linkage Several genome-wide scans have been performed on diverse ethnic populations in order to identify genes responsible for the development of CAD (Table 57.1). In a Finnish population, loci that correlated with CAD were identified on chromosomes 2q21.1–22 (LOD  3.2) and Xq23-26 (LOD  3.5) (Pajukanta et al., 2000). In a northeastern Indian population, a site on chromosome 16p13-pter (LOD  3.06) was suggestive for CAD (Francke et al., 2001). In an Australian population, a locus on chromosome 2q36-37.3 (LOD  2.63) was found to significantly correlate with the presence of CAD (Harrap et al., 2002). TABLE 57.1

A whole genome scan of 1406 individuals from 513 German families with a history of early CAD or MI identified a locus for MI on chromosome 14q12.3-13.0 (LOD  3.9) (Broeckel et al., 2002). The diversity in loci identified by these studies may be due in part to the phenotypic inconsistencies, diversity of the ancestry of the populations, and inadequate statistical power. The GENECARD study was a genome-wide scan for genetic regions linked to early-onset CAD in over 400 families with a history of early-onset CAD (Hauser et al., 2004). This analysis identified a region on chromosome 3q13 (LOD  3.5) that was associated with early-onset CAD. Other regions of chromosome 3q have been associated with CAD in three earlier studies (Broeckel et al., 2002; Francke et al., 2001; Harrap et al., 2002). The Diabetes Heart Study also identified linkage of this region of chromosome 3q13 with CAD in type 2 diabetics (Bowden et al., 2006). A genome-wide scan for susceptibility genes for MI in a population of European-American ancestry from 428 multiplex families with familial premature CAD and MI identified a significant locus for MI on chromosome 1p34-36 (LOD  11.68) but did not detect a significant locus for CAD (Wang et al., 2004). The chromosome 1p34-36 locus contains the gene for connexin-37 (CX37), which is a gap junction protein expressed in the arterial endothelium and is involved in vascular growth, aging and regeneration after injury. The C allele of the CX37 SNP P319S has previously been shown to be associated with CAD in a Taiwanese population, as well as carotid intima thickening in Swedish men (Boerma et al., 1999; Yeh et al., 2001). The T allele of this same CX37 SNP has also been associated with risk for MI in a case-control study of Japanese men (Yamada et al., 2002). Whether connexin-37 or another gene(s) accounts for the linkage peak remains unresolved.

Genome-wide scans for CAD/MI

Study (ref.), year

Population

No. of families

Mean age (years)

Locus/Loci

Candidate gene

Pajukanta et al. (2000)

Finnish

156

55

2q21, Xq23

NTD

Francke et al. (2001)

Mauritian

99

47

16p13

NTD

Broeckel et al. (2002)

European

513

52

14q32

NTD

Harrap et al. (2002)

Australian

61

62

2q36

NTD

Ozaki et al. (2002)

Japanese

1133

62.5

6p21

LTA

Wang et al. (2004)

American

428

44

1p34-36

CX37

Hauser et al. (2004); Connelly et al. (2006)

European-American

438

56

3q13

GATA2

Helgadottir et al. (2004)

Icelandic

296

13q12-13

ALOX5AP

9p21

NTD

WTCCC (2007); Helgadottir European, Icelandic, American et al. (2007); McPherson et al. (2007); Samani et al. (2007)

CX37  connexin-37; LTA  lymphotoxin alpha; GATA2  GATA2 transcription factor; ALOX5AP  arachidonate 5-lipooxygenase-activating protein; NTD  none to date. Reproduced with permission from Topol (2005) with modification.

Predisposition

Accordingly, studies are targeting the smaller population of individuals with CAD who ultimately suffer MI. Evidence is mounting that some of the genes responsible for progression to MI may differ from the genes responsible for CAD. Although multigenetic interactions are likely responsible for the majority of cardiovascular risk, some exceptions have more recently been described with findings of significant risk being directly attributable to variants in a single gene. The development of the International Haplotype Map is allowing researchers to utilize single nucleotide polymorphisms (SNP) to hone in on the areas of interest identified by linkage studies and identify specific genes that are associated with CAD and MI (Table 57.2).

TABLE 57.2

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Association Studies The GENEQUEST study was the first high-throughput SNP study of individuals with premature MI (Topol et al., 2001). This study examined 72 SNPs of 62 vascular genes in 398 families identified three members of the thrombospondin (TSP) gene family of matricellular proteins, TSP-1, -2 and -4, which were associated with familial premature MI. The findings of significant association of TSP-2 and TSP-4 with premature MI have subsequently been replicated in other studies and by different investigators (Boekholdt et al., 2002; McCarthy et al., 2004; Wessel et al., 2004). The TSP-1 SNP results in a substitution of serine for asparagine at residue 700 of the protein and although rare (1% frequency),

Specific genes associated with MI risk

Study (ref.), year

Gene

SNP

Estimated Risk

Topol et al. (2001)

TSP-1 TSP-2 TSP-4

N700S 3’UTR region A387P

OR 11.9, p  0.041 OR 0.31, p  0.0018 OR 1.89, p  0.002

Wang et al. (2003)

MEF2A

D15S120

OR/RR/HR not available

Yamada et al. (2002)

CX37 PAI-1 Stromelysin-1

C1019T 4G-668/5G 5A-1171/6A

OR 1.4, p  0.002 OR 1.6, p  0.001 OR 4.7, p  0.001

Ozaki et al. (2002)

LTA

T26N A252G

OR 1.78, p  0.001 OR 1.69, p  0.001

Ozaki et al. (2004)

Galectin-2

C3279T

OR 1.57, p  0.001

Helgadottir et al. (2004)

ALOX5AP

HapA

RR 1.8, p  0.001

Shiffman et al. (2005)

ROS1 Palladin TAS2R50 OR13G1

rs619203 rs12510359 rs1376251 rs1151640

OR 1.23, p  0.012 OR 1.25, p  0.0028 OR 1.28, p  0.0018 OR 1.19, p  0.013

Helgadottir et al. (2006)

LTA4H

HapKa

European-Americans: RR 1.31, p  0.037 African-Americans: RR 4.39, p  0.008

Ozaki et al. (2006)

PSMA6

rs1048990a

OR 1.36, p  0.002

Cohen et al. (2006)

PCSK9

Nonsense variant: Y142X C679X Missense variant: R46L

HR 0.11, p  0.03 HR 0.5, p  0.003

Connelly et al. (2006)

GATA2

rs2713604a rs3803a

OR 1.5, p  0.011 OR 0.7, p  0.028

Shiffman et al. (2006)

VAMP8 HNRPUL1

rs1010 rs11881940

OR 1.75, p  0.025 OR 1.92, p  0.0043

a

TSP  thrombospondin; MEF2A  myocyte-enhancing factor 2A; CX37  connexin-37; PAI-1  plasminogen activator inhibitor type-1; LTA  lymphotoxin alpha; ALOX5AP  arachidonate 5-lipooxygenase-activating protein; LTA4H  leukotriene A4 hydrolase; PSMA6  proteasome subunit type 6; PCSK9  proprotein convertase subtilisin/ kexin type 9 serine protease; HapA  4 SNP HapA haplotype; HapK  10 SNP HapK haplotype; GATA2  GATA2 transcription factor; VAMP8  vesicle-associated membrane protein 8; HNRPUL1  heterogeneous nuclear ribonucleoprotein U-like 1; OR  odds ratio; RR  relative risk; HR  hazard ratio. a Indicates validated in more than one cohort.

CHAPTER 57

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(b)

50 μm

1

60

50

40

1

2

70

Light transmission

30

20

(a)

Genomics of Myocardial Infarction

10

668

2

Figure 57.1 TSP-1 variants and platelet aggregation: (a) Platelet aggregation in the absence (curve 1) or presence (curve 2) of TSP-1 variant Asn-700 induced by 20 M ADP and monitored by decreased light transmission. (b) Aggregates of ADP-stimulated platelets in the absence (panel 1) or presence (panel 2) of purified rTSP-1 at 100 M. (Reproduced with permission from Narizhneva et al., 2004.) (A387) TSP-4

(A387) TSP-4

Figure 57.2 Effect of TSP-4 variants on adhesion of HUVECs. Cells were plated on plastic preincubated with purified TSP-4 variants (50,000 cells/well of 24-well plate) and photographed after 24 h of culture. The presence of the P387 variant interferes with HUVEC cell binding and proliferation. (Reproduced with permission from Stenina et al., 2003.)

homozygous individuals had the highest association with MI (OR  8.66). The TSP-1 SNP has been shown functionally to increase surface expression and platelet aggregation, which may account for its high association with MI (Narizhneva et al., 2004) (Figure 57.1). The TSP-4 SNP is more common with the minor allele present in over 30% of individuals and confers a near twofold risk of MI (OR  1.89). The TSP-4 SNP results in a substitution of proline for alanine, which has been correlated with gain-of-function, proatherogenic effects by interfering with endothelial cell adhesion and proliferation (Stenina et al., 2003) (Figure 57.2), along with activation of neutrophils (Pluskota et al., 2005). Another gene that has been identified as a heritable risk for MI is the gene for the transcription factor myocyte-enhancing factor 2A (MEF2A) (Wang et al., 2003). In a large pedigree study of a family with a history of premature CAD and MI, a genome-wide scan identified a locus on chromosome 15q26 that was highly associated with risk (Figure 57.3). An analysis of the candidate genes in this area revealed a 21-bp deletion

mutation in exon 11 of the MEF2A gene that imparts an autosomal dominant inheritable risk of MI. This deletion was shown to prevent localization of MEF2A in the nucleus and disrupts transcription (Figure 57.4). A subsequent study of over 400 nonrelated MI cases and controls found MEF2A nonsynonymous point mutations in exon 7 in MI cases but not controls (Bhagavatula et al., 2004). These point mutations also appear to limit the transcription activity of MEF2A and provide further evidence of the importance of this gene in the development of CAD and risk for MI. Recently, further analysis of the GENECARD data has identified two SNPs in the 3 untranslated region of the transcription factor GATA2 gene as being significantly associated with early-onset CAD (Connelly et al., 2006). GATA2 is expressed in hematopoietic stem cells but also in locations susceptible to the development of atherosclerosis such as aortic endothelial cells and smooth muscle cells. The association of the GATA2 SNPs with early-onset CAD was identified first in the familial early-onset CAD cohort of GENECARD

Predisposition

l.1

ll.1 

ll.2 ll.3

■

669

l.2

ll.4 ll.5  

ll.6 

ll.7

ll.8 ll.9 

ll.10 ll.11 ll.12 ll.13    

lll.6 lll.1 lll.2 lll.3 lll.4    

lll.5 

lll.7 

lll.8 

lll.9 

lll.10 

Figure 57.3 MEF2A intragenic deletion cosegregates with CAD in kindred QW1576. The pedigree shows genetic status: indicates presence of the 21-bp deletion of MEF2A (heterozygous); indicates the absence of the deletion. Individuals with CAD are indicated by solid squares (males) or circles (females). Individuals under 50 years of age without known CAD are shown in light gray. The proband is indicated by an arrow and deceased individuals are indicated by a slash (/). (Reproduced with permission from Wang et al., 2003.)

(a)

HUVEC

(b)

HVSMC

Figure 57.4 Functional characterization of wild type and 21 bp MEF2A proteins by immunoflourescence demonstrating the defect in nuclear localization of the mutant MEF2A in (a) human umbilical vascular endothelial cells (HUVEC); and (b) human aortic smooth muscle cells (HVSMC). Cells were transfected with expression constructs for wild type and mutant MEF2A proteins tagged with a FLAG epitope (green signal). Cell nucleus stained with 4 6-diamidino-2-phenylindole (blue signal). (Reproduced with permission from Wang et al., 2003.)

and was subsequently validated in a separate nonfamilial young affected CAD case-control cohort. The functional significance of the GATA2 SNPs is unknown, but these findings suggest that GATA2 regulated genes may be significantly involved in earlyonset CAD and its progression.

A number of inflammation-mediating molecules, such as the cytokine lymphotoxin-alpha (LTA), have been implicated in the process of plaque rupture. A massive genome-wide SNP study of Japanese patients analyzed over 65,000 SNPs in 13,738 genes and found 2 functional SNPs in the LTA gene that were

670

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Genomics of Myocardial Infarction

highly associated with risk of MI (Ozaki et al., 2002) (Figure 57.5). These SNPs were shown to increase expression of vascular cell adhesion molecule and E-selectin which creates a proinflammatory environment. The PROCARDIS Consortium later confirmed the association of a functional allele of LTA (N26 [804A]) with CAD in white Europeans (2004). LTA protein binds to galectin-2, a member of the galactosebinding lectin family, and this binding is critical for the extracellular expression of LTA (Figure 57.6). Recently a SNP in LGALS2 encoding galectin-2 has been shown to be significantly associated

(a)

(b) 1.20

*

25 Relative mRNA expression

Relative luciferase activity

* 0.96 0.72 0.48 0.24

20 15 10 5 0

0.00 10G–252A

10A–252G

10A–252A

None

26Thr

26Asn

Figure 57.5 Functional SNPs of LTA gene. (a) Transcriptional regulatory activity is affected by the SNP in Intron 1 of LTA (252A → G) as measured by relative luciferase activity (*p  0.01); (b) Induction of adhesion molecules such as VCAM1 is differentially affected by 26Asn-LTA and 26Thr-LTA in HCASMC cultures following treatment with medium only (white) or with 20 ng/ml of 26Thr-LTA (gray) or 26Asn-LTA (black) for 4 h (*p  0.01). (Reproduced with permission from Ozaki et al., 2002.)

Galectin-2

with susceptibility to MI by affecting the transcriptional level of galectin-2 leading to altered secretion of LTA (Ozaki et al., 2004). LTA and galectin-2 were shown to be expressed in smooth muscle cells and macrophages in the intima of atherosclerotic plaques but not in normal medial smooth muscle cells. The TT allele of galectin-2 was shown to confer protection while the CC allele confers susceptibility to MI (Ozaki et al., 2004). A key regulator of the inflammatory cascade, the 26S ubiquitin-proteasome system, has also recently been implicated in susceptibility to MI, further emphasizing the role of inflammation in the pathogenesis of MI. A SNP (exon 1-8C/G) located in the 5 untranslated region of exon 1 of the proteasome subunit  type 6 (PSMA6) gene was identified in a whole-genome case-control association study of Japanese patients as being significantly associated with MI (OR  1.36) (Ozaki et al., 2006). The exon 1 -8C/G SNP was shown to alter the transcription levels of the PSMA6 gene both in vitro and in vivo. The PSMA6 gene codes for an  subunit of the 20S proteasome, which is the core particle for the 26S ubiquitin-proteasome system that degrades IB protein. IB inhibits the activation of the nuclear factor B (NF-B) which regulates the expression of many genes in the inflammatory pathway. A recent genome-wide association study not limited to candidate genes examined 11,053 SNPs in 6891 genes and found four gene variants that were associated with MI. These gene variants encode a tyrosine kinase (ROS1 [OR 1.75]), the cytoskeletal protein paladin (KIAA0992 [OR 1.40]), and two G protein–coupled receptors (TAS2R50 [OR 1.58] and OR13G1 [OR 1.40]). At this point, the underlying biological mechanism for these associations is unknown and warrants further investigation, which could yield new therapeutic strategies (Shiffman et al., 2005).The same group published a genome-wide

LTA

Merge

Figure 57.6 LTA binds to galectin-2. Colocalization of endogenous LTA with galectin-2 in U937 cells (top row) with enlarged images of representative cells in the upper panels (bottom row). (Reproduced with permission from Ozaki et al., 2004.)

Predisposition

association study for early-onset MI and identified a platelet degranulation gene – VAMP8 – as having an odds ratio of 1.75 in 3 separate cohorts (Shiffman et al., 2006). Adding to the complexity of the molecular landscape of CAD and MI is the finding of an untranslatable but presumably functional RNA transcript that has been associated with MI in a large case-control genome-wide association study using 52,608 SNPs in a cohort of Japanese patients (Ishii et al., 2006). This functional RNA has been designated the myocardial infarction associated transcript (MIAT), and one SNP in exon 5 was shown to affect transcriptional levels of the gene. The mechanism by which this RNA transcript contributes to susceptibility to MI remains to be identified but examples of other noncoding functional RNA molecules have been demonstrated to influence important transcriptional processes in other models of disease. The Leukotriene Pathway More recently, variants of the arachidonate 5-lipoxygenaseactivating protein (ALOX5AP) gene have been shown to confer a significant risk for MI. ALOX5AP plays a key role in the inflammatory leukotriene biosynthetic pathway. A genome-wide scan of 296 Icelandic families identified a four-SNP haplotype, called HapA, in the ALOX5AP gene that confers a nearly twofold risk of MI and stroke (Helgadottir et al., 2004). This association was further verified in a cohort of British patients with another haplotype, called HapB, of the ALOX5AP gene which also conferred a nearly twofold risk of MI. Individuals with these at-risk haplotypes were shown to have a significantly increased neutrophil production of leukotriene B4 (LTB4) supporting the role of this gene in the inflammatory process implicated in atherosclerosis and plaque rupture. Another gene in the same leukotriene biosynthetic pathway as ALOX5AP has recently been implicated in risk of MI. A study of 1553 Icelandic individuals with history of MI demonstrated that a haplotype, called HapK, of the leukotriene A4 hydrolase (LTA4 H) gene was associated with a relative risk of 1.45 (p  0.035) for MI and additional cardiovascular disease (Helgadottir et al., 2006). This association was also confirmed in three separate cohorts from the United States population. The HapK haplotype was also shown to correlate with increased neutrophil production of LTB4. Of further interest, in the United States cohorts tested the relative risk of MI for AfricanAmerican carriers of the HapK haplotype was much higher (RR  3.57) although the haplotype was less frequent in this population. This suggests that the LTA4 H variant may interact with other gene variants more common in African-Americans, although to date these other gene variants have yet to be determined. PCSK9 – A Key Player in LDL Cholesterol Receptor Modulation Genetic variations in the proprotein convertase subtilisin/kexin type 9 serine protease (PCSK9) gene have been shown to reduce

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671

LDL cholesterol levels. The results of a massive 15 year longitudinal study of nonsense and missense genetic variants of PCSK9 were recently published (Cohen et al., 2006). The presence of a nonsense mutation (2.6% of the African-American cohort) was associated with a 28% reduction in mean LDL cholesterol and an 88% reduction in risk of cardiovascular events (p  0.008; HR  0.11) (Figure 57.7). The presence of a missense variation of PCSK9 was associated with a relatively lower 15% reduction in LDL cholesterol and a 47% reduction in cardiovascular risk (p  0.003; HR  0.50). Nonsense mutations of PCSK9 were rare (6 of 9537 subjects) in the Caucasian cohort. This study elegantly demonstrates two important concepts. First, ethnic background can be used as a crude marker to identify patients with underlying genetic susceptibilities. Secondly, the benefits conveyed by modification of known conventional risk factors such as LDL cholesterol levels are likely determined by underlying genetic mechanisms and inheritable susceptibilities. Chromosome 9p21 Identified as a Hotspot for Risk of MI One of the most exciting developments in the field of cardiovascular genomics is the recent discovery of a strong association of a region of chromosome 9p21 with MI. What makes this discovery so important is that it has been replicated in four separate genome-wide association studies (GWAS) spanning multinational cohorts including thousands of cases and controls (Helgadottir et al., 2007; McPherson et al., 2007; Samani et al., 2007; WTCCC, 2007). These studies have shown that over 20% of Caucasians are homozygous for this genetic variation and that it carries a greater than 20–30% risk of MI. The chromosome 9p21 region of interest contains the genes for two cyclin-dependent kinase inhibitors, CDKN2A (p16INK4a) and CDKN2B (p15INK4b). These genes are involved in cell cycle regulation, and CDKN2B expression is induced by TGF-, which may play a role in the pathogenesis of atherosclerosis. Another gene in this region is methylthioadenosine phosphorylase, which is involved in adenine and methionine salvage. Further adding to the excitement surrounding this area is the additional finding of an association of this 9p21 region with type 2 diabetes mellitus, which is a strong conventional risk factor for CAD and MI and suggests a common mechanism of risk for these diseases (Saxena et al., 2007; Scott et al., 2007; Zeggini et al., 2007). However the SNPs which have been associated with CAD and MI are not within these genes, and to date it is not clear what the mechanism is by which these genetic variations might influence susceptibility to CAD and MI. It is important to note that in each GWAS there were findings of loci of interest that were not replicated in the other studies. This demonstrates the need for cautious interpretation of the findings from a single GWAS and the critical need for replication of findings by independent validation studies. The elucidation of the association between chromosome 9p21 and MI is an elegant example of the degree of scrutiny required for the investigation of other GWAS findings.

672

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Genomics of Myocardial Infarction

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(a)

(b) No nonsense mutation (n = 3278)

50th percentile

30 12

Frequency (%)

10

0 0

50

100

150

200

250

300

PCSK9142x or PCSK9 679x (n  85) 30

Coronary heart disease (%)

20

8

0 20

p  0.008

4

No

Yes

PCSK9142x or PCSK9679x

10

0 0 50 100 150 200 250 300 Plasma LDL cholesterol in black subjects (mg/dl)

Figure 57.7 Distribution of plasma LDL cholesterol levels (a) and the incidence of coronary heart disease (b) among AfricanAmerican subjects, according to the presence or absence of a PCSK9142X or PCSK9679X allele. (Reproduced with permission from Cohen et al., 2006.)

Conventional Risk Factors and Environmental Influences Although genetic factors may play a significant role in the development of CAD and MI, the fact is that 80–90% of patients with CAD have conventional risk factors such as hypertension, hyperlipidemia, diabetes mellitus or cigarette smoking (Khot et al., 2003). Environmental influences such as diet have also been shown to play an important role in the development of CAD as well. Results of the Nurse’s Health Study demonstrated trans-fat intake was associated with increased risk of CAD, whereas there was an inverse association with polyunsaturated fat intake (Oh et al., 2005). What remains to be determined is to what extent the effect of these conventional risk factors and environmental influences is dependent upon underlying genetic interactions. It has been shown that increased dietary arachidonic acid significantly enhanced atherogenesis in carriers of variant 5-lipoxygenase genotypes, whereas increased dietary intake of n-3 fatty acids appeared to reduce atherogenesis in this population (Dwyer et al., 2004). As mentioned previously, carriers of nonsense and missense mutation of the PCSK9 gene show significantly reduced levels of LDL cholesterol and enjoy a protective benefit against cardiovascular risk (Cohen et al., 2006). This suggests that susceptibility to conventional risk factors may ultimately depend upon complex genetic and environmental interactions.

SCREENING STRATEGIES Currently, screening for cardiovascular risk is based on various models that incorporate gender, age, ethnicity and behavioral and disease risk factors. Obviously, risk associated with gender and ethnicity imply a genetic component, but it is likely that the susceptibility to behavioral and disease risk factors is also based on genetic heritability. Ongoing whole-genome SNP association studies will lay the groundwork for more extensive and definitive identification of SNPs and haplotypes associated with MI. These haplotypes will likely allow us to identify patients at increased risk for CAD and MI before the onset of disease and regardless of exposure to conventional risk factors. The identification of genetic susceptibility traits will allow for more accurate risk stratification of patients than is achievable with current clinical models. The presence of a particular trait may carry such significant risk as to directly identify patients at risk, as is the case for BRCA1 gene variant carriers who are at high risk for breast and ovarian cancer. More commonly a genetic trait may be integrated into the risk assessment calculation of more conventional models to improve the accuracy of the risk prediction. Early identification of patients at risk can prompt risk factor modification and early genetically tailored pharmacological interventions, which could greatly decrease the morbidity and

Pharmacogenomics of MI

mortality associated with these diseases. Future models of cardiovascular risk will likely be based on these complex multigenetic and environmental interactions. However, the technical difficulties and financial burden associated with genetic analysis must be overcome before these traits can be economically and efficiently incorporated into every day clinical practice. Although genetic variation can result in the presence or absence of a particular protein and thereby result in a disease state, it is more common that these variants result in differential expression of a particular protein. For example, a SNP in LGALS2 has been associated with increased risk of MI (Ozaki et al., 2004).The LGALS2 SNP encodes for the protein galectin-2 and results in alterations in the transcriptional levels of galectin-2 which binds to LTA. This could result in altered levels of LTA secretion and intravascular inflammation which is thought to be associated with CAD and MI. The expression levels of a number of other proteins, such as C-reactive protein, have also been shown to correlate with risk of MI and it is certain that these expression levels are similarly genetically determined. Findings such as these may lead to the development of protein-based assays, which are faster and more economical than DNA analysis and are more amenable to every day clinical use.

DIAGNOSIS OF ACUTE MI The diagnosis of acute MI is primarily based on the clinical symptoms and electrocardiographic changes at the time of presentation. Adjunctive noninvasive imaging modalities such as transthoracic echocardiography and the emerging use of cardiac multislice-CT can aid in the diagnosis of MI but also delay the initiation of therapy. The development of specific serum biomarkers for acute myocardial injury has increased our appreciation and detection of non-ST elevation MI. However, elevated levels of these biomarkers are not detectable for up to 6 h after the onset of injury and also delay the initiation of time-dependent intervention. A better understanding of the cascade of molecular events at the time of plaque rupture and thrombosis may yield more specific biomarkers that can be detected immediately after the onset of injury. This could decrease morbidity and mortality due to infarction by decreasing the delay to intervention.

PROGNOSTIC IMPLICATIONS OF MI Heart Failure and Ventricular Remodeling Greater than 20% of patients treated for initial MI will develop acute heart failure and the incidence increases with recurrent MI. Post-MI heart failure conveys a three- to sixfold increase in risk of death and is the result of the complex processes of myocardial necrosis and ventricular remodeling. Studies have shown that female gender as well as complex phenotypic traits, such as hypertension and diabetes, is associated with increased risk

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673

of post-MI heart failure, which suggests a genetic component to the process. The incidence of post-MI heart failure has been shown to decrease dramatically 8 days after MI (Thomas et al., 2005). Identification of the early molecular mechanisms responsible for the development of post-MI heart failure may yield targets for future therapies. Risk for Repeat Myocardial Infarction A recent analysis of atherosclerosis-related gene SNPs in 1586 Japanese patients from the Osaka Acute Coronary Insufficiency Study revealed that G allele carriers at the position 252 of the LTA gene were independently associated with an increased risk of death after acute MI compared to noncarriers who also suffered acute MI as well as recurrent MI when (hazard ratio  2.46; 95% CI  1.24–4.86) (Mizuno et al., 2005). This study would suggest that although risk for MI may be a function of multiple genetic polymorphisms, the presence of a select group of polymorphisms may confer an even higher risk for carriers. A better understanding of the functional influences of these polymorphisms as they are discovered will allow for more accurate risk stratification. Response to Post-MI Medical Therapy Administration of -blocker therapy post-MI has been shown to reduce infarct size and mortality, however all patients may not derive equivalent benefit. A recent prospective study of response to -blocker therapy in patients with two common polymorphisms of the  2-adrenergic receptor demonstrated a significant difference in 3-year survival which was genotype specific (Lanfear et al., 2005). Similar results have previously been shown for risk of incident coronary events (Heckbert et al., 2003). Genetic markers such as these may allow us to discriminate between treatment responders and nonresponders and should allow for individualized therapies as well as better risk stratification.

PHARMACOGENOMICS OF MI HMG-CoA Reductase Inhibitors HMG-CoA reductase inhibitors, commonly referred to as “statins”, reliably lower atherogenic lipoproteins and have been shown to decrease the incidence of MI in numerous studies (Davidson, 2005; Schwartz et al., 2005). These drugs have also been shown to have significant anti-inflammatory effect and lower serum levels of inflammatory markers such as C-reactive protein (Ridker et al., 1999). These effects appear to correlate with a reduction in the rate of atherosclerotic plaque progression and possibly even plaque regression (Ridker et al., 2005; Nissen et al., 2005; Nissen et al., 2006). Studies have also suggested an effect of statin therapy on ventricular remodeling post-MI. Simvastatin was shown to reduce the incidence of heart failure in patients with CAD in a retrospective analysis of the Scandinavian Simvastatin Survival Study (4S) trial (Kjekshus et al., 1997). However, this finding

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was not replicated with the use of pravastatin in the CARE study (Lewis et al., 2003). Although overall significant benefit has been demonstrated with the use of statin therapy, considerable interindividual variation exists in the response to statin therapy. It is likely that genetic factors are responsible for this variation in individual response. Multiple genes have been implicated as potential modulators of statin response but few of the findings have been replicated (Kajinami et al., 2004). Identification of genetic haplotypes that correlate with increased response to statin therapy will allow responsive individuals to be targeted for therapy while nonresponders may avoid the side effects of a nonbeneficial and costly therapy. Antiplatelet Therapies Aspirin Resistance Aspirin irreversibly binds cyclooxygenase, which plays a key role in the process of platelet aggregation. The benefit of aspirin antiplatelet therapy for the reduction of all-cause mortality, nonfatal MI and recurrent MI has been well established (Antiplatelet Trialists Collaboration, 2002). However, not all patients appear to receive equal benefit from aspirin therapy and have been classified as aspirin resistant. Several mechanisms of aspirin resistance have been proposed but one of the most studied mechanisms involves polymorphisms of the IIIa subunit of the platelet glycoprotein IIb–IIIa receptor, which is the final common pathway in platelet aggregation. The PLA2 polymorphism of the glycoprotein IIb–IIIa receptor is present in over 30% of patients with CAD. Heterozygous carriers of the PLA2 allele have also been shown to be aspirin resistant (Cooke et al., 2006; Hanjis et al., 2006). This polymorphism may represent a genetic component for aspirin resistance, and there are likely others that may affect other steps in the platelet activation cascade. Recently a significant association with a synonymous SNP in the platelet gene for the adenosine 5-diphosphate (ADP) receptor P2Y1 was found in an aspirin-resistant population (Jefferson et al., 2005). Identification of patients with gene-mediated aspirin resistance could further reduce the incidence of MI by the use of different antiplatelet therapies in this population. Clopidogrel Resistance One of the major advances in the treatment of acute MI has been the use of the ADP receptor antagonist clopidogrel. ADP is released by activated platelets and by binding to the platelet P2X1, P2Y1 and P2Y12 receptors greatly accelerates platelet aggregation. Clopidogrel irreversibly binds the P2Y12 receptor and blunts ADP-mediated platelet aggregation. However, just as is the case with aspirin therapy, there is significant interpatient variability in the response to clopidogrel. One suggested mechanism for this difference in response is polymorphisms of the P2Y12 receptor gene. Although the known mutations of this gene have been shown to result in congenital bleeding disorders, one study has identified a polymorphism in healthy subjects that was associated with increased platelet response to ADP stimulation as well

as peripheral artery disease (Fontana et al., 2003). Also, clopidogrel is a prodrug that requires activation by the hepatic cytochrome P3A4 and cytochrome P3A5 enzymes. Numerous SNPs have been identified in these genes, which could affect clopidogrel metabolism and lead to decreased activation. Polymorphisms of the P2Y12 receptor and cytochrome P3A genes could account for some of the observed resistance to clopidogrel (Nguyen et al., 2005).

NOVEL AND EMERGING THERAPIES Drug-Eluting Intracoronary Stents Currently there is some debate regarding the use of drugeluting stents for the treatment of acute MI (Saia et al., 2003; Valgimigli et al., 2005); however numerous studies have shown a significant reduction in the rate of in-stent restenosis with the introduction of this technology (Bavry et al., 2005; Hill et al., 2004). These stents are coated with immunosuppressant or cytotoxic agents that reduce the incidence of restenosis. Currently, the two most commonly used agents in the United States are sirolimus and paclitaxel, but a variety of other agents are currently in development. Sirolimus is a cytostatic macrolide antibiotic which inhibits smooth muscle proliferation by halting cell cycle progression. Smooth muscle proliferation is a key component of the neointimal proliferation process which leads to restenosis. Paclitaxel is a cytotoxic agent which disrupts microtubule assembly and kills proliferating or dividing cells. In addition, these drugs may also inhibit apoptosis of the existing smooth muscle cells and contribute to the stabilization of vulnerable plaque (Faber et al., 2002; Smith et al., 2003). As more is discovered about the molecular mechanisms that promote restenosis, novel therapeutic agents may be developed to more specifically inhibit neointimal proliferation while preserving the cellular mechanisms of healing and plaque stabilization. Leukotriene Inhibitors Variants in the ALOX5AP gene and the LTA4 H gene have been shown to be associated with risk of MI (Helgadottir et al., 2004, 2006). Recently, in a randomized trial of 191 patients at risk for MI with ALOX5AP or LTA4 H gene “gain-of-function” haplotypes, an ALOX5AP inhibitor was studied for its effects on common biomarkers associated with MI risk. A 4-week trial of the ALOX5AP inhibitor resulted in a significant and dose-dependent reduction in serum C-reactive protein and amyloid A. Also, the use of the ALOX5AP inhibitor attenuated the generation of leukotriene B4 and myeloperoxidase by activated neutrophils and the urinary metabolite of LTB4. However, there was a significant 9% increase in serum plasma Lp-PLA2 levels with a corresponding 8% increase in LDL cholesterol noted at the highest dose of the ALOX5AP inhibitor. Currently, a clinical trial is underway to determine if the use of this ALOX5AP inhibitor will affect the overall risk of MI in this at-risk patient

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population (Hakonarson et al., 2005). A study of dietary modification in over 400 patients with ALOX5AP gene promoter polymorphisms demonstrated that increased intake of n-3 fatty acids appeared to reduce atherogenesis in this population (Dwyer et al., 2004). This suggests that in addition to gene targeted pharmacological treatment, gene targeted behavioral modification strategies may yield further reduction in risk of MI.

increase in LVEF compared to placebo (p  0.36) (Janssens et al., 2006). Although stem cell therapy for acute MI is in its infancy, these preliminary studies suggest significant future potential and a better understanding of the molecular mechanisms involved in targeted myocardial regeneration may one day revolutionize the treatment of acute MI.

Stem Cell Therapy The mobilization of stem cells to repair myocardial damage and restore function after MI remains an emerging field with a history of variable results. One approach has been the administration of granulocyte-colony stimulating factor (G-CSF) to patients in an attempt to recruit stem cells to sites of myocardial injury. Several preliminary studies have failed to demonstrate benefit from this approach. The Stem Cells in MI trial failed to demonstrate a reduction in infarct size or improvement in ventricular function with the subcutaneous administration of G-CSF to patients post-MI (Ripa et al., 2006). In another randomized, placebo-controlled trial of 114 acute MI patients who underwent successful percutaneous revascularization, GCSF was administered subcutaneously daily for 5 days. Again, no significant difference was seen on size of infarct, left ventricular function or coronary restenosis between the treatment group and controls at 4–6 months after infarction (Zohlnhofer et al., 2006). Results of trials utilizing intracoronary injection of stem cells in acute MI have also been variable. The TOPCARE-AMI trial demonstrated that 59 acute myocardial infarction (AMI) patients who received infusions of progenitor cells into the infarct artery showed significant improvement in cardiac function and reduced infarct size after 4 and 12 months (Schachinger et al., 2004). In the BOOST clinical trial, 60 acute MI patients received successful percutaneous revascularization and were randomly assigned to optimum medical treatment or optimum medical treatment and intracoronary infusion of autologous bone-marrow stem cells (BMSC) 4.8 days (sd = 1.3) after percutaneous revascularization. After 6 months, the mean global LV ejection fraction (LVEF) was shown to be significantly increased in the BMSC group compared to controls (p  0.0026) (Wollert et al., 2004). An analysis of the BOOST trial patients at 18 months however demonstrated no significant difference in LVEF between groups (p  0.27) (Meyer et al., 2006). Likewise the results of the ASTAMI trial, where 100 patients with anterior wall MI were randomized after percutaneous revascularization to usual care versus autologous BMSC intracoronary infusion showed no significant difference in LV function at 6 months (Lunde et al., 2005). In another small but randomized, double-blind clinical trial of 67 patients, the group receiving intracoronary infusion of autologous BMSC 1 day after successful percutaneous revascularization for acute MI demonstrated a significant reduction in infarct size compared to placebo (p  0.036). Despite this reduction in infarct size, the BMSC infusion did not result in a significant

CONCLUSION Numerous chromosomal loci have been identified that appear to contain genes that are associated with the development of CAD and MI. Unlike CAD, which is nearly endemic in our society, MI appears to be a more restrictive phenotype, and its heritability has been demonstrated by several studies that have identified specific genes that confer risk of MI in carriers. Although these genes do not account for the entirety of MI, they demonstrate the importance of genetic factors and help us to begin to develop better models of risk assessment. As the number of candidate genes and chromosomal loci linked to CAD and MI continues to grow, we are faced with the increasing dilemma that, with the exception of chromosome 9p21, the majority of these interesting findings have not been independently replicated. The problem revolves around the inconsistency in the definitions of the phenotypes for CAD, MI and “normal” controls, as well as the heterogeneity of ancestry and population stratification used in each study. The phenotype of CAD can be especially troublesome given that opinions differ as to what is a pathological amount of CAD for a specific age or gender and that significant occult disease may exist in asymptomatic patients. Even the more restrictive phenotype of MI can prove problematic as there are important differences between the processes that lead to ST segment elevation MI and non-ST segment elevation MI. Also, only a small fraction of the patients with the CAD phenotype will go on to experience MI, and the genes responsible may not be common to both processes. Defining the phenotype of the “normal” control is also challenging because the problem of occult disease necessitates coronary visualization by selective angiography or multidetector CT to confirm the absence of significant CAD. Additionally, due to the generally slow progression of the disease, some younger patients classified as unaffected controls may ultimately develop significant disease at a later age. Standardized phenotypic definitions for CAD, MI and controls have been proposed and if adopted for future studies could better harness global research efforts by facilitating the replication of findings across cohorts of differing ancestries (Luo et al., 2007). Early identification of individuals who carry genes that increase MI susceptibility will allow for early premorbid interventions and hopefully significantly reduce the morbidity and mortality associated with this disease. In addition to risk assessment, identification of genes that influence therapeutic response will allow for individualized drug therapies and behavioral modifications while preventing costly and possibly harmful side effects in nonresponsive individuals. And as the complex molecular

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mechanisms of MI are being elucidated, novel targets for new therapies are being identified. With a clearer understanding of the complex genetic and environmental interactions that lead to the

catastrophic consequences of MI it is possible that in the coming decade we will see a drastic reduction in the overwhelming burden of this disease on our society.

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A functional SNP in PSMA6 confers risk of myocardial infarction in the Japanese population. Nat Genet 38(8), 921–925. Pajukanta, P., Cargill, M.,Viitanen, L., Nuotio, I., Kareinen, A., Perola, M., Terwilliger, J.D., Kempas, E., Daly, M., Lilja, H. et al. (2000). Two loci on chromosomes 2 and X for premature coronary heart disease identified in early- and late-settlement populations of Finland. Am J Hum Genet 67(6), 1481–1493. Pluskota, E., Stenina, O.I., Krukovets, I., Szpak, D., Topol, E.J. and Plow, E.F. (2005). Mechanism and effect of thrombospondin-4 polymorphisms on neutrophil function. Blood 106(12), 3970–3978. Porto, I., Leone, A.M., Crea, F. and Andreotti, F. (2005). Inflammation, genetics, and ischemic heart disease: Focus on the major histocompatibility complex (MHC) genes. Cytokine 29(5), 187–196. Ridker, P.M., Rifai, N., Pfeffer, M.A., Sacks, F. and Braunwald, E. (1999). Long-term effects of pravastatin on plasma concentration of C-reactive protein. The Cholesterol and Recurrent Events (CARE) Investigators. Circulation 100(3), 230–235. Ridker, P.M., Cannon, C.P., Morrow, D., Rifai, N., Rose, L.M., McCabe, C.H., Pfeffer, M.A. and Braunwald, E. (2005). C-reactive protein levels and outcomes after statin therapy. N Engl J Med 352(1), 20–28. Ripa, R.S., Jorgensen, E., Wang, Y., Thune, J.J., Nilsson, J.C., Sondergaard, L., Johnsen, H.E., Kober, L., Grande, P. and Kastrup, J. (2006). Stem cell mobilization induced by subcutaneous granulocyte-colony stimulating factor to improve cardiac regeneration after acute ST-elevation myocardial infarction. Result of the double-blind, randomized, placebo-controlled stem cells in myocardial infarction (STEMMI) trial. Circulation 113(16), 1983–1992. Saia, F., Lemos, P.A., Lee, C.H., Arampatzis, C.A., Hoye, A., Degertekin, M., Tanabe, K., Sianos, G., Smits, P.C., McFadden, E. et al. (2003). Sirolimus-eluting stent implantation in ST-elevation acute myocardial infarction: A clinical and angiographic study. Circulation 108(16), 1927–1929. Samani, N.J., Erdmann, J., Hall, A.S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R.J., Meitinger, T., Braund, P., Wichmann, H.E. et al. (2007). Genomewide association analysis of coronary artery disease. N Engl J Med 357(5), 443–453. Saxena, R.,Voight, B.F., Lyssenko,V., Burtt, N.P., de Bakker, P.I., Chen, H., Roix, J.J., Kathiresan, S., Hirschhorn, J.N., Daly, M.J. et al. (2007). Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316(5829), 1331–1336. Schachinger, V., Assmus, B., Britten, M.B., Honold, J., Lehmann, R., Teupe, C., Abolmaali, N.D., Vogl, T.J., Hofmann, W.K., Martin, H. et al. (2004). Transplantation of progenitor cells and regeneration enhancement in acute myocardial infarction: Final one-year results of the TOPCARE-AMI trial. J Am Coll Cardiol 44(8), 1690–1699. Schwartz, G.G. and Olsson, A.G. (2005). The case for intensive statin therapy after acute coronary syndromes. Am J Cardiol 96(5A), 45F–53F. Scott, L.J., Mohlke, K.L., Bonnycastle, L.L.,Willer, C.J., Li,Y., Duren,W.L., Erdos, M.R., Stringham, H.M., Chines, P.S., Jackson, A.U. et al. (2007). A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316(5829), 1341–1345. Shiffman, D., Ellis, S.G., Rowland, C.M., Malloy, M.J., Luke, M.M., Iakoubova, O.A., Pullinger, C.R., Cassano, J., Aouizerat, B.E., Fenwick, R.G. et al. (2005). Identification of four gene variants associated with myocardial infarction. Am J Hum Genet 77(4), 596–605. Shiffman, D., Rowland, C.M., Louie, J.Z., Luke, M.M., Bare, L.A., Bolonick, J.I., Young, B.A., Catanese, J.J., Stiggins, C.F.,

Pullinger, C.R. et al. (2006). Gene variants of VAMP8 and HNRPUL1 are associated with early-onset myocardial infarction. Arterioscler Thromb Vasc Biol 26(7), 1613–1618. Smith, E.J. and Rothman, M.T. (2003). Antiproliferative coatings for the treatment of coronary heart disease. What are the targets and which are the tools? J Interv Cardiol 16(6), 475–483. Stenina, O.I., Desai, S.Y., Krukovets, I., Kight, K., Janigro, D., Topol, E.J. and Plow, E.F. (2003). Thrombospondin-4 and its variants: Expression and differential effects on endothelial cells. Circulation 108(12), 1514–1519. Thom,T., Haase, N., Rosamond,W., Howard,V.J., Rumsfeld, J., Manolio,T., Zheng, Z.J., Flegal, K., O’Donnell, C., Kittner, S. et al. (2006). Heart disease and stroke statistics – 2006 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 113(6), e85–ee151. Thomas, K.L. and Velazquez, E.J. (2005). Therapies to prevent heart failure post-myocardial infarction. Curr Heart Fail Rep 2(4), 174–182. Topol, E.J., McCarthy, J., Gabriel, S., Moliterno, D.J., Rogers,W.J., Newby, L.K., Freedman, M., Metivier, J., Cannata, R., O’Donnell, C.J. et al. (2001). Single nucleotide polymorphisms in multiple novel thrombospondin genes may be associated with familial premature myocardial infarction. Circulation 104(22), 2641–2644. Valgimigli, M., Percoco, G., Malagutti, P., Campo, G., Ferrari, F., Barbieri, D., Cicchitelli, G., McFadden, E.P., Merlini, F., Ansani, L. et al. (2005). Tirofiban and sirolimus-eluting stent vs abciximab and bare-metal stent for acute myocardial infarction: A randomized trial. JAMA 293(17), 2109–2117. Wang, L., Fan, C., Topol, S.E., Topol, E.J. and Wang, Q. (2003). Mutation of MEF2A in an inherited disorder with features of coronary artery disease. Science 302(5650), 1578–1581. Wang, Q., Rao, S., Shen, G.Q., Li, L., Moliterno, D.J., Newby, L.K., Rogers, W.J., Cannata, R., Zirzow, E., Elston, R.C. et al. (2004). Premature myocardial infarction novel susceptibility locus on chromosome 1P34-36 identified by genomewide linkage analysis. Am J Hum Genet 74(2), 262–271. The Wellcome Trust Case Control Consortium (WTCCC). (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145), 661–678. Wessel, J., Topol, E.J., Ji, M., Meyer, J. and McCarthy, J.J. (2004). Replication of the association between the thrombospondin-4 A387P polymorphism and myocardial infarction. Am Heart J 147(5), 905–909. Wollert, K.C., Meyer, G.P., Lotz, J., Ringes-Lichtenberg, S., Lippolt, P., Breidenbach, C., Fichtner, S., Korte, T., Hornig, B., Messinger, D. et al. (2004). Intracoronary autologous bone-marrow cell transfer after myocardial infarction: The BOOST randomised controlled clinical trial. Lancet 364(9429), 141–148. Yamada,Y., Izawa, H., Ichihara, S., Takatsu, F., Ishihara, H., Hirayama, H., Sone, T., Tanaka, M. and Yokota, M. (2002). Prediction of the risk of myocardial infarction from polymorphisms in candidate genes. N Engl J Med 347(24), 1916–1923. Yeh, H.I., Chou, Y., Liu, H.F., Chang, S.C. and Tsai, C.H. (2001). Connexin37 gene polymorphism and coronary artery disease in Taiwan. Int J Cardiol 81(2–3), 251–255. Yusuf, S., Hawken, S., Ounpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J. et al. (2004). Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Casecontrol study. Lancet 364(9438), 937–952.

Recommended Resources

Zdravkovic, S., Wienke, A., Pedersen, N.L., Marenberg, M.E.,Yashin, A.I. and de Faire, U. (2004). Genetic influences on CHD-death and the impact of known risk factors: Comparison of two frailty models. Behav Genet 34(6), 585–592. Zeggini, E., Weedon, M.N., Lindgren, C.M., Frayling, T.M., Elliott, K.S., Lango, H., Timpson, N.J., Perry, J.R., Rayner, N.W., Freathy, R.M. et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316(5829), 1336–1341. Zhang, B., Ye, S., Herrmann, S.M., Eriksson, P., de Maat, M., Evans, A., Arveiler, D., Luc, G., Cambien, F., Hamsten, A. et al.

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(1999). Functional polymorphism in the regulatory region of gelatinase B gene in relation to severity of coronary atherosclerosis. Circulation 99(14), 1788–1794. Zohlnhofer, D., Ott, I., Mehilli, J., Schomig, K., Michalk, F., Ibrahim, T., Meisetschlager, G., von Wedel, J., Bollwein, H., Seyfarth, M. et al. (2006). Stem cell mobilization by granulocyte colony-stimulating factor in patients with acute myocardial infarction: A randomized controlled trial. JAMA 295(9), 1003–1010.

RECOMMENDED RESOURCES Topol, E.J. (2005). Simon Dack Lecture. The genomic basis of myocardial infarction. J Am Coll Cardiol 46(8), 1456–1465. Wang, Q. (2005). Molecular genetics of coronary artery disease. Curr Opin Cardiol 20(3), 182–188. Jefferson, B.K. and Topol, E.J. (2005). Molecular mechanisms of myocardial infarction. Curr Probl Cardiol 30(7), 333–374.

The Wellcome Trust Case Control Consortium (WTCCC). (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145), 661–678.

CHAPTER

58 Acute Coronary Syndromes L. Kristin Newby

INTRODUCTION

PREDISPOSITION

At least 2 million individuals are hospitalized each year with acute coronary syndromes (ACS), and 1 in 5 deaths in the United States is a result of ischemic heart disease (Rosamond et al., 2007). Despite its common occurrence and severe consequences, the exact molecular mechanisms that trigger an ACS are not fully elucidated, and the prognostic tools to identify which individuals are at risk for a first or subsequent ACS event are limited. Although it is clear the there is a genetic component to the occurrence of coronary artery disease (CAD) and acute coronary syndromes, most clinically applicable work thus far has fallen into the use of protein biomarkers measured from peripheral blood for use as diagnostic or prognostic tools. However, few examples exist of the use of these biomarkers to actually guide treatment. With rapid advances in technology, ACS and its treatment are ripe for exploration using genomics and related techniques such as proteomics and metabolomics. This chapter will briefly review what is known of the genetics and genomics of ACS, examine protein-based biomarkers and their combinations as an example of the forerunner of clinical proteomics, and conclude with an examination of the potential applications of “omic” techniques for refinement of diagnosis, risk stratification and management of ACS in the future.

Clinical Risk Factors and Risk Prediction The development of CAD and the occurrence of myocardial infarction (MI) are complex processes. Although several clinical characteristics that predispose to these processes are now commonly known, including male sex, cigarette smoking, diabetes, hypertension, hyperlipidemia, and obesity, it is increasingly recognized that these characteristics provide only a superficial understanding of what predisposes to acute coronary syndromes. In addition, the ability to use these characteristics to discriminate individuals at risk from those who are not is at best modest. For example, the most widely used instrument for prediction of the risk for death or MI over a 10-year time horizon among individuals without known coronary disease, the Framingham Risk Score, which considers many of these clinical factors, has a c-index of only 0.69 in men and 0.72 in women (Ohman et al., 2000; Wilson et al., 1998). That is, for pairs of individuals, one who has a death or MI event and one who does not, the Framingham Risk Score will correctly select the one with an event only 69% of the time among men and only 72% of the time among women. Clinical models that assess the risk of a second event after the occurrence of an ACS, such as Global

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Copyright © 2009, Elsevier Inc. All rights reserved.

Screening

Registry of Acute Coronary Events (GRACE) model, which estimates the risk for in-hospital and 6-month death or MI (Eagle et al., 2004; Granger et al., 2003), have somewhat better predictive ability. The discriminative capability of the GRACE model (c-index 0.84) was enhanced by the addition of biomarker information (serum creatinine and creatine kinase [CK]-MB) to clinical characteristics. Still, the ability to discriminate which individuals will have an ACS event could be improved further, opening the door for the use of omic technologies, including RNA expression, proteomics, and metabolomics, to better characterize disease state and risk for future events. Genetic Predisposition Consensus on the existence of genetic predisposition for CAD is well-established. Family history has been shown repeatedly to be a robust, independent risk factor for CAD (Ciruzzi et al., 1997; Schildkraut et al., 1989; Shea et al., 1984), even after adjustment for shared environmental factors (Schildkraut et al., 1989; Zureik et al., 1999), and the heritability of CAD is particularly strong in early-onset forms, where the relative risk of developing early-onset CAD in a first-degree sibling is between 3.8 and 12.1, depending on the age-of-onset in the proband (Hauser et al., 2004). Despite this, no single genetic variant has been identified that accounts for a major fraction of the large burden of CAD and cardiovascular events. Further, despite the wealth of published literature implicating genes in CAD, most association studies have been plagued by low strength of association and lack of replication. For summaries of the body of work in the genetics of coronary disease and MI, the reader is referred to excellent reviews in references by Ginsburg et al. (2005) and Chen et al. (2007). Most recently, variants of 2 genes in the 59-lipoxygenase (5-LO) pathway were associated with cardiovascular events in an Icelandic population: 5-LO activator protein (FLAP) associated with a twofold increase in MI and stroke (Helgadottir et al., 2004) and a haplotype (HapK) spanning the leukotriene A4 hydrolase (LTA4 H) gene with a relative risk of 1.45 for MI among patients with other vascular disease (Helgadottir et al., 2005). When HapK was validated in cohorts from the United States, there was an even stronger, threefold increase in risk among African Americans. Two recent, independent case-control genome-wide association studies, which were replicated in multiple independent populations, provided the first evidence of a common genetic variant (risk allele frequency ~45%) located on chromosome 9p21 that is associated with substantial risk of coronary heart disease or MI (Helgadottir 2007; McPherson et al., 2007). Homozygotes for the risk allele comprise 20–25% of the Caucasian population. Of note, this variant, in the region of tumor suppressor genes CDKN2A and CDKN2B, was associated with MI with an odds ratio of 1.26 for heterozygotes for the risk allele, and 1.64 for homozygotes. Still, these associations are modest and at this point provide little useful information to guide contemporary clinical care.

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SCREENING There are currently no purely genomic tools that have been identified to screen the general population for risk of future ACS, identification of current ACS events or for risk of death or recurrent ischemic events after an ACS. However, early results with expression profiling suggest potential applications for screening. RNA expression profiling using micorarray technology has been shown to accurately classify both the presence and severity of atherosclerotic lesions in aortic tissue (Seo et al., 2004) and to identify genes potentially involved in plaque rupture (Faber et al., 2001), a pathophysiological precursor to the clinical syndrome of ACS. Importantly, since access to tissue is challenging for screening, diagnosis or prognostic testing in ACS, studies have shown that gene expression profiling from circulating monocytes and peripheral blood leukocytes correlates with the extent of carotid vascular disease (Patino et al., 2005) and CAD (Ma and Liew, 2003), suggesting that blood can be used as a reporter tissue for events occurring in the vessel wall. Unbiased metabolomic profiling of human serum by proton nuclear magnetic resonance (NMR) analysis also has predicted the presence and severity of CAD (Brindle et al., 2002). In fact, in this study, NMR-based analysis of human serum was better at differentiating individuals with one-, two-, and three-vessel CAD than a model of traditional clinical and laboratory risk factors. In addition, in a study of 53 cases with angiographic CAD and 53 controls without angiographic CAD, large-scale pooled plasma proteomics using LC/MS/MS technology identified differential expression of proteins and peptides between groups (Donahue et al., 2006). Of 731 proteins and peptides identified, 95 were differentially expressed in cases and controls. Among these were proteins involved in natural host defense mechanisms, growth, inflammation, and coagulation. These results suggest the potential for development as biomarkers, in addition to their use to further explore the mechanistic underpinnings of CAD and ischemic events. More recently, a study using urine proteomics identified biomarker patterns that correlated with the presence of angiographically severe CAD (Zimmerli et al., 2007). From more than 1000 polypeptides characterized per sample, 15 characterized a unique biosignature for CAD, which when used to predict CAD in a second cohort did so with a sensitivity of 98% and a specificity of 83%. Interestingly, after coronary intervention and increasing physical activity, the pattern reverted towards that of healthy controls, an observation that suggests potential not only for screening and diagnosis, but also for monitoring disease progression. In summary, although genomic approaches to screening for CAD or quantifying future risk for ACS are not yet ready for clinical application, this is an active area of research with early results that suggest promise for applications to clinical care, development of biomarkers of disease state and risk and use as research tools to enhance understanding of the molecular underpinnings of ACS.

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DIAGNOSIS Conventional Diagnostic Tools The acute coronary syndromes encompass a spectrum of pathophysiological processes manifest as plaque instability and plaque rupture or erosion, coronary thrombosis with varying degrees of coronary artery and microvascular occlusion and reductions in blood flow leading to myocardial ischemia and in some cases, myocardial necrosis (Fuster et al., 1992a, b). The diagnosis of ACS is largely a clinical one, relying on a patient’s description, or history, of the event that led him to seek medical attention and a small number of readily available objective diagnostic tools. The first and most readily available objective assessment of a patient with suspected ACS is the 12-lead electrocardiogram (ECG). Based on the ECG, patients can be stratified into 2 primary groups, those with ST-segment elevation MI (STEMI) and those with non-ST-segment elevation (NSTE) ACS. About twothirds of patients will have NSTE ACS and 1/3 will have STEMI (Figure 58.1; Morrow et al., 2007). This electrocardiographic distinction is clinically important as it immediately defines a divergence in treatment strategy. Patients with STEMI generally have a totally occluded major coronary artery and benefit from rapid reperfusion therapy with percutaneous revascularization or administration of intravenous fibrinolytic therapy. For an excellent review and current recommendations for the management of STEMI, the reader is referred to the American College of Cardiology/American Heart Association (ACC/AHA) guidelines for management of patients with STEMI (Antman et al., 2004). In a small case-control study, platelet RNA expression analysis implicated 2 candidate proteins (CD69 and myeloid related protein-14) that differentiated STEMI patients from those with stable CAD (Healy et al., 2006). However, in the context of STEMI, these findings are mostly of interest as a research tool given the central role of the ECG in diagnosis and management of STEMI patients. This work does offer proof of principle and, as discussed subsequently, could have more relevance for application to identification of NSTE ACS patients.

Acute coronary syndrome Electrocardiogram

Biomarker of necrosis

UA

NSTEMI

Confirmatory

STEMI

NSTEACS

STEMI

Figure 58.1 Diagnostic flow and distribution of types of acute coronary syndromes.

Patients whose initial ECG does not reveal ST-segment elevation are further subdivided into 2 groups, largely on the basis of measurement of levels of biomarkers released into the blood stream as a result of ischemic myocardial injury and necrosis. Although a number of markers have been used historically (SGOT, LDH, total CK), over the past 2 decades mass assays for the cardiac-specific isoenzyme of creatine kinase (CK-MB) and more recently, cardiac-specific isoforms of troponin T and I have been the recognized diagnostic gold standards for MI. With the publication of the joint ESC/ACC Task Force recommendations for MI redefinition in 2001, troponin, because of its enhanced sensitivity and specificity for myocardial necrosis, became the preferred diagnostic marker of MI in most clinical situations (Alpert et al., 2000). The 2007 revision of this document refined the definition of MI further in an attempt to achieve consensus, particularly on appropriate clinical situations for the use of troponins and/or CK-MB for MI diagnosis and diagnostic parameters of the assays, that would be applicable and practical across medical practice and clinical trials around the world (Thygesen et al., 2007). Along with the National Academy of Clinical Biochemistry (NACB) Practice Guidelines for Laboratory Medicine (Morrow et al., 2007), these documents summarize the base of evidence and recommendations for the use of biochemical markers in the diagnosis of ACS. Key recommendations for the use of biochemical markers to diagnose MI are shown in Table 58.1. Need for Novel Biomarkers for Diagnosis of ACS While CK-MB and troponins are useful diagnostic tools for MI, they only become elevated after irreversible myocardial injury has occurred. Ideally, one would identify patients with an acute coronary syndrome early, at a point when therapeutic intervention might mitigate myocardial damage. Further, the group of patients without ECG changes or biomarker elevations on presentation is a particularly challenging group to manage, especially when the clinical presentation is atypical. Over 6 million Americans present to emergency departments each year for evaluation of chest pain syndromes; clearly the vast majority are not ultimately diagnosed with an ACS. Admitting all of these individuals to the hospital is not practical from the perspective of resource use and costs, but the medico-legal risk associated with missing a patient with MI and discharging him from the emergency department is high. Missed MI, which carries a twofold increased risk of death (Pope et al., 2000), is the leading cause of malpractice claims against emergency department and primary care physicians (Karcz et al., 1996; Rusnak et al., 1989). Therefore, there is a tremendous need for refined diagnostic testing to identify who among this large number of patients with normal ECG and troponin levels at presentation is having an ACS and to identify as early as possible those who need treatment or who can be safely sent home. While this is an area of great diagnostic need, few such tests are commercially available, making this clinical situation poised for the application of omic technologies to identify unique molecular signatures that

Diagnosis

distinguish patients with unstable angina or pre-infarction from those with non-cardiac diagnoses. In current practice, serial testing of multiple necrosis markers with different release kinetics after myocardial injury (CKMB, troponin, and myoglobin) has been shown to identify MI in more patients in a general emergency department cohort and to do it as much as an hour earlier than conventional single marker testing with troponin or CK-MB (Newby et al., 2001a). However, even earlier detection, before necrosis occurs, could be clinically beneficial for triage and treatment. Ischemia modified albumin (IMA) and myeloperoxidase are FDA-approved assays for use as adjuncts to ECG and troponin testing in this situation, but each has limitations to its widespread adoption. The albumin cobalt binding (ACB) test takes advantage of modification of the N-terminus of albumin in the setting of ischemia (Bar-Or et al., 2001), reducing its ability to bind cobalt. The increased free cobalt can then be detected with special assays (Bar-Or et al., 2000). Importantly, in human angioplasty models of myocardial ischemia, IMA becomes positive within a few minutes after the onset of ischemia (Sinha et al., 2003), suggesting that it could be an ideal tool for early differentiation of chest pain patients in the emergency department. Unfortunately, the sensitivity, specificity, and positive predictive value of this test are far too low for use as a diagnostic tool for ischemia or pre-infarction or to guide treatment decisions in those with a positive test. However, it does have excellent negative predictive value, and when combined with negative troponin testing and non-diagnostic ECG can exclude the diagnosis of ischemia with a negative predictive value of over 97% (Peacock et al., 2006). Myleoperoxidase is an enzyme activated in and released by activated neutrophils and monocytes; it is believed to have a

TABLE 58.1

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pathophysiological role in ACS and may be a marker of plaque instability (Apple et al., 2005; Malech and Naussef, 1997). A study published in 2003 suggested a potential role for myeloperoxidase in early emergency department diagnosis and risk stratification (Brennan et al., 2003). In this study, among 604 patients presenting with chest pain to the emergency room, myeloperoxidase levels were elevated at baseline among patients who were initially troponin-negative, but subsequently troponin-positive, even when fewer than 3 hours had elapsed since the onset the symptoms. Further, there was a correlation of myeloperoxidase with both short and long-term outcomes. However, leukocyte and monocyte activation and myeloperoxidase release can occur in the setting of a number of disease states (Apple et al., 2005); therefore, like the ACB test, the low specificity of elevated myeloperoxidase levels for ACS precludes its use as a stand alone marker for diagnosis of ischemia. It must be interpreted in the context of clinical findings and other laboratory data. Omic Technologies in Diagnosis of ACS One of the most intriguing investigations that foreshadows the potential application of omic technologies to diagnostic test development for use in management of patients with suspected ACS involved metabolomics. In a small case-control study of 36 patients who underwent stress testing with nuclear myocardial perfusion imaging, unbiased metabolomic profiling of blood obtained pre- and post-test using LC/MS technology identified small molecules that changed in abundance differentially between patients with ischemia and those without (Figure 58.2) (Sabatine et al., 2005). After statistical analysis of the output, a score reflecting the presence or absence of change in the 6 most discordantly regulated metabolites predicted ischemia with a

Class I recommendations for use of biochemical markers for diagnosis of myocardial infarction

Recommendation

Level of evidence

Biomarkers of myocardial necrosis should be measured in all patients who present with symptoms consistent with ACS.

Level of evidence: C

The patient’s clinical presentation (history, physical exam) and ECG should be used in conjunction with biomarkers in the diagnostic evaluation of suspected MI.

Level of evidence: C

Cardiac troponin is the preferred marker for the diagnosis of MI. Creatine kinase MB (CK-MB) by mass assay is an acceptable alternative when cardiac troponin is not available.

Level of evidence: A

Blood should be obtained for testing at hospital presentation followed by serial sampling with timing of sampling based on the clinical circumstances. For most patients, blood should be obtained for testing at hospital presentation and at 6–9 h.

Level of evidence: C

In the presence of a clinical history suggestive of ACS, the following are considered indicative of myocardial necrosis consistent with MI a. Maximal concentration of cardiac troponin exceeding the 99th percentile of values (with optimal precision defined by total CV  10%) for a reference control group on at least 1 occasion during the first 24 h after the clinical event (observation of a rise and/or fall in values is useful in discriminating the timing of injury). b. Maximal concentration of CK-MB exceeding the 99th percentile of values for a sex-specific reference control group on 2 successive samples (values for CK-MB should rise and/or fall).

Level of evidence: C

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0.000001 Lactic acid

CASES (Statistical significance of change from baseline)

MET41

0.0001

MET120 MET334

0.01 MET221 ME

MET50

Fructose

Inosine MET2

MET193

T2 13 Homo angle Xanthosis acid MET 282 Vsee MET50 Glyedsae MET 301 Mychocy MET 136 Tryptophan profile MET 256

Homoserline

1.0

Alanine

Hypoxanthine

MET185

0 19 Uridine MET 89 T2 MET37 E xxxxxx M MET203 xxxxxx xxxxxx ME Hypporic acid T1 Ma 7 CH lic AP acid

MET23J

MET 286 MET 264 MET200 Cloulo MET 276 acetese MET268MET191 MET264 Argin Uric acid 36 6 succirateMET31 Citric acid T3 31 05 ME MET ET3 MET 292 M xxxxxx MET206 Glucuromata

Sorbitol

MET309

0.01

MET292 CABA MET258

MET272 MET 218

0.0001 0.0001

0.01

1.0

0.01 0.0001 CONTROLS (Statistical significance of change from baseline)

0.000001

Figure 58.2 Scatterplot of the statistical significance changes in metabolite levels according to case (ischemia) or control (no ischemia) status. Significant changes (p  0.05) are represented as colored circles; no significant change as black dots. Red indicates and increase in concentration and green a decrease in concentration. The rim of the circle reflects change in the controls and the center reflects changes in the cases.

Discovery Candidate proteins and profiles selected from specific well-defined human clinical cohorts and animal studies

Validation Specificity and selectivity determined using large clinically well-defined human cohorts

Implementation Clinical tests developed via platform adaptation and regulatory approval

Figure 58.3 Conceptual framework for clinical proteomics efforts as proposed by the National Heart, Lung and Blood Institute clinical proteomics working group.

c-index of 0.83. Much work remains to translate this finding to practical clinical utility (e.g., validation in unselected emergency department populations with suspected ischemia, development of assays that are practical for use in hospital clinical laboratories and in real time), but the concept highlights the possibilities for application of omic technologies to the early detection of ACS. The use of proteomics, both candidate-based approaches and unbiased approaches, as a platform for discovery and development

of novel biomarkers of cardiovascular disease has been widely espoused (Anderson, 2005a, b; Arab et al., 2006; Granger et al., 2004). Figure 58.3 shows a conceptual framework for such efforts proposed by the NHLBI clinical proteomics working group. Despite this enthusiasm, the use of proteomics to distinguish ACS patients from those without ACS is in its infancy. Recently, however, in a small study of 11 patients with acute MI, 8 with unstable angina and 9 age-matched controls without known coronary

Prognosis

Phases:

Phase 1 Preclinical exploratory

Phase 2 Clinical characterization and assay validation

Phase 3 Clinical association: Retrospective repository studies

Phase 4 Clinical association: Prospective screening studies

685

Phase 5 Disease control

Objective

Target Biomarker identification, feasibility

Study assay in people with and without disease

Case-control studies using repository specimens

Longitudinal studies to predict disease

Clinical use

Site

Biomarker development lab

Biomarker validation lab

Clinical epidemiologic centers

Cohort studies

Community

Design

Cross-sectional

Cross-sectional

Case-control

Prospective

RCT

Sample size

Small

Small

Modest

Medium

Large

Validity

Content and construct validity

Criterion validity

Predictive validity

Efficacy of strategy

Effectiveness

Result

Assay precision reliability, sensitivity

Reference limits, intra-individual variation

Screening characteristics, true and falserates

ROC analyses

No.-needed-to screen/treat

Figure 58.4

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A 5-step process that is essential to move from biomarker discovery to clinical utility.

disease, Mateos-Cáceres and colleagues published a seminal report on the used of 2d gel-based proteomics to identify changes in the protein map that discriminated patients with acute MI from those with unstable angina based on changes relative to controls (Mateos-Cáceres et al., 2004). These observations provide support that proteomics may lead to usable clinical tools to distinguish disease state in the clinical setting of suspected ACS. In an excellent review of the development of biomarkers in cardiovascular disease, Vasan has outlined a 5-step process that is essential to move from discovery findings such as this to clinical utility (Figure 58.4; Vasan, 2006). Finally, as mentioned above, the use of platelet RNA expression profiling (or possibly peripheral blood RNA expression analysis), either directly or to identify candidate protein biomarkers of ACS, may prove to be a useful tool for differentiating difficult to diagnose NSTE ACS presentations from non-ACS presentations or to identify NSTE ACS earlier after presentation.

PROGNOSIS The goals of “biomarker” testing are multifold. As described in previous sections screening for disease and diagnosis are important applications. However, equally important is the ability to use the

information from biomarker testing to discern not only the presence or absence of disease, but more importantly to relate biomarker information to prognosis or downstream meaningful clinical outcomes. Although the concept of using genetic and genomic, proteomic, and metabolomic approaches for risk stratification and to refine prognosis in ACS is appealing, substantial work remains to bring this conceptual construct to clinical utility. That there is a need for such advances to refine clinical models of risk is undenied, and numerous examples of the development and use of biomarker assessment for prognosis or risk stratification abound in the ACS literature. Although hundreds of individual proteins, in addition to routine laboratory tests, have been identified as predicting clinical outcome, only a few have sufficient evidence to support their use in routine clinical practice. Guidelines for the use of biomarker testing for risk stratification in ACS were recently published by the NACB, and class I recommendations are summarized in Table 58.2 (Morrow et al., 2007). In this regard, there is substantial evidence for use of troponin (I or T), b-type natriuretic peptide (BNP) or N-terminal proBNP, and high-sensitivity c-reactive protein (hsCRP) for risk stratification of ACS patients. Each of these biomarkers contributes incremental information to that from easily assessable clinical and laboratory parameters and there are commercially available, high-quality assays for clinical use. Although many

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TABLE 58.2

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Class I recommendations for use of biochemical markers for risk stratification in ACS

Recommendation

Level of evidence

Patients with suspected ACS should undergo early risk stratification based on an integrated assessment of symptoms, physical exam findings, ECG findings, and biomarkers.

Level of evidence: C

A cardiac troponin is the preferred marker for risk stratification and, if available, should be measured in all patients with suspected ACS. In patients with a clinical syndrome consistent with ACS, a maximal (peak) concentration exceeding the 99th percentile of values for a reference control group should be considered indicative of increased risk of death and recurrent ischemic events.

Level of evidence: A

Blood should be obtained for testing on hospital presentation followed by serial sampling with timing of sampling based on the clinical circumstances. For most patients, blood should be obtained for testing at hospital presentation and at 6–9 h.

Level of evidence: B

other novel protein markers have been identified (Ginsburg et al., 2005; Morrow et al., 2007), their independent contribution to risk stratification remains to be proved and development of assays for routine use in hospital laboratories remains. As a forerunner to the application of proteomics (or other multidimensional omic technologies) to risk stratification of ACS patients, many studies have now demonstrated the utility of combining information carried by multiple markers of risk into a common risk prediction model. For example, in one study, a simple score based on measurement of troponin, BNP and hsCRP provided additive, complementary information to that from any marker alone in the context of clinical markers of risk (Sabatine et al., 2002). Additionally, investigators from the CAPTURE trial used a candidate protein approach, to identify six proteins (hsCRP, troponin T, CD40 ligand, myeloperoxidase, vascular endothelial growth factor (VEGF), placental growth factor, and pregnancyassociated plasma protein [PAPP]-A) that together refined prediction of 6-month death or MI compared with use of any single marker in both ACS and general emergency department patients with suspected ACS (Heeschen et al., 2005). In another report (Baldus et al., 2003), five markers each had independent additive value, and in a multivariable model, each protein marker had more predictive information than any clinical variable. Finally, one of the most important insights into the use of protein biomarkers in ACS risk stratification came from investigators in a substudy of the GUSTO-IV trial. They examined use of markers of multiple processes (necrosis, troponin; inflammation, hsCRP, and neurohormonal activation, NT-proBNP) in the context of clinical characteristics and routine laboratory testing (Westerhout et al., 2006), and made the important observation that the prognostic utility of a given marker depended on the outcome of interest. NT-proBNP, troponin T, and hsCRP were all independently associated with mortality at 1 year, in addition to heart rate, creatinine clearance, and ST-segment depression. However, only troponin T, creatinine clearance and ST-segment depression predicted risk for future MI. In addition, the relative strengths of the association of a biomarker with risk for subsequent events varied according to the time frame in which it was assessed. Thus, protein biomarkers may vary in their utility for short- and long-term risk stratification after ACS, but protein

biomarkers consistently comprise several of the most important independent predictors of risk.

PHARMACOGENOMICS In considering biomarkers for diagnosis or risk stratification in ACS, one must always keep in mind that the ultimate goal is to identify clinically useful tools that can guide the selection of therapeutic agents or management strategies that uniquely benefit individuals with the identified risk profile. In this regard, troponin has emerged as a prototype for a biomarker of personalized medicine in ACS. Not only has troponin become solidly positioned to define MI (Thygesen et al., 2007), it is a powerful predictor of risk for death or recurrent MI among patients with ACS (Antman et al., 1996; Heidenreich et al., 2001; Ohman et al., 1996; Ottani et al., 2000), and also appears to identify which patients derive greatest benefit from certain therapies, including glycoprotein IIb/IIIa inhibitors (Boersma et al., 2002; Hamm et al., 1999; Heeschen et al., 1999; Newby et al., 2001b), low-molecular-weight heparins (Lindahl et al., 1997; Morrow et al., 2000), and the early invasive catheterization strategy (Morrow et al., 2001;Wallentin et al., 2000). However, even with the success of troponin as a biomarker to target aggressive ACS therapy to those most likely to benefit, some troponin-positive patients still have events, some troponinnegative patients may derive benefits, and troponin does not provide insight into avoidance of risks such as bleeding. Thus, because many of our existing prevention and ACS treatment strategies are expensive and associated with potential adverse consequences, refining our understanding of how to target these treatments to individuals who will most benefit from them with the least risk of side effects remains an important challenge. In this context, there is currently much enthusiasm for the prospect of applying information from transcriptomic, proteomic and metabolomics techniques to refine our ability to guide therapeutic decision making. However, as discussed previously, substantial work remains before these approaches reach clinical applicability in ACS. Incorporation of sample ascertainment in clinical trials and single- and multi-center registries, along with

Acknowledgements

adequate funding sources (private and public) for omic analyses from these sample repositories, will ensure that the lessons learned from the experience with troponin as a diagnostic tool, prognostic indicator and marker of therapeutic response will be expanded into the development of advanced, omic-based biomarker assessments for the future.

MONITORING Because an ACS is a transient event there is little monitoring used beyond routine chemistry, hematology and coagulation laboratory testing. One might envision potential applications for genomics in this regard – for example, surveillance of drug compliance or drug response post-discharge, early detection of bleeding before clinically evident, assessment for the likelihood of or actual response (or lack thereof) to standard medical therapy (aspirin, statins, angiotensin converting enzyme inhibitors and bets-blockers) that might be used to guide use, dosing or decisions to continue or discontinue use or to add additional therapies.

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monocytes (Huang et al., 2004), and promote smooth muscle cell migration and proliferation (Back et al., 2005). Further, expression of 5-LO is increased in unstable carotid plaques compared with stable plaques, which also show increased levels of LTB4 and matrix metalloproteinases 2 and 9, implicating a role for this pathway in plaque instability (Cipollone et al., 2005). LTB4 levels are also increased in circulating white blood cells of MI patients compared with age-matched controls, and increased levels of LTB4 in atherosclerotic plaque has been associated with increased risk for MI and stroke (Helgadottir et al., 2004). These findings in conjunction with the previously described association of genetic variants within this pathway with the occurrence of MI led to the selection of an inhibitor of the 5-LO pathway (DG-301, an inhibitor of FLAP) for development as a therapeutic agent to prevent ACS. In phase II testing, this agent decreased circulating levels of both myeloperoxidase and hsCRP (Hakonarson et al., 2005), biomarkers of inflammation that have been associated with ACS events.

CONCLUSION NOVEL AND EMERGING THERAPEUTICS Although randomized clinical trials have provided abundant evidence for therapies that substantially reduce the population risk of first or subsequent MI or death, acute coronary events continue to occur (Antithrombotic Trialists Collaboration, 2002; CAPRIE Steering Committee, 1996; The Clopidogrel in Unstable Angina to Prevent Recurrent Events Trial Investigators, 2001; Heart Outcomes Prevention Evaluation Study Investigators, 2000; The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group, 1998). Both the US Food and Drug Administration (FDA) and the National Institutes of Health (NIH) have encouraged use of advanced molecular and cellular biomarkers in drug development and clinical trials as a principle means to facilitate pre-marketing risk assessment and to enhance the efficiency of clinical trials (Becker and Andreotti, 2006; Administration FDA, 2004). Such a convergence of genetic, genomic, proteomic and metabolomic approaches with drug development and clinical testing should improve targeting of existing therapies to those at greatest risk while minimizing adverse responses. At the same time, a more complete understanding of the relative importance and intersection of biological pathways involved in the occurrence of ACS will also lead to novel therapeutic targets and treatment options that can further reduce the incidence of acute coronary events beyond existing therapies. One example of such convergence of genetics and omics approaches in identification of a new therapy for ACS comes from the previously described studies of the 5-LO pathway of arachadonic acid metabolism. Leukotriene B4 (LTB4), a product of the 5-LO pathway is one of the most potent mediators of arterial inflammation and atherosclerosis, acting to increase monocyte chemoattractant protein 1 (MCP-1) production in

The application of genomics to fundamental research and the development of clinical tools for use in diagnosis, risk stratification and treatment in ACS is in its infancy, but examples of its potential abound. However, to achieve that potential, a number of challenges will have to be overcome. Most current studies are small and without longitudinal follow-up, rendering them prone to false positive associations due to the large number of comparisons within and across studies, unable to assess the independent contribution of their findings in the context of readily available clinical information, and limited in their ability to assess prognostic ability for future events. Few studies have been replicated and none have progressed to the point of testing for utility in general clinical practice. Collaborative research networks that both focus efforts in a coordinated fashion and assure sufficient sample sizes and appropriate populations with longitudinal follow-up for discovery and validation, availability of adequate research funding, development of high-throughput methodologies that speed assays times and reduce costs and continued advances in bioinformatics and biostatistics to manage these large, multidimensional datasets are but a few of the hurdles that must be cleared to ensure continued advances in understanding the systems biology of ACS and the development of genomics and related disciplines to practical clinical application in ACS.

ACKNOWLEDGEMENTS Within the past 2 years, Dr Newby has received research grant support from Schering Plough, Millennium Pharmaceuticals, Medicure, Roche Diagnostics, Inverness Medical, and Adolor; and has received consulting honoraria from Johnson and Johnson/Scios, Proctor & Gamble, Novartis, Astra-Zeneca/ Atherogenics, Biosite, Roche Diagnostics, and CV Therapeutics.

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Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875. Schildkraut, J.M., Myers, R.H., Cupples, L.A., Kiely, D.K. and Kannel, W.B. (1989). Coronary risk associated with age and sex of parental heart disease in the Framingham Study. Am J Cardiol 64, 555–559. Seo, D., Wang, T., Dressman, H., Herderick, E.E., Iversen, E.S., Dong, C., Vata, K., Milano, C.A., Rigat, F., Pittman, J. et al. (2004). Gene expression phenotypes of atherosclerosis. Arterioscler Thromb Vasc Biol 24, 1922–1927. Shea, S., Ottman, R., Gabrieli, C., Stein, Z. and Nichols, A. (1984). Family history as an independent risk factor for coronary artery disease. J Am Coll Cardiol 4, 793–801. Sinha, M.K., Gaze, D.C., Tippins, J.R., Collinson, P.O. and Kaski, J.C. (2003). Ischemia modified albumin is a sensitive marker of myocardial ischemia after percutaneous coronary intervention. Circulation 107, 2403–2405. Thygesen, K., Alpert, J.S. and White, H.D. (2007). on behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. Eur Heart J 28, 2525–2538. Vasan, R.S. (2006). Biomarkers of cardiovascular disease: Molecular basis and practical considerations. Circulation 113, 2335–2362. Wallentin, L., Lagerqvist, B., Husted, S., Kontny, F., Stahle, E. and Swahn, E. (2000). for the FRISC II Investigators Outcome at 1 year after an invasive compared with a non-invasive strategy in unstable coronary-artery disease: The FRISC II invasive randomised trial. Lancet 356, 9–16. Westerhout, C.M., Fu, Y., Lauer, M.S., James, S., Armstrong, P.W., Al-Hattab, E., Califf, R.M., Simoons, M.L., Wallentin, L. and Boersma, E. (2006). for the GUSTO-IV ACS Trial Investigators. Short- and long-term risk stratification in acute coronary syndromes: The added value of quantitative ST-segment depression and multiple biomarkers. J Am Coll Cardiol 48, 939–947. Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H. and Kannel, W.B. (1998). Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847. Zimmerli, L.U., Schiffer, E., Zürbig, P., Good, D.M., Kellmann, M., Mouls, L., Pitt, A.R., Coon, J.J., Schmieder, R.E., Peter, K. et al. (2007 19). Urinary proteomic biomarkers in coronary artery disease. Mol Cell Proteomics. [Epub ahead of print] Zureik, M., Touboul, P.J., Bonithon-Kopp, C., Courbon, D., Ruelland, I. and Ducimetiere, P. (1999). Differential, association of common carotid intima-media thickness and carotid atherosclerotic plaques with parental history of premature death from coronary heart disease: The EVA study. Arterioscler Thromb Vasc Biol 19, 366–371.

RECOMMENDED RESOURCES The following 3 articles represent state of the art summaries and recommendations for biomarker testing in the diagnosis of myocardial infarction and prognostication in ACS patients: Thygesen, K., Alpert, J.S. and White, H.D. (2007). On behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. Eur Heart J 28, 2525–2538. Morrow, D.A., Cannon, C.P., Jesse, R.L., Newby, L.K., Ravkilde, J., Storrow, A.B., Wu, A.H.B. and Christenson, R.H. (2007). National

Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines: Clinical characteristics and utilization of biochemical markers in acute coronary syndromes. Circulation 115, e356–e375. Apple, F.S., Jesse, R.L., Newby, L.K., Wu, A.H.B. and Christenson R.H. (2007). National Academy of Clinical Biochemistry and IFCC Committee for Standardization of Markers of Cardiac Damage Laboratory Medicine. Practice Guidelines: Analytical issues for biomarkers of acute coronary syndromes. Circulation 115, e352–e355.

Recommended Resources

This paper presents an excellent perspective on the potential for application of genomics to personalized cardiovascular care: Ginsburg, G.S., Donahue, M. and Newby, L.K. (2005). Prospects for personalized cardiovascular medicine: The impact of genomics. J Am Coll Cardiol 46, 1615–1627. These 2 papers provide expert opinion and recommendations for the application of proteomics to cardiovascular medicine and biomarker development: Granger, C.B., Van Eyk, J.E., Mockrin, S.C. and Anderson N.L. (2004). National Heart, Lung, and Blood Institute Clinical Proteomics

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Working Group. National Heart, Lung, and Blood Institute Clinical Proteomics Working Group Report. Circulation 109, 1697–1703. Vasan, R.S. (2006). Biomarkers of cardiovascular disease: Molecular basis and practical considerations. Circulation 113, 2335–2362.

CHAPTER

59 Heart Failure in the Era of Genomic Medicine Ivor J. Benjamin and Jeetendra Patel

INTRODUCTION Heart failure has been conveniently subdivided according to abnormalities in the cardiac cycle: namely, systolic heart failure (SHF) and diastolic heart failure (DHF). SHF is associated with decreased cardiac output and ventricular contractility, termed systolic dysfunction, and is attributed to a loss of ventricular muscle cells. Dilated cardiomyopathy (DCM) is characterized by impaired systolic function and myocardial remodeling and enlargement of one or both ventricles. Idiopathic dilated cardiomyopathy (IDCM) refers to primary myocardial disease in the absence of coronary, valvular or systemic disease. The ventricular remodeling of DHF, however, is characterized by normal chamber size with impaired ventricular filling from abnormal myocardial stiffness during the relaxation phase. More recently, the clinical syndrome of heart failure with preserved ejection fraction (HFPEF) – left ventricular ejection fraction  50% – has been recognized in several cross-sectional studies (Bhatia et al., 2006; Owan et al., 2006).

PREDISPOSITION (GENETIC AND NON-GENETIC) In western societies, ischemic heart disease (~60%) and hypertension are the most common causes of ventricular systolic dysfunction but there is now irrefutable evidence for genetic

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 692

defects whose onset and progression occur in adulthood (e.g., familial cardiomyopathy) (Benjamin and Schneider, 2005; Morita et al., 2005). Beginning in the late 1950s, distinct alterations in the size and geometry of the left ventricle, termed “ventricular remodeling,” were being recognized, but the ensuing debate for over three decades was primarily focused on morphological classifications. Hypertrophic cardiomyopathy (HCM) is characterized by predominant and marked thickening of the left circumferential ventricular wall (i.e., hypertrophy), small LV cavity size and hypercontractility. Such patients including young athletes were prone to sudden cardiac death attributed pathophysiologically to subaortic stenosis and cavity obliteration triggering inadequate cardiac output and lethal arrhythmias. In contrast, dilatation of left ventricular cavity and reduced systolic function are the hallmarks of dilated cardiomyopathy (DCM). In 1991, the Seidmans’ laboratory at Harvard Medical School reported for the first time that mutations in the gene encoding the -myosin heavy chain, a major structural and contractile protein, was the genetic basis for familial HCM associated with sudden death, ushering in the present era of cardiovascular genomic medicine. This seminal discovery permanently shifted the paradigm from the morphological to the molecular, enabling basic insights into disease pathogenesis to be viewed from how single gene defects orchestrate profound alterations at the biochemical, metabolic, hemodynamic, and physiological levels. In parallel, genetically engineered animals models became the state-of-the art for establishing causality

Copyright © 2009, Elsevier Inc. All rights reserved.

Screening

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693

Remodeling pathways Dilated

Ca2ⴙ cycling proteins KATP channel

Desmosome Lamin A/C Metavinculin Cypher/ZASP Desmin Dystrophin-complex

Hypertrophic

Sarcomere proteins

Myosin binding protein C Myosin light chains Metabolic/ storage

Z-disc proteins Mitochondria

Nkx2.5

Heart failure

Figure 59.1 Human gene mutations can cause cardiac hypertrophy (blue), dilation (yellow), or both (green). In addition to these two patterns of remodeling, particular gene defects produce hypertrophic remodeling with glycogen accumulation (pink) or dilated remodeling with fibrofatty degeneration of the myocardium (orange). Sarcomere proteins denote -myosin heavy chain, cardiac troponin T, cardiac troponin I,-tropomyosin, cardiac actin, and titin. Metabolic/storage proteins denote AMPactivated protein kinase  subunit, LAMP2, lysosomal acid  1,4-glucosidase, and lysosomal hydrolase -galactosidase A. Z-disc proteins denote MLP and telethonin. Dystrophin-complex proteins denote -sarcoglycan, -sarcoglycan, and dystrophin. Ca2 cycling proteins denote PLN and RyR2. Desmosome proteins denote plakoglobin, desmoplakin, and plakophilin-2. (Reprinted with permission from Morita et al., 2005.)

and, ultimately, a basis to test proof-of-concept leading to disease prevention. Many more single genetic defects are routinely being linked to familial heart failure (Figure 59.1) but an important future challenge is to establish how inherited and acquired factors conspire to drive the growing epidemic of heart failure. Severe occlusive coronary disease is the substrate for acute coronary syndromes, myocardial infarctions and subsequent pump failure as shown in Figure 59.2. The high prevalence of heart failure in African-Americans with hypertension underscores potential gene–environment interactions in selected populations. Infectious etiologies (e.g., rheumatic heart disease) are declining but valvular heart disease from iatrogenic causes (e.g., diet pills, toxins) remains an important risk factor (Figure 59.2). Viruses (e.g., Coxsackie’s B3, parvovirus) are the major suspected culprits for idiopathic dilated cardiomyopathy (IDCM) in which the postviral sequalae of inflammation and apoptosis trigger ventricular remodeling and dilation (Liu and Mason, 2001). IDCM accounts for 30% of cases of DCM. Heart failure on presentation in the peri-partum or post-partum period has a variable clinical course from severe pump failure to complete recovery. The most common cause of right ventricular heart failure (RVHF) is left ventricular systolic dysfunction. In addition, RVHF is associated with congenital heart disease (e.g., tetralogy of fallot), primary pulmonary hypertension, and arrhythmogenic right ventricular dysphasia and right ventricular infarction. Stress cardiomyopathy is a rare reversible form of left ventricular dysfunction associated clinically with emotional stress, angiographically with “apical ballooning,” and pathophysiologically with excess sympathetic

activation (Wittstein et al., 2005).This entity remains a diagnosis of exclusion, which mimics ST segment elevation MI (STEMI) on presentation, has a much more favorable clinical outcome than STEMI. Lastly, thyrotoxicosis, Paget’s disease and severe chronic anemia are rare causes of high output heart failure. Individuals afflicted with HFPEF are more commonly older age, female gender and have a history of hypertension and atrial fibrillation.

SCREENING The New York Heart Association (NYHA) functional classification scheme, an older but widely used screening tool, assesses the severity of functional limitations of individuals afflicted with heart failure. The four classes of the NYHA classification are linked to increasing severity of signs and symptoms and correlate well with prognosis. This classification scheme, however, has important limitations since diverse pathophysiological processes leading to symptomatic heart failure are overlooked (Dunselman et al., 1988). Accordingly, the American College of Cardiology and American Heart Association (ACC/AHA) Classification of Chronic Heart Failure was developed to account for the multiple stages and predisposition conditions associated with the clinical syndrome. Designed to encompass emerging scientific evidence, an expert panel periodically assembles these updates, which are the most widely used and authoritative sources on the evaluation, management, performance measures, and outcomes on heart failure (Bonow et al., 2005; Hunt et al., 2005; Radford et al., 2005). In turn, these guidelines incorporating preclinical

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Heart Failure in the Era of Genomic Medicine

Current view Inheritable disorders

Toxins alcohol doxurubicin

Myocarditis

Chagas’

Ischemic heart disease

Pressure overload (Hypertension)

Metabolic disorders

Volume overload (MR, AR)

Hemodynamic decompensation (Neurohumoral dysfunction)

Myocardial remodeling

Clinical heart failure

Figure 59.2 The schematic diagram illustrates the different etiologies and multiple compensatory and adaptive pathways implicated in the clinical syndrome of heart failure.

stages, risk factors, pathophysiologic stages, and clinical recognition of heart failure are further subdivided into four stages. Stage A patients are at high risk for developing heart failure, but have had neither symptoms nor evidence of structural cardiac abnormalities. Major risk factors include hypertension, diabetes mellitus, coronary artery disease and family history of cardiomyopathy. In selected patients, the administration of angio-tensin converting enzyme (ACE) inhibitor is recommended to prevent adverse ventricular remodeling. Stage B patients have structural abnormalities from previous myocardial infarction, LV dysfunction or valvular heart disease but have remained asymptomatic. Both ACE inhibitors and beta-blockers are recommended. Stage C patients have evidence for structural abnormalities along with current or previous symptoms of dyspnea, fatigue and impaired exercise tolerance. In addition to ACE inhibitors and beta-blockers, optimal medical regimen may include diuretics, digoxin, and aldosterone antagonists. Stage D patients have end-stage symptoms of heart failure that are refractory to standard maximal medical therapy. Such patients are candidates for left ventricular assist devices (LVADs) and other sophisticated maneuvers for myocardial salvage or end-of-life care.

PATHOPHYSIOLOGY Neurohumoral Mechanisms Low-cardiac output and systemic hypoperfusion elicit a cascade of compensatory mechanisms but predominantly activation of the neurohumoral pathway for augmenting fluid retention (Figure 59.2). Sympathetic nervous system activation increases

heart rate and peripheral vasoconstriction from the release of catecholamines, triggering increased afterload and myocardial oxygen consumption. Catecholamines also increase renin secretion, cell death, fibrosis, and myocardial irritability, underlying substrates for lethal arrhythmias and sudden death. In contrast, natriuretic peptides released from specialized cells in the atria exert hormonal actions in distant vascular beds, stimulating vasodilation, and diuresis. Afterload reducing agents and betaadrenergic blockers have significantly reduced the morbidity and mortality while improving the survival of patients with heart failure. Likewise, antagonists of aldosterone, which promotes salt and water retention, have proven clinical benefits. Myocardial Remodeling Left ventricular dysfunction and systolic heart failure secondary to myocardial infarction or ischemia are the prerequisites of low-ejection fraction and elevated pulmonary pressures with congestion. Acquired or inherited conditions that either decrease cardiomyocyte viability and/or increase cell death will ultimately trigger pump failure and symptomatic heart failure. Given the heart’s limited capacity for regeneration, terminally differentiated ventricular cardiomyocytes may undergo hypertrophy in response to increase metabolic and homodynamic demands. Activation of the “fetal gene program” orchestrates transcriptional upregulation of genes encoding contractile and cytoskeletal proteins – the prerequisite for compensatory hypertrophy. Recruitment of such adaptive mechanisms provides a variable but stable and asymptomatic interval – perhaps lasting years – before cardiac decompensation. The ensuing ventricular dilatation is a pathologic form of adaptation, termed “ventricular remodeling,” affecting intrinsic cardiac mass, the extracellular matrix, collagen deposition and fibrosis as shown in Figures 59.2 and 59.3. Whereas low levels of reactive oxygen species (ROS) serve as stress signals in

Diagnosis

(a)

695

Ventricular remodeling after acute infarction

Initial infarct (b)

■

Expansion of infarct (hours to days)

Global remodeling (days to months)

Ventricular remodeling in diastolic and systolic heart failure

Normal heart

Hypertrophied heart (diastolic heart failure)

Dilated heart (systolic heart failure)

Figure 59.3 Ventricular remodeling after infarction (a) and in diastolic heart failure (b). (Adapted from Jessup et al. N Engl J Med 348, 2007–2018.)

redox-dependent regulation, elevated levels of ROS caused by mitochondrial dysfunction may alter myocardial energetics, cardiac metabolism, and trigger the release of cytochrome c, thereby activating cell survival/death pathways. Endothelial dysfunction gives rise to the aberrant release of nitric oxide, a potent vasodilator, and/or reactivity with ROS to form peroxynitrite, which causes oxidative damage and cellular injury. Progressive remodeling, in attempts to maintain systolic function and homeostasis (Stage B), leads to valvular regurgitation from inadequate apposition of the mitral leaflets, increasing myocardial stress and, ultimately, decompensated heart failure (Stage C and Stage D). Mechanisms of Cell Death in Heart Failure Progressive loss of cardiomyocytes from either necrosis or apoptosis with diverse pathogenic states contributes to the pathogenesis of heart failure as shown in Table 59.1 and Figure 59.4 (Liew and Dzau, 2004; Wencker et al., 2003). Apoptosis or programmed cell death is activated by signaling cascades, via either the extrinsic or intrinsic cell survival/death pathways (Danial and Korsmeyer, 2004). Ligands such as TNF-, which bind to cognate receptors at the plasma membrane, mediate cell death through the extrinsic pathway, whereas the Bcl-2 family – consisting of both pro- and anti-apoptotic proteins – regulates the intrinsic pathway. Mitochondria play a central role in cell survival/death principally from the initiation of stress signals (e.g., ROS) and release of mitochondrial cytochrome c, which initiates complex formation and the activation of apoptotic

proteases (e.g., caspase-9) (Danial and Korsmeyer, 2004).The role of apoptosis in chronic heart failure, which ranges between 80 and 250 myocytes per 100,000 nuclei in failing human hearts was elegantly validated by Wencker and coworkers using transgenic mice harboring a fusion protein FKBP fused with a conditionally active caspase (Wencker et al., 2003). In contrast, low-level inhibition of apoptosis prevented DCM and death, suggesting possible therapeutic strategies for combating heart failure.

DIAGNOSIS Newly diagnosed patients with heart failure most commonly seek medical attention for either gradual or abrupt onset of the classical signs and symptoms with pulmonary congestion. The clinical spectrum varies widely but dyspnea on exertion, peripheral edema, orthopnea, and paroxysmal nocturnal dyspnea are not uncommon. Exertional chest pain or angina at rest requires an immediate evaluation to determine if biochemical evidence of myocardial damage demands more aggressive management for acute coronary syndromes. Elevated jugular venous distension from right ventricular failure, ascites, and cachexia are more ominous signs for low-cardiac output and decompensation, requiring urgent attention, preferably, from a provider who specializes in heart failure management. Routine diagnostic studies include an electrocardiogram, chest radiograph, and B-type natriuretic peptide, the latter having the best predictive value for distinguishing between CHF and

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Heart Failure in the Era of Genomic Medicine

TABLE 59.1

The genetic basis of cardiomyopathies

Symbol

Chromosome

Gene product

Cardiomyopathy type

ACTC

15q11–14

Cardiac muscle -actin

Hypertrophic and dilated

ABCC9

12p12.1

Member 9 of the superfamify C of ATP-binding cassette (ABC) transporters

Dilated

CSRP3, MLP

11p15.1

Cysteine- and glycine-rich protein 3

Dilated

DES

2q35

Desmin

Dilated

DSP

6p24

Desmopfakin

Dilated

LMNA

1q21.2–21,3

Lamin A/C

Dilated

VCL

10q22.1–q23

Metavlnculin

Dilated

MYBPC3

11p11.2

Cardiac myosin-binding protein C

Hypertrophic and dilated

MYH6

14q12

Cardiac muscle a-isoform of myosin heavy chain (heavy polypeptide 6)

Hypertrophic

MYH7

14q12

Cardiac muscle a-isoform of myosin heavy chain (heavy polypeptide 7)

Hypertrophic and dilated

MYL2

12q23–24.3

Myosin regulatory light chain associated with cardiac myosin-(or slow) heavy chain

Hypertrophic

MYL3

3p21.2–21.3

Myosin light chain 3

Hypertrophic

PLN

6q22.1

Phospholamban

Dilated

PRKAG2

7q35–36

2 non-catalytic subunit of AMP-activated protein kinase

Hypertrophic

SGCB

4q12

-Sarcoglycan (43kDa dystrophin-associated glycoprotein)

Dilated

SGCD

5q33–34

-Sarcoglycan (35kDa dystroph in-associated glycoprotein)

Dilated

TAZ, G4.5

Xq28

Tafazzin

Dilated

TTN

2q31

Titin

Hypertrophic and dilated

TCAP

17q12

Titin-cap

Dilated

TPM1

15q22.1

Tropomyosin 1 ()

Hypertrophic

TNNI3

19q13.4

Troponin I, a subunit of the troponin complex of the thin filaments of striated muscle

Hypertrophic

TNNT2

1q32

Cardiac isoform of troponin T2, tropomyosin-binding subunit of the troponin complex

Hypertrophic and dilated

Adapted from Dzau et al., Nat Rev Genet 5, 811–825 (2004).

non-CHF patients (Maisel and McCullough, 2003). Noninvasive echocardiography is the most commonly used diagnostic tool for the assessment and follow-up of patients with heart failure with or without preserved ejection fraction. Coronary angiography should be performed to exclude reversible causes for left ventricular dysfunction or to guide prompt revascularization. If the coronary vessels are widely patent in the setting of global dysfunction, then endomyocardial biopsy should be considered to assess for reversible causes including viral myocarditis (Liu and Mason, 2001). Equilibrium radionucleotide angiography (ERNA) is another noninvasive diagnostic study that assesses both left and right ventricular systolic function. Screening tools such as contrast computer tomographic angiography and

magnetic resonance imaging (MRI) are gaining attention as emerging technologies with equivalent sensitivity and specificity as the invasive angiogram for coronary arteriography. MRI may also uncover unsuspected infiltrative cardiomyopathy, arrhythmogenic right ventricular dysplasia, and is superior for the assessment of myocardial viability before revascularization.

PROGNOSIS Over 5 million Americans or 1.5% of the US population have chronic heart failure, and there is a similar prevalence at risk of undiagnosed left ventricular dysfunction (Braunwald and

Pharmacogenomics

■

697

Emerging view

Defective chaperones

Metabolic disorders

Apoptosis and necrosis

Tachycardia

Fibrosis

Ischemic heart disease

Pressure overload (hypertension)

Oxidative stress

Volume overload (MR, AR)

Calcium (etc.) dysregulation

Myocarditis

Sarcomeric protein mutations

Mitochondrial dysfunction

Clinical heart failure Figure 59.4 At the cellular and molecular levels, crosstalk among pathways related to oxidative stress and calcium dysregulation, for example, may contribute to secondary apoptosis and necrosis. In spite of considerable insights about mechanisms, current therapies focus on reversing neurohumoral imbalances but rarely on underlying mechanisms.

Bristow, 2000). With over 550,000 new cases of heart failure, the disproportionate health and economic burden exceeds 24 billion dollars annually (DiBianco, 2003). Soon, heart failure will become the number one cause of death worldwide, eclipsing infectious disease (Bleumink et al., 2004). Whereas new pharmacologic management and revascularization techniques continue to improve the survival after acute myocardial infarction, the prevalence of chronic heart failure appears to be increasing as the population ages (Braunwald and Bristow, 2000). Notwithstanding, heart failure accounts for 20% of all hospital admissions in patients older than 65, and the hospitalization rate has increased by 159% in the past decade (Jessup and Brozena, 2003). Available treatments for heart failure have only modestly improved the morbidity and mortality (Jessup and Brozena, 2003), and, for patients with advanced heart failure, the prognosis still remains grim with 1-year mortality rates between 20% and 45%, overshadowing the worse forms of some cancers (Jessup and Brozena, 2003).

PHARMACOGENOMICS Substantial phenotypic heterogeneity in heart failure and the variability of responses among individuals taking pharmacologic agents have been attributed to common polymorphisms in the genome (Liggett, 2001). A surmountable hurdle, however, is the robustness of the association between putative genetic markers and therapeutic response. Recent lessons from studies of human heart failure illustrate handsomely both the enormous potential and challenges for pharmacogenomics – a maturing discipline in which an individual’s genetic determinants are used to predict drug response and outcomes (Evans and Relling, 1999; Liggett, 2001). Liggett and coworkers have demonstrated that non-synonymous single-nucleotide polymorphisms of the

1-adrenergic receptor (1-AR), a member of the seven membrane-spanning receptor superfamily, alters the therapeutic response to -blockers during heart failure (Liggett et al., 2006). Stimulatory effects between 1-AR and heterotrimeric G proteins Gs mediate both beneficial and deleterious signal transduction pathways during the onset and progression of heart failure. Because 1-AR is the major subtype in cardiac myocytes, increased catecholamines exert potent cardiomyopathic effects, cardiac remodeling and abrogation of gene expression, which are antagonized by 1-AR blockers resulting in improved outcomes. A single nucleotide variation at nucleotide 1165 in the gene encoding 1-AR results in either Arg or Gly at position 389 residue (Liggett et al., 2006). In response to inotropic stimulation, human trabeculae muscle with the 1-Arg-389 residue from either nonfailing or failing hearts exhibited significantly greater contractility than 1-Gly-389 polymorphism. As shown in the -Blocker Evaluation of Survival Trial (BEST), which evaluated the -blocker bucindolol for the treatment of Class III/IV, insights into the mechanisms for pharmacogenomic phenotypes involving the Arg/Gly polymorphism of the 1-AR owe much credit to the DNA Study Group (Feldman et al., 2005; Liggett et al., 2006).The foresight of BEST investigators to recognize the power of genetic haplotyping underscores the importance for all future well-designed human trials to include contingencies for pharmacogenomics in an era of genomic medicine. Notwithstanding the success gleaned from a highly penetrant single-gene trait such as Arg/Gly polymorphism of the -AR, future advances will require undertaking the more formidable challenges related to multigene traits that influence drug metabolism and response for therapeutic individualization (Evans and McLeod, 2003). The recent African-American Heart Failure (A-HeFT) enrolled 1050 black patients with NYHA class III or IV to receive a fixed dose of two well-established medications, isosorbide

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dinitrate and hydralazine, in a placebo controlled ran-domized multicenter trial. Combination nitrates and hydralazine, termed BiDil, when added to standard therapy was efficacious improving survival in blacks. But the implications of this high-profile study have drawn considerable scientific and ethical scrutiny owing to the marketing strategy of this therapy, which under the proprietary label, was advanced as a novel approach for racebased management. Because physical and genetic traits are not interchangeable, A-HeFT per se might prove to be poor surrogate for studies of pharmacogenetics since neither BiDil’s efficacy in other racial and ethnic groups nor genetic markers for predicting the response of blacks to BiDil were ever tested. In contrast, polymorphisms in the ACE pathway have been extensively studied, especially the ACE DD polymorphism, which had significantly higher death and need for transplant compared to II and ID genotypes (McNamara et al., 2001). With concurrent -blocker treatment, patients with ACE DD polymorphism showed improved survival but benefited with a higher ACE dosage (McNamara et al., 2004), supporting the clinical utility of genetic information in clinical management. For at-risk populations, the pace for moving bidirectional bench  bedside and bedside  community-based practices should accelerate using evidence-based strategies emerging from disciplines such as health outcomes.

MONITORING Genomic Profiling Heart failure encompasses dynamic processes in which the activation or deactivation of distinct pathways at different stages in the pathogenesis suggest opportunities for intervention and even prevention before irreversible decompensation. A fundamental question, therefore, is how to develop improved diagnostic and prognostic indices that may guide improvements in treatment and outcomes for heart failure. A major goal of microarray-based analyses is to identify genes whose similar patterns of expression accurately represent the disease state or biological process. Such information, however, is often insufficient to identify the causal mechanisms but provides a comprehensive picture of the underlying process, which can predict responses to therapy or disease stage. Both unsupervised and supervised approaches are applied to determine if previously unrecognized or unexpected patterns of expression exist in the datasets. Hierarchical clustering, for example, is an unsupervised approach that may be used after gene expression profiling to identify interdependent pathways before the onset of overt heart failure. Identification and validation of genes or novel pathways that are activated earliest may improve early detection and, ultimately, will be essential for designing therapies that prevent the natural history and progression of disease. If individual genes have different predictive power, then a “weighted voting scheme,” based on the levels of gene expression, can be designed and tested before widespread application. Considerable caution in data interpretation is warranted, however,

as comparisons from different laboratories may be skewed considerably by patient selection, different treatments, and clinical stages. While many technical details and microarrays are beyond the scope of this chapter, considerable attention is being paid to standardization of data collection, normalization, and data reporting. Most peer-reviewed journals have instituted policies for primary sequence databases to be deposited before publication using the “minimal information about a microarray experiment” or MIAME standards (Ball et al., 2002; Ball et al., 2004; Brazma et al., 2001). We predict that meta analyses of large datasets hold particular promise for finding universal themes for classification, clinical staging, and predicting outcomes from gene expression profiles. Transcriptional Profiling of Heart Failure Neurohumoral, hemodynamic and environmental factors participate in remodeling the failing heart, but genetic, molecular, and cellular events are inscribed at the transcriptional level. Signaling pathways and biological processes implicated in the hypertrophic response of the heart are shown in Figure 59.4. Early genetic markers of cardiac hypertrophy include transcriptional reprogramming of genes encoding contractile proteins, oncogenes, neurohumoral factors, and transcription factors have been identified. Genes encoding proto-oncogenes c-jun, c-fos, c-myc, skeletal -actin and ANF are also activated in response to hypertrophic stimuli. (Izumo et al., 1988; Komuro et al., 1988; Mulvagh et al., 1987; Schwartz et al., 1986). In angiotensin II receptor type 1alpha knockout mice, cardiomyocytes were capable of evoking increased protein synthesis and mitogen-activated protein kinase (MAPK) activation when stretched, strengthening the primary role of mechanical stretch in maintaining the hypertrophic phenotype. The mechanisms by which mechanical stress is converted into biological response are yet to be fully elucidated. High-density oligonucleotide arrays have also identified multiple genes, representing diverse biological process (e.g., myocardial structure, myocardial assembly and degradation, metabolism, protein synthesis, and stress response), which were differentially expressed in nonfailing and failing human hearts (Yang et al., 2000). Other larger studies of human heart failure have confirmed the role for MAPKs, mechanical stress and neurohumoral pathways in heart failure (Kudoh et al., 1998). Likewise, genetic pathways identified during acute and chronic pressure overload reflect differential gene expression during distinct phases may represent potential target for therapy. A substantial limitation, however, remains that lack of reproducibility and reliability in the sample sets owing to selection bias and differences in etiology, age, sex, mode of onset, treatment regimens and clinical course. End-stage heart failure is associated with an increased activity and alterations of multiple gene products including the extracellular matrix/cytoskeletal (e.g., collagen types I and III, fibromodulin, fibronectin, and connexin 43) (Tan et al., 2002). When gene expression profiling was applied in a transgenic model of tumor necrosis factor- overexpression, a large number of immune response-related genes were over-expressed, along with a IgG deposition in myocardium, supporting activation of immune system and inflammatory mechanisms in the development and

Monitoring

progression of heart failure (Feldman and McTiernan, 2004; Kubota et al., 1997). Gene expression profiles of heart failure caused by alcoholic cardiomyopathy and familial cardiomyopathy suggest that the onset and disease progression may involve different genetic determinants. A provocative study by Blaxall and coworkers has reported that genomic profiling in a murine model of heart failure reverted to the normal phenotype after rescue by expression -adrenergic receptor kinase and suggested mild and advanced heart failure maybe similar in mice and humans (Blaxall et al., 2003). As previously mentioned, the conclusions must be interpreted cautiously owing to the complexities of heart failure related to the imprecision associated with genetic, physiologic, and clinical phenotypes. A Case for Biologic Reclassification of Heart Failure Gene expression profiling has significantly improved the diagnostic classification of specific conditions (e.g., breast cancer, chronic myelogenous leukemia) but remains a formidable challenge for deciphering meaningful insights about the biological mechanisms underlying disease pathogenesis (Quackenbush, 2006). Among inheritable forms of cardiovascular diseases, recent advances of single-gene disorders have fundamentally altered our understanding about the cellular processes, metabolic alterations and transcriptional reprogramming of the diseased heart (Seidman and Seidman, 2001). Much like the success seen for tumor classification and other improvements in cancer therapeutics (Bell, 2004; Quackenbush, 2006), and beyond the availability of genetic tests for disease-causing mutations of cardiomyopathy (Morita et al., 2005), the development of genomic tools that are causally linked to disease pathogenesis, termed a “molecular signature,” will likely accelerate progress for early detection, targeted therapy and disease monitoring of inheritable heart failure (Bell, 2004). We suggest that the opportunities exist for microarray-based profiling, proteomics, metabolomics, and genome-wide technologies to propel the transition from clinicopathologic to clinico-genomic classifications for heart failure. Different gene profiles for failing and nonfailing hearts have already permitted differentiation among heart failure with different etiologies, as shown recently by Donahue and colleagues in Table 59.2 (Donahue et al., 2006). Considerable discordance, however, exists as our ability to diagnose heart failure using genomic profile lags substantially behind clinical management. Important obstacles remain, such as limitations in procuring tissue samples needed for genomic profiles and their transition from use as research tools into the realm of clinical diagnostics. Biomarkers Versus Biosignatures for Heart Failure Considerable biological heterogeneity of heart failure demands more robust tools to guide clinical outcomes. Much recent attention has focused on biological markers, or biomarkers, which objectively measure and evaluate normal biological processes, pathologic process, or pharmacological response to therapeutic intervention (Vasan, 2006). Current enthusiasm for biomarker strategies, however, has also brought confusion and ambiguity for applications in clinical practice. Too often, highly fragmented

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information obtained from patients at different clinical stages precludes meaningful analysis and extrapolation to broader subclasses. Accordingly, we propose that an integrative approach – that encompasses our ability to predict the onset, rate of progression, and response to therapy and/or clinical outcome with reproducibility and reliability may circumvent such limitation of biomarkers – requires the development of a molecular signature or “biosignature.” In order to circumvent existing limitations of biomarkers, proof-of-concept for such biosignatures, however, may require tissue sampling and serial analysis for identification and validation (Figure 59.5). Among eight individuals with IDCM but with similar clinical characteristics for chronic heart failure at baseline, Lowes and colleagues reported that serial sampling was superior to cross-sectional gene expression profiling since there was less variance in the differences on gene chip analysis of endomyocardial biopsies (EMB) from the same patient than among the different subjects with similar phenotypes (Lowes et al., 2006). Because these biological processes may precede the transition into heart failure and premature death, future work should address the intriguing possibility that distinct metabolic pathways might be linked to novel molecular signatures in disease pathogenesis (Figure 59.5). Protein aggregation cardiomyopathy (PAC) (also termed desmin-related myopathy – DRM) is a multi-system disease, caused by the missense R120G mutation in the gene encoding the human small HSP B-crystallin (hR120GCryAB).To understand the pathogenic mechanisms by which hR120GCryAB expression causes cardiotoxicity and heart failure, our group has shown in recent genetic studies in mice that selective hR120GCryAB expression in the heart induces a novel toxic gain-of-function mechanism involving reductive stress, apparently emanating from increased activity of glucose 6-phosphate dehydrogenase (G6PD) (Rajasekaran et al., 2007). Reductive stress refers to an abnormal increase in the amounts of reducing equivalents (e.g., glutathione, NADPH), which has been demonstrated in lower eukaryotes (Simons et al., 1995;Trotter and Grant, 2002) but has not been commonly shown in the mammals and/or in disease states (Chance et al., 1979;Gores et al., 1989). Such genetic evidence, that dysregulation of G6PD activity is a causal mechanism for R120GCryAB cardiomyopathy, forms the rationale for ideas related to metabolic and genetic pathways that might codify biosignatures. What metabolic changes occur before the onset of detectable myopathic or pathologic alterations, and how does such imbalance contribute to cardiomyopathy and heart failure? Does reductive stress exert direct or indirect consequences on mitochondrial (dys)function? Applications in redox proteomics and multiplex protein markers are presently being pursued to determine if glutathionylation, for example, of key components in mitochondrial and other metabolic pathways are causally linked to disease pathogenesis. Molecular Diagnosis of Allograft Rejection Although peripheral blood mononuclear cells (PBMCs) are abundant and highly accessible sources of genomic material, a potential for diagnostic inaccuracy and therapeutic failure exists

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TABLE 59.2

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Heart Failure in the Era of Genomic Medicine

Discovery projects in heart failure

Comparison

Subjects

Platform

Finding

Failing versus non-failing

2 cases (1 ICM and 1 DCM) 2 control cases

Affymetrix Hu 6800

Alterations of expression of cytoskeletal and myofibrillar genes, genes encoding stress proteins, and genes involved in metabolism, protein synthesis, and protein degradation

Failing versus non-failing

7 cases (DCM) 5 control cases

Cardiochip (custom array)

Upregulation of genes for atrial natriuretic peptide, sarcomeric and cytoskeletal proteins, stress proteins, and transcription/translation regulators Down-regulation of genes regulating calcium signaling pathways

Failing versus non-failing

8 cases (DCM) 7 control cases

Affymetrix Hu 6800

103 differentially expressed genes with most prominent being atrial natriuretic factor and brain natriuretic peptide

Failing versus non-failing

10 cases (DCM) 4 control cases

Custom arrays

364 differentially expressed genes Up-regulation being most prominent in genes for energy pathways, muscle contraction, electron transport, and intracellular signaling Down-regulation was most prominent in genes for cell cycle control

Failing versus non-failing

9 cases (5 ICM and 4 DCM) 1 control case

Affymetrix HG-U95A

95 differentially expressed genes with notable upregulation of atrial natriuretic peptide and brain natriuretic peptide Prominent pathways up-regulated include cell signaling and muscle contraction

Failing versus non-failing

6 cases (DCM) 5 control cases

Affymetrix HG-U133A

165 differentially expressed genes, the most prominent being structural and metabolic genes

Failing versus non-failing

5 cases (DCM) 5 control cases

Custom array for apoptotic pathways

Differentially expressed genes in apoptotic pathways

Pre- and post-left ventricular assist device

6 cases (3 DCM and 3 ICM)

Affymetrix Hu 6800

530 differentially expressed genes (295 up and 235 down) with prominent changes in genes for metabolism

Pre- and post-left ventricular assist device

7 cases (DCM)

Affymetrix HG-U133A

179 differentially expressed genes (130 up and 49 down There was prominent up-regulation in nitric oxide pathways and down-regulation of inflammatory genes

Pre- and post-left ventricular assist device

19 cases (8 DCM and 11 ICM)

Affymetrix HG-U133A

107 differentially regulated genes (85 up and 22 down) Prominent was the up-regulation of genes regulating vascular networks and down-regulation of genes regulating myocyte hypertrophy

HCM and DCM versus non-failing

3 DCM 2 HCM 3 control cases

Cardiochip (custom array)

Multiple genes and pathways up- and down-regulated some common to DCM and HCM some distinct to each

DCM  dilated cardiomyopathy; HCM  hypertrophic cardiomyopathy; ICM  ischemic cardiomyopathy.

if there is discordance between the information in PBMCs and underlying condition in the diseased tissues. Significant progress has been made for patients after cardiac transplantation, which could change existing paradigms for clinical decision-making and management of allograft rejection. Standard protocols after heart transplantation requires patients to undergo serial EMB as a means to monitor for rejection and to guide immunosuppressive

therapy. Such surveillance maneuvers are invasive, expensive and carry considerable risks such as perforation of the ventricular wall and hemopericardium. Analysis of the histological data by expert pathologists is subject to inter-observer variability and the diagnosis of acute rejection has been controversial (Nielsen et al., 1993; Winters and McManus, 1996). Horwitz and colleagues were among the first to demonstrate that gene expression

Novel Therapeutics and Future Directions

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701

New view Defective chaperones

Metabolic disorders

Force generation and transmission

Tachycardia

Pressure overload (hypertension)

Ischemic heart disease

Dysregulation of GSH metabolism

Stress response pathways

Volume overload (MR, AR)

Catabolic energy transfer

Myocarditis

Sarcomeric protein mutations

Oxidative phosphorylation

Biosignatures for clinical heart failure ? Personalized medicine Figure 59.5 New diagnostic approaches, based on information that integrates genes and molecular pathways at the onset, progression and end stages are needed to improve heart failure classification. “Biosignatures” for heart failure – developed from microarray analysis technologies, proteomics and genomic technologies – are proposed here to integrate the biological processes and molecular mechanisms for rationale drug design and treatment in the post genomic era of personalized medicine.

profiles of PBMCs might provide an alternative approach of the diagnosis of allograft rejection (Horwitz et al., 2004). Patients who subsequently developed acute rejection had a distinct genomic profile compared with patients without any rejection and, after treatment for rejection, the majority (98%) of differentially expressed genes returned to baseline. The CARGO (Cardiac Allograft Rejection Gene Expression Observational) study prospectively investigated gene expression analysis from PMBCs as a diagnostic tool to predict transplant rejection (Mehra, 2005). From the core group of 11 genes associated with immune response pathways, which were identified by quantitative real-time polymerase chain reaction (QT-PCR) and assigned weighted scores, CARGO investigators were able to predict rejection with a sensitivity and specificity of 80% and 60%, respectively (Deng et al., 2006). Owing to reduced sensitivity and specificity immediately after transplantation, the test may also be unreliable for the diagnosis of low/intermediate grade rejection. Now commercially available (AlloMap®), this landmark study provides proof-of-concept that gene expression profiling in PBMCs are predictive for acute rejection pathways in cardiac transplant patients. One important implication is that genomic profiling of specific targets expressed in peripheral blood will increasingly be used as a sensitive marker for transplant rejection but direct evidence that such monitoring should guide therapeutic management awaits further independent validation.

NOVEL THERAPEUTICS AND FUTURE DIRECTIONS A Primer on Cardiac Progenitor Cells for Myocardial Regeneration In the era of genomic medicine, among the many approaches being investigated are efforts to exploit the resident population of cardiac progenitors for regeneration or repair of damaged myocardium. Cardiac embryogenesis proceeds from the parallel contributions of two separate progenitor cell populations derived from a common progenitor at gastrulation: namely, the primary and secondary “heart fields” (Garry and Olson, 2006; Kelly et al., 2001). Genetic and morphogenic programs give rise to the mesoderm-derived primary heart field, the earliest population of cardiac progenitors, originating in the splanchnic mesoderm and subsequently migrating into cardiac crescent. Cells from the cardiac crescent proceed to form the midline linear heart tube, consisting of the inner endocardial and outer myocardial layer. The four-chambered heart arises from rightward looping, differential growth and from contributions of the secondary or anterior heart field (Buckingham et al., 2005). Whereas precursors of the primary heart field ultimately contribute to the developing left ventricle and atria, the right ventricle and outflow track are derived from secondary heart field. A landmark study by Laugwitz and colleagues has challenged existing dogma by demonstrating isl1 cardiac progenitor cells with replicative

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capacity can be isolated from the adult mouse, rat and human heart (Laugwitz et al., 2005). Fate mapping studies indicate isl11 progenitor cells migrate into the outflow region and contribute into the right atrium, right ventricle and partial left ventricle. Embryonic precursors of distinctive origins give rise to three cardiac lineages – cardiac, smooth muscle, and endothelial cell – constituting the mature heart. Until recently, plasticity of the adult heart, however, was widely disbelieved due to the prevailing dogma that this postmitotic, terminally differentiated organ was devoid of regenerative capacity. A new era for cardiac biology has emerged with the discovery of a resident subpopulation of myocardial cells with the capacity for replication, termed “cardiac progenitors,” in both normal and pathological hearts. The discovery of cardiac progenitor cells in the adult mammalian heart has intensified recent efforts to develop strategies for repairing or regenerating injured myocardial tissues after hearts attacks. If cardiac progenitor cells isolated from patients undergoing selective cardiac procedures have the characteristics for clonagenicity, self-renewal and multipotentiality, then tissue samples will be obtained from patients undergoing coronary artery bypass surgery, surgical repair or replacement for valvular heart disease and LVAD become optimal sources for regenerative therapies. Based on published reports in mice, the left ventricular apex and left atrium regions are the optimal sites for harvesting cardiac progenitors but cardiac niches with progenitors have been identified in the midregion and basal regions (Urbanek et al., 2006). Future studies are needed to determine if symmetric and asymmetric cell division will promote the differentiation of pluripotent progenitor cells into distinct lineages of the mature human heart. If age, gender, risk factors, and other disease status will have an impact on the plasticity, proliferation or cellular functions of cardiac progenitors are currently outstanding questions. Another future goal is to determine if genetic determinants of cardiac progenitor cells will enhance their cellular and molecular properties to survive and reconstitute the welldifferentiated myocardium in vivo. Cardiac Cell Therapy Because existing therapies for heart failure are palliative and do not address the root cause of the disease, considerable enthusiasm has been generated recently with the prospects of cardiac cell therapy for myocardial regeneration (Orlic et al., 2001) or neovascularizaton (Kocher et al., 2001). Is the administration of progenitors cells derived from bone marrow or circulating blood feasible, safe, and efficacious? To date, however, results of several human trials are either equivocal or show modest improvement in selected endpoints but not clinical outcomes. In the Reinfusion of Enriched Progenitor Cells and Infract Remodeling in Acute Myocardial Infarction (REPAIR-AMI) trial, the largest study of cardiac stem therapy, patients who received an intracoronary infusion of progenitors derived from bone marrow cell (BMC) after successful percutaneous intracoronary intervention for acute myocardial infarction showed an absolute improvement in LVEF at 4 months compared with the placebo group (Schachinger et al., 2006). In contrast, results

of the Bone Marrow Transfer to Enhance ST-Elevation Infarct Regeneration (BOOST) trial have dampened enthusiasm since the relative improvement in LVEF at 6 months was abolished at 18 months (Meyer et al., 2006). An important question for clinical and translational scientists is whether cardiac cell therapy has efficacy for the current victims with chronic heart failure. The TOPCARE-CHD trial – Transplantation of Progenitor Cells and Recovery of LV Function inpatients with Chronic Ischemic Heart Disease – enrolled patients with LV dysfunction but healed scars an average of 6 years after myocardial infarction. Patients receiving BMC showed greater improvement in global and regional LV function compared with a similar cohort receiving progenitors derived from circulating blood cells (Assmus et al., 2006). Notwithstanding, effective local regeneration by either BMC or circulating progenitors appears limited owing to follow-up animals studies indicating that the majority of infused cells (97%) exit the myocardium (Hofmann et al., 2005). While the proof-of-concept for cardiac cell therapy appears to be valid, an immediate challenge for the field is to provide evidence for improvement in clinical outcomes for patients with chronic heart failure.

CONCLUSIONS AND RECOMMENDATIONS Current therapeutic interventions for heart failure primarily target the end-stage manifestations (e.g., volume overload), without regard for the etiology and, often, with unpredictable consequences for the individual patient. If the goals of personalized medicine will soon be realized, then significant breakthroughs in prevention, early detection, targeted therapies, and enhanced monitoring of disease states are essential to stem the looming heart failure epidemic in the genomic era. From the Human Genome Project and HapMap Consortium (Collins et al., 2003), computational analyses and molecular methods are beginning to influence diagnostics, guide therapies and, ultimately, inform us about preventative measures. Although specific therapies for many heritable cardiac diseases have lagged substantially behind advances in other fields, new opportunities for improving their diagnosis, prognosis and clinical outcomes now seem within reach. Recommendations 1. Codify and foster stronger relationship among patients, providers, and institutional review boards (IRB) as the “tripod” for genomic medicine; 2. Accelerate the collection of tissue samples from patients with unexplained heart failure; 3. Focus more attention on discovery of basic disease mechanisms to identify “biosignatures” as the new basis for molecular classifications; 4. Encourage investments on “high quality” therapeutic targets to inevitably drive personalized medicine; 5. Use of such targeted therapies will supplant gene replacement therapy for inherited heart disease.

References

ACKNOWLEDGEMENTS

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Lori Kaumans provided excellent editorial assistance during preparation of this manuscript.

An award from NHLBI (5R01 HL63874) and Christi T. Smith Foundation provided support for this work. Krista Boeger and

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reduces remodeling and improves cardiac function. Nat Med 7, 430–436. Komuro, I., Kurabayashi, M., Takaku, F. and Yazaki,Y. (1988). Expression of cellular oncogenes in the myocardium during the developmental stage and pressure-overloaded hypertrophy of the rat heart. Circ Res 62, 1075–1079. Kubota, T., McTiernan, C.F., Frye, C.S., Slawson, S.E., Lemster, B.H., Koretsky, A.P., Demetris, A.J. and Feldman, A.M. (1997). Dilated cardiomyopathy in transgenic mice with cardiac-specific overexpression of tumor necrosis factor-alpha. Cir Res 81, 627–635. Kudoh, S., Komuro, I., Hiroi, Y., Zou, Y., Harada, K., Sugaya, T., Takekoshi, N., Murakami, K., Kadowaki, T. and Yazaki, Y. (1998). Mechanical stretch induces hypertrophic responses in cardiac myocytes of angiotensin II type 1a receptor knockout mice. J Biol Chem 273, 24037–24043. Laugwitz, K.L., Moretti, A., Lam, J., Gruber, P., Chen, Y., Woodard, S., Lin, L.Z., Cai, C.L., Lu, M.M., Reth, M. et al. (2005). Postnatal isl1 cardioblasts enter fully differentiated cardiomyocyte lineages. Nature 433, 647–653. Liew, C.C. and Dzau, V.J. (2004). Molecular genetics and genomics of heart failure. Nat Rev Genet 5, 811–825. Liggett, S.B. (2001). Pharmacogenetic applications of the Human Genome project. Nat Med 7, 281–283. Liggett, S.B., Mialet-Perez, J., Thaneemit-Chen, S., Weber, S.A., Greene, S.M., Hodne, D., Nelson, B., Morrison, J., Domanski, M.J., Wagoner, L.E. et al. (2006). A polymorphism within a conserved beta(1)-adrenergic receptor motif alters cardiac function and beta-blocker response in human heart failure. Proc Natl Acad Sci USA 103, 11288–11293. Liu, P.P. and Mason, J.W. (2001). Advances in the understanding of myocarditis. Circulation 104, 1076–1082. Lowes, B.D., Zolty, R., Minobe, W.A., Robertson, A.D., Leach, S., Hunter, L. and Bristow, M.R. (2006). Serial gene expression profiling in the intact human heart. J Heart Lung Transplant 25, 579–588. Maisel, A.S. and McCullough, P.A. (2003). Cardiac natriuretic peptides: a proteomic window to cardiac function and clinical management. Rev Cardiovas Med 4(Suppl 4), S3–S12. Mehra, M.R. (2005). The emergence of genomic and proteomic biomarkers in heart transplantation. J Heart Lung Transplant 24, S213–S218. Meyer, G.P., Wollert, K.C., Lotz, J., Steffens, J., Lippolt, P., Fichtner, S., Hecker, H., Schaefer, A., Arseniev, L., Hertenstein, B. et al. (2006). Intracoronary bone marrow cell transfer after myocardial infarction: eighteen months’ follow-up data from the randomized, controlled BOOST (BOne marrOw transfer to enhance ST-elevation infarct regeneration) trial. Circulation 113, 1287–1294. Morita, H., Seidman, J. and Seidman, C.E. (2005). Genetic causes of human heart failure. J Clin Invest 115, 518–526. Mulvagh, S.L., Michael, L.H., Perryman, M.B., Roberts, R. and Schneider, M.D. (1987). A hemodynamic load in vivo induces cardiac expression of the cellular oncogene, c-myc. Biochem Biophys Res Commun 147, 627–636. Nielsen, H., Sorensen, F.B., Nielsen, B., Bagger, J.P., Thayssen, P. and Baandrup, U. (1993). Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System. J Heart Lung Transplant 12, 239–243. Orlic, D., Kajstura, J., Chimenti, S., Jakoniuk, I., Anderson, S.M., Li, B., Pickel, J., McKay, R., Nadal-Ginard, B., Bodine, D.M. et al. (2001). Bone marrow cells regenerate infarcted myocardium. Nature 410, 701–705.

Owan, T.E., Hodge, D.O., Herges, R.M., Jacobsen, S.J., Roger, V.L. and Redfield, M.M. (2006). Trends in prevalence and outcome of heart failure with preserved ejection fraction. New Engl J Med 355, 251–259. Park, J., Choe, S.S., Choi, A.H., Kim, K.H., Yoon, M.J., Suganami, T., Ogawa, Y. and Kim, J.B. (2006). Increase in glucose-6-phosphate dehydrogenase in adipocytes stimulates oxidative stress and inflammatory signals. Diabetes 55, 2939–2949. Quackenbush, J. (2006). Microarray analysis and tumor classification. New Engl J Med 354, 2463–2472. Radford, M.J., Arnold, J.M., Bennett, S.J., Cinquegrani, M.P., Cleland, J.G., Havranek, E.P., Heidenreich, P.A., Rutherford, J.D., Spertus, J.A., Stevenson, L.W. et al. (2005). ACC/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with chronic heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Heart Failure Clinical Data Standards): developed in collaboration with the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: endorsed by the Heart Failure Society of America. Circulation 112, 1888–1916. Schachinger, V., Erbs, S., Elsasser, A., Haberbosch, W., Hambrecht, R., Holschermann, H.,Yu, J., Corti, R., Mathey, D.G., Hamm, C.W. et al. (2006). Intracoronary bone marrow-derived progenitor cells in acute myocardial infarction. New Eng J Med 355, 1210–1221. Schwartz, K., de la Bastie, D., Bouveret, P., Oliviero, P., Alonso, S. and Buckingham, M. (1986). Alpha-skeletal muscle actin mRNA’s accumulate in hypertrophied adult rat hearts. Cir Res 59, 551–555. Seidman, J.G. and Seidman, C. (2001). The genetic basis for cardiomyopathy: from mutation identification to mechanistic paradigms. Cell 104, 557–567. Tan, F.L., Moravec, C.S., Li, J., Apperson-Hansen, C., McCarthy, P.M., Young, J.B. and Bond, M. (2002). The gene expression fingerprint of human heart failure. Proc Natl Acad Sci USA 99, 11387–11392. The International HapMap Consortium (2005). A haplotype map of the human genome. Nature 437, 1299–1320. Urbanek, K., Cesselli, D., Rota, M., Nascimbene, A., De Angelis, A., Hosoda, T., Bearzi, C., Boni, A., Bolli, R., Kajstura, J. et al. (2006). Stem cell niches in the adult mouse heart. Proc Natl Acad Sci USA 103, 9226–9231. Vasan, R.S. (2006). Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 113, 2335–2362. Wencker, D., Chandra, M., Nguyen, K., Miao, W., Garantziotis, S., Factor, S.M., Shirani, J., Armstrong, R.C. and Kitsis, R.N. (2003). A mechanistic role for cardiac myocyte apoptosis in heart failure. J Clin Invest 111, 1497–1504. Winters, G.L. and McManus, B.M. (1996). Consistencies and controversies in the application of the International Society for Heart and Lung Transplantation working formulation for heart transplant biopsy specimens. Rapamycin Cardiac Rejection Treatment Trial Pathologists. J Heart Lung Transplant 15, 728–735. Wittstein, I.S., Thiemann, D.R., Lima, J.A., Baughman, K.L., Schulman, S.P., Gerstenblith, G., Wu, K.C., Rade, J.J., Bivalacqua, T.J. and Champion, H.C. (2005). Neurohumoral features of myocardial stunning due to sudden emotional stress. New Engl J Med 352, 539–548. Yang, J., Moravec, C.S., Sussman, M.A., DiPaola, N.R., Fu, D., Hawthorn, L., Mitchell, C.A., Young, J.B., Francis, G.S., McCarthy, P.M. et al. (2000). Decreased SLIM1 expression and increased gelsolin expression in failing human hearts measured by high-density oligonucleotide arrays. Circulation 102, 3046–3052.

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60 Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection Michael Pham, Mario C. Deng, Jay Wohlgemuth and Thomas Quertermous

INTRODUCTION The development of highly parallel assessment of genome-wide patterns of gene expression and the development of statistical and bioinformatics algorithms for evaluation of these large datasets have provided great insight into the molecular basis of physiology and disease. While less widely appreciated, it was apparent early on that large gene expression databases, if collected in a clinically and epidemiologically appropriate fashion, could provide the basis for diagnosis and monitoring tools capable of informing on human disease. Attention has focused on tissue samples obtained through biopsy for cancer profiling and monitoring and on gene expression in circulating leukocytes to gain information about the broad array of immune-associated diseases. Although technically demanding with respect to reproducibility of measuring changes in gene expression and appropriate handling of data, gene expression measurements have nonetheless provided the basis for a small number of clinically useful and recognized tests that are now available to physicians for disease monitoring, augmenting more classical laboratory data to guide patient management decisions.

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

CARDIAC ALLOTRANSPLANTATION AS A DEFINITIVE THERAPY FOR END-STAGE HEART FAILURE Heart failure (HF) is a leading cause of morbidity and mortality in the United States. In 2001, approximately five million Americans were affected by this condition, and over half a million new cases are diagnosed annually. The disease accounts for the number one cause of hospitalization for individuals aged 65 or greater. In patients with end-stage HF and severe functional limitation, 1-year mortality approaches 75%, rivaling the effects of most cancers (Rose et al., 2001). Despite major advances in the treatment of this disease over the past decade, a sizable number of patients with terminal or progressive myocardial dysfunction are fated to die or to be severely limited by symptoms. In these patients, biological replacement of the heart with human organs (allotransplantation) has become standard therapy and is widely accepted as a modality for prolonging life and improving its quality in carefully selected patients. Currently, approximately 4000 heart transplants are performed worldwide each year with survival rates of 81%, 74%, and 68% at 1, 3, and 5 years, respectively (Taylor et al., 2006).

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THE PROBLEM OF ALLOGRAFT REJECTION Heart transplantation is the definitive therapy for end-stage HF but is complicated by recipient immune responses to the transplanted heart (allograft), often resulting in cardiac allograft rejection. Different forms of rejection are recognized, including hyperacute rejection (Kemnitz et al., 1991), acute cellular rejection (ACR) (Billingham et al., 1990; Rodriguez, 2003; Stewart et al., 2005; Winters and McManus, 1996; Winters et al., 1998), acute antibody-mediated rejection (AMR), mixed rejection (Book et al., 2003; Michaels et al., 2003a), and chronic rejection, also termed cardiac allograft vasculopathy (CAV). Hyperacute rejection is rare but may occur in the setting of circulatingpreformed antibodies to the ABO blood group (in cases of ABO blood group incompatability) or to major histocompatability antigens on the cardiac allograft. This form of rejection manifests as severe graft failure within the first few minutes to hours after transplantation. ACR is the most common form of rejection and occurs in 30–50% of heart transplant recipients in the first year following transplantation (Hershberger et al., 2005). Most episodes occur within the first 3–6 months. This type of rejection is primarily mediated by T-lymphocytes and is characterized by lymphocytic infiltration within the myocardium, direct myocyte damage, and subsequent graft dysfunction. The severity of cellular rejection reflects the distribution and extent of inflammation and the presence or absence of myocyte damage. Acute AMR is mediated by B-lymphocytes and is characterized by antibody deposition on the cardiac allograft microvasculature, complement activation, and graft dysfunction. It is more likely to be associated with hemodynamic compromise compared with ACR, carries a worse prognosis, and is a strong risk factor for the early development of CAV (Michaels et al., 2003b). The prevalence of AMR has been reported to be between 15% and 20%, and it can occur independently of or in combination with ACR (Hammond et al., 1989; Michaels et al., 2003a). Chronic rejection occurs months to years after transplantation and is typically manifested as CAV and late graft failure. The mechanisms of chronic rejection are incompletely understood but may involve a proliferative response to both immunologically and nonimmunologically mediated endothelial injury with progressive intimal thickening within the coronary vessels.

IMMUNOSUPPRESSION STRATEGIES TO PREVENT REJECTION Most clinically used immunosuppressive regimens consist of a combination of several agents used concurrently and utilize several general principles. The first general principle is that immune reactivity and tendency toward graft rejection are highest early (within the first 3–6 months) after graft implantation and decrease with time. Thus, most regimens employ the highest levels of immunosuppression immediately after transplantation and decrease those levels over the first year, eventually settling

on the lowest maintenance levels of immune suppression that are compatible with preventing graft rejection and minimizing drug toxicities. The second general principle is to use low doses of several drugs without overlapping toxicities in preference to higher (and more toxic) doses of fewer drugs whenever feasible. The third principle is that too much or too intense immunosuppression is undesirable, because it leads to a myriad of undesirable effects, such as susceptibility to infection and malignancy. Finding the right balance between over- and underimmunosuppression in an individual patient is truly an art that utilizes science. Most modern immunosuppressive protocols employ a threedrug regimen consisting of a calcineurin inhibitor (cyclosporine or tacrolimus), an antiproliferative agent (mycophenolate mofetil or azathioprine), and corticosteroids. The calcineurin inhibitors inhibit production of the cytokine interleukin-2, therefore preventing T-lymphocyte differentiation and proliferation. The antiproliferative agents exert their immunosuppressive effects by blocking purine synthesis and inhibiting proliferation of both T- and B-lymphocytes. Corticosteroids are non-specific antiinflammatory agents that interrupt multiple steps in immune activation, including antigen presentation, cytokine production, and proliferation of lymphocytes. They are used in relatively high doses in the early post-transplant period and are tapered to low doses or discontinued after the first 6–12 months. A new class of agents called proliferation signal inhibitors (sirolimus and everolimus) work by inhibiting both T- and B-lymphocyte proliferation through G1 cell cycle blockade. These newer agents have become increasingly popular over the past 5 years due to their ability to prevent and retard progression of CAV and calcineurin-inhibitor-associated nephrotoxicity. Significant advances have been made over the past two decades in moving from drugs that provide broad and nonspecific immunosuppression to newer agents that provide more targeted immunosuppression through inhibition of lymphocyte activation and proliferation. Although use of these newer agents has decreased the incidence of both rejection and life-threatening infections, modern immunosuppressive regimens are inherently associated with drug- and class-specific toxicities, including metabolic derangements (hypertension, dyslipidemia, diabetes mellitus) and renal dysfunction. Additionally, heart transplant patients continue to have higher risk of developing opportunistic infections and malignancy compared to the general population due to suppression of their immune system. The risk of infection and neoplasm is typically related to the intensity and duration of immunosuppression. Given the continued inherent toxicity of immunosuppression regimens, the variability of immunosuppression and susceptibility to drug toxicities among different individuals, and the constant aim to find a regimen that provides optimal immunosuppression with minimal side effects, there continues to be a great need for ways of monitoring immune suppression. Better tools for monitoring relative immunosuppression would allow for tailoring of immunosuppressive regimens to individual patients and aid in the development of new therapeutics (Table 60.1).

Current Strategies for Monitoring Transplant Rejection

TABLE 60.1

TABLE 60.2

Immunosuppression therapies

Method

Target

Selectivity

Total body irradiation

Bone marrow



Steroids

Lymphocytes/RES



Thoracic duct drainage

Lymphocytes



Antilymphocyte globuline

Lymphocytes



Azathioprine

Lymphocytes



Plasmapheresis

Antibodies



Cyclophosphamide

B-lymphocytes



Antithymocyte globuline

T-lymphocytes



Monoclonal CD3 antibodies

CD3 T-lymphocytes



Monoclonal CD4 antibodies

CD4 T-lymphocytes



Mycophelonate

De novo purine synthesis in lymphocytes



Cyclosporine

IL2 inhibition in T-lymphocytes



Tacrolimus

IL2 inhibition in T-lymphocytes



Daclizumab

IL2 receptor antibodies



CURRENT STRATEGIES FOR MONITORING TRANSPLANT REJECTION Most patients with acute rejection are asymptomatic and have no clinical findings of cardiac allograft dysfunction. Additionally, the signs and symptoms of rejection, when present, are nonspecific and may only manifest in the late stages of rejection. Thus, close surveillance of heart transplant recipients for acute rejection is critical. Patients are typically monitored for rejection using a combination of clinical assessment, imaging and/or quantification of allograft function [echocardiography, multiple gated acquisition (MUGA) scan, measurement of intracardiac pressures and flows], in addition to sampling of the myocardium via the technique of endomyocardial biopsy (EMB). Protocols for the timing of rejection surveillance are variable among transplant programs but are generally chosen to match the observed frequency of rejection episodes, which is clearly highest in the early postoperative period. Most programs perform rejection

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Grading system for endomyocardial biopsy

ISHLT Standardized cardiac biopsy grading Acute cellular rejection Old (1990) Grade

Revised (2004) Grade

Description

0

0R

No rejection

1A 1B 2

1R (mild)

Interstitial and/or perivascular mononuclear cell infiltrate with up to one focus of myocyte damage

3A

2R (moderate)

Two or more foci of mononuclear cell infiltrate with associated myocyte damage

3B 4

3R (severe)

Diffuse mononuclear and/or mixed inflammatory cell infiltrates with multiple foci of myocyte damage, with or without edema, hemorrhage, or vasculitis

Antibody-mediated rejection AMR 0

Negative for acute AMR

AMR 1

Positive immunofluorescence or immunoperoxidase staining for AMR (positive CD68, C4d).

R denotes “revised” grade. Adapted from Stewart et al. (2005).

surveillance on a weekly basis for the first 4–6 postoperative weeks and then with diminishing frequency in a stable patient but at a minimum of every 3 months for the first postoperative year and at 3–6 months intervals after the first year. The gold standard for diagnosing ACR has remained the EMB. In this procedure, several pieces of heart tissue from the inner portion (endomyocardium) of the right ventricle are removed with a rigid catheter advanced into the heart via a vein in the patient’s neck or groin area. The specimens are then evaluated under a microscope for evidence of inflammatory infiltrates and myocyte injury. A uniform and standardized grading scheme for grading of ACR was developed by the International Society of Heart and Lung Transplantation (ISHLT) in 1990 and recently revised in 2004 (Stewart et al., 2005) (Table 60.2). Rejection therapy in the form of augmented immunosuppression is typically given for rejection grades of 3A/2R or higher and for rejection episodes that are associated with allograft dysfunction. The EMB, compared to other modalities of rejection surveillance, has the advantage of identifying rejection prior to the development of cardiac allograft dysfunction. However, the procedure is invasive, expensive, subject to sampling error, inter-observer variability, and causes morbidity (0.5–1.5%)

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Figure 60.1 Progression and diagnosis of acute cellular rejection within the cardiac allograft.

(Nielsen et al., 1993; Winters and McManus, 1996; Winters et al., 1998). Biopsy-related complications include damage to the tricuspid valve, myocardial perforation, and excessive bleeding. Although non-invasive alternatives to EMB are clearly needed, methods such as echocardiography, ultrasonic myocardial backscatter, radionuclide imaging, magnetic resonance imaging, intramyocardial electrograms, measurement of serum cardiac markers such as cardiac troponin I and B-type natriuretic peptide, and multiparametric immune monitoring have been difficult to validate and implement (Deng et al., 2005).

Most of the currently employed strategies identify cellular rejection long after immune activation has occurred. In many cases, myocyte injury and allograft dysfunction have already occurred (Figure 60.1). Despite activation of a broad range of immune pathways occurring in rejection, it has not been possible to identify specific molecular clues that inform on the degree of rejection in the transplanted organ. Clearly, greater information regarding the genes and proteins that are specifically activated in relation to transplant rejection is needed to provide a measure of rejection activity. Based on advances in the human

Development of a Gene Expression Signature for Cardiac Transplant Rejection

Phase I Discovery

Phase II Development

Phase III Clinical validation

Figure 60.2

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Candidate gene selection 285 CARGO samples tested with a leukocyte microarray Database and literature mining dentification of 252 candidate genes

Algorithm development Highly sensitive and reproducible real-time PCR methods Development of a 20-gene algorithm to distinguish rejection from quiescence (AlloMap molecular expression testing)

Validation Prospective, blinded, statistically powered study (n  270) Additional samples tested to further define performance (n  1000)

Approach to the development of a gene expression profiling test for cardiac transplant rejection.

genome project and based on the development of microarray technology and related computational methodology, a group of clinical and molecular scientists hypothesized that peripheral blood leukocyte expression profiling might reflect differential activity in the two states of acute rejection and quiescence within the allotransplanted heart.

THE CARGO CLINICAL STUDY As circulating peripheral blood mononuclear cells (PBMC) reflect the status of a host immune system and responses to the allograft, measurement of PBMC gene expression should provide useful diagnostic or prognostic information for cardiac transplant recipients. Based on this hypothesis, studies have now been performed (Deng et al., 2006a; Horwitz et al., 2004; Schoels et al., 2004) to identify genes and pathways predictive of rejection in peripheral blood and to develop a molecular test based on this information. To identify the correlation between gene expression in circulating mononuclear cells and the relative degree of cardiac allograft rejection, a multicenter prospective study called the Cardiac Allograft Rejection Gene Expression Observational (CARGO) was initiated. Based on the genes and pathways identified as correlates of rejection, the CARGO study resulted in the development and validation of a peripheral blood gene expression test for rejection. The test was developed using DNA microarray technology and real-time PCR. The CARGO study had as a primary objective the development of a molecular diagnostic test from PBMC samples to discriminate between a quiescent state (original ISHLT Grade 0 rejection; revised ISHLT Grade 0R) and moderate/severe rejection (original ISHLT Grade 3A; revised ISHLT Grade 2R) in cardiac transplant recipients. Patients at eight heart transplant centers were

followed prospectively with blood sampling performed at the time of post-transplant visits. Biopsies were graded by both local pathologists and three independent core pathologists blinded to clinical data. The scheme for gene discovery, test development, and validation from CARGO is shown in Figure 60.2.

DEVELOPMENT OF A GENE EXPRESSION SIGNATURE FOR CARDIAC TRANSPLANT REJECTION Microarrays were used to identify genes in PBMC samples (n  285) from cardiac transplant recipients that are associated with moderate/severe rejection or the absence of rejection. A set of 252 genes were selected for real-time PCR assay development from the microarray data and from genes in the published literature with known or hypothesized roles in cardiac allograft rejection (knowledge-based genes). While an empirical genomic approach should provide a comprehensive look at the genome, technical limitations of microarray technologies suggested that additional genes from existing knowledge should also be evaluated even if they were not selected from the microarray experiments. The limitations of microarrays include low sensitivity for genes expressed at low levels or in small subsets of circulating cells and nonspecificity of array probes for individual transcripts or splice variants. The 62 genes from the real-time PCR panel found to be reproducibly and differentially expressed between rejection and quiescence states were evaluated in a training set of 145 PBMC samples, and a multigene algorithm was developed using linear discriminant analysis for the ability to classify samples as having no rejection (ISHLT 0R) or moderate/severe rejection (ISHLT 2R). The optimal number of genes required for best classification was determined to be 11, with 9 genes being

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included as controls. A gene expression profiling (GEP) test for detecting cardiac allograft rejection was then developed using PCR-based quantification of expression of these genes and their relative contribution to a probability score, based on the classification algorithm (Table 60.2). Finally, a validation cohort of independent samples from CARGO (63 samples from 63 patients) was used to validate the performance of the multigene test. These samples were independent from those used for the gene selection and algorithm development studies described above. This approach is critical for genomic studies that are fraught with false positive results due to the very large excess of genes to samples (Deng et al., 2006a). In this study, the test was shown to accurately detect the absence of moderate/severe rejection and thus identify a state of “quiescence” in the allograft. Using a single, pre-defined threshold of 20 (scores below 20 indicating a low probability of rejection), the test distinguished moderate/severe biopsy-defined rejection from quiescence (p  0.0018) and had an agreement of 84% (95% CI 66–94%) with ISHLT Grade 3A/2R rejection compared with EMB. Beyond 1-year post-transplant, patientGEP scores below the threshold also had a negative predictive value (NPV) for Grade 3A/2R rejection of 99%. Based on these data, the GEP has been commercialized by XDx, Inc., Brisbane, CA. Their AlloMap™ test, which became available in January 2005, employs quantitative PCR of the 20 genes in triplicate and provides a score ranging from 0 to 40, with lower scores being associated with a very low likelihood of moderate/severe cardiac allograft rejection. A score of 30–34 has been used by many heart transplant centers to identify patients with a low probability of acute rejection. Details on development and validation of the GEP test have been published elsewhere (Deng et al., 2006b).

PATHWAYS MONITORED BY THE GEP (AlloMap™) TEST A total of 62 genes were identified by real-time PCR in the validation phase of the CARGO study as being able to distinguish quiescent cardiac transplant patients from those who had moderate/severe biopsy-defined rejection. Study of the GeneOntology (http://www.geneontology.org) annotations associated with these genes, and in particular the 11 GEP test genes (Deng et al., 2006b), identified several different pathways (Table 60.3) associated with the rejection process. The pathways include T-cell priming, state of immunosuppression, changes in platelet phenotype, and systemic responses to inflammation in the allograft. T-cell receptor chains were upregulated. Another cluster involved hematopoiesis, reflecting an increase in erythroid progenitors, potentially stimulated by circulating byproducts of immune activity in the allograft (Hammer et al., 1998). Alternatively, a certain level of hematopoietic activity may be required to have or sustain an acute rejection response. Downregulation of several genes was also observed. A number of genes of unknown function showed significant up- or down-regulation with rejection, requiring further work to elucidate their roles. In

T A B L E 6 0 . 3 Genes and pathways represented in the final GEP (AllomapTM) diagnostic test Genes in AlloMap test

Corresponding pathways

SEMA7A

Macrophage activation/PMNs

IL1R2, FLT3, ITGAM

Steroid responsiveness

PF4, G6B

Platelet production

MIR, WDR40A

RBC production (hematopoiesis)

PDCD1, ITGA4

T-cell activation and regulation

RHOU

Cell morphology

the context of the phenotypic changes that a naive T-cell acquires when it is primed in the lymph node (Mempel et al., 2004), PDCD1, a negative costimulatory molecule that minimizes the autoreactivity of an aggressive effector T-cell, is one of those candidates in the GEP classifier. Primed T-cells need to be able to traffic into sites of inflammation and acquire new trafficking molecules including ITGA4 (another GEP classifier gene).

VARIABILITY OF THE BIOPSY GOLD STANDARD AND RELATIONSHIP TO THE GEP (AlloMap™) SCORE The CARGO study design originally assumed a “gold standard” clinical endpoint of biopsy-based detection of ACR. However, results from CARGO demonstrated that this “gold standard” was limited by considerable inter-observer variability among local pathologists, and use of GEP testing may help reduce the variability of diagnosing ACR inherent in the biopsy (Marboe et al., 2005). Therefore, in the development and evaluation of the GEP test, rejection was defined by a local pathologist’s grading and by interpretation by a panel of three experienced, independent, blinded pathologists (central pathologists) who reread each case. GEP scores were progressively higher, on average, as the rate of concordance among all pathologists’ readings increased for identifying Grade 3A/2R rejection. When 1 of 4 pathologists diagnosed Grade 3A/2R, the average GEP score was 28.5. When 4 of 4 agreed on Grade 3A/2R, the average GEP score was 33 (Marboe et al., 2006).

DISCORDANCE BETWEEN BIOPSY GRADE AND MOLECULAR SCORE Certainly the situation can arise where the GEP score is discordant from the histologic grading. A “positive” biopsy (ISHLT

Relationship of GEP Scores to Cytomegalovirus Infection

Grade 3A/2R) and low GEP score (34) are uncommon but may hypothetically be seen when local pathologists misdiagnose rejection, either overgrading or misinterpreting (e.g., Quilty lesions) the histology (Marboe et al., 2005). Additionally, a subset of focal rejection may be benign; in one study, more than 90% of Grade 3A/2R biopsies with two foci of focal moderate rejection diagnosed after 1-year post-transplant, resolved without therapy (Winters et al., 1995). Finally, molecular testing and biopsy measure different processes which may be discordant (e.g., lagging clearance of infiltrate in “resolving rejection”). In the CARGO study, rates of positive biopsy and low molecular score were low. For example, with a threshold score of 34 beyond 1-year post-transplant, the NPV of the test for Grade 2R/3A rejection was 99.2%. Thus, a Grade 2R/3A would be expected to occur with a low GEP score in 8 of 1000 tests in a population similar to CARGO. Conversely, a “negative” biopsy and high molecular score may be observed. Several hypotheses may explain this phenomenon, including early or focal rejection that may not be detected on the biopsy due to sampling error, alloimmune activation in the absence of cellular rejection on the biopsy, immune activation relating to conditions other than ACR (e.g., AMR, CAV/chronic rejection, or infection), or a quiescent state in a chronic and clinically stable heart transplant recipient. In the CARGO study, the rate of samples tested that had a negative biopsy with a high GEP score increased with time post-transplantation.

EFFECT OF TIME POSTTRANSPLANTATION ON PERFORMANCE OF THE GEP TEST Samples from CARGO were used to derive the performance characteristics for the GEP test across a range of scoring thresholds. Since rejection rates and average GEP scores are known to vary with time post-transplant, performance characteristics were reported for defined time intervals (6, 6–12, and 12 months). NPV and positive predictive value (PPV) were calculated for each threshold with respect to Grade 3A/2R rejection as defined by both local and central pathologists. The percent above threshold is the estimated overall rate of positive tests for an outpatient population. When interpreting the PPV for the GEP test for Grade 3A/2R rejection, it must be recognized that this parameter is highly dependent on the time-dependent prevalence of rejection in this population. In the first 6 months post-transplant, the risk of rejection is significantly higher than in later periods, and the PPV of the GEP test can be as high as 20–40% with high scores. Beyond 1-year post-transplant, the rate of Grade 3A/2R rejection drops to low levels (3% of biopsies in CARGO), and the PPV of the test declines as well. This phenomenon is important to consider when interpreting high scores in patients 1 year posttransplant, who are at low risk for rejection. The expectation in the majority of these patients is that they do not have

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concurrent Grade 3A/2R rejection on biopsy. The GEP score associated with quiescence also rises with time post-transplant, probably related to down titration of corticosteroids and overall immunosuppression. Since the risk of rejection decreases with time and the NPV of the GEP score remains high over the range of scores between 30 and 35, the higher GEP score thresholds may be used to identify patients at very low risk for rejection during later periods post-transplant. Thus, implementation guidelines based on early clinical experience with the test recommend that GEP thresholds should vary by time posttransplant (0–6, 6–12, and 12 months post-transplant) (Starling et al., 2006).

RELATIONSHIP OF GEP SCORE TO CORTICOSTEROID DOSE Quiescent GEP scores rise with time post-transplant throughout the first year, with the steepest part of this rise in the first 6 months (Figure 60.3). Corticosteroid dosing is the clinical variable most strongly associated with this pattern (Starling et al., 2005). Expression of a number of genes in the GEP algorithm correlates with steroid dose (ITGAM, IL1R2, FLT3). The pattern of change in expression of these genes with decreasing steroid dose is the same as the pattern observed with rejection. Steroid dosing is rapidly reduced in the first 6 months and progressively lowered during the remainder of the first year and beyond. Available data from the CARGO registry suggest that prednisone doses of 20 mg/day do not significantly influence the GEP score.

RELATIONSHIP OF GEP SCORES TO CYTOMEGALOVIRUS INFECTION A critical early question was whether the GEP would be influenced by systemic infections common in transplant patients, and studies were performed to evaluate this possibility. Of 171 samples from 104 CARGO patients tested, 13 (8%) had cytomegalovirus (CMV) detected by quantitative PCR. Eighteen genes correlated with CMV viremia (p  0.05). These genes were involved in T-cell activation (granzyme B, LAG3) and anti-viral response and host defense (viperin, IFN). A subset of these genes was previously known to correlate with CMV infection. No correlation was detected between plasma CMV PCR positivity and acute rejection (ISHLT Grade 2/1R) at the time of sample acquisition or at subsequent follow-up times. Initial analysis showed that the pattern of gene expression from subjects with PCR-detectable CMV did not significantly overlap with that of acute rejection, and CMV status had no significant impact on the acute rejection gene expression pattern or GEP score (Deng et al., 2004). The impact of other infections has not been studied; however, the GEP genes do not overlap with genes and pathways identified in a recent review of the host molecular response to infection (Jenner et al., 2005).

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Application of Genomic Methodology for Assessment of Cardiac Transplant Rejection

50

40

40 AlloMap

30

30

20

20 Prednisone

Figure 60.3

10

10

0

Prednisone dose [mg/day]

AlloMap score, quiescent samples

Allomap score and steroid dose vs. days post transplant 50

0

100

200

300 400 Days post transplant

500

600

700

0

The GEP (AlloMap™) score and steroid dose versus days post-transplant.

PREDICTION OF FUTURE ACR BY MOLECULAR SCORE

CLINICAL USE OF THE AlloMap™ TEST

Peripheral blood gene expression patterns in 104 clinically stable heart transplant patients without initial histological evidence of acute rejection (ISHLT Grade 0 or 1A/1R) were evaluated in a nested case-control study within CARGO, to assess the ability of the GEP test to predict rejection in the subsequent 12 weeks. The cases included 39 patients who developed subsequently moderate to severe rejection (ISHLT Grade 3A/2R) within 12 weeks, and the controls included the remaining 65 patients who did not experience rejection during this time period. In addition, changes in individual gene expression patterns and their relationship with rejection therapy were studied. The gene expression score was significantly higher in patients with future moderate/ severe rejection (p  0.01), and the difference was more statistically significant within 180 days post-transplant (p  0.0004). In this period, no patients with gene expression scores 20 developed moderate/severe rejection within 12 weeks, while 58% of patients with gene expression scores 30 did go on to develop rejection. Individual genes that most significantly predicted future rejection included IL1R2 and FLT3, both corticosteroid responsive genes that decreased in expression before rejection, and PCD1, a marker of T-cell activation, that increased prior to rejection. Anti-rejection therapy resulted in a significant decrease in gene expression score (p0.01). These data suggest that peripheral blood gene expression patterns can anticipate future moderate/severe cardiac allograft rejection in advance of EMB and may allow pre-emptive augmentation of baseline immunosuppression (Mehra et al., 2007).

AlloMap™ is commercially available through the CLIA-certified XDx reference laboratory for use in heart transplant recipients who are 2 months or beyond post-transplantation and has been used clinically by US transplant centers since January 2005. Experience with rejection surveillance protocols incorporating GEP testing has provided additional insight into the performance characteristics of this test when used in a real-time clinical setting and across a wide spectrum of time periods posttransplantation. To date, transplant centers have used the AlloMap™ test in conjunction with the biopsy or in lieu of the biopsy in patients who are 3 months post-transplant. Indications for the GEP test vary according to the time period in which it is used. Centers have used the test within the first year to identify individuals at future risk of rejection and to assist in weaning of corticosteroids and immunosuppression. Beyond the first year post-transplantation, the test is typically used to identify patients at low risk of rejection that may safely be managed without routine biopsies. Finally, the AlloMap™ test has been used to noninvasively exclude rejection in patients with ambiguous signs or symptoms, in patients with inadequate biopsy specimens, and in patients with difficult vascular access. The pooled data from early clinical implementation of GEP testing at several large US transplant centers (follow-up date March 31, 2006) have been analyzed and reported (Starling et al., 2004). Two hundred and forty-three clinical AlloMap™ measurements were included, 32 (13.2%) during the 6–12month period, 192 (79.0%) during the 1–5-year period, and 19 (7.8%) during the 5-year period. The most important

Further Application of Genomic Science to Transplant Rejection

observation was the CARGO study confirmation of a high NPV (100%) with respect to ISHLT 2R/3A biopsy grades. It should be recognized that the mean duration post-transplant in this cohort was longer than the 14.5 months in the CARGO study. The data suggest that the test characteristics, when applied to patients who are 6 months post-transplant, are similar to those derived from the CARGO study. However, in this patient population, a higher test threshold (34), which still maintains excellent NPV of 99%, may be appropriate as the AlloMap™ scores tend to rise with time post-transplant in clinically stable patients with histologically confirmed “quiescence” (Starling et al., 2006). A second important observation derived from clinical experience is that a subset of patients have consistently high longitudinal AlloMap™ scores but yet do not have associated Grade 3A/2R rejection on their biopsies. Therefore, some transplant centers have discontinued biopsies or transitioned to non-invasive rejection surveillance protocols in patients with consecutively high scores and quiescent biopsies. When used as part of a non-invasive strategy of rejection surveillance, the GEP test is typically combined with clinical and echocardiographic assessment of graft function to identify patients that may safely be managed without a heart biopsy. A clinical algorithm utilizing GEP testing to noninvasively manage heart transplant patients who are beyond 1-year post-transplantation has been published (Pham et al., 2007). While the performance of the AlloMap™ test has been validated in a large number of transplant patients, the clinical outcomes associated with using a gene expression-based strategy to monitor for rejection are currently unknown. A multicenter randomized clinical trial is underway to evaluate a GEP-based strategy, compared to a biopsy-based strategy, for assessing rejection in heart transplant recipients who are 6 months–5 years posttransplant. The “Invasive Monitoring Attenuation through Gene Expression (IMAGE)” will evaluate the impact of these two strategies with respect to clinically meaningful outcomes, such as graft dysfunction, rejection with hemodynamic compromise, and death. Additionally, the study will evaluate the incidence of biopsy-related complications, quality of life, and resource utilization among the two groups of patients.

FUTURE DIRECTIONS AND ONGOING RESEARCH WITH GEP TESTING Studies evaluating the role of the AlloMap™ test in various clinical scenarios are currently in progress, and their findings should provide additional insight into use of the test in these conditions. For example, the CARGO II study is currently underway at 13 transplant centers in North America and Europe. The goals of this large observational study, with a targeted enrollment of 500 patients, are to evaluate the ability of the AlloMap™ test to predict both current and future ACR, to detect AMR and

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transplant vasculopathy, to validate the AlloMap™ test in an international patient population, and to investigate its potential for tailoring and individualizing immunosuppressive medications. Future studies will also be needed to evaluate the clinical significance of persistently high scores in the absence of biopsy-detected rejection, to determine the impact of proliferation signal inhibitors and newer immunosuppressive agents on AlloMap™ scores, and to validate the performance of the test in patients 15 years of age. Finally, while use of the GEP test is currently limited to heart transplant recipients, recently presented data from the ongoing Lung Allograft Rejection Gene expression Observational (LARGO) study provided compelling evidence that GEP testing will successfully be applied to lung transplant recipients in the near future (Keshavjee, 2007).

FURTHER APPLICATION OF GENOMIC SCIENCE TO TRANSPLANT REJECTION Genomic data from the CARGO study provides a unique opportunity for gathering molecular insights regarding the genes and pathways that are active in transplant rejection. In a preliminary study of all 285 focused leukocyte arrays utilized in the CARGO study, we applied the ARACNe algorithm to identify highly connected genes in the peripheral PBMC network of heart transplant recipients. From the 7370 genes represented on the focused microarray used in our original CARGO study, we retained those that had 70% completeness of information per gene, imputed missing values for those genes which were retained by using the k-nearest neighbor method implemented in Significance Analysis of Microarrays (SAM) (Tusher et al., 2001) (http://www-stat-class.stanford.edu/sam). Four thousand six hundred and eighty-eight genes were therefore represented in the ARACNe analysis of the 285 CARGO arrays. Compatible with the previous results on the B-cell network, we identified several hubs that have been extensively implicated in the regulation of the immune response. From these preliminary data on the CARGO dataset, a scale-free topology – probably secondary to small numbers – could not be inferred. The ARACNe-generated network allows identifying the subnetworks associated with specific genes of interest. We ranked all genes by their connectivity. We identified several transcription factors that have been extensively implicated in the regulation of immune response for further analysis, including the wellcharacterized transcription factor CREB (cAMP-responsive element binding protein; (Deng et al., 2006c; Muller et al., 1995). By developing this systems biological approach in an iterative manner, we envision that the reconstructed CD4 T-cell network can be used to identify molecular targets for immunosuppression and allow for improved clinical decisions on tailoring of medication in a more personalized approach to prevent acute rejection and immunosuppression side effects.

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REFERENCES Billingham, M.E., Cary, N.R., Hammond, M.E., Kemnitz, J., Marboe, C., McCallister, H.A., Snovar, D.C., Winters, G.L. and Zerbe, A. (1990). A working formulation for the standardization of nomenclature in the diagnosis of heart and lung rejection: Heart Rejection Study Group. The International Society for Heart Transplantation. J Heart Transplant 9, 587–593. Book, W.M., Kelley, L. and Gravanis, M.B. (2003). Fulminant mixed humoral and cellular rejection in a cardiac transplant recipient: A review of the histologic findings and literature. J Heart Lung Transplant 22, 604–607. Deng, M.C., Mehra, M., Hunt, S., Valantine, H., Miner, R., Phillips, J., Wohlgemuth, J.G., Chernoff , D., Woodward, E. and Eisen, H.J. (2004). Leukocyte gene expression signature of CMV viremia in cardiac allograft recipient is distinct from that seen during allograft rejection. Am J Transplant 4E(Suppl 8), 455. Deng, M.C., John, R., Baron, H., Itescu, S. and Suciu-Foca, N. (2005). Principles of transplantation immunology. In Bone Disease of Organ Transplantation (E. Shane and J. Compston, eds.), Elsevier, New York, pp. 3–30. Deng, M.C., Eisen, H.J. and Mehra, M.R. (2006). Methodological challenges of genomic research – the CARGO study. Am J Transplant 6, 1086–1087. Deng, M.C., Eisen, H.J., Mehra, M.R., Billingham, M., Marboe, C.C., Berry, G., Kobashigawa, J., Johnson, F.L., Starling, R.C., Murali, S. et al. (2006). Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant 6, 150–160. Deng, M.C., Cadeiras, M., Lim, W.K., Bayern, M., Sinha, A., Li, J.F., Baron, H.M., Rosenberg, S., Dedrick, R., Klingler, T. et al. (2006). cAMP-responsive element binding protein CREB molecular network is differentially enriched during rejection and quiescence in heart transplant recipients. Circulation 114, 55. Hammer, C., Reichenspurner, R., Meiser, B. and Reichart, B. (1998). Cytoimmunology in monitoring: The Munich experience. Transplant Proc 30, 873–874. Hammond, E.H.,Yowell, R.L., Nunoda, S., Menlove, R.L., Renlund, D.G., Bristow, M.R., Gay, W.A., Jr, Jones, K.W. and O’Connell, J.B. (1989). Vascular (humoral) rejection in heart transplantation: Pathologic observations and clinical implications. J Heart Transplant 8, 430–443. Hershberger, R.E., Starling, R.C., Eisen, H.J., Bergh, C.H., Kormos, R.L., Love, R.B.,Van Bakel, A., Gordon, R.D., Popat, R., Cockey, L. et al. (2005). Daclizumab to prevent rejection after cardiac transplantation. N Engl J Med 352, 2705–2713. Horwitz, P.A., Tsai, E.J., Putt, M.E., Gilmore, J.M., Lepore, J.J., Parmacek, M.S., Kao, A.C., Desai, S.S., Goldberg, L.R., Brozena, S.C. et al. (2004). Detection of cardiac allograft rejection and response to immunosuppressive therapy with peripheral blood gene expression. Circulation 110, 3815–3821. Jenner, R.G. and Young, R.A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nat Rev Microbiol 3, 281–294. Kemnitz, J., Cremer, J., Restrepo-Specht, I., Haverich, A., Ziemer, G., Heublein, B., Borst, H.G., Uysal, A. and Georgii, A. (1991). Hyperacute rejection in heart allografts. Case studies.. Pathol Res Pract 187, 23–29. Keshavjee, S., Berry, G., Marboe, C.C.,Wilt, J.S.,Trulock, E.P., Corris, P.A., Dolyce, R.L., McCurry, K.R., Arcasoy, S.M., Davis, R.D. et al.

(2007). Refining the identification of discriminatory genes for rejection in lung transplantation: The LARGO study. J Heart Lung Transplant 26, S185–S186. Marboe, C.C., Billingham, M., Eisen, H., Deng, M.C., Baron, H., Mehra, M., Hunt, S., Wohlgemuth, J., Mahmood, I., Prentice, J. et al. (2005). Nodular endocardial infiltrates (Quilty lesions) cause significant variability in diagnosis of ISHLT Grade 2 and 3A rejection in cardiac allograft recipients. J Heart Lung Transplant 24, S219–S226. Marboe, C., Deng, M.C., Berry, G., Billingham, M., Eisen, H.J., Pauly, D., Baron, H., Klingler, T.M., Lai, P.G., Mahmood, I. et al. (2006). Increased molecular testing scores associated with agreement among cardiac pathologists for the diagnosis of ISHLT 3A and higher rejection. J Heart Lung Transplant 25, 105–106. Mehra, M.R., Kobashigawa, J.A., Deng, M.C., Fang, K.C., Klingler,T.M., Lal, P.G., Rosenberg, S., Uber, P.A., Starling, R.C., Murali, S., Pauly, D.F., Dedrick, R., Walker, M.G., Zeevi, A. and Eisen, H.J. (2007). CARGO Investigators. Transcriptional signals of T-cell and corticosteroid-sensitive genes are associated with future acute cellular rejection in cardiac allografts. J Heart Lung Transplant 12, 1255–1263. Mempel, T.R., Henrickson, S.E. and Von Andrian, U.H. (2004). T-cell priming by dendritic cells in lymph nodes occurs in three distinct phases. Nature 427, 154–159. Michaels, P.J., Fishbein, M.C. and Colvin, R.B. (2003). Antibody-mediated rejection of human organ transplants. Springer, New York. Michaels, P.J., Espejo, M.L., Kobashigawa, J., Alejos, J.C., Burch, C., Takemoto, S., Reed, E.F. and Fishbein, M.C. (2003). Humoral rejection in cardiac transplantation: Risk factors, hemodynamic consequences and relationship to transplant coronary artery disease. J Heart Lung Transplant 22, 58–69. Muller, F.U., Boknik, P., Horst, A., Knapp, J., Linck, B., Schmitz, W., Vahlensieck, U., Bohm, M., Deng, M.C. and Scheld, H.H. (1995). cAMP response element binding protein is expressed and phosphorylated in the human heart. Circulation 92, 2041–2043. Nielsen, H., Sorensen, F.B., Nielsen, B., Bagger, J.P., Thayssen, P. and Baandrup, U. (1993). Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System. J Heart Lung Transplant 12, 239–243. Pham, M.X., Deng, M.C., Kfoury, A.G., Teuteberg, J.J., Starling, R.C. and Valantine, H. (2007). Molecular testing for long-term rejection surveillance in heart transplant recipients: Design of the Invasive Monitoring Attenuation through Gene Expression IMAGE) trial. J Heart Lung Transplant 26, 808–814. Rodriguez, E.R. (2003). The pathology of heart transplant biopsy specimens: Revisiting the 1990 ISHLT working formulation. J Heart Lung Transplant 22, 3–15. Rose, E.A., Gelijns, A.C., Moskowitz, A.J., Heitjan, D.F., Stevenson, L.W., Dembitsky, W., Long, J.W., Ascheim, D.D., Tierney, A.R., Levitan, R.G. et al. (2001). Long-term mechanical left ventricular assistance for end-stage heart failure. N Engl J Med 345, 1435–1443. Schoels, M., Dengler, T.J., Richter, R., Meuer, S.C. and Giese, T. (2004). Detection of cardiac allograft rejection by real-time PCR analysis of circulating mononuclear cells. Clin Transplant 18, 513–517. Starling, R.C., Deng, M.C., Kobashiganva, J.A.,Walther, D.,Wohlgemuth, J., Rosenberg, S. and Mehra, M. (2005). The influence

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Tusher, V.G., Tibshirani, R. and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98, 5116–5121. Winters, G.L. and McManus, B.M. (1996). Consistencies and controversies in the application of the International Society for Heart and Lung Transplantation working formulation for heart transplant biopsy specimens. Rapamycin Cardiac Rejection Treatment Trial Pathologists. J Heart Lung Transplant 15, 728–735. Winters, G.L., Loh, E. and Schoen, F.J. (1995). Natural history of focal moderate cardiac allograft rejection. Is treatment warranted? Circulation 91, 1975–1980. Winters, G.L., Marboe, C.C. and Billingham, M.E. (1998). The International Society for Heart and Lung Transplantation grading system for heart transplant biopsy specimens: Clarification and commentary. J Heart Lung Transplant 17, 754–760.

CHAPTER

61 Hypertrophic Cardiomyopathy in the Era of Genomic Medicine J. Martijn Bos, Steve R. Ommen and Michael J. Ackerman

INTRODUCTION Hypertrophic cardiomyopathy (HCM) is a primary disorder of the myocardium associated with increase in cardiac mass and typically, asymmetric, predominantly left ventricular hypertrophy. Affecting 1 in 500 people, HCM is a disease of profound phenotypic and genotypic heterogeneity. Clinically, HCM can be characterized by a completely asymptomatic course to severe cardiac symptoms or even sudden cardiac death (SCD). In 1989, the molecular underpinnings of HCM were exposed with the discovery of the first locus of familial HCM, which was followed in 1990 with the identification of a mutation in the MYH7-encoded beta myosin heavy chain. Since then, hundreds of mutations scattered amongst at least 10 myofilament genes confer the pathogenetic substrate for this “disease of the sarcomere/myofilament.” More recently, the genetic spectrum of HCM has expanded to encompass mutations in Z-disc–associated genes (Z-disc HCM). Furthermore, seemingly unexplained cardiac hypertrophy mimicking HCM can be the (primary) feature of some syndromes or metabolic diseases. However, the underlying genetic mechanism for approximately 30–50% of HCM remains to be elucidated and although many genotype–phenotype relationships have been observed, only few carry enough clinical significance to aid physicians predicting genotype status or course and prognosis of the disease. Current treatment for HCM is mostly aimed at symptom relief, although studies are underway to design pharmacologic

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 716

treatments for prevention of hypertrophy or individualized treatment based on one’s genetic fingerprint.

DEFINITIONS, CLINICAL PRESENTATION, AND DIAGNOSIS Background Hypertrophic cardiomyopathy is defined as unexplained left ventricular hypertrophy (LVH) in the absence of precipitating factors such as hypertension or aortic stenosis. HCM is a disease of enormous phenotypic and genotypic heterogeneity. Affecting 1 in 500 people, it is the most prevalent genetic cardiovascular disease, and more importantly the most common cause of SCD in young athletes (Maron, 2002). HCM can manifest with negligible to extreme hypertrophy, minimal to extensive fibrosis and myocyte disarray, absent to severe left ventricular outflow tract obstruction (LVOTO), and distinct patterns of hypertrophy. Nomenclature Hypertrophic cardiomyopathy was described fully for the first time by Teare in 1958 as “asymmetrical hypertrophy of the heart in young adults” (Teare, 1958). Over the past half century, HCM has since been known by a confusing array of names, reflecting its clinical heterogeneity and uncommon occurrence in daily

Copyright © 2009, Elsevier. Inc. All rights reserved.

Definitions, Clinical Presentation, and Diagnosis

practice. In 1968, WHO defined cardiomyopathies as “diseases of different and often unknown etiology in which the dominant feature is cardiomegaly and heart failure” (Abelmann, 1984). In 1980, cardiomyopathies were newly defined as “heart muscle diseases of unknown cause,” thereby differentiating it from specific identified heart muscle diseases of known cause such as myocarditis. Throughout the years, names such as idiopathic hypertrophic subaortic stenosis (IHSS), (Braunwald et al., 1964), muscular subaortic stenosis (Pollick et al., 1982), and hypertrophic obstructive cardiomyopathy (HOCM) (Schoendube et al., 1995) have been used widely and interchangeably to define the same disease. In 1995, the WHO/International Society and Federation of Cardiology Task Force on cardiomyopathies classified the different cardiomyopathies by dominant pathophysiology or, if possible, by etiological/pathogenetic factors (Richardson et al., 1996).The four most important cardiomyopathies – dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and hypertrophic cardiomyopathy (HCM) – were recognized, next to a number of specific and mostly acquired cardiomyopathies, like ischemic- and inflammatory cardiomyopathies (Richardson et al., 1996). Accordingly, HCM is described as left and/or right ventricular hypertrophy, usually asymmetric and involving the interventricular septum with predominant autosomal dominant inheritance that can be caused by mutations in sarcomeric contractile proteins (Richardson et al., 1996). On a microscopic level, HCM is characterized by the classical triad of cardiomyocyte hypertrophy, fibrosis, and myofibrillar disarray. Clinical Presentation and Diagnosis The clinical presentation of HCM is underscored by extreme variability from an asymptomatic course to that of severe heart failure, arrhythmias, and SCD. Many patients remain asymptomatic or only mildly symptomatic throughout the course of life. HCM commonly manifests between the second and the fourth decades of life but can present at the extremes of age. Infants and young children may present with severe hypertrophy leading to heart failure, and these patients have poor prognosis. More often, SCD can be the tragic sentinel event for HCM in children, adolescents, and young adults. The most common symptoms at presentation of disease are exertional dyspnea, chest pain, and syncope or presyncope. Approximately 5% of patients with HCM progress to “end-stage” disease characterized by LV dilatation and heart failure. In such cases cardiac transplantation may be considered. Other serious life-threatening complications include embolic stroke and cardiac arrhythmias. Echocardiography Conventional two-dimensional echocardiography is the diagnostic modality of choice for the clinical diagnosis of HCM. The disease is characterized by otherwise unexplained and usually asymmetric, diffuse or segmental hypertrophy associated with a non-dilated left ventricle (LV) independent of presence or absence of LV outflow obstruction. A left ventricular wall thickness of 12 mm is typically

TABLE 61.1

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Septal morphologies in HCM

Septal morphology

Description

Sigmoid septum

– Septum concave to cavity with pronounced septal bulge – Ovoid LV cavity

Reverse septal curvature

– Predominant mid-septal convexity toward LV cavity – Crescent-shaped cavity

Apical variant

– Predominant apical distribution of hypertrophy

Neutral variant

– Overall straight or variable convexity, predominantly neither convex nor concave

regarded as normal, with measurements of 13–15 mm labeled as “borderline hypertrophy.” A maximal LV end-diastolic wall thickness exceeding 15 mm represents the absolute dimension generally accepted for the clinical diagnosis of HCM in adults (in children, 2 or more standard deviations from the mean relative to body surface area) (Maron et al., 2003). Echocardiography can also provide details of location and degree of hypertrophy. In general, while there are innumerable morphologic appearances of the heart, the following four different morphological subtypes of HCM can be recognized in most cases (Table 61.1, Figure 61.1): sigmoid septum, reverse septal curvature, apical-, and neutral contour variant. Dynamic LVOTO is a common feature of HCM but is not required for the diagnosis of HCM. The existence of LVOTO is diagnosed by demonstration of a resting or provocable Doppler gradient of 30 mmHg. LVOTO is produced by the interaction of the hypertrophied septum and systolic anterior motion (SAM) of the mitral valve. The latter results from abnormal blood flow vectors across the valve, and abnormal anterior positioning of the valve and its support structures. Variable severity, posteriorly directed mitral regurgitation is a common finding. Most patients at presentation manifest some degree of impaired diastolic function ranging from abnormal relaxation to severe myocardial stiffness, elevated left ventricular end-diastolic pressure (LVEDP), elevated atrial pressure, and pulmonary congestion leading to exercise intolerance and fatigue. Systolic cardiac function, as measured by ejection fraction, is usually preserved. It is notable that more elegant measures of systolic performance, such as tissue velocity and strain imaging, suggest a decrease in systolic function in patients with HCM, and may even be present in gene-mutation carriers before hypertrophy can be detected. “End-stage disease” is characterized by LV dilatation, poor systolic function, and heart failure. Magnetic Resonance Imaging Magnetic resonance imaging (MRI) constitutes an important additional diagnostic tool especially in patients with suboptimal echocardiography or unusual segmental involvement of myocardium. Delayed gadolinium enhancement in MRI is an excellent

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Hypertrophic Cardiomyopathy in the Era of Genomic Medicine

Sigmoid septum (47%) 8% Myofilament Gene

Reverse septal curvature (35%) 79% Myofilament Gene

Apical (10%)

Neutral (8%)

32% Myofilament Gene

41% Myofilament Gene

Figure 61.1 Septal morphologies in HCM. Shown are the most common septal morphologies in HCM, their distribution in a large cohort of patients with HCM as well as the yield of genetic testing for each morphological subgroup.

tool to identify areas of scarring, replacement fibrosis, or irreversible myocardial injury. There are some recent reports of higher risk of SCD associated with these finding. In a cohort of 42 patients, myocardial hyper-enhancement was found in 79%. Extent of hyper-enhancement was greater in patients with progressive disease (28.5% versus 8.7%, p  0.001) and in patients with two or more risk factors for SCD (15.7% versus 8.6%, p  0.02) (Moon et al., 2003).

MOLECULAR GENETICS OF HCM Sarcomeric/Myofilament HCM Since the sentinel discovery of the first locus for familial HCM on chromosome 14 (1989) and first mutations involving the MYH7-encoded beta myosin heavy chain (1990) as the pathogenic basis for HCM (Geisterfer-Lowrance et al., 1990; Jarcho et al., 1989), over 400 mutations scattered among at least 24 genes encoding various sarcomeric proteins (myofilaments and Z-disc– associated proteins) and calcium-handling proteins have been identified (Table 61.2, Figure 61.2). The most common genetically mediated form of HCM is myofilament HCM, with hundreds of disease-associated mutations in eight genes-encoding proteins critical to the sarcomere’s thick myofilament (beta myosin heavy chain [MYH7] [Geisterfer-Lowrance et al., 1990]; regulatory myosin light chain [MYL2] and essential myosin light chain [MYL3] [Poetter et al., 1996]), intermediate myofilament (myosin-binding protein C [MYBPC3] [Watkins et al., 1995]), and thin myofilament (cardiac troponin T [TNNT2], alphatropomyosin [TPM1] [Thierfelder et al., 1994], cardiac troponin I [TNNI3] [Kimura et al., 1997]), troponin C [TNNC1] [Landstorm et al., 2008] and actin ([ACTC] [Mogensen et al., 1999; Olson et al., 2002a]). Targeted screening of giant sarcomeric TTN-encoded titin, which extends throughout half of the sarcomere, has thus far revealed only one mutation (Satoh et al. 1999). More recently, mutations have been described in the myofilament protein alpha myosin heavy chain encoded by MYH6

(Niimura et al., 2002). The prevalence of mutations in the eight most common myofilament-associated genes, currently comprising the commercially available HCM genetic tests in different international cohorts, ranges from 30% to 61%, leaving still a large number of patients with genetically unexplained disease (Van Driest et al., 2005a). Z-disc and Calcium-Handling HCM Over the last few years, the spectrum of HCM-associated genes has expanded outside the myofilament to encompass additional subgroups that could be classified as “Z-disc HCM” and “calcium-handling HCM.” Due to its close proximity to the contractile apparatus of the myofilaments and its specific structure–function relationship with regard to cyto-architecture, as well as its role in the stretch-sensor mechanism of the sarcomere, recent attention has been focused on the proteins that comprise the cardiac Z-disc. Initial mutations were described in CSRP3-encoded muscle LIM protein (Geier et al., 2003) and TCAP-encoded telethonin (Hayashi et al., 2004), an observation replicated in a large cohort of unrelated patients with HCM (Bos et al., 2006). Recently, mutations in patients with HCM have been reported in LDB3-encoded LIM domain binding 3, ACTN2-encoded alpha-actinin-2, and VCL-encoded vinculin/ metavinculin (Theis et al., 2006). Interestingly, although the first HCM-associated mutation in vinculin was found in the cardiacspecific insert of the gene, yielding the protein called metavinculin (Vasile et al., 2006a), the follow-up study also identified a mutation in the ubiquitously expressed protein vinculin (Vasile et al., 2006b). In 2007, a mutation in the Z-disc–associated MYOZ2-encoded myozenin 2 was reported as a novel gene for HCM (Osio et al., 2007). As the critical ion in the excitation–contraction coupling of the cardiomyocyte, calcium and proteins involved in calciuminduced calcium release (CICR) have always been of high interest in the pathogenesis of HCM. Although with very low frequency, mutations have been described in the promoter – and coding region of PLN-encoded phospholamban – an important

Molecular Genetics of HCM

TABLE 61.2

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Summary of HCM susceptibility genes Gene

Locus

Protein

Estimated frequency (%)

Giant filament

TTN

2q24.3

Titin

1

Thick filament

MYH7a MYH6 MYL2a MYL3a

14q11.2–q12 14q11.2–q12 12q23–q24.3 3p21.2–p21.3

-myosin heavy chain -myosin heavy chain Ventricular regulatory myosin light chain Ventricular essential myosin light chain

15–25 1 2 1

Intermediate filament

MYBPC3a

11p11.2

Cardiac myosin-binding protein C

15–25

TNNT2 TNNI3a TPM1a ACTCa TNNC1a

1q32 19p13.4 15q22.1 15q14 3p21.3–p14.3

Cardiac troponin T Cardiac troponin I -tropomyosin -cardiac actin Cardiac troponin C

5 5 5 1 1

LBD3 CSRP3 TCAP VCL ACTN2 MYOZ2

10q22.2–q23.3 11p15.1 17q12–q21.1 10q22.1–q23 1q42–q43 4q26–q27

LIM binding domain 3 (alias: ZASP) Muscle LIM protein Telethonin Vinculin/metavinculin -actinin 2 Myozenin 2

1–5 1 1 1 1 1

RYR2 JPH2 PLN

1q42.1–q43 20q12 6q22.1

Cardiac ryanodine receptor Junctophilin-2 Phospholamban

1 1 1

Myofilament HCM

Thin filament

a

Z-disc HCM

Calcium-handling HCM

a

Available as part of commercially available genetic test for HCM.

inhibitor of cardiac muscle sarcoplasmic reticulum Ca(2)ATPase (SERCA) (Haghighi et al., 2006; Minamisawa et al., 2003), the RyR2-encoded cardiac ryanodine receptor (Fujino et al., 2006), and the JPH2-encoded type 2 junctophilin (Landstrom et al., 2007). Metabolic and Syndromal HCM-mimicry The last genetic subgroup of unexplained cardiac hypertrophy is the one comprising metabolic and syndromal HCM-mimickers – in which cardiomyopathy is a sometimes sole presenting feature (Table 61.3). In 2001, the first mutations in PRKAG2-encoded AMP-activated protein kinase gamma 2, a protein involved in the energy homeostasis of the heart, were described in two families with severe HCM and aberrant AV-conduction in some individuals (Blair et al., 2001). Further studies showed that patients with mutations in this gene lack the HCM characteristics of myocyte – and myofibrillar disarray – but show newly formed vacuoles filled with glycogen-associated granules. This glycogen storage disease therefore seemed to mimic HCM, distinguishing itself by electrophysiological abnormalities, particularly ventricular pre-excitation (Arad et al., 2002, 2005). In 2005, Arad et al. described mutations in lysosome-associated membrane protein2 encoded by LAMP2 (Danon’s syndrome) and protein kinase gamma 2 encoded by PRKAG2 in glycogen storage disease-associated genes mimicking

the clinical phenotype of HCM (Arad et al., 2005). A recent community-based study showed that in 50 healthy individuals with idiopathic LVH, participants with sarcomere gene protein and storage mutations essentially were indistinguishable clinically from those without mutations (Morita et al., 2006). In 2005, a mutation in FXN-encoded frataxin associated with Friedrich ataxia was described in a patient with HCM. Although this patient also harbored a myofilament mutation in MYBPC3-encoded myosin binding protein C, functional characterization showed significant influence of the FXN-mutant on the phenotype, suggesting that the observed alterations in energetics may act in synergy with the present myofilament mutation (Van Driest et al., 2005b). Akin to PRKAG2 and LAMP2, Fabry’s disease can express predominant cardiac features of seemingly unexplained LVH. Over the years, mutations in GLAencoded alpha-galactosidose A have been found in patients with this multisystem disorder (Nakao et al., 1995; Sachdev et al., 2002; Sakuraba et al., 1990). Genotype–Phenotype Relationships in HCM For over the past decade, multiple studies have tried to identify phenotypic characteristics most indicative of myofilament HCM to facilitate genetic counseling and strategically direct clinical genetic testing (Ackerman et al., 2002; Richard et al.,

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Metabolic HCM (1%)

Calcium-handling HCM (1–5%)

SR ATP

Ca2+

Frataxin

Lysosome

Tropomyosin Troponin

Myosinbinding protein C

ADP

I-Band

Actin

CapZ

Tropomyosin

Z-Disc Desmin

Ankyrin Obscurin

S100 Myopalladin CARP

MLP

Nebulette

β-myosin heavy chain

Myosin light chain

Myofilament HCM (40–70%)

m

Calsarcin gamma-Filamin

PKC ZASP Calcineurin

Myopodin ALP MinK T-Cap Myostatin alpha-Actinin

Tiltin

Z-disc HCM (1–5%)

Figure 61.2 Subgroups of genetic HCM. Shown are the most important functional subgroups of genetically mediated HCM. Blue arrows indicate the functional relationship between the different elements.

2003;Van Driest et al., 2002, 2003, 2004a, b; Woo et al., 2003). Up until 2001, it was thought that specific mutations in these myofilament genes were inherently “benign” or “malignant” (Anan et al., 1994; Coviello et al., 1997; Elliott et al., 2000; Moolman et al., 1997; Niimura et al., 2002; Seidman et al., 2001; Varnava et al., 1999; Watkins et al., 1992). However, these studies were based on highly penetrant, single families with HCM, and later genotype–phenotype studies involving a large cohort of unrelated patients have indicated that great caution must

be exercised with assigning particular prognostic significance to any particular mutation (Ackerman et al., 2002; Van Driest et al., 2002, 2004b). Furthermore, these studies demonstrated that the two most common forms of genetically mediated HCM – MYH7-HCM and MYBPC3-HCM – were phenotypically indistinguishable (Van Driest et al., 2004b). While several phenotype–genotype relationships have emerged to enrich the yield of genetic testing, these patient profiles have not been particularly clinically informative on an individual level.

Molecular Genetics of HCM

TABLE 61.3 Gene

Metabolic and Syndromal-HCM-mimickers

Locus

Protein

Syndrome

7q35-q36.36

AMP-activated protein kinase

WPW/HCM

LAMP2a

Xq24

Lysosome-associated membrane protein 2

Danon’s syndrome

GAA

17q25.2-q25.3 Alpha-1,4-glucosidase Pompe’s deficiency disease

GLAa

Xq22

Alphagalactosidase A

Fabry’s disease

FXN

9q13

Frataxin

Friedrich’s ataxia

PTPN11b

12q24.1

Protein tyrosine phosphatase, nonreceptor type 11

Noonan’s syndrome

PRKAG2

a

Note: In patients with cardiac hypertrophy secondary to Noonan’s syndrome, PTPN11 mutations are quite uncommon (10%) compared to 50% yield expected for Noonan’s syndrome without LVH. a

Available as part of commercially available genetic testing.

b

PTPN11 is also associated with LEOPARD syndrome and is allelic to Noonan’s syndrome.

Recently, an important discovery, linking the echocardiographically determined septal morphology to the underlying genetic substrate, was made (Binder et al., 2006). The first link to be drawn between septal morphologies was a result of a HCM study by Lever and colleagues in the 1980s, where septal contour (Table 61.1) was found to be age-dependent with a predominance of sigmoidal-HCM noted in the elderly (Lever et al., 1989). In the early 1990s, an early genotype–phenotype observation involving a small number of patients and family members showed that patients with mutations in the beta myosin heavy chain (MYH7-HCM) generally had reversed curvature septal contours (reverse curve-HCM) (Solomon et al., 1993). Analysis of the echocardiograms of 382 previously genotyped and published patients (Van Driest et al., 2003, 2004b, c), revealed that sigmoidal-HCM (47% of cohort) and reverse curve-HCM (35% of cohort) were the two most prevalent anatomical subtypes of HCM and that the septal contour was the strongest predictor for the presence of a myofilament mutation, regardless of age (Binder et al., 2006). In fact, multivariate analysis in this cohort demonstrated that septal morphology was the only, independent predictor of myofilament HCM with an odds ratio of 21 (p  0.001) when reverse curve morphology was present (Figure 61.1; Binder et al., 2006). The yield from the commercially equivalent HCM genetic research test for myofilament HCM was 79% in reverse curveHCM but only 8% in patients with sigmoidal-HCM. Of the smaller subgroup of patients with apical HCM, 32% had a mutation in one of the elements of the cardiac myofilament (Binder et al.,

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2006). These observations may facilitate echo-guided genetic testing by enabling informed genetic counseling about the a priori probability of a positive genetic test based on the patient’s expressed anatomical phenotype of HCM (Figure 61.1). With the majority of known myofilament proteins studied, except for a complete analysis of the giant protein TTNencoded titin, recent research has focused on proteins beyond the cardiac myofilaments, especially proteins involved in the cytoarchitecture and cardiac stretch-sensor mechanism of the cardiomyocyte localized to the cardiac Z-disc (Figure 61.2). The Z-disc is an intricate assembly of proteins at the Z-line of the cardiomyocyte sarcomere. Proteins of the Z-disc are important in the structural and mechanical stability of the sarcomere as they appear to serve as a docking station for transcription factors, Ca2 signaling proteins, kinases, and phosphatases (Frank et al., 2006; Pyle et al., 2004). In addition, this assembly of proteins seems to serve as a way station for proteins that regulate transcription by aiding in their controlled translocation between the nucleus and the Z-disc (Frank et al., 2006; Pyle et al., 2004). A main implication for the Z-disc is its involvement in the cardiomyocyte stretch sensing and response systems (Knoll et al., 2002). Mutations in these proteins have been implicated as HCM (Bos et al., 2006; Geier et al., 2003; Hayashi et al., 2004; Vasile et al., 2006a, b) and DCM susceptibility genes (Bos et al., 2006; Geier et al., 2003; Hayashi et al., 2004; Mohapatra et al., 2003; Olson et al., 2002b; Vasile et al., 2006a). Additionally, it has become appreciated that these divergent cardiomyopathic phenotypes of HCM and DCM are partially allelic disorders with ACTC, MYH7, TNNT2, TPM1, MYBPC3, TTN, MLP, TCAP, and VCL established as both HCM and DCM susceptibility genes (Daehmlow et al., 2002; Geier et al., 2003; Gerull et al., 2002; Hayashi et al., 2004; Kamisago et al., 2000; Mohapatra et al., 2003; Olson et al., 2000, 2001;Vasile et al., 2006a). Recently, mutations in ACTN2-encoded alpha-actinin-2 (ACTN2) and LDB3-encoded LIM domain binding 3 (LDB3) as novel HCM susceptibility genes (Theis et al., 2006) were described. Linking reverse curve-HCM to the presence of myofilament mutation, and recognizing that the Z-disc may transduce multiple-signaling pathways during stress, translating into hypertrophic responses, cell growth and remodeling (Frey et al., 2004), it was observed that Z-disc HCM, in contrast to myofilament HCM, is preferentially sigmoidal. In fact, 11 out of 13 patients with Z-disc HCM had a sigmoidal septal contour and no reverse septal curvatures were seen (Theis et al., 2006). It is speculated that Z-disc HCM leads to a hypertrophic response that is expressed in the areas of highest stress (i.e., LVOT) and therefore predisposes to a sigmoidal septal contour. New Insights and Approaches to Genomics of HCM Not only in molecular genetics but also in other fields of “-omics,” novel pathways underlying the pathophysiology of this heterogeneous disease have been identified using several new techniques to study large-scale transcriptional changes (Churchill, 2002; Holland, 2002;Velculescu et al., 1997). A transcriptomic approach

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Clinic HCM proband

Genetics-based screening A priori chance of positive genetic test based on septal contour Reverse septal curvature Neutral Apical Sigmoidal

Echocardiography-based screening Screen first-degree relatives and athletic second-degree relatives

79% 41% 32% 8%

Genetic testing performed ?

No

Yes

Positive echocardiogram ?

No

Yes Mutation identified?

No Repeat echocardiogram every 5 years

Yes Confirm mutation in first-degree relatives

Mutation identified?

No

HCM confirmed Perform annual clinical and echocardiographic exam

(annual for young adults and athletes)

Yes No further screening

Figure 61.3 Genetic- and echocardiographic-based screening in HCM. Flow-chart showing decision to follow in genetic- and echocardiography-based screening in HCM. Noted are the a priori chances for a positive genetic test result based on the echocardiographic-scored septal contour, as well as the steps to follow if a patient chooses not to pursue genetic testing.

using microarray, a technique that enables one to give a snapshot view of gene expression, combined with complex analytic tools, can identify genes that seem to be co-regulated and thereby form a transcriptional network of genes and pathways. Microarray chips can hold over 10,000 genes and can be utilized to compare expression levels in certain disease states with healthy controls. In 2002, Hwang et al. studied RNA from heart failure patients with either HCM or DCM and found 192 genes to be upregulated in both, as well as several genes differentially expressed between the two diseases providing information on different pathways and genes involved in the pathogenesis (Hwang et al., 2002). More recently, Rajan et al. performed microarray analysis on ventricular tissue of two previously developed transgenic HCM mice carrying mutations in alpha-tropomyosin (TPM1). Studying 22,600 genes, they discovered 754 differentially expressed genes between transgenic and non-transgenic mice, of which 266 were differentially regulated between the two different mutant hearts showing most significant changes in genes belonging to the “secreted/ extracellular matrix” (upregulation) and “metabolic enzymes” (downregulation) (Rajan et al., 2006). Microarray techniques were also used by Sayed et al. when they studied the effect of aortic constriction on expression of microRNAs, fundamental regulators consisting of non-coding RNA molecules that silence genes through post-transcriptional regulation first described by Lee et al. in 1993 (Lee et al., 1993). In this recent study, they found that microRNAs, especially

microRNA-1 (miR-1), play an important role in the development of cardiac hypertrophy, where downregulation of miR-1 leads to relief of its growth-related target genes, protein synthesis, and increased cell size showing a completely new approach to the -omics of HCM (Sayed et al., 2007). In 2007, a novel-sensitive messenger RNA (mRNA) profiling technology, PMAGE (polony multiplex analysis of gene expression), which detects mRNAs as rare as one transcript per three cells, was developed (Kim et al., 2007). Using this new technique, early transcriptional changes preceding pathological manifestations were identified in mice with HCM-causing mutations, including low-abundance mRNA encoding signaling molecules and transcription factors that participate in the disease pathogenesis (Kim et al., 2007).

SCREENING AND TREATMENT FOR HCM Family Screening in HCM Genetic and clinical screening of family members with HCM plays an important role in the early diagnosis of HCM. Figure 61.3 shows one possible algorithm to follow after diagnosis of a proband with HCM. In general, all first-degree relatives and probably “athletic” second-degree relatives of an index case of HCM

Screening and Treatment for HCM

should be screened by an ECG and an echocardiogram. Annual screenings are recommended for young persons (12–25 years) and athletes and thereafter every 5 years. If the HCM-causing mutation is known, first-degree relatives should have confirmatory genetic testing of the mutation in addition to the screening ECG and echocardiogram. Depending on the established familial versus sporadic pattern, confirmatory genetic testing should proceed in concentric circles of relatedness. For example, if the mutation is established in the patient’s father, the patient’s paternal grandparents should be tested, and if necessary, the paternal aunts and uncles and so forth. If the putative HCM-associated mutation is not found in a phenotype-negative family member, screening can be stopped. However, a decision to cease surveillance for HCM in a relative hinges critically on the certainty of the identified gene/ mutation and its causative link as well as the complete absence of any traditional evidence used to diagnose HCM clinically (i.e., asymptomatic and normal echo). Follow-up and Sports Participation Intense physical exertion can potentially trigger SCD in individuals with HCM. According to the 2005 Bethesda Conference recommendations, athletes with HCM should be excluded from participation in contact sports as well as most organized competitive sports, with the possible exception of low-intensity sports classified as class IA sports (i.e., golf, bowling, cricket, billiards, and riflery) (Maron et al., 2005). Per the guidelines, the presence of an implantable cardioverter defibrillator (ICD) does not alter these recommendations. This restrictive approach is loosened for the patient with genotype-positive but phenotype-negative HCM. All patients with HCM should undergo, on an annual basis, careful personal and family history record, two-dimensional echocardiography, 12-lead ECG, 24–48-h ambulatory Holter electrocardiogram, and exercise stress testing (for evaluation of exercise tolerance, blood pressure, and ventricular tachyarrhythmias). Pharmacological Therapy for Obstructive HCM The primary goal of pharmacologic therapy in obstructive HCM is to decrease symptoms. Understanding the pathophysiology of the LVOTO is crucial to devising appropriate medication recommendations. The LVOTO in HCM is dynamic and highly load-dependent. The obstruction is augmented with decreases in preload (volume status) and afterload (vasodilation) and with increases in contractility. Simple physical exertion such as walking can cause all of these to occur simultaneously. As patients with HCM typically only have symptoms with effort, the goal of medical therapy is to decrease the effort-related augmentation of LVOTO. Beta Blockers Due to negative inotropic and negative chronotropic effects, beta blockers are the traditional mainstay of HCM therapy. Beta blockers are used in symptomatic patients with or without obstruction to control heart failure and anginal chest pain. The dose–response relationship of these medications varies

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significantly from patient to patient. Commonly used beta blockers include propranolol, atenolol, metoprolol, and nadolol (Elliott et al., 2004; Spirito et al., 2006). Calcium-Blocker Therapy The calcium channel antagonist verapamil is another drug used in HCM for its negative inotropic effect. It should be avoided in infants and used with caution in patients with heart failure and/or very significant obstruction (Maron et al., 2003). While both verapamil and diltiazem have negative inotropic and negative chronotropic effects, in some patients, vasodilatory action can predominate and paradoxically increase the severity of symptoms. Dihydropyridine-type calcium antagonists are pure vasodilators that should be avoided in patients with HCM. Disopyramide Disopyramide is a negative inotrope and type 1-A antiarrhythmic agent, which may help some patients with obstruction. It decreases cardiac output in nonobstructive HCM and is used primarily in patients not responding to beta blocker and/or calcium channel blocker therapy. There are no data that beta blockers, calcium channel blockers, or disopyramide alter the risk of sudden death (Maron et al., 2003). Drugs to Be Used with Caution in HCM Angiotensin-converting enzymes inhibitors (ACE inhibitors), angiotensin II blockers, nifedipine, and other pure afterload reducing agents should be used with caution, as afterload reduction may worsen LVOTO (Roberts et al., 2005; Spirito et al., 2006). Beta adrenergic agents like dopamine, dobutamine, or epinephrine and agents with increased inotropic activity may worsen LVOTO (Maron et al., 2003). Likewise, rapid or aggressive diuresis can decrease preload and worsen LVOTO. Pharmacogenomics Currently, there is no therapy available specifically designed for specific gene mutations underlying the disease nor a therapy that has been shown to reverse the hypertrophic process. Although polymorphisms in the renin-angiotensin-aldosterone system (RAAS) modify the phenotype of HCM, particularly MYBPC3-HCM (Perkins et al., 2005), a direct correlation with genotype-specific drug treatment has not been shown. However, with growing evidence that ACE inhibitors especially combined with low doses of aldosterone receptor blockers can prevent the progression of hypertrophy and fibrosis (Fraccarollo et al., 2003, 2005; Kalkman et al., 1999; Kambara et al., 2003; Monteiro de Resende et al., 2006), one can envision that, with increasing knowledge of the genomic background of HCM, specific therapies will emerge in the near future. In other cases for example, the proper and prompt recognition of an HCM phenocopy such as cardiac Fabry’s disease can facilitate gene-specific pharmacotherapy including in this particular example, enzyme-replacement therapy. Albeit rare, such clinical sleuthing can enable early treatment and prevent the progression of the disease.

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The Role and Impact of Non-Pharmacological Therapy Septal Myectomy Surgery Ventricular septal myectomy remains the gold standard for treating drug refractory, symptomatic obstructive HCM; a procedure during which a piece of hypertrophied septum is removed in order to relieve the obstruction (Maron et al., 2004; Nishimura et al., 2004; Spirito et al., 2006). Surgery is usually indicated in patients with peak instantaneous LVOT Doppler gradient of 50 mmHg or higher under rest or provocation and/or severely symptomatic patients (NYHA class 3 or 4, (Maron et al., 2003, 2004; Spirito et al., 2006). This profile represents approximately 5% of patients with HCM (Elliott et al., 2004). More extensive, extended septal myectomy involving the anterolateral papillary muscle and mitral valvuloplasty may be needed in patients with abnormal papillary muscle apparatus and mitral valve abnormalities (Ommen et al., 2005). The surgical mortality is 1% in most major centers (Maron et al., 2004; Poliac et al., 2006). Long-term survival after surgical myectomy is equal to that observed in the general population (Ommen et al., 2005). Surgery provides long-term improvement in LVOT gradient, mitral valve regurgitation, and symptomatic improvement (Maron et al., 2004; Ommen et al., 2005; Poliac et al., 2006). Septal Ablation Alcohol septal ablation technique using ethanol (95% alcohol 1–3 ml) is injected in specific septal branches of the left anterior descending artery producing a controlled septal infarction often providing dramatic symptomatic improvement in some patients (Faber et al., 1998; Gietzen et al., 1999; Kimmelstiel et al., 2004;

Knight et al., 1997; Lakkis et al., 1998). The criteria for patient selection for alcohol septal ablation are similar to myectomy with the following caveat – the impact of alcohol septal ablation on SCD risk is unknown. Scarring associated with alcohol septal ablation may create a permanent arrhythmogenic substrate (Maron et al., 2003). Complications include complete atrioventricular block requiring permanent pacemakers (5–10% of patients), large myocardial infarction, acute mitral valve regurgitation, ventricular fibrillation (VF), and death (2–4%, Nishimura et al., 2004; Roberts et al., 2005; Spirito et al., 2006). Alcohol septal ablation is not suitable for patients with LVOTO secondary to abnormal mitral valve apparatus and unusual location of hypertrophy away from the area supplied by septal perforator. Given the unknown future risks of alcohol septal ablation, it is not recommended in children or young adults (Maron et al., 2003). Implantable Cardioverter Defibrillator The implantable cardiac defibrillator (ICD) plays an important role in primary and secondary prevention of sudden death of patient with HCM. The three main functions of the ICD are detection of arrhythmia; delivery of appropriate electrical therapy; and storage of diagnostic information, including electrocardiograms and details of treated episodes. In a multicenter study of ICDs in patients with HCM, the device intervened appropriately, terminating VT/VF, at a rate of 5% per year for those patients implanted as primary prevention and 11% per year for secondary prevention, over an average follow-up of 3 years (Maron et al., 2000). The indications for implantation of an ICD are listed in Figure 61.4 and can be summarized as secondary prevention for all patients with a

Secondary prevention For all patients with a history of OHCA, documented sustained VT or VF

Primary prevention Two or more major risk factors Individualized consideration with only one major risk factor

Major risk factors • Abnormal blood pressure response during exercise • Extreme hypertrophy (30 mm) • Family history of sudden cardiac death • Non-sustained ventricular tachycardia • Unexplained syncope (exercise-induced)

Possible Modifiers • Gadolinium hyper-enhancement on CMR • LVOTO

Figure 61.4 The major indications for implantation of an ICD in HCM: secondary prevention or primary prevention, and major risk factors. CMR, cardiac magnetic resonance; LVOTO, left ventricular outflow tract obstruction; OHCA, out of hospital cardiac arrest; VF, ventricular fibrillation; VT, ventricular tachycardia.

References

history of out of hospital cardiac arrest (OHCA), documented sustained VT or VF, or as primary prevention in patients with two or more major risk factors. Dual-Chamber Pacing There has been a great deal of debate surrounding the use of pacing as a means of relieving ventricular obstruction. Some studies have shown a beneficial effect, while others demonstrated significant placebo effect (Maron et al., 2003). The average decrease in LVOTO gradient with pacing ranged from a modest 25% to 40% and varied substantially (Maron et al., 2003). There is evidence to suggest that appropriately used dual-chamber pacing may decrease LVOT gradient and provide symptomatic relief (Maron et al., 1999). Thus, there may be a limited role of dual-chamber pacing in a select group of patients, for example patients with advanced age (65 years) and higher surgical risk. There is no evidence to suggest any change in SCD risk or disease progression (Boriani et al., 2004). Maze Procedures Surgical Maze procedure combined with myectomy may be a feasible therapeutic option in HCM with LVOTO and atrial

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fibrillation (AF). There are small case series reporting low operative mortality and morbidity and a high likelihood of patients remaining in sinus rhythm post procedure (Chen et al., 2004). Larger studies with longer follow-up are needed to better define the risks and benefits of surgical Maze procedure in HCM.

CONCLUSIONS Hypertrophic cardiomyopathy is a disease underscored by profound genotypic and phenotypic heterogeneity. Over last two decades many discoveries have exposed some of the pathogenic mechanisms of the disease with the elucidation of many HCM susceptibility genes. One of the most important genotype– phenotype relationships percolating from these discoveries and best translated to clinical practice is that of the strong relationship between the reverse curve-HCM and the presence of a myofilament mutation. Future research should show whether these discoveries can be translated further into pathogenetic-based strategies for the treatment and prevention of this disease.

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CHAPTER

62 Genetics and Genomics of Arrhythmias Jeffrey A. Towbin and Matteo Vatta

INTRODUCTION Arrhythmias typically are thought to occur due to primary or secondary abnormalities in cardiac electrophysiology. These abnormalities can include primary alterations in myocardial conduction and repolarization or those occurring as a result of structural heart disease. A significant portion of the individuals found to have abnormalities of rhythm and conduction are now known to have a genetic basis, with familial inheritance notable. Familial inheritance of arrhythmias and conduction disorders indicates that genetic factors play an integral role in development of these abnormalities. Understanding the underlying genetic defects responsible for these disorders has indeed provided insights into the mechanisms leading to the clinical picture and promises to impact the therapeutic strategies used in the care of these patients. Over the course of the last 15 years, our understanding of the genetic abnormalities in a variety of cardiomyopathies, long QT syndrome (LQTS), Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia (CPVT), and newer disorders has led to general concepts of cardiovascular disease. In this chapter, the clinical features and management of arrhythmia disorders and conduction disease will be discussed and the current understanding of the genetic and genomic abnormalities associated with these disorders will be reviewed.

SPECIFIC CARDIAC ARRHYTHMIAS The entire cardiac electrical system can be affected by genetic abnormalities, leading to atrial and ventricular tachyarrhythmias, Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

sinus node dysfunction (SND), or atrioventricular block (AV block). Primary and secondary arrhythmias (i.e., those associated with structural heart disease) are discussed below.

PRIMARY ABNORMALITIES IN CARDIAC RHYTHM: VENTRICULAR TACHYARRHYTHMIAS Long QT Syndromes The long QT syndromes (LQTS) are primary disorders of cardiac repolarization (Schwartz et al., 2000a) in which prolongation of the QT interval corrected for heart rate (QTc) is seen on the surface electrocardiogram (ECG) along with abnormalities of T-wave morphology and sinus bradycardia (Figure 62.1). Syncope, seizures, and sudden death are the clinical features that are commonly seen, occurring due to ventricular tachycardia (VT), especially polymorphic VT or torsades de pointes (Figure 62.2), which can degenerate into ventricular fibrillation (VF). Torsades de pointes is common in all forms of LQTS, and is defined as “turning of the points” describing the varying axis of the QRS complex during VT (Schwartz et al., 2000a). This arrhythmia is a subset of polymorphic VT, to be distinguished from monomorphic VT which has a different morphology and mechanism of development (El-Sherif et al., 1997; Viskin et al., 1996). Monomorphic VT usually results from reentrant mechanisms and typically can be induced by programmed electrical stimulation. On the other hand, polymorphic VT cannot be induced using programmed electrical stimulation, suggesting the mechanism is unlikely to be Copyright © 2009, Elsevier Inc. All rights reserved. 729

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primary reentry. Torsades de pointes, in fact, is thought to be initiated by abnormal automaticity and then maintained by reentrant mechanisms (Antzelevitch, 1999). Development of early afterdepolarizations (EADs) appear to be an important mechanism whereby drug-induced action potential prolongation initiates torsades (January and Riddle, 1989; Verduyn et al., 1997). Under normal conditions, the ventricle is activated from subendocardium to epicardium by impulses arising in the subendocardial Purkinje network. Mapping data in animal models support the idea that the initial beat in torsades arises in the subendocardium, consistent with a triggered beat arising from an EAD in the Purkinje system (El-Sherif et al., 1996).The conditions that evoke EADs markedly prolong repolarization in the mid-myocardium (M cell region) as well, resulting in a situation where propagation of EAD-related triggered beats from the subendocardium may be blocked in regions where M cell action potentials have become especially long, setting up intramural reentrant excitation with a circuit that varies from beat to beat (Antzelevitch et al., 1995). This perhaps accounts for the distinctive morphology of torsades de pointes (Asano et al., 1997; El-Sherif et al., 1997).

Recently, the concept has emerged that defects in currents important for repolarization prolong the action potential but are not directly arrhythmogenic. Rather, action potential duration creates a milieu in which genetically normal, drug-unmodified ion channels or other electrogenic phenomena further prolong repolarization and precipitate arrhythmias. The LQTS have been classified into acquired and genetically inherited forms. Acquired long QT syndrome (aLQTS) is the most common form of LQTS, with drug-induced LQTS particularly common. Drugs implicated in aLQTS include antiarrhythmic agents such as quinidine or sotalol, tricyclic antidepressants, antibiotics (especially macrolide antibiotics such as erythromycin), antihistamines such as terfenidate, and inhalational anesthetics. Acquired LQTS has also been seen in association with metabolic derangements including hypokalemia, hypomagnesemia, and hypercalcemia. Additionally, aLQTS has been identified in patients with other cardiac diseases such as cardiomyopathies and myocardial ischemia, as well as under circumstances of intracranial disease (i.e., intracranial surgery, subarachnoid hemorrhage, and increased intracranial pressure).

KVLQT1

HERG (I Kr)

SCN5A (I Na) Figure 62.1

Electrocardiograms demonstrating LQTS associated with the three major genes causing LQTS (with permission).

Figure 62.2

Torsade de pointes polymorphic ventricular tachycardia.

Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias

Four forms of inherited LQTS are known: Romano-Ward LQTS (Romano et al., 1963;Ward, 1964) and Jervell and LangeNielsen syndrome (Jervell and Lange-Nielsen, 1957) are the classically described forms of LQTS. More recently, two other disorders, Andersen syndrome (Andersen et al., 1971) and Timothy syndrome (Marks et al., 1995; Splawski et al., 2004) have been described, studied and their underlying causes determined. Another disorder, sudden infant death syndrome (SIDS) has also been found to be “LQTS-like” in some babies (Schwartz et al., 1998).

TABLE 62.1

Diagnostic criteria in LQTS

Clinical finding

Points a

Electrocardiographic findings QTcb 480 ms1/2

3 1/2

460–470 ms

2

450 (male) ms1/2

1

Torsades de pointes c

2

T-wave alterans

1

Notched T wave in three leads

1

Low heart rate for aged

0.5

Clinical history Syncopec With stress

2

Without stress

1

Congenital deafness

0.5

Family historye Family members with definite LQTS f

1

Unexplained sudden cardiac death below age 30 among immediate family members

0.5

Scoring: 1 point  low probability of LQTS; 2–3 points  intermediate probability of LQTS; 4 points  high probability of LQTS. a In the absence of medications or disorders known to affect these ECG features. b QTc calculated by Bazett’s formula, where QTc  QT/ RR . c Mutually exclusive. d Resting heart rate below the second percentile for age. e The same family member cannot be counted twice. f Definite LQTS is defined by an LQTS score 4.

Figure 62.3

T-wave alternans in a child with LQTS.

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Romano-Ward syndrome is characterized by autosomal dominant inheritance with reduced penetrance and is the most common inherited form of LQTS, estimated to have an incidence of 1 in 10,000 live births worldwide (Priori et al., 1999a, b; Vatta et al., 2000). Jervell and Lange-Nielsen syndrome is a seemingly rare condition, with an estimated incidence of 1 in 1.6 million live births (Priori et al., 1999a, b;Vatta et al., 2000). This disorder, which is defined as LQTS associated with sensorineural deafness, has been described as having autosomal recessive inheritance since its initial description in 1957 (Jervell and Lange-Nielsen, 1957). In the past decade, molecular genetic studies, have clarified its inheritance, suggesting it to be autosomal dominant LQTS associated with autosomal recessive deafness. Finally, SIDS has been shown to be another presentation of inherited LQTS (Schwartz et al., 1998). Supportive data was provided by the identification of mutations in the cardiac sodium channel gene, SCN5A, by Ackerman et al. (2001), followed by identification of HERG/KCNH2 mutations in other infants with SIDS (Christiansen et al., 2005). Clinical Features of LQTS LQTS is typically identified in individuals presenting with syncope, seizures or sudden cardiac death which results from episodic ventricular tachydysrhythmias, particularly torsades de pointes and VF (Priori et al., 1999a, b; Schwartz et al., 2000a;Vatta et al., 2000). Cardiac arrhythmias have been reported in up to 24% of cases, but the risk of sudden death has been estimated to be less than 1% per year. These estimates, however, are almost certainly inaccurate, since a significant percentage of the 300,000–400,000 sudden deaths occurring in the United States yearly (Myerburg, 1997) are likely to be the result of this disorder (which goes unrecognized) and many living patients are asymptomatic and can have normal QT intervals on screening ECGs due to reduced penetrance. Today, many asymptomatic family members are being identified via screening ECGs and molecular genetic family screening, and, therefore, better estimates are likely in the future. Since LQTS may be difficult to diagnose, a set of criteria have been developed by Schwartz et al. (1985, 1993, 2000a). These criteria use a point system in the diagnostic scheme, relying heavily on classic features (Table 62.1). This approach is thought to improve diagnostic accuracy by including major criteria (prolonged QTc 440 ms; stress-induced syncope; family history of LQTS) and minor criteria (congenital deafness; T-wave alternans; relative bradycardia; abnormal ventricular repolarization) (Figure 62.3). The clinical features of LQTS, which occur due to arrhythmias, are typically associated with triggering events. The most

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well described triggers include exercise, anxiety or excitement, auditory events (i.e., telephone or alarm clock ringing), swimming or diving into a pool, and being postpartum. Some patients have events or die during sleep, which is thought to be associated with bradycardia. In most cases of LQTS, no other abnormalities occur. However, sensorineural deafness is associated with the Jervell and Lange-Nielsen form of LQTS, distinguishing it from the Romano-Ward syndrome. LQTS has been reported to occur in approximately 1% of children with congenital deafness and this has led to the recommendation for screening ECGs in this group of children. A study from Thailand demonstrated a possible prevalence of 0.7% of Jervell and Lange-Nielsen in children with sensorineural deafness at a school for the deaf (Sopontammarak et al., 2003). These children also appear to have more severe ECG abnormalities than Romano-Ward syndrome patients and have a worse prognosis. Other associated abnormalities have rarely been reported in more complex patients with LQTS. Marks et al. (1995) described LQTS patients with syndactyly; in this patient subset, LQTS appears to be quite severe, with a high percentage dying in infancy. In most of these patients, the initial presentation included 2:1 AV block due to marked QT prolongation. These patients were initially found in sporadic cases, with both genders being represented, but more recently families have been identified and a new syndrome termed Timothy syndrome (Splawski et al., 2004), has been labeled (see section on “Timothy Syndrome”). Other patients with LQTS have been described with associated diabetes mellitus or asthma (Bellavere et al., 1988; Rosero et al., 1999), as well as those with potassium-sensitive periodic paralysis, dysmorphic features and skeletal abnormalities termed Andersen syndrome (Andersen et al., 1971). Genetics of LQTS As previously noted, four forms of inherited LQTS have been described, including autosomal dominant (Romano-Ward syndrome), autosomal recessive (Jervell and Lange-Nielsen syndrome), and other complex forms (Andersen syndrome and Timothy syndrome), in addition to sporadic cases. Over the past 15 years, the genetic aspects of all four forms of LQTS have been unraveled. In the case of the most common inherited form, Romano-Ward syndrome, the key genes have been identified for all of the mapped subtypes. In 1991, Keating and colleagues identified genetic linkage to the short arm of chromosome 11 (11p15.5) in several families with Romano-Ward syndrome (Keating et al., 1991a, b). Shortly thereafter, we demonstrated genetic heterogeneity and this was confirmed by several laboratories subsequently (Benhorin et al., 1993; Curran et al., 1993; Towbin et al., 1992, 1994). In a collaborative effort (Jiang et al., 1994), linkage was shown for several families to two new loci, the long arm of chromosome 7 (7q35-36) and the short arm of chromosome 3 (3p21). The three loci were later termed LQT1 (11p15.5), LQT2 (7q35-56), and LQT3 (3p21). A fourth locus (LQT4) on chromosome 4q (4q25-27) was later described as well (Schott et al., 1995). Two other genes, both located on

chromosome 21q22 (LQT5, LQT6), were later identified (Abbott et al., 1999; Barhanin et al., 1996; Sanguinetti et al., 1996a). More recently, other candidate genes and their loci were also identified (Table 62.2). Penetrance in Romano-Ward syndrome is reduced and in some families Romano-Ward syndrome appears to occur in a recessively inherited pattern (Priori et al., 1998, 1999c). Gene Identification in Romano-Ward Syndrome: Ion Channel Gene Mutations KVLQT1 or KCNQ1: The LQT1 Gene

The first of the genes mapped for LQTS, termed LQT1, required 5 years from the time that mapping to chromosome 11p15.5 was first reported to gene cloning (Table 62.2). This gene, originally named KVLQT1, but more recently called KCNQ1, is a potassium channel gene that consists of 16 exons, spans approximately 400 kb, and is widely expressed in human tissues including heart, inner ear, kidney, lung, placenta, and pancreas, but not in skeletal muscle, liver, or brain. Analysis of the predicted amino acid sequence of KCNQ1 demonstrated that the gene encodes a potassium channel α-subunit (Barhanin et al., 1996; Sanguinetti et al., 1996a;Wang et al., 1996) with a conserved potassium-selective pore-signature sequence flanked by six membrane-spanning segments similar to shaker-type channels (Figure 62.4). A putative voltage sensor is found in the fourth membrane-spanning domains (S4) and the selective pore loop is between the fifth and sixth membranespanning domains (S5, S6). Biophysical characterization of the KCNQ1 protein confirmed that KCNQ1 is a voltage-gated potassium channel protein subunit that requires coassembly (Barhanin et al., 1996; Sanguinetti et al., 1996a) with a -subunit called mink or KCNE1 to function properly (Figure 62.4). Expression of either KCNQ1 or KCNE1 alone results in inefficient (or no) current development. When both subunits are coexpressed in either mammalian cell lines or Xenopus oocytes, however, the slowly activating potassium current (IKs) is developed in cardiac myocytes. Combination of normal and mutant KVLQT1 subunits forms abnormal IKs channels, and these mutations are believed to act through a dominant-negative mechanism (the mutant form of KVLQT1 interferes with the function of the normal wild-type form through a “poison pill”type mechanism) or a loss-of-function mechanism (only the mutant form loses activity) (Demolombe et al., 1998; Shalaby et al., 1997; Wollnick et al., 1997). In some cases, the protein is not normally trafficked to the membrane. The vast majority of mutations in KCNQ1 are heterozygous mutations in Romano-Ward syndrome patients (Chouabe et al., 1997, 2000; Duggal et al., 1998; Li et al., 1998; Splawski et al., 1997a; Towbin, 2006), and KCNQ1 appears to be the most commonly mutated gene in LQTS. HERG or KCNH2: The LQT2 Gene

After the initial localization of LQT2 to chromosome 7q3536 by Jiang et al. (1994) (Table 62.2), candidate gene screening

Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias

TABLE 62.2

Long QT and related arrhythmia syndromes

Gene

Locus

Syndrome

Protein and subunit

Function and abnormality

KCNQ1

11p15.5

LQTS1

KV7.1 

IKs↓KvLQT1

KCNH2

7q35

LQTS2

KV11.1 

IKr↓ HERG

SCN5A

3p21

LQTS3

NaV1.5 

INa↑

ANKβ

4q25

LQTS4

Ankyrin-B

INa,K↓ INCX↓

KCNE1

21q22.1

LQTS5

minK 

IKs↓

KCNE2

21q22.1

LQTS6

MiRP1 

IKr↓

KCNJ2

17q23

LQTS7

Kir2.1 

IK1↓

CACNA1C

12p13.3

LQTS8

CaV1.2 1c

ICa,L↑

CAV3

3p25

LQTS9

Caveolin3

INa↑

SCN4B

11q23

LQTS10

NaV1.5 4

INa↑

KCNQ1

11p15.5

JLNS1

KV7.1 

IKs↓ KvLQT1

KCNE1

21q22.1

JLNS2

minK 

IKs↓

SCN5A

3p21

LQTS3, SIDS1

NaV1.5 

INa↑

KCNQ1

11p15.5

LQTS1, SIDS2

KV7.1 

IKs↓↑ KvLQT1

KCNH2

7q35

LQTS2, SIDS3

KV11.1 

IKr↓ HERG

CAV3

3p25

LQTS9, SIDS4

Caveolin3

INa↑

KCNE2

21q22.1

LQTS2, SIDS5

MiRP1 β

IKr↓

LQT1:11p15.5

IKs

LQT5:21q22

KvLQT1

LQT6:21q22

HERG minK

MiRP1

INa

LQT3:3p21-23

IKr

LQT2:7q35-36

SCN5A

IK1

LQT7:17q23 KCNJ2

Figure 62.4

Chromosomal loci and genes responsible for LQTS.

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identified mutations in HERG (human ether-a-go-go-related gene), a cardiac potassium channel gene originally cloned from a brain cDNA library (Warmke and Ganetzky, 1994) and that is expressed in neural crest-derived neurons (Arcangeli et al., 1997), microglia (Pennefather et al., 1998), a wide variety of tumor cell lines (Bianchi et al., 1998), and the heart (Curran et al., 1995). The KCNH2 gene consists of 16 exons and spans 55 kb of genomic sequence (Curran et al., 1995). The predicted topology of HERG is shown in Figure 62.4 and is similar to KCNQ1 but, unlike KCNQ1, KCNH2 has extensive intracellular aminoand carboxyl-termini, with a region in the carboxyl-terminal domain having sequence similarity to nucleotide binding domains (NBDs). Electrophysiologic and biophysical characterization of KCNH2 expressed in Xenopus oocytes established that this protein encodes the rapidly activating delayed-rectifier potassium current IKr (Sanguinetti et al., 1995; Trudeau et al., 1995) and electrophysiologic studies of LQTS-associated mutations demonstrated a loss-of-function or a dominant-negative mechanism (Sanguinetti et al., 1996b) of action. In addition, protein trafficking abnormalities have been shown to occur (Furutani et al., 1999; Zhou et al., 1998). More recently, Zhang et al. (2004) identified an intronic variant in KCNH2 (T1945  6C) in which splicing assay analysis showing downstream intron retention and complementary DNA with the retained intron 7 failed to produce functional channels, consistent with potential disease-causing dysfunction. This finding potentially expands the disease-causing mechanisms in LQTS. This channel has been shown to coassemble with -subunits for normal function, similar to that seen in IKs. Abbott et al. (1999) identified MiRP1 (KCNE2) as a -subunit for KCNH2 (see section on “MiRP1: The LQT6 Gene”). The identification of these initial genes which encoded ion channels, suggested that the “final common pathway” of LQTS is ion channel disruption (Towbin, 2000). SCN5A: The LQT3 Gene

Utilization of the candidate gene approach established that the gene responsible for chromosome 3-linked LQTS (LQT3) (George et al., 1995) is the cardiac sodium channel gene SCN5A (Wang et al., 1995a, b) (Table 62.2). SCN5A is highly expressed in human myocardium and brain, but not in skeletal muscle, liver, or uterus (Hartmann et al., 1999; Wang et al., 1995a). It consists of 28 exons that span 80 kb and encodes a protein of 2016 amino acids with a putative structure that consists of four homologous domains (DI to DIV), each of which contains six membrane-spanning segments (S1 to S6) similar to the structure of the potassium channel α-subunits (Figure 62.4) (Gellens et al., 1992). Mutation analysis identified three mutations initially (Wang et al 1995a, b); and when expressed in Xenopus oocytes, all mutations generated a late phase of inactivation-resistant, mexiletine- and tetrodotoxin-sensitive whole-cell current via different mechanisms (Bennett et al., 1995; Dumaine et al., 1996). Two of the three mutations showed dispersed reopening after the initial transient, but the other

mutation showed both dispersed reopening and long-lasting bursts. These results suggested that SCN5A mutations act through a gain-of-function mechanism (the mutant channel functions normally, but with altered properties such as delayed inactivation) and that the mechanism of chromosome 3-linked LQTS is persistent nonactivated sodium current in the plateau phase of the action potential. An et al. (1998) also showed that not all mutations in SCN5A are associated with persistent current and demonstrated that SCN5A interacted with -subunits. Other mutations were later identified (Benhorin et al., 1998). MinK or KCNE1: The LQT5 Gene

The minK (IsK or KCNE1) gene was initially localized to chromosome 21 (21q22.1) and found to consist of three exons that span approximately 40 kb (Honore et al., 1991). It encodes a short protein consisting of 130 amino acids and has only one transmembrane-spanning segment with small extracellular and intercellular regions (Figure 62.4). When expressed in Xenopus oocytes, it produces potassium current that closely resembles the slowly activating delayed-rectifier potassium current IKs in cardiac cells (Barhanin et al., 1996; Sanguinetti et al., 1996a), but requires coexpression cardiac slowly activating delayed-rectifier IKs current to develop (Barhanin et al., 1996; Sanguinetti et al., 1996a). Bianchi et al. (1999) also showed that mutant KCNE1 results in abnormalities of IKs and IKr and in protein trafficking abnormalities. McDonald et al. (1997) showed that KCNE1 also interacts with KCNH2, regulating IKr. Splawski et al. (1997b) demonstrated that KCNE1 mutations cause LQT5 when they identified mutations in two families with LQTS (Table 62.2). In both cases, mutations were identified that reduced IKs by shifting the voltage dependence of activation and accelerating channel deactivation. This was later confirmed by others (Chouabe et al., 2000; Duggal et al., 1998) and further supported by the fact that a mouse model with mutant minK (Vetter et al., 1996) developed a phenotype (that included deafness). The functional consequences of these mutations include delayed cardiac repolarization and hence, an increased risk of arrhythmias (Chouabe et al., 2000; Duggal et al., 1998; Franqueza et al., 1999; Vatta and Towbin, 2006). MiRP1 or KCNE2: The LQT6 Gene

The MiRP1 gene (the minK-related peptide 1 or KCNE2 gene) is a potassium channel gene encoding a small integral membrane subunit protein that assembles with KCNH2 (LQT2) to alter its function and enable full development of the IKr current (Figure 62.4). This is 123-amino acid channel protein has a single predicted transmembrane segment similar to that described for KCNE1. Chromosomal localization studies mapped the KCNE2 gene to chromosome 21q22.1, within 79 kb of KCNE1 (minK) and arrayed in opposite orientation. The open reading frames of these two genes share 34% identity and both are contained in a single exon, suggesting that they are related through gene duplication and divergent evolution (Abbott et al., 1999). Three missense mutations associated with dysrhythmias were initially identified (Abbott et al., 1999) and biophysical analysis demonstrated that these mutants form channels

Primary Abnormalities in Cardiac Rhythm: Ventricular Tachyarrhythmias

that open slowly and close rapidly, thus diminishing potassium currents. Therefore, like KCNE1, this channel protein acts as a -subunit but, by itself, leads to risk of ventricular arrhythmia when mutated (Table 62.2).

TABLE 62.3 Gene

Locus

Syndrome

Protein and subunit

Function and abnormality

SCN5A

3p21

BrS1, CoD

NaV1.5 

INa↓

GPD1L

3p24

BrS2

G3PD1L

INa↓

Ankyrin-β or ANKβ: The LQT4 Gene

SCN5A

3p21

SUND

NaV1.5 

INa↓

Initially mapped in 1995 to chromosome 4q25-25 by Schott and colleagues in a large French family with autosomal dominant LQTS associated with sinus bradycardia due to SND and atrial fibrillation (AF), the gene remained elusive until 2003. Mohler et al. (2003) identified ankyrin-β or ankyrin-2 (ANKβ) as the disease-causing gene (Table 62.2). This gene encodes a protein with three major isoforms with molecular weights ranging from 440, 220, and 150 kDa generated by alternative splicing. The major isoform in the heart is the 220 kDa form. Ankyrins are adapter proteins that link integral membrane proteins to the spectrin-based cytoskeleton (Bennett and Baines, 2001) and contain three functional domains that consist of the membrane binding domain, the spectrin binding domain, and the regulatory domain. These proteins bind to several ion channel proteins, including the voltage-sensitive sodium channel (INa), anion exchanger (Cl/HCD3 exchanger), sodium–potassium ATPase, sodium–calcium exchanger (NCX or INaˆCa), and calcium release channels including those mediated by the ryanodine receptor (RyR2) and inositol triphosphate (IP3) receptor. Mutations in ANKβ appear to act by a loss-of-function mechanism. It is likely that mutations in this gene cause clinical phenotype by disrupting the cytoskeletal framework which results in compromise of channel function or of the trafficking of channels to their proper locale. A murine model of mutant ANKβ results in clinical similarities to that seen in humans and findings indicative of calcium handling abnormalities (Mohler et al., 2003) and sodium channel dysfunction (Chauhan et al., 2000) have been reported.

SCN5A

3p21

Progressive CoD

NaV1.5 

INa↓

SCN5A

3p21

BrS1, CoD, AA (SSS)

NaV1.5 

INa↓

SCN5A

3p21

BrS1, LQTS3

NaV1.5 

INa↓

SCN5A

3p21

BrS1, LQTS3, CoD

NaV1.5 

INa↓

SCN5A

3p21

iVF, CoD

NaV1.5 

INa↓

SCN5A

3p21

DCM, CoD, AA (AF)

NaV1.5 

INa↓

SCN5A

3p21

BrS1, SIDS1

NaV1.5 

INa↓

SCN5A

3p21

BrS1, CoD, SIDS1

NaV1.5 

INa↓

Caveolae are 50–100-nm omega-shaped microdomains of the plasmalemma which are particularly abundant in the cardiovascular system, including cardiomyocytes, endothelial cells, smooth muscle cells, macrophages, and fibroblasts. These proteins are involved in a variety of functions including vesicular trafficking and serve as platforms to organize and regulate a variety of signal transduction pathways. Caveolins, the principal proteins in caveolae, are found as three different isoforms (CAV1–CAV3) that are encoded by separate genes. Although CAV1 and CAV2 are expressed in most cell types, only CAV3 is specifically expressed in cardiomyocytes and skeletal muscle. Some cardiac ion channels have been specifically localized to caveolae, including the SCN5A-encoded voltage-gated sodium channel (Nav1.5), the voltage-dependent potassium channel (Kv1.5), the sodium-calcium exchanger, and the L-type calcium channel. In the heart, a variety of other signaling molecules have been found

735

Brugada and related arrhythmia syndromes

Gene Identification in Romano-Ward Syndrome: Non-Ion Channel-Encoding Genes

Caveolin-3 or CAV3: The LQT9 Gene

■

in caveolae, including the 2-adrenergic receptor. Vatta et al. (2006) identified multiple mutations in familial and sporadic cases of LQTS (Table 62.2) and electrophysiological analysis of these mutations demonstrated a two- to three-fold increase in the late sodium current compared with wild-type CAV3. In addition, CAV3 was shown to directly interact with SCN5A, the cardiac sodium channel. This gain-of-function of the cardiac sodium channel induced by mutations in CAV3, is similar to the SCN5A mechanism of disease. Caveolin-3 is the second non-ion channel protein (after ankyrin-B) implicated in the pathogenesis of congenital LQTS. Consistent with the “final common pathway” hypothesis (Bowles et al., 2000; Towbin, 1998, 2000), genes encoding cardiac channel-interacting proteins, which secondarily disrupt ion channel function, may confer genetic susceptibility for LQTS. Caveolin-3 represents one of a potentially large group of such proteins. In addition, NaV1.5 appears to be disrupted in a variety of arrhythmia-associated disorders, including LQTS and Brugada syndrome (Table 62.3) both possible substrates of SIDS (Ackerman et al., 2001; Towbin and Ackerman, 2001) and AV block (Benson et al., 2003). SCN4B: The LQT10 Gene

Sodium channel -subunits are critical regulatory proteins, and four of these -subunits have been described, 1–4. These regulatory proteins are encoded by the sodium channel -subunit gene family known as Nav and the four protein subunits include SCN1B–SCN4B. These -subunits are all detectable in cardiac tissue and play a crucial role in cell adhesion, signal transduction, channel expression at the sarcolemma, and voltage dependence

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Genetics and Genomics of Arrhythmias

of channel gating. These proteins are characterized by an extracellular N-terminal cleaved region, a transmembrane segment, and a cytoplasmic domain with a C-terminal tail. Studies have shown that Nav subunits contain an extracellular Ig-like fold, often found in cell adhesion molecules that target ion channels to the sarcolemma and mediate interactions with signaling molecules. Nav1 and Nav3 are similar in sequence and associate noncovalently with -subunits while Nav2 and Nav4 are related proteins that are disulfide-linked to -subunits. Immunohistochemical studies in murine hearts indicate that the primary cardiac sodium channel in ventricular myocytes is composed of Nav1.5 (SCN5A) plus 2 and/or 4 subunits. The 4-subunit cytoplasmic tail is thought to interact with the S6-binding site within the inner cavity of Nav1.5. Recently, Madeiros-Domingo et al. (2007) have identified a mutation in the gene encoding the 4-subunit (SCN4B) in a multigenerational family with severe LQTS in several family members (Table 62.2), as well as with reduced penetrance in others. In addition, AV block, sudden cardiac death, and bradycardia were clinically apparent in this family. The authors identified a missense mutation in the family (L179F) which occurred in the transmembrane-spanning region of the protein. Biophysical analysis of this mutation expressed with the SCN5A -subunit demonstrated an eightfold increase in late sodium current compared with SCN5A alone and threefold increase compared with SCN5A  WT-4. This is consistent with the LQT3 biophysical phenotype and is similar to that seen in CAV3-related LQTS. Hence, this further confirms the “final common pathway hypothesis” that arrhythmias and conduction system disease occur as a consequence of ion channel dysfunction occurring either directly through ion channel mutations or secondarily due to dysfunction resulting from abnormal binding partner function or other indirect channel abnormality (such as, occurs with drugs) (Bowles et al., 2000;Towbin, 2000).

same location led to premature termination at the C-terminal end of the KVLQT1 channel. At the same time, Splawski et al. (1997a) identified a homozygous insertion of a single nucleotide that caused a frameshift in the coding sequence after the second putative transmembrane domain (S2) of KCNQ1.Together, these data strongly suggested that at least one form of JLNS is caused by homozygous mutations in KCNQ1, which was confirmed by others (Chen et al., 1999; Chouabe et al., 1997; Schulze-Bahr et al., 1997;Tyson et al., 1997;Wollnick et al., 1997). As a general rule, heterozygous mutations in KCNQ1 cause Romano-Ward syndrome (LQTS only), whereas homozygous (or compound heterozygous) mutations in KCNQ1 cause JLNS (LQTS and deafness). The hypothetical explanation suggests that although heterozygous KCNQ1 mutations act by a dominantnegative mechanism (Mohammad-Pannah et al., 1999), some functional KCNQ1 potassium channels still exist in the stria vascularis of the inner ear. Therefore, congenital deafness is averted in patients with heterozygous KCNQ1 mutations. For patients with homozygous mutations, no functional KCNQ1 potassium channels can be formed. It was shown by in situ hybridization that KCNQ1 is expressed in the inner ear (Neyroud et al., 1997), suggesting that homozygous KCNQ1 mutations can cause the dysfunction of potassium secretion in the inner ear and lead to deafness (Vetter et al., 1996). However, it should be noted that incomplete penetrance exists and not all heterozygous or homozygous mutations follow this rule (Priori et al., 1999c). As with Romano-Ward syndrome, if KCNQ1 mutations can cause the phenotype, it could be expected that KCNE1 mutations could also be causative of the phenotype (JLNS). Schulze-Bahr et al. (1997), in fact, showed that mutations in KCNE1 result in JLNS syndrome as well (Table 62.2), and this was confirmed subsequently (Duggal et al., 1998; Tyson et al., 1997). Hence, abnormal IKs current, whether it occurs due to homozygous or compound heterozygous mutations in KCNQ1 or KCNE1, results in LQTS and deafness.

COMPLEX FORMS OF LQTS

Andersen-Tawil Syndrome (LQT7) Clinical Aspects Andersen et al. (1971) identified a complex phenotype including ventricular arrhythmias, potassium-sensitive periodic paralysis, and dysmorphic features. The dysmorphisms included hypertelorism, broad nasal root, defects of the soft and hard palate, as well as short stature. More recently, skeletal abnormalities have broadened the phenotype (Andelfinger et al., 2002). These skeletal features include micrognathia, clinodactyly, syndactyly, and scoliosis. The associated cardiac abnormalities include QTc prolongation, VT, VF, and atrial arrhythmias. Torsades de pointes and bidirectional VT have been seen. In addition, repolarization abnormalities affecting late repolarization and resembling giant U waves are common. Sudden death has not been reported as a major risk in this disorder. Andelfinger et al. (2002) also reported sex-specific variable expression, as well as other clinical features including unilateral dysplastic kidney and congenital heart disease (bicusopid aortic valve, coarctation of the aorta, valvular pulmonic stenosis).

Jervell and Lange-Nielsen Syndrome Clinical Features As noted previously, patients with Jervell and Lange-Nielsen syndrome ( JLNS) have severe QT interval prolongation, episodic tachydysrhythmias including torsades de pointes, syncope and/or sudden death, and sensorineural deafness (Jervell and Lange-Nielsen, 1957; Schwartz et al., 2000). The deafness is autosomal recessive and severe while the LQTS is autosomal dominant (Vatta et al., 2000). Genetics Neyroud et al. (1997) reported the first molecular abnormality in patients with JLNS when they reported on two families in which three children were affected by JLNS, finding a novel homozygous deletion–insertion mutation of KCNQ1 (Table 62.2). A deletion of 7 bp and an insertion of 8 bp at the

Complex Forms of LQTS

TABLE 62.4

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737

Ca2 dependent arrhythmia syndromes

Gene

Locus

Syndrome

Protein and subunit

Function and abnormality

CACNA1CA

12p13.3

TS1, ASD

CaV1.2 1C

ICa,L↑

CACNA1C

12p13.3

TS2, ASD

CaV1.2 1C

ICa,L↑

RyR2

1q42

CPVT1

RyR2 

SR Ca2leak ↑

CASQ2

1p13.3

CPVT2

Calsequestrin

SR Ca2 leak ↑

ANKβ

4q25

CPVT3

Ankyrin-B

SR Ca2 leak ↑

KCNJ2

17q23

CPVT4, ATS1

Kir2.1 

IK1↑

RyR2

1q42-q43

CPVT1, LQTS

RyR2 

SR Ca2 leak ↑

RyR2

1q42-q43

CPVT1, ARVC2

RyR2 

SR Ca2 leak ↑

Genetic Aspects Kir 2.1 or KCNJ2: The LQT7 Gene

Andersen-Tawil syndrome was originally mapped to chromosome 17q23-q24.2 by Plaster et al. (2001) using genome-wide linkage analysis. Candidate gene mutation screening identified mutations in KCNJ2 (Table 62.2), which encodes an inward rectifier potassium channel called Kir2.1 (Tristani-Firouzi et al., 2002). This channel is highly expressed in the heart and plays a role in phase 4 repolarization and in the resting membrane potential. Multiple gene mutations have been identified to date with relatively high penetrance noted. Functional studies have demonstrated reduction or suppression of IK1 by a haploinsufficiency or dominant-negative effect (Tristani-Firouzi et al., 2002; Lange et al., 2003). Lange et al. (2003) generated known KCNJ2 mutants which did not yield any measurable potassium currents in CHO cells consistent with failure to form functional homomultimeric complexes and non-functional channels. In addition, Bendahhou et al. (2003) demonstrated that defective Kir 2.1 channels may not traffic to the membrane properly. This gene may play a role in developmental signaling pathways as well, which is believed to be the cause of the dysmorphisms (Andelfinger et al., 2002). Analysis of a variety of KCNJ2 mutations demonstrated that many of these mutations included residues implicated in binding membrane-associated phoephatidylinositol 4,5biphosphate (PIP2) (Donaldson et al., 2003). It should be noted that nearly 40% of cases do not segregate with this gene, suggesting that genetic heterogeneity exists (Donaldson et al., 2004). Timothy Syndrome Clinical This complex disorder is characterized by multisystem dysfunction including webbing of fingers and toes (syndactyly), immune deficiency, intermittent hypoglycemia, a cognitive immune deficiency, intermittent hypoglycemia, cognitive abnormalities, autism, dysmorphic features, various forms of congenital heart disease, and lethal arrhythmias associated with LQTS (Marks et al., 1995; Splawski et al., 2004).

Genetics This autosomal dominant disorder was described initially by Marks et al. (1995), and more recently the genetic basis of this syndrome was described by Splawski et al. (2004). Mutations in Ca(V)1.2, the L-type calcium channel, which is expressed in all affected tissues, was shown to produce maintained inward Ca2 overload in multiple cell types and, in the heart, causes prolonged Ca2 current delaying cardiomyocyte repolarization and increased arrhythmia risk (Table 62.4). Genotype–Phenotype Correlations in LQTS Moss et al. (1995) showed that the ECG manifestations of LQTS were in great part determined by which channel is mutated. Different T-wave patterns were clearly evident when comparing tracings from patients with mutations in LQT1, LQT2, and LQT3. More recently, Zareba et al. (1998) showed that the mutated gene may result in a specific clinical phenotype with different triggers and may predict outcome. For instance, these authors suggested that mutations in LQT1 and LQT2 result in early symptoms (i.e., syncope) but the risk of sudden death was relatively low for any event. In contrast, mutations in LQT3 resulted in a paucity of symptoms but when symptoms occurred they were associated with a high likelihood of sudden death. Zareba et al. (2003a) showed that the location of mutations in KCNQ1 plays no role in clinical course. However, Shimizu et al. (2004) showed mutation site-specific differences in arrhythmic risk and sensitivity to sympathetic stimulation in LQT1 patients with those having transmembrane mutations having a greater risk of early-onset cardiac events and greater sensitivity to sympathetic stimulation compared with patients having C-terminal mutations. Moss and colleagues showed that pore mutations in LQT2 have higher risk effects of age and gender based on genotype were also reported (Zareba et al., 2003b). In this case, Zareba and colleagues showed that during childhood, the risk of cardiac events was significantly higher in LQT1 and LQT3 males than females with no gender-related differences in the risk of cardiac events among LQT2 and LQT3 carriers. In adulthood, LQT2 and LQT1 females had a significantly higher

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risk of cardiac events than respective males. The lethality of cardiac events was highest in LQT3 males and females, and higher in LQT1 and LQT2 males than females. Compound mutations, which reportedly occurs in 8% of subjects, appears to cause longer QTc intervals, worse and more frequent cardiac events and, in general, a more severe phenotype than other mutations and worse disease than expected (Westenskow et al., 2004). Neonates appear to have gene-specific clinical features as well. Lupoglazoff et al. (2004) studied 23 neonate probands and demonstrated that 2:1 AV block is common and usually associated with LQT2 mutations, with a poor prognosis during the first month of life. In contrast, LQT1 mutations in this patient subgroup appeared correlated with sinus bradycardia and good short-term prognosis with -blocker therapy. Ackerman et al. (1999) and Moss et al. (1999) showed that swimming is a common trigger for symptoms in LQT1 patients while Wilde et al. (1999) have found auditory triggers to be common in LQT2. LQT3, on the other hand, appears to be associated with sleepassociated symptoms. More recently, Choi et al. (2004) evaluated 388 subjects with LQTS for LQT gene mutations and identified 43 individuals with swimming-related events (11%). Among this group, 65% had mutations in LQT1, with 21% having ryanodine receptor mutations (RyR2), and 5% with LQT2 mutations. Coupled with the findings by Moss and colleagues, it could be suggested that understanding the underlying cause of LQTS in any individual could be used to improve survival by prevention and gene-specific therapy. Management of LQTS At present, there are four classical modalities for treatment of LQTS that have withstood the test of time: (1) -blockers (Moss, 1998, Moss et al., 2000; Priori et al., 2004; Villain et al., 2004; ), (2) pacemakers (Eldar et al., 1987; Moss et al., 1991, Viskin, 2000), (3) left cervicothoracic sympathetic ganglionectomy (Moss and McDonald, 1970; Schwartz et al., 1991, 2004), and, most recently, (4) internal cardioverter defibrillator (ICD) (Ten Harkel et al., 2005; Zareba et al., 2003c). The mortality of untreated symptomatic patients with LQTS exceeds 20% in the year after their first syncopal episode and approaches 50% within 10 years of initial presentation (Schwartz, 1985). With institution of the classical therapy, this can be reduced to 2–4% in 5 years after initial presentation (Moss et al., 2000; Schwartz, 1985). Despite the lack of a placebo-controlled, randomized clinical trial, strong evidence supports the use of antiadrenergic interventions as the mainstay of therapy for most patients. The trigger for many life-threatening events appears to involve sudden increases in sympathetic activity (i.e., emotional or physical stress), and therefore, antiadrenergic therapy makes physiological sense. The -adrenergic blocking agents prevent new syncopal episodes in approximately 65–75% of patients (Moss et al., 2000). Suppression of complex ventricular arrhythmias (i.e., couplets and VT) seems desirable. Villain et al. (2004) showed that a low incidence of cardiac events occur in children with LQTS treated with -blockers. In the 122 children

studied, only 4 deaths occurred. Of these, 111 children (92%) were treated with -blockers alone, with no deaths and only 5 non-fatal cardiac events (4.5%) noted. None of these children had LQT1 mutations. Priori et al. (2004) studied 335 patients treated with -blockers for an average of 5 years and found 10% of LQT1 patients, 23% of LQT2 patients, and 32% of LQT3 patients had events while being treated with the highest risk of events in LQT2 and LQT3 patients. Chatrath et al. (2004) found that 25% of LQTS probands had cardiac events, with LQT1 most prevalent. In addition, they found that the highest rate of events occurred during treatment with atenolol while propranolol appeared more protective. The addition of an IB agent (mexiletine) to -blocker therapy may be helpful, particularly in patients with the LQT3 genotype (Schwartz et al., 1995). Highrisk patients with drug resistant, symptomatic VT are referred for left cardiac sympathetic denervation which apparently provides additional protection (Moss and McDonald, 1970; Schwartz et al. 1991, 2004). In addition, the use of cardiac pacing as an adjunct to -blockers appears to be most rational in patients with evidence of pause-dependent or bradycardia-dependent arrhythmias (Eldar et al., 1987; Moss et al., 1991;Viskin, 2000). Symptomatic bradycardia due to LQTS or induced by -blocker therapy, should also be considered an indication for elective pacing. Beta-blocker therapy is monitored with treadmill exercise testing with the desired result a blunting of the heart rate response to exercise. However, Kaltman et al. (2003) suggested that little difference occurs between pretreated and treated patients regarding QTc at any phase, QTc dispersion or other QTc measures. Unfortunately, in some patients, this comes at the expense of excessive sinus bradycardia at rest and with minimal levels of exertion. Excessive fatigue, inattentiveness and irritability may result in discontinuance of therapy by the patient. Compliance, especially in the adolescent population, may be enhanced by returning the patient to a relatively normal lifestyle by the elimination of chronotropic incompetence with a pacemaker. Other less time-tested therapies are also available. In some rare cases, torsades de pointes persists despite therapy with the classical modalities. The ICD has been used successfully in this setting (Groh et al., 1996; Platia et al., 1985). Up until recently, it had not been considered to be first-line therapy because shocks from the device can precipitate further emotional stress and set off a circuitous response of persistent malignant arrhythmias. However, the Multicenter Automatic Defibrillator Implantation Trial (MADIT), which demonstrated dramatic superiority of therapy with automatic implantable defibrillators over “best conventional therapy” in patients with coronary disease at high risk for ventricular arrhythmias (Moss et al., 1996), has made this therapeutic approach somewhat appealing. Automatic implantable defibrillators have been used more commonly in LQTS patients due to the results of the MADIT trial. However, whether this is the best approach is still not clear as long-term data is needed to determine the answer to this question. Zareba et al. (2003c) has shown 3-year follow-up analysis with 1.3% death in ICD patients versus 16% non-ICD patients. Reports of efficacy in neonates have been published as well (Ten Harkel et al., 2005).

Complex Forms of LQTS

Another new approach to treating patients with LQTS is the so-called “gene-specific” approach. With the identification of the precise molecular defect in some patients with LQTS, specific mechanism-based therapies have been devised and small therapeutic trials performed. Schwartz et al. (1995) were the first to use this approach when they used the sodium channel blocker mexiletine in patients with mutations in the sodium channel gene SCN5A (LQT3). In these patients the QTc was dramatically shortened in a statistically significant manner. Patients with potassium channel mutations (HERG, LQT2) treated with mexiletine had no change in the QTc. However, no data currently exists which demonstrates clinical efficacy of this approach in either decreasing the number of syncopal events or improving survival. Other sodium channel blockers have had similar effects (Rosero et al., 1999). Recently, Benhorin et al. (2000) showed that flecainide shortened the QTc in patients with a D1790G SCN5A mutation while lidocaine was ineffective suggesting that allele-specific therapies may be needed. Although the data is intriguing, use of sodium channel blockers alone for patients with SCN5A mutations should still be considered experimental. Other gene-specific trials have also been performed with similar results. Compton et al. (1996) used intravenous potassium to elevate the serum potassium to 4.8 in patients with LQT2 and found significant shortening of the QTc. Again, no data on survival or symptom improvement exists for this therapy, but this has been confirmed (Choy et al., 1997). Etheridge et al. (2003) showed that QTc reduction may be large, QT dispersion and

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739

T-wave morphology improved, but outcome data was not available. Potassium channel openers may have the same effect (Shimizu et al., 1998). Other gene-specific therapeutic approaches are currently being developed. Brugada Syndrome Clinical Features This disorder is characterized by ST-segment elevation in the right precordial leads (V1-V3) with or without right bundle branch block (RBBB) (Figure 62.5) and episodic VF (Brugada et al., 2005). The first identification of the ECG pattern of RBBB with ST-elevation in leads V1-V3 was reported by Osher and Wolff (1953). Shortly thereafter, Edeiken (1954) identified persistent ST-elevation without RBBB in 10 asymptomatic males and Levine et al. (1956) described ST-elevation in the right chest leads and conduction block in the right ventricle in patients with severe hyperkalemia. The first association of this ECG pattern with sudden death was described by Martini et al. (1989) and later by Aihara et al. (1990) and further confirmed in 1991 by Pedro and Josep Brugada (1991) who described four patients with sudden death and aborted sudden death with ECGs demonstrating RBBB and persistent ST-elevation in leads V1-V3. In 1992, these authors characterized what they believed to be a distinct clinical and ECG syndrome (Antzelevitch et al., 2003; Brugada et al., 1992, 1997). In some patients the surface ECG appears normal, even in familial cases. Provocation studies in the catheterization laboratory using ajmaline, flecainide, or procainamide

Figure 62.5 Brugada syndrome in a 4-year old. Note the right ventricular conduction delay and ST-segment elevation in leads V1, V2. Procainamide infusion worsened the ST-elevation.

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Genetics and Genomics of Arrhythmias

will result in ST-segment elevation in the right precordial leads in affected patients (Brugada et al., 2000, 2003a; Hong et al., 2004a; Priori et al., 2002a; Rolf et al., 2003). Long term followup (Brugada et al., 2002) of patients and families with Brugada syndrome demonstrates sudden death is a persistent risk. The finding of ST-elevation in the right chest leads has been observed in a variety of clinical and experimental settings and is not unique or diagnostic of Brugada syndrome by itself. Situations in which these ECG findings occur include electrolyte or metabolic disorders, pulmonary or inflammatory diseases, abnormalities of the central or peripheral nervous system. In the absence of these abnormalities, the term idiopathic ST-elevation is often used and may identify Brugada syndrome patients (Antzelevitch, 1999). Two consensus conferences have been held to define the clinical signs, diagnostic approaches, therapies, and diagnostic studies (Antzelevitch et al., 2005;Wilde et al., 2002a,b,c). The ECG findings and associated sudden and unexpected death has been reported as a common problem in Southeast Asia where it most commonly affects men during sleep (Nademanee et al., 1997). This disorder, known as Sudden and Unexpected Death Syndrome (SUDS) or Sudden Unexpected Nocturnal Death Syndrome (SUNDS), has many names in Southeast Asia including bangungut (to rise and moan in sleep) in the Philippines; nonlai-tai (sleep-death) in Laos; lai-tai (died during sleep) in Thailand; and pokkuri (sudden and unexpectedly ceased phenomena) in Japan (Nademanee et al., 1997; Sangwatanaroj et al., 2002). General characteristics of SUDS include young, healthy males in whom death occurs suddenly with a groan, usually during sleep late at night. No precipitating factors are identified and autopsy findings are generally negative (Gotoh, 1976). Life-threatening ventricular tachydysrhythmias as a primary cause of SUDS have been demonstrated, with VF occurring in most cases (Hayashi et al., 1985). Clinical Genetics Most of the families identified to date with Brugada syndrome have autosomal dominant inheritance (Antzelevitch, 1998; Brugada et al., 1997; Gussak et al., 1999; Kobayashi et al., 1996). It appears as if penetrance is reduced, as many patients have a normal ECG and no symptoms until provocation studies are performed or fever unmasks the ECG abnormalities (Dumaine et al., 1999; Wakita et al., 2004). Although the number of families reported has been small, it is likely that this is due to underrecognition as well as premature and unexpected death. Molecular Genetics In animal studies, blockade of the calcium-independent 4-aminopyridine-sensitive transient outward potassium current (Ito) results in surface ECG findings similar to that seen in Brugada syndrome and includes elevated, downsloping ST-segments (Antzelevitch, 1999) and occurs due to greater abbreviation in the epicardial action potential compared with the endocardium (which lacks a plateau phase). Loss of the action potential plateau (or dome) in the epicardium but not endocardium would be expected to cause ST-segment elevation. Because loss of the dome is caused by an outward shift in the balance of currents

active at the end of phase I of the action potential (principally Ito and ICa), autonomic neurotransmitters such as acetylcholine facilitate loss of the action potential dome by suppressing calcium current and augmenting potassium current, whereas -adrenergic agonists (i.e., isoproterenol, dobutamine) restore the dome by augmenting ICa. Sodium channel blockers also facilitate loss of the canine right ventricular action potential dome as a result of a negative shift in the voltage at which phase I begins (Antzelevitch, 1999; Di Diego et al., 2002). Based on this information, candidate genes (Ito, ICa, and INa) were selected for study. In 1998, our laboratory reported the findings on six families and several sporadic cases of Brugada syndrome using candidate gene screening (Chen et al., 1998;Table 62.3), with mutations in SCN5A identified (Chen et al., 1998; Vatta et al., 2002a; Figure 62.6). Since then, many groups have confirmed these findings and mutations have been identified throughout the channel topography (Hong et al., 2004a, b). A second locus on chromosome 3 at 3p22-25 was subsequently identified (Weiss et al., 2002) and the gene, GPD1L, was demonstrated (Table 62.3). In the case of SUNDS, Vatta et al. (2002b) demonstrated mutations in SCN5A are responsible for the disease and that the disease is allelic to Brugada syndrome. In our original study, biophysical analysis of the mutants in Xenopus oocytes demonstrated a reduction in the number of functional sodium channels in some mutations, which promote development of reentrant dysrhythmias, while in other mutations sodium channels recovered from inactivation more rapidly than normal, and were temperature-dependent (Dumaine et al., 1999; Wakita et al., 2004). In this case, the presence of both normal and mutant channels in the same tissue would be expected to promote heterogeneity of the refractory period, a well-established mechanism causing dysrhythmias. Inhibition of the sodium channel INa current causes heterogeneous loss of the action potential dome in the right ventricular epicardium, leading to a marked dispersion of depolarization and refractoriness, an ideal substrate for development of reentrant dysrhythmias. Phase 2 reentry produced by the same substrate is believed to provide the premature beat necessary for initiation of the VT and VF responsible for symptoms in these patients. Interestingly, however, Kambouris et al. (1998) identified a mutation (R1623H) in essentially the same region of SCN5A as on of the original (T1620M) mutations, but the clinical and biophysical features of this mutation were found to be consistent with LQT3 and not Brugada syndrome. The same authors also demonstrated these mutant channels to have a propensity to inactivate without ever opening (i.e., closed state inactivation), suggesting novel physiologic mechanisms can occur in a heterogeneous manner in similar regions of this ion channel (Kambouris et al., 2000). In some cases, LQT3 and Brugada syndrome occur in the same family (Grant et al., 2002; Kyndt et al., 2001). Therapy No good medical therapy has been clearly identified for these patients thus far. Belhassen et al. (1999, 2004) have argued that quinidine has a role in chronic therapy for these patients but

Familial VT/CPVT

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741

I Na Sodium channel (SCN5A)

Extracellular I

II

III

Domains

IV

Intracellular

SIDS

Figure 62.6

Lev syndrome

Isolatedhconduction disease

Brugada

Mutations in SCN5A in patients with Brugada syndrome (black).

this is based on a small number of clinical vignettes. In addition, Glatter et al. (2004) suggested that sotalol may be effective. Currently, implantation of an ICD is the therapy of choice but this remains controversial. The prognosis of these patients appears to be good (Eckhardt et al., 2005). Ajiro et al. (2005) has suggested that late potentials on signal average ECG identify patients at risk irrespective of mutation. This has not been confirmed by others (Brugada et al., 2003b; Smits et al., 2002).

SHORT QT INTERVAL SYNDROME Clinical Features In 2000, Gussak and colleagues identified a new familial clinical syndrome characterized by an abbreviated QTc interval (300 ms), predisposition to life-threatening arrhythmias and a high rate of sudden death. AF may occur and on electrophysiologic evaluation, short refractory periods may be identified. Age of onset of symptoms may be young (1 year of age) and in some cases may be responsible for SIDS. Genetics of Short QT Syndrome Short QT syndrome (SQT) was initially shown by Gussak et al. (2000) to have autosomal dominant inheritance. Multiple families and sporadic cases have been reported (Bellocq et al., 2004; Brugada et al., 2004; Gaita et al., 2003; Gussak et al., 2000; Priori et al., 2004). Mutations in three ion channel genes (SQT1, HERG/KCNH2; SQT2, KvLQT1/KCNQ1; SQT3, Kir 2.1/ KCNJ2; Table 62.5) have been reported to date (Bellocq et al., 2004; Brugada et al., 2004; Priori et al., 2005). In the case of SQT1, mutations dramatically increased IKr, leading to heterogeneous abbreviation of action potential duration and refractoriness and reduced the affinity of the channels to IK blockers. Bellocq et al. performed functional studies on the KCNQ1 mutations which revealed a pronounced shift of the half-activation kinetics

TABLE 62.5 Gene

Short QT syndromes

Locus

Syndrome

Protein and subunit

Function and abnormality

KCNH2

7q35

SQTS1

KV11.1 

IKr↑HERG

KCNQ1

11p15.5

SQTS2

KV7.1 

IKs↑KvLQT1

KCNJ2

17q23

SQTS3

Kir2.1 

IK1↑

which led to a gain-of function of IKs and repolarization shortening. In the case of the SQT3-causing mutations in KCNJ2, whole-cell patch-clamp studies of this inward rectifying potassium channel Kir2.1 (IK1) demonstrated a larger outward IK1 than wild type and shifting of peak current, as well as acceleration of the final phase of repolarization leading to shortened action potential duration. Clinically, this leads to tall and asymmetric T waves, a finding that appears to be distinct for SQT3 (Priori et al., 2005). Therapy in SQT Syndrome Since sudden death occurs commonly in this disorder, defibrillator implantation is indicated. Gaita et al. (2004) evaluated the role of medical therapy, testing QTc interval response to flecainide, sotalol, ibutilide, and quinidine with only quinidine producing QTc prolongation (normalization). However, no definitive treatment approaches have been described to date. Wolpert et al. (2005) evaluated the role of quinidine in SQTS due to HERG mutations as well.

FAMILIAL VT/CPVT Clinical Features This inherited form of VT was first described in detail by Rubin et al. (1992) and is another form of VT that occurs in

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Figure 62.7

■

Genetics and Genomics of Arrhythmias

Complete AV block in this individual with bradycardia.

the absence of structural heart disease. Clinically, these patients present with frequent runs of nonsustained VT at rest, which may decrease or extinguish with exercise (Rubin et al., 1992; Sacks et al., 1978; Vlay, 1987) and may occur during childhood with episodes of Adams Strokes syndrome (Coumel et al., 1978; Leenhardt et al., 1995). In some cases, salvos of nonsustained VT may be incessant, causing palpitations, dizziness or syncope. The ECG is usually normal during sinus rhythm, with a normal QT interval. Electrophysiologic evaluation may not consistently show inducible VT in these patients, suggesting that the mechanism for this dysrhythmia is not reentry, as with more common forms of VT. Instead, it is believed that this form of inherited dysrhythmia is due to enhanced automaticity (i.e., an increased rate of electrical firing of a ventricular myocyte) or triggering (i.e., results from secondary depolarizations that occur during or immediately after repolarization). Sumitomo et al. (2003) described the ECG characteristics of CPVT and clinical features in detail. The initial manifestations included young age of presentation (10.3 years, mean age of onset), syncope (79%), cardiac arrest (7%), and family history (14%). ECGs demonstrated sinus bradycardia and normal QTc. The CPVT morphology included polymorphic (62%), polymorphic and bidirectional (21%), bidirectional (10%) or polymorphic with VF (7%). Cohen et al. (1989) demonstrated a sudden death risk with familial bidirectional ventricular tachycardia. Genetics and Management This inherited tachyarrhythmia is transmitted as an autosomal dominant trait (Tables 62.2 and 62.4; Glikson et al., 1991; Wren et al., 1990). Mutations in the ryanodine receptor (RyR2) have been identified as causative in CPVT (Laitinen et al., 2001; Marks et al., 2002; Priori et al., 2001, 2002b; Swan et al., 1999). In addition, mutations of the calsequestrin gene have been found in CPVT (Eldar et al., 2003; Lahat et al., 2002; Postma et al., 2002) appears to be relatively uncommon but the incidence and prevalence are not known. The therapy of this disorder appears to rely on the use of -adrenergic blocking agents, which effectively suppresses this rhythm disturbance. However, Sumitomo showed complete -blocker control in only 31% of cases (2003). In addition, the use of class I and class III antiarrhythmics have reportedly been successful in treating this disorder. Sumitomo and colleagues also showed calcium channel antagonists partially suppressed CPVT in some familial autosomal dominant cases. The use of ICDs have not been reported in this disorder but this is certainly a likely option in malignant families or difficult cases.

PRIMARY CONDUCTION ABNORMALITIES Lev-Lenegre Progressive Cardiac Conduction Disease Clinical Features This syndrome, also known as progressive cardiac conduction defect (PCCD), is amongst the most common cardiac conduction disorders in the world and represents the major cause for pacemaker implantation worldwide at 0.15 pacemaker implantations per 1000 inhabitants per year in developed countries (Schott et al., 1999). PCCD is characterized by progressive worsening of cardiac conduction through the His-Purkinje system, resulting in right or left bundle branch block and widening of the QRS complex (Lenegre and Moreau, 1963; Lev, 1964; Lev et al., 1970). Patients with this disorder ultimately develop complete AV block (Figure 62.7) and in many instances present with syncope or sudden death. Etiologically, PCCD has been considered to be a primary degenerative disease or an exaggerated age-related process in which sclerosis of the conduction system occurs (Gault et al., 1972; Gazes et al., 1965). Genetics The first gene for PCCD was mapped to chromosome 19q13.3 (Table 62.6) in families with autosomal dominantly transmitted disease (Brink et al., 1995; Connor et al., 1959; De Meeus et al., 1995; Waxman et al., 1975). Genetic heterogeneity was identified when Schott et al. (1999) demonstrated linkage to chromosome 3p21 and identified mutations in the cardiac sodium channel gene SCN5A (Tables 62.3 and 62.6; Figure 62.6). This gene had previously been shown to cause a form of long QT syndrome (LQT3) as well as Brugada syndrome (Chen et al., 1998; Wang et al., 1995a, b). Other supportive evidence of SCN5A mutations causing AV block have been reported. Miura et al. (2003) identified SCN5A mutations in congenital LQT and 2:1 AV block while Shirai et al. (2002) showed that mutations in this gene may cause overlapping features of Brugada syndrome and cardiac conduction disease. Tan and colleagues showed that mutations in SCN5A results in isolated conduction system disease as well, and this was confirmed by Wang et al. (2002). In addition, Viswanathan et al. (2003) showed that a common SCN5A polymorphism (H558R) mitigates the effects of mutations on channel function and its clinical response. Mechanisms responsible for these varying clinical features, patient sudden death, have been speculated to be based on the biophysical responses to the mutations in SCN5A (Towbin, 2001a, b).

Primary Conduction Abnormalities

TABLE 62.6

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Atrial arrhythmia syndromes

Gene

Locus

Syndrome

Protein and subunit

Function and abnormality

KCNQ1

11p15.5

ATFB1, LQTS1

KV7.1 

IKs↑ KvLQT1

KCNQ1

11p15.5

ATFB1, SQTS2

KV7.1 

IKs↑ KvLQT1

KCNE2

21q22.1

ATFB1

MiRP1 

IKs↑

KCNE1

21q22.1

AF

minK 

IKs↓

KCNA5

12p13

AF

KV1.5 

IKur↓

KCNJ2

17q23

ATFB1

Kir2.1 

IK1↑

ANKβ

4q25

AF, Bradycardia, LQTS4

Ankyrin-B

INa,K↓ INCX↓

KCNH2

7q35

AF, SQTS2

KV11.1 

IKr↑ HERG

SCN5A

3p21

Congenital SSS

NaV1.5 

INa↓

GJA5

1q21.1

ATFB4

Connexin 40

Coupling ↓

SCN5A GJA5 GJA5

3p21 1q21.1

Atrial standstill (Coinheritance)

NaV1.5  Connexin 40

INa↓ Coupling ↓

RyR2

1q42

AT, CPVT1

RyR2 

SR Ca2 leak ↑

HCN4

15q24

SSS, AF, Bradycardia

HCN4 

If↓

Management Patients with PCCD require pacemaker therapy once bradycardia occurs. In cases where syncope precedes diagnosis, immediate implantation is appropriate. Prior to pacemaker implantation, serial ECGs and Holter monitor studies are necessary for close monitoring. Sinus Node Dysfunction/Sick Sinus Syndrome Clinical Features Congenital absence of sinus rhythm (Surawicz and Hariman, 1988) or SND (Caralis and Varghese, 1976; Spellberg, 1971) occurs in familial forms. The clinical presentation is generally due to symptomatic bradycardia, but it also may be associated with paroxymal AF and other atrial tachydysrhythmias. Histologic data are limited, but one report of a single member of a family who suffered sudden death showed mononuclear cell infiltration of the sinus node; fibrosis of the sinus, AV nodes, and atrial tissue; and atrophy of the right and left bundle branches (Bharati et al., 1992). Genetics and Management The inheritance pattern of familial congenital absence of sinus node function is autosomal dominant with a high degree of penetrance (Table 62.6).The gene for this disorder remains unknown. However, Benson et al. (2003) identified homozygous mutations in the cardiac sodium channel gene SCN5A (Figure 62.6) in individuals with SSS. Treatment consists of permanent pacing for symptomatic bradycardia and, unlike persistent atrial standstill, in which the atria are unable to be paced due to inability to produce electrical excitation, atrial or dual-chamber pacing may be used. If

AF is a prominent feature, antiarrhythmic medications and/or chronic anticoagulation may be indicated. Atrial Fibrillation Clinical Aspects Atrial fibrillation (AF), first described in 1906, is a cardiac rhythm disorder characterized by rapid, irregular activation of the atria, resulting in loss of coordinated atrial contraction and therefore reduced ventricular filling and stasis of blood in the atria (Fye, 2006). This predisposes to heart failure and thromboembolic stroke (Albers, 1994; Wang et al., 2003;Wolf et al., 1991). It is currently believed to be the most common sustained cardiac arrhythmia worldwide, accounting for 15% of all strokes and over 30% of all strokes in subjects 65 years of age or older (Wolf et al., 1991). The prevalence of AF reportedly has a age dependence, ranging from 1% in young adults to approximately 10% in individuals older than 80 years of age (Kannel et al., 1998). In the United States, more than 3 million people are estimated to have persistent AF (Feinberg et al., 1995; Go et al., 2001). AF is an independent risk factor for death, as well (Benjamin et al., 1998). AF may be classified as paroxysmal, persistent or permanent (Boston Area Anticoagulation Trial, 1990). The paroxysmal form of AF accounts for 35–40% of all AF, and there is a 30–50% chance of paroxysmal AF converting to a permanent form (Boston Area Anticoagulation Trial, 1990). It is frequently observed as a complication of a variety of cardiovascular and systemic disorders, such as hypertension, coronary artery disease, valvular heart disease, various forms of congenital heart disease, as well as cardiomyopathies. Thus, AF is typically considered to be a sporadic, non-genetic form of disease. In approximately 10–20% of cases, however, no underlying associated defect is identified

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Genetics and Genomics of Arrhythmias

and, therefore, it is termed “idiopathic” or “lone” AF (Brand et al., 1985). In rare occasion, fetal onset has been identified (Tikanoja et al., 1998). Electrophysiology of AF Atrial conduction depends on the connections between cells, the structural properties of the atrial wall, and ion flux. Cardiomyocytes link in an end-to-end fashion by intercalated disks and therefore form a syncytium of elongated branching fibers. The structural components of atria are required to function properly for normal electrical activity. Atrial electrophysiology is a complex process which requires coordinated interactions between multiple ionic and structural factors in order to generate normal impulse formation and propagation (Weiss et al., 2005). Genetics of AF The first case of apparent inherited AF was described in 1936 and subsequently a small number of families have been published in the literature (Arnar et al., 2006; Bedi et al., 2006; Beyer et al., 1993; Darbar et al., 2003; Ellinor et al., 2005; Fox et al., 2004; Orgain et al., 1936). Over the past decade, it has become apparent that familial aggregation of AF is relatively common and, for this reason, genetic analyses were able to be pursued. The first genetic locus for AF was reported in 1997 by Brugada and colleagues, identifying chromosome 10q22-q24 linkage. A second locus was later identified by Oberti et al. (2004) on chromosome 5p13 in a family with neonatal-onset autosomal recessive AF. Later, Chen et al. (2003) identified linkage in a single family with AF on chromosome 11p15 and, using a candidate gene screening approach, identified a mutation in KCNQ1. This was subsequently confirmed in a small number of subjects and appears to be an uncommon cause of AF (Ellinor et al., 2004, 2006; Otway et al., 2007). Another uncommon cause of AF includes mutations in the -subunits of the cardiac IKs channel, including KCNE2 and possibly KCNE3 (Yang et al., 2004; Zhang et al., 2005). The KCNJ2 gene located on chromosome 17q23-q24, which encodes the Kir2.1 protein that forms the α-subunit of the cardiac IKI, has also been identified in a small number of subjects (Xia et al., 2005). A novel and promising gene, KCNA5, an atrial-specific gene located on chromosome 12p13, has been reported to be mutated and causative of AF in a single family with AF completes the genes identified to date that cause predominant AF (Olson et al., 2006). An additional group of genes have been found to carry mutations in subjects with combined atrial and ventricular phenotypes, and these include KCNH2, SCN5A, and LMNA. In the case of KCNH2, an N588K missense mutation was identified in one family with an overlapping phenotype including AF and SQT syndrome (Hong et al., 2005). This same mutation was identified in two other families only presenting with SQTS alone (Brugada et al., 2004). Similarly, another ion channel-encoding gene that is well known as a cause of arrhythmia disorders, SCN5A, has been shown to cause complex phenotypes that

include AF, in particular those associated with dilated cardiomyopathy and conduction system disease (McNair et al., 2004; Olson et al., 2005). Finally, another pleiomorphic gene, lamin A/C (LMNA), has also been shown to cause AF in patients and families with dilated cardiomyopathy and conduction disease (Fatkin et al., 1999; Sebillon et al., 2003). Another group of mutations that have been described in subjects are so-called “somatic mutations.” This group of abnormalities differs from the germline mutations described above. Typically, genetic-based disorders are inherited in families by transmission of gene mutations in the germ cells. Mutations can also arise de novo in discrete cell lineages during embryonic development or postnatally. Germ cell de novo mutations can be transmitted to future generations while somatic mutations cannot, but in this case can give rise to mixed populations of mutant and normal cells within specific tissues, known as mosaicism. Mosaic mutations may have variable functional consequences and hence the clinical phenotype that develops may be unpredictable. In AF, somatic mutations have been associated in a small number of studies. Gollob et al. (2006) identified mutations in the GJA5 gene, encoding the cardiac gap junction protein connexin40, in DNA from atrial tissue from 4 subjects; however, none of these variants were found in DNA extracted from blood. In one other subject, a mutation was identified in DNA extracted from both atrial tissue and blood. Functional studies was performed on the mutations and showed reduction of gap junction formation and/or coupling properties. Genetic Mechanisms in AF The majority of mutations in subjects with AF have occurred in genes encoding potassium channels. Mutations in KCNQ1 and KCNE2 appear to increase IKs channel activation while mutations in KCNJ2 and KCNH2 lead to increased activation of cardiac IKI and IKr currents, respectively. These gain-of-function mutations all result in shortening of the action potential duration and effective refractory period, thereby promoting AF by creating an electrical substrate for reentry. Genome-Wide Association Studies Recently, a trend in the studies performed to identify novel genetic linkage of new disease-causing genes has included genetic association studies (GAS) and genome-wide association studies (GWAS) (Thomas and Witte, 2002). In the case of GAS, these studies are a form of candidate gene screen that aims to assess the association between disease status and genetic variants (polymorphisms, single nucleotide polymorphisms or SNPs) in a population, while GWAS is an unbiased, hypothesis-free approaches that involve large scans of the genome using a dense set of SNPs (up to 500,000 or more) in an experiment aimed at identifying causal variants (Donahue and Allen, 2005). GWAS studies generate vast datasets, leading to challenges both in primary analysis and meta-analysis (Hirschhorn and Daly, 2005; Visscher et al., 2008; Zintzaras and Lau, 2008). Over the years, GAS and GWAS studies have come under scrutiny due to that lack of replication of data by independent

References

studies. Major methodological issues that have surfaced include small sample size and insufficient power to detect minor contributing roles of one or more alleles. Other confounding factors may include population stratification and experimental design flaws have been problematic. More recently, many of these challenges have been overcome and new genes and pathways have been identified using GWAS. However, effect sizes are small in these studies, with relative genotype risks typically 1.5. In the case of quantitative traits, the individual effect sizes are consistent with 1% of the phenotypic variance being explained by a single polymorphism. Hence, while challenges exist for this approach, successes have been forthcoming and excitement appears to exist in regard to the future impact of these methods. A variety of cardiac disorders have successfully utilized GWAS recently. Larson et al. (2007) evaluated the Framingham study population and identified chromosome 9p21 associating with major cardiovascular disease, as well as associating with AF.

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745

Gudbjartsson et al. (2007) identified variants conferring risk of AF on chromosome 4q25. These and other studies (Ellinor et al., 2008) are quickly referring new targets responsible for the phenotype. In the case of AF, SNPs in several cardiac ion channel genes have been linked with the disease. In several reports, IKs channel components have been linked with AF, including KCNE1, KCNE2, and KCNE5 (Chen et al., 2007; Ehrlich et al., 2005; Fatini et al., 2006, 2007; Lai et al., 2002; Ravn et al., 2005; Zeng et al., 2007). In addition, SNPs in genes that alter regulation of ion channel function (Bedi et al., 2006; Firouzi et al., 2004; Nyberg et al., 2007; Schreieck et al., 2004), intracellular handling of calcium (Firouzi et al., 2004), gap junction formation (Juang et al., 2007;Yamashita et al., 1997), and activation of the rennin–angiotensin system (Fatini et al., 2006; Tsai et al., 2004) have been reported. These associated genes include GNB3 and NOS3, which regulate channel function, and GJA5 which is involved in the formation of gap junctions, amongst others.

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et al. (2002b). Proposed diagnosic criteria for the Brugada syndrome. Circulation 106, 2514. Wilde, A.A., Antzelevitch, C., Borggrefe, M., Brugada, J., Brugada, R., Brugada, P., Corrado, D., Hauer, R.N.W., Kass, R.S., Nademanee, K., Priori, S.G., Towbin, J.A.: Study Group on the Molecular Basis of Arrhythmias of the European Society of Cardiology (2003). Proposed diagnostic criteria for the Brugada syndrome. Euro Heart J 23, 1648–1654. Wolff, L. (1943). Familial auricular fibrillation. N Engl J Med 229, 396–397. Wolf , P.A., Abbott, R.D. and Kannel, W.B. (1991). Atrial fibrillation as an independent risk factor for stroke: The Framingham Heart Study. Stroke 22, 983–988. Wollnick, B., Schreeder, B.C., Kubish, C., Esperer, H.D., Wieacker, P. and Jensch, T.J. (1997). Pathophysiological mechanisms of dominant and recessive KVLQTI K channel mutations found in inherited cardiac arrhythmias. Hum Mol Genet 6, 1943–1949. Wolpert, C., Schimpf, R., Giustetto, C., Antzelevitch, C., Cordeiro, J., Dumaine, R., Brugada, R., Hong, K., Bauersfeld, U., GAita, F. et al. (2005). Further insights into the effect of quinidine in short QT syndrome caused by a mutation in HERG. J Cardiovasc Electrophysiol 16, 54–58. Wren, C., Rowland, E., Burn, J. and Campbell, R.W.F. (1990). Familial ventricular tachycardia: A report of four families. Br Heart J 63, 169–174. Xia, M., Jin, Q., Bendahhou, S., He, Y., Larroque, M.M., Chen, Y., Zhou, Q., Yang, X., Liu, Y., Liu, B. et al. (2005). A Kir2.1 gainof-function mutation underlies familial atrial fibrillation. Biochem Biophys Res Comm 332, 1012–1019. Yamashita, T., Hayami, N., Ajiki, K., Oikawa, N., Sezaki, K., Inoue, M., Omata, M. and Murakawa, Y. (1997). Is ACE gene polymorphism associated with lone atrial fibrillation? Jpn Heart J 38, 637–641. Yang,Y., Xia, M., Jin, Q., Bendahhou, S., Shi, J., Chen,Y., Liang, B., Lin, J., Liu, Y., Liu, B. et al. (2004). Identification of a KCNE2 gain-offunction mutation in patients with familial atrial fibrillation. Am J Hum Genet 75, 899–905. Zareba, W., Moss, A.J., Schwartz, P.J. et al. (1998). Influence of the genotype on the clinical course of the long-QT syndrome. N Engl J Med 339, 960–965. Zareba, W., Moss, A.J., Sheu, G., Kaufman, E.S., Priori, S.,Vincent, G.M., Towbin, J.A., Benjorin, J., Schwartz, P.J., Napolitano, C. et al. (2003a). Location of mutation in the KCNQ1 and phenotypic presentation of long QT syndrome. J Cardiovasc Electrophysiol 14, 1149–1153. Zareba, W., Moss, A.J., Locati, E.H., Lehmann, M.H., Peterson, D.R., Hall,W.J., Schwartz, P.J.,Vincent, G.M., Priori, S.G., Benhorin,J. et al. (2003b). International Long QT Syndrome Registry. Modulating effects of age and gender on the clinical course of long QT syndrome by genotype. J Am Coll Cardiol 42, 103–109. Zareba, W., Moss, A.J., Daubert, J.P., Hlal, W.J., Robinson, J.L. and Andrews, M. (2003c). Implantable cardioverter defibrillator in high-risk long QT syndrome patients. J Cardiovasc Electrophysiol 14, 337–341. Zeng, Z., Tan, C., Teng, S., Chen, J., Su, S., Zhou, X., Wang, F., Zhang, S., Gu, D., Makielski, J.C. et al. (2007). The single nucleotide polymorphisms of I (Ks) potassium channel genes and their association with atrial fibrillation in a Chinese population. Cardiology 108, 97–103.

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Zhang, D., Liang, B., Lin, J., Liu, B., Zhou, Q.S. and Yang, Y.Q. (2005). KCNE3 R53H substitution in familial atrial fibrillation. Chin Med J 118, 1735–1738. Zhang, L., Vincent, G.M., Baralle, M., Baralle, F.E., Anson, B.D., Benson, D.W.,Whiting, B.,Timothy, K.W., Carlquist, J., January, C.T. et al. (2004). An intronic mutation causes long QT syndrome. J Am Coll Cardiol 44, 1283–1291.

Zhou, Z., Gong, Q., Miles, L. et al. (1998). HERG channel dysfunction in human long QT syndrome. J Biol Chem 263, 21061–22106. Zintzaras, E. and Lau, J. (2008). Trends in meta-analysis of genetic association studies. J Hum Genet 53, 1–9.

CHAPTER

63 Hemostasis and Thrombosis Richard C. Becker and Felicita Andreotti

Extrinsic pathway occurs on TF–bearing cells

INTRODUCTION Blood coagulation is a cell surface, biochemical event designed not only to stem the loss of blood following vascular injury (hemostasis), but also to provide the necessary molecular, cellular, and protein constituents for growth and repair as well. In addition, coagulation occurring within medium-sized arteries and veins can have detrimental effects, ranging from end-organ damage to death. Conceptually, blood coagulation represents a complex, yet well-coordinated series of events that involve tissue factor-bearing cells and platelets. The initiation and propagation phases of coagulation, under biological conditions are catalyzed by thrombin in small (nM) and large concentrations, respectively (Figures 63.1 and 63.2) (Monroe and Hoffman, 2006). The complex catalysts that participate in tissue factormediated thrombin generation each consist of a serine protease interacting with a receptor and/or cofactor protein, which collectively are anchored to a specific cellular surface. One must recognize, however, that coagulation is regulated tightly by stoichiometric and dynamic systems of inhibition. Specifically, the tissue factor concentration threshold for thrombin generation is steep, and the resulting product is dependent largely on the concentration of plasma proteins and surface inhibition, including tissue factor pathway inhibitor and antithrombin III. Thus, the blood coagulation proteome can be studied in biochemically quantifiable terms (Mann et al., 2006).

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

(b)

(a)

X

Extrinsic pathway Vlla TF

VIIa

ll Fibrinogen

Xa

Va

Xa

IIa

TF

Xa Va Lipid

X

IIa

Tissue factor–bearing cell

TF

lla Fibrin

IX IXa

Figure 63.1 Traditional (a) and contemporary (b) paradigms of coagulation highlighting both the biochemical and cell-based components required for thrombin generation. From Monroe and Hoffman (2006).

GENETICS OF COAGULATION The complex network of integrated biochemical events regulating mammalian coagulation comprises, in essence, five proteases (f II or prothrombin, f VII, f IX, f X, and protein C) that interact with five cofactors (tissue factor, f VIII, f V, thrombomodulin, and membrane proteins) to generate fibrin (Davidson et al., 2003b). Although each component of the network has unique functional properties, data derived from gene organizations, protein structure,

Copyright © 2009, Elsevier. Inc. All rights reserved. 755

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Intrinsic pathway occurs on activated platelets (a)

(b) Intrinsic pathway lXa

HK XII PK XIa

X

ll lla

XI lX IX

IXa VIIIa Lipid Xa Va Lipid II Fibrinogen

IXa Xa

X Xla IIa

Vllla Va Activated platelet

Fibrin

Figure 63.2 The assembly of coagulation proteins on activated platelets leads to a burst of thrombin (IIa) generation – a prerequisite event in thrombus growth and propagations, and fibrin formation. From Monroe and Hoffman (2006). Vertebrates Proposed genome duplications 450MYA Chordates

als

X

X X X X X X X

??? ?*

#

mm

X

X X X X X X

ds

X

Ma

e.g. e.g. Lamprey Fugu Hagfish Zebrafish

Tissue factor Factor VII Factor IX Factor X Protein C Prothrombin Factor V Factor VIII Thrombomodulin Protein S Tissue factor pathway inhibitor Antithrombin Fibrinogen

Bir

les

t Tur

fish

bia

phi

Am

ss

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e Tel

le Jaw

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and sequence analysis suggest that coagulation-hemostatic regulatory proteins may have emerged more than 400 million years ago from duplication and diversification of only two gene structures: a vitamin K–dependent serine protease, composed of a -carboxylated glutamic acid-epidermal growth factor (EGF)– like domain structure (common to f VII, f IX, f X, and protein C), and the A1-A2-B-A3-C1-C2 domain structure common to f V and f VIII. Prothrombin is also a vitamin K–dependent serine protease; however, it contains kringle domains rather than EGF domains, suggesting a replacement during gene duplication and exon shuffling. Analysis of active-site amino acid residues reveals distinguishing characteristics of thrombin from other serine proteases, supporting its position as the ancestral blood enzyme (Figures 63.3 and 63.4) (Davidson et al., 2003a, b; Krem and Di Cera, 2002; McLysaght et al., 2002; Van Hylckama Vlieg et al., 2003). There is evidence that large regional or genome duplications have contributed to the overarching structure of mammalian coagulation genomes. Similarly, local duplication and translocation could have contributed to the evaluation of multigene families on separate chromosomal regions (Abi-Rached et al., 2002). The evolution of complex mammalian coagulation pathways from invertebrate and early vertebrate species is perhaps best illustrated in Zebrafish, whose cDNA/gene orthologues for major coagulant, anticoagulant and fibrinolytic proteins bare striking homology to mammalian sequences (Hanumanthaiah et al., 2002). The only difference, which requires further investigation, is a f VII-like gene in Zebrafish that clusters with f VII and f X genes and functions as a inhibitor (f VIIi) of coagulation. Interestingly, the f VII, f VIIi, and f X gene cluster is homologous to a Drosophilia trypsin gene cluster, supporting a rapid path of evolution from invertebrates to vertebrates, and ultimately, mammals.

e.g. e.g. Chicken Man Ostrich

X

X

X

X

X X X X X

X X X X X X

X X X X X X X

X X X X X X X

Figure 63.3 Chordate phylogenetic tree illustrating proposed genome duplication and the complement of coagulation proteins identified to date. Reprinted, with permission, from Davidson et al. (2003a, b).

HUMAN HEMOSTATIC VARIABILITY de Lange and colleagues (de Lange et al., 2001) performed a class twin study, including 1002 female twins, 149 pairs of

Genotype–Phenotype Influences

HagfishPT FuguPC

0.75 0.73

0.60

0.48

FX clade

FuguFIX FIX clade FuguFIXB ChickenFIX CowFIX

0.53

757

PC clade A1

0.1

ChickenFVII FVII clade HumanFVII MouseFVII RabbitFVII 0.37 CowFVII 0.56 FuguFVIIC FuguFVII FuguFVIIB RabbitFX CowFX HumanFX MouseFX RatFX ChickenFX FuguFX

■

FuguFV RatCP HumanHP MouseHP MouseCP HumanCP FuguCP

MouseFV HumanFV CowFV 0.55 FuguFV

MouseHP HumanHP FuguCP

A2

RatCP MouseCP HumanCP MouseFV 0.63 CowFV HumanFV FuguFV

PigFV 0.55 MouseFV HumanFV 0.80 DogFV

DogFV 0.50

MouseFV PigFV HumanFV

0.70 HumanHP MouseHP HumanCP FuguCP 0.75 MouseCP 0.76 RatCP FuguFV CowFV ChickenFV FuguFV PigFV MouseFV DogFV HumanFV 0.72 HumanFV ChickenFV MouseFV

A3

FuguFV

HumanFIX RabbitFIX 0.57 MouseFIX 0.75

0.1

0.48

FuguPCB ChickenPC MousePC RatPC CowPC RabbitPC HumanPC

PC clade

Figure 63.4 Consensus tree illustrating the phylogenetic relationship of vitamin K–dependent proteases and A domain–containing proteins. The number at each internal node represents the posterior probability that the taxa in the corresponding subtree form a clade in the recovered consensus tree. Reprinted, with permission, from Davidson et al. (2003a, b).

monozygotic and 352 pairs of dizygotic twins. Quantitative genetic model fitting revealed that genetic factors were responsible for 41–75% of the variation in fibrinogen, factor VII, factor VIII, plasminogen activator, factor XIII A-subunit and B-subunit, and von Willebrand factor (vWF). Factor XIII activity showed higher (82%) and factor XII lower (38%) heritability. A higher monozygotic than dizygotic twin correlation was seen for all factors, supporting further the influence of genetics on hemostatic proteins. Because the genetic contribution to cardiovascular disease and related thrombotic phenotypes decreases with advancing age, the heritability of hemostatic proteins was determined in 130 monozygotic and 155 dizygotic same-sex twin pairs (ages 73–94 years) participating in the Longitudinal Study of Aging of Danish Twins. Genetic factors accounted for 33% (D-dimer) to 71% (Thrombin Activatable Fibrinolysis Inhibitor). In a linage analysis, polymorphisms explained a very small proportion of genetic variations in hemostatic variables (Bladbjerg et al., 2006). Thus, age has a modest effect on hemostatic proteins, explaining anywhere from 1.5% to 14.5% of the variance in plasma concentrations (de Lange et al., 2001).

GENOTYPE–PHENOTYPE INFLUENCES The work of Rosendaal and colleagues (Van Hylckama Vlieg et al., 2003) underscores the diversity of hemostasis as a biologic system. The structural homologies among coagulation factors suggests similarities in their biosynthesis (transcription, posttranslational processing) and in the mechanisms that govern their plasma levels and clearance. In a group of healthy subjects participating in the Leiden Thrombophilia Study (Van Hylckama Vlieg et al., 2003), clustering was found among the plasma concentrations of vitamin K–dependent factors (II, VII, IX, and X) and those of factors XI and XII. Factors V and VIII clustered with fibrinogen and D-dimer (a measure of fibrin formation and its subsequent degradation). The anticoagulant factors (protein C, protein S, and antithrombin III) clustered together, whereas f XIII remained independent. The identification of several independent clusters within the group of procoagulant and anticoagulant factors suggests that the basis for individual fluctuations in plasma levels may lie outside the genes coding for these factors. This example highlights the complexity of linking genotypes to phenotypes and the relevance of understanding the

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regulation of gene expression (in particular, the role played by environmental factors) and posttranslational protein modifications (Romualdi et al., 2003). Completion of the human genome sequence, although one of the most important achievements in science, medicine, and human biology, highlights the importance of proteins in determining biological function and disease (reviewed in reference [Loscalzo, 2003]). Indeed, gene sequences cannot fully predict the complex quaternary structure of proteins (nor their function or dysfunction). The regulation of proteins, in fact, escapes genetic control through posttranslational modifications and protein–protein interactions. Proteomics is defined as the identification and functional characterization of complete sets of proteins expressed by complex biological systems. Posttranslational modifications of proteins, including proteolysis, oxidation, phosphorylation, nitrosylation, glycation, and sulfuration may well participate in the development and clinical expression of thrombophilias. Many of these modifications are the consequence of environmental influences on gene expression (reviewed in reference [Loscalzo, 2003]). Examples include the correlation between the oxidative state of plasma proteins (determined by measurement of carbonyl content) and several markers of thrombin generation and activity (De Cristofaro et al., 2002); oxidation of thrombomodulin at methionine 388, which reduces its capacity to down-regulate coagulation (through activated protein C), but not its capacity to inhibit fibrinolysis [through Thrombin Activatable Fibrinolysis Inhibitor (Nesheim, 2001)]; oxidation of low-density lipoprotein, a process that involves modification of the amino acid side chain of apoprotein B, which changes the protein moiety’s identity, rendering it prothrombotic (Vlassara et al., 1995); nonenzymatic glycation of protein and lipid macromolecules and their condensation to advanced glycation end products (AGEs), which promote atherothrombosis (Loscalzo, 2003); and, the mixed disulfide-bond formation between cysteinyl side chains of proteins and homocysteine, which may induce protein modifications that favor vascular injury and thrombosis (Van Hylckama Vlieg et al., 2003). The Framingham investigators (Wang et al., 2006) measured 10 biomarkers in 3209 participants attending a routine examination cycle of the Framingham Heart Study. During a median follow-up of 7.4 years, 207 participants died and 169 had a first major cardiovascular event. Neither fibrinogen, D-dimer or plasminogen activator inhibitor type 1 strongly predicted death or major cardiovascular events by Cox proportional hazards models adjusted for conventional risk factors, suggesting that protein modifications, to include their metabolic byproducts, may represent more accurately, pathobiological events and provide more direct links between genotype and phenotype.

GENE-ENVIRONMENT INFLUENCES ON HEMOSTASIS Fibrin, as summarized previously, is the predominant protein constituent of blood clots formed from fibrinogen, a large

TABLE 63.1

Variants of fibrinogen and factor XIII

Variants

Function

Relation to disease

Fibrinogen

Binds thrombin and f XIII; reduces fiber diameter

Increases risk for MI

Fibrinogen AEC

Unknown

None?

Fibrinogen A Taq1

–

–

Fibrinogen B Bcl1

Increases fibrinogen level

More common in CAD

Fibrinogen B-148C/T

–

–

Fibrinogen B-448G/A

Alters nuclear protein binding

More common in CAD

Fibrinogen A Thr312Ala

Changes fibrin structure/ function and FXIII cross-linking

Atrial fibrillation/ pulmonary embolism

Fibrinogen BArg448Lys

Changes fibrin structure/function

Macrovascular disease

Factor XIIIA Val34Leu

Changes FXIII activation rate and fibrin structure/function

MI, Deep vein thrombosis (DVT)

Factor XIIIB His95Arg

Unknown

MI

Splice variants

Noncoding polymorphisms

Coding polymorphisms

From Scott et al. (2004). MI  myocardial infarction; CAD  coronary artery disease.

glycoprotein present within the circulation. Because fibrin clot architecture plays an important role in hemostasis and vascular repair, variations have important clinical implications. In turn, variability in fibrin strand width, branch points, mass-to-length ratio, density and cross-linking among healthy individuals, as well as those with atherosclerosis, metabolic disorders, and other prothrombotic disease states, support both genetic and environmental influences (Table 63.1) (Scott et al., 2004). Alterations in gene expression and coding function, splice variants, and posttranslational modifications each influence fibrin structure and functionality. Vascular Bed–Specific Hemostasis and Thrombosis A well-recognized feature of thrombophilias, whether inherited or acquired (or both), is the focal and vascular bed–specific

Circulating Cellular and Protein Influences on Hemostasis and Thrombosis

TABLE 63.2

■

759

Vascular bed–specific thrombosis Organ bed Heart

Lung

Spleen

Kidney

Brain

Clinical disorders/ associated syndromes

Myocardial infarction Acute coronary syndromes

Pulmonary hypertension Pulmonary embolism/infarction

Splenic infarction

Nephropathy Renal thrombosis

Stroke

Clinically associated factors

Activated factor IX peptide, factor XIIa Fibrinogen Plasminogen activator inhibitor-1 Tissue-type plasminogen activator antigen von Willebrand factor

Fibrinopeptide A Hereditary spherocytosis Soluble thrombomodulin

Protein C Sickle cell anemia

Factor V Leiden Homocysteine Plasminogen activator inhibitor-1 4G/4G

Antithrombin III Factor V Leiden Homocysteinuria

From Edelberg et al. (2001) with permission.

expression of thrombosis. This distinct feature suggests that local regulatory pathways and intrinsic vessel-determined responses to prothrombotic stimuli are distinguishing factors (Mackman, 2005). Moreover, from the clinical perspective, it argues strongly in favor of a patient/vascular bed–specific approach to screening, diagnostic testing, and management rather than a more generalized strategy (Table 63.2) (Edelberg et al., 2001). The regulation of platelet–vessel wall interactions, coagulation proteases, and fibrinolytic factors takes place on the endothelial surface, suggesting strongly that overall hemostatic regulation and the heterogeneity of clinical expression observed under conditions favoring thrombosis, is based on site-specific differences in endothelial cell structure, functionality, and molecular responsiveness to both biologic and rheologic conditions (Bavendiek et al., 2002; Braddock et al., 1998; Chien et al., 1998; Lin et al., 1997; Sokabe et al., 2004). Endothelial cells derived from venous, arterial, and microvascular beds exhibit distinct phenotypes in mitosis rates (Beekhuizen and van Furth, 1994), growth responses (Rupnick et al., 1988), signaling pathways (Chang et al., 2000), and expression of nitric oxide synthase (Guillot et al., 2000), vWF (Edelberg et al., 1998), and tissue-type plasminogen activator (Christie, 1999; Rosenberg, 1999). In addition, the functional relationship between vascular endothelial cells and surrounding tissues (cortex, myocardium, myometrium, connective tissue/ skeletal muscle) and hemostatic regulation, may be particularly important in “site-specific” responses to prothrombotic stimuli (Christie et al., 1999; Le Flem et al., 1999; Nishida et al., 1993; Tabrizi et al., 1999). An area of considerable interest and relevance to the subject of thrombophilias and vascular bed–specific thrombotic potential is endothelial cell repair and death (apoptosis). An ability to restore endothelial integrity (structural and functional) following injury and the prompt regulation of apoptotic cell–mediated prothrombotic activity may, in fact, be the most critical part of the biologic equation (Figure 63.5).

The clear distinction between vascular beds in response to injury, regulation of coagulation and perhaps thromboprophylaxis with antithrombotic drugs has implications for investigation of medical genomics in general, and the impact of disease predisposition attributable to traditional risk factors in particular. Indeed, the major risk factors for clinical atherosclerosis, including dyslipidemia, hypertension, glucose intolerance, adiposity, cigarette smoking, sedentary life style, inflammatory markers and, to a lesser degree, hemostatic factors accounted for 85% of the cardiovascular disease arising within the Framingham population (Kannel and Wolf, 2006).

CIRCULATING CELLULAR AND PROTEIN INFLUENCES ON HEMOSTASIS AND THROMBOSIS A traditionally held view that thrombosis, particularly involving the arterial circulatory system, is governed solely by factors intrinsic to the vessel wall at sites of injury requires reconsideration. Tissue factor is found in high concentrations within atherosclerotic plaques, activated endothelial cells, fibroblasts, macrophages, and vascular smooth muscle cells; however, tissue factor antigen is also present within the circulating blood of patients with coronary artery disease or with hematologic disorders characterized by heightened thrombogenicity (Falciani et al., 1998). Tissue factor–containing neutrophils, monocytes, and microparticles (Giesen et al., 1999), circulating in peripheral blood, can be delivered to sites of vascular injury where they contribute directly to both the initiation of thrombus formation and its subsequent propagation. This evolving construct emphasizes an important interface between leukocytes and activated endothelial cells, activated platelets, and the vessel wall, where essential substrate for thrombin generation already exists (Giesen et al., 1999; McEver, 2001). Adherent leukocytes enhance fibrin deposition by

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Vascular bed–specific hemostatic regulation

Platelet response

Coagulation protease response

Fibrinolytic response

Nitric oxide prostacycline

Thrombomodulin tissue factor expression

t-PA u-PA PAI-1

Vascular repair

Vascular death (apoptosis)

Prothrombotic stimuli

Regulation

Figure 63.5 Vascular bed–specific hemostasis and thrombosis is regulated by endothelial cell-based vascular responses to prothrombotic stimuli involving platelets, coagulation proteases, and fibrin. Cell–cell and cell–protein interactions, coupled with programmed cellular events, play a particularly important role in vascular repair.

CD18-dependent capture of fibrin protofibrils that are flowing in plasma, and by f XII–dependent thrombin generation. They also activate platelets, providing a fully functional platform for fibrin formation under flow conditions (Goel and Diamond, 2001). A blood-borne propensity for thrombosis, not entirely dependent on vascular pathology, provides a biology-based explanation for the observed disparities between “degree” or “extent” of atherosclerosis and risk of thrombotic events of the arterial vascular bed (Karnicki et al., 2002), as well as for the propensity toward venous thrombosis with malignancy and following trauma or major surgery.

RACE-RELATED INFLUENCES ON HEMOSTASIS AND THROMBOSIS The first examination of the Atherosclerosis Risk in Communities (ARIC) Study (Ohira et al., 2006) included 14,448 men and women between the ages of 45 and 64 years who were free of clinical stroke. During the average follow-up of 13.4 years, 531 incident ischemic strokes occurred (105 lacunar, 326 nonlacunar, and 100 cardioembolic). African Americans had a threefold higher multivariate-adjusted risk of lacunar stroke compared to White individuals. No racial difference in nonlacunar or cardioembolic stokes was observed after adjusting for prevalent risk factors. Nontraditional risk factors to include lipoprotein A (LP[a]) and vWF were associated with an

increased risk of nonlacunar stroke; whereas, lacunar stroke was related to only one nontraditional risk factor, white blood cell count. The population attributable fraction for vWF was greater than that for smoking with cardioembolic stroke. Combined data from the National Hospital Discharge Survey and the Indian Health Service were used to determine the rate of venous thromboembolism in American Indians and Alaskan Natives. The diagnosis of VTE was 71 per 100,000 per year in American Indian/Alaskan Natives; 155 per 100,000 per year in African Americans and 131 per 100,000 per year among White Americans, suggesting differences in genetic and/or environmental risk factors for venous thrombosis (Stein, 2004). An interest in personalized medicine creates both opportunities and challenges when one considers the potential importance and contribution of culture, race, and ethnicity. Clearly, the major challenge is defining culture and race from a purely genetic perspective. Indeed, genetic variability in most parts of the world is increasing, and ethnic diversity translates to genetic diversity within culturally defined groups. Similarly, group differences in pathology and/or response to a particular treatment is not necessarily genetic in nature. Nevertheless, it is important to acknowledge ethnic differences in the phenotypic expression of disease that may have, at least in part, a genetic basis, and intermediate phenotypes that must be accounted for in population studies (Moral et al., 2003). Healthy Black Africans have lower protein S, protein C, and antithrombin III levels compared to healthy Whites and also have higher diluted Russell’s Viper Venom Times (a test used

Race-Related Influences on Hemostasis and Thrombosis

■

761

TABLE 63.3 Results of studies examining racial/ethnic differences in selected hemostatic factors and endothelial markers Factor or marker

Author, year

Finding

Fibrinogen

Mack, 1998 Meade, 1978 Meade, 1986 Is, 1989 Bao, 1992 Folsom, 1992 Tarcy, 1992 Green, 1994 Cook, 2001 Matthews, 2005

US Hispanic  US White  US Chinese UK Black  UK White Gambian European US White  Japanese in USA and Japan US Black  US White US Black  US White US Black  US White US Black  US White UK White and UK South Asian immigrants  African immigrants US Black  US White, Hispanic, Chinese, Japanese

Factor VIII

Mack, 1998 Meade, 1978 Meade, 1986 Iso, 1989 Folsom, 1992 Tarcy, 1992 Green, 1994

US Black  US Chinese  US Hispanic  US White UK Black  UK White Gambian  European US White  Japanese in USA and Japan US Black  US White US Black  US White US Black  US White

D-dimer

Mack, 1998 Sakkienen, 1999

US Black  US Hispanic  US White  US Chinese US “non-White”US White

PAP

Mack, 1998 Sakkinen, 1999

US Black  US White  US Hispanic  US Chinese US White  Hawaii Japanese Americans

PAI-1

Mack, 1998 Iso, 1993 Festa, 2003 Matthews, 2005

US White  US Chinese  US Chinese  US Black UK White  Japanese in Japan US Hispanic  US White  US Black US Hispanic  US White, Black, Chinese, Japanese

ICAM-1

Mack, 1998 Hwang, 1997 Miller, 2003

US Hispanic  US White  US Black  US Chinese US White  US Black UK White and UK South Asian Immigrants  UK African Immigrant

VWF

Mack, 1998 ISO, 1989 Folso, 1992 Green, 1994

US Black  US Chinese  US Hispanic  US White US White  Japanese in USA and Japan US Black  US White US Black  US White

STM

Mack, 1998 Saloaa, 1999

US Hispanic  US White  US Black  US Chinese US White US Black

E-Selection

Mack, 1998 Hwang, 1997 Miller, 2003

US Black  US Hispanic  US White  US Chinese US Black US White UK White  African Immigrants  South Asian Immigrants

PAP, plasmin-antiplasmin; PAI-1, plasminogen activator inhibitor-1; ICAM-1, intercellular adhesion molecule 1, WVF, von Willebrand factor; STM, soluble thrombomodulin. From Mack (1998) with permission.

to screen for the presence of a circulating lupus anticoagulant) (Jerrard-Dunne et al., 2003). Hemostatic and endothelial cell markers among White, Black, Hispanic, and Chinese Americans were determined in the Multi-Ethnic Study of Atherosclerosis (MESA) Study (Lutsey et al., 2006). Black Americans had the highest levels of f VIII, D-dimer, plasmin-antiplasmin complexes, and vWF. Whites and

Hispanics had intermediate levels of most markers, while Chinese participants had among the highest levels of PAI-1, but the lowest levels of all other factors and markers. The observations persisted after adjustment for traditional cardiovascular disease risk factors. A summary of published studies investigating race difference in selected hemostatic factors and endothelial markers appears in Table 63.3.

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In general, loss-of-function mutations that predispose to thrombosis are relatively rare (1:200–500 individuals affected). The low prevalence may reflect a loss of mutant alleles from the gene pool among homozygous individuals who either die in utero or early in life. In contrast, gain-of-function mutations, including factor V Leiden and prothrombotic gene mutations are better tolerated and, as a result, relatively prevalent (8–10%), particularly among Caucasian populations (Reitsma, 2000). Although, both mutations are uncommon in Africa, the available data suggest a 5–6% incidence among African Americans (Mack et al., 1998) highlighting the potential impact on thrombotic risk of race diversity. Emerging data suggest that African Americans possess the highest burden of VTE disease, and Asians the lowest, compared with the White population. In both racial groups, factor V Leiden and prothrombin gene mutations are less common, suggesting the existence of disease modifiers (Itakura, 2005). The Pharmacogenetic Optimization of Anticoagulation Study (Limdi et al., 2006) determined racial differences in prothrombotic genotype frequency among European American and African American patients receiving anticoagulant therapy. The factor V Leiden GA genotype was documented in 8.6% and 1.4% of patients, respectively, and contributed to the risk of VTE among European Americans, but equally for venous and arterial thrombosis in African Americans. The risk of VTE associated with surgical procedures, in combined tomoxifen and chemotherapy regimens and with systemic prothrombotic disorders such as paroxysmal nocturnal hemoglobinuria is similar and maybe higher among African Americans compared to populations of European descent. The potential contribution of a novel prothrombin gene variant, prothrombin C20209T, as a modifier of thrombotic risk in African Americans is under investigation (Itakura, 2005). Cerebral venous thrombosis with distinct clinical presentations has been reported widely. Camargo and colleagues (Camargo et al., 2005) prospectively studied 50 patients from Brazil, comparing clinical and laboratory data among White and African-Brazilian patients. Multiple venous thromboses, deep cerebral venous thromboses and worse clinical outcomes were more frequent in African-Brazilian than White individuals, with higher frequencies of protein C deficiency and factor V Leiden and prothrombin gene mutations, respectively. The evidence supporting distinct frequencies of prothrombotic gene mutations among race-related groups suggests a requirement for race-specific studies to more accurately define inherent risk. A large-scale analysis of plasma protein C, protein S, antithrombin III, and plasminogen activities in Japan identified a prevalence of deficiencies of 0.13%, 0.15%, 1.12%, and 4.29%, respectively. The protein S mutation K196E conferred the greatest risk of VTE, with an estimated allele frequency of 0.009, suggesting that 1 in 12,000 Japanese may be homozygous for the E allele and at heightened risk (Miyata et al., 2006). The very low prevalence of factor V Leiden and prothrombin gene mutations among Chinese patients with VTE suggests that unique genetic and environmental profiles contribute risk ( Jun et al., 2006).

LINKAGE STUDIES IN THROMBOSIS Linkage studies differ from association studies by their dependence on transmission from parents to offspring (of a gene marker) and functional genetic variant. Accordingly, linkage investigation requires sibling pairs, nuclear families or extended families (Souto, 2003). Unlike association studies that are prone to false positive and false negative (failure to identify the candidate gene despite the identification of polymorphisms [unrelated to the phenotype in question]) results, linkage studies are more technically challenging but critical to uncovering new genes that causally influence the observed phenotype. In the Genetic Analysis of Idiopathic Thrombosis (GAIT) family-based study, an additive genetic heritability of 60% for thrombosis was estimated, suggesting the gene mutations would represent the single largest causal mechanism in the pathobiology of disease (Blangero et al., 2003). Genome-wide scans were carried out using 325 pedigrees with 1144 individuals participating in the Framingham Heart Study (Lin et al., 2007). Using variance-component linkage methods, heritabilities were estimated at greater than 50% for red blood cell count, mean corpuscular volume and mean corpuscular hemoglobin. For red blood cell count, a maximum LOD score of 3.2 on chromosome 19 was identified in close proximity to several genes known to influence cellular differentiation and possibly thrombogenicity-erythropoietin and erythroid Kruppel-like factor. A variance-component linkage analysis of C-reactive protein (CRP), IL-6, MCP-1, and soluble ICAM was performed in 304 extended families from the Framingham Heart Study (Dupuis et al., 2005). Heritability estimates ranged from 14% to 44%. A significant linkage to MCP-1 was found on chromosome 1 (LOD 4.27), in a region containing several candidate genes, including E-selectin, P-selectin, and CRP. The findings suggest that genes on chromosome 1 may influence inflammation and may have a potential role in atherothrombosis. Based on available information derived from investigations among individuals with thrombotic disorders, a preferred approach is to utilize linkage studies for gene identification, followed by association studies to further examine polymorphisms in the positional candidate genes (Broeckel et al., 2002; Cooper et al., 2002). If prior linkage studies have not been performed, candidate genes, at the very least, should be based on known functionality.

ASSOCIATION STUDIES IN THROMBOSIS The study of complex diseases to include venous and, perhaps to an even greater extent, arterial thrombosis has taken several unique paths. Association studies, conducted among unrelated subjects are undertaken with a basic premise that genetic variants are either related directly to a predetermined phenotype or closely linked to a causative variant. Using either a cohort or case-control approach, the association between genotype and phenotype can be examined.

A Personalized Approach to Hemostasis and Thrombosis

Although population admixture is a potential confounder in genetic association studies, careful selection of a “control” population may address the problem (Wacholder et al., 2000). In contrast, the generalizability and discovery of intermediate phenotypes that can then be used to identify candidate gene or tested in linkage studies is a potential strength of association studies. A linkage disequilibrium-based genetic approach was utilized to investigate common gene sequence variance in five thrombosis-related genes and related plasma hemostatic proteins among 1811 unrelated Framingham Heart Study participants (Kathiresan et al., 2006). Forty-one tag single nucleotide polymorphisms (SNPs) were genotyped and revealed associations between a fibrinogen-beta SNPs with circulating fibrinogen levels and 7 f VII SNPs and plasma f VII levels. In a step-wise analysis, a single fibrinogen-beta variant explained 1% of the residual variance in fibrinogen levels, and 2 f VII SNPs together explained 10% of the residual variance in f VII levels. The availability of genome-wide surveys of genetic variants and decreasing cost of genotyping provides an ideal environment for using association studies to unravel complex human conditions. Programs in biostatistical methods and informatics will undoubtedly play a critical role in realizing the full potential of this technique (Lin et al., 2006).

HERITABILITY AND THROMBOSIS: EXISTING COMPLEXITIES Heritability, defined as the proportion of the total phenotypic variation within a population, is attributed to genetic variance – also referred to as shared variance. The challenge in complex diseases, including thrombotic disorders, is driven by a lack of independence for genetic and environmental components. In many cases,

Controls with VTE

APS

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763

there is an interaction between the two, with one or more environmental factors modifying the genetic effect. A common illustration is an individual with an inherited thrombophilia who experiences trauma or undergoes major surgery. Under these circumstances, an existing predisposition, coupled with an acquired risk may culminate in a thrombotic event. There are several subtypes of genetic variance: additive (variance resulting from individual alleles and best represented in parent–offspring studies); dominance (variance stemming from pairs of homologous alleles as determined in twin studies); and epistasis (variance from genes that affect the expression of other genes). Accordingly, heritability is itself complex and may be most suitable for detecting the presence (or absence) of genetic variance (van Asselt et al., 2006).

A PERSONALIZED APPROACH TO HEMOSTASIS AND THROMBOSIS A recognized priority area for thrombosis-related investigation is identifying patients who will ultimately experience the clinical phenotype. This knowledge allows a clear distinction between individuals in whom treatment is required to prevent life-altering or life-threatening events, and those who potentially incur undue risk from antithrombotics with little likelihood of benefit. Numerous studies have demonstrated the ability of geneexpression profiles to identify subtle distinctions that define important clinical phenotypes. In a study performed at Duke University Medical Center, Potti and colleagues (Potti et al., 2006) identified gene-expression profiles that predicted thrombotic events among patients with anticardiolipin antibodies (Figures 63.6 and 63.7). An analysis of 50 genes who expression

Normal

aPLA

Figure 63.6 Patterns of gene expression that characterize clinical phenotypes. Hierarchal clustering of the initial patient samples based on gene-expression patterns. Each gene is represented by a single row, and each sample is represented by a single column. The color heat map represents genes in a graded fashion along a spectrum of activation, extending from strongly upregulated genes in red to the down-regulated in blue. VTE, venous thromboembolism; APS, antiphospholipid antibody syndromes; aPLA, antiphospholipid antibody. From Potti et al. (2006) with permission.

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Validation cohort (n  32)

Training cohort (n  57)

(a)

APS

Controls

APS

Controls

5

3

10 2 15 1

Genes

20 25

0

30 1 35 40

2

45

3

50 10

20

30

40 50 Sample number

60

70

80

(b) 1.00 (c)

1.00

0.75 0.50

Probability

Probability

0.75

0.25

0.50

0.25 0.00 5 4 3 2 1 0 1 2 Metagene score Aps

3

4

5

Controls with VTE

0.00 4

3

2 1 0 1 Metagene score

2

3

Figure 63.7 (a) Gene-expression profiles that classify and predict APS phenotype; (b) leave- one out cross validation probabilities in the training cohort; (c) Validation of binary regression model in a blinded clot. APS, antiphospholipid antibody syndrome; VTE, venous thromboembolism. From Potti et al. (2006).

patterns provided the power to discriminate and predict thrombosis included APOE, coagulation factor X, thomboxane, matrix metalloproteinase 19, interleukin 22 receptor, and hematopoietic progenitor cell antigen (CD34) precursor. An ability to identify polymophisms in high-porosity genes may, in turn, lead to novel diagnostic tools for determining patients at risk, and intervening prior to a clinical event.

PATIENT SCREENING: A TRADITIONAL PARADIGM Whom to Investigate In patients with arterial thrombosis, the search for an underlying thrombophilic condition is justified in the presence of at least

Patient Screening: A Traditional Paradigm

TABLE 63.4

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765

Conditions that predispose to thromboembolism

Venous

Arterial

Inherited

Inherited

1. Gene polymorphisms of the hemostatic system: – factor V Leiden (G1691A) – G20210A prothrombin variant – gain-of-function variants of factor VIII, IX, XI

1. Gene polymorphisms of the hemostatic system:b – factor V Leiden (G1691A) – G20210A prothrombin variant – gain-of-function variants of fibrinogen, factor VII, plasminogen activator inhibitor type-1 – glycoprotein IIIa (Leu33Pro)

2. Deficiencis of antithrombin, protein C, protein S

2. Family history of arterial thrombosisa

3. Dysfibrinogenemia

3. Homocystinuria

4. Family history of venous thrombosisa

4. Congenital dyslipidemias

5. Homocystinuria and MTHF C677T 6. Varicose veins Physiologic

Physiologic

1. Pregnancy, pueperium 2. Aging

1. Male gender 2. Aging

Environmental

Environmental

1. 2. 3. 4. 5.

Surgery, trauma, immobilization Oral contraceptives, HRT Heparin-induced thrombocytopenia Antifibrinolytic agents, prothrombin complex concentrates Endotoxemia

Other 1. 2. 3. 4. 5. 6. 7. 8. 9.

Previous venous thromboembolism Obesity Malignancies Antiphospholipid antibodies Polycythemia vera, essential thrombocytosis Congestive heart failure Nephrotic syndrome Behcet’s disease, other vasculitides Paroxysmal nocturnal hemoglobinuria

1. 2. 3. 4. 5.

Smoking, cocaine use Oral contraceptives, HRT Heparin-induced thrombocytopenia Antifibrinolytic agents, prothrombin complex concentrates Thienopyridine-related TTP

Other 1. 2. 3. 4. 5. 6. 7. 8. 9.

Previous arterial thrombosis Atherosclerosis, vasculitis Hypercholesterolemia, metabolic syndromec Congestive heart failure, renal failure Atrial fibrillation Antiphospholipid antibodies, rheumatoid arthritis Polycythemia vera, essential thrombocytosis Sickle cell anemia, macroglobulinemia Malignancies

Modified from Voetsch et al. (2004) and Levine et al. (2002) MTHF  methylenetetrahydrofolate reductase; HRT  hormone replacement therapy; TTP  thrombotic thrombocytopenic purpura. a May be defined as at least 1 first-degree relative affected 50 years if male and 55 years if female. b Relation between genotype and thrombosis variable in different studies. c Components of the metabolic syndrome include hypertension, diabetes or glucose intolerance, obesity, reduced HDL-cholesterol, and hypertriglyceridemia.

one of the following: (1) recurrent thromboembolic event; (2) young age (50 years if male, 55 years if female); (3) lack of significant arterial stenosis at angiography; (4) age 55 years if male or 60 years if female and no apparent cause (i.e., lack of traditional cardiovascular risk factors, systemic illnesses, malignancies, offending drugs); or (5) age 55 years if male or 60 years if female and strong family history of thrombosis (Table 63.4) (Figure 63.8) (Andreotti and Becker, 2005). A venous thromboembolic (VTE) event should prompt further evaluation in the

following individuals: age 50 years, thrombosis at unusual sites (cerebral, mesenteric, hepatic, portal veins), recurrent venous thrombosis, venous thrombosis and a strong family history of thrombotic disease, women with recurring miscarriages and/or puerperal complications. A targeted approach to testing influences strongly the likelihood of detecting an inherited mutation with biological linkage (Table 63.5) (Seligsohn and Lubetsky, 2001). The importance of gathering a family history, a simple and frequently underutilized tool available to all and useful

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Proposed pathway to search for arterial thrombophilia Patient with arterial thrombosis and at least one of the following: 1. Previous arterial thrombosis 2. Age 50 if male or 55 if female 3. No significant artery stenoses at angiography 4. Age 55 if male or 60 if female  no apparent cause 5. Age 55 if male or 60 if female  strong family history * Additionally screen for: – Antiphospholipid antibodies: – lupus anticoagulant – anticardiolipin – anti-beta2 glycoprotein I – ET, PV, malignancies – Cocaine metabolites in urine (within 36 h of onset of thrombosis)

Assess: – Factor V genotype at 1691 locus – Prothrombin genotype at 20210 locus – Other functional candidate gene variants – Homocysteine in serum or plasma

Figure 63.8 Proposed criteria for the selection of patients in whom it may be justified to perform a number of nonroutine tests for underlying thrombophilic conditions. Asterisk denotes at least 1 first-degree relative affected at age 50 years if male of 55 years if female. ET indicates essential thrombocytosis; PV, polycythemia vera. From Andreotti and Becker (2005).

TABLE 63.5 The prevalence of factor V Leiden and prothrombin gene mutations in healthy, unselected and selected populations with venous thrombosis Genetic variant

Healthy subjects

Unselected patients with venous

Selected patients with venous thromboembolism

# Examined

% with variant

# Examined

% with variant

# Examined

% with variant

Factor V Leiden, heterozygote

16,150

4.8

1142

18.8

162

40

Prothrombin G20210A, heterozygote

11,932

2.7

2884

7.1

551

16

Selected patients, age less than 50, a family history of venous thrombosis, a history of recurrent events, and the absence of acquired risk factors except pregnancy of the use of oral contraceptives. Adapted from Seligsohn (2001).

in assessing the risk for common complex diseases, has been stressed by the Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention (Yoon et al., 2003). Selective testing for common inherited thrombophilias is more cost-effective than universal screening (Wu, 2006). What and When to Investigate The tests summarized in Figure 63.8 may be performed during hospitalization, even during the initial stages of a thrombotic episode. Evaluations for an acquired thrombophilia, including those associated with underlying malignancy, systemic disorders, and drug-induced prothrombotic states, should also begin without delay. A definitive diagnosis of myeloproliferative disorder or

the search for occult malignancy may require serial office visits and carefully selected imaging studies. Implications for Treatment The approaches to inherited and acquired thrombophilias differ at several levels. The former raises questions of susceptibility to recurrent events, treatment duration, and whether to perform testing among related family members (who may themselves carry the trait) (Seligsohn and Lubetsky, 2001). The latter is based on concomitant illnesses and identification of offending drugs or conditions in which diagnosis and treatment of the predisposing disorder have a major impact on the overall thrombotic risk.

Patient Screening: A Comprehensive and Population-Based Approach

■

767

Introductory letter to patients with scheduled well-visits

Study coordinator contacts and assesses interest Not interested 

Interested 

Voluntary refusal survey



Phone consent and baseline survey Schedule study visit 40 min prior to appointment Study visit

Informed consent and completion of genetic passport

Physician reviews risk assessment

Consider speciality referral (high risk)

Genetic counselor  Counsels about risk  Testing offered, if appropriate Positive genetic test Referred to disease specialist for:  Evaluation  Risk-based recommendations  Educational materials

Negative test or refused testing

Discuss risk management strategies (moderate risk)

 

Manages risk/offers riskbased recommendations Offers educational materials

Follow routine guidelines (average risk)



Patient reminded to follow standard of care for prevention/monitoring

Summary letter to referring Physician Updates assessment tool at next scheduled visit

Figure 63.9 A population-based approach to thrombophilias emphasizes family and patient history of thrombotic events, carefully selected tests, genetic counseling when appropriate and educated disease management.

PATIENT SCREENING: A COMPREHENSIVE AND POPULATION-BASED APPROACH Individuals who have experienced an initial VTE event incur a significant risk for recurrence. Traditional risk factors including stasis (from immobility and surgery), vascular injury and either inherited or acquired thrombophilias contribute to sustained risk. In the last 10–15 years it has become apparent that there are number of genetic mutations that are present variably within populations that predispose some individuals to recurring events. Accordingly,

attention to personal and family history of thrombosis, with testing according to established guidelines is recommended. A thrombosis risk intake system, modeled for use in a community setting, is summarized in Figure 63.9. The algorithm and targeted risk management recommendations ideally should be based on the ACMG and CAP consensus statements with respect to genetic testing for thrombophilias and expert opinion. There should be shared decision made before proceeding with the test, as well as education posttest. This particular approach is based on existing concern for patients with inherited thrombophilias. A recent study specifically investigated the knowledge and education needs of individuals with the factor

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V Leiden mutation (Hellmann et al., 2003). Several observations are worthy of careful consideration: 79% incorrectly estimated their thrombosis risk (2–8 years posttesting); 64% stated that they were given little information about factor V Leiden (pre-or posttesting); 68% had remaining questions at the time of the survey; 53% believed their health care providers to be inadequately informed about the test implications; and 88% preferred knowing the results (of genetic testing).

PROGNOSTIC CONSIDERATIONS The most prevalent phenotype of a thrombophilia is venous thromboembolism – a continuum from deep vein thrombosis (DVT) to pulmonary embolism (PE). The annual incidence of venous thromboembolism is approximately 1 in 1000 patients, similar to the annual incidence of stroke. The mortality of PE is particularly high with approximately one in six cases resulting in death. As previously described, individuals who have experienced an initial VTE event have a significant risk of recurrence even after an initial course of treatment. The declining risk over time suggests that concomitant illness and factors related to the thrombus itself contribute. Recent data, based on patients with unprovoked (spontaneous or idiopathic) VTE, suggest a 10–15% rate of recurrence. The incidence is particularly high following a PE (GonzalezPorras et al., 2006). Although the presence of an inherited thrombophilia is associated with increased risk, common mutations such as factor V Leiden, prothrombin 20210, and methylene tetrahydrofolate reductase (MTHFR), pose the greatest concern when combined (double defects), or exist as homozygous or double heterozygous forms. The case fatality rates for recurrent PE during anticoagulant therapy vary from 8% to as high as 26%. Recurrence after a course of therapy, on average for 3–6 months, is fatal in approximately 5% of cases (Gonzalez-Porras et al., 2006). The risk of recurrent arterial thrombosis is determined by several factors, include age, vascular bed origin, coexisting disease in other vascular beds, concomitant risk factors, the presence of a defined thrombophilia, and the use and associated effectiveness of existing treatments (that may included percutaneous and/or surgical interventions).

EMERGING PLATFORM FOR HEMOSTASIS AND THROMBOSIS RESEARCH Scientific advances have provided a strong knowledge base for research agendas in the fields of hemostasis and thrombosis. Coupled with great strides in molecular biology, the next decade promises many opportunities to translate fundamental science to patient care. Several emerging platforms will be instrumental and include: proteomic analysis capabilities for defining

signaling cascades in platelets (Garcia, 2006), systems-based models of individual pro- and anticoagulant factor levels, and related in silico prediction models of thrombotic capacity (hemostatic proteome) (Brummel-Ziedins et al., 2005), expression profiles of circulating blood components (Potti et al., 2006) and thrombus modulation achieved through inhibition of cellular adhesive events ( Jackson et al., 2005). A pyrosequencing-based genotype platform that analyzes common prothrombotic, hemostatic, and treatment-modulating mutations may represent an optimized diagnostic strategy for analysis (Holmberg et al., 2005), with potential application in both epidemiologic studies and clinical diagnosis (Mooser et al., 2003). Pharmacogenomics Antithrombotic therapy has improved patient outcomes across a broad range of thrombotic disorders; however, not all patients in all clinical settings respond favorably. Accordingly, efforts must be undertaken to better distinguish “good responders” from “bad responders” or “non-responders.” Pharmacogenomics represents an important part of an emerging paradigm for drug treatment given gene-based influences on enzymes, transporters, and receptors involved with several fundamental properties, including drug absorption, metabolism, excretion, mechanism of action, and toxicity (reviewed in reference [Palkimas et al., 2003]). An illustration of pharmacogenomics as a platform for patient care is well represented with the anticoagulant warfarin (a vitamin K antagonists used widely in the management of thrombotic disorders). Genetic variations in the hepatic metabolism of S-warfarin, the more potent isomer that accounts for 70% of the overall anticoagulant response, by P450 2C9 have important implications in drug dose requirements and risk of bleeding (reviewed in reference [Aithal et al., 1999]) (Table 63.6) (Dang et al., 2005; Higashi et al., 2002; Loebstein et al., 2001; Tabrizi et al., 2002;Taube et al., 2000). Although the prevalence of 2C9 polymorphisms varies among ethnic groups, this may not fully explain differences in warfarin dose requirements (Dang et al., 2005). One must acknowledge, despite the likely importance of pharmacogenomics in clinical practice, that the role of genetic testing in therapy decisions must be defined through carefully designed clinical trials. The Bloodomics project at Cambridge University, financed by the European Community, has a primary objective to discover genetic markers within platelets that predict arterial thrombosis risk. An overarching plan is to identify and catalog SNPs in 300 genes, and establish a basis for population genetics (reviewed in reference [Nurden, 2006]). Polymorphisms in platelet receptors, coagulation proteins and fibrin; coupled with concomitant genetic polymorphisms affecting the metabolism, disposition, transporter proteins, and target binding of antithrombotic drugs emphasizes the importance of ongoing pharmacogenomic research within clinical trials. Application of bioinformatics, genomics, and pharmacogenomics will play a critical role in the unraveling of complex interactions (Iqbal and Fareed, 2006).

References

TABLE 63.6

■

769

Genetics of warfarin metabolism

Author and n

Genetic variants

Effect on dosage requirements

Increased risks

Higashi et al. n  185

CYP2C9*1/*1(n  127) CYP2C9*1/*2(n  28) CYP2C9*1/*3(n  18) CYP2C9*2/*2(n  4) CYP2C9*2/*3(n  3) CYP2C9*3/*3(n  5)

Mean dose is significantly related to genotype being highest in CYP2C9*1/*1 (5.63 mg) and lowest for CYP2C9*3/*3 (1.60 mg) (p  0.001)

Increased risk of high INR, longer time to achieve stable dosing, and increase serious bleeding events

Tabrizi et al. n  153

CYP2C9*1/*1(n  107) CYP2C9*1/*2(n  22) CYP2C9*1/*3(n  21) CYP2C9*2/*2(n  1) CYP2C9*2/*3(n  1) CYP2C9*3/*3(n  1)

Significant decrease in weekly dose in CYP2C9*2 (28.3 mg) and CYP2C9*3 (27.9 mg) compared to wild type allele (40.1 mg) (p  7690.0021 and p  0.0016, respectively)

Not available

Taube et al. n  561

CYP2C9*1/*1(n  392) CYP2C9*1/*2(n  107) CYP2C9*1/*3(n  53) CYP2C9*2/*2(n  3) CYP2C9*2/*3(n  6) CYP2C9*3/*3(n  0)

Dose is related to genotype: homozygous CYP2C9*2 had the lowest maintenance dose (3.04 mg) compared to the wild type (5.01 mg) (p  0.001)

No increased risks found

Loebstein et al. n  156

Age 65 CYP2C9*1(n  49) CYP2C9*2(n  15) CYP2C9*3(n  10) Age  65 CYP2C9*1(n  59) CYP2C9*2(n  13) CYP2C9*3(n  10)

The dose was lowest in patients 65 years old with the CYP2C9*3 variant

Not available

Aithal et al. n  188

Low dose patients CYP2C9*1/*1(n  7) CYP2C9*1/*2(n  12) CYP2C9*1/*3(n  10) CYP2C9*2/*2(n  2) CYP2C9*2/*3(n  5) CYP2C9*3/*3(n  0)

A strong correlation between CYP2C9 and warfarin sensitivity was demonstrated. In addition, there were a higher percentage of variants in the low dose group

Difficulties at initiation of warfarin therapy due to supratherapeutic INR and increased episodes of major bleeding

Random dose patients CYP2C9*1/*1(n  32) CYP2C9*1/*2(n  9) CYP2C9*1/*3(n  10) CYP2C9*2/*2(n  1) CYP2C9*2/*3(n  0) CYP2C9*3/*3(n  0) Adapted from Palkimas, et al. (2003) with permission.

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Andreotti, F. and Becker, R.C. (2005). Atherothrombotic disorders: New insights from hematology. Circulation 111, 1855–1863. Bavendiek, U., Libby, P., Kilbride, M., Reynolds, R., Mackman, N. and Schonbeck, U. (2002). Induction of tissue factor expression in human endothelial cells by CD40 ligand is mediated via activator protein 1, nuclear factor kappa B, and Egr-1. J Biol Chem 277, 25032–25039.

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64 Peripheral Arterial Disease Ayotunde O. Dokun and Brian H. Annex

INTRODUCTION The completion of the human genome project and availability of the complete genetic sequences of mouse strains in which preclinical models of human disease have been described has made available novel approaches to investigate the role of genetics in human diseases. The knowledge and techniques emerging from these projects have revolutionized the approach to mapping and identifying new disease-related genes. The use of genomic methodologies is providing insight leading not only to identification of novel disease-related genes but also to understanding the effect of gene modulation in disease processes. Moreover, it is providing novel insight into the mechanism of action following certain therapies. While the use of genomic methodologies has lead to great advances in knowledge in some fields as cancer research, its use is still in its infancy in others. In the field of vascular biology and more specifically in studies of peripheral arterial disease (PAD), investigators are just beginning to take advantage of the unique opportunities these approaches provide. Here we describe epidemiology, risk factors, clinical presentations of PAD and some of the current and potential future avenues through which genomic methodologies can be used to further our understanding of this disease and its management.

EPIDEMIOLOGY AND RISK FACTORS FOR PAD PAD is a term that encompasses atherosclerosis in arterial beds other than the coronary arteries and the most common site is Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

the lower extremity where occlusive disease leads to impaired perfusion to the lower extremities. The major established risk factors for the development of PAD are essentially the same as those recognized as important in generalized atherosclerosis and include increasing age after 40 years, cigarette smoking, diabetes mellitus, hyperlipidemia and hypertension (Fowkes, 1990; Hiatt et al., 1995). Elevated levels of C-reactive protein and homocysteine may also be important risk factors (Darius et al., 2003; Gerhard et al., 1995; Graham et al., 1997). Although previously under-recognized and under-diagnosed by the medical community and therefore viewed as less important than heart disease, PAD is now recognized to have a prevalence that is similar to that of ischemic heart disease (Gerhard et al., 1995; Kannel and McGee, 1985). It affects about 6% of people between 50 and 60 years old and 10–20% of those over 70 years old, suggesting an increased prevalence with age (Hirsch et al., 2001).

CLINICAL MANIFESTATIONS OF PAD The presence of PAD should be suspected in every patient with multiple risk factors for the disease even in the absence of symptoms. Most patients with PAD lack the classic symptoms of PAD and thus are often considered asymptomatic; some estimates suggest as many as 50% of PAD patients fall into this category (Hirsch et al., 2001). It is currently recommended that patients with multiple risk factors, especially smokers and diabetics, with other risk factors, should undergo non-invasive testing such as ankle brachial index (ABI) detected by Doppler probe to make Copyright © 2009, Elsevier. Inc. All rights reserved. 773

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the diagnosis. ABI measures the ratio of systolic blood pressure in the ankle to that of the brachial vessels; an ABI 0.9 is considered diagnostic for PAD (Hirsch et al., 2001; Newman et al., 1999). Once the diagnosis of PAD is made, further testing with duplex ultrasonography, segmental Doppler pressure or volume pletthysmography, MRA or angiography, may be utilized depending on the clinical situation. There are two major clinical manifestations of PAD, intermittent claudication (IC) and critical-limb ischemia (CLI) (see Table 64.1 for comparison of IC versus CLI). IC manifests as reduced blood flow to the extremities during exercise resulting in pain relieved only by rest, while CLI describes pain at rest that may be associated with non-healing leg ulcers or gangrene. Interestingly, while patients with IC have amputation and annual mortality rate of 1–2%, those with CLI have 6-month amputation risk of 25–40% and an annual mortality of 20% (Ouriel, 2001). The diagnosis of IC versus CLI is based upon time-tested clinical classification schemes, namely, the Rutherford and the Fontaine classifications. In the Rutherford classification, IC encompasses categories 1–3 (mild, moderate and severe claudication respectively), while CLI includes categories 4–6 (ischemic rest pain, minor tissue loss and ulceration or gangrene). The Fontane classification is more commonly used in Europe, with stages IIa and IIb describing IC while stages III–IV are categories of CLI. There is no biomarker or hemodynamic measure that is pathopneumonic for either of the two major clinical classifications.

THERAPEUTIC STRATEGIES FOR PAD Treatment strategies for PAD share certain similarities to those of coronary atherosclerotic disease (CAD). Disease management strategies are geared toward prevention of disease progression by addressing underlying risk factors and interventions to treat symptoms. Strategies for prevention of disease progression focus primarily on smoking cessation in smokers and good glycemic control in diabetic patients. Treatment of hypertension and dislipidemia, as well as use of anti-platelet therapy, is also highly recommended. Interventions aimed at reducing symptoms and increasing walk-time in patients includes medical therapy, with use of the phosphodiesterase inhibitor, cilostazol (Hiatt, 2001). The methyxanthine derivative, pentoxyphilline, which improves deformity of red blood cells, is also used but has been shown to be less effective (Dawson et al., 2000). Interestingly one of the most effective therapies for PAD is actually not pharmacologically based. Exercise training has been shown through numerous studies to be effective in improving the symptoms and increasing walk-time in patients with PAD (Christman et al., 2001; Leng et al., 2000). As a result, a supervised exercise program is now recommended as a key component of the management of PAD (Christman et al., 2001; Hirsch et al., 2006). However, the molecular mechanism by which exercise improves symptoms in PAD is poorly understood. One of the leading

TABLE 64.1

Comparison of IC and CLI Intermittent claudication (IC)

Critical limb ischemia (CLI)

Diabetes

Known risk factor

Known risk factor

Smoking

Known risk factor

Known risk factor

Hypertension

Known risk factor

Known risk factor

Hyperlipidemia

Known risk factor

Known risk factor

C reactive protein

Known risk factor

Known risk factor

Pain with ambulation

Usually present

Usually present

Pain at rest

Usually not present

Usually present

Ulceration or Gangrene

Usually not present

May be present

ABI 0.9

Usually present

Sometimes associated with lower ABI’s but low ABI does not predict disease

Annual mortality

1–2%

20%

Amputation rate

1–2%

25–40%

Risk factors

Clinical characteristics

Biochemical characteristics β2 macroglobulin

Higher levels in PAD patient but not shown to differentiate between IC and CLI

Angiogenic factors

Potential use as biomarker under investigation

Vascular Endothelia cell Growth Factor A (VEGF)-A

No difference in serum levels compared to non PAD patients (Findley et al., 2008).

Soluble Tie 2

No difference in serum levels compared to non PAD patients (Findley et al., 2008).

Higher levels detected in serum of CLI patients compared to IC and non PAD patients (Findley et al., 2008). Higher levels detected in serum of CLI patients compared to IC and non PAD patients (Findley et al., 2008).

Gene Polymorphisms Contributing to Atherosclerosis and PAD

hypotheses is that exercise training leads to improved perfusion of the extremities in PAD patients through increased angiogenesis. Other possibilities include a role for mitochondria biogenesis within skeletal muscles. The potential role of these mechanisms and factors that modulate these mechanisms (e.g., growth factors) are currently being investigated. In patients with CLI or with severe IC revascularization with surgical bypass grafting or percutaneous peripheral intervention (PPI) is more typical. The less invasive nature of PPI is making it more common as a treatment option in these patient populations. However, not all patients are candidates for revascularization due to a variety of reasons including small target vessels, diffuse PAD and presence of other co-morbidities that preclude them from undergoing surgery or PPI. This has lead to the development of other therapeutic options currently in the experimental phase, such as therapeutic angiogenesis. Therapeutic angiogenesis is an experimental approach in which vascular growth factors are delivered to ischemic tissues in an attempt to improve tissue ischemia. A variety of vascular growth factors are currently being investigated and these include vascular endothelial growth factor (VEGF), fibroblast derived growth factor (FGF), hepatocyte growth factor, and angiopoietins (Jones and Annex, 2007). Interestingly some of these vascular growth factors have multiple isoforms (e.g., VEGF). Identifying which isoform or combination of isoforms will be most effective in therapy adds another level of complexity to their use as therapeutic agents. Recently a novel approach was developed that involves delivery of a plasmid that encodes an engineered zinc finger protein (ZFP) transcription factor that can target transcription of endogenous genes (Dai et al., 2004; Liu et al., 2001; Rebar et al., 2002). This approach has the key advantage of being capable of inducing expression of multiple isoforms of the growth factors being investigated. Our laboratory has successfully tested the effectiveness of ZFP-VEGF in preclinical model of PAD (Li et al., 2007; Xie et al., 2006) and is now investigating its safety in humans through phase I clinical trials.

IC AND CLI ARE DISTINCT CLINICAL OUTCOMES OF PAD Major advances in the field of ischemic heart disease followed the recognition that ischemic heart disease is a continuum; however, the two major clinical manifestations of PAD (IC and CLI) appear to be quite distinct. The observation that progressively lower ABIs are associated with worsening symptoms of claudication (McDermott et al., 2002) has made some assume that IC and CLI are due to a continuum of reduced blood flow. In this line of thought, CLI is simply a worse form of IC. However, the ultimate symptomatic presentation of this disease is heterogeneous with some individuals presenting with claudication while others present de novo with CLI. Moreover, many patients with claudication never progress to CLI and many presenting with CLI do not report antecedent claudication (Boyd, 1962; Cronenwett et al., 1984; Imparato et al., 1975). Interestingly, even

TABLE 64.2

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Genes associated with PAD

Gene polymorphisms contributing to atherothrombosis and PAD

Gene polymorphisms contributing to atherosclerosis and PAD

Gene polymorphisms contributing to PAD with unknown gene function

Factor II (FII G20210A)

APO E

PAOD1

P2Y12 (H2 allele)

APO B

Fibrinogen (beta)

174 G/C (IL-6 promoter)

in patients with similar risk factors, atherosclerotic burden, and virtually the peripheral hemodynamics, some will present with IC while others present with CLI. These observations suggest that there are factors other than the currently known risk factors for PAD that may influence these clinical syndromes.

GENETIC BACKGROUND AS A RISK FACTOR FOR PAD The well established risk factors for PAD are nearly identical to those for atherosclerosis. There is, however, an emerging body of evidence that suggest that the genetic background of an individual may be important in the pathogenesis of PAD. In one study, the prevalence of PAD in various ethnic backgrounds was evaluated and the results suggest higher prevalence in African Americans (AA) even after adjusting for age and other traditional risk factors for PAD (Kullo et al., 2003). Since individuals of the same ethnicity are likely to share certain ancestral genes, the higher prevalence of PAD in AA suggests that there may be gene polymorphisms contributing to PAD. Further evidence supporting the possible role of genetic risk factors in PAD comes from both association and linkage studies. Sequence variations in a variety of genes have shown statistically significant association with PAD. These genes can be classified into three different categories: pro-atherosclerotic, pro-atherothrombotic or unknown, based on the function of the gene products (Table 64.2). Each category is discussed in greater detail below.

GENE POLYMORPHISMS CONTRIBUTING TO ATHEROSCLEROSIS AND PAD One of the major risk factors for atherosclerosis is hyperlipidemia; hence it is not surprising that polymorphisms in genes involved in lipid metabolism may be associated with PAD. Monsalves and coworkers evaluated polymorphisms within the APOB gene

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among 205 patients with a diagnosis of PAD. Their data showed higher prevalence of an R2 and X1 allele among the PAD patients compared to non-PAD controls; however since most of the individuals also had arterial disease other than PAD, they concluded that variations at the APOB locus contribute to predisposing individuals to the development of arterial disease but could not discriminate where in the arterial system the disease develops (Monsalve et al., 1991). Similarly, an association study from the Honolulu Asian-aging study involving 3161 Japanese-American men aged 71–93 showed an association of APO E polymorphisms with PAD (Resnick et al., 2000). However, at this point the specific mechanisms by which these gene polymorphisms contribute to the pathogenesis of PAD is poorly understood. Inflammation is becoming increasingly recognized as an important factor in the pathogenesis of atherosclerosis. IL-6 is a pro-inflammatory, multifunctional cytokine produced by various cell types including monocytes, adipocytes and endothelial cells. Elevated levels of IL-6 have been found in patients with atherosclerotic disease (Biasucci et al., 1996; Milei et al., 1996). Sequence variations in the promoter region of the IL-6 gene (-174) have been reported with two different alleles identified resulting in three genotypes GG, GC and CC (Flex et al., 2002). These described polymorphisms influence the level of transcription of IL-6 and IL-6 protein concentration in serum. Flex and coworkers in their study of 84 patients with PAD, found that the GG genotype was more common in individuals with PAD, suggesting a role for this pro-inflammatory cytokine in pathogenesis of PAD (Flex et al., 2002). PAD is a consequence of atherosclerosis in the lower extremities; therefore, it is not unexpected that sequence variations in genes contributing to the development of atherosclerosis may be also associated with PAD.

POLYMORPHISMS IN PROATHEROTHROMBOTIC GENES AND PAD It is fairly well established that thrombosis plays an important role in the pathogenesis of atherosclerosis (Yee et al., 2001). Thrombin is not only important in fibrin formation and platelet aggregation but thrombin is also important in endothelial activation, platelet and leukocyte recruitment (Coughlin, 2000). Consequently, various groups have hypothesized that polymorphisms in genes encoding hemostatic proteins may contribute to development of atherosclerosis. Indeed studies looking at fibrinogen gene polymorphisms found that certain genotypes were associated with an increased risk of peripheral atherosclerosis (Fowkes et al., 1992). Similarly, Reny and coworkers investigated an association of factor II and V polymorphisms in PAD (Reny et al., 2004). Although no association was found for factor V polymorphisms, a statistically significant association was shown between the FII G20210A allele of factor II and PAD. The FII G20210A polymorphism appears to increase local generation of thrombin and thus contribute to the progression of atherothrombosis.

The importance of platelet aggregation in arterial thrombosis is also well known. The platelet ADP receptor P2Y12 is a seven transmembrane receptor which, upon activation, promotes platelet aggregation (Conley and Delaney, 2003; Gachet, 2001). Blockade of P2Y12 by thienopyridines has been shown to be beneficial in patients with cardiovascular disease. Polymorphisms in the P2Y12 gene have been described (Fontana et al., 2003a); one of the alleles (H2) results in a gain of function haplotype on ADP-induced platelet aggregation. Hence it was hypothesized that this allele may be associated with increased risk of PAD (Fontana et al., 2003b). Indeed an association was found for the presence of H2 allele and PAD even after adjustment for traditional PAD risk factors.

GENETIC LOCUS CONFERRING SUSCEPTIBILITY TO PAD Human studies of the genetics of PAD are quite limited outside of genes contributing to atherosclerosis or thrombosis. Despite an extensive review, we were able to identify only a single family-based linkage study that has identified a genetic locus conferring susceptibility to PAD. This study of Icelandic families with multiple family members exhibiting PAD, identified a locus termed PAOD1 that mapped to human chromosome 1p31 (Gudmundsson et al., 2002). Interestingly, other risk factors for PAD such as hypertension, hyperlipidemia and diabetes did not contribute to the positive linkage. One of the key strengths of this study is the fact that the investigators took an unbiased approach that is rather than starting from a known gene and looking for association the investigators started with a phenotype and performed a linkage study. Despite these strong and convincing genetic data, the genes responsible for PAOD1 has not been identified.

IDENTIFICATION OF NOVEL GENE POLYMORPHISMS INVOLVED IN PAD As earlier described, sequence variations in certain genes are associated with the development of PAD. However, these gene polymorphisms appear to contribute to the development of atherosclerotic and or atherothrombotic disease in general rather than specifically to PAD. The consequence of atherosclerosis and atherothrombosis is vessel occlusion leading to diminished perfusion of the target organ, which in PAD is the skeletal muscle of the lower extremities. It is therefore possible that polymorphisms in genes involved in skeletal muscle function and adaptation to ischemic stress may be important in the pathogenesis of PAD. This hypothesis is supported by the observation that in spite of similar atherosclerotic burden, some individuals present with IC while others present de novo with CLI. Furthermore, in a mouse preclinical model of PAD in which the mouse femoral artery is ligated and excised to introduce ischemic stress (hindlimb

Future Potential Use of Genomic Methodologies in PAD

ischemia or HLI) our group and others have shown that recovery is different among mouse strains (Dokun et al., 2008; Fukino et al., 2003; Scholz et al., 2002). Since the ischemic stress in this model is independent of atherosclerosis and yet recovery is strain dependent, this suggests a role for polymorphisms in genes other than those contributing to atherosclerosis. Investigating the role of genetic polymorphisms involved in such novel mechanisms in PAD is quite challenging. For instance it requires taking an unbiased approach in which one starts with a phenotype then through association or linkage studies identify genes that are involved in the observed phenotype. Moreover, even when such an approach is taken, identifying the specific genes involved could still be quite challenging.This is perhaps best exemplified by the Gudmundsson study described earlier in which PAOD1 was identified. Although they took an unbiased approach starting with a phenotype leading to the identification of a genetic locus, the specific gene(s) conferring the PAOD1 phenotype remains unknown. Nevertheless current advances in genomic methodologies is allowing novel approaches to be taken to investigate polymorphisms in genes that may contribute to PAD via mechanisms different from those involved in development of arthrosclerosis or atherothrombosis.

IDENTIFICATION OF A QUANTITATIVE TRAIT IN A PRECLINICAL MODEL OF PAD One approach to identifying novel gene polymorphisms important in PAD is to take advantage of the strain specific difference in recovery following HLI described above, to MAP and identify candidate genes. Several strains of mice can be screened to identify their recovery phenotype. Strains at the phenotypic extremes can then be crossed and back crossed as necessary to generate progeny for further screening and mapping. Whereas previous approaches have involved the use of microsatellite markers to genotype and map involved genes, this approach is time consuming and costly. An alternative approach involves use of commercially available mouse single nucleotide polymorphism (SNP) linkage panels. This allows for rapid scanning of the entire mouse genome with subsequent identification of quantitative trait locus (QTLs) that are associated with the phenotype of interest.

REFINING A QTL USING HAPLOTYPE ANALYSIS Once a QTL has been identified, it may be important to further refine the identified locus from a region containing hundreds of genes to that containing a handful of genes; making it easier to identify the specific gene(s) responsible for the phenotype of interest. Traditionally this would have involved making congenic mice strains generated by successive crosses between the F1 (generated from a cross between the two parental strains) and the parental strain with the phenotype of interest. While

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this approach has other benefits including allowing for independently testing the role of the locus in the absence of other genetic components from the parent strain, it is quite time consuming and costly. An alternative to this approach is to perform haplotype analysis. Most of the genetic variations between mice inbred strains are found in ancestral haplotype blocks that are shared between strains. Although sequence variations unique to specific strains do exist, they are less frequent (Wade et al., 2002). Therefore if the phenotype of multiple mouse strains are known then one can take advantage of ancestral haplotype sharing patterns within the inbred mouse lineage to quickly identify high priority areas within a QTL interval that are likely to harbor the polymorphism involved in a given phenotype (Peters et al., 2007). This approach has been successfully used to quickly refine the boundaries of QTLs covering otherwise large genomic intervals, into regions containing only a few genes (Sheehan et al., 2007;Wang et al., 2004).

IDENTIFICATION OF CANDIDATE GENES To identify the gene(s) that may be involved in the differential recovery of mouse strains following experimental HLI, one approach as been to use gene expression profiling. In this approach the differential expression of genes from post– hindlimb ischemic tissue from different mouse strains are sought. Gene(s) that are highly expressed in association with a given phenotype are then selected as candidate gene(s) conferring a given phenotype. Unfortunately, as is the case with most studies using gene profiling, multiple differentially expressed genes may be identified. This creates the challenge of determining which gene(s) are the most relevant in conferring the phenotype. To avoid the above predicament, gene expression profiling can be combined with QTL mapping and haplotype analysis. This allows one to focus only on genes within an identified QTL that are differentially expressed between strains. This methodology may be applicable not only in preclinical models as has already described, but also in human studies as well. For instance gene expression profiling can be applied to tissue samples from patients with and without PAD. Candidate genes can then be identified among differentially expressed genes that localize within a QTL (e.g., PAOD1) known to be associated with PAD.

FUTURE POTENTIAL USE OF GENOMIC METHODOLOGIES IN PAD The use of genomic methodologies in studies of PAD is in its infancy. However, investigators in vascular biology are now beginning to take advantage of the novel approaches that these genomic methodologies offer. As described in detail above, genomic approaches are currently being used to identify novel genetic polymorphisms that may be important in understanding other mechanisms contributing to the pathogenesis of PAD in

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ways previously unappreciated. There are numerous areas in studies of PAD where genomic methodologies may have significant impact. For instance gene expression profiling may be applied to gain insight into candidate gene(s) whose differential expression might be contributing to the distinct clinical outcomes in patient with PAD. Novel insights into disease pathogenesis may be obtained by performing gene expression analysis on tissue samples from IC and CLI patients matched for anatomic distribution of disease, ABI’s, and risk factors for PAD. Another area where genomic strategies may be used in PAD is in modulation of gene expression as a consequence of altered tissue environment and the pathogenesis of PAD. A classic example of tissue environment alteration is the metabolic derangements seen in type II diabetes. Diabetes is a major risk factor for PAD and along with smoking accounts for about 80% of the risk associated with development of PAD. Interestingly, other than through its contribution to increased risk of atherosclerosis, how diabetes contributes to development of PAD is poorly understood. Recently our laboratory showed that there is alteration in the mRNA and protein expression of the potent angiogenic factor VEGF and its receptors in the skeletal muscle of mice with high fat diet induced type II diabetes (Li et al., 2007). This suggests that alterations in angiogenic factors may be important in the pathogenesis of PAD in diabetics. To identify additional angiogenic factors whose expression may be altered in diabetes; analysis of differential expression of all known angiogenic and vasculogenic factors can be sought in normal versus diabetic tissues via microarray analysis. Once specific genes with altered expression are identified, the mechanism via which their altered expression contributes to PAD can then be studied. The potential use of methodologies afforded by current genomics is not limited to understanding disease pathogenesis in

PAD but may be extended to providing solutions to some of the challenges faced with current therapeutic approaches. Supervised exercise training has been shown to be an effective therapy for PAD patients (Gardner et al., 2000). However, it can be quite expensive and patient compliance is typically a major challenge. Therefore understanding the molecular mechanisms via which exercise training contributes to symptom improvement in PAD patients may lead to the development of less expensive and more practical therapeutic options. One possible approach is to investigate potential mechanisms involved by studying genes that may be differentially expressed in exercised versus non-exercised tissues. Of course it is possible that exercise training will lead to increased transcription of multiple genes, thus creating the challenge of identifying which genes are most relevant. Should this occur, priority can be given to those genes with known or presumed function in angiogenesis or tissue adaptation to ischemia. The specific role of these genes can then be tested in preclinical models of PAD by targeted gene silencing or over-expression. Ultimately, the role of the human orthologs of these genes can be assessed in humans with PAD. In conclusion, although the use of genomic methodologies is still in its infancy in studies of vascular disease especially in PAD; its use is growing exponentially and will likely continue to positively impact our understanding of the disease and its therapies.

ACKNOWLEDGEMENT This was supported by R01 HL75752 from the National Institute of Health, National Heart, Lung, and Blood Institute and the Office of Research on Women’s Health, Office of the Director to BHA. None of the authors have any conflicts of interest.

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growth factor transcription factor plasmid DNA induces therapeutic angiogenesis in rabbits with hindlimb ischemia. Circulation 110, 2467–2475. Darius, H., Pittrow, D., Haberl, R., Trampisch, H.J., Schuster, A., Lange, S., Tepohl, H.G., Allenberg, J.R. and Diehm, C. (2003). Are elevated homocysteine plasma levels related to peripheral arterial disease? Results from a cross-sectional study of 6880 primary care patients. Eur J Clin Invest 33, 751–757. Dawson, D.L., Cutler, B.S., Hiatt, W.R., Hobson, R.W., 2nd, Martin, J.D., Bortey, E.B., Forbes, W.P. and Strandness, D.E., Jr (2000). A comparison of cilostazol and pentoxifylline for treating intermittent claudication. Am J Med 109, 523–530. Dokun, A.O., Keum, S., Hazarika, S., Li, Y., Lamonte, G.M, Wheeler, F., Marchuk, D.A., Annex, B.H. (2008). A quantitative trait locus (LSq-1) on mouse chromosome 7 is linked to the absence of tissue loss after surgical hindlimb ischemia. Circulation 117(9), 1207– 1215. Epub 2008 Feb 19. Findley, C.M., Mitchell, R.G, Duscha, D.D., Annex, B.H., Kontas, C.D, (2008). Plasma levels of soluble Tie2 and VEGF distinguish critical limb ischemia from intermittent claudication in patients with peripheral arterial disease. J Am Coll Cardiol (in press).

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Flex, A., Gaetani, E., Pola, R., Santoliquido, A., Aloi, F., Papaleo, P., Dal Lago, A., Pola, E., Serricchio, M., Tondi, P. et al. (2002). The -174 G/C polymorphism of the interleukin-6 gene promoter is associated with peripheral artery occlusive disease. Eur J Vasc Endovasc Surg 24, 264–268. Fontana, P., Dupont, A., Gandrille, S., Bachelot-Loza, C., Reny, J.L., Aiach, M. and Gaussem, P. (2003a). Adenosine diphosphate-induced platelet aggregation is associated with P2Y12 gene sequence variations in healthy subjects. Circulation 108, 989–995. Fontana, P., Gaussem, P., Aiach, M., Fiessinger, J.N., Emmerich, J. and Reny, J.L. (2003b). P2Y12 H2 haplotype is associated with peripheral arterial disease: A case-control study. Circulation 108, 2971–2973. Fowkes, F.G. (1990). Peripheral vascular disease: A public health perspective. J Public Health Med 12, 152–159. Fowkes, F.G., Connor, J.M., Smith, F.B., Wood, J., Donnan, P.T. and Lowe, G.D. (1992). Fibrinogen genotype and risk of peripheral atherosclerosis. Lancet 339, 693–696. Fukino, K., Sata, M., Seko, Y., Hirata, Y. and Nagai, R. (2003). Genetic background influences therapeutic effectiveness of VEGF. Biochem Biophys Res Commun 310, 143–147. Gachet, C. (2001). ADP receptors of platelets and their inhibition. Thromb Haemost 86, 222–232. Gardner, A.W., Katzel, L.I., Sorkin, J.D., Killewich, L.A., Ryan, A., Flinn, W.R. and Goldberg, A.P. (2000). Improved functional outcomes following exercise rehabilitation in patients with intermittent claudication. J Gerontol A Biol Sci Med Sci 55, M570–M577. Gerhard, M., Baum, P. and Raby, K.E. (1995). Peripheral arterialvascular disease in women: Prevalence, prognosis, and treatment. Cardiology 86, 349–355. Graham, I.M., Daly, L.E., Refsum, H.M., Robinson, K., Brattstrom, L.E., Ueland, P.M., Palma-Reis, R.J., Boers, G.H., Sheahan, R.G., Israelsson, B. et al. (1997). Plasma homocysteine as a risk factor for vascular disease. The European Concerted Action Project. Jama 277, 1775–1781. Gudmundsson, G., Matthiasson, S.E., Arason, H., Johannsson, H., Runarsson, F., Bjarnason, H., Helgadottir, K., Thorisdottir, S., Ingadottir, G., Lindpaintner, K. et al. (2002). Localization of a gene for peripheral arterial occlusive disease to chromosome 1p31. Am J Hum Genet 70, 586–592. Hiatt, W.R. (2001). Medical treatment of peripheral arterial disease and claudication. N Engl J Med 344, 1608–1621. Hiatt, W.R., Hoag, S. and Hamman, R.F. (1995). Effect of diagnostic criteria on the prevalence of peripheral arterial disease. The San Lvis Valley Diabetes Study. Circulation 91, 1472–1479. Hirsch, A.T., Criqui, M.H., Treat-Jacobson, D., Regensteiner, J.G., Creager, M.A., Olin, J.W., Krook, S.H., Hunninghake, D.B., Comerota, A.J., Walsh, M.E. et al. (2001). Peripheral arterial disease detection, awareness, and treatment in primary care. JAMA 286, 1317–1324. Hirsch, A.T., Haskal, Z.J., Hertzer, N.R., Bakal, C.W., Creager, M. A., Halperin, J.L., Hiratzka, L.F., Murphy, W.R., Olin, J.W., Puschett, J.B. et al. (2006). ACC/AHA 2005 Practice Guidelines for the management of patients with peripheral arterial disease (lower extremity, renal, mesenteric, and abdominal aortic) a collaborative report from the American Association for Vascular Surgery/Society for Vascular Surgery. Society for Cardiovascular Angiography and Interventions, Society for Vascular Medicine and Biology, Society of Interventional Radiology, and the ACC/AHA Task Force on Practice

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associated with peripheral arterial disease: Results of a case-control study. J Thromb Haemost 2, 1334–1340. Resnick, H.E., Rodriguez, B., Havlik, R., Ferrucci, L., Foley, D., Curb, J.D. and Harris, T.B. (2000). Apo E genotype, diabetes, and peripheral arterial disease in older men: The Honolulu Asia-aging study. Genet Epidemiol 19, 52–63. Scholz, D., Ziegelhoeffer, T., Helisch, A., Wagner, S., Friedrich, C., Podzuweit, T. and Schaper, W. (2002). Contribution of arteriogenesis and angiogenesis to postocclusive hindlimb perfusion in mice. J Mol Cell Cardiol 34, 775–787. Sheehan, S.M., Tsaih, S., King, B.L., Stanton, C., Churchill, G.A., Paigen, B. and Dipetrillo, K.J. (2007). Genetic analysis of albuminuria in a cross between C57BL/6J and DBA/2J mice. Am J Physiol Renal Physiol 293, F1649–F1656.

Wade, C.M., Kulbokas, E.J., Kirby, A.W., 3rd, Zody, M.C., Mullikin, J.C., Lander, E.S., Lindblad-Toh, K. and Daly, M.J. (2002). The mosaic structure of variation in the laboratory mouse genome. Nature 420, 574–578. Wang, X., Korstanje, R., Higgins, D. and Paigen, B. (2004). Haplotype analysis in multiple crosses to identify a QTL gene. Genome Res 14, 1767–1772. Xie, D., Li, Y., Reed, E.A., Odronic, S.I., Kontos, C.D. and Annex, B.H. (2006). An engineered vascular endothelial growth factor-activating transcription factor induces therapeutic angiogenesis in ApoE knockout mice with hindlimb ischemia. J Vasc Surg 44, 166–175. Yee, K.O., Ikari, Y. and Schwartz, S.M. (2001). An update of the Grutzbalg hypothesis: The role of thrombosis and coagulation in atherosclerotic progression. Thromb Haemost 85, 207–217.

CHAPTER

65 Genomics of Congenital Heart Disease Jessie H. Conta and Roger E. Breitbart

INTRODUCTION Congenital heart disease (CHD) refers to structural malformations of the heart and great vessels that result from abnormal embryonic and fetal development. They comprise a broad spectrum of anatomic and clinical severity, ranging from relatively simple lesions such as atrial and ventricular septal defects to highly complex malformations of the cardiac chambers and their venous and arterial connections. As a group, cardiovascular malformations are the most common form of major birth defect, occurring at a frequency of 8 per 1000 live births (excluding bicuspid aortic valve, which has an estimated prevalence of 2% to 3%). They are also among the most frequent causes of death in infants beyond the neonatal period. The genetic basis of CHD has been the subject of intense investigation and increasing understanding in the past decade. CHD has long been recognized as a key feature of certain genetic syndromes, a prime example being Down syndrome (trisomy 21). In contrast, the importance of genetic mutation in non-syndromic CHD has emerged relatively recently through the elucidation of single gene defects in certain families with inherited CHD. This work, undertaken largely before the completion of the Human Genome Project, has relied principally on conventional genetic approaches, that is linkage analysis and positional cloning, as well as candidate gene sequencing. As it happens, however, families with mendelian transmission of CHD are actually quite rare. Non-mendelian patterns with variable penetrance and expressivity are more the rule in families with manifestly inherited CHD. Furthermore, most cases of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

CHD are, or at least appear to be, sporadic, occurring in patients with no known family history of CHD. On this basis there is growing expectation that the bulk of CHD will ultimately be attributed to more complex genetics involving polymorphisms, modifying loci, and gene-gene and gene-environment interactions. Ongoing investigation of these mechanisms relies necessarily on genomic approaches. This chapter reviews the current knowledge of the molecular genetics and genomics of CHD. Future directions for this research, and the associated challenges, are also considered. Recommendations for genetic evaluation of patients and families with CHD are offered, including screening, molecular diagnosis, and counseling. Of note, the genetics of cardiac arrhythmias and of the cardiomyopathies, both also affecting children, are beyond the scope of this chapter but are addressed elsewhere in this volume (see Chapters 61 and 62).

CHD GENE DISCOVERY BY CONVENTIONAL GENETICS Salient human genes with established roles in the etiology of CHD are summarized in Table 65.1. This list includes the most well studied and illustrative examples of CHD genes but is by no means exhaustive. Indeed, a search of the Online Mendelian Inheritance in Man database (www.ncbi.nlm.nih.gov/omim/) yields more than 900 additional genes or loci linked to a range of CHD at this writing. CHD genes have been identified largely through conventional genetic approaches including linkage Copyright © 2009, Elsevier Inc. All rights reserved. 781

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TABLE 65.1

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Genomics of Congenital Heart Disease

Salient human congenital heart disease genes

Gene

Typea

Syndrome

Cardiac Lesionsb

References

TBX5

TF

Holt-Oram

ASD, others

Basson et al. (1997); Li et al. (1997b); Bruneau et al. (2001)

TBX1

TF

DiGeorge

Conotruncal defects

Merscher et al. (2001); Lindsay et al. (2001); Jerome and Papaioannou (2001); Yagi et al. (2003)

NKX2-5

TF

Non-syndromic

ASD, VSD, others

Schott et al. (1998); Benson et al. (1999); McElhinney et al. (2003); Elliott et al. (2003)

GATA-4

TF

Non-syndromic

ASD, VSD, others

Garg et al. (2003)

ZFMP2/FOG2

TF

Non-syndromic

TOF

Pizzuti et al. (2003)

SALL4

TF

Okihiro

ASD, VSD, TOF

Al-Baradie et al. (2002); Kohlhase et al. (2002)

ZIC3

TF

Heterotaxy

Complex malformations

Casey et al. (1993); Gebbia et al. (1997); Ware et al. (2004)

TFAP2B

TF

Char

PDA

Satoda et al. (1999, 2000); Zhao et al. (2001)

JAG1

CS

Alagille

PS, TOF

Oda et al. (1997); Li et al. (1997a); Eldadah et al. (2001)

NOTCH2

CS

Alagille

PS, TOF

McDaniell et al. (2006)

NOTCH1

CS

Non-syndromic

BAV

Garg et al. (2005)

PTPN11

CS

Noonan

PS, HCM

Jamieson et al. (1994); Tartaglia et al. (2001, 2002)

FIBRILLIN-1

EM

Marfan

MVP, AD

Kainulainen et al. (1990); Magenis et al. (1991); Dietz et al. (1991a, b); Lee et al. (1991)

ELASTIN

EM

Williams

SVAS, others

Ewart et al. (1993)

a

TF, transcription factor; CS, cell signaling factor; EM, extracellular matrix protein.

b

ASD, atrial septal defect; VSD, ventricular septal defect; TOF, tetralogy of Fallot; PDA, patent ductus arteriosus; PS, pulmonary stenosis; HCM, hypertrophic cardiomyopathy; MVP, mitral valve prolapse; AD, aortic dilation; SVAS, supravalvar aortic stenosis.

analysis, positional cloning, and candidate gene sequencing. Some were discovered in the context of clinical genetic syndromes that include CHD phenotypes while others have been found in families with non-syndromic CHD. Re-sequencing of candidate genes has also demonstrated mutations or polymorphisms associated with sporadic CHD. Transcription Factor Genes Among the fourteen CHD genes in Table 65.1, eight encode nuclear transcription factors. Five of these, Tbx5, Nkx2-5, GATA4, Zfpm2/Fog2, and Sall4, have been demonstrated to interact cooperatively with each other to regulate gene expression during cardiac embryogenesis. TBX5 (Holt-Oram Syndrome) Holt-Oram syndrome is an autosomal dominant disorder characterized by skeletal malformations of the upper extremities and CHD, most commonly secundum atrial septal defects but also ventricular septal defects and tetralogy of Fallot. TBX5, encoding a T-box nuclear transcription factor, was identified as the disease gene using linkage analysis and detailed positional cloning, and several missense and nonsense mutations were identified

in multiple affected families (Basson et al., 1997; Li et al., 1997b). TBX5 was found expressed in embryonic human heart and limb, and targeted deletion of Tbx5 in the mouse resulted in defective cardiac development, supporting the conclusion that human TBX5 mutation is responsible for the CHD in Holt-Oram patients (Basson et al., 1997; Bruneau et al., 2001; Li et al., 1997b). TBX1 (DiGeorge Syndrome) DiGeorge syndrome, now synonymous with velo-cardio-facial syndrome, Shprintzen syndrome, and CATCH22, involves a range of variable clinical phenotypes including CHD, neonatal hypocalcemia, cellular immune deficiency, palatal defects, characteristic facies, and cognitive and psychiatric disorders. The cardiac defects are typically conotruncal or outflow tract malformations associated with defective neural crest migration, including interrupted aortic arch, truncus arteriosus, and tetralogy of Fallot, especially tetralogy with pulmonary atresia (McDonaldMcGinn et al., 1999). This is a well established contiguous gene syndrome caused by microdeletion of a critical region located at chromosome 22q11.2 (a minority of DiGeorge patients have an alternate deletion at 10p14–13). The human TBX1 gene,

CHD Gene Discovery by Conventional Genetics

encoding another T-box transcription factor and expressed in neural crest and the developing cardiac outflow tract (conotruncus), was mapped to the center of the DiGeorge critical region (Chieffo et al., 1997). Mice with targeted heterozygous Tbx1 deletion developed cardiac outflow tract malformations and certain extra-cardiac phenotypes characteristic of human DiGeorge syndrome (Jerome and Papaioannou, 2001; Lindsay et al., 2001; Merscher et al., 2001). Furthermore, both the cardiac and extracardiac phenotypes could be rescued by the introduction of an extrachromosomal copy of Tbx1. These findings lend strong support to the hypothesis that human TBX1 mutations may be sufficient to cause the cardiac defects that are characteristic DiGeorge syndrome and, indeed, similar lesions in patients who do not have the syndrome. However, rather surprisingly, isolated TBX1 mutations or deletions have not been found in large numbers of non-deleted patients with conotruncal malformations (Conti et al., 2003; Gong et al., 2001). There is a sole report of TBX1 frameshift and missense mutations in a small kindred and two unrelated individuals with DiGeorge-like phenotypes and no 22q11.2 deletion (Yagi et al., 2003). Thus, a role for TBX1 mutation in non-syndromic conotruncal malformations seems very likely but awaits more definitive proof. NKX2-5 The mammalian homeobox transcription factor Nkx2-5 was first identified as a homolog of the Drosophila tinman gene, essential for specification of heart muscle progenitors (Bodmer, 1993; Komuro and Izumo, 1993). Targeted disruption of Nkx2-5 in the mouse was found to cause arrested cardiac development at the linear heart tube stage (Lyons et al., 1995). Subsequent studies showed that Nkx2-5 interacts with Tbx5, the Holt-Oram factor, to promote cardiomyocyte differentiation (Hiroi et al., 2001). Human NKX2-5 was recognized as a likely candidate gene in the linked region on chromosome 5q35 following genome-wide scans in families with non-syndromic, multigenerational CHD (Schott et al., 1998). Direct sequencing identified a series of mutations that segregated with the CHD. Subsequently, re-sequencing in large cohorts of CHD patients has revealed an appreciable frequency of NKX2-5 mutations or polymorphisms in association with sporadic, apparently non-familial CHD involving a broad range of cardiac defects (Benson et al., 1999; Elliott et al., 2003; Goldmuntz et al., 2001; McElhinney et al., 2003). Somatic mutations of NKX2-5 have also been identified in the hearts of patients with cardiac malformations although causation has not been established (Reamon-Buettner et al., 2004). GATA-4 GATA-4, one of a family of closely related zinc-finger transcription factors, is expressed in the developing heart. Targeted homozygous deletion of Gata4 in the mouse abrogated normal formation of the primitive heart tube in the early embryo (Kuo et al., 1997; Molkentin et al., 1997), while haploinsufficiency and conditional myocardial deletion resulted in a range of cardiac malformations (Pu et al., 2004; Zeisberg et al., 2005).

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Human GATA4 was identified as a candidate gene in an interval at 8p22–23 that was linked to CHD, principally atrial septal defects, in a genome-wide scan in a large multigenerational kindred (Garg et al., 2003). Direct sequencing identified mutations that segregated with non-syndromic CHD in this and a second independent kindred. GATA4 interacts with Tbx5, the HoltOram factor, and with Nkx2-5 to regulate cardiac gene expression, and disease-causing mutations abrogate these interactions (Durocher et al., 1997; Garg et al., 2003). Somatic mutations of GATA4 have also been identified in the hearts of patients with cardiac malformations although again causation has not been established (Reamon-Buettner and Borlak, 2005). ZFPM2/FOG2 FOG2 or ZFPM2, one of a family of zinc finger transcription factors, is expressed in the embryonic and adult heart, co-localizes with GATA-4, and interacts physically with GATA4 to downregulate its activity (Svensson et al., 1999). Mice with targeted homozygous deletion of Zfpm2/Fog2 develop cardiac lesions including tetralogy of Fallot, complete atrioventricular canal, and tricuspid atresia (Svensson et al., 2000; Tevosian et al., 2000). In a candidate gene approach, two missense mutations in ZFPM2/FOG2 were identified in two of 47 patients with nonsyndromic tetralogy of Fallot (Pizzuti et al., 2003). However, these mutations had at most a slight effect on protein function in vitro and, moreover, co-existing mutations in either GATA-4 or NKX2-5 were not excluded. Additional investigation will be required if ZFPM2/FOG2 is to be implicated more definitively in CHD. SALL4 (Okihiro Syndrome) Okihiro syndrome is an autosomal dominant disorder involving multiple congenital anomalies including a characteristic eye movement disorder (Duane anomaly), forelimb defects, deafness, and CHD, typically atrial septal defects, ventricular septal defects, or tetralogy of Fallot. Linkage analysis in three affected pedigrees mapped the disease gene to a 21.6 cM region on chromosome 20 containing SALL4, a novel member of a zinc finger transcription factor family, in which nonsense and frameshift mutations were identified (Al-Baradie et al., 2002). Another group, working contemporaneously, also identified SALL4 as the Okihiro disease locus using a candidate gene approach when they recognized certain non-cardiac phenotypic similarities between Okihiro syndrome and another syndrome caused by mutations in SALL1 (Kohlhase et al., 2002). Recent work has shown that in the developing mouse, Sall4 expression is regulated by Tbx5, the Holt-Oram gene, and the two interact in cardiac and forelimb patterning (Koshiba-Takeuchi et al., 2006). ZIC3 (Heterotaxy) Heterotaxy refers to ambiguous visceral situs in which the positions of normally asymmetric organs such as the liver and spleen are disordered with respect to the midline. Cardiac lesions associated with heterotaxy are often among the most severe

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Genomics of Congenital Heart Disease

encountered, including complex malformations of the atria and ventricles and their venous and arterial connections. Many cases of heterotaxy appear to be sporadic but familial cases, notably with X-linked transmission, have been described. Linkage analysis in one such family mapped the heterotaxy phenotype to a locus at Xq24-q27.1 (Casey et al., 1993), and mutation in the gene ZIC3, a zinc finger transcription factor, was identified by positional cloning (Gebbia et al., 1997). Subsequently, additional ZIC3 mutations have been identified by re-sequencing in other heterotaxy kindreds and isolated individuals with similar CHD phenotypes (Ware et al., 2004). TFAP2B (Char Syndrome) Char syndrome is an autosomal dominant disorder comprising facial dysmorphism, hand anomalies, and patent ductus arteriosus. Linkage analysis in two large multigenerational kindreds mapped the phenotype to a 3.1 cM region on chromosome 6 (Satoda et al., 1999). Candidate gene analysis then revealed missense mutations in TFAP2B, encoding a transcription factor expressed in neural crest, a contributor to the embryogenesis of the ductus arteriosus (Satoda et al., 2000). Examination of genotype–phenotype correlations among several TFAP2B mutations revealed that those in the DNA-binding domain of the protein produce the full Char phenotype while one in the trans-activation domain is associated with patent ductus arteriosus but only mild facial and no limb abnormalities (Zhao et al., 2001). Cell signaling genes Four CHD genes in Table 65.1 encode proteins involved in cell signal transduction, three of which are in the Notch pathways that regulate cell fate decisions during embryonic development. JAG1 and NOTCH2 (Alagille Syndrome) Alagille syndrome is an autosomal dominant disorder comprising hepatic, cardiac, ocular, and vertebral abnormalities in addition to a characteristic facial appearance (Alagille et al., 1987). The cardiac defects are typically right-sided, with valvar pulmonary stenosis the single most common finding and tetralogy of Fallot the most common complex lesion. Linkage analysis and positional cloning in a series of affected families led to the definition of a locus on chromosome 20p12 that included JAG1, encoding a Notch ligand, in which a series of mutations were identified (Li et al., 1997a; Oda et al., 1997). Analysis of JAG1 during mammalian embryogenesis showed expression in the developing heart and vessels, consistent with a critical role in patterning of the right heart and pulmonary vasculature (Loomes et al., 1999). Subsequently, in a candidate gene approach, a JAG1 missense mutation was identified as the cause of non-Alagille autosomal dominant tetralogy of Fallot segregating in a large kindred, shown in Figure 65.1 (Eldadah et al., 2001). This finding further validated the hypothesis that genes associated with certain cardiac phenotypes in genetic syndromes may also be found to be mutated in non-syndromic patients with similar cardiac defects. Recently, in a candidate gene approach, mutations

in the JAG1 receptor NOTCH2 were found to segregate with cardiac and other phenotypes in two rare Alagille families without JAG1 mutations, strengthening the understanding that these phenotypes are attributable to defective Notch pathway signaling (McDaniell et al., 2006). NOTCH1 (Bicuspid Aortic Valve) Bicuspid (two-leaflet) aortic valve is the most prevalent congenital cardiac malformation, estimated to occur in as many as 2–3% of individuals. It is inconsequential in many children, but associated with stenosis of the valve in others. Further, bicuspid valves have a predilection to calcify in later life leading to progressive aortic stenosis and regurgitation. Although most cases appear to be sporadic, families with autosomal dominant bicuspid aortic valve have been identified. Whole genome linkage analysis in one such family and additional studies in other kindreds led to the identification of mutations in the NOTCH1 transmembrane receptor that appear to be responsible both for the congenital valve deformity and later de-repression of osteoblast calcium deposition in these valves (Garg et al., 2005). PTPN11 (Noonan Syndrome) Noonan syndrome is an autosomal dominant disorder comprising dysmorphic facial features, skeletal malformations, short stature, and cardiac abnormalities of which pulmonary stenosis and hypertrophic cardiomyopathy are most characteristic. Genome-wide linkage analysis in a large kindred mapped the Noonan locus to the long arm of chromosome 12 (Jamieson et al., 1994). PTPN11, encoding the SHP-2 intracellular protein tyrosine phosphatase, was identified in this region and, in a candidate gene approach, was found to be mutated in approximately 50% of Noonan cases (Tartaglia et al., 2001); a locus or loci for the remaining cases remains to be mapped. Genotype-phenotype analyses showed that pulmonary stenosis was seen more frequently, and hypertrophic cardiomyopathy less frequently, in Noonan patients with PTPN11 mutations compared to those without (Tartaglia et al., 2002). Extracellular Matrix Protein Genes Mutations in two genes encoding extracellular matrix proteins cause congenital syndromes involving arteriopathies of different forms. FIBRILLIN-1 (Marfan Syndrome) Marfan syndrome is an autosomal dominant disease of connective tissue principally involving the cardiovascular, skeletal, and ocular systems. Cardiovascular manifestations include mitral valve prolapse and regurgitation, presenting in infancy in the most severe cases, and progressive aneurismal dilation of the aortic root with the potential for catastrophic aortic dissection and rupture. Marfan syndrome was first mapped to chromosome 15 using traditional genetic linkage analysis (Dietz et al., 1991b; Kainulainen et al., 1990). At about the same time, immunohistochemical studies performed on skin and dermal fibroblasts from Marfan patients revealed that abnormal microfibrils were

CHD Gene Discovery by Conventional Genetics

(a) l

1

■

785

Tetralogy of Fallot

2

Ventricular septal defect with aortic dextroposition Peripheral pulmonic stenosis 1

ll

2 D20S894 G274D C/T

lll

1

D20S894 G274D C/T

1 3 D G C T 1

lV D20S894 G274D C/T

1 2 D G C C

(b)

2

3

[1] [5] [D] [G] [C] [C]

Congenital heart disease (unspecified)

3 5 G G T T

Obligate carrier

3

4

5

6

7

8

2 4 1 5 G G D G C T C T

GG C T

D G C T

G G C T

G G C T

D G C T

G G C T

D G G G C T C T

G G C T

4

5

6

7

8

9

G G T C

D G C T

G G G G T T T T

G G T T

2

3

9

D G C C

10

11

12

13 1 5 D G C T 10

D G C C

14

15

16

1 2 G G D G G G T C C T C T 11

G G T C

17 1 5 D G C T

12 1 2 D G C C

II-3 III-1 III-2 III-3 III-4 III-5 III-8 III-10 III-12 III-13 III-15 III-16 III-17 IV-1 IV-4 IV-5 IV-6 IV-7 IV-8 IV-9 IV-10 IV-11 IV-12

G274D allele wild-type allele

Figure 65.1 Alagille kindred. (a) Pedigree of four generations of a family with Alagille syndrome. Haplotypes include marker alleles of D20S894 (1–5), glycine (G) or aspartic acid (D) at the mutation site (G274D), and cytosine (C) or thymidine (T) at nucleotide 765, a silent polymorphism (information in brackets was inferred). All individuals with a clinical phenotype inherited the D274 allele. (b) Genomic DNA from the indicated individuals was hybridized with radiolabeled probes specific for the mutant G274D and wild-type coding sequences. (Reprinted, with permission, from Eldadah et al., 2001.)

deficient in the protein fibrillin (Hollister et al., 1990). Then, taking a candidate gene approach, it was possible to localize FIBRILLIN-1 to 15q21.1 (Magenis et al., 1991) and to demonstrate conclusively that mutations in this gene were tightly linked to the Marfan phenotype in several affected families (Dietz et al., 1991a; Lee et al., 1991). Recognizing this, fibrillin has long been assumed to be critical in the aortic wall and other connective tissues as a structural protein. However, recent work has revealed that fibrillin has a regulatory role in TGF- signaling and dysregulation of this pathway may instead underlie Marfan pathogenesis (Habashi et al., 2006; Neptune et al., 2003). ELASTIN (Williams Syndrome) Williams or Williams-Beuren syndrome is a contiguous gene syndrome involving a 1.5–2 Mb microdeletion of chromosome 7q11.23. The phenotype comprises characteristic endocrine, cognitive, and facial features in association with areas of arterial narrowing, most typically supravalvar aortic stenosis (SVAS). SVAS is a constriction of the proximal aorta above the valve that increases the pressure work of the left ventricle, leading potentially to ventricular hypertrophy and congestive heart failure. On histopathological examination the architecture of the aortic wall elastic lamina is disrupted. The specific gene responsible for

SVAS in Williams syndrome remained unknown until a nonsyndromic form of autosomal dominant SVAS was recognized in a number of families. Linkage studies and candidate gene analysis in one such kindred identified ELASTIN as the disease gene in non-syndromic SVAS (Ewart et al., 1993). As this gene resides in the Williams critical region on 7q11.23, ELASTIN hemizygosity appears to underlie the characteristic arteriopathy of Williams syndrome. Chromosomal Aneuploidies Associated with CHD Down syndrome (trisomy 21) is the most prevalent genetic syndrome causing congenital heart malformations. It is most closely associated with endocardial cushion defects such as complete atrioventricular canal. The gene or genes on chromosome 21 that underlie the predilection for cardiac defects in Down syndrome are the subject of ongoing intense investigation (Reeves, 2006). Putative cardiac genes for trisomy 13 and trisomy 18, much less common than trisomy 21 but also involving heart defects, similarly remain to be elucidated. In Turner syndrome (45,XO), haploinsufficiency of one or more as yet unidentified genes on the X chromosome is inferred to cause coarctation of the aorta, the cardiovascular lesion most frequently associated with this condition.

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Genomics of Congenital Heart Disease

GENOMIC STRATEGIES FOR CHD GENE DISCOVERY It is evident from the foregoing overview that conventional genetic methods have been effective tools in the discovery of CHD genes and mutations. However, these methods are limited to the extent that they allow, in general, for the study of only one or a few genes at a time. The advent of genomic technologies has afforded opportunities in CHD, as in other fields, to analyze thousands of genes if not the entire genome simultaneously with the promise of accelerated discovery. Large Scale cDNA Sequencing The first systematic effort to sequence large numbers of cardiac cDNAs was pioneered in the early 1990s by Liew and coworkers (Hwang et al., 1994; Liew et al., 1994). A cDNA library was constructed from human fetal heart and more than 3000 clones identified, nearly half of which were novel at the time by comparison to existing public databases. Early experiments in array transcription profiling, in the form of dot blot filters, demonstrated differential fetal versus adult expression of some of these genes. These investigators expanded their analysis to encompass multiple cardiac libraries and found more than 43,000 expressed sequence tags using automated sequencing; an additional 41,000 were culled in silico from existing databases (Hwang et al., 1997). Further bioinformatics analyses allowed for the identification of potential tissue-, developmental stage-, and disease-specific expression of subsets of these genes. Subtractive Hybridization and Differential Display Understanding that developmental stage-specific genes are crucial to cardiac morphogenesis, additional genomic strategies have been brought to bear to identify them. Srivastava and coworkers developed a novel subtractive hybridization technique, termed subtractive and selective PCR amplification, to select genes expressed specifically in the earliest embryonic cardiac precursor cells (Gottlieb et al., 2002). Among these was Bop, a transcriptional repressor that they then showed to be necessary for normal development of the right ventricle, potentially through regulation of the HAND2 transcription factor, also critical for formation of the right ventricle (Srivastava et al., 1997). These and other investigators also used subtractive and differential display techniques to compare gene expression in Hand2/ versus wild-type mice and elucidate cardiac molecular pathways downstream of HAND2 (Yamagishi et al., 1999). One such pathway gene mapped to human chromosome 10p13-p14, which is deleted in patients with a variant of DiGeorge syndrome that also includes cardiac malformations (Villanueva et al., 2002). Microarray Transcriptional Profiling More recently microarray technology has been used to study gene expression patterns related to CHD. Some of these

experiments have focused on regional differences in cardiac gene expression in the early vertebrate embryo or in the adult heart (Afrakhte and Schultheiss, 2004; Zhao et al., 2002). Quertermous and coworkers undertook a comprehensive analysis of gene expression patterns in the four chambers and interventricular septum of the mouse heart using a 42,300 element mouse cDNA microarray representing more than 25,000 unique genes and expressed sequence tags (Tabibiazar et al., 2003). This strategy yielded large sets of chamber-specific genes, some in gene families well established to be involved in cardiac development and others more novel. In the first genome-wide array analysis comparing normal and congenitally malformed human hearts, Sperling and coworkers hybridized human unigene cDNA arrays with right ventricular and/or right atrial RNA probes prepared from normal donors and from patients with simple ventricular septal defects, tetralogy of Fallot, and other defects leading to right ventricular hypertrophy (Kaynak et al., 2003). As represented in Figure 65.2, the levels and probability values for the differentially expressed genes constitute lesion- and chamber-specific genomic fingerprints. Similar microarray analyses of gene expression in tetralogy of Fallot and other lesions producing right ventricular outflow obstruction have followed (Konstantinov et al., 2004; Sharma et al., 2006). The identification of transcriptional patterns associated with particular congenital malformations is intriguing. The extent to which these reflect the primary genetic etiologies of these lesions versus secondary perturbations – due to cyanosis, pressure overload, volume overload, chamber interactions, for example – merits further investigation. Microarray analysis is an increasingly important tool for the elucidation of molecular pathways that lie downstream of key transcriptional regulators of cardiac development and CHD, including Nkx2-5, Tbx5, and GATA6. In a study of gene expression in the myopathic ventricles of conditional Nkx2-5/ knockout mice, genes associated with myocardial cell proliferation and trabeculation were markedly dysregulated (Pashmforoush et al., 2004). Among these was a gene previously established to lie downstream of Nkx2-5, as well as a series of others newly associated with this pathway. Other investigators have profiled gene expression in a mouse model of DiGeorge syndrome with Tbx1 haploinsufficiency (Prescott et al., 2005) and in cardiacderived cell lines with or without overexpression of TBX5 or GATA6, revealing multiple putative downstream target genes in each pathway (Plageman and Yutzey, 2006; Alexandrovich et al., 2006). Mutagenesis and Phenotypic Screens Genomic strategies for CHD gene discovery are complemented by “phenomic” approaches in the mouse. The Jackson Laboratory has undertaken a phenotype-driven project to identify new murine mutations leading to the development of cardiovascular disease (Svenson et al., 2003). In collaboration with the Laboratory of Developmental Biology at the National Heart Lung and Blood Institute, non-invasive two-dimensional and Doppler echocardiography was used to screen 7546 mouse fetuses from

(1) (1) (1)

(1)

(2)

(2)

(2)

(2)

RV vs LV

A vs V

VSD in RA

RVH in RV

TOF in RV

Cytogenetic and Molecular Genetic Testing

RPL36 KDELR2 H19 RPS7 PSMB1 RPS11 FLJ10407 ARHGDIG FKSG14 AMMECR1 PDE1A S100A13 TBXAS1 SYTL2 LOC51189 THRSP OGDH LRPPRC FLJ10350 DKFZP434P1735 BECN1 KIAA0010 CS GNAS HIRIP3 KLF12 ADD2 GSN OAZ2 SLC16A5 KIAA0846 MGC20504 INPPL1 LW-1 SNX2 RPS6KA5 COX7A2L PRX MGC2664 ATP5F1 FLJ20312 KIAAO144 FSTL1 TNFRSF6 ST13 HSPC072 MGC2731 CARD11 RCD-8 INE2 FLJ13153 SIAT9 LOC55831 SCD LOC64116 FLJ10808 EEF1A1 IL1ORB CPE S100A11 SULT1C1 D10S170 RPS11 GAPD RIPK3 RPL36 SLC4A7 NDUFB9 RPL18A SIAHBP1 GABARAPL1 ARVCF KCNS2 FLJ14600 RAB38 PIPPIN SEC236 OA48-18 HSA011916 D1S155E HYPK FH PER2 RPLPO ARTS-1 COX6B CFL2 CAPNS1 PPAP2C LGALS1 COX8 SECRET

(2) CALU VWF DKFZP560D1346 KIAA1200 RPL13A RPL31 (1) FLJ10350 (1) DIA1 (1) NDUFS7 (1) VPS35 RPS14 SOHA (1) RPL37A (1) NDUFB10 (1) NHP2L1 NDUFB4 (1) TNNI3 (1) TNNI1 DNAH9 MGC12921 KIAA1437 COX6C ADRM1 TESK1 DKFZP564K247 RTN4 RECQL4 GDI1 MGC10600 FLJ22623 PLA2G2A Z391G C3 IFI30 PAM TAGLN DF ADAM9 LOC58496 MYL4 DKK3 HSKM-B MYL2 FHL2 MYL3 PVR CKMT2 C14orf3 FABP3 ATP5F1 C20orf35 FHL1 TPM1 PFKP KIAA1695 LPL LPL NUDT4 LPL KIAA1909 FER1L4 MGC3207 SLC26A8 PCL1 PTPN18 ELAC2 HSF28P PROX1 C7 LXN ACAC8 TRAM MAFF PTPN4 MGC4562 IFITM3 UBE2G2 FLJ14431 ING5 EPSBR1 TBC1D2 DKFZP76112123 MacGAP PAWR LOC51316 PFAS TEKT2 FLJ20245 BRDG1 H3FF HOOK2

6 4 2 up-regulated

0 -

2 4 6 down-regulated

Figure 65.2 Transcription profiling. Graphic display of gene expression levels and probability values profiled across several cardiac phenotypes using microarray analysis. Gene names are listed. Each comparison is represented by a column and each gene by a row. The –log10(P) for each gene in each comparison is color coded in yellow-to-red for upregulated and yellow-to-green for downregulated genes, for example a value of 2 represents P0.01 and is color coded in red if the gene is upregulated. (Reprinted, with permission, from Kaynak et al., 2003.)

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262 chemically (N-ethyl-N-nitrosourea) mutagenized families (Yu et al., 2004). Remarkably, congenital heart defects were identified in 124 of these families, among them several with virtual phenocopies of human genetic syndromes including HoltOram and DiGeorge syndromes. Chromosomal localization of these mutations using polymorphic microsatellite markers revealed that they mapped to loci other than Tbx5 and Tbx1, the genes previously linked respectively to these syndromes. Thus it is likely that novel CHD genes, some in established molecular pathways and others in entirely new pathways, will be discovered in these and other chemically mutagenized mouse models.

CYTOGENETIC AND MOLECULAR GENETIC TESTING Cytogenetic and molecular techniques used in the discovery of genes associated with CHD also have practical utility when evaluating the underlying cause of CHD in a patient. Table 65.2 provides a comparison of available diagnostic genetic testing platforms and their applications in CHD. Structural chromosome anomalies may explain a finding of CHD in conjunction with other congenital anomalies or developmental issues. A standard metaphase karyotype (450–550 bands) is useful for identifying extra or missing chromosomes, whereas high-resolution banding (550–800 bands) is effective at identifying more subtle structural abnormalities including deletions, duplications, translocations, and inversions. Fluorescence in situ hybridization (FISH), another cytogenetic technique, has greater specificity and resolution for identification of microdeletions or duplications that could be missed in a karyotype analysis. FISH is routinely used to detect microdeletion syndromes that are associated with CHD including velocardiofacial syndrome (deletion of 22q11.2) and WilliamsBeuren syndrome (deletion of 7q11.23). Array-based comparative genomic hybridization (CGH) is increasingly performed as an adjunct to standard high-resolution karyotype analysis. Array-CGH can detect abnormalities as small as 80 kb whereas high-resolution chromosome analysis has a minimum resolution of 5 mb. Array-CGH typically includes subtelomeric evaluation to analyze the most distal ends of the chromosomes. Array-CGH cannot detect balanced rearrangements or abnormalities in the genome that are not covered by the selected DNA segments in the microarray. However, arrayCGH may be a useful means of evaluating a patient with CHD for multiple genetic abnormalities at once. Lastly, targeted mutation analysis of a known gene, whether by sequencing, array hybridization, or multiplex ligationdependent probe amplification (MLPA), can be useful in confirming a clinical diagnosis in a patient whose features are highly suggestive of a particular genetic syndrome. Targeted mutation analysis is complicated by locus heterogeneity, where it is likely that other causal genes exist but have not been discovered. Thus,

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TABLE 65.2

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Genomics of Congenital Heart Disease

Cytogenetic and molecular diagnostic testing in CHD

Assay platform

Genetic abnormality

Resolution

Examples

Standard metaphase karyotype (450–550 bands)

Aneuploidies (monosomies, trisomies)

5–10 Mb

Trisomy 13, 18, 21

Karyotype with highresolution banding (550–850 bands)

Chromosome structural anomalies: duplications, translocations, interstitial or terminal deletions

3–5 Mb

Trisomy 13, 18, 21, other chromosome structural anomaly syndromes

Fluorescence in situ hybridization (FISH)

Subtle chromosome structural anomalies: microdeletions, microduplications, subtle translocations

30–40 kb in clinical setting; as small as 1 kb in research setting

Deletion syndromes such as Velocardiofacial Syndrome (del22q11.2), Williams-Beuren Syndrome (del7q11.23), some instances of Noonan syndrome (PTPN11)

Array comparative genomic hybridization (array-CGH)

Identifies deletions and duplications in regions of the genome that are represented on the microarray

80 kb clinical setting, smaller in research setting

Ability to evaluate multiple deletion/duplication syndromes simultaneously

Multiplex ligation-dependent Detects very small genetic probe amplification (MLPA) aberrations, to the level of a single nucleotide change

Single base pair alterations

Small deletions below level of detection by FISH

Targeted DNA mutation detection (sequencing)

Single base pair alterations

Holt-Oram syndrome (TBX5), Alagille syndrome (JAG1), Char syndrome (TFAP2B), Noonan syndrome (PTPN11)

Single gene mutations, including point mutations, deletions, duplications within the gene

a negative test does not rule out the condition and provides limited recurrence risk information to the family.

MEDICAL EVALUATION AND COUNSELING RECOMMENDATIONS A thorough medical evaluation, including family history, physical exam, and genetic testing, can aid in distinguishing isolated or sporadic CHD from syndromic CHD. This distinction can provide critical information for cardiac management as well as screening for extra-cardiac issues. Identification of an underlying genetic cause provides answers to the family and aids in recurrence risk counseling. Lastly, given the variability in phenotypic expression of many of the genetic syndromes associated with CHD, knowing the genetic cause and inheritance pattern helps identify at-risk relatives who may need cardiac evaluation and potential intervention. The following approach is recommended to the treating clinician, also outlined in Figure 65.3. Family history: A three-generation family history can help to direct genetic testing. Documentation of details related to CHD in other family members is critical. It may be useful to request cardiac evaluations of other family members to rule out subtle cardiac defects. Additionally, it is important to document any history of other birth defects, learning disabilities, mental retardation, multiple miscarriages, and still births. Physical exam and clinical imaging: During the physical exam, attention should be paid to subtle dysmorphic facial features,

as well as the involvement of other organ systems. The exam should also focus on ear and eye abnormalities, skeletal issues such as limb reduction defects, gastrointestinal and urologic defects. Standard radiological studies may provide unique diagnostic clues; spine anomalies, aortic arch anomalies, and stomach situs can often be seen in a routine chest radiograph. For example, a right aortic arch, in combination with the diagnosis of tetralogy of Fallot and a family history of learning disabilities is suggestive of velocardiofacial syndrome. Genetic testing: If the diagnosis of CHD was made prenatally, the patient may have had a standard karyotype performed via amniocentesis to rule out obvious chromosomal abnormalities. It is reasonable to order a high-resolution chromosome analysis if this has not been done, particularly if the infant or child has dysmorphic features or other congenital anomalies. If a chromosome abnormality is detected, it is recommended that the family consult with a genetic specialist for counseling and evaluation of other family members. Genetic counseling: In the case of syndromic CHD, the clinician can provide referrals to appropriate specialists who can help evaluate and manage non-cardiac manifestations. A discussion about the genetic cause, inheritance pattern and available genetic testing for at-risk family members is imperative, as well as options for prenatal diagnosis in future pregnancies. A referral to a genetics specialist may be valuable in addressing the various questions and concerns of these families. If the family history is not indicative of familial CHD and the individual does not have features that are suggestive of a syndromic cause, the clinician is

Conclusion

 

 

 





Figure 65.3

  

High resolution cytogenetic tests Specific molecular genetic tests as clinically indicated



789

Detailed family medical history Comprehensive physical exam

Family history positive for CHD Multiple congenital anomalies  Dysmorphic facial features  Likely genetic syndrome

Abnormal result

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Family history negative for CHD No additional anomalies/disease  No dysmorphic features Apparently isolated/sporadic CHD

 

Discuss empiric recurrence risks Consider fetal echocardiogram for future pregnancy

Normal result

Referral to genetics specialist for counseling and possible further genetic testing

Strategy for genetic evaluation of patient with CHD.

encouraged to discuss empiric evidence of an increased risk of CHD in subsequent children. The family can also be informed about fetal echocardiography to monitor future pregnancies.

CONCLUSION Although there has been substantial progress in the identification of CHD genes and mutations using conventional genetic approaches, the relative paucity of informative families with multigenerational CHD and the phenotypic heterogeneity within these families represent significant challenges. Also challenging for large scale CHD gene expression analyses are the exceedingly limited numbers, amounts, and quality of relevant cardiac tissues available to genomics investigators at this time. The challenges of recruitment of informative CHD families and collection of biological reagents are being met, in part, through the creation of cardiovascular genetic registries. These are repositories of patient and family DNA samples and patient cardiac tissues when available.These biological materials are highly annotated with exhaustive clinical phenotypic information and detailed family medical histories stored in searchable databases. These resources are then made available to laboratory-based investigators for disease gene discovery and genotype-phenotype correlation.

Genomic research in CHD is in many ways in its infancy. Although a substantial number of causative genes have been identified, it is clear that others remain to be discovered. Obvious examples include those responsible for the cardiac defects in the chromosomal aneuploidies trisomy 13, trisomy 18, trisomy 21, and Turner syndrome (45,X). But even in non-syndromic CHD there are likely to be multiple other causative genes and mutations yet to be discovered. Large scale genetic studies including trio analyses and genome-wide association studies are being brought to bear in CHD using robust collections of clinically annotated DNAs where available. Even where single gene mutations have been identified, it is also clear that CHD represents complex genetic traits. Within affected families there is typically incomplete penetrance (not every individual who carries the mutation has CHD) and variable expressivity (different individuals with the same mutation may have different CHD lesions). There is a widely held expectation that gene-gene interactions, involving modifying loci, will be responsible at least in part for this phenotypic heterogeneity. Future genomic investigation will need to focus on the identification of these genes, many residing within established CHD molecular pathways and others possibly in novel pathways. Gene-environment interactions may also underlie phenotypic heterogeneity. Elucidation of these mechanisms may reveal an

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entirely new dimension in the understanding of CHD etiology and potentially its prevention. There is also growing expectation that genetic factors will be pivotal not only in the development of CHD but also in its outcomes and response to therapy. In many instances the same CHD genes that are expressed in the early embryo are also expressed in the adult heart; thus, the same mutations may impact the development of critical problems in the longer term including ventricular dysfunction and arrhythmia (Pashmforoush et al., 2004). In this regard, it is noteworthy that among patients who are born with apparently identical cardiac defects and who undergo identical operations, late outcomes may be very different. These outcomes may be viewed as components of complex CHD phenotypes; thus, there is again every expectation that they will be determined at least in part by modifying genes. Similarly, it is likely that genetic factors play an important role in the response to and recovery from cardiopulmonary bypass surgery, and in the efficacy and toxicity of key drugs – inotropes, afterload reducers, diuretics, anti-thrombotics,

anti-arrhythmics – which vary considerably in CHD patients. Discovery of these genes and their sequence variants will be the goal of future genomic and pharmacogenomic investigation in CHD. Now more than just tools for the research laboratory, genomic technologies have rapidly increasing applications in the detection and diagnosis of CHD. Translation of genetic knowledge into prevention and treatment of CHD, for example by direct manipulation of genes and their molecular pathways in patients, is a promise for the future.

ACKNOWLEDGEMENTS The authors thank Christine Seidman and Jon Seidman for helpful discussions and Kristin Tassinari for expert administrative assistance. This work was supported in part by NIH P50 HL074734 and by the Boston Children’s Heart Foundation. The authors have no conflicts of interest related to this publication.

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66 Genomics of Perioperative and Procedural Medicine Simon C. Body, Mihai V. Podgoreanu and Debra A. Schwinn

INTRODUCTION Perioperative medicine is a unique setting where acute, robust, and generalized (whole body) proinflammatory cascades are initiated on a reproducible basis (Chang, 2003; Gaudino et al., 2003b). Ambulatory chronic diseases such as coronary artery disease (CAD) (Erren et al., 1999), diabetes (Duncan and Schmidt, 2006), and congestive heart failure are increasingly recognized as having inflammatory components (Sampietro et al., 2005; Weber, 2005); however such inflammation is generally lowgrade, chronic, and often well-localized. In contrast, acute surgical interventions such as heart surgery with cardiopulmonary bypass (CPB) increase serum catecholamine levels 2–10 fold above baseline (Mielck et al., 2005;Voisine et al., 2004), superimposing potent acute inflammation on top of pre-existing pathophysiology associated with chronic disease (Podgoreanu and Schwinn, 2005). Procedures such as major surgery, CPB, and intra-aortic balloon counterpulsation (used to support circulation during severe heart failure or myocardial ischemia) are all capable of initiating such extensive and generalized inflammatory responses. As previous chapters have described, cardiovascular disease is highly influenced by individual patient genomic, transcriptomic, proteomic, and metabolomic profiles. Inflammatory pathways are no exception. In the setting of surgical and traumatic procedures, multiple single nucleotide polymorphisms (SNPs) present in inflammatory signaling pathway genes lead to Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 794

altered expression of relevant proteins such as increased C-reactive protein (CRP) (Grocott et al., 2005), interleukin-6 (IL-6) (Grocott et al., 2005; Stafford-Smith et al., 2005), platelet membrane glycoprotein IaIIa (GPIaIIa) (Welsby et al., 2005), tissue factor (TF) (Welsby et al., 2005), platelet glycoprotein Ib (GPIb) (Welsby et al., 2005), as well as decreased tissue factor pathway inhibitor (TFPI) (Welsby et al., 2005), and others (Brull et al., 2001; Burzotta et al., 2003; Cotton et al., 2000; Galley et al., 2003; Koch et al., 2003; Li et al., 2005; Rinder et al., 2002). Recent studies on the role of genetic variability in the perioperative period suggest that while inflammation is a necessary requisite for healing (Serhan and Chiang, 2004; Serhan and Savill, 2005), patients with proinflammatory SNPs (and the respective biomarkers) are at higher risk for adverse outcomes after their procedure (Podgoreanu and Schwinn, 2005). Characteristic phenotypes studied by perioperative genomics include immediate postoperative adverse events (incidence/severity of organ dysfunction) (Table 66.1), as well as long-term outcomes. Overall, an individual’s genetic susceptibility to adverse perioperative events stems not only from genetic contributions to the development of co-morbid risk factors (like CAD) during the patient’s lifetime, but also from genetic variability in specific biological pathways participating in pathophysiological events during and after surgery. Thus, the term perioperative genomics is justified by a combination of unique environmental insults and postoperative phenotypes that characterize surgical and critically ill patient populations. This chapter reviews these findings and explores their implication for genomic medicine, notably in the postoperative environment. Copyright © 2009, Elsevier Inc. All rights reserved.

Why Perioperative Insults are not Equivalent to Chronic Ambulatory Disease

TABLE 66.1 Phenotypes

Categories of Immediate Perioperative

Immediate perioperative outcomes Mortality Myocardial infarction Heart failure Stroke or cognitive dysfunction Arrhythmia Bleeding Renal dysfunction or failure Lung injury Sepsis

WHY PERIOPERATIVE INSULTS ARE NOT EQUIVALENT TO CHRONIC AMBULATORY DISEASE In evolution and individual lifetimes, inflammation plays a crucial role in the physiologic and adaptive response to morbidity and mortality due to hemorrhage, infection, tissue injury, and repair. These phylogenetically important adaptive processes are protective and usually appropriate. However, failure to regulate the inflammatory response can be maladaptive. For example, an inability to develop a sufficient inflammatory response may predispose an individual to infection and some cancers, to a lesser extent than seen in some of the severe genetic immunodeficiencies (Coussens and Werb, 2002). Similarly, these short-term appropriate inflammatory responses can be deleterious when persistent, excessive or generalized, such as their role in atherosclerosis and myocardial ischemia (Libby, 2002; Tracey, 2002). Furthermore, individual variation in these responses is evident in the clinic and laboratory, as described in the remainder of this text. It has long been recognized that the inflammatory response to surgery and invasive procedures varies between individuals. Modulation of perioperative inflammation may be one mechanism by which genetic variation influences the occurrence of adverse postoperative outcomes. In ambulatory populations, polymorphisms of inflammatory genes have been shown to be associated with an increased risk of cardiovascular disease. For example, the proinflammatory cytokine IL-6 is important in the pathogenesis of atherosclerosis via its role in stimulating endothelial activation (Yudkin et al., 1999), vascular smooth muscle cell proliferation (Nabata et al., 1990), leukocyte recruitment (Romano et al., 1997), and complement activation (Montz et al., 1991). Furthermore, elevated IL6 levels have been correlated with the development and severity of CAD (Erren et al., 1999; Ridker et al., 2000), atherosclerotic

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plaque instability (Biasucci et al., 1999), and adverse cardiovascular outcomes (Biasucci et al., 1999). One potential mechanism for interindividual variability is that the C allele of the G–174C IL-6 promoter polymorphism is associated with elevated serum IL-6 levels and an increased risk of adverse cardiovascular outcomes in several populations and diseases (Humphries et al., 2001; Jones et al., 2001). This polymorphism has also been linked to increased IL-6, fibrinogen, and CRP levels and atherosclerosis in elderly patients (Jenny et al., 2002). This latter observation emphasizes the strong, known, inter-relationships between various inflammatory pathways with hemostasis pathways. Similarly the A allele of the TNF- gene G–308A promoter polymorphism is associated with elevated levels of the proinflammatory cytokine TNF- (Warzocha et al., 1998) and an increased risk of CAD in women with type 2 diabetes (Vendrell et al., 2003). Thus, the complexity of pathway inter-relationships adds a level of difficulty in analysis of perioperative genomic findings. Although many studies have demonstrated the influence of genetic variation on the inflammatory response and risk of cardiovascular disease in ambulatory patient populations (Marenberg et al., 1994; Murata et al., 1998), there is considerably less knowledge of similar relationships between genetic influences and perioperative morbidity in patients undergoing cardiovascular surgery, angioplasty, or other cardiac procedures. Coronary artery bypass graft (CABG) surgery with CPB is well known to invoke a systemic inflammatory response characterized by complement, leukocyte and platelet activation, and the formidable expression of numerous proinflammatory mediators. Activators of this immune response include direct surgical or device trauma, exposure of circulating blood to the bioincompatible surfaces of the extracorporeal circuit and devices (Laffey et al., 2002), gut endotoxin release (Rocke et al., 1987), and reperfusion of ischemic tissues (Collard and Gelman, 2001; Shernan, 2003). Although inflammation in general is thought to be an adaptive response, in certain individuals the systemic inflammatory response to surgery may be severe enough to be associated with significant perioperative and long-term clinical morbidity, including impaired hemostasis, ventricular failure, myocardial infarction (MI), stroke, and multisystem organ dysfunction (McBride et al., 1996; Ross, 1999; Salo, 1992). Although important genetic determinants of complex cardiovascular diseases have been identified using genome-wide linkage analysis, often such studies rely on data from multigenerational families. This is impractical in studies investigating perioperative outcomes because entire families do not undergo the operative insult. Furthermore, perioperative adverse events likely involve multiple disease genes and complex environmental factors. The only available studies published thus far have utilized candidate polymorphisms in cohorts of individuals undergoing the same operation, examining identified cases and unaffected controls within the same cohort. Candidate genes have generally been selected based on a priori hypotheses about potential etiological roles in adverse outcomes, current understanding of pathophysiological processes from literature reviews, biochemical/physiologic pathway analysis, and expert opinion.

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Further candidate genes are identified in more unbiased ways using microarray gene expression profiling in human tissues, surgical animal models, and genome-wide scans. Growing evidence suggests that variability in gene expression levels underlies susceptibility to complex disease and is determined by regulatory polymorphisms affecting transcription, splicing, and translation efficiency in a tissue- and stimulus-specific manner. Identification of disease-causing genes involves integrating data on DNA sequence variation and gene expression variation in well-phenotyped surgical patient populations.

PERIOPERATIVE ATRIAL FIBRILLATION Heritable forms of atrial fibrillation (AF) occur in the ambulatory non-surgical population (Brugada, 2005; Fox et al., 2004; Hong et al., 2005; Ogimoto et al., 2004; Olson et al., 2005; Ravn et al., 2005; Xia et al., 2005; Zhang et al., 2005) and levels of circulating markers of inflammation such as CRP are strongly correlated with development of AF (Aviles et al., 2003), although this may not be a causal process. Estimates of 5–30% of ambulatory AF having a genetic origin have been made (Brugada, 2004; Fox et al., 2004). Rare dominant mutations of cardiac potassium channel and gap junction proteins make up the majority of identified genes but explain a low proportion of the attributable genetic risk in the ambulatory population (Gollob et al., 2006; Roberts, 2006a, b). Such limited identification encourages further work. Several groups have investigated transcriptional responses to AF using right atrial appendage tissue obtained at the time of cardiac surgery. mRNAs derived from genes in oxidative pathways, remodeling, contractile proteins, and ion channels have been observed to be differentially regulated (Kim et al., 2003; Ohki-Kaneda et al., 2004; Ohki et al., 2005). Although it is tempting to ascribe observed differences in transcriptional profile to being causative of AF, it is far more likely that these differences are a response to AF, rather than etiologic (Barth et al., 2005; Kim et al., 2005). Prominent responses to AF are the appearance of apoptosis, a transcriptome that resembles ventricular tissue, and upregulation of injury pathways causing hypertrophy and fibrosis (Barth et al., 2005; Bukowska et al., 2006; Kim et al., 2003, 2005; Korantzopoulos et al., 2006; Thijssen et al., 2002). Further issues are the high incidence of coronary and valvular heart diseases in the source cardiac surgical populations that impose an altered transcriptional background. New onset AF occurs in approximately 30% of patients undergoing cardiac surgery, despite most regimens of drug prophylaxis (Kailasam et al., 2005; McKeown and Epstein, 2005). AF is associated with longer hospital stays, increased cost, higher incidence of stroke, and poorer long-term survival (Albahrani et al., 2003; Lahtinen et al., 2004; Quader et al., 2004). Nongenetic risk factors for development of AF after cardiac surgery include older age, poor left ventricular function, valvular heart disease, type of venous cannulation, use of CPB, and right coronary artery atherosclerosis (Amar et al., 2004; Hill et al., 2002a, b;

Hogue et al., 2005; Likosky et al., 2003; Mathew et al., 2004). Because CABG is a profound inflammatory stimulus, inflammatory genes and others influencing the severity of inflammation are natural targets for study. Further indirect evidence of an inflammatory role in the genesis of AF is provided by the observation that an exaggerated rise in white blood cell count after CABG surgery is associated with a greater risk of developing AF (Abdelhadi et al., 2004). The pathogenesis of perioperative AF is complex, even more so than AF developing in the ambulatory population. This is because perioperative AF is associated with older age, surgical technique, coronary and valvular heart disease, along with electrolyte, inflammatory and possibly genetic factors. Specific evidence for a genetic role in postoperative AF is sparse. Promoter polymorphisms of the IL-6 gene are strongly related to circulating levels of IL-6 in both ambulatory and post-CABG surgery populations (Brull et al., 2001; Burzotta et al., 2001; Gaudino et al., 2003a; Kelberman et al., 2004; Lai et al., 2003). A single research group has assessed the association between IL-6 and AF (Gaudino et al., 2003a). These investigators have shown a relationship between the G–174C IL-6 promoter polymorphism, IL-6 levels, and several clinical outcomes after CABG surgery including AF (Burzotta et al., 2001; Gaudino et al., 2003a). GG homozygosity of the G–174C promoter polymorphism independently predicted postoperative AF (risk ratio  3.3). In addition, GG homozygotes had higher circulating levels of IL-6 2 days after surgery, but surprisingly, not at any other measured time-point. Problems in this study’s power, design and analysis, limit its ability to establish a strong association between the polymorphism and the risk of AF. There is a contradictory lack of association between CRP, which is strongly regulated by IL-6, and AF in women having cardiac surgery (Hogue et al., 2006). Overall, this field is barely touched, yet would appear to be highly amenable to investigation because of the high incidence of postoperative AF and strong inflammatory regulation of transcription.

PERIOPERATIVE VENOUS AND ARTERIAL THROMBOSIS Extensive effort has been devoted to identifying genotypes associated with hypercoagulable states that increase the risk of postoperative deep venous thrombosis, limb ischemia, vascular graft occlusion, pulmonary embolism, and stroke. One example is the Leiden point mutation in factor V (Franco and Reitsma, 2001) (FVL), the most common inherited prothrombotic factor in Caucasians (Donahue, 2004a). FVL is associated with an increased risk of venous thromboembolism, stroke, and CABG thrombosis (Franco and Reitsma, 2001). Rarer mutations within the anti-thrombin III, protein C, and protein S genes along with FVL, together account for approximately 60% of deep venous thromboses (Donahue, 2004a, b). The most common form of arterial thrombosis, MI, is a multifarious intersection of endothelial injury, lipid metabolism, inflammatory, and thrombotic pathways. These have been

Perioperative Venous and Arterial Thrombosis

investigated in ambulatory populations using linkage studies with extraordinarily little replication and identification of only two genes (Topol et al., 2006). Similarly, over 200 candidate gene studies have examined ambulatory MI and have been cursed with similar lack of replication. To date, there are only two published genome-wide studies of MI although other adequately powered studies are underway (Ozaki et al., 2002; Shiffman et al., 2005). Reports from animal models, linkage, twin, and population association studies reveal high heritability (Zdravkovic et al., 2002) in incidence and progression of CAD (Fischer et al., 2005) and death from CAD (Zdravkovic et al., 2002). Similarly, genetic susceptibility to MI has been established (Broeckel et al., 2002;Yamada et al., 2002). Though these studies do not directly address heritability of adverse perioperative myocardial events, they suggest a strong genetic contribution to risk of adverse cardiovascular outcomes in general (Figure 66.1). Postoperative MI is a common (7–15%), albeit not assiduously identified, event after cardiac and vascular surgery (Mangano, 1997), with a high mortality (30–50%) (Lindenauer et al., 2005). The “multifarious intersection” of endothelial injury, lipid metabolism, inflammatory and thrombotic pathways in MI is markedly altered by surgery, and especially cardiac surgery, with endothelial injury, prothrombotic processes, and inflammation all present after surgery. However, there are a paucity of studies directly relating genetic risk factors to adverse perioperative myocardial outcomes, specifically after CABG surgery (Botto et al., 2004; Delanghe et al., 1997; Ortlepp et al., 2001; Volzke et al., 2002, 2005). In the setting of cardiac surgery, postoperative MI involves three major converging pathophysiological processes (Figure 66.2) (Podgoreanu et al., 2006). In non-cardiac surgery, pathophysiology of postoperative MI is not as clearly understood, but a combination of two mechanisms appear predominant: (1) plaque rupture and coronary thrombosis triggered by perioperative endothelial injury from hyper-adrenergic, proinflammatory

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and prothrombotic states (Bottiger et al., 2005; Colagrande et al., 2005; Kertai et al., 2004; Wright and Lefer, 2005), and (2) prolonged stress-induced ischemia and tachycardia in the context of compromised perfusion (Devereaux et al., 2005a, b; Landesberg, 2003; Landesberg et al., 2001; Newby and Nimmo, 2004; Priebe, 2005). Extensive genetic variability exists in each of these mechanistic pathways, which may combine to modulate magnitude of myocardial injury. Several genotypes associated with an increased risk of acute and delayed graft occlusion following CABG surgery have been identified. Plasminogen activator inhibitor-1 (PAI-1) is a serine protease inhibitor of tissue plasminogen activator, and is an important inhibitor of fibrinolytic activity. Circulating PAI-1 levels are regulated in part by an insertion/deletion polymorphism (4G/5G) in the promoter region of the PAI-1 gene and are increased in individuals expressing this genotype, causing reduced fibrinolysis. These genotypes have been correlated with venous and arterial graft occlusion after cardiac surgery (Rifon et al., 1997). Similarly, the PlA2 polymorphism of the GPIIbIIIa

Surgical Trauma Ischemia-Reperfusion Injury CPB Systemic Effects Early Conduit Failure Incomplete Revascularization Cytokines Complement EC/PMN interactions Endotoxemia ROS, RNS Bioincompatibility Contact activation

Platelets Intrinsic/Extrinsic coagulation Fibrinolysis

Adrenergic response Acute β-AR desensitization Insulin resistance

Protease Activation Apoptosis Drug Interactions

“Vulnerable” Blood

Systemic and Local Inflammation

Neuroendocrine Stress

Myocardial injury Adverse event

Disease Perioperative or Peri-Procedure Insult Health

Disease

Normal response

Ischemic/ Pharmacologic preconditioning

Heat shock proteins

Cardioplegia techniques

Cardioprotection Adverse event

Figure 66.1 Effect of acute peri-procedural injury. A spectrum of phenotypic expression is seen in chronic disease. Superimposed application of peri-procedural stress results in another bell-shaped curve of adverse outcomes. Both have underlying genetic components contributing to the variability seen. While sometimes related, peri-procedure outcomes are not necessarily related to underlying chronic disease mechanisms. (Modified with permission from Podgoreanu and Schwinn, 2005.)

Figure 66.2 The degree of perioperative myocardial injury. The degree of perioperative myocardial injury is a result of the balance between injurious and cardioprotective (endogenous and exogenous) biological mechanisms, mediated via a wide array of biochemical pathways with extensive genetic variability. AR  adrenergic receptor; CPB  cardiopulmonary bypass; EC  endothelial cells; PMN  polymorphonuclear neutrophils; RNS  reactive nitrogen species; ROS  reactive oxygen species. (Modified with permission from Podgoreanu and Schwinn, 2005.)

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platelet receptor has been associated with graft thrombosis, MI, and death after CABG surgery (Zotz et al., 2000). In a recent rigorously performed study, other genetic variants that predict perioperative MI have been found to include the proinflammatory cytokine IL-6 -572G  C (odds ratio [OR] 2.47), as well as 2 adhesion molecules – intercellular adhesion molecule-1 (ICAM1 Lys469Glu; OR 1.88) and E-selectin (SELE 98G  T; OR 0.16) (Podgoreanu et al., 2006). In this study, the inclusion of genotypic information from these polymorphisms improves prediction models for post-cardiac surgery MI based on traditional risk factors alone (C-statistic 0.764 versus 0.703) (Podgoreanu et al., 2006). Deficiency of mannose-binding lectin (MBL), a circulating liver-synthesized protein and important recognition molecule in the lectin complement activation pathway (Turner and Hamvas, 2000), has been associated with increased risk of severe atherosclerosis among non-surgical patients with variant MBL genotypes (Best et al., 2004; Madsen et al., 1998). Interestingly, MBL deficiency has also been associated with arterial thrombosis in systemic lupus erythematosus (Ohlenschlaeger et al., 2004) and with early venous bypass graft occlusion following CABG (Limnell et al., 2002). Another etiology of perioperative thrombotic complications is heparin-induced thrombocytopenia (HIT). This disorder presents either as a benign isolated thrombocytopenia (type I) or as a decrease in platelet count associated with vascular thrombosis (type II). Approximately 25–50% of cardiac surgical patients develop heparin-dependent IgG 5–10 days after surgery, but only 1–3% develop HIT (Warkentin and Greinacher, 2003). The mechanism of type II HIT involves immunoglobulin type G (IgG) bound to platelet factor 4 and heparin, forming a complex with the FcIIA platelet receptor, resulting in platelet activation and hypercoagulability. It is not understood why some patients with heparin antibodies develop severe thrombotic complications while others remain asymptomatic. The FcIIA receptor is the only platelet receptor known to bind IgG and has a G-to-A polymorphism at position 131 of its amino acid sequence that encodes either arginine (Arg131) or histidine (His131). Studies of the role of this polymorphism in HIT have produced markedly conflicting results (Trikalinos et al., 2001). These data highlight the need for additional studies seeking to identify genetic variants associated with the risk of perioperative thrombotic complications.

PERIOPERATIVE STROKE AND NEUROCOGNITIVE DYSFUNCTION Stroke is a devastating and too-frequent (1–3%) consequence of cardiac surgery that is associated with much poorer quality of life and 10-fold greater risk of 30-day mortality. Although often less devastating, the more frequent (30–50%) postoperative neurocognitive dysfunction after CPB is problematic with a quarter of individuals still having persistent deficits 6 months later (Newman et al., 2001a, b). Furthermore, post-CPB patients

whose neurocognitive deficits persist beyond 5 years have significantly reduced quality of life (Newman et al., 2001b). The etiology of post-CPB neurocognitive dysfunction is multifactorial and non-genetic risk factors include older age, fewer years of education (Newman et al., 1995), and a history of diabetes (Selnes et al., 1999). Intraoperative predictors include difficulty identifying an atheroma-free aortic cross-clamp site (Selnes et al., 1999), increased number of cerebral emboli, elevated CPB rewarming temperature, and lower jugular bulb oxygenation during CPB (Newman et al., 1995). Recent evidence suggests that genetic factors may contribute to perioperative cerebral vascular thrombosis and related cerebral ischemia, neuronal resistance to ischemia and to neuronal repair after the ischemic insult via inflammatory mechanisms. Grocott and coworkers examined the effect of 26 SNPs upon the frequency of stroke in 1635 patients and found that the interaction of minor alleles of the CRP 3 UTR 1846C/T and the IL-6 promoter SNP -174G/C polymorphisms were significantly associated with stroke (OR  3.3; 95% CI, 1.4–8.1; p  0.0023) (Grocott et al., 2005). The results support the expected pivotal role of inflammation in cell injury and post-cardiac surgery stroke, perhaps in etiology or reaction to ischemic penumbra. Further work is needed to elucidate the precise biological mechanism involved. Platelet aggregation is key to the generation of intravascular thrombosis. It is mediated by binding of fibrinogen or von Willebrand factor to the platelet GPIIbIIIa receptor, amongst other pathways. Based on the notion that much of the neurocognitive decline seen post-CPB is due to exacerbated thrombosis and atherosclerotic plaque, one study investigated the relationship between the prothrombotic P1A2 polymorphism of the platelet GPIIbIIIa receptor and early neurocognitive decline after cardiac surgery (Mathew et al., 2001). Multivariate analysis showed that patients with one or more P1A2 alleles were more likely than P1A1 homozygotes to have significant neurocognitive impairment. Additionally, although both groups had similar intraoperative platelet activation, the P1A2 group had more prolonged postoperative platelet activation. These findings suggest that further study of the inter-relations among the P1A2 allele, platelet activation, and short- and long-term neurocognitive decline after CPB are warranted. Considerable attention has been focused on the role of the apolipoprotein E (APO-E) gene in determining neurocognitive outcomes after CPB. APO-E is an important regulator of cholesterol metabolism and lipid transport and appears to play a role in neuronal repair. In animal models, nerve injury has been shown to stimulate APO-E secretion by non-neuronal cells (Snipes et al., 1986), and APO-E and its receptor regulate phospholipid and cholesterol transport during early reinnervation of the injured hippocampus (Poirier, 1994). The APO-E gene has 3 common alleles (2, 3, 4). The 4 allele has been associated with increased risk of late-onset Alzheimer’s disease (Edwardson and Morris, 1998) and worse traumatic head injury (Jordan et al., 1997; Teasdale et al., 1997), whereas homozygosity for the APO-E 3 allele has been found to predict more favorable neurologic outcomes after stroke

Dynamic Genomic Markers of Perioperative Outcomes

in mice (Sheng et al., 1998) and after cardiopulmonary resuscitation in humans (Schiefermeier et al., 2000). Studies of the APO-E 4 allele in relation to adverse neurocognitive outcomes in CABG patients undergoing CPB have produced inconsistent findings. Patients carrying the APO-E 4 allele, particularly those with less education, were significantly more likely than 4 non-carriers to experience a decline in cognitive performance, both at hospital discharge and 6 weeks postoperatively (Tardiff et al., 1997). However, two later studies of CABG patients reported no effect of the 4 allele on adverse neurocognitive outcomes (Askar et al., 2005; Steed et al., 2001). A third study of 86 CABG patients found that those with the APO-E 4 allele had poorer postoperative verbal fluency but did not differ from patients carrying other APO-E alleles with regard to overall cognitive test scores (Robson et al., 2002). Although the mechanism by which the APO-E 4 allele might influence perioperative neurocognitive dysfunction in CABG patients undergoing CPB has yet to be determined, the APO-E 4 allele does not seem to affect global cerebral blood flow or oxygen metabolism during CPB (Ti et al., 2001). It is possible that mild neurocognitive effects related to the APO-E 4 allele have increased manifestation with older age. In other words, CPB “unmasks” the long-term effects of APO-E 4 on the aging brain. A study of infants who underwent cardiac surgery at less than 6 months of age showed that the APO-E 2 (rather than the APO-E 4) allele was associated with decreased psychomotor development at 1 year of age (Gaynor et al., 2003). Alternatively, the effect of the APO-E 4 allele may be due to increasing atheroma burden affecting cerebral embolic load, rather than a specific effect on neuronal homeostasis (MacKensen et al., 2004). These findings mandate larger studies of neurocognitive function with longer follow-up to assess the effect of the APO-E 4 allele. Finally, a recent study suggests that P-selectin and CRP genes both contribute to modulating susceptibility to cognitive decline following cardiac surgery (Mathew et al., 2007). Specifically, the minor alleles of CRP 1059G/C and the SELP 1087G/A are associated with reduction in the observed incidence of postoperative cognitive decline (OR 0.37 and 0.51; absolute risk reduction for carriers 20.6% and 15.2%, respectively) (Mathew et al., 2007). These findings have implications for identifying populations at risk who might benefit from targeted perioperative anti-inflammatory strategies.

HEMORRHAGE AND CARDIAC SURGERY Depending on institutional practices, between 30% and 75% of patients receive a transfusion during or after routine CABG surgery. Higher transfusion rates occur with more complex or reoperative surgery. Patients who require reoperation for bleeding have higher rates of sternal wound infection, organ system dysfunction, resource utilization and death. Patients with highpenetrance, low-frequency mutations that cause bleeding, such as the hemophiliac, are easily identified. In contrast, it is likely

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that the majority of genetic variation in blood loss is due to low-penetrance, high-frequency polymorphisms that do not cause significant bleeding outside of the operative environment. The largest and best-conducted study of genetic causes of bleeding after CABG surgery examined 19 candidate platelet glycoprotein and coagulation pathway SNPs in 780 US patients (Welsby et al., 2005). Six SNPs (GPIaIIa-52C  T and 807C  T, GPIb alpha 524C  T, TF -603A  G, prothrombin 20210G  A, TFPI -399C  T) and the angiotensin converting enzyme (ACE) I/D showed significant association with bleeding (p  0.01), independent of clinical predictors. As previously mentioned, the Pro (P1A2) amino acid substitution of the GPIIbIIIa receptor is associated with increased platelet activation. In a study of 102 patients undergoing CABG, PlA1 homozygotes had more bleeding (1138  656 versus 760  231 ml, p  0.05) (Morawski et al., 2005). Interestingly, in aspirin-treated patients, PlA2 carriers had greater blood loss than PlA1 homozygotes (1858  932 ml versus 1216  525 ml, p  0.05). Such studies should be extended, especially because of the high prevalence of anti-platelet drug use in the peri-procedure period and the likely effect of gene-by-drug interactions.

DYNAMIC GENOMIC MARKERS OF PERIOPERATIVE OUTCOMES While most existing studies summarized above have focused on DNA sequence variants as predictors of perioperative outcomes, important complementary approaches are beginning to explore the dynamic changes in gene and protein expression resulting from the interaction of structural DNA variations with multidimensional surgical stimuli in the perioperative period. Such dynamic genomic markers can be used clinically to improve preoperative risk stratification and monitor postoperative recovery (Hopf, 2003), as well as prior to surgery to improve mechanistic understanding of perioperative stress responses and identify novel organ protection strategies. For example, Feezor and coworkers (Feezor et al., 2004) used peripheral blood collected preoperatively to identify an integrated genomic and proteomic pattern that could discriminate patients who developed multisystem organ failure after thoracoabdominal aortic aneurysm repair from those who did not. Using a similar concerted approach of transcriptomic and proteomic analyses, Tomic and coworkers (Tomic et al., 2005) characterized the molecular response signatures to cardiac surgery with and without CPB, a robust trigger of systemic inflammation. The authors demonstrated that, rather than being the primary source of serum cytokines, peripheral blood mononuclear cells assume a “primed” phenotype upon contact with the extracorporeal circuit that facilitates their trapping and subsequent tissue-associated inflammatory response. Interestingly, some mediators achieved similar systemic levels following off-pump surgery, but with delayed kinetics, offering novel insights into the concepts of contact activation and compartmentalization of inflammatory responses to major surgery. Clearly, future studies are needed to

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correlate perioperative gene expression response patterns in end organs (e.g., myocardium) to those in readily available potential surrogate tissues such as peripheral blood leukocytes. Identifying gene- and protein-based biomarkers in peripheral blood to non-invasively monitor, diagnose, and predict perioperative cardiac allograft rejection and survival is an area of rapid scientific growth. While several polymorphisms in genes involved in alloimmune interactions, the renin-angiotensin-aldosterone system and the transforming growth factor- superfamily have been associated with cardiac transplant outcomes, their relevance as useful clinical monitoring tools remains uncertain. However, peripheral blood mononuclear cell multigene-based molecular assays have shown the most promise for monitoring the dynamic responses of the immune system to the transplanted heart, discriminating immunologic allograft quiescence, and predicting future rejection (Mehra et al., 2006). Furthermore, several clinically available protein-based biomarkers of alloimmune activation, microvascular injury (troponins), systemic inflammation (CRP), and wall stress and remodeling (BNP) correlate well with allograft failure and vasculopathy and have good negative predictive values, but require additional studies to guide their clinical use. Similarly, molecular signatures of functional recovery of end-stage heart failure following left ventricular assist device (LVAD) support using gene expression profiling have been reported (Hall et al., 2006), and could be used to monitor patients who received an LVAD as destination therapy or assess the timing of potential device explant. Several preclinical studies have used transcriptomic and proteomic profiling to improve the mechanistic understanding of processes involved in ischemic and anesthetic myocardial pre- and postconditioning (Sergeev et al., 2004), as well as neurologic injury associated with cardiac surgery (Sheikh et al., 2006). The epidemiologic framework for assessing the applicability of previously identified biomarkers of perioperative/procedural morbidity is contingent upon demonstrating their clinical validity, analytical validity, and clinical utility (Khoury et al., 2004). Perioperative genomic investigators are currently conducting replication studies in different surgical patient populations to formally assess the sensitivity, specificity, and predictive values of initial markers and establish their clinical validity. For genomic classifiers the emphasis during external validation is placed on prospectively testing the accuracy of the entire molecular pattern in a new patient population rather than corroborating results in individual genes. In this regard, analytical reproducibility of microarray data

across different laboratories raises potential challenges (Bammler et al., 2005). Converting the classifier to a more stringent, readily available platform already validated in clinical diagnostic testing (e.g., real-time RT-PCR) could enhance the acceptance of molecular signature analysis into clinical practice. This is particularly important for the extremely dynamic perioperative period, where test turnaround times of several hours are required for meaningful therapeutic interventions to take place. Clinical utility (targeted interventions to reduce perioperative morbidity among patients with a certain genetic/genomic profile) remains to be evaluated in future genomically stratified perioperative controlled trials. Equally important is the integration and translation of static and dynamic genomic data into information that can be used by busy clinicians at the bedside or in the operating room. Risk stratification scores and computerized decision support tools incorporating genomic information may impact surgical/procedural treatment pathways at multiple points. Potential preoperative uses range from assessment of cardiopulmonary fitness (Lee et al., 2006) and susceptibility to exaggerated proinflammatory or prothrombotic responses to surgery and adverse anesthetic drug effects, to optimum timing of cardiac valvular procedures (e.g., based on a proteomic profile predictive of transition from compensatory myocardial adaptation to overt heart failure) (Matt et al., 2007). At risk patients can be optimized preoperatively and receive targeted intraoperative immuno- and thrombo-modulatory therapies. Monitoring repair processes and the immuno-inflammatory status postoperatively using dynamic genomic “vital signs” may facilitate early pre-clinical detection of sepsis or multiple organ dysfunction syndrome.

CONCLUSION Genomic medicine has ushered in a new era of mechanistic understanding for many medical diseases. Recognizing the role of acute injury and stress responses in amplifying inflammatory signaling has also increased understanding of post-procedure adverse events. Such information goes a long way toward making personalized medicine a reality and overall medical care safer. However, unique study designs and analysis methods for these non-family based studies must be further developed and used in appropriate populations before a complete mechanistic picture of this paradigm is assured.

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67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82.

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Cancer Genes, Genomes, and the Environment Immune Cells and the Tumor Microenvironment Lymphomas Genomics in Leukemias Genomics of Lung Cancer Breast Cancer and Genomic Medicine Colorectal Cancer Prostate Cancer Molecular Biology of Ovarian Cancer Pancreatic Neoplasms The Multiple Endocrine Neoplasia Syndromes Genomics of Head and Neck Cancer Genomic Medicine, Brain Tumors and Gliomas Molecular Therapeutics of Melanoma Emerging Concepts in Metastasis Diagnostic-Therapeutic Combinations in the Treatment of Cancer

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67 Cancer Genes, Genomes, and the Environment

F

Robert L. Strausberg

INTRODUCTION Over the past several decades, genetic studies have revealed the complexity of cancer as well as the principles that emerge with respect to how those changes guide tumor development and progression. In the individual chapters in this section, details are presented with respect to genetic knowledge of specific cancers. Here I present a brief discussion of some overall principles guiding our current view of cancer as well as future research directions.

ACQUIRED FUNCTIONS OF CANCER CELLS The acquisition of cancer results from the dynamic nature of our genome, both as inherited from our parents and from changes that we acquire during our lifetime. Several features of cancer are important to note as we consider how and why this disease develops and progresses, and how we might practice better prevention and intervention methods. The world of cancer literature is complex and reflects the numerous genes that contribute to or prevent disease formation, the diversity of cellular signaling networks, and the multitude of approaches available to cells to achieve a particular phenotype. As with any journey, the road to cancer can involve different paths, some more direct than others.The paths chosen reflect the specific environment in which a cell functions, including the multitude of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 808

interactions with the host. However, from complexity has emerged simplicity in our understanding of the features that cancer cells acquire. Many of these were elegantly summarized by Hanahan and Weinberg (Hanahan and Weinberg, 2000), who noted several distinct functions common to all tumors. Examples of these functions are the ability to be self-sufficient in growth signals, to attain limitless growth potential, resistance to biological means that are used to prevent cancer cell formation such as anti-growth signaling, as well as controlled cell death (apoptosis), a process in which cells that have accumulated genome damage are eliminated. A very important principle is that tumors are comprised of more than cancer cells and include a multitude of cells that support cancer development, such as the endothelial cells that form the stroma (Alghisi and Ruegg, 2006; Bissell and Radisky, 2001; Bissell et al., 2002; Folkman, 1996). In addition, cancers are often (perhaps always) antigenic and are recognized by host immune systems, and these host defenses must be evaded (Simpson et al., 2005) (see Chapter 68). Finally, ultimate damage from most solid tumors results from their ability to metastasize to new sites (Crawford and Hunter, 2006) (see Chapter 81).

CHROMOSOMAL ABERRATIONS AND CANCER Peering into the genome of solid tumors reveals an enormous complexity of change, some of which can be viewed even by Copyright © 2009, Elsevier Inc. All rights reserved.

Inherited Predisposition

microscopic examination of whole chromosomes (Knutsen et al., 2005; Mitelman, 2000; Porter et al., 2006; Rowley, 1998; Speicher and Carter, 2005;Wang et al., 2002). These cytogenetic studies reveal that the chromosomes that we inherit from our parents undergo remarkable change, resulting in large regions of deletion, amplification, as well as extensive breakage and rejoining of chromosomes (translocation). This principle of somatic change extends from large chromosomal rearrangements to specific nucleotide substitutions that alter the functions of cellular proteins (Figure 67.1). The importance of chromosomal aberrations in cancer is highlighted by examples from liquid tumors (leukemia and lymphoma) where the rearrangements are typically more limited than observed in solid tumors, and where specific translocations have been observed in many patients (Mitelman, 2000; Rowley, 1998) (see Chapters 69 and 70).

Normal cell

Cancer cell

Gene A

Gene A

Gene B

Gene B

Gene C

Gene C

Gene D

Gene D

Figure 67.1 Illustrated are changes in gene expression that can be causative to cancer initiation and progression. For Gene A, there is reduced expression of a tumor suppressor, leading to a shift in balance toward oncogenic events. In the case of Gene B, expression has increased, as might be the case for an oncogene that might be produced in excess compared with tumor suppressors that normally would provide a balance. Reduced expression of gene A and overexpression of gene B might occur though several distinct molecular mechanisms such as promoter alterations and including epigenetic effects (Esteller, 2006; Jones and Laird, 1999). Gene C illustrates a gene structure change based on a chromosomal translocation (such as in BCR-ABL) that might also lead to activating or inactivating events. In the example provided by Gene D, a specific mutation occurs within the protein coding sequence. This can result for example, in protein self activation (such as in receptor tyrosine kinases) leading to oncogenic properties, or inactivation (as in tumor suppressors such as TP53).

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The significance of these noninherited chromosomal aberrations has been highlighted recently by the successful introduction of the molecularly targeted small molecule, imatinib mesylate, known as Gleevec (Druker, 2004) (see Chapter 82). This targeted therapeutic was based on the initial discovery in 1960 of a chromosomal aberration in chronic myelogenous leukemia. Subsequently called the Philadelphia chromosome, this specific translocation of chromosomes 9 and 22 was shown to generate a hybrid gene (Bcr/Abl) with oncogenic properties, resulting in cancer development (see also review of cytogenetics technologies and history by Speicher and Carter) (Speicher and Carter, 2005).

CANCER GENES AND THEIR FUNCTIONS A major focus of the biomedical research during the past three decades has been the cataloging of cancer genes and their activities, including genes that are directly involved in the initiation, development, maturation, and metastasis of cancers. Identification of these genes has been accomplished through several approaches such as mapping of genes for predisposition in affected families, and identification of tumor-specific somatic changes within cancer development. Interestingly, the discovery and characterization of the highly important TP53 gene, first receiving attention was based on a different approach, involving the observation of attenuation of TP53 protein function through direct association with oncogenic viruses (Levine et al., 1991). Certain themes have emerged with respect to the functions of cancer genes and several excellent reviews are available that discuss the roles of cancer genes in detail. In very general terms these genes include oncogenes (directly involved in promoting cancer development and progression), tumor suppressors (with activities linked restricting the development of cancer), and genes associated with alteration of genome stability, thereby promoting changes that might support cancer development (Vogelstein and Kinzler, 2004). The checks and balances of these genes and their products assure that a single change does not result in a cancerous phenotype. In addition, the biological context in which these genes function determines the timing of potential changes in these genes, whether changes are viable only within specific tissues with restricted impact (somatic changes) or can be inherited (and therefore present in all cells). The requirement for multiple genetic changes, and the presence of certain gene products (such as TP53) that serve to identify potential harmful genetic changes, presents the opportunity to repair damage or eliminate cells carrying deleterious mutations (through programmed cell death, apoptosis), thereby assuring that cancer is indeed a very rare phenotype among the trillions of cells within a human.

INHERITED PREDISPOSITION It is now clear that cancer-initiating events can derive from genetic variation inherited from our parents, environmental

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effects, somatic mutations, or a combination. Examples of wellknown genes contributing to inherited predispositions include adenomatous polyposis coli (APC) in colorectal cancer (Bodmer, 2006; Rowley, 2005), BRCA1 in breast cancer (Narod and Foulkes, 2004) and CDKN2 (p16) in melanoma (Gruis et al., 1995). These genes, with variants identified in families with high frequency of familial cancer, can significantly increase the likelihood that an individual will develop cancer during their lifetime. The search for genes that predispose to cancer is a highly active area of research that now benefits from the human genome sequence, an extensive set of single nucleotide polymorphisms (SNPs), as well as technologies that facilitate whole genome association studies (Baffoe-Bonnie et al., 2006; Crockford et al., 2006; Ellis et al., 2006; Kemp et al., 2006; Smith et al., 2006). For example, recent studies have focused on the identification of loci beyond BRCA1 and BRCA2 that might be associated with predisposition in breast and ovarian cancer (Ellis et al., 2006). In a very recent study, genome-wide association was successfully employed to identify intronic alleles of the FGFR2 gene that are associated with increased risk for breast cancer in postmenopausal women of European ancestry (Hunter et al., 2007). In general, such inherited forms of cancer occur earlier in life than noninherited forms, and their effect can be substantially modified by environment factors. An illustrative example of the interplay of genetics and environment in cancer predisposition is the recent report of Dogan and colleagues (Dogan et al., 2006) of the clear association of predisposition to mesothelioma in Turkish villages, but only manifested in the presence of asbestos fibers; whereas even with the same exposure to asbestos fibers among persons lacking the predisposition, the incidence of this cancer is very low (Burke and Press, 2006).

CELLULAR PROGRESSION TOWARD CANCER THROUGH SOMATIC CHANGES Cancer development is typically a multiyear process, requiring several distinct events before the ultimate phenotype is achieved

Normal cell

(Figure 67.2). Based on the ability of researchers to observe physical changes in certain tissues as they advance from normal to cancer, it has been possible to correlate certain genetic changes with stages of disease progression. This has especially been the case in colorectal cancer (Fearon and Vogelstein, 1990) (see Chapter 73) and melanoma (Miller and Mihm, 2006) (see Chapter 80) for which physical changes, from early lesions to cancer can be observed over a period of years. Although the oncogenic process progresses in a manner specific to the organ of origin, illustrative of this process is colon cancer development (Fearon and Vogelstein, 1990). Colorectal cancer arises from polyps that can be observed in colonic mucosa and monitored as disease progresses (or does not progress). Cancer development proceeds in a multistep process in which the normal colonic epithelium becomes hyperplastic then proceeds through stages of adenoma, and finally becoming cancerous, invasive, and metastatic. Fearon and Vogelstein (Fearon and Vogelstein, 1990) described the common somatic genome changes associated with progression through these stages, thereby establishing the remarkable notion that cancer stages could be described not only in histological context, but also as a molecular disease with a logical progression. Illustrative examples of genes that have been described with roles in transitional steps in progression are APC/B-catenin, K-Ras/B-Raf, and TP53. The initial molecular change in many colorectal cancers is mutation of the APC gene (Jeter et al., 2006) (now recognized as a “gatekeeper” gene), leading to cellular growth promotion because the protein encoded by the mutant form of APC fails to degrade beta-catenin, a protein involved in the transcriptional activation of the cyclin D1 and c-myc oncogenes. APC mutations often derive from somatic change, but inherited mutations in APC lead to familial adenomatous polyposis (FAP), a form of colon cancer characterized by extensive polyp formation, consistent with presence of the altered protein in each of the colonic cells. As with other mutations that predispose to cancer, the inherited APC mutation accounts for only a small fraction of all colorectal cancer; however, when present it is highly penetrant, and virtually all patients express cancer by age 40. A second form of inherited and highly penetrant colorectal cancer,

Progressive genomic changes

Cancer cell

Shift in environment over time

Figure 67.2 Cancer progression through multiple genomic alterations. Depicted are a series of genomic alterations (these can range from single gene events such as point mutations to large genomic changes including chromosomal translocations) that drive cellular phenotype from normal to premalignant, and eventually to cancer. The first mutation is sometimes inherited, but most frequently occurs by somatic change. Subsequent changes are somatic, and lead to increasing genomic instability, especially in solid tumors. The range of mutations depends upon the type of cancer and environmental influences.

Comprehensive Sequencing of the Kinome

hereditary nonpolyposis colon cancer (HNPCC) arises through a very distinct mechanism in that the predisposing mutations are present in mismatch repair genes such as MSH2, MLH1, and PMS2. Although APC and HNPCC together only account for about 5% of colorectal cancers, in individual families the risk can be much higher, as the chance of inheriting a mutant allele from a heterozygous parent is 50%, and these alleles are highly penetrant. Among the interesting features of colon cancer genetic progression is the interplay of the KRAS and BRAF genes that mutate post-initiation but that are present in large polyps and that appear to play a role in polyp growth. Rajagopalan and colleagues (Rajagopalan et al., 2002) demonstrated that mutations in these genes are equivalent in their tumorigenic effects and that mutations in only one of these genes is present within an individual tumor. This is an example of a much more general phenomenon: within pathways individual mutations can be sufficient for activation, thereby eliminating the need and selection for mutations in other genes that can also activate that pathway. It is also important to note that while this discussion has focused on discrete mutations in the exons of specific genes, other more global genomic changes are also associated with colorectal cancer (as they are in all other cancers). For example, extensive chromosomal losses, such as in chromosome 17p are observed, facilitating the development of phenotypic effects of recessive tumor suppressor genes. In addition, extensive epigenetic changes to the genome are observed in polyps, in the form of both hypo- and hypermethylation. Hypomethylation is often associated with oncogene activation and hypermethylation can reduce the expression of tumor suppressor genes. Finally, even with these substantial genetic changes, it is still difficult for cells to become cancerous. In particular, the actions of the TP53 protein serve to protect cells from cancerous growth by blocking the cell cycle in damaged cells and stimulation of programmed cell death (apoptosis) (Vogelstein et al., 2000). Clearly, a functioning TP53 protein is very limiting to cancer development, and mutations in this gene are observed late in tumorigenesis and TP53 mutations are observed in most colorectal cancers (and many human cancers). Even in cases in which TP53 is not altered, it may be that other changes within its pathway result directly in altered TP53 function or in related proteins, thereby enabling tumor progression. The key genomic changes associated with cancer metastasis are less well known overall, and the involvement of inherited predisposition to metastasis, from initiation through colonization at new sites and proliferation is a subject of intense research interest (Crawford and Hunter, 2006). For example, recent evidence indicates that a specific transcription factor, twist, is involved both in normal development as well as epithelial-mesenchymal transition critical to metastasis (Yang et al., 2006).

FROM THE GENOME TO THE CLINIC Our knowledge of the acquired functions of cancers (Hanahan and Weinberg, 2000), the pathways that support those functions, as

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well as the interplay of genetics and tumor progression (Fearon and Vogelstein, 1990), provides an important framework for increased understanding of cancer, and our ability to intervene to prevent and reduce the impact of cancer to patients. Indeed, the notion that a well-defined understanding of the molecular differences of normal and cancer cells can lead to new interventional strategies is now supported by several clinical examples. In recent years there has been great interest in the development of targeted small molecules and monoclonal antibodies directed to activated receptor tyrosine kinases (Drevs et al., 2003), enzymes that regulate cell signaling both in the cancerous cells as well as in the support cells, such as endothelial cells associated with angiogenesis. Advances in our understanding of these kinases reflect increased comprehensive knowledge of the genomic alterations of cancer, including not only point mutations, but also translocations, amplification/deletion events, as well as epigenetic changes. As discussed above, the targeted small molecule Gleevec (Druker, 2004) was developed based on the identification and molecular characterization of the Philadelphia chromosome (9:22 translocation) present in most cases of chronic myelogenous leukemia. Herceptin (Sawyers, 2002), a monoclonal antibody-based therapy, was developed based on overexpression of the Her2/neu protein in a subset of breast cancers, often resulting from tandem amplification of this gene. Similarly, Iressa (Blackledge, 2004), a small molecule developed to treat lung cancer, was also based on overexpression of a receptor kinase, the epidermal growth factor receptor (EGFR) (Blackledge and Gefitnib, 2004). Puzzling at first was that tumor response was observed only in about 10% of patients. Subsequently it was determined that tumor responsiveness was actually not based on gene overexpression, but instead to the presence of somatically derived activating mutations with EGFR in those patients (Paez et al., 2004). This example highlights the importance of definition of the target to the precision of single amino acid variation, and the importance of gaining comprehensive knowledge of genomic and proteomic data to discern the most informative features. Finally, the recent introduction of Avastin (Ferrara, 2005) highlights the opportunity to target oncogenic kinases in tumor components that are not epithelial cancer cells but rather part of the supportive infrastructure, such as the endothelium. In this case the target is vascular endothelial growth factor (VEGF), an angiogenic inducer that operates through Flt-1 (required for endothelial cell morphogenesis) and the KDR gene that is involved in mitogenesis.

COMPREHENSIVE SEQUENCING OF THE KINOME The realization that at least some receptor tyrosine kinases are “druggable” targets suggests that a better knowledge of alterations in this gene family might identify additional targets for therapeutic intervention. The completion of human genome draft sequences (Lander et al., 2001; Venter et al., 2001) provided a context for

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cataloging all of the human kinase genes (Manning et al., 2002), thereby setting the stage for assessment of these genes and their products as targets for existing or new therapeutics. In a groundbreaking study, Bardelli and colleagues (Bardelli et al., 2003) selected 138 genes including 133 tyrosine kinase and tyrosine kinase-like genes, as well as 5 receptor guanylate cyclases. PCR-directed sequence analysis of over 800 exons derived from these genes in 35 colorectal cancer cell lines generated a rich dataset of previously unknown somatic variation. Remarkably, this study represented the first comprehensive analysis of somatic variation in any gene family in cancer. This effort established several important principles that could only be gleaned from comprehensive analysis. First, although the tyrosine kinases were among the best studied of cancer genes, most of the mutations were in genes not previously associated with human cancer. Second, although mutations in individual tyrosine kinase genes were generally not present in a large proportion of the cancers, in sum more than 30% of the colorectal cancers examined had non-synonymous changes in a tyrosine kinase gene family member. Finally, it does not appear that the changes observed were random, since a high proportion of the mutations encoded non-synonymous variants, more likely to be associated with effects on biological function. Subsequently several groups have expanded the search for somatic variation in the kinase genes in various cancers in order to identify additional potential targets for intervention (see e.g., Bignell et al., 2006; Davies et al., 2005; Futreal et al., 2005; Rand et al., 2005). In at least two instances these analyses have revealed frequently occurring mutations in receptor tyrosine kinase genes (Davies et al., 2002; Samuels et al., 2004) with potential utility as therapeutic targets. For example, Davies and colleagues discovered that the serine/threonine kinase BRAF is very commonly altered in melanoma (66% of malignant melanomas were found to have somatic missense mutations in this gene) and mutations in this gene were also found in other cancers. Importantly, the altered BRAF proteins were found to have elevated kinase activity that is transforming in cultured cells. In their study of the colorectal cancer kinome, Bardelli et al. (Bardelli and Velculescu, 2005) identified frequent somatic mutations (in 32% of the tumors studied) in the lipid kinase phosphatidylinositol 3-kinase gene (PIK3CA). Analysis in other cancers revealed additional mutations in this gene although at lower frequency (Bardelli and Velculescu, 2005; Gallia et al., 2006). Functional analysis of cells with these mutations (Parsons et al., 2005; Samuels et al., 2005; Samuels and Ericson, 2006), revealed increased enzymatic activity, resulting in enhanced AKT signaling, cell growth independent of growth factors, as well as increased invasion and metastasis. Therefore, for both the BRAF and PIK3CA, the frequency of mutations and observed functional effects suggest that these may be suitable targets for intervention.

EXPANDING THE SEARCH The successful identification of potential new targets based on cancer kinome sequence analysis has set the stage for a much

more comprehensive vision of the molecular characterization of cancer genomes (Bardelli and Velculescu, 2005). Recently, Sjoblom and colleagues (Sjoblom et al., 2006) reported on a study of somatic mutation in eleven of colorectal and breast cancer cell lines or xenografts, and encompassing over 13,000 verified full-length human genes based on the human genome sequence (Lander et al., 2001; Venter et al., 2001) as well as high quality full-length cDNA sequences (Imanishi et al., 2004; Strausberg et al., 2002). As in the case of comprehensive kinome sequencing this unbiased survey of mutations in the wellcharacterized human genes yielded new insights to sequence variation specifically associated with tumor DNA. In total, somatic mutations were identified in over 1400 of the genes surveyed, and nearly 200 of these were validated in an additional panel of 24 tumors from each tumor type, indicating that these genes are somatically mutated relatively frequently. The results suggested that the typical breast tumor and colorectal tumor has more than 70 and 50 non-synonymous somatic mutations in protein-encoding genes, respectively. Interestingly, the nucleotide changes in these mutations and their frequency in specific genes differed markedly in breast and colorectal cancer, indicating tissue specific mutation patterns. The molecular analysis of cancer is complicated by genomic heterogeneity within tumors. Therefore, digital technologies that can reveal the molecular state of individual genomes within the cancer will greatly benefit our understanding of this complex disease. Recently, Thomas et al. (Thomas et al., 2006) demonstrated that rare mutations within a tumor could be discerned through the use of pyrosequencing-based technology, of particular importance as these mutations were associated within functional differences for response to therapeutic intervention. The ultimate sequencing achievement toward the comprehensive molecular analysis of cancers will be the complete sequencing of tumor genomes, which will provide a complete view of changes that are in protein-encoding genes, regulatory regions, as well as regions of unknown function. Such analysis will provide an integrated view of point mutations, insertions/ deletions, gene amplifications and loss, as well as translocations. While now technically feasible, sequencing of complete human genomes is still quite expensive, and it will be important to analyze thousands of cancer genomes. Sequencing technologies are advancing rapidly (Rogers and Venter, 2005;Wicker et al., 2006) and the attention of the community has been focused toward the achievement of human genomes at a cost of approximately $1000 (Pennisi, 2006).

MULTIPLE MOLECULAR MECHANISMS FOR ONCOGENE ACTIVATION As illustrated by the discovery of current drugs targeted to activated tyrosine kinases, many routes are available for genomic change that can activate oncogenes or reduce the activity of tumor suppressors. In addition to activating mutations, oncogenic activity can be enhanced by producing excess amounts of

Cancer Genomic Databases Expedite Progress

these proteins by gene copy number amplification, epigenetic changes, or through activation based on chromosomal translocations. Therefore, it is important that molecular analysis of cancer integrates information about each of these potential events within the context of the same cells or tumors.

MICROARRAYS AND CANCER GENOMICS The establishment of microarray (Fodor et al., 1991; Schena et al., 1995) and sequenced-based (Meyerson et al., 2004; Porter et al., 2006) technologies set in motion visions of comprehensive molecular analysis of cancer, resulting in new insights not only to mechanisms of cancer development and progression, but also providing a basis for new classifications of cancers complementary to traditional approaches. Examples of successful applications of microarray technology include the development of predictors of patient outcome through analysis of gene expression patterns (Bloom et al., 2004; Carr et al., 2003; Kapp et al., 2006; Meyerson et al., 2004; van’t Veer et al., 2003) as well as by copy number variation using oligonucleotide or BAC-based arrays (Bignell et al., 2004; Chung et al., 2004; de Leeuw et al., 2004). In a recent example, Bergamaschi and colleagues (Bergamaschi et al., 2006) described the classification of breast cancer subtypes based on copy number variations as discerned by genome-wide array comparative genomic hybridization platform. Importantly, copy number alterations were associated with distinct tumor grades, estrogen receptor status, TP53 mutation, and overall survival. In addition, different types of copy number variation were linked with tumor subtypes previously defined by gene expression analysis, suggesting different molecular mechanisms with distinct cancer subtypes. An additional example is the report of de Leeuw and colleagues (de Leeuw et al., 2004) in which array CGH revealed not only in previously identified regions of copy number variation associated with mantle cell lymphoma, but also in 13 novel regions with recurrent alterations that were not observed with traditional cytogenetics, and that serve as regions to search for new oncogenes and/or tumor suppressors. Recently Bild et al. (Bild et al., 2006) utilized gene expression analysis to determine signatures that are based on patterns of oncogenic pathway regulation as well as disease outcomes. Using this approach across multiple pathways, this team identified specific patterns of pathway activation that discriminate tumor subtypes. One of the important findings of this study was the notion that linkage of pathway deregulation with sensitivity to targeted therapeutics provides a guide for how these therapeutics can be chosen most effectively for the specific tumor and patient.

ENVIRONMENTAL CANCER GENOMICS Genomic analysis has also provided new insights to the interactions of the genomic and environmental interface. For example, Swede

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et al. (Swede et al., 2006) described a comparison based on array CGH of genomic alterations in lung cancer of smokers and nonsmokers, revealing for example, recurring loss of heterozygosity on chromosomes 14 and 18 in smokers, whereas in nonsmokers the LOH patterns were more dispersed. Interestingly, the overall level of amplifications and deletions was more extensive in the smoking population. Array CGH, using a cDNA-based platform, was also utilized to discern the effects of asbestos exposure in lung cancer patients (Nymark et al., 2006). While the traditional cytogenetic approach to CGH revealed specific DNA damage profiles associated with asbestos exposure, including a higher number of aberrations on average in the asbestos exposed population as well as a specific site of aberration on chromosome 2, array CGH revealed 18 asbestos-related sites of chromosomal aberration, including 11 fragile sites, which may be more sensitive to asbestos damage.

CANCER GENOMIC DATABASES EXPEDITE PROGRESS Perhaps the biggest impact of the genomics revolution on cancer research is the establishment of databases that provide rapid access to the research community to the molecular changes associated cancers. Although just a beginning, already such databases have been developed that give a glimpse of the future. An important early launching of cancer genomics database development was the National Cancer Institute’s Cancer Genome Anatomy Project (CGAP) (http://cgap.nci.nih.gov), initiated in 1997 (Strausberg et al., 1997) with a vision of populating integrated databases encompassing cancer chromosomal aberrations (Knutsen et al., 2005; Mitelman and Mertens, 2006), linkage of the cytogenetic map with the human genome sequence (Cheung et al., 2001), genetic variation (Buetow et al., 2001), and gene expression patterns (Boon et al., 2002; Brentani et al., 2003). Concurrent with the development of CGAP, the Human Cancer Genome Project (HCGP) (Brentani et al., 2003) of the Ludwig Institute for Cancer Research (LICR) developed a public gene expression database of cancer based on a cDNA method termed ORESTES (Sakabe et al., 2003), designed to focus on the protein-encoding regions of cDNAs. Also concurrent with CGAP, the Cancer Genome Project (CGP) of the Sanger Centre provided early vision for the comprehensive gene sequencing of human cancers (Greenman et al., 2007), resulting in the Catalog of Somatic Mutations in Cancer (COSMIC) database (Forbes et al., 2006). Ongoing studies in the CGP have been focused on both copy number alterations as well as somatic changes in cancer including point mutations and insertion/deletion events. An early success of this project was the identification of common somatic mutations in the BRAF gene in melanoma (Davies et al., 2002). An overview of the distinct, but complementary, features of the CGAP, CGP, and HCGP projects has previously been published (Strausberg et al., 2003).

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The National Institutes for Health recently announced a new cancer genomic consortium that builds from the projects described above (Collins and Barker, 2007). The goal of The Cancer Genome Atlas (TCGA) is to facilitate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. Currently this project is in a pilot stage in which alterations in three cancers including glioblastoma, lung, and ovarian are being explored (http://cancergenome.nih.gov/about/ cancers_studied_by_tcga.asp.) Key to the success of these projects and associated databases going forward will be the integration of phenotypic information, and the seamless interface of cancer research from basic (including animal models of human cancer) to the clinic. At present, although many databases have been or are being established ranging from the comprehensive to the very specific, the integration of information generated by the cancer research community is still very disjointed, and this presents an important challenge and opportunity toward advancing patient care based on the genomic science. Underpinning the success of these efforts will be integration through the human genome sequence and its genes, and the incorporation of highly curated information such as for specific genes (Olivier et al., 2002).

MOUSE MODELS OF CANCER Important to our progress in understanding the roles of genes and environment in cancer development and progression are animal model that are increasingly sophisticated in their mimicry of human cancers. For example, Kuperwasser and colleagues (Kuperwasser et al., 2004) constructed an orthotopic xenograph model with epithelial cells as well as stromal cells that are of human derivation in order to provide a tumor development model closely aligned with human tumors. More recently this team (Kuperwasser et al., 2005) reported on the development of a mouse model of breast cancer in which the cancer cells and their natural human bone metastasis target both function, resulting in human species specific metastasis within the mouse. Moreover, the combined use of improved models of mouse cancer together with advanced molecular imaging technologies holds much promised toward understanding the mechanisms and pathways that drive cancer development and progression, and to better direct the development of early detection and therapeutic intervention (Kim et al., 2005).

FUTURE DIRECTIONS The sequencing of the human genome and identification of its genes set the stage for a new era of cancer intervention based on the specific molecular nature of the tumor and genetic characteristics of the patient. It is certainly early to speculate how rapidly this new era of “personalized medicine” will become the norm in medical care (see Chapter 22). Already, some wonderful

successes have occurred, and for certain cancers patients receive therapies based on specific gene changes in their tumors. Successes are expected to be greatly expedited through close integration of basic and clinical research, both in the academic setting and industry (Strausberg et al., 2004). New targeted therapies will be developed in tandem with specific molecular diagnostic tests that inform the most appropriate intervention strategy for the patient, or at least best inform those choices (Papadopoulos et al., 2006) (see Chapter 82). Accomplishment of these goals will require continued technological advances as well as new considerations for how research teams are organized to address this mission (Stoughton and Friend, 2005; Strausberg et al., 2004). Although the complexity of tumors has been appreciated, the complexity of the intermolecular interactions, and the interplay of regulation between cancer cells and other tumor components including endothelial cells and the immune system can now be better examined with newer technologies (Hu et al., 2005). Discovery of genomic variation in patients and tumors is a platform for cancer research, but still only a portion of the required infrastructure. The expression products of the genome ultimately drive tumor development and progression, and understanding the complex manner in which these products interact within cells and between cells in a tumor, as well as the interface with their microenvironment and the physiology of the host, remains a great challenge. Therefore, model systems become increasingly important. Essential to this complete picture will be the comprehensive characterization of epigenetic changes (Bodmer, 2006; Hu et al., 2005) associated with or causative of the cancer as well as the functional effects that can be observed through molecular imaging (Jordan and Cheng, 2007; Weissleder, 2006) and physiological features as revealed metabolomic analysis (Claudino et al., 2007; Griffin and Kauppinen, 2007). Increasing dataset integration, including basic and clinical research and incorporating data on molecules that target cancers and their specific effects in cells and organisms, will bring new perspectives to the design of intervention strategies (Buetow, 2005; Buetow et al., 2002; Bussey et al., 2006; Strausberg and Schreiber, 2003). As we learn how to target specific molecules, the consequences of that targeting for the short- and long-term will drive the cost-effective and timely appearance of the pharmacy of cancer drugs that will be required to practice personalized medicine. Importantly, the emergence of molecularly targeted imaging technologies will allow the visualization of specific enzymatic activities and biological functions such as metastasis, as well as playing a major role in increased knowledge of cancer within humans and the development of personalized intervention strategies (Weissleder, 2006). The prospect of a pharmacy of molecularly targeted therapeutics accessible to patients based on their specific cancer seems quite bright. It is also important to note that perhaps the biggest impact on cancer can derive from a better understanding of the environmental factors that are associated with cancer development and the specific molecular mechanisms that are

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of cancer. Integration of omic technologies and databases in prevention and early detection studies will be enabling to this goal. With that knowledge perhaps the incidence of cancer might decrease, and hopefully those individuals that develop cancer will have better prospects for long-term survival in good health. It is a vision that can be brought to fruition.

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Strausberg, R.L., Simpson, A.J. et al. (2004). Oncogenomics and the development of new cancer therapies. Nature 429(6990), 469–474. Swede, H., Bartos, J.D. et al. (2006). Genomic profiles of colorectal cancers differ based on patient smoking status. Cancer Genet Cytogenet 168(2), 98–104. Thomas, R.K., Nickerson, E. et al. (2006). Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing. Nat Med 12(7), 852–855. Trujillo, E., Davis, C. et al. (2006). Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc 106(3), 403–413. van ‘t Veer, L.J., Dai, H. et al. (2003). Expression profiling predicts outcome in breast cancer. Breast Cancer Res 5(1), 57–58. van Breda, S.G., de Kok, T.M. et al. (2007). Mechanisms of colorectal and lung cancer prevention by vegetables: A genomic approach. J Nutr Biochem. Venter, J.C., Adams, M.D. et al. (2001). The sequence of the human genome. Science 291(5507), 1304–1351. Vogelstein, B. and Kinzler, K.W. (2004). Cancer genes and the pathways they control. Nat Med 10(8), 789–799. Vogelstein, B., Lane, D. et al. (2000). Surfing the p53 network. Nature 408(6810), 307–310. Wang, T.L., Maierhofer, C. et al. (2002). Digital karyotyping. Proc Natl Acad Sci USA 99(25), 16156–16161. Weissleder, R. (2006). Molecular imaging in cancer. Science 312(5777), 1168–1171. Wicker, T., Schlagenhauf, E. et al. (2006). 454 sequencing put to the test using the complex genome of barley. BMC Genomics 7, 275. Yang, J., Mani, S.A. et al. (2006). Exploring a new twist on tumor metastasis. Cancer Res 66(9), 4549–4552.

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68 Immune Cells and the Tumor Microenvironment David S. Hsu, Michael Morse, Timothy Clay, Gayathri Devi and H. Kim Lyerly

INTRODUCTION Cancer is derived from sequential genetic and epigenetic changes in the cellular genome (Vogelstein and Kinzler, 2004) that eventually manifests itself as “self-sufficient in growth signals, insensitivity to growth-inhibitory (antigrowth) signals, evasion of programmed cell death (apoptosis), limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis” (Hanahan and Weinberg, 2000). Currently, the tumor cells are now viewed “as complex tissues in which mutant cancer cells have conscripted and subverted normal cell types to serve as active collaborators in their neoplastic agenda” (Hanahan and Weinberg, 2000). This view shifts the focus of scientific inquiry to the cancer microenvironment that consists not only of tumor cells (and possibly tumor progenitor cells or tumor “stem cells”), but also to the surrounding stromal elements, including the extracellular matrix, fibroblasts, immune and inflammatory cells, adipocytes, smooth muscle cells, and vascular cells. The importance of a whole microenvironment view is underscored by several observations: First, the interaction between cancer cells and their microenvironment can largely determine the behavior

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of the tumor in vivo. For example, recent studies have shown that the establishment of human breast tumor xenografts in mice depends on the presence of human tumor-derived stromal fibroblasts (Kuperwasser et al., 2004). Second, the various components of the tumor microenvironment have been independently linked to outcome. It has also been reported that increased microvascular density and vascular endothelial growth factor expression in tumors, the presence of myofibroblasts (Surowiak et al., 2006), and the presence of tumor-infiltrating macrophages (Bingle et al., 2002; Pollard, 2004) has been linked to more aggressive tumors, higher rates of relapse and/or inferior survival (Des Guetz et al., 2006; Uzzan et al., 2004). Recent reports of gene expression profiling of tumors are consistent with the view that gene expression in the tumor cells and stromal cells provide information about tumor behavior. Analysis of bulk gene transcription profiles of the tumor and stromal including immune cells have been found to be predictive of metastasis or survival (Ramaswamy et al., 2003;Wang et al., 2005). Metastatic capacity of the tumor appear to be determined by some of the same early changes that lead to tumorigenesis (Bernards and Weinberg, 2002) and that can be predicted from the gene expression profile of the bulk tumor tissue (Ramaswamy

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Immune Cells of the Tumor Microenvironment

et al., 2003; van’t Veer, et al., 2002; Ye et al., 2003). In addition, while the gene expression signature of primary tumors may be similar to that of their corresponding metastases, the gene expression signature differs significantly between metastasis-free primary tumors and tumors with accompanying metastases (D’Arrigo et al., 2005; Ye et al., 2003). These data suggest that the metastatic propensity of tumors is an early feature of tumors that is influenced by host factors in the local environment of the primary tumor and metastatic sites suggesting the importance of the host immune and inflammatory response in the tumor microenvironment. Because there are a number of excellent reviews on the tumor microenvironment in general (Kiaris et al., 2004; Liotta and Kohn, 2001; Mueller and Fusenig, 2004; Witz and LevyNissenbaum, 2006), this chapter will focus on the immune and inflammatory cell component of the microenvironment and in particular the genomic study of immune system and tumor cell interactions and their applicability for clinical practice.

IMMUNE CELLS OF THE TUMOR MICROENVIRONMENT Immune and Inflammatory Cells Tumors are frequently infiltrated with immune and inflammatory cells including T cells, dendritic cells, neutrophils, macrophages, eosinophils, and mast cells (Figure 68.1). Although one might assume that the presence of cells with antitumor activity would lead only to tumor destruction, well established clinical

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scenarios suggest that chronic inflammation is associated with the development and progression of malignancy-gastric cancer associated with H. pylori infection (Sugiyama, 2004), colorectal cancer associated with inflammatory bowel diseases (Pohl et al., 2000), and hepatocellular carcinoma (HCC) associated with hepatitis B and C infection (Chan and Sung, 2006). Considerable experimental evidence supports a role for macrophages and mast cells in abetting tumor progression (Coussens et al., 1996; Lin et al., 2001). Tumor cells frequently overexpress inflammatory cytokines such as CSF1 (a growth and differentiation factor for macrophages) (Kacinski, 1997; Lin et al.,2002; Scholl et al., 1994) and chemokines such as CC chemokine ligand 2 CCL2/ MCP-1 (monocyte chemoattractant protein 1 (Ueno et al., 2000; Valkovic et al., 1998) that cause macrophage infiltration into tumors (so called tumor infiltrating or tumor associated macrophages (TAM)) (Bingle et al., 2002; Lee et al., 2004; Leek and Harris, 2002). Overexpression of these cytokines and macrophage infiltration have typically been linked to poor prognosis. This paradox – poorer prognosis despite the ability of TAMS to destroy tumor cells directly – results from their production of growth factors (TNF- (Szlosarek and Balkwill, 2003), IL1 , and IL-6 (Balkwill et al., 2005) that maintain tumor proliferation and survival, matrix metalloproteinases that remodel the extracellular matrix (Egeblad and Werb, 2002), IL-10, PGE2, and TGF- that inhibit adaptive immune responses, and VEGFs and FGF that stimulate angiogenesis and lymphangiogenesis (Barbera-Guillem et al., 2002; Esposito et al., 2004; Yano et al., 1999). Microarray analysis of tumor cells co-cultured with macrophages demonstrate the upregulation of 50 genes including proangiogenic genes (IL-6, IL-8, and MMP-9), supporting the

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Tumor Ag Pro-angiogenic factors. MMPs

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Growth factors Th2 cytokines

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Endothelium Granulocyte (eosinophil)

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Th

cytokines

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Figure 68.1 Interaction between inflammatory/immune cells and tumor microenvironment. Tumors secrete chemoattractants for inflammatory cells (tumor-associated macrophages, eosinophils, mast cells) that in turn secrete factors that cause proliferation of tumors and infiltration of the tumor microenvironment with new blood vessels. In addition, tumors secrete cytokines and factors that inhibit dendritic cells and T cell activation and promote regulatory T cell development. Dendritic cells may have T cell stimulatory or inhibitory activity depending on the cytokine milieu.

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concept that TAM regulate the production of pro-angiogenic factors in tumors (Chen et al., 2005). Furthermore, the data supported the concept that the upregulation of the pro-angiogenic factors was in part mediated through NF-B. An important finding in this study was that anti-inflammatory agents (celecoxib, dexamethasone, pyrrolidine dithiocarbamate (PDTC, an NF-B-specific inhibitor), and aspirin) could revert the gene expression pattern towards the state of tumor cells not cultured with macrophages, likely through NF-B-mediated mechanisms. Nonetheless, the results also showed that the inhibitory mechanisms of the anti-inflammatory agents in cancer cell/macrophage co-cultures can be discriminated from each other by gene expression profiles and thus are different. These data could therefore provide clues towards determining more comprehensively the functional mechanisms of anti-inflammatory drugs, as well as for evaluating the potential activity of new drugs for interfering with the inflammation-mediated pro-tumorigenic activity of TAM. Already there is interest in combining COX-2 inhibitors with anticancer therapy because of the association of COX-2 overexpression by tumors and increased angiogenesis, invasion, metastasis, resistance to apoptosis and diminished tumor immunity. The COX-2-dependent effects on tumor immunity appear to be partly mediated by prostaglandin E2 (PGE2) and include skewing of T-cell responses toward TH2 responses and suppression of dendritic cell (DC) functions (Betz and Fox, 1991; Huang et al., 1996, 1998; Sharma et al., 2003; Stolina et al., 2000). Myeloid Cells Other cellular components of the microenvironment have direct effects on suppressing antitumor immunity. A population of immature myeloid cells (iMC) [characterized in mice as Gr-1 CD11b cells and in humans as CD33 mature myeloid and lymphoid marker negative and HLA-DR negative (reviewed in Kusmartsev and Gabrilovich, 2006)], among other functions in tumor bearing animals, inhibit antigen-induced interferon- (IFN-) production by CD8 T cells (Gabrilovich et al., 2001) and prevent development of cytolytic T cells (CTC) in vitro (Liu et al., 2003). Mechanisms for tumor-induced T-cell dysfunction in cancer mediated by iMC include the production of TGF- (suppressive of T-cell function), reactive oxygen species (toxic to T cells), and peroxynitrite, (an oxidant that can inhibit T-cell activation and proliferation by impairment of tyrosine phosphorylation and apoptotic death), depletion of arginine by arginase (reduced T-cell proliferation) (reviewed in Kusmartsev and Gabrilovich, 2006). We are not aware of microarray analyses yet on the immature myeloid cells, although because they likely represent a collection of more than one cell type, genomic analysis may be less fruitful until the different subgroup members can be better delineated. Regulatory T Cells Another subset of inhibitory cells-infiltrating tumors are the CD4CD25 regulatory T cells (Treg) that were originally described by their ability to suppress activation and expansion of self-reactive effector T cells (peripheral tolerance) in order to

prevent autoimmunity (Thornton and Shevach, 1998), but more recently they have been described to enable tumors to elude the host antitumor immune responses. Treg numbers are greater in the peripheral blood of patients with breast (Liyanage et al., 2002), pancreas (Liyanage et al., 2002), liver (Ormandy et al., 2005) gastric, and esophageal cancer (Ichihara et al., 2003) compared with healthy controls, and within tumors of patients with ovarian (Curiel et al., 2004), hepatocellular (Ormandy et al., 2005) gastric, and esophageal cancer (Ichihara et al., 2003) and lung cancer (Petersen et al., 2006). Disease free and/or overall survival are negatively impacted by greater Treg numbers within tumor (Bates et al., 2006; Curiel et al., 2004; Petersen et al., 2006, Sato et al., 2005). Treg cells require antigen-specific activation or polyclonal TCR stimulation to exert their suppressive function, but once activated, they can suppress T cells (proliferation and IL-2 secretion of naïve or effector T cells) in an antigen-nonspecific manner through a cell-contact mechanism. TGF- expressed on the surface of Tregs and secreted IL-10 may also mediate suppression of proliferation of both naïve CD4 T cells and CD8 T cells (Reviewed in Wang, 2006). Recently, Pfoertner et al. (2006) designed a Human Treg Chip and identified 62 genes differentially expressed in Treg, including those well known to be involved in Treg function (FOXP3, CTLA4, and CCR7), as well as a large number of genes not previously associated with Treg. These data suggest that it may be possible to identify Treg infiltration into tumors by scanning microarray data for the Treg signature. Also, by studying the pathways to which many of the previously unassociated genes belong, it may be possible to identify pathways in Treg that could be modulated to eliminate their negative impact on antitumor immune response. This analysis has been aided by studies that have analyzed the different expression profiles between activated T cells, anergic T cells (incapable of responding to antigen), and Treg (Knoechel et al., 2006) induced by the same self-Ag. Dendritic Cells Some infiltrating immune cells may have both immunosuppressive and immunostimulatory activity depending on the conditions in the tumor milieu. DC are central to the initiation and modulation of T-cell responses (Banchereau et al., 2000). DC originating from bone marrow-derived precursors infiltrate peripheral sites, including tumors where they capture and process antigen (Guermonprez et al., 2002). In response to local inflammatory signals (CD40L, lipopolysaccharides (LPS), interleukin 1 (IL-1), or tumor necrosis factor (TNF)), DC mature resulting in their migration to secondary lymphoid organs and presentation of antigen to T cells (Lanzavecchia and Sallusto, 2001) and the secretion of IL-12, IL-18, and IL-10. Mature DC are characterized by their potent capacity to activate naive T lymphocytes due to the high expression of HLA class II and costimulatory molecules (CD40, CD80, and CD86.) Many, but not all studies (Ambe et al., 1989; Coventry and Morton, 2003; Eisenthal et al., 2001; Furihata et al., 1992; Goldman et al., 1998; Iwamoto et al., 2003; Lespagnard et al. 1999; Okuyama et al., 1998) suggest that increased infiltration of DC into tumors or in

Immune Cells of the Tumor Microenvironment

the peri-tumoral tissue is associated with an improved prognosis. The reasons for nonuniformity in this observation may be due to the fact that DC may have immunostimulatory or immunotolerizing activity. The negative effects of tumor on DC and the resulting DC mediated tumor-induced immune suppression have been extensively reviewed (Fricke and Gabrilovich, 2006). In brief, defects in DC maturation occur such that there are fewer mature DC within tumors and an accumulation of immature DC precursors (immature myeloid cells). This is thought to be in part related to VEGF secretion by tumors. A decrease in functional antigen presenting cells reduces the activation of antigen-specific T cells; iDCs do not induce antitumor immune responses but instead induce T-cell tolerance. Furthermore, an immunotolerizing subset of DCs (plasmacytoid DC) accumulate in tumors and tumor draining lymph nodes and suppressed T-cell responses to other antigen presenting cells. Furthermore, DC may produce indoleamine 2, 3-dioxygenase (IDO), an enzyme catalyzing the initial and rate-limiting step in the catabolism of tryptophan (Uyttenhove et al., 2003). By depleting tryptophan locally, IDO blocks the proliferation of T lymphocytes which are arrested in the G1 phase of the cell cycle during tryptophan shortage. The transcription profile of DC with interferon gamma-mediated IDO production has been studied to create a “toleragenic” signature (Orabona, et al., 2006). A number of microarray studies of DC at different maturational states have also been performed (Ahn et al., 2002; Dietz et al., 2000; Granucci et al., 2001; Ju et al., 2003; Le Naour et al., 2001; Messmer et al., 2003;Tureci et al., 2003). Although genes expected to be upregulated in DC such as cytokines (IL-1, TNF-, IL-6, etc.), growth factors receptors (IL-7R, IL-13R, GM-CSF-R, etc.) antigen processing/presentation (UBH1, TAP1, and 2-microglobulin) CD83, and molecules involved in CD40 signaling were strongly induced (ERK3, MAP3K4) (Tureci et al., 2003), these studies have demonstrated at times surprising results. DC matured with TNF- and polyI:C were found to upregulate IDO and other components of the tryptophan metabolism system using a DCdedicated microarray (called the “DC Chip”), designed to incorporate genes differentially regulated during DC maturation and other genes with known or probable immune functions (McIlroy et al., 2005), as had been observed by others using different maturational stimuli (Hwu et al., 2000). This would suggest the mature DC may have immunosuppressive effects under certain conditions. These data combined with the observation that tumors also secrete IDO (Brandacher et al., 2006) would suggest that IDO should be a target for enhancing antitumor immune responses. Such data is now being generated in murine models. T Cells Because they are central to the destruction of tumors expressing their cognate antigens, higher numbers of CD8 CTL within tumors are, not surprisingly, associated with an improved prognosis. CD4 helper T cells have also been correlated with outcome (Alvaro et al., 2006; Fukunaga et al., 2004; Haanen et al., 2006; Hiraoka et al., 2006; Karja et al., 2005; Sato et al., 2005; Schumacher et al., 2001; Wakabayashi et al., 2003). Most studies

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used immunohistochemistry to enumerate theseT cells. Galon et al. 2006 demonstrated that human colorectal cancer with a high density of infiltrating memory and effector memory T cells were less likely to disseminate to lymphovascular and perineural structures and to regional lymph nodes. They followed up this work using tissue microarrays to quantitate total T lymphocytes (CD3), CD8 T cell effectors and their associated cytotoxic molecule (GZMB), and memory T cells (CD45RO) within the center and invasive margin of tumors (Galon et al., 2006). Tumors from patients without recurrence had higher immune cell densities (CD3, CD8, GZMB, and CD45RO) within each tumor region than did those from patients whose tumors had recurred. This relationship held after adjusting for tumor invasion, tumor differentiation, and lymph node invasion. An important aspect of their analysis was the use of a genomic analysis to evaluate the expression levels of genes related to inflammation, Th1 adaptive immunity, and immunosuppression. They identified a dominant cluster of comodulated genes for Th1 adaptive immunity (genes encoding T-box transcription factor 21, interferon regulatory factor 1, IFN-, CD3-, CD8, granulysin, and granzyme B (GZMB)). There was an inverse correlation between the expression of these genes and tumor recurrence rate. In this study, the inflammatory and immune suppression clusters were not correlated with outcome. These data suggest that it is possible to identify a Th1 signature in tumors and that it has prognostic significance, likely by a direct affect on tumors. Others have utilized quantitative real-time PCR for assessing immune cell density. Mocellin et al. (2003) demonstrated that genes for CD4, CD8, CD14, CD56 could serve as indicators of helper and cytotoxic T-lymphocytes, macrophages and NK cells, respectively, under resting conditions and after cell stimulation. Others (Willinger et al., 2005) have characterized the gene expression and cytokine signaling profiles of CD8 naïve T cells, effector memory, and effector memory CD45RA  cells. They observed that effector memory and effector memory CD45RA are closely related, but central memory have a gene expression and cytokine signaling signature that lies between that of naïve and effector memory or effector memory CD45RA cells. Furthermore, a survey of the genes expressed by these subgroups indicated that effector memory and the effector memory CD45RA cells strongly express genes with known relevance to CD8 T cell effector function (such as granzyme A (GZMA), granzyme B (GZMB), granzyme H (GZMH), and perforin (PRF1) as well as TNFSF10 (TRAIL) and TNFSF6 (FASL) that mediate perforin-independent apoptosis of target cells as well as EOMES, TBX21 (T-BET), REL, NFATC2, and NFATC3, transcription factors that control effector function in CD8 T cells. Thus, it may be possible to search tumor microarray data for these immune cell signatures as a way of determining which immune cell types might predominate within the tumor and to determine whether they have prognostic significance. Natural Killer Cells One other cell type with prognostic significance within the tumor environment is the natural killer (NK) cell. Extensive

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infiltration of NK cells in human cancers is associated with improved prognosis (Coca et al., 1997; Ishigami et al., 2000). Two distinct human NK cell subsets have been identified, based on expression of FcRIII (CD16), the CD16-CD56bright NK (10%) and CD16 CD56dim NK (90%) The CD56bright have poor cytolytic activity but secrete large amounts of cytokines, and CD56dim have potent natural cytotoxicity, but produce significantly less cytokines (reviewed in Wallace and Smyth, 2005). Activated NK cells secrete IFN-, GM-CSF, G-CSF, M-CSF, TNF-, IL-5, IL-10, IL-13, and chemokines which can have a direct effect on tumor growth, but some of these cytokines may also prime immune effector cells that are crucial for subsequent adaptive immune responses. The expression profile of the different NK cell subsets (Hanna et al., 2004) suggests that CD16–CD56bright, CD16CD56dim, and activated NK cells indeed represent three different NK cell subsets. Molecular explanations for the behavior of the different NK cell subsets was determined by the differences in gene expression. For example, it was determined that possible reasons that CD16 CD56brightNK are less cytotoxic than CD56dimCD16NK include lower levels of the cytotoxic molecules GZMB and CTLA-1 expressed by the CD56brightCD16 subset. Furthermore, the CD3 chain was down-regulated more than threefold in CD56brightCD16NK and this is also noted in T cells with decreased cytolytic activity. Regarding trafficking of NK cells, the CD16CD56brightNK cell population preferentially expressed CD62L and chemokine receptors CCR7 and CXCR4 explaining why this subset is enriched in various human secondary lymphoid organs (lymph nodes, tonsils, and spleen) while the CD16CD56dimNK preferentially traffic to sites of inflammation. They also observed that CD58 (LFA-3), ICAM2, and integrin E were upregulated in the CD16 CD56dim subset, while integrin 5, integrin M, integrin X, ICAM3, and CD44 were upregulated in the CD16CD56bright NK subset. The activated NK cells possessed further gene expression changes including inducers of cell cycle progression and proliferation, tetraspanin molecules involved in adhesion, chemokine regulatory molecules, and effector molecules such as specific granzymes. These data provide potential signatures of NK cells that could be applied to the study of the tumor microenvironment to determine if NK cells may be playing an important role in the host response to the tumor.

EXAMPLES OF TISSUE OR GENE MICROARRAYS USED TO STUDY TUMORS Genomic analysis of the tumor microenvironment has been helpful in determining the events important for tumor progression. Lymphomas represent a special scenario in which immune cells within the tumor microenvironment directly enhance tumor progression by secreting cytokines stimulatory to the tumor cells. The pathogenesis of some types of lymphoma are particularly related to the immune response in the microenvironment. For gastric MALT lymphomas, related to H. pylori infection, the

growth of lymphoma cells is not fully autonomous and survival and proliferation depend on antigen-mediated Th2 cytokines produced by T cells responding to the infectious organism (Isaacson and Du, 2004). Other host factors also appear to be important, such as certain polymorphisms of the T-cell inhibitory molecule, CTLA4 (Cheng et al., 2006). In follicular lymphomas (FL), it has been proposed that the tumor cells recruit a cellular microenvironment that facilitates their own growth and the resulting immunologic makeup appears to influence prognosis. An initial event (e.g., t14;18) results in B cells with resistance to apoptosis. In poor prognosis FL, additional genetic events in B cells induce activated follicular dendritic cells (FDC) and activated T-cells producing Th1 cytokines that cause an increased component of interfollicular tumor cells. Subsequent transformation to diffuse large B-cell lymphoma may result in this context from genomic instability either due to other genetic defects within the tumor cells or from activated immune cells producing mutagenic oxidative radicals (de Jong, 2005). In good prognosis FL, the genetic events in the malignant B cells induce relatively inactivated FDC and T-cells that do not lead to further genomic instability. Microarray analysis of FL has demonstrated that the infiltrating immune cells predict prognosis (Dave et al., 2004). Dave and colleagues identified two signatures, one of which (Immuneresponse 1 signature) correlated with improved prognosis when overexpressed and a second (Immune-response 2 signature) associated with a poorer prognosis when overexpressed. By mathematically combining the two a score for recurrence could be obtained that predicted survival. The Immune-response 1 signature includes genes encoding T-cell markers (e.g., CD7, CD8B1, ITK, LEF1, and STAT4) and genes that are highly expressed in macrophages (e.g., ACTN1 and TNFSF13B). The authors note that Immune-response 1 signature is not merely a surrogate for the number of T cells in the tumor-biopsy specimen since many other standard T-cell genes (e.g., CD2, CD4, LAT, TRIM, and SH2D1A) were not associated with survival. The Immuneresponse 2 signature includes genes known to be preferentially expressed in macrophages, DC, or both (e.g., TLR5, FCGR1A, SEPT10, LGMN, and C3AR1). They further determined that these gene signatures reflected gene expression by nonmalignant tumor-infiltrating immune cells rather than the malignant clone. This study therefore provides a molecular signature of the type of immune response that is associated with long-term survival. The actual reason for the longer survival, though, can only be hypothesized. For example, it is possible that the immune response embodied by Immune response-1 signature leads to an antitumor response and Immune response-2 genes are associated with an inflammatory and pro-tumorigenic response; but, Dave et al. (2004) also suggested that the lymph-node cells responsible for the Immune-response 1 signature provide trophic signals that promote the survival or proliferation of the malignant cells, but the dependence of the malignant cells on these environmental signals may prevent them from leaving the lymph node, possibly accounting for prolonged survival (Dave et al., 2004). Similarly, the study by Monti et al. (2005) used wholegenome array analysis in diffuse large B-cell lymphoma to identify

Genomic Studies of Immune Stimulation within the Microenvironment

three distinct clusters. The first cluster consisted of genes involved in oxidative phosphorylation while the second involved more proliferative type genes such as genes encoding cell cylcle regulation, DNA repair, and B-cell receptor signaling. The third group was termed “host response” (HR) and consisted of genes involved in the host immune response such as T/NK cell receptor, macrophages and DC markers, inflammatory mediators such as IL-2, and genes involved in the complement cascade. Within this group, CD2/CD3 tumor infiltrating-lymphocytes were also identified. However, patient survival was not improved in the HR group, suggesting the role and interaction of the host immune response is highly dependent on the tumor type and the microenvironment. The picture of immune-mediated tumor progression is also likely different in non-hematologic malignancies. Budhu et al. (2006) evaluated the role of the microenvironment in metastases of HCC by comparing the gene expression profiles of 115 noncancerous surrounding hepatic tissues from two HCC patient groups, those with intrahepatic or extrahepatic metastases [which the authors called a metastasis-inclined microenvironment (MIM)], and those with HCC without detectable metastases [which the authors termed a metastasis-averse microenvironment (MAM)] sample. Using this patient cohort, they conducted gene expression profiling studies of a subset of MIM and MAM samples using cDNA microarray and demonstrated two gene clusters (called inflammation/immune response cluster A and B, respectively) most significantly differentiated MIM from MAM samples. Cluster A contains 38 underexpressed genes, while cluster B contains 68 overexpressed genes in the MIM group. The authors note that over 30% of the genes in these two clusters are associated with either inflammation and/or immune response functions. For example, MHC class II HLA-DR, found on antigen presenting cells such as macrophages, was most significantly upregulated in noncancerous MIM samples. Using qRT-PCR to analyze the expression of cytokine genes, the authors showed that MIM had a significant increase in Th2 cytokines (IL4, IL5, IL8, and IL10) and a concomitant decrease in Th1 cytokines (IL1, IL1, IL2, IFN, and TNF-) compared to a normal liver pool. A 17 gene “liver microenvironment venous metastasis” signature (12 Th1/Th2 cytokines, HLA-DR, HLA-DPA, ANXA1, PRG1, and CSF1) was more than 92% accurate in correctly predicting venous (intrahepatic) metastases of an independent HCC cohort and also predicted recurrence after resection and distant metastases. This profile was more accurate than a signature developed from HCC tumor tissue and than traditional clinical predictors. The 17 gene signature did not overlap with that developed by Dave et al. (2004) to predict recurrence of FL, suggesting that the prognostic genes may differ among different tumor types. In the future, it would be of interest to understand what treatments recapitulate the more favorable gene clusters as an indicator of their likely efficacy. For example, attempting to enhance Th1 cytokines (e.g., application of interferons) might improve outcome in some patients with HCC as has been suggested by studies demonstrating reduced risk of HCC in patients with vial hepatitis treated with HCC.

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GENOMIC STUDIES OF IMMUNE STIMULATION WITHIN THE MICROENVIRONMENT Immunotherapy strategies targeting tumors have included nonspecific strategies such as the systemic administration of IL-2 intended to activate T and NK cells as well as cancer vaccines intended to activate antigen-specific T cell and antibody responses. Studying the “tumor microenvironment signature” that predicts response to these modalities or the changes in the microenvironment signature following application of these strategies could help identify methods for augmenting their efficacy. An important demonstration of the utility of genomic analysis of cytokine effects on tumor environment was made by Panelli et al. (2002) who analyzed peripheral blood mononuclear cells and FNA samples from tumors of patients treated with interleukin-2 (IL-2). They carried out their analysis in two ways: First, they determined a set of genes whose expression was consistently altered in PBMCs following IL-2 administration and then analyzed these genes in a set of FNAs obtained from the same patients. They sampled lesions 3 hours after one dose and four doses of high dose IL-2. The post-IL-2 FNA material was cohybridized with reference RNA obtained pre-IL-2 from each lesion. The few genes consistently upregulated in FNA samples belonged to subclusters consisting of genes for inflammatory chemokines (MCP-1, MCP-3, IP-10, LD-78) and interferon- (IFN-)-related genes (guanylate-binding protein IFN-inducible, MxA, IP-10). Expression of these genes was found to be related to activation of either monocytes or T cells. They also analyzed genes with the highest level of upregulation or down-regulation in FNA samples following IL-2 administration. These genes included those for several chemokines (GRO-1, MIG, MCP-1, MCP-3, MIP1-, MIP-, PARC, IL-8), cytokine receptors (CCR-1, IL-R, IL-1R antagonist, IL-2R, transforming growth factor- receptor (TGFR), IFNR  chain), adhesion molecules associated with mononuclear cell migration (CD62L, VCAM-1, CD64, CD29), cytotoxic proteins in granules produced by activated monocytes (grancalcin and calgranulin) and several genes associated with IFN- activity, such as HLA class II molecules and IFN-regulatory or responsive genes such as those for MxA, MxB, and IRF-1. Interestingly, no genes associated with apoptosis or cell proliferation were identified. In addition, no evidence of migration of various immune-cell subsets was observed by analyzing changes in frequency of genes constitutively expressed by immune cells (such as, CD8, CD4, CD14). Thus, the early effects of IL-2 administration are more associated with an inflammatory modulation of the tumor microenvironment rather than with specific migration, activation and/or proliferation of immune cells at the tumor site. By analyzing a lesion that underwent partial regression, the authors found 19 genes with the highest expression. Among these 19 genes (TIAR, nucleolysin cytotoxic granule; NK4, natural killer cell protein 4; NKG5, granulysin; EBI3; TCR alpha; DAG kinase; HLA class II region expressed gene KE4; MHC class II DR beta; SERPINB1, Leukocyte elastase

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inhibitor; MIP-1 delta; FGF-13; STIM1, Stromal interaction molecule 1; VEGF; CD62; P-selectin; GALECTIN 1; N-MYC; DAP-1; 53BP1, p53 binding protein), those for NKG5, T-cell receptor  chain, and HLA class II related transcripts are of particular interest, particularly because they have also been found to be upregulated in the context of acute rejection of renal allografts (Sarwal et al., 2001a, b). In addition, three were associated with IL-2-dependent NK-cell activation: NK4, CD62 P-selectin, and galectin 1. The authors explained their data as follows: “IL-2 first affects leukocytes in the peripheral circulation where the IL-2 receptor is immediately upregulated on lymphocytes, monocytes and NK cells. Lymphocytes and NK cells directly respond to IL-2 by releasing IFN, whereas monocytes upregulate CD64, a typical marker of phagocytic activity, the receptor for the inflammatory cytokine IL-1RI, and most probably begin to release IL1/ in a stimulatory autocrine feedback loop. Further amplification of the biological effects of IL-1 is achieved by the ability of IL-1 to upregulate receptor expression for itself, IL-2R, IFNR and GM-CSF/IL-3/IL-5R. The circulating pro-inflammatory cytokines IL-6 and IFN- further increase monocyte activation by triggering the release of MCP-3 and MCP-1 and/or inducing monocyte mobilization to the tumor microenvironment. Macrophages expressing IL-1RI and IFNR that reach the tumor microenvironment, along with resident macrophages, release additional chemokines and cytokines (MCP-3, MIP-1 and , MIG, PARC/MCP-4, IL-8), affecting the trafficking and/or activation of monocytes themselves, lymphocytes/ NK cells, DC and fibroblasts, which in turn can release inflammatory factors. Migration to the tumor microenvironment is also reflected by the active transcription of molecules involved in adhesion of mononuclear cells (CD62L selectin) to vascular endothelial cells (V-CAM). In addition, upregulation of mRNA expression for the cytolytic granule proteins calgranulin/grancalcin and NKG5 underlies the induction of a cytotoxic response by monocytes/macrophages and NK cells, respectively. The net effect of the IL-2-induced inflammatory cascade in the tumor microenvironment is the dominant expression of IFN--dependent genes (MHC class II, HSP70, MxA, MxB) and/or IFN- regulatory genes (IRF-1, IF1616, GBP1 interferon inducible, IEF protein). These data suggest that addition of other cytokines (such as interferons) at the appropriate time point and possibly the adoptive transfer of certain cell subtypes such as NK cell may result in enhanced activity.”

Thus far such approaches have had questionable activity, but will need to be studied genomically to determine if the expected changes in the tumor environment indeed occur. Similar to genomic analysis of cytokine strategies, it will be important to characterize the gene expression patterns associated with protective cancer vaccine strategies. In a murine model of vaccines targeting murine neu antigen (Astolfi et al., 2005), expression profiles from the tumor demonstrated increased expression of IFNinduced genes (Stat1, Ifi1, Ifi47, Igtp, Gbp1-3, CXCL9, PSMB9) and chemokine genes (Chemokine receptor 5 (CCR5), chemokine

ligand 8 (CCL8) and a T cell-specific GTPase (TGTP)) in those with more efficient vaccine responses. An important finding was that chronic vaccination is needed to maintain an active IFNgamma-mediated response in the mammary gland. Thus far, there have been few other analyses of the tumor microenvironment after anticancer immunization on a genomic level, but such experiments are clearly needed to help guide this field.

GENOMIC ANALYSIS OF TUMOR MICROENVIRONMENT IN IMMUNOTHERAPY STUDIES Genomic approaches to study of the immune component of the tumor microenvironment offer the possibility of identifying which cytokines and signaling molecules are important for limiting pro-tumorigenic responses and enhancing antitumor immune responses. In some cases, molecules previously unassociated with immune responses may be identified in the analyses providing new avenues for modulating immune responses. This excitement must be tempered by the challenges inherent in such complicated systems. First, because the changes induced by cytokines may be rapid in onset and short-lived (e.g., McIlroy et al., 2005 study demonstrated that DC undergoing maturational stimuli undergo rapid changes in mRNA expression; Li et al., (2006) observed that generations of T-cells responding to a stimulus activated cell cycle and surviving gene pathways, while late generations of T cells had more dramatic changes in transcription of cytokine genes), the time for sampling tumors must be carefully planned and dependent on the change most important to measure. Nonetheless, if it can be demonstrated that there are not dramatic changes over time, then sampling need not be as well-timed. For example, Panelli et al. 2002 found that samples from the same lesion clustered together independently of time, suggesting that profiles of IL-2-induced genes tend to be lesion specific throughout IL-2 therapy. Second, polymorphisms in a variety of genes may influence how individuals respond to immune stimuli, with some individuals not responding at all. Therefore, it is important that, as much as possible, samples be obtained before and after the stimulus for comparison in an individual subject. Because individual tumor sites may also have different patterns of gene expression, it is important to sample the same tumor site in serial analyses. For example, Panelli et al. (2002) compared gene expression from the same tumor obtained by FNA before and after IL-2 therapy in order to minimize differences due to heterogeneity of lesions or of patients and to maximize their ability to detect difference between the pre- and post-IL-2 expression profile. Third, some immune stimuli cause rapid and dramatic changes in the frequency of cellular subsets which could potentially overwhelm the ability to detect small changes in gene expression within individual cells. As Panelli et al. (2002) points out “observed variations in gene expression profile could be due to differential gene expression in individual cells, shifting proportions of different cell subsets, or a combination of the two.” Therefore, it is important to confirm that immune-response signatures are not merely a surrogate for

Conclusion

the number of immune cells in the tumor-biopsy specimen, by comparing induced genes with genes constitutively expressed by immune cells. This type of analysis was performed by Dave et al. (2004) in their analysis of predictive signatures for FL. Fourth, the limited amount of material obtained from some tumors may allow analysis of the expression of only a few gene products, which might miss relevant genes. Marincola’s group developed a method of high fidelity anti-sense RNA (aRNA) amplification that “uses a combination of template switching and in vitro transcription (Wang et al., 2000) and yields 1:10,000–1:100,000 amplification of transcripts from conventional total RNA preparations that maintain gene expression profiles comparable to that of conventional poly(A) and total RNA based sources in cDNA microarrays.” Using this approach, they observed a good correlation between transcript detection of cellular markers in aRNA, total RNA, and at the protein level (Ohnmacht et al., 2001). Therefore, although the ability to confirm genomic results with immunohistochemistry would also be helpful, when it is not possible, there can be some degree of confidence in the transcript expression results. Fifth, one must consider how selective to be in tumor analyses, both in the portion of the tumor sampled and in the genes samples. For example, there has been a trend towards the use laser capture microdissection of tumors. Microarrays generated from these specimens, if they do not include immune cells, will obviously not be helpful for the study of immune interactions in the tumor environment. Given the expense of microarrays and the scarcity of tissue, in our opinion, the most information should be obtained from microarrays to allow other research questions to be asked with that available information. Furthermore, some have argued that wholegenome microarrays are not well-suited for routine analyses, especially when one is interested in one cell type or cytokine pathway. For this reason, some have developed specific arrays (such as DC Chip). Again, we believe that this misses genes of previously unknown association which may ultimately be relevant in understanding the immune cell-tumor interaction.

PROTEOMICS OF IMMUNE CELLS AND THE TUMOR MICROENVIRONMENT The identification of tumor antigens using proteomics have contributed to identify candidate targets for cancer immunotherapy. For the past two decades, the use of two dimensional gel electrophoresis (2DE) has been the basis for proteomic research (O’Farrell and Gold, 1973). 2DE first separates proteins according to their isoelectric point and then by their molecular mass allows for better separation and resolution of protein components. Two methods employing the use of 2DE methods are Serological Proteome Analysis (SERPA) and Matrix-assisted laser-desorption ionization (MALDI). SERPA was developed to identify proteins that induce antibody response in cancer patients (Klade et al., 2001). Two gels with identical protein contents are probed with serum from a cancer patient and normal patient. Spots that are unique from the blot of the cancer patients are

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identified using mass spectrometry to identify tumor-specific antigens. In a similar technique, proteins from serum and blood of clinical patients are separated by 2DE. After 2DE, proteins on the gel are digested with a protease like trypsin to create smaller protein peptides. Combining mass spectrometry and MALDI, a protein/peptide map is created which can be used as a diagnostic fingerprint (Stoeckli et al., 2001; Xu et al., 2002). Recently, protein microarray have been developed to further facilitate the identification of cancer biomarkers. Antibody microarrays have been used to screen sera from prostate cancer patients and normal controls to identify five proteins (von Willebrand Factor, iGM, alpha1-antichymotrypsin, villin and IgG) that differ between the two groups (Miller et al., 2003). Similarly, proteins from prostate cancer cell lines were immobilized on microarray and then used to screen antibodies from prostate cancer patients to identify tumor-specific antigens (Qin et al., 2006). These approaches have now been used to create custom made protein microarrays that are now commercially available (Invitrogen ProtoArray). The development of these techniques has facilitated the identification of tumor associated antigens for cancer prediction, prevention and treatment. However, these results are still preliminary and still remain to be validated in clinical settings.

CONCLUSION The tumor microenvironment is now known to involve interactions between the stroma and tumor cells and the immune component represents a dynamic and modifiable component. Recent genomic studies have demonstrated the importance of characterizing and understanding immune cells of the tumor microenvironment and future goals will be the incorporation of this knowledge into clinical medicine. Although, genomic analysis of the immune cell infiltration into tumors has been fairly limited, it has already demonstrated that “immune response signatures” can be identified that correlate with prognosis (Budhu et al., 2006; Dave et al., 2004; Monti et al., 2005) and can be used to identify patients with poorer prognosis. However, the key next step is to determine how the immune cells and their cytokines truly impact tumor cells to identify new targets for therapy. For example, in patients with FL who are treated with rituximab, microarray analyses have shown that FL from nonresponders to rituximab had a higher expression of the genes involved in cellular immune response and inflammation, specifically those encoding cytokine (e.g., STAT4), TNF (e.g., JunB, Fos), and T-cell receptor (T-cell receptor beta) signaling, and complement proteins (Bohen et al., 2003). Furthermore, genomic signatures can also be used to help understand the pathobiology of immune/inflammatory responses that might contribute to tumorigenesis. For example, in tumors where a Th2 signature seems to predominate, attempts to modify the immune environment by the addition of cytokines that enhance a Th1 environment may be attempted. In tumors where a regulatory T-cell signature seems to predominate, attempts to

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deplete regulatory T cells may be attempted. Insights from these analyses will help design strategies for modifying the immune component of the microenvironment, identify new targets and to enhance efficacy of immunotherapy (Nagorsen et al., 2002). Finally, the use of genomics and proteomics can be used to identify tumor antigens for the development of cancer vaccine. Currently, vaccines are based on the idea of stimulating the human immune system to fight off disease by exposing the immune system to an inactivated form of an infectious agent such as a bacterium or virus. The immune system cells recognize a part of the infectious agent, known as an antigen, as being foreign to the body and produce antibodies which bind

to the antigen and target it for destruction by other parts of the immune system. Consequently, if the body is then infected by a real (i.e., live) infectious agent the body has pre-prepared immune defenses ready to attack the infectious agent. For example the concept of cancer vaccines is design to induce an immune response against tumor cells that the immune system has previously failed to recognize as dangerous. Most cancer vaccines uses whole tumor cells as the vaccinating agent, but the results have been mixed. The use of genomics and proteomics to identify potential tumor antigens using patient samples may eventually result in a more efficacious vaccine to specifically target tumors.

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CHAPTER

69 Lymphomas Lisa Rimsza

INTRODUCTION “Lymphoma” is not a single disease but a general term encompassing nearly 40 different types of lymphoid malignancies broadly divided into Hodgkin and non-Hodgkin categories, the latter including precursor B or T-cell neoplasms and mature B or T-cell neoplasms, as well as rare natural killer (NK) cell neoplasms. Each type of lymphoma has unique histogenesis. In other words, each type of lymphoma has been derived from a particular lymphocyte subset defined by stage of differentiation, immune compartment, function, activation status, and/or exposure to antigens. The diversity of the immune system is reflected by the many types of lymphoma ranging from indolent to aggressive, highly curable to almost invariably lethal diseases. Any age, race, or sex of patients can be affected, as can any location in the body. Lymphoid neoplasms including lymphoma (non-Hodgkin and Hodgkin lymphoma), chronic lymphocytic leukemia and multiple myeloma accounted for 93,420 new cases and 38,000 deaths in the United States in 2005. Over one-half of new cases are non-Hodgkin lymphoma (Morton et al., 2006). The incidence of lymphoma has increased 50% from 1970 to 1990, although the rate of increase appears to be slowing. Increased lymphoma incidence is partly attributable to the AIDS epidemic and increased numbers of immunocompromised patients and partly unexplained.The cause of the increased incidence in non-immunocompromised patients is unknown

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but thought to be due to unidentified factors in the environment such as viral or other infections, or exposure to toxins. Table 69.1 contains a list of Hodgkin and non-Hodgkin lymphoma types according to the World Health Organization ( Jaffe et al., 2001). Predisposition Certain groups of patients are more likely to get lymphoma than others. In general, lymphoma is a disease of adults. As mentioned above, immunocompromised patients, either due to congenital immune deficiency, HIV infection, or iatrogenic immunosuppression (for the purpose of preventing organ or tissue rejection or for treatment of autoimmune and other types of disease) are at increased risk. Presumably, in immunosuppressed states, immunosurveillance by T-cells is compromised, allowing growth of a malignant lymphoid clone. Other risk factors include genetic diseases that promote genomic instability, exposure to ionizing radiation or chemotherapy, and exposure to certain infectious agents. Epstein-Barr virus (EBV) is particularly associated with Burkitt lymphoma (BL), Hodgkin lymphoma, and lymphomas in immunocompromised patients. Human T-cell leukemia virus-1 (HTLV1) is highly associated with adult T-cell leukemia/lymphoma. Kaposi’s Sarcoma Human Virus-8 (KSHV8) is associated with primary effusion lymphoma (PEL). Chronic bacterial infections such as Helicobacter pylori in the stomach or Borrelia burgdorferi in the skin can create situations of

Copyright © 2009, Elsevier Inc. All rights reserved.

TABLE 69.1

Lymphoma disease categories, characteristic chromosomal aberrations, and involved genes

Lymphoma

Characteristic chromosome aberrations

Involved genes

Lymphoplasmacytic/Waldenstrom’s macroglobulinemia

t(9;14)(p13;q32)

PAX5, IgH

Splenic Marginal Zone

Loss of 7q21-32

Extranodal marginal zone (MALT)

t(11;18)(q21;q21) t(14;18)(q32;q21) t(1;14)(p22;q21) 5(1;2)(p22;p12)

AP12, MALT1 MALT1, IgH BCL-10, IgH BCL-10, Igκ

Follicular

t(14;18)(q32;q21)

BCL-2, IgH

Mantle cell

t(11;14)(q13;q32)

CCND1 (cyclin D1), IgH

Diffuse large B cell

t(14;18)(q32;q21) t(3;14)(q27;q32) t(3;22)(q27;q11) t(2;3)(p12;q27) 2p13–15 amplification

BCL-2, IgH BCL-6, IgH BCL-6, Igλ BCL-6, Igκ REL amplification

Mediastinal (thymic) large B cell

Gains at 9p

JAK amplification

t(8;14)(q24;q32) t(8:22) (q24;q11) t(2;8) (p11;q24)

C-MYC, IgH C-MYC, Igλ C-MYC, Igκ

B-cell

Nodal marginal zone

Intravascular large B cell Primary effusion Burkitt

Lymphomatoid granulomatosis T/NK cell Adult T-cell leukemia/lymphoma Extranodal NK/T-cell, nasal type Enteropathy-type T-cell Hepatosplenic T-cell

Isochromosome 7q

Subcutaneous panniculitis-like T-cell Blastic NK-cell Mycosis fungoides/Sezary syndrome Primary cutaneous CD30-positive T-cell lymphoproliferative disorders Angioimmunoblastic T-cell Peripheral T-cell, unspecified Anaplastic large cell

Hodgkin Nodular lymphocyte predominant Nodular sclerosis Mixed cellularity Lymphocyte rich Lymphocyte depleted Immunodeficiency associated lymphoproliferative disorders Primary immune disorder-related Human immunodeficiency virus-related Post-transplant Methotrexate-associated

t(2;5)(p23;q35) other translocations involving ALK

ALK, NPM ALK, others

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chronic inflammation which in turn lead to lymphoid proliferation and an increased risk of lymphoma (Aster, 2005). Screening At this time, due to the diversity of types of lymphoma, extremely variable presentation in patients, and relative rarity of the disease, no screening tests have been developed. Thus, outside of the known risk groups described above, there is no way of predicting who will get lymphoma. Patients are therefore typically diagnosed after they have the disease. Diagnosis The diagnosis of lymphoma is a highly specialized branch of medicine practiced by pathologists, often with subspecialty training (hematopathologists). The diagnosis of lymphoma, and even lymphoma type, can be suspected based on clinical history, laboratory data, or imaging studies, but the definitive diagnosis can only be made from a tissue biopsy. The histologic appearance of the biopsy alone is frequently insufficient for current classification of lymphoma. Additional studies to determine the lineage and subtype of the malignant lymphoid cells as well as any characteristic genetic changes are often necessary. These studies may include immunophenotyping by flow cytometry or immunohistochemistry, cytogenetics, molecular studies such as Southern blot, PCR, RT-PCR, or FISH to assess lymphoid clonality or to discover characteristic chromosomal aberrations. Molecular diagnostic techniques are almost routine in the medical diagnosis of hematopoietic neoplasms. Table 69.1 includes a description of the most common known genetic changes in lymphomas identified by karyotyping or FISH, summarized from a recent review article (Spagnolo et al., 2004). Because of the extreme complexity of diagnosis in lymphomas and the present need to integrate information from numerous different testing modalities, a search for a single unifying technique for diagnosis is particularly appealing. Gene expression profiling (GEP) is therefore being used extensively in studies aimed at establishing a precise clinical diagnosis of particular lymphoma subtypes based on characteristic GEP patterns or “signatures.” Prognosis In a patient with a recent diagnosis of lymphoma, prognosis is the next question to be considered. It is imperative to know what type of clinical course the patient might be facing in order to plan therapy. Therapy for lymphoma can range from careful observation without treatment, to local radiation, immunomodulation, chemotherapy, and even hematopoietic stem cell transplant. The International Prognostic Index (IPI) is commonly used in patients with diffuse large B-cell lymphoma (DLBCL) to determine the likely clinical outcome of the patient (Shipp et al., 1993). This IPI score includes age, serum lactate dehydrogenase (LDH) level, performance status, stage of disease, and number of extra-nodal sites. Patients with high IPI scores usually have a worse outcome and may be selected for more aggressive or alternative therapies. However, even within groups of patients with similar IPI scores, the outcome is variable (from cure to

death). Thus, there are clearly additional factors about each patient’s tumor which influence the course of the disease. GEP has been recently used to identify gene expression patterns that have additional prognostic information for an individual patient independent of the clinical IPI score (Rosenwald et al., 2002; Shipp et al., 2002). Monitoring Monitoring of patients with lymphoma may be performed using a variety of methods including serum markers of tumor burden and radiological imaging. Samples of bone marrow, blood, or tissue may also be examined for residual disease after treatment. When disease is not morphologically apparent in such samples, more sensitive techniques including FISH or PCR for characteristic molecular genetic findings known to be present in the original tumor or immunophenotyping to identify an abnormal lymphoid population may be used. At this point in time, GEP studies have not been applied to the issue of minimal residual disease detection. Novel and Emerging Therapeutics After the questions of diagnosis and prognosis, a search for therapeutic targets has been the next information sought in lymphoma GEP studies. As stated previously, even within groups of patients with the same diagnosis and with similar IPI scores, there is still variability in patient outcome. Many of the known disease subtypes have a dismal prognosis. Therefore, a search for new therapeutic targets is a pressing concern. Recent studies have identified the NF-B pathway (Lam et al., 2005), PKC, MAPK (Elenitoba-Johnson et al., 2003), or immune modulation (Dave et al., 2004; Rimsza et al., 2004) as likely targets. GEP in Lymphoma Research Lymphoma and leukemia are particularly amenable to GEP analysis due to the relative homogeneity of tissue compared to solid tumors and to the lack of strong cohesion between the cells which makes homogenization of tissue samples relatively easy. Because of these factors, the early use of cytogenetics helped to define the genetic basis of the different diseases. The cytogenetics of these diseases in turn led to development of PCR, RT-PCR, and FISH tests to identify the characteristic genomic changes with more sensitivity and with shorter turnaround time to test completion. Consequently, acquisition of fresh tissue for flow cytometry and/or snap freezing became standard practice for hematologic malignancies in many medical centers, which stands in contrast to most of the non-hematologic solid tumors for which tissue biopsy specimens are immediately placed in fixatives to preserve tissue appearance under the microscope after paraffin embedding and staining. Therefore, when GEP was developed as a new technique, research groups interested in lymphoma had access to high quality frozen tissue. Many groups also had experience performing molecular techniques such as flow sorting, RNA/DNA extraction and qualitative or quantitative PCR and RT-PCR for diagnostic purposes. This eased the way for

Diffuse Large B-Cell Lymphoma

lymphoma researchers to apply GEP in their studies. In 1998, after completion of the Human Genome Project with a draft of the entire human DNA sequence, Dr. Robert Klausner, then director of the National Institutes of Health (NIH), announced the Director’s Challenge Program to establish the molecular diagnosis of cancer. The Leukemia and Lymphoma Molecular Profiling Project (LLMPP) got underway immediately with the development of the “LymphoChip.” This chip was a spotted oligonucleotide microarray containing numerous elements known to represent genes expressed by B or T lymphoid cells, genes involved in the immune response, or genes expressed by lymphoma and leukemia cell lines. These genes included cytokines, chemokines, chemokine receptors, adhesion molecules, cell surface differentiation molecules, signal transduction molecules, transcription factors, cell cycle regulators, apoptosis genes, tumor suppressor genes, oncogenes, viral genes, and genes induced/ repressed during the immune response (Alizadeh et al., 1999). Under the NIH Director’s Challenge Program, this group examined a series of snap frozen lymph node biopsies from different types of lymphomas and benign tissues. The results demonstrated the striking differences in GEP of the several lymphomas from each other and from benign tissues (Alizadeh et al., 2000). This was the first demonstration of the potential of GEP in the investigation of lymphoma. Other papers by the LLMPP and other research groups, which will be detailed under particular disease subtypes in the following chapters, soon followed. Besides the Lymphochip, other groups have used different array platforms including high density oligonucleotide arrays with focused gene sets (sub-genomic) such as HY6800 or U95 chips and later on total genome chips such as U133A  B (all from the Affymetrix corporation, Santa Clara, CA). Subsequent studies have also used tumor samples (sorted, unsorted, or microdissected) or cell lines representing different types of lymphomas with a focus on diagnosis, identification of genes involved in lymphomagenesis, discovery of new subtypes, prognostically relevant genes, and therapeutic targets. The different diseases, techniques, analysis strategies, specimen sources and questions asked have led to an explosive amount of information over the last 5 years. The major advances in the most common diseases or findings illustrating a general principle will be discussed in the sections below.

DIFFUSE LARGE B-CELL LYMPHOMA DLBCL is the most common of the aggressive lymphomas, accounting for nearly 40% of lymphomas overall. With multiagent chemotherapy, the overall survival is approximately 50% at 5 years including some patients who are completely cured and some who die of initial or relapsed disease. The clinical IPI score for DLBCL, described above, helps to stratify patients into risk categories based on clinical features. However, even within categories, patient outcome is variable. There are three common chromosomal alterations in DLBCL. The first involves translocations of the BCL-6 gene at the 3q27 locus with the

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immunoglobulin heavy chain (IgH) or light chain (Ig or Ig) genes (see Table 69.1). Since BCL-6 is a critical gene in germinal center formation, alterations in BCL-6 fundamentally influence the developmental program of the B-cells. Secondly, the t(14;18)(q32;q21) translocation places the anti-apoptotic gene BCL-2 under the regulation of IgH enhancers possibly leading to increased anti-apoptosis signals. Thirdly, amplification of chromosomal region 2p13–15 leads to amplification of the REL gene which is a member of the NF-B family. DLBCL can occur at any anatomic location and is characterized histologically by sheets of large B-lymphocytes which may have a variety of cytologic appearances. Several subtypes have been described based on histologic appearance (centroblastic, immunoblastic, anaplastic) or sites of presentation (immune-privileged sites, intravascular) (Jaffe et al., 2001). Because of the variable patient outcome, it has long been suspected that other biologically relevant subtypes may exist. Diagnosis and Histogenesis In the first GEP paper published on lymphoma, the investigators used the Lymphochip to analyze 96 samples of several subtypes of lymphoid malignancies including DLBCL, benign lymphoid cell samples, and DLBCL cell lines. They discovered that in otherwise histologically indistinguishable cases, there were at least two different subtypes of DLBCL termed the germinal center B-cell (GCB) and activated B-cell (ABC) subtypes with better and worse survival respectively. This was the first documentation that DLBCL tumors with the same appearance could have unique gene expression signatures which yielded prognostically useful information. Other researchers used Affymetrix HU6800 oligonucleotide arrays to differentiate DLBCL from follicular lymphoma (FL). DLBCL had increased expression of LDH and transferrin (known prognostic markers in lymphoma) as well as increased cellular proliferation genes, invasion and metastasis genes (cathep-sins B and D), myc-targeted genes, hematopoietic cell kinases (induced with CD44 signaling) and apoptosis inhibitors. The GCB subtype was later found to correlate with the presence of the t(14;18)(q32;q21) translocation (23–35% of the cases) which made intuitive sense because the GCB subtype of DLBCL is of follicular cell origin and may share the translocation as an early pathogenic event similar to FL in which the t(14;18) is the characteristic genetic abnormality. These findings lent credence to the importance of the GCB and ABC distinction since these subtypes could be related to a previously well known prognostically important genetic translocation (Huang et al., 2002; Iqbal et al., 2004). A short panel of only 3 immunohistochemical stains was then developed from the GEP signatures, which was capable of dividing DLBCL into GCB and non-GCB subtypes yielding similar prognostic information to the GEP analysis (Chang et al., 2004; Hans et al., 2004). This latter study demonstrated that the strength and complexity of GEP analysis is reducible into a format which can be put into immediate clinical use for patient diagnosis.

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Prognosis The LLMPP group then expanded on the initial set of 40 DLBCL cases with a follow-up study of 240 cases of untreated, untransformed DLBCL with an emphasis on gene expression patterns related to prognosis. They discovered four key gene expression signatures that were highly related to patient survival: “germinal center,” “major histocompatibility (MHC) class II,” “lymph node,” and “proliferation.” Two of the four signatures, “MHC Class II” and “lymph node,” contain antigen presenting molecules, T-cell-associated genes, inflammatory genes, and stromal genes, which directly reflect the critical role of tumor immunosurveillance and host response in relation to patient outcome. Representative genes from these four signatures with the addition of the gene BMP6 were used to create a 17-gene outcome predictor score, which provide additional prognostic value independent of the clinical IPI score. A follow-up study on the same cases used comparative genomic hybridization (CGH) to assess chromosomal losses, gains, or amplifications and found that this added prognostic information to the GEP results. In particular, chromosomal gains involving 3p11–12 correlated with a worse outcome and were related to decreased MHC Class II signature while loss at 6q21 also correlated with worse outcome but with increased proliferation signature (Bea et al., 2005). Shipp and colleagues, however, found no prognostic significance based on the cell-of-origin (GCB versus ABC) in their data using the HU6800 chip or in their re-analysis of the Lymphochip data. However, they did find that the Lymphochip data confirmed the prognostic significance of the PKC-2A isoform. Since these two datasets appeared to have different conclusions, the LLMPP investigators downloaded and re-analyzed the Shipp dataset. Using a different analysis technique, they were able to reconcile somewhat the two different datasets and conclusions (Wright et al., 2003). This exercise emphasizes the importance of making GEP data available to the research community for interactive re-analysis to validate scientific conclusions. It also underscores that different conclusions can be reached as a result of different technology (gene arrays, samples) and analysis strategies. The Shipp research group then used the Affymetrix total genome chip (U133A  B) on 175 DLBCL cases and identified three distinct GEP-defined subgroups termed “oxidative-phosphorylation (Ox-Phos),” “B-cell receptor/proliferation” and “host response.” The “B-cell receptor/proliferation” cluster is characterized by increased expression of genes related to not only signaling and proliferation but also replication, DNA repair, and transcription factors. The “host response” cluster showed high expression of T-cell, dendritic cell, macrophage, and antigen presentation genes which may reflect the prominent inflammatory background seen in some types of DLBCL (T-cell/ histiocyte rich large B-cell lymphoma). The “Ox-Phos” cluster included genes related to oxidative-phosphorylation, mitochondrial function, and the electron transport chain (Monti et al., 2005). Comparison of the consensus clusters identified in this study with the GCB and ABC cell-of-origin signatures described by Rosenwald et al. indicated that these two classification schema identify different aspects of tumor biology. Some

overlap of gene expression patterns was identified in that there was enrichment of genes from the “lymph node” signature in the “host response” consensus cluster as well as increased “proliferation” signature genes in the “B-cell receptor/signaling” consensus cluster. Furthermore, the “B-cell receptor/signaling” consensus cluster genes were enriched in the ABC subtype while the “Ox-Phos” consensus cluster genes were enriched in the GCB subtype. The importance of the loss of the “MHC Class II” gene expression signature as reported by Rosenwald et al. was further documented by a reanalysis of the LLMPP dataset. This analysis revealed that for every incremental decrease in MHC Class II expression, there was an incremental increase in the hazard ratio of death and that loss of MHC Class II expression was associated with a decreased percentage of tumor infiltrating lymphocytes. The same investigators later used GEP data to create a positional expressional profiling map which used gene expression data organized by chromosomal position to create a “virtual” CGH with implications about the genetic structure of the MHC Class II region. These “virtual” results correlated well with actual laboratory CGH results demonstrating that complete mRNA data can be used assess the possibility of DNA alterations (Rimsza et al., 2006). In order to create a more practical platform for outcome prediction in DLBCL, Lossos and co-workers analyzed 66 cases using quantitative real-time RT-PCR to assess mRNA levels of 36 genes identified by univariate analysis in either the Shipp or LLMPP GEP data or in previous studies reported in the literature using a variety of investigational methods. These investigators identified six genes (LM02, BCL-6, FN1, CCND2, SCYA3, and BCL-2) as most important to overall survival. Using these six genes, they created a multivariate model with prognostic predictive ability independent of the clinical IPI score (Lossos et al., 2004). This work again demonstrated that GEP data can be reduced to a small number of gene analyses that might be quickly applicable in the clinical laboratory setting to determine patient prognosis.

Additional Subtypes Other investigators have used GEP to investigate phenotypically different subtypes of DLBCL in order to determine whether these represent different diseases. One group focused on DLBCL which either does or does not express the usually T-cell associated antigen CD5 (Kobayashi et al., 2003). A second group of investigators compared site-specific DLBCL either involving or outside of the central nervous system (Fujii et al., 2005). Both of these groups discovered GEP patterns suggesting that there were additional DLBCL subtypes yet to be identified. Another group analyzed cases of DLBCL with primary involvement of the skin. There has been a long debate over whether those cases of cutaneous DLBCL which involve the lower leg were a different disease from those involving other areas of the skin. These investigators discovered that those tumors involving the leg were frequently of the ABC subtype while those tumors involving

Hodgkin Lymphoma

other areas of the skin were generally related to the GCB subtype (Hoefnagel et al., 2005). Their results correlated well with the known worse outcome in DLBCL of the leg compared to other sites. Why the ABC subtype of cutaneous DLBCL would involve the lower leg more frequently than other skin locations is currently a matter for speculation. Therapeutic Targets Using the GEP data on the ABC subtype of DLBCL, the LLMPP group identified activation of the NFB pathway as a potential therapeutic target not typically seen in the GCB subtype of lymphoma. They then treated ABC and GCB DLBCL cell lines with IB kinase inhibitors and demonstrated toxicity only in the ABC cell lines. This work mined the GEP data, identified an abnormal pathway, and demonstrated the in vitro effects of targeting that pathway, illustrating the strength of GEP for identification of new therapeutic targets.

PRIMARY MEDIASTINAL LARGE B-CELL LYMPHOMA Primary Mediastinal Large B-Cell Lymphoma (PMBCL) has been a controversial entity that was originally thought to be an otherwise typical DLBCL that happened to involve the mediastinum, while others described this as a unique subtype of DLBCL derived from thymic B-cells in the mediastinum. Clinical and pathologic evidence to support the latter view included a propensity of occurrence in young women, a characteristic morphologic pattern including large lymphoid cells with polylobated nuclei and abundant clear cytoplasm in a background of sclerosis, and a unique immunophenotype including lack of surface immunoglobulin and expression of FIG1 (Copie-Bergman et al., 2003). Characteristic genomic changes included chromosomal gains at 9p and amplification at 2p13– 15, which includes the REL and BCL-11A loci (Jaffe et al., 2001). However the relationship between DLBCL and PMBCL remained unclear. Diagnosis and Lymphomagenesis The LLMPP groups used GEP with the Lymphochip to establish a molecular signature of PMBCL. Using 35 PMBCL cases defined by a 6 cm mediastinal mass at clinical presentation, they found a GEP signature which was different from either the ABC or GCB subtypes of DLBCL. Characteristic genes that were over expressed included two genes previously described as over-expressed in PMBCL, MAL, and FIG1, as well as genes under-expressed as compared to DLBCL. 21/35 cases were true PMBCL while the others appeared to be ABC or GCB subtypes of DLBCL involving the mediastinum. Surprisingly, the GEP analysis also discovered similarities to Hodgkin lymphoma (HL) cell lines and microdissected malignant HL cells from patient lymph nodes. In particular, loss of expression of genes associated with the B-cell receptor signaling pathway were noticed in both

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lymphomas implying a similar lymphomagenic mechanism. HL is a different lymphoma in histologic appearance and classification, but some overlapping clinicopathologic features with PMBCL are apparent. After the GEP discovery, it was reinforced that both lymphomas often had involvement of the mediastinum, predominance in young women for cases with mediastinal involvement, frequent lack of surface immunoglobulin, genomic gains at 9p and 2p, and expression of CD30 (Rosenwald et al., 2003a). Simultaneously, the Shipp research group, using the Affymetrix U133A  B platform described similar findings: that PMBCL was a separate entity from DLBCL that had overlapping features with HL. These similarities included decreased expression of molecules involved in B-cell receptor signaling and activation of the NF-B pathway (Savage et al., 2003). Excitingly, these landmark studies reached the same conclusions using different microarray platforms, samples, and analysis methods, indicating the strength of the conclusions. The discovery that PMBCL and HL may share a similar pathogenic mechanism shed light on the origins of the controversial cases of so-called “mediastinal gray zone” lymphomas. These rare lymphomas have features of both diseases and have been difficult to diagnose as either Hodgkin or non-Hodgkin lymphoma. Perhaps the “gray zone lymphomas” represent the missing link between PMBCL and HL (Calvo et al., 2004). Therapeutic Targets Further investigation of the NF-B pathway as a potential therapeutic target in PMBCL was confirmed by demonstration of REL protein localization to the nucleus, high levels of NF-B binding activity, and NF-B inhibition (achieved by transduction with a super-repressor form of IB) of proliferation in a PMBCL cell line. The investigators further defined an “NF-B activation” signature by re-analysis of the GEP data. This signature included increased expression of genes promoting cell survival and anti-apoptotic signaling via TNF which was significantly different from the NF-B signature exhibited by the ABC type DLBCL. They also noted partial overlap with the “host response” consensus cluster in DLBCL which may reflect the rich stromal background characteristic of PMBCL (Feuerhake et al., 2005). NF-B activation is frequently but not invariably associated with gene amplification, suggesting the possibility of multiple pathogenic mechanisms. Taken together, these data clearly identify NF-B as a potential therapeutic target in PMBCL. Therapies lowering NF-B activity, such as the proteasome inhibitor, bortezomib, which decreases degradation of the NF-B inhibitor IB, are under development for clinical use in lymphomas.

HODGKIN LYMPHOMA HL has traditionally been kept separate from the non-Hodgkin lymphomas because of very characteristic clinical and pathologic features. HL always spreads in a contiguous fashion from

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one lymph node group to the adjacent lymph nodes on the same side of the diaphragm (above or below), next through the lymphatic system to lymph nodes on the opposite side of the diaphragm, then to the spleen, liver, and bone marrow. Involvement outside of the hematolymphoid system is therefore rare. Because of this predictable pattern of spread, radiotherapy can be curative in the early stage disease. The most common subtype of HL is the Nodular Sclerosis variant, which occurs classically in young women and involves lymph nodes above the diaphragm. This disease has a generally good prognosis. The Mixed Cellularity variant of HL is more often seen in male and HIV patients, has less mediastinal involvement, a higher stage at diagnosis, and a generally less favorable outcome (Jaffe et al., 2001). Histologically, HL is also very unusual in that the malignant cells, termed Reed-Sternberg cells, and their variants are uncommon in the tumor tissue. It is estimated that the malignant cells make up fewer than 1% of all cells present with the remaining cells being inflammatory cells: lymphocytes, eosinophils, plasma cells, and stromal elements. To further complicate the diagnosis, Reed-Sternberg cells generally do not express the characteristic immunophenotype of any normal hematopoietic cells. In fact, they lack CD45 (leukocyte common antigen), specific T-cell antigens, and have weak or no expression of specific B-cell antigens. For a long time, the lineage of Reed-Sternberg cells was therefore a matter for speculation. Investigators used laser microdissection to isolate primary Reed-Sternberg cells directly from frozen tissue sections. PCR analysis of these cells demonstrated that they were B-cells with rearrangements of the immunoglobulin heavy and light chain genes (Kanzler et al., 1996; Marafioti et al., 2000). GEP studies of HL have often relied on HL cell lines derived from Reed-Sternberg cells since the process of microdissection to obtain enough mRNA for complete analysis is difficult and time consuming. However, some investigators have first used cell lines, which allows analysis of homogenous samples, then confirmed key findings using primary tissue samples (Kuppers et al., 2003). Cell sorting has also been used to isolate primary Reed-Sternberg cells from tissues (Cossman, 2001). Lymphomagenesis Building on the knowledge that immunoglobulin heavy and light chain genes could be successfully rearranged in HL but the proteins were not expressed, investigators used Affymetrix U95 microarrays on HL cell lines and discovered a loss of the B-lineage specific expression program which was confirmed by immunohistochemistry. These results agreed with those described by other research groups studying PMBCL previously described in this chapter. Interestingly, continued expression of the Pax-5 gene, a transcription factor essential for B-cell commitment in early development and maintenance of the mature B-cell phenotype, was noted, although many genes known to be transcriptionally activated by Pax-5 (CD19, CD21, CD22, CD79a, BLNK) were down-regulated. No mutations in the Pax-5 gene were found to account for this phenomenon, indicating that there must be another mechanism of loss of the

B-cell phenotype (Schwering et al., 2003b). In the same year, the same group compared HL cell lines to GCB DLBCL cell lines using U95 Affymetrix microarrays and confirmed some of the findings with SAGE and RT-PCR on microdissected ReedSternberg cells from patient lymph nodes. They found that Reed-Sternberg cells had an altered expression of hundreds of genes including potential oncogenes (rhoC, L-myc, and PTP4A) and transcription factors (ATF-5, ATBF1 and p21SNFT) and most closely resembled the ABC type of DLBCL. (Schwering et al., 2003a). Comparison of HL to NHL (non-Hodgkin lymphoma) cell lines identified expression of the activating transcription factor, ATF3, exclusively in HL. RNA interference assays selectively diminished growth of the HL cell lines compared to the NHL lines indicating the importance of ATF3 in HL growth (Janz et al., 2005). GEP using chemokine, chemokine receptor, and cytokine DNA chips on whole lymph nodes of HL identified a mixed T helper1/T helper2 response by the background T-cells and found differing chemokine expression profiles mainly related to presence or absence of EBV infection rather than histologic subtype (Ohshima et al., 2003). Prognosis and Subtypes Another group analyzed whole lymph nodes with GEP using their own spotted microarrays and found 3 subtypes of HL; these corresponded to the known Mixed Cellularity and Nodular Sclerosis variants with the latter divided into good and poor outcome groups. The poor outcome genes related to angiogenesis, proliferation, cell adhesion, and growth factor receptors. Good outcome genes were involved with increased apoptosis, cell signaling, cytokines, cytokine receptors and transduction molecules (Devilard et al., 2002).

FOLLICULAR LYMPHOMA FL is the most common of the low-grade lymphomas, accounting for nearly 40% of all NHL in the United States. This disease occurs mainly in adults and is thought to originate from GCB-cells. Patients present with one or multiple sites of lymph node involvement. The tumor cells express markers of germinal center differentiation and recapitulate normal germinal center architecture. The genetic hallmark of the disease is the t(14;18)(q32;q21) that brings the anti-apoptosis gene, BCL-2, to the vicinity of the immunoglobulin heavy chain gene enhancers resulting in the over expression of BCL-2 protein and prolonged cell survival. The disease follows a variable waxing and waning course over several years (Jaffe et al., 2001). Therapy has been generally reserved for those patients with discomfort or other symptoms related to the bulk or location of their disease. However, new monoclonal antibody therapies directed against tumor cell surface markers may be changing the treatment paradigm (Fisher et al., 2005). About 25–35% of patients’ tumors will undergo “transformation” to DLBCL which will then require multi-agent chemotherapy (Jaffe et al., 2001). Efforts to predict the clinical course of individual patients have relied on clinical

Follicular Lymphoma

features similar to the IPI score developed for DLBCL (SolalCeligny et al., 2004). Histologic grading based on counting the number of large “centroblastic” cells per high power microscopic field has also been used, but with variable reproducibility (Jaffe et al., 2001;The Non-Hodgkin’s Lymphoma Classification Project, 1997). However, speculation has remained high regarding whether there are other features of FL useful in guiding clinical treatment decision making. Diagnosis One of the first papers on GEP in FL compared normal GCBcells to malignant B-cells, both enriched from involved lymph nodes by negative selection. This procedure is different from most previously discussed procedures because it compared cell types rather than complete lymph nodes. Using the Atlas cDNA Expression array (Clontech, Mountain View, CA) containing 588 cDNAs and confirming their results with quantitative RT-PCR, it was reported that FL, compared to benign B-cells, upregulated genes related to cell cycle regulation, transcription factors including Pax-5, cell–cell interactions, tumor necrosis factor, interleukin-2 receptor, and interleukin-4 receptor while decreasing expression of genes related to adhesion including MRP8 and MRP14. BCL-2 expression was increased as expected from the characteristic chromosomal translocation in FL involving BCL2. Some of these genes mapped to regions previously reported to be altered in FL such as 6p21.1CIP1 and 6p21.3TNF (Husson et al., 2002). Other investigators analyzed DLBCL cell lines with t(14;18)(q32;21) compared to mantle cell lymphoma (MCL) cell lines harboring the t(11;14)(q13;q32) involving the immunoglobulin heavy chain gene and CCND1 which encodes the cyclin D1 protein. Using a cDNA spotted microarray with 4364 genes printed at their own facility, they compared the t(14;18)containing cell lines to purified B-cells from benign tonsils and described increased expression of genes related to cellular proliferation, survival, and metabolism in the cell lines. Numerous under-expressed genes identified in the t(14;18) containing cell lines included negative regulators of cell activation and growth. Not surprisingly, t(11;14) and t(14;18) containing cell lines had distinctly different GEP profiles, with t(11;14) cell lines expressing increased cyclin D1 and associated downstream genes as expected in MCL (Robetorye et al., 2002). Transformation In order to investigate the molecular basis for the transformation of low grade FL into DLBCL, investigators compared 7 paired tumor samples from the same patients taken pre- and posttransformation. They found increased expression of 36 genes related to proliferation and metabolism and decreased expression of 66 genes including T-cell genes, dendritic and stromal cell genes, and transcription factors previously known to be involved with transformation, along with 25 novel genes (de Vos et al., 2003a). Other researchers evaluated 12 matched pairs proven to be clonally related to each other using an in-house spotted microarray and identified increased expression of p38-mitogen-activated

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protein kinase (MAPK) and growth factor cytokine receptors in the transformed samples. Results were confirmed with quantitative RT-PCR and an independent, blinded set of FL and DLBCL cases yielded similar results supporting the validity of the observations. The same paper described that pharmacologic manipulation of phosphorylated MAPK in a t(14;18)/MAPK over-expressing FL cell line inhibited growth in vitro and in vivo in a NOD/SCID mouse model. These results suggested MAPK as a therapeutic target in FL. A third group of investigators studied transformed FL compared to untransformed cases using array CGH to assess genomic alterations and compared these results to their previously reported GEP results on the same cases. Interestingly, they described 2 different GEP signatures in transformed cases that were related to over- or under-expression of the oncogene, c-myc. Heterogeneous acquired chromosomal aberrations were also observed in the transformed FL. For example, gains at 18q21.3 were observed, but not related to BCL-2 levels, implicating other genes in that region as important in transformation. Gains or amplifications at the 2p16 locus implicated the REL and BCL11A genes. This work supported the idea that there may be more than one genetic mechanism for transformation to a higher grade neoplasm (Lossos et al., 2002; Martinez-Climent et al., 2003). Prognosis Due to the variable clinical outcome of patients with survival ranging from 1 to 20 years, there has always been a medical interest in identifying prognostic factors in FL. The LLMPP group analyzed a training set of 95 samples, using the Affymetrix whole genome oligonucleotide arrays (U133A  B) to define a prognostic GEP signature. Two different signatures related to patient outcome, independent of clinical risk factors, were described, termed immune response 1 (IR1) and immune response 2 (IR2). The IR1 gene signature is enriched in transcripts related to T-cell mediated immune responses including monocytes and cytotoxic T-cell genes while the IR2 signature is enriched in transcripts expressed by histiocytes and dendritic cells. Both signatures can be combined to create a survival predictor score. When patients were split into 4 quartiles based on this score, median survival varied from 3.9 to 13.6 years. Most interestingly, the tumor microenvironment, particularly the nonmalignant immune cells, not the tumor cells, predicted patient survival. Differences in IR1 and IR2 between individual patients may reflect genetic variation in immune response and regulation between different individuals. Or, the differences in IR1 and IR2 between different tumors may reflect variation in the molecular biology of the malignant FL cells. There are 2 hypotheses about why IR1 and IR2 are associated with good and bad prognosis, respectively. In the “immune response” hypothesis, IR1 reflects an effective antitumor response by the adaptive immune system. In contrast, IR2 reflects genes expressed in the innate immune system implying that FL cases with increased IR2 signature have learned to evade the adaptive immune system. A second hypothesis termed “immune cell dependence” suggests that the tumorassociated immune cells supply trophic survival signals to the

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malignant cells. The malignant cells may therefore have difficulty leaving the lymph node. FL cases with increased IR2 signature may have lost dependence on the immune cells and be able to grow without such requirements, resulting in a more aggressive tumor and poorer prognosis. Further investigation will be necessary to dissect these interactions (Staudt and Dave, 2005). An independent study compared microdissected follicles from 10 FL and 10 lymph nodes with benign reactive follicular hyperplasia using a cytokine and chemokine cDNA microarray. They identified interleukin receptor 3-alpha as increased in FL follicles among several other increased and decreased genes. Since this receptor is derived from activated follicular dendritic cells, these findings also imply the importance of the tumor microenvironment in FL growth. Other investigators used paired samples from patients with low grade (Grade 1 or 2) FL compared to aggressive disease (Grade 3b or DLBCL) based on current histologic criteria ( Jaffe et al., 2001). They constructed an 81-gene model to represent these categories. In aggressive disease, genes involved in cell cycle control, DNA synthesis, and metabolism are upregulated. Genes increased in indolent disease included T-cell and macrophage-derived genes, which could either implicate host immune and microenvironmental factors in good prognosis FL or just be a reflection of higher abundance of stromal elements in low grade versus high grade lymphomas. This model accurately classified 93% of the FL samples in an independent validation set of 58 cases. The authors noted that development of diagnostic assays to make the low versus high grade distinction using custom-made mini-chips or multiplex RT-PCR may be clinically feasible (Glas et al., 2005).

MANTLE CELL LYMPHOMA MCL is a NHL of B-lymphocytes thought to originate from the mantle zone surrounding the germinal center. Patients are middle aged to elderly, predominantly male, and have frequent involvement of the gastrointestinal tract. These lymphomas have very poor outcomes. Patient survival is variable ranging from less than 1 to over 10 years, with a median of approximately 3 years. Most patients cannot be cured ( Jaffe et al., 2001). Therefore in MCL, similar to DLBCL and FL, there is a real clinical need to identify patients with a poor prognosis who might be candidates for alternate therapy as well as patients with a good prognosis who may require less aggressive treatment. MCL has a characteristic translocation t(11;14)(q12;q32), which brings the gene encoding the cell cycle protein, cyclin D1, to the vicinity of the immunoglobulin heavy chain enhancers leading to overexpression of cyclin D1 (Jaffe et al., 2001). This deregulation of cell cycle control presumably confers a proliferative advantage to these cells. Diagnostically, the presence of the t(11;14)(q13;q32) translocation, high expression levels of cyclin D1, and characteristic immunophenotype have made MCL a fairly uniform diagnostic category. Whether or not MCL can occur without the characteristic translocation and whether the sometimes variable nuclear cytologic appearance (blastoid variant (BV) represents

transformation or different disease subtypes are other diagnostic questions. Diagnosis The initial two GEP papers on MCL focused on the differences between MCL and benign lymphoid populations. The first paper evaluated whole MCL lymph nodes versus whole benign hyperplastic nodes. Differential expression of apoptotic genes were identified and confirmed with quantitative RT-PCR in addition to the expected over-expression of cyclin D1 and associated genes (Hofmann et al., 2001). The second paper compared MCL to pre-germinal center (antigen-naïve) and post-germinal center (antigen-experienced) B-cells sorted from normal tonsils. They described normal levels of expression of the CCR7 receptor but decreased expression of other receptors that are expressed during transition of B-cells from primary follicles to GCBcells. They challenged the previous belief that MCL originates from antigenically naïve, pre-GCB-cells suggesting a slightly later functional stage. MCL was characterized by decreased trafficking and differentiation genes, deregulated growth factors and growth factor receptors and oncogenes (Cyclin D1, BCL-2, and MERTK), increased metastasis and angiogenesis genes, defective apoptosis, and differential expression of neurotransmitter receptors. Two GEP patterns were identified indicating that there might be two different subtypes of MCL (Ek et al., 2002). Two other groups compared MCL to flow cytometrysorted normal mantle zone lymphocytes. Altered apoptosis and signaling pathways were identified compared to normal populations. Gene expression signatures for pathologically defined subtypes included signatures for cases with somatic mutations of the immunoglobulin heavy chain gene, a proliferation signature, and a blastoid cytology signature (Martinez et al., 2003; Rizzatti et al., 2005). De Vos and colleagues investigated the BV of MCL and identified altered progress through the G1/S and G2/M checkpoints along with cyclin D1-associated genes indicating that the BV cases may represent MCL with additional alterations in the gene expression pattern related to cell cycle (de Vos et al., 2003b). A comparison of MCL with 2 other types of lymphoma, small lymphocytic lymphoma and marginal zone lymphoma, which sometimes have overlapping clinicopathologic variables, demonstrated that MCL had an upregulation of cell cycle control genes, as anticipated from the known association with t(11;14)(q13;q32), as well as multidrug resistance genes. This latter finding may have implications for treatment options (Thieblemont et al., 2004). Prognosis The LLMPP group analyzed 101 cases of MCL to identify a diagnostic and prognostic signature unique to this type of lymphoma. Genes associated with outcome were analyzed to develop a survival predictor model. A large proportion of the survival predictor genes (24/48, 58%) formed a signature related to proliferation. However, these proliferation genes were different than the proliferation signature genes that constitute an important survival predictor in DLBCL. In MCL, the proliferation

Miscellaneous Lymphomas

genes were associated with cell cycle progression and DNA synthesis, but not with c-myc as seen in the DLBCL proliferation signature. Furthermore, the higher the expression of cyclin D1 transcripts, the worse the patient outcome. Independent of the elevated cyclin D1, deletion of the INK4a/ARF locus encoding the p16INK4a and p14ARF tumor suppressor genes related to cell cycle progression was more frequent in MCL with high expression of the proliferation signature. Thus, a quantitative model based on the proliferation signature was able to predict patient outcome (Rosenwald et al., 2003b). Interestingly, approximately 5% of cases submitted to the study but not included in the above analysis, had otherwise typical clinical pathologic features but did not overexpress the cyclin D1 mRNA or protein. However, they had other diagnostic, morphologic, and immunophenotype features of MCL as well as a characteristic MCL GEP. These cases expressed high levels of cyclin D2 or D3, perhaps as an alternative molecular mechanism of pathogenesis (Fu et al., 2005).

BURKITT LYMPHOMA BL is a type of B-cell NHL which is thought to originate from germinal center centroblasts. Classically, the malignant cells are medium-sized, round, and uniform with frequent mitotic figures and frequent apoptosis. The apoptotic cells and necrotic debris are often engulfed by macrophages resulting in the characteristic “starry sky” effect. By immunophenotyping, the cells strongly express B-cell antigens, show light chain restriction, and have markers of germinal center differentiation including CD10 and BCL-6 as well as high proliferation (Ki-67 antigen expression approaching 100%). BCL-2, found in many DLBCL, is typically negative. Genetically, BL is defined by a translocation between the c-myc oncogene and either the immunoglobulin heavy chain gene or one of the light chain genes. This translocation results in over expression of c-myc and dysregulated cell cycle. BL is a relatively common lymphoma in children, but much rarer in adults. BL is associated with EBV infection, particularly in the endemic form, which is found in children in Africa. Sporadic BL is less often associated with EBV and more common in adults. BL also occurs in immunocompromised patients ( Jaffe et al., 2001). Because of the extremely high proliferation rate, BL is exquisitely sensitive to chemotherapy using cell-cycle specific drugs. Intensive chemotherapy regimens can be curative, but are also difficult to tolerate, particularly for adults. Thus, the clinical distinction between BL and DLBCL, which is more common and receives a less intensive chemotherapy regimen, is critical. Currently, diagnosis is based on a combination of morphologic and immunophenotypic features along with evidence of the characteristic c-myc translocation. Atypical Burkitt cases or “Burkitt-like” cases have also been recognized that have most features of BL but with cytologic features outside what is typically encountered (Jaffe et al., 2001). Cases with otherwise typical DLBCL morphology can also harbor the c-myc translocation. Cases with overlapping features are not infrequent,

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particularly in adult patients, a situation that can lead to a difficult diagnostic dilemma with significant clinical consequences. Diagnosis Because of the real-world difficulty in accurately diagnosing BL, two research groups independently pursued definition of a characteristic GEP pattern for classic BL as compared to DLBCL. Their results were published simultaneously in the same journal (Dave et al., 2006; Hummel et al., 2006). The first paper was published by the LLMPP group using Affymetrix’s U133A  B chips. Their analysis of 303 cases of aggressive B-cell lymphomas clearly identified a characteristic gene expression signature associated with classic BL cases as diagnosed by a panel of expert hematopathologists with morphology, immunophenotyping, and FISH for the c-myc translocation. In addition, they found discrepant cases with either the clinicopathologic diagnosis of BL and GEP of DLBCL or the reverse, a clinicopathologic diagnosis of DLBCL or high grade DLBCL, but with the GEP signature of BL. Of interest, they found that cases with the BL GEP signature most likely benefited from more intensive chemotherapy regimens, regardless of the clinicopathologic classification. The genes that were highly expressed in BL included a subset of GCB-cell associated genes and targets of c-myc, while there was diminished expression of NFkappa-B targets and MHC class I genes. The second study used Affymetrix U133A chips to study 220 aggressive B-cell lymphomas to develop a GEP signature for the disease. FISH and CGH studies were also performed. They defined a GEP signature for BL which was predominantly associated with Ig-c-myc rearrangement and low chromosomal complexity score. Other cases without the characteristic signature (mainly DLBCL) were usually without c-myc rearrangment. Intermediate cases with a mixture of features were also identified. Both of these papers examined large numbers of cases and arrived at very similar conclusions: BL is a molecularly definable group of lymphoma that can be characterized by GEP and translocation status, that the classic pathologic diagnostic approach can be enhanced with the addition of GEP information, and that intermediate cases with features of both DLBCL and BL do occur. This latter group of cases will require further study to define exact categories and implications for therapy. It was anticipated by both groups that the GEP definition of BL in some form will be taken forward and applied to clinical research and practice.

MISCELLANEOUS LYMPHOMAS There are numerous other types of lymphomas with interesting GEP findings which are outside the limits of this chapter, but a few additional studies can be briefly mentioned. Primary effusion lymphoma (PEL) is an unusual lymphoma occurring, not as a mass, but as a fluid collection in various body cavities including the peritoneal, pericardial, and pleural spaces. An association with Kaposi’s sarcoma virus and EBV has been identified. GEP has been used to define the histogenesis of this disease as arising from plasmablastic cells (Jenner et al., 2003; Klein et al., 2003)

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with mechanisms to avoid immunosurveillance (Suscovich et al., 2004). The role of the Kaposi’s sarcoma virus and viral latent versus lytic genes was also investigated (Fan et al., 2005; Jenner et al., 2001). The incredibly complex world of peripheral NK and T-cell lymphoma (PTCL) will be a fertile area of GEP analysis and is only just beginning to be defined. These lymphomas are less common than the B-cell lymphomas and consequently less is known about their immunophenotypic or genetic characteristics. Most likely, the category of PTCL represents several diseases which have yet to be distinguished. Definition of new entities, investigation into lymphomagenesis, and prognostic features are just beginning to be reported in a scattering of T cell lymphomas (Ballester et al., 2005; Choi et al., 2004; Fillmore et al., 2002; Mahadevan et al., 2005; Martinez-Delgado et al., 2004; Murakami et al., 2001; Ohshima et al., 2004; Thompson et al., 2005). The most common and best defined PTCL is the cutaneous lymphoma, mycosis fungoides (MF). GEP has identified two potential subclasses of MF (Tracey et al., 2003) as well as altered gene expression related to transformation (Li et al., 2001), dissemination to peripheral blood (Kari et al., 2003; van Doorn et al., 2004), and sensitivity to chemotherapy (Tracey et al., 2002).

CLINICAL APPLICATIONS OF MOLECULAR ASSAYS IN LYMPHOMA Currently, molecular applications in lymphoma diagnosis surround the use of FISH, PCR, or karyotyping to identify characteristic translocations associated with specific diagnoses. PCR, Southern blot, and in situ hybridization can be used to detect lymphoid clonality to support a malignant versus benign lymphoid process. The extensive use of GEP as detailed in this chapter has opened up a whole new vista for the way in which GEP may be applied in the diagnostic setting. At this time, GEP remains a research tool to identify pathways of lymphomagenesis, characterize known disease entities, search for new entities and relationships between entities, and identify therapeutic targets. The dream of using GEP technology, in the way of customized microarray chips for lymphoma diagnosis, prognosis, and treatment selection, is just beginning to be realized. Incorporation of GEP information into diagnosis, clinical treatment trials, and therapeutic practice should follow just behind the development of a robust, regulatory agency-cleared, testing platform.

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70 Genomics in Leukemias Lars Bullinger, Hartmut Dohner, and Jonathan R. Pollack

INTRODUCTION Recently, our understanding of the hematopoietic system has expanded dramatically, revealing leukemias to exhibit an extraordinary biologic and clinical heterogeneity. While the broad classification of leukemias is still based on the cell of origin (e.g. myeloid or lymphoid) and the rapidity of the clinical course (e.g., acute or chronic), over the past several years efforts have been made to characterize additional biologically and clinically relevant leukemia entities. At the end of the 1990’s, the World Health Organization (WHO) summarized consensus practices to formulate a revised classification of tumors of hematopoietic and lymphoid tissues (summarized in Table 70.1) (Harris et al., 1999). This classification, for example, divides acute myeloid leukemias (AML) into four large subclasses (AML with recurrent cytogenetic abnormalities, with multilineage dysplasia, therapy related, and not otherwise categorized), which are further subdivided into distinct AML subtypes. However, for many leukemia classes the genetic or pathogenic events are still unknown, and within well-defined leukemia subgroups, like AML cases with a t(8;21) or an inv(16), considerable clinical heterogeneity is observed (Schlenk et al., 2004). Thus, there is a need for a refined classification based on an improved understanding of the molecular mechanisms leading to leukemia. Leukemia Cytogenetics and Molecular Genetics Leukemias have a long tradition of being “first”, with chronic myelogenous leukemia (CML) being the first malignancy in which a recurring chromosomal abnormality, the Philadelphia Genomic and Personalized Medicine, 2-vol set by Willard & Ginsbing 844

chromosome, was found to result from a translocation of genetic material from one chromosome to another (Rowley, 1973). The fusion gene resulting from this translocation t(9;22), BCRABL, was then shown to be responsible for the myeloproliferation observed in CML (Konopka et al., 1985; Shtivelman et al., 1985). Representing still the gold standard for the investigation of aberrations in leukemias, chromosome banding analyses have since led to the discovery of well over 100 chromosome translocations and the identification of a number of recurring chromosomal gains and losses in leukemic cells, thereby transforming our understanding of the genetic mechanisms involved in leukemogenesis. At the molecular level, many of these chromosomal translocations result in the deregulated expression of oncogenes like e.g. MYC in acute lymphoblastic leukemia (ALL). Alternatively, translocations can result in the creation of chimeric fusion proteins, many of which alter transcriptional programs to block cell differentiation. For example, in AML, t(8;21) and inv(16) create the AML1-ETO and CBFB-MYH11 fusions, respectively, both of which deregulate the activity of the transcription factor complex Core Binding Factor (CBF), altering the expression of genes and disrupting cell differentiation (Frohling et al., 2005; Licht and Sternberg, 2005). Nevertheless, while many such aberrations like e.g. t(8;21) or inv(16) can block the differentiation of myeloid cells, they are not by themselves sufficient to cause a myeloid leukemia. On the other hand, it has been shown that constitutively activated signaling molecules, such as FLT3 or RAS family members, can induce the complementary myeloproliferative phenotype. Today, Copyright © 2009, Elsevier. Inc. All rights reserved.

Introduction

TABLE 70.1

Lymphoid Origin

TABLE 70.2 a

“Acute” clinical course

“Chronic” clinical course

AML with recurrent genetic abnormalities AML with t(8;21)(q22;q22), (AML1/ETO) AML with inv(16)(p13q22), (CBF/MYH11) AML with t(15;17)(q22;q12), (PML/RAR) AML with 11q23 (MLL) abnormalities

Myeloproliferative diseases Chronic myelomonocytic leukemia Atypical chronic myeloid leukemia Juvenile myelomonocytic leukemia etc.

AML with multilineage dysplasia AML, therapy related AML not otherwise categorized

Chronic myeloproliferative diseases CML with t(9;22)(q34;q11), (BCR/ABL) Chronic neutrophilic leukemia Chronic eosinophilic leukemia, etc.

Precursor B-cell neoplasms Precursor B-ALL Burkitt leukemia

Mature B-cell neoplasms CLL B-cell prolymphocytic leukemia Hairy cell leukemia etc.

Precursor T-cell neoplasms Precursor T-ALL

Mature T-cell neoplasms T-cell prolymphocytic leukemia T-cell granular lymphocytic leukemia, etc.

Cytogenetic and Molecular Aberrations in AML

Risk category

Cytogenetic findings [Genes involved]

Associated molecular abnormalities

Favorable

t(8;21)(q22;q23) [ETO, AML1/RUNX1]

KIT mutations (Exon 8 and 17, codon 816) CEBPA downregulation

inv(16) (q13q22)/t(16;16) [MYH11, CBFB]

KIT mutations (Exon 8 and 17, codon 816) NRAS and KRAS mutations CEBPA downregulation CEBPA mutation FLT3 ITD and FLT3 activating loop mutation

del(9q) t(15;17) (q22;q11q21) [PML, RARA]b

Unfavorable

845

WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues – Leukemias

Myeloid Origin

Intermediate

■

Normal karyotype

Trisomy 21 Trisomy 11

MLL PTD FLT3 ITD and FLT3 activating loop mutation CEBPA mutation NPM1 mutation BAALC over expression RUNX1 mutation MLL PTD

t(6;9)(p23;q34) [DEK, CAN] complex karyotype (3 or more aberrations)

FLT3 ITD TP53 mutation

a

Risk categories for overall survival according to the CALGB (Cancer and Leukemia Group B).

b

Not included in the CALGB study, but favorable risk category in other studies.

there are further lines of evidence implicating a multistep leukemogenesis, and based on advances in molecular genetics many pathogenetically relevant mutations have been identified both in myeloid (Table 70.2) (Frohling et al., 2005; Licht and Sternberg, 2005) and lymphoid (Armstrong and Look, 2005; Pui and Evans, 2006) leukemias. Leukemia Treatment Regarding leukemias’ tradition of being “first”, acute promyelocytic leukemia (APL) was one of the first malignancies to be successfully treated with a molecularly targeted therapy, all-trans

retinoic acid (ATRA). ATRA specifically targets the transforming potential of the fusion gene product, PML-RARA, that results from a t(15;17), which is commonly detected in APL. Likewise, CML was the first disorder in which a small molecule inhibitor had been designed to specifically target the diseasecausing underlying molecular defect, the BCR-ABL fusion protein. Since then, additional novel drugs have been shown to be effective in leukemias, including numerous tyrosine kinase inhibitors (e.g. FLT3 inhibitors like PKC412), farnesyltransferase inhibitors (e.g. tipifarnib), demethylating agents (e.g. decitabine), histone deacetylase inhibitors (e.g. valproic acid), and monoclonal

846

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TABLE 70.3

AML

■

Genomics in Leukemias

Prognostic Factors in Leukemias

Clinical/Laboratory parameters

Cytogenetics

increasing age (↓)

t(15;17), inv(16), t(8;21) (↑) normal karyotype, t(9;11), others ()

Abnormal organ function (↓)a

complex karyotype, inv(3) (↓)

Poor performance status (↓)a

5/del(5q), 7, others (↓)

↑ Sokal and Hasford prognostic scores (↓)

del(9q) (↓)

OCT1 expression (↑)

High WBC (↓)

t(4;11)/ALL1-AF4 (↓)

MDR1 function (↓)

Increasing age (↓)

t(1;19)/PBX-E2A (↓)

Secondary AML/t-AML (↓)

CML

Hematologic response (↑)

Molecular factors MRD1 expression (↓)

NPM1, CEBPA mutations (↑)

FLT3, MLL mutations (↓)

↑ BAALC, ERG expression (↓)

MRD persistence (↓) ↑ C50 imatinib (↓)

ALL

Late achievement of CR (↓) MRD persistence (↓)

In-vitro resistance (↓) Immunophenotype: Pro-B-ALL CLL

Binet/Rai stage Response to therapy (↓) ↑ lymphocyte doubling time ↑ 2-microglobulin (↓) ↑ Thymidine-kinase(↓)

t(9;22)/BCR-ABL (↓)

complex aberrant karyotype (↓)

del (13q14) (↑)

del(17p13), del(11q22q23) (↓)

Unmutated IgVH statusb (↓)

V3.21 expression (↓)

↓ CD38 expression(↓) ↑ ZAP70 expression (↓)

(↓) Unfavorable;, (↑) favorable; () intermediate prognostic marker; a

Applies to all leukemias.

antibodies (e.g. the anti-CD33 antibody gemtuzumab ozogamicin) (Tallman, 2005). While these agents are currently being investigated within clinical treatment trials, the combination therapies of cytotoxic drugs with or without stem-cell transplantation are still the gold standard and have increased the cure rate of leukemia, especially in childhood acute lymphoblastic leukemia (ALL) with an overall cure rate of over 80% (Pui and Evans, 2006). However, cure rates for adults remain much lower, thereby signaling the need for treatment approaches incorporating the above mentioned novel targeted therapies. This should be based on an improved risk stratification that reflects the biological and clinical heterogeneity of leukemias in order to guide efficient patient management. Prognostic Factors in Leukemias In AML, patients are assigned to risk-groups based on the underlying leukemia karyotype (Byrd et al., 2002; Grimwade et al., 1998; Slovak et al., 2000), which represents one of the most powerful prognostic factors in this disease (Table 70.3). However, the identification of novel molecular markers has recently permitted to further dissect existing prognostic leukemia subclasses, like for example the large group of AML patients

presenting with normal karyotype disease (Frohling et al., 2005; Licht and Sternberg, 2005). Internal tandem duplications (ITD) of the FLT3 gene, partial tandem duplications (PTD) of the MLL gene, as well as mutations of CEBPA and NPM1 are of prognostic relevance in this AML subgroup, as is the expression level of BAALC. In chronic lymphoid leukemias (CLL), cytogenetic aberrations also provide prognostic information in addition to the immunoglobulin variable heavy chain gene (VH) mutational status (Seiler et al., 2006). Nevertheless, despite this recent progress there is still no commonly accepted risk stratification for many leukemia subtypes as the mechanisms of leukemogenesis are not yet fully understood.

GENOMICS IN LEUKEMIAS: INSIGHTS INTO LEUKEMIA BIOLOGY Genomics-Based Class Prediction in Leukemias Over the past several years, genomics methods have offered a range of experimental approaches to capture the molecular variation underlying the biological and clinical heterogeneity of

Genomics in Leukemias: Evaluation of Drug Effects

leukemias. Since genome-wide gene expression profiling (GEP) based on DNA microarrays represents one of the most powerful genomics tools, we will mainly focus on the impact of this technology on leukemia management, especially as it was the leukemias in which the utility and promise of GEP was first demonstrated. By analyzing AML and ALL samples, Golub et al., were able to distinguish AML and ALL without previous knowledge of these leukemia classes, and, by developing a supervised class predictor, new leukemia cases could be accurately assigned to one of these two leukemia classes (Golub et al., 1999). Since then, DNA microarray technology has already contributed significantly to the field of leukemia research (Ebert and Golub, 2004; Staudt, 2003), including the identification of a general gene dosage effect for the expression of genes located in areas of chromosomal gains and losses (Haslinger et al., 2004; Rucker et al., 2006). Based on supervised analytical approaches, in childhood ALL distinct expression profiles were identified to correlate with prognostically important leukemia subtypes like e.g. T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and ALL with a hyperdiploid karyotype (50 chromosomes) (Yeoh et al., 2002), and these findings have since been confirmed in other studies of both childhood and adult ALL (Armstrong et al., 2002; Ross et al., 2003). In AML, a gene expression-based discrimination of the cytogenetically defined subgroups inv(16), t(8;21), t(15;17) and t(11q23)/MLL could also be demonstrated (Bullinger et al., 2004; Ross et al., 2004;Valk et al., 2004) (Figure 70.1). Notably, in acute leukemia the same classifiers have been shown to apply to both pediatric and adult leukemia cases exhibiting identical genetic aberrations (Kohlmann et al., 2004; Ross et al., 2004), and it has been demonstrated that the respective signatures are quite robust (Kohlmann et al., 2005; Mitchell et al., 2004). Thus, gene expression profiling may provide a useful alternative approach to classify known leukemia subgroups with high accuracy (Haferlach et al., 2005). Likewise, gene expression patterns associated with molecular genetic aberrations like the VH mutational status in CLL have been demonstrated and led to the identification ZAP-70 (Rosenwald et al., 2001), which is now used as surrogate marker for the VH mutational status. In AML gene expression patterns associated with e.g. FLT3 ITD (Bullinger et al., 2004;Valk et al., 2004), CEBPA (Valk et al., 2004), and NPM1 mutations (Alcalay et al., 2005) have been described. However, in contrast to translocations involving the MLL gene (Armstrong et al., 2002), no characteristic pattern has been identified for cases with MLL PTD (Bullinger et al., 2004; Ross et al., 2004), thereby suggesting that cases with MLL PTD might be more heterogeneous at a molecular level and that not all genetic alterations in leukemia result in definably altered gene expression patterns. Genomics-Based Class Discovery in Leukemias By analyzing 285 AML with unsupervised analytical approaches Valk et al., identified sixteen AML subgroups that included novel prognostically-relevant subtypes of leukemia (Valk et al., 2004). While cases with favorable cytogenetics generally exhibited “homogeneous clustering”, novel clusters were often

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847

characterized by specific molecular alterations, like for example MLL abnormalities or increased EVI1 expression. On the other hand, “homogenously grouped” classes, like cases with an inv(16) or a t(8;21), were also characterized by molecular variation depending on the probe sets included in the analysis (Valk et al., 2004). Using a different unsupervised clustering approach this molecular heterogeneity within t(8;21) and inv(16) cases was also observed in a separate study (Bullinger et al., 2004), thereby suggesting that distinct patterns of gene expression within the t(8;21) and inv(16) subgroups reflect alternative cooperating events leading to transformation. Interestingly, in this study, cases with normal karyotype also segregated mainly into two distinct groups (Figure 70.2), each of which included a small number of cases from other cytogenetic classes (Bullinger et al., 2004). While FLT3 aberrations were more highly represented in one subgroup, FAB (French American British) M4/M5 morphologic subtypes were more prevalent in the other subgroup, and Kaplan-Meier analysis identified a statistically significant difference in overall survival between the two subclasses (Bullinger et al., 2004). In agreement, Valk and colleagues also identified normal karyotypepredominated clusters associated with FLT3 ITD, as well as a cluster including mainly specimens from AML patients whose blasts displayed FAB M4 or M5 morphology (Valk et al., 2004).

GENOMICS IN LEUKEMIAS: EVALUATION OF DRUG EFFECTS Molecular Signatures of Anti-Leukemic Drugs Leukemia was again one of the first diseases in which DNA microarray technology was applied for monitoring drug effects by analyzing the effect of all-trans retinoic acid (ATRA) treatment in APL-derived cell lines (Tamayo et al., 1999). These analyses showed that ATRA-regulated genes included members of the tumor necrosis factor (TNF) pathway, suggesting that this pathway might intersect with ATRA signaling and play a role in regulating cell survival in response to ATRA. GEP has also been successfully used to evaluate the sensitivity of CML to imatinib mesylate (Tipping et al., 2003), which targets the ABL kinase activity of the BCR-ABL fusion. Using a cell line model, differentially-expressed genes correlated with imatinib mesylate resistance could be identified, suggesting that alternative pathways maintain viability and promote growth independently of BCR-ABL. Similarly, Rosenwald et al., have recently investigated the molecular consequences of fludarabine treatment of CLL patient samples using GEP (Rosenwald et al., 2004). Both in vitro and in vivo exposure to fludarabine, a purine analog currently used in standard CLL treatment regimens, resulted in a consistent “response signature” characterized by p53 target genes and genes involved in DNA repair, thus, providing a molecular explanation for the drug resistance and aggressive clinical course often seen in p53-mutated CLL patients. This analysis further suggested

848

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Genomics in Leukemias

t(11q23) R/G Ratio

inv(16)

t(8;21)

t(15;17)

2

AML 20 AML 50 AML 69 AML 83 AML 68 AML 111 AML 84 AML 79 AML 97 AML 64 AML 103 AML 81 AML 25 AML 73 AML 77 AML 90 AML 70 AML 89 AML 34 AML 65 AML 53 AML 38 AML 62 AML 26 AML 4 AML 16 AML 48 AML 114 AML 105 AML 44 AML 98 AML 95 AML 29 AML 117 AML 60 AML 104 AML 119 AML 99 AML 101 AML 86 AML 2 AML 33

(log2-based)

1 0 1

PLAUR PLAUR DTR S100B BIRC5 DNA2L B3GNT6

2 MT2A

t(15;17)

MT1G MT1G MT1H MT1L MT3 HT011 C17orf26 EFEMP2 CLTCL1 ERP70 ARMET P4HB P4HB GABRE AGRN CDC42EP4 CDC42EP4 AKAP2 GPM6B EFA6R

t(8;21)

KIAA1395 KIAA1395 PLCG1 MLLT4 BMP4 GLUL VAMP5 CLIPR-59 NAV1

inv(16)

SLC9A3R2 PTPRM PTPRM NT5E SPARC SPARC SPARC LRP6

t(11q23)

LRP6 LRP6 LOC92235 CYP2S1 TM4SF1 IGSF4 IGSF4 DACH PDCD6IP MYLE eIF2a KIAA0992 HSPC019 GAGED2 AKR7A2 SIAT6 TPO ADCY9

Figure 70.1 Gene expression signatures of leukemia cytogenetic classes. Shown is a “heat map” representation of selected genes identified by supervised analysis whose expression is significantly correlated with specific AML cytogenetic aberrations, like t(11q23), inv(16), t(8;21), and t(15;17). Gene-expression levels are depicted in a pseudo color scale as indicated (red indicates higher expression levels). The analysis shown was performed using publicly available microarray data that were based on a supervised analysis (Bullinger et al., 2004). Gene-expression signatures such as these can be used to classify the cytogenetic group of new specimens with high accuracy. AML 64 was initially characterized as “normal karyotype” by cytogenetic banding analysis, but for which RT-PCR subsequently identified the diagnostic CBFB-MYH11 fusion transcript characteristic of inv(16) cases.

the importance of only treating patients that warrant therapy, as fludarabine treatment might select for p53 mutant CLL cells (Rosenwald et al., 2004). Another recent study sought to provide a better molecular understanding of resistance to L-asparaginase, an important component of most treatment regimens for ALL.

In vitro exposure to L-asparaginase in cell lines and pediatric ALL samples followed by gene expression profiling revealed changes that reflect a consistent coordinated response to asparagine starvation, which is independent of asparagine synthetase base-line expression levels (Fine et al., 2005). Thus, targeting

Genomics in Leukemias: Clinical Outcome Prediction

0.2

0.1

0.0 0.1 0.0 0.1

0.1

0.2

Normal karyotype group I

Normal karyotype group II

0.2 0.1

0.0

0.1

0.2

Figure 70.2 Discovery of new molecular subtypes of leukemia. Shown are the results of an unsupervised principal components analysis (PCA) displaying a projection of the first three principal components of variable gene expression for AML specimens with normal karyotype. Based on distinct patterns of genes expression, PCA identifies two novel subgroups of normal karyotype AML cases (indicated by red and blue dots, respectively). Group II cases exhibit more frequent myelomonocytic differentiation, while group I cases more often harbor FLT3 mutations and are associated with shorter overall survival. Data are from Bullinger et al. (2004).

particular “amino acid starvation response” genes might provide a novel way to overcome L-asparaginase resistance of ALL cells. Drug Response Prediction in Leukemia To determine whether the cellular responses provoked by chemotherapeutic agents can be predicted, Cheok and colleagues profiled gene expression in childhood ALL cells before and after in vivo treatment with methotrexate and mercaptopurine, given either alone or in combination (Cheok et al., 2003). A gene expression pattern consisting of 124 genes, which included genes involved in apoptosis, mismatch repair, cell cycle control and stress response, accurately discriminated among the assigned treatments. Notably, this signature showed differences in cellular response to drug combinations versus single agents and indicated that common pathways of genomic response to the same treatment are shared by different ALL subtypes. Testing for in vitro sensitivity to prednisolone, vincristine, asparaginase, and daunorubicin, the identification of differentially expressed genes in drug-sensitive and drug-resistant ALL leukemia cells allowed the creation of a combined gene-expression score of resistance, which was shown to be of prognostic relevance in childhood ALL (Holleman et al., 2004).

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849

Discovery of Novel Anti-Leukemic Drugs Today, genomics approaches including DNA microarray technology play an important part in the drug target discovery process, not only by providing a valuable tool for the optimization and clinical validation of novel compounds. While at the beginning of the process GEP allows the identification and prioritization of potential therapeutic targets, subsequent expression profiling assists in drug discovery and toxicology. Then, various bioinformatics approaches are used to deduce from expression profiles the mechanism of action of new drugs as well as off-target effects. However, it is important to keep in mind the limitations of evaluating drug responses through measurements of mRNA abundance alone. Nevertheless, GEP provides a powerful tool in pharmacogenomics that promises an improved prediction of prognosis and drug response (Walgren et al., 2005). In a recent example of the utility of GEP in drug discovery, a GEP-based high-throughput screening approach was applied to screen for chemical compounds with differentiation-inducing activity in leukemia (Stegmaier et al., 2004). Following definition and validation of a microarray-based differentiation signature, a high throughput screening method was designed to detect the respective gene pattern. Using this approach, treatment of a leukemia cell line with 1,739 different compounds revealed eight chemicals that reliably induced the differentiation signature (Stegmaier et al., 2004). Interestingly, one of these compounds inhibited epidermal growth factor receptor (EGFR) kinase activity, and the authors could show in a subsequent study that the Food and Drug Administration (FDA)–approved EGFR inhibitor gefitinib similarly promoted the differentiation of AML cell lines and primary patient–derived AML blasts in vitro (Stegmaier et al., 2005). Notably, the analyzed AML cells did not express EGFR, indicating an EGFR-independent mechanism of gefitinib induced differentiation in AML, thereby suggesting the presence of additional yet unknown key targets.

GENOMICS IN LEUKEMIAS: CLINICAL OUTCOME PREDICTION Identification of Novel Surrogate Markers in Leukemia Many groups have now demonstrated that GEP allows the identification of specific signatures correlated with and the subsequent prediction of known “favorable-risk” cytogenetic aberrations in both AML like e.g. cases with inv(16), t(8;21), or t(15;17) (Bullinger et al., 2004; Haferlach et al., 2005;Valk et al., 2004), and in ALL like e.g. cases with t(12;21) or hyperdiploidy (more than 50 chromosomes per leukemia cell) (Yeoh et al., 2002). Similarly, “unfavorable-risk” cytogenetics subgroups can be predicted, as well as prognostically relevant molecular genetic aberrations (Bullinger and Valk, 2005). Such analyses led to the identification of novel surrogate markers among which ZAP-70 represents the first to be clinically implemented. Coding for a

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tyrosine kinase essential for T cell signaling, ZAP-70 exhibited five-fold higher expression levels in patients with unmutated VH genes compared to those in which the B cell clone had undergone the rearrangement of the immunoglobulin heavychain variable region (Rosenwald et al., 2001). The clinical usefulness of this novel marker, which can be readily measured at the protein level by flow cytometry, has recently been validated (Orchard et al., 2004). Furthermore, GEP-based prediction of leukemias sensitive or resistant to chemotherapeutic agents can provide significant information regarding treatment outcome in leukemias. The fact that initial findings could be confirmed in independent sets of patients in ALL (Holleman et al., 2004; Lugthart et al., 2005) and AML (Heuser et al., 2005) suggests the feasibility of an improved treatment management in leukemias based on GEP. Furthermore, expression profiling might be used to predict leukemia cases with minimal residual disease (MRD) following treatment at high accuracy. Based on the hypothesis that this treatment resistance is reflected by an intrinsic feature of leukemia cells at the time of diagnosis, a prognostic signature predicting the MRD load following therapy could be defined (Cario et al., 2005).

AML samples

1

Training set

2

Class Class 1 2

3

Test set

4

Evaluation of predictive value

Class predictor

Class prediction

Cross validation

(b)

Good outcome 100 Survival probability in %

Identification of Novel Prognostic Markers and Signatures in Leukemias Using supervised approaches, many groups have tried to generate signatures correlated with good and poor outcome. However, in contrast to signatures correlated with resistance to chemotherapy, in our experience supervised signatures predictive of good and poor outcome have generally not been validated in independent data sets. This might be secondary to the limitation of strictly supervised approaches in outcome prediction as survival and survival time are impacted by many things other then the tumor cells themselves, and thus are likely to be very noisy surrogates for the underlying prognostically relevant tumor subclasses. In order to discover gene-expression signatures with prognostic value in addition to signatures correlated with cytogenetics, novel semi-supervised strategies combining the strengths of supervised and unsupervised approaches might be helpful in leukemia research (Bair and Tibshirani, 2004). Semi-supervised approaches use the subset of genes correlating with survival time for supervised clustering (or supervised principal components analysis) of specimens to reveal the underlying prognostically relevant tumor subtypes, and then to build a predictor for these subtypes (Figure 70.3). Applying this approach to a cohort of AML patients, a 133 gene signature could be defined and validated as a significant independent outcome predictor, both across all cytogenetic classes and within the large subset of clinically-important AML cases with normal karyotype (Bullinger et al., 2004). Recently, this signature has been validated by applying it to an independent set of AML cases with normal karyotype (Marcucci et al., 2006). While these findings are definitely encouraging, further validation of results in larger cohorts and in independent studies are required before clinical implementation becomes feasible in leukemias.

(a)

Poor outcome

80

60

40

20 p  0.006 0 0

500 1000 Survival time in days

1500

Figure 70.3 Semi-supervised approach for leukemia outcome prediction. (a) Schematic overview of a supervised clustering strategy. (1) AML specimens are randomized into separate training and test sets. (2) In the training set, genes whose expression correlates with survival are used to cluster samples into favorable and unfavorable outcome classes. (3) An optimal gene expression predictor is constructed for these outcome classes. (4) The outcome class predictor is then validated by predicting outcomes in the independent test set. (b) Evaluation of outcome predictor. Kaplan-Meier survival analysis of the independent test set validates the gene expression classifier (here comprising 133 genes) as a significant predictor of overall survival. Data are from Bullinger et al. (2004).

Conclusions

CONCLUSIONS Genomics in Leukemias – Important Approaches to Study Leukemias During the last decade, leukemias have been an attractive area of research using genomic approaches like DNA microarray technology, as leukemia samples can be easily obtained (Ebert and Golub, 2004; Staudt, 2003). Since its invention, GEP has contributed an important new facet to the exploration of these hematologic malignancies (Bullinger et al., 2005; Chiaretti et al., 2005), with the existing knowledge in hematopoiesis and leukemia genetics guiding data interpretation and facilitating the generation of biologically meaningful hypotheses. This technology will further contribute to a comprehensive molecular leukemia classification in the future, and characteristic expression patterns will support individualized leukemia treatment by enabling physicians to discern cases with a high relapse risk or a high probability of resistance to therapy. Ultimately, microarray assays might be sufficient to adequately diagnose leukemias, predict their course, and guide individualized treatment approaches. Validation of Microarray-Based Findings in Leukemias The correlation of gene expression patterns with patient outcome provides increased challenges for the translation of initial research findings into robust diagnostics that are validated to be of clinical benefit. Studies that are based on sophisticated algorithms including a multiple random validation strategy, as results might otherwise be overoptimistic (Michiels et al., 2005), represent a prerequisite for successful validation attempts. However, there remain unresolved data analysis issues that merit further research, but if appropriately addressed they might facilitate validation of findings, such as the examination of intersections between sets of findings (Allison et al., 2006). First analyses have already demonstrated that by using adequate data normalization algorithms, cross-platform classification is feasible with high consistency and reproducibility in both childhood ALL and adult AML data sets (Nilsson et al., 2006). As recently an increasing number of well-annotated gene expression data sets has become publicly available, this powerful approach will significantly contribute to a successful exploration and comparison of existing data sets, thereby offering a prerequisite for the future contribution of GEP towards a comprehensive molecular classification and improved risk-adapted leukemia management. Clinical Applications of Genetic and Molecular Technologies While many genetic technologies like Fluorescence-In-SituHybridization (FISH), which has been shown to be a powerful tool in CLL prognostication (Dohner et al., 2000), have already found entrance into routine leukemia diagnostics (Binet et al., 2006), markers first identified by GEP studies are starting to enter the routine leukemia work-up. For example, ZAP-70, detected to be associated with unmutated VH genes (Rosenwald et al., 2001), has recently been validated to be a useful prognostic marker in

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851

CLL (Orchard et al., 2004), and ZAP-70 is now being widely used to guide CLL patient management (Binet et al., 2006; Krober et al., 2006). Similarly, an increasing number of prognostic markers identified from recent studies using GEP, like for example MN1 in AML (Heuser et al., 2006), will be evaluated within prospective trials in the future. While many challenges remain ahead before the largescale clinical implementation of GEP-based outcome predictors becomes feasible, GEP might well become a reliable diagnostic tool in the near future. It has been impressively demonstrated that GEP provides a useful alternative approach to classify known leukemia subgroups with high accuracy (Haferlach et al., 2005). In addition, the European Leukemia Network (ELN) consensus guidelines for microarray gene expression analyses in leukemia have been established (Staal et al., 2006), thereby laying the basis for the widespread application of this technology. Furthermore, within the international MILE (Microarray Innovations in Leukemia) study, investigators had begun prospectively analyzing over 4000 leukemia cases in 11 participating centers to define the role of GEP in the diagnostic panel for the work-up of leukemia (Haferlach et al., 2006). Using standardized protocols and identical equipment, this represents the first study to show a high reproducibility of microarray findings within an international multi-center research trial. The leukemia class prediction accuracy was greater than 95% for identical leukemia samples analyzed at different centers. Thus, this high reproducibility of results has laid the cornerstone for an ongoing international clinical research initiative with the aim to evaluate the potential application of microarrays in leukemia diagnosis and classification (Haferlach et al., 2006). Future Challenges of Genomics in Leukemias Today, primary analyses have begun to dissect the molecular heterogeneity of leukemia. However, an outstanding challenge for the future will be the integration of GEP and other whole genome approaches which recently have begun being used on a broader basis, to validate the numerous biologic hypotheses suggested by gene expression data. For example, in leukemias microarray-based genomics technologies have been used to profile the expression of micro-RNAs (Calin et al., 2005; Lu et al., 2005), and genome-wide single nucleotide polymorphism analyses have revealed large-scale cryptic regions of acquired homozygosity in the form of segmental uniparental disomy (Fitzgibbon et al., 2005). Microarrays also offer the possibility to screen for global methylation changes in leukemias (Gebhard et al., 2006) and to delineate genomic DNA copy number alterations (CNAs) at high resolution (Pollack and Iyer, 2002). Parallel analysis of global gene expression changes and CNAs has been shown to provide useful information regarding the delineation of candidate genes in the respective genomic regions, as recently demonstrated in AML with complex aberrations (Figure 70.4) (Rucker et al., 2006). In the future, additional integrative analyses that evaluate the leukemia transcriptome in the context of other data sources, such as SNP (single nucleotide polymorphism) arrays, tiling arrays, promoter arrays, and proteomics, will allow the extraction of additional biological insights from the data. However, a prerequisite for a successful integration of these technologies in

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(a) 0

(b) 0.5

1

1.5

2

(c)

Chromosome 7

GEP Chromosome 7q

7p

7q11

(d) GEP Chromosome 7q22q36

1p

7q22.1 | PMPC 7q22.3 | MLL5 7q22.3 | SYPL 7q22.3 | 1PBEF1 7q22.3 | PIK3C 7q31.1 | IMMP2L 7q31.1 | LRRN 7q31.2 | CAV2 7q31.2 | CAV1 Centromere 7q11

7q32.1 | NAG8 7q32.1 | MGC50844 7q32.2 | UBE2H 7q32.2 | TSGA1 7q32.3 | PODX 7q33 | AKR1B 7q33 | CALD

7q36 Y

7q36

2 1 0.5 0 0.5 1 2

7q33 7q34

| CREB3L2 | TIF1

7q34

| BRAF

7q34

| CLECSF5

7q34 7q34

| GSTK | EPHA1

7q36.1 | RARRES2 7q36.1 | ABP1 7q36.1 | CDK 7q36.3 | UBE3C

Figure 70.4 Parallel Analysis of CNA and GEP in an AML Case with Loss of 7q. (a) Enlarged view of an array comparative genomic hybridization (CGH) profile of an inv(16) AML case with del(7)(q22qter) using an 8k-BAC/PAC microarray platform. Ratio scale for CNA is indicated. (b) Enlarged view of deleted region with deleted clones colored green. (c) and (d) As previously reported (Rucker et al., 2006), correlation of array CGH (b) and GEP (d) findings helps delineate candidate genes located in the deleted region like MLL5 or PBEF1 in the proximal region of the 7q deletion. Genomic aberrations and gene expression levels are color coded according to the indicated pseudocolor scale.

leukemia research will be to define a common language for the communication of genomic profiles across different experimental systems, and the development of integrative bioinformatics tools for sharing and analyzing such leukemia profiles.

ACKNOWLEDGMENTS The authors have no conflicts of interest and have nothing to disclose.

REFERENCES Alcalay, M., Tiacci, E., Bergomas, R., Bigerna, B., Venturini, E., Minardi, S.P., Meani, N., Diverio, D., Bernard, L., Tizzoni, L., et al.. (2005). Acute myeloid leukemia bearing cytoplasmic nucleophosmin (NPMc AML) shows a distinct gene expression profile characterized by up-regulation of genes involved in stem-cell maintenance. Blood 106, 899–902.

Allison, D.B., Cui, X., Page, G.P. and Sabripour, M. (2006). Microarray data analysis: From disarray to consolidation and consensus. Nat Rev Genet 7, 55–65. Armstrong, S.A. and Look, A.T. (2005). Molecular genetics of acute lymphoblastic leukemia. J Clin Oncol 23, 6306–6315.

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chronic myelogenous leukemia patients express c-abl proteins with a common structural alteration. Proc Natl Acad Sci USA 82, 1810–1814. Krober, A., Bloehdorn, J., Hafner, S., Buhler, A., Seiler, T., Kienle, D., Winkler, D., Bangerter, M., Schlenk, R.F., Benner, A. et al. (2006). Additional genetic high-risk features such as 11q deletion, 17p deletion, and V3–21 usage characterize discordance of ZAP-70 and VH mutation status in chronic lymphocytic leukemia. J Clin Oncol 24, 969–975. Licht, J.D. and Sternberg, D.W. (2005). The molecular pathology of acute myeloid leukemia. Hematology (Am Soc Hematol Educ Program), 137–142. Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A. et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435, 834–838. Lugthart, S., Cheok, M.H., den Boer, M.L., Yang, W., Holleman, A., Cheng, C., Pui, C.H., Relling, M.V., Janka-Schaub, G.E., Pieters, R. et al. (2005). Identification of genes associated with chemotherapy crossresistance and treatment response in childhood acute lymphoblastic leukemia. Cancer Cell 7, 375–386. Marcucci, G., Radmacher, M.D., Ruppert, A.S., Mrozek, K., Kolitz, J.E., Whitman, S.P., Edwards, C.G., Vardiman, J.W., Caligiuri, M.A., Carroll, A.J. et al. (2006). Independent Validation of Prognostic Relevance of a Previously Reported Gene-Expression Signature in Acute Myeloid Leukemia (AML) with Normal Cytogenetics (NC): A Cancer and Leukemia Group B (CALGB) Study. Blood, ASH Annual Meeting Abstracts 106, 755. Michiels, S., Koscielny, S. and Hill, C. (2005). Prediction of cancer outcome with microarrays: A multiple random validation strategy. Lancet 365, 488–492. Mitchell, S.A., Brown, K.M., Henry, M.M., Mintz, M., Catchpoole, D., LaFleur, B. and Stephan, D.A. (2004). Inter-platform comparability of microarrays in acute lymphoblastic leukemia. BMC Genomics 5, 71. Nilsson, B., Andersson, A., Johansson, M. and Fioretos, T. (2006). Crossplatform classification in microarray-based leukemia diagnostics. Haematologica 91, 821–824. Orchard, J.A., Ibbotson, R.E., Davis, Z., Wiestner, A., Rosenwald, A., Thomas, P.W., Hamblin, T.J., Staudt, L.M. and Oscier, D.G. (2004). ZAP-70 expression and prognosis in chronic lymphocytic leukaemia. Lancet 363, 105–111. Pollack, J.R. and Iyer, V.R. (2002). Characterizing the physical genome. Nat Genet 32(Suppl), 515–521. Pui, C.H. and Evans, W.E. (2006). Treatment of acute lymphoblastic leukemia. N Engl J Med 354, 166–178. Rosenwald, A.,Alizadeh, A.A.,Widhopf, G., Simon, R., Davis, R.E.,Yu, X., Yang, L., Pickeral, O.K., Rassenti, L.Z., Powell, J. et al. (2001). Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia. J Exp Med 194, 1639–1647. Rosenwald, A., Chuang, E.Y., Davis, R.E., Wiestner, A., Alizadeh, A.A., Arthur, D.C., Mitchell, J.B., Marti, G.E., Fowler, D.H.,Wilson,W.H. et al. (2004). Fludarabine treatment of patients with chronic lymphocytic leukemia induces a p53-dependent gene expression response. Blood 104, 1428–1434. Ross, M.E., Zhou, X., Song, G., Shurtleff , S.A., Girtman, K., Williams, W.K., Liu, H.C., Mahfouz, R., Raimondi, S.C., Lenny, N. et al. (2003). Classification of pediatric acute lymphoblastic leukemia by gene expression profiling. Blood 102, 2951–2959.

Ross, M.E., Mahfouz, R., Onciu, M., Liu, H.C., Zhou, X., Song, G., Shurtleff, S.A., Pounds, S., Cheng, C., Ma, J. et al. (2004). Gene expression profiling of pediatric acute myelogenous leukemia. Blood 104, 3679–3687. Rowley, J.D. (1973). Identificaton of a translocation with quinacrine fluorescence in a patient with acute leukemia. Ann Genet 16, 109–112. Rucker, F.G., Bullinger, L., Schwaenen, S., Lipka, D.B., Wessendorf , S., Frohling, S., Bentz, M., Miller, S., Scholl, C., Schlenk, R.F. et al. (2006). Disclosure of candidate genes in acute myeloid leukemia with complex karyotypes using microarray-based molecular characterization. J Clin Oncol 24, 3887–3894. Schlenk, R.F., Benner,A., Krauter, J., Buchner,T., Sauerland, C., Ehninger, G., Schaich, M., Mohr, B., Niederwieser, D., Krahl, R. et al. (2004). Individual patient data-based meta-analysis of patients aged 16 to 60 years with core binding factor acute myeloid leukemia: A survey of the German Acute Myeloid Leukemia Intergroup. J Clin Oncol 22, 3741–3750. Seiler, T., Dohner, H. and Stilgenbauer, S. (2006). Risk stratification in chronic lymphocytic leukemia. Semin Oncol 33, 186–194. Shtivelman, E., Lifshitz, B., Gale, R.P. and Canaani, E. (1985). Fused transcript of ABL and BCR genes in chronic myelogenous leukaemia. Nature 315, 550–554. Slovak, M.L., Kopecky, K.J., Cassileth, P.A., Harrington, D.H., Theil, K.S., Mohamed, A., Paietta, E., Willman, C.L., Head, D.R., Rowe, J.M. et al. (2000). Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: A Southwest Oncology Group/Eastern Cooperative Oncology Group Study. Blood 96, 4075–4083. Staal, F.J., Cario, G., Cazzaniga, G., Haferlach, T., Heuser, M., Hofmann,W.K., Mills, K., Schrappe, M., Stanulla, M.,Wingen, L.U., et al. (2006). Consensus guidelines for microarray gene expression analyses in leukemia from three European leukemia networks. Leukemia 20, 1385–1392. Staudt, L.M. (2003). Molecular diagnosis of the hematologic cancers. N Engl J Med 348, 1777–1785. Stegmaier, K., Ross, K.N., Colavito, S.A., O’Malley, S., Stockwell, B.R. and Golub, T.R. (2004). Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat Genet 36, 257–263. Stegmaier, K., Corsello, S.M., Ross, K.N., Wong, J.S., Deangelo, D.J. and Golub, T.R. (2005). Gefitinib induces myeloid differentiation of acute myeloid leukemia. Blood 106, 2841–2848. Tallman, M.S. (2005). New strategies for the treatment of acute myeloid leukemia including antibodies and other novel agents. Hematology (Am Soc Hematol Educ Program), 143–150. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S. and Golub, T.R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96, 2907–2912. Tipping, A.J., Deininger, M.W., Goldman, J.M. and Melo, J.V. (2003). Comparative gene expression profile of chronic myeloid leukemia cells innately resistant to imatinib mesylate. Exp Hematol 31, 1073–1080. Valk, P.J., Verhaak, R.G., Beijen, M.A., Erpelinck, C.A., Barjesteh van Waalwijk van Doorn-Khosrovani, S., Boer, J.M., Beverloo, H.B., Moorhouse, M.J., van der Spek, P.J., Lowenberg, B. et al. (2004). Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 350, 1617–1628.

Recommended Resources

Walgren, R.A., Meucci, M.A. and McLeod, H.L. (2005). Pharmacogenomic discovery approaches: Will the real genes please stand up?. J Clin Oncol 23, 7342–7349. Yeoh, E.J., Ross, M.E., Shurtleff , S.A., Williams, W.K., Patel, D., Mahfouz, R., Behm, F.G., Raimondi, S.C., Relling, M.V.,

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RECOMMENDED RESOURCES American Society of Hematology http://www.hematology.org/ European Hematology Association http://www.ehaweb.org/

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National Cancer Institute (NCI) http://www.cancer.gov Gene expression omnibus http://www.ncbi.nlm.nih.gov/geo/

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71 Genomics of Lung Cancer Hasmeena Kathuria, Avrum Spira and Jerome Brody

INTRODUCTION In the early 1900s, lung cancer was a rare disease; today it is the most common cause of cancer death in the United States and in the world. Worldwide in 2006, there will be 1.2 million lung cancer deaths. In the United States alone, there will be 174,000 new cases of lung cancer in 2006, with 162,000 deaths, accounting for 31% of all cancer deaths in men and 26% of all cancer deaths in women. The incidence of lung cancer tracks, with a 30–40 year lag, the frequency of cigarette smoking in a population. Smoking incidence and lung cancer rates are similar to the United States in Western Europe, but are 30% higher in Eastern Europe and in China where 70% of men smoke, and an epidemic of lung cancer is predicted in the mid21st century. The fact that only 10–15% of smokers develop lung cancer suggests there are genetic factors that influence individual susceptibility to the carcinogenic effects of cigarette smoke. However, the late age at which lung cancer usually develops limits traditional genetic studies, since parents and siblings are not often available to study. Unfortunately, survival rates for newly diagnosed lung cancer remain at 15%, virtually unchanged in the past 4–5 decades, largely because the disease is most often diagnosed at an advanced stage and there is presently no way to determine which of the 10–15% of current and former smokers are at highest risk for developing lung cancer.

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This chapter summarizes the progress that has been made in understanding the molecular mechanisms and pathogenesis of lung cancer and attempts to describe how gene expression profiling on human lung cancer, airway, and peripheral blood specimens is leading to new approaches in lung cancer screening, classification, diagnosis, prognosis, and treatment. This review focuses mainly on the genomic studies of lung cancer, including a brief discussion on the findings of methylation and microRNA (miRNA) arrays in lung tumorigenesis. Studies using single nucleotide polymorphism (SNP) and protein arrays are beyond the scope of this review, as are studies of in vitro cell lines and mouse models. Although we have attempted to be as inclusive as possible, our list of studies reviewed is likely to be incomplete. The section on “Early Diagnosis/Screening of Lung Cancer” provides a brief overview of the hereditary factors that may explain susceptibility to the carcinogenic effects of smoking and describes the polymorphisms in genes involved in the metabolism of the toxic components of cigarette smoke. This section also summarizes the current studies that use genomic profiling to identify the subset of smokers who are at high risk for developing lung cancer and highlights the importance of developing biomarkers and chemoprevention strategies aimed at high-risk current or former smokers. Although this section briefly discusses epigenetic changes that have been associated with smoking, discussions on other genetic changes, including cytogenetic changes and somatic mutations, are beyond the scope of this chapter.

Copyright © 2009, Elsevier. Inc. All rights reserved.

Early Diagnosis/Screening of Lung Cancer

The section on “Classification and Prognosis” provides a brief overview of the different categories of genes known to be involved in tumorigenesis, including oncogenes, growth factors and their receptors, and tumor suppressor genes. Given the complexity of lung cancer, these tumors are likely dependent on more than one oncogenic signaling pathway. This section sites examples of how genomics has advanced our ability to predict the dysregulation of various oncogenic pathways in lung cancer (both the mutated gene product itself and its downstream targets), thus potentially offering an opportunity for developing new therapeutic drugs that are pathway-specific. This section focuses in some detail on recent advances in understanding epidermal growth factor receptor (EGFR)-mediated survival signals. The section on “Pathogenesis and Treatment of Lung Cancer” begins by describing the tumor, node, metastases (TNM) tumor classification system. Although tumor staging is currently the most important prognostic variable for predicting survival, some of the limitations of the current system include the fact that patients diagnosed with similar stage and treated using similar protocols, often respond quite differently and have varying survival rates. This section describes how genomic expression profiling of lung cancer has identified prognostic genes and identified subtypes of adenocarcinomas, thus potentially guiding clinical decision-making in the future by identifying “high risk” lung cancer patients that would benefit from improved diagnostic and treatment options. Gene expression profiling, combined with genetics, clinical information, proteomics, and imaging studies can be applied to developing tools for risk-assessment, early diagnosis, and new approaches for individualized treatment. With stronger working relationships and collaborations between bench scientists and their clinical counterparts and establishment of large databases with standardized methods for data collection and analyses, modern genomic technology promises continued improvement in diagnostic and therapeutic options for lung cancer patients.

EARLY DIAGNOSIS/SCREENING OF LUNG CANCER Smoking, Lung Cancer, and Genetics In the late 1920s, it was first suspected that cigarette smoking caused lung cancer, and in the 1950s and 1960s a variety of studies linking lung cancer to cigarette smoking were published. In the 1960s, over 40% of men in the United States smoked until the Surgeon General’s report linked smoking to lung cancer. Since then, smoking rates in men have decreased to 20–25% with a subsequent leveling off and then fall in lung cancer incidence in the 1990s. Smoking rates in women, however, began to rise in the 1950s and lung cancer became the leading cause of death from cancer in women in the 1980s (reviewed by Spiro and Silvestri, 2005). By 2030, it is projected that smoking incidence will plateau at 20% of the adult US population in both men and

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women, ensuring a constant high rate of lung cancer throughout the first half of the 21st century. There is controversy about whether lung cancer is now occurring more frequently in never-smokers. Environmental tobacco exposure or second-hand smoke may cause lung cancer in life-long non-smokers. There have been reports of lung cancer in large numbers of non-smoking Chinese women who have been exposed to fumes from cooking with charcoal in poorly ventilated dwellings (Luo et al., 1996; Zhou et al., 2000). These observations raise the question of whether lung cancer in other never-smokers might result from a variety of unrecognized toxic exposures especially in genetically susceptible individuals. We now know that although 80–85% of patients with lung cancer have a history of smoking, only 10–15% of patients who smoke actually develop lung cancer suggesting that hereditary factors may explain susceptibility to the carcinogenic effects of smoking. It is well established that smokers who are first-degree relatives in families with a history of lung cancer have a two- to threefold increased risk of developing lung cancer (Thun et al., 2002). The risk is further increased with a family history of early onset lung cancer. Hereditary occurrence is also suggested since both smokers and non-smokers with a positive family history are at higher risk for developing lung cancer. Epidemiological studies show that Native American and African American smokers are more susceptible to lung cancer than whites, while Latinos, and Japanese Americans are less susceptible (Haiman et al., 2006). These differences are accentuated in those who have smoked fewer than 10 cigarettes per day and tend to disappear in those who have smoked more than 30 cigarettes per day, suggesting genetically determined carcinogenic susceptibility that is masked at high levels of smoke exposure (Haiman et al., 2006). The reasons for the observed racial/ethnic differences in response to the carcinogenic effects of cigarette smoke are not yet known, but it is likely that both genetic and environmental differences are involved. African Americans, for example, tend to smoke menthol cigarettes or ones with higher levels of nicotine and tar (Okuyemi et al., 2004). There have been a large number of studies that have attempted to define heritable causes of lung cancer susceptibility. Polymorphic variants in almost 50 genes have been claimed to be associated with either a reduced or elevated risk of lung cancer (reviewed by Cooper, 2005). Early studies focused on polymorphisms in genes involved in the metabolism of the toxic components of cigarette smoke. Most of the compounds in cigarettes are activated by phase I drug metabolizing enzymes (DME), such as cytochrome p450, to become active and carcinogenic. These phase I genes code for substances that convert smoke constituents to highly reactive intermediates that can bind to and mutate DNA. Phase II DMEs, such as N-acetyltransferases, sulfotransferases, and glutathianone S-transferases, detoxify carcinogenic products by conjugating these reactive species to less reactive, excretable products. While some studies support the importance of polymorphisms in both phase I and II genes, others have found no relation between polymorphisms in these genes and the incidence of lung cancer

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(Perera et al., 2006; Raunio et al., 1999;Taioli et al., 2003;Wikman et al., 2001). It is likely that there are a number of genes, some involved in interacting pathways that account for individual susceptibility to the carcinogenic effects of smoking. Recent studies have described polymorphisms in cell cycle checkpoint genes in African American smokers with lung cancer and in DNA repair genes in smokers both with and without lung cancer (DavidBeabes and London, 2001; Wenzlaff et al., 2005). A promoter polymorphism that increases promoter activity in caspase-9, a cysteine protease involved in the caspase-mediated apoptotic pathway, was recently found to contribute to genetic susceptibility in lung cancer (Park et al., 2006). In addition, a number of studies have demonstrated linkage to lung cancer on chromosome 6q23 (Bailey-Wilson et al., 2004) and on chromosome 12q (Sy et al., 2004). Mouse modeling studies have identified similar linkage between lung cancer and chronic obstructive pulmonary disease (COPD), and many of the genes in the area of linkage are involved in inflammation, a process that is likely central in the pathogenesis of both diseases (Bauer et al., 2004). Identifying Smokers at Risk The risk for developing lung cancer increases with accumulated exposure to cigarette smoke, most often expressed as pack-years (calculated by multiplying the number of packs of cigarettes smoked per day by the number of years the person has smoked). As noted earlier, even in a high-risk population of smokers, however, the incidence of lung cancer is only 15% over a lifetime. The incidence increases slightly in those over the age of 60 with greater than 30 pack-years, previous lung cancer, and/or atypia of bronchial epithelial cells in the sputum. Because of the lack of effective diagnostic biomarkers that identify which of the current and former smokers are at the greatest risk for developing cancer, lung cancer is most often diagnosed at a late stage after it has spread. In contrast to most cancers, 5-year survival rates for lung cancer, 15%, have not changed appreciably over the past 4–5 decades. Previous screening trials with frequent chest x-rays and sputum cytology have not demonstrated an effect on lung cancer mortality (reviewed by Jett and Midthun, 2004). Spiral computerized tomography (CT) scan screening can detect lung tumors at an early stage. The I-ELCAP study, a systematic case–control observational study that included more than 31,000 subjects who were at risk for lung cancer, found that screening by CT resulted in a diagnosis of lung cancer in 484 participants, of which 412 (85%) had clinical stage I lung cancer (Henschke et al., 2006). Spiral CTs, however, while highly sensitive, can be non-specific and many newly detected small lesions have proven on resection to be nonmalignant scar tissue or old granulomas rather than early lung cancers ( Jett, 2005). Thus, it is not yet known if this approach will alter lung cancer mortality or justify the relatively high cost of large-scale screening. Current trials, including the National Lung Cancer Screening Trial (NLST), will help answer these questions. Developing biomarkers that are highly sensitive, specific, and target individuals with early stages of cancer is clearly

necessary to improve lung cancer mortality. Several groups have used genomic profiling to identify the subset of smokers who are at higher risk for developing lung cancer. Powell et al. (2003) compared the gene expression profiles of tumor and matched normal tissues from smokers and non-smokers. Although hierarchical clustering did not separate tumors from smokers versus non-smokers, it did separate tumor and non-tumor tissue. Four times more genes were altered between tumor and lung in nonsmokers as compared with smokers. Their findings demonstrate that underlying normal tissues from smokers and non-smokers differ, consistent with the concept of “field cancerization” in smokers where genes are altered in the entire respiratory epithelium. To begin to understand the mechanisms by which some individuals protect themselves from the carcinogenic effects of smoking, Spira et al. (2004) used high-throughput genomic and bioinformatic tools to define the genome-wide impact of smoking and smoking cessation on bronchial airway epithelium. They demonstrated changes in both antioxidant and drug metabolizing genes in airway epithelial cells as well as increases in putative oncogenes and decreases in tumor suppressor genes. They also noted that the expression level of smoking-induced genes among former smokers began to resemble that of never smokers after 2 years of smoking cessation. Genes that reverted to normal within 2 years of cessation tended to serve metabolizing and antioxidant functions. In addition the authors found that several genes, including putative oncogenes and tumor suppressor genes, failed to revert to never-smoker levels years after smoking cessation, perhaps explaining the continued risk for developing lung cancer many years after individuals have stopped smoking (Spira et al., 2004). This same group has reported a lung cancer-specific diagnostic gene expression profile in histologically normal airway epithelial cells of patients being evaluated for the diagnosis of lung cancer (Spira et al., 2007). These genes also predicted lung cancer in previously published studies of normal and lung cancer tissue, raising the possibility that changes in airway gene expression of smokers might not only serve as biomarker for risk of developing lung cancer, but may reflect to some extend the genomic changes that occur in the actual lung cancers. These studies support the possibility that measuring gene expression in easily accessible airway epithelial cells may provide a biomarker that identifies smokers at high risk of developing lung cancer. Detection of mutations or aberrant methylation in sputum or serum is a promising approach to the early diagnosis of lung cancer. Detection of promoter hypermethylation in the CDKN2A, DAPK1, RASSF1, CDH1, GSTP1, RAB, CDH13, APC, MLH1, MSH2, and MGMT genes have been demonstrated using sputum or bronchial lavage (reviewed by Cooper, 2005). Aberrant promoter hypermethylation has been shown to occur frequently in patients with resected lung cancers and concordance between methylation patterns in tumor specimens and bronchial epithelial cells has been demonstrated. In addition, recent studies demonstrated that gene promoter hypermethylation in sputum could identify people at high risk for lung cancer incidence. One study demonstrated FHIT methylation in preneoplastic lesions from smoking-damaged bronchial epithelium

Classification and Prognosis

(Zochbauer-Muller et al., 2001). Another study demonstrated hypermethylation of the CDKN2A gene promoter prior to clinical evidence of lung carcinoma (Kersting et al., 2000). Lastly, Palmisano et al. (2000) showed that p16 and/or MGMT could be detected in DNA from sputum up to 3 years before clinical diagnosis of squamous cell cancer. Reports documenting the clinical potential of detecting tumor-related genes, mutations, loss of heterozygosity, polymorphisms, and promoter hypermethylation of circulating tumor DNA in serum are also surfacing. In addition high-throughput methods for analyzing the methylation status of hundreds of genes simultaneously are being applied to the discovery of methylation signatures that distinguish normal from cancer tissue samples (Bibikova et al., 2006;Wilson et al., 2006). However, none of these studies has tested the diagnostic potential in prospective multicenter trials. Future Directions Lung cancer is one of the few cancers for which screening is not recommended, even in high-risk individuals. Although spiral CT and autofluorescent bronchoscopy may increase the detection rate of early lesions, these procedures are costly and/or relatively invasive, have not been shown to be specific for the presence of lung cancer, and it is not yet known if screening will alter mortality. Since molecular, genetic, and epigenetic abnormalities precede morphological changes in bronchi and alveoli, biomarkers may help select a group of high-risk patients that would benefit from spiral CT and/or fluorescent bronchoscopy. To develop screening profiles that could potentially predict those at risk for developing cancer and/or detect lung cancer at an earlier stage, profiles must be capable of identifying the few abnormal cells among many normal cells and samples ideally should be obtained in a relatively non-invasive fashion. The availability of a number of agents that might be effective in reversing the pre-malignant changes in airway epithelial cells (see below) has driven the search for biomarkers that identify individuals at highest risk for developing lung cancer. Genetic abnormalities can be detected from bronchial biopsies, respiratory cells from sputum, and circulating DNA, and gene expression profiles generated from these specimens offer a wide area of investigation for biomarker development. To be applied in a screening program, these biomarkers must be specific and cost effective with a high efficiency: the focus has been on intermediate markers that indicate risk but might be reversible with appropriate treatment. A recent review has identified the benefits of and requirements for the ideal biomarker (Dalton and Friend, 2006). Benefits include (1) predict who will develop lung cancer and/ or detect cancer at an early treatable stage, (2) serve as guides for treatment decisions (3) and serve to identify new targets for drug development. The authors in this review point out the need to develop biomarkers that deal with the molecular diversity of lung cancer that monitor disease progression and that predict and/or monitor response to preventative therapy. A biomarker that meets even the majority of these requirements remains to be discovered but will almost certainly evolve with modern genomic technology.

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A relatively new field, “genetical genomics,” which attempts to link gene expression in affected tissues with genetic polymorphisms promises to provide insights into heritable factors in lung cancer (Li and Burmeister, 2005). This field is derived from recent technological and computational advances that allow one to measure levels of gene expression in the affected tissue and relate them to high-density SNP discovery in genomic DNA (Pastinen et al., 2006). Patterns of gene expression in smokeexposed tissue and linkage to SNPs in the affected genes are likely to lead to new insights into heritable cause of lung cancer. Chemoprevention of high-risk current or former smokers represents one of the most important areas of current research in the prevention of lung cancer. It has been a topic of great interest for over 20 years. Despite numerous trials beginning with high doses of retinoids in the 1980s, there have been no studies to date that have demonstrated a positive outcome in terms of mortality from lung cancer and few studies that have demonstrated an effect of intermediate end points, such as sputum atypia or genomic markers of epithelial cell damage. Several recent publications review the results of previous randomized trials and the rationale for use of new chemopreventative agents (Hirsch and Lippman, 2005; Kelloff et al., 2006; Khuri and Cohen, 2004). Chemoprevention (chemoprophylaxis) has assumed increasing importance as former smokers now account for 50% of new lung cancer cases in the United States. Since there are approximately 45 million former smokers in the United States, identifying which former smokers are at highest risk for developing lung cancer in the future is the most important first step in any chemoprevention program. Many new chemopreventative agents are being explored using agents that fall into several broad categories: (1) anti-inflammatory/antioxidants, (2) epigenetic modulators of methylation and/or acetylation, and (3) modulators of signal transduction, particularly those that affect the EGFRsignaling pathways. A recent clinical study conducted in 10 patients to assess the potential chemopreventive effect of myoinositol in smokers with bronchial dysplasia showed a significant increase in the rate of regression of preexisting dysplastic lesions (Lam et al., 2006). The potential impact of chemoprevention is large, but the field awaits the emergence of intermediate markers of cancer risk that must be validated in prospective studies.

CLASSIFICATION AND PROGNOSIS Histological Classification of Lung Tumors and TNM Staging Lung cancers are classified as small-cell lung carcinomas (SCLC) or non–small-cell carcinomas (NSCLC). Small-cell lung cancers have neuroendocrine features that are identified by immunohistochemistry and histology. NSCLC are subcategorized as adenocarcinomas (most common), squamous cell carcinomas, and large-cell carcinomas, and are clinically distinct from SCLC. The pathological distinction between SCLC and NSCLC is very important since these tumor types are treated differently (reviewed by Spira and Ettinger, 2004).

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Pre-malignant lesions termed low and high-grade dysplasia, carcinoma in situ (CIS), and atypical adenomatous hyperplasia (AAH) are associated with increased risk for developing lung cancer (reviewed by Kerr, 2001). When the airways are exposed to carcinogens, cellular and molecular changes occur over broad mucosal surfaces, a concept termed “field cancerization.” Accumulation of mutations and epigenic alterations ensue, eventually leading to invasive lung cancer, a process called “multistep carcinogenesis” (reviewed by Cooper, 2005). Histological analysis is currently used to identify and classify cancers, yet definitive diagnoses are often difficult to make. Furthermore, tumors with the same histological classification behave differently, and morphological classification has not been effective in predicting the aggressiveness of a cancer or how the cancer will respond to therapeutic agents. In NSLC, tumor staging (International System for Staging Lung Cancer) is the most important prognostic variable for predicting survival. TNM staging takes into account the degree of spread of the primary tumor (T), the extent of regional lymph node involvement (N), and the presence or absence of metastases (M) (summarized by Hoffman et al., 2000). Recently adopted revisions to TNM staging include (1) the splitting of stage I into IA and IB; (2) the splitting of stage II into IIA and IIB; (3) reclassifying T3N0M0 from Stage IIIA to IIB; and (4) classifying multiple pulmonary tumor nodules as T4 if the satellite nodule(s) are in the same lobe, or M1 if the ipsilateral nodules(s) are in the nonprimary lobe. Despite these changes and improvements in diagnosis including both non-invasive methods (CT/PET), and invasive methods (EUS, EBUS), patients diagnosed with similar stage and treated using similar protocols, often respond quite differently and have varying survival rates. Some tumors, though found at an early stage, will rapidly progress to metastatic disease. Furthermore, in patients who have undergone surgical resection, recurrence rates are high with 5-year survival rates only 40% for stage IIB NSCLC. Although several combined clinical, histological, and laboratory variables such as age, stage or grade of tumor, and serum protein levels can be used to assess a patient’s prognosis with variable accuracy, these criteria are not able to provide important information about the prognostic diversity within each stage such as how aggressive a particular subtype will be, or how a patient will respond to therapy. By combining clinical variables and histopathology with gene expression profiling, predicting a patient’s prognosis could theoretically be improved. Molecular Classification and Prognostic Value of Lung Cancer Genomics Morphological tumor classification does not always accurately predict the patient’s clinical behavior since lung cancer is genetically heterogeneous. For example, patients with stage IA lung cancer resected for cure still have 30% mortality from local recurrence, distant metastases, and/or new occurrence. Within each clinical stage, there is variability in the presence of specific mutations, deletions of tumor suppressor genes, amplifications of oncogenes, and chromosomal abnormalities. Genomics is a powerful tool for classifying tumor subtypes. Lung cancer

patients with biomarkers that predict a poor outcome could be selected for adjuvant chemotherapy while those that predict a good prognosis may be able to avoid the toxicity and cost of unnecessary chemotherapy. Recently, microarray studies have identified and validated specific genes whose expression differs between normal and tumor tissue. Using cDNA arrays, Wikman et al. (2002) compared gene expression profiles of 14 pulmonary adenocarcinoma patients with normal lung tissue. These authors demonstrated marked differences in gene expression level between normal lung and adenocarcinomas. Another study by Yamagata et al. (2003) showed that a profile based on gene expression could be used on blinded samples to differentiate primary NSCLC from normal lung and lung metastases. Furthermore, groups of genes were identified that were able to identify known histological subgroups of NSCLCs. Genomic high-throughput technologies have been used in many studies to identify gene expression signatures that predict patient survival and/or relapse rates. A considerable number of expression profiling studies have been performed on clinical lung cancer specimens (summarized by Granville and Dennis, 2005; Kopper and Timar, 2005; and Cooper, 2005). Not only can gene expression profiles group tumor samples consistent with classical histology, but can also identify subgroups within histologic subclasses. In an attempt to discover previously unrecognized subtypes of lung cancer, two microarray studies aimed at class discovery by hierarchical clustering using primary lung cancer specimens were published in 2001 (see Figure 71.1). Garber et al. (2001) identified gene subsets that are characteristic of each of the known morphological subtype in NSCLSs. In addition, these authors found that these tumors could be further divided into subgroups with significant differences in patient survival. Another study by Bhattacharjee et al., (2001) showed by gene expression analysis of 186 lung carcinomas that biologically distinct subclasses of lung carcinomas exist. In their study, a subclass of adenocarcinomas defined by having neuroendocrine gene expression had a less favorable outcome, whereas a subset of patients with predominantly type II pneumocyte expression, had a more favorable outcome. In a study designed to identify whether gene expression patterns could predict survival, Beer et al. (2002) demonstrated that expression profiles based on microarray analysis could be used to predict disease progression and clinical outcome in early-stage lung adenocarcinomas. Subdivision of the lung tumors based on gene expression patterns matched the morphological classification of tumors into squamous, large-cell, adenocarcinoma, and small-cell lung cancer. Furthermore, adenocarcinomas could be further subclassified based on their gene expression profiles that correlated with the degree of tumor differentiation and patient survival. They demonstrated by gene profiling that a list of 50 genes were most effective at dividing patients with stage I lung adenocarcinoma into high- and low-risk groups for mortality, thus potentially identifying a subset of stage I NSCLC cancer patients who would benefit from adjuvant therapy. This group has recently extended their observations to the squamous cell

Classification and Prognosis

1995 Description of cDNA array (Schena et al. Science 1995)

1996

1997

1998

2000

2001

2002

2003

2004

2005

2006

1995

Description of oligonucleotide array (Wodicka et al. at Nat Biotechnology 1997)

861

2006 Development and validation of clinically relevant early-stage lung cancer outcome predictor (Potti et al. NEJM. 2006)

2001 First microarray studies of lung cancer tissue: class discovery related to lung cancer outcomes (Bhattacharjee et al. MIAS 2001: Garber et al. MIAS. 2001)

1999

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2010

First clinical application of microarray technology (Golub et al. Science. 1999)

Class prediction model developed for lung cancer prognosis (Beer et al. Nat. Med 2002)

Ongoing randomized clinical trial to evaluate performance of genomic biomarkers for lung cancer outcome

Figure 71.1 Timeline for the development of DNA microarray technology and its application to lung cancer. The key advances in the field of microarray technology and their clinical applications to lung cancer tissue for prognostic purposes are highlighted.

Future Directions Currently, lung cancer patients within a given clinical stage and tumor type receive the same treatment despite the genomic heterogeneity that exists between patients. Although expression profiling of lung cancer has identified prognostic genes and identified subtypes of adenocarcinomas, until recently, these profiles have not been ready to be incorporated into clinical practice. Comparison of array studies has been difficult because the array platforms, sample preparation, and technical factors have been different. In addition, many molecular classification studies do not match the classification based on tumor histology, perhaps because use of whole lungs for microarray studies may not accurately reflect gene expression in the cancer cells because of contamination and differences in abundance of stromal and surrounding normal cells. Although laser-captured microdissection (LCM) can be used to obtain a homogeneous population of

Stage IA, predicted low risk of recurrence (n  47)

100

80 Stage IA (n  68) Survival (%)

carcinoma subset of NSCLC and have generated a similar risk of progression stratifier which combined with the adenocarcinoma tool, will cover 80% of all NSCLC (Raponi et al., 2006). Recently, a clinically relevant prognostic tool that may alter clinical decision-making in early-stage lung cancer was developed using gene expression profiling (Potti et al., 2006a, b). Potti et al. (2006a, b) identified a gene expression profile that predicted the risk of recurrence in 89 patients with early-stage lung cancer (Figure 71.2). The authors then validated the model, termed the “lung metagene” model, in a group of 134 patients. The test predicted recurrence for individual patients significantly better than did clinical prognostic factors. This predictor also identified a subgroup of patients with stage IA lung cancer who were at high risk of recurrence and would potentially benefit from adjuvant therapy. Based on these findings, a multicenter clinical trial has been initiated. This upcoming clinical trial is the first to use a genomic test to select treatment options for individual lung cancer patients.

60

40

20 Stage IA, predicted high risk of recurrence (n  21)

p0.001 0 0

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50

75 Months

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Figure 71.2 Application of the gene expression model to determine prognosis in stage IA NSCLC. Kaplan-Meier survival estimates from three cohorts of patients with stage IA disease. The middle black Kaplan-Meier curve represents the median survival estimate in all cohorts. The top and bottom KaplanMeier curves represent survival estimates for subjects predicted to have low and high risk of recurrence respectively by the gene expression model, demonstrating the ability of the genomic model to accurately predict survival in stage IA disease. Figure reproduced from Potti et al. NEJM, 2006.

epithelial cancer cells, excluding surrounding stroma may eliminate cells that contribute to the tumor environment. Redefining tumor classification from strictly morphologybased schemes to molecular-based classifications using a variety of parameters including histological patterns, gene expression profiles including the presence or absence of oncogenes, tumor

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suppressor genes, and miRNAs, and cell surface protein and receptor status, promises to provide clinically important information on tumor subsets within morphological classes. A recent expression profile analysis of lung adenocarcinomas, for example, classified tumors into two major subtypes, terminal respiratory unit (TRU) and non-TRU subtypes (Takeuchi et al., 2006). TRU type tumors retained features of normal peripheral lung features and had higher frequency of EGFR mutations. The presence of EGFR mutations in this adenocarcinoma subtype predicted a shorter post-operative survival. A large study is underway in which participating centers are pooling and analyzing lung adenocarcinoma specimens, the results of which will hopefully lead to a gene expression signature that will differentiate the adenocarcinoma subtypes (Dobbin et al., 2005). Prognostic profiles and eventually, a prognosticspecific gene chip, may help guide clinical decision-making by identifying “high risk” lung cancer patients that would benefit from improved diagnostic and treatment options.

PATHOGENESIS AND TREATMENT OF LUNG CANCER Molecular Alterations in Lung Cancer Proto-oncogenes Proto-oncogenes such as Ras and Myc were first discovered in the 1970s and 1980s, and it was only in the 1980s and 1990s that these genes were integrated into signaling pathways and that mutations in these genes were linked to human cancers. The Ras genes (Hras, Kras, and Nras) encode GTPase proteins that help transduce survival and growth-promoting signals. When oncogenic mutations occur, the normal abrogation of RAS signaling by hydrolysis of bound GTP to GDP is impaired, resulting in persistent signaling (reviewed by Singhal et al., 2005). Point mutations (found most frequently in codon 12, 13, and 61) are detected in 20–30% of lung adenocarcinomas (Slebos et al., 1990) and 90% of these mutations are found in Kras (see Table 71.1). Mutations in Kras are markers for poor prognosis in NSCLCs (Graziano et al., 1999) as mutations rarely occur in SCLC. Myc genes (MYCL, MYCN, and CMYC ), which encode transcription factors that regulate genes involved in cell cycle regulation, DNA synthesis, and RNA metabolism, become activated by loss of transcriptional control or by gene amplification, resulting in MYC protein overexpression. C-Myc amplification occurs in 5–10% of NSCLCs (Richardson and Johnson, 1993). Growth Factors and Their Receptors Lung tumors often express growth factors and their receptors, and the resulting regulatory loops can stimulate tumor growth. EGFR, also known as ERBB-1, is highly expressed in many epithelial tumors, including a subset of lung adenocarcinomas (Reissman et al., 1999). When ligand (EGF or TGF-) binds to EGFR, there is receptor tyrosine kinase activation and a series of downstream signaling events, including the mitogen-activated protein kinase (MAPK), PI3/Akt, and Jak/Stat pathways (see Figure 71.3) that

TABLE 71.1 cancer

Common molecular alterations in lung Type of mutation

Frequency

k-Ras

Point mutation

NSCLC: 20–30% (mostly adeno)

MYC

Translocation amplification

SCLC: 30–40% NSCLC: 5–10%

Bcl-2

Translocation

NSCLC: (30%) Squamous: 25%; Adeno: 10%

EGFR (ERBB-1)

Deletion/Mutation amplification

SCLC: 0% NSCLC: 10% (BAC: 25%)

HER2 (erbB2)

Translocation amplification

NSCLC 30–40%

p53

Deletion/LOH

SCLC: 75% NSCLC: 50%

Rb gene

Point mutation

SCLC: 90% NSCLC: 15–30%

P16 (CDKN2)

Deletion/LOH

SCLC: 80% NSCLC: 30–50%

FHIT

Deletion/LOH

SCLC: 100% NSCLC: 60%

Proto-oncogenes

Growth factors

Tumor suppressors

can result in cellular proliferation, increased cell motility, tumor invasion, anti-apoptosis, and resistance to chemotherapy summarized by Baselga, 2006). EGFR tyrosine kinase, therefore, was proposed as a target for cancer therapy 20 years ago. Anti-EGFR drugs approved for cancer treatment include the monoclonal antibodies directed against the extracellular domain of the receptor (anti-EGFR Mabs) and small-molecule inhibitors of EGFR’s tyrosine kinase activity (TKIs). In NSCLCs, two trials have demonstrated that in unselected patient populations, anti-EGFR TKIs have modest levels of antitumor activity. In 10% of patients, however, tumor response is often dramatic, but responses tend to be transient. Responsive patients are most often non-smoking Asian woman with adenocarcinomas, often bronchioloalveolar carcinoma (BAC) (reviewed by Baselga, 2006; Dziadziuszko et al., 2006). Subsequent analyses demonstrated that lung cancers with somatic mutations in EGFR correlate with a positive clinical response to EGFR inhibitors (reviewed by Janne et al., 2005; Lynch et al., 2006; and Thomas et al., 2006). Unfortunately, these initial responders often develop resistance to these drugs. A second EGFR somatic mutation, T790M, occurs in some cases of NSCLC that recur after an initial response to TKIs (Kobayashi

Pathogenesis and Treatment of Lung Cancer

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EGF TNF- EGFR Drugs

P

PI3K

Mutations

1. Small-molecule TKIs 2. Monoclonal antibodies 3. Irreversible EGFR TKI 4. Combined therapy

P

RAS

1. Exon 19 deletions del E746-A750 2. Exon 20 duplication 3. Exon 21 mutation L858R 4. Second-point mutation T790M

PKC Frequency

AKT

MAPK

JAK/STAT

Cell proliferation Cell survival Metastases

Mutations SCLC 0% NSCLC 10% Adeno 6% BAC 26% Large 0% Squamous 0%

Figure 71.3 When ligand (EGF or TGF-) binds to EGFR, there is receptor tyrosine kinase activation and a series of downstream signaling events, including the MAPK, PI3/Akt, and Jak/Stat pathways, that can result in cellular proliferation, increased cell motility, tumor invasion, anti-apoptosis, and resistance to chemotherapy. Receptor expression can be increased by gene duplication (*). Most of the tyrosine kinase domain and all activating EGFR mutations thus far are in exons 18–21. Deletions in exon 19 and substitution mutations in exon 21 account for the majority of mutations. Mutations occur most frequently in bronchoalveolar carcinoma (BAC), but not in squamous cell carcinoma or SCLC (Marchetti et al., 2005; Shigematsu et al., 2005). Anti-EGFR drugs approved for cancer treatment include the monoclonal antibodies directed against the extracellular domain of the receptor (anti-EGFR Mabs) and small-molecule inhibitors of EGFR’s tyrosine kinase activity (TKIs). Examples of anti-EGFR Mabs include Cetuximab (Erbitux), Panitumumab, and Pertuzumab. Examples of TKIs include Gefitinib (Iressa) and Erlotinib (Tarceva).

et al., 2005). Germ line T790M mutations have been described in a family with multiple cases of lung cancer (Bell et al., 2005). Clinical trials using alternate EGFR inhibitors, such as irreversible EGFR inhibitors, are underway in NSCLC patients. Some centers have begun to sequence EGFR in all resected tumors in order to tailor drug therapy to specific mutations, although further research is needed to more completely define the appropriate population for EGFR testing (reviewed by Sequist et al., 2006). The complexity of EGFR mutations is highlighted by a recent report identifying different EGFR mutations in the primary tumor versus lesions metastatic from the primary site (Italiano et al., 2006). Furthermore, Engelman et al. (2006) recently reported that allelic dilution of a biologically significant resistance mutation in EGFR-amplified lung cancer may be undetected by direct sequencing. Tumor Suppressor Genes The role of p53, a tumor suppressor gene, is to help maintain genomic integrity after DNA damage. When cells undergo stress, such as from carcinogen exposure, UV radiation, and/or hypoxia, p53 becomes upregulated and then acts as a transcription

factor to increase genes such as p21 (which in turn controls G1/2 cell cycle transition) and induces apoptosis by activating genes such as BAX, PERP, and others (reviewed by Fong et al., 2003; Singhal et al., 2005). In lung cancer, missense mutations can occur and cause loss of p53 function in 75% of SCLCs and 50% of NSCLCs (Toyooka et al., 2003). Decreased expression of p16 by promoter hypermethylation, mutations, or allelic loss occurs in 30–50% of NSCLCs. The p16INK4A-cyclin D1-CDK4-RB pathway controls G1/S cell cycle transition, and loss of p16 releases the tumor cells from RB-mediated cell cycle arrest (reviewed by Fong et al., 2003). Alternatively, the Rb gene can be inactivated directly by deletions, point mutations, or alternative splicing, and are more commonly found in SCLCs (90%), than NSCLCs (15–30%) (see Table 71.1). Oncogenic Pathway Signatures The malignant process is complex, involving multiple mutations leading to deregulation of signaling pathways (see above). In addition, the role of miRNA, very small non-coding RNA products that play a key role in regulatory networks by regulating the translation and regulation of mRNAs, are emerging

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(reviewed by Calin and Croce, 2006). Recent studies have demonstrated altered miRNA gene expression in lung cancers. Dicer, a component of the miRNA machinery has been shown to be downregulated in NSCLCs (Karube et al., 2005). Reduced expression of let-7 miRNA family members have been found to correlate with shorter post-operative survival in potentially curative lung cancer resection (Takamizawa et al., 2004). The feasibility and utility of monitoring miRNAs has been recently demonstrated in a study by Lu et al. (2005). These authors demonstrated, using a bead-based miRNA detection method, that a relatively small number of miRNA genes (200 miRNAs) may be sufficient to classify human tumors. A study by Yanaihara et al. (2006) showed that lung cancer specimens have extensive alterations of miRNA expression that may dysregulate cancer-related genes. Furthermore, miRNA microarray analysis using a microchip for genome-wide miRNA profiling (developed by Liu et al., 2004) identified a signature that could discriminate lung cancer from non-cancer tissue, and could also correlate lung cancer specimens with patient survival. In their study, high miR-155 and low let-7a-2 correlated with poor survival in lung adenocarcinomas (Yanaihara et al., 2006). let-7 miRNA family members have been shown to directly regulate Ras genes (Johnson et al., 2005). These combined studies suggest that let-7 may be a promising therapeutic agent to treat lung cancers caused by activating mutations in Ras genes (reviewed by Esquela-Kerscher and Slack, 2006). DNA microarray–based gene expression signatures have been developed that have the ability to define oncogenic pathways. The ability to predict the deregulation of various oncogenic pathways (both the mutated gene product itself and its downstream targets) offers an opportunity for new therapeutic drugs that are pathway-specific. Sweet-Cordero et al. (2005) recently developed a KRAS-mutant signature from analysis of expression array data using the KRAS-mutant mouse model of lung adenocarcinoma, which was then transferred to a human KRAS-mutant signature. The signature accurately identified KRAS-mutant lung tumors. These results were confirmed using siRNA and real-time RT-PCR. Another study by Bild et al. (2006a, b) demonstrated that DNA microarray–based gene expression signatures can not only predict cells expressing oncogenic activity from control cells, but can also predict deregulation of various oncogenic pathways in specific tumor types derived from mouse cancer models and human cancer specimens. These authors used multiple experimental models including functional cell-based assays, mouse lung cancer models, and human cancer specimens to demonstrate that the Ras pathway status clearly correlates with lung adenocarcinoma relative to squamous cell carcinomas subtypes. Furthermore, independent of tumor histology, patients displaying deregulation of multiple pathways (Ras with Src, Myc, catenin) had a poor survival. Future Directions Molecularly targeted therapy to reduce lung cancer mortality biomarkers provide an opportunity to identify subpopulations of

patients who are most likely to respond to a given therapy and identify new targets for drug development. A study by Olaussen et al. (2006) for example, showed that in lung tumor specimens, the absence of ERCC1 (an enzyme that participates in the repair of DNA damage caused by cisplatin) was associated with a survival benefit from cisplatin-based adjuvant chemotherapy, whereas patients whose tumor expressed the enzyme failed to benefit from chemotherapy. Several clinical studies have now shown the therapeutic efficacy and safety of tyrosine kinase inhibitors for specific tumor subtypes. Many of the 90 proteins with tyrosine kinase domains encoded by the human genome are aberrantly activated in human tumors. Intriguingly, after treatment with tyrosine kinase inhibitors in some tumor subtypes, there is marked reduction not only in tumor growth, but also in tumor viability. This concept of dependence of the tumor mutated oncogenes and/or on EGFRmediated survival signals is referred to as “oncogene dependence or oncogene addiction” (Varmus, 2006; Weinstein, 2002). Oncogene dependence is also demonstrated in mouse models overexpressing constitutively active K-ras, where the mutant Kras is then silenced. In this model, lung tumors rapidly regress as a result of apoptosis when K-ras is silenced even in the absence of important tumor suppressor genes (Fisher et al., 2001). An alternative hypothesis to explain oncogene addiction is through differential signal attenuation of multiple pro-apoptotic and pro-survival signals, a term coined “oncogenic shock” (Sharma et al., 2006). The improved survival in NSCLC patients taking EGFR TKIs seems not to only be limited to patients with EGFR mutations. Markers such as EGFR gene amplification, ErbB3 levels, and Her2 mutations may also be predictors of responsiveness (reviewed by Engelman and Cantley, 2006). Ongoing studies to investigate EGFR TKIs as first line therapy in selected patients with mutations in exons 18–21, increased EGFR copy number or protein expression, and with clinical characteristics associated with response are needed (reviewed by Johnson, 2006). Although the majority of lung tumor cells acquire resistance mutations during therapy with EGFR TKIs, these cells seem to be to be sensitive to a new group of TKIs that covalently cross-link the receptor (Carter et al., 2005). In addition, a recent study showed that microarray gene expression profiling demonstrated a pattern of gene expression associated with sensitivity to EGFR (Coldren et al., 2006). Mouse lung models with inducible expression of mutations in EGFR have been generated and may help in further understanding the pathogenesis of human lung cancer and aid in the validation of cancer therapeutics (reviewed by Dutt and Wong, 2006). Given the complexity of NSCLC, it is likely that these tumors are dependent on more than one oncogenic signaling pathway. Alterations in cancer-specific copy number and loss of heterozygosity (LOH) are important changes found in cancer cells and SNP array analyses may reveal pathways disrupted in tumorigenesis. SNP arrays have been used in genomic studies for detecting LOH in lung cancer and have both detected previously unknown regions of copy number change as well as known regions of both amplification and homozygous deletion.

Conclusion

It is likely therefore that combination targeted therapy directed at multiple oncogenic pathways may not only prove more effective than single agents alone, but may also prevent or delay secondary resistance. Most tumors express multiple mutant genes, but the relative importance of mutated genes in maintaining the cancer phenotype is not yet known, nor is the role of growthpromoting signals by non-cancerous stromal cells. The success of anti-angiogenic strategies in NSCLC suggests that targeting specific proteases and growth signals supplied by the tissue environment may prove useful as therapeutics. Establishing tumor banks to study the molecular phenotype of patients who are sensitive and resistant to therapy in order to identify the appropriate patients to treat is therefore crucial. Recently, gene expression signatures from tumor biopsy specimens have been developed that can predict sensitivity to individual chemotherapeutics. Furthermore, these chemotherapy response signatures are being integrated with signatures of oncogenic pathway deregulation to potentially identify new therapeutic strategies (Potti et al., 2006a, b). The considerable potential of using gene expression profiling of tumors to define causal molecular pathways and potential therapeutic targets for individuals is summarized in a recent review from Nevins’ group (Bild et al., 2006a).

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Although promising, few of the molecular advances have yet reached the stage for clinical application. Better understanding of cancer heterogeneity, including the variability in routine prognosis and patient response to therapy requires stronger working relationships and collaborations between bench scientists and their clinical counterparts, including oncologists, pathologists, thoracic surgeons, in both the community and in academia. Large databases will need to be constructed that have standardized methods and study design for data collection and analysis. Clinical trials (several are in phase II trials) need to be designed to verify results before being incorporated to routine clinical practice. Combinations of data sets on lung cancer specimens including gene expression array data, SNP array data, and sequencing data are being collected by some researchers so that key molecular changes may be identified more readily. The contributions of modern genomic technologies, particularly those that provide measures of global gene expression, are depicted in Figure 71.4. Gene expression profiling provides a new approach to assessing risk of developing lung cancer, potential tools for early diagnosis, new approaches to determining prognosis and oncogenic pathways that will lead to individually targeted therapies. Combined with genome-wide SNP screens that hold the promise of determining heritable predispositions to developing lung cancer and defining pathway-based phamacogenomics, there is promise that lung cancer may no longer be the number one cause of cancer death in the world.

CONCLUSION To date, significant advances in lung cancer treatment have come from the addition of adjuvant therapy in early-stage disease and from trials combining chemotherapy and radiation in stage IIIA disease. Yet, the mortality rate from lung cancer is higher than the next three major cancers combined. A recent review stressed the importance of a multi-targeted approach focusing on prevention, early detection, and molecularly targeted therapy to optimize lung cancer diagnosis and treatment (reviewed by Shepherd, 2005). Tobacco use, particularly in underdeveloped countries where smoking is still on the rise, must be the first target, since the most effective way to reduce lung cancer mortality is prevention. Early detection using state-of-the-art computer tomography (CT) scanning and positron emission tomography (PET) combined with molecular profiling to identify therapeutically exploitable differences between normal, precancerous, and cancer cells must be the second target. These approaches must be combined with genomic and genetic biomarkers that identify current and former smokers at highest risk for developing lung cancer. High-risk former smokers may well benefit from one of the many chemopreventative medications that are now being tested in clinical trials. The final target must be better treatment. Already, based on discoveries in the research lab, molecular targets, particularly agents that interfere with the EGFR pathway and those that block endothelial growth factors, have been developed. Clinical presentations of patients along with molecular characteristics of their lung cancers will have a significant effect on response and survival.

Gene expression profiling (global) (epigenetics) (miRNAs)

Genetical genomics

Genetics (SNPs)

Proteomics

Integrate Molecular imaging

Risk/Biomarkers (prophylaxis)

Clinical information

Early diagnosis

Pathways Prognosis (targeted therapy) (adjuvant therapy)

Figure 71.4 Diagram depicting contributions of gene expression profiling, combined with genetics, clinical information, proteomics, and imaging studies that can be applied to developing (a) risk-assessment tools that can be used to identify current and former smokers at highest risk for developing lung cancer and who might benefit from chemopreventative therapy; (b) tools for early diagnosis of current and former smokers with lung stage I-potentially resectable lung cancer; (c) tools that define molecular pathways that have lead to individual lung cancers and that predict what pharmacological approaches might be used to define best approaches to treatment of individual lung cancers; and (d) prognosis of resected cancers defining which patients have a high probability of recurrence or metastases and therefore should receive adjuvant therapy.

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72 Breast Cancer and Genomic Medicine Erich S. Huang and Andrew T. Huang

INTRODUCTION Patient Presentation Soon after her 55th birthday Joan Smith has her first screening mammogram. Her radiologist notes a small, 1 cm radiodensity associated with microcalcifications in the upper outer quadrant of her right breast. She cancels a planned vacation and spends her next week in and out of appointments with surgical, medical, and radiation oncologists. During the many hours she spends in clinic waiting rooms, she sees many women who share her fears and uncertainty. Some are younger, some older. Some are black, some white.The women she sees cover the range of size, shape, and race, but all share similar apprehensions about what is in store for them. Later, her core needle biopsy reveals an invasive carcinoma. From this point, Mrs. Smith is funnelled into a choreographed series of steps. Her surgeon performs a lumpectomy, the radiation oncologist begins planning her outpatient radiotherapy, and the medical oncologist initiates her chemotheraphy Herceptin treatments. In spite of the variety of women Mrs. Smith sees in the clinic waiting rooms, the treatment they undergo are generally variations on a theme. For the most part, the diagnostic and therapeutic maneuvers they undergo are validated by large studies such as NSABP B-13, B-14, and B-21 or HERA (Table 72.1). All of these studies are established “dogma.” They represent the best available evidence in treating women with breast cancer. The approach her doctors take is algorithmic; she is carried stepwise through a sequence of steps according to what is best supported in medical literature. Genomic and Personalized Medicine, 2-vol set by Willard

A patient support group becomes a source of great comfort for Mrs. Smith. There she learns of the revolutionary impact of estrogen blockade for hormone receptor-positive tumors, and Herceptin for HER2-positive tumors. Each member has a unique story to tell about her struggle with the disease; yet ironically, many of them have followed the same path in diagnosis and treatment. How is that, Mrs. Smith wonders? There is one woman in the group, Mrs. Brown, who was diagnosed with a small cancer 6 years ago, had a lumpectomy with clear margins and a negative axillary node dissection, yet she is now struggling with metastasis to her spine. She was initially told that her prognosis was quite good. Having been told the same thing, Mrs. Smith wonders what are the odds that she will find herself in the same situation at a time she will be looking forward to grandchildren and retirement? If Mrs. Brown’s physicians could have foreseen her recurrence, how might they have used this information? Would they have treated her differently? In 8 cases out of 10, the standard treatment would have sufficed and cured her of disease, but this is little comfort to Mrs. Brown who proved to be among the 2 out of 10 exceptional cases.

THE PROMISE Experiences like Mrs. Smith’s are shared by more than 200,000 women in the United States yearly. And every year more than 40,000 of such women die from breast cancer – the second leading cause of death among females (Jemal et al., 2006). As the most common cancer diagnosis in women, one out of every six Copyright © 2009, Elsevier Inc. All rights reserved. 869

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TABLE 72.1

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Large Clinical Trials for Management of Breast Cancer.

Trial

Treatment

Outcome

NSABP B-04

Total mastectomy versus total mastectomy with XRT versus radical mastectomy

No significant difference in disease-free or overall survival rates

NSABP B-06

Total mastectomy versus lumpectomy versus lumpectomy with XRT

No significant difference in disease-free or overall survival rates; addition of XRT to lumpectomy reduced local recurrence rate from 39% to 10%

NSABP B-13

Surgery alone versus surgery plus adjuvant chemotherapy in nodenegative patients with estrogen receptor-negative tumors

Improved disease-free survival rate for adjuvant chemotherapy group

NSABP B-14

Surgery alone versus surgery plus adjuvant tamoxifen

Improved disease-free survival rate with adjuvant tamoxifen group

NSABP B-18

Neoadjuvant chemotherapy with doxorubicin, cyclophosphamide, or both for 4 cycles versus the same regimen given postoperatively

No significant difference in overall survival or disease-free survival rates (53% and 70% at 9 years in postoperative group and 69% and 55% in the preoperative group)

NSABP B-21

Lumpectomy plus tamoxifen versus lumpectomy plus tamoxifen plus XRT versus lumpectomy plus XRT for nonenegative tumors 1 cm

Combination of XRT and tamoxifen was more effective than either alone in reducing ipsilateral breast tumor recurrence

NSABP B-27

Neoadjuvant chemotherapy comparing AC  4 cycles then surgery versus AC  4 cycles, docetaxel  4 cycles then surgery versus surgery between 4 cycles of AC and 4 cycles of docetaxel

Groups I and III were combined and compared with group II; clinical and pathological complete response rates increased significantly among patients who received preoperative AC and docetaxel

NSABP B-32

SLN biopsy followed by axillary dissection versus SLN biopsy alone for clinical node-negative patients

SLN indentification rate was similar in both groups, accuracy was high for both, negative predictive value was high for both

SLN: sentinel lymph node; Reproduced from Anderson, Surgical Oncology Handbook, 4th edition, 39–40.

American women will confront breast cancer during her lifetime. The disease is more common in industrialized Northern America and Europe and is rising in developed Asia (Cheng et al., 2000). In the past 30 years, mortality has decreased as screening mammography has become common practice. Screening mammography is a surveillance measure for clinically occult disease consisting of two views of each breast for asymptomatic women. Standardized criteria known as the BI-RADS (breast imaging reporting and data system) implemented by the American College of Radiology aid clinical decisions based on identified masses, their morphology and calcifications, their size, number, and morphology. Not without some controversy, studies best support the notion that screening impacts mortality for women 50 yearsold and greater, decreasing mortality by 20–30%, while strong evidence suggests similar mortality reduction in 40–49-year-old women. Current recommendations from the National Cancer Institute, American Cancer Society, and the US Preventative Services Task Force are that women between 40 and 50 years seek screening every 1–2 years with annual exams after 50.

Concomitantly, as more women have historically sought screening, incidence has increased due to broader detection, with the largest component of this increase being early Stage I cancers (Chu et al., 1996). Risk factors include (1) Gender: 99% of breast cancers are found in women. (2) Age: incidence increases with age until menopause, when it plateaus (Peto et al., 2000). (3) Race: the diagnosis is more common among Caucasians, followed by African Americans, then Hispanic and Asian Americans (Jemal et al., 2006). (4) Family history, for which two factors dominate: number of first degree relatives and age. The presence of a first degree relative diagnosed at a younger age (40 years) essentially doubles one’s risk compared to having an elderly relative with breast cancer (Collaborative Group on Hormonal Factors on Breast Cancer, 2001). Familial breast cancers are estimated to comprise approximately 10% of breast cancers; hence, the large majority of breast cancers are sporadic events. (5) Hormonal status: a well-accepted risk modifier. Patients such as women with early menarche and late first full term pregnancy who have longer exposure to estrogen demonstrate higher risk, while those with shorter lifetime exposure are at reduced risk (Clemons et al., 2001).

Molecular Bases

GENETIC BASES Genes implicated in familial breast cancers are classically the BRCA1 and 2 tumor suppressors, as well as the ubiquitously significant p53 proto-oncogene. Ashkenazi Jews, for example, are known to have specific BRCA1 mutations at a much higher frequency than other populations. This is attributed to a founder effect when the population of Ashekenazim was small. As groundbreaking their discovery has been, the BRCA genes account for a narrow segment of the disease. BRCA1 and 2 gene products are involved in DNA repair of double-stranded breaks. This normally occurs via homologous recombination. In the absence of normal BRCA1 and 2 activity, cells demonstrate morphologically abnormal chromosomes attributed to impaired replication. Clinically, mutation of these genes is responsible for 5–6% of all breast cancers and a significant portion of inherited breast and ovarian cancers (Venkitaraman, 2002). Unfortunately, multiple mutations of these loci are implicated in breast cancer, and widespread testing is controversial in its costeffectiveness even for patients with suggestive family histories. With a cost of over $3000, most recommendations for primary care providers involve genetic counseling before ordering a test. If testing is performed and a patient demonstrates a significant mutation, the options include: (1) prophylactic mastectomy, which is estimated to reduce risk of developing breast cancer between 90% and 95% (Hartmann et al., 1999; Rebbeck et al., 2004). Or (2) surveillance with yearly mammograms and MRI scanning. Clearly, when indicated and when providing useful results, BRCA testing provides information that may be acted upon and impacts developing breast cancer. Other Mendelian disorders include mutation of the p53 tumor suppressor. Familial mutations are associated with LiFraumeni syndrome for which there is high penetrance for breast cancer. Epistatically related is mutatiom of an activator of p53, ATM, which results in Ataxia-Telangiectasia, a syndrome of cerebellar ataxia, immune deficiencies, and oculocutaneous telangiectasias associated with elevated propensity for malignancies, including breast cancer. Other germline mutations include those of the PTEN gene, a tumor suppressor whose mutation results in Cowden syndrome manifest in mucocutaneous hamartomas, dermatologic abnormalities and early onset thyroid and breast cancer. And Peutz-Jeghers syndrome, secondary to mutation of STK11, results in gastrointestinal hamartomatous polyps and mucocutaneous pigmentation and increased risk for malignancies, including breast. The sporadic derangements that contribute to 90% of the remaining breast cancer cases are little understood.

MOLECULAR BASES Molecular characteristics of breast tumors that have accepted predictive and prognostic impact in breast cancer include hormonal status and HER2.

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Observations of the linkage between hormonal status and breast cancer date back to the late 19th century. This, in combination with the modern concept and discovery of the estrogen receptor has led to the paradigm of estrogen modulation in managing receptor-positive breast cancers. Hormonal status is predictive and arguably prognostic. Multiple studies indicate that the presence of estrogen receptors in breast tumors is strongly predictive of response to hormonal therapy, while an overwhelming minority of receptor-negative tumors demonstrate response (Bezwoda et al., 1991; Manni et al., 1980). Estrogen receptor-positive tumors tend to be more well-differentiated by histopathologic criteria and demonstrate lower rates of recurrence at 5 years. This paradigm demonstrates the profound clinical impact that a single clinical marker can have on managing a disease. The importance of molecular markers is reinforced by the finding that amplification of HER2 in breast tumors has negative impact on survival and relapse (Slamon et al., 1987). Approximately one-fifth of tumors are found to overexpress this member of the epidermal growth factor receptor family. Studies also suggest that such overexpression predicts improved response to anthracycline-based chemotherapy (Thor et al., 1998; Pritchard et al., 2006). Though there is laboratory-based evidence that there are intersections between hormone-mediated pathways and HER2-based pathways, the clinical impact of overexpression in predicting response to hormone modulation is equivocal. In any case, directed therapy against HER2 with Trastuzumab (Herceptin), a humanized monoclonal antibody to the receptor, represents a significant advance in identifying key molecular characteristics of a tumor and designing a therapy based on these data. The apprehension that Mrs. Smith feels is reflective of the current state of breast cancer treatment. Accepted treatment modalities have gone a great distance to improve the care and outlook of breast cancer patients, but they do not take account of the heterogeneity of individual tumor biology. While AJCC cancer staging is undoubtedly useful for making treatment decisions, there are many cases where a presumably low-risk patient who receives definitive therapy develops recurrent disease, and cases where patients with presumably advanced tumors survive longer than expected. HER2 and estrogen and progesterone receptor status prove that understanding molecular aspects of someone’s disease can dramatically impact their treatment; yet therapy tailored to the full spectrum of molecular biology in a patient’s individual tumor is still a promise rather than a reality. The full complexity of biologic response to treatment does not stop with individual tumors, superimposed on these are the differences in how a patient responds to treatment. One to 4% of women who receive the Herceptin monoclonal antibody develop congestive heart failure, and 10% experience significant decrease in cardiac function (Chien, 2006). The ultimate goal of all medical interventions, whether for cancer or essential hypertension, is to tailor therapy to the individual patient and her manifestation of disease. The current state

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of the art is in designating a patient as being part of a population rather than as a unique individual. Our patient Mrs. Smith, by St Gallen’s criteria, is a member of a low-risk population when using anatomic and pathologic parameters such as tumor size or axillary node status, but how she will fare based on the inherent properties of her particular manifestation of disease is unknown to us. As she enters a treatment system that is protocol-bound, Mrs. Smith wants desperately to feel as if she were being treated based on the behavior of her own cancer – that her treatment is calibrated according to her biology. Unfortunately, cancer care as it stands today is not amenable to fine gradations in biology because there are no technical means to identify such molecular particularities. In theory, the richness and diversity of information about a tumor that genome-scale technology provides is truly reflective of the biology of a particular tumor. This will provide an avenue, among others such as proteomics, to reaching the ultimate goal of individualized cancer treatment.

PROGNOSIS AND PREDICTION Oncologic therapy is an exercise in minimizing morbidity and maximizing efficacy. In order to accomplish this, one needs to calibrate treatment, which is often morbid, with extent or aggressiveness of disease. Consequently, staging methods provide standardized criteria for measuring the anatomic extent of disease. National and international cancer organizations embrace these clinicopathologic yardsticks with the hope that clinicians can use them as universal standards for prognosis, treatment, and outcomes. Staging is anatomic and pathologic: tumor size, number of regional nodes, and the presence of metastasis are the sole components of the TNM system. This reflects a historical understanding of cancer beginning in one location, spreading locally, then accessing lymphatics or invading vascular structures. Implicit in this is that anatomic extent is a surrogate for a tumor’s behavior. It is generally accepted as a first approximation that more aggressive tumors will manifest themselves at the time of diagnosis by the presence of lymph node metastasis, invasion into surrounding tissues or by distant metastases; while more indolent tumors are likely small and restricted to their site of origin. Obviously, the weakness of TNM staging is this very assumption. While the TNM system serves admirably for allowing clinicians to gauge how aggressive treatment should be to how advanced or aggressive a particular patient’s disease. Most clinicians acknowledge that it is a surrogate, an approximation of tumor behavior. For instance, modern practice has discarded removal of the pectoral muscle from Halsted’s radical mastectomy as originally described, because this aggressive approach to local control ultimately does not save any more lives than the “modified” radical procedure that spares the muscle. The biology of breast cancer suggests that some point of its natural history involves leapfrogging past adjacent tissue – that it becomes a systemic disease. And at the time of surgery, clinicians

simply concede their inability to gauge whether micrometastasis has already occurred when there is no gross evidence of invasion. Likely for this reason, while most women with curative resections of small tumors and negative axillary nodes are considered cured, a significant fraction returns years later with metastatic disease.

MOLECULAR MARKERS Hormone receptor status and presence of HER2 represent molecular markers that undoubtedly impact breast cancer clinical management. The power of adjuvant and neo-adjuvant therapies directed against these entities suggests that therapies specifically directed against growth pathways, whether actuated by hormones or oncogenes (or both) can provide individualized therapy for the characteristics of a specific tumor. A natural outgrowth of this thinking is that identifying more molecular markers will highlight more pathways and more avenues for developing specific therapies. Until recently, there were no technically facile methods to survey and identify differentially expressed genes. Classical molecular biology required a hypothesis and painstaking analysis on a gene-by-gene basis. A graduate or post-doctoral fellow might spend a year assaying the acti-vity of a handful of genes by transferring them to nylon membranes and “blotting” them with radioisotopes to generate a hundred datapoints. With the draft completion of the Human Genome Project, microarrays represent a viable technology for analyzing the activity of several orders of magnitude more genes. Microarrays are technological descendents of the Southern blot. Fixing a nucleic acid to a substrate provides a method for taking advantage of complimentary hybridization and various fluorescent or radioactive tagging schemes to establish the presence and abundance of a particular nucleic acid sequence from a sample. What a Southern or Northern blot can do for a handful of sequences, a microarray can do in multiplicity. The greater the number of distinct nucleic acids sequences represented on an array, the broader the survey of the genes being expressed in a tissue at the time it is harvested. Therefore tagged substrate derived from RNA harvested from a particular tumor when hybridized to an array can provide an estimate of how actively genes are being expressed at the time of harvesting. Technology has merely provided a complete genome sequence and the means to increase the physical density of sequences to a point where many thousands of sequences can be assayed in a timely manner. Current generation arrays encompass most of the human genome. Therefore the scale of data provided by microarray experiments expands by orders of magnitude from the 100s available with traditional molecular biology, to the hundreds of thousands or even millions afforded by genome-scale assays. This explosion of data requires methods to analyze it. Researchers are embracing many methodologies for contending with data on such a large scale.

Netherlands Cancer Institute Study

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873

van’t Veer data set

GENOMIC INSIGHTS

Luminal and Basal Tumors A follow-up study from the same investigators demonstrated the expression data derived from ER-positive tumors fell into two categories, Luminal A and B, while estrogen receptor-negative tumors also possessed distinct patterns and could be classified as Normal, Basal-like and Her2-related. More importantly, these classifications corresponded to clinical differences between patients. The term “luminal” was applied to ER-positive tumors because ER-responsive genes such as trefoil factor 3, LIV-1, and GATA-binding protein thought to typify luminal epithelium were well represented. The distinction between Luminal A and B lay in lower expression of ER-responsive genes in the B subgroup. Particularly compelling were differences in survival between women whose breast cancers fell into either

0.8

Probability

Individual Tumors are Identifiable in Principle A variety of studies in the late 1990’s and early 2000’s demonstrated that microarray data from neoplastic tissues provided sufficient information to distinguish biological differences. Work from investigators such as Golub or Alizadeh respectively demonstrated not only that hematologic malignancies such as AML and ALL might be distinguished by “class prediction” tools, but that genome-scale microarray data could differentiate between chemotherapy-responsive and unresponsive large B cell lymphomas even if they were microscopically indistinguishable (Alizadeh et al., 2000; Golub et al., 1999). Extending on this work, investigators soon realized that the richness and complexity of microarray data could provide “molecular portraits” of breast tumors. These portraits were so variegated and distinct that patterns of gene expression data from tumors after the presumed seismic changes of chemotherapy possessed enough of the characteristics of the tumors before therapy to be traced back to original patient. This finding held true when comparing a single individual’s primary tumor and tissue from a lymph node metastasis. When using hierarchical clustering schemes, gene expression patterns from an individual, whether from tumor samples taken before chemotherapy or after, or from an individual’s primary tumor and lymph node metastases, were more similar to one another than to samples between individuals, suggesting that genome-scale expression data possesses enough information to characterize an individual’s particular tumor, even after the changes in that particular tissue’s biology represented by exposure to doxorubicin or metastasis to a new microenvironment (Sorlie et al., 1999). From a technical standpoint, this finding supports that varying expression levels of RNA even when derived from samples taken at different times from the same patient represent genuine biology about that patient. It is how to interpret these data and rigorously link biology with these “pervasive” gene expression patterns, as the authors put it, that continues to be the subject of active investigation.

1

0.6

p  0.01

0.4 0.2 0 0

24 48 72 96 120 144 168 192 Time to distant metastasis (9months)

Censored Basal

Luminal A

Luminal B

ERBB2

Figure 72.1 Kaplan–Meier analysis of disease outcome. Time to development of distant metastasis in the 97 sporadic cases from van’t Veer et al. Patients were stratified according to the subtypes of Luminal A, Luminal B, Basal and ERBB2 (adapted from Sorlie et al, 2001).

of these taxonomies. Women classified as having Luminal A tumors tended to have better prognosis than those with Luminal B. Carrying the analysis further, patients with Basal-like and Her2-related tumors had poorer prognosis than either Luminal subtype with Her2-related tumors possessing the steepest Kaplan-Meier curves. This type of study suggested that gene expression data could highlight subtle and clinically significant differences between breast tumors in a manner that can extend on conventional histopathologic diagnosis. It also demonstrated that a link might be drawn between the clinical behavior of a tumor and the patterns of gene expression within it (Sorlie et al., 2001) (Figure 72.1).

NETHERLANDS CANCER INSTITUTE STUDY A prominent application of genome-scale expression studies to prognostication is a study of 78 tumors from lymph nodenegative patients younger than 55 from the Netherlands Cancer Institute. A 70-gene classification signature was developed for distinguishing patients with poor prognosis defined as recurrence or metastasis within 5 years from patients with good prognosis (disease-free survival up to 5 years). This study was promptly followed with a validation of this same signature in a more diverse group of tumor samples from a patient group with a mixture of lymph node positive and negative stage I and II disease. The classifier performed very well in patients with good prognosis; at 5 years it correctly identified 95% with distant metastasis-free disease. When taken out to 10

Log10 (expression ratio) 0.6

Figure 72.2 0

0.6 Clustering of 96 breast tumours

(a) Clustering of5,000 significant genes

(c)

The Mammaprint 70 “prognosis classifier genes” across 295 consecutive patients.

(b) Morasta add.

Agioirnasion

Lymphocytic Infiltration

Grade 3

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BRCA1

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Contig 27915RC Contig 14390RC POU2AF1 PIM2 LOC51237 LOC57823 LOC57823 AJ249377 X93006 U96394 X79782 AF063725 IGLL1 IGL@ IGL@ AJ225092 IGKV3D-15 AF103458 AJ225093 Contig 10268RC Contig 44195RC AF058075 IGL@ IGKC TLX3 Contig 42547 Contig 20907RC ICAP-1A FLJ20340 AF103530 MTR1 CD19 CD19 IGHM VPREB3 BM040 KIAA0167 TRD@ IRF5 Contig 50634RC

Contig 37571RC KIAA0882 CA12 ESR1 GATA3 MYB P28 FLJ20262 AL133619 Contig 56390RC CELSR1 Contig 58301 RC UGCG AL049265 BCL2 EMAP-2 HSU79303 Contig 51994RC Contig 237RC Contig 47045RC XBP1 HNF3A VAV3 Contig 54295RC AL133074 Contig 53968RC Contig 49342RC ZFP103 AL110139 FLJ12538 ERBB3 FBP1 Contig 50297RC FLJ20273 AL080192 TCEB1L D5S346 AL137761 TEGL Contig 41887RC

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NSABP Study

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years, 85% of patients were correctly classified. When applied to poor prognosis, however, its predictive capabilities were less satisfying. Five year accuracy was only 61%. By 10 years, this rate had declined to 51% (van de Vijver et al., 2002). Accordingly, an EORTC-sponsored prospective randomized clinical trial based on this classifier, titled MINDACT (Microarray for Node Negative Disease May Avoid Chemotherapy), is in accrual in Europe. Its primary focus is assessing whether patients with clinically high-risk disease, whose 70-gene classifier suggests that they should actually be in a low-risk category can be “safely spared chemotherapy without affecting distant metastasisfree survival.” Such a study represents the logical next step after numerous retrospective studies of relatively limited numbers of patients. It should be noted in this particular approach is that gene expression data and clinical parameters are placed in opposing camps. In the United States, the 70-gene signature is now approved for marketing by the Food and Drug Administration as MammaPrint. It is not yet available for clinical use (Figure 72.2).

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In a similar vein, a collaborative study involving investigators at Duke University and the Koo Foundation Sun Yat-Sen Cancer Center takes a different approach to using gene expression data to develop classifiers for understanding lymph node metastasis and 3-year recurrence in 89 cases. Lymph node status being the strongest clinical predictor of prognosis in breast cancer, it was used as a proof-of-principle for using gene expression data in breast cancer. Rather than utilizing a single classifier, their analysis is based on multiple classifiers or “metagenes” that are used in aggregate via recursive partitions of the sample set. In other words, a single classifier metagene may correctly separate a large part, but not all of a dataset into lymph node positive or negative patients, and by successively “partitioning” the data with additional classifiers, the prediction of lymph node status is sharpened. A final predictive model is ultimately a composite of many different metagenes or classifiers. An additional advantage to such an approach is that metagenes provide a basis to understand the disparate biological processes in lymph node metastasis rather than attempting to compress a complex biological phenomenon into a single classifier. Using this methodology, appropriately identifying whether a patient was likely to be lymph node positive or negative was achieved 90% of the time. Extending the analysis to recurrence at 3 years after diagnosis demonstrated similar accuracy (Huang et al., 2003a, b) (Figure 72.3).

17 49 10 12 15 22 20 29 1823 16 37 36 27

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Figure 72.3 Differential contributions of separate metagenes in classifying high from low-risk patients. High risk are labeled in red, while low risk are in blue. Note how different metagenes segregate these patients in different manners.

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estrogen-receptor positive breast cancers. Successfully making this prediction could potentially obviate chemotherapy for the 85% of patients for whom tamoxifen after surgery is sufficient for preventing 10-year distant recurrence. In a departure from many such studies, the investigators use RT-PCR from paraffin-embedded samples up to 10 years old. Their assay consists of a set of 21 genes (including 5 reference genes) derived from an original set of 250 genes studied across three previous investigations. Their selection was based on their technical robustness in the process of RT-PCR and their performance as predictors. Genes within this set included known proliferation and cell cycle entities, as well as genes linked to tissue invasion and estrogen. An empirically derived “recurrence score” from these RT-PCR data determines whether a patient is low, intermediate, or high risk based on data from three training studies including samples from NSABP B-20 (Figure 72.4). Clinical data from this last study were used to establish the cutoff points for each of these recurrence categories. As applied to 668 samples in the NSABP collection, the investigators tested several hypotheses: first, whether the a low recurrence score accurately anticipated whether a patient would be free of distant recurrence more than 10 years after surgery, and whether there were a statistically significant relation between these two. By these criteria, in the setting of predicting 10-year recurrence in a population of node-negative, estrogen-receptor positive women, the recurrence score was accurate. In comparison to traditional clinical risk factors such as age and tumor size, the score demonstrated a higher degree of statistical significance (Paik et al., 2004). This predictor (commercialized as the Oncotype Dx assay by Genomic Health), is being validated in a Phase III trial (TAILORx Breast Cancer Trial) sponsored by the National Cancer Institute and guided by the Eastern Cooperative Oncology Group. The trial will analyze whether patients within a low to intermediate risk range will benefit from addition of chemotherapy to hormonal therapy; and whether a particular threshold recurrence score can be used to anticipate whether chemotherapy would be beneficial or not. Patients below the risk range will receive hormonal therapy alone, while patients above this range will receive combined chemotherapy and hormonal therapy. Patients in the risk range will be randomized to hormonal therapy alone versus combined therapy. As this trial accrues, Oncotype DX is finding use among medical oncologists on a day-to-day basis to inform the process of placing estrogen-receptor positive, node-negative patients on chemotherapy (Figure 72.4).

PATHWAY PREDICTION Another promising application of genome-scale information is for pathway prediction. Early pilot studies indicated that overexpression of oncogenic activities in cell culture could evoke transcriptional profiles that both predicted and quantified oncogenic pathway activation in a fashion robust enough to be applied in vivo and appropriately identify the dysregulated activities evoking tumorigenesis in transgenic mouse models.

Freedom from Distant Recurrence (% of patients)

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No. at Risk Low risk 338 328 313 298 276 258 231 170 38 Intermediate 149 139 128 116 104 96 80 66 16 risk High risk 181 154 137 119 105 91 63 13

Figure 72.4 Likelihood of distant recurrence using the Oncotype DX assay to identify node-negative, estrogen-receptor positive patients to distinguish low versus high-risk patients.

This provides a facile approach to interrogating the potentially numerous growth activation, local invasion, metastatic, and oncogenic pathways that may be present in breast cancer. Analysis of the effects of chemotherapeutic drugs on lung cancer and breast cancer tissue culture lines suggest that this strategy identify metagenes that appropriately predict response to pharmacologic intervention (Huang et al., 2003a, b; Bild et al., 2006); Potti et al., 2006). This line of work will potentially provide clinicians tools based on tumor biology to decide whether patients for whom the decision to institute chemotherapy is equivocal by conventional clinico-pathologic criteria should undertake the toxicities of systemic therapy for maximal benefit.

THE REALITY OF CLINICAL GENOMICS At this point in its development, the application of gene expression analysis to clinical decision-making in breast cancer is promising. The studies outlined above provide good evidence that genomics can potentially unmask the molecular particularities of a woman’s breast cancer, but until the time-tested standard of a randomized, prospective trial actually validates this possibility, it remains only a promise. There is little doubt that the massively multivariate data provided by genome-scale expression analysis of breast tumors potentially revolutionizes our understanding and our clinical approach to breast cancer. This depends on robust and reproducible measurement of the expression of thousands of genes.Yet at this point in time, it is arguable that microarray data alone are neither robust nor reproducible. The strong movement to using RT-PCR data on a handful of selected genes, as with the Oncotype DX product from Genomic Health, Inc. is a byproduct of skepticism about the consistency of genome-scale expression data derived

References

from microarrays. On the other hand, 5 years of experience with gene expression data demonstrate that their richness and complexity are concordant with the heterogeneity of cancer biology. Even early studies with a limited number of genes being studied– with first generation methodologies for interpreting expression data–show that there are pervasive expression patterns that unambiguously link a tumor to a single individual regardless of whether samples were taken before or after chemotherapy. Many groups ultimately treat microarrays as a convenient screening tool for developing a restricted subset of genes that can be used as a predictor for a single outcome measure. Among the best known, the 70-gene predictor that is currently being tested in European clinical trials, and the 21-gene RT-PCR Oncotype DX assay that is currently commercially available to patients, divide patients into two to a few subgroups. Dividing patients into a “high risk” versus “low risk” groups, while representing a potential refinement in clinical decision-making, does not do justice to the potential of gene expression data. The strongest challenge to such methodology is that further study of the dataset that produced the 70-gene predictor by an independent group reveals that there are many 70-gene predictors utilizing different genes that can predict risk with no less accuracy (Ein-Dor et al., 2005). If this is the case, why choose a particular group of 70 genes? It is tempting to take this analysis as evidence that the field need be reassessed and that predictors based on microarrays merely represent an arbitrary choice of what genes to include. On the other hand, a new study in which data from 295 patients were analyzed utilizing previously published prognostic and predictive gene expression models showed that there was a remarkable degree of “concordance” – similar classification ability – between models even when the gene sets diverged significantly. The authors concluded that similar biological phenotypes were being tracked, even if constituent genes in a particular classification scheme might differ (Fan et al., 2006). Such a finding suggests a different interpretation: one may approach the data by extending the analysis beyond making prediction on a single outcome. Surely a group of patients can be divided into “high risk” versus “low risk”, luminal versus basal, high probability for recurrence or low, and surely multiple gene expression-based models do equally well in making such divisions. So what does the presence of so many essentially “indistinguishable”predictors mean? Where the answer lies is in the fact that the questions being asked may be extended further. If microarrays afford one a dataspace of thousands of patients and hundreds and thousands of genes, why stop at one division of the data? Does a single classification of “high risk” versus “low risk” do justice to the

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data? Why not apply every possible predictor to subdivide this complex dataspace as many ways as possible? In theory, by successively applying different predictors to a group of breast cancer patients, one can segregate the set down to small, homogeneous groups of patients with individualized prognosis. One might argue that “similar biological phenotypes” is an oversimplification. If one only wanted to divide a group of patients in two and stop, different gene expression models may perform similarly, but if one wanted to get at the biological particularities of particular patients, successive partitioning with different models (what our group would call metagenes) might carry us to that point. Taking a step in this direction, one group found that a “core serum response” (CSR) profile derived from analysis of quiescent fibroblasts stimulated with serum appeared to be a useful way to study the behavior of tumors (Chang et al., 2004). By segregating a patient population into high risk and low risk first with the 70-gene predictor, and then applying the CSR. They found that patients who were high risk in addition to having an active CSR signature, were at much higher risk for metastatic disease than those with an inactive or “quiescent” profile. This method of successively partitioning genome-scale data maximizes one’s opportunity for using all the useful data present in a dataset (Chang et al., 2005). Genomics bears great promise as a technologically sophisticated interrogation of the multitudinous activities present in breast tumor tissue. The greatest challenge for investigators is appropriately and responsibly harnessing the richness of gene expression data for developing robust and reproducible tools for breast cancer prognosis and prediction. Ironically the same richness that supplies the possibility of individualizing our understanding of the disease, provides such a wealth of information that separating “noise” from “signal” is difficult. With the current state of the art, there is little common ground for making this distinction. While clinical trials for several genomic tools that preliminarily provide prognosis above and beyond standard clinicopathologic staging are accruing, it is likely that more advanced statistical and data-mining methodologies will sharpen our ability to predict the behavior of a particular patient’s tumor. Gene expression is ultimately not a better way to understand breast cancer. It provides an avenue for understanding how a complex, high-dimensional, data rich environment provides a deeper understanding of the disease. Each level of complexity, whether via gene expression, polymorphisms or proteomics, provides yet more opportunity to augment our understanding of the particularity of a tumor’s behavior, hopefully to a point where we will be able to reproducibly individualize a patient’s disease and develop strategies that are tailored to that patient.

REFERENCES Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson, J., Jr, Lu, L., Lewis, D.B.,Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R.,

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Huang, E., Ishida, S., Pittman, J., Dressman, H., Bild, A., Kloos, M., D’Amico, M., Pestell, R.G., West, M. and Nevins, J.R. (2003). Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 34(2), 226–230. Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., Smigal, C. and Thun, M.J. (2006). Cancer statistics. CA Cancer J Clin 56(2), 106–130. Manni, A., Arafah, B. and Pearson, O.H. (1980). Estrogen and progesterone receptors in the prediction of response of breast cancer to endocrine therapy. Cancer 46(12 Suppl), 2838–2841. Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F. L., Walker, M.G., Watson, D., Park, T., Hiller, W., Fisher, E.R., Wickerham, D.L., Bryant, J. and Wolmark, N. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27), 2817–2826. Peto, J. and Mack, T.M. (2000). High constant incidence in twins and other relatives of women with breast cancer. Nat Genet 26(4), 411–414. Potti, A., Dressman, H.K., Bild, A., Riedel, R.F., Chan, G., Sayer, R., Cragun, J., Cottrill, H., Kelley, M.J., Petersen, R., Harpole, D., Marks, J., Berchuck, A., Ginsburg, G.S., Febbo, P., Lancaster, J. and Nevins, J.R. (2006). Genomic signatures to guide the use of chemotherapeutics. Nat Med 12(11), 1294–1300. Pritchard, K.I., Shepherd, L.E., O’Malley, F.P., Andrulis, I.L., Tu, D., Bramwell, V.H. and Levine, M.N.National Cancer Institute of Canada Clinical Trials Group (2006). HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 354(20), 2103–2111. Rebbeck, T.R., Friebel, T., Lynch, H.T., Neuhausen, S.L., van’t Veer, L., Garber, J.E., Evans, G.R., Narod, S.A., Isaacs, C., Matloff , E., Daly, M.B., Olopade, O.I. and Weber, B.L. (2004). Bilateral prophylactic mastectomy reduces breast cancer risk in BRCA1 and BRCA2 mutation carriers: the PROSE Study Group. J Clin Oncol 22(6), 1055–1062. Slamon, D.J., Clark, G.M., Wong, S.G., Levin, W.J., Ullrich, A. and McGuire, W.L. (1987). Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235(4785), 177–182. Sørlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Thorsen, T., Quist, H., Matese, J.C., Brown, P.O., Botstein, D., Eystein Lønning, P. and Børresen-Dale, A.L. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19), 10869–10874. Thor, A.D., Berry, D.A., Budman, D.R., Muss, H.B., Kute, T., Henderson, I.C., Barcos, M., Cirrincione, C., Edgerton, S., Allred, C., Norton, L. and Liu, E.T. (1998). erbB-2, p53, and efficacy of adjuvant therapy in lymph node-positive breast cancer. J Natl Cancer Inst 90(18), 1346–1360. van de Vijver, M.J., He, Y.D., van’t Veer, L.J., Dai, H., Hart, A.A., Voskuil, D.W., Schreiber, G.J., Peterse, J.L., Roberts, C., Marton, M.J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E.T., Friend, S.H. and Bernards, R. (2002). A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25), 1999–2009. Venkitaraman, A.R. (2002). Cancer susceptibility and the functions of BRCA1 and BRCA2. Cell 108(2), 171–182.

CHAPTER

73 Colorectal Cancer G.L. Wiesner, T.P. Slavin and J.S. Barnholtz-Sloan

INTRODUCTION Over the next year, approximately 112,340 new cases of colorectal cancer (CRC) will be diagnosed in the United States and over one million cases across the globe, making CRC one of the most common cancers worldwide (ACS, 2007; Curado et al., 2007). Accounting for 10% of cancer-related deaths, CRC is the third most common cause of cancer mortality in both men and women in the United States (ACS, 2007). CRC occurs in all races and all peoples, but the rates vary among people with different racial and ethnic backgrounds. African Americans have an increased incidence and mortality of CRC compared to European Americans in the United States, while Hispanic Americans and American Indian/Alaskan Natives have the lowest incidence rates (Ries et al., 2007). Survival rates also differ among racial/ethnic groups and are highly dependent on stage of disease at diagnosis; when the cancer is diagnosed at an early stage the 5-year relative survival can be as high as 90%. However, only about a third of all CRC cases are diagnosed in the early stages (Jemal et al., 2007). Despite this dismal statistic, both CRC incidence and mortality rates have steadily decreased over the last two decades (Ries et al., 2007). The simultaneous decline in these rates is most likely a reflection of an increase in screening practices that interrupt the development of invasive cancer by removing benign colon polyps or early stage disease in screened individuals (Burt, 2000; Vogelaar et al., 2006; Winawer et al., 2003). Screening may also decrease the incidence of CRC Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

by reducing the time that the colon epithelium is exposed to potential environmental risk factors. Many studies have shown that there are both genetic and environmental causes for CRC, as reviewed (Giovannucci and Wu, 2006; Potter and Hunter, 2002). Several inherited genetic syndromes that involve a high risk for CRC have been discovered such as hereditary nonpolyposis colorectal cancer (HNPCC), familial adenomatous polyposis (FAP), and other rare inherited polyposis syndromes (Rustgi, 2007). However, these syndromes taken together only account for 5% or less of all CRC diagnoses (Figure 73.1; Burt and Neklason, 2005). Important for public health is the approximately 15–20% of CRC cases with a family history of CRC and/or polyps (Burt, 1996a; Sandhu et al., 2001). The family members in this group have an elevated lifetime risk for CRC, implying additional, yet undiscovered, genetic and/or common environmental susceptibility factors underlying colon neoplasia. The current theory of CRC causation states that multiple sequential mutations in cellular regulatory genes of the colon epithelium occur before an invasive cancer can develop (Figure 73.2; Fearon and Vogelstein, 1990; Vogelstein et al., 1988). This multi-step model of cancer development is also influenced by environmental factors, such as diet or smoking, and modifier genes, which are presumed to additionally alter key cellular functions. It is notable that much of the theoretical framework for understanding the root causes of carcinogenesis have stemmed from genetic and genomic research in CRC. A wider appreciation Copyright © 2009, Elsevier. Inc. All rights reserved. 879

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incr

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Minor genes: 95%

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Familial

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Figure 73.1 Genetic predisposition to CRC. The risk for CRC increases along a continuum; risk is lowest with a sporadic/ isolated family history (75% of cases), moderate with clear positive family history of disease (20% of cases), and highest in known monogenic inherited CRC syndromes (5% of cases).

of the multi-step phenomena, the pathways involved, and the global epigenetic alterations in cancer were gained using a genomic viewpoint of disease causation. The increasing availability and affordability of genetic/genomic tools will only improve the diagnosis, screening, treatment, and monitoring of patients in the future. With broader application of these technologies, genomics will be the mainstay of personalized medical care, rather than a private application for the unique patient or family. This chapter will review the current understanding of genetic predisposition, somatic genomic alterations and environmental influences in the development of CRC. The application of genomic technologies for the patients with CRC or at risk for CRC will also be discussed. Lastly, the emerging genomicbased technologies that promise to personalize and improve healthcare will be reviewed.

GENOMIC MODEL OF CRC Most malignant tumors of the colon arise from an adenomatous polyp, a benign growth of the colon that is easily identified and removed using colonoscopic screening and biopsy methods. Seminal studies of colonic karyotypes demonstrated the adenoma to carcinoma sequence, in which specific chromosomal and/or genetic alterations correlated with progressive growth of colon tumors from benign adenoma to overt cancer (Figure 73.2; Fearon and Vogelstein, 1990;Vogelstein et al., 1988). In fact, two separate progression pathways for CRC development have been discovered and validated: (1) the chromosomal instability (CIN) pathway and (2) the mismatch repair pathway (MMR) pathway.

The multi-step process of carcinogenesis is complex, involving as many as six discrete molecular clonal alterations in the adenomatous tissue over a period of 20 or more years before transforming into an invasive carcinoma (Knudson, 2001). The earliest step in the CIN pathway is a mutation in the APC gene, a key factor in the WNT/-catenin pathway, which is associated with the formation of small, microscopic clusters of proliferating cells called an aberrant crypt which then develops into a benign adenoma. About 15% of CRC have underlying errors in the mismatch repair (MMR) mechanism, identified by microsatellite instability (MSI), loss of MMR proteins and/ or mutations in various DNA repair genes and growth factors (Figure 73.2; Ionov et al., 1993; Thibodeau et al., 1993). The newly described CpG island methylator phenotype (CIMP) may constitute an additional third CRC progression pathway in which epigenetic alterations in promoter regions of regulatory genes that cause the development of polyps and cancer (Toyota et al., 1999). The key initiating event in the CIN pathway is a mutation in the adenomatous polyposis coli (APC; OMIM 611731) gene, which normally forms a stabilizing complex for -catenin in the WNT signaling pathway (Albuquerque et al., 2002; Powell et al., 1992). Typically described as a tumor suppressor gene with a “gatekeeper” function, pathologic mutations in APC interrupt the normal APC–-catenin interaction, which in turn, allows for unregulated nuclear signaling by -catenin to downstream messengers (Chung, 2000; Kinzler and Vogelstein, 1996). Even though germline mutations in the APC gene cause a small fraction of CRC, approximately 85% of sporadic CRC harbor APC mutations, either by point mutations or overt 5q chromosome deletions (Powell et al., 1992; Rajagopalan et al., 2003; Vogelstein et al., 1988). Non-random gains or losses of chromosomal material is a consistent feature in the CIN pathway, with chromosome 18q and 17p frequently altered, presumably causing haploinsufficiency of gene targets like deleted in colorectal carcinoma (DCC; OMIM 120470) and TP53 (OMIM 191170), respectively, that then confers a selective advantage for specific clones (Rajagopalan et al., 2003). The factors that initiate, promote, and favor metastasis are not completely understood, but most likely involve acquired mutations in specific gene targets, such as mutations in the K-ras oncogene and the TP53 tumor suppressor gene. Approximately 30–50% of large adenomas and CRC have an acquired mutation in the K-ras gene, a member of the tyrosine kinase super family that links extracellular signals to targeted genes in growth and differentiation pathways (Martinez et al., 1999;Vogelstein et al., 1988). Germline alterations in other Ras genes are often found in somatic tumors and are also associated with cancer development in rare inherited developmental disorders, indicating the pleiomorphic nature of this transcription factor (Schubbert et al., 2007). Similarly, germline mutations in the TP53 gene cause the inherited Li-Fraumeni syndrome (OMIM 151623), while acquired mutations occur in a wide variety of cancer types. For CRC, acquired TP53 mutations are found to be a late event in the adenoma to carcinoma sequence (Kinzler and Vogelstein,

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Dietary components: Red Meat, multivitamins, fruits/ vegetables, fiber, calcium

TGF-βRII mutation

Lifestyle components: Obesity, smoking, alcohol use, physical activity

Figure 73.2 Multi-step progression of CRC development. There are many interacting factors, both genomic and environmental, that influence the transformation of a normal colonic epithelial cell into a metastatic cancer. The nuclear progression of the adenoma to carcinoma sequence is illustrated within the cell (i.e., depicted by the oval), denoting the multiple genetic alterations that occur in the CIN or MMR pathways. Outside the cell, are the many environmental dietary and lifestyle factors that promote the initiation and progression of the cancer. These factors may work through the constitutional genome of the individual that encode metabolizing proteins, such as NAT1/2 and MTHFR, that work to modify the course of cancer development. On the edge of the cell, are genes thought to be modifiers that could be involved with factors inside or outside the cell.

1996). As gene discovery in cancer advances, additional somatic mutations in other oncogenes, such as AKT1, will deepen the molecular understanding of CIN pathway progression in CRC (Carpten et al., 2007). In contrast to CIN tumors, up to 15% of CRC has little, if any, evidence of aneuploidy. MMR-deficient tumors have a recognizable cellular phenotype with poorly differentiated mucinous histopathology with a peritumoral lymphocytic infiltration (Messerini et al., 1996). This type of CRC that evolves along the described MMR pathway (Figure 73.2) is thought to be caused by the loss of repair function of single basepair mismatches formed during S-phase in the epithelial colon cell, either through an inherited or acquired inactivation of one of

the MMR genes. This inactivation renders the cell susceptible to further somatic mutations throughout the genome, increasing the cellular mutation rate from 100 to 1000 fold in monoand di-nucleotide sequences (de la Chapelle, 2004; Gryfe, 2006). Molecular analysis of these sequences can reveal a unique profile known as MSI that correlates with cellular loss of specific proteins encoded by the MMR genes, hMSH2, hMLH1, hMSH6, hPMS1, and hPMS2 (OMIM 609309, 120436, 600678, 600258, and 600259, respectively) (Ionov et al., 1993; Thibodeau et al., 1993). The increased mutation rate in mono- or di-nucleotide tracts could explain the high frequency of acquired mutations in TGFRII (OMIM 190182) and other genes that appear to be targeted in MSI tumors, as well as the more rapid rate of adenoma

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to carcinoma progression compared to the CIN pathway CRC (Boland et al., 2007). TGFRII (OMIM 190182) is an extracellular receptor and a member of the serine/threonine protein family containing a mono-tract of A10 that is somatically mutated in MMR deficient tumors, causing a loss of the natural inhibitory signal to the TGF/SMAD pathway and proliferation of the aberrant clone (Boland et al., 2007; Ionov et al., 1993; Markowitz et al., 1995; Samowitz et al., 2005) The CIN and MMR pathways appear to have little overlap, with the CIN tumors exhibiting distal location in the bowel, non-random chromosomal aneuploidy, and mutations in APC, K-ras and TP53 genes and the MSI tumors primarily exhibiting a proximal location, mucinous histopathology, MSI, loss of MMR proteins, and acquired mutations in TGFRII. While the initial observation of tumor instability was in tumors from patients with germline mutations in the MMR genes, it is now clear that the majority of MSI tumors are sporadic and may be due to widespread epigenetic inactivation of CpG islands in the promoter regions of specific genes, such as hMLH1 and others. Researchers have proposed that the CIMP is an additional third CRC pathway, with older age of onset and tumors that exhibit a serrated histopathology, MSI, and increased mutations in specific oncogenes, K-ras and BRAF (OMIM 164757) (Deng et al., 2004; Minoo et al., 2007; Samowitz et al., 2005). Serrated adenomas are a newly recognized type of polyp with cancerous potential and hyperplastic and adenomatous features. When advanced, these lesions can develop areas of dysplasia with somatic CpG island methylation and mutations in the BRAF oncogene (OMIM 164757). BRAF mutations can be used to differentiate a tumor into CIMP or CIN, as a correlative study of 126 colon tumors using six CIMP-related markers, six tumor suppressor genes, MSI, and loss of heterozygosity (LOH), demonstrated that the BRAF V600E mutation is highly predictive of the CIMP phenotype (Minoo et al., 2007). Promoter methylation of MLH1 and other genes, such as p16INK4, p14ARF (OMIM 600160) is also now recognized as an important mechanism of colon carcinogenesis and may cause wider dysregulation in the cancer development process, providing further molecular evidence of heterogeneity in CRC carcinogenic pathways (Goel et al., 2007; Minoo et al., 2007). Nonetheless, it is not clear if the CIMP phenotype is a discrete and separate pathway from the MMR deficiency pathway and further work will need to clarify whether specific genes underlie these genomic mechanisms (O’Brien et al., 2006; Ogino et al., 2007; Xu et al., 2004). The search for the genes in the CIN, MMR and CIMP pathways is ongoing using the current genomic tools of comparative genomic hybridization (CGH) and gene expression microarrays on cancer tissues (Vendrell et al., 2005). Ried and colleagues examined tissue from 14 normal colons, 12 adenomas, 14 high grade adenomas, and 16 CRCs using florescent microarray probes and found increases of chromosome 20q, 13q, 8q, 7p, 1q, and 5p and losses on chromosomes 4, 8p, 18q, and 17p (Ried et al., 1996). Advances in technology have improved the resolution of CGH studies for copy number variations. One recent study examined 42 primary CRC and 37 tumor cell

lines, using an oligonucleotide array of 22,500 elements mapping to 16,097 loci. Fifty minimal common regions or MCRs, defined by the occurrence of overlapping events in two or more samples, were identified. Of the 28 amplified and the 22 deleted copy number MCRs, 5 regions held previously known genes, 15 recurring MCR regions were more focal and contained less than 12 genes, and 10 regions contained a total of 65 identifiable genes linked to cancer. Importantly, this method identified 21 new candidate genes that had not previously been linked to CRC (Martin et al., 2007). Correlating gains and losses of chromosomal material with cancer transformation is important, but whether these copy number variants alter the expression pattern and activity of known cancer susceptibility genes is not known. For CRC, this issue has begun to be explored by analyzing colon biospecimens with expression arrays. For example, Nosho and colleagues examined 550 known susceptibility genes for at least a threefold elevation or threefold diminution of activity in 36 adenomas and 14 early invasive cancers and found 13 upregulated and 19 downregulated genes (Nosho et al., 2005). This study confirms the progression of colon adenomas to carcinomas in CRC, but also suggests that the expression of a relatively small number of genes is altered in colon neoplasias. In order to identify a set of susceptibility genes associated with acquired cancer development, an alternative approach of high-throughput sequencing of over 13,000 candidate genes was used to systematically categorize the genetic alterations in 11 breast and 11 colon tumors. Individual tumors had on average a large number (90) of different mutated genes even though a small subset of 189 genes was consistently mutated in all of the tumors (Sjoblom et al., 2006). Using a pathway analysis of the global sequencing data, the team developed a “landscape” of breast and colon tumor non-random mutational events and showed that, while some genes were consistently mutated in all tumors examined, the majority of genetic defects in cancer were not frequently mutated. Nonetheless, taken together, these mutations caused defects in specific pathways that provided an overall fitness advantage to that specific clone (Wood et al., 2007). In the future, there will be more reliance on genomic tools in the discovery and delineation of cancer pathways in CRC, with future directions focused on developing and improving personalized tests and treatments.

PREDISPOSITION FOR CRC Predisposition for CRC is a continuum of increasing risk and is dependent on several risk factors, including age, gender, nationality, existing medical conditions (i.e., previous polyps or inflammatory bowel disease) and family history. From a healthcare perspective, CRC is one of the most common tumors of adulthood, and categorizing the cancer into inherited monogenic, familial and sporadic/isolated forms is useful to determine the level of risk for disease (Figure 73.1). Sporadic CRC is a disease of aging, as the risk is low in younger individuals and increases later in life. The lifetime risk for CRC development in

Predisposition for CRC

the United States is 6% compared to 80–100% in individuals who harbor mutations in monogenic familial syndromes (Burt, 1996b; Doxey et al., 2005; Lynch and de la Chapelle, 1999; Ries et al., 2007). Up to 20% of CRC cases will have a family history of the disease, which confers a moderate two- to three-fold elevation in risk compared to the general population. Although the risk increases for these individuals with earlier onset of disease and with more than one family member affected, there is less precision in determining the level of risk (Fletcher et al., 2007; Sandhu et al., 2001; St John et al., 1993; Winawer et al., 1996). Genetic researchers use these categories to identify midto high-risk kindreds for gene discovery and other genomic studies (Wiesner et al., 2000, 2003). Further, these major categories also form the basis for clinical care for individuals at risk for CRC, determining the level of screening, and identification of those patients and specific family members who should consider genetic testing (Lynch et al., 2007). Inherited Monogenic Syndromes The characterization of the monogenic CRC syndromes (Table 73.1) has provided an essential understanding of the genomic natural history of cancer. Altogether, these syndromes have been recognized for over 100 years, even though they account for only 5% of all CRC cases (see Figure 73.1; Burt and Neklason, 2005). Primarily divided into polyposis and nonpolyposis syndromes, most monogenic forms are caused by highly penetrant germline alleles in genes that encode proteins in the CIN or MSI pathways (Figure 73.2). Following the multi-hit cancer paradigm, cancer development arises in individuals harboring a germline mutation after acquired alterations of specific regulatory genes occur in the colon epithelium. Clinically, most of these syndromes are inherited as autosomal dominant traits with variable expressivity and incomplete penetrance, with an extremely high risk for individuals and families who carry the mutated allele(s). However, because of the rarity of pathologic alleles in the general population, these mutations do not confer a high attributable risk for CRC in the general population. Thus, recognition of these monogenic syndromes is essential for proper risk management, which begins with genetic evaluation and testing (Doxey et al., 2005; Lagerstedt et al., 2007; Rustgi, 2007). Familial Adenomatous Polyposis Familial adenomatous polyposis (FAP; OMIM 175100) was the first recognized form of monogenic inherited CRC accounting for about 1% of all CRC (Rustgi, 2007). Inherited as an autosomal dominant condition, individuals typically develop multiple adenomatous polyps in adolescence, which, if left untreated, will ultimately progress to invasive CRC (Jo and Chung, 2005; Kwak and Chung, 2007). Gastrointestinal manifestations include the development of hundreds to thousands of adenomatous polyps, invasive CRC, gastric cancer, and peri-ampullary duodenal cancer. If prophylactic surgery is not performed, CRC occurs in nearly 100% of mutation carriers, with the average age at onset of initial CRC by 40 years or less (Jo and Chung, 2005;

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Kwak and Chung, 2007). There are several extra-gastrointestinal manifestations of FAP, some that are useful in diagnosis of the condition, like congenital hyperplasia of the pigment epithelium (CHRPE), and some, such as osteomas and desmoid tumors, that also contribute to the morbidity and mortality of the disorder. FAP is caused by germline mutations in the APC gene, which has been shown to be a key mutational target in sporadic CIN CRC (Figure 73.2). Karyotypic analysis of two patients with polyposis and developmental delay allowed researchers to identify the location of the gene on the long arm of chromosome 5, illustrating the historical impact of early genomic technology in delineating the causes of CRC (Bodmer et al., 1987; Herrera et al., 1986). After the APC gene was identified using positional cloning, mutational analyses of families with FAP have shown that germline mutations occur throughout the gene, with most mutations causing premature stop codons and more severe disease (Groden et al., 1991). Further genetic analysis of families has also demonstrated several allelic variants of FAP, such as Gardner syndrome, Turcot syndrome, and the attenuated form of FAP (AFAP; OMIM 175100). AFAP is characterized by later onset of CRC, a lesser number of polyps termed oligopolyposis, and a lack of extra-gastrointestinal manifestations. Patients with AFAP have mutations clustering in the 5 or 3 segment of the APC gene, while patients with Gardner syndrome have mutations primarily between codons 767 and 1513 (Bisgaard and Bulow, 2006). Patients with profuse and extensive polyposis have been identified with APC mutations near the -catenin binding site (Nieuwenhuis and Vasen, 2007). Further, some APC alleles, such as the I1307K allele found in up to 28% of patients with Ashkenazi Jewish descent and a family history of disease, do not manifest a high rate of polyp formation, again emphasizing the need to fully explore the genomic mechanism(s) of polyp and cancer formation (Laken et al., 1997, 1999). Genotype–phenotype correlation studies have also shown that up to 30% of patients with polyposis have no identifiable mutations in the APC gene (Moisio et al., 2002). Using a pathway analysis approach, a possible autosomal recessive form of oligopolyposis called Mut Y Homologue gene (MYH)-associated polyposis (MAP; OMIM 604933) was recognized in 2002, with the description of a sibship with multiple adenomatous polyps and CRC but without detectable mutations in the APC gene. Candidate gene analysis of the excision repairs genes in this kindred identified mutations in the MYH gene located on chromosome 1p (Al-Tassan et al., 2002). Defects in the MYH gene leads to the accumulation of G:C to T:A transversions in genes with susceptible GAA sequences. In fact, APC is an example of a gene that is highly predisposed to these type of transversions (Al-Tassan et al., 2002; Cheadle and Sampson, 2003; Lipton and Tomlinson, 2004). MAP has been identified in 7–8% of APC negative polyposis families and is associated with early-onset colon neoplasia, with the average polyp onset of 46 years and CRC of 49.7 years (Doxey et al., 2005; Jo and Chung, 2005). Interestingly, a recent population-based case control study suggests that the risk for polyps and cancer may not be strictly recessive, as both monoallelic and biallelic mutation carriers

TABLE 73.1

Monogenic colorectal cancer syndromes Gene

OMIM

Gene map

Major syndrome featuresa

FAP

APC

175100

5q21–22

100 Colorectal adenomatous polyps or  100 polyps with family history

Attenuated FAP

APC

175100

5q21–22

100 Colorectal adenomatous polyps; oligopolyposis

Gardner

APC

175100

5q21–22

Polyposis, osteoma, desmoid, CHRPE

Turcot

APC

175100

5q21–22

Polyposis, glioblastoma, meduloblastoma

MYH-associated

MYH

604933

1p32–34

Autosomal recessive adenomatous oligopolyposis and pilomatricomas

SMAD4

174900

18q21.1

Juvenile polyps, pulmonary arterio-venous malformations, digital clubbing

BMPR1A

174900

10q22.3

Cowden

PTEN

158350

10q23.31

Papillomatous papules,acral keratoses, facial trichemommas, mucosal lesions breast/uterine/thyroid/brain/renal cell cancers, fibroids

BannayanRuvalcaba Riley

PTEN

153480

10q23.31

Lipomas, glans penis macules, macrocephaly, pectus excavatum, scoliosis, hyperextensibility, mental retardation

Peutz-Jeghers

STK11 / LKB

175200

19p13.3

Mucosal hyperpigmentation, thyroid cancer, sex cord/gynecomastic tumors, ovarian cysts/cancer, breast/bladder/lung cancers

Basal cell nevus

PTCH

109400

9q22.3

Coarse facies, basal cell cancer, jaw keratocysts, macrocephaly, forehead bossing, facial milia, medulloblastoma, cardiac and ovarian fibromas

Multiple endocrine neoplasia 2B

RET

162300

10q11.2

Mucosal neuromas, enlarged lips, medullary thyroid cancer, pheochromocytoma, “marfanoid” body habitus

610069

15q15

Typical juvenile polyps, colonic adenomas, and colorectal carcinomas

MSH2

120435/ 609309

2p21–22

Early-onset MSI CRC diagnosed younger than 50 years; presence of synchronous, metachronous CRC, gastrointestinal cancer, uterine, ovarian cancer. Refer to Bethesda Criteriac

MLH1

120436

3p21.3

As above

MSH6

600678

2p16

As above, with a higher rate of uterine cancer

MLH3

604395

14q24.3

No kindreds have been identified with germline mutations to date

PMS1/2

600258/ 600259

2q31/ 7p22

MLH1 or PMS2

276300

Syndrome Polyposis Adenomatous polyposis

Hamartomatous polyposis Juvenile polyposis

Mixed polyposis Hereditary mixed HMPS1 polyposisb Non-polyposis HNPCC (Lynch)

Turcot Muir-Torre Familial CRC

Early-onset CRC and brain cancers, primarily glioblastoma

MSH2 or MLH1 609309 b

Early-onset CRC and sebaceous skin tumor

CRCS1

608812

9q22–23

CRCS2

611469

8q24

Early-onset MSS CRC

a Clinical manifestations from Online Mendelian Inheritance in Man (OMIM) (http://www.ncbi.nlm.nih.gov/sites/entrez?dbOMIM; Accessed December 2007). b c

Linkage analysis identified chromosomal location; specific gene(s) not identified.

Bethesda criteria for HNPCC include CRC in an individual at 50 years or less without a family history or kindreds meeting Amsterdam I or II guidelines (see Box 73.1, Umar et al., 2004 and Lindor et al., 2006).

Predisposition for CRC

of the most common MYH Y165C or G382D variants had a twofold increased risk compared to controls (Croitoru et al., 2004). A later population-based study found a similar result for the MYH Y165C variant, but not for other identified variants, suggesting that MYH variants are low-penetrant alleles (Balaguer et al., 2007). Hereditary Nonpolyposis Colorectal Cancer Hereditary nonpolyposis colorectal cancer (HNPCC) or Lynch syndrome comprises 1–3% of CRC and is characterized by early-onset proximal CRC exhibiting MSI (de la Chapelle, 2005; Kwak and Chung, 2007). Inherited as an autosomal dominant trait, HNPCC individuals face a 50–70% lifetime risk of developing CRC, in addition to other malignancies (Hampel et al., 2005). Other HNPCC-associated cancers include tumors of the gastrointestinal tract, hepatobiliary system, urinary collecting system, and female reproductive system. Women with HNPCC have a lifetime risk for endometrial cancer as high as 40–60% and up to a 12% lifetime risk of developing ovarian cancer (Hampel et al., 2006; Kwak and Chung, 2007). Clinical variants of HNPCC also exist (Table 73.1), including Muir-Torre syndrome with skin keratoacanthomas and sebaceous neoplasms, and the Turcot syndrome with brain glioblastomas (Kwak and Chung, 2007; Rowley, 2005; Rustgi, 2007). That these syndromes overlap with similar phenotypes caused by APC/CIN pathway mutations reinforces the concept of genetic and genomic heterogeneity in CRC.

BOX 73.1

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HNPCC is caused by germline mutations in one of five MMR genes, hMSH2, hMLH1, hMSH6, hPMS1, or hPMS2 (Table 73.1). Several guidelines have been developed, including the Amsterdam I, Amsterdam II, and the Bethesda criteria, to assist in the recognition of kindreds with HNPCC (see Box 73.1; Rodriguez-Bigas et al., 1997; Vasen, 2000; Vasen et al., 1991). Initially proposed as eligibility criteria for gene discovery studies, the Amsterdam I guidelines are the most stringent and have been described simply as the 3-2-1 rules, requiring the occurrence of three individuals with CRC in two generations, one of whom developed CRC less than 50 years and is an immediate relative of the other two (Vasen et al., 1991). Amsterdam II family phenotype allows the substitution of other extracolonic tumors, such as endometrial cancer, renal cancer or small bowel cancer in assessing families (Vasen et al., 1999). Because families have been identified with MMR gene mutations who do not meet these criteria, the less stringent Bethesda criteria were developed to clinically identify individuals and families who should consider germline testing (Umar et al., 2004). Other Major CRC-Associated Genetic Syndromes The inherited hamartomatous syndromes comprise an additional set of CRC polyposis syndromes distinct from the adenomatous polyposis or nonpolyposis syndromes. The hamartomatous syndromes are characterized by a malformed overgrowth of mesodermal, endodermal, or ectodermal cellular colonic elements (Schreibman, 2005). These rare syndromes carry a large risk of

Criteria used for HNPCC testing

Amsterdam criteria I: 1. One member diagnosed with CRC before age 50 years. 2. Two affected generations. 3. Three affected relatives, one of them a first-degree relative of the other two. 4. FAP should be excluded. 5. Tumors should be verified by pathological examination.

3.

Amsterdam criteria II:

5.

1. There should be at least three relatives with a Lynch syndromeassociated cancer (CRC or cancer of the endometrium, small bowel, ureter, or renal pelvis). 2. One should be a first-degree relative of the other two. 3. At least two successive generations should be affected. 4. At least one should be diagnosed before age 50 years. 5. FAP should be excluded in the CRC cases. 6. Tumors should be verified by pathological examination. Revised Bethesda Guidelines for Colorectal Tumor MSI Testing: 1. CRC diagnosed in an individual younger than 50 years. 2. Presence of synchronous, metachronous colorectal, or other Lynch syndrome-associated tumors (i.e., endometrial, stom-

4.

ach, ovarian, pancreas, ureter and renal pelvis, biliary tract, and brain tumors; sebaceous gland adenomas and keratoacanthomas; and carcinoma of the small bowel), in an individual regardless of age. CRC with MSI-high pathologic associated features diagnosed in an individual younger than 60 years. CRC or Lynch syndrome-associated tumor diagnosed in at least one first-degree relative younger than 50 years. CRC or Lynch syndrome-associated tumor diagnosed at any age in two first- or second-degree relatives.

Reviewed in: Umar, A., Boland, C.R., Terdiman, J.P., et al. (2004). Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J Natl Cancer Inst 96(4), 261–268. Lindor, N.M., Petersen, G.M., et al. (2006). Recommendations for the care of individuals with an inherited predisposition to Lynch syndrome: A systematic review. JAMA 296(12), 1507–1517. Genetics of Colorectal Cancer (PDQ®) (http://www.cancer. gov/cancertopics/pdq/genetics/colorectal/healthprofessional)

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developing CRC as well as other types of cancer (Table 73.1). Together, they occur at approximately 1/10th the frequency of the adenomatous syndromes and therefore account for less than 1% of CRC (Schreibman, 2005). The approximate incidence of each syndrome listed is 1 in 200,000 births, with the exception of juvenile polyposis syndrome, which is seen 1 in 100,000 births (Schreibman, 2005). While a comprehensive review is beyond the scope of this chapter, each of these syndromes have been associated with mutations in specific pathways, such as the PTEN pathway (OMIM 601728) in Cowden and BannayanRuvalcabe-Riley syndromes and the LKB1 pathway (OMIM 602216) in Peutz-Jeghers syndrome, which again highlights the importance of a genomic approach in delineating the mechanisms of CRC development (Rustgi, 2007). Familial CRC Familial CRC accounts for about 20% of all CRC cases and is loosely defined as a positive history of at least one family member with CRC but not meeting the criteria of any of the cancer syndromes discussed previously (Figure 73.1 and Table 73.1). However, this group is heterogeneous and includes families with multiple affected members as well as families with a single affected individual. For example, the label syndrome X has been used to describe kindreds with early-onset CRC and microsatellite stable (MSS) tumors, suggesting that there are as yet unidentified single gene defects that account for familial clustering of CRC (Lindor et al., 2005; Lipkin and Afrasiabi, 2007). Genome linkage studies scans support this notion and have identified multiple regions of interest, including chromosome 9q21.3 (CRCS1, OMIM 608812), chromosome 8q24 (CRCS2, OMIM 611469), chromosome 15 (HCRA/CRAC1, OMIM 610069), and a variety of other loci (Daley et al., 2008; Jaeger et al., 2003; Haiman et al., 2007a; Tomlinson et al., 2007; Wiesner et al., 2003). The analysis of the HCRA/CRAC1 region is instructive of the improvements in genomic technology in gene discovery, as kindreds with oligopolyposis were first linked and verified to this region using microsatellite markers (Daley et al., 2008; Jaeger et al., 2003). The region was next examined with single nucleotide polymorphism (SNP) variant analysis in CRC cases and controls and found statistical evidence for variants near GREM1 and SCG5 with a P value of 4.44  1014 (Jaeger et al., 2008). Rather than a discrete autosomal dominant or recessive condition, most familial forms of CRC are complex genetic traits involving the interaction of many variables. Risk assessment is based on epidemiologic studies that show a two- to four-fold increase in risk for family members, depending on the number of close relatives affected with CRC and the age at onset of disease. Thus, for a person who has a single parent with CRC, the lifetime risk doubles that of the general population (Sandhu et al., 2001). However, the risk is fourfold the general population risk for individuals with three close relatives with CRC or if the family member developed cancer at an early age (Boardman et al., 2007; St John et al., 1993). Importantly, the

National Polyp Study demonstrated that the risk for CRC also increases with a family history of adenomatous polyps, indicating that there are familial risk factors in the initiation of colon neoplasias (Winawer et al., 1996). Causes are likely to include undiscovered low penetrance or recessive alleles as well as undefined gene–gene interactions and gene–environmental interactions, among others. Future genome-wide association studies may identify links that can be then further studied. Sporadic or Isolated CRC Approximately 75% of all CRC occurs in individuals with no known genetic predisposing factors for disease and about 80% are caused by somatic defects in the CIN pathway as shown in Figure 73.2 (Gryfe, 2006). This set is often designated as the “general population” and includes a wide spectrum of individuals who may or may not have a combination of modifiable environmental factors and/or somatic genetic changes that have been shown to modify one’s risk for CRC or affect prognosis and response to treatment. Epidemiologists have also studied many environmental risk factors to better understand risk of CRC, beyond reporting of a family history of CRC. The factors that have been shown to be associated with an increased risk of CRC include obesity (insulin resistance), limited physical inactivity, smoking, heavy alcohol use, a diet high in red or processed meat and low in fruits and vegetables (folate), fiber, and calcium/dairy foods, non-multivitamin use, non-steroidal antiinflammatory drug use (NSAIDs), and non-post-menopausal hormone replacement therapy (Giovannucci and Wu, 2006; Potter and Hunter, 2002). In addition, other underlying colon inflammatory diseases, such as Crohns disease and ulcerative colitis, have been shown to have a significant increased risk of CRC development, although the lifetime estimates vary widely from 7% to 30% (Eaden et al., 2001; Ekbom et al., 1990). The overall risk of CRC development is similar for each of these inflammatory diseases (Gillen et al., 1994; Greenstein et al., 1981). After excluding inherited monogenic and familial forms of CRC, much of the remaining variation in genetic risk for CRC is most likely explained by combinations of common low-risk variants. This “common disease – common variant” hypothesis is believed to hold true for most human complex diseases, including cancer. Several researchers have suggested that studying SNPs for association with CRC risk should be a powerful research strategy and several candidate gene and whole genome linkage scans have been performed seeking to identify these putative common variants (Botstein and Risch, 2003). Historically, genetic risk factor studies have focused on single variants in single genes involved in metabolism of heterocyclic amines from dietary factors and folate metabolism pathways (Potter and Hunter, 2002). These candidate genes have been shown to be associated with risk of CRC when considered together with certain environmental exposures (Potter, 1999). Polymorphisms in three relevant enzymes for metabolism of heterocyclic amines and polycyclic aromatic hydrocarbons, N-acetyltransferases (NAT1; OMIM 108345 and NAT2: OMIM

Risk Assessment, Evaluation, and Genetic Testing

243400) and cytochrome-P1A2 (CYP1A2; OMIM 124606), have been studied extensively for their role in risk of CRC associated with meat consumption, cigarette smoking and alcohol use with inconsistent results (Brockton et al., 2000; Chen et al., 1998; de Jong et al., 2002; Potter et al., 1999; Vineis and McMichael, 1996;Ye and Parry, 2002). One of the possible nutrients responsible for the association between vegetables and multivitamin intake and reduced CRC risk is folic acid (Giovannucci, 2002a). Therefore, the association between CRC risk, folate intake and variants in key folate metabolism genes, such as methylenetetrahydrofolate reduc-tase (MTHFR; OMIM 607093) and thymidylate synthase (TS; OMIM 188350), has been studied repeatedly (Chen et al., 1998; Curtin et al., 2004; Hubner et al., 2007; Slattery et al., 1999; Ulrich et al., 2002). The data from the studies of MTHFR polymorphisms show that individuals with the TT genotype for the polymorphism 677C → T are “hyper-responders” to folate and to alcohol. If the diet was high in folate and low in alcohol, then the patients were at a significantly lower risk of CRC compared to those individuals with the CC or CT genotype. Additionally, a growing body of evidence suggests that individuals with higher circulating levels of IGF-1 have an increased risk of CRC; the association between another IGF candidate, IGF-binding protein 3 (IGFBP3), and CRC risk is less consistent (Giovannucci, 2001; Giovannucci et al., 2000; Kaaks et al., 2000; Ma et al., 1999). These data suggest that common variants exist in these genes, altering predisposition to CRC in the general population. However, genomic technology does not yet have the capacity to test for all such variants simultaneously and to determine the exact level of statistical risk for an individual. Thus translating these variants into clinical risk algorithms must wait for the complete identification of variants and improved study designs to determine the attributable risk for each predisposing or protective factor. With the invention of genotype microarrays and other high-throughput genotyping techniques, the capacity to genotype more than 500,000 SNPs on a single individual at one time is now available. Hence, many genome-wide association (GWA) studies of various complex diseases are utilizing this technology to assess genetic risk on a genome-wide global scale. CRC is no exception, where several case-control studies have now identified a region on the long arm of chromosome 8 (8q24) that may confer increased genetic risk for CRC (Broderick et al., 2007; Poynter et al., 2007; Tomlinson et al., 2007; Zanke et al., 2007). This region has also been implicated in prostate cancer susceptibility, supporting the notion that there is variability in the disease phenotype from defects in cancer pathways that overlap in tumor development (Gudmundsson et al., 2007; Haiman et al., 2007b;Yeager et al., 2007). There are multiple different interesting CRC candidate genes in this region that warrant further study. In addition, GWA studies have demonstrated that SNPs in the SMAD7 gene located on chromosome 18, which is part of the TGF/WNT signaling pathway, is associated with CRC (Broderick et al., 2007). Key to understanding common variants and risk for CRC will be the correlation of copy number

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variants and gene expression variants on sporadic CRC within the context of specific cellular pathways. As whole-genome approaches become more accessible and more affordable and bioinformatics techniques advance, large-scale, genome-wide studies of gene–gene and gene–environment interactions will be able to better delineate factors associated with CRC risk.

RISK ASSESSMENT, EVALUATION, AND GENETIC TESTING Personalized medicine will rely on improved genomic and proteomic tests for diagnosis treatment, including new drugs and therapies. The practical application of genomic information in healthcare today is primarily in risk assessment and direct genetic testing. As knowledge of genetic susceptibility to cancers increases, researchers, healthcare providers, and the public are finding it increasingly difficult to incorporate recommendations for healthcare based on genomic discoveries (Acheson et al., 2000; Burke, 2005). Several studies have shown that physician knowledge about genetic and genomic concepts is limited, even though a family history of cancer is among the most commonly encountered genetic issue encountered in primary care (Acheson et al., 2000; Carroll et al., 2003; Giardiello et al., 1997). New tools are needed to assist clinicians to recognize at-risk individuals and families with hereditary cancer susceptibility, to tailor cancer screening and prevention and to screen for eligibility to participate in cancer genetic research (Acheson and Wiesner, 2004; Acheson et al., 2006). One such tool under development will categorize families by level of risk for cancer, including CRC, and will generate a report for patients and physicians with screening and genetic testing recommendations (Acheson et al., 2006). Computerized programs have been developed for breast cancer and are used to assist the clinician in evaluating the individual’s risk and in considering genetic testing (Berry et al., 2002; Evans et al., 2004; Kelly and Sweet, 2007; Lindor et al., 2007). Risk algorithms similar to BRCAPRO called MMRPRO are based on known risk factors for colon cancer of family history and other risk factors (Chen et al., 2006). Genetic testing for FAP and HNPCC are conditions in which genomic technology has been successfully translated into clinical care. Various guidelines for genetic testing for colon cancer susceptibility have been suggested by professional organizations and experts in the field, with the consistent recommendation that genetic counseling and evaluation should be provided to patients considering genetic testing (ASCO, 2003; Levin et al., 2006; Lynch et al., 2007; NCCN, 2007). Genetic evaluation includes a detailed medical history including the cancer and pre-neoplastic diagnoses, the construction of a three to four generation family tree, physical examination, risk assessment, and consideration of genetic testing, including a full discussion of possible genetic discrimination. Genetic testing is generally warranted if the personal or family history is suggestive of an inherited monogenic syndrome, the testing can be adequately interpreted and/or the results can be used to guide medical and

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surgical management for the patient and other family members (ASCO, 2003). One of the most difficult issues in genetic testing for cancer is in the interpretation of results, as adequate studies to determine the clinical utility of the test are often not available, due to the rarity of the individual disorders and the lack of power in the statistical analyses needed to determine sensitivity and specificity of the tests (Burke et al., 2002). Further, most tests based on medical sequencing can also identify variants with indeterminate clinical significance and, thus, may not provide a diagnosis or further clarification of risk for the individual or family. Clinicians must also recognize that locus and allelic genetic heterogeneity will alter the final interpretation, as a negative result may be a reflection of incomplete testing. Risk assessment of the family medical tree is the first step in deciding what test to offer a patient of family who may be at risk for colorectal neoplasia (Figure 73.3). The family tree should be examined for evidence of autosomal dominant inheritance and any occurrences of early-onset CRC or polyposis to determine whether the family member represents a polyposis

syndrome or nonpolyposis syndrome. Verifying the pathology of the reported CRC and colon polyps and other cancers in the family is often recommended (Box 73.1 and Table 73.1). If the individual has multiple adenomatous polyps and a suggestive family history, genetic testing for APC mutations is warranted, However, the clinician should recall that up to 20–30% of FAP patients are affected due to spontaneous de novo APC mutations in the germline, without a family history of the disorder in either parent (Burt and Neklason, 2005). Thus, genetic testing of the APC gene is important for any patient with multiple adenomatous polyps at an early age regardless of family history (NCCN, 2007). Additionally, FAP is one disorder in which the testing of children may be warranted because screening would commence by age 10, if a pathologic mutation is identified (ASCO, 2003). The evaluation of HNPCC begins with analysis of the family tree using the Amsterdam I, Amsterdam II, and Bethesda criteria (see Box 73.1). HNPCC testing is first done with tumor tissue screening for loss of immunohistochemical (IHC) staining

Individual with CRC

Family history criteria

Multiple polyps Adenomatous Hamartomatous

Yes

None to few polyps Meets AM-I, AM-II, Bethesda

No

No

Yes

Tumor testing with MSI and IHC

Gene testing APC MYH others (Table 73.1)

No

Yes

MMR gene testing

No

FAP or AAFP Syndromic follow-up

Familial risk follow-up

Yes

HNPCC follow-up

Figure 73.3 Clinical decision tree for the systematic evaluation of suspected inherited CRC syndromes. Evaluation begins with a detailed family history of CRC and/or polyps for each patient and the progresses to either the appropriate long term follow-up and observation or genetic testing depending on the family history and polyp status of the patient.

Prognosis and Treatment

for proteins encoded by hMSH2, hMLH1, hMSH6, hPMS1, or hPMS2, and DNA polymerase chain reaction assay for MSI (NCCN, 2007). This strategy is based on the observation that a high level of MSI (MSI-H) in HNPCC tumors are also accompanied by loss of the MMR protein that corresponds to the gene mutation. The Bethesda guidelines recommend a standard set of mono- and di-nucleotide tracks that are particularly sensitive to MMR deficiency, with MSI-H defined as instability in 30% or more markers (Rodriguez-Bigas et al., 1997; Umar et al., 2004). As acquired BRAF mutations are highly associated with sporadic MSI-H tumors, testing tumor DNA for BRAF V600E mutation will help to categorize the patient (Minoo et al., 2007). If the screening tests are indicative of an inherited MMR deficiency, direct sequencing of MMR genes is next performed looking for germline mutational error in the family (Hendriks et al., 2006; Kwak and Chung, 2007).

SCREENING AND SURVEILLANCE Many studies have proven that screening for CRC with fecal occult stool testing and colonoscopy methods has reduced the incidence, morbidity, and mortality of the disease (Burt, 1996b; USPSTF, 2002;Winawer et al., 2003). Despite this, less than 35% of adults follow screening guidelines, and there is great interest in genomic screening tools that may improve compliance and or clinical utility for identifying CRC in average risk and high-risk populations (CDC, 1996). Standard recommendations for average risk individuals have been published by the American Cancer Society (ACS), and other professional organizations, such as the National Comprehensive Cancer Network (NCCN) (ACS, 1999; NCCN, 2007; Winawer et al., 2006). In general these recommendations suggest colonoscopic screening starting at age 50, and that occult blood testing of the stool with left sided endoscopic screening or double contrast barium enema be performed if colonoscopy is not available (Davila et al., 2005; Smith et al., 2001). However, if the individual has a modest family history and no other risk factors, colonoscopy screening should commence at least a decade earlier at age 40 (Fletcher et al., 2007). Specific guidelines have been developed for FAP and HNPCC with screening and surveillance recommendations starting at a younger age for specific tumor manifestations of the condition (Lindor et al., 2006; NCCN, 2007). In general, at risk family members for FAP and HNPCC begin a screening and surveillance program at 10 and 20 years, respectively (Lindor et al., 2006; Rustgi, 2007). While a review of these guidelines is beyond the scope of this chapter, the importance of identification and screening cannot be overstated. One study demonstrated a 70% reduction in the standardized mortality rate in 140 HNPCC families enrolled in a large scale surveillance program in the Netherlands (de Jong et al., 2006). Newer non-invasive genetic technologies, such as stool and serum testing, have been developed to reduce the need for invasive colonoscopic screening and/or increase the sensitivity

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of the examination (Hundt et al., 2007; Levin, 2006). Several studies have shown that fecal material retains the cellular remnants of the colonic epithelium and that the nuclear DNA from these cells remains intact. Molecular analysis of genes known to be altered in CRC progression were first examined in patients with CRC finding that three genes, TP53, BAT26, and K-Ras, accurately identified such patients (Dong et al., 2001). Multiplex tests for specific genes and methylation signatures are now available commercially; one early study examined 21 gene mutations in 80 cases compared to 212 controls and identified 63% of CRC cases and 57% of advanced adenomas, with a calculated specificity of 94% in individuals without colon neoplasia or with small adenomas (Tagore et al., 2003). Aberrant methylation of three genes in stool DNA found nearly 94% sensitivity but only 77% specificity in a small sample of 52 patients with CRC, 35 patients with benign colon disease (21 with adenomas), and 24 patients with negative colonoscopies (Huang et al., 2007). The identification of potential serum biomarkers for CRC diagnosis has also received some attention, given the ease of collecting serum as compared to other invasive diagnostic techniques for CRC. Newer approaches, mostly mass spectrometry (MS) based surface-enhanced laser desorption/ionizing time-of flight (SELDI-TOF) or matrix-assisted laser desorption/ionizing time-of flight (MALDI-TOF) methodologies, can produce high-throughput proteomic “signatures” from a variety of biospecimens. The ability of the measured protein intensities to distinguish cancer from non-cancerous biospecimens is assessed with multivariate statistical analyses. A recent proteomic study of CRC by Ward and colleagues (2006) identified a combination of four proteins that were significantly different between CRC cases and controls and then used this signature to classify serum samples from 62 CRC cases and 31 controls with a 95% sensitivity and 91% specificity. However, further validation of these proteomic techniques in sera of CRC cases compared to controls is necessary in order to advance CRC personalized medicine.

PROGNOSIS AND TREATMENT Prognosis and treatment preferences after CRC diagnosis are highly dependent on stage of disease. The early stages of CRC (stage II or less) are treated primarily with surgery with or without chemotherapy and can have a 5-year survival 90% (Ries et al., 2007). However, the 5-year survival for late-stage disease is 10% and treatment can include a combination of surgery, chemotherapy, and/or radiotherapy. Additional significant independent predictors of survival are age at diagnosis, race/ethnicity, and family history of CRC. Whether a colon tumor develops along the CIN or MSI pathway may also have therapeutic implications. Sporadic MSI tumors have a significantly improved prognosis compared to MSS tumors (Bettstetter et al., 2007; Lim et al., 2004; Popat et al., 2005; Samowitz et al., 2001). Although the results are inconsistent, patients with stage II or III

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MSI tumors may not benefit from 5-Flouorouracil (5-FU) adjuvant chemotherapy, a standard agent used in CRC (Barratt et al., 2002; Jover et al., 2006; Ribic et al., 2003). In contrast, CIN CRCs have been observed to be associated with poor prognosis, but not as consistently as the improved prognosis finding for MSI tumors (Bardi et al., 2004; Risques et al., 2003;Vendrell et al., 2005). Resolution of these inconsistencies in clinical outcome may need to wait until genomic methodologies mature more fully. As most sporadic colorectal tumors are MSS, progression is linked to abnormalities of chromosome number, allelic imbalance or LOH, which are independently associated with poor survival (Bardi et al., 2004; Lanza et al., 1998; Risques et al., 2003;Vendrell et al., 2005). Chromosomes 5q, 17p, and 18q show the most frequent LOH in sporadic CRC, therefore, studies of alterations in specific genes in these chromosomal locations have been studied for an association with prognosis with conflicting results (Munro et al., 2005; Popat and Houlston, 2005). A systematic review of TP53 abnormalities and CRC outcome from 241 studies involving 19,000 individuals showed that the presence of a TP53 mutation conferred a 30% decrease in survival compared to those with no mutation (Munro et al., 2005). Another review of genetic changes and outcome focused on another common genetic event in CRC, 18q loss and/or loss of DCC expression, showing that individuals with loss of DCC expression have twice the risk of dying compared to individuals without these genetic changes (Popat and Houlston, 2005). MSI status, p53 mutation and DCC expression are commonly identified in CRC; however it is still not well understood whether the association seen with clinical outcome is indicative of an underlying biological pathway or a specific defective gene (Gryfe, 2006). Promising research in breast cancer has recently shown the utility of gene-specific expression profiling and clinical outcome, and this technology has been rapidly accepted by clinicians to guide chemotherapy (Morris and Carey, 2007). Similar utility of whole-genome gene expression profiling in predicting prognosis after CRC diagnosis has also been sought (Barrier et al., 2007; Eschrich et al., 2005;Wang et al., 2004). Two studies, both using the Affymetrix Human U133A gene expression chips, have identified a discrete set of expressed genes that predicted survival with 78% accuracy (Barrier et al., 2007;Wang et al., 2004). However, there was little overlap in the genes included in these two predictive gene sets, and only Wang and colleagues validated their findings for prediction of recurrence. A third study, using gene expression information from spotted cDNA arrays, found an optimal gene set of 43 genes with 93% sensitivity and 84% specificity in predicting CRC outcome (Eschrich et al., 2005). Gene expression profiling has also been shown to be able to discriminate different histological types of CRC, different stages of CRC and CRC from control colon tissue (Joyce and Pintzas, 2007). Again, there was little to no overlap in expressed genes with the previously described gene sets, highlighting the significant challenges faced in developing this technology. Differences in microarray platforms, data analysis techniques

and, in particular, the inclusion/exclusion criteria for study subjects could have contributed the observed differences. Although gene expression profiling has yielded some interesting prognostic candidates, many other types of genetic changes could also be significantly associated with prognosis. Genome-wide analyses of copy number changes and methylation status have been used in other cancers to predict clinical outcomes and could also prove useful in CRC outcomes analysis. Additionally, the role of environmental risk factors in prognosis is poorly understood and needs further study particularly studying gene–environment interactions and how these might affect prognosis.

PHARMACOGENETICS/GENOMICS OF CHEMOPREVENTION AND CHEMOTHERAPY Pharmacogenetics/genomics seeks to discover the genotype or genotypes that predict overall drug response in terms of clinical outcomes and toxicity and to tailor this information for patients who have been categorized to have good drug response and low toxicity to a particular treatment (Marsh, 2007). These discoveries would also help in identifying patients at risk for adverse clinical outcomes or toxicities and as a result treat them with a different agent. Agents have been developed as chemopreventative agents for individuals at risk for CRC as well as treatment therapies for patients diagnosed with CRC. The multiple mutational events in both sporadic and inherited forms of CRC (Table 73.1 and Figure 73.2) have been explored for predictive and prognostic potential with a few showing promising results (Allen and Johnston, 2005). Some of the factors that are associated with CRC, such as diet, are modifiable and have therefore been targets for prevention and intervention studies or have been used as potential chemopreventative agents (Boursi and Arber, 2007; Giovannucci, 2002b). The most well-established agent in CRC chemoprevention is NSAID use, in the form of regular over-the-counter aspirin (Markowitz, 2007; Raju and Cruz-Correa, 2006). The molecular basis of NSAID chemoprevention is mostly due to the inhibition of cyclooxygenase (COX) enzymes, which have two isoforms COX-1 and COX-2, but NSAIDs may also inhibit CRC through non-COX mediated pathways (Brown and DuBois, 2005; Hawk and Levin, 2005). The strongest evidence for a protective effect of NSAID use on risk of CRC comes from two double-blind randomized trials, showing a 20–45% reduced risk of CRC depending on aspirin dose (Baron et al., 2003; Sandler et al., 2003). Sulindac, a non-steroidal agent, has also been used in chemoprevention trials for high-risk patients with FAP, with modest success (Keller et al., 1999). Other chemopreventative agents that show promise are calcium carbonate, selenium and hormone replacement therapy; however, the molecular mechanisms of these agents are not well understood and evidence from randomized trials of these agents is inconsistent (Gryfe, 2006).

Pharmacogenetics/Genomics of Chemoprevention and Chemotherapy

Systemic combination chemotherapy is now the mainstay of treatment for patients with advanced CRC, and includes the combination of 5-FU with other agents, such as irinotecan or oxaliplatin (Kelly and Goldberg, 2005). From a genomic standpoint, several genes have been identified that play a role in either predicting response or side effects to specific drugs used for individuals with CRC. Many of these genes are specific drug targets with known variants or genes and/or enzymes involved in drug metabolism pathways. Table 73.2 lists the drugs commonly used to treat CRC and their associated genetic target(s) (Kruzelock and Short, 2007; O’Dwyer et al., 2007). The most well understood of these chemotherapy agents and its biological markers for drug response are 5-FU or capecitabine, which is metabolized to become 5-FU (Parker and Cheng, 1990). 5-FU is given intravenously, while capecitabine is dosed orally (Diasio, 2001). The four key enzymes that can influence the toxicity and/or efficacy of 5-FU (and capecitabine) are: thymidylate synthetase (TS), dihydropyrimidine dehydrogenase (DPD), thymidine phosphorylase (TP), and methylenetetrahydrofolate reductase (MTHFR). In general, low levels of TS, DPD, or TP have been shown to confer better drug response and hence better survival (Kruzelock and Short, 2007). Interestingly, when intratumoral gene expression levels of the combination of TS, DPD, and TP were analyzed, 92% of responders to 5-FU had very low levels of all three enzymes compared to non-responders, who had at least one enzyme expressed at above normal threshold levels (Salonga et al., 2000). Two specific SNP variants in MTHFR (677C → T and 1298A → C) are functionally linked to reduced activity of this enzyme; harboring the T allele of the

TABLE 73.2

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677 variant may be associated with increased response rates to 5-FU (Cohen et al., 2003; Etienne et al., 2004; Weisberg et al., 1998). Newer chemotherapeutic medications for advanced CRC, such as the topoisomerase inhibitor, irinotecan, also appear to have functional variants that are associated with the development of serious side effects (Table 73.2). The UGT1A1*28 7/7 variant was significantly associated with the development of grade III/IV neutropenia in a study of 141 patients with advanced CRC treated with the combination chemotherapy FOLFIRI. In addition, patients who harbored both the TS 3 -UTR 6  /6 and XRCC3-241 C/C genotypes had an increased risk of disease progression compared to similar staged non-carrier patients (Ruzzo et al., 2007). Most of the current knowledge in CRC predicting response to treatment is based on gene expression transcriptional analysis (i.e., at the mRNA level only). However, it is the protein that plays the crucial active role in all cells in the body. Quantification of gene expression from mRNA analysis is not always directly correlated with measured protein level as there can be translational and post-translational modifications that determine the amount of cellular protein. Therefore, proteomic profiling has great potential to better understanding the relationship between protein levels and response to treatment (Nishizuka et al., 2003). Ma and colleagues used a welldescribed panel of 60 human cancer cell lines (NCI-60), which includes 7 CRC cell lines, to predict response to a wide array of anticancer drugs using reverse-phase protein lysate microarrays (Ma et al., 2006; Paweletz et al., 2001). They were able to

Drugs commonly used to treat CRC and their associated genetic target(s)

Drug

Genetic targets

5-FU and Capecitabine

Thymidylate synthetase (TS)

Dihydropyrimidine dehydrogenase (DPD)

Thymidine phosphorylase (TP)

Methylenetetrahydrofolate reductase (MTHFR)

Irinotecan

Uridine diphosphateglucuronosyltranferase 1A (UGT1A)

Cytochrome P450s (specifically the CYP3A family)

Carboxylesterases (CES)

Adenosine-triphosphate binding cassettes (ABC transporters)

Oxaliplatin

Excision repair crosscomplementing 1 and 2 (ERCC1 and ERCC2)

Glutathione-Stransferase-P1 (GSTP1)

Bevacizumab

Vascular endothelial growth factor (VEGF)

Cetuximab

Epidermal growth factor receptor (EGFR)

Panitumumab

Epidermal growth factor receptor (EGFR)

Transforming Growth Factor alpha (TGF)

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identify eight protein markers that predicted response to 5-FU (CDH1, CDH2, KRT8, ERBB2, MSN, MVP, MAP2K1, and MGMT), where all but one of these was known to be involved in colon cancer development. Additionally the CRC cell lines clustered into two separate groups, which also differed from the other cell lines using these eight protein markers, showing the utility of this set to not only predict chemosensitivity to 5-FU, but to distinguish the CRC cell lines from other cancer types. Proteomic analysis shows tremendous promise not only as a diagnostic tool but also as a clinical outcome prediction tool. While these studies hold the promise of profiling patients to tailor the selection of drug treatment, there is a lack of consensus among cancer specialists as to the use of pharmacogenomic profiling for patients with CRC. The American Society of Clinical Oncology guidelines currently support genetic testing for variants in TS, DPD, and TP genes for prognostic information, but not for selection of chemotherapeutic agents, such as 5-FU (Locker et al., 2006). Validation of additional predictive markers of response will require randomized clinical trials by tumor markers status, which would require large sample sizes since the prevalence of some of these markers is low in the population and the individual markers may have small effects individually but larger overall effects in combination with other markers (Sargent et al., 2005).

copy number change, epigenetics (i.e., methylation) and SNP genotype variants in a single individual. Several studies have already shown the utility of whole genome expression analysis to predict CRC diagnosis and prognosis and have attempted to characterize a genetic prognostic signature for these tumors and others have shown the utility of whole-genome SNP association analyses to localize regions of the genome that may harbor CRC risk genes (Broderick et al., 2007; Joyce and Pintzas, 2007; Tomlinson et al., 2007; Zanke et al., 2007). However, comparison between these studies can be difficult because of differences in study eligibility criteria, sample collection and storage, incomplete correlative demographic and clinical information, nucleic acid extraction methods and quantification, diversity of microarrays tools used and hybridization techniques, and small sample sizes. In addition, bioinformatics tools are just now being developed and tested that allow for the incorporation of genomic information from both RNA- and DNA-based techniques in addition to environmental exposure information. It is necessary for bigger studies to be performed utilizing the most recent pathway driven bioinformatics techniques to validate and replicate current findings, in order to meaningfully translate them to the clinic to directly affect patient care.

CONCLUSION NOVEL AND EMERGING THERAPEUTICS The future of personalized genetic/genomic medicine that will comprehensively and accurately predict risk, overall survival and/or response to treatment will need to integrate both whole genome and whole proteome information. Current technologies under investigation range from stool DNA for screening, virtual colonoscopy and serum proteomic analysis and gene expression profiling for prognosis and therapeutic management. Additionally, analysis of microRNAs (miRNA), a family of short noncoding small RNAs, has emerged as an area that may hold significant diagnostic biomarker and therapeutic target potential in cancer research (Zhang et al., 2007). The miRNA microarray expression profiling of paired CRC tissue and normal colon tissue from 84 CRC patients was recently tested for an association with survival (Schetter et al., 2008). This study found that 37 miRNAs were differentially expressed in the CRC tissue and that one of these, miRNA-21, was associated with a 2.5fold increased risk of death in both the initial set of 84 CRC tissue/normal pairs and the validation set of 113 separate individuals with CRC, independent of other covariates, such as stage of disease, age at diagnosis, gender, race and tumor location. Interestingly, miRNA-21 is expressed at high levels in most cancerous solid tumors and has been shown to act an as antiapoptotic factor (Volinia et al., 2006). Whole-genome technology is rapidly changing and will soon allow for simultaneous analysis of gene expression, chromosomal

The molecular and proteomic basis of CRC was firmly established when seminal studies provided the conceptual framework of the adenoma to carcinoma sequence by demonstrating that multiple mutations produced proliferating neoplastic clones over time. The progress in genomic technology is evident in the analysis of colorectal tumors, beginning with standard solid tumor G-banding cytogenetic methods, and the addition of flow cytometry, DNA fingerprinting, CGH techniques, highthroughput sequencing, and expression profiling (Vendrell et al., 2005). Further important work identified the importance of gene–protein pathways that regulate patterns of cell growth and the interaction of these pathways with environmental factors. The simplistic genetic notion of defects in a single gene determining the development of disease has been swept away with a richer and more complicated view of disease causation. This view includes the constitutional and somatic “genomotype” of the individual – the genotypes of major and modifying genes plus the epigenetic alterations in proliferating cellular clones over time – plus the mutation rate of key regulatory pathways, and the action of environmental factors. The future holds great promise that the genomic understanding of CRC will lead to the personalization of medical care of people at risk for cancer, as well as those being treated for colon neoplasia. Translating such predictive and diagnostic information into clinically relevant care paths is the work of the near future. Once in place, genomic medicine will be the cornerstone of healthcare for people at risk for CRC or those being treated for colon neoplasias.

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74 Prostate Cancer Phillip G. Febbo and Philip W. Kantoff

INTRODUCTION Prostate cancer is the second leading cause of cancer death in men living in Western countries. The management of men diagnosed with prostate cancer remains predicated on a combination of clinical and pathological characteristics of the individual and the cancer. Molecular features of prostate cancer remain largely of academic interest and are not used in the delivery of care. However, the dramatic response of metastatic prostate cancer to castration demonstrates the profound impact of successful molecularly targeted therapy, and increasing insight into the molecular pathogenesis of prostate cancer undoubtedly holds further therapeutic promise (Balakumaran and Febbo, 2006; Nelson et al., 2003). The genetic and epigenetic events that result in the transformation of prostate epithelial cells are becoming better understood, and genomic tools have fueled many of the recent discoveries. The synchronous development of multiple, highquality models of prostate cancer, together with sophisticated genomic technologies, have accelerated our molecular understanding of prostate cancer over the past decade and have been instrumental in the identification of key molecular events driving prostate cancer initiation, progression, and metastasis. The molecular story of prostate cancer, however, remains far from complete. A comprehensive molecular characterization of the approximately 220,000 men diagnosed each year in the United States of America remains untenable. As the sequence of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 898

the human genome and technologies for global assessment of DNA, RNA, and protein patterns are now available, it is hoped that the many molecular insights established over the past decade will be effectively woven together into a molecular map of prostate cancer to characterize and define therapy for each individual diagnosed. This chapter will highlight recent prostate cancer discoveries empowered by our nascent ability to perform global analysis on DNA variation, RNA expression, and protein expression.

GENETIC PREDISPOSITION AND ALTERATIONS IN PROSTATE CANCER Highly Penetrant Prostate Cancer Genes Despite significant efforts, there have been limited gains in the identification of germline mutations accounting for families with a significantly increased risk of prostate cancer. Likely due to a combination of disease heterogeneity and high disease prevalence, few highly penetrant prostate cancer-causing genes have been identified to date. Genes located in regions in linkage disequilibrium with prostate cancer that have confirmed mutations in at least some families at increased risk include ELAC2, MSR1, and RNAseL (reviewed in [Schaid, 2004]), but there are no genes that are the equivalent to those such as BRCA1 and breast cancer (see Chapter 72). Copyright © 2009, Elsevier Inc. All rights reserved.

Genetic Predisposition and Alterations in Prostate Cancer

In 2002, germline mutations within the ribonuclease L gene (RNAseL) gene were found to account for some families with a high risk of prostate cancer (Carpten et al., 2002). Subsequent work has found that a variant of RNAseL, the non-synonymous change Arg462Gln, confers decreased enzymatic activity and is associated with an increased risk of prostate cancer (Casey et al., 2002). While the mechanism(s) behind the association between prostate cancer risk and RNAseL remains unclear, a screen for viral DNA in prostate cancers identified a novel gammaretrovirus in prostate cancers occurring primarily in individuals with a variant of RNAseL associated with decreased activity (Urisman et al., 2006). The virus, now referred to as xenotropic murine leukemia virus-related virus (XMRV), has been isolated and shown to infect human cells (Dong et al., 2007). Further studies are currently underway to determine if infection with the virus is associated with an increased risk for prostate cancer. This intriguing finding suggests a provocative mechanism connecting the RNaseL variant with an increased risk of prostate cancer through compromised immunity and resulting viral infection. Germline Variation and Prostate Cancer Risk Variation of many germline polymorphisms have been associated with an individual’s risk of developing prostate cancer (Pomerantz et al., 2007). Most early studies focused on genetic polymorphisms, oligonucleotide repeat (di-, tri- etc.), or single nucleotide repeat polymorphisms, with potential functional impact on proteins within the androgen metabolism pathways (reviewed in [Singh et al., 2005]). However, as the paradigm of genomic assessment has shifted from single polymorphism to allele structure (haplotypes), more comprehensive analyses are now possible. In 2006, analysis of germline polymorphisms and their association with prostate cancer identified a region on chromosome 8 in significant linkage disequilibrium with the disease (Amundadottir et al., 2006). Although the association was identified initially in an Icelandic population, two variants were found to have a strong association with prostate cancer risk across multiple populations, with the largest population attributable risk observed in African-American men (Amundadottir et al., 2006). Using an alternative approach of admixture mapping in order to identify prostate cancer risk alleles in African-American men, an independent group also identified this locus (Freedman et al., 2006). Subsequently, multiple studies have confirmed the association of this region with risk of prostate cancer and have determined that there are two independent loci that impact risk of prostate cancer located within 8q24 (Gudmundsson et al., 2007; Haiman et al., 2007a, b; Schumacher et al., 2007; Wang et al., 2007; Yeager et al., 2007). While the proximity of these polymorphisms to the MYC oncogene (~150 KB) suggests that they may act through modulation of MYC expression, the specific markers are within non-transcribed regions of the genome and the mechanism underlying the association of these loci with prostate cancer risk remains unknown.

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With the advent of technologies to rapidly assay hundreds of thousands of SNPs (see Chapter 8), genome-wide association studies are becoming more prevalent and are likely to result in additional insights. For example, using a 100K SNP array from Affymetrix, investigators looked for loci with strong association with prostate cancer for incident cases within men participating in the Framingham heart study (Murabito et al., 2007). While no loci met statistical criteria for genome-wide significance, association with prostate cancer was found for two SNPs within the MSR1 gene (discussed above as being associated with a familial risk of prostate cancer). Thus, in time, more granular technologies and assessment of large cohorts with different racial and ethnic constitution will likely result in additional insights as to the genetic influence on the development of prostate cancer. Mitochondrial DNA Another compelling finding is the association of mutations within mitochondrial genes and prostate cancer risk (Petros et al., 2005). When the cytochrome oxidase subunit I gene (COI) was sequenced in patients with prostate cancer, 11–12% were found to have mutations that altered conserved amino acids compared to 2% individuals without cancer (Petros et al., 2005). Both germline and somatic mitochondrial mutations have been found to contribute to the risk of prostate cancer, underscoring their potential importance (Gomez-Zaera et al., 2006; Petros et al., 2005). A plausible mechanistic explanation involves the role these mutations may play to increase reactive oxygen species and oxidative stress. Increased oxidative stress is a commonly associated mechanism with prostate cancer development and may explain the observed associations between prostate cancer onset and NKX3.1 loss (Ouyang et al., 2005), as well as GST-Pi methylation (Parsons et al., 2001) in prostate cancer. Thus, changes in the mitochondrial genome may further highlight the importance of oxidative stress on the development and progression of prostate cancer. Epigenetic Changes in Prostate Cancer Along with genetic changes in the DNA sequence, epigenetic modifications (see Chapters 5 and 11) have been associated with the development and progression of prostate cancer. Methylation of GST-Pi is one of the earliest and most consistent findings in prostate cancer (Lee et al., 1994). Subsequently, many additional genes with CpG-rich promoters have been found to be preferentially methylated with the development of prostate cancer. A few relatively recent examples include 14-3-3sigma (Pulukuri and Rao, 2006), P4501A1 (Okino et al., 2006), TIMP-2 (Pulukuri et al., 2007), and RASFF1A (Kawamoto et al., 2007). Clearly, epigenetic regulation plays a role in the development and progression of prostate cancer, but the relative importance of the individual genes thus far identified remains largely unknown. Investigations using technology platforms with greater coverage of the human genome are currently ongoing and are likely to continue to identify specific genomic regions altered with

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prostate cancer progression (reviewed in [Nelson et al., 2007]). Already patterns of post-translational acetylation and dimethylation of histones H3 and H4 have been shown to identify two groups of low-grade prostate cancers with different risks of recurrence independent of tumor stage, preoperative prostate-specific antigen levels, and capsular invasion (Seligson et al., 2005). Importantly, the availability of high-throughput methods such as DNAse hypersensitivity that assess high-order chromatin structure (Crawford et al., 2006) represents yet another outstanding opportunity for discovery. Somatic DNA Alterations Genomic screens for DNA-based changes have recapitulated previous findings from targeted analyses. In one study, a microarray genotyping single nucleotide polymorphisms (SNPs) identified loss of heterozygosity in regions on chromosomes 8p (22%), 8q (24%), 16q (20%), 13q (18%), 10q (12%), and 4q (12%) (Lieberfarb et al., 2003). With the exceptions of 8q and 4q, each of these regions had been previously implicated in prostate cancer (Nelson et al., 2003). Similarly, in a recent study using array

TABLE 74.1

comparative genomic hybridization (aCGH), gains were seen at 1q, 7, 8q, 16p and 17q and losses at 2q, 4p/q, 6q, 8p, 13q, 16q, 17p, and 18q (Saramaki et al., 2006). With the increased resolution offered by the 16,000-feature microarray used for this study, smaller regions of gains or loss could be discerned. Genomic analyses of somatic changes in prostate cancer have highlighted the role of specific oncogenes and pathways in prostate cancer (Table 74.1). A few are consistently associated with prostate cancer development and progression and deserve specific mention. Importantly, the detailed knowledge of these pathways in prostate cancer and specific details with respect to their role in prostate cancer provide important molecular paradigms with which to consider the role of novel molecular alterations in prostate cancer as they are identified. PTEN/PI3K/mTOR PTEN antagonizes the growth-promoting effects of the family of proteins constituting the dimeric complex with PI3K activity by removing the phosphate moiety from the 3 position of phosphotidylinositol 3,4,5-triphosphate (PIP3) (Li et al., 1997).

Common chromosomal alterations in prostate cancer

Gene

Chromosomal location

Change observed

Function

Reference

ETS family members

21,7,others

Translocation downstream of AR regulated genes (primarily TMPRSS2)

Transcription factors

(Tomlins, 2006)

GSTPi

11

Methylation of promoter

Responds to oxidative stress

(Nelson, 1994)

PTEN

10q23

Loss

Tumor suppressor gene involved in regulation of the phosphatidylinositol 3-kinase pathway. Loss of the PTEN enzyme can lead to rapid cell growth and proliferation.

Hughes (2005)

NKX3.1

8p21

Loss/Inactivation

Tumor suppressor gene that is prostate specific and regulates epithelial growth and differentiation.

Hughes (2005); Shand (2006)

c-myc

8q

Gene amplification

Encodes a transcription factor that regulates the expression of multiple genes leading to increased cell proliferation.

Hughes (2005); Jenkins (1997)

Rb

13q

Loss (LOH, mutation)

Tumor suppressor gene that prevents replication of damage DNA through the cell cycle.

Hughes (2005); Phillips (1994)

p53

17p

Loss (LOH, mutation)

Tumor suppressor gene that prevents entry of damaged DNA into the cell cycle and promotes apoptosis.

Grignon (1997); Hughes (2005)

AR

X

Amplification, mutation in hormone refractory disease

Transcription factor

Koivisto (1995)

Prostate Cancer Detection

Homozygous loss of PTEN is common in advanced prostate cancer (Sansal and Sellers, 2004), and loss of one allele is likely to be an early event (Bello-DeOcampo and Tindall, 2003). Inhibition of PI3K by targeted molecules devastates prostate cancer cells with PTEN loss largely through pro-apoptotic effects (Lin et al., 1999). It is now clear that PTEN has gene dosage effects in prostate cancer that are profound. The two transgenic mice thus far developed with prostate-specific Pten knockout (Trotman et al., 2003;Wang et al., 2003) have provided remarkable insight into PTEN biology. Although complete inactivation of PTEN is observed in a significant number of cases of advanced prostate cancer, only one allele is lost in many patients at presentation. Trotman and coworkers created a Pten hypomorphic allele to generate a series of Pten transgenic mice that had progressively decreasing levels of the PTEN protein expressed in the prostate. The incidence of prostate cancer, latency and progression correlated with Pten dose in the prostate, thus providing definitive evidence that PTEN copy number is important in prostate cancer and that the hemizygous state of PTEN may play a key role in the initiation of prostate cancer (Trotman et al., 2003). The impact of this work has been extended with the recent demonstration of cooperativity between PTEN and p53 (Chen et al., 2005c). PTEN protects p53 protein from Mdm2-mediated degradation (Mayo et al., 2002), and p53 enhances PTEN trans-cription (Stambolic et al., 2001). In transgenic mice, conditional inactivation of p53 fails to produce a prostate tumor phenotype in mice, and in the same strain complete PTEN inactivation in the prostate triggers tumor formation with a long latent period. However, combined inactivation of Pten and p53 leads to formation of invasive prostate cancer as early as 2 weeks after puberty and is often lethal by 7 months of age (Chen et al., 2005c). This work demonstrated that acute PTEN inactivation induces growth arrest through the p53-dependent cellular senescence pathways both in vitro and in vivo, which can be fully rescued by combined loss of p53. This could potentially explain observations in human prostate cancer where evidence of cellular senescence is seen in early stages (by strong beta-gal staining in regions of prostatic intraepithelial neoplasia (PIN) but not in frank tumor) where cells presumably have lost p53. p53 appears to be an important checkpoint that restricts Pten-deficient tumors, and lysates from pten-null mice prostates showed a 10-fold induction of p53 (Chen et al., 2005c). Along with major genetic modifiers such as p53, germline genetic variation is also likely to impact the phenotype of PTEN loss in prostate epithelium. This possibility is suggested by the somewhat disparate phenotypes of two prostate-specific, complete Pten knockout mice. The Pten-null C57Bl/6  129/Balb/ c F2 mice developed by Wang and coworkers using a cre-lox knockout approach (Wang et al., 2003) exhibit a latent period for PIN formation of 8–10 months for heterozygotes mice and as short as 1.5 months for the homozygous mutant mice, which further supports the dose-dependent effects of PTEN. In addition, homozygous mutants develop invasive adenocarcinoma that progresses to metastasis and does not respond to castration.

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However, in a pure C57Bl/6 background, PTEN loss results in invasive cancer but not metastasis (Trotman et al., 2003). MYC c-Myc is a transcriptional factor that is required for expressing many genes involved in cell-cycle transition events and proliferation (Adhikary and Eilers, 2005). Overexpression of MYC will result in increased apoptosis unless there are mutations in MYC itself (Hemann et al., 2005) and/or deficits in homeostatic mechanisms that facilitate escape from p53 and p19ARF mediated apoptosis (Dang et al., 2005). Frequent amplification of chromosome 8q24 and/or the MYC oncogene has been demonstrated in androgen-independent prostate cancer with greater frequency of amplification noted as prostate cancer progresses (Edwards et al., 2003). p53 and Rb While distinct in their biology, p53 and Rb have both been implicated in late-stage prostate cancer and have a demonstrated causative role in transgenic mouse models. Loss of heterozygosity at the Rb loci is frequent in early prostate cancer (60% in [Phillips et al., 1994]). Rb loss results in dysregulation of the E2F transcription factors which activate genes critical for cell-cycle progression and proliferation. p53 is best characterized as causing cell-cycle arrest and apoptosis in the setting of DNA damage. p53 mutations have been associated with prostate cancer. In addition, both p53 and Rb immunohistochemistry have prognostic importance in prostate cancer (Theodorescu et al., 1997). Fascinating recent work has further demonstrated significant interaction between Rb loss and p53 signaling (Hill et al., 2005). While the supportive role of the prostatic stroma in cancer development and progression has been recognized for some time (Cunha et al., 2002), somatic genetic changes have inconsistently been found and there was little direct support for processes whereby genetic or epigenetic events preferentially occur in stromal adjacent to malignant epithelial cells. In transgenic mice with Rb loss, there is paracrine selective pressure on the stromal fibroblasts to escape from p53 growth arrest. As a result, investigators found p53 mutations occurring within the stromal cells (Hill et al., 2005). While this specific model provides only a single example of somatic genetic changes in the stroma driven by epithelial malignancy, it likely represents a common phenomenon that helps explain the stable phenotypic differences observed between stromal cells adjacent to either benign epithelial glands or malignant epithelial glands (Cunha et al., 2003).

PROSTATE CANCER DETECTION The discovery of circulating prostate-specific antigen (PSA) and its subsequent use as a screening test has resulted in earlier detection, but PSA screening has yet to demonstrate a survival benefit. Importantly, while PSA can diagnose prostate cancer earlier than symptoms, physical exam, or other blood chemistries, it

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remains limited as a predictive marker and provides little insight as to the specific biology of an individual’s prostate cancer. The goal of many genomic projects in prostate cancer is to improve upon the specificity of serum PSA and to discover and develop biomarkers that provide additional predictive or biological insight (Figure 74.1). Urine Biomarkers Prostate cancer cells slough into the prostatic glands and ducts and can be found in the urine. As high-grade prostate intraepithelial neoplasia often is associated with prostate cancer, the identification and characterization of prostate cells within the urine may provide an opportunity. In addition, degraded proteins derived from the serum and passing into the urine may also provide diagnostic, prognostic, or predictive information (M’Koma et al., 2007). Single molecular markers demonstrate correlation between biopsy results and urine protein levels. For example, there is good correlation between the presence of -methylmalonyl co-A racemase (AMACR) detected in the urine and biopsy results in men undergoing diagnostic evaluation for prostate cancer (Rogers et al., 2004). When PCR-based promoter methylation assays were used to compare cells collected in the urine to prostate biopsies, correlation was very high

Circulating cells Blood

Prostate

Nucleated WBC – Host Genomic DNA, RNA expression Circulating tumor cells – Number, Somatic DNA, RNA and protein expression Circulating endothelial cells – Number

Serum or plasma

PSA – Value, status (Free, complexed, etc.), and rate of change Molecular biomarkers – EPCA3, KLK2, other novel biomarkers Serum proteomics – Mass spec-based, reverse-phase, others Auto-antibodies – “Immunomics”

DNA

ETS family member translocations – FISH AR gene alterations – FISH, sequencing, etc. Other somatic DNA alterations – aCGH, SNP chips, sequencing Epigenetic changes – Methylation patterns

Cancer Cells

RNA

Expression patterns or “signatures” microRNA patterns RNA splice variants

Protein

Molecular biomarkers – immunohistochemistry, AMACR, EZH2, others Tissue proteomics – Mas spec-based, reverse-phase, “phosphoproteome”

Sediment cells

Molecular markers – DNA alterations, GSTPi methylation patterns

Urine

RNA Protein

Figure 74.1

for three genes (94% for GST-pi and APC, 82% for EDNRB) (Rogers et al., 2006). Additional urine biomarkers of potential clinical utility currently under study include PCA3 (de Kok et al., 2002; Tinzl et al., 2004) and the TMPRSS2/ERG translocations (Hessels et al., 2007). Thus, molecular events detected in cells or prostate-specific proteins present in the urine seem to reflect changes within the prostate and may serve as biomarkers to provide a means for earlier diagnosis. Broader proteomic approaches have demonstrated some early success in correlating protein patterns with prostate pathology. Using a mass spectrometry approach (MALDI-TOF-MS), investigators have identified calgranulin B in the urine of men with prostate cancer following prostatic massage (Rehman et al., 2004). Alternatively, by adsorbing urine proteins onto a reversephase resin and subsequently directly spotting them onto a specific matrix (-cyano-4-hydroxycinnamic acid), another group demonstrated peaks that could be used to distinguish between individuals with benign prostatic hypertrophy (BPH), PIN, and prostate cancer with approximately 75% specificity and 70% sensitivity (M’Koma et al., 2007). These early analyses require validation, but suggest that urine holds some promise as a source for informative biomarkers. The relative utility of urine-based analysis compared to serum-based analysis remains to be fully explored.

Biomarkers in prostate cancer.

Molecular markers – PCA3 Molecular biomarkers – Calgranulin B Urine proteomics – Mas spec-based, reverse-phase, “phosphoproteome”

Prostate Cancer Detection

Serum Biomarkers There has been great interest in improving our ability to detect and anticipate the behavior of prostate cancer beyond that currently enabled by serum PSA. PSA kinetics (e.g., PSA velocity or PSA doubling time) has been recently broadly recognized to improve the prognostic accuracy of PSA (D’Amico et al., 2005; Freedland et al., 2005; Partin et al., 1994), but still provide relatively limited insight into the molecular biology of an individual’s cancer. To this end, multiple groups have performed proteomic analysis of serum samples in an attempt to identify more sensitive and/or specific markers. Comprehensive analysis of circulating serum proteins in patients at risk or diagnosed with prostate cancer have used two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), MS-based technologies (e.g., matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) or surface enhanced laser desorption ionization time-of-flight (SELDI-TOF)), and reverse-phase protein arrays (Ornstein and Tyson, 2006). There have been early suggestions that proteomics can distinguish patients with prostate cancer from patients with a normal PSA (Petricoin et al., 2002) and can distinguish patients with advanced prostate cancer from patients with advanced breast or bladder cancer (Villanueva et al., 2006). Significant effort is now focused on determining the best technological platforms and clinical applications for use in the care of men diagnosed with prostate cancer. However, blood collection and processing remains a significant challenge to open-ended proteomic technologies such as MALDI-TOF or SELDI-TOF, as small changes in storage or protein isolation can significantly impact proteomic profiles (Gelmann and Semmes, 2004). When sample processing is standardized, differential patterns have been identified that correlate with the presence of prostate cancer (Villanueva et al., 2006) and the presence of bone metastasis (Li et al., 2005), but these findings require more extensive testing before their true prognostic or predictive value is known. An alternative approach, referred to as reverse-phase proteomics, has also demonstrated promise (Wang et al., 2005). In a specific example, prostate cancer cDNA library is used to create a bacteriophage library and single clones expressing prostate cancer antigens are spotted onto glass microarrays. Patient’s serum is labeled and hybridized to the arrays in the presence of an alternatively labeled “normal” serum. This technique determines differential presence of auto-antibodies directed against prostate cancer antigens. Investigators found that this approach had greater sensitivity and specificity than PSA in men with serum PSA measured between 4 and 10 ng/dl (Wang et al., 2005). These reverse-phase proteomic approaches may be less impacted by sample processing and be deployable clinically. Currently, many associations between proteomic analysis and prostate cancer remain limited to single investigator teams, and no single marker or proteomic-based model has been incorporated into clinical practice. However, with improved standardization of processing and platforms, serum proteomics have great promise to improve prostate cancer care. Early studies have already provided proof of concept by identifying serum proteins

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903

that may improve upon PSA for prostate cancer diagnosis and treatment and prospective evaluation and clinical implementation of the best markers is likely to occur over the next few years. Circulating Tumor Cells As tumors progress, malignant cells can be found within the circulation. Surrogate measures of circulating tumor cells (CTC), such as RT-PCR for PSA from whole blood (Seiden et al., 1994), have been associated with prostate cancer risk (Ghossein et al., 1999). The development of methods to isolate CTC from blood has created further opportunity to determine if the CTC number and nature provides an emerging opportunity for biomarker development. Patients with prostate cancer seem to have a relatively heavy burden of CTC, and increased numbers of CTC are associated with more advanced disease (Moreno et al., 2001) and survival (Moreno et al., 2005). Decreases in CTC numbers in response to therapy have recently been found to be associated with survival in men treated with castrationrefractory prostate cancer (Moreno et al., 2007). However, it remains to be seen if the biology of an individual’s CTC significantly reflects that of their metastatic disease. Given the relatively small numbers (a median of 5 CTC/ 7.5 ml of blood in patients with advanced prostate cancer [Moreno et al., 2005]), the isolation and analysis of CTC has proved to be a significant challenge. Thus far, analysis has been largely restricted to specific proteins or genes (PSA, PSMA, etc.) (Chen et al., 2005a). However, investigators have demonstrated the ability to detect molecular markers such as EGFR expression, DNA ploidy, and/or androgen receptor (AR) amplification in samples with 5 CTC/7.5 ml of blood (Shaffer et al., 2007). As cells are more efficiently isolated and methods of DNA or RNA amplification improved, it is possible that genomic analysis of this compartment may provide surrogate prognostic or predictive biomarkers. Imaging Prostate Cancer Imaging of the prostate is performed routinely, but thus far has demonstrated limited utility in defining intraprostatic disease burden, and no imaging modality has provided strong predictive correlates with prostate cancer behavior. Imaging modalities that have been in routine use and tested for their ability to define either intraprostatic tumor burden or histopathological correlates include transrectal ultrasound, pelvic CT, pelvic MR, pelvic MR with endorectal coils, Prostascint scans, and positron-emission tomography scans. A few imaging modalities are successful in suggesting extraprostatic disease (CT, pelvic MR / endorectal coils) or enlarged regional lymph nodes (MR / endorectal coil). In a recent study, MR evidence of non-organ confined disease (either extracapsular, seminal vesicle, or lymph node involvement) contributed to the prognostic nomograms (Kattan nomogram) that are frequently used in clinical practice (Wang et al., 2006). However, no modality has been proven to provide sufficient clinically useful information about intraprostatic

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tumor burden, pathology, or biology so as to warrant widespread adoption. MR spectroscopy is an emerging technology that has yet to be fully evaluated for its potential to provide prognostic and/or predictive information about cancer (Sorensen, 2006). Preliminary studies focused on prostate cancer have shown considerable promise (Hricak, 2005). MR spectroscopy of prostates after removal have demonstrated spectroscopic correlates with pathology (Cheng et al., 2005) and in vivo studies are underway. MR spectroscopy was found to contribute to the anatomical information provided by MR in the study by Wang and coworkers although the contribution was not found to be statistically significant (Wang et al., 2006). While widespread use of MR spectroscopy has been somewhat limited because of concerns regarding technical reproducibility, emerging work has started to identify spectroscopic approaches that are more readily performed across institutions. In addition, while there has been relatively poor correlation between MR imaging and intraprostatic tumor volume or pathology, few studies have addressed the correlation of MR spectroscopy and intratumor volume and/or pathology. Finally, there have been no studies bringing together MR spectroscopy and genomic analyses of prostate cancer to determine if there are spectroscopic findings that identify prostate cancer tumors with poor prognostic or specific predictive genomic signatures.

GENOMIC CHANGES ASSOCIATED WITH PROSTATE CANCER BEHAVIOR The progression of the prostate epithelial cells from benign to malignant and from local to metastatic is associated with genetic, epigenetic, RNA, and protein alterations. Significant effort has lead to our understanding how specific molecular events impact the development and progression of prostate cancer. More recently, global approaches of genomic interrogation have been applied to discover and define the most important molecular events in prostate cancer. Prostate Cancer Initiation Early expression analyses of prostate cancer focused on global differences between benign tissue (either “normal” prostate glands or BPH) and “cancer.” Multiple groups have demonstrated significant differential gene expression between benign and malignant localized prostate cancer specimens using microarrays (Dhanasekaran et al., 2001; Lapointe et al., 2004; Luo et al., 2001; Singh et al., 2002;Welsh et al., 2001). The difference in global gene expression between tissue samples comprised of benign epithelium and those containing malignant prostate cells were profound and several genes (e.g., AMACR and hepsin) were common to multiple groups. Importantly, microarray analysis demonstrated that AMACR had consistently increased expression in prostate cancer (Luo et al., 2001). AMACR expression was subsequently validated by immunohistochemistry (Luo et al., 2002; Rubin et al., 2002) and is currently used

clinically in some situations when the diagnosis of malignancy is unclear. Perhaps one of the most significant findings resulting from microarray analysis is the identification of recurrent chromosomal rearrangements associated with prostate cancer (Tomlins et al., 2005). By looking at data from gene expression microarrays, this group identified outlier genes whose expression was uncharacteristically high in individual cases of prostate cancer compared to other cancers. This approach identified the frequent aberrant expression of two transcription factors belonging to the ETS family, ERG and ETV1. When the genomic location and organization of these genes were further interrogated, they were found to be fused to the 5 end of the prostate-specific, androgen-regulated gene TMPRSS2, with one or the other fusion present in a very high proportion of localized prostate cancer specimens (23/29) (Tomlins et al., 2005), suggesting that fusion of an ETS family member with TMPRSS2 may be a common initiating event in prostate cancer. Multiple groups have likewise identified these translocations in a high percentage of prostate cancer patients (Hermans et al., 2006; Iljin et al., 2006;Tu et al., 2007). In addition, there have been early reports that the presence of ETS translocations are associated with more aggressive pathology (Mosquera et al., 2007) and higher prostate cancer specific mortality (Demichelis et al., 2007). The translocation between TMPRSS2 and an ETS family member appears to be an important early event in prostate cancer and, while it may have less impact on late-stage disease (Hermans et al., 2006), targeting these translocations is likely to be a clinically important approach. Biochemical Relapse Following Surgery As an imperfect but feasible measure of progression, many groups have analyzed the difference in RNA expression patterns between prostate cancers from men who were cured (or remained biochemically free of disease for at least 4 years) versus men who had biochemical recurrence (Glinsky et al., 2004; Henshall et al., 2003; Lapointe et al., 2004; Singh et al., 2002). Interestingly, while each independent group could consistently find expression patterns anticipating biochemical recurrence using multi-gene predictive models, there was little overlap in the genes used in each group’s specific model. This can be interpreted in many ways, but is likely due to the consistent presence of expression structure associated with biochemical relapse that is subtle (i.e., low signal to noise ratio) yet deep (many genes). The consistency of such an outcome signature suggests that genetic events in the primary tumors do determine a propensity to spread; however, the biology driving such expression patterns remains far from clear, and recurrence signatures may not be unique to prostate cancer. The pre-existence of genetic or epigenetic events that determine aggressive behavior is also supported by DNA-based analysis. Using aCGH, investigators have found a higher frequency of DNA copy number changes associated with recurrent disease and specifically identified loss of 8p being associated with advanced stage and gain of 11q13 with biochemical recurrence

Genomic Changes Associated with Prostate Cancer Behavior

(Paris et al., 2004). The specific regions found to be amplified contained the MEN1 gene, and subsequent analysis confirmed a difference in RNA expression. Integrative Analysis While preliminary genomic analysis of prostate cancer was largely restricted to single molecular compartments (i.e., DNA, RNA, etc.), there is an increasing utilization of integrative analysis that uses more than one molecular compartment to investigate tumor biology. An integrative approach using extensive genomic and proteomic analysis of prostate cancer found that, compared to benign prostate, 64 proteins were altered in localized prostate cancers, and an additional 156 proteins were detected in metastatic cancer. Only 48–64% concordance was observed between the RNA and proteomic analyses but the genes that were correlated with outcome by either both methods served as good predictors of clinical outcome in prostate cancer as well as other tumors (Varambally et al., 2005). Such integrative analyses are likely typical of future genomic studies and will help to determine molecular sub-types of prostate cancer in order to provide a more comprehensive understanding of prostate cancer biology and improve prostate cancer treatment. Another approach, while not involving more than one molecular compartment, is to use differential gene expression as a phenotype and to assess the similarity between gene expression as prostate cancer progresses with a broad range of expression phenotypes derived experimentally. In a recent paper, gene expression was assessed in prostate samples ranging from benign to malignant disease including a number of metastatic samples (Tomlins et al., 2007). Differential gene expression between stages of progression was used to assess similarity with gene expression changes associated with other expression-based phenotypes. For example, this process – termed the molecular concept map (MCM) – found that genes associated with proliferation in cell lines had a coordinated increase in expression as prostate cancer transitioned from local to metastatic cancer. While somewhat limited due to their analysis of only 44 individuals were, this analytic method has started to identify the biology underlying prostate cancer progression and metastasis and suggests further analysis of larger datasets will be fruitful. Correlates with Pathology There are also gene expression signatures associated with prostate cancer pathology (Singh et al., 2002). Genes found to have increased expression in tumors of increasing pathological grade (as measured using the Gleason grading system) were found to be consistent across independent sets of prostate cancer specimens (Singh et al., 2002). Interestingly, a large number of these genes were downstream targets of TGF-beta signaling and were among the top genes correlated with Gleason score (Febbo and Sellers, 2003). Subsequent work has further supported strong gene expression associations with Gleason grade (Halvorsen et al., 2005) and found that gene expression patterns can be used to distinguish Gleason grade 3 from Gleason grade 4/5 with 76% accuracy (True et al., 2006).

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These analyses suggest that a biology associated with aggressive behavior can be found in the localized tumors and likely predates the spread of cancer. While genes such as MUC1 (Lapointe et al., 2004), monoamine oxidase A (True et al., 2006) and many others have been associated with Gleason score, the specific biology driving prostate cancer aggressive disease is not fully understood. Changes Associated with Progression to Metastatic Prostate Cancer An alternative approach to identifying molecular changes associated with aggressive prostate cancer is to compare local to metastatic disease. By measuring gene expression in hormonerefractory prostate cancer and comparing it to local disease, RNA and protein expression of enhance of zeste homologue 2 (EZH2) was found to be elevated (Varambally et al., 2002). Increased expression of EZH2 in localized tumors was subsequently associated with recurrence. Interestingly, the prognostic importance of genes differentially expressed between local and metastatic tumors may be independent of tumor type. When local tumors from a variety of solid cancers were compared to a variety of metastatic samples, a 17-gene signature of metastasis was derived. When this signature was applied to prostate, breast, and meduloblastoma, the signature could anticipate disease recurrence greater than expected by chance alone (Ramaswamy et al., 2003). However, when the prognostic importance of single genes such as EZH2 are assessed in independent datasets and in the context of multivariable models, they seldom hold greater prognostic value than established clinical and pathological features (Rhodes et al., 2003). Literature Mining In another example of integrative analysis, the interpretation of differential gene expression between different stages of prostate cancer can be aided by high-throughput annotation programs, such as those that perform text-based literature mining. In a recent example, genes found to have differential expression between local and metastatic prostate cancer were used as a set to find pathways that cooccurred in the corpus of medical literature more frequently than expected by chance alone. This approach identified FOSB as being associated with the genes differentially expressed in metastatic prostate cancer, and subsequent immunohistochemistry found increased nuclear FOSB staining in metastatic prostate cancer compared to local prostate cancer (Febbo et al., 2007). Additional annotation approaches using Gene Ontology or Pathway membership are being used with increasing frequency to help interpret genomic findings. Prostate Cancer Response to Treatment Genomics to Inform Clinical Trials Genomics have already been incorporated into clinical trials so as to investigate the biological response to novel treatments in prostate cancer and identify potential mechanisms of resistance. In the setting of a phase II neoadjuvant Docetaxel (Taxotere)

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trial for high-risk localized prostate cancer, microarray-based gene expression was measured in tumors following docetaxel (Taxotere) therapy and compared to untreated prostate tumors matched for Gleason grade (Febbo et al., 2005). While there were no genes with large differences in expression (5 fold between treated and untreated tumors), gene set enrichment analysis (GSEA) (Subramanian et al., 2005) identified altered expression of genes involved in androgen metabolism. This finding further underscores the potential importance of androgen signaling in prostate cancer and has helped to inform the development a large multi-center phase III trial (CALBG [cancer and leukemia group B] 90203) that compares neoadjuvant docetaxel (Taxotere) and androgen ablation followed by surgery to surgery alone for men with similar high-risk prostate cancer (Eastham et al., 2003). In another example, patterns of gene expression were determined in localized prostate cancer before and after neoadjuvant Imatinib Mesylate (Gleevec) given to men with intermediate to high-risk prostate cancer prior to prostatectomy (Febbo et al., 2006). Microarray analysis following laser capture microdissection and RNA amplification was used to determine gene expression changes associated with therapy from nine patients. The study revealed large gene expression differences, and the gene most differentially expressed, MPK1, was validated by immunohistochemistry. GSEA comparing Imatinib Mesylate (Gleevec)-treated prostate cancer with untreated or pre-treatment biopsies highlighted a potential impact of treatment on apoptosis of cells associated with tumor microvasculature. Additional studies are now underway that look to improve the impact of targeted therapy on microvasculature by either using agents that target multiple receptor tyrosine kinases on the tumor vasculature (Neoadjuvant Sunitinib [Sutent], Duke University Medical Center, Daniel George, P.I., and Neoadjuvant Sorafenib (Nexavar), Fred Hutchinson Cancer Research Center, Evan Yu, P.I., which targets both PDGFR and VEGF) or combining targeted therapy with chemotherapy (Neoadjuvant Bevacizumab [Avastin] and Docetaxel [Taxotere], Dana Farber Cancer Institute, William Oh, P.I.). Further studies using laser capture microdissection and RNA amplification with targeted agents have the potential to lead to the discovery of potential mechanisms of targeted therapy in cancer. Guiding Cytotoxic or Targeted Therapy In 2004, two pivotal trials, TAX 327 and SWOG 99-16, demonstrated a survival benefit for docetaxel in metastatic prostate cancer (Petrylak et al., 2004; Tannock et al., 2004). While this represents a significant advance, fully 40% of patients treated with docetaxel progress by the sixth cycle of docetaxel, and improved therapeutic options are required. Multiple phase III trials testing docetaxel-doublets are underway aimed at improving the outcome for men treated with docetaxel, but an alternative approach remains improved selection of patients who receive the greatest benefit from docetaxel. As men who normalize their PSA in response to docetaxel have median life expectancies that reach almost 3 years, additional therapies may

only add toxicity. Recently, in vitro data from the NCI-60 panel of cell lines with Affymetrix microarray data and chemotherapy sensitivity data have found that chemotherapy sensitivity signatures can be developed that predict clinical response in patients with breast, lung, and ovarian cancer (Potti et al., 2006). Studies are now underway to determine if these genomic signatures can predict response to docetaxel in prostate cancer. Along with chemotherapy sensitivity genomic signatures, pathway activity signatures have been published that may help identify patients most likely to respond to targeted therapy (Bild et al., 2006). Important questions remain regarding the most effective means of developing predictive pathway signatures to help guide therapy, but multiple studies are currently underway that use genomics to enrich for responsive patients. At Duke University, a single arm Phase II study of RAD001 (Everolimus – Novartis) in men with HRPC is underway in which CT-guided biopsies are performed prior to and following treatment with RAD001 (Everolimus) to evaluate the molecular, genetic, and genomic effects of RAD001 in these tumor specimens and to determine if response to RAD001 can be predicted based upon the molecular state an individual’s tumor.

GENOMIC CHANGES ASSOCIATED WITH HORMONE-REFRACTORY PROSTATE CANCER AR Activity Prostate cancer’s dependence on androgens has been known for over a half a century since Huggins and Hodges demonstrated the dramatic palliative effects of orchiectomy for patients with metastatic prostate cancer (Huggins and Hodges, 1941). Classically, androgens act by binding to the intracellular AR, a ~110 kD nuclear transcription factor with a central DNA binding domain, a ligand binding domain, and both ligand-dependent and ligand-independent transcriptional activation domains (reviewed in [Febbo and Brown, 2002]). The regulation of AR activity involves a complex dance of protein–protein and protein–ligand binding eventually resulting in transcription of a subset of genes containing androgen response elements (AREs). While this classic mechanism of action remains valid, complexities including non-transcriptional effects of androgens, AR co-regulators and transcriptional specificity, and non-autonomous AR effects have built upon the “classic” understanding of AR actions (Taplin and Balk, 2004). AR Transcriptional Targets It is clear that identification of critical downstream targets of the AR likely holds significant biological and therapeutic importance in prostate cancer. The precise identification of AR targets is complicated by the ubiquitous presence of the degenerate AR response element in the genome and the complex interaction of AR with regulatory proteins. Of the known AR chaperones, Hsp90, has, perhaps, the most significant impact on AR activity. Inhibition of Hsp90 function results in proteosomal degradation

Future Prospects of Genomics in Prostate Cancer Care

of proteins that require this chaperone for stability, including AR (Solit et al., 2002). In prostate cancer models, inhibition of hsp90 has been shown to modify the effect of dihydrotestosterone and inhibit the growth of hormone-sensitive and resistant tumors (Harashima et al., 2005; Solit et al., 2003). Interestingly, when the gene expression patterns following androgen exposure in LNCaP cells, an androgen sensitive prostate cancer cell line, was used as a “signature” with which to identify small molecules with expression antagonistic expression profiles, inhibitors of hsp90 were among the top chemicals on the list (Hieronymus et al., 2006). Recent studies have looked at the global expression changes induced by castration in men with prostate cancer so as to glean biological insight. Interestingly, many of the genes identified in cancer cell lines were found to decrease following chemical castration when intraprostatic androgen levels were sufficiently reduced (Mostaghel et al., 2007). However, often some AR signaling was maintained as determined by the continued expression of putative AR target-genes and serum androgen levels did not correlate strongly with intraprostatic hormone levels. Thus, proactive assessment of continued AR activity and more aggressive AR inhibition is likely to be a fruitful area of future clinical research. AR Addiction Androgen ablation is the first line of therapy in prostate cancer. The significant biochemical and/or symptomatic response by most patients to castration demonstrates oncogenic addiction to AR signaling. However, most patients develop resistance to the treatment and prostate cancer progresses to the “hormonerefractory” stage. Importantly, it is now clear that while prostate cancer is progressing in the setting of low circulating levels of testosterone, most cancers are still dependent on AR signaling (reviewed in [Scher and Sawyers, 2005]). In approximately onethird of patients with hormone-refractory prostate cancer, the AR gene is amplified (Bubendorf et al., 1999; Koivisto et al., 1995; Visakorpi et al., 1995). For patients treated with specific AR-inhibitors, a subset will develop mutations in the AR gene that enable the mutant AR to be activated by additional androgens as well as progesterone and estrogen (Taplin et al., 1995, 1999). Similarly, a prostate cancer cell line, MDA PCa, established from a patient who had undergone castration, had two mutations in the AR receptor that made it more responsive to a sub-family of circulating steroids not effected by castration (Krishnan et al., 2002; Zhao et al., 1999). Recently, in hormone-resistant prostate cancer xenografts, increased AR RNA expression was the most consistent RNA change associated with hormone-refractory growth (Chen et al., 2004). Subsequently, the authors demonstrated that increased AR expression was both sufficient and necessary for “hormone-refractory” growth of the xenografts (Chen et al., 2004).This work is supported by analysis of metastatic, androgenindependent human tumors that have very high expression of the AR and upregulation of metabolic enzymes that increase bioactive androgens when compared to local, untreated tumors using microarrays (Stanbrough et al., 2006). Thus, it has become

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very clear that continued AR signaling is a critical mechanism of hormone-refractory prostate cancer. Ongoing investigations are focused on the mechanisms by which prostate epithelial cells can maintain AR signaling in the setting of low circulating androgens.

FUTURE PROSPECTS OF GENOMICS IN PROSTATE CANCER CARE Prostate cancer is common, biologically heterogeneous, and protean in its clinical manifestations. Through the use and analysis of isogenic cell lines, xenografts, transgenic mice, and human tumors, one begins to deconvolute the precise biological mechanisms that combine to create the native complexity and heterogeneity of this disease. Here, we have underscored compelling recent genomic discoveries in prostate cancer so as to provide the reader with molecular paradigms with which to interpret future insights into the biology of prostate cancer. While we inevitably had to omit a significant amount of important research in prostate cancer, the work discussed here is representative of current prostate cancer research. Already our molecular understanding of prostate cancer has changed due to the discovery fostered by genomic technologies. The identification of genetic translocations involving ETS family members is probably the most important molecular discovery in prostate cancer over the past 10 years and has fundamentally altered how investigators characterize prostate cancer. Yet, while this discovery underscores the power of genomic tools for discovery, it underscores the remaining limitations resulting from our reductionist approach to genomic-based discoveries. In order for most genomic discoveries to be clinically deployable, they have to be reduced in complexity. This may be due to limitations of tissue availability or concerns regarding technical variation in a clinical setting, but, regardless, such a reduction of complexity limits the true potential for genomics to impact the clinic. In addition, there are still significant unknowns regarding the role of tumor heterogeneity, temporal variation, and host health status on our ability to reproducibly assay a genomic state of prostate cancer either through serum, urine, CTC, or biopsies of tumors. That being said, many screening, cohort, and treatment trials are incorporating standard operating practices so as to facilitate robust genomic analysis of clinical samples and these practices will help in the future translation of genomics to the clinic. Looking forward, it is hoped that the collective work mapping genetic and biological interactions between key regulators of prostate epithelial cells, epithelial-stromal interactions, host immune system, and host genetics will eventually result in a comprehensive understanding of prostate cancer. While it is likely that the molecular characteristics of an individual’s prostate cancer will be analyzed using a relatively limited molecular tools in the near future, eventual application of genomic technologies and nanotechnology offer a promise of robust future characterization. Such a characterization is likely to be required in order to maximize our ability to optimize and individualize preventative and treatment strategies.

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genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum Mol Genet 13(13), 1303–1313. Parsons, J.K., Nelson, C.P., Gage, W.R., Nelson, W.G., Kensler, T.W. and De Marzo, A.M. (2001). GSTA1 expression in normal, preneoplastic, and neoplastic human prostate tissue. Prostate 49(1), 30–37. Partin, A.W., Pearson, J.D., Landis, P.K., Carter, H.B., Pound, C.R., Clemens, J.Q., Epstein, J.I. and Walsh, P.C. (1994). Evaluation of serum prostate-specific antigen velocity after radical prostatectomy to distinguish local recurrence from distant metastases. Urology 43(5), 649–659. Petricoin, E.F., 3rd, Hackett, J.L., Lesko, L.J., Puri, R.K., Gutman, S.I., Chumakov, K., Woodcock, J., Feigal, D.W., Jr, Zoon, K.C. and Sistare, F.D. (2002). Medical applications of microarray technologies: A regulatory science perspective. Nat Genet 32(Suppl), 474–479. Petros, J.A., Baumann, A.K., Ruiz-Pesini, E., Amin, M.B., Sun, C.Q., Hall, J., Lim, S., Issa, M.M., Flanders, W.D., Hosseini, S.H. et al. (2005). mtDNA mutations increase tumorigenicity in prostate cancer. Proc Natl Acad Sci USA 102(3), 719–724. Petrylak, D.P., Tangen, C.M., Hussain, M.H., Lara, P.N., Jr, Jones, J.A., Taplin, M.E., Burch, P.A., Berry, D., Moinpour, C., Kohli, M. et al. (2004). Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer. N Engl J Med 351(15), 1513–1520. Phillips, S.M., Barton, C.M., Lee, S.J., Morton, D.G., Wallace, D.M., Lemoine, N.R. and Neoptolemos, J.P. (1994). Loss of the retinoblastoma susceptibility gene (RB1) is a frequent and early event in prostatic tumorigenesis. Br J Cancer 70(6), 1252–1257. Pomerantz, M.M., Freedman, M.L. and Kantoff, P.W. (2007). Genetic determinants of prostate cancer risk. BJU Int 100(2), 241–243. Potti, A., Dressman, H.K., Bild, A., Riedel, R.F., Chan, G., Sayer, R., Cragun, J., Cottrill, H., Kelley, M.J., Petersen, R. et al. (2006). Genomic signatures to guide the use of chemotherapeutics. Nat Med 12(11), 1294–1300. Pulukuri, S.M. and Rao, J.S. (2006). CpG island promoter methylation and silencing of 14-3-3sigma gene expression in LNCaP and Tramp-C1 prostate cancer cell lines is associated with methylCpG-binding protein MBD2. Oncogene 25(33), 4559–4572. Pulukuri, S.M., Patibandla, S., Patel, J., Estes, N. and Rao, J.S. (2007). Epigenetic inactivation of the tissue inhibitor of metalloproteinase2 (TIMP-2) gene in human prostate tumors. Oncogene 26(36), 5229–5237. Ramaswamy, S., K. Ross, N., Lander, E.S. and Golub, T.R. (2003). A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1), 49–54. Rehman, I., Azzouzi, A.R., Catto, J.W., Allen, S., Cross, S.S., Feeley, K., Meuth, M. and Hamdy, F.C. (2004). Proteomic analysis of voided urine after prostatic massage from patients with prostate cancer: A pilot study. Urology 64(6), 1238–1243. Rhodes, D.R., Sanda, M.G., Otte, A.P., Chinnaiyan, A.M. and Rubin, M.A. (2003). Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer. J Natl Cancer Inst 95(9), 661–668. Rogers, C.G., Yan, G., Zha, S., Gonzalgo, M.L., Isaacs, W.B., Luo, J., De Marzo, A.M., Nelson, W.G. and Pavlovich, C.P. (2004). Prostate cancer detection on urinalysis for alpha methylacyl coenzyme a racemase protein. J Urol 172(4 Pt 1), 1501–1503. Rogers, C.G., Gonzalgo, M.L., Yan, G., Bastian, P.J., Chan, D.Y., Nelson, W.G. and Pavlovich, C.P. (2006). High concordance of gene methylation in post-digital rectal examination and post-biopsy urine samples for prostate cancer detection. J Urol 176(5), 2280–2284.

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75 Molecular Biology of Ovarian Cancer Tanja Pejovic, Matthew L. Anderson and Kunle Odunsi

INTRODUCTION The normal human ovary is lined by a cuboidal epithelium. These cells remain quiescent for most of their lifespan, proliferating only to repair the ovarian surface after ovulation. Epidemiologic evidence demonstrates a clear correlation between the frequency of ovulation and the incidence of epithelial ovarian cancer. As a result, particular attention in the search for precursors to ovarian cancer has focused on small inclusion clefts that persist after repair of the ovarian surface. The molecular environment influencing these clefts is unique, the epithelia lining these clefts express specific cell adhesion molecules, including cadherins, not normally observed in other areas of the ovarian surface epithelium. While in other organs, such as colon, distinct pre-malignant lesions have been identified and found to accumulate, genetic defects that ultimately result in the pre-malignant ovarian change has not yet been clearly identified. Histologic findings consistent with a pre-invasive lesion for ovarian cancer have been described by a number of investigators in ovaries from high-risk women undergoing prophylactic oophorectomy and in areas of ovarian epithelium adjacent to early stage ovarian cancers that demonstrate a transition from normal to malignant cells (Schlosshauer et al., 2003). The hypothesis that these lesions are pre-malignant is strengthened by observations that regions of epithelial irregularity express levels of p53 and Ki-67 intermediate between those found in normal ovary epithelium and ovarian cancers. Each of these observations is consistent with the hypothesis that, similar to cancers originating in other organs, Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

ovarian cancer evolves from an intraepithelial precursor. If so, improved means to detect and/or eradicate these lesions may prove fruitful for preventing ovarian cancer. Research efforts, designed to improve the early detection of ovarian cancer or optimize its clinical management at later stages, are likely to be more successful based on the understanding of molecular events responsible for this disease.

INHERITED OVARIAN CANCER SYNDROMES Linkage analysis of familial breast and ovarian cancers provided some of the first insights into the molecular basis of ovarian cancer. These efforts ultimately identified two genes, BRCA1 and BRCA2, each clearly associated with an increased incidence of ovarian cancer (Miki et al., 1994;Wooster et al., 1994). Although only a minority (8–10%) of diagnosed ovarian cancers are familial, about 60% of familial ovarian cancers are associated with mutations at the BRCA1 locus, located on 17q21 (Easton et al., 1995). Hundreds of mutations in BRCA1 have been identified, most commonly loss of function nonsense or frameshift mutations. Two of the three specific founder mutations, 185delAG and 5382insC, are found in 1% and 0.1% of Ashkenazi Jewish women. The population-based studies have suggested that the lifetime risk of ovarian cancer in BRCA1 mutation carriers is about 20–30%, but this increased risk is not manifest until the age of 40. The functional consequences of loss of function mutations in BRCA1 are potentially profound. BRCA1 regulates Copyright © 2009, Elsevier Inc. All rights reserved. 913

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p53, an oncogene frequently implicated in ovarian cancer. Thus, loss of BRCA1 function allows DNA damage to accumulate via a loss of its activation of p53 check point. Other mechanisms of BRCA1-associated ovarian carcinogenesis such as sitespecific dysregulation of X-linked gene expression in BRCA1associated epithelial ovarian malignancies have been described. BRCA2 gene plays a role in DNA repair by homologous recombination. About 35% of hereditary ovarian cancers may be attributed to BRCA2 mutations and both female and male carriers of the mutation are at risk for breast cancer. The third founder mutation 6174delT occurs in BRCA2. Mutations in BRCA1 and BRCA2 are only rarely observed in sporadic, non-familial ovarian cancers. Characterization of genome-wide patterns of gene expression in sporadic breast cancers has allowed investigators to classify these tumors as either BRCA1-like or BRCA2 like in the patterns of their gene expression. These observations implicate alterations in other components of the BRCA1/2regulated pathways that contribute to sporadic ovarian cancer.

TABLE 75.1 population

OPTIONS FOR SCREENING AND PREVENTION

OR

The standard recommendations for women at increased risk for ovarian cancer include pelvic examination, serum tumor markers, and pelvic ultrasound. Unfortunately, none of these strategies have proven effective as a screening modality. Most cancers detected in high-risk populations are detected at an advanced stage. The high risk combined with the ineffectiveness of current screening methods has led to the recommendation that women with high risk undergo risk-reducing salpingo-oophorectomy (RRSO) after completion of child-bearing. To be considered at high-risk for ovarian cancer, subjects must satisfy one of the following criteria (Table 75.1) (US Preventive Services Task Force recommendations, 2005). Unfortunately risk-reducing surgery is not fail-safe. Cases of peritoneal surface serous carcinoma have been described in 2% of cases occurring from 1 to 27 years after risk reducing oophorectomy (Offit, 1998).

GENOMIC INSTABILITY AND OVARIAN CANCER Genomic instability is a hallmark of all cancer, including epithelial ovarian cancers. In order to become genetically unstable the cell has to become intolerant to DNA damage. The cell can achieve this by inactivating in any of the six major DNA repair pathways: base excision repair (BER), mismatch repair (MRM), nucleotide excision repair (NER), homologous recombination (HR), non-homologous recombination (NHR), and translesion DNA synthesis (TLS). The specific DNA pathway affected often predicts the specific type of mutations observed in particular cancers, its sensitivity to drugs, as well as clinical outcome of affected patients.

1.

Criteria for high-risk for ovarian cancer

The family of the subject has a documented deleterious BRCA1 or BRCA2 mutation – in either the subject herself, or a first- or second-degree relative.

OR 2.

For non-Ashkenazi Jewish women: The family of the subject contains at least two ovarian and/or breast cancers among the first- or second-degree relatives within the same lineage. This condition can be satisfied by multiple primary cancers in the same person. Where breast cancer is required to meet this criterion, at least one breast cancer must have been diagnosed at the age of 50 years or younger.

OR 3.

4.

For non-Ashkenazi Jewish women: A combination of three or more first- or second-degree relatives with breast cancer regardless of age at diagnosis.

For non-Ashkenazi Jewish women: A first-degree relative with bilateral breast cancer.

OR 5.

For non-Ashkenazi Jewish women: A history of breast cancer in a male relative.

OR 6.

For women of Ashkenazi Jewish heritage: Any firstdegree relative or two second-degree relatives within the same lineage with breast or ovarian cancer.

OR 7.

The subject is of Ashkenazi Jewish heritage and has had breast cancer herself. To meet this criterion, her breast cancer must have been diagnosed at the age of 50 years or younger.

FANCONI/ANEMIA PATHWAY Studies on the pathogenesis of rare inherited DNA repair disorders, such as Fanconi anemia (FA), have helped define the molecular basis of defective DNA damage responses linked to cancer risk. FA is a rare genetic disorder characterized by skeletal anomalies, progressive bone marrow failure, cancer susceptibility and cellular hypersensitivity to DNA cross-linking agents. To date, thirteen FA genes have been cloned: FANCA, -B, -C, -D1, -D2, -E, -F, -G, -J, -L, -M, -N, and -I. Of these, FANCA, FANCB, FANCC, FANCE, FANCF, FANCG, FANCL, and FANCM form a nuclear core complex. While the functional scope of this complex has not been fully defined, it is clear that it must be completely intact to facilitate monoubiquitination of the downstream FANCD2 protein, a change that permits

Somatic Mutations in Ovarian Cancer

Complex I A

B

C F E

M 5 3

915

Complex II I

G

■

D2

L

?J/BA

CH1

Ub

Ub

D2

D1/BRC

N

o

A2

Parental DNA

Leading strand Lagging strand 3 5

Stalled Replication fork at crosslinked DNA

Figure 75.1 The Fanconi anemia DNA repair pathway. After DNA damage, Complex 1 is turned on. Complex 1 functions as an E3 ubiquitin ligase and monoubiquitinates FANCD2. Monoubiquitinated (activated) FANCD2 interacts with FANCD1/BRCA2 and other DNA repair proteins to form Complex 2. After DNA repair, FANCD2 is deubiquitinated by USP1 and the DNA replication fork proceeds.

the protein to co-localize with BRCA1, BRCA2, and RAD51 in damage-induced nuclear foci (Garcia-Higuera et al., 2001) (Figure 75.1). Several lines of evidence link FA pathway with ovarian carcinogenesis. First, BRCA2 has been identified as the FA gene FANCD1 (Howlett et al., 2002). As a result, heterozygotes for BRCA2 mutations have a high risk of tissue-specific epithelial cancers such as breast and ovarian cancer, while homozygotes develop FA. Second, an increased prevalence of epithelial cancers, including ovarian malignancies, has been observed in FANCD2-nullizygous mice (Houghtaling et al., 2003). Third, functionally significant silencing of FANCF through promoter hypermethylation has also been described in ovarian cancer (Taniguchi et al., 2003). Lastly, reduced levels of FANCD2 protein are found in ovarian surface epithelium from women at risk for ovarian cancer (Pejovic et al., 2006). Taken together, these data suggest that the FA pathway is important in defining predisposition to ovarian cancer and that aberrations of FA genes may account for some familial ovarian cancer cases not accounted for by BRCA1 and BRCA2 mutations and that berrations in FANCD2 may be associated with ovarian cancer predisposition. In sporadic ovarian cancers, the epigenetic silencing of Fanconi pathway through methylation of the promoter region is one of the frequent mechanisms of inactivation. One study found that 4/19 primary ovarian carcinomas had FANCF methylation, although a larger study of 106 ovarian tumors did not identify loss of FANCF expression (Taniguchi et al., 2003).

Epigenetic silencing of BRCA1 through methylation was found in 23% of advanced ovarian carcinomas. Similar to the FA/BRCA pathway, disruption of other DNA repair pathways has been observed in ovarian cancer. A small fraction of ovarian cancer cases occur in women with hereditary non-polyposis colorectal cancer (HNPCC), also known as Lynch syndrome II. Affected individuals carry germline mutations in one of the DNA mismatch repair genes MSH2, MSH1, PMS1, and PMS2.

SOMATIC MUTATIONS IN OVARIAN CANCER Ovarian carcinoma is a monoclonal disease that progresses through a series of genetic alterations that successively accumulate and transform a normal to a neoplastic cell. The molecular complexity of ovarian cancer has become apparent through the use of novel technologies that explore the genome, transcriptome, and proteome. These novel high throughput technologies are expected to provide information that allows for earlier diagnosis and individualized treatment of ovarian cancer. Biomarkers could be also used to stratify ovarian cancer into disease groups of varying aggressiveness. Stage III and IV ovarian cancer has a consistently high mortality rate, while Stage I and II ovarian cancer has a widely variable rate of mortality. It may be possible to stratify ovarian cancer in its earliest stages into groups of cancer with varying degrees of aggressiveness and subsequent mortality.

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Gene Expression Profiling of Ovarian Cancer Ovarian cancers have been subjected to transcriptional profiling using complementary DNA (cDNA) or oligonucleotide array types. Alterations in gene expression have been suggested as useful markers of histologic differentiation in ovarian cancer. Marquez and coworkers (2005) showed distinct expression profiles of different ovarian cancer histologic subtypes and identified several markers of mucinous tumors, including TFF1, AGR2, LGALS4, CEACAM6, and CTSE. Several of these genes are also upregulated in normal colon and are important for protection and healing of the human gastrointestinal tract (Devine et al., 2000). In addition, Wamunyokoli et al. (2006) identified upregulation of NET1 and ERBB3 in low-malignant potential mucinous ovarian tumors and invasive mucinous carcinoma of the ovary leading to the hypothesis that these two genes may participate in the initiation of the transformation process in mucinous ovarian cancer. ERB-B2/HER-2/NEU was also found to be differentially upregulated in clear cell ovarian cancer. This gene encodes the target for the humanized anti-HER2/neu antibody, trastuzumab (Herceptin), that is showing promise for treatment of patients with ovarian cancers showing overexpression of Her2/neu protein (Wang et al., 2006). Recently, gene expressing profiling has successfully been used to predict response to platinum-based chemotherapy (Dressman et al., 2007). The accuracy of detecting platinum resistant disease based on expression profiling was 89%. Within this group expression signatures consistent with SRC and Rb/ E2F pathways were identified and successfully targeted in vitro. Proteomics Proteomics is the study of the proteome of a population of cells. The proteome is a result of many factors including DNA alterations, mRNA splicing, post-translational modifications, and functional regulation of gene expression (An et al., 2006; Banderal et al., 2003). Technology platforms incorporating mass spectrometry (MS) for proteomic biomarker discovery include both pattern-based methods that produce MS-derived protein pattern via SELDI (surface-enhanced laser desorption and ionization), MALDI, or electrospray and identity-based methods that yield lists of sequence-identified peptides from LC-MS/MS analysis of proteolytically digested proteins (Zhang et al., 2006). Pattern and identity combine in MS/MS analysis of selected spots from differential protein displays such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). Each method has strengths and limitations. The SELDI technique was used by Petricoin et al. (2002) for the search for ovarian cancer detection markers. This group studied serum samples of 50 ovarian cancer patients and 50 unaffected women. A protein pattern specific to ovarian cancer was identified and applied to a set of 116 serum samples from 50 women with cancer and 66 unaffected women. The proteomic pattern correctly identified all patients including 18 Stage I cancers. Only 3 of the 66 unaffected women were wrongly diagnosed as having ovarian cancer. This study awaits further validation.

ONCOGENES AND GROWTH FACTORS Increased mutagenic signaling by receptor tyrosine kinases play a major role in ovarian carcinogenesis. Overexpression of ERBB1, ERBB2/HER2/neu, and c-FMS has been reported repeatedly in ovarian cancer. One of the major downstream mediators of signaling initiated by these receptors is the phosphatidylinositiol 3kinase (PIK3K)-AKT/mammalian target of rapamycin (mTOR) pathway. This pathway is activated by multiple genomic aberrations in up to 70% of ovarian carcinomas. For example, with the PIK3CA gene at chromosome 3q26 being amplified in 25–40% of the cases. This proto-oncogene encodes the p110 catalytic subunit of PI3K and, when amplified or mutated, activates signaling through the PI3K/AKT/mTOR pathway. This pathway is thought to be a critical target for therapy of ovarian cancer. Indeed PI3K inhibitor molecules decrease ovarian cancer proliferation and ascites formation and increase chemotherapy induced apoptosis (Table 75.2). It has been reported that 75% of ovarian carcinomas are resistant to transforming growth factor- (TGF-) (Hu et al., 2000), and the loss of TGF- responsiveness may play an important role in the pathogenesis and/or progression of ovarian cancer. In addition, it has been shown that TGF-1, the TGF- receptors (TR-II and TR-I), and the TGF--signaling component Smad2 are altered in ovarian cancer (Wang et al., 2006). Alterations in TR-II have been identified in 25% of ovarian carcinomas (Lynch et al., 1998) whereas mutations in TR-I were reported in 33% of such cancers (Chen et al., 2001). Loss of function mutations of TGF-1, TR-I, and TR-II can lead

TABLE 75.2 carcinoma

Genetic alterations in sporadic ovarian

Gene

Function

Mechanism

Frequency (%)

HER2/neu

Tyrosine kinase

Amplification/ overexpression

20–30

EGFR

Tyrosine kinase

Amplification/ overexpression

12–17

KRAS

G-protein

Mutation

15–60

CMYC

Transcription factor

Overexpression

30

PIK3CA

Kinase

Amplification

25–40

AKT2

Kinase

Amplification

10

p53

Tumor suppressor

Mutation/ deletion Overexpression

30–60

p16

Tumor suppressor

Homozygous deletion

15

Ovarian Cancer Metastases

to disruption of TGF--signaling pathways and subsequent loss of cell cycle control novel TGF--signaling component, termed km23. Sequence alterations in human km23 in epithelial ovarian cancer were found in 42% of ovarian carcinomas (Ding et al., 2005). The expression of IGF-II and IGFBP-3 also vary substantially by clinical and pathologic features of ovarian cancer. High levels of IGF-II expression are associated with unfavorable prognostic indicators of the disease, whereas high expression of IGFBP-3 is related to favorable prognostic variables. Patients with high IGF-II expression tend to have higher risk of disease progression and death regardless of the level of IGFBP-3 expression (Lu et al., 2006). However, the IGF-II does not seem to be an independent marker for ovarian cancer prognosis. Ras pathway also appears to be activated in ovarian cancers. The KRAS is mutated in 15% of borderline serous ovarian tumors and 47% of mucinous neoplasm. While the mutation rate is higher in borderline mucinous (30–60%) than in invasive mucinous cancers (19%), it appears that these mutations play a role in the progression of the mucinous adenoma to borderline tumor to invasive mucinous carcinoma (Ren et al., 2006). KRAS activates PKCi on 3q26.2, a key regulator of a complex required for localization of E-cadherin to cell junctions, for maintenance of tight junctions and for cell polarity, loss of which would allow aberrant interactions of cell signaling molecules and autocrine cell activation (Macara, 2004). In ovarian cancer PKCi is associated with cyclin E induction. Amplification of the CMYC oncogene also occurs in about 30% of ovarian cancers, however the functional significance of this amplification is unclear if overexpression of the corresponding protein occurs at the same time.

TUMOR SUPPRESSOR GENES Loss of tumor suppressor gene function plays a major role in the development of most cancers. This involves a two-step process in which both copies of tumor suppressor gene allele are inactivated. In most cases there is mutation of one allele of the gene and loss of the other allele due to deletion of the large region of the chromosome where the gene localizes. Some tumor suppressor genes are inactivated via epigenetic changes, such as hypermethylation of GpC islands in the promoter area, while the gene structure remains intact. Aberrations of p53 tumor suppressor gene are the most frequent genetic event in ovarian carcinoma (Berchuck et al., 1994). The frequency of p53 mutations is higher in advanced versus early ovarian carcinomas (50–60% versus 10–20%) and is uncommon in borderline ovarian tumors. While in most cases of ovarian cancer, single amino acid change in the DNA binding domain of p53 (missense mutation) result in overexpression of non-functional protein, 20% of advanced stage ovarian carcinoma contain mutations that result in a truncated protein that is not over-expressed. Other tumor suppressor genes known to be inactivated in ovarian cancer include PTEN and p16 (Table 75.2). PTEN is

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inactivated in some endometrioid ovarian carcinomas (Obata et al., 1998). In addition p16, an inhibitor of cyclin-dependent kinases, may be inactivated via homozygous deletion or promoter methylation in a small fraction of ovarian cancer (Havrilesky et al., 2001).

EPIGENETICS IN OVARIAN CARCINOGENESIS It has become increasingly apparent that epigenetic events can lead to cancer as frequently as loss of gene function due to mutations or loss of heterozygosity. The overall level of genomic methylation is reduced in cancer (global hypomethylation), but hypermethylation of promoter regions of specific genes is a common event (Jones and Baylin, 2002) that is often associated with transcriptional inactivation of specific genes (Costello et al., 2000). This is critical because the silenced genes are often tumor suppressor genes and their loss of function can be evident in early stages of cancer, and can also drive neoplastic progression and metastasis. Epigenetic gene silencing is a complex series of events that includes DNA hypermethylation of CpG-islands within gene promoter regions, histone deacetylation, methylation or phosphorylation, recruitment of methyl-binding domain proteins and other chromatin remodeling factors to suppress gene transcription. Global hypermethylation of CpG islands appears to be prevalent but highly variable in ovarian cancer tissue (Ahluwalia et al., 2001). Specific aberrant methylation of cancer-associated genes such as p16INK4A, RASSF1A, BRCA1, and hMLH1 have been reported for ovarian tumors or cell lines, albeit in various degrees and in a non-tumor type-dependent fashion (Esteller et al., 2000; Rathi et al., 2002). While a higher degree of DNA methylation is associated with drug resistance and believed to be the reason for treatment failure and death of 90% of patients with metastatic disease, the demethylation activity of chemotherapeutic drugs can elevate the expression of proteins such as MDR1 (Bell et al., 1985) that lead to a more frequent disease recurrence after chemotherapy (Wei et al., 2002). Thus, the specificity of demethylation of select genes is important to ensure the success of treatment and prevent disease recurrence.

OVARIAN CANCER METASTASES Metastasis is the functional hallmark of all cancer. In contrast to cancers where metastasis clearly depends on the ability of cells to invade blood or lymphatic vessels, direct dissemination within the peritoneal cavity plays a critical role in the progression of ovarian cancer. A wide variety of gene products have been implicated in the metastasis of ovarian cancer. These include growth factor receptors such as epidermal growth factor receptor (EGFR), insulin-like growth factor receptors (IGFRs) and kinases, such as jak/ stat, focal adhesion kinase, PI-3 kinase and c-met. Comparisons of primary and metastatic ovarian cancers by transcriptional profiling have failed to reveal significant differences in the expression of gene products likely related to the metastatic process.

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Particular attention has recently focused on the role of lysophospatidic acid (LPA) in promoting the metastasis of ovarian cancers. LPA is constitutively produced by mesothelial cells lining the peritoneal cavity; its levels are increased in the ascites of women with both early and late stage ovarian cancers (Ren et al., 2006). At a molecular level, exogenous LPA enhances ovarian cancer invasiveness both by activating matrix metalloproteinase2 via membrane-type-1-matrix metalloproteinase (MT1-MMP) and downregulating the expression of specific tissue inhibitors of metalloproteinases (TIMP-2 and -3) (Sengupta et al., 2006). LPA also promotes dissemination of ovarian cancer by loss of cell adhesion (Do et al., 2007). However, LPA has also been shown to promote the invasiveness of ovarian cancers by additional mechanisms dependent on interleukin-8 (IL-8) (So et al., 2004). The G12/13-RhoA and cyclooxygenase pathways have also been implicated in the LPA – induced migration of ovarian cancers. These mechanisms appear to be independent of the ability of LPA to induce changes in MMP2 expression. Until recently, the metastasis of ovarian cancer has been almost exclusively studied as a process involving individual cells. However, multicellular clusters of self-adherent cells, known as spheroids, can be isolated from the ascitic fluid of women with ovarian cancer. Spheroids readily adhere to both extracellular matrix proteins, such as collagen IV, and mesothelial cells in monolayer culture using beta1 integrins. Once adherent, the cells contained in spheroids disaggregate, allowing them to invade underlying mesothelial cells and create invasive foci. These observations are consistent with the hypothesis that ovarian cancer spheroids play an important role in the metastasis of ovarian cancer. Recent evidence has shown that a loss of circulating gonadotropins result in a dose-dependent decrease in the expression of VEGF in their outer proliferating cells of ovarian cancer spheroids, that these cell cluster remain responsive to signals in their microenvironment that may further promote metastasis (Schiffenbauer et al., 1997). The presence of spheroids in ascites may also help to explain the frequent persistence and frequent recurrence of ovarian cancer after treatment. Spheroids express high levels of p27 and P-glycoprotein which contribute, at least in part, to their relative resistance to the cytotoxic effects of paclitaxel when compared to ovarian cancer cells in monolayer culture (Xing et al., 2007). However, the mechanisms by which the aggregation of malignant cells promote or enhance cell survival remain unclear. However, these observations are consistent with in vitro studies that demonstrate that the signals generated by adhesion to specific components of the extracellular matrix, such as collagen IV, can modify the sensitivity of ovarian cancers to chemotherapy. It is also unclear, at present, how the aggregation of these malignant cells might promote or enhance the migration, attachment or invasion of ovarian cancer cells. However, insight into these questions is likely to come from genetic models, such as the migration of the border cell cluster in Drosophila. Analyses of border cell migration indicate specific shifts in epithelial polarity and changes in the patterns of signals arising at junctional

proteins are necessary for the invasion and migration of epithelial clusters. Signals arising from these junctional proteins appear to be integrated by specific steroid receptor coactivators, known as AIB1 (SRC3). Overexpression of AIB1 is a frequent feature of ovarian cancers suggesting that the pathways regulated by this transcriptional coactivators may indeed play a critical role in promoting ovarian cancer metastasis.

ANGIOGENESIS Angiogenesis is critical for the development of ovarian cancer and its peritoneal dissemination. This process mechanistically involves both the branching of new capillaries as well as the remodeling of larger vessels. Other processes, such as vasculogenic mimicry, have also been implicated in tumor angiogenesis (Sood, 2001). Angiogenesis is tightly regulated by a balance of pro- and anti-angiogenic factors. These include growth factors, such as TGF-, vascular endothelial growth factor (VEGF), and plateletderived growth factor (PDGF); prostaglandins, such as prostaglandin E2 (PGE2); cytokines, such as IL-8; and other factors, such as the angiopoetins (Ang-1, Ang-2) and hypoxia inducible factor-1alpha (HIF-1). Many of these angiogenic factors have been implicated in ovarian cancer. For example,VEGF is a family of secreted polypeptides with critical roles in both normal development and human disease. Many cancers, including ovarian carcinomas, release VEGF in response to the hypoxic or acidic conditions typical in solid tumors. Near universal, albeit variable, levels of VEGF expression have been reported in ovarian cancers, where higher levels correlate with advanced disease and poor clinical prognosis (Kassim et al., 2004). Circulating levels of VEGF have also been reported to be higher in the serum of women with ovarian cancers when compared to those with benign tumors (Cooper et al., 2002). Expression of hypoxia-inducible factor (HIF-1) correlates well with microvessel density in ovarian cancers and has been proposed to upregulate VEGF expression (Jiang and Feng, 2006). Culturing ovarian cancer cell lines under hypoxic conditions stimulates the expression of both HIF-1alpha and VEGF expression in ovarian cancer cell lines; addition of PGE2 potentate the ability of hypoxia to induce the expression of both proangiogenic factors (Zhu et al., 2004). However, the specific roles of different pro- and anti-angiogenic factors in ovarian cancer often remain unclear. For example, investigators have reported that expression of HIF-1 a pro-angiogenic factor, correlates inversely with the sensitivity of ovarian cancers to primary combination chemotherapy, but is associated with improved survival after suboptimal debulking (Nakai et al., 2007). Investigators also continue to implicate new factors in tumor angiogenesis, although specific functional roles for these gene products have often yet to be defined. For example, a series of 12 novel tumor vascular markers (TVMs) have been recently identified by microdissection of tumor vasculature from ovarian cancers and genome-wide transcriptional profiling

References

(Buckanovich et al., 2007). Overexpression of these TVMs was observed only in ovarian cancer, but not any of the normal tissues tested, including healthy tissues from the female reproductive tract with ongoing physiologic angiogenesis. The expression of these TVMs in other cancers suggests that these factors may play a wider role in tumor-induced angiogenesis. Many of the molecules implicated in regulating angiogenesis in cancer also regulate other processes critical for cancer metastasis, such as cell migration and invasiveness. Inhibition of PI-3 kinase (PI3K) decreases transcription of VEGF in ovarian cancer cells, an effect that is reversed by the forced expression of AKT (Skinner et al., 2004). Such observations are consistent with reports that hypoxia not only induces angiogenesis, but also increases the invasiveness of ovarian cancer cells.

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SUMMARY Ovarian cancer is a genetic disease. The ongoing research focusing on the identification of the genetic pathways involved in the development of ovarian cancer will contribute to the understanding of the process of the malignant transformation of the normal ovarian surface epithelium and the regulatory mechanisms of proliferation, apoptosis and metastases. Research advances are likely to accelerate the development of novel biomarkers and targeted therapies for these cancers. This requires well coordinated and discovery-oriented translational research that can quickly assess novel biomarkers for early detection and targeted treatment strategies that could result in benefit to patients with ovarian cancer.

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76 Pancreatic Neoplasms Asif Khalid and Kevin McGrath

INTRODUCTION Pancreatic cancer is a lethal disease, where yearly prevalence equals mortality. It currently is the fourth leading cause of cancer-related death in the United States ( Jemal et al., 2006). Unfortunately, despite medical and surgical advances, overall survival has not changed over the last several decades. Owing to the insidious onset of non-specific symptoms, pancreatic cancer generally presents in an advanced state, where the overall 5-year survival rate remains at 5% ( Jemal et al., 2006). Since detection at the earliest stage provides the best chance for cure, recent research efforts have focused on the improved diagnosis, early detection and screening for pancreatic cancer. There is abundant literature supporting the development of pancreatic cancer in parallel with accumulation of genetic alterations. As such, molecular analysis has emerged as an area of intense interest in this regard. Pancreatic intraductal precursor lesions have been re-classified as pancreatic intraepithelial neoplasia (PanIN) based on degree of cellular atypia (Hruban et al., 2001). The prevalence of genetic mutations appears to increase with increasing atypia of these precursor lesions, temporally correlating to PanIN grade progression (Figure 76.1) (Hruban et al., 2000). Understanding the genetic basis of the pancreatic cancer progression model will hopefully provide targets for innovative molecular-based screening and diagnostic testing. Oncogene activation (e.g., K-ras), tumor-suppressor gene losses (e.g., p53, p16, DPC4, HER-2/neu) and genomemaintenance gene mutations (e.g., BRCA2, microsatellite instability, telomerase) appear to parallel the cellular evolution of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

pancreatic cancer. Histologically, these changes progress through the PanIN classification, where PanIN-1A is characterized by flat mucinous epithelium, PanIN-1B by papillary change, PanIN2 by atypical papillary change, and PanIN-3 by carcinomain-situ. K-ras oncogene point mutations and over-expression of the HER-2/neu gene product appear to be early events in pancreatic carcinogenesis, as these are prevalent in lower grade PanIN lesions (Day et al., 1996; Hruban et al., 1993). p16/INK4a tumor-suppressor gene inactivation is found more frequently in higher grade PanIN lesions, suggesting it is an intermediate event (Wilentz et al., 1998). Inactivation of p53, DPC4/SMAD4 and BRCA2 is prevalent in PanIN-3 lesions and rarely found in low-grade lesions, representing late molecular events (Goggins et al., 2000; Luttges et al., 1999;Wilentz et al., 2000; ). Thus, this temporal model of molecular carcinogenesis can serve as a template to allow integration of histologic or cytologic information with that derived from mutational analysis of DNA present in cellular samples, cyst fluid and pancreatic juice with the ultimate goal of increasing early detection rates and diagnostic accuracy. There is a wealth of information rapidly accumulating regarding the role of molecular analysis in the early detection and diagnosis of pancreatic cancer. This chapter will discuss current molecular capabilities for screening, early detection and improved diagnosis of pancreatic cancer. The focus will be on direct clinical applicability in this rapidly evolving field, amounting to translational research in motion. Diagnosis and evaluation of mucinous cystic neoplasms (MCN), which are premalignant pancreatic cysts, will also be discussed, along with new insights into molecular prognostication and tumor-directed gene therapy. Copyright © 2009, Elsevier Inc. All rights reserved. 921

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Normal

PanIN-1A

PanIN-1B

Her-2/neu K-ras

PanIN-2

PanIN-3

p16

p53 DPC4 BRCA2

Figure 76.1 The pancreatic carcinogenesis model of PanIN progression. From Hruban, R.H., Goggins, M., Parsons, J., Kern, S.E. (2000). Progression model for pancreatic cancer. Clin Cancer Res 6, 2969–2972, with permission.

PREDISPOSITION (GENETIC AND NON-GENETIC) It is estimated that up to 10% of pancreatic cancers may be linked to an inherited component (Lynch et al., 1996). Two distinct groups of patients are considered to be at risk for pancreatic cancer: (1) familial pancreatic cancer, requiring at least two firstdegree relatives to be afflicted with the disease in the absence of other familial cancers, and (2) hereditary tumor syndromes carrying an increased incidence of pancreatic cancer. Unfortunately, there has been no definitively identified gene responsible for familial pancreatic cancer, and there is likely phenotypic influence in addition to genetic predisposition. Additionally, the absolute risk is unknown. In the latter group of hereditary tumor syndromes, genetic alterations have been discovered, therefore at-risk patients can be readily identified based on genetic testing. Hereditary syndromes with an increased incidence of pancreatic cancer include familial atypical multiple mole melanoma (FAMMM), Peutz-Jeghers syndrome (PJS), hereditary non-polyposis colorectal carcinoma (HNPCC), familial breast and ovarian cancer (FOBC), cystic fibrosis (CF), ataxia-telangiectasia (AT) and familial adenomatous polyposis (FAP) (Hahn and Bartsch, 2004). The vast majority of pancreatic cancers are sporadic, without an apparent genetic linkage. Risk factors for sporadic pancreatic cancer include increasing age, tobacco use, chronic pancreatitis and long-standing diabetes mellitus. Additionally, dietary factors such as high intake of meat and fats appear to increase the risk.

SCREENING Pancreatic Cancer The ability to reliably screen patients for pancreatic cancer would be a major clinical advance. As with other clinical screening tests, the goal is early detection of disease, ideally in the

premalignant stage, as only early detection offers any chance of curative treatment with this deadly disease. Given the relatively low prevalence of pancreatic cancer (33,700 cases estimated for 2006) (Jemal et al., 2006), screening of the general population is not practical. However, secondary screening of high-risk patients, such as those with hereditary pancreatic carcinoma syndromes (up to 10% of cases) may prove feasible. Current screening strategies employ various imaging modalities (computed tomography [CT], endoscopic retrograde cholangiopancreatography [ERCP], endoscopic ultrasound [EUS]) in high-risk patients, such as those with hereditary pancreatic carcinoma syndromes. An early experience using EUS in high-risk families histologically correlated subtle EUS and ERCP findings suggestive of chronic pancreatitis with dysplasia, or PanIN lesions. Twelve patients from high-risk families were referred for pancreatic resection based on EUS and ERCP findings. Ten had total pancreatectomy and two had distal pancreatectomy; all had widespread dysplasia by surgical pathology, mostly involving small- and medium-sized ducts. CT and serum markers (CEA, CA19-9) had low sensitivity for pancreatic dysplasia (Brentnall et al., 1999; Kimmey et al., 2002). The Seattle group’s current screening protocol employs EUS beginning 10 years before the earliest age of cancer development in the affected relative. Normal EUS findings are followed up every 2–3 years; an abnormal EUS is evaluated with ERCP. As the age of the surveyed patient approaches that of the affected relative, the surveillance interval increases to yearly. It is acknowledged that the ideal surveillance interval remains unknown. Given the high prevalence of PanIN lesions in pancreata (Stelow et al., 2006), this aggressive surgical strategy should only be reserved for those high-risk individuals with hereditary pancreatic carcinoma syndromes. In a larger prospective controlled series, 78 asymptomatic high-risk patients were evaluated with both CT and EUS at baseline and 12 months (Canto et al., 2006). ERCP was performed if EUS detected an abnormality. To date, eight patients have undergone surgery with a confirmed diagnosis of pancreatic neoplasia: six benign Intraductal papillary mucinous neoplasm

Diagnosis

(IPMN), one malignant IPMN and one focal PanIN. Thus, there was a 10% yield in screening this high-risk population. EUS findings of chronic pancreatitis were more commonly seen in the study group as compared to matched controls. EUS detected more pancreatic lesions than multidetector CT; however, CT scan discovered extra-pancreatic neoplasms not evident on EUS. Hence, the Johns Hopkins group currently recommends both CT and EUS for screening these high-risk patients, as the tests appear to be complementary. Surveillance intervals for lesions detected but not resected are not currently defined, however three patients in this study had IPMN enlargement over the course of 1 year (Canto et al., 2006). Screening programs for pancreatic cancer, such as the above mentioned, currently depend on imaging modalities to detect radiographic abnormalities. Ideally, molecular analysis of pancreatic juice to detect key mutations involved in pancreatic carcinogenesis could serve as a stand-alone screening test or an adjunct to EUS examination. To this end, the oncogene K-ras has been the most thoroughly studied in pancreatic juice. Studies to date report a widely variable sensitivity and specificity for detection in juice of pancreatic cancer patients, ranging from 30% to 100% (Furuya et al., 1997; Kondo et al., 1994; Kondoh et al., 1998; Matsubayashi et al., 2006;Tada et al., 1993;Trumper et al., 2002;Watanabe et al., 1998; Yamashita et al., 1999). Further troubling has been the detection of K-ras in pancreatic juice of control patients and those with chronic pancreatitis (Furuya et al., 1997; Trumper et al., 2002), where it may reflect the existence of PanIN lesions. Mutational and methylation status analysis of p16 and p53 have also been performed; however, in isolation, these have a low sensitivity for detecting pancreatic cancer (42%) (Fukushima et al., 2003; Klump et al., 2003; Matsubayashi et al., 2003; Yamaguchi et al.,1999). It therefore seems unlikely that any one marker will have a future role as a stand-alone screening or diagnostic test. A recent study evaluated a combination of K-ras, p16 and p53 mutations for predicting the presence of pancreatic cancer (Yan et al., 2005). Pancreatic juice was aspirated at the time of ERCP in 48 patients with pancreatic cancer, 49 patients with chronic pancreatitis and 49 patients with biliary stone disease. p53 mutations were detected in 42% of cancer cases, 4% of patients with chronic pancreatitis, and 0% of controls. K-ras mutations were detected in 54%, 34% and 21% of these cohorts, respectively. Sixty-two percent of cancer patients had p16 promoter methylation levels 12%, compared to 8% of those with chronic pancreatitis and 13% of controls. Although individual mutational analysis lacked sensitivity and specificity, combination analysis increased the discrimination between benign and malignant disease. The authors concluded that the level of discrimination would allow high-risk patients to be stratified from a negligible risk to over a 50% probability of harboring malignancy (Yan et al., 2005). Although case-controlled, the above study was conducted in symptomatic pancreatic cancer patients. The level of discrimination was modeled for screening high-risk patients; however, it remains unproven in asymptomatic patients at risk. The Johns Hopkins group analyzed pancreatic juice to detect aberrantly methylated DNA for use as a marker of pancreatic

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neoplasia, as promoter methylation is a common mechanism for gene silencing during carcinogenesis (Matsubayashi et al., 2006). Patients with known pancreatic cancer, IPMN, chronic pancreatitis and controls were evaluated, where juice was obtained via pre-operative ERCP or at surgical resection. Pancreatic juice DNA was analyzed for promoter methylation using methylation-specific PCR assays for 17 genes. There was a significantly higher percentage of genes methylated in the juice of pancreatic cancer patients as compared to the controls and those with chronic pancreatitis. Additionally, a higher percentage of genes were methylated in pancreatic juice obtained at ERCP as compared to surgically obtained juice. Reasoning for this is unclear, but may reflect the use of secretin to stimulate juice production during ERCP (Matsubayashi et al., 2006). Deserving of further study, this analysis may serve as an adjunct to aid in the diagnosis of pancreatic cancer, and may also have screening applications. Molecular analysis of pancreatic juice for pancreatic cancer screening is currently an intense area of translational research. It is likely that broad-based molecular panels targeting multiple mutations commonly involved in pancreatic carcinogenesis will have the highest yield, and results of ongoing studies are anxiously awaited. It is presumed that these analyses will be performed on pancreatic juice collected at the time of screening EUS examinations.

DIAGNOSIS Pancreatic Cancer Short of surgery, pancreatic cancer can prove difficult to diagnose in a subset of patients. Current modalities for diagnosing pancreatic cancer include ERCP with bile duct or pancreatic duct brush cytology, percutaneous ultrasound- (US) or CT-guided fine needle aspiration (FNA) and endoscopic ultrasound-guided FNA (EUS-FNA). Definitive pre-operative diagnosis of pancreatic malignancy is thus dependent on morphologic cellular criteria of cytologic specimens. Cytology-based diagnosis of pancreatic cancer from bile duct brushings has a diagnostic sensitivity of 60% (Farrell et al., 2001; Glasbrenner et al., 1999; Macken et al., 2000; Pugliese et al., 2001; Stewart et al., 2001), where EUS-FNA has a diagnostic sensitivity of 60–95% based on precise targeting of the tumor (Brandwein et al., 2001; Eloubeidi et al., 2003; Shin et al., 2002; Wiersema et al., 1997). In a percentage of cases, sampling error, paucicellular samples, confounding inflammation and desmoplasia and cell drying artifact can result in an indeterminate diagnosis, necessitating repeat invasive procedures which may result in the delay of a definitive diagnosis and ultimately treatment for several months. Molecular applications applied to indeterminate cytology have potential to increase the diagnostic accuracy for pancreatic cancer. Microdissection-Based Genotyping Khalid and colleagues developed an innovative method to evaluate indeterminate cytologic specimens to increase diagnostic

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yield (Khalid et al., 2004, 2006). Microdissection-based genotyping (MBG) coupled with loss of heterozygosity (LOH) analysis was used to evaluate for a broad panel of tumor-suppressor genelinked microsatellite markers and k-ras point mutations. This application involves dissecting individual cell aggregates from existing slides and subjecting them to polymerase chain reaction (PCR) to generate DNA for broad panel genotyping of microsatellite allele loss markers. Tumor-suppressor gene loss is based on determination of LOH for informative loci situated within or adjacent to specific genes of interest using fluorescent capillary electrophoresis. Fractional allelic loss (FAL) or fractional mutational rate (FMR), defined as the number of mutations (k-ras-2 point mutation / significant allelic imbalance) divided by the total number of informative microsatellite markers plus 1 for kras status, is used to measure overall mutation accumulation. As each individual possesses a unique panel of informative polymorphic microsatellite markers, the FAL or FMR allows comparison across cohorts with respect to cumulative acquired LOH. This technique was first applied to biliary stricture brush cytology obtained at ERCP, which has an overall sensitivity 60% for diagnosing pancreatic cancer. LOH and k-ras codon 12 mutational analysis of PCR amplified DNA from brush cytology specimens discriminated reactive from malignant cells with 100% sensitivity, specificity and accuracy (Khalid et al., 2004). MBG has since been applied to EUS-FNA cytology samples from pancreatic mass lesions. Benign inflammatory pseudotumors, due to focal chronic pancreatitis or autoimmune pancreatitis, can be impossible to differentiate from malignancy based on imaging alone. All too frequently, EUS-FNA cytology samples are indeterminate due to cellular atypia, and patients are subjected to surgical resection for a definitive diagnosis. Indeed, approximately 10% of pancreaticoduodenectomies performed for presumed malignancy reveal benign disease on pathological evaluation (Abraham et al., 2003; Weber et al., 2003). If surgical pathology proves a benign entity, resection in retrospect is considered unnecessary. LOH analysis of FNA samples shows promise in accurately differentiating benign from malignant pancreatic masses and increasing the diagnostic sensitivity of inconclusive cytologic samples (Khalid et al., 2006). Based on comparison to final surgical pathology, LOH analysis of indeterminate cytology specimens, as compared to positive controls, accurately differentiated benign pseudotumors from pancreatic cancer with high-statistical significance. It additionally improved diagnostic accuracy (100%) for suspicious but inconclusive samples. The FMR for cases ultimately proven malignant was significantly higher than benign cases. Five of six benign cases carried no mutations, where one case of autoimmune pancreatitis harbored a k-ras mutation in the setting of coexisting PanIN lesions (Khalid et al., 2006). Although a currently small experience in this regard, the adjunct genomic analysis will prove to be a powerful tool in the clinical evaluation and management of these difficult cases. Telomerase Telomerase activity has been reported to correlate with the progression of malignant change in many cancers. Telomeres are repetitive DNA sequences at the end of chromosomes. Telomere

lengths shorten with each cell division, leading to cell senescence. Telomerase, a ribonucleoprotein enzyme, functions to maintain telomere lengths, thereby promoting cell immortality. Telomerase is absent in normal tissues, but is upregulated in many cancers. Telomerase activity has been studied in pancreatic cancer and has been detected in surgically resected specimens (Hiyama et al., 1997), aspirated pancreatic juice (Suehara et al., 1997; Uehara et al., 1999; Uemura et al., 2003), brush cytology (Morales et al., 1998) and CT-directed FNA samples (Pearson et al., 2000). The sensitivity for diagnosis has varied from 35% to 100%, depending on the sample type evaluated and the telomerase assay employed. The telomeric repeat amplification protocol (TRAP) is the most commonly employed assay, which provides semi-quantitative information about the activity of telomerase. However, real-time quantitative PCR (RT-PCR) appears to be a more sensitive assay when compared to TRAP (Buchler et al., 2001). The main value in telomerase activity detection is in the differentiation of malignant versus benign disease, as specificity appears to be 100% (Hiyama et al., 1997; Uehara et al., 1999; Uemura et al., 2003). As EUS-FNA is being more commonly employed for diagnosis of pancreatic cancer, a recent study evaluated the utility of telomerase activity detection as an adjunct to cytologic diagnosis (Mishra et al., 2006). EUS-FNA cytology had a high sensitivity of 85% in this study, but the addition of telomerase assay increased the sensitivity to 98% while maintaining the specificity at 100%. Telomerase results were positive in six of seven patients with negative cytology who were ultimately proven to have cancer; however, telomerase was not detected in patients with proven benign disease. Thus, much like LOH analysis, telomerase appears to be an adjunct diagnostic tool for differentiating benign from malignant disease and increasing sensitivity for cancer diagnosis. Proteomics Proteomics is an emerging field incorporating large-scale analysis of proteins in biologic fluids or cells by biochemical techniques. This is an attractive arena for pancreatic cancer research, specifically early detection and diagnosis. Candidate biomarkers have been discovered by analyzing resected pancreatic cancer, pancreatic juice and serum. Analysis of pancreatic juice seems the appropriate strategy when screening high-risk patient groups, whereas serum analysis would be most appropriate for wide-based screening. Although still in its early stage, the use of proteomic profiling for pancreatic cancer biomarker discovery is encouraging. Two-dimensional electrophoresis for protein separation followed by mass spectrometric identification of proteins has been utilized in analysis of pancreatic cancer and pancreatic juice. However, this method is limited to a relatively low-throughput scale of research (Chen et al., 2005). A more appealing approach for disease biomarker development uses proteomic pattern analysis. This approach uses the pattern of signals within a mass spectrum to identify differentially abundant peaks within normal and disease samples for distinguishing the two groups.

Diagnosis

Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) has been employed in this regard, whereby MS is used to generate proteomic patterns in biological fluids and then pattern recognition algorithms are applied to distinguish cancer patients from normal controls. This technique is suited for high-throughput research and can be applied to body fluids such as pancreatic juice or serum using biochip technology and antibody microarrays to enable multiplexed and rapid protein measurements (Chen et al., 2005). SELDI-TOF has also been applied to pancreatic tissue to identify protein peaks to differentiate benign from malignant states. In one study, a training model was developed that could accurately distinguish pancreatic cancer from benign pancreatic tissue based on the protein peak pattern (Scarlett et al., 2006). A similar study found a large number of proteins differentially expressed in chronic pancreatitis and pancreatic cancer as compared to normal pancreatic tissue (Crnogorac-Jurcevic et al., 2005). It is unclear whether this technology will enhance preoperative diagnostic accuracy in this regard, as these methodologies have yet to be applied to EUS-FNA cytology samples. More importantly, these protein profiles may potentially be applied to pancreatic juice or serum to screen high-risk patients. As such, the first comprehensive study of the pancreatic juice proteome revealed numerous proteins associated with pancreatic cancer (Chen et al., 2006). This study identified candidate biomarkers differentially expressed in cancer patients as compared to normal controls. This group has since evaluated pancreatic juice proteins in the setting of pancreatitis, in hopes of eliminating false-positive results for cancer given the potential for overlapping biomarkers (Chen et al., 2007). Similar preliminary work has also been applied to serum via plasma protein profiling. Honda and colleagues compared plasma proteomes between pancreatic cancer patients and matched controls using SELDI-TOF MS. A learning algorithm was applied to discriminate proteomic patterns, identifying a set of four mass peaks that differentiated sera from pancreatic cancer patients with high accuracy rates (Honda et al., 2005). Although exciting, this work will need to be confirmed in a large-scale multi-center trial to truly determine clinical utility. There is also a potential role for proteomics to monitor for disease recurrence in patients who have undergone pancreatic cancer resection. An innovative study using two-dimensional electrophoresis identified a group of plasma proteins that consistently change following resection. Furthermore, this group identified proteins that correlated with recurrence of disease (Lin et al., 2006). Although a small study, the clinical applications of this type of technology could revolutionize post-operative monitoring and surveillance. Pancreatic Cystic Neoplasms Incidental pancreatic cystic lesions are being detected with increasing frequency due to the widespread use of high-quality abdominal imaging. Contrary to previous belief, the majority of cysts detected today are MCN (Fernandez-del Castillo et al., 2003). MCN are considered premalignant and encompass IPMN and MCN. Surgical resection of mucinous pancreatic cysts is generally recommended given the concern for malignant degeneration.

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However, the natural history regarding the frequency and timing of malignant change of MCN remains unknown. Evaluation of pancreatic cysts involve cross-sectional imaging (CT or MRI) and EUS-FNA with cyst aspirate analysis. Unfortunately, in the absence of an associated solid mass, imaging modalities cannot reliably differentiate benign from malignant cysts (Ahmad et al., 2001, 2003; Brugge et al., 2004). Given the paucicellular nature of the cyst aspirate, the sensitivity of cytologic analysis has proven suboptimal. Currently, an elevated cyst fluid CEA level is considered the most reliable indicator of a mucinous cyst; however it does not distinguish premalignant from malignant state (Brugge et al., 2004). Given the unknown natural history of MCN and associated morbidity of pancreatic resection, better tools to assess presence of malignancy are desirable. This is especially important in marginal surgical candidates with small incidental cystic neoplasms. As the cellular content of pancreatic cyst aspirates is frequently suboptimal, molecular analysis of the cyst fluid itself is attractive in hopes of detecting mutations that correlate with malignant degeneration. It is hypothesized that cyst epithelial cells undergoing malignant change would have a higher turnover rate, thereby releasing more DNA into the fluid that bathes the cells. PCR amplification of DNA from these whole or lysed cells shed into the fluid from the cyst lining may therefore be predictive of the cyst pathology, where a high level of accumulated mutational damage would reflect an underlying malignancy, and similar alterations would not be seen in benign cysts. To investigate this hypothesis, a single center study was conducted, applying LOH and K-ras mutational analysis to pancreatic cyst aspirates targeting markers for pancreatic carcinogenesis (Khalid et al., 2005). Based on comparison to surgical pathology of the resected cyst, LOH analysis of the cyst fluid aspirate accurately predicted the existence of malignancy. Thirty-six cysts with confirmed histology were analyzed: 11 malignant, 15 premalignant and 10 benign cysts. The malignant cysts could be differentiated from premalignant cysts based on the number of mutations and the temporal sequence in which the mutations were acquired (p  0.001). Early K-ras mutation followed by allelic loss was the most predictive of a malignant cyst (sensitivity 91%, specificity 93%) (Khalid et al., 2005). Thus, it appears that pancreatic cyst fluid contains adequate DNA to allow mutational analysis that can serve as an ancillary tool to the conventional workup of pancreatic cysts. A multi-center trial to further evaluate this technology has been completed, with the interim analysis showing that the quantity of DNA and temporal sequence of mutations continue to predict cyst pathology (Khalid et al., 2006a, b). Several studies have also investigated whether telomerase activity can serve as a marker for malignancy in cystic neoplasms. In one small study evaluating IPMN, pancreatic juice aspirated at the time of ERCP was analyzed. Cytology alone diagnosed malignancy in 4/13 patients, but when combined with analysis for telomerase activity, the yield increased to 11 of 13 patients being correctly diagnosed with malignant IPMN. As telomerase activity was not detected in benign tumors, the authors suggest telomerase may be a useful adjunct to standard cytologic analysis (Inoue et al., 2001).

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In another study analyzing a spectrum of cystic lesions, Yeh and colleagues found expression of telomerase activity in malignant and borderline malignant cysts, but not in benign cysts (mucinous or serous) or pseudocysts. The sensitivity of telomerase for predicting at least borderline malignant change was 67% with a specificity of 100% (Yeh et al., 1999). Thus, it appears that telomerase activity takes part in malignant transformation and may be a useful marker to distinguish malignant from benign cystic neoplasms. The Johns Hopkins group has evaluated gene expression profiles to help differentiate invasive from non-invasive IPMNs. Employing RT-PCR and oligonucleotide microarrays, they were able to identify genes that were overexpressed in IPMNs associated with invasive carcinoma. Immunohistochemical validation revealed that claudin 4, CXCR4, S100A4 and mesothelin were associated with the invasive phenotype. This analysis was performed on resected specimens; thus further work is required to see if these results can be reproduced pre-operatively on aspirated cyst fluid, as they could be used to guide management regarding surgery or observation (Sato et al., 2004).

PROGNOSIS Pancreatic Cancer Pre-operative prognostication for malignant disease is currently based on the cancer stage and grade. Prognostic markers are desired to stratify treatment protocols, to enable individualized therapy, and to develop new treatment strategies. For pancreatic adenocarcinoma, in which the majority of cases present with locally advanced and unresectable disease, prognostication unfortunately has not been of significant clinical importance to date given the high-mortality rate with only few long-term survivors (Jemal et al., 2006). However, gene expression profiling of the tumor may predict clinical course and outcome in select patients with potentially resectable disease. In a recent study from MD Anderson Cancer Center, single nucleotide polymorphisms (SNPs) in DNA repair genes were evaluated to see if they affected clinical prognosis. Previous studies have shown that individual variation in DNA repair capacity can affect response to therapy and overall survival. All patients underwent neoadjuvant therapy with gemcitabine and radiotherapy after gene expression was determined from whole blood. Genotypes RecQ1 A159C, RAD54L C157T, XRCC1 R194W and ATM T77C had a significant effect on overall survival. The overall mean survival of the group was 20.2 months. However, patients with none of the adverse genotypes had a mean survival of 62.1 months. Those with one, two or three or more at risk alleles had median survival times of 27.5, 14.4 and 9.9 months, respectively (Li et al., 2006). Thus, for these earlier stage tumors, it appears that polymorphic variants of DNA repair genes affect the clinical course of disease, which hopefully will translate to novel targeted therapy in the future. Other studies have been performed ex vivo, where resected pancreatic cancer has been profiled and compared to the outcome of the patient. Cytokeratins (CK) 7 and 20 were found to be

overexpressed in pancreatic cancer, where CK 20 expression defined a subset of tumors with a worse prognosis (Matros et al., 2006). Peroxisome proliferator-activated receptor gamma (PPARgamma) is a ligand-activated transcription factor shown to be overexpressed and associated with a higher tumor stage and grade, thus correlating to a worse prognosis (Kristiansen et al., 2006). Secreted protein acidic and rich in cysteine (SPARC), a protein involved in cell migration and cell matrix interactions, is frequently silenced in pancreatic cancer but expressed in stromal fibroblasts. Peritumoral SPARC expression has also been associated with a poor clinical prognosis (Infante et al., 2007). Thus, many genes have been identified that are differentially expressed in pancreatic cancer, some of which are predictive of clinical outcome. If this could be determined routinely in a preoperative fashion, a patient could be counseled as to the true benefit of major pancreatic surgery. A study evaluating EUSFNA cytology samples revealed that RT-PCR detected increased expression of lipocalin 2 (LCN 2) and tissue-type plasminogen activator (PLAT) in pancreatic cancer (Laurell et al., 2006). Not only could this technology increase diagnostic accuracy, but more importantly, could provide pre-operative prognostic information. The field of gene expression profiling is expanding quickly in the pancreatic cancer arena and will continue to evolve rapidly. Not only is it anticipated to provide reliable prognostic information, it may also give insight into the pancreatic carcinogenesis pathway and provide novel targets for therapy. Pancreatic Endocrine Tumors In less aggressive pancreatic tumors, the ability to pre-operatively prognosticate may be very significant, especially for marginal surgical candidates given the morbidity associated with pancreatic surgery. Pancreatic endocrine tumors (PET) have been reported to occur with an incidence of 1 per 100,000 persons per year (Barakat et al., 2004); however, it is expected that incidental PETs will be discovered with increasing frequency given widespread use of cross-sectional imaging, much like the phenomenon occurring with incidental pancreatic cystic lesions (Warner, 2005). The biological behavior of PETs can vary widely from clinically indolent to highly aggressive. Elevated Ki-67 proliferative index 2%, mitotic rate 2, size 4 cm, nuclear atypia, capsular penetration with local invasion, and/or metastatic disease define a malignant PET. The Ki-67 index and mitotic rate also correlate with survival (La Rosa et al., 1996; Panzuto et al., 2005). Unfortunately, these indices and histologic assessment come from surgical resection specimens. Similar to pancreatic adenocarcinoma, the developmental progression and malignant transformation of PET also appear to correlate with accumulation of genetic alterations. Recent studies indicate that chromosomal losses occur more commonly than gains or amplifications, and the LOH profile correlates with tumor size, extent and malignant phenotype (Barghorn et al., 2001a, b; Guo et al., 2002a, b; Hessman et al., 1999, 2001; Speel et al., 1999, 2001; Stumpf et al., 2000; Zhao et al., 2001). Furthermore, molecular markers such as telomerase activity, Her2/neu over-expression, hMLH1 methylation and microsatellite

Conclusion

instability may predict PET behavior independent of the tumor’s functional status or histopathologic features (Furlan et al., 2004; Goebel et al., 2002; House et al., 2003; Lam et al., 2000). These data suggest that molecular analysis of PET may provide relevant information of clinical and prognostic utility. To date, however, these analyses have only been performed on surgically resected specimens. The molecular investigation of pre-operative EUS-FNA samples from PET are very appealing for predicting the clinical course specifically in an era where more incidental lesions are being detected. To that effect, the Pittsburgh group performed broad panel microsatellite loss analysis on pre-operative cytologic samples obtained via EUS-FNA. Twenty-five patients were studied; 13 with “benign” or indolent and 12 with malignant disease, respectively, based on pathologic assessment and clinical course. As previously reported, tumor size greater than 3 cm and high Ki-67 index and mitotic rate correlated well with disease progression. However, FAL appeared to be the strongest factor associated with disease progression. Four of 13 “benign” PETs contained a single microsatellite loss; the other nine were without mutation (mean FAL 0.028). All 12 malignant PETs harbored multiple allelic losses (mean FAL 0.37). The LOH profile not only differentiated benign from malignant PET (FAL 0.2, sensitivity 83%, specificity 100%), but also correlated best with disease progression and survival. Thus, for the first time, this molecular information can be used for clinical decision-making and prognostication pre-operatively, especially in those patients who are marginal surgical candidates (Nodit et al., 2006).

PHARMACOGENOMICS As mentioned previously, early detection offers the best chance for curative treatment in pancreatic cancer. Unfortunately, the majority of patients present with locally advanced and unresectable disease. Chemotherapy, of which gemcitabine is considered the standard, only increases survival by an average of 3 months. It is also currently debatable whether neoadjuvant therapy is advantageous. In the evolving era of genomic medicine, chemoresistance-related gene profiles of cancer specimens using DNA microarray assays may guide selection of chemotherapeutic regimens. An early report shows this profiling is possible using EUS-FNA cytology samples (Ashida et al., 2006), which can be obtained pre-operatively. Whether an individualized approach to chemotherapeutic selection translates to improved outcome and survival is yet to be determined.

MONITORING Cyst Surveillance Given the unknown natural history of pancreatic cystic neoplasms and the morbidity of pancreatic surgery, some cysts are surveyed rather than resected. Commonly, diminutive cysts and those occurring in marginal surgical candidates frequently are

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surveyed via cross-sectional imaging and/or EUS. Resection is generally recommended if the cyst enlarges over time, or becomes symptomatic. However, it is unclear whether an increase in cyst size correlates with malignant transformation. In this era of rapidly evolving genomic medicine, molecular analysis can be applied to cyst fluid aspirates during surveillance EUS exams, to hopefully follow mutational profiles and direct surgical intervention for those patients who evolve worrisome patterns. Not only will this practice facilitate resection of appropriate cystic neoplasms, it will also avoid unnecessary surgery in those who do not demonstrate neoplastic progression. This surveillance approach is under current study.

NOVEL AND EMERGING THERAPIES TNFerade™ is a replication-deficient adenoviral vector, a DNA carrier, which contains the human gene for tumor necrosis factor-alpha (TNF-alpha), an immune system protein with welldocumented anti-cancer effects. The gene is regulated by a chemoradiation-inducible promoter, Egr-1. TNFerade™, after direct injection into a tumor, stimulates the local production of TNF-alpha with resultant tumor necrosis. A phase II clinical trial of TNFerade™ in patients with locally advanced pancreatic cancer is currently ongoing, with encouraging results reported from the dose-escalation and safety phase (Farrell et al., 2006). TNFerade™ was injected directly into pancreatic tumors via US-guided percutaneous injection, or EUSguided fine needle injection. Patients underwent weekly injection of TNFerade™ into the tumor for five consecutive weeks while receiving continuous infusion 5-fluorouracil and external beam radiation. Patients treated with the maximum tolerated dose (n  11) had greater locoregional control of their tumors, longer progression-free survival, a greater percentage (45%) undergoing surgical resection, and improved median survival of 11.2 months. Three of the patients in this cohort survived more than 24 months (Farrell et al., 2006).These results are very encouraging, and results of the randomized phase of the trial are anxiously awaited.

CONCLUSION The application of genomic analysis is quickly impacting the field of pancreatic oncology. It has already demonstrated clinical applicability in improved accuracy for the diagnosis of pancreatic cancer and cystic neoplasms. Surveillance strategies are evolving for cystic neoplasms based on molecular profiling, and it is hoped that genomic-based individualized therapy for pancreatic cancer dependent on chemoresistance profiles will improve outcome. Directed gene therapy is currently a reality, and results of ongoing trials are anxiously awaited. The biggest hope is for a reliable screening and early detection algorithm, where it is believed that genomic analysis will play a paramount role – this will be the Holy Grail for genomic applications in pancreatic oncology. Given the rapid evolution of this intriguing field, it will only be a matter of time until the ultimate goal is achieved.

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77 The Multiple Endocrine Neoplasia Syndromes Y. Nancy You, Vipul Lakhani and Samuel A. Wells

INTRODUCTION Most often tumors of the endocrine glands develop sporadically and make themselves known from the systemic effects of excess hormone production, rather than from symptoms related to complications resulting from an enlarging mass, which is the case with most non-endocrine, solid organ neoplasms. About 70 years ago it was recognized that distinct clusters of endocrine tumors were inherited in a familial pattern representing a class of diseases that came to be known as the Multiple Endocrine Neoplasia (MEN) syndromes. The first of these disease complexes described was the McCune-Albright syndrome (MAS), which consisted of the familial occurrence of café-au-lait pigmented skin lesions, polyostotic fibrous dysplasia and endocrine dysfunction, particularly precocious puberty (Albright et al., 1937; McCune, 1936). Subsequently, other endocrine disorders were reported in association with MAS, including pituitary tumors secreting growth hormone (GH) or prolactin, hyperthyroidism, and autonomous adrenal hyperplasia. In 1985 Carney described the familial occurrence of dark macules on the skin and mucosa, café-au-lait spots, blue nevi and other pigmented lesions, and a variety of non-endocrine and endocrine tumors, including: myxomas in various organs, primary pigmented nodular adrenocortical disease (causing Cushing’s syndrome), GH-producing pituitary adenomas and Sertoli and

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

Leydig cell tumors (Carney et al., 1985). Because neither the MAS nor the Carney complex are commonly thought of as MEN syndromes they are mentioned for historical interest only and will not be discussed further. At present, when speaking of the MEN syndromes, one is referring to MEN1 or MEN2, the latter of which is further sub-classified into MEN2A, MEN2B, or the related disease, Familial Medullary Thyroid Carcinoma (FMTC). MEN1 and MEN2 each has an estimated frequency of 1 in 30,000 (Gagel and Marx, 2002; Kouvaraki et al., 2005). Almost always more than one endocrine tumor occurs in patients with the MEN syndromes, and rather than being solitary, they are multicentric, presenting as several neoplastic foci within a single endocrine gland. Each of the MEN syndromes is caused by a specific genetic mutation, which can be detected by direct mutational analysis of germline DNA. Information gained by genetic testing is of little use in guiding the clinical management of patients with MEN1. Such is not the case, however, in patients with MEN2, where molecular genetic data provide the basis, on the one hand for preventive therapy in patients with early disease, and on the other hand for targeted therapy in patients with advanced disease. There is no better example of the translation of molecular genetic information into clinical medical practice than that represented by the MEN2 syndromes.

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THE MULTIPLE ENDOCRINE NEOPLASIA SYNDROMES Multiple Endocrine Neoplasia Type 1 Description The syndrome of multiple endocrine adenomatosis, subsequently known as MEN1, was first described in 1954 in a family with tumors of the parathyroid glands, the pituitary, and the pancreatic islet cells (Wermer, 1954). Although for practical purposes the MEN1 syndrome refers to a patient with two of the three above-mentioned endocrine tumors, it is now known that combinations of over 20 separate endocrine or non-endocrine tumors may be associated with MEN1 (Table 77.1) (Brandi et al., 2001). Because of insufficient space only the three most common diseases associated with MEN1 will be described. Hyperparathyroidism

The most common endocrinopathy in patients with MEN1 is hyperparathyroidism, which occurs in close to 100% of patients by age 50 years. The parathyroid tumors are benign and

TABLE 77.1 years

usually all four glands are diseased, although each gland might not be enlarged. The hyperparathyroidism is managed surgically and it is important that the surgeon realizes that all four glands are diseased, regardless of their size. The proper management is either subtotal (3 ¹- gland) parathyroidectomy or total parathyroidectomy with a ²parathyroid autograft. Regardless of the operation performed, the majority of patients will develop recurrent hyperparathyroidism within 10–15 years postoperatively. Calcimimetics, a new class of drugs that are calcium-sensing receptor agonists, inhibit the release of parathyroid hormone. These agents are currently in clinical trial and may prove useful in the treatment of patients with MEN1, as well as in patients with hypercalcemia from other causes (Silverberg et al., 1997). Pancreatic Islet Cell Tumors

Approximately half of the patients with MEN1 develop malignant pancreatic islet cells tumors, most of which secrete the hormone, gastrin. Whereas formerly the hyperacidity caused by excess gastrin secretion was difficult to manage, the advent of pharmacological agents that block gastric acid secretion has simplified treatment. Currently, almost all patients with gastrinomas

Characteristic features of multiple endocrine neoplasia type 1 (MEN 1) with estimated penetrance at age 40

Endocrine Features

Estimated penetrance (%)

Parathyroid adenoma

90

Entero-pancreatic tumor a

Non-endocrine Features

Estimated penetrance (%)

Facial angiofibroma

85

Collagenoma

70

Gastrinoma

40

Lipoma

30

Insulinoma

10

Pheochromocytoma

1

20b

Ependymoma

NF (e.g., pancreatic polypeptide)a a

a

Other (e.g., glucagonoma , VIPoma , somatosatinomaa)

1

2

Anterior pituitary tumor Prolactinoma

20

Other (e.g., GH, GH and prolactin, NF, ACTH, TSH)

5, 5, 5, 2, rare

Foregut carcinoid Thymic carcinoid NFa

2 a

Bronchial carcinoid NF

2

Gastric enterochromaffin-like tumor NF

10

Adrenal cortex NF Modified from Brandi et al. (2001). Copyright 2001, The Endocrine Society. NF  non-functioning tumors which may synthesize peptide hormones or other factors but do not usually over-secrete enough to produce a hormonal expression, VIP  vasoactive intestinal peptide, GH  growth hormone, ACTH  adrenocorticotrophic hormone, TSH  thyroid stimulating hormone. a Indicates that the particular tumor type harbors substantial (25%) malignant potential. b Prevalence of non-functioning and clinically silent pancreatic tumors may be nearly 100%.

The Multiple Endocrine Neoplasia Syndromes

are managed initially by medical therapy, reserving surgical resection of the tumor for those with poorly controlled disease, or for those with pancreatic tumors larger than 2 cm where the risk of metastasis is thought to be increased. The less commonly occurring islet cell tumors either secrete insulin, glucagon, vasoactive intestinal polypeptide or pancreatic polypeptide, and surgical resection is indicated as the initial treatment. Even though virtually all of the pancreatic islet cell tumors associated with MEN1 are malignant, patient morbidity and mortality are related more to excess hormone production and secretion than to progressive tumor growth, local invasion or distant metastases. The timing and extent of surgical resection depends on the specific type of islet cell tumor. Pituitary Tumors

Benign pituitary tumors, most commonly prolactinomas, occur in approximately 25% of patients with MEN1 and can be managed successfully by the administration of a dopamine agonist such as bromocriptine. The less commonly occurring

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pituitary tumors are treated surgically, usually by transphenoidal hypophysectomy. Molecular Genetics of MEN1 In 1997 a group of investigators at the National Institutes of Health discovered a genetic mutation causative of MEN1 (Chandrasekharappa et al., 1997). The gene spans a 9.8 kb segment of chromosome 11q13 and consists of 10 exons with a 1830-bp region that encodes a novel highly conserved 610amino acid protein, menin. Menin, a putative tumor suppressor, mainly resides in the nucleus but is also found in the cytoplasm. Menin interacts with the N-terminus of the AP1 transcription factor, JunD, but the biological significance of this interaction is unknown. Approximately 400 unique germline or somatic MEN1 mutations of the menin gene have been described. Almost 75% of the mutations cause truncation of the protein and result from frameshift (deletions, insertions, or splice site defects) and nonsense mutations (Figure 77.1).

Truncation (2)

(2)

(2)

(2) (2)

(2) (6)

100 bp

(7)(2)

(2)

(2) (5) (2)

(5)(4) (2)

(2)

(2)(2)

(2)

(2)(3)(3)(3)

(2) (3)(2) (2)

2

1

3

ATG

(4)

(6)

(2)

(2)

(2)

(2)

(2)

(2)(2)(6)

4

(2)

5

6

7

(3)

8

(2)

9

(3) (3) (2)

NLS

(5)(5)

10

(2)

(2) (2)

(2)

(2) (2) (2)

NLS

TGA

(3)

(2)

Germline Parathyroid Gastrinoma (2)

(2)

Insulinoma Neuroendocrine Carcinoid Pituitary

Codon change

Skin

Figure 77.1 A schematic diagram of the germline and somatic mutations on the menin gene which is responsible for multiple endocrine neoplasia type 1 (MEN1). Mutations shown above the exons cause menin truncation; those shown below the exons cause an amino acid or codon change. Missense mutations in a region of menin (amino acids 139–242, identified by blue shading) prevented interaction with the AP1 transcription factor JunD. The location of the two nuclear localization signals (NLS), at codons 479–497 and 588–608, are indicated. The green-shaded areas indicate the untranslated regions. Numbers in parentheses represent multiple reports of the same mutation in apparently unrelated individuals. (Modified from Schussheim, D.H., Skarulis, M.C., Agarwal, S.K., Simonds, W.F., Burns, A.L., Spiegel, A.M. and Marx, S.J. (2001). Multiple endocrine neoplasia type 1: New clinical and basic findings. Trends Endocrinol Metab 12, 173–178, with permission.)

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T A B L E 7 7 . 2 A representative schedule for screening patients who have inherited MEN1. (Identified by direct DNA analysis or other criteria) Tumor

Age to begin (year)

Laboratory tests (annually)

Imaging test (if laboratory test positive)

Parathyroid hyperplasia

20

Serum calcium

Sestamibi scan

Gastrinoma

20

Fasting serum gastrin

Octreotide scan

Insulinoma

10

Fasting glucose

Selective measurement of pancreas vein insulin during arterial calcium infusion

Other pancreatic islet cell tumors

20

ChromograninA; glucagon; proinsulin

Octreotide scan; CT or MRI

Anterior pituitary

15

PRL; IGF-1

MRI

Foregut carcinoid

20

None

CT

Modified from Brandi et al. (2001) with permission. CT  computed tomography scan, MRI  magnetic resonance imaging, PRL  prolactin, IGF-1  Insulin like growth factor – 1.

Approximately 10% of germline MEN1 mutations arise de novo. Furthermore, in 10–30% of patients with MEN1, no mutations in menin have been demonstrated, and presumably they are not detectable by current sequencing methods. Direct DNA analysis provides useful clinical information in two ways. In kindreds with a demonstrated menin mutation, patients shown not to have inherited a mutated allele need no further testing, as neither they nor their descendents will develop MEN1. Conversely, in kindred members who have a demonstrated menin mutation, a screening program can be designed to detect the development of endocrine tumors characteristic of the syndrome (Table 77.2). There are, however, substantial limitations to genetic testing for patients with this syndrome. There is no relationship between the molecular genotype and the disease phenotype and thus no way of predicting which patients are likely to develop one or more of the tumors characteristic of MEN1. More importantly, for any tumor associated with MEN1, genetic testing provides no useful information regarding the timing of therapy, either for patients with occult disease (prophylactic therapy) or for patients with symptomatic disease. Even if it were known that a family member had inherited a mutation in menin, there would be no indication to prescribe medical or surgical therapy until there were symptoms

secondary to a pancreatic islet cell tumor, a pituitary tumor, or hyperparathyroidism. It is of interest that somatic mutations in menin characteristic of MEN1 also occur to varying degrees in sporadic endocrine tumors not associated with MEN1. For example, the mutation frequency in sporadic gastrinomas is approximately 35%, compared to 15% in other sporadic pancreatic islet cell tumors, except for insulinomas where no mutations have been reported. Mutations occur in approximately 15% of sporadic parathyroid adenomas but mutations have been found in less than 1 of patients with pituitary tumors (Schussheim et al., 2001). Multiple Endocrine Neoplasia Type 2 Description MEN type 2 was first described in 1968 (Steiner et al., 1968). It was subsequently appreciated that the type 2 MEN syndrome represented three distinctly different diseases: MEN2A (the syndrome initially described in 1968), MEN2B and FMTC (Chong et al., 1975; Farndon et al., 1986). Patients with MEN2A develop medullary thyroid carcinoma (MTC), pheochromocytomas and hyperparathyroidism, whereas patients with MEN2B develop MTC, pheochromocytomas, a generalized ganglioneuromatosis, and a characteristic physical appearance. Patients with FMTC develop only MTC. Of patients with the type 2 MEN syndromes, 75–80% have MEN2A, 5–15% have FMTC and 5% have MEN2B. The characteristic patterns of disease in patients with sporadic MTC, MEN2A, MEN2B and FMTC are shown in Table 77.3. Medullary Thyroid Carcinoma

Virtually all patients with MEN2A, MEN2B and FMTC develop MTC and it is the most common cause of death in patients with these syndromes. The MTC is not derived from thyroid follicular cells, rather it originates from the neural crest “C-cells” that are incorporated within the thyroid parenchyma during embryogenesis of the lateral thyroid complex. MTC is multi-centric and located in the upper portions of the thyroid lobes. The only curative therapy is surgical resection, performed prior to the time that the MTC develops or spreads beyond the thyroid gland. The MTC cells have great biosynthetic capability and secrete a number of biogenic agents, including the hormones: calcitonin (CTN) and adrenocorticotrophic hormone (ACTH), and the glycoprotein, carcinoembryonic antigen (CEA). CTN is an excellent tumor marker and its secretion can be markedly enhanced by stimulation with the intravenous infusion of calcium and pentagastrin (Wells et al., 1978). Before the discovery of the genetic mutations causative of MEN2A, MEN2B and FMTC, measurement of stimulated plasma CTN levels was the primary method of establishing the diagnosis of hereditary MTC and for determining the timing of thyroidectomy. Even in the era of molecular genetic testing, measurement of plasma CTN levels still has value in planning the timing of thyroidectomy in some patients. Plasma CTN measurements, however, are most useful in the assessment of patients after thyroidectomy, where elevated plasma levels indicate persistent or recurrent MTC, even though it may not be apparent clinically.

The Multiple Endocrine Neoplasia Syndromes

TABLE 77.3

Clinical presentation of medullary thyroid carcinoma

Type

Thyroid distribution

Familial pattern

Associated abnormalities

Sporadic

Unilateral

No

None

3

MEN 2A

Bilateral

Yes

Pheochromocytoma Hyperparathyroidism

2

MEN 2B

Bilateral

Yes/No

Pheochromocytoma Mucosal neuroma Ganglioneuroma Characteristic phenotype

4

FMTC

Bilateral

Yes

None

1

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Biologic virulence

MEN  multiple endocrine neoplasia, FMTC  familial medullary thyroid carcinoma.

The primary treatment of patients with MTC is total thyroidectomy and resection of lymph nodes in the central compartment of the neck. If enlarged lymph nodes are detected in the lateral neck, a modified neck dissection is indicated. Pheochromocytomas

Approximately half of patients with MEN2A and MEN2B develop pheochromocytomas, tumors of the medullary (central) portion of the adrenal gland. As with MTC, the pheochromocytomas are multi-centric and commonly bilateral, even though they may develop simultaneously or asynchronously. If enlarged, both adrenals should be removed at one operation; however, if only one adrenal gland is enlarged, it should be removed and the other adrenal gland left intact and resected later if it becomes enlarged. The catecholamines, epinephrine and norepinephrine, are synthesized in the adrenal medulla, and excess secretion of these hormones causes hypertension. Great care must be taken in the diagnosis and treatment of patients with these tumors. This is especially true in the setting of a thyroidectomy, where induction of general anesthesia in the presence of an occult pheochromocytoma may be associated with severe hypertension and even death. Hyperparathyroidism

Approximately 30% of patients with MEN2 develop hyperparathyroidism, and as with MEN1 all parathyroid glands are diseased and should be treated by either subtotal parathyroidectomy or total parathyroidectomy with a parathyroid autograft. Variants of MEN2A

There are two distinct variants of MEN2A: Hirschsprung’s Disease (HCRD), characterized by the absence of autonomic ganglia in the hindgut (Decker et al., 1998), and Cutaneous Lichen Amyloidosis (CLA) a pruritic skin lesion usually located on the upper back (Gagel et al., 1989). Molecular Genetics of the MEN Type 2 Syndromes The RET (Rearranged during Transfection) gene was discovered by Takahashi and associates (Takahashi et al., 1985). The gene is located in the pericentromeric region of chromosome 10q11.2

and includes 21 exons. RET encodes a receptor tyrosine kinase, which is expressed in neuroendocrine cells (including thyroid C cells and adrenal medullary cells), neural cells (including parasympathetic and sympathetic ganglia), urogenital tract cells, and the branchial arch cells, which give rise to the parathyroid glands. In 1993 and 1994 it was shown that germline point mutations in RET can cause MEN2A, MEN2B and FMTC (Carlson et al., 1994b; Donis-Keller et al., 1993; Mulligan et al., 1993). The RET gene is structured with an extracellular portion, which contains four cadherin-like repeats, a calcium binding site, and a cysteine-rich region, a transmembrane portion, and an intracellular portion, which contains a tyrosine kinase domain (Figure 77.2). RET is subject to alternative splicing, which results in three protein isoforms with either 9, 43 or 51 amino acids at the C terminus (Myers et al., 1995; Tahira et al., 1990). One of four glial-derived neurotrophic factor (GDNF) family ligands (GFLs), GDNF, neurturin, persephin, or artemin, binds RET in conjunction with one of four glycosyl-phosphatidylinositol-anchored co-receptors, designated GDNF family receptors (GFR): GFR-1, GFR-2, GFR-3 and GFR-4 (Baloh et al., 1998; Creedon et al., 1997; Sanicola et al., 1997). The GFL-GFR complex causes dimerization of two RET molecules with activation of autophosphorylation and intracellular signaling. The C-terminus of RET contains 16 tyrosine residues, among which Y905 is a binding site for Grb7/10 adaptors, Y1015 a binding site for phospholipase, C, Y981 a binding site for c-Src and Y1096 a binding site for Grb2 (Ichihara et al., 2004). Tyrosine 1062 is a multi-docking binding site for such proteins as SHC, SHCC, IRS1/2, FRS2, DOK1/4/5/ and Enigma. The RET receptor may activate various signaling pathways through Y1062, which thereby serves as a prerequisite for initiating transformation of RET-derived oncogenes in cell cultures and transgenic animals (Ichihara et al., 2004). Indications for Genetic Testing in Patients with MTC In patients presenting with a thyroid mass, diagnosed on tissue biopsy as MTC, it is important to determine if the disease is hereditary or sporadic. Lacking a family history of hereditary MTC, this may be difficult, although the development of MTC

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FMTC

MEN 2A

MEN 2B

Codon

Exon

X X

532 533

8

X X X X X X X

600 603 606 609 611 618 620

10

X X

X X X X X X X X

630 634 635 637 649 666

X X X X X X X

X

X X

X

X

X

X X

X X X

GDNF

11

768 778 781 790 791

13

804 852

14

883 891

15

912 918

16

ATP

Substrate

Figure 77.2 A schematic showing the RET tyrosine kinase receptor and ligand complex as well as genotype-phenotype correlations for patients with type 2 multiple endocrine Neoplasia syndromes including MEN2A, MEN2B and FMTC. The RET gene product is divided into intracellular (purple), transmembrane (orange), and intracellular domains (blue) containing tyrosine kinase activity. The exons coding for each domain are shown with corresponding colors. Known RET codon mutations are listed and grouped according to the exons. Phenotypically expressed clinical syndromes corresponding to each codon mutation are listed. (Adapted from You, Y.N., Lakhani V., Wells, S.A. and Moley J.F. (2006). Medullary thyroid cancer. Surg Oncol Clin N Am 15, 639–660, with permission.)

at an early age and the presence of microscopic C cell hyperplasia in the thyroid parenchyma adjacent to the MTC, make hereditary MTC more likely. Even in a patient with no family history of MTC, hereditary disease is possible, as he or she may have either a de novo mutation or a low penetrance RET mutation, such as occurs in codons 804 or 768 (Pasini et al., 1997). Regardless of the historical and clinical findings, the best evidence-based medical practice is to routinely test all patients with the histological diagnosis of MTC for the presence of RET germline mutations. It is imperative that genetic counselors be involved in decisions about genetic testing, genetic diagnosis, and recommended treatment. Correlation Between Genotype and Phenotype More than 95% of patients with the type 2 MEN syndromes have mutations in RET. There is a distinct correlation between genotype and phenotype in patients with MEN2A, MEN2B and FMTC both as concerns the pattern of expression of clinical disease and the severity of disease (Brandi et al., 2001; Mulligan et al., 1993, 1994).

The mutations for MEN2A are most often located in the extracellular cysteine-rich region (exons 10 and 11) of the gene and over 85% of patients have a mutation in codon 634 (Eng et al., 1996). All reported cases of MEN2A and CLA have had a mutation at codon 634. Patients with MEN2A who have hyperparathyroidism most often have codon 634 mutations, particularly a C634R mutation (Mulligan et al., 1994). In patients with FMTC, germline mutations have been reported in exon 8 (codons 532 and 533), exon 10 (codons 609, 611, 618 and 620), exon 11 (codons 630 and 634), exon 13 (codons 768, 790 and 791), exon 14 (codons 804 and 844), exon 15 (codon 891), and exon 16 (codon 912) (Eng et al., 1996; Jimenez et al., 2004). The V804 M mutation appears to be unique to FMTC, whereas patients with the V804L mutation also have MTC and pheochromocytoma (Nilsson et al., 1999). Regardless of the codon mutation, great care must be taken in the diagnosis of FMTC, since the studied pedigree may be of insufficient size to exclude with confidence the occurrence of hyperparathyroidism or a pheochromocytoma. Ninety-five percent of patients with MEN2B have a point mutation in codon 918 (exon 16) within the intracellular

The Multiple Endocrine Neoplasia Syndromes

TABLE 77.4

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937

Mutation-phenotype relation in multiple endocrine neoplasia type 2A No. Patients (%)

Phenotype

No. Patients

Codon 618

Codon 620

Codon 634

p-value

Pheochromocytoma

161

9/42 (21)

8/12 (67)

58/107 (54)

0.001

Hyperparathyroidism

213

4/48 (8)

3/53 (6)

56/112 (50)

0.0001

Pheochromocytoma and Hyperparathyroidism

141

0/38 (0)

0/11 (0)

31/92 (34)

0.0001

Data from Mulligan et al. (1994).

domain of RET. A small number of patients with MEN2B have a mutation in codon 883 (exon 15) (Smith et al., 1997). The correlation of specific RET codon mutations with the phenotypic expression of hereditary MTC can be seen in Figure 77.2 and Table 77.4. Very rarely, compound heterozygous mutations in V804 M with either Y806C or S904C have been reported in patients with a phenotype resembling MEN2B (Miyauchi et al., 1999). In a study of 25 patients with de novo MEN2B, Carlson and associates found that the new mutation was of paternal origin in all cases. The investigators also observed a distortion of the sex ratio in both de novo MEN2B patients and the affected offspring of MEN2B transmitting males, suggesting a possible role for imprinting (Carlson et al., 1994a). Correlation Between Genotype and Severity of Disease At the MEN Consortium Meeting in 2000, a consensus panel evaluated the relationship between the RET codon mutation and the biological aggressiveness of hereditary MTC (Brandi et al., 2001). Based on combined clinical data the panel defined three levels of thyroid cancer severity. Patients with mutations in RET codons 609, 768, 790, 791, 804 or 891 (Level 1) are at risk for developing MTC; however, their tumors are generally more indolent and develop at a later age than is the case in patients with other RET codon mutations. Recommendations for the timing of thyroidectomy in this group are controversial. In patients with mutations in RET codons 611, 618, 620 or 634 (Level 2), thyroidectomy is recommended at or before 5 years of age. Patients with MEN2B and mutations in RET codons 883 or 918 (Level 3) have the most severe form of MTC, and thyroidectomy is recommended within the first 6 months of life (Table 77.5). MTC occurs in virtually all patients with MEN2A, MEN2B and FMTC, and it is the most common cause of death. Family members who have inherited a mutated allele characteristic of one of these syndromes will develop MTC, and they can be identified by direct DNA analysis of white blood cell DNA, early in life, or even prenatally. Thus, the rational strategy of removing the thyroid gland prophylactically, or while the MTC is still confined to the gland, has made the type 2 MEN and related

T A B L E 7 7 . 5 Recommendations for prophylactic thyroidectomy based On RET codon mutation Risk Level for MTC

1 High

2 Higher

3 Highest

Codons

609, 768, 790, 791, 804, 891

611, 618, 620, 634

883, 918, or known MEN 2B

Thyroidectomy (Age)

No consensus: By 5 to 10 years; or at first abnormal stimulated calcitonin

By 5 years

By 6 months; preferably within first month of life

Data from Brandi et al. (2001); Kouvaraki et al. (2005).

syndromes the archetype of genomic medicine. Assuming that very young patients who will develop MTC can be identified and that the disease can be prevented by prophylactic removal of their thyroid gland, one must decide the ideal time for the operation. The primary consideration is to remove the thyroid gland before the MTC has spread beyond the thyroid capsule and to do so with minimal morbidity. As mentioned above, it has been recommended that the timing of thyroidectomy be based on a patient’s specific RET codon mutation (Brandi et al., 2001; Machens et al., 2001). While such a strategy is plausible, it is known that there are certain factors that modify the severity of the MTC, even within individual families. For example, it has been shown in some kindreds with codon 804 RET mutations (generally associated with a non-aggressive form of MTC) that a concomitant somatic 918 codon mutation in MTC cells confers a highly malignant phenotype (Lombardo et al., 2002). Furthermore, it has been proposed that certain specific single-nucleotide polymorphisms (SNPs) influence the clinical behavior of the MTC; however, at present this relationship is unclear (Weber and Eng, 2005).

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The molecular basis for these genotype-phenotype correlations existing between the various RET mutations and the resulting disease phenotype remains poorly understood. However, in vitro studies have reproduced these genotype-phenotype correlations and suggested that, in general, mutations in RET codons conferring level 1 risk (Table 77.5) are less oncogenic than those conferring level 2 or 3 risks (Iwashita et al., 1999, Mise et al., 2006). For example, among cysteine mutants observed in MEN2A and FMTC, the less oncogenic RET mutants exhibit reduced ability to form covalent disulfide-bond homodimers, a mechanism known to mediate the transforming ability of this type of mutation (Chappius-Flament et al., 1998; Ito et al., 1997). The molecular mechanism sustaining the high oncogenic potential of the MEN2B-associated M918T RET mutation is also unclear. This mutation targets the P  1 loop of the kinase, a domain that establishes physical contact with the protein substrate. Accordingly, it has been reported that the M918T mutation results in a shift in peptide substrate specificity of the RET kinase (Songyang et al., 1995). Recently, studies of the X-ray structure of the RET kinase domain have demonstrated that the RET kinase domain forms inactive dimers that are disrupted during receptor activation. Intriguingly, M918 maps at the protein interface that stabilizes the conformation of the inactive dimmer. Therefore, it is likely that mutation at codon 918 disrupts the inactive conformation leading to a potent activation of the RET kinase (Knowles et al., 2006). Recently, gene expression studies relating to MEN2A and MEN2B have been reported by two groups (Myers and Mulligan, 2004) and (Jain et al., 2004). Myers and Mulligan used cDNA microarray analysis of cell lines that expressed either the RET9 or the RET51 protein isoform to study RET-mediated gene expression patterns. They found that cells expressing RET have altered intercellular interactions correlated with increased expression of a number of cell surface molecules. The most striking expression pattern observed, however, was the upregulation of stress response genes, specifically heat shock protein (HSP) 70 family members: HSPA1A, HSPA1B AND HSPA1L. Additionally, other members of several HSP families associated with stress response were upregulated. The increased expression of HSPs, particularly of the HSP70 and HSP90 families, has been documented in breast cancer, gastrointestinal cancer, and endometrial cancer and is associated with a poor prognosis. Conversely, HSP70 levels correlate with malignancy in osteosarcomas and renal cell carcinomas, however, its expression is associated with an improved prognosis and a positive response to chemotherapy (Jaatt et al., 1999). Jain and associates performed microarray expression analysis from pheochromocytomas and MTCs in patients with MEN2A, MEN2B and sporadic MTC. They found 118 probe sets that were differentially regulated in MEN2B tumors compared to MEN2A tumors (20 were upregulated in MTCs from patients with MEN2A and 98 were upregulated in MTCs from patients with MEN2B). Five genes, were most discriminating by significance analysis microarray, and correctly classified all of the cases of MEN2A and MEN2B MTCs.

Realizing the criticalness of removing the thyroid gland while the MTC is curable, and understanding that it is impractical to establish strict guidelines for the timing of thyroidectomy based on the various RET codon mutations, clinicians should err on the side of advising thyroidectomy too early rather than too late. This approach is strengthened by the fact that once the MTC has spread beyond the thyroid gland it is virtually incurable, as no chemotherapy or radiotherapy regimen has proven effective. In a recent study, Skinner and colleagues evaluated 50 young children, ranging in age from 3 to 19 years, who were known to have inherited a mutated RET allele characteristic of MEN2A. The patients had no clinical evidence of MTC and were treated by prophylactic total thyroidectomy and resection of lymph nodes in the central zone of the neck. Each patient was evaluated at least 5 years after thyroidectomy by clinical evaluation and measurement of plasma CTN levels following calcium and pentagastrin stimulation. In 44 (88%) of the 50 children (and in all children less than 8 years of age at the time of thyroidectomy), there was no evidence of recurrent or persistent MTC as basal and stimulated plasma CTN levels were undetectable (Skinner et al., 2005). Our current practice in patients with hereditary MTC is influenced to some degree by a patient’s specific RET mutation. In patients with MEN2B (mutations at either codon 883 or 918), thyroidectomy is advised at the time of diagnoses, even in the first months of life. In patients with MEN2A or FMTC, regardless of the patient’s codon mutation, thyroidectomy is advised at or before 5 years of age. We advocate this general and simple approach to increase the likelihood that hereditary MTC can be cured or prevented in these young patients. If clinicians choose to follow children (with known RET mutations characteristic of MEN2A or FMTC) beyond 5 years of age, stimulated plasma CTN levels may be useful in determining the time of thyroidectomy. One should note, however, that there are few data correlating stimulated plasma CTN levels with stage of disease and curability of MTC. The Role of the RET Protooncogene in Patients with Sporadic MTC Somatic mutations of RET have been detected in 23–70% of patients with sporadic MTC (Eng et al., 1995; Zedenius et al., 1995), with mutations at codon 918 being the most common. In studies of small numbers of patients, there has been suggestive evidence that the sporadic MTC is more aggressive in patients with somatic RET mutations, compared to those without them; however, this needs to be confirmed in larger studies (Marsh et al., 1996; Romei et al., 1996). RET mutations in codon 918, 620, 630 and 634 are found infrequently in sporadic pheochromocytomas, however, RET mutations have not been found in sporadic parathyroid tumors or in tumors originating from other neural crest-derived cells. To date, a somatic RET mutation is the sole genetic abnormality consistently found in sporadic MTC. Oncogenic proteins are believed to function in signaling cascades. However, there has been no report of genetic alteration in the oncogenic

The Multiple Endocrine Neoplasia Syndromes

proteins functioning along the RET signaling cascade, such as RAS (Bockhorn et al., 2000; Moley et al., 1991), BRAF (Xing, 2005) and PIK3CA (Wu et al., 2005), for MTC. In contrast (discussed below), these mutations have been described in follicular-cell-derived thyroid carcinomas. Deletions in several chromosomes and, particularly, 1p and 22q have been reported in MTC, indicating that potential tumor suppressors mapping at these loci might be involved in MTC formation (Khosla et al., 1991, Marsh et al., 2003). Finally, transgenic mouse models have provided compelling evidence that proteins working along the “Rb (retinoblastoma) pathway” and in particular p110Rb itself might be involved in MTC formation, as their disruption in the germline predisposes transgenic mice to MTC (Williams et al., 1994). Whether these genes are involved also in human MTC is still unknown. However, intriguingly, MTC formed in Rb-null animals harbor somatic mutations in RET that overlap with those present in human MEN2 patients, providing an interesting similarity between human and mouse diseases (Coxon et al., 1998). The Role of RET in Patients with Papillary Thyroid Carcinoma (PTC) At the somatic level heterologous gene partners joining the C-terminal RET tyrosine kinase-encoding domain to the promoter and N-terminal portion of unrelated genes results in the illegitimate expression of a constitutively active chimeric form of the receptor in thyroid cells. The first thyroid-specific oncogene RET/PTC was described in 1987 (Fusco et al., 1987). To date more than 10 molecular fusion oncogenes have been identified and form more than 15 different hybrid oncogenes, all of which differ according to the 5 -terminal region of the heterologous gene (Figure 77.3). The most common of these chimeric oncogenes are RET/PTC1 (60–70%) and RET/PTC3 (20– 30%). The reported incidence of these hybrid genes in sporadic PTCs varies widely, from less than 5% to almost 70%, depending on technical and geographic factors. The prevalence of RET/ PTC in the thyroid cancer of children is greater than 50%, and in youngsters who developed PTC following exposure to radiation from the Chernobyl accident, the prevalence of such rearrangements in the thyroid cancers is 67–87% (Klugbauer et al., 1995; Nikiforov et al., 1997;Williams et al., 1996). Similar rearrangements have not been described in patients with follicular carcinoma or anaplastic carcinomas, although they have been reported in Hürthle cell carcinomas and trabecular adenomas. The RET/PTC oncogenes, found in PTC, like the RET mutations in the MEN type 2 syndromes, potentiate the intrinsic tyrosine kinase activity of RET and thereby the downstream signaling events. It should be noted that the most common mutation in PTC is the BRAFT17699A mutation (V600E), which occurs in almost 70% of tumors (Kimura et al., 2003). There is no overlap between the presence of BRAF mutations and RET/PTC gene rearrangements. Compared to the RET/PTC rearrangements, the BRAF mutations are associated with the more aggressive forms of papillary carcinomas, characterized by extrathyroidal invasion, lymph node metastases, advanced tumor stage, and tumor recurrence (Xing et al., 2005). The relationship

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Figure 77.3 A schematic showing chimeric forms of the RET receptor formed after joining the C-terminus of RET to the promoter and N-terminus of unrelated genes. These chimeric products, termed RET/PTC oncogenes, result in constitutive activation of RET and have been identified in patients with papillary thyroid carcinoma.(Modified from Santoro, M., Melillo, R.M., Carlomagno, F., Fusco, A. and Vecchio, G. (2002). Molecular mechanisms of RET activation in human cancer. Ann N Y Acad Sci 963, 116–121, with permission.)

between RET/PTC rearrangements and the biology of PTC is unclear. The Development of Target-Based Therapy for MTC and PTC The human genome contains more than 90 protein tyrosine kinases (PTKs), which are involved in essential cellular processes such as proliferation, differentiation and anti-apoptotic signaling. Unregulated activation of PTKs through mechanisms such as point mutations, in hereditary MTC, or gene rearrangements, in patients with PTC, can lead to oncogenesis. The importance of PTKs as therapeutic targets was highlighted by the use of imatinib mesylate (Gleevec; Novartis, Basel, Switzerland) in the treatment of patients with chronic myelogenous leukemia (CML) and gastrointestinal stromal tumors (GIST) (Demetri et al., 2002; Kantarjian et al., 2002). A translocation adding a 3 segment of the ABL gene from chromosome 9q34 to the 5 portion of the BCR gene on chromosome 22q11, creates a constitutively activated tyrosine kinase (BCR-ABL) that causes CML. In GISTs, gain-of-function mutations in the KIT gene result in constitutive activation of KIT signaling and uncontrolled cell proliferation. Imatinib inhibits the following

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Figure 77.4 In a patient with MEN2A and metastatic hereditary MTC, dramatic decrease in the size of soft tissue (breast) metastatic lesion was noted after 3 and 6 months of therapy on ZD6474.

PTKs: platelet-derived growth factor receptor  (PDGFR), the chimeric BCR-ABL fusion oncoprotein, and the transmembrane receptor KIT. In a recent international study, 454 patients with CML who had failed therapy with interferon were treated with 400 mg/day of oral imatinib. Major cytogenetic responses were noted in 60% of the patients, and complete hematological responses were seen in 95%. After a median follow-up of 18 months, 95% of the patients were alive (Kantarjian et al., 2002). In a study of 147 patients treated with imatinib for metastatic GISTs, 79 (53.7%) had a partial response and 41 (27.9%) had stable disease. There were no complete responses, however, and early resistance to imatinib was noted in 13.6% of the patients. A genotype-phenotype correlation was noted in GIST patients, as those with KIT mutations in exon 11 had the best response to imatinib, whereas patients without mutations in either KIT or PDGFRa failed to respond (Demetri et al., 2002). The dependence of GISTs on KIT mutations for survival appears incomplete, indicating the influence of other genetic factors on tumor cell viability and progression. The RET gene encodes a PTK, which when mutated plays a causative role in hereditary MTC, and when translocated plays a causative role in PTC. Thus, it would appear that patients whose thyroid cancers are caused by derangements in RET would be candidates for targeted therapy. This is a highly important issue, since to date, MTC has been largely unresponsive to either standard or experimental chemotherapeutic regimens or to radiotherapy. In 2002 Carlomagno and associates demonstrated that ZD6474 (Zactima; AstraZeneca, Wilmington, USA), an inhibitor of Kinase insert Domain-containing Receptor (KDR) PTK activity, blocked RET kinases (Carlomagno et al., 2002). Subsequently, a Phase II clinical trial was initiated, evaluating ZD6474 in 30 patients with metastatic hereditary MTC (Wells et al., 2006). To date 30 patients have been entered on the study and 30% have experienced an objective partial remission, as measured by RECIST criteria (Therasse et al., 2000) (Figure 77.4). There have been no complete remissions. Plasma levels of the tumor markers CTN and CEA have decreased following treatment with ZD6474 indicating a biochemical response (Figure 77.5). At present other compounds, some of which are PTKs inhibitors, are being studied in clinical trials of patients

with hereditary MTC, sporadic MTC and PTC. Even though the use of targeted therapy for patients with thyroid cancer is at an early stage, there seems little doubt that these agents will benefit patients with advanced disease for whom previously there was no treatment. The process of gaining Food and Drug Administration (FDA) approval of a specific targeted therapy for patients with locally advanced or metastatic MTC depends on the evaluation of data from carefully conducted Phase I and Phase II clinical trials. If these studies demonstrate that the compound in question is safe and that it is associated with a significant objective response rate, the FDA may grant accelerated approval (under subpart H [21CRF 314 subpart H]). Such approval, based on a surrogate endpoint (response rate) from a trial even with a modest number of patients, would be based on the lack of other effective chemotherapy or radiation therapy for patients with this clinical stage of disease. Upon completion of the current Phase II study with ZD6474, the data will be submitted to the FDA. The problems with clinical trials evaluating targeted therapy for patients with hereditary MTC concern the limited number of patients with MTC who would meet the eligibility criteria for the trial and the relatively slow growth of MTC in patients with certain RET codon mutations. Also, the long-term side effects of ZD6474 are unknown and will only become apparent after prolonged periods of treatment. Targeted therapies such as ZD6474 and others would potentially have usefulness in patients other than those with hereditary MTC. Currently, a clinical trial with ZD6474 is being planned for patients with hereditary or sporadic MTC who have locally advanced or metastatic disease. Approximately 40% of patients with sporadic MTC have somatic RET mutations and one would expect that they their tumors would respond similarly to patients with germline RET mutations. Clinical trials are also being planned to evaluate the efficacy of ZD6474 in patients with metastatic PTC, since approximately 40% of these patients have chromosomal translocations, which activate RET. Finally, a clinical trial is being planned for patients with MEN2A and MEN2B associated pheochromocytomas. The majority of patients with sporadic MTC and many patients with hereditary MTC are not cured by surgical

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Figure 77.5 In a Phase II clinical trial of ZD6474, decreases in the levels of tumor markers calcitonin (a) and CEA (b) were observed in the majority of patients with metastatic hereditary MTC after therapy.

resection, as indicated by an elevated plasma CTN level following thyroidectomy. Many of these patients have no other clinical evidence of recurrent or persistent MTC, may also be candidates for effective and low toxicity therapies, which target RET.

CONCLUSION The MEN1 and MEN2 syndromes are fascinating disorders, together representing the entire spectrum of endocrine tumors, excepting the gonadal system. Moreover, the diseases exemplify the fundamental importance of the human genome project as a construct for understanding, at the molecular level, how certain malignancies arise and how they behave clinically. Most importantly, molecular genetic discoveries have profoundly improved the diagnosis and treatment of patients with the MEN syndromes and many other diseases. There is no better demonstration of the translation of molecular genetic information to the practice of clinical medicine than that represented

by the discovery that mutations in the RET proto-oncogene cause MEN2A, MEN2B and FMTC. Following this observation, it became possible to identify members of an affected family who had inherited a mutated RET allele and to institute preventive thyroidectomy for MTC, the hallmark of these syndromes. The diagnosis and treatment of patients with MEN2A, MEN2B and FMTC have been improved markedly and clinicians are now entering a phase where targeted therapy of patients with advanced MTC is becoming a reality.

ACKNOWLEDGEMENTS The authors would like to thank Professor Joseph Nevins, Institute for Genome Sciences & Policy at Duke University and Professor Massimo Santoro, Department of Cellular and Molecular Biology and Pathology, at the University of Naples for their many helpful suggestions in the preparation of this manuscript.

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proto-oncogene in the same allele in a patient with multiple endocrine neoplasia type 2B without codon 918 mutation. Jpn J Cancer Res 90, 1–5. Moley, J.F., Brother, M.B., Wells, S.A., Spengler, B.A., Biedler, J.L. and Brodeur, G.M. (1991). Low frequency of ras gene mutations in neuroblastomas, pheochromocytomas, and medullary thyroid cancers. Cancer Res 51, 1596–1599. Mulligan, L.M., Kwok, J.B., Healey, C.S., Elsdon, M.J., Eng, C., Gardner, E., Love, D.R., Mole, S.E., Moore, J.K., Papi, L. et al. (1993). Germ-line mutations of the RET proto-oncogene in multiple endocrine neoplasia type 2A. Nature 363, 458–460. Mulligan, L.M., Eng, C., Healey, C.S., Clayton, D., Kwok, J.B., Gardner, E., Ponder, M.A., Frilling, A., Jackson, C.E., Lehnert, H. et al. (1994). Specific mutations of the RET proto-oncogene are related to disease phenotype in MEN 2A and FMTC. Nat Genet 6, 70–74. Myers, S.M. and Mulligan, L.M. (2004). The RET receptor is linked to stress response pathways. Cancer Res 64, 4453–4463. Myers, S.M., Eng, C., Ponder, B.A. and Mulligan, L.M. (1995). Characterization of RET proto-oncogene 3 splicing variants and polyadenylation sites: A novel C-terminus for RET. Oncogene 11, 2039–2045. Nikiforov, Y.E., Rowland, J.M., Bove, K.E., Monforte-Munoz, H. and Fagin, J.A. (1997). Distinct pattern of ret oncogene rearrangements in morphological variants of radiation-induced and sporadic thyroid papillary carcinomas in children. Cancer Res 57, 1690–1694. Nilsson, O., Tisell, L.E., Jansson, S., Ahlman, H., Gimm, O. and Eng, C. (1999). Adrenal and extra-adrenal pheochromocytomas in a family with germline RET V804L mutation. JAMA 281, 1587–1588. Pasini, A., Geneste, O., Legrand, P., Schlumberger, M., Rossel, M., Fournier, L., Rudkin, B.B., Schuffenecker, I., Lenoir, G.M. and Billaud, M. (1997). Oncogenic activation of RET by two distinct FMTC mutations affecting the tyrosine kinase domain. Oncogene 15, 393–402. Romei, C., Elisei, R., Pinchera, A., Ceccherini, I., Molinaro, E., Mancusi, F., Martino, E., Romeo, G. and Pacini, F. (1996). Somatic mutations of the ret protooncogene in sporadic medullary thyroid carcinoma are not restricted to exon 16 and are associated with tumor recurrence. J Clin Endocrinol Metab 81, 1619–1622. Sanicola, M., Hession, C., Worley, D., Carmillo, P., Ehrenfels, C., Walus, L., Robinson, S., Jaworski, G.,Wei, H., Tizard, R. et al. (1997). Glial cell line-derived neurotrophic factor-dependent RET activation can be mediated by two different cell-surface accessory proteins. Proc Natl Acad Sci USA 94, 6238–6243. Schussheim, D.H., Skarulis, M.C., Agarwal, S.K., Simonds, W.F., Burns, A.L., Spiegel, A.M. and Marx, S.J. (2001). Multiple endocrine neoplasia type 1: New clinical and basic findings. Trends Endocrinol Metab 12, 173–178. Silverberg, S.J., Bone, H.G., III, Marriott, T.B., Locker, F.G., Thys-Jacobs, S., Dziem, G., Kaatz, S., Sanguinetti, E.L. and Bilezikian, J.P. (1997). Short-term inhibition of parathyroid hormone secretion by a calcium-receptor agonist in patients with primary hyperparathyroidism. N Engl J Med 337, 1506–1510. Skinner, M.A., Moley, J.A., Dilley, W.G., Owzar, K., Debenedetti, M.K. and Wells, S.A., Jr (2005). Prophylactic thyroidectomy in multiple endocrine neoplasia type 2A. N Engl J Med 353, 1105–1113. Smith, D.P., Houghton, C. and Ponder, B.A. (1997). Germline mutation of RET codon 883 in two cases of de novo MEN 2B. Oncogene 15, 1213–1217. Songyang, Z., Carraway, K.L., III, Eck, M.J., Harrison, S.C., Feldman, R.A., Mohammadi, M., Schlessinger, J., Hubbard, S.R.,

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Smith, D.P., Eng, C. et al. (1995). Catalytic specificity of protein-tyrosine kinases is critical for selective signalling. Nature 373, 536–539. Steiner, A.L., Goodman, A.D. and Powers, S.R. (1968). Study of a kindred with pheochromocytoma, medullary thyroid carcinoma, hyperparathyroidism and Cushing’s disease: Multiple endocrine neoplasia, type 2. Medicine (Baltimore) 47, 371–409. Tahira, T., Ishizaka, Y., Itoh, F., Sugimura, T. and Nagao, M. (1990). Characterization of ret proto-oncogene mRNAs encoding two isoforms of the protein product in a human neuroblastoma cell line. Oncogene 5, 97–102. Takahashi, M., Ritz, J. and Cooper, G.M. (1985). Activation of a novel human transforming gene, ret, by DNA rearrangement. Cell 42, 581–588. Therasse, P., Arbuck, S.G., Eisenhauer, E.A., Wanders, J., Kaplan, R.S., Rubinstein, L., Verweij, J.,Van Glabbeke, M., van Oosterom, A.T., Christian, M.C. et al. (2000). New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92, 205–216. Weber, F. and Eng, C. (2005). Editorial: Germline variants within RET: Clinical utility or scientific playtoy?. J Clin Endocrinol Metab 90, 6334–6336. Wells, S.A., Jr, Baylin, S.B., Linehan, W.M., Farrell, R.E., Cox, E.B. and Cooper, C.W. (1978). Provocative agents and the diagnosis of medullary carcinoma of the thyroid gland. Ann Surg 188, 139–141. Wells, S.A., You, Y.N., Lakhani, V., Hou, J., Langmuir, P., Headley, D., Skinner, M., Morse, M., Burch, W. and Schlumberger, M. (2006). A phase II trial of ZD6474 in patients with hereditary metastatic

medullary thyroid cancer. J Clin Onc, 2006 ASCO Annu Meet Proc 24, 5533. Wermer, P. (1954). Genetic aspects of adenomatosis of endocrine glands. Am J Med 16, 363–371. Williams, B.O., Remington, L., Albert, D.M., Mukai, S., Bronson, R.T. and Jacks, T. (1994). Cooperative tumorigenic effects of germline mutations in Rb and p53. Nat Genet 7, 4804. Williams, G.H.,Rooney, S.,Thomas, G.A.,Cummins, G. and Williams, E.D. (1996). RET activation in adult and childhood papillary thyroid carcinoma using a reverse transcriptase-n-polymerase chain reaction approach on archival-nested material. Br J Cancer 74, 585–589. Wu, G., Mambo, E., Guo, Z., Hu, S., Huang, X., Gollin, S.M., Trink, B., Ladenson, P.W., Sidransky, D. and Xing, M. (2005). Uncommon mutation, but common amplifications, of the PIK3CA gene in thyroid tumors. J Clin Endocrinol Metab 90, 4688–4693. Xing, M. (2005). BRAF mutation in thyroid cancer. Endocr Relat Cancer 12, 245–262. Xing, M., Westra, W.H., Tufano, R.P., Cohen, Y., Rosenbaum, E., Rhoden, K.J., Carson, K.A., Vasko, V., Larin, A., Tallini, G. et al. (2005). BRAF mutation predicts a poorer clinical prognosis for papillary thyroid cancer. J Clin Endocrinol Metab 90, 6373–6379. Zedenius, J., Larsson, C., Bergholm, U., Bovee, J., Svensson, A., Hallengren, B., Grimelius, L., Backdahl, M.,Weber, G. and Wallin, G. (1995). Mutations of codon 918 in the RET proto-oncogene correlate to poor prognosis in sporadic medullary thyroid carcinomas. J Clin Endocrinol Metab 80, 3088–3090.

RECOMMENDED RESOURCES http://www.prenhall.com/lewin This website features the complete electronic version of the classic textbook Genes VIII with Flash illustrations. The text is continuously updated online, providing coverage of the most recent advances in the field of genomics. http://archive.uwcm.ac.uk/uwcm/mg/search/120346.html This website of the Human Gene Mutation Database includes all mutations of the RET gene reported to date with corresponding pheonotype and reference articles. Brandi, M.L., Gagel, R.F., Angeli, A., Bilezikian, J.P., Beck-Peccoz, P., Bordi, C., Conte-Devolx, B., Falchetti, A., Gheri, R.G., Libroia, A., et al. (2001). Guidelines for diagnosis and therapy of MEN type 1 and type 2. J Clin Endocrinol Metab 86, 5658–5671. This article represents a current summary of the clinical features, molecular pathogenesis, and management guidelines of patients with the MEN syndromes.

Marx, S.J. (2005). Molecular genetics of Multiple Endocrine Neoplasia types 1 and 2. Nat Rev Cancer 5, 367–375. This article comprehensively reviews our current genetic understanding of these diseases. Kouvaraki, M.A., Shapiro, S.E., Perrier, N.D., Cote, G.J., Gagel, R.F., Hoff, A.O., Sherman, S.I., Lee, J.E. and Evans, D.B. (2005). RET proto-oncogene: a review and update of genotype-phenotype correlations in hereditary medullary thyroid cancer and associated endocrine tumors. Thyroid 15, 531–544. This article contains an updated review of the role of RET protooncogene in human endocrine tumors.

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78 Genomics of Head and Neck Cancer Giovana R. Thomas and Yelizaveta Shnayder

INTRODUCTION Head and neck squamous cell carcinoma (HNSCC) of the upper aerodigestive tract (UADT) represent approximately 4% of all cancers, with an estimated 40,500 new cases of oral cavity, pharynx and larynx cancer and 12,000 expected deaths in the United States in 2006 (Ries et al., 2006). Approximately half of all patients with HNSCC have advanced stage disease at the time of diagnosis, with an expected 5-year survival rate between 10% and 40%. Despite treatments that may consist of mutilating surgery, radiotherapy, and/or chemotherapy, overall long-term survival remains low due to uncontrollable persistent or recurrent HNSCC. The low rate of survival of patients with locoregional and distant recurrences has highlighted the need for new approaches for diagnosis and treatment. As a result of exposure to carcinogens, molecular analyses of normal, precancerous, and head and neck cancers have revealed over-time accumulation of specific genetic alterations in protooncogenes and tumor suppressor genes (TSG) that are associated with potential for locoregional spread, time to recurrence, and overall survival. Several experimental models of molecular epithelial carcinogenesis have been proposed. However, there are certain genetic alterations common to these models. Epithelial carcinogenesis and progression has been characterized by promoter hypermethylation or loss of heterozygosity (LOH) at 9p21 leading to loss of TSG p16, mutation at 17p13

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

leading to loss of TP53 TSG function, and gene amplification at 11q13 leading to overexpression of cyclin-D1. Importantly, changes in expression levels of EGFR and VEGF have also been associated with malignant progression. DNA microarray technology has been useful in identifying a specific pattern of deregulated network of genes and proteins that may provide a means for early detection, a unique target therapeutic intervention or used to monitor HNSCC disease progression. In this chapter, we summarize and discuss recent advances in our knowledge concerning the pathogenesis, diagnosis, monitoring, prognosis, and treatment of head and neck cancer obtained by using modern molecular biological tools to study gene expression and chromosomal aberrations. In addition, we discuss novel molecular targeting strategies in the treatment of HNSCC and suggest where future progress may occur.

HEAD AND NECK SQUAMOUS CELL CARCINOMA Predisposition The predominant environmental risk factors for developing HNSCC identified to date are the use of alcohol and tobacco, immunosuppression, chewing betel quid nuts, and exposure to highrisk human papilloma virus (HPV). However, not all smokers and drinkers develop cancers; therefore, genetic predisposition and other host factors may play an equally important role in tumorigenesis.

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Benzo(a)pyrene diol epoxide (BPDE) is a toxic metabolite of benzo(a)pyrene, the main carcinogenic component of tobacco smoke. BPDE has been shown to exert its mutagenic effect mostly by irreversibly binding to DNA, forming BPDE-DNA adducts, or by DNA oxidation. Other components of tobacco smoke such as catechols and nitrosamines produce free radical compounds known to cause DNA fragmentation. The individual capacity for nucleotide excision and repair of normal DNA after BPDE-induced damage has been shown to correlate with HNSCC development independent of age, sex, ethnicity, smoking status, or alcohol use in a case-control study (Cheng et al., 1998). Mutagen hypersensitivity or individual’s capacity of DNA repair against free radical damage and its relevance to the development of HNSCC was examined by Schantz et al. (1997). The relationship between the risk of HNSCC, nutrition, and mutagen hypersensitivity as determined by the quantity of bleomycin-induced chromosomal breaks within peripheral blood lymphocytes was examined in HNSCC patients and controls. Dietary intake of some antioxidants such as vitamins C, E, and carotenoids; cigarette smoking; alcohol drinking; and body mass index (BMI) were also examined. The study found that mutagen hypersensitivity was strongly associated with increased risk of HNSCC. Also, high intakes of vitamins C and E and some carotenoids were independently related to a decreased risk of HNSCC. The role of genomic instability on the development of HNSCC was further elucidated in a study of patients with Fanconi anemia, a rare autosomal recessive disorder characterized by a high degree of spontaneous chromosomal aberrations (Kutler et al., 2003). The overall incidence of HNSCC in Fanconi anemia patients was 3% as compared to the incidence of 0.038% in general population, and median age of these patients with HNSCC was 31. The cumulative rate of disease recurrence after treatment by the age of 40 was 50% in this patient population. In recent years, there has been mounting epidemiologic and experimental evidence of a role for HPV as the etiologic agent in a small proportion of HNSCC, mostly in non-drinkers and non-smokers. A recent study identified high-risk HPV16 DNA inside the nuclei of cancer cells of 90% of HPV-positive oropharyngeal HNSCC specimens by in situ hybridization (Gillison et al., 2000). These cancers, as compared to non-HPV-associated oropharyngeal HNSCC, tended to be in non-drinkers, non-smokers, have basaloid features on histology, not associated with p53 mutation, and have a 59% reduction in risk of dying from cancer, after adjustment for stage of disease, morbidity, and other confounding factors. In addition, the association between HPV16 infection and HNSCC in specific sites suggests the strongest and most consistent association is with tonsil cancer, and the magnitude of this association is consistent with an infectious etiology (Hobbs et al., 2006). Screening The multistep carcinogenesis process results in epigenetic and metabolic changes that give rise to histologically distinct

precursor phenotypes that harbor specific genetic alterations. Pre-malignant lesions of the UADT can present as leukoplakia (white patch) or erythroplakia (red patch). Histologically, leukoplakia may demonstrate benign hyperkeratosis of the surface epithelium, epithelial hyperplasia, dysplasia, or invasive carcinoma. Up to 8% of leukoplastic lesions may demonstrate invasive squamous cell carcinoma (SCC), whereas erythroplakia has a much greater potential for malignancy. Approximately 90% of erythroplastic lesions may demonstrate severe dysplasia, carcinoma in situ or invasive SCC. The transformation rate of dysplasia to cancer has been reported as high as 36.4%. Improvements in overall survival in patients with HNSCC rests on early identification of pre-malignant lesions and intervention in patients at risk prior to development of advanced stage disease. Since epithelial carcinogenesis is a multistep process directed by complex molecular events that involve specific genetic defects in proto-oncogenes and TSG, early identification of genetic alterations that may represent early transition into malignant phenotype may be possible through various measures recently described. Early detection of oral and oropharyngeal SCC using brush biopsies to detect exfoliated cytology and DNA cytometry have been investigated. Cytologic diagnosis combined with DNAimage cytometry to measure DNA aneuploidy has proven to have high specificity and sensitivity for malignancy in studies of patients evaluated with pre-malignant lesions in the oral cavity (Maraki et al., 2004; Remmerbach et al., 2001). In fact, these investigators have shown that cytology combined with DNA-image cytometry may predict malignant transformation up to 15 months before its histologic confirmation. The measurement of DNA ploidy in epithelial cells of oral leukoplakia has been studied by Sudbo and colleagues (reviewed in Sudbo and Reith, 2005). These investigators have shown that patients with aneuploid dysplastic oral lesions had a 96% rate of oral cancer with 70% rate within 3 years, an 81% rate of subsequent cancer, and 74% rate of death from cancer. Moreover, newer molecular methods such as microsatellite analysis to detect genetic alterations in exfoliated oral mucosal cells (Spafford et al., 2001) or in serum (Nawroz-Danish et al., 2004) also suggest that this approach is feasible to detect occult carcinoma in patients at risks for developing HNSCC. These studies suggest that biomarkers of genomic instability, such as aneuploidy and allelic imbalance, can accurately measure the cancer risk of oral pre-malignant lesions. The use of molecular imaging studies for detection of HNSCC is still in the very early pre-clinical stages. Less than a handful of studies have addressed this potential method for detection of HNSCC. Hsu et al. (2005) using a simple fluorescence spectroscopy system developed a molecular-specific contrast agent targeted against EGFR as a screening technique for oral cancer. The system was tested in four in vitro models including fresh tissue slices from normal and abnormal oral cavity biopsies and whole normal and abnormal oral cavity biopsies. These investigators proved that an inexpensive and simple spectroscopy system can be used in biological models of living

Head and Neck Squamous Cell Carcinoma

systems to detect the optical signal from a contrast agent targeted toward a cancer-related biomarker with good signal-tonoise ratios. This system has the potential to improve the early detection of oral neoplasia by providing a low-cost screening tool. Scher (2007) analyzed whether in vivo videomicroscopy (IVVM) is useful for the study of distant metastasis and studied the possible role of nitric oxide in the development of metastasis from HNSCC. Videomicroscopic images of a human squamous cell carcinoma cell line (FaDu) labeled with an intracytoplasmic fluorescent marker were analyzed in the microcirculation for cell adhesion, morphology, deformation, circulation, location of adhesion within the microcirculation, and alteration of microvascular circulation. He concluded that IVVM allows direct assessment of circulating HNSCC with the microcirculation and is a powerful model for the study of distant metastasis. In addition, he found that nitric oxide and IL-1 play a role in increasing the arrest of HNSCC in the liver and are important in the generation of distant metastasis in patients with HNSCC. In a study by El-Sayed et al. (2005), nanoparticle technology was used as potential biosensors in malignant oral epithelial cells. These investigators used a simple and inexpensive technique to record surface plasmon resonance (SPR) scattering images and SPR absorption spectra from both colloidal gold nanoparticles and from gold nanoparticles conjugated to monoclonal anti-epidermal growth factor receptor (anti-EGFR) antibodies after incubation in cell cultures with a nonmalignant epithelial cell line (HaCaT) and two malignant oral epithelial cell lines (HOC 313 clone 8 and HSC 3). They showed that the anti-EGFR antibody conjugated nanoparticles specifically and homogeneously bind to the surface of the cancer type cells with 600% greater affinity than to the noncancerous cells and suggested that SPR scattering imaging or SPR absorption spectroscopy generated from antibody conjugated gold nanoparticles can be useful in molecular biosensor techniques for the diagnosis and investigation of oral epithelial living cancer cells in vivo and in vitro. Diagnosis Because the probability of curing patients with HNSCC is related to the stage of disease, the importance of diagnosing HNSCC at an early stage cannot be overemphasized. Visual inspection is the most common method of detecting oral and oropharyngeal squamous carcinoma. The diagnosis of HNSCC, however, is frequently delayed because symptoms for which patients will seek medical attention such as pain, dysphagia, and shortness of breath occur late in the stage of disease. Laryngeal squamous cell carcinoma remains one of a few subsite that is likely to be detected at earlier stages due to the presenting symptom of persistent hoarseness, which can occur with mild alterations of the vibratory surfaces of the true vocal cords. Radiologic imaging modalities such as computed tomography (CT), magnetic resonance imaging, positron-emission tomography (PET), combined PET/CT, ultrasound, and lymphoscintigraphy are critical tools that provide information on extent of tissue invasion, involvement of regional lymph nodes, and presence of distant metastatic disease. The information

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provided by imaging modalities is critical for staging and subsequent treatment planning. However, there remains significant variability in outcomes for patients within the same tumor-nodemetastasis (TNM) stage. Therefore, many attempts have been made to identify high-risk patients through molecular markers. Exposure to carcinogens such as tobacco and alcohol may result in pre-malignant epithelial changes over a wide field of epithelial surface within the aerodigestive tract. This clinical phenomenon has been called “field cancerization” and may lead to frequent occurrence of multiple primary tumors in epithelial areas affected by widespread pre-malignant disease and possibility of distant-related primary tumors in UADT. For this reason, it is imperative to evaluate mucosa of the UADT including the esophagus and trachea with directed biopsies of suspicious areas during endoscopic examination. By identifying patients at risk for cervical lymph node metastasis and extracapsular spread without the need for surgical node dissection, tissue or serum biomarkers can play a vital role in clinical decision-making. The molecular profiling of primary tumors from HNSCC has been investigated for the potential of predicting the presence of lymph node metastasis at the time of diagnosis. DNA microarray gene expression of primary tumors of the oral cavity and oropharynx found that signature or predictor gene sets can detect local lymph node metastases using material from primary HNSCC with better performance than current clinical diagnosis (O’Donnell et al., 2005; Roepman et al., 2005). Additionally, the gene expression profile of 53 genes with roles in cell differentiation, adhesion, signal transduction, and transcription regulation have been associated with depth of invasion in patients with oral SCC (Toruner et al., 2004). A large number of genes that are abnormally expressed compared to control have been found in tumors from patients with oral SCC. A large percentage of these were upregulated proteins including metalloproteinases (MMPs), proteins involved in regulation of cell adhesion and cell-cycle-related proteins; and downregulation of potential tumor suppressors, suggesting that oral SCC show aberrant expression of genes involved in proliferation, apoptosis, extracellular matrix degradation, and other cellular pathways (Kornberg et al., 2005). Although expression signatures may potentially be able to accurately identify subjects harboring HNSCC and may represent an advance in the classification of these tumors, application of this methodology to the clinical setting has not yet been successfully put to practice. Prognosis The most important factor correlating with prognosis of HNSCC currently remains TNM staging. Large primary tumor size, presence of lymph node metastasis, positive margins after surgical excision, and perineural invasion have been reliable indicators of poor clinical outcome in patients with HNSCC. However, the single most important factor that determines survival is the metastatic status of the cervical lymph nodes at the time of diagnosis. Particularly, the presence of extracapsular spread in cervical lymph node metastasis remains the most

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TABLE 78.1

Prognostic markers in HNSCC

Chromosome

Function

Mutation in HNSCC

Result of mutation

Method of study in HNSCC tumors

Correlation with:

Possible clinical applications

References

p53

17p13

Tumor suppressor

50–69%

Overexpression

IHC

Decreased disease-free survival, overall survival in larynx SCC

Cisplatin resistance: consider other agents Increased radiosensitivity

Cabelquenne et al. (2000); Ogawa et al. (1998)

EGFR

Transmembrane tyrosine kinase

34–80%

Overexpression

IHC

Short disease free survival and overall survival

New chemotherapy agents: 1. Inhibitors of tyrosine kinase (Gefitinib, Erlotinib), 2. Antibodies against EGFR (Cetuximab)

Grandis andTweardy (1993); Bonner et al. (2006)

VEGF

Angiogenic factor

Overexpression

IHC

Poor overall survival

Antibodies against VEGF-A (Bevacizumab)

Teknos et al. (2002); Caponigro et al. (2005)

Gene amplification Overexpression

IHC FISH RT-PCR

Tumor extension, regional lymph node metastases, advanced clinical stage

Capaccio et al. (2000); Bova et al. (1999)

Loss of expression

IHC RT-PCR

Disease relapse

Bazan et al. (2002); Yuen et al. (2002)

Cyclin D1

11q13

Proto-oncogene

17–79%

P16

9p21

Tumor suppressor 52–82%

Genomics of Head and Neck Cancer

Marker

Head and Neck Squamous Cell Carcinoma

significant clinical prognostic indicator of survival, local–regional recurrence and distant metastasis in patients with HNSCC. Although these clinical prognostic parameters provide the best possible criteria for deciding treatment modalities, they are limited in discerning future behavior of aggressive HNSCC. The search for novel molecular prognostic markers with potentially significant predictive value for biological aggressiveness of HNSCC has exploded in the recent years. Better prediction of the risk of developing distant metastases would help introduce a more selective treatment approach according to the biological aggressiveness of the tumor. Several molecular mediators of tumor progression, invasion, and metastasis, which function in growth factor signaling, metastasis, and suppressor genes have been well investigated in HNSCC (Table 78.1) and will be described below. TP53 is a tumor suppressor gene located on 17p13 and consists of 11 exons that encode a protein, p53, and function in carcinogenesis by initiating G1 arrest in response to certain DNA damage and apoptosis. The prevalence of TP53 mutations is 50–69% in HNSCC. A number of studies have shown that TP53 gene mutations are associated with increased risk for locoregional recurrence and poor outcome (Erber et al., 1998; Mineta et al., 1998). Mutated p53 protein overexpression has also been associated with tumor recurrence, poor disease-free survival rate, poor overall survival rate, increased rates of local control, and decreased disease-free survival in HNSCC. A significant correlation between p53 expression and clinical outcome appears to be the strongest in the subsite of patients with laryngeal SCC. In these patients, overexpression of mutated p53 predicts poor disease-free and poor overall survival rates. Epidermal growth factor receptor (EGFR) is a transmembrane tyrosine kinase capable of promoting neoplastic transformation. The downstream signaling events upon ligand binding include activation of tyrosine kinase and activation of intracellular Ras, Raf, and mitogen-activated protein kinase (MAPK) cascades. EGFR expression has been extensively studied in HNSCC and its overexpression reported in 34–80% of HNSCC using IHC (Beckhardt et al., 1995; Grandis and Tweardy, 1993). This marker has been significantly associated with short diseasefree survival, overall survival, and poor prognosis in patients with HNSCC. The EGFR family includes EGFR (c-erbB1 or Her1), c-erbB2 (Her2-neu), c-erbB3 (Her3), and c-erbB4 (Her4), which have the ability to form receptor heterodimers and crosstalk between them. Both EGF and transforming-growth factor-alpha (TGF-) are ligands that bind to EGFR. Co-expression of Her2 and Her3, which do not have intrinsic tyrosine kinase activity, is reported to increase transforming activity and their expression has been strongly associated with shortened patient survival. In addition, the co-expression of Her2 and Her3 significantly improved the predictive power in patients with oral SCC and suggest a common regulatory mechanism. Vascular endothelial growth factor (VEGF) induces proliferation, migration, and survival of endothelial cells during tumor growth by binding to specific tyrosine receptor kinases.

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Thus far, six members of the VEGF family have been identified; VEGF-A/vascular permeability factor (four isoforms),VEGF-B/ VEGF-related factor (VRF), VEGF-C/VEGF-related protein (VRP), VEGF-D/c-fos-induced growth factor (FIGF), VEGF-E and placenta growth factor (PlGF) and abnormal regulation of angiogenic factors have been implicated in the pathogenesis of cancer. Overexpression of VEGF-A and VEGF-C has been correlated to poor overall survival in patients with advanced stage HNSCC (Teknos et al., 2002). Furthermore, pre-treatment levels of VEGF-A and/or VEGF-C in early stage disease appear to be independent predictors of presence of cervical lymph node metastasis (O-Charoenrat et al., 2001). Cyclin D1 (CCND1) is a proto-oncogene located on chromosome 11q13 that modulates a critical step in cell cycle control progression. Amplification and/or overexpression of CCND1 have been demonstrated in 17–79% of tumor specimens from patients with HNSCC with immunohistochemistry, FISH, or RT-PCR. CCND1 amplification and/or overexpression of the structurally normal protein has been shown to correlate significantly with tumor extension, regional lymph node metastases and advanced clinical stage of HNSCC (Capaccio et al., 2000). In addition, many recent studies show convincing data of CCND1 aberration as a prognostic marker for diseasefree survival and overall survival in patients with this disease (Bova et al., 1999; Michalides et al., 1995). The role of P16 in HNSCC carcinogenesis and progression has not been clearly established. The p16 protein, which is encoded by the CDKN2A (MTS1, INK4A) tumor suppressor gene on chromosome 9p21, inactivates the function of cdk4cdk6-cyclin D complexes. Using fluorescence in situ hybridization (FISH) or PCR-based techniques, P16 deletion or mutation was detected in 48–52% of tumor specimens from patients with HNSCC and has been significantly associated with decreased survival, development of distant metastases, and as an independent predictive factor for disease relapse and death (Bazan et al., 2002). However, downregulation of p16, although found in 48% of tumors and associated with a more locally advanced tumor, had no prognostic significance for nodal metastasis and survival in studies examining p16 expression by immunohistochemical techniques or P16 gene alterations by RT-PCR in specimens of patients with HNSCC (Danahey et al., 1999; Yuen et al., 2002) . The contrast in these study outcomes may not only reflect differences in the type of tissue and methods used for examination, but also depend on differences in treatment protocols. In larger studies of patients with HNSCC, DNA microarray analysis has been used to identify distinct gene expression signatures associated with clinical outcome in HNSCC. Distinct subtypes of HNSCC with different clinical outcomes based on gene expression patterns obtained from tumor samples from patients with HNSCC were recently described. These subtypes had significant differences in clinical outcomes including recurrence-free survival and overall survival, and were able to identify patterns of expression that could predict the presence of lymph node metastases in HNSCC tumors (Chung et al., 2004).

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Pharmacogenomics The response to chemotherapy, radiation and their side effects in patients with head and neck cancer is dependent on several factors including pharmacogenetics (see Chapter 27). Organ preservation therapy using chemotherapy with cisplatin-based chemotherapy and 5-fluorouracil combined with radiotherapy have been used effectively in patients with advanced HNSCC. In addition, studies have shown that patients that achieve a complete response to chemotherapy have a better prognosis than those that do not. Ideally, molecular markers could be used to determine individual pharmacogenetic profiles to identify patients most likely to have chemotherapeutic benefit and patients with the highest risk of suffering genotoxic side effects. These profiles will ideally lead to individualized therapies, improved treatment outcomes, and a movement toward clinically applied pharmacogenetics. Cisplatin (cis-diamminedichloroplatinum [CDDP])-based chemotherapy as part of multimodality treatment has shown significant activity against HNSCC, as a DNA-damaging agent. However, its effectiveness in the treatment of recurrent HNSCC has been limited due to acquired or intrinsic resistance. The mechanisms of resistance to cisplatin-based chemotherapy are not well understood. Molecular markers involved in cisplatin resistance phenotype has been investigated by various groups in cisplatin resistant and sensitive HNSCC cell lines using cDNA microarray analysis. Upregulation of the gene expression of glycoprotein hormone alpha subunit and downregulation of human folate receptor and tumor-associated antigen L6 were described (Higuchi et al., 2003). Decreased expression of caveolin-1, a novel TSG correlated with cisplatin resistance in patients with oral SCC (Nakatani et al., 2005). Resistance to cisplatin chemotherapy has also been shown to be significantly correlated to expression of mutant p53 (Bradford et al., 2003), which is frequently present in head and neck cancer. Tumor cell lines from patients with HNSCC with mutant p53 have shown significantly more sensitivity to cisplatin than those that do not contain these mutations. The authors of these studies state that tumors that are resistant to cisplatin also overexpress anti-apoptotic proteins Bcl-2 and Bcl-xL. Other studies have shown that p53 mutated tumors was higher in the group of patients with nonresponse to cisplatin-fluorouracil neoadjuvant chemotherapy than in the group of responders (Cabelquenne et al., 2000). Overexpression of mutant-type p53 expression in HNSCC was also shown to be associated with increased sensitivity to ionizing radiation (Ogawa et al., 1998; Servomaa et al., 1996). Recently, low expression of the apoptosis-blocking protein family members has been shown to be a good predictor of chemotherapy response in patients with head and neck cancer. Low expression of Bcl-xL in tumor specimens from patients with HNSCC enrolled in the Department of Veterans Affairs Laryngeal Cancer Group Study correlated with response to induction chemotherapy (Bradford et al., 2003). In addition, it was shown that induction of mutant p53 in HNSCC lines resulted in decreased expression of Bcl-2 and increased susceptibility to

cisplatin-induced apoptosis and implicates Bcl-2 in the deregulation of p53-induced apoptosis (Andrews et al., 2004). Increased tumor resistance to cytotoxic agents, including radiotherapy (Milas et al., 2003, 2004; Baumann and Krause, 2004) has been associated with EGFR overexpression in HNSCC in addition to its association with more aggressive tumor behavior. This suggest that evaluation of EGFR status at the time of diagnosis may help identify subset of patient who are at increased risk of neck node metastasis, may have an unfavorable treatment outcome with radiotherapy and may, therefore, benefit from more aggressive treatments. Monitoring Patients treated for HNSCC are followed clinically for evidence of recurrent disease, development of second primary lesions or distant metastasis. The chance of developing a second primary tumor has been estimated at 2–3% per year in patients with HNSCC. In addition, 20–30% of patients treated for HNSCC will develop recurrent disease at primary site and is the most common cause of treatment failure. Because prognosis of late stage recurrent disease is dismal, early detection is imperative. Distant metastatic disease occurs in 11–15% of patients treated for HNSCC and, at this stage, treatment is palliative. Identifying molecular markers in primary tumors that are associated with locoregional relapse may allow for early identification of patients needing additional surveillance and treatment and may have the potential of decreasing probability of distant disease. For example, EGFR overexpression has been shown to be an independent prognostic factor for neck node relapse in primary specimens of patients with laryngeal SCC undergoing primary resection (Almadori et al., 1999). In addition, when surgical margins of primary HNSCC are examined for mutational changes, there is an increased risk of local recurrence when positive margins demonstrating clonal alterations in TP53 are observed (van Houten et al., 2004). Promoter hypermethylation is an important mechanism to silence TSG in cancer. Aberrant DNA methylation of P16, O6-methylguanine-DNA-methyltransferase, glutathione Stransferase P1, and death-associated protein kinase (DAP-kinase), key genes involved in critical pathways in head and neck tumor progression, have been detected in the serum and in the saliva of patients with HNSCC (Sanchez-Cespedes et al., 2000; Rosas et al., 2001). Thus, promoter hypermethylation of key genes may be promising serum and saliva markers for monitoring affected patients with HNSCC. Gene expression signatures using DNA microarray technology have potential utility as biomarkers to predict patients at risk for locoregional recurrence. Several gene expression signatures from HNSCC tumors from various anatomical sites in the head and neck have been identified. In one study, deregulated gene expression of the met-proto-oncogene and its ligand hepatocyte growth factor/scatter factor; snail homologue 2 (SNA12/ SLUG), a transcriptional repressor involved in epithelial/mesenchymal transitions; and genes important for tumor cell/extracellular matrix interactions such as laminins and integrins were associated with risk of local treatment failure (Ginos et al.,

Head and Neck Squamous Cell Carcinoma

2004). In addition, six novel poorly characterized differentially genes potentially involved in acquisition of metastatic potential were identified when tumors from patients with SCC of the hypopharynx were studied for identification of biomarkers of aggressive clinical behavior (Cromer et al., 2004). These studies show the feasibility of monitoring HNSCC patients at risk for developing locoregional recurrence by molecular characterization of genes in primary tumors or in serum and saliva of patients with HNSCC. Because surgical salvage rates are greatly diminished when occult nodal disease becomes clinically manifest, planned posttreatment neck dissection is advocated but may not be necessary in all patients. The role of PET-CT in this scenario remains unproven but holds promise in being able to identify which patients with advanced nodal disease prior to treatment may be harboring residual disease in the neck after chemoradiotherapy. These patients may then go on to planned post-treatment neck dissection. The implementation of as yet unidentified molecular tumor markers in combination with PET-CT may ultimately prove to be effective in identifying patients who will best benefit from post-therapy neck dissection. Proteomics and Metabolomics of Head and Neck Cancer Whereas genomics offers the opportunity to examine gene expression or variation in gene sequence, proteomics encompasses evaluation of protein expression, activation, modification, and degradation and targets protein function. Likely both proteomics and genomics will provide clinically useful and complementary information that will speed scientific understanding of HNSCC. Proteomic profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an emerging technique to identify new biomarkers in biological fluids and to establish clinically useful diagnostic computational models. Proteomics of HNSCC have revealed significant findings. Baker et al. (2005) combined laser-capture microdissection (LCM) with liquid chromatography-tandem mass spectrometry (LCMS/MS) to identify proteins expressed in histologically normal squamous epithelium and matching SCC. Immunohistochemical analysis of HNSCC tissues revealed lack of keratin 13 in tumor tissues and strong staining in normal epithelia, and high expression of Hsp90 in tumors. Likewise, cellular proteomes of 30 matched normal squamous epithelial cells and carcinoma specimens were analyzed after tissue microdissection using microarray composed of 83 different antibodies by Weber et al. (2007). Of the 83 proteins examined, 14 showed differential expression between HNSCCs and normal epithelium. The protein microarray approach revealed an upregulation of eight proteins and a downregulation of six proteins. Bag-1, Cox-2, Hsp-70, Stat3, pescadillo, MMP-7 (matrilysin), IGF-2, and cyclin D1 were identified to be significantly upregulated, whereas suppressor of cytokine signaling 1, thrombospondin, TGF-beta1, Jun, Fos, and Fra-2 were downregulated. Upon correlation of differentially regulated proteins with the clinicopathologic data of their patients, MMP-7

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(matrilysin) was found to be associated with survival in univariate, but not multivariate, analysis. Protein expression profiles from two laryngeal carcinoma specimens and corresponding adjacent normal tissue were recently investigated as potential biomarkers which may contribute to the pathogenesis of laryngeal carcinoma using two-dimensional differential in-gel electrophoresis and mass spectroscopy. Differentially expressed proteins were identified, and they included stratifin, S100 calcium-binding protein A9, p21-ARC, stathmin, and enolase (Sewell et al., 2007). Protein expression profiles were also analyzed by Roesch-Ely et al. (2007) in 113 HNSCCs and 73 healthy, 99 tumor-distant, and 18 tumor-adjacent squamous mucosa by SELDI-TOF-MS on IMAC30 ProteinChip Arrays. Calgizarrin (S100A11), the Cystein proteinase inhibitor Cystatin A, Acyl-CoA-binding protein, Stratifin (14-3-3 sigma), Histone H4, alpha- and beta-Hemoglobin, a C-terminal fragment of betahemoglobin and the alpha-defensins 1-3 were identified by mass spectrometry. Comparison of the protein profiles in the tumordistant-samples with clinical outcome of 32 patients revealed a significant association between aberrant profiles with tumor relapse events. Zhou et al. (2006) studied 100 serum samples including 48 from HSCC patients and 52 from normal controls by SELDITOF-MS. They found that 45 potential biomarkers could differentiate HSCC patients from normal controls. Metabolomics, the study of metabolite changes in a biological system, is believed to be a good reflection of the phenotype of any cell or tissue, and studies have reported that tumoral tissue differ in metabolic content from their benign counterpart. Proton nuclear magnetic resonance (HNMR) spectroscopy is emerging as a rapid and noninvasive method in identifying new markers potentially useful for clinical diagnosis in biofluids, such as urine and plasma. However, this new -omic science has not yet been significantly evaluated in clinical studies in HNSCC. Although, metabolomics may be a complimentary tool to genomics and proteonomics in diagnosing cancer, refining the process of targeted drug discovery, defining surrogate markers that may predict response, toxicity, and prognosis, several years of study will be necessary before the impact of this new technology in HNSCC can be truly assessed. Novel and Emerging Therapeutics The EGFR and the vascular endothelial growth factor receptor (VEGFR) are the two most promising new targets for the treatment of head and neck cancer. As single agents or in combination with chemotherapy, agents that bind to EGFR or VEGF have demonstrated clinical activity in Phase I and II trials. The epidermal growth factor receptor is a member of the ErbB family of receptors and is composed of ligand-binding extracellular domain, a hydrophobic transmembrane region and a tyrosine kinase containing cytoplasmic region. There are two classes of anti-EGFR agents: monoclonal antibodies (mAb) directed at the extracellular domain of the receptor and small molecule adenosine triphosphate competitive inhibitors of the receptor’s tyrosine kinase (TK). Combining EGFR targeted

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therapy with mAb and TK inhibitors is appealing and is being studied in patients with HNSCC. In pre-clinical trials, combined treatment with two agents resulted in a greater inhibition of EGFR signaling, synergistic effect on cell proliferation, and induction of apoptosis. The Food and Drug Administration (FDA) has recently approved the use of three EGFR inhibitors for cancer therapy; the mAb Cetuximab (ErbituxTM) and the small molecule TK inhibitors gefitinib (IressaTM) and erlotinib (TarcevaTM). Cetuximab, which blocks the binding of EGF and TGF, is a recombinant DNA-derived, chimeric human-murine mAb that binds to EGFR with similar affinity to natural ligand. It induces antibody-mediated receptor dimerization, which results in downregulation of EGFR and inhibition of growth, enhanced apoptosis and reduced angiogenesis, invasion and metastasis. It has been studied as single agent or in combination with chemotherapy in active and completed trials (Bourhis et al., 2006; Herbst et al., 2005). A recently reported Phase III trial combining Cetuximab with high-dose radiation on locoregional control and survival in 424 patients with locally advanced HNSCC demonstrated a statistically significant prolongation in 3-year survival rate (44% in control arm and 56% in Cetuximab arm) and improved 2-year locoregional control from 48% to 56% (Bonner et al., 2006). A recent Phase II trial combining Cetuximab with or without definitive chemoradiotherapy for advanced HNSCC is encouraging (Pfister et al., 2006). The use of gefitinib as a single agent in 47 patients with locally advanced HNSCC who failed or were determined unfit for standard chemotherapy, demonstrated 36% rate of disease control (partial response or stable disease). This result is more favorable than that achieved with standard palliative therapy in this setting, with the added benefits of reduced treatment toxicity and the ease of oral administration (Kirby et al., 2006). Studies of human HNSCC cell lines in mice also suggest that the combination of gefitinib and cyclooxygenase-2 (COX-2) inhibitor, celecoxib, exhibit a cooperative effect on progression and growth of tumors through blocking both EGFR- and COX-2-related pathways (Zhang et al., 2005). Angiogenesis has been linked to tumor progression of HNSCC and poor clinical outcome. The combination of bevacizumab and erlotinib is presently being evaluated in clinical trials in patients with HNSCC (Caponigro et al., 2005). Bevacizumab is a human mAb against VEGF-A and prevents binding of all VEGF isoforms to all VEGF receptors. In addition, targeting multiple pathways simultaneously may be an appropriate strategy for the treatment of HNSCC. However, accurately identifying patients who will benefit from this novel therapeutics may require the expression of the target agent. Clinical trial data, however, show no correlation between tumor expression of EGFR and treatment response to EGFR inhibitors (Harari and Huang, 2006). However, the current evaluation of EGFR and VEGF is limited by technology. Newer technologies such as genomic polymorphisms may allow for better detection of these molecular targets. Nevertheless, these treatments are emerging as promising new targeted therapies for the treatment of HNSCC.

Genomics of HNSCC: Clinical Applications Genomic technologies have the potential to change the basis of clinical oncologic practice in various ways. Information on the disease pathology of HNSCC generated from genomic technologies could be translated into: (1) diagnostic tests of biomarkers of cellular proliferation, cell damage or death, and cellular metabolism, and (2) therapeutic products, as is seen with the development of EGFR antagonists, which are presently in clinical practice for treatment of HNSCC. Genomic approaches are also changing the conduct of clinical trials. Many clinical trials of HNSCC are now being designed with genomic testing in mind. Importantly, biomarkers in tissue or body fluids identified by genomics offer a means for detecting early or recurrent disease in patients that are at risk for HNSCC. This technology has the added potential to characterize individuals that are likely to have a response to chemotherapy and/or radiotherapy, so that treatment can be tailored to the individual patient. As a preventive approach, genome-based information of the potential risk for HNSCC may allow early intervention with preventive measures, which may ultimately reduce healthcare costs. The economic value of using genomic technology in clinical setting and in clinical trials have not been fully addressed. However, because of the potential advantages in targeted drug development and drug-safety concerns, significant growth of molecular approaches to clinical medicine will continue. Therefore, the combination of genomic, proteomic, and metabolomic technologies may allow us to monitor a large number of key cellular pathways simultaneously and may provide a better understanding of HNSCC.

CONCLUSION The present knowledge of head and neck cancer molecular pathogenesis favors the existence of deregulated and redundant pathways that may be targets for disease diagnosis, monitoring, and treatment. Proper validation of new biomarkers is of paramount importance; however, standardization of scientific techniques is still missing. Many of the genetic alterations involved in the development and progression of HNSCC such as TP53 and EGFR have been well-characterized. However, association with survival for many such as TP16 has been called into question. Nevertheless, in recent years, the field of head and neck cancer therapy has witnessed the emergence of novel targeted therapies such as against EGFR and VEGFR that inhibit specific pathways and key molecules in growth and progression of squamous carcinoma of the head and neck. Phase II clinical trials using EGFR in advanced HNSCC has shown advantage in survival and locoregional control in the group receiving Cetuximab. However, correlation between EGFR expression on tumors and treatment response has not been demonstrated thus far. Perhaps specific mutations in the EGFR genes may offer powerful new opportunities to predict those patients more likely to benefit from the use of targeted treatments.

References

Although we have seen recent success in the targeted therapy of head and neck cancer, several major gaps in our knowledge remains regarding the predictive potential of many other biomarkers in HNSCC. Understanding of specific molecular and cellular mechanisms whereby biomarkers in HNSCC continue to carcinogenesis or progression of HNSCC is critical. New genomic technologies such as DNA and tissue microarray coupled with advanced bioinformatics tools may make it feasi-

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ble to study biomarkers that are able to reliably and accurately predict outcome during cancer management and treatment. Future work in this field should address studies that incorporate complimentary advanced imaging technology with molecular surveillance in the monitoring of patients with HNSCC. Methods to identify mutations or molecular footprints to predict those patients most likely to respond favorably to targeted therapy in combination with tradition treatments are needed.

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Recommended Resources

Schantz, S.P., Zhang, Z.F., Spitz, M.S., Sun, M. and Hsu, T.C. (1997). Genetic susceptibility to head and neck cancer: Interaction between nutrition and mutagen sensitivity. Laryngoscope 107(6), 765–781. Scher, R.L. (2007). Role of nitric oxide in the development of distant metastasis from squamous cell carcinoma. Laryngoscope 117(2), 199–209. Servomaa, K., Kiuru,A., Grenman, R., Pekkola-Heino, K., Pulkkinen, J.O. and Rytomaa, T. (1996). p53 mutations associated with increased sensitivity to ionizing radiation in human head and neck cancer cell lines. Cell Prolif 29(5), 219–230. Sewell, D.A.,Yuan, C.X. and Robertson, E. (2007). Proteomic signatures in laryngeal squamous cell carcinoma. ORL J Otorhinolaryngol Relat Spec 69(2), 77–84. Spafford, M.F., Koch, W.M., Reed, A.L., Califano, J.A., Xu, L.H., Eisenberger, C.F.,Yip, L., Leong, P.L.,Wu, L., Liu, S.X. et al. (2001). Detection of head and neck squamous cell carcinoma among exfoliated oral mucosal cells by microsatellite analysis. Clin Cancer Res 7(3), 607–612. Sudbo, J. and Reith, A. (2005). The evolution of predictive oncology and molecular-based therapy for oral cancer prevention. Int J Cancer 115(3), 339–345. Teknos, T., Cox, C.,Yoo, S., Chepeha, D., Wolf, G., Bradford, C., Carey, T. and Fisher, S. (2002). Elevated serum vascular endothelial growth factor and decreased survival in advanced laryngeal carcinoma. Head Neck 24(11), 1004–1011.

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Toruner, G.A., Ulger, C., Alkan, M., Galante, A.T., Rinaggio, J., Wilk, R., Tian, B., Soteropoulos, P., Hameed, M.R., Schwalb, M.N. et al. (2004). Association between gene expression profile and tumor invasion in oral squamous cell carcinoma. Cancer Genet Cytogenet 154(1), 27–35. Weber,A., Hengge, U.R., Stricker, I.,Tischoff, I., Markwart,A.,Anhalt, K., Dietz, A., Wittekind, C. and Tannapfel, A. (2007). Protein microarrays for the detection of biomarkers in head and neck squamous cell carcinomas. Hum Pathol 38(2), 228–238. van Houten,V.M., Leemans, C.R., Kummer, J.A., Dijkstra, J., Kuik, D.J., van den Brekel, M.W., Snow, G.B. and Brakenhoff, R.H. (2004). Molecular diagnosis of surgical margins and local recurrence in head and neck cancer patients: A prospective study. Clin Cancer Res 10(11), 3614–3620. Yuen, P.W., Man, M., Lam, K.Y. and Kwong, Y.L. (2002). Clinicopathological significance of p16 gene expression in the surgical treatment of head and neck squamous cell carcinomas. J Clin Pathol 1, 58–60 Zhang, X., Chen, Z., Choe, M.S., Lin, Y., Sun, S-Y., Wieand, H.S., Shin, H.J.C., Chen, A., Khuri, F.R. and Shin, D.M. (2005). Tumor growth inhibition by simultaneously blocking epidermal growth factor receptor and cyclooxygenase-2 in a xenograft model. Clin Cancer Res 11(17), 6261–6269. Zhou, L., Cheng, L.,Tao, L., Jia, X., Lu,Y. and Liao, P. (2006). Detection of hypopharyngeal squamous cell carcinoma using serum proteomics. Acta Otolaryngol 126(8), 853–860.

RECOMMENDED RESOURCES Slaughter, D.P., Southwick, H.W. and Smejkal, W. (1953). Field cancerization in oral stratified squamous epithelium; clinical implications of multicentric origin. Cancer 6(5), 963–968. This is a landmark article describing the concept of field cancerization and its implications in development of second primaries in the upper aerodigestive tract. Thomas, G.R., Nadiminti, H. and Regalado, J. (2005). Molecular predictors of clinical outcome in patients with head and neck squamous cell carcinoma. Int J Exp Pathol 86(6), 347–363. This presents a comprehensive review of molecular markers involved in carcinogenesis and progression of head and neck squamous cell carcinoma and their associations with clinical outcome.

Patmore, H.S., Cawkwell, L., Stafford, N.D. and Greenman, J. (2005). Unraveling the chromosomal aberrations of head and neck squamous cell carcinoma: a review. Ann Surg Oncol 12(10), 831–842. This is an in-depth evaluation of specific chromosomal aberrations from data generated from comparative genomic hybridization analysis of head and neck squamous cell carcinoma.

CHAPTER

79 Genomic Medicine, Brain Tumors and Gliomas Sean E. Lawler and E. Antonio Chiocca

INTRODUCTION Approximately 20,000 new patients with primary brain tumors are diagnosed in the United States each year (De Angelis, 2001). These constitute the most common solid tumors in children, and rank first among all cancer types in average years lost. They rarely metastasize outside the central nervous system; however, more than 100,000 patients per year die with symptomatic intracranial metastases due to systemic primary cancer. Tumor type is currently determined by histopathologic analysis of biopsy samples and graded on a scale of I (benign) to IV (highly malignant) based on a range of histological tumor features (frequency of mitotic figures, necrosis, nuclear atypia and vascularity) according to the WHO classification of nervous system tumors (Kleihues and Cavenee, 2000; Louis et al., 2007). The various brain tumor types show similarities in clinical presentation, diagnosis and treatment. Most are treated by aggressive surgical resection when possible, followed by chemo and/or radiotherapy. Brain tumors are especially challenging, because they are often resistant to therapies, progress rapidly, and infiltrate normal brain tissue. Even a benign brain tumor may seriously compromise normal brain function, and surgical tumor excision must be carried out without damaging vital brain structures. Delivery of drugs to the central nervous system, and therapy induced neurotoxicity present further obstacles to effective treatment.

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This article will focus on gliomas, the best studied and most common primary brain tumor type, accounting for approximately half of all cases. Gliomas are usually classified as either astrocytic or oligodendroglial (summarized in Table 79.1). Low-grade gliomas such as pilocytic astrocytomas are benign and have a good prognosis. Survival range for Grade II and III astrocytomas is broad, and these usually progress to higher-grade tumors. The most common and most aggressive astrocytic tumor is the Grade IV glioblastoma multiforme, among the deadliest of human cancers with a median survival of around 12 months, even with aggressive treatment. Oligodendrogliomas and the mixed oligoastrocytomas are less common than pure astrocytomas, and typically have longer survival times. Most low-grade oligodendrogliomas progress to a higher-grade tumor. With early diagnosis by MRI and chemotherapy, the mean survival is 16 years. There are several well established molecular alterations commonly seen in gliomas, which are also observed in many other cancer types (reviewed in Louis, 2006; Schwartzbaum et al., 2006). Gliomas are typically characterized by increased tyrosine kinase receptor activation through activation of platelet-derived growth factor receptor (PDGFR) or epidermal growth factor receptor (EGFR) signaling. This is reflected in amplified levels of receptors and/or their ligands, or the presence of mutated constitutively active receptors such as EGFRVIII. This leads to activation of downstream intracellular signaling pathways, the

Copyright © 2009, Elsevier Inc. All rights reserved.

Predisposition

TABLE 79.1

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Common subtypes of glioma and their basic characteristics

Tumor type

Characteristics

Peak incidence

Survival

Pilocytic Astrocyoma (I)

Slow growing, often cystic

Children

Infrequently fatal

Diffuse Astrocytoma (II)

Slow growing, invasive tendency to progress

Young adults

Mean 6–8 years highly variable

Anaplastic Astrocytoma (III)

tendency to progress

40–60 years

Mean TTP 2 years

Glioblastoma (IV)

De novo, or secondary mitotic figures, anaplasia, necrosis, vascularity

45–70 years

12 months

Grow diffusely in cortex and white matter

50–60 years

3–15 years

Oligodendrocytic and astrocytic characteristics

35–50 years

4–7 years

Astrocytic

Oligodendroglial Oligodendroglioma (II) and Anaplastic oligodendroglioma (III) Mixed Oligoastrocytoma (II) and anaplastic oligoastrocytoma (III)

PI3K/Akt pathway being considered as extremely important by promoting cell survival. High-grade gliomas are also associated with loss of the tumor suppressor phosphatase and tensin homolog (PTEN), which acts by further activating PI3K signaling. The cell cycle is deregulated through disruption of p53 (alterations in p53, HDM2, and p14ARF) and RB (through alterations in p16 and RB) pathways. Based on age of onset and pathology, two different kinds of glioblastoma exist, with characteristic genetic alterations, although they are clinically similar (Kleihues and Cavenee, 2000). Glioblastoma in older patients shows no sign of previous low-grade tumors, and is known as either de novo or primary glioblastoma (95%). Typically these tumors show EGFR amplification, p16 deletions and PTEN deletions. Secondary glioblastoma occurs in younger patients (5%) and is due to progression from lower-grade astrocytoma. These are characterized by p53 mutation and amplification of PDGF signaling. In contrast to astrocytomas, oligodendrogliomas are characterized by allelic loss of 1p and 19q. However, tumor progression is associated with changes similar to those seen in astrocytomas (see Figure 79.1). Emerging data from the use of high-throughput, chip-based global molecular profiling techniques such as array-CGH and gene expression profiling suggest that these changes represent only a fraction of the alterations in gliomas, as described in the following sections.

PREDISPOSITION Relatively few genetic and environmental factors that influence brain tumor development have been clearly identified (reviewed in Schwartzbaum et al., 2006), and only a small minority of tumors can be attributed to inherited predisposition. For example, a shared

susceptibility to breast cancer, brain tumors, and Fanconi anemia was reported in four families with germline BRCA2 mutations (Offit et al., 2003). In addition, genetic predisposition is associated with various familial cancer syndromes (Turcot’s, NF1, NF2, and Li-Fraumeni), accounting for 1–2% of all brain tumors. Inherited polymorphisms that may influence brain tumor formation involve oxidation, DNA repair, and immune function. For example, the DNA repair gene XRCC7 G7621T variant, leads to a 1.8-fold increased risk of glioblastoma (Wang et al., 2004) and the hTERT MNS16A allele results in a twofold increase in survival, due to higher expression levels of this allele (Wang et al., 2006). A consistent inverse association between self-reported allergic conditions and glioma has been observed. Asthma-associated polymorphisms in IL-4RA and IL-13 were inversely associated with glioblastoma incidence. This is a provocative observation because these factors have also been shown to inhibit glioma growth, and suggests a link between glioblastoma and immune function (Schwartzbaum et al., 2005). Epidemiological studies have shown that glioblastoma has higher incidence in the white population and is more common in men than women (ratio 3:2), for unknown reasons. Many environmental risk factors have been investigated, including the use of cell phones, electromagnetic fields, viral infection, diet, tobacco, and previous head trauma/injury (Schwartzbaum et al., 2006). Gamma-radiation is the most widely accepted risk factor for gliomas. Indeed, genetic sensitivity to radiation or environmental carcinogens may play a role in brain tumor pathogenesis (Bondy et al., 2001). The limited findings on genetic and environmental factors involved in brain tumor development may reflect the complexity of genetic interactions with environmental factors. In addition, there is a need for studies to be carried out

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Tumor originating cell – Astrocyte, Oligodendrocyte, Glial progenitor p53 mutation (65%) LOH 1p/19q (70%)

Oligodendroglioma (grade II)

PDGFRA overexpression (60%) Astrocytoma (grade II)

CDKN2A loss

EGFR ampification (40%) MDM2 increase (50%)

LOH 19q (50%) RB alteration (25%)

CDKN2 loss (40%) LOH10p

Oligodendroglioma (grade III)

Astrocytoma (grade III)

PTEN mutation (30%) RB alteration

LOH 10q PTEN mutation (5%)

Secondary glioblastoma

Primary glioblastoma De novo

Figure 79.1 Pathways to glioma formation. Three pathways of genetic alteration are currently known that define Oligodendroglioma, primary glioblastoma and secondary glioblastoma. The major genetic alterations associated with tumor progression are shown, with their approximate frequency.

in large sample groups to confirm many preliminary findings, that have been reported in small studies. A brain tumor epidemiology consortium has recently been established in order to further understanding in this area (Schwartzbaum et al., 2006).

SCREENING Brain tumors are comparatively rare, tissue is not easily accessible, predisposition is poorly understood, and reliable serum markers have not been established; therefore screening is not currently carried out. However, in the future, improved imaging techniques may lead to earlier detection of pre-symptomatic lesions, providing the opportunity for effective surgical intervention. Effective screening for brain tumors would be greatly facilitated by the identification of consistent robust serum markers. Through efforts in gene expression profiling and proteomics candidate markers are emerging as described in later sections.

DIAGNOSIS AND PROGNOSIS Brain tumor diagnosis is currently based largely on histologic analysis of tumor biopsies, according to WHO guidelines. Major prognostic factors in gliomas are tumor type and grade, patient age, symptom duration, degree of surgery and neurological deficit. The few long-term survivors of glioblastoma are young, in otherwise good health, and able to undergo gross total resection followed by chemo and radiotherapy. The current WHO tumor

classification system suffers from well recognized problems due to interobserver variability, tumor heterogeneity and ambiguous tumor types (Louis et al., 2001). Very few further molecular markers are in common use at present, although codeletion of 1p and 19q is useful in predicting clinical responsiveness in oligodendroglioma, as described below. The incorporation of molecular information would allow less subjective diagnosis, and more precise prognostic information. Chromosomal Alterations in Brain Tumors A range of characteristic genetic changes have been observed, which in high-grade gliomas are variable and complex (reviewed by Bayani et al., 2005). Benign tumor types are characterized by fewer changes, the most prominent being alteration of chromosome 22. Cytogenetic analysis often reveals a normal karyotype for grade I astrocytomas, whereas grade II gliomas often have mutations in the TP53 gene and overexpression of PDGFRA. The most commonly observed genetic alteration in glioblastoma is loss of chromosome 10 (60–80%), resulting in reduced PTEN expression, although it is possible that other genes on chromosome 10 are important. Gain of chromosome 7 (EGFR), and loss of 9p (p16 and P14ARF), are also consistently observed (shown in Figure 79.1). Other consistently observed chromosomal alterations are shown in Table 79.2. Recent, detailed microarray-based high-resolution genomic mapping studies have confirmed the presence of the commonly observed genetic changes in brain tumors and are revealing many more alterations whose significance is not yet understood, which may also eventually become useful diagnostic tools. Kotliarov

Diagnosis and Prognosis

TABLE 79.2 tumors

Common genetic alterations seen in glial

Region

Alterations

Candidate glioma genes

1p (34-pter) (various)

Gains and deletions

Unknown

1q32

Gains

RIPK5, MDM4, PIK3C2B

4q

Deletions

NEK1, NIMA

7p11.2 – p12

Amplification/gain

EGFR

9p21 – p24

Deletions

CDKN2

10q23

Deletions

PTEN

10q25 – q26

Deletions

MGMT

12q13.3 – q15

Amplification

MDM2, CDK4

13p11 – p13

Loss

RB

19q13

Loss

GLTSCR1, GLTSCR2, LIG1, PSCD2

22q11.2 – q12.2

Loss

28 genes including INI1

22q13.1 – 13.3

Loss

Not known

Adapted from Schwartzbaum et al. (2006).

et al. (2006) reported the use of Affymetrix high density 100 K SNP arrays to analyze genomic alterations in 178 glioma samples at an unprecedented resolution of 25 Kb. Array-CGH analysis of glioblastoma has uncovered numerous novel changes and can easily distinguish primary from secondary glioblastoma, and also showed that secondary glioblastoma falls into two distinct groups (Maher et al., 2006). Each tumor group had many more unique events than shared and therefore may benefit from quite different therapeutic approaches. cDNA microarray-based CGH was able to predict tumor type successfully based on common genetic alterations (Bredel et al., 2005). All these studies revealed large numbers of previously unidentified alterations. Other studies have reported deletions in chromosomes 6, 21, and 22 (Lassman et al., 2005). Two regions in 6q26 were found to be commonly deleted, containing three novel genes, which may have tumor suppressor functions (Ichimura et al., 2006). Furthermore, the NIH Cancer Genome Atlas Project promises to carry out largescale genomic sequencing to fully understand genetic alterations in cancer. Glioblastoma is one of the initial three cancer types that will be analyzed in this project (http://cancergenome.nih. gov). This analysis promises to revolutionize the level of understanding of this cancer. The most commonly used molecular diagnostic tool based on chromosomal alterations at present is in oligodendroglioma. Oligodendrogliomas frequently show a remarkable sensitivity

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to PCV chemotherapy, (procarbazine, CCNU, and vincristine), with a response rate of approximately 75%. Thus, within a histologically indistinguishable entity, there exist subgroups which show different biological behavior. Molecular genetic studies revealed that LOH of chromosome 1p which is found in 60–80% of oligodendrogliomas, and often accompanied by LOH of 19q (Cairncross et al., 1998), was the underlying reason for the increase in sensitivity. This occurs in both high and lowgrade oligodendrogliomas and also applies to radiotherapy and temozolomide (Hoang-Xuan et al., 2004), the current chemotherapeutic drug of choice in glioma. Combined loss of 1p and 19q are strong correlates of longer survival in oligodendroglioma (Cairncross et al., 1998) and has been associated with increased survival in glioblastoma patients (Ino et al., 2000). Therefore genetic analysis for 1p and 19q is now commonly performed for oligodendroglioma and can be used to determine the most suitable therapeutic strategy. The prognostic significance of 1p/19q was confirmed in large recent trials (Van den Bent et al., 2006). Identification of the key genes involved at 1p and 19q may lead to novel treatment strategies broadly applicable to gliomas in general. It has recently been shown that 1p/19q co-deletion is a result of an imbalanced translocation. This finding may lead to a better understanding of the functional importance of 1p/19q co-deletion (Jenkins et al., 2006). Studies also show that oligodendrogliomas and oligoastrocytomas may be further subgrouped on the basis of distinct genetic changes in addition to 1p and 19q alterations. CGH revealed a hemizygous deletion in 500 kb region in 11q13 and 300 kb region in 13q12 in virtually all low-grade oligodendrogliomas, regardless of 1p/19q status (Rossi et al., 2005), suggesting that further markers exist. Other molecular diagnostic tools include analysis of EGFR amplification, seen commonly in gliomas. Glioblastomas with amplified EGFR often occur in older patients and can be difficult to distinguish from anaplastic oligodendroglioma due to their small cell appearance. Assessment of EGFR along with 1p and 19q can therefore be helpful in this situation (Burger et al., 2001). In summary, at present, to distinguish between different glioma groups, the following changes are the most typical and some are used diagnostically. Loss of chromosomes 1p/19q is typical of oligodendrogliomas, whereas gains of chromosome 7 in the setting of intact 1p/19q are more typical of astrocytomas. The detection of amplified EGFR favors the diagnosis of highgrade astrocytomas over anaplastic oligodendroglioma, which is especially relevant for small cell astrocytomas. Transcriptional Alterations in Brain Tumors Global transcriptional profiling using microarrays has revealed many novel changes in brain tumors (reviewed by Mischel et al., 2004). This initial burst of studies has revealed many alterations of potential utility. However, the vast majority of this data has not been replicated in independent patient cohorts. Expression profiles can readily distinguish different histologically classified tumor types. For example, microarray data could separate glioblastoma from oligodendroglioma on the basis of a 170

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(Shai et al., 2003) or a 70 gene signature (Nutt et al., 2003). EGFR amplified glioblastoma was distinguished from nonamplified EGFR tumors using a 90 gene signature (Mischel et al., 2003), grade II and grade III oligodendrogliomas by a 200 gene subset (Watson et al., 2001) and also oligodendrogliomas with and without 1p loss (Mukasa et al., 2002). Gene expression analysis has also identified tumor subgroups that are indistinguishable histologically. For example, clustering analysis separated oligodendrogliomas into two survival groups based on gene expression (Huang et al., 2004). Two microarray studies of glioblastoma identified gene signatures corresponding to different survival groups, and suggest that increased expression of genes that promote infiltration can lead to a poor prognosis (Liang et al., 2005, Rich et al., 2005). Furthermore, microarrays were better predictors of survival than histological grading for oligodendrogliomas (Nutt et al., 2003). Gene expression profiling therefore may provide useful diagnostic and prognostic information. In a comprehensive study, gene expression profiles were obtained from 76 resected astrocytomas with known survival data (Phillips et al., 2006). Tumors were separated into either short or relatively long survival groups, and cluster analysis then segregated tumors into 3 distinct discrete sample sets based on 35 signature genes. These were defined by the predominant class of gene expressed, as either proneural (PN), proliferative (Prolif), or mesenchymal (Mes). The PN subset was associated with substantially longer survival, was more prominent in younger patients, and showed activated Notch signaling as seen by elevated delta-like protein 3 (DLL3) expression. Poor prognosis was associated with upregulation of proliferative markers such as proliferating cell nuclear antigen (PCNA), topoisomerase 2a (TOP2A), and angiogenesis markers such as vascular endothelial growth factor (VEGF) and its receptors, and platelet endothelial cell adhesion molecule 1 (PECAM1). Markers of neural stem cells were also associated with poor prognosis. All WHO classified grade II tumors were PN, whereas for grade IV tumors 31% were PN, 20% were Prolif and 49% were Mes, with expression profiles shifting toward the Mes class after recurrence. This suggests that the identified subtypes represent stages of tumorigenesis rather than different tumors. This study also correlated gene expression profiles with genetic abnormalities demonstrating that losses of chromosome 10 and gains on chromosome 7 are associated with Prolif and Mes phenotypes (around 80%), compared with 20% for the PN class. The authors therefore propose a model in which PN phenotype progresses to a Prolif phenotype, and ultimately the Mes phenotype, with the poorest prognosis. This kind of study demonstrates that useful information could be obtained from a small subset of signature genes. A similar study identified a 44 gene expression signature to classify gliomas into previously unrecognized biological and prognostic groups which outperformed histology-based classification in survival prediction (Freije et al., 2004). In agreement with Phillips et al. key genes identified include DLL3 (good prognosis), TOP2A (poor) leukemia inhibitory factor (LIF) (poor), S100A4 (poor), and VEGF (poor).

The most common alterations seen in gene expression profiling studies of gliomas are in genes involved in immune system regulation, hypoxia, cell proliferation, angiogenesis, neurogenesis, and cell motility. Ten of the most commonly reported upregulated genes in glioblastoma are summarized in Table 79.3. Many of these changes were not predicted by analysis of chromosomal alterations. Of these highly upregulated genes, the transmembrane glycoprotein GPNMB (Kuan et al., 2006) and YKL40 (also known as chitinase 3 like-1 (CHI3L1)) (Hormigo et al., 2006), have been proposed as prognostic markers. YKL-40 has emerged from gene screening studies as one of the most robust and consistently observed markers in glioblastoma (Freije et al., 2004; Phillips et al., 2006). YKL-40 mRNA was on average 82fold higher in glioblastoma than anaplastic oligodendrogliomas (Nutt et al., 2003). A further study showed that that YKL-40 may be a better marker than glial fibrillary acidic protein (GFAP) which is currently the standard in distinguishing diagnostically challenging gliomas, and suggests that combining YKL-40 and GFAP staining is the best approach at present (Nutt et al., 2005). YKL-40 expression is also associated with radioresistance

TABLE 79.3 The “top ten” overexpressed genes in glioma as judged by their reported appearance in microarray studies cited in this chapter Gene

Symbol(s)

Function

Fibronectin 1

FN1

ECM – angiogenesis, invasion

Insulin like growth factor binding protein 2

IGFBP2

Promotes invasion

Collagens (IV and VI)

COL6A/ COL4A

ECM, promotes invasion

Topoisomerase 2A

TOP2A

DNA replication and transcription

Biglycan

BGN

ECM proteoglycan. Role in glioma unknown

Chitinase 3-like-1

CHI3LI

ECM, invasion, angiogenesis, survival

Vascular endothelial growth factor

VEGF

Angiogenesis, invasion

Vimentin

VIM

Intermediate filament, invasion

Epidermal growth factor receptor

EGFR

Survival, growth, invasion

TGF-1

TGFB1

Proliferation, differentiation, invasion

Pharmacogenomics

(Pelloski et al., 2005). Serial analysis of gene expression (SAGE) analysis of glioma samples revealed that it is the most upregulated gene compared with normal brain (Boon et al., 2004). Taken together the data described shows that gene expression profiling may predict tumor type, patient outcome, and is providing the data to identify new therapeutic targets (Horvath et al., 2006). However, there are a large number of potential markers, and it is not yet clear which of these will finally be used clinically. A potential drawback of transcriptional profiling is that mRNA levels in a given sample may not be representative of the whole tumor, and also that they do not provide an accurate indication of the translation into protein. mRNA must be bound to polysomes in order to be translated, and a study of polysomal RNA in glioma cell lines revealed marked differences to global RNA in the same sample (Rajasekhar et al., 2003). In addition, the recently discovered non-protein coding RNA molecules such as microRNAs can have a profound impact on gene expression by either causing degradation of specific transcripts, or by preventing translation of the mRNA. MicroRNA expression is altered in many cancers (Volinia et al., 2006), including brain tumors, with mir-21 being particularly over-represented in glioblastoma (Chan et al., 2005; Ciafre et al., 2005). This also has functional importance because knockdown of mir-21 leads to apoptosis in glioma cells (Chan et al., 2005). Proteomic Studies in Brain Tumors Proteomic studies on brain tumors typically have used the 2-dimensional gel electrophoresis and mass spectrometry approach. Comparison of glioblastoma with non-tumor tissue identified 11 upregulated and 4 down-regulated proteins including the fatty acid binding protein FABP7 – also identified as a prognostic marker in microarrays (Hiratsuka et al., 2003, Liang et al., 2005). The study closely examined SIRT2, a tubulin deacetylase, downregulated in 12 out of 17 gliomas, and suggested it may act as a tumor suppressor. Another study used 2d-gel-based proteomics to molecularly classify and predict survival in 85 samples including various tumors and normal brain. Expression patterns were identified that could distinguish tumor types and predict survival (Iwadate et al., 2004). Proteomics may find utility in the identification of serum biomarkers that could lead to much simpler diagnosis and monitoring. Few studies have yet been performed in this area, although cathepsin D was found to be elevated in the serum of glioblastoma patients, and associated with poor prognosis (Fukuda et al., 2005). Matrix metalloprotease-9 (MMP-9) and YKL-40 have also been detected in the serum of glioblastoma patients (Hormigo et al., 2006). However, like other studies in this area, independent analysis on further patient groups is not yet available. Direct mass spectrometry of tissue samples is emerging as a powerful tool and may become increasingly influential in the diagnostic arena (Caprioli, 2005). Analysis of 162 patient biopsies from glioma patients using this approach, known as direct tissue matrix-assisted laser desorption ionization mass spectroscopy (MALDI-MS), was used to generate a novel classification

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scheme based on protein expression profiles, which segregated long-term and short-term survivors (Schwartz et al., 2005). The authors were able to distinguish normal brain from glioma biopsies and also to distinguish various grades of tumor by protein expression in these samples. MALDI-MS technology is also amenable to high-throughput tissue screening in a clinical setting. Significant developments can be expected in this area in the near future.

PHARMACOGENOMICS At present, many brain tumors are treated in a similar way regardless of their classification, but this situation is changing as patient responsiveness to various therapies is correlated with genetic changes in the tumor. For example patients with combined 1p and 19q loss may opt for chemotherapy but withhold radiation until recurrence, therefore avoiding the problematic effects of extensive radiation to the brain in long surviving patients. On the other hand, a patient with intact chromosome 1p and EGFR amplification may opt to forego PCV chemotherapy, and have radiation and a novel therapy, thereby avoiding the myelotoxic effects of PCV (Louis, 2006). The benefit of chemotherapy in glioblastoma and anaplastic astrocytoma is small, with response plus stabilization in 20–50% of patients, because these tumors have intrinsic chemoresistance. Esteller et al. (2000) first described the mechanism by which some gliomas are resistant to nitrosourea alkylating agents such as carmustine (BCNU). These agents kill by alkylation of the O6 position of guanine, thereby crosslinking adjacent DNA strands. These crosslinks can be repaired by the DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT), which rapidly reverses alkylation. Around 30% of gliomas lack MGMT, and this correlates closely with chemosensitivity. However, mutations in MGMT are rare, and it was found that methylation of the promoter region of the gene, leading to transcriptional silencing, accounts for the variation. 12 of 19 highgrade glioma patients with methylated MGMT promoters had a partial or complete response to treatment, whereas only one of 28 patients with an unmethylated promoter had a response. MGMT promoter methylation also is linked to responsiveness to temozolomide (Hegi et al., 2005). A second example of pharmacogenomics is in the use of EGFR kinase inhibitors for glioma treatment. Molecularly targeted therapeutics such as EGFR inhibitors are an attractive option for glioma because of the frequently observed amplification of EGFR in these tumors. However, trials so far have shown only a 20% response rate (Mellinghoff et al., 2005). Molecular determinants of responsiveness were analyzed, and it was found that combined EGFRVIII mutation and the presence of PTEN sensitizes tumor, suggesting that secondarily targeting PI3K signaling may improve this kind of therapy, and that patients with the appropriate profile should be enlisted in further trials for these drugs. As glioma treatment becomes increasingly sophisticated further details and examples of chemoresistance

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mechanisms, which can affect therapeutic decision making are sure to emerge.

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MONITORING Currently, the determination of treatment response is monitored using imaging techniques such as MRI or computed tomography. Patients’ cognitive abilities and other neurological symptoms are also examined. For low-grade tumors monitoring is carried out once or twice yearly by MRI scanning. Often extensive tumor progression occurs in the interval between different imaging studies and/or goes undetected due to lack of contrast enhancement. Alternative markers of tumor burden may potentially permit early detection of treatment failure and allow for more rapid changes in therapeutic strategy. Effective screening, monitoring and diagnosis may be enhanced by the identification of serum markers, which can be readily detected in the presence of a brain tumor. For example,YKL-40 is over-expressed in glioblastoma and can be detected in the serum of patients, and has been proposed as a potentially useful surrogate marker of tumor burden, response to treatment, or relapse (Hormigo et al., 2006). Other markers have been proposed such as low-molecular weight caldesmon (Zheng et al., 2005), and cathepsin D (Fukuda et al., 2005). The utility of these markers for monitoring and prognostic purposes needs to be established in prospective studies with large patient numbers.

NOVEL AND EMERGING THERAPEUTICS The fact that brain tumors can be clearly separated into molecular subgroups with different prognosis or drug response, suggests that ultimately specific therapies may be necessary, tailored according to the molecular alterations seen in an individual tumor. An effective therapeutic strategy must eliminate tumor cells, with minimal neurological damage, cross the blood brain barrier (unless directly delivered to the tumor), overcome elevated interstitial tumor pressure, and active resistance mechanisms. The existence of various cell types within a tumor suggests that a cocktail of drugs may be required. A wide range of novel approaches are being examined for brain tumor treatment, in both clinical trials and in experimental animal models, generally using glioma cells intracranially implanted into the brains of mice or rats. The important current strategies are described below and can be summarized as follows: ● ● ●

●

Otimizing and enhancing existing strategies. Improved drug delivery. Targeted therapies in which molecules and pathways and key processes involved in gliomagenesis are targeted with either specific small molecule inhibitors, immunotoxins, or gene therapy agents. Vaccines, and immunostimulatory methods, to enhance immune recognition and destruction of the tumor.

Oncolytic viruses engineered to specifically replicate in and destroy tumor cells. Combinations of therapeutic methods designed to target multiple processes.

The current standard of care for glioblastoma employs aggressive resection, followed by radiotherapy and chemotherapy with temozolomide. This treatment doubles the 2-year survival rate compared with radiotherapy alone (Stupp et al., 2005). The effectiveness of temozolomide and other chemotherapeutic agents is limited by chemoresistance, due at least partly to MGMT expression. Therefore, inhibition of DNA repair pathways may be effective in conjunction with alkylator chemotherapy in patients expressing MGMT. For example, the MGMT inhibitor O6-benzyl guanine enhances the effectiveness of alkylator therapy in glioma cells (Liu and Gerson, 2006). In addition, other approaches that can build on the conventional approaches or combine effectively would seem sensible. For example, enhancement of temozolomide with perifosine (an Akt inhibitor) reported additive effects in a mouse glioma model (Momota et al., 2005). Some problems, such as systemic toxicity and the presence of the blood-brain barrier, may be overcome by local drug delivery directly to the resected tumor, thereby achieving high local concentration of the therapeutic agent. This can be done by implanting chemotherapeutic infused wafers into the resected tumor cavity (e.g., Gliadel). Another approach being tested in patients is to deliver the therapeutic agent from an external reservoir, by a method known as convection enhanced delivery (CED) in which a drug is slowly infused into the tumor area over a period of days – both conventional and novel therapeutics such as gene therapy agents and immunotoxins may be suited to this method (reviewed by Chiocca et al., 2004). Due to the limited progress made with chemotherapy and radiotherapy over the years, researchers have begun to investigate the development of more specific, targeted treatment modalities that exploit the molecular pathogenesis of cancers in the brain. The identification of molecular alterations by traditional molecular techniques and now by high-throughput methods is leading to the identification of many potential targets. The development of small molecule inhibitors and monoclonal antibodies designed to target key enzymes on which cancer cells depend, raises the possibility that rational approaches can be used. Targets such as EGFR, PDGFR, and VEGFR tyrosine kinase domains have been considered promising for the development of small molecule inhibitors, such as erlotinib and gefitinib (Mellinghoff et al., 2005). Monoclonal antibodies such as bevacizumab (humanized VEGF monoclonal antibody) and trastazumab (Her-2/neu monoclonal antibody) have been examined. These inhibitors are being used in a wide range of combinations (e.g., traditional therapies or other pathway inhibitors such as rapamycin) to improve their efficacy. Recent reports indicate that bevacizumab in combination with chemotherapy leads to a very high response rate in glioblastoma (Pope et al., 2006). In order to improve the prospects for these therapies it will be

Conclusions

important to profile genetic and transcriptional alterations in patients involved in trials. It is becoming clear that these agents are highly dependent upon the profile of genetic alterations in a given tumor. For example, inhibitors of Notch or Akt could be improved significantly by enrolling patients with PN or Prolif/ Mes signature respectively (Phillips et al., 2006). Tumor targeted immunotoxins consist of a tumor cell receptor ligand conjugated to a highly potent toxin and have been associated with encouraging long term survivals (Rainov and Heidecke, 2004; Sampson et al., 2005). Antibodies to cell surface markers highly expressed on gliomas such as IL-13, EGFR, IL-4 have been examined, with toxin conjugated IL-13 involved in a large-scale multi-institutional clinical trial for recurrent glioblastoma at the time of writing. Strategies that harness the power of the immune system hold some promise in glioma treatment (reviewed by Hussain and Heimberger, 2005). A major feature of gliomas is immunosuppression, which prevents normal immune function and may facilitate tumor growth. In a particularly interesting study, Steiner et al. (2004) explored the use of immunotherapy as an adjuvant to standard radiotherapy in patients with glioblastoma. A vaccine prepared from patient tumor cells, infected with Newcastle disease virus (an avian paramyxovirus) as a nonspecific immunostimulant, were administered to patients every 3–4 weeks, doubling median overall survival, although this needs verification in randomized trial. Viral and gene-based therapies have been explored for many brain tumors and have been in clinical trials for around 15 years, showing safety but a lack of efficacy. Research in this area is ongoing, and improved approaches are slowly coming to the clinic. A recent trial using non-replicating adenovirus to deliver the thymidine kinase gene, showed improved patient survival, indicating that progress is still being made in this area (Immonen et al., 2004). Viruses with oncolytic properties have also been exploited (Chiocca, 2002). These agents either naturally target tumor cells, or can be engineered to do so; furthermore, they can be armed with therapeutic transgenes or used in combination with other therapeutic strategies, leading to increased antitumor efficacy. Recently it was reported that temozolomide can synergize with oncolytic herpes virus therapy in a mouse glioma model, indicating that these two separate approaches may be compatible (Aghi et al., 2006). A final area of great interest is in the targeting of so-called brain tumor stem cells, which may constitute the root of the tumor and whose elimination could be required for successful therapy. Until recently, brain tumors were thought to arise from glial cells residing in the brain parenchyma. However, recent evidence in human and animal studies suggests that neural stem cells are an alternative cellular origin. In experimental animal models both astrocytes and neural progenitor cells can give rise to neoplasms that recapitulate the histopathological hallmarks of human gliomas (discussed in Louis, 2006). The adult forebrain contains abundant neural stem cells, and human glioblastomas contain tumorigenic neural stem-like cells (Singh et al., 2004). Moreover, stem-like cells are much more tumorigenic than non-stem-like

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cells from the same tumor. It would seem that unless the stemlike component of gliomas can be effectively targeted, then treatment failure is inevitable. One of the reasons for the failure of traditional therapies may be due to the fact that they are designed to target dividing cells, which only represent a small proportion of the actual tumor cells, and stem cells are thought to divide slowly. It has been reported that stem-like cells in brain tumors are radio-resistant, indicating that special measures may be needed to take care of this population therapeutically (Bao et al., 2006). CD133 positive cells also express higher MGMT and are more resistant to chemotherapy (Liu et al., 2006). These data suggest that the stem-like component of gliomas is a major reason for the difficulty in treatment, and that novel approaches may be required for their elimination.

CONCLUSIONS The prognosis for many brain tumors remains dismal. However, the wealth of data currently being generated by microarraybased studies in particular should have a profound effect on brain tumor management. Headway is currently being made in four main areas: 1. Identification of robust molecular markers and signatures that define tumor types: Recent studies have revealed that molecular signatures readily distinguish tumor types, and can also further subdivide histologically similar tumors into previously unknown groups. This opens up the possibility that molecular information will be increasingly useful in brain tumor diagnosis. 2. Correlation of molecular signatures with patient prognosis: Molecular information may provide the ability to predict patient survival more reliably than at present. Recent studies show that molecular classification can improve prediction of patient outcome compared with histology in some cases. 3. Correlation of molecular data with therapeutic response: Molecular alterations play a role in drug responsiveness. The identification of these alterations is beginning to impact clinical decision making; 1p/19q is now widely used, and MGMT and EGFR status may be used in the near future. These represent the first steps towards the development of patienttailored therapies. 4. Identification of novel therapeutic targets: Improved understanding of brain tumor biology is allowing the development of new therapeutic strategies based on novel molecular targets revealed by global genetic analyses. The most promising approaches so far may involve tumor classification by gene expression profiling using a panel of relevant genes. The identification of the PN, Mes and Prolif tumor subgroups (Phillips et al., 2006) provides a strong foundation for such an approach. Microarray studies have identified many candi dates that could be of use in prognosis and monitoring – the most promising of these so far may be YKL-40, which can differentiate

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astrocytic from oligodendroglial tumors and is associated with poor prognosis. The association between MGMT promoter methylation and improved treatment response in glioblastoma patients has provoked much interest in this area. The incorporation of MGMT expression analysis may therefore soon emerge as a useful predictive clinical tool. The list of molecular alterations is growing, but is still far from complete. In addition the role of epigenetic changes has

barely been studied as yet in gliomas. The major challenge at this point is to translate this increasingly large amount of data into clinically useful tools. This should lead to the development of a new genomic approach to glioma treatment, using molecular signatures to provide accurate diagnosis, and to stratify patients for the most effective therapy, or for targeted therapies. This should finally lead to a better outlook for these cancers.

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multiforme: A pilot study to assess feasibility, safety, and clinical benefit. J Clin Oncol 22, 4272–4281. Stupp, R., Mason, W.P., van den Bent, M.J., Weller, M., Fisher, B., Taphoorn, M.J., Belanger, K., Brandes, A.A., Maros, C., Bogdahn, U. et al. (2005). Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352, 987–996. Van den bent, M.J., Carpentier, A.F., Brandes, A.A., Sanson, M., Taphoorn, M.J., Bernsen, H.J., Frenay, M.,Tijssen, C.C., Grisold,W., Sipos, L. et al. (2006). Adjuvant procarbazine, lomustine, and vincristine improves progression-free survival but not overall survival in newly diagnosed anaplastic oligodendrogliomas and oligoastrocytomas: A randomized European Organisation for Research and Treatment of Cancer phase III trial. J Clin Oncol 24, 2715–2722. Volinia, S., Calin, G.A., Liu, C.G., Ambs, S., Cimmino, A., Petrocca, F., Visone, R., Iorio, M., Roldo, C., Ferracin, M. et al. (2006). A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 103, 2257–2261. Wang, L.E., Bondy, M.L., Shen, H., El-Zein, R., Aldape, K., Cao, Y., Pudavalli, V., Levin, V.A., Yung, W.K. and Wei, Q. (2004). Polymorphisms of DNA repair genes and risk of glioma. Cancer Res 64, 5560–5563. Wang, L., Wei, Q., Wang, L.E., Aldape, K.D., Cao, Y., Okcu, M.F., Hess, K.R., El-Zein, R., Gilbert, M.R., Woo, S.Y. et al. (2006). Survival prediction in patients with glioblastoma multiforme by human telomerase genetic variation. J Clin Oncol 24, 1627–1632. Watson, M.A., Perry, A., Budhraja, V., Hicks, C., Shannon, W.D. and Rich, K.M. (2001). Gene expression profiling with oligonucleotide microarrays distinguishes World Health Organization grade of oligodendrogliomas. Cancer Res 61, 1825–1829. Zheng, P.P., Hop, W.C., Sillevis Smitt, P.A., van den Bent, M.J., Avezaat, C.J., Luider, T.M. and Kros, J.M. (2005). Low-molecular weight caldesmon as a potential serum marker for glioma. Clin Cancer Res 11, 4388–4392.

CHAPTER

80 Molecular Therapeutics of Melanoma Jiaqi Shi, Yonmei Feng, Robert S. Krouse, Stanely Leong and Mark A. Nelson

INTRODUCTION Metastatic melanoma is a malignancy in which very little therapeutic progress has been made in the last 30 years. Treatment with chemotherapy alone or in combination with cytokines have been very disappointing, with modest response rates that have not really impacted survival rates. Part of the reason for this treatment failure is that melanomas are highly resistant to radiation and chemotherapeutic agents. New strategies need to be designed. Advances in the molecular genetics and genomics of melanoma are leading to insights into the mechanism responsible for the development of this disease. In this chapter we discuss key genetic abnormalities involved in the etiology of melanoma and discuss the rationale for targeting signaling pathways using smallmolecule inhibitors.

DIAGNOSIS Accurate risk assessment is critically important in establishing management criteria. The present American Joint Committee on Cancer (AJCC) staging system for melanoma encompasses characteristics of the tumor (T), nodal status (N), and the presence or absence of metastases (M) to stratify prognostic groups (Balch et al., 2001). Features of the primary tumor evaluated in this system include tumor thickness and tumor ulceration. The nodal status is determined by the presence or absence of tumor Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

cells within draining lymphatics and regional lymph nodes, as well as the number of positive nodes and the presence of microscopic versus macroscopic nodal involvement. The presence of any metastasis beyond regional lymph node qualifies as Stage IV disease. While individuals with Stage I and Stage II disease (patients who have not yet developed lymphatic involvement or metastases, respectively) have good and intermediate prognoses. However, considerably heterogeneity exists in the prognosis for individuals with Stage III disease. Presently, lymph node status is determined by selective sentinel lymph node (SLN) biopsy in all patients, with tumor greater than 1 mm thick and without clinical evidence of nodal disease. Several studies demonstrate the stepwise evolution of melanoma cells to one or two regional nodes, thus making the disease ideal for staging by selective SLN dissection (Morton et al., 1999; Reintgen et al., 1992, 1994; Thompson et al., 1995). The introduction of sentinel lymphadenectomy allows for increased identification of submicroscopic disease while reducing patient morbidity (Morton et al., 1999; Reintgen et al., 1992, 1994; Thompson et al., 1995); since only those patients with a positive sentinel node need to undergo complete regional node dissection. The SLN is thought to contain more tumor than the other lymph nodes in the same region; therefore additional steps are taken to increase detection of cancer cells. Presently, there is no universally accepted approach to the pathologic examination of SLNs. Therefore, at most institutions, serial section of SLN at 2–4 mm intervals Copyright © 2009, Elsevier Inc. All rights reserved. 967

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and staining of consecutive sections at each level with H&E is performed with two or more immunohistochemical markers such as S-100, HMB-45, and melan A/MART1. Although routine histologic examine using H&E has the capacity to detect one melanoma cell in 104 lymphocytes (Sung et al., 2001) and immunohistochemical techniques can improve detection of disease, a significant proportion of micrometastasis are likely missed due to the fact only between 1% and 5% of the node is examined, even when stringent SLN protocols are utilized (Yu et al., 1999). The fact that metastasis in patients with histological negative SLNs supports the notion that some of these histological negative SLNs may harbor microscopic deposits of tumor. Genetic analysis of a SLN from a melanoma patient has the potential to improve diagnosis and treatment of disease. Presently, reverse transcription-polymerase chain reaction (RT-PCR) has been the most extensively applied molecular technique to detect micrometastases in SLN. Attempts to identify the Stage I and II patients at greatest risk for recurrence have focused primarily on detection of melanoma-specific mRNA using RTPCR. RT-PCR for metastatic melanoma has been studied in both SLNs (Blaheta et al., 2001; Cook et al., 2003; Davids et al., 2003; Goydos et al., 2003) and blood (Smith et al., 1991;Wascher et al., 2003). Typically melanoma-specific mRNAs such as tyrosinase, MART1, MAGE, and GP100 have been studied (Baruch et al., 2005). Several studies have applied RT-PCR for the detection of micrometastases in SLNs and the conclusions from these studies vary (reviewed in [Baruch et al., 2005]). Rimoldi and coworkers investigated Melan-A/MART 1 and tyrosinase detection in SLN positive and negative cases and showed disease recurrence in 12% of the RT-PCR positive immuno-pathology negative SLN, indicating that extensive immunohistochemistry analysis may underestimate the presence of micrometastases. They also concluded that molecular detection of disease, may be more sensitive but needs to be further improved in order to attain better specificity (Rimoldi et al., 2003). The development of quantitative real-time PCR (qRT) for detection of occult tumor cells is a further advancement for the field. In a retrospective study of 215 clinically nodenegative patients who underwent lymphatic mapping and sentinel lymphadenectomy for melanoma, qRT PCR was applied for the detection of four melanoma-associated genes: MART-1, MAGEA3, GalNAc-T, and Pax3. Of the 162, patients with histopathology-negative SLNs, 48 (30%) had nodes that expressed at least one of the four qRT genes and there was a significantly increased risk of disease recurrence (Takeuchi et al., 2004). However, in a prospective multi-institutional study of 1446 patients with histologically negative SLNs using conventional RT-PCR there was no additional prognostic information beyond standard histopathologic analysis of SLNs (Scoggins et al., 2006). Although the data are conflicting from many reports, a recent systematic review and meta analysis of 22 studies enrolling 4019 patients who underwent SLN biopsy for clinical Stage I to II cutaneous melanoma suggests that PCR-based detection of melanoma cells in SLNs may have clinical value (Mocellin et al., 2007). However, appropriate quality control and quality assurance measures need to be taken to assure the specificity of the assays. Furthermore, the types

of genes investigated need to be expanded to genes involved in the transformation (i.e., B-raf, MITF, NEDD9,TWIST, etc.) and metastatic spread of melanoma. Recent work on cDNA microarray analyses of fresh melanomas demonstrated the differential expression of several genes in metastatic versus primary melanomas (Haqq et al., 2005). This study identified the nuclear receptor coactivator3 (NCOA3) gene to be overexpressed in melanoma metastases when compared with unrelated primary melanomas as controls. NCOA3 is a member of the steroid receptor coactivator1 family and is also known as AIB1 or SRC-3. The NCOA3 gene maps at a chromosomal region at 20q12 that is frequently amplified in human breast cancers (Anzick et al., 1997). Its overexpression has been correlated with poor clinical outcome in patients with breast cancer (Zhao et al., 2003). Increased gene copies of 20q have been found in melanoma specimens and cell lines (Barks et al., 1997; Bastian et al., 1998). In a separate study, it has been found that overexpression of NCOA3 by immunohistochemistry was significantly predictive of SLN metastasis (p  .013) and the mean number of metastatic SLNs (p  .031). Kaplan-Meier analysis demonstrated a significant correlation between NCOA3 overexpression and decreased RFS (p .0021) and DSS (p  .030). Logistic regression analysis showed that increased level of NCOA3 to be an independent predictor of SLN status (p  .017). Multivariate Cox regression analysis showed that increased NCOA3 expression had an independent effect on RFS (p  .095) and DSS (p  .021). NCOA3 was found to be a more powerful predictor of DSS than tumor thickness and ulceration. Thus, NCOA3 may be used as a molecular prognostic marker for melanoma (Rangel et al., 2006). Based on the cDNA microarray analysis of fresh melanomas (Haqq et al., 2005), the osteopontin gene has also been found to be a marker of disease progression in melanoma patients because of its overexpression in the microarray study. In another study, it has been found by multivariate logistic regression analysis that osteopontin overexpression by immunohistochemistry was an independent predictor of SLN status (p  .0062). Further, multivariate Cox regression analysis showed that osteopontin overexpression was an independent predictor of DSS (p  .049) (Rangel et al., 2007). In summary, cDNA microarray analyses of fresh melanomas provide candidate genes associated with melanoma. By correlating their protein products with SLN status and clinical outcome, important molecules are identified to further define the process of melanoma progression with respect to SLN metastasis and systemic dissemination. In the future, different subgroups of melanoma patients may be more accurately defined by molecular markers. In other words, more precise staging of melanoma may be achieved by molecular taxonomy.

GENETICS OF MELANOMA Genetic abnormalities differentiate melanomas from benign melanocytic proliferative lesions. Genetic changes in melanoma play a key role in the development of disease. However, unlike

Novel and Emerging Therapeutics

many epithelial tumors, mutations affecting the tumor suppressor genes – p53, Rb, PTEN or Ras, appear to be rare or at least occur relatively late in melanoma progression. Mutations of BRAF, which lead to sustained mitogen-activated protein kinase activity (MAPK), and inactivation of p16/p14ARF are detected in the majority of melanoma (Daniotti et al., 2004; Goding, 2000). Indeed, activating mutations of BRAF and loss of p16 and ARF are found in the majority of melanomas. Moreover, loss of function mutations that occur at the CD2KA locus (p16INK/ p14 ARF) confer susceptibility to melanoma and are found in approximately 25% of melanoma-prone families (Carlson et al., 2003; Hayward, 2003). Melanomas also display aberrations in developmental signaling pathways common to melanocyte differentiation. Specifically, signaling by receptor tyrosine kinases (e.g., Kit, the Wnt signaling pathway), melanocortin signaling pathway (a-MSH/melanocortin-1 receptor/cAMP), and loss of the p16INK4a cyclin-dependent kinase inhibitor signaling events that can regulate the expression or function of microphthalmia transcription factor (Mitf), which plays as an essential role in melanocyte development and survival (Weeraratna et al., 2002; Widlund and Fisher, 2003). For example, increased Wnt5a in human melanomas with low motility and invasiveness leads to a more aggressive phenotype and Wnt ligand expression correlates with histologic features (Pham et al., 2003). In addition oncogenic activation of the Ras signaling pathway and disruption of PTEN function appear to cooperate in a reciprocal fashion to contribute to melanoma (Wu et al., 2003). Activation of the MAPK kinase pathway (which includes Ras/BRAF/MEK/ ERK) appears to play a key role in the oncogenic behavior of melanoma. Activating mutations affecting B-RAF, mostly in the kinase domain (80% V599E) have been found in greater than 50% of melanomas (Brose et al., 2002; Dong et al., 2003; Pavey et al., 2004; Pollock et al., 2003; Smalley, 2003;Yazdi et al., 2003). Despite the high prevalence of B-RAF mutations in cutaneous melanoma, in general, genetic aberrations of melanoma are heterogeneous and complex and correlate with aggressive disease and poor survival (Daniotti et al., 2004; Pavey et al., 2004). For example, Acral melanomas show frequent and early gene amplification; lentigo maligna melanomas display more frequent gain/loss of 15q, 17q/13q, and 17p than superficial spreading and nodular melanomas (Bastian et al., 2003), and melanomas with p16/ARF  B-RAF mutational profile are associated with longer survival than those with more complex mutational profiles (Daniotti et al., 2004). These genotypic differences highlight the need for more intensive study as the discovery of unique molecular markers may make it possible more accurate diagnosis, more precise prediction of outcome, and potentially lead to the development of unique therapies. Recent discoveries using genome-wide technologies enable systematic documentation of tumor heterogeneity at high resolution are revealing opportunities to improve stratification and ultimately management of melanoma patients. Expression profiling studies demonstrate distinct molecular classes of melanoma (Bittner et al., 2000; Haqq et al., 2005; Mocellin et al., 2007; Onken et al.,

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2004; Segal et al., 2003;Tschentscher et al., 2003). Similarly, at the genomic level CGH arrays studies provide evidence of the nonrandom nature of the chromosome abnormalities in different subtypes of melanoma. Indeed CGH studies have identified that distinctive patterns of genetic alterations occur in melanomas arising in different anatomic sites and with varying sun exposure histories (acral, mucosal, or with and without chronic sun damage) (Curtin et al., 2005). Similar genetic and biological heterogeneity are likely to characterize metastatic melanoma lesions.

PHARMACOGENOMICS The goal of pharmacogenomic analysis of cancer is to identify and develop agents that selectively target key pathways, while taking into account the genetic background of the patient. Most cancer-related pharmacogenomic studies focus on identification of host genetic factors that alter tolerance or response to chemotherapy (Sellheyer and Belbin, 2004; Tsai and Hoyme, 2002). The development of high-throughput technologies (such as cDNA microarray, comparative genomic hybridization array [CGHa], single nucleotide polymorphism analysis [SNP]) have the potential to permit comprehensive genetic characterization of individuals and their cancer as customize therapy. For cDNA microarray analysis, several new refined applications have been developed, such as gene-set-enrichment analysis (GSEA) and experimental pathway analysis (Bild et al., 2006). Beyond the simple clustering and correlation of gene expression data, these mining tools generate patterns (or signatures) to identify the most effective drug combinations for individual patients. One example is the gene expression signatures identified by Potti et al. (2006) that predict sensitivity to individual chemotherapeutic drugs. Indeed, recent findings strongly support that targeting specific signaling pathways may be critical for the development and progression of melanoma might be promising. For example, microphthalmia-associated transfer factor (MITF) is accepted as a differentiation factor in the melanocytic lineage (Levy et al., 2006). MITF is often overexpressed human melanoma metastases and may regulate melanoma cell viability by increasing Bcl2 and CDK2 transcriptional activity (Du et al., 2004; McGill et al., 2002) Preclinical studies indicate that reducing MITF expression leads to an increased sensitivity of human melanoma cells to conventional therapy. MITF may become a novel avenue for therapy, together with its transcription targets Bcl2 and CDK2. Other potential molecular targets based on melanoma tumor immunology and cellular signaling pathways are discussed below.

NOVEL AND EMERGING THERAPEUTICS Molecular-Targeted Therapy Surgery is the mainstay of therapy for melanoma. However, for late-stage patients with thick lesions or regional metastatic

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lymph nodes, there is a high risk for relapse. For this group of patients, there is to date no single agent has significantly changed survival rates. Dacarbazine remains the only “standard” agent with response rates of 10% (Flaherty, 2006). Development of adjuvant therapies that increase survival beyond that achieved following surgery alone has been a long-standing goal of melanoma researchers and clinicians. An increasing number of potential therapeutic targets have been identified. Targeting Antiapoptotic Mediators Bcl-2, Bcl-XL, IAPs (inhibitor of apoptosis protein), and nearly all members of the BH3-only class of antiapoptotic proteins have been reported as overabundant in melanoma and may confer resistance to chemotherapy (Tang et al., 1998). It is not clear that any one antiapoptotic protein functions as a master suppressor of apoptosis in melanoma. Therefore, the therapeutic value of inhibiting one member of this large protein family in melanoma is a matter of speculation. The Bcl-2 gene was discovered as a proto-oncogene found at the breakpoints of t(14;18) chromosomal translocations (Tamm, 2006). Oblimersen (Genta), an antisense oligonucleotide against Bcl-2, has been tested extensively in clinical trials. This agent had been shown preclinically to downregulate Bcl-2 mRNA and to enhance the cytotoxicity of chemotherapy. In a phase I/II trial with advanced melanoma, most patients showed downregulation of Bcl-2 in tumor biopsy specimens (Jansen et al., 2000). Data from this small trial were the basis for a phase III trial among 771 patients with metastatic melanoma (Flaherty, 2006;Tamm, 2006). Patients were randomly assigned to receive dacarbazine alone or in combination with oblimersen. Addition of oblimersen to dacarbazine prolonged disease-free survival, but not overall survival. As a result, the FDA did not approve Bcl-2 antisense for the treatment of metastatic melanoma. The failure of this trial leaves more questions than answers. As inhibitors of other antiapoptotic proteins are developed clinically, some of the questions raised by the Bcl-2 trials will reappear and need to be answered. Targeting Multiple Signaling Pathways The MAPK Pathway

The MAPK pathway is activated in virtually all melanomas (Smalley, 2003). This pathway regulates numerous cellular activities in melanoma, including survival. Effective disruption of this pathway, either through pharmacologic inhibition or the deletion of one or more key pathway components, results in cell death. B-Raf is the most frequently mutated oncogene identified to date in melanoma, present in 60–70% of cases (Davies et al., 2002). In another 15% of melanomas, N-Ras, which is immediately upstream of B-Raf, is mutated and constitutively activated (Alsina et al., 2003). The therapeutic value of agents that target the MAPK pathway is still uncertain. N-Ras inhibition remains a therapeutic challenge. The only clinical agents used to date that affect Ras activity are the farnesyltransferase inhibitors (Table 80.1). These agents impair the posttranslational modification of the Ras proteins and prevent their

membrane localization, which is required for signaling activity (Johnston, 2001). This class of agents is limited by lack of specificity. Because activating mutations in B-Raf bypass upstream N-Ras activity, N-Ras is an unattractive target in B-Raf mutant melanoma. Raf inhibition is a focus of several ongoing clinical trials. The only agent with B-Raf inhibitory activity that has reached clinical trials is sorafenib (Table 80.1). This agent was selected for clinical development before BRAF was identified as an oncogene relevant to melanoma and is most potent against CRAF (Wilhelm et al., 2004). In the clinical development of sorafenib, it was found to be also a potent inhibitor of several receptor tyrosine kinases involved in neovascularization and tumor progression, including VEGFR-2, VEGFR-3, PDGFR-, Flt-3, and c-kit. In melanoma cell lines, sorafenib induces cell cycle arrest and apoptosis (Karasarides et al., 2004). The activity of the MAPK is clearly inhibited. However, sorafenib administration to an immunodeficient mouse bearing a mutant B-Raf melanoma xenograft only slows tumor growth compared with controls. In clinical trials, single-agent sorafenib has been associated with few objective responses and a modest degree of tumor stabilization (Ahmad and Eisen, 2004). Two clinical trials combining sorafenib with chemotherapy are completed or in progress. The most extensively evaluated combination has been

T A B L E 8 0 . 1 Compounds that target multiple signaling pathways in clinical trials in melanoma Compound

Protein target

Development phase

Sorafenib

B-Raf, VEGFR-2, VEGFR3, PDGFR-, Flt-3, c-kit

Phase III

RAD001

mTOR

Phase II/III Phase I/II

Imatinib mesylate

c-kit, PDGFR-

Phase II

PS-341

Proteasome, NF-B (indirectly)

Phase II

BMS-345541

IB kinase 

Preclinical

R115777 (farnesyl transferase inhibitor)

Ras

Preclinical

PD0325901

MAPK kinase

Phase I/II

ISIS 345794

STAT-3

Preclinical

G4460

c-myb

Preclinical

Cilengitide

V3 and V5 integrin

Phase II

Vitaxin (LM609)

V3 integrin

Phase II

CNTO 95

V3 and V5 integrin

Phase I

Rapamycin, CCI-779

Novel and Emerging Therapeutics

that of sorafenib with carboplatin and paclitaxel (Flaherty, 2006). In a large, single-arm phase II trial, this regimen was associated with an objective response rate and progression-free survival that far exceeds that reported in similarly large trials of other agents in melanoma. A randomized phase III trial comparing this regimen with the chemotherapy agents alone is currently under way (Lejeune, 2006). This double-blind trial among 800 patients with metastatic melanoma will definitively address the contribution of sorafenib to this chemotherapy. Preliminary results of this trial are very promising. In parallel, a phase II/III trial combining sorafenib with dacarbazine is ongoing. In the mean time, identification of new B-Raf inhibitory compounds is under way (Niculescu-Duvaz et al., 2006). An alternative strategy to targeting B-Raf is the inhibition of MEK (MAPK/extracellular signal-regulated kinase kinase), the immediate downstream signaling component in the MAPK pathway. Preclinical data support the induction of apoptosis in vitro and xenograft growth inhibition in vivo (Zhang et al., 2003). The only clinical evidence in melanoma comes from a recently reported phase I trial of PD-0325901 in which melanoma patients were well represented (Zhang et al., 2003). Nearly all patients had nearly complete inhibition of the MAPK pathway. Yet only 2 of 27 melanoma patients showed objective responses. A single-agent phase II trial is under way and includes patients with melanoma. Based on preclinical data, the combination of MEK inhibition with chemotherapy is justified but remains to be clinically investigated (Zhang et al., 2003). Integrin Signaling Pathway

Cell adhesion and migration are essential for tumor development. The link between the extracellular matrix (ECM) and the actin cytoskeleton is mainly mediated by receptors of the integrin family. Integrins are also responsible for signaling between the cells and the environment. Integrins can roughly be grouped into four subfamilies: 1, 2 and 7, 5, and 3. Integrin family plays key roles in regulating tumor growth and metastasis as well as tumor angiogenesis. During melanoma development, changes in integrin expression, intracellular control of integrin functions and signals impact upon the ability of tumor cells to interact with their environment and enable melanoma cells to convert to an invasive phenotype. In situ, 1, 3, 5, 2, 3, and 4 levels have been proven to be increased in primary and metastatic melanomas, whereas 4 and 6 levels are decreased (Kuphal et al., 2005). Antagonists of several integrins are now under evaluation in clinical trials to determine their potential as therapeutics for malignant melanoma (Kuphal et al., 2005). Currently available inhibitors of integrins are functionally blocking monoclonal antibodies, peptide antagonists, and cyclic peptides which mimics matrix. Those compounds not only affect angiogenesis, but also suppress cell migration and invasion of transformed cells, block tumor metastasis and induce apoptosis. Cilengitide is a cyclic peptide that inhibits 53 and 55 integrin function. In preclinical models it blocks ligand

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binding to 53 integrins at nanomolar concentrations, thus altering the interaction of endothelial cells with the ECM. This results in the induction of apoptosis in activated endothelial cells and causes the tumor to “starve.” Cilengitide is currently in phase II clinical trials. Integrin 51 and 53 blocking peptides with antitumor angiogenesis and tumor metastasis activities are currently in preclinical development (Carron et al., 1998; Reinmuth et al., 2003; Stoeltzing et al., 2003). A second class of integrin inhibitors is the disintegrins. These proteins possess both a remarkable sequence homology and an equally notable variability in potency and selectivity in their interactions with integrin receptors. The small proteins bind with various degrees of specificity to integrins 2b3, 51, and 53 expressed on cells. The 5 binding antagonist Vitaxin (LM609) is a humanized monoclonal antibody that reacts with 53 and is currently tested in phase II clinical trials (Patel et al., 2001; Posey et al., 2001). CNTO95 is a new fully humanized monoclonal antibody that inhibits 5 integrins. CNTO95 inhibited growth of human melanoma tumors in nude mice by approximately 80%. Based on these preclinical data, a dose-escalating phase I clinical trial in malignant melanoma patients has been initiated (Trikha et al., 2004). Furthermore another humanized antibody against 51 is in phase I of clinical trials for cancer (Jin and Varner, 2004). Integrin-targeted nanoparticles are another novel therapy. The nanoparticles were coupled with an integrin antagonist. This compound specifically binds to the 53, if the integrin is upregulated in endothelial or tumor cells. Furthermore, an alternative compound against the integrin receptors is Ajoene. It is a garlic-derived compound with inhibitory effects on the expression of the 41 integrin and the growth of melanoma cells (Ledezma et al., 2004). PI3 Kinase/Akt Pathway

PI3K/Akt pathway is another pathway that contributes to melanoma resistance to cytotoxic chemotherapy. This pathway is a key regulator of survival of cancer cells and has been shown to be constitutively active in melanoma (Luo et al., 2003), 43–50% of melanomas have selective constitutive activity in Akt3 (Stahl et al., 2004). Inhibition of Akt in melanoma, using either PI3K inhibitors or Akt3 RNAi, reduces growth and induces apoptosis (Krasilnikov et al., 1999; Stahl et al., 2004). Inhibitors of Akt are in preclinical development (Stahl et al., 2004). One Akt inhibitor, API-2, has high selectivity and an impressive in vitro profile against a wide range of cancer cell lines (Yang et al., 2004). One downstream target of Akt is the mammalian target of rapamycin (mTOR). mTOR enhances cell growth through activation of S6K1 and inhibition of eIF4E-BP. Inhibition of mTOR with agents such as CCI-779 (phase II/III) and RAD001 (phase I/II) shows great promise (Atkins et al., 2004; Boulay et al., 2004; Margolin et al., 2005). Preclinical studies also indicate that there is synergistic inhibition of melanoma cell proliferation by the combination of rapamycin, an inhibitor of mTOR kinase, and BAY 43-9006, an inhibitor of B-Raf kinase (Molhoek et al., 2005).

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Src and STAT3 Signaling Pathway

The Src family of non-receptor tyrosine kinases were among some of the first oncogenes to be identified. A novel Src kinase inhibitor, SU6656, has been shown to reduce the growth of three early-stage RGP-melanoma cell lines, but had little effect on either VGP or metastatic lines (Smalley and Herlyn, 2005). Some of the downstream effects of Src are mediated by the signal transducers and activator of transcription (STAT). Constitutive STAT-3 activation is found in both melanoma cell lines and tissue samples (Niu et al., 2002). ISIS 345794 (Table 80.1) is an antisense drug targeting STAT-3. In preclinical studies, antisense inhibition of STAT-3 significantly delayed tumor growth and increased cancer cell death in multiple cell and animal models of cancer (Tamm, 2006). Inhibitors of c-kit and PDGFR-

Imatinib mesylate is an agent that is potent against both c-kit and PDGFR-. Single-agent imatinib did not achieve significant activity in phase II trials in melanoma (Wyman et al., 2006). The value of this agent in combination with chemotherapy is unknown. However, imatinib seems to have limited capacity for combination with chemotherapy due to an enhancement of myelosuppression presumably derived from c-kit inhibition. Antiangiogenic Cancer Therapies

Over the past three decades, the dependence of tumor growth on neovascularization has been firmly established by extensive experimental evidence. As a result, tumor starvation through interference with tumor blood supply has become a wellrecognized approach of cancer therapy (Gille, 2006; Kerbel and Folkman, 2002). Among the several crucial angiogenic growth factor receptor pathways identified to date, the vascular endothelial growth factor (VEGF) family of proteins and receptors has been the major focus of targeted drug development in oncology (Hicklin and Ellis, 2005). In February 2004, the humanized anti-VEGF antibody bevacizumab (BEV) was the first antiangiogenic compound that was approved by the US Food and Drug Administration for use in conjunction with standard chemotherapy in advanced colorectal cancer (CRC) patients (Ferrara et al., 2004). The VEGF family of proteins and receptors plays a primary role in angiogenesis-dependent growth of most cancer types (Ferrara et al., 2004; Hicklin and Ellis, 2005). The PDGF family of growth factors is increasingly being recognized as a complementary target of antiangiogenic therapy. PDGF members not only increase tumor growth by autocrine stimulation of cancer cells via PDGF receptor (PDGFR) activation and overexpression but also by enhancing tumor angiogenesis (Ostman, 2004). It is therefore assumed that combined VEGFR and PDGFR inhibition constitutes a more effective antiangiogenic approach for cancer therapy (Bergers et al., 2003; Pietras and Hanahan, 2005). In addition, the EGF receptor (EGFR) has been established as an important therapeutic target in a large number of epithelial tumors (Baselga and Arteaga,

2005). Aberrant EGFR activation leads to cell cycle progression, reduced apoptotic capacity, and enhanced angiogenesis. Various preclinical studies have demonstrated that VEGF/VEGFRtargeted therapies alone are capable of not only suppressing the growth of established tumors but also of inducing remarkable tumor regressions or even eradication of metastatic disease (Hicklin and Ellis, 2005). The impressing results of these preclinical studies created expectations as to the potential of such targeted antiangiogenic approaches, which could not be met in the clinical setting (Garber, 2002). When VEGF/VEGFR-directed therapies were administered as single agents, only modest objective responses were seen without yielding long-term survival benefits (Hicklin and Ellis, 2005). On the basis of preclinical evidence indicating that antiangiogenic agents can act synergistically with traditional chemo- and radiotherapy, angiogenesis inhibitors were increasingly studied in conjunction with standard cytotoxic regimens in clinical trials. The strategy to target VEGF-A in combination with chemotherapy finally proved successful, as evidenced by the approval of the anti-VEGF antibody BEV in combination with standard chemotherapy in advanced CRC patients (Hurwitz et al., 2004). This was the first study to definitively show a benefit of an antiangiogenic compound when combined with chemotherapy, thereby reinstalling the confidence in antiangiogenic cancer therapy. In the era of targeted-cancer therapy, the development of orally available small-molecule kinase inhibitors has emerged as an attractive alternative to humanized monoclonal antibodies (Dancey and Sausville, 2003). However, orally available smallmolecule kinase inhibitors may significantly increase toxicity of chemotherapy protocols. For example, compound SU11248 can target both tumor cells (via inhibition of c-kit and PDGFR-) and the endothelial cell compartment (via inhibition of VEGFRs and PDGFR-). Likewise, sorafenib (BAY 43-9006) can inhibit B-Raf, VEGFRs, PDGFR-, Flt-3 and c-kit. On the basis of encouraging data from phase I/II trials, the addition of sorafenib to carboplatin and paclitaxel chemotherapy is hoped to yield survival benefits in advanced melanoma patients (Gille, 2006). Sorafenib as multitargeted tyrosine kinase inhibitor that also inhibits RAF kinases was initially expected to be particularly useful for melanoma therapy, as in more than 60% of all affected melanoma patients activating B-RAF mutations are detected. However, the remarkable responses that were seen with sorafenib were independent of the B-RAF mutational status. In conclusion, two trends of targeted antiangiogenic cancer therapy have become apparent. Firstly, antiangiogenic compounds with therapeutic activity in advanced disease are increasingly being tested in the adjuvant clinical setting. In particular, BEV is currently evaluated in large randomized phase III clinical trials, either alone or in addition with chemotherapy. Secondly, there is a clear tendency at present to combine different antiangiogenic agents to accomplish a more comprehensive approach in blocking tumor angiogenesis. Not only the anti-VEGF antibody BEV, but also the multitargeted inhibitors SU11248 and sorafenib are currently tested in combination with EGFR inhibitors in phase I/II trials to complement their modes of action.

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CONCLUSIONS As the incidence of melanoma increases, so does the need for better rational treatments and more précised pathologic staging. The application of such techniques as real-time PCR, global array based assays (i.e., cDNA microarray and SNP analysis), and

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proteomics are beginning to elucidate key pathways involved in the growth and resistance of melanomas to therapy. However, the integration of molecular markers of melanoma pathogenesis and prognosis into drug development for novel agents against melanoma is central for success and should be viewed as a high priority for investigational studies.

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81 Emerging Concepts in Metastasis Nigel P.S. Crawford and Kent W. Hunter

INTRODUCTION Cancer is a major public health concern among Western populations and is the second leading cause of death in all age groups. The vast majority of cancer-related mortality is a consequence of metastasis, the process by which tumors spread throughout the body, and in spite of therapeutic advances, metastatic disease is frequently incurable in many commonly occurring forms of cancer. Mechanistically, metastasis is an enormously complex process (Figure 81.1). To successfully colonize a distant site, tumor cells must escape the primary lesion by invading the surrounding stroma and basement membrane, penetrate into the vasculature, lymphatics or cross the peritoneal cavity, survive anchorage independence, arrest in a secondary site either by receptor-mediated adhesion or physical trapping, and adapt to a novel microenvironment to either initiate growth within the blood or lymphatic vessel or to extravasate into the surrounding tissue, before proliferating into a clinically relevant lesion. Although the initial steps of this process (tumor escape through arrest in the secondary site) may take place in a relatively short period of time (minute-to-hours), clinical manifestation may be delayed by months, years and in some instances, by decades. During the intervening period, it is currently believed that the disseminated cells are proliferatively quiescent, which renders them relatively refractory to current cytotoxic therapies. The

fact that adjuvant therapy does produce clear clinical benefits in terms of increased survival does, however, suggest that a fraction of the disseminated cells are likely proliferating in subclinical masses. Nevertheless, shedding of large numbers of tumor cells into the vasculature and ability to recover viable disseminated cells from tissues long after the initial arrest implies that a potentially large reservoir of disseminated tumors cells exists throughout the body, which might subsequently develop into clinically relevant metastatic lesions. The insidiousness of the metastatic process is compounded by the relatively refractory nature of metastatic disease to current chemotherapeutic regimens and the recurrence of secondary masses years after resection and treatment of the primary tumor mass. These characteristics make this aspect of the oncogenic process potentially the most difficult to understand and manage. Solid primary tumors are thought to be able to shed millions of malignant cells into the bloodstream every day (Butler and Gullino, 1975). This implies that although metastasis is observed clinically with an all too high frequency, it is thankfully an extremely inefficient process. This in turn raises the question as to why very few clinically relevant metastases form in most individuals. Many processes are believed to contribute to this phenomenon, termed “metastatic inefficiency,” although its precise origins are rather poorly defined. For example, the destruction of intravasated tumor cells by hemodynamic and shearing

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(b) Invasion and intravasation

(a) Primary tumor growth

(f) Growth at secondary site

(e) Extravasation

(c) Dissemination

(d) Arrest at secondary site

Figure 81.1 The process of metastasis (Fidler, 2003). Metastasis is a complex process that enables a neoplastic cell to leave a primary tumor, disseminate from its origin either by lymphatic or blood vessels, and form a viable lesion in a distant organ. It is typically a stepwise process, and inability to perform any of these steps inhibits the ability of the tumor cell to form a metastasis. The process begins with the growth of a primary tumor resulting from neoplastic transformation of a normal cell (a). Continued growth causes the tumor to produce angiogenic factors, which facilitates growth of new blood vessels, or vascularization. Without this, and the consequent increase in nutrient supply to the tumor, it would be unable to grow beyond 1–2 mm in diameter. The tumor begins to encroach upon blood and lymphatic vessels as growth continues. Entry of tumor cells into the circulation is facilitated by the ability of tumor cells to invade local stroma, and typically occurs at thin-walled venules that offer little resistance to intravasation (b). Cells can enter the bloodstream as either in aggregates or in isolation. Following entry into the circulation, tumor cells disseminate widely, but are typically detected in those organs with a rich blood supply (c). Tumor cell arrest occurs at in the capillary beds of secondary organs, and is a reflection of adhesion molecule-mediated interactions between the tumor cell and either the capillary endothelial cells or exposed subendothelial basement membrane (d). Following arrest, the tumor cell leaves the circulation by the process of extravasation (e), which is believed to occur by similar mechanisms that facilitate invasion. The final step in the metastatic cascade is proliferation at the secondary site (f), which requires the tumor cell to be able to develop an independent vascular supply and to evade local host defenses.

forces may account for a great deal of the observed metastatic efficiency (Weiss et al., 1992), yet tumor cells have been demonstrated to arrest in capillary beds, extravasate with high efficiency, and subsequently reside dormant in secondary sites for prolonged periods (Holmgren, 1996; Hunter, 2003). However, the number of tumor cells in the bloodstream is not proportional to the frequency of observed metastasis, a point elegantly demonstrated by observations of metastatic frequency in ovarian

cancer patients with peritovenous shunts to manage ascites (Tarin et al., 1984). Metastases were observed at the same frequency as in women without shunts, in spite of the fact that vast amounts of tumor cells were introduced into the venous circulation daily. The lack of correlation between clinical metastases and the capability of large numbers of tumor cells to enter and exit the vasculature suggests that metastatic capacity is not solely dependent upon the properties of the tumor cell itself. Rather,

Tools to Investigate the Mechanisms of Metastasis

how tumor cells interact with host stroma (e.g., Kaplan et al., 2005) and differential functionality of both the tumor cell and stroma as a consequence of hereditary variation (e.g., Park et al., 2005) are all factors that appear to play important roles in metastasis formation. Cancer is not a single disease, but a family of diseases that share a number of common characteristics, most notably uncontrolled cellular proliferation. However, specific genetic and genomic events leading to neoplastic transformation and tumor progression are distinct in different primary tumors. The same principles hold true for metastasis, and owing to the heterogeneous nature of metastatic progression in different organs, specific aspects will not be covered in significant detail here. Rather, the aim of this chapter is to inform the reader of advances in current understanding of the fundamental principles of metastasis common to solid tumors. This will be achieved by examining a number of prominent technologies used to investigate the mechanism of metastasis and the insights these have afforded researchers and clinicians as to the underlying processes involved in secondary tumor formation. Finally, the potential ramifications of this knowledge upon determination of prognosis and the ability to derive novel treatments will then be briefly discussed.

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TOOLS TO INVESTIGATE THE MECHANISMS OF METASTASIS

loci (QTL) or susceptibility loci that encompass polymorphic gene(s) that modify the risk of cancer. Numerous examples of these types of cancer-associated loci exist in the literature, with hundreds of QTLs mapped in the mouse (Demant, 2003). In addition to modifying susceptibility to cancer development, these loci influence a wide variety of pathological conditions common in human populations, including diabetes (Leiter, 1984), and hypertension (Kantachuvesiri et al., 1999). Very simply, QTLs are defined by correlating a measurable trait (e.g., tumor size, metastasis frequency) with allelic variation in linked polymorphic genetic markers (e.g., microsatellite alleles) in a given population. This type of study frequently utilizes animals generated by back- or intercrosses, since these models have been shown to be a robust means of generating low-resolution localization of genes that modulate phenotypes (Hunter and Williams, 2002). Following identification of a QTL, the aim is then to determine the identity of the gene(s) within the QTLs that are responsible for the observed linkage. This task is frequently laborious, primarily because it necessitates the use of positional cloning within a locus that is often many millions of base pairs in length. Given that genomic regions of this size typically contain many thousands of genes, the reader can appreciate the magnitude of the task facing researchers, although the difficulty of such approaches has been alleviated by the publication of the complete genome sequence. It is therefore likely that in the future many more cancer susceptibility genes will be identified in this manner.

The Genetics of Metastasis: Mouse Models Cancer, Animal Models, and the Identification of Cancer Susceptibility Genes The study of cancer at the genetic level has traditionally focused on defining somatic mutations within individual cells that promote neoplastic transformation and tumor progression. These mutations are facilitated by “genomic instability,” which results in deletion of tumor suppressor genes and activation of oncogenes (reviewed in Boland and Goel, 2005), which in turn promote tumor cells to gain an increasingly severe neoplastic phenotype. These types of defects are well documented in many tumor types, and increasingly, the study of the genetic basis of neoplasia has been edging towards identifying variant genes that increase susceptibility to cancer development. Mouse models of human cancer have proven a useful means of investigating cancer susceptibility (reviewed in Demant, 2003), which is primarily a reflection of the ability to control environmental and genetic variation in animal models, issues that continue to prove significant confounding variables in the study of disease susceptibility in human populations. Specifically, the study of human cancer susceptibility is hampered by both the low penetrance of cancer susceptibility genes in the general population and disparate levels of uncontrollable factors, including differences in environmental exposure (Hunter and Williams, 2002). Mouse models of human cancer have proven particularly useful in defining genomic regions harboring quantitative trait

Mouse Models and Metastasis Susceptibility As discussed above, many examples of QTLs that increase susceptibility to the development of specific forms of cancer have been described in the literature (see Demant, 2003). Fewer studies, however, have aimed to determine whether other elements of tumor growth and progression, including metastasis, are influenced by genetics factors. In our experience, one of the most useful tools in the study of the genetics of metastasis has been the highly metastatic transgenic mouse mammary tumor model FVB/N-TgN(MMTV-PyMT)634Mul (PyMT). As is the case with human cancer, tumorigenesis in mice is a disease of aging, and substantial periods of time are inevitably required for wild-type mice to develop disease, and even after a lengthy aging process, it is by no means certain that the mouse will develop cancer. The use of transgene-driven mouse models, like the PyMT breast cancer model described here is therefore desirable for a number of reasons, the most prominent of which is the degree of uniformity of mammary tumor development: PyMT model animals develop palpable mammary tumors with 100% penetrance by approximately 60 days of age, with 苲85–95% developing pulmonary metastasis by 100 days of age (Guy et al., 1992). The use of transgenic mice is therefore a powerful means of improving experimental consistency and reproducibility. To investigate the role of germline variation in metastasis development, a set of breeding experiments were performed where PyMT transgenic animals were crossed with different

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inbred strains to introduce genetic heterogeneity (Lifsted et al., 1998). It is important to point out two salient facts here: first, the heterozygous F1 progeny acquired the transgene by breeding, meaning that each has the same number of copies of the PyMT antigen gene integrated into the same genomic site; and second, the high degree of consistency at which the PyMT antigen-driven tumors produce pulmonary metastases in the parental strain (85%). Consideration of both of these leads to the assumption that the F1 mice would develop pulmonary metastases at the same rate if metastasis were not influenced by germline variation. However the converse was observed; some of the F1 progeny developed 苲10-fold fewer pulmonary metastases compared to the parental PyMT strain, while others displayed a 苲2–3-fold increase in pulmonary lesions. The most plausible explanation of these results is that polymorphic loci within the germline modify the efficiency of metastasis in different inbred mouse strains. It therefore follows that in addition to the presence of polymorphic genes that influence tumor latency and growth kinetics within the genome, polymorphic genes that subtly influence the process of metastasis are also likely to exist. Quantitative trait mapping was performed to investigate the origins of this phenomenon. These experiments initially demonstrated that metastasis efficiency modifier QTLs exist on chromosomes 6 and 19 (Hunter et al., 2001), with a subsequent study showing that further loci are present on chromosomes 7, 9, and 17 (Lancaster et al., 2005). The most extensively studied of these metastasis efficiency modifier loci, Mtes1 (Hunter et al., 2001), is on proximal mouse chromosome 19. Evaluation of Mtes1 involved a strategy known as “Multiple Cross Mapping” (MCM) (Hitzemann et al., 2002), a technique that exploits shared haplotypes amongst different inbred mouse strains. MCM was used to construct a medium resolution map of a 10 Mbp of mouse chromosome 19. By identifying haplotype blocks that were common to inbred mouse strains with high-metastatic potentials, the number of potential candidate genes within the original chromosome 19 QTL was lowered from approximately 500 to 23 (Park et al., 2003). The list of potential Mtes1 candidate genes was further narrowed using a combination of categorization based upon their molecular function or known association with the metastatic process. DNA sequence analysis was then performed to determine whether identified polymorphisms had a potential functional relevance, and segregation analysis of genetically linked variable tandem repeats (Park et al., 2005). This strategy enabled the list of tangible candidate genes to be narrowed to one, a gene called “signal-induced proliferation-associated gene 1” (Sipa1, also known as Spa1), which encodes a protein containing a C-terminal leucine zipper motif and an N-terminal GTPase activating protein (GAP) domain homologous to the human RAP1GAP. Subsequent functional analysis demonstrated an amino acid polymorphism within Sipa1 altered both enzymatic function and the metastatic capacity of a highly metastatic mammary tumor cell line. This is the first evidence, to the best of our knowledge, that germline variation modifies the efficiency of the metastatic process.

The Genomics of Metastasis Microarray Analysis of Gene Expression in Cancer Tissue In recent years, microarray analysis of gene expression in both primary and secondary tumors has come to play a pivotal role in research into the mechanistic aspects of metastatic progression. Indeed, data gained from microarray analysis of many different cancer tissues has led many researchers to question the conventional theory of metastasis, the “somatic progression model.” This model, initially proposed by Nowell (1976), states that tumor cell subpopulations gain the ability to metastasize through the sequential accumulation of somatic mutations that activate metastasis promoter and inactivate metastasis suppressor genes. The biological basis of this theory confirmed a number of experiments, including those performed by Fidler and Kripke 1977, which involved intravenously introducing clonal murine malignant melanoma into mice. They demonstrated that each clonal variant possessed different metastatic capacities, which was concluded to be a result of the heterogeneous composition of the parental tumor, which in turn was a reflection of the fact of the existence of highly metastatic tumor cell variants in the parental population. Later studies determined that the molecular mechanism of this heterogeneous metastatic behavior was a consequence of differing genetic stability coupled with preferential selection of subpopulations of tumor cells (Welch and Tomasovic, 1985). These observations have, however, been challenged by a number of studies of the in vivo characteristics of metastasizing tumor cells (Giavazzi et al., 1980; Mantovani et al., 1981; Milas et al., 1983). Most recently, microarray analysis of tumor gene expression has thrown doubt upon the somatic progression model of metastasis (reviewed by Weigelt et al., 2005). Specifically, a number of studies involving microarray analysis of human primary breast carcinomas and paired metastatic tumors were performed to evaluate patterns of gene expression common to both primary and secondary lesions. The outcome of these studies was somewhat surprising in that bulk tumor tissues typically displayed patterns of gene expression, or expression signatures, that enabled determination of the metastatic propensity of a primary tumor with a high degree of accuracy (Ramaswamy et al., 2003; van de Vijver et al., 2002; van’t Veer et al., 2002). Furthermore, when expression patterns of the relevant genes were examined in primary tumor tissue prior to the development of clinically detectible metastasis, the same predictive gene expression signatures were evident. The latter finding is of particular significance when one considers the mechanistic aspects of metastatic progression. Specifically, this observation is in apparent conflict with the somatic progression model, since the microarray data imply that the metastatic potential is encoded as early somatic mutational events in tumorigenesis, and not a late event caused by the sequential acquisition of mutations. Interestingly, the same expression signature patterns are evident in PyMT-induced mouse mammary tumors (Hunter et al., 2003; Qiu et al., 2004), and show a high degree of correlation to those seen in the humans (Ramaswamy et al., 2003).

Tools to Investigate the Mechanisms of Metastasis

This implies that the mechanisms of metastatic progression are comparable between PyMT mice and humans, the potential significance of which will be determined later. There are a number of ways to interpret these data. A plausible explanation, at least on initial consideration, is that metastatic potential is encoded shortly after tumor development, possibly as a result of early somatic “founder” mutations within the primary tumor (Bernards and Weinberg, 2002; Ramaswamy et al., 2003). This would appear to explain why small primary tumors display microarray gene signatures that are predictive of metastasis. It would also provide an explanation for the clinical phenomenon of unknown primary cancer metastatic disease, where individuals present with symptomatic metastatic disease, yet either have no clinical evidence of a primary tumor, or possess a small, well-differentiated lesion. Indeed, this phenomenon is not uncommon and is estimated to constitute approximately 5% of cancer cases (Riethmuller and Klein, 2001). However, the biological plausibility of this hypothesis is limited since it implies that metastasis-promoting early somatic events occurring shortly after initiation of tumorigenesis should prime all tumor cells within a primary lesion to be highly metastatic. Thankfully, this is not the case, and cells from primary tumors that exhibit poor prognosis microarray gene expression signatures typically display very low metastatic efficiencies. We therefore argue that, in isolation, early somatic mutational events cannot provide an adequate explanation for the observed differences in metastatic potential, and that these data imply that other factors must play a role in modulating the process of metastasis (Figure 81.2). Microarray Analysis of Gene Expression in Normal Tissue: Mechanistic Implications An important question that now needed to be posed is what are the origins of these metastasis-predicted gene expression profiles? Specifically, do these expression profiles arise as a consequence of early events in the process of tumorigenesis, or are they evident in host tissues prior to the onset of tumorigenesis? The implication of the latter is that host factors, most likely in the form of constitutional polymorphism, are in some manner driving the propensity of a primary tumor to metastasize. It would therefore follow that the impact of somatic mutation upon metastatic propensity, although an integral component of metastatic progression, is not the sole causative factor, at least in the early stages of tumorigenesis. Furthermore, this would imply that the microarray expression signatures observed in high-metastatic propensity primary tumors exist at least partially as a consequence of the combined effects of multiple germline-encoded polymorphisms. To address this, further experimentation was performed to analyze gene expression patterns in disease-free mammary tissues derived from the F1 progeny of transgenic PyMT mice crossed with either a high-metastatic potential mouse strain (FVB) or low-metastatic potential strains (either DBA or NZB) (Yang et al., 2005). Global gene expression was examined in these tissues using microarray analysis. Quantitative real-time PCR (qPCR) was also performed to test the expression of constituent

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genes of a previously described metastasis expression signature (Ramaswamy et al., 2003) in the same tissues. The eventual outcome of this study was to demonstrate that differential expression of the same poor prognosis gene expression signatures observed in humans are indeed present between the normal tissues of the high- and low-metastatic potential mice. The importance of these findings become apparent when one considers the role of germline-encoded Sipa1 variation in the modulation of metastatic efficiency, as described above (Park et al., 2005). The Sipa1 study shows that, at least in our mouse model, constitutional polymorphisms can alter metastatic potential. Microarray analysis of gene expression in disease-free normal tissues demonstrates the existence of gene expression signatures that correlate with metastatic potential prior to the onset of tumorigenesis. The implication is therefore that expression signatures probably exist as a consequence of (as yet undefined) constitutional variation. These results therefore allow us to offer our interpretation of the mechanism of metastatic progression at the molecular level. We believe that the outcome of the Sipa1 study is consistent with microarray expression studies of gene expression patterns in tumor tissue as described previously (Ramaswamy et al., 2003; van de Vijver et al., 2002; van’t Veer et al., 2002), in that the ability of a primary tumor to metastasize is indeed determined before the occurrence of clinically detectable metastasis. However, the innate metastatic propensity of any tumor is not solely related to early somatic mutation, but also to differential functionality of multiple host genes caused by coding, splice site and/or transcriptional regulatory region polymorphism, acting in tandem with environmental and somatic factors. As currently available means of analyzing both genetic and genomic data increase in both power and accuracy, it will be possible to determine the validity of this mechanism, which as discussed below might have important consequences for the assessment of prognosis. At present, the utility of microarray gene expression analysis as a means of assessing prognosis and outcome in breast cancer is currently under investigation in the clinical setting (van de Vijver, 2005). It is hoped that tumor gene expression profiling will significantly enhance the ability of clinicians to identify those patients at risk of progression, and the likely degree to which any given individual will respond to commonly employed treatment modalities. Furthermore, it is entirely possible that tumor expression profiling will prove a powerful tool in other solid malignancies, since it has been demonstrated that predictive gene expression signatures also exist in other primary tumor types (Budhu et al., 2006; Ramaswamy et al., 2003). Future refinements in profiling may include the use of proteomics as well as gene expression profiling of tumor tissues to more directly interrogate the molecular state of the cell (Yanagisawa et al., 2003). If genetic polymorphism is a major contributor to these predictive profiles then the possibility also exists of identifying at-risk patients through profiling of normal, nonneoplastic tissues.The constitutional polymorphisms that enhance metastasis susceptibility are obviously not only present prior to the onset of disease, but will be detectable throughout all bodily

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Environmental influences

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METASTASIS MICROARRAY GENE EXPRESSION SIGNATURE

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Figure 81.2 Germline modification of metastasis. There is a growing body of evidence implicating hereditary variation as a factor involved in modulation of metastatic propensity within a primary tumor. Specifically, susceptibility to metastasis following development of a solid tumor is governed, at least in part, by the same factors that modulate susceptibility to other complex, non-Mendelian genetic disorders: through the combined effects of metastasis efficiency modifier gene polymorphism and poorly-defined environmental factors. The result of these modifying factors is that metastasis susceptibility varies between individuals, implying that it may well be possible to classify individuals as possessing either a “high” or “low” metastatic genotype. Metastasis-associated microarray gene expression signatures serve as a quantifiable biological manifestation of this pro-metastatic germline variation. These signature profiles are apparent either in early primary tumors (Ramaswamy et al., 2003; van de Vijver et al., 2002; van’t Veer et al., 2002), or in normal tissues prior to the onset of tumorigenesis (Yang et al., 2005). It should be noted, however, that the existence of these signatures is likely also attributable to somatic changes within the primary tumor and the aforementioned environmental factors. Dysregulation of expression signature genes driven by germline polymorphism leads to primary tumors having an increased propensity to develop metastasis-enhancing somatic changes within the primary tumor. It is likely that it is these somatic mutations that allow tumor cells to complete the metastatic cascade.

Tools to Investigate the Mechanisms of Metastasis

tissues. This implies that the impact of germline-encoded metastasis susceptibility alleles and the expression profiles driven by such alleles should theoretically be discernable in blood, which is both a readily available and routinely collected clinical tissue. Such gene signatures would likely be rather different from those observed in the solid tumors due to tissue-specific differences in gene expression, but still potentially present, permitting risk stratification and outcome prediction from this relatively noninvasive and easily obtainable tissue. In addition to assessing clinical outcome, metastasis-risk profiling raises the possibility of identification of pharmacological agents that inhibit the outgrowth of dormant tumor cells when given chronically. Chemopreventative agents could be screened to identify those that “reprogram” the transcriptome of high-risk patients to appear more akin to that of a low-risk individual, thereby potentially reducing the risk of recurrence or progression. While this possibility is at present theoretical, some work from our laboratory suggests that this is by no means an unrealistic goal for future research. In our hands, chronic exposure to caffeine was found to suppress the metastatic capacity of PyMT-induced mammary tumors. Furthermore, caffeine also significantly altered tumor gene expression profiles and caused the expression signature of the highly metastatic FVB mouse to develop characteristics more closely resembling those of the lowmetastatic capacity [(DBA × PyMT)F1 or (NZB × PyMT)F1] animals (Yang et al., 2004). However, these are very much preliminary observations and extensive research will be required to determine whether a similar approach is feasible in humans. Pathway-based analysis may further permit targeted therapeutic strategies to ameliorate the impact of secondary disease. Recent work has demonstrated that microarray analysis can identify expression signatures that are induced upon activation of specific molecular pathways (Bild et al., 2006). As we begin to elucidate the signaling pathways critical to metastatic progression, and their no doubt complex and diverse interconnections, it is highly likely that we will become more reliant upon the use of bioinformatics to isolate prognosis-predictive signature profiles. To be able to identify such a metastasis-related signaling nexus would itself be a major breakthrough in the understanding of metastasis. Furthermore, it may facilitate the development of targeted therapies capable of disrupting these critical pathways, thereby reducing or even eliminating the secondary tumors that are by far the most frequent cause of cancer-related deaths. Genomic Microarrays and the Assessment of DNA Copy Number Although our data suggest that metastatic capacity is encoded at least partially prior to transformation, currently available data suggest that somatic mutation also plays a pivotal role in many aspects of metastatic progression. Most importantly, the manifestation of clinically overt metastatic disease appears to be dependant upon somatic events, a fact that is clearly illustrated by recent studies examining the phenomenon of metastatic tissue tropism, a phenomenon where tumor cells derived from different primary lesions will preferentially colonize specific secondary sites.

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With the recent descriptions of subpopulations of metastatic cells that mediate metastasis to bone (Kang et al., 2005) and the lung (Minn et al., 2005), the importance of somatic mutation in metastasis pathobiology becomes apparent: the occurrence of such events, and the microarray gene signatures that accompany them, are almost certainly due to the somatic events (either mutational or epigenetic) that are observed as tumorigenesis progresses to a point where secondary metastatic lesions are clinically obvious (Fidler and Kripke, 1977). The importance of being able to assess somatic mutations within cancer genomes is therefore evident, with a number of technologies being currently available to facilitate this. One of the most widely employed methodologies has been comparative genomic hybridization (CGH). CGH was originally described by Kallioniemi et al. (1992) as a means of detecting and mapping copy number changes. This is achieved by differentially labeling test and reference genomes and hybridizing these to a metaphase chromosome spread to detect gains and losses based on changes in signal ratios. This in turn allows for a number of aberration types to be characterized, including interstitial deletions and duplications, non-reciprocal translocations and gene amplifications (Albertson and Pinkel, 2003). Those aberrations that do not cause a change in copy number, and are therefore not apparent upon CGH analysis, are frequently detected by other methodologies including chromosome banding, spectral karyotyping (SKY) or M-FISH and loss of heterozygosity or allelic imbalance. The principal disadvantage of CGH, however, is that its resolution is limited and only has the ability to detect chromosomal aberrations at intervals of 苲10–20 Mb (Albertson, 2003), which is primarily a reflection of its dependence on the use of metaphase chromosomes to map aberrations. This approach is gradually being superseded by microarray-based formats, or array CGH (aCGH), which has a number of advantages over the use of chromosomes (Figure 81.3). These include higher resolution and dynamic range when compared to conventional CGH, and it allows for direct mapping of aberrations to genomic sequence, as well as higher throughput (Albertson and Pinkel, 2003). Array CGH experimentation utilizes microarrays that possess representations of the genome spotted on the array surface. The spots are typically one of a number of commonly utilized formats including bacterial artificial chromosomes, cDNA clones, and oligonucleotides, the advantages and disadvantages of which are beyond the scope of this chapter (for a comprehensive review see Albertson, 2003; Davies et al., 2005). However, the resolution of probes represented on each type of array is significantly greater than is possible by conventional CGH, and it is estimated that commercially available oligonucleotide arrays possess an average genomic spatial resolution of 35 kb and are able to detect single copy number aberrations (Barrett et al., 2004). In many respects the experimental procedures for CGH and aCGH are very similar and typically involve differential labeling of test and reference genomes followed by hybridization and visualization. Array CGH has been extensively utilized in the analysis of segmental genomic alterations in primary tumors to investigate chromosomal regions involved in cancer etiology and progression

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11 16x4x2 x 02x4x16

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Figure 81.3 Array comparative genomic hybridization (aCGH) is a powerful means of assessing differences in genomic copy number between samples. The format of microarrays varies (see text), but all involve differentially labeling of two genomic DNA samples (e.g., genomic DNAs derived from a primary tumor and a metastatic lesion). Following this, the two differentially labeled samples are combined, hybridized to a DNA microarray and the fluorescent intensity of each probe feature on the microarray determined by scanning. Probes are printed on the microarrays in a manner that allows for scanned feature intensity to be correlated with probe genomic location. Subsequent analysis of the relative florescence of the reference and experimental dye intensities for each microarray feature allows for an estimation of probe copy number differences between these two samples. Software packages allow for a genome-wide assessment of probe copy number, which in turn can be used to assess gross chromosomal aberrations. For example, the output shown here (CGH Analytics©, Agilent Technologies, Palo Alto, CA) shows a number of statistically significant chromosomal abnormalities between the two mouse experimental and reference genomic DNA samples, the location of which are denoted by solid blue lines adjacent to the relevant chromosomes.

(reviewed in Davies et al., 2005). However, there has been less widespread application of aCGH to investigate the origins of specific aspects of the cancer process, including metastasis, although refinements in currently available array-based technologies are making this approach a more attractive prospect for researchers. Examples of tumor types where novel chromosomal aberrations have been successfully characterized in metastatic lesions using aCGH include metastatic colorectal (Buffart et al., 2005; Tanami et al., 2005), endocervical (Hirai et al., 2004) and nasopharyngeal cancers (Yan et al., 2005). Such studies have shown that a wide range of chromosomal aberrations are commonly found in metastatic lesions, some of which are tumor-specific while others appear to be commonly associated with metastatic progression. A particularly powerful means of approaching aCGH-based chromosomal aberration analysis is to integrate genomic copy

number data with microarray-based analysis of the transcriptome. As an example of this type of study, Gysin et al. (2005) correlated microarray-derived mRNA expression patterns in a panel of human pancreatic cancer cell lines with differing metastatic capacities with aCGH data from the same cell lines. This approach enabled them to identify a promising set of candidate genes, many of which are regulated by RAS signaling, which may be involved in invasion and metastasis. This approach is, however, computationally intensive with analysis being particularly complex, and necessitates the use of powerful analytical systems and the expertise to be able to utilize such platforms. In spite of this, aCGH analysis holds much promise given its power and versatility as an analytical tool, and it is likely that it will be seen with increasing frequency in the literature in years to come as a means of investigating the complexities of metastasis.

Tools to Investigate the Mechanisms of Metastasis

The Proteomics of Metastasis Since the completion of the human genome, attention has increasingly turned towards the systematic study of proteins, including protein structure, different isoforms, post-transcriptional modifications and protein–protein interactions. This field, known as proteomics, has a number of advantages over more established research areas, which has led to the increase in interest in investigating cancer, as well as many other diseases, at the proteomic level. One of the most apparent advantages of studying proteomics is that that most cellular functions are carried out by proteins rather than by DNA or RNA, although an increasing wealth of research has begun to implicate short RNAs in regulatory functions. Another significant benefit of the study proteomics over genomics is that recent studies have shown that mRNA levels poorly correlate with protein levels, implying that post-transcriptional levels play an important role in determining the amount of protein that is actually transcribed (Everley and Zetter, 2005). Finally, it appears that proteomic diversity is far higher then genomic diversity, since the human genome contains 苲30,000 genes but the human proteome is estimated to consist of over 100,000 proteins, which again, is primarily as a consequence of post-transcriptional and post-translational modifications. Proteomics is, however, not without its disadvantages, and suffers for a number of reasons including that no methodology is currently able to address all analytical challenges in isolation. In addition, the ability to detect low-abundance proteins is severely limited, which is largely a reflection of the fact that an amplification technology analogous to the polymerase chain reaction to amplify DNA/RNA does not exist. A variety of technologies are routinely employed in proteomic analysis, and as a generalization include protein separation technologies coupled with mass spectrometry (MS) and protein microarray methods (for a comprehensive review of these technologies see Cai et al., 2004; Everley and Zetter, 2005; Hoehn and Suffredini, 2005). Broadly speaking, the application of these technologies to the problem of metastasis can be divided into two areas of research. The first of these is “quantitative proteomics,” where sample protein compositions are compared and contrasted to facilitate to the identification of new diagnostic and prognostic markers as well the evaluation of new therapeutic targets (Everley and Zetter, 2005). The second of these domains is “functional proteomics,” which includes, but are not limited to, the study of protein–protein interactions and the use of chemical probes to selective isolate and characterize enzyme activity levels (Everley and Zetter, 2005). Quantitative Proteomics The use of proteomic technology for discerning molecular markers that are hallmarks of metastatic progression frequently necessitates the quantification of protein expression. One of the most widely used strategies to achieve this is to employ twodimensional polyacrylamide gel electrophoresis (2DE) followed by MS and peptide mass fingerprinting (Cai et al., 2004). 2DE is a commonly employed method to determine protein expression

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differences between two samples, and involves gel separation of combined sample in two dimensions, the first dimension separating based on the isoelectric charge of the protein, and the second within the gel matrix by size or molecular weight (Hoehn and Suffredini, 2005). Individual spots are then cut from the gel, digested with proteolytic enzymes, and the resulting fragments analyzed with high-resolution MS. This approach has been successfully applied in a number of studies that have examined differences between primary and metastatic tumors at the proteomic level (reviewed in Cai et al., 2004). For example, 2D-PAGE has been successfully utilized to examine proteomic differences between primary colorectal and secondary hepatic metastases (Tachibana et al., 2003), identifying protein expression differences that implicate Apolipoprotein A1 as a potential marker of aggressive primary colorectal tumors. Protein microarrays are an alternative means of quantifying expression of a variety of different proteins in a given sample at the same time. They utilize antibodies targeted against well-characterized proteins, and generally exist in one of two formats: either as antibodies printed on a glass slide in array format (rather analogous to DNA microarrays), or adherent to fluorescent beads (Hoehn and Suffredini, 2005). This technology is primarily used for protein quantification rather than biomarker discovery since prior knowledge of the molecule one wants to measure is required (Hoehn and Suffredini, 2005). Although there are few examples of implementation of this technology to study metastatic progression, it has been shown that protein microarrays have the ability to discern proteomic differences between normal and malignant breast tissues (Hudelist et al., 2004). This makes this approach a potentially attractive prospect to be able to evaluate prognosis and therapeutic options, which will likely generate increased interest as the technology is refined. Functional Proteomics The most apparent difference between functional and quantitative proteomics is that functional protein levels are much more challenging to determine accurately owing to the frequency of post-translational modifications. This is especially true of enzymes, which are often activated by post-translational peptide cleavage. However, exploring the proteome at the functional level employs many of the same methodologies used to investigate protein abundance, although methodologies that allow the investigation of protein–protein interactions and enzyme activity levels used far more frequently (Everley and Zetter, 2005). A number of studies have sought to utilize functional proteomic methodologies to investigate dysregulated aspects of protein function in metastasis (reviewed in Cai et al., 2004). For example, Jessani et al. (2002) used a process known as activitybased protein profiling, which involves the covalent linkage of chemical probes to the active sites of enzymes and enables direct measurement of the levels of the active and inactive forms of specific enzymes. The proteomes of a panel of human breast and melanoma cancer cell lines were labeled using rhodaminecoupled fluorophosphonate, which was followed by SDS/PAGE to separate labeled proteins. Individual bands were then excised

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from the gel, digested with trypsin, and their composition analyzed using MS and microcapillary liquid chromatography. This enabled this group to demonstrate that this type of functional proteomic approach could be used to accurately delineate the invasiveness of different cell lines based on the activity of a cluster of proteases, lipases, and esterases. Specifically, the most invasive cell lines displayed marked down-regulation of the activity of these groups of enzymes, with concurrent up-regulation of a distinct set of secreted and membrane-associated enzyme activities. It is therefore evident that this type of approach, although technically challenging, has the power to grant new insights into the mechanisms of metastasis, insights that are not currently possible through the use of more familiar technologies. Complementary Approaches: Genomics and Proteomics In spite of these successes, the application of proteomics is limited compared to genomics in that proteomic technologies are not yet sufficiently advanced to enable examination of protein expression and functionality at a comprehensive proteome-wide scale. This is most definitely not the case with DNA microarrays, which typically generate such an abundance of data that the vast majority is never examined in any depth whatsoever. This makes it particularly attractive to integrate proteomic and genomic data to enable a more holistic picture of a particular system to be constructed. Despite the lack of consistent correlation between protein and mRNA levels in vivo (Anderson and Seilhamer, 1997), this approach has been used with some success. Varambally et al. (2005) utilized such a strategy where high-throughput immunoblotting was used in parallel with mRNA expression profiling in different prostate tissues ranging in pathological classification from benign to hormone-refractory metastatic tissues. Through an immunoblotting assay, it was determined that a total of 156 proteins were dysregulated between clinically localized and metastatic disease. However, when expression of these proteins was compared to their mRNA expression levels using microarray analysis, they found that protein and mRNA levels correlated very poorly, with only 48–64% concordance between mRNA and protein levels. Most strikingly, however, when clinical outcome was considered as a variable, it was demonstrated that those mRNA transcripts that did correlate with protein levels in metastatic prostate cancer could be used as gene predictors of progression in clinically localized disease. The significance of this latter finding has yet to be fully explained, and further experimentation will be required to determine whether this is an isolated effect, or whether this is in fact a characteristic of metastatic disease in general. Our laboratory has also successfully utilized a combination of genomic and proteomic approaches to investigate metastasis. Proteomic techniques were used in the study described above, where microarray analysis was used to examine gene expression signatures in normal tissues derived from inbred mice possessing differing metastatic potentials (Yang et al., 2005). Here, MS was used to analyze the protein composition of salivary gland secretions in different inbred mouse strains, with an aim of being able to develop an alternative means of developing a predictive metastasis signature. Interestingly, it was found that it is possible

to prospectively identify those animals within a genetically heterogeneous population that are at high risk of metastasis by analyzing salivary peptide secretions using MS.

ASSESSEMENT OF PROGNOSIS AND NEW TREATMENTS FOR METASTASIS: THE ROLE OF NEW TECHNOLOGIES Recent advances aside, cancer relapse and death rates remain unacceptably high, and there is a clear need for clinicians have access to investigative technologies that will allow both the degree of treatment required and overall prognosis to be determined with improved accuracy. The necessity to develop new technologies to predict the likelihood of metastasis formation and allow for the evaluation of treatments for secondary disease could have substantial benefits in the clinical arena and might even allow for treatment regimens to be developed that are tailored towards the individual rather than to the type of tumor they possess. New Perspectives on the Assessment of Prognosis The mainstay of staging solid tumors has traditionally relied upon pathological examination of tumor specimens (e.g., primary tumor, sentinel lymph nodes, distant metastatic lesions) coupled with both clinical observation and the findings of various radiological techniques. However, the emergence of new immunological and molecular technologies has augmented currently available approaches to cancer staging in many different tumor types, and has enabled clinicians to detect metastatic disease and assess prognosis with increasing accuracy (reviewed in Timar et al., 2002).Yet the potential of many of these technologies remain, for the most part, untapped, with the efficacy of many immunological and molecular tools to detect micrometastases at best unclear at the present time. As a result of this, their appearance in current staging protocols is sparse to say the least, especially given the range of technologies currently available, a point that emphasizes the necessity to perform new clinical trials to determine the value of such technologies in assessing metastatic progression in patient populations. The potential for using microarray analysis of tumor gene expression patterns as a prognostic tool has been the subject of much discussion in the literature recently. As discussed above, a number of prominent studies have shown that primary tumors with a higher propensity to metastasize display expression signatures that enable prospective assessment of the risk of metastasis formation (Ramaswamy et al., 2003; van de Vijver et al., 2002; van’t Veer et al., 2002). Although this implies that microarray expression analysis theoretically could give an indication of likely clinical outcome, implementation of this technology in a clinical setting as an adjunct to more traditional means of assessing prognosis is, however, somewhat limited. This is primarily a consequence of the nature of microarray technology, with limitations including that it is costly, both in regards to the requirements for expensive reagents and time required for personnel to

Conclusion

perform experimentation, and that microarray analysis requires the use of tissue as a means of generating prognostic data. The latter point raises a number of other concerns in that the tumor tissue must first be accessible (i.e., either by biopsy or surgical resection), and tissue must be collected in a manner so as not to allow tumor RNA to degrade, meaning tissue must be promptly preserved following its removal from the body since RNA typically undergoes rapid degradation. Recent studies have also questioned the reliability of microarray data, with a significant lab-to-lab variation shown to exist in experimental observations in some studies (Irizarry et al., 2005). These difficulties, although not insurmountable with improvements in currently available technologies, highlight the need to develop tools that are equally as accurate as microarray analysis in predicting clinical outcome yet do not suffer from the associated technical limitations. Intriguingly, our recent experiences with SIPA1 as described above might prove valuable in this respect. We have previously demonstrated that a polymorphism within mouse Sipa1 is associated with an altered metastatic potential (Park et al., 2005). As a means of continuing our investigation the role of this gene in metastasis, a pilot study was performed where polymorphisms within human SIPA1 were characterized in a cohort of breast cancer patients (Crawford et al., 2006). Interestingly, data from this preliminary study appears to show that the presence of these SIPA1 polymorphisms is associated with poor outcome in breast cancer. Although substantial work will be required to validate these preliminary results, it does hold some hope that it might prove possible to utilize germline variation in the form of single nucleotide polymorphisms (SNPs) as a tool to determine the likelihood of metastatic disease development. It should be noted, however, that it is exceedingly unlikely that any single SNP or combination of SNPs within an individual gene will possess sufficient prognostic power to be used as a stand-alone predictive assay. More likely, a clinically viable prognostic assay will require the development of a panel SNPs within a collection of genes, with each gene represented shown independently to be a modifier of metastatic efficiency. If this can be achieved then SNP genotyping will likely prove a robust means of assessing prognosis that, along with more conventional modalities, could augment, or even replace, microarray-based gene expression profiling as an assay to assist in tumor staging. The fact that SNP genotyping does not possess many of the technical constraints of microarray gene expression analysis is a significant advantage, especially since the material required to perform this type of assay (e.g., blood) is far more accessible than the tumor specimens required for microarray analysis. In addition, the relative simplicity of SNP genotyping assays means that there is significantly less lab-to-lab and platform-to-platform variability. New Treatment Possibilities for Metastatic Disease Germline-encoded susceptibility to metastasis development is very much a new concept in the field of cancer research, and the possibility of determining metastasis susceptibility from tissues harboring heritable material raises a number of possibilities

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for novel treatments for this most lethal consequence of cancer. As discussed above, it may well prove possible to identify individuals within a population who are at increased risk of metastasis by screening for metastasis risk alleles. Furthermore, if a test could be developed with sufficient prognostic power, it follows that it could be performed prior to the initiation of tumorigenesis, which in turn would allow for those individuals identified as being at high risk of both cancer and subsequent metastasis to be entered into chemoprevention regimens to reduce both cancer risk and potential metastatic burden. To determine whether this is a feasible strategy, we investigated the effects of chronic caffeine exposure on tumor progression in our highly metastatic mouse model (Yang et al., 2004). Caffeine exposure was commenced at two different time points in the PyMT transgenic mouse model: prior to the appearance of palpable mammary tumors and shortly after palpable tumor development. Caffeine was shown to reduce both total tumor burden and metastatic colonization prior to tumor development, while suppressing only the overall incidence of metastasis with no concurrent effect on overall tumor burden when given after palpable tumor development. Genomic and proteomic technologies were also used in this study, and gave some insight into the mechanism of action of caffeine, and showed that it may act by inhibiting the malignant transformation of mammary epithelial cells, by inhibiting conversion of dormant tumor cells to micrometastases, or micrometastases to macrometastases, or by inhibiting tumor cell adhesion and motility. Overall, these data are highly encouraging in that they suggest that caffeine or similar small molecules might improve clinical outcome if initiated in high-risk individuals prior to or shortly after diagnosis.

CONCLUSION Owing to the eventual outcome of metastatic disease in many forms of solid cancer, the quest to gain fresh insights into the molecular mechanisms of this process remains at the forefront of cancer research. New technologies that allow ever deeper insights into the genetics, genomics and proteomics of this complex process are affording researchers just this opportunity. Each of these related disciplines utilizes an array of technologies that possess ever-increasing power and unfortunately, a concomitant increase in analytical and technological complexity. The future of metastasis research, as with all branches of biological science, lies in finding a means of integrating these technologies to enable a more complete picture of a particular system or disease to be assembled. This approach, also known as “transomics” is a double-edged sword: with the increase in the ability to gain fresh insights into biological systems comes the increase in complexity of experimentation. It is likely, however, that this approach will be implemented far more widely in the future as associated computational power increases. This will hopefully enable researchers to gain a more integrative understanding of the process of metastasis, as well as many other aspects of cancer.

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Hirai, Y., Utsugi, K., Takeshima, N., Kawamata, Y., Furuta, R., Kitagawa, T., Kawaguchi, T., Hasumi, K. and Noda, T. (2004). Putative gene loci associated with carcinogenesis and metastasis of endocervical adenocarcinomas of uterus determined by conventional and array-based CGH. Am J Obstet Gynecol 191, 1173–1182. Hitzemann, R., Malmanger, B., Cooper, S., Coulombe, S., Reed, C., Demarest, K., Koyner, J., Cipp, L., Flint, J., Talbot, C. et al. (2002). Multiple cross mapping (MCM) markedly improves the localization of a QTL for ethanol-induced activation. Genes Brain Behav 1, 214–222. Hoehn, G.T. and Suffredini, A.F. (2005). Proteomics. Crit Care Med 33, S444–S448. Holmgren, L. (1996). Antiangiogenis restricted tumor dormancy. Cancer Metastasis Rev 15, 241–245. Hudelist, G., Pacher-Zavisin, M., Singer, C.F., Holper, T., Kubista, E., Schreiber, M., Manavi, M., Bilban, M. and Czerwenka, K. (2004). Use of high-throughput protein array for profiling of differentially expressed proteins in normal and malignant breast tissue. Breast Cancer Res Treat 86, 281–291. Hunter, K.W. (2003). Allelic diversity in the host genetic background may be an important determinant in tumor metastatic dissemination. Cancer Lett 200, 97–105. Hunter, K.W. and Williams, R.W. (2002). Complexities of cancer research: mouse genetic models. ILAR J 43, 80–88. Hunter, K.W., Broman, K.W., Voyer, T.L., Lukes, L., Cozma, D., Debies, M.T., Rouse, J. and Welch, D.R. (2001). Predisposition to efficient mammary tumor metastatic progression is linked to the breast cancer metastasis suppressor gene Brms1. Cancer Res 61, 8866–8872. Hunter, K.W.,Welch, D.R. and Liu, E.T. (2003). Genetic background is an important determinant of metastatic potential. Nat Genet 34, 23–24. Irizarry, R.A., Warren, D., Spencer, F., Kim, I.F., Biswal, S., Frank, B.C., Gabrielson, E., Garcia, J.G., Geoghegan, J., Germino, G. et al. (2005). Multiple-laboratory comparison of microarray platforms. Nat Meth 2, 345–350. Jessani, N., Liu,Y., Humphrey, M. and Cravatt, B.F. (2002). Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness. Proc Natl Acad Sci USA 99, 10335–10340. Kallioniemi, A., Kallioniemi, O.P., Sudar, D., Rutovitz, D., Gray, J.W., Waldman, F. and Pinkel, D. (1992). Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science 258, 818–821. Kang, Y., He, W., Tulley, S., Gupta, G.P., Serganova, I., Chen, C.R., Manova-Todorova, K., Blasberg, R., Gerald, W.L. and Massague, J. (2005). Breast cancer bone metastasis mediated by the Smad tumor suppressor pathway. Proc Natl Acad Sci USA 102, 13909–13914. Kantachuvesiri, S., Haley, C.S., Fleming, S., Kurian, K., Whitworth, C.E., Wenham, P., Kotelevtsev, Y. and Mullins, J.J. (1999). Genetic mapping of modifier loci affecting malignant hypertension in TGRmRen2 rats. Kidney Int 56, 414–420. Kaplan, R.N., Riba, R.D., Zacharoulis, S., Bramley, A.H., Vincent, L., Costa, C., MacDonald, D.D., Jin, D.K., Shido, K., Kerns, S.A. et al. (2005). VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche. Nature 438, 820–827. Lancaster, M., Rouse, J. and Hunter, K.W. (2005). Modifiers of mammary tumor progression and metastasis on mouse chromosomes 7, 9, and 17. Mamm Genome 16, 120–126. Leiter, E.H. (1984). Genetic control of pathogenesis of diabetes in C3H mice. Influence of the major histocompatibility complex. Diabetes 33, 1068–1071.

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82 Diagnostic-Therapeutic Combinations in the Treatment of Cancer Jeffrey S. Ross

INTRODUCTION The regulatory approvals in the United States and Europe of trastuzumab (Herceptin®) for the treatment of HER2 overexpressing metastatic breast cancer (Figure 82.1) and imatinib mesylate (Gleevec®) for the treatment of patients with bcr/abl translocation positive chronic myelogenous leukemia (Figure 82.2) and gastrointestinal stromal tumors (GISTs) featuring an activating c-kit growth factor receptor mutation has created enthusiasm for anticancer targeted therapy in both the scientific and public communities (Mauro and Druker, 2001; O’Dwyer and Druker, 2001). Recent major news magazines and other public media have highlighted interest in new anticancer drugs that exploit diseasespecific genetic defects as the target of their mechanism of action (Brown, et al., 2001; Lemonick and Parl, 2001). It is now widely held that the integration of molecular oncology and molecular diagnostics will further revolutionize oncology drug discovery and development; customize the selection, dosing, and route of administration of both previously approved traditional agents and new therapeutics in clinical trials, and individualize medical care for the cancer patient (Amos and Patnaik, 2002; Bottles, 2001; Evans and McLeod, 2003;Weinshilboum, 2003).

TARGETED THERAPIES FOR CANCER From the regulatory perspective, “targeted therapy” has been defined as a drug in whose approval label there is a specific Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 990

reference to a simultaneously or previously approved diagnostic test that must be performed before the patient can be considered eligible to receive that specific drug. The co-approvals of the antibreast cancer antibody trastuzumab (Herceptin®) and the required tissue-based tests for patient eligibility (Herceptest®, Pathway®, InSite®, and Pathvysion®) are examples of this strict definition of targeted therapy. However, for many scientists and oncologists, anticancer drugs are considered to “targeted” when they feature a focused mechanism that specifically acts on a well-defined target or biologic pathway that, when inactivated, causes regression or destruction of the malignant process. Examples of this less rigorous definition of targeted therapy include hormonal-based therapies for breast cancer, small-molecule inhibitors of the epidermal growth factor receptor (EGFR), blockers of invasion and metastasis enabling proteins and enzymes, antiangiogenesis agents, proapoptotic drugs and proteasome inhibitors. Finally, another definition of targeted therapy involves anticancer antibody therapeutics that seek out and kill malignant cells bearing the target antigen.

THE IDEAL TARGET The ideal cancer target (Table 82.1) can be defined as a macromolecule that is crucial to the malignant phenotype and is not significantly expressed in vital organs and tissues; that has biologic relevance that can be reproducibly measured in readily obtained clinical samples; that is definably correlated with clinical outcome; and that interruption, interference, or inhibition of Copyright © 2009, Elsevier Inc. All rights reserved.

(a) 0

1

15–25,000 Receptors 1.0–1.2 gene ratio

80–110,000 Receptors 1.2–1.4 gene ratio

2

3

370–630,000 Receptors 1.4–2.4 gene ratio

2–10,000,000 Receptors 3.4–5.6 gene ratio (c)

(b)

Figure 82.1 (a) HER-2/neu Protein Expression in Infiltrating Ductal Breast Cancer Measured by Immunohistochemistry using the HerceptestTM Slide Scoring System. Upper Left: 0 (negative) staining for HER-2/neu protein. This level of staining is typically associated with 15,000–25,000 surface receptor molecules per cell and HER-2/neu gene copy to chromosome 17 copy ratios measured by FISH of 1.0–1.2. Upper Right: 1 staining associated with 80,000–110,000 receptors and a gene ratio of 1.2–1.4. Lower Left: 2 staining with membranous distribution, but no total cell encirclement associated with 370,000–630,000 receptors and a gene ratio of 1.4–2.4. Lower Right: 3 staining with diffuse positive membranous distribution, total cell encirclement and “chicken wire” appearance associated with 2,000,000–10,000,000 receptors and a gene ratio of 3.4–5.6. (peroxidase-anti-peroxidase with HerceptestTM antibody X 200). [Receptor count and FISH gene ratio data provided by Dr. Kenneth Bloom, USLabs, Inc., Irvine, CA). (b) HER-2/neu Gene Amplification in Infiltrating Ductal Breast Cancer Detected by Fluorescence In Situ Hybridization (FISH). Left: HER-2/neu gene amplification demonstrated by the Abbott-Vysis PathvysionTM method showing significant increase in HER-2/neu gene signals (red) compared to chromosome 17 signals (green) with a HER-2/neu gene ratio of 3.9. Right: HER-2/neu gene amplification using the Ventana InformTM method showing another breast cancer specimen with an absolute (raw) HER-2/neu gene copy number of 24. (c) HER-2/neu gene Amplification in Infiltrating Breast Cancer Detected by Chromogenic In Situ Hybridization (CISH) using anti- HER-2/neu probe and IHC with diaminobenzidine chromagen (SpotLightTM HER-2/neu probe, Zymed Corp., South San Francisco, CA). Reprinted from Ross, J.S, Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher.

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such a macromolecule yields a clinical response in a significant proportion of patients whose tumors express the target with minimal to absent responses in patients whose tumors do not express the target. For antibody therapeutics, additional important criteria include the use of cell surface targets that when complexed with the therapeutic naked or conjugated antibody, internalize the antigen–antibody complex by reverse pinocytosis, thus facilitating tumor cell killing.

THE FIRST DIAGNOSTICTHERAPEUTIC COMBINATION IN CANCER THERAPY: HORMONAL THERAPY FOR BREAST CANCER

Figure 82.2 Chronic myelogenous leukemia and imatinib (Gleevec®) therapy. Photomicrograph demonstrates a bcr/abl translocation detected by FISH in a patient with a packed bone marrow biopsy diagnostic of chronic myelogenous leukemia (inset). Note the yellow “fusion” gene product, indicating the apposition of one green (chromosome 22) and one red (chromosome 9) resulting from the translocation. This patient was treated with single agent imatinib (Gleevec®) and achieved complete remission of bone marrow histology and absence of bcr/abl by routine cytogenetics and FISH assessment.Reprinted from Ross J.S. and Hortobagyi, G. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc. Sudbury, MA with permission by the publisher.

TABLE 82.1

Features of the ideal anti-cancer target

■

Crucial to the malignant phenotype

■

Not significantly expressed in vital organs and tissues

■

A biologically-relevant molecular feature

■

Reproducibly measurable in readily obtained clinical samples

■

Correlated with clinical outcome

■

When interrupted, interfered with or inhibited, the result is a clinical response in a significant proportion of patients whose tumors express the target

■

Responses in patients whose tumors do not express the target are minimal

Reprinted from Ross, J.S. and Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher.

Targeted therapy for cancer began in the early 1970s with the introduction of the estrogen receptor (ER) biochemical assay to select patients with painful metastatic breast cancer for surgical ablation of estrogen producing organs (ovaries, adrenals) (Figure 82.3) (Osborne, 1998). The ER assay was followed by a similar dextran coated charcoal biochemical assay for the progesterone receptor (PR) and subsequently converted to an immunohistochemistry (IHC) platform when the decreased size of primary tumors associated with self-examination and mammographybased screening programs prevented the use of the biochemical test (Wilbur et al., 1992). The drug tamoxifen (Nolvadex®), which has both hormonal and nonhormonal mechanisms of action, has been the most widely prescribed antiestrogen for the treatment of metastatic breast cancer and chemoprevention of the disease in high risk women (Ciocca and Elledge, 2000; Jordan, 2003a, b, c). Although, ER and PR testing is the front line for predicting tamoxifen response, additional biomarkers, including HER-2/neu (HER-2) and cathepsin D testing, have been used to further refine therapy selection (Locker, 1998). The introductions of specific estrogen response modulators and aromatase inhibitors such as anastrozole (Arimidex®), letrozole (Femara®), and the combination chemotherapeutic, estramustine (Emcyt®) (Buzdar et al., 2006; Ibrahim and Hortobagyi, 1999; Jordan, 2003a, b, c; Miller et al., 2002) have added new strategies for evaluating tumors for hormonal therapy. Most recently, the Oncotype Dx® (Genomic Health, Redwood City, CA) multigene RT-PCR multiplex assay using a 21-gene probe set and mRNA extracted from paraffin blocks of stored breast cancer tissues was introduced as a new guide to the use of tamoxifen in ER positive node negative breast cancer patients (Paik et al., 2004). The assay features 16 cancer-related genes and 5 reference genes that were selected based on a series of transcriptional profiling experiments. The cancer-related genes include markers of proliferation including Ki-67, markers of apoptosis including survivin, invasion-associated protease genes including MMP11 and cathepsin L2, ER and HER2/neu gene family members, the glutathione-S-transferase genotype M1, CD68, a lysosomal monocyte/macrophage marker and BAG1, a co-chaperone glucocorticoid receptor associated with bcl-2 and apoptosis. Using a cohort of 688 lymph node negative,

The First Diagnostic-Therapeutic Combination in Cancer Therapy: Hormonal Therapy for Breast Cancer

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Breast Cancer Kd  2.0  1010 Number of binding sites  276 fmol/mg total protein

Bound/Free ratio 0.6 0.8 1.0 1.2

1.4

1.6

Make tumor cytosol

0.4

Incubate with radiolabeled estradiol

Relative ER mRNA expression

0

0.2

Centrifuge and separate fluid from protein pellet, determine radioactive CPM in each and produce scatchard data plot

0 0.5

0.1

0.2

0.3

0.4

Bound pmol/ml

25 20 15 10 5 0 ⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙⴙ ⴚⴚⴚⴚⴚⴚⴚⴚⴚ ⴚ ⴚ ⴚ ⴚ ⴚ ⴚ ⴚ ⴚ ⴚ ⴚⴙⴚ ⴙ ⴚⴚ

ER status by IHC on core needle biopsies

Figure 82.3 ER status determination. (a) Biochemical competitive binding assay for ER status determination. (b) Comparison of ER messenger RNA expression detected by microarray profiling and corresponding ER protein expression measured by IHC. The concordance between ER levels determined by IHC and ER levels determined by gene expression profiling was about 95%. Reprinted from Ross, J.S, Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher.

ER tumors obtained from patients enrolled in the NSABP B-14 clinical trial treated with tamoxifen alone, the 21-gene assay produced three prognosis scores of low, intermediate and high risk. The recurrence rates for these patients at 10 years follow-up was 7% for the low risk, 14% for the intermediate risk and 31% for the high risk groups. The difference in relapse rates between the low risk and high risk patients was highly significant (p  0.001). On multivariate analysis this assay predicted adverse outcome independent of tumor size and also predicted overall survival (Paik et al., 2004). Although not currently approved by the FDA, the interest in this new assay has been intense and it has become commercially available in a centralized format for new patients. Recent data presented at the 2005 ASCO Meeting showed that the Oncotype Dx® is also capable of performing as a stand-alone prognostic test based on the test results in an

untreated patient population (Paik et al., 2005). Detailed evaluation of the gene set in the Oncotype Dx® assay indicates that the mRNA levels of ER appear to be the most significant predictors in the node-negative ER-positive population (by IHC). Further studies are needed to validate the assay, learn its best uses and limitations given the evolving approach to hormonal therapy with non-tamoxifen drugs, the wide use of cytotoxic agents in the adjuvant setting for node-negative patients and the availability of both RT-PCR based and non-RT-PCR approaches to predicting breast cancer response to anti-estrogen and other anti-neoplastic agents used for treatment of the disease (Bast and Hortobagyi, 2004). In current practice the routine testing of tumor samples for androgen receptor status has not been incorporated into the selection of hormonal therapy for the disease.

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DIAGNOSTIC-THERAPEUTIC COMBINATIONS FOR LEUKEMIA AND LYMPHOMA The introduction of immunophenotyping for leukemia and lymphoma was followed by the first applications of DNA-based assays, the polymerase chain reaction, and RNA-based molecular technologies in these diseases that complemented continuing advances in tumor cytogenetics (Gleissner and Thiel, 2001; Rubnitz et al., 1999). In addition to the imatinib (Gleevec®) targeted therapy for chronic myelogenous leukemia, other molecular targeted therapies in hematologic malignancies includes the use of all-trans-retinoic acid (ATRA) for the treatment of acute promyelocytic leukemia (Grimwade and Lo Coco, 2002); anti-CD20 antibody therapeutics targeting nonHodgkin lymphomas, including rituximab (Rituxan®) (Kiyoi and Naoe, 2005); and the emerging Flt-3 target for a subset of acute myelogenous leukemia patients (see below) (Ross et al., 2003a).

HER-2 POSITIVE BREAST CANCER AND TRASTUZUMAB (HERCEPTIN®) After the introduction of hormone receptor testing, some 30 years then elapsed before the next major targeted cancer chemotherapy program for a solid tumor was developed. In the mid-1980s, the discovery of the HER-2 (c-erbB2) gene and protein and subsequent association with an adverse outcome in breast cancer provided clinicians with a new biomarker that could be used to guide adjuvant chemotherapy (Slamon et al., 2001). The development of trastuzumab (Herceptin®), a humanized monoclonal antibody designed to treat advanced metastatic breast cancer that had failed first- and second-line chemotherapy, caused a rapid wide adoption of HER-2 testing of the patients’ primary tumors (Schnitt and Jacobs, 2001). However, soon after its approval, widespread confusion concerning the most appropriate diagnostic test to determine HER-2 status in formalin-fixed paraffin-embedded breast cancer tissues substantially impacted trastuzumab use (Hayes and Thor, 2002; Hortobagyi, 2001; Masood and Bui, 2002, Paik et al., 2002; Tanner et al., 2000; Wang et al., 2000, 2001; Zhao et al., 2002). Since its launch in 1998, trastuzumab has become an important therapeutic option for patients with HER-2-positive breast cancer (Bast et al., 2000; Ligibel and Winer, 2002; McKeage and Perry, 2002, Shawver et al., 2002). In general, when specimens have been carefully fixed, processed and embedded, there has been excellent correlation between HER-2 gene copy status determined by Fluorescence In Situ Hybridization (FISH) and HER-2 protein expression levels determined by IHC (Slamon et al., 2001). The main use of either method in current clinical practice is focused on the negative prediction of response to trastuzumab. Currently, both the American Society of Clinical Oncology and the College of

American Pathologists consider HER-2 testing to be part of the standard work-up and management of breast cancer (Hammond et al., 2000; Pawlowski et al., 2000). Recently, the chromogenic (non-fluorescent) in situ hybridization technique has been used to determine the HER-2 gene amplification status with promising results (Figure 82.1) (Hortobagyi, 2001; Zhao et al., 2002). Non-morphologic approaches for determining HER-2 status have also been developed. The RT-PCR technique which has been predominantly used to detect HER-2 mRNA in peripheral blood and bone marrow samples, has correlated more with gene amplification status than IHC levels of primary tumors, but has been less successful as a predictor of survival (Bieche et al., 1999; Dressman et al., 2003;Tubbs et al., 2001). With the advent of laser capture microscopy and the acceptance of RT-PCR as a routine and reproducible laboratory technique, the use of RT-PCR for the determination of HER-2 status may increase in the future. The cDNA microarray-based method of detecting HER-2 mRNA expression levels has recently received interest as an alternative method for measuring HER-2/neu status in breast cancer (Fornier et al., 2005; Pusztai et al., 2003). Finally, the serum HER-2/ELISA test measuring circulating HER-2 (p185neu) protein is an FDA-approved test that has seen increased clinical use as a method for monitoring the response to trastuzumab (Ross et al., 2005). A summary of HER-2 testing methods in breast cancer is shown in Table 82.2.

TABLE 82.2 Methods of detection of HER-2/neu status in breast cancer Method IHC

Target Protein

FDA-approved

Slide-based

a

Yes

a

Yes

Yes

FISH

Gene

Yes

CISH

Gene

No

Yes

Southern blot

Gene

No

No

RT-PCR

mRNA

No

No

Microarray TP

mRNA

No

No

Tumor ELISA

Protein

No

No

Serum ELISA

Protein

Yesb

No

Reprinted from Ross, J.S. and Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher. IHC  immunohistochemistry FISH  fluorescence in situ hybridization CISH  chromogenic in situ hybridization RT-PCR  reverse transcriptase polymerase chain reaction TP  transcriptional profiling ELISA  enzyme-linked immunosorbent assay a For prognosis and prediction of response and eligibility to receive trastuzumab therapy b For monitoring response of breast cancer to treatment

Other Targeted Anticancer Therapies Using Antibodies

OTHER TARGETED ANTICANCER THERAPIES USING ANTIBODIES An unprecedented number and variety of targeted small molecule and antibody-based therapeutics are currently in early development and clinical trials for the treatment of cancer. Therapeutic antibodies have become a major strategy in clinical oncology because of their ability to specifically bind to primary and metastatic cancer cells with high affinity and create antitumor effects by complement-mediated cytolysis and antibodydependent cell-mediated cytotoxicity (naked antibodies) or by the focused delivery of radiation or cellular toxins (conjugated antibodies) (Carter, 2001; Goldenberg, 2002; Hemminki, 2002; Milenic, 2002; Reichert, 2002; Ross et al., 2003b, ). Currently, there are eight anticancer therapeutic antibodies approved by the US Food and Drug Administration (FDA) for sale in the United States (Table 82.3). Therapeutic monoclonal antibodies are typically of the IgG class containing two heavy and two light chains. The heavy chains form a fused “Y” structure with two light chains running in parallel to the open portion of the heavy chain. The tips of the heavy-light chain pairs form the antigen binding sites, with the primary antigen recognition regions known as the complementarity determining regions. The early promise of mouse monoclonal antibodies for the treatment of human cancers was not realized because (1) unfocused target selection led to the identification of target antigens that were not critical for cancer cell survival and progression, (2) there was a low overall potency of naked mouse antibodies as anticancer drugs, (3) antibodies penetrated tumor cells poorly, (4) there was limited success in producing radioisotope and toxin conjugates, and (5) the development of human antimouse antibodies (HAMA) prevented the use of multiple dosing schedules (Reilly et al., 1995). The next advance in antibody therapeutics began in the early 1980s when recombinant DNA technology was applied to antibody design to reduce the antigenicity of murine and other rodent-derived monoclonal antibodies. Chimeric antibodies were developed where the constant domains of the human IgG molecule were combined with the murine variable regions by transgenic fusion of the immunoglobulin genes; the chimeric monoclonal antibodies were produced from engineered hybridomas and Chinese Hamster Ovary (CHO) cells (Merluzzi et al., 2000; Winter and Harris, 1993). The use of chimeric antibodies significantly reduced the HAMA responses but did not completely eliminate them (Kuus-Reichel et al., 1994; Merluzzi et al., 2000). Although several chimeric antibodies achieved regulatory approval, certain targets required humanized antibodies to achieve appropriate dosing. Partially humanized antibodies were then developed where the six complementarity determining regions of the heavy and light chains and a limited number of structural amino acids of the murine monoclonal antibody were grafted by recombinant technology to the complementarity determining region depleted human IgG scaffold (Milenic, 2002). Although this process further reduced or eliminated the

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HAMA responses, in many cases significant further antibody design procedures were needed to reestablish the required specificity and affinity of the original murine antibody (Jones et al., 1986, Isaacs, 2001; Pimm, 1994;Watkins and Ouwehand, 2000). A second approach to reducing the immunogenicity of monoclonal antibodies has been to replace immunogenic epitopes in the murine variable domains with benign amino acid sequences, resulting in a deimmunized variable domain. The deimmunized variable domains are genetically linked to human IgG constant domains to yield a deimmunized antibody (Biovation, Aberdeen, Scotland). Additionally, primatized antibodies were subsequently developed that featured a chimeric antibody structure of human and monkey that, as a near exact copy of a human antibody, further reduced immunogenicity and enabled the capability for continuous repeat dosing and chronic therapy (Reff et al., 2002). Finally, fully human antibodies have now been developed using murine sources and transgenic techniques (Reff et al., 2002). Using modern antibody design and deimmunization technologies, scientists and clinicians have attempted to improve the efficacy and reduce the toxicity of anticancer antibody therapeutics (Carter, 2001; Chester and Hawkins, 1995; Reff Heard, 2001; Reilly et al., 1995; Nielsen and Marks, 2000). The bacteriophage antibody design system has facilitated the development of high affinity antibodies by increasing antigen binding rates and reducing corresponding detachment rates (Nielsen and Marks, 2000). Increased antigen binding is also achieved in bivalent antibodies with multiple attachment sites, a feature known as avidity. Modern antibody design has endeavored to create small antibodies that can penetrate to cancerous sites but maintain their affinity and avidity. A variety of approaches has been used to increase antibody efficacy (Carter, 2001). Clinical trials have recently combined anticancer antibodies with conventional cytotoxic drugs, yielding promising results (Carter, 2001; Goldenberg, 2002; Hemminki, 2002; Milenic, 2002; Reichert, 2002; Ross et al., 2003b). The applications of radioisotope, small molecule cytotoxic drug, and protein toxin conjugation have resulted in promising results in clinical trials and achieved regulatory approval for several drugs now on the market (see below). Antibodies have also been designed to increase their enhancement of effector functions of antibody-dependent cellular cytotoxicity. Another cause of toxicity of conjugated antibodies has been the limitations of the conjugation technology, which can restrict the ratio of the number of toxin molecules per antibody molecule (Carter, 2001; Goldenberg, 2002; Watkins and Ouwehand, 2000). Methods designed to overcome the toxicity of conjugated antibodies include the use of antibody targeted liposomal small molecule drug conjugates and the use antibody conjugates with drugs in nanoparticle formats to enhance bonding strength that enable controlled release of the cytotoxic agent. Another technique that uses site selective prodrug activation to reduce bystander tissue toxicity is the antibody directed enzyme prodrug therapy. An antibody-bound enzyme is targeted to tumor cells. This allows for selective activation of a nontoxic prodrug to a cytotoxic agent at the tumor site for cancer therapy.

Antibody therapeutics for cancer Type

Target

Indication(s) (both approved and investigational)

Alemtuzumab Campath®

05/01

BTG

Monoclonal antibody, humanized Anticancer, immunological Multiple sclerosis treatment Immunosuppressant

CD52

Cancer, leukemia, chronic lymphocytic Cancer, leukemia, chronic myelogenous Multiple sclerosis, chronic progressive

Daclizumab Zenapax®

03/02

Monoclonal IgG1 Chimeric Immunosuppressant Antipsoriasis Antidiabetic Ophthalmological Multiple sclerosis treatment

IL2R

Transplant rejection, general Transplant rejection, bone marrow Uveitis Multiple sclerosis, relapsing-remitting Multiple sclerosis, chronic progressive Cancer, leukemia, general Psoriasis Diabetes, type I Asthma Colitis, ulcerative

Monoclonal IgG1 Chimeric Anticancer, immunological Antiarthritic, immunological Immunosuppressant

CD20

Cancer, lymphoma, non-Hodgkin’s Cancer, lymphoma, B-cell Arthritis, rheumatoid Cancer, leukaemia, chronic lymphocytic Thrombocytopenic purpura

Monoclonal IgG1 Humanized Anticancer, immunological

p185neu

Cancer, breast Cancer, lung, non-small cell Cancer, pancreatic

ILEX Oncology Schering AG Protein Design Labs Hoffmann-La Roche

Rituximab Rituxan®

11/97

IDEC Genentech HoffmannLa Roche Zenyaku Kogyo

Trastuzumab Herceptin®

09/98

Genentech

Gemtuzumab Mylotarg®

05/00

Wyeth/AHP

Monoclonal IgG4 Humanized

CD33 / coleacheamycin

Cancer, leukemia, AML (patients older than 60 years)

Ibritumomab Zevalin®

02/02

IDEC

Monoclonal IgG1 Murine Anticancer

90

CD20 / Yttrium

Cancer, lymphoma, low grade, follicular, Transformed non-Hodgkin’s (relapsed or refractory)

Tositomumab Bexxar®

06/03

Corixa

Anti-CD 20 Murine Monocolonal antibody with 131I conjugation

CD20

Cancer, lymphoma, non-Hodgkin’s

Cetuximab Erbitux®

02/04

Imclone Bristol Myers Squibb

Anti-EGFR monoclonal antibody

EGFR

Approved for third line treatment of metatstatic colorectal cancer that has failed primary chemotherapy

Bevacizumab Avastin®

02/04

Genentech

Anti-VEGF (ligand)

VEGF

Avastin is approved for use in combination with intravenous 5-fluorouracil-based chemotherapy as a treatment for patients with first-line – or previously untreated – metastatic colorectal cancer.

Edrecolomab PanorexTM

01/95 (Europe Glaxo-SmithOnly, nut Kline FDA-approved)

Monoclonal IgG2A Murine Anticancer

Epithelial cell Cancer, colorectal adhesion molecule (EpCAM)

Hoffmann-La Roche ImmunoGen

Adapted from Ross, J.S. and Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher.

Diagnostic-Therapeutic Combinations in the Treatment of Cancer

Source Partners

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FDA approval date

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Name

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TABLE 82.3

Other Targeted Anticancer Therapies Using Antibodies

A variety of factors can reduce antibody efficacy (Reilly et al., 1995): (1) limited penetration of the antibody into a large solid tumor or into vital regions such as the brain, (2) reduced extravasations of antibodies into target sites due to decreased vascular permeability, (3) cross-reactivity and nonspecific binding of antibody to normal tissues reduces targeting effect, (4) heterogeneous tumor uptake results in untreated zones, (5) increased metabolism of injected antibodies reduces therapeutic effects, and (6) HAMA and human antihuman antibodies form rapidly and inactivate the therapeutic antibody. Toxicity has been a major obstacle in the development of therapeutic antibodies for cancer [Carter, 2001; Goldenberg, 2002; Ross et al., 2003b;Watkins and Ouwehand, (2000)]. Crossreactivity with normal tissues can cause significant side effects for unconjugated (naked) antibodies, which can be enhanced when the antibodies are conjugated with toxins or radioisotopes. Immune-mediated complications can include dyspnea from pulmonary toxicity, occasional central and peripheral nervous system complications, and decreased liver and renal function. On occasion, unexpected toxic complications can be seen, such as the cardiotoxicity associated with the HER-2 targeting antibody trastuzumab. Radioimmunotherapy with isotopic-conjugated antibodies can also cause bone marrow suppression (see below). Unconjugated or naked antibodies include a variety of targeting molecules both on the market and in early and late clinical development. A variety of mechanisms have been cited to explain the therapeutic benefit of these drugs, including enhanced immune effector functions and direct inactivation of the targeted pathways as seen in the antibodies directed at surface receptors such as HER-1 (EGFR) and HER-2 (Amos and Patnaik, 2002; Brown et al., 2001; Lemonick and Parl, 2001; Mauro and Druker, 2001). Surface receptor targeting can reduce intracellular signaling, resulting in decreased cell growth and increased apoptosis (Reff et al., 2002). As seen in Table 82.3, of the nine anticancer antibodies on the market in the United States, two are conjugated with a radioisotope 90Y-ibritumomab tiuxetan (Zevalin®) and 131 I-tositomumab (Bexxar®) and one is conjugated to a complex natural product toxin gemtuzumab ozogamicin (Mylotarg®). Conjugation procedures have been designed to improve antibody therapy efficacy and have used a variety of methods to complex the isotope, toxin, or cytotoxic agent to the antibody (Carter, 2001; Goldenberg, 2002). Cytotoxic small molecule drug conjugates have been widely tested, but enthusiasm for this approach has been limited by the relatively low potency of these compounds (Carter, 2001). Fungal derived potent toxins have yielded greater success with the calicheamicin conjugated antiCD33 antibody gemtuzumab ozogamicin approved for the treatment of acute myelogenous leukemia and a variety of antibodies conjugated with the fungal toxin maytansanoid (DM-1) in preclinical development and early clinical trials. The interest in radioimmunotherapy increased significantly in 2001 with the FDA approvals of the 90Y-conjugated anti-CD20 antibody 90Y-ibritumomab tiuxetan and the 131I-conjugated anti-CD20 antibody 131 I-tositumomab. A variety of isotopes are under investigation

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in addition to 90Y as potential conjugates for anticancer antibodies (Goldenberg, 2002). Radioimmunotherapy features the phenomenon of the bystander effect, in which if antigen expression is heterogeneous, extensive tumor cell killing can still take place, even on nonexpressing cells, but can also lead to significant toxicity when the neighboring cells are vital non-neoplastic tissues such as the bone marrow and liver. Antibody Therapeutics for Hematologic Malignancies The earliest and most successful clinical use of antibodies in oncology has been for the treatment of hematologic malignancies (Burke et al., 2002; Carter, 2001; Goldenberg, 2002; Linenberger et al., 2002; Reff et al., 2002; Ross et al., 2003b; Stevenson et al., 2002; Watkins and Ouwehand, 2000; Wiseman et al., 2002). By taking advantage of improved recombinant technologies generating more specific and higher affinity monoclonal antibodies with reduced immunogenicity after humanization or deimmunization and the emerging conjugation capabilities, antibody therapeutics have become a major weapon in the treatment of leukemias and lymphomas (Burke et al., 2002; Linenberger et al., 2002; Stevenson et al., 2002;Wiseman et al., 2002). Rituximab (Rituxan®) Approved in 1997, rituximab (Rituxan®) is arguably the most commercially successful anticancer drug of any type since the introduction of taxanes. Rituximab sales exceeded $ 700 million in sales in the United States in 2001 (Reichert, 2002). Targeting the CD20 surface receptor common to many B cell nonHodgkin lymphoma subtypes, rituximab is a chimeric monoclonal IgG1 antibody that induces apoptosis, antibody-dependent cell cytotoxicity, and complement-mediated cytotoxicity (Reff et al., 2002) and has achieved significantly improved disease-free survival rates compared with patients receiving cytotoxic agents alone (Dillman, 2001; Coiffier, 2002; Grillo-Lopez, 2002; GrilloLopez et al., 2002). 90

Y-ibritumomab Tiuxetan (Zevalin®) 90 Y-ibritumomab tiuxetan (Zevalin®) consists of the murine version of the anti-CD20 chimeric monoclonal antibody, rituximab, which has been covalently linked to the metal chelator, MD-DTPA, permitting stable binding of 111In when used for radionucleotide tumor imaging and 90Y when used to produce enhanced targeted cytotoxicity (Dillman, 2002; Gordon et al., 2002; Krasner and Joyce, 2001; Wagner et al., 2002). In early 2002, 90Y-ibritumomab tiuxetan became the first radioconjugated antibody therapeutic for cancer approved by the FDA. Since its FDA approval, numerous patients who have received 90Y-ibritumomab tiuxetan after becoming refractory to a rituximab-based regimen have achieved significant responses (Dillman, 2001; Gordon et al., 2002). Gemtuzumab ozogamicin (Mylotarg®) The approval of gemtuzumab ozogamicin (Mylotarg®) by the FDA in 2000 marked the first introduction of a plant toxin

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conjugated antibody therapeutic (Bross et al., 2001; Larson et al., 2002; Nabhan and Tallman, 2002; Sievers and Linenberger, 2001; Stadtmauer, 2002). Gemtuzumab ozogamicin is targeted against CD33, a surface marker expressed by 90% of myeloid leukemic blasts but absent from stem cells, armed with calicheamicin, a potent cytotoxic antibiotic that inhibits DNA synthesis and induces apoptosis (Stadtmauer, 2002). The current indication for use of gemtuzumab ozogamicin is in acute myelogenous leukemia patients older than 60 years with the recommendation that before the initiation of therapy, the leukemic blast count is below 30,000/mL (Bross et al., 2001; Nabhan and Tallman, 2002; Sievers and Linenberger, 2001). Alemtuzumab (Campath®) Alemtuzumab (Campath®), a humanized monoclonal antibody, was approved in mid-2001 for the treatment of B-cell chronic lymphocytic leukemia in patients who have been treated with alkylating agents and who have failed fludarabine therapy (Dumont, 2002; Pangalis et al., 2001). Daclizumab (Zenapax®) Daclizumab (Zenapax®) is a chimeric monoclonal antibody that targets the interleukin-2 receptor. This antibody is primarily used to prevent and treat patients with organ transplant rejection but has also been used in a wide variety of chronic inflammatory conditions, including psoriasis, multiple sclerosis, ulcerative colitis, asthma, type I diabetes mellitus, uveitis, and also in a variety of leukemias (Carswell et al., 2001, Kreitman et al., 1992). 131

I-tositumomab (Bexxar®) I-tositumomab (Bexxar®) is a radiolabeled anti-CD20 murine monoclonal antibody approved in 2003 for the treatment of relapsed and refractory follicular/low grade and transformed non-Hodgkin lymphoma (Cheson, 2002; Zelenetz, 2003). 131

Antibody Therapeutics for Solid Tumors Interest in the development of antibody therapeutics for solid tumors among many commercial organizations and universities has been significantly impacted by the technologic advances in antibody engineering and the approval and recent clinical and commercial success of trastuzumab, the only therapeutic antibody approved by the FDA for the treatment of solid tumors (edrecolomab is approved in Germany, but not in the United States). Trastuzumab (Herceptin®) Trastuzumab (Herceptin®) has been described above. During the 6 years since the FDA approval of trastuzumab, two additional antibodies have been approved for the treatment of solid tumors (cetuximab and bevacizumab). In addition, continuing progress has been made in this field, and there are a number of both late stage and early stage products in development which show substantial promise.

Cetuximab (Erbitux®) The epidermal growth factor receptor (EGFR), also known as HER-1, is the target of two FDA-approved small molecule drugs (see below) and one FDA-approved antibody (Mendelsohn and Baselga, 2000). Cetuximab (Erbitux®) is a chimeric monoclonal antibody that binds to the EGFR with high affinity, blocking growth factor binding, receptor activation, and subsequent signal transduction events and leading to cell proliferation (Baselga, 2001). Cetuximab enhanced the antitumor effects of chemotherapy and radiotherapy in preclinical models by inhibiting cell proliferation, angiogenesis, and metastasis and by promoting apoptosis (Baselga, 2001). Cetuximab has been evaluated both alone and in combination with radiotherapy and various cytotoxic chemotherapeutic agents in a series of phase II/III studies that primarily treated patients with either head and neck or colorectal cancer (Baselga, 2001, Herbst and Langer, 2002). Breast cancer trials are also underway (Leonard et al., 2002). Although the FDA approval process for cetuximab was initially slowed because of concerns over clinical trial design and outcome data management (Reynolds, 2002), the antibody was approved for use in the treatment of advanced metastatic colorectal cancer in February 2004. Similar to trastuzumab, the development of cetuximab also included an immunohistochemical test for determining EGFR overexpression to define patient eligibility to receive the antibody (Wong, 2005). Thus, cetuximab has joined trastuzumab as an FDA-approved targeted therapy featuring an unconjugated antibody. However, there have been conflicting reports suggesting that the use of a pharmacodiagnostic test (EGFR immunostaining) is unnecessary for the selection of cetuximab in colorectal cancer therapy (Saltz, 2005). Recent clinical trials have found significant efficacy for cetuximab in the treatment of head and neck squamous cell cancers often in combination with radiation treatment (Harari and Huang, 2006). Bevacizumab (Avastin®) Bevacizumab (rhuMAb-VEGF) is a humanized murine monoclonal antibody targeting the vascular endothelial growth factor ligand (VEGF) approved by the FDA in 2004 for the front line or first line treatment in combination with chemotherapy of metatstatic colorectal cancer. VEGF regulates both vascular proliferation and permeability and functions as an antiapoptotic factor for newly formed blood vessels (Chen et al., 2000; Ferrara, 2005; Rosen, 2002). In addition to its approved indication in colorectal cancer, bevacizumab has shown promising efficacy in combination with cytotoxic drugs for the treatment of nonsmall cell lung cancer (Wakelee and Belani, 2005), renal cell carcinoma (Stadler, 2005), pancreatic cancer (Bruckner et al., 2005), breast cancer (de Gramont and Van Cutsem, 2005) and prostate cancer (Berry and Eisenberger, 2005). Unlike cetuximab, the development of bevacizumab has not included a diagnostic eligibility test. Neither direct measurement of VEGF expression in tumor, circulating VEGF levels in serum or urine or assessment of tumor microvessel density have been incorporated into the clinical trials or linked to the response rates to the antibody. To date a number of theories have been proposed as to the actual

Selected Targeted Anticancer Therapies Using Small Molecules

mechanism of action of bevacizumab and the relative contributions of direct anti-angiogenesis and other tumor vasculature stabilization and cytotoxic chemotherapy potentiation effects of the antibody (Blagosklonny, 2005; Hurwitz and Kabbinavar, 2005). In summary, currently used without an integrated diagnostic eligibility test, bevacizumab cannot be considered a true targeted therapy, and further development of this agent for use in prostatic, breast, lung, renal, and other cancers may will be inhibited by the inability to individually select patients who will be more likely to benefit from its use, either alone or in combination with other traditional cytotoxic drugs, antibodies and novel drugs.

(a)

Edrecolomab (Panorex®) Edrecolomab is a murine IgG2A monoclonal antibody that targets the human tumor-associated antigen Ep-CAM (17-1A). Edrecolomab has been approved in Europe (Germany) since 1995 to date has not been approved by the FDA. In a study of 189 patients with resected stage III colorectal cancer, treatment with edrecolomab resulted in a 32% increase in overall survival compared with no treatment (P  0.01) (Schwartzberg, 2001). Edrecolomab’s antitumor effects are mediated through antibody-dependent cellular cytotoxicity, complement-mediated cytolysis, and the induction of an antiidiotypic network (Haller, 2001). Edrecolomab is also currently being tested in large multicenter adjuvant phase III studies in stage II/III rectal cancer and stage II colon cancer. Edrecolomab was well tolerated when used as monotherapy and added little to chemotherapy-related side effects when used in combination. Sequential treatment of patients with metastatic breast cancer with edrecolomab after adjuvant chemotherapy reduced levels of disseminated tumor cells in the bone marrow and eliminated Ep-CAM-positive micrometastases (Kirchner et al., 2002).

(b)

huJ-591 (Anti-PSMAEXT) Prostate-specific membrane antigen (PSMA) is a membranebound glycoprotein restricted to normal prostatic epithelial cells, prostate cancer, and the endothelium of the neovasculature of a wide variety of non-prostatic carcinomas and other solid tumors (Figure 82.4) (Israeli et al., 1994; Liu et al., 1997; Ross, 2005). PSMA expression per cell progressively increases in primary prostate cancer, metastatic hormone sensitive prostate cancer, and hormone refractory metastatic disease. PSMA expression is increased further in association with clinically advanced prostatic cancer, particularly in hormone refractory disease, and appears to be an ideal molecule for use in targeting prostatic cancer cells. Increasing expression levels of PSMA in resected primary prostate cancer is associated with increased rates of subsequent disease recurrence (Ross et al., 2003c). Humanized and fully human antibodies specific for the extracellular domain of PSMA have been developed. A phase I clinical trial of one these antibodies, huJ591 conjugated with 90Y, has yielded promising results (Milowsky et al., 2004). Programs using toxin conjugates with anti-PSMA antibodies have completed preclinical development (Fracasso et al., 2002) and are currently showing promising results in early stage clinical trials for hormone-refractory

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Figure 82.4 PSMA expression in non-prostate cancer. (a) Traditional bone scan demonstrating bilateral activity in the femur indirectly indicating the presence of metastatic renal cell carcinoma. (b) 111I-huJ591EXT diagnostic immunoscintiscan of the same patient showing direct localization of the anti-PSMA antibody conjugate to the sites of metastatic renal cell carcinoma that feature PSMA expression in the tumor neovasculature. Reprinted from Ross, J.S. and Foster, C.S. eds. The Molecular Oncology of Prostate Cancer. 2006. Sudbury, MA: Jones and Bartlett, Inc. with permission by the publisher.

advanced metastatic prostate cancer (Milowsky et al., 2005). Finally, antibodies to PSMA have been used as diagnostic imaging agents (Figure 82.4), including the commercially available Prostascint® Freeman et al., 2002).

SELECTED TARGETED ANTICANCER THERAPIES USING SMALL MOLECULES Table 82.4 lists selected small molecule drugs designed to target specific genetic events and biologic pathways critical to cancer growth, invasion, and metastasis. Targeted Small Molecule Drugs for Hematologic Malignancies ATRA Arguably the first truly targeted therapy after the development of hormonal therapy for breast cancer was the development of

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Selected small molecule dugs designed to target specific genetic events and biologic pathways critical to cancer growth and progression Drug

Source

Clinical development status

Comment

PML-RAR- in PML

ATRA

Promega

Approved

First true targeted therapy since the introduction of ER testing and hormonal therapy for breast cancer

Bcr/abl in CML

Imatinib

Novartis

Approved

Has emerged as standard of care for early stage CML

c-Kit in GIST PDGF-

Imatinib

Novartis

Approved

Responses in relapsed/metastatic GIST can be predicted by the location of the activating c-kit mutation

Flt-3 in AML

SU5416 PKC412 MLN-518

Pfizer Novartis Millennium

Early Stage Clinical Trials

Small molecule drugs that target the flt-3 internal tandem duplication seen in 30% of AML

EGFR in NSCLC

Gefitinib

Astra Zeneca

Approved/withdrawn

No survival benefit. Returned to clinical trials.

EGFR in NSCLC and pancreatic cancer

Erlotinib

Genentech/OSI

Approved

Survival benefit demonstrated. No diagnostic test current used to select patients.

Anti-angiogenesis in renal cell carcinoma

BAY 43-9006

Bayer

Approved

Raf kinase inhibitor also targets PDGFR and VEGFR.

Anti-angiogenesis in Myelodysplastic Syndrome

Lenolidamide

Celgene

Approved

Also in clinical trials for the treatment of multiple myeloma

Other anti-angiogenesis

Thalidomide Sunitinib

Celgene Pfizer/Sugen

Approved Approved

Multiple myeloma Gastrointestinal tumor

Bcl-2

G3135

Genta

Pending

Anti-sense oligonucleotide targets the antiapoptotic gene, bcl-2

Proteasome in multiple myeloma

Bortezomib

Millennium

Approved

Proteasome inhibition effective in hematologic malignancies, but of uncertain potential for the treatment of solid tumors

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TABLE 82.4

Diagnostic-Therapeutic Combinations in the Treatment of Cancer

Adapted from Ross, J.S. and Hortobagyi, G.H. eds. The Molecular Oncology of Breast Cancer. Jones and Bartlett, Inc., Sudbury, MA with permission by the publisher.

Selected Targeted Anticancer Therapies Using Small Molecules

ATRA for the treatment of acute promyelocytic leukemia, a subset of acute nonlymphocytic leukemia featuring a diseasedefining retinoic acid receptor activating t(15:17) reciprocal translocation (Fang et al., 2002; Parmar and Tallman, 2003). For these selected patients, direct targeting of the retinoic acid receptor with ATRA has resulted in very high response rates, delay in disease progression, and long-term cures for these patients (Fang et al., 2002; Parmar and Tallman, 2003). Imatinib (Gleevec®) The development of imatinib for patients with chronic myelogenous leukemia in 2001 ushered in a new excitement both in the scientific and public communities for targeted anticancer therapy. Imatinib received fast-track approval by the FDA as an ATP-competitive selective inhibitor of bcr-abl and has unprecedented efficacy for the treatment of early stage chronic myelogenous leukemia typically achieving durable complete hematologic and complete cytogenetic remissions, with minimal toxicity (Druker, 2003; Goldman and Melo, 2003; O’Brien et al., 2003). Imatinib is a true targeted therapy for leukemia in that a test for the bcr/abl translocation must be performed before a patient will be considered as eligible to receive the drug. The prediction of resistance to imatinib in early phase Chronic Myeloid Leukemia (CML) has been the subject of numerous studies (Lange et al., 2005; O’hare et al., 2005). It is the current goal to predict resistance emergence with gene mutation testing and employ novel tyrosine kinase inhibitors to attempt to overcome blast cells that have lost the ability to bind imatinib to the ATP binding pocket of the fusion gene (Lange et al., 2005; O’hare et al., 2005). Imatinib has also achieved regulatory approval for the treatment of relapsed and metastatic GISTs, which characteristically feature an activating point mutation in the c-kit receptor tyrosine kinase gene (von Mehren, 2003). For GISTs, the response to imatinib treatment appears to be predictable based on the location of the c-kit mutation (Verweij et al., 2003). The use of imatinib in GIST is also an example of targeted therapy as a measurement of c-kit expression usually performed by IHC, required to confirm the diagnosis and render the patient eligible for treatment. Interestingly, most commercially available antibodies for c-kit recognize the total c-kit and do not distinguish the activated or phosphorylated version, which is the actual target of imatinib. Currently, the high treatment failure rate is directly linked to the test used to characterize the patients. It is anticipated that either the use of specific antibodies designed to identify the activated c-kit gene or directed sequencing of the c-kit gene may be required before imatinib is prescribed for patients with recurrent or metastatic GIST. An alternative to c-kit mutation testing for the prediction of resistance to imatinib, functional imaging after initial dosing of the drugs has been employed for patients with metastatic GIST (Heinicke et al., 2005). In early 2006, the anti-angiogenesis agent sunitinib was approved by the FDA for the treatment of advanced GIST that has become resistant to imatinib therapy.

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Flt-3 Targeted Therapy In approximately 30% of cases of acute myelogenous leukemia and less frequently in other forms of leukemia, a flt-3 gene mutation creates an internal tandem duplication that creates an abnormal FLT3 receptor that promotes the growth and survival of the leukemic cells (Advani, 2005; Gilliland and Griffin, 2002; Kelly et al., 2002; Sawyers, 2002). Three small molecule compounds are in clinical trials for the treatment of acute myelogenous leukemia by targeting the flt-3 internal tandem duplication. These drugs are also examples of potential true targeted therapies in that a test for detecting an internal tandem duplication that causes the flt-3 gene activation will likely be required and incorporated into the FDA drug approval label should these agents be successful in future clinical trials. Targeted Small Molecule Drugs for Solid Tumors Gefitinib (Iressa®) Gefitinib was originally approved by the FDA in 2003 as a monotherapy for the treatment of patients with locally advanced or metastatic non-small cell lung cancer after failure of both platinum based and docetaxel chemotherapies (Ranson, 2002; Schiller, 2003). Gefitinib is a small molecule drug that targets the EGFR. In contrast with the approval of trastuzumab, this approval of gefitinib did not include an eligibility requirement reference to a specific tumor diagnostic test designed to select patients that were more likely to respond to the drug. Overexpression of EGFR typically identified by IHC is extremely common in both lung and breast cancers (Campiglio et al., 2004; Ranson, 2002; Schiller, 2003), but in contrast with HER-2 overexpression, which is virtually limited to cases with gene amplification, multiple mechanisms of dysregulation of EGFR and associated activation of signaling pathways have been described for both of these tumors (Campiglio et al., 2004; Ranson, 2002; Schiller, 2003). Thus, it has been difficult to develop this drug for expanded indications or combination therapies in the absence of a well-defined efficacy test. However, more recently, two independent groups reported their similar discovery of a specific activating mutation in the tyrosine kinase domain of the EGFR receptor that was associated with a high response of patients with non-small cell lung cancer to gefitinib (Lynch et al., 2004; Paez et al., 2004). Of interest have been the consistent observations that both a bronchioloalveolar histology and a persistent skin rash have been the best clinical signals of gefitinib response in lung cancer (Dudek et al., 2005). In addition, although specific activating mutations in the EGFR gene have been reproduced in a number of studies (Chan et al., 2006), some studies have failed to demonstrate this association and other biomarkers including EGFR gene amplification have also been found to be predictive of tumor response (Carbone, 2004; Kobayashi et al., 2005). Most recently, follow-on studies of gefitinib in lung revealed that the increased response rates that led to the approval of the drug were not accompanied by a clinical survival advantage (Twombly, 2005). This has led to a current withdrawal of the drug while further research and clinical trials are performed. It is possible

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the gefitinib will reappear on the market for the treatment of lung cancer with an integrated diagnostic test designed to boost the response rates by limiting the treatment to tumors with specific histologic and molecular features. ®

Erlotinib (Tarceva ) Erlotinib is another targeted small molecule inhibitor of EGFR that was approved by the FDA in 2005 for the treatment of non-small cell lung cancer and pancreatic cancer (Moore, 2005; Smith, 2005). To date, similar to gefitinib, the clinical trials and FDA approval for erlotinib have not included an assessment of the EGFR status or other diagnostic test for eligibility to receive the drug. In lung cancer the predictors of tumor response including skin rash and brochioloalveolar histology have also applied to erlotinib as have the somewhat conflicting associations of both activating EGFR mutations and EGFR gene amplification as predictors of drug response (Chan et al., 2006; Silvestri and Rivera, 2005). Clinical trials have demonstrated that erlotinib does add a survival benefit to the treatment of both lung and pancreatic cancers and the drug remains on the market currently without an integrated diagnostic eligibility test. BAY 43-9006 (Sorafenib®) BAY 43-9006 is a RAF kinase inhibitor that also inhibits the VEGFR and PDGFR growth factor receptors. It is thus considered to be an anti-angiogenesis drug. This oral agent was approved in late 2005 by the FDA for the treatment of metastatic renal cell carcinoma (Staehler et al., 2005). Currently, there are no diagnostic tests associated with the selection of this agent and clinical trials for other types of cancer are on-going. Other Small Molecule Antiangiogenesis Agents (SU5416, Thalidomide [Thalomid®], Lenalidamide [Revlimid®], Endostatin/Angiostatin, and Marimastat) A variety of small molecule drugs are currently in clinical trials for the treatment of solid tumors that target the establishment and growth of tumor blood vessels (Khalil et al., 2003; Mendel et al., 2000; Thomas and Kantarjian, 2000; Zogakis and Libutti, 2001). Additional compounds that target matrix metalloproteases, such as the drug marimastat, are also considered to be angiogenesis inhibitors (Brown, 2000; Dell’Eva et al., 2002; Miller et al., 2002). The anti-angiogenesis drug, lenalidomide (Revlimid®) was approved by the FDA in late 2005 for the treatment of myelodysplastic syndrome (List, 2005). To date, none of these compounds has a linked diagnostic test such as tumor microvessel density or the expression of an angiogenesis promoting gene or protein in their clinical development plans. G3139 (Genasense®) Another strategy in anticancer therapy is the targeting of chemotherapy resistance by overcoming the antiapoptosis mechanisms of cancer cells. An example of this approach is the novel antisense oligonucleotide G3139, which targets the antiapoptotic gene bcl-2 (Tamm et al., 2001; Tolcher, 2002). This

agent has been the most widely tested antisense therapy and has been mostly focused in hematologic malignancies (Stein et al., 2005). Bortezomib (Velcade®) Recently, drugs targeting the proteasome have been developed that are designed to impact downstream pathways regulating angiogenesis, tumor growth, adhesion, and resistance to apoptosis (Adams, 2002; Elliott and Ross, 2001). One of these agents, bortezomib (PS-341), has recently been approved for the treatment of advanced refractory multiple myeloma (Richardson et al., 2003). Bortezomib has shown both preclinical activity in animal studies and biologic activity in early clinical trials involving patients with a variety of solid tumors, but, to date, no trials using this agent alone or in combination with other drugs has progressed to Phase III. Although pharmacogenomic studies of bortezomib use in multiple myeloma have been conducted, to date, no specific pattern of gene expression or other specific test has emerged that could be a guide to the selection of patients for treatment.

PHARMACOGENOMICS Targeted therapy in oncology has been a major stimulus for the evolving field of pharmacogenomics. In its broadest definition, pharmacogenomics can encompass both germline and somatic (disease) gene and protein measurements used to predict the likelihood that a patient will respond to a specific single or multiagent chemotherapy regimen and to predict the risk of toxic side effects (Weinstein, 2000; Ross et al., 2004). In breast cancer, whole genome transcriptional profiling has been used as a technique for classification and prognosis (Bertucci et al., 2000; Sorlie et al., 2001; van de Vijveret et al., 2002; van ‘t Veer et al., 2002). Gene expression profiles can define cellular functions, biochemical pathways, cell proliferation activity and regulatory mechanisms. The hierarchical clustering technique of data analysis from transcriptional profiling of clinical samples known to have responded or been resistant to a single agent or combination of anticancer drugs has recently been employed as a guide to anticancer drug therapy in cancers of the breast and other organs (Ntzani and Ioannidis, 2003). Using transcriptional profiling, the microarray technique has been able to generate an 81% accuracy for predicting the presence or absence of pathologic complete response after preoperative chemotherapy with sequential weekly paclitaxel and 5-FU, doxorubicin and cyclophosphamide (FAC) in breast cancer (Ayers et al., 2004). Interestingly, the highest rated single gene predictor in this study has also predicted paclitaxel response in an on slide IHC format (Rouzier et al., 2005). Currently, there is great interest in both the scientific and commercial communities in learning the high density genomic microarrays will ultimately be used as diagnostic assays themselves or yield to more familiar technologies testing small subsets of the discovered markers on platforms already entrenched in the clinical laboratory (Ross et al., 2005).

References

CONCLUSION During the next several years, the field of oncology drug development and cancer medicine will see numerous products developed with integrated diagnostic tests. These diagnostic – therapeutic combinations will enter the market designed to

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“personalize” their use, dosage, route of administration, and length of treatment for each patient, one at a time. Only time will tell whether this new approach to anticancer pharmaceuticals will yield breakthrough results, reducing morbidity and mortality and improving outcomes for the new cancer patients of the personalized medicine era.

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Section

Inflammatory Disease Genomic Medicine

83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94.

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Environmental Exposures and the Emerging Field of Environmental Genomics Molecular Basis of Rheumatoid Arthritis “Omics ” in the Study of Multiple Sclerosis Inflammatory Bowel Disease Glomerular Disorders Spondyloarthropathies Asthma Genomics Genomic Aspects of Chronic Obstructive Pulmonary Disease Genomic Determinants of Interstitial Lung Disease Peptic Ulcer Disease Cirrhosis in the Era of Genomic Medicine Systemic Sclerosis

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83 Environmental Exposures and the Emerging Field of Environmental Genomics David A. Schwartz

INTRODUCTION Completed in 2003, the Human Genome Project (HGP) is by all measures a resounding success. The HGP achieved its mission of producing an accurate and complete sequence of the human genome and did it two years early with costs substantially less than the original estimate. Yet despite this enormous achievement, we are still far from the original purpose of the HGP: being better able to diagnose, treat, and prevent disease through an improved knowledge of the genetic underpinnings of disease. Although the HGP has successfully mapped the human genome and has developed innovative technology for genomic studies, we remain limited in how this information can be used to improve clinical medicine and public health. This limitation arises from the simple fact that genetics is not the sole determinant of health or disease. In fact, although an emerging consensus suggests that many of the complex and prevalent diseases that humans develop occur as a result of multiple biologically unique gene–gene and gene–environment interactions, even this conceptual framework is limited. The development of disease in humans, environmental and otherwise, is simply far more complex. Environmental exposures affect those that are vulnerable temporally (age), spatially (geographically), and by unique circumstance (co-morbid disease, nutritional status, economic Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1010

status, race, and genetics). Even this paradigm fails to address the complex interaction of endogenous and exogenous risks that ultimately cause disease. Moreover, diseases are not usually single entities; rather, most diseases represent several or many specific pathophysiologic processes that can only be fully understood by focusing on the genetic and environmental contributions to etiology and pathogenesis. Environmental health research and genomic research are logical, even necessary, partners. Ultimately, the discoveries that are made in environmental genomics will lead to better diagnosis, treatment, and prevention of these common, complex human diseases.

IMPORTANCE OF ENVIRONMENTAL EXPOSURES IN HUMAN HEALTH Individual variation in response to environmental exposures is a major impediment to understanding the environmental contribution to disease. These variations in response arise from different susceptibilities, including genetic susceptibilities, susceptibilities arising from developmental stages of life, co-morbidity with other diseases or other exposures, and lifestyle differences such as varying nutritional status and physical activity levels. Despite the difficulties inherent in teasing apart environmental

Importance of Environmental Exposures in Human Health

BOX 83.1 Environmental Health Sciences and Environmental Economics A field of science that examines how the environment, interacting with numerous susceptibility factors (e.g., genetic makeup, age of organism, comorbidities) influences both health and disease. Since the interactions by which environmental agents elicit their responses occur at the molecular level, this science also affords a powerful way to identify early cellular events that elicit disease pathogenesis and culminate in human illness. In this context, “environment” refers to pollutants and chemicals (e.g., lead, mercury, ozone), useful commercial products that may enter the environment (e.g., pesticides), and natural toxins that are part of our everyday life (e.g., toxins produced by molds and dust mites). Other factors, such as diet and exercise, can also interact with these types of environmental agents to influence health and disease. Although environmental exposures can play a role in almost every human disease, individual responses to environmental agents are highly variable and dependent, in part, on genetic susceptibilities. Genomic technologies now provide sophisticated tools to better define the gene–environment interactions that underpin most common human diseases, a new field that is often referred to as “environmental genomics.”

contributions to human disease, a number of studies have shown both that non-genetic factors are significant components of disease risk and that environmental exposures, particularly during fetal development, can profoundly affect subsequent genetic expression. Comparing disease risk in monozygotic and dizygotic twins provides some of the most compelling evidence of the importance of environment in human health. In one study evaluating risk for developing several types of cancer, genes accounted for less than 50% of disease risk; environmental factors presumably played a role in the remaining cancer cases (Lichtenstein et al., 2000). In a review of autoimmune diseases, genetics appeared to account for less than 50% of disease risk and, in the case of systemic lupus erythematosus, only 25–40% of disease risk, with environment accounting for the remaining 60–75% of risk (Powell et al., 1999). In a study of Parkinson’s disease, early onset (before age 50) appeared to be controlled predominantly by genetic factors. In the more common late onset cases, however, an environmental trigger was suspected of accounting for roughly 85% of the cases (Tanner et al., 1999). None of these studies identified any specific environmental agents that were important for these diseases. In fact, for the purpose of these studies, the “environment” would include diet and other broad environmental factors. Nonetheless, they serve to highlight the importance of gene–environment interaction in disease etiology and the need to better capture the environmental components of disease if we are to advance public health. The influence of environmental exposures on transcriptional regulation of genes is clearly highlighted by the field of epigenetics. Skinner and colleagues recently demonstrated the potential transgenerational adverse effects of intrauterine exposure

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TABLE 83.1 Some environmental agents that revealed important biological pathways Environmental agent

Biological pathway revealed

Observed health effects in vertebrates or humans

Dioxins, dibenzofurans

Ah receptor

Epithelial changes, porphyria, liver damage, cancer, teratogenicity

Endotoxin

Polymorphism in Toll-like Receptor 4 (TLR-4); role in innate immunity

Asthma, atherosclerosis, septis, lung transplant rejection

Ultraviolet light

Nucleotide excision repair system

Cancer

Environmental estrogen and androgen mimics

Estrogen and androgen receptor signaling

Infertility, alterations in sexual differentiation, feminization, demasculization, premature (advanced) puberty, cancer

to endocrine-disrupting pesticides on male fertility (Anway et al., 2005). Exposure of pregnant mice to either an antiandrogenic compound, vinclozolin, or and estrogenic compound, methoxychlor, both decreased spermatogenic capacity and increased infertility in the males exposed in utero. Furthermore, these effects were passed through the male germline through all generations studied (F1–F4). The effects on reproduction correlated with altered DNA methylation patterns in the germline. Findings from Randy Jirtle’s laboratory indicate that exposure through maternal diet to common methylating agents found in vegetables and vitamin supplements can have profound effects on gene expression in offspring and these effects on gene expression continue to be inherited in subsequent generations (Waterland and Jirtle, 2003). This same laboratory found that in utero exposure to the soy component, genistein, also altered methylation status and that this effect persisted into adulthood and protected the mice from developing obesity (Dolinoy et al., 2006). Moreover, since monozygotic twins diverge in the concordance of methylation as a function of age (Fraga et al., 2005), it is abundantly clear that methylation is a dynamic process, subject to a lifetime of environmental influences (Table 83.1). These findings underscore the role that intrauterine exposures could potentially have on common complex diseases that involve developmentally vulnerable organ systems. Such research also indicates that environmental exposures may serve as biological clues to understanding the regulation of gene expression and the role that transcriptional regulation may have on the

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risk of developing disease, as well as point to novel therapeutic interventions.

IMPORTANCE OF ENVIRONMENTAL EXPOSURES IN STUDYING DISEASE PROCESSES Environmental exposures provide a controlled method for targeting and manipulating cellular machinery in ways that provide insight into both basic biology and the mechanistic events leading to clinical disease. Because environmental agents can operate early in the disease process, they provide a useful technique for uncovering very early events in disease pathogenesis that can be used to identify methods to diagnose diseases before they are clinically evident, to develop early interventions that prevent progression to end-stage disease, and to identify targets for screening additional environmental agents. In this way, environmental agents have tremendous potential for use as probes in understanding the processes of common chronic diseases, as well as suggesting possible routes for intervention. For instance, the discovery of the aryl hydrocarbon receptor (AhR) occurred as a direct result of the known toxicity of dioxin and polycyclic aromatic hydrocarbons. Not only did this discovery demonstrate the biological role of the AhR in mediating the toxicity to these agents, it also revealed the role of the AhR in homeostasis and basic pathophysiologic processes. Most importantly, however, the identification of the AhR led to the ultimate discovery of the PAS (PER-ARNT-SIMS) superfamily of receptors that mediate response to various forms of environmental stress such as hypoxemia and circadian rhythm, and control basic physiologic activities such as vascular development, learning, and neurogenesis (Kewley et al., 2004; Nebert et al., 2004). Likewise, environmental exposures can be used to simplify complex disease processes by narrowing the pathophysiologic phenotype to elucidate the genetics and biology that underlie a particular condition. For example, diseases such as asthma arise from dozens of etiologic agents. Since asthma caused or exacerbated by dust mites, endotoxin, or ozone involves different genes and different biological mechanisms, the disease can be better studied by focusing the investigation on a specific etiologic type of asthma.

COMPARATIVE ENVIRONMENTAL GENOMICS Identifying and studying environmentally responsive genes across animal species is one of the most powerful tools in environmental genomics research. Given that an extensive number of animal genomes have been sequenced and have demonstrated the evolutionary conservation of biology and genetic structure, comparative environmental genomics will be an important tool for identifying the genes that control response to specific environmental agents, which in turn will accelerate our discoveries

in environmental health sciences. For instance, the discovery of the importance of the toll-like receptors in innate immunity in mammals occurred as a direct result of the observation that a defective receptor in flies caused them to be much more susceptible to Aspergillus fumigatus (Lemaitre et al., 1996; Medzhitov et al., 1997). The importance of this finding is clearly illustrated in the variations in the toll-like receptors that alter the response to microbial pathogens (Arbour et al., 2000) and modify the risk of developing a variety of diseases that are associated with innate immunity (Cook et al., 2004). The ease with which we can observe and apply knowledge across model systems must be exploited so that we can efficiently understand the biological and clinical importance of environmentally responsive genes. The field of comparative genomics is at a very early stage of development (Kruglyak and Nickerson, 2001), and characterization systems like Gene Ontology functional classifications (Harris et al., 2004) are helping us to make these comparisons between species. In mice, recombinant inbred strains (Snell, 1978) between two different strains that vary considerably between their phenotype and response to environmental insult allow one to map quantitative trait loci (QTLs) so that one has the ability to identify and localize on the genome different regions of genes that affect the characteristic being explored. In addition, screening large numbers of inbred strains of mice has facilitated the identification of disease modifying and disease causing genes (Grupe, et al., 2001). These represent powerful approaches to identify the environmental and genetic contributions to disease. However, to translate information with any confidence from one species to another requires that the species have orthologous genes and pathways. Although there are many critical genes and pathways relevant in both the developing organism and the adult, the class of ligand-induced transcription factors can conceptually intersect many pathways in an organism. In this respect, one finds considerable homology between human, mouse, zebrafish, and even Caenorhaiditis elegans for those factors (e.g., humans, mice, and zebrafish have estrogen receptors that can be responsive to environmental estrogen-like compounds; AhRs that can use PCBs and TCDD as ligands for inducing a variety of cytochrome p450s involved in the processing of foreign molecules in the body; and retinoic acid receptors which play significant roles in the development of their embryos). In addition, C. elegans possesses orthologs of many of the receptor and cognate signal transduction pathways present in higher organisms. Biological and mutant evidence with these receptor systems in the different species confirm the overlap in functions. As each of these receptor systems can serve as “sensors” for an environmental challenge, the homology among species then allows one to use each species to its own technical advantage. As an example, the neural tube of humans, mice, and zebrafish is generated during the segmentation phase of embryonic development. Transient structures, somites, form from paraxial mesoderm and give rise to vertebrae and ribs, skeletal muscle and dermis of the skin. They also provide the migration paths of neural crest cells and axons from spinal nerves. Somitic segments are added on caudally and the neural tube develops in this caudal fashion flanked by the somites. In retinoid

Exposure Assessment in the Gene-Environment Paradigm

signaling, a crucial enzyme, Raldh2 is expressed in the somites and is responsible for converting retinal to retinoic acid. This small ligand can then be transferred to the developing neural tube to signal events in its differentiation. At the growing caudal end of the organism, another enzyme, cytochrome p450RAI (or cyp26a1) is synthesized and can metabolize retinoic acid providing a retinoic acid-free zone at the growing caudal end near the neural tube. Mouse mutants of cyp26a1 produce phenotypes which include an open caudal neural tube (spina bifida) and, are at a lower risk of caudal fusions and truncations (Abu-Abed et al., 2001; Sakai et al., 2001). Crossing a heterozygous Raldh2 mutant allele into this homozygous mutant cyp26a1 background suppresses these phenotypes (Niederreither et al., 2002). This fact suggests that lowering the retinoic acid concentration 50% during the development of the neural tube can have a significant effect. In zebrafish, Linney and others have shown that the Raldh2 gene is itself repressed by retinoic acid and the cyp26a1 gene is induced by retinoic acid (Dobbs-McAuliffe et al., 2004). Anterior to the developing trunk neural tube, retinoid signaling also plays significant roles. As the hindbrain begins to segment into structures (rhombomeres), another retinoid metabolizing enzyme cyp26b1 appears (Abu-Abed et al., 2002) and basically creates a retinoid-free zone in the middle of the hindbrain that can play a significant role both in the expression of retinoid responsive genes such as the homebox genes and also in the derived neural crest cells that migrate from the hindbrain region. Therefore, these retinoid pathway genes, plus additional ones yet to be as closely studied, could be genes involved in neural tube defects or genes whose expression might be affected by environmental toxicants. Within this context, there is developing evidence of potential intersection of retinoid events and those mediated by the AhR. Although this has yet to be developed in embryos, in human airway epithelial cells exposed to the AhR ligand TCDD, a series of genes within the retinoid pathway are affected (Martinez et al., 2002) though synthetic retinoids have been shown to have an impact upon AhR-driven gene expression (Gambone et al., 2002). A genomic approach using microarray analysis in different species is currently the best way to examine these interactions between these two receptor pathways and ligands the affect them. With transcription factors such as these ligand inducible receptors, the complete molecular repertoire of interacting genes in any one species has yet to be completely defined. Therefore, a comparative genomics approach among species using microarray analysis and bioinformatics approaches that allow the hypothetical creation of pathways should allow one to determine the functional homology of these different regulatory genes. The zebrafish model then allows one to efficiently knock-down individual genes in the pathway to test its efficacy. This type of approach has been elegantly used to dissect developmental pathways in the sea urchin (Davidson et al., 2002a, b). However, performing such analyses across species requires a considerable collaborative effort between laboratories having expertise in human conditions and a working knowledge with each model system being compared. Clearly, then, the field of comparative genomics, though in its infancy, holds tremendous promise for identifying the critical

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pathway architectures that are common across species and that, therefore, have been evolutionarily conserved and are likely candidates for common elements of disease etiology. Additionally comparative genomics affords a way to use toxicological studies in laboratory models to link with human disease. In fact toxicological responses of model organism systems can be studied in parallel with human studies to identify and predict human subgroups that may be particularly susceptible to adverse effects of environmental toxicants. The human studies themselves would involve a broad spectrum of protocols, including epidemiological studies, association studies in cohort or case-control studies, human cell culture studies, or human clinical or experimental studies.

EXPOSURE ASSESSMENT IN THE GENE-ENVIRONMENT PARADIGM Recent work by Stephanie London and her colleagues at the Mexican National Institute of Public Health highlights the enormous potential of environmental genomics in dealing with common diseases (Romieu et al., 2004). These investigators examined asthma incidence in a cohort of children living in Mexico City. Knowing that Mexico City had high levels of ozone and that ozone caused airway inflammation through oxidative stress, this study examined the benefit of antioxidant supplementation in asthmatic children. They found that supplementation with antioxidant vitamins C and E did, in fact, counteract the decreased lung function arising from ozone. There was, however, substantial variability in the effect seen among different children. Thinking that genetic differences might account for some of this variability, the scientists aggregated the study group by their genetic ability to produce glutathione S-transferase (GSTM1), one of the enzymes that play a major role in protecting cells against oxidative damage. GSTM1-deficient children given antioxidant vitamins C and E showed the greatest protection against ozone-induced decreases in lung function. GSTM1-deficient children who were given a placebo showed no such protection. Those children who were GSTM1 positive did not exhibit ozone-induced decreases in lung function, regardless of antioxidant status. These results help establish the critical role of GSTM1 in protecting the lungs against oxidative stress, as well as identifying a genetic subtype of asthmatic children that would benefit from antioxidant supplementation. Studies such as this one have become possible because of the substantial advances made in genomic science and technology. This particular study also benefited from the fact that levels of ozone in the air can be measured and these measurements are a reliable surrogate for exposure levels in people. Of equal importance was that the lung is immediately exposed to inhaled pollutants and, in the case of asthma, the clinical effects are fairly immediate. For many environmental exposures, however, this scenario does not apply. There are usually long latency periods between exposure and observed effects in diseases such as cancer, neurodegeneration, and autoimmunity. Another confounding factor is that people differ significantly in their uptake of many

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environmental agents, thus preventing researchers from using exposures in the environment as reliable surrogates for individual exposure levels. Finally, environmental exposures, particularly those of a lifetime, arise as multiple exposures to chemicals, such as metals and solvent, biological agents, such as toxins released from mold and bacteria, and lifestyle factors that affect our ability to handle these exposures, such as diet and physical activity levels. Thus, although current genomic tools have greatly increased our ability to assess individual genetic susceptibility, current exposure assessment tools do not provide the same level of precision or sophistication when we need to assess individual environmental exposures. Until this situation improves, the research community will be unable to identify the gene–environment interactions that account for many important diseases and disabilities. The full potential of genomics will never be realized until the exposure assessments needed for environmental genomics research can be made. Without more precise measures of exposure, it will be very difficult to figure out why certain people develop disease and others do not. To determine how our environment, diet, and physical activity contribute to illness, new technologies are needed, such as small, wearable sensors that can measure environmental agents that come in contact with the body. New devices also are needed to measure changes in human biology as a result of exposure, even when exposure occurred many years previously. Given the recent advances in biomedical research, this is the right time to take on this challenge. Capabilities currently exist for the global analysis of genes (genomics), gene transcripts (transcriptomics), proteins (proteomics), and metabolites (metabolomics) in biological samples of blood and urine. Emerging fields, such as nanotechnology, molecular imaging, and sensor technology are beginning to yield products that can be adapted for biomedical research. These emerging technologies represent important opportunities for providing new tools to measure the biological response

to multiple environmental exposures while they are occurring or long after they have occurred. Ideally, these new technologies will generate measurements of personal exposure at multiple points in the continuum from exposure to disease. This continuum is illustrated in Figure 83.1, where the initial environmental exposure is external, is absorbed or ingested and becomes the effective internal dose. From this exposure arise subtle pathologic changes that ultimately develop into overt, clinical symptoms. Technology development is needed that would improve our ability to study this process in its early stages before clinical signs arise. Specifically, technologies are needed that will provide measurements of exposures that come into contact with the body by the skin, nose or lung, and measurements of early response represented by pathophysiological indicators of disrupted biological functions that ultimately lead to disease. To move the environmental genomics field further, the Secretary of Health and Human Services and the Director of the National Institutes of Health (NIH) have created the Genes and Environment Initiative (GEI) (Kuehn, 2006). This initiative will focus on the role that both genetic variation and environmental exposures play in the development of complex human diseases. Importantly, a component of this initiative will be devoted to improving the precision and utility of exposure assessment tools – both personal environmental sensors and homeostatic responses to various forms of environmental stress. Ideally these new technologies will be able to simultaneously monitor physical activity (such as heart rate, respiratory rate, and oxygen exchange), measures of physiologic response to exposures, geographic location, and environmental conditions using wireless devices that provide automated data retrieval. These personalized measures of exposure will be combined with genomic information, as well as dietary information, to decipher environmental and genetic risk factors for disease development and progression. Ultimately this knowledge will enable researchers and clinicians to devise ways to prevent, diagnose, and treat common diseases.

Conceptual approach to environmental biology

Environmental/ dietary exposure

External contact

Body of contact measurements (Environmental sensors)

Internal dose

Early biological preclinical response

Improved measures of body burden

Phenotype or clinical response

Biological measurements (Biological sensors)

Technology development

Figure 83.1 Conceptual approach to environmental biology: Highlights the opportunities to develop more precise measures of environmental exposures and to further characterize the specific biologic response that links the exposure to the disease process.

References

BOX 83.2

Genes and Environment Initiative

On February 6, 2006, the Secretary of the US Health and Human Services announced that the National Institutes of Health would receive funding beginning in fiscal year 2007 for the Genes and Environment Initiative (GEI), a project that will be ongoing for 5 years. The GEI seeks to accelerate research to uncover the genetic and environmental interactions of such human diseases as asthma, arthritis, Alzheimer’s disease, and cancer. The GEI will be managed by a coordinating committee headed by the Directors of the National Institute of Environmental Health Sciences (NIEHS) and the National Center for Human Genome Research (NCHGR). The project will perform genotyping studies for several dozen common diseases, which will be selected by peer review. It also will develop new environmental monitoring tests and devices that measure toxicant exposures, dietary intake, and physical activity, as well as determining individuals’ biological responses to these influences. In addition, the National Library of Medicine will develop databases to manage the vast amount of genetic, medical, and environmental information that is expected to be generated from this initiative.

CHALLENGES AND FUTURE OF ENVIRONMENTAL GENOMICS The major challenges for environmental genomics will be in the arena of exposure assessment. Issues of particular concern are (1) how to provide meaningful measures of long-term exposures, (2) how to assess the consequences of multiple exposures (particularly over a lifetime), and (3) how to assess exposure to non-persistent environmental agents. An additional layer of complexity will be in determining how diet and exercise can modify responses to these exposures. In fact, the genomics part of environmental genomics is the least difficult aspect of the field. It will be some time before the ability to assess environmental exposures will even come close to the sophistication and accuracy currently possible for assessing components of the genome. Despite the challenges, the field is well worth pursuing. As past experience has shown, defining environmental components of disease has ramifications far beyond the initial finding. Insight from future discoveries will reveal new pathways of effects, will probably identify very early events in disease pathogenesis, and would ideally suggest ways to use this information for early therapeutic interventions that could prevent disease progression. Furthermore, advances in environmental assessment, when coupled with genomic understanding of individual susceptibilities,

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will have benefit in the field of environmental policy. Currently the high variability in responsiveness to environmental exposures has prevented unambiguous extrapolation of data from laboratory studies into exposure standards that are suitable to protect the public’s health. It is this ambiguity that has led to the courts being the primary arbiters of what environmental policy should be. This situation would improve, however, if advances where made in the various fields of study described above. Thus, results from comparative genomics would strengthen our ability to extrapolate from animal studies of environmental exposures to the anticipated human health consequences of these exposures. More sophisticated tools to assess human environmental exposures would provide scientists with more precise measures of actual human exposures. Finally, being able to account for the variability in human responsiveness to environmental agents, be it from genetic susceptibilities, differences in nutrition status, or changes in physical activity, would tremendously enhance the ability of scientists to determine the actual contribution of different exposure scenarios to human disease. Such improvements would greatly enhance the science upon which environmental policy is based and might well reduce the contentiousness that currently surrounds many of these policies.

CONCLUSION The vision of environmental genomics is to define the geneenvironment underpinnings of human disease in ways that can lead to improved human health. Although in its infancy, new genomic tools have helped this field make significant contributions to our understanding of common diseases. Its full potential, however, will only be realized when the methodology for assessment of individual exposures can achieve the level of precision currently available for the assessment of individual genetic susceptibilities. While this will not be easy, the impact of advancing personalized exposure assessment would be profound. Improved exposure assessment technology would allow researchers to decipher the environmental and genetic risk factors for disease development and progression, specifically the interaction between environmental exposures and gene sequence differences. It would provide a means to determine very early pathophysiologic measures of disease initiation, thus allowing for better screening and intervention strategies. Ultimately, the ability to develop, validate, and correlate exposure-response indicators with genetic variation will be critical to the medical community’s success in reducing the burden of common diseases such as obesity, asthma, neurodegenerative diseases, and cancer.

REFERENCES Abu-Abed, S., Dolle, P., Metzger, D., Beckett, B., Chambon, P. and Petkovich, M. (2001). The retinoic acid-metabolizing enzyme, CYP26A1, is essential for normal hindbrain patterning, vertebral identity, and development of posterior structures. Genes Dev 15, 226–240.

Abu-Abed, S., MacLean, G., Fraulob, V., Chambon, P., Petkovich, M. and Dolle, P. (2002). Differential expression of the retinoic acidmetabolizing enzymes CYP26A1 and CYP26B1 during murine organogenesis. Mech Dev 110, 173–177.

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Anway, M.D., Cupp, A.S., Uzumcu, M. and Skinner, M.K. (2005). Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science 308, 1466–1469. Arbour, N.C., Lorenz, E., Schutte, B.C., Zabner, J., Kline, J.N., Jones, M., Frees, K., Watt, J.L. and Schwartz, D.A. (2000). TLR4 mutation is associated with endotoxin hyporesponsiveness in humans. Nat Genet 25, 187–191. Cook, D.N., Pisetsky, D.S. and Schwartz, D.A. (2004). Toll-like receptors in the pathogenesis of human disease. Nat Immunol 5, 975–979. Davidson, E.H., Rast, J.P., Oliveri, P., Ransick, A., Calestani, C.,Yuh, C. H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C. et al. (2002a). A genomic regulatory network for development. Science 295, 1669–1678. Davidson, E.H., Rast, J.P., Oliveri, P., Ransick, A., Calestani, C.,Yuh, C. H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C. et al. (2002b). A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo. Dev Biol 246, 162–190. Dobbs-McAuliffe, B., Zhao, Q. and Linney, E. (2004). Feedback mechanisms regulate retinoic acid production and degradation in the zebrafish embryo. Mech Dev 121, 339–350. Dolinoy, D.C., Weidman, J.R., Waterland, R.A. and Jirtle, R.L. (2006). Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome. Environ Health Perspec, 114, 567–572. Fraga, M.F., Ballestar, E., Paz, M.F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suner, D., Cigudosa, J.C., Urioste, M., Benitez, J. et al. (2005). Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A 102, 10604–10609. Gambone, C.J., Hutcheson, J.M., Gabriel, J.L., Beard, R.L., Chandraratna, R.A., Soprano, K.J. and Soprano, D.R. (2002). Unique property of some synthetic retinoids: Activation of the aryl hydrocarbon receptor pathway. Mol Pharmacol 61, 334–342. Grupe, A., Germer, S., Usuka, J., Aud, D., Belknap, J.K., Klein, R. F., Ahluwalia, M.K., Higuchi, R. and Peltz, G. (2001). In silico mapping of complex disease-related traits in mice. Science 292, 1915–1918. Harris, M.A., Clark, J., Ireland, A., Lomax, J., Ashburner, M., Foulger, R., Eilbeck, K., Lewis, S., Marshall, B., Mungall, C. et al. (2004). The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32. Database issue, D258–D261. Kewley, R.J., Whitelaw, M.L. and Chapman-Smith, A. (2004). The mammalian basic helix-loop-helix/PAS family of transcriptional regulators. Int J Biochem Cell Biol 36, 189–204. Kruglyak, L. and Nickerson, D.A. (2001). Variation is the spice of life. Nat Genet 27, 234–236. Kuehn, B.M. (2006). NIH initiatives to probe contribution of genes, environment in disease. JAMA 295, 1633–1634.

Lemaitre, B., Nicolas, E., Michaut, L., Reichhart, J.M. and Hoffmann, J. A. (1996). The dorsoventral regulatory gene cassette spatzle/Toll/ cactus controls the potent antifungal response in Drosophila adults. Cell 86, 973–983. Lichtenstein, P., Holm, N.V., Verkasalo, P.K., Iliadou, A., Kaprio, J., Koskenvuo, M., Pukkala, E., Skytthe, A. and Hemminki, K. (2000). Environmental and heritable factors in the causation of cancer– analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 343, 78–85. Martinez, J.M., Afshari, C.A., Bushel, P.R., Masuda, A., Takahashi, T. and Walker, N.J. (2002). Differential toxicogenomic responses to 2,3,7,8-tetrachlorodibenzo-p-dioxin in malignant and nonmalignant human airway epithelial cells. Toxicol Sci 69, 409–423. Medzhitov, R., Preston-Hurlburt, P. and Janeway, C.A. (1997). A human homologue of the Drosophila Toll protein signals activation of adaptive immunity. Nature 388, 394–397. Nebert, D.W., Dalton, T.P., Okey, A.B. and Gonzalez, F.J. (2004). Role of aryl hydrocarbon receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer. J Biol Chem 279, 23847–23850. Niederreither, K., Abu-Abed, S., Schuhbaur, B., Petkovich, M., Chambon, P. and Dolle, P. (2002). Genetic evidence that oxidative derivatives of retinoic acid are not involved in retinoid signaling during mouse development. Nat Genet 31, 84–88. Powell, J.J., Van de Water, J. and Gershwin, M.E. (1999). Evidence for the role of environmental agents in the initiation or progression of autoimmune conditions. Environ Health Perspect 107, 667–672. Romieu, I., Sienra-Monge, J.J., Ramirez-Aguilar, M., Moreno-Macias, H., Reyes-Ruiz, N.I., del Rio-Navarro, B.E., Hernandez-Avila, M. and London, S.J. (2004). Genetic polymorphism of GSTM1 and antioxidant supplementation influence lung function in relation to ozone exposure in asthmatic children in Mexico City. Thorax 59, 8–10. Sakai, Y., Meno, C., Fujii, H., Nishino, J., Shiratori, H., Saijoh, Y., Rossant, J. and Hamada, H. (2001). The retinoic acid-inactivating enzyme CYP26 is essential for establishing an uneven distribution of retinoic acid along the anterio-posterior axis within the mouse embryo. Genes Dev 15, 213–225. Snell, G. (1978). Congenic resistant strains of mice. In Origins of inbred mice (H. Morse, ed.),Academic Press, New York, pp. 119–155. Tanner, C.M., Ottman, R., Goldman, S.M., Ellenberg, J., Chan, P., Mayeux, R. and Langston, J.W. (1999). Parkinson disease in twins: An etiologic study. JAMA 281, 341–346. Waterland, R.A., and Jirtle, R.L. (2003). Transposable elements: Targets for early nutritional effects on epigenetic gene regulation. Mole Cell Biol 23(15), 5293–5300.

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84 Molecular Basis of Rheumatoid Arthritis Robert M. Plenge and Michael E. Weinblatt

INTRODUCTION Rheumatoid arthritis (RA) is a systemic, chronic inflammatory disorder whose root cause is unclear. The clinical hallmark of RA is an inflammatory arthritis with a predilection for specific diarthrodial (freely movable) joints. It is the most common form of inflammatory arthritis, with an estimated prevalence of up to 1% in the adult population. Females are at greater risk than males for developing the disease, with a female:male ratio of 2.5:1. While the disease can occur at any age, the peak age on onset is in the 40s, with an increasing incident with age. As with many complex diseases – those influenced by multiple genes and environmental exposures – there is substantial clinical heterogeneity. The clinical features of new-onset RA are highlighted in Table 84.1. With longstanding disease, articular erosions and joint deformities occur. Autoantibodies (RF and CCP) have important diagnostic and prognostic features, and have proven very useful in clinical management of RA. Most efforts aimed at understanding the molecular basis of RA have focused of genetic studies of disease susceptibility. To date, there are only two genes that have been convincingly demonstrated to influence risk of RA (PTPN22 and HLA-DRB1), although only a small fraction of the genome has been adequately interrogated using available molecular genetic techniques. Other genomic technologies such as large-scale expression and proteomic profiling in RA are less mature, but offer promise in understanding disease etiology. “Genomic medicine” – translating genomic information into prediction of disease susceptibility, characterization of gene– environment interactions, identification of new therapeutic Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

T A B L E 8 4 . 1 Common clinical features of new-onset rheumatoid arthritis Symptoms ● ● ● ●

Joint swelling Pain/stiffness (commonly in morning and lasting 1 h) Fatigue Malaise

Articular characteristics ● ● ● ●

Palpation tenderness Synovial thickening Effusion Erythema

Distribution ● ● ●

Symmetrical Distal (e.g., hands and feet) more commonly than proximal (e.g., spine) PIP, MCP/MTP, wrist/ankle more commonly than elbow/ knee, shoulder/hip

PIP  proximal interphalangeal joint, MCP  metacarpophalangeal joint, MTP  metatarsophalangeal joint

targets, and development of novel gene-based diagnostics – has had very little impact thus for on the clinical management of patients with RA.The association of susceptibility to HLA-DRB1 alleles has been known since the 1970s, yet has not translated Copyright © 2009, Elsevier Inc. All rights reserved. 1017

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into novel diagnostics or therapeutic treatments. The most remarkable advancement in the clinical management of RA, which emerged from intense basic science research, is the development of inhibitors of an important cytokine, tumor necrosis factor-alpha (TNF). Advances in genomic medicine hope to parallel the success of TNF-inhibitors, thus leading to improved patient care for this debilitating disease.

CLINICAL FEATURES The range of presenting clinical symptoms in RA is quite variable, but the sine qua non of RA is an inflammatory arthritis, manifested by symmetrical joint pain, stiffness, and swelling of diarthrodial joints. In most cases, onset of symptoms is insidious, and several months of symptoms are required to establish a diagnosis. Other systemic diseases may mimic RA (including arthritis secondary to inflammatory bowel disease, Lyme disease or psoriasis), although the clinical presentation, joint distribution, and radiographic changes are often sufficient to eliminate these disorders from the differential diagnosis. The American College of Rheumatology (ACR) has established criteria for the diagnosis of RA (Arnett et al., 1988). These criteria have 90% sensitivity and specificity for RA when compared with non-RA rheumatic disease control subjects. The clinical course of RA is extremely variable: some patients suffer mild, self-limiting arthritis while others develop progressive multi-system inflammation with profound morbidity and mortality (Pincus, 1995). Approximately 15% of patients who meet the ACR criteria for RA become free of symptoms at one year’s time. For the majority of patients, however, chronic disease ensues; if left untreated, joint destruction from synovitis occurs. Radiographic evidence of RA is present on standard X-rays in more than 70% within two years. More sensitive techniques such as MRI can identify changes such as synovial hypertrophy, bone edema, and erosive changes as early as 4 months after disease onset (McGonagle et al., 1999; McQueen et al., 1998). Ultimately, irreversible articular damage leads to physical deformity and functional disability, which carries an economic burden to society. For those patients with chronic disease, RA can be divided generally into those who are “seropositive” or “seronegative” based upon the presence of circulating autoantibodies. Classically, Rheumatoid Factor (RF) is the autoantibody that establishes whether a patient is seropositive or negative. More recently, antibodies directed against cyclic citrullinated proteins (anti-CCP antibodies) have been identified. These autoantibodies demonstrate higher sensitivity and specificity for RA when compare to RF. In general, seropositive patients progress more rapidly and have more severe disease than seronegative patients. (See below for a more detailed discussion on autoantibodies.) These patient characteristics are important when interpreting genetic and epidemiological studies in RA. Conclusions from a collection of new-onset, early arthritis cohort may be quite different than a collection of patients with longstanding disease.

PREDISPOSITION Despite decades of research, the root cause of RA is unclear (Firestein, 2003). Genes and environment together contribute to development of RA, although only two genes (PTPN22 and HLA-DRB1), and one environmental factor (smoking), have been associated with RA susceptibility across multiple, independent studies. Antibodies directed against cyclic citrullinated peptides (anti-CCP antibodies) have emerged as a specific marker for RA. That anti-CCP antibodies predate the diagnosis of RA by years suggests that these autoantibodies are pathogenic rather than simply a marker of chronic inflammation. Together, these risk factors suggest a hypothetical model that an environment trigger (e.g., smoking) invokes a generalized inflammatory response in genetically susceptible hosts, which leads to the formation of autoantibodies and eventually RA. Genetic Basis of RA The genetic contribution to RA susceptibility in humans has been demonstrated through twin studies (MacGregor et al., 2000), family studies (Bali et al., 1999), and genome-wide linkage scans (Amos et al., 2006; Cornelis et al., 1998; Etzel et al., 2006; Jawaheer et al., 2001, 2003; MacKay et al., 2002; Shiozawa et al., 1998). Heritability refers to the amount of phenotypic variation due to additive genetic factors, rather than common environmental factors, stochastic variation, gene–environment interactions, and gene–gene interactions. One such study demonstrated that approximately 60% of disease variability is inherited (MacGregor et al., 2000). Another measure of genetic contribution to disease activity is to compare prevalence of disease in family members compared to the general population. Whereas the population risk of RA is 1%, the monozygotic (MZ) twin of a patient with RA has a risk of 15% (Aho et al., 1986; Jarvinen and Aho, 1994; Silman et al., 1993). Moreover, the relative risk (RR) to the sibling of a proband with RA is 5 for RA (Deighton et al., 1989; Hasstedt et al., 1994; Wolfe et al., 1988), although the number varies depending on the population studied (Jawaheer et al., 2001). The MHC-Region and HLA-DRB1 Susceptibility Alleles The major histocompatibility complex (MHC) region spans 3.6 megabases (Mb) on the short arm of human chromosome 6, and contains hundreds of genes, including many involved in immune function (Horton et al., 2004; Stewart et al., 2004). It has been estimated that the MHC-region of the human genome accounts for approximately one-third of the overall genetic component of RA risk (Deighton et al., 1989; Rigby et al., 1991). Genome-wide linkage scans using both microsatellite (Cornelis et al., 1998; Jawaheer et al., 2001; MacKay et al., 2002; Shiozawa et al., 1998) and single nucleotide polymorphism (SNP) markers (Amos et al., 2006) have identified consistently this region as important in RA pathogenesis. These genomewide scans have demonstrated that the MHC region has the largest genetic contribution in RA, and the relative contribution

Predisposition

of MHC genes (MHC) was found to be 1.75 (Cornelis et al., 1998; Jawaheer et al., 2001). Much, but probably not all, of the risk attributable to the MHC-region is associated with alleles within the HLA-DRB1 gene. An association between RA and the Class II HLA proteins was first noted in the 1970s, when the mixed lymphocyte culture (MCL) type Dw4 (related to the serological subtype DR4) was observed to be more common among patients with RA compared to controls (Stastny, 1978; Stastny and Fink, 1977). Subsequently, investigation of the molecular diversity of Class II proteins (subunits of HLA-DR, -DQ and -DP) localized the serological Dw4 subtype to the HLA-DRB1 gene (Gregersen et al., 1986a, b). When the susceptible DR subtypes were considered as a group, Gregersen et al., noted a shared amino acid (a.a.) sequence at positions 70–74 of the HLA-DRB1 protein (Gregersen et al., 1987). These residues are important in peptide binding, and thus it was hypothesized that RA-associated alleles bind specific peptides, which in turn facilitates the development of auto-reactive T cells. These alleles are now known collectively as “shared epitope” alleles due to the related sequence composition in the third hypervariable region (Table 84.2): the susceptibility alleles result in missense a.a. changes, where the shared susceptibility a.a. motif is 70Q/R-K/R-R-A-A74. The HLA-DRB1 gene encodes for a protein that is part of MHC Class II molecules. These molecules, heterodimers of alpha and beta proteins, are found on professional antigen-presenting cells (APCs), and display peptides derived from extracellular proteins to CD4 T cells (T “helper” cells). The genes that encode the Class II molecules are found in three subregions (DR, DQ, and DP) spanning 1 Mb within the MHC region. Within the DR subregion, one alpha- and three betachain genes have been described; the a-chain gene and two beta-chain genes, DRB1 and DRB2, are clearly expressed. The DQ subregion contains two sets of alpha and beta-chain genes, DX and DQ-alpha and beta. With the exception of DR-alpha, all of the expressed genes display considerable allelic diversity. Classification of HLA-DRB1 molecules includes serological nomenclature (e.g., DR1, DR4, and DR10), MCL nomenclature (e.g., Dw4) and DNA-sequence based nomenclature (see below). Since the initial observation, a large number of population studies have confirmed the association between RA and allelic variants at HLA-DRB1 (Ollier and Thomson, 1992). At the level of DNA, the most common (5% population frequency) HLA-DRB1 shared epitope susceptibility alleles include *0101, *0401, and *0404 in individuals of European ancestry, and *0405 and *0901 in individuals of Asian ancestry; less common shared epitope alleles include *0102, *0104, *0408, *0413, *0416, *1001, and *1402. Of note, the *0901 allele observed among Asian populations does not strictly conform to the SE a.a. sequence motif (70R-R-R-A-E74, see Table 84.3), and the classic SE alleles may not contribute to risk in African-American and Hispanic-American RA populations (McDaniel et al., 1995; Teller et al., 1996). Thus, additional exploration of the molecular basis of HLA-DRB1 susceptibility alleles is needed in the future.

TABLE 84.2 DRB1 alleles

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RA HLA-DRB1 “shared epitope” alleles Low-resolution

a.a (location) 70

71

72

Q

R

R

73 A

74 A

DRB1*0101

DR1

Q

R

R

A

A

DRB1*0102

DR1

–

–

–

–

–

DRB1*0103

DR1

D

E

–

–

–

DRB1*03

DR3

–

K

–

G

R

DRB1*0401

DR4

–

K

–

–

–

DRB1*0402

DR4

D

E

–

–

–

DRB1*0403

DR4

–

–

–

–

E

DRB1*0404

DR4

–

–

–

–

–

DRB1*0405

DR4

–

–

–

–

–

DRB1*0407

DR4

–

–

–

–

E

DRB1*0408

DR4

–

–

–

–

–

DRB1*0411

DR4

–

–

–

–

E

DRB1*07

DR7

D

–

–

G

Q

DRB1*08

DR8

D

–

–

–

L

DRB1*0901

DR9

R

–

–

–

E

DRB1*1001

DR10

R

–

–

–

–

DRB1*1101

DR11

D

–

–

–

–

DRB1*1102

DR11

D

E

–

–

–

DRB1*1103

DR11

D

E

–

–

–

DRB1*1104

DR11

D

–

–

–

–

DRB1*12

DR12

D

–

–

–

–

DRB1*1301

DR13

D

E

–

–

–

DRB1*1302

DR13

D

E

–

–

–

DRB1*1303

DR13

D

K

–

–

–

DRB1*1323

DR13

D

E

–

–

–

DRB1*1401

DR14

R

–

–

–

–

DRB1*1402

DR14

–

–

–

–

–

DRB1*1404

DR14

R

–

–

–

E

DRB1*15

DR2

–

A

–

–

–

DRB1*16

DR16

D

–

–

–

–

Genes associated with RA HLA-DRB1“shared epitope” alleles are classified by amino acid (a.a.) sequence at positions 70–74. The consensus a.a. sequence (QRRAA) is shown at the top; the identical a.a. is indicated by a dash (-) and variable a.a. indicated by appropriate nomenclature. Alleles in bold are associated with RA susceptibility.

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Molecular Basis of Rheumatoid Arthritis

While HLA-DRB1 susceptibility alleles are often considered as a group, the strength of the genetic association to RA susceptibility differs across the DRB1 alleles. There are at least two classes of HLA-DRB1 risk alleles (high and moderate). In general, DRB1*0401 and *0405 alleles exhibit a high level or risk, with a RR of approximately 3. The DRB1*0101, *0404, *0901, and *1001 alleles exhibit a more moderate RR in the range of 1.5. It is becoming increasingly clear that HLA-DRB1 shared epitope alleles only influence the development of seropositive RA, and more specifically anti-CCP RA (see below for discussion on autoantibodies) (Huizinga et al., 2005; Irigoyen et al., 2005). Collectively, the shared epitope alleles have an odds ratio (OR) of over 5 if CCP RA patients are compared to matched healthy controls. Because these alleles are quite common in the general population (collectively, allele frequency 40% in individuals of European ancestry), the attributable risk for SE alleles is quite high. Several investigators have proposed a refined classification of shared epitope alleles, as this hypothesis alone cannot explain all of the genetic risk attributable to the HLA-DRB1 locus (de Vries et al., 2002; Gao et al., 1991; Michou et al., 2006; Zanelli et al., 1998). No consensus has emerged, however. Some of these studies suggest that a protective allele may be in linkage disequilibrium with the HLA-DRB1 alleles. It has been hypothesized that the presence of an asparagine amino acid at position 70 of the HLA-DRB1 protein (D70) may be associated with protection from the development of RA (once the effect of the shared epitope alleles has been taking into consideration) (Mattey et al., 2001a; Ruiz-Morales et al., 2004). Numerous studies have shown that HLA-DRB1 susceptibility alleles influence disease severity in longstanding disease, particularly the development of bony erosions (e.g., Chen et al., 2002; Gorman et al. 2004; Moxley and Cohen, 2002). More recently, however, it has been suggested that this association is primarily due to the presence of CCP autoantibodies (Huizinga et al., 2005). It remains to be determined whether HLA-DRB1 alleles contribute additional risk of developing erosive disease independent of CCP autoantibodies (van der Helm-van Mil et al., 2006). This more recent observation may be an important explanation for why some studies have demonstrated that SE alleles predict erosive changes, but only in RF- patients (ElGabalawy et al., 1999; Mattey et al., 2001b). One hypothesis to explain this observation is that RF- patients in these older studies are actually CCP [and it is known that SE alleles have a stronger association with CCP than RF RA (Irigoyen et al., 2005)]. In the future, it will be important to assess the relationship between HLA-DRB1 alleles and clinical outcome, controlling for the effect of CCP as well as other important clinical variables. Despite decades of research, it is not fully known how the HLA-DRB1 alleles cause risk of RA, and direct functional proof has been difficult (Goronzy and Weyand, 1993; Nepom, 2001). Hypotheses include that the SE alleles influence (1) thresholds for T-cell activation [based on avidity between the T-cell receptor, MHC, and peptide, especially in the context of post-translational

modification events important in RA pathogenesis (Hill et al., 2003)]; (2) thymic selection of high-affinity self-reactive T cells (based on the T-cell synovial repertoire) (Yang et al., 1999); and (3) molecular mimicry of microbial antigens (Albani et al., 1992). It is worthwhile noting that the third hypervariable region of the protein (location of SE allelic variants) contains a peptide-binding groove that serves to present peptides to CD4 T cells (Seyfried et al., 1988), and that citrullination of certain peptides triggers a strong immune response to citrullinated peptides in HLA-DRB1 *0401 transgenic mice (Hill et al., 2003). Other MHC-Region Genes Several studies suggest that additional genes within the MHC likely contribute to disease susceptibility, once the effect of HLADRB1 has been taken into consideration (Jawaheer et al., 2002; Kochi et al., 2004; Mulcahy et al., 1996; Singal et al., 1999; Zanelli et al., 2001). For example, an extended haplotype that includes HLA-DRB1 DR3 alleles may be associated with RA (Jawaheer et al., 2002). The associated haplotype spans 500 kb, and contains Class III MHC genes, including the TNF-alpha region implicated in other studies (Mulcahy et al., 1996; Ota et al., 2001; Waldron-Lynch et al., 2001). One study suggests that this association is restricted to CCP- patients (Irigoyen et al., 2005). No single study has tested comprehensively DNA variants within the MHC in a patient population large enough to detect subtle effects beyond HLA-DRB1 alleles. Only recently have genetic linkage disequilibrium maps become available to thoroughly test the hypothesis that non-HLA-DRB1 alleles influence the risk of developing RA (Miretti et al., 2005;Walsh et al., 2003). Application of high-density SNP genotyping in large patient collections should provide additional insight into the role of the MHC region in susceptibility and severity of RA. Non-MHC Genes The identity of genes contributing to RA that lie outside the MHC region has been more elusive. A sizeable portion of genetic variation is attributable to such non-HLA genes – up to two-thirds in some studies. The gene with the most convincing evidence of replication, PTPN22, influences threshold of T-cell activation (Begovich et al., 2004;Vang et al., 2005). The statistical evidence of association for other genes (e.g., CTLA4, PADI4, and SLC22A4) is not yet conclusive, and therefore it is not possible to draw broader conclusions on functional classification of genes associated with RA beyond HLA-DRB1 and PTPN22 (which implicate class II presentation and T-cell activation). The most convincing non-HLA gene associated with RA is PTPN22 (Begovich et al., 2004), a finding that has been replicated across multiple independent studies (e.g., Dieude et al., 2005; Harrison et al., 2006; Hinks et al., 2005; Lee et al., 2005; Orozco et al., 2005; Plenge et al., 2005; Steer et al., 2005;Viken et al., 2005; Zhernakova et al., 2005). The susceptibility allele is a missense variant that changes an arginine to tryptophan amino acid (R620W), resulting in alteration of T-cell activation (Begovich et al., 2004;Vang et al., 2005). The magnitude of the genetic effect, as measured by the OR, is substantially less than

Predisposition

TABLE 84.3

Non-MHC associations in RA

Gene

OR

Comments

PTPN22

1.75

Clear association with missense SNP (rs2476601) and CCP RA

CTLA4

1.20

Possible association with CT60 SNP (rs3087243) and RA

PADI4

1.20

Possible association with haplotype tagged by SNP (rs2240340) and RA

SLC22A4

1.25

Possible association with SNP (rs2073838) and RA

for HLA-DRB1*0401 but similar to other SE alleles (PTPN22 OR 1.75). Interestingly, this allele is absent in East Asians, and thus not associated with susceptibility in Japanese populations (Ikari et al., 2006). Extensive genotype-phenotype correlations have not been conducted for PTPN22 (as they have for HLA-DRB1). Like HLA-DRB1 alleles, PTPN22 only associates with seropositive RA. There is some evidence that PTPN22 influences age of onset (Plenge et al., 2005), and may have a more significant effect in males compared to females (Pierer et al. 2006; Plenge et al., 2005), but no evidence that it influences disease activity or radiographic erosions (Harrison et al., 2006;Wesoly et al., 2005). Additional genes have been implicated in genetic association studies of RA, but have not yet reached the level of statistical significance required to solidify their position as true RA-susceptibility genes (see Table 84.3). Some encouraging candidate genes include CTLA4, PADI4, and SLC22A4. 1. The association between variants with CTLA4 and susceptibility to autoimmunity is most convincing in type 1 diabetes and autoimmune thyroiditis (Ueda et al., 2003). CTLA4 is a negative regulator of T-cell activation. In these diseases, an allele in the 3 untranslated region (UTR) of the gene causes a modest increase in disease risk (OR 1.2–1.5). Several studies have extended these findings to RA (Lei et al., 2005; Plenge et al., 2005), where the magnitude of the genetic effect is again modest (1.20). 2. PADI4 encodes for an enzyme that post-translationally modifies arginine to citrulline, and may therefore be important in generating anti-CCP autoantibodies. An initial report in Japanese patients implicated a common variant (population allele frequency 35%) in disease risk (Suzuki et al., 2003); subsequent reports have been less convincing statistically, but nonetheless support an association with RA susceptibility (Iwamoto et al., 2006). 3. Finally, a common allele within the SLC22A4 gene may be associated with RA susceptibility in East Asian populations (Tokuhiro et al., 2003), but this result has not been replicated in RA patients of European ancestry (Plenge et al., 2005). The protein product of SLC22A4 is an organic cation transporter expressed in hematological and immunological tissues. Of note, the putative causal allele, which

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1021

disrupts a RUNX1-binding site, is at reduced population frequency in RA patients of European ancestry compared to East Asian ancestry (0.08 versus 0.30 minor allele frequency), thus limiting power to detect an association in RA patients with European ancestry. Candidate gene studies in RA, as with many other complex diseases, highlight the current challenge of genetic association studies (Plenge and Rioux, 2006). First, most studies to date have tested a small fraction of genetic variation in the human genome. It is estimated that over 10 million common variants (population allele frequency 1%) exist in the human genome, yet most studies test a vanishingly small fraction of these variants. Second, the expected genetic effect for most disease alleles is quite modest (OR 1.5). Therefore, thousands of patients are required to detect the genetic effect – and most studies have been conducted on far fewer, limiting power to detect a true positive association. In the immediate future, genome-wide association studies, which test the majority of common genetic variants in the human genome, will be conducted in RA and other autoimmune diseases. If appropriately designed and interpreted (Hirschhorn and Daly, 2005), it is expected that these studies will greatly expand the list of RA-susceptibility genes. Several genome-wide association studies are underway with genotyping platforms that capture 60% of common genetic variants (as estimated by Phase II HapMap). Non-Genetic Risk Factors Sex Bias and Hormonal Factors Perhaps the strongest non-genetic risk factor for the development of RA is female sex: females are more than twice as likely to develop RA compared to males, and this disparity is even greater at a younger age (Linos et al., 1980; Symmons et al., 1994). In females, the risk of developing RA increases with age and peaks around the time of menopause (Doran et al., 2002; Goemaere et al., 1990; Karlson et al., 2004). These observations have lead to considerable effort in examining the role of hormonal and pregnancy factors in disease occurrence. These studies include: 1. Conditions associated with excess estrogen and/or progesterone may be protective against developing RA. The risk of developing RA is increased in the 12-month postpartum period (when serum hormone levels fall) (Silman et al., 1992), and symptoms are less severe during the post-ovulatory phase of the menstrual cycle and during pregnancy (when levels are elevated) (Latman, 1983; Ostensen et al., 1983). 2. Duration of breast-feeding may be associated with risk of developing RA. Women with 24 total months of breast-feeding have a twofold reduced risk of developing RA (Karlson et al., 2004). 3. Cross-sectional studies of serum androgen levels consistently demonstrate lower serum testosterone levels in RA patients (Cutolo and Accardo, 1991; Masi et al., 1995). 4. The hypothalamic-pituitary axis (HPA) is altered in RA patients (Chikanza et al., 1992), and conditions leading to

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Molecular Basis of Rheumatoid Arthritis

panhypopituitarism and hypoadrenalism are associated with the onset of RA (Wilder, 1996). 5. Castration of female mice exaggerates arthritis (Holmdahl et al., 1986), and estrogen replacement therapy suppresses arthritis in the collagen-induced mouse model of arthritis (Holmdahl et al., 1987) and in adjuvant arthritis in the rat (Kappas et al., 1963), consistent with the role of estrogen in the human. 6. Finally, a subset of RA patients undergoes striking remission during pregnancy. Although fetal-maternal genetic relationships have been invoked to explain this phenomenon (Nelson et al., 1993), other data fails to fully support this hypothesis (Brennan et al., 2000; Gregersen, 2000), and thus it is likely that hormonal changes during pregnancy at least partially explain disease remission in pregnancy. While it has been proposed that genes within sex steroid hormone pathways may contribute to RA inherited risk, no gene has been conclusively identified to date. Environmental Factors Smoking is the environmental risk factor with strongest association with developing RA. Over the past 20 years, many studies have convincingly shown that smoking increases risk of developing RA in both males and females by a factor of 2.0 (Gorman, 2006; Stolt et al., 2003). Most of these studies demonstrate that the increase risk is in developing autoantibody positive RA, and that the risk is greatest for heavy, current smokers; the risk remains, however, for 10 years following smoking cessation. Because the link between smoking and autoantibody positive RA is reminiscent of the genetic association between HLA-DRB1 “shared epitope” alleles and autoantibody positive RA, Padyukov et al. investigated the risk of developing disease in carriers of these two established risk factors: a 16-fold risk of developing RF RA in smokers who carry two copies of the SE alleles was observed (Padyukov et al., 2004). Other putative environmental risk factors that have not been as widely replicated as smoking include blood transfusions, obesity, occupational silica and mineral oil exposure, and socioeconomic class (Silman and Pearson, 2002; Symmons, 2003). It is interesting that several of these putative environmental factors are inhaled through the lungs. Silica dust and mineral oil exposure, similar to exposure to cigarette smoke, was a risk factor only for seropositive RA. Mineral oil can also act as an adjuvant capable of inducing experimental arthritis in rodent models. Infectious Agents There is indirect, but certainly not conclusive evidence that exposure to an infectious agent(s) may trigger the development of RA (Silman and Pearson, 2002). Despite decades of investigation, however, no single infectious agent has emerged as influencing risk of RA. Support for an infectious etiology contributing to RA risk include: (a) certain forms of arthritides in humans are triggered

by bacteria, including enterogenic and urogenic infections (e.g., reactive arthritis, rheumatic fever, Lyme disease, and Whipple’s disease); (b) bacterial components are able to induce chronic arthritis closely resembling RA in animal models; (c) antibodies, T-cell clones, or cellular immune responses specific to certain bacteria as well as bacterial components have been observed in synovial fluid or peripheral blood in patients with RA; and (d) and paleontological (Rothschild et al., 1988) and epidemiological data (Doran et al., 2002; Kaipiainen-Seppanen et al., 1996; Shichikawa et al., 1999) demonstrating the first appearance of RA in modern man, yet with a decrease in incidence over the last 40 years (with the improvement in public health measures and the widespread use of antimicrobial therapy). If an infectious agent is causal, one could imagine different mechanisms by which it might lead to RA. Exposure might lead to a generalized over-active immune system, thus clearing the infection but resulting in autoimmunity later in life. A more specific response to a single infectious agent might induce an immunological response against the infection, yet lead to crossreactivity with antigens in human synovial tissue (“molecular mimcry”). Autoantibodies RF and CCP Autoantibodies Autoantibodies have proven useful in the diagnosis and prognosis of RA patients. Autoantibodies are detected in approximately two-thirds of patients with RA and predict severe disease. The two major types of autoantibodies used clinically to create RA subsets are RF, which is an immunoglobulin specific to the Fc region of IgG, and anti-cyclic citrullinated peptide (CCP) antibodies, which are antibodies directed against peptides that have arginine posttranslationally modified to citrulline (Schellekens et al., 2000). These autoantibodies are strongly correlated but may represent distinct clinical subsets of RA. RF autoantibodies are part of the diagnostic criteria for RA (Arnett et al., 1988). The RF assay, however, remains suboptimal as a diagnostic test, as it lacks sensitivity (50–90%) and specificity (50–90%) (Shmerling and Delbanco, 1991). Furthermore, it is present in many other disease states (including those that mimic RA) and patients that smoke, and its incidence increases with age. In contrast, anti-CCP antibodies have moderate sensitivity (60–80%) but increased specificity for RA (90%), and predict functional status and radiographic erosions in patients with early-onset RA (van Jaarsveld et al., 1999; van Zeben et al., 1992; Kroot et al., 2000). There has been much debate about whether these autoantibodies are causal or whether they represent a non-specific response to systemic inflammation. Several lines of evidence suggest that these autoantibodies – and CCP in particular – are pathogenic: (1) anti-CCP antibodies appear before disease onset (Rantapaa-Dahlqvist et al., 2003; Schellekens et al., 2000); (2) the presence of CCP is very specific to RA; (3) antibodies against citrullinated proteins enhance tissue injury in a murine

Predisposition

model of arthritis (collagen-induced arthritis) (Kuhn et al., 2006); and (4) genetic variation in an enzyme, PADI4, involved in the citrullination pathway appears associated with RA susceptibility (Suzuki et al., 2003) and see “Non-MHC genes” above). Finally, presence of RF and CCP antibodies may facilitate the identification of genes in RA. As with other complex human diseases, substantial clinical heterogeneity exists. Because presence or absence of RF and CCP antibodies provides a rationale clinical subset, these have been used to subset patients in the search for RA genes. Indeed, the two genes with a clear association to RA (HLA-DRB1 and PTPN22) are both association with CCP patients, but not CCP- patients. Hypothetical Model of RA Predisposition Based upon the above information, it is tempting to speculate about a hypothetical model of RA pathogenesis. One model, put forth by Klareskog et al (Klareskog et al., 2006), is that smoking or other air pollutants lead to inflammation and citrullination of proteins in the lungs in genetically susceptible individuals. This event leads to the formation of anti-CCP antibodies, which have a direct pathogenic role in the joint. Under this hypothesis, citrullinated proteins are presented in class II MHC molecules to induce an immune response, and HLA-DRB1 variants augment the autoimmune response. The PTPN22 missense allele may also augment the autoimmune response, or may allow autoreactive T-cells to escape selection in the thymus. While aspects of this model may be correct, it almost certainly does not explain RA pathogenesis in all patients. Continued integration of clinical and basic science research is necessary to revise, or refine, models of RA pathogenesis. The advent of genome-wide association studies should facilitate identification of new RA-related genes, facilitating our understanding of disease pathogenesis. Other Genomic Studies Other genomic resources are available to study predisposition to RA, including mRNA expression profiling and examination of serum protein levels, although neither has been widely implemented in large patient collections. The field of proteomics is still limited by the availability of cost-effective, high-throughput assays. The search for auto-antigens that predispose to RA has also been challenging. The goals of these efforts include identifying novel biomarkers that allow early and specific disease diagnosis or predict severe disease, or biomarkers that may serve as therapeutic targets. The success of RF and anti-CCP autoantibodies provide proof-of-concept that a novel biomarker might have direct clinical utility. mRNA Expression Profiling Genome-wide analysis of mRNA expression patterns has been performed in peripheral blood and synovial fluid derived from RA patients.This approach has proven insightful in the peripheral blood of patients with systemic lupus erythematosis (SLE)

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(Baechler et al., 2003), but has been less fruitful to date in RA. Studies in peripheral blood have compared RA patients versus normal controls (Batliwalla et al., 2005; van der Pouw Kraan et al., 2007), RF versus RF- patients (Bovin et al., 2004), and early versus chronic RA (Olsen et al., 2004). Similar techniques have been applied to synvovial tissue, where inflammatory RA synovium is compared to non-inflammatory OA synovium (Devauchelle et al., 2004; van der Pouw Kraan et al. 2003a, b). No clear consensus has emerged from these studies, but there is some evidence that there may be an alteration in the number or activation state of monocytes (Batliwalla et al., 2005) or a type I interferon signature (similar to SLE, [van der Pouw Kraan et al., 2007]). Many studies are too small (e.g., 15 patients) to draw any significant conclusions given the large number of hypotheses (i.e., expressed genes) tested (Bovin et al., 2004; Devauchelle et al., 2004; Haas et al., 2006; Szodoray et al., 2006). Proteomics Genome-wide genetic association studies and mRNA expression profiling allow for exploration of novel hypotheses, effectively exploring hundreds of thousands of genes for influence on disease risk. These platforms are becoming increasingly costeffective and robust. In contrast, the field of proteomics has been hampered by equivalent technical capacity. Mass spectrometry offers such a platform, but is not yet widely in use (Domon and Aebersold, 2006). Consequently, most “proteomics” research in RA and other complex diseases has been limited to ELISAbased methodologies that are not capable of high-throughput multiplexing – and thus limited to a small number (hundreds not thousands) of proteins of known relevance. It is clear that cytokines play a crucial role in RA pathogenesis (Lee and Weinblatt, 2001). Cytokines are small soluble proteins that mediate intercellular communication within the immune system. Important examples include the proinflammatory cytokines interleukin 1 (IL1) and tumor necrosis factor alpha (TNF-alpha), as well as their soluble receptors (p55 and p75 for TNF; IL1R1, IL1R2, and IL1Ra for IL1). These cytokines are secreted primarily by macrophages within the synovium, and lead to inflammation and stimulation of synovial tissue effector functions (e.g., cellular proliferation, expression of metalloproteinases and adhesion-molecules, and secretion of prostaglandins and other cytokines). Perhaps the most incriminating evidence for the role of cytokines in RA pathogenesis is in neutralizing therapies directed at the proteins themselves in patients with active RA: antibodies directed against TNFalpha (or its soluble receptor) have been remarkably useful in the treatment of RA; anti-IL1 therapies are somewhat effective, but not to the same degree as anti-TNF-alpha therapies. While cytokines are important in RA, what is not clear is whether alterations in cytokine secretion per se leads to the development of RA, or whether dysfunction is simply the result of an over-active immune system. No longitudinal cohort study has yet demonstrated alterations in these molecules prior to the development of RA – evidence that would be necessary

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to establish causality. An alternate strategy to establish causality would be to demonstrate that genetic variation within a cytokine gene leads to the development of RA. Numerous studies have suggested that alleles within the TNF gene influence RA risk, but it has yet to be conclusively shown that this effect is independent of linkage disequilibrium with HLA-DRB1 or neighboring genes within the 3.6 Mb MHC region. In theory, all alleles that influence secretion of a given cytokine could be mapped and then tested to determine whether these alleles also influence the development of RA. To date, no such comprehensive list of alleles exists. It is worth highlighting studies that have implemented newer proteomic techniques such as mass spectrometry (Saulot et al., 2002; de Seny et al., 2005; Liao et al., 2004; Uchida et al., 2002), multiplex cytokine arrays (Hueber et al., 2006) and antigen microarrays (Hueber et al., 2005) to analyze synovial fluid and serum in RA patients. A study by Liao et al. used a twostep mass spectrometry approach to generate protein profiles of synovial fluid from patients with either erosive or non-erosive RA (Liao et al., 2004). Among 33 proteins elevated in the synovial fluid of patients with erosive RA were C-reactive protein (CRP) and 6 members of the S100 protein family of calciumbinding proteins. The authors demonstrated that levels of CRP, S100A8 (calgranulin A), S100A9 (calgranulin B), and S100A12 (calgranulin C) proteins were also elevated in the serum of patients with erosive RA. A different study by de Seny et al., which included 34 patients with longstanding disease, identified 5 proteins elevated in the serum of patients with CCP RA (compared to patients with other inflammatory diseases), one of which was hypothesized to be myeloid-related protein 8 (MRP8) (de Seny et al., 2005). The MRP8 protein may be enriched in the synovial fluid of RA compared to osteoarthritis patients (Uchida et al., 2002). Finally, Hueber et al. used microarrays in a set of early RA patients to show that autoantibody reactivity against citrullinated epitopes is more common in patients with high-serum levels of TNF, IL-1, IL-6, IL-13, IL-15, and GM-CSF (Hueber et al., 2006).

SCREENING No reliable methods to screen a population prior to disease onset exist for RA. It may be that the presence of CCP autoantibodies together with genetic susceptibility variants (PTPN22 and HLA-DRB1) will increase risk substantially to the point where screening the healthy population because cost-effective (Berglin et al., 2004; Johansson et al., 2005). Under this scenario, it remains to be determined whether effective intervention to prevent RA will emerge – currently, RA therapies are directed at symptoms rather than a cure. Moreover, this simple approach does nothing to screen the 1/3 of RA patients who develop autoantibody negative disease. Overall, no clear paradigm has emerged as to how screening the population would influence clinical decision-making, and additional attention should be paid to this area in the future.

DIAGNOSIS, PROGNOSIS, AND MONITORING The diagnosis of RA is based on established clinical criteria (Arnett et al., 1988). The clinical course for any individual patient is highly variable. Extent of synovial inflammation and presence of autoantibodies at the time of initial diagnosis portend a poor prognosis, but cannot alone predict prognosis in any individual patient. Similarly, the presence of HLA-DRB1 SE alleles predicts more severe disease, but does not routinely enter into decision-making in clinical patient care. Because treatment strategies have improved dramatically over the last decade with the advent of TNF-alpha inhibitors, prognosis is more influenced by response to treatment rather than aggressiveness of the underlying disease in an untreated patient. A major goal of therapy is early institution of diseasemodifying anti-rheumatic drugs, or DMARDs. The most popular disease-modifying therapy is low dose weekly methotrexate (MTX). This drug is used as a mono-therapy or in combination with other synthetic molecules (antimalarials, sulfasalazine, leflunomide) or with biologic response modifiers. Biologics that neutralize the pro-inflammatory cytokine TNF-alpha have been a major advance in the therapy of RA. Monoclonal antibodies (infliximab and adalimumab) or the p75 TNF-alpha soluble receptor fusion protein (etanercept) are effective as a mono-therapy or in combination with MTX in reducing the signs and symptoms of the disease, as well as improving quality of life and slowing the rate of radiographic progression. These therapies are very effective when combined with MTX. Newer biologics recently approved for use in RA include abatacept (blocks the CTLA4 co-stimulation pathway) and rituximab (a B-cell depleting strategy). Early intervention with MTX and combination therapy (including anti-TNF-alpha therapy) have had a major impact on this disease.

PHARMACOGENOMICS In contrast to literature on RA susceptibility, the literature on pharmacogenomics is less mature. No single gene is clearly associated with response to therapy, although variants in the MTHFR gene may influence MTX response and side-effect profile, and variants within the MHC region may influence response to anti-TNF- agents. MTHFR Variants and Methotrexate MTX is one of the most widely used DMARDs in the treatment of RA. Variability in efficacy and toxicity exist in clinical practice, suggesting that genetics may influence drug activity. MTX acts on the pathway that generates bioactive folate compounds, possibly through the inhibition of dihydrofolate reductase. While a principal agent in the treatment of RA, MTX nonetheless is not effective in all patients: ~40–60% of patients respond to MTX, and the dose required to suppress RA activity differs widely among patients. An important MTX toxicity is liver cirrhosis, occurring in less than 1% of RA patients, and

Pharmacogenomics

routine laboratory monitoring is required to prevent serious damage. To date, it is not possible to predict which patients will respond or succumb to adverse events during MTX treatment. The enzyme 5,10-methylenetetrahydrofolate reductase (MTHFR) is central to folate metabolism. Several common polymorphisms within the gene are associated with reduced enzyme activities (C677T and A1298C). It has been hypothesized that these genetic variants may also influence the development of efficacy or toxicity of MTX. Support for this hypothesis comes from two studies: (a) a prospective study of 236 RA patients in which the T allele of the C677T polymorphism was associated with an increased rate of the discontinuation of MTX treatment because of toxicities, primarily due to an increased risk of elevated liver enzyme levels (RR 2.38; 95% CI: 1.06–5.34) (van Ede et al., 2001); and (b) a retrospective study of 106 RA patients where an overall higher rate of MTX toxicity was observed in patients with the 677T allele than those without it (RR 1.25; 95% CI: 1.05–1.49) (Urano et al., 2002). Other studies have failed to demonstrate a clear association with these single SNPs, however (e.g., Kumagai et al., 2003;Weisman et al., 2006), either because of limited statistical power, heterogeneity in the toxicity phenotypes, or because the original results are actually false positive associations. MHC Variants and Response to Anti-TNF-␣ Therapy A remarkable advance in the treatment of RA occurred with the introduction of monoclonal antibodies that block TNF-. As with MTX, up to half of patients do not respond adequately to anti-TNF- treatment. Furthermore, the toxicity profile of these agents includes life-threatening infections, and possibly an increase rate of malignancy. Thus, a better understanding of who responds and who develops serious toxicity would be a significant advance in patient care.

TABLE 84.4

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The entirety of TNF pharmacogenetic studies performed to date is shown in Table 84.4. Many of the candidate genes (and alleles) were chosen based on knowledge of RA-susceptibility alleles, as well as known biology of the TNF- pathway. The most consistent signal observed is across the MHC (Criswell et al., 2004; Kang et al., 2005; Martinez et al., 2004; Seitz et al., 2007), although this effect is not observed in all populations (Marotte et al., 2006; Padyukov et al., 2003). Furthermore, no single allele within the MHC was associated across all studies. (For simplicity we list in Table 84.4 whether an association was identified with any allele within the MHC region, rather than a specific MHC allele.) Most of the studies are small and underpowered to detect modest genetic effects. Only three studies have examined more than 100 patients (which is still underpowered to detect modest effects).The main results from these studies are summarized below: 1. The largest published study (n  301 RA patients on etanercept, [Criswell et al., 2004]) demonstrated (a) patients with 2 copies of SE alleles were more likely to respond to treatment than those with 0 or 1 copies (OR 4.3 [95% CI 1.8–10.3]), and (b) a haplotype created by SE alleles plus TNF-LTA SNPs at positions 308, 238, and 488 (TNF), and positions 249, 365, and 720 (LTA) were more likely to respond to TNF treatment. 2. The next largest study (n  198 RA patients on infliximab, [Marotte et al., 2006]) demonstrated (a) no association between SE alleles and treatment response, and (b) no association with single marker analyses of two TNF SNPs (308 and 238). 3. The last study with 100 patients (n  123 RA patients on etanercept, [Padyukov et al., 2003]) demonstrated (a) no association between SE alleles and treatment response (data not shown), and (b) no association with single marker analyses of a TNF SNP (308).

Pharmacogenetic studies of response to anti-TNF-␣ therapy

Publication

Population

n

Duration

MHC association

Criswell (2004)

North American

301

12 months

Yes

Marotte (2006)

French

198

30 weeks

No

Padyukov (2003)

Swedish

123

12 weeks

No

Martinez (2004)

Spanish

78

12 weeks

Yes

Fabris (2002)

Italian

78

6 months

Yes

Kang (2005)

Korean

70

12 weeks

Yes

Mugnier (2003)

French

59

22 weeks

Yes

Seitz (2007)

Swiss

54

24 weeks

Yes

Fonseca (2005)

Portuguese

22

24 weeks

Yes

Cuchacovich (2004)

Chilean

20

22 weeks

No

Tutucnu (2005)

North American

30

12 weeks

Not tested

For each study, we highlight the number of patients (n), treatment duration at which time response was assessed (duration), and whether an association was observed with any allele within the MHC (published before February 2007).

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In summary, the available pharmacogenetic studies performed to date do not yet conclusively establish that genetic variation within any gene is associated with response to DMARD treatment. No study has conducted a genome-wide search for DNA variants that influence DMARD outcome, and all studies to date have been underpowered to detect modest effects (OR 1.5). Large-scale clinical studies that include thousands of patients with detailed clinical outcomes data are needed to determine whether a DNA variant is associated with response to MTX and TNF-alpha monoclonal antibodies, respectively.

CONCLUSIONS While “genomic medicine” has yet to have a large impact on the clinical management of RA patients, the field is still in its

infancy. The major bottleneck to date has been in identifying genes that influence RA susceptibility and severity. The advent of large, genome-wide association studies powered to detect modest genetic effects promise to identify new genes in the very near future. Once a gene is identified, the next immediate challenge will be to identify the causal allele(s), incorporate genetic knowledge into evolving models of RA pathogenesis, and correlate genotypes to subclinical phenotypes such as radiographic erosions, disease severity, and treatment response. Ultimately, the true measure of success of genomic medicine will be whether genetic information improves patient care, through novel diagnostic or therapeutic interventions. The goal of the field should be nothing short of a cure for RA.

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85 “Omics” in the Study of Multiple Sclerosis Francisco J. Quintana and Howard L. Weiner

INTRODUCTION Multiple sclerosis (MS) is an autoimmune disorder in which the central nervous system (CNS) is targeted by the dysregulated activity of the immune system, resulting in focal lesions and progressive neurological dysfunction. MS is heterogeneous in its clinical symptoms, rate of progression and response to therapy, probably reflecting the existence of several pathogenic mechanisms that make different contributions to the disease. MS is a T cell-mediated autoimmune disease thought to result from a combination of genetic and environmental factors (Weiner, 2004). In this chapter we will analyze the contribution of genomics, transcriptomics, immunomics and proteomics in delineating these factors, as well as their utility for monitoring disease progression and response to therapy, identifying new targets for therapeutic intervention and detecting individuals at risk of developing the disease later on in life.

GENOMICS IN MS The first observation suggesting a genetic contribution to MS susceptibility was the identification of familial aggregation: first, second and third degree relatives of MS patients have an increased risk of developing the disease (Mackay, 1950); a sibling of an MS patient, for example, has a 20-times greater lifetime risk of developing MS than an individual from the general population Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1032

(Sawcer et al., 2005). Further studies have showed that familial aggregation in MS results from sharing predisposing genetic elements and not from the exposure to environmental factors (Dyment et al., 2004). MS is considered a complex genetic disease in which many polymorphic genes have small or at most moderate effects on the overall MS risk, disease severity, rate of progression and age of onset among several clinical outcomes. To date, the major histocompatibility complex (MHC) locus on chromosome 6p21 MHC remains the strongest and most convincingly chromosomal region linked to MS: The DRB1*15 and the CRB1*17 alleles increase the risk for MS while the CRB1*14 has a disease-protective effect (Games, 2003; Jersild et al., 1972; Oksenberg and Barcellos, 2005). Several non-MHC candidate loci have also been linked to MS (Oksenberg and Barcellos, 2005), but it has proven difficult to validate their association in independent studies. The difficulty in the identification of nonMHC genes associated to MS might result from the genetic heterogeneity existing among MS patients, meaning that different combinations of genes might lead to the same end phenotype: MS. In this scenario, methods like linkage analysis might not be sensitive enough for the detection of genes bearing only modest effects on MS susceptibility, and thus association studies in large cohorts of patients and controls might be needed. Briefly, two approaches are used for the identification of genes linked to MS pathogenesis and progression: linkage and association mapping. Copyright © 2009, Elsevier Inc. All rights reserved.

Genomics in MS

Linkage mapping is based on the study of the co-inheritance of genetic markers and phenotypes in families over several generations. Linkage mapping is successful in finding genes for rare Mendelian monogenic diseases inherited in a dominant fashion. However, in diseases like MS where several loci have a small contribution to the phenotype under study, linkage studies only identify those loci that have the strongest influence. In addition, one of the premises implicit in linkage studies is that all the families studied have their susceptibility determined by the same genes, an assumption at odds with mounting evidence suggesting that the susceptibility to MS is genetically heterogeneous. A recent study used 4506 genetic markers to analyze 2692 individuals in 730 families of Northern European descent looking for the co-inheritance of genetic markers and MS (Sawcer et al., 2005). Multipoint non-parametric linkage analysis could only find one significant linkage, which unsurprisingly pointed to the MHC locus on chromosome 6p21. This study therefore confirms the identification of the MHC locus as a genetic determinant of the susceptibility to MS, but it also highlights two limitations of linkage mapping. First, linkage mapping tends to identify large chromosomal fragments because of the small number of recombination events analyzed in familial pedigrees; the MHC locus, for example, contains more than 200 genes. Second, the authors also found suggestive linkage on chromosomes 17q23 and 5q33 and 19p13, but the data were inconclusive about these loci even when a high density of genetic markers was used. Other mapping strategies are needed to identify loci with modest effects on MS. Association mapping looks for genetic markers with higher frequencies in MS patients than controls, suggesting an association between a disease phenotype and allelic variation (Hafler and De Jager, 2005). Although genetic susceptibility to MS has been linked to the MHC locus, the particular gene or genes underlying susceptibility to MS within this locus are a matter of discussion, particularly because of the punctuated pattern of linkage disequilibrium observed for this chromosomal location (Jeffreys et al., 2001). As a result, other genes within the MHC locus besides HLA-DRB1 might be associated with MS; tnf and other loci in HLA class III and HLA class I are logical candidates. Lincoln and coworkers genotyped 4203 individuals from Finland and Canada with a high-density SNP panel to identify the gene or genes in the MHC locus responsible for the increased susceptibility to MS (Lincoln et al., 2005). Their results identified the DRB1 allele in the HLA class II region as the single major susceptibility locus in the MHC region, although there is still a small chance that a closely adjacent locus contains the true susceptibility variant. Thus, association mapping confined the MS susceptibility to the approximately 100 kb of sequence flanking HLA-DRB1. On the one hand, this work illustrates the increased power of association over linkage mapping studies. On the other hand, it highlights the dependency of association mapping on linkage disequilibrium: the uneven distribution of recombination events in this region leads to the identification of a locus for MS susceptibility big enough to contain several genes.

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Admixture mapping is an association mapping strategy based on: (a) a difference on the prevalence of a disease between two ethnic groups and (b) the existence of a third admixed population (Smith and O’Brien, 2005). Northern Europeans and Africans have different susceptibilities to many autoimmune, circulatory and metabolic disorders, among them MS (Smith and O’Brien, 2005). This differential susceptibility is also reflected in North America where MS is more prevalent in European Americans than African Americans. On average, there have been only six generations since African and European populations came into contact in North America, resulting in little recombination between chromosomes of African and European ancestry in the history of African American populations. Thus, the chromosomal segments of one or the other ancestry are long in the admixed population, and few genetic markers can be used to classify the genome of an African American into sections with African or European origins. It is therefore possible to study African Americans to identify genomic regions where individuals with MS tend to have an unusually high proportion of ancestry from either Europeans or Africans, indicative of the presence of an MS variant that differs in frequency between the two ancestral populations. This mapping strategy was initially suggested several years ago (Chakraborty and Weiss, 1988), but it could only be fully implemented with the recent advent of genetic markers that identify human populations of different ancestry (Sachidanandam et al., 2001). Admixture mapping led to the identification of a new locus associated with MS risk around the chromosome 1 centromere as a result of the analysis of 1166 genetic markers in 1648 samples (605 MS patients and 1043 controls) (Reich et al., 2005). Notably, this linkage could not be replicated using an independent set of 143 African Caribbeans with MS from Martinique and the United Kingdom, suggesting that either the locus in chromosome 1 does not have a role in African Caribbean populations or that the African Caribbean cohort was not large enough. Future studies should attempt to clone the susceptibility gene on chromosome 1. Genome scans aimed at identifying non-MHC genes linked to MS usually fail when initial promising candidates cannot be validated on independent populations. New experimental techniques and analytical methods, however, are trying to change this trend. A recently published study combined the results of whole genome screens for linkage or association in 18 populations and superimposed them in a combined genomic map (Abdeen et al., 2006). The regions identified by this meta-analysis were then verified in a different set of samples (Abdeen et al., 2006), leading to the identification of several non-MHC candidate genes that modify the risk for MS. The list of candidates includes genes involved in CNS development and regeneration (NTN1, NCAM1, ADAM22 and ADAMTS10) in addition to genes directly linked to inflammation (TGFA, TGFBR, IL18 and IL10RA) (Abdeen et al., 2006). Although these new gene candidates are still awaiting independent validation, the metaanalysis used for their identification might constitute a new method for shortlisting new candidates to be targeted in independent replication studies.

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TRANSCRIPTOMICS IN MS Large-scale studies of mRNA expression in MS have been directed at characterizing the two main components of the disease: the lesion and the immune response. Characterization of the Lesion The hallmark of MS is the presence of demyelinated plaques in the white matter area of the CNS (Lassmann, 1998). These lesions are heterogeneous, reflecting the contribution of diverse mechanisms to disease progression (Lassmann et al., 2001). The study of the transcriptional activity in the lesions is therefore of importance for the characterization of the processes that drive MS, and for the identification of new targets of immune intervention. Lindberg and coworkers studied the transcriptional activity of lesions and normal appearing white matter (NAWM) in samples taken from 6 secondary progressive MS (SPMS) patients and 12 matched controls (Lindberg et al., 2004). Within the lesion, only 21% of the upregulated genes were associated to the immune response; 77% of those immune-related transcripts corresponded to the cellular response. However, the Four most significant immune upregulated genes were linked to humoral immunity (immunoglobulins). Notably, tertiary lymph nodes that support the maturation of antibody-secreting plasma cells within the CNS have been identified in SPMS brains (Aloisi and Pujol-Borrell, 2006; Corcione et al., 2005). In addition, those genes that showed a decreased expression mainly belonged to one of two categories: genes with well-known anti-inflammatory activities (pointing to a deficit in immunoregulatory mechanisms), and genes involved in neural homeostasis. Interestingly, the NAWM showed a significant upregulation in immune-related genes, mainly involved in signaling and effector functions like blood–brain barrier (BBB) disruption and lymphocyte activation.These data provide a molecular insight into the pathological mechanisms driving MS and confirm the presence of physiological abnormalities on NAWM. The study of the transcriptional profile in the MS lesion can lead to the identification of new targets for immuno intervention. Lock and coworkers studied the expression profile in the acute MS lesions (with signs of inflammation) and in silent lesions (without inflammation but showing clear signs of demyelination and scarring) (Lock et al., 2002). Both types of lesions showed an upregulated expression of genes associated with MHC class II antigen presentation, immunoglobulin synthesis, complement and pro-inflammatory cytokines. Neuron-associated genes and those associated with myelin production were underexpressed. Different expression profiles were found in active and silent lesions. -integrin was found to be elevated in chronic silent MS lesions; notably antibodies to 4-integrin reverse and reduce the rate of relapse in relapsing-remitting experimental autoimmune encephalomyelitis (EAE) (Yednock et al., 1992), and a humanized version of this antibody showed promising effects in the treatment of human MS (Polman et al., 2006).

The expression of IgE and IgG Fc receptors was upregulated in chronic silent MS lesions (Lock et al., 2002). Accordingly, mice harboring impaired IgE and IgG Fc receptors develop a milder EAE than their wild type counterparts (Lock et al., 2002). These effects were stronger from day 20 onwards following the induction of the disease, in accordance with microarray data showing that Fc receptor transcripts are elevated in chronic but not acute MS lesions. Granulocyte colony-stimulating factor (GCSF) was found to be upregulated in acute active lesions; its administration to mice before challenge with an encephalopathogenic peptide also leads to an amelioration of the disease, suggesting that endogenous GCSF might participate of the natural regulation of acute attacks (Lock et al., 2002). In a separate study, the large-scale sequencing of nonnormalized cDNA libraries derived from plaques dissected from brains of patients with MS showed an increased frequency of transcripts coding for osteopontin (OPN), a Th1 cytokine involved in the immune response to infectious diseases (Chabas et al., 2001). OPN transcripts were detected exclusively in the MS mRNA population but not in control brain mRNA. The upregulated expression of OPN was confirmed by immuno-histochemistry in human MS plaques, which showed OPN expression in microvascular endothelial cells and macrophages, and in the white matter areas adjacent to plaques. Similar patterns of expression were seen in mice and rats representing a model of relapsing remitting and monophasic EAE, respectively.To test the relevance of OPN in MS, OPN-deficient mice were generated.These mice showed a reduced severity in EAE. Moreover, neutralization of OPN with neutralizing antibodies also led to an amelioration of EAE (Blom et al., 2003), validating the use of cDNA microarrays in the search for new therapeutic targets for MS.The upregulation of OPN levels in MS plaques (Tajouri et al., 2005) and in the circulation (Comabella et al., 2005;Vogt et al., 2003, 2004) of MS patients was replicated in independent studies, prompting the search for polymorphisms in the opn gene associated with MS. Although some controversy still remains (Caillier et al., 2003) polymorphisms in the opn locus have been associated with increased levels of circulating OPN and disease course (Chiocchetti et al., 2005). OPN is therefore an example of how results obtained in transcriptomics studies might lead to the identification of genetic polymorphisms linked to MS. Characterization of the Immune Response The analysis of the transcriptional profile could also be applied to study the immune response in MS, to monitor the progression of the disease and the response to therapy. Two points, however, should be kept in mind when considering the use of cDNA arrays for the analysis of the immune response in MS patients: First, these studies assume that changes in the periphery somehow reflect the ongoing situation within the CNS. Second, these studies are limited by the normal “noise” that exists in basal gene expression, originated from diverse factors such as the relative proportion of different blood cell subsets, gender, age and the time of day at which the sample was taken (Whitney et al., 2003). This variation is likely to impose severe limitations for the use of cDNA microarrays in a clinical setting.

Immunomics in MS

Follow up of Disease Activity A recent study by Achiron and co-workers followed the transcriptional activity of peripheral blood mononuclear cells (PBMC) prepared from relapsing-remitting MS (RRMS) patients during the relapses and remission of MS (Achiron et al., 2004). The authors identified a transcriptional signature associated to the relapse, that included genes involved in the recruitment of immune cells, epitope spreading and escape from immune-regulation. Although encouraging, these results should be validated using an independent set of samples and in longitudinal studies to asses their predictive value. Response to Therapy Gene expression profiling can also be used to classify patients according to different clinical criteria, such as responder or nonresponders to therapy. Interferon (IFN) is widely used for the treatment of MS (Kappos and Hartung, 2005), however, its precise mechanism of action is not known, and neither are biomarkers that would allow the identification of patients that will benefit from it. Weinstock-Guttman and colleagues used cDNA microarrays to study the effects of IFN therapy on the transcriptional activity of monocyte-depleted PBMC (Weinstock-Guttman et al., 2003).This pharmaco-dynamic study found that upon 1 h of IFN administration, significant changes are detected in the expression of genes involved in the antiviral response, IFN signaling and markers of lymphocyte activation. These studies provided a molecular description of the effects of IFN on RRMS patients and were later on extended to identify transcriptional signatures associated with a favorable response to treatment with IFN (Sturzebecher et al., 2003). Moreover, they demonstrated that MS patients showing a positive response to treatment with IFN, as assessed by longitudinal gadolinium-enhanced MRI scans and clinical disease activity, are characterized by specific patterns of gene expression (Sturzebecher et al., 2003). Based on these observations, Oksenberg and coworkers identified groups of genes whose expression has a prognostic value for the identification of MS patients likely to respond to treatment with IFN (Baranzini et al., 2005). The work of Oksenberg and coworkers is remarkable for two reasons: First, it demonstrates that gene expression profiling can be used in the management of MS to select a therapeutic regime suited to the patient’s metabolism. Second, it used a methodology (RTPCR) accessible to clinical laboratories, facilitating the translation of their results into daily medical practice. The combination of the data generated in transcriptomics and genomics studies can be an invaluable source of information and new hypotheses. Aune et al. compared the genes differentially expressed by lymphocytes in rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), insulin-dependent diabetes mellitus (IDDM) and MS, concluding that they are clustered within chromosomal domains in the genome (Aune et al., 2004). Strikingly, they found that the chromosomal domains containing the genes differentially expressed in autoimmune disorders could be mapped to disease susceptibility loci associated to those diseases by genetic linkage studies (Aune et al., 2004).

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These results suggest that the expression of disease-associated genes is co-regulated as a result of shared genetic regulatory elements or local patterns of chromatin condensation. Recently Baranzini and coworkers studied the genetic concordance between gene expression and genetic linkage in MS (Fernald et al., 2005). They first compiled the data on gene expression available for MS and EAE, and superimposed it with the all the known susceptibility loci identified in MS and EAE. In their study, Baranzini and coworkers identified the MS susceptibility genes located in the MHC locus as overlapping with clusters of differentially expressed genes in MS and murine EAE. However, they could also identify an interesting region on chromosome X that might contribute to the sexual dimorphism observed in MS. The integration of the data generated by different platforms, like transcriptomics, genomics and proteomics, is therefore likely to deepen our understanding of the mechanisms driving MS.

IMMUNOMICS IN MS The autoimmune nature of MS suggests that the study of the immune response should be useful for the early diagnosis, prognosis and monitoring of MS patients. With this aim, new tools have been developed, allowing a high-throughput analysis of the T cell- and antibody-mediated immune response. T Cell Response MHC arrays have been recently developed based on the immobilization of peptide/MHC tetramers used to activate peptidespecific T cells and follow cytokine secretion or adhesion (Chen et al., 2005; Soen et al., 2003). Their use for the study of human immunology is limited by the high frequency of polymorphisms existing on the MHC locus. Nevertheless, these arrays have been recently used to characterize the cellular response in tumor-vaccinated humans (Chen et al., 2005). Reverse phase arrays consist of arrays of spotted lysates prepared from primary cells, which are interrogated with antibodies specific for phosphorylated or de-phosphorylated proteins involved in signaling transduction pathways of interest (Chan et al., 2004). By combining different antibodies and inhibitors of specific signal transduction pathways, detailed signaling maps can be constructed describing the molecular events that lead to the activation of a specific cell population. This approach have been recently be used to study CD4CD25 regulatory T cells (Treg) (Chan et al., 2004). Since defects in Treg function have been described in MS patients (Viglietta et al., 2004), these arrays could be useful in the identification of signaling defects in the immune system of MS patients, and how these pathways are modified in immuno-modulatory regimes. Multidimensional FACS is based on the simultaneous computation of the signals produced by more than 16 colors, and their analysis using advance mathematical algorithms (Perfetto et al., 2004). This technique could be useful to interrogate the immune response to specific antigens using a combination of tetramers, intracellular cytokine staining and surface markers (Chattopadhyay et al., 2005).

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Cell migration arrays (Kuschel et al., 2006). To reach the CNS and cause tissue destruction, autoimmune T-cells have to express surface molecules that allow them to cross the BBB and initiate inflammation (Luster et al., 2005). The importance of this process for MS progression is shown by the beneficial effects that natalizumab, a humanized antibody to 4 integrin that interferes with T cell extravasation to the CNS and has showed promising preliminary results in the treatment of MS (Polman et al., 2006). Thus, the characterization of the adhesion properties exhibited by the different cell populations in MS patients might be of help to predict and monitor response to treatment and predict and prevent the development of relapses. B Cell Response Antibodies are of interest in MS because they might have a pathological role in MS (Genain et al., 1995), and more importantly, because antibody responses are thought to reflect the activity of the T cell compartment (Robinson et al., 2002b). These antibodies can be produced by B cells in the periphery and make their way to the inflamed CNS via the disrupted BBB, but they are also produced by intrathecal germinal centers that seem to form tertiary lymph nodes (Aloisi and Pujol-Borrell, 2006; Corcione et al., 2005). It is easier to assay antibody reactivity than to follow antigen-specific T-cell responses, thus new efforts have been invested in the development of new technologies for monitoring the humoral response in MS patients and autoimmunity (Quintana et al., 2004; Robinson et al., 2002a). Antigen arrays can be used to detect changes in the repertoire of antibodies reflecting the antigen spreading that accompanies EAE progression (Robinson et al., 2003). The information obtained about the degree of antigen spreading observed in each mouse was used to design tailored immunomodulatory vaccines to control EAE (Robinson et al., 2003). In humans, have been used to identify new lipid targets of antibodies present in the CSF of MS patients; some of the new lipid targets found were also validated on EAE (Kanter et al., 2006). Future experiments should study the antibody response in the serum of MS patients, searching for patterns of antibody reactivity that predict the progression of MS or the response to therapy, as has been shown for other autoimmune disorders, such as RA (Hueber et al., 2005), autoimmune diabetes (Quintana et al., 2004) and SLE (Li et al., 2005). Antigen arrays have also been shown to identify the individual mice that will develop autoimmune diabetes later in life by studying an experimental model that shows incomplete penetrance. Thus, antigen arrays might be useful for the identification of patients at risk of developing MS, before the overt onset of the symptoms (Quintana et al., 2004). In addition, antigen arrays might be used to interrogate the antibody repertoire in the search of new targets of the MS autoimmune attack and therefore, new targets for immuno-modulation. Indeed, we have preliminary data suggesting that antigen arrays might be used to identify antibody patterns linked to the different forms of MS and the pathology at the site of the lesion (Quintana and Weiner, unpublished results).

PROTEOMICS IN MS Proteomic studies in MS can identify new targets of the autoimmune process complementing the information provided by antigen array; they can also identify new processes contributing to disease pathology, and biomarkers for the early diagnosis and monitoring of MS patients. Identification of New Targets of Autoimmunity Using 2D gels on brain extracts, Almeras and coworkers identified 14 new targets recognized by antibodies present in CSF (Almeras et al., 2004). The targets identified by this study included heat shock proteins, structural proteins and enzymes involved in glucose metabolism (Almeras et al., 2004); although these results have been partially validated in the experimental model of MS EAE (Zephir et al., 2006), they are still waiting validation in an independent patient cohort. Identification of New Pathogenic Processes Proteomic studies have also identified new mechanisms that contribute to MS pathology. The existence of a link between Epstein-Barr Virus infection (EBV) and MS has been recently strengthened by the work of Cepok and co-workers, who used protein expression arrays to characterize the reactivity of antibodies in the CSF of MS patients, most of those antibodies recognized EBV epitopes (Cepok et al., 2005). These results suggest that EBV reactivation might elicit and abnormal immune response in susceptible individuals that contributes to MS (Sundstrom et al., 2004). Brinkmeier and coworkers analyzed the CSF of MS and Guillain-Barre Syndrome (GBS) patients to characterize molecules that might affect ion-channel function (Brinkmeier et al., 2000). They identified and endogenous pentapetide (QYNAD) that works as a reversible Na channel blocker (Brinkmeier et al., 2000). This pentapetide is present in the CSF of healthy individuals, but its levels are upregulated 3–14-fold in MS and GBS patients.This blocking peptide might be involved in the fast exacerbations and relapses commonly seen in demyelinating autoimmune diseases. Moreover, it might become a valuable marker of disease activity, and the target of future therapeutic interventions. These observations, however, could not be replicated by independent researchers (Cummins et al., 2003), highlighting the need for independent validation of proteomic findings. Identification of Biomarkers Proteomics can identify biomarkers useful in the monitoring of the different processes that contribute to MS pathology. In this direction, several studies have suggested that cytokines, chemokines, complement and adhesion molecules can be used as indicators of the inflammatory process in MS; while the levels of actin, tubulin, neurofilaments, tau, GFAP and S-100 proteins can be takes as indicators of axonal loss and gliosis (reviewed in Bielekova and Martin, 2004; Miller, 2004;Teunissen et al., 2005). However, for these to be of widespread clinical use, these markers

References

should be easy to collect and provide reproducible results. This motivation has boosted efforts aimed at finding markers of inflammation and neurodegeneration detectable in blood (Bielekova and Martin, 2004), urine (Bielekova and Martin, 2004) and tears (Devos et al., 2001); and simultaneously it has fostered the development of new technologies aimed at detecting minute amounts of proteins in body fluids (Zhang et al., 2006).

TABLE 85.1

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Use of different “Omics” applications in MS

Application

Use in MS

Genomics

Identification of genes controlling MS susceptibility and progression

Transcriptomics

Identification of new pathogenic mechanisms Follow up of disease activity

CONCLUSION How can we apply the information provided by genomics, transcriptomics, immunomics and proteomics to the early diagnosis, prevention, monitoring and therapy of MS? A first step is the adaptation of the technologies mentioned in this chapter to a clinical setting, in terms of costs and practicality. The personal genome project, for example, aims to develop within the next 10 years cheap, fast and reliable methods to sequence the whole genome of an individual for US$1000 or less (Shendure et al., 2004). This would facilitate the early detection of individuals at risk of developing MS, and while identifying genetic markers known to influence disease progression or the response to therapy. Similar projects are aimed at facilitating the proteomic analysis of clinical samples (Newcombe et al., 2005); a summary of the possible contribution of the specific “omics” applications to MS is depicted in Table 85.1. Note that each one of these technologies is focused on only one of the biochemical processes or layers that describe the organism in a particular physiological state. Thus, if we want to predict the behavior of such a complex biological system like the human body (its response to environmental stimuli, therapy, etc.) then it is imperative not only to identify all of the relevant cellular an molecular layers that make it up, but also to describe qualitatively and quantitatively how these layers interact. In other words, we need to know how the genomics, transcriptomics, immunomics and proteomics of an individual influence each other. How are the genomics and transcriptomics of MS-linked loci related (Aune et al., 2004; Fernald et al., 2005)? How is the transcriptional activity reflected in the immune response? How is that immune response related to the proteomic profile of the individual? How does the genomic makeup of an MS patient influence her/his response to therapy (Danesi et al.,

Prediction of the response to therapy Immunomics

MHC arrays: Study of T cell immunity Reverse phase arrays: Study of signaling pathways Multidimensional FACS: Characterization of cellular immunity Cell migration arrays: Analysis of factors controlling the chemotaxis Antigen arrays: Investigation of the antibody response

Proteomics

Identification of new pathological processes Biomarkers for disease monitoring/early diagnosis

2000)? Addressing these points will require the development of computational tools for the integration of networks and pathways into accurate quantitative models (Bauch and Superti-Furga, 2006; Hwang et al., 2005); and it will rely on graphical tools that facilitate the visualization of these models (Efroni et al., 2003). MS results from a complex dialog between a susceptible individual and a fostering environment. This dialogue is likely to be unique to each individual, and many of its words are currently unknown. However, genomics, transcriptomics, immunomics and proteomics will allow us to construct accurate models, to identify those missing words and prevent, diagnose and cure MS.

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Devos, D., Forzy, G., de Seze, J., Caillez, S., Louchart, P., Gallois, P. and Hautecoeur, P. (2001). Silver stained isoelectrophoresis of tears and cerebrospinal fluid in multiple sclerosis. J Neurol 248, 672–675. Dyment, D.A., Ebers, G.C. and Sadovnick, A.D. (2004). Genetics of multiple sclerosis. Lancet Neurol 3, 104–110. Efroni, S., Harel, D. and Cohen, I.R. (2003). Toward rigorous comprehension of biological complexity: Modeling, execution, and visualization of thymic T-cell maturation. Genome Res 13, 2485–2497. Fernald, G.H., Yeh, R.F., Hauser, S.L., Oksenberg, J.R. and Baranzini, S.E. (2005). Mapping gene activity in complex disorders: Integration of expression and genomic scans for multiple sclerosis. J Neuroimmunol 167, 157–169. Games, (2003). A meta-analysis of whole genome linkage screens in multiple sclerosis. J Neuroimmunol 143, 39–46. Genain, C.P., Nguyen, M.H., Letvin, N.L., Pearl, R., Davis, R.L., Adelman, M., Lees, M.B., Linington, C. and Hauser, S.L. (1995). Antibody facilitation of multiple sclerosis-like lesions in a nonhuman primate. J Clin Invest 96, 2966–2974. Hafler, D.A. and De Jager, P.L. (2005). Applying a new generation of genetic maps to understand human inflammatory disease. Nat Rev Immunol 5, 83–91. Hueber, W., Kidd, B.A., Tomooka, B.H., Lee, B.J., Bruce, B., Fries, J.F., Sonderstrup, G., Monach, P., Drijfhout, J.W., van Venrooij, W.J. et al. (2005). Antigen microarray profiling of autoantibodies in rheumatoid arthritis. Arthritis Rheum 52, 2645–2655. Hwang, D., Rust, A.G., Ramsey, S., Smith, J.J., Leslie, D.M.,Weston, A.D., de Atauri, P., Aitchison, J.D., Hood, L., Siegel, A.F. et al. (2005). A data integration methodology for systems biology. Proc Natl Acad Sci USA 102, 17296–17301. Jeffreys, A.J., Kauppi, L. and Neumann, R. (2001). Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nat Genet 29, 217–222. Jersild, C., Svejgaard, A. and Fog, T. (1972). HL-A antigens and multiple sclerosis. Lancet 1, 1240–1241. Kanter, J.L., Narayana, S., Ho, P.P., Catz, I., Warren, K.G., Sobel, R.A., Steinman, L. and Robinson, W.H. (2006). Lipid microarrays identify key mediators of autoimmune brain inflammation. Nat Med 12, 138–143. Kappos, L. and Hartung, H.P. (2005). 10 years of interferon beta-1b (Beta feron therapy. J Neurol 252(Suppl 3), iii1–iii2. Kuschel, C., Steuer, H., Maurer,A.N., Kanzok, B., Stoop, R. and Angres, B. (2006). Cell adhesion profiling using extracellular matrix protein microarrays. Biotechniques 40, 523–531. Lassmann, H. (1998). Neuropathology in multiple sclerosis: New concepts. Mult Scler 4, 93–98. Lassmann, H., Bruck, W. and Lucchinetti, C. (2001). Heterogeneity of multiple sclerosis pathogenesis: Implications for diagnosis and therapy. Trends Mol Med 7, 115–121. Li, Q.Z., Xie, C., Wu, T., Mackay, M., Aranow, C., Putterman, C. and Mohan, C. (2005). Identification of autoantibody clusters that best predict lupus disease activity using glomerular proteome arrays. J Clin Invest 115, 3428–3439. Lincoln, M.R., Montpetit, A., Cader, M.Z., Saarela, J., Dyment, D.A., Tiislar, M., Ferretti, V., Tienari, P.J., Sadovnick, A.D., Peltonen, L. et al. (2005). A predominant role for the HLA class II region in the association of the MHC region with multiple sclerosis. Nat Genet 37, 1108–1112. Epub 2005 Sep 25. Lindberg, R.L., De Groot, C.J., Certa, U., Ravid, R., Hoffmann, F., Kappos, L. and Leppert, D. (2004). Multiple sclerosis as a generalized

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CHAPTER

86 Inflammatory Bowel Disease Ad A. van Bodegraven and Cisca Wijmenga

INTRODUCTION Inflammatory bowel diseases (IBD) are a group of chronic inflammatory disorders of the gastrointestinal tract of unknown origin; they comprise three main entities: ulcerative colitis (UC), Crohn’s disease (CD), and indeterminate colitis. Indeterminate colitis represents a patient group that cannot be classified as either UC or CD, but which constitutes about 10% of IBD patients. Follow-up studies have shown that the majority of these patients eventually develop the features of UC. In addition, collagenous colitis and microscopic colitis have been described, although they will not be discussed in this chapter. Celiac disease is also a chronic inflammatory disorder of the gastrointestinal tract, but this disease is not usually included in IBD. The incidence of CD has increased 8- to 10-fold over the past 50 years (Bach, 2002), whereas the incidence of UC has remained stable. CD is mainly seen in urbanized, developed countries and the change in incidence is associated with improved hygiene or social standards in Western societies. In contrast, UC is a more global disease (Karlinger et al., 2000). The median age of patients with IBD at diagnosis has increased over time, partly due to a higher proportion of elderly patients (65 years of age), especially in UC. However, the incidence of CD is also increasing in patients younger than 17 years of age. The lifetime risk for Caucasians to develop UC is twice that of developing CD (0.15%) (Schreiber et al., 2005). Hence, the incidence of IBD varies with geographic location, industrialization and the availability of diagnostic means. It ranges from 0.5 to 24.5

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per 100,000 people per year for UC, and from 0.1 to 10 per 100,000 per year for CD (Gallop et al., 1988; Moum et al., 1996; Russel et al., 1998). There is an evident north–south gradient, with the highest incidence of IBD occurring in northern countries, such as Scandinavia and Scotland, followed by Northwest Europe (the Netherlands, United Kingdom) and the United States, and with the lowest reported incidences in southern, Mediterranean Europe (Greece, Crete, Italy). Recent data from developing countries show similar trends (Jiang et al., 2006). Apart from the differences in incidence rates of IBD around the world, national, and regional differences have also been observed (Ekbom et al., 1991; Latour et al., 1998; Timmer and Goebell, 1999; Timmer et al., 1999). Reported prevalence rates of UC and CD also vary widely in different studies, ranging from 21 to 234 per 100,000 for UC and from 12 to 146 per 100,000 for CD. Colorectal CD seems to be increasing, and in UC, proctitis and left-sided UC seem to be becoming more prevalent. Both diseases show variation in the individual clinical presentation and outcome that is most likely due to differences in genetic susceptibility, exposure to environmental factors, the commensal bacteria in the intestine, and the intestinal immune system. Even though CD and UC share some features, they also show major differences and it is still not clear whether both disorders are related at the molecular level, which would justify lumping them together as IBD. However, more and more evidence is emerging that a dysregulated mucosal immune response underlies the chronic intestinal inflammation, potentially initiated by a dysfunctional (immunological) intestinal barrier.

Copyright © 2009, Elsevier Inc. All rights reserved.

Predisposition (Genetic and Non-Genetic)

PREDISPOSITION (GENETIC AND NON-GENETIC) Current theories suggest that multiple factors, each of relatively weak effect, may act together to influence disease risk in IBD. Apart from genetic factors, there is evidence that supports an association between IBD and a large number of seemingly unrelated environmental factors, including smoking, oral contraception, diet, breastfeeding, drugs, geographical and social status, stress, microbial agents, intestinal permeability, and appendectomy (Corrao et al., 1998; Danese et al., 2004; Garcia Rodriguez et al., 2005; Loftus, 2004). However, only smoking has been confirmed as a clear environmental risk factor for IBD, protecting against UC but increasing the risk for CD (Cosnes, 2004). In addition, IBD has a strong genetic component, although this seems to be more pronounced in CD than in UC. The derived heritability in CD is higher than for many other common complex diseases. Evidence for a strong inherited predisposition to IBD susceptibility comes from twin studies and studies of familial aggregation (Peeters et al., 1996; Satsangi et al., 1994; Tysk et al., 1988). There is, however, no discernable Mendelian inheritance pattern; thus we must assume a complex pattern of inheritance in which both susceptibility genes and environmental factors may contribute to IBD pathogenesis. Twin studies provide a powerful means of assessing the contribution of both genetic and environmental factors to disease susceptibility (Martin et al., 1997). There are different measures of concordance used in such studies, but those based on National Twin Registries allow the individual twins to be selected independently of disease, so that they can then be assessed separately. In a recent study based on 38,507 identified twins born in Denmark from 1953 to 1982, the proband-wise concordance rates for UC and CD among monozygotic and dizygotic twins were estimated and found to be 58.3% for CD and 18.2% for UC among the monozygotic pairs, and 0% and 4.5%, respectively among the dizygotic pairs (Halfvarson et al., 2003; Orholm et al., 2000). The sibling relative risk provides a further measure of the heritability of IBD and is defined as the risk to an affected patient’s sibling divided by the population risk (prevalence). Estimates from German (Kuster et al., 1989) and northern France/Belgium (Laharie et al., 2001) populations suggest sibling relative risks for CD of 15–35. For a sibling of an UC patient, this risk is substantially lower: a Danish (Orholm et al., 1991) and Italian (Meucci et al., 1992) study showed relative sibling risks of 6–9 for UC. Another argument for a genetic contribution to IBD pathogenesis stems from differences in prevalence among different ethnic groups, with the highest rates found amongst Caucasians and people with an Ashkenazi Jewish background (Roth et al., 1989;Yang et al., 1993). Over the past 10 years substantial progress has been made in identifying susceptibility genes for IBD, using both genomewide linkage scans in affected sibling pairs and genetic association studies. To date, 14 genome-wide linkage scans have been

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conducted that have resulted in the identification of at least 9 loci (IBD1-IBD9). A recent meta-analysis on data from 10 genome-wide scans also revealed the IBD1, IBD3, IBD5, and IBD6 loci, as well as many novel loci (Van Heel et al., 2004). Unfortunately not all IBD susceptibility loci have been replicated consistently. Silverberg and colleagues showed that IBD is frequently misdiagnosed, thereby reducing the ability to detect linkage (Silverberg et al., 2001). A recent study using a phenotyped cohort of 904 affected relative pairs uncovered novel loci and also demonstrated that the IBD2 locus is an extensive UC locus (Achkar et al., 2006). The next step, to go from a linkage region to a disease susceptibility gene, has also proved to be difficult. The usual course is to switch from a family-based linkage design to a population-based genetic association design (Wild and Rioux, 2004). The latter requires large numbers of case/ control pairs and polymorphic markers able to capture all the genetic variation. The identification of the vast majority of common (1%) single nucleotide polymorphisms (SNP) and the correlation between them (“HapMap”) (Altshuler et al., 2005) will revolutionize such studies in future. From the established IBD linkage regions, only the disease susceptibility genes from IBD1 (CARD15/NOD2) and IBD5 (SLC22A4/SLC22A5) have been uncovered (Table 86.1). Both these genes confer risk of CD. The current status of genetic research in IBD has been discussed in excellent reviews by Bonen and Cho (2003), Wild and Rioux (2004), Newman and Siminovitch (2005), and Vermeire and Rutgeerts (2005). The CARD15/NOD2 gene was identified through both a classical positional cloning strategy (Hugot et al., 2001) and a positional and functional candidate gene approach (Hampe et al., 2001; Ogura et al., 2001). Mutations in CARD15/NOD2 are found in approximately 30% of CD patients. Although multiple variants in the gene can increase susceptibility only to CD, the most commonly identified mutations are the R702W, G908R, and L1007fsinsC. The increased risk conferred by mutations in the CARD15/NOD2 gene depends on the number of mutant alleles: heterozygosity increases the risk 2- to 3-fold, whereas homozygosity is associated with a 20- to 40-fold higher risk of developing CD. However, the frequencies of mutant CARD15/ NOD2 alleles vary considerably across European populations (Arnott et al., 2004) and show low frequencies in northern European countries (Arnott et al., 2004; Helio et al., 2003; Medici et al., 2006;Thjodleifsson et al., 2003). CARD15/NOD2 mutations are associated with a more severe form of the disease, an early age at onset, and ileal lesions (Abreu et al., 2002; Hampe et al., 2002; Lesage et al., 2002). CARD15, the caspase recruitment domain (CARD) family member 15 protein, is a member of the Nod1/Apaf-1 family and encodes a protein with two CARDs and six leucine-rich repeats (LRRs). The protein is primarily expressed in the peripheral blood leukocytes, but also in the intestinal epithelial layer, in particular in Paneth cells and associated with mucosal alpha-defensin expression (Ogura et al., 2003; Wehkamp et al., 2004). It plays also a role in the immune response to intracellular bacterial lipopolysaccharides (LPS) by recognizing the muramyl dipeptide (MDP) derived from them

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CHAPTER 86

TABLE 86.1

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Inflammatory Bowel Disease

Genes known or suspected of being involved in IBD pathogenesis

Replicated IBD genes/loci

Chromosomal location

Disease

Relative risk heterozygote/ homozygote

Alleged mechanism of action

Genotype/ phenotype correlation

CARD15/NOD2 (IBD1)

16q13

CD

2–4 versus 20–40

Innate immunity, balance of immunological response

Ileal involvement and earlier clinical presentation

DLG5

10q23

CD

1.5

Maintenance of epithelial barrier integrity

Ileal (combined with peri-anal)

SLC22A4/OCTN1, SLC22A5/OCTN2 (IBD5)

5q31–q33

CD

2.5 versus 4

Epithelial barrier

Multi-site, more severe phenotype

MYO9B

19p13

UC

1.2

Epithelial barrier

IL23R

1p31

IBD

0.26

Innate immunity

ATG16L1

2q37.1

CD

1.45

Mediating innate immune response to bacteria, interaction with NOD2

IBD2

12q14

UC

IBD3

6p21

IBD4

14q11–12

CD

MDR1/ABCB1

7q22

IBD?

Membrane transporter (xenobiotic handling)

IBD

Response to xenobiotics

IBD

Innate immunity

Both small intestinal (ileal CD) and large intestinal (UC) inflammation

Suggested IBD genes/loci

PXR NOD1/CARD4

7p14

TLR-9

3p21

TLR-4

?

Extensive UC

MHC associated immunological responses

Both small intestine and colonic disease locations Interaction with smoking

Early age at onset (25 y)

Mediating innate response, interaction with NOD2 IBD

TLR-5

Mediating innate immune response to gram-negative bacteria. Interaction with NOD2 Mediating innate immune response to flagellin

NF-kB1

4q

IBD

Pivotal mediator of generation proinflammatory cytokines

ICAM-1 (IBD6?)

19p13

IBD

Function of pivotal adhesion molecule

PPARg (IBD9?)

IBD

Innate response to intestinal bacteria

COX2

IBD

Inflammatory response

TNFSF15

Refractory (and extensive) disease

Pivotal pro-inflammatory cytokine

In CD: fistulizing disease. In UC: limited disease extent

Screening

and activating the NF-kB protein. Although its role in CD is not entirely elucidated, many leads point at involvement with the innate immune response. Fine-mapping of the IBD5 locus initially led to the identification of a unique haplotype of 250 kb (Rioux et al., 2001). Since the SNPs within the haplotype were in strong linkage disequilibrium, they all conferred risk to CD. The association to the 250 kb haplotype has been replicated extensively, thereby confirming the importance of the IBD5 locus. Subsequently, extensive sequence analysis revealed two variants within the organic cation transport genes SLC22A4/OCTN1 and SLC22A5/ OCTN2, which have been proposed to alter the gene function and expression and to form a haplotype associated with CD risk (Peltekova et al., 2004). Because of the strong linkage disequilibrium across the entire 250 kb haplotype, it remains to be proven if the two identified variants act independently of the surrounding SNPs (reviewed in Reinhard and Rioux, 2006). SLC22A4/ OCTN1 and SLC22A5/OCTN2 are involved in the active cellular uptake of carnitine across the intestinal epithelial layer. The role of the SLC22A4/OCTN1 and SLC22A5/OCTN2 genes in CD pathology is not clear, but they are thought to act through an inappropriate inflammatory host response to commensal flora. The fine-mapping of an IBD locus on 10q23 (Hampe et al., 1999) resulted in the identification of the DLG5 gene, a member of the family of discs large (DLG) homologs (Stoll et al., 2004) and the membrane-associated guanylate kinase (MAGUK) superfamily. DLG5 localizes to the plasma membrane and cytoplasm and interacts with components of adherens junctions and the cytoskeleton. A function in the transmission of extracellular signals to the cytoskeleton and in maintaining epithelial cell structure has been proposed. The association with DLG5 varies significantly across populations (Buning et al., 2006; Daly et al., 2005; Ferraris et al., 2006; Gazouli et al., 2005; Medici et al., 2006; Newman et al., 2006; Noble et al., 2000a;Tenesa et al., 2006; Torok et al., 2005; Tremelling et al., 2006;Vermeire et al., 2005;Yamazaki et al., 2004). More recently, the celiac disease-associated gene MYO9B (Monsuur et al., 2005), which localizes to the IBD6 locus, was shown to be associated with IBD in four different populations (Van Bodegraven et al., 2006). Table 86.1 gives an overview of the different genes known or suspected of being involved in IBD pathogenesis. The picture that is emerging suggests that genes involved in IBD are either important in the innate immune response to commensal bacteria or involved in the maintenance of the epithelial barrier. More recently it has become possible to perform genomewide genetic association studies. Using 300,000 SNPs a highly significant association was found between CD and SNPs in the gene encoding a subunit of the proinflammatory cytokine IL23 (IL-23R) (Duerr et al., 2006). An uncommon coding variant within the IL-23R gene strongly protects against CD. The proinflammatory cytokine IL23 is considered to be a driver of innate immune pathology in the intestine. A second genome-wide study using nearly 20,000 nonsynonymous SNPs, found a strong association between CD and the autophagy-related 16-like 1 gene

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(ATG16L1) encoding a protein in the autophagosome pathway that processes intracellular bacteria (Hampe et al., 2007). The association of both IL23R and ATG16L1 to CD fits in the emerging picture that genes involved in IBD are important in the innate immune response to commensal bacteria.

SCREENING Screening for IBD can either be performed in high-risk families, or in the population at large. Apart from the general remarks on screening in a specific cohort, in particular for a disease with no known cure, risk factors have to be available. In affected families we can use the genetic risk factors to identify people at risk of developing IBD. A number of potential genetic risk factors have already been described that predispose to IBD, with CARD15/NOD2 being the most commonly-associated gene. Unfortunately, the three common CARD15/NOD2 risk variants have low sensitivity (38.5%) and rather low specificity (88.6%) for preventive screening of CD (Chamaillard et al., 2006). As there are no strategies for influencing the risk profile to attract disease, screening for this or any other risk gene does not yet contribute to diagnostic or treatment strategies for a (potential) patient. In addition, several biomarkers have been described that are present in both IBD patients and in unaffected family members; these include increased intestinal permeability, anti-Saccharomyces cerevisiae antibodies (ASCA), and perinuclear antineutrophil cytoplasmatic antibodies (pANCA). In addition to these three markers, antibodies against microbial substances such as anaerobic coccoid rods, Pseudomonas fluorescens (I2), flagellins, and the outer membrane porin C of Escherichia coli (OmpC) have also been described in affected IBD patients (Bouma et al., 1999; Oudkerk Pool et al., 1993; Shanahan et al., 1992;Vermeire et al., 2001). It is interesting that these markers all point to the underlying etiopathogenetic hypothesis of IBD, namely a dysfunctional intestinal barrier function, leading to a dysfunctional handling of macromolecules and (intestinal) bacterial antigens, finally leading to an exaggerated immunological intestinal mucosal response, whether at the level of antigen-presenting cells or of cells involved in acquired immunological reactions (Fasano et al., 2005; Schreiber et al., 2005). Whereas serum pANCAs are mainly associated with UC (Oudkerk Pool et al., 1993), ASCA have particularly been associated with CD. S. cerevisiae, present in baker’s yeast, is found in a wide variety of foods, and thus are an antigenic challenge to most people. Although it has been hypothesized that individuals characterized by an increased leakage of S. cerevisiae antigens generate ASCA, this hypothesis does not hold true for other commonly present, similar antigens, such as Candida albicans, which are not regularly found in CD patients. Although these markers can be used in combination for some degree of differentiation between UC and CD (see also the Diagnosis section below), the presence of these types of markers has not been associated with a particular course of the disease, nor do they have enough sensitivity and specificity to help delineate non-affected people at risk of developing IBD (Yang et al., 1995).

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DIAGNOSIS UC and CD are clinically distinct disorders that are also differentially diagnosed. Recent research is aimed at genotype– phenotype associations in order to improve clinical assessment and treatment of patients. To interpret these genotype–phenotype data, phenotypical assessment has to be of outstanding and reproducible quality. Efforts to clearly define different subtypes of IBD have been undertaken by a group of IBD-experts at the World Congress of Gastroenterology 2005, Montreal, leading to the Montreal classification (Silverberg et al., 2005). Ulcerative Colitis UC is clinically characterized by urgency, bloody stools, a high-defecation frequency and abdominal discomfort, and it is commonly associated with fever and weight loss. In general, the severity of symptoms parallels the intensity and extent of inflammation. The inflammatory reaction primarily involves the mucosa of the large bowel, characteristically appearing as a continuous inflammation from the anal verge to the more proximal colon with a sharp demarcation to apparently normal mucosa. Microscopic examination reveals erosions, ulcerations, cryptitis, crypt abscesses, and infiltration of polymorphonuclear granulocytes, plasma cells, lymphocytes, and eosinophils in the lamina propria, accompanied by mucin depletion. UC can be diagnosed by a combination of characteristic clinical, endoscopic, and pathohistological findings. Corroborating findings may be observed in blood chemistry (inflammatory response, iron-deficiency or chronic-disease associated anemia), in feces analysis (markers of inflammation, such as calprotectin), in the absence of microbial pathogens, and in serological findings, in particular the presence of pANCA. Endoscopic findings are invaluable for the subclassification of the disease, because this depends on disease extent. The role of radiological examination is limited in UC patients (Hanauer, 2004). Classification of UC depends on disease extent, with subclasses of distal disease confined to the rectum (proctitis) or rectum and sigmoid colon (proctosigmoiditis), left-sided disease (from anal verge to splenic flexure), and universal or pancolitis, referring to disease proximal to the splenic flexure and usually involving the complete colon (pancolitis), in the new Montreal classification referred as E1-3. Severity of disease is classically assessed by the Truelove and Witts score, but several other combined scores are available. Crohn’s Disease CD is an inflammatory disease of the whole intestinal tract, thus leading to a broad spectrum of clinical presentations. Its most classical course is a localized inflammation of the terminal ileum, causing periods of relapsing abdominal pain with gradual weight loss, followed after months to years by postprandrial pain with colitis due to intestinal obstruction. Involvement is limited to the colon, which was only acknowledged in the middle of the 20th century, although it presents with the same symptoms as UC,

except that rectal bleeding is less pronounced. Increasingly, colorectal disease has been reported over time in several epidemiological studies. Furthermore, oral (granulomatous) ulceration or cheilitis, esophageal inflammation with difficulty of food passage and retrosternal pain, epigastrial pain and nausea due to gastritis or duodenal inflammation may all occur. Inflammation due to CD may extend over the whole bowel wall, but is frequently discontinuous, with alternating normal and affected areas. The transmural inflammation may lead to fistula formation between bowel segments, or between bowel and skin, bladder, or vagina. Peri-anal fistulas are relatively common.The macroscopic appearance encompasses areas of inflammation with erythema, exudate and aphtoid ulceration that may confluence and form large, deep tramline ulcers, or – when ulcers fuse transversely – cobblestones. Subsequent healing may lead to strictures. Characteristically, these areas of inflammation are patchy, whereas continuous inflammation, especially of the colon, may occur. The hallmark early microscopic lesion is an aphtoid ulcer; histopathologic ulcer features in a surrounding with unaffected, adjacent crypts. Focal mucosal disease is a pivotal histopathologic finding: focal cryptitis, focal chronic inflammation, isolated (acute) terminal ileitis all fit in with CD. Granulomas, localized well-formed aggregates of epitheloid histiocytes preferably in the presence of giant cells or a surrounding cuff of lymphocytes, are regularly found. CD can be diagnosed by the combination of characteristic clinical, endoscopic, and pathohistological findings. Again, additional findings may be found in blood chemistry (inflammatory response, iron deficiency or chronic disease-associated anemia), in feces (calprotectin), in the absence of microbial pathogens, and in serological findings, in particular the presence of ASCA. The role of radiological examination is complimentary to endoscopic findings in CD patients. Small bowel diagnostics still rely on classical radiological methods such as small bowel follow-through and enteroclysis. Recently, newer techniques have begun replacing the old ones. These new techniques include CT scans and MRI scans, preferentially enhanced by intra-intestinal and vascular contrast. In addition, a video capsule has been developed, introducing visual assessment of the small bowel and leading to a more accurate small bowel assessment, while double-balloon endoscopy has provided endoscopic access to the complete small bowel since 2001, allowing sampling for biopsy specimens as well as visual assessment. CD is usually classified by a combination of age, disease localization, and disease behavior, detailed in the Vienna classification (Gasche et al., 2000). In 2005 the Vienna classification was updated to become the Montreal classification (Silverberg et al., 2005). Severity of CD is most commonly assessed by the CD activity index (CDAI), a score that calculates disease activity for eight components, some of which are monitored over 7 days: stool frequency, general well-being, abdominal discomfort, erythrocyte sedimentation rate (ESR), weight, extra intestinal symptoms, use of antidiarrheal drugs, and fever. The validity of such a subjective, 7 days’ score has been discussed extensively. An instant bedside or bureau-side method is the Harvey-Bradshaw (HB) score that is highly related with CDAI, comprising frequency of liquid stools,

Prognosis

general well-being, abdominal pain, complications (extra-intestinal symptoms), and presence of an abdominal mass.This handy score is less widely used than CDAI in clinical (scientific) practice, partly due to EMEA and FDA regulations on drug registration. In order to avoid any subjective measure in a severity assessment score, a Disease activity score (DAI) of laboratory values and calculable physical signs was drawn up. The main problem with this score is its cumbersome calculation. CDAI and DAI are only related to a small extent. It is of interest that in recent clinical trials with biologicals patients were stratified for elevated CDAI score as well as an increased C-reactive protein (CRP) concentration as an indicator the acute phase response. Apparently, CDAI calculation underscores inflammatory variables. A solely subjective score reflecting the quality of life of IBD patients has been validated with the same purpose; assessment of disease activity and response to therapy. The most regularly used score is the Inflammatory Bowel Disease Questionnaire (IBDQ) a 32-item questionnaire that evaluates quality of life using four dimensions: bowel complaints, general malaise, social impact, and emotional burden. Serological markers may be helpful in identifying patients with IBD. pANCA is expressed in the majority of UC patients, varying from 30–85%, whereas only 4–30% of CD patients show this profile, and then, particularly, in patients with CD confined to the colon, making differentiation between UC and CD more difficult. Patients suffering from concurrent primary sclerosing cholangitis (PSC) have a higher prevalence of pANCA. Positive reactions against ASCA, identifying antibodies against yeast cell wall phosphopeptidomains, are more commonly found in CD patients (50–70%) than in UC patients (6–14%), and healthy controls (0–5%). IgA type antibodies appear to have a higher specificity for CD, but ASCA is also present in celiac disease (up to 30%), ankylosing spondylitis, autoimmune hepatitis, and others. The presence in various IBD populations – and their family members – of other detected microbial antibodies, such as against the outer membrane porin C of E. coli (OmpC), antibodies against P. fluorescens (I2) and several more has not been studied sufficiently to be of use in diagnosing disease. The origin and significance of these antibodies are yet unknown. The genetic background of IBD patients varies greatly, making the presence of the susceptibility genes already described insufficient to diagnose IBD, even in cohorts of people at risk, such as family members. Two well-described examples of these risk genes that are (as yet) unsuitable for establishing a diagnosis are HLA-DRB1*0103 for UC in Caucasians, HLA-DRB1*1502 in Japanese and CARD15/NOD2 in CD. Genetic markers are therefore not currently used in the diagnostic process of IBD. Despite the differences in clinical characteristics between CD and UC, some 10% of patients are diagnosed as unclassified or indeterminate colitis (depending on whether surgery, i.e., proctocolectomy, has been performed) as they cannot be classified as either CD or UC. Up till now, no serological, genetic or other characteristics can distinguish with enough accuracy between the three IBD-diagnoses, UC, CD, and unclassified colitis. A recent study showed that gene expression on peripheral blood monocytes can provide biomarkers to distinguish CD and UC patients

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(Burczynski et al., 2006). Although these results need to be validated and replicated in independent and prospective cohorts, this type of diagnostic tools holds great promise for the future.

PROGNOSIS The course of UC is usually relapsing, a disease pattern occurring in about 90% (Langholz et al., 1994). About 10% will have only one period of active disease in a 25-year time span and the probability of chronic active UC is also low (20%) (Langholz et al., 1994; Witte et al., 2000). Risk factors precipitating relapses are use of aspirin or NSAID, enteric infection, and seasonal variation, possibly related to stimulation of the (eosinophils-mediated) immune system. Smoking and appendectomy appear to be protective (Andres and Friedman, 1999). About 25–40% of UC patients will require surgery in the short- or long-term (Turner et al., 2007). Chronic active, medical-therapy refractory UC is considered to be a proper indication for surgery, usually proctocolectomy in combination with an ileo-anal pouch reconstruction. Prior to surgery, high-dose intravenous corticosteroid therapy up to 60 mg prednisolone-equivalents daily is usual and welldocumented to be effective in about two-third of patients (Turner et al., 2007). Predictors of surgical intervention are disease extent, clinical signs of severe systemic disease involvement (such as stool frequency, temperature, heart rate), and inflammatory variables like serum CRP and albumin concentration, whereas fecal calprotectin concentration holds promise for the future (Turner et al., 2007). In addition, fulminant active disease, concurrent development of colonic adenocarcinoma, and complications due to active disease such as intractable colonic bleeding or stenosis are other indications for surgery. Although observed in large cohorts, the increased risk of developing colonic adenocarcinoma is relatively limited. Risk factors for colonic adenocarcinoma comprise the presence of primary sclerosing cholangitis (PSC), and non-responsive disease of the universal colon. This increased risk comes on top of general markers of an increased risk for the development of colonic carcinoma, such as a positive family history. The debate of an increased risk of lymphoma in IBD patients is still ongoing, with confounding factors of chronic disease, use of immunosuppressive and other methodological difficulties. Markers for the course of UC comprise both serological and genetic markers. Although the presence of pANCA has neither been associated with disease localization, disease severity, nor response to therapy, it is more commonly found in patients with concurrent PSC. The latter has been associated with development of colonic (and cholangio-) adenocarcinoma. Genetic markers of disease severity include HLA DRB1*0103, IKBL738, and hMLH1 655A  G. The HLA DRB1*0103allele has been associated with a severe course of UC, including non-response to corticosteroids and necessity of surgery (Satsangi et al., 1996; Roussomoustakaki et al., 1997), and similar findings were reported in a Spanish UC cohort for the presence of IKBL738 (de la Concha et al., 2000). The hMLH1 gene might be involved in genetic susceptibility to refractory UC as the GG

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genotype at position 655 of the hMLH1 gene was almost 5 times more frequent in refractory UC patients compared with nonrefractory patients (Bagnoli et al., 2004). These and other combinations of genes have been associated with clinical course; however most studies could not be reproduced or concerned rather small patient cohorts, limiting the clinical significance in daily practice. The course and prognosis of CD has been described extensively by Munkholm et al., based on large population-based cohorts from Denmark (Munkholm et al., 1994, 1997; Langholz et al., 1997). These population studies are excellent for extracting data on disease course, but refer mainly to this particular Nordic population so that extrapolation to other populations remains difficult. Nevertheless, these data reveal that the course of disease during the first year after diagnosis predicts the course in the following 5 years. Being quiescent in the first year gives rise to a quiescent disease course in subsequent years in 44% of CD patients; only 8% have an active course of disease. The remaining 48% suffers from a relapsing disease course. In patients with an active disease in the first year following diagnosis, these numbers are 5% and 45%, with 50% having a relapsing course. A second approach is to subclassify according to the proposals from a working group of IBD experts in Vienna, the so-called Vienna classification (Gasche et al., 2000), later updated in Montreal (Silverberg et al., 2005). In particular, patients with a diagnosis at young age and those suffering from penetrating disease have a worse disease outcome. Surgery, as a measure of failure of medical therapy, and thus of a disadvantageous course of CD, is still necessary in about 70% of patients. This high percentage has not yet dropped, notwithstanding new therapies, showing that truly diseasemodifying treatment approaches still need to be developed. Many efforts have been undertaken to identify predictors of disease course allowing for adapted therapeutic approaches. Most, however, have restricted clinical value. ASCA positivity is associated with small intestinal localization of disease (terminal ileum), and correlated with young age of diagnosis of CD. Presence of IgG and IgA antibodies is associated with a stenotizing or penetrating type of disease behavior and less often with a chronic inflammatory colonic type of CD (Abreu et al., 2002; Mow et al., 2004). In phenotype-genotype studies, the course of CD is usually classified according to anatomic localization, disease behavior, and age of onset (according to the Vienna classification). There is no evidence, however, that this subclassification constitutes nosologic entities. In addition, the Vienna classification varies over time due to the developing course of disease (Louis et al., 2001). However, the validity and rigor of phenotyping are major factors in the adequate interpretation of correlations with an alleged genetic background. Thus interpretation of genotype studies to predict disease course is proving difficult, and fixed time spans have been suggested in the Montreal classification before a phenotype can be reliably assessed. Immediately following the description of CARD15/NOD2, several groups reported a phenotypic correlation of one of the major mutated CARD15/NOD2 alleles with localization of CD in the ileum, a finding that has been corroborated repeatedly. Surgery and a stricturing phenotype of CD have also been reported in several

series (Alvarez-Lobos et al., 2005; Russell et al., 2005), whereas, interestingly, the CARD15/NOD2 genotype did not predict response to the potent anti-TNF-alpha drug infliximab. Other genes reported to be associated with CD, including the infrequent HLA-DRB1*03, SLC22A4/OTCN1 and SLC22A5/ OCTN2 (Noble et al., 2005;Vermeire et al., 2005), and the IBD 5 (Latiano et al., 2006) locus, have been related to a variety of phenotypes, including earlier age of disease onset, peri-anal (fistulizing) disease, ileal or colonic disease, or reduced need of surgery; there are no corroborating data from large cohorts. Extra-intestinal manifestations have been reported since the very beginning that IBD has been recognized as a disease entity. Extra-intestinal complications comprise inflammatory changes of joints, eyes, cutis, and liver, but also thrombo-embolic manifestations and osteoporosis. Several studies have reported an overall incidence in 25–35% of IBD patients. Symptoms may be confined to any single organ system, although combinations of organ systems can also occur. A wide variety of rare manifestations have been reported. Most frequently the joints are affected with arthralgia and peripheral or axial arthritis. Other regular manifestations involve the skin, showing erythema nodosum or pyoderma gangrenosum, the eyes with inflammatory reactions such as (epi)scleritis and uveitis, the bone with osteopenia or even osteoporosis, the liver with PSC, leading to an increased risk of cholangiocarcinoma, the hematological tract with anemia and thrombocytopenia, and finally the vascular system with thrombo-embolic events. In addition, many autoimmune disorders may be associated with IBD. Some of these manifestations or concurrent diseases may reflect a common intestinal pathology, with a common pathway immunological response. Hence, IBD might be considered a systemic disorder disease, which may have implications for the proper recognition of patients at risk for IBD, especially when the extra-intestinal manifestations or concurrent diseases precede the development of IBD. It is likely that genetic factors contribute to expression of this highly variable clinical picture, but few studies have been published. A correlation between the phenotype of IBD-associated arthropathy and HLA alleles has been reported, with HLA-B27 and HLA-B35 related to complaints of large joints and HLAB44 to small joints (Orchard et al., 2000). The same group of investigators ascribed a role for HLA alleles in the occurrence of ocular and cutaneous symptoms (Orchard et al., 2002). Other genetic factors have been associated with bone loss in IBD patients. Non-carriage of the 240-base pair allele of the IL-1ra gene and carriage of the 130-base pair allele of IL-6 were independent of clinical severity of disease and application of corticosteroids associated with increased bone loss in a group of 83 IBD patients (Schulte et al., 2000). It is likely that larger, well-phenotyped cohorts of IBD patients will allow for better study of genetic markers for extraintestinal manifestations, as may be deduced from efforts with specific IBD-associated containing chips, the so-called “IBDChip” (Sans et al., 2006). The IBDchip is the world’s first diagnostic DNA chip and is currently being validated in the European community. The chip contains 61 known polymorphic alleles,

Monitoring

with alleged association with diagnosis and course of IBD and is aimed at a better prediction of prognosis and response to therapy of patients suffering from IBD.

PHARMACOGENOMICS A broader understanding of the IBD disease pathology will deliver novel drug targets for disease intervention. It is anticipated that genomics and proteomics technologies will assist in compound identification in drug discovery. Infliximab, a chimeric monoclonal antibody against TNF-alpha, is such a target that neutralizes one of the critical inflammatory mediators in IBD. Nevertheless, the drug is only effective in 40–66% of patients after 2 years of treatment (Hanauer et al., 2002; Present et al., 1999; Sands et al., 2004). It is clear that drug response, in general, is partly genetically determined. Carriage of CARD15/ NOD2 seems to play no role in response to infliximab (Mascheretti et al., 2002;Vermeire et al., 2002), whereas another study on CD patients who had received infliximab showed that patients homozygous for the V allele of the FcgammaRIIIa-158 polymorphism had a better biological and possibly better clinical response to infliximab (Louis et al., 2004). In addition, there has been a suggestion that the IBD5 locus is involved in response to infliximab (Urcelay et al., 2005). Recently there has also been a report suggesting that the lack of response to infliximab can, in part, be attributed to certain genetic polymorphisms in two apoptosis genes, Fas ligand and caspase-9 (Hlavaty et al., 2005). Additional studies are needed to corroborate these findings. IBD patients homozygous for the methylenetetrahydrofolate reductase (MTHFR) 1298C allele are more likely to experience side effects than patients homozygous for the wild-type A allele (21.0% versus 6.3%, P 0.05) when treated with the immunosuppressor methotrexate (MTX) (Herrlinger et al., 2005). This is in contrast to what was found in patients with rheumatoid arthritis where the MTHFR 1298A allele was associated with MTX-related adverse events in both Caucasians and African-Americans (Odds Ratio [OR] 15.86, 95% CI 1.51-167.01; p  0.021) (Hughes et al., 2006). Similar results were observed in a Japanese study on rheumatoid arthritis, which showed that subjects with the 1298A allele had a higher frequency of side effects from MTX (P 0.05, RR  1.42, 95% CI 1.11-1.82) (Urano et al., 2002). These conflicting results raise some concern and require additional validation studies as well as a better understanding of how MTX works. A more well-established polymorphism influencing drug response is the thiopurine methyl transferase gene (TPMT) in relation to the outcome of therapy with thiopurines, of which azathioprine and 6-mercaptopurine are frequently used in IBD patients. The metabolism of azathioprine or 6-mercaptopurine is rather complicated, involving various enzymatic steps with interindividual differences in genotype and phenotype. Essentially, the therapeutic activity of thiopurines is related to the concentration of the metabolite 6-thioguanine nucleosides (6-TGN), probably mediated by the phosphorylated forms of this metabolite (Neurath et al., 2005; Poppe et al., 2006). The

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pivotal enzyme TPMT methylates 6-mercaptopurine and its metabolite 6-thioinosinemonophosphate (6-TIMP) into 6methylmercaptopurine (6-MMP) and 6-methyl-thioinosinemonophosphate (6-MTIMP), respectively. The activity of TPMT is genetically regulated (Krynetski et al., 1996;Yates et al., 1997). Approximately 1 in 9 patients is heterozygous for the common polymorphisms, and 1 in 300 patients is homozygous. Patients with one mutant (dysfunctional) TPMT allele have a diminished TPMT activity and patients with two non-functional mutant alleles have no TPMT activity. Low TPMT activity will result in an increased amount of azathioprine or 6-mercaptopurine being metabolized by hypoxanthine phosphoribosyl transferase (HGPRT), resulting in high levels of 6TGN. Hence, TPMT deficiency leads to a potentially life-threatening myelo-suppression as 6-TGN accumulates. TPMT with a higher than average activity leads to generation of high levels of 6-MMP, which has been associated with hepatotoxicity in one study in a specific group of children suffering from CD (Dubinsky et al., 2000), and 6-MTIMP, and low levels of 6TGN. This is associated with therapeutic inefficacy. Taken together, determination of the TPMT alleles may be helpful in predicting response to thiopurine therapy and in assessment of risk of myelotoxicity or hepatotoxicity. Its determination prior to treatment with thiopurines has therefore been advocated. Nevertheless, other factors contribute to adverse events of thiopurines, as has been shown by retrospective analysis of more than 40 IBD patients in whom thiopurines had to be withdrawn due to myelotoxicity. Of these the majority had wild-type TPMT alleles, although most patients with myelodepression in the first weeks of treatment were homozygous for mutant alleles (Colombel et al., 2000). The response to glucocorticosteroids also seems to be partly genetically determined. Polymorphisms in the multidrugresistance gene 1 (MDR-1) are excellent candidates to affect the absorption and concentrations of MDR-1 substrates (Farrell et al., 2000; Hoffmeyer et al., 2000). In a study in Hungarian patients it has been suggested that the DLG5 113A allele – which was shown not to be associated with disease susceptibility – may confer resistance to steroids (Lakatos et al., 2006). With the advanced understanding of human genetic variation, it is now possible to perform genome-wide association studies in large cohorts of responders and non-responders to identify genetic risk and efficacy factors that are related to the effects of such drugs; this is similar to what is being done to identify disease susceptibility genes. Such research may have broad implications since it is likely that the response to a particular drug will not be restricted to the IBD phenotype. Hence, in future, these genetic profiles may help tailor disease treatment and patient care by increasing efficacy and preventing adverse drug reactions (Egan et al., 2006).

MONITORING Disease profiling by genetic characteristics has not been established as a clinical option for IBD. Although various predictors

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of disease susceptibility and disease course have been identified, they do not have enough sensitivity and specificity to be useful in daily clinical practice (Mascheretti et al., 2005; Vermeire, 2004). Nevertheless, genomics holds promise for disease management: in particular in identifying patients with a severe disease course, in whom a more aggressive treatment approach may be justified. For this type of patients it might be more beneficial to initiate top-down strategies and start with biological and immunosuppressive treatment immediately following diagnosis of disease, in order to improve the disease course (Hommes et al., 2006). The current treatment strategies start by using less aggressive drugs such as mesalazine, before treatment with corticosteroids or immunosuppressives). In addition, patients at risk of developing intestinal adenocarcinoma due to chronic inflammatory disease may be characterized by genetic profiles of adenomatous intestinal tissue harvested during surveilance endoscopy, or by examination of the genetic characteristics of feces. The latter development is currently being examined in surveillance programs of the general population.

NOVEL AND EMERGING THERAPEUTICS Approximately 25% of UC patients and up to 70% of patients with CD will undergo intestinal surgery in the course of their disease. In UC, this usually involves proctocolectomy followed by intra-anal pouch reconstruction. In CD, stenotized, inflamed, or fistulized tissue has to be removed while employing surgical techniques to rescue as much intestine as possible. Remarkably, the course of IBD has not been changed during the last few decades, and more intensified use of immunosuppressives in recent years has not led to a change in the percentage of patients being operated upon. The current medical strategy particularly involves immunomodulation, usually apoptosis inducing, with corticosteroids, immunosuppressives, such as azathioprine, 6-mercaptopurine, methotrexate and cyclosporine, and more recently biologicals, of which the archetypical anti-TNF-alpha drug infliximab has become firmly established. Enhanced understanding of IBD pathogenesis may result in novel therapies. New therapy strategies involve the different aspects of the alleged pathogenesis of IBD. It is evident that potential new avenues of therapy will focus on those aspects of the disease that can be modulated: the commensal bacteria in the intestine and the intestinal immune system. Several approaches have been investigated to dampen the intestinal and systemic immunological response. One approach involves diminishing the exposure of antigens to the immunological system. Because IBD is at least partly associated with increased intestinal leakage, we can correct the intestinal barrier dysfunction, as demonstrated by using drugs such as infliximab (Suenaert et al., 2002), by probiotics (reviewed in Jenkins et al., 2005; Penner et al., 2005), or by improving the nutritional status (Hulsewe et al., 2004). Induction of oral tolerance, inducing immune tolerance or systemic hyporesponsiveness, by feeding

proteins is another approach, with beneficial effects demonstrated in mice colitis-models (Neurath et al.,1996) and small human series (Israeli et al., 2005; Margalit et al., 2006). However, most therapeutic strategies in IBD patients are directed at rebalancing the exaggerated immunological response, either by inducing apoptosis or by modifying the leukocyte response or leukocyte trafficking. These concepts are based on resetting the balance between various potential T-cell responses of the lamina propria of the intestine against antigens. This is usually simplified into a T-helper 1 (Th1) and T-helper 2 (Th2) scheme, both T-cell types characterized by their specific cytokines. This T-cell repertoire is directed by pivotal immunological players such as antigen-presenting cells (dendritic cells), T-regulatory cells and many others. This emerging field of cytokine interaction is expanding rapidly and holds the promise of new therapeutic developments (Tato and O’Shea, 2006). Immunomodulation by T-cell repertoire redirection using cytokines and anti-cytokines is an avenue of IBD treatment that can be expanded, especially since a proportion of patients does not respond to the current immunomodulatory therapies. Current (anti-)cytokine therapies involve IL-10, IL-11, IL-12, and TGF-B, amongst others. This type of therapy is characterized by its aim to dampen an exaggerated Th1-balanced or Th2-balanced immunological response (Bouma and Strober, 2003). Many various ways of cytokine interaction have been proposed and examined, including use of monoclonal antibodies or RNA silencing by means of oligonucleotides. An exciting new approach is intestinal IL-10 delivery by means of genetically engineered, transgenic Lactococcus lactis-species (Braat et al., 2005). Providing a blockade of chemokines may result in similar immunomodulation, either by response modification (from Th2 to Th1 or vice versa) or tolerance. Chemokine therapy, whether by blockade, monoclonal antibodies or silencing oligonucleotides, is currently being investigated in various ways. It has been well established that the migration of leukocytes from blood circulation into the intestinal tract is mediated in part by 4-integrins. In animal models antibody-mediated inhibition of 47-integrins reduces inflammation (Hesterberg et al., 1996). In humans natalizumab, an antagonist that blocks 4-integrin, has shown to be effective in active CD patients (Ghosh et al., 2003). However, the drug exhibits mechanism-based toxicities which makes the drug an unattractive therapeutic agent (Sandborn et al., 2005). Recently, mice carrying a mutated 4-integrin have been developed that allow normal development of the immune system, but they were defective in 4-integrin signaling that normally triggers the inflammatory reaction (Feral et al., 2006). This work suggests that proteins that interfere with 4-integrin signaling might be more promising targets for drug development. Anti-ICAM therapy (alicaforsen) uses a similar type of blockade, which is directed against intracellular adhesion molecules (ICAM-1) and contributes to leukocyte adhesion and migration, local lymphocyte stimulation, and which is responsible for T lymphocyte trafficking in the intestine. Interestingly, an ICAM-1 polymorphism has been associated with IBD course and PSC, but not with treatment or treatment success (Low et al.,

Conclusion

2004; Yang et al., 2004). The efficacy of alicaforsen has been investigated in both UC and CD and shown to be beneficial when used locally in UC patients, but had hardly any benefit for CD patients (Van Assche and Rutgeerts, 2002). Apoptosis, a pivotal physiological mechanism in re-addressing exaggerated immunological responses, can be induced by classical drugs as discussed earlier, but recently, strong apoptosis-inducing strategies have been proposed, such as using monoclonal antibodies against CD3. These monoclonal antibodies have been developed in many forms; some have a xenobiotic background (–ximabs), some are humanized proteins (-zumabs), and some are completely human (-mumabs). These monoclonal antibodies may be directed against many leukocyte markers for studies in animals, but several are being studied in human trials.Visilizumab is one of the agents that is being investigated in both phase II and III trials. Other antibodies that have been generated against monoclonal antibodies comprise cytokines or cytokine receptors like TNF with its classical example infliximab. The most drastic immunological reset is (autologous) bone marrow transplantation, which has been employed in end-stage CD when no other therapeutic options are left. The results are conflicting, and additional research is warranted (Hawkey, 2004). An alternative therapeutic strategy is directed at the (commensal) bacteria in the intestine, as these are considered to be involved in the pathogenesis of IBD (Fiocchi, 1998). The concept of changing the microbial flora of the intestine in order to influence the course of IBD has been demonstrated by means of antibiotics in fistulous CD and in maintenance therapy of CD (Guslandi, 2005). Contrasting an antibiotic approach is introduction of (beneficial) bacteria into the intestine to modulate the immunological response; a new area of scientific investigation. The main scientific challenge now is to characterize the ‘normal’ intestinal microbial contents, after which diseasespecific changes can be detected and eventually followed by bacterial manipulation. This concept of using prebiotics or probiotics in order to change disease course has recently been boosted by interesting findings in the treatment of pouchitis. A specific (probiotic) mixture of bacteria was shown to reduce the recurrence of pouchitis following inflammation and the incidence of pouchitis directly following surgery (Gionchetti et al., 2003; Hart et al., 2004; Mimura et al., 2004). This complex probiotic mixture appears to modify the intestinal microbial flora and probably has a secondary action on the immunological response (Kuhbacher et al., 2006). Probiotics may prove to be a promising new treatment modality for IBD if specific mixtures of bacteria can be found that influence the mucosal immunological response in a predictable manner. However, so far the results from treating IBD patients with probiotics are still conflicting (Bibiloni et al., 2005;Venturi et al., 1999). As most current therapies only work in a proportion of patients, future studies on treatment efficacy should also consider the patient’s genetic background. Another interesting avenue that has not yet been exploited is the use of gene expression profiles to guide therapeutic intervention in IBD, similar to that proposed for certain types of cancer (Bild et al., 2006). As gene

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expression profiles are normally determined at the site of action, colonic biopsy material may be required for this type of study. Similarly, changes in gene expression profiles may indicate the response to certain treatment modalities. Finally, gene expression can be used to identify potential new targets for therapy. A recent microarray analysis (Costello et al., 2005) on biopsies from both UC and CD patients may provide new leads for novel therapeutic targets.

CONCLUSION Many studies have been performed to identify biomarkers, including genetic ones, for IBD diagnosis, prognosis and to follow the course of disease. Risk analysis by means of biomarkers in non-affected IBD-family members is another field of interest. Although for CD, firm disease-gene associations have been established that induce the risk for disease 40 times, neither these genetic markers nor other bioparameters have been useful in clinical practice. This is in part due to their insufficient test characteristics (diagnostic accuracy), as well as their insufficient means to prevent disease or to change the disease course. Combination of several biomarkers may potentially hold promise, but in order to be properly studied extensive and phenotypically well-described, monomorphic populations are needed. National and multinational biobanks are necessary to provide for these type of large and coherent patient series. Results of successful therapeutic strategies in rheumatoid arthritis patients in which early initiation of therapy with aggressive immunosuppression avoids development of irreversible damage of joint, have stimulated similar approaches in CD. Early data on this type of therapy, indicative of beneficial effects on IBD-course, warrant further study (d’Haens, 2008). The recent identification of the IL-23R gene suggests the IL-23 signaling pathway as a promising therapeutic target in IBD. Implementation of new types of therapy, but also ongoing immunosuppression therapy, holds certain risks for severe adverse events and even mortality. Therefore, adequate identification of patients at risk for a severe disease course is mandatory before implementation in daily practice may be anticipated. Investigation of all types of biomarkers, and in particular genetic markers, needs to be continued, since the different approaches have shown promising but weak signals. For the near future it can be foreseen that tools like IBDchip will become available for molecular diagnostics and patient management. In addition, gene expression (i.e., microarrays) or protein biomarkers may also become available to sub-stratify patients with IBD into various clinical prognostic groups, to follow the course of disease, or the response to therapy. Such tools will only be useful when they can be applied to peripheral blood or even less invasive materials such as saliva. To determine the validity and the prognostic value of biomarkers well-defined population-based prospective cohorts have to be available. The many worldwide initiatives for building large population biobanks will become essential in this.

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87 Glomerular Disorders Tadashi Yamamoto, Hidehiko Fujinaka and Visith Thongboonkerd

INTRODUCTION It is estimated that more than 1.4 million people worldwide are treated with dialysis due to end-stage renal disease (ESRD), and the number has been growing annually at a rate of approximately 6–7% (Grassmann et al., 2006). Glomerular disorders, such as diabetic nephropathy and IgA nephropathy, are the leading causes of chronic kidney disease (CKD) progressing to ESRD. However, the pathogenic molecular mechanisms of these progressive glomerular disorders are still unclear. Since the mechanisms are expected to be mediated by gene transcription and/or mutations, serial studies have been conducted to analyze their involvement. Analyses of mRNA expression in experimental animal models and in human clinical samples, such as renal biopsy tissues and urine samples, have demonstrated up- and down-regulation of some genes in glomerular disorders. These changes may be used as diagnostic and/or prognostic tools for glomerular diseases. Comprehensive analyses of gene expression profiles using recent advanced technology such as cDNA and SNP microarrays have been done to understand and to explore molecular mechanisms underlying the progressive glomerular disorders both in humans and in experimental animals. In this chapter, we briefly summarize several molecular techniques used for detection and quantification of mRNA expression levels in glomerular disorders. Recent findings of genome-wide profiling

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and genome variations in glomerular disorders are also provided. Finally, perspectives of genomic medicine for gene therapy, genome-based drug discovery and development, as well as regeneration of the kidney or glomerular cells for the treatment of glomerular disorders are discussed.

TECHNIQUES FOR DETECTION, QUANTIFICATION, AND PROFILING OF mRNA EXPRESSION IN THE KIDNEY Histopathological examinations of renal biopsy tissues provide the key information for diagnosis and therapeutic management of patients with CKD. However, the conventional histopathology-based analysis offers only descriptive diagnostic categories (classifications) and gives limited information on the prognosis or disease progression. To obtain more insights into the disease processes in individual patients, additional information is needed. With the emerging tools of molecular and genomic medicine, comprehensive studies of mRNA expression in renal biopsy tissues have become feasible. Especially in animal models of glomerular disorders, quantitative analysis of mRNA expression by Northern blotting or ribonuclease protection assays (RPA) has been frequently done since the glomeruli can be easily

Copyright © 2009, Elsevier Inc. All rights reserved.

Techniques for Detection, Quantification, and Profiling of mRNA Expression in the Kidney

isolated by a standard sieving method. RPA is an excellent technique for simultaneously quantitative analysis of multiple transcripts, together with an internal control of housekeeping genes. It is about 10-times more sensitive than Northern blot analysis. Increased and decreased glomerular mRNA expression of various genes have been clearly demonstrated in experimental models of glomerular disorders (Fujinaka et al., 1997). However, both RPA and Northern blotting require a considerable amount of RNA samples and are not practical to apply for analysis of human glomeruli obtained by renal biopsies. Because the amounts of mRNA obtained from small renal biopsy specimens are often below the detection levels of RPA and Northern blotting, quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) has been introduced. This advanced technique was a solution for rapid and reliable mRNA measurement in minute amounts of biopsy or glomerular specimens. Preparation and storage conditions for mRNA have been standardized to effectively prevent the loss or degradation of mRNA. With this approach, quantitative evaluation of mRNA expression from a single microdissected glomerulus and even from a single podocyte seems possible (Schroppel et al., 1998). The short amplicons in real-time PCR have enabled the mRNA quantification in RNA samples derived not only from fresh frozen tissues but also from formalin-fixed paraffin-embedded sections. However, the choice of an internal reference is somewhat crucial, since regulation of the housekeeping genes may confound the expression ratio with the mRNA of interest. A number of transcripts are frequently used as the reference for qRT-PCR of renal biopsy specimens, including glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18S rRNA and cyclophilin A (Schmid et al., 2003a). For some instances, evaluation of several housekeeping genes, in parallel, may be needed. In addition, we should also pay attention to the variability of quality of mRNA in renal biopsy samples obtained at different times or by different procedures. This may seriously interfere with the results of qRT-PCR. In situ hybridization has been employed to detect sites of specific mRNA expression within the kidney. Simple and reproducible results of mRNA expression have been demonstrated in paraformaldehyde-fixed, paraffin-embedded sections. Figure 87.1 shows an example of in situ hybridization to examine podocalyxin mRNA expression in human kidney tissues. Unlike Northern blot analysis, RPA and qRT-PCR, in situ hybridization does not require the procedures for RNA extraction and separation by electrophoresis. However, it is generally considered that quantification of mRNA expression by in situ hybridization is limited, and procedures for in situ hybridization are quite complicated, laborious and time-consuming. Recently, an automated in situ hybridization method has been introduced to make all the complicated steps (from baking through signal detection) automatic within 1 or 2 days. A further advancement is the simultaneous analysis of gene expression, targeting multiple transcripts.The aims for characterization of a tissue-specific gene expression profile or transcriptome are to establish the relationship among a variety of transcripts and to determine coordinated changes in clusters of genes under

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(a)

(b)

Figure 87.1 Podocalyxin mRNA expression in human kidney biopsy samples. In situ hybridization shows that the expression of podocalyxin mRNA in podocytes is not altered by glomerular injury. (a) Renal tissue from IgA nephropathy with proteinuria. (b) Renal tissue from minor glomerular abnormalities without proteinuria.

physiological and/or pathological conditions. Gene expression profiling or transcriptome analysis is based on serial and partial sequencing of cDNAs, or on parallel hybridization of labeled cDNAs to specific probes immobilized on a slide (cDNA microarrays). Some methods have been designed specifically to compare gene expression under different conditions or between health and disease conditions. The first study of comprehensive gene expression profiling in the field of nephrology was conducted by the large-scale sequencing of a 3 -directed regional cDNA library in cultured human mesangial cells (Yasuda et al., 1998). In this study, the most abundant mRNA was that of fibronectin, which accounted for

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47 out of 1193 sequenced clones. Moreover, a new mesangiumpredominant gene namely “megsin” (mesangial cell-specific gene with homology to serpin) was also reported in a subsequent study by the same group (Miyata et al., 1998). Serial analysis of gene expression (SAGE) is an alternative technique for gene expression profiling that permits simultaneous and quantitative analysis of 9–13 bp sequence tags that correspond to unique mRNAs. In the human kidney, SAGE analysis has been performed using microdissected glomeruli and only seven nephron segments (Chabardes-Garonne et al., 2003). Identification of genes preferentially expressed in the glomerulus may be potentially useful for characterization of glomerular diseases. By analyzing a region at 6q22-q23, which has been shown to be closely associated with the incidence of IgA nephropathy, three candidates including those encoding connective tissue growth factor (CTGF), transcription factor 21, podocyte-expressed, and Gap junction protein alpha1 43 kD have been identified as the disease-related genes (Chabardes-Garonne et al., 2003). However, SAGE seems to be less effective in evaluating genes expressed at low levels, for example genes encoding cytokines, chemokines, and their receptors. Moreover, it requires a considerable amount of mRNA and has limited applicability for the study of human glomerular disorders. Differential display and subtractive hybridization methods have been introduced to screen for mRNA profiles and to identify genes differentially expressed between samples. Using differential display RT-PCR, a panel of mRNAs differentially expressed in the isolated glomeruli of the Finnish-type congenital nephrotic syndrome has been obtained (Haltia et al., 1999). However, this technique is hindered by a high rate of false-positive results and requires careful confirmation by other independent techniques. Recently, the arrival of cDNA microarray technology has offered the possibility to simultaneously measure mRNA expression of thousands (or more) of genes in the glomerulus. This advanced technology is a simple and quick method for determining which ones among the large number of genes should be selected for further analysis. However, its hybridization-related variation often demands validation by other methods such as qRT-PCR, RPA, or Northern blot analysis. On a microarray commercially available, numerous genes (approximately 22,000 sets of cDNA molecules, each of which encodes for a different gene) are printed on an array. Either cDNA or cRNA synthesized from the RNA extracted from the kidney tissue is labeled with a fluorescence dye and allowed to hybridize with the cDNA spotted onto the array. Then, fluorescence intensity of the hybridized cDNA or cRNA is monitored at each gene spot and used as an indicator for the amount of mRNA of singular transcripts present in the RNA sample. By this microarray technique, unique or highly distinctive gene expression patterns can be demonstrated in human renal glomeruli, cortex, medulla, papillary tips, and pelvis (Higgins et al., 2004). In this study, 139 out of 1548 differentially expressed genes have been shown to be predominant in the glomeruli, including several transcripts known to be expressed in podocytes (actinin alpha 4, glomerular epithelial protein 1 [GLEPP1], tight junction protein ZO-1,

and osteonectin), glomerular endothelium (tyrosine kinase and endothelial differentiation gene-1 [EDG-1]), and mesangial cells (endoglin), although many of the other genes such as those encoding bone morphogenetic protein-7 (BMP-7), mothers against decapentaplegic homolog 6 (MADH6), and ficolin 3 (hakata antigen) have not previously been reported as predominant genes in the glomeruli. RNA expression profiling of thousands of genes in renal biopsy tissues may identify genes that are involved in development and progression of glomerular disorders. A limitation of the microarray technique is that relatively large amounts of total RNA (generally at least 5–10 g) are needed for hybridization, whereas the yield of glomerular RNA recovery from renal biopsy specimens is considerably lower. In the cDNA microarray analysis of laser microdissected glomeruli of renal biopsy specimens obtained from 11 patients with lupus nephritis and 2 controls, heterogeneous mRNA expression phenotypes in the lupus glomeruli have been demonstrated (Peterson et al., 2004). These include mRNAs that indicate the presence of B cells, different myelomonocytic lineages, fibroblast and epithelial cell proliferation, matrix alterations, and expression of type I IFN-inducible genes (Peterson et al., 2004). Although only 1–5 ng of total RNA was obtained from each laser microdissected glomerulus in this study, the RNAs were amplified by two rounds of T7 promoter-based linear amplification technique prior to the microarray analysis. Laser microdissection combined with cRNA amplification may make possible the detection of transcription profiles down to the single cell level, but may also carry the significant risk of sampling error. Obtaining renal tissue by biopsy is considerably risky. Thus, other sources of clinical samples obtained from noninvasive procedures are required. For instance, peripheral blood leukocytes have been used for the cDNA microarray analysis to segregate patients with autoimmune-mediated renal disorders into several subgroups (Yang et al., 2002) and to evaluate mRNA expression profiles in patients with IgA nephropathy (Preston et al., 2004). Urinary sediment has been also used as a mRNA source to evaluate renal damage in patients with CKD (Szeto et al., 2006). More recently, microRNAs (miRNAs) have been shown to play important roles in mammalian gene expression. They are short noncoding RNAs of 19–25 nt to induce post-transcriptional gene repression by blocking protein translation (by binding to the 3 UTR of their target genes) or by inducing mRNA degradation, potentially playing central roles in physiological and pathological conditions. An miRNA database is currently available on the website (http://microrna.sanger.ac.uk/). The field of miRNAs is now exploding to discover gene targets and their relevance in diseases by using miRNA microarray. A recent report has shown that smad-interacting protein 1 (SIP1) is a target of microRNA-192 (miR-192), a key miRNA highly expressed in the kidney (Kato et al., 2007). Transforming growth factor- (TGF-)-mediated collagen regulation has been also demonstrated to be a consequence of a crosstalk between E-box repressors (dEF1 and SIP1) and miR-192 that could be relevant to the pathogenesis of diseases such as diabetic nephropathy.

mRNA Expression Profiles of Glomerular Disorders

mRNA EXPRESSION PROFILES OF GLOMERULAR DISORDERS A series of studies in experimental models of glomerular diseases disclose several essential events of mRNA expression in glomerular or inflammatory cells. Cytokine and chemokine mRNA expression has been extensively investigated in experimental models of immune-mediated glomerulonephritis, and several cytokines and chemokines have been reported as the common players in glomerular disorders, including TGF-, platelet-derived growth factor (PDGF), CTGF, fibroblast growth factor-2 (FGF-2), epidermal growth factor (EGF), insulinlike growth factor-I (IGF-I), interleukins, tumor necrosis factor-alpha (TNF-), interferon-gamma (IFN-), macrophage colony-stimulating factor (M-CSF), monocyte chemoattractant protein-1 (MCP-1), and others. Diabetic Nephropathy mRNA expression levels of extracellular matrix (ECM) components and ECM-regulating cytokines have been studied in human diabetic nephropathy and upregulation of alpha-2 type IV collagen mRNA has been identified in sclerotic glomeruli of diabetic nephropathy (Esposito et al., 1996). An elevation of mRNA expression for glomerular TGF-1 has been also demonstrated in the early stage of diabetic nephropathy by a competitive PCR method (Iwano et al., 1996). To localize gene expression sites, in situ hybridization analysis has been applied to renal biopsies obtained from patients with diabetic nephropathy. The results show upregulation of interleukin-6 (IL-6) mRNA expression (Suzuki et al., 1995) and downregulation of metalloproteinase-3 (MMP-3) and tissue inhibitor of metalloproteinase-1 (TIMP-1) RNAs in the diabetic glomeruli (Suzuki et al., 1997). Experimental models of diabetic nephropathy and glomerular cells stimulated with high-glucose concentrations in culture have been also studied to examine mRNA expression profiles for the understanding on development of human diabetic nephropathy. By suppression-subtractive hybridization or mRNA differential display method, altered gene expression induced by high-glucose conditions has been reported in cultured human mesangial cells and in diabetic mice (Clarkson et al., 2002; Holmes et al., 1999; Sun et al., 2002). In addition, several analyses using cDNA microarray techniques have been performed in human and experimental diabetic nephropathy (Baelde et al., 2004; Morrison et al., 2004; Susztak et al., 2004; Wada et al., 2001; Wilson et al., 2003). The up- and down-regulated genes in diabetic nephropathy reported in these studies are summarized in Table 87.1. IgA Nephropathy In several studies on IgA nephropathy, the expression of specific cytokine and/or chemokine genes in the kidney has been studied in association with the degree of cellular proliferation and ECM production. By competitive PCR, 12 genes encoding cytokines and growth factors have been examined in renal biopsy samples from patients with IgA nephropathy and a prominent increase in expression of IFN- mRNA has been demonstrated (Yano et al.,

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1997). Expression of other immune-related cytokine/growth factor genes has been also demonstrated in kidney tissues obtained from patients with IgA nephropathy. The increased ratio of IFN/IL-10 transcript, an indicator of Th1 predominance, is associated with renal dysfunction and severe glomerular sclerosis in patients with IgA nephropathy (Lim et al., 2001). Comprehensive gene expression profiling has been performed in renal biopsy samples from patients with IgA nephropathy, and the results show 13 upregulated genes (out of a total of 860 genes evaluated) in IgA nephropathy (Waga et al., 2003). Moreover, microarray analysis using peripheral white blood cells obtained from patients with IgA nephropathy has identified 15 genes as candidate biomarkers for the disease activity (Preston et al., 2004; Schena et al., 2007). Other Glomerular Disorders Levels of mRNA expression of unique genes may be used as effective predictors for renal outcome. Early upregulation of mRNAs of ECM components and ECM-regulating cytokines has been shown to correlate with outcome in later phase of diseases. mRNA levels of collagen type I 1, collagen type IV 1, and TGF- are increased in an anti-glomerular basement membrane (GBM) glomerulonephritis model (Merritt et al., 1990), and the increase of collagen type I 1 mRNA expression predicts for the extent of fibrosis at day 7 and the percentage of fibrous crescents at day 30 (Coimbra et al., 1991). This predictive power has been shown to be higher than that found in conventional measures such as serum creatinine levels, urinary protein excretion levels, and histopathological analysis at day 7 (Lee et al., 1997). A considerable number of studies have been also conducted for measuring glomerular mRNA levels in animals and humans using qRT-PCR techniques. As prognostic tools, it is difficult to use mRNA levels in human renal biopsy samples since they already contain histological abnormalities to some extent. A comparison to mRNA levels in renal biopsies with normal histology, or with only minimal lesions, is required. As diagnostic tools in acquired human proteinuric glomerular disorders, the effectiveness of mRNA quantification of podocyte-specific markers by real-time RT-PCR has been reported. Using synaptopodin as a reference, the mRNA expression ratio of podocin to synaptopodin in microdissected glomeruli has allowed the segregation of focal segmental glomerulosclerosis (FSGS) from minimal change disease (MCD) (Schmid et al., 2003b). Additionally, mRNA expression of nephrin and podocin in urinary sediment significantly differs among proteinuric disease categories and correlates well with the rate of decline in renal function (Szeto et al., 2005). Comprehensive gene expression analyses have been done in experimental models of several glomerular disorders. In a murine model of systemic lupus erythematosus, enhanced expression of 11 genes (SPARC, TMSB10, S100a6, ANXA2, OPN, LCN2, COL3A1, MGLAP, C3, B2M, and LYZS) has been demonstrated in the renal cortex by cDNA microarray analysis (Ka et al., 2006). Similarly, genes encoding osteopontin, kidney injury molecule-1, and thymosin 10 have been identified as markedly upregulated

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TABLE 87.1

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Up- and down-regulated genes in diabetic nephropathy

Genes or Gene Products

Expression

Materials

References

ZNF236

Upregulated

Human mesangial cells cultured in high-glucose medium

Holmes et al. (1999)

Unc-18 homolog, POU domain transcription factor 2, lunatic fringe gene homolog, fibrous sheath component 1, Sox-17, fibulin 2, MRJ

Upregulated

Streptozotocin-induced diabetic mice

Wada et al. (2001)

CTGF, gremlin, caldesmon

Upregulated

Human mesangial cells cultured in high-glucose medium

Clarkson et al. (2002)

UbA52

Upregulated

Diabetic newborn mice

Sun et al. (2002)

Gpx3

Upregulated

Non-obese diabetic (NOD) mice

Wilson et al. (2003)

Thiol antioxidative genes: glutathione peroxidase 1, peroxiredoxin 6, thioredoxin 2

Upregulated

Rat mesangial cells cultured in high-glucose medium

Morrison et al. (2004)

Aquaporin 1, calpain 3, hyaluronoglucosidase, PECAM

Upregulated

Patients with diabetic nephropathy

Baelde et al. (2004)

Hydroxysteroid dehydrogenase-3beta isotype 4, osteopontin

Upregulated

db/db or streptozotocin-induced diabetic mice

Susztak et al. (2004)

SCD1

Downregulated

Non-obese diabetic (NOD) mice

Wilson et al. (2003)

BMP-2, VEGF, FGF-1, IGFBP-2, nephrin

Downregulated

Patients with diabetic nephropathy

Baelde et al. (2004)

genes in the irreversible anti-Thy-1 nephritis model (Tsuji et al., 2006). However, comprehensive gene expression analyses in human renal biopsy tissues are still limited. Analysis of cDNA microarray assays on renal biopsy specimens from 10 FSGS patients and 5 controls has shown differential expression of a group of 429 genes (Schwab et al., 2004). Although differences in gene expression have been also shown in samples from subsets of patients with either nephrotic syndrome or glomerular disorders, expression sites of these genes have not been identified since whole renal biopsy specimen were used for this analysis. To understand the pathogenesis and pathophysiology through gene expression analysis, we should have at least information on the site of gene expression by in situ hybridization or by immunohistochemistry of proteins translated from these genes.

GENOME VARIATIONS IN GLOMERULAR DISORDERS Detection of mutations in the genome has important implications for diagnosis, prognosis, and therapy of a number of diseases. Techniques such as restriction fragment length polymorphism (RFLP) and single-strand conformation polymorphism have been used for detection of genetic alterations. However, the screening for all possible mutations in one gene using such approaches is mostly cumbersome. The use of microarray technology for large-scale

screening of mutations and gene polymorphisms might be the solution, and this has been initiated in the field of cancer research. By the completion of the Human Genome Project, human genome sequence encoding approximately 20,000–25,000 proteins has been determined (International Human Genome Sequencing Consortium, 2004). Among these, approximately 0.1% varies in different individuals and single nucleotide polymorphism (SNP) is the most frequent variant. Thus, the investigation of SNPs in relation to disease occurrence and susceptibility has gained considerable attention in the past few years. SNPs appear in one nucleotide per several hundred or thousand nucleotides. It can be considered that the occurrence of gene polymorphisms in the DNA leads to alterations of amino acids and thereby alters the function of protein products. In addition, polymorphisms in the promoter region and other regulatory regions of a gene have an influence on the rate of mRNA transcription. In both diabetic nephropathy and IgA nephropathy, a contribution of genetics has been described (Chow et al., 2005). The application of microarray technology to large-scale screening for SNPs of candidate genes in members of families, in which glomerular disorders frequently occur, is very useful because DNA can be easily extracted from peripheral blood samples. SNP analysis can be performed with the aid of probes hybridizing to relatively short nucleotides printed on the arrays. Another fact is that such analyses can be done in blood samples is advantageous and complementary to the study of renal biopsy tissues.

Genome Variations in Glomerular Disorders

Diabetic Nephropathy The inheritance of type 1 diabetes mellitus is genetically determined in humans, although the consequences are complicated. Genome-wide scans for linkage in type 1 diabetes have identified more than 20 candidate regions closely related to the disease susceptibility, including IDDM1 (the HLA region on chromosome 6p21) and IDDM2 (the insulin gene region on chromosome 11p15) (Ewens et al., 2002) (see Chapter 96). In type 2 diabetes mellitus, contribution of genetic factors to the onset and progression of chronic diabetic complications is highly convincing, but the genes conferring susceptibility to this disease remain to be identified. A genome-wide analysis of gene-based SNPs has been performed in a large cohort of Japanese patients with diabetes mellitus (Maeda, 2004). In this case-control association studies, patients with type 2 diabetes mellitus were divided into two groups; one had retinopathy and overt nephropathy, whereas the other had diabetic retinopathy but without signs of renal involvement (the control group). Genotyping of these patients with 80,000 SNP loci has suggested several (~1600) distinct regions to be potential candidates of the susceptibility to diabetic nephropathy. By analyses of these regions, genes encoding solute carrier family 12 member 3 (SLC12A3) (Tanaka et al., 2003) and engulfment and cell motility 1 (ELMO1) (Shimazaki et al., 2005) have been subsequently reported to be associated with the susceptibility to diabetic nephropathy. Substitution of Arg to Gln at the 913th residue of SLC12A3 might reduce the risk to develop diabetic nephropathy, suggesting that this gene product might be a potential target for the prevention or treatment of diabetic nephropathy (Tanaka et al., 2003). Other SNPs have been reported as candidate genes to be involved in the pathogenesis of type 2 diabetic nephropathy. Transcription factor 7-like 2 (TCF7L2) has been identified as a strong candidate gene for type 2 diabetes susceptibility, and five SNPs and one tetranucleotide repeat polymorphism within this gene have a strong association with the disease in Caucasian populations (Grant et al., 2006). Studies on SNPs in TCF7L2 gene have been published repeatedly. In addition, some polymorphisms in the genes related to the renin-angiotensin system have been reported to be associated with diabetic nephropathy. These studies on genome variation of diabetic nephropathy patients are summarized in Table 87.2. Genome-Wide Association Studies (GWAS) are currently on going with support by the Foundation for the National Institutes of Health (FNIH). Also the Genetics of Kidneys in Diabetes (GokinD) study supported by Juvenile Diabetes Research Foundation International (JDRF) has analyzed a repository of DNA and clinical information of type 1 diabetes patients, with or without kidney disease, along with their parents (Mueller et al., 2006). PVT1 has been identified as a candidate gene for renal complication of type 2 diabetes, by a genomewide analysis of 115,352 SNPs in pools of 105 cases and 102 control subjects (Hanson et al., 2007). In addition, the European Rational Approach for the Genetics of Diabetic Complications (EURAGEDIC) study has been designed to identify genetic

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T A B L E 8 7 . 2 Polymorphisms in the genes encoding the renin-angiotensin system (RAS) associated with diabetic nephropathy Reference

Gene

Polymorphism

Mizuiri et al. (2001)

Angiotensin converting enzyme

An insertion/ deletion (I/D) polymorphism in the 16th intron

Jacobsen et al. (2003)

Angiotensinogen Angiotensin II receptor type 1

M235T A1166C

Osawa et al. (2007)

Angiotensin converting enzyme

Eight SNPs, including five SNPs that were almost in complete linkage disequilibrium with the I/D polymorphism within the 16th intron Three SNPs including M235T One SNP within the second intron. The A1166C polymorphism was not associated with diabetic nephropathy

Angiotensinogen Angiotensin II receptor type I

risk factors for diabetic nephropathy in type 1 diabetes patients of European populations (Tarnow et al., 2008). IgA Nephropathy Undoubtedly, there are genetic components of the pathogenesis and clinical manifestations of IgA nephropathy. This has been inferred from the existence of its familial forms, the presence of elevated serum IgA levels and overproduction of IgA by cultured B cells in unaffected family members, and the geographic differences in prevalence (higher in the Pacific Rim and Southern Europe, as compared to Northern Europe and North America). However, most studies in regard to this aspect have been done with a relatively small number of IgA nephropathy cases and control subjects, using analysis of SNPs only in single candidate gene. The lack of concordance across many of these studies reflects both small sample sizes and methodological limitations of such a strategy in studying a complex polygenic disease. This is compounded by uncertainty as to whether IgA nephropathy is truly a single disease entity and the realization that IgA nephropathy may exist as a subclinical condition in apparently normal control populations. Genome-wide linkage analysis of familial IgA nephropathy has revealed a close association with DNA in 6q22-23

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(IGAN1) (Gharavi et al., 2000), although no proteins encoded by IGAN1 have been identified. Certain candidate genes associated with IgA nephropathy have been also reported, such as genes encoding human leukocyte antigen, major histocompatability complex (MHC), uteroglobin, selectin, polymeric immunoglobulin receptor, immunoglobulin m-binding protein 2, and angiotensin converting enzyme (ACE) (Suzuki et al., 2005) and protein kinase CK2 (Yamada et al., 2005). An interaction between the ACE insertion/deletion and -adducin Gly460Trp polymorphisms has been shown to be associated with blood pressure regulation and progression of renal dysfunction (Narita et al., 2003).

GENETICS OF CONGENITAL GLOMERULAR DISORDERS Alport syndrome is a hereditary glomerulonephritis variably associated with neural hearing loss and ocular abnormalities. This genetically transmitted disease is heterogeneous – the gene COL4A5 is mutated in the X-linked dominant form, which is the most frequent form (~85%) of the disease, whereas COL4A3 or COL4A4 are mutated in the autosomal recessive or dominant form. The distinction of the mutated gene is important because the prognosis and genetic counseling differ. In general, families with autosomal dominant Alport syndrome have a relatively mild phenotype, as indicated by a slower rate of progression to ESRD comparing to most of patients with X-linked Alport syndrome. In contrast, all boys and girls with autosomal recessive Alport syndrome develop ESRD usually by their teens or young adult ages. Mutation screening of the genes for making early diagnosis is difficult, because of the genetic heterogeneity of the disease, the large size of the three genes, and the random distribution of mutations along these huge genes. For these reasons, evaluation of expression of type IV collagen chains in the skin, and if necessary in the GBM, remains a useful tool for the diagnosis. A RNAbased method, using COL4A5 mRNA extracted from hair roots as the primary material for mutation screening, followed by traditional exon by exon screening strategy, has been reported to increase the rate of identifying mutations (King et al., 2006). Nephrotic syndrome found at birth or during the first 3 months, or occurring later in childhood may be congenital. A high proportion of children with nephrotic syndrome occurring before the age of three years are steroid resistant. Several genes such as NPHS1,NPHS2, and WT1 have been implicated in severe forms of nephrotic syndrome in children. Recent studies have shown that congenital nephrotic syndrome may be secondary to mutations of one of these three genes, and that some patients have a digenic inheritance of NPHS1 and NPHS2 mutations. Congenital nephrotic syndrome of Finnish type, characterized by autosomal recessive inheritance, is caused by mutations in NPHS1, the nephrin gene (Lenkkeri et al., 1999). Nephrin is a member of immunoglobulin super family proteins working as a cell adhesion molecule, and is a major component of slit diaphragm at foot processes of podocytes playing a crucial role in glomerular ultrafiltration. Because the extrarenal sites of nephrin

expression have been reported in brain, spinal cord and pancreatic -cells, its extrarenal malfunctions have been suggested to induce neurological symptoms observed in some patients with congenital nephrotic syndrome (Beltcheva et al., 2003). The Finnish-type congenital nephrotic syndrome is often accompanied by the absence of both nephrin protein itself and the slitdiaphragm structure between foot processes of the podocytes. Most infants are born prematurely with low birth weight for gestational age, and edema is present at birth or appears within a few days due to severe nephrotic syndrome. Massive proteinuria is accompanied by severe hypoalbuminemia and hypogammaglobulinemia. Terminal renal failure occurs most often within 3–8 years. NPHS1 consists of 29 exons spanning 26 kbp in the chromosomal region 19q13.1. Most of the mutation (~78%) observed in the Finnish NPHS1 gene is a 2 bp deletion in exon 2 (Finmajor) that causes a frameshift and a translation stop at the end of exon 2, resulting in the absence of nephrin at the slit-diaphragm sites. A nonsense mutation in exon 26 (Fin-minor) has been also observed in ~16% of Finnish NPHS1 gene mutation. These two mutations lead to truncated proteins and are rare in non-Finnishtype congenital nephrotic syndrome, in which more than 60 different mutations in NPHS1 gene, including deletions, insertions, splicing, missense and nonsense mutations have been reported. NPHS2 mutations have been first described in children with familial steroid-resistant idiopathic nephrotic syndrome (Boute et al., 2000). NPHS2 gene, which encodes podocin, has been identified by a positional cloning approach and mapping a locus to chromosome 1q25-q31. Podocin, a 383-amino acid protein, is localized at the filtration slit and is demonstrated in a lipid raft-associated protein fraction. Podocin has been shown to interact with nephrin and another protein, CD2AP. Deletion of CD2AP gene has been also demonstrated to be associated with congenital nephrotic syndrome. Moreover, podocin has been also demonstrated to play a major role in the structural integrity and function of the slit diaphragm, which is crucial for the maintenance of glomerular perm-selectivity. More than 30 types of different mutations have been found in the NPHS2 gene, including missense mutations, frameshift mutations, in-frame deletions, and nonsense and splice-site mutations. The R138Q substitution (Arg to Gln) is the most frequent mutation, which induces congenital nephrotic syndrome (Schultheiss et al., 2004). These findings confirm the genetic heterogeneity of congenital nephrotic syndrome and also indicate that the disease activity is mostly due to the variety of NPHS2 mutations.

GENOMIC MEDICINE FOR GLOMERULAR DISORDERS Gene therapy may be one of the ideal treatments for hereditary or even for non-hereditary diseases in the future, since the specifically therapeutic effect can be determined, whereas the harmful effects to other tissues can be limited. However, trials of gene therapy in glomerular disorders lag behind because of the low efficiency of gene transfer or delivery and the difficulty in targeting

Genomic Medicine for Glomerular Disorders

to specific cells in the glomerulus. Several viral vector systems are currently being studied; however, they still have limitations for clinical use not only because of the aforementioned difficulties but also due to uncertainty regarding their toxicity and immunogenicity. Adenovirus vector-mediated gene transfer is most commonly used to introduce genes into cells in experimental glomerulonephritis (El Shemi et al., 2004). The introduction of the gene encoding type IV collagen 5 chain into the glomeruli of a swine model of Alport syndrome using an adenoviral vector system has been successful to generate the triple helix of the type IV collagen in the GBM with collagen 3 and 4 chains (Heikkila et al., 2001). However, simple injection of the vector into renal artery or through the ureter does not usually achieve efficient gene introduction into the glomerular cells. Some maneuvers to improve the efficiency of transfer need to be developed. Viral vectors may elicit immune reactions and have a potential risk of mutagenesis. Therefore, non-viral vectors such as the hemagglutinating virus of Japan (HVJ)-liposome method have been devised. By delivery of TGF- antisense oligonucleotides in the renal artery, a suppressive effect has been reported on the expression of TGF- and ECM expansion in the glomerulus (Akagi et al., 1996). Skeletal muscle targeted gene therapy for glomerular disorders seems to be less invasive and more practical if secreting proteins expressed in the muscle are beneficial, because it provides high efficiency and long-lasting gene expression compared with the kidney-targeting methods.The efficiency of gene transfer to the kidney is 10- to 100-fold less compared with that to the skeletal muscle. Decorin derived from skeletal muscle after inoculation of a plasmid vector carrying decorinencoded gene has been reported to inhibit TGF- expression and ameliorate glomerulosclerosis in rats (Isaka et al., 1996). Transplantation-based gene therapy using stem cells is also promising. Bone marrow-derived cells can be transduced with an IL-1 receptor antagonist (IL-1ra) gene and transfused into experimental rats with anti-GBM glomerulonephritis (Yokoo et al., 1999).The transduced cells expressing macrophage markers (CD11b and CD18) have been shown to accumulate within the inflamed glomeruli. Prophylactic injection with the cells expressing IL-1ra before induction of the glomerulonephritis can suppress subsequent inflammatory events in the glomeruli and can preserve the renal function and glomerular structure. The synthetic small interfering RNA (siRNA) duplexes have recently been introduced to suppress gene expression selectively in somatic mammalian cells without nonselective toxic effects of double-stranded RNA (dsRNA). Although a selective in vivo delivery of siRNA to the glomeruli has not been reported, attempts have been made to examine whether injection of synthetic siRNAs via renal artery followed by electroporation can be effective and therapeutic in silencing-specific genes in the glomeruli (Takabatake et al., 2005). siRNA targeting against TGF-1 significantly suppresses TGF-1 mRNA and protein expression, and ameliorates the progression of ECM expansion in experimental glomerulonephritis. The effect of siRNA has been compared with that of antisense oligodeoxynucleotide in cultured rat mesangial cells and shown more potent

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effect (1000-fold) in siRNA sequence-specific suppression of transgene expression. Current therapies for CKD or chronic glomerular disorders by suppression of rennin-angiotensin system may retard the progression of renal failure at some degrees, but can not completely stop their progression to ESRD. Identification of other therapeutic targets with more efficacies is therefore crucially required. There are essentially two ways to search for target genes for the treatment of diseases. One is the research on intervention of known genes that are thought to be involved in the pathogenesis of the disease. Excellent examples in the field of glomerular disorders are genes encoding angiotensin II, TGF-, MCP-1, and others, since they have been demonstrated as the key molecules involved in some progressive glomerular disorders and their blockade may be valuable as the effective treatment. The other one is the search for the novel target genes as the new molecules for the treatment of glomerular disorders by the comprehensive gene expression profiling. The cDNA and SNP microarray analyses of human glomerular or kidney tissues with glomerular diseases are expected to provide candidate genes for the new therapeutic targets. Since organ donation for kidney transplantation is limited worldwide, research on kidney regeneration is required for the new therapy of ESRD. To regenerate the kidney, stem cells and growth factors for the kidney have been extensively studied. Because it is presumably difficult or impractical to regenerate a whole mature kidney in an in vitro system, several researchers have considered transplantation of undifferentiated or partially developed kidney precursor, such as metanephros, as a possible alternative. In animal studies, human or pig embryonic metanephros has been transplanted into the immune-deficient mice in the subcapsular space of the kidney, and has subsequently developed to the mature kidney of a small size (Dekel et al., 2003). The developed kidneys can be functional as evidenced by efficient urine production, although the ureter and major renal vessels are not well developed. There are several concerns that remain to be solved for the regeneration of the kidneys including: construction of renal vessels and ureter, immunogenicity of the transplants, methods to induce mesenchymal cells and ureteric buds from stem cells, and methods to propagate them to metanephros and normal size kidney. It is reasonable to expect that some stem cells contribute to the renal tissue repair after injury, although the presence of tissue stem cells in the kidney has not yet been convincing. Stem cells such as embryonic and bone marrow stem cells are the other candidates for the source to regenerate a part of the kidney. Mouse embryonic stem cells injected into the kidney have been reported to develop into a ureteric bud-like structure and express many genes that are normally expressed in the developing kidney (Yamamoto et al., 2006). Human bone marrow stem cells have been also reported to differentiate into functional complex structures of the new kidney in intact mouse embryos in the whole-embryo culture (Yokoo et al., 2005). However, there are still several problems such as ethical issues for the use of stem cells for renal regeneration, as well as the problem of rejection of the regenerated tissues by the host.

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It is conceivable that the kidneys in developmental stages or during the recovery from renal injury express various developmental and growth factors, and that these factors may be potentially useful for induction of the kidney regeneration. Several factors important for renal development and growth; including, Pax-2, Pax-8, glial cell line-derived neurotrophic factor (GDNF), hepatocyte growth factor (HGF), WT-1, Wnt-4, and BMP-7, have been identified and deserve further investigations for clinical use in the future.

CONCLUSIONS Several studies have extensively examined gene and mRNA expression profiles of glomerular disorders, both in humans and in

experimental animals. The genetic backgrounds are revealed not only in hereditary but also in non-hereditary glomerular disorders. A part of the pathogenic molecular mechanisms of the progressive glomerular disorders have been recently clarified by genomic medicine. Nevertheless, our current knowledge on the pathogenesis and pathophysiology of glomerular disorders categorized as CKD is still insufficient. Since significant advances in genomerelated mega sciences, from genomics to other “omics” areas, are currently introduced to medicine, it is fortunate for nephrology researchers to analyze CKD by these sciences to disclose the disease pathogenesis and pathophysiology, and to develop new diagnostic tools and prognostic indicators. Discovery of new therapies for better therapeutic outcome and successful prevention of CKD and ESRD is our goal. Genomic medicine and related sciences are considerably mature for our voyage to such an ultimate goal.

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88 Spondyloarthropathies Dirk Elewaut, Filip De Keyser, Filip Van den Bosch, Dieter Deforce and Herman Mielants

INTRODUCTION Spondyloarthropathy (SpA) refers to a group of chronic autoimmune disorders (Schumacher and Bardin, 1998) including ankylosing spondylitis (AS) (van der Linden and van der Heijde, 1998), reactive arthritis (Keat, 1999), psoriatic arthritis (PsA; Espinoza et al., 1992), arthritis associated with inflammatory bowel disease (IBD; De Keyser et al., 1998), acute anterior uveitis (AAU) (Rosenbaum, 1992), and undifferentiated SpA (Zeidler et al., 1992). A childhood form juvenile SpA also exists. SpA share common clinical, radiological, and genetic features that are clearly distinct from other inflammatory rheumatic diseases. Wright and Moll (1976) introduced the concept initially using the term seronegative polyarthritis, which was eventually changed to SpA. The term relates not only to the spine and the peripheral joints but also refers to other structures that are involved in the disease process (the enthesis, the eye, the gut) (Braun and Sieper, 1996; Francois et al., 1995). The adjective “seronegative”, since the absence of the rheumatoid factor is the primary characteristic of patients included in the concept, has been abandoned as the term is confusing with its most common use in relation to HIV infection.

CHARACTERISTICS OF SpA The common characteristics that are essential in the recognition of the SpA concept are listed in Table 88.1. Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

Absence of the Rheumatoid Factor and Rheumatoid Nodules The co-existence of spondyloarthropathy (SpA) and rheumatoid arthritis (RA) in the same patient may occur (Luthra et al., 1976). Histologically proven rheumatoid nodules are not, however, found in SpA and remain characteristic for RA. Peripheral Arthritis The peripheral arthritis in the SpA is generally pauciarticular, asymmetrical, and involves preferentially the small and large weight-bearing joints of the lower limbs. The number of involved joints is clinically less important than the asymmetry, keeping in mind that the fewer involved joints, the better the chance of observing an asymmetrical pattern; the larger the number of involved joints, the smaller the chance of finding an asymmetrical pattern. In general, the arthritis is non-erosive and self-resolving, in some patients the arthritis becomes chronic and erosive (Mielants et al., 1990a). In contrast to RA, distal interphalangeal joint involvement in SpA is common. Dactylitis is a specific peripheral manifestation of the SpA and comprises tenosynovitis of the flexor tendon, often accompanied by arthritis of the proximal interphalangeal and distal interphalangeal joint of the same digit or toe (sausage finger or toe). Peripheral arthritis is the key feature of undifferentiated SpA, reactive arthritis, and juvenile SpA. In AS, peripheral joint involvement varies according to authors but a prevalence of up to 40% has been reported (Gran Copyright © 2009, Elsevier Inc. All rights reserved. 1067

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TABLE 88.1 1. 2. 3. 4. 5. 6. 7.

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Characteristics of the SpA concept

Absence of the rheumatoid factor and rheumatoid nodules Peripheral arthritis Spinal inflammation: inflammatory back pain, sacroiliitis with or without spondylitis Peripheral enthesitis Clinical overlap between different entities of the group Familial aggregation Association with HLA-B27

and Skomsvoll, 1997). The hips and the shoulders are the most frequently involved peripheral joints in AS. Peripheral arthritis other than hip and shoulders in SpA forms a distinct entity from the axial involvement not related to the presence of HLA-B27. The pattern of the peripheral arthritis is clearly asymmetrical even in patients in whom the peripheral involvement is polyarticular, as in a majority of the cases with long-standing PsA. Spinal Inflammation: Inflammatory Back Pain and Sacroiliitis with or without Spondylitis Inflammatory back pain is often the earliest sign of inflammatory spinal involvement in SpA. Inflammatory back pain is defined by insidious onset before the age of 45 years, improvement by exercise, association with morning stiffness, and duration of at least 3 months (Calin et al., 1977). Inflammatory back pain is present in more than 90% of patients with AS, in 50–80% of patients with undifferentiated SpA (Zeidler et al., 1992), in 11% of the patients with IBDs (Protzer et al., 1996), and in 70% of patients with reactive arthritis (Fox et al., 1979). Sacroiliitis is the cornerstone for the classification of AS; without sacroiliitis the definite diagnosis of AS cannot be made. Few patients with typical spinal lesions of AS without sacroiliitis have been described (Khan et al., 1985). Sacroiliitis affects up to 40% of the patients with reactive arthritis (Leirisalo-Repo et al., 1994), 20% of the PsA patients (Torre Alonso et al., 1991), 20% of the patients with IBD (de Vlam et al., 2000), and 50–70% of the patients with undifferentiated SpA (Mau et al., 1988). Sacroiliitis in AS is not different from the sacroiliac involvement in other forms of SpA, except for the more frequent unilateral involvement in the latter diseases. The spondylitis is characterized radiologically by the presence of squaring, erosions, syndesmophytes, zygapophyseal joint involvement, discitis, and ankylosis. Usually the spondylitis develops at a later stage of the disease than the sacroiliitis. The spondylitis in AS cannot be distinguished from the spondylitis in the other forms of SpA, except for the asymmetrical appearance of the syndesmophytes in the latter diseases. Radiological signs of spondylitis are seen in 62% of the AS patients with a higher frequency in male compared to female patients (Gran

and Skomsvoll, 1997). Differences are seen between HLA-B27 positive and HLA-B27 negative AS patients (Mielants et al., 1993b). Spondylitis evolves to bamboo spine in about 20% of the AS patients but less frequently in females than in males (Gran and Skomsvoll, 1997). Spondylitis affects up to 26% of the patients with reactive arthritis (Leirisalo-Repo, 1998), 20% of the patients with PsA (Scarpa et al., 1988), and 20% with IBD (de Vlam et al., 2000). Peripheral Enthesitis Inflammation at the enthesis, the attachment of a tendon, joint capsule, ligament, or fascia to bone, is a specific pathological feature of the SpA (Ball, 1971). Inflammation at the enthesis causes a focal osteitis with local destruction, followed by the formation of granulation tissue, which is replaced by bone in the final phase (Ball, 1971). Repeated episodes of inflammation, destruction, and osseous repair result in bone apposition, the enthesophyte. In the spine, the enthesitis is localized at the intervertebral disk, the zygapophyseal joints. The most frequently involved peripheral entheses are the insertion of the fascia plantaris at the calcaneum, the insertion of the Achilles’ tendon at the posterior surface of the calcaneum, and the insertion of the ligamentum patellae at the tuberositas tibiae. Peripheral enthesitis can cause pain but may be also asymptomatic. Peripheral enthesitis occurs in 20% of the patients with SpA and is seen in all forms of SpA. Isolated enthesitis in association with HLA-B27 is reported as the presenting symptom in some cases (Olivieri et al., 1989). In AS, the prevalence varies from one study to the other from 25% to 54% (Gerster et al., 1977; Resnick et al., 1977) and may precede peripheral arthritis and spinal symptoms in juvenile onset forms (Burgos-Vargas et al., 1997). Peripheral enthesitis is found in up to 40% of the patients with reactive arthritis (Kvien et al., 1994), in 20–50% of the patients with pauciarticular juvenile arthritis (Gerster and Piccinin, 1985; Mielants et al., 1993a), in 6% of the patients with IBD (de Vlam et al., 2000; Scarpa et al., 1992), and in 20% of patients with PsA (Oriente et al., 1994). Overlap Between the Different Clinical Entities of the SpA Concept There is a definite clinical overlap between the different diseases which are included in the SpA concept (Moll et al., 1974). The diseases share not only many clinical and radiological locomotor manifestations, such as inflammatory back pain, peripheral arthritis, enthesitis, sacroiliitis, and spondylitis, but also extra-articular manifestations in the eye, at the mucosal level, and in the skin. AAU which is linked to HLA-B27 is the most common extraarticular manifestation of SpA and occurs in 20–40% of the patients during the course of the disease (Banares et al., 1998). Mucosal involvement in patients with SpA is seen in the gastrointestinal and urogenital tracts. Mucocutaneous changes in the mouth are seen in 10% of patients with chlamydia-induced reactive arthritis (Amor et al., 1983). Inflammatory gut lesions are found in 65% of the patients with undifferentiated SpA, in 90% of the patients with intestinal triggered reactive arthritis,

Characteristics of SpA

and in 60% of the patients with AS, with a higher prevalence in patients with peripheral involvement (Mielants et al., 1989). Gut inflammation is also observed in PsA (Schatteman et al., 1995), in late onset pauciarticular juvenile chronic arthritis (JCA; Mielants et al., 1993a), and in AAU (Mielants et al., 1990b). Other mucosal localization (urethritis, balanitis, cervicitis) occurs, especially in urogenital reactive arthritis, but it is rare in other subgroups. Prostatitis is quite common in urogenital reactive arthritis and in AS (Wollenhaupt et al., 1995). Balanitis, cervicitis, and urethritis are often asymptomatic. Similar lesions can also be observed in the oral mucosa. Skin involvement, such as erythema nodosum, keratoderma blennorrhagica, pyoderma gangrenosum, and psoriatic-like lesions occur with variable frequencies (Wollenhaupt et al., 1995). Familial Aggregation Evidence for familial aggregation in each of the disorders may be derived from multiple pedigree studies (Hochberg et al., 1978; Wright, 1978). Sixteen percent of the patients with SpA have a first- or second-degree relative with inflammatory axial pain or peripheral arthritis (Hochberg et al., 1978). An increased prevalence of AS and subclinical sacroiliitis is reported in relatives of patients with IBD, reactive arthritis, and JCA (Calin and Fries, 1975; Mielants et al., 1986). Association with HLA-B27 The discovery of an association between HLA-B27 and AS and related disorders broadened the interest and understanding of these diseases (Brewerton et al., 1973; Schlosstein et al., 1973). The strong association of the SpA with HLA-B27 reinforces the familial aggregation and the clinical overlap amongst the different entities of the concept. The prevalence of AS and SpA in a population correlates directly with the prevalence of HLA-B27 and there may be differences amongst HLA-B27 and disease association according to the different ethnic groups. The association of HLA-B27 seems especially linked to the presence of spondylitis and sacroiliitis rather than to the presence of peripheral arthritis. The frequency of HLA-B27 in the various diseases of the SpA concept is given in Table 88.2. While the role of HLA-B27 as a predisposing gene in AS has been established for more than 30 years, it is now clear that several other genes also contribute to disease susceptibility. Twin studies for example have shown that only 20–30% of the total genetic risk for the disease can be accounted for by HLA-B27 (Brown et al., 1997), whereas the whole MHC contributes about 40–50%. For example, other MHC genes such as HLA-B60 and HLA-DR1 seem to be associated with AS although their contribution is rather limited. Genome-wide linkage screens have indicated that additional genetic markers are distributed on different chromosomes (Laval et al., 2001; Zhang et al., 2004). A non-MHC susceptibility locus for SpA was found to map to 9q31-34 (Miceli-Richard et al., 2004). The suggested gene markers included genes that have been linked to diseases

TABLE 88.2

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Frequency of HLA-B27 in the different SpA

Disease

HLA-B27 positivity (%)

Ankylosing spondylitis Reactive arthritis

90 70–90

Psoriatic arthritis With peripheral arthritis With sacroiliitis or spondylitis

a to 25 70

Inflammatory bowel diseases With peripheral arthritis With spondylitis or sacroiliitis

a 50–70

Undifferentiated spondyloarthropathy

80

Idiopathic anterior uveitis

70

Late onset pauciarticular chronic juvenile arthritis Without sacroiliitis With sacroiliitis

25 40–60

“a” denotes equal to normal population.

predisposing to SpA such as psoriasis and IBD, or markers that include genes important in regulating immune responses (e.g., antigen processing and presentation or cytokine responses). Hence, AAU may be linked to a gene region on chromosome 9 (Martin et al., 2005), while the interleukin-1 gene cluster is involved in AS (Maksymowych et al., 2003, 2006; Timms et al., 2004). Other candidate gene analyses including TGF- and IL-6 polymorphisms revealed no firm association. Classification Criteria Classification criteria for several disorders belonging to the SpA already exist; for AS: the Rome criteria (Kellgren, 1962), the New York criteria (Bennett and Burch, 1968), the van der Linden criteria (van der Linden et al., 1984); for PsA (Vasey and Espinoza, 1984). There is a consensus that these criteria are too restricted, as there is a need to emphasize the existence of a much wider disease spectrum. This radiographically detected sacroiliitis in the absence of symptoms would not be included in the existing classifications. Some patients who clearly are part of the SpA concept, such as those who, for example, present an asymmetric sacroiliitis together with a dactylitis or an anterior uveitis, would also be excluded from these classifications. For this reason the European Spondyloarthropathy Study Group (ESSG) has proposed a set of criteria for the entire SpA group of patients (Dougados et al., 1991). Patients with clearly defined disease entities such as reactive arthritis or AS on the one hand and those with undifferentiated SpA on the other hand would be selected by these criteria. Patients with inflammatory spinal pain or asymmetrical synovitis predominantly of the lower limb, together with at least one of

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the following: positive family history, psoriasis, IBD, enthesopathy, alternate buttock pain, or sacroiliitis correspond to the ESSG criteria and should be classified as SpA (Table 88.3). Parallel to the ESSG criteria (Amor et al., 1991), have developed a point-scale with comparable sensitivity and specificity. By their simplicity, the ESSG criteria are more useful in general medicine. Diseases Belonging to the SpA Concept The diseases included in the SpA concept are listed in Table 88.4. Previously, late onset pauciarticular JCA was considered as a disease belonging to the concept like the others, but it seems preferable to consider that this group is as heterogeneous as in the adult, in which we must recognize different subgroups as juvenile AS, juvenile PsA, juvenile IBD, juvenile AAU, and juvenile undifferentiated SpA. At present Whipple’s disease is no longer included in the concept since the incidence of sacroiliitis and spondylitis as well as the relationship with HLA-B27 is controversial (Dobbins, 1987; Feurle et al., 1979). Moreover, the pattern of peripheral joint is mostly polyarticular and symmetrical in contrast with the pauciarticular asymmetric involvement in the SpA (Helliwell and Wright, 1987). There is some debate as to whether or not Behçet’s syndrome is a SpA. Lack of familial association with other diseases

TABLE 88.3

ESSG criteria

Inflammatory spinal pain or Synovitis (asymmetricala or predominantly in the lower limbs a) and One or more of the following: Positive family history Psoriasis Inflammatory bowel disease Enthesopathy Alternate buttock pain Sacroiliitis Without sacroiliitis, sensitivity 77%, specificity 89%; with sacroiliitis, sensitivity 86%, specificity 87%. a

T A B L E 8 8 . 4 Diseases included in the spondyloarthropathy concept ●

Ankylosing spondylitis

●

Reactive arthritis (enterogenic and urogenital)

●

Psoriatic arthritis

●

Inflammatory bowel disease

●

Acute anterior uveitis

●

Synovitis acne pustulosis hyperostosis osteomyelitis (SAPHO) syndrome

●

Undifferentiated spondyloarthropathies

●

Juvenile spondyloarthropathies

included in the concept, the association with HLA-B51 rather than with HLA-B27, the lack of low back pain and the intermittent reports about the incidence of sacroiliitis resulted in the exclusion of Behçet’s disease from the SpA concept (Hamuryudan et al., 1997). Nonetheless, the simultaneous presence of SpA and Behçet’s disease may occur in the same patient. The same question can be asked about the classification of the arthritis occurring in celiac disease. The peripheral involvement is polyarticular and symmetric, without predominance of the lower limbs, the arthritis disappears when the patient is kept on a gluten-free diet and does not relapse if adherence to the diet. Axial involvement is rare (Lubrano et al., 1996) and the relation with HLA-B27 is inconstant (Bourne et al., 1985). Consequently, the arthritis in celiac disease should probably be excluded from the SpA concept. SAPHO (synovitis, acne pustulosis, hyperostosis, and osteomyelitis) syndrome groups joint and bone involvement associated with dermatological disorders, such as palmoplantar pustulosis and pustular psoriasis (Chamot et al., 1987). The association with sacroiliitis, bowel disease, and psoriasis links SAPHO with the concept of SpA (Kahn and Khan, 1994). A number of patients correspond to the characteristics of the SpA but cannot be classified in one of the diseases listed in Table 88.4. They are included in the concept and are labeled as undifferentiated SpA (Khan and van der Linden, 1990; Mielants et al., 1989). Careful investigation for associated conditions like psoriasis (by careful clinical observation in the hair region and umbilical region and of the nails) and IBD (by performing ileocolonoscopy with biopsy) and the discovery of other unexpected disease associations will reduce the number of patients with undifferentiated SpA and will enable their classification into one of the well-defined subgroups. Clinical research and further improvement of the knowledge of the pathogenesis of the SpA in the future may end with the disappearance of the subgroup of the undifferentiated SpA.

ROLE OF BOWEL INFLAMMATION The gut is clearly implicated in the concept of SpA as gut induced reactive arthritis (ReA) and IBD are part of this concept. AS and the different subtypes of SpA are associated with several extra-articular manifestations, including inflammatory gut lesions that can evolve to IBD, AAU, and skin lesions such as psoriasis and erythema nodosum. Interestingly, there is an important overlap between the different disease manifestations (axial involvement, peripheral arthritis, IBD, psoriasis, etc.) in a single patient as well as in families, pointing to a strong genetic predisposition for SpA. HLA-B27, the best-known genetic factor, is found in 90% of the AS patients versus 8% in the overall population. The strong linkage with HLA-B27 provides an essential clue to our understanding of the cellular and molecular pathophysiology of SpA. Inflammatory Gut Lesions in SpA Gut Involvement in SpA Since the first report on subclinical gut inflammation in SpA patients, revealed by ileocolonoscopy, exploring the whole

Role of Bowel Inflammation

colon, cecum, and terminal ileum, several authors have confirmed these findings in different forms of SpA (SpA) (reviewed in [De Keyser et al., 1998]). Histological gut inflammation was found as well in reactive arthritis, undifferentiated SpA and AS patients between 30% and 60% of the cases. Prevalence of gut inflammation in AS was higher in patients with associated peripheral arthritis than in those only presenting axial involvement (De Keyser et al., 1998). Gut inflammation was also found in other diseases included in the concept of SpA. In JCA, ileocolonoscopy was performed in the subgroup presenting a pauciarticular late onset, on 12 children with pauciarticular late-onset JCA; gut inflammation was found in nine of them (75%) (Mielants et al., 1993a). In PsA, gut inflammation was found only in those groups related to the SpA concept, that is, in the axial and the pauciarticular group and not in the polyarticular group (Schatteman et al., 1995), however, the prevalence of gut lesions was lower than in the other SpA (26%). A systematic study analyzing a group of 27 patients with AAU with or without axial joint inflammation revealed gut inflammation in 66% of cases (Banares et al., 1995). Histology of Gut Inflammation Two histological types of gut inflammation can be distinguished in SpA: acute and chronic inflammation (Cuvelier et al., 1987). Importantly, this classification refers to the morphological characteristics, and not to the onset or duration of the disease. The acute type resembles acute bacterial enterocolitis. The mucosal architecture is well preserved. The ileal villi and crypts are infiltrated by polymorphonuclear cells. In the lamina propria, there is an increased number of inflammatory cells, consisting of a mixture of granulocytes, lymphocytes, and plasma cells. The chronic type resembles chronic ileocolitis, almost indistinguishable from Crohn’s disease. In this type, the mucosal architecture is clearly disturbed. The villi are irregular, blunted, and fused. The crypts are distorted and the lamina propria is edematous and infiltrated by mononuclear cells. Basal lymphoid follicles occur. In some cases of chronic lesions, aphtoid ulcers, branching of the crypts, the ulcer-associated cell lineage, and sarcoid-like granulomas are present. Whereas acute lesions are mainly seen in patients with reactive arthritis, chronic lesions are slightly more prevalent than acute lesions in undifferentiated SpA and AS (Mielants et al., 1995). Clinical Overlap Between Spondylartropathies and IBDs Although the presence of chronic gut lesions resembling the histology of Crohn’s disease in an important fraction of the SpA patients was already suggestive for a pathogenic relation with classical Crohn’s disease, these findings awaited further evidence to link them more formally with Crohn’s disease. On the one hand, in a prospective study involving 123 patients with SpA who had undergone initial endoscopy, the clinical evolution and the course of the intestinal inflammation was studied (De Vos et al., 1996). Evolution to clinically overt IBD was observed in 7% of patients. Mainly patients with initial chronic inflammation were at risk. Other risk factors included persistent high C-reactive protein (CRP), and radiological sacroiliitis in the absence

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of HLA-B27. On the other hand, a significant proportion of IBD patients depict clinical and radiological signs compatible with SpA. Clinical Evidence of a Relation Between Gut and Joint Inflammation Several lines of clinical evidence also point to the fact that gut and joint inflammation are related in SpA and thus that the gut could have an important pathogenic role. Firstly, in AS the prevalence of gut inflammation was higher in patients with associated peripheral arthritis than in patients without arthritis (De Keyser et al., 1998). Secondly, chronic lesions on gut histology were associated with more advanced radiological signs of sacroiliitis and spondylitis, and with more erosive and destructive peripheral articular disease (Cuvelier et al., 1987). Thirdly, upon follow-up of patients with SpA in whom a second ileocolonoscopy was performed, remission of joint inflammation was associated with a disappearance of the gut inflammation, whereas persistence of inflammation of the locomotoric system mostly was associated with the persistence of gut inflammation, confirming the strong relationship between gut and joint inflammation (Mielants et al., 1995). Gut Inflammation in SpA is Pathogenetically Related to Crohn’s Disease Early Immune Alterations in SpA Gut Mucosa Demetter et al. (2002) investigated the presence of lymphoid follicles and the expression of leukocyte adhesion molecules in gut mucosa of SpA patients without macroscopic or microscopic gut inflammation and controls. The number of lymphoid follicles was increased in both the ileum and the colon of SpA patients. Macrophages, characterized by the expression of CD68, were more numerous in colonic mucosa from SpA patients. The amount of lymphoid follicles and lamina propria mononuclear cells expressing CD11a, CD11c, and VCAM-1 was increased in non-inflamed gut mucosa from SpA patients. These findings clearly demonstrate the presence of changes of the gut-associated immune system in SpA preceding histological signs of inflammation and suggest an increased antigen handling and presentation and augmented maturation of naïve T cells toward memory T cells in the SpA gut. Also, the colons of patients with SpA and Crohn’s disease, but not ulcerative colitis, showed increased numbers of macrophages expressing the scavenger receptor CD163 (Demetter et al., 2005). These findings again highlight the presence of early immune alterations in the SpA gut mucosa which are reminiscent of Crohn’s disease and bring up a strong argument for a role of macrophages in this group of diseases. Immune Features in the Gut Mucosa of Patients with SpA Reminiscent of Crohn’s Disease Besides these immune features preceding the occurrence of microscopic bowel inflammation, a number of histological alterations related to inflammation also appeared to be common in SpA and Crohn’s disease. E-cadherin mediates homotypic,

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homophilic intercellular adhesion in epithelial cells. An upregulation of E-cadherin and its associated catenins was demonstrated in clinically overt IBD (Demetter et al., 2000b). In SpA, expression of the proteins of the E-cadherin/catenin complex in acute and chronic subclinical gut inflammation was found increased as well (Demetter et al., 2000a), especially at the sites of active inflammation. There is a predominance of Th1-producing mucosal T cells in both SpA and Crohn’s disease, with a proportional decrease of IFN- and IL-2 producing CD3 and CD3CD8 lymphocytes (Van Damme et al., 2001a, b). Common Gut-Related Serological Markers in SpA and Crohn’s Disease ASCA (anti-Saccharomyces cerevisiae antibodies) were first described in patients with Crohn’s disease (both IgG and IgA antibodies), and IgA ASCA were recently reported to be elevated in patients with AS (Hoffman et al., 2003). Adding proof to the concept that SpA and IBD are immunologically related diseases. No correlation between the presence of subclinical bowel inflammation and ASCA IgA levels was noted (although the relevant study groups in that respect were small). These data were recently confirmed by Torok et al., 2004. It remains speculative whether these IBD-related antibodies in SpA are associated with development of IBD. Common Gut-Related Genetic Polymorphisms A correlation has been reported between polymorphisms in the CARD15 gene and an increased susceptibility for Crohn’s disease (Hugot et al., 2001; Ogura et al., 2001). Three independent single nucleotide polymorphisms (SNPs) in CARD15 are associated with Crohn’s disease in about 30 to 46% of patients. These variants increase the risk for Crohn’s disease with a factor 3 for heterozygous and factor 38 or 44 for respectively homozygous or compound heterozygous individuals. The CARD15 protein is involved in NF-B activation and in apoptosis by two N-terminal Caspase Recruitment Domains (hence the term CARD), although the precise pathogenetic role in Crohn’s disease remains to be determined. Most studies have not demonstrated an association with SpA or AS in particular. In view of the apparent correlation between gut inflammation in SpA and clinical evolution to Crohn’s disease, Laukens et al. (2005) investigated the relation between the presence of polymorphisms in this susceptibility gene for Crohn’s disease with gut inflammation in SpA patients. The carrier frequency of the variants in the SpA populations was similar as in the control population, but increased in the subgroup of SpA patients with chronic gut inflammation (42%), being significantly higher than in the other SpA subgroups (7%) and the control group (17%), with no significant difference from the prevalence in Crohn’s disease (45%). These findings demonstrate that the presence of CARD15 polymorphisms in patients with SpA is associated with a higher risk for development of chronic gut inflammation.

HISTOPATHOLOGY OF SYNOVITIS IN SpA The synovial membrane consists of two compartments: the synovial lining layer and the sublining layer. The lining layer plays a role in the production of synovial fluid components, the absorption of fluid and substances from the cavity and the blood/ synovial fluid exchanges (Hendersen, 1982). Histologically, it is composed of one or two cell layers of synovial intimal cells, called synoviocytes. Two types of synoviocytes have been identified (Graabaek, 1984; Palmer et al., 1985). The type A or “macrophage-like” synoviocytes are derived from monocytic cells of the bone marrow and can be considered as resident CD68 macrophages. Accordingly, they possess the capacity to phagocyte and to present antigens in a MHC class II context. The type B or “fibroblast-like” synoviocytes are of mesenchymal origin (Barland et al., 1982). The two cell types are imbedded in a specialized extracellular matrix (ECM; Revell et al., 1995). Beneath the synovial lining, but not separated from it by a basal membrane, lies the sublining layer, which is composed of loose connective tissue with scattered blood vessels and relatively few cells, mainly fibroblasts, macrophages, and fat cells. In inflammatory arthritis, a number of prominent changes of the synovium are observed, including vascular changes, inflammatory infiltration and pannus formation. A first characteristic of inflammatory synovitis is an increase in the number of blood vessels in the sublining layer (FitzGerald and Bresnihan, 1995). Beside the increase in the number of blood vessels, the inflamed synovial tissue is characterized by endothelial activation, as indicated by the increased endothelial expression of adhesion molecules such as PECAM-1, ICAM-1, and E-selectin (Johnson et al., 1993; Kriegsmann et al., 1995b; Szekanecz et al., 1994; Tak et al., 1995). Both the endothelial activation (Fischer et al., 1993; Grober et al., 1993;, Lazarovits and Karsh, 1993) and the expression of chemotactic factors (Deleuran et al., 1994; Harigai et al., 1993; Volin et al., 1998) are critical steps in the infiltration of the synovial membrane by inflammatory cells. The main infiltrating cell populations in inflamed synovium are lymphocytes and macrophages. The lymphocyte population consists predominantly of CD4 memory T cells (Poulter et al., 1985). Two patterns of T cell infiltration can be distinguished: perivascular lymphocytic aggregates forming occasionally lymphoid follicles with germinal centers, and diffuse lymphocytic infiltrates without further microarrangements. Beside CD4 T cells, the inflamed synovium contains CD8 T cells, NK cells, and different populations of B lymphocytes: memory B cells, mainly located in lymphoid aggregates, and plasma cells, which are observed more diffusely in the synovium (Brown et al., 1995). Macrophage infiltration is another prominent feature of inflamed synovium. Infiltrating CD68 cells are found diffusely in the sublining layer and probably also migrate to the synovial lining (Sack et al., 1994). The pathophysiological relevance of infiltrating macrophages in rheumatoid synovium is highlighted by their presence in preclinical synovitis and the correlation of

Histopathology of Synovitis in SpA

the degree of macrophage infiltration with both disease activity and joint damage (Kraan et al., 1998; Mulherin et al., 1996; Tak et al., 1997;Yanni et al., 1994;Youssef et al., 1999). The synovial lining layer in inflammatory arthritis is characterized by a manifest hyperplasia, resulting in an increased lining thickness of 3 to more than 10 cell layers. Several factors contribute to lining hyperplasia: increased numbers of CD68 type A synoviocytes, proliferation of fibroblast-like synoviocytes, and impaired apoptosis of type B synoviocytes. The partial transformation of the synoviocytes, the attachment to cartilage (Ishikawa et al., 1996; Kriegsmann et al., 1995a) and the production of degrading enzymes (Ahrens et al., 1996; Keyszer et al., 1998; Konttinen et al., 1999a, b) all contribute to the formation of an aggressive pannus tissue. Together with multinucleated osteoclasts present in the synovial lining at the site of invasion (Ashton et al., 1993; Gravallese et al., 2000), the pannocytes mediate the cartilage and bone destruction in autoimmune arthritis (Pap et al., 2000; Zvaifler et al., 1997). The histopathology of SpA synovium was recently reviewed (Baeten and De Keyser, 2004). A first striking characteristic in SpA synovium is that the macro- and microscopic hypervascularization, which is a general hallmark of synovitis, is even more pronounced in SpA than in RA (Baeten et al., 2000; Ceponis et al., 1996; Reece et al., 1999; Veale et al., 1993). Several factors implicated in this process were identified in SpA synovium: VEGF, angiopoietins, TGFbeta, and MMP-9 (Fearon et al., 2003; Fraser et al., 2001). The endothelium of the synovial blood vessels is highly activated as illustrated by the expression of ICAM1, and PECAM. Interestingly, the expression of E-selectin appears to be lower in SpA than in RA, which could contribute to infiltration with different subsets of inflammatory cells. As to the inflammatory infiltration of SpA synovium, it is essentially composed of lymphocytes and macrophages. The number of infiltrating CD3 T cells, CD4 T cells, and CD20 B cells is slightly lower in SpA than in RA (Baeten et al., 2000). Similarly, there are fewer lymphoid aggregates, which is paralleled by a relative scarcity of follicular dendritic cells in SpA synovium (Baeten et al., 2002). A number of phenotypical and functional studies indicate that the synovial T cells in SpA have a different expression of adhesion molecules such as the beta7 integrins than in RA (Elewaut et al., 1998) and have a decreased Th1/Th2 ratio (Baeten et al., 2001b; Canete et al., 2000; Rudwaleit et al., 2001) (Figure 88.1). Another feature of SpA synovium is that the global number of CD68 macrophages is similar or even lower than in inflamed RA synovial tissue, possibly depending on the SpA subtype (Baeten et al., 2000; Kraan et al., 1999; Smeets et al., 1998;Veale et al., 1993). However, a particular subset of macrophages expressing the scavenger receptor CD163 is selectively increased in SpA synovium (Baeten et al., 2002). This macrophage subset is also increased in non-inflamed gut mucosa of patients with SpA, highlighting the relation between gut and synovium and indicating that even histologically normal intestine depicts already subclinical immune alterations in SpA. This macrophage subset could influence the local inflammation

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by at least two pathways. Firstly, these cells produce large amounts of the pro-inflammatory cytokine TNF but not of the anti-inflammatory cytokine IL-10, which could contribute to the impairment of the Th1 response of T cells and the defense against intracellular bacteria. Secondly, CD163 macrophages in the SpA joint contribute to the local production of soluble CD163, which will then inhibit the proliferation and activation of T lymphocytes. Of interest, another scavenger receptor, the host defense gene MARCO that also contributes to the clearance of bacteria, is selectively downregulated in the joint of SpA patients (Seta et al., 2001). These data support the concept that, beside specific T cell activation, alterations of the innate immune system create an environment permissive for persistence of bacterial antigens and for inappropriate inflammatory responses in the joint of SpA patients. PsA is probably the best-studied SpA subtype. One of the first studies using comparative histology indicated already the decreased lining layer hyperplasia and the increased vascularity in PsA versus RA (Veale et al., 1993). Similar to SpA, the inflammatory infiltration in PsA consists of mixed population of infiltrating leukocytes, with again as specific features a paucity of dendritic cells and an increased presence of activated granulocytes secreting the pro-inflammatory protein S100A12 (ENRAGE) (Foell et al., 2003; Konig et al., 1997). One single study compared systematically PsA synovitis with AS-USpA synovitis, leading to several important observations (Kruithof et al., 2005a). Firstly, analysis of a wide panel of synovial features (including CD4-CD8, S100A12, MRP8/ MRP14, etc.) failed to show any significant difference between PsA and AS-USpA. Secondly, both groups show the same

Figure 88.1 The histopathology of SpA synovium reveals changes in both lining layer and stroma. As in most types of chronic arthritis, the lining layer in SpA may be thickened, yet to a lesser degree than in RA. Another feature is the hypervascularization, which is a general hallmark of synovitis, yet more pronounced in SpA than in RA. Also, neutrophil infiltration is more pronounced in SpA than RA.

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SpA-like features that distinguish them from RA: moderate lining layer hyperplasia, marked hypervascularity, presence of polymorphonuclear cells and CD163 macrophages, and paucity of follicular aggregates and dendritic cells. Thirdly, synovitis in oligoarticular and polyarticular PsA appears to be very similar. Collectively, these data indicate that PsA synovium displays all major characteristics of SpA synovitis. Nevertheless, further investigation of potential differences in specific cellular or molecular pathways remains warranted to explain the different phenotype of PsA arthritis, especially in the context of the more aggressive joint destruction.

GUT AND SYNOVIUM TRANSCRIPTOMES Over the past years, microarray studies have been very useful in identifying novel mediators in several diseases. In an attempt to identify new genes involved in SpA pathogenesis, several groups have initiated such genomic approaches. While there are some differences in the criteria set for selection of candidate transcripts and statistical analyses applied (Rihl et al., 2004), the studies enforced that a microarray-based strategy is able to identify candidate genes. One study focussed on the gene expression pattern in peripheral blood mononuclear cells (PBMC) of patients with SpA, RA, and PsA versus normal controls (Gu et al., 2002). A 588-gene microarray was used as a screening tool to select a panel of such genes from PBMC of these subjects and controls and they were afterward validated by RT-PCR. This proof of principle study indicated that in all groups studied, amongst others, the macrophage differentiation marker MNDA (myeloid nuclear differentiation antigen), MRP8 and MRP14, signaling molecules JAK3 and MAP kinase p38, chemokine receptors CCR1 and CXCR4 were upregulated in the arthritic patients versus healthy controls. Interestingly, some differences in gene expression level among the different inflammatory diseases were noted. Thus, CXCR4 levels were markedly higher in RA versus SpA and PsA patients. Likewise, the levels of IL-8 were higher in RA patients compared to SpA and PsA. Other genes such as IL-1, by contrast were upregulated in RA and AS patients but not in PsA patients (Gu et al., 2002). A major challenge in bulk analysis of samples is the presence of heterogenous cell types, so the above identified genes might appear to be highly expressed only because certain cell types are preferentially enriched. This is especially obvious when analysing tissue samples such as synovium. Some studies therefore postulated that any observed microarray transcripts will be acceptable as candidates only if the serum concentrations of the corresponding proteins correlate with the disease activity index in SpA. Yang et al. reported that in synovial tissue of SpA patients only five genes appeared to be upregulated compared to PBMC: Macrophage colony stimulating factor (M-CSF), IL-7, Matrix Metalloproteinase-3 (MMP-3), Bcl-Xl/Bcl-2 associated death promoter (BAD) protein, and the neurotrophin gene (Yang et al.,

2004). Interestingly, M-CSF and MMP-3 levels in serum correlated with the disease activity in AS. Infusions of infliximab in AS patients led to a significant decrease in the values of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) as well as of the serum MMP-3. These findings were confirmed in a parallel study (Vandooren et al., 2004). No change in serum M-CSF was observed. While MMP-3 has been previously been studied in inflammatory arthritides, particularly RA (Ribbens et al., 2000), M-CSF was unexpected. It may contribute to inflammatory processes by proliferation and activation of monocytes. One interesting observation is that both MMP-3 and M-CSF had not been linked to SpA prior to the microarray study described here, because the serum levels in SpA patients are within the range of healthy subjects (Yang et al., 2004).These findings indicate that MMP-3 and M-CSF are potentially useful markers of AS disease activity and illustrate the potential of microarrays in identifying biomarkers. In follow-up studies it was shown that neurotrophins and their receptors play a role in spondyloarthritis synovitis; and that these genes displayed a relation to inflammation and response to anti-TNF treatment (Rihl et al., 2005). More recently, a similar approach was evaluated in the intestinal mucosa (Laukens et al., 2005). Given the role of intestinal inflammation in SpA and Crohn’s disease, and the observation that a fraction of them develops overt Crohn’s disease prompted us to evaluate whether subclinical gut lesions in SpA patients are associated with transcriptome changes comparable to those seen in Crohn’s disease. We, therefore examined global gene expression in non-inflamed colon biopsies, and screened for differentially expressed genes. This approach allowed examination of noninflamed areas which offers the possibility of identifying early markers for Crohn’s disease, eventually allowing prediction of evolution to Crohn’s disease in SpA patients.Therefore, a microarray analysis was used as an initial genome-wide screen for selecting a comprehensive set of genes relevant to Crohn’s disease and SpA, leading to the identification of 2625 expressed sequence tags (ESTs) that are differentially expressed in the colon of Crohn’s disease and/or SpA patients. These clones, with appropriate controls, were used to construct a glass-based microarray. This was used to analyze colon biopsies from 15 SpA patients, 11 Crohn’s disease patients, and 10 controls. It appeared that 95 genes were differentially expressed in SpA patients with a history of subclinical chronic gut inflammation as well as in Crohn’s disease patients. Among these genes, two of them had been previously related to Crohn’s disease. Acylcoenzyme A oxidase (ACOX1), an enzyme of the fatty acid beta-oxidation pathway, donates electrons to oxygen resulting in hydrogen peroxidase production. ACOX1 activity was found to be reduced in Crohn’s disease (Aimone-Gastin et al., 1994). In addition, one of the four types of seleniumdependent glutathione peroixidases, glutathione peroxidase 2, an enzyme with strict expression in the gastrointestinal tract and involved in maintaining a barrier against absorbing dietary hydroperoxides and against damage of endogenously formed hydroxyl peroxides, has also been found to be disturbed in the context of IBD. We found that its expression was upregulated in

Novel and Emerging Therapeutics and Biomarkers

normal colon tissue of Crohn’s disease and SpA patients with a history of chronic gut inflammation, suggesting it may act as a marker expressed at non-pathological sites in the intestine in Crohn’s disease and Crohn’s disease susceptible SpA patients. Principal component analysis of this filtered set of 95 genes successfully distinguished colon biopsies from the three groups studied. SpA patients with subclinical chronic gut inflammation cluster together, and were found to be more related to Crohn’s disease. Overall these findings indicate that SpA patients have an aberrant gene expression profile in comparison to healthy controls, indicating that alteration of gene expression in the colon of SpA patients is a biologically relevant phenomenon. Further studies are underway to explore the involvement of these genes in both Crohn’s disease and SpA patients with a history of chronic gut inflammation to find new (genetic) markers for detection of early Crohn’s disease in SpA.

PROTEOME ANALYSIS Although proteomic studies are booming, the number of reported proteome studies in SpA is very limited. Proteomics can be defined as the large-scale characterization of the entire protein complement of a cell, tissue or organism, rather than the study of a particular protein in a disease setting. The aim of most proteome studies is to identify new potential biomarkers and/ or protein targets for therapeutic intervention and/or to obtain more insights into the biology of the disease. In general there are different levels at which a “proteome analysis” can be performed: expression proteomics, structural proteomics, and functional proteomics and there are two main technological platforms utilized in the field of proteome analysis: the classical gel-based approach (combined with mass spectrometry for identification of proteins) and the gel-free approaches. The current status of proteome analysis and on the technology utilized, focused on rheumatologic diseases, has been recently reviewed (Tilleman et al., 2005a). Proteome analysis can be performed on several kinds of biological samples from patients: two studies analyzed synovial fluid and plasma, one study comparing reactive arthritis patients to RA patients (Sinz et al., 2002) and the other in the search of markers for erosive RA (Liao et al. 2004).Yet another study used 20 PsA patients as an inflammatory control group for a serum-based RA biomarkers discovery study (de Seny et al., 2005). Our group analyzed the proteome of synovial tissue of SpA and RA patients in comparison with tissue from osteoarthritis patients (Tilleman et al., 2005b). Sinz and colleagues used the classical gel-based approach. They, included, however only one reactive arthritis patient together with three OA patients as the control group, which was used for comparison with the proteome of synovial fluid and plasma from six RA patients (Sinz et al., 2002). As only one reactive arthritis patient was included, this study was not designed to examine the SpA proteome, but should be considered as a study investigation the synovial fluid proteome of RA patients. The same holds on for the other study (de Seny et al., 2005).

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de Seny and colleagues used the gel-free approach of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). This technology is not considered to be a proteome analysis sensu stricto, since the technology is not designed nor capable of studying the entire protein complement and in general the proteins were not identified. This study revealed some mass markers of which their combination could be indicative for RA. These mass markers were not further identified, with the exception of a suggestive identification of MRP-8. MRP-8, however, was shown not to be RA specific, but rather it was mentioned as a possible marker for the inflammatory process. Using another gel-free approach, based on two-dimensional liquid chromatography-coupled tandem mass spectrometry, Liao et al., 2004 studied the proteome of the synovial fluid of erosive versus non-erosive RA patients. Some of the proteins they found to be differentially expressed in the synovial fluid of these patients were confirmed to be differential also in the serum of patients with erosive versus non-erosive RA or healthy controls. Noteworthy is that besides CRP, calgranulin B and calgranulin C also calgranulin A (MRP-8) was among these proteins. Our study (Tilleman et al., 2005b) demonstrated that a proteome analysis of synovial tissue from biopsies, which consist of heterogeneous cell populations, is a feasible approach. We utilized the classical gel-based approach and concluded that the synovial proteome of SpA patients reveals a distinct protein expression pattern in comparison to the synovial proteome of RA patients. We identified fructose bisphosphate aldolase A and alpha-enolase as proteins with higher expression levels in SpA versus OA synovial tissue. In addition we also identified calgranulin A (MRP-8), a well known biomarker for inflammatory arthritis, to be highly upregulated in SpA and RA in comparison to OA. While additional studies are warranted to elucidate the significance of these identified proteins in SpA, this proof of concept study suggests that proteomics-based technologies may unmask novel players in the pathogenesis of SpA.

NOVEL AND EMERGING THERAPEUTICS AND BIOMARKERS Conventional Disease Modifying Anti-Rheumatic Drugs Sulfasalazine, which has been successfully used to treat colonic inflammation in UC and Crohn’s disease, has been found to be effective in the treatment of the peripheral arthritis accompanying SpA, especially if intestinal inflammation is present (Mielants et al., 1996). It may also have a favorable effect on the peripheral arthritis in IBD, but will not influence axial symptoms. Although frequently inducing a clinical remission in SpA, sulfasalazine does not prevent the development of IBD. Leflunomide is an isoxazole derivative approved for the treatment of RA. It has only been studied in a small open-label trial of 12 patients with Crohn’s disease that were intolerant of azathioprine (Prajapati et al., 2003): clinical improvement was

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noted in 8 of 12 patients suggesting that further study in controlled trials would be warranted. The drug has also been studied in a small open-label trial of 20 patients with AS: no significant effect was observed with regard to axial disease; a modest improvement was seen on the peripheral arthritis (Haibel et al., 2003). TNF Blockade in Crohn’s Disease and SpA: Clinical Response and Imaging It has been demonstrated that treatment with a single infusion of chimeric monoclonal antibody infliximab directed against tumor necrosis factor-alpha (TNF) was highly effective in the shortterm treatment of intestinal involvement in treatment-resistant Crohn’s disease (Targan et al., 1997), even resulting in the closure of enterocutaneous fistulae (Present et al., 1999). Moreover, the results of the Crohn’s disease clinical trial evaluating infliximab in a new long-term treatment regimen study (ACCENT I) showed that maintenance therapy with infliximab in moderateto-severe Crohn’s disease prolonged the response and remission of the disease (Hanauer et al., 2002; Rutgeerts et al., 1999). The first observation that infliximab therapy might be useful for the treatment of resistant peripheral joint and axial manifestations in patients with Crohn’s disease, came from an open pilot study (Van den Bosch et al., 2000): four patients with therapy-resistant or fistulising Crohn’s disease, but at the same time also active SpA, were treated with 5 mg/kg infliximab. Besides remission of gut inflammation, a significant improvement of articular and axial symptoms was observed in all patients. Two patients suffered, besides from Crohn’s disease, from an HLAB27 positive AS; in one of them, and in the two other patients the SpA manifestations consisted of oligoarticular peripheral arthritis. In all patients inflammatory variables such as CRP normalized after treatment with infliximab. Axial inflammatory pain disappeared after a single infusion. In two patients peripheral synovitis went into full remission after one infusion, while a second treatment was necessary in the third patient. In one patient the disease flared after 3 months, but retreatment with the same dose of infliximab induced a new remission. As a consequence of these initial findings, TNF blockade with infliximab (loading dose regimen 5 mg/kg) was explored in a number of open studies in patients with different forms of active SpA: in total, more than 100 patients with AS have been treated in short-term open studies with infliximab; invariably, a high success rate was reported. Based on the data of the open studies, two double-blind, placebo-controlled trials were conducted simultaneously in Ghent (Van Den Bosch et al., 2002) and Berlin (Braun et al., 2002): in these studies the fast and significant improvement of TNF blockade was for the first time confirmed in a placebo-controlled way. Although no formal placebo-controlled study has been performed in Crohn’s disease spondyloarthritis, there is little doubt that infliximab is also highly efficacious in this indication. In the above-mentioned ACCENT I trial, evaluating the efficacy of a retreatment regimen of infliximab in patients with active Crohn’s disease, maintenance therapy turned out to be helpful in also resolving extraintestinal manifestations, such as arthritis (Hanauer et al., 2001). Recently an Italian open study (Generini

et al., 2004) evaluated the efficacy of a loading dose regimen of 5 mg/kg infliximab in 24 patients with SpA associated with active (n16) or quiescent (n8) Crohn’s disease. Patients were retreated with either 3 mg/kg (in case of bowel disease remission after the loading dose) or 5 mg/kg when gut symptoms were persisting. The retreatment period varied between 12 and 18 months. Infliximab improved both gastrointestinal and overall articular symptoms (axial disease, peripheral arthritis, enthesitis). In patients with inactive Crohn’s disease at baseline, infliximab prevented IBD flares during the follow-up period. With regard to these findings, a special scientific challenge is the fact that TNF blockers not effective in the treatment IBD can be effective in AS. Etanercept is an example of such a drug with discordant efficacy in both diseases. Efficacy measures (both for axial and peripheral disease) in three placebo-controlled trials randomizing patients with AS to placebo or subcutaneous etanercept 25 mg twice weekly, improved in the AS group with the same impressive magnitude as observed with infliximab treatment (Brandt et al., 2003; Davis et al., 2003; Gorman et al., 2002). However, in an 8-week, randomized, double-blind, placebo-controlled trial, etanercept showed no signs of efficacy in patients with active Crohn’s disease (Sandborn et al., 2001b). Moreover, Marzo-Ortega et al. (2003) treated two patients with Crohn’s disease spondyloarthritis with etanercept and observed complete remission of axial symptoms, whereas their Crohn’s disease either persisted or flared during treatment. The biological basis of this discrepancy is currently still under research. Reasons for such a discrepancy may include differences in bioavailability and pharmacodynamics, as well as cell biological effects (induction of apoptosis) that may differ between different TNF blocking agents. At least in Crohn’s disease, there are data suggesting that only infliximab and not etanercept is able to bind to activated peripheral blood and lamina propria lymphocytes derived from the gut of Crohn’s disease patients (Sandborn et al., 2001a). In addition, infliximab but not etanercept, induced apoptosis of these lymphocytes, providing a biological basis, at least in Crohn’s disease, for the difference in the efficacy of infliximab and etanercept. Although conventional radiography is sufficiently sensitive in established disease in SpA, osteoproliferative changes that are typically identified on plain X-rays only occur later in the disease. Therefore, there has been much interest in the use of magnetic resonance imaging (MRI) in SpA, especially because of its ability to visualize active inflammation which allows to increase the diagnostic benefit early in the disease. MRI has proven to be very useful for identification of early sacroiliitis and spondylitis, also in patients with undifferentiated SpA (Brandt et al., 1999; Braun et al., 1995, 1998). MRI is also useful in peripheral joints and entheses, which may also be well assessed by ultrasonography (Balint et al., 2002; McGonagle et al., 1998). Because of these features, some studies have addressed the efficacy of TNF blockers on modifying active spinal inflammation by MRI (Baraliakos et al., 2005; Braun et al., 2003, 2006; Haibel et al., 2006). In these studies a significant decrease of inflammation was observed. By contrast, whether anti-TNF therapy is able to stop radiographic progression as assessed by conventional X-rays remains an open question. Altogether, it appears that MRI has

Conclusions

become a well-established imaging tool to evaluate therapeutic responses in SpA. Synovial Tissue Biomarkers in the Spondyloarthropathies Synovial biopsies were studied at baseline and at week 12 in 8 SpA patients treated with infliximab (Baeten et al., 2001a). This study indicated a significant reduction of the vascularity and the expression of VCAM-1, which is a ligand for 47 integrins. As to the inflammatory infiltration, there was a global trend toward reduction of all infiltrating cell types, including macrophages, CD3 and CD4 T cells, lining layer fibroblasts, and neutrophils. Surprisingly, there was a significant increase in both B cells and plasma cells, with these cells becoming the main cell population infiltrating the synovial membrane in four out of the eight patients. These findings have been recently confirmed in an independent placebo-controlled study with infliximab (Kruithof et al., 2002). The pooled data from these studies (20 SpA synovial samples at baseline and week 12 of infliximab therapy) indicate a profound effect on lining layer hyperplasia, vascularity, and inflammatory infiltration (with the exception of B cells and plasma cells), suggesting that the effect is partially mediated by downregulation of the hypervascularity and endothelial activation, leading to a reduction of the influx of inflammatory cells. Similar observations were made after etanercept treatment (Kruithof et al., 2005b). Synovial biopsies were obtained at baseline, and at weeks 12 and 52 from patients with SpA. Etanercept induced a rapid and sustained decrease in clinical and histologic measures of inflammation. The most prominent histologic change was a decrease in the different macrophage subsets (CD68  , CD163  , myeloid-related protein (MRP) 8 and 14). There was normalization of lining layer hyperplasia, and a moderate decrease in vascularity. Synovial expression of MMP-3 and MMP-9 was down-modulated. Patients with active psoriasis and PsA were subjected to arthroscopic synovial biopsy before and 48 h after being randomized to receive a single infusion of infliximab 3 mg/kg (Goedkoop et al., 2004b). Changes in the histologic appearances of the synovial tissue were compared to changes in biopsy samples from patients who received a placebo infusion. Significant decreases in sublining CD3 and CD68 cell populations were observed after 48 h in the treatment group, and not in the placebo group. Another study evaluated patients with active PsA who received infliximab 3 mg/kg at baseline and at weeks 2, 6, 14 and 22, combined with stable MTX therapy, in an open-label study (Canete et al., 2004). Arthroscopic biopsies were obtained at baseline and at week 4, and the primary aim of the study was to evaluate the effects of anti-TNF therapy on vascularity and angiogenesis regulation. Significant improvements in all disease activity measures were observed at week 24. Moreover, significant decreases in measures of vascularity, angiogenesis, and adhesion molecule expression were observed at week 4. In addition, an open-label study of infliximab 5 mg/kg administered at baseline and at weeks 2 and 6, modulators of angiogenesis were evaluated in serial biopsies obtained at baseline and at week 8 (Goedkoop et al., 2004a). Rapid and significant clinical

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improvements were observed. Consistent with previous reports, infliximab therapy produced significant decreases in tissue macrophages, but not T cells, and in several markers of vascularity and angiogenesis, measured by both immunohistochemistry and RT-PCR. MMP and inhibitors are central mediators of joint destruction and participate in ECM degradation by cleavage of ECM constituents such as collagen and proteoglycans. In SpA, it has been suggested that MMP3 may have a role as a tissue biomarker of therapeutic intervention (Vandooren et al., 2004). In a recent extensive study of SpA, including patients with PsA, AS, and USpA, the expression of MMPs 1, 2, 3, and 9, and TIMPs 1 and 2 in synovial tissue was evaluated before and after treatment with infliximab administered at baseline and at weeks 2 and 6. Immunohistochemical analysis demonstrated a significant downregulation of MMP-3 and TIMP-1 in the synovial lining layer, and of all MMPs and TIMPs in the sublining layer following infliximab treatment. A collaborative study was specifically designed to identify synovial tissue biomarkers for early phase clinical trials in SpA (Kruithof et al., 2006). A total of 52 patients with SpA (AS, 19; PsA, 16; USpA, 17) were evaluated. Twenty patients received infliximab 5 mg/kg at baseline and at weeks 2 and 6, 20 received etanercept 25mg twice weekly, and 12 received no effective therapy. Synovial biopsies were obtained at baseline and at week 12. The clinical outcome measures improved significantly in the two TNF blockade cohorts but not in the control group. Of the 52 patients, 35 were categorized as responders and 17 as non-responders. Following an analysis of 10 different cell surface markers and tissue proteins, changes in tissue macrophage subsets, polymorphonuclear leukocytes, and MMP3 expression significantly reflected the response to treatment, supporting a potential role for these mediators as tissue biomarkers of therapeutic intervention in early phase clinical trials in SpA.

CONCLUSIONS Over the past years remarkable progress has been made in the understanding of SpA: the link with IBD has led to the introduction of anti-TNF therapies in this disease which has revolutionized the therapeutic management of SpA. In addition, we have gained insight into the cellular events that are accompanied with SpA-associated synovitis and are beginning to identify and validate other important predisposing genes besides HLA-B27. However, little information is currently available from genomic and proteomic approaches and even more so to translate these findings into the daily management of our patients. In the future one would envision to risk-stratify individual patients at an early stage of their disease according to a combination of clinical parameters, genetic predisposing genes and perhaps even a set of markers defining the composition of the synovial tissue infiltrate. Therefore, much additional work needs to be conducted to validate current findings, identify new predisposing genes or disease-associated proteins and combine this with state of the art bioinformatics approaches to allow translation into the clinic.

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by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. Arthritis Rheum 48, 2011–2018. Maksymowych, W.P., Rahman, P., Reeve, J.P., Gladman, D.D., Peddle, L. and Inman, R.D. (2006). Association of the IL1 gene cluster with susceptibility to ankylosing spondylitis: an analysis of three Canadian populations. Arthritis Rheum 54, 974–985. Martin, T.M., Zhang, G., Luo, J., Jin, L., Doyle, T.M., Rajska, B.M., Coffman, J.E., Smith, J.R., Becker, M.D., Mackensen, F., Khan, M.A. et al. (2005). A locus on chromosome 9p predisposes to a specific disease manifestation, acute anterior uveitis, in ankylosing spondylitis, a genetically complex, multisystem, inflammatory disease. Arthritis Rheum 52, 269–274. Marzo-Ortega, H., McGonagle, D., O’Connor, P. and Emery, P. (2003). Efficacy of etanercept for treatment of Crohn’s related spondyloarthritis but not colitis. Ann Rheum Dis 62, 74–76. Mau, W., Zeidler, H., Mau, R., Majewski, A., Freyschmidt, J., Stangel, W. and Deicher, H. (1988). Clinical features and prognosis of patients with possible ankylosing spondylitis. Results of a 10-year followup. J Rheumatol 15, 1109–1114. McGonagle, D., Gibbon, W., O’Connor, P., Green, M., Pease, C. and Emery, P. (1998). Characteristic magnetic resonance imaging entheseal changes of knee synovitis in spondylarthropathy. Arthritis Rheum 41, 694–700. Miceli-Richard, C., Zouali, H., Said-Nahal, R., Lesage, S., Merlin, F., De Toma, C., Blanche, H., Sahbatou, M., Dougados, M., Thomas, G. et al. (2004). Significant linkage to spondyloarthropathy on 9q31-34. Hum Mol Genet 13, 1641–1648. Mielants, H., Veys, E.M., Joos, R., Suykens, S., Cuvelier, C. and De Vos, M. (1986). Familial aggregation in seronegative spondyloarthritis of enterogenic origin. A family study. J Rheumatol 13, 126–128. Mielants, H.,Veys, E.M., Cuvelier, C. and De Vos, M. (1989). Subclinical involvement of the gut in undifferentiated spondylarthropathies. Clin Exp Rheumatol 7, 499–504. Mielants, H., Veys, E.M., Goethals, K., Van Der Straeten, C. and Ackerman, C. (1990a). Destructive lesions of small joints in seronegative spondylarthropathies: Relation to gut inflammation. Clin Exp Rheumatol 8, 23–27. Mielants, H.,Veys, E.M.,Verbraeken, H., De Vos, M. and Cuvelier, C. (1990b). HLA-B27 positive idiopathic acute anterior uveitis: A unique manifestation of subclinical gut inflammation. J Rheumatol 17, 841–842. Mielants, H., Veys, E.M., Cuvelier, C., De Vos, M., Goemaere, S., Maertens, M. and Joos, R. (1993a). Gut inflammation in children with late onset pauciarticular juvenile chronic arthritis and evolution to adult spondyloarthropathy – a prospective study. J Rheumatol 20, 1567–1572. Mielants, H., Veys, E.M., Goemaere, S., Cuvelier, C. and De Vos, M. (1993b). A prospective study of patients with spondyloarthropathy with special reference to HLA-B27 and to gut histology. J Rheumatol 20, 1353–1358. Mielants, H., Veys, E.M., Cuvelier, C., De Vos, M., Goemaere, S., De Clercq, L., Schatteman, L. and Elewaut, D. (1995). The evolution of spondyloarthropathies in relation to gut histology. II. Histological aspects. J Rheumatol 22, 2273–2278. Mielants, H., Veys, E.M., Cuvelier, C. and De Vos, M. (1996). Course of gut inflammation in spondylarthropathies and therapeutic consequences. Baillieres Clin Rheumatol 10, 147–164. Moll, J.M., Haslock, I., Macrae, I.F. and Wright, V. (1974). Associations between ankylosing spondylitis, psoriatic arthritis, Reiter’s disease, the intestinal arthropathies, and Behcet’s syndrome. Medicine (Baltimore) 53, 343–364.

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Mulherin, D., Fitzgerald, O. and Bresnihan, B. (1996). Synovial tissue macrophage populations and articular damage in rheumatoid arthritis. Arthritis Rheum 39, 115–124. Ogura,Y., Bonen, D.K., Inohara, N., Nicolae, D.L., Chen, F.F., Ramos, R., Britton, H., Moran, T., Karaliuskas, R., Duerr, R.H. et al. (2001). A frameshift mutation in NOD2 associated with susceptibility to Crohn’s disease. Nature 411, 603–606. Olivieri, I., Gemignani, G., Braccini, G., Romagnoli, C. and Pasero, G. (1989). Isolated HLA-B27 associated peripheral enthesitis. J Rheumatol 16, 1519–1521. Oriente, P., Biondi-Oriente, C. and Scarpa, R. (1994). Psoriatic arthritis. Clinical manifestations. Baillieres Clin Rheumatol 8, 277–294. Palmer, D.G., Selvendran,Y., Allen, C., Revell, P.A. and Hogg, N. (1985). Features of synovial membrane identified with monoclonal antibodies. Clin Exp Immunol 59, 529–538. Pap,T., Shigeyama,Y., Kuchen, S., Fernihough, J.K., Simmen, B., Gay, R.E., Billingham, M. and Gay, S. (2000). Differential expression pattern of membrane-type matrix metalloproteinases in rheumatoid arthritis. Arthritis Rheum 43, 1226–1232. Poulter, L.W., Duke, O., Panayi, G.S., Hobbs, S., Raftery, M.J. and Janossy, G. (1985). Activated T lymphocytes of the synovial membrane in rheumatoid arthritis and other arthropathies. Scand J Immunol 22, 683–690. Prajapati, D.N., Knox, J.F., Emmons, J., Saeian, K., Csuka, M.E. and Binion, D.G. (2003). Leflunomide treatment of Crohn’s disease patients intolerant to standard immunomodulator therapy. J Clin Gastroenterol 37, 125–128. Present, D.H., Rutgeerts, P., Targan, S., Hanauer, S.B., Mayer, L., van Hogezand, R.A., Podolsky, D.K., Sands, B.E., Braakman, T., DeWoody, K.L. et al. (1999). Infliximab for the treatment of fistulas in patients with Crohn’s disease. N Engl J Med 340, 1398–1405. Protzer, U., Duchmann, R., Hohler, T., Hitzler, W., Ewe, K., Wanitschke, R., Meyer zum Buschenfelde, K.H. and Marker-Hermann, E. (1996). [Enteropathic spondylarthritis in chronic inflammatory bowel diseases: Prevalence, manifestation pattern and HLA association]. Med Klin (Munich) 91, 330–335. Reece, R.J., Canete, J.D., Parsons, W.J., Emery, P. and Veale, D.J. (1999). Distinct vascular patterns of early synovitis in psoriatic, reactive, and rheumatoid arthritis. Arthritis Rheum 42, 1481–1484. Resnick, D., Feingold, M.L., Curd, J., Niwayama, G. and Goergen, T.G. (1977). Calcaneal abnormalities in articular disorders. Rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, and Reiter syndrome. Radiology 125, 355–366. Revell, P.A., al-Saffar, N., Fish, S. and Osei, D. (1995). Extracellular matrix of the synovial intimal cell layer. Ann Rheum Dis 54, 404–407. Ribbens, C., Andre, B., Jaspar, J.M., Kaye, O., Kaiser, M.J., De Groote, D. and Malaise, M.G. (2000). Matrix metalloproteinase-3 serum levels are correlated with disease activity and predict clinical response in rheumatoid arthritis. J Rheumatol 27, 888–893. Rihl, M., Baeten, D., Seta, N., Gu, J., De Keyser, F., Veys, E.M., Kuipers, J.G., Zeidler, H. and Yu, D.T. (2004). Technical validation of cDNA based microarray as screening technique to identify candidate genes in synovial tissue biopsy specimens from patients with spondyloarthropathy. Ann Rheum Dis 63, 498–507. Rihl, M., Kruithof , E., Barthel, C., De Keyser, F.,Veys, E.M., Zeidler, H., Yu, D.T., Kuipers, J.G. and Baeten, D. (2005). Involvement of neurotrophins and their receptors in spondyloarthritis synovitis: relation to inflammation and response to treatment. Ann Rheum Dis 64, 1542–1549.

Rosenbaum, J.T. (1992). Acute anterior uveitis and spondyloarthropathies. Rheum Dis Clin North Am 18, 143–151. Rudwaleit, M., Siegert, S., Yin, Z., Eick, J., Thiel, A., Radbruch, A., Sieper, J. and Braun, J. (2001). Low T cell production of TNFalpha and IFNgamma in ankylosing spondylitis: Its relation to HLA-B27 and influence of the TNF-308 gene polymorphism. Ann Rheum Dis 60, 36–42. Rutgeerts, P., D’Haens, G., Targan, S., Vasiliauskas, E., Hanauer, S.B., Present, D.H., Mayer, L., Van Hogezand, R.A., Braakman, T., DeWoody, K.L. et al. (1999). Efficacy and safety of retreatment with anti-tumor necrosis factor antibody (infliximab) to maintain remission in Crohn’s disease. Gastroenterology 117, 761–769. Sack, U., Stiehl, P. and Geiler, G. (1994). Distribution of macrophages in rheumatoid synovial membrane and its association with basic activity. Rheumatol Int 13, 181–186. Sandborn, W.J., Feagan, B.G., Hanauer, S.B., Present, D.H., Sutherland, L.R., Kamm, M.A., Wolf, D.C., Baker, J.P., Hawkey, C., Archambault, A. et al. (2001a). An engineered human antibody to TNF (CDP571) for active Crohn’s disease: A randomized doubleblind placebo-controlled trial. Gastroenterology 120, 1330–1338. Sandborn,W.J., Hanauer, S.B., Katz, S., Safdi, M.,Wolf, D.G., Baerg, R.D., Tremaine, W.J., Johnson, T., Diehl, N.N. and Zinsmeister, A.R. (2001b). Etanercept for active Crohn’s disease: A randomized, double-blind, placebo-controlled trial. Gastroenterology 121, 1088–1094. Scarpa, R., Oriente, P., Pucino, A.,Vignone, L., Cosentini, E., Minerva, A. and Biondi Oriente, C. (1988). The clinical spectrum of psoriatic spondylitis. Br J Rheumatol 27, 133–137. Scarpa, R., del Puente, A., D’Arienzo, A., di Girolamo, C., della Valle, G., Panarese, A., Lubrano, E. and Oriente, P. (1992).The arthritis of ulcerative colitis: Clinical and genetic aspects. J Rheumatol 19, 373–377. Schatteman, L., Mielants, H., Veys, E.M., Cuvelier, C., De Vos, M., Gyselbrecht, L., Elewaut, D. and Goemaere, S. (1995). Gut inflammation in psoriatic arthritis: A prospective ileocolonoscopic study. J Rheumatol 22, 680–683. Schlosstein, L., Terasaki, P.I., Bluestone, R. and Pearson, C.M. (1973). High association of an HL-A antigen, W27, with ankylosing spondylitis. N Engl J Med 288, 704–706. Schumacher, H.R. and Bardin, T. (1998). The spondylarthropathies: classification and diagnosis. Do we need new terminologies?. Baillieres Clin Rheumatol 12, 551–565. Seta, N., Granfors, K., Sahly, H., Kuipers, J.G., Song, Y.W., Baeten, D., Veys, E.M., Maksymowych, W., Marker-Hermann, E., Gu, J. et al. (2001). Expression of host defense scavenger receptors in spondylarthropathy. Arthritis Rheum 44, 931–939. Sinz, A., Bantscheff, M., Mikkat, S., Ringel, B., Drynda, S., Kekow, J., Thiesen, H.J. and Glocker, M.O. (2002). Mass spectrometric proteome analyses of synovial fluids and plasmas from patients suffering from rheumatoid arthritis and comparison to reactive arthritis or osteoarthritis. Electrophoresis 23, 3445–3456. Smeets, T.J., Dolhain, R.J., Breedveld, F.C. and Tak, P.P. (1998). Analysis of the cellular infiltrates and expression of cytokines in synovial tissue from patients with rheumatoid arthritis and reactive arthritis. J Pathol 186, 75–81. Szekanecz, Z., Haines, G.K., Lin,T.R., Harlow, L.A., Goerdt, S., Rayan, G. and Koch, A.E. (1994). Differential distribution of intercellular adhesion molecules (ICAM-1, ICAM-2, and ICAM-3) and the MS-1 antigen in normal and diseased human synovia. Their possible pathogenetic and clinical significance in rheumatoid arthritis. Arthritis Rheum 37, 221–231.

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factor alpha (infliximab) versus placebo in active spondylarthropathy. Arthritis Rheum 46, 755–765. van der Linden, S. and van der Heijde, D. (1998). Ankylosing spondylitis. Clinical features. Rheum Dis Clin North Am 24, 663–676, vii. van der Linden, S., Valkenburg, H.A. and Cats, A. (1984). Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum 27, 361–368. Vandooren, B., Kruithof, E., Yu, D.T., Rihl, M., Gu, J., De Rycke, L., Van Den Bosch, F.,Veys, E.M., De Keyser, F. and Baeten, D. (2004). Involvement of matrix metalloproteinases and their inhibitors in peripheral synovitis and down-regulation by tumor necrosis factor alpha blockade in spondylarthropathy. Arthritis Rheum 50, 2942–2953. Vasey, F.B. and Espinoza, L.R. (1984). Psoriatic Arthritis. Grune and Stratton, Orlando FL. Veale, D.,Yanni, G., Rogers, S., Barnes, L., Bresnihan, B. and Fitzgerald, O. (1993). Reduced synovial membrane macrophage numbers, ELAM1 expression, and lining layer hyperplasia in psoriatic arthritis as compared with rheumatoid arthritis. Arthritis Rheum 36, 893–900. Volin, M.V., Shah, M.R., Tokuhira, M., Haines, G.K., Woods, J.M. and Koch, A.E. (1998). RANTES expression and contribution to monocyte chemotaxis in arthritis. Clin Immunol Immunopathol 89, 44–53. Wollenhaupt, J., Kolbus, F., Weissbrodt, H., Schneider, C., Krech, T. and Zeidler, H. (1995). Manifestations of Chlamydia induced arthritis in patients with silent versus symptomatic urogenital chlamydial infection. Clin Exp Rheumatol 13, 453–458. Wright,V. (1978). Seronegative polyarthritis: A unified concept. Arthritis Rheum 21, 619–633. Wright, V. and Moll, J.M. (1976). Psoriatic Arthritis. In Seronegative Polyarthritis. New Holland Publishing Co,Amsterdam. Yang, C., Gu, J., Rihl, M., Baeten, D., Huang, F., Zhao, M., Zhang, H., Maksymowych, W.P., De Keyser, F., Veys, E.M. et al. (2004). Serum levels of matrix metalloproteinase 3 and macrophage colonystimulating factor 1 correlate with disease activity in ankylosing spondylitis. Arthritis Rheum 51, 691–699. Yanni, G., Whelan, A., Feighery, C. and Bresnihan, B. (1994). Synovial tissue macrophages and joint erosion in rheumatoid arthritis. Ann Rheum Dis 53, 39–44. Youssef , P., Roth, J., Frosch, M., Costello, P., Fitzgerald, O., Sorg, C. and Bresnihan, B. (1999). Expression of myeloid related proteins (MRP) 8 and 14 and the MRP8/14 heterodimer in rheumatoid arthritis synovial membrane. J Rheumatol 26, 2523–2528. Zeidler, H., Mau, W. and Khan, M.A. (1992). Undifferentiated spondyloarthropathies. Rheum Dis Clin North Am 18, 187–202. Zhang, G., Luo, J., Bruckel, J., Weisman, M.A., Schumacher, H.R., Khan, M.A., Inman, R.D., Mahowald, M., Maksymowych, W.P., Martin, T.M. et al. (2004). Genetic studies in familial ankylosing spondylitis susceptibility. Arthritis Rheum 50, 2246–2254. Zvaifler, N.J., Tsai, V., Alsalameh, S., von Kempis, J., Firestein, G.S. and Lotz, M. (1997). Pannocytes: Distinctive cells found in rheumatoid arthritis articular cartilage erosions. Am J Pathol 150, 1125–1138.

CHAPTER

89 Asthma Genomics Scott T. Weiss, Benjamin A. Raby and Juan C. Celedón

INTRODUCTION Asthma is a complex disease affecting over 300 million individuals in the developed world (Palmer and Cookson, 2000). Ninety percent of all asthma cases, including asthma in adults, have their origin in childhood. Of concern are the increases in asthma prevalence (CDC, 1995) and hospitalization rates (Weiss et al., 1993). Between 1980 and 1994, the self-reported prevalence of asthma in the United States of America increased from 30.7 to 53.8 per 1000, an increase of 75% (CDC, 1995). This increase has been accompanied by a similar increase in health care utilization and mortality over the same time period (CDC, 1995). An estimated 12.6 billion dollars were spent on the diagnosis and management of asthma in the United States in 1998, of which 58% were direct medical expenditures (Sullivan and Strassels, 2002). Despite the availability of several classes of therapeutic agents for asthma, it has been estimated that as many as one-half of asthmatic patients do not respond to treatment with 2-agonists, leukotriene antagonists, or inhaled corticosteroids (Drazen et al., 2000; Liggett, 2001; Silverman et al., 2002). It is quite likely that genetics and genomics will significantly impact asthma in the next 5 years particularly in the area of prediction of clinical events.

ASTHMA: BASIC PATHOBIOLOGY Asthma is a clinical syndrome of unknown etiology characterized by reversible episodes of airflow obstruction, airway Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1084

hyperresponsiveness, and a chronic inflammatory process of the airways of which mast cells, eosinophils, T-lymphocytes, epithelial cells, and airway smooth muscle cells play a prominent role (Elias et al., 2003). Figure 89.1 shows important pathobiologic features of asthma. CD4 lymphocytes produce IL-3, IL-4, IL-5, IL-13, and GM-CSF and thereby promote the synthesis of IgE, an important allergic effector molecule. Chemokines, such as eotaxin, RANTES, and IL-8 produced by epithelial and inflammatory cells, serve to amplify and perpetuate the inflammatory events. Several bronchoactive mediators, such as histamine, leukotrienes, and neuropeptides are released into the airways and precipitate an asthma attack by causing airway smooth muscle constriction, mucus secretion, and edema. Over time, there is smooth muscle growth and the deposition of subepithelial connective tissue, a process referred to as airway remodeling. Clinically, asthmatics have difficulty exhaling air because of an increase in airway resistance that is a consequence of smooth muscle contraction, inflammation, and remodeling (Figure 89.2). Physiological impairment is quantitated most commonly by the forced expiratory volume in 1 s (FEV1) (Figure 89.2). FEV1 is the volume of air a person can “blow out” in 1 s and is very useful as a measurement of lung function because it is easily obtained, reproducible, and correlated with asthma severity and therapeutic responses (ATS, 1987). While it is difficult to be precise, it is reasonable to estimate that although there is general agreement on the physiology described above, the actual genetic and genomic architecture of these events is just beginning to be described. The above description of events, while true, is likely Copyright © 2009, Elsevier Inc. All rights reserved.

Genome-Wide Linkage Analyses of Asthma and its Intermediate Phenotypes

Growth factors endogenous GC Early environmental factors Allergens Pollution Virus Cigarettes Stress

Cytokines

TH2 endogenous GC immune deviation

Acute inflammation

Tissue remodeling

Leukotrienes Histamine Endothelins/Chemokines Complement Endogenous GC

st on oc ch on Br

Susceptibility genes ADAM33 Fcε RI IL4Rα TGFβ GPRA GSTP1

ric n tio

Figure 89.1

Chronic Inflammation Allergens Pollution Virus Cigarettes Stress

Symptoms Wheezing Dyspnea Cough

Asthma pathobiology 1.

Volume (l)

nonasthmatic

• Airway inflammation • Airway remodeling • Airway responsiveness • Reversible airflow obstruction

Figure 89.2

asthmatic

1

2 3 Time (s) Forced expiratory FEV1  Volume at 1 s

Asthma pathobiology 2.

to only represent 15–20% of the total pathobiology that will be ultimately elucidated by genomic methods.

PREDISPOSITION (GENETIC AND NON-GENETIC) TO ASTHMA Non-Genetic Predisposition Eighty percent of childhood asthmatics exhibit hypersensitivity to indoor aeroallergens (Sears et al., 1991), and high concentrations of indoor allergens can worsen asthma symptoms in sensitized individuals (Rosenstreich et al., 1997). In a small follow-up study of atopic children of atopic parents, exposure to high concentrations of dust mite allergen in the first year of life was associated with higher risk for sensitization and asthma at age 11 (Sporik et al., 1990). Although certain home characteristics are significant predictors of dust mite, cat, and cockroach allergen

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exposure, they may be poor at predicting whether low levels of allergen are present in a home (Chew et al., 1998). Since “low levels” of indoor allergens may lead to sensitization in susceptible individuals, direct measurement of allergen concentrations in house dust have been recommended for use in epidemiologic studies (Chew et al., 1998).Viral respiratory infections are a cause of asthma exacerbations but not a proven cause of disease onset. Passive (primarily in utero) and active cigarette smoking are the other major environmental cause of the disease. It is likely that the response to these environmental exposures has a genetic and or genomic basis; however, virtually nothing is known about the important genetic predictors of adverse response to these environmental agents. Polymorphisms in IL13 and ADRB2 seem to predict response to passive and active cigarette smoking, respectively, but nothing is known about the relationship of genetic polymorphisms to other important environmental exposures. Genetic Predisposition Data from a significant number of studies indicate familial aggregation of asthma (Jenkins et al., 1997;Van Arsdel and Motulsky, 1959). A method frequently used to establish the existence of familial aggregation of a trait is to calculate the recurrence risk to relatives of type R ( ). Using data from Sibbald et al. (1980) resulted in a of 3.3 for asthma. Familial aggregation, however, could result from genetic factors or a shared environment. Estimates of the heritability of asthma in several twin studies conducted around the world have ranged from 36% to 79% (Duffy et al., 1990; Edfors-Lubs, 1971; Hopp et al., 1984; Lubs, 1972; Nieminen et al., 1991; Sibbald et al., 1980), with the highest values coming from studies that had a more comprehensive phenotypic assessment of asthma (Sandford et al., 1996).

GENOME-WIDE LINKAGE ANALYSES OF ASTHMA AND ITS INTERMEDIATE PHENOTYPES To date, 16 groups have reported results of genome-wide linkage analysis for asthma phenotypes (Table 89.1) (Bleecker et al., 1999; Celedon et al., 2007; CSGA, 1997; Daniels et al., 1996; Dizier et al., 2000; Evans et al., 2004; Ferreira et al., 2005; Haagerup et al., 2002; Hakonarson et al., 2002; Hersh et al., 2007b; Koppelman et al., 2002; Laitinen et al., 2001; Ober et al., 1998; Pillai et al., 2006;Wjst et al., 1999; Xu et al., 2000, 2001a, b;Yokouchi et al., 2000). Several of these groups have published second-generation surveys with larger numbers of subjects and/ or markers (Blumenthal et al., 2004; Brasch-Andersen et al., 2006; Dizier et al., 2005; Huang et al., 2003; Meyers et al., 2005; Ober et al., 2000a, b; Xu et al., 2001a, b). In spite of obvious heterogeneity in the design of these studies, 13 regions have shown significant evidence of linkage to asthma or its intermediate phenotypes: chromosomes 2p16 (to airway hyperresponsiveness (AHR), in families from Europe, Australia, and the United States) (Pillai et al., 2006), 2p25 (to AHR, in Chinese families) (Xu et al., 2001a, b), 2q33 (to eosinophil count in Australian

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TABLE 89.1

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Asthma Genomics

Results of genome-wide linkage analyses of asthma and/or its intermediate phenotypesa

Reference

Population

Number of families (subjects)

Linkage to asthma**

Linkage to intermediate phenotypes of asthma

Daniels et al. (1996)

Australian

80 (363)

Not assessed

Skin test index11q12; AHRb  4q, 7p; eosinophil count  6p; atopy  6p, 13q; total serum IgE 16q

Xu et al. (2001a, b); Huang et al. (2003); Blumenthal et al. (2004)

North American (white, Hispanic, African American)

266–287 (885–1931)

6p (whites), 1p (Hispanics), 11q13 (African Americans)

Dust mite allergy19p13 (whites); atopy  21q (Hispanics)

Ober et al. (2000)

Hutterites

1 (693)c

1p, 3p, 5q, 8p

AHR  5p, 19p; dust mite allergy  2q; mold allergy  3q, 11p

Wjst et al. (1999)

German and Swedish

97 (415)

Xu et al. (2000); Koppelman et al. 2002; Meyers et al. (2005)

Dutch

200 (1174)

3p, 5q

AHR  3p, 5q; total serum IgE: 2q, 3q, 5q, 7q21, 12q, 13q; eosinophil count: 2q, 15q, 17q

Yokouchi et al. (2000)

Japanese

47 (197)

4q, 5q31-q33, 6p, 12q, 13q

Not assessed

Dizier M et al. (2000, 2005)

French

107–295 (not available)

1p (asthma and allergic rhinitis)

Eosinophil count: 12q

Laitinen et al. (2001)

Finnish

86 (443)

4q, 7p14-p15 (asthma and increased total serum IgE)

Total serum IgE7p14-p15

Xu et al. (2001a, b)

Chinese

533 (2551)

Not assessed

AHR2p25, 19q; total serum IgE: 1q; cockroach allergy  4q

Haagerup et al. (2002); Brasch-Andersen et al. (2006)

Danish

100 (424)

1p, 5q, 6p, 12q24

Total serum IgE: 3q, 5q, 6p; atopy  6p

Hakonarson et al. (2002)

Icelandic

175 (1134)

14q24

Not assessed

Van Eerdewegh et al. (2002)

North American and British

460 (920)

20p13

Not assessed (results for AHR not presented separately from asthma)

Ferreira et al. (2005)

Australian

202 (591)

1q, 4p, 11p, 17q, 18p, 19p

AHR  6p, 20q; atopy  2q, 3q, 6p, 17q, 20q; dust mite allergy20q13; total serum IgE  10q

Evans et al. (2004)

Australian

539 (2360)

Not assessed

Eosinophil count2q33, 8q

Pillai et al. (2006)

North American, European, Australian

414 (1555)

Celedon et al. (2007); Raby et al. (2007)

Costa Rican

8 (638)

AHR2p16, 4p 12q

AHR12q24 (nonsmokers); total serum IgE20p12 (males)

** Complete linkage results for all chromosomes not published. a

Only regions showing suggestive or significant evidence of linkage (as defined by Lander and Kruglyak) to asthma and/or its intermediate phenotypes (other than measures of lung function) are shown. Regions with significant evidence of linkage to asthma and/or its intermediate phenotypes are bold.

b

AHR  airway hyperresponsiveness.

c

Large pedigree divided into 10–20 sub-pedigrees for data analysis.

Candidate-Gene Association Studies of Asthma

twins) (Evans et al., 2004), 5q31-q33 (to mite-sensitive asthma in a Japanese population) (Yokouchi et al., 2000), 7p14-p15 (to total serum IgE in a Finnish population) (Laitinen et al., 2004), 7q21 (to total serum IgE in a Dutch population) (Xu et al., 2000), 11q12-q13 (to atopy in Australians and asthma in African Americans) (Huang et al., 2003; Laitinen et al., 2004), 12q24 (to asthma in Denmark (Brasch-Andersen et al., 2006) and AHR in Costa Rica (Celedon et al., 2007), 14q24 (to asthma in an Icelandic population) (Hakonarson et al., 2002), 19p13 (to dust mite allergy in whites in the United States) (Blumenthal et al., 2004), 20p12 (to total IgE among Costa Rican males) (Raby et al., 2007), 20p13 (to asthma in families from the United States and United Kingdom) (Van Eerdewegh et al., 2002), and 20q13 (to dust mite allergy in Australians) (Ferreira et al., 2005). In spite of the relatively large number of genomic regions linked to asthma-related phenotypes, only five potential asthmasusceptibility genes (PDH finger protein 11 [PHF11], dipeptidylpeptidase 10 [DPP10], disintegrin and metalloprotease 33 [ADAM 33], G protein-coupled receptor for asthma susceptibility [GPR154 or NPSR1], and human leukocyte antigen G [HLA-G]) have been identified by a positional cloning approach (Allen et al., 2003; Laitinen et al., 2004; Nicolae et al., 2005;Van Eerdewegh et al., 2002; Zhang et al., 2003). In all cases, significant linkage between a chromosomal region and asthma phenotypes in a genome-wide linkage analysis was followed by fine-mapping studies of linkage and association in the linked region. Replication studies of the association between asthma phenotypes and these potential asthma-susceptibility genes have yielded inconsistent results, and no specific asthma-susceptibility variants have been identified within these genes (Hersh et al., 2007a, b). However, results of multiple association studies (Hersh et al., 2007a, b), as well as functional data in rodents and humans provide strong support for a significant role of GPR154 in asthma pathogenesis (Laitinen et al., 2004).

CANDIDATE-GENE ASSOCIATION STUDIES OF ASTHMA Although there are more than 500 genetic association studies for asthma, most suffer from methodological problems including small sample size, non-comprehensive coverage of the gene(s) of interest, failure to correct for multiple testing, and (for casecontrol studies) lack of adjustment for population stratification. To date, there have been reports of positive associations between variants in over 100 genes and asthma phenotypes (Ober and Hoffjan, 2006). Using the gene as the unit of replication (a very liberal criterion), more than 40 associations were replicated in at least two populations and 17 associations were replicated in at least 5 populations (Allen et al., 2003; Hersh et al., 2007a, b; Ober and Hoffjan, 2006). On the other hand, few associations have been replicated for specific polymorphisms in genes for a particular phenotype and in the same direction, suggesting that LD with adjacent variants and/or bias explains a significant proportion of these findings.

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Variants in nine positional candidate genes (IL4, IL4RA, IL13, ADRB2, CD14, TNFA, HLA-DRB1, HLA-DQB1, and FCERIB) have been associated with asthma phenotypes in at least ten populations (Ober and Hoffjan, 2006). Through activation of its receptor, interleukin 4 (IL4) stimulates production of total serum IgE. The IL4 and IL4 receptor α chain (IL4RA) loci are on genomic regions linked to asthma phenotypes (5q31 for IL4 and 16p12 for ILRA). A functional SNP in the promoter of IL4 has been associated with total serum IgE (Rosenwasser et al., 1995), asthma (Rosenwasser et al., 1995), rhinitis (Zhu et al., 2000), asthma severity (Sandford et al., 2000), and atopic dermatitis (Novak et al., 2002). SNPs in exons of IL4RA have been associated with asthma (Mitsuyasu et al., 1998), total serum IgE (Howard et al., 2002), atopic dermatitis (Hershey et al., 1997), and asthma severity (Rosa-Rosa et al., 1999). The interleukin 13 (IL13) and monocyte differentiation antigen CD14 (CD14) genes are on chromosome 5q31-33, a region linked to asthma phenotypes. Experiments in rodents and humans support a critical role of interleukin 13 in asthma pathogenesis (Grunig et al., 1998). Functional SNPs in the promoter and coding regions of IL13 have been associated with asthma (Heinzmann et al., 2003; Howard et al., 2001; van der Pouw Kraan et al., 1999), airway responsiveness (Howard et al., 2001), atopy (DeMeo et al.,2002; Howard et al., 2001), eosinophilia (Hunninghake et al., 2007), and total serum IgE (Graves et al., 2000; Hunninghake et al., 2007). CD14 is a receptor for bacterial cell wall components that may influence immune responses (Guerra et al., 2004). A functional SNP in the promoter of CD14 has been associated with total serum IgE (Baldini et al., 1999), atopy (Ober et al., 2000a, b), food allergy and non-atopic asthma (Woo et al., 2003), and airway responsiveness (O’Donnell et al., 2004). Tumor necrosis factor alpha (TNFA) is a proinflammatory cytokine (Wang et al., 2004), and HLA class II molecules are candidates for controlling immune responses to allergens. The TNFA and human leukocyte antigen DRB1 (HLA-DRB1) genes are on chromosome 6p21, a genomic region linked to asthma phenotypes. A functional variant in the promoter of TNFA has been associated with asthma (Moffatt and Cookson, 1997), airway responsiveness (Moffatt and Cookson, 1997), atopy (Castro et al., 2000), and total serum IgE (Shin et al., 2004). Functional SNPs and/or haplotypes in HLA-DRB1 have been associated with sensitization to specific allergens (Ansari et al., 1989), atopy (Aron et al., 1996), asthma (Di Somma et al., 2003), and total serum IgE (Moffatt et al., 2001). The gene for the beta chain of the high affinity receptor for IgE (FCERIB) is on chromosome 11q13, a genomic region linked to atopy. SNPs and/or haplotypes in FCERIB have been associated with asthma (Cox et al., 1998; Green et al., 1998), total serum IgE (Li and Hopkin, 1997; Shirakawa et al., 1994), and atopy (Shirakawa et al., 1994). Although there are studies refuting the association between each of these nine positional candidate genes and asthma phenotypes, many of these negative studies lacked adequate statistical power to exclude weakto-moderate genetic effects. In addition, multiple studies have found an association between known functional SNPs in seven

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of these genes (IL4, IL4RA, CD14, IL13, TNFA, ADRB2, and HLA-DRB1) and asthma phenotypes. Thus, current evidence suggests that these genes influence asthma pathogenesis.

GENOME-WIDE ASSOCIATION STUDIES OF ASTHMA To date, only one genome-wide association study of asthma has been published (Moffatt et al., 2007) After testing for association between a panel of 317,000 SNPs and asthma in subjects with and without asthma, Cookson and coworkers identified a gene on chromosome 17q21 (ORMDL3) as a potential susceptibility gene for asthma. These findings were then replicated in two independent cohorts. Other genome-wide association studies of asthma and its intermediate phenotypes are ongoing.

ASTHMA GENOMICS Complementing these genetic surveys are a handful of studies examining global gene expression patterns in experimental models of asthma and allergic inflammation, in addition to several clinical studies (Brutsche et al., 2002; Guajardo et al., 2005; Hansel et al., 2005; Karp et al., 2000; Laprise et al., 2004; Lilly et al., 2005; Zimmermann et al., 2003; Zou et al., 2002a, b). Akin to genome-wide SNP association studies, the primary strength of genome-wide microarray surveys of gene expression is that they are hypothesis-free in that no assumptions regarding the underlying pathobiology of the conditions under study are placed on data gathering, analysis or interpretation. As such, these approaches have the potential to reveal previously unsuspected molecules or pathways relating to asthma and allergy. Table 89.2 summarizes microarray studies in asthma assessing in vivo gene expression (studies of cultured asthmarelevant cells, transgenic mouse models of asthma, or pharmacogenetics are not included). In one of the first studies, Karp and colleagues combined pulmonary tissue gene expression data with SNP genotypes (an example of integrative genomics) in segregating backcross mice to identify C5 as a primary mediator of allergic response (Karp et al., 2000). They demonstrated that: (1) C5 was the only differentially expressed gene following allergen challenge among high- (A/J) and low- (C3H/ HeJ) responder mice that also mapped to an AHR locus on chromosome 2; (2) C5 expression is strain dependent and correlates with a 2-bp missense mutation; and (3) blockade of C5 signaling inhibits allergen-mediated responses. Human studies suggest that C5 expression is increased in allergic airway inflammation (Krug et al., 2001) and that C5 genetic variation influences asthma susceptibility (Hasegawa et al., 2004). In a second study, Zimmermann and colleagues (2003) studied patterns of differential gene expression in two models of allergic pulmonary inflammation and identified a common set of differentially expressed genes relating to arginine metabolism, thus implicating this previously unsuspected pathway in the pathogenesis of

allergic inflammation. It is interesting that initial genetic association studies of arginase I and II suggest that genetic variants in this pathway are also related to asthma susceptibility (Li et al., 2006). In contrast to the studies in mouse models, the results from six published studies of gene expression in human populations of asthmatics have been somewhat less exciting in that few truly novel biologic pathways have been reported. We note that these studies evaluated relatively small number of individuals and varied greatly in the tissues studied, analytic methods used, and definitions of significant differential gene expression. It is, therefore, not surprising that the gene signatures identified have little in common with each other. However, it is notable that each of these studies identified genes previously implicated in asthma pathobiology, and several of these genes harbor genetic variation previously associated with asthma-susceptibility and severity (see Table 89.2). It is therefore likely that many of the other genes identified in these studies play previously unrecognized roles in the pathogenesis of asthma. The challenge remains separating the wheat from the chaff. Given the size of these differentially expressed asthma gene sets – as many as 324 genes in one study of nasal epithelium (Guajardo et al., 2005) – experimental validation using animal models is not feasible on a large scale. One approach would be to test all of the identified genes for evidence of genetic association in asthma cohorts. As an example, Laprise and colleagues have demonstrated association to asthma with five polymorphisms (including one non-synonymous variant) in the CX3CR1 fractalkine receptor (Tremblay et al., 2006). This gene was among those demonstrating greatest differential expression in bronchial epithelium derived from asthmatics (Laprise et al., 2004). The most important cumulative result of these early studies is that together they demonstrate the feasibility of using expression profiling for the study of asthma, and should prompt a second generation of genomic epidemiologic studies. We suggest that the first step include repeating some of these earlier study designs but using considerably larger sample size, with a focus on identifying gene expression signatures and global patterns of gene expression (rather than specific genes). These studies should be large enough to enable splitting of the cohort in two to allow follow-up validation studies of the predictive accuracy of any identified signatures – such as assessment of the predictive power of identified sets in differentiating asthma affection status, atopic status, or disease severity. In addition, these studies should include collection of DNA from all subjects to enable integration of gene expression data with SNP genotype data for the identification of asthma-associated regulatory variation. In this way, investigators will not only have a catalog of differentially expressed asthma genes, but also a set of putative functional variants for follow-up in well-designed genetic association studies.

SCREENING Because asthma is a clinical syndrome, it is subject to potential diagnostic bias by physicians. Factors such as age, presence

Screening

TABLE 89.2

■

Published expression microarray studies in asthma**

Study

Organism

Tissue/cell type

Phenotype assessed

Sample size

Major findings

Karp et al. (2000)

A/J and C3H/HeJ mice

Lung tissue

Ovalbumin challenge

4 A/J, C3H/HeJ, F1 8 F1xA/J backcross

21 genes overlapping all cross-strain comparisons, including C5, on chromosome 2 (airways responsiveness QTL)

Zou et al. (2002a, b)

Cynomolgus monkeys

Lung tissue

IL4 or Ascaris suum inhalational challenge

Controls  6 IL4  1, Ascaris  6

149 genes (2.5-fold change), including eotaxin, MCP-1, PARC, COL2A1

Zimmermann et al. (2003); Munitz et al. (2007)

Balb/c mice

Lung tissue

Allergen challenge with ovalbumin or A. Fumigatus

Ova  3 A. Fumigatus  3 Control  5

291 genes overlapping. larginine metabolism genes identified: CAT2, Arginase I and Arginase II. CD48 antigen later implicated following allergen challenge

Brutsche et al. (2002)

Human

Peripheral blood mononuclear cells

Atopic status

Atopy and Asthma  18 Developed composite score (10 genes) Atopy, no asthma  8 Controls  14 with 96% sensitivity and 92% specificity for differentiating atopy from controls. Included IL1RA1, IL6, RET, CD71

Laprise et al. (2004)

Human

Bronchial wall

Asthma versus normal pre- and post-ICS

Asthma  4 Normal  4

79 genes, including 21 previously implicated in asthma, including ALOX15, NOS2A, TRAa, MUC5A, COL2A1

Hansel et al. (2005)

Human

Peripheral blood CD4 lymphocytes

Asthma severity

Mild, no ICS  5 Mild, ICS  5 Severe  5

40 genes, including TGFB1, UTS2, TRDa@, JUND

Guajardo et al. (2005)

Human

Nasal epithelium

Asthma exacerbation

Control  10 Stable  10 Exacerbation  10

324 genes (2-fold change) including IL1b, Defensinb1, IL1R1, retinoic acid receptor

Lilly et al. (2005)

Human

Bronchial epithelium

Segmental airway allergen challenge

5 asthmatics pre- and post- challenge

149 genes (2-fold change) including IL1RN, IL1b, PTGFR, MCP-1, NFKB, MIP1b

Youssef et al. (2007)

Human

Peripheral blood basophils

Response to IgE receptor cross-linking

3 nonreleasers and 5 releasers, pre- and post- cross-linking

253 differentially expressed genes following “releaser” basophil stimulation, including RANTES, MIP1, VEGF, heatshock proteins

** Studies of cultured asthma-relevant cells, transgenic mouse models of asthma, or pharmacogenetics are not included.

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or absence of allergy, presence or absence of reversible airflow obstruction, a family history of the disorder, and the presence or absence of cigarette smoking may influence whether a physician will actually diagnose asthma or another condition. Thus, the exclusive use of a physician diagnosis of asthma in genetic studies may result in significant misclassification of true asthmatic subjects. Rigorous clinical criteria for the definition of asthma, include airway responsiveness, respiratory symptoms, and physician-diagnosis of asthma; such rigorous criteria will likely minimize the misclassification of subjects which can compromise the power of genetic analysis methods to identify diseaserelated genes. One of the first genomic applications in the area of screening will be for asthma diagnosis. The clinical situation is such that 40% of children wheeze in the first year of life and 20% continue to wheeze at age 3 and at age 6, but only half of these are the same ones at these latter two ages. A genomic predictive test to predict which of the 1-year olds will go on to get asthma at age 6 would have great clinical utility and would advance clinical practice. Such a test is feasible in the next 5 years. The advantages of intermediate phenotypes are that they are objectively defined and that they may be influenced by a smaller number of genes than the disease of interest. Asthma intermediate phenotypes can be divided into two groups, based on the definition of asthma. The first group includes lung function phenotypes: airway responsiveness, FEV1, and bronchodilator response. The second group includes factors influencing airway inflammation. A key component in the definition of asthma is “bronchial hyperresponsiveness to a variety of stimuli.” Although the population prevalence of increased airway responsiveness may be two- to three-fold higher than the prevalence of asthma, studies in both adults and children now demonstrate that increased airway responsiveness antedates the occurrence of asthma. Airway responsiveness is an integrated physiologic trait that depends on airway geometry, airway epithelium, autonomic nervous system function, and smooth muscle. Population-based studies have demonstrated that airway responsiveness is closely related to the level of FEV1. Environmental exposures, such as allergens, viral infections, and cigarette smoke, increase airway responsiveness in asthmatic subjects. Increased airway responsiveness is an independent predictor of subsequent accelerated decline in FEV1. Airway responsiveness testing can be expressed as either PD20 (i.e., the provocative dose eliciting a 20% drop in FEV1), or as dose-response slope (i.e., the rate of decrease in FEV1 from the first to the last concentration of methacholine administered). Both dichotomous and continuous phenotypes have been utilized in a variety of genetic studies. FEV1 is a physiological measure of lung function that is thought to reflect the pathobiology of airway obstruction. Age, height, and gender are all important determinants of FEV1 in both normal and diseased subjects. In young asthmatics, the level of FEV1 is usually normal or, if reduced, it does increase with treatment with a bronchodilator (reversible airflow obstruction).

There is, however, a difference in the FEV1 of asthmatics relative to normal subjects, and it is now clear that asthmatics have reductions in growth of FEV1 in childhood and accelerated decline in FEV1 in adulthood. FEV1 is highly predictive of asthmatic attacks, is correlated with airway responsiveness and bronchodilator response, predicts asthma severity, and is correlated with peripheral blood eosinophil count. Bronchodilator response is usually assessed as a response to single or multiple doses of an inhaled beta agonist with preand post-measurement of FEV1 (delta FEV1). Bronchodilator response is correlated with FEV1 and airway responsiveness cross-sectionally, but probably represents a distinct phenotype. There is no agreement, however, on how bronchodilator response should be expressed. There are at least five different methods: (1) delta FEV1 divided by baseline FEV1 in liters, expressed as a percentage; (2) delta FEV1 expressed as an absolute value in liters; (3) delta FEV1 divided by predicted FEV1, expressed as a percentage; and (4) delta FEV1 divided by the difference between predicted and initial FEV1 value, expressed as a percentage, and (5) as standardized residuals. Each of these indices of bronchodilator response has problems associated with it. It has been argued, for example, that using delta FEV1 as a percentage of the baseline level spuriously amplifies the recorded bronchodilator response in patients with a very low FEV1. Other investigators believe that this measurement does reflect the greater clinical benefit in the subject with poor initial FEV1 compared to a subject with a relatively normal baseline FEV1. In addition to controversy concerning “the best index” of bronchodilator response, there is little consensus on what constitutes a significant bronchodilator response. As a result, various cut-off values for the different indices of delta FEV1 have been proposed. Whereas Douma and colleagues chose an increase of 9% in delta FEV1 percent predicted as a “significant” bronchodilator response, the ATS has defined a significant bronchodilator response as a delta FEV1 percent baseline 12%. In view of the arbitrariness of any cut-off value, Brand and colleagues have proposed to use this intermediate phenotype as a continuous variable. Serum total and allergen-specific IgE levels increase with increasing age up until age 15–20, at which point they decline steadily throughout adult life. The distributions are similar in males and females. Environmental factors associated with higher levels include bacterial and parasitic infections, allergen exposure, and cigarette smoking. Serum total IgE levels are log normally distributed and there is no consistent cut-off associated with the development of symptoms. Burrows documented an association of total IgE with airway responsiveness, level of lung function, and symptoms in asthmatic subjects. The prevalence of skin test reactivity to common aeroallergens is more common in asthmatic than non-asthmatic children. Skin test reactivity is an immediate type hypersensitivity reaction in the skin to a prick test with a variety of environmental aeroallergens. Although there are no significant gender differences in skin test response, there are significant age differences, with peak prevalence at about age 15–20 and a progressive decline

Pharmacogenetics

afterward. Skin test reactivity is usually assessed by measuring the perpendicular diameter (in millimeters) of the skin wheal and subtracting a negative control. A variety of skin test indices have been developed based on the size of reactions to a battery of antigens. Some individuals have simply summed the skin test wheals and taken the mean wheal size as a continuous index of skin test reactivity; others have used an ordinal scale. Skin test reactivity is correlated cross-sectionally with serum total and allergen-specific IgE levels, airway responsiveness, and asthma. In addition, skin test reactivity predicts decline in FEV1 in older adults. Peripheral blood eosinophilia is known to be associated with increased airways responsiveness and higher serum total IgE levels. Eosinophil counts vary with increasing age, are higher in women than in men, and increase in response to cigarette smoking exposure, parasitic infection, and bacterial infection. The distribution is log normally distributed. Other potentially relevant variables include age, gender, season of the year, smoking, and indoor allergens. Age, gender, season, and smoking history can be readily assessed with a questionnaire, but assessment of indoor allergen exposures likely require direct measurements. All of the intermediate phenotypes mentioned here, airway responsiveness, FEV1, eosinophils, bronchodilator response, total and specific IgE are all amenable to genomic approaches or joint genetic and genomic approaches to define the genes involved in each phenotype. Then systems biology and KEGG pathways can be employed to find the overlap between them.

DIAGNOSIS Because Asthma is a clinical syndrome there is no gold standard test for the diagnosis. Active doctor diagnosed asthma has a close to 100% correlation with a positive test for airway responsiveness. However, the test is overly sensitive and not specific, with many non-asthmatics having a positive response. Similar results can be obtained for all of the potential screening tests noted above. For this reason parental or personal self-report remains the diagnostic standard for genetic and clinical studies often confirmed by additional physiologic testing of bronchoconstrictive or bronchodilator response.

PROGNOSIS Most asthma is diagnosed in childhood with prospective studies suggesting that 90% of all cases are diagnosed by age 6 years. As the lung grows and airways enlarge as many as half of all children with asthma outgrow overt disease yet may retain the intermediate phenotypes of allergy or airway responsiveness. These are the genetically susceptible individuals that are most likely to have a recurrence of symptoms with a significant environmental exposure in adult life such as viral respiratory infection or cigarette smoking. Often if the early life event is in the

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distant past the adult will not recall the presence of symptoms before the age of 6 and will presume the onset is incident in adulthood. Allergic asthma in childhood is less likely to remit than nonallergic asthma and can progress to chronic obstructive lung disease in an unknown percentage of cases. Again omics can be used to define the various subtypes of disease, the natural history of these subtypes and any potential genetic interaction with age.

PHARMACOGENETICS The two main types of asthma drugs are the so-called “reliever” drugs that target the acute bronchoconstriction and the socalled “controller” drugs that are used to reduce the severity of airway inflammation and the severity and frequency of obstruction (CSGA, 1997). The main reliever drugs are rapid-acting β2-agonists (e.g., albuterol, metaproterenol, pirbuterol, levalbuterol), which relax the bronchial smooth muscle by activating β2-adrenergic receptors. This is the treatment of choice for very mild asthma. For moderate and severe asthma, the reliever treatment is usually combined with controller treatment. The two commonly used classes of controller agents are the inhaled glucocorticoids and the leukotriene inhibitors. Inhaled glucocorticoids (e.g., budesonide, beclomethasone, flunisolide, and fluticasone) and leukotriene inhibitors (e.g., montelukast and zafirlukast) modify the inflammatory micro-environment of the airway to reduce airway obstruction and hyperresponsiveness. There is large interindividual variation in the treatment response to asthma medications (Drazen et al., 2000; Szefler et al., 2002). For example, in a study by Malmstrom and colleagues comparing the efficacy of the inhaled steroid beclomethasone (200ug bid) with the leukotriene antagonist montelukast (10 mg/qd), it is clear that both drugs are effective over a 12week course of treatment with a mean increase in FEV1 of 13.1% for the beclomethasone group and 7.4 % for the montelukast group (Figure 3a) (Malmstrom et al., 1999). However, when these same data are viewed from a different perspective, focusing on the number of individuals as a function of percent change in FEV1 from baseline, it is clear that many patients had little response (Figure 3b). In fact, 22% of patients appear to have had an adverse response to treatment with a decline in FEV1 at 12 weeks compared with baseline. The mean therapeutic improvement in FEV1 for all patients is driven by a dramatic increase in FEV1 in a minority of trial subjects. In a similar study, 38% of patients randomized to inhaled budesonide or fluticasone demonstrated improvements in FEV1 of under 5% over the course of 24 weeks (Szefler et al., 2002). A unique subset of up to 25% of the non-responders can be classified as having glucocorticoid-resistant asthma (Chan et al., 1998). These patients are non-responsive to even high doses of oral steroids. Furthermore, a careful analysis of these studies indicates that individuals responsive to one class of asthma drug may not necessarily be the individuals responsive to a different class of asthma drug. Despite these studies, there is no universally accepted definition

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Figure 89.3a Standard approach to clinical trials data showing mean effect by treatment group.

Figure 89.3b Histogram of individual response to beclomethasone subjects in first arm of 3a.

of a steroid (or β2-agonist) non-responder. These types of data illustrating variable drug efficacy are not limited to asthma drug trials but can be found for almost all classes of drugs. The degree to which this interindividual variability is genetic remains uncertain and probably differs with each class of asthma drug. Moreover, interindividual variability may depend on the particular preparation within a class of asthma drug. Calculations of the repeatability (r) of the treatment response for all three classes of asthma drugs, defined as the fraction of the total population variance that results from within individual differences, show values for r between 60% and 80% indicating the upper limit of the genetic component and suggesting that a substantial fraction of the variance of the treatment response could be genetic (Drazen et al., 2000). The leukotrienes (LT) are a family of lipoxygenated eicosatetraenoic acids derived from arachidonic acid and produced

in the airways of asthmatics that are potent bronchoconstrictors. Of the many enzymes involved in the formation of the leukotrienes, polymorphisms in two, ALOX5 and LTC4S have been associated with altered response to LT inhibitors. ALOX5 catalyzes the conversion of arachidonic acid to LTA4, which is a rate limiting step involved in the synthesis of all leukotrienes (Silverman and Drazen, 1999). In 221 patients with asthma who received either high-dose ABT-761, an ALOX5 inhibitor similar to zileuton, (n114) or placebo (n107) treatment, we found that approximately 6% of asthmatic patients had no wildtype allele at the ALOX5 promoter locus and had a diminished response to ABT-761 treatment (Drazen et al., 1999). These findings were consistent with the hypothesis that repeats of the –GGGCGG- sequence other than the wild type are associated with decreased gene transcription and ALOX5 product production. This has recently been confirmed in cells obtained from patients with asthma (Kalayci et al., 2003, 2006). Another enzyme of the leukotriene pathway, LTC4S, is responsible for the adduction of glutathione at the C-6 position of the arachidonic acid backbone to form LTC4, a potent bronchoconstrictor. There is a known SNP in the LTC4S promoter, A-444C, with a C allele frequency of 0.19 in normal subjects and of 0.27 in patients with severe asthma (Sayers et al., 2003). The 444C allele creates an activator protein2 binding sequence that appears to be functional (Sayers et al., 2003), suggesting that the 444C variant is associated with enhanced cysteinyl leukotriene production. Sampson and colleagues found that, among asthmatic subjects treated with zafirlukast (20 mg bid), those homozygous for the A allele (n10) at the 444 locus had a lower FEV1 response than those with the C/C or C/A (n13) genotype (Sampson et al., 2000). These findings provide possible evidence of a second pharmacogenetic locus in addition to the ALOX5 promoter locus modulating the leukotriene pathway. Studies evaluating the 2-agonist pathway have focused largely on the 2-adrenergic receptor gene (ADRB2). Numerous clinical (Israel et al., 2000, 2001; Reihsaus et al., 1993) and cellular (Liggett, 2000) studies, including one recent prospective, genotype-stratified, clinical trial (Israel et al., 2004), support the association of variation in this gene with response to inhaled 2agonist therapy. Other genes in the 2-agonist pathway are now being actively investigated (Tantisira et al., 2005). Genetic associations have also been reported for response to corticosteroids in asthma with the following phenotypes: lung function change (Tantisira et al., 2004b), airway constriction (Tantisira et al., 2004a) and relaxation (Tantisira et al., 2005), severe exacerbations (Tantisira et al., 2005), and steroid resistance.

MONITORING During an asthma attack, patients experience shortness of breath, cough, and/or wheezing. Between attacks, patients may be asymptomatic or they may have chronic symptoms of breathlessness with mild to moderate exertion or episodes of nocturnal

References

awakening due to airway narrowing at night. There are two primary ways to monitor asthma patients either in the clinical setting or in a drug trial: symptoms or lung function testing. Spirometry is described above. In clinical drug trials, asthma symptoms are commonly graded and recorded by the patient to give an index of the symptomatic response to asthma treatment (Juniper et al., 1994). Other potentially useful indices of the effects of asthma treatment are measures of healthcare utilization including doctor visits, urgent care emergency room visits, and hospitalizations. Changes in FEV1, symptom scores, and healthcare utilization that occur during the course of therapy are important outcome indicators that are used in clinical trials. It is possible proteomics or genomics will contribute in this area by providing reliable and useful for monitoring clinical performance biomarkers of inflammation that are reliable and useful for monitoring clinical performance.

NOVEL AND EMERGING THERAPEUTICS With the exception of the anti IgE drug Omalizamab, there have been no new drugs for asthma in the past 20 years. Much work is currently being done in creating combinations of inhaled steroids and beta agonists to be given concurrently. Pharmacogenetics holds the promise of identifying the subsets of patients responding (or not responding) to all four classes of

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asthma medications. Predictive tests are currently in development based on genomic approaches to treatment response for inhaled steroids and short acting beta agonists and should be deployed in clinical practice in the next 5 years. Recently, there has been some interest in cytokine blocking agents and their potential use in asthma for example, anti-Il4 or -Il5; however, toxic side effects have precluded their movement into clinical trials.

CONCLUSIONS Asthma genomics has the potential to have a major impact in the next 5–10 years: first, by the development of genomic predictors of disease occurrence, response to asthma medication, and asthma severity (exacerbations). Prediction will be based on genomics of CD4 T lymphocytes in peripheral blood or whole genome scan SNP data or the integration of these two data sources.

ACKNOWLEDGEMENTS The authors wish to acknowledge their colleagues in the Channing Laboratory Respiratory Genetics Group, and the Center for Genomic Medicine at Brigham and Women’s Hospital.

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association and functional studies of promoter polymorphisms in the leukotriene C4 synthase gene (LTC4S) in asthma. Thorax 58, 417–424. Sears, M., Burrows, B., Flannery, E., Herbison, G., Hewitt, C. and Holdaway, M. (1991). Relation between airway responsiveness and serum IgE in children with asthma and in apparently normal children. N Engl J Med 325, 1067–1071. Shin, H.D., Park, B.L., Kim, L.H., Jung, J.H., Wang, J.H., Kim, Y.J., Park, H.S., Hong, S.J., Choi, B.W., Kim, D.J. et al. (2004). Association of tumor necrosis factor polymorphisms with asthma and serum total IgE. Hum Mol Genet 13, 397–403. Shirakawa, T., Li, A., Dubowitz, M., Dekker, J.W., Shaw, A.E., Faux, J.A., Ra, C., Cookson, W.O. and Hopkin, J.M. (1994). Association between atopy and variants of the beta subunit of the high-affinity immunoglobulin E receptor (see comments). Nat Genet 7, 125–129. Sibbald, B., Horn, M.E., Brain, E.A. and Gregg, I. (1980). Genetic factors in childhood asthma. Thorax 35, 671–674. Silverman, E.S. and Drazen, J.M. (1999). The biology of 5-lipoxygenase: Function, structure, and regulatory mechanisms. Proc Assoc Am Physicians 111, 525–536. Silverman, E.S., Hjoberg, J., Palmer, L.J., Tantisira, K.G., Weiss, S.T. and Drazen, J.M. (2002). Application of pharmacogenetics to the therapeutics of asthma. In Therapeutic Targets of Airway Inflammation (D. Huston, ed.), Marcel Dekker, New York. Sporik, R., Holgate, S., Platts-Mills, T. and Cogswell, J. (1990). Exposure to house-dust mite allergen and the development of asthma in childhood. N Engl J Med 323, 502–507. Statement of the American Thoracic Society (1987). Standardization of spirometry – 1987 update. Am Rev Respir Dis 136, 1285–1298. Sullivan, S.D. and Strassels, S.A. (2002). Health economics. In Asthma and COPD. Basic Mechanisms and Clinical Management (N.C. Thomson, ed.),Academic Press, San Diego, pp. 657–671. Szefler, S.J., Martin, R.J., King, T.S., Boushey, H.A., CHerniack, R.M., Chinchilli,V.M., Craig, T.J., Dolovich, M., Drazen, J.M., Fagan, J.K. et al. (2002). Significant variability in response to inhaled corticosteroids for persistent asthma. J Aller Clin Immuno 109, 410–418. Tantisira, K., Small, K., Litonjua, A., Weiss, S. and LIggett, S. (2005). Molecular properties and pharmacogenetics of a polymorphism of adenylyl cyclase type 9 in asthma: Interaction between beta-agonist and corticosteroid pathways. Hum Mol Genet 14, 1671–1677. Tantisira, K.G., Hwang, E., Raby, B., Silverman, E.S., Lake, S.L., Richter, B.G., Peng, S.L., Drazen, J.M., GLimcher, L.H. and Weiss, S.T. (2004). TBX21: A functional variant predicts improvement in asthma with the use of inhaled corticosteroids. Proc Natl Acad Sci USA 101, 18099–18104. Tantisira, K.G., Lake, S., Silverman, E.S., Palmer, L.J., Lazarus, R., Silverman, E.K., Liggett, S.B., Gelfand, E.W., Rosenwasser, L.J., Richter, B. et al. (2004). Corticosteroid pharmacogenetics: Association of sequence variants in CRHR1 with improved lung function in asthmatics treated with inhaled corticosteroids. Hum Mol Genet 13, 1353–1359. Tremblay, K., Lemire, M., Provost, V., Pastinen, T., Renaud, Y., Sandford, A.J., Laviolette, M., Hudson, T.J. and Laprise, C. (2006). Association study between the CX3CR1 gene and asthma. Genes Immun 7, 632–639. Van Arsdel, P.P. and Motulsky, A.G. (1959). Frequency and heritability of asthma and allergic rhinitis in college students. Acta Genet Med Gemellol 9, 101–114. van der Pouw Kraan, G.C.T.M, van Veen, A., Boeije, L.C.M, van Tuyl, S.A.P, de Groot, E.R., Stapel, S.O., Bakker, A.,Verweij, C.L.,Aarden, L.A. and

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van der Zee, J.S. (1999). An IL-13 promoter polymorphisms associated with increased risk of allergic asthma. Genes Immunity 1, 61–65. Van Eerdewegh, P., Little, R.D., Dupuis, J., Del Mastro, R.G., Falls, K., Simon, J., Torrey, D., Pandit, S., McKenny, J., Braunschweiger, K. et al. (2002). Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature 418, 426–430. Wang,T.N., Chen,W.Y.,Wang,T.H., Chen, C.J., Huang, L.Y. and Ko,Y.C. (2004). Gene–gene synergistic effect on atopic asthma: tumour necrosis factor-alpha-308 and lymphotoxin-alpha-Ncol in Taiwan’s children. Clin Exp Allergy 34, 184–188. Weiss, K.B., Gergen, P.J. and Wagener, D.K. (1993). Breathing better or wheezing worse? The changing epidemiology of asthma morbidity and mortality. Annu Rev Public Health 14, 491–513. Wjst, M., Fischer, G., Immervoll, T., Jung, M., Saar, K., Rueschendorf, F., Reis, A., Ulbrecht, M., Gomolka, M., Weiss, E.H. et al. (1999). A genome-wide search for linkage to asthma. German Asthma Genetics Group. Genomics 58, 1–8. Woo, J.G., Assa’ad, A., Heizer, A.B., Bernstein, J.A. and Hershey, G.K. (2003). The -159C T polymorphism of CD14 is associated with nonatopic asthma and food allergy. J Aller Clin Immunol 112, 438–444. Xu, J., Postma, D.S., Howard, T.D., Koppelman, G.H., Zheng, S.L., Stine, O.C., Bleecker, E.R. and Meyers, D.A. (2000). Major genes regulating total serum immunoglobulin E levels in families with asthma. Am J Hum Genet 67, 1163–1173. Xu, J., Meyers, D.A., Ober, C., Blumenthal, M.N., Mellen, B., Barnes, K.C., King, R.A., Lester, L.A., Howard, T.D., Solway, J. et al. (2001). Collaborative Study on the Genetics of Asthma. Genomewide screen and identification of gene-gene interactions for asthma-susceptibility loci in three US populations: Collaborative study on the genetics of asthma. Am J Hum Genet 68, 1437–1446. Xu, X., Fang, Z., Wang, B., Chen, C., Guang, W., Jin, Y., Yang, J., Lewitzky, S., Aelony, A., Parker, A. et al. (2001). A genome-wide search for quantitative-trait loci underlying asthma. Am J Hum Genet 69, 1271–1277.

RECOMMENDED RESOURCE Respiratory Genetics. (2005). Silverman, E.K., Shapiro, S.D., Lomas, D.A., Weiss, S.T. (eds.) Hodder Arnold. Comprehensive textbook of respiratory genetics.

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Yokouchi,Y., Nukaga,Y., Shibasaki, M., Noguchi, E., Kimura, K., Ito, S., Nishihara, M., Yamakawa-Kobayashi, K., Takeda, K., Imoto, N. et al. (2000). Significant evidence for linkage of mite-sensitive childhood asthma to chromosome 5q31-q33 near the interleukin 12B locus by a genome-wide search in Japanese families. Genomics 66, 152–160. Youssef , L.A., Schuyler, M., Gilmartin, L., Pickett, G., Bard, J.D.,Tarleton, C.A., Archibeque, T., Qualls, C., Wilson, B.S. and Oliver, J.M. (2007). Histamine release from the basophils of control and asthmatic subjects and a comparison of gene expressions between “releaser” and “nonreleaser” basophils. J Immunol 178, 4584–4594. Zhang, J., Rowe, W.L., Clark, A.G. and Buetow, K.H. (2003). Genomewide distribution of high-frequency, completely mismatching SNP haplotype pairs observed to be common across human populations. Am J Hum Genet 73, 1073–1081. Zhu, S., Chan-Yeung, M., Becker,A.B., Dimich-Ward, H., Ferguson,A.C., Manfreda, J., Watson, W.T., Pare, P.D. and Sandford, A.J. (2000). Polymorphisms of the IL-4, TNF-alpha, and Fcepsilon Rlbeta genes and the risk of allergic disorders in at-risk infants. Am J Respir Crit Care Med 161, 1655–1659. Zimmermann, N., King, N.E., Laporte, J.,Yang, M., Mishra,A., Pope, S.M., Muntel, E.E., Witte, D.P., Pegg, A.A., Foster, P.S. et al. (2003). Dissection of experimental asthma with DNA microarray analysis identifies arginase in asthma pathogenesis. J Clin Invest 111, 1863–1874. Zou, J., Young, S., Zhu, F., Gheyas, F., SKeans, S., Wan, Y., Wang, L., Ding, W., BIllah, M., McClanahan, T., Coffman, R.L. et al. (2002). Microarray profile of differentially expressed genes in a monkey model of allergic asthma. Genome Biol 3, research0020. Zou, J., Young, S., Zhu, F., Xia, L., Skeans, S., Wan, Y., Wang, L., McClanahan, T., Gheyas, F., Wei, D. et al. (2002). Identification of differentially expressed genes in a monkey model of allergic asthma by microarray technology. Chest 121, 26S–27S.

CHAPTER

90 Genomic Aspects of Chronic Obstructive Pulmonary Disease Peter J. Barnes

INTRODUCTION Chronic obstructive pulmonary disease (COPD) is characterized by progressive development of airflow limitation that is not fully reversible (Barnes, 2000a). The term COPD encompasses chronic obstructive bronchiolitis with obstruction of small airways and emphysema with enlargement of airspaces and destruction of lung parenchyma, loss of lung elasticity and closure of small airways (Hogg, 2004). Chronic bronchitis, by contrast, is defined by a productive cough of more than 3 months duration for more than 2 successive years; this reflects mucous hypersecretion and is not necessarily associated with airflow limitation. COPD is common and is increasing globally. It is now the fourth leading cause of death in the United States and the only common cause of death that is increasing. This is likely to be an underestimate as COPD is likely to be contributory to other common causes of death. It is predicted to become the third most common cause of death and the fifth most common cause of chronic disability worldwide in the next few years (Lopez et al., 2006). It currently affects more than 5% of the adult population and is underdiagnosed in the community (Chapman et al., 2006). COPD consumes an increasing proportion of health care resources, which currently exceed those devoted to asthma by more than threefold. There is persuasive evidence that the susceptibility to develop COPD is genetically determined, although the genes have not

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yet been identified. Genomics and proteomics are currently in use to understand the abnormal protein expression in this disease as a way of understanding its complex pathophysiology. There are clearly different phenotypes of COPD that need to be better defined. Finally pharmacogenomics may have an impact on COPD therapy in the future. This chapter gives an overview of COPD and highlights where genomic medicine is relevant to the future understanding and management of this common disease.

PREDISPOSITION Several environmental and endogenous factors, including genes, increase the risk of developing COPD (Table 90.1). Environmental Factors In industrialized countries, cigarette smoking accounts for most cases of COPD, but in developing countries other environmental pollutants, such as particulates associated with cooking in confined spaces, are important causes. It is likely that there are important interactions between environmental factors and a genetic predisposition to develop the disease. Air pollution (particularly sulfur dioxide and particulates), exposure to certain occupational chemicals such as cadmium and passive smoking may all be additional risk factors.The role of airway hyperresponsiveness and allergy as risk factors for COPD is still uncertain.

Copyright © 2009, Elsevier Inc. All rights reserved.

Pathophysiology

TABLE 90.1

Risk factors for COPD

Environmental factors

Endogenous (host) factors

Cigarette smoking Active Passive Maternal Air pollution Outdoor Indoor: biomass fuels Occupational exposure Dietary factors High salt Low antioxidant vitamins Low unsaturated fatty acids Infections

1-Antitrypsin deficiency Other genetic factors Ethnic factors Airway hyperresponsiveness?

Low birth weight

Atopy, serum IgE and blood eosinophilia are not important risk factors. However, this is not necessarily the same type of abnormal airway responsiveness that is seen in asthma. Low birth weight is also a risk factor for COPD, probably because poor nutrition in fetal life results in small lungs, so that decline in lung function with age starts from a lower peak value. Genetic Factors Longitudinal monitoring of lung function in cigarette smokers reveals that only a minority (15–40% depending on definition) develop significant airflow obstruction due to an accelerated decline in lung function (2- to 5-fold higher than the normal decline of 15–30 ml FEV1/year) compared to the normal population and the remainder of smokers who have consumed an equivalent number of cigarettes (Figure 90.1). The rate of decline in lung function in the general population (Framingham Study) has a heritability of about 50% (Gottlieb et al., 2001). This strongly suggests that genetic factors may determine which smokers are susceptible and develop airflow limitation. Further evidence that genetic factors are important is the familial clustering of patients with early onset COPD and the differences in COPD prevalence between different ethnic groups (Sandford and Silverman, 2002). Patients with 1-antitrypsin deficiency (proteinase inhibitor (Pi) ZZ phenotype with 1-antitrypsin levels 10% of normal values) develop early emphysema that is exacerbated by smoking, indicating a clear genetic predisposition to COPD (Carrell and Lomas, 2002). However, 1-antitrypsin deficiency accounts for 1% of patients with COPD, and many other genetic variants of 1-antitrypsin that are associated with lower than normal serum levels of this proteinase inhibitor have not been clearly associated with an increased risk of COPD, although analysis of several studies indicates a small risk, with an odds ratio from COPD in Pi MZ versus Pi MM individuals of approximately two (Hersh et al., 2004). This has lead to a search for associations between COPD and single nucleotide polymorphisms (SNPs) of other candidate genes that may be involved in its pathophysiology (Cookson,

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2006). Various SNPs have been associated with COPD, as defined by a reduced FEV1, but there is emerging evidence that different aspects of COPD may relate to different genotypes (Demeo et al., 2007; Hersh et al., 2006a). For example, a reduction in gas diffusion is correlated with SNPs of the microsomal epoxide hydrolase gene, reduced exercise performance is correlated with SNPs of the latent transforming growth factor-beta binding protein-4 (LTBP4), whereas dyspnea was linked to three SNPs in transforming growth factor 1 (TGF-1). This suggests that more careful phenotyping is required in the future to sort out susceptibility genes. A 10-fold increased risk of COPD in individuals who have a polymorphism in the promoter region of the gene for tumor necrosis factor- (TNF-) that is associated with increased TNF- production has been reported in a Taiwanese population but not confirmed in Caucasian populations. So far, few significant associations have been detected between SNPs and disease, and even those reported have not usually been replicated in other studies or have no obvious functional effects. Several other genes have been implicated in COPD, but few have been replicated in different populations. One SNP that has been replicated is heme oxygenase-1 (HMOX1), which is linked to protection against oxidative stress (Hersh et al., 2005) (Table 90.2). In addition to candidate genes that have been linked to the progressive lung disease, genetic factors are also important in addictive behavior, such as the nicotine addition of smokers, which has a high degree of heritability (Goldman et al., 2005). For example, SNPs of the dopamine transporter gene SLC6A3 and the dopamine receptor 2 gene have been associated with nicotine addiction and inability to quit (Erblich et al., 2005). Positional cloning is more likely to identify novel genes, and genome-wide linkage studies have identified some areas of weak linkage. The most convincing evidence is for linkage on chromosome 2q in the region of the SERPINE2 gene, which encodes an antiprotease (Demeo et al., 2006). Several SNPs of the SERPINE2 gene have been associated with increased susceptibility to COPD. Various high density genome-wide screens are currently in progress, with the aim of identifying novel genes involved in COPD susceptibility. This approach is now possible with high throughput genotyping of thousands of SNPs in very large populations.

PATHOPHYSIOLOGY COPD includes chronic obstructive bronchiolitis with fibrosis and obstruction of small airways, and emphysema with enlargement of airspaces and destruction of lung parenchyma, loss of lung elasticity and closure of small airways. Chronic bronchitis, by contrast, is defined by a productive cough of more than 3 months duration for more than 2 successive years; this reflects mucous hypersecretion and is not necessarily associated with airflow limitation. Most patients with COPD have all three pathological mechanisms (chronic obstructive bronchitis, emphysema and mucus plugging) as all are induced by smoking, but may

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FEV1 (% predicted at age 25)

100 Nonsmoker or non-susceptible smoker 75 Susceptible smoker Stopped smoking aged 50 years

50 Disability 25

Stopped smoking aged 60 years

Death 0 25

50

75

Age (years)

Figure 90.1 Natural history of COPD. Annual decline in airway function showing accelerated decline in susceptible smokers and effects of smoking cessation. Patients with COPD usually show an accelerated annual decline in forced expiratory volume in 1 second (FEV1), often greater than 50 ml/year, compared to the normal decline of approximately 20 ml/year, although this is variable between patients. The classic studies of Fletcher and Peto established that 10–20% of cigarette smokers are susceptible to this rapid decline. However, with longer follow-up more smokers may develop COPD. The propensity to develop COPD amongst smokers is only weakly related to the amount of cigarettes smoked and this suggests that other factors play an important role in determining susceptibility. Most evidence points toward genetic factors, although the genes determining susceptibility have not yet been determined.

TABLE 90.2 susceptibility

Some of the genes associated with COPD

Candidate genes

Risk

1-Antitrypsin

ZZ genotype high risk MZ, SZ genotypes small risk

1-Chymptrypsin

Associated in some populations

Matrix metalloproteinase-1, 2, 9, 12

Associated in some studies

Microsomal epoxide hydrolase

Increased risk

Glutathione S-transferase

Increased risk

Heme oxygenase-1

Small risk but consistent

Interleukin-13

Small risk

Vitamin D binding protein

Inconsistent

TNF- promoter

Inconsistent

TGF- promoter

Inconsistent

differ in the proportion of emphysema and obstructive bronchitis (Figure 90.2). Small Airways There has been debate about the predominant mechanism of progressive airflow limitation and recent pathological studies

suggest that is closely related to the degree of inflammation, narrowing and fibrosis in small airways (Barnes, 2004c; Hogg et al., 2004). Emphysema may contribute to the airway narrowing in the more advanced stages of the disease, with disruption of alveolar attachments facilitating small airway closure and gas trapping. This combined effect of small airway disease and early closure on expiration results in lung hyperinflation, which results in progressive exertional dyspnea, the predominant symptom of COPD. Emphysema Emphysema describes loss of alveolar walls due to destruction of matrix proteins (predominantly elastin) and loss of type 1 pneumocytes as a result of apoptosis. Several patterns of emphysema are recognized: centriacinar emphysema radiates from the terminal bronchiole; panacinar emphysema involves more widespread destruction and bullae are large airspaces. Emphysema results in airway obstruction by loss of elastic recoil so that intrapulmonary airways close more readily during expiration. Emphysema with loss of gas-exchanging surface also leads to progressive hypoxia and eventually to respiratory failure. Pulmonary Hypertension Chronic hypoxia may lead to hypoxic vasoconstriction, with structural changes in pulmonary vessels that eventually lead to secondary pulmonary hypertension (Naeije, 2005). Inflammatory changes similar to those seen in small airways are also seen in pulmonary arterioles. Only a small proportion of

Cellular and Molecular Mechanisms

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COPD

Normal

Disrupted alveolar attachments (emphysema)

Mucosal inflammation, fibrosis Mucus hypersecretion

Airway obstructed by Airway held open by alveolar attachments

• Loss of attachments • Mucosal inflammation + fibrosis • Mucus obstruction of lumen

Figure 90.2 Mechanisms of airflow limitation in COPD. The airway in normal subjects is distended by alveolar attachments during expiration, allowing alveolar emptying and lung deflation. In COPD these attachments are disrupted because of emphysema thus contributing to airway closure during expiration, trapping gas in the alveoli and resulting in hyperinflation (Hogg, 2001). Peripheral airways are also obstructed and distorted by airways inflammation and fibrosis (chronic obstructive bronchiolitis) and by occlusion of the airway lumen by mucous secretions which may be trapped in the airways because of poor mucociliary clearance.

COPD patients develop pulmonary hypertension, and it is likely that genetic susceptibility may play a role. Systemic Features Patients with severe COPD also develop systemic features, which may have an adverse effect on prognosis (Agusti et al., 2003). The most common systemic feature is weight loss due to loss of skeletal muscle bulk. This may contribute to the muscle weaknesses as a result of impaired mobility due to dyspnea. Other systemic features include osteoporosis and depression. These systemic features may be due to overspill of inflammatory mediators from the lung into the systemic circulation. There appears to be a difference in susceptibility to systemic features between patients as they do not always correlate with disease severity and this may be genetically determined. Patients with COPD also have comorbidities, particularly cardiovascular diseases. Smoking is a common risk factor for ischemic heart disease and COPD, but there may be shared genetic susceptibilities. There may be common genetic predispositions to COPD and cardiovascular diseases. COPD patients with coexisting cardiovascular disease have increased circulating levels of C-reactive protein (CRP) (Gan et al., 2004). There is evidence for genetic determinants of plasma CRP concentrations in COPD patients (Hersh et al., 2006b). Exacerbations An important feature of COPD are exacerbations, with worsening of dyspnea and an increase in sputum production. This may lead to hospitalization and accounts for a high proportion

of the costs of COPD. Exacerbations are usually due to infections, either due to bacteria (especially Haemophilus influenzae or Steptococcus pneumoniae ) or to upper respiratory tract virus infections (especially rhinovirus or respiratory syncytial virus) (Papi et al., 2006). Exacerbations of COPD tend to increase as the disease progresses, but some patients appear to have more frequent exacerbations than others, which may suggest genetic predisposing factors.

CELLULAR AND MOLECULAR MECHANISMS There is chronic inflammation predominantly in small airways and the lung parenchyma, with an increase in numbers of macrophages and neutrophils in early stages of the disease indicating an enhanced innate immune response, but in more advanced stages of the disease there is an increase in lymphocytes (particularly cytotoxic CD8 T cells), including lymphoid follicles that contain B- and T-lymphocytes, indicating acquired immunity (Hogg et al., 2004) (Figure 90.3). Macrophages Alveolar macrophages play a critical role in the orchestration of this pulmonary inflammation, since they are activated by inhaled irritants such as cigarette smoke and release chemokines that attract inflammatory cells, such as monocytes, neutrophils and T cells, into the lungs (Barnes, 2004a; Barnes et al., 2003). Monocytes are attracted into the lung to differentiate into tissue

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Cigarette smoke (and other irritants) Alveolar macrophage

Epithelial cells TGFCTGF

Fibroblast

Chemotactic factors CD8 + lymphocyte Neutrophil

PROTEASES Fibrosis (COB)

Alveolar wall destruction (Emphysema)

Monocyte Neutrophil elastase Cathepsins MMPs

Mucus hypersecretion (Chronic bronchitis)

Figure 90.3 Inflammatory mechanisms in COPD. Cigarette smoke (and other irritants) activate macrophages in the respiratory tract that release neutrophil chemotactic factors, including interleukin-8 (IL-8) and leukotriene B4 (LTB4). These cells then release proteases that break down connective tissue in the lung parenchyma, resulting in emphysema, and also stimulate mucus hypersecretion. These enzymes are normally counteracted by protease inhibitors, including 1-antitrypsin, secretory leukoprotease inhibitor (SLPI) and tissue inhibitor of matrix metalloproteinases (TIMP). Cytotoxic T cells (CD8) may also be recruited and may be involved in alveolar wall destruction.

and alveolar macrophages, the number of which are increased more than 20-fold in COPD compared to cigarette smokers without COPD (Retamales et al., 2001). These cells then all release a variety of inflammatory mediators and proteases, which collectively result in the typical pathology of COPD (Barnes, 2004b). Mediators Prominent mediators are those that amplify inflammation, such as TNF-, interleukin(IL)-1 (IL-1) and IL-6, and chemokines which attract inflammatory cells such as CXCL8 (IL-8), CXCL1 (GRO), CXCL10 (IP-10), CCL1 (MCP) and CCL5 (RANTES) (Donnelly and Barnes, 2006). Elastolytic enzymes account for the tissue destruction of emphysema and include neutrophil elastase and matrix metalloproteinase-9 (MMP-9). There is an imbalance between increased production of elastases and a deficiency of endogenous antiproteases, such as 1-antritrypin, secretory leukoprotease inhibitor and tissue inhibitors of MMPs. MMP-9 may be the predominantly elastolytic enzyme causing emphysema and also activates TGF-, a cytokine that is expressed particularly in small airways that may result in the characteristic peribronchiolar fibrosis. Oxidative stress is a prominent feature of COPD and is due to exogenous oxidants in cigarette smoke and endogenous oxidants release from activated inflammatory cells, such as neutrophils and macrophages (Bowler et al., 2004). Endogenous antioxidants may also be defective. Oxidative stress enhances inflammation and may lead to corticosteroid resistance. There is also increase production of nitric oxide (NO) in peripheral lung

of COPD patients, with increased formation of peroxynitrite (Brindicci et al., 2005; Ricciardolo et al., 2005). Differences from Asthma Although both COPD and asthma involve chronic inflammation of the respiratory tract, there are marked differences in the inflammatory process between these diseases, which are summarized in Table 90.3 (Barnes, 2000b). While there are striking differences between the inflammation in mild asthma and COPD, patients with severe asthma become much more similar to patients with COPD, with involvement of neutrophils, macrophages, TNF-, CXCL8, oxidative stress and a poor response to corticosteroids (Barnes, 2006a). This has suggested that there may be similarities in genetic predisposition to COPD in smokers and severe asthma. Several novel susceptibility genes, including DPP10, GPRA, PHF11 and ADAM33, that have been identified in severe asthma have now also been identified in COPD patients (van Diemen et al., 2005). Gene Regulation Proinflammatory mediators, such as TNF- and IL-1, activate the transcription factor nuclear factor-B (NF-B), which is activated in the airways and lung parenchyma of COPD patients, particularly in epithelial cells and macrophages (Di Stefano et al., 2002). There is further activation of NF-B during exacerbations (Caramori et al., 2003). NF-B switches on many of the inflammatory genes that are activated in COPD lungs, including chemokines, adhesion molecules such as ICAM-1 and E-selectin,

Cellular and Molecular Mechanisms

T A B L E 9 0 . 3 Differences between inflammation in COPD and asthma Inflammation

COPD

Asthma

Inflammatory cells

Neutrophils

Eosinophils

CD8 T cells

Mast cells

CD4 T cells

CD4 T cells

Macrophages

Macrophages

LTB4

LTD4, histamine

TNF-

IL-4, IL-5, IL-13

CXCL1, CXCL8

CCL11

Oxidative stress

Oxidative stress

Epithelial metaplasia

Epithelial shedding

Fibrosis

Fibrosis

Mucus secretion

Mucus secretion

AHR

AHR

Peripheral airways

All airways

Predominantly

No parenchymal effects

Inflammatory mediators

Inflammatory effects

Location

Parenchymal destruction Response to corticosteroids





LT, leukotriene; TNF, tumor necrosis factor; IL, interleukin; GRO, growth-related oncogene; AHR, airway hyperresponsiveness.

inflammatory enzymes such as cyclo-oxygenase-2 and inducible nitric oxide synthase, elastolytic enzymes such as MMP-9 and proinflammatory mediators such as TNF- and IL-1 which themselves activate NF-B. NF-B-activated genes result in acetylation of core histones (particularly histone-4), which is necessary for activation of inflammatory genes and this is reversed by histone deacetylase-2 (HDAC2) (Barnes et al., 2005). There is a marked reduction in HDAC2 activity and expression in lung parenchyma, airways and alveolar macrophages of COPD patients (Ito et al., 2005). This is a mechanism that can account for the amplified pulmonary inflammation seen in COPD compared to smokers with normal lung function and also explains why COPD patients are not responsive to corticosteroids, since HDAC2 is the mechanism whereby corticosteroids switch off activated inflammatory genes (Barnes, 2006b) (Figure 90.4). The reduction in HDAC2 expression in COPD appears to be secondary to increased oxidative and nitrative stress and the

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formation of peroxynitrite, which nitrates tyrosine residues on HDAC2. Genomics Better knowledge of the complex underlying pathophysiology has identified many candidate genes that appear to be involved in the molecular mechanisms of COPD, and polymorphisms of several of these have been investigated for association with COPD. With gene microarrays it is now possible to study the global expression of all human genes in cells and tissues (transcriptomics). Few studies of global gene expression have been conducted in COPD, but several laboratories are now looking at the differences in gene expression between COPD patients and smokers exposed to similar numbers of cigarettes who have normal lung function. These studies are using alveolar macrophages, circulating monocytes, airway epithelial cells and peripheral lung. Serial analysis of gene expression and microarray analysis in peripheral lung from mild to moderate COPD patients versus normal smokers has demonstrated that a total of 261 genes showed differential expression (Ning et al., 2004). Many of these genes encode inflammatory transcription factors and growth factors. The transcription factor Egr-1 was notably upregulated and shown to increase the activity of mitogen-activated protein kinase pathways (MAPK), which regulate multiple inflammatory genes. A microarray study of human bronchial epithelial cells exposed to nicotine has identified upregulation of several genes in the MAPK pathway (Tsai et al., 2006). Gene expression profiling of alveolar macrophages has demonstrated 40 genes upregulated and 35 downregulated in smokers compared to nonsmokers. Most of these genes belong to the functional categories of immune/inflammatory response, cell adhesion and extracellular matrix, proteases/antiproteases, antioxidants, signal transduction and regulation of transcription (Heguy et al., 2006). The amplification of inflammation in COPD patients compared to equivalent smokers with normal lung function may be genetically determined, through amplification of inflammatory genes or defective expression of anti-inflammatory or protective mechanisms. Studies looking at susceptibility to the effects of cigarette smoke in different strains of mice may also be informative. For example, the susceptible strain AKR/J shows increased expression of cytokines linked to T helper 1 cells after exposure to tobacco smoke compared to non-susceptible strains (C57BL/6/J and NZWlac/J) (Guerassimov et al., 2004). Molecular genomics may identify markers of risk, but may also reveal novel molecular targets for the development of treatments of the future. A whole genome-wide linkage scan in relatives of patients with familial early-onset COPD found evidence for linkage of airway obstruction with chromosome 2 (LOD 2.60 at 216 cM). In a smokers-only analysis, evidence for linkage was also observed with chromosome 12 (LOD 5.03 at 35 cM) and chromosome 2 (LOD 4.13 at 229 cM), but the specific genes involved have not been identified (Demeo et al., 2004).

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COPD

Normal

Cigarette smoke

Stimuli

Corticosteroids Oxidative stress

Alveolar macrophage Peroxynitrite GR

NF-κB

NF-B ↑HDAC2 Histone acetylation

↓HDAC2 Histone acetylation

-

↑ TNF↑ IL-8 ↑ MMP-9

↓Histone acetylation

TNFIL-8 MMP-9

Figure 90.4 Proposed mechanism of corticosteroid resistance in COPD patients. Stimulation of normal alveolar macrophages activates nuclear factor-B (NF-B) and other transcription factors to switch on histone acetyltransferase leading to histone acetylation and subsequently to transcription of genes encoding inflammatory proteins, such as TNF- and IL-8. Corticosteroids reverse this by binding to glucocorticoid receptors (GR) and recruiting histone deacetylase-2 (HDAC2). This reverses the histone acetylation induced by NF-B and switches off the activated inflammatory genes. In COPD patients cigarette smoke activates macrophages, as in normal subjects, but oxidative stress (acting through the formation of peroxynitrite) impairs the activity of HDAC2. This amplifies the inflammatory response to NF-B activation, but also reduces the anti-inflammatory effect of corticosteroids as HDAC2 is now unable to reverse histone acetylation.

Proteomics While gene microarray analysis gives considerable information about proteins that are transcriptionally regulated it is now recognized that many genes involved in inflammation are regulated post-transcriptionally and that it is necessary to measure protein expression using proteomic approaches (Bowler et al., 2006). A recent study used a proteomic approach to investigate the plasma concentrations of multiple inflammatory and immune proteins during COPD exacerbations compared to baseline measurements (Hurst et al., 2006). CRP was found to be the most discriminant marker and was linked to various proteins associated with monocytic and lymphocytic inflammatory pathways. In another study 143 plasma proteins were assayed in COPD patients compared to control subjects and 25 found to show some correlation with clinical parameters in the COPD patients (Pinto-Plata et al., 2006). Proteomic analysis may also be applied to cells, lung tissue or sputum (Barnes et al., 2006).

DIAGNOSIS AND SCREENING Diagnosis Diagnosis is commonly made from the history of progressive dyspnea in a chronic smoker and is confirmed by spirometry,

which shows an FEV1/VC ratio of 70% and FEV180% predicted. Staging of severity is made on the basis of FEV1, but exercise capacity and the presence of systemic features may be more important determinants of clinical outcome (Celli et al., 2004). Measurement of lung volumes by body plethysmography shows an increase in total lung capacity, residual volume and functional residual capacity, with consequent reduction in inspiratory capacity, representing hyperinflation as a result of small airway closure. This results in dyspnea which may be measured by dyspnea scales and reduced exercise tolerance, which may be measured by a 6 min or shuttle walking test (Figure 90.5). Carbon monoxide diffusion is reduced in proportion to the extent of emphysema. A chest X-ray is rarely useful but may show hyperinflation of the lungs and the presence of bullae. High resolution computerized tomography demonstrates emphysema, but is not used as a routing diagnostic test. Blood tests are rarely useful; a normocytic normochromic anemia is more commonly seen in patients with severe disease than polycythemia due to chronic hypoxia. Arterial blood gases demonstrate hypoxia and in some patients hypercapnia. Screening COPD is grossly underdiagnosed, as symptoms present late in the progression of disease (Chapman et al., 2006). Population screening with measurement of FEV1 and FEV1/VC ratio

Management

Emphysema + Small airway obstruction Mucus hypersecretion Lung hyperinflation ↑ Trapped gas

Dyspnea

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disease, including exercise performance and muscle weakness, which signal a greater mortality (Celli et al., 2004). Patients who develop right heart failure (cor pulmonale) also have poor survival, although this may be improved by long-term oxygen therapy. It is hoped in the future that genetic approaches will help to improve the prediction of prognosis and identify what the most effective management strategies may be.

Cough and sputum

↓ Exercise tolerance

MANAGEMENT COPD is managed according to the severity of the disease, with a progressive escalation of therapy as the disease progresses (Barnes and Stockley, 2005; Rennard, 2004).

Deconditioning

Poor health status

Figure 90.5 Symptoms of COPD. The most prominent symptom of COPD is dyspnea, which is largely due to hyperinflation of the lungs as a result of small airways collapse due to emphysema and narrowing due to fibrosis, so that the alveoli are not able to empty. Hyperinflation induces an uncomfortable sensation and reduces exercise tolerance. This leads to immobility and deconditioning and results in poor health status. Other common symptoms of COPD are cough and sputum production as a result of mucus hypersecretion, but not all patients have these symptoms and many smokers with these symptoms do not have airflow obstruction (simple chronic bronchitis).

would pick up early COPD in smokers over 40 years. However, this would not identify COPD due to nonsmoking causes, which constitute 10–20% of the total. This would require screening spirometry of all individuals over 40 years in addition to the 20–40% of the population who smoke. It is hoped that gene expression studies might result in a biomarker that is correlated with COPD susceptibility, but this is unlikely as so many genes are likely to be involved (Barnes et al., 2006).

PROGNOSIS COPD is slowly progressive with an accelerated decline in FEV1, leading to slowly increasing symptoms, fall in lung function and eventually to respiratory failure (Figure 90.1). The only strategy to reduce disease progression is smoking cessation, although this is relatively ineffective once FEV1 has fallen below 50% predicted and the patient is symptomatic. Patients with more severe exacerbations develop acute exacerbations, which have a prolonged effect on quality of life for many months. There is still debate about the role of acute exacerbations on disease progression, but the decline in lung function may be accelerated further following an acute exacerbation (Donaldson et al., 2002). Factors other than FEV1 are important in the prognosis of the

Anti-Smoking Measures Smoking cessation is the only measure so far shown to slow the progression of COPD, but in advanced disease stopping smoking has little effect and the chronic inflammation persists. Nicotine replacement therapy (gum, transdermal patch, inhaler) helps in quitting smoking, but bupropion, a noradrenergic antidepressant, is more effective. More effective anti-smoking therapies, including partial nicotinic agonists and cannabinoid receptor antagonists are currently being evaluated. As discussed above, there may be genetic determinants of smoking cessation that make it very difficult for some patients to quit. Bronchodilators Bronchodilators are the mainstay of current drug therapy for COPD. The bronchodilator response measured by an increase in FEV1 is limited in COPD, but bronchodilators may improve symptoms by reducing hyperinflation and therefore dyspnea, and may improve exercise tolerance, despite the fact that there is little improvement in spirometric measurements. Previously short-acting bronchodilators, including 2-agonists and anticholinergics, were most widely used, but more recently longacting bronchodilators have been introduced. These include the inhaled long-acting 2-agonists (LABA) salmeterol and formoterol and the once daily inhaled anticholinergic tiotropium bromide. In patients with more severe disease, these therapies appear to be additive. Theophylline is also used as an add-on bronchodilator in patients with very severe disease, but systemic side effects may limit its value. Antibiotics Acute exacerbations of COPD are commonly assumed to be due to bacterial infections, since they may be associated with increased volume and purulence of the sputum. However, it is increasingly recognized that exacerbations may be due to upper respiratory tract viral infections or may be non-infective, questioning the place of antibiotic treatment in many patients. Controlled trials of antibiotics in COPD show a relatively minor benefit of antibiotics in terms of clinical outcomes and lung function. Although antibiotics are still widely used in exacerbations of

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COPD, methods that can reliably diagnose bacterial infection in the respiratory tract are needed so that antibiotics are not used inappropriately. There is no evidence that prophylactic antibiotics prevent acute exacerbations. Oxygen Home oxygen accounts for a large proportion (over 30% in the United States) of health care spending on COPD. Long-term oxygen therapy was justified by two large trials showing reduced mortality and improvement in quality of life in patients with severe COPD and chronic hypoxemia (PaO2  55 mmHg). More recent studies have demonstrated that patients with less severe hypoxemia do not appear to benefit in terms of increased survival, so that selection of patients is important in prescribing this expensive therapy. Similarly, nocturnal treatment with oxygen does not appear to be beneficial in terms of survival or delaying the prescription of long-term oxygen therapy in patients with COPD who have nocturnal hypoxemia. Corticosteroids Inhaled corticosteroids are now the mainstay of chronic asthma therapy and the recognition that chronic inflammation is also present in COPD provided a rationale for their use in COPD. Indeed, inhaled corticosteroids are now widely prescribed in the treatment of COPD. However, the inflammation in COPD shows little or no suppression even by high doses of inhaled or oral corticosteroids. This may reflect the fact that neutrophilic inflammation is not suppressible by corticosteroids as neutrophil survival is prolonged by steroids. There is also evidence for an active cellular resistance to corticosteroids, with no evidence that even high doses of corticosteroids suppress the synthesis of inflammatory mediators or enzymes. This is related to a decreased activity and expression of HDAC2 (Barnes, 2006b). Approximately 10% of patients with stable COPD show some symptomatic and objective improvement with oral corticosteroids, and it is likely that these patients have concomitant asthma, as both diseases are very common. Furthermore, these patients have elevated sputum eosinophils and exhaled NO, which are features of asthmatic inflammation. Long-term treatment with high doses of inhaled corticosteroids fails to reduce disease progression, even at the early stages of the disease. However, there is a small protective effect against acute exacerbations (approximately 20% reduction) in patients with severe disease. In view of the risk of systemic side effects in this susceptible population, inhaled corticosteroid are only recommended in patients with FEV1  50% predicted who have two or more severe exacerbations a year. There is a small beneficial effect of systemic corticosteroids in treating acute exacerbations of COPD, with improved clinical outcome and reduced length of hospital admission. The reasons for this discrepancy between steroid responses in acute versus chronic COPD may relate to differences in the inflammatory response (increased eosinophils) or airway edema in exacerbations.

Other Drug Therapies Systematic reviews show that mucolytic therapies reduce exacerbation by about 20%, but most of the benefit appears to derive from N-acetylcysteine which is also an antioxidant. A controlled trial, however, did not show any overall benefit in reducing exacerbations or disease progression (Decramer et al., 2005). Pulmonary Rehabilitation Pulmonary rehabilitation consists of a structured program of education, exercises and physiotherapy and has been shown in controlled trials to improve the exercise capacity and quality of life of patients with severe COPD, with a reduction in health care utilization. Pulmonary rehabilitation is now an important part of the management plan in patients with severe COPD. There is debate about the duration and frequency of pulmonary rehabilitation (Troosters et al., 2005). Most of the benefits appear to relate to exercise so that modified simplified programs are now often used. Lung Volume Reduction Surgical removal of emphysematous lung improves ventilatory function in carefully selected patients (Wouters, 2004). The reduction in hyperinflation improves the mechanical efficiency of the inspiratory muscles. Careful patient selection after a period of pulmonary rehabilitation is essential. Patients with localized upper lobe emphysema with poor exercise capacity do best, but there is a relatively high operative mortality, particularly with patients who have a low diffusing capacity. Significant functional improvements include increased FEV1, reduced total lung capacity and functional residual capacity, improved function of respiratory muscles, improved exercise capacity and improved quality of life. Benefits persist for at least a year in most patients, but careful long-term follow-up is needed in order to evaluate the long-term benefits of this therapy. More recently nonsurgical bronchoscopic lung volume reduction has been achieved by insertion of one-way valves by fiberoptic bronchoscopy. This gives significant improvement in some patients and appears to be safe, but collateral ventilation reduces the efficacy of this treatment so that significant deflation of affected lung may not be achieved. Management of Acute Exacerbations Acute exacerbations of COPD should be managed by supplementary oxygen therapy, initially 24% oxygen and checking that there is no depression of ventilation. Antibiotics should be given if the sputum is purulent or there are other signs of bacterial infection, High doses of corticosteroids reduce hospital stay and are routinely given. Chest physiotherapy is usually given, but there is little objective evidence for benefit. Noninvasive ventilation is indicated for incipient respiratory failure and reduces the need for intubation. Pharmacogenomics There is no information about the impact of pharmacogenomics on COPD therapy. Polymorphism of the 2-receptor affect the

Conclusions

bronchodilator response to short- and long-acting 2-agonists in asthma, with reduce responses seen in patients with the Arg–Arg16 polymorphism (Liggett, 2002). However this is not a large effect and is unlikely to have a major clinical impact. It is possible that polymorphism in corticosteroids signaling pathways may contribute to the corticosteroids resistance in COPD but this has not yet been investigated. Using genomics to better define the clinical phenotypes may lead to a more rational use of specific therapies in the future.

NEW TREATMENTS Apart from quitting smoking, no other treatments, including corticosteroids, slow the progression of COPD. Yet this disease is associated with an active inflammatory process and progressive proteolytic injury of lung tissues even in its most advanced stages, suggesting that pharmacological intervention may be possible. A better understanding of the cellular and molecular mechanisms involved in COPD provides new molecular targets for the development of new drugs and several classes of new drug are now in development (Barnes and Hansel, 2004; Barnes and Stockley, 2005). Mediator Inhibitors Several inflammatory mediators are implicated in COPD, providing logical targets for the development of synthesis inhibitors or receptor antagonists. These include 5-lipoxygenase inhibitors that prevent the synthesis of leukotriene B4 and specific leukotriene B4 antagonists, several of which are now being evaluated in COPD. Specific antagonists of CXCR2, one of the receptors on neutrophils that is activated by CXCL8 and related chemokines, have been developed and humanized antibodies and soluble receptors that block TNF- have already been developed for use in other chronic inflammatory diseases. More potent and stable antioxidants are also in development. However, it is not certain that antagonizing a single mediator will have a major clinical effect, since many mediators with overlapping actions are involved in COPD. Protease Inhibitors Several inhibitors of neutrophil elastase are now in clinical development. Only one has so far been reported in a clinical study which showed no benefit, but this may reflect the fact that several proteases are involved or the fact that it may be difficult to monitor efficacy in such a slowly progressive disease. Several nonselective MMP inhibitors have been developed, and there is now a search for more selective MMP-9 inhibitors in order to avoid the musculoskeletal side effects that have hampered the development of this class of drug. Another approach is supplementation of endogenous antiproteinases using human recombinant 1-antitrypsin, secretory leukoprotease inhibitor or elafin or even gene therapy, although there are doubts that sufficient protein can be delivered by either approach.

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Novel Anti-Inflammatory Drugs While corticosteroids are ineffective, other anti-inflammatory treatments, particularly those that inhibit neutrophilic inflammation, might be effective. The most promising amongst these are phosphodiesterase (PDE)4 inhibitors, which have an inhibitory effect on key inflammatory cells involved in COPD, including macrophages, neutrophils and cytotoxic T lymphocytes (Fan, 2006). Several PDE4 inhibitors are in development for COPD, although a limitation of drugs in this class is the common side effect of nausea and headaches also caused by PDE4 inhibition. Other novel anti-inflammatory approaches in development include inhibitors of NF-B, inhibitors of p38 MAPK, although none of these have reached clinical trials. Impacts of Genomics on Therapy As indicated above, molecular genetics and genomics may identify novel targets for the development of new therapies. Pharmacogenomics might have an impact on choice of therapy in the future. It is unlikely that gene-based therapies will be useful, but silencing of inflammatory genes by inhaled interfering RNAs and antisense oligonucleotides might be a feasible approach in the future (Ulanova et al., 2006). Micro-RNAs (miRNA) are small regulatory RNAs that inhibit the translation of several activated genes and may have application to the suppression of multiple inflammatory genes in the future (Ying et al., 2006).

CONCLUSIONS COPD is a major global disease that is increasing in frequency. Although the major causes are cigarette smoking and biomass fuels, relatively little is understood about the underlying inflammatory and immune mechanisms. The accelerated decline in lung function in the 10–20% of smokers who develop COPD is likely to be genetically determined, but there is no agreement on which genes are involved. More studies, particularly high density genome-wide approaches, are needed to identify susceptibility genes and to link these to different patient phenotypes. Identification of susceptibility genes may facilitate screening and early identification of disease. Microarray analysis is now revealing upregulation of various inflammatory and immune genes and downregulation of other genes that may be protective in peripheral lung, macrophages and epithelial cells. More studies are needed in relevant cells from carefully phenotyped patients. At the moment it is not possible to give any guidance to family members of COPD patients, apart from those with PiZ 1-antitrypsin deficiency, about genetic risks since the genes determining risk have not yet been identified. Pharmacogenetics needs to be applied to COPD therapies in order to select patients that would benefit more from one treatment compared to another. Current therapy for COPD is unsatisfactory and there are no effective anti-inflammatory treatments. Identification of novel genes involved in COPD may also lead to the discovery of new drug targets.

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Comprehensive gene expression profiles reveal pathways related to the pathogenesis of chronic obstructive pulmonary disease. Proc Natl Acad Sci USA 101, 14895–14900. Papi, A., Luppi, F., Franco, F. and Fabbri, L.M. (2006). Pathophysiology of exacerbations of chronic obstructive pulmonary disease. Proc Am Thorac Soc 3, 245–251. Pinto-Plata, V., Toso, J., Lee, K., Bilello, J., Mullerova, H., De Souza, M., Vessey, R. and Celli, B. (2006). Use of proteomic patterns of serum biomarkers in patients with chronic obstructive pulmonary disease: Correlation with clinical parameters. Proc Am Thorac Soc 3, 465–466. Rennard, S.I. (2004). Treatment of stable chronic obstructive pulmonary disease. Lancet 364, 791–802. Retamales, I., Elliott, W.M., Meshi, B., Coxson, H.O., Pare, P.D., Sciurba, F.C., Rogers, R.M., Hayashi, S. and Hogg, J.C. (2001). Amplification of inflammation in emphysema and its association with latent adenoviral infection. Am J Respir Crit Care Med 164, 469–473. Ricciardolo, F.L., Caramori, G., Ito, K., Capelli, A., Brun, P., Abatangelo, G., Papi, A., Chung, K.F., Adcock, I., Barnes, P.J. et al. (2005). Nitrosative stress in the bronchial mucosa of severe chronic obstructive pulmonary disease. J Allergy Clin Immunol 116, 1028–1035. Sandford, A.J. and Silverman, E.K. (2002). Chronic obstructive pulmonary disease. 1: Susceptibility factors for COPD the genotype– environment interaction. Thorax 57, 736–741. Troosters, T., Casaburi, R., Gosselink, R. and Decramer, M. (2005). Pulmonary rehabilitation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 172, 19–38. Tsai, J.R., Chong, I.W., Chen, C.C., Lin, S.R., Sheu, C.C. and Hwang, J.J. (2006). Mitogen-activated protein kinase pathway was significantly activated in human bronchial epithelial cells by nicotine. DNA Cell Biol 25, 312–322. Ulanova, M., Schreiber, A.D. and Befus, A.D. (2006). The future of antisense oligonucleotides in the treatment of respiratory diseases. BioDrugs 20, 1–11. van Diemen, C.C., Postma, D.S., Vonk, J.M., Bruinenberg, M., Schouten, J.P. and Boezen, H.M. (2005). A disintegrin and metalloprotease 33 polymorphisms and lung function decline in the general population. Am J Respir Crit Care Med 172, 329–333. Wouters, E.F. (2004). Management of severe COPD. Lancet 364, 883–895. Ying, S.Y., Chang, D.C., Miller, J.D. and Lin, S.L. (2006). The microRNA: Overview of the RNA gene that modulates gene functions. Methods Mol Biol 342, 1–18.

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91 Genomic Determinants of Interstitial Lung Disease Paul W. Noble and Mark P. Steele

INTRODUCTION Interstitial lung diseases (ILDs), also referred to as diffuse parenchymal lung diseases (DPLDs), are a diverse group of lung diseases that can be classified according to clinical, radiologic, physiologic, or pathologic criteria that result in fibrosis of the alveolar interstitium and impairment of gas exchange. The term DPLD more accurately describes these entities, since among the ILDs there is substantial variation in the degree of involvement of lung structures other than the alveolar interstitium, including capillaries, terminal and respiratory bronchioles, and lymphatics along the bronchovascular bundle and interlobular septae. A general classification scheme of ILD/DPLD categorizes DPLD related to connective tissue diseases, drug-induced diseases, occupational and environmental exposures, granulomatous diseases, inherited conditions such as familial interstitial pneumonia (FIP) and Hermansky-Pudlak syndrome, unique conditions such as eosinophilic granuloma and amyloidosis, and the idiopathic interstitial pneumonias (IIPs). While the pathogenic mechanisms are known or inferred in some of the DPLDs such as those related to environmental exposure, drug exposure, or autoimmune mechanisms, the pathogenesis of most of these entities is poorly understood. Furthermore, it is well recognized that there is considerable individual variation in the natural history of DPLD, particularly regarding the susceptibility to agents known to cause pulmonary fibrosis, such as radiation or

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asbestos. Since the lung responds to these injuries in a limited fashion, when using standard radiologic and pathologic evaluation, there is significant overlap and diagnostic uncertainty with DPLD. Consequently, there is considerable interest in the study of the genetics, genomics, and proteomics of ILD/DPLD to better understand the pathogenesis of these diseases and individual disease susceptibility, to discover biomarkers for disease diagnosis and prognosis, as well as to develop effective treatment interventions. Four lines of evidence suggest that the development of pulmonary fibrosis is, at least in part, determined by genetic factors. First, clustering of pulmonary fibrosis, an uncommon disease, has been reported in monozygotic twins raised in different environments (Bonanni et al., 1965; Javaheri et al., 1980; Solliday et al., 1973), in genetically related members of several families (Bitterman et al., 1986; Bonanni et al., 1965; Hughes, 1964; Swaye et al., 1969), in consecutive generations of the same families (Bonanni et al., 1965; Hodgson et al., 2002; Lee et al., 2005), and in family members separated at an early age (Swaye et al., 1969). While a single report suggests that FIP is inherited as an autosomal recessive trait (Tsukahara and Kajii, 1983), other pedigrees demonstrate an autosomal dominant pattern of inheritance (Adelman et al., 1966; McKusick and Fisher, 1958; Swaye et al., 1969), perhaps with reduced penetrance (Adelman et al., 1966; Bitterman et al., 1986; Hughes, 1964; Javaheri et al., 1980; Marshall et al., 2000; Musk et al., 1986a, b; Solliday et al.,

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Genetic Determinants of Sarcoidosis

1973; Swaye et al., 1969). Second, pulmonary fibrosis is observed in genetic disorders with pleiotropic presentation, including Hermansky-Pudlak syndrome (Depinho and Kaplan, 1985), neurofibromatosis (Riccardi, 1981), tuberous sclerosis (Harris et al., 1969; Makle et al., 1970), Neimann-Pick disease (Terry et al., 1954), Gaucher disease (Schneider et al., 1977), familial hypocalciuric hypercalcemia (Auwerx et al., 1985), and familial surfactant protein C mutation (Thomas et al., 2002). Third, considerable variability exists in the development of pulmonary fibrosis among workers exposed to similar concentrations of fibrogenic dusts or organic antigens. For instance, following exposure to asbestos, similarly exposed individuals may experience very different outcomes (Polakoff et al., 1979; Selikoff et al., 1979). Fourth, inbred strains of mice differ in their susceptibility to fibrogenic agents. In comparison to BALB/c or 129 mice, C57BL/6 mice develop more lung fibrosis when challenged with either bleomycin (Ortiz et al., 1998; Rossi et al., 1987) or asbestos (Corsini et al., 1994;Warshamana et al., 2002). This review will focus on the genetic, genomic, and proteomic approaches to identify disease susceptibility genes that predispose to DPLD or to aid in the diagnostic classification of DPLD. To better understand the pathogenesis of pulmonary sarcoidosis, both candidate gene and genome-wide linkage strategies have been utilized, as well as proteomic approaches to identify pathogenic antigens causing granulomatous inflammation. Surfactant protein C deficiency has been linked to familial cases of pediatric and adult interstitial pneumonia and represents the most successful candidate gene approach to studying genetic susceptibility to DPLD. Microarray-based expression profiling is being successfully utilized to aid in the classification of DPLDs and to identify novel disease-susceptibility genes. Recombinant inbred and congenic mouse strains have been successfully used to map susceptibility loci related to radiation-, bleomycin-, and asbestos-induced pulmonary fibrosis. Finally, genome-wide scans are being utilized to identify susceptibility loci that predispose to the development of IIPs.

GENETIC DETERMINANTS OF DPLD IN MOUSE STRAINS Investigation of inbred mouse strains that differ in their susceptibility to pulmonary fibrosis can be used to identify pulmonary fibrosis susceptibility genes. No mouse strain has been identified that spontaneously develops pulmonary fibrosis that resembles any IIP, but there are several mouse strains that have been identified as being susceptible or resistant to pulmonary fibrosis following various stresses to the lung. The most commonly studied model is pulmonary fibrosis following intratracheal administration of bleomycin in the susceptible C57BL6 strain compared to the more resistant C2Hf/Kam and C3H/ HeJ strains. One limitation of the bleomycin mouse model is that the lung injury induced by bleomycin tends to resolve over time with relatively little pulmonary fibrosis compared to the permanent damage seen in humans. Differences in susceptibility to

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radiation-induced (Franko et al., 1996; Haston and Travis, 1997; Haston et al., 2002b; Sharplin and Franko, 1989), bleomycininduced, and paraquat-induced pulmonary fibrosis in different inbred mouse strains have been used to identify loci linked to pulmonary fibrosis (Haston et al., 1996, 2002a, 2005; Lemay and Haston, 2005). In the bleomycin-induced pulmonary fibrosis model, the bleomycin hydrolase gene appears to be at least one of the candidate genes. In these studies, lung fibrosis is histologically scored as a quantitative trait for QTL analysis. Given that linked QTL intervals typically span large regions up to 10–50 Mb, microarray analysis can be targeted to linked regions to identify differentially expressed genes that are potential candidate genes (Haston et al., 2005; Katsuma et al., 2001). Results of these studies are summarized in Table 91.1.

GENETIC DETERMINANTS OF SARCOIDOSIS Evidence for Genetic Basis Sarcoidosis represents a complex disease with racial and ethnic differences in disease prevalence, an association with environmental exposures, and a genetic predisposition. A genetic basis for pulmonary sarcoidosis is suggested by familial clustering of sarcoidosis (Buck and Mc, 1961; Harrington et al., 1994; Headings et al., 1976; Moura et al., 1990; Wiman, 1972) and racial differences in disease prevalence (Rybicki et al., 2001a, b). A large multicenter case control study investigating the familial aggregation of sarcoidosis in the United States demonstrated a 5.8-fold increase in the relative risk of developing sarcoidosis among first degree relatives (Rybicki et al., 2001a, 2005a). Candidate Gene Studies Sarcoidosis demonstrates characteristic granulomatous inflammation with morphologic features of the granuloma having a concentration of CD4 positive T cells at the central core, CD8 positive T cells at the periphery, and a host of associated immunologic abnormalities (Statement on sarcoidosis, 1999). Immunologic abnormalities observed in patients with sarcoidosis include expansion of T cells bearing restricted T cell receptor suggesting oligoclonality; increased expression of members of the TNF-ligand and TNF-receptor superfamilies by T cells; B cell hyperactivity and spontaneous in situ production of immunoglobulin; and accumulation of monocytes/macrophages with antigen presenting capacity associated with increased release of macrophage-derived cytokines (IL-1, IL-6, IL-8, IL-15, TNF-, IFN-, GM-CSF), chemokines (RANTES, MIP-1, IL-16), and fibrogenic cytokines (TGF-, PDGF). Genes involved in these pathways are plausible biologic candidate genes. Given the known racial and ethnic differences in disease prevalence, the characteristic immunologic features of the disease, attention has been directed to HLA region genes that predispose to the development of sarcoidosis. HLA class II alleles have been most frequently reported to be associated with the risk of

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TABLE 91.1

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Mouse models of DPLD

Animal model

Mouse strain

Locus

Result

Reference

Bleomycin

C57BL6

Chr 17

LOD score 2.8, marker D17Mit198/D17Mit16 localized to 2.7 cM region of MHC accounting for 40% of genetic risk LOD score 3.3, D11Mit272/D11Mit310 with evidence for interactions between Chr 17 and 11

Haston et al. (2002a)

Chr 11

Haston et al. (2002a)

Bleomycin

C57BL6 and A/J congenic strain

Chr 9

LOD 4.9 at D9Mit236, 246 differentially expressed genes mapped to the interval

Lemay and Haston (2005)

Bleomycin

C3H-H2 reduced congenic

MHC susceptible

Reduced levels of bleomycin hydrolase activity in the fibrosis-prone strains

Haston et al. (2002a)

Radiation

C57BL/6JChr 17

Chr 17

LOD 4.2 at D17Mit16 within the bleomycin linked region LOD 4.5 at D1Mit206 LOD 4.6 at D6Mit254

Haston et al. (2002b)

Chr 1 Chr 6

developing sarcoidosis. Studies performed in different populations have produced conflicting results demonstrating either increased risk or protection from various HLA alleles. There is a consistent association with HLA-DR3 haplotypes and a more favorable prognosis in Czech, German, Italian, Japanese, Polish, and Scandinavian populations (Berlin et al., 1997; BoguniaKubik et al., 2001; Foley et al., 2001; Gardner et al., 1984; Ina et al., 1989; Ishihara et al., 1994; Martinetti et al., 1995; Swider et al., 1999). The HLA-DRB1 and HLA-DQB1 alleles have been associated with milder forms of the disease (erythema nodosum, Löfgren’s syndrome, stage 0/1 CXR findings) in patients from Scandinavia, UK, and the Netherlands (Berlin et al., 1997; Sato et al., 2002). Of note, HLA-DQB1 is in linkage disequilibrium (LD) with HLA-DRB1, which is in close proximity to non-HLA-related genes such as TNF that may be similarly influencing outcomes in sarcoidosis (Mrazek et al., 2005). The favorable outcome associated with increased TNF- production based on the A2 promoter allele may be related to a common haplotype shared by HLA-DR3 (Seitzer et al., 2002). Other HLA loci that confer disease susceptibility include HLA-A1, -B8, -B22, -B13, -DR15, and -DR16, whereas protection from disease or milder forms of the disease have been associated with HLA-DR17 and -DRw52 (Berlin et al., 1997; Grunewald et al., 2004; Maliarik et al., 1998a; Martinetti et al., 1995; Rossman et al., 2003). A number of non-HLA genes have been investigated for an association with sarcoidosis. The most heavily investigated is an insertion/deletion located in intron 16 of the gene for angiotensin converting enzyme (ACE), which is known to affect serum ACE levels. While this mutation affects serum ACE levels, there appears to be no relationship to disease susceptibility (Alia et al., 2005; Maliarik et al., 1998b; McGrath et al., 2001). Other non-HLA candidate genes studied in sarcoidosis and their associations are summarized in Table 91.2. In interpreting these data, it is important to look closely at the method of choosing control

Haston et al. (2002b)

populations to avoid spurious associations due to population stratification, as well as statistical genetic methods such as verification that genetic markers are in Hardy–Weinberg equilibrium, and correction for multiple comparisons. It is also important to look for evidence of linkage disequilibrium (LD) flanking putative disease susceptibility single nucleotide polymorphisms (SNPs) since unknown mutations in LD with the putative disease susceptibility SNP may be responsible for the apparent association. In general, replication studies in multiple populations are necessary before a candidate gene SNP is unequivocally linked as a sarcoidosis susceptibility gene. Currently, utilizing mutation screening in candidate genes, only HLA alleles have been validated unequivocally as susceptibility genes for sarcoidosis. Genetic Epidemiology Evidence that sarcoidosis aggregates in some families, and the ability to accurately identify and phenotype these families, is critical to the successful completion of family-based linkage studies. The most comprehensive study of the familial aggregation of sarcoidosis comes from A Case Control Etiologic Study of Sarcoidosis (ACCESS) (Rybicki et al., 2001a). The study population was drawn from 10,862 first-degree and 17,047 seconddegree relatives identified by 706 sarcoidosis case-control pairs. Controls were matched to cases on race, sex, age, and three-digit phone numbers. The familial relative risk of sarcoidosis was 5.8 (OR  5.8, [2.1, 15.9]) for sibs, and 3.8 (OR  3.8, [1.2, 11.3]) for parents. Linkage Analysis The first published genome-wide linkage study in sarcoidosis was from 63 German families consisting mostly of affected sibling pairs having 138 affected siblings and 95 first-degree relatives (Schurmann et al., 2001). This genome-wide scan utilized

Genetic Determinants of Sarcoidosis

TABLE 91.2

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Non-HLA candidate gene polymorphisms in sarcoidosis

Candidate gene

Polymorphism

Result

Reference

ACE Vitamin D receptor IL-1 cluster TGF 3 HSP-70 hom BTNL2 TLR4 Nod2/Card15 TNF CCR5 BTNL2 HSP70-hom TGF

Intron 16 in/del BsmI RFLP IL--889 4785A 2763, 2437 10 intron/exon 5 SNP A299G, T399I R702W, G908R, 1007FsinC A2 promoter allele HHC haplotype 3 locus haplotype C2437T 4875A

Population specific Increased risk Increase risk 2X Fibrotic sarcoid Löfgren’s syndrome Increased risk Increased in chronic sarcoid No effect Favorable prognosis Persistent lung disease Increased risk Lofgren’s syndrome Fibrotic sarcoid

Berlin et al. (1997); McGrath et al. (2001) Niimi et al. (2000b) Niimi et al. (2000a) Kruit et al. (2006) Bogunia-Kubik et al. (2006) Rybicki et al. (2005b) Pabst et al. (2006) Kanazawa et al. (2005); Martin et al. (2003) Sabounchi-Schutt et al. (2003) Spagnolo et al. (2005) Akahoshi et al. (2004) Zorzetto et al. (2002) Takada et al. (2001)

225 microsatellite markers, and was analyzed using the NPL score (Genehunter 2.0), a nonparametric, model-free approach where modes of inheritance and penetrance values are not required. The highest NPL score was 2.99, p0.001, obtained with marker D6S1666, which resides in the MHC class II gene. The genome-wide significant NPL score is approximately 3.6, p0.001. Therefore, the NPL score of 2.99 is very suggestive but not conclusive evidence for linkage to the MHC class II gene. Other chromosome regions showing minor peaks (p0.05) include NPL scores of 2.39 on chromosome 3p21, 1.87 on chromosome 1p22, 1.82 for chromosome 9q33, 1.64 on chromosome X, and 1.92 on chromosomes 7q22 and 7q36. The second linkage study in sarcoidosis is from the Sarcoidosis Genetic Analysis Consortium (SAGA) reporting linkage analysis of sibling pairs from 229 African American families utilizing 380 microsatellite markers. These investigators reported p-values based on Haseman-Elston regression. The smallest p-value was 0.0005 on chromosome 5 at marker D5S2500. Interestingly, despite using a higher density of markers in the MHC class II region, the SAGA investigators did not find evidence for linkage in the MHC class II region. The different linkage results in the German and African American populations would be consistent with ethnicity-related locus heterogeneity. Also, the sample sizes in these two studies are relatively small for a complex disease, and may account for the different results. Nevertheless, these studies indicate that genome-wide linkage studies can be utilized to identify disease susceptibility genes in sarcoidosis. Based on the initial linkage analysis of the 63 German families demonstrating linkage to chromosome 6p21, SNP-based fine mapping of the region was performed using extended families and trios to conduct transmission disequilibrium test (TDT), and case-control association analysis (Valentonyte et al., 2005). The results demonstrated an association with SNP rs2076530 (PTDT  3  106, Pcasecontrol  1.1  108) located in the butyrophilin-like2 gene (BTNL2) located adjacent to HLA-DR1.

Additional analysis demonstrated G to A transition in BTNL2 that leads to the use of a cryptic slice site resulting in a 4 bp deletion that generates a premature stop codon, and the resulting protein lacks the C-terminal immunoglobulin-like constant domain and transmembrane domain. The authors demonstrate that the disease allele encodes for a protein that is located in the cytoplasm rather than on the plasma membrane. The odds ratio (OR) for developing sarcoidosis when heterozygous for the susceptibility allele is 1.6, and 2.75 in homozygotes. The authors did not comment on the overall frequency of the susceptibility allele in their population. Given the complexity of sarcoidosis, and the modest OR in the range of 1.6–2.75, one would expect that BTNL2 is one of the several sarcoid susceptibility genes, and further investigations of BTNL2 in other non-German populations will be important. Additional studies of the role of the BTNL2 gene in sarcoidosis may identify novel mechanisms predisposing individuals to sarcoidosis, or other granulomatous inflammation.

Genomic Medicine and Sarcoidosis The identification of BTNL2 mutations, or mutations in other genes that lead to the susceptibility of sarcoidosis, will likely aid several aspects of the clinical approach to patients with sarcoidosis. A very clinically relevant application of sarcoid susceptibility alleles would be the development of a diagnostic test that distinguishes chronic progressive forms of sarcoidosis from spontaneous remitting disease. Another useful risk allele would be one that identifies risk for extra-pulmonary sarcoidosis such as cardiac or central nervous system involvement since involvement of these organs results in substantial morbidity and mortality. The identification of risk alleles that distinguish progressive forms of the disease, or forms of the disease associated with higher morbidity would identify a group of patients needing aggressive anti-inflammatory therapy while sparing those with milder,

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self-limited disease the toxicity of these therapies. In addition, the identification of sarcoid susceptibility genes might provide insight into development of new pharmacologic therapies.

Proteomics in Sarcoidosis Sarcoidosis is characterized by granulomatous inflammation with expansion of T cells bearing restricted T cell receptor suggesting oligoclonality, and polyclonal antibody production. The intradermal injection of the Kveim reagent, homogenates of diseased sarcoid tissue obtained from spleen, or lymph nodes, induces characteristic granulomatous inflammation known as the Kveim reaction (Siltzbach, 1961). The biochemical features of the Kveim reagent include neutral detergent insolubility; heat, acid, and protease resistance; and sensitivity to potent denaturants, suggesting a poorly soluble protein or protein aggregate (Chase and Siltzbach, 1967; Lyons et al., 1992). Proteomic approaches have been applied to better characterize the inflammatory response of sarcoidosis, and to identify potential antigenic proteins present in the Kveim reagent. Several studies have analyzed sarcoid bronchoalveolar lavage fluid (BALF) protein by 2-D gel electrophoresis and mass spectroscopy. These studies demonstrate increased concentrations of many plasma proteins in sarcoid BALF (Magi et al., 2002; Rottoli et al., 2005; Sabounchi-Schutt et al., 2003). The largest study to date compared alveolar proteins in patients with acute forms of sarcoidosis with chest radiographs having only bilateral hilar adenopathy to those with more chronic forms of sarcoidosis (Kriegova et al., 2006). Forty differentially expressed proteins were seen and some were identified as albumin, alpha-1-antitrypsin, and protocadherin-2 precursor. These data demonstrate that BALF analyzed by SELDI-TOF or MALDI-TOF mass spectroscopy can identify unique disease-related protein profiles, and that these protein profiles may change with the stage of the disease or disease progression. Epidemiologic studies suggest an association between infective agents and sarcoidosis based on seasonal variations and case clustering (Baughman et al., 2003). Several lines of evidence suggest mycobacteria as a candidate organism. First, while mycobacteria in general cannot be isolated from sarcoid tissue using conventional microbiologic techniques, cell wall deficient L forms persisting as an intracellular organism can be identified. Second, several PCR-based studies have identified mycobacterium DNA in sarcoid tissue homogenates (Song et al., 2005). Utilizing biochemical techniques similar to that used to isolate the Kveim reagent and immunoblotting using immunoglobulin obtained from the serum of sarcoid patients, investigators identified immunoreactive proteins unique to sarcoid tissue homogenates. These antigenic bands were excised from gels, subjected to trypsin digestion, and analyzed by MALDI-TOF mass spectroscopy. The peptide fingerprints were identified as mycobacterium tuberculosis catalase–peroxidase. These authors hypothesize that insoluble aggregates of mycobacteria-derived catalase– peroxidase drive the sarcoid immune response in genetically predisposed individuals.

SURFACTANT PROTEINS AND DPLD Pulmonary surfactant Pulmonary surfactant is a complex mixture of phospholipids and proteins that functions to reduce surface tension at the alveolar air interface preventing atelectasis. Deficiency of pulmonary surfactant is the principal cause of respiratory distress syndrome in premature infants (Whitsett and Weaver, 2002). Four surfactantassociated proteins, surfactant proteins A, B, C, and D, have been described, and two have been associated with DPLD. Surfactant protein C (SP-C) is a highly hydrophobic protein that enhances the surface tension lowering properties of pulmonary surfactant. Familial cases of neonatal respiratory distress have been associated with surfactant protein B deficiency, but respiratory distress of neonates is not considered a form of DPLD/ILD (Nogee et al., 2000). Genetic variants of SP-A have been associated with increased risk of idiopathic pulmonary fibrosis (IPF; Lawson et al., 2004).

Family Studies with SP-C Nogee et al. (2001) reported a full-term baby girl born to a woman who had desquamative interstitial pneumonia, a type of IIP, at 1-year of age. The infant’s maternal grandfather died of an unknown lung disease. The infant developed respiratory distress at the age of 6 weeks, and surgical lung biopsy demonstrated non-specific interstitial pneumonia (NSIP). Both the infant and the mother had minimal SP-C by either immunohistochemical staining, or immunoblotting of lung tissue. DNA sequence analysis of the SP-C gene demonstrated a heterozygous substitution of A to G at the first base of intron 4 that abolished the normal donor splice site resulting in a truncated mRNA. Subsequently, there have been several other similar families with SP-C mutations and interstitial pneumonia (Chibbar et al., 2004; Thomas et al., 2002). In the largest kindred, a heterozygous T to A substitution was identified in exon 5 causing glutamine for threonine substitution. In this pedigree, there was both adult-onset interstitial pneumonia of the usual interstitial pneumonia (UIP) histology and children with the cellular NSIP (Thomas et al., 2002). Immunohistochemical analysis of these patients demonstrated intracellular aggregates of SP-C and in vitro expression studies demonstrated abnormal intracellular processing of SP-C in alveolar type II cells.

SP-B and SP-C Genetic Variants Associated with IPF In a single study by Selman and colleagues, the SP-A1 6A4 haplotype is associated with a substitution of 3 amino acids at positions 19, 50, and 219, with the 219 variant being a tryptophan for arginine substitution (Lawson et al., 2004; Selman et al., 2003). The amino acid 219 variant is associated with IPF in smokers and nonsmokers (OR  3.67 [1.34, 10.07], p  0.01).

Genetic Determinants of FIP

GENETIC DETERMINANTS OF PULMONARY FIBROSIS IDENTIFIED IN RARE INHERITED DISORDERS Pulmonary fibrosis is observed in genetic disorders with pleiotropic presentation, including Hermansky-Pudlak syndrome, neurofibromatosis, tuberous sclerosis, Neimann-Pick disease, Gaucher disease, familial hypocalciuric hypercalcemia, familial SP-C mutation, and most recently in dyskeratosis congenital. Recently, mutations associated with the dyskeratosis congenita syndrome have been in a small percentage of FIP (Armanios et al., 2007). Specifically, 8% of 73 families with more than one case of IIP were found to have heterozygous mutations in telomerase reverse transcriptase, resulting in shortening of telomeres. These authors suggest that telomere shortening may cause apoptosis of the alveolar epithelium. Emerging Concepts from Genomic Studies of Associated DPLD An interesting feature of these studies is the variable histopathologic features among family members sharing the identical SP-C mutation, suggesting modification of disease phenotype by other unknown factors. Mutations in SP-C and telomerase reverse transcriptase also suggest that abnormalities of the alveolar epithelium, particularly alveolar type II epithelial cells, the major source of pulmonary surfactant and also the progenitor cell for alveolar type I epithelial cell, are critical for the development of pulmonary fibrosis and interstitial pneumonia. In the instance of SP-C mutations, intracellular aggregates of abnormally processed SP-C may induce functional abnormalities of alveolar epithelial cells. These studies have resulted in a paradigm shift in the mechanism and the pathogenesis of IIP, away from an inflammatory hypothesis, toward one of abnormal injury and repair of the alveolar epithelium and the fibroproliferative response. These data support the lack of efficacy of glucocorticoid and cytotoxic treatments of IPF.

GENETIC DETERMINANTS OF FIP Clinical Features The interstitial pneumonias can be classified as sporadic or idiopathic (IIP), in which no positive family history can be identified, or FIP. There are no data on the relative proportion of interstitial pneumonias that are familial, but estimates are in the range of 5%. Familial aggregation has been reported in a variety of studies in twins, siblings raised apart, and multigenerational families. Steele et al. (2005) reported the largest collection of families, identifying 111 families from the United States, and 20 multigenerational pedigrees were consistent with autosomal dominant inheritance. Forty-five percent of the families demonstrated phenotypic heterogeneity with some families having bronchiolitis obliterans or NSIP and UIP within the same pedigree. Cigarette smoking was associated with affection status among siblings (OR  3.6, [1.3, 9.8], p  0.01). The histopathologic heterogeneity in these

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families has been confirmed in a subsequent study with multiple independent pathologists. UIP is found in 40% of these families, but the predominant histopathologic pattern (60%) for the FIP cohort was difficult to classify pattern characterized by advanced fibrosis with a high incidence of microscopic honeycombing, smooth muscle proliferation and fibrosis, and variable diffuse alveolar septal and airway-centered scarring. Similar to the findings with SP-C mutations, these data suggest that histologically distinct forms of pulmonary fibrosis may have common pathogenic mechanisms, and that cigarette smoking may contribute to the development of pulmonary fibrosis in individuals who are genetically prone to this disease. Candidate Genes Identified by Microarray Analysis in Familial Pulmonary Fibrosis Yang and colleagues performed microarray analysis of 16 cases of sporadic IIP (14 UIP, 2 NSIP), and 10 cases of familial IIP (6 UIP, 4 NSIP), compared to 9 normal lung controls. RNA was extracted from diseased lung tissue (specimens from surgical lung biopsy, autopsy, or explanted lung at the time of lung transplant), or normal controls (9 lung samples taken from donor lung at the time of lung transplant). cRNA was synthesized and hybridized onto whole human genome arrays modified with an additional 657 probes for genes/ESTs that would be potentially informative based on preliminary linkage data (Yang et al., 2005). Expression profiling was performed using standard protocols (co-hybridized with human universal cell line reference RNA; replicates with the two dyes swapped; Lowess-normalized intensities; analysis using the TGR MIDAS and MeV software), and differentially expressed genes were identified using significance analysis of microarrays (SAM) with 100 permutations. 558 differentially expressed transcripts between cases compared to controls were identified, with 135 genes being up- or downregulated greater than 1.8-fold. When hierarchical clustering was applied to the set of 135 genes, all but two samples clustered according to disease versus no disease, and familial disease segregated from sporadic disease (Figure 91.1). Sixty-nine differentially expressed genes were identified that distinguish sporadic and familial IP, and these are broadly grouped into functional classes (Figure 91.2), with a wide variety of chemokines, extracellular matrix, and growth-related genes that are differentially expressed. These data indicate that familial and sporadic IIP are transcriptionally distinct and also suggest many similarities between the histologic subtypes of UIP and NSIP. This study indicates that additional candidate genes that may be important in the pathogenesis of IIP, or that might be useful as biomarkers of disease activity, can be identified using whole-genome microarray analysis. Relevance of Genomics and Microarrary Studies to DPLD/ILD A number of clinical challenges exist that impede better understanding of disease mechanisms in DPLD and are potentially addressed using expression profiling, proteomic, and genomic approaches. First, even in the hands of expert clinicians,

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Genomic Determinants of Interstitial Lung Disease

Figure 91.1 Hierarchical clustering of 35 cases and 16 controls. Hierarchical clustering of 35 samples based on 135 transcripts that best differentiate patients with pulmonary fibrosis from normal controls. Samples are color-coded according to the disease inheritance status (orange – normal, blue – sporadic IIP, magenta – familial IIP) or histological features (orange – normal, green – NSIP, red – UIP, gray – no histological evaluation). Average linkage clustering with Euclidian distance metric was used.

radiologists, and pathologists specializing in DPLD, a definitive consensus diagnosis is achievable in about 85% of patients due to the clinical, radiologic, and pathologic overlap of these entities. Microarray-based expression profiling has the capability to improve diagnostic accuracy. A recent study by Selman and colleagues demonstrates that a distinct microarray expression signature exists for hypersensitivity pneumonitis, a clinical entity

with well-recognized radiologic and pathologic overlap with IIP, when compared to IPF (Selman et al., 2006). Linkage Studies in Pulmonary Fibrosis The only published study performing genome-wide linkage analysis in FIP comes from Finland (Hodgson et al., 2006). Using 6 pedigrees, NPL scores of 1.7 on chromosome 3 (marker

Conclusion

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Figure 91.2 Mean Expression Ratios in normal, sporadic IIP, and familial IIP. Groups of 69 genes with known function that best differentiate familial IIP from sporadic IIP. Genes are arranged and color-coded according to their function: dark green – calcium/potassium ion binding and transport; yellow – cell adhesion; orange – cell proliferation and death; red – coagulation cascade; blue – cytokines, chemokines, and their receptors; purple – ECM degradation; gray – ECM structural component; bright green – growth factors and their receptors; magenta – immune response; turquoise – metabolism; navy – motor activity; lavender – proteases and inhibitors; brown – regulation of I-kappaB/NF-kappaB cascade; and black – other.

D3S1278) and chromosome 4 (marker D4S424), and NPL score of 1.6 on chromosome 13 (D13S265) were obtained. On chromosome 4, a shared haplotype was identified among 8 of 24 multiplex families. A candidate gene located in the region of interest, ELMOD2, was further investigated by resequencing of exons and exon/intron boundaries. No mutations in ELMOD2 in these locations were identified. RT-PCR and in situ hybridization demonstrated decreased levels of ELMOD2 mRNA in six cases of sporadic IPF compared to controls. Genome-wide

linkage analysis of the 111 US families is in progress, and preliminary results indicate linkage (LOD score 3.0) on chromosome 11p15 not identified in the Finland study (unpublished data).

CONCLUSION The ILDs/DPLDs are a heterogeneous group of diseases with complex pathogenesis, diverse histopathology, and variable natural

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history. It is becoming increasingly clear that these entities occur in genetically susceptible individuals combined with other triggers such as environmental and drug exposure. Currently, obtaining a thorough family history and environmental history will identify at-risk individuals. In the future, at-risk individuals will likely be identified by genotyping for high-risk alleles. Highrisk alleles for the development of sarcoidosis have been recently identified, and the application of these risk alleles to the diagnosis, prognosis, and treatment of sarcoidosis will likely evolve rapidly over the next several years. Also, proteomic approaches have demonstrated success in identification of mycobacteria species as a potential environmental trigger to the development of sarcoidosis, and similar approaches are likely to identify environmental triggers in other DPLDs. In the near future, linkage and candidate genes studies will identify additional susceptibility genes for the development of pulmonary fibrosis. Mutations in SP-C and telomerase identified to date suggest that mutations affecting alveolar epithelial cell function cause susceptibility to pulmonary fibrosis. Microarray expression profiling and proteomic profiling may play an increasing role in the diagnosis of ILDs/DPLDs. It is likely that in the future, the diagnosis of a specific type of ILD/ DPLD will include the use of a panel of disease-specific antibodies to stain lung tissue in a manner similar to the histopathologic

diagnosis of carcinoma or lymphoma, or the use of other biomarkers identified using genomic approaches. Secondly, substantial variation in rates of disease progression exists for most of the IIPs. Some patients experience stable disease lasting several years, and then experience acute exacerbations characterized by rapid disease progression resulting in precipitous drop of lung function and death. This variable natural history confounds several relevant clinical problems such as the appropriate timing of referral for lung transplantation, an operation with a clear survival advantage in IPF and end-stage pulmonary sarcoidosis, but not uniformly successful in all patients. The variable natural history of these diseases also confounds the appropriate design of clinical treatment trials. The identification of biomarkers using genomic and proteomic profiling that accurately predict prognosis or treatment responsiveness would be of great clinical value. Proteomic profiling of lung tissue or bronchoalveolar lavage fluid may clarify environmental exposure, and assist in the diagnosis of hypersensitivity pneumonitis since the triggering antigen is sometimes not identified by the environmental history. Currently, we have little insight into the mechanism of acute exacerbations of IPF, or the pathogenesis of idiopathic interstitial pneumonias in general. Through the use of genomic medicine, it is likely that better insight into disease mechanisms will be obtained, leading to better treatment strategies of these disorders.

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92 Peptic Ulcer Disease John Holton

INTRODUCTION Peptic ulcer disease (PUD) comprises both duodenal ulcer (DU) and gastric ulcer (GU). Duodenal ulcers occur most often in the first part of the duodenum or in the pre-pyloric region of the stomach (antrum). Gastric ulcers are most frequently seen on the lesser curve of the stomach at the junction of the body and antrum (angularis) (Figure 92.1). Acute stress ulcers involve the body of the stomach and are often multifocal and transient. Histologically, the ulcer is a break in the mucosa with loss of epithelial cells, exposure of the basement membrane and involvement of the muscularis mucosae. Ulcers develop because of an imbalance between the normal protective attributes of the stomach and the potentially damaging secretions in the lumen of the stomach. This imbalance may be caused by a number of factors, the principal one being colonization by Helicobacter pylori (Figure 92.2). Ulceration may also occur associated with a number of other conditions such as Crohns disease, vascular insufficiency, hypersecretory states such as gastrinoma (Zollinger Ellison syndrome), antral G cell hyperplasia, mastocytosis and multiple endocrine neoplasms (MEN-1). Acute stress ulcers are caused by excess alcohol use, non-steroidal anti-inflammatory agents (NSAIDS), burns, trauma to the central nervous system, cirrhosis, chronic pulmonary disease, renal failure, radiation and chemotherapy. Pre-Helicobacter Management of PUD Prior to the isolation of H. pylori, ulceration was thought to be caused by psychological stress and excess spicy foods. It was Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1122

recognized that smoking was a contributory factor and that excess acid in the stomach was the primary pathology. The treatment was mainly surgical. The first operation was carried out in 1882 with antral resection and duodenogastrostomy (Billroth I) followed a few years later by antral resection and gastrojejunostomy (Billroth II). The aim of these surgical procedures was to reduce the antral phase of acid production. In 1943, Dragstedt introduced truncal vagotomy to reduce the cephalic phase of acid production. However, vagotomy also reduced the drainage from the stomach and had to be combined with a pyloroplasty in order to allow free drainage from the stomach. Frequently both these procedures (antral resection, either Billroth I or II; vagotomy with pyloroplasty) were combined. In the 1970s highly selective vagotomy was introduced, which specifically reduced acid secretion. Side effects of these surgical procedures included diarrhea, a dumping syndrome and anemia. In addition to surgical procedures, in 1976 long-term pharmacological acid suppression with H2-receptor antagonists was introduced to ameliorate symptoms and allow ulcer healing. However, if the medication was stopped, the ulcer frequently recurred. Isolation of H. pylori and Post-Helicobacter Management Curved or spiral shaped bacteria had been seen in the stomach of various animals and humans as far back as 1888 and had been thought of as commensals and of no significance. Since this period, however, two technical advances set the ground for the subsequent isolation of H. pylori: in the 1970s the development of endoscopes allow stomach biopsies to be taken, and the Copyright © 2009, Elsevier Inc. All rights reserved.

Clinical and Physiological Aspects of PUD

Oesophagus

Site of gastric ulcer

Pylorus

Body

Antrum Duodenum

Site of duodenal ulcer

Figure 92.1 This diagram shows the usual location of gastric and duodenal ulcers. Duodenal ulcers are found in the antrum of the stomach or the first part of the duodenum. Gastric ulcers are located at the junction of the antrum and fundus or body of the stomach.

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isolating the organism on artificial media by culturing it under microaerobic conditions similar to that used to isolate the gastrointestinal pathogen Campylobacter jejuni. The organism was initially called Campylobacter pylori but in 1989 was transferred to a separate Genus called Helicobacter (Goodwin, 1994). The initial suggestion that gastritis and ulceration may be caused by this organism was met with scepticism, leading both Marshall and Morris to separately swallow a culture of the organism in order to fulfill Koch’s Postulates. Both individuals developed gastritis, which resolved on eradication of the organism. In 1997 the genome sequence of one strain of the organism was determined (Tomb et al., 1997). Eventually, the causal nature of at least 90% of cases of PUD as being H. pylori was widely accepted, and both Warren and Marshall received the Nobel Prize for Medicine and Physiology in 2005 (Megraud, 2005). The recognition that H. pylori is the principal cause of PUD has led to a dramatic change in the management of disease: from surgery or lifelong H2-receptor antagonists to a short course of an acid suppressive (usually a proton pump inhibitor, PPI) combined with two antibiotics – usually amoxycillin and clarithromycin or amoxicillin and metronidazole (European Helicobacter Study Group, 2005).

CLINICAL AND PHYSIOLOGICAL ASPECTS OF PUD PUD typically presents with epigastric pain, usually occurring in the early hours of the morning and relieved by food. There may be nausia and vomiting, and there may also be melena with symptoms of anemia. Alternatively, ulcers may initially present with hematemesis or signs of perforation without any prior symptoms. A key factor in the pathophysiology of ulcer disease is acid regulation.

Figure 92.2 This is a Giemsa stained section of stomach showing Helicobacter pylori located within a gastric gland, Some of the bacteria are located on the surface of the epithelial cells.

recognition of cultural conditions necessary for the isolation of microaerophilic bacteria. In 1982–1983, Warren, a histopathologist from Perth, Australia, had noted an association between the presence of these organisms and gastritis. He and his colleague Marshall, a gastroenterologist, studied a large series of endoscopic biopsies demonstrating the almost perfect association between gastritis and the presence of this curved bacterium (Warren and Marshall, 1983) that was initially called a campylobacter-like organism. Additionally, they were successful in

Acid Regulation The pH of the stomach is about 1.5–2.0, although gastric acidity both shows a diurnal variation and bears a relationship to food intake. Patients who develop DU typically have high basal and meal-stimulated acid secretion whilst those who develop GU typically have hyposecretion of acid. The control of acid release comprises three arms: neural, hormonal and paracrine. Orexigenic neuropeptides modulate the action of the vagus nerve, which stimulates the parietal cells to secrete acid in response to food intake or the thought of food. The stimulus by the vagus nerve also activates G cells in the antrum of the stomach to release gastrin. Gastrin stimulates enterochromaffin-like cells (ECL) in the stomach to release histamine, which in turn stimulates parietal cells (found principally in the fundus of the stomach) to secrete acid (H). Gastrin also stimulates the growth of ECL as well as directly stimulating parietal cells to secrete acid. Other hormones that modulate acid release are ghrelin and cholecystokinin. Negative feedback regulation

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is through the acid stimulating the release of somatostatin from D cells in the stomach, which inhibits the release of gastrin from G cells (Moss et al., 1992). Added complexity of control of acid secretion is provided by the action of a pituitary adenyl cyclase activating polypeptide (stimulation), galanin (inhibition), transforming growth factor alpha (gastrin agonist) and importantly, IL-1 beta (strongly inhibitory) (Schubert, 2005). Normal Mucosal Defense Mechanisms Because the luminal contents of the stomach (low pH and digestive enzymes) would damage the epithelium, the stomach has a number of defense mechanisms, which include the secretion of a vicid mucus layer from goblet cells and the production of bicarbonate ion, which neutralizes the acid of the stomach within the mucus layer. A key factor in the production of mucus and regulating blood flow is the action of prostaglandins which are thus cytoprotective. Secretion of bicarbonate is an active process and is regulated by the vagus nerve. The acid is secreted from parietal cells, which lay beneath the protective mucus barrier, but exposure of the adjacent epithelial cells to the high acid is prevented by viscus fingering within the mucus barrier by rapid ejection of the acid from the parietal cells. Additional protection to the mucous layer is provided by surfactants and sulfhydryl compounds at the apical cell membrane. The normal cellular turnover is a further protection, as is the motility of the gastrointestinal tract with the stomach contents periodically discharged into the small intestine. Within the lamina propria the normal blood flow maintains the integrity of the epithelial layer by supplying nutrients and oxygen and diluting toxic factors.

PATHOPHYSIOLOGY OF ULCER FORMATION The two most common causes of PUD are infection with H. pylori and taking NSAIDS. Helicobacter-Induced Ulcers H. pylori causes damage to the host by five principal mechanisms: 1. H. pylori causes direct damage to the protective mucus barrier and the epithelial cell layer by inhibiting mucus secretion, secreting phospholipase A (which disrupts the micellar structure of the mucus) and generating ammonium which, in addition to disrupting the protective mucus layer, is also directly cytotoxic for the gastric epithelial cells. H. pylori also secretes a vacuolating cytotoxin which is directly cytotoxic (Harris et al., 1996; Kubota et al., 2004) and injects the cagA protein (see later) into the epithelial cell by means of a type IV secretion apparatus. This latter protein disrupts intracellular signaling pathways in the epithelial cell which in part leads to secretion of the chemokine (IL-8). 2. H. pylori induces a Th-1 mediated acute inflammatory response by stimulating IL-8 secretion from the epithelial

cells and recruiting granulocytes into the lamina propria. Products from H. pylori induce the release of free oxygen radicals from the granulocytes, thus causing bystander damage to the epithelial cells (Brisslert et al., 2005). 3. H. pylori inhibits the negative feedback loop in the control of acid regulation by means of its lipopolysaccharide inhibiting the production of somatostatin. This leads to continuous production of gastrin (and thus acid) and a characteristic hypergastrinaemia (Moss et al., 1992). 4. H. pylori affects the balance of cell division/apoptosis, thus affecting ulcer development and healing as well as having pathological implications for the long-term complication of colonization by H. pylori – gastric adenocarcinoma (Cai et al., 2005). 5. During infection with Helicobacter pylori auto-antibodies are produced, which target the acid secreting mechanism of the parietal cells and eventually may lead to loss of parietal cells and thus loss of the ability to produce acid (Lo et al., 2005). NSAIDS-Induced Ulcers Non-steroidal anti-inflammatory agents damage the stomach by two mechanisms, a local mechanism and a systemic effect on mucus production. 1. NSAIDS are both weak acids and lipophilic, and as such the hydrophobic surfactant layer on the apical surface of the epithelial cells and in the mucus does not present such a barrier to penetration. Once they have penetrated the protective mucus, NSAIDs cause direct damage to the epithelial cell by altering cell permeability and damaging mitochondria, leading to apoptosis (Nagano et al., 2005). 2. When absorbed into the blood, NSAIDS inhibit the enzyme cyclo-oxygenase 1 (COX-1), which produces prostaglandins from arachidonic acid metabolism. The effect of prostaglandins is to increase blood flow in the stomach, increase mucus and bicarbonate production and inhibit acid production. Reduction in prostaglandin secretion diminishes three of the major defense mechanisms of the stomach: bicarbonate and mucus production and blood flow (Gambero et al., 2005). Ulcer Formation The net effect of these various insults is the loss of protective mucus and death of the epithelial barrier, resulting in exposure of the damaged epithelium and underlying lamina propria to the effects of acid and proteolytic enzymes resulting in an ulcer (Figures 92.3 and 92.4). If Helicobacter spreads from the antrum to give a pan-gastritis, the acid-producing cells suffer damage, and eventually an atrophic gastritis develops with a decrease in acid production and an increase in intraluminal pH. Atrophic gastritis and the subsequent intestinal metaplasia that develops are pre-malignant risk factors that in a percentage of individuals will lead onto the development of gastric adenocarcinoma. Similar damage results from NSAID usage but by a different mechanism.

The Helicobacter Genome

Figure 92.3 This is a photograph of the macroscopic appearance of a duodenal ulcer in the antrum of the stomach.

Mucosa

Smooth muscle

Ulcer Base Blood vessel

Serosal fat

Figure 92.4 This is a photomicrograph of a hematoxylin and eosin stained section of stomach illustrating an ulcer that has eroded the epithelium exposing the underlying tissues.

THE HELICOBACTER GENOME Genome Structure and Diversity In 1997, the genome sequence of H. pylori 26695 was sequenced by The Institute for Genomic Research (TIGR) (Tomb et al., 1997) and 2 years later that of another strain, J99, was sequenced by AstraZeneca in collaboration with Genome Therapeutics Corporation, United States of America.

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Strain 26695 has 1667867 base pairs (bp) an average GC content of 39% and 1590 predicted coding sequences, of which 1091 were annotated. Well developed systems for motility, iron scavenging, restriction and modification and outer membrane proteins (OMPs)/adhesions were noted with a relatively restricted repertoire of metabolic and biosynthetic systems or regulatory networks. An abundance of homopolymeric tracts was found, underscoring the potential for antigenic variation by slipped strand mispairing. In this strain there were five areas with a different GC ratio, one of which has been identified as a pathogenicity island (PAI), and two others in relation to the presence of insertion sequences (IS). Two ISs were identified with 13 copies of one (IS605) and 4 copies of the other (IS606). In all 95 paralogous gene families (16% of the total genome) are present, the largest family with 32 members related to porins/OMP. Three secretion pathways are present: the SecA-dependent one found in many bacteria, a Type IV secretion system associated with the PAI and related to that of Agrobacterium and a flagellar export pathway. The large number of homopolymeric tracts, dinucleotide repeats and paralogous families explains the potential for the high degree of variation known to occur in the H. pylori genome, reflecting its ability to survive and evolve in its restricted ecological niche. Although placed in the gamma Proteobacteria it also carries genes more closely related to nonProteobacteria testifying to lateral gene transfer at some point in the organism’s evolutionary history. The second H. pylori isolate to be sequenced allowed a comparison of both J99 and 26695 (Alm et al., 1999). H. pylori J99 has 1643831 bp, which is smaller than that of 26695 by 24036 bp. The number of predicted coding sequences in J99 is 1495, which is fewer than the revised number of predicted coding sequences in 26695 (now estimated to be 1552 rather than the original 1590). Each strain has unique genes not found in the other strain. J99 has 89 unique genes, of which 56 are of unknown function whilst 26695 has 117 unique genes with 91 of unknown function. There are a predicted 1406 genes common to both strains. J99 has fewer complete ISs. Like 26695, J99 carries a complete PAI and also like strain 26695 has a large number of paralogous gene families. One region of both genomes carries 46–48% of the strain-specific genes and has been termed the plasticity zone (PZ). In J99 this PZ is continuous but in strain 26695 it is split into two sections separated by about 600 kb. Allelic variation occurs in both strains with differences found mainly at the third codon position. Moderate macrogenome diversity also exists between the strains, with about 85% of orthologs having the same neighbor at both sides, 10% with the same neighbor on one side and a strain-specific gene on the other and about 3% flanked by strain-specific genes. Despite these genomic variations the proteome of both strains are very similar. Following the sequencing of the second H. pylori genome ( J99), it was necessary to re-annotate both genomes to a common classification (Boneca et al., 2003). H. pylori has one of the highest allelic diversity of any organism, and comparison of both sequenced genomes indicates a high degree of size variation between orthologs due to a variety of

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mechanisms. The diversity is generated by frequent recombination, a high mutation rate, slipped strand mispairing, frame shifts and the presence of pseudogenes (Tomb et al., 1997; Suerbaum et al., 1998;Wang et al., 1999). Much of the diversity is localized within the PAI or PZ. On a macrogenomic scale, however, the sequences of the two genomes indicate a similar organization. The population genomic structure of H. pylori is regarded as panmictic rather than clonal (Go et al., 1996) although isolates from an individual or family group are clonal (Han et al., 2000). A study of 23 isolates using electrophoresis of 16 enzymes indicated an allelic diversity ranging from 2–11 alleles with no obvious clustering of disease-related strains (Hazell et al., 1997). A whole-genome microarray analysis of 15 strains utilizing 1660 unique sequences derived from the strains J99 and 26695 showed a high degree of genetic diversity between the strains (Salama et al., 2000). One thousand two hundred and eighty one sequences were common to all the strains and 362 were missing from at least one of the isolates. At a conservative estimate, about 12–18% of the genes of an individual isolate were strain-specific. This genetic diversity is probably a reason for the ability of H. pylori to become a chronic pathogen in the stomach despite a vigorous local and systemic immune response. In part, it explains the different clinical outcomes of infection with H. pylori, and it also reflects the continuing evolution of the organism over decades within the human stomach of an individual. Several studies (Han et al., 2000; Israel et al., 2001; Lundin et al., 2005) have demonstrated the presence of related sub-strains of H. pylori within the stomach of a single individual. In one study the person who was infected with the original sequenced isolate, J99, remained infected despite appropriate therapy, and after 6 years additional cultures were isolated from the same patient. The new isolates were subjected to random amplified polymorphic DNA-PCR (RAPD-PCR), sequencing of unlinked genes and micro-array analysis. The results demonstrated that all the isolates were related to the original strain, although the isolates varied in genetic content from the original strain and co-isolates by about 3%. Current isolates did not hybridize with some open reading frames (ORFs) from the original strain with a loss of 0.28–1.52% ORFs and yet some ORF sequences from current isolates hybridized with specific sequences from the other strain, H. pylori 26695, of which the whole genome sequence is known. A second study confirmed the presence of closely related sub-clones isolated from a single individual (Bjorkholm et al., 2001) and additionally went on to demonstrate the genetic and phenotypic stability of these two clones in an experimental system using transgenic mice expressing human Lewis b, the ligand for the BabA adhesion on the organism. Of the two original isolates, one had lost the PAI and, although this isolate could colonize germ-free transgenic mice along with the PAI positive isolate, only the PAI positive isolate could colonize conventional mice harboring a normal flora. Over a 3- or 10-month period of study, no nucleotide substitutions or deletions were detected in either strain indicating that in this model system either there was insufficient selective pressure or time for divergence to occur.

Allelic Diversity and Disease H. pylori colonizes over half the population of the world. In some countries the colonization rate is as high as 90% even by the age of 10 years. In others it is about 30% overall, with only 5% being colonized within the first decade. This geographical difference in prevalence is caused principally by variation in availability of a good public health infrastructure and prevailing social conditions. Of those colonized, the vast majority are without symptoms although they do have gastritis. A small proportion, about 5–20%, will develop PUD and an even smaller percentage, about 1%, which develop gastric cancer. The difference in clinical outcome is the results of the interaction of three factors: environmental, host susceptibility and strain variation. Strain Variation and Virulence As mentioned, H. pylori possesses a number of virulence characteristics that are not present or not expressed in all strains. Some strains have acquired a PAI that carries 31 different genes, some of which relate to the synthesis of a Type IV secretion system, and a gene (cagA) coding for a protein that the Type IV secretion system transfers to gastric epithelial cells – the cytotoxin associated gene product (CagA). Nearly all strains carry another locus vacA, separate from the PAI, that secretes a vacuolating cytotoxin (VacA). This latter product, however, is secreted in varying amounts depending on the presence of allelic diversity within the vacA gene and possibly the presence of the cagA PAI. Strains can thus be broadly divided into two main types: Type I that co-express cagA and vacA and Type II that do not (Xiang et al., 1995). The clinical significance of this is that Type I strains are more often isolated from patients with serious gastroduodenal disease such as PUD and gastric cancer. Vacuolating Cytotoxin

About 50% of isolates produce a vacuolating cytotoxin. The vacA gene encodes a 139–140 kDa protein which is cleaved into a 33 amino acid signal sequence and a 90 kDa toxin. The C-terminal domain of the toxin comprises 14–16  strands that inserts into the bacterial membrane and acts as an autotransporter (Type V secretion system) for the toxin (Fischer et al., 2001) that is secreted. After secretion, the toxin is further cleaved into a 34 kDa N-terminal region and a 58 kDa C-terminal region that nevertheless remain associated. The toxin monomer is activated by acid, undergoing a conformational change and then oligomerizes to form a 900 kDa protein, as it associates with lipid rafts in the cytoplasmic membrane of epithelial cells (Lupetti et al., 1996). In the cytoplasm of the epithelial cell, its mode of action is to disrupt the endocytic pathway leading to enlargement of late endosomes. The toxin forms anion-selective channels in membranes allowing the accumulation of weak bases, stimulated by the acidification of the vacuole by V-ATPase. Additionally, transfection experiments show that the p34 peptide becomes associated with the mitochondria releasing cytochrome C from the mitochondria and thus activating the caspase cascade (Galmich et al., 2000). When given directly onto gastric epithelium, the cytotoxin induces damage causing necrosis and

The Helicobacter Genome

ulceration. A further consequence of the action of the toxin is the inhibition of processing of antigens in antigen-presenting cells, thus affecting T-cell responses. Although most strains carry the vacA gene, there is a large diversity in the sequence of the mid region (MR) and the leader sequence (LS). This diversity has consequences in relation to secretion of toxin, binding to cell types and geographical distribution of strains. The general structure of the VacA protein is illustrated in Figure 92.5.There are four variations of the LS designated S1a,S1b,S1c and S2 and several variations of the MR designated m1a, m1b, m1T, m2a m2b and chimeric types, for example, m1b-m2 (Atherton et al., 1995; Pan et al., 1998; Strobel et al., 1998; van Doorn et al., 1998; van Doorn et al., 1999; Wang et al., 1998). The primers used to detect these variations are given in Table 92.1. Strains with an S1 LS have a different cleavage site from that of S2, and these strains are more often found associated with PUD than S2-containing strains. They produce high levels of cytotoxin and are more frequently found in cagA positive strains (Atherton, 1997). The MR is also an independent marker for cytotoxin activity in vitro with m1 stains being more cytotoxic than m2 strains. The MRs are responsible for binding to cells and depending upon the cell type m2 strains are just as toxic as m1 strains (Ji et al., 2000; Pagliaccia et al., 1998).Various combinations of the LS and MR alleles have been isolated from patients, giving strains with different phenotypes of cytotoxin activity. Strains with the S1/m1 alleles appear to produce more cytotoxin compared to S2/m2 strains, which apparently do not produce detectable cytotoxin activity, although the gene is

LS Vac - S1 polymophism S1a S1b S1c

Urea

MR M

S2

M1 M1

M

M1 M1

LS

Dimeric vacuolating sytotoxin

Urease

NH3CO2

Oligomerization

Mitochondria NH3

N

NH4 Vacuole

V-ATP ase

H2O

Gastric epithelial call Immune cells

Figure 92.5 This diagram illustrates the polymorphisms found in the vac A gene in the LS and mid region (MR) of the gene. It also illustrates the secretion and oligomerization of the vacA protein and its action on the epithelial/immune cell to disrupt normal endosomal trafficking leading to the production of large vacuoles and its effect on the mitochondria leading to apoptosis.

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transcribed. This lack of association between H. pylori S1/m2 strains in countries with high levels of PUD and gastric cancer is explicable by the lack of binding of m2 strains to cell types used in testing cytotoxic activity. However, there may also be a modulation of toxin production based on levels of transcription of the vacA gene (Forsyth et al., 1998), which could affect the phenotype of the strain and clinical outcomes. Cag PAI and the CagA Gene Product

CagA is a marker for the cag PAI: a 40 Kb DNA fragment containing 31 genes, of which 6 encode a Type IV secretion system, by which the CagA protein is injected into host cells. Once inside the gastric epithelial cell, it is phosphorylated at a tyrosine residue, present in EPIYA motifs, by Src-family kinases and binds to the SH2 domain of SHP-2 phosphatase, thus deregulating its activity (Hatakeyama, 2003). The immediate result of this is the alteration of cell morphology, with the prolonged activation of Erk signaling and the production of the ‘humming bird’ phenotype. The cell cycle is also interfered with, caused by an increasing expression of cyclin D3, the phosphorylation of the tumor suppressor Rb and the expression of the transcription factor c-jun, leading to the increase in cell proliferation of gastric epithelial cells, probably caused by a deregulation of the G1/S checkpoint of the cell cycle (De Luca et al., 2003). These effects may have long-term consequences in relation to gastric carcinogenesis. Additionally, CagA recruits both scaffolding protein ZO-1 and the junctional adhesion protein to its site of attachment (inducing loss of villi and pedestal formation) and affects epithelial barrier functions (Amieva et al., 2003). Finally, engagement of the Type IV secretion apparatus leads to secretion of IL-8 from the epithelial cell with consequent recruitment of granulocytes (Fischer et al., 2001). The presence of the cag PAI is not an “all or none” phenomenon. Several studies have shown that the PAI can exist as a single contiguous entity or can be divided by an insertion element (IS 605) or chromosomal DNA into two parts; cag I and cag II. Various deletions of the cag PAI have also been recognized (Censini et al., 1996; Covacci et al., 1997). The insertion element is an additional variable as it may be completely absent or it may be present in variable numbers located in different parts of the genome (Figure 92.6). The cagA protein is also variable in size (120–140 kDa) due to a different number of repeats of sequences present in the 3 -end of the gene. Analysis of this region of the cagA gene revealed four types designated A-D based on the nucleotide sequence and organization of the locus (Yamaoka et al., 1999). These repeats are illustrated in Figure 92.6. Geographic differences can also be found in the 3’ region of the genome. Studies of European strains compared to East Asian strains have identified nucleotide differences leading to two amino acid sequences denoted as (1) Western cagA specific Sequence (WSS) and (2) East Asian cagA specific sequence (EASS) (Azuma et al., 2002; Yamaoka et al., 1999), namely: 1. FPLKRHDKVDDLSKVGRSVSPEPIYATIDDLG

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TABLE 92.1

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Primers for some polymorphic regions of Helicobacter pylori

Primer

Polymorphic locus

References

5 GTCAGCATCACACCGCAAC3 5 CTGCTTGAATGCGCCAAAC3

vacA s1aSS1-F VA1-R

Atherton et al. (1995)

5 AGCGCCATACCGCAAGAG3

vacAs1bSS3-F/VA1-R

Atherton et al. (1995)

5 CTCTCGCTTTAGTGGGGYT3

vacA s1c S1C-F/VA1-R

Yamazaki et al. (2005)

5 ATGGAAATACAACAAACACAC3

vacA s2 VA1-F/VA1-R

Atherton et al. (1995)

5 GGTCAAAATGCGGTCATGG3 5 CCATTGGTACCTGTAGAAAC3

vacA m1aVA3-F VA3-R

Atherton et al. (1995)

5 GGAGCCCCAGGAAACATTG3 5 CTGCTTGAATGCGCCAAAC3

vacA m2 VA4-F VA4-R

Atherton et al. (1995)

5 CAATCTGTCCAATCAAGCGAG3 5 GCGTCAAAATAATTCCAAGG3

vacA m1/m2 VAG-F VAG-R

Atherton et al. (1999)

5 GGCCCCAATGCAGTCATGGAT 3 5 GCTGTTAGTGCCTAAAGAAGCAT3

vacA m1b VAm-F3 Vam-R3

Pan et al. (1998)

5 GTGTTTTTAACCAAAGTATC3 5 CTATAGCCASTYTCTTTGCA3

IceA1 iceA1F iceA1R

van Doorn et al. (1998)

5 CAACGATAATGGCACAAACT3 5 GTCGTATCAATAACAGAAGTTG3

Type I hopQ OP5136 OP4829

Cao et al. (2002)

5 GATAACAGGCAAGCTTTTGAGG3 5 CTGCAAAAGATTGTTTGGCAGA3

cagA CAGAF CAGAR

van Doorn et al. (1998)

5 GGCAATGGTGGTCCTGGAGCTAGGC3 5 GGAAATCTTTAATCTCAGTTCGG3

5 end cagA cagA5 cagA2

Mukhopadhyay et al. (2000)

5 AGGATTTCAGCAAGGTAACGCAAGC3 5 TAAGATTTTTGGAAACCACCTTTTGTAT3

3 end cagA cagA-F40481 cagA-R41660

Mukhopadhyay et al. (2000)

5 ACCCTAGTCGGTAATGGGTTA3 5 GTAATTGTCTAGTTTCGC3

3 end cagA CAG1 CAG2

Yamaoka et al. (1998b)

5 AATCCAAAAAGGAGGAAAAACATGAAA3 5 TGTTAGTGATTTCGGTGTAGGACA3

BabA2

Rad et al. (2002)

2. ESSAINRKIDRINKIASAGKGVGGFSGAGRSASPEPIY ATIDFDEANQAG Differences of nucleotide sequence also exist in the less variable, 5 end, of the cagA gene. Two types have been identified, cagA1 and cagA2, the former found in Europe, America and Australia whilst the latter was found in isolates from East Asia. Isolates with the cagA2 strains were principally vacA S1c, and cagA1 was more commonly linked to vacA m1 strains (van Doorn et al., 1999b). Potential additional complexity is provided by the presence of EPIYA motifs in the cagA protein, whose number and phosphorylation status may vary. Other Virulence Markers

Several other virulence characteristics have also been identified. Some of these characteristics, such as the urease enzyme and the

flagella, are important for establishing colonization. Urease also acts as a toxin by hydrolyzing urea to produce ammonia, which is cytotoxic, and by diminishing the viscous nature of the mucus layer, which is protective. Several adhesins have been identified, the best characterized one being BabA, which binds the Lewis b blood group antigen expressed on gastric epithelial cells. Some strains have a nonfunctional pseudogene (BabA1) whilst other strains only have one functional gene, BabA2, and others have a separate functional allele, BabB. BabA has several polymorphisms located in the MR of the protein leading to variation in degree of binding to Lewis b. Strains expressing a chimeric protein BabA2/BabA1 have also been identified that show phase variation of expression of the protein, and this reported heterogeneity may have clinical implications (Backstrom et al., 2004; Hennig et al., 2004). Other adhesins are SabA (which binds sialyl Lewis x and which is

The Helicobacter Genome

PAI  Pathogenicity island OM  Outer membrane PPS  Peri plasmic space PG  peptidoglycan IS  insertion sequence

Cag A

Peptidoglycan

OM PSS PG Cag A protrin Cag II

IS605

Cag I

Cag A

Cag II

Deletion of PAI

Cag I

Cag II

NH3CO2 Polymorphisms C Type A A B A of 3’ end of CagA Type B A B A B A B A Type C A B A Type D A

A

C C

A

C

C

A A

C

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A second study (Graham et al., 2002) investigated the in vivo whole genome expression from the human stomach. In order to overcome the instability of RNA isolated from tissue and the comparatively low numbers of bacteria found in the stomach, the method of selective capture of transcribed sequences (SCOTS) combined with microarray analysis was used. In all, 14 genes were differentially expressed in vivo compared to in vitro grown bacteria, including the VirB4 homolog (part of the Type IV secretion apparatus). The majority of the genes expressed in the human stomach encoded H. pylori-specific proteins of unknown function. This approach of analyzing in vivo gene expression is likely to be valuable in identifying disease-related genes, and these results encourage further studies in this area.

A A

Figure 92.6 This diagram illustrates the PAI and its deletions and the polymorphisms found with in the cagA gene based upon the variation in the number of repeats of sequences A, B and C. The diagram also illustrates the inoculation of both bacterial peptidoglycan from the cell wall of Helicobacter pylori and the cagA protein into a gastric cell leading to disruption of intercellular signaling. The materials are transferred into the eukaryotic cell by a Type IV secretion system which is coded for by genes on the pathogenicity island.

found in inflamed tissue), AlpA and AlpB, HpA and HopZ. Less information is available concerning the other adhesions. The ligands for AlpA/B are unknown. HpA is the coding sequence for an hemaglutinin that binds N-acetylneuraminyl-lactose and HopZ exists as two alleles and its expression is possible regulated by slipped strand mispairing. Other virulence factors that have been identified are: an OMP HopQ that is present as two alleles Type I and Type II, an outer inflammatory protein OipA associated with increased expression of IL-8, and a locus called iceA (induced by contact with epithelium), which has two alleles iceA1 and iceA2, the former encoding a putative restriction endonuclease (Peek et al., 1998; Yamaoka et al., 2000). Whole Genome Expression Studies In order to understand the changes in genome expression following infection, analysis of genome expression by microarrays have been performed. In one study, the effects of in vitro acid stress were investigated (Ang et al., 2001). A microarray containing 1534 ORFs from strain 26695 was prepared and the genome expression at pH 7.2 and 5.5 analyzed. Four hundred and forty five ORFs were expressed under both pH conditions, 80 had increased expression under acid conditions and 4 had reduced expression. Of those that had increased expression, one group coded for proteins recognized to be involved in the acid-stress response, one group had no obvious connection to acid stress and one group was of unknown function.

Geographic Distribution and PUD Studies of the genome sequence of the hypervariable regions of the H. pylori genome isolated in different geographical locations show that different strains have geographical predominance. These differences can be used to analyze population migrations and also suggest that H. pylori may have been transmitted to the New World from European invaders. In Northern and Eastern Europe S1a alleles predominate; in France and North America S1a and S1b are equal in number; in Iberia and South America S1b predominate; and in East Asia S1c is the major strain type. The MR alleles are equally distributed in these regions except for South America where m1 predominates. These studies also confirm that East Asian strains are quite distinct from European isolates (Kersulyte et al., 2000; van der Ende et al., 1998; Yamaoka et al., 2000b). Markers for PUD Genome Analysis

The high level of genetic variation within the genome of H. pylori explains, in part, the variation in clinical outcome of infection and why the majority of individuals colonized by the organism remain asymptomatic whilst other will develop serious gastroduodenal disease including PUD. Type I strains (presence of S1 vacA alleles and production of the vacuolating cytotoxin and presence of the cag PAI) are more often isolated from patients with PUD compared to Type II strains, which do not have the cag PAI and are not phenotypically cytotoxic but possess the vacA S2 allele. Similarly the presence of BabA2, oipA, iceA1 and Type I HopQ alleles have all been linked to PUD in some studies. In Europe and North America, S1a/m1, cagA positive strains are more common in patients with PUD compared to non-ulcer dyspepsia (NUD), but in China nearly all strains are vacA and cagA positive and are equally distributed between PUD and NUD (Pan et al., 1998). In Korea, S1c strains are more common in PUD (Choe et al., 2002), and in a Japanese study the S1a/m1a WSS 3 cag region was significantly associated with PUD whereas the S1c/m1b ESS 3 cag region were

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not (Yamazaki et al., 2005). The Far East genotype, however, was correlated with gastric atrophy in a study in Japan (Azuma et al., 2004). In Brazil, cagA was correlated with DU but the subtypes of the 3 cagA region were not correlated with disease groups (Rota et al., 2001). On the other hand, in Malaysia in a study of the three different ethnic groups (Malay, Chinese, Indian) no correlation could be found between cagA, or vacA and PUD (Tan et al., 2005). In Europe, the association of VacA (s1), cagA, BabA2 or Lewis expression by Hp (Thoreson et al., 2000) is correlated with PUD. The association is more so in strains that are positive for all three alleles (Olfat et al., 2005), but in Brazil a study could not show any correlation between BabA2 and PUD although the locus was more common in chronic gastritis (Gatti et al., 2005). A large study of 827 strains from four countries, however, found that of the three loci, only BabA2 could be considered as a marker for separating PUD from gastritis although none were particularly valuable as predictive markers (Yamaoka et al., 1999; Yamaoka et al., 2002). A further locus identified as a marker for PUD is iceA (van Doorn et al., 1998), although once again a study from Japan could not confirm this (Ito et al., 2000). These conflicting results demonstrate that other bacterial markers may be important and that interaction with host factors are also important in disease outcome. A study of two strains (both caAvacAs1, iceA1) isolated from either DU (G1.1) or GU (B128) were investigated by microarray and for the induction of inflammation in a gerbil model (Israel et al., 2001). The GU isolate induced more inflammation, IL-8 secretion and apoptosis compared to the DU isolate. The microarray analysis demonstrated that the DUinducing strain had a large deletion covering cag6-cag23 (which includes cagE). This study emphasizes both the importance of the cag PAI in pathogenesis of serious disease and also the utility of microarray analysis in identifying potential markers for specific clinical outcomes. A study of three genes in the PZ (JHP940; JHP947; HP986) from 200 Brazilian patients identified JHP947 as being associated more strongly with DU and gastric carcinoma (99%) as opposed to gastritis (44%) (Santos et al., 2003), but again, as with other studies, it was not specific for PUD. Proteome Analysis

An immunoproteomic approach using the strain 26695 and sera from 24 H. pylori positive patients (Haas et al., 2002) yielded 310 antigenic protein spots, of which some were differentially recognized in patients with gastritis or PUD. Further work by this group (Krah et al., 2003) identified peptides within a subset of spots using MALDI-TOF. The protein GroEL was identified as a component in 15 of the spots and was recognized antigenically. A proteome analysis of H. pylori isolates from two patients (Pereira et al., 2006), one with gastritis and the other with DU showed that four spots were uniquely associated with each condition and six spots had variable expression. Further work is required to confirm this study and to identify the proteins.

Serological Markers of PUD

A number of serological studies have identified low molecular weight proteins as possible markers of PUD. A study of 108 patients with gastric cancer, PUD or NUD showed that antibodies to a 26 kDa protein were more common in PUD and that when combined with sero-prevalence to the cagA protein the sensitivity and specificity was 76% and 62% respectively (Kuo et al., 2003). A similar study of 156 patients from Thailand (Vilaichone et al., 2003) showed that antibodies to a 35 kDa protein were associated with GU. A study of 80 Japanese patients with different clinical conditions showed the 35 kDa antigen was more common (97%) in GU or DU compared to gastritis (70%) (Yamaoka et al., 1998).

HUMAN POLYMORPHISM AND PUD The first demonstration that polymorphisms in the human genome could affect the clinical outcome of Helicobacter infection was provided by the increases risk of developing gastric adenocarcinoma in patients who had certain polymorphisms in IL-1 (El-Omar et al., 2000). It was subsequently shown that a synergistic effect was seen between bacterial and human polymorphisms (Rad et al., 2003). Thus, the VacA s1 genotype was linked to a greater risk of developing severe gastroduodenal disease, especially if in combination with other ‘high risk’ host markers such as the host IL-1 genotype IL-1–511T/-31C and the genotype of its receptor IL-1RN*2. When examined singly, in relation to the development of DU in 278 individuals, the alleles IL-1B  3954 and IL-RN*2 were not associated with the development of DU; however, there was a strong allelic link between the two loci in patients with DU (Garcia-Gonzales et al., 2001). On the other hand, a study of 315 individuals showed that the presence of IL-1RN*2 was a risk factor, with an odds ratio (OR) of 22.6 in the presence of colonization by Helicobacter (Hsu et al., 2004). A smaller study of 215 individuals could not confirm that any polymorphisms in IL-1B, IL-1RN, TNF-A TNF-B were related to the development of DU (Garcia-Gonzales et al., 2005). However, when looked at in combination: IL-1RN*2; IL-1B-31; IL1B-511C; IL-1B  3954C; TNF-haplotypeE was found with a slight (non-significant) increase in the patients with DU. An earlier study by the same group showed that genotype LTA (lymphotoxin, TNF-B) NcoI 2.2 and TNF-I were both more common in GU than DU (Lanas et al., 2001). Further confirmation of tumor necrosis factor (TNF) polymorphisms associated with PUD was provided (Lu et al., 2005), showing that the alleles TNF-A-1031C and TNF-A -863A have an OR of 2.4 for the loci individually and 6.0 in combination. A significant association between IL-8 A/T heterozygote and duodenal ulceration (Gyulai et al., 2004) or gastric ulceration (Ohyauchi et al., 2005) has been identified compared to the wild type T/T allele. Studies of polymorphisms in IL-6, IL-12 and CD14 have not shown any association (Garcia-Gonzales et al., 2005; Gyulai et al., 2004; Lobo Gatti et al., 2005). Finally,

Genomics in the Management of Disease

associations between CD11c exon 15 and intron 31 G/A in combination show an OR of 2.4 for GU disease (Hellmig et al., 2005) and polymorphisms in the myloperoxidase (468 A/A) locus show an OR of 8.7 for DU when infected with H. pylori (Hsu et al., 2005). An inability to mount an effective immune response may be more likely to lead to chronic infection and thus indirectly to PUD. A study of microsatellite polymorphisms in the T-cell receptor V 6 locus has identified a genotype (TCRBV6S1B/B) that is not able to eliminate Helicobacter and thus is more susceptible to chronic infection although in the cohort studied this genotype did not correlate with clinical outcome (Kunstmann et al., 2000). A study of 20 single nucleotide polymorphisms (SNPs) in the genes for matrix metalloproteases 1, 3, 7 and 9 in 599 isolates from infected patients showed a strong association between GU and a promoter variants of MMP 7 and 9 (Hellmig et al., 2006). Polymorphisms in genes related to the action of NSAIDS have been identified although relationships to clinical outcome and ulcer formation have not been so far linked. Cyclo-oxygenase 1 (COX-1) converts arachidonic acid to prostaglandin H2, and heterozygosity for the polymorphisms A-842G/C50T are more likely to show a greater inhibition of prostaglandin synthesis by aspirin than is the homozygous (Halushka et al., 2003).

GENOMICS IN THE MANAGEMENT OF DISEASE Cytochrome P450 (CYP) represents a group of enzymes involved in metabolism of drugs, which are found principally in the liver. The enzymes are coded for by at least 50 genes with a number of pseudogenes identified. In one study it was found that nearly 60% of over 300 drugs were metabolized by CYP (Bertz and Granneman, 1997). Polymorphisms in these enzymes are recognized to be important in treatment of diseases, as some polymorphisms may metabolize different drugs at different rates thus having different clinical outcomes, that is, success or failure. H. pylori is treated with PPI, for example, omeprazole, and a combination of clarithromycin and metronidazole (or amoxicillin). CYP2C19 is important in metabolism of PPIs and CYP3A4 in metabolism of clarithromycin. In one study (Sapone et al., 2003) of 143 patients with H. pylori infection, lack of eradication was correlated with homozygous extensive metabolizers (HomEM) (CYP2C19*1/*1) or heterozygous extensive metabolizers (HetEM) (CYP2C19*1/*2 or *1/*3). All patients with CYP2C19*2/*2 had their organism successfully eradicated. Those individuals who were HetEM in CYP2C19 and carried the polymorphisms CYP3A4*1B and CYP3A4*2 were more likely to have Helicobacter eradicated compared to the wild type CYP3A4, suggesting some synergistic action between the two cytochrome P450s. A cost analysis of genotyping patients for CYP2C19 polymorphisms prior to commencing eradication therapy in the United States showed a savings ranging from $495–$2125 for every ulcer prevented (Lehmann et al., 2003).

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Interleukin-1beta is also an important determinant of successful eradication of Helicobacter using triple therapy (Furtura et al., 2004; Take et al., 2003). In a study of 336 patients, IL-1B-511T/T had the highest eradication rate (94.7%) compared to IL-1B-511C/C with 77.3%, with the heterozygote C/T having an intermediate eradication rate. This is mediated by the intragastric pH (related to the IL-1 polymorphism) as this affects the activity of particularly clarithromycin in the stomach. An additional Helicobacter-associated factor that affects the eradication rate is resistance to clarithromycin. Resistance is related to polymorphisms within the 23S ribosome subunit to which clarithromycin binds. Resistant isolates have A2124G or A2143G polymorphisms, and, if present, the eradication rate is only 48% compared to 87% in the absence of these polymorphisms (Furtura et al., 2005). Results of a study including 684 subjects showed a reduced risk of developing DU if they carried the TGFb  869C/C polymorphism and that the T/C polymorphism was involved in susceptibility to developing DU (Garcia-Gonzalez et al., 2006). A correlation between susceptibility to developing DU and NOD1 A796A was present in 20% of patients with DU compared to 6% in controls (Hofner et al., 2007). In children with DU, the G238A of the TNF gene was present in 31% of cases also carrying the strain marker iceA1, compared to 1.6% in controls (Wilschanski et al., 2007). In adults, iceA1 compared to iceA2 was also more frequent in DU cases (Caner et al., 2007). In rats, real-time PCR and RT-PCR were used to show that of 8000 genes studied in relation to cysteamine-induced ulceration, 40 genes had marked changes in expression, suggesting the interaction of many genes in the development of ulceration (Deng et al., 2007). The effect of host polymorphisms on eradication of H. pylori showed that MDR1 T3435T polymorphism had an eradication rate of 67% when given lansoprazole  amoxicillin  clarithromycin, compared to C/C or C/T which had eradication rates of 81–82% (Furtura et al., 2007). The effects of host polymorphisms in the cytochrome P450 2C9 (CYP2C9) locus on ulceration and bleeding in association with NSAID use were shown in a study of 26 patients compared to 52 controls, all of the study population being H. pylori negative. Higher frequencies of bleeding were found in patients with CYP2C9*1/*3 (34/5.8%) and CYP2C9*1/*2 (27/15.4%) compared to controls (Pilotto et al., 2007). Genomics in Relation to Diagnosis A number of molecular techniques have been applied to the detection, typing and assessment of eradication of H. pylori. Detection of markers of virulence and antibiotic resistance has been achieved largely by the polymerase chain reaction (PCR) with appropriate primers. The use of real-time PCR allows direct detection and quantification of the target of interest and the development of robotic workstations can increase throughput of analytes. Specimens used for the detection and genotyping

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of Helicobacter has largely been gastric biopsies although stool specimens have also been used. A number of different methods of typing H. pylori have been used such as ribotyping, RAPD-PCR, PCR-RFLP and pulse field gel electrophoresis (PFGE) (Ge and Taylor, 1998) in both coding and non-coding areas (Bereswell et al., 2000).

FUTURE DEVELOPMENTS IN THE USE OF GENOMIC TECHNIQUES IN RELATION TO PUD There are several potential developments from the wealth of genomic information derived from H. pylori. In addition to typing of Helicobacter for epidemiological reasons and the detection of specific virulence markers, bioinformatic data could also be used to identify genes that encode potential vaccine candidates (Chakravarti et al., 2001). Surface exposed or secreted proteins produced in abundance and that are relatively conserved are likely to be good candidates for vaccines. Using a multiparameter model to identify potential candidates based on surface location; amount produced; homology to proteins in related organisms; extent of presence in Helicobacter isolates and predicted number of epitopes, 15 proteins were identified of which 6 were known already to be protective whilst a further 2 proteins conferred protective immunity in a mouse model (Sabarth et al., 2002). Further, mouse models have been shown to be valid for antigen screening of potential human vaccines (Bumann et al., 2002). An immunoproteomic analysis of human sera identified several new proteins that were immunogenic in all infected patients and could possibly be of value in vaccine development. Also in this study, the intensity of staining of the proteome differed for 23 proteins when gastritis was compared to PUD, which implies they could be involved in aetiopathology of PUD and that they may also be of value as diagnostic markers for PUD (Haas et al., 2002). A comparison of the two sequenced genomes of H. pylori (Doig et al., 1999) and an in silico analysis using the pathway prediction algorithm PathoLogic and two databases EcoCyc E. coli database and MetaCyc pathway database (Paley and Karp, 2002), plus the use of extreme pathway analysis (Price et al., 2002), has aided the understanding of the physiology of H. pylori. An understanding of the physiology of the organism can be used to identify potential targets for intervention with the development of new anti-microbial agents. Novel nucleic acid amplification methods are becoming available, which may supplant traditional PCR with thermal cycling. Loop-mediated isothermal amplification methodology (LAMP) is already in use for human genotyping and may be introduced as a point of care method (Iwasaki et al., 2003). This will greatly facilitate the targeted therapy of patients with Helicobacter to maximize eradication rates. Finally, the use of urine as a specimen (Botezatu et al., 2000; Umansky and Tomei, 2006) to detect H. pylori transrenal DNA is also under investigation. Preliminary results indicate that the

organism can be detected and the cagA status can be determined (Vaira, D. Personal Communication).

CONCLUSIONS Currently there is no test that can identify a person with PUD whether from H. pylori or NSAID consumption – other than performing an endoscopy and having a look in the stomach (Table 92.2). The presumption, embodied in the test and treat approach, is that someone with symptoms of an ulcer and evidence of colonization by Helicobacter (the main cause of PUD) has Helicobacter-associated disease and, if under 55 years without alarm symptoms, is given a course of Helicobacter eradication therapy. The ratio of pepsinogen I/II, combined with anti-H. pylori antibodies and levels of gastrin, can identify patients with atrophic gastritis, which is a risk factor for GU and gastric cancer but does not per se identify someone with PUD. Identification of a person colonized by a Type I strain (either using PCR on a gastric biopsy (and hence having an endoscopy) or serologically using a cagA ELISA) will identify someone of greater risk for having serious gastroduodenal disease – but not specifically PUD. Constellations of virulence markers on the H. pylori genome (cagA, vacA s1, iceA, BabA2) make it more likely that the person may have serious disease particularly if combined with certain host polymorphisms (e.g., IL-1RN*2, TNF-A-1031C and IL-8 heterozygotes). On the other hand patients do get PUD when colonized by Type II strains also.

T A B L E 9 2 . 2 Current diagnostic and management uses of genomic and proteomic information Methods

Diagnosis

PCR

HP risk markers identified

Microarray

HP few studies, NSAID no studies. Will be of use in understanding pathogenesis and identifying markers

Immunoproteomic

Early results encouraging. Will be of use in understanding pathogenesis and identifying markers

SNP

HP/NSAIDS risk factors identified

Serology

HP/NSAIDS – specific tests for colonization by HP; no specific tests for PUD; early studies indicating specific PUD-associated proteins require verification Management

SNP

HP  H. pylori.

HP markers identified of use in tailoring treatment. NSAIDS provision risk marker identified requires verification

References

Studies involving microarray analysis of whole genome expression and immunoproteomic analysis in different clinical conditions including NSAID use, may reveal specific markers for PUD as opposed to cancer or gastritis. Similarly analysis of human SNPs may also identify risk factors for the development of ulceration whatever the insult. Indeed early evidence from immunoproteomic studies of colonization by H. pylori are encouraging in identifying specific proteins associated with PUD. In respect of management of patients, currently human SNPs are of greater value in identifying patients at risk from ulceration due to NSAID use or at greater risk of failed therapy for Helicobacter eradication. Human and Helicobacter genomic information is likely to be of increasing value in the management of PUD. Identification of Helicobacter virulence traits and human susceptibility markers will

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help predict the possible outcome of colonization and allow tailored therapy in order to maximize eradication. It is also evident that identification of single loci may be of limited value but the identification of constellations of Helicobacter and human loci may have predictive value. It is also clear that as yet unrecognized markers for disease outcome may be of importance. In silico analysis of genome data may yield novel therapeutic targets and, newly introduced molecular tests using transrenal DNA will both speed up and make office diagnosis a possibility.

ACKNOWLEDGEMENTS I would like to thank Prof Sampurna Roy of Calcutta University, India for permission to use Figures 92.1–92.4.

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Yamaoka, Y., Kodama, T., Kashima, K., Graham, D.Y. and Sepulveda, A.R. (1998b). Variants of the 3 region of the cagA gene in Helicobacter pylori isolates from patients with different H. pylori associated diseases. J Clin Micro 36, 2258–2263. Yamaoka, Y., El-Zimaity, H.M.T., Gutierrez, O., Figura, N., Kim, J.K., Kodama, T., Rappuoli, R. and Covvaci, A. (1999). Relationship between CagA 3 repeat region of Helicobacter pylori, gastric histology and susceptibility to low pH. Gastroenterology 117, 342–349. Yamaoka, Y., Kodama, T., Gutierrez, O., Kim, J.G., Kashima, K. and Graham, D.Y. (1999). Relationship between Helicobacter pylori iceA, cagA and vacA status and clinical outcome: Studies in four different countries. J Clin Microbiol 37, 2274–2279. Yamaoka, Y., Kwon, D.H. and Graham, D.Y. (2000). A M(r) 34,000 proinflammatory outer membrane protein (oipA) of Helicobacter pylori. Proc Natl Acad Sci USA 97, 7533–7538. Yamaoka,Y., Osato, M.S., Sepulveda, A.R., Gutierrez, O., Figura, N., Kim, J.G., Kodama, T., Kashima, K. and Graham, D.Y. (2000). Molecular epidemiology of Helicobacter pylori: Separation of H. pylori from East Asian and non-Asian countries. Epidemiol Infect 124, 91–96. Yamaoka,Y., Souchek, J., Odenbreit, S., Haas, R., Arnqvist, A., Boren, T., Kodama, T., Osato, M.S., Gutierrez, O., Kim, J.G. et al. (2002). Discrimination between cases of duodenal ulcer and gastritis on the basis of putative virulence factors of Helicobacter pylori. J Clin Microbiol 40, 2244–2246. Yamazaki, S., Yamakawa, A., Okuda, T., Ohtani, M., Suto, H., Ito, Y., Yamazaki, Y., Keida, Y., Higashi, H., Hatakeyama, M. et al. (2005). Distinct diversity of vacA, cagA, cagE genes of Helicobacter pylori associated with peptic ulcer in Japan. J Clin Microbiol 43, 3906–3916.

RECOMMENDED RESOURCES Websites http://www.tigr.org/tigr-scripts/CMR2gene_table.spl?dbghp As given on the website: The Comprehensive Microbial Resource (CMR) is a free website used to display information on all of the publicly available, complete prokaryotic genomes.

MG1655. The long-term goal of the project is to describe the molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists, and for biologists who work with related microorganisms.

http://www.helicobacter.org The website of the European Helicobacter Study Group

http://www.histopathology-india.net/PepUlc.htm The gastroenterology website of Prof Sampurna Roy.

http://genolist.pasteur.fr/PyloriGene As given on the website: The purpose is to collate and integrate various aspects of the genomic information from H. pylori. PyloriGene provides a complete dataset of DNA and protein sequences derived from two different strains: 26695 and J99, linked to the relevant annotations and functional assignments.

Book

http://ecocyc.org:1555/HPY/organism-summary?objectHPY As given on the website: EcoCyc is a bioinformatics database that describes the genome and the biochemical machinery of E. coli K-12

Axon, A. (ed). Best Practice & Research: Clinical gastroenterology. vol. 21(2) Helicobacter pylori, Elsevier. Description from the Elsevier website: In practical paperback format, each 200 page topic-based issue of Best Practice & Research Clinical Gastroenterology will provide a comprehensive review of current clinical practice and thinking within the specialty of gastroenterology.

CHAPTER

93 Cirrhosis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison

INTRODUCTION The liver has been called the “the custodian of the milieu interieur.” Consistent with its many varied metabolic functions, the liver has a complex transcriptome and proteome. The normal liver has many diverse functions including synthesis of vitamins and proteins, bile production, immune defense, as well as metabolism of carbohydrates, lipids and toxins. Despite being only 2.5% of body weight, the liver receives 25% of the cardiac output that is essential for maintaining its metabolic and synthetic functions. The functional unit of the liver is the hepatic lobule, which is arranged in an organized repeating fashion around a central venule to form the intact organ (Figure 93.1). Liver injury is characterized by progressive fibrosis tissue deposition within the lobule, leading to eventual disruption of normal lobular architecture that is characteristic of cirrhosis (Figure 93.2) (Friedman, 2000). In the genomic era, the molecular classification of fibrosis and cirrhosis development is predominantly characterized by morphological changes. Cirrhosis is the pathogenic hallmark of advanced liver injury. Cirrhosis is morphologically defined by distortion of hepatic architecture by dense bands of fibrosis “scar” leading to “islands” or nodules of hepatocytes (Friedman, 2000). The

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1138

causes of cirrhosis are varied with many etiologies (Table 93.1). The development of cirrhosis is a premalignant condition, with virtually all cases of liver cancer (also known as hepatocellular carcinoma [HCC]) developing in individuals with prior cirrhosis. Various intrahepatic cell populations are essential in understanding the development of cirrhosis. The pivotal cell involved in fibrosis leading to cirrhosis is the hepatic stellate cell (HSC). However, the functional unit of the liver is the hepatocyte, which is the cell type from which HCC develops. The mechanisms of fibrosis leading to cirrhosis will be discussed in the context of understanding the pathogenic mechanisms by which this process evolves. Genomic medicine promises new insights into the pathogenesis of fibrosis and the promise of individualized predictive medicine aiming to prevent cirrhosis and the sequelae of liver failure and HCC.

LIVER STRUCTURE Disruption of normal hepatic structure is a hallmark of liver injury. The liver is a complex organ made up of many heterogenous cell types. The functional cell of the liver is the 20–30 m diameter hepatocyte that is arranged in interconnecting plates

Copyright © 2009, Elsevier Inc. All rights reserved.

Liver Structure

(a)

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1139

(b) Hepatocyte

Liver plates

Central vein

Stellate cell

Space of Disse

Bile canaliculus Fat-storing cell Sinusoidal capillary

Kupffer cells Endothelial cells of sinusoid

Fat-storing cell Hering’s canal Inlet arteriole Inlet venule

Inlet venule Distributing vein

Hepatic artery Portal vein

Bile duct

Distributing vein (c)

Endothelial cell

Endothelial cell fenestrae

(d)

PT

PT FB

FB

PT

CV

FB PT

PT PT

Figure 93.1 Hepatic structure.The liver is composed of plates of hepatocytes surrounded by sinusoids (a vascular space) through which blood flows from either the portal vein or hepatic artery located in the portal triad to a central vein (Panel A). Sinusoids consist of hepatocyte plates surrounded by a fenestrated endothelial layer (Panel B). The space between the endothelial layer and the hepatocyte is the space of Disse in which the HSC is found (Panel B). A normal liver consists of multiple hepatic lobules with portal triads (PT) at the periphery and a central vein (CV) within the middle of the lobule (Panel C). Within a cirrhotic liver there is disruption of normal lobular architecture by dense connecting bands of fibrosis (FB) (Panel D, contrast with normal liver in Panel C).

within the structural unit of the liver, the hepatic lobule (Figure 93.1). There are 50,000 to 100,000 lobules in the adult liver. A vascular space known as the hepatic sinusoid is lined by fenestrated sinusoidal endothelial cells (SEC) and Kupffer cells (Figure 93.1). The SEC performs a central role in the recruitment and retention of inflammatory cells within the liver. Kupffer cells are intrahepatic macrophages attached to the sinusoidal walls. These cells are preferentially located around portal tracts and are essential cells involved in modulating intrahepatic immune responses. The HSC is the main mediator of intrahepatic fibrogenesis and is located between the SECs and the hepatocytes in the space of Disse. Bile from hepatocytes is secreted into bile canaliculi formed between hepatocytes, which drain into epithelium lined bile ducts. The cellular interactions

within the liver determine the normal homeostatic functions and the response to injury. The space of Disse between the endothelium and hepatocytes in which the HSC are located is the initial site of intrahepatic fibrogenesis. In the normal liver, the space of Disse is composed of non-fibril forming collagens, particularly types IV, VI and XIV, proteoglycans (principally percelan) and glycoproteins (principally fibronectin, laminin and tenascin). In contrast, during liver injury the space of Disse is enlarged as the extracellular matrix (ECM) is remodeled with an initial increase in fibronectin and tenascin. Subsequently, fibrogenesis proceeds with type III then I collagens, elastin and laminin deposition in the space of Disse. The molecular pathogenesis of abnormal matrix production is poorly understood.

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Normal Liver Hepatocyte microvilli and SEC fenestrations

Injured Liver Loss of hepatocyte microvilli and SEC fenestrations

Kupffer Cell

Sinusoid SEC

HSC

SEC

ECM

Kupffer Cell

Space of Disse

Hepatocyte Hepatocyte

HSC = Hepatic Stellate Cell SEC = Sinusoidal Endothelial Cell ECM = Extracellular Matrix

Hallmarks Loss of hepatocyte microvilli Loss of SEC fenestrations Proliferation of HSC Deposition of abnormal ECM

Figure 93.2 Liver fibrogenesis.In liver fibrosis development the quiescent stellate cell is transformed into an activated phenotype with ECM changes characterized by the deposition of a scar-like matrix within the space of Disse. This change is accompanied by a loss of hepatocyte microvilli and endothelial fenestration.

FIBROSIS AND CIRRHOSIS Despite the adoption of genomic medicine approaches in recent years, the hallmark of liver injury remain the morphological changes. In end-stage fibrosis there is “encapsulation” of islands of hepatocytes by bands of fibrosis, known as cirrhosis (Figure 93.2). The hepatic fibrotic response represents a wound healing response with morphological features of matrix remodeling, contraction and “scaring” (Eng and Friedman, 2000). The HSC is central to this fibrogenic process (Figure 93.2). The fibrogenic process can be regarded in stages based on morphology or on molecular pathogenic events (Friedman, 2000). Fibrosis of the liver has classically been regarded as an irreversible disease process. However, it is now clear that advanced fibrosis, possibly even cirrhosis, can significantly improve and possibly resolve completely in some cases (Benyon and Iredale, 2000; Corbett et al., 1993; Dufour et al., 1998; Tsushima, et al., 1999). Molecular studies of intrahepatic fibrogenesis in progressive injury indicated that this process is dynamic and characterized by distinct events associated with initiation, perpetuation and regression (Bataller and Brenner, 2005). The remodeling of the ECM in fibrogenesis is clearly a dynamic process, and an improved understanding of the molecular pathways involved in this process will help in developing future targeted therapeutic agents. Liver injury characterized by fibrosis is extremely rare in the absence of intrahepatic inflammatory changes. The evolution of both innate and adaptive immune response in chronic liver

injury is an essential factor perpetuating and driving intrahepatic injury resulting in liver fibrogenesis. Therefore, the separate study of fibrogenic pathways characterized by ECM remodeling and intrahepatic inflammatory response is often completely arbitrary.

DIAGNOSIS OF CIRRHOSIS An accurate assessment of cirrhosis and the preceding evolution of intrahepatic fibrosis is a frequent diagnostic challenge. Routine biochemical analysis, as well as clinical signs, are at best suggestive but not diagnostic of the grade of fibrosis or the development of cirrhosis. To-date, liver biopsy has been the principal approach to the assessment of fibrosis. However, liver biopsy is an invasive procedure that is prone to sampling error, significant observer variability and may be associated with morbidity and mortality (Siegel et al., 2005). This has led to the development of non-invasive measures of fibrosis including the appraisal of panels of profibrogenic serum markers and assessment of intrahepatic elasticity or ‘stiffness’ changes using modified ultrasonography (elastometry). Other imaging techniques of the liver with ultrasound, computer tomography or magnetic resonance imaging can provide indicators of cirrhosis, but interpretation is often operator-dependent and lacks sensitivity for accurate staging of advanced disease. However, imaging and biochemistry may be useful in providing indicators of worsening hepatic function from cirrhosis, such as ascites or the development of HCC.

Treatment of Cirrhosis

TABLE 93.1 disease

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1141

Causes of cirrhosis and/or chronic liver

To-date there are no established molecular markers of fibrosis progression or cirrhosis.

Infectious diseases Brucellosis Capillariasis Echinococcosis Schistosomiasis Toxoplasmosis Viral hepatitis (hepatitis B, C, D; cytomegalovirus; Epstein–Barr virus) Inherited and metabolic disorders 1-Antitrypsin deficiency Alagille’s syndrome Biliary atresia Familial intrahepatic cholestasis (FIC) types 1–3 Fanconi’s syndrome Galactosemia Gaucher’s disease Glycogen storage disease Hemochromatosis Hereditary fructose intolerance Hereditary tyrosinemia Wilson’s disease Drugs and toxins Alcohol Amiodarone Arsenicals Oral contraceptives (Budd-Chiari) Pyrrolidizine alkaloids (Veno-occlusive disease) Other causes Biliary obstruction (chronic) Cystic fibrosis Graft-versus-host disease Jejunoileal bypass Non-alcoholic steatohepatitis (NASH) Primary biliary cirrhosis Primary sclerosing cholangitis Sarcoidosis Causes of non-cirrhotic hepatic fibrosis Idiopathic portal hypertension (Non-cirrhotic portal fibrosis, Banti’s syndrome); three variants: Intrahepatic phlebosclerosis and fibrosis; Portal and splenic vein sclerosis and Portal and splenic vein thrombosis Schistosomiasis (“pipe-stem” fibrosis with pre-sinusoidal portal hypertension) Congenital hepatic fibrosis (may be associated with polycystic disease of liver and kidneys)

TREATMENT OF CIRRHOSIS The treatment options in cirrhosis are limited, and in all cases the best treatment option is the minimization or avoidance of the causative agent. Significant advances have been made in the treatment of liver injury with the event of antiviral therapy that can both control and eradicate viral hepatitis B and C (see Chapter 112). However, antiviral therapy is frequently ineffective. Additionally, the pathobiology of many causes of liver disease is poorly understood. As a result research has intensified in attempting to develop agents that may lessen the progression of fibrosis, with a view to delaying or avoiding the development of cirrhosis. Translational approaches to control intrahepatic damage are frequently shown to be effective in vitro, but they are often limited by a lack of specificity for liver injury. Minimization of intrahepatic inflammation is a promising approach to the control of liver injury. The use of interleukin (IL)-10 (a Th2 cytokine) opposes the pro-inflammatory Th1 cytokine profile frequently observed in intrahepatic inflammation driving profibrotic liver injury. IL-10 has been successfully used in a number of animal models as well as individuals with chronic HCV infection and shown to decrease the development of fibrosis (Boyer and Marcellin, 2000). Further, anti-inflammatory agents that target the Kupffer cell such as inhibitors of the 5lipooxygenase pathway show promise in animal models of fibrosis. However, presently there is little clinical data supporting the use of anti-inflammatory agents to treat intrahepatic fibrosis. The prevention of liver injury through modulation of apoptosis is another approach to control liver injury (Kaplowitz, 2000; Liu et al., 2004; Sancho-Bru et al., 2005). This concept is supported by the development and progress of caspase inhibitors into phase II clinical trials. In theory, whilst the avoidance of hepatocyte apoptosis may be a reasonable approach to the management of liver injury, the absence of cell-specific delivery means that individuals treated this way may be exposed to a plethora of potentially adverse effects including an increased risk of developing malignancies. Non-specific approaches including the use of antioxidants such as alpha-tocopherol, the flavonoid sylimarian and the Japanese herb Sho-saiko-to show promise in in vitro and animal studies, but the results to date in clinical studies have been disappointing (Rockey, 2005). An approach to the treatment of liver injury receiving a lot of attention are measures designed to stop or eliminate activated HSCs. The most promising of these approaches opposes the effects of the renin angiotensin system, which activates HSC (Warner et al., 2007). With abundant angiotensin (AT) receptors on HSCs that are important in HSC activation, the use of ACE and ATII receptor inhibitors appears to be a logical and promising approach. Initial pre-clinical studies have suggested

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that both ACE and ATIIR inhibitors are effective in decreasing cirrhosis. Further, as these agents have already been extensively used in other diseases, their safety profile is well known and they are likely to be readily tolerated by patients with liver disease. Other possible approaches designed to target the HSC are at best promising but clinically unproven concepts, and this includes the use of selective endothelin receptor antagonists, leptin antagonists, adiponectin agonists and cannabinoids in treating intrahepatic fibrosis (Lotersztajn et al., 2007; Mallat and Lotersztajn, 2006; Rockey, 2005). The use of PPAR / agonists also shows promise with thiazolindiones being shown in animal models to be effective in reducing liver fibrosis (Rockey, 2005). These drugs have been extensively used in diabetes mellitus, with known safety profiles, and are currently being actively investigated in clinical trials as possible liver antifibrotics in nonalcoholic steatohepatitis. Immune modulators such as interferon  and  have been shown to have an antifibrotic effect in vitro (Rockey, 2005). However, the lack of specificity is a major limitation in the use solely as these agents as antifibrotics. Similarly strategies such as TGF- antagonism whilst promising also lack specificity and are likely to be complicated by significant adverse side effects.

T A B L E 9 3 . 2 Candidate gene and polymorphisms involved in cirrhosis Gene

Candidate Gene gene polymorphism

Effect of protein on fibrosis

ADH



Unknown

ALDH



Unknown

Angiotensinogen



Increases

ApoE



Increases

CD14



Increases

CPT1A



Decreases

CTLA-4



Increases

CYP2E1



Unknown

DDX5



Increases

Fas

 

HLA-II Haplotypes IFN-

Increases 

HFE 

IL-1 receptor IL-10



IL-13



Unknown Variable Decreases



Increases



Decreases Unknown

GENETICS OF CIRRHOSIS

IL-1



Cirrhosis pathogenesis is characterized by many conserved as well as divergent pathways involved in the development of progressive fibrosis. There are clearly a number of important host genetic factors that influence the development of fibrosis, rate of progression and the subsequent development of complications (Bataller et al., 2003). Older individuals and males are known to have significantly greater rates of liver fibrogenesis and subsequent development of cirrhosis (Poynard et al., 2005). Multiple candidate genes thought to be central to cirrhosis development have been identified using rodent models, and many human gene polymorphisms are likewise associated with fibrosis development (Table 93.2) (Bataller et al., 2003). Genetic polymorphisms have been implicated at all stages of fibrosis development including disease susceptibility (i.e., HCV and haptoglogin), injury (i.e., LPS interaction with CD14), immune responses (i.e., HLA-II alleles), activation of HSC (i.e., angiotensinogen) and increase in fibrogenic ECM (i.e., plasminogen and TGF-) (Bataller et al., 2003). The single most comprehensive analysis of genetic polymorphisms in fibrosis analyzed 24,832 putative functional, single nucleotide polymorphisms (SNPs) in 916 individuals with HCV infection. This study identified a missense mutation in the DEAD box polypeptide 5 (DDX5) gene associated with two POLG2 SNPs that are linked with an increased risk of advanced fibrosis (Huang et al., 2006). The same study identified another missense SNP in carnitine palmitoyltransferase 1A (CPT1A) associated with a decreased risk of fibrosis. The fibrosis association of these polymorphisms was validated in a separate cohort of 483 individuals (Huang et al., 2006). However, it is unclear what the function of these genes is in HCV induced liver injury. Further,

IL-6



Unknown

Leptin



Increases 

MnSOD

Increases

Increases

NOS-2



Decreases

OB-R



Increases

Plasminogen



Decreases

SMAD-3



Increases 

TAP2 Telomerase



TGF-1



Unknown Decreases



Increases

TIMP-1



Increases

TNF



Unknown

it is unclear if the identified polymorphisms are important only in HCV liver injury or more generally in other types of liver injury. The identification of candidate genes and polymorphisms has seen a number of novel molecular approaches adopted in an attempt to treat liver disease (Prosser et al., 2006). These have included the use of caspase inhibitors, TGF- blockade, TNF blockade, PDGF blockade and inhibition of Kupffer cell activity (Prosser et al., 2006). Further approaches have attempted to change the ECM composition in fibrosis via the administration of matrix metalloproteinases and plasminogen activator (Prosser et al., 2006). Although modern molecular approaches have identified multiple genes involved in the pathogenesis of cirrhosis,

The Liver Proteome

most of these findings have yet to lead to therapeutic agents in the clinical trial setting. The identification of genetic loci associated with the development of progressive liver injury has focused on the underlying disease rather than the common pathway leading to the development of fibrosis and eventual cirrhosis (Table 93.3). The immunogenetics of autoimmune liver disease and viral hepatitis are partially characterized with a number of genetic loci being associated with disease severity and/or progression (Donaldson, 2004). However, the immunogenetics of other forms of liver injury is poorly characterized. To-date the precise association of many of these markers with liver disease severity remains to be determined. However, in the future the determination of the genetic haplotypes conferring susceptibility to fibrosis or predicting the progression of liver disease is likely to be incorporated into routine clinical practice.

THE LIVER TRANSCRIPTOME Evolving genomic medicine approaches to liver disease require an understanding of the complexity of genome expression within the normal and diseased liver. There are estimated to be approximately 20–25,000 protein-encoding genes in the human genome. Further, there are an unknown number of functionally significant alternately spliced transcripts arising from these genes that may exceed 100,000 in number. How many of these mRNA transcripts are expressed in the liver is unknown. Methods of identifying and comparing organ transcriptomes are also uncommon. One method of inferring complexity is to examine GenBank human UniGene clusters of non-redundant gene sets (Yuan et al., 2001). These UniGene clusters are compiled from annotated and uncharacterized mRNA sequences and as a group represent a species’ transcriptome (Yuan et al.,

TABLE 93.3

Immunogenetics of liver disease

Disease

Dominant haplotype

Autoimmune liver disease AIH

DRB1*0301, DRB1*0401, DRB1*1501, DRB1*0405, DRB1*1301

PSC

MICA*008, DRB1*0301, DBQ1*0603, DBQ1*0602, DBQ1*0302, DBQ1*0303, MICA*002

PBC

DRB1*0803, DRB1*0801

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1143

2001). Currently the human UniGene assembly of clusters (Build 196) has approximately 7 million sequences representing 83,896 non-redundant transcripts. Parsing key word searches1, approximately 20% of transcripts (representing 16,950 clusters) were identified in liver tissue; this compares to brain (37%), lung (31%), kidney (25%), colon (22%) and heart (16%). Coulouarn et al. (2004) used a similar approach and identified 12,638 nonredundant clusters from liver tissue (UniGene Build 129). An alternate approach in which Serial Analysis of Gene Expression (SAGE) libraries were examined can also provide insights into the complexity of the liver transcriptome (Yamashita et al., 2000, 2004a, b). Two normal human liver SAGE libraries identified 15,496 and 18,081 unique transcripts from a total number of 66,308 and 125,700 tags, respectively (Yamashita et al., 2000, 2004a, b). However, in a SAGE comparison of multiple organs, 32,131 unique tags were identified (from a total of 455,325 tags) of which 56% were expressed in the liver compared to brain (75%), breast (81%) and colon (91%) (Yamashita et al., 2004a, b). Therefore, it is clear that the normal liver has a complex transcriptome expressing many thousands of mRNA transcripts. Normal liver transcriptome expression varies to account for phenotype differences such as sex and age variation (Cao et al., 2001; Tadic et al., 2002; Yang et al., 2006). Microarray analysis of normal human liver by Yano et al. (2001) highlights the variability of the non-diseased liver transcriptome. A total of 2418 genes were examined in five normal patients, with only half of these transcripts being detected in four out of five patients. Further, only 27% of genes had co-ordinate expression in these non-diseased, apparently normal patients. Therefore, in addition to the liver having a complex transcriptome, there appears to be significant individual variability in transcript expression. This is further highlighted by the observation of Enard et al. (2002) that duplicate liver samples from the same individual differed by 12% (technical variation) but that intraspecies variation was as pronounced as interspecies variation in hepatic mRNA transcript expression when comparing chimpanzees and humans. Focused specialized arrays such as the Liverpool nylon array targeting the liver transcriptome have now been synthesized and include in excess of 10,000 target genes (Coulouarn et al., 2004). However, such approaches fail to detect differential gene expression for transcripts not expressed in normal liver that are subsequently expressed with the development of liver pathobiology. This is a particularly important consideration in genomic medicine, as transcriptomes in disease can markedly increase in complexity, especially in the presence of neoplastic transformation and inflammation (Feezor et al., 2005; Scriver, 2004).

Viral hepatitis HAV

DRB1*1301

THE LIVER PROTEOME

HBV

DRB1*1301, DRB1*1302

HCV

DRB1*0101, DRB1*0301

The human proteome is complex and variable depending on the organ or cell population being studied. Proteins can be

AIH: auto-immune hepatitis; PSC: primary sclerosing cholangitis; PBC: primary biliary cirrhosis; HAV: hepatitis A virus; HBV: hepatitis B virus; HCV: hepatitis C virus.

1

Parsing string used (“liver” or “hepatic”) and “human” for UniGene Build 180.

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subject to in excess of 100 different types of post-translational modifications (Cantin and Yates, 2004). Therefore, the estimated 25,000 genes in the human genome may give rise to greater than a million distinct proteins (Neverova et al., 2005; Righetti et al., 2005). Utilizing an approach of parsing GenBank with key words, we can gain insight into human organ proteome complexity2. Proteomes from human brain, liver, lung, kidney, bowel/colon, heart and serum express 40%, 13%, 10%, 18%, 6%, 7% and 6% respectively, of the proteins found in all of these organs. The number of GenBank human protein entries for each organ was: brain (53,091), liver (17,610), lung (12,770), kidney (23,283), bowel/colon (7749), heart (8961) and serum (8122). This highlights the complexity of the proteome within the liver and other solid organs. Contained within a cell, approximately 90% of the cellular protein mass is due to the 100 most abundant proteins and a further 1200 proteins account for another 7% of the protein mass (Lefkovits et al., 2000, 2001). However, the remaining 3% of the protein mass includes 2800 proteins (over 50% of the different protein species) and frequently is below the detection limit of most proteomic detection methods (Lefkovits et al., 2000, 2001). Therefore, it is important to consider the frequency of protein expression within a homogeneous cellular sample (i.e., cell lines) compared to heterogeneous cellular sample (i.e., organs). This is an especially important consideration in genomic medicine, as the non-parenchymal cell subpopulation abundance in biopsy specimens is low and sample representation of the whole organ is often inaccurate, a problem known to occur in upward of 15% of liver biopsy samples (Ratziu et al., 2005; Regev et al., 2002). Genomic medicine approaches often assume that changes in gene/mRNA expression reflect changes in corresponding protein expression. However, there are multiple examples where protein expression or function is not controlled by mRNA expression. Indeed, in the intact non-diseased liver tissue, approximately 25% of the changes in the mRNA transcript expression are not accompanied by changes in protein expression (Anderson and Seilhamer, 1997). Studies comparing mRNA and protein expression are rare in all organs, including the liver. Anderson et al showed a poor correlation of the liver tissue abundance of 19 proteins and corresponding mRNA transcripts (correlation coefficient of only 0.48) (Anderson and Seilhamer, 1997). Additionally, they isolated 50 abundant mRNA transcripts, of which 29 encoded secreted proteins (Anderson and Seilhamer, 1997). However, this result contrasted with the 50 most abundant proteins they isolated, as none were secreted (Anderson and Seilhamer, 1997). Overall, within mRNA

2

In November 2006, the GenBank protein entries were parsed with the key word human and one of the following: brain, liver, lung, kidney, bowel or colon, heart, kidney and serum. A total of 131,586 protein entries across all of these organs were identified. The percentage for each organ was then calculated to give an estimate of relative protein abundance.

transcriptomes compared to the corresponding proteomes, there is a bias in expression toward an over-representation of mRNA transcripts encoding secreted proteins, and high abundance mRNA transcripts such as G3PDH have been repeatedly demonstrated (Anderson and Seilhamer, 1997; Jansen and Gerstein, 2000; Miklos and Maleszka, 2001a, b; ter Kuile and Westerhoff, 2001). However, it is salient to remember that the protein expression in every cell is controlled by the transcriptome, although the relationship between individual gene transcripts and the corresponding protein expression may not at first be apparent.

DEVELOPMENT OF LIVER FIBROSIS Liver fibrosis is characterized by activation of the HSC. Invariably, there is associated inflammation and an intrahepatic immune response driving the perpetuation of the pro-fibrogenic phenotype of the HSC. Therefore, fibrosis development can be examined by focusing on the pro-fibrogenic HSC or alternatively by studying the various types of liver injury, associated inflammatory response and the pathways of HSC activation. Hepatic Stellate Cell The HSC is the principal cell type mediating the matrix remodeling and degradation of fibrosis (Figure 93.3). The HSC normally constitutes approximately 5% of the intrahepatic cell mass in the non-diseased liver and increases to 10–15% in the diseased liver (Friedman, 2000; Mehal et al., 2001). The HSC is likely to have evolved from the neural crest origin in contrast to the endoderm origin of hepatic parenchymal cells, as suggested by the presence of markers such as glial fibrillary acidic protein, nestin and N-CAM (Levy et al., 1999; Nakatani et al., 1996; Niki et al., 1999;Vogel et al., 2000). The use of gene array analysis has led to the identification of a number of additional neural markers in cirrhosis including BDNF, GDNF and neuromodoluin, which are now thought to be previously unrecognized markers of HSC (Shackel et al., 2002). However, heterogeneity in the intrahepatic HSC population suggests that the intrahepatic stellate cells may not derive from a single embryonic source (Levy et al., 1999; Nakatani et al., 1996; Sell, 2001). The normal stellate cell exists in a quiescent state, and with injury it is transformed into a proliferative, fibrogenic and contractile myofibroblast, a response known as stellate cell activation (Friedman, 2000). The activation of the HSC involves both an initiation and perpetuation of the activated cell phenotype. Additionally, the activated phenotype can resolve in the liver with cessation of injury. It is now clear that distinct molecular pathways are involved in the process of initiation, perpetuation and resolution of the activated HSC (Friedman, 2000). Overall, fibrosis is characterized by a three- to six-fold increase in collagen and non-collagenous components of the ECM (Gressner, 1998). Activated stellate cells produce hyaluronan, fibronectin, entactin, tenescin, undulin, elastin and laminin

Development of Liver Fibrosis

Cirrhotic

Normal

1145

Normal

Apoptosis

Injury

Quiescent Stellate Cell

■

Acitvated Stellate Cell

Resolution

(Effector of Cirrhosis)

Perptuation

Figure 93.3 HSC activation.Intrahepatic injury is accompanied by activation of the HSC. This transformation sees the quiescent stellate cell, which stores retinoid, transformed into a profibrogenic cell that actively remodels the ECM leading to cirrhosis. Activation of the stellate cell leads to state in which the activated cell phenotype is perpetuated by both the initial injurious insult and also by autocrine cell activation. Resolution of fibrosis is due to removal of the activated stellate cell through the process of apoptosis.

(Gressner, 1998). The quiescent stellate cell produces predominantly type IV collagen, whereas the activated myofibroblast produces predominantly collagen type I as well as collagen type IV and III (Friedman, 2000; Gressner, 1998). Additionally, myofibroblasts produce proteoglycan, with chondroitin sulfate being the predominant proteoglycan, as well as LTBP sulfate and heparin sulfate (Gressner, 1998). The phenotypic changes associated with HSC activation include matrix remodeling, proliferation and contractility. Upon activation the morphology of the HSC changes with a loss of intracellular vitamin A stores, development of more pronounced cytoplasmic processes and flattening of the cell (Friedman, 2000; Gressner, 1998). This is accompanied by increased HSC contractility, especially due to the production of NO and endothelin-1, which is an important determinant of the increased portal venous pressure during liver injury (Rockey, 1997). Proliferation of the activated HSC occurs in response to a number of growth factors, most of which signal through receptor tyrosine kinases (Ankoma Sey et al., 1998; Friedman, 2000). Growth factors such as PDGF, TGF- and EGF are initially secreted from adjacent Kupffer cells, hepatocytes and cells of the inflammatory infiltrate (Callahan et al., 1985; Marra et al., 1994; Pinzani et al., 1996). Subsequently, an autocrine loop is established by the activated HSC that produces these growth factors, especially PDGF and TGF- (Friedman, 2000; Callahan et al., 1985; Marra et al., 1994). The dynamic nature of the fibrotic response is apparent given the documented resolution seen in many animal models of fibrosis and the improvement seen with treatment of some

causes of human liver disease. The resolution of the activated HSC is unlikely to involve the “retrodifferentiation” or transition back to the quiescent phenotype but appears to involve activated HSC elimination by apoptosis (Benyon and Iredale, 2000). In animal models of fibrosis, there is a significant increase in the rate of activated HSC apoptosis. Activated HSC are more sensitive to Fas-ligand-induced apoptosis compared to quiescent HSC. Intrahepatic natural killer (NK) cells in response to liver injury upregulate Fas-ligand (FasL) expression (Benyon and Iredale, 2000; Moroda et al., 1997; Tsutsui et al., 1996). Persistence of the activated HSC despite increased Fas-ligand and Fas-ligand sensitivity seems to be due to the concurrent increase in proliferative factors, especially PDGF, that are antiapoptotic (Benyon and Iredale, 2000; Simakajornboon et al., 2001; Staiger and Loffler, 1998). Similarly other autocrine factors may act to enhance activated HSC apoptosis including expression of IL-10 and members of the Bcl-2 family (Galle, 1997; Kanzler and Galle, 2000; Ockner, 2001). This pro- and antiapoptotic response seen with HSC activation may be a means of autocrine regulation limiting “scar” formation. Genomic Studies of HSCs Genomic studies of HSC have led to the development of ex vivo cell culture systems and a better understanding of in vivo injury attributable to HSC. Microarray analysis has been utilized to identify differential gene expression in activated HSCs from in vitro culture models (Lee et al., 2004; Liu et al., 2004). In one study, a number of novel and previously recognized genes were identified, including MAPK pathway genes, osteopontin

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and ERK-1 (Lee et al., 2004; Qiang et al., 2006). Further, ERK-1 was subsequently shown in RNA interference experiments to be necessary for HSC proliferation as well as being anti-apoptotic with expression maintaining the activated cell phenotype (Qiang et al., 2006). Differentially expressed genes in murine-activated HSC included those involved in protein synthesis (RP16), cell-cycle regulation (Cdc7), apoptosis (Nip3) and DNA damage response (MAT1) (Liu et al., 2004). Further, genomic studies addressing the perpetuation of the activated HSC phenotype identified expression of the telomerase catalytic subunit (human telomerase reverse transcriptase [hTERT]) in human activated HSCs, which immortalizes these cells and maintains an activated HSC phenotype (Schnabl et al., 2002). Senescent HSCs expressed reduced levels of ECM proteins, including collagens, tenascin and fibronectin. Furthermore, maintenance of telomere length represents an important survival factor for activated human HSCs (Schnabl et al., 2003). Therefore, an immortalized human HSC line has been generated by infecting primary human HSCs with a retrovirus overexpressing hTERT (Schnabl et al., 2002). The hTERT positive HSCs do not undergo oncogenic transformation and exhibit morphologic and functional characteristics of in vivo activated HSCs. Subsequent GenechipTM and RT-PCR analysis showed that mRNA expression patterns in telomerase-positive HSCs are similar to those in primary in vivo activated human HSCs (Schnabl et al., 2002). Similarly, another two immortalized HSC lines LX-1 and LX-2 were characterized by microarray analysis and determined to have a gene expression profile similar to that of in vivo activated human primary HSC (Xu et al., 2005). These newly developed cell lines are proving to be valuable tools to study the biology of human HSCs. Importantly, although microarray and GenechipTM studies have demonstrated that these ex vivo HSC are similar to in vivo activated HSC with a greater than 70% similarity in transcript expression, a number of differences have been identified. Therefore, ex vivo cell culture of stable HSC may not be completely representative of in vivo injury highlighting the importance of intracellular interactions within the liver in determining transcriptome expression.

TRANSCRIPTOME ANALYSIS OF LIVER DISEASE Presently there are hundreds of published gene array studies of human liver disease or studies that utilize human liver tissue. Most of these studies attempt to understand liver by examining mRNA transcript expression. There are few publications in human liver disease where gene expression is correlated with clinical outcome. Two of the most common causes of liver injury globally, viral hepatitis B and C, are discussed in more detail in Chapter 112. Unfortunately, many forms of liver injury cannot be studied in commonly used laboratory animal models and most animal studies focus on the pathways of fibrosis development.

Hepatitis B Virus Infection Functional genomics studies of acute hepatitis B virus (HBV) infection in the chimpanzees has led to unique insights into how this virus evades the immune response and causes injury (Wieland et al., 2004). Initially, there is no apparent significant immune/ inflammation-associated differential gene expression during the early phase of HBV infection and viral replication. Therefore, HBV infection acts in the initial phase as a “stealth virus” failing to induce a significant innate immune response (Wieland and Chisari, 2005;Wieland et al., 2004). Intrahepatic gene induction is first seen during the phase of attempted viral clearance. Gene expression during the early phase of infection was associated with T cell receptor and antigen presentation. Following this T cell effector function (granzymes), T cell recruitment (chemokines) and monocyte activation-associated gene expression has been demonstrated. A later phase of clearance was associated with the expression of B cell-related genes. Chronic HBV infection is characterized by gene expression profiles consistent with active inflammatory response (increased IRF-6 and CCL16), cell proliferation (increased cyclin-H and p53) and cellular apoptosis (14-3-3 interacting gene increased) (Honda et al., 2001, 2006). Hepatitis C Virus Infection Acute and chronic hepatitis C virus (HCV) infection has been studied using functional genomics techniques. These experiments have examined acute HCV infection in primates, as well as the sequelae of chronic infection. The results from microarray studies of acute HCV infection in the chimpanzee are intriguing. Acute HCV infection is characterized by a rapid (within 2 weeks) as well as a delayed induction (up to 6 weeks) of genes involved associated with the innate immune response (Bigger et al., 2001). Most of these genes are associated with interferon gene expression and are known as interferon response genes (IRG’s; including ISG15, ISG16, CXCL9, CXCL10, Mx-1, stat1, 2 5 -oligoadenylate synthetase and p27).Viral clearance appears to be associated with rapid induction of these IRG’s. Overall HCV persistence appears to be associated with comparatively less induction of IRG’s compared to viral clearance. Further, chronic HCV-related liver injury appears to be characterized by an IRGassociated chronic Th1 immune response, which is insufficient to clear the virus but is chronic and responsible for ongoing liver injury. The situation with interferon treatment of HCV infected individuals, which is aimed at viral eradication, is similar to acute infection as an immune response, characterized by a significant increase in IRG expression following treatment, is associated with a greater likelihood of a sustained long-term therapy response. Clearly, the immune response to HCV drives fibrogenesis, as interferon administration is associated with a reduction intrahepatic inflammation and fibrosis even in the absence of a long-term virological clearance following treatment. Alcoholic Liver Disease Globally, alcohol is a leading cause of progressive liver fibrosis leading to cirrhosis. Intrahepatic gene profiling using microarrays

Transcriptome Analysis of Liver Disease

in ethanol-fed baboons has identified increased expression of multiple annexin-related genes (including A1 and A2) that were not previously implicated in the progression of fibrosis in alcoholic liver disease (ALD; Seth et al., 2003). Further, the intrahepatic transcriptome profile in alcohol-associated liver injury is significantly different from other forms of liver disease (Deaciuc et al., 2004; Seth et al., 2003). Transcriptome profiling has allowed differentiation of alcoholic hepatitis from alcoholic steatosis. Genes known to be involved in alcohol injury such as alcohol dehydrogenases, acetaldehyde dehydrogenases, interleukin-8, S-adenosyl methionine synthetase, phosphatidylethanolamine N-transferase and several solute carriers have been shown to be differentially expressed in alcoholic hepatitis versus alcoholic steatosis. In alcoholic hepatitis, many novel differentially expressed genes have been identified, including claudins, osteopontin, CD209, selenoprotein, annexin A2 and genes related to bile duct proliferation (Seth et al., 2003). Differentially expressed genes involved cell adhesion, ECM proteins, oxidative stress and coagulation that were common to alcoholic hepatitis and end-stage ALD. Importantly, genes associated with fibrosis, cell adhesion, ECM remodeling are increased in human advanced ALD, consistent with the fibrotic nature of ALD. However, many of these genes are not specific to alcohol-induced liver injury and have been reported in other forms of liver cirrhosis such as primary biliary cirrhosis (PBC) (Shackel et al., 2001, 2002). Non-Alcoholic Fatty Liver Disease Non-alcoholic fatty liver disease (NAFLD) is the “western” liver disease related to obesity and form part of the metabolic syndrome characterized by increased BMI, hypertension and insulin resistance. Non-alcoholic steatohepatitis (NASH) is the clinicopathological syndrome in NAFLD in which lipid deposition within the liver is accompanied by inflammation and is widely studied using gene array analysis of mRNA transcript expression. The inflammation in NAFLD that results in NASH is of considerable importance as NASH, not NAFLD, is characterized by progressive injury and eventual cirrhosis. Studies have identified differentially expressed genes in end-stage NASH cirrhosis compared to other disease states (Sreekumar et al., 2003; Younossi et al., 2005a, b). Decreased expression of genes associated with mitochondrial function and increased expression of genes associated with the acute phase response are observed (Sreekumar et al., 2003). The latter increases were speculated to be associated with insulin resistance, a feature of NAFLD (Sreekumar et al., 2003). Further differential expression of genes involved in lipid metabolism, ECM remodeling, regeneration, apoptosis and detoxicification have all been observed in NASH following microarray analysis (Younossi, et al., 2005a, b). Autoimmune Hepatitis Autoimmune hepatitis (AIH) is an uncommon autoimmune disease affecting the hepatic lobule with a number of morphological similarities to the injury caused by HBV and HCV. The only available data on AIH are a comparison between HCV- and AIH-associated cirrhosis (Shackel et al., 2002). One of the key

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findings in this study was the observation that three inhibitors of apoptosis (IAP) genes were selectively differentially expressed in AIH. This is an intriguing finding. If this gene expression was identified to be in the intrahepatic lymphocyte population, then lack of apoptosis of such cells may be an important pathogenic pathway in the perpetuation of AIH. In this comparison to HCV, AIH was associated with an inflammatory gene pathway that consisted of a mix of Th1- and Th2-associated genes. Therefore, an intrahepatic Th1 immune response appears to be more fibrogenic, as evident from this work, HCV liver injury and animal models of liver injury (Shi et al., 1997). Biliary Liver Injury Viral hepatitis and alcoholic liver injury typically affect the hepatic lobule within the liver and are referred to classically “hepatitic” or “lobular” liver injury. However, a number of other types of liver injury are characterized by insults primarily to the bile ducts and are commonly referred to a “biliary” disease. Classically, biliary diseases include autoimmune-mediated bile duct injury of PBC and the poorly understood condition of bile ducts known as primary sclerosing cholangitis (PSC) in which inflammation, infection and fibrosis initially affects the bile ducts and then the liver parenchyma. One of the major findings in a gene array examination of PBC end-stage liver disease was the identification of a subset of genes associated with the Wnt pathway (Shackel et al., 2001). In particular, Wnt13, Wnt5A and Wnt12 were shown to be differentially expressed. Other genes particularly upregulated in PBC included transcription initiation factor 250 kDa subunit (TAFII 250), PAX3/forkhead transcription factor and patched homolog (PTC). A consistent feature of the gene array analysis of PBC was the repeated identification of Drosophila genes homologs that were differentially expressed (Wnt genes, hedgehog pathway, notch pathway) (Shackel et al., 2001). The only available data on PSC come in a comparison with PBC (Shackel et al., 2001). Compared with PBC, there were a far greater number of genes showing differential expression in PSC versus non-diseased liver (compared with PBC, and nondiseased liver). These include genes associated with epithelial biology (amphiregulin, bullous pemphigoid antigen), inflammation (T cell Secreted Protein P I-309, CTLA4), apoptosis-related genes (Bcl-2 interacting killer, Bcl-x, Death associated protein 3) and intracellular kinases such as CDK7 and JAK1. A disease in which bile ducts are absent, biliary atresia (BA), has been studied comparing gene expression in embryonic versus perinatal forms of the disease (Zhang et al., 2004). Gene profiling clearly separated these two conditions. The most remarkable difference was in the expression of so-called regulatory genes. In embryonic BA, 45% of differentially expressed genes were in this category versus 15% in the perinatal form. Included in these genes were imprinting genes, genes associated with RNA processing and cell cycle regulation that were not present in the perinatal form of BA. Overall, the results from genomic studies of biliary liver disease are consistent with distinct patterns of injury each associated with unique gene signatures.

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The first clinically adopted genomic medicine approach in liver disease arose from the development of a gene array resquencing chip capable of diagnosis of a number of rare inherited inborn errors of metabolism resulting in neonatal hyperbilirubinemia (Liu et al., 2007;Watchko et al., 2002). Hepatocellular Carcinoma Liver injury characterized by cirrhosis is a premalignant condition. Despite the many diverse causes of liver injury, the development of HCC invariably occurs in the setting of cirrhosis. Indeed, the development of HCC without cirrhosis is extremely rare. Functional genomics methodologies are advancing the understanding of how fibrosis progression to cirrhosis is required for malignancy development. However, this sequence of events is still poorly understood. Neoplastic proliferation in HCV-associated HCCs has been studied by microarray analysis. A plethora of potential novel tumor markers have been identified. These include the serine/ threonine kinase 15 (STK15) and phospholipase A2 (PLA2G13 and PLA2G7) that were found to be increased in over half of the tumors identified (Smith et al., 2003). However, different studies identify different gene groups in HCV-associated HCC such as: cytoplasmic dynein light chain, hepatoma derived growth factor, ribosomal protein L6, TR3 orphan receptor and c-myc (Shirota et al., 2001). The clustering analysis in this study showed that the expression of 22 genes in HCC related to differentiation of the malignancy, with over half of these genes being transcription factors or related to cell development or differentiation (Shirota et al., 2001). Although many of these genes can be implicated in HCC development, they are often identified in gene sets obtained from end-stage diseased tissue. Therefore, whether these genes represent cause or effect is unknown. HBV-associated HCC has been studied by several groups (Iizuka et al., 2002, 2004; Kim et al., 2004a, b). Genes associated with cell proliferation, cell cycle, apoptosis and angiogenesis were dysregulated in HCC tissues. Increased expression of cyclin-dependent kinases was seen whilst several cell cycle negative regulators were decreased. Metastatic development has also been studied using gene arrays (Pan et al., 2003; Qin and Tang, 2004;Tang et al., 2004;Ye et al., 2003). Genes identified with metastatic development include osteopontin, and in vivo neutralizing antibodies to osteopontin block tumor invasion (Ye et al., 2003). A study of unsupervised gene profiling of patients with HCC revealed a set of genes associated with decreased survival including RhoC (Wang et al., 2004). These genes include a subset of pro-proliferative, anti-apoptotic genes as well as genes involved in ubiquitination and histone modification. Gene profiles in HCC have also demonstrated patterns of gene expression associated with tumor differentiation, vascular invasion as well as recurrence after surgery (Tang et al., 2004). Gene Arrays in Animal Models Animal models studied have included acute liver regeneration, drug toxicity, liver fibrosis, fatty liver, biliary obstruction, liver transplantation and carcinogenesis. Drug toxicity studies are

numerous and include effects induced by clofibrate, PPAR alpha agonists, carbon tetrachloride, amiodarone, arsenic and methotrexate (Bulera et al., 2001; Cunningham et al., 2000; Huang et al., 2004; Jiang et al., 2004; Jolly et al., 2005; Jung et al., 2004; Minami et al., 2005; Shankar et al., 2003; Ulrich et al., 2004; Waring et al., 2001a, b). In one study, a novel cDNA library highly enriched for genes expressed under a variety of hepatotoxic conditions was created and used to develop a custom oligonucleotide library (Waring et al., 2003). An expression signature for rat liver fibrosis was identified using a 14,814 cDNA gene microarray (Utsunomiya et al., 2004). The “genetic fibrosis index” identified consisted of 95 genes (87 upregulated, 8 downregulated). These included genes associated with cytoskeletal proteins, cell proliferation and protein synthesis. Bile obstruction in the mouse identified three sequential main biological processes. At day 1, enzymes involved in steroid metabolism were overexpressed. This was followed by an increase in cell cycle/proliferation-associated genes at day 7, occurring at a time of maximum cholangiocyte proliferation. From days 14–21, genes associated with the inflammatory response and matrix remodeling were identified. Similar temporal gene expression was identified in the model of acute liver regeneration. Steroid and lipid metabolism genes were downregulated as early as 2 h post-hepatectomy, whilst genes associated with cytoskeletal assembly and DNA synthesis became upregulated by 16 h and remained elevated at the 40-h time point at the peak of S phase. ALD has been studied in the mouse chronic enterogastric ethanol infusion model (Deaciuc et al., 2004). A total of 12,422 genes were analyzed and several cytochrome P450 genes were shown to be upregulated, whilst several genes involved in fatty acid metabolism and fatty acid synthase were downregulated. In contrast, genes associated with glutathione-S-transferase were markedly upregulated. Interestingly, a novel intestinal factor was 50-fold downregulated. Therefore, it is postulated that chronic alcohol ingestion may affect healthy intestinal epithelium and downregulate this gene, causing intestinal permeability changes.

PROTEOMIC STUDIES OF LIVER DISEASE Proteomic studies of liver disease have fallen into the following four groups: (1) discovery of previously unrecognized proteins in a cell population or disease state (Eng and Friedman, 2000; Kawada et al., 2001; Kristensen et al., 2000), (2) biomarker discovery (Schwegler et al., 2005; Seow et al., 2001), (3) hepatic toxicological prediction/profiling (Fella et al., 2005; Fountoulakis and Suter, 2002; Gao et al., 2004; Guzey and Spigset, 2002; Kaplowitz, 2004; Low et al., 2004; Meneses-Lorente et al., 2004; Merrick and Bruno, 2004; Nordvarg et al., 2004; Roelofsen et al., 2004) and (4) studies of known proteins or classes proteins (Greenbaum et al., 2002; Joyce et al., 2004) (see Tables 93.1 and 93.2). Proteomics has successfully been used in biodiscovery of proteins in hepatocytes, HSCs (Eng and Friedman, 2000; Kawada et al., 2001; Kristensen et al., 2000), HCC (Seow

Proteomic Studies of Liver Disease

et al., 2001) and viral hepatitis (Comunale et al., 2004; Garry and Dash, 2003; Kim et al., 2003; Lu et al., 2004; Rosenberg, 2001; Scholle et al., 2004). In these studies tens to hundreds of proteins were identified. However, these studies are limited as they sample rather than profile the proteome. Further observed changes in protein expression may reflect weak associations rather than a direct role in the development of pathobiology. The biodiscovery approach has been successfully used in toxicological models and used to develop characteristic profiles of protein expression that may predict intrahepatic toxicology responses (Fella et al.,2005; Fountoulakis and Suter, 2002; Gao et al., 2004; Guzey and Spigset, 2002; Kaplowitz, 2004; Low et al., 2004; Merrick and Bruno, 2004; Meneses-Lorente et al., 2004; Nordvarg et al., 2004; Roelofsen et al., 2004). This is an area of intense research focus for pharmaceutical companies as they strive to reduce development cost and aim to predict drug toxicity earlier in the drug development cycle. Biomarker discovery is another area receiving attention using proteomic methods. However, for years there has not been a newly FDA-approved serum marker as this is an immense research challenge. Biological sample protein concentrations vary by 12–15 orders of magnitude, and specific serum markers are likely to be expressed at nanomolar or lower concentrations. One approach to overcome these limitations is to use a combination of potential markers that are easier to detect but with each protein marker alone having a lower specificity but high sensitivity. This is an approach currently used in serum tests of hepatic fibrosis, and proteomic methods are being used to try and identify new serum markers of hepatic fibrogenesis (Henkel et al., 2005; Poon et al., 2005; Xu et al., 2004). One of the most promising approaches is the use of accurate mass tags (AMT) or suicide substrates that selectively and reproducibly target a subproteome (Bogdanov and Smith, 2005; Greenbaum et al., 2002; Joyce et al., 2004; Pasa-Tolic et al., 2004). This has the added advantage of aiding prefractionation and increasing resolution of proteins as the tag can be captured on an affinity surface (Greenbaum et al., 2002; Joyce et al., 2004). Hepatitis B Virus Infection In contrast to gene array experiments, there are a number of studies that use proteomics on sera to examine different stages of chronic HBV infection. In one study, altered proteomic profiles were identified for haptoglobin beta and alpha 2 chain, apolipoprotein A-1 and A-1V, alpha-1 antitrypsin, transthyretin and DNA topisomerase 11 beta (He et al., 2003). Some of these proteins are amongst the most abundant serum proteins secreted by the liver and are generally associated with acute phase inflammatory responses. What was apparent in this study was that different isoforms of some of these proteins showed distinct changes in HBV infection itself and differed at times between patients with low inflammatory scores versus high inflammatory scores. Some examples include a decrease in cleaved haptoglobin beta peptides and ApoA-1 fragments in patients with higher inflammatory scores. In comparison, some alpha-1-antitrypsin fragments were increased in patients with higher inflammatory scores. An

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alternate approach studied serum protein profiles and correlated this with disease severity using a SELDI Protein Chip analysis and artificial neural network (ANN) models (Poon et al., 2005). They found 6 fragments with a positive and 24 with a negative prediction of fibrosis stage and subsequently developed a fibrosis index with excellent precise values for significant fibrosis and cirrhosis based on the Ishak fibrosis score. The inclusion of clinical biochemical parameters such as ALT, bilirubin, total protein, hemoglobin and INR strengthened the accuracy of their predictive model (see Chapter 112). Hepatitis C Virus Infection Proteomic methodologies have been applied to a number of aspects of HCV-related liver injury. These include the study of HCV-related HCC development in which overexpression of alpha enolase was identified and correlated with poorly differentiated HCC (Kuramitsu and Nakamura, 2005; Takashima et al., 2005). The response of hepatocyte cell lines to IFN gamma treatment has uncovered over 54 IFN response genes including many novel targets, an approach that may pave the way for novel therapies. Examination of protein extracts that bind to the HCV IRES has identified a number of novel protein targets such as Ewing Sarcoma breakpoint 1 region protein EWS and TRAF-3. The final aspect of HCV liver injury receiving attention is the study of potential biomarkers such as heat shock protein HSP-70 associated with HCV infection progression to HCC (Takashima et al., 2003) (see Chapter 112). AIH, PBC and PSC Associated Liver Disease There are few published proteomic studies addressing the pathophysiology of these diseases. Cholangiocarcinoma that is associated with PSC has been studied using proteomics techniques (Koopmann et al., 2004). Using tandem mass spectroscopy, Koopman and colleagues identified Mac-2-binding protein (Mac-2BP) as a diagnostic marker in biliary carcinoma. The diagnostic accuracy of serum Mac-2BP expression in biliary carcinoma was superior to the established marker CA19-9. This study highlights the progression of proteomic research in liver disease; a focus initially on malignancy and biomarker discovery is followed by studies of pathophysiology. ALD and NAFLD Proteomic studies of alcoholism have, like the gene array studies, been an eclectic mix of research examining HCC development associated with alcohol, studies of hepatocyte alcohol-related biology and neural aspects of alcohol addiction. Studies of the intrahepatic toxic effects using proteomics have helped outline toxicology profiles that can be used for screening as well as trying to understand the alcohol-associated liver injury. Mitochondrial ethanol hepatoxicity is thought to involve modification of protein thiol redox state. Using 2D gel proteomic studies, Venkatraman et al. (2004) were able to demonstrate a decrease in the reduced thiols on aldehyde dehydrogenase and glucose regulated protein 78 (Venkatraman et al., 2004). The change in aldehyde dehydrogenase-reduced thiols was accompanied by a reduction in specific

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activity of the enzyme. The term “alcoholomics” has been coined to refer to the study of those proteins (i.e., the sub-proteome) that are directly or indirectly affected by alcohol. There has been to date only a single study evaluating proteomic profiling in NAFLD (Younossi et al., 2005a, b). This study used SELDI-TOF MS to profile serum samples from 91 patients with NAFLD and 7 obese controls. Twelve unique protein peaks were identified that associated with NALFD (4 associated with steatosis, 4 with steatosis with non-specific inflammation and 4 with NASH). Unfortunately, although the peak mass was shown, SELDI-TOF MS lacks the accuracy required to give mass determination enabling equivocal protein identification. Hepatocellular Carcinoma To date, biomarker profiling has predominantly focused on studies of malignant tissue (Liu et al., 2005; Wiesner, 2004; Wong et al., 2004). In one study, HCC development in chronic HBV infection was characterized by a significant decrease in a fragment of complement-3 and an isoform of apolipoprotein A-1 (Steel et al., 2003). In tissue studies, expression of variants of aldehyde dehydrogenase and tissue ferritin light chain has been identified in HCC, but not surrounding tissues. Similar studies have also identified fructose-bisphosphatase arginosuccinate synthetase and cathepsin B pre-protein as downregulated in HCC tissues. In an extensive study using laser capture microscopy, Li et al. (2004) identified 261 proteins differentially expressed between HCC and non-HCC hepatocytes. Kinases in the Eph family were identified along the Ras-like family of Rho proteins. In addition a DEAD box polypeptide was downregulated whilst three members of the spliceosome and heterogeneous nuclear ribonucleoprotein K were upregulated. Also SELDITOF MS has examined the sera of 82 patients with cirrhosis (38 without and 44 with HCC). An algorithm including the six highest scoring peaks allow the prediction of HCC in over 90% of cases (Paradis et al., 2005). The highest discriminating peak was a C terminal peptide of vitronectin. Proteomic Analysis of Blood As a Marker of Liver Disease: “Next Generation” Liver Function Tests? Several studies have evaluated proteomic analysis of serum protein as a diagnostic test to assess the severity of liver disease and in particular for non-invasive assessment of liver fibrosis. These studies are in their infancy, with basic methodologies still unresolved. However, the early studies show the technique has promise. In a pilot study of 46 patients with chronic hepatitis B, an ANN model was derived from the proteomic fingerprint and used to derive a fibrosis index (Poon et al., 2005). The ANN fibrosis index strongly correlated with Ishak scores and stages of fibrosis. The area under the ROC curve for significant fibrosis (Ishak score 2) and cirrhosis (Ishak score 4) were both 0.90. Inclusion of International Normalized Ratio (INR), total protein, bilirubin, alanine aminotransferase and hemoglobin in the ANN model improved the predictive power, giving

accuracies 90% for the prediction of fibrosis and cirrhosis. Another study found that pre-treatment of serum proteins to remove N-glycosylation enhanced the resolution of serum polypeptide profiles (Comunale et al., 2004). This technique has the potential to improve diagnostic serum proteomics. Chen et al. (2004) developed a method of glycoproteomic analysis in an attempt to discover serum markers that can assist in the early detection of HBV-induced liver cancer. The authors showed that woodchucks diagnosed with HCC have dramatically higher levels of serum-associated core alpha-1,6-linked fucose. One glycoprotein, Golgi Protein 73 (GP73), was found to be elevated and hyper-fucosylated in the serum of animals and humans with a diagnosis of HCC. Serum profiling was used to distinguish HCC from earlier stages of HCV-related liver disease (Schwegler et al., 2005). The proteomic model distinguished chronic HCV from HCV to HCC with moderate sensitivity and specificity. Inclusion of known serum markers alpha fetoprotein, des-gamma carboxyprothrombin, along with GP73, significantly improved the diagnostic accuracy of detecting HCC.

PROTEOMICS IN OTHER LIVER DISEASE The metalloproteome is defined as the set of proteins that have metal-binding capacity by being metalloproteins or having metal-binding sites. The copper and zinc metalloproteomes were defined in human hepatoma lines (She et al., 2003). Although the gene for Wilson disease has been identified, the mechanisms by which excess copper leads to oxidative stress, acute liver failure or cirrhosis are not fully understood. Using an in vitro model of copper loading, novel copper-binding proteins were isolated using proteomic techniques (Roelofsen et al., 2004). Although there has been limited proteomic analysis of liver tissue in models of disease, liver-associated pathobiological processes have been examined. One study of the liver aging process identified 85 differentially expressed proteins comprised of antioxidation glucose/amino acid metabolism signal transduction and cell cycle systems (Cho et al., 2003). In aging, the antioxidation system showed a large increase in glutathione peroxidase and a decrease in glutathione-S-transferase. Similarly, levels of t-glycludic enzymes were decreased in the aging animal. Furthermore, levels of proteins associated in signal transduction/ apoptosis, for example, cathepsin B, were decreased in the aging process. However, it is unclear if the identified genes explain the increased rate of fibrosis progression seen with increasing age. Proteomics has been used to also identify genes associated with LPS-induced liver injury. Proteins such as TRAIL receptor 2 were downregulated in the liver of LPS treated mice, whilst TNFAIP1 was significantly upregulated. Four different proteins were novel in the fatty liver proteome (aconitase succinate dehydrogenase, propanol Co-A carboxylase alpha chain and 3-hydroxyanthrilate 3-4-dioxygenase). LPS is thought to be an important mediator of injury in a number of conditions such as alcohol-related liver injury.

References

FUTURE IMPACT OF GENOMICS STUDIES Although genomics studies have already made significant contributions to our understanding of liver injury, there still remain many unanswered questions. In particular the canonical molecular pathways mediating fibrosis progression and the development of cirrhosis are poorly understood. In the next decade genomics studies will help to predict the susceptibility to fibrosis and the likelihood of developing sequelae such as liver failure and HCC. Long-term, proteomics will help in the identification of noninvasive markers of liver injury and will help to screen individuals for HCC. In the future, genomics approaches are likely to result in the development of newer therapeutic options based on our understanding of the molecular events involved in the development of cirrhosis. Further, the identification of genomic susceptibility markers will help to further individualize risk assessment.

CONCLUSION

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progressive fibrosis tissue deposition and eventual disruption of normal hepatic architecture. The hallmark of liver injury is activation of the HSC, which mediates the development of fibrous tissue and change in the composition of the ECM. Inflammation and immune responses are the driving force behind the transition of the quiescent HSC to an activated phenotype. Additionally, the liver response to injury includes changes in cellular proliferation and apoptosis that may explain the premalignant potential of cirrhosis. Although remarkably consistent in its development, cirrhosis can arise from many diverse causes, is often difficult to diagnose and once established is often difficult to treat. Unfortunately, it is clear that better pharmaceutical agents are needed to alter the natural history of fibrosis and subsequent development of cirrhosis. Importantly, the promise of genomics approaches is now being realized as these technologies are being used to better understand liver disease pathogenesis, determine susceptibility and develop novel therapies. Consequently, individualized patient assessment and tailored therapy will be possible in liver disease in the era of genomic and personalized medicine.

In conclusion, cirrhosis is the pathogenic consequence of a remarkably conserved response to injury within the liver, characterized by

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CHAPTER

94 Systemic Sclerosis Ulf Müller-Ladner

INTRODUCTION Systemic sclerosis (SSc) is a chronic connective tissue disorder that affects predominantly the skin but also numerous internal organs. Although the pathophysiology is not completely elucidated, SSc results finally in an excessive accumulation of extracellular matrix components by activated fibroblasts, which is also the histopathologic hallmark of the disease. Dominant clinical features are skin sclerosis, reduced acral microcirculation including severe Raynaud’s syndrome, cutaneous ulceration, esophageal dysfunction, pulmonary fibrosis and hypertension as well as renal failure. In early stages, additional characteristics of SSc are perivascular inflammatory infiltrates and alterations of the terminal capillaries, which frequently precede the development of fibrosis. The microvasculature of SSc patients, specifically the capillaries of the extremities, shows a reduced density and an irregular architecture, resulting in a decreased capillary blood flow causing malnutrition and severe tissue hypoxia. Aside from skin ulceration, these microvascular abnormalities in combination with tissue hypoxia, as demonstrated in Figure 94.1, contribute also to severe dysfunction of internal organs such as renal failure and pulmonary hypertension.

PREDISPOSITION Risk Factors At present, there are no valid markers that are able to predict the development of SSc in a given individual nor are there Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

established risk factors (Denton and Black, 2005). Exposure to toxic agents such as alkylating molecules, silica dust, vinyl chloride and l-tryptophan have been proposed as potential initiating factors similar to other fibrosing diseases, but none of them has been proven to initiate the disease in humans. Recent research data support these clinical observations by failing to establish a link between acetylator status via arylamine-N-acetyltransferase 2 polymorphisms and overt SSc (Skretkowicz et al., 2005). Although disease-specific genes or a distinct genetic profile have also not yet been identified, the individual ethnic background influences significantly disease manifestation and progression. For example, pulmonary interstitial fibrosis can be less frequently observed in Caucasian patients as compared to Afro-American and Japanese patients. Similarly, there exists no known infectious agent that bears the potential to trigger SSc, although enhanced levels of anti-CMV antibodies have been found in the majority of SSc patients (Neidhart et al., 1999). Occurrence SSc is regarded a rare or even orphan disease with incidence rates approximately between 5 and 20 new cases per million in Europe and the United States (Denton and Black, 2005). Although exposure to warmer temperatures usually improves acral perfusion; with regard to the occurrence of digital ulcers, no differences can be observed between countries with cold or warm climates or different regions within a single country. However, the prevalence in the United States (up to 20 per million) appears to be higher than in other countries (up to 4 per million). In contrast, a prospective study from Estonia revealed an unexpected high prevalence of more than 2000 per million, Copyright © 2009, Elsevier Inc. All rights reserved. 1155

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Figure 94.1 Typical clinical phenomena of a patient with active SSc. Acral hypoperfusion (Raynaud’s phenomenon) is associated with skin sclerosis and ulcerations.

which can be potentially explained by a less stringent definition of “scleroderma spectrum disorders” (Valter et al., 1997). The age of onset, especially of the first non-Raynaud’s symptom, is between the age of 30 and 50, and the disease is basically not observed prior to puberty. Similar to other connective tissue disorders, SSc is predominant in females with ratios of women to men between 5 and 14:1. Twin Studies Twin studies revealed that less than 5% of monozygotic and dizygotic twins are concordant for SSc. On the other hand, when gene expression is compared by high-throughput microarray techniques, the pattern of dermal fibroblasts of unaffected monozygotic twins was not significantly different from SSc patients. In addition, in this study functional experiments could show that healthy fibroblasts that were incubated with serum from an SSc-affected patient or with serum of the unaffected monozygotic twin developed a typical SSc pattern with an increased expression of collagen1A2, SPARC (secreted protein, acidic and rich in cysteine, osteonectin) and CTGF (connective tissue growth factor) (Zhou et al., 2005). Genetic Markers Although being performed for a variety of disease entities (Trcka and Kunz, 2006; Xiong et al., 2005), at present no whole genome or proteome analysis has revealed an unique pattern in SSc patients, tissues or cells (Ahmed and Tan, 2003; FeghaliBostwick, 2005; Strehlow 2000). On the other hand, as outlined below, several cell types have been screened for gene expression abnormalities and candidate genes have been examined for polymorphisms and disease associations. Most likely, knowledge from genetics addressing target genes and protein groups such

as the topoisomerase I complex will provide additional data in this field (Czubaty et al., 2005). In addition, cDNA array techniques were successfully used to identify the gene expression of disease-related cell types such as endothelial cells. When compared to normal skin endothelial cells, about 3% of the 14,000 examined genes were dysregulated in SSc patients (Giusti et al., 2006). In another approach, cDNA arrays were used to compare gene expression profiles of peripheral blood mononuclear cells of patients with early SSc, which revealed a distinct upregulation of 18 interferon-inducible genes, selectins and integrins supporting the idea of an infectious trigger in the early phases of the disease (Tan et al., 2005). Gene expression profiling of CD8positive lung T cells, on the other hand, resulted in two distinct gene cluster groups, with one showing a type II T cell activation in combination with profibrotic factors and matrix metalloproteinases (MMPs) (Luzina et al., 2003). With regard to genetic markers, a limited but increasing number of studies examined the presence and pattern of genetic polymorphisms and single nucleotide polymorphisms (SNPs) in SSc (Assassi et al., 2005). In addition, as SSc is known not to be inherited in a Mendelian fashion, experimental and clinical research has focused on genetic alterations in numerous genes known to be operative in SSc pathophysiology, which revealed interesting aspects especially with regard to growth factors, matrix-related molecules and inflammation markers (Table 94.1).

T A B L E 9 4 . 1 Loci of interest of genetic variants of different molecules involved in SSc pathophysiology Molecule/gene

Genomic variant

TGF

Codon 10, -1133bp in promoter

Fibrillin-1

SNP in 5’-untranslated region, CT insertion in exon A

SPARC

Alteration in numerous SNPs

TNF

TNF13 microsatellite, -863A allele

TNF receptor type II

GG genotype in exon 6

TNF

252 in exon 1

CTLA-4

49A locus, A/G polymorphism in exon 1

IL-1

CTG/CTG diplotype SNP, 889 allele polymorphism

CXCR2

Polymorphisms

MCP-1

2518 polymorphism in promoter

CD19

499G  T polymorphism

TAP-1/TAP-2

Polymorphisms

ACE

Insertions/deletions in chromosome 17

eNOS

Polymorphisms

Predisposition

The initial analysis of the TGF gene revealed no strong genetic abnormalities, which were also not found for plateletderived growth factor (PDGF) (Zhou et al., 2000), but detailed analysis at codon 10 showed that SSc patients are prone to high TGF synthesis, irrespective of limited or diffuse disease (Crilly et al., 2002). In Asia, polymorphisms within the TGF gene appear to depend on the patient cohort and the region as genetic variations, but no unique polymorphism could be found within Japan and Korea (Lee et al., 2004; Ohtsuka et al., 2002; Sugiura et al., 2003). Of interest, adenoviral gene transfer of TGF receptor type I into fibroblasts in combination with cDNA array revealed in a distinct TGF- RI-induced profibrotic phenotype with upregulation of collagen type I and CTGF (Pannu et al., 2006). Another promising candidate for detailed gene and polymorphism analysis is CTGF (Leask et al., 2004; Zhu et al., 2004). However, further research in larger cohorts of patients is needed as data derived from the tight-skin mouse (TSK) show a distinct G  T polymorphism at 1133bp in the TGF1 promoter of murine fibroblasts (Zhu et al., 2004). As TSK pathophysiology is based – in part – on an in-frame duplication of the fibrillin-1 (FBN-1) gene, which leads to larger FBN proteins, it was interesting to observe also a strong association of an SNP in the 5’-untranslated region of the FBN-1 gene not only in Choctaw Indians, but also in Japanese SSc patients (Tan et al., 2001, 2003). Specifically, a CT insertion in this gene region of exon A was negatively associated with the disease (Kodera et al., 2002). Further research on matrix metabolism-regulating genes showed an association of the stromelysin promoter with SSc (Marasini et al., 2001), a link for fibronectin polymorphisms with fibrosing alveolitis (Avila et al., 1999) but no association of the MMP-1 promoter with the disease (Johnson, K.L. et al., 2001). Conversely, the SPARC gene, which is upregulated by TGF and involved in assembly of extracellular matrix proteins, appears to be altered significantly in distinct SNPs. This alteration appears not to be restricted to ethnic groups with high risk for SSc such as the Choctaw Indians (Zhou et al., 2000, 2002), although this finding has recently been challenged by a subsequent study on European SSc patients (Lagan et al., 2005). Tumor necrosis factor (TNF) is one of the driving proinflammatory and pluripotent molecules in autoimmune disease. Based on the knowledge of the inflamed initial stages in SSc pathophysiology, numerous groups examined the presence of TNF gene polymorphisms in SSc patients. In the first intron at locus 252, the two homozygous genotypes of TNF were significantly associated with SSc in Japanese patients. In the TNF gene, however, statistical power was not sufficient to prove a similar association (Pandey and Takeuchi, 1999). When compared to German patients, however, the TNF13 microsatellite polymorphism appeared to be a genetic marker in Japanese Scl70 positive (diffuse) SSc patients (Takeuchi et al., 2000). In contrast, the TNF-863A allele showed a strong association with anticentromere-positive (limited) European SSc patients (Sato et al., 2004) and the rare GG genotype in exon 6 of the TNF receptor type II was found to be more frequent in another European

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diffuse SSc cohort (Tolusso et al., 2005). Similarly, Japanese patients with elevated levels of anti-RNP antibodies appear to contain more 49A alleles in the CTLA-4 gene (Takeuchi et al., 2002), whereas in African-Americans, this A/G heterozygotic polymorphism in the CTLA-4 exon 1 was associated with the disease itself (Hudson et al., 2004). Genomic evaluation of the interleukin-1 (IL-1) gene revealed distinct genetic aberrations in Japanese SSc patients, and SNP analyses showed a distinct CTG/CTG diplotype associated strongly with the development of interstitial lung disease in these patients (Kawaguchi et al., 2003). In addition, Czech patients revealed a polymorphism in the IL-1a gene at position 889 (Hutyrova et al., 2004). Accumulation of both inflammatory cells as well as direct profibrotic properties has been associated with chemokines in SSc pathophysiology. In the IL-8 receptor CXCR-2, two polymorphisms could be located, which are linked to the disease (Renzoni et al., 2000), and the 2518G promoter polymorphism in the MCP-1 gene of SSc fibroblasts affects MCP-1 synthesis in these cells (Karrer et al., 2005), which revealed novel insights into the role of this pluripotent molecule (Figure 94.2). Of note, MCP-1 serum levels were found to correlate with levels of the vasoconstrictive and profibrotic molecule endothelin-1 (ET-1) (Peterlana et al., 2006).

Th0

MCP-1

Th2

MCP-1 MCP-1 MCP-1 MCP-1 MCP-1

IL-4

IL-4

MCP-1

Proteoglycans

IL-4

MCP-1

Fibroblasts Collagen type I

Collagen type I

Figure 94.2 Role of MCP-1 in the development of fibrosis. MCP-1 does not have direct effects on dermal fibroblasts due to the lack of functional MCP-1 receptors. Instead, MCP-1 favors the differentiation of IL-4 producing T cells. Soluble IL-4 in turn induces the synthesis of collagens in resident dermal fibroblasts via binding to IL-4R. In this model, glycosaminoglycans, which are increased in the fibrotic skin, bind MCP-1 via ionic–ionic interactions and act as a local reservoir for MCP-1 further enhancing its profibrotic effects (adapted from Distler et al., 2006).

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With regard to cellular immunology, CD19-positive B cells appear to bear a 499G  T polymorphism in the CD19 coding region in SSc patients, which was also associated with susceptibility to the disease (Tsuchiya et al., 2004). In Korean patients, transporter associated with antigen processing (TAP)-1 and -2 polymorphisms were found to be independent of other HLADR associations (Takeuchi et al., 1994), suggesting different roles of genomic alterations in antigen-presenting cells in SSc (Song et al., 2005). However, distinct HLA alleles appear to be linked directly to SSc subtypes, as it could be shown that in SSc men, HLA class II allele DQA1*0501 was associated with diffuse but not limited disease (Lambert et al., 2000a). In this population, maternal HLA compatibility was not a significant risk factor for development of the disease (Lambert et al., 2000b). Alterations in genes regulating microvasculature development, intravascular thrombosis, dysregulated fibrinolysis and perivascular fibrosis have also been addressed by genomic analyses. Patients with SSc appear to have a higher prevalence of angiotensin converting enzyme (ACE) insertion/deletions on chromosome 17 and polymorphisms within the endothelial nitric oxide synthase (eNOS) gene in Italian patients (Fatini et al., 2002, 2004). In contrast, other groups showed that eNOS polymorphisms do neither influence the course of SSc nor do they enhance susceptibility in the French Caucasian population (Allanore et al., 2004;Tikly et al., 2005). Moreover a recent investigation performed in Korea could also not find a difference in the frequencies of all ACE insertion/deletion genotypes between patients and controls, nor between diffuse and limited and diffuse SSc patients (Joung et al., 2006).

SCREENING Typical Clinical Phenomena Although most clinical symptoms such as localized or diffuse scleroderma are unique within internal medicine diseases, several clinical phenomena require a distinct differential diagnosis such as Raynaud’s syndrome, which can also be associated with systemic lupus erythematosus (SLE) and other connective tissue diseases (Table 94.2).

T A B L E 9 4 . 2 Prevalence of Raynaud’s syndrome in patients with different rheumatic and connective diseases Disease

Occurrence

Systemic sclerosis

90%

Other connective tissue diseases

90%

Systemic lupus erythematosus

Up to 40%

Polymyositis/dermatomyositis

Up to 30%

Rheumatoid arthritis

Up to 20%

Sjögren’s syndrome

Up to 15%

Adapted from Grader-Beck and Wigley (2005).

Increase of cutaneous fibrosis and skin thickening can be measured by the modified Rodnan Skin Score (mRSS), which is a validated tool that scores various regions of the body by 0 (no thickening) to 3 (severe thickening), giving a maximum score of 51 (Valentini, 2003). In active diffuse SSc, mRSS usually exceeds values of 18–20. On the other hand, deterioration of organ failure such as pulmonary fibrosis or renal dysfunction requires frequently detailed examination including evaluation of histologic samples. Less overt but still frequent complications include involvement of all parts of the gastrointestinal (GI) tract with esophageal dysfunction as the most prominent and intestinal pseudoobstruction as the most severe and even life-threatening complication. Laboratory Markers Activation of the immune system is usually marked by the presence of anti-nuclear antibodies (ANA), of which the two main subtypes associated with the limited form and the diffuse form are anti-centromere- and anti-Scl70- (anti-topoisomerase I) antibodies, respectively. Anti-topoisomerase-antibodies are associated with pulmonary fibrosis. Other antibodies frequently found in SSc patients, which are also subject to genomic analysis, are antibodies against fibrillin-1, other RNA polymerases, PmScl, U1RNP and Th/Ku antigens (Denton and Black, 2005; Valentini, 2003). Inflammatory parameters including ESR and CRP are normally not significantly elevated and rather indicate infectious complications, in contrast to other connective tissue diseases. Moreover, antiphospholipid antibodies are very rare in SSc patients. Imaging Tools Although proposed continuously, at present no imaging system (e.g., ultrasound with high-resolution probes) exists that allows reliable and reproducible illustration and quantification of skin thickness in SSc patients (Nouveau-Richared et al., 2004; Valentini et al., 2003a, b). On the other hand, capillaroscopy of the nailfold microvasculature has proven to be a valuable tool to diagnose early stages of the disease (Cutolo et al., 2004). The normal nailfold capillaroscopic pattern shows a regular disposition of the capillary loops along with the nailbed. In contrast, in patients suffering from secondary RP, structural disorganization, giant and numerically reduced capillaries, hemorrhages and avascular areas can be found in the majority of patients with SSc. These alterations are accompanied by a distinct histopathologic pattern showing dilated postcapillary venules located in the papillary and superficial reticular dermis and vessel walls consisting of non-fenestrated endothelial cells surrounded by a variable number of pericytes and smooth muscle cells. Of interest, on an ultrastructural level, no specific features could be found and with regard to molecular analysis, in these patients no enhanced endothelial expression of endoglin, endothelin, E-selectin and ICAM-1 could be found (Walker et al., 2005). Recently, Cutolo et al. reclassified three defined major nailfold videocapillaroscopic (NVC) patterns that presently are considered useful in assessing the appearance and progression of

Monitoring and Genomic Factors

the sclerodermic microangiopathy (Cutolo et al., 2004). These three patterns include “early”, “active” and “late” characteristic pictures (Figure 94.3). Of note, interesting capillaroscopic alterations have also been observed in patients with SLE, Sjögren’s syndrome, antiphospholipid syndrome and other connective tissue diseases. For evaluation of organ involvement, especially of pulmonary fibrosis, performing high-resolution CT are standard procedures together with the “direct” techniques such as bronchoscopy, GI endoscopy, cardiac and GI ultrasound and right heart catheterization.

DIAGNOSIS Classification Criteria Classification criteria for SSc are rather simple and include one major criterion (sclerodactyly proximal of the metacarpophalangeal joints) and three minor criteria (sclerodactyly, acral ulceration or defects and bilateral basal pulmonary fibrosis). SSc consists of two different subtypes: diffuse SSc, in which cutaneous fibrosis exceeds the elbow or knee region to proximal parts of the body; and limited SSc, which keeps restricted to the distal parts of the body. Of note, facial sclerosis can be found in both entities, and involvement of internal organs – though different in severity of involvement – is also independent of the type of SSc. The formerly separated CREST syndrome (calcinosis, Raynaud’s syndrome, esophageal dysmotility, sclerodactyly, teleangiectasia) is nowadays subsumed under the limited SSc type. Some patients, however, show fibrosis of internal organs typical for SSc without any sign of scleroderma. These rare patients are classified as SSc sine scleroderma (Denton and Black, 2005). Differential Diagnosis and Overlap Syndromes Overlap syndromes are defined by a combination of key features of more than one rheumatic disease present in the same patient and often defined by a specific immunologic laboratory parameter such as anti-U1RNP antibodies for true Sharp syndrome and Jo1-antibodies for a distinct form of polymyositis. Arthritis, Raynaud’s phenomenon and sclerodactyly are common features; thyroiditis, polymyositis and fibrosing alveolitis belong to the more serious manifestations. Owing to the diversity of clinical phenomena, there exist no reliable data addressing the prevalence of overlap syndromes. In general, patients with an overlap syndrome appear to occur less frequent than patients with SLE, but more frequent than patients with SSc or inflammatory myopathy.

PROGNOSIS In general, prognosis is dependent on the subtypes of SSc. Patients with limited disease have a more benign long-term outcome than do patients with diffuse disease. This does not only apply to the extent of skin thickening, which is also reflected by the

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course of the values for the Rodnan Skin Score, but also to the extent of organ involvement, morbidity and mortality (Denton and Black, 2005). After an initial inflammatory phase at onset, diffuse SSc shows the most rapid progression within the first 2 years, whereas skin sclerosis and fibrosis of patients with limited disease usually proceeds slowly over years. Of the laboratory parameters available, serum levels of growth factors (e.g., VEGF), adhesion molecules (VCAM, E-selectin) and ET-1 appear to correlate with the extent of fingertip ulcers and organ involvement (Distler et al., 2004; Kuryliszyn-Moskal et al., 2005). With regard to development of secondary malignant disease, which is frequently observed in autoimmune diseases, SSc showed predominantly a higher SIR (standard incidence ratio) of 5.9 for lung cancer and within the SSc subset an overall SIR for all cancers of 2.7 for diffuse and 1.9 for limited disease (Hill et al., 2003).

PHARMACOGENOMICS Pharmacogenetic data are very rare, not only in diseases associated with sclerotic processes, with cystic fibrosis (Sangiuolo et al., 2004) and multiple sclerosis (Cunningham et al., 2005) being the best examined diseases. But there are first hints that genomic alterations may influence the outcome of treatments in SSc. For example, a recent study could show that analysis of the IL-1 promoter (i.e., the T-889C polymorphism) revealed a link to a less favorable outcome of SSc patients that needed to undergo cyclophosphamide treatment for fibrosing alveolitis (Beretta et al., 2007).

MONITORING AND GENOMIC FACTORS Monitoring of General Disease In the past years, numerous efforts have been undertaken to establish parameters to monitor established disease. At present, the most widely used tool is the mRSS (Valentini et al., 2003a), which measures the extent of skin involvement in a given patient. Further developments in order to include all affected organs are the disease activity score as proposed by Valentini et al. (2003b). This score is composed of different organ and laboratory parameters, which are summarized to an overall activity score, which can be used to estimate the current disease activity and as baseline for outcome and therapy studies. Table 94.3 shows the composition of the activity score. Microchimerism Less than a decade ago, first data were published on the presence of microchimerism in female SSc patients and its influence on initiation and severity of the disease has since then been subject to an intensive and controversial discussion about the potential mechanism by which these cells might contribute to the pathogenesis of the disease (Jimenez and Artlett, 2005). However, a number of research groups have published the identification or

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Normal

(a)

(b)

Early

(c)

(d)

Active

(e)

(f)

Late

(g)

(h)

Figure 94.3 Representation of the normal NVC pattern (a, b) and the three nailfold videocapillaroscopy scleroderma patterns (c–h) when magnified 200×. The early pattern (c, d) is characterized by well-preserved capillary architecture and density, the presence of enlarged capillaries, giant capillaries (c, arrow) and hemorrhages (d, arrows). The active pattern (e, f ) is characterized by frequent giant capillaries and hemorrhages, moderate loss of capillaries and disorganization of capillary architecture, with rare ramified capillaries (f, arrow). The late pattern (g, h) is characterized by severe capillary architecture disorganization with loss of capillaries, the presence of ramified capillaries (g, arrow), very few giant capillaries, absence of hemorrhages, and large avascular areas (adapted from Cutolo et al., 2004).

Monitoring and Genomic Factors

TABLE 94.3 for SSc

Proposed EScSG disease activity indices

Criteria

Whole series

TTS  20 (Kahaleh’s method)*

1.0

Scleredema

0.5

Skin

2.0

Digital necrosis

0.5

Vascular

0.5

Arthritis

0.5

Articular/muscular

dcSSc

lcSSc

0.5 3.0

2.5

2.0

1.0 1.0

1.0

↓TLCO

0.5

DCardiopulmonary

2.0

ESR 30 mm/1st hour

1.5

2.5

Hypocomplementemia (C3 and/or C4)

1.0

1.0

Total maximum disease activity index

10.0

4.0

10.0

1.5

10.0

Adapted from Valentini et al. (2003b). TTS, total skin score; TLCO, carbon monoxide transfer factor; ESR, erythrocyte sedimentation rate. * To be changed to mRTSS 14.

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quantification of microchimeric cells in the peripheral blood or tissues of SSc patients, their appearance in different stages of the disease and one study has also investigated their function. With regard to HLA association, fetal microchimerism among T lymphocytes was found to be associated with the HLA DQA1*0501 genotype of the mother (Lambert et al., 2000a), and in another cohort analyzing HLA-Cw antigens, cellular microchimerism of either female or male origin could be found in approximately two-thirds of the SSc patients versus one-third of the healthy controls (Artlett et al., 2000). In this context, one of the most challenging questions addresses the role of male microchimerism in female SSc patients, because the male cells from the offspring might trigger certain if not key mechanisms in the (imminent) SSc pathophysiology of the mother. For example, there appear to be more microchimeric cells in unaffected than affected skin of SSc patients supporting the idea of microchimerism preceding the initiation of fibrosis (Sawaya et al., 2004). In female patients, who had died from SSc complications, male cells were predominantly located in the spleen of the SSc patients and in various other organs except the pancreas. In contrast, in the control women, who had died of reasons other than autoimmune disease, in none of the organs could male cells be detected (Johnson, R.W. et al., 2001). Such male cells, especially T cells derived from SSc female patients (Figure 94.4), frequently are of the Th2 type and produce IL-4, which supports

(a)

(b)

(c)

(d)

Figure 94.4 Presence of Y chromosomes in T cell clones generated from the skin of women with SSc. X chromosomes are labeled in green and the centromeric region of Y chromosome is labeled in orange. Male cells show yellow and red signals and female cells show two yellow signals. (a) T cell blasts from one clone showing Y chromosome. (b) A mitotic T cell blast from the same clone. (c) T cell blasts from one clone showing no Y chromosome. (d) Mitotic T cell blasts from the same clone (adapted from Scaletti et al., 2002).

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the hypothesis of a graft-versus host-like status mediated by these cells in active disease (Scaletti et al., 2002). Of note, microchimerism in SSc patients is cell based as no free microchimeric DNA could be detected (Lambert et al., 2002); this status appears to be a long-term phenomenon (Selva-O’Callaghan et al., 2003). However, as in most genetic studies, the confounding population and ethnicity are critical for the outcome of the study as another study examining female SSc patients in Spain could not confirm an enhanced rate of fetal microchimerism when compared to healthy women, who had also given birth to at least one son (Selva-O’Callaghan et al., 2003). It needs to be determined, however, if microchimerism correlates also with disease activity, a phenomenon, which could not be observed in patients with other autoimmune diseases such as SLE (Lambert et al., 2005). Moreover, there has always been the hypothesis that the severity of SSc is directly dependent on the number of pregnancies. With regard to the latter, real-time quantitative polymerase chain reaction (PCR) targeting the Y chromosome-specific sequence DYS14 was used to test DNA extracted from peripheral blood mononuclear cells. SSc patients who had never given birth to a son were studied, and male DNA were found in an equivalent percentage of 15% of women with SSc as compared to 13% of healthy women. Based on this finding, other potential sources of male DNA include sexual intercourse as well as unrecognized male pregnancy or twins (Lambert et al., 2005). Monitoring of Organ Involvement Although cutaneous fibrosis is the most obvious and impressive alteration in SSc, the prognosis quoad vitam and/or reduction in quality of life is frequently determined by pulmonary, cardial, intestinal and visceral involvement (Denton and Black, 2005; Walker et al., 2007). Aside general organ dysfunctions as mentioned above, some complications require more intensive attention. Progressive interstitial pulmonary fibrosis and consecutive pulmonary hypertension, right and left heart failure, scleroderma renal crisis and intestinal pseudoobstruction with subsequent ileus symptomatic can all develop rapidly into a life-threatening situation. For example, in intestinal pseudoobstruction, rapid conservative non-surgical decompression using a nasogastral suction in combination with prokinetics can avoid surgical intervention. Moreover, untreated reflux esophagitis can lead to acute esophageal bleeding and chronic esophagitis can be the initiation of Barrett’s dysplasia and adenocarcinoma.

THERAPEUTIC STRATEGIES Unlike other rheumatic diseases and the primarily fibrotic character of SSc, most of the therapies effective in mixed connective diseases have failed to prove efficacy in SSc patients (Denton and Black, 2005; Lin et al., 2003). It can be speculated, whether this is due to targeting the wrong pathways or just being too late in the development of the disease. However, for distinct situations, disease-modifying anti-rheumatic drugs (DMARDs)

may be effective. But at present, no evidence-based diseasemodifying regimen for SSc exists although most of the patients have received or are taking at least low-dose steroids and/or d-penicillamine to inhibit overall disease activity. The role of DMARDs in SSc Therapy For skin sclerosis, d-penicillamine was regarded the therapy of choice for an extended period of time until a milestone study revealed that no significant effect can be expected from this drug independent of its dosage (Clements et al., 2004). Methotrexate demonstrated some positive effects in randomized trials. In a 48-week randomized double-blind trial, 68% of the patients responded favorably with improvement of skin score, patients general assessment, grip strength and erythrocyte sedimentation rate (van den Hoogen et al., 1996). In another randomized controlled trial, after 12 months patients in the MTX group showed a tendency for improved skin scores and diffusion capacities as well as physician global assessments, but these parameters were not significantly different from placebo treated patients. In contrast, the intent-to-treat analysis showed a significant improvement of skin scores with MTX (Pope et al., 2001). Cyclosporine A, on the other hand, revealed also a significant reduction in skin thickening and microvascular blood flow in two studies, but no consistent effect on pulmonary and cardiac involvement could be shown in a long-term setting. Unfortunately, higher doses of cyclosporine A resulted also in higher toxicity of the drug in SSc patients (Clements et al., 1993). Therapy of Organ Dysfunction For Raynaud’s syndrome, aside consequent thermoprotection of hands and feet, calcium antagonists and prostaglandins, especially when applied intravenously, have shown to be effective. As outlined below, novel developments include ET receptor antagonists and phosphodiesterase-5 inhibitors (Boin and Wigley, 2005; Colglazier et al., 2005; Selenko-Gebauer et al., 2006). Figure 94.5 illustrates the effect of bosentan on the perfusion of hands and fingers. For interstitial lung fibrosis and alveolitis the situation for treatment appears to be more promising. In a retrospective analysis, azathioprine and low-dose steroids were able to improve mean dyspnea score from a baseline of 1.5–0.5 at 12 months and to 0.43 at 18 months. However, the improvement of FVC (%) from a baseline of 54.3–63.4% at 12 months and 60.0% at 18 months was not statistically significant (Dheda et al., 2004). SSc and alveolitis treated with cyclophosphamide 750 mg and a pulse of 125 mg methylprednisolone every 3 weeks resulted in a modest improvement of FVC and DLCO, without reaching significance. On the other hand, a pooled analysis of the data from 53 patients reported from other groups reached statistical significance for improvement of pulmonary function tests (Airo et al., 2004). Steroid dosage in this combination therapy appears to be critical as another trial showed that high-dose steroids with 1 mg/kg for 4 weeks tapered to 5 mg/kg improved significantly parenchymal lung involvement, FVC, DLCO, dyspnea severity

Therapeutic Strategies

Baseline

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Week 16

Patient 1

Patient 2

Patient 3

Figure 94.5 Thermography of hands of patients with SSc (adapted from Selenko-Gebauer et al., 2006) was performed at room temperature (22°C) at baseline and at week 16 after continuous treatment with the dual ET receptor antagonist bosentan. Note the increase in perfusion not only in the forearms and hands, but also in numerous digits.

and skin involvement (Pakas et al., 2002). In addition, a large American multicenter trial presented recently at the American Thoracic Society Meeting, the Scleroderma Lung Study, which included 162 patients with acute alveolitis that were treated with cyclophosphamide or placebo revealed a good efficacy for cyclophosphamide with minor toxicity. Similar results were obtained by a low-dose regimen (Valentini et al., 2006). Several therapeutic approaches for the individual organs of the GI tract have proven to be effective (Denton and Black, 2005). A (secondary) sicca syndrome requires treatment with artificial saliva, and due to the high risk for development of caries, these patients should see a dentist on a 6-monthly basis. Gastroesophageal reflux symptoms can be treated effectively with proton pump inhibitors. However, in most of the patients, high doses (e.g., up to 80 mg pantoprazole) are needed to resolve the majority of symptoms including reflux-associated asthma attacks. In some patients with severe reflux, addition of H2 blockers at nighttime (“sequential acid blocking”) has an

additional effect, although in preclinical studies, this effect was not found to be significant in the majority of patients. Reduced intestinal motility has become more problematic since cisapride has been taken off the market due to the risk of cardiac arrhythmia. Therefore the effect of other prokinetic drugs such as metoclopramide, domperidone and erythromycin needs to be evaluated for each patient separately, especially in cases suffering from intestinal pseudoobstruction. Stenoses of the GI tract, regardless whether in the esophagus, stomach, small and large intestine, and the anus, can be treated mechanically with balloon dilatation as other strategies such as botox applications are usually much less effective than in other GI diseases. Cardiac involvement based on inflammation such as perimyocarditis and pericardial effusion frequently indicates an overlap to connective tissue diseases and can be treated by immunosuppressants and steroids, whereas left or right heart failure or arrhythmias should be treated according to the general recommendations of the respective cardiac societies. A similar

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situation holds true for involvement of the kidney with the only exception of scleroderma renal crisis, which occurs frequently following high-dose or long-term steroid therapy. In case of rapid deterioration of renal function, immediate high-dose therapy with ACE inhibitors can prevent life-long dialysis (Denton and Black, 2005). Extracorporal photochemotherapy (ECP) for therapy of SSc patients has been evaluated by numerous groups and is frequently been performed in dermatologic centers. One report showed beneficial effects for skin scores (Rook et al., 1992), another report did not show any improvement in ECP treated patients (Enomoto et al., 1999), a most recent controlled study showed an improvement in skin score when compared to sham treated patients after 12 months, but did not have enough power to reach statistical significance, altogether resulting in no general recommendation for this therapy (Knobler et al., 2006). Discussed other options for therapy are intravenous immunoglobulins, leflunomide or rapamycin. Interferon-γ treatment of SSc was commonly associated with flu-like adverse events and a tendency of improved skin changes was not significant (Grassegger et al., 1998).

NOVEL AND EMERGING THERAPEUTICS Following the bench-to-bedside idea with matrix-producing fibroblasts in the center of interest (Figure 94.6), targeting TGF as driving factor for fibrogenesis appears to be an attractive approach. However, in a phase I/II trial, 45 patients with recent

Apoptosis

Hy

po

-1 lin the do En

xia

Endothelial cells

Autocrine Loop (CTGF, TGF-)

Fibroblasts

Matrix Synthesis Collagens Proteoglykans

Reduced Matrix Degradation (?)

Figure 94.6 Pathophysiology of the SSc fibroblast provides both the basis for explanation of the role of the various molecules such as growth factors, cytokines and matrix-remodeling enzymes involved in progressive fibrosis as well as ideas for novel treatment strategies including FGF-, TGF- and CTGF-depending pathways as well as the ET–ET receptor interaction.

onset SSc randomly allocated to three different concentrations of anti-TGF1 or placebo infusions did neither show improvement of serum markers nor skin scores with 5 and 10 mg/kg of antiTGF antibodies (Denton et al., 2007). Other potential antifibrotics as shown in liver and lung fibrosis include the tyrosine kinase inhibitor imatinib but at present, only pilot studies have been published (Denton and Black, 2005). Blocking proinflammatory cells such as thymocytes may also be a promising approach and application of anti-thymocyte globulin (ATG) in a pilot study of 13 patients with recent onset in combination with mycophenolate mofetil (MMF) decreased the skin score from 28 at baseline to 17 after 12 months. On the other hand, hand contractures worsened during the study (Stratton et al., 2001). Amongst the novel developments for therapy, the ET–ET receptor interaction is currently the best examined pathway in SSc angioregulation and fibrosis, which has also led to the introduction of various ET receptor antagonists such as bosentan, sitaxentan and ambrisentan into clinical medicine. These developments are based on the discovery of the soluble factor ET more than a decade ago, which was found to be a very strong vasoconstrictor synthesized by the endothelium of the microvasculature (Kahaleh, 1991). Histologic examinations revealed that in SSc skin, the binding sites of ET (i.e., the ET receptors type A and B) are located at microvessels of the subepidermal plexus but also on larger vessels, glands, epidermis and hair follicles. In addition, already the first studies could show that ET synthesis not only is stimulated by exposure to low temperatures similar to the situation in Raynaud’s syndrome, but also by fibroblast proliferation and collagen synthesis (Kahaleh, 1991). Of the three ET receptors, ET-1 appears to mediate the strongest mitogenic effect on fibroblasts (Kikuchi et al., 1995), and further research addressing the distribution of ET receptors in tissue showed also an intensive expression of ET-1 in scleroderma lung tissue and in the kidney of patients with scleroderma renal crisis, aside the skin (Tabata et al., 1997), the primary sites of development of fibrosis in SSc patients (Abraham et al., 1997). Molecular analysis of the specific pathways triggered by the ET–ET receptor system revealed that ET-1 induced the synthesis of collagen-1 and -3 in skin fibroblasts, although the effect on SSc skin fibroblasts was lower in this study when compared to SSc fibroblasts (Xu et al., 1998). The profibrotic activity of ET-1 is further enhanced by its capability to downregulate expression of MMPs such as MMP-1 (collagenase-1). This matrix remodeling was dependent on the intracellular activation of tyrosine kinases and protein kinase C. Similar to the above-mentioned experiments, SSc fibroblasts did not only have less ET receptors on their surface, they were also less responsive to these triggers than normal skin fibroblasts supporting the hypothesis of an already activated fibrotic phenotype unresponsive to additional stimuli. Further research showed that ET-1 was also able to induce a contractile phenotype in lung fibroblasts, which was illustrated by the upregulation of α-smooth muscle actin, ezrin, moesin and paxillin. This protein synthesis was mediated intracellularly by the akt/PI3-kinase pathway (Shi-Wen et al., 2001, 2004). Of interest, an increasing body of data demonstrate also a significant role from adhesion molecules in accumulation of

References

profibrotic fibroblasts (Kupper, 1995). ET-1 is directly involved in this process as it could be shown that the integrin ICAM-1 was upregulated by ET-1 in SSc fibroblasts, which could be inhibited by the ET receptor antagonist bosentan (Xu et al., 1998). Of interest, ICAM-1 upregulation seen by ET-1 can also be achieved by IL-1 using the same ICAM-1 promoter region and intracellularly the same NFkB site, although sensitivity to the stimuli differed between skin fibroblasts (Waters et al., 2006). With regard to clinical application of inhibition of the ET pathway, the most prominent advances have been made with single (Barst et al., 2006; Galie et al., 2005) and dual ET receptor antagonists such as sitaxentan, ambrisentan and bosentan for pulmonary hypertension and fibrosis (Channick et al., 2001; Humbert and Simmoneau 2005; Rubin et al., 2002; Sfikakis et al., 2007), improving significantly survival of the patients from 47% to 71% after 2 years (Williams et al., 2006). In addition, ET receptor antagonists appear also to prevent the reoccurrence of digital ulcers in SSc patients, although healing itself was not different from control patients (Korn et al., 2004). Similar studies addressing the role of phosphodiesterase-5 inhibitors such as sildenafil, tadalafil and vardenafil, which have shown to be beneficial in pulmonary fibrosis and pulmonary hypertension (Singh et al., 2006; Wilkins et al., 2005), are currently under way for SSc patients, and it will be interesting to see if the effects are equivalent or even superior to the ET receptor antagonists (Ghofrani et al., 2004; Singh et al., 2006). SSc patients with refractory and rapid progressive disease might benefit from a high-dose chemotherapy followed by an autologous stem cell transplantation (SCT).This therapeutic strategy is currently being performed and evaluated in the European ASTIS trial and the North American SCOT trial. Preliminary

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results from about 90 patients with severe SSc receiving SCT in the ASTIS trial indicate that this therapy is feasible and does not show an increased mortality (van Laar et al., 2005).

CONCLUSIONS In the field of autoimmune and fibrotic diseases, SSc belongs to the most challenging and puzzling entities. On the other hand, research of the past years including molecular, genomic and genetic strategies elucidated various novel pathways operative in SSc such as specific growth factors and vasculoactive molecules that provide the basis for novel therapeutic strategies including TGF and CTGF inhibitors as well as anti-ET antagonists. In addition, emerging worldwide collaborative research and patient databases such as EUSTAR (www.eustar.org) and SCTC (www. sctc-online.org) will facilitate cutting-edge future research by providing the necessary large-scale patient cohorts and samples. With regard to genetic and genomic testing for diagnosis, prognosis and treatment of SSc, these large-scale databases and patient cohorts will also be the best and maybe the only opportunity to realize the practical use of individual parameters derived from the genome – a challenging goal, which has not yet reached the practicing physician in an everyday setting.

ACKNOWLEDGEMENTS The work has been supported – in part – by funding of the German Scleroderma Foundation and the German Research Society (DFG).

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Section

Metabolic Disease Genomic Medicine

95. 96. 97. 98.

Genomic Medicine and Obesity Diabetes Metabolic Syndrome Nutrition and Diet in the Era of Genomics

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95 Genomic Medicine and Obesity J. Alfredo Martínez

INTRODUCTION Genetics of obesity have only achieved quantitative standards in the last decades (Marti et al., 2008). However, the first attempts to link the genetic inheritance to body composition were reported in the early 1920s by Davenport, in “Body built and its heritance”, which were pursued by Vague and coworkers in the 1940s when they hypothesized about the occurrence of different obesity subtypes associated to sexual dimorphisms. Also, the “thrifty gene” theory raised in the paper by Neel (1962), outlining the fact that genes that predispose to diabetes and obesity would have relative advantage in populations that often experienced starvations, contributed to establish a relationships between genetics and body weight control (Bell et al., 2005). However, the classic books published in the 1970s about Energy Balance and Obesity in Man by Garrow (1974) and The Obese Patient by Bray (1976) only made minor references concerning monogenic forms of obesity. In 1977, the Nutritional Heart, Lung and Blood Twins Study first indicated the possibility that the observed aggregation for obesity was due to genetic factors rather than environment (Feinleib et al., 1977). Indeed, only in the International Congress of Obesity held at Jerusalem in 1986, the pioneering works by Bouchard et al. concerning the role of genetic predisposition on obesity onset were debated, which was continued with the recognized study concerning weight gain in overfed twins (Bouchard and Tremblay, 1997). Later, other classic textbooks about obesity by Bjorntorp (2001), as well as by Bouchard, Bray, and James (1998 and 2004), reinforced the fundamentals to characterize the role of Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1170

inheritance, contributions and gene–lifestyle interactions on obesity. More recently the impact of Biomics, particularly genomics and transcriptomics, on obesity research is producing new and challenging breakthroughs by using specific tools and methodologies such a microarrays (Ommen, 2004). Up to 2006, 12 editions of the human obesity map have been published showing the continuous advances in the field (Rankinen et al., 2006).

OBESITY: CAUSES AND GENETIC PREDISPOSITION Obesity is a multifactorial disorder characterized by disproportionately high adipose tissue content in the body, accompanying a disequilibrium in the energy balance equation: energy intake  energy expenditure (Bray, 2003). A fat content higher than 25% of the body weight in men and 33% in women are often arbitrarily considered as cut-off points to identify a subject as obese (Seidell and Flegal, 1998), which is commonly associated with excessive weight-for-height. However, very muscular individuals or pregnant women may have a greater weight than expected for height according to the standards, without showing an increased adiposity (Kasper et al., 2005). Another aspect to be ascertained is the regional distribution of the weight as fat, since the abdominal fat deposition or “android obesity” is an increased risk factor for disease as compared to “gynoid obesity”, in which fat is more evenly and peripherally distributed around the body and upper regions of the legs (Lean, 1998). Copyright © 2009, Elsevier Inc. All rights reserved.

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CNS Neuropeptides

Energy met  Adipose tissue  BMR Thermogeneis Physical activity

Afferent signals

CHO Lipids Proteins Alcohol

A positive energy balance between the amounts of energy consumed over the energy spent in everyday life underlies weight gain. Indeed, the excessive fat accumulation in adipose tissue leading to obesity is the result of a chronic overconsumption of foods and drinks over the energy expenditure requirements, in which dietary and lifestyle habits, sociological factors, metabolic and neuroendocrine alterations as well as hereditary components are involved (Martínez, 2000). Actually, environmental and lifestyle influences promoting excessive caloric intake and sedentary patterns are known to induce a positive energy balance leading to weight gain (Marti et al., 2004). Indeed, the availability of energydense meals, and sedentary patterns facilitated by motorized transport and other common physically inactive pursuits (TV viewing, computer work, etc.) have markedly risen in the last decades (Martínez et al., 1999). On the other hand, cross-sectional data show a strong association between unhealthy eating habits and physical inactivity, which have contributed to explain the observed obesity pandemic (OMS Report, 2000). In this context, a high intake of non-starch polysaccharides/fiber has been considered as a protective factor against obesity, while a high consumption of fast food and sweetened sugar drinks or fruit juices is viewed as a obesity risk factor (Bes-Rastrollo et al., 2006). Furthermore, prospective studies provide additional evidence suggesting that an increase in physical activity and small changes in dietary behavior may help to prevent a disproportionate weight gain (Hill et al., 2003). Body weight stability and the associated regulatory processes depend upon nutrient intake and physical activity patterns, but are also influenced by compensatory genetic-dependent metabolic and neuroendocrine mechanisms (Martinez and Frühbeck, 1996). Thus, despite the daily fluctuations in both components of the energy balance equation (energy intake versus energy expenditure), body weight and adiposity remain in a dynamic steady state for long periods of time (often within  1% variation over many years), which has been attributed to the participation of a powerful control system to fine tune the dietary macronutrient supply to body fuel oxidation demands (Jequier and Tappy, 1999). The control of the maintenance of body composition has been the subject of a number of theories or hypotheses such as the occurrence of a physiological set point for body weight, glucostatic or glycogen drives for feeding, metabolic/nutrient partitioning approaches, the participation of the nervous system, an adipostat mediated by signals from the adipose tissue, all of which might be under genetic control and explain individual variability (Martínez, 2000). In this context, it has been hypothesized that the stability of body weight and composition depends upon an axis with three inter-related and self-controlled components: (1) food intake, (2) nutrient turnover and thermogenesis and (3) body fat stores (Figure 95.1). All three elements underlie complex inter-related feedback mechanisms, which are affected by the individual’s genetic background. Indeed, obesity is caused by perturbations of the balance between food intake and energy expenditure, which is regulated by a complex physiological system that requires the integration of several peripheral signals and controls coordination in the brain, (Solomon and Martínez, 2006) basically through hypothalamic

■

Efferent signals

Genetics/lifestyle/ neuroendocrine balance

Energy metabolism

Figure 95.1

Body weight regulation.

nuclei and different orexigenic or anorexigenic neuropeptides (NPY, AGRP, orexins, leptin, POMC, etc). Genes may determine afferent and efferent signals, as well as central mechanisms involved in body weight regulation (Bell et al., 2005). Thus, the transferable genetic information involved in short- and long-term stable body weight regulation and composition maintenance is acting via: (1) different peptides and monoamines involved in the regulation of the appetite, (2) variations in energy and nutrient utilization, resting metabolic rate or response to physical activity and (3) individual differences in adipocyte metabolism (Palou et al., 2003). The possible mechanisms through which the genetic susceptibility (Figure 95.2) could be acting include reduced rates of basal metabolism and macronutrients oxidation, alterations of adipogenesis and quantitative and qualitative deviations of food intake (Moreno et al., 2005). Obesity, similar to other chronic diseases with a recognized monogenic or polygenic origin, is associated with a number of pathological dysfunctions and disturbances with important implications for the individual’s and community health (hyperinsulinaemia, diabetes, hypertension, immunological alterations, certain cancer types, etc.), which is further worsened by the growing rates of obesity prevalence (20–40% of the EU population are overweight or obese and more than 50% of US subjects have problems of excessive weight gain) (OMS Report, 2000). Indeed, the health consequences of obesity are serious and diverse, ranging from premature death to different morbidities affecting quality of life (Kasper et al., 2005). Despite that relative risk estimations of the health problems associated with obesity are founded on a limited number of countries, some studies have reflected that the risk of suffering from NIDDM, gallbladder disease, dyslipemia, insulin resistance and sleep apnea are increased at least three times in the obese, while the risk of cardiovascular diseases and hypertension is moderately increased in the obese, and the risks of certain cancer and hormonal

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Genomic Medicine and Obesity

Gene (ADN)

Protein/Function

Appetite NPY LEP POMC MC4R

Energy expenditure UCP1 UCP2 UCP3

Lipid metabolism ADRβ2 ADRβ3 FABP LIPE

Adipogenesis Signaling PPAR RXR C/EBP

PAI IL-6 IRS

Figure 95.2 Some genes involved in body weight homeostasis categorized by processes.

disturbances as well as back pain associated with obesity show relative risks ranging between 1 and 2 (OMS Report, 2000). Also, severe obesity has been found to produce a 12-fold increase in mortality in 25–35 years old obese individuals when compared to lean subjects. The rapid increase in obesity incidence over recent years suggests that environmental and lifestyle influences in addition to other psychopathological or genetic determinants are independently affecting the energy balance equation adjustment (Bray and Bouchard, 2004). Actually, the growing prevalence of obesity around the world is mainly attributed to changes in lifestyle (increase of the consumption of high-energy-yielding foods enriched with carbohydrates and fats or reductions of the physical activity, etc.) that specifically may impact genetic susceptibility. Also, from an evolutionary point of view, the individuals with thrifty genes are more resistant to malnutrition, explaining why large proportions of diverse populations are susceptible to become obese (Bell et al., 2005). The mutual interactions between the genetic make-up and the environment undoubtedly complicate the understanding of the specific roles of genes and external influences in obesity (Schuldiner and Munir, 2003).

SEARCH FOR GENES INVOLVED IN OBESITY The interaction of genetic, environmental, physiological and psychosocial factors is involved in body weight homeostasis (Tremblay et al., 2004). Thus, the specific distribution of energy expenditure requirements and individual substrate partitioning are known to influence the energy balance equation depending on the genetic make-up (Moreno-Aliaga et al., 2005). In this context, the risk of excessive weight gain in children of some families with obese parents is increased two- to three-fold for moderate obesity and up to eight times for severe obesity (Marti et al., 2004). Moreover, twin and adoption studies substantiate a role for genetics in obesity (Bray and Bouchard, 2004). The discovery of populations such as Pima Indians with shared alterations in basal metabolism rates or in fat oxidation after food

intake corroborates such hypothesis, as well as the fact that genetic factors can modulate the effects of physical activity and diet on weight and body composition (Bray et al., 1998). Furthermore, several studies carried out in large numbers of families encompassing members of different degrees of relatedness have allowed the quantification of the statistical association regarding objective indicators of obesity (body mass index (BMI) or percentage of fat) depending on the degree of the familial relationship (Bell et al., 2005; Marti et al., 2004).The heritability of BMI is lower between husband and wife (0.10–0.19) and between uncle/aunt and nephews (0.08–0.14), and increases between parents and children (0.15–0.23) and among siblings (0.24–0.34). The concordance for BMI is higher in monozygotic (0.70–0.88) than in dizygotic twins (0.15–0.42). Also, studies of dietary intervention, based on positive and negative energy balances in identical twins, convincingly pointed out that the differences in the susceptibility to overfeeding or periods of semi-starvation seem to be partially explained by genetic factors (Bouchard and Tremblay, 1997). Family studies have largely revealed that the heritability of the Quetelet index is about 25–50%, while twin studies have mostly estimated the contribution of genetic factors at 70–80% (Bell et al., 2005; Comuzzie and Allison, 1998). However, this information is not enough to prove unequivocally the genetic origin of obesity, since the families share, besides genes, other factors implied in obesity such as lifestyle, dietary habits and environmental factors. In this context, genetic association studies search for statistical relationships among a gene polymorphism with a given phenotype, generally among non-related individuals (Rosmond, 2003). This research strategy can consider the comparison between cases and controls, analysis of the variability for specific loci or the discrimination between mutation carriers and non-carriers regarding a given character. On the other hand, genetic linkage studies imply the persistence or co-segregation of a genetic marker or locus and a phenotypical character within a family (Bouchard, 1997). Furthermore, quantitative genetics allow assessing the influence of environmental factors on the hereditary variability, which can be based on diverse genes (polygenic interaction). The studies of gene expression as affected by dietary and activity patterns constitute another approach to relate the specific participation, both quantitative and qualitative, of different genes in energy homeostasis processes (Viguerie et al., 2005a). A salient feature of a study concerning the role of the lipid/carbohydrate content or the calorie restriction in affecting the mRNA levels is that the energy intake is more important than the macronutrient distribution (Viguerie et al., 2005b). Additionally, animal models have been able to characterize the impact of fat intake on gene expression (Lopez et al., 2005), which have evidenced that a number of genes controlling many different metabolic pathways such as lipogenesis, thermogenesis, lipolysis, adipogenesis, differentiation, oxidative stress, inflammation, etc. are affected by lipid consumption, which may help in the future to define molecular phenotypes or subsets of obesity. Obesity as a complex syndrome with a multiple etiology may be explained in some circumstances by monogenic mutations, but in most cases appears as a polygenic condition, which may be additionally affected by a myriad of environmental influences

Search for Genes Involved in Obesity

TABLE 95.1 obesity ● ●

●

●

Approaches to study the genetics of

Studies within families, adoptions and twins Mendelian syndromes with obesity manifestations Autosomal recessive Autosomal dominant X-linked Animal models Spontaneous genetically obese animals Transgenics and KO animals Quantitative trait loci (QTL) studies Association and linkage studies Candidate genes Genome-wide scans

(Guy-Grand, 2003). A widely accepted hypothesis assumes that complex diseases such as obesity are likely to be based on a limited number of predisposing alleles, each conferring a small increase in the risk to the individual. Heterogeneity in complex phenotypes implies that the genetic predisposition may also result from any one of several rare variants in a number of genes (Marti et al., 2004). The role of a genetic predisposition in obesity has long been assumed to affect both sides (intake/expenditure) of the energy equation (Figure 95.2). In this context, evidence from human single-gene mutations (leptin: LEP; leptin receptor: LEPR; pro-opiomelanocortin: POMC; melanocortin 4 receptor: MCR4; protein convertase 1: PC1; etc) has solely implicated energy intake (Rankinen et al., 2006). However, Mendelian syndromes with obesity as a clinical feature (e.g., Prader–Willi syndrome) have also revealed reductions in energy expenditure as a contributing factor to obesity (Lyon and Hirschhorn 2005). In this context, evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) screening from animal cross-breeding experiments, association studies with candidate genes, and linkage studies from genome scans have been reported (Table 95.1). So far, at least, 176 human obesity cases due to singlegene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes as can be seen through specific websites (Table 95.2). There are about 250 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches more than 450 markers. The description of human obesity QTLs derived from genome scans continues to grow, and at least 250 QTLs for obesity-related phenotypes from genomewide scans have been reported. More than 50 genomic regions harbor QTLs supported by two or more investigations. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with more than 400 findings of positive associations with 127 candidate genes. A promising observation

TABLE 95.2 genomics

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1173

Selected web page resources for obesity

American College of Medical Genetics www.acmg.net ATCC Global Bioresource Center http://www.atcc.org/ Database of Gene Knockouts http://www.bioscience.org/knockout/knochome.htm DNA Repeat Sequences and Disease www.neutro.wustl.edu/neuromusuclar/mother/dinarep.htm Ensemble Genome Browser http://wwww.ensembl.org/ Gene Tests-Gene Clinics http://www.genetest.org GenLink http://www.genlink.wustl.edu HUGO Gene Nomenclature http://www.ucl.ac.uk/nomenclature Human Obesity Gene Map http://www.obesitygene.pbrc.edu Laboratory of Molecular Mouse Genetics http://www.zmg.uni-mainz.de/tetmouse/tet.htm MITOMAP, a Human Mitochondrial Genome Database http://www.mitomap.org Mouse Genome Resources www.ncbi.nlm.nih.gov/genome/guide/mouse/ Mouse Genome Sequencing Consortium http://www.ensembl.org/Mus_musculus/ National Center for Biotechnology Information (NCBI) http://wwww.ncbi.nlm.nih.gov/ National Human Genome Research Institute http://wwww.genome.gov/ Office of Biotechnology Activities National Institutes of Health www.4.od.nih.gov/oba/ Online Mendelian Inheritance in Animals (OMIA) www.angis.su.oz.au/Databases/BIRX/omia Online Mendelian Inheritance in Man (OMIM) www.ncbi.nlm.nih.gov/omim Rat Genome Database http://rgd.mcv.edu/ Ratmap http://ratmap.org/index.html The European Nutrigenomics Organisation (NUGO) http://www.nugo.org Transgenic Animal Model Core http://www.med.umich.edu/tamc/index Transgenic/Targeted Mutation Database http://tbase.jax.org/

is that 22 genes are each supported by at least five positive studies (Tables 95.3 and 95.4). The obesity gene map shows putative loci on all chromosomes except Y. The latest electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu. Overall more than 600 genes, markers or chromosomal regions have been associated with human obesity phenotypes (Rankinen et al., 2006). In this context, genetic epidemiology of obesity aims to screen and assess the phenotype traits associated to the genetic

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TABLE 95.3

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Genomic Medicine and Obesity

Selected list of genes that have been associated with obesity phenotypes

Gene

Name/Function

Associated phenotype

ACE

Angiotensin 1-converting enzyme

BMI, waist circumference, body fat

ADIPOQ

Adiponectin, C1Q and collagen containing domain

BMI, waist circumference

ADRB2

Adrenergic, beta 2 receptor

BMI, waist-to-hip ratio, body fat, skinfolds, lipolysis

ADBR3

Adrenergic, beta 3 receptor

BMI, abdominal visceral fat, waist-to-hip ratio

DRD2

Dopamine receptor D2

Body weight, reduced energy expenditure, skinfolds

GNB3

Guanine nucleotide-binding protein 3(G-protein)

BMI, reduced lypolysis, body fat

HTR2C

5-Hydroxtryptamine (serotonin) receptor 2C

BMI, weight change

IL-6

Interleukin 6

BMI (in men) waist circumference (in men) reduced, fasting energy expenditure

INS

Insulin

BMI, body weight, leptin

LDLR

Low-density lipoprotein receptor

BMI

LEP

Leptin

BMI, body weight

LEPR

Leptin receptor

BMI, fat-free mass, fat-free mass, body fat (%), leptin, abdominal total fat

LIPE

Lipase

BMI, body fat, skinfolds, waist-to-hip ratio, reduced lipolysis

MC4R

Melanocortin 4 receptor

BMI, body fat (%), resting energy expenditure

NR3CI

Nuclear receptor 3C 1 (glucocorticoid receptor)

BMI, waist-to-hip ratio (in men), leptin, skinfolds

PPARG

Peroxisome proliferative-activated receptor gamma

BMI, leptin, waist circumference, body fat (%), leptin, lipid oxidation

RETN

Resistin

BMI, waist circumference

TNFA

Tumor necrosis factor alfa

BMI, body fat (%), waist circumference

UCP1

Uncoupling protein 1

BMI, waist-to-hip ratio, body fat (%)

UCP2

Uncoupling protein 2

BMI, energy expenditure, macronutrient oxidation, body fat, skinfolds

UCP3

Uncoupling protein 3

BMI, waist-to-hip ratio, body fat (%), resting energy expenditure, leptin, skinfolds

VDR

Vitamin D (1,2,5-dihydroxyvitamin D3) receptor

Fat mass

make-up and their relationships with lifestyle factors (Hill et al., 2003). The research strategies to identify the genetic determinants of obesity are multiple (Tables 95.1 and 95.2). Indeed, the process of ascribing a gene to a phenotype is complex due to the low density of coded DNA (5%), the potential interactions of genes with environmental factors and the diversity of methods and tools to assess body fat and energy metabolism phenotypes, which have hampered the advances in this area (Marti et al., 2004). The identification of genes may follow ascending strategies from the genotype to the phenotype, in which a gene or genes are investigated to find out a relation with a given obesity marker, but also descending strategies in which from a clinical feature of excessive

fat accumulation, the genetic determinant is searched concerning the heritability of BMI, skinfolds and waist circumference (Bray et al., 1998). In Caucasian families, an estimate of 0.45–0.60 for measures of fat men such as BMI and 0.29–0.48 for measures of fat distribution has been calculated, which fit in well with the estimate from twin studies, where a minimum 0.40 for fatness or obesity was reported (Bell et al., 2005). The co-existence of obesity manifestations on various members of a family corroborates that the genetic make-up can play a role in obesity, which is reinforced by the fact that impairments in the thermogenesis, basal metabolic rate and sympathetic activity as well as specific effects on hyperphagia or physical activity performances are genetically controlled

Diagnosis and Characterization of Genes Associated with Obesity

TABLE 95.4 Chromosomal location of selected candidate genes with more than three positive association studies and two positive linkage studies Chromosome

Association studies

Linkage studies

1

AGT, ATP1A2, LEPR, LMNA, NROB2

LEPR, ATP1A2, LEPR

2

IRS1, POMC

ACP1

3

ADPQ, GHRL, PPARG

4

FABP2, PPAGC1A, UPC1

5

ADRAB2, NR3C1

6

ENPP1, ESR1, TNF-

SE30

7

IL-6, LEP, NPY, SERPINE1

LEP

8

ADRAB3, LPL

9 10

ADRA2A; GAD2

11

AD0A4; DRD2, IGF2, INS, UCP2,UCP3

ATA34 E8

12

GNB3, VDR

IGF-1

13 14 15

CYP19A1; PLIN

16

AGRP, FOXC2

17

ACE

18

MC4R

19

APOE, LDLR, LIPE, RETN

20 21 22

PPARA

X

AR, HTR2C, SLC6A14

Y

(Palou et al., 2003). However, these investigations do not discard a role for the shared environment conditioning dietary habits and physical activity patterns. Another aspect is that genetic predisposition may influence the android/gynecoid fat distribution (central or gluteo–femoral phenotypes), as has been proven in familial obesity risks studies (Katzmarzyk et al., 1999), which may be mediated in some cases by sexual gene-dependent differentiation of the adipose tissue muscle ratio (Vague et al., 1989).

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1175

DIAGNOSIS AND CHARACTERIZATION OF GENES ASSOCIATED WITH OBESITY Genetic diseases are usually categorized as chromosomal alteration, monogenic or mendelian and multifactorial syndromes (Kasper et al., 2005). The chromosome alterations involve absence, duplication or abnormal distribution of one of several chromosomes related to excessive or faulty material, which may affect body fat content as occurs in Down Syndrome (trisomy 21) or Turner syndrome (X monosomy), which are often accompanied by obesity and other abnormal anatomical manifestations. Obesity-related Mendelian disorders can be classified as autosomal dominant, autosomal recessive or X-linked (Table 95.5). Thus, a search (July 2006) in the obesity heading of the OMIM database produced 75 entries concerning autosomal dominant transmission, 74 entries concerning autosomal recessive transmission and 40 X-linked. Most of these syndromes have been already assigned to a specific locus. The clinical features of some of these metabolic disorders include, in addition to obesity, mental retardation and some craniofacial/ anatomical deformations as well as insulin resistance, hypertension, etc. Interestingly, 11 single-gene mutations with a specific obesity phenotype have been identified so far (CRH1, CRH2, GPR24, LEP, LEPR, MC3R, MC4R, NRTK2, POMC, PCSK1 and SIM1). The majority of the monogenic obesity cases reported in the literature remain those with a genetic defect in the MC4R gene (Hebebrand et al., 2003). Research on different animal models have produced valuable information concerning the identification of different genes, markers or chromosomal regions potentially involved in energy homeostasis (Campión et al., 2004). One approach is to overexpress, inactivate or manipulate specific genes playing a role in the regulation of body weight through transgenic or knock out (KO) animal models. Also, the RNA interference (RNAi) approach allows for the creation of experimental models to assess different biological functions and mechanisms concerning a number of genes. Some examples of spontaneous obesity models are the ob/ ob, db/db, CPE, Tubby and AY mice or the Zucker (fa/fa) rat, in which the homologous genes have been found in humans (LEP, LEPR, attractin, carboxypeptidase, tubby protein related to insulin actions and agouti signaling proteins, respectively). Polygenic animal models of obesity also have been described, such as the New Zealand, BSB, Osborne-Mendel or the dessert rat (Martinez et al., 2002). The murine obesity gene maps identify at least 248 genes potentially involved in excessive weight gain (Rankinen et al., 2006). In some cases, human homologs have been reported. Actually, the transgenic technology based either the pronuclear microinjection of a fertilized oocyte and the transfection of embryonic stem cells have allowed the study of the role on body weight regulation of a number of genes by overpressing or blocking specific genes (Table 95.6). Another possibility of investigating the role of the inheritance on obesity is based on traditional genetics selection strategies as occurs in plant biology, which has allowed one to identify more than 400 QTLs from cross breeding not solely in

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TABLE 95.5

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Genomic Medicine and Obesity

Some Mendelian syndromes with obesity manifestations Prader Willi

Bardet-Biedel

Alstrom

Borjesson Forssman Lenhman

Simpson Golabi Behmel

Inheritance

Dominant 15 q 11.2

Recessive ( 8 loci)

Recessive 2p13.1

X-linked Xq26.3

X-linked (2 loci)

Craniofacies abnormalities

Frequent

No distinctive

No distinctive

Frequent

Frequent

Limbs/hands abnormalities

Swell hands and limbs short stature

Polydactily Brachidactily Syndactyly

No distinctive

Short stature Short toes

Big hands and short finger Very tall syndachtyly

Obesity

Generalized onset (1–3 years)

Mild obesity onset (1–2 years)

Truncal onset (1–2 years)

Truncal obesity

Slight obesity

Mental retardation

Mild to moderate

Not always

No

Severe

Psychosocial impairment

Other common features

Hypogonadism Muscular hypotonia Depigmentation

Hypertension diabetes mellitus Hypogonadism Renal abnormalities Retina dystrophy

Diabetes blindness deafness

Hypometabolism epilepsy

Cardiac arrhythmias

rodent, but also in another species such a cow, chicken, pig, etc. (Rankinen et al., 2006). In any case, QTLs identified from phenotyping and genotyping of crosses between two strains define only statistical probability of a polymorphic gene residing in a defined genetic internal and continual generation of congenic animal is required to implicate specific genes with well characterized QTL regions (Perusse et al., 2005). The fundamental requirements for the candidate gene approach are the identification of a gene that is involved in the disease phenotype, a polymorphic maker within that gene and a suitable set of subjects to genotype for that marker (Bray et al., 1998). Association studies seek to establish statistical relations between a genetic polymorphism and a given phenotype. The statistical approach may search the comparison between cases and controls, the analysis of variations between cases and controls, the analysis of variations between specific loci and discrimination study between carriers and non-carriers for a trait (Rosmond, 2003). The association studies provide information in order to identify genes with low involvement, but they require establishing high statistical cut-offs (usually p  0.001) to avoid random or biased findings. Evidence for association between markers of candidate genes with obesity-related phenotypes (BMI, skinfolds, waist circumference, LEP, etc.) has been reported for at least 125 genes (Perusse et al., 2005). Thus, statistical relationships with p  0.0001 have been found for the following genes: ADA, ADBR2 and ADBR3, ATP1A2, ENPP1, GNB3, MRT2C, IL6R, INS, LEPR, MC3R, NR3C1, PPARG, PYY, RETN, UCP1, UCP2 and UCP3. (Tables 95.3 and 95.4). Linkage studies are designed to investigate the persistence or co-segregation during generations of a genetic marker or locus

within a family (Bray et al., 1998). This approach can be applied with candidate genes or genetic markers concerning specific polymorphisms. This protocol is commonly used on nuclear families or pedigrees in order to investigate the role of new genes through genome-wide scans (Bosse et al., 2004; Rankinen and Tiwari, 2004). Linkage studies with obesity phenotypes have been able to identify more than 200 QTLs related to excessive weight gain (Rankinen et al., 2006). Thus, linkage with obesityrelated phenotypes (LOD  2.0) has been reported for different genes or markers including the following: LEPR, AMPD1, ACP1, ISL1, NR3C1, BF, NPY, LEP, KEL, IGF1, MC5R, LDLR y ADA (Table 95.4). Genome-wide linkage scans involve the typing of family using polymorphic markers that are positioned across the whole genome followed by calculating the degree of linkage of the marker to a disease trait (Rankinen and Tiwari, 2004). In obesity, the sample sets are families representative of the general population and families that include at least an obese proband. The 2005 update of the human obesity gene map revealed that the number of genes or markers that have been directly and indirectly linked with human obesity are increasing rapidly and now are higher than 625 (Figure 95.3). Some of these genes or chromosomal regions, such as the uncoupling proteins (UCPs), LEP, LEPR, adrenergic receptors (ADRB2, ADRB3), peroxisome proliferator-activated receptors (PPARs), fatty acid binding protein (FABP), etc. have been related to energy metabolism control and may be specifically affected by dietary intake and composition, and also by physical activity. Other genes are specifically involved in control of food intake (NPY, POMC, CCK, MCH, etc.), while some others influence different

Diagnosis and Characterization of Genes Associated with Obesity

TABLE 95.6

Selected murine models of genetic obesity

Gene

Name/Function

Phenotype

Manipulation

Agouti

Agouti signaling protein

Obesity

Tg

ADRB3

Adrenergic receptor

Obesity

KO

AGRP

Agouti-related protein

Obesity

Tg

AQP7

Aquaporin 7

Obesity

KO

CART

Cocaine and amphetamine regulated transcript

Obesity

KO

CAV1

Caveolin 1

Obesity resistant

KO

CEBPB

CCAAT/enhancer binding protein

Reduced adiposity

KO

CIDEA

Cell death inducing DNA

Reduced adiposity

KO

CNR1

Cannabinoid receptor 1

Reduced adiposity

KO

CPE

Carboxyl peptidase

Obesity

Spontaneous

FABP4

Fatty acid-binding protein

Obesity

KO

FOXC2

Forkhead Box Co2

Reduced adiposity

Tg

GHRL

Ghrelin

Reduced adiposity

KO

HCRT

Orexin

Late-onset obesity

Tg

HSD11B1

Hydrosteroid 11B DH1

Obesity

Tg

HTR2C

Serotonin receptor 2C

Late-onset obesity

KO

LEP

Leptin

Obesity

Spontaneous

LEPR

Leptin receptor

Obesity

Spontaneous

LIPE

Hormone sensitive lipase

Reduced adiposity

KO

MC4R

Melanocortin receptor 4

Obesity

KO

NPY4R1

Neuropeptide 4 receptor 1

Obesity

KO

POMC

Pro-opio melanocortin

Obesity

KO

RETN

Resistin

Obesity

Tg

RXRG

Retinoid x receptor gamma

Resistant to obesity

KO

SCD1

Stearoyl CoA desaturase

Reduced adiposity

KO

SIN 1

Single minded 1

Obesity

Tg

TNFA

Tumor necrosis factor alfa

Obesity

KO

TUB

Tubby

Late-onset obesity

Spontaneous

UCP1

Uncoupling protein 1

Reduced adiposity

Tg

UCP2

Uncoupling protein 2

Reduced adiposity

tg

UCP3

Uncoupling protein 3

Reduced adiposity

tg

VLDL R

VLD lipoprotein receptor

Reduced adiposity

KO

tg: transgenic; KO: knock out.

■

1177

1400

■

600

11

KO/Tg

244

Mendelian disorders

Single gene mutations

800

50

408

Human QTI

(2005 detail)

1200 1000

Genomic Medicine and Obesity

317

Candidate genes with positive findings

CHAPTER 95

Animal QTL

1178

127

400 200 0 94 95 96 97 98 99 00 01 02 03 04 05

Figure 95.3 Evolution of the genes and chromosomal regions related to obesity between 1994–2005.

metabolic and signaling pathways, adipogenesis, etc. (PPAR, FABP, PKA, c/EBP, etc.), affecting the energy equation and, consequently, body weights of those individuals who are carriers of specific defective gene mutations/polymorphisms with an influence on fat deposition (Rankinen et al., 2006). The studies of interactions of the genotype with environmental factors constitute a new challenge to establish the role of diet and physical activity in the genetics of obesity as well as those investigations devoted to evaluate the impact of lifestyle on gene expression (Cheung and Spielman, 2002). Indeed, genes predisposing to obesity potentially have an impact on dietary intake as well as on physical activity performance, while gender and age have been also reported as effect modifiers on obesity risk in subjects carrying different gene polymorphisms, respectively (Marti et al., 2004). Several association studies have described interactions between macronutrient intake and different polymorphisms in candidate genes affecting the obesity trait in predisposed subjects (Martínez et al., 2003; Moreno-Aliaga et al, 2005; Nieters et al., 2002). Genes Involved in Appetite Regulation A number of genes and chromosomal regions have involved in the regulation of the food intake through association or linkage studies such as leptin, neuropeptide Y, ghrelin, AGRP, orexins, CART, etc. (Riccardi et al., 2004). In this context, a monogenic form of obesity in humans occurs as a consequence of very rare mutations of the leptin gene, which lead to undetectable levels of serum leptin (Clement, 2005). Also, members of a consanguineous family with a rare LEPR deficiency were identified, exhibiting severe early-onset obesity with several metabolic disturbances. Affected individuals were homozygous for a mutation that truncates the receptor before the transmembrane domain, and the mutated receptor circulates bound to leptin. Additionally, several more common mutations at the LEPR gene have been described, but only some of the studies have been able to show an association with overweight and fat mass excess (Perusse et al., 2005). Much attention has been focused on the role of the hypothalamic LEP–melanocortin system in body weight

regulation and obesity (Hebebrand et al., 2003). The importance of the melanocortin signaling pathway in humans has been suggested by a number of monogenic polymorphisms identified in genes involved in the synthesis or processing of the glycoprotein POMC or in mutations leading to defects in POMC signaling via melanocortin receptors. All mutations result in profound obesity; however, specific gene mutations in the LEP/POMC pathway account for 5% of cases of obesity. In this context, dominant inheritance of obesity conferred by different missense, nonsense and frame-shift mutations in the MC4R gene has been extensively reported in many populations including European and American individuals (Hebebrand et al., 2003). Genetic variation in serotonin receptors, NPY, orexins and other neuropeptides, has also been widely studied in obesity given its potential involvement on food intake and body weight gain (Solomon and Martínez, 2006). Genes Involved in Energy Expenditure Regulation There are a number of studies that have evaluated the role of specific polymorphisms in relation to the control of the energy expenditure (Bastarrachea et al., 2004). Thus, adrenergic receptor genes mediate the rate of lipolysis in response to endogenous and exogenous catecholamines. The Trp64Arg variant of the 3-adrenergic receptor have been associated in some studies to lipid accumulation in the adipose tissue and also there are studies concerning the 2 and 2 adrenoceptors in the same direction concerning different polymorphisms (Bray and Bouchard, 2004). Also genes controlling the expression of the inner mitochondrial uncoupling proteins (UCP1, UCP2 and UCP3) have been related to obesity phenotypes (Moreno et al., 2005). A number of human’s studies have found relationships between UCP polymorphisms and exercise efficiency resting energy expenditure, substrate oxidation, energy metabolism, BMI, fat accumulation and body weight changes (Macho et al., 2000). Genes Involved in Adipocyte Metabolism Some genes involved in the regulation of adipocyte growth and differentiation have been associated with regulation of metabolism and body weight control (Bray and Bouchard, 2004). Thus, PPAR particularly the adipose specific isoform (PPAR2) is a key transcription factor implicated in adipogenesis as well as glucose and lipid homeostasis. A higher BMI for Ala carriers was also observed compared to non-carriers in 3-year and 10-year follow-up studies (Moreno-Aliaga et al., 2005). Another example is adiponectin, which is an adipocyte expressed protein that participates in the homeostatic control of glucose, lipid, and energy metabolism. Adiponectin gene polymorphisms have been associated with obesity, insulin sensitivity and type 2 diabetes in some cross-sectional studies (Rubio, 2006). Also, PGC1 is a cofactor regulating the expression of UCPs, the mitochondrial biogenesis, and other processes related to adipocyte energy homeostasis, whose variants are being related to excessive weight gain (Bray and Bouchard, 2004). Furthermore, the renin-angiotensin system is involved in adipocyte growth and differentiation.

Screening and Diagnosis

Other Genes Related to Obesity Common allelic variants of genes related to the insulin signaling pathway have been evaluated in relation to energy metabolism control such as the insulin gene, insulin-like growth factor 1 receptor (IGF-1R), plasma cell membrane glycoprotein 1, insulin receptor substrates 1 and 2 (IRS-1 and IRS-2), and the phosphatidylinositol 3-kinase p85 gene. Also, resistin, PAI, and different genes involved in lipid metabolism have been shown to potentially play a role in energy homeostasis and obesity such those codifying hepatic lipase (HL), lipoprotein lipase (LPL) and several lipoproteins (Rubio, 2006). Polymorphism of angiotensin converting enzyme (ACE) gene has been associated with overweight and abdominal adiposity, insulin resistance and hypertension in humans. Moreover, heterotrimeric guanine nucleotide-binding proteins (G proteins) mediate many pathways including the -adrenergic signaling pathway. The C825T polymorphism in the gene coding for the 3 subunit of G proteins (GNB3) has been shown to be associated with several phenotypes such as hypertension, obesity, and diabetes mellitus comprising the metabolic syndrome, although the conclusions have been inconsistent (Moreno et al., 2005). The fat mass and obesity gene (FTO) has also shown to contribute to common forms of human obesity (Loos and Bouchard, 2008).

SCREENING AND DIAGNOSIS Epidemiologic, genetic and molecular studies suggest that there are people who are most susceptible than other to becoming overweight or obese (Bray and Bouchard, 1998). The degree of excess of fat, its distribution within the body and the associated health consequences depends on the genetic background (Moreno et al., 2005). Therefore, it is important to adequately assess the obesity phenotype not only for diagnosis, but also for therapeutic purposes. Thus, the graded classification of overweight and obesity aims for meaningful comparison of weight status among populations and identification of individuals/groups at risk, which helps to set up the intervention. Body weight for height and BMI (Kg/m2) are common measures to define the obesity situation, which are suitable for general population samples, despite being relatively crude (OMS Report, 2000). In this context, other anthropometric or body composition determinations can be used such as the waist–hip ratio, percentage body fat, circulating leptin levels, etc. Other tools to characterize the obesity phenotype are based on the application of methods to assess food intake such as 72-h recall, food frequency questionnaires, dietary records, etc. Additional screening/diagnostic protocols aiming to identify the etiology of excessive weight gain are focused to estimate the energy expenditure (macronutrient oxidation and BMR) on the energy devoted to physical activity, through pedometers or calorimetry techniques, heart rate monitoring, sedentarism questionnaires, etc. (Table 95.7). Indeed, both appetite and physical activity patterns are potentially involved in obesity onset and complications, which must be adequately assessed. Genomic medicine, aiming to improve the quality of medical care, is benefitting from the use of genotypic testing (DNA

TABLE 95.7 obesity

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Methods to phenotypically characterize

●

Body composition: BMI, waist/hip circumferences, isotope dilution, bioelectrical impendence, skinfolds, etc.

●

Anatomical distribution of fat: Imaging techniques (CT, NMR, DEXA, etc.) Energy expenditure: Stable isotopes, indirect/direct calorimetry, respiratory quotient, physical activity questionnaires, pedometers, heart rate, etc. Energy intake: Dietary records and recall, diaries, food frequency questionnaires, etc. Molecular phenotypes: Hormone levels, DNA, mRNA, and protein measurements

●

●

●

analysis) to identify genetic predisposition markers of obesity and to design individualized medical therapy based on the genotype (Challis and Yeo, 2002). Also, clinically oriented transcriptomics, focusing on the analysis of RNA by measuring the level of all or selected subset of genes based on the amount of mRNA in a given sample, are helping to understand and screen the causes of obesity (Moreno-Aliaga et al., 2001). DNA sequence analysis is increasingly used as a diagnostic tool for determining carrier status and prenatal testing in monogenic disorders (Kasper et al., 2005). A number of techniques described in this textbook are available for the detection of mutations in monogenic disorders, which include cytogenetics, fluorescence in situ hybridization (FISH), Southern blotting, polymerase chain reaction based methods (PCR and RT-PCR), DNA sequencing and restriction fragment length polymorphisms (RFLP). Other approaches that have been used to identify mutations related to obesity are simple-strand conformational polymorphism (SSCP), gradient gel electrophoresis (DGGE) RNAse cleavage and protein truncated test (PTT). Novel technologies for the analysis of mutations, genetic mapping and mRNA expression profiles are rapidly developing (Ross, 1998; Ommen, 2004). Thus, microarray-based methods allowing the hybridization of thousands of genes simultaneous are being clinically used for mutational analysis and, characterizations of gene expression patterns in both obesity screens/diagnosis and research in humans and animals models (Kasper et al., 2005; Moreno-Aliaga et al., 2001). The application of complex analytical techniques to encompass all the types of studies has led to the conclusion that the heritability estimate for BMI in large sample sizes was likely to be from 25 to 40%, while the amount of abdominal fat was influenced by a genetic component attaining about 50% of the individual differences (Bell et al., 2005; Comuzzie and Allison, 1998). Also, it should be stressed that the current epidemic of obesity is not really a consequence of the sudden appearance of mutations in genes regulating weight homeostasis, but the lack of adaptation in gene expression to environmental factors, such as the consumption of high-energy-yielding foods and sedentary habits.

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Possible mechanisms through which a genetic susceptibility may be operating is the low body metabolism rate, low levels of macronutrient oxidation rates, low fat-free mass, low appetite control or low RQ post-prandial adjustments to intake or low oxidative process associated to physical activity (OMS Report, 2000). Other factors involved in the development of obesity thought to be genetically modulated are associated to impairments on adipose tissue, lipid metabolism, adipogenesis, on nutrient partitioning as well as endocrine/neural disturbances related to energy homeostasis such as insulin sensitivity, GH status, leptin action, hypothalamic neuropeptides, etc. (Martinez et al., 2002). Indeed, it is envisaged that genetic analyses, specifically in highrisk individuals, may be used for gene-based diagnosis and more interestingly for gene-founded therapies concerning genotypes affecting food intake, thermogenesis, adipogenesis, etc. In this context, gene polymorphism analysis could orient clinical recommendations to high-risk patients, since physical activity programs may be more suitable for some genotypes (Corbalan et al., 2002) or changes in carbohydrate or lipid intake may be more convenient for some specific gene variants (Luan et al., 2001; Martínez et al., 2003).

PROGNOSIS AND GENE-BASED TREATMENTS The health consequences of obesity are diverse and serious, ranging from an increased risk of premature death to higher associated risks of suffering for diabetes, hypertension, inflammatory disturbances, cancer, etc. (Bray and Bouchard, 2004). The obesity prognosis depends upon a number of anatomical-linked phenotypical characteristics, such as the number of fat cells and fat distribution as well the causal factors, such a neuroendocrine disorders, drug-induced, weight gain sedentary lifestyles, psychological misbehaviors, which may be genetically dependent (Bray, 2003). Indeed, the prognosis and the treatments are affected by the genetic make-up, since genetic predisposition may influence the associated clinical manifestations and complications commonly found in obese subjects (Lean, 1998). This situation envisages that genetically based diagnosis and therapeutical approaches will be routinely applied in the obesity area as is occurring in some others diseases such as inborn errors of metabolism or diseases related to lipid metabolism (Basse et al., 2004; Kasper et al., 2005). Actually, it is of current interest to identify the involvement of some genetic polymorphism in relation to the ability of losing weight induced by different types of interventions commonly prescribed to obese patients such as diet therapy, pharmacotherapy and surgical approaches. Indeed, on the horizon is the possibility to individually treat obesity subjects depending on the genotype (Table 95.8) as reviewed by Moreno-Aliaga et al. (2005); a role for pharmacogenomics is not immediately clear since not many specific drugs to fight against obesity are currently in the market (Moreno et al., 2005). In this context, congenital leptin deficiency is an extreme case of genetic obesity, whose deleterious effects can be restored

by the administration of exogenous recombinant human leptin. In this special situation, the success of the pharmacological treatment with leptin would completely depend on the genetic make-up of the patient (O’Rahilly et al., 2003). By contrast, initial trials in obese patients with no leptin deficiency showed no significant benefits in terms of weight reduction unless substantially high doses were given. Using patients with polygenic (common) obesity, the response to low-calorie diet in relation with genetic polymorphisms in the promoter region of the LEP gene was studied (Mammes et al., 1998). At position C-2549A of the LEP gene, a weaker BMI reduction was found for –2549A allele carriers than the observed for homozygotes for the –2549C allele. Some attention has been paid to the potential effects of Trp64Arg polymorphism in the ADBR3 gene in weight reduction programs (Table 95.8). Initially, resistance to lose weight in homozygotes for the Arg64 allele compared with higher reductions either in heterozygotes or Trp64 homozygotes have been reported. On the contrary, other investigations did not find differences in weight loss by Trp64Arg genotypes in women submitted to a low-calorie diet. It is worth mentioning the differences on design in these studies according to the selection of participants (obese versus non-obese, diabetic versus non-diabetic) or the characteristics of the weight loss protocol (Moreno-Aliaga et al., 2005). Additionally, a lower ratio of visceral to subcutaneous fat has been reported in Arg64 carriers compared to non-carriers, especially in pre-menopausal women after a 3-month reduction period. Interestingly, a loss of visceral tissue has been found in Arg64 carriers compared with non-carriers after the intervention, but no differences in either body composition or energy expenditure by genotype groups were found. The response to exercise as a weight-lowering strategy appears to be affected by polymorphisms in the 2-adrenergic receptor gene (Macho-Azcarate et al., 2003). A high initial degree of body fat mass determined by the Gly16 allele for the 2-adrenoceptor polymorphisms predict those individuals who will have a rebound weight gain after their initial successful weight loss (Masuo et al., 2005). The effect of uncoupling proteins on weight loss has been assessed in different studies (Riccardi et al., 2004). Thus, a relevant role for the A-3826G polymorphism of the UCP1 gene in the magnitude of weight loss has been suggested (Table 95.8). In opposition to these results, no differences by genotype in weight loss of obese patients submitted to a gastroplasty were found. On the other hand, comparing proton leak and UCP2 and UCP3 expression in skeletal muscle of diet-resistant and diet-responsive overweight women, it was found that weight loss, mitochondrial proton leak-dependent respiration, and expression of UCP3 mRNA was greater in diet-responsive than in diet-resistant subjects, while no changes were found in UCP2 mRNA levels. Also, some UCP3 polymorphisms were associated with the changes in mRNA levels and body weight loss (Cha et al., 2006). Other studies have reported a possible synergistic effect of UCP1 and ADBR3 gene polymorphisms on obesity and body

Prognosis and Gene-Based Treatments

TABLE 95.8

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Candidate genes and prognosis of weight loss response to dietary programs

Gene

Polymorphism*

Comments

Leptin

C-2549 A (5-region)

Potential influence of BMI reduction

POMC

R236G

Weight loss is apparently unaffected

HTR2C

759C/T

Lower resistance to weight loss

ADB3R

Trp64Arg

Potential influence in weight and fat distribution losses

ADB2R

Gln27Glu

Participation in rebound weight gain

UCP1

A-3826G

Potential impact on the magnitude of weight loss

UCP3

55C→T (promoter)

Potential role on body fat distribution loss

Pro12Ala

Potential involvement in maintenance after weight loss

IRS-1

Gly972Arg

No apparent role

IRS-2

G1057D

No apparent role

IGF-1R

GAA1013GAA

No apparent role

LIPC

G-250G (Promoter)

Weight loss dependent on the genotype

LPL

Hind III polymorphism

Weight loss dependent on the genotype

Apolipoproteins

ApoE e4

Possible relation with responsiveness to diet

ApoB/VNTR

Possible relation with responsiveness to diet

ApoA-IV-1/2

Potential involvement in food intake

PAI-1

4G/5G (promoter)

Genotype dependent reduction in BMI

IL6

174 G  C

Potential involvement in maintenance after weight loss

Genes related to appetite control

Genes related to energy expenditure

Adipogenesis PPARG2 Genes related to insulin resistance insulin signaling pathway

Genes related to lipid metabolism

Other genes potentially related to obesity

*Modified from Moreno-Aliaga et al. (2005) and Martinez et al. (2008). Polymorphism nomenclature is shown as reported by the authors.

weight changes (Sivenius et al., 2000). Thus, subjects with mutations in ADBR3 and UCP1 genes lost less weight than either those with the Trp64Arg variant alone (ADRB3) or the GG genotype of the UCP1 gene. Other researchers found a lower weight reduction in subjects with both mutations compared with subjects with no mutations, and a faster weight gain after the ending of the very-low-calorie diet intervention, while similar weight reductions after a clinical intervention for the four defined genetic categories concerning the Trp64Arg polymorphism (ADRB3 gene) and a promoter polymorphism (55C→T) of the UCP3

gene in overweight-obese subjects with coronary artery disease or metabolic syndrome. However, a beneficial effect of the wild-type genotype for both variants was suggested for body fat distribution and glycemic control (Table 95.8). Also, some genotype-dependent food intake control related cases have been studied. A recent trial to compensate for the genetic lack of hypothalamic melanocortin function was conducted by the administration of adrenocorticotropic hormone 4–10 (ACTH4-10), a melanocortin fragment with anorexic effect. However, after 3 months of treatment with increasing

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doses of ACTH4-10, no change of body weight or metabolic rate was observed. On the other hand, it has been reported that the individual ability to lose weight was not hampered in three children heterozygous for the R236G variant of the POMC gene that underwent a weight reduction program during 11 months (Santoro et al., 2004). Also, the serotonin receptor HTR2C genotype (759C/T polymorphism) on weight loss program, was involved in the resistance to weight loss in the heterozygous genotype compared with either CC or TT homozygous genotypes and attributed such observation to a heterosis effect. Moreover, several intervention studies were aimed to explore the relationship between PPAR2 genotypes and weight loss (Nieters et al., 2002). Thus a 3-year study showed that subjects with the Ala12 allele lost more weight than subjects with the other genotypes, although a significant higher weight regain was achieved during subsequent follow-up (12 months) in carriers of the Ala12 allele in comparison with the Pro12 homozygotes (Lindi et al., 2002). These observations suggest that a general distinction should be made between genetic factors affecting weight loss per se and weight loss maintenance. Thus, also, it has been shown that a IL-6 polymorphism (174 G  C) may influence weight regain after a weight-lowering program based on a energy-restricted diet, which may interact the Pro12Ala polymorphism of the PPAR gene (Goyenechea et al., 2006). On the other hand, no significant differences in weight reductions linked to the genotype were detected, in the subjects carrying risk genotypes in the IGF-1R, IRS-1 and IRS-2 genes after lifestyle interventions (Moreno-Aliaga et al., 2005). However, a reinforced effect on weight loss with the presence of both the Gly972Arg polymorphism of the IRS-1 gene and Trp64Arg polymorphism in the ADRB3 gene in women who underwent a formal weight-loss program was described (Benecke et al., 2000). In the Finnish DPS, it was found that subjects with the G-250G promoter polymorphism of the HL gene (LIPC) tended to loss less weight than subjects with the 250A allele both in the control and in the intervention group (Todorova et al., 2004). In addition, having the G-250G genotype predicted the conversion from impaired glucose tolerance to Type 2 diabetes independently of weight at baseline and weight change. Furthermore, numerous polymorphisms of the apolipoprotein B gene have been described, which have been accompanied by alterations in lipid metabolism (Bosse et al., 2004). A meta-analysis indicated that the apoB EcoRI and MspI polymorphisms are associated with responsiveness to diet. A study investigating the effects of apoA-IV genotype during weight loss found that subjects with apoA-IV1/2 lost more weight than apoA-IV-1/1 subjects. On the contrary, it was found by others no significant differences in weight loss between both genotypes, although subjects with the apoA-IV-1/1 genotype showed a higher increase in HDL-C after in response to weight loss compared to the apoA-IV-1/2 subjects. Also the potential relationship between the PAI-1 promoter 4G/5G genotype and weight loss on the fibrinolytic system and lipid parameters in obese children was assessed and after a 3-month period

of treatment (nutritional counseling and physical activity). A decrease in PAI-1 levels was observed in obese children, who have reduced their BMI in comparison with those obese children who did not decrease their BMI (Estelles et al., 2001). On the other hand, a behavioral interventional activity in obese carriers of the Trp64Arg polymorphism of the ADBR3 gene demonstrated difficulties in losing weight (Shiwaku et al., 2003). Additionally, a large scale European intervention trial (NUGENOB) concerning the comparison of the impact of more than 40 genetic polymorphisms on weight loss revealed that much work is required to be performed in this area (Sorensen et al., 2006). Other studies have been reported, by using microarrays, that mRNA levels (gene expression) could be affected by the energy restriction, but not apparently by the composition of the hypocaloric diet (low versus moderate fat content), suggesting that more research is needed in this field (Viguerie et al., 2005b).

NOVEL AND EMERGING THERAPEUTICS: NUTRIGENOMICS, PHARMACOGENOMICS AND GENE THERAPY The -omics technologies including nutrigenomics, trascriptomics, proteomics and metabolomics will contribute to provide a more global view of diet–genome interactions concerning genetics, gene expression, protein and metabolic patterns and integral system biology approaches. Indeed, nutrigenomics is involved with the fusion of the traditional nutrition sciences with genome sequence-based data and methods (Marti et al., 2005). Thus, comprehensive genome data, DNA microarrays for transcriptional profiling, chromatin immunoprecipitation assays, and genome cataloging of DNA methylation patterns will revolutionize our understanding of the interplay between nutrient recommendations and genetic variations with impact on metabolic capacities, which will lead to more “personalized” dietary or drug treatments in situation of excessive body weight (Van Ommen, 2004). Other -omics approaches concerning protein or metabolic profiling of obese patients either as a metabolic classification/screening tool or as a means to determine markers of dietary drug response or discovery of new therapeutical approaches are still in their infancy, and are expected to be useful in the future (Kussmann et al., 2006) The potential implication of pharmacogenomics in clinical obesity medicine in that individuals could be treated according to genetic personal markers selecting specific medications and dosages, which could improve safety and efficiency in obesity therapy as well as to develop novel compounds aimed to obesity (Emilien et al., 2000). As an example, it has been reported that genotyping for the GNB3 C825T polymorphisms is highly predictive for the identification of obese individuals who will benefit from a centrally acting noradrenaline and serotonin re-uptake inhibitor such as sibutramine (Hauner et al., 2004).

Acknowledgements

Of course, the implementation of these gene-based treatments will require careful validation. Gene transfer strategies can be useful to introduce new genetic material into mammalian cells, which can be achieved by using “in vivo” or “in vivo” approaches (Campión et al., 2004). While in vivo gene transfer delivers the genetic manipulation directly into the animal, ex vivo gene delivery refers to the transfer of the gene manipulation into cells/organs removed from a donor, expanded in vitro, and then subsequently reintroduced into the organism. In both cases, the cells lose the capacity to pass the gene manipulation on to subsequent generations, but the time, cost, and complexity of the experiments, are reduced. The most commonly used methods for gene transfer in obesity research are viral transduction systems, mainly with adenovirus, and the direct injection of naked DNA. Theoretically, non-viral vectors have no limit concerning the size of DNA to be incorporated into the cells, and they are suitable for oligonucleotide delivery, which is also applicable for RNA transfer. Transfection in vivo is generally far less efficient than the adenoviruses and can induce an immunogenic response. Indeed, the use of non-viral vectors is extensive and generally applied, and multiple examples have been described. Typical non-viral techniques of gene transfer are the mechanical administration of naked DNA, electroporation, cationic, liposomes, and DNA– protein complex. Our group, for example, has performed muscle gene transfer of UCP1, UCP2, and LEP by in vivo injection of naked DNA into the rodent (Marti et al., 2003) affecting body weight and energy metabolism. Transfection by electroporation is also simple, inexpensive, and safe, and by using this technique it is possible to enhance the transfection in vivo of a direct DNA injection in brain, muscle or liver. Finally, recent studies have reported significant success in gene transfer by transfecting in vivo and ex vivo exogenous DNA with cationic liposomes or other composites, non-viral vector systems in liver, and other obesity target tissues of rodents, such as adipose tissue (Campión et al., 2004). Thus it has been induced UCP1 liver production after using a liposome system, which reduced mitochondrial energy efficiency (González-Muniesa et al., 2006). A new technology arising in the field of genetic and molecular manipulation is the antisense approach, which is being applied to inhibit the expression of a target gene in a sequencespecific manner (Campión et al., 2004). Thus, in cell cultures after the use of RNAi for silencing genes in insulin-sensitive adipocytes, the role of theses genes in insulin-signaling cascades has been demonstrated, while in vivo hypothalamic administration of RNAI against AGRP has proven that this peptide reduces metabolic rate independently of food intake. Delivery remains a major obstacle to deliver siRNA and si-encoding DNAs to the site of action on vertebrate cells. The role of gene expression regulation in obese subjects and in post-obese situations will contribute to better understated energy metabolism and the treatment of obesity (CourthesyTheulaz et al., 2005;Viguerie et al., 2005b). Indeed, gene therapy and other gene-related approaches may constitute powerful tools to treat genetically dependent disorders (Lander, 2000; Trujillo

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et al., 2006) in the future to personalize nutrition (Martinez et al., 2008) as well as epigenetic studies.

CONCLUSIONS Obesity, as a complex syndrome with a multifactorial origin, may be explained in some circumstances by monogenic mutations, but in most cases appears as a polygenic condition, which is additionally affected by a myriad of environmental influences (dietary and physical activity patterns). Family, adoption and twin studies as well as those of gene candidate association confirm that the risk of obesity has a genetic component. Additionally, the examination of online Mendelian inheritance in man database (OMIM) reveals that more than 300 entries are related to obesity and about 50 appear as monogenic Mendelian syndromes with obesity manifestations. On the other hand, at least several single-gene models of obesity have been reported in rodents and more than 400 QTL for different animal species (cow, rodent, chicken, pig, etc.) has been characterized. A number of studies recruiting important numbers of participants with different consanguinity degrees have been able to set up quantitative relationships among relatives concerning the heritability (r2) of objective markers of obesity. As many as 600 genes and genetic markers codifying for molecular related to appetite control, energy expenditure and adipogenesis have been characterized as involved in body weight control. Identification of candidate genes may allow providing individual specific recommendations (dietary advice and/or drug therapy) to achieve effective weight loss and successful long-term maintenance of weight loss, on the basis of an identified genetic susceptibility. However, at this moment it is premature to offer targeted obesity therapy based on the information of the genotype/ weight loss association studies published to date. In the future, the advance in molecular genetic biotechnology will ease the way to combine the search for new candidate genes, novel polymorphisms, and gene expression patterns putatively involved in gene–nutrient interaction concerning weight homeostasis given the complexity of weight loss responses. Supplementary efforts are needed to identify the interactions between the most relevant gene polymorphisms affecting both the amount and composition of the weight loss as well as the changes in obesityassociated risk factors.

ACKNOWLEDGEMENTS The author of the chapter wishes to express his appreciation and recognition to the following coworkers and colleagues by generous and valuable collaboration in this field: J. Bresan, J. Campion, B. de Fanti, P. González-Muniesa, A. Marti, M.A. Martínez-Gonzalez, F. Milagro, M.J. Moreno-Aliaga, M.D. Parra, P.C. Pérez-Matute, M.P. Portillo, J.L. Santos, A. Solomon and M.A. Zulet as well as to members of the NUGENOB and DIOGENES Consortia.

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CHAPTER

96 Diabetes Maggie Ng and Nancy J. Cox

INTRODUCTION Diabetes mellitus is a group of metabolic disorders characterized by high blood glucose levels resulting from defects in insulin secretion, insulin action or both. Chronic high blood glucose levels are associated with long-term tissue damage, dysfunction and failure of various organs especially the eyes, kidneys, nerves, heart and blood vessels. The most common forms of diabetes are type 1 diabetes, in which autoimmune destruction of the insulin-secreting pancreatic -cells leads to an absolute insulin deficiency (or complete absence of endogenous insulin), and type 2 diabetes, which is characterized by a relative deficiency of insulin attributable to defects leading to insulin resistance, defects in insulin secretion, or both. Type 1 diabetes commonly arises in childhood or adolescence, and the lifetime risk of type 1 diabetes in US populations of European descent is about one in 250 (0.004), but lower in US populations of recent Asian, African or Native American ancestry. Type 2 diabetes is more frequently diagnosed in late adulthood and is far more common, with 8–10% of Americans of European descent diagnosed with type 2 diabetes by age 60. Age-specific rates of type 2 diabetes are even higher in some US populations, including African Americans, Mexican Americans and Native Americans, and there are alarming increases in the incidence of type 2 diabetes in younger Americans, due at least in part to increasing levels of obesity in the United States coupled with increasingly sedentary lifestyles (Narayan et al., 2003). Some 1.6 million Americans have been diagnosed with type 1 diabetes and more than 16 million Americans with type Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

2 diabetes. And for many of these individuals, their family members are at a substantially increased risk relative to the population average; for example, sibs of a patient with type 1 diabetes have a 15-fold increased risk of disease (i.e., s15), while sibs of a patient with type 2 diabetes have a 1.8–2.8-fold increased risk of disease (i.e., s1.8–2.8) (Rich, 1990). Thus, because it is easy and inexpensive to diagnose, common and familial, diabetes has long been a model for exploring the genetic basis of complex disorders – those that cluster in families but do not have simple patterns of transmission (Florez et al., 2003). In addition, given especially the incidence of type 2 diabetes in the general adult population and the expectation that one or more genetic variants could alter individual risk of disease, it has become an attractive candidate for early thinking about the introduction of genetic and genomic concepts into medical practice. Despite the efforts of many investigators over many years, though, association studies at candidate genes and genome-wide linkage studies have, until very recently, yielded only a small number of loci having genetic variation reproducibly shown to affect risk of either type 1 or type 2 diabetes. Prior to recent GWAS (see Chapter 8), susceptibility loci for type 1 diabetes with broad support over multiple studies included the HLA region, with primary determinants thought to be variation at the HLADRB1, -DQA1 and -DQB1 loci (Davies et al., 1994), the insulin gene (INS) (Bell et al., 1984), the gene for cytotoxic T-lymphocyte associated protein 4 (CTLA4) (Ueda et al., 2003) and the protein tyrosine phosphatase-22 gene (PTPN22) (Bottini et al., 2004). All of these were originally identified through candidate gene studies. With respect to linkage, in a large type 1 diabetes Copyright © 2009, Elsevier Inc. All rights reserved. 1187

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consortium dataset with 1435 multiplex families (Concannon et al., 2005), the significance of the evidence for linkage at the HLA region was quite dramatic, at p  2  1052, with the next best region located near CTLA4, with p  9  105. Two other regions with notable evidence for linkage were 10p14-q11 with p  1.2  104, and 16q22-q24 with p  4.9  104. In contrast, there was only modest evidence for linkage at INS, and loci with relatively modest sibling risk ratios could be excluded from much of the rest of the genome ( s  1.3 excluded from 82% of the genome; s  1.5 excluded from 95% of the genome). Results of early candidate gene association studies as well as linkage and positional cloning studies in type 2 diabetes had yielded a similar paucity of genes and regions with widely reproducible linkage or association (for a recent review, see Permutt et al., 2005). Although a number of candidate genes, including candidates derived on the basis of effective treatments for type 2 diabetes such as KCNJ11 and PPARG, had been validated through large-scale meta-analysis (Altshuler et al., 2000; Gloyn et al., 2003), the vast majority of candidate gene studies in type 2 diabetes had not yielded reproducible associations. Similarly, while a few linkage studies yielded positionally cloned genes with at least some support from other studies, including CAPN10 (Horikawa et al., 2000) and HNF4A (Love-Gregory et al., 2004; Silander et al., 2004), only TCF7L2 (identified by investigators at deCode in Iceland in positional cloning studies based on one of their best conditional linkage signals [Grant et al., 2006]) has been widely replicated in multiple populations (Cauchi et al., 2006; Groves et al., 2006). As predicted by Risch and Merikangas (1997), GWAS have allowed identification of genetic risk factors with relatively modest effects while preserving the utility of the genome-wide context. The first of the GWAS in diabetes were published in 2007, and to date, some 10 such studies have been published for type 2 diabetes (Diabetes Genetics Initiative, 2007; Florez et al., 2007; Hanson et al., 2007; Hayes et al., 2007; Rampersaud et al., 2007; Salonen et al., 2007; Scott et al., 2007; Sladek et al., 2007; Steinthorsdottir et al., 2007; Zeggini et al., 2007), while three GWAS (Hakonarson et al., 2007; Smyth et al., 2007; WTCCC, 2007) and a follow-up study (Todd et al., 2007) have been published for type 1 diabetes. These studies have enabled the identification of new genetic risk factors for both disorders and have provided valuable additional context for understanding the relative importance of some of the previously identified risk factors. For example, TCF7L2, identified originally through positional cloning studies in an Icelandic sample (Grant et al., 2006), is clearly the largest individual genetic risk factor for type 2 diabetes, at least in type 2 diabetes cases from northern Europe. Similarly, HLA remains the undisputed major genetic risk factor for type 1 diabetes (WTCCC, 2007); as with the linkage studies, no other association comes close to the magnitude of effect detected at HLA. For both disorders, outside the locus with the largest individual effect, the remaining genetic risk factors have quite modest odds ratios (OR), with a few for type 1 diabetes being near 2.0, but most in the range of 1.1–1.2. Moreover, there are clearly many additional

genetic risk factors to be identified, as the established genetic risk factors account for only a small proportion (2.3%) of the interindividual variation in genetic risk for type 2 diabetes and just over half of the variation in genetic risk for type 1 diabetes (due mostly to the large effects in the HLA region). Given that the genetic risk factors yet to be identified are unlikely to have much larger effects than those already identified, but rather are likely to be of similar magnitude or smaller, it is clear that more studies, larger studies, or more sophisticated or novel approaches will be needed to identify additional loci and allow us to achieve a more comprehensive understanding of the genetic risk factors for diabetes. Indeed, it could be argued that the loci identified to date have added little to our fundamental understanding of the genetic component to either type 1 or type 2 diabetes. That is, type 1 diabetes was already known to be an autoimmune disease, and genetic risk factors identified to date merely confirm the importance of genes involved in T-lymphocyte development, regulation of the immune system and tolerance. Similarly, genetic risk factors identified to date for type 2 diabetes have provided no more insight than we have ever had toward a comprehensive understanding of its fundamental biological basis. Indeed, the more pessimistic viewer might reasonably argue that the modest effect sizes observed to date are the “big” ones – that whatever additional genetic risk factors are identified will have such modest effects as to be nearly indistinguishable from background loci and thus of limited scientific or clinical value. So, is the glass already half full and filling fast with diabetes susceptibility genes that will provide us that elusive and comprehensive understanding of the genetic component to diabetes? Or is the glass, at best, half empty, with little likelihood of filling no matter how much money we spend at the tap? As with almost all of the earlier genetic research in complex disorders, including candidate gene and linkage mapping studies, the GWAS in diabetes have been among the first, and are certainly among the most numerous with large sample sizes, to be conducted on complex traits; thus, the answers to these questions have implications not only for type 1 and type 2 diabetes, but for other complex disorders as well. In order to address these questions, it is important to examine critically the studies that have been conducted to date and to review what we have learned from them.

GWAS IN TYPE 2 DIABETES The GWAS for type 2 diabetes have been conducted in a variety of populations, using a variety of study designs and highthroughput platforms. Studies on type 2 diabetes using the first generation GWAS platforms (e.g., the Affymetrix 100K single nucleotide polymorphism [SNP] platform) were quite expensive and consequently included relatively modest numbers of samples. The low sample sizes coupled with the low density and high redundancy of the 100K platform precluded any of the studies on the 100K platform from identifying loci with genome-wide significant associations (Florez et al., 2007; Hanson et al., 2007; Hayes et al., 2007; Rampersaud et al., 2007).

GWAS in Type 2 Diabetes

TABLE 96.1 2007

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Samples, study designs and SNP genotyping platforms used in dense GWAS for type 2 diabetes published in

Study Sladek et al. (2007)

Samples French 690 cases 660 controls

Design Case/control Lean cases

Platform Illumina 300K  100K

Follow-up 57 SNPs 2617 cases 2894 controls

Steinthorsdottir et al. (2007)

Icelandic 1399 cases 5275 controls

Case/control

Illumina 300K

47 SNPs 1110 cases 2272 controls

Scott et al. (2007)

Finns 1161 cases 1174 controls

Case/control

Illumina 300K

80 SNPs 1215 cases 1258 controls

Diabetes Genetics Initiative (2007)

Finns 1464 cases 1467 controls

Case/control and family BMI matched

Affymetrix 500K

107 SNPs 5065 cases 5785 controls

WTCCC (2007)

English 1924 cases 2934 controls

Case/control

Affymetrix 500K

21  56 SNPs 2022 cases 2037 controls

Salonen et al. (2007)

European 500 cases 497 controls

Case/control

Illumina 300K

10 SNPs 2573 cases 2776 controls

This situation changed dramatically with the availability of higher density SNP platforms, and Table 96.1 summarizes population, sample size and study design information for the studies published in 2007 and conducted using the denser SNP genotyping platforms on type 2 diabetes. The scans conducted using sufficiently dense platforms to be considered truly genomewide have been focused exclusively on populations of recent European descent, primarily northern European populations, and it is only in these dense scans on very large samples that loci meeting genome-wide criteria for significance have been identified and confirmed (Table 96.2). Some studies utilized only lean cases (Sladek et al., 2007), matched cases to controls on the basis of body mass index (BMI) (Diabetes Genetics Initiative, 2007), or conducted analyses separately in lean and obese cases (Steinthorsdottir et al., 2007). Many of the studies enhanced power by utilizing cases with a family history of disease in their primary screen, their follow-up studies, or both. The varying designs have led to intriguing differences in results that help to extend the observed associations. For example, although most studies have reported the OR for TCF7L2 to be in the range of 1.35–1.4 even within studies in which cases were chosen to have a positive family history of type 2 diabetes, Sladek et al. (2007) estimated the OR for TCFL2 to be 1.65. This higher estimate may be attributable to the use of lean cases, as the allele at TCF7L2 increasing risk of type 2 diabetes is associated with lower BMI in both cases and controls; thus lean cases are likely to be particularly enriched for individuals carrying the high-risk allele for type 2 diabetes at TCF7L2. Similarly, while the FTO locus was easily detected in the WTCCC study

and was also apparent in the FUSION study, it had little signal in the DGI study, largely because the DGI study matched cases and controls on BMI. As FTO appears to affect risk of type 2 diabetes primarily through its effects on obesity and BMI, the failure to detect the effects of FTO in the DGI sample is easily understood; conditioning on BMI similarly reduced the FTO effects in the WTCCC samples (Frayling et al., 2007). The confirmed loci for type 2 diabetes listed in Table 96.2 have little in common with respect to known function, although the risk allele at several of the susceptibility loci (TCF7L2, CDKAL1, SLC30A8) has been shown to be associated with reduced insulin secretion. Clearly, however, considerable additional research into the physiological basis of the risk imparted by the genetic variation at these loci is necessary. Moreover, it is clear we have barely begun to identify genetic risk factors for type 2 diabetes. The WTCCC group noted that the combined effects of all of the risk factors identified in the combined samples of the DGI, FUSION and WTCCC studies together would yield an estimate of s of 1.07 (WTCCC, 2007), which contrasts substantially with the overall s value for type 2 diabetes estimated to be in the range of 1.8–2.8. Investigators from the DGI study noted that the eight loci identified in the combined DGI, FUSION and WTCCC studies account for only about 2.3% of the overall variance in type 2 diabetes risk among individuals (Diabetes Genetics Initiative, 2007). In addition, OR are modest overall and can be quite low within any individual study. As indicated in Table 96.2, the maximum OR for most loci is less than 1.2 and most of these loci have sufficiently low OR in at least one study that the locus did not meet the study’s threshold for reporting the result.

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T A B L E 9 6 . 2 Confirmed susceptibility loci/regions for type 2 diabetes Locus

TCF7L2 SLC30A8 CDKAL1 HHEX

Chromosome

10 8

Odds ratio (range across studies)

Risk allele frequency (controls)

1.34–1.71

0.30

a

0.70

a

1.01 –1.18

6

1.16 –1.20

0.32

10

1.15a–1.13

0.58

a

PPARG

3

1.02 –1.20

0.86

CDKN2A/B

9

1.12a–1.17

0.83

KCNJ11 IGF2BP2 FTO

11

a

0.45

a

1.14 –1.15

3

1.12 –1.18

0.32

16

1.03a–1.23

0.40

a

Lowest value found, but the lowest OR could not be determined because some studies did not report all results, and the locus indicated did not have OR greater than the threshold for reporting in at least one study.

As has been found in GWAS for other complex traits, most of the alleles associated with increased risk of type 2 diabetes are located in non-coding sequence without obvious function (sometimes between genes, sometimes within introns), a notable exception being an amino acid polymorphism in SLC30A8. Even when the associated allele is within an intron of a gene, however, we should be cautious about ascribing risk to the gene in which the SNP resides, and it is clearly premature to ascribe the function of a SNP between genes to the closest gene based only on physical proximity. It is possible that at least some of the risk alleles affect expression of genes outside the one in which they are located or near and may well exert their effects through a gene or genes on other chromosomes. Thus, it may be prudent to continue to refer to these as susceptibility loci rather than as susceptibility genes. It is also notable that many of the loci with confirmed associations have very common risk alleles (i.e., the frequency of the risk allele is greater than 0.5). While no one has claimed that the polymorphisms identified to date are the actual causal alleles driving the observed associations, it is unlikely that the true susceptibility alleles will be very different in frequency from those already identified. Even with D of 1.0 between the tag SNP and the causal variant, differences in allele frequencies of the causal and associated alleles would reduce the r2 value between them sufficiently to degrade the power to detect the association. The observation that the risk allele is the more common allele for several of the confirmed associations raises the possibility that it is the ancestral allele (i.e., the allele fixed in other primate species) at the causal site that increases risk of disease, with a more recent mutation at that site (i.e., the derived allele) reducing risk relative to the ancestral state. As noted by Di Rienzo and Hudson (2005), quite a number of the polymorphisms confirmed to affect risk to common disorders have ancestral susceptibility alleles. For any biallelic polymorphism

there is always one allele that increases and one allele that decreases risk relative to the other, and thus characterization of a susceptibility locus as a “risk” locus or a “protective” locus is often made in arbitrary ways. Considering risk versus protection in the context of ancestral versus derived alleles can substantially improve our understanding of associations. Patterns of variation in the vicinity of susceptibility loci with ancestral susceptibility alleles can be quite different from expectations shaped by the more familiar Mendelian paradigm in which alleles increasing risk of disease are the rare, recent mutations. Accordingly, geneticists may need to revamp their thinking to correctly interpret results of association studies involving such loci. While it is undeniably exciting to have identified so many susceptibility loci for type 2 diabetes, what we have is still far from the comprehensive understanding of the genetic component to type 2 diabetes that we seek. Given the sample sizes (on the order of 40,000 cases and 40,000 controls for all dense-platform studies combined) and total resources necessary to make the discoveries summarized in Table 96.2, it is daunting to consider what it might take to identify additional susceptibility loci.

FUTURE RESEARCH IN TYPE 2 DIABETES GENETICS There would clearly be value in combining results across all of the dense GWAS carried out to date. Fortunately, the longstanding collegiality of the diabetes genetics community continues to flourish, with the early collaboration of the DGI, FUSION and WTCCC groups providing an outstanding example of the value of collaborative science in the GWAS era, and it is likely that combined results from all available GWAS in type 2 diabetes will be completed in the near future. In addition to those that have been published, dense GWAS have been conducted in the Framingham cohort (9000 samples), and in smaller Chinese and Korean samples, and are underway in Pima Indian, Mexican American and African American samples, as well as a variety of additional populations of European descent. If combined analyses can be conducted with even the GWAS that will be available in 2008, it is likely that additional susceptibility loci for type 2 diabetes can be identified. Notably, studies completed by mid-2008 will provide additional context for the susceptibility loci that have been identified largely in northern European populations, as GWAS in many other populations will be available. Among these additional populations are some in which cases are likely to be leaner than those in northern European populations (e.g., Chinese, Korean), as well as some in which cases are likely to be more obese and insulin resistant than those in northern European populations (e.g., Mexican Americans, African Americans, Pima Indians). The similarities and differences in susceptibility loci detected in these populations may provide more insight into the genetic component of diabetes by highlighting different pathways or parts of pathways in different populations. For example, it is unlikely that TCF7L2 will be the most significantly associated susceptibility

GWAS in Type 1 Diabetes

locus in the Chinese, Korean, Pima or Mexican Americans populations, because the allele increasing risk at TCF7L2 has much lower frequency in Asian and Native American populations. In addition, as summarized in Table 96.2, only very modest numbers of SNPs have been included in follow-up studies conducted in GWAS on type 2 diabetes to date. Deeper follow-up may well lead to the identification of additional susceptibility loci as well. But it should not be necessary to rely solely on more studies in more samples to identify additional susceptibility loci for type 2 diabetes. It may be possible to use various types of “enrichment” analyses to determine whether the top signals are enriched for SNPs within genes in certain pathways or within genes associated with particular biological processes. Such analyses are challenging for a number of reasons. While our signals come as SNPs, these bioinformatics analyses require gene lists. As noted above, it may be perilous to annotate SNPs to genes without knowing what the causal polymorphisms actually are. Moreover, even if causal SNPs have been identified, those SNPs may lie outside genes and/or may affect the expression of genes outside the gene in which they are located. Additional studies on the association of genetic variation to expression phenotypes in tissues relevant to type 2 diabetes may help to annotate SNPs to genes for downstream bioinformatics studies. Regardless, enrichment analyses should be conducted in a way that explicitly allows for the uneven interrogation of genes that inevitably occurs even with dense SNP genotyping platforms. For example, genes might be weighted for bioinformatics enrichment studies according to the proportion of known variation that is interrogated on the platform used for analysis. Potential Clinical Utility Ideally, the accumulation of additional dense GWAS and the thoughtful application of bioinformatics approaches to these data will enable the identification of a sufficient number of type 2 diabetes susceptibility loci to obtain a better understanding of the nature of the genetic component to type 2 diabetes in all populations. What will the clinical utility of this information be? Type 2 diabetes is one of the few disorders in which there are confirmed associations in genes relevant to the mechanism of drug action for some of the drug therapies used to treat diabetes. This certainly raises the possibility that the identification of additional susceptibility loci will lead to discovery of new drug therapies for this disorder. It has also been argued that if we identify enough of the genetic risk factors, we may be able to develop sufficiently accurate predictive models to permit risk reduction (through behavior modification or even drug therapies) in those with the highest risk of disease. The problem with that idea is that we already have good models for predicting the risk of type 2 diabetes, and it is unclear that the inclusion of genetic risk factors will improve risk prediction any time soon, if at all (Wilson et al., 2007). These models use a variety of inexpensively measured complex traits (lipid levels, BMI, family history) that are likely to share some of both their genetic and their non-genetic determinants with type 2 diabetes. Thus, knowing the overall genetic risk is not really necessary

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to develop accurate predictions of risk for type 2 diabetes. Nonetheless, it is certainly possible that knowing specific genetic risk factors that an individual carries may help determine the most appropriate therapy for that individual. Thus, clinical trials of diabetes therapies should be incorporating information on genetic risk factors as quickly as associations become replicated. It is also possible that knowledge of the specific genetic risk factors will allow us to conduct more targeted epidemiological studies to identify non-genetic risk factors more specific than “Western diet and lifestyle” that increase risk of type 2 diabetes. These more specific non-genetic risk factors may be cost-effective targets for interventions that could prevent, or at least delay onset of disease.

GWAS IN TYPE 1 DIABETES The first GWAS to be published for type 1 diabetes was a survey of 6500 non-synonymous SNPs conducted in 2029 cases and 1755 controls (Smyth et al., 2006). In addition to confirming the previously known association at PTPN22, a SNP at the gene interferon induced with helicase C domain 1, IFIH1, was found to be strongly associated with type 1 diabetes in both the original and the replication samples of 2471 cases, 4593 controls and 2134 parent–child trios. Subsequent GWAS used dense SNP genotyping platforms to assess the entire genome for type 1 diabetes susceptibility loci. Hakonarson et al. (2007) reported results of a GWAS for type 1 diabetes conducted using the Illumina 550K SNP set on 563 cases, 1163 controls and 483 complete trios for primary analysis and 939 families including at least a trio for follow-up studies. Their studies confirmed associations at HLA, INS and PTPN22, and identified variation at KIAA0350 as contributing risk to type 1 diabetes. The initial genome-wide screen in the WTCCC (2007) included ~2000 cases with type 1 diabetes and ~3000 controls unphenotyped with respect to type 1 diabetes. These studies also confirmed the previous associations at HLA and PTPN22, and provided independent support for recent reports implicating variation at CD25. Subsequent studies reported on both follow-up to the findings in the WTCCC studies and follow-up to a study of 13,378 non-synonymous SNPs in 3400 cases and 3300 controls, with the follow-up being conducted in 4000 cases, 5000 controls and 2997 trios (Todd et al., 2007). Based on the initial WTCCC genome-wide studies, 11 SNPs from regions with p  1.64  105 and not previously implicated in type 1 diabetes were genotyped in all of the follow-up samples. Four of these regions yielded compelling replication of association: a region on 18p11 in which PTPN2 was the only gene, a region on 12q24 in which a single non-synonymous SNP in SH2B3 was sufficient to model the observed association, the 16p13 region containing KIAA0350, and the 12q13 region, in which a SNP near ERBB3 was the most strongly associated SNP. They also followed up 14 SNPs from the non-synonymous screen, which provided replication for a SNP in the T-lymphocyte co-stimulation gene CD226 as well as for SNPs in CAPSL, C20orf168, IL7R and CFTR. The results of genetic studies in type 1 diabetes provide a more cohesive picture of the genetic risk factors for this autoimmune

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disease. Genes involved in T-cell development, immune regulation and recognition of pancreatic autoantigens all appear to play a role in the development of type 1 diabetes. As with type 2 diabetes, very large samples were required to identify and confirm these susceptibility loci. OR for HLA approach 7, and for INS and PTPN22 are around 2.0, but are between 1.2 and 1.3 for many of the other loci that have been identified. Thus, the initial screens were only modestly powered to detect effects at these loci.

FUTURE STUDIES IN TYPE 1 DIABETES The clinical utility of genetic risk factors identified for type 1 diabetes rises considerably if it becomes possible to reduce the likelihood that individuals with high risk of developing type 1 diabetes actually develop the disease. Good predictive models for type 1 diabetes risk remain elusive, however, and we still lack understanding of the non-genetic triggers for type 1 diabetes. Thus, one may also harness genetic risk factors to improve epidemiological studies targeting identification of specific nongenetic risk factors for type 1 diabetes. While gene–environment interaction complicates the identification of genetic risk factors, it can be an ally for clinical studies, as only one element of the interaction need be disrupted to reduce the entire risk due to the interaction. Thus, prevention strategies might also focus on non-genetic risk factors that interact with genetic risk factors.

CLINICAL UTILITY OF GENETIC RESEARCH IN DIABETES The most immediate clinical utility of the genetic research in diabetes relates to the ability to use genetic information to improve the quality of diagnosis with respect to the particular subtype of diabetes, which in turn can have profound implications for how patients should be treated. The most dramatic examples of altered diagnosis and treatment regimens relate to

the subset of individuals who have been diagnosed with type 1 diabetes at a very early age (usually before 6 months of age), and therefore treated with insulin. Individuals diagnosed with diabetes at this early age are more likely to have a rare Mendelian form of diabetes called permanent neonatal diabetes (PND). The most common cause for PND is mutation at KCNJ11 within Kir6.2, the inwardly rectifying ATP-sensitive potassium channel. These patients can be safely and more effectively treated with sulfonylureas (taken orally) than with insulin (Pearson et al., 2006), and this change in treatment regimen dramatically alters the lifestyle of patients for the better. As with the rare monogenic forms of diabetes, we expect that better understanding of the genetic risk factors for diabetes will ultimately lead to new drugs targeting the new genes and pathways that are identified, ultimately leading to more individualized therapies based on primary etiology of disease. Pharmacogenetic studies are providing insights into other ways of individualizing therapies. For example, recent studies (Shu et al., 2006) indicate that genetic variation at OCT1 may contribute to interindividual variation in response to metformin, a commonly used agent for treatment of diabetes. OCT1 (organic cation transporter 1) has a role in hepatic uptake of metformin and thus was a candidate gene for generating such effects. The coming years are likely to see more direct investigation of pharmacogenetic effects for drugs used to treat diabetes, and translation of these findings will also personalize diabetes treatment.

CONCLUSION It is difficult not to be excited by the potential for improving our understanding of the primary etiology of diabetes that results of initial GWAS have promised. Improved understanding of primary etiology at the level of genes and their products should lead to more specifically targeted therapies, which together with results from pharmacogenetic studies may substantially alter the way that diabetes is diagnosed and treated.

REFERENCES Altshuler, D., Hirschhorn, J.N., Klannemark, M., Lindgren, C.M., Vohl, M.-C., Nemesh, J., Lane, C.R., Schaffner, S.F., Bolk, S., Brewer, C. et al. (2000). The common PPARg Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 26, 76–80. Bell, G.I., Horita, S. and Karam, J.H. (1984). A polymorphic locus near the human insulin gene is associated with insulin-dependent diabetes mellitus. Diabetes 33(2), 176–183. Bottini, N., Musumeci, L., Alonso, A., Rahmouni, S., Nika, K., Rostamkhani, M., MacMurray, J., Meloni, G.F., Lucarelli, P., Pellecchia, M. et al. (2004). A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat Genet 36, 337–338. Cauchi, S., Meyre, D., Dina, C., Choquet, H., Samson, C., Gallina, S., Balkau, B., Charpentier, G., Pattou, F., Stetsyuk, V. et al. (2006). Transcription factor TCF7L2 genetic study in the French population:

Expression in human beta-cells and adipose tissue and strong association with type 2 diabetes. Diabetes 55(10), 2903–2908. Davies, J.L., Kawaguchi, Y., Bennett, S.T., Copeman, J.B., Cordell, H.J., Pritchard, L.E., Reed, P.W., Gough, S.C.L., Jenkins, S.C., Palmer, S.M. et al. (1994). A genome-wide search for human type 1 diabetes susceptibility genes. Nature 371(8), 130–136. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, Saxena, R., Voight, B.F., Lyssenko, V., Burtt, N.P., de Bakker, P.I., Chen, H., Roix, J.J. et al. (2007). Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316(5829), 1331–1336. Di Rienzo, A. and Hudson, R.R. (2005). An evolutionary framework for common diseases: The ancestral-susceptibility model. Trends Genet 21(11), 596–601.

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Florez, J.C., Hirschhorn, J. and Altshuler, D. (2003). The inherited basis of diabetes mellitus: Implications for the genetic analysis of complex traits. Ann Rev Genomics Hum Genet 4, 257–291. Florez, J.C., Manning, A.K., Dupuis, J., McAteer, J., Irenze, K., Gianniny, L., Mirel, D.B., Fox, C.S., Cupples, L.A. and Meigs, J.B. (2007). A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: Replication and integration with other genome-wide datasets. Diabetes 56(12), 3063–3074. Frayling,T.M.,Timpson, N.J.,Weedon, M.N., Zeggini, E., Freathy, R.M., Lindgren, C.M., Perry, J.R., Elliott, K.S., Lango, H., Rayner, N.W. et al. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316(5826), 889–894. Gloyn, A.L., Weedon, M.N., Owen, K.R., Turner, M.J., Knight, B.A., Hitman, G., Walker, M., Levy, J.C., Sampson, M., Halford, S. et al. (2003). Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52(2), 568–572. Grant, S.F., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Manolescu, A., Sainz, J., Helgason, A., Stefansson, H., Emilsson, V., Helgadottir, A. et al. (2006). Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 38(3), 320–323. Groves, C.J., Zeggini, E., Minton, J., Frayling, T.M., Weedon, M.N., Rayner, N.W., Hitman, G.A.,Walker, M.,Wiltshire, S., Hattersley,A.T. et al. (2006). Association analysis of 6,736 U.K. subjects provides replication and confirms TCF7L2 as a type 2 diabetes susceptibility gene with a substantial effect on individual risk. Diabetes 55(9), 2640–2644. Hakonarson, H., Grant, S.F., Bradfield, J.P., Marchand, L., Kim, C.E., Glessner, J.T., Grabs, R., Casalunovo,T.,Taback, S.P., Frackelton, E.C. et al. (2007). A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature 448(7153), 591–594. Hayes, M.G., Pluzhnikov, A., Miyake, K., Sun, Y., Ng, M.C., Roe, C.A., Below, J.E., Nicolae, R.I., Konkashbaev, A., Bell, G.I. et al. (2007). Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies. Diabetes 56(12), 3033–3044. Hanson, R.L., Bogardus, C., Duggan, D., Kobes, S., Knowlton, M., Infante,A.M., Marovich, L., Benitez, D., Baier, L.J. and Knowler,W.C. (2007). A search for variants associated with young-onset type 2 diabetes in American Indians in a 100K genotyping array. Diabetes 56(12), 3045–3052. Love-Gregory, L.D., Wasson, J., Ma, J., Jin, C.H., Glaser, B., Suarez, B.K. and Permutt, M.A. (2004). A common polymorphism in the upstream promoter region of the hepatocyte nuclear factor-4 alpha gene on chromosome 20q is associated with type 2 diabetes and appears to contribute to the evidence for linkage in an ashkenazi jewish population. Diabetes 53(4), 1134–1140. Narayan, K.M., Boyle, J.P., Thompson, T.J., Sorensen, S.W. and Williamson, D.F. (2003). Lifetime risk for diabetes mellitus in the United States. JAMA 290, 1884–1890. Pearson, E.R., Flechtner, I., Njølstad, P.R., Malecki, M.T., Flanagan, S.E., Larkin, B., Ashcroft, F.M., Klimes, I., Codner, E., Iotova, V. et al. (2006). Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N Engl J Med 355(5), 467–477. Permutt, M.A., Wasson, J. and Cox, N. (2005). Genetic epidemiology of diabetes. J Clin Invest 115(6), 1431–1439.

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Rampersaud, E., Damcott, C.M., Fu, M., Shen, H., McArdle, P., Shi, X., Shelton, J.,Yin, J., Chang,Y.P., Ott, S.H. et al. (2007). Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: Evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes 56(12), 3053–3062. Rich, S.S. (1990). Mapping genes in diabetes. Genetic epidemiological perspective. Diabetes 39(11), 1315–1319. Salonen, J.T., Uimari, P., Aalto, J.M., Pirskanen, M., Kaikkonen, J., Todorova, B., Hyppönen, J., Korhonen,V.P., Asikainen, J., Devine, C. et al. (2007). Type 2 diabetes whole-genome association study in four populations: The DiaGen consortium. Am J Hum Genet 81(2), 338–345. Scott, L.J., Mohlke, K.L., Bonnycastle, L.L.,Willer, C.J., Li,Y., Duren, W.L., Erdos, M.R., Stringham, H.M., Chines, P.S., Jackson, A.U. et al. (2007). A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316(5829), 1341–1345. Shu,Y., Sheardown, S.A., Brown, C., Owen, R.P., Zhang, S., Castro, R.A., Ianculescu, A.G., Yue, L., Lo, J.C., Burchard, E.G. et al. (2006). Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest 117(5), 1422–1431. Silander, K., Mohlke, K.L., Scott, L.J., Peck, E.C., Hollstein, P., Skol, A.D., Jackson, A.U., Deloukas, P., Hunt, S., Stavrides, G. et al. (2004). Genetic variation near the hepatocyte nuclear factor-4 alpha gene predicts susceptibility to type 2 diabetes. Diabetes 53(4), 1141–1149. Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S. et al. (2007). A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445(7130), 881–885. Smyth, D.J., Cooper, J.D., Bailey, R., Field, S., Burren, O., Smink, L.J., Guja, C., Ionescu-Tirgoviste, C., Widmer, B., Dunger, D.B. et al. (2006). A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat Genet 38, 617–619. Steinthorsdottir, V., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Jonsdottir, T., Walters, G.B., Styrkarsdottir, U., Gretarsdottir, S., Emilsson, V., Ghosh, S. et al. (2007). A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39(6), 770–775. Todd, J.A., Walker, N.M., Cooper, J.D., Smyth, D.J., Downes, K., Plagnol, V., Bailey, R., Nejentsev, S., Field, S.F., Payne, F., et al. (2007) Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet 39(7), 857–864. Ueda, H., Howson, J.M., Esposito, L., Heward, J., Snook, H., Chamberlain, G., Rainbow, D.B., Hunter, K.M., Smith, A.N., Di Genova, G. et al. (2003). Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease. Nature 423(6939), 506–511. The Welcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678. Wilson, P.W., Meigs, J.B., Sullivan, L., Fox, C.S., Nathan, D.M. and D’Agostino, R.B., Sr. (2007). Prediction of incident diabetes mellitus in middle-aged adults: The Framingham Offspring Study. Arch Intern Med 167(10), 1068–1074. Zeggini, E., Weedon, M.N., Lindgren, C.M., Frayling, T.M., Elliott, K.S., Lango, H., Timpson, N.J., Perry, J.R., Rayner, N.W., Freathy, R.M. et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341.

CHAPTER

97 Metabolic Syndrome Rebecca L. Pollex and Robert A. Hegele

INTRODUCTION Since the turn of the millennium, the metabolic syndrome (MetS), defined as the clustering of multiple metabolic abnormalities, including abdominal obesity, dyslipidemia (elevated serum triglyceride and depressed serum high-density lipoprotein [HDL] cholesterol), dysglycemia and hypertension, has attracted considerable interest and debate as researchers and clinicians have weighed its validity and potential to identify individuals at risk for diabetes and/or cardiovascular disease (CVD) (Eckel et al., 2005; Ford, 2005). The MetS fits the profile of an archetypical complex trait, which is determined by the interplay of genetic and environmental factors influencing the development of obesity, insulin resistance, inflammatory processes, and cardiovascular intermediate traits (Eckel et al., 2005).

DIAGNOSIS: DEFINITION OF THE MetS PHENOTYPE At least five different phenotypic definitions, including those proposed by the World Health Organization (WHO), the US National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), and most recently the International Diabetes Federation (IDF), have attempted to pin down the essential components of this unique pathophysiological condition (Table 97.1) (Alberti et al., 2005; Balkau and Charles, 1999; Einhorn et al., 2003; NCEP, 2001;WHO, 1999). Each definition attempts to provide relatively simple clinical markers that can

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1194

be used to identify individuals with the complex bouillabaisse of metabolic, pro-inflammatory, and pro-thrombotic disturbances that are not routinely measured but which act as the likely pathogenic agents for CVD (Eckel et al., 2005). The IDF definition implicitly prioritizes abdominal obesity as the agent provocateur in MetS (Alberti et al., 2005), since only individuals with increased waist girth are eligible for subsequent assessment of the additional criteria (Table 97.1). By each definition, MetS is a discrete, non-continuous trait. And regardless of the phenotypic definition used, MetS is a common trait, affecting approximately one in four North Americans with similarly high prevalence in other subpopulations (Eckel et al., 2005). Furthermore, the NCEP ATP III MetS definition has heuristic value clinically as a prospective determinant of disease risk, as it has been associated with a moderate increase in the development of CVD and also with diabetes incidence (Ford, 2005). Recently, some have argued that the risk factor cluster represented by the “MetS” label does not necessarily impart a higher CVD liability than the simple sum of the individual risk factor components (Kahn et al., 2005). This position has support from studies where upon adjustment for MetS components, MetS was no longer a significant predictor, or its impact was greatly attenuated (Kahn et al., 2005). Additionally, the practical utility of MetS has been questioned, as its evaluation, in comparison to the traditional Framingham risk prediction model for CVD, seemed to demonstrate no greater predictive power (Stern et al., 2004). But the MetS definition does not include such risk factors such as age, smoking and total cholesterol, which

Copyright © 2009, Elsevier Inc. All rights reserved.

TABLE 97.1

Comparison of metabolic syndrome (MetS) definitions

WHO (1999)

EGIRa (1999)

Glucose intolerance/insulin resistance, defined by: ● Type 2 diabetes; ● Impaired fasting glucose (FBG 6.1 mmol/L); ● Impaired glucose tolerance (2 h PPG 7.8 mmol/L); or ● Insulin resistance (lowest 25% for hyperinsulinemic euglycemic clamp-glucose uptake).

Insulin resistance: ● Hyperinsulinemia (fasting insulin top 25% [non-diabetic population])

Plus 2 of the following: ● Obesity WHR 0.9 (M), 0.85 (F) and/or BMI 30 kg/m2 ●

Dyslipidemia TG 1.7 mmol/L and/or HDL-C 0.9 mmol/L (M), 1.0 mmol/L (F) Hypertension BP140/90 mmHg (and/or medication)

●

Microalbuminuria Urinary albumin excretion rate 20 mg/min or albumin:creatinine ratio 30 mg/g

●

●

●

AACE (2003)

IDF (2005)

Any three of the following: Impaired fasting glucose FBG 6.1 mmol/L

Clinical judgement based on the following: ● Glucose intolerance FBG 6.1 mmol/L or 2 h PPG 7.8 mmol/L

Central adiposity (waist circumference): ● European 94 cm (M), 80 cm (F) ● South Asian and Chinese 90 cm (M), 80 cm (F) ● Japanese 85 cm (M), 90 cm (F)

●

●

●

Central obesity Waist circumference 94 cm (M), 80 cm (F)

●

Dyslipidemia TG 2 mmol/L and/or HDL-C 1.0 mmol/L

●

Hypertension BP 140/90 mmHg (and/or medication)

Central obesity Waist circumference 102 cm (M), 88 cm (F)

●

Overweight/obesity BMI 25 kg/m2

●

Low HDL-C HDL-C 1.04 mmol/L (M), 1.29 mmol/L (F)

Hypertriglyceridemia TG 1.69 mmol/L

●

Hypertension BP 130/  85 mmHg (and/ or medication)

Low HDL-C HDL-C 1.04 mmol/L (M), 1.29 mmol/L (F)

●

Hypertension BP 130/  85 mmHg

●

Other risk factors: Family history of or high-risk ethnic group for T2DM, hypertension or CVD; polycystic ovarian syndrome; sedentary lifestyle; advancing age

Hypertriglyceridemia TG 1.69 mmol/L

Plus 2 of the following: Hypertriglyceridemia TG 1.7 mmol/L

●

●

Low HDL-C HDL-C 1.0 mmol/L (M), 1.3 mmol/L (F)

●

Hypertension BP 130/ 85 mmHg (and/or medication)

●

Glucose intolerance FBG 5.6 mmol/L or pre-existing diabetes

Abbreviations: AACE, the American College of Endocrinology; BP, blood pressure; BMI, body mass index; CVD, cardiovascular disease; EGIR, European Group for the Study of Insulin Resistance; F, female; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; IDF, International Diabetes Federation; M, male; NCEP ATP III, National Cholesterol Education Program Adult Treatment Panel III; PPG, post-prandial glucose; T2DM, type 2 diabetes; TG, triglyceride; WHO, World Health Organization; WHR, ratio of waist-to-hip circumference. a

Non-diabetic subjects only.

Diagnosis: Definition of the MetS Phenotype

●

Plus 2 of the following: ● Impaired fasting glucose FBG 6.1 mmol/L

NCEP ATP III (2001)

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Metabolic Syndrome

contribute largely to the predictive value of the Framingham score; in this context the MetS phenotype is actually complementary to the Framingham score. But despite these questions, the dissemination of the concept of the MetS phenotype has proven to be critical in focussing the attention of both clinicians and researchers toward the importance of obesity, insulin resistance, and related traits as contributors to CVD risk.

PATHOPHYSIOLOGY OF MetS IN BRIEF The predominant underlying risk factors for MetS include abdominal obesity and insulin resistance, with other associated conditions including chronic inflammation and physical inactivity (Eckel et al., 2005). The initial understanding of MetS stemmed from the observation that insulin resistance and hyperinsulinemia acted as the foundation for the clustering of disturbed metabolic symptoms (Reaven, 1988). This aggregation was at first referred to as the “insulin resistance syndrome”. As mentioned above, the more recent IDF definition focuses on the driving force of abdominal obesity in MetS expression (Alberti et al., 2005). Visceral fat accumulation, often due to overnutrition and physical inactivity, results in a milieu of excess circulating free fatty acids, which have lipotoxic effects on target tissues such as the pancreas and muscle, precipitating insulin resistance, and leading, eventually, to hyperglycemia (Eckel et al., 2005). In addition, visceral fat accumulation results in dysregulation of adipocytokine secretion including hyposecretion of adiponectin and hypersecretion of leptin, tumor necrosis factor-α and interleukin-6, which individually or in concert may also mediate many of the changes in MetS (Eckel et al., 2005). Clearly, nutrition is a key environmental factor involved in the pathogenesis and progression of diet-related diseases, such as the MetS. Overnutrition, quantity, underlies visceral fat accumulation and the development of hyperglycemia. Additionally, studies suggest a causal relationship between dietary fatty acid composition, quality, and insulin resistance (Laaksonen et al., 2002). Genetic susceptibility also plays into the equation, with gene–nutrient interactions modulating phenotypic outcomes (Luan et al., 2001). Furthermore, Western dietary patterns appear to promote a state of inflammation (Giugliano et al., 2006). Mechanistically, dietary fatty acids and other nutrient-derived factors can both directly and indirectly affect gene expression via a number of nutrient-sensitive transcription factors (Roche, 2004). Thus, from environmental factors to obesity, to inflammation and insulin resistance, the unraveling of the MetS so far indicates the dynamic interplay between a number of contributing pathogenic factors and pathways.

GENETICS OF MetS Evidence that the MetS Phenotype is Heritable Findings from twin and family studies suggest a heritable component in the clustering of MetS factors. For instance, among

2508 American male twin pairs, clustering of hypertension, diabetes, and obesity was found in 31.6% of monozygotic pairs but only in 6.3% of dizygotic pairs (Carmelli et al., 1994). Similar evidence for heritable factors was found in a twin study of female pairs (Edwards et al., 1997). Numerous family studies have also suggested a genetic influence on MetS, including a study among Japanese-American families that found significant genetic influences on all MetS components, especially dyslipidemia (Austin et al., 2004). The evidence for causative genes underlying MetS has encouraged investigators to study both the rare monogenic forms of MetS and also the common discrete trait using a variety of approaches, including genetic linkage and association analysis. Single Gene Human Models of MetS Study of monogenic forms of MetS might help to better understand the common MetS. Some very rare patients with certain single gene disorders express clusters of abnormalities that are seen in the common MetS (Hegele and Pollex, 2005). For instance, subjects with familial partial lipodystrophy (FPLD) have mutations in either LMNA or PPARG (peroxisome proliferator-activated receptor-γ) genes. These individuals display the defining features of MetS including insulin resistance, dyslipidemia, and hypertension (Cao and Hegele, 2000; Hegele and Pollex, 2005) and have markedly increased CVD risk, especially in women (Hegele, 2001). Detailed phenotypic evaluation of these patients has revealed distinct stages of disease evolution. For instance the initial metabolic disturbance in FPLD is insulin resistance, followed closely thereupon by development of dyslipidemia, with hypertension and diabetes occurring later in disease evolution, and CVD occurring later still (Hegele and Pollex, 2005). The clarified progression of abnormal constituent phenotypes in FPLD has suggested a rational, staged treatment regimen to modulate the natural history and development of the CVD end points. Furthermore, understanding these stages of disease progression might have value not only for patients with this rare condition, but perhaps also for patients with the common MetS phenotype. Genome-Wide Linkage Scans for MetS The strategy of interrogating the entire human genome to detect chromosomal segments that are linked with complex phenotypes has been recently applied to MetS; at least four genome-wide linkage scans have attempted to lay the foundation for finding genes associated with MetS. For instance, a study of 2209 individuals from 507 American families found MetS to be linked with loci on chromosomes 3q27 and 17p12. The quantitative trait locus (QTL) on chromosome 3q27 was strongly linked to six traits: weight, waist circumference, leptin, insulin, insulin/glucose ratio, and hip circumference (Kissebah et al., 2000). The locus on chromosome 17p12 was linked primarily with plasma leptin. A second study of 261 non-diabetic subjects from 27 Mexican-American families showed significant linkage for MetS to two unique regions on chromosome 6 (D6S403 and D6S264) and a region on chromosome 7

Genetics of MetS

(D7S479-D7S471) (Arya et al., 2002). A third study of American families found evidence for linkage on several chromosomal regions (1p34.1, 1q41, 2p22.3, 7q31.3, 9p13.1, 9q21.1, 10p11.2 and 19q13.4) (Loos et al., 2003). A key finding of this study was the observation of ethnic-group specific linkages. Finally, a study of Hispanic families reported that chromosome 1q23-q31 harbored at least one gene related to MetS (Langefeld et al., 2004). Importantly, no specific gene or mutation has been found as a result of any of these studies. Because some of these chromosomal regions have been previously linked with CVD and diabetes risk factors, these regions could harbor potential candidate genes for MetS. The results, however, must be interpreted in light of the caveats for all gene linkage studies involving complex traits such as MetS (Pollex and Hegele, 2005). For instance, replication of QTL linkage peaks even in the exact same study samples can sometimes be difficult for metabolic traits (Pollex and Hegele, 2006a, b). Genetic Mechanisms Underlying the MetS Phenotype Genes could influence the development of MetS in multiple ways (Hegele and Pollex, 2005). Each key component of MetS – obesity, dyslipidemia, dysglycemia, and high blood pressure – is itself a quantitative trait with a genetic basis, and candidate genes have been identified for some of these. For example, visceral obesity, a probable first step in the development of MetS, has been found to be associated with various gene polymorphisms, including variation in ARCP30 encoding adiponectin (Sutton et al., 2005). Such associations with obesity may be fundamental to the development of MetS. In addition, blood pressure has been associated with variation in AGT encoding angiotensinogen (Jeunemaitre et al., 1992) and plasma lipid concentrations have been associated with variation in APOE and APOC3 genes encoding apolipoprotein (apo) E and C-III, respectively (Sing and Davignon, 1985; Waterworth et al., 2000). Thus, variants associated with individual MetS components could underlie association with the entire syndrome. Furthermore, some candidate gene products might act within a common pathway that affects more than one MetS component. For example, GCCR encoding the glucocorticoid receptor has been associated with obesity, hypertension, and insulin resistance (Rosmond, 2002). ARCP30 has been associated with diabetes, hypertension, and dyslipidemia (Kondo et al., 2002; Ohashi et al., 2004). GNB3 encoding the β3 subunit of G protein has been associated with hypertension and obesity (Siffert et al., 1998; Siffert et al., 1999). Variations in genes encoding transcription factors such as FOXC2 and SREBP-1 have been associated with insulin sensitivity and plasma concentrations of triglycerides (Kotzka and Muller-Wieland, 2004; Ridderstrale et al., 2002). These genes might thus be candidates for association studies with the whole MetS phenotype. In addition, detailed profiling of all the metabolites or metabolic markers in a particular biological context, an approach that has sometimes been called “metabolomics”, could be another approach to identify key metabolic pathways and intermediate

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metabolites that can specify candidate genes for the MetS (Griffin and Nicholls, 2006) (see Chapter 15). This strategy, while conceptually appealing, needs to be validated and replicated. Genetic Association Studies for MetS Since 2002, more than 20 genetic association studies with MetS have been reported (Ambye et al., 2005; Andersen et al., 2006; Bing et al., 2005; Costa et al., 2002; Dallongeville et al., 2003; Erkkila et al., 2002; Fernandez et al., 2004; Frederiksen et al., 2002; Frederiksen et al., 2003; Grilo et al., 2006; Guettier et al., 2005; Hamid et al., 2005a, b; Kang et al., 2006; Lee and Tsai, 2002; McCarthy et al., 2003; Meirhaeghe et al., 2005a, b; Pollex et al., 2006; Rhee et al., 2006; Robitaille et al., 2004; Russo et al., 2006; Shen et al., 2006; Steinle et al., 2004), including 15 positive associations (Tables 97.2 and 97.3). Typically these studies involve single nucleotide polymorphism (SNP) variants in candidate genes for MetS. In general, reported associations between MetS and candidate SNP genotypes are modest and lack supportive evidence across multiple study samples. There are conflicting associations between MetS and polymorphisms in genes encoding angiotensinogen I-converting enzyme (ACE) (Costa et al., 2002; Lee and Tsai, 2002), fatty acid-binding protein (FABP2) (Erkkila et al., 2002; Guettier et al., 2005) and GNB3 (Andersen et al., 2006; Pollex et al., 2006). In contrast, positive associations of MetS with common SNPs in APOC3 (Guettier et al., 2005; Pollex et al., 2006) and PPARG (encoding peroxisome proliferator-activated receptor-γ) (Frederiksen et al., 2002; Meirhaeghe et al., 2005a, b) were replicated in more than one study sample. Most other single positive association studies have not yet been replicated. Consistent associations have been reported for MetS with the LMNA gene, encoding lamin A/C (LMNA). This nuclear envelope protein has been shown to harbor rare dysfunctional mutations which were causative for FPLD (see above) (Cao and Hegele, 2000; Hegele and Pollex, 2005). In population studies, LMNA SNPs have been associated with measures of obesity (Hegele et al., 2000; Hegele et al., 2001). In a study of Amish subjects, the common LMNA c.1908C  T polymorphism (trivial name p.H566H), located near the lamin A and lamin C transcript splice site was significantly associated with NCEPdefined MetS (Steinle et al., 2004). Another genetic association with NCEP-defined MetS was reported for the gene encoding the β2-adrenergic receptor (BAR2) in a study of 1195 French men and women (Dallongeville et al., 2003). Two common SNPs change the amino acid sequence at codons 16 and 27 (trivial names p.G16R and p.D27E). Both SNPs were associated with MetS in men but not in women, suggesting gender-dependency of the genetic susceptibility. Similarly, a large-scale association study examining 110 MetS candidate genes among coronary artery disease patients found several associations that were significantly genderdependent (McCarthy et al., 2003). Another gender-specific association has been observed between MetS and the APOC3 gene encoding apo C-III, a protein constituent of triglyceride-rich lipoprotein particles

1198

CHAPTER 97

TABLE 97.2

■

Metabolic Syndrome

Genetic studies showing positive associations with the metabolic syndrome

Gene

Protein product

Polymorphism name

ACE

Angiotensin I-converting enzyme

I/D (intron16)

AGT

Angiotensinogen

T174M

APOC3

Apolipoprotein C-III

BAR2

MetS definition

Sample N, (nationality)

Reference

WHO

1,461 (Chinese)

(Lee and Tsai, 2002)

Females only

NCEP ATPIII

515 (Oji-Cree)

(Pollex et al., 2006)

–455T  C

Females only

NCEP ATPIII

515 (Oji-Cree)

(Pollex et al., 2006)

2-adrenergic receptor

Gly16Arg Gln27Glu

Males only

NCEP ATPIII

1,195 (French)

(Dallongeville et al., 2003)

CAPN10

Calpain-10

Haplotype: SNP43,-19, -63

NCEP ATPIII modified

382 (Korean T2DM)

(Kang et al., 2006)

CAV1

Caveolin-1

22285C  T 22375-22375 del AC 1132T  C

IDF

460 (Spanish)

(Grilo et al., 2006)

FABP2

Fatty acid-binding protein 2

A54T APOC3/FABP2 combined haplotype

NCEP ATPIII

180 (Indian)

(Guettier et al., 2005)

GNB3

G protein 3 subunit

825C  T

NCEP ATPIII

515 (Oji-Cree)

(Pollex et al., 2006)

IL6

Interleukin 6

Promoter haplotype

WHO

2,828 (Danish)

(Hamid et al., 2005a, b)

LDLR

Low-density lipoprotein receptor

Silent SNP, exon 13

NCEP ATPIII

762 (Caucasian; coronary artery disease patients)

(McCarthy et al., 2003)

LMNA

Lamin A/C

H566H

NCEP ATPIII

971 (Amish)

(Steinle et al., 2004)

LRPAP1

Low-density lipoproteinrelated proteinassociated protein 1

Silent SNP, exon 5

NCEP ATPIII

762 (Caucasian; coronary artery disease patients)

(McCarthy et al., 2003)

NOS3

Endothelial nitric oxide synthase

1132T  C

NCEP ATPIII

199 (Spanish)

(Fernandez et al., 2004)

PPARG

Peroxisome proliferatoractivated receptor-

Haplotype including: –681C  G, –689C  T, Pro12Ala, 1431C  T

NCEP ATPIII

1,155 (French)

(Meirhaeghe et al., 2005a, b)

Pro12Ala

EGIR

2,245 (Danish)

(Frederiksen et al., 2002)

NCEP ATPIII

4,018 (Asian)

(Shen et al., 2006)

UCP2

Uncoupling protein 2

–866G  A

Comments

Hypertensive only

Females only

Hypertensive only

Indian men only

Abbreviations: EGIR, European Group for the Study of Insulin Resistance; F, female; NCEP ATP III, National Cholesterol Education Program Adult Treatment Panel III; NS, not significant; SNP, single nucleotide polymorphism; WHO, World Health Organization.

Genetics of MetS

TABLE 97.3

■

1199

Genetic studies showing no association with the metabolic syndrome

Gene

Protein product

Polymorphism name

MetS definition

Sample N (nationality)

Reference

ACE

Angiotensin I-converting enzyme

I/D (intron16)

WHO

643 (Brazilian T2DM)

(Costa et al., 2002)

ADRB3

3-adrenergic receptor

Trp64Arg

EGIR

2,117 (Danish)

(Frederiksen et al., 2003)

FABP2

Fatty acid-binding protein 2

Ala54Thr

NCEP ATPIII

414 (European CHD patients)

(Erkkila et al., 2002)

FATP1

Fatty acid transport protein

Intron 8 48G  A

NCEP ATPIII

1,134 (French)

(Meirhaeghe et al., 2005a, b)

GHRL

Ghrelin

Leu72Met

NCEP ATPIII

2,413 (Danish)

(Bing et al., 2005)

GNB3

G protein 3 subunit

825C  T 825C  T

WHO NCEP ATPIII

7,518 (Danish) 1,134 (French)

(Andersen et al., 2006) (Meirhaeghe et al., 2005a, b)

LEP

Leptin

5 UTR 19G  A

NCEP ATPIII

1,134 (French)

(Meirhaeghe et al., 2005a, b)

LTA

Lymphotoxin-

T60N

WHO

3,036 (Danish)

(Hamid et al., 2005a, b)

MTHFR

Methylene tetrahydrofolate reductase

677C  T

N/A

100 (Italian T2DM)

(Russo et al., 2006)

PPARA

Peroxisome proliferatoractivated receptor-

L162V

NCEP ATPIII

632 (French Canadian males)

(Robitaille et al., 2004)

PPARG

Peroxisome proliferatoractivated receptor-

161C  T Pro12Ala

NCEP ATPIII modified

253 (Korean females)

(Rhee et al., 2006)

PPARGC1A

Peroxisome proliferatoractivated receptor- co-activator 1

Gly482Ser

NCEP ATPIII

2,349 (Danish)

(Ambye et al., 2005)

TNFA

Tumour necrosis factor-

–308G  A

NCEP ATPIII

1,134 (French)

(Meirhaeghe et al., 2005a, b)

UCP3

Uncoupling protein 3

–55C  T

NCEP ATPIII

1,134 (French)

(Meirhaeghe et al., 2005a, b)

Abbreviations: as in Tables 97.1 and 97.2, plus I/D insertion/deletion; WHO, World Health Organization; NCEP ATP III, National Cholesterol Education Program Adult Treatment Panel III; EGIR, European Group for the Study of Insulin Resistance; T2DM, type 2 diabetes mellitus; CHD, coronary heart disease; N/A, not available.

that inhibits the action of lipoprotein lipase. SNPs within the APOC3 promoter have previously been found to be associated with elevated plasma triglycerides. More recently, APOC3 promoter SNPs have been associated with MetS in both aboriginal Canadian females (Pollex et al., 2006) and South Asians (Guettier et al., 2005). Another candidate gene for MetS is PPARG, which encodes a ligand-activated transcription factor that is involved in many biological processes ranging from lipid and glucose metabolism to fatty acid transport to adipocyte

differentiation. Two studies, each involving 1000 participants, have reported positive associations between SNPs in PPARG and MetS. Frederiksen et al. showed a decreased risk of EGIR-defined MetS in homozygotes for the A12 allele of the common PPARG Pro12Ala polymorphism (Frederiksen et al., 2002). While Meirhaeghe et al. found no association between MetS and the same SNP genotype in a French sample, haplotypes that were constructed using three other PPARG SNPs showed an association with NCEP-defined MetS (Meirhaeghe et al., 2005).

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Clinical Recommendations Genomic testing may be contemplated, and perhaps helpful diagnostically, for patients whose clinical phenotype is equivocal for single gene disorders such as lipodystrophy. However, it is premature to propose molecular genetic testing in the context of MetS diagnosis and/or treatment. The totality of evidence, mainly from association studies, indicates that DNA markers are simply not ready for routine clinical use. Unresolved issues include the role of gene–environment interactions, ethnic- and sex-specific associations and modifications to the MetS definition (Pollex et al., 2006).

CONCLUSION The concept of the MetS phenotype has represented a fundamental advance by focussing clinical attention on the importance of abdominal obesity as CVD risk factor. But somewhat paradoxically, MetS does not yet have a single clinically operative definition. It is clear that MetS is a complex trait with numerous features. Furthermore, it results from the interaction of environmental factors, such as caloric excess and physical inactivity, with probable underlying genetic susceptibility factors. While evidence for genetic determinants exists from observations in twins and families, genetic linkage and association studies have not yet identified genomic DNA markers that have even remote potential for clinical use. Furthermore, context-dependent factors such as ethnicity, diet, and gender (Knopp et al., 2005) strongly influence the pathogenesis of MetS. This complexity will further hinder efforts to identify replicable genetic associations that might one day form the basis of clinical predictive tests of susceptibility to, development of complications from, and/or response to interventions for MetS.

Finally, the modern revolution in molecular genetics and genomics has focused our attention on the genetic component of disease, at the expense of the environmental component. This is true not only for MetS but also for cancer, diabetes, obesity, and neuropsychological disorders. The implication that genetics is of prime importance in the etiology of disease is reminiscent of a similar situation a century ago. At that time, just after the discovery of bacteria, the appealing power of the fledgling field of microbiology lead overzealous investigators to naïvely attribute many diseases to microbial causes. Subsequent scientific progress helped to define strict criteria for attributing causation to infectious agents. This imposed a satisfying rigor on the concept of disease etiology and more importantly permitted the development of rational interventional strategies. The current focus on genetic etiologies can be viewed as part of a societal trend in which external, uncontrollable deterministic factors are seen to be of primary importance in any adverse outcome, at the expense of the component of personal responsibility. It is likely that, in many instances, personal decisions and actions will be shown to mitigate the impact of unfavorable genetics. Thus, even the person who carries genes predisposing to MetS can take personal responsibility for actions to avoid victimization by her or his genetic endowment.

ACKNOWLEDGEMENTS Supported by the Jacob J. Wolfe Distinguished Medical Research Chair, the Edith Schulich Vinet Canada Research Chair (Tier I) in Human Genetics, a Career Investigator award from the Heart and Stroke Foundation of Ontario, and operating grants from the Canadian Institutes for Health Research, the Heart and Stroke Foundation of Ontario, the Ontario Research and Development Challenge Fund (Project d0507) and by Genome Canada.

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Hamid, Y.H., Urhammer, S.A., Glumer, C., Borch-Johnsen, K., Jorgensen, T., Hansen, T. and Pedersen, O. (2005). The common T60N polymorphism of the lymphotoxin-alpha gene is associated with type 2 diabetes and other phenotypes of the metabolic syndrome. Diabetologia 48, 445–451. Hegele, R.A. (2001). Premature atherosclerosis associated with monogenic insulin resistance. Circulation 103, 2225–2229. Hegele, R.A., Cao, H., Harris, S.B., Zinman, B., Hanley, A.J. and Anderson, C.M. (2000). Genetic variation in LMNA modulates plasma leptin and indices of obesity in aboriginal Canadians. Physiol Genom 3, 39–44. Hegele, R.A., Huff, M.W. and Young, T.K. (2001). Common genomic variation in LMNA modulates indexes of obesity in Inuit. J Clin Endocrinol Metab 86, 2747–2751. Hegele, R.A. and Pollex, R.L. (2005). Genetic and physiological insights into the metabolic syndrome. Am J Physiol Regul Integr Comp Physiol 289, R663–R669. Jeunemaitre, X., Soubrier, F., Kotelevtsev,Y.V., Lifton, R.P.,Williams, C.S., Charru, A., Hunt, S.C., Hopkins, P.N.,Williams, R.R., Lalouel, J.M. et al. (1992). Molecular basis of human hypertension: Role of angiotensinogen. Cell 71, 169–180. Kahn, R., Buse, J., Ferrannini, E. and Stern, M. (2005). The metabolic syndrome: Time for a critical appraisal Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 48, 1684–1699. Kang, E.S., Nam, M., Kim, H.J., Kim, H.J., Myoung, S.M., Rhee, Y., Ahn, C.W., Cha, B.S. and Lee, H.C. (2006). Haplotype combination of Calpain-10 gene polymorphism is associated with metabolic syndrome in type 2 diabetes. Diabetes Res Clin Pract 73, 268–275. Kissebah, A.H., Sonnenberg, G.E., Myklebust, J., Goldstein, M., Broman, K., James, R.G., Marks, J.A., Krakower, G.R., Jacob, H.J., Weber, J. et al. (2000). Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci USA 97, 14478–14483. Knopp, R.H., Paramsothy, P., Retzlaff, B.M., Fish, B., Walden, C., Dowdy, A., Tsunehara, C., Aikawa, K. and Cheung, M.C. (2005). Gender differences in lipoprotein metabolism and dietary response: Basis in hormonal differences and implications for cardiovascular disease. Curr Atheroscler Rep 7, 472–479. Kondo, H., Shimomura, I., Matsukawa, Y., Kumada, M., Takahashi, M., Matsuda, M., Ouchi, N., Kihara, S., Kawamoto, T., Sumitsuji, S. et al. (2002). Association of adiponectin mutation with type 2 diabetes: A candidate gene for the insulin resistance syndrome. Diabetes 51, 2325–2328. Kotzka, J. and Muller-Wieland, D. (2004). Sterol regulatory elementbinding protein (SREBP)-1: Gene regulatory target for insulin resistance?. Expert Opin Ther Target 8, 141–149. Laaksonen, D.E., Lakka, T.A., Lakka, H.M., Nyyssonen, K., Rissanen, T., Niskanen, L.K. and Salonen, J.T. (2002). Serum fatty acid composition predicts development of impaired fasting glycaemia and diabetes in middle-aged men. Diabet Med 19, 456–464. Langefeld, C.D., Wagenknecht, L.E., Rotter, J.I., Williams, A.H., Hokanson, J.E., Saad, M.F., Bowden, D.W., Haffner, S., Norris, J.M., Rich, S.S. et al. (2004). Linkage of the metabolic syndrome to 1q23-q31 in Hispanic families: The Insulin Resistance Atherosclerosis Study Family Study. Diabetes 53, 1170–1174. Lee,Y.J. and Tsai, J.C. (2002). ACE gene insertion/deletion polymorphism associated with 1998 World Health Organization definition of metabolic syndrome in Chinese type 2 diabetic patients. Diabetes Care 25, 1002–1008.

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Loos, R.J., Katzmarzyk, P.T., Rao, D.C., Rice,T., Leon, A.S., Skinner, J.S., Wilmore, J.H., Rankinen, T. and Bouchard, C. (2003). Genomewide linkage scan for the metabolic syndrome in the HERITAGE Family Study. J Clin Endocrinol Metab 88, 5935–5943. Luan, J., Browne, P.O., Harding, A.H., Halsall, D.J., O’Rahilly, S., Chatterjee,V.K. and Wareham, N.J. (2001). Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes 50, 686–689. McCarthy, J.J., Meyer, J., Moliterno, D.J., Newby, L.K., Rogers, W.J. and Topol, E.J. (2003). Evidence for substantial effect modification by gender in a large-scale genetic association study of the metabolic syndrome among coronary heart disease patients. Hum Genet 114, 87–98. Meirhaeghe, A., Cottel, D., Amouyel, P. and Dallongeville, J. (2005). Association between peroxisome proliferator-activated receptor gamma haplotypes and the metabolic syndrome in French men and women. Diabetes 54, 3043–3048. Meirhaeghe, A., Cottel, D., Amouyel, P. and Dallongeville, J. (2005). Lack of association between certain candidate gene polymorphisms and the metabolic syndrome. Mol Genet Metab 86, 293–299. NCEP (2001). Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285, 2486–2497. Ohashi, K., Ouchi, N., Kihara, S., Funahashi, T., Nakamura, T., Sumitsuji, S., Kawamoto, T., Matsumoto, S., Nagaretani, H., Kumada, M. et al. (2004). Adiponectin I164T mutation is associated with the metabolic syndrome and coronary artery disease. J Am Coll Cardiol 43, 1195–1200. Pollex, R.L. and Hegele, R.A. (2005). Complex trait locus linkage mapping in atherosclerosis: time to take a step back before moving forward?. Arterioscler Thromb Vasc Biol 25, 1541–1544. Pollex, R.L. and Hegele, R.A. (2006a). Genetic determinants of the metabolic syndrome. Nat Clin Pract Cardiovasc Med 3, 482–489. Pollex, R.L. and Hegele, R.A. (2006b). Longitudinal differences in familial combined hyperlipidemia quantitative trait loci. Arterioscler Thromb Vasc Biol 26, e120. Pollex, R.L., Hanley, A.J., Zinman, B., Harris, S.B., Khan, H.M. and Hegele, R.A. (2006). Metabolic syndrome in aboriginal Canadians: Prevalence and genetic associations. Atherosclerosis 184, 121–129. Reaven, G.M. (1988). Banting lecture 1988 Role of insulin resistance in human disease. Diabetes 37, 1595–1607. Rhee, E.J., Oh, K.W., Lee, W.Y., Kim, S.Y., Oh, E.S., Baek, K.H., Kang, M.I. and Kim, S.W. (2006). Effects of two common polymorphisms of peroxisome proliferator-activated receptor-gamma gene on metabolic syndrome. Arch Med Res 37, 86–94. Ridderstrale, M., Carlsson, E., Klannemark, M., Cederberg,A., Kosters, C., Tornqvist, H., Storgaard, H., Vaag, A., Enerback, S. and Groop, L. (2002). FOXC2 mRNA Expression and a 5’ untranslated region polymorphism of the gene are associated with insulin resistance. Diabetes 51, 3554–3560.

Robitaille, J., Brouillette, C., Houde, A., Lemieux, S., Perusse, L., Tchernof, A., Gaudet, D. and Vohl, M.C. (2004). Association between the PPARalpha-L162V polymorphism and components of the metabolic syndrome. J Hum Genet 49, 482–489. Roche, H.M. (2004). Dietary lipids and gene expression. Biochem Soc Trans 32, 999–1002. Rosmond, R. (2002). The glucocorticoid receptor gene and its association to metabolic syndrome. Obes Res 10, 1078–1086. Russo, G.T.,Di Benedetto,A.,Alessi, E., Ientile,R.,Antico,A., Nicocia, G., La Scala, R., Di Cesare, E., Raimondo, G. and Cucinotta, D. (2006). Mild hyperhomocysteinemia and the common C677T polymorphism of methylene tetrahydrofolate reductase gene are not associated with the metabolic syndrome in Type 2 diabetes. J Endocrinol Invest 29, 201–207. Shen, H., Qi, L., Tai, E.S., Chew, S.K., Tan, C.E. and Ordovas, J.M. (2006). Uncoupling Protein 2 Promoter Polymorphism -866G/A, Central Adiposity, and Metabolic Syndrome in Asians. Obesity (Silver Spring) 14, 656–661. Siffert, W., Rosskopf, D., Siffert, G., Busch, S., Moritz, A., Erbel, R., Sharma, A.M., Ritz, E., Wichmann, H.E., Jakobs, K.H. et al. (1998). Association of a human G-protein beta3 subunit variant with hypertension. Nat Genet 18, 45–48. Siffert, W., Forster, P., Jockel, K.H., Mvere, D.A., Brinkmann, B., Naber, C., Crookes, R., Du, P.H.A., Epplen, J.T., Fridey, J. et al. (1999).Worldwide ethnic distribution of the G protein beta3 subunit 825T allele and its association with obesity in Caucasian, Chinese, and Black African individuals. J Am Soc Nephrol 10, 1921–1930. Sing, C.F. and Davignon, J. (1985). Role of the apolipoprotein E polymorphism in determining normal plasma lipid and lipoprotein variation. Am J Hum Genet 37, 268–285. Steinle, N.I., Kazlauskaite, R., Imumorin, I.G., Hsueh, W.C., Pollin, T.I., O’Connell, J.R., Mitchell, B.D. and Shuldiner, A.R. (2004). Variation in the lamin A/C gene. Associations with metabolic syndrome. Arterioscler Thromb Vasc Biol 24, 1708–1713. Stern, M.P., Williams, K., Gonzalez-Villalpando, C., Hunt, K.J. and Haffner, S.M. (2004). Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease?. Diabetes Care 27, 2676–2681. Sutton, B.S., Weinert, S., Langefeld, C.D., Williams, A.H., Campbell, J.K., Saad, M.F., Haffner, S.M., Norris, J.M. and Bowden, D.W. (2005). Genetic analysis of adiponectin and obesity in Hispanic families: The IRAS Family Study. Hum Genet 117, 107–118. Waterworth, D.M., Talmud, P.J., Bujac, S.R., Fisher, R.M., Miller, G.J. and Humphries, S.E. (2000). Contribution of apolipoprotein CIII gene variants to determination of triglyceride levels and interaction with smoking in middle-aged men. Arterioscler Thromb Vasc Biol 20, 2663–2669. WHO (1999). Definition, diagnosis and classification of diabetes mellitus and its complications. Part I: Diagnosis and classification of diabetes mellitus.

RECOMMENDED RESOURCES Website National Cholesterol Education Program: http://www.nhlbi.nih. gov/about/ncep/ Home of the most commonly used guidelines

for MetS diagnosis, the Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).

Recommended Resources

Journals Grundy S.M. (ed). (2004). Metabolic syndrome: Part I and II. Endocrinol Metab Clin North Am 33(2, 3). A collection of 19 review articles on the cause, prevalence, outcome and management of MetS.

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Reaven, G. M. (1988). Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37, 1595–1607. First paper which introduced the concept of a syndrome of abnormalities linked to insulin resistance, putting “syndrome X” on the map.

CHAPTER

98 Nutrition and Diet in the Era of Genomics Jose M. Ordovas and Dolores Corella

INTRODUCTION The genomic revolution has catapulted the development of several new technologies that can be applied in nutritional sciences. The potential benefits of harnessing the power of genomics for dietary prevention of disease are enormous, and this approach is considered the future of nutritional research in the postgenomic era. The prominent role of diet in the etiology of disease was recognized first for diseases related to micronutrient deficiency and then for multifactorial disorders related to over-nutrition. Therefore, the major practical translation of nutrition research to public health consists of defining optimal dietary recommendations aimed to prevent disease and to promote health for everybody and for each stage of human life. For this purpose, several dietary guidelines have been implemented to improve the health of the general population and of those at high risk for specific diseases (i.e., cardiovascular disease [CVD], cancer, hypertension, and diabetes). The characteristics of those dietary recommendations have been determined by expert committees following careful examination of the best scientific evidence available at the time. However, while some of these guidelines have been tailored to some population subgroups, for example, the elderly or women who are pregnant or breastfeeding, past and current dietary guidelines have not considered the dramatic differences on the individual’s physiological response to changes in nutrient intake. Moreover, specific dietary recommendations to treat specific diseases have not considered individual differences. Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1204

These differences in response may greatly affect the efficacy of these recommendations at the individual level (Corella and Ordovas, 2005a; Stover, 2006). The mechanisms responsible for the interindividual differences in dietary response are, expectedly, very complex and therefore far from being fully understood. Nevertheless, the significant role of genetic factors contributing to those individual differences in response to nutrients has been proposed for several decades (Holtzman, 1988) and successfully demonstrated for rare inborn errors of metabolism. More recently, and powered with new technologies, researchers began to comprehensively examine these nutrient–gene interactions at the molecular level for metabolic alterations that affect the population at large. The combined evidence supports the notion that these diseases are triggered because of interactions between specific genes and environmental factors (Tiret, 2002). These interactions are dynamic, beginning at conception and continuing through adulthood. The concept of “environment” is complex and broad, and it has been frequently associated with tobacco smoking, drug consumption, exposures to pollutants, education, and socioeconomic status (Corella and Ordovas, 2005b). However, food intake is the environmental factor to which we are all exposed, necessarily and permanently, from conception to death, and it has been a major driving force through species’ evolution. Therefore, dietary habits are the most important environmental factor modulating gene expression during one’s life span. Copyright © 2009, Elsevier Inc. All rights reserved.

Methodological Issues

The concept of gene–diet interaction describes the modulation of the effect of a dietary component on a specific phenotype (i.e., plasma lipid concentrations, glycemia, obesity) by a genetic variant. Alternatively, this notion refers to the dietary modification of the effect of a genetic variant on a phenotypic trait. The potential benefits of harnessing the power of genomics for dietary prevention of disease are enormous, and this approach will lead nutritional research in the postgenomic era (Afman and Muller, 2006; DeBusk et al., 2005; Keusch, 2006; Mensink and Plat, 2002). The genomic revolution has fostered the development of several complementary technologies that will greatly benefit nutritional sciences (Collins et al., 2003). In addition to genomics, techniques such as proteomics, metabonomics, and bioinformatics are already providing insights about gene–nutrient interactions at the cell, individual, and population level (Daniel, 2002). All these techniques can and should be combined to understand both the influence of specific nutrients and the whole dietary patterns on the metabolic behavior of cells, organs, and the whole organism (Corthesy-Theulaz et al., 2005; Kussmann et al., 2006; Roberts et al., 2001). This challenge can be accomplished by using bioinformatics and chemometrics that provide tools for managing the large and complex datasets provided by genomics, transcriptomics, proteomics, and metabolomics, and constitute what we know as functional genomics, also referred to as systems biology (van Ommen and Stierum, 2002). The development of systems biology transformed the concept of gene–nutrient interaction from the traditional reductionist approach of studying the effect of a nutrient over a specific metabolic event into a global one, in which a significant fraction of all regulated genes and metabolites can be queried simultaneously (Go et al., 2003). Holistically, the whole is the dynamic interaction of the parts. According to Hoffmann (2003), these goals can be accomplished if scientists have: (a) knowledge of the part (nutrients, food, and dietary patterns); (b) valid information: adequate experimental design, dietary assessments, and statistical methods; (c) tools to study and visualize more complex models of interactions; and (d) massive computer power to integrate information, and (e) an interdisciplinary approach by transgressing the boundaries between and beyond disciplines and institutions. Driven by these technologies and paradigms, nutrition science has embraced “nutritional genomics” (Go et al., 2003; Muller and Kerstenet, 2003; van Ommen and Stierum, 2002), promoting an increased understanding of (a) how nutrition influences metabolic pathways and homeostatic control, (b) how this regulation is altered in the early phase of a diet-related disease, and (c) to what extent individual sensitizing genotypes contribute to such disease. Nutritional genomics has already raised high interest and expectations, and some researchers (Haga et al., 2003) warn that genomic profiling and its interaction with environmental factors such as diet is not ready for prime time. It is true that evidence supporting health outcome benefits based on such testing is lacking, and that before this approach becomes valid and clinically useful, well-designed epidemiologic studies and clinical

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evaluations of recommended interventions based on genotype are required. This work provides the current state-of-the-art on nutritional genomics at the population level for both monogenic and multifactorial diseases. Because a major limitation of nutritional genomics is the lack of well designed and conducted epidemiological studies, major emphasis has been placed on applying epidemiological principles to the study of nutritional genomics, not only to interpret the results of published studies, but also to provide guidance on the design of new investigations in this area.

METHODOLOGICAL ISSUES Nutritional genomics is a concept that may revolutionize public health. As indicated above, one goal of nutritional genomics is to find genetic markers that reveal significant gene–diet interaction, thus providing tools for personalized and more successful dietary recommendations (“nutrigenomics”). Academic researchers, the public, industry, and some government agencies have a lot of interest in this increasingly popular topic. However, before this science can be translated into public use, findings need to be validated by solid scientific evidence, which at the present time is mostly absent. To avoid, or at least minimize, the adverse effects on both scientific and public confidence that current knowledge may create, one must understand the strengths and limitations of the published evidence. Therefore, it would be useful to apply the principles of evidence-based medicine (Guyatt et al., 2000; Oxman et al., 1993) and epidemiology (Haga et al., 2003) to nutritional genomics when causality is inferred from the results of association studies. Below, we discuss some of the limitations that need to be overcome to make this science more sound and reproducible. Dietary Assessment Dietary assessment plays a crucial role in our ability to detect relationships between dietary exposure and disease causation. Therefore, high-quality dietary information is a key to establishing causality in nutritional genomics. However, the uncertainties associated with the current instruments to assess dietary intake have been identified as the Achilles’ heel for the study of gene– diet interactions in population/observational studies. The best approach to ascertain true dietary intake is within the context of prospective dietary intervention studies carried out under highly controlled conditions. However, these wellcontrolled feeding studies have several important logistic limitations (Most et al., 2003), including their cost, the small number of participants, and the brief duration of the interventions. Therefore, a considerable proportion of our knowledge relating dietary intake to phenotypes and disease risk comes from population studies using, for the most part, self-reported dietary questionnaires. Diet records, diet-history questionnaires, 24-h recalls, or food-frequency questionnaires (FFQs) are the most common methods to get individual dietary intakes (Willett,

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1987). Each method has strengths and weaknesses. To date, FFQ has been the dietary assessment method most commonly used in large-scale studies; however, its validity has been increasingly questioned (Kipnis et al., 2002; Kristal et al., 2005; Schaefer et al., 2000). An important question for nutritional epidemiology studies that involve nutritional genomics is: Which type of dietary information is more relevant? Should we use foods, nutrients, or dietary patterns? Food preparation and cooking methods can significantly affect the final nutrient content in foods. Food items contain thousands of specific chemical compounds, some known and well quantified, some poorly characterized, and others subject to geographical and seasonal variability or still undefined. With the expanding knowledge of the role of nutrients and bioactive compounds in gene expression and cellular response, nutritional genomics needs a new definition of nutrients (Young, 2002). Jacobs and Steffen (2003) proposed complementary research methodology in which the study of foods, food patterns, and individual nutrients of food components are considered together. This integrative approach could be useful in nutritional genomics and it is currently being used by ongoing studies, including the Framingham Heart Study, in which dietary intake is measured in terms of foods, nutrients, and dietary patterns to explore the influence of diet and the possible genetic modulation in metabolic syndrome and CVD (Millen et al., 2005, 2006; Sonnenberg et al., 2005). This integrative approach in dietary assessment can be further improved by measuring some biochemical indicators to represent the more objective measures of dietary intake for specific nutrients (Bingham, 2002; Neuhouser et al., 2003). However, we still lack reliable biomarkers for many significant nutrients. These current limitations should be successfully resolved by incorporating the new analytical techniques of the postgenomic era. Thus, one goal for nutritional genomics is to identify biomarkers that will provide better guidance on the relationship between nutrition and health. Genetic Assessment Nutritional genomics studies must have a good measurement of diet as well as a good measurement of genotype. The study of genetic susceptibility requires a more careful definition of “genotype.” Currently, there are scores of single nucleotide polymorphisms (SNP) that can be easily determined at low cost, giving rise to the opportunity to study a large set of SNP combinations increasing the level of complexity (Suh and Vijg, 2005). Another relevant illustration of the complexity of studying genetic data in nutrition is the fact that variation in the human genome is not only present in the form of SNPs. Insertions, deletions, and recently, large-scale copy-number variations that involve gains or losses of several kilobases of DNA, have been reported to be common in the general population (Sebat et al., 2004). With the application of high-throughput methods (Barrett and Cardon, 2006; Evans and Cardon, 2006; Herbert et al., 2006; Nicolae et al., 2006), quality control procedures in the laboratory are particularly important. Misclassifying the genotype (i.e., a dataset containing less than 95% reproducibility) can bias measure of association between genotype and disease, and largely affect

gene–nutrient interactions. Quality control measures, including internal validation, blinding, duplicates, test failure rate, inspection of whether genotype frequencies conform to Hardy– Weinberg equilibrium, and blind data entry, must be reported in the methodology section (see Chapters 7–9). Another development is the use of haplotypes (Daly et al., 2001), instead of individual SNPs, for genomic analysis. Various statistical algorithms have been developed to estimate haplotypes from genotypic data in unrelated individuals (Salem et al., 2005). However, many limitations and inconsistencies are still present in these estimations. Equal concern evolves from the use of microarrays in nutritional genomics (Kidgell and Winzeler, 2005; Page et al., 2003; Potter, 2003).

GENE–NUTRIENT INTERACTIONS Despite the limited number of studies and the shortcomings of their experimental designs, the preliminary evidence about gene–diet interactions for CVD and cancer is both revealing and promising (Ordovas, 2006a; Ulrich, 2005). Currently, this area of research focuses first on identifying the genes and markers responsible for these interaction effects, and second on characterizing the mechanisms responsible for those gene–nutrient interactions. As previously mentioned, it is important to consider the dynamic nature of these interactions through the life span. First, fetal development and the “in uterus” conditions would be essential to establish the first gene–nutrient interactions. Second, in some conditions, as in the case of inborn errors of metabolism, nutrition in the first years of life is a key determinant of health or disease status. Third, for multifactorial diseases such as atherosclerosis and cancer, a long period of exposure to the same dietary pattern would be necessary to develop the disease phenotype (Leong et al., 2003). The hormonal environment could also be a major determinant of the interaction, and this is especially important in women’s health and could be the basis for future genome-based gender- and agespecific recommendations. Traditionally, genetic diseases have been classified as monogenic, usually rare and determined by a single gene, or multifactorial, common and determined by several genes in combination with other nongenetic factors. However, this classification is an oversimplification, and the reality is far from clear cut. This is evident from the dramatic phenotypic diversity of the so-called classical monogenic diseases reflecting the heterogeneity of mutations at the major locus, the action of some secondary and tertiary modifiers, and the influence of a wide range of environmental factors. Therefore, most monogenic traits share some of the features found on the multifactorial diseases. Diet may be the most influential environmental factor modulating the phenotypes for both monogenic and multifactorial diseases. Therefore, nutritional genomics provides the tools and the evidence to modulate the phenotypic expression of these diseases. Pragmatically, the goals of nutritional genomics will be easier to accomplish in monogenic than in polygenic

Gene–Nutrient Interactions

diseases. Therefore, understanding the genetic interactions that determine the phenotype for rare monogenic diseases should help us gain further insight into the more complex interactions between several genes and environmental factors involved in the phenotypic expression of multifactorial diseases. Examples of Gene–Diet Interactions in Monogenic Diseases Classical monogenic diseases in which diet plays a determinant role in the final phenotype (such is the case of phenylketonuria, galactosemia and lactose intolerance) illustrate the key role of nutrition on the clinical manifestation of disease. 1. Phenylketonuria (PKU) is an autosomal recessive disorder resulting from phenylalanine (Phe) hydroxylase (PAH, EC 1.14.16.1) deficiency. Phe is toxic to the brain, and if untreated, severe mental retardation occurs. PKU is considered one of the first and best examples of gene–diet interactions because the mental retardation in subjects with mutations in the PAH gene is preventable with dietary modification (Santos et al., 2006; Scriver and Waters, 1999). PKU is the most common inborn error of amino acid metabolism in Caucasians, with an average incidence of 1/10,000 (see Cederbaum, 2002, for review). This condition can be detected by measuring serum Phe levels, and the detection of hyperphenylalaninemia (HPA) is included in the newborn screening programs of most Western countries (Guthrie and Susi, 1963). A Phe-restricted diet (well-adjusted to the tolerance) has been the mainstay of treatment for PKU (Bickel et al., 1953). Currently, this intervention has the highest level of experimental evidence. Phe is an essential amino acid found in all protein foods, implementing a Phe-restricted diet after birth reduces blood Phe concentrations and avoids the severe consequences of untreated PKU. 2. Clinical galactosemia is a complex trait in which multiple developmental and metabolic pathways are involved (Ridel et al., 2005). As an inborn error of metabolism, it is defined as an autosomally inherited disorder of galactose metabolism, which occurs because of a deficiency of one of three principal enzymes involved in the metabolism of galactose, through its conversion to glucose (Novelli and Reichardt, 2000).These enzymes are galactose-1-phosphate uridyltransferase (GALT; EC2.7.7.10), galactokinase (GALK; EC2.7.1.6), and uridinediphosphate galactose-4 epimerase (GALE; EC 5.1.3.2). The most common deficiency in all communities is that of the transferase enzyme, and this is the enzyme deficiency associated with “classical galactosemia.” Incidence of classical galactosemia is approximately 1:40,000 live births. This condition usually presents in the neonatal period with failure to thrive, feeding difficulties, and prolonged conjugated hyperbilirubinemia. It can be fatal if a lactose-/galactose-restricted diet is not introduced. The therapeutic effects of this galactose-restricted diet in children with GALT deficiency

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constitute an example of gene–nutrient interaction with the highest level of evidence and with long tradition and widespread use in clinical practice. Thus, as general recommendation, as soon as there is evidence of galactosemia (either biochemical, genetic, or clinical), galactose must be excluded from the diet (Walter et al., 1999) to prevent the lethal consequences of this defect. This means a lifelong avoidance of normal milk or dairy products. However, this GALT deficiency remains an enigma. Instituting a galactose-restricted diet, which has been the therapy used for more than six decades, alleviates the neonatal toxicity syndrome but fails to prevent long-term complications. 3. Lactose intolerance is a disorder with a very high prevalence worldwide (Campbell et al., 2005). The digestive system of lactose-intolerant individuals cannot break down the lactose. This inability results from a shortage of the enzyme lactase, which is produced by the cells that line the small intestine. This disorder presents dramatic geographical differences. It is least common among people of northern European descent, where lactase deficiency is present in no more than 15% of the adult population. The prevalence increases up to 80% in African Americans and Hispanics, and even higher (95%) in American Indians and Asians. However, in most cases, lactase deficiency develops naturally over time when, after about the age of 2, the body begins to produce less lactase. Most people do not experience symptoms until they reach an older age. Because of this gene–nutrient interaction, subjects with specific mutations in the gene(s) involved should avoid lactose-containing foods. The degree of lactose intolerance varies greatly among patients with lactose intolerance, which suggests an important allelic effect of some particular mutations. However, to date, there are no results from studies examining this specific gene–nutrient interaction at the molecular level (Bodlaj et al., 2006). There are numerous other examples of gene–diet interactions among the classically known inborn errors of metabolism, which tend to be found in very low prevalence (Scriver and Waters, 1999). Early identification and treatment of these genetic diseases require prompt diagnosis and correction of metabolic abnormalities by dietary restriction of the offending substances and, in most cases, the consumption of various special formulas to meet the nutritional requirements (Prietsch et al., 2002). Gene–Diet Interactions in Multifactorial/ Age-Related Diseases Multifactorial diseases such as CVD, cancer, osteoporosis, and neurological diseases are usually associated with the aging process. Therefore, they are currently the major health problems in a world that is growing older (Tinker, 2002). Cardiovascular Disease CVD represents the paradigm of multifactorial disorders encompassing multiple genetic and modifiable risk factors. The

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current recommendations aim to reduce the classical modifiable risk factors, and much emphasis has been placed on controlling high-plasma cholesterol levels. However, this is just one of a constellation of risk factors associated with CVD. The link between dyslipidemia and the development of atherosclerosis was established a few decades ago and is now widely accepted (Figure 98.1). The National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) publishes updated guidelines for treating lipid disorders. The last version is the ATPIII.These guidelines consider dietary modification the cornerstone of primary prevention, with emphasis on reducing the high-saturated fat atherogenic diet, as well as controlling other behavioral factors such as sedentary lifestyle (Third Report NCEP, 2002). However, despite these and other guidelines, we are still far from accurately predicting disease and from knowing how many individuals can achieve the recommended goals using the proposed global approaches. The latter problem exists because the relationships between dietary changes and serum lipid changes are predictable for groups; however, due to the striking variability in the interindividual response of serum cholesterol to diet, we cannot predict individual response. However, there is evidence that the dramatic variation in individual plasma lipids in response to changes in dietary fat and cholesterol has a genetic component that may be accounted in part by lipidrelated candidate genes. Many of these genes have been explored in terms of gene–diet interactions. A detailed account of each of these studies and their interpretation would exceed the space available for this work, but they were the subject of recent comprehensive reviews (Loktionov, 2003; Masson et al., 2003; Rubin and Berglund, 2002). We highlight some of the loci, specifically APOE, APOA1, APOA5, and LIPC that provide relevant examples for different types of interactions with specific dietary components. Information about other genes that show significant gene–diet interaction in the modulation of plasma lipid were detailed in a systematic review (Masson et al., 2003). Apolipoprotein E

The Apolipoprotein E (APOE) gene has been the locus most intensively examined in terms of its potential to determine the individual variability in its low-density lipoprotein (LDL) cholesterol response to diet interventions. This interest is obvious, considering APOE’s pivotal role in lipoprotein metabolism. APOE in serum is associated with chylomicrons, very low-density lipoproteins (VLDL), and high density lipoproteins (HDL), and serves as a ligand for multiple lipoprotein receptors (Mahley, 1988). The best studied genetic variation at the APOE locus results from three common alleles in the population, E*4, E*3, and E*2, with frequencies in Caucasian populations of approximately 0.15, 0.77, and 0.08, respectively (Davignon et al., 1988). Population studies show that plasma cholesterol, LDL cholesterol (LDL-C), and APOB levels are highest in subjects carrying the E*4, intermediate in those with the E*3, and lowest in those with the E*2 alleles (Ordovas et al., 1987; Schaefer et al., 1994). However, these studies also pointed to the possibility that the higher LDL-C levels observed in subjects carrying the E*4 allele were manifested primarily in the presence of an atherogenic diet and brought up

the notion that the response to dietary saturated fat and cholesterol could differ among individuals carrying different APOE alleles. Previous findings related to this locus have been extensively reviewed (Ordovas, 1999, 2001, 2002; Rubin and Berglund, 2002). However, despite the numerous studies examining the relation between APOE genetic variability and LDL-C response to diet intervention, there is considerable inconsistency regarding the magnitude and significance of the reported associations, and this locus continues to be the subject of intense research. Rubin’s revision (Rubin and Berglund, 2002) includes 29 intervention studies that examine APOE–diet interactions. A total of 3224 subjects participated in these studies, ranging from 16 to 420 subjects per study. Of the 29 studies, 12 demonstrated no significant APOE–diet interactions, 15 reported significant interactions (E*4 was usually associated with increased dietary response), and 2 were undefined. Using the same available literature, but different selection criteria, Masson et al., (2003) reviewed 62 dietary intervention periods, including 3223 subjects. Again, the range of the studies varied between 8 and 210 subjects per dietary intervention. According to this review, 42 of the diet interventions did not demonstrate significant APOE–diet interactions, and only 19 provided evidence for significant interactions, clearly demonstrating the diversity of the results presented in the original papers as well as those obtained from review papers. This heterogeneity is expected on the basis of the multifactorial characteristics of the phenotypes examined and underscores the need for more comprehensive genetic panels combined with better assessment of the environmental factors. Although the obvious dietary factors implicated in gene– diet interactions affecting plasma lipid levels are dietary fats and cholesterol, other dietary components have revealed significant interactions. This is the case for alcohol intake. Although the raising effect of alcohol consumption on HDL-cholesterol levels is well established, the effect on LDL-C is still unclear. It is possible that the reported variability will be due to interactions between genetic factors and alcohol consumption. Our analyses in the Framingham Study (Corella et al., 2001) show that in male nondrinkers, LDL-C levels were not different across APOE groups; however, in male drinkers, there were differences in LDL-C, with E*2 subjects displaying the lowest levels.When LDL-C levels were compared among the APOE subgroups by drinking status, LDLC levels in E*2 male drinkers were lower than in E*2 nondrinkers. Conversely, in E*4 males, LDL-C was higher in drinkers than in nondrinkers. In women, the expected effect of APOE alleles on LDL-C levels was present in both drinkers and nondrinkers. These data suggest that in men variability at this locus modulates the effects of consuming alcoholic beverages on LDL-C levels. Apolipoprotein A-I

Apolipoprotein A-I (APOA1) is the major apolipoprotein of HDL and plays a central role in lipid metabolism and CVD risk (Segrest et al., 2000). Therefore, the APOA1 locus is a prime candidate for studying genetic variability in HDL levels. APOA1 maps to the long arm of chromosome 11, clustered with APOC3, APOA4, and APOA5. A common G to A transition (G/A) located 75-bp upstream from the transcription

Gene–Nutrient Interactions

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Dietary fat and cholesterol

Liver

Bile salts and cholesterol

Intestine FA

FABP2

CYP7A1

TG NCP1L1

UC Sit

ACAT2

CE

C LDLR

APOBEC1 B48

C

ABCG8

LRP Chylo-R B48,E,C3

ABCG5

FFA

Chylos B48,A1, A4,C2

LPL

Chylos B48,A1, A4,C2

TG

MTP

SCARB1

PLTP

HTGL

CETP

LDLR

HDL(AI,A2)

HDL2 AI,AII

Lp(a) B100 Apo(a)

CETP VLDL B100,E,C2

PLTP

FFA

ABCA1

Apo(a)

HDL3 AI,A2,E

FFA

Adipose tissue

SCARB1

CE

FFA

PLIN HSL

B100

ACAT2

APOA1

ADIPOQ

HMGCR

TG

MTP

LPL

CD36

IDL B100 E

HTGL LDL B100

APOA5 LDLR

Pheripheral tissue EL

Macrophage Foam cell

CD36

oxLDL

mmLDL

LDL

Figure 98.1 Human lipoprotein metabolism.Abbreviations: A1 (apolipoprotein A-I); A2 (apolipoprotein A-II); A4 (apolipoprotein AIV); A5 (apolipoprotein A-V); ABCA1 (ATP-Binding Cassette, Subfamily A, Member 1); ABCG5 (ATP-Binding Cassette, Subfamily G, Member 5); ABCG8 (ATP-Binding Cassette, Subfamily G, Member 8); ACAT2 (cytosolic acetoacetyl-CoA thiolase); apo (apolipoprotein); APOBEC1 (apolipoprotein B mRNA editing enzyme); B100 (apolipoprotein B-100); B48 (apolipoprotein B-48); C2 (apolipoprotein C-II); C3 (apolipoprotein C-III); CE (cholesteryl esters); CD36 (CD36 Antigen) CETP (cholesteryl ester transfer protein); Chylos (chylomicron); Chilo-R (chylomicron remnants); CYP7A1 (cholesterol-7-alpha-hydroxylase); E (apolipoprotein E); FA (fatty acids); FABP2 (intestinal fatty acid binding protein); FFA (free fatty acids); LDL (low-density lipoprotein); HMGCR (3-hydroxy-3methylglutaryl-CoA reductase); LDLR (LDL receptor); LPL (lipoprotein lipase); NPC1L1 (Niemann-Pick C1-like 1); TG (triglycerides); HDL (high-density lipoprotein); HTGL (hepatic triglyceride lipase); IDL (intermediate density lipoproteins); mmLDL (minimally modified LDL); Lp(a) (lipoprotein (a); MTP (microsomal triglyceride transfer protein); oxLDL (oxidized LDL); PLTP (phospholipid transfer protein); VLDL (very low-density lipoprotein); SCARB1 (scavenger receptor type I B); Sit (sitosterol); UC (unesterified cholesterol); PLIN (perilipin); HSL (hormone-sensitive lipase); ADIPOQ (adiponectin). Those components of the lipoprotein pathway that have been the subject of investigation related to gene–diet interactions are shown with white font over black background. Adapted from Corella and Ordovas (2005a).

start site of the APOA1 gene has been extensively studied. This polymorphism was associated initially with apoA-I and HDLC (Jeenah et al., 1990) concentrations, and individuals carrying the minor A-allele presented higher levels, compared with G/G subjects (Pagani et al., 1992). Subsequent studies examining this association have shown contradictory results (Juo et al., 1999). Environmental factors modulating the effect of this genetic variant could explain these disparities. Some studies investigated

the possible interaction with tobacco smoking (Talmud et al., 1994), whereas we focused on the potential interaction with diet using both intervention (Lopez-Miranda et al., 1994; Mata et al., 1998) and observational studies (Ordovas et al., 2002a). Our data supported a significant gene–diet interaction modulating the response of plasma lipids to dietary modification. Specifically, in women participating in the Framingham Study, HDL-C concentrations were associated with a significant interaction between

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PUFA intake and APOA1 genotype. Our data show that G/G women consuming 6% PUFA/day had higher HDL-C than A-carriers. Conversely, when consuming 6% PUFA/day, G/G had lower HDL-C concentrations than A-carriers. These findings suggest that the expression of the APOA1 gene could be regulated by PUFA. In men, the situation was more complex because the effects were observed using three-way interactions, including smoking and alcohol consumption, in the analyses. Apolipoprotein A-V

The Apolipoprotein A-V (APOA5) gene has been shown to play an important role in determining plasma triglyceride (TG) concentrations in humans (Pennacchio et al., 2001). Several polymorphisms within this locus (1131T  C, 3A  G, 56C  G, IVS3  476G  A, 1259T  C, etc.) have been identified, and their minor alleles have been reported to be significantly associated with increased plasma TG levels in several populations (Lai et al., 2003, 2004; Pennacchio et al., 2002). We investigated the interaction between APOA5 gene variation and dietary fat in determining plasma fasting TGs, remnant-like particle (RLP) concentrations, and lipoprotein particle size in the Framingham Heart Study (Lai et al., 2006). Polymorphisms 1131T/C and 56C/G, representing two independent haplotypes, were analyzed. Significant gene–diet interactions between the 1131T/C polymorphism and PUFA intake were found in determining fasting TGs, RLP concentrations, and particle size, but these interactions were not found for the 56C/G polymorphism.The 1131C allele was associated with higher fasting TGs (about 20%) and RLP concentrations (about 30%) in only those subjects consuming a high-PUFA diet (6% of total energy). Although the magnitude of the effect was slightly greater in men, similar directions of effects were observed and no statistically significant heterogeneity by sex was found. These interactions showed a dose–response effect when PUFA intake was considered as a continuous variable (p  0.01). Similar interactions were found for the sizes of VLDL and LDL particles. Only in carriers of the 1131C allele did the size of these particles increase (VLDL) or decrease (LDL) as PUFA intake increased (p  0.01). We further analyzed the effects of n-6 and n-3 fatty acids and found that the PUFA–APOA5 interactions were specific for dietary n-6 fatty acids. Thus, the potentially negative effects associated with elevated lipoprotein remnant concentrations observed in carriers of the APOA5 1131C allele who consume high n-6 PUFA were not observed for the consumption of n-3 PUFA. Hepatic Lipase

Hepatic lipase (HL) is a plasma lipolytic enzyme that participates in metabolizing intermediate-density lipoprotein (IDL) and large LDL into smaller, denser LDL particles, and in converting HDL2 to HDL3 during reverse cholesterol transport. HL has also been suggested to act as a ligand for cell-surface proteoglycans in the uptake of lipoproteins by cell-surface receptors. HL deficiency is characterized by mildly elevated concentrations of TG-rich LDL and HDL particles, as well as impaired metabolism of postprandial

TG-rich lipoproteins, which may result in premature atherosclerosis. Conversely, increased HL activity is associated with increased small, dense LDL particles and decreased HDL2 concentrations. Four common SNPs on the 5 -flanking region of the HL gene (LIPC) (763[A/G], 710[T/C], 514[C/T], and 250[G/A]) are in total linkage disequilibrium and define a unique haplotype that is associated with variation in HL activity and HDL-C levels (Jansen et al., 1997). We investigated gene–diet interactions between a tag SNP, the 514C/T and fat intake in Framingham Heart Study participants (Ordovas et al., 2002b). Our data show that homozygous for the major C allele at the 514(C/T) SNP react to higher contents of fat in their diets by increasing the concentrations of HDL-C, which could be interpreted as a “defense mechanism” to maintain the homeostasis of lipoprotein metabolism. Conversely, homozygotes for the minor T allele cannot compensate and experience decreases on the HDL-C levels. These data could identify a segment of the population especially susceptible to diet-induced atherosclerosis. Considering the higher frequency of the T allele among certain ethnic groups (e.g., African Americans), these data could shed some light on the impaired ability of certain ethnic groups to adapt to new nutritional environments, as clearly seen for Native Americans and Asian Indians. In this regard, we replicated the gene–diet interaction described above in a multiethnic cohort that consisted of Chinese, Malays, and Indians representing the population of Singapore (Tai et al., 2003). In addition to the significant gene– diet interactions reported in these papers, our data provide clues about the reasons why genotype–phenotype association studies fail to show consistent results. In theory, this polymorphism at the LIPC gene will show dramatically different outcomes in association studies depending on the dietary environment of the population studies. The impact of these interactions will be magnified in populations with a high prevalence of the T allele, as it is with Asians and African Americans. Significant gene–diet interactions for this polymorphism have also been reported by other investigators (Bos et al., 2005; Gomez et al., 2005) adding support to the promising role of this gene in future genetic panels aiming to provide personalized nutrition recommendations. Obesity, Diabetes, and Metabolic Syndrome The adoption of a modern, urban lifestyle has been associated with a rapid increase in the prevalence of obesity. Today, one in three Americans is considered obese. Many developing countries are experiencing similar increases in obesity. Obesity is associated with significant morbidity and mortality related to a number of chronic diseases including hypertension, dyslipidemia, type 2 diabetes mellitus, and cancer (Calle et al., 2003; Must et al., 1999). The pace with which the obesity pandemic has occurred suggests that a change in the environment is the likely cause. However, studies in twins and families support also the significant heritability ranging from 0.46 to 0.80 (Bell et al., 2005). Moreover, the degree of weight gain in response to overfeeding was highly correlated between pairs of twins. This has led to the belief that chronic diseases such as obesity result as a consequence of a complex interaction between environment (an abundant

Gene–Nutrient Interactions

supply of calories accompanied by reduced physical activity) and genetic factors, the latter of which connote genetic susceptibility to obesity in modern society (Manolio et al., 2006). Hundreds of genes have been cataloged as potential candidates related with the susceptibility to obesity (Rankinen et al., 2006). The description of each one of them is well beyond the space and needs of this work and we will focus on some recent examples for our own studies focusing on the gene for human perilipin (PLIN) which is on chromosome 15q26, in the region of a linkage locus for diabetes, hypertriglyceridemia, and obesity. In a study to examine the regulation of lipolysis in obese and nonobese women (Mottagui-Tabar et al., 2003), it was found that obese women exhibited two- to four-fold higher basal and nor-adrenaline induced rates of lipolysis in fat cells obtained through a biopsy in the abdominal subcutaneous area. It was also noted that adipocyte perilipin content was reduced in obese women. This study also examined the effect of a polymorphism (11482G  A) in intron 6 of the perilipin locus in relation to perilipin content as well as the rates of lipolysis. Adipose tissue from women who were homozygous for the A allele at position 11482 exhibited 50–100% higher rates of lipolysis and 80% lower perilipin content. Following this study, several polymorphisms at the perilipin locus have been examined for potential associations with BMI or obesity risk. We have examined four SNPs (PLIN1: 6209T  C, PLIN4:11482G  A, PLIN5: 13041A  G, and PLIN6:14995A  T) in several geographically and ethnically diverse populations to determine the patterns of linkage disequilibrium in the various populations and the associations between these polymorphisms with obesity-related traits. Our first report included 1589 white patient samples from a general Spanish population (Qi et al., 2004a). The above specified SNPs were common, with minor allele frequencies ranging from 0.26 to 0.38. Two of these (6209T  C and 11482G  A) were in strong linkage disequilibrium (D 0.96). The presence of one or both of these SNPs were associated with lower BMI and reduced risk of obesity in women. A significant gene–gender interaction was observed, however, and the effect was seen only in women. In addition, the 11482G  A polymorphism also showed significant associations with fasting glucose and triglyceride concentrations. Conversely, a trend was observed for the other two SNPs (13041A  G and 14995A  T) to be associated with increased BMI and risk of obesity. These same polymorphisms were then examined in 734 white patients (373 men and 361 women) who were attending a residential lifestyle modification program in California (Qi et al., 2004b). As with the study in Spain, the effects were seen primarily in women. As in Spanish women, the 6209T  C and the 11482G  A polymorphisms were associated with lower BMI. These associations, however, did not reach statistical significance. Instead, the presence of the rare alleles at positions 13041 and 14995 were associated with increased BMI. At this stage, it appeared that the rare alleles at positions 6209 and 11482 at the 50 end of the perilipin locus had opposite effects on BMI when compared with the minor alleles at positions 13041 and 14995, at the 3 end of the perilipin locus. Whereas the presence of the former was

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associated with reduced BMI, the latter was associated with increased BMI. In line with this notion, the haplotype that included the common alleles at the 6209T  C, 11482G  A SNPs and the rare alleles at the 13041A  G and 14995A  T SNPs had the greatest effect on obesity risk in the population in California. We subsequently proceeded to examine these SNPs in a multiethnic population living in Singapore, comprising 2763 Chinese, 726 Malays, and 598 Asian Indians (Qi et al., 2005). One interesting finding was that the intragenic linkage disequilibrium structure of the perilipin locus differed between populations that were white compared with populations of Asian extraction. Specifically, in whites, the 11482G  A polymorphism was in strong positive linkage disequilibrium with the 6209T  C polymorphism and negative linkage disequilibrium with the 13041A  G and 14995 A  T polymorphisms. In Chinese, Malays, and Asian Indians living in Singapore, the situation was reversed, with the 11482G  A polymorphism in negative linkage disequilibrium with the 6209T  C polymorphisms and in positive linkage disequilibrium with the 13041A  G and the 14995A  T polymorphisms. Given the differences in patterns of linkage disequilibrium observed, one would expect that the rare allele for the 11482G  A polymorphism, which was associated with lower levels of obesity in the white population, would be associated with increased levels of obesity in the Asian populations. In fact, this is exactly what was observed. Moreover, as in the two previous studies, the significant associations were observed only in females. They were only present in Malays and Asian Indians, however, not in Chinese. It is unclear at this time why no association was observed in Chinese ethnic groups despite being reasonably powered to detect an association, if present. If indeed complex traits such as obesity are a consequence of interactions between genetic and environmental factors (Manolio et al., 2006), such gene–environment interactions could explain the discrepant findings among populations. It could simply be that the French and Chinese are exposed to different environments when compared with whites in Spain or California, and Malays and Asian Indians in Singapore. Do such gene–environment interactions operate in relation to the association between polymorphisms at the perilipin locus and obesity? In fact, the interaction between polymorphisms at this locus and gender has already been described in whites and some of the Asian groups. Recently, one study (Corella et al., 2005) has reported that individuals carrying the A allele for the 11482G  A polymorphism are resistant to weight loss with caloric restriction. Another (Kang et al., 2006) reported resistance to weight gain following treatment with the peroxisome proliferator activator receptor  agonist rosiglitazone in individuals carrying this polymorphism. Therefore, overweight and obese individuals carrying this allele may not benefit from traditional dietary approaches to lose weight and alternative approaches may be needed. In this regard, an interesting observation was made regarding the potential regulatory effects of a plant extract on perilipin, hormone-sensitive lipase and other parameters of lipid metabolism in obese women (Abidov et al., 2006). Up to this point, we have dealt primarily with associations between polymorphisms at the perilipin locus and their

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associations with the degree of obesity per se, as opposed to the downstream effects of obesity, such as insulin resistance. Jang et al. (2006) studied 177 obese Korean men and women who were treated with caloric restriction (300 kcal/day) and found that the presence of the A11482 allele was associated with greater weight loss in response to caloric restriction and that this weight loss was primarily in the visceral fat compartment. Since visceral obesity is generally associated with increased plasma-free fatty acid concentrations, one may expect a greater reduction in plasma-free fatty acids in these individuals. Instead, these individuals experienced an increase in plasma-free fatty acids despite a greater reduction in visceral fat mass. It is important to remember that fatty acids serve not only as a source of energy but also as signaling molecules. Free fatty acids result in impaired glucose homeostasis and may increase the risk of type 2 diabetes mellitus. To test the hypothesis that dietary fat may interact with polymorphisms at the perilipin locus to modulate diabetes-related traits, we re-examined data from the Singapore population, taking into consideration dietary macronutrient intake (Corella et al., 2006). We found evidence of an interaction between dietary fat (specifically saturated fat) intake, polymorphisms at the perilipin locus (11482G  A and 114995 A  T) and insulin resistance. Greater intake of energy from saturated fats as associated with increased insulin resistance amongst individuals who were homozygous for the rare alleles but not individuals who carried the common allele. This effect was independent of BMI and consistent with the associations with anthropometric measures; these interactions were observed only in women. Cancer Over the past three decades, numerous epidemiological studies (ecological, case-control, and follow-up) have reported associations between diet and cancer (Gonzalez, 2006a; Uauy and Solomons, 2005). Based on this evidence, various health organizations formulated dietary guidelines to reduce cancer risk at the population level. These guidelines generally propose reducing fat intake, particularly saturated fat, including a variety of vegetables and fruits in the daily diet, being physically active and maintaining a healthy weight, consuming alcoholic beverages in moderation, and minimizing intake of salt-cured, salt-pickled, or smoked foods. Although these general recommendations have been generally accepted, there is concern about the fact that few reported associations between nutrients and cancer are consistently and convincingly replicated (Kritchevsky, 2003). One example is the association between a high fat diet and breast cancer. Although ecological and various case-control have reported a direct association between fat intake and breast cancer (Mazhar and Waxman, 2006), results from a recent randomized, controlled, primary prevention trial conducted at 40 US clinical centers from 1993 to 2005 in a total of 48,835 postmenopausal women did not support these findings (Prentice et al., 2006). One of the bases for the discrepancy between studies is the already mentioned measurement error of dietary components and the confounding from other factors, including other environmental and genetic factors and their interactions. Another area of concern is defining

the relevant time frame over which to measure diet. For cancer, it is generally hypothesized that the effect of diet may occur many years before diagnosis; thus, the ability to recall diet in the remote past is of considerable interest. An accurate measure of intake, but which covers an irrelevant time frame, is not valuable. This is the main limitation that is pointed out when interpreting the results of clinical trials with dietary interventions involving short periods. It is currently well accepted that prospective studies involving a considerable number of cases and using quantitative dietary assessment, biomarkers of food intake, and genetic measures is necessary to gain further understanding of the etiologic roles of dietary factors in the causation of cancer before making specific dietary recommendations (Vineis, 2001).The European Prospective Investigation into Cancer and Nutrition (EPIC) is one study that was designed with these considerations in mind (Gonzalez, 2006b). Considering the current limitations, it is clear that much remains to be learned about diet and cancer, in particular about gene–diet interactions, and that the level of evidence provided by the few studies in the area are not adequate to advise dietary modifications. However, during the past decade some pioneering studies have revealed interesting and promising gene–nutrient interactions that are guiding current investigations. Some examples of the most relevant gene–nutrient interaction in cancer research are presented below. Interaction Between Polymorphism Involved in Alcohol Metabolism and Alcohol Consumption and Cancer Risk

Alcohol consumption is another dietary factor that has been the focus of extensive gene–environmental interaction studies in determining cancer risk. Epidemiological data have identified alcohol consumption as a statistically significant risk factor for cancers of the oral cavity, pharynx, larynx, esophagus, liver, colon, rectum, breast, pancreas, and lung (Boffetta and Hashibe, 2006; Seitz et al., 2005; ). However, although the association between chronic alcohol consumption and cancer of the upper-gastrointestinal tract as well as of the liver is well established, associations of alcohol and other organ cancers are highly controversial and there is at present not enough evidence for a causal association. Although the exact mechanisms by which chronic alcohol ingestion stimulates carcinogenesis are not known, experimental studies in animals support the concept that ethanol is not a carcinogen but under certain experimental conditions is a co-carcinogen and/or tumor promoter (Purohit et al., 2005). On the other hand, results from recent studies suggest that the risk of organ-specific cancer for alcohol drinkers is highly modulated by genetic factors. Research has focused on variants in genes for alcohol metabolism and folate metabolism genes (Boffeta and Hashibe, 2006). The metabolism of ethanol leads to the generation of acetaldehyde and free radicals. Ethanol is oxidized to acetaldehyde by the enzyme alcohol dehydrogenase (ADH). Acetaldehyde is oxidized to acetate by the enzyme acetaldehyde dehydrogenase (ALDH). Evidence has accumulated that acetaldehyde is predominantly responsible for alcohol-associated carcinogenesis. Therefore, polymorphisms at ADH and ALDH have been the focus of numerous gene–alcohol interaction studies.

Gene–Nutrient Interactions

In particular, the ADH1C gene is implicated because the ADH1C*1 allele encodes for a rapidly ethanol metabolizing enzyme leading to increased acetaldehyde levels in comparison with the variant ADH1C*2 allele. Although it has been reported that heavy drinkers homozygous for the ADH1C*1 allele have a higher risk to develop upper aerodigestive tract cancer (Visapaa et al., 2004), no consistent ADH1C–alcohol interactions on cancer risk can be summarized (Peters et al., 2005;Wang et al., 2005). Moreover, some authors have pointed out the prominent role of other dietary factors in modulating the interaction between alcohol intake and genetic polymorphisms on cancer risk (Poschl et al., 2004). Thus, for example, heavy alcohol use might lead to nutritional deficiencies by reduced intake of foods rich in micronutrients, by impaired intestinal absorption, and by changes in metabolic pathways. The most relevant adverse effect of ethanol seems to be on folate metabolism (including vitamin B12 and vitamin B6) and resulting in further changes in DNA-methylation pathways. More details about these pathways are discussed below. Interaction Between Methylenetetrahydrofolate Reductase Gene Variation and Folate and Vitamin Intake in Cancer Risk

Methylenetetrahydrofolate reductase (MTHFR) catalyzes the reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrathydrofolate, the predominant circulatory form of folate and methyl donor for the remethylation of homocysteine to methionine. Kang et al. (1988) identified a thermolabile form of MTHFR that is associated with reduced enzyme activity and with elevated levels of plasma total homocysteine. The molecular basis of the thermolabile variant is a cytosine (C) to a thymine (T) substitution at nucleotide 677 (C677  T), which converts an alanine residue to a valine (Frosst et al., 1995). This mutation is relatively common and results in an enzyme that has decreased stability and specific activity. Homozygosity (TT) for this polymorphism is associated with hyperhomocysteinemia and higher CVD risk, particularly among those with low-folate intake (Kluijtmans et al., 1996; Ma et al., 1996). Paradoxically, the T allele has been associated with a lower risk of cancer in several studies (Chen et al., 1996; Ma et al., 1997; Sharp and Little, 2004). Folate may reduce carcinogenesis through various mechanisms, including maintenance of normal DNA synthesis and DNA methylation. 5-Methyltetrathydrofolate is essential for DNA methylation, and both hypo- and hypermethylation of DNA, which can cause either over- or underexpression of genes, respectively, may contribute to carcinogenesis (reviewed in Giovannucci et al., 1994). Reduced activity of MTHFR in T carriers may increase the likelihood of sufficient methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP), resulting in less incorporation of uracil into DNA, fewer DNA breaks, and a decreased risk for cancer. However, the lower risk of carriers of the T allele was only noted when folate intake was normal. This gene–diet interaction was specially found for colorectal adenomas and colon cancer risk, additional modulators have described for other cancer sites (Powers, 2005). Slattery et al. (1999) found that colon cancer risk was reduced 30–40% in TT

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individuals who consumed adequate dietary intake of folate, vitamin B12, and vitamin B6. The inverse association (a higher risk of cancer) was stronger in individuals over 60-years-old with a low-nutrient intake. Replicating these results, Ulrich et al. (1999) reported that low folate and low intake of vitamins B12 and B6 were associated with an increased risk of colorectal adenomas in individuals with the TT compared with the CC genotype. Folate status in individuals who chronically consume moderate amounts of alcohol may be impaired because alcohol causes malabsorption of folate, increased excretion, and abnormal folate metabolism (Bailey, 2003). Data from the Physicians Health Study (Ma et al., 1997) confirmed that individuals with the TT genotype and with a lower intake of folate are especially sensitive to the carcinogenic effect of alcohol in colorectal cancer. However, further studies in Caucasian and Asian populations have obtained controversial results (Le Marchand et al., 2005; Otani et al., 2005; van den Donk et al., 2005;Wang et al., 2006). Interaction Between Polymorphism in Genes Involved in the Detoxification of Dietary Carcinogens and their Main Food Sources in Cancer Risk

Common polymorphisms in genes involved in the metabolism and detoxification of dietary carcinogens may be related to different activities for the corresponding enzymes as well as with cancer risk. This has been observed in the P450 genes for the cytochrome P450 phase I enzymes, as well as in genes for the phase II enzymes that detoxify carcinogenic metabolites by producing readily excreted, hydrophilic conjugation molecules (Perera and Weinstein, 2000). N-acetyltransferase (NAT) is a phase II enzyme that is found in two isoforms (NAT1 and NAT2) and may be involved in the acetylation of aromatic and heterocyclic amine carcinogens such as those found in cooked proteins. The NAT2 polymorphism was discovered more than 40 years ago following differences observed in tuberculosis patients and isoniazid toxicity. The classical isoniazid slow acetylator phenotype(s) is due to a reduced NAT2 protein with a frequency that is approximately 30–50% in Caucasian populations but has striking geographical differences. This polymorphism is important in clinical pharmacology and toxicology because of its primary role in the activation or deactivation of many drugs (for review, see Hein et al., 2000). Several polymorphisms have been characterized in NAT1 and NAT2 and some of these DNA polymorphisms are related to NAT activity that has complex interactions with other environmental factors such as tobacco smoking. Based on polymorphisms in the NAT1 and NAT2 genes, individuals may be classified into one of the three categories: fast, slow, and intermediate rate metabolizers. Subjects carrying the rapid NAT2 acetylator allele have a higher risk of colon cancer when consuming larger amounts of meat. This reflects the greater ability of rapid acetylators to activate heterocyclic aromatic amines to carcinogenic derivatives within the colon mucosa. This higher risk was reported in the first case-control studies in Australia (Roberts-Thompson et al., 1996), as well as in prospective cohort studies in the United States (Chen et al., 1996). Huang et al. (2003) replicated these

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results in hepatocellular carcinoma (HCC) in a case-control study. They did not find significant associations between the susceptibility of HCC and the overall NAT2 genotypes. However, there was a trend of increased HCC risk in rapid acetylators from low to intermediate, and there was high red meat intake (p  0.016), even when adjusted for family history of HCC and habitual alcohol drinking. They also specifically tested the interaction between red meat intake and the NAT2 acetylator status, obtaining a significant p-value (p  0.007). Similar results were obtained by Tamer et al. (2006) in Turkey. They studied NAT2 genotypes in controls and patients of colorectal carcinoma and found a higher risk of colorectal carcinoma associated with a high protein intake in subjects with the fast NAT2 genotype, suggesting that exposure to carcinogens through consumption of a high-protein diet may increase the risk of colorectal carcinoma only in genetically susceptible individuals. In addition to these examples of gene–nutrient interaction on cancer risk, there are many other candidate genes that are being investigated in ongoing studies. The success of the results greatly depend on the replication and on the holistic integration of all “omic” technologies in the framework of nutritional genomics.

PATH FORWARD Despite the excitement brought up by an increasing number of findings related to nutritional genomics, the progress of the field is hampered by the inadequacy of current experimental approaches to efficiently deal with the biological complexity of the phenotype(s), the complexity of dietary intakes, differing genetic background among participants, and the limitations of low-statistical power of the studies. At the present time, nutritional scientists are not limited in their progress in this area by technology. Powerful genomic techniques allow the determination of hundreds of thousands of SNPs per individual efficiently and reliably. Such techniques are the basis for the whole genome association studies currently in use to identify new disease genes. However, no such approach has been yet used for gene–nutrient interactions due to the lack of proper statistical tools. Likewise, transcriptomics, proteomics, and metabolomics will provide global insights into biological effects of dietary interventions and they will be also key to inform about pathways and specific genes involved in individual response. The integration of these techniques into a systems biology approach (van Ommen and Stierum, 2002) will require statistical and approaches and complex modeling that needs to be fully developed to translate the power of these techniques into practical applications. We and others have proposed that only a comprehensive, international nutritional genomics approach (Kaput et al., 2005) will yield short- and long-term benefits to human health by: (i) revealing novel nutrient–gene interactions, (ii) developing new diagnostic tests for adverse responses to diets, (iii) identifying specific populations with special nutrient needs, (iv) improving the consistency of current definitions and methodology related to dietary assessment, and (v) providing the information for developing

more nutritious plant and animal foods and food formulations that promote health and prevent, mitigate, or cure disease. Achieving these goals will require extensive dialog between scientists and the public about the nutritional needs of the individual versus groups, local food availability and customs, analysis and understanding of genetic differences between individuals and populations, and serious commitment of funds from the public and private sectors. Although current evidence from both experimental and observational nutrigenetics studies is not enough to start making specific personalized nutritional recommendations based on genetic information, there are some examples of common SNPs modulating the individual response to diet as proof of concept of how gene–diet interactions can influence lipid metabolism. It is critical that these preliminary studies go through further replication and that subsequent studies be properly designed with sufficient statistical power and careful attention to phenotype and genotype. Despite the lack of maturity of the field, a number of “direct-to–consumer” nutritional testing services are being currently offered. It is important that these services inform the consumers of the still very limited application of these products and refrain from making unfounded claims. The many challenges that lay ahead are evident. This review has examined the vast world of nutrigenetics and nutrigenomics only through the small keyhole of lipid metabolism, obesity and cancer-related genes. These initial steps in understanding nutrigenomics will likely lead to fundamental breakthroughs that will both clarify today’s mysteries and pave the way for clinical applications. However, to arrive at the point where it is possible to assess the modulation by specific SNPs of the effects of dietary interventions on lipid metabolism, well designed, adequately powered, and adequately interpreted randomized controlled studies (or their equivalent) of greater duration than current studies are needed, with careful consideration given to which patients to include in such studies. This will require a bidirectional path to discovery going from epidemiological to GCRCbased studies and back. Moreover, research must also investigate the potential mechanisms involved in the gene–diet interactions reported by nutrigenetic studies (van Ommen and Stierum, 2002). These imperative needs can be achieved only through the collaboration of experts in the different fields involved, which must include nutrition professionals. In addition, a number of important changes in the provision of health care are needed in order to achieve the potential benefits associated with this concept, including a teamwork approach, with greater integration among physicians and nutrition professionals. Once more experience is gained from patients and/or individuals at high risk, these approaches could be applied toward primary prevention of CVD.

CONCLUSIONS Nutritional genomics is a fast-developing research area with tremendous potential to yield results that could change the way dietary guidelines and personal recommendations are established and carried out in the future. The notion is that nutrigenetics

References

will provide the basis for personalized dietary recommendations based on the individual’s genetic make-up and information from other environmental factors. This will probably require individual ascertainment of all informative SNPs or, as forecast by others, complete sequencing of the genome. Geneticists will use this data to forecast future genetic predisposition for disease, and it will guide the implementation of the proper preventive measures. For several decades, a very simplified version of this concept has been implemented in many countries. Through established programs to detect inborn errors of metabolism, millions of babies have been analyzed for the presence of rare monogenic disorders and, based on the results, many of those affected have been spared of the sometimes lethal consequences of their genetic defect. In many cases, the solution was as simple as providing them with the right dietary mix. From a genetics viewpoint, there is still a lot of work to be done, even for these relatively simple diseases, but there is excellent proof that the concept works. From the conceptual point of view, the situation with multifactorial disorders is more complex. However, the range and complexity are vastly different; the goal of nutrigenetics aims to detect predisposition for all diseases with a genetic component, and to provide the tools for its prevention decades before they could be manifested, instead of detecting and preventing monogenic disorders with very rare prevalence. Whether this is feasible remains to be seen. For now, this knowledge is developed from multiple small intervention studies that provide a body of observational findings that generally show little consistency.

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Nutrigenetics needs to move forward with nutrigenomics to translate observational findings into molecular mechanisms. To achieve these ambitious goals, it will be necessary to move toward strategies that will yield findings that are more robust. In summary, nutritional genomics will be the driving force of future nutritional research, and it has the potential to change dietary disease prevention and therapy and have a major impact on public health. However, the complexity of the goals set for nutritional genomics is tremendous, and their accomplishment will require breaking many of the molds of traditional research and seeking integration of multiple disciplines and laboratories working coordinately. Despite the difficulties described, preliminary evidence strongly suggests that the concept will work and that by using behavioral tools founded on nutrition, we will be able to harness the information contained in our genomes to achieve successful aging.

ACKNOWLEDGEMENTS Supported by NIH/NHLBI grant no. HL54776, contracts 53-K06-5–10 and 58–1950-9–001 from the US Department of Agriculture Research Service, and grants (CIBER OBN, CB06/03/035 from the Instituto de Salud Carlos III, Spain, and PR2006-0258 from Spanish Ministerio de Educación y Ciencia. D.C. has a contract from the University of Valencia, Spain.

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Manolio, T.A., Bailey-Wilson, J.E. and Collins, F.S. (2006). Genes, environment and the value of prospective cohort studies. Nat Rev Genet 7, 812–820. Martini, M.C., Campbell, D.R., Gross, M.D., Grandits, G.A., Potter, J.D. and Slavin, J.L. (1995). Plasma carotenoids as biomarkers of vegetable intake: The University of Minnesota Cancer Prevention Research Unit Feeding Studies. Cancer Epidemiol Biomarkers Prev 4, 491–496. Masson, L.F., McNeill, G. and Avenell, A. (2003). Genetic variation and the lipid response to dietary intervention: A systematic review. Am J Clin Nutr 77, 1098–1111. Mata, P., Lopez-Miranda, J., Pocovi, M., Alonso, R., Lahoz, C., Marin, C., Garces, C., Cenarro, A., Perez-Jimenez, F., de Oya, M. et al. (1998). Human apolipoprotein A-I gene promoter mutation influences plasma low density lipoprotein cholesterol response to dietary fat saturation. Atherosclerosis 137, 367–376. Mazhar, D. and Waxman, J. (2006). Dietary fat and breast cancer. QJM 99, 469–473. Mensink, R.P. and Plat, J. (2002). Post-genomic opportunities for understanding nutrition: The nutritionist’s perspective. Proc Nutr Soc 61, 401–404. Meyer, F. and White, E. (1993). Alcohol and nutrients in relation to colon cancer in middle-aged adults. Am J Epidemiol 138, 225–236. Millen, B.E., Quatromoni, P.A., Nam, B.H., Pencina, M.J., Polak, J.F., Kimokoti, R.W., Ordovas, J.M. and D’Agostino, R.B. (2005). Compliance with expert population-based dietary guidelines and lower odds of carotid atherosclerosis in women: The Framingham Nutrition Studies. Am J Clin Nutr 82, 174–180. Millen, B.E., Pencina, M.J., Kimokoti, R.W., Zhu, L., Meigs, J.B., Ordovas, J.M. and D’Agostino, R.B. (2006). Nutritional risk and the metabolic syndrome in women: Opportunities for preventive intervention from the Framingham Nutrition Study. Am J Clin Nutr 84, 434–441. Milner, J.A., Allison, R.G., Elliott, J.G., Go, V.L., Miller, G.A., Rock, C., Roy, R. and Wargovich, M.J. (2003). Opportunities and challenges for future nutrition research in cancer prevention: A panel discussion. J Nutr 133, 2502S–2504S. Milner, J.A. (2003). Incorporating basic nutrition science into health interventions for cancer prevention. J Nutr 133, 3820S–3826S. Most, M.M., Ershow, A.G. and Clevidence, B.A. (2003). An overview of methodologies, proficiencies, and training resources for controlled feeding studies. J Am Diet Assoc 103, 729–735. Mottagui-Tabar, S., Ryden, M., Lofgren, P., Faulds, G., Hoffstedt, J., Brookes, A.J., Andersson, I. and Arner, P. (2003). Evidence for an important role of perilipin in the regulation of human adipocyte lipolysis. Diabetologia 46, 789–797. Muller, M. and Kersten, S. (2003). Nutrigenomics: Goals and strategies. Nat Rev Genet 4, 315–322. Murray, J.A. (1999). The widening spectrum of celiac disease. Am J Clin Nutr 69, 354–365. Must, A., Spadano, J., Coakley, E.H., Field, A.E., Colditz, G. and Dietz, W.H. (1999). The disease burden associated with overweight and obesity. JAMA 282, 1523–1529. Nambi, V., Hoogwerf , R.J. and Sprecher, D.L. (2002). A truly deadly quartet: Obesity, hypertension, hypertriglyceridemia, and hyperinsulinemia. Cleve Clin J Med 69, 985–989. Neuhouser, M.L., Patterson, R.E., King, I.B., Horner, N.K. and Lampe, J.W. (2003). Selected nutritional biomarkers predict diet quality. Public Health Nutr 6, 703–709.

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The Genetic Approach to Dementia Parkinson’s Disease: Genomic Perspectives Epilepsy Predisposition and Pharmacogenetics Ophthalmology Genomic Basis of Neuromuscular Disorders Psychiatric Disorders Genomics and Depression Bipolar Disorder in the Era of Genomic Psychiatry

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99 The Genetic Approach to Dementia Robert L. Nussbaum

INTRODUCTION Dementia is a serious and growing problem as the average age of the population increases. As one example, the over-65 population of Canada has more than doubled over the past 70 years, from 5% to 12% of the population, while the fraction of the population over age 85 has quadrupled, from 0.2% to 0.8% (Hogan and Hogan, 2003); a similarly aging population is found throughout the developed world. The number of dementia patients is rising proportionately to the aging of the population, placing a tremendous economic and emotional burden on their families and society. Dementia is a serious problem in younger individuals as well, with an estimated half-million individuals under age 65 afflicted in the United States alone (Maslow, 2006). There is a clear need to understand the causes of dementia, to determine their pathogenetic pathways, and to develop methods to intervene to prevent or delay the onset. Dementia is a clinical syndrome, defined as a progressive impairment of cognitive function, particularly in the areas of memory, judgment, decision-making, attentiveness when communicating with others, orientation to familiar surroundings, and language (Dugue et al., 2003). Many different diseases with different pathological processes cause dementia. In 30% of cases, the dementia is secondary to systemic disease such as hypertension or atherosclerosis, leading to vascular-occlusive dementia with multiple small infarcts (Barker et al., 2002).

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Other secondary causes include paraneoplastic syndromes, alcoholism, and AIDS (Dugue et al., 2003). The majority of cases, however, reflect primary dementia; among these, the different forms can be roughly divided into two general categories based on pathological appearance of the postmortem brain: those with neurodegeneration and aggregates of protein, and those with vascular-occlusive disease and infarction. Many elderly individuals who die with dementia, however, have both vascular pathology and protein aggregation findings at autopsy. More than half of all primary dementia is due to Alzheimer disease (AD). Another 10% of dementia is due to diffuse Lewy body disease (DLBD), which presents as a dementia clinically similar to AD but is associated with the characteristic intracellular protein aggregates referred to as Lewy bodies (Lippa et al., 2007). Finally, there are a number of early-onset, autosomal dominant hereditary dementias, ranging from the relatively common, such as frontotemporal dementia (FTD), to the rare, such as CADASIL and the prion diseases (Figure 99.1). Minimal cognitive impairment (MCI) is a term used to describe individuals with mild difficulty with one component of cognition, such as short-term memory (“amnestic MCI”) (Dugue et al., 2003). Between 10% and 15% of amnestic MCI patients progress each year to true dementia, usually of the AD-type, which is five- to seven-times greater than the rate at which dementia occurs in a group of age-matched elderly without MCI (Petersen et al., 1995).

Copyright © 2009, Elsevier Inc. All rights reserved.

Incidence of Dementia

In this chapter, we focus on the various forms of primary dementia. We will describe the clinical features, incidence, and what is known about the underlying genetic bases for these diseases. The underlying pathophysiology leading to neuronal

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cell death is not completely understood for any of the primary dementias, but protein aggregation in or around neurons is a striking feature that is in common among nearly all the primary dementias (CADASIL being the exception, where protein aggregates occur in blood vessels, leading to micro-infarcts).

Secondary LOAD 15–25% Familial

FTD DLBD

CADASIL

SE EOAD 15% Autosomal dominant 40% Familial

Figure 99.1 Relative contributions of various causes of dementia to the overall population burden of disease. LOAD: late-onset Alzheimer disease; EOAD: early-onset Alzheimer disease; FTD: frontotemporal dementia; DLBD: diffuse Lewy body disease; SE: spongy encephalopathy (prion disease); CADASIL: cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Figures are approximate.

INCIDENCE OF DEMENTIA Over 95–98% of all dementias occur after age 65, an age that is often cited as the arbitrary dividing line between early- and late-onset diseases. Early-onset dementias (i.e., those that occur before the age of 65) are rare and have an incidence of approximately 2–5 per 1000 person-years in the age group 55–64 (Knopman et al., 2006; Maslow, 2006), whereas the incidence of dementia over the age of 65 is approximately 20 per 1000 person-years. The incidence of dementia increases steadily with increasing age, from nearly 5 per 1000 in the 65–69-year-old group to over 84 per 1000 person-years in individuals over 90 (Kukull and Ganguli, 2000; Kukull et al., 2002) (Figure 99.2). Over age 65, well over 95% of dementia is primary, caused by one of the neurodegenerations (principally AD) described in this chapter, or is secondary to vascular-occlusive disease of the brain (McMurtray et al., 2006). In contrast, below age 65, only 70% of dementia is either neurodegenerative or vascular in origin.

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Figure 99.2 Incidence per 1000 person-years of dementia of all types as a function of age (red line). Yellow and blue lines are the 95% confidence limits. Data from Dugue et al. (2003).

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PRIMARY DEMENTIAS Alzheimer Disease AD is a disorder of slowly progressive dementia associated with extracellular aggregates of beta-amyloid (“amyloid plaques”) and intracellular aggregates of the cytoskeletal protein tau (“neurofibrillary tangles”) (Figure 99.3). At autopsy, AD patients have plaques and tangles throughout the cortex, but most particularly in the entorhinal cortex and hippocampal CA1 layer. Approximately 5–7% of all AD has an early age of onset; among these early-onset cases, approximately one-sixth are inherited in an autosomal dominant manner while another 40% show some familial recurrence without a clear mendelian inheritance pattern (Ertekin-Taner, 2007; Rao et al., 1994). The majority of late-onset AD (LOAD) is sporadic (i.e., a single individual is affected in the family) although 15–25% do have other affected individuals in their families. Even when there are multiple AD patients in a family, however, the disease usually lacks a clear-cut mendelian inheritance pattern (Nussbaum et al., 2007). Early-Onset AD The majority of all of the autosomal dominant, early-onset forms of AD are caused by mutations in one of three genes: the amyloid precursor protein gene (APP) located on human chromosome 21, the presenilin 1 gene (PSEN1), and the presenilin 2 gene (PSEN2) (Rogaeva et al., 2006). Mutations in APP and PSEN1 cause a highly penetrant, predominantly early-onset disease. In contrast, PSEN2 mutations have more variable penetrance and an older age of onset (Rogaeva et al., 2006). APP, PSEN1, and PSEN2 mutations cause AD through their effect on the processing of the protein encoded by APP, the amyloid precursor protein. This protein is a single-pass

Figure 99.3 Pathology in AD. The large circular plaque in the lower left-hand corner is an extracellular amyloid plaque. The dark triangular shaped bodies are neurons filled with intracellular neurofibrillary tangles of tau protein. (Formalin-fixed brain treated with silver stain.)

transmembrane protein located at the cell surface. It can be cleaved by one of two pathways: either via an a-secretase or a b-secretase (Figure 99.4) (Nussbaum and Ellis, 2003). Further cleavage by c-secretase following b-secretase cleavage creates peptide fragments containing 40 or 42 amino acid residues, referred to as the Ab40 and Ab42 peptides. A large body of evidence implicates the Ab42 fragment, a highly amyloidogenic peptide, as a major toxic agent in the pathogenesis of AD (Eckman and Eckman, 2007). First, of the nearly two dozen missense mutations in APP that cause autosomal dominant, early-onset AD (EOAD), all result in amino acid substitutions near the a-, b- or c-secretase cleavage sites. Through mechanisms that are not completely understood, these mutations promote cleavage via the b- and c– secretase pathways and generate relatively or absolutely increased amounts of Ab42. Second, copy number mutations of the APP gene, ranging in size from a few hundreds of kilobases to the entire chromosome 21 (as in Down syndrome) lead to EOAD and also cause an increased production of the Ab42 peptide (Cabrejo et al., 2006; Lai and Williams, 1989; Rovelet-Lecrux et al., 2006, 2007; Schupf et al., 2007). Finally, elevated production of Ab42 also occurs in association with mutations in the PSEN1 and PSEN2 genes in families with autosomal dominant EOAD (Citron et al., 1997). Hundreds of different missense mutations have been found throughout the PSEN1 gene; in contrast, only a few mutations have been described in the much rarer families with PSEN2 mutations. How these mutations affect the activity of the various secretases and cause increased Ab42 production is not known. Late Age of Onset AD Genetics also contributes substantially to the risk for late age of onset AD, as documented in twin and family epidemiological studies. For example, in a large Swedish twin registry with longterm follow-up, a typical concordance rate for AD was 59% for monozygotic twins (Gatz et al., 2005). In contrast, concordance for dizygotic twins was typically between 24%, for unlikesexed, and 32%, for like-sexed twins. Such twin studies suggest that about half of the liability to develop AD is genetic in origin (Pedersen et al., 2004). Comparing the frequency of AD in the family members of AD patients versus the relatives of unaffected controls is a complementary approach to estimating the genetic contribution to LOAD. Among first-degree relatives of patients affected with AD, the average lifetime risk for AD is 40% up to age 96, with a somewhat higher risk applying to women, as compared to a lifetime risk of 15–20% in individuals without affected relatives (Lautenschlager et al., 1996; Seshadri et al., 1995). Based on family data, it is estimated that about half of the liability to develop late age of onset AD is genetic in origin (Martinez et al., 1998). A similar heritability was noted when MRI scans were used to detect AD endophenotypes in the relatives of AD patients (Lunetta et al., 2007). Although twin and family studies clearly support a major genetic contribution to LOAD, only one gene variant has so far been incontrovertibly shown to contribute significantly to AD disease risk, the 4 allele at the apolipoprotein E (APOE) gene.

Primary Dementias

There are three alleles at APOE, 2, 3, and 4, and the risk for AD increases substantially and additively with each 4 allele an individual carries (Corder et al., 1993; Saunders et al., 1993). Among Caucasians, the risk for AD increases approximately threefold in carriers of one 4 allele and approximately eightfold in 4/4 homozygotes. The impact of the 4 allele appears to be predominantly through its effect on the average age of onset: each 4 allele lowers the average age of onset of AD by approximately 10 years. The effect of the 4 alleles at the APOE locus appears to be the strongest in populations of European and Japanese origin. The data are much more equivocal for African American populations, with some studies suggesting an association between 4 alleles and AD and others showing no association (Murrell et al., 2006;Tang et al., 1998). The controversies surrounding the association between APOE 4 and AD in different ethnic or racial groups has been recently reviewed (Ertekin-Taner, 2007). The mechanism by which the APOE 4 allele increases the risk for AD has not been clearly determined. However, it

Normal processing of -amyloid percursor protein

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is clear that the apolipoprotein E is synthesized in neurons in response to injury and undergoes proteolytic cleavage (Mahley et al., 2006). The product of the 4 allele appears to be cytotoxic through an effect on the cytoskeleton and on mitochondrial function. Further work needs to be done to elucidate the role of APOE, and the 4 allele in particular, in neurodegeneration. It should be stressed, however, that the APOE genotype alone does not explain the entire genetic contribution to LOAD (Martinez et al., 1998). A large number of linkage and association studies have been performed in an attempt to find additional loci at which variants increase the risk for AD. Studies in many populations in the United States and Europe have identified more than 75 different loci with significant linkage (LOD scores) or statistically significant association (Ertekin-Taner, 2007). Unfortunately, some of these studies used overlapping sample sets and cannot be considered independently confirmed. Others have not been replicated, and there is some persistent skepticism as to whether these loci represent real linkage or association findings or false positive signals (Finckh, 2003). For

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Figure 99.4 Pathways of APP cleavage by the a-, b-, and c-secretases, demonstrating the steps by which the amyloidogenic, toxic Ab42 peptide is generated (Panel A). Panels B and C depict the effect of various missense mutations in the APP gene that lead to increased Ab42 production.Reproduced from Nussbaum and Ellis (2003) with permission.

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example, one large genome-wide scan using over 500,000 SNP markers in over 1000 autopsy-proven AD patients and controls confirmed that APOE itself is contributing to LOAD, but failed to find any other loci in which variants are significantly associated with AD (Coon et al., 2007). Nevertheless, a number of chromosomal regions stand out as possible locations for genes involved in AD risk because they have suggestive LOD scores or demonstrate significant association that appear to have been replicated in multiple, different studies that analyzed independent samples: chromosome 6 (regions 6p21-2 and 6q27), chromosome 10 (region 10q21.3), chromosome 12 (regions 12p11.23 and 12q13.2) and chromosome 9 (regions 9p21.2-p22.2 and 9q22.2) (Ertekin-Taner, 2007). Positional candidate genes within these regions have been analyzed for association to AD, and some genes demonstrate an association with AD that has been replicated in multiple studies. A database of such associations is available at the AlzGene website (Bertram et al., 2007). These genes and regions of the genome are, therefore, attractive candidates for further genetic and functional studies. Clues from the cell biology of Ab42 production have also been used to identify potential functional candidate genes in which variants might be associated with a risk for AD. Variants in one interesting class of genes, those that encode proteins involved in APP sorting and delivery to the various secretase pathways, have been analyzed for association. The sortilin-related receptor gene SORL1 has emerged from these studies as a candidate contributor to AD risk (Lee et al., 2007; Rogaeva et al., 2007). In cell culture systems, loss of SORL1 function causes an increase in the amount of APP that is sorted to the b- and c-secretase pathways, thereby increasing the production of Ab42. Variants in the non-coding regions upstream and downstream of the gene constitute two independent haplotypes that show significant association to AD in three independent populations. These same risk haplotypes were associated with a decreased expression of SORL1 mRNA in white blood cells, thereby providing a functional link to the genetic association. Despite extensive genetic analysis, much remains to be done to identify the genetic contributors to AD. The field of AD genetics is represented in the current deluge of genomewide association studies being carried out in many complex diseases (see Chapter 8). One hopes that the pathogenic variants in most of the genes, even those associated with modest increases or decreases in AD risk, will be discovered and confirmed. With these genes in hand, novel therapeutic targets will become evident for the potential development of interventions to combat the disease. Frontotemporal Dementia FTD is an early-onset form of dementia that is nearly as common a cause of early-onset dementia as is EOAD (Ratnavalli et al., 2002). The disease may, however, occur even after age 65 and may account for up to 5–10% of dementia in the elderly. The disease is often familial and can be inherited as an autosomal dominant trait with very high, age-dependent penetrance. FTD is clinically heterogeneous, and the dementia seen in FTD differs

in some respect from that seen in AD (Haugarvoll et al., 2007). For example, early in the course of FTD, the dementia is often associated with difficulties with social interactions that result from behavioral and personality changes, disinhibition, and neglect of personal hygiene. Disorientation to familiar surroundings and short-term memory loss are less prominent features early in FTD compared to AD. A Parkinsonian movement disorder is very common, as is occasional lower motor neuron dysfunction reminiscent of amyotrophic lateral sclerosis. FTD results from mutations in four different genes (Haugarvoll et al., 2007). The loci most commonly found mutated in FTD are the progranulin gene (GRN) and the gene encoding the microtubular-associated protein tau (MAPT), each accounting for an approximately equal number of cases of FTD. Together, they are responsible for just over half of all cases of FTD. At autopsy, FTD brains show loss of large cortical neurons in the prefrontal and anterior temporal lobes. In cases of FTD that are due to mutations in MAPT, there are aggregates of the microtubular protein tau associated with gliosis. In other FTD patients, those with mutations in GRN, neuronal inclusions contain ubiquitin-positive protein aggregates containing the TAR DNA binding protein-43, seen with microvacuolization of the neuropil and scant gliosis. The types of mutations causing FTD are very different in the cases due to GRN and those due to MAPT mutations. The mutations in GRN are overwhelmingly loss-of-function mutations, implying that haploinsufficiency is the dominant genetic mechanism (Haugarvoll et al., 2007). The precise biological role of progranulin and the mechanism by which haploinsufficiency of this gene causes highly penetrant FTD are unknown. In contrast, mutations in MAPT are toxic gain-of-function mutations (Hutton, 2001). The MAPT gene has four exons (exons 9 through 12) that contain copies of a microtubular-binding domain. MAPT undergoes extensive alternative splicing that generates at least six isoforms. These six MAPT isoforms can be grouped into two classes, those that include exon 10 and have four copies of the repeat and those that skip exon 10 and have only three copies of the repeat (Figure 99.5). Many of the mutations in MAPT that cause FTD interfere with the skipping of exon 10 and generate an excess of tau protein containing four microtubular binding domain repeats. Tau with four microtubular binding domains is much more prone to undergo fibrillization and aggregation. There are other MAPT mutations that do not affect splicing of exon 10 but alter the properties of the tau protein, rendering it more prone to aggregation and, presumably, lead to FTD that way. In addition to mutations in GRN or MAPT, rare missense mutations in the gene for the chromatin-modifying 2B protein (CHMP2B) can cause FTD associated with amyotrophic lateral sclerosis; nuclear inclusions are seen at autopsy. Another rare form of FTD results from missense mutations in the valosin-containing protein gene (VCP), often in association with an inclusion-body myopathy or Paget disease of bone. The pathogenic mechanism by which missense mutations in CHMP2B or VCP cause FTD is unknown.

Primary Dementias

(a) I260V G272V K257T

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Figure 99.5 MAPT mutations in FTD. (a) Schematic diagram of a segment of the gene containing exons 9 through 13. Exonic missense mutations and mutations in intron 10 are shown. (b) Schematic diagram of the stem-loop structure in the 5 end of intron 10. Mutations that disrupt the stem-loop reduce the inhibitory effect of the stem-loop on splicing that would exclude exon 10, leading to more frequent inclusion of exon 10 and, therefore, increased synthesis of tau protein with four microtubule binding domains. (c) Exonic mutations that strengthen a splice enhancer, lead to more frequent inclusion of exon 10 and, therefore, increased synthesis of tau protein with four microtubule-binding domains. Reproduced from Hutton (2001) with permission.

DLBD and Parkinson Disease with Dementia DLBD is another form of neurodegeneration, accounting for 10% of cases of dementia, although some estimates suggest it is nearly as frequent as AD as a cause of dementia in the elderly (Lippa et al., 2007). DLBD is clinically similar to AD but characteristically shows a higher frequency of psychosis with auditory and visual hallucinations and is often accompanied by the symptoms and signs of Parkinsonism (Lippa et al., 2007; McKeith et al., 2005; Walker et al., 2002). At autopsy, brains of DLBD patients have the characteristic a-synuclein aggregates known as Lewy bodies throughout the cerebral cortex as well as in the olfactory bulb/anterior olfactory nucleus and in subcortical regions such as the substantia nigra, locus coeruleus, and dorsal motor nucleus of the vagus (Figure 99.6). The Lewy bodies seen in DLBD are similar to the aggregates found in classic Parkinson disease (PD), although their distribution is more cortical in DLBD, while in PD they are concentrated more in subcortical areas, at least during early stages of the disease (Braak et al., 2003, 2004). A longstanding controversy exists over the relationship between DLBD and the dementia seen in PD patients (Parkinson disease with dementia or PDD). Dementia develops in 20–40% of patients with PD (Mayeux et al., 1992; Mindham et al., 1993) while clinical or subclinical abnormalities involving

the dopaminergic system of the substantia nigra are frequent in patients with DLBD (Walker et al., 2002). An arbitrary distinction used by clinicians to separate PDD from DLBD is the “1-year” rule. If dementia occurs before or up to 1 year following the onset of the Parkinsonian movement disorder, the patient is said to have DLBD. If dementia occurs more than 1 year after the onset of the movement abnormality, the diagnosis of PDD is made instead. Although there are some subtle differences in the dementia seen in PDD and DLBD, both disorders have similar a-synuclein aggregates. Furthermore, the occurrence of PDD and DLBD in different members of the same family carrying a duplication or a triplication of the a-synuclein gene (Chartier-Harlin et al., 2004; Farrer et al., 2004; Singleton et al., 2003) suggest that the underlying pathogenic mechanism is the same in both disorders, even if the clinical features and distribution of the Lewy body pathology is somewhat different. Mendelian DLBD is rare but does occur in families with autosomal dominant forms of PD. Families with mutations (Kruger et al., 1998; Polymeropoulos et al., 1997; Zarranz et al., 2004) or copy number changes (Chartier-Harlin et al., 2004; Farrer et al., 2004; Singleton et al., 2003) in the a-synuclein gene have early-onset, autosomal dominant disease, presenting clinically as PDD in some family members, but as DLBD in others. Patients with mutations in the LRRK2 gene, which is responsible

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Figure 99.6 Pathology in DLBD. The large circular structure in the center of the field is a Lewy body. (Formalin-fixed human brain section stained with hematoxylin and eosin.)

for a form of autosomal dominant PD with variable penetrance, can also present with DLBD (Ross et al., 2006). The vast majority of DLBD and PDD cases, however, are sporadic, with a late age at onset. Far less is known about the genetic contributions to Lewy body dementia than is known about AD. There is some suggestive evidence that the APOE 4 allele is increased in individuals with DLBD, as it clearly is in AD, but the evidence is less clear and somewhat contradictory (Jasinska-Myga et al., 2007). One confounding factor is that many patients have a mixed pathological picture at autopsy, with both diffuse cortical Lewy bodies and Alzheimer pathology of amyloid plaques and neurofibrillary tangles. The possible role of the APOE 4 allele in DLBD is difficult to determine when there is mixed pathology and when AD, in which the APOE 4 clearly plays a role, is also present. Prion Diseases Prion diseases (spongiform encephalopathies) are a rare cause of dementia, occurring in approximately one in a million individuals (Eggenberger, 2007). About 15% of all spongiform encephalopathy is familial and is referred to as familial Creutzfeldt–Jakob disease (fCJD). In this disorder, patients suffer a general loss of their sense of well being and undergo vague personality changes, confusion, and a form of dementia characterized by difficulties with judgment, memory, and reasoning. Ataxia, myoclonic jerking, and choreo-athetosis appear soon after the initial signs and symptoms. At autopsy, there is widespread neuronal loss with diffuse spongiform change; deposition of amyloid plaques that stain positive with antibodies against the prion protein may also be present. Although classical fCJD has a different clinical presentation and course than early-onset familial AD, there is enough difficulty in making the clinical distinction antemortem that the prion protein gene should be considered for sequencing along with APPl, PSEN1, and PSEN2 in all cases of early-onset, familial AD (Finckh et al., 2000).

Other rarer variants of familial prion disease are known that differ in their phenotypic manifestations from fCJD. These disorders, known as fatal familial insomnia (FFI) (Gambetti et al., 1995) and Gerstmann–Sträussler–Scheinker syndrome (GSSs) (Ghetti et al., 1996), occur in patients carrying different missense or frameshift mutations in the prion protein gene. In these disorders, severe autonomic and brainstem dysfunction (in FFI) or cerebellar and basal ganglia dysfunction (in GSSs) are far more prominent than is dementia, although cognition is generally not spared in these familial prion disease variants, especially late in the course. Prion diseases are caused by aggregation of the prion protein, a naturally occurring neuronal protein that, upon adoption of an abnormal conformation, has a very high propensity to aggregate. This conformational change is induced in normal prion protein molecules by prion molecules that have already adopted the aggregation-prone conformation, thus allowing for propagation of the abnormal conformation and acceleration of the protein aggregation. In familial prion diseases, mutations in the prion gene increase its propensity to adopt an aggregationprone conformation. Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a rare form of early-onset dementia inherited in an autosomal dominant manner. The disorder has a prevalence of approximately 1 per 50,000 and most commonly presents between age 20 and 60, with a peak in the fifth decade (Opherk et al., 2004; Razvi et al., 2005). Migraine headache is a frequent early symptom of CADASIL patients as are transient ischemic attacks, neuropsychiatric abnormalities, seizures, and, later, a progressive dementia with prominent defects in executive function, judgment, and language (Dichgans et al., 1998; Opherk et al., 2004). Patients generally survive 5–10 years after diagnosis with progressive dementia and neurological disability leading to death. As opposed to all the other hereditary forms of primary dementia discussed previously in this chapter, CADASIL is not a primary neurodegenerative disorder with protein aggregation in or around neurons. Instead, CADASIL is characterized by an angiopathy in which there is degeneration of vascular smooth muscle cells in association with electron-dense, extracellular particles, visible with the electron microscope, in and around degenerating smooth muscle cells in the media of arterioles. This characteristic finding can be seen in tissue obtained at autopsy or in biopsy material from skin. The angiopathy in CADASIL leads to a progressive, occlusive disease of small blood vessels, resulting in multiple small infarcts, called lacunae, throughout the brain. The neurological defects seen with lacunar infarcts are usually more circumscribed both clinically and pathologically than those seen with the typical large cerebral artery occlusive stroke (Mohr, 1983; van den Boom et al., 2002). Lacunar infarcts in the cortex may result in a clinical picture of a pure motor stroke, a pure sensory stroke, or a mixed stroke, while the classic syndrome of dysarthria/clumsy hand is seen with deep pontine

Future Prospects for Genomic Medicine in the Dementias

lacunae. The accumulation of lacunar infarcts throughout the cortex is the likely cause of the progressive dementia seen in three-quarters of patients with CADASIL (Dichgans et al., 1998; Opherk et al., 2004). The only molecular lesions known to cause CADASIL are mutations in NOTCH3, which encodes the cell surface receptor NOTC3 (Dotti et al., 2005; Federico et al., 2005; Joutel and Tournier-Lasserve, 1998). Most NOTCH3 mutations in CADASIL are missense or splicing mutations that result in an odd number, instead of the normal even number, of cysteine residues in the extracellular epidermal growth factor-like repeats of NOTC3. The abnormal conformation caused by having an odd number of cysteine residues in the NOTC3 extracellular domain leads to the accumulation of misfolded NOTC3 fragments that form the deposits in the arteriolar walls that are visible on electron microscopy (Joutel and Tournier-Lasserve, 1998; Schroder et al., 1995). How these mutations lead to the aggregation of NOTC3 fragments is not understood. CADASIL is inherited as an autosomal dominant disorder, nearly always from a carrier parent (i.e., new mutations are rare). CADASIL is one of the few human autosomal dominant diseases that is a true dominant rather than a semidominant condition, in that homozygotes and heterozygotes have a very similar clinical phenotype (Tuominen et al., 2001).

CLINICAL APPROACH TO THE DEMENTIAS The clinical approach to the dementias relies on careful history and neurological examination, neuropsychological testing, family history, brain imaging, and molecular diagnostics when appropriate. The rapidity of disease progression, the presence of neurological signs besides dementia, such as Parkinsonism as seen with DLBD or FTD, or ataxia, dysarthria and myoclonus as in fCJD, the pattern of cognitive losses (language, short-term memory, judgment or executive function), which differ among the FTDs and AD, the degree of atrophy of frontal versus temporal or hippocampal structures seen on brain MRI, the presence of vascular-occlusive lacunae on brain MRI, may all be helpful in making an antemortem diagnosis (Grossman et al., 2007; Mastrianni and Roos, 2000; van den Boom et al., 2002, 2003). Unfortunately, definitive diagnosis of the cause of dementia is not always clear prior to death and requires postmortem examination at autopsy. When the clinical presentation, family history, neurological examination, and neuropsychological profile are suggestive of one of the dementias in which the gene is known, testing the affected patient by DNA sequencing of the appropriate genes can provide a definitive diagnosis. Physicians who submit DNA for testing should be aware, however, that the test may show a gene variant that has not been seen before and is of unknown pathogenic significance. It is also possible that the sequencing will be normal in a patient with EOAD or FTD because the disease is due to mutations in a yet to be discovered gene.

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It is important to distinguish between diagnostic testing in an affected individual and presymptomatic testing of asymptomatic relatives of a patient shown to have a pathogenic mutation in a known gene. At-risk, asymptomatic relatives may choose to have testing to help with decision-making with regard to having more children or for the purposes of career or financial planning. For some of these disorders, such as CADASIL, FTD, or AD due to APP or PSEN1 mutations, penetrance is very high, although agedependent. A positive test result in these diseases has high predictive value for development of the disease, but not necessarily for when it will develop. In another familial dementia, such as AD due to PSEN2 mutations or DLBD/PDD due to LRRK2 mutations, penetrance is much lower and, as a consequence, a positive test has much lower positive predictive value. Because of the risk for serious psychological and potential financial (life or disability insurance) damage from receiving a positive test, pre-test counseling and posttest psychological and social support are mandatory. Most adults who choose such testing and test positive seem able to handle and use the information (Steinbart et al., 2001). Genetic professionals universally recommend against testing children for these disorders. The role of testing for the 4 allele at the APOE locus in AD is more controversial. In an affected individual in the 65–75 age range with suspected AD, the presence of one or two 4 APOE alleles has a positive predictive value for AD of 75% and 98% respectively and, therefore, may help to confirm a diagnosis of AD (Nussbaum et al., 2007). Most physicians, however, would still make sure to rule out one of the rare but treatable causes of dementia, such as depression or endocrine imbalance, regardless of APOE genotype. Predictive testing using APOE genotype is currently considered to be of little value because no intervention is currently available to prevent or delay the disease process. In the over 65 age group, when 1 in 50 individuals would be expected to develop dementia each year, the vast majority of people with one 4 allele and most of those with two 4 alleles will still not develop AD. However, it should be noted that the utility of identifying individuals at increased risk for AD through APOE genotyping would increase dramatically once a preventive intervention becomes available.

FUTURE PROSPECTS FOR GENOMIC MEDICINE IN THE DEMENTIAS The application of genetic and genomic methods to the clinical evaluation and care of patients with dementia is still at a very early stage. Besides the obvious utility of finding mutations in the single genes responsible for dementia in families with autosomal dominant disease, there is currently no other genomic or proteomic testing of proven value in any of the dementias. One major barrier to the development of applying such technology is the inaccessibility of the brain and lack of brain tissue for proteomic analysis or transcript expression profiling. Cerebrospinal fluid, however, is more readily accessible and a number of proteomic analyses are being done in an attempt to identify biomarkers for the various dementias (Davidsson and Sjogren,

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2005, 2006). These studies are still at the research stage and their utility for increasing sensitivity and specificity of diagnosis over current modalities (history, physical, neuropsychological testing, and brain imaging) will need to be rigorously demonstrated.

CONCLUSION There has been substantial progress in identifying the genes responsible for the familial, autosomal dominant dementias. The genetic contributions to the vast majority of dementias

that are not inherited in a familial, autosomal dominant manner are being actively sought with powerful new tools of genomewide association at higher and higher resolution. One common theme, that of protein aggregation, has emerged from the studies of dementia and raises the possibility that there may be a final common pathway in the way neurons react, sicken, and die in response to such aggregates. As the various forms of dementia are elucidated and the genes that contribute are discovered, we expect that the pathways leading to neuronal death will be understood, thus providing the basic science underpinnings needed to translate these discoveries into useful interventions.

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A., Vercelletto, M. et al. (2006). APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat Genet 38, 24–26. Rovelet-Lecrux, A., Frebourg,T.,Tuominen, H., Majamaa, K., Campion, D., Remes, A.M. (2007). APP locus duplication in a Finnish family with dementia and intracerebral haemorrhage. J Neurol Neurosurg Psychiatr 78, 1158–1159. Saunders, A.M., Strittmatter, W.J., Schmechel, D., George-Hyslop, P.H., Pericak-Vance, M.A., Joo, S.H., Rosi, B.L., Gusella, J.F., CrapperMacLachlan, D.R., Alberts, M.J. et al. (1993). Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology 43, 1467–1472. Schroder, J.M., Sellhaus, B. and Jorg, J. (1995). Identification of the characteristic vascular changes in a sural nerve biopsy of a case with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Acta Neuropathol (Berl) 89, 116–121. Schupf , N., Patel, B., Pang, D., Zigman, W.B., Silverman, W., Mehta, P.D. and Mayeux, R. (2007). Elevated plasma beta-amyloid peptide Abeta(42) levels, incident dementia, and mortality in Down syndrome. Arch Neurol 64, 1007–1013. Seshadri, S., Drachman, D.A. and Lippa, C.F. (1995). Apolipoprotein E epsilon 4 allele and the lifetime risk of Alzheimer’s disease. What physicians know, and what they should know. Arch Neurol 52, 1074–1079. Singleton, A.B., Farrer, M., Johnson, J., Singleton, A., Hague, S., Kachergus, J., Hulihan, M., Peuralinna, T., Dutra, A., Nussbaum, R. et al. (2003). alpha-Synuclein locus triplication causes Parkinson’s disease. Science 302, 841. Steinbart, E.J., Smith, C.O., Poorkaj, P. and Bird, T.D. (2001). Impact of DNA testing for early-onset familial Alzheimer disease and frontotemporal dementia. Arch Neurol 58, 1828–1831.

Tang, M.X., Stern, Y., Marder, K., Bell, K., Gurland, B., Lantigua, R., Andrews, H., Feng, L., Tycko, B. and Mayeux, R. (1998). The APOE-epsilon4 allele and the risk of Alzheimer disease among African Americans, whites, and Hispanics. JAMA 279, 751–755. Tuominen, S., Juvonen,V., Amberla, K., Jolma, T., Rinne, J.O., Tuisku, S., Kurki, T., Marttila, R., Poyhonen, M., Savontaus, M.L. et al. (2001). Phenotype of a homozygous CADASIL patient in comparison to 9 age-matched heterozygous patients with the same R133C Notch3 mutation. Stroke 32, 1767–1774. van den Boom, R., Lesnik Oberstein, S.A., van Duinen, S.G., Bornebroek, M., Ferrari, M.D., Haan, J. and van Buchem, M.A. (2002). Subcortical lacunar lesions: An MR imaging finding in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Radiology 224, 791–796. van den Boom, R., Lesnik Oberstein, S.A., Spilt, A., Behloul, F., Ferrari, M.D., Haan, J., Westendorp, R.G. and van Buchem, M.A. (2003). Cerebral hemodynamics and white matter hyperintensities in CADASIL. J Cereb Blood Flow Metab 23, 599–604. Walker, Z., Costa, D.C.,Walker, R.W., Shaw, K., Gacinovic, S., Stevens,T., Livingston, G., Ince, P., McKeith, I.G. and Katona, C.L. (2002). Differentiation of dementia with Lewy bodies from Alzheimer’s disease using a dopaminergic presynaptic ligand. J Neurol Neurosurg Psychiatr 73, 134–140. Zarranz, J.J., Alegre, J., Gomez-Esteban, J.C., Lezcano, E., Ros, R., Ampuero, I., Vidal, L., Hoenicka, J., Rodriguez, O., Atares, B. et al. (2004). The new mutation, E46K, of alpha-synuclein causes Parkinson and Lewy body dementia. Ann Neurol 55, 164–173.

RECOMMENDED RESOURCES AlzGene website. Go to http://www.alzforum.org/res/com/gen/alzgene/default.asp GeneReviews. Online Reviews of AD, FTD, CADASIL, and fCJD. Go to http://www.geneclinics.org/and click on GeneReviews.

Useful Reviews Dugue, M., et al. (2003). Review of dementia. Mt Sinai J Med 70, 45–53. Ertekin-Taner, N. (2007). Genetics of Alzheimer’s disease: A centennial review. Neurol Clin 25, 611–667.

CHAPTER

100 Parkinson’s Disease: Genomic Perspectives Shushant Jain and Andrew B. Singleton

INTRODUCTION Parkinson’s disease (PD) is the second most common neurodegenerative disease with at least 5 million people affected globally (Twelves et al., 2003). PD was first described in 1817 by James Parkinson in the Essay of Shaking Palsy. Subsequently Brissaud noted lesions within the substantia nigra (SN) and, together with Meynerts previous observation that the basal ganglia is involved in movement, concluded that injury of this region was responsible for the symptoms in PD. Nearly half a century later, dopamine was identified as a neurotransmitter in the basal ganglia and individuals with PD showed a deficiency in dopamine. This subsequently led to the administration of levodopa (L-dopa, metabolic precursor of dopamine) which remains the most effective symptomatic treatment for PD (Birkmayer and Hornykiewicz, 1962). Although the underlying physiological cause of the major movement symptoms of PD had been exposed, the explanation for specific nigral degeneration remains unclear. For many years it was believed that PD was primarily the effect of environmental insult as studies had recognized that individuals with exposure to certain chemicals such as 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) could lead to a disease with parkinsonism features. Further studies showed that a PD phenotype could arise from multiple different etiologies including vascular insults, infections (post-encephalitic Parkinsonism caused by the influenza virus) and frontal lobe tumors.

Research into PD was revolutionized when a genetic basis for PD was established with the identification of monogenic forms (Table 100.1). Elucidating the genetics and environmental causes of PD has highlighted biological pathways critical in disease pathogenesis. This in turn could allow the subdivision of the disease based on genetic rather than phenotypic information which could help explain the wide variation in this disease, including differences in clinical course, and response to treatment, and it might help clarify the role of environmental factors in disease cause or susceptibility.

CLINICAL CHARACTERISTICS OF PD PD belongs to a heterogeneous family of diseases referred to as parkinsonism syndromes. Within this group there are many diseases such as progressive supranuclear palsy (PSP), diffuse Lewy body disease (DLB) and environmentally induced parkinsonism, such as exposure to MPTP or other environmental insults. A clinical diagnosis of PD as opposed to other parkinsonism syndromes requires the presence of tremor, rigidity, and akinesia. In addition, there are inclusion and exclusion criteria which also must be fulfilled: (1) no detectable cause, (2) no cerebella deficits, (3) limited pyramidal signs, (4) no lower motor dysfunction, (5) limited gaze palsy, and (6) minor autonomic deficits. A pathological diagnosis dictates loss of dopaminergic cells in the SN and also the presence of intracytoplasmic eosinophilic inclusions called Lewy bodies (LB) in surviving neurons (Hardy and Lees, 2005).

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TABLE 100.1

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Parkinson’s Disease: Genomic Perspectives

Genetic loci implicated in Parkinson’s disease

Locus

Protein name

Inheritance pattern

Phenotype AOO40–50 years

PARK1 PARK4

-SYNUCLEIN

AD

Features of dementia with Lewy bodies AOO35 years

PARK2

PARKIN

AR

Slow disease progression L-dopa responsive No Lewy bodies AOO50 years

PARK3

Unknown

AD with reduced penetrance

Typical PD L-dopa responsive No pathology available AOO50 years

PARK5

UCHL-1

AD

Typical PD L-dopa responsive No pathology available AOO35–45 years

PARK6

PINK1

AR

Slow disease progression L-dopa responsive No pathology available AOO30–40 years

PARK7

DJ-l

AR

Slow disease progression L-dopa responsive No pathology available AOO50 years

PARK8

LRRK2

AD

Typical PDL-dopa responsive Variable pathology

PARK9

ATP13A2

AR

Levodopa-responsive parkinsonism with pyramidal degeneration, supranuclear gaze palsy, and dementia

AOO16 years

AOO50–60 years PARK10

Unknown

Risk factor

Typical PD No pathology available AOO50–60 years

PARK11

Unknown

Risk factor

Typical PD No pathology available AOO50–60 years

PARK12

Unknown

Risk factor

Chromosome 5 (5q23)

Unknown

Risk factor

Typical PD No pathology available AOO50–60 years Typical PD No pathology available

Abbreviations: AOO: Average age of onset, AD: Autosomal dominant, AR: Autosomal recessive.

PD is a late-onset disease, primarily occurring in the fifth or sixth decades, although some forms, particularly the recessive genetic diseases, can begin in childhood. Disease in individuals where a specific etiology is not known and where there is no clear

family history of PD are classified as sporadic PD. The early symptoms of PD are usually non-specific and may be seen in many other neurological syndromes or as a part of normal aging.To begin with, there is a general lethargy with possible mood and mild cognitive

Genetics of PD

impairment. Subsequently, an intermittent tremor often present only under stress can develop with asymmetrical rigidity moving to the other side of the body within 3–5 years. Within 5 years bradykinesia and postural instability ensue (Hughes et al., 1993). The average mortality rate in PD is approximately 1.5 above the general population. On average disease duration is 13 years, and the mean age at death has been reported at 73 years. The most common causes of death in PD patients is pneumonia through lack of activity, cardiovascular disease, or severe injury through falling (Hughes et al., 1993).

GENETICS OF PD Many diseases have a genetic component, whether it is due to inherited mutations or as a result of genetic variation controlling the response to environmental stresses such as viruses or toxins. The identification of the genetic causes of a disease allows one to isolate the primary pathogenic mechanism and/or contributors to a disease. The ultimate goal is to use this information to identify and develop new ways to treat, cure, or even prevent the disease. A common methodology used in the determination of the relative contribution of genetics to disease is the twin study, this is performed by comparing concordance of disease in monozygotic (MZ) twins (who share all genes) and dizygotic (DZ) twins (who share, on average, 50% of autosomal genes). Recent data from twin studies using the uptake of 18F-dopa and positron emission tomography (PET) imaging have suggested that genetics does play a role in disease (Piccini et al., 1997) but also suggest genetics is not the sole determinant of disease. These data are consistent with the most widely held hypothesis, that the majority of typical PD cases are a result of a complex interplay between genetic variability and environmental exposures. Many genes have been implicated in PD but the analysis of multiple nuclear families or isolated populations has led to the identification of multiple genes and loci that cause mendelian PD or increase risk for PD (Table 100.1). SNCA (PARK1; PARK4; -SYNUCLEIN) -synuclein was previously cloned as the non-Abeta () component of Alzheimer’s disease (AD) amyloid plaques. However, its role in PD became evident in 1997 when a mutation (A53T) within a Greek kindred was shown to cause autosomal dominant PD (Polymeropoulos et al., 1997). Subsequently, a further two additional missense mutations (A30P and E64K) have been identified as rare causes of disease as it has multiplication of the genomic segment containing the gene encoding -synuclein (SNCA) (Singleton et al., 2003). Soon after the discovery of mutations in the gene encoding -synuclein as the first genetic cause of PD, this protein was found to be the major component of LB, the pathological hallmark of PD (Spillantini et al., 1997). However, the pathology in individuals with -synuclein mutations is not typical of idiopathic PD; the pathology is usually more extensive with LB not only in the SN but also throughput the cortex, striatum and locus ceruleus; additionally -synuclein pathology may also be seen outside of neuronal cells, within glia, somewhat similar to

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the glial cytoplasmic inclusions noted in multiple system atrophy (Mukaetova-Ladinska and McKeith, 2006). The parkinsonism associated with -synuclein mutations presents at a relatively early age (30s to 50s) and is rapidly progressive, in many cases the disease in patients with -synuclein mutations progresses to include a prominent dementia, likely a reflection of the extensive cortical pathology noted in these patients (Kruger et al., 2001). -synuclein is part of a gene family including  and  synuclein.The function of -synuclein is not well understood but many hypothesizes exist regarding its role in PD pathogenesis. -synuclein is primarily located at synaptic membranes and therefore may have a role in maintaining synaptic function which in part has been supported by animal modeling; analysis of -synuclein knockout mice has suggested a role for -synuclein of long-term regulation and/or maintenance of presynaptic function (Kaplan et al., 2003). Because SNCA was the first gene implicated in PD and because its protein product is the major deposited species in the hallmark lesion of this disease, considerable resources have been used in an attempt to understand the pathophysiological process that results from -synuclein mutation. Firstly -synuclein can aggregate under a number of different conditions, the central hydrophobic region, where the missense mutations reside, tends to self-aggregate. The end product of -synuclein aggregation is the formation of heavily insoluble polymers of protein known as fibrils, which is promoted by both the A53T mutation and overexpression of -synuclein (Figure 100.1) (Volles and Lansbury, 2003). Conversely, A30P slows the rate of fibril accumulation but increases the rate of -synuclein protofibril formation. Because of this and other data it is now believed that the protofibril species of -synuclein are the toxic species (Cookson, 2005). -synuclein protofibrils have the ability to form poreslike structures (Volles and Lansbury, 2003) which can cause leakage of vesicles. Furthermore, PD associated mutations are able to increase the permeabilizing activity of -synuclein by increasing protofibril formation. The subsequent binding and formation of pores in the mitochondrial or vesicular membranes (Volles and Lansbury, 2003) or at the cell surface, could lead to disruption of numerous cellular activities and cell death. Another mechanism of -synuclein toxicity could be mediated in part by its post-translational modification. -synuclein is phosphorylated at Ser-129 and it is this form that is primarily deposited in LB. In addition, altering this residue to either prevent or mimic phosphorylation suppresses or enhances synuclein toxicity respectively in Drosophila transgenic models (Chen and Feany, 2005). If phosphorylation of -synuclein is a necessary event in its pathogenesis then inhibiting the kinases responsible would be a good target. Numerous studies are also underway to determine if synuclein levels are a good correlate for disease status and hence a biomarker for pre-symptomatic individuals (Miller et al., 2004). PRKN (PARK2; PARKIN) The gene encoding PARKIN (PRKN) was the first gene to be identified with mutations that underlie autosomal recessive PD and represents the most common known cause of early-onset parkinsonism (Kitada et al., 1998). PRKN mutations account

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PARKIN mutations, oxidative stresses Mutant proteins and normal proteins Alphasynuclein α-synuclein mutations PARKIN mutations

Oxidative stresses

Complex I

Oxidative stress e.g. MOTP

Protofirbrils

Mitochondria 26S Proteosome DJ-1

PINK1 Fibrils

Environmental toxins Dopamine synthesis and degradation

Lewy body

MtDNA mutation Mendelian mutations

Figure 100.1 A proposed model for mechanisms of cellular toxicity in PD. PRKN mutations and oxidative stress can inhibit PARKIN mediated ubiquitination of specific substrates leading to their accumulation. These substrates may inhibit both the proteasome and mitochondria. The formation of -synuclein protofibrils and aggregates can be toxic to both the mitochondria and proteasome. PINK1 and DJ-1 promote cell survival, either directly or indirectly by protecting mitochondria from oxidative stress.

for nearly 50% familial cases where the age of onset is below 40 years and the probability of a patients having PRKN-linked disease increases as age of onset decreases. The clinical picture of PRKN-linked disease is not that of typical PD patients commonly present with dystonia, other atypical features include hyperreflexia, slow progression, and early compilations from L-dopa treatment. Patients survive an average of 10 to 20 years and have a more symmetrical onset (Lohmann et al., 2003). Despite the relative abundance of PRKN-linked disease there remains a paucity of neuropathological analysis in patients with disease unequivocally caused by PRKN mutation. The majority of reports indicate a lack of LB pathology and because the presence of nigral LB is a hallmark pathological feature of PD and a large amount of resources have been invested in an attempt to determine the molecular pathway of disease related to PRKN mutation, there has been considerable debate over the relevance of PRKN-linked parkinsonism to typical PD (Hardy

and Lees, 2005). However, while the nature of LB pathology in this form of parkinsonism is unclear, there is certainly degeneration and dysfunction of the dopaminergic neurons and as such establishing the mechanism of the preferential vulnerability of this neuronal system in PRKN disease is likely to be directly relevant to typical PD. There are multiple types of mutations that cause loss of function mutations within PRKN: point mutations to large scale deletions spanning multiple exons, multiplications, frameshift (alters reading frame), truncating, and splice-site mutations. The scale and assortment of mutations within PRKN makes genetic diagnosis difficult as extensive screening has to be performed. Furthermore, there are several point mutations where pathogenicity is currently equivocal (von Coelln et al., 2004). PRKN encodes an E3 ubiquitin ligase which is responsible for the addition of ubiquitin molecules to specific target proteins that are subsequently recognized by the proteasome and degraded (Figure 100.1). As a consequence of the large deletions

Genetics of PD

and multiple mutations throughout the gene, lack of PARKIN may lead to the accumulation of one or more of its substrates and subsequently to cell death. In support of this hypothesis, when suspected substrates of PARKIN are overexpressed they can lead to dopaminergic cell loss which can subsequently be rescued by PARKIN but not its mutants (Kahle and Haass, 2004). As the accumulation of proteasomal substrates or proteasome inhibition is implicated in PD pathogenesis (McNaught and Olanow, 2006), individuals involved in small molecule proteasome inhibitor clinical trials (e.g. NPI-0052; Chauhan et al., 2006), will have to be carefully monitored to ensure they do not develop parkinsonism features. Another hypothesis suggests that loss of PARKIN may result in mitochondrial damage and apoptosis, as knockout of PARKIN homologs from both mice and Drosophila cause decreases in mitochondrial respiratory capacity demonstrated by reduced lifespan, locomotor defects due to apoptotic cell death and male sterility due to spermatid individualization defects; this hypothesis is gaining impetus as the genes mutated in other recessive Mendelian PD cases appear to link mitochondria to neuronal cell loss (Shen and Cookson, 2004). DJ1 (PARK7; DJ-1) DJ-1 mutations are found in young-onset autosomal recessive parkinsonism but are the rarest known genetic cause of parkinsonism (1–2% of familial PD; Bonifati et al., 2003); however, mutation of DJ-1 remains the rarest known genetic cause of parkinsonism. Given the rarity of DJ-1 mutations there is limited clinical data and to date no pathological data available in DJ-1-linked patients. Patients with DJ-1 mutations present with a young-onset disorder (mid-30s) and follows a relatively benign course (Bonifati et al., 2003). Consistent with loss of function, DJ-1 is recessively inherited and one of the original families possessed a large deletion encompassing the start codon of DJ-1. Subsequently, multiple point and splice mutations have been described within DJ-1. Although the current function of DJ-1 is unclear, some mutations (e.g. L166P) destabilize DJ-1 thus leading to increased degradation by the proteasome. As a consequence there is insufficient DJ-1 which is hypothesized to increase neuronal vulnerability to toxic insult and apoptosis (Figure 100.1). Several hypothesis exist as to how DJ-1 protects cells from toxic insult. DJ-1 is a 189 amino acid member of the ThiJ/PfpI/ DJ1 superfamily, ubiquitously expressed and localizes to the cytosol and mitochondria as well as the nucleus in dividing cells. Under oxidative stress conditions, such as exposure to paraquat or MPTP, DJ-1 undergoes an acidic shift in pI by modifying the side chain of cysteine 106 to form a sulfinic acid. This is correlated with the protein relocating from the cytosol to the outer mitochondria membrane. Overexpression of DJ-1 in culture can decrease sensitivity to specific stressors, such as paraquat and MPTP (CanetAviles et al., 2004). Conversely, loss of DJ-1 in mice, cell culture, and Drosophila models leads to increased sensitivity to oxidative stresses (Cookson, 2005; Shen and Cookson, 2004). DJ-1 only has a weak ability to scavenge free radicals and thus, DJ-1 is unlikely to primarily function as an anti-oxidant

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protein. Consequently, a role for DJ-1 as an oxidative stress sensor has been suggested. DJ-1 may have an analogous role to the DNA damage sensing enzymes (e.g. ATM, ATR, and RAD proteins) where specific enzymes recognize different types of DNA damage and are able to mediate the appropriate response (e.g. apoptosis, cell cycle arrest, and transcription). As DJ-1 was cloned as part of a RNA protein-binding complex, it is postulated that DJ1 may control transcription and/or translation of particular RNA species in response to oxidative stress (Abou-Sleiman et al., 2003). Alternatively, DJ-1 has been shown to bind to numerous proteins such as DAXX, preventing it from activating the apoptotic pathway and decreasing cell sensitivity to oxidative stresses. However, many of these DJ-1 interactors still require validation in vivo, both to confirm the interaction and to establish that they play specific roles in DJ-1 mediated cell survival (Abou-Sleiman et al., 2003). PINK1 (PARK6; Pten Induced Kinase 1) Mutations in the gene PINK1 were identified in four Italian families with recessive early-onset PD (Valente et al., 2004). Initial screens for PINK1 mutations in early-onset familial cases reveals that PINK1 mutations are a more common cause of young-onset PD than DJ-1 mutation, but not as prevalent as PRKN mutation. PINK1 mutations are estimated to cause 4% of familial recessive PD. The clinical course of individuals with PINK1 mutations resembles that of sporadic PD except the age of onset is earlier (approximately 35–45 years of age) and disease progression is slower. Similar to PRKN disease dystonia at onset appears to be more frequent in individuals with PINK1 mutations. No pathology data is available from any affected individuals (Healy et al., 2004a). PINK1 is predicted to be a serine-threonine kinase that is targeted to the mitochondria. Once PINK1 enters the mitochondria, the N-terminal mitochondrial targeting motif is cleaved. Although no substrates of PINK1 have been identified, the recessive nature of the disease and the presence of truncating mutations in PINK1-linked cases, suggest loss of kinase activity may result in cell loss. As PINK1 is a mitochondrial kinase and can protect cells against oxidative stresses such as paraquat and MPTP (Figure 100.1) (Beilina et al., 2005; Valente et al., 2004). PINK1 may phosphorylate multiple proteins to maintain mitochondrial function and inhibit apoptosis. In support of this observation, knockout of Drosophila PINK1 results in male sterility, apoptotic muscle degeneration, defects in mitochondrial morphology, and increased sensitivity to oxidative stress. As mutations within PINK1 were only recently identified in PD, more work is needed to determine what the endogenous function of PINK1 is and how mutations within PINK1 can cause selective degeneration of the SN. LRRK2 (PARK8; DARDARIN) In 2002, autosomal dominant PD within a large Japanese kindred from Sagamihara was linked to the pericentromeric region of chromosome 12 (Funayama et al., 2002). Affected members of this family presented with a clinically typical L-dopa responsive PD with an age at onset of approximately

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50 years. Neuropathologically, individuals exhibited pure nigral degeneration in the absence of LB. In addition the disease associated haplotype was carried by many unaffected individuals suggesting incomplete penetrance of the mutation (Funayama et al., 2002). Further linkage of multiple families to the region confirmed the locus and suggested that the gene could be a common cause of PD. However, the pathology of these families was heterogeneous with some individuals presenting with LB pathology while others presented with TAU pathology. In 2004, the gene for PARK8-linked PD was identified, LRRK2 was originally identified as part of the kinome project and the protein (DARDARIN-the Basque word for tremor) is predicted to be a tyrosine kinase (Paisan-Ruiz et al., 2004; Zimprich et al., 2004). DARDARIN is a very large protein (2527 amino acids) with multiple functional motifs. DARDARIN primarily belongs to a newly identified family of proteins referred to as ROCO that contains two conserved domains (1) a ROC (Ras in complex proteins) domain that belongs to the Ras GTPase superfamily and (2) a COR domain (C-terminal of ROC). In addition DARDARIN contains multiple protein interaction motifs such as WD40, armadillo, and a leucine rich repeat (Jain et al., 2005). The wild-type function of DARDARIN is unknown but from preliminary functional studies it appears that mutations within DARDARIN alter kinase activity and it is required with the formation of intracellular aggregates as well as with toxicity. In terms of genetic testing, LRRK2 is arguably the most important gene linked to PD as one single mutation (G2019S) accounts for 1–2% of sporadic PD and 5–6% of familial European PD (Jain et al., 2005). However, there is considerable variability associated with LRRK2-linked disease; this includes not only the pathological variability noted above but also variability in clinical presentation, age at onset and penetrance. Initial estimates of penetrance suggested that carrying a G2019S mutation resulted in an 80% chance of having disease; however, the bias of family based recruitment means that this estimate was high and it appears that the penetrance of the G2019S mutation is closer to 30%. This variability in the disease occurrence, presentation, progression, and endpoint suggests that there are other genetic, environmental or stochastic events that modulate the disease process. Unlike mutations in other genes that cause PD, the frequency with which mutations in LRRK2 occur affords us the opportunity to investigate these specific modulators of the disease, and one would hope these will also be relevant to idiopathic PD (Singleton, 2005). As common as LRRK2 mutations may be, one has to question the utility of clinical genetic testing in PD. Multiple mutations within LRRK2 (Brice, 2005) and several other genes implicated in PD have been described where the pathogenicity of the variants remain in doubt. Furthermore, identification of a mutation does not necessitate an individual will develop disease, or alter its prognosis or clinical treatment. At present, genetic testing in PD should only be used to confirm a clinical diagnosis of PD (McInerney-Leo et al., 2005).

GENETICS OF SPORADIC PD Mutations in known genes account for less than 10% of all PD thus research has been aimed at identifying genes associated with typical PD and has focused on the role of common genetic variation in modulating lifetime risk for disease. As many of the genes identified in mendelian forms of PD implicate the mitochondria in disease pathogenesis (Shen and Cookson, 2004), numerous studies have questioned if mutation of the mitochondrial genome (mtDNA) or the various components encoded by the nuclear genome, contribute to PD development or progression. Many lines of evidence support a role of mitochondrial damage in the pathogenesis of PD (Muqit et al., 2006). Mitochondrial complex I activity is systematically decreased in human PD brains and administration of complex I inhibitors (MPTP and rotenone) to rats, mice, and monkeys recapitulates many aspects of PD, including selective neurodegeneration of the SN and formation of LB type pathology (Betarbet et al., 2000). Recently two studies implicated specific mutation of SN mitochondria as causes for impairment of cellular respiration, specific neuronal vulnerability, and age-dependent risk associated with PD (Bender et al., 2006; Kraytsberg et al., 2006). Amplification of mitochondrial DNA revealed more somatic deletions within SN mitochondria than mitochondria from other brain regions and that deletions in SN mitochondria were higher in PD cases than controls, although this difference did not reach statistical significance. By the age 70, nearly all the SN neurons had elevated levels of mtDNA deletions, implying that these types of deletions might contribute to the age-dependent pathogenic processes seen in PD. It is feasible that mutation and damage of mitochondria contribute to the preferential vulnerability of SN neurons and disease progression, but it remains unclear if accumulation of mitochondrial mutations is the fundamental pathogenic event in the majority of PD. It is probable that genetic variability at different loci contributes and predisposes some individuals to accumulating higher levels of mitochondrial mutations and damage, thus leading to sufficient neuronal loss and clinical manifestation of disease. More traditional approaches have identified risk factors for PD. This involves a candidate gene association analysis, where typically a gene is chosen based on its function, expression or genomic position, common variants are assayed within the gene, and the frequency of these variants are compared between cases and controls. The ease and low cost of this approach has resulted in hundreds of candidate gene association studies being published in PD. The well-characterized genes in terms of genetic association with typical PD are likely those encoding -synuclein and TAU. When the SNCA triplication was discovered, not only did it validate many overexpression studies but also asked the question if smaller increases in SNCA could increase the risk for sporadic disease. Many studies have attempted to address this question but as with most studies looking at risk factors in complex diseases, they have been largely inconclusive. Even though a polymorphic multi-allelic repeat in the promoter of

Genetics of Sporadic PD

SNCA (Rep1) can negatively regulate -synuclein expression, genetic analysis of this marker has not determined if more subtle increases in -synuclein expression can increase risk for sporadic PD. Examination of common variability in other genes involved in monogenic forms of PD has failed to reveal a consistent association with sporadic PD (Jain et al., 2005). Perhaps the most robust genetic association with increased risk for PD comes from analysis of the microtubule associated protein TAU. Mutations in this gene cause frontotemporal dementia and Parkinsonism linked to chromosome 17 (FTDP17) (Hutton et al., 1998). TAU forms intraneuronal inclusion referred to neurofibrillary tangles (NFTs) in many diseases pathologically referred to as tauopathies such as AD, PSP and corticobasal degeneration (CBD) (Rademakers et al., 2004). Consequently TAU was considered as a candidate gene for these diseases and a specific haplotype (referred to as the H1 haplotype) has been consistently associated with increased risk for PSP and CBD. As a common feature of both PSP and CBD is an initial presentation of parkinsonism, a role for TAU in PD has been evaluated. Thus far, numerous studies demonstrate that individuals that are homozygous for the H1 haplotype are approximately 1.5 times at greater risk of developing PD (Healy et al., 2004b). Although TAU and SNCA have been extensively investigated, the role of common genetic variability in these genes in risk for PD is still arguable. There are many reasons why elucidating the genetics of typical PD has been problematic; some of these lie in study design, many studies are often underpowered, there may be poor selection of control populations and lack of correction for population stratification or multiple testing. All these problems result in the publication of positive association that is usually followed by failure to replicate papers (Botstein and Risch, 2003). Although statistical power will always be a problem, the advent of the HapMap project (www.hapmap.org) and the availability of technology for genome-wide association studies promise a more complete genetic analysis of PD (Maraganore et al., 2005). These data should reveal common genetic variability underlying disease and, in the absence of association, give a reasonable indication of single common genetic variants not underlying disease (Farrall and Morris, 2005). Therapeutics Currently the most effective non-surgical treatment for PD is the administration of L-dopa. The metabolic precursor to dopamine was first administered in 1960s to a group of individuals affected with post-encephalitic Parkinsonism (Birkmayer and Hornykiewicz, 1962). Within a short period of time individuals improved from a near catatonic state to relatively normal lives. This drug initially offers a remarkable treatment option to most patients with PD; however, as effective as L-dopa is in the early stages of PD, with continued use, approximately 60% of patients develop response fluctuations and dyskinesias. The half life of L-dopa and therefore the therapeutic benefit can be improved by the co-administration of inhibitors that break down L-dopa to non-useful, potentially harmful metabolites such as 3-MT or dopa quinine (COMT and MAO (monoamine oxidase) inhibitors, Figure 100.2).

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L-tyrosine Tyrosine hydroxylase

Dopa Dopa decarboxylase DAT mediated re-uptake

Dopamine

Catechol-omethyltransferase

3-MT

Monoamine oxidase

Monoamine oxidase

DOPACA

Catechol-omethyltransferase

Homovanillic acid

Figure 100.2 Mechanisms of dopamine synthesis and metabolism. 3-MT: 3-methoxytyramine, DOPAC: 3, 4-dihydroxyphenylacetic acid, COMT: Catechol-o-methyltransferase, MAO: Monoamine oxidase.

An alternative to dopamine replacement has been surgical intervention. The loss of dopaminergic cells causes the general deregulation of neurotransmission. The over activation of the subthalamic nucleus (STN) leads to an excessive inhibitory output from the globus pallidus interna (GPi) and SN to the cortex and motor systems (Figure 100.3). As a consequence bradykinesia, tremor, and rigidity arise. The aim of surgical intervention is to disrupt the striatum and other areas to allow increased signaling from the SN. This treatment can be effective at treating L-dopa induced dyskinesias, tremor, and rigidity. A more recent advancement in surgical intervention is deep brain stimulation (DBS). This is essentially the same as disrupting the STN and GPi, but as an alternative to ablation, electrodes are implanted to block the signaling via a high-frequency electrical current (Garcia et al., 2005). As a consequence, there is more flexibility in controlling neurotransmission. Current treatments only temporarily manage the symptoms of PD and do not halt or slow down the progression of dopaminergic cell loss. Since the primary movement disorder associated with PD involves the relative loss of a specific neuronal population, this aspect of the disease represents an excellent target for cell-based therapies. Transplantation of fetal mesencephilic dopaminergic neurons into the STN of PD patients has proven clinical effective with sustained relief from the symptoms (Correia et al., 2005). This therapy has the potential to cure PD if the

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Glutamate Cortex

Striatum D1 Receptor

D2 Receptor

Glutamate GABA and substance P Thalamus

Dopamine

Substantia nigra pars compacta Brainstem

GABA

GABA and enkephalin

Globus pallidus externa

GABA Globus pallidus interna Glutamate

SUbthalamic nucleus

Substantia nigra pars reticulata

Figure 100.3 Neuronal pathways in the basal ganglia. The overall effect of striatal dopamine release is to reduce basal ganglia output, leading to increased activity of thalamocortical projection neurons. Lack of dopamine results in increased activity of GPi, SN pars reticulata and the STN. This ultimately leads to disruption and inhibition of brain stem motor areas and thalamocortical motor system.

transplanted cells are not afflicted by the same mechanism of neuronal death as the patient’s neurons. However, at present a prohibitive number of neurons are required (3–6 fetuses per patient) per patient and current methods of delivery do not lend themselves to treatment of large numbers of patients. Research is underway to grow and differentiate and stem cells into dopamine cells but problems remain. The population of cells produced are not of a pure cell type (i.e. dopaminergic cells) and therefore when implanted into the striatum may actually be detrimental. Studies into creating a pure enriched population of dopamine neurons ex vivo and targeting endogenous stem cells for differentiation into dopaminergic neurons are advancing at a significant pace and represent a realistic therapeutic goal for PD. The Role of Genetics in the Development of Future Therapeutic Strategies In the near future it will become practical to analyze a person’s genome, in a high throughput standardized manner. As a result, identifying genetic variability that alters response to a drug and thus identifying markers that are likely predictors of drug response will gain momentum. Based on this information an individual’s drug regime can be tailored to their biological characteristics. This avenue of research should eventually be able to optimize the dose of a drug but also prevent the administration of drugs to individuals who would normally have an adverse potentially lethal reaction. From the opposite side, individuals that respond better to certain drugs may help elucidate biological components of a disease process and identify other potential

drug targets. As L-dopa is such an effective treatment for PD many of the preliminary pharmacogenomic approaches have aimed to identify biological factors which augment and prolong its result. Accordingly, genes involved in dopamine metabolism have been assessed for their genetic contribution to L-dopa efficacy, most notably including analysis of genetic variability within the genes encoding MAO, dopa decarboxylase and COMT among others (Figure 100.2; Maimone et al., 2001). While the initial results of these studies are inconclusive, the application of genome-wide genotyping techniques holds the promise of identifying easily assayable variants that offer predictive value in terms of response to treatment. The most successful strategies for treating PD will be those that aim to stop or significantly delay the underlying molecular disease processes. Etiology-based treatment will require not only the elucidation of these processes and identification of viable targets but also early diagnosis of the disease. This latter point ensures that protective therapy can be applied before too much irreversible damage has occurred and in the event that the successful therapy is one that slows down but does not halt the pathological process, the therapy can be applied early enough to ensure there is little effect on quality of life for the patient in later years of treatment. Numerous studies have sought to identify biological or genetic markers that may aid in the pre-symptomatic diagnosis or prognosis of PD sufferer’s. As yet no such genetic factors have been identified but 18F-dopa uptake with PET imaging (Khan et al., 2005) and levels of phospho-synuclein have the potential to be used as

References

pre-symptomatic diagnostic aids (Miller et al., 2004). However, before both can be put into clinical practice, they have to be put to rigorous and extensive testing in large patient cohorts to determine if they are truly diagnostic for disease development and prognosis. As noted above, it is hoped that highlighting genes involved in the pathogenesis of PD will allow effective modeling of disease in both cell and animal based systems. This in turn will provide a greater understanding of the underlying molecular mechanisms of the disease process and highlight pathways for therapeutic intervention. When these models produce a readily quantifiable endpoint believed to be related to the pathological processes of the disease, they also allow high-throughput screening of molecular libraries of compounds for inhibitors of pathogenic processes. Importantly, the recent discovery of LRRK2 mutations as a cause of PD has not only provided us with another tool to create model systems of disease, but has also provided clinical researchers with a large patient pool to study disease onset, progression, and response to treatment. Large cohorts of asymptomatic subjects will be relatively easy to identify by assessing siblings and

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children of patients with G2019S linked disease. This group of subjects affords us the opportunity to not only identify signs and symptoms of disease that may be used as specific early indicators of PD, but also provide a cohort of patients in whom the efficacy of neuroprotective agents can be tested.

CONCLUSION As light is shed upon the molecular mechanisms behind the pathogenesis of PD and advancements in both surgical and pharmacological treatments, it is expected that there will be a significant improvements in the treatment of symptoms in PD. Slowing or halting underlying neurodegeneration, on the other hand, will require the identification of an easy and affordable test(s) that allow early diagnosis. Genetic screening coupled with longitudinal studies investigating a selection of biological (e.g. phospho-synuclein levels, 18F-dopa uptake) and clinical traits will hopefully discriminate such factors.

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Chen, L. and Feany, M.B. (2005). Alpha-synuclein phosphorylation controls neurotoxicity and inclusion formation in a Drosophila model of Parkinson disease. Nat Neurosci 8, 657–663. Cookson, M.R. (2005). The biochemistry of Parkinson’s disease. Annu Rev Biochem 74, 29–52. Correia, A.S., Anisimov, S.V., Li, J.Y. and Brundin, P. (2005). Stem cellbased therapy for Parkinson’s disease. Ann Med 37, 487–498. Farrall, M. and Morris, A.P. (2005). Gearing up for genome-wide geneassociation studies. Hum Mol Genet 14(Spec No. 2), R157–R162. Funayama, M., Hasegawa, K., Kowa, H., Saito, M., Tsuji, S. and Obata, F. (2002). A new locus for Parkinson’s disease (PARK8) maps to chromosome 12p11.2-q13.1. Ann Neurol 51, 296–301. Garcia, L., D’Alessandro, G., Bioulac, B. and Hammond, C. (2005). High-frequency stimulation in Parkinson’s disease: More or less?. Trends Neurosci 28, 209–216. Hardy, J. and Lees, A.J. (2005). Parkinson’s disease: A broken nosology. Mov Disord 20(Suppl 12), S2–S4. Healy, D.G., Abou-Sleiman, P.M., Gibson, J.M., Ross, O.A., Jain, S., Gandhi, S., Gosal, D., Muqit, M.M., Wood, N.W. and Lynch, T. (2004a). PINK1 (PARK6) associated Parkinson disease in Ireland. Neurology 63, 1486–1488. Healy, D.G., Abou-Sleiman, P.M., Lees, A.J., Casas, J.P., Quinn, N., Bhatia, K., Hingorani, A.D. and Wood, N.W. (2004b). Tau gene and Parkinson’s disease: A case-control study and meta-analysis. J Neurol Neurosurg Psychiatr 75, 962–965. Hughes, A.J., Daniel, S.E. and Lees, A.J. (1993). The clinical features of Parkinson’s disease in 100 histologically proven cases. Adv Neurol 60, 595–599. Hutton, M., Lendon, C.L., Rizzu, P., Baker, M., Froelich, S., Houlden, H., Pickering-Brown, S., Chakraverty, S., Isaacs, A., Grover, A. et al. (1998). Association of missense and 5 -splice-site mutations in tau with the inherited dementia FTDP-17. Nature 393, 702–705. Jain, S., Wood, N.W. and Healy, D.G. (2005). Molecular genetic pathways in Parkinson’s disease: A review. Clin Sci (Lond) 109, 355–364.

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Kahle, P.J. and Haass, C. (2004). How does parkin ligate ubiquitin to Parkinson’s disease?. EMBO Rep 5, 681–685. Kaplan, B., Ratner, V. and Haas, E. (2003). Alpha-synuclein: Its biological function and role in neurodegenerative diseases. J Mol Neurosci 20, 83–92. Khan, N.L., Jain, S., Lynch, J.M.,Pavese, N.,Abou-Sleiman, P., Holton, J.L., Healy, D.G., Gilks, W.P., Sweeney, M.G., Ganguly, M. et al. (2005). Mutations in the gene LRRK2 encoding dardarin (PARK8) cause familial Parkinson’s disease: Clinical, pathological, olfactory and functional imaging and genetic data. Brain 128, 2786–2796. Kitada, T., Asakawa, S., Hattori, N., Matsumine, H., Yamamura, Y., Minoshima, S., Yokochi, M., Mizuno, Y. and Shimizu, N. (1998). Mutations in the parkin gene cause autosomal recessive juvenile parkinsonism. Nature 392, 605–608. Kraytsberg, Y., Kudryavtseva, E., McKee, A.C., Geula, C., Kowall, N.W. and Khrapko, K. (2006). Mitochondrial DNA deletions are abundant and cause functional impairment in aged human substantia nigra neurons. Nat Genet 38, 518–520. Kruger, R., Kuhn, W., Leenders, K.L., Sprengelmeyer, R., Muller, T., Woitalla, D., Portman, A.T., Maguire, R.P.,Veenma, L., Schroder, U. et al. (2001). Familial parkinsonism with synuclein pathology: Clinical and PET studies of A30P mutation carriers. Neurology 56, 1355–1362. Lohmann, E., Periquet, M., Bonifati, V., Wood, N.W., De Michele, G., Bonnet, A.M., Fraix,V., Broussolle, E., Horstink, M.W.,Vidailhet, M. et al. (2003). How much phenotypic variation can be attributed to parkin genotype?. Ann Neurol 54, 176–185. Maimone, D., Dominici, R. and Grimaldi, L.M. (2001). Pharmacogenomics of neurodegenerative diseases. Eur J Pharmacol 413, 11–29. Maraganore, D.M., de Andrade, M., Lesnick,T.G., Strain, K.J., Farrer, M.J., Rocca, W.A., Pant, P.V., Frazer, K.A., Cox, D.R. and Ballinger, D.G. (2005). High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet 77, 685–693. McInerney-Leo, A., Hadley, D.W., Gwinn-Hardy, K. and Hardy, J. (2005). Genetic testing in Parkinson’s disease. Mov Disord 20, 1–10. McNaught, K.S. and Olanow, C.W. (2006). Proteasome inhibitorinduced model of Parkinson’s disease. Ann Neurol 60, 243–247. Miller, D.W., Hague, S.M., Clarimon, J., Baptista, M., Gwinn-Hardy, K., Cookson, M.R. and Singleton, A.B. (2004). Alpha-synuclein in blood and brain from familial Parkinson disease with SNCA locus triplication. Neurology 62, 1835–1838. Mukaetova-Ladinska, E.B. and McKeith, I.G. (2006). Pathophysiology of synuclein aggregation in Lewy body disease. Mechanisms of Ageing and Development Dementias and Cognitive Disorders: New Insights and Approaches 127, 188–202.

Muqit, M.M., Gandhi, S. and Wood, N.W. (2006). Mitochondria in Parkinson disease: Back in fashion with a little help from genetics. Arch Neurol 63, 649–654. Paisan-Ruiz, C., Jain, S., Evans, E.W., Gilks, W.P., Simon, J., van der Brug, M., Lopez de Munain, A., Aparicio, S., Gil, A.M., Khan, N. et al. (2004). Cloning of the gene containing mutations that cause PARK8-linked Parkinson’s disease. Neuron 44, 595–600. Piccini, P., Morrish, P.K., Turjanski, N., Sawle, G.V., Burn, D.J., Weeks, R.A., Mark, M.H., Maraganore, D.M., Lees, A.J. and Brooks, D.J. (1997). Dopaminergic function in familial Parkinson’s disease: A clinical and 18F-dopa positron emission tomography study. Ann Neurol 41, 222–229. Polymeropoulos, M.H., Lavedan, C., Leroy, E., Ide, S.E., Dehejia, A., Dutra, A., Pike, B., Root, H., Rubenstein, J., Boyer, R. et al. (1997). Mutation in the alpha-synuclein gene identified in families with Parkinson’s disease. Science 276, 2045–2047. Rademakers, R., Cruts, M. and van Broeckhoven, C. (2004). The role of tau (MAPT) in frontotemporal dementia and related tauopathies. Hum Mutat 24, 277–295. Shen, J. and Cookson, M.R. (2004). Mitochondria and dopamine: New insights into recessive parkinsonism. Neuron 43, 301–304. Singleton, A.B., Farrer, M., Johnson, J., Singleton, A., Hague, S., Kachergus, J., Hulihan, M., Peuralinna, T., Dutra, A., Nussbaum, R. et al. (2003). alpha-Synuclein locus triplication causes Parkinson’s disease. Science 302, 841. Singleton, A.B. (2005). Altered alpha-synuclein homeostasis causing Parkinson’s disease: The potential roles of dardarin. Trends Neurosci 28, 416–421. Spillantini, M.G., Schmidt, M.L., Lee, V.M., Trojanowski, J.Q., Jakes, R. and Goedert, M. (1997). Alpha-synuclein in Lewy bodies. Nature 388, 839–840. Twelves, D., Perkins, K.S. and Counsell, C. (2003). Systematic review of incidence studies of Parkinson’s disease. Mov Disord 18, 19–31. Valente, E.M., Abou-Sleiman, P.M., Caputo,V., Muqit, M.M., Harvey, K., Gispert, S., Ali, Z., Del Turco, D., Bentivoglio, A.R., Healy, D.G. et al. (2004). Hereditary early-onset Parkinson’s disease caused by mutations in PINK1. Science 304, 1158–1160. Volles, M.J. and Lansbury, P.T., Jr. (2003). Zeroing in on the pathogenic form of alpha-synuclein and its mechanism of neurotoxicity in Parkinson’s disease. Biochemistry 42, 7871–7878. von Coelln, R., Dawson,V.L. and Dawson,T.M. (2004). Parkin-associated Parkinson’s disease. Cell Tissue Res 318, 175–184. Zimprich, A., Biskup, S., Leitner, P., Lichtner, P., Farrer, M., Lincoln, S., Kachergus, J., Hulihan, M., Uitti, R.J., Calne, D.B. et al. (2004). Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron 44, 601–607.

RECOMMENDED RESOURCES 1.

2.

http://www.alzforum.org/ – A website primarily for current research in Alzheimer’s disease, it also discusses the most important current findings in many other neurological disorders including Parkinson’s disease http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMDsearch &DBomim – A link to the Online Mendelian Inheritance in Man based on the NCBI website. This website gives brief

3.

descriptions of various diseases including Parkinson’s disease (#168600). From this, one can access all genetic factors associated with a specific disease. Healy, D.G., Abou-Sleiman, P.M. and Wood, N.W. (2004c). PINK, PANK, or PARK? A clinicians’ guide to familial parkinsonism. Lancet Neurol, 3, 652–662 – An excellent review on the impact of genetics on the clinical diagnosis of Parkinson’s disease.

CHAPTER

101 Epilepsy Predisposition and Pharmacogenetics Nicole M. Walley and David B. Goldstein

INTRODUCTION Epilepsy is a serious neurological condition that affects an estimated 0.5% of the population (MacDonald et al., 2000) and is associated with social marginalization, increased morbidity and premature mortality (Duncan et al., 2006). Currently, there is a lack of certainty concerning pathophysiological cause; however, epilepsy has been observed to run in families, suggesting a genetic component to the disease (Cansu et al., 2007). Indeed, several epilepsy syndromes have been found to have a strong, almost Mendelian genetic etiology; however, these account for a striking minority of epilepsy cases. The remainder of epilepsy syndromes, though extensively studied, currently has neither clear genetic explanations nor indisputable genetic risk factors. This chapter will briefly summarize the “Mendelian” epilepsies that have so far been characterized, the current state of genetic and genomic research in complex epilepsy and epilepsy treatment, and the future direction of genetic work in epilepsy.

predictable inheritance (Ottman, 1997). Though rare, several families have been characterized with autosomal dominant syndromes including autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), generalized epilepsy with febrile seizures plus (GEFS), childhood absence epilepsy (CAE), autosomal dominant juvenile myoclonic epilepsy (ADJME), autosomal dominant idiopathic generalized epilepsy (ADIGE) and benign familial neonatal seizures and benign familial neonatal–infantile seizures (Table 101.1). The causal mutations identified in these families largely follow an apparently autosomal dominant, almost Mendelian pattern of inheritance, and some mutations have been found in more than one family. These mutations are by no means common, however, and there are instances of unaffected probands harboring the mutation, suggesting incomplete penetrance caused by unidentified genetic or environmental factors. Despite this extensive list of mutations and genes that contribute to familial epilepsies, these discoveries are of limited clinical utility in several respects: ●

MENDELIAN EPILEPSIES Although epilepsy is known to run in families, only around 1% of epilepsy patients present with a clear family history and

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

Familial mutations apply only to a small proportion of patients, even when considered within a particular syndrome. For example, despite the identification of several mutations in four different genes that cause GEFS, fewer than 20% of cases with GEFS are estimated to have been Copyright © 2009, Elsevier Inc. All rights reserved. 1243

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TABLE 101.1

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Epilepsy Predisposition and Pharmacogenetics

List of genes and mutations leading to autosomal dominant familial epilepsy syndromes

Syndrome

Gene

Mutation

Report(s)

Molecular Effects (where characterized)

Autosomal dominant idiopathic generalized epilepsy

CLCN2

1-bp insertion (bp 597)

Haug et al. (2003)

Frameshift resulting in truncated protein; causes greatly reduced chloride currents (Haug et al., 2003)

IVS2−14del11

Haug et al. (2003)

37% reduction in wild-type channels due to abberant splicing and exclusion of exon 3 (Haug et al., 2003)

Glutamate 715 → Glycine

Haug et al. (2003)

Increased outward chloride current resulting in exon hyperexcitability (Haug et al., 2003)

GABRA1

Alanine 322 → Aspartate

Cossette et al. (2002)

Loss of function fue to reduced surface expression, reduced GABA-sensitivity and accelerated deactivation of the ligand-gated ion channel (Krampfl et al., 2005)

EFHC1

Arginine 182 → Histidine

Annesi et al. (2007)

Autosomal dominant juvenile myoclonic epilepsy

Suzuki et al. (2004) Phenylalanine 229 → Leucine, Aspartate 210 → Asparagine, and Aspartate 253 → Tyrosine

Autosomal dominant nocturnal frontal lobe epilepsy

CHRNA4

Proline 77 → Threonine and Arginine 221 → Histidine

Suzuki et al. (2004)

Arginine 353 → Tryptophan

Annesi et al. (2007)

Serine 252 → Phenylalanine

Steinlein et al. (1995, 1996)

Attenuation of the increase in R-type calcium currents, which are enhanced by wild-type EFHC1 (Suzuki et al., 2004)

Increased receptor desensitization and decreased recovery leading to diminished receptor activity (Weiland et al., 1996)

Saenz et al. (1999) 3-bp Leucine Insertion (aa264/bp788)

Steinlein et al. (1997)

Increased Ach affinity and decreased Ca permeability (Steinlein et al., 1997)

Serine 256 →Leucine

Hirose et al. (1999)

Increased desensitization and decreased Na /Ca permeability ratio resulting in decreased channel activity (Matsushima et al., 2002)

Rozycka et al. (2003) CHRNB2

Valine 287 → Leucine

De Fusco et al. (2000)

Decreased channel desensitization (De Fusco et al., 2000)

Valine 287 → Methionine

Phillips et al. (2001)

Increased affinity (10 fold) in ACh affinity (Phillips et al., 2001)

Mendelian Epilepsies

TABLE 101.1

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List of genes and mutations leading to autosomal dominant familial epilepsy syndromes (Continued)

Syndrome

Gene

Mutation

Report(s)

Autosomal dominant partial epilepsy with auditory features

LGI1

Cysteine 42 → Arginine

Ottman et al. (2004)

Cysteine 42 → Glycine

Berkovic et al. (2004b)

Cysteine 46 → Arginine

Gu et al. (2002)

Deletion 329 C

Hedera et al. (2004)

Alanine 110 → Aspartate

Ottman et al. (2004)

Serine 145 → Arginine

Hedera et al. (2004)

Leucine 154 → Proline

Pisano et al. (2005)

1-bp deletion (bp 835)

Kalachikov et al. (2002)

Isoleucine 298 → Threonine

Ottman et al. (2004)

Phenylalanine 318 → Cysteine

Fertig et al. (2003)

2-bp deletion (bp 1050–1051)

Kalachikov et al. (2002)

Glutamine 383 → Alanine

Kalachikov et al. (2002)

Serine 473 → Lysine

Berkovic et al. (2004b)

1-bp insertion (bp 1639)

Kalachikov et al. (2002)

IVS3(-3)C → A

Kalachikov et al. (2002)

Deletion 329 C

Singh et al. (1998)

2-bp insertion (aa 283–284)

Singh et al. (1998)

Arginine 214 → Tryptophan

Miraglia del Giudice et al. (2000)

Guanine 271 → Valine

Zhou et al. (2006)

Tyrosine 284 → Cysteine

Singh et al. (1998)

Alanine 306 → Threonine

Singh et al. (1998)

13-bp deletion (aa 522–534)

Singh et al. (1998)

10-bp deletion 1-bp insertion (bp 761–770)

Bassi et al. (2005)

Lysine 526 → Asparagine

Borgatti et al. (2004)

IVS14(6) C → A

de Haan et al. (2006)

5-bp insertion

Biervert et al. (1998)

1-bp deletion (bp 2513)

Lerche et al. (1999)

1187 (2) T → G

Lee et al. (2000)

2-bp deletion (bp1369)

Pereira et al. (2004)

Benign familial neonatal seizures

KCNQ2

Molecular Effects (where characterized)

Slower opening and faster closing kinetics and a decreased voltage sensitivity (Castaldo et al., 2002)

Protein truncation resulting in decreased current density (Bassi et al., 2005)

Abberant splicing adds 4-bp to the transcript resulting in a frameshift and truncated protein (de Haan et al., 2006)

(Continued)

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TABLE 101.1 Syndrome

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Epilepsy Predisposition and Pharmacogenetics

List of genes and mutations leading to autosomal dominant familial epilepsy syndromes (Continued) Gene

KCNQ3

SCN2A

Mutation

Report(s)

1-bp deletion (bp 1931)

Tang et al. (2004)

Glycine 263 → Valine

Charlier et al. (1998)

Tryptophan 309 → Arginine

Hirose et al. (2000)

Arginine 223 → Glutamine

Berkovic et al. (2004a)

Valine 892 → Isoleucine

Berkovic et al. 2004a

Asparagine 1001 → Lysine

Striano et al. (2006)

Leucine 1003 → Isoleucine

Berkovic et al. (2004a)

Arginine 1319 → Glutamine

Berkovic et al. (2004a)

Leucine 1330 → Phenylalanine

Heron et al. (2002)

Leucine 1563 → Valine

Heron et al. (2002)

Molecular Effects (where characterized)

Childhood absence epilepsy

GABRG2

IVS6  2T → G

Kananura et al. (2002)

Splice-site mutation that is predicted to result in the production of truncated/ non-functional GABRG2 proteins (Kananura et al., 2002)

Generalized epilepsy with febrile seizures plus (GEFS)

SCN1B

Cysteine 121 → Tryptophan

Wallace et al. (1998)

Disruption of a disulfide bridge that maintains an extracellular immunoglobulin-like fold; effectively a loss of function mutation ( Wallace et al., 1998)

Aspartic Acid 188 →Valine

Wallace et al. (2001b)

Tyrosine 779 → Cysteine

Annesi et al. (2003)

Threonine 875 → Methionine

Escayg et al. (2000)

Presence of inactivating current (persistent current) and a depolarizing shift in the voltage dependence of channel availability, resulting in hyperexcitability (Lossin et al., 2002)

Tryptophan 1204 → Argenine

Escayg et al. (2001)

Presence of inactivating current (persistent current) and a hyperpolarizing shift of channel activation, resulting in hyperexcitability (Lossin et al., 2002)

Lysine 1270 → Threonine

Abou-Khalil et al. (2001)

Valine 1353 →Leucine

Wallace et al. (2001b)

Absence of SCN1A current (Lossin et al., 2003)

Argenine 1648 → Histidine

Escayg et al. (2000)

Presence of inactivating current (persistent current), resulting in hyperexcitability (Lossin et al., 2002)

Mendelian Epilepsies

TABLE 101.1 Syndrome

●

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List of genes and mutations leading to autosomal dominant familial epilepsy syndromes (Continued) Gene

Mutation

Report(s)

Molecular Effects (where characterized)

SCN1A

Isoleucine 1656 →Methionine

Wallace et al. (2001b)

Decreased expression, decreased voltage sensitivity, and hyperpolarizing shift in steady state inactivation (Lossin et al., 2003)

Argenine 1657 → Cysteine

Lossin et al. (2003)

Decreased sodium current density, decreased expression, decreased voltage sensitivity, and accelerated recovery from activation (Lossin et al., 2003)

Alanine 1685 →Valine

Sugawara et al. (2001)

Absence of SCN1A current (Lossin et al., 2003)

Glycine 1742 → Aspartate

Pineda-Trujillo et al. (2005)

Methionine 1841 → Threonine

Annesi et al. (2003)

Aspartate 1866 →Tyrosine

Spampanato et al. (2004)

Weakened 1 subunit interaction resutling in decreased inactivation and increased persistent current (Spampanato et al., 2004)

SCN2A

Argenine 187 → Tryptophan

Sugawara et al. (2001)

Prolonged current due to slowing of inactivation (Sugawara et al., 2001)

GABRG2

Lysine 289 → Methionine

Baulac et al. (2001a)

Decreased GABAergic current (Baulac et al. 2001a)

Glutamine 351 → STOP

Harkin et al. (2002)

Reduction of GABA receptor density on the cell surface and decreased GABA-activated currents (Harkin et al., 2002)

Arginine 43 →Glutamine

Wallace et al. (2001a)

Loss of benzodiazepine sensitivity (Audenaert et al., 2006); alters receptor assembly and decreases receptor expression (Frugier et al., 2007; Sancar and Czajkowski 2004)

caused by these mutations (Pineda-Trujillo et al., 2005; Wallace et al., 2001b). The genes that are responsible for causing autosomal dominant epilepsies do not appear to host variation that predisposes to the common epilepsies. A number of studies have assessed the role that common variation in these genes plays in the risk/development of common epilepsy syndromes; however these studies consistently report no risk factors in these genes (Chou et al., 2003b; Steinlein et al., 1999).

●

Treatment of patients suffering from autosomal dominant epilepsy is not affected by the presence/absence of any of these identified genetic causes for epilepsy. With one known exception (Lucas et al., 2005), the mutations that cause familial epilepsies do not affect response to medications. Therefore, from a clinical standpoint, choice of pharmacological treatment is based on the diagnosis of syndrome/ epilepsy type without consideration of the underlying genetic causes.

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Epilepsy Predisposition and Pharmacogenetics

These identified familial mutations, along with a plethora of identified de novo mutations (Mulley et al., 2005), are sufficient to provide evidence for a genetic component to epilepsy and a known genetic diagnosis for some patients with epilepsy. However, such a genetic diagnosis neither alters treatment procedures nor improves therapeutic outcome. From a research standpoint it is most certainly prudent to continue to study familial epilepsies due to its potential to illuminate the pathophysiology – and maybe common genetic causes – of epilepsy. But due to the small number of patients likely to be affected by genetic diagnosis and the lack of effect it has on treatment, genetic testing for known familial mutations is neither commonly used nor warranted in a clinical setting at this point in time.

TABLE 101.2 List of genomic regions thought to associate with common forms of epilepsy through linkage studies Syndrome/epilepsy type Linkage peak

Reference

Febrile seizures

8q13–21

Wallace et al. (1996)

19p13.3

Johnson et al. (1998)

2q23–24

Peiffer et al. (1999)

5q14–15

Nakayama et al. (2000)

6q22–24

Nabbout et al. (2002)

18p11

Nakayama et al. (2004)

21q22

Hedera et al. (2006)

2q21–33

Lopes-Cendes et al. (2000)

2p24

Audenaert et al. (2005)

6p21.3

Greenberg (1988)

15q14

Elmslie et al. (1997)

6p11–12

Bai et al. (2002)

Mesial temporal lobe epilepsy

4q13.2–21.3

Hedera et al. (2007)

Temporal lobe epilepsy

1q25-31

Baulac et al. (2001b)

10q22–24

Brodtkorb et al. (2002)

12q22–23

Claes et al. (2004)

Childhood absence epilepsy

8q24

Fong et al. (1998)

Generalized tonic clonic seizures

11q22.1–23.3

Yang et al. (2003)

10q25–q26

Puranam et al. (2005)

COMMON EPILEPSIES The vast majority of patients suffering from epilepsy are not affected by rare single-gene mutations, but rather have common forms of the disorder, which are presumed to result from the complex interaction of genetic and environmental factors. Despite such large numbers of affected patients, very little is known concerning the pathophysiological etiology of the common epilepsies and even less is known concerning the genetic risk factors that predispose to disease development. Linkage and Association Studies Although linkage studies are best powered to detect monogenic susceptibility loci, population-based linkage studies have been carried out in large families (and/or groups of families) within specific seizures and syndrome types. From this body of work, several genomic regions have been identified as containing susceptibility loci (Table 101.2), which encompasses several of the specific syndromes. As is evident from the linkage peaks identified, there are multiple peaks for each syndrome, and there is very little overlap between peaks of the different syndromes. This confirms what was already expected of a complex disease: epilepsy is likely the result of multiple genetic influences; mutations in different genes/areas of the genome may result in the same clinical syndrome, and clinically dissimilar syndromes may have entirely different genetic risk factors. Despite a wealth of linkage evidence in complex epilepsy disorders, very few causal variants have been identified to date as the cause for a linkage peak. One potential exception is the linkage peak on chromosome 6p and its association with juvenile myoclonic epilepsy (JME) (Greenberg et al., 1988). In 2003, a single nucleotide polymorphism in the BRD2 gene was found to associate strongly within JME patients that exhibit familial linkage to 6p (Pal et al., 2003). Unfortunately, there is as yet no functional evidence supporting a role for BRD2 in epilepsy, and a recent purely population-based association study (i.e., in which the JME patient population was identified without regard for the presence/absence of linkage to 6p) does not clearly indicate a substantial role for BRD2 as a risk factor for JME in the general

GEFS

Juvenile myoclonic epilepsy

population (Cavalleri et al., 2007). Thus, linkage studies on the whole have not identified clear risk factors for epilepsy. Candidate Gene Studies By and large, the literature in epilepsy genetics is dominated by association studies, usually adopting a small-scale candidate gene approach, with gene selection methods that have been very limited in scope (see Chapter 8). A large proportion of studies

Common Epilepsies

focus on ion channels, a direction driven by the evidence of ion channels’ involvement in Mendelian epilepsy and the resulting consideration that epilepsy is a “channelopathy.” Other studies have selected candidate genes from animal studies (i.e., selecting genes whose knockout models result in seizures) or based on what is known about epilepsy pathology (or a combination of rationales). The results of these small-scale studies have been highly variable, with a flood of positive association results that are frequently followed up with failed replication attempts. Indeed, one single replication attempt for all five genes reported to associate with TLE (IL-1β (Kanemoto et al., 2000), PDYN (Stogmann et al., 2002), GABBR1 (Gambardella et al., 2003), PRNP (Walz et al., 2003), APOE (Briellmann et al., 2000)), and two genes reported to associate with febrile seizures (CHRNA4 (Chou et al., 2003a) and GABRG2 (Chou et al., 2003a)) failed on all counts, adding to an already conflicting body of replication attempts for these genes (Cavalleri et al., 2005). This situation is similar for all reported associations in common epilepsy syndrome types (i.e. initial reports of positive associations are followed by failed replication attempts); as such the understanding of genetic contributions to epilepsy is primitive. As a general approach, candidate gene studies may in fact be too limited in scope to identify real risk factors for epilepsy. A recent large-scale candidate gene study investigated all common variation in nearly 300 candidate genes in over 3000 cases and controls. The cohort was divided according to epilepsy syndrome and seizure type to provide clinically specific phenotypes, and was also divided into a primary association group and a replication group (Cavalleri et al., 2007). Unfortunately, even this study was unable to identify any clear, unambiguous, replicable associations with the genes included in the analysis. While the candidate gene list is almost certainly not exhaustive, the negative findings in this study indicate that straightforward, strong-effect risk factors simply may not exist in the candidate genes that have been studied thus far (Cavalleri et al., 2007). Of particular concern is that all studies, both small and large scale, are limited by a lack of understanding of the phenotypic complexity of epilepsy. As has already been emphasized, the etiology of the disorder is poorly understood. Also, it has been observed in the Mendelian studies that different gene mutations and therefore different pathophysiological causes can result in similar clinical phenotypes. Therefore, by extension, it is not difficult to imagine that clinical phenotypes as they are currently defined (by seizure and syndrome type) are genetically heterogeneous. Unfortunately, there have been very few attempts to identify appropriate endophenotypes, which could provide a more specific and homogeneous phenotype. Brain volume and atrophy of various brain structures has been posed as one endophenotype possibility (Ronan et al., 2007); however the value of this has yet to be assessed. What is most certainly necessary in the future of genetic studies in epilepsy is more stringent criteria for phenotype definition as well as the in-depth exploration of specific endophenotypes.

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Epilepsy Pharmacogenetics In addition to identifying risk factors for epilepsy and clarifying epilepsy pathophysiology, genetic studies may also make a substantial contribution to treatment. Epilepsy treatment is an ideal pharmacogenetic research target because there are key variable treatment responses that are both clinically important and amenable to genetic investigation (see also Chapter 27). Of particular importance are the variable-response phenotypes of efficacy, safety (i.e. adverse drug reactions, ADRs) and dosing. Efficacy Nearly a third of patients do not achieve adequate seizure control upon treatment with any of the available antiepileptic drugs (AEDs) (Sander, 1993), and there is currently little understanding of what mechanisms are responsible for this failure. While surgery may be an option (although it is not in a significant proportion of refractory cases), it is certainly considered by some to not be very desirable. A clear genetic and molecular understanding of drug resistance in epilepsy patients has the potential to guide the development of new therapeutic strategies in the context of genomic and personalized medicine. To this end, there has been a number of genetic investigations addressing the topic, with the ABCB1 gene (MDR1/P-gp) gaining the most attention. This gene encodes a drug transporter and was selected as an obvious candidate because it has several AED substrates (Loscher and Potschka, 2002). A silent polymorphism in exon 26 of the gene has been associated with protein levels in gut biopsies (Hitzl et al., 2001; Hoffmeyer et al., 2000), with a uptake of rhodamine in cellular assays (Hitzl et al., 2001), and with the pharmacokinetics of a number of drugs (Jiao et al., 2004; Kageyama et al., 2005; Wong et al., 2005). Most recently, the polymorphism has been shown to influence mRNA stability, suggesting that it may itself be a functional polymorphism (Wang et al., 2005). The associations of the ABCB1 exon 26 polymorphism with refractory epilepsy have been ambiguous, as have most associations across a spectrum of human diseases. Initial studies showed that patients with drug-resistant epilepsy were more likely to have the genotype that is associated with high expression (the C/C homozygote) of the ABCB1 drug transporter (Siddiqui et al., 2003). Subsequent analyses, however, have been mixed. There has been only one exact replication attempt of the initial study, which showed no effect of the polymorphism (Tan et al., 2004). Other studies reported an effect of the polymorphism on pharmacoresistance, but these cannot be viewed as replications because of differences in definitions of resistance (Hung et al., 2005; Zimprich et al., 2004). In the most recent study, drug-resistant patients in a Japanese population were more likely to harbor the T allele and the T/T genotype at this locus, results in direct contrast to previous reports (Seo et al., 2006). Safety Many patients suffer side effects that may be life threatening; even in less severe cases, such side effects may still have detrimental

Dosing One of the few validated pharmacogenetic discoveries exists in the field of epilepsy: a polymorphism in the SCN1A gene associates with dose, has been replicated, and has known functional consequences. This study serves as a useful model for considering other reported associations. For most AEDs, patients require dramatically different doses to control seizures (Figure 101.1), and it is not possible to predict what dose a patient is able to tolerate or what will be necessary to achieve seizure control. Determining the appropriate dose for patients can sometimes take months, during which time patients continue to suffer seizures. Carbamazepine (CBZ) and phenytoin (PHT) are two commonly used AEDs that require dramatically different doses between individual patients. Both drugs target the alpha subunit of the sodium ion channel, encoded by the brain-expressed gene, SCN1A. As part of a small candidate gene study, Tate et al. showed that an intronic polymorphism in the SCN1A gene (rs 3812718) is associated with maximum recorded dose used in clinic in a British cohort (Tate et al., 2005) in which patients with the major allele (A) require a higher dose of AED. The same correlation was found to exist between PHT dose and drug-serum levels in a Taiwanese cohort (Tate et al., 2006). Molecular studies subsequently showed that SCN1A rs 3812718 is located in a splice site involved in the mutually

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effects on quality of life. It has been estimated that 17% of emergency room visits due to ADRs (Pirmohamed et al., 2004; Prince et al., 1992) and 10% of ADRs leading to hospitalization are caused by AEDs (Wu and Pantaleo, 2003). ADRs were cited as the cause of discontinuation in nearly 40% of decisions to terminate treatment with a particular medication (Kwan and Brodie, 2000), and although patients exposed to high doses of AEDs are more likely to develop ADRs, side effects are not necessarily dose related. While there are several examples of clinically relevant ADRs that likely have a genetic basis, a common example of epilepsy pharmacogenetics concerns the HLA-encoding region and its contributions to AED hypersensitivity, mainly carbamazepine (CBZ) (Chung et al., 2004; Pirmohamed et al., 2001). Pirmohamed et al. and Chung et al. have both reported association of gene variants in this region with the severe reactions CBZ-induced Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Both studies considered haplotypes but did not identify the particular functional allele(s). Chung et al. reported a haplotype with 100% frequency in cases with CBZ-induced SJS and in only 3% of CBZ-tolerant patients, with positive and negative predictive values of 93.6% and 100%, respectively. This study was later extended to include more patients (n  60 patients with SJS/TEN) and results were confirmed in this extended cohort (Hung et al., 2006). A replication attempt in a Caucasian population, however, failed to replicate these findings, as the associated haplotype was absent from this population (Alfirevic et al., 2006).

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Figure 101.1 There is a wide distribution of doses used for many AEDs. Here, we show that dosing of phenytoin (PHT) commonly ranges between 100 and 1000 mg, and dosing of carbamazepine commonly ranges from 200 to 2600 mg.

Exon 5N

Exon 4

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Figure 101.2 The proposed effects of IVS5N  5 G  A on SCN1A Splicing. The GG genotype allows for more efficient splicing of the fetal exon 5 N, whereas the major allele, A, disrupts the splice site. We found that the GG genotype requires lower maintenance doses of carbamazepine and PHT (Heinzen et al., 2007).

exclusive inclusion/exclusion of exon 5 A or the alternative exon 5 N (Figure 101.2). Functionally, this polymorphism has a substantial effect on the proportion of transcripts containing exon 5 N (neonatal form) of the SCN1A gene (Figure 101.3). Individuals with the A/A genotype require higher drug doses and have, on average, 0.7  1% of SCN1A transcripts in the neonatal form, compared to subjects with the G/G genotype that have 41  9% of transcripts containing exon 5 N. The G allele elicits an apparently dominant effect, with the A/G genotype having 28  4% of transcripts in the neonatal form (Heinzen et al., 2007).

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the future program of work in the field of disease gene functional genomics and pharmacogenetics.

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Figure 101.3 (a) Correlation of the SCN1A IVS5N  5 G→A genotype with the percent of transcripts containing neonatal exon 5 N in the temporal neocortex of control (䊊) and mesial temporal lobe epileptic brain tissue (䊉). (b) Change in the proportion of 5 N expression with SCN1A IVS5N  5 G→ A genotype in HEK293 cells transfected with the genomic DNA fragment containing either GG, AG, AA (minigene system). Data are presented as the mean of two independent replications.

These results show that the genotype at SCN1A rs 3812718 that is associated with lower doses of drugs is also associated with higher proportions of mRNA coding for the 5 N form of the ion channel. This may indicate that the 5 N form of the sodium channel is more easily bound by CBZ and PHT and therefore is more responsive to medication. Future therapeutic attempts to increase the expression of this gene form may result in better response to CBZ, PHT and other sodium channel-acting drugs. Although the clinical implications of this discovery have not been assessed, this work demonstrates that retrospective pharmacogenetic studies can identify new functional polymorphisms. The functional evaluation of the role of this polymorphism illustrates the relevance of splice form variation in a leading target of AEDs. This combined use of genetics study with appropriate functional follow-up forms one of the key paradigms of

Large-scale Studies Despite the limited success of genetic studies in epilepsy, the field as a whole is now poised to address the sub-optimal study designs of past work, particularly the small number of variants considered. Candidate gene studies, which have been the anchor of genetic studies thus far, have produced, at best, mixed results. Even a single large-scale candidate gene study (Cavalleri et al., 2007) failed to definitively identify a significant risk factor. However, it is now technologically and economically possible to carry out whole genome association studies in large numbers of samples using between 300,000 and 1,000,000 tagging single nucleotide polymorphisms (SNPs) (see Chapter 8). Because the most obvious candidate genes have not given answers, it is now necessary to interrogate the entire genome in order to identify risk factors outside of the short candidate gene lists. In addition to study size, it is necessary to build large, carefully phenotyped cohorts of patients. Of particular issue is that: (i) in order to increase sample size, phenotypes may be compromised; and (ii) due to the unknown pathophysiology of the disease, phenotypes are selected according to clinical classifications. Both of these practices can contribute to phenotypic heterogeneity if patients with clinically similar syndrome/seizure types have different underlying pathophysiological and genetic causes, a factor that will obscure the outcomes of association studies. Pharmacogenetic studies also still have great potential to impact epilepsy treatment. Of particular importance is certainly the identification of genetic predictors of treatment-limiting or life-threatening ADRs: cognitive ADRs upon exposure to topiramate, rash/SJS upon exposure to lamotrigine, CBZ and/ or PHT, weight gain on valproic acid, etc. Refractory epilepsy is also an important consideration in pharmacogenetic studies because it (i) affects up to 30% of epilepsy patients, and (ii) may indicate molecular targets for future medications that will be effective in this patient population. Prospective Studies The future of genetics in epilepsy, particularly pharmacogenetics, will necessarily incorporate prospective study design – both to create and capitalize on carefully collected and phenotyped cohorts and to evaluate the clinical utility of genetic discoveries. For example, one polymorphism associated with dosing of the AEDs CBZ and PHT in the SCN1A gene has already been identified (Tate et al., 2005, 2006), but estimation of its effect size and potential clinical significance is difficult within a retrospective study design. A prospective data collection framework that involves the identification of seizure response as a function of dose for CBZ and PHT would serve to better assess the effects of the SCN1A rs 3812718 polymorphism in a clinical

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setting and could also identify new polymorphisms that also affect drug response. A prospective study design also allows for the incorporation of other significant patient information into the study design that could be difficult to obtain retrospectively, such as number and type of seizures, doses of all AEDs, compliance to medication, co-medication, alcohol consumption, smoking history, head injuries, hospital visits related to epilepsy, AED blood-serum levels, pregnancy and hormonal contraceptive use, and weight/body mass index. Using such a study design would allow for the identification of variants that affect dosing, response and incidence of adverse reactions, and also aid in the assessment of the value of associations in a clinical setting.

In all, there is great potential for genetics to identify risk and predict treatment outcomes. The assessment of these phenotypes on whole genome platforms will provide the genomic coverage needed to assess the genome outside of a candidate gene framework. Increasing sample size will provide the statistical power necessary when studying these large numbers of genotypes, and careful consideration and definition of phenotype, both with respect to predisposition and treatment response, will be necessary to ensure phenotypic homogeneity and allow for the identification of risk factors relevant to the patient population.

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Kanemoto, K., Kawasaki, J., Miyamoto,T., Obayashi, H. and Nishimura, M. (2000). Interleukin (IL)1beta, IL-1alpha, and IL-1 receptor antagonist gene polymorphisms in patients with temporal lobe epilepsy. Ann Neurol. 47, 571–574. Krampfl, K., Maljevic, S., Cossette, P., Ziegler, E., Rouleau, G.A. et al. (2005). Molecular analysis of the A322D mutation in the GABA receptor alpha-subunit causing juvenile myoclonic epilepsy. Eur J Neurosci 22, 10–20. Kwan, P. and Brodie, M.J. (2000). Early identification of refractory epilepsy. N Engl J Med 342, 314–319. Lee, W.L., Biervert, C., Hallmann, K., Tay, J., Dean, C. et al. (2000). A KCNQ2 splice site mutation causing benign neonatal convulsions in a Scottish family. Neuropediatrics 31, 9–12. Lerche, H., Biervert, C., Alekov, A.K., Schleithoff, L., Lindner, M. et al. (1999). A reduced K current due to a novel mutation in KCNQ2 causes neonatal convulsions. Ann Neurol 46, 305–312. Lopes-Cendes, I., Scheffer, I.E., Berkovic, S.F., Rousseau, M., Andermann, E. et al. (2000). A new locus for generalized epilepsy with febrile seizures plus maps to chromosome 2. Am J Hum Genet 66, 698–701. Loscher, W. and Potschka, H. (2002). Role of multidrug transporters in pharmacoresistance to antiepileptic drugs. J Pharmacol Exp Ther 301, 7–14. Lossin, C., Rhodes, T.H., Desai, R.R., Vanoye, C.G., Wang, D. et al. (2003). Epilepsy-associated dysfunction in the voltage-gated neuronal sodium channel SCN1A. J Neurosci 23, 11289–11295. Lossin, C., Wang, D.W., Rhodes, T.H.,Vanoye, C.G. and George, A.L., Jr. (2002). Molecular basis of an inherited epilepsy. Neuron 34, 877–884. Lucas, P.T., Meadows, L.S., Nicholls, J. and Ragsdale, D.S. (2005). An epilepsy mutation in the beta1 subunit of the voltage-gated sodium channel results in reduced channel sensitivity to phenytoin. Epilepsy Res 64, 77–84. MacDonald, B.K., Cockerell, O.C., Sander, J.W. and Shorvon, S.D. (2000). The incidence and lifetime prevalence of neurological disorders in a prospective community-based study in the UK. Brain 123(Pt 4), 665–676. Matsushima, N., Hirose, S., Iwata, H., Fukuma, G., Yonetani, M. et al. (2002). Mutation (Ser284Leu) of neuronal nicotinic acetylcholine receptor alpha 4 subunit associated with frontal lobe epilepsy causes faster desensitization of the rat receptor expressed in oocytes. Epilepsy Res 48, 181–186. Miraglia del Giudice, E., Coppola, G., Scuccimarra, G., Cirillo, G., Bellint, G. et al. (2000). Benign familial neonatal convulsions (BFNC) resulting from mutation in the KCNQ2 voltage sensor. Eur J Hum Genet 8, 994–997. Mulley, J.C., Scheffer, I.E., Petrou, S., Dibbens, L.M., Berkovic, S.F. and Harkin, L.A. (2005). SCN1A mutations and epilepsy. Human mutation 25(6), 535–542. Nabbout, R., Prud’homme, J.F., Herman, A., Feingold, J., Brice, A. et al. (2002). A locus for simple pure febrile seizures maps to chromosome 6q22-q24. Brain 125, 2668–2680. Nakayama, J., Hamano, K., Iwasaki, N., Nakahara, S., Horigome,Y. et al. (2000). Significant evidence for linkage of febrile seizures to chromosome 5q14-q15. Hum Mol Genet 9, 87–91. Nakayama, J., Yamamoto, N., Hamano, K., Iwasaki, N., Ohta, M. et al. (2004). Linkage and association of febrile seizures to the IMPA2 gene on human chromosome 18. Neurology 63, 1803–1807. Ottman, R., (1997). Family studies in epilepsy: A comprehensive textbook, edited by J. J. Engel. Lippincott-Raven Publishers, Philadelphia, pp. 177–183.

Ottman, R., Winawer, M.R., Kalachikov, S., Barker-Cummings, C., Gilliam, T.C. et al. (2004). LGI1 mutations in autosomal dominant partial epilepsy with auditory features. Neurology 62, 1120–1126. Pal, D.K., Evgrafov, O.V., Tabares, P., Zhang, F., Durner, M. et al. (2003). BRD2 (RING3) is a probable major susceptibility gene for common juvenile myoclonic epilepsy. Am J Hum Genet 73, 261–270. Peiffer, A., Thompson, J., Charlier, C., Otterud, B.,Varvil, T. et al. (1999). A locus for febrile seizures (FEB3) maps to chromosome 2q23-24. Ann Neurol 46, 671–678. Pereira, S., Roll, P., Krizova, J., Genton, P., Brazdil, M. et al. (2004). Complete loss of the cytoplasmic carboxyl terminus of the KCNQ2 potassium channel: a novel mutation in a large Czech pedigree with benign neonatal convulsions or other epileptic phenotypes. Epilepsia 45, 384–390. Phillips, H.A., Favre, I., Kirkpatrick, M., Zuberi, S.M., Goudie, D. et al. (2001). CHRNB2 is the second acetylcholine receptor subunit associated with autosomal dominant nocturnal frontal lobe epilepsy. Am J Hum Genet 68, 225–231. Pineda-Trujillo, N., Carrizosa, J., Cornejo, W., Arias, W., Franco, C. et al. (2005). A novel SCN1A mutation associated with severe GEFS in a large South American pedigree. Seizure 14, 123–128. Pirmohamed, M., James, S., Meakin, S., Green, C., Scott, A.K. et al. (2004). Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 329, 15–19. Pirmohamed, M., Lin, K., Chadwick, D. and Park, B.K. (2001). TNFalpha promoter region gene polymorphisms in carbamazepine-hypersensitive patients. Neurology 56, 890–896. Pisano, T., Marini, C., Brovedani, P., Brizzolara, D., Pruna, D. et al. (2005). Abnormal phonologic processing in familial lateral temporal lobe epilepsy due to a new LGI1 mutation. Epilepsia 46, 118–123. Prince, B.S., Goetz, C.M., Rihn, T.L. and Olsky, M. (1992). Drugrelated emergency department visits and hospital admissions. Am J Hosp Pharm 49, 1696–1700. Puranam, R.S., Jain, S., Kleindienst, A.M., Saxena, S., Kim, M.K. et al. (2005). A locus for generalized tonic-clonic seizure susceptibility maps to chromosome 10q25-q26. Ann Neurol 58, 449–458. Ronan, L., Murphy, K., Delanty, N., Doherty, C., Maguire, S. et al. (2007). Cerebral cortical gyrification: a preliminary investigation in temporal lobe epilepsy. Epilepsia 48, 211–219. Rozycka, A., Skorupska, E., Kostyrko, A. and Trzeciak, W.H. (2003). Evidence for S284L mutation of the CHRNA4 in a white family with autosomal dominant nocturnal frontal lobe epilepsy. Epilepsia 44, 1113–1117. Saenz, A., Galan, J., Caloustian, C., Lorenzo, F., Marquez, C. et al. (1999). Autosomal dominant nocturnal frontal lobe epilepsy in a Spanish family with a Ser252Phe mutation in the CHRNA4 gene. Arch Neurol 56, 1004–1009. Sancar, F. and Czajkowski, C. (2004). A GABAA receptor mutation linked to human epilepsy (gamma 2R43Q) impairs cell surface expression of alphabetagamma receptors. J Biol Chem 279, 47034–47039. Sander, J.W. (1993). Some aspects of prognosis in the epilepsies: a review. Epilepsia 34, 1007–1016. Seo, T., Ishitsu, T., Ueda, N., Nakada, N., Yurube, K. et al. (2006). ABCB1 polymorphisms influence the response to antiepileptic drugs in Japanese epilepsy patients. Pharmacogenomics 7, 551–561. Siddiqui, A., Kerb, R., Weale, M.E., Brinkmann, U., Smith, A. et al. (2003). Association of multidrug resistance in epilepsy with a

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CHAPTER

102 Ophthalmology Janey L. Wiggs

INTRODUCTION The function of the eye is to transduce light into an electrical signal and then transmit the electrical signal to the brain. A variety of tissues and specialized cells carry out these complex processes. The ocular globe is divided into two fluid-filled compartments, called the anterior and posterior chambers (Figure 102.1). The anterior chamber is filled with an aqueous fluid called aqueous humor and the posterior chamber is filled with a viscous substance called the vitreous humor. The globe is supported by a tough outer shell, the sclera, that also supports the optic nerve as it exits the eye. The cornea is a transparent tissue located on the anterior ocular surface that allows light to enter the eye and also helps focus the light on the retina. Inside the eye are a number of structures including the iris and pupil (regulates the amount of light entering the eye), the lens (focuses light on the retina), the ciliary body (makes aqueous humor) and trabecular meshwork (drains aqueous humor). Under normal circumstances the rate of production of aqueous humor equals the rate of removal. Light traveling through the cornea, pupil and lens is focused on the retina, which carries out the phototransduction of light to produce an electrical signal that is transmitted through the optic nerve to the brain. The retina is a complex tissue made up of 10 distinct layers (Figure 102.2). The most external cell layer is the retinal pigment epithelium, which provides metabolic support and is attached to a basement membrane (Bruch’s membrane). Next to the retinal pigment epithelium are the Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1256

Sclera

Choroid Retina

Cornea

Fovea Pupil

Lens Optic nerve

Iris Ciliary body Anterior chamber

Figure 102.1

Posterior chamber

Vertical sagittal section of the adult human eye.

rod and cone photoreceptors, which are the cells where phototransduction occurs. Connected to the photoreceptors are the amacrine, bipolar and horizontal cells that modulate the signal output from the rods (dim light) and cones (bright light). The signal from the photoreceptor goes through the bipolar cells Copyright © 2009, Elsevier Inc. All rights reserved.

Cornea

PE Pigment epithelium cell OS  IS ONL OPL

Rod photoreceptor Cone photoreceptor Horizontal cell Rod bipolar cell

INL Müller glia

Cone bipolar cell Amacrine cell

IPL

GCL NFL

Ganglion cell

Figure 102.2 Schematic diagram of the human retina. Abbreviations: PE: pigment epithelium; OS: outer segments; IS: inner segment; ONL: outer nuclear layer; OPL: outer plexiform layer; INL: inner nuclear layer; IPL: inner plexiform layer; GCL: ganglion cell layer; NFL: nerve fiber layer.

to the ganglion cells. The axons of the ganglion cells form the optic nerve and send the signal to their first synapse at the lateral geniculate body. The signal from the retina eventually forms an image in the occipital lobe of the brain. Eye movements are controlled by six muscles located on the outside of the globe (extraocular muscles) that are innervated by cranial nerves III, IV and VI. Inherited disorders causing visual disabilities have been described for all of the ocular structures. A comprehensive discussion of each of these diseases would not be within the scope of this chapter. Selected inherited ocular disorders that are representative of genetic eye diseases will be discussed in the following sections and are listed in Table 102.1. For the majority of these conditions, screening for mutations in causative genes will help define the diagnosis and in some cases will give insight into the prognosis and help assist therapeutic decisions.

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ROBO3 (Chan et al., 2006) and HOXA1 (Tischfield et al., 2005). A number of chromosomal syndromes are also associated with strabismus, including Rubenstein–Taybi syndrome (Allanson and Hennekam, 1997), Cornelia de Lange syndrome (Nallasamy et al., 2006) and Down’s syndrome (Yurdakul et al., 2006). Screening patients with eye movement disorders for mutations in these genes will help define the diagnosis. Duane’s syndrome is a congenital eye movement disorder characterized by abnormalities of horizontal eye movements, and indicates a problem with cranial nerve VI, the abducens nerve (Bagheri and Repka, 2005). The syndrome can be inherited as an autosomal dominant trait, and using three affected pedigrees a genome-wide scan identified a locus for the syndrome on chromosome 20q13 (Al-Baradie et al., 2002). A novel gene, LOC57167, subsequently identified as a new member of the SAL family (SALL4) was identified as the causative gene. SALL4 is a zinc finger protein that shares significant homology with SALL1 coding for a transcriptional repressor and responsible for a related developmental disorder Towne–Brocke syndrome (Botzenhart et al., 2005). SALL4 mutations causing Duane’s syndrome mainly result in truncated protein products and cause a loss-of-function of the protein (Terhal et al., 2006). The Duane’s phenotype suggests that this protein is likely to have an important role in abducens motor neuron development. A related condition, congenital fibrosis of the extraocular muscles type 1 (CFEOM1) is associated with absence of the superior division of the oculomotor nerve (cranial nerve III) and corresponding midbrain motoneurons and profound atrophy of the two muscles normally innervated by this nerve, the levator palpebrae superioris and superior rectus (Traboulsi, 2004). These two muscles elevate the eyelid and the globe, and their dysfunction accounts for the primary features of the CFEOM1 phenotype. Mutations in KIF21A, a kinesin, have been identified in patients affected with this condition (Yamada et al., 2003). Kinesins are molecular motors responsible for microtubule-dependent transport of cargo; in neurons, they are responsible for anterograde and retrograde axonal transport. CFEOM1 probably results from the inability of mutated KIF21A to successfully deliver a cargo essential to the development of the oculomotor axons, neuromuscular junction or extraocular muscles.

CORNEA EXTRAOCULAR MUSCLES Strabismus is the general term for ocular misalignment that results from dysfunction of one or more extraocular muscles. Strabismus can be inherited as a Mendelian trait, and there is at least one common disorder, esotropia, that exhibits complex inheritance (for review see Engle, 2006). Recent studies have demonstrated that mutations in genes necessary for the normal development and connectivity of the brainstem ocular motoneurons (cranial nerves III, IV, VI) are associated with these disorders, including PHOX2A (Nakano et al., 2001), SALL4 (Al-Baradie et al., 2002), KIF21A (Yamada et al., 2003),

The function of the cornea is dependent on the transparency of the tissue. Any process that causes opacification of the cornea results in a decrease in vision. The cornea can lose its transparency as a consequence of infection (both bacterial and viral), trauma (scar tissue formation) and inherited corneal dystrophies. Genetic causes of corneal disease can be inherited as autosomal dominant and autosomal recessive traits and are caused by mutations in at least nine genes (ARSC1, CHST6, COL8A2, GLA, GSN, KRT3, KRT12, M1S1 and TGFBI [BIGH3]). Three of these genes (GSN, M1S1, TGFBI) are associated with amyloid deposition in the cornea (review see Klintworth, 2003).

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TABLE 102.1

Selected ocular disorders and associated genes discussed in this chapter

Ocular tissue

Specific disorder

Inheritance

Gene – function

Ocular motor (eye movement)

Duane’s syndrome

AD

SALL4 – zinc finger protein; transcriptional regulator

CFEOM1 (congenital fibrosis of extraocular eye muscles type 1)

AD

KIF21A – kinesin; axonal transport

Groenouw’s (granular) dystrophy

AD

TGFB1/BIGH3 – keratoepithelin; extracellular matrix protein

Lattice type 1 dystrophy

AD

Avellino’s (combined granular-lattice) dystrophy

AD

ReisBückler’s dystrophy

AD

Pulverulent cataract

AD

Alpha3 (C  46) and Alpha8 (C  50) – connexins; gap junctions

Zonular cataract Posterior polar cataract

AD

Alpha A- and alpha B-crystallins – lens proteins

Iris

Abnormal development: Peter’s Anomaly Corneal keratitis Aniridia

AD

PAX6 – transcriptional regulator; gene expression

Trabecular meshwork

Early-onset glaucoma

AD

MYOC – Myocilin; extracelluar matrix

Optic nerve

Leber’s Hereditary Optic neuropathy

Mitochondrial

Missense mutations in complex I of the respiratory chain (mitochondrial DNA)

Kjer’s optic neuropathy

AD

OPA1 – dynamin-related GTPase

Retinitis pigmentosa

AD*

RHO – rhodopsin; rod photoreceptor pigment

Leber’s congenital amaurosis

AR

RPE65 – enzyme responsible for regeneration of 11-cis retinal

Retinoblastoma

AD

RB1 – tumor suppressor protein

Age-related macular degeneration (AMD)

Complex

CFH – complement factor H; LOC387715/HTRA1 – unknown function

Cornea (dystrophy)

Lens (cataract)

Retina

*Retinitis pigmentosa may also exhibit autosomal recessive, X-linked and digenic inheritance patterns.

Screening patients with inherited corneal opacities for mutations in this panel of genes will help define the diagnosis and the prognosis. Because some of these disorders are inherited as dominant traits and others as recessive traits, defining the causative gene in an affected individual will also define the disease risk in other family members. The four most common autosomal dominant corneal dystrophies are: Groenouw’s (granular) type 1 (Moller, 1989), lattice type 1 (Klintworth, 1967), Avellino’s (combined granular-lattice) (Folberg et al., 1988; Rosenwasser et al., 1993), and Reis–Bückler’s (Kuchle et al., 1995). Although all four of these corneal dystrophies affect the anterior stromal layer of the cornea and cause the formation of discrete white localized deposits that progressively obscure vision, the detailed clinical and

pathologic features differ. All four dystrophies had been genetically mapped to a common interval on chromosome 5q31 (Eiberg et al., 1994; Gregory et al., 1995; Small et al., 1996; Stone et al., 1994), and mutations in a single gene, TGFB1/BIGH3, were subsequently identified in individuals affected with one of the four conditions (Munier et al., 1997). An abnormal protein product of this gene, keratoepithelin, accumulates in patients carrying mutations. The normal protein product is probably an extracellular matrix protein that modulates cell adhesion. Four different missense mutations occurring at two different arginine codons, R124 and R555 have been found. Interestingly, different mutations at the codon R124 cause lattice dystrophy type I or Avellino’s dystrophy, the two dystrophies characterized by amyloid deposits (Korvatska et al., 1998). The mutations

Trabecular Meshwork

that cause Avellino’s and lattice dystrophies abolish a putative phosphorylation site that is probably required for the normal structure of keratoepithelin. Destruction of this aspect of the protein structure leads to the formation of the amyloid deposits that cause opacification of the cornea. As a result, the mutant protein is destructive to the normal tissue. Mutations at the R555 appear to result in either granular dystrophy or Reis– Bücklers dystrophy. These phenotype–genotype correlations demonstrate the variable expressivity of mutations in this gene and the significance of alteration of the arginine residues 124 and 555. Of interest, pathologic deposits caused by keratoepithelin accumulation have only been observed in the cornea and not in other tissues or organs (El Kochairi et al., 2006). Because the TGFB1/BIGH3 gene is expressed in other tissues (Sandgren et al., 1990), these results suggest a cornea-specific mechanism causing the accumulation of mutant keratoepithelin.

LENS The ocular lens is transparent and focuses the light coming into the eye from the cornea and pupil onto the retina. Cataract is an opacification of the lens that causes a loss of vision and can develop congenitally during childhood and most commonly as an adult (age-related cataract). Congenital cataract is a leading cause of visual disability in children and recently many causative genetic mutations have been identified. Inherited cataract is clinically and genetically heterogeneous with at least 11 different defined phenotypes. Cataracts may be inherited as autosomal dominant, autosomal recessive, or X-linked recessive traits, and at least 12 loci and 15 specific genes are currently identified, with more genes remaining to be discovered (for reviews see Hejtmancik and Kantorow, 2004; Reddy et al., 2004). Two general classes of proteins, the crystallins and the connexins have been associated with early onset cataracts. Screening for mutations in the causative genes will help define the diagnosis in affected individuals. Some forms of early onset cataract severely interfere with vision while others do not. Because of this spectrum of disease severity, in addition to clarification of the diagnosis, genetic screening could help establish a prognosis and also cataract-risk in family members. Intercellular gap junction channels, consisting of alpha3 (C  46), alpha8 (C  50) and alpha6 (C  43) connexin subunits have been implicated in lens development and maintenance. These channels probably transport metabolites, secondary messages and ions between lens cells (Krutovskikh and Yamasaki, 2000; Xia et al., 2006). Missense changes in these genes have been associated with cataract phenotypes, with changes in the alpha3 (C  46) and alpha8 (C  50) associated with a central pulverulent cataract than can have a better prognosis for vision (Berry et al., 1999; Li et al., 2004). Alpha A- and alpha B-crystallins are the major components of the lens. These proteins are also members of the small heatshock protein family, and they possess chaperone-like function. Interestingly, mutations in these genes can cause cataract and

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also various forms of myopathy (Horwitz, 2003). For example, missense and frame-shift mutations in alpha B-crystallin cause autosomal dominant congenital lamellar cataract (Liu et al., 2006), while other missense mutations have been associated with dilated cardiomyopathy (Inagaki et al., 2006). The underlying mechanism appears to be a dominant negative effect that interferes with the normal chaperone-like function of these proteins.

IRIS The iris contains the pupil, which functions as an aperture regulating the amount of light entering the eye. Disorders causing iris and pupil dysfunction are typically ocular developmental syndromes that lead to abnormal formation of the iris and pupil. These developmental abnormalities can lead to an elevation of intraocular pressure and glaucoma (see below) and also cataract. Most of the genes responsible for these conditions are transcriptional regulatory factors and other proteins that play regulatory roles in ocular developmental processes including PAX6, FOXC1, PITX2 and LMX1B (for review see Gould et al., 2004; Idrees et al., 2006). Most of the developmental syndromes resulting from mutations in these genes are inherited as autosomal dominant traits. Mutations in these genes are associated with variable expressivity, and there is extensive overlap between the phenotypes associated with the different causative genes. Screening a panel of these genes for mutations in affected patients will define the diagnosis and help identify other family members at risk. The PAX6 gene is a transcription factor essential for the development of tissues including the eyes, central nervous system and endocrine glands of vertebrates and invertebrates. It regulates the expression of a broad range of molecules, including other transcription factors, cell adhesion and short-range cell–cell signaling molecules, hormones and structural proteins. It has been implicated in a number of key biological processes including other cell proliferation, migration, adhesion and signaling both in normal development and in oncogenesis (Simpson and Price, 2002). Mutations in PAX6 are associated with a range of human phenotypes including aniridia (absence of the iris) (Tzoulaki et al., 2005), Peter’s anomaly (Singh et al., 1998) and corneal keratitis (Sale et al., 2002). Most mutations cause a truncated polypeptide or disruption of the critical paired box homeodomain (van Heyningen and Williamson, 2002).

TRABECULAR MESHWORK The trabecular meshwork and related outflow pathways remove aqueous humor from the anterior chamber of the eye. The intraocular pressure is dependent on the rate of fluid removal by the trabecular meshwork which under normal conditions matches the rate of formation. The intraocular pressure can become elevated when the trabecular meshwork no longer

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keeps pace with the rate of fluid formation and an elevation of intraocular pressure is a major risk factor for glaucoma (Gordon et al., 2002). Glaucoma is a common blinding disease that results in an irreversible degeneration of the optic nerve (see below). Despite the important function of the trabecular meshwork, very little is known about the molecular pathways involved in fluid removal. A number of genes have been associated with trabecular meshwork dysfunction and elevated intraocular pressure including those that cause abnormal development of the iris and anterior segment (see above and Wiggs, 2005 for review). The degeneration of the optic nerve associated with high intraocular pressure is an insidious process and significant vision can be irreversibly lost before the patient is aware of the symptoms. Screening for risk factors, including genetic risk factors, is necessary to identify individuals at risk so that pressure-lowering treatment can be initiated before irreversible damage to the optic nerve occurs. Although a major genetic risk factor for glaucoma or elevated intraocular pressure has yet to be discovered, genes that can confer risk in a small number of patients have been described. Missense mutations in the gene coding for myocilin (MYOC) are associated with an early onset of elevated intraocular pressure and severe glaucoma (Fingert et al., 2002). Some mutations cause more significant disease than others and probably cause a gain-of-function resulting in retention of the mutant protein in the cell and subsequent cell death (Liu and Vollrath, 2004). Mutations in another gene, WDR36 are insufficient to cause glaucoma, but patients who have glaucoma and also have changes in this gene have a more severe phenotype (Hauser et al., 2006). The function of the WDR36 protein is not known but may influence immune mechanisms suggesting a possible role for immune response in elevated intraocular pressure (Monemi et al., 2005).

OPTIC NERVE The optic nerve is the only path for communication between the eye and the brain. When the optic nerve is damaged, irreversible blindness occurs. The optic nerve contains the axons from the retinal ganglion cells that travel through a supporting structure, the lamina cribrosa, before reaching their first synapse in the lateral geniculate body. Inherited disorders of the optic nerve include degenerative processes (primarily glaucoma described above) as well as primary disorders causing optic nerve atrophy (for review see Newman, 2005). Mitochondrial function is a critical element in optic nerve disease: Leber’s hereditary optic neuropathy is caused by missense mutations in mitochondrial DNA (Valentino et al., 2004), while Kjer’s autosomal dominant optic atrophy is caused by mutations in OPA1 that also affect mitochondrial function (Olichon et al., 2006). Screening for mutations in genes known to contribute to optic nerve disease can identify individuals at risk, but therapeutic options are currently limited (Johns and Colby, 2002).

RETINA The phototransduction of light is carried out by the retina. Phototransduction occurs in the rod and cone photoreceptors and is dependent on rhodopsin and the cone opsins. The photoreceptors signal a cascade of retinal neural cells with the terminal axons of the retinal ganglion cells forming the optic nerve. Inherited blinding disorders that affect the structure and physiology of retinal cells participating in this complex process include: retinitis pigmentosa and other retinal degenerations (for review see Kennan et al., 2005); retinoblastoma (for review see Knudson, 2001); and age-related macular degeneration (for review see Tuo et al., 2004; Wiggs, 2006). The role of selected genetic factors in these retinal disorders will be discussed below. The first gene to be recognized as a cause of retinitis pigmentosa was the gene coding for rhodopsin, the primary pigment in rod photoreceptors. Missense mutations in rhodopsin cause an autosomal dominant form of retinitis pigmentosa (Dryja et al., 1991). In general the rhodopsin mutations associated with retinitis pigmentosa can be placed into two groups: those that affect rhodopsin synthesis, folding, or transport from the rod cell, and those that have detrimental effects on rhodopsin’s functions, such as photobleaching, photoactivation and deactivation (Kisselev, 2005). P23H, is the most common mutation associated with autosomal dominant retinitis pigmentosa, and this mutation causes protein misfolding or missorting in the endoplasmic reticulum (Olsson et al., 1992). Missense mutations that affect rhodopsin function, such as K296E cause congenital night blindness in addition to autosomal dominant retinitis pigmentosa (Robinson et al., 1992). Although definitive treatment for these conditions is not currently possible, screening for mutations will help identify individuals at risk. Leber’s congenital amaurosis is a severe blinding condition that leaves infants and children without any useful vision. The most common cause of this condition are mutations in RPE65 coding for a protein located in the retinal pigment epithelium that is a critical enzyme responsible for regeneration of 11-cis retinal, the chromophore needed for visual pigments (Galvin et al., 2005; Takahashi et al., 2005). Mutations in the gene cause a loss of function of the protein, and restoration of vision has been accomplished in both a canine model and a mouse model of the disease using an rAAV vector containing the normal RPE65 gene (Acland et al., 2005; Chen et al., 2006). Clinical trials are underway to evaluate the efficacy of injecting RPE65 viral constructs into human eyes. Retinoblastoma is the most common primary intraocular malignancy of childhood. Predisposition to the disease can be inherited as an autosomal dominant trait, and the mean age of diagnosis for inherited cases is 12 months of age. Affected children can present with a visible white reflex at the pupil (leukokoria) but tumors can also be located in the retina such that they are not detected by a typical clinical exam. If the tumors are not treated, nearly all patients die of intracranial extension and disseminated disease within 2 years. Careful examination

References

of the retina is difficult in young children, and usually requires general anesthesia. Children at risk must be examined every 3 months until they have reached an age where tumor development is unlikely (about age 5). The discovery of the RB1 gene as the causative gene for retinoblastoma made it possible to develop molecular diagnostic tests to detect carriers of mutant forms of the gene (Wiggs et al., 1988). Current methods include the protein truncation test (Tsai et al., 2004) and sequence detection strategies (Houdayer et al., 2004). Individuals identified as carriers of mutant forms of the gene can undergo increased surveillance and timely treatment which involves removal of the eye or treatment of the tumor with proton beam radiation (Abramson and Schefler, 2004). Age-related macular degeneration (AMD) affects over 10 million Americans and is the leading cause of blindness among the elderly. AMD is a complex disease that results from interactions between genetic and environmental factors. Studies have shown that the risk of macular degeneration increases with age, smoking and excess dietary lipids (Hyman and Neborsky, 2002). Significant genetic contributions have been demonstrated by increased concordance between identical twins, familial clustering and increased risk to first-degree relatives (Stone et al., 2001). Recently variants of two genes, complement factor H (Edwards et al., 2005; Haines et al., 2005; Klein et al., 2005) and a novel gene LOC387715 (Rivera et al., 2005) have been shown to substantially increase the risk of macular degeneration. These risks are further increased in individuals who smoke (Schmidt et al., 2006). These important results suggest that screening for these genetic factors can identify individuals at risk for the disease and identify individuals who can reduce their risk by behavior modification.

GENETIC TESTING FOR OCULAR DISORDERS The identification of genes responsible for inherited eye disease and the development of powerful molecular techniques for detecting genetic defects makes it likely that genetic testing

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for inherited ocular disorders will become commonplace over the next decade. Current genetic tests are available for many ocular disorders, including those discussed in this chapter. A number of CLIA-certified laboratories perform genetic testing for eye diseases in the United States (Stone, 2007), and a group of CLIA laboratories are participating in a genotype/phenotype project sponsored by the National Eye Institute (eyeGENE; http://www.nei.nih.gov). Current tests mainly provide diagnostic information, however, future testing is expected to provide prognostic information. With the additional development of pharmacogenomic tests predicting therapeutic responsiveness, gene-based tests could become an increasingly important component of an individualized treatment plan. Genetic testing is currently most useful for the numerous rare Mendelian ocular disorders. Recent advances have identified genetic risk factors for two common complex ocular conditions, macular degeneration and one form of glaucoma, pseudoexfoliation syndrome (Edwards et al., 2005; Fan et al., 2007; Haines et al., 2005; Klein et al., 2005; Rivera et al., 2005; Thorleifsson et al., 2007). As more genetic and environmental risk factors for complex diseases are identified, genetic testing will become an important part of establishing risk estimates for these common blinding conditions.

SUMMARY The number of genes known to cause Mendelian genetic disease in ophthalmology has increased dramatically over the past five years, with over 100 genes known to contribute to ocular disease (Blain and Brooks, 2007). Recent advances have also identified important genetic factors that contribute to complex eye disease. Future studies will reveal additional risk factors for other complex ocular disorders including glaucoma and myopia (Iyengar, 2007). As the genetic defects responsible for both Mendelian and complex ocular diseases are identified, the protein products of those genes can be characterized through additional proteomic studies that should provide a better understanding of underlying disease mechanisms.

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103 The Genomic Basis of Neuromuscular Disorders Erynn S. Gordon and Eric P. Hoffman

INTRODUCTION Neuromuscular disorders involve muscle or the motor neurons controlling the muscle. Motor neuron cell bodies are in the spinal cord, with nerve processes either running to the muscle (lower motor neuron), or to other motor neurons (inhibitory upper motor neurons). The connection (synapse) between the motor neuron and the muscle is the neuromuscular junction. While each neuromuscular disease is relatively rare, the combined prevalence of all neuromuscular diseases is significant, with between 28.6/100,000 and 48.2/100,000 people affected with over 40 independent neuromuscular diseases (Ahlstrom et al., 1993; Emery, 1991). Of the more than 40 conditions grouped within neuromuscular disorders, most show muscle weakness as the primary symptom. Some conditions are genetic or hereditary in nature (e.g., Duchenne muscular dystrophy (DMD), the most common genetic disorder worldwide), while others reflect a systemic disorder or autoimmune reaction. In this chapter, we provide examples of motor neuron (amyotrophic lateral sclerosis, ALS), and neuromuscular junction (myasthenia gravis) muscle (DMD) pathologies, and also give tables listing all known common neuromuscular disorders (Table 103.1).

MOTOR NEURON DISEASE Motor neurons are comprised of the lower motor neuron cell body, which originates in the brain stem or spinal cord and Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

terminates in the skeletal muscle, and the upper motor neuron, which originates in motor cortex of the brain and terminates in the spinal column or the medulla. Unlike the lower motor neuron, which runs throughout the body carrying communication from the nerves to the muscles, the upper motor neurons are confined to the central nervous system. Typically, damage to the lower motor neuron leads to muscle weakness, while damage to the upper motor neuron leads to spasticity (lack of inhibitor reflexes). Conditions caused by motor neuron disease include: spinal muscular atrophy, ALS, progressive bulbar palsy, and post-polio syndrome (Table 103.2). Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, is the result of both upper and lower motor neuron dysfunction. Symptoms typically begin in adulthood, with progressive weakness of voluntary muscles causing paralysis and death within a few years of diagnosis. Patients initially show either limb weakness, cramps, and muscle loss (atrophy) or facial and throat (bulbar) symptoms. Regardless of the initial symptoms, the disease will progress to include all voluntary muscles. While the time course is variable, death occurs on an average 3–5 years after the onset of the disease (Williams and Windebank, 1991). About 10% of all cases of ALS are familial cases (familial ALS or FALS), although several genes responsible for varying forms of FALS have been identified, the most common genetic cause (accounting for 20% of FALS cases), and the best characterized is dominantly inherited mutations of the superoxide dismutase 1 gene (SOD1). The gene mutations cause single amino acid changes in the SOD1 protein, and this leads to an Copyright © 2009, Elsevier Inc. All rights reserved. 1265

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TABLE 103.1

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The Genomic Basis of Neuromuscular Disorders

Genetic distribution of neuromuscular diseases

Disease category

# Causative genes/loci

# of AD conditions

# of AR conditions

# of XL conditions

# of allelic associated disease phenotypes

Muscular dystrophies

23

8

10

2 (XLR)

39

Congenital muscular dystrophies

11

0

11

0

20

Congenital myopathies

10

7

5

1 (XLR)

13

Distal myopathies

5

3

3

0

13

Other myopathies

15

13

1

1 (XLR)

29

7

5

3

0

15

12

15

3

0

34

6

5

0

0

9

Metabolic myopathies

11

1

8

2 (XLR)

14

Hereditary cardiomyopathies

30

27

2

1 (XLR)

57

Congenital myasthenic syndromes

7

4

10

Spinal muscular atrophies

8

3

4

1 (XLR)

21

Hereditary ataxias

17

15

5

0

30

Hereditary motor and sensory neuropathies (HMSN)

30

24

12

1 (XLD)

60

Hereditary paraplegias

23

10

9

4 (XLR)

31

Other neuromuscular disorders

9

6

2

1 (XLR)

18

Myotonic syndromes Ion channel muscle diseases Malignant hyperthermia

0

8

Adapted from Kaplain and Fontain (2006). AD  autosomal dominant; AR  autosomal recessive; XL  X-linked.

inappropriate increase in SOD1 function (gain-of-function mutations). It remains poorly understood how increased SOD1 activity leads to progressive motor neuron death, but this may involve stress of the motor neurons themselves, or associated cells (astrocytes, microglia). Mouse models of FALS have been produced by overexpressing mutant SOD1, although about 20 times greater expression than naturally occurs in the endogenous murine SOD1 genes is required to produce for clinical symptoms of ALS. Over 90% of ALS is sporadic, with no evidence of family inheritance or a specific causative gene. The cause of sporadic ALS remains obscure, although some cases have been associated with chemical or biological exposures (bat soup in Guam, and Gulf War Syndrome). Unfortunately, experimental therapeutics

developed on the FALS mouse model of ALS (SOD1) have not proven to help sporadic ALS patients in clinical trials.

DISORDERS OF THE NEUROMUSCULAR JUNCTION The lower motor neuron connects with the muscle fiber at the motor end plate, these components together make up the neuromuscular junction. The axon of the motor neuron branches into multiple endings allowing it to innervate multiple myofibers, although each myofiber has only a single motor neuron contacting it. Upon firing of the motor neuron, acetylcholine is released by the motor neuron endplate, where it travels across

TABLE 103.2

Common disorders of the motor neuron Protein product

Inheritance

Clinical features

Familial ALS

SOD1

21q22.1

Superoxide dismutase 1

AD

Accounts for 2–3% of all cases of ALS. Presenting symptoms usually include problems in dexterity or gait resulting from muscle weakness, or with difficulty speaking or swallowing. Patients become paralyzed and often require ventilation. Loss of respiratory function is ultimately the cause of death.

Juvenile familial ALS

ALS2

2q33.2

Alsin

AR

Disease onset usually occurs by 10 years of age. Primary symptoms include spasticity of facial muscles and paraplegia in limbs. Patients show chronic motor neuron disease with gradual weakness of voluntary muscles.

ALS-3

unknown

18q21

unknown

AR

Patients present with leg weakness. Both upper motor neuron and lower motor neuron symptoms are present over time. No cognitive involvement. Mean age of onset is 45 years.

Juvenile dominant amyotrophic lateral sclerosis

ALS4

9q34

Senataxin

AD

Patients present with gait disturbance in the second decade. Distal weakness and atrophy is common. Patients show very slow disease progression with wheelchair use in the 5th and 6th decades. Rarely do patients develop bulbar symptoms.

ALS-5

Unknown

15q15.1-q21.1

Unknown

AR

Juvenile onset with distal amyotrophy with spaticity. Hallmark features is the absence of bulbar features. Associated with long-term survival.

ALS-6

Unknown

16q12.1-q12.2

Unknown

AD

Adult-onset symptoms with rapid progression.

FTD-MND

Unknown

9q21-q22

Unknown

AD

Patients present with physical features of ALS and dementia (loss of inhibition, loss of activities of daily living).

FTDP-17

MAPT

17q21

Microtubular associated protein tau

AD

Patients experience symptoms of disinhibition, dementia, parkinsonism, and ALS.

Brown-Vialettovan Laere syndrome

Unknown

Unknown

Unknown

AD with AR variants

Childhood-onset disease with bulbar paralysis and progressive deafness.

Spinal muscular atrophy

SMN1

5q13

Survival motor neuron 1

AR

Clinically SMA can be divided into three main categories: Type 1 (Werdnig-Hoffmann) – onset by 6 months; diffuse weakness, poor feeding, death due to respiratory insuffiency by 18 months. Type 2 – onset before 18 months of age, patients never stand, death by respiratory deficiency after 2 years of age. Type 3 – onset after 18 months. Some children will walk and stand independantly, proximal symmetric weakness with slow disease progress, death in adulthood.

Kennedy disease

AR

Xq11.2–q12

Androgen receptor

XR

Average onset is in the 3rd decade with muscle cramps, fatigue, and gynecomastia often present since adolescence. With age, lower limb weakness becomes the prominent feature. Bulbar features and facial weakness are common.

Adapted from Strong et al. (2005) and Kaplain and Fontain (2006). AD  autosomal dominant; AR  autosomal recessive.

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Gene

Disorders of the Neuromuscular Junction

Disease

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The Genomic Basis of Neuromuscular Disorders

the synaptic cleft to the acetylcholine receptors (AChRs) on the myofiber plasma membrane. This initiates the action potential (firing) of the myofiber, with flow of sodium and potassium across the myofiber membrane (sarcolemma), resulting in the contraction of the myofiber. Genetic mutations or autoimmune disorders that result in inhibition of the release of acetylcholine across the synaptic cleft, or inhibition of the uptake of acetylcholine by the receptors on the myofiber, cause weakness due to loss of neuromuscular transmission (Table 103.3). Disorders of the neuromuscular junction can be caused by environmental toxins such as botulism or snake venoms, which can cause paralysis by inhibiting the release or uptake of acetylcholine. The only known genetic causes of failure of neuromuscular transmission include mutations in subunits of the AChR genes (congenital myasthenia gravis). Autoimmune disorders with antibodies directed against the AChRs also block neuromuscular transmission, and this is the cause of the relatively common neuromuscular disease myasthenia gravis. Myasthenia gravis has a characteristic clinical presentation of facial and distal weakness exacerbated following exertion, but improving after rest. Weakness is often most prominent in the extraocular muscles leading to diplopia, the levator palpebrae leading to ptosis, and muscles involved in speech and swallowing. Other voluntary muscles including muscles of the limbs and trunk may also exhibit weakness. Involvement of the respiratory muscles can be life-threatening and warrants regular monitoring. Weakness may stay localized to one area or present in a more generalized fashion. Significant variation between patients is seen both in clinical presentation and response to treatment. Treatment is accomplished by immune suppression, and often, thyroidectomy.

DISORDERS OF THE MUSCLE There are many types of muscle disease, both inherited and acquired (Table 103.4). There are four major groups: muscular dystrophies, congenital myopathies, metabolic myopathies, and inflammatory myopathies. The muscular dystrophies are cell autonomous defects within the muscle fiber, often of regulatory or structural proteins, leading to repeated bouts of myofiber degeneration (necrosis) and regeneration. Congenital myopathies are typically a problem with muscle development, leading to small and weak muscles at birth, but less evidence of myofiber necrosis. The metabolic myopathies involve defects in energy metabolism, and include the mitochondrial myopathies. Inflammatory myopathies are autoimmune disorders of muscle. Within the subgroup of the muscular dystrophies, there are over 23 causative genes (Table 103.4). Most forms show autosomal recessive (AR) or X-linked recessive inheritance patterns, with loss of a single protein product causing the corresponding disease (loss-of-function). The most common subgroup of the muscular dystrophies involve proteins that are components of the membrane cytoskeleton of the myofiber, a network of proteins that serve to increase plasma membrane stability (Figure 103.1). These include loss of intracellular dystrophin,

sarcoglycans (alpha, beta, gamma, and delta subunit genes), integrin alpha7, and extracellular laminin alpha 2 (also called merosin). Myofibers have particularly stringent requirements for structural support of the plasma membrane as force must be transmitted from the intracellular actin/myosin contractile apparatus to the extracellular connective tissue. Also, myofibers are larger than any other cell type, again putting unusual amounts of stress on the plasma membrane. Loss of some of the stabilizing proteins leads to membrane instability and leakage of material in and out of the myofiber in an unregulated manner. Myofibers can degenerate (necrosis) from this process, but myofibers are able to regenerate effectively from resident stem cells (myoblasts). Thus, muscle pathology in the muscular dystrophies typically shows muscle cells in varying stages of degeneration and regeneration. With advancing age, the regeneration can become less and less successful, leading to eventual loss of myofibers, weakness, and an early death. While there is a certain level of consistency between the clinical picture in all forms of muscular dystrophy (e.g., weakness), several distinct classes of disease have arisen based on age of onset, specific muscles affected, specific molecular defect, and inheritance patterns (Table 103.4). The most common class of muscular dystrophies is the dystrophinopathies (mutations of the very large dystrophin gene on the X chromosome). This group is comprised of any disorder caused by a primary (either partial or complete) deficiency of the dystrophin protein at the myofiber plasma membrane. Duchenne muscular dystrophy (DMD), is caused by complete loss of dystrophin, and is the most common of all genetic disorders worldwide. Other classes of muscular dystrophies include the congenital muscular dystrophies of which there are both syndromic (involving muscle, brain and eyes) and non-syndromic forms (muscle specific). The limb-girdle muscular dystrophies comprised of both autosomal dominant (AD) and AR muscular dystrophies presenting in childhood through adulthood. Additional forms of muscular dystrophy include Emery Dreifuss muscular dystrophy and fascioscapulohumeral muscular dystrophy (FSHD). In this chapter we will discuss ALS, myasthenia gravis and DMD as examples of motor neuron, neuromuscular junction, and muscle disease, respectively. A summary of the characteristics of most of the different neuromuscular disorders can be found in Tables 103.1–103.4.

PREDISPOSITION Neuromuscular diseases can be either genetic/hereditary or acquired. The majority of motor neuron diseases are sporadic with unknown causes; while a subset has a clear genetic pathway. Examples of genetic motor neuron diseases include spinal muscular atrophy which is caused, most commonly, by a homozygous deletion of exon 7 of the SMN gene and some cases of FALS that are caused by mutations in the SOD1 gene. Similarly, for disorders of the neuromuscular junction, while rare monogenic exceptions exist, such as congenital myasthenic syndrome with mutations in

TABLE 103.3

Common disorders of the neuromuscular junction

Disease

Gene

Locus

Protein product

Inheritance

Clinical features

Myasthenia Gravis

Unknown

Unknown

Unknown

Acquired/ autoimmune

Symptoms begin in the 2nd or 3rd decade with ocular weakness, oropharyngeal weakness, or limb weakness in a minority of patients. Disease severity increases with use and fatigue.

Myasthenia gravis

CHAT

10q11.2

Choline acetyltransferase isoform

AR

See above

Congenital myasthenia gravis (CMS): Acetylcholine receptor (AChR) deficiency

CHRNE CHRNB1 CHRND

17p13–p12 17p13.1 2q33–q34

AChR

AR

Ocular, bulbar or respiratory muscle weakness may be present from birth, worsening by crying or activity in the neonatal period. Patients may have normal or delayed mile stones. All are negative for anti AChR antibodies.

Slow channel syndrome

CHRNA1 CHRNB1 CHRND CHRNE

2q24–q32 17p13.1 2q33–q34 17p13–p12

AChR

AD (rare cases AR due to CHRNE)

Variable onset ranging from birth to 7th decade. Facial weakness including ptosis and ophthalmoplegia. Weakness and fatigability of forearm extensors, neck flexors, and respiratory muscles.

Fast channel syndrome

CHRNA1 CHRND CHRNE

2q24–q32 2q33–q34 17p13–p12

AChR

AR

Onset beginning at birth with diffuse weakness sometime requiring respiratory support.

Congenital myasthenic syndrome with end plate acetylcholine esterase deficiency (type 1C)

COLQ

3p25

Acetylcholinesterase collagen-like tail subunit

AR

Generalized weakness begins by 2 years of life, with slow motor milestones, respiratory, and facial muscle weakness. Weakness is symmetric and can be severe.

Congenital myasthenia gravis – post synaptic

RAPSN

11p11.2–p11.1

Rapsyn

AR

Symptoms begin at birth or in infancy with hypotonia and weakness, poor suck and a weak cry. Most patients have delayed motor milestones with respiratory involvement.

Predisposition

Adapted from Kaplain and Fontain (2006). AD  autosomal dominant; AR  autosomal recessive.

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1270

Common disorders of muscle Locus

Protein product

Inheritance

Clinical features

Duchenne/Becker MD

Dystrophin

Xp21.2

Dystrophin

XLR

Progressive proximal muscle weakness beginning by age 5 resulting in wheelchair use by age 12 and death in the 20s. BMD has a similar but slower progression.

Emery Dreifuss muscular dystrophy

EMD/LMNA

Xq28/1q21.2–q21.3

Emerin/Lamin A/C

XLR/AD

Weakness predominately in the shoulders upper arms and calves; contractures often precede weakness, cardiac complications are common.

Facioscapulohumeral muscular dystrophy

Unknown

4q35

Unknown

AD

Weakness of facial muscles (eyes and mouth) as well as shoulders, upper arms and lower legs initially, with slow progression.

Limb-girdle muscular dystrophy 1A-1G

3 known genes: TTID LMNA CAV3

7 known loci: 5q31 1q21.2 3p25 6q23 7q 7q31.1-q32.2 4q21

3 known protein products: Myotilin Lamin A/C Caveolin-3

AD

Weakness and wasting restricted to the limb musculature, proximal greater than distal. Relative sparing of the heart and bulbar muscles, although exceptions occur, depending on the genetic subtype.

Limb-girdle muscular dystrophy 2A-2K

11 known genes

11 known loci

11 known protein products

AR

Weakness and wasting restricted to the limb musculature, proximal greater than distal. Relative sparing of the heart and bulbar muscles, although exceptions occur, depending on the genetic subtype.

Merosin deficient congenital muscular dystrophy

LAMA2

6q22–q23

Laminin alpha 2

AR

Generalized hypotonia and weakness present from birth with changes on MRI but no cognitive involvement.

Syndromic congenital muscular dystrophy: Fukuyama CMD

FCMD

9q31–q33

Fukutin

AR

Generalized muscle weakness with hypotonia; mental retardation or learning disabilities, eye defects or seizures.

The Genomic Basis of Neuromuscular Disorders

Gene

■

Disease

CHAPTER 103

TABLE 103.4

Muscle eye brain disease

POMGNT1

1p34.1

O-linked mannose beta 1,2-Nacetylglycosaminyl transferase

Walker Warburg syndrome

POMT1

9q34.1

Protein-O-mannosyl transferase 1

MDC1C

FKRP

19q13.33

Fukutin related protein

AR

Calf pseudohypertrophy, dilated cardiomyopathy involving the left ventricle, and absence of white matter changes on MRI.

Ullrich syndrome/ Bethlem myopathy

COL6A2/3

21q22.3/2q37

Alpha 2 type VI collagen/ alpha 3 type VI collagen

AR

Weakness and hypotonia, proximal joint contractures (particularly finger flexion contractures), and striking hyperlaxity of distal joints. Some children acquire the ability to walk independently; however, progression results in later loss of ambulation. Early and severe respiratory involvement.

Nemaline myopathy

NEB ACTA1 TPM3 TPM2 TNNT1

2q22 1q42.13–q42.2 1q21–q23 9p13.2–p13.1 19q13.4

Nebulin Actin alpha 1 tropomyosin 3 Tropomyosin 2 Toponin T1

AR/AD

Weakness and hypotonia of facial muscles, neck and upper limbs; often affects respiratory muscles; swallowing and speech problems are common.

Myotonic dystrophy (Steinert disease)

DMPK

19q13.3

Myotonic dystrophy protein kinase

AD

Characterized by myotonia, cataracts, and cardiac involvement. Clinical presentation ranges from mild to severe in the typical adult onset form, but is always severe with MR in the congenital form.

Myotonia congenital– Thomsen disease– Becker disease

CLCN1

7q35

Chloride channel 1

AD/AR

Muscle stiffness presents in childhood affected all striated muscle, including facial muscles. Muscles are often hypertrophic in appearance. Stiffness may be relieved by repeated muscle contraction.

Predisposition

Adapted from Kaplain and Fontain (2006). AD  autosomal dominant; AR  autosomal recessive; XLR  X-linked recessive.

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Extracellular Basement membrane proteins Collagen VI

Laminin-2 (Merosin)

(Bethlem ulrich syndrome)

(MDCIA)

Agrin α–Dystroglycan

(LGMD 2C-F) Sarcoglycans β γ α δ

Sarcospan

Integnns

Caveolin-3 (LGMD 1C) Dysferlin (LGMD 2B)

β1

Gr2

Filamin 2 α–Dystrobrevin

nNOS

P

P

P nNOS

β1 P

α1

Syntrophins

Syncoilin

(LGMD 2I) FKRP (MDC 1C)

α7

β

(ITGA7)

Calmodulin

NH2

Dystrophin (DMD/BMD) OH

P

Calp ain-3

(LGMD 2A)

P

Trim32

(LGMD 2G) Telethonin CO

CO

(LGMD 2H)

OH

Myotilin

(LGMD 1A)

Golgi LaminA/c

Intracellular

F-Actin filaments

(LGMD 2J) Titin

Anain

(LGMD 1B) (EMD) Nuclenus

NH2

Figure 103.1 Schematic of the dystrophin associated protein complex and other proteins associated with the muscular dystrophies. Please see Table 103.4 for more information on the clinical features and inheritance of the muscular dystrophies (Courtesy of Diana Escolar, MD Children’s National Medical Center, Washington, DC).

the  subunit of the AChR gene or the rapsyn gene; the majority of neuromuscular junction disorders are sporadic, resulting from an autoimmune response or environmental toxins. Alternatively, myopathies, including all of the muscular dystrophies are monogenetic conditions caused by a mutation in a specific gene, in many cases these genes have been identified; however, some still elude identification. Amyotrophic Lateral Sclerosis ALS is a complex disorder caused by a combination of genetic and environmental factors resulting in a loss of motor neurons. Recent epidemiologic studies suggest an incidence between 1.4 and 2.7 per 100,000 (Worms, 2001); however for unknown reasons, both the incidence and prevalence appear to be on the rise (Neilson et al., 1994; Riggs, 1990; Worms, 2001). ALS can be divided into two main categories – sporadic and familial.

Although up to 10% of cases are thought to be familial cases, most of which are inherited in an AD fashion, the genetic cause for the majority of these cases remains unknown. The largest single cause of ALS identified to date, accounting for 2–3% of all cases (~20% of familial cases), is the gene encoding copper-zinc superoxide dismutase (SOD1) (Brown, 1995). Over 100 mutations in the SOD1 gene have been identified, all of which are inherited in an AD pattern except one, which has been observed to be an AR variant with incomplete penetrance (Andersen et al., 1997). Eleven additional loci for FALS have been identified; however, the causative gene has only been identified in three cases: ALSin (ALS2 inherited an AR fashion), Sentaxin (ALS4 inherited an in AD fashion), and microtubular associated protein tau (FTDP, frontotemporal dementia with parkinsonism inherited in an AD fashion) (Strong et al., 2005). While the majority of cases of ALS do not appear to be attributable to Mendelian genetics, there

Screening

are assumed to be “susceptibility genes” that increase the relative risk of developing ALS. Current genes identified as contributing to susceptibility to ALS include: vascular endothelial growth factor (VEGF), NF-H (specifically insertions and deletions in the KSP domain), microdeletions in mitochondrial DNA encoding cytochrome C oxidase, RNA processing errors in EAAT2, deletions of the NAIP gene and decreased number of SMN copies (Strong et al., 2005). In addition, different apolipoprotein E (APOE) alleles have been suggested to mediate the ALS phenotype. Specifically, APOE2 has been found to be associated with limb onset and longer survival while APOE4 was associated with bulbar onset occurring earlier in life (Moulard et al., 1996). No clear environmental agent or physical characteristic has been found to cause ALS; however, several risk factors have been identified including smoking, increased body mass index, recent mechanical injury, work in the agricultural industry, non-specialized factory work, and professionals sports (Bracco et al., 1979; Nelson et al., 2000a, 2000b). While it is believed that sporadic cases have a genetic component, it is not well understood at this time. Myasthenia Gravis Myasthenia gravis is an autoimmune disorder of the neuromuscular junction affecting between 0.25 and 2 per 100,000 individuals worldwide with a female to male ratio of 4:1(Kurtzke, 1978; Plauche, 1983). While myasthenia gravis is seen in all populations, there is a greater incidence of childhood myasthenia gravis in Asian populations than in Caucasian populations (Garlepp et al., 1983). The majority of cases are caused by highly specific antibodies against AChR; patients who test negative for this antibody have been found to have antibodies against muscle specific kinase (MuSK) (Hoch et al., 2001). Childhood-onset Caucasian patients test negative for antibodies to AChR with a greater frequency than other populations (Compston et al., 1980). Although no one gene has been found to be responsible for myasthenia gravis, strong evidence suggests a genetic component to the disease. Support for the genetic basis of myasthenia gravis is based on the finding that different human leukocytic antigens (HLA) compliments are associated with early-onset cases versus late-onset cases. Specifically, early-onset cases (defined as presenting prior to age 40 and seen with greater frequency in women) are HLA-B8 and DR3 positive in about 60% of cases (Compston et al., 1980) and late-onset cases (defined as presenting after age 40 with a slightly increased incidence in men) have been associated with HLA-B7 and DR2 (Maggi et al., 1991). In addition, family members of affected individuals have been found to have an increased risk of myasthenia gravis (1000 times population risk); higher concordance in monozygotic twins and an increased incidence of comorbidity with other autoimmune diseases (Maggi et al., 1991; Oosterhuis, 1989). Efforts to identify genetic factors involved in the development of myasthenia gravis continue. Recently the Fc immune complex receptor III (FcRIII) was implicated in the myasthenia gravis rat model (experimental autoimmune myasthenia gravis) (Tuzun et al., 2006).

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Approximately, 10% of patients are affected with myasthenia gravis due to a thymic tumor; while this cause accounts for the minority of cases, it is important to note that approximately 30– 60% of thymomas are associated with myasthenia gravis (Chiu et al., 1987; Oosterhuis, 1989). Duchenne Muscular Dystrophy Duchenne is the most common form of muscular dystrophy affecting approximately 1 in 3500 males equally worldwide (Emery, 1991). This X-linked recessive condition is caused by mutations in the dystrophin gene which results in the complete absence of the dystrophin protein. Approximately, 65% of mutations are large deletions or duplications of the gene comprising one or more exons; another 35% of mutations are point mutations, of which about half are nonsense mutations (Traverso et al., 2006). As in all X-linked recessive conditions, this disease affects males primarily, while females are often silent carriers. Rare manifesting females carriers, affected due to skewed X-inactivation exist but are often misdiagnosed as having limb-girdle muscular dystrophy. With each pregnancy there is a 50% risk that a carrier female will pass on the affected dystrophin gene on the X chromosome. As such, carrier females have a 25% risk to have an affected son or 25% risk to have a carrier daughter with each pregnancy. At 79 exons and 2.2 million base pairs, the dystrophin gene is the largest gene identified to date. The large size of this gene makes it vulnerable to a high de novo mutation rate which has been observed to be 1/10,000 egg or sperm cells. Approximately, 1/3 of all boys with DMD are born to non-carrier mothers due to a de novo mutation.

SCREENING There is currently no standard screening for neuromuscular diseases.While several states in the United States (Iowa, Pennsylvania) and several countries, including Wales and the United Kingdom have conducted research studies of newborn screening for DMD using creatine kinase (CK), and this approach remains controversial. Historically, in the United States in order for a condition to be considered for inclusion in any state newborn screening panel it had to be a treatable condition whereby early treatment would significantly alter the course of the disease. At this time, treatment for Duchenne does not meet these criteria. However, given the severity of the disease and number of years before diagnosis, Duchenne has long been considered for newborn screening as it is believed that early diagnosis of an affected child would open the door to reproductive options for DMD carriers that were not otherwise aware of their carrier status and potentially prevent the birth of other children in the family (Parsons et al., 2002). For those who oppose newborn screening for DMD, pediatric screening criteria include: any male child not walking by 18 months of age, delayed speech or global developmental delay, awkward gait, inability to run, or painful legs under the age of 4 years (Mohamed et al., 2000). Although screening criteria exist for Duchenne, they are not universally used. No standardized or population screening of any type currently exists for ALS or for myasthenia gravis.

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DIAGNOSIS Despite the differences between the various forms of neuromuscular disease, there are many commonalities to the diagnostic approach for this group of disorders. As with any diagnostic process, the diagnosis of neuromuscular disease requires a detailed clinical examination including past medical history, family history, laboratory tests (blood), manual muscle testing, and functional tests. Muscle and nerve biopsies were common component of the diagnostic process up to a few years ago, but recently, the increased use of genetic testing using patient blood DNA has led to less need for invasive biopsy procedures. Based on the outcome of the clinical examination, additional studies including electrophysiological studies, nerve or muscle biopsy, genetic testing, or metabolic studies may be needed. Amyotrophic Lateral Sclerosis The median age of diagnosis for ALS is 55 years. No definitive diagnostic tool, molecular, biochemical, or other currently exists for ALS. Diagnostic evaluations for ALS are based on the criteria set for by the World Federation of Neurology Research Group on Neuromuscular Disease (1994) and include signs of both lower motor neuron degeneration (by clinical, electrophysiological, or neuropathological evaluation) and upper motor neuron degeneration (by clinical examination) in addition to evidence of disease progression, spreading either within the affected area or to other regions of the body. These symptoms must be present in the absence of evidence of other diseases (by neuroimaging or electrophysiologic testing). New imaging and biochemical techniques are constantly being examined to determine if a clear diagnostic marker can be developed for ALS. One protein isoform, Nogo-A, has been found to be increased on Western blot of muscle biopsies from patients with ALS compared to healthy controls and control subjects with other denervation disorders and may ultimately prove to be a reliable diagnostic tool (Dupuis et al., 2002). Myasthenia Gravis Diagnosis of myasthenia gravis is usually made by a combination of clinical, interventional, and electrophysiological assessments. Clinical diagnosis is based on a history of weakness that improves with rest and worsens with activity. Intravenous injection of edrophonium (tensilon test), resulting in rapid improvement of clinical symptoms, can be used to further support the diagnosis with the third supporting feature coming from decreased compound muscle action potential during repetitive stimulation (Vincent, 2005). While the presence of this triad of features is ideal in confirming a diagnosis of myasthenia gravis, they can be substituted for serum AChR antibodies. A positive antibody test, as is seen in about 85% of patients, is considered diagnostic (Lange, 1997). Finally, the “ice test”, resulting in improvement in ptosis after application of ice to the eyelid has demonstrated near 100% sensitivity in diagnosing myasthenia gravis (Golnik et al., 1999). Disease severity can be established

based on the Myasthenia Gravis Foundation of America clinical classification. Duchenne Muscular Dystrophy DMD is typically diagnosed between the ages of 3 and 5 years (Zalaudek et al., 1999). Patients present with proximal muscle weakness and calf hypertrophy, difficulty climbing stairs, difficulty rising from the floor, toe walking, and markedly elevated CK levels. After a clinical diagnosis is suspected, confirmation of the diagnosis is usually made in one of three ways, genetic testing, biochemical analysis of muscle biopsy, or by family history and CK testing. Sixty percent of patients with DMD have large deletions in the dystrophin gene and another 5% have large duplications; these mutations can be detected using several methods including multiplex PCR (polymerase chain reaction), multiplex amplifiable probe hybridization (MAPH), and multiple ligation-dependent probe amplification (MLPA). Deletions and duplications predicted to shift the reading frame are consistent with a diagnosis of DMD (Traverso et al., 2006). For those patients who are deletion/duplication negative, gene sequencing can be performed. Sequencing will detect point mutations as well as small insertions and deletions in about 90–95% of patients who are deletion/duplication negative, leading to a combined sensitivity of approximately 97% of DMD cases (Buzin et al., 2005). For patients in whom a definitive diagnosis can not be established with molecular testing, biochemical analysis of dystrophin on muscle biopsy is still the gold standard in the diagnosis of DMD. Histologic features indicative of but not specific to DMD include fiber size variation with predominance of type 1 fibers and selective loss of type 2B fibers, necrosis, and basophilic regenerating fibers are common as are central nuclei, connective tissue, and fat increases with age. For a definitive diagnosis, a western blot must be done establishing a complete absence of dystrophin. Immunostaining for antibodies against different areas of the dystrophin protein is also widely used; however, it is generally considered to be less sensitive and more subjective than western blot (Hoffman et al., 1988). For those families with a positive maternal family history of DMD, confirmed by one of the above methods, markedly elevated CK levels in combination with clinical features of DMD is considered diagnostic.

PROGNOSIS Amyotrophic Lateral Sclerosis Patients with ALS live on average from 23 to 48 months from the time of diagnosis with 5-year survival rates estimated from 9–40% and 10 year survival rates dropping to 8–16% (Mitsumoto et al., 1998). During the course of the disease progression, patients will have a decline in respiratory function, necessitating respiratory support; dysphagia potentially leading to malnutrition; and dysarthria leading to a loss of speech.

Monitoring

These symptoms are seen in combination with the progressive muscle weakness that ultimately leads to paralysis (Simmons, 2005). There is increasing evidence that patients with ALS have cognitive involvement, usually exhibited by a loss of inhibition, distractibility, and other personality changes as well as deficits in executive functioning. These symptoms show significant overlap with frontotemporal dementia (Lomen-Hoerth et al., 2003). Myasthenia Gravis Due to the lack of standardized treatment for myasthenia gravis, prognosis is variable; however, a retrospective study of 844 patients found that pharmacologic remission rates for patients on treatment (corticosteroids and/or cholinesterase inhibitors), were 5% at 1 year and 41% by year 10, while complete remission rates (untreated) were 1% at year 1 up to 21% by year 10. Thymectomy has also been found to be predictive of a higher chance of complete remission with 5-year remission rates of 15% in thymectomy patients versus 8% in those not undergoing the procedure; the greatest outcome was seen for those operated on during the first year of disease. Early diagnosis is predictive of a greater chance of achieving complete remission in cases where initial symptoms lasted less than 1 year. While prognosis is not influenced by the sex of a patient, disease severity is correlated with decreased rates of remission (Beghi et al., 1991). In treated and untreated patients, symptoms typically fluctuate and can be impacted by environmental factors such as emotional distress, illness, dysregulation of the thyroid, increased body temperature (due to weather or illness), and drugs. Exacerbation of symptoms due to any of the above factors may result in a serious myasthenic crisis (acute respiratory crisis) (Ferrero et al., 2005). Patients with myasthenia gravis do appear to develop mild cognitive deficits during the course of the disease progression, specifically in the area of information processing (verbal and visual learning), the cause of which is not currently known (Paul et al., 2000). Duchenne Muscular Dystrophy Patients with DMD present with proximal muscle weakness, toe walking and delayed motor milestones; if not apparent at the time of diagnosis, patients will develop a waddling gait, difficulty climbing stairs and a gowers maneuver, all of which worsen over time. Muscle weakness begins in the lower limbs with upper limb weakness appearing in the later stages of the disease. Weakness will progress requiring the use of a wheelchair between age 7 and 13 years. Once wheelchair bound, several complications develop including, scoliosis, skeletal deformities, hip, and knee contractures. Patients will die, often due to respiratory complications (diaphragmatic and intercostals muscle weakness leading to respiratory failure), in the third decade (Emery, 1993). Recently, due to increased use of both glucocorticoids (prednisone) and ventilatory support, patients can survive into their 20s and older (Ishikawa et al., 1999). Other symptoms may include cognitive delays (30% of patients) and gastrointestinal discomfort with or without gastric dilation (Miller and Wessel, 1993).

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MONITORING Amyotrophic Lateral Sclerosis Given the rapid progression of ALS, constant monitoring to address changes in health status is needed. Of primary importance are respiratory issues, most commonly measured by force vital capacity (FVC) whereby shorter survival has been correlated with a lower FVC (Stambler et al., 1998). FVC and other indicators of respiratory function (nocturnal oximetry, maximal inspiratory pressure, maximal sniff nasal inspiratory force) can be used as indicators of when to initiate non-invasive positive pressure ventilation. Due to the bulbar component of ALS, either as an initial symptom, or acquired during the disease progression, nearly all patients will experience dysphagia leading to malnutrition and/or dehydration. This symptom can be managed initially by initiating changes in food consistency but later may necessitate the placement of a feeding gastrostomy tube (Simmons, 2005). Loss of communication due to dysarthria can have a significant effect on quality of life and should be monitored; alternative communication devices can help to alleviate this symptom. Patients with ALS experience progressive deterioration of muscle strength leading to frequent falling and ultimately a loss of independent ambulation. Changes in strength can be evaluated using manual muscle testing or with maximal voluntary isometric contractions and addressed with durable medical equipment – cane, walker, wheelchair, appropriate to the patient’s current, and future needs. Myasthenia Gravis Respiratory complications pose the largest risk for mortality in myasthenia gravis, therefore respiratory function should be monitored, ideally using FVC. All patients are at risk for thymic tumors, which even when treated may recur in the form of mediastinal or pleural metastases. In order to appropriately monitor for neoplasias, long-term follow-up including chest scans are needed. Patients with myasthenia gravis should be watched for an exaggerated response to anesthesia resulting in a prolonged need to mechanical ventilation. In addition, drugs such as anti-malarials,  blockers, verapamil, and aminoglycosides may worsen the disease state and should be used with caution. Finally, women of childbearing age with myasthenia gravis are at risk to have a child with neonatal myasthenia gravis (10% of babies born to affected women) due to transfer of maternal IgG AChR antibodies. Appropriate counseling should be provided and treatment, including thymectomy, plasma exchange and immunosuppression, should be prescribed to lower circulating antibodies and reduce risk to the fetus (Vincent et al., 2001). Duchenne Muscular Dystrophy Patients with DMD have three main areas of disease progression that warrant monitoring, muscle function, cardiac function, and pulmonary function. Muscle function should be monitored using manual muscle strength testing. Several measures are available for cardiac monitoring including, electrocardiogram,

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ventricular late potentials, ventricular arrythmias, and LV systolic function. Of these measures, LV systolic function has been found to be the greatest predictor of mortality (Corrado et al., 2002); however, due to the presence of scoliosis and skeletal deformities, cardiac imaging can be difficult and lack sensitivity. In lieu of more conventional imaging methods, cardiac hormones such as atrial natriuretic peptide, brain natriuretic peptide, and norephinephrine have been found to be good indicators of congestive heart failure and should be monitored. It is recommended that initial cardiac monitoring begin at age 9 with echocardiogram and followed by cardiac hormones as skeletal deformities set in (Ishikawa et al., 1999). Decreased lung function, indicated by decreased FVC is often the first sign of respiratory progression. This marker will continue to decline overtime and will worsen with the onset of scoliosis. FVC can be used as an indicator of when to initiate respiratory support such as bi-pap or invasive ventilation.

CURRENT, NOVEL, AND EMERGING THERAPIES Amyotrophic Lateral Sclerosis Riluzole, currently the only drug approved by the FDA in the treatment of ALS, is a glutamate antagonist shown to effectively slow the decline in muscle strength and extend survival by an additional 3–4 months (Simmons, 2005). Future alternatives to Riluzole will likely include combination therapies that address the occurrence of excitotoxicity in patients and mouse models with ALS. The glial glutamate transporter 1 (GLT1 or EAAT2) is reduced in affected patients, possibly leading to neuronal degeneration. Attempts to increase the expression of GLT1 with ceftriaxone is have been successful and result in moderately prolonged survival of the mouse model. Several other drugs have been successful in the SOD1G93A mouse model including, arimoclomol, a drug used to upregulate heat shock proteins work by preventing the pathogenic aggregation of proteins (Kieran et al., 2004); insulin-like growth factor (IGF)-1, glial cell line derived neurotrophic factor (GDNF) and VEGF. These three drugs have all been shown to have a neuroprotective effect to varying degrees possibly through their action in preserving the morphology of motor neurons and decreasing gliosis. Of interest, the method of delivery can significantly alter the therapeutic benefit. IGF-1 and VEGF yielded the best results when delivered through viral vectors injected directly into muscle (Carri, et al., 2006). One non-pharmacologic approach to treatment is repetitive transcranial magnetic stimulation (rTMS). This technique at low doses (1 Hz) decreases motor cortex excitability and has been shown in mouse models and small human trials to slow the disease progression. Alternatively, high doses (20 Hz) were found to increase the disease progression (Di Lazzaro et al., 2004). Several successful attempts at gene therapy have been made using the SOD1 mouse model. Upregulation of the cell death inhibitor, Bcl-2, using transgenes has resulted in a slower disease

progression and increased survival (Kostic et al., 1997). Delivery of IGF-1 and GNDF using adeno-associated virus to spinal motor neurons results in increased survival time. Delivery of GNDF is particularly effective as it localizes to the neuromuscular junction and is then taken up by the nerve terminal and delivered to the motor neuron (Kaspar et al., 2003; Lu et al., 2003). While pharmacogenomic options in ALS are not overly abundant, stem cells have been proposed as a mechanism to replace dead motor neurons, or protect existing motor neurons. Research using mouse models suggests that stem cells injected into the spinal cord of mice do result in neuroprotection at the injection site but remain local and therefore would not be effective with a site specific injection at ameliorating the disease (Carri, et al., 2006). Unfortunately, approaches showing success in rodent models have not yet shown success in human patients with ALS, and there is no therapy that is able to significantly increase lifespan and quality of life. Myasthenia Gravis Several treatments are currently available for the amelioration of symptoms in myasthenia gravis. All treatments target different aspects of the immune system while striving to reduce the presence of AChR antibodies. Current treatments include: acetylcholinesterase (AChE) inhibitors; propantheline; 3,4-diamino pyridine which blocks presynatic potassium channels increasing the release of AChR; and immunosuppressants which are effective in reducing AChR antibody levels. Non-pharmacologic therapies include: plasma exchange to eliminate autoantibodies and surgical thymectomy which is used with greater frequency in patients with early-onset disease (Vincent, 2005). Patients who do not respond to traditional immunosuppressants have had positive results when treated with FK506 (tacrolimus hydrate) which acts on the immune system by decreasing cytokines production to limit their interaction with T helper cells thus resulting in decreased antibody production by B cells (Schreiber and Crabtree, 1992). Successful treatment, resulting in stalled disease progression, of the rat myasthenia gravis model (EAMG) has been achieved using a fusion protein comprised of gelonin and amino acids 4-181 of the a subunit of the human AChR and may in the future prove effective in humans (Hossann et al., 2006). While immunosuppressive therapy and/or AChE inhibitors are the standard treatment for myasthenia gravis, studies have shown that targeting the AChE RNA over the protein may be more effective. Using an antisense oligonucleotide (EN101) targeted to AChE mRNA, therapeutic benefit has been observed both in the myasthenia gravis animal model (EAMG) and humans (Brenner et al., 2003; McKee et al., 2003). Additional proposed therapies include suppression of symptoms by mucosally administered recombinant AChR fragments; gene therapy to provide long-term immune modulating molecules that target the inflammatory response; and conversion of antigen presenting cells to apoptotic inducing cells that target AChR specific T cells (Drachman et al., 2003; Souroujon et al., 2003; Tarner et al., 2003).

Advances in Genomics and Proteomics

Duchenne Muscular Dystrophy Current therapies for DMD include prednisone to slow the disease progression and beta blockers in older patients to reduce the risk of mortality associated with dilated cardiomyopathy (Ishikawa et al., 1999). While prednisone has been proven effective and has been recommended as standard care in DMD by the American Academy of neurology (Moxley et al., 2005), it is not universally accepted, nor is it successful in halting the disease progression. As a result additional treatment options continue to be researched and tested. Transplantation of muscle precursor cells has been attempted in nine patients with DMD with varied responses – eight of the nine showed some dystrophin expression after transplantation; however, expression levels ranged from 3.5 to 26% (Skuk et al., 2006). Many attempts have been made to use gene therapy to correct or at least improve the phenotype in DMD. While there are several ways to get dystrophin into individual muscle fibers, the simplest way is to inject plasmid DNA encoding dystrophin directly into muscle, this approach has proved successful in mice and in a small human trial but the benefit is limited due to the site specific nature of the delivery (Zhang et al., 2004). Alternatively, many studies have attempted to use viruses to deliver the dystrophin gene. Originally the adenovirus vector was considered but was found to be problematic due to immune responses. More recently attention is on the adeno-associated viral vectors; however, they are not capable of carrying a full-length dystrophin cDNA. Success with systemic injections of mini dystrophin has been seen in the DMD mouse model and single site injections has been found to be successful with mini and micro dystrophin genes (Gregorevic et al., 2004). Most recently antisense oligonucleotide skipping has been proposed. This technique modifies the deletion by inducing exon skipping during pre-mRNA splicing, resulting in an in-frame deletion consistent with a milder Becker muscular dystrophy phenotype (Bremmer-Bout et al., 2004). As with ALS, none of the successes in rodent models have yet improved DMD patient lifespan or quality of life, although the number of human clinical trials has recently expanded dramatically.

ADVANCES IN GENOMICS AND PROTEOMICS The study of genomics and proteomics seeks to understand the molecular and biochemical basis of living organisms on a deeper and more complex basis than what is traditionally thought of as genetics. Although genomics, like genetics, seeks to study the genes, genomics looks at the entire genome at once and attempts to identify which genes (independently or together) may be involved in a disease process rather than focusing solely on a single disease causing gene. Whole-genome analyses often use single nucleotide polymorphism (SNP) arrays to look at 100,000-800,000 SNPs in a single sample. While the magnitude of the data that these techniques generate is startling, the statistical implications of multiple tests requires statistical correction

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often necessitating large sample populations and multiple validation sets to establish a significant association. Alternatively, proteomic profiling involves the identification of the expression, function and interaction of proteins with specific physical or biochemical characteristics. Comparative proteomics allows investigators to identify different protein fingerprints that may be associated with various disease states. Ultimately, the identification of disease specific proteins (or protein deficiencies) or genomic fingerprints may allow for the development of diagnostic tools and/or protein specific therapies not previously available. Amyotrophic Lateral Sclerosis When considering the commonality of ALS (lifetime risk of 1 in 800 to 1 in 2000), the paucity of information available on the diagnosis, treatment, and genetic causes is surprising. A few genes, SOD1 in particular, have been found to cause hereditary forms of the condition; but the majority of cases an estimated 90% of cases, which are known to be sporadic in nature, are still without any clear causative gene(s), pathway(s), or protein(s). Although no single gene, pathway, or protein has emerged through the research, progress had been made in the identification of associated genes and proteins as well as putative pathways. Whole-genome analysis of sporadic ALS has revealed multiple susceptibility genes. Dunckley et al. (2007) studied 766,955 unique SNPs in 386 sporadic ALS patients, followed by two independent validation groups for a total of 1287 ALS patients. Ten SNPs were found to be significantly associated in all three groups while an additional 41 SNPs were significant in two of three groups. After Bonferroni correction, one single SNP, rs6700125 remained significant in the Caucasian population. rs6700125 is in the FLJ10986 gene and was found to have an odds ratio of 1.38 (present in patients over controls). Follow-up studies of the FLJ10986 protein identified a 45 kDa and 48 kDa protein doublet which was present in the spinal cord of both patients and controls. The function of this protein still remains unknown, although it is recognized as part of the FGGY family of carbohydrate kinase domains which are involved in energy metabolism and glycolysis. A similarly designed, three tiered study, using a combined 1337 patients from the Netherlands, Belgium, and Sweden identified a second SNP, rs2306677 in the ITPR2 gene. This variant, like rs6700125, was the only one whose significance persisted after Bonferroni correction (p  0.012). ITPR2 works with ITPR1 and 3 to form the inosital 1,4,5 triphosphate receptor family. ITPR2 acts as a calcium channel on the endoplasmic reticulum of neurons controlling intracellular calcium levels. When ITPR2 is altered, increased intracellular calcium levels can lead to apoptosis and neuronal degeneration. Motor neurons specifically express higher levels of ITPR2 than other neurons; in addition, the limited ability of motor neurons to buffer calcium may predispose them to higher levels of damage in the presence of an altered ITPR2 than is seen in other cell types (van Es et al., 2007). While these two studies were able

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to identify two SNPs associated with sporadic ALS, a series of other studies have failed at the same task. Multiple possible and plausible explanations have been posed as explanations for the difficulty in successfully identifying the genetic basis of sporadic ALS: the sporadic ALS phenotype is not driven by a single gene or locus, the Bonferroni correction so commonly used to account for multiple tests is overly conservative and ignores linkage disequilibrium between SNPs, and finally, the possibility that the sporadic ALS phenotype is actually comprised of multiple independent conditions with different genetic influences and/or causes (Schymick et al., 2007). Although genome-wide association studies have not yet provided the boon of information the ALS community is awaiting, proteomic studies have made strides toward developing a diagnostic test for sporadic ALS. Many potential protein biomarkers for ALS have been identified in plasma including inflammatory markers (immunoglobulin G, complement C3, complement H), cytokines (transforming growth factor beta), carrier proteins (APOE), extracellular matrix components (fibronectin), biochemical signatures (alpha 2 macroglobulin and a panel of 55 metabolites), and lipid peroxidation products (oxidized coenzyme Q). While each category of biomarkers has a plausible rational for involvement in ALS, there are significant limitations to proteomic studies of plasma. The primary complication being biologic variation in concentration (dilution of biomarkers in large volume of blood, low abundance of cytokines and growth factors); this uncontrollable variability makes further delineation and validation of these associated markers difficult (Kolarcik and Bowser, 2006). Alternatively, since protein biomarkers from the brain and spinal cord are transferred to cerebrospinal fluid (CSF) through interstitial fluid, biomarkers detected in CSF are therefore thought to be a more accurate representation of the acute disease process then biomarkers detected in plasma. Five categories of biomarkers have been identified through proteomic studies of CSF in patients with sporadic ALS: lipid peroxidation products (malondialdehyde, 4-hydroxynonenal), markers of DNA damage (8-hydroxy-2 -deoxyguanosine), cytoskeletal proteins (tau, neurofilament), neutrophic factors (PEDF, VEGF), and inflammatory mediators (interleukin-6, C4d, MCP-1) (Kolarcik and Bowser, 2006). Recognizing that a single dysregulated protein is unlikely to be solely responsible for the complex cascade of cellular events seen in sporadic ALS, mass spectrometry based proteomics has been used to examine multiple proteins and peptides. Two independent groups using surface-enhanced last desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) have identified protein panels that are both highly sensitive and highly specific for the diagnosis of sporadic ALS. Ranganathan et al. (2005) report 19 m/z peaks that, when used together, have a 100% specificity and 91% accuracy for diagnosing sporadic ALS. Three peaks in particular were found to have high diagnostic predictive value, TTR, cystatin C, and neuroendocrine protein 7B2. These protein peaks were identified in patients whose CSF samples were collected, on average, within 385 days of the onset of symptoms. Follow-up studies of patients 1339–3913

days from the onset of symptoms did not find the same pattern suggesting that biomarker patterns may be influenced by disease progression. An independent study by Pasinetti et al. (2006) confirmed the reduction of cystatin C in patients with sporadic ALS (within 1.5 years of the onset of symptoms). Cystatin C is a regulator of cysteine protease activity and a known component of Bunina bodies (small intracellular inclusions in motor neurons of patients with ALS). Decreased levels of cystatin C in CSF of sporadic ALS patients may reflect aggregation of cystatin in Bunina bodies. In addition to the identification/confirmation of cystatin C, two other protein peaks, one identified as a proteolytic fragment of the neuroendocrine specific protein VGF, the other unidentifiable but known to be a 7.6 kDa protein were found, when taken together, to have similar sensitivity (91%) and specificity (97%) in the diagnosis of patients with sporadic to those identified by Ranganathan (2005). Although no FDA approved test has been developed for the diagnosis of sporadic ALS at this time, it is hoped that protein biomarkers discovered to date in combination with additional markers yet to be identified will provide clinicians with an effective diagnostic tool as well as aid in the identification of ALS subtypes, therefore providing insight into prognosis, average length of disease duration, and potentially therapeutic targets. Myasthenia Gravis Genomics and proteomics studies on myasthenia gravis have been limited to animal models. The site of the disease is the neuromuscular junction in muscle, and this is limited in both size and availability from patients. SNP association studies have been limited to candidate genes. Duchenne Muscular Dystrophy In DMD, the primary genetic and biochemical defect is already known, and high throughput genomics and proteomics approaches are not needed to further elucidate ‘cause’ of the disorder. However, Duchenne dystrophy is a progressive disorder, and the cascade of molecular networks and ensuing tissue remodeling is quite poorly understood. It is with regards to understanding the progressive pathophysiology of the disorder that high throughput genomics and proteomics can provide considerable new insights. In understanding the progression of the disease, novel therapeutics approaches may become available, targeting specific key aspects of the gene and protein networks driving the progressive weakness and muscle wasting. The initial mRNA microarray profiling study of muscle biopsies from DMD patients provided insight into a number of novel aspects of the pathogenesis of the disease, including metabolic failure of the muscle, de-differentiation of myofibers, and influx of dendritic cells (Chen et al., 2000). A follow-up study looked at mRNA profiles of patient muscle as a function of age, including fetal, pre-symptomatic infant, and later childhood symptomatic stages (Chen et al., 2005). The authors found that different gene and protein networks were invoked at different

References

stages of the disease process. NFkB inflammatory pathways and failure of acquisition of metabolic capacity were evident quite early in the disease (infants), while TGFbeta networks were commensurate with onset of symptoms. These data suggest that therapies targeted toward the development of weakness and wasting may change dependent on the age of the patient. Genome-wide studies of polymorphisms (SNPs) can be used to identify ‘genetic modifiers’ of Duchenne dystrophy. For example, some DMD patients respond quite positively to daily glucocorticoids, but some do not. This suggests that genetic polymorphisms unrelated to the dystrophin gene and gene mutation are modifying the patients’ response to glucocorticoids. Also, some patients show an early onset and rapid progression, while others show a milder phenotype, even though they may share the same loss-of-function gene mutation. Again, scans for genetic modifiers can provide a molecular understanding of variation in disease presentation and progression. Studies of genetic modifiers require large numbers of well-characterized DMD patients, and are currently underway (see http://www.wellstone-dc.org), but no publications are yet available.

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CONCLUSION We have compared and contrasted current knowledge regarding the cause and treatment of three distinct neurological disorders: ALS, myasthenia gravis, and DMD. The diagnosis of ALS remains predominantly clinically based, and existing treatments only marginally slow disease progression. However, new genome-wide SNP association studies promise new insights into disease pathogenesis and open new avenues for experimental therapeutics. In myasthenia gravis, the autoimmune process driving this disease is effectively treated with drugs and surgery in the large majority of patients, and genetic predispositions already known. For Duchenne dystrophy, genetic studies have identified the causative gene and protein, and heralded the routine application of molecular diagnostics into standard of care. Translation of the knowledge of disease molecular pathogenesis to improved patient care has proven difficult and slower than initially anticipated.

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Dunckley, T., Huentelman, M.J., Craig, D.W., Pearson, J.V., Szelinger, S., Joshipura, K., Halperin, R.F., Stamper, C., Jensen, K.R. & Letizia, D. et al. (2007). Whole-genome analysis of sporadic amyotrophic lateral sclerosis. New Eng J Med 357, 775–788. Dupuis, L., Gonzalez de Aguilar, J.-L., di Scala, F., Rene, F., de Tapia, M., Pradat, P.-F., Lacomblez, L., Seihlan, D., Prinjha, R. & Walsh, F.S. et al. (2002). Nogo provides a molecular marker for diagnosis of amyotrophic lateral sclerosis. Neurobiol Dis 10, 358–365. Emery, A.E. (1991). Population frequencies of inherited neuromuscular diseases – A world survey. Neuromuscul Disord 1, 19–29. Emery, A.E.H. (1993). Duchenne muscular dystrophy, In Oxford Monographs on Medical Genetics, 2nd edition, Vol. 24. Oxford University Press, Oxford, pp. 1–127. Ferrero, S., Pretta, S., Nicoletti, A., Petrera, P. & Ragni, N. (2005). Myasthenia gravis: Management issues during pregnancy. Eur J Obstet Gynecol Rep Biol 121, 129–138. Garlepp, M.J., Dawkins, R.L. & Christiansen, F.T. (1983). HLA antigens and acetylcholine receptor antibodies in penicillamine induced myasthenia gravis. BMJ 286, 338–340. Golnik, K.C., Pena, R., Lee, A.G. & Eggenberger, E.R. (1999). An ice test for the diagnosis of myasthenia gravis. Ophthalmology 106, 1282–1286. Gregorevic, P., Blankinship, M.J., Allen, J.M., Crawford, R.W., Meuse, L., Miller, D.G., Russell, D.W. & Chamberlain, J.S. (2004). Systemic delivery of genes to striated muscles using adeno-associated viral vectors. Nat Med 10, 828–834. Hoch, W., McConville, J., Helms, S., Newson-Davis, J., Melms, A. & Vincent, A. (2001). Auto-antibodies to the recentpro tyrosine kinase MuSK in patients with myasthenia gravis without acetylcholine receptor antibodies. Nat Med 7, 365–368. Hoffman, E.P., Fishbeck, K.H., Brown, R.H., Johnson, M., Medori, R., Loike, J.D., Harris, J.B., Waterston, R., Brooke, M. & Specht, L. et al. (1988). Characterization of dystrophin in muscle-biopsy specimens from patients with Duchenne’s or Becker’s muscular dystrophy. N Engl J Med 318(21), 1363–1368. Hossann, M., Li, Z., Shi,Y., Kreilinger, U., Buttner, J.,Vogel, P.D.,Yuan, J., Wise, J.G. & Trommer, W.E. (2006). Novel immunotoxin: A fusion protein consisting of gelonin and an acetylcholine receptor fragment as a potential immunotherapeutic agent for the treatment of myasthenia gravis. Prot Exp Purific 46, 73–84. Ishikawa, Y., Bach, J.R. & Minami, R. (1999). Cardiomyoprotection for Duchenne muscular dystrophy. Am Heart J 137, 895–902. Kaplain J.-C. and Fontain B. Updated 2006. www.musclegenetable.org. Kaspar, B.K., Llado, J., Sherkat, N., Rothstein, J.D. & Gage, F.H. (2003). Retrograde viral delivery of IGF-1 prolongs survival in a mouse ALS model. Science 301, 839–842. Kieran, D., Kalmar, B., Dick, J.R., Riddoch-Contreras, J., Burnstock, G. & Greensmith, L. (2004). Treatment with arimoclomol, a coinducer of heat shock proteins, delays in disease progression in ALS mice. Nat Med 10, 402–405. Kolarcik, C. & Bowser, R. (2006). Plasma and cerebrospinal fluid-based protein biomarkers for motor neuron disease. Mol Diag Ther 10(5), 281–292. Kostic, V., Jackson-Lewis, V., deBilbao, F., Dubois-Dauphin, M. & Przedborski, S. (1997). Bcl-2 prolonging life in a transgenic mouse model of familial amyotrophic lateral sclerosis. Science 277, 559–562. Kurtzke, J.F. (1978). Epidemiology of myasthenia gravis. Adv Neurol 19, 545–566.

Lange, D.J. (1997). Electrophysiologic testing of neuromuscular transmission. Neurology 48(Suppl 5), S18–S22. Lomen-Hoerth, C., Murphy, J., Langmore, S., Kramer, J.H., Olney, R.K. & Miller, B. (2003). Are amyotrophic lateral sclerosis patients cognitively normal?. Neurology 60, 1094–1097. Lu,Y.Y., Wang, L.J., Muramatsu, S., Ikeguchi, K., Fujimoto, K., Okada, T., Mizukami, H., Matsushita, T., Hanazono, Y. & Kume, A. et al. (2003). Intramuscular injection of AAV-GDNF results in sustained expression of transgenic GDNF, and its delivery to spinal motorneurons by retrograde transport. Neurosci Res 45, 33–40. Maggi, G., Casadio, C., Cavallo, A., Cianci, R., Molinatti, M. & Ruffini, E. (1991). Thymoma: Results of 241 operated cases. Ann Thorac Surg 51, 152–0156. McKee, D., Agus, D., Soreq, H., Ben Joseph, O., Brawer, S., Sussman, J. & Argov, Z. (2003). Antisense therapeutics in myasthenia gravis. J Neurol 250(Suppl 2), 43. Miller, G. and Wessel, H.B. (1993). Diagnosis of dystrophinopathies: review for the clinician. Pediatr Neurol 9(1), 3–9. Mitsumoto, H., Chad, A. & Pioro, E.P. (1998). Amyotrophic Lateral Sclerosis. FA Davis, Philadelphia. Mohamed, K., Appleton, R. & Nicolaides, P. (2000). Delayed diagnosis in Duchenne muscular dystrophy. Eur J Paediatr Neurol 4(5), 219–223. Moulard, B., Sefiani, A., Laamri, A., Malafosse, A. & Camu, W. (1996). Apolipoprotein E genotyping in sporadic amyotrophic lateral sclerosis: Evidence for a major influence on the clinical presentation and prognosis. J Neurol Sci 139, 34–37. Moxley III, R.T., Ashwal, S., Pandya, S., Connolly, A., Florence, J., Mathews, K., Baumbach, L., McDonald, C., Sussman, M., Wade, C., (2005). Quality Standards Subcommittee of the American Academy of Neurology; Practice Committee of the Child Neurology Society. Practice parameter: Corticosteroid treatment of Duchenne dystrophy: Report of the Quality Standards Subcommittee of the American Academy of Neurology and the Practice Committee of the Child Neurology Society. Neurology 64(1), 13–20. Neilson, S., Gunnarsson, L.G. & Robinson, I. (1994). Rising mortality from motor neuron disease in Sweden 1961–1990: The relative role of increased population life expectancy and environmental factors. Acta Neurol Scand 902, 150–159. Nelson, L.M., Matkin, C., Longstreth, W.T., Jr. & McGuire, V. (2000a). Population based case control study of amyotrophic lateral sclerosis in western Washington state: II. Diet. Am J Epidemiol 151, 164–173. Nelson, L.M., McGuire, V., Longstreth, W.T., Jr. & Matkin, C. (2000b). Population based case control study of amyotrophic lateral sclerosis in western Washington state: I. Cigarette smoking and alcohol consumption. Am J Epidemiol 151, 156–163. Oosterhuis, H.J.G.H. (1989). The natural course of myasthenia gravis: A long term follow up study. J Neurol Neurosurg Psychiatr 52, 1121–1127. Pasinetti, G.M., Ungar, L.H., Lange, D.J.,Yemul, S., Deng, H., Yuan, X., Brown, R.H., Cudkowicz, M.E., Newhall, K. & Peskind, E. et al. (2006). Identification of potential CSF biomarkers in ALS. Neurology 66, 1218–1222. Parsons, E.P., Clarke, A.J., Hood, K., Lycett, E. & Bradley, D.M. (2002). Newborn screening for Duchenne muscular dystrophy: A psychosocial study. Arch Dis Child 86, F91–F95. Paul, R.H., Cohen, R.A., Gilchrist, J.M., Aloia, M.S. & Goldstein, J.M. (2000). Cognitive dysfunction in individuals with myasthenia gravis. J Neurol Sci 179, 59–64.

Recommended Resources

Plauche, W.C. (1983). Myasthenia gravis. Clin Obset Gynecol 26, 592–604. Ranganathan, S.,Williams, E., Ganchev, P., Gopalakrishnan,V., Lacomis, D., Urbinelli, L., Newhall, K., Cudkowicz, M.E., Brown, R.H. & Bowser, R. (2005). Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis. J Neurochem 95(5), 1461–1471. Riggs, J.E. (1990). Longitudinal gompertzian analysis of amyotrophic lateral sclerosis mortality in the U.S., 1977–1986: Evidence for an inheritently susceptible population subset. Mech Ageing Dev 55, 207–220. Schreiber, S.L. & Crabtree, G.R. (1992). The mechanism of action of cyclosporine A and FK506. Immunol Today 13, 136–142. Schymick, J.C., Scholz, S.W., Fung, H.-C., Brittan, A., Arepalli, S., Gibbs, R., Lombardo, F., Matarin, M., Kasperaviciute, D. & Hernandez, D.G. et al. (2007). . Lancet Neurol 6, 322–328. Simmons, Z. (2005). Management strategies for patients with amyotrophic lateral sclerosis from diagnosis through death. Neurologist 11(5), 257–270. Skuk, D., Goulet, M., Roy, B., Chapdelaine, P., Bouchard, J.-P., Roy, R., Dugre, F.J., Sylvain, M., Lachance, J.-G. & Deschenes, L. et al. (2006). Dystrophin expression in muscles of Duchenne muscular dystrophy patients after high density injections of normal myogenic cells. J Neuropathol Exp Neurol 65(4), 371–386. Souroujon, M.C., Maiti, P.K., Feferman, T., Im, S.H., Raveh, L. & Fuchs, S. (2003). Suppression of myasthenia gravis by antigen-specific mucosal tolerance and modulation of cytokines and costimulatory factors. Ann N Y Acad Sci 998, 533–536. Stambler, N., Charatan, M. & Cedarbaum, J.M. (1998). Prognostic indicators of survival in ALS. Neurology 50, 66–72. Strong, M.J., Kesavapany, S. & Pant, H.C. (2005). The pathobiology of amyotrophic lateral sclerosis: A proteinopathy? J Neuropathol Exp Neurol 64(8), 649–664. Tarner, I.H., Slavin, A.J., McBride, J., Levicnik, A., Smith, R., Nolan, G.P., Contag, C.H. & Fathman, C.G. (2003). Treatment of autoimmune disease by adoptive cellular gene therapy. Ann N Y Acad Sci 998, 512–519.

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Traverso, M., Lamnati, M., Minetti, C., Regis, S., Tedeschi, S., Pedemonte, M., Bruno, C., Biassoni, R. & Zara, F. (2006). Multiplex real-time PCR for detection of deletions and duplications in dystrophin gene. Biochem Biophysiol Res Comm 339, 145–150. Tuzun, E., Saini, S.S.,Yang, H., Alagappan, D., Higgs, S. & Christadoss, P. (2006). Genetic evidence for the involvement of Fcg receptor III in experimental autoimmune myasthenia gravis pathogenesis. J Neuroimmunol 174, 157–167. van Es, M., Van Vught, P.W., Blauw, H.M., Franke, L., Saris, C.G., Andersen, P.M., Van Den Bosch, L., de Jong, S.W., van’t Slot, R., Birve, A., et al. (2007). ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: A genome-wide association study. Lancet Neurol, Published on-line September 7, 2007. Vincent, A. (2005). Mechanisms in myasthenia gravis. Drug Discov Today Dis Mech 2(4), 401–408. Vincent, A., Palace, J. & Hilton-Jones, D. (2001). Myasthenia gravis. Lancet 357, 2122–2128. Williams, D.B. & Windebank, A.J. (1991). Motor neuron disease (amyotrophic lateral sclerosis). Mayo Clin Proc 66, 54–82. World Federation of Neurology Research Group on Neuromuscular Disease (1994). El Escorial World Federation of Neurology criteria for the diagnosis of amyotrophic lateral sclerosis. J Neurol Sci 124, 96–107. Worms, P.M. (2001). The epidemiology of motor neuron diseases: A review of recent studies. J Neurol Sci 191, 3–9. Zalaudek, I., Bonelli, R.M., Koltringer, P., Reiscker, F. & Wagner, K. (1999). Early diagnosis in Duchenne muscular dystrophy. Lancet 353, 1975. Zhang, G., Ludtke, J.J., Thioudellet, C., Kleinpeter, P., Antoniou, M., Herweijer, H., Braun, S. & Wolff, J.A. (2004). Intraarterial delivery of naked plasmid DNA expressing full-length mouse dystrophin in the mdx mouse model of Duchenne muscular dystrophy. Hum Gene Ther 15, 770–782.

RECOMMENDED RESOURCES http://www.neuro.wustl.edu/neuromuscular/ Maintained by Alan Pestronk, MD at Washington University in St. Louis, MO; this site offers a comprehensive summary of all neuromuscular diseases including clinical features and laboratory findings searchable by disease, category, clinical, or laboratory features. www.genetests.org Funded by the National Institute of Health and maintained by University of Washington in Seattle, WA this website offers “geneReviews” for over 356 conditions. Each review is written by an expert in the field and peer reviewed. Appropriate for health professionals and students. In addition this website offers a search function to identify laboratories offering testing for over 1200 genetic diseases. http://www.wellstone-dc.org This website, maintained by the Research Center for Genetic Medicine at Children’s National Medical Center, describes some of the current

research into neuromuscular diseases (funded by the Wellstone Muscular Dystrophy Centers Grant) as well as lectures from experts in the field on topics such as “Muscular Dystrophy Targets” and “Drug Discovery for the Muscular Dystrophies”. www.mdausa.org The Muscular Dystrophy Association is a national organization which provides funding for research and aids people affected with neuromuscular diseases by providing funding for care, information, and emotional support. The MDA’s webpage offers disease summaries as well as links to research. www.alsa.org The ALS Association is a national organization dedicated to the fight against ALS. This website offers information on ALS for patients and family members, as well as practice parameters for health care professionals. Information on current research is also available.

CHAPTER

104 Psychiatric Disorders Stephan Züchner and Ranga Krishnan

INTRODUCTION Genetic factors play a fundamental role in the genesis of many mental disorders. With the sequence of the human genome now available, the majority of common variation identified, and new high-throughput technologies arriving in academic research laboratories it is widely expected that genes will be identified explaining a large proportion of the risk to develop mental disorders. The identification of the underlying genetic variation will, and already does, transform parts of psychiatry toward a neuroscience-based discipline. The modeling of mental disorders from genes, to cell culture and animals, to functional imaging and electrophysiologic studies produces new synergies and significantly enhances the discovery process. Genetic variations associated with mental disorders should provide new diagnostic tools and allow for preventive clinical interactions. Risk genes will likely provide new targets for drug development and it is anticipated that elements of personalized medicine will enter clinical psychiatry. Giving this prospect the enthusiasm to find the underlying genes is very high. Thomas Insel, the NIMH director, optimistically suggested to refer to the current decade as the “decade of discovery” (Insel and Quirion, 2005). However, the field of psychiatric genetics has not lived up to these high expectations, yet. In this chapter a variety of aspects of genomic medicine is discussed in regard to psychiatry. Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1282

GREAT PROSPECT, BUT ARE WE THERE YET? Although many psychiatric conditions have a strong heritability any major genes have not yet been identified. This comes of surprise given that medicine is in a “gold rush” of discovering new genes on a weekly basis. For example, dozens of genes have been identified for neurodegenerative diseases, including Alzheimer disease, Parkinson disease, and motor neuron disorders. The discussion for the underlying reasons ranges from an especially complex entanglement of multiple genetic and environmental factors to the impracticality of the current clinical phenotyping system for genetic studies. It is of course important to realize that genetics for complex diseases has limitations and might never be able to deliver a binary diagnosis for an illness such as schizophrenia. Nonetheless, genetic effects have been established for a number of genes, mainly from monoamine pathways. For instance, the serotonergic and norepinephrine systems are targets for antidepressant drugs and genetic variation in the serotonin neurotransmitter system contributes with no doubt to the risk of psychiatric disorder (Coppen and Wood, 1982; Kalia, 2005). However, it is likely that significant genes for mental disorders are not confined to the neurotransmitter system. Indeed, recent findings based on positional mapping strategies found a number of interesting genes that would not have been considered as obvious candidates (Owen et al., 2005). These findings Copyright © 2009, Elsevier Inc. All rights reserved.

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have prompted several functional studies on putative genes for schizophrenia. Therefore, whole genome-based strategies might be a more successful approach in psychiatric genetics than the candidate gene strategy. The available genomic analysis tools have improved dramatically over the last few years. The human genome is available at a very high accuracy, tagging singlenucleotide polymorphism (SNP) based association studies are now a standard, and whole genome association studies on a single chip are within reach for many academic laboratories. New approaches including enhanced expression studies, metabolomics, and brain imaging might provide an alternative or complementary means of phenotyping for future genetic studies.

From a purist genetic point of view, the identification of large, clinically well-characterized pedigrees are still the gold standard for gene mapping via linkage analysis. New methods have been developed in recent years to take advantage of dense and relatively cost-effective genotyping of SNPs across the entire genome. These latter approaches usually require large sample sizes (1000 cases and controls), in order to dissect the genetic heterogeneity in the presence of small odd’s ratios for each susceptibility gene. Within the next few years we will know whether the whole genome association approach has the power to advance the understanding of the complex genetics of psychiatric disease.

CLASSIFICATION RECONSIDERED

HOW COMPLEX CAN IT BE?

With the establishment of the Diagnostic and Statistical Manual of Mental Disorders (DSM) as the standard diagnostic reference during the last 25 years, psychiatry has increasingly attracted the interest of neuroscientists, geneticists, and brain imagers. A standardized nomenclature is the foundation of comparative empiric experimentation and promises the identification of biomarkers, genes, or structural brain equivalents for disease. This intricate classification system is accepted and practiced around the world. A reliable phenotyping system is the foremost condition for human genetic studies. Since the high expectations in psychiatric genetics have not yet yielded the awaited success, the current classification is object of a renewed debate. It has been argued that the manual fails to identify the underlying molecular structure of mental disorder (McHugh, 2005). Clinical phenotypes might not represent the involved genotypes well enough, which is a huge burden for any genetic study. The field has developed a validated series of instruments for psychiatric phenotyping such as the diagnostic interview for genetic studies (DIGS) and family interview for genetic studies (FIGS); yet these and others can be cumbersome to administer (Nurnberger, Jr. et al., 1994). The more subtle phenotypes of individuals may be difficult to assign accurately. Intelligent principles as to why certain mental disorders share characteristics with some but not with others cannot be derived from this classification. The discussion circles around the question whether a nominalist or essentialist approach would serve psychiatry better as a neuroscience discipline (Krishnan, 2005). Should psychiatric disorder be defined by more fundamental characteristics as suggested by McHugh or is it simply premature to raise this question? (Krishnan, 2005), (McHugh, 2005). Several DSM disorders have been found to co-locate among the relatives of individuals with schizophrenia, major depression, or bipolar affective disorder. In addition several genetic susceptibility loci appear to be common to more than one DSM defined disorder. For example, the chromosomal locus on 18p11 has been independently identified in linkage analysis studies of bipolar and schizophrenia samples (Berrettini, 2000). In any case, the identification of strong genetic effects will have the potential to deliver new arguments for this discussion.

Mental disorders are considered genetically complex with many genes involved possibly acting in an additive manner. In addition, twin studies and epidemiological research indicate that environmental factors modulate the genetic vulnerability toward the development of mental illness. Derived from successful genetic studies in neurodegenerative disorders, it is likely that dozens if not hundreds of genes are involved in mental disorders. Thus, the effect of each single gene might be rather small, although a few major genes are likely to exist. The detection of a significant effect for a minor gene and/or interaction between several genes does require a large sample of patients and controls. The analysis of several thousand DNA samples on a genomewide scale is still a difficult and costly task to achieve with today’s technology. The high rate of findings not confirmed in other studies is (in part) attributable to factors such as sample size, differences in genomic structure (population stratification), environmental exposures (e.g. culture), and diagnostic criteria (phenotyping). Meta-analyses of candidate genes for mental disorders have confirmed overall significance for a number of genes, but the effect on the vulnerability to develop disease is generally small. An example of the successful exploration of candidate genes is the serotonergic system. Considering the success of drugs based on selective serotonin uptake inhibitors (SSRI), much effort has been aimed at the genetics of the serotonergic system. The protein that regulates serotonin uptake into cells, 5-hydroxytryptamine transporter (5-HTT) or SLC6A4, is localized in dendrites of serotonin-releasing neurons. Meltzer et al. demonstrated that the serotonin uptake velocity in platelets is inherited and was low in unipolar and bipolar depressed patients as well as schizoaffective depressed subjects (Meltzer and Arora, 1988; Meltzer et al., 1981). Later, an insertion/deletion polymorphism in the promoter region of 5-HTT has been shown to be associated with personality traits such as anxiety, depression, and aggressiveness (Lesch et al., 1996). The shorter “S” variant causes lower 5-HTT expression than the long “L” allele (Heils et al., 1996). In their now classic study of gene × environment interaction in depression, Caspi et al. evaluated in a longitudinal study design, how life stresses will precipitate depression

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depending on the inherited 5-HTT alleles (Caspi et al., 2003). They determined whether subjects had undergone stressful life events and whether they had experienced a major depressive episode. The effects of life events on depressive symptoms were significantly stronger among SS and SL subjects than among LL subjects. Studies in mice and rhesus macaques and imaging studies of humans supported the connection between 5-HTT alleles and stress (Bennett et al., 2002; Hariri et al., 2002; Murphy et al., 2001). Serotonin synthesis in the brain is mainly controlled by tryptophan hydroxylase 2 (TPH2) (Zhang et al., 2004). Rare loss-of-function mutations in TPH2 have been associated with unipolar major depression (Zhang et al., 2005). Although other groups had difficulty confirming these findings (Blakely, 2005), a recent study identified a risk haplotype in TPH2 that was associated with suicide attempt and major depression in four populations (Zhou et al., 2005). These examples illustrate the difficulties one faces to unravel the underling genetic factors in psychiatry; but it also shows how the evaluation of pathways will yield interesting insights into the relevant molecular mechanisms.

THE VALUE OF RARE GENETIC VARIATION There is a real possibility that some genes for mental disorder act in a classic Mendelian fashion, e.g. autosomal dominant. Such genes would likely contribute to only a small proportion of metal disorders, but much could be gained by identifying new molecular pathways. For example, in Alzheimer disease, the early-onset forms are mainly caused by many different mutations in three genes: presenilins 1 and 2, and amyloid beta precursor protein. A wealth of studies in many disciplines followed these discoveries. In psychiatry, clinical and epidemiologic studies have confirmed that the families of probands are often enriched with mental illness. The study of such extended families have allowed for a number of classic linkage studies and the establishment of chromosomal loci. Although “segregation” of disorders in these pedigrees is often confounded because it may include a variety of conditions ranging from affective disorder, anxiety disorder, to substance abuse. Nevertheless, established chromosomal loci allow for selection of candidate genes based on positional information rather then pure hypothesis-driven criteria. Such genes, when identified, hold the promise to establish new pathways relevant for mental disorders. Interestingly, a number of chromosomal aberrations have been identified in schizophrenia patients and some of the best candidate genes are associated with these loci. A chromosomal translocation t(1;11)(q42;q14.3) in a single family segregated with mental illness that included schizophrenia and affective disorders (Blackwood et al., 2001). This translocation generated LOD scores between 3.6 and 7.1 depending on whether segregation of phenotypes was restricted to schizophrenia, affective disorders, or both. Subsequently, evidence was presented that the gene disrupted in schizophrenia 1 (DISC1) that is located in this

region causes schizophrenia by affecting neuronal functions, such as neuronal migration, neurite architecture, mitochondrial function, and intracellular transport (Millar et al., 2005a), (Miyoshi et al., 2003). The Ser704Cys change in DISC1 has also been associated with reduced hippocampal gray matter and cognitive function in healthy subjects (Callicott et al., 2005), (Thomson et al., 2005). An alternative translocation t(1;11)(q42;q14) has been identified in a small Scottish family that co-segregated with schizophrenia. This chromosomal aberration, disrupted phosphodiesterase 4B (PDE4B), a gene that has been shown to cause behavioral changes in mice and fruit flies (Millar et al., 2005b). A study by Abelson et al. identified in a Tourette’s syndrome patient a small chromosomal inversion at 13q31.1. This chromosomal area contained the gene slit and trk like 1 (SLITRK1) (Abelson et al., 2005). The authors also identified a frame-shift mutation and two microRNA target site mutations in SLITRK1 in additional families. All these changes were extremely rare and would not have been identified in association studies. This paper was celebrated by Science as one of the top ten scientific breakthroughs in 2005. Additional missense mutations in SLITRK1 have recently been identified by our group in trichotillomania patients, a disorder that also belongs to the so-called “obsessive– compulsive spectrum” of phenotypes. (Zuchner et al., 2006). This example illustrates the dramatic clinical overlap between DSM-based disorder categories that will be revealed by genetic studies. Finally, the chromosome 22q11 deletion syndrome encompasses velocardiofacial syndrome, DiGeorge syndrome, and conotruncal anomaly face syndrome. The variable phenotype on 22q11 also includes schizophrenia, mood disorder, anxiety, attention deficit, autistic, and substance abuse disorders (Pulver et al., 1994). Thus, genes located at this chromosomal locus are promising candidates for schizophrenia. Current studies favor the genes catechol-O-methyltransferase (COMT), proline dehydrogenase (PRODH), and DHHC-type containing 8 zinc finger (ZDHHC8).

CONVERGING METHODS It is now a general conception that the convergence of several methods will greatly enhance the ability to pin down the underlying pathways in psychiatric disorder. Some of the methods that are expected to have major relevance are discussed. Novel genetic/genomic methods A highly accurate version of the human genome is now available to the scientific community. Of the ~23,000 genes it is estimated that more than 80% are expressed in brain. Most genes code for a number of isoforms, which significantly increases the number of different translated proteins. The mapping of expression pattern of the whole transcriptome is now readily available via hybridization arrays (DNA chip). More advanced versions of these arrays will include all known isoforms. The mapping of expression in postmortem human brain and animal models

Personalized Medicine

under different paradigms will potentially narrow the long list of candidate genes for mental disorder. Regulatory sites in genes are of major importance for disease development, including promoter, splicing sites, transcription factor binding sites, and microRNA target sites. The comprehensive mapping of all regulatory sites joined with genomic variation data will greatly enhance the evaluation of candidate genes. Further development of bioinformatic prediction algorithms will play a major role. The HapMap project was finished in November 2005. The evaluation of the allele frequencies of a large number of SNPs in different populations is providing a powerful new tool. The use of haploblocks for association studies has become a standard in the last few years. Whole genome association DNA chips are largely based on this knowledge too. SNPs and microsatellites are the best studied types of genomic variation. Recent evidence suggests that small chromosomal aberrations such as small deletions/amplifications, inversions, etc. are a common feature in the human genome and can be associated with disease. Comparative genomic hybridization is a new method that allows for array based whole genome screening for these variations but its resolution is limited. As discussed above, chromosomal aberrations have already proved as valuable targets in schizophrenia and Tourette’s syndrome. All these methods can be converged onto chromosomal linkage regions in order to identify candidate genes for in-depth evaluation. Such multi-tiered approaches will become the gold standard in genetic research of mental disorders. Imaging The phenotypic expression of genetic variation upon brain structure and function is becoming accessible via modern imaging techniques. For example, using functional MRI (fMRI), Bookheimer et al. studied memory processes in people with and without genetic risk factors for dementia (Bookheimer et al., 2000). Healthy elderly individuals, who carried the epsilon 4 allele of the apolipoprotein E gene (APOE-4), which is associated with an increased risk of developing Alzheimer’s disease, showed increased activation of the hippocampus, the parietal cortex, and prefrontal cortex structures implicated in memory function. When a subgroup of subjects was tested two years later, their decline in memory performance correlated with the degree of base line brain activation. These results suggest that fMRI can detect subtle neural changes even prior to the development of clinically apparent memory deficits (Bookheimer et al., 2000). Volumetric neuroimaging in unipolar depression suggests abnormalities in the frontal lobe, basal ganglia, cerebellum, and hippocampus/amygdala complex (Beyer and Krishnan, 2002). In bipolar disorder, abnormalities in the third ventricle, frontal lobe, cerebellum, and possibly the temporal lobe are noted (Beyer and Krishnan, 2002). Anatomically abnormal orbital frontal regions and basal ganglia have been reported in obsessive–compulsive disorder, the temporal lobe was found to be reduced in size in panic disorder, and abnormal hippocampus shrinkage was shown

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in posttraumatic stress disorder (Brambilla et al., 2002). Magnetic resonance imaging (MRI) studies often have limited sample sizes and a cross-sectional design. Longitudinal MRI studies accompanied by genetic studies or larger sample sizes could provide powerful tools (see also Chapter 47). Metabolomics Metabolomics allows for measurement of global metabolite profiles in organic samples. Metabolites are the result of the interaction of the system’s genome with its environment. They are not merely the end product of gene expression but form part of the regulatory system in an integrated manner (Rochfort, 2005). Metabolomics is fast gaining attention with the majority of the papers in this field having been published only in the last 2 years. An early metabolomics study in schizophrenia used human brain tissue and revealed that half of the altered proteins were associated with mitochondrial function and oxidative stress responses (Prabakaran et al., 2004). Cluster analysis of transcriptional alterations were able to differentiate 90% of schizophrenia patients from controls and confounding drug effects could be ruled out (see also Chapter 15). Systems biology Systems biology is a new field that seeks to integrate different levels of information to understand how biological systems function (e.g., gene and protein networks involved in cell signaling, metabolic pathways, organelles, cells, physiological systems, organisms, etc.). By studying the relationships and interactions between various levels it is hoped that eventually an understandable model of a whole system can be developed. Originally introduced in cancer biology, systems biology is expected to gain a more prominent role in unraveling the causes of complex diseases (Liu et al., 2006; Lunshof, 2006). The now available large-scale gene, protein and metabolite measurements dramatically accelerates hypothesis generation and testing in disease models. Computer simulations integrating knowledge on different level will help prioritize targets for in-detail study (Butcher et al., 2004).

PERSONALIZED MEDICINE The development of genomic medicine and genetic testing has greatly advanced the diagnosis of relatively rare diseases. However, the impact of genetic has limited relevance in medicine and clinical psychiatry. This might change in the near future when strong genetic effects will be discovered that predict the susceptibility to develop mental disorder. In the best case scenario, clinical psychiatrists will be able to derive risk, course, and outcome for their patients from such tests. Psychiatry is the focus of another application of genome medicine – pharmacogenetics. Pharmacogenetics is defined as the study of the variability of drug response due to heredity (Pirmohamed, 2001). In fact the Food and Drug Administration (FDA) has recently approved a first test kit (AmplichipCYP450, Roche) that genotypes two cytochrome P450 genes (CYP2D6

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and CYP2C19). This product takes advantage of established gene chip technology and measures about 15,000 DNA features in those two genes simultaneously. The two genes have long been studied for their role in metabolizing many known drugs (Table 104.1), including commonly prescribed antipsychotics and antidepressants. This test allows for identification of so-called ultrarapid and poor metabolizers. Genetic variations in CYP2D6 and CYP2C19, among others, determine our metabolic enzyme activity, which will lead to differences in how efficient drugs in different people. Patients on the extreme sides of the spectrum either do not react to the given drug or they are in danger to overreact and to develop severe side effects (de Leon et al., 2006). Nevertheless, the genotypes in CYP2D6 and CYP2C19 account for only a fraction of ultrarapid and poor metabolizers. Also, certain drugs, called pro-drugs, cause the opposite phenomenon: ultrarapid metabolizers may suffer adverse events and poor metabolizers may not respond. Obviously, additional genetic factors play a role in these phenomena. Table 104.1 lists groups of drugs that have been implicated in CYP2D6 and CYP2C19 dependent metabolism. The table is based on a more detailed list and excellent web resource available from the Division of Clinical Pharmacology, Department of Medicine, Indiana University (http://medicine.iupui.edu/flockhart/). Interestingly, the relevant drugs are not confined to psychiatry, but are of interest to the whole of medicine. Beyond this so-called “safety pharmacogenetics” there will be “efficacy pharmacogenetics” that is expected to determine the best drug for a given patient (de Leon et al., 2006). An increasingly interesting approach will be to utilize drug response as a phenotype and then apply genome-wide association and/or linkage analyses. In order to obtain the necessary sample size for

TABLE 104.1

such an approach, future multi-center clinical trials have to build in genetic approaches from the beginning and rather than to treat them like a fancy add-on. There is every reason to expect tremendous heterogeneity underlying individual drug responses. Ethical issues might come up for genetic variation that confers susceptibility that is predominantly present only in specific ethnic groups. Pharmacogenetics has a long way to go, but inevitably, genetic testing in conjunction with pharmacotherapy will be part of the future of medicine.

CONCLUSION Although psychiatric genetics is characterized by unprecedented efforts to identify the underlying genetic basis very few “good” candidates have emerged – none of them explaining a major portion of the respective disorder. This is in contrast to most other field in medicine and neurology, where large numbers of genes have been identified. With the availability of new methods for genetic analysis on the genome level studies are within reach that were unthinkable of a few years ago. Whether psychiatric diseases are especially “complex”, the clinical classification system does not represent the underlying pathology, or for other reasons, the current efforts are likely to succeed. The prospect of alternative assessment of disorder in the psychiatric practice has the potential to transform this discipline. The public perception of metal disorders might significantly shift when major genetic effects are identified and their testing routinely applied. Aspects of genomic and personalized medicine will significantly benefit the patient afflicted with a mental disorder.

Clinically relevant drug substrates for metabolism based on published evidence

CYP2 D6 Beta-Blockers

Antidepressants

Antipsychotics

Others

Carvedilol

Amitriptyline

Haloperidol

Atomoxetine

Metoprolol

Clomipramine

Risperidone

Codeine

Propafenone

Desipramine

Thiroridazine

Dextromethorphan

Timolo

Imipramine

Flecainide

Mexiletine

Paroxetine

Ondansetron

Venlafaxine

Tamoxifen Tramadol

CYP2C19 Proton Pump Inhibitors

Anti-epileptics

Antidepressants

Others

Omeprazole

Diazepam

Amitriptyline

Cyclophosphamide

Lansoprazole

Phenytoin

Clomipramine

Progesterone

Pantoprazole

Phenobarbitone

References

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Lesch, K.P., Bengel, D., Heils, A., Sabol, S.Z., Greenberg, B.D., Petri, S., Benjamin, J., Muller, C.R., Hamer, D.H. and Murphy, D.L. (1996). Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274(5292), 1527–1531. Liu, E.T., Kuznetsov, V.A. and Miller, L.D. (2006). In the pursuit of complexity: systems medicine in cancer biology. Cancer Cell 9(4), 245–247. Lunshof , J. (2006). Teaching and practicing pharmacogenomics: a complex matter. Pharmacogenomics 7(2), 243–246. McHugh, P.R. (2005). Striving for coherence: psychiatry’s efforts over classification. JAMA 293(20), 2526–2528. Meltzer, H.Y. and Arora, R.C. (1988). Genetic control of serotonin uptake in blood platelets: a twin study. Psychiatry Res 24(3), 263–269. Meltzer, H.Y., Arora, R.C., Baber, R. and Tricou, B.J. (1981). Serotonin uptake in blood platelets of psychiatric patients. Arch Gen Psychiatry 38(12), 1322–1326. Millar, J.K., James, R., Christie, S. and Porteous, D.J. (2005a). Disrupted in schizophrenia 1 (DISC1): subcellular targeting and induction of ring mitochondria. Mol Cell Neurosci 30(4), 477–484. Millar, J.K., Pickard, B.S., Mackie, S., James, R., Christie, S., Buchanan, S.R., Malloy, M.P., Chubb, J.E., Huston, E., Baillie, G.S. et al. (2005b). DISC1 and PDE4B are interacting genetic factors in schizophrenia that regulate cAMP signaling. Science 310(5751), 1187–1191. Miyoshi, K., Honda, A., Baba, K., Taniguchi, M., Oono, K., Fujita, T., Kuroda, S., Katayama, T. and Tohyama, M. (2003). DisruptedIn-Schizophrenia 1, a candidate gene for schizophrenia, participates in neurite outgrowth. Mol Psychiatr 8(7), 685–694. Murphy, D.L., Li, Q., Engel, S., Wichems, C., Andrews, A., Lesch, K.P. and Uhl, G. (2001). Genetic perspectives on the serotonin transporter. Brain Res Bull 56(5), 487–494. Nurnberger, J.I., Jr, Blehar, M.C., Kaufmann, C.A., York-Cooler, C., Simpson, S.G., Harkavy-Friedman, J., Severe, J.B., Malaspina, D. and Reich, T. (1994). Diagnostic interview for genetic studies. Rationale, unique features, and training NIMH Genetics Initiative.. Arch Gen Psychiatr 51(11), 849–859. Owen, M.J., Craddock, N. and O’Donovan, M.C. (2005). Schizophrenia: genes at last?. Trend Genet 21(9), 518–525. Pirmohamed, M. (2001). Pharmacogenetics and pharmacogenomics. Br J Clin Pharmacol 52(4), 345–347. Prabakaran, S., Swatton, J.E., Ryan, M.M., Huffaker, S.J., Huang, J.T., Griffin, J.L.,Wayland, M., Freeman,T., Dudbridge, F., Lilley, K.S. et al. (2004). Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatr 9(7), 684–697, 643. Pulver, A.E., Nestadt, G., Goldberg, R., Shprintzen, R.J., Lamacz, M., Wolyniec, P.S., Morrow, B., Karayiorgou, M., Antonarakis, S.E. and Housman, D. (1994). Psychotic illness in patients diagnosed with velo-cardio-facial syndrome and their relatives. J Nerv Ment Dis 182(8), 476–478. Rochfort, S. (2005). Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. J Nat Prod 68(12), 1813–1820. Thomson, P.A., Harris, S.E., Starr, J.M., Whalley, L.J., Porteous, D.J. and Deary, I.J. (2005). Association between genotype at an exonic

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SNP in DISC1 and normal cognitive aging. Neurosci Lett 389(1), 41–45. Zhang, X., Beaulieu, J.M., Sotnikova, T.D., Gainetdinov, R.R. and Caron, M.G. (2004). Tryptophan hydroxylase-2 controls brain serotonin synthesis. Science 305(5681), 217. Zhang, X., Gainetdinov, R.R., Beaulieu, J.M., Sotnikova, T.D., Burch, L.H., Williams, R.B., Schwartz, D.A., Krishnan, K.R. and Caron, M.G. (2005). Loss-of-function mutation in tryptophan hydroxylase-2 identified in unipolar major depression1. Neuron 45(1), 11–16.

Zhou, Z., Roy, A., Lipsky, R., Kuchipudi, K., Zhu, G., Taubman, J., Enoch, M.A., Virkkunen, M. and Goldman, D. (2005). Haplotype-based linkage of tryptophan hydroxylase 2 to suicide attempt, major depression, and cerebrospinal fluid 5-hydroxyindoleacetic acid in 4 populations. Arch Gen Psychiatry 62(10), 1109–1118. Zuchner, S., Cuccaro, M.L., Trans Viet, K.N., Cope, H., Krishnan, R.R., Pericak-Vance, M.A., Wright, H.H., Ashley-Koch, A. (2006). Mutations in SLITRK1 cause Trichotillomania. Mol Psychiatr 11, 887–889.

CHAPTER

105 Genomics and Depression Brigitta Bondy

INTRODUCTION Depression is a highly prevalent, potentially life-threatening condition that affects hundreds of millions of people all over the world. It can occur at any age from childhood to late life and exerts a tremendous cost upon society. The psychopathological state involves a triad of symptoms with low or depressed mood, anhedonia and low energy or fatigue. Other symptoms, such as sleep and psychomotor disturbances, feelings of guilt, low selfesteem, suicidal tendencies as well as autonomous and gastrointestinal disturbances are also often present. Depression is not a homogenous disorder but a complex phenomenon with many subtypes. The differences in symptomatology range from mild symptoms to severe symptoms with or without psychotic features. Interactions with other psychiatric and somatic disorders are quite common. An interaction between depression and cardiovascular disease, with worsened prognosis after myocardial infarction and increased mortality rates, is especially well documented. The etiology of depression is multi-factorial, including non-genetic and genetic factors. As with most human complex disorders, the relationship between genes and the disorder is not straightforward but the result of complicated interactions between

Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

multiple genes and the environment. Similarly, the response to antidepressant medication varies among patients and is also at least partly influenced by genetic variants.

DIAGNOSIS, PREVALENCE AND COURSE OF DEPRESSION The clinical course of major depression (formerly unipolar depression) is characterized by one or more major depressive episodes without a history of manic, mixed or hypomanic episodes. According to the diagnostic criteria of DSMS-IV (American Psychiatric Association, 1994), five of the following symptoms have to be present for a minimum of two weeks: depressed mood, loss of interest or pleasure, significant alteration in weight and appetite, insomnia or hyposomnia, disturbances in psychomotor activity with either agitation or retardation, fatigue or loss of energy, feelings of worthlessness, diminished ability to think or concentrate and, last but not least, recurrent thoughts of death and suicidal ideation or acts. Recurrent episodes of major depressive disorders may differ in symptomatology and thus show pleomorphic manifestations within one individual (Oquendo et al., 2004).

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The lifetime prevalence of depression is between 10% and 20% in the general population worldwide, with a female to male ratio of about 5:2. Typically, the course of the disease is recurrent and most patients recover from major depressive episodes. However, a substantial proportion of the patients become chronic, and after 5 or 10 years of prospective follow-up, 12% and 7%, respectively, are still depressed (Keller et al., 1997). But patients who recover also have a high rate of recurrence, as approximately 75% of patients experience more than one episode of major depression within 10 years. There is a high level of comorbidity between anxiety and depressive disorders, with co-occurrence rates up to 60% (Gorman, 1996). This suggests that comorbid anxiety and depression are the rule rather than exception. Furthermore, there is high co-occurrence of neuroticism, which is characterized by dysphoria, tension and emotional reactivity and is often a premorbid personality structure and a robust predictor for future onset of depression. It is estimated that both anxiety and neuroticism share about 50% of the genetic factors with depression.

PATHOPHYSIOLOGICAL MECHANISMS Depressive disorders are complex phenomena in terms of symptomatology and multi-factorial in etiopathogenesis. Although the specific causes have not been elucidated in detail, there is overwhelming evidence that depression is caused by the interaction of multiple genetic risk factors and environmental and neurobiological factors (Mann and Currier, 2006) (Figure 105.1). Cross-influences can be seen between all these pathways, and genes contribute to all of them (Kendler et al., 2006).

neurotransmitters modulate many behavioral symptoms that are disturbed in depression, such as mood, vigilance, motivation, fatigue and psychomotor agitation or retardation. Abnormal function may arise from altered synthesis, storage or release of the neurotransmitters as well as from disturbed sensitivity of their receptors or sub-cellular messenger functions within the synapse (Figure 105.2). Transport proteins play a crucial role in neural transmission, as they reduce the availability of the neurotransmitters in the synaptic cleft and thus terminate their effect on pre- and postsynaptic receptors. It is also noteworthy that most of the available antidepressants have an impact on the 5-HT transporter. On the basis of recent evidence, which showed that the proteins of the postsynapse are involved in long-term adaptive mechanisms in response to altered transmission during disease or when drugs take effect, these proteins are now considered to be the main modulators of neuronal activity and pathophysiology of mental disorders (Manji and Duman, 2001). Newer hypotheses stress the importance of this adaptation or plasticity of neuronal systems and propose that depression could result from an inability to make the appropriate adaptive responses to stress or other aversive stimuli, and that antidepressants may act by correcting this dysfunction or by directly inducing the appropriate adaptive responses (Duman, 2004). Neurotrophic factors (e.g., the brain-derived neurotrophic factor (BDNF), and the BDNF receptor (trkB)) are involved in these processes and promote the function and growth of neurones in the adult brain that contain 5-HT.

Enzyme

Acute and chronic stress Somatic disorders Life style

Environmental and somatic factors

Genetically influenced susceptibility

Figure 105.1 depression.

Vesicle storage MAO

Presynaptic receptors

Neurotransmitter transporters

Monoamine hypothesis Neuroendocrine disturbances Signal transduction Morphologic alterations of brain Neurogenesis

Genetic susceptibility

Neurotransmitters

Metabolites

The Neurobiological Basis of Depression Most theories about the neurobiology of depression involve functional deficiencies of the brain monoaminergic transmitters, especially serotonin (5-HT) and norepinephrine (NE). Both

Neurobiological basis

Aminoacidprecursors

Presynapse

Hypothetical model for the etiology of

G-protein coupled receptors Effectors

Postsynapse

G-proteins

Second messengers Proteinkinases Gene expression

Figure 105.2 The signal transduction cascade in the synapse. All compartments may be disturbed, beginning with neurotransmitter production, storage and release within the presynapse, down to postsynaptic events, mediated via neurotransmitter– receptor coupling and the activation of postsynaptic effectors. According to recent knowledge, the long-term adaptation processes with regulation of gene expression are crucially involved in pathophysiology of disorders and drug response.

Pathophysiological Mechanisms

Despite these transmission processes within neurones,the stressresponse system involving the hypothalamus-pituitary-adrenal (HPA) axis is of paramount importance in the development of depression. One of the consistent findings in psychiatry is that a significant proportion of depressed patients hypersecretes hypothalamic-corticotropin-releasing hormone (CRH), which stimulates adrenocorticotropin (ACTH) secretion from the pituitary and finally leads to increased cortisol levels and inadequate glucocorticoid feedback (Holsboer, 2000). Furthermore, various stress hormones interact with the serotonergic system on several levels, as sustained CRH or cortisol overdrive down-regulate the serotonergic system in terms of turnover, activity of the neurons and alterations on the receptor basis (van Praag, 2005). Thus, a possible conclusion might be that disturbances of the 5-HT and stress hormone systems are of pathophysiological relevance in depression and are not merely an epiphenomenon of the condition. Non-Genetic Factors A detailed model of the etiology of major depression suggests that it results from the interaction of domains that act during development. These domains include not only neuroticism, low selfesteem and substance abuse, but also interpersonal difficulties such as low education, low social support and stressful life events (Kendler et al., 2006). The influence of chronic stress and adverse life events (acute stress) has been the subject of numerous investigations and most findings show an excess of severely threatening events prior to the onset of a depressive episode (Paykel, 2001). As such events do not always trigger depression, it has been suggested that certain people have a predisposition, probably on a genetic basis, toward adverse reactions. Furthermore, there are gender differences in the reaction to stress, with women being more susceptible to interpersonal stress and men to legal or work-related stressful life events (Kendler et al., 2001). A very important factor is early life stress, including childhood neglect or sexual abuse, both of which are major risk factors for the onset of depression in later life. An explanation for this delayed reaction could be an impact of stress on the formation of enduring effects on biological and psychological development (Mann and Currier, 2006) (see section: Genetics of emotional regulation and response to stress). Genetic Basis of Major Depression It is well known that depression runs in families. Numerous studies have documented that first-degree relatives of mood disorder patients have an approximately threefold risk of developing depression. There is further evidence that these relatives are also more susceptible to anxiety, substance abuse or social impairment compared to the offspring of non-depressed parents (Weissman et al., 2006). However, the familial loading could also be the result of shared environmental factors, thus suggesting that the vulnerability to depression could be due to nurture rather than nature. A number of studies comparing the prevalence of depression among mono- and dizygotic twins gave sufficient evidence that major depression (as a disease) is moderately heritable, and on the basis of these twin studies, estimated that heritability may be 40–50% (Malhi et al., 2000). Furthermore, there is no doubt that more

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than one single gene is responsible for the increased vulnerability and that considerable gene–gene and gene–environment interactions complicate the identification of relevant susceptibility genes (Levinson, 2005). Genetic Linkage Analyses As heritability of depression does not follow the Mendelian pattern, the available linkage analyses that investigated a major gene effect are less convincing, and genome scan results are not yet available. Nevertheless, in the last decade a number of different results were obtained for depression and related traits. Selected regions on chromosomes 3, 4, 6, 12, 15 and 18 (for review see Levinson, 2005), especially in combination with depression-related personality traits such as neuroticism and harm avoidance, have received support from more than one study. However, as is the case for all linkage analyses for complex disorders, one cannot predict which will be true positives in the long run and thus until now none of these chromosomal regions has really been accepted as a susceptibility gene for depression. As statistical power is one of the fundamental problems of most studies, the full impact of genetic linkage findings on the search for depression susceptibility genes might come from combined analyses of multiple datasets (Levinson, 2005). As major depression is such a complex disorder, association studies, despite all their well-known pitfalls, appeared to be more suitable than linkage analyses and it was hoped that this method might unravel minor effects of genes. The selection of candidate genes followed the neurobiological hypotheses of depression, which will be summarized briefly here. Association Studies with Different Candidate Genes Due to the prominent importance of the serotonergic system, association studies have so far focused on polymorphisms in genes encoding for the rate-limiting synthesizing enzyme tryptophan hydroxylase (TPH), the serotonin transporter (5-HTT), the different 5-HT receptors and the degrading enzyme, monoamine oxidase-A (MAOA). The majority of studies were carried out with the serotonin transporter, which is a crucial protein within the synapse as it cleaves 5-HT from the synaptic cleft and is the target protein of the selective 5-HT reuptake inhibitors and other antidepressants (see section: Psychopharmacogenetics). A polymorphism within the promoter region of the 5-HTT (5-HTTLPR), comprising a 44-base-pair insertion/deletion resulting in short (S) and long (L) alleles, was shown to affect the transcription and thus the function of the gene (Heils et al., 1995). Many studies followed the first findings that the S-allele is associated with anxiety (Lesch et al., 1996), but the results were discrepant and even meta-analyses yielded inconclusive results as to whether the Sallele confers a risk for depression. Methodological issues might be one explanation for the failure to find such an association, because in most studies the samples were small and clinically heterogeneous. In this respect it is remarkable that a recent study, which investigated a large and clinically well characterized sample, clearly demonstrated that the S-allele of the 5-HTTLPR was

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TABLE 105.1

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Genomics and Depression

Important association studies in major depression

Gene symbol

Gene

Result

BDNF

Brain derived neurotrophic factor

270CT, negative result

CRHR1

CRH receptor

One positive result, not replicated

DRD4

Dopamine receptor D4

48 bp repeat, questionable association

GNB3

G-protein 3

Questionable, partly replicated

5-HT1A

Serotonin receptor 1A

1019CG polymorphism affect serotonin system Predispose depression

5-HT2A

Serotonin receptor 2A

No association

5-HTTLPR

Serotonin transporter

S-allele associated with anxiety, neuroticism S-allele associated with depression in response to stress

TPH2

Tryptophan hydroxylase, brain specific

SNPs in introns 5 associated with depression

Adapted from Levinson, 2005.

significantly more frequent in depressed patients than in controls (Hoefgen et al., 2005). This finding could be independently replicated in a large cohort of the Spanish population (Cervilla et al., 2006). On the basis of these new and methodologically subtle studies, one might be tempted to speculate that the S-allele of the 5-HTTLPR might have a small but independent effect on the vulnerability for depression. A further interesting and promising gene of the serotonergic pathway is the recently identified, brain-specific TPH2 gene (Walther et al., 2003), which is the limiting enzyme in serotonin synthesis of the central nervous system (CNS). There is now evidence that a region in or around introns 5 of the TPH2 gene might be a susceptibility locus for depression. Zill and colleagues were the first to associate a haplotype in intron 5 with major depression (Zill et al., 2004a) and completed suicide (Zill et al., 2004b). Although the exact location and characterization of the relevant mutations have yet to be established, there are now three independent studies showing that haplotype blocks in regions overlapping with that reported by Zill et al. are associated with major depression, suicidality and bipolar disorder patients of different ethnic groups (Harvey et al., 2004;Van Den et al., 2006; Zhou et al., 2005). Many other genes of the serotonergic pathway were investigated by several groups, but although some positive results emerged the data were contradictory and even some meta-analyses did not give convincing evidence for a significant interaction between any of these investigated genes and the genetic liability for major depression (for review see Leonardo and Hen, 2006; Levinson, 2005). A summary of findings of genetic association studies is listed in Table 105.1. Genome-wide association studies could be very valuable and are currently being carried out, but the results are not yet available. Evaluating the results of both linkage and association studies it emerges that, besides the established heritability for depression, a susceptibility gene or set of genes could not be identified so

far. Beyond the fact that mood disorders are complex in nature, may represent a family of related but distinct conditions and are probably polygenic, the definition of the clinical phenotype of depression is a serious problem in molecular genetic studies. Although the diagnostic criteria seem to be clearly defined by the DSM-IV criteria, the co-occurrence of other psychiatric disorders, such as anxiety or alcohol/drug constitutes a major challenge in genetic studies. Genetics of Emotional Regulation and the Response to Stress The identification of biological mechanisms through which genes lead to differences in emotional behavior is paramount to understanding how genes confer risk for psychiatric disorders such as depression (Hariri and Holmes, 2006). As 5-HT is a key modulator of emotional behavior sub-serving anxiety and depression, the effects of gene variations in the serotonin transporter gene were investigated for their influence on temperament, character, and the regulation of emotion. After the first observation almost a decade ago (Lesch et al., 1996) that individuals carrying at least one S-allele of the 5-HTTLPR displayed higher levels of anxiety, neuroticism and harm avoidance, many studies were performed to confirm this observation. Although the results of the various studies were not consistent, meta-analyses have demonstrated a significant association between the S-allele and these personality traits (for review see Hariri and Holmes, 2006;Munafo et al., 2005). Emerging evidence from human and animal studies indicates that a relative loss in the 5-HTT gene function not only increases anxiety but also exerts a negative influence on the capacity to cope with stress, which further increases the risk for mood disorders. This was underlined by a study by Caspi et al., who investigated a representative cohort in a prospective, longitudinal study (Caspi et al., 2003). They could show that individuals with one or two copies of the S-allele exhibited more depressive symptoms, more diagnosable depression and more suicidality in relation to

Pharmacogenomics of Antidepressants

stressful life events than individuals who were homozygous for the L allele. This finding suggests that genetic variants may act to promote one’s resistance to environmental pathogens and that the S-allele moderates the “depressiogenic” influence of stressful life events. Similar studies followed and replicated these findings about an increased vulnerability to stress in S-allele carriers, and were further extended to the impact of the dopamine D4 receptor gene (Ebstein, 2006; Hariri and Holmes, 2006). In addition, the study by Grabe et al. (2005) demonstrated this gene–environment interaction in relation to the 5-HTTLPR genotypes in a cohort with severe mental and physical distress (e.g., myocardial infarction, stroke, diabetes and degenerative diseases). They found an interaction between the 5-HTTLPR S-allele and unemployment or chronic disease, but only in females. This finding goes beyond those of the previous studies, and indicates a higher mental vulnerability to social stressors and chronic disease. All these recent findings demonstrate that the 5-HTTLPR affects not only central 5-HTT function, but as a consequence seems also to be involved in the regulation of bio-behavioral characteristics. Recent studies have begun to investigate how the 5HTTLPR S-allele might mediate stress reactivity at the level of the neural pathway regulating emotion. The non-invasive method of neuroimaging was used to determine the effects of 5-HTTLPR on reactivity of the amygdala, a brain region that is crucial in mediating emotion, to stress. An amygdala hyper-reactivity to stress was shown in S-allele carriers in several ethnically different cohorts (Hariri and Holmes, 2006). Thus, these findings identify a possible neuroanatomical substrate for the negative affect associated with the S-allele in healthy volunteers.

PHARMACOGENOMICS OF ANTIDEPRESSANTS There is substantial unexplained interindividual variability in the response to treatment with psychoactive drugs, as a proportion of patients who receive a regular dose do not respond adequately or experience limiting side effects. Despite the availability of a vast variety of drugs, about 30–50% of patients are non-responders. The nature of drug response is a highly complex phenomenon involving genetic and non-genetic factors, the latter including age, gender, hepatic and renal status, nutrition, smoking and alcohol intake. Within the last decade, the concept of individualized drug therapy on the basis of genetic investigations has become a major issue in psychopharmacology. After early positive results there was much enthusiasm about the identification of a genetic make-up that would be ideal to optimally tailor drug treatment in psychiatry. Numerous association studies have been carried out since then, with genes coding for either the pharmacokinetic (i.e., the processes that influence bioavailability) or pharmacodynamic (targets of drug action) pathways. Most pharmacodynamic studies investigated candidate genes that were proposed either by the etiopathology of depression or by the putative pharmacological mechanisms of the drugs (Reynolds et al., 2006). However,

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despite some advances in various fields, the final goal of an optimally tailored therapy still remains elusive. Polymorphic Drug Metabolizing Enzymes and Pharmacokinetic Aspects All antipsychotics are subject to extensive metabolism by various enzymes of the cytochrome P-450 (CYP) family, which play a pivotal role in the elimination of these drugs and therefore influence their efficacy and toxicity. Factors that affect CYP function and expression, such as CYP pharmacogenetics and the processes of inhibition and induction, all influence the in vivo rates of drug elimination (Murray, 2006). Genetic variants in CYP enzymes constitute multi-allelic systems that express a variety of phenotypes. Patients with these phenotypes can be distinguished as poor (PM), intermediate (IM), extensive (EM) or ultrafast metabolizers (UM) (Brosen, 2004). The PMs lack an active form of the expressed enzyme due to an inactivating allelic variant, IMs have at least one copy of an active gene and UMs contain duplicated or amplified gene copies, thus leading to either increased, maybe toxic, or decreased, maybe ineffective, concentrations of the drug (Oscarson, 2003). Although many CYP enzymes are known, only a few of them are relevant for metabolism of psychoactive drugs, mainly the CYP1A2, CYP2D6, CYP2C19 and CYP3A4. Earlier studies focused on the interaction between CYP genotype, the plasma concentration of the drugs and the response to treatment. Although a relation between the CYP genotype and plasma concentration could be shown, a clear effect on drug response is missing. The processes of CYP induction and inhibition may have tremendous effects on drug elimination and thus on the incidence of adverse drug effects. Thus, inhibition of the metabolizing enzyme might convert an EM to a PM type. Since many if not most depressives are on a multi-drug regimen, there is a large potential for enzyme inhibitory reactions and an increased rate of adverse drug effects. Especially co-medication with antidepressants, but also with some antibiotics or -blockers, might considerably increase the plasma concentrations of the main drug (Table 105.2); this interaction is extremely important in those patients who are already PMs due to genetic variants (Grasmader et al., 2004). On the other hand, some drugs such as carbamazepine, phenytoin or hyperforin are known to up-regulate CYP expression in the liver; the respective substrates are then eliminated more rapidly via this enzyme induction (Grasmader et al., 2004). Together with environmental factors such as smoking and/or alcohol consumption, both of which are inducers of CYP activity, genetic factors might lead to different metabolism of a given drug. Pharmacodynamic Aspects: Response to Treatment The term pharmacodynamics encompasses all processes influencing the resulting effect, such as the interaction with target proteins or with mechanisms that are modulated by this interaction. The primary targets of antidepressants are known and are congruent with the etiological hypotheses. For this reason, mostly candidate genes from the monoaminergic pathway have so far been investigated.

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TABLE 105.2

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Major cytochrome P450 isoenyzmes (CYP), their substrates, enzyme inhibitors and inductors

Enzyme

Substrate

Inhibitor

Inducer

CYP1A2

TCAs (e.g., amitriptyline, clomipramine, imipramine), duloxetin, fluvoxamine, mirtazapine

Fluvoxamine, grapefruit juice, antibiotics

Carbamazepine, hyperforin, nicotine

CYP2C19

TCAs (e.g., amitriptyline, clomipramine, doxepin, imipramine, trimipramine); SSRIs (e.g., citalopram, fluoxetine, sertraline), moclobemide, venlafaxin,

Fluoxetine, valproic acid, fluvoxamine

Carbamazepine, phenytoin

CYP2D6

TCAs (e.g., amitriptyline, clomipramine, desipramine, imipramine, nortriptyline); SSRIs (e.g., fluoxetine, fluvoxamine, paroxetine),duloxetin, mianserin, venlafaxine,

Fluoxetine, paroxetine, citalopram, duloxetin, fluvoxamine, fluphenazine, moclobemide, haloperidol, perphenazin, propranolol, antibiotics

No inducer known

CYP3A4

TCAs (e.g., amitriptylin, clomipramin, imipramin), citalopram, reboxetine, venlafaxin,

Fluoxetine, fluvoxamine, olanzapine, grapefruit juice nicotine

Carbamazepine, hyperforin, phenytoin

One of the most consistent findings in pharmacogenetics is the involvement of the serotonin transporter (5-HTT), which is not only the initial target of the selective serotonin reuptake inhibitors (SSRIs), but is also affected by most of the other antidepressants. The functional variant in the 5’-regulatory region of this it was repeatedly shown in independent that the S-allele is associated with slower response to SSRIs such as paroxetine, citalopram and fluvoxamine, as demonstrated in more than two independent studies (for review see (Serretti et al., 2005). Interestingly, this polymorphism also seems to be involved in response to drugs beyond the SSRIs, for example lithium ions, which are widely used as a mood stabilizer (review (Binder and Holsboer, 2006). Other possible candidate genes of the serotonergic system were those coding for the different 5-HT receptors, the rate-limiting enzyme for the synthesis of tryptophan hydroxylase (TPH1), or monoamine oxidase (MAO), the degrading enzyme. Further investigated genes were of the norepinephrine and dopamine systems (Serretti and Artioli, 2004; Serretti et al., 2005), however, only a few consistent and replicated studies are available (Lefebvre et al., 2006) (see Table 105.3).

CURRENT CONCEPTS Signal Transduction and Neural Plasticity All the different genes coding for proteins which mediate the signaling pathway from neurotransmitter receptors via activation of kinases, transcription factors and finally the synthesis of proteins are good candidates both for pharmacogenetic investigations and for identifying susceptibility genes (see Figure 105.2). Heterotrimeric guanine nucleotide binding proteins (G-proteins), which represent essential regulatory components in transmembrane coupling in about 80% of the neurotransmitter receptors, with their internal second messenger systems

TABLE 105.3 Important genes tested for association with antidepressants drug response Gene symbol

Gene

Association with response

ACE

Angiotensin converting enzyme

Yes, not independently replicated

COMT

Catechol-Omethyltransferase

Yes, not independently replicated

CYP2D6

Cytochrome P450, family2, subfamily 6

Yes, independently replicated

DRD2

Dopamine receptor D2

No

FKBP5

FK506 binding protein

Yes, independently replicated

GNB3

G-protein 3

Yes, independently replicated

HTR1A

Serotonin receptor 1A

Yes, not independently replicated

HTR2A

Serotonin receptor 2A

Yes, independently replicated

MAOA

Monoamine oxidase A

No

HTT

Serotonin transporter

Yes, independently replicated

Adapted from Binder and Holsboer, 2006.

(adenylylcyclase and the phosphoinositide system), are thus key elements in the response to psychoactive drugs. But also secondary signaling mechanisms downstream from the receptors and G-proteins, such as the cAMP, phosphodiesterase, cAMP

Current Concepts

response element and cAMP binding proteins, are currently under investigation, although no clear associations have been reported yet (Licinio and Wong, 2005). In recent years increasing evidence was found that chronic treatment with antidepressants influences the expression of potential target genes, for example neurotrophic factors (BDNF factor), the BDNF receptor (trkB) or vesicle proteins (synapsin I-III, synaptophysin). These proteins are involved in neuronal or synaptic plasticity mechanisms, whereas the transcription factor CREB (cAMP response element binding protein) represents the link between the observed short- and long-term treatment effects (Duman, 2004). One of the targets of CREB is the upregulation of BDNF via the BDNF promoter, and a proposed mechanism for BDNF antidepressant action involves its effect on neural plasticity and survival (Paez-Pereda, 2005). Pre-clinical studies suggest that the expression of BDNF might be a downstream target of antidepressant treatment, and that BDNF exerts antidepressant activity in animal models of depression. Furthermore, BDNF protects against stress-induced neuronal damage, and might affect neurogenesis in the hippocampus, which is thought to be involved in the pathogenesis of mood disorders (Hashimoto et al., 2004). A common, functionally active polymorphism in the BDNF gene, the Val66Met polymorphism, has been repeatedly investigated in psychiatric and neurological disorders but the results are discrepant and do not allow any conclusion to be made about its participation in psychopathology (Rosa et al., 2006;Schumacher et al., 2005). Pharmacogenetic data are rare and do not really give a plausible indication of an interaction. The Stress Response System It has been postulated that the decreased BDNF levels seen in depressed patients may be secondary to increased cortisol levels, a phenomenon that has been repeatedly described in alterations of the stress-hormone regulation in affective disorders. Hyperactivity of the HPA-axis with elevated secretion of corticotrophin-releasing factor (CRF) and subsequently of cortisol, as well as a decreased glucocorticoid receptor (GR) sensitivity and disturbed feedback mechanisms, are well known (Holsboer, 2000). In this context, a recent study that investigated a possible association between genes regulating the HPA-axis and response to antidepressants and susceptibility for depression seems to be of importance. Several single nucleotide polymorphisms (SNPs) in genes regulating the HPA-axis activity were investigated in depressed patients and matched controls. A significant association between the response to antidepressants and SNPs in the FKBP5 gene, a GR-regulating co-chaperone of hsp-90, was found in two independent samples. Patients homozygous for the minor allele of the associated SNPs responded almost 2 weeks earlier to antidepressant drug treatment than patients with the other genotypes (Binder et al., 2004). Disturbances of the HPA axis are also mirrored by genetic findings concerning the angiotensin-converting-enzyme (ACE) gene. ACE is not only involved in blood pressure regulation but is also active within the CNS, where its primary function comprises degradation of neuropeptides, including bradykinin and

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substance-P. ACE is also supposed to modulate the regulation of the hypothalamic-pituitary-adrenal axis, thereby interacting with synthesis and production of neuroactive steroids (Moskowitz, 2002). Although an initial finding of an association of the Dallele of a functional insertion/deletion (I/D) polymorphism (D-allele associated with higher ACE levels and higher neuropeptide degradation capabilities) and depression (Arinami et al., 1996) could not be replicated, an association with response to antidepressant medication (independent of type of drug) or non-pharmacological treatment (Baghai et al., 2003, 2004, 2005) was observed. Moreover, the D-allele was associated with hyperactivity of the HPA-axis, determined by the combined Dex/ CRH test (Baghai et al., 2002). The possible importance of the ACE gene as a susceptibility gene for depression and stress reactions was further underlined by a recent study which demonstrated that an SNP in the promoter region (rs -4291 GT) was not only associated with major depression (in two large, independent samples), but also with the serum ACE concentration and with hypercortisolism during the acute state of the disorder (Baghai et al., 2006). This finding is important as it suggests that the ACE gene might be a missing link between affective disorders and cardiovascular diseases (Bondy et al., 2002). Gene and Protein Expression Studies New techniques such as proteomics and the cDNA micro-arrays represent prominent expression profiling techniques to investigate multi-factorial and polygenic complex traits. These techniques allow to study the expression of thousands of genes or proteins in one experiment and are thus powerful instruments for the analysis of pathophysiological mechanisms and for the search for new drug targets (Bertilsson et al., 2002). Most of the work currently carried out covered pharmacogenomic aspects and a recent review demonstrated certain novel candidate genes that may underlie the mechanism of action of antidepressants. Among them are genes affecting neurogenesis (via the HPA axis and related neuroendocrine systems, the cAMP messenger system as well as phopshorylation of CREB), neurotransmitter release and neurite outgrowth via cysteine string protein, as well as several neural cell adhesion molecules (for review see (Yamada et al., 2005)). These findings support the hypothesis that plasticity represents the mechanism of action of drugs. Concerning pathophysiological mechanisms of depression alteration of signaling-, oligodendroglial- and GABA-related genes have been observed and opened up new aspects for studying the pathophysiologic mechanisms and the treatment of the disorder (for review (Sequeira and Turecki, 2006). Proteomic analyses uses two-dimensional electrophoreses and subsequent mass spectrometric sequencing of proteins to investigate protein changes in relation to the disorder or drug treatment. Due to limited accessibility of postmortem brain samples and several technical problems (as e.g., postmortem delay), only few studies have been carried out with this technique, but they may be seen as a non-hypothesis driven screening method for the detection of new candidates genes in neurobiological research (Johnston-Wilson et al., 2000; Schlicht et al., 2006).

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FUTURE ASPECTS Although the findings described above are important, none of the positive results can fully account for the heterogeneity in depression or in response to antidepressant treatment. Despite the fact that most of the genetic studies evaluated small samples and only one or a few genetic variants, it appears almost impossible to identify a susceptibility gene for “depression” as a disorder. One of the main reasons for this is the involvement of complex emergent phenomena, including character, temperament and several neurobiological factors, all of which underlie a genetic contribution. The well-known comorbidity between anxiety, depression and neuroticism, with their interacting genetic contributions, further complicates the identification of a disease susceptibility gene as genes influence all levels of cognitive and emotional behaviors and all levels of biology, and thus transcend phenomenological diagnoses (Hariri and Holmes, 2006). After years of limited success the field is now conceptually evolving from trying to find a gene or genes that confer a risk for the development of depression or are involved in treatment response. Our increasing knowledge of the basic neurobiology and of the complex interactions and functional circuits will have an increasing impact on the search for relevant genes. Newer hypotheses about depression beyond the monoaminergic theory will produce new sets of candidate genes. According to recent hypotheses about the neurobiology of depression these candidates will emerge from the processes of brain development, the longterm response to stress, regulation of the HPA axis and cortisol secretion as well as from neurotrophic, neurotoxic or inflammatory processes, each of which are also genetically influenced. Similarly, it is still unclear which neurotransmitter systems are the ultimate target via which drugs produce a clinical effect. This means that the majority of genes responsible for drug response are still unknown.The pharmacogenomic approach uses the recent advances in experimental genomics and proteomics,

together with the available sequence information of the Human Genome Project. These developments will not only enable genome-wide screens of several millions of SNPs without the use of a candidate gene strategy, but also functional investigations of gene and/or protein expression on the whole genome or proteome level. The goal of pharmacogenomics is to study the mechanisms by which different genetic factors affect the organism’s drug response. This will help to find the most efficacious treatment for patients with specific genetic profiles (Paez-Pereda, 2005). Although there is no doubt that large-scale gene and/or protein expression analyses or whole genome analyses will provide new insight into the pathophysiology of depression and the mechanisms of therapeutic effects and adverse drug reactions, we are presently lacking a serious impact of genomics in daily clinical management. Considering the well-documented increased familial vulnerability for the disorder, an identification of persons at risk is highly warranted. However, as the vulnerability for the disorder might not directly be caused by a mutant gene but by an altered interaction between the mutant gene and other predisposing factors, future research will be directed toward these interactions. To give an example: the serotonin system plays an important mood mediating role, alterations in genes and their functional consequences on the protein might be among the first to be recognized as vulnerability markers. Although this serotonin innate vulnerability, by itself, is not sufficient to cause a depressive episode (Firk and Markus, 2007), it was shown that additional factors, such as environment might trigger the outburst of the disorder. Thus, healthy individuals with a positive family history of depression and a genetic susceptibility that may be due to a polymorphism in the 5-HT transporter which deteriorates stress coping mechanisms, are more prone to develop depression. This example is the beginning of current and future research and thus opens up interesting options in the future management of depression and the identification of high-risk subjects.

REFERENCES American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders. Washington DC. Arinami, T., Li, L., Mitsushio, H., Itokawa, M., Hamaguchi, H. and Toru, M. (1996). An insertion/deletion polymorphism in the angiotensin converting enzyme gene is associated with both brain substance P contents and affective disorders. Biol Psychiatr 40, 1122–1127. Baghai, T.C., Binder, E.B., Schule, C., Salyakina, D., Eser, D., Lucae, S. et al. (2006). Polymorphisms in the angiotensin-converting enzyme gene are associated with unipolar depression, ACE activity and hypercortisolism. Mol Psychiatr 11, 1003–1015. Baghai, T.C., Schule, C., Zill, P., Deiml, T., Eser, D., Zwanzger, P. et al. (2004). The angiotensin I converting enzyme insertion/deletion polymorphism influences therapeutic outcome in major depressed women, but not in men. Neurosci Lett 363, 38–42. Baghai, T.C., Schule, C., Zwanzger, P., Minov, C., Zill, P., Ella, R. et al. (2002). Hypothalamic-pituitary-adrenocortical axis dysregulation

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106 Bipolar Disorder in the Era of Genomic Psychiatry Ayman H. Fanous, Frank Middleton, Carlos N. Pato and Michele T. Pato

INTRODUCTION Bipolar disorder (BPD) is a chronic and often debilitating neuropsychiatric condition affecting approximately 1% of the population worldwide. Its annual cost in the United States alone has been estimated to be $24–45 billion (Kleinman et al., 2003). Disability can occur in a variety of contexts in the course of BPD. Affected patients experience episodes of depression and either mania or hypomania, with varying temporal patterns and episode severity. When depressed, patients can suffer from decreased energy, lack of interest, and poor concentration. When manic, they may become euphoric, hyperactive, and irritable, with unrealistically inflated self-esteem. This may lead patients to exercise poor judgment, become aggressive, and cause significant damage to their personal and professional lives. Throughout the course of illness, suicide risk is increased, as is the prevalence of substance misuse, with its attendant medical and psychosocial consequences. The prevalence of BPD and its potential morbidity and mortality has made uncovering its genetic etiology an important goal in biomedical research. In this chapter, we will review the current state of knowledge of the genetic basis of BPD. After a brief overview of clinical issues relevant to genetic studies, we will discuss what is currently known about the predisposition to BPD, first in family, twin, and adoption studies, and then in greater detail in molecular genetic studies. Finally, we describe our approach to gene Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

discovery in the Portuguese Island Collection, which utilizes new technologies in a convergent approach. These recent advances could increase the power to elucidate the genetic substrates of neuropsychiatric and other complex diseases.

DIAGNOSIS The concept of BPD has been evolving since the time of Hippocrates, who hypothesized that affective dysregulation was due to imbalances of bodily humors, as reviewed by Angst and Marneros (2001). At the current time, the diagnosis of BPD is purely clinical, based on operationalized diagnostic criteria. The most commonly used criteria are those included in the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) as well as the International Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). Despite studies of numerous neurobiological systems, including neuropathological studies, there are not enough robust and specific findings which could be used to develop a diagnostic “gold standard.” Compounding the difficulty of uncovering a unifying pathophysiology is the lack of a phenotypically isomorphic animal model of BPD. Although seizure induction and psychostimulant challenge have been used to model various symptomatic aspects of the illness, they lack the requisite episodic alternation between manic and depressive symptoms, which are the cardinal features of the illness. Copyright © 2009, Elsevier Inc. All rights reserved. 1299

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Two BPDs are often included in genetic studies. Bipolar I disorder (BPI) is marked by either manic episodes or mixed episodes, which are an admixture of manic and depressive symptoms. These by definition cause considerable dysfunction and usually require hospitalization. In addition, they can include psychotic features such as hallucinations and delusions. Bipolar II disorder (BPII) is marked by hypomanic episodes, which may have many of the same signs and symptoms as manic episodes, but are shorter, less severe, do not require hospitalization, and are not associated with psychotic features. Major depressive episodes almost always occur in the course of both BPI and BPII. Linkage and association studies of BPD have used variable definitions of affection, which has complicated the interpretation of their results. In general, “narrow” definitions have included BPI as well as schizoaffective disorder, bipolar type. “Intermediate” definitions have added BPII to the narrow definition, while “broad” definitions have added major depressive disorder to the intermediate definition. In the absence of a “gold standard,” there will likely be little consensus on the ideal phenotype for association or linkage studies of BPD in the near future. However, an optimistic view would hold that these studies themselves may inform the diagnosis and nosology of the illness. We have summarized some of the earliest uses of genetic information to explain phenotypic variation in BPD in section “Phenotype Studies.” Almost all patients who have a manic episode will have further episodes. BPD (then called manic-depressive illness) was first distinguished from schizophrenia by Kraepelin on the basis of its relatively good outcome. However, up to 18% of cases become chronic. This is often defined as the presence of significant affective symptoms for 2 years without a relapse. Longitudinal studies suggest an illness progression, with more spontaneous, frequent, and lengthy episodes with increasing duration of illness, suggesting a kindling-like mechanism (Post, 1992). Although several candidate genes have been associated with BPD, as discussed below, no causative variants have been identified in any one population. This has precluded the development of screening tests. However, recent work on genome-wide gene expression in lymphocytes, as will be outlined later in this chapter, has opened up the possibility of such tests being developed in the foreseeable future.

PREDISPOSITION Family, Twin, and Adoption Studies Family studies aim to determine whether biological relatives of ill individuals have a greater risk of illness than the general population. A summary estimate of the recurrence risk in family studies of BPD, weighting for sample size, was calculated at 8.7%, suggesting over a 10-fold increase in risk (Smoller and Finn, 2003).

However, since familial aggregation of illness can be due to environmental as well as genetic factors, twin and adoption studies have been conducted to disentangle these domains of risk factors. Only four twin studies of BPD have been undertaken using modern statistical methods and standardized diagnostic criteria. The largest of these calculated the heritability of BPD at 87% (Cardno et al., 1999). None supported the importance of environmental factors shared by family members, like education, nutrition, and parenting styles, in the liability to illness. Due to the difficulty in obtaining a statistically powerful sample of adopted offspring, there have been few adoption studies of BPD. Those that have been published did not observe differences in the risk of BPD among biological versus adopted relatives of BPD probands. However, when BPD was combined with unipolar depression, biological relatives of ill probands had a greater risk of illness than adopted relatives, suggesting the importance of genetic risk factors. Chromosomal Regions Harboring Susceptibility Genes In the last three decades, the number of known genetic markers has steadily increased, offering the possibility of increasingly dense genome-wide scans. To date, 23 known genome scans of BPD have been published, providing support for several genomic regions. Very few individual scans, however, have produced genome-wide significant linkage. Furthermore, although several regions have been implicated by studies from multiple samples, no region has been uniformly supported across studies. Explanations include between-sample genetic heterogeneity and false-negative results due to small sample sizes (Table 106.1). Three major meta-analyses of linkage studies of BPD have been published, which we will refer to below. These use different but complementary methods. In the first, Multiple Scan Probability (MSP) (Badner and Gershon, 2002), published p-values in specific regions are combined across studies to assess overall evidence for linkage. The second method, Genome Scan Meta-Analysis (GSMA), determines the empirical significance of a specific chromosomal region based on how often it is highly ranked, across genome scans (Segurado et al., 2003). Both of these methods may be influenced by study-specific weaknesses, such as low power. The most recent approach has been to pool marker data (PMD) from several existing studies using a common sex-averaged map, and to perform linkage analysis de novo (McQueen et al., 2005). This study combined data from 1067 families and 5179 individuals. As a complementary approach, association methods, testing for allele frequency differences between groups of cases and controls, have also been utilized. Family-based samples have been thought to provide protection against false positives due to population stratification. Numerous candidate genes have been tested. Most of these are involved in neural processes known to be involved in the regulation of mood, such as dopaminergic, noradrenergic, and serotonergic pathways. However, our fund

Predisposition

TABLE 106.1 Chromosomal regions with the most significant evidence of harboring susceptibility genes for bipolar disorder Chromosomal region

Linkage

Association

Linkage meta-analysis

1q



 (DISC1)

–

4q



–

–

6q



8p



 (Neuregulin 1)

8q



–



10q



–



11p



 (DRD4, BDNF)

–

13q



 (DAOA)



18



–



21q



 (TRPM2, TSPEAR)

–

22q



 (COMT)

–

 –

of knowledge about potentially important biological pathways is limited, making it difficult to generate useful candidates. Therefore, in addition to candidate gene studies, a number of groups have been engaged in positional cloning, testing a large number of markers in previously linked regions to attempt to identify susceptibility alleles in individual genes. Genes associated in this way could in turn suggest biological pathways relevant to etiology and treatment, thereby also suggesting additional testable candidates. In the following section, we will review linkage and association studies in regions with the most plausible evidence of harboring susceptibility genes. We will of necessity be selective in presenting results. Both in the interest of space as well as an attempt to exclude probable false positives, we will narrow our discussion to regions with either replicated linkage, association, or both, or that have been implicated in meta-analyses. In grouping reports together under genomic regions, genetic location will be defined broadly. This is because simulation studies strongly suggest that the spatial resolution of linkage is poor, with stochastic variation in location estimates ranging in the tens of centimorgans (Roberts et al., 1999). Chromosome 1q Linkage: Interest in this region began with a report of suggestive linkage to 1q32 in 22 multiplex pedigrees collected at the National Institute of Mental Health’s Center for Neurogenetics

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(CNG) (Detera-Wadleigh et al., 1999). This was followed by a report of linkage in a Scottish sample of 22 families, and an independent Scottish sample of 13 families (Macgregor et al., 2004), along with 5 British and 2 Icelandic families (Curtis et al., 2003). Linkage to 1q42 in families that segregated schizoaffective disorder was recently reported (Hamshere et al., 2005). Association: A large Scottish family with both bipolar and schizophrenic members was observed to have a balanced (1;11)(q42.1;q14.3) translocation. A large gene was discovered in this region and named Disrupted in Schizophrenia-1 (DISC1) (Millar et al., 2000), which was subsequently associated with schizophrenia in multiple samples (Craddock et al., 2006). In the first positive report in BPD some SNPs were associated in a more or less diagnosis-specific manner with BPD, schizophrenia, and schizoaffective disorder in a US Caucasian sample (Hodgkinson et al., 2004). In a Scottish sample, there was association between a region of DISC1 and BPD that was more specific to women (Thomson et al., 2005). Chromosome 4p Linkage to 4p16 was first reported in 12 Scottish families (Blackwood et al., 1996). In 1997, the first reports from the National Institute of Mental Health Genetics Initiative for BPD were published. This is a multicenter collaborative effort which collected samples in four “waves.” In Wave 1, which consisted of 97 families and 540 individuals, there was modestly increased allele sharing at two markers on 4p15 (Detera-Wadleigh et al., 1997). This was followed by linkage to 4p15 in three related Old Order Amish (OOA) pedigrees (Ginns et al., 1998). Chromosome 4q NIMH Wave 1 resulted in linkage to 4q32 (Detera-Wadleigh et al., 1997), followed by the OOA study, which reported linkage to 4q31 (Ginns et al., 1998). In 55 Australian pedigrees, multiple markers at 4q35 attained model-free LODs greater than 1.5 (Badenhop et al., 2003). In the same year, linkage was reported to 4q31 in a sample of 57 US and Israeli pedigrees (Liu et al., 2003), and to 4q32 in 65 Maryland and Iowa families (McInnis et al., 2003b). Chromosome 6q The first report of linkage on 6q was from the NIMH Wave 1 sample, in which two markers at 6q23.3–6q25 resulted in maximum LOD score of 2.37 (Rice et al., 1997). Evidence for linkage to this region has accumulated steadily since then. Additional reports from the NIMH samples included linkage to 6q24 in an analysis combining Waves 1 and 2 (McInnis et al., 2003a). The first publication from NIMH Wave 3, which contained 250 independent families, reported genome-wide significant linkage near marker D6S1021 (6q16) (Dick et al., 2003). Confidence in this linkage was increased the following year with two reports from the Portuguese Island Collection (PIC). First, an NPL of 2.02 was obtained, also at D6S1021, in 16

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families (Pato et al., 2004). After nine additional families were added, all 25 were genotyped using the GeneChip Human Mapping 10K Array (HMA10K), resulting in a substantial increase in the NPL score to 4.2 and considerable narrowing of the linkage peak (Middleton et al., 2004). In the last 2 years, studies suggest that this region is indeed one of confirmed linkage to BPD. Linkage to 6q24 was recently reported in Spanish, Bulgarian, and German samples (Schumacher et al., 2005), as well as 14 families from a population isolate in Northern Sweden (Venken et al., 2005). Despite replications in multiple samples, as well as several individual reports of genome-wide significant linkage, linkage to 6q has not been a universal finding. Perhaps more persuasive, however, of conferring “confirmed linkage” status on 6q have been the results of the PMD meta-analysis, in which a model-free LOD of 4.19 was observed around 6q22 (McQueen et al., 2005). To date, no positive association studies of genes on 6q have been published. However, this is a region in which more intensive searches for susceptibility genes are clearly warranted. Chromosome 8p Linkage: Linkage disequilibrium on 8p23.1 was reported in a Costa Rican sample, using a definition of affection of BPI with a history of multiple hospitalizations. A LOD of 3.46 at 8p21 was obtained in the US/Israeli sample (Park et al., 2004). In a sample of 75 German, Italian and Israeli families, a LOD of 2.3 was obtained using a narrow definition of affection and a recessive model (Cichon et al., 2001b). Association: Neuregulin 1 was identified in a positional cloning study of schizophrenia in an Icelandic sample (Stefansson et al., 2002), and replicated in multiple samples. The first positive report of association with BPD was from a UK sample (Green et al., 2005). In light of another gene, DAOA, having been identified in schizophrenia with multiple replications in BPD (Detera-Wadleigh and McMahon, 2006), further testing of neuregulin 1 is warranted. Chromosome 8q Linkage to 8q24 was first reported in the German/Italian/Israeli sample (Cichon et al., 2001b). Additional support has come from NIMH Wave 3 (Dick et al., 2003), as well as the Maryland/Iowa sample (McInnis et al., 2003b). The GSMA supported linkage from 8q24.1 to the qter using both intermediate and broad models. While the MSP analysis did not support this region, it was one of only two (along with 6q) that achieved genomewide significance in the PMD meta-analysis. Chromosome 10q A model-free LOD of 2.17 at 10q26 was observed in two Danish families using a narrow definition (Ewald et al., 2002). Additional support comes from linkage to 10q21–24 in the US/Israeli sample (Liu et al., 2003), and to the same marker in the German/Italian/Israeli sample (Cichon et al., 2001a).

The GSMA supported 10q11.21–22.1 under a very narrow definition. Chromosome 11p Linkage: Chromosome 11p has been of interest since the first report from the OOA sample (Egeland, 1988). Additional support was obtained from linkage to 11p13–14 in two highdensity Costa Rican pedigrees, as well as in NIMH Wave 2 at 11p15 (Zandi et al., 2003). Genome-wide significant linkage to 11p15 was obtained in the PIC using the HMA10K assay (Middleton et al., 2004). Association: Dopaminergic pathways have been implicated in the pathophysiology of both psychotic and affective illness, making dopamine receptor genes good candidates for association studies. Numerous studies have been published of a 48 bp polymorphism in the dopamine type 4 receptor gene, DRD4 (11p15.5). A meta-analysis of 917 cases and 1164 controls observed significant association, using the presence of either BPD or MDD as the definition of affection, with no evidence of publication bias or ethnic heterogeneity (Lopez et al., 2005). Brain-derived neurotophic factor (BDNF) (11p14.1), is part of the neurotrophins superfamily and is known to be involved the growth and survival of neurons as well as in synaptic plasticity (Labelle and Leclerc, 2000; Lindholm et al., 1996). A val66met polymorphism in BDNF has been tested in several family and case–control samples, which have resulted in three of the former being positive for association, while none of the latter was. In one European sample, there was no association overall, but the subset of cases with rapid cycling (i.e., having four or more mood episodes per year), was associated, as reviewed by Craddock et al. (2006). No meta-analyses of BDNF have been published to date. Chromosome 12q Linkage: Interest in this region was due to co-segregation of BPD and Darier’s disease in a British family, and the mapping of the Darier’s locus to 12q23–24. Two reports in 1999 supported this claim. These included a LOD of 1.24 in the CNG sample, as well as a model-free LOD of 5.05 in families from the founding population of the Saguenay–Lac-St-Jean region of Quebec (Shink et al., 2005). More recently, a heterogeneity LOD of 2.8 to these bands was reported in seven multiplex UK and Icelandic families (Curtis et al., 2003). Chromosome 13q Linkage: Increased allele sharing was reported on 13q13 in the OOA (Ginns et al., 1996). The following year, in the NIMH Wave 1 sample, increased allele sharing on 13q14–24 was reported, but subsequent NIMH collections did not replicate it (Rice et al., 1997). However, a maximum LOD of 3.4 on 13q32 was reported in the CNG sample (Detera-Wadleigh et al., 1999). This was further corroborated by suggestive linkage at 13q31– 34 in 20 families from San Diego and Vancouver (Kelsoe et al., 2001), as well as 13q12 in the Maryland/Iowa sample (McInnis

Predisposition

et al., 2003b). This region was supported in the MSP, but not the GSMA or PMD analyses. Association: Following up replicated linkage to schizophrenia, the 13q33.2 gene G72, now known as d-amino acid oxidase activator (DAOA), was identified in a French Canadian sample (Chumakov et al., 2002). Positive reports in BPD have resulted from multiple samples, with overall significance of several SNPs reported in a meta-analysis (Detera-Wadleigh and McMahon, 2006). Although different SNPs and haplotypes were associated in different samples, DAOA may be considered the first confirmed susceptibility gene for BPD. Phenotype studies: Association with both schizophrenia and BPD suggested the importance of detailed phenotypic analysis of DAOA. First, association with DAOA in BPD was predicted by the presence of persecutory delusions (Schulze et al., 2005). Second, in a case–control sample from the United Kingdom, association was reported with BPD, but not schizophrenia, using traditional diagnostic categories (Williams et al., 2006). Interestingly, schizophrenia with a history of a major depressive disorder was associated, but BPD with a history of psychosis was not. Although a clear pattern has yet to emerge from such studies, this gene is very likely a susceptibility gene for both illnesses, and is possibly also a modifier gene. Such studies may one day inform discussions of psychiatric diagnosis and nosology. Chromosome 18 A large region in which multiple markers were linked, from 18p11 to 18q12, was reported using model-free linkage in the CNG sample (Berrettini et al., 1994). Additional support came from a study of two Costa Rican pedigrees, which reported linkage to 18q22–q23 (McInnes et al., 1996). In the Maryland/ Iowa sample, there was excess allele sharing at two markers spanning 18q21–q22 (McMahon et al., 1997). Nominal significance was obtained in the GSMA analysis on 18p-q using three definitions of affection. Chromosome 21q Linkage: Linkage to 21q22 was first reported in an Eastern European Jewish family including 18 affected members (Straub et al., 1994). After follow up with 10 more families, a LOD of 3.35 was observed. Suggestive linkage has subsequently been reported at 21q11–13 in NIMH Wave 1 (Detera-Wadleigh et al.), a UK/Icelandic sample (Smyth et al., 1997), and the San Diego/Vancouver sample (Kelsoe et al., 2001). Association: Recently, two genes in 21q22.3 were reported to be associated with BPD in a case–control sample from the United Kingdom: the transient receptor potential gene melastatin 2 (TRPM2), as well as the TSPEAR, both of which are highly expressed in brain (McQuillin et al., 2006). TRPM2 in particular has a priori support, as it is involved in Ca homeostasis. As such, it may impact the mechanism of action of lithium, one of the most widely used drugs in the treatment of BPD. Lithium is known to affect the production of inositol 1,4,5 triphosphate, an important second messenger affecting Ca levels.

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Association with TRPM2 has recently been replicated in both a case–control and a family-based sample from Canada (Xu et al., 2006). Chromosome 22q Linkage: This chromosomal region has been of interest in genetic studies of schizophrenia due to the presence of the VCFS (velocardio-facial syndrome) locus on 22q11. VCFS, also known as DiGeorge syndrome, is associated with an increased prevalence of psychosis. Following up an earlier analysis of an overlapping sample, significant linkage in the San Diego/Vancouver sample at 22q12 was obtained (Kelsoe et al., 2001). Additional suggestive linkage was reported from the NIMH Wave 1 and CNPG samples (Detera-Wadleigh et al., 1999). This region was supported by the MSP, but neither the GSMA or PMD analyses. Association: Interest in catechol-O-methyltransferase (COMT) as a candidate gene for schizophrenia was generated by its role in metabolizing dopamine, along with its location in 22q. The COMT Val158Met polymorphism exists in two forms: Val is associated with greater thermostability and therefore greater function, as well as a consequent relative decrease in DA and NE levels. The Met allele is therefore associated with a relative increase in DA and NE. The Val allele has been associated with schizophrenia in multiple samples. Although most association studies in BPD have been negative, a recent study of 217 cases and around 3000 controls in an Israeli sample reported association with the same 3-marker haplotype that was associated with schizophrenia in this sample (Shifman et al., 2004). Interestingly, the association was stronger in female patients. This suggests that future studies of COMT in BPD should use haplotype analysis as well as stratify on the basis of gender. Studies in Italian and US samples have supported the association with COMT. In addition, the Met allele has been associated with rapid cycling within bipolar patients, suggesting that COMT may be a susceptibility, as well as a modifier gene for BPD (reviewed by Craddock et al., 2006). Studies of Additional Candidate Genes MAOA (Xp11.3) encodes monoamine oxidase, which is a key enzyme in the degradation of biogenic amines, including neurotransmitters. Although numerous studies have been undertaken, very few have been positive. However, a meta-analysis of European and Asian samples supported association with a CA repeat polymorphism (Furlong et al., 1999), which was further supported by a study of 272 cases and 122 controls in a French and Swiss sample (Preisig et al., 2000). 5HTT (SLC6A4, 17q11.2) encodes the serotonin transporter. As one of the main targets of antidepressant medications, especially serotonin-specific reuptake inhibitors (SSRI’s), it is a natural candidate gene for mood disorders. Three meta-analyses have been published, with two suggesting a small increase in risk associated with the short allele in a 44-bp insertion/deletion in the promoter region (5HTTLPR) which is thought to alter transcription levels (Cho et al., 2005; Lasky-Su et al., 2005).

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DRD2 (11q22.2–22.3) encodes the dopamine type 2 receptor, which is the target of typical, and some atypical, antipsychotics. It has been the subject of a number of studies. The most commonly studied polymorphism has been a CA repeat, with up to 5 common alleles. A recent study of a multicenter European collaborative sample of 358 cases and 358 controls reported significant association with the 5-repeat allele (Massat et al., 2002). Lessons Learned from Linkage and Association Studies The body of studies cited above has largely given credence to the notion that BPD is a more or less “typical” complex disease. Such disorders can be characterized by several features which distinguish them from “Mendelian” diseases. Some of these include locus and allelic heterogeneity; low penetrance; polygenic inheritance, in which common variants in several genes have combinatorial effects on risk; and phenocopies produced by environmental risk factors such as substance misuse. Variable expressivity and/or pleiotropy may explain the overlap between linkage and association results of BPD with those of schizophrenia as well as manic depressive disorder (MDD). Additional sources of genetic complexity, for which there is less evidence to date in BPD, include gene–gene (epistatic) and gene–environment interactions. All of these factors would be expected to contribute to the observed small effect sizes and non-replication of linkage, association, and haplotype studies across populations, in this disease which is clearly more common than Mendelian disorders. However, despite BPD’s apparent consistency with the “common disease–common variant” model, large-scale sequencing studies, which are required to discover low-frequency, highly penetrant risk alleles, which determine “Mendelian” diseases, have yet to be performed in BPD. Proteomic Studies Studies based exclusively on genotypes are able to suggest the involvement of individual genes in the etiology of a given disorder, but do not necessarily provide any information on the functional consequences of particular risk alleles or haplotypes. The majority of reported risk alleles for psychiatric illness have no known function, and furthermore, several reported associations are of different variants in different populations. This lack of specificity is compounded by the imprecise relationship between gene expression and protein levels. This imprecision is a result of variation in the efficiency of translation, rates of mRNA degradation, and post-translational modifications due to factors other than sequence variation in the genes in question. Proteomics, denoting the large-scale study of proteins themselves, is an approach that aims to overcome these limitations. Although numerous studies have examined the expression of individual proteins in the brains of bipolar subjects, it is only in the last year that large-scale studies have been published. Beasley et al. (2006) reported altered anterior cingulated cortical expression of 19 proteins in patients with schizophrenia, BPD

or MDD. These included five mitochondrial and five cytoskeletal proteins. Novikova et al. (2006) used ProteinChip methodology and reported increased expression of seven proteins in BPD, five of which also demonstrated altered expression in schizophrenia. This field clearly is in a nascent stage, and an important future direction will be to relate genomic and proteomic findings. Metabolomic studies of BPD have yet to be published at this time.

PHARMACOGENETICS The response to lithium carbonate, which has been a mainstay of both acute and prophylactic treatment of BPD for decades, is partial in one-third of patients and of negligible effect in another third. In recent years, the testing of polymorphisms in candidate genes mediating drug response has been promoted as a means to eventually being able to individually tailor pharmacotherapy according to patients’ genetic profiles. Compared to antidepressants, there have been relatively few pharmacogenetic studies of mood stabilizers, with the vast majority being of lithium. There have been very few replicated results. Contributing factors include smaller sample sizes due to the difficulty of assessing patient response long term, as well as varying definitions of treatment response. Future studies should examine patients being treated with the popular anticonvulsant mood stabilizers, such as valproic acid, carbamazepine, and lamotrigine, as well as atypical antipsychotics. Although there have been no measure-specific replications of 5HTT polymorphisms, results of two studies suggest that the short allele of the 5HTTLPR confers an overall worse clinical outcome, including associations with poorer lithium response (Serretti et al., 2001) and susceptibility to antidepressant induced mania (Mundo et al., 2001). These, in conjunction with the candidate gene studies described above, suggest that more attention should be given to this gene. Most studies of antidepressants in the depressive phases of BPD combine patients with both bipolar and unipolar depression, making it difficult to draw firm conclusions in BPD specifically. In general, pharmacogenetics in BPD should be considered to be in a preliminary phase, with significant methodological hurdles needing to be overcome. At the time of writing, the Systematic Treatment Enhancement for Bipolar Disorder (STEP-BD) study is being undertaken. It is a large, multicenter study of the treatment of BPD, which aims to reflect the population of bipolar patients who routinely present for clinical care (Sachs et al., 2003). Incorporating both naturalistic and randomized clinical trial strategies, it also has a concurrent pharmacogenetics component that has great potential to overcome the hurdles of sample size and patient assessment previously plaguing BPD pharmacogenetics research. To date, 1700 patients have donated blood for genetic tests, and it is hoped that such a study will enable the adequate testing of a number of important hypotheses.

The Role of New Technologies in Elucidating the Genetics of BPD

THE ROLE OF NEW TECHNOLOGIES IN ELUCIDATING THE GENETICS OF BPD In the sections that follow, we briefly review our multi-faceted approach to gene discovery for schizophrenia in the Portuguese population. The approach we use includes new high-throughput tools for SNP genotyping and haplotyping, with subsequent linkage, family-based association, and case–control association analyses of these data. The results of these studies are combined with high-throughput functional genomic screens, using peripheral blood leukocytes from the same subjects used in the genetic screens in an overall convergent approach to the analysis of complex neuropsychiatric disorders. We are beginning to apply this approach to BPD as well, in the Portuguese population. It is our hope that the convergent deployment of new technologies in multiple modalities will provide considerably greater sensitivity and specificity in a field that is fraught with a high risk of both Type I and Type II errors. Genetic Markers for Linkage and Family-Based Association In the past 3 years, technological developments have made it possible to perform highly accurate and rapid genotyping of tens of thousands to hundreds of thousands of SNPs in individual DNA samples. These SNP genotypes have proven to be as useful for genetic analyses as more traditional microsatellites, which take much longer to genotype. We have previously shown (Middleton et al., 2004) that there is greater power to detect linkage using high-density SNP genotyping panels (such as the Affymetrix 10K Human Mapping Assay; HMA) compared with traditional 10 cM microsatellite-based scans. Specifically, in a linkage study of BPD, we obtained genome-wide significance using scans of exactly the same families and individuals that failed to attain this level of significance using microsatellites. We have suggested that the most likely explanations for the reduced power of microsatellite panels are the presence of prominent gaps in coverage and the reduced information content. Other researchers reported similar findings. In a study of 157 families segregating for rheumatoid arthritis, John et al. (2004) used the same Affymetrix 10K assay we used in our earlier studies and compared the linkage results obtained for this platform with those found using a 10 cM microsatellite assay. Like our study, they obtained a genome-wide significant linkage peak with the SNP assay that failed to achieve this level of significance with the microsatellite panel. Moreover, four regions attained nominal significance in the SNP scan that had not been detected by the microsatellite scan. The SNP map also decreased the width of the 1-LOD support intervals under linkage peaks, and thus greatly reduced the number of potential candidate genes for follow up study. Finally, Schaid et al. (2004) compared the linkage results obtained for whole genome analysis of 467 men with prostate cancer from 167 families, using a panel of 400 microsatellites and the HMA10K assay. They reported a small number of

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linkage peaks in that study with LOD scores exceeding 2.0 (up to 4.2) that were obtained with the HMA10K but not detected in the microsatellite-based analyses, which they attributed to increase information content of the assay. The conclusions from these studies and ours, based on empirical studies of large pedigree sets in complex diseases, have been shown to have theoretical bases. Sawcer et al. (2004) compared the performance and information content of three different higher density platforms (from Applied Biosystems, Illumina, and Affymetrix) and concluded that there was a marked improvement in the informativeness and performance of each of these assays compared with microsatellite-based approaches (particularly for the Illumina and Affymetrix platforms). In another study, using a series of simulations with different intermarker intervals, for affected sib-pair studies, Evans and Cardon (2004) showed that linkage analysis with dense SNP maps extracts much more information than a 10 cM microsatellite map. They concluded “… the very low values of information content associated with sparse panels of microsatellite markers suggest that previous linkage studies that have employed these panels would benefit substantially from reanalysis with a dense map of SNPs. This is particularly true for sib-pair studies in which parents have not been genotyped (p. 691).” Genetic Markers for Linkage Disequilibrium Mapping In addition to the use of arrays that contain low and medium density SNP sets, we have also recently been utilizing highdensity SNP genotyping panels, which generate more than 500,000 genotypes on each subject. Currently, there are two major manufacturers of arrays of this density – Affymetrix, which markets the 500K Human Mapping Arrays (HMA500K) and Illumina, which offers up to 650,000 SNPs on a series of bead arrays. In close head-to-head comparisons, both of these high-density platforms perform quite similarly in terms of call rates and concordance rates and will certainly represent the industry standard for years to come. With arrays of this type, the goal is to define specific SNPs or blocks of SNPs (i.e., haplotypes) that are more common in affecteds compared with control subjects. The major limitation of a purely frequency-driven approach such as this is that there are too many differences to prove localizing by themselves. In fact, at an alpha level of 0.05, there are literally hundreds to thousands of associated SNPs on every chromosome (Figure 106.1). Based on results such as these, we feel it is inadequate to adopt a purely case–control driven approach, but rather much more advantageous to consider a convergent family-based and population-based approach. Analysis of Dense SNP Datasets In the studies we have performed, we focus on the data obtained using genotypes from either the Affymetrix 10K or 50K Human Mapping Assays for both family-based linkage and association. To date, there are very few software programs available that can incorporate dense SNP data and pedigree structures for both of these approaches. One free software platform that can accomplish these tasks is MERLIN (Multipoint Engine for Rapid

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1 2 3 4 5 6

1 2 3 4 5 6

7 8 9 10 11 12

7 8 9 10 11 12

13 14 15 16 17

13 14 15 16 17

18 19 20 21 22 x

18 19 20 21 22 x

431,440 annotated SNPs in genome screen

34,375 nominally associated SNPs (p  .05)

Figure 106.1 Lack of localization in case–control genome-wide association analysis of BPD. Left panel displays the positions of all the annotated SNPs on the HMA500K panel across the human genome used in this screen. Right panel shows the locations of all SNPs that showed nominally significant associations (p  .05) in a study using this panel to compare 100 familial cases of BPD with 100 psychiatric screens controls. We note that the total number of nominally associated SNPs equals 8.0% of the total number screened.

Likelihood Inference) designed by Abecasis and colleagues (2002). Another commercial software program that was specifically designed for these tasks is GeneSpring GT. The basic workflow for running these analyses is as follows. Briefly, after loading the genotyping data and pedigree structures into this program, we first clean the data, by checking for SNPs that violate Hardy–Weinberg equilibrium, or contribute to a disproportionate number of inheritance errors (greater than 0.1%). Then, we use an expectation–maximization (EM) algorithm to deduce haplotypes and construct a map of these haplotypes, using the complete set of pedigrees available. Once this is accomplished, it is possible to proceed with haplotypebased singlepoint (single haplotype block) and multipoint (mutliple blocks) downstream linkage analysis. Because of uncertainties regarding the mode of inheritance, disease allele frequency, and penetrance, we always begin our analyses using non-parametric linkage analysis. If a specific chromosomal region appears to be strongly implicated in a non-parametric linkage, then it should show even greater linkage using parametric testing, if a suitable inheritance model can be specified (dominant or recessive, with varying degrees of penetrance), and the specific families which share that linkage can be identified. Finally, family-based association testing is performed on these same families (or subsets of the families) using the same haplotype map used for linkage analysis, with the aim of identifying specific risk haplotypes that are overtransmitted to affected subjects from their affected parents. The types of linkage results that can be obtained from a multi-faceted analysis of complex disorders are illustrated in Figure 106.2, which presents data based on a study of 25 Portuguese pedigrees segregating for BPD that were analyzed using the HMA50K arrays. Our original whole genome scan

performed using the HMA10K identified chromosome 6q as a major candidate region (see Middleton et al., 2004). The results from our more recent haplotype-based SNP analysis reinforced chromosome 6q as a major locus in this population. Markers for Gene Expression in Same Subjects and Families While genetic studies often lead to the identification of putative candidate genes and risk haplotypes, this is only the first step, and greater support for specific candidates can be obtained by consideration of the functional consequences of genetic variability. One method of assessing function is to quantify the levels of expression of individual genes. We have been performing systematic studies on the utility and feasibility of high-throughput gene expression analysis of leukocyte samples from the PIC collection as assays of gene function in families with known patterns of linkage (Middleton et al., 2005; Petryshen et al., 2005a, b). As a result, we have developed an extensive database of gene expression data on more than 55 discordant sib-pairs from families with schizophrenia and BPD. These studies use primary leukocyte samples subjected to differential white blood cell counts. Only gender-matched, age-matched, and blood cell composition-matched discordant sib-pairs are included. One of our first general observations about this body of work was that both the brain and the leukocytes express approximately half of the 22,000 genes present on this array and that there is considerable overlap (mean 59%; up to 92%) in the genes that are expressed in these different sources depending on the loci or the functional gene group being studied. The commonly expressed genes are present at all cytogenetic loci. At the functional level, every neurotransmitter signaling group we examined was also

The Role of New Technologies in Elucidating the Genetics of BPD

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Combined linkage and association analyses of bipolar disorder 2.5

24

Haplotype NPL HHRR Chi Sq Case-Ctrl Chi Sq

2

22 20 18

NPL

14 12

1

Chi Sq

16

1.5

10 8 0.5

6

130,000,000

129,000,000

128,000,000

127,000,000

126,000,000

125,000,000

124,000,000

123,000,000

122,000,000

121,000,000

120,000,000

4 0

2

Base pair

Figure 106.2 Haplotype-based non-parametric linkage, family-based association, and case–control association support chromosome 6q as a bipolar candidate region.In this figure, the haplotype-based NPL score curve clearly supports non-parametric linkage to 6q with a peak NPL exceeding 1.5 in the 120–128 Mb region. In addition to the linkage scan, haplotype-based haplotype relative risk (HHRR) testing of family-based association lent further support for specific haplotype blocks in this region (red plot), as did a complementary case–control association study of 100 familial BP subjects compared with 100 screened controls (blue plot). In total, out of this 8 Mb candidate region, we were able to narrow it to 12 small regions flanking convergent linkage/association peaks (gray arrows). These 12 convergent peaks overlapped with a total of 8 different genes.

found to express many genes in common in the blood and the brain, and most groups even expressed a few genes in the blood that were not detectable in the brain. Other cellular function groups, such as the ribosomal group and certain metabolic pathways expressed almost all of the same genes (85%), as might be expected, given their essential roles in general cellular functions. We conclude that leukocyte expression analysis can provide information on the expression patterns of numerous genes that may play a critical role in brain function. This overlap implies that if any genetic variation affects the level of expression of a gene, then we would have approximately 60% power to detect changes in brain-enriched genes, merely through the screening of blood expression patterns. The utility of the primary leukocyte expression data to actually classify or differentiate affected subjects from control subjects is evident in our studies of 38 age- and gendermatched discordant sibs with schizophrenia or BPD (Middleton et al., 2005). In this study, out of the 22,283 probe sets on the U133A GeneChip, a list of 302 significantly changed genes was obtained after Robust Multiarray Averaging (RMA) and pairwise Mann–Whitney testing with Benjamini–Hochberg correction for multiple comparisons. This list was further refined to a set of 20 genes that could correctly classify each of the control subjects and affected subjects with complete accuracy. We noted

in that study that there were a number of differentially expressed genes that fell within a short distance of the linkage peak on chromosome 6q. Examination of the most changed genes in that study reveal small but potentially meaningful effects for two genes that fell just outside the peaks implicated by Figure 106.2, including alpha 2 laminin (which has been implicated in congenital muscular dystrophy). However, it should be pointed out that the use of only 5 sib-pairs is greatly underpowered to detect significant changes, and we only analyzed these pairs on the U133A array, which lacks probes for a subset of the genes in the 6q region. On the other hand, in our schizophrenia data, among the genes with the most consistent alterations in schizophrenic subjects were several with interesting pathophysiological roles in schizophrenia, including specific neuregulin 1 (NRG1) variants and a number of neurotransmitter-receptor transcripts, including several related to GABAergic, glutamatergic and serotoninergic transmission. Furthermore, the data on the NRG1 expression changes was extended to a larger haplotype analysis in a larger set of families from the PIC collection, and identified a pattern of polymorphisms associated with altered expression (Petryshen et al., 2005a). Moreover, in another parallel study, we have recently shown even more significant evidence of promoter haplotypes regulating the levels of GABA receptor subunit expression in the peripheral leukocytes of schizophrenic

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subjects (Petryshen et al., 2005b). Eventually, we hope to perform the same type of analysis for BPD. Although the gene expression approach that we have outlined above clearly shows great potential for prioritizing gene candidates within chromosomal regions showing strong linkage or association, the approach does have some limitations that merit attention. First, it is not always possible using primary cells to control for the effects of medication, or substances such as cigarettes that are used by a disproportionately high percentage of subjects with schizophrenia or BPD compared to healthy controls. Such potential confounders can be controlled if one has access to non-medicated affected subjects, or has a sufficiently large number of subjects on the same dosing regimens to systematically evaluate the effect of different treatments on expression signatures. Secondly, although more work is involved, it is also possible to perform several passages of primary cells, particularly if one first separates the different leukocyte fractions (T cells, B cells, neutrophils, etc.) to try and “wash out” the effects of medications or comorbid substances. Notably, many of these substances have the potential to exert profound effects on the epigenetic regulation of gene expression, and could still alter the expression signatures of these primary cells through several passages. A final method of attempting to control for such confounds involves establishing immortal cell lines (lymphoblasts) of Epstein–Barr virus-transformed B lymphocytes. Indeed, the practice of establishing these cell lines was widely adopted by the NIH Collaborative Genetics Initiative, for the majority of large-scale DNA collection projects involving serious mental illness, including our studies in the Portuguese islands. Thus, it is now possible for approved researchers to simply request lymphoblasts from the NIH Cell Repository from subjects with specific diagnoses, many of whom have medication histories

and other demographic information available. Clearly, the use of such a vast resource will greatly enhance our understanding of the potential effects of risk alleles or haplotypes on the expression of genes that may have great relevance for either gene discovery or diagnosis.

CONCLUSION Identifying the genetic basis of BPD has been fraught with many of the same difficulties that have plagued genetic studies of most other complex diseases. Nevertheless, remarkable progress has recently been made, which has been accelerating due to major technological advances in the last several years. The foregoing results raise hope that in the decade or decades to come, it will be possible to use classification methods based on genotypes as well as RNA and/or protein expression profiles to identify individuals at high risk of developing BPD. This could thereby allow intervention programs to be instituted. This, of course, presupposes that this genetic information is proven to be not only associated with BPD, but also predictive of its future onset. Longitudinal high-risk studies will therefore be crucial in testing such hypotheses. Additional future benefits could include improvements in the differential diagnosis of mood and psychotic disorders. It is well known, for example, that early in the course of illness, it is often difficult to differentiate between BPD and schizophrenia. More accurate early diagnosis could lead to more effective treatment planning that could ultimately improve patient outcomes. Finally, progress in pharmacogenetics promises to allow treatment programs to be individually tailored, which may considerably improve patient outcomes.

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Williams, N.M., Green, E.K., Macgregor, S., Dwyer, S., Norton, N., Williams, H. et al. (2006). Variation at the DAOA/G30 locus influences susceptibility to major mood episodes but not psychosis in schizophrenia and bipolar disorder. Arch Gen Psychiatr 63, 366–373. Xu, C., Macciardi, F., Li, P.P., Yoon, IS., Cooke, R.G., Hughes, B. et al. (2006). Association of the putative susceptibility gene, transient receptor potential protein melastatin type 2, with bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 141B, 36–43. Zandi, P.P.,Willour,V.L., Huo,Y., Chellis, J., Potash, J.B., MacKinnon, D.F. et al. (2003). Genome scan of a second wave of NIMH genetics initiative bipolar pedigrees: Chromosomes 2, 11, 13, 14, and X. Am J Med Genet B Neuropsychiatr Genet 119, 69–76.

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Genomic Approaches to the Host Response to Pathogens Genomic Medicine and AIDS Viral Genomics and Antiviral Drugs Host Genomics and Bacterial Infections Sepsis and the Genomic Revolution Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

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107 Genomic Approaches to the Host Response to Pathogens M. Frances Shannon

INTRODUCTION Following a period of relative disinterest in infectious disease research due to the enormous impact of vaccines and antibiotics on the spread of and mortality from these diseases, there is now renewed and growing interest in this area of research. This has been driven by several recent worldwide developments including: (i) the rising incidence of diseases such as Acquired Immune Deficiency Syndrome (AIDS) and antibiotic-resistant tuberculosis; (ii) antibiotic-resistant bacterial strains presenting a severe health threat in hospitals; (iii) the rapid spread of new pathogens such as Severe Acute Respiratory Syndrome (SARS); and (iv) the threat of bioterrorism. Indeed, nearly 25% of annual deaths worldwide are due to infectious disease (Morens et al., 2004). Thus, the need to develop new diagnostic methods, more effective vaccines and better therapeutic strategies is urgent. In order to effectively deal with infectious disease threats, it is important to understand both the pathogen and the response of the host, since the outcome of infection is determined by complex host–pathogen interactions. Pathogens are initially detected by the surveillance cells of the innate immune system using cell surface receptors known as Toll-like receptors (TLRs) (reviewed in [Cook et al., 2004]). These TLRs recognize specific components of the pathogen, for example, bacterial lipopolysaccharide (LPS) or double-stranded (ds) RNA from viruses. While many cell types express TLRs, cells of the innate immune system

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such as dendritic cells (DCs) and macrophages play particularly important roles in detecting and responding to pathogens. The response of these cells to a pathogen is determined by the specific pathogen component that interacts with the TLR and the specific TLR family member that is activated. Widespread changes in gene expression are detected following TLR activation and the activated cells produce a plethora of cytokines and chemokines that then activate the adaptive arm of the immune system. The specific cytokines and chemokines produced by the TLR-activated cells tailor the response of the adaptive immune system to deal with the specific pathogen (Cook et al., 2004). Thus, the initial host response to a pathogen through the TLRs determines the outcome of the infection. Host response to infection can be a double-edged sword in that sometimes the response itself can create an adverse outcome for the host. In addition, the aberrant response of the host to self instead of foreign pathogens can create severe pathologies involving chronic inflammatory and autoimmune diseases. The urgent need to better understand host–pathogen interactions has come at a time when genomics and related technologies are expanding rapidly. The availability of complete genomic sequences of an expanding number of pathogens, the human and mouse genome sequences and the advent of genome-wide genotyping and gene expression profiling has opened up new avenues of investigation in the field. The genotype of the pathogen plays a major role in the response of the host to infection with more virulent pathogenic

Copyright © 2009, Elsevier Inc. All rights reserved.

Genetic Susceptibility to Pathogens

strains often possessing the capability to interfere with the host immune response (Fitzgerald and Musser, 2001; Kato-Maeda et al., 2001; Schoolnik, 2002). In addition, different individuals in a population can have very different responses to a genetically identical pathogen. While there are many complex reasons for this, it is clear that part of the differential response is governed by underlying genetic differences between individuals (Clementi and Di Gianantonio, 2006; Zhang and Zhang, 2006). Studies in mouse models of infection have clearly demonstrated that these genetic differences are complex and may involve more than one genetic locus for a given susceptibility or resistance trait (e.g., [Delahaye et al., 2006]). While there are some classic examples of genetic mutations affecting the response of the host to a pathogen (e.g., malaria and sickle cell mutations) there is much to be learned before the genetics of host susceptibility is fully understood. The advent of genome-wide genotyping using single nucleotide polymorphisms (SNPs) or microsatellite markers, leading to major advances in molecular epidemiology, will revolutionize our ability to determine the complexities of the genetic component of pathogen–host interactions (Weiss and Terwilliger, 2000). It is well known that the cells of the host immune system are activated upon detection of a pathogen by TLRs as described above. This activation process includes widespread changes in the gene expression profile of the cells with hundreds of genes being either switched on or off in response to signals generated from the pathogen-detecting TLRs. The response of individual genes has been studied in minute detail for a handful of genes and while this has produced an understanding of some aspects of host response to infection it by no means gives us the total picture. Understanding the molecular response of the host to infection has been greatly improved by using microarray-based technologies and these technologies are opening up new diagnostic possibilities as well as presenting new therapeutic options (Aderem and Smith, 2004; Bryant et al., 2004; Feezor et al., 2005; Hedeler et al., 2006; Korth and Katze, 2002; Ng et al., 2006; Ricciardi-Castagnoli, 2005; Ricciardi-Castagnoli and Granucci, 2002; Smith and Bolouri, 2005). This chapter will focus on two aspects of the host response to pathogens where major advances are being made using genomics approaches and will describe the future impact of these approaches on the development of diagnostics and therapeutics for infectious disease. These are (i) defining the basis of genetic susceptibility to infection and (ii) the definition of the system-wide molecular response to a pathogen.

GENETIC SUSCEPTIBILITY TO PATHOGENS It is now relatively easy to map genes associated with genetic diseases that show a Mendelian pattern of inheritance. However, these diseases account for only a very small proportion of the human disease burden and many of the more common and fatal diseases have a complex etiology with many genetic and

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environmental contributions. While it is clear that most complex disease has a genetic component, defining that genetic component has been difficult to date since many complex diseases such as coronary heart disease, diabetes and others are polygenic with different genetic loci contributing in major or minor ways to disease susceptibility. In addition, in different populations or under different environmental conditions, distinct but overlapping sets of genetic loci are likely to contribute. The sequencing of the human genome and the genome-wide genetic and functional mapping that has followed have raised hopes of mapping the genetic component of complex disease and there are many large efforts around the world with this aim. Studies in both animal models and human populations have shown that infectious disease and the response of the host to a specific infection also has a complex genetic component (Clementi and Di Gianantonio, 2006; Lipoldova and Demant, 2006; Marquet et al., 1996; Mira et al., 2004). Thus, inbred mouse models have been developed that clearly show a genetic component to susceptibility for specific pathogens and in some cases at least part of the underlying genetic reason has been defined (Beck et al., 2000; Mak et al., 2001; Rogner and Avner, 2003). Mapping the genetic components of susceptibility to infection in human populations has been much more difficult due to the large natural variation in humans, the polygenic nature of this trait and the low penetrance of many of the susceptibility alleles. For infectious disease, this is complicated even more by the complex nature of the environmental influences particularly the fact that these diseases, unlike other complex diseases, are transmissible in populations. However, a combination of animal and human population studies, combined with the latest genomic technologies, is beginning to unravel the issues of genetic susceptibility to infection. The use of inbred and congenic strains of mice are well established systems for identifying susceptibility loci (Beck et al., 2000; Rogner and Avner, 2003). In recent years genetic manipulation of specific loci by deletion or mutation has provided many mouse models for screening (Mak et al., 2001). The use of ethylnitrosourea (ENU) mutagenesis to randomly create point mutations in the mouse genome has opened up a new forward genetics approach to identifying susceptibility loci (Papathanasiou and Goodnow, 2005). This chemical mutagen, when used at appropriate doses and at the correct stage of development, can introduce single point mutations into the mouse genome. By screening libraries of mutant mice for susceptibility to specific pathogens, it should be possible to identify genetic loci that dictate susceptibility or resistance to a range of pathogens on a large scale. It is relatively straightforward to identify a chromosomal region involved in susceptibility in these mouse strains by genotyping with microsatellite markers, but identifying the specific gene that is mutated is still very time-consuming. The speed with which this can be achieved depends on the presence of candidate genes within the chromosomal interval or the ability to resequence large amounts of DNA. The latter is becoming achievable with the advent of new rapid sequencing technologies and is set to revolutionize forward genetic

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approaches to disease understanding (Bennett et al., 2005; Serre and Hudson, 2006). The recent explosion in genetic information for the human genome including the complete genome sequence and detailed genetic and physical maps has increased our ability to find variation in the human genome and correlate it with disease. Family studies, especially twin studies, and population studies have clearly shown a genetic component to susceptibility to infectious disease (Frodsham and Hill, 2004; Lipoldova and Demant, 2006; Strunk and Burgner, 2006). Susceptibility generally follows a complex pattern of inheritance and there are two main methods of mapping and identifying genetic loci involved in such complex heredity. These are either association studies or linkage studies. Association studies involve screening populations for specific mutations in a candidate gene(s) in case–control studies or in family studies. This type of approach identified the link between Human Immunodeficiency Virus (HIV) resistance and the chemokine receptor, CCR5 described below (Dean et al., 1996; Samson et al., 1996). Genome-wide association studies although still quite expensive are now becoming feasible and being used to define linkage between specific markers and susceptibility. These linkage studies depend on the availability of markers and the density of these markers is rapidly increasing

TABLE 107.1

with the large scale identification of new SNPs across the human genome. The selection of which SNPs to use and the large numbers of samples needed to generate statistically significant associations for low penetrance alleles are still challenges. The Haplotype MAP (HapMap) project is starting to identify haplotypes within different population groups and together with improvements in large scale genotyping technology and bioinformatics should be useful in studies of complex disease inheritance. Table 107.1 summarizes the best studied genetic susceptibility loci for response to different infectious agents in both mouse models and human studies. One of the classical examples of genetic susceptibility to infection is the role of the hemaglobinopathies in the outcome of malaria infection (Patrinos et al., 2005). There are also certain chromosomal regions and families of genes that have attracted attention in terms of searching for susceptibility alleles or polymorphisms. Because the TLR family of receptors plays a major role in recognizing pathogens, it was speculated that genetic variation in these receptors or their signaling pathways might be responsible for some susceptibility phenotypes (reviewed in [Schroder and Schumann, 2005]). One of the best examples to date is the occurrence of a single polymorphism in the region of the human TLR4 gene encoding the extracellular domain of the

List of well-described susceptibility loci for resistance or susceptibility to infectious disease.

Pathogen HIV

Malaria

Genes

References

CCR5

Dean et al. (1996); Samson et al. (1996)

HLA Class I

Hendel et al. (1999); Li et al. (2007); Selvaraj et al. (2006)

CCR2

Magierowska et al. (1999); Su et al. (1999)

Globin locus

Reviewed in Patrinos et al. (2005)

HLA Class I

Migot-Nabias et al. (2001); Young et al. (2005); Reviewed in Hill (1996, 1999)

TNF-α promoter

McGuire et al., (1994); Ubalee et al. (2001)

TLR2

Kang and Lee (2002); Bochud et al. (2003)

HLA Class II

Shaw et al. (2001); Mehra et al. (1995)

TNF-α promoter

Roy et al. (1997); Shaw et al. (2001)

Legionella

TLR5

Hawn et al. (2003); Merx et al. (2006)

Tuberculosis

HLA Class I

Lombard et al. (2006); Vijaya Lakshmi et al. (2006)

Leprosy

NRAMP1/Slc11a1

Kusuhara et al. (2007); Li et al. (2006)

Typhoid fever

HLA Class II

Dunstan et al. (2001); Dharmana et al. (2002)

Leishmania

NRAMP1/Slc11a1

Bucheton et al. (2003); Mohamed et al. (2004)

HLA

Reviewed in Lipoldova and Demant (2006)

TNF

Bucheton et al. (2003)

IFNgR1

Mohamed et al. (2003)

IL-4

Mohamed et al. (2003)

Salmonella

NRAMP1/Slc11a1

Sebastiani et al. (1998)

Inhaled E. coli LPS

TLR4

Arbour et al. (2000); Feterowski et al. (2003)

Pyogenic bacteria

IRAK4

Picard et al. (2003)

This lists includes genes identified in both mouse and human studies.

Exploring the Host Response Through Expression Profiling

receptor which confers reduced sensitivity to inhaled Escherichia coli (E. coli) LPS (Arbour et al., 2000). Interestingly, when septic shock patients were compared with a control group, these lower-responding alleles were found only in the septic shock group and these individuals had a higher incidence of Gramnegative bacterial infection (Feterowski et al., 2003). Such studies need further confirmation since there are also a number of studies that failed to find any linkage between TLR4 mutations and response to various infections (Schroder and Schumann, 2005). There is also enormous variation in the response of individuals to LPS even in the absence of TLR4 mutations implying that variation may occur in other components of the TLR4 signaling system. An example of this is the link between IRAK4 mutations and increased susceptibility to infection with pyogenic bacteria (Picard et al., 2003).Variation in other TLR genes has also been associated with disease susceptibility. For example, a mutation in the extracellular domain of TLR2 is linked to susceptibility to leprosy (Alcais et al., 2005) and a mutation in TLR5 increases susceptibility to Legionella (Hawn et al., 2003). Taken together these data support the idea that variation in the innate immune recognition of pathogens play an important part in governing susceptibility to an array of infectious diseases. However, caution needs to be exercised until larger population groups have been studied. The extensive polymorphism at the chromosomal regions encoding major histocompatibility complex (MHC) proteins is thought to have arisen through natural selection in response to selective pressure from infectious disease. Although human leukocyte antigen (HLA) association with resistance or susceptibility to infectious disease has been difficult to identify because of the complex array of antigenic epitopes involved, a number of studies have implicated this locus in genetic susceptibility to infectious disease (Ghodke et al., 2005; Little and Parham, 1999). MHC molecules fall into two classes, Class I that present foreign antigens to CD8 cytotoxic T cells and Class II that play a similar role for CD4 helper T cells. Variation in specific Class I genes has been shown to confer susceptibility to pulmonary tuberculosis and to HIV whereas mutations in other Class I genes confer resistance to HIV and to severe malaria. Class II mutations that confer resistance to hepatitis B or hepatitis C have been identified and susceptibility to typhoid fever and leprosy are also associated with specific Class II mutations. Further molecular analysis of these and other associations may in the future have an impact on the development of new vaccines and immunotherapeutics. To date the most successful manner of identifying susceptibility genes in human populations has been the candidate gene approach. Candidate genes have emerged from many sources including mouse genetic studies as well as biochemical and function dissection of the immune system. Once a candidate gene is identified, the chromosomal region spanning this gene in the human genome is then scanned for the occurrence of specific mutations or for functional polymorphisms in case– control studies across populations or in linkage studies in family groups. Such studies have identified a number of well described

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susceptibility loci for infection with various pathogens. One of the most heralded example was the identification of a deletion in the chemokine receptor, CCR5, which was shown to confer resistance to HIV infection (Dean et al., 1996; Samson et al., 1996). Biochemically, this can be explained by the fact that CCR5 is a co-receptor for HIV on the surface of T cells (Dragic et al., 1996). A mutation in another chemokine receptor, CCR2, has also been shown to confer HIV resistance in certain Caucasian populations (O’Brien and Moore, 2000). Some genes have been associated with susceptibility or resistance to multiple pathogens. For example, variation in the NRAMP1/Slc11a1 gene is associated with susceptibility to Leishmania and to specific intracellular bacteria such as tuberculosis (Barton et al., 1999; Govoni et al., 1996; Lipoldova and Demant, 2006; Sebastiani et al., 1998). Mutations in the tumor necrosis factor (TNF) locus, mainly gene promoter mutations, have been linked with malaria and leprosy susceptibility (Lipoldova and Demant, 2006). Gene promoter or control region mutations have an impact on the level of protein produced from the gene rather than the function of the protein. This is an area of great interest but more difficult to study for several reasons, including the inability to identify control regions simply from sequence information and the complexity and flexibility of transcriptional control. A recent review detailing the genes associated with Leishmania susceptibility describes a number of genes that can affect disease outcome including the interferon-gamma receptor type 1 (IFNGR1), the interleukin-4 (IL-4) gene and the NRAMP-1/Slc11a1 gene (Lipoldova and Demant, 2006). These genes and others such as interleukin-12 (IL-12) and its receptor are also linked with Salmonella and certain mycobacterial infections (Lipoldova and Demant, 2006). Thus, it is likely that variation in many genes can contribute to disturbing the finely balanced tuning of the immune system and lead to an altered response to a pathogen. It is clear from such studies that the same genes may be involved in susceptibility to an array of pathogens indicating a core immune response critical for any pathogen. The identification of susceptibility loci for infection with various pathogens will aid in developing new diagnostic screens based on the detection of genetic variants in these loci. It could be envisaged that a person’s susceptibility or resistance to a pathogen could be defined by a simple genotyping screen either prior to exposure to any pathogen or upon presentation with an infection. It may also be possible to determine the likely outcome of the infection through a genotyping screen. The definition of susceptibility loci will also contribute to our ability to develop new vaccines and therapeutics.

EXPLORING THE HOST RESPONSE THROUGH EXPRESSION PROFILING The use of microarray technology to generate expression profiling data is becoming common place in biomedical research (reviewed in [Quackenbush, 2002; Sherlock, 2000]). This

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technology allows the documentation of mRNA levels for thousands of genes from total RNA prepared from cells or tissue samples. The data obtained can be compared from sample to sample allowing the changes between samples to be documented and quantified. The “expression profile” for any cell or tissue is simply the list of genes whose expression can be detected using microarrays. The differences in the expression profile from one cell or tissue to the next or in cells treated in a specific manner is a surrogate measure of the cell/tissue phenotype and shows how that phenotype responds to its environment. Expression profiling is most useful when large datasets become available and when the data is combined with other data types and detailed bioinformatics studies. For example, using functional clustering of expression profiling data can help identify pathways that are important for a particular process and co-expression clustering combined with other technologies can help define regulatory networks within the cell. Over the last 5–6 years, this technology has been applied to identifying the changes in gene expression that occur in response to infection by various pathogens (Aderem and Smith, 2004; Boyce et al., 2004; Bryant et al., 2004; Feezor et al., 2005; Foti et al., 2006; Jenner and Young, 2005; Korth and Katze, 2002; Korth et al., 2005; Ricciardi-Castagnoli and Granucci, 2002). To date there are more than 150 papers in the literature that describe gene expression changes that occur in response to infection with a plethora of pathogens and in many cell types (reviewed in [Jenner and Young, 2005]). Many of these are in vitro studies, taking specific cell types and infecting them with specific agents including bacteria, viruses, parasites and yeasts. In addition, cellular responses to bacterial components have also been documented, helping to identify pathogen-specific responses as well as determining the pathogenic component responsible for the major gene expression effects. Virulent or non-virulent strains of specific pathogens as well as mutant organisms have been used to determine the gene expression profile associated with a negative or positive clinical outcome. Few in vivo studies have been carried out and have shed light on the more complex responses seen in whole animals and helped to validate the in vitro data. Identifying a Common Host Response to Infection Several pioneering studies demonstrated that microarrays could be used to determine changes in the gene expression profile of cells in response to virus or bacterial infection (Boldrick et al., 2002; Gao et al., 2002; Huang et al., 2001; Nau et al., 2002). These studies paved the way for the analysis of the host response to a wide variety of infectious agents. The most significant of these studies compared the response of macrophages or DCs to a variety of infectious agents in a single study. In these studies a strong shared response to all infections, be they bacterial, viral or parasitic in nature, was identified. Not only was there commonality from one infectious agent to another but there was also some commonality across cell types. This expression signature has been interpreted as a general “alarm signal” for infection (reviewed in [Jenner and Young, 2005]). Studies of infection

with Gram-positive and Gram-negative bacteria also revealed a common expression signature in peripheral blood mononuclear cells. Recently, the Young lab has interrogated all of the publicly available expression profiling data related to the host response to infection (Jenner and Young, 2005). The dataset includes 785 experiments in cells ranging from macrophages and DCs to cells of the adaptive immune response, endothelial and epithelial cells and spanning a wide range of infecting agents. This meta-analysis revealed that a “common host response” can be detected across all of these cell types and infectious agents and show that although cells such as macrophages and DCs specialize in detecting infection, other cells of the body can mount the same “alarm response” as described above (Figure 107.1). Not surprisingly this expression signature contains many genes associated with the immune system particularly those encoding inflammatory cytokines, chemokines and their receptors. However, some more surprising patterns of expression were also detected. It has been long known that interferon-stimulated genes (ISGs) are regulated by virus infection, but it has only recently been recognized that bacteria and other infecting agents can also illicit the interferon response. This was borne out in these meta-analyses where upregulation of an ISG set is observed across a broad range of infecting agents and cell types. Not only do these cells change the expression of secreted factors and their receptors during infection but the intracellular milieu is also modified. Once again, there is a common pattern of change observed in all of these studies. The upregulation of signaling and transcription pathways that both augment and attenuate the immune response are observed leading to the interpretation that both positive and negative feedback loops operate within the cell to heighten or dampen the immune

Prostaglandin receptors Cytokine receptors Chemokine receptors

Adhesion molecules

Adaptors Signaling molecules

Phosphatases Transcription factors

Antigen processing

Apoptosis molecules

Co-stimulatory receptors

Antigen presentation

Interferon-stimulated genes Invasion molecules

Interferons Chemokines

Cytokines

Figure 107.1 Summary of the common host response to infection. This figure is adapted from Jenner and Young (2005) and summarizes their meta-analysis of gene expression changes in response to infection with various pathogens or activation of a variety of cells with agents that mimic infection. Genes are grouped into general functional categories.

Exploring the Host Response Through Expression Profiling

response. Temporal profiling can reveal extra layers of complexity and in one study a pro-inflammatory profile followed by an anti-inflammatory profile was identified in macrophages activated with LPS (Wells et al., 2005). Meta-analysis of temporal studies of activation through different TLRs also revealed that the inflammatory chemokines/cytokine signature was an early response while the ISG response was later, presumably reflecting the need for an indirect activation of the ISGs through interferon production (Jenner and Young, 2005). Changes in the expression level of genes involved in both activation and repression of apoptosis also fall under the “common signature” banner and this is interpreted as sending the cells into a state of high alert where apoptosis can be either initiated to eliminate infected cells or terminated if the infection resolves (Jenner and Young, 2005). Although these in vitro studies have provided an overview of the response of isolated cell types to pathogenic infection, in vivo studies are needed to validate any of these results before application to clinical medicine. A number of animal models have been used to profile the host response to infection with a variety of agents. These studies are complicated especially if whole tissue samples are used in that changes in gene expression can result not only from genuine changes within the cells of the tissue but also from the recruitment especially of immune cells into the infected or inflamed tissue. Nevertheless, in vivo studies have, in some cases, shown good correlation with the expression profiles found from in vitro studies. For example, profiling the brains of mice infected with virulent Sindbis virus revealed that ISGs as well as inflammatory chemokines were upregulated (Johnston et al., 2001) and in terms of diagnosis or treatment the exact reason behind these changes in expression signature may be irrelevant. In vivo studies also have some advantages in that the gene expression profiles detected in infected tissues will often make more sense when combined with other physiological or cell biology data from studies of the infected host. Thus an “infection signature” would not only describe the altered gene expression of the immune cells that are recruited to the sites of infection but also would include changes in the gene expression of the resident cells of the tissue and may provide a more robust profile of the infection process for use in diagnostic applications. What can be applied to clinical medicine from these studies? The ability to detect an “infection signature” using focused microarrays could potentially be used as a diagnostic tool. There would be an immediate need to identify a core set of genes with sufficiently robust changes in gene expression to form the basis of a diagnostic array. Arraying technology would need to be priced for diagnostic use and the technology would have to be deemed sufficiently robust to pass all the quality control requirements of a diagnostic laboratory. No doubt progress will be made toward these goals in the near future. Pathogen-Specific Responses In addition to the “common host response” described above, microarray studies have revealed that pathogen-specific responses also exist. This is not surprising since it has long been known that different pathogens induce distinct arms of the adaptive

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immune response. For example, distinct types of helper T cells are activated in response to bacterial and viral infection compared to parasite infection and DC products such as cytokines and chemokines control the differentiation of T helper cell into the correct response type (Mosmann et al., 2005). Thus, the fact that cells of the innate immune system display a pathogenspecific transcriptional response as well as a general alarm signal helps to dictate the subsequent immune response. Different TLRs are involved in recognizing and responding to different pathogens. For example, TLR2 is responsible for activation in response to Gram-positive bacteria while TLR4 responds to LPS, a component of Gram-negative bacteria (Beutler and Rietschel, 2003; Cook et al., 2004). TLR3 responds to dsRNA and thus dictates the viral immune response for many dsRNA viruses (Beutler and Rietschel, 2003; Cook et al., 2004). Studies using bacterial components such as LPS and flagellin as well as dsRNA have revealed that each TLR induces a specific as well as general transcriptional response. The transcriptional response of macrophages and peripheral blood mononuclear cells is more robust in response to Gram-negative (TLR4) compared with Gram-positive (TLR2) bacteria and the ISG response is considerably reduced for the Gram-positive expression signature (Boldrick et al., 2002; Nau et al., 2002). These differences are further observed when bacterial components are used to activate cells. For example, LPS from Gram-negative bacteria, a specific ligand for TLR4, can activate the ISG profile but TLR2 ligands such as LTA and MDP cannot (Jenner and Young, 2005). Not only the activation of specific gene sets but also the strength of the specific response signature may be important for the immune detection of the type of pathogen involved. E. coli infection of DCs strongly upregulates the chemokines/cytokine inflammatory cluster whereas infection with influenza or other single stranded (ss) RNA viruses (through TLR7) has a weaker ability to regulate this cluster but a stronger ability to regulate the ISG signature (Huang et al., 2001; Lund et al., 2004). These types of results raise the possibility that the diagnosis of the type of pathogen involved in an infection would be helped by the development of customized microarrays that could distinguish the gene expression profiles elicited by particular pathogens. Additionally, arrays that also detect RNAs produced by the pathogen may be even more significant as a diagnostic tool. Determining the Outcome of the Infection An infecting agent can either be cleared from the body by the immune system mounting an appropriate response or cause severe or terminal pathology. The outcome depends on a multiplicity of events ranging from the genotype of the host, that is, whether the host displays a resistant or susceptible phenotype, the genotype of the infectious agent, that is, virulent or non-virulent strains and many other less defined environmental factors. Can genomic approaches be used to determine the outcome of infection? Clearly, as discussed above, the technology is developing to define susceptible and resistant host genotypes especially in animal models of infection but the ability to do this routinely in human populations is some way into the future. Given the smaller

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genomes of pathogenic organisms, defining virulence genotypes is progressing at a faster rate (Chan, 2003; Dorrell et al., 2005; Fitzgerald and Musser, 2001; Kato-Maeda et al., 2001; MacFarlane et al., 2005; Schoolnik, 2002; Zhang and Zhang, 2006). Expression profiling studies have been used to investigate the differences in the host response to pathogenic and nonpathogenic strains of specific infectious agents. In one example, mice infected with a pathogenic strain of pneumonia virus upregulated the expected inflammatory chemokines/cytokine profile as well as the ISG profile but an attenuated strain of the same virus could not, although the virus replicated in the lungs of these mice to the same degree (Domachowske et al., 2001). Temporal profiling of the infection process in animals will also help to define the expression signatures associated with the ability of the host to clear specific pathogens.

GENETICAL GENOMICS AND SYSTEMS BIOLOGY: THE NEW FRONTIERS While considerable information and progress is being made with the approaches described above, there is now much interest in combining both a genetic screening approach with an expression profiling approach in a new area of investigation being dubbed “genetical genomics” (reviewed in [Cook et al., 2004; de Koning et al., 2005; Schadt et al., 2003]). The rationale behind this combination is twofold: first, expression profiling data can be used to identify those genes in a chromosomal region previously identified from genetic approaches, whose expression is altered between two different genotypes. These genes then immediately become strong candidates for a susceptibility gene at that genetic locus. In this way, mutations that affect the expression but not the function of a protein can also be identified. Second, expression profiling combined with genotyping can identify functional pathways that can be affected by a single mutation. For example, if a mutation affects a transcription factor that controls expression of a group of functionally related genes then not only the transcription factor but the entire pathway will be identified opening up better opportunities to design new therapeutics. In a prelude to these types of approaches, Hume and colleagues (Wells et al., 2003) carried out expression profiling studies on LPS-stimulated macrophages from various mouse strains that show differential response to infection. These studies showed that while there was a common core response to TLR4 activation, each strain of mice showed a unique gene expression

program implicating many different genetic loci in this variable response. Mapping the genetic loci responsible for these different transcriptional responses will help to shed light on genetic loci that may play a role in human susceptibility to infection. These type of approaches move into the realms of “systems biology” which is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions, focusing on all the components and the interactions among them, as part of one system. Given the complexity of the immune response to infection, ultimately this type of approach may provide more answers.

APPLICATION TO CLINICAL PRACTICE Although genomic approaches to understanding the host response to infectious disease are still very much at the developmental stage, it is likely that such approaches will be applied to diagnosis in the near future. In fact there are already some examples of the application of these approaches. A number of studies have been described using oligonucleotide-based arrays for the detection of multiple pathogens in a single experiment (Campbell and Ghazal, 2004; Dietel and Sers, 2006; Hervas, 2004; Kostrzynska and Bachand, 2006; Pompe et al., 2005). Recent studies have investigated the use of expression profiling of whole blood to determine human subjects at risk of recurrent tuberculosis (Mistry et al., 2007) and to identify high and low responders to lipopolysaccharide (Wurfel et al., 2005). A study in macaques has used whole blood expression profiling to examine molecular signatures of influenza infection (Baas et al., 2006). There are many efforts currently to optimize expression profiling for biomarker discovery and for clinical trials (Debey et al., 2006; Shou et al., 2005; Zheng et al., 2006). There are still many hurdles to be over come in terms of application to the clinical setting including standardization of sample preparation, robustness of the microarray platform and data analysis, cost quality assurance and so on (Abdullah-Sayani et al., 2006). The introduction of genetic or genomic screening into the clinical setting for infectious disease will no doubt occur in the near future.

ACKNOWLEDGEMENT I would like to thank Stephanie Palmer for help with preparation and proof-reading of the manuscript.

REFERENCES Abdullah-Sayani, A., Bueno-de-Mesquita, J.M. et al. (2006). Technology insight:Tuning into the genetic orchestra using microarrays–limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol 3(9), 501–516. Aderem, A. and Smith, K.D. (2004). A systems approach to dissecting immunity and inflammation. Semin Immunol 16(1), 55–67.

Alcais, A., Mira, M. et al. (2005). Genetic dissection of immunity in leprosy. Curr Opin Immunol 17(1), 44–48. Arbour, N.C., Lorenz, E. et al. (2000).TLR4 mutations are associated with endotoxin hyporesponsiveness in humans. Nat Genet 25(2), 187–191. Baas, T., Baskin, C.R. et al. (2006). Integrated molecular signature of disease: Analysis of influenza virus-infected macaques

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Recommended Resources

Sebastiani, G., Olien, L. et al. (1998). Mapping of genetic modulators of natural resistance to infection with Salmonella typhimurium in wildderived mice. Genomics 47(2), 180–186. Selvaraj, P., Swaminathan, S. et al. (2006). Association of human leukocyte antigen-A11 with resistance and B40 and DR2 with susceptibility to HIV-1 infection in south India. J Acquir Immune Defic Syndr 43(4), 497–499. Serre, D. and Hudson, T.J. (2006). Resources for genetic variation studies. Annu Rev Genomics Hum Genet. 7, 443–457. Shaw, M.A., Donaldson, I.J. et al. (2001). Association and linkage of leprosy phenotypes with HLA class II and tumour necrosis factor genes. Genes Immun 2(4), 196–204. Sherlock, G. (2000). Analysis of large-scale gene expression data. Curr Opin Immunol 12(2), 201–205. Shou, J., Dotson, C. et al. (2005). Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray. Biomarkers 10(4), 310–320. Smith, K.D. and Bolouri, H. (2005). Dissecting innate immune responses with the tools of systems biology. Curr Opin Immunol 17(1), 49–54. Strunk, T. and Burgner, D. (2006). Genetic susceptibility to neonatal infection. Curr Opin Infect Dis 19(3), 259–263.

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Ubalee, R., Suzuki, F. et al. (2001). Strong association of a tumor necrosis factor-alpha promoter allele with cerebral malaria in Myanmar. Tissue Antigens 58(6), 407–410. Weiss, K.M. and Terwilliger, J.D. (2000). How many diseases does it take to map a gene with SNPs?. Nat Genet 26(2), 151–157. Wells, C.A., Ravasi, T. et al. (2003). Genetic control of the innate immune response. BMC Immunol 4, 5. Wells, C.A., Ravasi, T. et al. (2005). Inflammation suppressor genes: Please switch out all the lights. J Leukoc Biol 78(1), 9–13. Wurfel, M.M., Park, W.Y. et al. (2005). Identification of high and low responders to lipopolysaccharide in normal subjects: An unbiased approach to identify modulators of innate immunity. J Immunol 175(4), 2570–2578. Young, K., Frodsham, A. et al. (2005). Inverse associations of human leukocyte antigen and malaria parasite types in two West African populations. Infect Immun 73(2), 953–955. Zhang, R. and Zhang, C.T. (2006). The impact of comparative genomics on infectious disease research. Microbes Infect 8(6), 1613–1622. Zheng, Z., Luo, Y. et al. (2006). Sensitive and quantitative measurement of gene expression directly from a small amount of whole blood. Clin Chem 52(7), 1294–1302.

RECOMMENDED RESOURCES Journals Jenner, R.G., Young, R.A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nat Rev Microbiol 3(4), 281–294. This review describes a meta-analysis of expression profiling data from the literature of cells infected with different pathogens or treated with pathogenic components. Lipoldova, M., Demant, P. (2006). Genetic susceptibility to infectious disease: Lessons from mouse models of leishmaniasis. Nat Rev Genet 7(4), 294–305. This paper reviews the literature on the genetic susceptibility to infection with Leishmania and compares susceptibility loci to those identified for other infections. Cook, D.N., Pisetsky, D.S., Schwartz, D.A. (2004). Toll-like receptors in the pathogenesis of human disease. Nat Immunol 5(10), 975–979. This review describes the role of Toll-like receptors in detection of pathogens and summarizes their involvement in infectious disease susceptibility.

Websites http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?dbunigene UniGene is an Organized View of the Transcriptome. Each UniGene entry is a set of transcript sequences that appear to come from the same transcription locus (gene or expressed pseudogene), together with information on protein similarities, gene expression, cDNA clone reagents and genomic location. http://www.ncbi.nih.gov/entrez/query.fcgi?dbOMIM Online Mendelian Inheritance in Man is a database catalog of human genes and genetic disorders. http://www.genome.jp/kegg/ Kyoto Encyclopedia of Genes and Genomes. http://pstiing.licr.org/ pSTIING (Protein, Signaling, Transcriptional Interactions and Inflammation Networks Gateway) is a publicly accessible knowledgebase about protein–protein, protein–lipid, protein–small molecules, ligand–receptor interactions, receptor–cell type information, transcriptional regulatory and signal transduction modules relevant to inflammation, cell migration and tumorigenesis.

http://www.genmapp.org/ Gene Map Annotator and Pathway Profiler is a computer application designed to visualize gene expression data on maps representing biological pathways and groupings of genes. http://www.ensembl.org/index.html Ensembl is a joint project between EMBL-EBI and the Sanger Institute to develop a software system which produces and maintains automatic annotation on selected eukaryotic genomes. http://www.informatics.jax.org/ MGD includes information on mouse genetic markers, molecular clones (probes, primers and YACs), phenotypes, sequences, comparative mapping data, graphical displays of linkage, cytogenetic and physical maps, experimental mapping data, as well as strain distribution patterns for recombinant inbred strains (RIs) and cross haplotypes. http://www.geneontology.org/ The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism. http://www.ncbi.nlm.nih.gov/geo/ Gene Expression Omnibus is a gene expression/molecular abundance repository supporting MIAME compliant microarray data submissions, and a curated, online resource for gene expression data browsing, query and retrieval. http://www.genome-www5.stanford.edu/ The Stanford MicroArray Database stores raw and normalized data from microarray experiments, and provides data retrieval, analysis and visualization. http://www.expression.microslu.washington.edu/expression/index. html Public Microarray Data Download Site powered by Expres-sion Array Manager. http://www.ncbi.nlm.nih.gov/projects/SNP/ The National Center for Biotechnology Information has established the Single Nucleotide Polymorphism (dbSNP) database to serve as a central repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms.

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108 Genomic Medicine and AIDS Thomas Hirtzig, Yves Lévy and Jean-François Zagury

INTRODUCTION The early 1980s saw the burst of a new field, molecular biology (Maniatis et al., 1982). At the same time, in 1983, the etiological agent of Acquired Immune Deficiency Syndrome (AIDS) (Gallo, 1984), the HIV-1 retrovirus, was discovered (Barre-Sinoussi et al., 1983). This parallel timing has lead AIDS research to expand and to benefit very closely from the advances in molecular biology. The genome of the HIV-1 was among the first genomes to be entirely sequenced in 1985 (Ratner et al., 1985; Wain-Hobson et al., 1985). In 1995, one of the first commercial tests based on the polymerase chain reaction (PCR) technique, the measure of HIV-1 viral load, was marketed. It has become a major monitoring test for AIDS patients. Large-scale genomic association studies on AIDS patients cohorts have been published as early as 1996 (Dean et al., 1996; Samson et al., 1996), which revealed new clues about the molecular mechanisms of HIV-1 infection. More recently, DNA micro-arrays (DNA chips) studies have been performed to study the effect of HIV-1 on the cellular expression of human genes and in 2007, genotyping chips targeting hundreds of thousands genetic polymorphisms have started to be used to unravel the human genetic factors influencing the progression to AIDS (Fellay et al., 2007). As one can see, AIDS research is intricately related to the emerging “genomic world”. In the present chapter we will Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg 1324

expose the state of the knowledge in the field of genomics and AIDS.

CONTEXT OF HIV AND AIDS HIV and the AIDS Pandemics In 1981, numerous cases of severe forms of Kaposi’s sarcoma were identified in young homosexual males in the United States (Gottlieb et al., 1981). This finding was remarkable since this disease was known to occur in older people only. Simultaneously, the Center for Disease Control (CDC) in Atlanta noticed a high number of a rare lung infection, Pneumocystis carinii pneumonia (CDC, 1981a). During that year, similar cases were reported in other countries, and also in heterosexual patients. A common link between these patients was the apparent destruction of their CD4 lymphocytes (CDC, 1981b) and, in 1982, the disease was named Acquired Immune Deficiency Syndrome (AIDS) (CDC, 1982). In 1983, Barre-Sinoussi et al. (1983) isolated a retrovirus from a patient, and called it Lymphadenopathy-Associated Virus (LAV). In 1984, Gallo et al. proved that this virus was indeed the etiologic agent of AIDS and gave it another name: Human T-cell Lymphotropic Virus 3 (HTLV-III) (Gallo et al., 1984). This latter work was the basis for the development of a blood test that screened for the virus and thus prevented its spread through Copyright © 2009, Elsevier Inc. All rights reserved.

Context of HIV and AIDS

blood banks. It was only in 1986 that the virus got its current name, Human Immunodeficiency Virus type 1 (HIV-1). In 1986, a second HIV-type virus, named HIV-2, was isolated from AIDS patients from West Africa (Clavel et al., 1986; Horsburgh and Holmberg, 1988), but it seemed globally less infectious and less prevalent than the first one. It is still today mostly limited to this area, while HIV-1 has expanded all over the world (McCutchan, 2006). In December 2005, the last UNAIDS/WHO AIDS report on the evolution of the epidemics was released (UNAIDS/WHO, 2005). The estimates for the worldwide number of patients were 40.3 million infected people, 17.5 million of which being women and 2.3 million being children under 15 years. The patients newly infected by HIV-1 in 2005 were 4.9 million, including 700,000 children. During the year 2005, 3.1 million people died of AIDS and its consequences, including 570,000 children under 15 years. Out of the 40.3 million people living with AIDS, 25 million live in Sub-Saharan Africa. Overall, more than 35 million infected people live in developing countries. More than 95% of the new infections occur in low- and middle-income countries. HIV Retroviruses Both HIVs are retroviruses, and belong to the lentivirus family. They follow the classical retroviral cycle of infection: (1) binding of the virus to receptors, (2) fusion and viral core inserted into the cell, (3) reverse transcription of the viral genome, (4) nuclear import of double stranded DNA, (5) integration of the proviral DNA into the host genomic DNA, (6) transcription of viral RNA and translation of viral proteins, (7) budding of the virus from the cell membrane and maturation and (8) new cycle of infection of uninfected cells. Once integrated in the genome, the provirus behaves essentially as a cellular gene and can be reactivated following an immune stimulation which can be provoked by undercurrent infections or by stress. (Oldstone, 1998; Zagury et al., 1986). The HIV-1 genome was first sequenced in 1985 (Ploegh, 1998; Wain-Hobson et al., 1985). It is about 9750 nucleotides long, and contains nine open reading frames (ORFs) (Figure

MA

CA

NC

p17

p24

p7 p6

Disease Description AIDS occurs generally many years after the initial infection. Three stages are distinguished in the evolution of HIV-1 infection (Figure 108.2): 1. The primary-infection is the first contact with the virus and is often clinically asymptomatic. During primary-infection

rev

vif

gag

tat LTR

vpr

pol PR

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108.1). Three of them are the classical retroviridae gag, pol and env ORFs, encoding polyproteins that need to be post-translationally processed to obtain nine functional proteins. The Gag polyprotein is composed of the Matrix (MA), Capsid (CA) and Nucleocapsid (NC) proteins, but its C-Terminal 55-amino acid-long peptide, called p6, is an uncommon feature of retroviruses. The Pol polyprotein is composed of the Protease (PR), Reverse Transcriptase (RT) and Integrase (IN) proteins. The presence of RT is the most obvious evidence that HIV-1 is a retrovirus. The Env polyprotein, also called gp160, is a transmembrane protein which is cleaved into its surface (SU or gp120) and transmembrane (TM or gp41) domains. The six other ORFs encode mostly intra-cellular regulators of the virus cycle: they are a TransActivator of Transcription (TAT), a Regulator of Virion protein expression (REV), a Negative regulatory factor (NEF), a Virion Infectivity Factor (VIF), and the Viral Proteins U and R (VPR and VPU) (Alizon et al., 1984; Luciw et al., 1984; Ratner et al., 1985). Due to numerous errors of the RT, each infection cycle generates new variants of HIV. Hence, HIV-1 exhibits multiple variants, and different strains have indeed been sorted in the different world regions (Frankel and Young, 1998; McCutchan et al., 1996; Subbarao and Schochetman, 1996). Today, several subtypes have been identified and named A, B, C, D, F, G, H and K, and the circulating recombined forms (CRF) coming from recombination events between several strains. The classification of these strains is made by a phylogenetic analysis of the Env sequences. Being infected by several different subtypes is predictive of a faster progression rate to AIDS (Kandathil et al., 2005), each subtype has its own pathogenic characteristics (Cheonis, 2006) and geographic area (Spira et al., 2003).

vpr LTR

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RT p66

p10

nef

env IN

SU

TM

p32

gp120

gp41

p51

Figure 108.1 Depiction of HIV-1 genes and their respective reading frames. Gag, pol, and env genes code for polyproteins which are further cleaved in smaller proteins. Tat and Rev proteins are produced after splicing of their mRNAs. CA: capsid, IN: integrase, LTR: long terminal repeat, MA: Matrix, NC: Nucleo-capsid, PR: Protease, RT: Reverse transcriptase, SU: Surface, TM: Transmembrane.

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one can observe a CD4 cell count decrease concomitant with a peak in viral load. This leads to the generation of an immune response which will control the virus replication and the patient will enter the next phase. 2. The latency phase, which can span over many years, and which exhibits no clinical signs of the virus infection. The virus may still replicate itself, specifically in its targets which are immune cells, and thus progressively colonize the immune system. When the immune system becomes no longer functional, this is the advent of the third phase, AIDS. 3. This last phase corresponds to an advanced stage of infection and one observes a very pronounced immunodeficiency witnessed by a low CD4 cell count and a high viral load. At this point, severe clinical symptoms can occur such as: cachexia, pneumocystosis in the lung, toxoplasmosis in the brain, lymphomas, etc.

Current Treatments In the last 10 years, the advent in industrialized countries of Highly Active AntiRetroviral Therapies (HAART) capable of containing HIV replication has changed the course of HIV infection by reducing the AIDS-related morbidity and mortality of patients. This clinical benefit is clearly related to thelimitation of the immunological damage that is caused by HIV replication and to the restoration of CD4 T-lymphocyte counts and specific responses against pathogens. It is agreed that the absence of realistic hopes of virus eradication points toward lifelong therapy and transformation of this lethal disease into a chronic infection. Since the introduction of the first drug proposed to patients in 1985 (Zidovudine, also known as AZido-Thymidine (AZT) (Berger et al., 1998), 20 antiretroviral drugs have been licensed for the treatment of HIV infection. Eight Nucleoside/nucleotide analogs (NRTI) and three Non-Nucleoside analogs (NNRTI) Reverse Transcriptase Inhibitors inhibit HIV replication before its integration. While NRTIs are incorporated in the reverse-transcribed viral genome and cause premature reversetranscription stop, NNRTIs interact with the catalytic domain of the RT, modifying its structure and impairing the fixation of the RT on its target. However, both kinds target the catalytic domain of the RT, so all the selective pressure applied to the virus is on a few amino acids. Eight protease inhibitors (PIs) prevent the maturation of virions. More recently a new class of antiviral drugs (Enfuvirtide) was shown to be capable of stopping

The time span between the HIV-1 initial infection and the appearance of AIDS can vary from a period of a few months to a period of 20 years or more. HIV-2 appears to be less pathogenic, with a larger time span between infection and the appearance of AIDS symptoms (Kandathil et al., 2005). A precise description of the various phases of the evolution of HIV-1 infection from the initial infection until the terminal phase, AIDS, has been provided by the Center for Disease Control (CDC, 1992): it is the famous 1993 CDC classification.

1200

Acute HIV syndrome wide dissemination of virus seeding of lymphoid organs

Primary infection

Death

107

1000

Opportunistic diseases

Clinical latency

900

106

800 Constitutional symptoms

700

105

600 500

104

400 300

HIV RNA copies per ml plasma

CD4 T lymphocyte count (cells/mm3)

1100

103

200 100 0

0

3

6 Weeks

9

12

1

2

3

4

5

6

7

8

9

10

11

102

Years

Figure 108.2 Standard evolution of the CD4 lymphocytes and of the viral load in the course of HIV-1 infection. The initial increase in viral load is buffered by the immune response within weeks. Then, without treatment, the evolution will vary from one individual to another, but on average, a seropositive subject will progress to the AIDS stage (immunodeficiency) in about 10 years.

Context of HIV and AIDS

the fusion process by targeting the gp41 region of the viral envelope. This drug, that needs to be administered by subcutaneous injections twice daily, is proposed mainly to advanced patients with drug resistant viral strains. The high rate of errors of the RT and the ability of viral strains to recombine lead to the diversity of HIV in infected subjects. This reinforces the need to combine potent antiviral drugs in order to prevent the emergence of HIV variants resistant to antiviral drugs. Although the long-term toxicities of HAART raised concerns about the use of these drugs over decades, the benefits of HAART clearly outweigh their potential side-effects (e.g.: renal, hepatic, cardiovascular, mitochondrial) and metabolic complications (diabetes, hyperlipidemia, lipodystrophy). These complications provide arguments for delaying the introduction of treatment in asymptomatic patients. However, the incomplete quantitative and qualitative immune restoration, the absence of reconstitution of lymphocyte populations in the gut, provide arguments for an early initiation of antiretroviral therapy. The development of immune-based strategies (cytokine therapy, therapeutic immunization) might help to contain viral replication during the period of treatment interruption (Levy, 2005; Levy et al., 2005, 2006; Mitsuya et al., 1985). Disease Transmission HIV-1 infection is primarily a sexually transmitted disease, and this sexual transmission route was the first described, essentially in the homosexual population in 1981–1982, but also in heterosexual patients since 1983 (Levy, 2006). Further studies on the transmission revealed other routes: intravenous drug use with contaminated syringes, infected blood transfusion and motherto-child transmission (Harris et al., 1983). The mother-to-infant transmission can occur either through blood contact at birth or through breastfeeding. Antiretroviral regimens can prevent intrapartum HIV transmission; however, these regimens do not prevent HIV transmission through breastfeeding. This is a crucial matter in countries where breastfeeding is commonly practiced for 1 or 2 years after birth. Furthermore, children who escape mother-to-infant transmission are again at risk of infection when they become sexually active. This is a strong rationale for the development of infant vaccine regimen that would represent an attractive strategy for long-term protection (Blanche et al., 1985). The transmission by blood transfusion has been highly limited since 1985, at least in developed countries, thanks to the systematic use of blood tests for screening the blood donations and also by heating procedures. Elements of Physiopathogenesis The first reports of AIDS cases were characterized by the presence of opportunistic diseases. This reflects the major effect of infection by HIV: a destruction of the immune system, which lets all pathogens or cancerous cells free to invade the body. The hallmark of HIV infection is a progressive decrease of naïve and memory CD4 T cells. AIDS occurs at the late stages of the depletion of the T cell pool. Recent data indicate that the

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depletion of memory CD4  CCR5 begins early after primary infection in the gut-associated lymphoid tissues (Luzuriaga et al., 2006). Interestingly the mechanisms of the restoration of this pool of CD4 T cells seem different to that of peripheral CD4 T cells, the latter being restored under HAART, while the depletion of gut lymphocytes persists over years. The mechanisms underlying the depletion of CD4 T cells are not well understood. One characteristics of HIV infection is the decrease of CD4 T cell survival (Kovacs et al., 2005; Mehandru et al., 2004; Mohri et al., 2001). Various direct and indirect mechanisms of T-cell depletion have been proposed since the direct infection of CD4 T cells cannot fully explain the destruction of the T cell pool. The only conclusive and reproducible observations to date point out the cytokine dysbalance, for instance the so-called Th1/Th2 shift (Kovacs et al., 2001), the greater fragility of the cells leading to their apoptosis (Ameisen and Capron, 1991; Clerici and Shearer, 1993) and the altered function of the antigen-presenting cells (Gougeon, 2003). Most recent data favor the role of chronic immune activation as a crucial mechanism of the chronic destruction of memory T cells (Hewson et al., 1999). The lack of reconstitution of the memory T-cell compartment from naïve CD4 T cell may stem from a defect of T-cell thymopoiesis as recently suggested by the observation of a low content of T-cell receptor excision circles, a molecular marker of recent thymic emigrants (Giorgi et al., 1999). Recent data suggest that HIV Nef protein may have a critical role in the destruction of CD4 T cells. Indeed, in contrast to Nef from non-pathogenic strains of SIV, HIV-1 Nef fails to reduce the degree of immune activation leading to an accelerated apoptotic death of infected cells (Dion et al., 2004). The CD4 antigen is the primary cell surface receptor for the virus (Schindler et al., 2006), but an interaction with additional receptors is required for the entry into the cell and the initiation of the infection. In vitro, the entry could be mediated by more than a dozen G-protein-coupled coreceptors from the chemokine receptor family (Berger et al., 1999; Dalgleish et al., 1984). However, there is little evidence that all these coreceptors are used in vivo (Clapham and McKnight, 2002). Schematically, the two most relevant coreceptors for HIV-1 replication in vivo are the -chemokine receptor CCR5 and the -chemokine receptor fusin CXCR4. The viruses that exclusively use CCR5 (receptor of -chemokines RANTES, MIP-1 and MIP-1) are known as R5 strains (generally non-syncytium-inducing), and those that exclusively use CXCR4 (receptor of -chemokine SDF-1) are called X4 strains (generally syncytium-inducing). R5 strains which target mainly macrophage-type cells are predominant in the initial infection for most individuals and they are progressively replaced by X4 variants, which target CD4 lymphocytes, and whose appearance has been associated with CD4 cell decline and disease progression (Berger et al., 1998). Several lines of evidence suggest that the immune system contributes to the long-term control of HIV-1 replication (Berkowitz et al., 1998; Borrow et al., 1994; Cao et al., 1995; Rosenberg et al., 1997). This is well illustrated by a subgroup of patients, called long-term non-progressors (LTNP), in whom

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Genomic Medicine and AIDS

preserved immune responses are associated with control of HIV replication to exceptionally low levels (Cao et al., 1995; Koup et al., 1994), in the absence of treatment. However, continued viral replication leading to a progressive immune destruction is the rule in a majority of patients. The level of viremia in untreated patients during this chronic phase is significantly reduced as compared to the peak viremia following primary infection. This reduction is related to specific immune responses detectable in patients since the early stages of infection. In the long term, although HIV-1 specific CD4 and CD8 responses may be detectable in patients at different time points of the disease, these cells are functionally impaired and fail to control viral replication after treatment discontinuation (Appay et al., 2000; Carcelain et al., 2001; Goepfert et al., 2000; Lieberman et al., 2001; Rosenberg et al., 1997). However, HIV-specific memory responses are not definitively lost and increase when the immune system is re-exposed to HIV antigens after discontinuation of HAART, but fail to contain HIV replication (Carcelain et al., 2001; Champagne et al., 2001). Structured treatment interruption (STI) may allow boosting of immune responses to HIV as the virus rebounds (Libois et al., 2006; Oxenius et al., 2002). However, this strategy does not lead to a significant control of viremia after a prolonged discontinuation of antivirals in patients chronically treated with antivirals (Choi et al., 2006; Lawrence et al., 2006; Ortiz et al., 1999). Moreover the absence of correlation between HIV-specific CD8 responses and the level of viremia once HAART is stopped (Silva et al., 2006) suggests that the immune system may need additional immune stimulation rather than “auto vaccination”.

PREDISPOSITION: SUSCEPTIBILITY TO HIV-1 INFECTION Genetic and Biological Markers Some individuals seem to resist infection by HIV-1 very efficiently: they are called Highly Exposed Persistently Seronegative individuals (HEPS). They can easily be identified among users of intravenous drugs, prostitutes and partners of seropositive patients engaged in frequent and unprotected sex. It is more difficult to estimate the proportion of such individuals in the general population. Winkler et al. described 79 potential HEPS in the American MACS cohort, composed of 5000 subjects considered “at risk” (Oxenius et al., 2002). Until now, only one genetic marker was clearly identified for its impact on susceptibility to infection: the homozygous presence of the CCR5-32 deletion. As CCR5 is the main coreceptor for R5-strains, the lack of a functional receptor highly limits the risk of successful infection by HIV-1. This was first demonstrated independently by 2 groups in 1996 (Dean et al., 1996, Samson et al., 1996, Winkler et al., 1998). McDermott et al. showed in 2000 that the RANTES-403A allele could be a risk factor for acquiring HIV-1 (Samson et al., 1996), but it still has to be confirmed by other studies. Zagury et al. showed that 16 out of 128 hemophiliacs transfused with massive amounts of infected blood during many

years, but who did not get infected (they were HEPS), produced higher levels of beta-chemokines. (Zagury et al., 1998) However, the RANTES polymorphism has not been tested in these 16 individuals. Kozlowski et al. suggested in 2003 that mucosal immunity may also play an important role in the resistance to HIV-1 infection (Zagury et al., 1998). Viral Strains According to Kunanusont et al., the E subtype is more infectious through the sexual transmission route than the B subtype (Kozlowski and Neutra, 2003). Their hypothesis is that in vivo this subtype more efficiently infects the mucosal Langerhans cells, as it does in vitro. This would contribute to the dramatic spread of the epidemics in South-East Asia where this strain is predominant (Kunanusont et al., 1995). The Environment: Pros and Cons Environmental factors can affect the susceptibility to HIV-1 infection. Many factors both increase the risk of infection and help the onset of AIDS. Among them, some are of socio-economic class, and the others are linked to the infectious environment. In reference to the socio-economics factors, malnutrition and poverty induce favorable conditions for the infection, by lowering the individual’s immune capacity. Another risk factor for children is the duration of breastfeeding, because the milk contains the virus. Each meal can represent a new opportunity of entry for the HIV-1, and for children who are already infected it brings a supplementary dose of virus. However, the quantitative effect of breast-feeding remains uncertain. The other class of risk factors is the infectious environment. The presence of other pathogens enhances a proinflammatory state which could facilitate the infection (Ford, 1996). However, the coinfection by HIV-1 and HIV-2 seems to decrease the infectivity of HIV-1, but no clear mechanism was found. A recent trial performed in South Africa confirms a protecting effect of circumcision against infection by HIV-1. The hypothesis proposed to explain this effect is that the soft and fragile mucosal surface is keratinized because of its constant exposition to external aggressions and thus becomes more resistant to viral infection. Also, the absence of prepuce reduces the risks of keeping a stock of infectious products and limits the surface of exchange with the sexual partners (Cohen, 2004; Dingman, 1996).

DIAGNOSIS Serology The diagnosis of HIV infection is based on the detection of specific antibodies and/or HIV antigens. Antibodies can be detected with a very high sensitivity through an enzyme-linked immunosorbent assay (ELISA test), but a confirmatory test by Western blot is needed. In HIV-1 infection, patients become seropositive within a few weeks after infection, whereas the first clinical symptoms related to immunodeficiency often appear much later. The serology tests are, to date, the sole reliable means for detecting HIV-1 infection. The direct detection of viral RNA

Prognosis

(viral load) is not adapted to make a diagnosis of infection since the number of viral RNA copies can be below the detection rate in many seropositive subjects. HIV-1 antibodies can migrate through the placenta to the fetus, and thus infants from HIV-1 infected mother can be seropositive without being themselves infected by HIV-1. Clinical Symptoms and CD4 Cell Decrease Serology is the major criterion for the identification of HIV-1 infection, but the definition of AIDS is based on the severe clinical manifestations described earlier and on a CD4 cells count below 200/mm3 (2005).

PROGNOSIS Human Genetics Among infected individuals, the susceptibility to the infection by HIV-1 is highly variable. The time span between the infection by HIV-1 and the AIDS stage presents inter-individual variations ranging from a few months to more than 20 years: some patients found seropositive at the beginning of the pandemics have not reached the AIDS phase yet, even without receiving any drug. In 1994, a classification of two extreme phenotypes was proposed, based only on the latency phase duration. A consensual definition for Long-Term Non-Progression (LTNP) (1992) corresponds to untreated patients who have remained asymptomatic for more than 8 years after their confirmed seropositivity, and have kept their CD4 cell count constantly higher than 500/mm3 blood. On the other hand, the Rapid Progression (RP) (Buchbinder et al., 1994) patients are those who have reached the AIDS stage in less than 3 years after their last seronegative test. The LTNP group and the RP group may represent respectively upto 5% and up to 10% of the infected patients. High throughput genotyping projects have focused on the identification of the genetic factors that could account for these particular phenotypes. For that, the distribution of the allele frequencies of each genetic marker in an extreme group is compared with that of a group of uninfected control subjects. These genomic projects based on extreme phenotypes are theoretically more powerful tools to unravel genetic factors involved in disease development/ progression than the study of the survival of seroconverter patients according to their genotypes by Kaplan–Meier analysis (Anzala et al., 1995). The LTNP cohorts known to date are GRIV (Do et al., 2006; Hendel et al., 1999; Huber et al., 2003; Vasilescu et al., 2003), ALT (Flores-Villanueva et al., 2003) and an Australian cohort (Magierowska et al., 1999). More recently, patients with a repeatedly undetectable viral load have been described : they correspond to a subgroup of LTNPs and are called “elite progressors”. Cohorts of elite progressors have been set-up in the United States (Clegg et al., 2000) with presently 150 subjects and in France (Bailey et al., 2006) with presently 50 subjects. GRIV is the only collection which also includes patients with an RP phenotype. Genotyping chips for genome-wide screenings (300K or 500 K single nucleotide polymorphisms,

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SNPs) are under way to be tested in various AIDS cohorts, but no result has been published yet. Up to now, only one study performed on a seroconverter cohort has been published, pointing out the protective effect associated with several polymorphisms in the HLA locus (Fellay et al., 2007). The genome scan study of the GRIV cohort has pointed out several gene polymorphisms involved in rapid progression and points out the initial role of mucosal immunity in progression (unpublished data). However, many genetic factors have previously been analyzed for their influence on progression to AIDS under the “candidate gene” approach. Consistent findings in the literature are the associations involving polymorphisms of CCR5, mainly the 32 deletion (Lambotte et al., 2005; Samson et al., 1996) and the P1 polymorphism in the promoter (Dean et al., 1996; Martin et al., 1998), and polymorphisms of class 1 HLA, mainly B35 associated with faster progression and B57 associated with resistance (Carrington et al., 1999; Hendel et al., 1999; McDermott et al., 1998).The putative HIV-1 protein epitopes associated with these HLA alleles have not been determined. An association has been suggested for specific combinations of KIR variants and HLA alleles (Gao et al., 2001). Several studies have associated the V64I mutation in the CCR2 coreceptor with a better resistance to progression (Lee et al., 1998; Martin et al., 2002). No mechanism has been discovered to explain the effect of this mutation. Associations with resistance to progression have been suggested for some RANTES promoter alleles: these alleles, 403A and 28G, were also shown to increase promoter activity suggesting that the increased RANTES expression within HIV-1 infected patients could help prevent viral spread and disease progression (Liu et al., 1999; Smith et al., 1997). Other associations between genetic polymorphisms and progression to AIDS have been described for cytokine-related genes such as IL4 (An et al., 2002;Vasilescu et al., 2003), IL10 (Nakayama et al., 2002; Vasilescu et al., 2003) or IL1_RN (Shin et al., 2000). It is important to stress that the apparent impact of all these polymorphisms on disease progression is generally limited to a few years (1–4 years) and it appears difficult to make a predictive test from these observations. If these genetic associations are validated in more cohorts, it will certainly be possible to combine some of this data and to derive reliable predictive tests, but the statistical power for such predictions is still insufficient. Much remains to be done in this field since it is suspected that no more than 10% of the genetic factors influencing disease progression are known (Do et al., 2006). Viral Load and CD4 Cells Count The most informative marker for the risk of AIDS progression is the number of viral RNA copies found in the plasma (viral load). As could be expected, a high viral load is associated with a shorter latency phase (O’Brien and Nelson, 2004). However, viral load alone is not fully relevant to describe the evolution of the infection: it has to be correlated to the CD4 cells count. As shown in Table 108.1, these two criteria must be taken together to allow a more precise description of the different observed phenotypes (Mellors et al., 1996, 2007).

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Genomic Medicine and AIDS

Viral Strains Being infected by several different subtypes is predictive of a faster progression rate to AIDS (Mellors et al., 1996). If the E subtype is more effective for the sexual transmission route (Cheonis, 2006), it has not been associated with a higher pathogenicity than the other strains.

TABLE 108.1

The Environment: Pros and Cons Environmental factors can affect the evolution of HIV-1 infection and AIDS onset. Most factors increase the risk of infection and help the onset of AIDS. Some of the factors which influence the susceptibility to infection also influence the progression to AIDS: malnutrition and poverty are classical factors in the

Relationship between CD4 count/viral load and AIDS progression*

CD4 count (cells/L)

Viral load (copies/mL)

200

Over 3 years

Over 9 years

10,000

14

64

10,000–30,000

50

90

30,000

86

100

50

85

7

66

10,000–30,000

36

85

30,000

64

93

36

81

7

54

10,000–30,000

15

74

30,000

40

85

21

71

MEAN 10,000 200–350

MEAN 10,000 350

MEAN Viral load (copies/mL)

CD4 count (cells/L)

Over 9 years

14

64

200–350

7

66

350

7

54

9

61

200

50

90

200–350

36

85

350

15

74

34

83

200

86

100

200–350

64

93

350

40

85

63

93

MEAN

10,000–30,000

MEAN

30,000

MEAN

AIDS progression in men (%) Over 3 years

200 10,000

AIDS progression in men (%)

*: adapted from Mellors et al. 1996 It seems that the viral load is a slightly better predictor for disease evolution than the CD4 lymphocyte count since there are more people evolving towards AIDS for high viral loads (30 000) than for low CD4 lymphocyte counts (200), and fewer people evoluting towards AIDS for low viral load (10 000) than for high CD4 lymphocyte counts (350). However such considerations are to be taken with a lot of caution since they may shift easily when one changes the cut-offs.

Pharmacogenomics

unrestrained development of the infection. The infectious environment also plays an important role: the presence of multiple pathogens generally accelerates the loss of CD4 cells. As for the infectivity, this was shown for hepatitis and herpes viruses, and the presence of HIV-2 lowers the progression rate to AIDS. This can be explained by the fact that the progression to AIDS consists mostly of an increase of the number of infected cells. If the infectivity is decreased/increased, the progression rate to AIDS follows the same rule.

MONITORING Viral Load and CD4ⴙ Cells Count The major parameters used to monitor the evolution of the infection are the CD4 cells count and the viral load. The CD4 cells count, apart from its prognostic value, has been the main criterion for the entrance in the AIDS stage of the infection. A regular follow-up of this count makes it possible to draw the progression line of the patient. Viral load, which is determined by RT-PCR on serum, has also become a major parameter for patient monitoring. This has stemmed from the work of Mellors et al. showing that the viral load had a very good prognosis value for the progression to AIDS (Kunanusont et al., 1995). Today, antiviral treatment is initiated when the CD4 cells count decreases significantly and is concomitantly associated with an increase in viral load. The increase of the CD4 cells count or at least its stabilization, together with the disappearance of viral load are the major monitoring test to assess the efficacy of a drug in a patient: when a treatment failure happens (persistently positive viral load), new drugs have to be used. The choice of the new drugs is described in the next paragraph. Adaptation of the Therapeutic Arsenal to the Viral Polymorphisms Another major concern in the monitoring of the infection is, beyond the follow-up of clinical features and CD4 cells count, the monitoring of viral genetic variation. The observation of resistance mutations can be summed up in resistance and therapeutic algorithms. Thus, the success of a therapeutic change after appearance of resistance in a patient’s virus population can be estimated by the identification of the resistance mutations that occurred in the viral genome. Indeed, these mutations can induce multiple drug resistances, and impair some therapeutic options. A recent illustration of this has been published by Yam et al. in 2006, applied to non-B subtypes of HIV-1 (Mellors et al., 1996). Every year a new release of the resistance mutations tables is published by the French Agence Nationale pour la Recherche contre le SIDA (ANRS) AC11 on the website http://www.hivfrenchresistance.org. The 2005 release is reported in Table 108.2. The International AIDS Society, USA also regularly updates their list of resistance-phenotype associated mutations (Yam et al., 2006). Several other algorithms can

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be found, and they were listed and compared by Sturmer et al. in 2003 (Johnson et al., 2005). They observed that except for one of these algorithms, all of them gave globally consistent information. With a little effort of standardization, they would all become equivalent in terms of clinical prediction.

PHARMACOGENOMICS Surprisingly, few large-scale studies have been published yet regarding the influence of HIV-1 or HIV-1 drugs on human cellular gene expression. We will mention the following studies: Li et al. (Sturmer et al., 2003) who found a differential expression of immune and inflammatory-related genes following HAART, Galey et al. (Li et al., 2004) who found that the expression of human genes was modified in astrocytes exposed to HIV-1 or gp120, Wen et al. (Galey et al., 2003) who identified many genes whose expression profiles were changed when macrophage or lymphocyte cell lines were infected by HIV-1, Solis et al. (Wen et al., 2005) who have studied the influence of the different viral strains on the expression of more than 3700 genes, Acheampong et al. (Solis et al., 2006) who have used micro-arrays to assess the effects of HIV-1 retrovirus on oral keratinocytes, Ross et al. (Acheampong et al., 2005) who have studied the genetic inflammatory cascade induced by HIV1 in human renal tubular epithelial cells, Wang et al. (Ross et al., 2006) who have studied the HIV-1 induced gene expression in astrocytes. It seems that no major breakthrough has arised from these studies, but this can be explained by the difficulties that still exist in interpreting reproducibly the results of micro-arrays. The analysis of AIDS drugs on gene expression should be promoted by pharmaceutical companies but they still have a lot of work to do in exploiting their candidate drugs targeting the RT and the viral protease. Overall, the most informative-omics approaches to date are cohort studies dealing with the genetic factors involved in susceptibility or resistance to HIV-1 infection as have been described in the diagnosis and prognosis parts of this chapter. Beside the development of CCR5 inhibitors, the application of these cohort studies can be found in vaccine clinical trials where HLA and CCR5 backgrounds are often genotyped. Hopefully the coming years will see the output of several -omics wide projects aiming at new therapeutic targets (see Emerging Therapeutics). Importantly, clinical practice has shown that some patients are intolerant to a drug, Abacavir, and it was found that these patients carry the HLA-B5701 genotype. As a consequence, before being treated by Abacavir, patients must be tested for their HLA-B alleles to avoid toxicity (Saag et al., 2008). More work has been done on the genomics of the virus, especially for the monitoring of patients taking drugs as seen in the previous chapter. The genetic differences between subtypes of HIV-1 can also change the probability of resistance mutations (Wang et al., 2004): for example, the subtype B has a lower genetic barrier to reach resistance to PI Indinavir than subtype G. There is another point to be aware of in the genetic

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TABLE 108.2

■

Genomic Medicine and AIDS

Algorithm for the choice of treatment according to the sequence of the RT

Drug

Condition

AZT

215YF

3

Select at least 3 from (41L, 67N, 70R, 210W, 215ACDEGHILNSV, 219QE)

3

151M

3

69i

3

215ACDEGHILNSV

2

75MSAT

3

215YF

3

Select at least 3 from (41L, 67N, 70R, 210W, 215ACDEGHILNSV, 219QE)

3

151M

3

69i

3

215ACDEGHILNSV

2

Exclude 70R and exclude 184VI and select at least 2 from (41L, 69D,74V, 215FY, 219QE)

3

Select at least 1 and not more than 1 from (70R,184VI) and select atleast 3 from (41L,69D,74V,215FY,219QE)

3

70R AND 184VI and select at least 4 from (41L,69D,74V,215FY,219QE)

3

151M

3

69i

3

74V and exclude 41L and exclude 69D and exclude 70R and exclude 184VI and exclude 215FY and exclude 219QE

3

65R

2

184VI

3

69i

3

65R

2

151M

2

184VI

3

69i

3

65R

2

151M

2

Select at least 5 from (41L,67N,74V,184VI,210W,215YF)

3

65R AND 74V AND 115F AND 184VI

3

151M

3

69i

3

Select at least 4 and not more than 4 from (41L,67N,74V,184VI,210W,215YF)

2

65R

2

D4T

DDI

3TC

FTC

ABC

Level

(Continued)

Novel and Emerging Therapeutics

TABLE 108.2 TDF

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Algorithm for the choice of treatment according to the sequence of the RT (Continued) Select at least 6 from (41L,44D,67N,69DNS,74V,210W,215YF) 65R

3

69i

3

Select at least 3 and not more than 5 from (41L,44D,67N,69DNS,74V,210W,215YF)

2

This algorithm indicates the risk of resistance to a drug or not according to the mutations found in the HIV-1 target protein. For instance for the drug AZT, a mutation of the RT at position 215 into a Tyrosine (Y) leads to a complete resistance while a mutation of the RT into an alalnine (A) at the same position leads possibly but not always to drug resistance. The Table presented here has been limited to the NRTI, but the complete Table which includes the resistance to the other drugs is available from the Web site: http://hivdb6.stanford.edu/asi/deployed/xmlTools/ algdisplay.pl?algnameANRS Level Definitions Order

Drug Classes Name

PI NRTI NNRTI EI

Original Susceptible Possible resistance Resistance Druglist

IDV: indinavir, SQV: saquinavir, NFV: nelfinavir, RTV: ritonavir, APV/r: fosamprenavir, LPV: lopinavir, ATV: atazanavir, TPV/r: tipranavir AZT: zidovudine, D4T: stavudine, DDI: didanosine, 3TC: lamivudine,FTC: emtricitabine, ABC: abacavir, TDF: tenofovir NVP: nevirapine, EFV: efavirenz T20

variability in HIV-1 strains: a single vaccine may not be protective against all the possible strains, considering the existence of multiple mutants. In that line, a risk for vaccine development is that it targets only the viral subtype B most prevalent in Europe and North America, without taking into account the variants existing elsewhere (Rowe, 1996; Spira et al., 2003).

NOVEL AND EMERGING THERAPEUTICS New viral proteins inhibitors New NRTIs, NNRTIs and PIs are still in development that are trying to decrease the toxicity of treatments and to diversify the targets (Osmanov et al., 1996). These aspects must be investigated further, as well as other ones (Fernandez et al., 2004), and may give birth to new kinds of NRTIs. In general terms, both competitive and non-competitive new inhibitors should be developed, to broaden the number of targe ts and the number of possible drug combinations. This could give more tools to limit the appearance of multiple resistant strains. The ideal would be drug combinations in which one is more efficient if the virus is resistant to the other drugs of the combination. Knowing that the resistance mutations decrease the virus fitness (Wainberg et al., 2005), such drugs could definitely impair the infection. If the expansion of RTIs and PIs classes is crucial, so is the targeting of the other viral proteins and functions. Among them, the IN (Andreoni, 2004) and the envelope protein (Hazuda et al.,

2004). Following interesting pre-clinical studies (Bonnenfant et al., 2004; Reeves and Piefer, 2005; Reinke et al., 2004), results of phase I/II clinical studies of IN inhibitors are encouraging (Di Santo et al., 2006; Grinsztejn et al., 2006). For Env inhibitors, one has already been licensed for therapeutic use called T-20 or enfuvirtide. The T-20 molecule, which targets the Env protein, is the first fusion inhibitor, and prevents the fusion of the virus with the cell membrane. It is used mostly by patients in therapeutical failure or with resistance to the other drugs. However, viruses resistant to T-20 have already been described (DeJesus et al., 2006), and their fitness measured (Bean, 2002). Other members of this molecule family are currently under development, with the idea, once again, of broadening the drug offers to limit the rise of resistant virions. Entry inhibitors New classes of drugs aiming at blocking the entry of the virus are currently under development and have even reached clinical trials phases 2 or 3 (Lu et al., 2004). A complete review of this question has been published in 2006 (Clay et al., 2006). These new classes consist mostly in two groups: antagonists of viral receptors and inhibitors of cell fusion (Clay et al., 2006). The antagonists of viral coreceptors target principally CCR5 for the moment, since the antagonists of CXCR4, despite promising anti-HIV-1 potential activity, still need chemical processing and optimization before entering a clinical trial (Cammack, 2001; De Clercq, 2005; Hatse et al., 2005). The antagonists of CCR5 have already shown a beneficial effect in short-term treatments

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(Feng et al., 2006), however, their potential hepatotoxicity has delayed the distribution license attribution. CD4 antagonists are also under development, but they are at the very beginning of the process (Fatkenheuer et al., 2005) and there has been a failure in the past of anti-CD4 mAb (Vermeire and Schols, 2003). Immune-based approaches Immune-based strategies aiming at controlling viral replication or improving immune recovery presently consist in the use of therapeutic immunization or immunomodulatory agents such as cytokines (Husson et al., 1992; Levy, 2005). Hopefully, these strategies may help to overcome the limitations of HAART by reducing the exposure to antiviral drugs. However, the scope of immune-based therapies studied so far is minor because of the absence of a large number of candidate agents. Among the immunomodulatory approaches, IL-2 is the most promising agent to date and has been under study for a number of years.The potential clinical benefit of this cytokine is currently under investigation in two large phase III international trials (ESPRIT and SILCAAT). Although these endpoint trials are ongoing, recent clinical studies have contributed to our understanding of the mechanisms of action of this cytokine and on its effects on the immune system (Marchetti et al., 2008). Studies evaluating the safety and the biological activity of IL-7 in HIV-infected patients are ongoing in France and USA. There is nowadays a consensus that developing a “sterilizing” vaccine – one that prevents the entrance of the virus in the body – will be very difficult due to the viral variations and researchers are instead looking for vaccines capable of controlling viral expansion (Levy, 2006). Among them, we must emphasize a genetically engineered vaccine with a deletion in the nef gene which was efficient in monkeys (Letvin, 2006). However, the detection of revertants with induction of AIDS has been described in vaccinated animals and questions the safety of attenuated vaccines (Daniel et al., 1992). Vaccine therapies at an advanced stage of disease are questionable since the immune system may be too surpressed. A promising research direction could be to combine various immune-based therapies with STI (Koff et al., 2006). The rationale is that during the treatment phase the viral-induced immunosuppression will be blocked and it will thus be possible to boost the immune system, and in turn, the immune system

will then be able to better control the virus after the interruption of treatment. Emerging ideas After targeting the viral proteins and the interactions between viral and host proteins, new pathways are being explored, where only host proteins or functions are targeted. For example, Song et al. recently presented an approach to enhance the antigen presentation on dendritic cells (Clumeck et al., 2006). Despite recurrent failure, vaccine projects are still under development, using a wide variety of immunogens (Song et al., 2006). The advent of genomic approaches (micro-arrays, cohort studies may lead to the identification of unsuspected targets and open to the development of new therapeutic and preventive strategies.

CONCLUSION This chapter has reviewed the various aspects of AIDS from the fundamental viral properties, its interactions with the human host, and the practical monitoring of patients, trying to emphasize the genomic aspects. As can be observed, it is premature to talk about a fully integrated genomic approach to AIDS. The use of AIDS-related DNA micro-arrays is still at a very early stage of development and a challenge will be to identify new therapeutic targets fitting with all the various viral subtypes. However, the existence of genetic tests such as the measure of viral load for prognosis of disease evolution, the search for viral mutations or HLA genotype to adapt the treatment, the identification of homozygous carriers of the CCR5-32 mutation to detect subjects likely to be resistant to infection, is proof that genomic means which target both the human and the viral genomes are already operational. New tests related to the genetic susceptibility to infection or to disease progression could emerge in the near future thanks to the extensive genotyping of large cohorts. A study has succeeded in correlating the viral genome evolution in infected subjects with their HLA class 1 genetic background (Peters, 2000): a major genomic challenge in the future will certainly be to combine on a large-scale knowledge of the human genetic background and that of the infectious agent in order to derive precise tools for disease treatment and prognosis.

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Levy, Y., Durier, C., Lascaux, A.S., Meiffredy, V., Gahery-Segard, H., Goujard, C., Rouzioux, C., Resch, M., Guillet, J.G. and Kazatchkine, M. (2006). Sustained control of viremia following therapeutic immunization in chronically HIV-1-infected individuals. Aids 20, 405–413. Levy, Y., Gahery-Segard, H., Durier, C., Lascaux, A.S., Goujard, C., Meiffredy, V., Rouzioux, C., Habib, R.E., Beumont-Mauviel, M., Guillet, J.G. et al. (2005). Immunological and virological efficacy of a therapeutic immunization combined with interleukin-2 in chronically HIV-1 infected patients. Aids 19, 279–286. Li, Q., Schacker, T., Carlis, J., Beilman, G., Nguyen, P. and Haase, A.T. (2004). Functional genomic analysis of the response of HIV-1infected lymphatic tissue to antiretroviral therapy. J Infect Dis 189, 572–582. Libois, A., Lopez, A., Garcia, F., Castro, P., Maleno, M.J., Garcia, A., Climent, N., Arnedo, M., Gallart, T., Gatell, J.M. et al. (2006). Dynamics of T cells subsets and lymphoproliferative responses during structured treatment interruption cycles and after definitive interruption of HAART in early chronic HIV type-1-infected patients. AIDS Res Hum Retroviruses 22, 657–666. Lieberman, J., Shankar, P., Manjunath, N. and Andersson, J. (2001). Dressed to kill? A review of why antiviral CD8T lymphocytes fail to prevent progressive immunodeficiency in HIV-1 infection. Blood 98, 1667–1677. Liu, H., Chao, D., Nakayama, E.E., Taguchi, H., Goto, M., Xin, X., Takamatsu, J.K., Saito, H., Ishikawa, Y., Akaza, T. et al. (1999). Polymorphism in RANTES chemokine promoter affects HIV-1 disease progression. Proc Natl Acad Sci USA 96, 4581–4585. Lu, J., Sista, P., Giguel, F., Greenberg, M. and Kuritzkes, D.R. (2004). Relative replicative fitness of human immunodeficiency virus type 1 mutants resistant to enfuvirtide (T-20). J Virol 78, 4628–4637. Luciw, P.A., Potter, S.J., Steimer, K., Dina, D. and Levy, J.A. (1984). Molecular cloning of AIDS-associated retrovirus. Nature 312, 760–763. Luzuriaga, K., Newell, M.L., Dabis, F., Excler, J.L. and Sullivan, J.L. (2006). Vaccines to prevent transmission of HIV-1 via breastmilk: scientific and logistical priorities. Lancet 368, 511–521. Magierowska, M., Theodorou, I., Debre, P., Sanson, F., Autran, B., Riviere, Y., Charron, D. and Costagliola, D. (1999). Combined genotypes of CCR5, CCR2, SDF1, and HLA genes can predict the long-term nonprogressor status in human immunodeficiency virus-1-infected individuals. Blood 93, 936–941. Maniatis, T., Fritsch, E.F. and Sambrook, J. (1982). Molecular Cloning, A Laboratory Manual. Cold Spring Harbor Laboratory, New York. Marchetti, G., Tincati, C., Monforte, A. and Gori, A. (2008). The challenge of IL-2 immunotherapy in HIV disease: “no through road” or turning point?. Curr HIV Res 6, 189–199. Marlink, R., Kanki, P., Thior, I., Travers, K., Eisen, G., Siby, T., Traore, I., Hsieh, C.C., Dia, M.C., Gueye, E.H. et al. (1994). Reduced rate of disease development after HIV-2 infection as compared to HIV-1. Science 265, 1587–1590. Martin, M.P., Dean, M., Smith, M.W., Winkler, C., Gerrard, B., Michael, N.L., Lee, B., Doms, R.W., Margolick, J., Buchbinder, S. et al. (1998). Genetic acceleration of AIDS progression by a promoter variant of CCR5. Science 282, 1907–1911. Martin, M.P., Gao, X., Lee, J.H., Nelson, G.W., Detels, R., Goedert, J.J., Buchbinder, S., Hoots, K., Vlahov, D., Trowsdale, J. et al. (2002). Epistatic interaction between KIR3DS1 and HLA-B delays the progression to AIDS. Nat Genet 31, 429–434.

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CHAPTER

109 Viral Genomics and Antiviral Drugs Roberto Patarca

INTRODUCTION Viral scourges continue to exact a huge toll (Osterholm, 2000). The disastrous acquired immunodeficiency syndrome (AIDS) virus pandemic is merely the most well known. Complications of hepatitis B and C kill approximately 1 million people each year, and measles continues to be the cause of deaths among children even though effective vaccines exist for measles and hepatitis B. Rotavirus, which causes diarrhea, takes the lives of hundreds of thousands, mainly children. It is not uncommon these days to hear news of gastrointestinal viral infections that affect cruise ship passengers. Other viral diseases seem to be restricted to specific areas. Ross River virus disease causes acute illness in parts of Australia, and the deadly Ebola and hantavirus occasionally emerge to wreak havoc in Africa and the American south. West Nile virus has terrorized New York and is responsible for several deaths. Many mysterious historical outbreaks of disease were probably viral, among them George Reinhold Forster’s description of the Tapanui flu, which sickened thousands of people in New Zealand in the 1970s, and Akureyri disease, which affected 1000 people in a small town in Iceland in 1948 and 1949. Some scientists have proposed, even as early as a century ago, that a much wider range of illnesses than is generally thought might actually be caused by viruses. Driven by advances

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in viral disease treatments secondary to the genomics revolution, these hypotheses have also been rekindled and challenge conventional wisdom to expand the realm of diseases of viral etiology to include pathological processes, such as atherosclerosis (Kullo et al., 2000) and autoimmune disease (Jones and Armstrong, 1995; Talal et al., 1990), which would not have been previously thought as secondary to infectious processes. Regardless of whether the latter hypotheses prove to be correct, the experience that is being garnered in this antiviral revolution also serves, in the light of the information now available on the genetic makeup of human beings and other living organisms, as an encouraging paradigm for the development of drugs to treat all kinds of human diseases and to understand complex biological functions. The current antiviral drug revolution can trace its first roots to the contributions of scientists such as Spallanzani, who compellingly challenged the theory of spontaneous generation supported by the Roman Catholic Church and revealed the existence of a biological microcosm invisible to the naked eye that could account for many occurrences that were until then shrouded by a cloud of mystical religious beliefs; from the growth of mold on a wet surface to the transmission of certain diseases. Although Girolamo Fracastoro taught, in 1530, that syphilis was a contagious disease spread by “seeds”, and in 1683 Anthony van Leeuwenhoek observed bacteria by using

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Viral Genomics and the Antiviral Drug Revolution Era

a crude microscope, it was the nineteenth century, with the contributions of scientists such as Louis Pasteur and Robert Koch, that saw the consolidation of the germ theory of disease and the flourishing of microbiology thanks to the definitive isolation of infectious organisms and the demonstration of their association with disease (Lederberg, 2000). Around that time, Dmitri Ivanowski and Martinus Beijerinck described viruses as small infectious agents that could pass through bacteria-stopping filters. Following the identification of infectious organisms, immunology was born as a discipline aimed at unraveling the mechanisms used by the body to control and defeat them. Shortly thereafter, modern infectious disease medicine was inaugurated with the discovery of antibiotics in the early part of the twentieth century, an accomplishment that was based on the observation that fungi produced substances that were able to kill bacteria. The isolation and medical use of penicillin and other naturally occurring antibiotics, as well as the development of their synthetic derivatives, has allowed to control the spread and severity of bacterial infections and to preclude reemergence of vast bacterial epidemics, such as the bubonic plague caused by the bacteria Yersinia pestis that killed a large portion of the human population in the Middle Ages. However, the misuse of antibiotics had also led to the emerging public health threat of antibiotic-resistant bacterial strains such as the invasive methicillin-resistant Staphylococcus aureus strains that are causing an unacceptable number of deaths among young nonhospitalized individuals (Klevens et al., 2007). Unlike the case with bacteria, viruses have no known natural enemies from which to isolate antibiotic-like substances, and, until the mid-1980s, viral infections were thought to be inherently preventable in some cases but generally untreatable. Many viral epidemics, such as polio, yellow fever, AIDS, and viral hepatitis, have received worldwide attention in the past and during this century. As mentioned above, there are also many historical accounts of diseases of presumed viral etiology. The first half of the twentieth century witnessed the first successful approach to control the spread of several viral infections: the development and worldwide use of vaccines. The concept of vaccination was originally developed by Jenner in eighteenth-century England based on the observation that milkmaids exposed to cows with cowpox were protected from smallpox. In this case, a subclinical infection with one virus was protective of an infection with a related one. The latter concept was also extended to the treatment of various infectious diseases by giving the patient even unrelated but more innocuous infectious diseases. Although in many cases the treatment was worse than the disease, the therapeutic approach was somehow useful with particular combinations of infectious agents. Outstanding triumphs of worldwide vaccination programs have been the eradication of smallpox and, predictably soon, of poliomyelitis (Marwick, 2000). After smallpox was eliminated as an infectious disease in Great Britain in 1962, two outbreaks occurred, one in 1973 and one in 1978, when smallpox virus under study in laboratories infected susceptible individuals. In both incidents, deaths resulted (Marwick, 2000). With the

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eradication of poliomyelitis throughout the world soon to be accomplished, steps are being taken to prevent polioviruses that remain in laboratories from escaping into the community and causing disease. These examples stress the need for universal availability of vaccines to effectively eradicate the diseases they cause. Unfortunately, we do not have vaccines against all viruses, and even in the cases for which we do have vaccines, the vaccine is not universally available. The dramatic success in immunizing children against childhood diseases stands in stark contrast to the much lower percentages of adults who are adequately immunized against common adult diseases. In the case of the flu vaccine, the influenza virus keeps changing, the vaccine has to be updated every year, and it is therefore not fully protective against all viral strains. One alternative to vaccines has been the use of injections of immunoglobulins, the natural bullets that the body produces to kill foreign invaders. Not too long ago, physicians advocated the use of immunoglobulin injections as a way to “boost” the body’s immune defenses and heighten resistance against microbes. The latter reasoning was perhaps again reflective of the old wisdom of using one infection to protect against another with the added refinement of using the natural mediators of the body’s attack machinery against infections instead of the infectious agent itself. The limitations of antibodies as antiviral therapeutic agents still leave us with having to treat viral infections. The Nobel laureate Paul Ehrlich preached in the early twentieth century about the usefulness of discovering chemical substances that would act as “magic bullets” against infectious agents with little or no untoward effects to humans. Although the “magic bullets” studied in Ehrlich’s days were too toxic, Ehrlich’s vision inspired the pharmaceutical industry’s search for therapeutic small molecules, a task that has now been rekindled with the help of modern biology.

VIRAL GENOMICS AND THE ANTIVIRAL DRUG REVOLUTION ERA Human Immunodeficiency Virus Viruses were the first microorganisms whose complete genetic makeup was characterized. The information on viral genes and their protein products has allowed the development of a series of chemicals with targeted antiviral activity. The advent of the AIDS pandemic in the second half of the twentieth century became the largest challenge to infectious disease medicine of the modern era. The discovery and characterization of the first human pathogenic retroviruses, the human T-cell leukemia/lymphoma virus type I (HTLV-I) (Josephs et al., 1984), facilitated the discovery of the etiological agent of AIDS, the human immunodeficiency virus type 1 (HIV-1). Determination of the primary structure of HIV-1, first known as HTLV-III or LAV (lymphadenopathy virus) and computerized analysis of the amino acid sequence of the gene products it encodes

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provided the targets and, at the same time, the reagents to develop rapid and sensitive assay systems for testing potential therapeutic agents with anti-HIV activity (Ratner et al., 1985). Therefore, anti-HIV therapeutic medicines were born from the marriage between molecular and cellular biology, traditional therapeutic small molecule screening, and the then-incipient discipline of bioinformatics (De Clercq, 1997; Elfassi et al., 1986; Haseltine and Patarca, 1986; Hirsch and Kaplan, 1987; Johnson and Hoth, 1993; Mitsuya and Broder, 1987; Patarca and Haseltine, 1984a, b, 1985, 1986; Patarca et al., 1982, 1987) a marriage that also fueled the vigorous resurgence of genomics research. The first databases of nucleic acid and protein sequences were created in the late 1970s. The computerization of algorithms for primary structure comparisons and secondary structure predictions (hydrophilicity and folding structures) and their use to analyze the genetic makeup of the AIDS virus in the framework of the knowledge garnered over decades for other known viruses quickly provided the targets for the development of anti-HIV agents. It is therefore the case that, although nonprimate viruses were the first microorganisms whose complete genetic makeup was characterized, it was not until the complete sequence of the AIDS virus became available in an unprecedented record time thanks to the strong public pressure for basic and clinical research that the information on viral genes and their protein products triggered an exponential growth in the development of chemicals with targeted antiviral activity. Before the AIDS epidemic, the only antiviral agent that had been widely introduced to clinical practice with modest acceptance was acyclovir. The drug acyclovir, which is used to treat infections by herpes simplex viruses, the causative agents of the most feared viral venereal disease before the AIDS era, inhibits the viral DNA polymerase, a protein that is needed for the virus to replicate. The drug, after chemical modification by the body, affects mainly the viral DNA polymerase because the latter is sufficiently different from its human cell counterpart. Similar in concept to acyclovir, the first medication introduced for AIDS and diseases related to HIV-1 infection was 3 -azido-2 , 3 -dideoxythymidine (formerly known as azidothymidine, AZT and currently known as zidovudine, ZDV), a nucleoside analog that inhibits reverse transcriptase, a critical enzyme for the replication of HIV (De Clercq, 1997; Hirsch and Kaplan, 1987; Johnson and Hoth, 1993; Mitsuya and Broder, 1987). It is noteworthy that the discovery of reverse transcriptase several decades before the characterization of the AIDS virus had demolished the prevailing dogma in molecular biology and showed that genetic information could also flow from RNA to DNA, as was exemplified by the life cycle of retroviruses. The initial success of AZT opened the door for the development of other antiviral agents, and no doubt exists today that antiretroviral chemotherapy can bring about reduction of viral load and clinical benefits to HIV-infected individuals. Besides AZT, a variety of 2 ,3 -dideoxynucleosides have been added to the anti-HIV armamentarium, among them ddI or didanosine, ddC or zalcitabine, d4T or stavudine, and 3TC or lamivudine. Many more are undergoing clinical or preclinical testing.

Non-nucleoside reverse transcriptase inhibitors, including nevirapine and delavirdine, have also become part of therapeutic routine and more will continue to emerge. The high mutation rate of HIV has allowed the selection of viral strains resistant to antiretrovirals, a feature that has fed a constant need for new viral therapeutic targets. As the first clinical application of what has been termed pharmacogenomics, the genomics era has also provided the intermediate- and high-throughput tools to genotype AIDS virus strains from patients to determine their drug resistance patterns. This approach has allowed the identification of particular mutations, insertions, and deletions whose presence is predictive of resistance to specific antiretroviral drugs (Masquelier et al., 2001). As shown in several clinical trials, including NARVAL and ARGENTA, changes in antiretroviral therapy choice triggered by increases in viral load and based on the viral resistance patterns allow better control of viral load and disease progression, a benefit that is continuously appreciable over time, even 3 years after treatment change (De Luca et al., 2006; Vray et al., 2003). The high rates of HIV drug resistance found among antiretroviral-naïve patients most likely as the result of both transmitted resistance and informal antiretroviral use suggest that routine resistance testing among these individuals also would be a cost-effective clinical practice (Smith et al., 2007). A virus that has approximately 15 genes, such as HIV, presents a much more limited drug target repertoire than bacteria such as the gut-dwelling bacterium Escherichia coli with approximately 1500 different proteins. The latter limitation in target variety has rendered the traditional random drug screening efforts for anti-HIV agents disappointing for the most part, a hurdle that has been the inspiration for the introduction of different drug development approaches. Approximately one decade after the introduction of AZT, the inhibitors of another viral enzyme, protease, were hailed on their way into clinics as the long-awaited panacea for AIDS. The viral protease is needed to cleave the original synthesis products of the virus to generate building blocks required for assembly of new viral particles. The successful development of HIV protease inhibitors is arguably the greatest achievement to date for the relatively new method of structure-based drug design (Erickson and Fesik, 1992; Wlodawer and Erickson, 1993). The latter design is possible when the structure of the molecular target has been determined by X-ray crystallography, nuclear magnetic resonance (NMR), or remodeling. Unbeknown to many clinicians, the presence in the viral genome and the start point for the generation of the protease gene had been originally predicted in the early 1980s with accuracy down to one amino acid in the first round of analysis of the HIV sequence. But it was not until the structure of this enzyme was determined that the first design studies with HIV-1 began with HIV protease in the early 1990s. Many protease inhibitors are currently available in the market: saquinavir, ritonavir, indinavir, and nelfinavir, and another large group is in the clinical and preclinical development. Recently, the protease inhibitor darunavir was introduced into the market and it binds tightly to protease proteins with common mutations that provide resistance against other protease inhibitors. Use of strains with antiviral drug resistance are helping to select other novel antiviral

Viral Genomics and the Antiviral Drug Revolution Era

compounds able to circumvent the genetic barriers introduced by viral mutation and this approach will continue to play a role in discovering and developing novel antiviral compounds. The initial experience with anti-HIV therapy has evinced the greater efficacy, as compared to monotherapy, of appropriately combining multiple classes of antiviral agents in patients with HIV infection. As structure-based drug design methods improve, new therapeutic agents will be effectively developed against novel antiviral targets for HIV-1 therapy. The X-ray crystallography and NMR structures of several HIV-1-encoded proteins have been determined, including the reverse transcriptase, RNase H, integrase, matrix, capsid, nucleocapsid, Tat protein, and a domain of gp41. In addition, the structures of the cell surface proteins with which HIV interact have also been characterized; that is, the envelope binding domains of CD4 (cluster designation 4) and that of certain chemokine receptors (CCR), such as CCR5. The need to continue to search for and develop novel antiviral agents for HIV therapy should not be underestimated, because the prevalence of new drug-resistant variants of HIV that are insensitive to even the best current regimens of triple and quadruple combination therapy is rising at an alarming rate; a situation made worse in many cases by patient’s noncompliance secondary to the complexity or financial burden of combined regimens. The discovery of protease inhibitors also illustrates another important strength of the genomics approach to drug discovery. The characterization of the genetic makeup of HIV allowed the development of target-based screens to identify novel lead compounds for specific targets that would otherwise have gone unidentified in cell culture-based assays. In this respect, highthroughput protease assays were responsible for identifying nonpeptidic lead compounds that were subsequently developed into potent protease inhibitors with anti-HIV activity, even though the initial lead compounds had no measurable antiviral activity in tissue culture assays. Conversely, compounds that exhibit antiviral activity in a cell culture-based screen can now be subjected to a battery of mechanism-based tests to profile their mode of action. The characterization of the genetic makeup of the AIDS virus is also helping to fine tune the development of AIDS vaccines aimed not only at triggering, as most conventional viral vaccines do, the production of antibodies, the natural bullets that cells of the body’s immunological defense system produce to kill foreign invading agents, but also at stimulating the so-called cellular immunity, that is, bringing into action other cells of the body’s defense system that can directly kill the virus or the virusinfected cells. Again, here the high mutation rate of the virus and the presence of different variants or clades of HIV in the major geographical areas affected by the pandemic have posed a formidable obstacle to the development of vaccines (Esparza, 1998). The development of vaccines as well as the preclinical testing of drugs and therapeutic biologicals with anti-HIV activity have found a strong ally in the availability of several naturally derived or man-made animal models, most prominent among which are simian immunodeficiency virus (SIV)-infected macaques; mice with genetically determined severe combined immunodeficiency engrafted with human hematolymphoid cells

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from fetal liver, thymus, and lymph node (SCID-hu mice); and SCID (severe combined immunodeficiency) mice resconstituted with human peripheral blood leukocytes (hu-PBL-SCID mice) (McCune et al., 1990; Mosier et al., 1991). The search for and generation of appropriate animal models for drug and vaccine testing is crucial to the success of the genomics approach for the discovery of new pharmaceuticals and is proving to be a major bottleneck for the pharmaceutical industry. In theory, antiviral drugs exert their effects by interacting with viral structural components, virally encoded enzymes, viral genomes, or specific host proteins such as cellular receptors, enzymes, or other factors required for viral replication. In principle, any virus-specific steps in the viral replicative cycle that differs from that in normal host cell function can serve as a potential target for the development of antiviral therapy. The final litmus test of this approach to drug development takes place at the clinic or the bedside, a process that involves both demonstration of safety and efficacy of a medication, as well as a learning curve for the health care professional in the use of a new therapeutic agent or modality. Viral Hepatitis and Dengue Virus The battle against the AIDS virus, despite its limitations, has habituated clinicians to the concept of treating viral infections with drugs, and agents similar in concept to those used for the AIDS virus are now being aimed against the hepatitis viruses. Interestingly, the initial analysis of the HIV primary sequence and its comparison to that of other viruses also put in evidence the existence in hepatitis B virus of a reverse transcriptase gene (Webster et al., 1985). Until then, the hepatitis B virus had been believed to be a doublestranded DNA virus, and now it is known that, similar to retroviruses, it replicates through an RNA intermediate. Based on this realization, the nucleoside analog lamivudine is being used for the treatment of chronic hepatitis B virus, while the nucleoside analog ribavirin (1-beta-D-ribofuranosyl-1,2,4-triazole-3-carboxamide) is part of the therapeutic arsenal for combating chronic infection with hepatitis C, an RNA virus. Ribavirin has also shown activity in vitro against dengue virus (Koff et al., 1982). The hepatitis C virus has been classified into six genotypes and although genotype does not appear to influence disease presentation or severity, it has been identified as a major predictor of response to interferon-based antiviral therapy, and this association has allowed optimization of antiviral therapy for infections with hepatitis C virus genotypes 1–4 while those for genotypes 5 and 6 have yet to be developed (Bowden and Berzsenyi, 2006). Genotyping allows a better understanding and more effective battle against the dynamic epidemiological evolution of viral infections as exemplified by the detection of a changing pattern of hepatitis C genotypes among intravenous drug users in France, which may require new therapeutic strategies and further study (Payan et al., 2005). Another study in France also stresses the need for appropriate standardization and validation of viral load quantification and genotyping techniques; the study revealed that among 14 specialized laboratories accuracies of genotype determinations ranged from 33 to 100% (Laperche et al., 2006).

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As mentioned before, appropriate monitoring, and treatment of viral infections although so far relatively expensive has important implications to morbidity and mortality that are yet to be factored into health economics estimations and morbidity and mortality assessments. For instance, a large, multicenter, casecontrol study supports a specific role of hepatitis C infection in the pathogenesis of diffuse large B-cell lymphoma (Nieters et al., 2007). Respiratory Syncytial Virus, Parainfluenza Virus, Adenovirus and Metapneumovirus The current use of nucleoside inhibitors extends to other viruses. For instance, ribavirin is used for treatment of respiratory syncytial virus (RSV) infection, the leading cause of lower respiratory tract infection (pneumonia and bronchiolitis) in normal infants and children (De Vincenzo, 1997). In the latter indication, a small particle aerosol generator delivers ribavirin and, to be effective, it must be started as early as possible. Although ribavirin aerosol has also been successfully used for the treatment of severe parainfluenza virus disease in some children with severe immunodeficiency, studies are thus far inadequate to establish efficacy. On the other hand, intravenous ribavirin has been used with successful responses in some cases of adenoviral infection. The latter experiences demonstrate another important area in the effective clinical use of the new drugs developed from the genomics revolution, namely their adequate delivery by several routes for different indications. In this respect, the genomics-based drug revolution has also fueled an exponential growth rate in the research on drug delivery systems, and many more innovative breakthroughs are on the horizon. Extensive genotyping among affected individuals is also helping to understand the epidemiology, link between genotype and disease severity, and development of vaccines and antiviral therapies for known and novel viral infections such as that by human metapneumovirus, which was first described in 2001 and like the RSV can cause respiratory illness ranging from mild self-limiting disease to bronchiolitis and pneumonia that may necessitate mechanical ventilation (Mackay et al., 2004). Influenza Virus The influence of using antiviral agents to control HIV infection extends further to the battle against the flu (Gubareva et al., 2000; Monto et al., 1999). Although two important proteins of influenza viruses were known for many years before the AIDS epidemic, it was not until recently that effective drugs that inhibited these enzymes were developed. Following the discovery in 1942 of an enzyme on the influenza virus surface that removed virus receptors from erythrocytes, the prediction was put forth that an inhibitor for said enzyme might be an effective antiviral agent. Although the first inhibitors of this viral enzyme known as neuraminidase were developed in 1969, it was not until 1993, after the crystal structure of the enzyme and improved understanding of the mechanism of catalysis had been achieved, that zanamivir was introduced as a potent and highly specific inhibitor of influenza neuraminidase activity. Inhaled zanamivir (Relenza;

Glaxo Wellcome, Inc.) entered clinical trials in 1994 and is now licensed around the world. The first orally active inhibitor, oseltamivir, was described in 1997 and also licensed around the world, while a second one entered clinical trials in 1999. The influenza neuraminidase inhibitors represent a significant advance over the hemagglutinin inhibitors amantadine (Symmetrel; DuPont) and rimantadine (Flumadine; Forrest Pharmaceuticals, Inc.) that were available for many years but rarely used in influenza therapy. Amantadine or rimantadine may be given orally early in the course for influenza type-A infections but are not effective for influenza type B. Ribavirin aerosol use has led to the reduction of symptoms in some patients with influenza, types A or B, infections. Amantadine, rimantadine, or zanamivir can also be used prophylactically in immunocompromised patients exposed to influenza-A virus infection. For those exposed to influenza B, zanamivir is recommended, using one dose daily during the exposure period (Monto et al., 1999). The neuraminidase as compared to the hemagglutinin inhibitors have a broader spectrum of antiviral activity (both influenza A and B as opposed to only A), less potential for emergence of clinically important resistance, better tolerability, and proven efficacy in reducing respiratory events leading to antibiotic use after influenza. One alternative approach for prophylaxis of influenza and RSV infections has been the use of immunoglobulins, in particular preparations enriched in those directed specifically at particular viruses. For example, seasonal prophylaxis of RSV infection in the form of monthly infusions of RSV-polyclonal antibody (palivizumab) has been effective in small infants with profound immunodeficiency, pulmonary compromise, and/or bone marrow transplant recipients. A series of antibodies against specific viral targets are being tested and more will be developed as target display libraries continue to allow selection of effective antibodies among samples with diversity in the thousands.

CONCLUSION Many other antiviral agents are in the market and there are different categories of antiviral agents at various stages of drug development. For instance, the broad-spectrum capsid-binding agent pleconaril (VP 63843) is aimed at the treatment of rhinovirus infection, the virus causing the common cold, but the drug is available on a compassionate protocol use. Pleconaril may also have therapeutic efficacy in enteroviral aseptic meningoencephalitis. An antiviral drug that could hardly be conceived of before the advent of genomics is based on the principle of “antisense”; the drug, fomivirsen, consists of nucleic acid sequences that bind to and neutralize a crucial component of the reproducing virus. It is approved in the United States for the treatment of cytomegalovirus retinitis. One drug being tested in patients with hepatitis C consists of a ribozyme; an RNA molecule that cuts specific viral RNA sequences. The appropriate use of antiviral agents is also being aided by the expanding use of viral genotyping and viral load determinations for a growing number of viral diseases, which in turn

References

is allowing to better control and understand their epidemiology and clinical consequences; barriers to their use such as cost and standardization are also being addressed. Studies of host–pathogen interactions using a genomic approach promises to also improve the treatment and control of spread of infectious agents. Besides the traditional viral infections, the antiviral agents being developed may also help in the control of diseases where the body loses its balanced control of internal processes. For instance, some viruses have become part of our genetic makeup, the so-called endogenous viruses, and their expression serves some functions that are being unraveled. The body appears to keep the expression of endogenous viruses in check, and it has been noted that unregulated expression of endogenous viruses may play a role in some autoimmune diseases, maladies in which the body attacks itself by making antibodies against its own tissues. One example of such a disease is Sjoegren’s syndrome, a malady in which the body makes antibodies against the salivary and lacrimal glands and the person suffers dry eyes and mouth. Overexpression of the endogenous viruses called intracisternal A particles have been associated with Sjoegren’s syndrome. The realm of antiviral therapy may soon extend to diseases that are not traditionally thought of as viral in origin. For instance, results from several studies in animals and humans have suggested that atherosclerosis, the clogging of blood vessels that can lead to heart attacks or strokes, may be influenced by microbial organisms including viruses such as cytomegalovirus, a herpesvirus family member, and bacteria, such as Chlamydia, Mycoplasma, and Helicobacter pylori. The latter hypothesis is in line with the change in pathogenetical thinking brought about by the link of a bacterium, H. pylori, to peptic ulcer disease and lymphoma associated with the gastrointestinal mucosa (Blaser, 1999). Therefore, antibiotics have joined antacids in the treatment of some forms of peptic ulcer disease. The recent molecular biology endeavors that have characterized most of the expressed genes in the human body are opening the doors for using natural proteins with antiviral activity, or even chemical substances directed at the proteins in the body that are the portals of invasion of viruses, as the new weapons in our continuing battle against these microbes. A revisit to old tradition brings to mind some useful paradigms. Many cultures around the world discovered that rubbing a frog on an infected wound would help clear the infection and heal the wound. The small proteins, defensins, which are responsible for this antibacterial activity in the frog’s skin were characterized in the past century. Later, similar proteins were discovered in human

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beings, some of which also have antiviral activity. Since the genetic makeup and expressed proteins of many different organisms are being deciphered around the world, it is likely that we will continue to discover other natural substances with antiviral activities and maybe even discover those elusive natural enemies of viruses. For instance, one bacterial organism, Mycoplasma, has been shown to help the AIDS virus, an observation that supports the hope that there might be another organism that may help kill or control it. In this line of thought, some scientists have proposed that certain populations of people that have proven to be particularly resistant to viral infections despite high-risk behavior may be infected with another organism that confers such protection. The characterization of the genetic makeup of viruses, of the proteins that they encode, and of the effects of the latter on cells have also helped to create testing systems for substances present in plants, and it is possible that new chemicals will be derived from the knowledge garnered over centuries in the field of herbal medicine. For instance, Louis Pasteur noted garlic’s antibiotic activity in 1858, and, more recently, the sulfur-containing component of garlic, allicin, has been shown to kill all viruses thus far tested in the laboratory (Hadley and Petry, 1999). Plant proteins with antiviral activities may also provide templates for the computational biochemistry search for homologous proteins in humans, a task that may help unravel new natural antiviral agents. In fact, over half of the top twenty-five prescription drugs in the market are derived from plants. Regardless of the source of drugs, whether naturally occurring or synthetic, the viral genomics and the antiviral drug revolution will continue to open new doors for novel approaches to viral and nonviral diseases. The determination of the sequence of the genome of viruses and of the protein products that they encode have created the targets for a booming pharmaceutical enterprise and represent a first success story of the genomics revolution. Antiviral drugs provide the first glimpse of the exciting new era of molecular medicine, a discipline that welcomes the twenty-first century as an infant for whom we have great expectations. One can only fathom by extrapolation that, if the limited number of viral targets so far worked out has generated a number of potential medications that is two orders of magnitude the number of targets, at least 1 million medications should in the near future be under investigation for the thousands of genes that express secretory proteins in humans. These medications will allow the regeneration of failing or aging organs, the restoration of deficient or the quelling of excessive bodily functions, and to help combat old and new challenges to human health.

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De Clercq, E. (1997). In search of a selective antiviral chemotherapy. Clin Microbiol Rev 10, 674–693. De Luca, A., Giambenedetto, S., Cingolani, A. et al. (2006). Three-year clinical outcomes of resistance genotyping and expert advice: Extended follow-up of the Argenta trial. Antivir Ther 11(3), 321–327.

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Mitsuya, H. and Broder, S. (1987). Strategies for antiviral therapy in AIDS. Nature 325, 773–778. Monto, A.S., Fleming, D.M., Henry, D. et al. (1999). Efficacy and safety of the neuraminidase inhibitor zanamivir in the treatment of influenza A and B virus infections. J Inf Dis 180(2), 256–261. Mosier, D.E., Gulizia, R.J., Baird, S.M. et al. (1991). Human immunodeficiency virus infection of human PBL-SCID mice. Science 251(4995), 791–794. Nieters, A., Kallinowsky, B., Brennan, P. et al. (2007). Hepatitis C and risk of lymphoma: Results of the European Multicenter CaseControl Study EPILYMPH. Gastroenterology 132(3), 1205–1207. Osterholm, M.T. (2000). Emerging infections – Another warning. New Engl J Med 342(17), 1280–1281. Patarca, R. and Haseltine, W.A. (1984a). Sequence similarity among retroviruses. Nature 309, 728. Patarca, R. and Haseltine, W.A. (1984b). Similarities among retrovirus proteins. Nature 312, 496. Patarca, R. and Haseltine, W.A. (1985). A major retroviral core protein related to EPA and TIMP. Nature 318, 390. Patarca, R. and Haseltine, W.A. (1986). Variation among the human T-lymphotropic virus type III (HTLV-III/LAV) strains. J Theor Biol 125, 213–217. Patarca, R., Dorta, B. and Ramirez, J.L. (1982). Creation of a database for sequences of ribosomal nucleic acids and detection of conserved restriction endonucleases sites through computerized processing. Nucl Acids Res 10(1), 175–182. Patarca, R., Haseltine, W.A., Webster, T. and Smith, T.F. (1987). Of how great significance. Nature 326, 749. Payan, C., Roudot-Thoraval, F., Marcellin, P. et al. (2005). Changing of hepatitis C virus genotype patterns in France at the beginning of the third millennium: The GEMHEP GenoCII study. J Viral Hepat 12(4), 405–413. Ratner, L., Haseltine, W.A., Patarca, R. et al. (1985). Complete nucleotide sequence of the AIDS virus, HTLV-III. Nature 313, 277–284. Smith, D., Moini, N., Pesano, R. et al. (2007). Clinical utility of HIV standard genotyping among antiretroviral-naïve individuals with unknown duration of infection. Clin Infect Dis 44(3), 456–458. Talal, N., Dauphinee, M.J., Dang, H. et al. (1990). Detection of serum antibodies to retroviral proteins in patients with primary Sjoegren’s syndrome (autoimmune exocrinopathy). Arthritis Rheum 34(10), 313–318. Vray, M., Meynard, J.L., Dalban, C. et al. (2003). Predictors of the virological response to a change in the antiretroviral treatment regimen in HIV-1-infected patients enrolled in a randomized trial comparing genotyping, phenotyping and standard of care (Narval trial, ANRS 088). Antivir Ther 8(5), 427–434. Webster, T., Patarca, R., Lathrop, R. and Smith, T.F. (1989). Potential structural motifs for reverse transcriptases. Mol Biol Evol 6, 317–320. Wlodawer, A. and Erickson, J.W. (1993). Structure-based inhibitors of HIV-1 protease. Annu Rev Biochem 62, 543–585.

CHAPTER

110 Host Genomics and Bacterial Infections Melissa D. Johnson and Mihai Netea

INTRODUCTION Bacterial pathogens have been known to cause infectious diseases in humans for centuries. Many of these pathogens are commensal organisms that are ubiquitous in the environment or colonize tissues within the host. Despite near constant contact with bacteria, only few of the exposed individuals actually develop clinical signs and symptoms associated with infection. The interplay between host and pathogen is complex, and infection may depend on genetically determined factors such as host immunity or virulence of the pathogen. Despite the long history of infectious diseases, genetic investigations of bacteria and host immunity have only recently been advanced to improve our understanding of the complex host–pathogen relationship. This area of study has experienced great advances since information from the Human Genome Project became available. Recent technologies such as genetic screening and expression analysis may help to better define the role of key features predisposing to infection, transition of a commensal microorganism into a pathogen, or response to infection once the pathogen has invaded normally sterile body tissue. Such advancements have been used to identify new therapeutic modalities, targeting overexpressed or deficient host immune factors, as well as components of pathogens that are critical for the development of resistance or survival. Collectively “omic” approaches to the study of microbial infections have been termed “infectomics” (Huang et al., 2007). Approaches may be centered on the pathogen, the host, or chemical gene or protein targets Genomic and Personalized Medicine, 2-vol set by Willard & Ginsburg

in an organism for potential therapeutic benefit. In this chapter, we will focus on host genomics, highlighting examples of studies aimed at unraveling the complex nature of immune response to infection. In the future, better understanding of genomics will hopefully enable us to better prevent and/or treat infections.

GENOMICS AND THE STUDY OF BACTERIAL INFECTIONS Bacterial Genomics Functional genomics of bacterial pathogens has been the focus of basic science research for some time. Scientists have long wondered what differentiates a commensal organism from an invading one, how some organisms develop resistance to various antibiotic treatments, and how organisms can so rapidly express the Darwinian extreme of “survival of the fittest”. At the heart of this is genetic makeup and expression within bacteria or fungi. On recent review, complete genomes have been mapped for more than several hundred species of bacteria, including Streptococcus pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Neisseria meningitidis (Celestino et al., 2004; J. Craig Venter Institute, 2006). These data are valuable in developing new targets for antibiotics, new diagnostic methods, and potential vaccination strategies as well as furthering our understanding of bacterial resistance mechanisms, bacterial pathogenicity and virulence, bacterial niche adaptation, and Copyright © 2009, Elsevier Inc. All rights reserved. 1347

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contribution of bacteria to certain chronic disease states (Fritz and Raczniak, 2002; Monaghan and Barrett, 2006). A discussion of microbial genomics is outside the scope of this text and will thus not be further considered. Nonetheless, many of the methods and procedures discussed in this text are applicable to the field of microbial genomics. Host Genomics The study of the association between the role of genes in susceptibility or outcome of infections is still in its infancy. Recent technological advancements have resulted in a surge of studies in this area. These advances include the availability of a large array of candidate genes that can be studied for association, as well as more affordable and fast technology to perform genome-wide screens to identify new genes that may have a role (Hill, 1998). Briefly, host defense to infectious pathogens depends upon both innate and acquired immunity (Emonts et al., 2003). Phagocytosis is an important first line of defense for some pathogens, particularly those that enter via mucosal surfaces. Cell types such as neutrophils, macrophages, dendritic cells, and monocytes are important components of response to invading pathogens, and CD4 expressing T lymphocytes differentiate into TH1, Prostagandin receptors PTGR,PTGER4

Interleukin receptors L2R, L7R, L12R2, L15R

Invasion MMP1,MMP7,MMP10 MMP12,MMP14, MMP19

Adhesion molecules ADRM1, CD6, CD38, CD44, CD53, ICAM1, ITGA5, ITGAX, ITGB8, LGALS9

Cytokine receptors COR7, CORL2, CXOR4

Antigen Presentation HLA-E, HLA-F, HLA-G

Co-stimulators CD40, CD80, CD83, CD86, CD137

Prostaglandin

TNF receptors TNFRSF1B

TH2, or TH17 cells that modulate the inflammatory process. Monocytes express a number of molecules that are important for antigen presentation and cell signaling, including FC receptors for IgG, complement receptors, and MHC Class II molecules. Complement is a critical part of the innate immune response. Three pathways are activated by either antibody–antigen and C-reactive protein (classical pathway), the interaction of C3 with factors B and D resulting in C3b (alternative pathway) or mannose-binding lectin (MBL) (innate activation pathway). Proteins important for cellular response to infection are depicted in Figure 110.1 (Jenner and Young, 2005). Pathogen recognition receptors (PRRs) are cell membrane receptors that recognize structures from pathogenic microorganisms called pathogen-associated molecular patterns (PAMPs). Four major classes of PRRs have been described to date: Tolllike receptors (TLRs), NOD-like receptors (NLRs), C-type lectin receptors (CLRs) and RigI-helicases: PRR members of the first three families are involved in the recognition of bacteria, and are involved in the triggering and activation of the innate host defense (Cambi and Figdor, 2005; Kawai and Akira, 2005; Murray, 2005). MBL is part of the collectin group of C-type lectins, which recognizes sugars, differentiating between foreign Chemotactic cytokines CCL3, CCL4, CCL5, CCL17, CCL19, CCL20, CCL22, CXCL1, CXCL2, CXCL3, CX3CL1 Pro-inflammatory molecules IL1, IL1, ILB, IL8, TNF

PTGS2 Antigen processing PSMA4, PSMB8, TAP1,2 PSMB9, PSMB10

BIRC2,3 CFLAR

Interferon-stimulated cytokines CCL8, CXCL9, CXCL10, CXCL11, TNFSF10

Other cytokines CSF1, CSF2,CSF3, IL2, IL3, IL4, IL5, IL10, IL11, IL12, IL15, LIF,LT, OSM, TGF, TNFSF9 Interferons IFN, IFN, IFN

CASP1,3,4,5,7 BCL2A1, MCL1

TRADD Adaptors

Antigen

Apoptosis

MYD88 IB-, IB-ε

TRAF1, TRAF6

Transcription factors MAP3K8

NF-B NF-B

A20, TNIP1, TANK

AP1

MAP3K4 Phosphatases DUSP1,2,4,5,6,8

IRFa/STATs IRF1,4,7,9 STAT1,4,5A

Interferon-stimulated ATF3,4, BATF, CREM G1P2, MX1,2, OAS1,2,L, PRKR, TRIM22

IFI16,ISG20, MX2, PML, SP110

Other TFs (BCL6, CEBPG, EGR4, HIF1, NFATC1, SMAD7, XBP1)

CRE

Figure 110.1 Cellular responses to infection (Jenner and Young, 2005). Gene products of the common host response and their functions inside and outside the cell. Arrows represent activatory connections between proteins in signaling pathways; lines ending in bars represent inhibitory connections. This figure does not indicate that these proteins are present in all cells at the same time.

Genomics and the Study of Bacterial Infections

TABLE 110.1

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TLR family ligands

TLR

Ligand

Entity commonly bearing ligand

TLR1

Triacyl lipopeptides

Numerous bacteria, mycobacteria

Soluble factors

Neisseria species

Lipoprotein/lipopeptides

Numerous pathogens

Peptidoglycan

Gram-positive bacteria

Lipoteichoic acid

Gram-positive bacteria

Lipoarabinomannan

Mycobacteria

A phenol-soluble modulin

Staphylococcus species

Glycoinositolphospholipids

Trypanosoma cruzi

Glycolipids

Treponema maltophilum

Porins

Neisseria spp., Shigella spp., Haemophilus influenzae

Zymosan

Fungi

Atypical LPS

Leptospira interrogans, Porphyromonas gingivalis

HSP70

Host

TLR3

Double-stranded RNA

Viruses

TLR4

LPS

Gram-negative bacteria

Taxol

Plants

Fusion protein

Respiratory syncytial virus

Envelope proteins

MMTV

HSP60

Chlamydia pneumoniae

F-protein

Cytomegalovirus

HSP60, HSP70, Type III repeat extra domain A of fibronectin, oligosaccharides of hyaluronic acid, polysaccharide fragments of heparin sulfate, fibrinogen

Host

TLR5

Flagellin

Numerous bacteria (i.e., Legionella)

TLR6

Diacyl lipopeptides

Mycoplasma species

TLR7

Imidazoquinoline

Synthetic compounds

TLR2

Loxoribine Bropirimine Guanine nucleoside analogues TLR8

R848/resiquimod

Single-stranded RNA

TLR9

CpG rich motifs of DNA

Bacteria

Chromatin: IgG complexes

Host

TLR10

Unknown

Unknown

TLR11

Profilin

T. gondii

Not determined

Uropathogenic bacteria

TLR12

Unknown

Unknown

TLR13

Unknown

Unknown

Based on Akira et al., 2006; Brikos and O’Neill, 2008; Dieffenbach and Tramont, 2005.

CHAPTER 110

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TLR5

TLR2 TLR5

TLR2

type I interferons. TRAM is only required in TLR-4 mediated signaling (Turvey and Hawn, 2006). These pathways are depicted in Figure 110.2. NLRs are related molecules that are more specific in their recognition of bacterial organisms in cytoplasm (Elson et al., 2007; Hill, 2006). CLRs are receptors for polysaccharide components such as glucans or mannans, especially in fungi, but they have also been involved in recognition of mycobacteria. CD14 is another pattern recognition receptor and may be important in recognition of both Gram-positive and Gramnegative bacteria. Additional mediators that aid in recruitment, activation or suppression of immune cells include cytokines such as IL-10, IL-6, interferon gamma, TNF, IL-12, and IL-4. Alterations in production or expression of these components

TLR4

TLR1

and self, and subsequently activates complement upon binding to foreign molecules. More than 10 TLRs have been identified, and of these TLR1, TLR2, TLR4, TLR5, TLR6, and TLR9 have proposed roles in recognition of certain bacteria (Table 110.1). Binding of a ligand to a TLR triggers two signaling pathways which differentiate on the basis of MyD88 involvement (Albiger et al., 2007a). MyD88 and another adaptor protein, TIR-associated protein (TIRAP or Mal) lead to induction of a pro-inflammatory cytokine response. With the exception of TLR3, MyD88-dependent pathways are activated by all TLRs. The MyD88-independent pathways are triggered by TLR3 and TLR4; TRIF (TIR domain containing adaptor protein inducing IFN-) and TRAM (TRIF-related adaptor molecules) activate

TLR11

1350

TRIF SOCS-1

Flih ST2

Cel

l me

End

oso

me

IRAK-N

P IRAK4

IRAK-10

TRIF

P

TBK1

P

R7

/9

P IRF3

RJP1

A20

TL

TAK1

P

-arrestin

6

Tolip

RIP1

P

RRAF

TBK1

TRL AD2 A (TL R9 **** )

MyD88s

IRAK1

TRAF3

mbr ane

TLR3

TLR7,6,9

TIRAR

TRAM MVDE8

RRAF-6

TRLA D3A

IRF7

IRF7 IKK MKK

1B

IRF3

P NFB

Nucle

ar m

IFN/

Figure 110.2

TLR signaling (Albiger, 2007a).

TNF COX2 IL-18

IFN

IFN

anbr ane

Host Genomics and Gram-Positive, Gram-Negative and Mycobacterial Infections

can lead to changes in susceptibility to an infecting pathogen or host response once actually infected. Based on investigations of various pathogens, it seems that there is not one standard panel of factors which predisposes to all infections. Rather, immunity to a given pathogen may depend on the nature of the infecting organism, site of entry, its growth characteristics and/or morphology, and its biochemical composition. Thus, it seems logical that in studies to date, varying host genetic factors have been implicated in either susceptibility to or prognosis with different infectious pathogens. Given that there have been numerous studies in host susceptibility to infections, this chapter will focus on the most common and well-studied Gram-positive and Gram-negative bacterial infections as well as mycobacterial infections. We will compare and contrast the available data for genetic associations and infection with selected pathogens, and highlight potential areas for future exploration. Methods of Study Much has been written regarding the different approaches to study host genomics as it relates to infectious diseases, and the limitations of certain study designs are well-recognized. Most infectious diseases can be classified as complex, with multifactorial contributions from environment, biological factors, and inheritance. Susceptibility to an infectious disease may follow a Mendelian pattern of inheritance, but the majority of infectious diseases are due to polygenic factors. It may be difficult to distinguish between these two origins, because other genes can influence expression of a Mendelian trait and expression of a polygenic trait may be heavily influenced by a single gene. While both linkage and association are important, association studies are more commonly employed in studies of genetics and infectious diseases susceptibility (Bochud et al., 2007). While powerful, linkage studies of infectious diseases are relatively uncommon for a number of reasons. First, it is challenging to identify and recruit affected sibling pairs or family members to establish well-characterized pedigrees during the time frame that the study is conducted. The rapid mortality associated with many infectious diseases also makes it difficult to identify affected persons and recruit them for study participation. In addition to these issues, most infectious diseases are expected to have a small contribution from individual genetic regions which would not necessarily be apparent given the limited sample size and power of such studies (Burgner et al., 2006). Thus, case–control association studies including unrelated affected versus non-affected persons are more common than linkage and association studies which might more closely explore genetics among family members. Association studies are not without problems, however. The biggest problem with these study designs is power, and almost all published data for genetic association and infectious diseases are based on underpowered studies. One group has suggested that at least 1500 case–control diads or case–parent triads are necessary for a sufficiently powered association study using a candidate-gene approach. A genome-wide association study could be sufficiently powered with 1000 cases and 1000 controls; however, studies of this kind are subject to a high false discovery rate.

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New technologies are making such studies more feasible, both from a technical and financial perspective. Given the issues with power and the potential for spurious findings, association studies should be validated in subsequent studies using independent cohorts (see Chapter 8). In addition to candidate-gene and genome-wide analyses, newer technologies are used to profile host-gene expression in response to different pathogens of interest (Campbell and Ghazal, 2004; Hossain et al., 2006). This approach can create a wealth of information about the functional and dynamic response to infection. For example, in a recent study murine liver cells were studied in response to challenge with E. coli or S. aureus. Seventeen genes were differentially expressed in response to these two different pathogens, including GBP-2, C-type lectin, adenylate cyclase, and genes involved in vesicle trafficking (Yu et al., 2004). The data suggested that sepsis due to these two pathogens may involve a common late host response, but initial responses to each of these bacteria were quite different. Studies such as this can yield a wealth of new information about pathways that might be important in susceptibility or response to infection. However, since they analyze so many data points, the statistical analysis must be carefully performed to minimize spurious findings. Since expression is a dynamic phenomenon, timing of the sampling and processing procedures used in the laboratory for these specimens are critical elements for this kind of research. Also, many such studies use only one particular cell line and study response of that line to challenge with the pathogen of interest in vitro. For many of these infections, multiple cell lines are involved in defense. Thus, incubation with only one kind of cell may not yield a complete picture of what is involved in susceptibility and response to infection in vivo. For these reasons, although studies such as these produce a tremendous amount of information, they should be considered hypothesis generating and generally require further validation (Modlin and Bloom, 2001).

HOST GENOMICS AND GRAMPOSITIVE, GRAM-NEGATIVE AND MYCOBACTERIAL INFECTIONS Gram-Positive Organisms A few Gram-positive infections have been investigated in human studies for genetic associations and immunity (Table 110.2). Importantly, these organisms lack lipopolysaccharides (LPS), so other mechanisms are responsible for host recognition and defense against these pathogens. In Gram positives, lipoteichoic acid (LTA) is a prominent player in triggering host recognition of the bacteria. LTA and peptidoglycan in Gram positives have been noted to be recognized by TLR2. Gene expression studies have suggested that the initial response to Gram-positive pathogens involves recognition by TLR2, which leads to a NFB mediated response with release of cytokines including IL-1 and TNF as well as IL-6 and IL-8. Subsequently, a later TLRindependent response arises in response to the pathogen or its

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T A B L E 1 1 0 . 2 Examples of host genetic factors implicated in association with select bacterial pathogens in the in vitro, animal model, or human studies Disease

Factors

Gram-positive organisms S. pneumoniae

TLR1, TLR2, TLR4, TLR6, TLR9, MyD88, CD14, LBP, NOD1, NOD2, IB, NFB-1 A, NFB-1E, MBL, FC-RIIA

S. aureus

TLR1, TLR2, TLR6, MyD88, CD14, CD36, NOD2, TNF- R1, peptidoglycan-recognition proteins

Listeria monocytogenes

TLR2, MyD88, NOD2

Gram-negative organisms Gram-negative sepsis/infections

TLR1, TLR2, TLR4, LPB, CD14, MyD88

Legionella

TLR2, TLR4, TLR5, MyD88, Naip5

Neisseria meningitidis

TLR4, MBL, properidin, ACE, FC-R, TNF, TNF, IL10, IL6, IL1, IL1-R antagonist

Mycobacteria M. tuberculosis

TLR1, TLR2, SLC11A1(NRAMP1), Class I and II HLA, MBL2, SP-A, SP-D, P2X7, DC-SIGN, DRB1, HLA-DR, INF-, VDR, IL12, IL-12R, IL-10, IL-1R antagonist, TNF-, TNF-R1, STAT-1

M. leprae

TLR1, TLR2, TAP-2, HSPA 1 A, DC-SIGN, TNF, VDR, MHC region 6p21, 10p13, HLA-DR, PARK2/PACRG

Based on Hirschhorn et al., 2002; Mira et al., 2004; Mira, 2006; Roy et al., 1997; Roy et al., 1999.

metabolites inside the host cell. These responses appear to be quite pathogen-specific, and differ among various microorganisms. Studies investigating host genomics and Streptococcus pneumoniae, Staphylococcus aureus, and Listeria monocytogenes have been completed and help further define the role of candidate genes for each of these infections. Of the Gram-positive pathogens, S. pneumoniae has been the most studied in regards to host immunity and susceptibility to infection. S. pneumoniae is a common cause of pneumonia, bacteremia, meningitis, and otitis media. TLR1, TLR2, TLR4, TLR6, and TLR9 have been associated with recognition and immune response to the pneumococcus (Albiger et al., 2007b; Echchannaoui et al., 2002; Echchannaoui et al., 2005; Knapp et al., 2004; Koedel et al., 2003; Malley et al., 2003; Schroder et al., 2003b; Srivastava et al., 2005; Yoshimura et al., 1999). Much of this evidence comes from animal models of pneumococcal infection. Knockout mice deficient in TLR2 appear to be more susceptible to S. pneumoniae meningitis. Reduced survival has also been reported among TLR2 knockout mice with intraperitoneal S. pneumoniae infection (Khan et al., 2005). However, in another study, knockout mice deficient in TLR2 did not appear to be at increased risk of pneumococcal pneumonia following intranasal infection. TLR4 has been suggested to have protective effects through its recognition of bacterial pneumolysin, which can potentially reduce colonization and subsequent infection. These benefits may also extend to pneumonia, as evidenced by experimental models in mice with pneumococcal pneumonia. Knockout mice deficient in TLR4 had higher

colony counts, although this did not result in increased mortality in this model (Branger et al., 2004). In a model of intravenous infection, TLR4 deficiency did not result in reduced survival or increased colony counts in infected mice (Benton et al., 1997), further suggesting that the role of TLR4 on immunity to S. pneumoniae may be limited to the airways. A recent study in human, however, did not find any significant associations between mutations in TLR2 or TLR4 and invasive pneumococcal infection (Moens et al., 2007). In this cohort, neither Caucasian Belgians with invasive pneumococcal disease nor healthy controls were homozygous for mutations in TLR2 R753Q, TLR2 P631H, or TLR2 R579H. Heterozygotes were identified in TLR2 R753Q (6.1% cases, 5% controls, p0.94) and TLR2 P631H (5.1% cases, 8.9% controls, p0.34), but were not significantly different between cases and controls. Similarly, there were no differences in either homozygous or heterozygous mutations in TLR4 D299G between cases and controls. Clinical outcomes were not different among those who were heterozygous or homozygous for TLR2 and TLR4 mutations at these sites, compared to those that had homozygous wild-type genotypes. In this cohort, cases and controls were not matched for gender or age, and the small number of subjects with homozygous mutations in TLR2 and TLR4 limited the power of the study. Additional studies in a larger, more well-defined population with carefully selected controls may be necessary to establish the role of TLR2 and TLR4 mutations and susceptibility to pneumococcal infection. Other factors such as myeloid differentiation factor 88 (MyD88) and CD14 have also been shown to increase risk of

Host Genomics and Gram-Positive, Gram-Negative and Mycobacterial Infections

S. pneumoniae infection in animal models (Albiger et al., 2005). MyD88 is important in the cell signaling cascade triggered by TLR or IL-1 family receptors, while CD14 serves as a co-receptor for LPS. Other proteins may also play a role in recognition and response to pneumococcus, including LPS-binding protein (LBP) and the nucleotide-binding oligomerization domains (Nod) Nod1 and Nod2 (Opitz et al., 2004). The role of these proteins in defense against pneumococcus, particularly in the context of other signaling pathways, has yet to be fully elucidated in animal models and humans. Recently, mutations in the IB family of receptors has been associated with invasive pneumococcal disease among hospitalized patients in the United Kingdom (Chapman et al., 2007a). This study included a large cohort of Caucasian UK residents with invasive pneumococcal disease who were identified as part of two other studies (Maskell et al., 2005; Roy et al., 2002) and one control group identified as part of a previous study as well as an independently collected cohort of 370 healthy adult volunteers from the United Kingdom. SNPs were identified in the first cohort of infected patients (including bacteremia, pneumonia, and meningitis) and controls, and then confirmed in the second set of Gram-positive infection (limited to empyema) cases and controls. The investigators identified 43 SNPs in three IB genes for analysis. Of these, six SNPs in NF-B-IA or NF-B-IE appeared to be associated with pneumococcal infection in the first analysis. In the second group of those with Gram-positive empyema (not limited to pneumococcus), the investigators did not find any association between the three most common SNPs and thoracic empyema. When this analysis was limited to the small number of subjects with pneumococcal empyema (42 subjects), mutations in NF-B-IA (rs3138053 and rs2233406) appeared to be protective, and were similar to that found in the first study cohort with invasive pneumococcal infection. Mutations in NF-B-IE (rs529948) were significant in the first invasive pneumococcal cohort, but did not appear so in the group of pneumococcal empyema subjects. These data are considered preliminary and should be validated in subsequent studies. MBL has been suggested to play a role in host defense against S. pneumoniae. This protein can phagocytose some bacteria by binding oligosaccharides on the bacterial cell surface (Bocchino et al., 2005). Homozygous mutations in the MBL gene on chromosome 10 have been associated with an increase in invasive pneumococcal infection (Garred et al., 2003; Kronborg et al., 2002; Moens et al., 2006a). In a large study of 225 white adults and children from the United Kingdom with invasive pneumococcal disease (blood, cerebrospinal fluid, joint fluid, etc.) and 353 white adult blood and transplant donors, homozygous mutations for MBL at codons 52, 54, or 57 occurred in 28 infected persons (12%) versus 18 controls (5%) (OR 2.59, 95% CI: 1.39–4.83).This finding was validated in a second cohort of 108 additional cases of invasive pneumococcal infection and 679 healthy neonates from the United Kingdom matched for ethnicity and geographical area of residence. Homozygotes among cases and controls were 10% and 5%, respectively (p  0.046). Frequency of heterozygotes as well as a functional promoter polymorphism at position 221

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in both cohorts were similar, and not different between cases and controls. In addition, homozygotes did not have significantly different sites of infection, lengths of stay, disease severity, or survival from those heterozygous for these mutations. The results of this study have been confirmed in two smaller studies. One limitation of this particular study is that cases and controls were not matched for age, despite the fact that the infected cohorts included both children and adults with a wide age range. Age was more closely matched in one of the subsequent studies. In addition, those with concomitant illnesses which would contribute to the risk of invasive pneumococcal disease were not excluded from some of the early studies. Such patients were excluded from the later confirmatory study. Vaccination status of study subjects with pneumococcal vaccine was not described by the investigators of these studies, and could potentially confound the study results. Given the time frame of the study, vaccination among children was probably rare, but from the time period of 1993 to 2003 uptake of pneumococcal vaccination was more than 20% among those 65 years of age and older (Noakes et al., 2006). However, the efficacy of pneumococcal vaccination in those with MBL deficiency is unknown. In a more recent study using samples from the same cohort of UK residents with invasive pneumococcal infection and non-infected controls, polymorphisms in the FCN2 gene were not found to be associated with this infection (Chapman et al., 2007b). FNC2 encodes for L-ficolin, which has been shown to bind Gram-positive organisms, and activates the lectin-complement pathway as well as working directly as an opsonin. Other studies in humans have investigated mutations in Fc-receptor IIA and pneumococcal disease, with conflicting results (Moens et al., 2006b). Fc-receptor IIA is thought to play an important role in phagocytosis of bacteria once they have been bound by IgG2. In very small studies, patients who were homozygous for Fc-receptor IIA polymorphisms at amino acid 131 appeared to be at increased risk of invasive pneumococcal infection (i.e., bacteremia) than controls (Yee et al., 1997; Yee et al., 2000; Yuan et al., 2003). In a more recent study of 55 Belgians with invasive pneumococcal disease and 100 gender and geographical matched non-infected controls of a wide age distribution, there was no difference in prevalence of Fc-receptor IIA genotypes between cases and controls (Fc-RIIA–R/ R131 genotype 21.8% versus 31% in cases and controls, respectively (p  0.047)). In general, all of these studies were limited by very small sample sizes. Although S. aureus also has LTA in its cell wall, lipoproteins and peptidoglycans may be even more important in its recognition by host TLR2 (Hashimoto et al., 2006). For S. aureus, knockout mice deficient in TLR2 experienced greater rates of mortality compared to wild-type mice with S. aureus infection. Similarly, mice deficient in MyD88, a downstream adaptor molecule involved in cell signaling, had substantially reduced survival which was even more pronounced than the TLR2 deficient mice. Colony counts in the organs of the Myd88 deficient mice indicated that these mutants were even more susceptible to S. aureus infection than the TLR2 deficient mice (Takeuchi et al., 2000). A study in 91 white French patients who had participated

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in a study of the role of TNF-alpha in septic shock found that 2 of 22 patients (9%) with Gram-positive septic shock had a mutation in TLR2 (Arg753Gln) while this mutation was not identified among 69 subjects with other causes of septic shock. Both subjects were heterozygous for Arg753Gln mutation, and had shock due to S. aureus infection. Allelic frequency of this mutation among non-infected controls was 3% (Lorenz et al., 2000). This finding has yet to be confirmed from other studies to date (Moore et al., 2004). Other important elements of host recognition and response to S. aureus may include TLR1, TLR6, CD14, CD36, NOD2, TNF-alpha receptor 1 (TNFR1), and peptidoglycan recognition proteins (Fournier and Philpott, 2005). The specific role and association of these molecules with S. aureus infection has yet to be established or studied in humans. Listeria monocytogenes may also be recognized by TLR2, and in animal models knockout mice deficient in TLR2 and MyD88 were more susceptible to this infection (Seki et al., 2002; Torres et al., 2004). In addition, NOD2 may play a role in defense against Listeria, as evidenced by knockout mice who were more susceptible to oral Listeria infection (Kobayashi et al., 2005). A number of other factors may be important to defense against L. monocytogenes and have been explored in animal models, but all of these findings have yet been validated in human studies of Listeria infection (Pamer, 2004). Gram-Negative Organisms Enterobacteriaceae and Other Gram-Negative Rods LPS in the cell wall of Gram-negative bacteria are responsible, once recognized by host-cell receptors, for triggering a cascade of events leading to adhesion of neutrophils to endothelial cells, clotting, and activation of secondary inflammatory mediators such as interleukins and leukotrienes. LPS is recognized by TLR4, LBP, and CD14. Some Gram-negative organisms also contain membrane proteins in the cytoplasm or outer cell membrane that are recognized by host-cell receptors such as TLR2 and TLR1. Several studies in mice have demonstrated that MyD88 signaling may be critical for the early host response to Ps. aeruginosa lung infections (Power et al., 2004; Skerrett et al., 2004). TLR4 may be important for a number of Gram-negative pathogens; however, results in human studies have yielded conflicting results. In one in vitro study using human cell lines, TLR4 but not TLR2 was the major factor mediating hostcell activation in response to Salmonella minnesota- and E. coliderived LPS (Tapping et al., 1993). This was recently validated in a study where TLR4 ligands seemed to predominate for Enterobacteriaceae. In contrast, responses to Ps. aeruginosa were dependent on both TLR4 and TLR2. Several small studies in humans have explored the role of TLR4 in Gram-negative infections. Among Europeans, TLR4 Asp299Gly and Thr399Ile SNPs have been found to cosegregate in 98% of individuals, while in African populations both an Asp299Gly and an Asp299Gly/Thr399Ile haplotype is present (Schroder and Schumann, 2005). In a case–control study of 91 Caucasian patients with septic shock and 73 healthy noninfected blood donors in France, a TLR4 Asp299Gly mutation

occurred exclusively among those with septic shock (5/91 cases (5.5%) versus 0/73 controls (0%)) (Lorenz et al., 2002). There was a similar rate of dual mutations at the 299 and 399 position (i.e., TLR4 Asp299Gly/Thr399Ile) in both cases and controls, 6/91 (6.6%) and 8/73 (11%) respectively. Among those with septic shock, solo mutations in 299 were present exclusively in those with Gram-negative infections (n  4) or polymicrobial infections (n  1). Survival and SAPS II scores of these subjects were similar to those with wild-type or dual mutation genotypes at the 299 and 399 position. Only one subject in this study was homozygous for the 299 mutation, and she died of rapidly progressive urosepsis and E. coli bacteremia. Based on these findings, the authors concluded that risk of Gram-negative septic shock may be higher among those with TLR4 299 mutations. Additional studies in a larger study population with more carefully selected controls should be performed to confirm these findings. In another study of 80 Spanish hospital inpatients with either acute (n  24) or chronic (n  56) osteomyelitis due to Gramnegative (n  23) or Gram-positive (n  57) organisms and 155 healthy blood donors matched by age and gender, polymorphisms in TLR4 Asp299Gly appeared to be associated with Gram-negative infections (Montes et al., 2006). In this cohort, three cases were homozygotes for polymorphisms at this site (Asp299Gly G/G) and two of these had Gram-negative infections. No controls were found to have homozygous polymorphisms in TLR4 Asp299Gly (p  0.038). Allelic frequencies did not suggest that patients with osteomyelitis had higher frequency of mutant carrier alleles for this site than controls (p  0.08). Similar results were found for TLR4 Thr399Ile. Polymorphisms in TLR2 (Arg753Gln) were not significantly different between cases and controls. Those with TLR4 Asp299Gly heterozygous or homozygous polymorphisms had higher rates of Gram-negative infections (60% versus 21.5% respectively, p  0.0086), higher incidence of hematogenous osteomyelitis (40% versus 11% respectively, p  0.013) and chronic osteomyelitis more frequently (93.3% versus 64.6% respectively, p  0.031) than those who did not have these mutant alleles. Functional significance of these polymorphisms in TLR4 Asp299Gly were explored, and the investigators observed reduced apoptosis of neutrophils after incubation with LPS among those with osteomyelitis compared to non-infected controls, and among those with mutant alleles. In addition, phosphorylation of IB after incubation with LPS was reduced among those homozygous for the Asp299Gly mutant alleles. Furthermore, neutrophils from those with these mutant alleles did not demonstrate increases in IL-6 secretion, as was observed in non-infected controls and those homozygous for the wild-type alleles. As with previous studies, there was a very low number of subjects who were homozygous for the TLR4 Asp299Gly mutation in this study, which limits its ability to make powerful statistical comparisons. Also, the authors did not apparently control statistically for the large number of comparisons used in this study. This may bias the study findings, especially considering its small sample size and rather heterogenous population. Thus, the Asp299Gly/Thr399Ile haplotype of TLR4 does not conclusively appear to increase risk of Gram-negative

Host Genomics and Gram-Positive, Gram-Negative and Mycobacterial Infections

infections. While several studies have implicated TLR4 haplotypes containing only the Asp299Gly (or Thr399Ile) SNPs, the small number of patients with these haplotypes in each of these studies limits our ability to make conclusions about these haplotypes and their role in Gram-negative infections. Legionella A few studies have investigated immunogenetic factors specific to Legionella spp. infections. Legionella is a flagellated Gram-negative rod, and in vitro TLR2, TLR4, and TLR5 have been shown to recognize this pathogen (Akamine et al., 2005; Girard et al., 2003; Hawn et al., 2003; Hawn et al., 2005; Kikuchi et al., 2004). In murine models TLR2 and MyD88 have been shown to be important (Hawn et al., 2006b). However, studies in animals have produced conflicting evidence regarding the role of TLR4 and Legionella. TLR4 polymorphisms have been suggested to play a protective role in a human cohort exposed to Legionella at a flower show in the Netherlands. In a study of 108 subjects who contracted Legionnaire’s disease following this exposure and 508 controls that were exposed but not clinically ill, Legionella pneumonia was significantly less common among those with the Asp299Gly SNP. In this study, only heterozygotes for Asp299Gly were found, and Thr399Ile cosegregated with Asp299Gly in this European population. As the authors note, it is not clear why TLR4 polymorphisms are associated with a protective effect against Legionella, especially since these polymorphisms have been associated with no effect or increased risk of infection due to other Gram-negative pathogens. This phenomenon may be due to the unique LPS structure of Legionella that results in differential cell recognition and signaling. However, additional studies are needed to validate these findings. Recently Naip5, which is important for intracellular signaling and the apoptosis mechanism of macrophages, has been suggested to play a role in the intracellular pathogenicity of Legionella (Wright et al., 2003). Naip5 can activate macrophages via TLR5 and MyD88-independent mechanisms (Delbridge and O’Riordan, 2007). Mice with polymorphisms in Naip5 have been shown to be at increased susceptibility to Legionella infection (Diez et al., 2003). No human studies to date have investigated Naip5 and Legionnaire’s Disease. Neisseria meningitidis Neisseria meningitidis is an encapsulated Gram-negative diplococcus that is a common cause of meningitis and sepsis. A study of siblings in the United Kingdom suggested that genetic factors in the host accounted for approximately 33% of risk of meningococcal disease among siblings of those with meningococcal infection (Haralambous et al., 2003). Multiple mechanisms appear to be involved with recognition and host immunity to meningococcus. Innate and adaptive immunity mechanisms have been demonstrated in response to meningococcal challenge, as well as complement activation and subsequent shock/coagulopathy. Studies of genetics and meningococcal disease have suggested that polymorphisms in key molecules can result in increased susceptibility to this infection or more severe phenotypes.

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Inherited complement deficiencies have been reported in association with meningococcal disease, but these are relatively rare in the general population and probably not the primary reason for susceptibility to this infection. Among candidate genes, polymorphisms in TLR4 have not been strongly associated with either increased susceptibility to or severity of infection (Read et al., 2001; Smirnova et al., 2003). A study of UK residents with meningococcal infection found no association between the TLR4 polymorphism Asp299Gly and meningococcal infection. Although most of the subjects with meningococcal infection (n  1047) were 1 year of age, the investigators used a control population consisting of 879 healthy adult blood donors. However, the investigators also stratified the infected cohort by age group and serotype and found no associations between TLR4 Asp299Gly polymorphism among these strata and fatality. A recent study suggested that polymorphisms in TLR4 Asp299Gly and The399Ile were associated with increased susceptibility to meningococcal disease among European Caucasian children less than 12 months of age. There was no significant difference in the rate of meningococcal disease among those 3 years of age and older who were heterozygotes or homozygotes for the Asp299Gly and The399Ile SNPs compared to healthy controls, however (Faber et al., 2006). The authors suggest these mutations may be particularly relevant among infants who have an immature immune system. Polymorphisms in TLR2 have not appeared to increase susceptibility to meningococcal disease, but have not been extensively studied. Studies of MBL polymorphisms have yielded more substantial associations for meningococcal infection (Hibberd et al., 1999). Heterozygotes and homozygotes for variant alleles of MBL have been shown to have substantially lower circulating MBL concentrations. Several reports have suggested that those with variant alleles are at increased risk for meningococcal infection (Bax et al., 1999; Salimans et al., 2004). A recent study also suggested that combined polymorphisms in properidin and MBL results in an increase risk of N. meningitidis meningitis (Bathum et al., 2006). Three properidin deficiency variant types have been identified and have been associated with increase severity of meningococcal infection and clinical outcome. However, additional genetic factors may influence mortality and severity of disease among individuals with properidin deficiencies (Densen et al., 1987; Fijen et al., 1999; Spath et al., 1999;Westberg et al., 1995). Angiotensin-converting enzyme (ACE) has pro-inflammatory properties and in one study, the DD genotype that imparts higher circulating levels of ACE was associated with increased severity of meningococcal disease (Harding et al., 2002). Other elements of acquired immunity may be important for susceptibility to and severity of meningococcal disease. For example, mutations in FC receptors have been associated with meningococcal sepsis and were more frequent in those with meningococcal disease than controls in some studies (Bredius et al., 1994; Platonov et al., 1998; van der Pol et al., 2001). However, these results were not consistent across investigations (Domingo et al., 2002; Fijen et al., 2000).

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Polymorphisms in cytokines such as TNF, TNF, IL10, IL6, IL1, and IL1 receptor antagonist may be important but have produced inconsistent results in human studies of meningococcal disease. Mycobacteria There are several indicators that TB infection might be associated with genetic susceptibility (Bellamy, 2005). These include a potential role of innate immunity of the disease, whereby only 30–40% of close contacts of an infected individual contract TB infection (National Center for HIV SATPC, 2005). In addition, numerous animal models and human investigations have demonstrated the potential role of interferon-,TNF-, reactive nitrogen intermediates, and other immunomodulators as well as CD4 and CD8 T cells in controlling the disease (Stead, 1992). Numerous reports provide supportive data for genetic susceptibility to tuberculosis. Some of the most convincing data come from a tragic accident involving BCG (Bacilli CalmetteGuerin) vaccine in Germany to 249 infants in 1926 (Dubos and Dubos, 1952). Unfortunately, one strain of live M. tuberculosis was administered to all of these children, resulting in subsequent death of 76 babies within 1 year. Since only one strain was involved, this incident suggested that there was a differential response to tuberculosis infection in this population. Other factors that could have impacted response to infection were rather limited in this population due to age and disease exposure. Other evidence comes from selective pressure within a relatively homogenous population of Qu’Appelle Indians who experienced an annual death rate of 10% of their population from tuberculosis infection in 1890. This rate declined to less than 0.2% after two generations, suggesting a selective advantage among families with the ability to fight M. tuberculosis (Motulsky, 1960). Further evidence for the role of genetics in TB comes from a study of more than 25,000 residents of racially integrated nursing home in Arkansas (Stead et al., 1990). Development of a new TB infection (as evidenced by skin test conversion with 60 days of a negative test) while in the nursing home was approximately twice as likely among blacks and whites, with 13.8 versus 7.2% experiencing new infections, respectively (relative risk 1.9, 95% CI: 1.7–2.1). Even when the source patient was white, blacks became infected more frequently. However, once infected, individuals from both races appeared to be at similar risk of progressing to clinical infection. Finally, other studies in monozygotic twins compared to dizygotic twins in older studies found TB concordance rates twice as high among monozygotic twins, further suggesting a genetic role in susceptibility to disease. Subsequent investigations in the genomic era have furthered our understanding of genetic factors and susceptibility to tuberculosis. In vitro studies and animal models of infection have been established to help describe the role of innate immunity against mycobacteria. Studies in humans have included analysis of individual patients with disastrous consequences following vaccination with other mycobacterial species, candidate-gene approaches using case–control designs, and genome-wide scans using families. The populations included in these various studies of genetic

association and linkages have mostly included African (Gambian or South African) and Asian (Japanese, Chinese, Koreans, Taiwanese, and Hong Kong) populations. Only a few have included Europeans, Indians, or Mexicans (Fernando and Britton, 2006). Based on these studies, a number of genes have been identified that may have a role in increased or decreased risk of mycobacterial diseases (Table 110.2). These include: interferon-, IL-10 and IL-12 receptor, TNF- receptor 1, STAT-1, vitamin D receptor (VDR), solute carrier 11a1 protein (SLC11A1) (formerly known as natural resistance-associated macrophage protein (NRAMP1)), Class I and Class II HLA, mannose-binding lectin (MBL2), pulmonary surfactant proteins SP-A and SP-D, the purinergic receptor P2X7, TLR2, IL-1 receptor antagonist, and TNF- (Soborg et al., 2007; Stein et al., 2007). To date, associations with heat shock protein 1A (HSPA1A) and transporter associated with antigen processing-2 (TAP-2) have only been reported in association with M. leprae infections. In addition to loci, microsatellite markers have been identified on the X chromosome, as well as chromosome 15 which suggest association with TB in Gambians (Bellamy et al., 2000). Genome-wide scan approaches in M. leprae infections identified other microsatellite markers on chromosome 6, 10, and 20 that may be important. A study in Turkish patients with tuberculosis and noninfected controls suggested that the Arg753Gln polymorphism of TLR2 was associated with increased susceptibility to TB. Homozygotes for the AA genotype were more frequent among those with TB (OR 6.04 (95% CI: 2.01–20.08)), and this remained significant when the analysis was restricted to those with pulmonary TB. Heterozygotes were also more frequent among those with TB, although this finding was less substantial (OR 1.6 (95% CI: 1.01–2.55)) (Ogus et al., 2004). A recent study also suggested that TLR2 genotype 597CC were associated with susceptibility to tuberculous meningitis in Vietnamese subjects (Thuong et al., 2007). This association was not found for pulmonary tuberculosis in this case–control study. Another analysis of this cohort suggested that a SNP in the TLR1 receptor domain (C558T) was associated with tuberculosis, and more strongly with tuberculous meningitis (Hawn et al., 2006a). In those with lepromatous leprosy, the Arg677Trp mutation of TLR2 was found in 10 of 45 subjects, but was absent in those with tuberculoid leprosy and healthy controls (Kang and Chae, 2001). This mutation was later shown to eliminate activation of NF-B mediated by TLR2, in response to challenge with M. leprae (Bochud et al., 2003). Subsequent studies have not replicated the findings of the Arg677Trp mutation in other cohorts, however, and additional studies are necessary to establish the role of this TLR2 mutation (Alcais et al., 2005; Sanchez et al., 2004; Schroder et al., 2003a). Another study has suggested that the isoleucine to serine mutation at position 602 on TLR1 was protective against leprosy among Turkish Caucasians (Johnson et al., 2007). TLR1 has been suggested to be an important co-receptor for TLR2. The 602S mutation was quite common among Caucasians and substantially more frequent than in those of African or East Asian descent. The authors suggest that these differences may

References

contribute to the increased and unexplained risk of tuberculosis among African Americans in previous studies. Several recent studies have also investigated variation in the C-type lectin DC-SIGN and mycobacterial diseases such as tuberculosis and leprosy. Numerous studies indicate that variation in repeats in the neck region of DC-SIGN had no association with susceptibility to tuberculosis infection among South African Coloureds, Colombians, and Tunisians (Barreiro et al., 2007; Ben-Ali et al., 2007; Gomez et al., 2006). Variations in the neck region of L-SIGN (or DC-SIGNR) were also not associated with susceptibility to tuberculosis among South Africans. Promoter polymorphisms in DC-SIGN have been implicated in tuberculosis susceptibility among South Africans in one study (Barreiro et al., 2006a) but not other studies of Tunisians and West Africans (Olesen et al., 2007). The correlation among South Africans may reflect Eurasian descent of the population, in contrast to the other populations studied. DC-SIGN variations were also recently studied in association with leprosy in a cohort of 194 infected Pakistanis and 78 noninfected controls (Barreiro et al., 2006b). 109 patients had lepromatous disease, while 85 had tuberculoid leprosy. In a cold binding assay, DC-SIGN expressing cells showed substantial binding to M. leprae, and the proportion was similar to binding of M. tuberculosis. However, there was no association between SNPs in DC-SIGN and susceptibility to leprosy. Since the number of cases/controls was relatively small, the study was potentially limited in its power to detect these differences. Future studies may be helpful to determine if DC-SIGN has a role in susceptibility to leprosy.

FUTURE DIRECTIONS What can Studies of Host Genomics Do for Us and Our Patients? There are many potential benefits of reliable, meaningful studies on host genomics and infection. First, information indicating

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increase in disease susceptibility and severity could be used to identify patients at greatest risk of infection and/or its most dire consequences. Such patients could be candidates for vaccination strategies and/or early screening/diagnosis. Second, information suggesting genomic protection from certain infectious diseases could lead to new therapeutic approaches or even individualized therapies for infected patients. These approaches may include development of new vaccines, or manipulation (i.e., repletion or blockade as appropriate) of endogenous compounds. Third, gene expression profiling may facilitate new diagnostic methods whereby a “signature” for host response to certain pathogens could be established, and potentially allow for more reliable and earlier identification of the infecting organism. A profile such as this might also enable prediction of those at highest risk of infectious complications or sepsis, facilitating earlier medical intervention.

CONCLUSIONS In summary, candidate-gene studies have identified a number of different molecules that may be important in host defense to Gram-positive, Gram-negative, and mycobacterial pathogens. Gene expression studies have assisted in identifying other molecules that may have a role in host defense, and represent a means by which functional implications of genetic mutations can be studied in a meaningful way over time. Together, these investigations will help unravel the mysteries of how the host interacts with the pathogen, and hopefully open the door to new therapeutic and diagnostic modalities for patients of the future. Although new technological advancements make such studies more feasible in terms of time and expense, we must consider limitations of these approaches and plan future investigations carefully with attention to model system, patient population, well-defined phenotypes, selection of appropriate controls, and use of appropriate statistical methods for analysis. This will aid in production of both meaningful and reliable information for the genomic era of medicine.

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Mira, M.T. (2006). Genetic host resistance and susceptibility to leprosy. Microbes Infect 8, 1124–1131. Mira, M.T., Alcais, A., Nguyen, V.T., Moraes, M.O., Di, F.C., Vu, H.T., Mai, C.P., Nguyen, T.H., Nguyen, N.B., Pham, X.K. et al. (2004). Susceptibility to leprosy is associated with PARK2 and PACRG. Nature 427, 636–640. Modlin, R.L. and Bloom, B.R. (2001). Immunology. Chip shots – Will functional genomics get functional?. Science 294, 799–801. Moens, L., Van Hoeyveld, E., Peetermans, W.E., De Boeck, C., Verhaegen, J. and Bossuyt, X. (2006a). Mannose-binding lectin genotype and invasive pneumococcal infection. Hum Immunol 67, 605–611. Moens, L., Van Hoeyveld, E., Verhaegen, J., De Boeck, K., Peetermans, W.E. and Bossuyt, X. (2006b). Fc[gamma]-receptor IIA genotype and invasive pneumococcal infection. Clin Immunol 118, 20–23. Moens, L., Verhaegen, J., Pierik, M., Vermeire, S., De Boeck, K., Peetermans, W.E. and Bossuyt, X. (2007). Toll-like receptor 2 and Toll-like receptor 4 polymorphisms in invasive pneumococcal disease. Microbes Infect 9, 15–20. Monaghan, R.L. and Barrett, J.F. (2006). Antibacterial drug discovery – Then, now and the genomics future. Biochem Pharmacol 71, 901–909. Montes, A.H., Asensi, V., Alvarez, V., Valle, E., Ocana, M.G., Meana, A., Carton, J.A., Paz, J., Fierer, J. and Celada, A. (2006). The Toll-like receptor 4 (Asp299Gly) polymorphism is a risk factor for Gramnegative and haematogenous osteomyelitis. Clin Exp Immunol 143, 404–413. Moore, C.E., Segal, S., Berendt, A.R., Hill, A.V.S. and Day, N.P.J. (2004). Lack of association between Toll-like receptor 2 polymorphisms and susceptibility to severe disease caused by Staphylococcus aureus. Clin Diagn Lab Immunol 11, 1194–1197. Motulsky, A.G. (1960). Metabolic polymorphisms and the role of infectious diseases in human evolution. Hum Biol 32, 28–62. Murray, P.J. (2005). NOD proteins:An intracellular pathogen-recognition system or signal transduction modifiers?. Curr Opin Immunol 17, 352–358. National Center for HIV SATPC (2005). Controlling tuberculosis in the United States. MMWR Recommendations and Reports 54, 1–81. Noakes, K., Pebody, R.G., Gungabissoon, U., Stowe, J. and Miller, E. (2006). Pneumococcal polysaccharide vaccine uptake in England, 1989–2003, prior to the introduction of a vaccination programme for older adults. J Public Health 28, 242–247. Ogus, A.C., Yoldas, B., Ozdemir, T., Uguz, A., Olcen, S., Keser, I., Coskun, M., Cilli, A. and Yegin, O. (2004). The Arg753GLn polymorphism of the human toll-like receptor 2 gene in tuberculosis disease. Eur Respir J 23, 219–223. Olesen, R., Wejse, C., Velez, D.R., Bisseye, C., Sodemann, M., Aaby, P., Rabna, P., Worwui, A., Chapman, H., Diatta, M. et al. (2007). DCSIGN (CD209), pentraxin 3 and vitamin D receptor gene variants associate with pulmonary tuberculosis risk in West Africans. Genes Immun 8, 456–467. Opitz, B., Puschel, A., Schmeck, B., Hocke, A.C., Rosseau, S., Hammerschmidt, S., Schumann, R.R., Suttorp, N. and Hippenstiel, S. (2004). Nucleotide-binding oligomerization domain proteins are innate immune receptors for internalized Streptococcus pneumoniae. J Biol Chem 279, 36426–36432. Pamer, E.G. (2004). Immune responses to Listeria monocytogenes. Nat Rev Immunol 4, 812–823.

Platonov, A.E., Shipulin, G.A., Vershinina, I.V., Dankert, J., van de Winkel, J.G. and Kuijper, E.J. (1998). Association of human Fc gamma RIIa (CD32) polymorphism with susceptibility to and severity of meningococcal disease. Clin Infect Dis 27, 746–750. Power, M.R., Peng, Y., Maydanski, E., Marshall, J.S. and Lin, T.J. (2004). The development of early host response to Pseudomonas aeruginosa lung infection is critically dependent on myeloid differentiation factor 88 in mice. J Biol Chem 279, 49315–49322. Read, R.C., Pullin, J., Gregory, S., Borrow, R., Kaczmarski, E.B., di Giovine, F.S., Dower, S.K., Cannings, C. and Wilson, A.G. (2001). A functional polymorphism of toll-like receptor 4 is not associated with likelihood or severity of meningococcal disease. J Infect Dis 184, 640–642. Roy, S., McGuire, W., Mascie-Taylor, C.G., Saha, B., Hazra, S.K., Hill, A.V. and Kwiatkowski, D. (1997). Tumor necrosis factor promoter polymorphism and susceptibility to lepromatous leprosy. J Infect Dis 176, 530–532. Roy, S., Frodsham, A., Saha, B., Hazra, S.K., Mascie-Taylor, C.G. and Hill, A.V. (1999). Association of vitamin D receptor genotype with leprosy type. J Infect Dis 179, 187–191. Roy, S., Knox, K., Segal, S., Griffiths, D., Moore, C.E., Welsh, K.I., Smarason, A., Day, N.P., McPheat, W.L., Crook, D.W. et al. (2002). MBL genotype and risk of invasive pneumococcal disease: A casecontrol study. Lancet 359, 1569–1573. Salimans, M.M., Bax, W.A., Stegeman, F., van, D.M., Bartelink, A.K. and van, D.H. (2004). Association between familial deficiency of mannose-binding lectin and mutations in the corresponding gene and promoter region. Clin Diagn Lab Immunol 11, 806–807. Sanchez, E., Orozco, G., Lopez-Nevot, M.A., Jimenez-Alonso, J. and Martin, J. (2004). Polymorphisms of toll-like receptor 2 and 4 genes in rheumatoid arthritis and systemic lupus erythematosus. Tissue Antigens 63, 54–57. Schroder, N.W. and Schumann, R.R. (2005). Single nucleotide polymorphisms of Toll-like receptors and susceptibility to infectious disease. Lancet Infect Dis 5, 156–164. Schroder, N.W., Hermann, C., Hamann, L., Gobel, U.B., Hartung, T. and Schumann, R.R. (2003a). High frequency of polymorphism Arg753Gln of the Toll-like receptor-2 gene detected by a novel allele-specific PCR. J Mol Med 81, 368–372. Schroder, N.W., Morath, S., Alexander, C., Hamann, L., Hartung, T., Zahringer, U., Gobel, U.B., Weber, J.R. and Schumann, R.R. (2003b). Lipoteichoic acid (LTA) of Streptococcus pneumoniae and Staphylococcus aureus activates immune cells via Toll-like receptor (TLR)-2, lipopolysaccharide-binding protein (LBP), and CD14, whereas TLR-4 and MD-2 are not involved. J Biol Chem 278, 15587–15594. Seki, E., Tsutsui, H., Tsuji, N.M., Hayashi, N., Adachi, K., Nakano, H., Futatsugi-Yumikura, S., Takeuchi, O., Hoshino, K., Akira, S. et al. (2002). Critical roles of Myeloid Differentiation Factor 88dependent proinflammatory cytokine release in early phase clearance of Listeria monocytogenes in mice. J Immunol 169, 3863–3868. Skerrett, S.J., Liggitt, H.D., Hajjar, A.M. and Wilson, C.B. (2004). Cutting edge: Myeloid differentiation factor 88 is essential for pulmonary host defense against Pseudomonas aeruginosa but not Staphylococcus aureus. J Immunol 172, 3377–3381. Smirnova, I., Mann, N., Dols, A., Derkx, H.H., Hibberd, M.L., Levin, M. and Beutler, B. (2003). Assay of locus-specific genetic load implicates rare Toll-like receptor 4 mutations in meningococcal susceptibility. Proc Natl Acad Sci USA 100, 6075–6080.

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Soborg, C., Andersen, A.B., Range, N., Malenganisho, W., Friis, H., Magnussen, P., Temu, M.M., Changalucha, J., Madsen, H.O. and Garred, P. (2007). Influence of candidate susceptibility genes on tuberculosis in a high endemic region. Mol Immunol 44, 2213–2220. Spath, P.J., Sjoholm, A.G., Fredrikson, G.N., Misiano, G., Scherz, R., Schaad, U.B., Uhring-Lambert, B., Hauptmann, G., Westberg, J., Uhlen, M. et al. (1999). Properdin deficiency in a large Swiss family: Identification of a stop codon in the properdin gene, and association of meningococcal disease with lack of the IgG2 allotype marker G2m(n). Clin Exp Immunol 118, 278–284. Srivastava, A., Henneke, P., Visintin, A., Morse, S.C., Martin, V., Watkins, C., Paton, J.C., Wessels, M.R., Golenbock, D.T. and Malley, R. (2005). The apoptotic response to pneumolysin is Tolllike receptor 4 dependent and protects against pneumococcal disease. Infect Immun 73, 6479–6487. Stead, W.W. (1992). Genetics and resistance to tuberculosis. Could resistance be enhanced by genetic engineering?. Ann Intern Med 116, 937–941. Stead,W.W., Senner, J.W., Reddick, W.T. and Lofgren, J.P. (1990). Racial differences in susceptibility to infection by Mycobacterium tuberculosis. New Engl J Med 322, 422–427. Stein, C.M., Zalwango, S., Chiunda, A.B., Millard, C., Leontiev, D. V., Horvath, A.L., Cartier, K.C., Chervenak, K., Boom, W.H., Elston, R.C. et al. (2007). Linkage and association analysis of candidate genes for TB and TNFalpha cytokine expression: Evidence for association with IFNGR1, IL-10, and TNF receptor 1 genes. Hum Genet 121, 663–673. Takeuchi, O., Hoshino, K. and Akira, S. (2000). Cutting edge: TLR2deficient and MyD88-deficient mice are highly susceptible to Staphylococcus aureus infection. J Immunol 165, 5392–5396. Tapping, R.I., Syroid, D.E., Bilan, P.T. and Capone, J.P. (1993). The 5 flanking sequence negatively modulates the in vivo expression and in vitro transcription of a human tRNA gene. Nucleic Acids Res 21, 4476–4482. Thuong, N.T., Hawn, T.R., Thwaites, G.E., Chau, T.T., Lan, N.T., Quy, H.T., Hieu, N.T.,Aderem, A., Hien,T.T., Farrar, J.J. et al. (2007). A polymorphism in human TLR2 is associated with increased susceptibility to tuberculous meningitis. Genes Immun 8, 422–428.

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Torres, D., Barrier, M., Bihl, F., Quesniaux, V.J.F., Maillet, I., Akira, S., Ryffel, B. and Erard, F. (2004). Toll-like receptor 2 is required for optimal control of Listeria monocytogenes infection. Infect Immun 72, 2131–2139. Turvey, S.E. and Hawn, T.R. (2006). Towards subtlety: Understanding the role of Toll-like receptor signaling in susceptibility to human infections. Clin Immunol 120, 1–9. van der Pol, W.L., Huizinga, T.W., Vidarsson, G., van der Linden, M.W., Jansen, M.D., Keijsers, V., de Straat, F.G., Westerdaal, N.A., de Winkel, J.G. and Westendorp, R.G. (2001). Relevance of Fcgamma receptor and interleukin-10 polymorphisms for meningococcal disease. J Infect Dis 184, 1548–1555. Westberg, J., Fredrikson, G.N.,Truedsson, L., Sjoholm,A.G. and Uhlen, M. (1995). Sequence-based analysis of properdin deficiency: Identification of point mutations in two phenotypic forms of an X-linked immunodeficiency. Genomics 29, 1–8. Wright, E.K., Goodart, S.A., Growney, J.D., Hadinoto, V., Endrizzi, M.G., Long, E.M., Sadigh, K., Abney, A.L., BernsteinHanley, I. and Dietrich, W.F. (2003). Naip5 affects host susceptibility to the intracellular pathogen Legionella pneumophila. Curr Biol 13, 27–36. Yee, A.M., Ng, S.C., Sobel, R.E. and Salmon, J.E. (1997). Fc gammaRIIA polymorphism as a risk factor for invasive pneumococcal infections in systemic lupus erythematosus. Arthritis Rheum 40, 1180–1182. Yee, A.M., Phan, H.M., Zuniga, R., Salmon, J.E. and Musher, D.M. (2000). Association between FcgammaRIIa-R131 allotype and bacteremic pneumococcal pneumonia. Clin Infect Dis 30, 25–28. Yoshimura, A., Lien, E., Ingalls, R.R., Tuomanen, E., Dziarski, R. and Golenbock, D. (1999). Cutting edge: Recognition of Gram-positive bacterial cell wall components by the innate immune system occurs via Toll-like receptor 2. J Immunol 163, 1–5. Yu, S.L., Chen, H.W., Yang, P.C., Peck, K., Tsai, M.H., Chen, J.J. and Lin, F.Y. (2004). Differential gene expression in Gram-negative and Gram-positive sepsis. Am J Resp Crit Care Med 169, 1135–1143. Yuan, F.F., Wong, M., Pererva, N., Keating, J., Davis, A.R., Bryant, J.A. and Sullivan, J.S. (2003). FcgammaRIIA polymorphisms in Streptococcus pneumoniae infection. Immunol Cell Biol 81(3), 192–195.

CHAPTER

111 Sepsis and the Genomic Revolution Christopher W. Woods, Robert J. Feezor and Stephen F. Kingsmore

INTRODUCTION An 18-year-old female college freshman who lives in a dormitory with 50 other women presents to her local acute care center with 12 h of fever and headache. She is referred to the emergency department and develops a petechial rash while in transit. In the emergency department she becomes hypotensive and lethargic. She is admitted to the intensive care unit (ICU). Blood and cerebrospinal fluid cultures taken at the time of admission grow Neisseria meningitidis, serogroup C. She dies 2 days later with multi-organ failure despite aggressive attempts at resuscitation and support. No other students in her dormitory become ill after prophylaxis is provided. Her devastated parents wish to know if her two siblings are at high risk of bad outcome with a similar infection. A 64-year-old hospitalized, African American veteran is admitted to the ICU after developing fever and hypotension subsequent to aortobifemoral bypass graft procedure. Blood cultures grow enteric Gram-negative rods. His fever persists and he develops disseminated intravascular coagulopathy, acute renal failure, acute lung injury, and succumbs to his illness after 3 weeks. His physicians considered drotrecogin alfa, but chose not to give it in the setting of recent surgery. A 52-year-old woman with a several month history of fever with negative evaluation including blood cultures presents with acute renal failure and severe aortic regurgitation. A transthoracic echocardiogram reveals vegetations on her aortic valve leaflet. Immunohistochemistry at the time of valve replacement

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confirms Bartonella henselae. The patient raises feral cats. Her primary care physician is frustrated that he was unable to diagnose the infection earlier. Each of these cases represents extremes of the heterogeneity of the sepsis spectrum and, yet, each highlights the potential benefits of the genomic revolution in the way we identify and care for patients presenting with the sepsis syndrome. In the past decade new techniques have been developed to investigate the causes of complex diseases. In this chapter, we will review the relevant literature and look toward the future. Sepsis is a common, heterogeneous clinical entity that is defined by the physiological changes known collectively as the systemic inflammatory response syndrome (SIRS) that occur in response to a presumed infectious etiology (Table 111.1) (Bone et al., 1989, 1992; Sands et al., 1997). The insulting agent may be bacterial, viral, fungal, or parasitic with more than 80% of cases originating from a pulmonary, genitourinary, or abdominal source (Opal and Cohen, 1999). However, sepsis is not a single disease, but rather a heterogeneous syndrome that is expressed through the interaction of networks of biochemical mediators and inflammatory cascades. Clinical expression is variable and its severity is influenced by the infectious etiology, the genetic background of the patient, comorbid conditions, the time to clinical intervention, and the supportive care provided by the physician. Patients with coincident acute organ dysfunction are considered to have severe sepsis. Patients with sepsis who fail to maintain their blood pressure despite adequate hydration

Copyright © 2009, Elsevier Inc. All rights reserved.

Genetic Polymorphisms Associated with Sepsis

TABLE 111.1 SIRS

● ● ●

Temp 38 C or 36 C Heart rate 90 beats per minute White Blood Cell count 12,000 cell/cc, 4000 cells/cc, or 10% band forms Respiratory rate 20 breaths per minute or PaCO232 torr

SIRS criteria and evidence of infection, or: ● ● ● ●

Severe Sepsis

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Criteria for systemic inflammatory response syndrome and sepsis ●

Sepsis

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White cells in normally sterile body fluid Perforated viscus Radiographic evidence of pneumonia Syndrome associated with high risk of infection

Sepsis criteria and evidence of organ dysfunction including: ● ● ● ● ●

Cardiovascular: SBP 90 mmHg, MAP  70 mmHg for at least 1 hour despite adequate volume resuscitation or the use of vasopressors Renal: urine output 0.5 mL/kg body weight/hour for 1 hour despite adequate volume resuscitation Pulmonary: PaO2/FiO2 250 if other organ dysfunction present or 200 if the lung is the only dysfunctional organ Hematologic: platelet count 80,000/cc or decreased by 50% within 3 days Metabolic: pH 7.3 or base deficit 5.0 mmol/L and plasma lactate 1.5 x upper limit of normal

are considered to have septic shock. Severe sepsis is a major cause of morbidity and mortality with an annual incidence of 50–100 cases per 100,000 persons in several industrialized nations (Martin et al., 2003). In the United States, there are approximately 750,000 new cases of severe sepsis annually with an economic impact approaching $17 billion (Angus and Wax, 2001). Despite an enormous investment in critical care resources, 20–50% of patients with sepsis died; it is the third leading cause of infectious death and tenth leading cause of death overall. The incidence of severe sepsis is increasing by approximately 9% annually. Between 1979 and 2000, the incidence increased from 82.7/100,000 to 240.4/100,000 (Martin, 2003). This increase is multifactorial, resulting from increased awareness and documentation of sepsis, “graying” of the population, greater use of invasive procedures for the diagnosis and monitoring of critically ill patients, emergence of antibiotic-resistant organisms, and increasing prevalence of immunocompromised patients (e.g., malignancy, AIDS, transplant recipients, diabetes mellitus, alcoholism, and malnutrition) (Parrillo et al., 1990). Furthermore, the relative contribution of etiological organisms has changed substantially over time. Gram-positive organisms superseded Gram-negative organisms in predominance in 1987, and fungal sepsis has increased by more than 200% since 1980 (Martin, 2003). Genomic medicine appears well situated to assist in the identification of individuals at substantial risk for certain infections, to stratify subsets of individuals who are likely to progress to adverse outcomes or most benefit from therapeutic intervention, and to facilitate the rapid identification of etiological organisms. However, the complex physiology and epidemiology of sepsis, together with the diversity of physicians confronted by this

illness, have slowed progress. Much of the literature focuses on the tremendous achievements in describing the genomes of various bacterial, viral, and fungal pathogens; however, this chapter focuses on the human genome and its response to the infectious perturbations that result in sepsis.

GENETIC POLYMORPHISMS ASSOCIATED WITH SEPSIS Microbiological infections and their complications, such as sepsis and severe sepsis, occur at the interface of host genes, microbial genes, and the environment. While exposure to a microbial agent is necessary, it is not sufficient to cause sepsis in the host. Regardless of etiology, sepsis stimulates the host’s immune, inflammatory, and coagulation responses (Figure 111.1). Although the general direction of activation is similar between persons, there are notable differences in response to infection that may have important clinical implications (Bellamy and Hill, 1998; Burgner and Levin, 2003; Choi et al., 2001). Therefore, sepsis progression depends on the relative weight of host defense versus microbe virulence. Several recent studies have shown evidence that host and pathogen molecular interactions drive adaptive evolution of the immune system – a host:pathogen genetic arms race (Hughes and Nei, 1988; Sackton et al., 2007; Schlenke and Begun, 2003). Accordingly, it is useful to evaluate how the genotype of patients determines their individual susceptibility and response to infection. Family and twin studies have demonstrated familial or twin aggregation of some, but not all sepsis outcomes and clinical presentations on a common genetic basis (Härtel et al., 2007; Jepson et al., 1995). There are several dramatic examples of lack

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Epithelial cells Excessive inflammatory response

Endothelial cells

Causative agents: Bacteria Trauma Shock

Pro-inflammatory cytokines  chemokines ROS production Enzyme release

PMN Macrophages

Vascular permeability Bacterial killing DIC Tachypnea Fever Leukocytosis Tachycardia Peripheral resistance

COMPLEMENT SYSTEM Intensity of inflammatory response

COAGULATION SYSTEM Serum proteins

HYPERREACTIVE IMMUNE RESPONSE

Edema Tissue damage Organ failure Leukocytopenia Shutdown of noutrophil & phagocytic cell function Susceptibility to infection HYPOREACTIVE IMMUNE RESPONSE, IMMUNE PARALYSIS

Dynamic time-course of the inflammatory response during sepsis

Figure 111.1 Time course and intensity of host immune inflammatory and coagulation responses during sepsis (reproduced with permission from Riedemann, N.C., Guo, P.-F. and Ward, P.A. (2003) “Novel Strategies for the treatment of sepsis”. Nat. Med. 9: 517–524). Microbiologic antigens and factors cause activation of the innate and cognate immune system, inflammatory response and coagulation system in sepsis, leading to production of proinflammatory cytokines, activation of the alternate pathway of complement and upregulation of adhesion and signaling molecules on white blood cells and blood vessel endothelium. ROS: Reactive oxygen species. DIC: Disseminated intracascular coagulation.

of host defense factors resulting from Mendelian mutations. For example, autosomal dominant inheritance in meningococcal meningitis susceptibility in a few large families due to a defective properdin gene in the alternative complement pathway (Densen et al., 1987). Mutations in ATP-sensitive K channels expressed in murine endothelial cells and coronary artery smooth muscle may cause profound susceptibility to mouse cytomegalovirus (Croker et al., 2007). Other examples of susceptibility alleles are variants in immune response genes, including the MHC, CCR5 in HIV infection, and Toll receptor 2 in Mycobacteria and non-immune response genes such as Nramp in Mycobacteria (Drennan et al., 2004; Liu et al., 1996; Samson et al., 1996; Skamene et al., 1998). Linkage-based methods and candidate gene-based association studies are being extensively applied to microbial infection outcomes and sepsis but, to date, have met with limited success in the identification of risk alleles for host gene:pathogen interaction (Botstein and Risch, 2003; Freimer and Sabatti, 2004; Hill, 2006). In sepsis, most studies have investigated candidate genes that are involved in pathogen detection (e.g., toll-like receptors, TLRs), the inflammatory response (e.g., tumor necrosis factor, TNF-) or coagulation (e.g., plasminogen activator inhibitor, PAI). Several

reviews of the associations between candidate gene polymorphisms and the risk and outcome of sepsis have been published (Arcaroli et al., 2005; Lin and Albertson, 2004; Majetschak et al., 2002; Mira et al., 1999). However, ultimately, complex diseases like sepsis represent the cumulative effect of many minor susceptibility alleles and/or a small number of alleles of large effect. Several of those alleles of potential large effect are discussed briefly in this chapter and in more detail in adjoining chapters in this volume. Limitations of Sepsis Gene-Association Studies The analysis of single nucleotide polymorphisms (SNPs) among various susceptibility genes, such as those coding for pro- and anti-inflammatory mediators, in the response to sepsis has the potential to nominate prediction tools that will permit the clinician to more precisely determine the type of therapy a particular patient should receive. In fact, early identification of a genotypic risk may even help guide the introduction of prophylactic therapy. However, unraveling the genetic variation in sepsis is complicated and close attention must be paid to study design issues, including the selection of an appropriate study population and sample size and understanding gene–environment interactions.

Genetic Polymorphisms Associated with Sepsis

A lack of replication among studies provides considerable concern in the interpretation of results. For example, an initial study may identify an allele with large estimated genetic effects, but subsequent studies fail to corroborate the results (Goring et al., 2001; Hirschhorn et al., 2002; Ioannidis et al., 2001; Lander and Kruglyak, 1995). Biological explanations of inconsistent results include unacknowledged confounding heterogeneity, such as poorly defined phenotypes, heterogeneous genetic sources for the phenotype (genocopies), population diversity (ethnic ancestry), population-specific linkage disequilibrium (LD), heterogeneous genetic and epigenetic backgrounds, or heterogeneous environmental influences (phenocopies). Analytic reasons for problems with reproducibility include failure to control the rate of false discoveries, lack of power, model misspecification, and heterogeneous bias in estimated effects among studies (Cardon and Bell, 2001; Cardon and Palmer, 2003; Redden and Allison, 2003; Sillanpaa and Auranen, 2004). Among these, the most frequent source of non-replication has been lack of power due to the limited number of individuals genotyped and phenotyped (Lohmueller et al., 2003; Risch, 2000). Pathogen Recognition/Signaling Toll-Like Receptors There has been substantial progress in defining variants of several genes and pathways implicated in infectious disease susceptibility; however, none have caused more excitement than investigation of genes affecting innate immunity, particularly newly discovered pattern recognition receptors and their associated signaling pathways. The TLRs have captured the attention of most investigators in this field. Innate immunity, recognition of invading microorganisms, is mediated by a set of soluble and membrane receptors that recognize conserved, pathogen-associated molecular patterns (PAMPs) shared among each class of infectious agents but absent in higher eukaryotes. Having a cytoplasmic domain that bears homology to the interleukin (IL)-1 receptor, TLRs have been conserved throughout diverse life forms including plants, insects, and mammals. Thus far, 10 human TLRs have been identified that play a role in sensing pathogens. Activation of TLRs stimulates macrophages resulting in the elaboration of proinflammatory cytokines and antimicrobial molecules such as nitric oxide and defensins. Concurrently, stimulated dendritic cells migrate to lymph nodes and over-express antigen MHC and costimulatory molecules (CD80/CD86). Therefore, TLRs are an essential link between innate and adaptive immunity. The discovery and characterization of TLRs as a key pattern recognition molecules for pathogens (or PAMPs, Beutler and Reitschel, 2003) and initiators of the innate immune responses (Akira et al., 2001; Creagh and O’Neill, 2006) stimulated many investigators to further characterize the relevance of polymorphisms in these receptors and susceptibility to infectious disease. Sepsis was an early target for characterization of the relevance of polymorphisms in TLRs. The TLR4 protein activated NF-kappa B and increased expression of the pro-inflammatory cytokines, IL-1, IL-6, and IL-8 in cultured human cells. and TLR4 deficient

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mice were resistant to systemic endotoxin exposure, but remained susceptible to Gram-negative infections (Oureshi et al., 1999). Asp229Gly and Thr399Ile are common TLR4 missense mutations that affect the extracellular domain and some reports suggest an association with varied human responses to inhaled endotoxin (Arbour et al., 2000). A number of small studies associated the Asp299Gly variant of the TLR4 gene with increased susceptibility to either Gram-negative infections (Agnese et al., 2002; Lorenz et al., 2002) or SIRS (Child et al., 2003). However, an evaluation of an intravenous lipopolysaccharide (LPS) challenge did not reveal an association with TLR4 mutations (Calvano et al., 2006). In addition, Smirnova et al. found an excess of rare TLR4 coding changes in meningococcal cases compared with controls (Smirnova et al., 2003). However, the study did not support an association with the functional Asp299Gly or Thr399Ile polymorphisms of TLR4. The absence of an association was also supported by a much larger case–control study (Read et al., 2001). Together, these studies of TLR4 polymorphisms suggest that individuals with the 299/399 polymorphisms may have an aberrant response to certain, but not all, Gram-negative bacterial diseases, resulting in an increased susceptibility to infection and severity of disease. TLR2 and TLR5 have also been targets of interest. TLR2 is most notable for detecting a wide repertoire of pathogens, including Gram-positive or Gram-negative bacteria, mycobacteria, fungi, viruses, and parasites. Largely this results from an ability to recognize ligands as a heterodimer with TLR1 or TLR6 (Lorenz, 2006). Of particular importance, TLR2 has been linked to the recognition of Gram-positive bacteria, which are now the leading cause of sepsis (Martin, 2003). TLR2 accomplishes this via response to peptidoglycan, lipoteichoic acid, and a variety of macromolecules in Gram-positive bacteria such as S. aureus. The relationship between TLR2 polymorphisms (primarily Arg753Gln) and S. aureus infection has been examined in several studies with contradictory results (Lorenz et al., 2000; Moore et al., 2004). Therefore, additional investigations are warranted to confirm this association. TLR5, which recognizes bacterial flagellin from both Grampositive and Gram-negative bacteria, activates NF-kappa B and the release of pro-inflammatory cytokines in response to this bacterial antigen (Hayashi et al., 2001). A stop codon polymorphism (Arg392TER) has been identified in the TLR5 gene and is associated with an increased susceptibility to Legionella pneumophila (Hawn et al., 2003). suggesting that this allele may increase susceptibility to pneumonia associated with flagellated organisms. NOD-Like and RIG-Like Receptors TLRs are involved in the recognition of all types of pathogens regardless of location. In contrast, NOD-like receptors (NLRs) primarily recognize cytoplasmic bacterial pathogens (Inohara and Nunez, 2003). Many NLRs have caspase recruitment domains (CARD). These domains are involved in the assembly of protein complexes that promote apoptosis. These CARD proteins may also participate in NF-kappa B signaling pathways.

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Hill et al. have studied a truncation variant of the human CARD8 gene and found that African children homozygous for this inactivating mutation are susceptible to non-typhoidal Salmonella bacteremia (Hill, 2006). A third family of pattern recognition receptors described more recently is the DExD/H Box RNA helicase family of RIG-I-like receptor (RLR) genes, which includes MDA5 as well as RIG-1 (Creagh et al., 2006). These receptors appear to differentially recognize double-stranded RNA from various viruses (Kato et al., 2006). An amino acid change in MDA5 has been associated with susceptibility to type 1 diabetes lending support to a viral etiology for this disease (Smyth et al., 2006). Mannose-Binding Lectin Mannose-binding lectin (MBL) is an acute-phase protein that can opsonize many bacterial and fungal pathogens and activate complement (Kuhlman et al., 1989). Approximately one-third of most human populations are heterozygous for one of the variants in the coding region of exon 1 that lead to lower MBL concentrations. Individuals with 2 copies of low MBL haplotypes have a higher risk of pneumococcal sepsis (Kronborg and Garred, 2002; Roy et al., 2002), and there is less well-replicated evidence for susceptibility to other bacterial pathogens and candidiasis. Although multiple studies indicate that MBL plays a role in mitigating certain pathogens, the high rates of haplotypes that specify low MBL levels in certain ethnic groups suggest that a relative lack of MBL might be beneficial to the host under other circumstances. In particular, studies of the MBL-MASP pathway suggest that it may play a role in reducing reperfusion injury in the heart and resulting in relative protection against the complications of myocardial infarction (Walsh et al., 2005). CD 14 CD14 functions as an anchor protein and is a ubiquitous pattern recognition receptor, specific for LPS and other ligands. Performing as a co-receptor for TLR4, it is shed by monocytes to facilitate LPS signaling for all other cells. Two polymorphisms at the promoter region of the CD14 gene have been widely explored. The -159 T allele has been associated with increased prevalence of Gram-negative infections and sepsis, but not with shock or survival (Gibot et al., 2002; Sutherland et al., 2005). However, other studies have yielded contradictory results. More recently, the -260 T allele has been linked to increased survival in a Brazilian sepsis population (D’Avila et al., 2006). Intracellular Signaling Molecules The IL1 receptor-associated kinase-4 (IRAK4) is an intracellular kinase that transduces intracellular signals conveyed by the TLRs and IL1 receptors. Patients with IRAK4 deficiency are at increased risk for invasive bacterial disease (Von Bernuth et al., 2005). Homozygous individuals for either of the two known variant alleles appear to have completely inhibited expression of IRAK4, thereby inhibiting activation of NF-kappa B and NF-kappa B-dependent pro-inflammatory mediators (Lasker et al., 2005).

Cytokine Polymorphisms Genetic variation in the pro-inflammatory cytokines TNF-, TNF-, IL-6, IL-8 and macrophage migration inhibitory factor (MIF) and the anti-inflammatory cytokines IL-10 and IL-1 RA are the most extensively studied in relation to sepsis. Patients predisposed to a balanced anti-, pro-inflammatory response appear to have better chances for survival (Figure 111.1). Defects in regulation occur when the balance is shifted and allows for unmitigated inflammation causing tissue and organ damage or the inability to extirpate invading pathogens. Pro-inflammatory Cytokines TNF- is a primary mediator in sepsis and an initial trigger of the immune response. Most studies have investigated the significance of the -308 A allele of the promoter region and have yielded mixed results on outcome of sepsis and no association with the expression of TNF- (Mira et al., 1999; Stüber et al., 1996). Potential associations with polymorphisms at 376 and position 238 are also not convincing. Bayley et al. reviewed the functional impact of genomic variations within the TNF locus and showed that most of the variation had no impact on the expression of TNF- or had contradictory results (Bayley et al., 2004). TNF- binds to the same receptor as TNF- and a number of polymorphisms have also been described. In particular, the TNF- 250 SNP has been the target of multiple investigations. However, LD with heat shock protein 70 alleles confounds interpretation and more advanced models will be necessary before inferences of TNF- variability in sepsis can be made. Both IL-1 and IL-1 engage the same receptor and are potent pro-inflammatory cytokines released by macrophages involved in the systemic inflammatory response and are capable of inducing the symptoms of septic shock and organ failure in animal models (Leon et al., 1992). Despite the finding that a homozygous TaqI genotype (511) correlates with expression of IL-1, no association with incidence or outcome of sepsis has been determined (Ma et al., 2002; Pociot et al., 1992). IL-6 plays a role in lymphocyte stimulation and its levels consistently associate with severity and mortality in sepsis (van der Poll and van Deventer, 1999). Allelic variation has been described in the promoter region (-174 G/C). A German study demonstrated improved survival with the -174 G/C SNP, but not incidence of sepsis (Schluter et al., 2002). It is possible that variation at the IL-6 gene may contribute to the outcome of sepsis, but variable association of specific SNPs with IL-6 levels and the potential for LD with several alleles obscure the clinical significance of these results. Similar studies for the identification of polymorphisms in IL-8 and MIF promoter regions and genes are ongoing. Anti-inflammatory Cytokines IL-10 is an integral part of the body’s anti-inflammatory processes and is involved in the suppression of innate and adaptive immune responses. Its expression is stimulated by inflammation,

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and it is a potent inhibitor of non-specific inflammatory response. In sepsis, high IL-10 levels have been consistently associated with disease severity (Wunder et al., 2004). This cytokine has several promoter variants with SNPs at 592, 819, and 1082. Several studies investigating the association of the promoter polymorphisms and sepsis severity, and survival found a higher frequency of the 1082 allele in patients with sepsis (particularly pneumococcal) and community-acquired pneumonia, but more well-designed studies are needed. The IL-1 receptor antagonist (IL1RN) inhibits the proinflammatory actions of IL-1 by binding to their receptor. Polymorphisms in IL1RN may be promising candidates for genetic associations, but should be explored in relation to the rest of IL-1 peptides.

Plasminogen Activator Inhibitor-1 and the Fibrinolytic System PAI-1 not only promotes clot stability, extension, and resistance to lysis, but also acts as an acute-phase reactant (Hoekstra et al., 2004). Increased PAI-1 is a risk factor for cardiovascular disease, but may confer a survival benefit in meningococcal sepsis (Kornelisse et al., 1996). A common PAI-1 promoter SNP is characterized by 4G and 5G alleles. 4G homozygosity is not only associated with an increase in PAI-1 transcription and higher rates of myocardial infarction, but also a higher risk for mortality and vascular complications among patients with sepsis (Haralambous et al., 2003; Hermans and Hazelzet, 2005; Zhan et al., 2005). Genetic polymorphisms of other constituents of the fibrin formation and degradation pathways also deserve further study.

Coagulation Activation of the coagulation cascade is a key event in the pathogenesis of sepsis. As a result, abnormalities in the antithrombin III, protein C, and tissue-factor inhibitor pathways have been implicated in the pathogenesis of sepsis. Recombinant human activated protein C (APC) is the only pharmacotherapy shown to be effective in the treatment of a subset of patients with severe sepsis (Bernard et al., 2001). Protein C 1641 AA genotype is associated with decreased survival, more organ dysfunction, and more systemic inflammation in patients having severe sepsis (Walley and Russell, 2007). APC not only inhibits factors Va, Xa, and PAI-1, but also neutrophil adherence, chemotaxis, and cytokine release. In animal models of septic shock genetic deficiency of tissue factor reduces mortality (Texereau et al., 2004). In contrast, genetic deficiencies of the anticoagulants thrombomodulin, antithrombin II, and protein C increase mortality. Several SNPs and other genetic polymorphisms have been described in genes of hemostatic factors, including thrombin fibrinogen, factor V, PAI-1, protein C, and endothelial protein C receptor.

Future Investigations To address the complexity of the sepsis response and to predict its outcome, multiple surrogate markers that reflect the nature and severity of the inflammatory response, the status of the coagulation and fibrinolysis systems, and the magnitude of organ injury are likely to be more effective in identifying patients at risk of an adverse outcome and those who may benefit from interventional therapies. Such strategies will inherently require the use of multiplex approaches that have sufficiently high throughput to be cost effective (Kingsmore, 2006). An example of the use of multivariable models for prediction of sepsis is shown in Figure 111.2. A combination of measurements of five serum or plasma

235 sepsis patients

3

Factor V and the Protein C Pathway Factor V influences protein C activation by promoting thrombin generation. Three independent SNPs of factor V have been described that all make factor Va partially resistant to inactivation by APC, resulting in a pro-thrombotic state (Nicolaes and Dahlback, 2003). The frequency of this polymorphism in certain populations suggests that it confers some evolutionary advantage. In a large study of children with meningococcal disease, factor V Leiden heterozygosity was not only associated with increased incidence of purpura fulminans, but also a trend toward reduced mortality (Kondaveeti et al., 1999). In a substudy of PROWESS, factor V Leiden carrier status was associated with lower 28-day mortality and with less vasopressor use (Bernard et al., 2001). However, it did not determine responsiveness to recombinant human APC infusion. Biomarker studies have demonstrated, however, that plasma protein C levels do predict usefulness of recombinant human APC infusion (Shorr et al., 2006).

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Figure 111.2 Multiple serum biomarkers effectively distinguish patients with sepsis from ill, non-septic patients (Kingsmore et al., 2005). Individual host protein biomarkers, such as IL6, demonstrate good diagnostic sensitivity for sepsis, but poor specificity. Principal component analysis with a quadratic decision surface (green curved surface) is shown for values of five serum protein biomarkers measured in 235 patients with sepsis (yellow spheres) and 56 patients without sepsis (red spheres), but with severe illness necessitating ICU admission. Multiplexed serum biomarkers exhibited a sensitivity of 100% and specificity of 99% (positive predictive value of 96%).

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cytokines showed high sensitivity and specificity in distinguishing between acutely ill, septic and non-septic patients, whereas individual cytokine measurements lacked specificity. Therefore, isolated, candidate gene-by-gene, or protein-by-protein approaches, may be rendered obsolete by these constraints.

MOLECULAR SIGNATURES AND SEPSIS The recognition and treatment of sepsis have posed a daunting challenge for clinicians despite the improvements in technology and the principles of critical care medicine. In addition to a broad array of infectious etiologies, many of the clinical signs of overwhelming sepsis such as fever, tachycardia, and leukocytosis are non-specific and can also be seen with systemic inflammation that is induced by non-infectious causes like mesenteric ischemia-reperfusion, pulmonary embolus, pancreatitis, or trauma. Regardless, empirical broad spectrum antibiotic use is the rule as a component of early goal-directed therapy (Rivers et al., 2001). The characterization of the human genome has provided a mechanism to study infection at the molecular level. Whereas former techniques have focused on obtaining fluid or tissue from a body compartment and incubating the sample to determine the cause of infection (if any), the study of genomics provides another means. The discovery of surface receptors that recognize microbial invasion and transmit signals to the immune system has led scientists to try to characterize the molecular and genetic changes in the host organism as it is invaded by exogenous infections. From as little as 100 ng of total cellular RNA isolated from whole blood or any tissue, the relative expression of all known genes, expressed sequence tags, and open-reading frames can be evaluated simultaneously. More importantly, the global interaction among these thousands of genes can be studied without any specific selection bias. By studying global expression, one can devise methods of class prediction based on global gene expression activity and this can be done without prior knowledge of the function of any of the genes. Functional genomics relates these technologies to clinical medicine. Although most investigations have focused on the changes in one or relatively few genes in response to a disease or treatment, these newer technologies are exploring the changes in gene expression of the entire genome. This has resulted in functional genomics offering two unique perspectives on sepsis biology: the ability to use “patterns” of gene, protein or metabolite expression to class predict or classify tissue responses (i.e., to develop a “signature” or “fingerprint” for a specific tissue, pathogen, or outcome) and to explore the underlying biological changes that occur in health and disease while not being limited to a subset of selected genes, proteins, or metabolites. Similarly, during the past year, a novel genetic approach – genome-wide association (GWA) – has demonstrated its potential to identify common genetic variants associated with complex diseases such as diabetes, inflammatory bowel disease,

and cancer (Kingsmore et al., 2008). GWA studies seek statistically significant associations between a disease phenotype and genotypes of hundreds of thousands of common single nucleotide variants distributed throughout the genome in hundreds or thousands of affected individuals and matched controls. GWA studies are anticipated to have broad impact on drug discovery and development by providing molecular understanding of common diseases and tools for molecular stratification of patients. As yet, no GWA studies of sepsis have been published, although several are underway. Challenges Associated with Applying Genomic Science to Sepsis Data acquisition from microarray analyses is not problematic; however, quality control, data analysis, and information extraction remain particularly challenging. Traditional statistical and bioinformational approaches are not appropriate for the simultaneous analyses of the large number of analytes on most microarrays. In addition to using probabilities to define confidence intervals among groups, false discovery rates are often applied (Steinmetz and Davis, 2004; Storey and Tibshirani, 2003). Equally problematic are the issues of how such vast quantities of data are analyzed or presented in a manner that can be readily understood. Heat maps, dendrograms, cluster, and principal component analyses, and network analyses are generally used to extract meaningful information (Eisen et al., 1998). As methods for accruing and analyzing data are improved, investigators can turn to other important issues to maximize the utility of genomic methodologies. The correlation between transcript abundance patterns in the peripheral blood and those within local cells at the site of infection is unclear. Clearly, the boundaries are not distinct. Furthermore, human clinical samples other than blood, cells, and tissue from the primary site of infection might be useful for genomic analysis. Pathogen Signatures Recognition of microbial invasion is one of the hallmarks of innate immunity. Antigen-presenting cells, as well as cells of epithelial and endothelial origin, express surface receptors that can recognize PAMPs. With the recent description of TLRs, considerable effort has been directed at examining the genome-wide expression response to different microbial pathogens. Nau et al. found that human macrophages have a profound genetic perturbation that is demonstrated following infection by any of a number of exogenous bacteria (Nau et al., 2002). The shared responses alter the baseline expression of genes that encode for other cell surface receptors, signal transduction proteins, and transcription factors. Other investigators have examined the genomic responses to exogenous microbial stimuli using varying populations of cells. Calvano et al. examined the genome-wide response of leukocytes from healthy volunteers inoculated with endotoxin. Out of 44,000 genes studied, there were 3714 genes whose expression signal changed significantly with in vivo stimulation (Calvano et al., 2005). This proved, on a genomic level, that the systemic perturbations of infection were more vast than clinically evident.

Molecular Signatures and Sepsis

Even with proof of the vast alterations in gene expression that infection can induce, genomic technology has been used to discriminate among types of infections. Huang et al. not only confirmed a large shared response among dendritic cells exposed to E. coli, Candida albicans, and influenza virus, but they also showed a differential response based on the pathogen (Huang et al., 2001). Feezor et al. stimulated whole blood from human volunteers with E. coli LPS or heat-killed Staphylococcus aureus (SAC) and found that there were 758 distinct genes whose expression level differentiated between the Gram-negative and Gram-positive exposures (Feezor et al., 2003). These investigators demonstrated that the families of genes affected by different pathogenic stimuli were strikingly unique (Figure 111.3). For instance, Gram-negative infection tended to alter the expression of genes involved in global immune response, signal transduction, and plasma membrane function while Gram-positive infection altered the expression of genes that are believed to control ribosomal proteins and cell cycle regulation. Chung et al. sought to characterize a genomic profile that would indeed distinguish between sepsis and presumably sterile systemic inflammation (Chung et al., 2006). In parallel analyses of murine and human models, they compared these two cohorts. Genomic data from spleens of septic and injured patients were used to create a septic profile, the accuracy of which was 67.1%; in the murine model, the accuracy of the genomic predictor profile was as high as 96%. Most recently, Ramilo and colleagues have demonstrated distinctive gene expression patterns in peripheral blood leukocytes from patients with confirmed systemic bacterial (E. coli, S. aureus, S. pneumoniae) and viral (influenza A) infections (Ramilo et al., 2007). Furthermore, distinctive gene expression patterns were observed in patients with respiratory infections of different etiologies demonstrating the utility of blood in the evaluation of infections of different organ systems. Similar results were documented in a cohort of military trainees with febrile respiratory illness (Thach et al., 2005). These studies are the first to show that molecular characterization can be performed to distinguish between the infected and non-infected states, even in the face of very similar clinical presentations. These data also serve to highlight the grossly insensitive criteria used in clinical medicine to determine infection or sepsis, and the power of genomic science to provide a molecular characterization of a clinical dilemma. On a macrosystem level, the whole body response to either stimuli has been well established: fever, leukocytosis, tachycardia, with the potential of devolving into multi-system organ failure. Functional genomics is a better test of the cause of the patient’s global dysfunction and may one day be used to guide initial therapy or to tailor ongoing therapy. Progression Signatures The need for rapid, accurate identification of disease progression in sepsis has increased dramatically with the upcoming availability of several novel treatment regimens. While novel sepsis

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therapies are improving sepsis outcomes, they are creating new patient management and diagnostic challenges for physicians. In 2001, the Food and Drug Administration (FDA) approved APC (Xigris, Eli Lilly and Company, Indianapolis, IN) for treatment of patients with severe sepsis (SS) and Acute Physiology and Chronic Health Evaluation II score of 25 — one of the first examples of inclusion of a biomarker in the indication for a therapy. In the pivotal phase III trial of APC in SS (PROWESS), 28-day mortality was decreased by 6% (Bernard et al., 2001). The greatest reduction in mortality (13%) and cost effectiveness was observed in the most seriously ill patients (those with APACHE II score 25). APC benefit was also observed in patients with pneumonia. In contrast, APC exhibited modest survival benefit and cost-ineffectiveness in patients with APACHE II score 25. However, APC therapy is associated with a 1–2% incidence of major bleeding. For these reasons, appropriate use of APC is most likely to occur following development of an objective, accurate, rapid diagnostic test for progression to severe sepsis. Diminution of protein C activity in citrated plasma was recently shown to be a surrogate marker for APC efficacy in patients with severe sepsis (Shorr et al., 2006). Prompt and accurate diagnosis is especially important for effective sepsis management and contributes significantly to positive outcomes (Rivers et al., 2001). Rates of progression of severe sepsis to organ failure, septic shock and death heterogeneous are largely independent of the specific underlying infectious disease process. For example, case-fatality rates in those with culture-negative severe sepsis are similar to those with positive cultures (Rangel-Frausto et al., 1995). Case-fatality rates in sepsis are, however, critically dependent upon disease staging. Current differentiation of SIRS, sepsis, severe sepsis, and septic shock relies exclusively on clinical assessment (Balk, 2000). Thus, a key unmet diagnostic need is for rapid, quantitative, objective determination of the stage of sepsis development and likelihood of progression to severe. Several currently available clinical indices do provide quantitative assessment of staging and severity of sepsis, but with limitations. They include sequential organ failure assessment (SOFA) score, APACHE II score, and blood lactate level (Pacelli et al., 1996). APACHE II score, although quantitative, is largely subjective, complex, cumbersome, and has a relatively narrow dynamic range. Blood lactate levels are quantitative, but limited by false negative normal values in elderly patients and by confounding comorbidities such as liver failure, diabetes mellitus, and the use of certain medications. The clinical picture in sepsis patients is highly dynamic, and assessment of such indices tends to be subjective and to occur with insufficient frequency. Furthermore, sepsis is very heterogeneous in terms of pathogen, source of infection, associated comorbidity, course and complications. Delayed or errant diagnosis of severe sepsis may result in failure of timely treatment. Thus, an early, rapid, objective diagnostic of severe sepsis would be a significant adjunct to these clinical indices and would significantly advance patient management.

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CON r1 CON r2 CON r3 LPS r1 LPS r2 LPS r3 SAC r1 SAC r2 SSC r3

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Up-regulated by SAC; down-regulated by LPS (n  208 genes)

Figure 111.3 Heat map and gene ontologies of leukocytes when ex vivo whole blood is stimulated with either LPS or heat-killed S. aureus. (a) K-means cluster analysis on 780 genes whose expression significantly changed in response to ex vivo stimulation. Patterns of gene expression could be classified into bins on the basis of the similar and disparate responses to Gram-negative and Gram-positive pathogens. CON, control. (b) Differences in gene expression between the two stimuli based on the ontologies for the 780 genes.Grampositive (Gram) stimulus (heat-killed S. aureus) preferentially altered the expression of ribosomal and mitochondrial proteins and cell cycle proteins, whereas the Gram-negative (Gram) stimulus altered the expression of genes involved in signal transduction and the immune response. Figure is from Feezor et al. (2003).

Elevations of IL-6, IL-8, IGFBP1, IL-2sR and MIF in sepsis have been widely documented. Furthermore, levels of IL-6, IL-8 and IL-2sR have variously shown correlation with sepsis severity, progression to septic shock, organ failure, and mortality. However, many other conditions elevate blood levels of these analytes, and diagnostic sensitivity and specificity of individual analyte levels has been insufficient for diagnosis of sepsis or severe sepsis (Delogu et al., 1995; Martin et al., 1994; Selberg et al., 2000).

Using an antibody microarray on specimens from patients from the PROWESS study (Bernard et al., 2001; Perlee et al., 2004), including sepsis non-survivors, 133 analytes were measured in 139 matched serum and plasma samples (between day 0 and 28) from 12 severe sepsis patients (7 survivors and 5 who died) and 8 normal individuals. Sixty-three candidate sepsis biomarkers were identified (35 in serum and 48 in plasma); each exhibited greater than twofold change between severe sepsis patients and controls on day zero. The top biomarker was IL-6

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(26-fold increase) (Kingsmore et al., 2005). Multivariable models were more effective than single analytes in prediction of sepsis (Figure 111.2). A combination of measurements of five serum or plasma cytokines showed high sensitivity and specificity in distinguishing between acutely ill, septic, and non-septic patients, whereas individual cytokine measurements lacked specificity. In an additional study, biomarkers predictive of sepsis death were identified by at least a twofold difference in average level between sepsis survivors and non-survivors in the days immediately preceding death (days 0–4). A total of 10 candidate sepsis mortality biomarkers were identified. The top biomarker was follistatin (15-fold difference). DNA microarray-based gene transcript profiling of the responses of primates to infection has begun to yield new insights into host–pathogen interactions; however, this approach remains plagued by challenges and complexities that have yet to be adequately addressed. The rapidly changing nature over time of acute infectious diseases in a host, and the genetic diversity of microbial pathogens present unique problems for the design and interpretation of functional-genomic studies in this field. In addition, there are more common problems related to heterogeneity within clinical samples, the complex non-standardized confounding variables associated with human subjects and the complexities posed by the analysis and validation of highly parallel data. Various approaches have been developed to address each of these issues, but there are still significant limitations that need to be overcome. The resolution of these problems should lead to a better understanding of the dialog between the host and pathogen.

influence drug response and toxicity and the discovery of new disease pathways that can be targeted with tailor-made drugs. Pharmacogenetics is the study of the genetic factors involved in the differential response between patients to the same medicine. Polymorphisms account for our differential susceptibility to disease and the variable outcome of treatments. The study of these variants in the human genome should enable pharmacogenetics to define the optimal treatment regimens for subsets of the population, allowing a wider range of patients to be treated and more effective outcomes to be produced with any given drug (see Chapter 27). Host response to severe sepsis is multifactorial and its pathogenesis is complex. Therefore, it is unlikely that a successful treatment will be a single drug or intervention. Rather, the trend in therapeutic interventions has been to focus on multimodal therapies targeting specific pathological components of the host response. It is probably naïve to conclude that the immunologic monitoring of patients with sepsis will rely on the concentration of any one marker or protein or on the expression of any one gene. In fact, prognostic studies conducted over the past 20 years have clearly shown that the measurement of single plasma analytes generally lack the sensitivity or specificity to predict outcome of severe trauma, infection, and sepsis. Although numerous individual mediators have plasma concentrations as a group that differ between patients with and without sepsis and between those who survive or die from sepsis (e.g., TNF-, IL-1, IL-6, IL-10, and procalcitonin), such measurements have not proven effective in predicting which individual patients will survive or respond to therapy.

THERAPEUTICS

CONCLUSION

The inflammatory component (SIRS) to sepsis may represent either an exaggeration of a normal physiological response to the infectious process or an imbalance between the pro- and antiinflammatory processes of the host. To that end, multiple immunotherapies have been investigated in an effort to modulate the inflammatory response and alter the clinical course of the patient with sepsis. These approaches have been relatively successful in animal models, but have largely failed in clinical trials, with the exception of APC (Bernard et al., 2001). In general, clinical studies have failed because of the heterogeneity of the clinical population, the inability to recognize the early clinical signs, and an incomplete understanding of the underlying pathophysiology of the septic response. Eichacker et al. have even speculated that the failure of these trials was inevitable given the severity of the illness in the patient populations. Their analysis of the clinical trials showed that antiinflammatory agents were more effective in patients with a higher complication risk and were potentially harmful in less severely ill patients (Eichacker et al., 2002). Knowledge of the entire human genome sequence is also the basis of the fields of pharmacogenetics and pharmacogenomics. Pharmacogenomics seeks an understanding of how genes

Sepsis is a heterogeneous, polygenic, and genetically complex syndrome that is initiated by infection and is characterized by a systemic inflammatory response and variable progression. Genetic polymorphisms in the immune response to infection have been shown to be associated with clinical outcomes. Association and functional studies involving genetic polymorphisms in essential genes have provided important insights into the mechanisms involved in the pathogenesis of sepsis-induced organ dysfunction. Recent advances in GWA studies and highthroughput candidate gene analysis of SNP genotypes provide valuable information on the interaction of multiple allelic variants and clinical outcome. More precise categorization of patients based on genetic background is likely to lead to individualized treatment. In summary, the prevalence, disparate presentation, rapid and unpredictable evolution, high mortality and availability of complex or costly new therapies for sepsis combine to create a significant and growing need for accurate, rapid, early identification of severe sepsis. Provision of such diagnostics on a widely available platform will enable physicians to select patients rapidly and objectively for early intensive sepsis therapy, decreasing morbidity, mortality, and associated patient care costs.

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112 Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine N.A. Shackel, K. Patel and J. McHutchison

INTRODUCTION

VIROLOGY OF HEPATITIS VIRUSES

Viral hepatitis is a significant global health problem with hepatitis B (HBV) and hepatitis C (HCV) infecting in excess of 300 million people. HBV and HCV are complicated by chronic persistent infection characterized in a proportion of patients by progressive hepatic injury leading to complications of endstage liver disease including hepatocellular carcinoma (HCC). HCC is the fifth most prevalent human malignancy, and the majority of cases can be directly attributed to liver injury secondary to chronic HBV and/or HCV infection. Although both HAV and HEV are significant health problems they are typically characterized by a self-limiting course and are not complicated by significant clinical sequelae in the majority of cases. Therefore, research into infectious hepatitis has focused mainly on HBV and HCV pathogenesis, including the development of liver fibrosis, the immune response in acute infection, mechanisms of viral persistence and the development of HCC. The use of functional genomics approaches has significantly advanced our understanding of viral hepatitis pathogenesis and as well as our understanding of therapeutic strategies.

The hepatitis viruses are characterized by specificity for the liver and in particular the hepatocyte (Figure 112.1). However, the mechanism by which these viruses are specific for the liver is largely unknown but is thought to involve hepatocyte receptor and co-receptor interactions and possible involvement of liverspecific pathways such as lipoprotein trafficking and synthesis. Hepatitis A (HAV) is an RNA virus of the Hepatovirus genus belonging to Picornaviridae family (Flehmig, 1990; Lee, 2003; Martin and Lemon, 2006). HAV is a positive strand RNA virus that has a 7.5 kb genome that is translated into a 2225 to 2227 polyprotein that gives rise to a number of structural and nonstructural proteins. The viral particles are 27–32 nm in diameter and there are distinct genotypes and sub-genotypes of HAV. In contrast to another hepatotrophic RNA virus, HCV, HAV has a high degree of genomic and resultant antigenic conservation. The spontaneous mutation rate of HAV is low and antibodymediated immunity from previous exposure or vaccination is effective in preventing HAV infection. HAV is characterized by slow replication, and it is rarely cytopathic and is stable in the environment for at least a month.

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(a) Virus

Family

Length RNA/DNA

Transmission

Chronic

Comments

HAV

Hepatovirus

2.2 kb

RNA

Faeco-oral

No

Can cause fulminant liver failure

HBV

Hepadnaviruses

3.2 kb

DNA

Blood borne Sexual Vertical

Yes

Can cause fulminant liver failure

HCV

Flaviviridae

3.0 kb

RNA

Blood borne Vertical

Yes

Fulminant liver failure is rare

HDV

Deltavirus

1.6 kb

RNA

Blood borne Sexual

Yes

Requires HBV surface antigen Can cause fulminant liver failure

HEV

HEV-like viruses

7.5 kb

RNA

Faeco-oral

No

Can cause fulminant liver failure

(b) H BV RNAs 2.1 kb RNA 2.4 kb RNA

Pre-S1 Pre-S2 -strand

ORF-S

+strand 3.5 kb RNA

AAA A AAAA A AA A

DR1

ORF-C 5’

5’ ORF-P DR2

Pre-C ORF-X

(c)

IRES 5

C

E1

Structural proteins

E2

NS2

0.7 kb RNA

NS3

NS4

NS5

IRES

3

Non-structural proteins

Figure 112.1 Hepatitis virus virology. (a) The virology of viral hepatitis (HAV to HEV) is summarized. (b) The HBV circular DNA and the multiple overlapping reading frames. (c) In contrast to HBV the HCV has a linear RNA genome encoding viral proteins. The HCV 5 and 3 internal ribosome re-entry site (IRES) are important viral regulatory elements.

HBV is a circular DNA virus that belongs to the hepadnaviruses that include woodchuck hepatitis virus and ground squirrel hepatitis virus (Chisari, 1992; Lee, 1997; Lok, 2000). The partially double stranded HBV genome is 3.2 kb in length and is organized with multiple overlapping open reading frames (ORF) (Chisari, 1992; Lee, 1997; Lok, 2000) (Figure 112.1). Greater than half of the genome is translated in more than one ORF and this limits the viral mutations that will be tolerated. HBV DNA incorporates into the host genome (Lau and Wright, 1993; Moyer and Mast, 1994). The viral proteins include the surface (envelope) protein, core, polymerase and X-proteins.

Variations of the nucleotide sequence and corresponding amino acids that constitute the HBV surface protein give rise to 8 common HBV genotypes (A–H). HCV is a positive strand RNA virus, a member of the Flaviviridae family that includes flaviviruses and pestiviruses (Shimotohno, 2000). There is considerable diversity in the HCV genome with at least 6 distinct genotypes, more than 50 subtypes and a propensity of the virus to mutate giving rise to “quasi species” (Forns and Bukh, 1999; Forns et al., 1999). The virus genome is 9.5 kb that encodes a large single polyprotein of 3010 to 3033 amino acids (Forns and Bukh, 1999) (Figure 112.1). The 5 and 3

Acquisition and Predisposition to Viral Hepatitis

untranslated regions are important for the replication of the virus and translation of the polyprotein. The HCV proteins comprise the structural proteins; core protein and the envelope glycoproteins E1, E2 and p7 followed by the non-structural proteins; proteases, helicase (NS2 and NS3), protease cofactor (NS4A), NS4B, replication associated phosphoprotein (NS5A) and the RNAdependent polymerase (NS5B) (Shimotohno, 2000; Simmonds, 1996). Possible receptors for HCV have been identified and these include CD-81, SRB-1 and Claudin-1. Hepatitis D (HDV also known as delta hepatitis) is unlike any other transmissible agent in animals (Taylor, 2006a, b). This minus strand circular RNA virus requires the hepatitis B surface antigen (HBsAg) for encapsulation and entry into hepatocytes. HDV only co-infects up to 5% individuals with HBV infection. Importantly, the prognosis of HDV co-infection is worse than with HBV alone. Hepatitis E (HEV) is related to Calciviradie but now has been classified into a separate genus of hepatitis E-like viruses (Krawczynski et al. 2000;Worm et al., 2002). The HEV genome consists of a single positive RNA strand of 7.5 kb that is translated into three polyproteins as there are three overlapping ORF. Consequently HEV has stable genome sequence and exists as a single serotype and at least four unique genotypes. HEV is stable in the environment and is common waterborne pathogen especially in developing countries.

ACQUISITION AND PREDISPOSITION TO VIRAL HEPATITIS HAV is characterized by an enteric route of infection and classically is transmitted via personal contact, illicit drug use or ingestion of contaminated food or water (Martin and Lemon, 2006). The incubation period following exposure can be up to 90 days. HAV infection typically occurs in sporadic outbreaks as well as isolated cases and can be endemic in poorly developed countries with poor sanitation. Although molecular phenotyping of the virus helps trace outbreaks, there is very little known about individual genetic factors that may predispose to HAV infection. Host genetic factors may not be that important as indicated by the epidemiologic studies showing that HAV infection is often transmitted to persons with similar risk factors. HEV is similarly transmitted to HAV and is characterized by a great propensity for waterborne transmission. Like HAV, HEV is endemic in many poorly developed countries. HEV has the propensity to be associated with miscarriage and an increased mortality in pregnant women especially during the third trimester. The basis for this is unclear, but a number of host factors are clearly important in determining susceptibility to infection. HBV or HCV enter the host through blood contact by direct inoculation (i.e., needle or transfusion) or via a disrupted percutaneous barrier (i.e., sexual or perinatal transmission) (Shimotohno, 2000; Simmonds, 1996). Entry of HBV or HCV into liver hepatocytes is not understood, and there are extra-hepatic reservoirs of infection in peripheral blood leukocytes (Cabrerizo et al., 2000;

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Yoffe et al., 1990). Receptor-based cell entry has been implicated in the both HBV and HCV pathogenesis (Cocquerel et al., 2006; Pileri et al., 1998;Yerly et al., 2006). The immunopathogenesis of both HBV and HCV infection is characterized by both an innate and adaptive immune response to the virus resulting in predominantly non-specific inflammation. Typically, neither HBV nor HCV are directly cytopathic as viral load does not correlate with organ damage and the level of antigen expression in hepatocytes does not correlate with hepatocyte injury (Chisari and Ferrari, 1995). Importantly, both HBV and HCV in vivo infectivity is limited to higher primates (chimpanzees and humans). Predisposing factors to viral hepatitis include a number of non-genetic and genetic factors (Thomas, 2000; Wasley and Alter, 2000; Yee, 2004) (see Table 112.1). HBV and HCV are more prevalent in communities with increased rates of intravenous drug use, unsafe therapeutic injections and with the use of unscreened blood products (Maddrey, 2000; Merican et al., 2000; Wasley and Alter, 2000). Fortunately, the recognition of routes of transmission, the use of safe injecting practices and the adoption of volunteer donor blood transfusion services are widely instigated global public health measures used to control the spread of viral hepatitis. Vertical transmission of HBV is dependent on the stage of maternal HBV infection and the viral antigen expression. Approximately, 10–20% of surface antigen positive mothers transmit the virus to their offspring. However, both surface and HBeAg expression is associated with a 90% rate of transmission. Maternal acute HBV infection results in a 10% neonatal rate of infection in the first trimester, which increases to 80–90% if acute infection occurs in the third trimester. HCV infection is characterized by lower rates of vertical transmission with only 2.7–8.4% of offspring being infected. The presence of HIV coinfection increases the rate of vertical transmission of both HBV and HCV. Transmission via sexual activity with mucosal disruption is more prevalent in HBV than HCV infection. Genetic factors predispose to persistence of HBV infection given the rate of concordance for surface antigen expression is greater in monozygotic compared to dizygotic twins (Table 112.1). The twin concordance data in HCV infection are not as convincing. There are a number of HLA alleles which are associated with both the persistence and the clearance of both HBV and HCV (Wang, 2003; Yee, 2004). Interestingly, in contrast to HIV progression homozygosity for HLA class II locus increases the risk of HBV persistence.The HLA class II locus DRB1*1302 is associated with HBV clearance and the DQB1*0301 locus is associated with a self-limiting course of HCV infection. Non-HLA immunogenic is also implicated in viral hepatitis with TNF promoter polymorphisms resulting in higher TNF secretion being associated with HBV clearance. The killer cell immunoglobulin-like receptors (KIR) genes interact with HLA class I molecules and specific KIR heliotypes are associated with HCV clearance (Martin and Carrington, 2005; Williams et al. 2005). Importantly, the majority of genetic predispositions identified in viral hepatitis is linked to viral persistence or clearance and can be directly implicated in the adaptive immune response. The current documented genetic disease associations with viral hepatitis are of limited use

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TABLE 112.1

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Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

Viral hepatitis genetic susceptibility associations

Allele/Polymorphism

Hepatitis C

Hepatitis B

HLA-DRB1*1302 and HLA-DRB1*0301

Spontaneous elimination of infection

HLA-DQA1*0501, DQB1*301, HLA-DRB1*1102 and HLA-DRB1*0301

Persistence of infection

HLA-DRB1*0101, HLADRB1*0401, HLA-DRB1*15, HLA-DRB1*1101, HLADRB1*0301, HLA-A*2301, HLAA*1101, HLA-A*03, HLA-B*57 and HLA-Cw*0102

Spontaneous elimination of infection

HLA-DRB1*0701, HLA-A*01B*08-Cw*07-DRB1*0301DBQ1*0201, HLA-Cw*04, HLA-Cw*04-B*53

Persistence of infection

TNF promoter

Viral replication and clearance

Viral replication and clearance

Interleukin-10

Spontaneous elimination of infection

Vitamin D receptor

Control of viral replication

GDNF family receptor alpha 1

At risk of HCC in HCV

Chemokine (C-X-C motif) ligand 14

At risk of HCC in HCV

Comment

Polymorphisms at -308 and -238 best characterized

Expressed on monocytes and lymphocytes

Previously known as SCYB14

HCC: Hepatocellular carcinoma.

in clinical practice. Presently genome-wide association studies are being undertaken to comprehensively characterized hepatitis genetic susceptibility. Therefore, future clinical practice is likely to see panels of genetic susceptibility markers being screened to determine prognosis and the likelihood of a treatment response.

SCREENING AND DIAGNOSIS OF VIRAL HEPATITIS HAV and HEV are characterized by jaundice, fever and a diarrhea illness. Given the propensity for epidemic outbreaks and known endemic regions of the globe (mainly developing countries) establishing the diagnosis is not difficult. HAV and HEV are readily diagnosed using serology with acute infection distinguished by the presence of antiviral IgM (Acharya and Panda, 2006; Panda et al., 2006). The diagnosis of HDV is also made on serology and anti-HDV IgM is detectable by 30 days following infection (Fiedler and Roggendorf, 2006; Weston and Martin, 2001). HBV and HCV infections results in a comparatively nonspecific cluster of symptoms ranging from asymptomatic viral infection (most common in HCV), jaundice and fever (30–50% of HBV infections) to fulminant hepatic failure and death (1%

of HBV and exceedingly rare in HCV). Importantly, both HBV and HCV viral replication can be associated with a normal liver panel. Although routine liver tests such as transaminases are used to monitor disease effects on the liver, they do not provide prognostic information and cannot be used for the diagnosis of viral hepatitis. The cornerstone of screening and diagnosis is serology for viral-specific antibodies and antigens. The commonly available serology of HBV is complex with the pattern of antibody and antigen expression determining the nature and timing of infection (Table 112.2). HCV antibody production is a simpler test of exposure which becomes positive within 4 weeks of exposure (Bhandari and Wright, 1995). The commonly used HCV antibody screening is performed with using an enzyme immunoassay (EIA) and is prone to false positive readings. Specificity of the EIA has improved with successive generations of test but confirmation of a positive result with supplemental methodology is considered mandatory. A positive HCV EIA is routinely confirmed using a recombinant immunoblot assay in which serum reactivity to a number of HCV recombinant viral proteins is assessed. Detection of HBV DNA and HCV RNA is not recommended routinely to establish the diagnosis (Acharya and Panda, 2006; Mondelli et al. 2005; Patel et al., 2006; Servoss and Friedman, 2004). However, as viral replication can occur with

Screening and Diagnosis of Viral Hepatitis

TABLE 112.2

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Hepatitis B virus serology and DNA quantitation

Serologic markers

Definition/clinical use

HBsAg – Hepatitis B surface antigen

● ●

General marker of infection First serologic marker to appear after infection Persistence for 6 months  chronic infection

HBeAg – Hepatitis B e antigen

●

Indicates active replication of virus

Anti-HBs – Antibody to hepatitis B s antigen

● ●

Documents recovery and/or immunity to HBV Detectable after immunity conferred by HBV vaccination

Anti-HBe – Antibody to hepatitis B e antigen

●

Marker of reduced level of replication

Anti-HBc (IgM) – Antibody to hepatitis B core antigen

●

Marker of acute HBV

Anti-HBc (IgG) – Antibody to hepatitis B core antigen

●

Marker of current or past infection

HBV DNA – Hepatitis B virus genomic DNA

●

Marker of HBV replication Used for monitoring response to therapy

●

●

normal biochemistry and antibody titers do not demonstrate viral replication quantitative assessment of viral nucleotide presence is used in many centers as the final confirmatory diagnostic test for active HBV or HCV infection. Quantitation of HBV DNA or HCV RNA is used in planning and following treatment of the infection. HBV DNA can be quantitated using an approach of capturing HBV DNA on a full length HBV RNA transcript and using antibodies to detect the RNA:DNA hybrid (known as capture assays or “Digene”TM). An alternative approach uses quantitative PCR methodologies to amplify the HBV DNA (COBAS AmpliorTM) or a variation of the capture approach using branched DNA (“Versant”TM). HBV and HCV viral levels are typically determined once the infection has been confirmed as they carry prognostic significance as well as being used to monitor treatment responses. HBV genotyping is not routinely performed in the clinical setting unless the patient is being considered for interferon-alpha therapy. An important aspect of screening and diagnosis of viral hepatitis involves an assessment of the extent of liver injury attributable to the virus. In particular the extent of hepatic inflammation and fibrosis has implications for the progression and the likely response to treatment. Routine biochemistry gives only limited information about the extent of inflammation or fibrosis, and most centers will use imaging of the liver with ultrasound or other modalities combined with a liver biopsy in assessing virus induced liver injury. However, biopsy is an invasive procedure prone to significant sampling and observer error (Siegel et al., 2005). This has led to the development of a number of new noninvasive screening modalities. The most promising approaches use multiple variables to assess for liver injury, and these methods are likely to become commonplace in the next decade. Viral Hepatitis Prognosis and Natural History The clinical course and progression of HBV and HCV are highly variable (Chen and Morgan, 2006; Chu and Liaw, 2006; Fattovich, 2003; Ghany and Seeff, 2006; Thomas and Seeff, 2005). The importance of individually tailored management

strategies is paramount, as there is a marked variation in the spectrum of disease even when all other host and viral factors appear equal. HBV infection results in acute self-limiting infection in 90% of cases. The rate of fulminant hepatic failure is 0.1–0.5%. Only 10% of cases go onto develop chronic infection with a spectrum of disease that varies from the relative benign chronic surface antigenemia through to more aggressive forms of the disease in which individuals have active viral replication characterized by surface antigen expression, e-antigen (HBeAg) expression and high viral DNA titers. However, the propensity of HBV to mutate is 10-fold greater than other DNA viruses, and mutations at position 1762 and 1764 of the precore protein promoter can give rise to precore mutants in which active HBV infection is not associated with HBeAg expression. Chronic hepatitis can spontaneously clear with active HBeAg expression being associated with both more rapid disease progression and spontaneous seroconversion rate (switching from HBeAg to HBeAb expression) of 8–15% per year. However, precore mutants, although characterized by active replication with high titers of HBV DNA, have spontaneous clearance rate of only 0.5% per year. The presence of normal liver biochemistry is associated with a spontaneous clearance of only 2–5% per year. Surface antigen (HBsAg) carrier state is lowest in individuals with vertical transmission acquired HBV and is associated with spontaneous clearance of only 1–2% per year. Active HBV viral replication is associated with intrahepatic inflammation and the progression to cirrhosis and sequelae including liver failure and the development of hepatocellular carcinoma (HCC). In HBeAg antigen positive individuals, cirrhosis develops at a rate of 2–5.4% per year with the 5-year cumulative risk of 8–20%. The rate of progression to cirrhosis is even higher in HBeAg antigen negative individuals with active viral replication. The risk of hepatic decompensation with HBV-associated cirrhosis is 16% at 5 years. In comparison to compensated cirrhosis, the development of decompensated cirrhosis results in a decrease in survival from 70% to 55% at 1 year and 2–14% decrease at 5 years. In the absence of cirrhosis the

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TABLE 112.3

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Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

Key findings arising from genomic studies of viral hepatitis Hepatitis C

Hepatitis B

Comments

Acute infection

Interferon stimulated gene expression (Mx1, ISG15) correlated with viral clearance

“Stealth virus” Innate immune response is abrogated Immune evasion

Acute HBV or HCV infection is poorly characterized by genomic studies

Chronic infection

Th1 immuno-phenotype perpetuating chronic injury

T-cell effector function activated and latter B-cell related gene expression observed

Treatment responses

Interferon stimulated gene induction correlate with treatment responses

Hepatocellular carcinoma

HCV core protein oncogenic

HBV x (HBx) protein oncogenic

Bio-markers

A number of non-specific inflammatory markers identified Potential tumor markers identified

A number of non-specific inflammatory markers identified

Future studies

Predicting HCV IFN treatment responses

Predicting HBV IFN and nucleotide/nucleoside treatment responses

incidence of liver-related deaths is low and ranges from 0 to 1.06 per 100 person-years. The development of HCC is uncommon in the absence of cirrhosis but develops at a rate of 2.8% per year with cirrhosis. The risk of HCC increases with age 45, males, HBeAg antigen expression, detectable HBV DNA and first-degree relatives with HCC. The prognosis of HCV infection has been the topic of considerable controversy as the natural history is highly variable (Chen and Morgan, 2006; Ghany and Seeff, 2006; Thomas and Seeff, 2005). The estimates of chronic infectivity range from 55% to 80% with most studies between 75% and 80%. The age of infection affects progression and the rate of spontaneous HCV clearance. Infected children have a spontaneous rate of clearance of 40–45% and develop cirrhosis in only 2–4% of individuals after 20 years of infection. In contrast, adult infection is characterized by 20% rate of spontaneous clearance and cirrhosis in 20–30% of individuals after 20 years. HCV cirrhosis is associated with 3–4% annual rate risk of liver decompensation and an annual incidence of 1.4–6.9% of developing HCC. It is clear from the natural history and prognosis that the aim of chronic HBV and/or HCV treatment is to stop the development of cirrhosis and the sequelae of hepatic decompensation and HCC. Genomic medicine is likely to have a significant impact in the future determination of viral hepatitis outcomes and identification of at risk individuals for development of cirrhosis and sequelae such as HCC. Genomic studies have made significant contributions to our understanding of HBV and HCV pathogenesis and treatment responses (see Table 112.3).

HBV treatment response have been poorly characterized by genomic studies

Determining the pathogenesis of and predicting chronicity, disease progression and development of sequelae such as HCC

PATHOGENESIS OF VIRAL HEPATITIS The pathogenesis of viral hepatitis is unique for HBV and HCV (Chisari and Ferrari, 1995; Lee and Locarnini, 2004; Tanikawa, 2004). However, there are similarities in the mechanisms of HBV and HCV clearance and persistence. The initial innate immune response characterized by interferon (IFN) and IFN stimulated gene (ISG) expression. Chronic infection with either virus is characterized by both antigen-specific and nonspecific CD4 and CD8 T-cell responses that are pivotal in virus clearance or in chronic infection responsible for ongoing inflammation resulting in liver injury. Further, both HBV and HCV employ the strategy of mutational escape to evade the adaptive immune response. The mode of entry of HBV or HCV into the hepatocytes is unknown. Receptor-mediated viral entry is thought likely, given the restricted cell population infected (Cocquerel et al., 2006; Pileri et al., 1998;Yerly et al., 2006). In HBV carboxypeptidase D (gp180) has been shown to interact with the preS portion of the large viral surface protein (Yerly et al., 2006). In HCV CD-81, scavenger receptor class B type 1 and claudin-1 have all been implicated in viral entry (Cocquerel et al., 2006; Pileri et al., 1998). Currently, no conclusive evidence of a single receptor or the presence of a receptor complex has been demonstrated for either HBV or HCV (Cocquerel et al., 2006;Yerly et al., 2006). There are marked differences in the pathogenesis of both HBV and HCV. Innate immune responses are blunted and do not appear to play an important role in HBV clearance.

Pathogenesis of Viral Hepatitis

In contrast a strong innate immune response is thought to be important in HCV clearance. Finally, antibody-derived immunity is present in HBV infection whilst it appears to be of no importance in HCV infection. HBV Pathogenesis HBV infection results in the formation of a double-stranded HBV genome in the nucleus that is converted into a covalently closed circular double-stranded DNA (cccDNA). Further, HBV DNA integrates into the host genome and this has significant implications in the long-term management of HBV, as the complete elimination of the virus is not possible and viral reactivation characterized by active replication is possible in the future. The cccDNA acts as a template for the formation of an RNA replicative intermediate, which is prone to a high rate of mutation of 1 in 105 bases. However, HBV replication after infection is characterized by low level replication reaching levels of 102– 104 genome equivalents per ml for up to 6 weeks after infection. Once established HBV is associated with extremely high viral titers of 108–1013 genome equivalents per ml. This has led to the assertion that HBV initially evades the immune response before becoming established. Importantly, once established, active HBV replication can result in infection of 100% of the intrahepatic hepatocytes. The innate immune activation in HBV infection is abrogated. However, HBV clearance can occur prior to the induction of an adaptive immune response. This is thought to be mediated by IFN- and - (type I IFN) in a non-classical manner that is proteosome dependent. Further antigen independent natural killer (NK) cell activation is thought to be responsible for IFN induction and induction of an adaptive immune response. Adaptive immunity in HBV is characterized by CD8 Tcell response to surface antigen epitopes with secretion of IFN- and TNF which have direct antiviral effects principally by controlling HBV replication at the stage of formation of the RNA replicative intermediate. In contrast to HCV, HBV antibodymediated humoral immunity effectively neutralizes the virus. However, antibody production is often absent in chronic HBV infection by mechanisms that are poorly understood. Viral protein such as the X-protein inhibit proteosome dependent control of virus replication also both surface antigen and precore protein act as tolerogens abrogating the T-cell response to the virus. Functional Genomics Studies of HBV pathogenesis Acute HBV infection in the chimpanzee has been analyzed using microarrays (Wieland et al., 2004a). Importantly, there was no differential gene expression during the initial phase of HBV infection and the first phase of HBV replication. This is in direct contrast to HCV infection and suggests that HBV infection acts in the initial phase as a “stealth virus” failing to induce a significant innate immune response (Wieland and Chisari, 2005; Wieland et al., 2004a). Intrahepatic gene induction is first seen during the phase of viral clearance (Wieland et al., 2004a). Gene expression during the early phase of infection was associated

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with T-cell receptor and antigen presentation. Following this T-cell effector function (granzymes), T-cell recruitment (chemokines) and monocyte activation-associated gene expression was observed (Wieland et al., 2004a, b). A later phase of clearance was associated with the expression of B-cell-related genes. This gene expression profile is consistent with initial innate immune response evasion and a subsequent induction of cell-mediated and humoral immune responses. In vitro cell models have been utilized to study HBV pathogenesis. However, this approach is impeded by the absence of a suitable model of in vitro viral replication. To determine the oncogenic role of HBV X-protein in the development of HCC, gene expression profiles in primary adult human hepatocytes and an HCC cell line (SK-Hep-1) ectopically expressing HBx via an adenoviral system has been studied (Wu et al., 2001). Many genes including a subset of oncogenes (such as c-myc and c-myb) and tumor suppressor genes (such as APC, p53, WAF1 and WT1) were differentially expressed and cluster analysis showed distinctive gene expression profiles in the two cell types. Therefore, HBx protein altered gene expression as an early event and favors hepatocyte proliferation that may contribute to liver carcinogenesis (Wu et al., 2001). Proteomics has also been used to examine different stages of chronic HBV infection. Serum proteomic profiles in chronic HBV infection identified increased haptoglobin beta and alpha 2 chain apolipoprotein A-1 and A-1V, apha-1 antitrypsin, transthyretin and DNA topisomerase 11 beta expression (He et al., 2003). Some of these proteins are amongst the most abundant serum proteins secreted by the liver and are generally associated with acute phase inflammatory responses. Proteins such as cleaved haptoglobin beta and ApoA-1 fragments are decreased in HBV patients with higher inflammatory scores. In contrast alpha-1-antitrypsin fragments were increased in patients with higher inflammatory scores. It is apparent from this study that different isoforms of some of these proteins showed distinct changes in HBV infection, which differed at times between patients with low inflammatory scores versus high inflammatory scores. An alternate approach studied serum protein profiles and correlated this with disease severity using a SELDI ProteinChip analysis and artificial neural network models (Poon et al., 2005). They found 6 fragments with a positive and 24 with a negative prediction of fibrosis stage and subsequently developed a fibrosis index with excellent predictive values for significant fibrosis and cirrhosis based on the Ishak fibrosis score. The inclusion of clinical biochemical parameters such as ALT, bilirubin, total protein, hemoglobin and INR strengthened the study accuracy. However, in general, potential markers of disease severity identified to date are untested prospectively in large clinical cohorts of individuals. HCV Pathogenesis HCV replication involves formation of a negative sense replicative RNA strand and the subsequent formation of dsRNA. This has several important implications for HCV pathogenesis. First,

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Diagnosis, Management and Disease Mechanisms of Hepatitis in the Era of Genomic Medicine

the formation of RNA intermediates means that there is no stable genomic replicative form of the virus and as a result HCV must produce new viral RNA and proteins to maintain persistence. Secondly, the formation of dsRNA associated with HCV replication is a target for endogenous RNA interference and elicits the endogenous IFN response (Yu et al., 2000). Further, the NS5B RNA polymerase of HCV lacks proof reading activity and as a result virus replication is highly error prone (1 in 103 bases) resulting in remarkable genetic diversity. HCV is currently divided into six major genotypes with many subtypes that differ by up to 35% in their nucleotide sequence. HCV infection is characterized by a rapid increase in circulating levels to 105–107 equivalents per ml. The rapid induction of an immune response means that not all hepatocytes are infected although the true proportion of hepatocytes infected is unknown. The innate immune response is characterized by induction of type I IFN, interferon-stimulted genes (ISGs) and a NK response. The IFN gene expression is induced by the induction of endogenous RNA interference pathways, the formation of dsRNA that binds to the RNA helicases RIG-1 and MDA5 and binding of the phagocytosed infected cell fragments to toll-like receptor (TLR)-3 (Honda et al., 2006; Kato et al., 2006;Yoneyama et al., 2005). These upstream events then signal through IRF-3 phosphorylation resulting in IFN gene transcription. The IFN gene expression then signals via cognate receptors and activates the JAK/stat pathway resulting in the induction of ISGs including protein kinase R, RNA–specific adenosine deamsinase-1 (ADAR-1), P56 and 2 -5 oligoadenylate synthetase. Most of these ISGs act on the formation of the negative replicative strand of HCV. The cellular innate immune response is characterized by induction of an NK cell response. NK cells destroy infected cells in an antigen independent manner via cytotoxic cell lysis. These activated NK cells secrete large amounts of IFN-, which activates and maintains a cellular adaptive immune response. Adaptive immune responses in HCV infection is characterized by the virus-specific CD4 and CD8 T-cell response to multiple HCV epitopes, including many highly promiscuous epitopes formed due to the high spontaneous rate nucleotide mutation rate associated with HCV replication (Bowen and Walker, 2005a, b). This T-cell response is accompanied and maintained by induction of IFN- and TNF, both of which can directly inhibit viral replication without killing an infected cell. Although HCV antibody production is universal in immunocompetent individuals, it does not prevent infection or correlate with outcome. Further the virus-specific T-cell response is maintained for decades after HCV clearance in contrast to the antibody responses which can become undetectable. Functional Genomics Studies of HCV pathogenesis In the chimpanzee, microarray technology has been used to study acute HCV infection (Bigger et al., 2001; Su et al., 2002). Viral clearance is associated with a marked early induction of IFN- induced genes such as 2 -5 oligoadenylate synthetase, Mx1, ISG15 and ISG16, with the later induction of immune

Th1 response-associated transcripts such as MIG (CXCL9) and IP10 (CXCL10) (Shackel et al., 2002). However, in chronic human HCV infection there is a persistent intrahepatic IFN- antiviral response, but the virus itself escapes the response via an inhibition of the effector arm (Bowen and Walker, 2005a, b). HCV evades the immune response in many ways. The protein products of viral replication have a number of inhibitory effects; core protein inhibits Fas-mediated apoptosis, E2 inhibits NK cell activation, E2 and NS5a inhibit PKR and NS3 inhibits IRF-3 (Karayiannis, 2005). The evasion from the adaptive response is less well defined, but chronically infected individuals have only weak, oligo-/mono-specific or no virus-specific CD4 or CD8 T-cell responses. The mechanism of evasion of this adaptive response is unclear. However, the induction of a persistent non-specific inflammatory response in chronic HCV infection results in the induction of genes associated with a Th1 immune response. Therefore, in chronic HCV the immune response is characterized by persistent but non-specific immune activation that damages the liver whilst being insufficient to clear the virus. The intrahepatic IFN- induced gene response is variable amongst individual patients. This response has been identified, by microarrays, to be higher in patients not responding to pegylated IFN and ribavirin therapy suggesting an increased resistance to the effector arm that cannot be amplified by exogenous therapy (Chen et al., 2005). In contrast, patients who had a sustained viral response (SVR) to pegylated IFN therapy had a lower expression of IFN genes that, by inference, can be amplified by exogenous therapy resulting in viral clearance. Comparison of chimpanzees that cleared acute HCV infection compared to an animal that had virus persistence has provided further insight into the balance between viral clearance and persistence (Su et al., 2002). In these experiments Su et al. observed upregulation genes associated with the early response (which correlated with viral load) including many IFN- induced genes; STAT 1, 2 -5 oligoadenylate synthetase, Mx1, ISP15 and p27 (Su et al., 2002). Interestingly, there was the induction of lipid pathway genes such as Fatty Acid Synthetase, Sterol Response Element Binding Protein (SREBP), downregulation of PPAR as well as hepatic lipase C and flotillin 2. Importantly, the lipid pathway genes are associated with viral replication and studies using in vitro replicon experiments has demonstrated altered viral replication with geranylgeranylation (Kapadia and Chisari, 2005). Further, the reduction in PPAR would be expected to be associated with insulin resistance, a feature of chronic HCV, but prior to this it was not an expected aspect of acute HCV infection. As noted previously (Bigger et al., 2001), clearance of HCV was associated with the late induction of Th1 transcripts such as CXCL9 and CXCL10, MHC expression and T-cell molecules such as CD8 and granzyme A. Although the induction of IFN- induced genes early in infection has been observed by others, the timing did not correlate with clearance as high levels of these transcripts continued in the animal with viral persistence (Bigger et al., 2001; Lanford

Pathogenesis of Viral Hepatitis

et al., 2001). Further, functional studies in HCV replicon systems have shown that the NS3/4a was able to inhibit IFN- antiviral effector function by blocking the phosphorylation of IRF-3, a key protein in the antiviral response (Karayiannis, 2005). Therefore, chronic HCV infection induces a persistent intrahepatic IFN- antiviral response, but the virus itself escapes this response via inhibition of the effector arm. However, microarray studies of the intrahepatic IFN- induced gene response show that this is variable and observed to be higher in patients not responding to pegylated IFN and ribavirin therapy, consistent with resistance of the effector arm of the immune response to amplification by exogenous therapy (Chen et al., 2005). In contrast, patients who had an SVR to pegylated IFN therapy had a lower expression of IFN genes, consistent with amplification of the effector arm of the immune response by exogenous therapy resulting in viral clearance. Chronic HCV infection has been studied in a number of ways using genomic analysis. Gene expression in liver biopsy material from individuals with chronic HBV has been compared to a non-diseased control group (Honda et al., 2001). Chronic HCV infection was associated with a predominant anti-inflammatory, pro-proliferative, anti-apoptotic intrahepatic gene profile (Honda et al., 2001). However, the results demonstrated widespread upregulation of pro-inflammatory genes such as IL-2 Receptor, CD69, CD44, IFN- inducible protein, MHC Class 1 genes and monokine induced by IFN-. These findings were similar to another study of HCV cirrhosis in which a pro-inflammatory Th1 associated transcript expression predominated (Shackel et al., 2002). Therefore, a Th1 immune response is thought to be responsible for the accelerated fibrogenesis of HCV liver injury with fibrosis-associated gene expression in HCV-associated fibrosis has included upregulation of a wide variety of genes including PDGF and TGF-beta 3 (Shackel et al., 2002). The premalignant potential of intrahepatic HCV infection has been studied by comparing HCV cirrhosis with and without HCC by gene array analysis (McCaughan et al., 2003). The upregulation of many oncogenes (i.e., TEL oncogene), immune genes (IFN- associated), fibrosis genes (integrins) as well as cell signaling (G-coupled receptor kinase) and proliferation associated genes (Cyclin K) was demonstrated in cirrhosis complicated by HCC. This is consistent with a premalignant cirrhotic response in HCV infection. Further, the data suggest that there is more cellular proliferation, immune activation and fibrosis in the liver of patients with HCC than those with cirrhosis alone. A key area of future research will be to ascertain whether such a profile can be recognized before HCC develops. This approach has a direct clinical application in identifying and screening high-risk patients. Gene array studies of HCV infection have revealed new insights into the development of HCC in HCV, structural analysis of the HCV RNA genome and identified novel markers of HCV intrahepatic injury and HCV associated HCC.The study of Smith et al. utilized 13,600 gene microarrays to profile patients with HCV cirrhosis, HCV and HCC and normal liver (Smith et al.,

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2003).The results identified 87 upregulated and 45 downregulated genes that appear to be markers of HCV liver injury (Smith et al., 2003). Importantly, the analysis aimed to exclude genes expressed in normal liver, other forms of cirrhosis or HCC. Genes such as ILxR (IL-13 receptor a2), CCR4 and cartilage glycoprotein 39 (GP-39) were identified (Smith et al., 2003). However, the study of Smith et al. highlights the problems with the interpretation of these large datasets using small numbers of patient samples; does the identified gene expression represent unique disease or phenotype associated gene expression or the stochastic probability of identifying a small cohort of genes from the many thousands being analysed? Cleary studies such as these, as powerful as they are, need to be validated by alternative methodologies in large patient groups. One approach to validation has been to confirm important gene expression identified in these studies by realtime RT-PCR in a larger cohort of patients (Shackel et al., 2001, 2002). HCC proliferation in HCV-associated liver injury has been studied by array analysis. This has resulted in a plethora of potentially novel tumor markers being identified. These include the serine/threonine kinase 15 (STK15) and phospholipase A2 (PLA2G13 and PLA2G7) that were shown to be increased in over half of the tumors identified (Smith et al., 2003). However, a different study implicated different gene groups in HCV-associated HCC; cytoplasmic dynein light chain, hepatoma-derived growth factor, ribosomal protein L6, TR3 orphan receptor and c-myc (Shirota et al., 2001). The clustering analysis in this study showed that the expression of 22 genes in HCC related to differentiation of the malignancy with over half of these genes being transcription factors or related to cell development or differentiation (Shirota et al., 2001). Although many of these genes can be implicated in HCC development, they are often identified in large gene sets in end-stage disease. Therefore, whether these genes represent cause or effect is unknown. Additionally, the number of differing gene sets being examined by the gene arrays being utilized is almost as great as the number of studies using them. Further, as these gene sets still only represent a fraction of the transcriptome being examined, they selectively identify differentially expressed genes. Gene array analysis of HCV recurrence in liver transplant allografts has provided novel insights into the molecular mechanisms of viral recurrence (Mansfield and Sarwal, 2004; McCaughan and Zekry, 2004). HCV recurrence in the liver graft is associated with expression of IFN- associated genes such as CXCL10 (IP-10), CXCL9 (HuMIG) and RANTES (McCaughan and Zekry, 2004). Further, antiviral IFN- associated gene expression is seen in chronic HCV recurrence and during acute rejection associated with HCV recurrence (McCaughan and Zekry, 2004). Additionally, upregulation of NF-kappa  pathway during acute rejection in association with HCV recurrence appears to alter cellular apoptosis via changes in the expression of TRIAL-associated genes (McCaughan and Zekry, 2004). Importantly chronic HCV recurrence in grafts is associated with Th1 associated gene expression similar to that seen in chronically HCV infected individuals that have not

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been transplanted (McCaughan and Zekry, 2004). In contrast cholestatic HCV recurrence, which follows an aggressive course, is associated with a Th2 cytokine profile (McCaughan and Zekry, 2004). This suggests that the Th1 immune response suppresses viral replication whilst being profibrogenic (McCaughan and Zekry, 2000, 2004; Shackel et al., 2002). In cholestatic HCV recurrence, the unchecked viral replication is directly fibrogenic (McCaughan and Zekry, 2004; McCaughan and Zekry, 2000). A particular challenge in the study of the effect of viruses on liver cells is the difficulty in infecting liver cells with virus. The studies described below have involved models in which cultured cells are infected with viral proteins or viral genome. Progress in this field has been rapid and most recently, a cellular model of HCV infection has been reported that is likely to stimulate further study (Heller et al., 2005; Lindenbach et al., 2005). Proteomic methodologies have been applied to a number of aspects of HCV-related liver injury. However, to date most proteomic studies have focused on the identification of a number of biomarkers of disease rather than trying to unravel aspects of HCV pathobiology. Proteomic studies have defined potential protein therapeutic targets that interact with HCV in detail. Large-scale proteome analysis of a full-length HCV replicon revealed prominent expression of proteins involved in lipid metabolism (Jacobs et al., 2005). Several in vitro proteomic studies have identified proteins that interact with specific HCV proteins. Heat shock protein 27 (Hsp27) was shown to specifically interact with NS5A via the N-terminal regions (Choi et al., 2004). Fourteen cellular proteins binding to the core protein were identified by proteomics (Kang et al., 2005). These proteins include DEAD-box polypeptide 5 (DDX5) and intermediate microfilament proteins, including cytokeratins (cytokeratin 8, cytokeratin 19 and cytokeratin 18) and vimentin. Interestingly, DDX5 gene polymorphisms are associated with accelerated fibrosis development in HCV infected individuals (see Chapter 93) (Huang et al., 2006). The development of HCC and IFN treatment response are two further aspects of HCV infection studied using proteomics. In the study of HCV-related HCC development, over-expression TABLE 112.4

of alpha enolase was identified and correlated with poorly differentiated HCC (Kuramitsu and Nakamura, 2005; Takashima et al., 2005). The response of hepatocyte cell lines to IFN- treatment has uncovered over 54 IFN response genes, including many novel targets an approach that may pave the way for novel therapies. Examination of protein extracts that bind to the HCV IRES has identified a number of novel protein targets such as Ewing Sarcoma breakpoint 1 region protein EWS and TRAF3. The final aspect of HCV liver injury receiving attention is the study of potential biomarkers such as heat shock protein 70 Hsp-70 associated with HCV infection progression to HCC (Takashima et al., 2003).

THERAPEUTICS AND PHARMACOGENOMICS The principal treatment goal in viral hepatitis is clearance of the virus with a secondary goal of averting or delaying the onset of cirrhosis, hepatic decomposition and HCC. Immune modulators in the form of IFN treatment have been the mainstay of treatment for years (see Table 112.4). Antiviral therapy has now become an effective treatment option in HBV, but in HCV is useful only when combined with IFN treatment. Finally, HAV and HBV infection are reliably protected against by immunization. There is no prospect in the foreseeable future of a vaccine for HCV. The treatment options for viral hepatitis are summarized in Table 112.4. Predicting individual’s treatment responses based on gene expression is likely to be an area in which genomic medicine will enable highly directed individual therapy in treatment of viral hepatitis. Treatment of HBV Infection IFN is the only treatment shown to clear HBV infection in chronically infected individuals without the development of drug resistance. In both HBeAG positive and negative individuals with chronic HBV infection, IFN treatment for 4–6 months has been shown to normalize liver function abnormalities, result in clearance of HBsAg and HBeAg and result in a sustained loss

Treatment of viral hepatitis

Virus

Chronicity

Treatment

Vaccine

Comments

HAV

No

Immune globulin

Yes

HBV

Yes

Immune globulin Nucleoside Analogues (target DNA polymerase) Immune mediators (i.e., IFN-)

Yes

Viral resistance to nucleoside analogues common

HCV

Yes

Immune mediators (i.e., IFN- and Ribavirin)

No

Small molecular inhibitors in clinical trials

HDV

Yes

Treatment of HBV

No

HEV

No

None

No

No specific treatment Avoidance

Therapeutics and Pharmacogenomics

of HBV DNA. In a meta-analysis of 15 studies, suppression of HBV DNA was seen in 37% of patient’s loss of HBeAg was seen in 33% of subjects and HBeAg seroconversion was seen in 18% of patients. Subjects responded to treatment if they had lower pretreatment HBV DNA levels and higher pretreatment liver transaminase levels. The advent of long-lasting IFN preparations using pegylated IFN has been shown to have an additional benefit over conventional IFN in treating HBV infection resulting in improved HBsAg and HBeAg seroconversion. However, the current meta-analysis of the treatment outcomes with IFN in HBV does not support its use in preventing HCC. Compared to nucleoside analogs, discussed below, one principle advantage of IFN therapy for HBV is the durability of the treatment response with 10% of individuals having a relapse in HBeAg expression up to 8 years latter. Nucleoside analogs are now being increasingly used to treat HBV infection and these agents target the HBV DNA polymerase. Lamivudine is the most widely used nucleoside analog and effectively suppresses HBV replication as evident by a greater than two log decline in HBV viral DNA titers. Lamivudine results in 16–18% HBeAg seroconversion in 1 year and 50% after 5 years. The durability of the response is 77% at 3 years. However, the sustained virological response in other studies has been reported as 39% at 4 years, and sustained response to lamivudine following cessation of therapy is significantly less than IFN. Continued treatment with lamivudine results in sustained suppression of HBV viral replication but is limited by the appearance of mutant forms of the HBV polymerase typically in a conserved YMDD motif at methoine 204 of the enzyme. After 1 year resistance develops in 14–32% of cases and this increase to 50% at 2 years and 74% at 5 years. Other approved nucleoside analogs include adefovir, tenofovir, telbirudine and entecavir. All have significant activity against HBV replication although both tenofovir and entecavir would appear to have greater activity against HBV. Importantly resistance with all of these newer agents is uncommon, with entecavir resistance being less than 5% at 2 years. Unfortunately resistance to these newer agents appears inevitable. In a situation analogous to HAART treatment of HIV-1 infection, combination therapy is now being studied in HBV infection. The conclusive outcome of these studies is not yet available but the initial results are promising especially in cases of lamivudine resistance. Treatment of HCV Infection IFN treatment is the only effective antiviral therapy available for the treatment of HCV. The current recommendations are for a 24–48 week course of treatment using pegylated IFN combined with the antiviral ribavirin. Ribavirin is a guanasine analog able to inhibit the replication of viruses but in the absence of IFN has no significant effect on HCV RNA levels. The overall chance of a sustained virological response (SVR) varies according to HCV genotype. In genotype 1 infection, SVR can be achieved in 42–46% of patients, with better response rates of 76–88% for those with genotype 2 or 3 infection.Virologic response to treatment can be predicted from the decline in HCV RNA at 12 weeks; The absence of a 2 log drop or undetectable HCV RNA

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at week 12 has a high negative predictive value for the absence of SVR in genotype 1 patients with continued therapy. In genotype 2 and 3 the decline in viral load at 4 weeks may be predictive of achieving SVR with only 12–16 weeks of therapy. The treatment of patients with cirrhosis is controversial but there appears to be a benefit in avoiding progression of disease, decompensation, and the development of HCC. However, in the presence of hepatic decompensation, IFN-based treatment is contraindicated and patients should be referred for transplant evaluation. Functional Genomics Studies Related to the Treatment of Viral Hepatitis IFN- is currently part of the standard of care treatment for HCV infection. Several studies have used microarray analysis to identify the mechanisms by which IFN- acts on hepatocytes and the HCV. IFN- activated the multiple signal transducer and activator of transcription factors (STAT) 1, 2, 3, 5 in cultured hepatocytes (Radaeva et al., 2002). Other upregulated genes include a variety of antiviral and tumor suppressors/pro-apoptotic genes. Downregulated genes include c-myc and c-Met and the hepatocyte growth factor (HGF) receptor (Radaeva et al., 2002). In a second and comparable study, IFN- antiviral efficacy was associated with 6–16 (G1P3) expression. Involvement of STAT3 in IFN- signaling was confirmed (Zhu et al., 2003). Resistance to IFN- antiviral activity may be mediated the HCV viral protein, NS5A. To identify the mechanisms through which NS5A blocks IFN activity, gene expression profile was studied in IFN-treated Huh7 cells expressing NS5A. The strongest effect of NS5A on IFN response was observed for the OAS-p69 gene (Girard et al., 2002). Another key response of hepatocytes to the HCV virus is cellular proliferation. Gene array studies identified upregulation of growth-related genes, in particular wnt-1 and its downstream target gene WISP (Fukutomi et al., 2005). In another study, CDK activity, hyperphosphorylation of Rb, and E2F activation was shown to be associated with hepatocyte proliferation induced by a full-length HCV clone (Tsukiyama-Kohara et al., 2004). Global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN was performed to examine liver-specific responses to IFN and the mechanisms of IFN inhibition of virus replication (Yan et al., 2004). Fifty-four proteins were induced by IFN and 24 were repressed, representing several novel and liver-specific key regulatory components of the IFN response. Molecular markers used on an individual patient basis might improve prediction of treatment responses prior to commencement of therapy. Previously Chen and colleagues examined liver biopsies prior to IFN- and ribavirin therapy from 16 responders, 15 non-responders and 20 normal individuals by gene array analysis and determined that 18 genes were predictive of an SVR (Chen et al., 2005). These investigators identified a gene expression signature of 8 genes that could predict the likelihood to achieve an SVR in 30 of 31 individuals (GIP2/IFI15/ISG15, ATF5, IFIT1, MX1, USP18/UBP43, DUSP1, CEB1, and RPS28) (Chen et al., 2005). The striking outcome from this study was that these genes, known to be involved in IFN responsiveness, were overexpressed

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in non-responders and formed part of the predictive gene signature profile. Additionally, two genes (ISG15/IFI15 and USP18/UBP43) were identified as part of a previously unrecognized novel IFN regulatory pathway (Chen et al., 2005; Randall et al., 2006). In a further study of peripheral blood mononuclear cells (PBMC), a relative lack of ISG expression was associated with a poor response to antiviral therapy with pegylated IFN (Taylor et al., 2007) and Feld and colleagues present work demonstrating unique patterns of liver gene expression that correlate with IFN and ribavirin treatment responses in HCV genotype 1 infection (Feld et al., 2007). Pharmacogenomics of Viral Hepatitis Pharmacogenomic studies of viral hepatitis are currently evolving as the treatment options become better and the understanding of the disease pathobiology improves. In HBV infection a group of 82 chronic active carriers received standard IFN- treatment for 6 months (King et al., 2002; Randall et al., 2006). These patients were concurrently studied for single nucleotide polymorphisms (SNPs) in genes involved in the JAK/stat signaling of IFN and in genes leading to the expression of ISGs (King et al., 2002; Randall et al., 2006). Two SNPs were identified that appeared to predict response; one in the promoter region of the ISG MxA and the other in the IFN regulated eIF-2 gene. This study is significant in demonstrating that host polymorphisms may correlate with treatment response in HBV infection (King et al., 2002; Randall et al., 2006). In HCV infection the nonstructural protein NS5a is known to bind viral RNA and alter HCV replication (Feld et al., 2007; Goyal et al., 2006; Kohashi et al., 2006;Taylor et al., 2007). Amino acid substitutions within the interferon sensitivity-determining region (ISDR) of NS5A correlates with IFN- treatment responses (Feld et al., 2007; Goyal et al., 2006; King et al., 2002; Kohashi et al., 2006;Taylor et al., 2007;Watanabe et al., 2005). A meta-analysis of ISDR NS5a mutations demonstrates a relative risk of 4.66 to 5.73 of IFN- treatment response compared to non-mutant ISDR HCV (Goyal et al., 2006; Schinkel et al., 2004). However, this effect is more pronounced in Japanese compared to European patients (Goyal et al., 2006; Kohashi et al., 2006; Pascu et al., 2004; Schinkel et al., 2004). These studies highlight the gaps in our knowledge of disease pathogenesis and host genomics that

influence disease and the lack of high-specific treatment options means that pharamacogenomic measures are still in their infancy.

FUTURE IMPACT OF GENOMIC STUDIES Although genomic studies have already made significant contributions to our understanding of viral hepatitis pathogenesis, there are still many unanswered questions. In particular the molecular pathways mediating acute HBV and HCV infection and the development of chronicity are poorly understood. In the next 5–10 years genomic studies will help to predict viral hepatitis treatment responses as well as helping to predict the development of sequelae such as HCC. Proteomics promises to identify non-invasive markers of liver injury and will help to screen individuals for HCC. Further, the identification of genomic susceptibility markers will help to further individualize risk assessment. Individualized patient assessment and tailored therapy will hopefully be possible in viral hepatitis due to genomics approaches.

CONCLUSION Clearly viral hepatitis represents a major global health burden resulting in significant morbidity and mortality as well as accounting for the majority of primary liver cancers. Our understanding of the pathogenesis of chronic viral HBV and HCV indicates that each of these viruses has developed ways of evading the immune system and injury is characterized by chronic intrahepatic immune activation that fails to eliminate the virus. The diagnosis of viral hepatitis was previously based on serology, but there is now an increasing reliance on nucleic acid quantification in both HBV and HCV. The treatment of viral hepatitis is limited and whilst eradication is possible for HCV with immune mediators such as IFN, we clearly have a long way to go in attempting to eradicate these viruses, and prevent disease progression. The goal in the future is the development of newer therapeutic compounds based on our understanding of the molecular events involved in the immunopathogenesis of these chronic viral infections.

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Glossary

2D-DIGE An analytical method in which different samples are covalently labeled with distinct fluorophores and then separated by 2D-PAGE to determine the relative abundance of individual species from two or more samples. 2D-PAGE A gel-based separation method in which proteins are fractionated based on their isoelectric points and molecular weights. Accuracy It describes how close to a true population value a measurement lies. ACE Angiotensin 1-converting enzyme; exopeptidase that catalyses the conversion of angiotensin I to angiotensin II, a potent vasoconstrictor. ACE is also involved in overweight and abdominal obesity. ACS Acute coronary syndromes; a constellation of clinical syndromes defined by characteristic symptoms (e.g., chest pain or pressure, shortness of breath, and/or profuse sweating), electrocardiographic (ECG) changes (principally in the ST segments) and the results of measurement of protein biomarkers of myocardial necrosis in the peripheral blood (creatine kinase [CK]-MB or troponin I or T). (1) Patients with symptoms and persistent ST-segment elevation on the ECG,whether or not the biomarkers of necrosis are elevated in the blood, have ST-segment elevation myocardial infarction; (2) patients with symptoms with or without ST-segment depression or transient elevation on the ECG but with elevated levels of CK-MB or troponin have non-ST-segment elevation myocardial infarction; (3) patients with symptoms without ECG changes or elevation in biomarker levels have unstable angina. Action potential Wave of electrical activity that travels along a neuron carrying a message.

Active demethylation Removal of methyl groups from methylated non-replicating DNA. Adduct A complex that forms when a chemical covalently binds to a biological molecule, such as DNA or protein. ADHD “Attention Deficit Hyperactivity Disorder” is a condition that becomes apparent in some children in the preschool and early school years. ADIPOQ Adiponectin, C1Q; is a hormone secreted by adipocytes that regulates energy homeostasis as well as glucose and lipid metabolism. Admixed population A population formed by the recent mixture of two previously distinct populations. ADRB2 Adrenergic beta2 receptor; the adrenergic receptors are a class of G protein-coupled receptors that are targets of the catecholamines, which are involved in lipid metabolism. ADRB3 Adrenergic beta3 receptor; the adrenergic receptors are a class of G protein-coupled receptors that are targets of the catecholamines.The 3 subtype is found in adipose tissue, where agonists enhance lipolysis. AGRP Agouti-related protein; this brain protein is a potent antagonist of MC3-R and MC4-R and is involved in the metabolic processes that regulate feeding behavior and body weight. It has been found in the hypothalamus. Allele One of two forms of a gene that occupy the same position on a specific chromosome. Alpha software release The alpha build of the software is the build delivered to the software testers that are persons different 1391

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from the software engineers, but usually internal to the organization or community that develops the software.

B0 Main static magnetic field used in MRI experiments; MR resonance frequency is proportional to B0.

Analyte The measured candidate protein.

BAC Bacterial artificial chromosome; a DNA construct for cloning DNA fragments of 120–150 kb in length.

Analytic validity The ability of a genetic test to accurately and reliably measure the genotype of interest. The four specific elements of analytic validity include analytic sensitivity (or detection rate), analytic specificity (correct classification of normal genotype), laboratory quality control, and assay robustness (resistance of the assay is to changes in pre-analytic and analytic variables). Analytical performance (of candidate protein test) Includes the sensitivity, specificity, accuracy, and precision of the test. Analytical validation (used interchangeably with analytical evaluation) Assessment of analytical performance characteristics of the candidate protein test, including sensitivity, specificity, accuracy, and precision. Annotation Descriptions about a biospecimen. Anonymised samples Biospecimens where personal data and research data are not linked by any key codes. Apolipoprotein Major protein component of lipoproteins; there are multiple apolipoproteins with functions. AQP7 Aquaporin 7; are a class of integral membrane proteins that form pores in the membrane of biological cells and selectively conduct water molecules in and out, while preventing the passage of ions and other solutes. The expression of this gene has been related to obesity. Array Ordered representation of biological material, in order to capture a molecular portrait of a living cell or tissue at the moment of sampling. DNA fragments as well as oligonucleotides of various lengths have been arrayed on substrates such as membranes, glass, plastic, silicon waffles, and metal alloys. ASIP Agouti signaling protein; the human homolog of mouse agouti. Association studies Genetic analysis based upon the relationship of a candidate gene with a given trait or phenotypical character. Association test A test of whether a polymorphism has a disease role by evaluating its frequency in affecteds versus nonaffecteds. Astrocytes Star shaped cells in the brain that help regulate the environment of the brain. Atrial septal defect Opening in the wall dividing the right and left atria. Atrophy Progressive loss of muscle mass. Attenuation A process by which the intensity of electromagnetic radiation is reduced as it passes through different materials, such as different tissues of a subject.

Bandwidth The range of usable frequencies in a communication system. Beta software release A beta version is the first version released outside the organization or community that develops the software, for the purpose of evaluation or real-world testing. The process of delivering a beta version to the users is called beta release. Biallelic Having two alleles (two versions, or two states) of a gene or DNA segment. Biobank A collection of biospecimens with associated annotation. Common information stored includes demographics, clinical history, family history, treatment, and outcome. Collections of biospecimens without annotation are not considered biobanks. Bioinformatics The science and practice of using computers and computer programs to solve information and analytic problems in the life sciences. Biomarker A measure of a chemical, cellular, molecular, immunologic, genetic, or physiologic signal, biologic event, or biologic state, measured in biological material. Biomarker An indicator of a specific biological state, physical, physiological or genomic in origin (e.g., DNA variation, transcript, protein, and metabolomic profiles) quantifiable in an analytical test system useful for the detection, diagnosis, prognosis, and monitoring of disease. Biospecimen General term for all biomaterials which includes but not limited to subcellular molecules such as DNA, RNA, cells, tissue, organs, blood, and other biological fluids (e.g., urine). BLAST Basic Local Alignment Search Tool; a computational algorithm that identifies similarities between a protein or nucleotide sequence and other sequences in a user-selected database. The query sequence is compared to every entry in the database, and results are provided as a prioritized list with the most similar sequences presented first. BLAT BLAST-like alignment tool; a different computational algorithm for identifying similarities between a protein or nucleotide sequence and other sequences. The major advantage of this method is its faster speed, which is mainly achieved by producing an index of non-overlapping sequence fragments of various sizes for each sequence being queried. BMI Body mass index; statistical measure of the weight of a person scaled according to height. It is used as a simple means of classifying inactive individuals of an average body composition according to their body fat content. As a rough guideline for adults a BMI of less than 20 implies underweight, over 25 is

Glossary

overweight, and over 30 is obese. It is calculated by taking the weight of the individual in kilograms and dividing by the squared height in meters. BMR Basal metabolic rate; amount of energy expended while at rest in a neutrally temperate environment and in the postabsorptive state (meaning that the digestive system is inactive, which requires about 12 h of fasting in humans). BPDE Benzo (a) pyrene diol epoxide; a toxic metabolite of benzo (a) pyrene, the main carcinogenic component of tobacco smoke. BRCA1 Breast cancer type 1, early onset; is a human gene that belongs to a class of genes known as tumor suppressors, which regulate the cell cycle and prevent uncontrolled proliferation. BRCA2 Breast cancer type 2 susceptibility protein; is a human gene that is involved in the repair of chromosomal damage and belongs to a class of genes known as tumor suppressor genes.Tumor suppressor genes regulate the cycle of cell division by keeping cells from growing and dividing too rapidly or in an uncontrolled way. BRCAPRO model A model and software for genetic counseling of women at high risk of hereditary breast and ovarian cancer. Bulbar symptoms Symptoms pertaining to eye movements, muscle of facial expression, speaking, and swallowing.

C5 The fifth factor of complement, a protein system important in allergic immune responses. caBIGTM Cancer biomedical informatics grid; developed by the National Cancer Institute, the caBIGTM is an infrastructure to facilitate collaborative grid-like sharing of research data and standards, tools and applications for cancer research through an open source access. Open to NCI’s supported programs and other international organizations, the caBIGTM is envisioned to be the next World Wide Web of cancer research. Candidate gene A gene that is suspected of underlying a disease based on its location in a chromosomal region that has been linked to the disease (positional candidate), and/or based on what is known about the biological function of its protein (functional candidate). CART Cocaine and amphetamine regulated transcript; is an anorexigenic peptide widely expressed in the central and peripheral, including the enteric and nervous systems, with a putative role on food intake. Case–control studies Studies that compare differences between disease and healthy individuals to identify diseaserelated or causal factors including demographics, past exposures, genetic, and epigenetic influences. CAV1 Caveolin-1 is an essential protein component of the plasma caveolar membrane and regulator of caveolae-dependent

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signaling and endocytosis, which has been related to weight regulation. cDNA microarray Micorarrays consisting of probes that are cDNAs spotted and arrayed. These are best exemplified by the microarrays first pioneered at Stanford University. CDS system A system that provides CDS to its end-users. A CDS system may be a module within a broader clinical software application such as an electronic health record system or a computerized provider order entry system. CDS Clinical decision support; the act of providing clinicians, patients, and other health care stakeholders with pertinent knowledge and/or person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. CEBPB CCAAT/enhancer binding protein; is a family of related basic region leucine zipper transcription factors involved in the regulation of various aspects of cellular differentiation and function in multiple tissues, including fat cells. CETP Cholesteryl ester transfer protein; is a protein that transfers lipids among lipoproteins, especially cholesteryl ester from HDL to VLDL in exchange for triglycerides. Chemical genomics A genomic response to chemical compounds. Chromatin DNA is packaged in the nucleus around a repeating unit of an assembly of proteins called nucleosomes.The overall configuration of this packaging of DNA in the nucleus is termed chromatin. Chromatin determines the accessibility of specific genes to the machinery, which transcribes them and enables their expression. Chromatin modification Histones are modified by certain chemical residues. Dedicated proteins (enzymes) are responsible for this modification.The state of chromatin modification defines its configuration. Chromatin remodeling An energy-dependent repositioning of nucleosomes in relation to specific gene regulatory regions during transitions in state of gene expression. Chromosome aberration Any type of change in the chromosome structure or number. Chylomicron Intestinal triglyceride-rich lipoprotein; elevated when triglycerides are 1000 mgm/dl. CIDEA Cell death inducing DNA; CIDEA belongs to a family of proapoptotic proteins that has five known members in humans and mice. The action of CIDEA is unknown, but CIDEA-null mice are resistant to obesity and diabetes. cis-Activating Activation of a gene at one locus by direct effect at that particular locus. CK Creatine kinase; is an enzyme in muscle that leaks into the blood stream causing elevated levels with muscle damage or disease.

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Claus model A computer program that uses statistics to predict a person’s risk for developing breast cancer based on family history. Clinical endpoint It is the ultimate consequence of a disease, which would occur without therapeutic intervention, for instance death, amputation, organ transplantation, blindness. Clinical utility The ability in specific clinical circumstances of a test to assist in clinical decision-making and improve health outcomes. Clinical validity The ability of a genetic test to detect or predict the associated phenotype.The elements of clinical validity are clinical sensitivity (or detection rate), clinical specificity, prevalence of the specific phenotype, positive and negative predictive values, penetrance, and modifiers (gene or environmental). Clinician A physician, physician assistant, nurse practitioner, or other caregiver who makes independent decisions for patient management. CNR1 Cannabinoid receptor 1; one of the two known receptors in the endocannabinoid system associated with the intake of food and tobacco dependency. Blocking the cannabinoid receptor 1 may reduce dependence on tobacco and the craving for food. CNV Copy number variant; a type of structural variation involving changes in the number of copies of segments of DNA 1 kb in size. Coalescence A point in a genealogical tree marking the common ancestor of two lineages. Coarctation of the aorta Narrowing of the aortic arch. Coded samples Samples with associated code rather than identifiable information. Coded samples is a means of protecting personal identifiers, for example, name, insurance number or social security number from improper use and disclosure. Cohort Originally defined as a group of people born during a particular period (a “birth cohort”), in epidemiology studies also refer to group of people with a common trait that are followed over time. Combinatorial chemistry The synthesis of compounds by combining a number of starting compounds in a variety of ways in order to build up a large number of product compounds. Common disease/common variant hypothesis The hypothesis that common genetic diseases are due to common polymorphisms. Complete atrioventricular canal Complex cardiac malformation resulting from defective development of the endocardial cushions that give rise to portions of the atrial and ventricular septa and the tricuspid and mitral valves. Complex disorder A disease where multiple genes interact with environment to produce the final disease phenotype.

Complex trait A trait that owes its existence to the interplay of multiple factors including both genetic and environmental determinants. Examples include obesity, hypertension, diabetes, and cancer. Compound heterozygote An individual possessing two abnormal alleles at a given locus, each with a different polymorphism or mutation. Computerized provider order entry (CPOE) system A clinical software application that allows a clinician to use a computer to directly enter medical orders. Concordance Similar or sameness in appearance or phenotype. Confidentiality The ethical principle of keeping information secret unless consented by owner of the information to be released or shared (e.g., in a doctor–patient relationship). Congenic strain A recombinant genetically derived strain in which a segment of a chromosome (usually incorporating a QTL) is transposed from one strain to another. Congenital heart disease Structural malformations of the heart and great blood vessels present at birth. Conotruncal Pertaining to the pulmonary and aortic cardiac outflow tracts. Consomic strain A recombinant genetically derived strain in which an entire chromosome is transposed form one strain to another. Controlled vocabulary A collection of preferred or authorized terms that are used to assist in more precise retrieval of information. Controlled vocabulary terms can be used for categorizing and indexing content, building coding systems, and creating style guides and database schema. One familiar type of a controlled vocabulary is taxonomy which is a hierarchical system for describing, naming and classifying plants and animals. CPE Carboxypeptidase E; also known as carboxypeptidase H and enkephalin convertase, is found as both a membrane-bound and a soluble glycoprotein in neuroendocrine tissues and adrenalgland chromaffin granules, which has been associated to obesity. CpG islands Regions in the genome which are highly enriched by the dinucleotide sequence CG. CG islands are found in the vicinity of regulatory regions of many genes, especially housekeeping genes, that are ubiquitously expressed. CSA Comparative sequence analysis; a general term for the cross-comparison of related nucleotide or protein sequences, with the goal of identifying similarities or differences among them. For applications in human genetics, this typically involves comparing human sequences to orthologous sequences from other species, for example, to assess the level of conservation of a nucleotide found to be mutated in a disease or to identify conserved sequences that might be candidates for being relevant functional elements.

Glossary

CT Computerized tomography; medical imaging method employing tomography where digital geometry processing is used to generate a three-dimensional image of the internals of an object from a large series of two-dimensional X-ray images. CTP© Current Procedural Terminology is a code set that accurately describes medical, surgical, and diagnostic services. It is maintained by the American Medical Association through an Editorial Panel. The CPT code set and is used in the United States for billing and reimbursement purposes. Use of CPT requires payment of a license fee. CYP2C19 “Cytochrome P450 2C19” is a member of the cytochrome P450 mixed-function oxidase system, is involved in the metabolism of xenobiotics in the body. It is involved in the metabolism of several important groups of drugs including many proton pump inhibitors and antiepileptics. CYP2D6 “Cytochrome P450 2D6” is a member of the cytochrome P450 mixed-function oxidase system, is one of the most important enzymes involved in the metabolism of xenobiotics in the body.

D Diffusion coefficient of water molecules, determines the rate of dephasing of MR signals in diffusion-weighted MRI. DEAL DNA-Encoded Antibody Libraries; a novel in vitro assay technology in which antibodies conjugated to single-stranded DNA are used as intracellular probes; microarrays containing DNA complementary to both conjugated antibodies and messenger RNA can then perform both proteomic and transcriptional profiling with a single prepared sample. Demographics Grouping of human subpopulations based on statistical characteristics (e.g., age, gender, societal class, lifestyle, etc.). DEXA Dual energy X-ray absorptiometry; means of measuring bone mineral density (BMD). Also, it is widely used for assessing body composition (fat and fat-free mass). DGGE Denaturing gradient gel electrophoresis; is a method used to identify changes of bases in a DNA segment. In this type of electrophoresis, the double fiber of DNA is put under a gradient process mediated by a denaturalization agent. Diplopia Double vision. Discovery-based research Research in which large amount of data are examined, without prior hypothesis, to discover markers or patterns that might discriminate among groups of individuals. Dizygotic twins Nonidentical or fraternal twins derived from the fertilization of different eggs. DNA “Deoxyribonucleic acid” is a nucleic acid molecule that contains the genetic instructions used in the development and functioning of all known living organisms.

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DNA binding proteins Dedicated proteins that recognize methylated DNA and recruit other chromatin-modifying enzymes to create a silencing chromatin configuration. DNA methylation A chemical coating of DNA by methyl residues. DNA methylation pattern In vertebrates, methylation occurs at cytosines, which are found in the dinucleotide sequence CG. Around 80% of CGs are methylated; however, the distribution of methylated cytosines differs from cell to cell. Different CG sites are methylated in different tissues. DNMT DNA methyltransferase; proteins (enzymes) that catalyze the addition of methyl residues at specific positions in DNA. Dominant inheritance (autosomal) Inheritance of a trait in which just one of two copies of the gene must be altered or inactive to express the trait. Donors A person that gives specimens voluntarily. A donor can be either living or deceased. DRD2 Dopamine receptor D2; family of G protein-coupled receptors of two general classes: D1 stimulation increases intracellular cAMP, whereas D2, D3, or D4 stimulation decreases cAMP, increases K flux, or decreases Ca2 flux, which have been related to weight control. Dysarthria Difficulty speaking. Dysphagia Difficulty swallowing.

Electronic health record (EHR) system A system that includes: (i) longitudinal collection of electronic health information for and about persons; (ii) immediate electronic access to person- and population-level information by authorized, and only authorized, users; (iii) provision of knowledge and decision support that enhance the quality, safety, and efficacy of patient care; and (iv) support of efficient processes for health care delivery. Endogamy A tendency for marriages to occur within a distinct group. Endogenous

Something produced within the body.

Epigenetics Long-term programming of gene expression by chromatin configuration and DNA methylation in the absence of a change in the DNA sequence. Epigenetic changes might be heritable. Epigenome The pattern of distribution of chromatin configurations and DNA methylation throughout the genome. Epistasis The masking of the phenotypic effect of alleles at one gene by alleles of another gene. A gene is said to be epistatic when its presence suppresses the effect of a gene at another locus. eQTL Expression QTL, a locus involved in regulating gene expression.

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FABP Fatty acid binding protein; is a protein responsible of getting fatty acid transport into the adipocyte. False discovery rate Expected proportion of accepted type 1 errors within a list of tested hypotheses, and controls for the number of tests being made. False positive rate Also known as a type 1 error in statistics, the chance of rejecting the null hypothesis in a single test, when the null hypothesis is true.

the functional units of heredity.The majority of eukaryotic genes contain coding regions (exons) that are interrupted by noncoding regions (introns). Gene–environment interaction “A different effect of an environmental exposure on disease risk in persons with different genotypes,” or, alternatively, “a different effect of a genotype on disease risk in persons with different environmental exposures.” Gene expression Viral RNA expression monitoring.

Familial aggregation Demonstration of an increased risk of a disease or trait among relatives of affected individuals. Often used to show that a phenotype is genetic.

Gene flow The movement of allele copies (via migration and/or interbreeding) from one subpopulation to another.

FEV1 Forced expiratory volume in 1 s. A physiologic measure of airflow obstruction obtained from spirometry.

Gene therapy To treat a disease by altering a person’s genes.

Fluorophore Fluorescent substance. FOXC2 Forkhead Box C2; the Forkhead Box (Fox) proteins are an extensive family of transcription factors that shares homology in the winged helix DNA-binding domain and whose members play essential roles in cellular proliferation, differentiation, transformation, longevity, and metabolic homeostasis. Framingham risk score A commonly used algorithm that predicts total coronary heart disease (CHD) risk over 10 years (i.e., risk of developing angina pectoris, myocardial infarction, or CHD death) using gender-specific equations that include age, blood total cholesterol, high-density lipoprotein (HDL) cholesterol, blood pressure, cigarette smoking, and diabetes mellitus. Functional genomics The study of genes, their resulting proteins, and the role played by the proteins the organism’s biochemical processes.

Gail model A computer program that uses personal and family history to estimate a woman’s chance of development breast cancer. GAIN “Genetic Association Information Network” is a public–private partnership of the Foundation for the National Institutes of Health, Inc. (FNIH) that currently involves the National Institutes of Health (NIH), Pfizer, Affymetrix, Perlegen Sciences, Abbott, and the Eli and Edythe Broad Institute (of MIT and Harvard University).

Gene pool The sum of all allele copies within a population.

Genetic association A significant difference in the frequency of a specific genetic polymorphism between affected and unaffected populations. Genetic linkage Correlation between the inheritance of a trait and chromosomal regions studied within family units. Genetic markers Difference in the genome from one individual that has the potential to affect the function of the gene and hence the phenotype of the individual. Genetic polymorphism Genome segment (locus), within or outside a gene, in which alternate forms (alleles) are present. In population genetics, variation is polymorphic if all alleles are found at frequencies 1%. Genetic testing Analyzing an individual’s genetic material to determine predisposition to a particular health condition or to confirm a diagnosis of genetic disease. Genocopy When the same variant in a particular gene leads to two different outcomes (traits) in two different patients; modifier genes and environmental factors contribute to this effect. Genome Whole hereditary information of an organism that is encoded in the DNA (or, for some viruses, RNA). This includes both the genes and the non-coding sequences. Genome-wide association Association testing using a densely spaced panel of markers spanning the entire genome.

Gamma camera An imaging device for visualizing radionuclides that decay by emitting gamma rays.The images may be planar images, thus two-dimensional, or the camera may be rotated for three-dimensional imaging (SPECT).

Genome-wide linkage scan A method to identify quantitative trait loci. Hundreds of marker loci, spanning all chromosomes, are genotyped in a large collection of family members, including probands with the trait of interest, with the intent to identify, through statistical methods, a significant linkage signal or peak.

Gamma ray Electromagnetic radiation (photon) emitted from the nucleus during the decay process of radionuclides. It may be detected by gamma camera.

Genome-wide scan Analysis of panels of microsatellite markers spaced at intervals of approximately 10 cM across the genome.

Gd Gadolinium, paramagnetic metal used in MR contrast agents. Gene Specific sequences of nucleotides along a molecule of DNA (or, in the case of some viruses, RNA), which represent

Genomic medicine The optimization of individuals’ health through the use of data on a patient’s genome and its downstream products, including messenger RNA, proteins, and metabolites.

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Genomics The study of genes and their function in relation to the environment.

HAT Histone acetyltransferase; enzymes that catalyze the transfer of acetyl groups to a lysine amino acid in histones.

Genotype The genetic constitution of an organism, as distinguished from its physical appearance.

HCRT Orexin; also called hypocretins, are the common names given to a pair of highly excitatory neuropeptide hormones, which participates in feeding, integrate metabolic functions, and regulates circadian and sleep states.

Genotyping Looking for sequence variation that has previously been characterized. Germline The continuation of a set of genetic information from one generation to the next. GHRL Ghrelin; a 28 amino acid hormone produced by X/A endothelial cells lining the fundus of the human stomach that stimulate appetite. Global multi-sequence alignment An alignment of multiple sequences based on similarity over the entire length of the sequences. This alignment method places more importance on maintaining a consistent long-range organization of the genomic region among species and less importance on local sequence identity. Glyco-peptide capture A method for isolating specific fractions of the proteome based on glycolyzation profile; often used to enrich secreted or cell-surface proteins from a gross lysate sample for further sequencing or analysis. GNB3 Guanine nucleotide-binding protein B polypeptide 3; also known as seven transmembrane receptors, heptahelical receptors, or 7TM receptors, are a protein family of transmembrane receptors that transduce an extracellular signal into an intracellular signal (G protein activation), which have been associated to obesity phenotypes. Gowers maneuver The process used to rise from the floor that involves using the arms to walk up the body. Commonly seen in patients with muscular dystrophy. GSEA Gene set enrichment analysis; a computational method for analyzing microarray data. It determines whether a set of genes show similar differences between two biological states of interest (e.g., normal versus disease). GWAS Genome-wide association study; a genetic-mapping study in which hundreds of thousands of DNA sequence variants across the genome are assessed simultaneously for association with a dichotomous or quantitative phenotype of interest.

Haplotype A sequential set of polymorphisms on one chromosome, or part of it, which are linked and tend to be inherited together. Haplotype block a chromosomal region that contain specific alleles at several different genetic loci that are inherited together more often than would be expected by chance alone.

HDACs Histone deacetylases; enzymes that reverses state of acetylation of histone tails by removing acetyl groups. HDL High-density lipoprotein; a biomarker whose plasma concentration has been epidemiologically associated with inverse CHD risk; the protective mechanism from HDL is thought to be “reverse cholesterol transport” or mobilization and disposal of cholesterol deposited within atherosclerotic plaques. Health literacy The degree to which individuals (consumers, patients, and lay persons) have the capacity to obtain, process and understand health information, and services needed to make appropriate medical or health decisions. Heterozygosity The expected frequency of heterozygotes in a population. Hierarchical clustering A type of unsupervised machine learning algorithm that results in the branched partitioning of a set of elements (e.g., genes) based on the distance or similarity between those elements. HIS/HIR Retrieval.

Health Information Search/Health Information

Histone acetylation Lysines (K) at different positions located at the tails of histones H3 and H4 are modified by acetyl groups. Histone demethylases from histones.

Enzymes that remove methyl groups

Histone methylation Modification of lysine or arginine residues in histone by methylation. Histone tails Amino termini of histones H3 and H4 assume a structure independent of the highly ordered structure of the rest of the histone protein. The tails are positively charged and tightly bind the negatively charged backbone of DNA. The tails are subjected to different chemical modifications, which change the affinity of their interaction with DNA. HL Hepatic lipase; endothelial-anchored enzyme in liver primarily responsible for hydrolysis of triglycerides and phospholipids in IDL and HDL. HLA Human leukocytic antigens which vary from person to person and allow a body to recognize cells as belonging in the body.

HapMap A map of haplotype blocks in a genome.

HMG-CoA reductase Rate-limiting enzyme in cholesterol biosynthesis; inhibition by “statins” results in reduction of plasmaBLDL-cholesterol levels.

Hardy–Weinberg proportions The expected frequencies of genotypes within a randomly mating neutral population.

HMT Histone methyltransferase; enzymes that transfer methyl groups onto lysine or arginine amino acids in histones.

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Homologous sequence Two or more sequences that originate from a common ancestor.These are typically subdivided into orthologous and paralogous sequences based on their origin. HTR2C 5-Hydroxtryptamine (serotonin) receptor 2C; the 5HT1C receptor is a G protein-coupled receptor that stimulates phospholipase C (PLC)-catalyzed hydrolysis of phosphatidylinositol bisphosphate, leading to the mobilization of intracellular calcium and to the activation of protein kinase C and participation in appetite control. HTR2C Serotonin receptor 2C; the 5-HT2C receptor is expressed in many brain regions, which is implicated in anorectic effects of brain serotonergic systems. Hypermethylated CG islands Most CG islands in vertebrate DNA are not methylated. In many cancers certain CG islands found in tumor suppressor genes are aberrantly highly methylated resulting in gene silencing. Hypertrophic cardiomyopathy Abnormal thickening of the ventricular wall involving pathological enlargement of cardiac myocytes and fibrous proliferation. Hypertrophy

Increase in size.

ICD-9 International Classification of Diseases – Ninth Revision is the official system of assigning codes to diagnoses and procedures associated with hospital utilization in the United States. The ICD-9-C consists of: (a) a numerical list of the disease code numbers in tabular form; (b) an alphabetical index to the disease entries; and (c) a classification system for surgical, diagnostic, and therapeutic procedures. IDL Intermediate-density lipoprotein; is formed by hydrolysis of triglycerides in VLDL; elevated in type III hyperlipoproteinemia. IL6 Interleukin-6; a glycoprotein produced by activated T-cells and a variety of other cells, which is considered as an adipokine, participating in body weight maintenance. In/del Polymorphism involving insertion or deletion of one or more basepairs. In silico Performed on computer or via computer simulation. In vitro An artificial environment outside the living organism, here also including cells cultured outside an organism. In vivo Studies within a living organism. Incidentaloma An asymptomatic non-functional tumor that is clinically and biochemically silent and discovered “incidentally” in a patient undergoing diagnostic imaging for a totally unrelated problem.The concept is a by-product of evolving diagnostic techniques that are increasingly powerful and comprehensive. Incomplete penetrance The failure of a disease or trait to manifest despite the presence of a causative gene change.

Informatics system A system, including hardware and software, that collects and stores data. In a biobank, the informatics system includes documents, tracking tools and data storage systems pertaining to sample inventory, processes and phenotypic data, and any other data formats. Information retrieval Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or hypertextually-networked databases such as the World Wide Web. There is a common confusion, however, between data retrieval, document retrieval, information retrieval, and text retrieval, and each of these has its own bodies of literature, theory, practice, and technologies. IR is interdisciplinary, based on computer science, mathematics, library science, information science, cognitive psychology, linguistics, statistics, and physics. Informed consent A dialog between would be donors, nextof-kin or legal representatives to communicate on the donation process including risk and benefits, confidentiality, voluntary nature, answer questions, and obtain approval for donation. INS Insulin from Latin insula, “island”; it is produced in the Islets of Langerhans in the pancreas being a polypeptide hormone that regulates carbohydrate metabolism. Apart from being the primary effector in carbohydrate homeostasis, it has effects on fat and energy metabolism. Institutional review board A selected body of individuals to review, approve, and monitor human subject research, acting as an oversight body. Interactome The collection of all interactions between individual biomolecules of an organism, including protein–protein interactions, transcriptional regulation, and post-transcriptional processing; the theoretical sum of all regulatory, metabolic, and signal transduction networks. International HapMap Consortium The consortium responsible for identifying common genetic variation within four different populations which lead to a haplotype map for much of the human genome in these populations. Interoperability The ability to communicate, exchange, and share information between different systems. This can be both technical and organizational, involving standardization, product design, IP access, and forming collaborations and partnerships. Interrupted aortic arch Discontinuity of the aortic arch proximal to the insertion of the ductus arteriosus. ISO9001 ISO9001 was created through the International Organization for Standardization (ISO), founded in 1946 to develop common standards for manufacturing, trade and communications. ISO is a worldwide federation of national standards bodies with headquarters in Geneva, Switzerland. ISO 9001 was prepared by Technical Committee ISO/TC 176, Quality management and quality assurance, Subcommittee SC2, Quality systems.

Glossary

ISO9001 is an internationally recognized quality standard for biorepositories, similar to cGMP, encouraging collaboration between organizations with the same standards. Two key features of ISO 9001 are that of ensuring customer satisfaction and provision for continuous improvement. Isolated population A population which has a high degree of endogamy. iTRAQ Isobaric Tagging for Relative and Absolute Quantification; a method for quantitative proteomic analysis in which target peptides from different experimental conditions are each tagged with a common mass balance isotope and a unique reporter group, with relative peptide concentrations determined by tandem mass spectrometry.

Kaplan–Meier analysis A type of statistical analysis used to determine whether a test variable (e.g., gene) is associated with a difference in the rate of survival (e.g., after diagnosis with a disease). Kilobase A measure of genetic distance (in 1000s of base pairs) between sites along the genome. Kinase Enzyme capable of catalyzing the transfer of a phosphate group from adenosine triphosphate (or other nucleoside triphosphate) to another molecule. KO Knockout animal; animals whose genetic material has been altered by genetic manipulation. Kolmogorov–Smirnov statistic A statistical test to determine whether two probability distributions are similar or different.

Laboratory information management systems Databases customized to track and automate sample and information work flow. LCAT Lecithin-cholesterol acyltransferase; enzyme that converts free cholesterol to cholesteryl ester on HDL. LCM Laser capture microdissection; a technique for sampling individual cells from a heterogeneous population of cells. LC-MS An analytical method in which a mixture of molecules is separated using liquid chromatography coupled online with a mass spectrometer for detection and characterization.

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2\-macroglobulin receptor and low-density lipoprotein receptor related protein, which are involved in lipoprotein signaling. LEP Leptin; a 16 kDa protein hormone that plays a key role in energy intake and energy expenditure, including the regulation of appetite and lipid metabolism. LEPR Leptin receptor; receptor involved in leptin signaling, which participates in energy metabolism control. Lexical Referring to morphological variations of words in a language or dictionary. In text analysis, lexical variations may include alternative spellings of a word in the same language or frank misspellings/typographical errors. Line of response A line joining two detectors for localizing a positron annihilation event in PET imaging. Linkage Genetic mapping by testing the co-inheritance of a phenotype and a marker panel in multiple generations. Linkage analysis Method to infer the positions of two or more loci by examining patterns of allele transmissions from parent to offspring, or patterns of allele sharing by relatives.The association in inheritance of two or more non-allelic genes is due to being located more or less closely on the same chromosome. LIPE Lipase hormone-sensitive; a water-soluble enzyme that catalyzes the hydrolysis of ester bonds in water-insoluble lipid substrates. Functional impairments have been associated to obesity. Lipoprotein Complexes responsible for transporting lipids (cholesterol, triglyceride, phospholipids) within the blood. Local multi-sequence alignment An alignment of multiple sequences based on similarities of sub-regions within each sequence.This alignment method places more importance on local sequence identity and less importance on maintaining a consistent long-range organization of the genomic region among species. Locus (pc.loci) Gene location or a marker in a chromosome responsible for a trait. Also makes reference to the DNA in that position. Sometimes, locus terminology is used to denominate itself to regions of DNA that are expressed. LOD Logarithm of odds; statistical approach to state the likelihood of occurrence in a given event. Longitudinal data Clinical data acquired over time from following the same individual or individuals.

LD Linkage disequilibrium; a measure of whether alleles of two genes, genetic markers or DNA segments tend to segregate together within a population in a non-random manner. Alleles that are in LD are usually found together on the same haplotype than would be expected by chance alone.

LPL Lipoprotein lipase; endothelial-anchored enzyme primarily responsible for hydrolysis of chylomicron and VLDL triglycerides, especially in muscle and adipose tissue.

LDL Low-density lipoprotein; major cholesterol-containing lipoprotein and major atherogenic lipoprotein.

mAb Monoclonal antibodies are often used as a high affinitybinding domain of the targeted MR contrast agent.

LDLR Low-density lipoprotein receptor; family of receptors, whose members include low-density lipoprotein receptor, alpha

Machine learning The ability for a computer program to improve or generalize from previous experience.

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Macroarray

Spot size over 300 micron.

Maintenance methylation Accurate copying of the DNA methylation pattern during cell division onto the nascent strand of DNA by DNMT1. Marker A known DNA sequence (e.g., a gene or part of gene) that can be identified by a simple assay, associated with a certain phenotype. A genetic marker may be a short DNA sequence, such as a sequence surrounding a single base pair change (single nucleotide polymorphism), or long one, like microsatellites. Mass spectrometer A device consisting of an ion source, mass analyzer, and detector that is used to measure the mass-tocharge ratio of ions. Mass spectrometry An analytical technique in which the mass-to-charge (m/z) ratio of ions is measured. Matrix The environment in which a measurand resides. MC4R Melanocortin 4 receptors; are members of the rhodopsin family of 7-transmembrane, G-protein coupled receptors. There are five known members of the melanocortin receptor system each with differing in specificities for melanocortins, which participates in food intake control. Measurands The measured candidate protein. Mendelian Refers to conditions or characteristics that show inheritance patterns in accordance with the laws of inheritance set out by the 19th century biologist and monk, Gregor Mendel. MeSH Medical Subject Heading is a comprehensive collection of medical terms created and maintained by the US National Library of Medicine and used as the de facto standard for indexing of biomedical literature. Metabolic syndrome A commonly occurring cluster of disorders, including abdominal obesity, dysglycemia, hypertension and dyslipidemia that are related to increased risk of cardiovascular disease and diabetes. Metabolome The metabolites of an organism. Metabolomics The unique small-molecule metabolite profiles or fingerprints associated with physiological processes or disease conditions. Metathesaurus The principal component of UMLS that contains linkages of terms between MeSH terms and numerous Controlled vocabularies. MIAME Minimal Information About a Microarray Experiment; a standard for reporting the primary microarray experimental data. Microarray A collection of microscopic DNA spots attached to a solid surface (glass, plastic or silicone) forming an array for the purpose of monitoring the expression level of thousands of genes simultaneously. Microglia Immune cells found within the brain.

Micronuclei Chromosome fragments or whole chromosomes that are not included in the main daughter nuclei during nuclear division. Microsatellite Polymorphism due to differing lengths of simple nucleotide repeat sequences. Mitral valve prolapse Abnormality involving redundant valve tissue, abnormal valve closure, and often regurgitation. MMPs Metalloproteinases proteins involved in regulation of cell adhesion and cell-cycle-related proteins. Molecular subtyping Using molecular/genomic approaches to distinguish between apparently similar phenotypes. Monogenic disease A disease resulting from the influence of a single gene, inherited in a Mendelian fashion (i.e., autosomal dominant, autosomal recessive, X-linked dominant). Monozygotic twins Identical twins derived from a single egg or zygote. Motor neuron A neuron that sends messages from the brain to muscle fibers. MR Magnetic resonance phenomena is the basis for MRI, magnetic resonance imaging, and MRS, magnetic resonance spectroscopy. MRI Magnetic resonance imaging; formerly referred to as magnetic resonance tomography (MRT) or nuclear magnetic resonance (NMR), is a method used to visualize the inside of living organisms. It is a common form of medical imaging and body composition assessment. mRNA “Messenger Ribonucleic Acid” is a molecule of RNA encoding a chemical “blueprint” for a protein product. mRNA is transcribed from a DNA template, and carries coding information to the sites of protein synthesis the ribosomes. MSA Molecular signature analysis; a molecular signature is defined as any composition of molecules, which is specific for a certain phenotype. Molecular signatures can be transcripts, proteins, or metabolites and are often used as clinical biomarkers. In this sense, we created the term “transcriptomic-based biomarkers” (TBB). Multiallelic Having more than two alleles (more than two versions, or more than two states) of a gene or DNA segment. Multiplexing technology Simultaneous measurement of multiple peptides/proteins in a single chamber or a chip. Multi-sequence alignment A computational process that maps multiple-related nucleotide or protein sequences relative to one another. Two major approaches are global and local multi-sequence alignment methods; both yield a line-by-line alignment file, with the similarities annotated on the reference sequence.

Glossary

Nearest shrunken centroid A standardized class centroid is a composition of mean values of expression of all individual genes in a given class divided by the within-class standard deviation.The resulting class centroids are further shrunken toward the overall centroid by an optimized threshold. The overall centroid is calculated by dividing the average expression of each gene in all classes by the pooled within class standard deviation. Thus, the shrunken centroids are “de-noised” versions of centroids that act as prototypes for each class. Necrosis Cell or tissue death. Negative predictive value Percent of patients with negative genetic test results (i.e., carrying normal genotypes) who do not exhibit a phenotype of interest. Neuromuscular junction The junction between a nerve cell and the muscle fiber that it activates. NHEJ Non-homologous end joining; a repair mechanism for double-stranded DNA (dsDNA) that does not require recombination between aligned homologous DNA sequences but instead sometimes uses short stretches of similar DNA to recreate a double strand from a single strand. NHEJ can result in the formation of CNVs. NIH “National Institutes of Health” is a part of the US Department of Health and Human Services and is the primary Federal agency for conducting and supporting medical research. Non-allelic Homologous Recombination (NAHR) Crossing-over (recombination) that occurs between two sequencesimilar strands of DNA that are not alleles of the same gene (nonallelic). NAHR resulting in copy number variation of intervening sequences and is generally associated with larger CNV structures. NPY4R1 Neuropeptide 4 receptor 1; receptor for Neuropeptide Y, which is known to be involved in feeding behavior through orixigenic actions. NR3CI Nuclear receptor subfamily 1, group C1 glucocorticoid receptor; superfamily that comprises all eukaryotic transcription factors and includes receptors for steroids, vitamins, and numerous orphan receptors, which translate hormonal signals into transcription responses. Nucleosomes The nucleosome is formed of an octamer of histone proteins containing a tetramer composed of two H3-H4 dimers, which are flanked on either side with an H2A-H2B dimer.

Oligonucleotide microarray Microarrays consisting of probes that are pre-synthesized oligonucleotides ranging in size from 25- to 60-mers created by in situ synthesis (e.g., Affymetrix GeneChip) or deposition.

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Oncogene A gene whose products may in certain circumstances transform a cell containing them into a tumor cell. Ontology Defines a common vocabulary, classification, and shared understanding. Open source A program in which the source code is available to the general public for use and/or modification from its original design free of charge. Common sharing of improved programs is part of the features of an open source resource. Orthologous sequences Homologous sequences that originated due to speciation. For example, speciation events eventually yielded the human CFTR and mouse Cftr genes.

P4 medicine Predictive, personalized, preventive, and participatory medicine; a new domain for clinical medicine incorporating novel and multivariate diagnostic and therapeutic technologies derived from advanced systems-level study of individual patients, allowing physicians to predict and treat patientspecific pathological processes. P450 CYP “Cytochrome P450” (abbreviated CYP, P450, infrequently CYP450) is a diverse superfamily of hemoproteins found in bacteria, archaea, and eukaryotes. Cytochromes P450 use a plethora of both exogenous and endogenous compounds as substrates in enzymatic reactions. Paralogous sequences Homologous sequences that originated due to genome-duplication events in a given species. For example, genome-duplication events likely yielded the HOX gene cluster expansion in vertebrates. Passive demethylation Loss of methylated sites during the copying of DNA methylation onto the nascent strand of DNA due to absence, inhibition or blocking of DNMT1. Patent ductus arteriosus Abnormal persistence of the fetal ductus arteriosus connecting the main pulmonary artery and aorta. PCR Polymerase chain reaction; in vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.

OMIM “Online Mendelian Inheritance in Man” is a catalog of human genes and genetic disorders.

Penetrance The frequency, under given environmental conditions, with which a specific phenotype is expressed by those individuals with a specific genotype.

OMS/WHO Specialized agency of the United Nations designed as a coordinating authority on international health issues.

Peripheral blood eosinophilia Usually defined as an absolute count of 200 eosinophils/mm3. The eosinophil is a type

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of white blood cell increased in subjects with allergy or parasitic infection.

511 KeV photons traveling in opposite directions that may be detected by a PET scanner.

Personalized medicine The optimization of individuals’ health through the use of all available patient data, including data on a patient’s genome and its downstream products . By this definition, genomic medicine is a subset of personalized medicine.

Power (1-) It is the probability of being able to demonstrate a statistically significant difference between the samples if a true difference of the estimated size actually exists in a larger population.

PET Positron emission tomography; a three-dimensional imaging technique for visualizing radionuclides that decays by emitting positrons. The radionuclides are usually injected into a subject for visualizing biologic substances or processes.

PPAR Peroxisome proliferative-activated receptor; group of nuclear receptor isoforms that are transcription factors for different processes, some of them are related to metabolic pathways and adipogenesis.

Phagocytosis A mechanism by which single cells engulf and carry particles into the cytoplasm.

ppm Parts per million, used to measure chemical shifts in MR studies. For example, for the resonance frequency of 100 MHz, 10 ppm would mean 1000 Hz.

Pharmacogenetics The study of how genetic variation influences the response to drug therapy. Pharmacogenomics The study of the interaction of an individual’s genetic makeup and response to a drug. Phenocopy An individual organism, diseased organ or cell showing features which are characteristic of a genotype other than its own (especially of a particular abnormal mutant), and could be produced environmentally or genetically.

Precision One of two common measures of the effectiveness of Information retrieval. Precision is technically defined as the ratio of the number of retrieved documents that are relevant to the total number of retrieved documents.

Phenotype A variety of an organism distinguished by observable characteristics rather than underlying genetic features.

Principal components analysis A mathematical method to reduce a high dimensional dataset (e.g., many samples plotted using many gene measurements) to a lower dimensional dataset (e.g., many samples plotted using few combinations of gene measurements), where each of the lower dimensions captures a significant degree of the variation of the data.

Phosphatase Enzymes which bring about the hydrolysis of esters of phosphoric acid, opposite to a kinase.

Privacy The right to a personal sphere, free from public attention and interference.

Plasma membrane Layer that surrounds a cell.

Prospective cohort studies Identify individuals’ exposure and follow them through time. Different from case–control studies, where individuals are often identified for outcome and previous exposure traced.

Polymorphism Sequence DNA differences among individuals with potential impact on function. The concept includes differences in genotypes ranging in size from a single nucleotide site (SNP) to large nucleotide sequences visible at a chromosomal level. POMC Pro-opio melanocortin; anorectic precursor polypeptide with 241 amino acid residues. It is synthesized in different cells of the hypothalamus, pituitary, and brain stem, which regulates food intake. Population stratification The type of confounding which occurs when individuals are selected from populations with different allele frequencies in different proportions in cases and controls. Positional cloning or linkage disequilibrium mapping A technique used to identify genes, usually those that are associated with disease, based on their location on a chromosome. Positive predictive value Percent of patients with positive genetic test results (i.e., carrying abnormal genotypes) exhibiting a phenotype of interest. Positron A subatomic particle of equal mass but opposite charge of an electron that is emitted by the nucleus upon radioactive decay. After traveling a short distance, it undergoes an annihilation reaction upon contacting an electron resulting in two

Prospective health care A personalized, predictive, and preventative approach to health care. Proteome The proteins of an organism. Proteomics The analysis of the entire protein complement of a cell, tissue, or organism under a specific, defined set of conditions. PTEN “Phosphatase and tensin” homolog (mutated in multiple advanced cancers 1) acts as a tumor suppressor gene, which means that it helps regulate the cycle of cell division by keeping cells from growing and dividing too rapidly or in an uncontrolled way. Ptosis Drooping of the upper eye lid. Pulmonary atresia ventricular outflow.

Imperforate pulmonary valve and right

QA Quality assurance; activities that provides evidence that a product or service is of the quality that satisfies customer requirements. Common paradigm for QA management follows the PDCA (plan, do, check, act) cycle.

Glossary

QC Quality control; the tests designed to ensure products meet specifications. In a biobank, it refers to tests that monitors biobanking processes, for example, sample processing, preservation, data storage, equipment performance, and ensuring samples meet standards expected as defined by the quality management system and includes but not limited to monitoring supplies, equipment maintenance, and checks on sample and data integrity and purity. QMS Quality management system; a management system to direct and control an organization with regard to quality. QMS includes quality planning, implementation, control (QC), quality assurance (QA), quality improvement, and documentation.

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Reference sequence The sequence of particular interest in a multi-sequence alignment that is used as the basis of all comparisons (e.g., the human sequence in the case of alignments generated in human genetic studies). Typically, similarities with other sequences are annotated relative to positions of the reference sequence, thus facilitating downstream analyses. Reference standard (“gold standard”) The state of the art method or procedure that differentiates health from disease. Regurgitation Backflow leakage, as of the aortic or mitral valve.

QTL Quantitative trait loci; region of DNA that is associated with a particular trait (e.g., height). Though not necessarily genes themselves, QTLs are stretches of DNA that are closely linked to the genes that underlie the trait in question.

Reporter gene A gene that encodes for a product that can be assayed, for example, one that can be imaged directly or one that binds, alters, and/or entraps a substance that can be imaged.

Qualification studies The confirmation of differential abundance of the candidate protein in the human plasma.

Resistin Circulating hormone that plays apparently a role in insulin resistance.

Quality standards Criteria for quality that is widely recognized and employed. Quality Conformance of a specimen or process with preestablished specifications or standards. Query Typically a word, or short string of words, representing a question that a person seeks to answer to; the starting point for Health Information Search and Retrieval (HIS/HIR). Q-values The expected false discovery rate attributed to a specific hypothesis in a set of tested hypotheses, if the null hypothesis for the specific test – and all those at a more significant level – are all rejected.

Randomization Ensures that each individual of a population to be sampled has an equal chance of being selected.With proper randomization there is no bias, that is, on the average, the estimates of the population parameters will be accurate. Recall One of the two common measures of the effectiveness of Information retrieval. Recall is technically defined as the ratio of the number of retrieved documents that are relevant to the number of relevant documents in the corpus. Recessive inheritance (autosomal) Inheritance of a trait in which both copies of the gene must be altered or inactive to express the trait. Reference materials Serum- or plasma-based materials in which the concentration of the measurand of interest is determined by the reference method. Reference method A thoroughly investigated method, in which exact and clear descriptions of the necessary conditions and procedures are given for the accurate determination of a measurand; the results of the reference method are traceable to those of the definitive method. Primary reference materials are used in the development and validation of the reference method.

Resequencing Looking for novel sequence variation.

Reverse Medical Dictionary Collection of common words and phrases used by lay persons mapped to their professional synonyms or counterparts. RF Radiofrequency, electromagnetic radiation typically in the range of 10–1000 MHz. RFLP Restriction fragment length polymorphism; individual differences in the fragmentation pattern of DNA after treatment with a restriction enzyme, due to fine details of genetic coding. Ribozyme Any RNA molecule capable of acting as an enzyme, often used in the context of cleavage of RNA. RXRG Retinoid X receptor gamma; subfamily of the steroid/thyroid nuclear receptor superfamily. There are three RXR genes: alpha, beta, and gamma, each with a distinct expression pattern and chromosomal location that have been associated to energy metabolism.

Sample It is a subset of the entire population. The sample elements – the experimental units – are drawn randomly from the entire population and the sample size N should be sufficiently large in order to be representative. SCD1 Stearyl CoA desaturase; an enzyme that catalyzes the formation of oleoyl-CoA and water from stearoyl-CoA, with potential participation in lipid metabolism related to obesity. Segmental duplication A DNA segment that is 1 kb in length (or longer) that occurs in two or more copies in a haploid genome, with different copies sharing more than 90% sequence similarity. Segmental duplication may or may not be CNVs, depending on whether their diploid copy number varies between individuals. Semantic Referring to the meanings assigned to words and symbols, or sets of words or symbols, in a language.

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SIN 1 Single Minded 1; a murine member of the family helixloop-helix/Per-Arnt-Sim including dioxin receptor (DR) that behave as transcriptional repressor and that have been ascribed as a regulating signal in energy homeostasis. siRNA Small interfering RNA or short nucleotide sequences that block the expression of specific messenger RNA (mRNA) in terms of protein synthesis. Skewed-X-inactivation Skewing from the normal random X inaction process in which half of all X chromosomes in a female body are turned off, usually resulting in a female using the X chromosome from her mother in half of her cells and that of her father in the other half. SNOMED Systematized Nomenclature of Medicine, a widely used, multi-axial diagnostic coding scheme originally developed by pathologists. Some of the axes include topography, morphology, etiology, and function. SNP Single nucleotide polymorphism; substitution of a nucleotide base along a DNA sequence occurring at a frequency of at least 1% in the population. SNPs comprise 90–95% of DNA variant sites. SNR

Signal-to-noise ratio used to evaluate quality of images.

Spasticity Involuntary muscle tightness, usually leading to stiffness and rigidity. Specimen Specifies a tissue, urine or blood sample. SPECT Single photon emission computed tomography; a three-dimensional imaging technique for visualizing radionuclides that decay by emitting gamma rays. The radionuclides are usually injected into a subject for visualizing biologic substances or processes. SPIO Superparamagnetic iron-oxide nanoparticles, MRI contrast agent that provides very efficient shortening of MR relaxation times.

retrieval. Some examples include the simple prepositions “the,” “a,” “and,” etc. When a text parser encounters one, it stops and eliminates them from further processing. Stratification A population made up of several subpopulations, with restricted gene flow between them. Structural genomic variant Balanced and unbalanced genetic variation involving DNA segments of , 1 kb and larger. Unbalanced structural genomic variants are referred to as CNVs. Balanced structural genomic variants can include inversions, translocations, and insertions. This term refers to segmental chromosomal aberrations and therefore excludes whole chromosomal imbalances or rearrangements. Supervised machine learning algorithm Analysis method that takes training data, with input (e.g., gene expression microarray measurements) and desired output (e.g., correct diagnosis), and can apply induced generalizations to unknown test data (e.g., new microarray measurements). Support vector machine A type of supervised machine learning algorithm used to find a classifier between two groups (e.g., types of disease), where the classifier can be a non-linear combination of features (e.g., genes). Surface plasmon resonance imaging of antibody arrays A high-throughput proteomic technique in which arrays coated with numerous antibodies to blood-borne or intracellular peptides are scanned optically to determine the concentration of immobilized proteins; used for biomarker discovery and serum protein profiling. Surrogate endpoint It is a biomarker that is intended to substitute for a clinical endpoint. It is expected to correlate strongly with clinical outcome requiring shorter study duration than clinical endpoint studies. Synapse The space between two neurons across which messages are transferred from one neuron to another.

SSCP Single strand conformation polymorphism; method to detect mutations (sequence changes) in DNA by shifts in electrophoretic mobility of DNA (under non-denaturing conditions) due to conformational changes induced by the mutation.

Synthetic lethal A genetic phenomenon in which two non-lethal mutations yield a lethal phenotype when combined, or were non-lethal mutation sensitize a cell to a compound that without the mutation is non-lethal.

Standard operating procedures (SOP) manual Quality documents of written standard procedures in a biobank for biobank personnels to adhere to for achieving uniformity of performance.

Systems biology The study of biology as an informational science, considering the transmission, modulation, and integration of information within interacting biological networks throughout an organism. Defining features of contemporary systems biology include: (1) measurements that are as global as possible; (2) different levels of information (DNA, RNA, protein, etc.) are integrated to capture biological responses based on the digital information of the genome interacting with the environment; (3) all biological systems (e.g., networks) must be studied dynamically as they capture, transmit, integrate, and utilize biological information; (4) all measurements must be quantitatively determined to the greatest extent possible; and (5) the global and dynamic data from the variety of information hierarchies must be integrated and modeled.

Stem cell A cell from which other types of cells can develop. Stenosis

Narrowing, as of the aortic or pulmonary valve.

STK11 gene “Serine/threonine kinase 11” is a tumor suppressor gene, which means that it regulates the cycle of cell division by keeping cells from growing and dividing too fast or in an uncontrolled way. Stop word Word in a text that are low in semantic content and are generally not useful as part of a query for information

Glossary

T1, T2, T2* MR images.

MR relaxation times that determine contrast in

Tricuspid atresia ventricular inflow.

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Imperforate tricuspid valve and right

Tagging The process of picking out a special set of SNPs that still largely represents all the common variation in a given genomic region.

Truncus arteriosus Complex cardiac malformation arising from failed septation of the embryonic arterial outflow (truncus) into separate pulmonary and aortic roots.

Tagging SNP An SNP that can represent other associated SNPs in the region. These associated SNPs are in high linkage disequilibrium with the tagging SNP.

TUB Tubby candidate gene; functional candidate gene with potential relation with the obesity onset.

Tandem mass spectrometry An analytical technique in which the mass-to-charge ratios of ions after they have been fragmented are measured in order to determine their structural formula. TBB Transcriptomic-based biomarker; is a more specific term for a molecular signature comprising RNA transcripts. TDT Transmission disequilibrium test; a test for the role of a polymorphism in a disorder by evaluating its frequency in affected versus non-affected siblings. Tetralogy of Fallot Most prevalent cyanotic (blue) congenital heart defect comprising pulmonary outflow obstruction, a ventricular septal defect, overriding (rightward malposition) of the aortic valve, and right ventricular hypertrophy. Tg Transgenic animal; the term transgenic animal refers to an animal in which there has been a deliberate modification of the genome – the material responsible for inherited characteristics – in contrast to spontaneous mutation. Therapeutic index The ratio between the toxic dose and the therapeutic dose of a drug, used as a measure of the relative safety of the drug for a particular treatment. Throughput The amount of data transmitted through a communication channel in a given time. TISSUE A collection of interconnected cells including extracellular and intercellular components. TNF Tumor necrosis factor; it is an important cytokine involved in systemic inflammation and the acute phase response, which could apparently play a role in energy metabolism. Total Serum IgE A marker of immediate-type hypersensitivity or allergy. IgE is a type of immunoglobulin produced by B lymphocytes after stimulation with certain cytokines. trans-Activation Activation of a gene at one locus by the presence of a particular gene at another locus. Transcriptome Entire complement of transcripts in a cell or tissue at a given time. Transcripts are complementary copies of genes, which can be explored by transcriptomics. Given the fact that every gene (as conventionally defined) has to be transcribed into RNA before becoming effective, the transcriptome directly reflects the activity of genes.

Type I error () It is the risk of concluding that conditions or treatments differ when, in fact, they are the same. The  level is usually set at 5%. Type II error () It is the risk of erroneously concluding that the conditions or treatments are not significantly different when, in fact, a difference of a given size or greater exists. Commonly chosen values of  are between 5% and 20%.

UCP1 Uncoupling protein 1;uncoupling proteins are mitochondrial transporters present in the inner membrane of mitochondria. UCP1 acts as a proton carrier activated by free fatty acids and creates a shunt between complexes of the respiratory chain and ATP synthase. Activation of UCP1 enhances respiration, and the uncoupling process results in a futile cycle and dissipation of oxidation energy as heat. UCP2 Uncoupling protein 2; is ubiquitous and highly expressed in the lymphoid system, macrophages, and pancreatic islets that are apparently related with heat loss. UCP3 Uncoupling protein 3 mitochondrial, proton carrier; is mainly expressed in skeletal muscles and it is also related to energy metabolism efficiency. UMLS Unified Medical Language System is a terminology systems developed by the US National Library of Medicine to produce a common structure that integrates vocabularies that are used in various biomedical knowledge domains. The principal component of UMLS is the Metathesaurus. Unsupervised machine learning algorithm Analysis method that induces a model or pattern (e.g., set of clusters) from input data (e.g., genes measured across many samples).

Validation To confirm by establishing the truth of a said matter. This may include but not limit to accuracy and reproducibility of certain scientific findings. VDR Vitamin D 1,25-dihydroxyvitamin D3 receptor; proteins, usually found in the cytoplasm, that specifically bind calcitriol, migrate to the nucleus, and regulate transcription of specific segments of DNA, which have been also related to weight control.

Transfection The introduction of free nucleic acid into a cell.

Ventricular septal defect the right and left ventricles.

Translational research The process of linking basic research to therapies and patient care.

Verification The assessment of the specificity of the candidate protein.

Opening in the wall dividing

1406

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Glossary

Vertical search A search that is “deep” rather than “wide.” It bypasses or excludes unrelated data by design and usually involves online resources that are pre-selected for their relevance to particular target audiences. A special instance of this is a design that includes resource descriptions and structured metadata with the intent to improve recall and precision. Viral DNA chip Microarray that holds viral DNA/oligonucleotide probes. VLDL Very low-density lipoprotein; major triglyceridecontaining lipoprotein when fasting triglycerides are 1000 mgm/dl, made by the liver. VLDLR VLD lipoprotein receptor; the low-density lipoprotein receptor gene family is a growing group of endocytic receptors that bind and internalize various ligands, which include apolipoprotein E (apoE)/lipoproteins and several proteins that are involved in coagulation and hemostasis and putatively in weight gain control.

WD40 A protein binding motif that contain 7 regions 40 AA long containing a conserved W & D.

X-linked recessive inheritance inheritance of a trait for which the gene is on the X chromosome, conditions are almost exclusively seen in males.

Yeast two-hybrid system A method for screening protein– protein interactions in which a yeast transcription factor is divided and fused into two separate target proteins, such that a downstream reporter gene is activated only when the target proteins interact avidly to bring the entire transcription factor in proximity to the upstream activating sequence.

Index Page numbers in bold refer to Volume 1; page numbers followed by f indicate a figure; page numbers followed by t indicate tabular material.

A Abacavir hypersensitivity reactions, 352 genetic basis, 372, 415 Abatacept, rheumatoid arthritis, 1024 ABCA1, 641–642 blood lipid associations, 644 common gene variants, 641 deficiency (Tangier disease), 641 high-density-lipoprotein metabolism, 636 therapeutic targeting, 647 ABCB1 antiepileptic drug pharmacogenomics, 1249 lapatinib pharmacogenomics, 350 methylation status, tumor treatment response prediction, 138 ABCG2 (BCRP), lapatinib pharmacogenomics, 350 ABCG5 mutations, sitosterolemia, 638–639 ABCG8 mutations, sitosterolemia, 639 Abdominal aortic aneurysm, 483 Abducens nerve disorders, 1257 Abetalipoproteinemia, 639, 647 Absolute risk, 463 ABT-737, 198 Academy of Managed Care Pharmacy (AMCP), 426, 429, 431 ACCE framework, 450 ACCENT I, 1076 ACCESS (A Case Control Etiologic Study of Sarcoidosis), 1112 Access to genetic information, 396 database policies, 392

Access to research results by participants, 391–392 Accuracy, 277 biomarkers diagnostic performance, 313–316, 314f protein immunoassay, 309–310 as surrogate endpoints, 300 experimental design for improvement, 277–280, 281 Acetaldehyde dehydrogenase alcoholic liver disease, 1147 polymorphism-related cancer risk, 1212–1213 Acetylcholine, neuromuscular junction, 1266, 1268 Acetylcholine receptors, 1268 antibodies, myasthenia gravis, 1268, 1273, 1274 mutations, congenital myasthenic syndrome, 1272 Acetylcholinesterase antisense oligonucleotides, 1276 Acetylcholinesterase inhibitors Alzheimer’s disease, 348 myasthenia gravis, 1276 N-Acetylcysteine, chronic obstructive pulmonary disease, 1106 N-Acetyltransferase 2 (NAT2) polymorphism bladder cancer susceptibility, 54 isoniazid phamacogenomics, 383 N-Acetyltransferases carcinogen metabolism, 1213 cigarette smoke, 857

colorectal cancer risk, 886, 1213–1214 Aciclovir, 1342 Acousto-optic tunable filters, 529 ACP1, obesity, 1176 ACTC mutations, hypertrophic cardiomyopathy, 718 Actin hypertrophic cardiomyopathy, 718 multiple sclerosis biomarker, 1036 Actinin, podocyte expression, 1058 Activated protein C, sepsis therapy, 1367, 1369, 1371 Activity-based protein profiling, 985 ACTN2 mutations (alpha-actinin-2), hypertrophic cardiomyopathy, 718, 721 Acute anterior uveitis, 1067 genetic factors, 1069 HLA-B27, 1068, 1069 spondyloarthropathies association, 1067, 1068, 1070 Acute coronary syndromes, 680–687 biochemical risk stratification markers, 685–686, 686t diagnosis, 682–685, 682f metabolomics, 683–684, 684f protein biomarkers, 682–683, 683t proteomics, 684–685, 684f, 685f leukotriene inhibitor therapy, 674–675, 687 monitoring, 687 pharmacogenomics, 686–687 risk factors see Cardiovascular risk screening, 681 1407

1408

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Index

Acute lymphoblastic leukemia L-asparaginase treatment, 848–849 corticosteroid-resistant, 167 sirolimus response study, 211 cytogenetics, 844, 849 gene expression profiling, 160, 162, 208, 208f, 847 drug response studies, 211, 849, 850 prognostically important subtypes, 268, 847 6-mercaptopurine response, TPMT pharmacogenetics, 331 treatment, 846 Acute myeloid leukemia copy number variation (CNVs), 851, 852f cytogenetics, 844, 849 Flt-3 targeted therapy, 1001 gemtuzumab ozogamicin therapy, 998 gene expression profiling, 162, 208, 208f, 268, 847, 848f, 851, 852f drug response prediction, 850 following intensive chemotherapy, 199 genomic data reporting, 234 MOZ–CBP fusion protein, 64 prognostic factors, 846 molecular signature analysis, 151 subtypes, 268, 844 Acute promyelocytic leukemia all-trans retinoic acid treatment, 845, 994, 1001 pharmacogenomics, 847 Acute stress ulcers, 1122 Acylcarnitines tandem mass spectrometry, medium-chain acyl-CoA dehydrogenase deficiency newborn screening, 475–476 targeted metabolic profiling, 185 insulin resistance, 187, 188f Acylcoenzyme A, targeted metabolic profiling, 185 Acylcoenzyme A oxidase (ACOX1) Crohn’s disease, 1074 spondyloarthropathies, 1074 ADA, obesity, 1176 Adalimumab, rheumatoid arthritis, 1024 ADAM22, multiple sclerosis, 1033 ADAM33 asthma, 1087 chronic obstructive pulmonary disease, 1102 ADAMTS10, multiple sclerosis, 1033 ADBR2, obesity, 1176, 1178 ADBR3, obesity, 1176, 1178, 1180–1181 Adducin, antihypertensive agent pharmacogenomics, 630, 631 Adefovir, hepatitis B treatment, 1385 Adeno-associated virus vectors, 612–613 Adenomatous polyposis coli, 810 see also APC; Familial adenomatous polyposis Adenomatous polyps, 880, 883

adenoma to carcinoma sequence, 880–882, 881f APC mutation-negative, 883 criteria for genetic testing, 888 familial adenomatous polyposis, 883 family history, 886 see also Colorectal adenoma Adenosine, aptamer biosensors, 595 Adenosine deaminase deficiency, gene therapy, 610, 616 S-Adenosyl methionine synthetase, alcoholic liver disease, 1147 S-Adenosyl-L-methionine (SAM; AdoMet), 62, 64 Adenovirus, 612 antiviral agents, 1344 host immune response, 539, 612 vectors, 612 cancer vaccines, 582 glomerular disorders, 1063 obesity, 1183 ADH see Alcohol dehydrogenase Adhesins, Helicobacter pylori, 1128–1129 Adipocyte metabolism fatty acids, 635 genetic factors, 1178 Adiponectin, 1196 Adipose tissue, lipoprotein metabolism, 635 Adjuvant Online!, 383 Adjuvants, cancer vaccines, 582–583 Admixed populations, 23, 237 association test artifacts, 38 Mexican Genome Project, 390 Admixture mapping, 11, 28 multiple sclerosis, 1033 ADRB2 polymorphism asthma, 1085, 1087 β2-agonist pharmacogenomics, 1092 see also β2 adrenergic receptor Adrenocorticotropic hormone 4–10 (ACTH4-10), obesity management, 1181–1182 Adrenocorticotropin (ACTH), depression, 1291 Adult T-cell leukemia/lymphoma, 830 Advanced glycation end products (AGEs), 758 Adverse drug reactions, 321, 343, 344, 347, 1286 antiepileptic agents, 1249–1250, 1251 antihypertensive agents, 631 drug development process, 421 pharmacogenetics applications, 371, 415 preventve testing, 421 Affymetrix, 435, 438, 440 Affymetrix HU6800 oligonucleotide array, 833, 834 lymphoma, 833 Affymetrix Mapping 500K array, 103, 105, 114, 1305, 1306

Affymetrix microarrays, 28, 104, 143, 144, 157, 227, 441, 833, 1188 Burkitt lymphoma, 839 colorectal cancer, 890 comparative aspects, 105 copy number variation (CNVs) detection, 105, 114 diffuse large B-cell lymphoma subtype differentiation, 833, 834 DNA processing steps, 104, 104f drug response biomarker studies, 346 fabrication, 545 fibroblast gene expression profiling, 162 follicular lymphoma, 837 GeneChip, 104, 104f, 105, 144, 158, 545, 549 gliomas, 958–959 hepatic stellate cells, 1146 Hodgkin lymphoma, 836 normalization, 146 primary mediastinal large B-cell lymphoma, 835 probe length, 105 quality control, 145 rapid single nucleotide polymorphism (SNP) genotying, 34 SARS resequencing, 549 schizophrenia gene discovery in Portuguese population, 1305, 1306 tiling arrays, 166 transcriptomic analysis in complex disease, 40 viral gene expression detection, 550 whole genome amplified DNA, 106 Affymetrix U95 microarray, 833, 836 Affymetrix U133 microarray, 890 Affymetrix U133 Plus 2.0 GeneChip Array, 144 Affymetrix U133A  B, 833, 835, 837, 839 African Americans heart failure BiDil treatment, 321, 698 hypertension relationship, 693 hemostatic factor/endothelial marker levels, 761, 761t linkage disequilibrium studies, 28 lung cancer, 857, 858 myocardial infarction risk, 671 LTA4H HapK haplotype association, 681 PCSK9 nonsense/missense variants, 671, 672f peripheral arterial disease risk, 775 population genomics, 23, 28 prostate cancer risk, 899 stroke risk, 760 thrombosis susceptibility genes, 762 venous thrombosis risk, 760 African human origins, 26–27 African-American Heart Failure (A-HeFT), 698

Index

AFT-5, Hodgkin lymphoma/Reed-Sternberg cells, 836 Age associations fibroblast gene expression profiling, 162 hypertension, 625, 627t peripheral arterial disease, 773 sporadic colorectal cancer, 882 see also Aging process Age-related macular degeneration see Macular degeneration Aggressiveness, 5-HTT polymorphism association, 1283 Agilent microarrays, 157, 441 oligonucleotide arrays, 113 Aging process gene expression profiles, 162 liver, 1150 see also Age associations AGR2, ovarian cancer, 916 AGRP (agouti-related protein) body weight regulation, 1171 obesity, 1178 RNA interference, 1183 AHEAD, 134t AIB1 (SRC3) melanoma metastases, 968 ovarian cancer metastasis, 918 Air pollution biomarkers of exposure, 303 chronic obstructive pulmonary disease, 1098 Ajoene, melanoma treatment, 971 AKT colorectal cancer, 881 melanoma therapeutic targeting, 971 ovarian cancer, 916, 919 see also P13 kinase/Akt pathway Akt3 interference RNA, melanoma treatment, 971 Akureyri disease, 1340 Alagille syndrome (JAG1/NOTCH2), 785f congenital heart disease, 784 Albumin cobalt binding (ACB) test, 683 Albumin, sarcoid bronchoalveolar fluid, 1114 Albuterol, asthma, 1091 Alcohol consumption cancer risk, 1212–1213 colorectal, 886, 887 head and neck, 945 folate intake effects, 1213 low-density lipoprotein cholesterol, 1208 Alcohol dehydrogenase (ADH) alcoholic liver disease, 1147 mouse model, 1148 polymorphism (ADH1C), cancer risk, 1212–1213 Alcoholic liver disease proteomics, 1149–1150 transcriptomics, 1146–1147

Alcoholism, 33 secondary dementia, 1222 Alcoholomics, 1150 Aldehyde dehydrogenase, alcoholic liver disease, 1147, 1149–1150 ALDH2 polymorphism, 360 Alemtuzumab, 998 chronic lymphocytic leukemia, 998 Alicaforsen, inflammatory bowel disease, 1048–1049 Alignment of sequences, local/global methods, 123 Alignment tools, 121 Alkaline lysis plasmid isolation, 90 Alkaptonuria, 1 Alkylating agents, DNA methylation profiles in tumor response prediction, 138 All-trans retinoic acid, acute promyelocytic leukemia, 845, 847, 994, 999, 1001 Allele-specific primer extension (ASPE), 103 Allelic imbalance, 12 Allergen hypersensitivity, asthma, 1085 AlloMap test (cardiac allograft rejection) clinical use, 712–713 corticosteroid dose effects, 711, 712f current/future research, 713 cytomegalovirus infection effects, 711 development, 709 endomyocardial biopsy “gold standard” relationship, 710–711 pathways monitored, 710, 710t post-transplant time effects, 711 rejection predicition, 712 Allopurinol side effects, HLA B*5801 genotype, 372 Alosetron, 344 ALOX5, leukotriene inhibitor pharmacogenomics, 1092 ALOX5AP HapA/HapB haplotypes myocardial infarction risk, 671, 674 therapeutic inhibition, 674–675 AlpA/B, Helicobacter pylori adhesins, 1129 α4-integrins, inflammatory bowel disease therapeutic targets, 1048 Alpha enolase, hepatocellular carcinoma, 1384 Alpha myosin heavy chain (MYH6) mutations, hypertrophic cardiomyopathy, 718 α-1 antitrypsin hepatitis B infection, 1149, 1381 sarcoid bronchoalveolar fluid, 1114 α-1 antitrypsin deficiency, 48 chronic obstructive pulmonary disease, 1099 gene therapy, 615 α-2 adrenoceptors, antihypertensive agent pharmacogenomics, 630 Alpha-2 laminin, psychiatric disease associations, 1306 Alpha-actinin-2 (ACTN2) mutations, hypertrophic cardiomyopathy, 718, 721

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1409

α-methylmalonyl co-A racemase, prostate cancer urine biomarker, 902 Alpha-satellite DNA, 8 α-synuclein gene mutations diffuse Lewy body dementia/Parkinson disease with dementia, 1227 Parkinson’s disease (SNCA; PARK1; PARK4), 1235 Lewy bodies, 1235 Alpha-tropomyosin (TPM1), hypertrophic cardiomyopathy, 718, 721, 722 αβ crystallin gene mutations autosomal dominant congenital lamellar cataract, 1259 dilated cardiomyopathy, 1259 protein aggregation cardiomyopathy (PAC; desmin-related myopathy), 700–701 Alphaviruses, viral chip technology, 551 Alport syndrome, 1062 gene therapy, 1063 ALS2 (ALSin), familial amyotrophic lateral sclerosis, 1272 ALS4 (Sentaxin), familial amyotrophic lateral sclerosis, 1272 AlzGene, 1226 Alzheimer’s disease, 50, 568, 1222, 1223, 1224–1226, 1229 acetylcholinesterase inhibitor treatment, 348 APOE4 association, 38, 348, 357, 648, 798, 1224–1225, 1226, 1229 neuroimaging, 1285 biomarker applications, 300 chemical genomic approaches, 197–198 early-onset, 1224 amyloid precursor protein processing abnormalities, 1224, 1225f gene mutations, 1224, 1284 gene therapy, 614 information database, 227 inheritance patterns, 1224 late onset, 1224–1226, 1229 candidate genes, 1226 genetic factors, 1224 pathology, 1224, 1224f rosiglitazone treatment, pharmacogenomics, 347, 348, 349f AMACR, gene expression in prostate cancer, 904 Amantadine, 1344 Ambrisentan, systemic sclerosis, 1165 American Indians lung cancer, 857 venous thrombosis risk, 760 American Society of Testing and Materials International Continuity of Care Record, 487 Americans with Disabilities Act, 365 Amino acids, targeted metabolic profiling, 185 Amiodarone hepatotoxicity, 1148

1410

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Index

AML1-ETO fusions gene, acute myeloid leukemia, 844 Amoxicillin, Helicobacter pylori eradication, 1123, 1131 AMPD1, obesity, 1176 AmpliChip Cytochrome P450 test, 331, 360, 361, 371, 441, 1286 AmpliTaq Gold, 93 Amygdala reactivity neurobiology, 533 serotonin transporter (5-HTT) gene polymorphism, 535–536 Amyloid precursor protein α-/β-/γ-secretase cleavage, 1224, 1225f candidate gene studies, 1226 Alzheimer’s disease Aβ42 fragment toxicity, 1224 APP mutations, 1224, 1229, 1284 processing abnormalities, 1224, 1225f Amyloidosis, interstitial lung disease, 1110 Amyotrophic lateral sclerosis (Lou Gehrig’s disease), 1265, 1272–1273 application of genomics/proteomics, 1277–1278 diagnosis, 1274 protein biomarkers, 1278 environmental factors, 1266, 1273 familial, 1266, 1272 causative/susceptibility genes, 1272–1273, 1277 superoxide dismutase 1 (SOD1) mutations, 1266, 1268, 1272, 1277 frontotemporal dementia association, 1226 gene therapy, 615, 1276 monitoring, 1275 prognosis, 1274–1275 sporadic, 1266, 1272 whole-genome analysis, 1277–1278 treatment, 1276 Analysis of variance, 213 Analyte-specific reagents (ASRs), 361 regulatory issues, 417, 418 Anastrozole, 992 Ancestry informative markers (AIMs), 11 Ancestry testing, 11 Andersen syndrome see Andersen-Tawil syndrome Andersen-Tawil syndrome, 731, 732, 737 clinical features, 737 genetics, 732, 737 LQT7 (Kir 2.1; KCNJ2), 737 23andMe Inc., 442 Androgen receptor prostate cancer, 906 circulating tumor cells, 903 hormone-refractory disease, 907 signaling inhibition, 198 transcriptional targets, 906–907 Androgen response elements (AREs), 906 Androgens

ablation therapy, 907 prostate cancer dependence, 906 Anemia, chronic with heart failure, 693 Aneuploidy, 6 Angiogenesis multiphoton in vivo microscopy, 526 therapeutic targets head and neck cancer, 952 melanoma, 972 ovarian cancer, 918–919 peripheral arterial disease, 775 Angiogenic factors, tumor microenvironment, 820 Angiopoietins inflammatory synovitis, 1073 ovarian cancer angiogenesis, 918 Angiotensin I (AT1) receptor variants, hypertension-related stroke, 629 Angiotensin II receptor blockers cirrhosis, 1142 hypertrophic cardiomyopathy, 723 pharmacogenomics, 630 Angiotensin II, therapeutic targeting in glomerular disease, 1063 Angiotensin-converting enzyme (ACE) inhibitors adverse drug reactions, 631 cirrhosis, 1142 endothelial reactive oxygen species effects, 655 hypertrophic cardiomyopathy, 723 pharmacogenomics, 630 heart failure, 698 systemic sclerosis, 1164 Angiotensin-converting enzyme (ACE) polymorphism ACE inhibitor-related cough, 631 depression, 1295 heart failure treatment response, 698 host response to Neisseria meningitidis, 1355 metabolic syndrome, 1197 obesity, 1179 sarcoidosis, 1112 systemic sclerosis, 1158 ANGPTL4 gene variants, 641, 642, 643 Aniridia, PAX6 mutation, 1256 Ankle brachial index (ABI), 774, 775 Ankryn-β (LQT4; Ankβ), 732, 735 Ankylosing spondylitis, 1067 anti-Saccharomyces cerevisiae antibodies (ASCA), 1072 classification, 1069 clinical features, 1068 bowel inflammation, 1071 extra-articular manifestations, 1069, 1070, 1071 eternacept treatment, 1076 gene expression profiles, 1074 genetic factors, 1069 HLA-B27, 1068, 1069

HLA-B60, 1069 HLA-DR1, 1069 infliximab treatment, 1074 Annexin A2, alcoholic liver disease, 1147 Annexin-V, cancer treatment efficacy evaluation, 497 Anopheles gambiae genome sequencing, 439 Anterior chamber, 1256 Antiangiogenic agents melanoma, 972 small molecules, 1002 Antibiotics chronic obstructive pulmonary disease, 1105–1106 drug-induced long QT syndromes, 731 Antibodies cancer immune response, 573 effector functions, 573–574 microarrays, surface plasmon resonance imaging (SPRI), 79–80 viral infection immune response, 539 Antibody-dependent cell-mediated cytotoxicity (ADCC), 573 Anti-cancer agents antibody-based, 995–999, 996t COX-2 inhibitor combined therapy, 820 drug metabolizing enzyme measurement, 360 molecular imaging in development, 497 small molecule-based, 999–1002, 1000t susceptibility bioinformatics applications, 209–210 DNA methylation markers, 138 gene microarray measurements relationship, 209–210, 210f pharmacogenomics, 849 Anticardiolipin antibodies, thrombotic event prediction, 763, 763f, 764f Anti-centromere antibodies, systemic sclerosis, 1158 Antidepressants cytochrome P450 metabolism, 1293, 1294t long-term effects, 1295 pharmacogenomics, 1286, 1293–1294, 1294t dosage recommendations, 349 Antiepileptic drugs adverse drug reactions, 1249–1250, 1251 dosing, 1250–1251 efficacy, 1249, 1251 pharmacogenomics, 1249–1251, 1250f, 1251t prospective study design, 1251–1252 Antigen arrays, antibody repertoire investigation in multiple sclerosis, 1036 Antigen presentation, cancer vaccines nucleic acid-based, 579, 580 peptide T-cell epitopes, 577, 578 proteins, 578 Antihistamines, drug-induced long QT syndromes, 731 Anti-hormonal therapies, DNA methylation profiles in tumor response prediction, 138

Index

Antihypertensive agents adverse drug reactions, 631 pharmacogenomics, 630–631 Antimalarials, rheumatoid arthritis, 1024 Anti-nuclear antibodies, systemic sclerosis, 1158 Antioxidants asthma response, 1013 cardioprotection, 658 Antiplatelet agents peripheral arterial disease, 774 pharmacogenomics, 674 Antipsychotic agents, pharmacogenomics, 1286 Antiretroviral drugs, 1326 Anti-Saccharomyces cerevisiae antibodies (ASCA) ankylosing spondylitis, 1072 inflammatory bowel disease, 1043, 1044, 1045, 1046 Crohn’s disease, 1072 Anti-Scl70 antibodies, systemic sclerosis, 1158 Antisense oligonucleotides, positron emission tomography (PET), 504, 504f Antisocial personality, 55 Antithrombin III, 755 racial/ethnic variation, 760, 762 sepsis pathogenesis, 1367 Antithrombin III deficiency, 762 Antithrombotic therapy, pharmacogenomics, 768 see also Warfarin Anti-thymocyte globulin, systemic sclerosis, 1164 Anti-U1RNP antibodies, Sharp syndrome diagnosis, 1159 Antiviral agents, 1340–1345 hepatitis B, 1343 hepatitis C, 1343 influenza, 1344 parainfluenza, 1344 respiratory syncytial virus, 1344 Antral G cells gastric acid secretion regulation, 1123 hyperplasia, peptic ulceration, 1122 ANXA2, glomerular disorders, 1059 Anxiety biomarkers imaging approaches, 532 depression comorbidity, 1290 5-HTT polymorphism association, 1283 Aortic dissection, Marfan syndrome, 784 AP-1, reactive oxygen species activation, 657–658, 657f APACHE II score, 1369 APC (adenomatous polyposis coli), 810 mutations colorectal cancer, 136, 810, 880, 882, 888 familial adenomatous polyposis, 883 promoter hypermethylation in lung cancer, 858 API-2, melanoma therapeutic targets, 971 APOA1 (apolipoprotein A-1) colorectal cancer, 985

gene–nutrient interactions, 1208–1210 hepatitis B infection proteomics, 1381 APOA4 (apolipoprotein A-1 V), hepatitis B infection proteomics, 1381 APOA5 (apolipoprotein A-V) blood lipid genome-wide association studies, 644 gene–nutrient interactions, 1210 variants, 641, 642, 643 ApoA-I, 634, 636 deficiency/structural mutation, high-density lipoprotein cholesterol reduction, 639, 641 cardiovascular risk, 644 hepatitis B, 1149, 1150, 1381 ApoA-IMilano, 639, 641 ApoA-II, 634 ApoA-V deficiency, 642 APOB mutation, familial hypobetalipoproteinemia, 639 variants, 642, 643, 644 low-density lipoprotein C interindividual variation, 639 obesity, 1182 peripheral arterial disease, 775–776 ApoB, 634–635, 636, 758 levels, APOE allele effects, 1208 therapeutic targeting, 646 antisense oligonucleotides, 646–647 ApoB-48, 635 ApoB-100, 635 mutation (familial defective apolipoprotein B-100), 637 ApoC-I, 635 ApoC-II, 635 mutation, familial chylomicronemia syndrome, 642, 644 ApoC-III, 635 metabolic syndrome, 1197, 1199 ApoE, 635 amyotrophic lateral sclerosis, 1273 atherosclerosis, 654, 655 lipoprotein metabolism, 1208 APOE (apolipoprotein E), 36, 91, 568 blood lipid genome-wide association studies, 644 diet interactions, 1208 E2 allele, 36, 38 E4 allele see APOE4 mutations, hyperlipoproteinemia type III (familial dysbetalipoproteinemia), 642, 648 post-cardiac surgery neurocognitive dysfunction, 798–799 thrombotic event prediction with anticardiolipin antibodies, 764 variants LDL cholesterol interindividual variation, 639

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1411

LDL cholesterol response to diet intervention, 1208 peripheral arterial disease, 775–776 ApoE2, 642, 648, 1208 amyotrophic lateral sclerosis, 1273 psychomotor development associations, 799 ApoE3, 642, 1208 APOE4, 38, 55, 357, 648, 1208 Alzheimer’s disease association, 38, 348, 798, 1224–1225, 1226, 1229 neuroimaging, 1285 amyotrophic lateral sclerosis, 1273 diffuse Lewy body dementia, 1228 low-density lipoprotein cholesterol (LDLC) level influence, 1208 post-cardiac surgery neurocognitive dysfunction, 799 Apolipoprotein(a), 635 Apolipoproteins, 634 Apoptosis cardiomyocytes in heart failure, 695–696, 697f kinases/phosphatases identification with RNA interference, 196 APP mutations, Alzheimer’s disease, 1224, 1229, 1284 Appetite regulation, genetic factors, 1178 Applera, 438, 440 Applied Biosystems (ABI), 434, 438 Aptamer biosensors, 594–595, 595f nanoparticle conjugates, 596, 596f Apurinic/apyrimidinic endonuclease (APE1/ APEX1), cancer susceptibility, 305 AR Chip Epoxy, 544 AR methylation status, tumor treatment response prediction, 138 Arachidonate 5-lipoxygenase-activating protein see ALOX5AP ARACNe, 713 Arden Syntax, 249 ARF, protate cancer methylation markers, 138 Argonaute (Ago) protein, RNA interference pathway, 194 ARH mutations, autosomal recessive hypercholesterolemia, 637 Arimoclomol, amyotrophic lateral sclerosis, 1276 Aromatic amine carcinogen metabolism, N-acetyltransferase (NAT) polymorphismrelated colon cancer risk, 1213–1214 Array comparative genomic hybridization cancer metastasis, 983–984, 984f congenital heart disease, 787 copy number variation (CNVs) detection, 112–114, 113f clinical cytogenetic diagnostics, 117, 117f gliomas, 957, 959 melanoma, 969 prostate cancer biochemical relapse after surgery, 904

1412

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Index

Array Designer 2.02 software, viral chip design, 541 Array Express, 152, 214, 215 Array Track, 422 Arrayed primer extension (APEX) cancer susceptibility biomarker studies, 305 hepatitis B precore/basal core promoter mutations detection, 551 Arrhythmias, 729–745 atrial arrhythmia syndromes, 743t familail ventricular tachycardia/ catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 primary conduction abnormalities, 742–745 short QT interval syndrome, 741–742 systemic sclerosis, 1163 see also Long QT syndromes; Short QT interval syndrome Arrhythmogenic right ventricular cardiomyopathy, 693, 717 ARSC1, corneal disease, 1257 Artemin, 935 Arterial thrombosis cardiac-surgery perioperative period, 796–798 recurrence risk, 768 risk factors, 765t risk prediction (Bloodomics project), 768 screening investigations, 764–765 Arthritis biomarkers, 301 Arthropathy, inflammatory bowel disease association, 1046 Artificial neural networks, 160, 213 proteomics hepatitis B infection, 1149, 1381 liver function, 1150 Aryl hydrocarbon receptor (AhR), 1012, 1013 Arylamine exposure-related bladder cancer, N-acetyltransferase 2 (NAT2) polymorphism influence, 54 Asbestos exposure, 1111 L-Asparaginase, 167 pharmacogenomics, 848–849 response prediction, 849 Aspirin colorectal cancer chemoprevention, 890 pharmacogenomics, 674 resistance, 674 Assembly (genome assembler software), 91 Assessing Genetic Risk: Implications for Health and Social Policy, 402 Association studies, 462 Alzheimer’s disease (late onset), 1225 asthma, 1087–1088 bipolar disorder, 1300, 1301, 1302, 1303 candidate genes, 464 copy number variation (CNVs), 116 correcting for population structure, 29 depression, 1291–1292, 1292t epilepsy, 1248, 1248t

gene–environment interactions, 52 genetic susceptibility biomarkers, 304 genotyping errors, 464–465 hypertension, 627 infectious diseases susceptibility, 1316, 1351 measures of association, 463–464, 463t meta-analysis, 465 metabolic syndrome, 1197–1199, 1198t, 1199t multiple comparisons, 465 multiple sclerosis, 1033 myocardial infarction, 667–671 neurobiology, behavioral variation functional imaging approaches, 534–535 obesity, 1173, 1176 probabilistic results, 466 replicability, 1365 search for medically relevant variants (linkage disequilibrium), 28, 29 sepsis, 1364–1365 single nucleotide polymorphisms (SNPs), 88 thrombosis, 762–763 translation into medical practice, 263 see also Genome-wide association studies; Linkage studies (genome scans) ASTAMI, 675 Asthma, 50, 67, 68, 88, 457, 1084–1093 airway hyperresponsiveness, 1084, 1085f, 1090 testing, 1090 allergen hypersensitivity, 1085 antioxidant supplementation response, 1013 bronchodilator response, 1090 diagnosis, 1091 family history, 1085, 1090 gene–environment interactions, 55–56, 457, 1085 genomics, 1088, 1089t candidate-gene association studies, 1087–1088 diagnostic applications, 1090 genome-wide association studies, 39, 1088 linkage analysis, 1085–1087, 1086t susceptibility genes, 1087 glutathione S-transferase M1 (GSTM1) polymorphism, 56, 457, 1013 hygiene hypothesis, 55–56 IL-4RA/IL-13 polymorphism, 957 monitoring, 1092–1093 pathobiology, 1084–1085, 1085f chronic obstructive pulmonary disease comparison, 1102, 1103t pharmacogenomics, 1091–1092 pharmacotherapy, 1091–1092, 1092f, 1093 prognosis, 1091 screening, 1088, 1090–1091 skin test reactivity, 1090–1091 ASTIS trial, 1165

Astrocytoma, 956, 957t anaplastic, alkylating agent chemoresistance, 961 cytogenetics, 958 gene expression profiles, 960 genetic alterations, 957, 958f progression, 957 Ataxia-telangiectasia breast cancer association, 871 pancreatic cancer association, 922 ATBF1, Hodgkin lymphoma/Reed-Sternberg cells, 836 ATG16L1, Crohn’s disease, 1043 Atherosclerosis, 33 biomarker applications, 301 DNA methylation in pathogenesis, 136 epigenetic alterations, 68 fibrin variation, 758 genetic risk factors, 775–776, 775t interleukin 6 (IL-6) in pathogenesis, 795 leukotriene pathway associations, 671 lipid disorder-related risk, 1208, 1210 plaque rupture risk biomarkers, 673 genetic influence, 665, 666, 671, 681 matrix metalloproteinases, 666, 687 tissue inhibitors of metalloproteinases (TIMPs), 666 reactive oxygen species, 652–660 endothelial inflammatory gene expression, 653–654 gene polymorphisms, 658 modulatory effects, 654–655 pharmacological targeting, 658–659 risk factor associations, 655 signaling in advanced disease, 658 thrombosis risk, 760 vascular endothelial dysfunction, 653 vascular-occlusive dementia, 1222 Atherosclerosis Risk In Communities (ARIC) study, 301, 760 Athletes, hypertrophic cardiomyopathy, 723 Atlas assembler, 91 Atlas cDNA Expression array, follicular lymphoma, 837 ATM mutations, breast cancer, 871 Atomoxitine, 421 ATP1A2, obesity, 1176 ATP-binding cassette protein A1 see ABCA1 ATP-binding cassette protein B1 see ABCB1 ATP-binding cassette protein G2 see ABCG2 (BCRP) ATP-binding cassette protein G5 see ABCG5 ATP-binding cassette protein G8 see ABCG8 Atrial arrhythmia syndromes, 743t Atrial fibrillation, 744–745 cardiac-surgery perioperative period, 796 clinical features, 744 electrophysiology, 744 genetics, 744–745, 796

Index

mechanisms, 745 somatic mutations, 745 genome-wide association studies, 745 sick sinus syndrome, 744 transciptional response, 796 Atrial natriurteic peptide gene, hypertensionrelated stroke association, 629 Atrial septal defects, 782 Atrial tachyarrhythmias, 729 Atrioventricular block, 729 Lev-Lenegre progressive cardiac conduction disease, 742–743, 743f Attenuated familial adenomatous polyposis, 883 Attenuation correction, 502 Attributable fraction, 463–464 Autism, 33 copy number variation (CNVs), 39, 361 Autism Genetic Resource Exchange (AGRE), 295 Autoimmune disease DNA methylation alterations, 68, 136 environmental factors, 49, 1011 Autoimmune liver disease immunogenetics, 1143, 1143t proteomics, 1149 transcriptomics, 1147 Autologous stem cell transplantation, systemic sclerosis, 1165 Autosomal dominant congenital lamellar cataract, 1259 Autosomal dominant hypercholesterolemia, 638, 643, 647 Autosomal dominant idiopathic generalized epilepsy (ADIGE), 1243, 1244t Autosomal dominant juvenile myoclonic epilepsy (ADJME), 1243, 1244t Autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), 1243, 1244t Autosomal dominant partial epilepsy with auditory features, 1245t Autosomal recessive hypercholesterolemia, 637–638, 643 Avastin see Bevacizumab Avellino’s (combined granular-lattice) corneal dystrophy, 1258, 1259 Avian influenza type H5N1, microarray tests, 370 AY mouse, 1175 Azathioprine, 706 inflammatory bowel disease, 1048 pharmacogenomics, 1047 response biomarkers, 331, 338 systemic sclerosis, 1162 B B2M, glomerular disorders, 1059 B7-H1, regulatory T cells, 574 B cells chronic obstructive pulmonary disease, 1101

inflammatory synovitis, 1072, 1073 multiple sclerosis immune response, 1036 sarcoidosis, 1111 systemic sclerosis, 1158 transplant rejection heart, 706 kidney, 211 B-type natriuretic peptide acute coronary syndromes risk stratification, 685, 686 cardiac allograft rejection biomarker, 800 heart failure diagnosis, 308, 696–697 test performance, 313, 313t, 314–315, 314t, 315f, 316, 317t BAALC, acute myeloid leukemia prognosis, 846 BabA1, 1128 BabA2, Helicobacter pylori adhesins, 1128, 1129 peptic ulcer disease correlations, 1129, 1130 Bacillus anthrax, 590 biosensor detection, 590, 592 proteomics, 568 Bacteria cancer vaccine vectors, 582 core genome, 565 DNA methylation, 61 in vivo expression technology (IVET), 566–567 pan-genome, 565 virulence gene identification, 566 Bacterial infections, 14, 1347–1357 genomics bacterial, 1347–1348 host, 1348–1351 host susceptibility Gram-negative organisms, 1354–1356 Gram-positive organisms, 1351–1354 IRAK4 mutations, 1317 study methods, 1351 see also Infectious disease Bacterial inhibition assay, 472 BAG1/Bag-1 head and neck cancer, 951 Oncotype Dx assay, 992 Bannyan-Ruvalcabe-Riley syndrome, PTEN pathway mutations, 886 BAR2, metabolic syndrome, 1197 Bardet–Biedl syndrome, 220 BAT26, colorectal cancer, 889 Bayesian methods, 213 post-test probability calculation, 314, 315–316 Bayesian networks, 213 Bcl-2 binding compounds, chemical genomics, 198 cardiomyocyte apoptosis in heart failure, 695 diffuse large B-cell lymphoma, 834

■

1413

follicular lymphoma, 836, 837 head and neck cancer, 950 lung cancer, 496 melanoma, 969, 970 therapeutic targeting, 970, 1002 Bcl-6, diffuse large B-cell lymphoma, 833, 834 Bcl-Xl, head and neck cancer, 950 Bcl-Xl/Bcl-2 associated death promoter (BAD), spondyloarthropathies, 1074 BCR-ABL fusions gene acute lymphoblastic leukemia, 847 Philadelphia chromosome (chronic myelogenous leukemia), 809, 844 BCR-ABL tyrosine kinase, 372 chronic myelogenous leukemia, 845, 847 imatinib mesylate inhibition, 372, 845, 847, 939, 940, 1001 BCRP (ABCG2), lapatinib pharmacogenomics, 350 BDNF see Brain-derived neurotrophic factor Bead-based multiplexing arrays, 369 Bead-based sequencing methods, 36 Beclomethasone, asthma management, 1091, 1092f Behavioral variation, 533, 533f functional imaging approaches, 532 candidate gene selection, 534 conceptual basis, 533–534 non-genetic factor influences, 534–535 serotonin transporter (5-HTT) gene polymorphic region, 535–536 task selection, 535 Behçet’s syndrome, 1070 Bengin prostatic hypertrophy, urine biomarkers, 902 Benign familial neonatal seizures, 1243, 1245–1246t Benzene exposure, 49 biomarkers, 303 chromosomal aberrations, 303 Benzo(a)pyrene carcinogenesis head and neck cancer, 946 myeloperoxidase polymorphism, 54 NAD(P) quinine oxidoreductase 1 (NQO1) polymorphism, 54 Benzo(a)pyrene diol epoxide, 946 O6-Benzyl guanine, chemoresistant brain tumor management, 962 β1-adrenergic receptor, pharmacogenomics antihypertensive agents, 630 beta blocker response in heart failure, 698 β2 adrenergic receptor heart, 736 pharmacogenomics antihypertensive agents, 630 β2-agonists, 1092 post-myocardial infarction therapy response, 673

1414

■

Index

β2-agonists asthma, 1091 non-responsers, 1091, 1092 chronic obstructive pulmonary disease, 1105 pharmacogenomics, 1092 β myosin heavy cain mutations (MYH7), hypertrophic cardiomyopathy, 692, 716, 718, 720, 721 β-Blocker Evaluation of Survival Trial (BEST), 698 Beta-blockers catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 hypertrophic cardiomyopathy, 723 long QT syndromes, 738–739 pharmacogenomics, 630 heart failure, 698 post-myocardial infarction response, 673 β-defensins, 115 11β-hydroxylase/aldosterone synthase fusion gene, glucocorticoid-remediable aldosteronism, 628 11β-hydroxysteroid dehydrogenase mutations, syndrome of apparent mineralocorticoid excess, 628 β-interferon, multiple sclerosis treatment, 1035 Betet quid chewing, 945 Bevacizumab, 575, 811, 972, 998–999 brain tumors, 962 head and neck cancer, 952 prostate cancer trials, 906 BF, obesity, 1176 Bias case–control studies, 323 diagnostic biomarker trials, 316 experimental accuracy impact, 277 genomic studies, 280–281 measurement, 276, 282 sample collection, 281 selection, 276, 280, 285 Bicuspid aortic valve (NOTCH1), 784 BiDil (nitrate/hydralazine combination), heart failure management in AfricanAmericans, 321, 698 Bile acid sequestrants, 646 familial hypercholesterolemia, 637 Bile canaliculus, 1139 Bile ducts, 1139 Biliary atresia, 1147 Biliary disease transcriptomics, 1147–1148 Biobank Information Management System (BIMS), 287, 287f Biobank Japan, 286 Biobanks, 236, 237, 267, 284–296, 466–467 automated procedures, 287 best practice guidelines, 286, 286f, 287, 295 centralized, 288, 288t clinical utility, 285 collection size, 287, 294

consent issues, 292 definition, 284 electronic medical records, 237 evolution, 285 existing models, 286–288 Singapore Tissue Network (STN), 288, 289–294 future developments, 294–296 harmonization requirement, 287 information management, 284 international initiatives, 285–286, 286f past organizational limitations, 285 policy issues, 389, 391 privacy issues, 284, 291–293, 295 prospective longitudinal cohort studies, 285 public consultation approaches, 391 public health applications, 450, 451 quality assurance, 285 reversibly de-identified data, 292–293 standard operating procedures (SOPs), 285 Bioconductor, 218 Bioinformatic sequence markup language (BSML), 248 Bioinformatics, 206–221, 226–230, 268–269 analytic methods, 213–214 challenges, 269 FDA Critical Path Initiative, 421 gene–diet interactions, 1205 genomics firm business activity, 438 historical background, 226 integrative biology, 220 medical applications, 207–212, 207t diagnosis, 207–208 histopathology, 211 nosology, 211–212 schizophrenia gene discovery in Portuguese population, 1305–1306 sepsis studies, 1368 therapeutics, 209–211, 337 proteomics, 176–179 data display, 178 modified peptide identification, 177 public health genomics, 447 software development, 230 software tools (free), 216–219 analytic, 217–218 interpretation, 218–219 viral chip design, 541 vocabularies/ontologies, 215–216, 215t, 229–230 Biological imaging, 370 Biological processes, Gene Ontology (GO), 216 The Biomarker Consortium, 422 Biomarkers, 269 amyotrophic lateral sclerosis, 1278 applications, 299–302, 380 biological variability, 309, 312 candidate protein frequency distribution, 311 candidate protein immunoassay, 308–309 accuracy, 309–310

analytical evaluation, 309–310 analytical measurement range, 310–311 limit of detection, 311 precision, 309, 310 variation, 309 candidate protein reference intervals, 311 clinical evaluation, 312–313 diagnostic accuracy, 313–316, 314f predictive value, 315–316 clinical event prediction, 380 clinical trial populations, 282 genome-based trial design, 269 colorectal cancer, 889 commercial development, 317–318 coronary artery disease acute coronary syndromes, 680, 682, 684–685, 685f myocardial infarction, 673 prognostic stratification, 685–686, 686t screening, 681 definition, 299 diagnostic research studies, 316 early disease, 76, 303–304 experimental design, 275–276, 316 FDA Critical Path Initiative, 421, 422 genetic susceptibility markers, 304–306 head and neck cancer, 947, 949 locoregional recurrence prediction, 950–951 heart failure, 699–701, 701f in vivo fluorescent imaging, 530 inflammatory bowel disease, 1043, 1045 leukemia outcome prediction, 849–850 liver disease, 1149, 1150 lung cancer diagnosis, 858, 859 molecular imaging, 497 multiple sclerosis, 1036–1037 neuropsychiatric disorder imaging, 532 population studies, 299–306 exposure to causal factors, 302–303, 302f molecular epidemiology, 302, 302f pre-analytical variation, 311–312, 312f prostate cancer, 902f circulating tumor cells, 903 serum, 903 urine, 902 regulatory requirements, 318 sample storage conditions, 312 sepsis, 1367, 1368f staging/severity assessment, 1369–1371 specimen collection/handling, 311–312 spondyloarthropathies, 1077 systems biology approach, 76, 78 emerging quantitative techniques, 78–81 in vitro measurement technology, 78, 79 in vivo techniques, 80–81 nanotechnology, 78 organ-specific fingerprints, 78 proteomics, 79–80 transferability of test performance, 317

Index

validation, 300, 308–318 economic aspects, 429 Biomarkers Consortium, 295 Biopterin deficiency, 473 Biorepositories see Biobanks BioSense, 592 Biosensors, 590–596 applications, 590, 591, 591f bioterrorism detection, 592 cancer detection, 591–592 nucleic acids, 592–596 aptamers, 594–595 molecular beacons, 592–594, 593f, 594f nanoparticles, 595–596 targets, 590 transduction mechanism, 590, 591f virus detection, 592 Biotechnology firm profitability, 440 Bioterrorism detection, 592 BioWatch, 592 Bipolar disorder, 441, 1283, 1299–1308 association studies, 1300, 1301, 1302, 1303, 1304 candidate genes, 1300–1301, 1303–1304 chromosomal regions harboring susceptibility genes, 1300–1303, 1301t 4p, 1301 6p, 1301–1302 11p, 1302 18p-q, 1303 1q, 1301 4q, 1301 10q, 1302 12q, 1302 13q, 1302–1303 21q, 1303 22q, 1303 DAOA (D-amino acid oxidase activator) linkage, 1303 Darier’s disease co-segregation, 1302 definitions, 1300 in genetic studies, 1303 diagnosis, 1299–1300 family studies, 1300 genome-wide association studies, 39 linkage studies, 1301, 1302, 1303, 1304 pharmacogenomics, 1304 prognosis, 1300 proteomics, 1304 screening, 1300 subtype I/II, 1300 twin/adoption studies, 1300 Birth defects, copy number variation (CNVs), 361 Bispecific antibodies, 574 Bisulfite DNA conversion, methylation analysis, 132, 132f, 133, 134, 135 Bladder cancer copy number variation (CNVs), 369

N-acetyltransferase 2 (NAT2) polymorphism, 54 serum protein antibody microarrays, 79 BLAST (Basic Local Alignment Search Tool), 121 BLAT (BLAST-Like Alignment Tool), 121 Bleomycin-induced pulmonary fibrosis, 1111 Blinding, experimental design, 280 Blocking, experimental design, 28, 277 Blood coagulation, 755, 755f, 756f, 1367 genetics, 755–756 proteins, 755–756 phylogenetic analysis, 756, 756f, 757f vitamin K-dependent, 756, 757 Blood glucose detector, 590 Blood lipids genome-wide association studies, 644, 645t screening, 637 variation, 636–637 Blood oxygen level-dependent (BOLD) fMRI, neuropsychiatric disorders, 532 Blood pressure, hypertension diagnosis, 627–628 screening, 627 Blood samples, DNA methylation assessment, 135–136, 138 Bloodomics project, 768 Bmi1, 603 BMP-7 see Bone morphogenetic protein-7 Body mass index, data extraction from electronic medical records, 238–239, 239t Body weight regulation, 1171, 1171f genetic factors, 1171, 1172f Bone morphogenetic protein-7 (BMP-7), glomerular expression, 1058 Boolean networks, 213 BOOST, 675, 702–703 Bop, ventricular development regulation, 786 Bordetella parapertussis, 371 Bordetella pertussis acellular vaccine, 563 molecular diagnosis, 371 Borrelia burgdorferi cutaneous lymphomas, 830 molecular diagnosis, 371 Bortezomid, 605, 1002 Bosentan, 1162, 1163f, 1165 Bottlenecks isolated population studies, 39 linkage disequilibrium, 28 BRAC Analysis test, 372 BRAF colorectal cancer, 136, 810, 811, 812, 882, 889 melanoma, 812, 813, 969 therapeutic targeting, 970, 971 Brain tumor stem cells, therapeutic targeting, 963 Brain tumors, 956–964 chromosomal alterations, 958–959

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1415

diagnosis, 958–961 epidemiology, 957–958 gene expression profiles, 959–961, 960t genomic mapping, 958 microRNAs, 961 monitoring, 962 pharmacogenomics, 961–962 predisposition, 957–958 prognosis, 958 gene expression signatures, 960 molecular signatures, 81, 963 proteomics, 961 screening, 958 therapeutic targets, 962–963 transcriptional alterations, 959–961 treatment strategies, 962 gene therapy, 963 targeted immunotoxins, 963 Brain-derived neurotrophic factor (BDNF) antidepressant effects, 1295 bipolar disorder, 1302 depression, 1290 hepatic stellate cells, 1144 Brain-derived neurotrophic factor receptor (trkB), 1290 antidepressant effects, 1295 Branched-chain α-ketoacid dehydrogenase complex defect, newborn screening, 185 Brazil, 440 BRCA1 biosensor detection, 591 breast cancer, 362, 382, 462, 810, 871 risk of second primary cancer, 357–358 DNA methylation status, 67, 915, 917 tumor treatment response prediction, 138 DNA sequence data, electronic medical records, 234–235 genetic testing, 263, 362, 372, 393, 403 costs, 363 direct-to-consumer marketing, 364, 393, 458 family history criteria for referral, 483 mutation probability estimation (BRCAPRO model), 482 ovarian cancer, 362, 382, 403, 913–914 aberrant methylation, 915, 917 DNA repair defects, 915 BRCA2 biosensor detection, 591 brain tumor predisposition, 957 breast cancer, 362, 382, 393, 403, 458, 462, 810, 871 risk of second primary cancer, 357–358 DNA sequence data, electronic medical records, 234–235 Fanconi anemia (FANCD1), 915 gene mutation probability estimation (BRCAPRO model), 482 genetic testing, 362, 372, 393, 403 costs, 363

1416

■

Index

BRCA2 (Continued) direct-to-consumer marketing, 364, 393, 458 family history criteria for referral, 483 ovarian cancer, 362, 382, 403, 810, 913, 914 DNA repair defects, 915 pancreatic cancer, 921 BRCA3, breast cancer risk, 382 BRCAPRO model, 482 BRCATA, breast cancer risk, 382 BRD2 juvenile myoclonic epilepsy (JME), 1248 see also β2-adrenergic receptor Breast cancer, 357, 869–878 alcohol consumption-related risk, 1212 BRCA1/BRCA2, 362, 372, 382, 393, 403, 462, 810, 871 management options, 871 risk of second primary cancer, 357–358 cell line microarrays, drug response investigations, 210–211 copy number variation (CNVs), 369, 813 dietary factors, 1212 DNA methylation analysis, 138 estrogen/progesterone-receptor status, 871, 872 luminal A/B subgroups, 873 targeted hormonal therapy, 992–993, 993f treatment responsiveness, 871 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET) staging, 496 treatment efficacy evaluation, 496 familial risk, 382, 481, 870–871 assessment, 482, 483 prevention strategies, 483, 485–486t FGFR2 association, 810 gene expression assay, 242 see also MammaPrint; Oncotype Dx assay gene expression profiles, 160, 162, 165, 167, 390, 873, 874f, 875t, 1002 cost-effectiveness, 427–428, 428t distant recurrence, 875–876 Duke-Taipei study, 875 lymph node status, 875, 876f Netherlands Cancer Institute study, 873 NSABP study, 875–876 pathway prediction, 876–877 prognostic applications, 164, 164f, 166–167, 209, 873, 875 risk stratification, 877 subtype differentiation, 873, 876f tissue sampling, 159 treatment planning, 167, 390 genetic factors, 263, 382, 871 predictors of metastasis, 371 genetic testing, 393, 394, 403, 871 access/reimbursement issues, 394 diagnostic-therapeutic combinations, 992–993

prognosis, 417 genome-wide association studies, 39 Genomic Health tumor characterization, 360 genomics applications, 877–878 Her2 receptor assay, 373–374 magnetic resonance imaging (MRI), 518, 519f Her2/neu positive, 870, 871, 872 biosensors, 591 clinical trial design, 340 diagnostic tests, 990, 991f, 994, 994t pharmacogenomics, 329, 331, 338 trastuzumab therapy, 242, 329, 331, 338, 360, 373–374, 811, 871, 990, 991f, 994 treatment approach, 264 hormonal status associations, 871 lapatinib pharmacogenomics, 350, 352, 352f metabolomics in diagnosis, 43 molecular basis, 871–872 prognosis, 164, 164f, 166–167, 209, 872 molecular signature analysis, 81, 151 receptor negative tumor subgroups, 873 recurrence risk, 426, 427, 428 regulatory T cells (Treg), 820 risk assessment, 382–383, 870–871 screening, 870 somatic mutations, 812 systems medicine approach, 76 TAILORx, 390, 427 TNM staging, 872 treatment, 264, 869 clinical trials, 869–870t BRG1, 61 BRIP1, breast cancer risk, 382 Bromocriptine, 933 Brugada syndrome, 729, 739–741 clinical features, 739–740 genetics clinical, 740 molecular, 736t, 740–741 mutations, 736t therapy, 741 BTNL2 (butyrophilin-like 2), sarcoidosis, 1113 Bucindolol, 698 Budesonide, 1091 Bupropion, 1105 Burkitt lymphoma, 839 Epstein–Barr virus association, 830, 839 gene expression profile, 839 BWSCRIA, breast cancer risk, 382 C C3, glomerular disorders, 1059 C4, systemic lupus erythematosis-related copy number variation, 116 C5 asthma, 1088

gene polymorphism, 665 C6 deficiency, meningococcal meningitis susceptibility, 569 C-Path Institute, 421 11 C-raclopride, dopamine-2 receptor positron emission tomography (PET), 506 C-reactive protein, 310, 310f acute coronary syndrome risk stratification, 685, 686 atherosclerosis pathogenesis, 795 atrial fibrillation, 796 cardiac allograft rejection, 800 chronic obstructive pulmonary disease, 1101, 1104 complement activation, 1348 Crohn’s disease, 1045, 1071 peripheral arterial disease, 773 post-cardiac surgery neurocognitive dysfunction, 799 stroke risk, 798 rheumatoid arthritis, 1024 spondyloarthropathies, 1075 surgical inflammatory response, 794 thrombosis gene linkage studies, 762 C-type lectin receptors (CLRs), 1348, 1350 CA19-19, pancreatic cancer monitoring, 308 CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarctas and leucoencephalopathy), 1222, 1228–1229 Caffeine, 987 Cag A (Helicobacter pylori cytotoxin associated gene product), 1126, 1127–1128, 1129f peptic ulcer disease correlations, 1129, 1130 CALBG trial, 906 Calcitonin, medullary thyroid carcinoma, 934 Calcium channel antagonists catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 hypertrophic cardiomyopathy, 723 pharmacogenomics, 630 Calcium ions, reactive oxygen species signaling pathway regulation, 655–656 Calcium-dependent arrhythmia syndromes, 738t Calcium-handling hypertrophic cardiomyopathy, 718–719 Calgranulin A see MRP8 Calgranulin B prostate cancer urine biomarker, 902 spondyloarthropathies, 1075 Calgranulin C, spondyloarthropathies, 1075 Calpain 10 gene, diabetes type 2, 38 Calsequestrin mutations, catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 Canadian Agency for Drugs and Technologies in Health (CADTH), 426 Canary pox virus, cancer vaccine vectors, 582 Canavan disease, gene therapy, 615

Index

Cancer, 33, 50, 178, 371, 415, 808–815 biosensors, 591–592 chromosomal aberrations, 808–809, 809f clinical decision support systems, 246 copy number variation (CNVs), 369 cost-effectiveness of genetic tests, 394 diagnostic-therapeutic combinations, 990–1003 diet–gene interactions, 1206, 1212–1214 alcohol consumption, 1212–1213 dietary carcinogen detoxification, 1213–1214 folate metabolism, 1213 vitamin intake, 1213 dietary factors/preventive guidelines, 1212 DNA methylation aberrations/chromatin modification, 66–67, 68f, 69, 370 therapeutic implications, 68–69 DNA methylation profiles, 69, 136 early detection/diagnosis, 136, 138 prognosis, 138 treatment response predicition, 138–139 DNA repair enzyme polymorphisms, 305 early effect biomarkers, 303 environmental factors, 49, 1011 gene interactions, 813 epigenetic gene silencing, 917 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 507, 507f family history, 246, 481, 486, 488, 809–810 gene discovery, 809 gene expression profiles, 151, 154–165, 813, 818 diagnostic applications, 371 oncogene expression signatures, 212, 212f prognostic applications, 162–163, 166, 209 subgroups identificaiton, 162, 268, 371 treatment otpimization, 360–361 gene therapy trials, 614–615 genetic susceptibility biomarkers, 304, 305 genome sequencing, 812 genomic databases, 813–814 immunosuppressive effects, 574–576, 808 microarray experiments, 151, 152, 157 data analysis, 160–161 sample size, 152 tumor tissue samples, 152, 159 microRNAs (miRNAs) expression profiling, 166 therapy, 604–605 molecular imaging, 494 detection, 494, 496 treatment efficacy evaluation, 496–497 molecular signature analysis, 151, 263 outcome predicition, 81, 382 molecular subtyping, 41 mouse models, 814 obesity association, 1172

pharmacogenomics, 372, 1002 receptor magnetic resonance imaging (MRI), 517–519, 519f single cell analysis, DNA-encoded antibody libraries (DEAL), 80 somatic mutations, 812 detection, 15 spread see Metastasis systems medicine approach to diagnosis, 76 targeted therapies, 990–1003 antibody-based, 995–999, 996t definitions, 990 ideal target, 990, 992, 992t small molecule-based, 999–1002, 1000t Cancer Biomedical Informatics Grid (caBIG), 286, 287–288 Cancer cells acquired functions, 808 microarray experiments, 162 receptor tyrosine kinases, 811 regulatory T cell induction, 574 single cell analysis, DNA-encoded antibody libraries (DEAL), 80 tumor microenvironment interactions, 818 Cancer Genome Anatomy Project (CGAP), 813 The Cancer Genome Atlas (TVGA), 814 glioblastoma, 959 Cancer Genome Project (CGP), 813 Cancer Outlier Profile Analysis (COPA), 160, 161 Cancer vaccines, 569, 573–584 adjuvants, 582–583 brain tumor treatment, 963 carbohydrates, 579 formulations, 582–583 genomic analysis of response, 823, 824 ideal properties, 576, 577f immunomonitoring, 583–584 messenger RNA (mRNA), 579–582 molecularly undefined, 576–577 peptides, 577–578 plasmid DNA (pDNA), 579–582 prophylactic, 574 proteins, 578 regulatory T cells (Tregs), 575 routes of application, 583 therapeutic aims, 574 tumor antigen selection, 576 viral/bacterial vectors, 582 Candida albicans infection, dendritic cell gene expression response, 1369 Candidate genes, 38, 1226 association (epidemiological) studies, 464 asthma, 1087–1088 bipolar disorder, 1300–1301, 1303–1304 chronic obstructive pulmonary disease, 1099, 1100t, 1103 cirrhosis, 1142, 1142t colorectal cancer, 886–887

■

1417

complex disease causal variants identification, 36, 38 depression, 1291–1292 diabetes type 1, 1187 diabetes type 2, 1188 diabetic nephropathy, 1061 drug response biomarkers, 325–326, 344, 346 Environmental Genome Project (EGP), 50, 51 epilepsy, 1248–1249, 1251 familial pulmonary fibrosis, 1115 HIV infection progression to AIDS, 1329 hypertension, 627 salt-sensitivity, 626 IgA nephropathy, 1058, 1062 infectious disease susceptibility, 1317, 1351 inflammatory bowel disease, 1041 multiple sclerosis, 1033 neurobiology, functional imaging approaches, 534 obesity, 1174t, 1175t, 1176 Parkinson’s disease, 1238 peripheral arterial disease, 777 psychiatric disorders, 1283 rheumatoid arthritis, 1021 sarcoidosis, 1111–1112, 1113t schizophrenia, 1285 sepsis, 1364 Capecitabine, 891 CAPN10, diabetes type 2 1188 CAPSL, diabetes type 1, 1191 CAPTURE, 686 Capture assay, hepatitis B DNA quantitation, 1379 Carbamazepine adverse drug reactions, 1250, 1251 HLA B*1502 genotype, 372 metabolism, 1293 pharmacogenomics, 1250 dose variations, 1250, 1251 Carbohydrate cancer vaccines, 579 Carbon nanowire, viral chip technology, 553 Carbon tetrachloride hepatotoxicity, 1148 Carboplatin/sorafenib, melanoma trials, 971 Carcinogen-metabolizing enzymes biomarkers of carcinogen exposure, 303 gene–diet interactions, 1213–1214 polymorphisms, 48, 49 bladder cancer susceptibility, 54 lung cancer susceptibility, 54 Carcinogenesis, 48, 810–811, 810f colorectal cancer, 879–880 adenoma to carcinoma sequence, 880–882, 881f DNA methylation patterns, 132, 136 Carcinogens biomarkers of exposure, 302–303 detoxification, gene–diet interactions, 1213–1214

1418

■

Index

CARD8, Salmonella infection susceptibility, 1366 CARD15/NOD2 Crohn’s disease, 38, 39, 1045, 1072 phenotype relationship, 1046 inflammatory bowel disease association, 1041, 1043 Cardiac calcium ion channel mutations, 738t Cardiac embryogenesis, 702 Cardiac ion channels interacting protein gene mutations, long QT syndromes, 736, 736t mutations long QT syndrome ECG manifestation correlations, 738 Romano-Ward syndrome, 733–735 see also Cardiac potassium ion channel mutations; Cardiac sodium channel gene (SCN5A; LQT3) mutations Cardiac perioperative medicine, 794–800 atrial fibrillation, 796 dynamic genomic outcome markers, 799–800 neurocognitive dysfunction, 798–799 stroke, 798–799 surgical inflammatory response, 794, 795–796 thromboembolism, 796–798 see also Coronary artery bypass grafting Cardiac potassium ion channel mutations Andersen-Tawil syndrome, 737 atrial fibrillation, 744, 745 Jervell and Lange-Nielsen syndrome, 737 Romano-Ward syndrome, 733–734, 735 short QT interval syndrome, 742, 742t Cardiac progentior cells, 702 Cardiac ryanodine receptor mutations see RyR2 mutations Cardiac sodium channel gene (SCN5A; LQT3) mutations, 732, 734–735, 736, 738, 739 atrial fibrillation, 744, 745 Brugada syndrome, 741, 741f gene-specific treatment approach, 739 Lev-Lenegre progressive cardiac conduction disease, 743 Romano-Ward syndrome, 732, 734–735 sinus node dysfunction/sick sinus syndrome, 744 sudden infant death syndrome, 731 Cardiac transplantation, 705–713, 800 heart failure, 705, 717 immunosuppressive regimens, 706, 707t monitoring, 707, 708f rejection, 382, 800 acute antibody-mediated, 706 acute cellular, 706, 707, 708f chronic (cardiac allograft vasculopathy), 706 gene expression network studies, 713

gene expression signature test see AlloMap test grading system, 707, 710t molecular diagnosis in peripheral blood mononuclear cells (CARGO study), 701–702, 707–709, 710, 711, 712, 713 prevention, 706 Cardiac troponin I (TNNT13) mutations, hypertrophic cardiomyopathy, 718 Cardiac troponin T (TNNT2) mutations, hypertrophic cardiomyopathy, 718 CardioChip, 143 Cardiogenomics database, 152 Cardiomyocytes apoptosis in heart failure, 695–696, 697f β1-adrenergic receptor polymorphism, 698 Cardiomyopathy, microarray experiments, 162 sample size, 152 tissue sources, 152 Cardiopulmonary bypass, 794, 796 molecular response, 799 postoperative neurocognitive dysfunction, 798, 799 systemic inflammatory response, 795 Cardiovascular disease, 50 chronic obstructive pulmonary disease comorbidity, 1101 DNA methylation profiles, 136 environmental factors, 49 epigenetic alterations, 68 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 507 gene therapy, 616–617 gene–diet interactions, 1206, 1207–1210 genome-wide association studies, 39, 644–645, 645t hypertension association, 629 metabolic profiling, 187 microarray analyses, tissue sources, 152 microRNA regulatory functions, 151 molecular imaging, 496 molecular signature analysis, 151 obesity association, 1172 risk factors see Cardiovascular risk see also Atherosclerosis Cardiovascular dynamics, 624–625, 626f Cardiovascular risk, 680–681, 759 cholesterol plasma level, 636 PCSK9 nonsense/missense variant effects, 671, 672f chromosome 9p21 region of interest, 671, 681 clinical decision support using electronic medical records, 236, 236f environmental factors, 672 familial partial lipodystrophy, 1196 family history, 665 genetic factors, 665–666, 681 genetic lipid disorders, 634, 643–644

high-density lipoprotein cholesterol, 639, 642 low-density lipoprotein cholesterol, 637, 639, 643 triglycerides, 642 genetic lipid variants, 644 homocysteine plasma levels, 1213 hypertension, 629, 655 metabolic syndrome, 1194 pro-inflammatory gene polymorphisms, 795 screening, 672–673 CARE study, 674 CARGO (Cardiac Allograft Rejection Gene Expression Observational) study, 701–702, 707–709, 710, 711, 712, 713 CARGO II, 713 Carmustine oligodendroglioma, 959 tumor response prediction, 138 Carney complex, 931 Carnitine palmitoyltransferase 1 A (CPT1A), cirrhosis, 1142 Carotid artery atherosclerosis biomarkers, 301 CART, obesity, 1178 CARTaGENE, 286 Case-control studies, 462–463, 465 association measures, 463, 463t bias, 323 case criteria, 464 computational analysis, 213 disease frequency measures, 463, 463t gene–environment interactions, 52–53 complex disease susceptibility, 53–56 genome-wide association studies, 101–102, 237 control genotyping databases, 101 population stratification, 103 sample size, 102, 102t genotyping errors, 464–465 infectious diseases susceptibility, 1351 odds ratios, relative risk estimation, 463 pharmacogenetics, 323–324, 325f population-based samping, 464 sample size, 464 thrombosis, 762–763 Case-only studies, gene–environment interaction, 465 Caspase inhibitors, cirrhosis, 1142 Caspase-9, lung cancer, 858 Caspases, cardiomyocyte apoptosis, 695–696 Catalase, 652 Catalog of Somatic Mutations in Cancer (COSMIC), 813 Cataract, inherited forms, 1259 CATCH22, 782 Catechol-O-methyltransferase see COMT Catecholaminergic polymorphic ventricular tachycardia (CPVT), 729, 742 genetics, 742 management, 742

Index

Cathepsin D brain tumor monitoring, 962 breast cancer, 992 glioblastoma, 961 Cathepsin L2, Oncotype Dx assay, 992 Ca(V)1.2 (L-type calcium channel) mutations, Timothy syndrome, 737 Caveolae, 735 cardiac ion channels, 736 Caveolin-3 (LQT9; CAV3), Romano-Ward syndrome, 735–736 Caveolins, 735 CBF (core binding factor), acute myeloid leukemia, 844 CBFB-MYH11 fusions gene, acute myeloid leukemia, 844 CCI-779, melanoma clinical trials, 971 CCL1 see MCP-1 CCL3L1 copy number variation, HIV/AIDS, 116 CCL5 see RANTES CCL16, hepatitis B infection, 1146 CCND1 see Cyclin D1 CCND2, diffuse large B-cell lymphoma prognosis, 834 CCR1, spondyloarthropathies, 1074 CCR2 mutation HIV infection progression to AIDS, 48, 1329 human immunodeficiency virus (HIV) resistance, 1317 CCR5 HIV infection/AIDS, 116, 1364 HIV-1 co-receptor, 1327 polymorphism studies, 29 HIV progression to AIDS, 48, 1329 HIV-1 resistance-related deletion, 1316, 1317, 1328, 1329 viral entry inhibitors, 1334 CCR7, regulatory T cells (Treg), 820 CD3 monoclonal antibodies, inflammatory bowel disease treatment, 1049 CD4 antagonists, 1334 HIV-1 cell surface receptor, 1327 CD4T cells asthma, 1084 hepatitis C, 1380, 1382 HIV infection/AIDS, 1327 inflammatory synovitis, 1072, 1073 response to infection, 1348 sarcoidosis, 1111 tumor microenvironment, 821 CD8T cells chronic obstructive pulmonary disease, 1101 hepatitis B, 1380, 1381 hepatitis C, 1380, 1382 inflammatory synovitis, 1072, 1073 sarcoidosis, 1111 tumor microenvironment, 821 CD14, 1350

host pathogen recognition/signaling, 1366 Enterobacteriacea, 1354 Staphylococcus aureus, 1354 Streptococcus pneumoniae, 1352, 1353 promoter polymorphism, 56 asthma, 56, 1087, 1088 CD25, diabetes type 1, 1191 CD36, host response to Staphylococcus aureus, 1354 Cd36 gene (fatty acid translocase), 40 CD40 ligand, acute coronary syndromes risk stratification, 686 CD81, hepatitis C receptor, 1377, 1380 CD209, alcoholic liver disease, 1147 CD226, diabetes type 1, 1191 Cdc7, hepatic stellate cells, 1146 CDH1 DNA methylation breast cancer, 138 lung cancer, 858 CDH3 DNA methylation, breast cancer, 138 CDH13 DNA methylation, lung cancer, 858 CDKAL1, diabetes type 2, 265, 1189 CDKN2A cardiovascular risk, 671, 681 diabetes type 2, 265 DNA methylation lung cancer, 858, 859 protate cancer, 138 CDKN2B cardiovascular risk, 671, 681 diabetes type 2, 265 cDNA microarrays, 157, 158f, 159 diabetic nephropathy, 1059 disease diagnosis/classification, 162 melanoma, 968 multiple sclerosis immune response, 1034 ovarian cancer, 916 renal biopsy tissue, 1058 systemic sclerosis, 1156 transcriptomics, 143, 144, 144f cDNA-mediated anneling selection extension and ligation (DASL) assay, 166 CEACAM6, ovarian cancer, 916 CEBPA, acute myeloid leukemia, 846, 847 Celastrol, 198 Celera Diagnostics, 438 Celera Genomics, 438, 439 Cell adhesion molecules, atherosclerosis, 654 Cell tracking, magnetic resonance imaging (MRI), 517, 518f Cell-based microarrays, viral chip technology, 553 Cell-based therapy Duchenne muscular dystrophy, 1277 heart failure/myocardial infarction, 675, 702–703 Parkinson’s disease, 1239–1240 stem cells, 604–606 renal regeneration, 1063–1064

■

1419

Cellular components, Gene Ontology (GO), 216 Center for Devices and Radiological Health (CDRH), 416 Center for Information Biology Gene Expression (CIBEX) database, 152 Centers for Medicare and Medicaid Services (CMS) Clinical Laboratory Improvement Amendments (CLIA) program administration, 418 genomic medicine regulation, 414 Central nervous system tumors, molecular signature analysis, 151 Centre de recherche en droit public (CRDP), 450 Centromeres, repetitive DNA, 8 Ceramides, targeted metabolic profiling, 185 Cerebral venous thrombosis, racial/ethnic variation in incidence, 762 Cerivastatin, 344 Cervical cancer, human papillomavirus, 374 vaccine, 374, 574 CETP blood lipid genome-wide association studies, 644 mutations, 641 variants, 641, 642, 644 Cetuximab, 998 head and neck cancer, 952 CFTR (cystic fibrosis transmembrane conductance regulator) mutations, 88, 125 diabetes type 1, 1191 G551D mutation comparative sequence analysis, 125, 125f multiple mutations, 476 newborn screening, 474 splice-site, 125f, 126 Char syndrome (TFAP2B), congenital heart disease, 784 Charcot-Marie-Tooth disease, CX32 promoter mutations, 126f, 127 Chemical cleavage of mismatched DNA, 96 Chemical exchange saturation transfer (CEST), 514, 515 contrast agents, 516–517 molecular magnetic resonance imaging, 519 Chemical genomics, 193–200 parallel RNA interference screens, 199 phenotypic screening, 198–199, 198t principles of approach, 196f, 197 Chemical Genomics project, 389, 389t Chemical individuality, 1, 2 Chemokines asthma, 1084 host response to infection, 1314 sarcoidosis, 1111 systemic sclerosis, 1157 Chemoprevention, lung cancer, 859

1420

■

Index

ChemSpider database, 183 CHFR methylation status, tumor treatment response prediction, 138 Chicken genome sequencing, 120 Childhood absence epilepsy (CAE), 1243, 1246t Chimpanzee genome sequencing, 120 China, 440 Chitinase 3 like-1 (CH12L1) see YKL-40 Chlamydia trachomatis, molecular diagnosis, 371 CHMP2B mutations, frontotemporal dementia, 1226 Cholangiocarcinoma, 1149 Cholecystokinin, 1123 Cholesterol intestinal absorption, 635 lipoprotein transport, 634 metabolism, 635, 636 plasma level cardiovascular risk, 636 screening, 637 Cholesterol absorption inhibitors, 646 Cholesteryl ester transfer protein (CETP), 636 deficiency, 641, 644 therapeutic inhibition, 647 see also CETP Choline, magnetic resonance spectroscopy, 519 Chromatin, 11, 12, 60 active/inactive, 61, 63–64 DNA methylation relationship, 61, 62, 65–66, 66f, 131 histone modifications, 63–64 stem cells, 602, 603 epigenetic patterns, 12 modification, 63–65, 67f cancer-related aberrations, 66–67 remodeling complexes, 61, 64 Chromatin immunoprecipitation (ChIP), stem cells, 602 Chromatin-modifying 2B protein (CHMP2B) mutations, frontotemporal dementia, 1226 Chromatin-remodeling genes, stem cells, 602 Chromatographic DNA conformation polymorphism detection, 96 Chromosomal aberrations brain tumors, 958–959, 959t cancer, 808–809, 809f array comparative genomic hybridization, 983–984, 984f congenital heart disease, 785 cytokinesis-block micronucleus (CBMN) assay, 303–304, 304f early disease biomarkers, 303 leukemia, 844–845 lymphoma, 831t, 832 diffuse large B-cell, 833 obesity, 1175 prostate cancer, 900–901, 900f, 904

schizophrenia, 1284, 1285 see also Cytogenetics Chromosomal syndromes, strabsimus, 1257 Chromosome 21, DNA sequencing by hybridization, 95 Chromosome Abnormality Database, 110 Chromosomes gene distribution, 8 human genome, 6t, 7f variation, 6t study methods, 6 three-dimensional organization, 13, 13f Chronic bronchitis, 1099 see also Chronic obstructive pulmonary disease Chronic disease pharmacogenetic research targets, 322 see also Complex disease Chronic granulomatous disease, gene therapy, 616 Chronic lymphocytic leukemia, 830 alemtuzumab treatment, 998 fludarabine treatment, 847–848 gene expression profiling, 847 prognostic factors, 846 molecular signature analysis, 151 ZAP-70 marker, 847, 849–850, 851 Chronic myelogenous leukemia, 193, 372 BCR-ABL fusion protein, 372 imatinib mesylate, 360, 372, 809, 811, 939, 940, 1001 diagnostic test of patient eligibility, 990, 992f targeted therapy, 990, 992f, 994 Philadelphia chromosome, 809, 811, 844 treatment, 845 pharmacogenomics, 847 Chronic obstructive bronchiolitis, 1099 see also Chronic obstructive pulmonary disease Chronic obstructive pulmonary disease, 343–344, 1098–1107 clinical features, 1105f systemic, 1101 corticosteroid resistance, 1103, 1104f, 1107 diagnosis, 1104, 1105f environmental factors, 1098–1099, 1099t exacerbations, 1101, 1105 treatment, 1106 familial clustering, 1099 genetic factors, 1099, 1099t, 1100t genomics, 1103 inflamatory mechanisms, 1101–1104, 1102f asthma comparison, 1102, 1103t macrophages, 1101–1102 mediators, 1102, 1107 NK-κB activation, 1102 management, 1105–1107 anti-smoking measures, 1105 antibiotics, 1105–1106

bronchodilators, 1105 corticosteroids, 1106 lung volume reduction, 1106 mucolytics, 1106 oxygen, 1106 pumonary rehabilitation, 1106 natural history, 1100f new treatment targets, 1107 pathophysiology, 1099–1101, 1101f emphysema, 1100 pulmonary hypertension, 1100–1101 small airways, 1100 pharmacogenomics, 1106–1107 prognosis, 1105 proteomics, 1104 screening, 1104–1105 CHST6, corneal disease, 1257 Chylomicrons, 634, 1208 apolipoproteins, 635 metabolism, 635, 636 Cigarette smoke carcinogenic components, 946 exposure, 48, 49 biomarkers, 302–303 bladder cancer risk, acetylator phenotype influence, 54 in utero, lung disease following, 56 lung cancer risk, myeloperoxidase polymorphism influence, 54 metabolising enzyme gene polymorphisms, 857 Cigarette smoking amyotrophic lateral sclerosis risk, 1273 apolipoprotein A-1 (APOA1) effects, 1209 asthma associations, 1085, 1090 chronic obstructive pulmonary disease, 1098, 1099, 1100f, 1102 colorectal cancer risk, 886, 887 familial interstitial pneumonia, 1115 gene expression profiles, 1103 head and neck cancer risk, 945, 946 incidence in USA, 857 inflammatory bowel disease association, 1041 lung cancer association, 856 at-risk smoker identification, 858–859 genetic influences, 856, 857–859 preventive approaches, 865 nicotine addiction-related genes, 1099 pancreatic cancer risk, 922 peripheral arterial disease risk, 773, 774 rheumatoid arthritis association, 1018, 1022 Cigarette smoking cessation chronic obstructive pulmonary disease management, 1105 gene expression profiles, 858 Cilengitide, melanoma clinical trials, 971 Cilostazol, 774 Circadian fluctuation in biomarkers, 311

Index

Cirrhosis, 1138–1151 causes, 1141t diagnosis, 1140–1141 genetics, 1142–1143 candidate genes, 1142, 1142t hepatitis B, 1379, 1380 hepatitis C, 1380, 1383 liver structural changes, 1139–1140, 1140f progression to hepatocellular carcinoma, 1138, 1148 treatment, 1141–1142 see also Liver disease Cisplatin head and neck cancer, 950 resistance, 950 tumor response prediction, 138 Citrate, plasma sample effects, 311 Citrate/isocitrate carrier (CIC), 185 Clarithromycin Helicobacter pylori eradication, 1123, 1131 resistance, 1131 pharmacogenomics, 1131 Classification of disease, 215–216, 215t Claudin-1, hepatitis C receptor, 1377, 1380 Claudin-4, pancreatic mucinous cystic neoplasms, 926 Claudins, alcoholic liver disease, 1147 Cleavase fragment length polymorphism analysis, 96 Clinical Data Interchange Standards Consortium (CDISC), 229 Clinical Decision Information System (CDIS), 239 Clinical decision support, 242–249 applications, 243, 244f, 244t, 245f genomic/personalized medicine, 245, 246t deployment barriers, 243 enabling stategies, 247–249, 248t information technology issues, 247 limitations, 246 effectiveness, 243–244 system attributes, 244–245 electronic medical records, 235–236, 236f, 243, 244, 247 history of development, 243 machine-executable knowledge resources costs of development/maintenance, 247, 249 reusability (knowledge sharing), 249 patient data format in IT systems, 247 standardization, 248–249 standard software interface, 247, 249 Clinical event prediction, 380 Clinical Laboratory Improvement Amendments (CLIA), 361, 393, 410, 418 laboratory certification, 414, 417, 418 Clinical laboratory services, 393 newborn screening tests, 476

Clinical Molecular Genetics Society, genetic testing audits, 393 Clinical practice guidelines (CPGs), 235 Clinical trials cost-effectiveness analysis, 425 data management, 228–229 honest broker approach, 229, 229f data modeling, 229–230 design, 282, 340 diagnostic biomarkers/in vitro diagnostics, 316, 417 drug development safety, 350–352 drug-diagnostic test combinations, 420 FDA Critical Path Initiative, 421 genome-based, 269 patient randomization, 263 genotype-guided, 328 response biomarkers, 282 results disclosure to participants, 391–392 surrogate endpoints, 300, 300t use of biomarkers, 299, 300 Clinical utility of genomic tests, 362 ClinicalTrials.gov, 227 ClinSeq project, 441 Clofibrate hepatotoxicity, 1148 Clone Tracker, 548 Clopidogrel, 674 CLUSTALW, 123, 124 Cluster analysis, 213, 1318 disease sub-type discovery, 214 CNTO95, melanoma clinical trials, 971 Coalescence, genealogies, 23–24 Coalition of 21st Century Medicine in the United States, 442 Cocaine aptamer biosensors, 595 dopamine-2 receptor effects, 506 Codeine pharmacogenomics, 371 Cohort studies, 284, 462 absolute risk related to environmental exposure, 463 association measures, 463, 463t disease frequency measures, 463, 463t gene–environment interactions, 52, 53, 465, 465t genotype–phenotype data access, 295 pharmacogenetics, 325 prospective longitudinal design, 285 relative risk estimation, 463 COL3A1, glomerular disorders, 1059 COL4A5 mutations, Alport syndrome, 1062 COL8A2, corneal disease, 1257 Collagens hepatic stellate cell production, 1145, 1146 liver fibrosis, 1139 Colorectal adenoma, 15, 810, 811 adenoma to carcinoma sequence, 880–882, 881f chromosomal instability pathway, 880–881, 882

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1421

mismatch repair pathway, 880, 881–882, 889, 890 Colorectal cancer, 879–892 N-acetyltransferase (NAT) polymorphismrelated risk, 1213–1214 alcohol consumption-related risk, 1212 bevacizumab treatment, 998 biomarkers, 889 candidate genes, 886–887 causation, 879 chemoprevention, 890–892 chemotherapy, 889, 890 genetic targets, 891, 891t response prediction, 891–892 copy number variation (CNVs), 882, 887 dietary factors, 57 folate, methylenetetrahydrofolate reductase polymorphism-related risk, 1213 DNA methylation analysis, 136, 138, 880, 882 early diagnosis, 879 edrecolomab treatment, 999 epidemiology, 879 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET) staging, 496 family history/familial forms, 393, 481, 487, 488, 810–811, 879, 883, 886, 888 inherited monogenic syndromes, 879, 883–886, 884t prevention strategies, 483, 484t gene expression profiles, 882, 890 genetic predisposition, 879, 880f, 882–887 genetic testing, 263, 887–889 genome-wide association studies, 887 genomic model, 880–882 inflammatory bowel disease association, 819 ulcerative colitis, 855, 1045 K-ras mutations biosensor detection, 591 panatimumab treatment, 340 metastasis, 880 proteomics, 985 microsatellite instability, 881–882 multistep development process, 810, 810f, 811 pharmacogenomics, 890–892 plasma methylation markers, 138 prognosis, 889–890 molecular signatures, 81 risk assessment, 887–889, 888f risk factors, 882–883, 886 screening, 879, 889 somatic mutations, 812 sporadic/isolated, 882, 886–887, 889 tumor vaccines plasmid DNA-based, 581 viral vectors, 582 tyrosine kinase gene mutations, 812 Colorectal polyps see Colorectal adenoma

1422

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Index

CombiMatrix microarrays, 165–166 Commercial exploitation, 16–17 biomarkers, 317–318 genomics, 434–435 Commercial genomics sector see Genomics firms Common allele common disease hypothesis, 13, 14, 39 colorectal cancer, 886 Common host response to infection, 1318–1319, 1318f, 1348f Comparative genomic hybridization, 369 Burkitt lymphoma, 839 colorectal cancer, 882 diffuse large B-cell lymphoma, 834 prostate cancer, 900 see also Array comparative genomic hybridization Comparative sequence analysis, 120–128 applications to genomic medicine, 121, 128 conserved sequences computational analysis, 124 experimental analysis, 124 identification, 124 disease-associated sequence variants, 124–128 compex genetic disease, 127–128 distal regulatory element function, 127 mRNA processing, 125–126 promoter function, 127 protein function, 125 drug target identification, 336 environmentally-responsive genes, 1012–1013 multi-sequence alignments, 123–124 orthologous sequences, 121, 1012 process, 121–124, 122f sequence acquisition, 121 repetitive elements masking, 123 resources, 121, 123t species selection, 121 Complement, 573, 1348 Complete atrioventricular canal, 782 Complex disease, 378, 379f ancestral susceptibility alleles, 1190 association studies, 29, 88–89 baseline risk assessment, 382 clinical utility of genetic testing, 266, 267 common allele common disease hypothesis, 13, 14, 39 comparative sequence analysis, 127–128 copy number variation (CNVs), 39, 116 environmental factors, 48–50 case-control studies, 53–56 interactions, 49, 49f, 50 research approaches, 52 epigenetic changes, 66–67 familial risk assessment, 481–483, 482t modifiable risk factors, 487 public health approaches, 488

family history, 481 gene/DNA sequence variants, 37–38t genome-wide association studies see Genome-wide association studies genomic approaches, 33–43, 34f, 50, 263–264, 568 data integration, 267–268, 269f DNA variant relationship with phenotype, 36, 37–38t, 38–40 genetical genomics, 40–41, 42 interactome, 42 intrauterine environmental exposure effects, 1011 isolated population studies, 39 metabolomics, 42–43, 180 molecular subtyping, 41–42 pharmacogenetic research targets, 322 predictive factors, 380–382 healthcare approaches, 264 proteomics, 42 rare allele involvement, 39 susceptibility gene identification, 441 systems biology, 40, 41t transcriptomic analysis, 40 twin studies, 49 Computational analysis, 206–221 methods, 213–214 molecular signature classifiers, 81 Computerized provider order entry (CPOE) system, 243–244 Computerized tomography (CT), 370 brain tumor monitoring, 962 cirrhosis, 1141 Crohn’s disease, 1044 head and neck cancer, 947 heart failure, 697 lung cancer screening, 858, 859 pancreatic cancer screening, 922–923 pancreatic mucinous cystic neoplasms, 925 prostate cancer, 903 systemic sclerosis, 1159 see also Positron emission tomographycomputerized tomography (PET-CT) Computerized tomography (CT)-guided fine-needle aspiration, pancreatic cancer diagnosis, 923 COMT (catechol-O-methyltransferase), 1239, 1240 bipolar disorder studies, 1303 schizophrenia candidate genes, 1285, 1303 Conduct disorders, 55 Confederation of Cancer Biobanks, 286 Confidentiality of data biobanking, 391 public health genomics, 450 Confocal laser microarray scanning systems, 547 Confocal microscopy, 525–526, 525f, 526f flexible fiber probes, 526, 527f Confoundings factors, 277

case–control studies, 323, 464 Congenital adrenal hyperplasia 11β-hydroxylase deficiency, 628 CYP11B1 mutations, 628 hypertension, 628 newborn screening, 359, 471 Congenital disease, molecular signature analysis, 151 Congenital fibrosis of extraocular muscles type 1 (CFEOM1), 1257 Congenital heart disease, 781–790 cytogenetic/molecular genetic testing, 787–788, 788t family history, 788 gene discovery conventional genetics, 781–785 large scale cDNA sequencing, 786 microarray transcriptional profiling, 786, 787f mutagenesis and phenotypic screens, 786–787 subtractive hybridization/differential display, 786 genetic counseling, 788–789 genetic factors, 781–782, 782t cell signaling genes, 784 chromosomal aneuploidies, 785 extracellular matrix protein genes, 784–785 transcription factor genes, 782–784 imaging investigations, 788 medical evaluation approach, 788–789, 789f physical examination, 788 right ventricular heart failure, 693 Congenital myopathies, 1268 Congenital night blindness, rhodopsin mutations, 1260 Conjugate vaccines, 562–563 Connective tissue growth factor (CTGF) glomerular disorders, 1059 IgA nephropathy, 1058 systemic sclerosis, 1157 Connectivity, cardiac allograft rejection gene expression network studies, 713 Connectivity Map, 167, 199–200, 200f drug discovery applications, 337 Connexins, early onset cataract, 1259 Conotruncal anomaly face syndrome, 1285 Consent access to genetic information, 396 biobanking, 292, 391 genetic testing, 363 investigatory in vitro diagnostic studies, 417 newborn screening, 455 public health genomics, 450 sample collection, 353 Conserved sequences, 12 comparative sequence analysis disease-associated sequence variants, 125 identification, 124

Index

non-coding sequences, 125–127 protein-coding sequences, 125 genome browser data, 124 Consumer education, 266, 364 genomic literacy, 402–404 Consumer health literacy, 253 genomic medicine, 402–404 Consumer online health information retrieval, 252–256 Contrast agents, magnetic resonance imaging (MRI), 515–517 Control groups comparative trials, 280 experimental design, 277 bias, 280 Convection enhanced delivery, brain tumor chemotherapy, 962 Convenience samples, 464 Cooperative Human Tissue Network (CHTN), 290–291 Copy number variation (CNVs), 10, 96, 108–118, 361 biallelic, 108, 109f cancer, 369, 813 causal genomic rearrangements, 108–109 non-allelic homologous recombination, 108–109, 110, 110f non-homologous end joining, 109–110, 111f clinical cytogenetic diagnostics, 117–118, 117f colorectal cancer, 882, 887 complex disease studies, 39 databases, 110, 112 definition, 108 detection, 112–114 array-based comparative genomic hybridization, 112–114, 113f genotyping arrays, 105, 114, 369 whole-genome sequence comparisons, 114 disease susceptibility associations, 114–116 functional impact, 112 genomic databases development, 26 genomic distribution, 110, 112, 112f HuRef genome analysis, 10 local regulatory variants, 12 multiallelic, 108, 109f neurodevelopmental disorders, 110, 114, 117 pharmacogenetics, 116–117 prostate cancer biochemical relapse after surgery, 904 single nucleotide polymorphisms (SNPs) tagging, 116 Cornea, 1256 genetic disorders, 1257–1259 Corneal dystrophies, 1257, 1258–1259 Corneal keratitis, PAX6 mutation, 1256 Cornelia de Lange syndrome, 1257

Coronary artery bypass grafting, 796 genetic factors in acute/delayed graft occlusion, 797–798 hemorrhage, 799 inflammatory response, 795 postoperative myocardial infarction, 797 postoperative neurocognitive dysfunction, 799 see also Cardiac perioperative medicine Coronary artery disease, 441, 665 biomarkers, 301 endothelial cell adhesion molecules, 654 environemntal risk factors, 55 familial risk, 481, 486, 487, 488 assessment, 483 modifiable risk factors, 487 prevention strategies, 483 fibrinogen polymorphism influence, 55 gene therapy, 616–617 genetic predisposition, 681 genome-wide association studies, 39, 265, 666–667, 666t heart failure, 692, 693, 694f metabolic profiling, 184 twin studies, 665 see also Acute coronary syndromes; Myocardial infarction Coronaviruses, protein microarray detection/ genotyping, 370 Corticosteroids asthma management, 1091, 1092 gluocorticoid resistance, 1091 non-responsers, 1091–1092 cardiac transplantation rejection prevention, 706 AlloMap test performance, 711, 712f chronic obstructive pulmonary disease, 1106 unresponsiveness, 1103, 1104f, 1107 pharmacogenomics, 1047, 1092 systemic sclerosis, 1162–1163 scleroderma renal crisis, 1164 ulcerative colitis, 1045 Corticotrophin-releasing hormone, depression, 1291 Cortisol metabolism, depression, 1291, 1295 Cost-benefit analysis, 425 Cost-consequence analysis, 425 Cost-effectiveness analysis, 424–426 genomic medicine, 424, 425t genomic testing, 425t, 427 access/reimbursement issues, 394 pharmacogenetics, 415 resource allocation decision-making, 425–426 Cost-minimization, 425 Cost-utility analysis, 425, 426 Costs drug development, 344 genetic/genomic tests, 362–363 third-party payments, 363

■

1423

genomic medicine, 266, 267 pharmacogenetic test clinical utility, 329 Cowden syndrome breast cancer, 871 PTEN mutations, 382, 886 Cox Proportional Hazards Model, 214 Coxsackie B3 virus, idiopathic dilated cardiomyopathy, 693 CPE mouse, 1175 CpG islands, 60, 131 DNA methylation, 131 cancer cells, 370 colorectal cancer, 136, 880, 882 ovarian cancer, 917 see also DNA methylation CpG methylation-sensitive restriction enzymes, 132 CRAF, melanoma, 970 Creatine kinase cardiac-specific isoenzyme, acute coronary syndrome diagnosis, 682 CREB (cAMP response element binding protein) antidepressant effects, 1295 cardiac transplantation rejection involvement, 713 CREST syndrome, 1159 CRF, depression, 1295 CRH1/2, obesity, 1175 Critical Path Initiative, 368, 421–422 Critical-limb ischemia, 774, 775 Crohn’s disease, 14, 96, 1040 anti-Saccharomyces cerevisiae antibodies (ASCA), 1043, 1044, 1045, 1046, 1072 CARD15/NOD2, 38, 39, 1043, 1045, 1072 phenotype relationship, 1046 classification, 1044, 1046 clinical features, 1044, 1071 colorectal cancer risk, 886 DEFB4 copy number variation, 115 diagnosis, 1044–1045 eternacept treatment, 1076 gene expression profiling, 162 genetic factors, 1041, 1042t, 1043 disease course prediction, 1046 genome-wide association studies, 39, 265, 1043 incidence, 1040 infliximab treatment, 1046, 1076 pharmacogenomics, 1047 peptic ulceration, 1122 prognosis, 1046 severity assessment, 1044–1045 spondyloarthropathy relationship, 1071–1072, 1074 surgical management, 1046 therapeutic tumor necrosis factor-α blockade, 1076 twin studies, 1041 see also Inflammatory bowel disease

1424

■

Index

Cross-linked iron-oxide (CLIO) contrast agents, 516, 516t cell surface receptor imaging, 518 cell tracking applications, 517 Cross-over design, 279, 280 placebo-controlled, 280 Cross-sectional studies, 462 association measures, 463, 463t disease frequency measures, 463, 463t Cross-validation, molecular signature analysis, 150–151 Crystallins, early onset cataract, 1259 CSF1, cancer cell expression, 819 CSRP3 mutations (LIM protein), hypertrophic cardiomyopathy, 718 CTLA4 diabetes type 1, 1187, 1188 gastric MALT lymphoma, 822 natural killer cells, 822 regulatory T cells (Treg), 574, 575, 820 rheumatoid arthritis, 1020, 1021 systemic sclerosis, 1157 CTSE, ovarian cancer, 916 Cumulative incidence, 463 Cutaneous diffuse large B-cell lymphoma, 834 Cutaneous lichen amyloidosis, 935 CX3CR1 polymorphism, asthma, 1088 CX32 promoter mutations, Charcot-MarieTooth disease, 126f, 127 CX37 polymorphism, coronary artery disease, 666 CXCL1 (GROα), chronic obstructive pulmonary disease, 1102 CXCL8, chronic obstructive pulmonary disease, 1102 CXCL9 (MIG), hepatitis C infection, 1382, 1383 CXCL10 (IP10) chronic obstructive pulmonary disease, 1102 hepatitis C infection, 1146, 1382 CXCR2, systemic sclerosis, 1157 CXCR2 antagonists, chronic obstructive pulmonary disease, 1107 CXCR4 human immunodeficiency virus 1 (HIV-1) co-receptor, 1327 pancreatic mucinous cystic neoplasms, 926 spondyloarthropathies, 1074 viral entry inhibitors, 1334 Cyanide fluorophores quantum dot reporter probes, 552 viral DNA target labeling, 546, 547 CYBA polymorphism, atherosclerosis, 658 Cyclic citrullinated peptide antibodies, rheumatoid arthritis, 1017, 1018, 1020, 1022–1023 Cyclin D1 (CCND1) carcinogenic compound metabolism, 303 gene expression profiling in cancer progression, 164

head and neck cancer, 945, 949, 951 mantle cell lymphoma, 838 Cyclin-dependent kinase inhibitor genes, cardiovascular risk, 671, 681 Cyclin-H, hepatitis B infection, 1146 Cyclooxygenase pathway, ovarian cancer metastasis, 918 Cyclooxygenase-2 (Cox-2) chronic obstructive pulmonary disease, 1103 head and neck cancer, 951 Cyclooxygenase-2 inhibitors anti-cancer agent combined therapy, 820 myocardial infarction risk, 199 Cyclophosphamide pharmacogenomics, 1002, 1159 systemic sclerosis, 1162, 1163 Cyclosporine, 706 inflammatory bowel disease, 1048 systemic sclerosis, 1162 CYP1A1 polymorphism, lung cancer risk in nonsmokers, 304–305 promoter methylation, prostate cancer, 899 CYP1A2 polymorphism antidepressants pharmacogenomics, 1293 colorectal cancer risk, 887 CYP2A6 polymorphism, 360 CYP2C9 polymorphism, 349 non-steroidal anti-inflammatory agent (NSAID)-induced peptic ulcer disease, 1131 pharmacogenomics antidepressants, 1293 phenytoin, 327 warfarin response variability, 326, 329, 349, 371, 768, 769t CYP2C19 polymorphism, 383 AmpliChip test, 331, 360, 371, 441 pharmacogenomics lapatinib, 350 proton pump inhibitors, 1131 psychiatric disorders, 1286 CYP2D6 polymorphism, 383 AmpliChip test, 331, 360, 371, 441 copy number variation (CNVs), 116 drug response variability, 349, 350 pharmacogenomics, 371 antidepressants, 1293 atomoxitine, 421 codeine, 371 healthy volunteer studies, 325 paroxetine, 327 psychiatric medication, 1286 CYP3A4 polymorphism pharmacogenomics antidepressants, 1293 clarithromycin, 1131 lapatinib, 350 CYP3A5 polymorphism, lapatinib pharmacogenomics, 350

CYP11B1 mutations, congenital adrenal hyperplasia, 628 CYP19 polymorphism, prostate cancer, 568 CYP26a1, 1013 CYP26b1, 1013 CYPs (cytochrome P450 isoenzymes), 49, 326 AmpliChip, 331, 360, 371, 441 antidepressants metabolism, 1293, 1294t cigarette smoke metabolism, 857 pharmacogenomics, 371–372 drug dosage adjustment, 349–350 Helicobacter pylori eradication, 1131 vascular reactive oxygen species generation, 653 Cystatin A, head and neck cancer, 951 Cystatin C, amyotrophic lateral sclerosis, 1278 Cystic fibrosis, 88, 125, 126, 447 carrier detection, 474 family history, 461, 481 gene therapy, 615 newborn screening, 359, 474–475, 476 platform, 474 sensitivity, 474–475 pancreatic cancer association, 922 Pseudomonas aeruginosa genetic adaptation during chronic infection, 569 see also CFTR (cystic fibrosis transmembrane conductance regulator) mutations Cystic fibrosis transmembrane conductance regulator see CFTR Cytochrome C oxidase mutations, amyotrophic lateral sclerosis, 1273 Cytochrome oxidase subunit I, prostate cancer association, 899 Cytochrome P450s see CYPs Cytogenetic assays copy number variation (CNVs), 117–118, 117f early disease biomarkers, 303 Cytogenetics acute lymphoblastic leukemia, 844, 849 acute myeloid leukemia, 844, 849 leukemia, 844–845, 845t lymphoma, 831t, 832 see also Chromosomal aberrations Cytokines asthma, 1084 glomerular disorders, 1059 infectious disease response, 1314, 1350 common host response, 1318, 1319 Gram-positive organisms, 1351 polymorphisms, 1366–1367 inflammatory bowel disease therapeutic target, 1048, 1049 nucleic acid-based cancer vaccine potentiation, 580, 581 rheumatoid arthritis proteomics, 1023, 1024 sarcoidosis, 1111 tumor cell secretion, 575, 819

Index

Cytokinesis-block micronucleus (CBMN) assay, 303–304, 304f Cytomegalovirus cardiac transplantation rejection AlloMap test performance, 711 microarray diagnostics, 547 susceptibility genes, 1364 systemic sclerosis-related antibodies, 1156 viral chips, 542, 551 viral gene expression detection, 550 Cytotoxic T cells regulatory T cell (Treg) suppression, 575 tumor microenvironment, 576, 821 virally-infected cell elimination, 538–539 D Dacarbazine melanoma, 970 tumor response prediction, 138 Daclizumab, 998 Danon’s syndrome, hypertrophic cardiomyopathy, 719 DAOA (D-amino acid oxidase activator), 1302 bipolar disorder, 1303 schizophrenia, 1303 DAPK1, promoter hypermethylation in lung cancer, 858 Darier’s disease, 1302 Data access, 270 policy issues, 391–392 public health genomics, 450 see also Confidentiality of data Data acquisition, 268–269 Data analysis microarray information, 160–161 mining, 160–161 supervised learning, 160, 161f unsupervised learning, 160, 161f viral chip technology, 548 Data repositories, 214–215, 227, 239 search problems, 215 Data standards, 268–269 Data-sharing policies, 392 Data-withholding practices, 392 Database of Genomic Variants, 110 Database of Genotype and Phenotype (dbGAP), 26, 227 Databases, 227–228, 230, 230f, 230t access policies, 392 cancer genomics, 813–814 data federations, 239–240 management, 228–229, 228t, 229f modeling, 229–230 submission, 228 warehouses, 239 drug discovery applications, 337 funding, 228 genomic sequences for comparative sequence analysis, 121

T-cell epitopes, 577 DATATOP study, 301 Daunorubicin pharmacogenomics, 849 db/db mouse, 1175 dbGaP, 26, 227 DC Chip, 821 DC-SIGN, host response to mycobacteria, 1357 DCC (deleted in colorectal carcinoma), 880, 890 DDX5 (DEAD-box polypeptide-5) cirrhosis, 1142 hepatitis C proteomics, 1384 Death-associated protein kinase (DAP-kinase), head and neck cancer monitoring, 950 DECIPHER, 110, 118 Decision analysis, economic evaluation, 425 Decision trees, 213 Decitabine, 845 deCode Genetics Inc., 236, 286, 295, 391, 440 Decorin, glomerular disorder gene therapy, 1063 Deep brain stimulation, Parkinson’s disease, 1239 Deep vein thrombosis, 768, 796 DEFB4, Crohn’s disease-related copy number variation, 115 Deleted in colorectal carcinoma (DCC), 880, 890 Deletions, 6, 9 see also Insertion/deletion (in/del) polymorphism Delta hepatitis see Hepatitis D Delta-like protein 3 (DLL3), astrocytomas, 960 Dementia, 1222–1230 antemortem diagnosis, 1229 application of genomics, 1229–1230 causes, 1222, 1223f clinical approach, 1229 genetic testing, 1229 incidence, 1223, 1223f neuroimaging, 1285 primary, 1222, 1224–1229 pathophysiology, 1223 secondary, 1222 Denaturing gradient gel electrophoresis, 96 Denaturing high-performance liquid chromatography, 96 Dendrimer technology, viral target DNA labeling for microarray diagnostics, 547 Dendritic cells adjuvant stimulation, 583 antigen presentation nucleic acid-based cancer vaccines, 579 protein cancer vaccines, 578 cancer immune response, 574 in vitro transfection for cancer vaccine delivery, 580, 581

■

1425

innate immune response, 1348, 1365 pathogen-specific responses, 1314, 1319 regulatory T cell (Treg) effects, 574, 575 response to infection, gene expression profiles, 1318, 1369 T cell activation, 820 tumor microenvironment, 576, 819, 820–821 Dengue viruses, viral chip technology, 551 Depression, 55, 1289–1296 anxiety comorbidity, 1290 biomarker imaging approaches, 532 causes, 1290, 1290f clinical course, 1289 diagnosis, 1289–1290 environmental factors, 1291 5-HTT gene interactions, 1293 gene expression studies, 1295 genetic factors, 1291–1292 association studies, 1291–1292, 1292t emotional regulation/stress response, 1292–1293 linkage analysis, 1291 5-HTT (5-hydroxytryptamine transporter; SLC6A4) polymorphism, 1283, 1284 hypothalamus-pituitary-adrenal axis involvement, 1291 neurobiology, 1290–1291, 1290f neural plasticity, 1294–1295 signal transduction, 1294–1295 stress response system, 1295 pharmacogenomics, 1293–1294 cytochrome P450 polymorphisms, 1293, 1294t prevalence, 1290 proteomics, 1295–1296 response to antidepressants, 1293–1294, 1294t, 1295 TPH2 (tryptophan hydrolase) mutations, 1283 see also Bipolar disorder Developmental verbal dyspraxia, 96 DG-301, 687 Diabetes, 33, 357, 1187–1192 blood glucose detector, 590 epidemiology, 1187 family history, 481, 486, 487, 488, 1187 hyperglycemia-induced reactive oxygen species, 655 information database, 227 pancreatic cancer risk, 922 peripheral arterial disease risk, 773, 774, 778 Diabetes Heart Study, 666 Diabetes type 1, 441, 1187 applications of genetic research, 1192 candidate gene studies, 1187 gene expression profile comparison with other autoimmune disorders, 1035 gene-environmental factor interactions, 1192

1426

■

Index

Diabetes type (Continued) genetic risk, 1187, 1188, 1192 genome-wide association studies, 39, 1188, 1191–1192 linkage studies, 1061, 1187–1188 newborn screening, 477 permanent neonatal diabetes, 1192 prospective health care, 382 Diabetes type 2, 39, 67, 88, 441, 1187 applications of genetic research, 1192 calpain 10 gene, 38 candidate gene studies, 1188 cardiovascular risk, chromosome 9p21 region of interest, 671 DNA methylation alterations, 68 epidemiology, 1187 farglitazar pharmacogenomics, 353, 353t future genetic research, 1190–1191 gene expression data analysis, 160 gene–nutrient interactions, 1212 genetic risk, 1188, 1189 genome-wide association studies, 39, 265, 1188–1191, 1189t, 1190t blood lipids, 644, 645t linkage studies, 1061 metabolic syndrome-associated risk, 1194 nephropathy, 1061 obesity association, 1172, 1187, 1189 TCF7L2 polymorphism, 265, 457 Diabetic nephropathy, 1056 candidate genes, 1061 renin-angiotensin system, 1061t gene expression profiles, 1059, 1060t genome-wide association studies, 1061 Diagnostic applications, 207–208, 264 Diagnostic interview for genetic studies (DIGS), 1283 Diagnostic markers see Biomarkers Diagnostic tests accuracy, 374 clinical utility, 362 clinical validation, 362, 447 costs, 362–363 third-party reimbursement, 363 value-based reimbursement, 430–431 economic incentives for development, 428–430 genomics firm business activity, 438 laboratory standards, 361–362 new technologies, 368–370, 369t applications, 370–373 newborn screening programs, 471 performance, 313–316 likelihood ratios, 314–315 predictive values, 315–316, 328 quality control, 328 ROC curve, 315 sensitivity, 313–314, 328 specificity, 313–314, 328 transferability, 317

pharmacogenomics, 328–329 drug combinations development, 419– 421, 429 regulatory issues, 420 policy issues, 390 pre-analytical variation effects, 311–312, 312f, 328 public health genomics, 447, 450 regulatory issues, 414–415, 416, 429 research studies, 316 results communication, 362 targeted therapies, 990 translational challenges, 374–375, 375t, 390 see also In vitro diagnostics Diagonalized linear discriminent analysis (DLDA), molecular signature analysis, 148 3,4-Diamino pyridine, myasthenia gravis, 1276 Dicer1, 603 Dicer, siRNAs generation, 195 Didanosine, 1342 Dietary assessment, 1205–1206 time frame, 1212 Dietary factors, 56–57 breast cancer, 1212 cancer risk, 1212, 1213 cardiovascular risk, 672 colorectal cancer, 886, 887 preventive strategies, 890 gene interactions, 1204, 1205, 1206–1214 head and neck cancer, 946 intrauterine effects on DNA methylation, 1011 metabolic syndrome, 1196 obesity, 1171, 1210–1211 pancreatic cancer, 922 see also Nutrition Dietary methyl donors, 1213 Differential display, 157 Differential hybridization, 157 Differential methylation hybridization (DMH), 133 Diffuse large B-cell lymphoma, 833–835 activated B-cell subtype, 833, 834 chromosomal aberrations, 833 diagnosis, 833 gene expression profiles, 822–823 prognostic applications, 151, 162, 164, 834 skin locations, 834–835 subtypes discrimination, 209, 209f, 268, 833, 834–835 therapeutic target identification, 835 germinal center B-cell subtype, 833, 835 histogenesis, 833 International Prognostic Index (IPI), 832 Diffuse Lewy body dementia, 1222, 1227– 1228, 1229, 1233 Lewy bodies (α-synuclein aggregates), 1222, 1227, 1228f Diffuse parenchymal lung disease, 1110–1118

classification, 1110 genetic factors, 1110–1111 inbred mouse studies, 1111, 1112t surfactant protein C mutations, 1114, 1115 genomics, 1115 microarray studies, 1115–1116 pathogenesis, 1115 sarcoidosis see Sarcoidosis surfactant proteins, 1114 see also Familial interstitial pneumonia Diffusion transfer imaging, 514, 514f Digene, 440 DiGeorge syndrome, 786, 787, 1285, 1303 congenital heart disease, 782–783 Dihydropyrimidine dehydrogenase, pharmacogenomics, 360 5-fluorouracil, 891, 892 Dilated cardiomyopathy, 692, 717, 721 alpha B-crystallin, 1259 Diltiazem, hypertrophic cardiomyopathy, 723 Dioxin toxicity, 1012 Dipeptidylpeptidase 10 see DPP10 Diphtheria vaccines, 562 Direct-to-consumer genetic test marketing, 403, 442 BRCA1 testing, 458 policy issues, 393–394 DISCI (disrupted in schizophrenia), 1284– 1285, 1301 bipolar disorder association, 1301 Discovery Partners International, 440 Discrimination, genetic, 267, 365 policy issues, 395–396 public health genomics, 451 Disease frequency measures, 463–464, 463t Disease sub-type discovery, bioinformatics, 213–214 Disease susceptibility, 13–15 Disease tracking, 380, 382 predictive models, 380 Disease-modifying anti-rheumatic drugs (DMARDS) rheumatoid arthritis, 1024 spondyloarthropathies, 1075–1076 systemic sclerosis, 1162 Disintegrins, melanoma therapeutic targets, 971 Disopyramide, hypertrophic cardiomyopathy, 723 Diuretics, pharmacogenomics, 630 DJ-1 (PARK7), Parkinson’s disease, 1237 DLBCL, RT-PCR gene expression profiling, 167 DLG5, inflammatory bowel disease, 1043 corticosteroid pharmacogenomics, 1047 DNA adduct biomarkers of exposure, 302, 303 conformation polymorphism detection, 96

Index

delivery for gene therapy direct naked DNA, 613–614 DNA-protein complexes, 614 host immune response (autoimmune disorder association), 614 liposomes, 614 melting temperature, 96 replication, positron emission tomography (PET), 503 DNA demethylation, 63 DNA methylation, 12, 60, 61, 62f, 131–139, 370 assessment technology, 132–136, 132t clinical applications, 136–139 methylation “content”/“pattern”/“level”, 133, 134f methylation-sensitive restriction enzymes, 132, 133 methylation-specific antibodies/proteins, 132–133 methylation-specific bisulfite modification, 132, 133, 134, 135 screening sample analysis, 135–136 tissue sample anaylsis, 133–135 bacterial systems, 61 cancer-related aberrations, 66–68, 68f therapeutic implications, 68–69 cell type specificity, 62 chromatin structure correlations, 61, 62, 65, 131 gene activity state determination, 65–66, 66f, 67f colorectal cancer, 880, 882 screening/surveillance, 889 colorectal polyps, 811 disease associations, 136, 137t early detection/diagnosis, 136, 138 late-onset, 68 prognosis, 138 treatment response prediction, 138–139 environmental factor influences, 62, 69 gene silencing, 65, 65f genome-wide epigenetic analysis, 133, 134t head and neck cancer monitoring, 950 intrauterine effects of environmental exposures, 1011 lung cancer early diagnosis, 858–859 marker discovery methods, 133 5-methyltetrahydrofolate metabolism, 1213 ovarian cancer, 917 pancreatic cancer screening, 923 pattern, 61–63, 133 as biomarker, 370 establishment/maintenance, 62 prostate cancer, 899 reversibility, 63 stem cells, 602, 603 vertebrate methylation sequence (CG), 62 DNA methyltransferase 1 (DNMT1), 62, 63, 131

cancer-related upregulation, 67–68 functional domains, 66 histone-modifying enzyme interactions, 65 DNA methyltransferase 1 (DNMT1) inhibitors, 69 DNA methyltransferase 2 (DNMT2), 63 DNA methyltransferase 3a (DNMT3a), 63, 65 DNA methyltransferase 3b (DNMT3b), 63, 131 gene mutations, 131 DNA methyltransferase 3L (DNMT3L), 63 DNA methyltransferase inhibitors, 69, 370 DNA methyltransferases (DNMTs), 61, 62, 131 chromatin-modifying enzyme interactions, 65–66 de novo activities, 63, 63f, 66, 131 maintenance activities, 62–63, 63f, 66, 131 methylation-independent biological effects, 66 sequence-specific targeting, 63 DNA repair enzyme genes defects brain tumors, 957 head and neck cancer, 946 ovarian cancer, 914, 915 methylation status, 139 polymorphisms cancer susceptibility biomarkers, 305 pancreatic cancer diagnosis, 926 DNA sequence imaging, positron emission tomography (PET) direct methods, 504, 504f indirect methods, 504–506 DNA sequencing, 88–97 automation, 434 by synthesis, 94–95 commercial R&D (“Next-Gen Sequencing”), 440, 441 congenital heart disease gene discovery, 786 copy number variation (CNVs) detection, 114 electronic medical records, 234–235 future goals, 96–97 genomics firm business activity, 438 hybridization on microarrays, 95 methodologies, 89, 90f, 95 PCR amplicon re-sequencing, 92–94 polymorphisms detection, 89, 95 whole genome shotgun method see Whole genome shotgun sequencing see also Comparative sequence analysis; Genome sequencing;Viral sequence determination DNA-adenine methylase (dam) gene, Salmonella typhimurium pathogenicity, 566 DNA-encoded antibody libraries (DEAL), blood biomarker analysis, 80

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1427

DNAStar, 438 DNMT3B mutations, ICF disorder, 131 Docetaxel, prostate cancer, 905–906 Domains, 8, 13 Donor lymphocyte infusions, 574 Dopa decarboxylase, 1240 Dopamine metabolism Parkinson’s disease therapeutics, 1239, 1239f pharmacogenomics, 1240 Dopamine-2 receptor polymorphism, bipolar disorder studies, 1304 positron emission tomography (PET), 505 using radioligand, 506–507 Dopamine-4 receptor polymorphism, psychiatric disorders, 1302 Dopaminergic pathways, psychiatric disorders, 1302 Down syndrome see Trisomy 21 Doxorubicin, pharmacogenomics, 1002 DPC4/SMAD4, pancreatic cancer, 921 DPP10 (dipeptidylpeptidase 10) asthma, 1087 chronic obstructive pulmonary disease, 1102 DRD2 see Dopamine-2 receptor DRD4 see Dopamine-4 receptor Drosha, 603 Drug abuse, 33 Drug development, 389, 418–419 biomarker applications, 299 surrogate endpoints, 300 clinical studies, 347–348 clinical trial safety, 350–352 critical path, 344 economics, 344, 426, 429 incentives, 428–430 failure rates, 343 molecular imaging, 497 monoclonal antibody anti-cancer agents, 995 pharmacodynamic markers, 338–340 pharmacogenomics, 337–338, 338t, 343–355, 346f, 415, 429 applications, 329, 340 drug-diagnostic test combinations, 419–420, 429 efficacy, 348–349 efficacy/safety profile tuning, 349–350 FDA guidance, 344 genetic biomarkers identification, 344, 347 regulatory issues, 340, 415, 418–421, 429 Voluntary Genomic Data Submission, 419, 422 process, 346–348, 347f RNA interference (RNAi) applications, 337, 352, 352f

1428

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Index

Drug discovery, 335–341 bioinformatics, 337 gene expression profiling, 167 genomics applications, 269–270, 337f drug identification, 337 target identification, 269–270, 336–337 genomics firm business activity, 438 high-throughput screening, 337 lead compounds, 337 pharmacodynamic markers, 338–340 pharmacogenomics, 347f target selection, 346 process, 335–336, 336f stem cell studies, 605 Drug pricing, 429 Drug response biomarkers, 337–338, 338t, 354f, 383 candidate gene approaches, 325–326, 344, 346 clinical studies, 347 whole-genome association studies, 325–326, 344 Drug response pathways, pharmacogenetic studies, 327 Drug therapy bioinformatics applications, 209–210 anti-cancer agent susceptibility, 209–210 cancer cell line drug responses, 210–211, 211f side-effects prediction, 210 clinical decision support, 243 Drug-allergy contraindications, 243 Drug–drug interactions, 243 Drug-eluting intracoronary stents, 674 Drug-induced long QT syndromes, 731 Drug-metabolizing enzymes, 360, 383 cigarette smoke metabolism, 857 genetic tests clinical acceptance, 419 drug labels, 418, 421 regulatory issues, 415, 416 pharmacogenetic studies, 326, 346, 371 drug response variability, 349–350 Duane’s syndrome, SALL4 mutations, 1257 Duchenne muscular dystrophy, 368, 1265, 1268, 1273 application of genomics/proteomics, 1278–1279 cell therapy, 1277 diagnosis, 1274 dystrophin mutations, 1268, 1272f, 1273, 1274 gene expression profiles, 1278–1279 gene therapy, 1277 genetic modifiers identification, 1279 monitoring, 1275–1276 newborn screening, 1273 prognosis, 1275 treatment, 1277 Duodenal ulcer, 1122, 1122f

susceptibility, 1130, 1131 see also Peptic ulcer disease Duplications, 6 segmental, 9 Dyskeratosis congenital, pulmonary fibrosis, 1115 Dystrophin mutations, muscular dystrophies, 1268, 1272f, 1273, 1274 E E2A-PBX1, acute lymphoblastic leukemia, 847 E-cadherin cancer-related hypermethylation, 67 inflammatory bowel disease, 1072 spondyloarthropathies, 1072 E-Predict, 548 E-selectin carotid artery atherosclerosis biomarker, 301 chronic obstructive pulmonary disease, 1103 inflammatory synovitis, 1072, 1073 postoperative myocardial infarction, 798 systemic sclerosis, 1159 EAAT2, amyotrophic lateral sclerosis, 1273 Early disease detection, 264, 457 biomarkers, 303–304 colorectal cancer, 879 lung cancer, 858–859 population genomic screening, 348–359 Early treatment, 242 Ebola virus, 1340 Echocardiography cardiac allograft rejection monitoring, 707 heart failure, 697 hypertrophic cardiomyopathy, 717, 721 screening, 722f, 723 septal morphology, 717, 717t, 718f, 721 Economic evaluation, 424–426 genomic medicine, 424, 426–432 genomic technologies, 426–428 breast cancer, 427–428 cost-effectiveness framework, 425t, 427 disease risk testing, 426 pharmacogenomics, 426 methodological approaches, 425 Edrecolomab, 999 EDTA, plasma sample effects, 311 Education consumers, 402–404 probabilistic information evaluation, 402–403 genomic literacy, 364, 402–404 genomic medicine, 401–411 health professionals, 363–364, 404–411, 451 competencies, 395 essential skills, 410–411, 411t genetic counselors, 408–409 laboratory geneticists, 410 medical geneticists, 408 medical specialists/subspecialists, 408

nurse geneticists, 409 pharmacists, 409–410 policy issues, 394, 395 primary care physicians, 405–408 public health genomics, 446, 451 newborn screening programs, 471 translational research, 368 EED, 603 Efficacy Proof of Concept studies, drug development, 347 EGFR see Epidermal growth factor receptor Egr-1, chronic obstructive pulmonary disease, 1103 Eicosanoids, targeted metabolic profiling, 185 EIGENSTRAT, 29 ELAC2, prostate cancer, 898 Elastin hepatic stellate cell production, 1144 liver fibrosis, 1139 ELASTIN (Williams syndrome), congenital heart disease, 785 Electrocardiogram acute coronary syndrome diagnosis, 682 Brugada syndrome, 739, 740, 740f catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 heart failure, 696 hypertrophic cardiomyopathy screening, 723 Lev-Lenegre progressive cardiac conduction disease, 743f long QT syndromes, 729, 730f, 732f cardiac ion channel mutation correlations, 738 Electronic medical records, 228–229, 233–240 biorespositories, 236, 237 clinical decision support, 235–236, 236f, 243, 245, 247 data sources, 234 family history, 235, 486–487 genomic data integration, 234–235, 235t genomic medicine, 234–235, 235t genomic research applications, 236–240 data extraction, 238–239, 239t genome-wide association studies, 236–237 patient information, 234 strategies for promoting adoption, 247–248 Electrophoresis DNA conformation polymorphism detection, 96 neuronal stem cell proteomics, 603 see also Gel electrophoresis, twodimensional; Polyacrylamide gel electrophoresis, two-dimensional (2D-PAGE) Electroporation, nucleic acid-based cancer vaccination, 579 Electrospray ionization-tandem mass spectrometry, metabolic profiling, 181, 183

Index

ELISA, 79 candidate protein biomarkers, 309 HIV infection diagnosis, 1328 pharmacodynamic marker assays, 340 virus identification, 539 ELISpot monitoring of cancer vaccines, 583 ELMO1, diabetic nephropathy, 1061 ELMOD2, familial interstitial pneumonia, 1117 Embryonic stem cells, 599 cell therapies, 604 DNA methylation, 603 gene expression profiles, 600 gene silencing, 603 proteomics, 603, 604 Emery Dreifuss muscular dystrophy, 1268 Emotional behavior, 1292 Emphysema, 1099 pathophysiology, 1100 surgical lung volume reduction, 1106 see also Chronic obstructive pulmonary disease Empirical Bayes methods, 147 Employment discrimination, 267, 365, 395 ENaC (epithelial sodium channel) mutations, Liddle’s syndrome, 628 Encapsulation, nucleic acid-based cancer vaccines, 579 ENCODE (Encyclopedia of DNA Elements) Project, 128, 134t, 440 End-stage renal disease, 1056 hypertension association, 629 Endogamy, 24 Endoglin, mesangial cell expression, 1058 Endometrial cancer, 885 Endomyocardial biopsy cardiac allograft rejection AlloMap test score relationship, 710–711 diagnosis, 701 grading system, 710t interobserver variability, 710 molecular score discordance, 710–711 monitoring, 707 gene chip analysis, 699–700 heart failure, 697 Endoscopic retrograde cholangiopancreatography, pancreatic cancer diagnosis, 923 screening, 922, 923 Endoscopic ultrasound pancreatic cancer screening, 922–923 pancreatic cystic neoplasm monitoring, 927 Endoscopic ultrasound-guided fine-needle aspiration pancreatic cancer diagnosis, 923 combined telomerase assay, 924 microdissection-based genotyping, 924 pancreatic mucinous cystic neoplasms, 925 Endothelial cells

activation, 759 inflammatory synovitis, 1072 atherosclerosis pathogenesis, 653 liver sinusoids, 1139 phenotypic variation, 759 prothrombotic stimuli response, 759, 760f reactive oxygen species, 652–653, 653f cell adhesion molecule expression induction, 654, 655, 656 inflammatory gene expression induction, 653–654 response to infection, gene expression profiles, 1318 tumor stroma, 808 vascular bed-specific thrombosis, 759 Endothelial differentiation gene-1 (EDG-1), glomerular epithelium expression, 1058 Endothelial lipase, 636 Endothelin, antihypertensive agent pharmacogenomics, 630 Endothelin receptor antagonists Raynaud’s syndrome, 1162 systemic sclerosis, 1164, 1165 Endothelin, -1 ET receptor interactions, 1164 hepatic stellate cells, 1145 systemic sclerosis, 1157, 1159, 1164, 1165 Endotoxin exposure hygiene hypothesis of asthma pathogenesis, 55–56 see also Lipopolysaccharide Energy expenditure regulation, genetic factors, 1178 Enfurvirtide, 1326 Enhancers, 12 mutation detection using comparative sequence analysis, 127 ultraconserved elements, 12 Enolase, laryngeal carcinoma, 951 eNOS polymorphism see Nitric oxide synthase ENPR1, obesity, 1176 Ensembl, 121, 123, 124 Entactin, hepatic stellate cell production, 1144 Entecavir, hepatitis B treatment, 1385 Enterobacteriacea, genetics of host response, 1354–1355 Enterovirus, viral chip technology, 551, 552 Entrez Gene, 219 Entrez Genome and Nucleotide Databases, 541 Environment–gene interactions, 47–57, 461, 1010–1015, 1011t amyotrophic lateral sclerosis, 1266, 1273 autoimmune disease, 1011 buffering, 51 cancer, 810, 813, 1011 cardiovascular risk, 672 chronic obstructive pulmonary disease, 1098–1099 cohort studies, 52, 53, 465, 465t absolute risk determination, 463

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1429

colorectal cancer, 879 comparative genomics, 1012–1013 complex disease, 48–50 case-control studies, 53–56 susceptibility gene interactions, 49, 50 depression, 1291 5-HTT gene polymorphism, 1293 disease process investigations, 1012 DNA methylation influences, 62 prophylactic strategies, 69 epidemiological studies, 465–466 epigenetics, 56–57, 60, 67 exposure biomarkers, 302–303, 302f measures, 53, 1013–1014, 1014f, 1015 health impact, 1011–1012 heart failure, 693 hemostasis, 758–759 HIV infection/AIDS susceptibility, 1328, 1330–1331 hypertension, 624, 625, 625f inflammatory bowel disease, 1041 Mendelian randomization approach, 466 mouse genome center studies, 51–52 neurobiology, functional imaging approaches, 534–535 neuromuscular junction disorders, 1268 obesity, 1171, 1172, 1178 Parkinson’s disease, 1233, 1235, 1237 population-based studies, 49, 50 susceptibility screening, 359 psychiatric disorders, 1283 public health genomics, 447–448, 448f, 457 research approaches, 52–53 rheumatoid arthritis, 1018, 1022 statistical analysis, 53 systems biology approach, 75 twin studies, 1011 types, 48 viral hepatitis, 1377 see also Nutrition The Environmental Determinants of Diabetes in the Young (TEDDY), 477 Environmental factors see Environment–gene interactions Environmental Genome Project (EGP), 48, 50–52 Environmental health sciences, 1010–1011 Environmental sensor genes, 112 Enzyme immunoassay hepatitis C diagnosis, 1378 see also ELISA Enzyme substrates, positron emission tomography (PET), 507, 507f Eosinophilic granuloma, 1110 Eosinophils, tumor microenvironment, 819 Ep-CAM (17-1A), monoclonal antibody targeted therapy, 999 Epi-illumination planar fluorescence imaging, 527, 528f

1430

■

Index

EPIC (European Prospective Investigation into Cancer and Nutrition), 286, 1212 EpiCare system, 234 Epidemiology, 461–467 association measures, 463–464, 463t disease frequency measures, 463–464, 463t evidence base, 466–467 gene–environment interaction, 465–466 Mendelian randomization approach, 466 objectives, 462 population-based samping, 461, 464 risk measures, 463–464 study designs, 462 see also Association studies; Genome-wide association studies Epidermal growth factor (EGF), glomerular disorders, 1059 Epidermal growth factor receptor (EGFR) brain tumor therapeutic targeting, 962 glioblastoma, 958, 959 gene expression profiles, 960 gliomas, 956, 957 immunotherapy, 963 head and neck cancer, 945, 949 cytotoxic agent resistance, 950 relapse prognosis, 950 therapeutic targeting, 951, 952 lung cancer, 95, 496, 862, 863f therapeutic targeting, 862–863, 864 melanoma antiangiogenic therapy, 972 monoclonal antibody targeted therapy, 998 ovarian cancer, 916, 917 prostate cancer circulating tumor cells, 903 Epidermal growth factor receptor inhibitors, 862–863, 864, 952 brain tumor pharmacogenomics, 961–962 small molecules, 990 erlotinib, 1002 gefitinib, 1001 Epigenetics, 8, 370, 1011 definition, 2 environmental exposure effects, 56–57 gene–environment interactions, 60, 67 involvement in complex disease, 66–67 ovarian cancer, 917 prostate cancer, 899–900 stem cells, 603 Epigenome, 60 definition, 2 Epigenomics, 60–70 definition, 2 Epilepsy, 1243–1252 common forms, 1248–1249 candidate genes, 1248–1249, 1251 linkage/association studies, 1248, 1248t future research approaches, 1251–1252 Mendelian (familial) forms, 1243, 1244– 1247t, 1247–1248 pharmacogenomics, 1249–1251, 1250f treatment, 1247, 1248

efficacy, 1249 safety, 1249–1250 Epistatic associations, pharmacogenetic studies, 327 Epithelial sodium channel (ENaC) mutations, Liddle’s syndrome, 628 Epstein–Barr virus Burkitt lymphoma, 830, 839 host cytokine system effects, 539 lymphomas association, 830 multiple sclerosis association, 1036 viral chips, 541, 551 viral gene expression detection, 550 Equilibrium radionuclide angiography, heart failure diagnosis, 697 ERBB1 see Epidermal growth factor receptor ERBB2 see Her2/neu ERBB3, ovarian cancer, 916 ERG, rearrangements in prostate cancer, 161 ERG-TMPRSS2 fusion, prostate cancer, 904 ERK1/2, 656 hepatic stellate cells, 1146 utilization as pharmacodynamic marker, 340 Erlotinib, 1002 brain tumors, 962 head and neck cancer, 952 lung cancer, 338 Erythroplakia, 946 Escherichia coli genome mapping, 1347 host gene expression in response to infection, 1351, 1369 outer membrane porin C antibodies, inflammatory bowel disease, 1043, 1045 Escherichia coli O157.H7, 590 nanoparticle biosensors, 595 Esophageal cancer 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 496 regulatory T cells (Treg), 820 Esotropia, 1257 ESPRIT, 1334 ESR1/2 methylation status, tumor treatment response prediction, 138 Essential hypertension see Hypertension Essential myosin light chain (MYL3) mutations, hypertrophic cardiomyopathy, 718 Estonia, 440 Estonia Genome Project (EGP), 286 Estramustine, 992 Estrogen receptor breast cancer targeted hormonal therapy, 992–993, 993f Oncotype Dx assay, 992, 993 Eternacept rheumatoid arthritis, 1024 spondyloarthropathies, 1076, 1077

Ethical issues, 364–365 genomics research policy, 389 newborn screening, 455 pharmacogenetic tests, 329 public health genomics, 447, 451 research results disclosure to participants, 392 Ethnic/racial variation, 364 breast cancer, 870 cardiovascular risk, 671 chronic obstructive pulmonary disease, 1099 colorectal cancer, 879 diet-induced atherosclerosis, 1210 genomics research analytical variables, 390 policy issues, 390–391 hemostasis, 760–762 coagulation factor/endothelial marker levels, 761, 761t hypertension, 627 nephrosclerosis association, 629 lactose intolerance, 1207 lung cancer, 857 multiple sclerosis, 1033 peripheral arterial disease, 775 pharmacogenomics and heart failure management, 321, 698 population genomics, 27–28 sarcoidosis, 1111 thrombosis, 760–762 susceptibility gene incidence, 762 Ethylnitrosurea mutagenesis, 1315 ETS translocations, prostate cancer, 904 ETV1, rearrangements, prostate cancer, 161 ETV1-TMPRSS2 fusion, prostate cancer, 904 Eurogentec, 440 Europe genomics firms, 439 genomics research policy, 389, 389t European Agency for the Evaluation of Medicinal Products (EMEA), pharmacogenomics information sharing, 419 European Bioinformatics Institute (AMBLEBI) Array Express repository, 152, 214, 215 EV11, acute myeloid leukemia, 847 Evaluation of genomic applications in practice and prevention (EGAPP), 374, 450, 458 Everolimus see RAD001 Evidence base for genomic medicine, 266, 267 Evidence-based guidelines, genomics translation into medical practice, 263 EWS, hepatitis C, 1149 ExactPlus, 124 EXP, immunomodulatory vaccines, 1036 Experimental autoimmune encephalomyelitis, 1034, 1035 Experimental design, 275–282 accuracy, 277–280, 281 bias, 277, 279, 280–281, 282, 316

Index

measurement, 276 selection, 276 biological variation considerations, 276 blinding, 280 blocking variables, 277, 281 concepts, 276–277 conditions/treatments, 277 allocation to experimental units, 279 confounding factors, 277 control groups/treatments, 277, 280 diagnostic biomarker trials, 316 gene discovery, 147 genomic studies, 280–281 epidemiological, 462 timing of tissue specimen collection, 281 see also Genome-wide association studies maximization of information content, 280 measurement error minimization, 276 molecular signature analysis, 148 Monte Carlo simulation, 278–279, 278f, 278t multiple endpoints, 276 nuisance variables, 277, 281 optimization, 279–280 patient eligibility criteria, 279, 280 pharmacogenetics, 323–325, 324f planning phase, 275 pooled samples, 147, 148 population sampling, 276 precision, 277–280, 281 randomization, 147, 148, 277, 281 replication, 147, 148, 277–278, 281 reproducibility, 151–152 sample size, 278–279 calculation, 279 statement of objectives, 275 statistician input, 275 symmetry, 280 viral chip microfabrication parameters, 543 written protocols, 275 Experimental (measurement) error, 276 Experimental units, 276 Expression quantitative trait loci (eQTLs), 40–41, 189 Extracellular matrix cirrhosis, therapeutic targets, 1143 liver fibrosis, 1144 remodeling, 1139, 1140 protein genes, congenital heart disease, 784–785 Extracorporeal photochemotherapy, systemic sclerosis, 1164 Extraocular muscles, genetic disorders, 1257 Eye genetic disorders, 1258t movements, 1257 structure, 1256, 1256f eyeGENE, 1261

Ezetimibe, pharmacogenomics, 646 EZH2, 63, 65, 603 prostate cancer metastasis, 905 F F 1-(2 -deoxy-2 -fluoro-β-D-arabinofur anosyl)thymidine (18F-FMAU), DNA replication positron emission tomography, 503 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET) cancer detection, 496 enzyme substrate imaging, 507, 507f treatment efficacy evaluation, 496 18 F 3 -deoxy-3 -fluorothymidine (18F-FLT), DNA replication positron emission tomography, 503 19 F magnetic resonance spectroscopy (MRS), 519, 520 F-tests, 147 FAB2 (fatty acid-binding protein), metabolic syndrome, 1197 FABP, obesity, 1176 FABP7, glioblastomas, 961 Fabry’s disease, hypertrophic cardiomyopathy, 719 Factor II see Prothrombin Factor V, 755, 756 polymorphism, effects on protein C pathway, 1367 sepsis, 1367 Factor V Leiden, 762 meningococcal disease susceptibility, 1367 patient information needs, 767–768 racial/ethnic variation in incidence, 762 targeted screening, 766t thromboembolism risk, 362 cerebral venous thrombosis in racial groups, 762 perioperative, 796 Factor VII, 755, 756 genetic variation, 757 thrombosis-associated variants, 763 Factor VIII, 755, 757 genetic variation, 757 Factor IX, 755, 756 Factor X, 755, 756 thrombotic event prediction with anticardiolipin antibodies, 764 Factor XII, 760 genetic variation, 757 Factor XIII, genetic variation, 757, 758t Factor H, age-related macular degeneration association, 39 FAD-dependent amine monooxygenases, 64 False discovery rate, 213 False negative result biomarker protein immunoassay, 310 diagnostic test accuracy, 314 pharmacogenetic tests, 328 18

■

1431

False positive rate, 213 False positive result association studies, 465 biomarker protein immunoassay, 310 diagnostic test accuracy, 314 newborn screening programs, 471 pharmacogenetics case–control studies, 324 tests, 328 Familial adenomatous polyposis, 810, 879, 883, 885 allelic variants, 883 APC gene mutations, 883 APC mutation-negative, 883, 885 clinical features, 883 colorectal cancer chemoprevention, 890 screening, 889 extra-gastrointestinal manifestations, 883 genetic testing, 393, 887, 888 pancreatic cancer association, 922 Familial atypical multiple mole melanoma, pancreatic cancer association, 922 Familial breast and ovarian cancer, pancreatic cancer association, 922 Familial chylomicronemia syndrome, 642, 644 Familial combined hyperlipidemia, 39, 643 USF1 gene, 38 Familial Cretzfeldt–Jacob disease, 1228, 1229 Familial defective apolipoprotein B-100, 637 Familial dysbetalipoproteinemia see Hyperlipoproteinemia type III Familial hyperalphalipoproteinemia, 643 Familial hypercholesterolemia, 637, 643, 647 gene therapy, 615, 616 novel therapeutic targets, 646, 647 prenatal diagnosis, 647 Familial hypertriglyceridemia, 643 Familial hypoalphalipoproteinemia, 643 Familial hypobetalipoproteinemia, 639, 647 Familial hypocalciuric hypercalcemia, pulmonary fibrosis, 1111, 1115 Familial interstitial pneumonia, 1110 clinical features, 1115 genetic determinants, 1115–1116 genome-wide linkage analysis, 1116–1117 microarray studies, 1115, 1116f, 1117f Familial medullary thyroid carcinoma, 931, 934 clinical features, 925t, 934 medullary thyroid carcinoma, 934–935 prophylactic thyroidectomy recommendations, 937, 938 RET gene mutations, 935 genotype–phenotype correlations, 936, 937, 937t Familial partial lipodystrophy, 1196

1432

■

Index

Familial risk assessment, 481–483, 482t integrated risk assessment strategy, 483, 484f stratification, 487–488 Familial study design, pharmacogenetics, 324–325 Familial surfactant protein C mutation, pulmonary fibrosis, 1111 Familial ventricular tachycardia see Catecholaminergic polymorphic ventricular tachycardia (CPVT) Family history, 481–488 adenomatous polyps, 886 Alzheimer’s disease, 1224 asthma, 1085, 1090 breast cancer, 382, 870–871 management approach, 485–486t cancer risk prediction, 246 cardiovascular risk, 665 chronic obstructive pulmonary disease, 1099 clinical utility, 483 disease management/prevention interventions, 483, 487 public health applications, 487–488 risk factor modification, 487 colorectal cancer, 879, 883, 886, 888 management approach, 484t confirmation, 486 congenital heart disease, 788 consumer education, 403 data collection, 483, 486 patient self-reports, 486 depression, 1291 electronic health records, 235, 486–487 epilepsy syndromes, 1243, 1244–1247t, 1247–1248 evaluation tools, 483, 486 lung cancer, 857 population approach, 487 prospective health care, 382 sarcoidosis, 1111, 1112 thromboembolism, 765–766, 767 Family interview for genetic studies (FIGS), 1283 Family studies, 52, 461–462 Alzheimer’s disease, late onset, 1224 bipolar disorder, 1300 infectious diseases susceptibility, 1316 inflammatory bowel disease, 1041 rheumatoid arthritis, 1018 sepsis outcome, 1364 FANCA-M, 914 FANCD1/D2, 914, 915 FANCF methylation status, tumor treatment response prediction, 138 Fanconi anemia BRCA2 (FANCD1) homozygotes, 915

DNA repair pathway ovarian cancer, 914–915, 915f promoter methylation-related inactivation, 915 gene therapy, 615 head and neck cancer risk, 946 Farglitazar, pharmacogenomics, 353, 353t Farnesyltransferase inhibitors, leukemia, 845 Fascioscapulohumeral muscular dystrophy, 1268 Fasting-induced adipose factor (FIAF), 184 Fasting/non-fasting state, biomarker specimen collection, 311 Fat-soluble vitamins, intestinal absorption, 635 Fatal familial insomnia, 1228 Fatty acids metabolism, 635 targeted metabolic profiling, 185 FCERIB, asthma, 1087 Fcγ receptor mutations, host response to Neisseria meningitidis, 1355 FcγRIIA, host response to Streptococcus pneumoniae, 1353 FcγRIII experimental autoimmune myasthenia gravis, 1273 infliximab pharmacogenomics, 1047 Fcgr3, glomerulonephritis-related copy number variation, 115 FDA see Food and Drug Administration Ferritin-expressing tumors, magnetic resonance imaging (MRI) of cell surface receptors, 518–519, 519f Fetal dopaminergic neuron transplantation, Parkinson’s disease, 1239–1240 FGFR1 mutations, glioblastoma, 95 FGFR2, breast cancer association, 810 FHIT, methylation in lung pre-neoplastic lesions, 858 Fibrates (PPARα agonists), 646, 658, 659 cirrhosis, 1142 pharmacogenomics, 646 reactive oxygen species effects, 659, 659f FIBRILLIN-1 (Marfan syndrome), congenital heart disease, 784–785 fibrillin-1, TSK (tight-skin) mouse, 1157 Fibrin, 755, 759–760 variation, 758 Fibrinogen, 758 sepsis, 1367 variants, 757, 758, 758t coronary heart disease, 55 Helicobacter pylori interactions, 55 hypertension-related stroke, 629 peripheral arterial disease, 776 thrombosis, 763 Fibroblast gene expression profiles, 161–162 tissue of origin differences, 211 Fibroblast growth factor (FGF) glomerular disorders, 1059

peripheral arterial disease treatment, 775 tumor microenvironment, 819 Fibronectin hepatic stellate cell production, 1144, 1146 liver fibrosis, 1139 renal biopsy tissue, 1057 Ficolin 3 (hakata antigen), glomerular expression, 1058 FIF21A, strabsimus, 1257 Filtered back projection, positron emission tomography (PET) image reconstruction, 502 Fisher–Wright (standard neutral) model, 24 FKBP5, antidepressant response variation, 1295 Flaviviruses, viral chip technology, 551 Flecainide, gene-specific long QT syndrome treatment, 739 FLJ10986, amyotrophic lateral sclerosis, 1277 Flow cytometry pharmacodynamic marker assays, 340 T cell cancer vaccine response monitoring, 583 FLT3 acute myeloid leukemia, 847, 993 prognosis, 846 targeted therapy, 1001 leukemias, 844 FLT3 inhibitors, leukemia treatment, 845 Fludarabine, pharmacogenomics, 847 Fluidic chips, viral chip technology, 554 Fluidics workstations, viral chip technology, 546–547 Flunisolide, asthma management, 1091 Fluorescence fluctuation spectroscopy, 553f, 554 Fluorescence imaging, 524–530 applications, 530 cancer detection, 496 hyper-spectral, 529–530 intravital microscopy, 525–526, 525f, 526f macroscopic, 526–527 methods, 525–530 multi-spectral, 529–530 planar, 527–528 tomography, 528–529, 528f, 529f Fluorescence molecular tomography (FMT), 528–529, 528f, 529f Fluorescence resonance energy transfer (FRET) dyes, 90 Fluorescence spectroscopy, oral cancer screening, 946–947 Fluorescence-activated cell sorting, 159 cell response analysis in multiple sclerosis, 1035 Fluorescence-in-situ-hybridization (FISH), 530 Burkitt lymphoma, 839 chromosomal aberration as early disease biomarkers, 303 congenital heart disease, 767

Index

lymphoma, 832 Fluorescent biosensors fluorescent-dye-doped silica nanoparticles, 595 molecular beacons, 592–594, 593f, 594f Fluorophores, 498 viral DNA microarray target labeling, 546–547 5-Fluorouracil, 167 colorectal cancer, 890, 891 head and neck cancer, 950 pharmacogenomics, 360, 891, 1002 treatment response protein markers, 891–892 Fluticasone, asthma management, 1091 FMS, ovarian cancer, 916 FN1, diffuse large B-cell lymphoma prognosis, 834 Focal segmental glomerulosclerosis, gene expression profiles, 1059, 1060 Folate intake colon cancer risk, 1213 supplements, 56–57, 69 Folate metabolism colorectal cancer studies, 887 gene–diet interactions, 1213 leukemia susceptibility, 51 Follicular lymphoma, 822, 836–838 diagnosis, 837 gene expression profiles, 822, 837 immune response, 822 prognosis, 822, 837–838 transformation, 837 treatment, 836 Food and Drug Administration (FDA) analyte-specific reagent (ASR) Guidance document, 418 approved diagnostic platforms, 361 Critical Path Initiative, 368, 421–422 genetic testing regulation, 393 laboratory-developed tests, 417, 418 genomic medicine regulation, 414 Guidance documents, 414 in vitro diagnostics regulation, 416 Guidance documents, 416, 417 multivariate index assays, 592 Medical Device Amendments, 318 Modernization Act, 416 pharmacogenetics drug development, 419 drug labels, 421 test policy, 329, 330t, 345t translational medicine promotion, 368, 421–422 Voluntary Genomic Data Submission, 419, 422 Food-frequency questionnaires (FFQs), 1205, 1206 Formalin-fixed paraffin-embedded samples DNA methylation assessment, 133

gene expression profiling, 166 genotyping technologies, 106 Formoterol, chronic obstructive pulmonary disease, 1105 Fos, head and neck cancer, 951 FOSB, prostate cancer, 905 Foundation for Genomics and Population Health, 447 Fourier transform ion cyclotron mass spectrometers (FT-ICR-MS), 183 Fowlpox virus, cancer vaccine vectors, 582 FOXC1, ocular development, 1256 FOXP3, regulatory T cells (Treg), 820 Fra-2, head and neck cancer, 951 Fragile X syndrome, 234, 368 newborn screening, 359 Framingham Heart Study, 758, 762, 1206, 1208, 1209, 1210 Framingham risk score, 680 Frataxin (FXN) mutations, hypertrophic cardiomyopathy, 719 Free fatty acids, targeted metabolic profiling, 185 Friedrich ataxia (FXN mutations), hypertrophic cardiomyopathy, 719 Frontotemporal dementia, 1222, 1226, 1229, 1239 clinical heterogeneity, 1226 gene mutations, 1226, 1227f inheritance patterns, 1226 FST, 24, 25 FTO diabetes type 2, 1189 obesity, 265 Functional genomics, 166 vaccines development, 565–566, 567f Functional magnetic resonance imaging (fMRI) dementia, 1285 neuropsychiatric disorders, 532 serotonin transporter (5-HTT) gene polymorphism, 535–536 Functional pathways approach drug discovery, 335 pharmacodynamic markers in drug develpment, 340 Fungal infection, 14 pathogen recognition, 1350 sepsis, 1362 FUSION study, 1189, 1190 FXN (frataxin) mutations, hypertrophic cardiomyopathy, 719 G G3139, 1002 G-proteins, antihypertensive agent pharmacogenomics, 630 GABA receptor expression, schizophrenia, 1306–1307 Gadolinium-based contrast agents, 515 Galactose-1-phosphate uridyl-transferase deficiency, 1207

■

1433

mutations, 476 Galactosemia, 1207 dietary management, 1207 newborn screening, 359, 471 second-tier genotyping, 476 Galanin, gastric acid secretion regulation, 1124 Galectin-2 gene polymorphism, myocardial infarction association, 670, 673 lymphotoxin-α binding, 670, 670f, 673 GalNAc-T, melanoma, 968 Gardasil recombinant vaccine, cervical cancer, 374 Gardner syndrome, 883 Garrod, Archibald, 1 Gas chromatography-mass spectrometry (GCMS), metabolic profiling, 181, 183, 185, 186, 187 Gastric acid secretion, 1123–1124 Gastric cancer Helicobacter pylori association, 819 information database, 227 regulatory T cells (Treg), 820 Gastric lymphoma, Helicobacter pylori association, 822, 830 Gastric mucosa bacterial community, 569 defense mechanisms, 1124 Gastric ulcer, 1122, 1122f susceptibility, 1130, 1131 see also Peptic ulcer disease Gastrin, 1123, 1124 Gastrinoma multiple endocrine neoplasia 1 (MEN1), 932 peptic ulceration, 1122 somatic menin mutations, 934 see also Zollinger Ellison syndrome Gastrointestinal stromal tumors, 360 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 507 treatment efficacy evaluation, 496 imatinib mesylate, 507, 939, 940, 1001 sunitinib, 1001 GATA2, coronary artery disease, 668–669 GATA4, congenital heart disease, 782, 783 Gaucher disease gene therapy, 615 pulmonary fibrosis, 1111, 1115 GCN5 N-acetyltransferases, 64 GD2 ganglioside, 573 Gedunin, 198 Gefitinib, 849, 1001–1002 brain tumors, 962 genetic markers of response, 337–338 head and neck cancer, 952 lung cancer, 336, 1001 small-cell, 360 Geisinger Clinic MyCode biorespository, 237–238, 238f

1434

■

Index

Gel electrophoresis, two-dimensional proteomics acute coronary syndrome diagnosis, 685 brain tumors, 961 cancer metastasis, 985 multiple sclerosis, 1036 pancreatic cancer diagnosis, 924 tumor microenvironment immune cells, 825 see also Polyacrylamide gel electrophoresis, two-dimensional (2D-PAGE) Geldanamycin, 605 Gemtuzumab ozogamicin, 846, 997–998 acute myeloid leukemia, 998 GenBank, 226 liver proteome, 1144 Gendicine, 614 Gene discovery, 146–147, 457 cancer, 809 experimental design, 147 statistical methods, 147, 148f transcriptomics/gene expression microarrays, 146–148, 146t, 151 validation, 148 Gene expression, 11 allelic imbalance, 12 data analysis, 160–161 mining, 160–161 supervised learning, 160, 161f unsupervised learning, 160, 161f data repositories, 214–215 genetic control of level, 12 genomic three-dimensional organization, 12–13, 13f genomic/epigenomic aspects, 12 see also Gene expression profiles; Transcriptomics Gene Expression Omnibus (GEO), 152, 214, 214f, 215 Gene expression profiles, 369 acute lymphoblastic leukemia, 847, 849 acute myeloid leukemia, 847, 848f, 851, 852f ankylosing spondylitis, 1074 asthma, 1088, 1089t brain tumors, 959–961, 960t breast cancer see Breast cancer cancer see Cancer cancer metastasis, 980–983 prognostic applications, 986–987 cardiac allograft rejection diagnosis from peripheral blood mononuclear cells, 701–702, 707–709, 709f chronic lymphocytic leukemia, 847 chronic obstructive pulmonary disease, 1103 colorectal cancer, 882, 890 congenital heart disease gene discovery, 786, 787f The Connectivity Map, 167 data validation, 165 dendritic cells, 821

depression, 1295 diffuse parenchymal lung disease, 1115–1116 drug response prediction, 849 drug target identification, 336 Duchenne muscular dystrophy, 1278 familial pulmonary fibrosis, 1115, 1116f, 1117f gliomas, 957, 959–961, 960t glomerular disorders diabetic nephropathy, 1059, 1060t IgA nephropathy, 1059 head and neck cancer, 945, 947, 949, 950–951 heart failure monitoring, 698, 699 Helicobacter pylori, 1129 hepatic stellate cells, 1145–1146 hepatitis B infection, 1381 hepatitis C infection, 1382 infectious disease responses, 1317–1320, 1351 infectious disease (sepsis), 1368 pathogen signatures, 1368–1369, 1370f progression signatures, 1371 leukemias, 846–847 liver tissue, 1143 lung cancer see Lung cancer lymphoma, 832–833 Burkitt’s, 839 diffuse large B-cell, 834–835 follicular, 837–837 Hodgkin/Reed-Sternberg cells, 836 mantle cell, 838–839 primary mediastinal large B-cell, 835 multiple endocrine neoplasia type 2, 938 multiple sclerosis, 1034, 1035 ovarian cancer, 916 pancreatic mucinous cystic neoplasms, 926 prostate cancer see Prostate cancer regulatory T cells (Treg), 820, 821 renal biopsy tissue, 1057 rheumatoid arthritis, 162, 1023, 1035, 1074 schizophrenia, gene discovery in Portuguese population, 1306–1038 spondyloarthropathies, 1074–1075 stem cells, 600–601 systemic sclerosis, 1156–1157 thrombotic event prediction with anticardiolipin antibodies, 763, 763f, 764f Gene expression-based high-throughput screening, drug discovery, 167 Gene flow, 24, 27 Gene functional analysis, 193–194 animal models, 199 clinical trials, 199 Gene identifier (NCBI), 216 Gene Ontology (GO), 216, 229, 1012 Gene patenting, 363 Gene regulatory networks stem cells, 602 systems biology approach, 75

Gene set enrichment analysis (GSEA), 40, 160 Gene silencing cancer, 917 regional hypermethylation, 67 DNA methylation, 65, 65f in heterochromatin, 64 microRNAs, 603 RNA interference, 194, 195, 195f small interfering RNAs (siRNAs), 370 stem cells, 603 Gene therapy, 610–617 amyotrophic lateral sclerosis, 1276 brain tumors, 963 clinical trials, 614–617, 615t AIDS, 614, 616 cancer, 614–615 cardiovascular disease, 616–617 inborn errors of metabolism, 615–616 delivery vehicles, 610, 611–614, 1063 direct naked DNA/plasmid DNA, 613–614 DNA-protein complexes, 614 liposomes, 614 viral vectors, 610, 611–613, 1063 Duchenne muscular dystrophy, 1277 glomerular disorders, 1062–1063 integration at ‘safe genomic sites’, 606 obesity, 1183 stem cell therapy combinations, 605–606 see also Plasmid DNA-based cancer vaccines Gene–environment interactions see Environment–gene interactions Genealogy, 23–24, 23f GENECARD, 666, 668 GeneCards, 219 GeneChip studies see Affymetrix microarrays GenePattern, 217, 218f GENEQUEST, 667 Generalized epilepsy with febrile seizures plus (GEFS), 1243, 1246–1247t, 1247 Genes coding/non-coding, 8 conservation in vertebrates, 120 human genome, 6–8 Genes and Environment Initiative (GEI), 1014, 1015 GeneSpring, 548, 1306 Genetic Analysis of Idiopathic Thrombosis (GAIT), 762 Genetic Association Information Network (GAIN), 382, 440, 467 Genetic counseling, 455 cystic fibrosis, 474 hemoglobin variants, 474 Genetic counselors, 363, 394, 395, 405 consumer education, 403 training, 408–409 Genetic effectors (perturbagens), 193, 194, 194t Genetic exceptionalism, 448

Index

Genetic Information Nondiscrimination Act, 365, 396 Genetic medicine definition, 2 see also Genomic medicine Genetic Nursing Credentialing Commission, 409 Genetic reductionism, 448 Genetic testing, 357, 402, 455 consent, 363 current levels, 392–393, 414 economic evaluation, 426 evaluation framework, 430, 430t family history criteria for referral, 483 family members, 426 genomics firm business activity, 438 integration into clinical practice, 263, 270, 271t regulation, 416–418 validation, 426 value-based reimbursement, 430–431, 431f Genetic variation see Variation Genetical genomics, complex disease, 40–41, 42 Genetics consumer education, 403 teaching resources, 403–404 definition, 2, 454 medical school curriculum, 406 Genetics Association Information Network, 295 Genetics in Primary Care project, 407 Genetix, 440 Genistein, intrauterine effects on DNA methylation, 1011 GenMAPP, 219, 219f Genome, 4–18, 435 composition, 8–9 definition, 2 microorganisms, 14 systems biology approach, 74, 75 three-dimensional organization, 12–13, 13f Genome assembler software, 91 Genome Database, 226 Genome Medicine Database of Japan (GeMDBJ), 227 Genome segment arrays (tiling paths), 6, 7f Genome sequencing automation of pyrosequencing technique (454 technology), 563 electronic medical records, 234 Sanger technique (shotgun sequencing), 563 systems medicine applications, 79 vaccine development applications, 563–564 whole genome shotgun method see Whole genome shotgun sequencing see also DNA sequencing Genome-based Research and Population Health International Network (GRaPHInt), 447, 451

Genome-wide association studies, 14, 39, 95, 101–106, 357, 441, 464–465 Alzheimer’s disease (late onset), 1226 amyotrophic lateral sclerosis, 1277–1278 asthma, 1088 atrial fibrillation, 745 blood lipids, 644, 645t cardiovascular disease, 644–645, 645t chromosome 9p21 region of interest, 671 case–control studies, 101–102 control genotyping databases, 101 colorectal cancer, 887 complex disease, 264–266, 265t coronary artery disease/myocardial infarction, 666–667 diabetes type 1, 1188, 1191–1192 diabetes type 2, 1188–1191, 1189t, 1190t enrichment analysis, 1191 diabetic nephropathy, 1061 electronic medical records, 236–237 accessing populations, 237 experimental design, 101–103 continuous phenotype studies, 102 disease phenotype characterization, 102–103 genotyping technologies, 103 population stratification, 103 power calulations, 102 sample size, 102, 102t study focus, 101–102 infectious diseases susceptibility, 1316, 1351 inflammatory bowel disease, 1043 pharmacogenetics, 325–326, 344, 346 prostate cancer, 899 psychiatric disorders, 1283 raw data quality control, 105–106 sample collection/processing, 106 viral hepatitis susceptibility, 1378 Genome-Wide Association Studies (GWAS) Project, 227 Genomic Health, 360 Genomic imprinting, 131–132 Genomic literacy, 364 consumers, 402–404 health professionals, 451 public policy makers, 402–404 Genomic medicine, 455, 455t, 568–569 applications, 266 role of public health, 455–456, 456f clinical decision support, 242–249 comparative sequence analysis, 121, 128 complex disease, 267–268, 269f cost issues, 266, 267 definition, 2, 401 direct-to-consumer marketing, 393–394 economic evaluation, 424–432 educational strategies, 266, 401–411 core competencies, 364 healthcare professionals, 363–364 medical geneticists, 408

■

1435

primary care physicians, 405–408, 406t, 407t electronic medical records, 234–235, 235t genomic research, 236–240, 239t federal regulation, 414–422 genetic risk assessment, 402 health professional delivery, 363–364, 394–395, 395t policy issues access/reimbursement, 394 services integration, 392–396 population genomics applications, 28–29 privacy issues, 266–267 public–private interactions, 434–442 translation into medical practice see Translational genomics Genomic peptide libraries, vaccine development, 567 Genomic testing see Genetic testing Genomics applications, 448 consumer education, 403 teaching resources, 403–404 definition, 2, 435, 454 personalized medicine approach, 15–16, 16f, 255–256 research policy issues, 389–391, 389f race as analytical variable, 390–391 results disclosure to participants, 391–392 study design principles, 275–282 Genomics firms, 434–435 business activity, 437–438, 438t business model, 435 characteristics, 435–436 future trends, 441–442 historical development, 434–435 market capitalization, 437, 437f productivity, 438–439, 439f intellectual property, 438, 439, 439t profitability, 440 taxonomy, 436t see also Private sector genomics Genone Research for Human Health, 389 Genotype data management, 228–229, 228t Genotyping technologies, 103–106 comparative aspects, 105 copy number variation (CNVs) detection, 105, 114 formalin-fixed paraffin-embedded tissue samples, 106 raw data quality control, 105–106 sample processing/throughput, 106 Gen-Probe, 438, 440 Germline genomic screening, 349 Gerstmann–Sträussler–Scheinker syndrome, 1228 Ghrelin gastric acid secretion regulation, 1123 obesity, 1178

1436

■

Index

Gilbert’s syndrome, UGT1A1 polymorphism, 350 GJA5, atrial fibrillation, 745 GKCR gene, common variants, 642 GLA, corneal disease, 1257 Glaucoma, 1260 Gleevec see Imatinib mesylate Glial fibrillary acidic protein (GFAP) glioma prognosis, 960 hepatic stellate cells, 1144 multiple sclerosis biomarker, 1036 positron emission tomography, 504 Glial-derived neurotrophic factor (GDNF), 935 amyotrophic lateral sclerosis treatment, 1276 hepatic stellate cells, 1144 Glial-derived neurotrophic factor receptor (GFR), 935 Glioblastoma, 956 alkylating agent chemoresistance, 961 chromosomal alterations, 958, 959 de novo/primary tumors, 957 FGFR1 gene mutation detection, 95 gene expression profiles, 959, 960 genetic alterations, 957, 958f mir-21 alterations, 961 monitoring, 962 proteomics, 961 risk factors, 957 secondary, 957 therapeutic strategies, 962 Gliomas, 956–964 chromosomal alterations, 958–959, 959t classification/grading, 956, 957t DNA methylation profiles, treatment response prediction, 138 epidermal growth factor receptor kinase inhibitors, 961 gene expression profiles, 957, 959–961, 960t glial fibrillary acidic protein, positron emission tomography, 504 molecular alterations, 956–957 pharmacogenomics, 959 predisposition, 957–958 prognostic factors, 958, 960 proteomics, 961 Global Registry of Acute Coronary Events (GRACE), 680–681 Glomerular disorders, 1056–1064 congenital, 1062 gene therapy, 1062–1063 genome variations, 1060–1062 mRNA expression, 1059–1060, 1060t analytic techniques, 1056–1058, 1057f small interfering RNA treatment, 1063 stem cell-based regeneration therapies, 1063 therapeutic targets, 1062–1063 Glomerular epithelial protein 1 (GLEPP1), podocyte expression, 1058 Glomerular gene expression profiles, 1058

Glomerulonephritis, Fcgr3 copy number variation, 115 Glucagon-secreting pancreatic islet tumors, multiple endocrine neoplasia 1 (MEN1), 933 Glucocorticoid receptor gene promotor active demethylation, 63 mental illness-related methylation alterations, 68 Glucocorticoid resistant asthma, 1091 Glucocorticoid-induced TNFR-related protein (GITR), 574 Glucocorticoid-remediable aldosteronism, hypertension, 628 Glucokinase mutations, maturity onset diabetes of the young type 2 (MODY2), 358 Glucokinase regulatory protein (GCKR) polymorphism, 644 Glucoproteomics, liver function, 1150 Glucose-stimulated insulin secretion, 185–186, 186f Glutaraldehyde, viral oligonucleotide immobiliztion, 543, 544 Glutathione peroxidase, 652 Glutathione peroxidase, 2 Crohn’s disease, 1075 spondyloarthropathies, 1074–1075 Glutathione S-transferase M1 (GSTM1) asthma response to antioxidant supplementation, 1013 susceptibility, 56, 457 cancer susceptibility lung cancer in non-smokers, 304–305 malignant pleural mesothelioma, 305 pooled studies, 305 NAD(P)H:quinone receptor oxidoreductase 1 (NQO1) gene interaction, 56 Glutathione S-transferase P1 (GSP1) aberrant DNA methylation head and neck cancer monitoring, 950 lung cancer, 858 prostate cancer, 138, 899 asthma susceptibility influence, 56 Glutathione S-transferase T1 (GSTT1) cancer susceptibility lung cancer in non-smokers, 304–305 pooled studies, 305 myeloperoxidase polymorphism interaction, 54 Glutathione S-transferases, cigarette smoke metabolism, 857 Glycans, 369 Glycomics, 369 Glycopeptide capture technique, blood protein biomarker analysis, 80 Glycoprotein IIb/IIIa inhibitors, pharmacogenomics, 686 GNAT family (histone acetyltransferases), 64

GNB3 atrial fibrillation, 745 metabolic syndrome, 1197 obesity, 1176 Gold particle-coated nucleic acid-based cancer vaccines, 579–580 Gordon’s syndrome see Pseudohypoaldosteronism type 2 GP73 (golgi protein 73), hepatocellular carcinoma, 1150 GP100, melanoma, 968 GPD1L, Brugada syndrome, 741 GPNMB, glioma prognosis, 960 GPR154/NPSR1 (G protein-coupled receptor for asthma susceptibility), 1087 GPRA, chronic obstructive pulmonary disease, 1102 Gradient echo (GE) magnetic resonance imaging, 514–515, 514f Graft-versus-leukemia effect, 574 Gram-negative bacteria gene expression response to infection, 1369 host detection, 1365 host genetic susceptibility, 1352t, 1354– 1356, 1365 sepsis, 1362 Gram-positive bacteria gene expression response to infection, 1369 host genetic susceptibility, 1351–1354, 1352t, 1365 host recognition, 1351, 1365 sepsis, 1362 Granulocyte colony-stimulating factor, multiple sclerosis lesion expression, 1034 Granulocyte macrophage colony-stimulating factor (CM-CSF) dendritic cell stimulation, 583 nucleic acid-based cancer vaccine potentiation, 580, 581 Granzyme A (GZMA), 821 Granzyme B (GZMB), 821 natural killer cell expression, 822 Granzyme H (GZMH), 821 Grateful Med, 254 Graves’ disease, radioiodine therapy, 508 “GRIP” population, 28 GRN (progranulin) mutations, frontotemporal dementia, 1226 GroEL, Helicobacter pylori infection marker, 1130 Groenouw’s (granular) type 1 corneal dystrophy, 1258, 1259 GRP24, obesity, 1175 GSN, corneal disease, 1257 GSTM1 see Glutathione S-transferase M1 GSTP1 see Glutathione S-transferase P1 GSTT1 see Glutathione S-transferase T1 Guillain-Barre syndrome, 1036 GUSTO-IV, 686 Gut microbiota, 14, 569

Index

contribution to metabolic profile in obesity, 184 metagenomic analysis, 569 H Haemophilus influenzae chronic obstructive pulmonary disease exacerbations, 1101 genome sequencing, 563 Haemophilus influenzae type b (Hib) vaccine, 562 Hairpin structure RNA (shRNA), RNA interference, 195, 196 Hamartomatous syndromes, colorectal cancer risk, 885–886 HAND2, ventricular development regulation, 786 Hantavirus, 1340 Haplotypes, 10, 227 Environmental Genome Project (EGP) candidate genes, 51 mouse studies, 52 HapMap project see HapMap project HapMap project, 26, 39–40, 88, 101, 227, 382, 390, 441, 464, 1285, 1316 coronary artery disease studies, 666 pharmacogenetic marker selection, 327–328 Haptoglobin, hepatitis B infection proteomics, 1149, 1381 Hardy-Weinberg proportions, 24 Harvard Bioscience, 440 HDM2 mutations, gliomas, 957 Head and neck cancer, 945–953 alcohol consumption-related risk, 1212 alcohol dehydrogenase (ADH) polymorphism, 1212–1213 applications of genomics, 952 bevacizumab treatment, 998 biomarkers, 947 distant metastases, 949 cytotoxic agent resistance, 950 diagnosis, 947 environmental risk factors, 945–946 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 496 field cancerization, 947 gene expression profiles, 945 clinical outcome associations, 949 locoregional recurrence prediction, 950–951 lymph node metastasis, 947 genomic instability/DNA repair defects, 946 human papilloma virus association, 945, 946 metabolomics, 951 monitoring, 496, 950–951 pharmacogenomics, 950 pre-malignant lesions, 946 prognosis, 947–949, 948t proteomics, 951

screening, 946–947 cytology with DNA-image cytometry, 946 fluorescence spectroscopy, 946–947 staging, 496 TNM, 947 therapeutic targets, 951–952 angiogenesis, 952 clinical trials, 952 Health economics, 424 Health insurance discrimination, 267, 365, 395, 396 Health Insurance Portability and Accountability Act (HIPPA), 365 Health Level 7 (HL7) Clinical Document Architecture, 487 Decision Support Service Standard, 249 Retrieve, Locate and Update Service, 248 standards, 248 data, 268–269 Health professionals education, 266, 394, 395, 404–411, 451 essential skills, 410–411, 411t genetic counselors, 408–409 laboratory geneticists, 410 medical geneticists, 408 medical specialists/subspecialists, 408 newborn screening programs, 471 nurse geneticists, 409 pharmacists, 409–410 primary care physicians, 405–408, 406t, 407t public health genomics, 446 genomic literacy, 451 genomic medicine delivery, 394–395, 395t policy issues, 394 Health promotion, 457 Healthcare Information Technology Standards Panel (HITSP), 249 Heart failure, 692–703 B-type natriuretic peptide biomarker, diagnostic performance, 308, 313, 313t, 314–315, 314t, 315f, 316, 317t BiDil adjunct treatment, population stratification by race, 321, 698 biomarkers/biosignatures, 699–701, 701f cardiac transplantation, 705 cardiomyocyte necrosis/apoptosis, 695–696, 697f cell therapy, 702–703 chronic obstructive pulmonary disease, 1105 classification, 693–694 clinico-genomic, 699, 700t diagnosis, 696–697 diastolic, 692 environment–gene interactions, 693 etiology, 692–393 genetic basis, 692–693, 693f, 696t genomic profiling, 698 hypertrophic cardiomyopathy, 717

■

1437

left ventricular assist device (LVAD) support, 800 monitoring, 698–702 myocardial infarction, 673 neurohumoral mechanisms, 694–695 pharmacogenomics, 697–698 with preserved ejection fraction, 692, 697 prognosis, 697 right ventricular, 693 systemic sclerosis, 1163 systolic, 692 transcriptional profiling, 698–699 ventricular remodeling, 692, 695, 695f Heat shock proteins, multiple sclerosis proteomics, 1036 HeavyMethyl (HM) assays, methylation assessment in disease screening samples, 136 Hedgehog pathway genes, primary biliary cirrhosis, 1147 Helicobacter pylori, 569 adhesins, 1128 BabA polymorphism, 1128, 1129 SabA, 1128–1129 clarithromycin resistance, 1131 colonization rates, 1125 cytotoxin associated gene product (CagA), 1126, 1127–1128 cag PAI-encoded Type IV secretion system, 1127, 1129 geographic differences, 1127 polymorphisms, 1127–1128, 1129f eradication, 1123, 1131 host polymorphisms influence, 1131 pharmacogenomics, 1131 therapeutics, 1123 flagella, 1128 gastric cancer association, 819, 1127 lymphomas, 822, 830 genome, 1125–1130 allelic diversity, 1125, 1126 pathogenicity islands, 1125, 1126 plasticity zone, 1125 structure, 1125–1126 molecular diagnostic techniques, 1131–1132 myocardial infarction risk, fibrinogen polymorphism influence, 55 peptic ulcer disease, 1122, 1123 applications of genomics, 1132, 1132t genomic markers, 1129–1130 geographic strain differences, 1129 proteomic markers, 1130 serological markers, 1130 ulcer formation, 1124 primers for polymorphic regions, 1128t serological proteome analysis (SERPA), 568 urease, 1128 vacuolating cytotoxin (VacA), 1126–1127 vacA polymorphism, 1126, 1127, 1127f, 1129

1438

■

Index

Helicobacter pylori (Continued) virulence determinants, 1125–1129 Type I/Type II strains, 1126, 1129 whole genome expression studies, 1129 Helper T cells pathogen-specific activation, 1319 tumor microenvironment, 821 see also CD4 T cells; Th1/Th2 cells Hemagglutinating virus of Japan (HVJ)liposome vectors, 1063 Hemaglutinin inhibitors, 1344 Hematologic malignancies monoclonal antibody therapy, 997–998 small molecule-targeted therapy, 999, 1001 Hematopoietic progenitor cell antigen (CD34) precursor, thrombotic event prediction with anticardiolipin antibodies, 764 Hematopoietic stem cells, 599–600 developmental plasticity, 604 gene expression profiles, 600 transplantation, 600 Heme oxygenase-1 (HMOX1), chronic obstructive pulmonary disease, 1099 Hemochromatosis gene polymorphism, 665 Hemoglobin S mutation, 474 Hemoglobin Sβ thalassemia, 474 Hemoglobin SC disease, 474 Hemoglobin SE disease, 474 Hemoglobin variants isoelectric focusing, 473 newborn screening, 473–474 second-tier genotyping, 476 Hemoglobinopathies, malaria outcome, 1316 Hemolytic anemia, acute, 48 Hemophilia A/B, gene therapy, 615 Hemostasis, 755–769 circulating cellular/protein influences, 759–760 coagulation protein genotype–phenotype relationships, 757–758 gene–environment interactions, 758–759 instrumentation research, 768 personalized approach, 763–764 pharmacogenomics, 768 racial/ethnic variation, 760–762 twin studies, 756–757 vascular bed-specific, 758–759 Heparin, plasma sample effects, 311, 312 Heparin-induced thrombocytopenia, 798 Hepatic lipase (LIPC) deficiency, 642 downregulation, hepatitis C persistent infection, 1382 gene–nutrient interactions, 1210 lipoprotein metabolism, 635, 636, 1210 genome-wide associations, 644 obesity association, 1179, 1182 variants, 641, 644, 1210 promotor polymorphism, 55 Hepatic steatosis, metabolic profiling, 184

Hepatic stellate cells, 1138 activation, 1144, 1145 genomic studies, 1145–1146 liver fibrosis, 1139, 1140, 1144–1146, 1145f proteomics, 1148 therapeutic targeting, 1142 Hepatitis A, 1375 diagnosis, 1378 predisposition, 1377 transmission, 1377 vaccine, 574 virology, 1375, 1376f Hepatitis B, 1340, 1375 adaptive immune response/antibody production, 1381 chronic persistent infection, 1375, 1379 cirrhosis, 1379, 1380 liver fibrosis assessment, 1379 cytokine system effects, 539 diagnosis, 1378 DNA quantitation, 1379 serology, 1378, 1379t environmental factors, 1377 gene expression profiles, 1146, 1381 genetic susceptibility, 1377–1378, 1378t HLA Class II genes, 1317 genome, 1376, 1376f genomic studies, 1343–1344, 1380, 1380t functional genomics, 1381–1382 genotypes A-H, 1376 hepatitis D (delta hepatitis) co-infection, 1377 hepatocellular carcinoma, 819, 1148, 1150, 1375, 1379, 1380, 1381 hepatocyte entry, 1380 HIV co-infection, 1377 innate immune response, 1380, 1381 evasion, 1381 natural history, 1379–1380 nucleic acid amplification testing (NAAT), 370 pathogenesis, 1377, 1380, 1381 piezoelectric gene sensor, 547 precore/basal core mutations, 551, 1379 prognosis, 1379–1380 proteomics, 1149, 1381–1382 screening, 1378, 1379 transmission, 1377–1378 vertical, 1377, 1379 treatment, 1384–1385, 1384t antiviral agents, 1141, 1343 interferon, 1384–1385 nucleoside analogs, 1385 pharmacogenomics, 1386 vaccine, 563, 574 viral chip technology, 552 virology, 1376, 1376f Hepatitis C, 1340, 1375 adaptive immune response, 1382

chronic persistent infection, 1375, 1380, 1382 genomic analysis, 1383 cirrhosis, 1380, 1383 diagnosis, 1378 RNA quantitation, 1379 serology, 1378 environmental factors, 1377 gene expression profiles, 1146, 1382 genetic susceptibility, 1377–1378, 1378t HLA Class II genes, 1317 genome, 1376–1377, 1376f genomic studies, 1343–1344, 1380, 1380t functional genomics, 1382–1384 genotypes/subtypes, 1343–1344, 1376, 1382 hepatocellular carcinoma, 819, 1148, 1375, 1380, 1383, 1384 hepatocyte entry, 1380 HIV co-infection, 1377 immune response evasion, 1382 innate immune response, 1380, 1381, 1382 liver fibrosis, 1384 assessment, 1379 susceptibility genes, 1142 natural history, 1380 nucleic acid amplification testing (NAAT), 370 pathogenesis, 1377, 1380, 1382–1384 prognosis, 1380 proteomics, 1149, 1150, 1384 receptors, 1377 recurrence in liver transplants, 1383–1384 screening, 1378, 1379 transmission, 1377–1378 vertical, 1377 treatment, 1385 antiviral agents, 1141, 1343 interferon, 1385 resistance, 1385 viral chip technology, 552 viral gene expression detection, 550 virology, 1376–1377, 1376f Hepatitis D (delta hepatitis) diagnosis, 1378 hepatitis B co-infection, 1377 virology, 1376f, 1377 Hepatitis E, 1375 diagnosis, 1378 genome, 1377 predisposition, 1377 transmission, 1377 virology, 1376f, 1377 Hepatitis, metabolomic approaches to diagnosis, 43 Hepatobiliary tumors, hereditary nonpolyposis colorectal cancer, 885 Hepatocellular carcinoma, 1375 N-acetyltransferase (NAT) polymorphismrelated risk, 1214 cirrhosis relationship, 1138, 1148 gene expression profiles, 823, 1148

Index

hepatitis B, 819, 1150, 1379, 1380 functional genomics, 1381 hepatitis C, 819, 1380, 1383 microarray analysis, 1383 proteomics, 1384 metastasis, 823 proteomics, 1149, 1150 regulatory T cells (Treg), 820 tumor markers, 1383 Hepatocytes, 1138–1139 hepatitis B/C virus entry, 1377, 1380 hepatitis virus infection, 1375 interferon-α response, functional genomic studies, 1385 proteomics, 1148 Hepsin, gene expression in prostate cancer, 904 Her3, head and neck cancer, 949 HER-2/neu, 338 breast cancer see Breast cancer, HER-2/neu positive head and neck cancer, 949 lung cancer, 496 magnetic resonance imaging (MRI), 518, 519f monoclonal antibody treatment see Trastuzumab Oncotype Dx assay, 992 ovarian cancer, 916 pancreatic cancer, 921 pancreatic endocrine tumors, 927 utilization as pharmacodynamic marker, 340, 352 HERA, 869 Herceptin see Trastuzumab Hereditary hemochromatosis (HFE), 481 population screening, 359, 450 Hereditary nonpolyposis colorectal cancer (Lynch syndrome), 372, 811, 879, 885 clinical variants, 885 colorectal cancer screening, 889 gene mutations, 885 genetic testing, 263, 393, 887, 888–889 indications, 885 microsatellite instability testing, 372 non-colorectal malignancy risk, 885 ovarian cancer risk, 915 pancreatic cancer risk, 922 Heredity, definition, 2 HERG/KCNH2 (LQT2), 732, 734, 738 short QT interval syndrome, 742 sudden infant death syndrome, 731 Heritability, 464 Hermansky-Pudlak syndrome, pulmonary fibrosis, 1110, 1111, 1115 Herpes simplex (HDV-1/2), viral chip technology, 541, 547, 548, 551, 553 Herpes simplex virus type 1-thymidine kinase (HSV1-TK), positron emission tomography reporter applications, 505

Herpes viruses, viral chip technology, 441, 541, 550 Heterochromatin, gene silencing, 64 Heterochromatin-associated protein (HP-1), 61, 64 Heterocyclic amine-metabolising enzyme polymorphisms, colorectal cancer risk, 886–887 N-acetyltransferase (NAT), 1213–1214 Heteroduplex analysis, 96 Heterotaxy (ZIC3), congenital heart disease, 783–784 Heterozygosity, 11 genetic variance measurement, 24 HuRef genome analysis, 11 population genomic modeling, 24 HFE mutations see Hereditary hemochromatosis HGDP-CEPH database, 26 Hierarchical clustering, 213 B-cell mediated kidney transplant rejection, 211 disease sub-type discovery, 214 diffuse large B-cell lymphoma, 209 familial pulmonary fibrosis candidate genes, 1115, 1116f gene expression profiles data analysis, 160 heart failure, 698 lung cancer, 858 viral chip image analysis, 548 HIF-1α ovarian cancer angiogenesis, 918 positron emission tomography of promoter– reporter constructs, 505, 505f High performance liquid chromatography peptide cancer vaccines, 577 sickle-cell hemoglobin screening, 473–474 High-density lipoprotein cholesterol, 636 alcohol intake effects, 1208 cardiovascular risk, 636, 643–644 genetic disorders elevation, 641 reduction, 639, 641 genetic influences, 636 common variants, 640t, 641–642 rare variants, 15 novel therapeutic targets, 647 screening, 637 High-density lipoproteins (HDL), 634 apolipoproteins, 634 ApoA-1, 1208–1210 ApoE, 1208 metabolism, 635–636, 636f remodeling, 636 High-throughput techniques, 206 chemical genomics, 197, 198 DNA methylation patterns, lung cancer early diagnosis, 858, 859 drug discovery, 337

■

1439

gene discovery colorectal cancer, 882 schizophrenia, 1305–1308 genotyping, 441 stem cell gene function analysis, 601–602 Highly active antiretroviral therapies (HAART), 1326, 1327 pharmacogenomics, 1331 side effects, 1327 Hirschsprung’s disease, 127, 935 RET gene enhancer mutations, 126f, 127 Histamine, gastric acid secretion regulation, 1123 Histone acetyltransferases (HATs), 61, 64, 65 stem cells, 602 superfamilies, 64 Histone code, 61, 63–65 protein–DNA interactions regulation, 64 Histone deacetylase 1 (HDAC1), 62, 65 Histone deacetylase 2 (HDAC2), 62, 65 chronic obstructive pulmonary disease, 1103 Histone deacetylase inhibitors, 66, 69, 370, 845 stem cell regulation, 605 Histone deacetylases (HDACs), 61, 64, 65 classes, 64 Histone demethylases, 61, 64, 65 Histone methyltransferases (HMTs), 61, 64, 65 Histones, 11, 12, 60 acetylation, 60, 61, 63, 64, 370, 602 chronic obstructive pulmonary disease, 1103 DNA methylation relationship, 66, 66f gene-specific, 66 prostate cancer, 899 H2A-H2B dimers, 60 H3-H4 tetramers, 60 methylation, 60, 61, 63, 64, 602, 603 prostate cancer, 899 N-terminal modifications, 60–61, 63–65 bivalent domains, 603 enzymatic regulation, 64–65 ovarian cancer, 917 protein–DNA interactions regulation, 64 stem cells, 602, 603 phosphorylation, 60, 63 sumoylation, 63 ubiquitination, 60, 63 Histopathology, bioinformatics applications, 211 HLA alleles cancer vaccines peptide, 577–578 response monitoring, 583 carbamazepine pharmacogenomics, 1250 diabetes type 1, 1187, 1188, 1191, 1192 infectious disease susceptibility, 1317 hepatitis B, 1377 hepatitis C, 1377 HIV infection/AIDS, 1364 mycobacterial disease, 1357 myasthenia gravis, 1273

1440

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Index

HLA alleles (Continued) pharmacogenomics, 328, 372 sarcoidosis, 1111–1112 HLA Class I genes HIV infection progression to AIDS, 48 susceptibility, 1317 malaria susceptibility, 1317 nucleic acid-based cancer vaccines, 580 peptide cancer vaccines, 578 T-cell epitopes, 577 tuberculosis susceptibility, 1317 HLA Class II genes dendritic cell expression, 820 hepatitis B persistence, 1377 hepatitis B/C susceptibility, 1317 leprosy susceptibility, 1317 nucleic acid-based cancer vaccines, 580 peptide cancer vaccines, 577, 578 typhoid fever susceptibility, 1317 HLA-A*01, peptide cancer vaccines, 578 HLA-A*02, peptide cancer vaccines, 578 HLA-A*03, peptide cancer vaccines, 578 HLA-A*0101, 577 HLA-A*0201, 577 HLA-A*24, peptide cancer vaccines, 578 HLA-A1, sarcoidosis, 1112 HLA-B*07, peptide cancer vaccines, 578 HLA-B*0701, 577 HLA-B*1502, carbamazepine adverse reactions, 372 HLA-B*4402, 577 HLA-B*5701, abacavir hypersensitivity reactions, 328, 352, 372 HLA-B*5801, allopurinol adverse reactions, 372 HLA-B7, myasthenia gravis, 1273 HLA-B8 myasthenia gravis, 1273 sarcoidosis, 1112 HLA-B13, sarcoidosis, 1112 HLA-B22, sarcoidosis, 1112 HLA-B27 acute anterior uveitis, 1068 ankylosing spondylitis, 1068 spondyloarthropathies, 1068, 1069, 1069t inflammatory bowel disease association, 1046 HLA-B35 arthropathy, inflammatory bowel disease association, 1046 HIV infection progression to AIDS, 1329 HLA-B44, arthropathy, inflammatory bowel disease association, 1046 HLA-B51, Behçet’s syndrome, 1070 HLA-B57, HIV infection progression to AIDS, 1329 HLA-B60, ankylosing spondylitis, 1069 HLA-BRB1, multiple sclerosis association, 1032, 1033

HLA-CRB1*14, multiple sclerosis association, 1032 HLA-CRB1*17, multiple sclerosis association, 1032 HLA-Cw, systemic sclerosis, 1161 HLA-DP, 1019 HLA-DQ, 1019 HLA-DQA1, diabetes type 1, 1187 HLA-DQA1*0501, systemic sclerosis, 1158, 1161 HLA-DQB1 asthma, 1087 diabetes type 1, 1187 sarcoidosis, 1112 HLA-DQB1*0301, hepatitis C, 1377 HLA-DR, 665, 1019 systemic sclerosis, 1158 HLA-DR1, ankylosing spondylitis, 1069 HLA-DR2, myasthenia gravis, 1273 HLA-DR3 myasthenia gravis, 1273 sarcoidosis, 1112 HLA-DR15, sarcoidosis, 1112 HLA-DR16, sarcoidosis, 1112 HLA-DR17, sarcoidosis, 1112 HLA-DRB1 asthma, 1087, 1088 diabetes type 1, 1187 rheumatoid arthritis, 1017, 1018–1020, 1023, 1024 shared epitope alleles, 1019, 1019t, 1020 sarcoidosis, 1112 HLA-DRB1*0103, ulcerative colitis, 1045 HLA-DRB1*03, Crohn’s disease, 1046 HLA-DRB1*1302, hepatitis B clearance, 1377 HLA-DRB1*1502, ulcerative colitis, 1045 HLA-DRw52, sarcoidosis, 1112 HLA-G, asthma, 1087 HMB-45, melanoma diagnosis, 968 HMG-CoA lyase defect, newborn screening, 184–185 HMG-CoA reductase inhibitors see Statins HMGCR, low-density lipoprotein cholesterol interindividual variation, 639 HMO Research Network data federation, 240 HMP database, 183 HNF4A, diabetes type 2, 1188 Hodgkin lymphoma, 830, 835–836 gene expression profiles, 836 prognosis, 836 treatment, 994 Holt-Oram syndrome (TBX5), congenital heart disease, 782, 787 Homocysteine, cardiovascular risk, 1213 peripheral arterial disease, 773 HopQ, Helicobacter pylori virulence factors, 1129 HopZ, Helicobacter pylori adhesins, 1129 Host–microbe interactions (metagenomics), 569

HpA, Helicobacter pylori adhesins, 1129 Hsp27, hepatitis C proteomics, 1384 Hsp70 head and neck cancer, 951 multiple endocrine neoplasia type 2, 938 polymorphism, 665 Hsp90 androgen receptor regulatory activity, 906–907 head and neck cancer, 951 prostate cancer, 907 Hsp90 inhibitors chemical genomics, 198 pharmacodynamic markers, 340 prostate cancer treatment, 167 HSPA1A, Mycobacterium leprae susceptibility, 1357 HTR2C, obesity, 1182 huJ-591 (anti-PSMAEXT), 999, 999f Human bone marrow-derived multipotent stem cells (hBMSCs), 600 Human Cancer Genome Project (HCGP), 813 Human Epigenome Project, 133, 134t Human Gene Mapping Conferences, 226 Human genetic history (population genomics), 26–27 Human genome, 6–9, 6t, 7f, 434 conserved regions, 7 epidemiology, 461–467 expression, 11–13 genes, 6–8 individual sequences, 17t “reference sequence”, 9, 114 sequencing, 88, 568 variation, 9–11, 9f, 10t HuRef genome analysis, 10–11 Human Genome Epidemiology Network (HuGE Net), 304, 305, 450 Human Genome Sciences (HGS), 434, 438 Human herpes virus 6 (HHV-6), viral chips, 542, 551 Human immunodeficiency virus (HIV), 1325 biosensor detection, 590 clinical aspects see Human immunodeficiency virus (HIV) infection/AIDS gene therapy vectors, 611 viral chip technology, 552 see also Human immunodeficiency virus-1 (HIV-1); Human immunodeficiency virus-2 (HIV-2) Human immunodeficiency virus-1 (HIV-1), 1325, 1341–1343 CCR5 co-receptor, 1327 polymorphism studies, 1329 CD4 cell surface receptor, 1327 CXCR4 co-receptor, 1327 drug resistance mutations, 242, 415, 1331, 1332–1333t, 1342

Index

G-protein-coupled co-receptors, 1327 genes, 1325, 1325f genome sequence, 1324, 1325, 1342 reverse transcriptase, 1325, 1342 subtypes, 1325, 1327, 1329 Human immunodeficiency virus-2 (HIV-2), 1325 progression to AIDS, 1326 Human immunodeficiency virus (HIV) infection/AIDS, 1324–1334, 1340 abacavir hypersensitivity reaction genetic factors, 352, 372 HLA-B*5701 genotyping, 328 CCL3L1 copy number variation, 116 CD4 counts, 1326, 1326f, 1327, 1329, 1330t, 1331 response to therapy, 1326 therapy initiation, 1331 clinical features, 1325–1326 AIDS definition, 1329 diagnosis, 1328–1329 environmental factors, 1328, 1330–1331 epidemiology, 1324–1325 hepatitis B/C co-infection, 1377 highly exposed persistently seronegative (HEPS) individuals, 1328 immune response, 1327–1327 long-term non-progressors (LTNP), 1327–1327, 1329 lymphomas association, 830 monitoring, 1331 nucleic acid amplification testing (NAAT), 370 pharmacogenomics, 1331, 1342 physiopathogenesis, 1327–1328 prognosis, 1329–1331 progression to AIDS, 29, 48, 1325, 1326f genetic studies, 1329 Rapid Progression (RP), 1329 time span, 1326, 1329 secondary dementia, 1222 serology, 1328–1329 subtype E sexual infectivity, 1328, 1329 subtypes, 1328, 1329 susceptibility genetics, 1328, 1364 CCR2 mutations, 1317 CCR5 deletion-related resistance, 29, 48, 1316, 1317, 1328, 1329 chemokine receptor polymorphism, 569 HLA Class I gene, 1317 transmission, 1327 circumcision in prevention, 1328 treatment, 1326–1327, 1331, 1342 gene therapy, 614, 616 highly active antiretroviral therapies (HAART), 1326, 1327 immune-based, 1334 novel therapeutics, 1333–1334, 1343 structured interruptions, 1328 viral entry inhibitors, 29, 1333–1334

viral protein inhibitors, 1333 viral resistance mutations in selection, 242, 415, 1331, 1332–1333t, 1342 vaccine development approaches, 1334, 1343 viral load, 1326f, 1328, 1331 late phase disease, 1326 measurement, 370, 373, 1324 nucleic acid biosensors, 592 primo-infection, 1326 progression risk, 1329, 1330t repeatedly undetectable (“elite progressors”), 1329 Human metapneumovirus (hMPV) infection, Virochip detection, 551 Human papillomavirus cervical cancer, 374 DNA biosensor, 592 head and neck cancer, 945, 946 molecular diagnosis, 374 vaccine, 374, 574 Human parainfluenza 4 (HPIV-4),Virochip detection, 551 Human parology database, 110 Human T-cell leukemia virus-1 (HTLV1), adult T-cell leukemia/lymphoma, 830 Human Tissue Bill, 451 Human tissues use, public health genomics, 450, 451 Human Treg Chip, 820 Human–mouse antibodies (HAMA), 310 HumGen database, 450 HUNT-2 (Norway), 286 Hunter syndrome, gene therapy, 615 Huntington’s disease, 447 biomarker applications, 300 HuRef genome, analysis of variation, 10–11 Huseq, 434 Hyaluronan, hepatic stellate cell production, 1144 Hydralazine, pharmacogenomics, 698 Hydrogen peroxide, 652 5-Hydroxytryptamine transporter (5-HTT; SLC6A4), 1283 antidepressant drug response, 1290, 1294, 1294t, 1303 functional neuroimaging, 535–536 lithium carbonate response, 1304 psychiatric disorder associations, 55, 1283–1284 bipolar disorder, 1303 depression, 1291–1292 emotional regulation/stress responses, 1292–1293 trait-like behavior variations, 533 Hyperbilirubinemia, drug-related, 352 UGT1A1 polymorphism, 350, 351f Hyperforin metabolism, 1293

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1441

Hyperlipoproteinemia type III (familial dysbetalipoproteinemia), 642, 643, 644 APOE2 association, 36, 38 genetic testing, 647–648 Hyperparathyroidism multiple endocrine neoplasia type 1, 933 multiple endocrine neoplasia type 2A/B, 935 genotype–phenotype correlations, 936 Hyper-spectral fluorescence imaging, 529–530 Hypertension, 11, 441, 624–632 age associations, 625, 627t cardiovascular risk, 629, 655 reactive oxygen species influence, 655 diagnosis, 627–628 end-organ damage, 625, 628 environment–gene interactions, 624, 625, 625f epidemiology, 624 essential (common form), 624, 625 familial partial lipodystrophy, 1196 heart failure, 692 information database, 227 metabolic syndrome, 1194, 1197 monitoring, 631 monogenic forms, 624, 628 nephrosclerosis, 629 novel therapeutics, 631 obesity association, 1172 peripheral arterial disease risk, 773, 774 pharmacogenomics, 630–631 pheochromocytoma, 935 predisposition, 625–627 candidate gene approach, 627 salt-sensitivity, 625–626 primary/secondary, 628 prognosis, 628–630 renin-angiotensin-aldosterone system, 629, 655 screening, 627 stroke, 628–629 vascular-occlusive dementia, 1222 Hypertriglyceridemia, 40 Hypertrophic cardiomyopathy, 692, 716–725 calcium-handling, 718–719 clinical presentation, 717–718 septal morphology, 717, 717t, 718f, 721 definition, 716 diagnostic imaging, 717–718 dual-chamber pacing, 725 family screening, 722–723, 722f follow-up, 723 genetic couseling/echocardiography-guided genetic testing, 721 genotype–phenotype relationships, 719–721 genotypic/phenotypic heterogeneity, 716, 717 implantable cardiverter defibrillators, 724–725 indications, 724f

1442

■

Index

Hypertrophic cardiomyopathy (Continued) Maze procedure, 725 metabolic/syndromal, 719, 721t Noonan syndrome (PTPM11), 784 molecular genetics, 718–722, 719t, 720f MYH7 (beta myosin heavy chain) mutations, 692, 716, 718, 720, 721 nomenclature, 716–717 pharmacogenomics, 723 pharmacological therapy, 723 sarcomeric/myofilament, 717, 718, 720f septal ablation, 724 septal myectomy surgery, 724 sports participation, 723 transcriptomics, 721–722 Z-disk, 716, 718–719, 721 Hypomanic episodes see Bipolar disorder Hypothalamus, body weight regulation, 1171 Hypothalamus-pituitary-adrenal axis, depression, 1291, 1295 Hypothyroidism, newborn screening, 359, 455, 472 Hypoxia inducible factor-1α see HIF-1α I I-ELCAP study, 858 131 I-tositumomab, 997, 998 non-Hodgkin lymphoma treatment, 998 IBD1, 1041 IBD1-IBD9, 1041 IBD2, 1041 IBD3, 1041 IBD5, 1041, 1043 infliximab pharmacogenomics, 1047 IBD6, 1041, 1043 IBDCHip, 1046 ICAM-1 (intercelluler cell adhesion molecule 1), 656 atherosclerosis biomarker, 301, 654, 655 chronic obstructive pulmonary disease, 1102 inflammatory bowel disease therapeutic target, 1048–1049 inflammatory synovitis, 1072, 1073 post-cardiac surgery myocardial infarction, 798 systemic sclerosis, ET-1 upregulation, 1165 thrombosis, 762 ICD see International Classification of Diseases (ICD) iceA, Helicobacter pylori virulence factors, 1129 Iceland, 440 biobanking policy issues, 391 ICF disorder, DNMT3B mutations, 131 iControl database, 26 IDDM1, genome-wide linkage scans, 1061 IDDM2, genome-wide linkage scans, 1061 Idiopathic dilated cardiomyopathy, 692 biomarkers from serial samples, 699 infectious etiologies, 693

Idiopathic hypertrophic subaortic stenosis, 717 Idiopathic interstitial pneumonia, 1110 Idiopathic pulmonary fibrosis surfactant protein A variants, 1114 surfactant protein B variants, 1114 Idiopathic ST-elevation, 740 see also Brugada syndrome IFIH1, diabetes type 1, 1191 IgA nephropathy, 1056 candidate genes, 1058 gene expression profiles, 1059 genomics, 1061–1062 peripheral blood leukocyte analysis, 1058 IGAN1, IgA nephropathy, 1062 IgE receptor expression, multiple sclerosis lesions, 1034 IGF-1 (insulin-like growth factor) amyotrophic lateral sclerosis treatment, 1276 colorectal cancer, 887 glomerular disorders, 1059 obesity, 1176, 1179 prostate cancer, 568 IGF-2 genomic imprinting, 132 head and neck cancer, 951 ovarian cancer, 917 IGF-binding protein 1, sepsis, 1369 IGF-binding protein, 3 colorectal cancer, 887 ovarian cancer, 917 IGSF2BP2, diabetes type 2, 265 IκB, 656 host response to Streptococcus pneumoniae, 1353 IκB kinase inhibitors, diffuse large B-cell lymphoma, 835 IKBL  738, ulcerative colitis severity marker, 1045 Illumina, 435 control genotyping databases, 101 Illumina arrays, 28, 103–104, 103f, 104f, 227, 441 BeadChip, 165 comparative aspects, 105 copy number variation (CNVs) detection, 105, 114 diabetes type 1, 1191 probe length, 105 rapid single nucleotide polymorphism (SNP) genotying, 34 schizophrenia gene discovery approach in Portuguese population, 1305 transcriptomic analysis in complex disease, 40 whole genome amplified DNA, 106 Illumina HumanHap300 array, 103, 105 Illumina HumanHap550 BeadChip, 105 Illumina Infinium assay, 103–104, 103f, 104f BeadChip, 103, 103f, 105 processing steps, 104 type I, 103 type II, 103, 104

IMAGE (Invasive Monitoring Attenuation through Gene Expression), 713 Imaging investigations brain tumor monitoring, 962 congenital heart disease, 788 head and neck cancer, 947 prostate cancer, 903–904 psychiatric disorders, 1285 systemic sclerosis, 1158–1159 Imaging reagents, 498 Imaging systems, 495t Imatinib mesylate, 193, 360, 415, 420, 605, 809, 811 BCR-ABL tyrosine kinase inhibition, 372, 939–940, 1001 chronic myelogenous leukemia, 372, 939, 990, 992f, 994, 1001 diagnostic test of patient eligibility, 990, 992f gastrointestinal stromal tumors, 507, 939, 1001 melanoma clinical trials, 972 pharmacogenomics, 847 prostate cancer trials, 906 systemic sclerosis, 1164 Immature myeloid cells, tumor microenvironment, 820 Immune response follicular lymphoma gene signatures, 837 multiple sclerosis high-throughput analysis, 1035–1036, 1037t viral infections, 538 Immunoassay pharmacodynamic markers, 340 virus identification, 539 Immunophenotyping, lymphoma, 832 Immunophilins, 197 Immunoreactive trypsinogen, cystic fibrosis newborn screening, 474, 475 Immunosuppression glioma-associated, 963 head and neck cancer risk, 945 lymphoma risk, 830 Immunosurveillance, 573 Implantable cardioverter defibrillators hypertrophic cardiomyopathy, 724–725, 724f long QT syndromes, 738, 739 short QT interval syndrome, 742 In situ hybridization, renal biopsy tissue mRNA expression, 1057, 1057f In utero exposure see Intrauterine environmental exposure In vitro diagnostics, 414 clinical studies, 417 definition, 416 investigational device exemptions (IDEs), 417 multivariate index assays (IVDMIAs), 417, 418, 441 Premarket Approval Application, 416–417

Index

Premarket Notification Submission/510(k) application, 416, 417 regulatory issues, 318, 416–417, 416f regulation as medical devices, 416 risk-related classification, 416–417 In vivo expression technology (IVET), vaccines development, 563, 566 In vivo videomicroscopy, head and neck cancer screening, 947 Inborn errors of metabolism gene therapy, 615–616 gene–diet interactions, 1207 neonatal hyperbilirubinemia, 1148 Incidence rate, 463 Incidental genomic findings, 256 Incomplete Freund’s adjuvant, 583 Incremental cost-effectiveness ratio (ICER), 425 Incyte, 434, 438 India, 440 Indinavir, 1342 pharmacogenomics, 1331 Infectious agents, rheumatoid arthritis, 1022 Infectious disease, 370–371, 1314 adaptive immune response, 1314, 1348 applications of genomics, 1320 chronic, 569 cytokine polymorphisms, 1366–1367 gene expression profiling, 1317–1320 common host response, 1318–1319, 1318f, 1348f outcome of infection, 1319–1320 pathogen-specific responses, 1319 progression signatures, 1369, 1371 host genetic susceptibility, 569, 1315–1317, 1316t, 1352t candidate gene studies, 1317 future research approaches, 1357 Gram-negative organisms, 1354–1356 Gram-positive organisms, 1351–1354 mouse models, 1315 mycobacteria, 1356–1357 host susceptibility study methods, 1351 host–microbe interactions (metagenomics), 569 innate immune response, 1314, 1348, 1365, 1368 pathogen recognition/signaling, 1365–1366, 1368 CD14, 1366 intracellular signaling molecules, 1366 mannose-binding lectin, 1366 NOD-like receptors, 1365–1366 RIG-like receptors, 1365–1366 Toll-like receptors (TLRs), 1314, 1315, 1365 systems biology, 1320 see also Bacterial infections; Sepsis;Viral infections

INFGR1 (interferon-gamma receptor type 1), Leishmania infection susceptibility, 1317 Inflammatory bowel disease, 357, 1040–1049 anti-microbial antibodies, 1043, 1045 arthritis, 1067, 1068, 1070, 1071 family history, 1069 biomarkers, 1043, 1045 colorectal cancer association, 819 diagnosis, 1044–1045 environmental factors, 1041 epidemiology, 1040 genetic factors, 1041, 1042t, 1045 extra-intestinal manifestations, 1046–1047 genome-wide association studies, 1043 intestinal barrier dysfunction, 1043 lymphoma association, 1045 microbial flora as therapeutic targets, 1049 monitoring, 1047–1048 novel therapeutics, 1048–1049 pharmacogenomics, 1047 prognosis, 1045–1047 screening, 1043 treatment, 1048, 1075 infliximab, 1047, 1048 see also Crohn’s disease; Ulcerative colitis Inflammatory myopathies, 1268 Inflammatory response cell surface receptor magnetic resonance imaging (MRI), 518 post-cardiac surgery stroke risk, 798 tumor microenvironment, 819 Infliximab ankylosing spondylitis, 1074 Crohn’s disease, 1046, 1076 inflammatory bowel disease, 1047 pharmacogenomics, 1047 psoriatic arthritis, 1077 rheumatoid arthritis, 1024 spondyloarthropathies, 1076, 1077 Influenza antiviral agents, 1344 cancer vaccine vectors, 582 diagnosis, 539 hemaglutinin inhibitors, 1344 host gene expression in response to infection, 1369 microarray tests, 370, 371 neuraminidase inhibitors, 1344 prophylaxis, 1344 vaccines, 562, 1341 Influenza A virus, viral chip technology, 552 Influenza B virus, viral chip technology, 552 Ingenuity, 178 Inhibitors of apoptosis (IAP genes), autoimmune hepatitis, 1147 Inkjet printing oligonucleotide microarray fabrication, 159 viral chip technology, 543 Innate immune response, 1365

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1443

hepatitis B, 1380, 1381 hepatitis C, 1380, 1382 infectious disease, 1314, 1348, 1365, 1368 Toll-like receptor (TLR) signaling, 1365 INS (insulin gene) diabetes type 1, 1187, 1191, 1192 obesity, 1176 INS-1 cells, glucose-stimulated insulin secretion, 185–186 Insertion/deletion (in/del) polymorphism, 9, 9f complex disease association studies, 89 HuRef genome analysis, 10 resequencing process, 94 whole genome shotgun sequencing, 92, 92f Insertions, 9 see also Insertion/deletion (in/del) polymorphism Insightful, 440 InSNP, 94 Institute of Medicine Committee for the Study of the Future of Public Health, 449 Insulin gene see INS Insulin receptor substrate 1/2 (IRS-1/2), obesity, 1179 Insulin resistance, 40 DNA methylation alterations, 68 familial partial lipodystrophy, 1196 metabolic profiling mass spectrometry, 186–187, 188f nuclear magnetic resonance spectroscopy, 184 obesity, 187 metabolic syndrome, 1194, 1196 perilipin (PLIN gene) polymorphism, 1212 Insulin-like growth factor receptors (IGFRs), ovarian cancer metastasis, 917 Insulin-like growth factors see IGF-1; IGF-2 Insulin-secreting pancreatic islet tumors, multiple endocrine neoplasia 1 (MEN1), 933 Integrating the Healthcare Enterprise (IHE), 248–249 Integrative biology, 220 Integrin inhibitors, melanoma clinical trials, 971 Integrins melanoma therapeutic targets, 971 muscular dystrophies, 1268 systemic sclerosis, 1156 Intellectual property rights, 429, 435 biobanking, 391 data-sharing policies, 392 genomics firms, 438–439, 439t policy issues, 396 Inter-SPORE Prostate Biomarker Study (IPBS), 287 14-3-3 Interacting gene, hepatitis B infection, 1146

1444

■

Index

Interactome (protein–protein interactions) complex disease, 42 systems biology approach, 81 Intercelluler cell adhesion molecule 1 see ICAM-1 InterDom, 216 Interferon response genes hepatitis C infection, 1146, 1149 systemic sclerosis, 1156 Interferon therapy functional genomics, 1385–1386 hepatitis B, 1384–1385 hepatitis C, 1385 pharmacogenomics, 1386 proteomics, 1385 systemic sclerosis, 1164 Interferon-stimulating genes (ISGs) common host response to infection, 1318, 1319 hepatitis B/C infection response, 1380 pathogen-specific activation, 1319 Interferonγ, 1350 active gene demethylation, 63 ELISpot monitoring of cancer vaccines, 583 glomerular disorders, 1059 IgA nephropathy, 1059 hepatitis B response, 1381 hepatitis C response, 1382 mycobacterial disease, 1357 natural killer cell production, 574 Interferons hepatitis B response, 1380, 1381 hepatitis C response, 1380, 1381, 1382, 1383 infectious disease response, 1350 Interleukin-1 (IL-1) Gram-positive organism infection response, 1351 polymorphism ankylosing spondylitis, 1069 sepsis, 1366 systemic sclerosis, 1157 rheumatoid arthritis pathogenesis, 1023 Interleukin-1α, tumor microenvironment, 819 Interleukin-1β chronic obstructive pulmonary disease, 1102, 1103 gastic cancer susceptibility, 1130 gastric acid secretion regulation, 1124 peptic ulcer disease, 1130 polymorphism-related Helicobacter pylori eradication rate, 1131 Interleukin-1 receptor (IL-R) polymorphism gastic cancer susceptibility, 1130 HIV infection progression to AIDS, 1329 sepsis, 1367 Interleukin-2 (IL-2) active gene demethylation, 63 nucleic acid-based cancer vaccine potentiation, 581

therapy genomic analysis of effects, 823–824 HIV infection/AIDS, 1334 Interleukin-2 receptor (IL-2R) follicular lymphoma, 837 sepsis, 1369, 1370 Interleukin-4 (IL-4) glioma immunotherapy, 963 host response to pathogens, 1350 polymorphism asthma, 1087, 1088 HIV infection progression to AIDS, 1329 Leishmania susceptibility, 1317 Interleukin-4 receptor (IL-4R) asthma-related polymorphism, 1087, 1088 brain tumor associations, 957 follicular lymphoma, 837 Interleukin-6 (IL-6) atherosclerosis pathogenesis, 653, 654, 795 chronic obstructive pulmonary disease, 1102 diabetic nephropathy, 1059 host response to pathogens, 1350, 1351 metabolic syndrome, 1196 obesity, 1182 polymorphism, 795 coronary artery bypass grafting outcome, 796 inflammatory bowel disease, 1046 perioperative atrial fibrillation, 796 peripheral arterial disease, 776 post-cardiac surgery stroke risk, 798 postoperative myocardial infarction, 798 sepsis, 1366 sepsis severity biomarker, 1369, 1370 surgial inflammatory response, 794 thrombosis gene linkage studies, 762 tumor microenvironment, 819 Interleukin-6 receptor (IL-6R), obesity, 1176 Interleukin-7 (IL-7) HIV infection/AIDS treatment, 1334 spondyloarthropathies, 1074 Interleukin-7 receptor (IL-7R) polymorphism, diabetes type 1, 1191 Interleukin-8 (IL-8) alcoholic liver disease, 1147 chronic obstructive pulmonary disease, 1102 Gram-positive organism response, 1351 Helicobacter pylori infection, 1127, 1129 ovarian cancer angiogenesis, 918 peptic ulcer disease, 1130 sepsis, 1366 severity biomarker, 1369, 1370 Interleukin-10 (IL-10) cirrhosis therapy, 1141 dendritic cell inhibition, 576 host response to pathogens, 1350 inflammatory bowel disease treatment, 1048 polymorphism HIV infection progression to AIDS, 1329 sepsis, 1366–1367

regulatory T cell secretion, 574, 575 tumor microenvironment, 819, 820 Interleukin-10 receptor (IL-10R) polymorphism multiple sclerosis, 1033 mycobacterial disease susceptibility, 1357 Interleukin-12 (IL-12) host response to pathogens, 1350 polymorphism, Salmonella infection susceptibility, 1317 Interleukin-12 receptor (IL-12R) polymorphism, mycobacterial disease susceptibility, 1357 Interleukin-13 (IL-13) asthma-related polymorphism, 1085, 1087, 1088 brain tumor associations, 957 glioma immunotherapy, 963 Interleukin-18 (IL-18) polymorphism, multiple sclerosis, 1033 Interleukin-22 receptor (IL-22R) polymorphism, thrombotic event prediction with anticardiolipin antibodies, 764 Interleukin-23 receptor (IL-23R) polymorphism, Crohn’s disease, 1043 Intermediate colitis, 1040, 1045 see also Inflammatory bowel disease Intermediate-density lipoproteins (IDL), 634 apolipoproteins, 635 metabolism, 635 hepatic lipase, 1210 Intermittent claudication, 774, 775 International Classification of Diseases (ICD) with Clinical Modification, 216 Health Level 7 (HL7) patient data standards, 248 ICD-9, 253 ICD-9-CM, 216 ICD-10-CM, 216 International Prognostic Index (IPI), diffuse large B-cell lymphoma, 832 International Society of Biological and Environmental Repositories, best practice guidelines, 286 International Society of Nurses in Genetics (ISONG), 409 Internet health information, 252 consumer health vocabularies, 253 consumer search characteristics, 252–253 information sites, 253–255, 254t, 255t personalized genomics, 255–256, 256t quality assurance, 255 InterPro, 216 Interrupted aortic arch, 782 Interspersed repetitive elements, 123 Interstitial cystitis, diagnostic marker, 178 Interstitial lung disease see Diffuse parenchymal lung disease Intraocular pressure elevation, 1259–1260

Index

Intrauterine environmental exposure, 1011 cigarette smoke asthma associations, 1085 lung disease following, 56 Intravital microscopy, 525–526, 525f, 526f, 527f inv(16), acute myeloid leukemia, 844, 847 Investigational device exemptions (IDEs), 417 Invitrogen, 438, 440 Ion channel function, multiple sclerosis/ Guillain-Barre syndrome, 1036 IP10 see CXCL10 Ipsogen, 438 IRAK4 (IL-1 receptor-associated kinase-4) pyogenic bacterial infection susceptibility, 1317 sepsis-related intracellular signaling, 1366 IRF-6, hepatitis B infection, 1146 Irinotecan adverse reactions, 891 colorectal cancer, 891 drug-metabolizing enzyme polymorphisms, 360, 372 pharmacogenetics, 891 drug labels, 421 genotype-guided clinical cancer trial, 328 UGT1A1 companion diagnostic, 372 Iris, 1256 genetic disorders, 1256 Ischemia modified albumin, acute coronary syndrome biomarker, 683 ISG15, hepatitis C infection, 1146, 1382 ISG16, hepatitis C infection, 1146, 1382 ISGs see Interferon-stimulating genes ISIS 345794, melanoma therapeutic targets, 972 ISL1, obesity, 1176 Isochores, 8 Isoelectric focusing, hemoglobin screening α thalassemia, 474 β thalassemia, 474 sickle-cell anemia, 473 Isolated populations complex disease studies, 39 linkage disequilibrium studies, 28 Isoniazid pharmacogenomics, Nacetyltransferase 2 polymorphism, 383 Isosorbide dinitrate pharmacogenomics, 698 Isotope-coded affinity tags (ICAT), quantitative proteomics, 175, 175f Isotopic tagging for relative and absolute quantification (iTRAQ), blood protein biomarkers, 80 Iterative image reconstruction, positron emission tomography (PET), 502 Itga6, stem cell expression, 600 ITPR2, amyotrophic lateral sclerosis, 1277–1278

J JAG1 (Alagille syndrome), congenital heart disease, 784 JAK3, spondyloarthropathies, 1074 Jak/stat kinases lung cancer, 862 ovarian cancer metastasis, 917 Janus kinase inhibitors, 336 Jervell and Lange-Nielsen syndrome, 731, 736–737 clinical features, 736 sensorineural deafness, 732, 736 genetics, 732, 733t, 737 KCNE1 mutations, 737 KCNQ1 mutations, 737 Jewish ancestry, population genomic studies, 23 JHDM1, 64 JmjC domain demethylases, 64 JNKs/SAPKs, 656 reactive oxygen species activation, 658 Jo1-antibodies, polymyositis diagnosis, 1159 join technique for database management, 210 JPH2 mutations (junctophilin type 2), hypertrophic cardiomyopathy, 719 Jun, head and neck cancer, 951 Junctional epidermolysis bullosa, gene therapy, 615 Junctophilin type 2 (JPH2) mutations, hypertrophic cardiomyopathy, 719 Juvenile chronic arthritis, 1070 extra-articular manifestations, 1069 bowel inflammation, 1071 family history, 1069 peripheral enthesitis, 1068 Juvenile myoclonic epilepsy, 1248 Juvenile polyposis syndrome, colorectal cancer risk, 886 Juvenile spondyloarthropathy, 1067, 1070 clinical features, 1068 K k-means clustering, 213 gene expression data analysis, 160 k-nearest neighbors microarray data analysis, 160 molecular signature analysis, 148 K-ras colorectal cancer, 136, 811, 880, 882, 889 biosensor detection, 591 development process, 810 panatimumab treatment, 340 lung cancer, 496, 862, 864 ovarian cancer, 917 pancreatic cancer, 921, 923, 924 pancreatic cystic neoplasms, 925 positron emission tomography (PET), 504 Kaplan–Meier analysis, 209, 214 Kaposi’s sarcoma, 1324 Kaposi’s sarcoma human herpes virus-8

■

1445

primary effusion lymphoma (PEL) association, 830, 839, 840 viral chip identification, 551 Kappa gene, B-cell-specific demethylation, 66 Karolinska Institutet Biobank, 286, 287 Biobank Information Management System (BIMS), 287, 287f KARs (killing activation receptors), 562 Karyotyping, 6, 7f congenital heart disease, 767 KCNA5, atrial fibrillation, 744 KCNE1 atrial fibrillation, 745 Jervell and Lange-Nielsen syndrome, 737 KCNE2/3, atrial fibrillation, 744, 745 KCNE5, atrial fibrillation, 745 KCNH2, atrial fibrillation, 744, 745 KCNJ2 atrial fibrillation, 745 LQT7, 737 KCNJ11 diabetes type 2, 265, 1188 permanent neonatal diabetes, 1192 KCNQ1, 738 atrial fibrillation, 744, 745 Jervell and Lange-Nielsen syndrome, 737 KEGG see Kyoto Encyclopedia of Genes and Genomes KEL, obesity, 1176 Kidney injury molecule-1, glomerular disorders, 1060 Kidney regeneration, 1063–1064 KIF21A mutations, congenital fibrosis of extraocular muscles type 1 (CFEOM1), 1257 Killed viral vaccines, 562 Kir 2.1/KCNJ2 mutations atrial fibrillation, 744 LQT7, 737 short QT interval syndrome, 742 KIRs (killing inhibitory receptors hepatitis C clearance, 1377 HIV infection progression to AIDS, 1329 natural killers cells, 539 c-kit therapeutic targets gastrointestinal stromal tumors, imatinib mesylate responsiveness, 939, 940, 1001 melanoma, 972 KIT transmembrane receptor, imatinib mesylate inhibition, 940 Kjer’s autosomal dominant optic atrophy, OPA1 mutations, 1260 Klebsiella pneumoniae, signature-tagged mutagenesis (STM), 567 Kolmogorov–Smirnov test statistic, 160, 211 Krabbe disease, newborn screening, 359 KRT3, corneal disease, 1257 Kuppfer cells, 1139 therapeutic inhibition in cirrhosis, 1142 Kveim reaction, 1114

1446

■

Index

KvLQT1/KCNQI mutations Jervell and Lange-Nielsen syndrome, 737 LQT1, 732, 733, 738 short QT interval syndrome, 742 Kyoto Encyclopedia of Genes and Genomes (KEGG), 183, 219, 221f, 1091 L L-myc, Hodgkin lymphoma/Reed-Sternberg cells, 836 L-type calcium channel (Ca(V)1.2) mutations, Timothy syndrome, 737 Laboratory geneticist training, 410 Laboratory Information Management System (LIMS), 289, 548 Laboratory standards, 361–362 Laboratory-developed tests, 368 regulation, 414, 417–418 Lactase deficiency, 29, 1207 Lactate levels, sepsis staging/severity assessment, 1369 Lactose intolerance, 29, 1207 Lamin (LMNA A/C) mutations atrial fibrillation, 744, 745 familial partial lipodystrophy (FPLD), 1196 metabolic syndrome, 1197 Laminin hepatic stellate cell production, 1144 liver fibrosis, 1139 muscular dystrophies, 1268 Lamivudine, 1342 hepatitis B treatment, 1343, 1385 Lamotrigine, adverse reactions, 1251 LAMP2 mutations, hypertrophic cardiomyopathy, 719 Langat virus, viral chip technology, 551 Lapatinib, breast cancer pharmacogenomics, 350, 352, 352f LARGO (Lung Allograft Rejection Gene expression Observational), 713 Laryngeal squamous cell carcinoma diagnosis, 947 protein expression profiles, 951 see also Head and neck cancer Laser capture microdissection glomerular tissue, 1058 head and neck cancer proteomics, 951 tissue sampling DNA methylation assessment, 133 microarray experiments, 159 LATS1/LATS2 DNA methylation, breast cancer prognosis, 138 Lattice type 1 corneal dystrophy, 1258, 1259 LCAT gene mutations, 641 LCN2, glomerular disorders, 1059 LDB3 mutations, hypertrophic cardiomyopathy, 718, 721 Lead compounds drug discovery, 337 selection, 299

Lead optimization, 337 Learning disability see Mental retardation Leber’s congenital amaurosis, RPE65 mutations, 1260 Leber’s hereditary optic neuropathy, 1260 Lecithin-cholesterol acyltransferase (LCAT), 636 deficiency, 641, 644 therapeutic upregulation, 647 Lectin-like receptors, natural killers cells, 539 Leflunomide rheumatoid arthritis, 1024 spondyloarthropathies, 1075–1076 systemic sclerosis, 1164 Left cervicothoracic sympathetic ganglionectomy, long QT syndromes, 738 Legal issues, 364–365 genetic discrimination, 395–396 genomics research policy, 389 public health genomics, 447, 450 Legionaire’s disease, molecular diagnosis, 371 Legionella genetics of host response, 1355, 1365 infection susceptibility, TLR5 mutations, 1317 molecular diagnosis, 371 Leiden Thrombophilia Study, 757 Leigh syndrome, 220 Leishmania infection susceptibility, 1317 Lenalidomide, 1002 Lens, 1256 genetic disorders, 1259 Lentiviral vectors, 611–612 LEP see Leptin Leprosy susceptibility, 1357 HLA Class II genes, 1317 TLR2 mutations, 1317 TNF gene mutations, 1317 Leptin (LEP) body weight regulation, 1171 gene transfer, 1183 metabolic syndrome, 1196 obesity, 1173, 1175, 1176, 1178, 1180 Leptin receptor (LEPR) mutations, obesity, 1173, 1175, 1176, 1178 leptinob/ob mouse, 189 let-7 microRNAs, lung cancer, 864 Letrozole, 992 Leukemia, 51, 844–852 biomarkers identification, 849–850 classification, 208, 844, 845t copy number variation (CNVs), 851, 852f cytogenetics, 844–845, 845t diagnostic-therapeutic combinations, 994 gene expression profiling, 162, 163f, 846– 847, 848f, 851 validation, 851 genomics applications, 851–852 class prediction, 846–847, 849f microRNAs, 851

molecular genetics, 844–845, 845t pharmacogenomics, 847–849 drug discovery, 849 drug response prediction, 849 prognostic factors, 846, 846t, 849–850 treatment, 845–846 Leukemia inhibitory factor (LIF), glioma prognosis, 960 Leukemia and Lymphoma Molecular Profiling Project (LLMPP), 833 Leukocyte adherence deficiency, gene therapy, 616 Leukotriene A4 hydrolase (LTA4H), myocardial infarction risk, 674 HapK haplotype association, 671, 681 Leukotriene B4, myocardial infarction risk, 671, 687 Leukotriene inhibitors asthma, 1091, 1092 chronic obstructive pulmonary disease, 1107 myocardial infarction, 674–675, 687 pharmacogenomics, 1092 Leukotriene pathway, myocardial infarction, 671 Lev-Lenegre progressive cardiac conduction disease clinical features, 742–743, 743f genetics, 743 management, 744 Levalbuterol, asthma, 1091 Levodopa Parkinson’s disease, 1233, 1239 pharmacogenomics, 1240 Lewy bodies, 1222 α-synuclein, 1227, 1228f, 1235 Parkinson’s disease, 1233, 1236 LGALS2 polymorphism, myocardial infarction, 670, 673 LGALS4 polymorphism, ovarian cancer, 916 Li-Fraumeni syndrome brain tumors, 957 breast cancer, 871 p53 gene mutations, 382, 880 Licensing fees, 363 Liddle’s syndrome, hypertension, 628 LIF (leukemia inhibitory factor), glioma prognosis, 960 Life Gene (Sweden), 286 Life insurance discrimination, 267, 395 454 Life Sciences, 441 Light-driven viral oligonucleotide immobiliztion techniques, 544–545 LightCycler, 541 Likelihood ratios, diagnostic test performance evaluation, 314–315 LIM domain binding 3 (LDB3 mutations), hypertrophic cardiomyopathy), 718, 721 LIM protein (CSRP3 mutations), hypertrophic cardiomyopathy, 718 Limb girdle muscular dystrophy, 1268

Index

gene therapy, 615 Linkage disequilibrium, 10, 24–26, 25f Environmental Genome Project (EGP), 51 medically relevant variants identification, 28–29, 38 perilipin (PLIN gene) polymorphism in obesity, 1211 pharmacogenetic candidate gene approaches, 326 prostate cancer studies, 898 sarcoidosis, 1112 schizophrenia gene discovery in Portuguese population, 1305 thrombosis-related genes, 763 Linkage studies (genome scans) Alzheimer’s disease (late onset), 1225 asthma, 1085–1087, 1086t bipolar disorder, 1300, 1301, 1302, 1303, 1304 chronic obstructive pulmonary disease, 1103 depression, 1291 diabetes type 1, 1187 diabetes type 2, 1188 diabetic nephropathy, 1061 epilepsy, 1248, 1248t familial colorectal cancer, 886 familial interstitial pneumonia, 1116–1117 genomics firm business activity, 438 IgA nephropathy, 1061–1062 infectious diseases susceptibility, 1316, 1351 inflammatory bowel disease, 1041 metabolic syndrome, 1196–1197 multiple sclerosis, 1033 obesity, 1173, 1176 psychiatric disorders, 1283 rheumatoid arthritis, 1018 sarcoidosis, 1112–1113 thrombosis, 762 LIPC see Hepatic lipase Lipid disorders, 634–648 cardiovascular risk, 634, 637, 639, 642, 643–644, 1208 common genetic variants, 639, 640t, 641– 642, 643 familial partial lipodystrophy, 1196 genetic disorders, 638t genetic testing, 647–648 genome-wide association studies, 644, 645t high-density lipoprotein, 639, 641–642 low-density lipoprotein, 637–639 management guidelines, 483, 1208 metabolic syndrome, 1194, 1197 screening, 637 triglycerides, 642–643 Lipid-modulating therapies, pharmacogenomics, 646 Lipocalin 2, pancreatic cancer, 926 Lipopolysaccharide gene expression response, 1369

host Gram-negative bacteria recognition, 1354 TLR4 response, 1319, 1320 sepsis-related signaling, 1366 Lipopolysaccharide-binding protein (LBP), host response to infection Enterobacteriacea, 1354 Streptococcus pneumoniae, 1353 Lipoprotein lipase (LPL) chylomicrons metabolism, 635 mutations, familial chylomicronemia syndrome, 642, 644 polymorphism, 55, 641 blood lipid genome-wide association studies, 644 common variants, 642 obesity association, 1179 very-low-density lipoprotein metabolism, 635 Lipoprotein(a), 635 stroke associations, 760 Lipoproteins classes, 634 genetic factors in phenotypic variance, 636–637 metabolism, 634–636, 1209f, 1210 endogenous pathway, 635, 635f exogenous pathway, 635, 635f therapeutic targets, 646–647 Liposomes, gene delivery vehicles, 614 Lipoteichoic acid, 1351 5-Lipoxygenase activator protein (FLAP), cardiovascular risk, 681 5-Lipoxygenase inhibitors chronic obstructive pulmonary disease, 1107 cirrhosis, 1141 5-Lipoxygenase pathway genes, cardiovascular risk, 681, 687 Lipoxygenases, 653 atherosclerosis, 655 Liquid chromatography–mass spectrometry acute coronary syndrome metabolomics, 683 quantitative proteomics, 176 Liquid chromatography–tandem mass spectrometry (LC-MS/MS), proteomics head and neck cancer, 951 ovarian cancer, 916 spondyloarthropathies, 1075 Liquid crystal tunable filters, hyper-spectral imaging, 529 Listeria monocytogenes cancer vaccine vectors, 582 genetics of host response, 1354 Literature mining, prostate cancer, 905 Lithium carbonaate metabolism, 1303 pharmacogenomics, 1304 Live-attenuated vaccines, 562 in vivo expression technology (IVET) in development, 566

■

1447

Liver aging process, 1150 lipopolysaccharide-induced injury, 1150 lipoprotein metabolism, 635, 636 proteome, 1143–1144 structure, 1138–1139, 1139f transcriptome expression, 1143 Liver cancer alcohol consumption-related risk, 1212 see also Hepatocellular carcinoma Liver disease biomarkers, 1149, 1150 proteomics, 1148–1150 metalloproteome, 1150 transcriptomics, 1146–1148 animal models, 1148 Liver fibrosis, 1138 development, 1139–1140, 1140f, 1144–1146 hepatitis C, 1384 proteomic index development, 1150 see also Cirrhosis Liver function tests, proteomics, 1150 LKB1 pathway mutations, Peutz-Jeghers syndrome, 886 LM02, diffuse large B-cell lymphoma prognosis, 834 LMNA mutations see Lamin mutations LMX1B, ocular development, 1256 Logical Observation Identifiers, Names and Codes (LOINC), 248 Long interspersed nuclear elements (LINEs), 9 Long QT syndromes, 729–741 acquired/drug-induced forms, 731 clinical features, 731–732, 732f complex forms, 736–741 costs of testing, 363 diagnostic criteria, 731, 731t genetics, 732–735, 733t, 734f see also Romano-Ward syndrome genotype–phenotype correlations, 738 inherited forms, 731 management, 738–739 gene-specific approach, 358, 739 torsade de pointes, 729, 730–731, 730f Long-chain L-3-hydroxyacyl CoA dehydrogenase (LCHAD) defect, newborn screening, 185 Longitudinal Study of Aging of Danish Twins, 757 Loop-mediated isothermal amplification methodology (LAMP), Helicobacter pyloriinduced peptic ulcer disease, 1132 Lou Gehrig’s disease see Amyotrophic lateral sclerosis Low birth weight, chronic obstructive pulmonary disease association, 1099 Low molecular weight caldesmon, brain tumor monitoring, 962

1448

■

Index

Low-density lipoprotein cholesterol alcohol intake effects, 1208 cardiovascular risk, 636 genetic factors modulating, 643, 671 genetic common variants, 639, 640t genetic disorders, 643 elevation, 637–639 reduction, 639 genetic influences, 636 APOE alleles, 1208 PCSK9 polymorphism, 671, 672, 672f lipid-modulating therapies, 646 novel therapeutic targets, 646–647 screening, 637 Low-density lipoprotein receptor (LDLR) atherosclerosis, 655 loss of function mutations, 647 familial hypercholesterolemia, 637 low-density lipoprotein cholesterol interindividual variation, 639 obesity, 1176 therapeutic targeting, 646 Low-density lipoprotein receptor-mediated endocytosis, 635, 636 Low-density lipoproteins (LDL), 634 apolipoproteins, 635 metabolism, 635 hepatic lipase, 1210 oxidation, 652, 654, 758 atherosclerosis, 655 hyperglycemia, 655 Low-molecular-weight heparin, pharmacogenomics, 686 Lower motor neurons, 1265 LPL see Lipoprotein lipase LQT1 (KVLQT1/KCNQ1), 732, 733, 738, 739 LQT2 (HERG/KCNH2), 732, 734, 738, 739 LQT3 see Cardiac sodium channel gene (SCN5A) mutations LQT4 (ankryn-β), 732, 735 LQT5 (minK/KCNE1), 732, 733, 735 LQT6 (MiRP1), 732, 734, 735 LQT7 (Kir 2.1; KCNJ2), 737 LQT9 (caveolin-3), 735–736 LQT10 (SCN4B), 736 LRRK2 (PARK8; dardarin) diffuse Lewy body dementia, 1227–1228, 1229 Parkinson’s disease, 1237–1238, 1241 LTA4H see Leukotriene A4 hydrolase LTBP4 (latent transforming growth factor-β binding protein-4), chronic obstructive pulmonary disease, 1099 LTC4S, leukotriene inhibitor pharmacogenomics, 1092 Lumera Nanocapture Gold, 80 Lung cancer, 48, 856–865 alcohol consumption association, 1212 biomarkers, 858, 859

of carcinogen exposure, 303 cigarette smoking association, 856 at-risk smoker identification, 858–859 genetic influences, 856, 857–859 classification, 859–862 histological, 859–860 molecular, 860–861 early diagnosis, 858 epidemiology, 856, 857, 858 never smokers, 857 family history, 857 field cancerization, 858, 860 gene expression profiles, 858, 859, 860, 862, 864–865, 865f “lung metagene” model, 861 prognostic value, 209, 860–861, 861f subset identification, 860, 861f gene polymorphism studies, 857–858 metabolic genes and risk in non-smokers, 304 myeloperoxidase, 54 NAD(P) quinine oxidoreductase 1 (NQO1), 54–55 growth factors/growth factor receptor alterations, 95, 862–863, 863f microRNA alterations, 863–864 molecular alterations, 862–863, 862t oncogene activation, 862 molecular imaging, 496 pathway signatures, 863–864 pathogenesis, 862–865 pre-malignant lesions, 860 prevention/chemoprevention, 859, 865 prognosis, molecular signature analysis, 151 screening, 858, 859 serum protein antibody microarrays, 79 small molecule-targeted therapy erlotinib, 1002 gefitinib, 336, 1001 stage at diagnosis, 856, 858 staging 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 496 TNM, 860 therapeutic targets, 862–863, 864–865 tumor suppressor gene mutations, 863 Lupus anticoagulant, ethnic variation, 761 Lupus nephritis, glomerular gene expression profiles, 1058 Lyme disease, molecular diagnosis, 371 Lymph node status, 496 breast cancer gene expression profiles, 875, 876f LymphoChip, 143, 833 Lymphocyte cytosolic protein-1 (LCP1), NSC 624004 anti-cancer agent susceptibility, 210 Lymphoma, 368, 830–840 chromosome aberrations, 831t, 832 diagnosis, 832

molecular, 840 diagnostic-therapeutic combinations, 994 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 496 gene expression profiling, 832–833 data analysis, 160 inflammatory bowel disease association, 1045 monitoring, 832 novel therapeutics, 832 predisposition, 830 prognosis, 832 screening, 832 types, 830, 831t Lymphoscintigraphy, head and neck cancer diagnosis, 947 Lymphotoxin-α galectin-2 binding, 670, 670f, 673 polymorphism, 665 heart disease, 39 myocardial infarction, 669–670, 670f, 673 Lynch syndrome see Hereditary nonpolyposis colorectal cancer Lynx, 435 Lysine-specific demethylase-1 (LSD1), 64 Lysophosphatidic acid (LPA), ovarian cancer metastasis, 918 Lysosomal storage disorders, newborn screening, 477 Lysosome-associated membrane protein 2 (LAMP2) mutations, hypertrophic cardiomyopathy, 719 LYZS, glomerular disorders, 1059 M M1S1, corneal disease, 1257 Mac-2BP (Mac-2-binding protein), cholangiocarcinoma, 1149 McCune-Albright syndrome, 931 Machine learning, 213 see also Supervised learning; Unsupervised learning Machine-executable knowledge resources, clinical decision support, 247, 249 Macrophage colony-stimulating factor (M-CSF) glomerular disorders, 1059 spondyloarthropathies, 1074 Macrophage migration inhibitory factor (MIF), sepsis, 1366 Macrophages chronic obstructive pulmonary disease, 1101–1102 Crohn’s disease, 1071 inflammatory synovitis, 1072, 1073 pathogen responses, 1314 gene expression profiles, 1318, 1319, 1368 innate immune response, 1348, 1365 sarcoidosis, 1111

Index

spondyloarthropathy, 1071 TLR-mediated responses, 1319 tumor microenvironment (tumor infiltrating macrophages), 819, 820 Macroscopic fluorescence imaging, 526–527 Macular degeneration, 88, 357, 441, 1260, 1261 complement factor H association, 39 genome-wide association studies, 39, 265 information database, 227 MADH6, glomerular expression, 1058 MAGE-A3, melanoma, 968 Magnetic nanoparticles, 496 Magnetic resonance imaging (MRI), 370, 512–521 advantages, 512 Alzheimer’s disease, 1224 applications, 520t brain tumor monitoring, 962 cell tracking, 517, 518f cirrhosis, 1141 contrast, 513–514 diffusion, 513–514, 514f, 515 T1/T2 relaxation, 513, 515 water exchange, 514, 515 contrast agents, 515–517 CEST/PARACEST, 516–517 paramagnetic, 515 superparamagnetic, 515–516, 517f Crohn’s disease, 1044 gradient echo (GE) imaging, 514–515, 514f head and neck cancer diagnosis, 947 heart failure, 697 hypertrophic cardiomyopathy, 717–718 lymph node metastases, 496 magnetic properties of nuclei, 513, 513t pancreatic mucinous cystic neoplasms, 925 positron emission tomography (PET) multimodal imaging, 509 prostate cancer, 903 receptor imaging with targeted contrast agents, 517–519 ferritin-expressing tumors, 518–519, 519f rheumatoid arthritis, 1018 source of signal, 513 spin echo (SE) imaging, 514–515, 514f spondyloarthropathies, 1076–1077 see also Functional magnetic resonance imaging (fMRI) Magnetic resonance spectroscopy (MRS), 519–520, 520t head and neck cancer, 951 prostate cancer, 904 Major histocompatibility region, 1018–1019 microarrays, T cell response analysis in multiple sclerosis, 1035 Malaria role of hemoglobinopathies in outcome, 1316

susceptibility HLA Class I genes, 1317 TNF gene mutation, 1317 Malignant pleural mesothelioma, genetic susceptibility, 305 Malmo Diet and Cancer Study–Cardiovascular Cohort, 639, 640t, 642, 643, 644 Malondialdehyde (MDA), carcinogen exposure biomarker, 302–303, 303f Malonyl-CoA decarboxylase, diet-induced insulin resistance, 186, 187 MammaPrint, 167, 417, 427, 441, 874f, 875 economic evaluation, 427 Managed care, 426 Manganese-based contrast agents, 515 Manic episodes see Bipolar disorder Mannose-binding lectin (MBL), 1348 complement activation, 1348 deficiency, cardiac surgery thrombotic complications, 798 mycobacterial disease susceptibility, 1357 pathogen recognition/signaling, 1366 response to Neisseria meningitidis, 1355 response to Streptococcus pneumoniae, 1353 Mantle cell lymphoma, 838–839 copy number variation (CNVs), 813 diagnosis, 838 gene expression profiles, 838–839 prognosis, 838–839 Manual microdissection, tissue sampling for microarray experiments, 159 MAOA see Monoamine oxidase MAPK see Mitogen-activated protein kinase Maple syrup urine disease, newborn screening, 185 MAPT see Microtubular-associated protein tau MARCO, spondyloarthropathies, 1073 Marfan syndrome (FIBRILLIN-1), congenital heart disease, 784–785 Marimastat, 1002 Marrow-isolated adult multilineage inducible (MIAMI) cells, 600 Marshfield Clinic Personalized Medicine Research Project, 227 MART1, melanoma, 968 MASCOT, 176 Mass spectrometry biomarker identification, 308 glycopeptide capture, 80 isotopic tagging for relative and absolute quantification (iTRAQ), 80 protein mixture analysis, 79, 80 glycan analysis, 369 instruments, 176, 177t metabolic profiling, 181, 182–183, 182t animal models of disease, 186–187 cultured cell regulatory/signaling mechanisms, 185–186 disease mechanisms, 184–187

■

1449

targeted profiling methods, 183, 184–187 newborn screening, 359 peptide cancer vaccines, 577 proteomics, 173, 174, 175–176, 177f, 369 brain tumors, 961 cancer metastasis, 985, 986 colorectal cancer, 889 neuronal stem cells, 603 ovarian cancer, 916 pancreatic cancer, 924 prostate cancer, 902, 903 rheumatoid arthritis, 1023, 1024 software packages, 176–177 modified peptide identification, 177–178 viral chip detection methods, 547 see also Tandem mass spectrometry (MS/MS) Massively parallel sequencing-by-synthesis, 94–95 Massively parallel signature signaling (MPSS) single nucleotide polymorphisms (SNPs) identification, 36 transcriptomics, 153 Mast cells, tumor microenvironment, 819 Mastocytosis, peptic ulceration, 1122 MAT1, hepatic stellate cells, 1146 Maternal phenylketonuria, 473 Maternal PKU Collaborative Study, 473 Matrix metalloproteinase 2 (MMP-2), ovarian cancer metastasis, 918 Matrix metalloproteinase 3 (MMP-3) diabetic nephropathy, 1059 spondyloarthropathies, 1074, 1077 Matrix metalloproteinase 7 (MMP-7; matrilysin) head and neck cancer, 951 peptic ulcer disease, 1131 Matrix metalloproteinase 9 (MMP-9) chronic obstructive pulmonary disease, 1102, 1103 glioblastoma, 961 inflammatory synovitis, 1073 peptic ulcer disease, 1131 Matrix metalloproteinase 11 (MMP-11), Oncotype Dx assay, 992 Matrix metalloproteinase 19 (MMP-19), thrombotic event prediction with anticardiolipin antibodies, 764 Matrix metalloproteinases atherosclerosis, 658 plaque rupture risk, 666 head and neck cancer, 947 systemic sclerosis, 1156 tumor microenvironment, 819 Matrix-assisted laser desorption ionization (MALDI), proteomics brain tumors, 961 ovarian cancer, 916 tumor microenvironment, 825

1450

■

Index

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) proteomics colorectal cancer, 889 Helicobacter pylori infection markers, 1130 prostate cancer, 902, 903 sarcoid bronchoalveolar fluid, 1114 single nucleotide polymorphism (SNP) genotying, 34 tumor-specific blood biomarkers, 178 viral chip detection methods, 547 Matrix-comparative genomic hybridization see Array comparative genomic hybridization Maturity onset diabetes of the young (MODY), molecular subtype diagnosis, 358 Maturity onset diabetes of the young type 2 (MODY2), glucokinase mutations, 358 Maximum likelihood expectation maximization (MLEM), positron emission tomography image reconstruction, 502 Maxygen, 440 Maytansanoid (DM-1), 997 Maze procedure, hypertrophic cardiomyopathy, 725 MCH, obesity, 1176 MCP-1 (monocyte chemoattractant protein-1; CCL1) cancer cell expression, 819 chronic obstructive pulmonary disease, 1102 glomerular disorders, 1059 therapeutic targeting, 1063 systemic sclerosis, 1157, 1157f thrombosis gene linkage studies, 762 MDA5, 1366 MDR-1 (multidrug-resistance gene 1), corticosteroid pharmacogenomics, 1047 Measles, 1340 cancer vaccine vectors, 582 vaccines, 562 Measurement bias, 276, 282 MeCP2, 65, 66, 68 Medical device regulations analyte-specific reagents (ASR), 418 in vitro diagnostics, 416 laboratory-developed tests, 417 risk-related classification, 416–417 Medical geneticists, 363, 394, 395, 405 training, 408 Medical genetics, 454–455, 455t definition, 2 Medical history-taking, 207 Medical informatics, 226 Medical school curriculum, primary care physician education, 405–406, 406t Medical Sequencing Project, 389 Medical specialist/subspecialist education, 408

Medical Subject Headings (MeSH), 216 Medication errors prevention, 244 Medium-chain acyl-CoA dehydrogenase deficiency newborn screening, 359, 475–476 platform, 475 treatment, 475 MEDLINE searches, 254 MedlinePlus, 254 Medullary thyroid carcinoma calcitonin tumor marker, 934 familial, 934–935 genetic testing indications, 935–936 multiple endocrine neoplasia type 2A/B, 934–935 RET mutations genotype–disease severity correlations, 937–938 prophylactic thyroidectomy recommendations, 937, 937t, 938 somatic in sporadic cases, 938–939 RET protein tyrosine kinase targeted therapy, 939–940 ZD6474 clinical trials, 940, 940f, 941f surgical treatment, 934, 935 MEF2A (myocyte-enhancing factor 2A), myocardial infarction, 668, 669f Megsin, renal biopsy tissue, 1058 MEK, melanoma, 969 therapeutic targeting, 971 Mel18, 603 Melan A/MART1, melanoma, 968 Melanocortin melanoma, 969 receptor mutations, obesity, 1173, 1175, 1176 Melanoma, 967–973 diagnosis, 967–968 metastasis, 968 sentinel lymph node biopsy, 967–968 GD2 ganglioside expression, 573 gene expression profiles, 164, 209, 969 gene therapy, 615 genetics, 968–969 BRAF mutations, 812, 813 CDKN2(p16) association, 810 lymph node status, 967–968 micrometastases detection, 968 pharmacogenomics, 969 plasmid DNA-based anti-tumor vaccines, 581 staging, 967 18 F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 496 T cell response, 576 therapeutic targets, 969–972, 970t angiogenesis, 972 antiapoptotic proteins, 970 c-kit, 972 integrin signaling pathway, 971

MAPK pathway, 970–971 P13 kinase/Akt pathway, 971 platelet-derived growth factor receptor-β (PDGFRβ), 972 Src/STAT3 signaling pathway, 972 Membrame-type-1 matrix metalloproteinase (MT1-MMP), ovarian cancer metastasis, 918 MEME, 124 Mendelian Inheritance in Man, 226 Menin mutations, multiple endocrine neoplasia 1 (MEN1), 933, 933f, 934 Meningococcus see Neisseria meningitidis Mental illness see Psychiatric disorders Mental retardation (learning disability) copy number variation (CNVs) associations, 361 DNA sequencing, 95 public health genomics, 450 6-Mercaptopurine acute lymphoblastic leukemia, 331 inflammatory bowel disease, 1047, 1048 pharmacogenomics, 331, 338, 1047 dose adjustment, 415 drug labels, 421 response prediction, 849 Mercator Genetics, 434 MERLIN, 1305 Merosin, muscular dystrophies, 1268 Mesenchymal stem cells, 600 developmental plasticity, 604 proteomics, 603 Mesothelin, pancreatic mucinous cystic neoplasms, 926 Mesothelioma, 810 Messenger RNA (mRNA), 8 amplification for microarray experiments, 159–160 cancer vaccines, 579–582 antigen processing enhancement, 580 clinical results, 581 immune system stimulation enhancement, 580–581 regulatory issues, 581 vaccination efficacy enhancement, 579–580 vector design, 580 positron emission tomography (PET), 506 processing sequence mutations, 125–126 systems medicine, 76 Mestizo population, 390 c-Met, ovarian cancer metastasis, 917 Metabolic disorders tandem mass spectrometry screening, 475 see also Inborn errors of metabolism Metabolic myopathies, 1268 Metabolic profiling, 180–189 targeted, 181 animal models of disease, 186–187 disease mechanisms, 184–187

Index

human disease pathogenesis, 187 mass spectrometry, 183, 184–187 unbiased, 181 comparison of methods, 182–183 see also Metabolomics Metabolic syndrome, 1194–1200 association studies, 1197–1199, 1198t, 1199t clinical applications of genomics, 1200 definition, 1194–1196, 1195t diagnosis, 1194 family/twin studies, 1196 gene–nutrient interactions, 1212 genetic mechanisms, 1197 genome-wide linkage scans, 1196–1197 monogenic forms, 1196 pathophysiology, 1196 Metabolites, 181 targeted mass spectrometry-based assay modules, 185 Metabolomic quantitative trait loci (mQTL), 189 Metabolomics, 2 acute coronary syndromes, 683, 684f advantages in disease research, 180 combined genetic/transcriptomic analyses, 187, 189 complex disease, 42–43, 268 coronary artery disease, 681 cultured cell regulatory/signaling mechanisms, 185–186 diagnostic applications, 209 electronic medical records, 234 gene–diet interactions, 1205 head and neck cancer, 951 impact on health–disease continuum, 264 mass spectrometry, 181, 182–183, 182t measurement technology, 181–182, 182t metabolomic quantitative trait loci (mQTL), 189 newborn screening, 359 nuclear magnetic resonance spectroscopy, 181, 182–183 disease research, 184 personalized medicine applications, 15, 16t psychiatric disorders, 1286 public health genomics, 447 schizophrenia, 1286 stable isotope-labeled internal standards, 181–182, 181f see also Metabolic profiling Metagenomics, 14 host–microbe interactions, 569 Metallic nanoparticle biosensors, 595 Metalloproteome, liver disease, 1150 Metapneumovirus, 1344 Virochip detection, 551 Metaproterenol, asthma management, 1091 Metastasis, 808, 811, 977–987 colorectal cancer, 880, 985 genomics, 980–984, 982f

array comparative genomic hybridization, 983–984, 984f gene expression profiles, 164, 165, 818, 980–983 17-gene expression signature, 905 genetic predictors, 371 integrative approaches, 986 susceptibility genes, 979–980 hepatocellular carcinoma, 823 melanoma, 968 mouse models, 979–980 multiphoton in vivo microscopy, 526 ovarian cancer, 917–918, 919, 978 primary diagnosis, 211, 212f process, 977–979, 978f “inefficiency”, 978 somatic progression model, 980 prognosis assessment, 986–987 prostate cancer, 905, 986 proteomics, 985–986 functional, 985–986 quantitative, 985 treatment approaches, 987 tumor microenvironment effects, 819 Metavinculin (VCL) mutations, hypertrophic cardiomyopathy, 718 Metformin, pharmacogenomics, 1192 Methicillin-resistanct Staphylococcus aureus (MRSA), 14 Methotrexate drug-metabolizing enzyme polymorphisms, 360 hepatotoxicity, 1148 inflammatory bowel disease, 1047, 1048 pharmacogenomics, 1024–1026, 1047 response prediction, 849 rheumatoid arthritis, 1024–1026, 1047 systemic sclerosis, 1162 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) exposure, 1233 Methyl-CpG-binding domain (MBD) proteins, 131 Methyl-supplemented diets, 56, 69 Methylated DNA binding domain protein 2 (MBD2), 63, 65 Methylated DNA binding domain protein 4 (MBD4), 63 Methylation-sensitive restriction enzymes, 132 DNA methylation marker discovery, 133 Methylation-specific antibodies, 132–133 Methylation-specific polymerase chain reaction (MSP), methylation assessment in tissue samples, 135 Methylation-specific single nucleotide primer extension (MS-SNuPE), tissue sample DNA methylation assessment, 134–135 5-Methylcytosine DNA glycosylase, 63 Methylenetetrahydrofolate reductase (MTHFR) polymorphism, 51 colorectal cancer, 887

■

1451

gene–diet interactions in cancer risk, 1213 hypertension/nephrosclerosis, 629 pharmacogenomics, 360 5-fluorouracil, 891 inflammatory bowel disease, 1047 methotrexate, 1024–1026 rheumatoid arthritis, 1047 O6-Methylguanine-DNA methyltransferase (MGMT) cancer-related hypermethylation, 67 head and neck cancer, 950 lung cancer, 858, 859 protate cancer, 138 tumor treatment response prediction, 139 glioblastoma chemoresistance, 961 therapeutic strategies, 962 MethyLight assay, methylation assessment disease screening samples, 136 protate cancer, 138 tissue samples, 135, 135f 5-Methyltetrahydrofolate metabolism, 1213 Methylthioadenosine phosphorylase gene, cardiovascular risk, 671 Metronidazole, Helicobacter pylori eradication, 1123, 1131 Mexican Genome Project, 390 Mexico, 440 Mexiletine, gene-specific long QT syndrome treatment, 739 MGLAP, glomerular disorders, 1059 MGMT see O6-Methylguanine-DNA methyltransferase MIAT (myocardial infarction associated transcript), 671 MICA, 574 MICB, 574 Microarray and gene expression markup language (MAGE-ML), 248 Microarray Innovations in Leukemia (MILE), 851 MicroArray Quality Control (MAQC) project, 165, 422 Microarray-in-a-tube system, viral chip technology, 551–552 Microarrays, 157–168, 368–369, 872–873 bead-based multiplexing, 369 brain tumor genomic mapping, 958–959 cancer diagnostics, 371 susceptibility population studies, 305 cellular biology applications, 164–165 clinical expression profiling, 166–167 see also Gene expression profiles copy number variation (CNVs) detection, 369 data analysis, 160–161 bioinformatics, 208, 209 mining, 160–161

1452

■

Index

Microarrays (Continued) supervised/unsupervised learning, 160, 161f data repositories, 152, 160, 214–215 diagnostic applications, 162, 207, 208–209 diffuse large B-cell lymphoma subtype differentiation, 833 disease classification, 162, 209, 212 DNA methylation marker discovery, 133 DNA sequencing by hybridization, 95 drug target discovery, 167 genomics firm business activity, 438 heart failure monitoring, 698 Helicobacter pylori genomics, 1125 hepatic stellate cell studies, 1145–1146 histopathology applications, 211 historical development, 541 hypertrophic cardiomyopathy, 721–722 limitations, 165 MIAME data standards, 152, 160 molecular biology applications, 164 normal tissue taxonomy, 161–162 pathogen detection/genotyping, 370 platforms, 157–159, 158f new technologies, 165–166 production batch variation, 281 prognostic applications, 162–163, 209 prostate cancer, 900 renal allograft rejection biomarkers, 300 reproducibility, 165, 282 sample preparation, 159–160 RNA isolation, 159–160 tissue sampling, 159 sample size, 152–153 schizophrenia gene discovery in Portuguese population, 1305 stem cell gene regulatory networks, 602 study power analysis, 152–153 tissue sources, 152 transcriptomics see Transcriptomics tumor microenvironment analysis, 822–823 vaccine development applications, 567–568, 567f validation, 281–282 virus identification see Viral chip technology Microbe–host interactions, 569 see also Infectious disease; Pathogen–host interactions Microbial genomes, 14, 370 whole genome analysis, vaccine development, 564–566 Microbial horizontal gene transfer, 464 Microbiome, 14 Microdissection-based genotyping, pancreatic cancer diagnosis, 924 Microdosing studies, 497 Microfluidic cell sorting techniques, 79 Microfluidic chips, 80 Micronucleus (MN) assay cancer susceptibility biomarkers, 305

early disease biomarkers, 303 MicroRNAs (miRNAs), 8 brain tumors, 961 cancer therapy, 604–605 cardiac remodeling, 151 chronic obstructive pulmonary disease, 1107 expression profiling, 166 glomerular disorders, 1058 hypertrophic cardiomyopathy, 722 leukemia, 851 lung cancer, 863–864 RNA interference, 195 stem cells, 603 Microsatellite instability colorectal cancer, 136, 880, 881–882, 890 hereditary nonpolyposis colorectal cancer, 372, 885, 889 pancreatic cancer, 921 pancreatic endocrine tumors, 927 Microsatellite markers complex disease causal variants identification, 38 head and neck cancer screening, 946 rheumatoid arthritis, 1018 see also Simple sequence repeat polymorphisms (SSRPs) Microsatellite repeats, 34 Microsomal expoxide hydrolase polymorphism, chronic obstructive pulmonary disease, 1099 Microsomal transfer protein (MTP) inhibitors, 647 mutations, abetalipoproteinemia, 639 Microsomal triglyceride transfer protein (MTO), 635 Microtubular-associated protein tau (MAPT) familial amyotrophic laterla sclerosis, 1272 frontotemporal dementia, 1226, 1227f Microwell biosensors, 594 MIF (macrophage migration inhibitory factor), sepsis, 1366, 1369 Mift, melanoma, 969 MIG (CXCL9), hepatitis C infection, 1382, 1383 Millenium Genome Project, 227 Millenium Pharmaceuticals, 434, 438 MINDACT (Microarray in Node negative Disease may Avoid ChemoTherapy), 167, 301, 873 Mineral oil exposure, rheumatoid arthritis risk, 1022 Minimal change glomerulonephritis, 1059 Minimal cognitive impairment, 1222 Minimum Information About a Microarray Experiment (MIAME) standards, 152, 160, 698 Mining, gene expression data analysis, 160–161 minK/KCNE1 (LQT5), 732, 733, 735 MiRP1 (LQT6), 734, 735 Mismatch pair detection, 96

Mitochips, mitochondrial genome sequencing, 361 Mitochondrial cytochrome c, cardiomyocyte apoptosis in heart failure, 695 Mitochondrial DNA mutations, optic nerve disorders, 1260 Mitochondrial genome damage, Parkinson’s disease, 1238 sequencing, 361 variation, 361 Mitochondrial myopathies, 1268 Mitochondrial trifunctional protein (MTP) defect, newborn screening, 185 Mitogen-activated protein kinase (MAPK) cardiomyocytes in heart failure, 699 chronic obstructive pulmonary disease, 1103 hepatic stellate cells, 1145 lung cancer, 862 melanoma, therapeutic targeting, 970–971 reactive oxygen species activation, 656 see also p38 MAPK Mitral valve prolapse/regurgitation, 784 Mitrazapine, pharmacogenetic studies, 325 MLH1 colorectal cancer, 881 methylation aberrations, 136, 882 mutations, 136 hereditary nonpolyposis colorectal cancer, 811, 885, 889, 915 lung cancer, promoter hypermethylation, 858 pancreatic endocrine tumors, 927 promotor methylation status, tumor treatment response prediction, 138 ulcerative colitis severity associations, 1045–1046 MLL acute lymphoblastic leukemia, 847 acute myeloid leukemia, 846, 847 MMRPRO, 887 MNDA (myeloid nuclear differentiation antigen), spondyloarthropathies, 1074 Modification of Diet in Renal Disease (MDRD), 629 Modifiers, pharmacogenetic test clinical validity, 328 Modular data analysis, 160 Molecular beacon biosensors, 592–594, 593f, 594f array biosensors, 594 immobilization strategies, 593 microsphere approach, 594 Molecular concept map, prostate cancer, 905 Molecular Devices, 440 Molecular epidemiology biomarkers, 302, 302f Molecular function, Gene Ontology (GO), 216 Molecular imaging, 494–498 cancer detection, 494, 496

Index

treatment efficacy evaluation, 496–497 drug development, 497 Molecular signature analysis, 146t, 148–151 cancer research applications, 151 cardiovascular disease applications, 151 experimental design, 148 statistical methods, 148–150 systems medicine, 81 validation, 150–151 Moloney murine leukemia virus retroviral vector, 611 Monoallelic gene expression, 12 Monoamine oxidase (MAOA), 1239, 1240 depression, 1291 prostate cancer, 905 psychiatric disorder associations, 55 antidepressant drug response effects, 1294, 1294t bipolar disorder studies, 1305 Monoclonal antibody therapy, cancer, 995– 999, 996t hematologic malignancies, 997–998 solid tumors, 998–999 treatment efficacy evaluation, 497 Monocrystalline iron-oxide nanoparticle (MION) contrast agents, 516 cell surface receptor imaging, 418 cell tracking applications, 517 Monocyte chemoattractant protein-1 see MCP-1 Montanide IAS-51, 583 Monte Carlo simulation, 278–279, 278f, 278t Montelukast, asthma management, 1091, 1092f Morpholino antisense oligonucleotides, stem cell gene function analysis, 602 Morphosys, 440 Motor neuron disease, 1265–1266, 1267t genetic, 1268 sporadic, 1268 Motor neurons, 1265 Mouse genome sequencing, 120 MOZ, 64 MPTP exposure, Parkinson’s disease, 1237 MRP8 (calgranulin A) follicular lymphoma, 837 rheumatoid arthritis, 1024 spondyloarthropathies, 1074, 1075, 1077 MRP14 follicular lymphoma, 837 spondyloarthropathies, 1074 MRT2C, obesity, 1176 MSH2 colorectal cancer, 881 hereditary non-polyposis colorectal cancer, 811, 885, 889, 915 promoter hypermethylation in lung cancer, 858 MSH6 colorectal cancer, 881

hereditary nonpolyposis colorectal cancer, 885, 889 MSR1, prostate cancer, 898, 899 Mtes1, cancer metastasis susceptibility, 980 MTHFR see Methylenetetrahydrofolate reductase polymorphism mTOR inhibition, melanoma therapeutic targets, 971 MUC1, prostate cancer, 905 Mucopolysaccharidosis, gene therapy, 615 MudPIT (multidimensional protein identification technology), quantitative proteomics, 175 Muir-Torre syndrome, 885 Multicenter Automatic Defibrillator Implantation Trial (MADIT), 739 Multi-Ethnic Study of Atherosclerosis (MESA), 761 Multiexperiment Viewer (MeV), 217, 217f Multifactor Dimensionality Reduction (MDR), 327 Multiphoton in vivo microscopy, 526, 527f MultiPipMaker, 123, 124 Multiple endocrine neoplasia syndromes, 931–941 Multiple endocrine neoplasia type 1 (MEN1), 905, 931, 932–934 clinical features, 932, 932t hyperparathyroidism/parathyroid tumors, 932 menin mutations, 933, 933f, 934 clinical screening schedule, 934, 934t somatic, 934 molecular genetics, 933–934 pancreatic islet tumors, 932–933 peptic ulceration, 1122 pituitary tumors, 933 Multiple endocrine neoplasia type 2 (MEN2), 934–941 RET gene mutations, 935, 936, 936f genotype–disease severity correlations, 937–938 genotype–phenotype correlations, 936– 937, 937t, 938 Multiple endocrine neoplasia type 2A (MEN2A), 931, 934 clinical features, 934, 935t gene expression profiles, 938 hyperparathyroidism/parathyroid adenomas, 935, 936 medullary thyroid carcinoma, 934–935 prophylactic thyroidectomy recommendations, 937, 938 pheochromocytomas, 935 RET gene mutations, 935 genotype–phenotype correlations, 936, 937t variants, 935 Multiple endocrine neoplasia type 2B (MEN2B), 931, 934 clinical features, 934, 935t

■

1453

gene expression profiles, 938 hyperparathyroidism/parathyroid adenomas, 935 medullary thyroid carcinoma, 934–935 prophylactic thyroidectomy recommendations, 937, 938 pheochromocytomas, 935 RET gene mutations, 935 genotype–phenotype correlations, 936– 937, 937t Multiple ligation-dependent probe amplification (MLPA), dystrophin mutations, Duchenne muscular dystrophy diagnosis, 1274 Multiple myeloma, 830 bortezomid therapy, 1002 Multiple Risk Factor Intervention Trial (MRFIT), 629 Multiple sclerosis, 33, 1032–1037 biomarkers applications, 300, 301 identification, 1036–1037 Epstein–Barr virus association, 1036 gene expression profiles, 162, 1034–1035 autoimmune disorder comparisons, 1035 lesion characterization, 1034 relapse, 1035 reponse to therapy, 1035 genomics, 1032–1033, 1037t admixture mapping, 1033 association studies, 1033 linkage mapping, 1033 whole genome scans, 1033 immune response high-throughput analysis, 1034, 1035–1036, 1037t B cell response, 1036 T cell response, 1035–1036 ion channel function, 1036 pharmacogenetic research, 323 proteomics, 1036–1037, 1037t susceptibility genes, 1032–1033 transcriptomics, 1034–1035, 1037t Multiplex amplifiable probe hybridization (MAPH), dystrophin mutations, 1274 Multiplex ligation-dependent probe amplication (MLPA), congenital heart disease, 787 Multipotent adult progenitor cells (MAPCs), 600 Multi-spectral fluorescence imaging, 529–530 Multivariate index assays, FDA regulation, 592 Mumps vaccines, 562 Muscle disorders, 1268, 1270–1271t, 1272 lipoprotein metabolism, 635 Muscular dystrophies, 1268, 1272 genes, 1268 newborn screening, 359 Muscular subaortic stenosis, 717

1454

■

Index

Mut Y Homologue gene (MYH)-associated polyposis, 883, 885 Mutagenesis and phenotypic screens, congenital heart disease gene discovery, 786–787 Mutation, 22, 23 cancer cells, 809, 809f heterozygosity frequencies, 24 linkage disequilibrium breakdown, 24–25 positron emission tomography (PET), 504 Mutation Surveyor, 94 Mx-1, hepatitis C infection, 1146, 1382 Myasthenia gravis, 1265, 1268, 1273, 1278 acetylcholine receptor antibodies, 1268, 1273, 1274 maternal transfer, 1275 therapeutic targeting, 1276 diagnosis, 1274 genetic factors, 1273 monitoring, 1275 neonatal, 1268, 1275 prognosis, 1275 thymoma associations, 1273 treatment, 1276 acetylcholinesterase antisense oligonucleotides, 1276 MYBPC3 mutations (myosin-binding protein C), hypertrophic cardiomyopathy, 718, 720, 721 MYC, 65 acute lymphoblastic leukemia, 844 lung cancer, 862 ovarian cancer, 917 prostate cancer, 901 Mycobacteria genetics of host response, 1356–1357 host genetic susceptibility, 1352t, 1364 interferon-gamma receptor 1 gene polymorphism, 569 host recognition, 1350 Mycobacterium leprae infection see Leprosy Mycobacterium tuberculosis catalase-peroxidase, sarcoidosis involvement, 1114 molecular diagnosis, 371 signature-tagged mutagenesis (STM), 567 see also Tuberculosis MyCode biorespository, 237–238, 238f Mycophenolate mofetil, 706 systemic sclerosis, 1164 Mycoplasma genitalium genome, 94, 563 Mycosis fungoides, 840 MyD88, 1350, 1352 host infection response Legionella, 1355 Listeria monocytogenes, 1354 Pseudomonas aeruginosa, 1354 Staphylococcus aureus, 1353 Streptococcus pneumoniae, 1352, 1353

Myeloid cells, tumor microenvironment, 820 Myeloid-related protein 8 see MRP8 (calgranulin A) Myeloperoxidase, 653 acute coronary syndromes diagnostic biomarker, 683 risk stratification, 686 atherosclerosis, 654–655 peptic ulcer disease, 1130 polymorphism glutathione S-transferase T1 (GSTT1) locus interaction, 54 lung cancer susceptibility, 54 variant allele A, 54 MYH6 mutations (alpha myosin heavy chain), hypertrophic cardiomyopathy, 718 MYH7 (beta myosin heavy chain) mutations, hypertrophic cardiomyopathy, 692, 716, 718, 720, 721 MYL2 mutations (regulatory myosin light chain), hypertrophic cardiomyopathy, 718 MYL3 mutations (essential myosin light chain), hypertrophic cardiomyopathy, 718 Mylotarg see Gemtuzumab ozogamicin MYO9B, inflammatory bowel disease, 1043 MYOC (myocilin), glaucoma/intraocular pressure elevation, 1260 Myocardial infarction, 39, 665–676 association studies, 667–671 genome-wide scans, 666–667, 666t leukotriene pathway, 671 proprotein convertase subtilisin/kexin type 9 serine protease (PCSK9), 671 specific genes, 667, 667t cardiac surgery perioperative period, 796–797, 797f, 798 cell therapy, 675, 702–703 chromosome 9p21 region of interest, 671, 681 diagnosis, 673 protein biomarkers, 682–683, 683t ST-segment/non-ST-segment elevation, 682 drug-eluting intracoronary stents, 674 environmental risk factors, 672 genetic influence on risk, 665–666 heart failure, 673, 693, 694f leukotriene inhibitor therapy, 674–675, 687 pharmacogenomics, 673–674 post-infarction therapy response, 673 prognostic implications, 673 risk factors see Cardiovascular risk risk stratification, 673 ST segment elevation, 682, 693 ventricular remodeling, 673, 695, 695f see also Acute coronary syndromes Myocardial infarction associated transcript (MIAT), 671 Myocyte-enhancing factor 2A (MEF2A), myocardial infarction, 668, 669f

Myocytes, fatty acids metabolism, 635 Myofiber cytoskeleton abnormalities, muscular dystrophies, 1268 Myofilament-associated gene mutations, hypertrophic cardiomyopathy, 718 Myopathies, 1268, 1272 myosin gene, active demethylation, 63 Myosin-binding protein C (MYBPC3) mutations, hypertrophic cardiomyopathy, 718, 720, 721 Myozenin 2 (MYOZ2) mutations, hypertrophic cardiomyopathy, 718 Myriad Genetics, 434 MYST family (histone acetyltransferases), 64 N N-Ras mutations, melanoma, 970 NAD(P)H oxidase AP-1 activation, 658 atherosclerosis-related polymorphism, 658 matrix metalloprotein-2 secretion regulation, 658 reactive oxygen species generation, 652 cardiovascular disease, 654 NAD(P)H:quinone receptor oxidoreductase 1 (NQO1) polymorphism, 51 glutathione S-transferase M1 (GSTM1) interaction, 56 lung cancer susceptibility, 54–55 Nailfold capillaroscopy/videocapillaroscopy, systemic sclerosis, 1158–1159, 1160f Naip5, Legionella susceptibility, 1355 NAIP deletions, amyotrophic lateral sclerosis, 1273 Nanoparticle biosensors, 595–596, 596f head and neck cancer screening, 947 molecular beacons, 594 Nanoparticles cancer treatment efficacy evaluation, 497 magnetic resonance imaging (MRI) contrast agents, 516 Nanosphere Verigene Warfarin Metabolism Nucleic Acid Test, 329 Nanotechnology, 370 integrin antagonists, melanoma treatment, 971 viral chips, 552–553 carbon nanowires, 553 quantum dots, 552 semiconducting nanowires, 552 silicon nanowires, 552–553 stripped nanowires, 552 Natalizumab, 344 inflammatory bowel disease, 1048 National Biospecimen Network Blueprint, 286, 287 National Center for Biotechnology Information (NCBI), 121, 226, 227 Gene Expression Omnibus (GEO), 152, 214, 214f, 215

Index

Gene identifier, 216 Taxonomy, 216 UniGene identifier, 216, 1143 National Cholesterol Education Program (NCEP) guidelines, 243 National Coalition for Health Professional Education in Genetics (NCHPEG), 364, 395, 410 National Genetics Education and Development Centre, 395 National Hospital Discharge Survey, 760 National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) Data Repository, 227 National Institute for Health and Clinical Excellence (NICE), 426, 430 National Lung Cancer Screening Trial (NLST), 858 National Office of Public Health Genomics, 447, 450 National reference laboratories, 368 Natural killer cells antibody-dependent cytotoxicity, 573 cancer immune response, 573, 574 cell killing mechnism, 574 hepatitis C innate immune response, 1382 killing activation receptors (KARs), 539 killing inhibitory receptors (KIRs), 539 lectin-like receptors, 539 lymphomas, 840 tumor microenvironment, 821–822 virally-infected cell elimination, 538–539 NCBI see National Center for Biotechnology Information (NCBI) NCI60 database, gene microarray measurements–drug susceptibility relationships, 209–210, 210f Neimann-Pick disease, pulmonary fibrosis, 1111, 1115 Neisseria gonorrhoea genome mapping, 1347 molecular diagnosis, 371 Neisseria meningitidis (meningococcus) genetics of host response, 1355–1356 microarray expression profiling, 567–568 sepsis, 1367 serogroup B, reverse vaccinology, 564–565, 565f signature-tagged mutagenesis (STM), 567 susceptibility C6 complement deficiency, 569 genetic factors, 1364, 1365 vaccines, 562, 563, 567 meningococcus C, 563 Nelfinavir, 1342 Neonatal hyperbilirubinemia, 1148 Neonatal myasthenia gravis, 1275 Neonatal screening see Newborn screening Nephrin (NPHS1) mutations, congenital nephrotic syndrome, 1062

Nephrotic syndrome, congenital, 1062 Nestin, hepatic stellate cells, 1144 NET1, ovarian cancer, 916 Network discovery, 213 Neural plasticity, depression, 1294–1295 Neural stem cells, 600 brain tumor therapeutic targets, 963 proteomics, 603 Neural tube defects, 57, 1013 Neuraminidase inhibitors, 1344 Neuroblastoma, GD2 ganglioside expression, 573 Neurocognitive dysfunction, post-cardiac surgery, 798–799 Neurodegenerative diseases, biomarker applications, 300 clinical trial surrogate endpoints, 301 Neurodevelopmental disorders, copy number variation (CNVs) associated syndromes, 114 clinical cytogenetic diagnostics, 117 database, 110 Neurofibromatosis brain tumor predisposition, 957 pulmonary fibrosis, 1111, 1115 Neurofilaments, multiple sclerosis biomarker, 1036 Neuromodulin, hepatic stellate cells, 1144 Neuromuscular disorders, 1265–1279, 1266t application of genomics/proteomics, 1277–1279 diagnosis, 1274 monitoring, 1275–1276 predisposition, 1268, 1272–1273 prognosis, 1274–1275 screening, 1273 treatment, 1276–1277 Neuromuscular junction, 1265 disorders, 1266, 1268, 1269t Neuronal ceroid lipofuscinosis, gene therapy, 615 Neuropeptide Y (NPY) body weight regulation, 1171 obesity, 1176, 1178 Neuropsychiatric disorders see Psychiatric disorders Neuroregulin 1, psychiatric disease associations, 1306 bipolar disorder, 1302 Neurotrophins, spondyloarthropathies, 1074 Neurturin, 935 Neutrophil elastase ephysema inflamatory mechanisms, 1102 inhibitors, 1107 New chemical entities (NCEs), 269 see also Drug development Newborn health, 470 Newborn screening, 207, 359–360, 455, 470–477 biopterin deficiency, 473

■

1455

components of system, 471 cystic fibrosis, 474–475 direct DNA analysis, 476, 477 disorder inclusion criteria, 470–471, 472, 475, 477 Duchenne muscular dystrophy, 1273 ethical issues, 455 expanded metabolic disorder screening, 475, 476, 477 ‘ACMG 29’ panel of disorders, 475–476 follow-up services, 471 information sources, 471 interpretation of results, 477 medium-chain acyl-CoA dehydrogenase deficiency, 475–476 phenylketonuria, 472–473 quality assurance, 471 sickle-cell anemia, 473–474 technology, 471–476, 472t new developments, 476–477 tandem mass spectrometry, 184–185 variability among programs, 476 Newcastle disease virus, 576 glioma cancer vaccine, 963 NF-H, amyotrophic lateral sclerosis, 1273 NF-κB inhibitors, chronic obstructive pulmonary disease, 1107 NF-κB pathway chronic obstructive pulmonary disease, 1102–1103 diffuse large B-cell lymphoma therapeutic target, 835 primary mediastinal large B-cell lymphoma, 835 reactive oxygen species activation, 656–657, 657f sepsis-related signaling, 1365, 1366 tumor pro-angiogenic factor expression regulation, 820 NFATC2, 821 NFATC3, 821 NHS National Genetics Education and Development Centre, 451 Nicotine addiction, 1099 Nicotine replacement therapy, 1105 Nicotinic acid (niacin), 646 Nifidipine, hypertrophic cardiomyopathy, 723 NimbleGen oligonucleotide arrays, 113 Nip3, hepatic stellate cells, 1146 Nitric oxide, 652 atherosclerosis, 654 chronic obstructive pulmonary disease, 1102 hepatic stellate cell production, 1145 Nitric oxide synthase, 652 chronic obstructive pulmonary disease, 1103 constitutive endothelial (eNOS), 652 ischemic heart disease, 658 systemic sclerosis, 1158 inducible (iNOS), 652

1456

■

Index

Nitrosamine carcinogens, head and neck cancer, 946 NKX2-5 congenital heart disease, 782, 783 microarray analysis, 786 NKX3.1, prostate cancer, 899 NlaIII, serial analysis of gene expression (SAGE), 153 Nod1/Nod2, host response to Streptococcus pneumoniae, 1353 NOD2, 38, 39 host infection response Listeria monocytogenes, 1354 Staphylococcus aureus, 1354 NOD-like receptors (NLRs), 1348, 1350 caspase recruitment domains (CARD), 1365 pathogen recognition/signaling, 1365–1366 Nomenclature standards, 268 Non-alcoholic fatty liver disease, 1147 proteomics, 1150 transcriptomics, 1147 Non-coding RNAs (ncRNAs), 8 Non-Hodgkin lymphoma, 830 131 I-tositumomab treatment, 998 Non-homologous end joining, copy number variation generation, 109–110, 111f Non-nucleoside analog reverse transcriptase inhibitors (NNRTI), 1326, 1342 novel agents, 1333 Non-parametric tests, 213 Non-small-cell lung carcinoma, 859 TNM staging, 860 Non-steroidal anti-inflammatory agents (NSAIDs) cancer chemoprevention, 199 colorectal cancer, 890 miscarriage risk, 199 peptic ulcer disease mechanisms, 1124 susceptibility polymorphism, 1131 Non-viral gene delivery, 605, 611 direct naked DNA/plasmid DNA, 613–614 DNA-protein complexes, 614 liposomes, 614 Noonan syndrome (PTPN11), congenital heart disease, 784 Norepinephrine neurotransmission, 1282 depression, 1290 Northern blotting, 157 renal biopsy tissue examination, 1056, 1057 Norwegian Mother and Child Cohort (NMCC), 286 NOS3, atrial fibrillation, 745 Nosology (disease ontology), 211–212, 215– 216, 215t NOTCH1 (bicuspid aortic valve), 784 NOTCH2 (Alagille syndrome), congenital heart disease, 784 NOTCH3 mutations, CADASIL, 1229 Notch pathway genes

astrocytomas, 960 primary biliary cirrhosis, 1147 novoSNP, 94 NPHS1 (nephrin) mutations, congenital nephrotic syndrome, 1062 NPHS2 (podocin) mutations, congenital nephrotic syndrome, 1062 NPM1, acute myeloid leukemia, 846, 847 NPSR1, asthma, 1087 NR3C1, obesity, 1176 NRAMP1 see SLC11A1 NRTK2 mutations, obesity, 1175 NSABP B-13, 869 NSABP B-14, 869 NSABP B-21, 869 NSC 624004 anti-cancer agent, lymphocyte cytosolic protein-1 (LCP1) susceptibility association, 210 NTN1, multiple sclerosis, 1033 Nuclear buds, 303, 304 Nuclear magnetic resonance spectroscopy, metabolic profiling, 181, 182–183 cultured cell regulatory/signaling mechanisms, 185–186, 186f disease research, 184 Nuclear medicine-based imaging, 500, 512 Nuclear myocardial perfusion imaging, acute coronary syndrome diagnosis, 683 Nuclear receptor coactivator-2 (NCOA3) see AIB1 (SRC3) Nucleic acid amplification testing (NAAT), viral load measurement/viral genotyping, 370 Nucleic acid biosensors, 592–596 aptamers, 594–595, 596 bioterrorism detection, 592 molecular beacons, 592–594, 593f, 594f nanoparticles, 595–596 virus detection, 592 Nucleoplasmic bridges, 303, 304 Nucleoside/nucleotide analog reverse transcriptase inhibitors (NRTI), 1326, 1342 novel agents, 1333 Nucleosomes, 60, 63 stem cells, 602 NUGENOB, 1182 Number of genes, 6 Nurse geneticists, 395 training, 409 Nurses’ Health Study, 672 Nutrition, 1204–1215 dietary assessment, 1205–1206 gene–nutrient interactions, 1204, 1205, 1206–1214 cancer, 1212–1214 cardiovascular disease, 1207–1210 diabetes type 2, 1212 metabolic syndrome, 1212 monogenic diseases, 1207

multifactorial/age-related diseases, 1207–1214 obesity, 1210–1212 individual variation in response, 1204 research approaches, 1214 Nutritional genomics, 1182, 1205 study methods, 1206 NY-ESO-1, 578 Salmonella typhimurium vector, 582 O ob/ob mouse, 1175 Obesity, 14, 15, 39, 357, 1170–1183 animal models, 1172, 1175, 1177t causes, 1170–1172 colorectal cancer risk, 886 definition, 1170 diabetes type 2 association, 1187, 1189 diagnosis, 1179–1180, 1179t environmental factors, 1171, 1172, 1178, 1210–1211 family studies, 1172 gene therapy, 1183 gene-based treatments, 1180–1182 gene–nutrient interactions, 1210–1212 genetic factors, 1170–1172, 1172f, 1211–1212 adipocyte metabolism, 1178 appetite regulation, 1178 association studies, 1173, 1176 candidate genes, 1174t, 1175t, 1176 characterization, 1175–1176 chromosomal aberrations, 1175 energy expenditure regulation, 1178 food intake control, 1181–1182 gene map, 1173, 1175, 1176, 1178f genome-wide association studies, 265 identification, 1172–1175 linkage studies, 1176, 1178 single-gene disorders, 1173, 1175 study approaches, 1173t syndromic associations, 1175, 1176t websites, 1173t weight reduction program response, 1180, 1181t health consequences, 1172–1173 integrative biology approaches, 220–221 measurement, 1174 metabolic profiling, 187 nuclear magnetic resonance spectroscopy, 184 metabolic syndrome, 1194, 1196, 1197 molecular subtyping, 42 novel treatment approaches, 1182–1183 pharmacogenomics, 1182–1183 drug development, 347 prognosis, 1180–1182 regional distribution of weight gain, 1170, 1175 rheumatoid arthritis risk, 1022

Index

screening, 1179–1180 thrifty gene theory, 1170, 1172 Oblimersen, melanoma trials, 970 Observational units, 276 Obsessive–compulsive disorder, SLITSK1 (slit and trk like 1) mutations, 1285 OCT1, metformin pharmacogenomics, 1192 Ocular disorders, 1258t genetic testing, 1261 Oculomotor nerve disorders, 1257 Odds ratios, relative risk estimation, 463 Office of Genetics (Genomics) and Disease Prevention, 446, 447 Office of In vitro Diagnostic Device Evaluation and Safety (OIVD), 416 OipA, Helicobacter pylori virulence factors, 1129 Okhiro syndrome (SALL4), congenital heart disease, 783 2’-5’ Oligoadenylate synthetase, hepatitis C infection induction, 1382 Oligodendroglioma, 956, 957t chromosomal alterations, 959 gene expression profiles, 959, 960 genetic alterations, 957, 958f pharmacogenomics, 959 progression, 956, 957 Oligonucleotide microarrays, 157, 158–159, 158f copy number variation (CNVs) evaluation, 361 disease diagnosis/classification, 162 experimental design, 147 hybridization detection, 158–159 ovarian cancer, 916 transcriptomics, 143–144, 145f Omalizumab, asthma, 1093 Omega 3 fatty acids, 646 Omeprazole, Helicobacter pylori eradication, 1131 Omic space, 35f Oncogenes, 809 activation, 811 molecular mechanisms, 812–813 cancer, 979 expression signatures, 212, 212f head and neck cancer, 945 heart failure, 699 lung cancer, 858, 862, 862t ovarian cancer, 916–917 pancreatic cancer, 921 OnCore UK, 286 Oncotype Dx assay, 167, 383, 394, 426, 592, 876, 877, 992–993 economic evaluation, 427, 428 TAILORx, 167, 390, 427, 876 tamoxifen response prediction, 992 Online health information consumer retrieval, 252–256 information sites, 253–255, 254t, 255t quality assurance, 255

search characteristics, 252–253 personalized genomics, 255–256, 256t Online medical libraries, 254 Online Mendelian Inheritance in Man (OMIM), 227, 382, 781, 1175 ONTAK, messenger RNA-based anti-tumor vaccine adjuvant therapy, 581 Ontologies biobank information management, 284 consumer on-line health information retrieval, 253 diseases, 215–216, 215t, 230 genomic, 216 OPA1 mutations, Kjer’s autosomal dominant optic atrophy, 1260 Open Biomedical Ontologies (OBO), 216 Ophthalmology, 1256–1261 OPN, glomerular disorders, 1059 Optic nerve, 1256 genetic disorders, 1260 Optical fiber biosensors, 594 Oral cancer alcohol consumption-related risk, 1212 see also Head and neck cancer Oral leukoplakia, 946 Ordered subset expectation maximization (OSEM), positron emission tomography image reconstruction, 502 Orexins body weight regulation, 1171 obesity, 1178 Organic acids, targeted metabolic profiling, 185 Organophosphates, paraoxonase detoxification, 49, 51 Ornithine transcarbamylase deficiency, gene therapy, 615 Orthologous sequences comparative sequence analysis, 121 drug target identification, 336–337 environmentally-responsive genes, 1012 Orthopox viruses, viral chip technology, 551 Osaka Acute Coronary Insufficiency Study, 673 Osteoarthritis, biomarkers, 301 Osteonectin, podocyte expression, 1058 Osteopontin alcoholic liver disease, 1147 glomerular disorders, 1059 hepatic stellate cells, 1145 multiple sclerosis lesions, 1034 Out-of-Africa human expansion, 26–27 Ovarian cancer, 913–919 angiogenesis, 918–919 BRCA1/2, 357–358, 362, 810, 913 biosensor detection, 591 risk assessment, 403 chemotherapy response prediction, 916 DNA repair defects, 914, 915 Fanconi anemia DNA repair pathway, 914–915, 915f

■

1457

epigenetics, 917 gene expression profiling, 916 genetic alterations, 914, 916t genomic testing, 263, 403 growth factors, 916–917 hereditary nonpolyposis colorectal cancer (Lynch syndrome), 885, 915 high-risk criteria, 914, 914t inherited syndromes, 913–914 metabolomic approaches to diagnosis, 43 metastasis, 917–918, 919, 978 spheroids, 918 oncogenes, 916–917 origin/pre-malignant lesions, 913 prevention, 914 prognostic molecular signatures, 81 proteomics, 916 regulatory T cells (Treg), 820 screening, 914 somatic mutations, 915–916 tumor suppressor genes, 917 Ovarian cyst, metabolomic approaches to diagnosis, 43 8-Oxoguanine DNA glycosylase (OGG1), cancer susceptibility, 305 Oxygen therapy, chronic obstructive pulmonary disease, 1106 Ozone exposure, 1013 P P2Y12 see Platelet ADP receptor P3/P4 medicine, 75, 81–82, 378, 401 see also Prospective health care P3G Consortium, 286, 450 P3G Observatory, 450 P13 kinase/Akt pathway gliomas, 957 lung cancer pathogenesis, 862 ovarian cancer, 916, 917, 919 therapeutic targeting brain tumors, 961 melanoma, 971 p14ARF gliomas, 957, 958 mantle cell lymphoma, 839 melanoma, 969 methylation in colorectal cancer, 882 p16/INK4A cancer-related aberrant methylation, 67, 882, 917, 950 cardiovascular risk associations, 671, 681 colorectal cancer, 882 gliomas, 957, 958 head and neck cancer, 945, 949, 950 lung cancer, 863 mantle cell lymphoma, 839 melanoma, 810, 969 ovarian cancer, 917 pancreatic cancer, 921, 923 p21-ARC, laryngeal carcinoma, 951

1458

■

Index

p21SNFT, Hodgkin lymphoma/ReedSternberg cells, 836 p27, hepatitis C infection, 1146, 1382 p38 MAPK, 656 follicular lymphoma therapeutic target, 837 melanoma, 969 spondyloarthropathies, 1074 p38 MAPK inhibitors, 336 chronic obstructive pulmonary disease, 1107 p47phox, 656 atherosclerosis, 654, 655 hypertension, 655 p53, 809 acute myeloid leukemia, 199 apoptosis regulation, 811 biosensor detection of mutations, 591 breast cancer, 871 cell cycle regulation, 196 colorectal cancer, 136, 880, 882, 889, 890 development process, 810, 811 fludarabine pharmacogenomics, 847–848 gliomas, 957, 958 head and neck cancer, 945, 949 cisplatin resistance, 950 hepatitis B infection, 1146 Li-Fraumeni syndrome, 382, 880 lung cancer, 863 ovarian cancer, 914, 917 pancreatic cancer, 921, 923 prostate cancer, 901 PTEN cooperative interactions, 901 simian virus-40 T antigen interaction, 509 31 P magnetic resonance spectroscopy (MRS), 520 P-selectin, post-cardiac surgery neurocognitive dysfunction, 799 Pacemaker implantation Lev-Lenegre progressive cardiac conduction disease, 742, 744 long QT syndromes, 738 sinus node dysfunction/sick sinus syndrome, 744 Paclitaxel drug-eluting intracoronary stents, 674 pharmacogenomics, 1002 sorafenib combination, melanoma trials, 971 PAD14, rheumatoid arthritis, 1020, 1021 Paget’s disease, heart failure, 693 PAI-1 see Plasminogen activator inhibitor-1 Paladin gene polymorphism, myocardial infarction, 670 Palivizumab, 1344 Pan-HLA-DR epitope (PADRE), nucleic acid-based cancer vaccine potentiation, 581 Panatimumab, KRAS-positive colorectal cancer treatment, 340 Pancreatic β-cells, glucose-stimulated insulin secretion, 185–186 Pancreatic cancer, 921–928

alcohol consumption-related risk, 1212 antibody microarray serum protein profiling, 79 CA19-19 monitoring, 308 carcinogenesis/progression, 921, 922f diagnosis, 923–925 microdissection-based genotyping, 923–924 proteomics, 924–925 telomerase activity, 924 DNA repair gene expression, 926 erlotinib therapy, 1002 familial, 922 hereditary syndromes, 922 molecular analysis, 921 pharmacogenomics, 927 precursor lesions, 921 prognosis, 926 regulatory T cells (Treg), 820 risk factors, 922 screening, 922–923 mutational analysis, 923 TNFerade(TN) therapy, 927 Pancreatic carcinoma in-situ, 921 Pancreatic cystic neoplasms, 921 diagnosis, 925–926 gene expression profiles, 926 malignant potential, 925 monitoring, 927 Pancreatic endocrine tumors multiple endocrine neoplasia 1 (MEN1), 932–933 prognosis, 926–927 Pancreatic intraepithelial neoplasia (PaIN), 921 Pancreatic polypeptide-secreting tumors, multiple endocrine neoplasia 1 (MEN1), 933 Pancreatitis, chronic, pancreatic cancer risk, 922 PAOD1, peripheral arterial disease, 776, 777 Papillary thyroid carcinoma RET/PTC fusion oncogene, 939 target-based therapy, 939–940 Parainfluenza virus antiviral agents, 1344 Virochip detection, 551 Parallel group design, 279, 280 Paralogs, 121 Helicobacter pylori genome, 1125 Paramagnetic contrast agents, 515 Paramagnetic enhanced chemical exchange saturation transfer (PARACEST) contrast agents, 516–517 molecular magnetic resonance imaging, 519 Parametric tests, 213 Paraoxonase (PON1) gene polymorphism, 49, 51 Paraquat exposure, Parkinson’s disease, 1237 Parathyroid adenomas multiple endocrine neoplasia type 1 (MEN1), 932

type 2A/B, 935 somatic menin mutations, 934 PARK1 (SNCAα-synuclein), 1235 PARK2 (PRKN; PARKIN), 1235–1237, 1236f PARK4, 1235 PARK6 (PINK1; Pten induced kinase 1), 1237 PARK7 (DJ-1), 1237 PARK8 (LRRK2; dardarin), 1237, 1241 Parkinson disease with dementia, 1227–1228, 1235 α-synuclein aggregates, 1227 Parkinsonian syndromes, 1233 Parkinson’s disease, 50, 1233–1241 biomarker applications, 300, 301 causes, 1233 cell-based therapy (fetal dopaminergic neuron transplantation), 1239–1240 clinical features, 1233–1235, 1234t dopamine replacement therapy levodopa, 1233, 1239 metabolic pathways, 1239, 1239f environmental factors, 49, 1011, 1233, 1235, 1237 gene therapy, 614 genetics, 1011, 1233, 1234t, 1235–1238 candidate genes, 1238 DJ-1 (PARK7), 1237 LRRK2 (PARK8; dardarin), 1237–1238, 1241 microarray experiments, 95 PINK1 (PARK6; Pten induced kinase 1), 1237 PRKN (PARK2; PARKIN), 1235–1237, 1236f SNCA (PARK1; PARK4;α-synuclein), 1235 sporadic cases (typical Parkinson’s disease), 1238–1239 information database, 227 Lewy bodies, 1233, 1236 surgical interventions, 1239 basal ganglion neuronal pathways, 1240f deep brain stimulation, 1239 treatment, 1239–1240 future role of genetics, 1240–1241 Paroxetine, pharmacogenetics, 327 Parvovirus, idiopathic dilated cardiomyopathy, 693 PAS (PER-ARNT-SIMS) receptors, 1012 Patent ductus arteriosus, 784 Patenting genes/genomes, 396 public health genomics, 450 genomic technology, 450 Pathogen genotyping, 14, 370 Pathogen recognition receptors (PRRs; pathogen-associated molecular patterns), 1348, 1365, 1368 Pathogen recognition/signaling, 1365–1366, 1368

Index

CD14, 1366 mannose-binding lectin, 1366 NOD-like receptors, 1365–1366 RIG-like receptors, 1365–1366 Toll-like receptors, 1365 Pathogen virulence, 1314–1315 Pathogen-associated molecular patterns (PAMPs) see Pathogen recognition receptors Pathogen–host interactions, 1314–1320 host genetic susceptibility see Infectious disease Pathogenicity islands, 564 Helicobacter pylori, 1125, 1126 Patient preparation, biomarker specimen collection, 311–312 Patient record identifiers, 228, 229 PAX3 melanoma, 968 primary biliary cirrhosis, 1147 PAX5 follicular lymphoma, 837 Hodgkin lymphoma, 836 PAX6, ocular development, 1256 PCA3, prostate cancer urine biomarkers, 902 PCAF, 64 PCSK1 mutations, obesity, 1175 PCSK9 see Proprotein convertase subtilisin/ kexin type 9 PCV chemotherapy, brain tumor pharmacogenomics, 959, 961 PD-1, regulatory T cells, 574 PDE4B (disrupted phosphodiesterase 4B), schizophrenia, 1285 PECAM-1 (platelet endothelial cell adhesion molecule 1) astrocytomas, 960 inflammatory synovitis, 1072, 1073 Penetrance, pharmacogenetic test clinical validity, 328 D-Penicillamine, systemic sclerosis, 1162 Peptic ulcer disease, 1122–1133 applications of genomics, 1132, 1132t clinical features, 1123 Helicobacter pylori, 1122, 1123, 1123f, 1124 eradication, 1123, 1131 genomic markers, 1129–1130 geographic strain differences, 1129 proteomic markers, 1130 serological markers, 1130 historical management, 1122–1123 molecular diagnostic techniques, 1131–1132, 1132t non-steroidal anti-inflammatory agent (NSAID)-induced, 1124 pathophysiology, 1123 acid regulation, 1123–1124 mucosal defense mechanisms, 1124 ulcer formation, 1124, 1125f susceptibility polymorphisms, 1130–1131

Peptide cancer vaccines, 577–578 T-cell epitopes, 577 PeptideAtlas, 215 Peptidoglycans, TLR2 recognition of pathogens, 1353 Perforin (PRF1), 821 Perforins, 574 Pericardial effusion, systemic sclerosis, 1163 Perilipin (PLIN) insulin resistance, 1212 obesity, 1211–1212 Perimyocarditis, systemic sclerosis, 1163 Perinuclear antineutrophil cytoplasmic antibodies (pANCA) inflammatory bowel disease, 1043, 1044, 1045 primary sclerosing cholangitis, 1045 Perioperative genomics, 794 see also Cardiac perioperative medicine Peripheral arterial disease, 773–778 clinical features, 774, 774t epidemiology, 773–774 exercise training response, 774–775, 778 genetic factors, 775, 775f candidate gene identification, 777 polymorphisms, 775–777 pro-atherothrombotic genes, 776 quantitative trait locus identification, 777 susceptibility loci, 776 genomics applications, 777–778 heterogeneity of ultimate symptomatic presentation, 775 percutaneous peripheral intervention, 775 risk factors, 773–774, 774t treatment, 774–775 Peripheral blood mononuclear cells, microarray analyses cardiac allograft rejection, 701–702, 707–709, 709f cardiovascular disease, 151, 152 Peripheral T-cell lymphomas, 840 Permanent neonatal diabetes, 1192 Peroxisome proliferator-activated receptor α (PPARα) agonists see Fibrates Peroxisome proliferator-activated receptors (PPARs), 658–659 pancreatic cancer, 926 Persephin, 935 Personal Genome Project, 441 Personalized genomics for consumers, 255–256 direct-to-consumer initiatives, 255, 266 incidental findings, 256 online information, 256t physician advice, 266 Personalized medicine, 15–17, 16f, 16t colorectal cancer risk assessment, 887, 888f commercial exploitation, 16–17 definition, 2 epidemiological study applications, 466 genetic stratification of disease, 357–358

■

1459

health risk assessment, 380 hemostasis, 763–764 implementation issues see Translational genomics psychiatric disorders, 1286 role of information technology, 242–243 targeted cancer treatment, 814 thrombosis, 763–764 see also Prospective health care Personalized Medicine Coalition, 442 Perturbagens (genetic effectors), 193, 194, 194t Pescadillo, head and neck cancer, 951 Pesticides, intrauterine exposure effects on male fertility, 1011 Peter’s anomaly, PAX6 mutation, 1256 Peutz-Jeghers syndrome breast cancer, 871 LKB1 pathway mutations, 886 pancreatic cancer, 922 STK11 mutation, 382 Phage integrase systems, non-viral gene vectors, 605 Pharmaceutical Benefits Advisory Committee (PBAC), 426 Pharmacist education, 409–410 Pharmacodynamics labeled anti-cancer agent imaging, 497 pharmacogenetic markers, 326–327, 346 drug discovery/develpment, 338–340 Pharmacogenetic Optimization of Anticoagulation Study, 762 Pharmacogenetics, 48, 209, 270, 321–332 candidate gene approaches, 325–326, 344, 346 case–control studies, 323–324, 325f clinical implementation, 360 clinical utility of analytic testing, 328–329 cohort multiple dosing studies, 325 copy number variation (CNVs), 116–117 definition, 270, 321 epistatic associations, 327 familial studies, 324–325 FDA approved biomarkers, 330t, 345t future developments, 331–332, 332t HapMap markers, 327–328 health professionals’ role, 394–394 healthy volunteer challenge studies, 325 objectives, 321, 323 pathway-based analyses, 327 pharmacokinetic versus pharmacodynamic markers, 326–327, 346 placebo response effects, 325 prospective analyses, 328 regulatory requirements, 329 research study design, 323–325, 324f phenotype definition, 323 research question definition, 323 research targets, 322–323, 322t test validity, 328

1460

■

Index

Pharmacogenetics (Continued) validation of associations, 328 whole-genome association studies, 325–326, 344, 346 see also Pharmacogenomics Pharmacogenetics Research Network (PGRN), 227 Pharmacogenomics, 227, 270, 321–332, 371–373, 568–569 acute coronary syndromes, 686–687 adverse drug reactions, 371, 1286 antidepressants, 1286, 1293–1294, 1294t drug interactions, 1293 asthma, 1091–1092 bipolar disorder, 1304 brain tumors, 961–962 cancer, 372, 1002 chronic obstructive pulmonary disease, 1106–1107 clinical decision support systems, 246 colorectal cancer, 890–892 commercial applications, 441 consumer education, 266 corticosteroids, 1092 data management, 228 definition, 270 diabetes, 1192 dopamine metabolism, 1240 drug development, 337–338, 338t, 343–355 drug efficacy, 348–349, 371, 1286 viral chip technology, 551 drug response biomarkers, 337–338, 338t drug-diagnostic test combinations clinical trial design, 420 diagnostic test development, 420 dosing adjustments, 420–421 drug labels, 420 economic value, 429 patient selection/monitoring, 419–420 safety/effectiveness demonstration, 420, 421 economic evaluation, 415, 426, 427 economic incentives, 428–430 epilepsy, 1249–1251, 1250f FDA Critical Path Initiative, 422 gliomas, 959 head and neck cancer, 950 healthcare professional education, 266 pharmacists, 409–410 heart failure, 697–698 Helicobacter pylori eradication, 1131 hemostasis, 768 HIV infection/AIDS, 1331, 1342 hypertension, 630–631 hypertrophic cardiomyopathy, 723 impact on health–disease continuum, 264 inflammatory bowel disease, 1047 leukemias, 847–849 melanoma, 969

myocardial infarction, 673–674 obesity, 1182–1183 oligodendrogliomas, 959 pancreatic cancer, 927 population genomic screening, 359 principles, 321–322 prospective health care, 383 prostate cancer, 905–906 psychiatric disorders, 1284t, 1286 regulatory issues, 329, 415, 418–421, 429 rheumatoid arthritis, 1024–1026, 1025t sample collection, 352–354 systemic sclerosis, 1159 thrombosis, 768 translational barriers, 419 uptake of diagnostic testing, 372–373 viral hepatitis, 1386 Voluntary Genomic Data Submission (VGDS), 228, 329, 344, 415 see also Pharmacogenetics Pharmacokinetics labeled anti-cancer agent imaging, 497 pharmacogenetics, 48 markers, 326–327, 346 PharmGKB (Pharmacogenetics Knowledge Base), 227 Phase I trials diagnostic biomarkers, 316 drug development, 346, 347 Phase II trials diagnostic biomarkers, 316 drug development, 347 safety, 350 drug-diagnostic test combinations, 420 Efficacy Proof of Concept studies, 347 Phase III trials diagnostic biomarkers, 316 drug development, 347 drug-diagnostic test combinations, 420 Phase IV trials, diagnostic biomarkers, 316 Phenotypic plasticity, 12 Phenylalanine hydroxylase deficiency, 1207 newborn screening, 185 Phenylketonuria, 48, 448, 1207 dietary management, 473, 1207 expression variability, 473 newborn screening, 185, 359, 455, 472–473 platform, 472 pregnancy management (maternal phenylketonuria), 473 Phenytoin adverse drug reactions, 1251 metabolism, 1293 pharmacogenomics, 327 dose variations, 1250, 1251 Pheochromocytoma, multiple endocrine neoplasia type 2A/B, 935 genotype–phenotype correlations, 936 PHF11 (PDH finger protein 11) asthma, 1087

chronic obstructive pulmonary disease, 1102 phiC31, non-viral gene vectors, 605–606 Philadelphia chromosome, 809, 811, 844 Phosphatidylethanolamine N-transferase, alcoholic liver disease, 1147 Phosphodiesterase-4 inhibitors, chronic obstructive pulmonary disease, 1107 Phosphodiesterase-5 inhibitors Raynaud’s syndrome, 1162 systemic sclerosis, 1165 Phospholamban (PLN) mutations, hypertrophic cardiomyopathy, 718 Phospholipase A2, hepatocellular carcinoma, 1148, 1383 Phospholipid transfer protein (PLTP), 636 Phospholipids, targeted metabolic profiling, 185 Photolithography, 34, 104 microarray generation oligonucleotide microarrays, 158, 158f optimization requirements, 546 viral chips, 545–546 PHOX2A, strabsimus, 1257 Phusion, 91 Phylogenetic analysis, blood coagulation proteins, 756, 756f, 757f Physicians’ Health Study, 301 Piezoelectric gene sensor microarrays, viral chip technology, 547 Pilot trials, pharmacogenetic tests, 329 PiMZ/PiMM, chronic obstructive pulmonary disease, 1099 PINK1 (PARK6; Pten induced kinase 1), 1237 PipMaker, 123, 124 Pirbuterol, asthma, 1091 Pituitary adenomas, multiple endocrine neoplasia type 1 (MEN1), 933 Pituitary adenyl cyclase activating polypeptide, gastric acid secretion regulation, 1124 PITX2 DNA methylation, breast cancer prognosis, 138 ocular development, 1256 PLA2 polymorphism, aspirin resistance, 674 Placebo groups, 280 Placebo response, pharmacogenetic studies, 325 Placental growth factor, acute coronary syndromes risk stratification, 686 Planar fluorescence imaging, 527–528 epi-illumination mode, 527, 528f reflectance mode, 527 Plasma specimen collection, 311 Plasmid DNA (pDNA) cancer vaccines, 579–582 antigen processing enhancement, 580 clinical results, 581 immune system stimulation enhancement, 580–581 regulatory issues, 581

Index

vaccination efficacy enhancement, 579–580 vector design, 580 direct delivery for gene therapy, 613–614 Plasminogen activator, genetic variation, 757 Plasminogen activator inhibitor-1 (PAI-1) polymorphism coronary artery bypass graft occlusion, 797 obesity, 1179, 1182 sepsis, 1367 Plasmodium falciarum, microarray expression profiling, 568 Platelet ADP receptor (P2Y12) polymorphism aspirin resistance, 674 clopidogrel resistance, 674 peripheral arterial disease, 776 Platelet endothelial cell adhesion molecule 1 see PECAM-1 Platelet glycoprotein IIb–IIIa receptor polymorphism aspirin resistance, 674 coronary artery bypass grafting complications, 797–798, 799 post-cardiac surgery neurocognitive dysfunction, 798 Platelet-derived growth factor (PDGF) aptamer biosensor detection, 594, 595f glomerular disorders, 1059 hepatic stellate cell production, 1145 ovarian cancer angiogenesis, 918 systemic sclerosis, 1157 Platelet-derived growth factor (PDGF) antagonists, cirrhosis, 1142 Platelet-derived growth factor receptor (PDGFR) brain tumor therapeutic targets, 962 gliomas, 956, 958 melanoma antiangiogenic therapy, 972 sorafenin inhibition, 1002 Platelets activation, 759, 760 post-cardiac surgery stroke risk, 798 biomarkers arterial thrombosis risk (Bloodomics project), 768 specimen collection, 312 RNA expression profiling, acute coronary syndromes, 685 Platinum compounds, DNA methylation profiles in tumor response prediction, 138 PLAU-promoter DNA methylation, breast cancer prognosis, 138 PLIN see Perilipin PLN mutations (phospholamban), hypertrophic cardiomyopathy, 718 PML-RAR fusion protein, 63, 66, 67–68 acute promyelocytic leukemia, 845 PMS1 colorectal cancer, 881

hereditary nonpolyposis colorectal cancer (Lynch syndrome), 885, 889, 915 PMS2 colorectal cancer, 881 hereditary nonpolyposis colorectal cancer (Lynch syndrome), 811, 885, 889, 915 Pneumocystis carinii pneumonia, 1324 Podocin (NPHS2) mutations, congenital nephrotic syndrome, 1062 Podocyte gene expression profiles, 1058 glomerular disorders, 1059 POLG2, cirrhosis, 1142 Policy issues, 388–396, 388f biobanking, 389, 391 data disclosure, 391–392 direct-to-consumer marketing, 393–394 genetic discrimination, 395–396 genetic exceptionalism avoidance, 448 genomic database access, 392 genomic literacy of policy makers, 402–404 genomic medicine access, 394 integration into healthcare, 392–396 health professional education, 394 intellectual property, 396 medical privacy, 395 public health genomics, 450–451 service development/evaluation, 450 reimbursement, 394 research, 389–391, 389f allocation/prioritization, 389–390 large-scale studies, 391–392 race as analytical variable, 390–391 results disclosure to participants, 391–392 PolicyDB database, 450 Poliomyelitis, 1341 vaccines, 562 Polony multiplex analysis of gene expression (PMAGE) hypertrophic cardiomyopathy, 722 single nucleotide polymorphism (SNP) discovery, 36 transcriptomics, 153 Polyacrylamide gel electrophoresis, twodimensional (2D-PAGE) proteomics ovarian cancer, 916 prostate cancer, 903 quantitative, 174, 174f Polyamide dendrimers, viral oligonucleotide immobiliztion, 544 Polycomb group (PcG) gene family, stem cells, 602–603 Polycyclic aromatic hydrocarbon toxicity, 1012 Polymerase chain reaction amplicon resequencing methodology, 92–94 biosensors, 592

■

1461

DNA sequencing by synthesis, 94 dystrophin mutations, Duchenne muscular dystrophy diagnosis, 1274 hepatitis B DNA quantitation, 1379 lymphoma, 832 microdissection-based genotyping, pancreatic cancer diagnosis, 924 molecular epidemiology applications, 304 mRNA amplification for microarray experiments, 159–160 Reed-Sternberg cells, 836 viral load assessment, 592 virus identification, 541 see also Real-time polymerase chain reaction; Reverse transcription– polymerase chain reaction Polymyositis, diagnosis, 1159 Polyphred, 94 Polysaccharide vaccines, 562 PON1 (paraoxonase) polymorphism, 49, 51 Pooled samples, transcriptomics experimental design, 147, 148 Population attributable fraction, 463–464 Population genomics, 11, 22–30 applications to genomic medicine, 28–29 concepts, 22–26 genealogical approach, 23–24, 23f human data sources, 26 human genetic history, 26–27 human “races”, 27–28 linkage disequilibrium, 24–26, 25f public health approaches, 447, 455, 457–458 standard neutral model, 24 Population sampling, 461, 464 case-control studies, 464 electronic medical records utilization, 237 experimental design, 276 Monte Carlo simulation, 278–279, 278f, 278t Population structure, 24–25, 29, 237 association test artifacts, 38 case-control study confounding, 464 genome-wide association studies, 103 principal component analysis, 27, 27f, 29 Population-based registries, 199 see also Biobanks Population-based studies biomarkers, 299–306 cohort studies, 52, 53 environmental factors, 49, 50 genomic screening, 348–359 human genomic epidemiology, 461, 462 Portuguese Island Collection (PIC), bipolar disorder linkage studies, 1301–1302 Poser, 465 Positional cloning bipolar disorder, 1301 diabetes type 2, 1188 inflammatory bowel disease, 1041 schizophrenia, 1302

1462

■

Index

Positron emission tomography (PET), 370, 500–510, 532 attenuation correction, 502 cancer detection, 496 2D imaging, 501, 502f 3D imaging, 501, 502f data acquistion modes, 501 data correction, 501–502 direct genomic DNA sequence imaging, 504, 504f DNA replication imaging, 503 head and neck cancer diagnosis, 947 image reconstruction, 502 sharpness/noise trade-off, 503 image resolution determinants, 502 depth of interaction, 502–503, 503f in vivo molecular diagnostics, 81 indirect DNA sequence imaging, 504–506 promoter amplification, 505–506 promoter–reporter constructs, 505, 505f reporter genes, 504–505 multimodal imaging, 509–510, 509f Parkinson’s disease, 1235, 1240–1241 physics, 500–501, 501f protein imaging, 506–509 enzyme substrates, 507, 507f ligands, 506–507 protein–protein interactions, 508–509, 508f pumps, 508 radiopharmaceuticals, 503, 503t RNA imaging, 506 scanners, 501 intrinsic resolution, 502 transcriptional analysis (promotor activation), 505 Positron emission tomography-computerized tomography (PET-CT), 370, 507f, 510 head and neck cancer diagnosis, 947 post-treatment neck dissection planning, 951 imaging protocol, 509f, 510 scanner design, 502 Positron emission tomography-magnetic resonance (PET-MR), 510 Post-polio syndrome, 1265 Post-test probability, calculation from pre-test probability, 314 Post-translational modification, 173, 369, 758 glycomics, 369 Post-trascriptional stem cell regulation, 603 Posterior chamber, 1256 Posture, biomarker specimen collection, 311 Power analysis genome-wide association studies, 102 microarray experiments, 152–153 PPARα agonists see Fibrates PPARG diabetes type 2, 265, 1188

familial partial lipodystrophy (FPLD), 1196 metabolic syndrome, 1197, 1199 obesity, 1176, 1178, 1182 PPARs see Peroxisome proliferator-activated receptors Prader–Willi syndrome, 1173 Pravastatin, 674 PRC1, 603 PRC2, 603 Pre-analytical variation biomarkers, 311–312, 312f genetic tests, 328 Prebiotic bacteria, inflammatory bowel disease treatment, 1049 Precision, 277 biomarkers protein immunoassay, 309, 310 surrogate endpoints, 300 experimental design improvement, 277–280, 281 replication influence, 277–278 Prediction Analysis of Microarrays (PAM), molecular signature analysis, 148–150, 149f, 150f Predictive factors, 466 prospective health care, 380–382 models, 380, 381f Predictive healthcare, 264 see also Prospective health care Predictive value biomarker diagnostic performance, 315–316 pharmacogenetic tests, 328 Prednisolone, pharmacogenomics, 849 Prednisone, Duchenne muscular dystrophy, 1277 Pregnancy-associated plasma protein A (PAPP-A) acute coronary syndromes risk stratification, 686 EDTA effects, 311–312 Preimplantation diagnosis, molecular signature analysis, 151 Premarket Approval Application, in vitro diagnostics, 416–417 Premarket Notification Submission, in vitro diagnostics, 416, 417 Prenatal testing current levels, 393 preimplantation diagnosis, 151 Presenilin 1/2 (PSEN1/2) mutations, Alzheimer’s disease, 1224, 1229, 1284 Prevalence, 463 of specific phenotype, pharmacogenetics test clinical validity, 328 Prevention, 242, 264, 378 familial risk level-related strategies, 483, 487 breast cancer, 485–486t colorectal cancer, 484t public health approaches, 488

genotypic versus phenotypic interventions, 448 population genomic screening, 348–359 public health approaches, 447, 457 see also Prospective health care Primary biliary cirrhosis, 1147 proteomics, 1149 transcriptomics, 1147 Primary care physician education, 405–408 continuing medical education, 407–408 medical school curriculum, 405–406, 406t residency training, 406–407, 407t Primary effusion lymphoma, 839 Kaposi’s sarcoma human herpes virus-8 association, 830, 839, 840 Primary mediastinal large B-cell lymphoma, 835 diagnosis, 835 therapeutic targets, 835 Primary pulmonary hypertension, 693 Primary sclerosing cholangitis, 1046, 1147 perinuclear antineutrophil cytoplasmic antibodies (pANCA), 1045 proteomics, 1149 transcriptomics, 1147 PRIMER3, 93 Principal component analysis, 213 cancer gene expression profiles, 212 metabolic profiling cardiovascular disease, 187 mass spectrometry, 183 nuclear magnetic resonance spectroscopy, 183 obesity, 187 metabolomic approaches to complex disease, 43 population structure analysis, 27, 27f, 29 PRINTS, 216 Prion disease (spongiform encephalopathies), 76, 77f, 78, 1222, 1228 Privacy issues, 16, 266–267 biobank data, 267, 284, 295, 391 reversibly de-identified data, 292–293 Singapore Tissue Network (STN), 291–293 genomic information databases, 392 genetic exceptionalism avoidance, 448 policy, 395 Private genomics research funding, 435 Private health R&D, 435 Private sector genomics business activity, 437–438, 438t financial/market factors, 437, 437f global scope, 439–441, 440t publicly funded genomic research interaction, 440 see also Genomics firms

Index

PRKAG2 mutations, hypertrophic cardiomyopathy, 719 PRKN (PARK2; PARKIN), Parkinson’s disease, 1235–1237, 1236f Probiotic bacteria, inflammatory bowel disease treatment, 1049 Procarbazine, oligodendrogliomas, 959 Processing bodies (P-bodies), RNA interference pathway, 194 PRODH (proline dehydrogenase), schizophrenia, 1285 Progesterone receptor assay, breast cancer targeted therapy, 992–993 Progranulin (GRN ) mutations, frontotemporal dementia, 1226 Progressive bulbar palsy, 1265 Progressive cardiac conduction defect see Lev-Lenegre progressive cardiac conduction disease Progressive supranuclear palsy, 1233 Prolactinomas, multiple endocrine neoplasia type 1 (MEN1), 933 Proliferating cell nuclear antigen (PCNA), astrocytomas, 960 Promoters, 12 aberrant methylation Fanconi anemia DNA repair pathway inactivation, 915 head and neck cancer, 950 ovarian cancer, 917 pancreatic cancer, 923 local regulatory variants, 12 mutation, comparative sequence analysis, 126f, 127 transcriptional analysis, positron emission tomograpy, 505, 505f Pro-opiomelanocortin (POMC) body weight regulation, 1171 gene mutations, obesity, 1173, 1175, 1176, 1178, 1182 Propantheline, myasthenia gravis, 1276 Properidin gene mutation, Neisseria meningitidis susceptibility, 1364 Proprotein convertase subtilisin/kexin type 9 (PCSK9) deficiency, 639 mutation autosomal dominant hypercholesterolemia, 638 familial hypobetalipoproteinemia, 639 polymorphism low-density lipoprotein cholesterol reduction, 639, 671, 672, 672f myocardial infarction risk, 644, 671, 672f nonsense/missense variants, 671, 672f therapeutic inhibition, 647 Prospective health care, 378–383, 379f, 380f baseline risk assessment, 382 key features, 380 phamacogenomics, 383

predictive factors, 380–382 predictive models, 380, 381f Prostaglandin E2 ovarian cancer angiogenesis, 918 tumor microenvironment, 819, 820 Prostaglandins, targeted metabolic profiling, 185 Prostate cancer, 11, 23, 568, 898–907 androgen ablation therapy, 907 androgen signaling, 906 biomarkers, 902f circulating tumor cells, 903 serum, 903 urine, 902 chromosomal rearrangements, 904 detection, 901–904 systems medicine approach, 76, 78 epigenetic changes, 899–900 histones acetylation/demethylation, 899 promoter methylation, 138, 899 gene expression profiles androgen receptor target-genes, 907 biochemical relapse after surgery, 904–905 cancer initiation, 904 correlates with pathology/aggressive disease, 905 literature mining, 905 metastatic progression, 905 response to treatment, 905–906 targeted cytotoxic therapy, 906 genome-wide association studies, 265, 899 genomic changes, 904–907 integrative analysis, 905 response to treatment, 905–906 germline polymorphisms, 899 highly penetrant genes, 898–899 hormone-refractory disease, 906–907 imaging, 903–904 messenger RNA-based anti-tumor vaccines, 581 metastasis, 986 mitochondrial gene mutations, 899 pharmacogenomics, clinical trials, 905–906 plasmid DNA-based anti-tumor vaccines, 581 prognostic molecular signatures, 81, 151 proteomics, 905 serum protein profiling, 79 screening, 901–902 somatic DNA alterations, 900–901, 900f Myc, 901 p53, 901 PTEN/P13K/mTOR, 900–901 Rb, 901 susceptibility marker clinical utility, 265 therapeutic HSP90 inhibition, 167 TMPR22–ERG/ETV1 gene fusion, 161 UGT2B17 copy number variation, 115–116, 115f

■

1463

xenotropic murine leukemia virus-related virus (XMRV) association, 550, 899 Prostate Specialized Program of Research Excellence National Biospecimen Network (Prostate SPORE NBN0 Pilot), 286, 287–288 Prostate-specific antigen (PSA), 901–902 nanoparticle biosensors, 595 nucleic acid-based cancer vaccines, 581 prostate cancer, 903 Prostate-specific membrane antigen (PSMA), monoclonal antibody targeted therapy, 999, 999f Prostatic intraepithelial neoplasia (PIN), 138 urine biomarkers, 902 Protease inhibitors, 1326, 1342, 1343 chronic obstructive pulmonary disease, 1107 novel agents, 1333 Proteasome subunit α type 6 (PSMA6) polymorphism, myocardial infarction, 670 Protein aggregation cardiomyopathy (PAC; desmin-related myopathy), 700–701 Protein C, 755, 756 factor V polymorphism effects, 1367 racial/ethnic variation, 760 sepsis, 1367 Protein C deficiency, racial/ethnic variation, 762 cerebral venous thrombosis risk, 762 Protein cancer vaccines, 578 Protein convertase 1 (PC1) mutations, obesity, 1173 Protein kinase drug targets, 336 Protein microarrays bipolar disorder, 1304 blood biomarker analysis, 79 cancer diagnostics, 371 metastasis, 985 head and neck cancer, 951 multiple sclerosis antibody reactivity, 1036 pathogen detection/genotyping, 370 proteomic analysis, 369 tumor microenvironment analysis, 825 Protein S deficiency, racial/ethnic variation, 762 Protein S, racial/ethnic variation, 760 Protein tyrosine kinases reactive oxygen species activation, 656 therapeutic targeting imatinib mesylate, 939–940 medullary thyroid carcinoma, 939 papillary thyroid carcinoma, 939 Protein tyrosine phosphatases, reactive oxygen species activation, 656 Protein–protein interactions, 369 positron emission tomography (PET), 508–509, 508f systems biology approach, 75

1464

■

Index

Proteins adduct biomarkers of exposure, 302 expression profiles, 369 molecular function, Gene Ontology (GO), 216 polymorphisms, 1 positron emission tomography (PET), 506–509 post-translational modification, 369, 758 glycomics, 369 Proteomics, 2, 173–179, 369 acute coronary syndromes diagnosis, 684–685, 684f, 685f risk stratification, 686 alcoholic liver disease, 1149–1150 amyotrophic lateral sclerosis, 1278 autoimmune hepatitis, 1149 bioinformatics, 176–178 data display, 178 modified peptide identification, 177 biomarkers, 178, 301 bipolar disorder, 1304 blood coagulation proteins, 758 brain tumors, 961 cancer metastasis, 985–986 cardiac surgery molecular response, 799 cholangiocarcinoma, 1149 chronic obstructive pulmonary disease, 1104 colorectal cancer screening/surveillance, 889 treatment response prediction, 891–892 complex disease, 42, 268 coronary artery disease screening, 681 data repositories, 215 depression, 1295–1296 diagnostic applications, 209 electronic medical records, 234 gel-based techniques, 174–175 gene–diet interactions, 1205 head and neck cancer, 951 hepatitis B, 1149, 1381–1382 hepatitis C, 1149, 1150, 1384 hepatocellular carcinoma, 1150, 1384 liver, 1143–1144 disease, 1148–1150 interferon therapy specific response, 1385 mass spectrometry, 173, 174, 175–176, 177f multiple sclerosis, 1036–1037, 1037t non-alcoholic fatty liver disease, 1150 ovarian cancer, 916 pancreatic cancer diagnosis, 924–925 personalized medicine applications, 15, 16t primary biliary cirrhosis, 1149 primary sclerosing cholangitis, 1149 prognostic applications, 209 prostate cancer, 905 serum biomarkers, 903 urine biomarkers, 902 psoriatic arthritis, 1075 public health genomics, 447

rheumatoid arthritis, 1023–1024, 1075 sample preparation methods, 176t sarcoid bronchoalveolar fluid, 1114 solution-based techniques, 174–175 spondyloarthropathies, 1075 stem cells, 603–604 systems medicine applications, 79–80 tumor microenvironment immune cells, 825 undersampling issues, 174 vaccines development, 568 Proteomics Identifications Database (PRIDE), 215 Prothrombin (factor II), 755, 756 polymorphism, 762 cerebral venous thrombosis risk, 762 peripheral arterial disease, 776 racial/ethnic variation, 762 targeted screening, 766t Protocadherin-2 precursor, sarcoid bronchoalveolar fluid, 1114 Proton pump inhibitors Helicobacter pylori eradication, 1123, 1131 pharmacogenomics, 1123, 1131 systemic sclerosis, 1163 PROWESS, 1369, 1370 PSEN1/2 (presenilin 1/2) mutations, Alzheimer’s disease, 1224, 1229, 1284 Pseudoexfoliation syndrome, 1261 Pseudohypoaldosteronism type 2 (Gordon’s syndrome), hypertension, 628 Pseudomonas aeruginosa genetic adaptation during chronic infection, 569 genetics of host response, 1354 genome mapping, 137 in vivo expression technology (IVET), 566 Pseudomonas fluorescens antibodies, inflammatory bowel disease, 1043, 1045 PSMA6 (proteasome subunit α type 6) polymorphism, myocardial infarction, 670 Psoralen-based viral oligonucleotide immobilization techniques, 544–545 Psoriatic arthritis, 1067 classification, 1069 clinical features, 1068 gene expression profiles, 1074 infliximab treatment, 1077 proteomics, 1075 synovitis histopathology, 1073 Psychiatric disorders, 1282–1286 candidate genes, 534, 1283 classification, 1283 DNA methylation alterations, 68 functional imaging approaches, 532–536 conceptual basis, 533–534 gene selection, 534, 535–536 non-genetic factor influences, 534–535 task selection, 535 genome-wide association studies, 1283

genomics, 1282–1283, 1285 linkage analysis, 1283 metabolomics, 1286 monoamine oxidase A polymorphism, 55 neuroimaging, 1285 personalized medicine, 1286 pharmacogenomics, 1284t, 1286 rare genetic variants, 1284–1285 serotonin transporter (5-HTT gene) polymorphism, 55, 535–536 systems biology, 1286 PTEN Bannyan-Ruvalcabe-Riley syndrome, 886 breast cancer, 871 Cowden syndrome, 382, 886 gliomas, 957, 958 melanoma, 969 ovarian cancer, 917 p53 cooperative interactions, 901 prostate cancer, 900–901 PTP4A, Hodgkin lymphoma/Reed-Sternberg cells, 836 PTPN11 (Noonan syndrome), congenital heart disease, 784 PTPN22 diabetes type 1, 1187, 1191, 1192 rheumatoid arthritis, 1017, 1018, 1020–1021, 1023, 1024 Public education, newborn screening programs, 471 Public Health Genetics Unit, 446, 447, 450, 451 Public health genomics, 446–452, 454, 455–456, 456f communication, 450 core activities, 449–451 definition, 447 family history-based approaches health initiatives, 488 screening activities, 487–488 gene–environment interactions as determinants of health, 447–448, 448f, 457 genetic exceptionalism avoidance, 448 health professional education, 451 information sharing, 450, 451 key concepts, 447–448 knowledge base, 452, 452t basic research, 449 knowledge integration, 449–450, 458 networks, 451 nutritional genomics approach, 1205 origins, 446–447 policy issues, 450–451 service development/evaluation, 450 societal attitudes, 449 stakeholder engagement, 450 translational strategy, 448–449, 449f, 458 workforce training, 451 Public Health Genomics European Network (PHGEN), 451

Index

Public health professionals, 455 genomics specialist training, 451 Public Population Project In Genomics (P3G) Consortium, 286, 450 Public sector genomics business activity, 437–438, 438t financial/market factors, 437, 437f see also Genomics firms Public–private interactions genomic medicine, 434–442 large-scale genomic projects, 440 PubMed, 254 Pulmonary embolism, 768 Pulmonary fibrosis, 1110–1111 genetic determinants in inherited disorders, 1115 see also Diffuse parenchymal lung disease Pulmonary hypertension, chronic obstructive pulmonary disease, 1100–1101 Pulmonary stenosis, 784 Pulmonary surfactant proteins, 1114 Pupil, developmental abnormalities, 1256 Purine nucleoside phorphorylase deficiency, gene therapy, 616 PVT1, diabetic nephropathy, 1061 PyMt mouse model, 980–981, 983 cancer metastasis susceptibility studies, 979–980 Pyrosequencing cancer somatic mutations, 812 single nucleotide polymorphism (SNP) discovery, 36 tissue sample DNA methylation assessment, 135 Pyruvate cycling pathways, glucose-stimulated insulin secretion, 185–186, 186f PYY, obesity, 1176 Q Quality assurance diagnostic assays, 361 newborn screening programs, 471 pharmacogenetics test validity, 328 viral chip microfabrication, 541–542 Quality-adjusted life years (QALYs), costutility analysis, 425, 426 Quantitative trait loci (QTLs), 189 cancer susceptibility genes, 979 metastasis, 980 environmentally-responsive gene discovery, 1012 mapping in model organisms, 40 metabolic syndrome, 1196, 1197 obesity, 1173 animal models, 1175–1176 peripheral arterial disease, 777 haplotype analysis, 777 salt-sensitivity, 626 Quantum dots biosensors, 595

nanocrystals, 370 viral chip technology, 552 Quinidine Brugada syndrome, 741 drug-induced long QT syndromes, 731 short QT interval syndrome, 742 Quinones exposure, leukemia susceptibility, 51 R R, 217–218 RAB, promoter hypermethylation in lung cancer, 858 Race see Ethnic/racial variation RAD001 (Everolimus) melanoma clinical trials, 971 prostate cancer targeted therapy, 906 Radiofrequency (RF) signals, 513 Radioimmunoassay candidate protein biomarkers, 309 newborn screening tests, 472 Radioimmunotherapy, 997 Radioiodine therapy, 508 Radiolabeled nucleosides, cancer treatment efficacy evaluation, 497 Radiopharmaceuticals, positron emission tomography, 503, 503t Random sampling, 276, 277 Monte Carlo simulation, 278–279, 278f, 278t Randomization assignment to treatment, 277 blocking, 277, 281 experimental design, 28, 277 tissue sampling, 281 transcriptomics, 147, 148 RANTES (CCL5) chronic obstructive pulmonary disease, 1102 hepatitis C, 1383 HIV infection progression to AIDS, 1329 susceptibility, 1328 Rapamycin (FK506; sirolimus), 167, 197 acute lymphoblastic leukemia, 211 drug-eluting intracoronary stents, 674 systemic sclerosis, 1164 RARB methylation status, tumor treatment response prediction, 138 Rare variants, 13, 14–15 complex disease studies, 39 Ras cancer metastasis, 984 colorectal cancer, 880 leukemias, 844 lung cancer, 862, 864 melanoma, 969, 970 ovarian cancer, 917 RASSF1 methylation status lung cancer, 858 tumor treatment response prediction, 138

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1465

RASSF1A methylation status breast cancer, 138 ovarian cancer, 917 prostate cancer, 899 Rat genome sequencing, 120 Raynaud’s syndrome management, 1162 prevalence in connective tissue disorders, 1158t systemic sclerosis, 1155, 1155f, 1158 RB1CC1, breast cancer, 382 Rb cancer-related hypermethylation, 67 mutations gliomas, 957 lung cancer, 863 medullary thyroid carcinoma, 939 prostate cancer, 901 retinoblastoma, 1261 RBP1 methylation status, tumor treatment response prediction, 138 Reactive arthritis, 1067 bowel inflammation, 1070, 1071 clinical features, 1068 extra-articular, 1068–1069 Reactive oxygen species atherosclerosis, 652–660 gene polymorphisms, 658 modulatory effects, 654–655 pharmacological targeting, 658–659 signaling in advanced disease, 658 chronic obstructive pulmonary disease, 1102 endothelial cells inflammatory gene expression induction, 653–654 sources, 652–653, 653f head and neck cancer, 946 hyperglycemia-induced, 655 hypertension, 655 prostate cancer associations, 899 signaling pathway regulation, 655–656 calcium ions, 655–656 mitogen-activated protein kinase, 656 protein tyrosine kinases/phosphatases, 656 transcription factor regulation, 656–658, 657f AP-1, 657–658 NF-κB, 656–657 tumor microenvironment, 820 Real-time polymerase chain reaction Bordetella pertussis/Bordetella parapertussis (whooping cough) diagnosis, 371 cardiac allograft rejection gene expression profiles, 708, 709 Helicobacter pylori diagnosis/monitoring, 1131–1132 metastasis susceptibility genes, 981 rapid single nucleotide polymorphism (SNP) genotyping, 34

1466

■

Index

Real-time polymerase chain reaction (Continued) telomerase activity, pancreatic cancer diagnosis, 924 tumor microenvironment T cells, 821 viral load monitoring, 539 Realizing the Promise of Pharmacogenomics: Opportunities and Challenges, 243 Receiver operating characteritic (ROC) curves, 466 diagnostic test performance, 315 Receptors, magnetic resonance imaging (MRI) with targeted contrast agents, 517–519 Recombinant vaccines, 563 Recombination linkage disequilibrium influence, 24, 25, 28 non-allelic homologous, copy number variation (CNV) generation, 108–109, 110, 110f population-specific variation, 28 region-specific variation, 28 Recombination-based in vivo expression technology (RIVET), vaccines development, 566 Rectal cancer irinotecan treatment, genotype-guided clinical trial, 328 see also Colorectal cancer Red-shifted fuorescent proteins, 530 Reductionist approach to disease, 378, 379f Reed-Sternberg cells, 836 REELIN, 68 Reference laboratory tests, 368 Reflectance planar fluorescence imaging, 527 Regulatory issues, 414–422 biomarkers (in vitro diagnostics), 318 genomic tests, 393, 416–418 laboratory-developed tests, 417–418 nucleic acid-based cancer vaccines, 581 pharmacogenetic tests, 329, 340 public health genomics, 450 targeted therapies, co-approval of diagnostic test, 990 Regulatory myosin light chain (MYL2) mutations, hypertrophic cardiomyopathy, 718 Regulatory T cells (Tregs), 574–576 cancer immunotherapy, 575 effector T cell interactions, 575 gene expression profiles, 820 induction, 574 phenotype 574 reduction with anti-CD25 monoclonal antibodies, 575 tumor microenvironment, 575, 820 Reimbursement genomic tests, 430–431, 431f policy issues, 394 Reis–Bückler’s corneal dystrophy, 1258, 1259 REL, 821

diffuse large B-cell lymphoma, 833 primary mediastinal large B-cell lymphoma, 835 Relative expression reversals, 81 Relative risk, 463 Relevance networks, 213 Renal biopsy tissue, mRNA expression analysis, 1056–1058, 1057f Renal cell carcinoma cancer vaccines messenger RNA-based, 581 peptide, 578 copy number variation (CNVs), 369 sorafenib therapy, 336, 1002 Renal disease hypertension association, 629 see also Glomerular disorders Renal transplant rejection, 211 biomarkers, 300 Renin-angiotensin system diabetic nephropathy candidate genes, 1061t hypertension, 629, 655 pharmacogenomics, 630 hypertrophic cardiomyopathy, 723 therapeutic targeting in cirrhosis, 1142 REPAIR-AMI, 702 Repeatability see Reproducibility RepeatMasker, 123 Repetitive DNA, 8–9 comparative sequence analysis, 123 microsatellite markers, 34 segmental duplications, 9 whole genome assembly process, 91 Repetitive transcranial magnetic stimulation, amyotrophic lateral sclerosis, 1276 Reporter technology fluorescence imaging, 525 molecular beacon biosensors, 593–594 positron emission tomography, genomic DNA sequence imaging, 505–506 Reproducibility association studies, 462, 1365 epilepsy, 1249 pharmacogenetics, 328 biomarker protein immunoassay, 310 experimental design, 147, 148, 277–278, 281 genomic results, 282, 877 transcriptomics, 147, 148, 151–152 viral diagnostic chip design, 543 Research policy, 389–391, 389f Resequencing, 14–15 affordability, 35 environmental response genes, 51 polymorphisms detection, 92 process, 92, 92f electrophoresis, 93–94 PCR set-up and clean-up, 93 primer design, 93, 93f

sequencing reaction set-up and clean-up, 93 trace analysis, 93–94, 94f single nucleotide polymorphisms (SNPs) identification, 34, 35 viral chip technology, 548–549, 550t Resistin, obesity, 1179 Respiratory distress syndrome, 1114 Respiratory syncytial virus antiviral agents, 1344 prophylaxis, 1344 Respiratory viruses microarray detection, 371 Restriction fragment length polymorphisms (RFLPs), 33, 95 molecular epidemiology applications, 304 Restrictive cardiomyopathy, 717 RET, 935, 936f Hirschsprung disease, 126f, 127 MEN2 mutations, 935, 936–937, 936f, 937t genotype–disease severity correlations, 937–938 genotype–phenotype correlations, 936– 937, 937t, 938 somatic mutations, sporadic medullary thyroid carcinoma, 938–939 RET receptor tyrosine kinase, 935, 936f glial-derived neurotrophic factor (GDNF) ligands, 935 therapeutic targeting, 940 RET/PTC fusion oncogene, 939, 939f papillary thyroid carcinoma, 939 Retina, 1256–1257, 1257f disorders, 1260–1261 gene therapy, 615 Retinitis pigentosa, 1260 Retinoblastoma, 1260–1261 Rb1 mutations, 1261 Retinoic acid metabolism, 1013 Retinoids DNA methylation profiles, tumor response prediction, 138 signaling in neural tube development, 1013 RETN, obesity, 1176 Retroviruses, 611, 1325 vectors, 611–612 Rett syndrome, 68 Reverse phase arrays prostate cancer serum proteomics, 903 T cell response in multiple sclerosis, 1035 Reverse transcriptase inhibitors, 1326 Reverse transcription–polymerase chain reaction breast cancer, 875, 876 expression profiling clinical applications, 167 follicular lymphoma, 837, 838 lymphoma diagnosis, 832 mantle cell lymphoma, 838 melanoma micrometastases detection, 968

Index

renal biopsy tissue, 1057 virus identification, 541 Reverse vaccinology, 563, 564–565, 565f Reversed phase high-performance liquid chromatography (RPLC), quantitative proteomics, 175 mass spectrometry coupling, 176 Rheumatic heart disease, 693 Rheumatoid arthritis, 441, 1017–1026 auto-antibodies, 1017, 1018, 1022–1023 anti-cyclic citrullinated peptide (antiCCP), 1017, 1018, 1020, 1022– 1023, 1024 rheumatoid factor (RF), 1017, 1018, 1022–1023 clinical features, 1017t, 1018 diagnosis, 1024 diagnostic criteria, 1018 disease-modifying anti-rheumatic drugs (DMARDS), 1024 environmental risk factors, 1018, 1022 gender-related risk/hormonal factors, 1021–1022 gene expression profiles, 162, 1023, 1074 comparison with other autoimmune disorders, 1035 genetic factors, 1017, 1018–1021 genome-wide association studies, 39, 265 genomics in clinical trial design, 340 HLA-DRB1 association, 1018–1020, 1023, 1024 shared epitope alleles, 1019, 1019t, 1020 infectious agent associations, 1022 monitoring, 1024 non-HLA-DRB1 MHC genes, 1020 non-MHC genes, 1020–1021, 1021t candidate genes, 1021 pharmacogenomics, 1024–1026, 1025t, 1047 predisposition model, 1023 prognosis, 1024 proteomics, 1023–1024, 1075 PTPN22 associations, 1017, 1018, 1020– 1021, 1023, 1024 rheumatoid nodules, 1067 screening, 1024 tumor necrosis factor-α inhibitors in management, 1018, 1024 response polymorphism, 1025–1026, 1025t Rheumatoid factor (RF), 1017, 1018, 1022–1023, 1067 RhoA, ovarian cancer metastasis, 918 RhoC Hodgkin lymphoma/Reed-Sternberg cells, 836 melanoma, 164 Rhodopsin mutations congenital night blindness, 1260 retinitis pigentosa, 1260

Ribavirin hepatitis C, 1343, 1385 respiratory syncytial virus infection, 1344 Ribonuclease, 96 Ribonuclease protection assay, renal biopsy tissue, 1056–1057 Ribosomal RNAs (rRNAs), 8 RIG-like receptors, pathogen recognition/ signaling, 1365–1366 RigI-helicases, 1348 Riluzole, amyotrophic lateral sclerosis treatment, 1276 Rimantadine, 1344 RISC/Argonaute, 603 Risk measures, 463–464 Ritonavir, 1342 Rituximab, 574, 997 Hodgkin’s lymphoma, 994 rheumatoid arthritis, 1024 RNA interference, 193–200, 370 chronic obstructive pulmonary disease treatment, 1107 drug discovery/development applications, 337, 352, 352f effector delivery, 195–196 gene expression profiling studies, 166 loss-of-function library screens, 196, 196f obesity, 1183 parallel chemical genomic screens, 199 pathway, 194, 195f RNA interference silencing complex (RISC), 194 RNA isolation, microarray experiments, 159–160 RNA-mediated fingerprinting with MALDI MS detection, 96 RNAseL polymorphism, prostate cancer, 898, 899 ROBO3, strabsimus, 1257 Rofecoxib, 344 Romano-Ward syndrome, 731, 732 genetics, 732 ion channel mutations, 733–735 LQT1 (KVLQT1/KCNQ1), 732, 733 LQT2 (HERG/KCNH2), 732, 734 LQT3 (SCN5A), 732, 734–735 LQT4 (ankryn-β), 732, 735 LQT5 (minK/KCNE1), 732, 733, 735 LQT6 (MiRP1), 734, 735 LQT9 (caveolin-3), 735–736 LQT10 (SCN4B), 736 non-ion channel-encoding genes, 735–736 ROS1 gene polymorphism, myocardial infarction, 670 Rosiglitazone, pharmacogenomics, 347, 348, 349f Ross River virus, 1340 Rotavirus, 539, 1340 vaccines, 562

■

1467

RP16, hepatic stellate cells, 1146 RPE65 mutations, Leber’s congenital amaurosis, 1260 Rubella vaccines, 562 Rubenstein–Taybi syndrome, 1257 RxNorm, Health Level 7 (HL7) patient data standards, 248 RyR2 mutations (cardiac ryanodine receptor) catecholaminergic polymorphic ventricular tachycardia (CPVT), 742 hypertrophic cardiomyopathy, 719 S S, 217, 218 S-100 proteins glioma prognosis, 960 glomerular disorders, 1059 head and neck cancer, 951 melanoma diagnosis, 968 multiple sclerosis, 1036 pancreatic mucinous cystic neoplasms, 926 psoriatic arthritis synovitis, 1073 rheumatoid arthritis, 1024 SabA, Helicobacter pylori adhesins, 1129 Saccharomyces cerevisiae, epigenetic regulation, 62 Salivary biomarkers, head and neck cancer locoregional recurrence, 950–951 SALL1, Towne–Brocke syndrome, 1257 SALL4 mutations congenital heart disease, 782, 783 Duane’s syndrome, 1257 strabsimus, 1257 Salmeterol, chronic obstructive pulmonary disease, 1105 Salmonella infection, susceptibility genes, 1317 Salmonella typhi vaccine, 562 Salmonella typhimurium cancer vaccine vectors, 582 DNA-adenine methylase (dam) gene, 566 in vivo expression technology (IVET), 566 live vaccine design, 566 signature-tagged mutagenesis (STM), 567 Salt-sensitivity/resistance genetic basis, 625–626 hypertension predisposition, 625 Sample experimental design, 276 population genomic studies (genealogies), 23, 24 Sample collection bias, 281 biomarkers, 311–312 fasting/non-fasting state, 311 patient preparation, 311–312 storage conditions, 312 timing, 311 consent, 353 pharmacogenomics, 352–354

1468

■

Index

Sample size, 275–276 case-control studies, 464 experimental design calculation, 279 Monte Carlo simulation, 278–279, 278f, 278t genome-wide association studies, 102, 102t microarray experiments, 152–153 pharmacogenetic research, 323 validation studies, 282 Sanger sequencing technique, 89 resequencing, 92 whole genome shotgun sequencing, 90 SAPHO syndrome, 1070 Saquinavir, 1342 Sarcoglycans, muscular dystrophies, 1268 Sarcoidosis, 1111–1114 candidate gene studies, 1111–1112, 1113t familial clustering, 1111, 1112 genetic factors, 1111 Kveim reaction, 1114 linkage analysis, 1112–1113 Mycobacterium tuberculosis catalase-peroxidase involvement, 1114 prognostic applications of genomics, 1113–1114 proteomics, 1114 Sarcomas, systems medicine approach to diagnosis, 76 Sarin, paraoxonase detoxification, 49 Sau3A, 153 Scandanavian Simvastatin Survival Study (4S), 673 Scavenger receptor class BI (SR-BI), 636 Schizophrenia, 39, 67, 403, 1283 association studies, 1301 candidate genes, 1285 chromosomal aberrations, 1284, 1285 DAOA (D-amino acid oxidase activator) linkage, 1303 DISCI (disrupted in schizophrenia), 1284–1285 DNA methylation alterations, 68 gene discovery approach in Portuguese population, 1305–1308 gene expression markers in families, 1306–1038 genome-wide linkage scans, 1305 linkage disequilibrium markers, 1305, 1306f single nucleotide polymorphism (SNP) dense dataset analysis, 1305–1306, 1307f metabolomics, 1286 microarray experiments, 162 PDE4B (disrupted phosphodiesterase 4B), 1285 positional cloning, 1302 Scleroderma Lung Study, 1163

SCN1A polymorphism antiepileptic drug pharmacogenomics, 1250–1251, 1250f, 1251f phenytoin response influence, 327 SCN4B (LQT10), 736 SCN5A (LQT3) see Cardiac sodium channel gene mutations SCNN1B polymorphism, farglitazar-related fluid retention, 353 Scoring ALgorithm for Spectral Analysis (SALSA), 177 SCOT trial, 1165 Screening, 242, 264 acute coronary syndromes, 681 asthma, 1088, 1090–1091 bipolar disorder, 1300 brain tumors, 958 breast cancer, 870 cardiovascular risk, 672–673 chronic obstructive pulmonary disease, 1104–1105 colorectal cancer, 879, 889 Duchenne muscular dystrophy, 1273 early disease detection, 457 familial risk assessment, 482 family history-based approach, 487–488 genomic, 348–359 germline, 359 head and neck cancer, 946–947 hepatitis B, 1378–1379 hepatitis C, 1378–1379 hereditary hemochromatosis, 450 hypertension, 627 hypertrophic cardiomyopathy, 722–723, 722f inflammatory bowel disease, 1043 intraocular pressure elevation, 1260 lipid disorders, 637 lung cancer, 858, 859 lymphoma, 832 newborn see Newborn screening obesity, 1179–1180 ovarian cancer, 914 pancreatic cancer, 922–923 prostate cancer, 901–902 public health genomics, 450 rheumatoid arthritis, 1024 systemic sclerosis, 1158–1159 thrombophilias, 764–765, 766, 766f population-based, 767–768, 767f viral hepatitis, 1378–1379 SCYA3, diffuse large B-cell lymphoma prognosis, 834 SEBASTIAN, 249 Segmental duplications, 9 Seizure focus, 18F 2-deoxy-D-glucose-positron emission tomography (FDG-PET), 507 SELDI ProteinChip, hepatitis B infection proteomics, 1149, 1381 Selectins, systemic sclerosis, 1156 Selection, 22

causal variant influences, 29 Selection bias, 276, 280, 285 Selective serotonin reuptake inhibitors, pharmacogenomics, 1294 SELEX, aptamer generation, 594 Self-organizing maps, 213 disease sub-type discovery, 214 Semiconducting nanowires, viral chip technology, 552 Sensitivity, diagnostic test, 313–314 newborn screening tests, 471 viral diagnostic chip design, 543 Sensorineural deafness, Jervell and Lange-Nielsen syndrome, 732, 736 Sentaxin (ALS4), familial amyotrophic lateral sclerosis, 1272 Sepsis, 1362–1371 applications of genomics, 1368 associated genetic polymorphisms, 1363–1367 association studies, 1364–1365 candidate gene studies, 1364 clinical critera, 1362t clinical staging/severity indices, 1369 coagulation, 1367 cytokine polymorphisms, 1366–1367 diagnosis, 1369 gene expression profiles pathogen signatures, 1368–1369, 1370f progression signatures, 1369–1371 host response, 1363, 1364f multiple serum biomarkers, 1367, 1368f pathogen recognition/signaling, 1365–1366 CD14, 1366 intracellular signaling molecules, 1366 mannose-binding lectin, 1366 NOD-like receptors, 1365–1366 RIG-like receptors, 1365–1366 Toll-like receptors, 1365 severe (with acute organ dysfunction), 1362 therapeutics, 1371 activated protein C, 1367, 1369, 1371 Septic shock, TLR4 polymorphism influence, 1317 Sequenom, 434 Sequential organ failure assessment (SOFA), 1369 SEQUEST, 177 SEREX technology, 584 Serial analysis of gene expression (SAGE), 153, 157 liver tissue, 1143 renal biopsy tissue, 1058 Serine/threonine kinase 15 (STK15), hepatocellular carcinoma biomarker, 1383 Serological proteome analysis (SERPA) tumor microenvironment proteomics, 568 vaccine candidate identification, 568

Index

Seronegative polyarthritis, 1067 Serotonin (5-HT) neurotransmission, 1282 candidate gene studies, 1283–1284 depression, 1290, 1291–1292, 1292t emotional behavior regulation, 1292 stress hormone interactions, 1291 Serotonin transporter gene polymorphism see 5-Hydroxytryptamine transporter (5-HTT; SLC6A4) Serum biomarkers, 301 head and neck cancer recurrence prediction, 950–951 prostate cancer, 903 specimen collection, 311 Severe acute respiratory syndrome (SARS) virus protein microarrays detection/genotyping, 370 resequencing GeneChips, 549 viral chip technology, 552 Severe combined immunodeficiency, gene therapy, 610, 616 SH2B3, diabetes type 1, 1191 Sharp syndrome, 1159 Shigella flexneri, proteomics, 568 Short interfering RNAs (siRNA) delivery, 195–196 design, 194–195 gene silencing, 370 in vivo gene inhibition, 196–197 loss-of-function library screens, 196 off-target effects, 197 parallel chemical genomic screens, 199 RNA interference pathway, 194, 195f therapy apoB targeting, 647 chronic obstructive pulmonary disease, 1107 glomerular disorders, 1063 Short interspersed nuclear elements (SINEs), 9 Short QT interval syndrome, 741–742 clinical features, 741–742 genetics, 742, 742t treatment, 742 Shprintzen syndrome, 782 Shrimp alkaline phosphatase/exonuclease I (SAP/Exo I) mix, 93 Sibutramine, pharmacogenomics, 1182 Sickle-cell anemia, 1, 378 carrier screening, 473 comprehensive infant care, 473 family history, 481 hemoglobin S mutation, 474 newborn screening, 473–474 platform, 473 phenotypic variability, 474 14-3-3Sigma promoter methylation, prostate cancer, 899 Signature-tagged mutagenesis (STM), vaccines development, 563, 567

Significance Analysis of Microarrays (SAM), 147, 148f, 713 familial pulmonary fibrosis, 1115 Significance measures, 213 SILCAAT, 1334 Sildenafil, systemic sclerosis, 1165 Silica exposure, rheumatoid arthritis, 1022 Silicon nanowires, viral chip technology, 552–553 SIM1 mutations, obesity, 1175 Simple sequence repeat polymorphisms (SSRPs), 95–96 see also Microsatellite markers Simvastatin, 673 Singapore, 440 national biobank see Singapore Tissue Network (STN) Singapore Bioethics Advisory Committee, 288–289, 289f, 292 Singapore Biomedical Sciences Initiative, 288, 289 Singapore Consortium of Cohort Studies (SCCS), 286, 295 Singapore Tissue Network (STN), 286, 288, 289–294 biospecimen collections, 290–291, 291t consent issues, 292 data privacy, 291–293 biomedical record linkage, 293, 294f Data Protection Framework (DPF), 293, 294f experience, 293–294, 295t expertise, 289 governance, 291, 292f infrastructure, 289, 290f Laboratory Information Management System (LIMS), 289 sample tracking, 289–290, 290f reversibly de-identified data, 292–293 Single cell analysis DNA-encoded antibody libraries (DEAL), 80 systems medicine applications, 79 Single molecule biosensors, molecular beacons, 594 Single molecule DNA sequencing, 36 Single nucleotide polymorphisms (SNPs), 9, 10, 33, 34, 88, 101, 227, 568 blood lipid variation, 644 high-density lipoprotein cholesterol, 640t low-density lipoprotein cholesterol, 639, 640t triglycerides, 640t commercial profiling, 16–17 comparative sequence analysis, 128 complex disease association studies, 88–89, 263–264 coronary artery disease/myocardial infarction studies, 666, 667–669, 667t databases, 26, 227

■

1469

HapMap dataset, 26 Environmental Genome Project (EGP), 50, 51 genetic susceptibility biomarkers, 28, 304, 382 genome-wide association studies, 14, 103, 264–265, 441 clinical utility, 265 genotyping technologies, 34, 36t, 101, 103 raw data quality control, 105–106 HuRef genome analysis, 11 local regulatory variants, 12 population studies, 305 heterozygosity estimates, 11 rheumatoid arthritis, 1018 schizophrenia gene discovery in Portuguese population, 1305 dense dataset analysis, 1305–1306 sequencing technologies, 34–36, 36t tagging, 28, 40, 101, 103 copy number variation (CNVs) characterization, 116 epidemiological studies, 464 HapMap project, 327 pharmacogenetic studies, 326, 346 thrombosis-related gene studies, 763 whole genome shotgun sequencing, 91, 91f Single photon emission computed tomography (SPECT), 500 multimodal imaging, 510 Single strand conformation polymorphism, 96 Single-base extension (SBE), Illumina Infinium II assay, 103 Single-molecule fluorescence fluctuation spectroscopy, 553f, 554 Sinus node dysfunction/sick sinus syndrome, 729, 744 clinical features, 744 genetics, 744 Sipa1/Spa1, cancer metastasis susceptibility, 980, 981, 987 Sirolimus see Rapamycin SIRT2, glioblastomas, 961 Sirtuine, 64 Sitaxentan, systemic sclerosis, 1165 Sitosterolemia, 638–639 Skills for Health Service, 395 SLC6A3, nicotine addiction, 1099 SLC6A4 see 5-Hydroxytryptamine transporter SLC11A1 (NRAMP) Leishmania infection susceptibility, 1317 mycobacterial infection susceptibility, 1317, 1357, 1364 SLC12A3, diabetic nephropathy, 1061 SLC22A4/OCTN1 inflammatory bowel disease, 1041, 1043, 1046 rheumatoid arthritis, 1020, 1021 SLC22A5/OCTN2, inflammatory bowel disease, 1041, 1043, 1046

1470

■

Index

SLC30A8, diabetes type 2, 1189, 1190 Sleeping Beauty (SB) non-viral gene vector, 605 SLITSK1 (slit and trk like 1) mutations, psychiatric disorders, 1285 Slow acetylator phenotype see NAcetyltransferase 2 (NAT2) polymorphism SMAD2, ovarian cancer, 916 SMAD7, colorectal cancer, 887 Small interfering RNAs see Short interfering RNAs (siRNA) Small nucleolar RNAs (snoRNAs), 8 Small-cell lung cancer, 859 gefitinib treatment, 360 Smallpox, 1341 SMN copy number variation, amyotrophic lateral sclerosis, 1273 deletions, spinal muscular atrophy, 1268 SNCA (PARK1; PARK4; α-synuclein), Parkinson’s disease, 1235, 1238–1239 SNOMED Clinical terms, 216, 229 SNPdetector, 94 Social issues, 364–365 genomics research policy, 389 public health genomics, 447, 451 Society for Genomics, Policy and Population Health, 451 Sodium iodide symporter, positron emission tomography (PET), 505, 508 Software, bioinformatics development, 230 free tools, 216–219 SOLAR, metabolic profiling in cardiovascular disease, 187 Solexa, 435, 441 Solid tumors monoclonal antibody therapy, 998–999 small molecule-targeted therapy, 1001–1002 Somatic mutation, 360–361 cancer metastasis, 980 array comparative genomic hybridization, 983–984, 984f medical resequencing, 15 Somatostatin, gastric acid secretion regulation, 1124 Somatostatin receptor type 2 (SSTR2), positron emission tomography (PET) reporter applications, 505 Sorafenib (BAY 43-9006), 1002 melanoma trials, 970–971, 972 prostate cancer trials, 906 SORL1, Alzheimer’s disease, 1226 Sotalol Brugada syndrome, 741 drug-induced long QT syndromes, 731 South Korea, 440 Southern blotting, 34, 368 SPARC glomerular disorders, 1059

systemic sclerosis, 1157 Specificity of diagnostic test, 313–314 newborn screening, 471 viral diagnostic chip design, 543 SpectroChip, 547 Sphingolipids, targeted metabolic profiling, 185 Spin echo (SE) magnetic resonance imaging, 514–515, 514f Spinal muscular atrophy, 1265 newborn screening, 359 SMN deletions, 1268 SPIO, cell surface receptor imaging, 418 Spliceosome, 125–126 RNAs, 8 Spondyloarthropathies, 1067–1077, 1070t bowel inflammation, 1070–1072 gene expression profiles, 1074–1075 histology, 1071 characteristic features, 1067–1069, 1067t classification, 1069–1070, 1070t disease modifying anti-rheumatic drugs, 1075–1076 extra-articular manifestations, 1068–1069 familial aggregation, 1069 gut-associated immune system alterations, 1071 historical aspects, 1067 HLA-B27 associations, 1068, 1069, 1069t inflammatory back pain, 1068 peripheral arthritis, 1068 peripheral enthesitis, 1068 proteomics, 1075 sacroiliitis, 1068 spondylitis, 1068 synovial tissue biomarkers, 1077 synovitis histopathology, 1072–1074, 1073f terminology, 1067 therapeutic tumor necrosis factor-α blockade, 1076 transcriptomics, 1074–1075 treatment response monitoring, 1077 undifferentiated, 1070 Spongiform encephalopathies see Prion disease Spotted cDNA microarrays, 157, 159 SRBP-1, hepatitis C receptor, 1377 SRC3 see AIB1 Src kinase inhibitors, melanoma, 972 SREBP, hepatitis C persistent infection, 1382 SSEA1-expressing stem cells, 600 Standard neutral (Fisher–Wright) model, 24 Standards for reporting of diagnostic accuracy (STARD) initiative, 374 Stanford Microarray Database (SMD), 214–215 Staphylococcus aureus genetics of host response, 1351, 1353–1354, 1365, 1369 genome mapping, 1347

genomic peptide libraries, 567 in vivo expression technology (IVET), 566 serological proteome analysis (SERPA), 568 signature-tagged mutagenesis (STM), 567 STAT1 hepatitis C persistent infection, 1382 mycobacterial disease susceptibility, 1357 STAT3 atherosclerosis pathogenesis, 654 head and neck cancer, 951 melanoma therapeutic targets, 972 Stathmin, laryngeal carcinoma, 951 Statins, 51, 646 familial hypercholesterolemia, 637 pharmacogenomics, 646, 673–674 post-myocardial infarction ventricular remodeling, 673 reactive oxygen species effects, 658, 659f Statistical methods experimental design planning, 275 transcriptomics differential gene expression, 147, 148f molecular signature analysis, 148–150 STATS, interferon-α activation, 1385 Stavidien, 1342 Stein estimators, 147 Stem cells, 599–606 adult, 599–600 cardiac progentior cells, 702 therapeutics, 604 cell therapies, 604–606 gene therapy combinations, 605–606 definition, 599 DNA methylation, 602, 603 embryonic, 599 gene function analysis animal models, 601–602 cell culture models, 602 gene targeting approaches, 601–602 histones modification, 602–603 microRNAs, 603 molecular signature, 600–601 posttrascriptional regulation, 603 protein–DNA interactions, 602–603 proteome, 603–604 self-renewal, 603 small molecule regulation studies, 605 types, 599–600, 601t Stevens-Johnson syndrome, 421 carbamazepine hypersensitivity, 1250 STK11 mutations breast cancer, 871 Peutz-Jegher syndrome, 382 STK15, hepatocellular carcinoma, 1148 Strabsimus, 1257 Stratagene, 440 Strategic health planning, 264 Stratifin, head and neck cancer, 951 Streptococcus agalactiae pan-genome, 565

Index

proteomics, 568 vaccine development, 565 Streptococcus group A genome, 565 Streptococcus group B, vaccine development, 565 Streptococcus pneumoniae chronic obstructive pulmonary disease exacerbations, 1101 genetics of host susceptibility, 1352–1353, 1369 genome, 94 mapping, 1347 sequencing, 563 microarray expression profiling, 568 signature-tagged mutagenesis (STM), 567 Streptococcus pyogenes, 565 proteomics, 568 Stress cardiomyopathy, 693 Stripped nanowires, viral chip technology, 552 Stroke biomarker applications, 300 family history, 481, 486, 487 hypertension association, 628–629 post-cardiac surgery, 798–799 racial/ethnic variation in risk, 760, 762 Stromelysin promoter, systemic sclerosis, 1157 Strong cation exchange (SCX), quantitative proteomics, 175 SU11248, melanoma clinical trials, 972 Subtelomeric regions, 8–9 Subtractive hybridization/differential display, 157 congenital heart disease gene discovery, 786 Subunit vaccines, 563 Succinylcholine, pharmacogenomics, 371 Sudden infant death syndrome, long QT syndrome-like, 731, 736 Sudden and Unexpected Death Syndrome (SUDS), 740 Sudden Unexpected Nocturnal Death Syndrome (SUNDS), 740 Sulfasalazine inflammatory bowel disease, 1075 rheumatoid arthritis, 1024 spondyloarthropathies, 1075 Sulfotransferases, cigarette smoke metabolism, 857 Sulindac, colorectal cancer chemoprevention, 890 Sunitinib gastrointestinal stromal tumors, 1001 prostate cancer trials, 906 renal cell carcinoma, 336 Superoxide, 652 Superoxide dismutase, 652 atherosclerosis, 655 Superoxide dismutase 1 (SOD1) mutations, familial amyotrophic lateral sclerosis, 1266, 1268, 1272, 1277

mouse model, 1276 Superparamagnetic iron-oxide (SPIO) contrast agents, 515–516, 517f cell tracking applications, 517 Supervised learning, 213 acute leukemias classification, 208 gene expression data analysis, 160, 161f acute lymphoblastic leukemia, 847 heart failure, 698 methods, 213 prognostic marker identification in leukemia, 850, 850f Support vector machines, 81, 213 cancer metastates primary diagnosis, 211, 212f microarray data analysis, 160 Suppressor-mutator (Spm) transposon demethylation, 66 Supravalvular aortic valve stenosis, 785 Surface plasmon resonance imaging blood biomarker analysis, 79–80 head and neck cancer screening, 947 Surface-enhanced laser desorption/ionizing time-of-flight mass spectrometry (SELDI-TOF-MS) amyotrophic lateral sclerosis, 1278 colorectal cancer, 889 head and neck cancer, 951 non-alcoholic fatty liver disease, 1150 ovarian cancer, 916 pancreatic cancer, 925 prostate cancer, 903 sarcoid bronchoalveolar fluid, 1114 spondyloarthropathies, 1075 Surfactant protein A variants idiopathic pulmonary fibrosis, 1114 mycobacterial disease susceptibility, 1357 Surfactant protein B deficiency, neonatal respiratory distress, 1114 Surfactant protein B variants, 1114 Surfactant protein C variants, 1114 diffuse parenchymal lung disease, 1115 familial pulmonary fibrosis, 1115 family interstitial pneumonia/neonatal respiratory distress, 1114 Surfactant protein D variants, 1114 mycobacterial disease susceptibility, 1357 Surgery, inflammatory response, 794, 795–796 Surrogate endpoints, 300, 300t biomarkers, 299, 300 Susceptibility biomarkers, 304–306 clinical utility, 265 genome-wide association studies, 265 Susceptibility genes asthma, 1087 cancer metastasis, 979–980 environmental factor interactions, 49 buffering, 51 research approaches, 52–53 statistical analysis, 53

■

1471

family studies, 461–462 infectious disease, 1315–1317, 1316t, 1364 public health genomics, 447–448, 448f SUV3-9, 65, 66 SUZ12, 603 SWOG 99-16, 906 Synapsin, antidepressant effects, 1295 Syndrome of apparent mineralocorticoid excess, hypertension, 628 Syndrome X, colorectal cancer, 886 Synovial inflamation, tissue biomarkers, 1077 Synoviocytes, 1072 inflammatory synovitis, 1072, 1073 Systemic inflammatory response syndrome (SIRS), 1362 clinical critera, 1362t Systemic lupus erythematosus complement component C4 gene copy number variation, 116 DNA methylation aberrations, 68, 136 environmental factors, 1011 gene expression profiles comparison with other autoimmune disorders, 1035 glomerular, 1059 Raynaud’s syndrome, 1158 Systemic sclerosis, 1155–1165 anti-centromere antibodies, 1158 anti-nuclear antibodies, 1158 anti-Scl70 antibodies, 1158 cardiac symptoms treatment, 1163–1164 cellular immunology, 1158 classification criteria, 1159 clinical features, 1155, 1155f diagnosis, 1159 disease activity score, 1158, 1161t epidemiology, 1156 gastrointestinal symptom treatment, 1163 genetic markers, 1156–1158, 1156t imaging investigation, 1158–1159 nailfold capillaroscopy/videocapillaroscopy, 1158–1159, 1160f inflammatory markers, 1158 interstitial lung fibrosis/alveolitis treatment, 1162 microchimerism, 1159, 1161–1162, 1161f modified Rodnan Skin Score (mRSS), 1158, 1159 monitoring, 1159, 1161–1162 organ involvement, 1162 therapy, 1162–1163 overlap syndromes, 1159 pharmacogenomics, 1159 prognosis, 1159 Raynaud’s syndrome, 1155, 1155f, 1158, 1158t management, 1162, 1163f renal function disorders, 1164 risk factors, 1155–1156 screening, 1158–1159

1472

■

Index

Systemic sclerosis (Continued) secondary malignant disease, 1159 therapeutic strategies, 1162–1164 autologous stem cell transplantation, 1165 novel approaches, 1164–1165, 1164f TSK (tight-skin) mouse model, 1157 twin studies, 1156 Systemized Nomenclature of Medical-Clinical Term (SNOMED-CT), 215, 216, 229, 253 Health Level 7 (HL7) patient data standards, 248 Systems biology, 40, 41t, 74–82, 368 asthma screening, 1091 biological networks, 75, 81 blood protein diagnostic markers, 76, 78 organ-specific fingerprints, 78 computational/mathematical challenges, 81 defining features, 74 emerging technologies, 78–81 gene–nutrient interactions, 1205 in vitro measurements, 78–79 infectious disease, 1320 medical applications, 75–76 molecular signature classifiers, 81 psychiatric disorders, 1286 viral chip technology, 550 Sysytemic Treatment Enhancement for Bipolar Disorder (STEP-BD), 1304 T t(1;11)(q42;q14) bipolar disorder, 1301 schizophrenia, 1284, 1285, 1301 t(8;21), acute myeloid leukemia, 844, 847 t(9;22), chronic myelogenous leukemia, 844 t(11;14)(q12;q32), mantle cell lymphoma, 838 t(11;14)(q13;q32), mantle cell lymphoma, 837 t(11;q23), acute myeloid leukemia, 847 t(14;16)(q32;q21), diffuse large B-cell lymphoma, 833 t(14;18)(q32;q21), follicular lymphoma, 836, 837 t(15;17) acute myeloid leukemia, 847 acute promyelocytic leukemia, 845, 1001 T cells cancer immune response, 573, 574 cancer vaccine response ideal response, 576 monitoring, 583 cancer-related dysfunction, 820 cardiac allograft acute rejection, 706 chronic obstructive pulmonary disease, 1101 cytotoxic see Cytotoxic T cells dendritic cell activation, 820 follicular lymphoma prognostic markers, 822 gastric MALT lymphoma, 822

helper see Helper T cells multiple sclerosis immune response, 1035–1036 regulatory see Regulatory T cell (Tregs) sarcoidosis, 1111, 1114 systemic sclerosis, microchimerism, 1161– 1162, 1161f tumor microenvironment, 576, 819, 821 see also CD4T cells, CD8T cells T-cell epitopes databases, 577 peptide cancer vaccines, 577 t-tests, 147, 213 Tacrolimus, 706 myasthenia gravis, 1276 Tadalafil, systemic sclerosis, 1165 TAFII, primary biliary cirrhosis, 1147 TAILORx (Trial Assigning Individualized Options for Treatment), 167, 390, 427, 876 Taiwan, 440 Tamoxifen, 138 biomarkers of response, 992 breast cancer targeted therapy, 992 Tandem mass spectrometry (MS/MS) metabolic disorder screening, 475, 476 full-scan mode, 475 metabolic profiling, 181, 183, 185, 186, 187 newborn screening, 184–185, 359, 472, 475–476 proteomics, 173, 174, 176, 177f Tandemly repeated repetitive elements, 123 Tangier disease, 641, 644, 647 TAP1, systemic sclerosis, 1158 TAP2 Mycobacterium leprae susceptibility, 1357 systemic sclerosis, 1158 Tapanui flu, 1340 TaqMan, 34, 541 TAS2R50 gene polymorphism, myocardial infarction, 670 Tau (microtubule associated protein) multiple sclerosis, 1036 Parkinson’s disease, 1239 TAX, 327, 906 Taxanes, DNA methylation profiles in tumor response prediction, 138 Taxonomy, 211–212 TBX1, congenital heart disease, 782–783 TBX5 cardiac gene expression interactions, 783 congenital heart disease, 782 microarray analysis, 786 SALL4 regulation, 783 TCAP mutations (telothonin), hypertrophic cardiomyopathy, 718, 721 TCF7L2 diabetes type 2 susceptibility, 265, 457, 1188, 1189, 1191

diabetic nephropathy, 1061 TEL-AML1, acute lymphoblastic leukemia, 847 Telomerase activated hepatic stellate cells, 1146 pancreatic cancer, 921, 924 pancreatic cystic neoplasms, 925–926 pancreatic endocrine tumors, 927 Telomeres, repetitive DNA, 8 Telomeric repeat amplification protocol (TRAP), pancreatic cancer diagnosis, 924 Telothonin (TCAP) mutations, hypertrophic cardiomyopathy, 718, 721 Temozolomide glioblastoma, 962 oligodendrogliomas, 959 tumor response prediction, 138 Tenascin hepatic stellate cell production, 1144, 1146 liver fibrosis, 1139 Tenofovir, hepatitis B treatment, 1385 N-Terminal pro-B-type natriuretic peptide, acute coronary syndrome risk stratification, 685 Terminologies, 248 Tetanus vaccines, 562 Tetrahydrobiopterin (BH4) biosynthesis, 473 Tetralogy of Fallot, 782, 783, 784, 786 TFAP2B (Char syndrome), congenital heart disease, 784 TFF1, ovarian cancer, 916 TGFB1/BIGH3 (keratoepithelin), corneal dystrophies, 1257, 1258, 1259 Th1/Th2 cells Crohn’s disease, 1072 inflammatory bowel disease, 1048 inflammatory synovitis, 1073 spondyloarthropathies, 1072 Theophylline, chronic obstructive pulmonary disease, 1105 Therapeutic index, 322 Thiazide diuretics, pharmacogenomics, 630 Thiazolindiones, cirrhosis, 1142 Thiopurine methyltransferase (TPMT) haplotypes, 327–328 polymorphism, thiopurines metabolism, 338, 360, 372, 421 acute lymphoblastic leukemia, 331 inflammatory bowel disease, 1047 Thiopurines drug-metabolizing enzyme polymorphisms, 360, 372 pharmacogenomics, 371 inflammatory bowel disease, 1047 Third-party reimbursement for genetic/ genomic tests, 363 Thrombin, 755, 756, 759, 760, 776 activation, 758 sepsis, 1367 Thrombomodulin, 755

Index

oxidation, 758 sepsis, 1367 Thrombophilias acquired, 766 coagulation protein posttranslational modification, 758 prognosis, 768 screening, 764–765, 766, 766f population-based, 767–768, 767f vascular bed-specific thrombosis, 758–759 Thrombosis, 755–769 circulating cellular/protein influences, 759–760 family history, 765–766 gene association studies, 762–763 gene linkage studies, 762 heritability, 763 instrumentation research, 768 personalized approach, 763–764 gene expression profiles, 763, 763f, 764f pharmacogenomics, 768 post-cardiac surgery, 796–798 stroke risk, 798 racial/ethnic variation, 760–762 screening, 655f, 764–766 population-based, 767–768 targeted approach, 765–766, 766f, 766t vascular bed-specific, 758–759, 759t Thrombospondin head and neck cancer, 951 polymorphism, myocardial infarction, 667 Thromboxane, thrombotic event prediction with anticardiolipin antibodies, 764 Thymidine analogs, DNA replication positron emission tomography (PET), 503 Thymidine phosphorylase, 5-fluorouracil pharmacogenomics, 891, 892 Thymidylate synthase polymorphism, 51 colorectal cancer, 887 5-fluorouracil pharmacogenomics, 891, 892 Thymosin β10, glomerular disorders, 1060 Thyroid cancer radioiodine therapy, 508 see also Medullary thyroid carcinoma; Papillary thyroid carcinoma Thyrotoxicosis, heart failure, 693 Tick-borne encephalitis vaccines, 562 Tight junction protein ZO-1, podocyte expression, 1058 Timing, biomarker specimen collection, 311 Timothy syndrome, 731, 732, 737 clinical features, 737 genetics, 732, 737, 738t tinman, 782 Tiotropium bromide, chronic obstructive pulmonary disease, 1105 Tip60, 64 Tipifarnib, 845 TIR-associated protein (TIRAP; Mal), 1350

Tissue factor, 755, 759 Tissue factor pathway inhibitor (TFPI), 755 procedure-related inflammatory response, 794 Tissue inhibitor of metalloproteinases 1 (TIMP-1) atherosclerotic plaque, 666 diabetic nephropathy, 1059 Tissue inhibitor of metalloproteinases 2 (TIMP-2) atherosclerotic plaque, 666 ovarian cancer metastasis, 918 promoter methylation, prostate cancer, 899 Tissue inhibitor of metalloproteinases 3 (TIMP-3), ovarian cancer metastasis, 918 Tissue inhibitors of metalloproteinases (TIMPs) atherosclerotic plaque, 666 cancer-related hypermethylation, 67, 899 Tissue microarrays, cancer diagnostics, 371 Tissue-type plasminogen activator, pancreatic cancer, 926 Titin (TTN mutation), hypertrophic cardiomyopathy, 718, 721 TLR1, 1348 host response to infection Enterobacteriacea, 1354 mycobacteria, 1356–1357 Staphylococcus aureus, 1354 Streptococcus pneumoniae, 1352 TLR2, 56, 1319, 1348 genetics of infection susceptibility, 1365 leprosy, 1317 Gram-negative bacteria detection, 1365 Gram-positive bacteria detection, 1351, 1365 host response to infection Enterobacteriacea, 1354 Legionella, 1355 Listeria monocytogenes, 1354 mycobacteria, 1356, 1364 Neisseria meningitidis, 1355 Staphylococcus aureus, 1353, 1354 Streptococcus pneumoniae, 1352 ligand, 583 lipoteichoic acid, 1351 TLR3, 1319, 1350 hepatitis C recognition, 1382 TLR4, 56, 1319, 1320, 1348, 1350 genetics of infection susceptibility, 1365 lipopolysaccharide response variation, 1316–1317 Gram-negative bacteria detection, 1365 host response to infection Enterobacteriacea, 1354–1355 Legionella, 1355 Neisseria meningitidis, 1355 Streptococcus pneumoniae, 1352 lipopolysaccharide co-receptor, 1366 TLR5, 1348

■

1473

genetics of infection susceptibility, 1317, 1365 Gram-negative bacteria detection, 1365 Gram-positive bacteria detection, 1365 host response to Legionella, 1317, 1355 TLR6, 1348 host response to infection Staphylococcus aureus, 1354 Streptococcus pneumoniae, 1352 TLR7, 579, 1319 TLR8, 579 TLR9, 579, 1348 host response to Streptococcus pneumoniae infection, 1352 ligand, 583 TLRs (Toll-like receptors), 1012, 1348 common host response to infection, 1319 hygiene hypothesis of asthma pathogenesis, 56 infectious diseases susceptibility associations, 1316–1317 ligands, 1349t dendritic cell activation, 583 nucleic acid-based cancer vaccine actions, 579, 580 pathogen recognition, 1314, 1315, 1348, 1350, 1365, 1368 pathogen-specific response to infection, 1319 signaling, 1350, 1350f TMPRSS2/ERG translocation prostate cancer, 904 urine biomarker, 902 TMSB10, glomerular disorders, 1059 TNF see Tumor necrosis factor-α, polymorphism TNFerade therapy, pancreatic cancer, 927 TNFSF10 (TRAIL), 821 TNNT2 mutations (cardiac troponin T), hypertrophic cardiomyopathy, 718, 721 TNNT13 mutations (cardiac troponin I), hypertrophic cardiomyopathy, 718 Toll-like receptors see TLRs TOPCARE-AMI, 675 TOPCARE-CHD, 702–703 Topiramate, adverse reactions, 1251 Topoisomerase 1, systemic sclerosis, 1156 anti-Scl70 antibodies, 1158 Topoisomerase 2a (TOP2A), astrocytomas, 960 Topoisomerase 2beta, hepatitis B, 1149, 1381 Torsades de pointes Andersen-Tawil syndrome, 737 Jervell and Lange-Nielsen syndrome, 736 long QT syndromes, 729, 730, 730f Tourette’s syndrome, SLITSK1 (slit and trk like 1) mutations, 1285 Towne–Brocke syndrome, SALL1, 1257 Toxic epidermal necrolysis, carbamazepine hypersensitivity, 1250 Toxicogenomics, 50, 53, 340, 415, 421

1474

■

Index

TP53 see p53 TPH2 see Tryptophan hydrolase TPM1 mutations (alpha-tropomyosin), hypertrophic cardiomyopathy, 718, 721, 722 TPMT polymorphism see Thiopurine methyltransferase Trabecular meshwork, 1259–1260 TRAF-3, hepatitis C, 1149 TRAG3 methylation status, tumor treatment response prediction, 138 TRAM (TRIF-related adaptor molecules), 1350 Tranilast trial, 350 Transcription factors binding sites, conserved non-coding sequences, 124 cardiac transplantation rejection, 713 congenital heart disease susceptibility genes, 782–784 cytotoxic T cell regulation in tumor microenvironment, 821 DNA methylation regulation, 65, 66 follicular lymphoma, 837 histone modification regulation, 65 Hodgkin lymphoma/Reed-Sternberg cells, 836 prostate cancer-related rearrangements, 904 reactive oxygen species regulation, 656–658 stem cells, 602, 603 Transcriptome map, 12 Transcriptomics, 2, 143–154, 144f, 166 alcoholic liver disease, 1146–1147 applications, 79, 146, 146t, 151, 567–568 autoimmune hepatitis, 1147 biliary disease, 1147–1148 cardiac surgery molecular response, 799 complex disease, 40 congenital heart disease gene discovery, 786, 787f current research issues, 151–153, 152t definition, 143 experimental design, 147, 148 reproducibility, 151–152 gene discovery, 146–148, 146t, 151 gene expression microarrays, 143–144, 144f, 145f, 146, 151–152, 166 normalization, 145–146 quality control, 144–145, 146f heart failure monitoring, 698–699 hepatitis B infection, 1146 hepatitis C infection, 1146 hepatocellular carcinoma, 1148 hypertrophic cardiomyopathy, 721–722 information management, 151–152 liver, 1143 disease, 1146–1148 massively parallel signature signaling (MPSS), 153 molecular signature analysis, 146t, 148–151

multiple sclerosis, 1034–1035, 1037t non-alcoholic fatty liver disease, 1147 personalized medicine applications, 15, 16t polony multiplex analysis of gene expression (PMAGE), 153 primary biliary cirrhosis, 1147 primary sclerosing cholangitis, 1147 serial analysis of gene expression (SAGE), 153 statistical methods, 147, 148–150, 148f stem cell studies, 600–601 systems medicine, 79 tumor tissue sources, 152 vaccine development, 567–568 TRANSFAC, 124 Transfection in vitro, nucleic acid-based cancer vaccinations, 580 Transfer RNAs (tRNAs), 8 Transforming growth factor-α, gastric acid secretion regulation, 1124 Transforming growth factor-β, 42 chronic obstructive pulmonary disease, 1099, 1102 colorectal cancer, 882 dendritic cell inhibition, 576 glomerular disorders, 1059 diabetic nephropathy, 1059 gene therapy, 1063 head and neck cancer, 951 hepatic stellate cell production, 1145 inflammatory synovitis, 1073 ovarian cancer, 916–917 angiogenesis, 918 regulatory T cell secretion, 574, 575 systemic sclerosis, 1157, 1164 tumor microenvironment, 819, 820 Transforming growth factor-β antagonists cirrhosis, 1142 systemic sclerosis, 1164 Transforming growth factor-β receptor mutations colorectal cancer, 881, 882 ovarian cancer, 916 Translational diagnostics, 270, 367–375 definition, 270 Translational genomics, 262–272 candidate health applications, 263 challenges, 266–267 clinical implementation, 357–365, 358f economic incentives, 428–430 enabling competencies/capabilities, 267–270 evidence-based guidelines development, 263 genomic testing, 263 impact on health–disease continuum, 263–264, 264f implementation strategies, 270–272, 271f personal genomes utility, 266 pharmacogenomics, 419 public health aspects, 263 research framework, 263, 263t Translational medicine, 367

Translational research, 456, 456t access/reimbursement issues, 394 FDA promotion, 421–422 policy issues, 389, 390 public health genomics, 449, 458 Translocations, 6 Transmission disequilibrium test (TDT), 38 Transthyretin, hepatitis B infection proteomics, 1149, 1381 Trastuzumab, 415, 420, 574, 990, 998 adverse effects, 872 brain tumor treatment, 962 breast cancer treatment, 360, 373–374, 811, 871, 990, 991f, 994 drug response biomarkers, 338 pharmacogenetics, 329, 331 diagnostic tests of patient eligibility, 990, 991f, 994t ovarian cancer treatment, 916 Tregs see Regulatory T cells Trichotillomania, SLITSK1 (slit and trk like 1) mutations, 1285 Tricuspid atresia, 782 Tricyclic antidepressants, drug-induced long QT syndromes, 731 TRIF (TIR domain containing adaptor protein inducing interferonβ), 1350 Triglycerides cardiovascular risk, 636 genetic disorders, 642 genetic variants, 15, 640t, 642–643, 644 intestinal absorption, 635 lipid-modulating therapies, 646 lipoprotein transport, 634 metabolism, 635, 636 plasma levels APOA5 polymorphism influence, 1210 gene–nutrient interactions, 1210 genetic factors, 636 screening, 637 transport/clearance, 635 Tripos, 440 Trisomy 13, congential heart disease, 785, 789 Trisomy 18, congential heart disease, 785, 789 Trisomy 21 (Down syndrome) APP (amyloid precursor protein gene) copy number variation, 1224 congenital heart disease, 781, 785, 789 obesity association, 1175 strabismus, 1257 trkB see Brain-derived neurotrophic factor receptor Troglitazone, 344 Troponin I, 317 acute coronary syndromes diagnosis, 682 risk stratification, 685, 686 TNNT13 mutations, hypertrophic cardiomyopathy, 718 Troponin T, 317

Index

acute coronary syndromes diagnosis, 682 risk stratification, 685, 686 TNNT2 mutations, hypertrophic cardiomyopathy, 718, 721 Troponins, cardiac transplant rejection biomarkers, 800 TRPM2 (transient receptor potential melanostatin 2), bipolar disorder, 1303 Truncus arteriosus, 782 Tryptophan hydrolase (TPH2), psychiatric disorder associations, 1283 antidepressant drug response effects, 1294, 1294t depression, 1291, 1292 TSC1/2 DNA methylation, breast cancer prognosis, 138 TSP-1 polymorphism, myocardial infarction, 667–668, 668f TSP-2 polymorphism, myocardial infarction, 667 TSP-4 polymorphism, myocardial infarction, 667, 668, 668f TSPEAR, bipolar disorder, 1303 TTN mutation (titin), hypertrophic cardiomyopathy, 718, 721 Tubby mouse, 1175 Tuberculosis, 378 genetic susceptibility, 1356–1357 HLA Class I genes, 1317 NRAMP1/Slc11a1, 1317 see also Mycobacterium tuberculosis Tuberous sclerosis, pulmonary fibrosis, 1111, 1115 Tubulin, multiple sclerosis biomarker, 1036 Tumor DNA sequencing, 95 Tumor infiltrating macrophages, 819, 820 Tumor microenvironment, 818–826 dendritic cells, 576, 820–821 genomic analysis, 824–825 immune stimulatory interventions, 823–824 immune cells, 819–822, 819f proteomics, 825 immune editing, 575 microarray experiments, 822–823 myeloid cells, 820 natural killer cells, 821–822 peripheral immune tolerance mediation, 575 T cells, 576, 821 regulatory (Tregs), 575, 820 Tumor necrosis factor-α asthma, 1087 atherosclerosis pathogenesis, 653 cardiomyocyte apoptosis in heart failure, 695 chronic obstructive pulmonary disease, 1102, 1103 follicular lymphoma, 837

glomerular disorders, 1059 hepatitis C infection, 1382 host response to pathogens, 1350 Gram-positive organisms, 1351 metabolic syndrome, 1196 polymorphism, 665, 795 asthma, 1087, 1088 chronic obstructive pulmonary disease, 1099 hepatitis B clearance, 1377 malaria/leprosy susceptibility, 1317 multiple sclerosis studies, 1033 peptic ulcer disease, 1130 rheumatoid disease, 1024 sepsis, 1366 rheumatoid arthritis, 1023, 1024 sarcoidosis, 1112 systemic sclerosis, 1157 tumor microenvironment, 819 Tumor necrosis factor-α inhibitors cirrhosis, 1142 rheumatoid arthritis, 1018, 1024 pharmacogenomics, 1025–1026, 1025t see also Eternacept; Infliximab Tumor necrosis factor-α receptor host response to Staphylococcus aureus, 1354 mycobacterial disease susceptibility, 1357 Tumor stroma, 808, 818 Tumor suppressor genes, 809, 811, 979 cancer-related aberrant silencing, 60, 67 therapeutic targeting, 69 head and neck cancer, 945, 950 local CpG island hypermethylation, 132 lung cancer, 858, 863 ovarian cancer, 917 pancreatic cancer, 921, 924 Tumor-associated antigens, 573, 575 antibody response, 584 cancer vaccine viral vectors, 582 T cell recognition, 574 T-cell epitopes, 577 Tumor-infiltrating T-cell infusions, 574 Tumor-specific antigens, 573 T cell recognition, 574 T-cell epitopes, 577 Tumor-specific biomarkers, 178 Turcot syndrome, 883, 885 brain tumor predisposition, 957 Turner syndrome congential heart disease, 785, 789 obesity association, 1175 Twin studies, 1011 age-related macular degeneration, 1261 Alzheimer’s disease, late onset, 1224 behavioral phenomena, 534 bipolar disorder, 1300 complex disease, 49 hemostasis, 756–757 hepatitis B/C susceptibility, 1377

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1475

infectious diseases susceptibility, 1316 inflammatory bowel disease, 1041 major depression, 1291 metabolic syndrome, 1196 obesity, 1170, 1172 Parkinson’s disease, 1235 pharmacogenetics, 324–325 psychiatric disorders, 1283 pulmonary fibrosis, 1110 rheumatoid arthritis, 1018 sepsis outcome, 1364 systemic sclerosis, 1156 tuberculosis susceptibility, 1356–1357 Twist, cancer metastasis, 811 Two-photon in vivo microscopy, 525f, 526 Type I error (α), 279, 465 Type II error (β), 279 Typhoid fever susceptibility, HLA Class II genes, 1317 Tyrosinase, melanoma, 968 Tyrosine kinase inhibitors, 336 head and neck cancer, 952 leukemia, 845 lung cancer, 862, 864 melanoma, 972 Tyrosine kinase receptors gene sequencing, 811–812 gliomas, 956 lung cancer pathogenesis, 862 melanoma, 969 ovarian cancer, 916 therapeutic targeting in cancer, 811 brain tumors, 962 Tyrosine kinases, glomerular epithelium expression, 1058 TZD (PPARγ) agonists, reactive oxygen species effects, 659, 659f U UCP1 gene transfer, 1183 obesity, 1176, 1178, 1180, 1181 UCP2 gene transfer, 1183 obesity, 1176, 1178, 1180 UCP3, obesity, 1176, 1178, 1180, 1181 UDP-glycosyltransferase I, pharmacogenomics, 360 UGT1A1 polymorphism irinotecan metabolism, 421 pharmacogenomics, 372 clinical trial safety, 350, 351f UGT2B17, prostate cancer-related copy number variation, 115–116, 115f UHRF1, 63 UK Biobank, 236, 286–287, 295, 391, 467 UK Genetic Testing Network, 450 Ulcerative colitis, 14, 1040 classification, 1044 clinical features, 1044

1476

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Index

Ulcerative colitis (Continued) colorectal cancer risk, 886, 1045 corticosteroid therapy, 1045 diagnosis, 1044 disease severity genetic markers, 1045–1046 genetic factors, 1041, 1042t HLA-DRB1 alleles, 1045 incidence, 1040 perinuclear antineutrophil cytoplasmic antibodies (pANCA), 1043, 1044, 1045 prognosis, 1045 surgery, 1048 indications, 1045 twin studies, 1041 see also Inflammatory bowel disease Ultrasmall superparamagnetic iron-oxide (USPIO) contrast agents, 516 cell tracking applications, 517 Ultrasound cirrhosis, 1141 head and neck cancer, 947 prostate cancer, 903 UMLS Metathesaurus, 253 Undulin, hepatic stellate cell production, 1144 UNG-mediated T-sequencing, 96 Unified Medical Language System (UMLS), 216 UniGene identifier, 216, 1143 United States genomics firms, 439 genomics research policy, 389, 389t University of California at Santa Cruz (UCSC) Genome Browser, 121, 123, 124 Unrestricted somatic stem cells (USSCs), 600 Unsupervised learning, 213 disease sub-type discovery, 214 gene expression data analysis, 160, 161f acute myeloid leukemia, 847 heart failure, 698 methods, 213 Upper motor neurons, 1265 Urea cycle intermediates, targeted metabolic profiling, 185 Uridine-diphosphate galactose-4 epimerase deficiency, 1207 Urinary colecting system tumors, hereditary nonpolyposis colorectal cancer (Lynch syndrome), 885 Urine biomarkers, 301 prostate cancer, 138, 902 Urokinase-type plasminogen activator, breast cancer metastasis, 138 USF1 gene, 38 V VacA (Helicobacter pylori vacuolating cytotoxin), 1126–1127, 1127f peptic ulcer disease correlations, 1129, 1130

Vaccines, 562–570 computer-driver design strategy, 563 development, 564–566 bacterial pan-genome concept, 565 functional genomics, 565–566, 567f genomic peptide libraries, 567 genomic technology applications, 563, 566t in vivo expression technology (IVET), 563, 566–567 microarray expression technology, 567–568 multiple genome analysis, 565 pathogen genome sequencing, 563–564 proteomics, 568 reverse vaccinology, 563, 564–565, 565f signature-tagged mutagenesis (STM), 563, 566–567 history of design approaches, 562–563 see also Cancer vaccines Vaccinia virus (ankara form), cancer vaccine vectors, 582 Validation biomarkers, 300, 308–318, 429 diagnostic tests, 362, 447 gene discovery, 148 gene expression profile data, 165, 851 genomic tests, 362, 426 microarrays, 281–282 molecular signature analysis, 150–151 pharmacogenetic associations, 328 sample size issues, 282 Valosin-containing protein (VCP) mutations, frontotemporal dementia, 1226 Valproic acid, 845 adverse reactions, 1251 Valvular heart disease, 693 VAMP8 polymorphism, myocardial infarction, 671 Vardenafil, systemic sclerosis, 1165 Variant detection, 33–36 whole genome shotgun sequencing, 91–92, 91f, 92f Variation complex disease phenotype relationships, 36, 37–38t, 38–40 copy number see Copy number variation (CNVs) human populations, 9–11, 22, 227 HuRef genome analysis, 10–11 impact on disease susceptibility, 13–15 population structure, 24 types, 9–10, 9f, 10t Varicella zoster vaccines, 562 viral chips, 541, 551 viral gene expression detection, 550 Variola virus, viral chip technology, 551 Vascular cell adhesion molecule 1 (VCAM-1), 656, 657

atherosclerosis, 653, 655 systemic sclerosis, 1159 Vascular endothelial growth factor (VEGF) acute coronary syndrome risk stratification, 686 amyotrophic lateral sclerosis, 1273, 1276 astrocytomas, 960 cancer therapeutic targeting, 811, 951 coronary artery disease, gene therapy, 616–617 head and neck cancer, 945, 949, 951 inflammatory synovitis, 1073 monoclonal antibody targeted therapy, 998 ovarian cancer angiogenesis, 918 peripheral arterial disease treatment, 775 systemic sclerosis, 1159 tumor microenvironment, 819, 821 Vascular endothelial growth factor receptor (VEGFR) sorafenin inhibition, 1002 therapeutic targeting brain tumors, 962 melanoma, 972 Vascular-occlusive dementia, 1222, 1223 Vasoactive intestinal polypeptide-secreting pancreatic islet tumors, multiple endocrine neoplasia 1 (MEN1), 933 VCL mutations (vinculin/metavinculin), hypertrophic cardiomyopathy, 718, 721 VCP mutations, frontotemporal dementia, 1226 Vector design, nucleic acid-based cancer vaccines, 580 Velocardiofacial syndrome, 782, 787, 788 psychiatric disorder associations, 1285, 1303 Venezuelan equine encephalitis, viral chip technology, 551 Venous thromboembolism family history, 765–766, 767 post-cardiac surgery, 796–798 prognosis, 768 racial/ethnic variation in risk, 762 recurrence, 767, 768 risk factors, 765t screening investigations, 765 see also Deep vein thrombosis; Pulmonary embolism Ventricular development genes, 786 Ventricular remodeling, heart failure, 692, 695, 695f Ventricular septal defects, 782, 786 Ventricular tachyarrhythmias, 729 Verapamil, hypertrophic cardiomyopathy, 723 Vertebrate genomes, 120 Very low-density lipoproteins (VLDL), 634, 1208 apolipoproteins, 635 metabolism, 635, 636 VH mutational status, chronic lymphocytic leukemia, 847, 850, 851

Index

Vibrio cholerae in vivo expression technology (IVET), 566 signature-tagged mutagenesis (STM), 567 Vincristine oligodendrogliomas, 959 pharmacogenomics, 849 Vinculin (VCL) mutations, hypertrophic cardiomyopathy, 718, 721 Viral binding receptors, 538, 539t Viral chip technology, 541–554 bioelectronic detection, 547 cell-based microarrays, 553 diagnostic applications, 550–552 fluidic chips, 554 fluidics workstations, 546–547 grabbing (electronic addressing technology), 546 growing oligonucleotide/DNA sequences (photolithographic generation), 545–546 image analysis, 548 labeling viral target DNA, 546–547 dendrimer technology, 547 microarray-in-a-tube system, 551–552 microfabrication, 541–552, 542f diagnostic chip design, 543 hybridization efficiency, 542–543 oligonucleotide sequence design, 541 quality control, 541–542 nanofabrication, 552–553 carbon nanowires, 553 quantum dots, 552 semiconducting nanowires, 552 silicon nanowires, 552–553 stripped nanowires, 552 scanning systems, 547–548 detector spatial resolution, 547 single molecule detection, 554 spotting, 543–545 immobilization techniques, 543, 544, 544t substrates, 543 viral gene expression, 549–550, 550t multipel pathogen detection, 550 single pathogen detection, 550 viral genotyping, 548, 549t viral load quantitation, 551 viral particle quantity in patient samples, 547 viral sequence determination, 548–549, 550t Viral hepatitis, 1375–1386 diagnosis, 1378–1379 immunogenetics, 1143, 1143t natural history, 1379–1380 pathogenesis, 1380–1384 pharmacogenomics, 1386 predisposition, 1377–1378 prognosis, 1379–1380

proteomics, 1149 screening, 1378–1379 transmission, 1377–1378 treatment, 1384–1386, 1384t functional genomics studies, 1385–1386 response prediction markers, 1385–1386 virology, 1365f, 1375–1377 Viral infection, 14, 538–539, 540t, 1340–1345 associated immune dysfunction, 539 diagnostic methods, 539, 541 historical aspects, 1340–1341 immune response, 538–539, 1314 effector cell antigenic epitope recognition, 539 molecular diagnosis, 370 TLR3 response, 1319 vaccines/vaccination, 562, 1341 Viral load assessment, 370 HIV infection/AIDS see Human immunodeficiency virus (HIV) infection/AIDS nucleic acid biosensors, 592 real-time polymerase chain reaction, 539 viral chip technology, 551 Viral myocarditis diagnosis, 697 heart failure, 693, 697 Viral sequence determination, 548–549, 550t vaccine development, 563–564 Viral vectors, 611–613 cancer vaccines, 582 development, 610 Virochip, 551 Virtual Data Warehouse (VDW), 240 Virulence factors, 564 Virulence gene identification, 566 Viruses, 538, 539t, 1341–1344 antigenic variation, 539 biosensor detection, 592 effector cell recognition, 539 gene expression, 549–550, 550t genotyping, 548, 549t identification methods, 539, 541 chip technology, 541 isolation/cultivation, 539 viral antigens, 541 viral genomic features, 541 virus-specific antibodies, 539, 541 Visilzumab, inflammatory bowel disease, 1049 VISTA, 123, 124 Vitamin D receptor (VDR), mycobacterial disease susceptibility, 1357 Vitamin intake, gene–diet interactions, 1213 Vitaxin (LM609), melanoma clinical trials, 971 VKORC1 polymorphism, warfarin response variability, 326, 327, 329, 349, 371 Vocabularies, 268 consumer on-line health information retrieval, 253

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1477

Voluntary Genomic Data Submission, 419, 422 goals, 419 pharmacogenomics, 329, 344, 415 Von Willebrand factor genetic variation, 757 stroke risk associations, 760 W Warfarin dose adjustment, 415 genotype-guided, 328, 421 pharmacogenomics, 326–327, 329, 349–350, 371, 372, 768, 769t WDR36, glaucoma, 1260 WebMCS, 124 Wellcome Trust Case Control Consortium (WTCCC), 295, 464, 467, 1189, 1190 West Nile virus, 1340 nucleic acid amplification testing (NAAT), 370 viral chip technology, 551 Western Australian Genome Health Project (WAGHP), 286 Western blot analysis, 79 biomarker antibodies for immunoassay, 309 pharmacodynamic marker assays, 340 Whipple’s disease, 1070 Whole genome amplified DNA, genotyping technologies, 106 Whole genome microarray analysis Helicobacter pylori, 1125, 1129 microbial vaccine development, 564–566 Whole genome shotgun sequencing, 89–92 colony plating/picking, 90 library construction, 89 process automation, 89, 90f sequence detection, 90–91 sequencing reactions, 90 template preparation, 90 variant detection, 91–92, 91f, 92f whole genome assembly process, 91 Whole-genome association studies see Genome-wide association studies Whole-genome sequence comparisons, copy number variation (CNVs) detection, 114 Whooping cough, molecular diagnosis, 371 Williams syndrome congenital heart disease, 785 cytogenetics, 787 Williams-Beuren syndrome see Williams syndrome Wilson disease, 1150 WNK kinase 1/4 mutations, pseudohypoaldosteonism type 2 (Gordon’s syndrome), 628 Wnt5a, melanoma, 969 Wnt pathway genes colorectal cancer, 880 primary biliary cirrhosis, 1147 WT1, congenital nephrotic syndrome, 1062

1478

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Index

X X chromosome inactivation, 131 X-linked severe combined immunodeficiency, gene therapy, 616 X-ray repair cross-complementing group 1 (XRCC1), cancer susceptibility, 305 Xanthine oxidase, reactive oxygen species generation, 652 atherosclerosis, 654 Xenotropic murine leukemia virus-related virus (XMRV), prostate cancer association, 550, 899 Y Y chromosome population genomic studies, 23 repetitive DNA, 8

T-cell chimerism in systemic sclerosis, 1161, 1162, 1162f 90 Y-ibirtumomab tiuxetan, 997 Yersinia enterocolitica, signature-tagged mutagenesis (STM), 567 YKL-40 glioblastoma, 961 treatment response monitoring, 962 glioma prognosis, 960–961, 963 Z Z-disk hypertrophic cardiomyopathy, 718–719, 721 Zafirlukast, asthma management, 1091 Zalcitabine, 1342 Zanamivir, 1344

ZAP-70, chronic lymphocytic leukemia, 847, 849–850, 851 ZD6474, medullary thyroid carcinoma treatment, 940, 940f, 941f ZDHHC8 (DHHC-type containing 8 zinc finger), schizophrenia candidate genes, 1285 ZFPM2/FOG2, congenital heart disease, 782, 783 ZIC3 (heterotaxy), congenital heart disease, 783–784 Zidovudine, 1326, 1342 Zinc finger protein transcription factor, peripheral arterial disease therapeutic angiogenesis, 775 Zollinger-Ellison syndrome (gastrinoma), 1122 Zucker (fa/fa) rat, 1175