Drug Resistance in Cancer Cells

Drug Resistance in Cancer Cells

Kapil Mehta · Zahid H. Siddik Editors

Drug Resistance in Cancer Cells

Foreword by Susan E. Bates

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Editors Kapil Mehta Department of Experimental Therapeutics M.D. Anderson Cancer Center University of Texas 1515 Holcombe Blvd. Houston TX 77030 USA [email protected]

ISBN 978-0-387-89444-7 DOI 10.1007/978-0-387-89445-4

Zahid H. Siddik Department of Experimental Therapeutics M.D. Anderson Cancer Center University of Texas 1515 Holcombe Blvd. Houston TX 77030 USA [email protected]

e-ISBN 978-0-387-89445-4

Library of Congress Control Number: 2009927661 c Springer Science+Business Media, LLC 2009  All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com

Foreword

It was estimated that in 2008, 1,437,180 patients would receive a new cancer diagnosis and 565,650 individuals would die of cancer (Jemal et al. 2008). Since the vast majority of patients dying of cancer will have had anticancer therapy, both conventional chemotherapy and novel targeted therapy, it can be concluded that these patients are dying with drug resistant cancer. The term multidrug resistance is also apt – in that these patients die after having undergone multiple rounds of different and structurally unrelated cancer therapies. However, for some, the concept of multidrug resistance is a worn out idea, stemming from disappointment with the drug resistance reversal strategies that were carried out in the 1990s using pump inhibitors to block drug resistance mediated by P-glycoprotein, product of the MDR-1 gene. However, if one takes the larger definition – multidrug resistance as simultaneous resistance to multiple structurally unrelated anticancer therapies – its existence cannot be denied. The purpose of this book is to explore new concepts related to drug resistance in cancer, including resistance to the new molecularly targeted agents. Perhaps new terminology is needed for resistance that occurs following therapy with the targeted agents: Novel Targeted Agent Resistance (NTR). Alternatively, we can return to the original definition of multidrug resistance as simply the resistance to multiple agents that occurs in the course of normal cancer progression. This resistance is likely to be mediated by many factors. Figure 1 presents a schematic that is meant to represent the complexity of our current understanding of drug resistance. At this, the schematic is still an oversimplification. The multifactorial nature of drug resistance is unquestioned now, but there was a time when a major goal of cancer investigation was to identify a single mechanism of resistance. Today, we can think of drug resistance in two classes: target-specific and target-nonspecific. These are very closely aligned with the older terms: acquired and intrinsic. Target-specific mechanisms relate to the development of resistance mechanisms that are specific to the drug target. These resistance mechanisms are often acquired – whether due to a new event or to selection of pre-existing events. An example of this type of resistance is found in the subset of breast cancers demonstrating loss of estrogen receptor following emergence of tamoxifen resistance (Clarke et al. 2003). Another excellent example is the development of BcrAbl kinase domain mutations such as the T315I, which renders chronic myelogenous leukemia (CML) cells resistant to

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Cellular Mechanisms of Drug Resistance: Too Numerous to Count?

Drug Uptake

RFC

Damage Repair

Drug

Drug Metabolism

R P1

hEN T

M

1 TR hC

CN T

Drug Target P GSH bcr-abl GSH P bcr-abl GST GS-Drug

MMR Proteins

OAT

Drug

Pgp

Topo-II Survival p53 Topo -I

2

AB CA 2

Glu-Drug

UGT Drug Drug

Metabolism

Tubulin

G2 ABC

Glu Glu

MRP

Drug Efflux

Fig. 1 Cellular mechanisms of drug resistance: too numerous to count? Six categories of resistance mechanisms are depicted: Drug Uptake, Drug Efflux, Drug Metabolism, Drug Target, Damage Repair, and Survival. The broken arrows indicate impaired drug binding to target such as might occur with an acquired mutation in Bcr-Abl, reduced levels of topoisomerase, or altered isotype composition in tubulin

imatinib (Gorre et al. 2001). Similarly, the T790M EGFR mutation renders NSCLC resistant to gefitinib (Pao et al. 2005). Turning to in vitro model systems, increased expression and a markedly increased phosphorylation of EGFR has been observed in trastuzumab resistant breast cancer cells (Ritter et al. 2007). The converse has also been demonstrated – upregulation of HER2 following suppression of EGFR with cetuximab suggests an alternate proliferative pathway for cancer maintenance (Wheeler et al. 2008). These target-specific mechanisms are difficult to predict in advance, and a single “fix” cannot be identified that would circumvent this type of resistance. Target-nonspecific mechanisms, on the other hand, are often intrinsic or constitutive – that is, they are present a priori and may be expropriated to promote drug resistance. They include those general mechanisms that were identified beginning in studies of resistance to “cytotoxic” agents, and they are often upregulated or preferentially selected after exposure to anticancer agents. These include p21-, p27-, or p53-induced cell cycle arrest; reduction in cell proliferation through cell adhesion signaling; upregulation of Bcl-2 or other pro-survival molecules; upregulation of enzymes that increase metabolic inactivation, such as glucuronidation or glutathione

Foreword

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conjugation; upregulation of drug efflux transporters; and downregulation of drug influx transporters (Mellor and Callaghan 2008). However, it would be a mistake to characterize every drug failure as drug resistance. Consider a clear and obvious example, the use of trastuzumab in HER2+ breast cancer. Early clinical trials in breast cancer demonstrated the activity of trastuzumab in the subset of patients whose cancers expressed high levels of HER2. Enrichment of patients with overexpressing cancers demonstrated a clinical benefit that may have been missed had the drug been developed in the entire patient population. The response rate in patients with breast cancer staining 2+ for HER2 was 6%, while it was 18% in patients with breast cancers staining 3+ for HER2 (Cobleigh et al. 1999). A later study suggested that immunohistochemical assessment incorrectly classified some of the patients. Among patients found to have cancers positive by fluorescence in situ hybridization (FISH), which documents amplification of HER2, responses were noted in 20%, relative to the absence of responses noted in FISH-negative tumors (Baselga 2001). Beyond the use of biomarkers to indicate that tumors are either HER2 positive or not, genomic analysis demonstrated that gene expression profiles can be used to cluster invasive breast cancers into six different subtypes, with HER2 positive tumors representing one distinct subtype (Sorlie et al. 2001). This suggests that there is not only HER2 overexpression but also that distinctive downstream signaling events result from activation of HER2. Tumors of this subtype are dependent on HER2 signaling for maintenance of the malignant phenotype. The remaining subtypes are equally distinctive but do not cluster with cells exhibiting HER2 overexpression. Do we then classify the cancers without HER2 expression as being “drug resistant?” In these tumors, the target is absent. Herceptin will not work in these tumors, and only confusion would result if investigators began to study mechanisms of resistance to Herceptin in such tumors. Thus, we need to be careful about defining a tumor as drug resistant. Are the 60–70% of renal cell cancers that do not achieve a partial remission with sunitinib drug resistant (Motzer et al. 2007)? Sorafenib, which at its FDA-approved dose appears to be a less potent TKI in comparison to sunitinib, achieves partial remissions in less than 10% of cases (Escudier et al. 2007). Are these tumors drug resistant? Similarly, gefitinib was found to be more tolerable than erlotinib, perhaps because a lower effective dose was selected for development (Lorusso 2003; Soulieres et al. 2004). As with the sorafenib/sutent pairing, it would be hard to argue that a tumor responding to erlotinib and not to gefitinib was indeed drug resistant. A strong argument could be made for a definition of drug resistance in the era of novel-targeted agents that requires presence of the target and effective drug concentrations. While an “effective drug concentration” may be related to a drug and its potency, another aspect that should be considered is interpatient variability in drug activation, absorption (for oral drugs), drug metabolism, and drug excretion. The expanding field of pharmacogenomics has already lent considerable insights into the potential impact of single nucleotide polymorphisms in altering drug clearance. As one example, tamoxifen is hydroxylated by cytochrome P450 (CYP) 2D6 to the potent

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metabolites 4-hydroxytamoxifen (4OHtam) and 4-hydroxy-N-demethyltamoxifen (4OHNDtam). Carriers of CYP2D6 alleles with reduced enzyme activity metabolize tamoxifen more poorly and have lower levels of active metabolites. These alleles have been associated with a poorer outcome to tamoxifen therapy (Goetz et al. 2007). Inhibitors of CYP 2D6 have been shown to have similar effects. Together with the presence of other tamoxifen metabolizing pathways, these findings further increase the complexity of “personalized medicine”. Low drug concentrations due to impaired activation or low plasma levels due to rapid clearance generate pharmacologic drug resistance, a problem deserving intensive study in oncology. Trough levels of imatinib above 1002 ng/ml have been associated with better and deeper responses to treatment in CML (Picard et al. 2007). The genotypes involved in mediating differences in imatinib levels have not been worked out in detail, some have postulated a role for the multidrug transporter ABCB1. Similarly, a polymorphic variant in the multidrug transporter ABCG2 that results in impaired transport has been related to increased gefitinib levels and toxicity (Cusatis et al. 2006; Li et al. 2007). These studies to date have been carried out on a small scale, and the impact of this variant on efficacy is not known. However, it will not come as a great surprise to find greater efficacy in patients carrying the variant. Finally, just as in the early 1990s, an overly simplistic view of multidrug resistance led to numerous ill-conceived trials attempting to overcome drug resistance (see Sakacs et al. in this book), so it would be overly simplistic to now consider resistance to gefitinib or to imatinib or to any other targeted agent as due solely to a single mechanism (Engelman and Janne 2008). Rather, it will be important to evaluate tumor samples for multiple mechanisms of drug resistance. Just as the field has set as its goal personalized approaches to cancer therapy (hoping to avoid intrinsic drug resistance by selecting appropriate therapies at the start), so we should develop strategies to identify individual mechanisms, or sets of mechanisms, of drug resistance. Table 1 lists some examples of multiple mechanisms of drug resistance that have been identified in the setting of both traditional agents and novel targeted agents. This list for each drug is not exhaustive – rather the emphasis is on unique mechanisms that have been identified. General pro-survival mechanisms such as cell cycle arrest allowing time for repair of damage have not been included here. Since it will not be possible to clinically address every possible mechanism of drug resistance for a given agent, the personalized medicine of the future will need to order the resistance mechanisms in terms of importance. For example, the secondary BCR-ABL mutation T315I, which renders CML cells more than 20-fold resistant to imatinib, is all that is needed to provoke clinical imatinib failure (Apperley 2007). However, BcrAbl mutations that render CML cells only 2–3-fold resistant to imatinib will need to coexist with another mechanism of resistance, in order to provoke clinical failure. In medicine, we attempt to distill a group of patient symptoms into a single diagnosis, and skilled diagnosticians are prized. Yet, in considering drug resistance as a diagnosis, we must function in a counterintuitive fashion. The phenotype is simple –

Foreword

ix Table 1 Multiple mechanisms of drug resistance Imatinib in CML: BCR-ABL kinase domain mutation BCR-ABL gene amplification Interindividual variation: Trough levels <1000 ng/ml ABCB1/Pgp ABCG2/BCRP Tamoxifen in Breast Cancer: Estrogen Receptor loss Low Cytochrome 2A6 expression and failure to generate active metabolite Gefitinib in NSCLC: Lower potency Lack of activating EGFR mutation Secondary EGFR T790M mutation Increased PI3K pathway and downstream survival signaling MET amplification ABCG2/BCRP Cisplatin: Metallothionines Decreased uptake transporters Increased DNA repair Increased glutathione conjugation Doxorubicin: Decreased topoisomerase II levels Increased oxygen radical scavenging Increased glutathione conjugation Increased ABCB1/Pgp expression

the drug fails to provide clinical benefit. But the underlying pathways to drug resistance are manifold and complex and very likely dependent on cell context. Rather than having a form of “stage migration” as one newly identified drug resistance mechanism subverts another in our collective imaginations, it will be important to begin to catalogue them all and to determine how to identify those operating in a particular tumor type. This book is an effort to describe in one place the array of drug resistance mechanisms confronting clinical oncologists today. Susan E. Bates National Cancer Institute Bethesda, MD, USA

References Apperley, J. F. 2007. Part II: management of resistance to imatinib in chronic myeloid leukaemia. Lancet Oncol. 8: 1116–1128. Baselga, J. 2001. Herceptin alone or in combination with chemotherapy in the treatment of HER2-positive metastatic breast cancer: pivotal trials. Oncology. 61 Suppl 2: 14–21.

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Foreword Clarke, R., Liu, M. C., Bouker, K. B., Gu, Z., Lee, R. Y., Zhu, Y., Skaar, T. C., Gomez, B., O’Brien, K., Wang, Y. and Hilakivi-Clarke, L. A. 2003. Antiestrogen resistance in breast cancer and the role of estrogen receptor signaling. Oncogene. 22: 7316–7339. Cobleigh, M. A., Vogel, C. L., Tripathy, D., Robert, N. J., Scholl, S., Fehrenbacher, L., Wolter, J. M., Paton, V., Shak, S., Lieberman, G. and Slamon, D. J. 1999. Multinational study of the efficacy and safety of humanized anti-HER2 monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic disease. J. Clin. Oncol. 17: 2639–2648. Cusatis, G., Gregorc, V., Li, J., Spreafico, A., Ingersoll, R. G., Verweij, J., Ludovini, V., Villa, E., Hidalgo, M., Sparreboom, A. and Baker, S. D. 2006. Pharmacogenetics of ABCG2 and adverse reactions to gefitinib. J. Natl. Cancer Inst. 98: 1739–1742. Engelman, J. A. and Janne, P. A. 2008. Mechanisms of acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer. Clin. Cancer Res. 14: 2895–2899. Escudier, B., Eisen, T., Stadler, W. M., Szczylik, C., Oudard, S., Siebels, M., Negrier, S., Chevreau, C., Solska, E., Desai, A. A., Rolland, F., Demkow, T., Hutson, T. E., Gore, M., Freeman, S., Schwartz, B., Shan, M., Simantov, R. and Bukowski, R. M. 2007. Sorafenib in advanced clear-cell renal-cell carcinoma. N. Engl. J. Med. 356: 125–134. Goetz, M. P., Knox, S. K., Suman, V. J., Rae, J. M., Safgren, S. L., Ames, M. M., Visscher, D. W., Reynolds, C., Couch, F. J., Lingle, W. L., Weinshilboum, R. M., Fritcher, E. G., Nibbe, A. M., Desta, Z., Nguyen, A., Flockhart, D. A., Perez, E. A. and Ingle, J. N. 2007. The impact of cytochrome P450 2D6 metabolism in women receiving adjuvant tamoxifen. Breast Cancer Res. Treat. 101: 113–121. Gorre, M. E., Mohammed, M., Ellwood, K., Hsu, N., Paquette, R., Rao, P. N. and Sawyers, C. L. 2001. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science. 293: 876–880. Jemal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Murray, T. and Thun, M. J. 2008. Cancer statistics, 2008. CA Cancer J. Clin. 58: 71–96. Li, J., Cusatis, G., Brahmer, J., Sparreboom, A., Robey, R. W., Bates, S. E., Hidalgo, M. and Baker, S. D. 2007. Association of variant ABCG2 and the pharmacokinetics of epidermal growth factor receptor tyrosine kinase inhibitors in cancer patients. Cancer Biol. Ther. 6: 432–438. Lorusso, P. M. 2003. Phase I studies of ZD1839 in patients with common solid tumors. Semin. Oncol. 30: 21–29. Mellor, H. R. and Callaghan, R. 2008. Resistance to chemotherapy in cancer: a complex and integrated cellular response. Pharmacology. 81: 275–300. Motzer, R. J., Hutson, T. E., Tomczak, P., Michaelson, M. D., Bukowski, R. M., Rixe, O., Oudard, S., Negrier, S., Szczylik, C., Kim, S. T., Chen, I., Bycott, P. W., Baum, C. M. and Figlin, R. A. 2007. Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N. Engl. J. Med. 356: 115–124. Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., Kris, M. G. and Varmus, H. 2005. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2: e73. Picard, S., Titier, K., Etienne, G., Teilhet, E., Ducint, D., Bernard, M. A., Lassalle, R., Marit, G., Reiffers, J., Begaud, B., Moore, N., Molimard, M. and Mahon, F. X. 2007. Trough imatinib plasma levels are associated with both cytogenetic and molecular responses to standard-dose imatinib in chronic myeloid leukemia. Blood. 109: 3496–3499. Ritter, C. A., Perez-Torres, M., Rinehart, C., Guix, M., Dugger, T., Engelman, J. A. and Arteaga, C. L. 2007. Human breast cancer cells selected for resistance to trastuzumab in vivo overexpress epidermal growth factor receptor and ErbB ligands and remain dependent on the ErbB receptor network. Clin. Cancer Res. 13: 4909–4919. Sorlie, 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.,

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Botstein, D., Eystein Lonning, P. and Borresen-Dale, A. L. 2001. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci., U S A. 98: 10869–10874. Soulieres, D., Senzer, N. N., Vokes, E. E., Hidalgo, M., Agarwala, S. S. and Siu, L. L. 2004. Multicenter phase II study of erlotinib, an oral epidermal growth factor receptor tyrosine kinase inhibitor, in patients with recurrent or metastatic squamous cell cancer of the head and neck. J. Clin. Oncol. 22: 77–85. Wheeler, D. L., Huang, S., Kruser, T. J., Nechrebecki, M. M., Armstrong, E. A., Benavente, S., Gondi, V., Hsu, K. T. and Harari, P. M. 2008. Mechanisms of acquired resistance to cetuximab: role of HER (ErbB) family members. Oncogene. 27: 3944–3956.

Contents

1 Multidrug Resistance Mediated by MDR-ABC Transporters . . . . . . . . Gergely Szak´acs, Kenneth Kin Wah To, Orsolya Polg´ar, Robert W. Robey, and Susan E. Bates

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2 Metastasis and Drug Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Dominic Fan, Sun-Jin Kim, Robert L. Langley, and Isaiah J. Fidler 3 The Role of Autophagy and Apoptosis in the Drug Resistance of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Tomohisa Yokoyama, Yasuko Kondo, Oliver B¨ogler, and Seiji Kondo 4 Mechanisms of Resistance to Targeted Tyrosine Kinase Inhibitors . . . 73 Stacey J. Baker and E. Premkumar Reddy 5 Targeting Transglutaminase-2 to Overcome Chemoresistance in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Kapil Mehta and Jansina Y. Fok 6 Extracellular Matrix-Mediated Drug Resistance . . . . . . . . . . . . . . . . . . . 115 P.S. Hodkinson and Tariq Sethi 7 Oxidative Stress and Drug Resistance in Cancer . . . . . . . . . . . . . . . . . . . 137 Dunyaporn Trachootham, Wan Zhang, and Peng Huang 8 Nuclear Factor-κB and Chemoresistance: How Intertwined Are They? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Ajaikumar B. Kunnumakkara, Preetha Anand, and Bharat B. Aggarwal 9 Drug Resistance and the Tumor Suppressor p53: The Paradox of Wild-Type Genotype in Chemorefractory Cancers . . . . . . . . . . . . . . . 209 Zahid H. Siddik xiii

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Resistance to Differentiation Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Bulent Ozpolat

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MicroRNAs and Drug Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Elisa Barbarotto and George A. Calin

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Molecular Signatures of Drug Resistance . . . . . . . . . . . . . . . . . . . . . . . . . 271 Melissa A. Troester, Jason I. Herschkowitz, and Katherine A. Hoadley

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Assessment of Drug Resistance in Anticancer Therapy by Nuclear Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Natalie Charnley, Catharine West, and Pat Price

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Overcoming Drug Resistance by Phytochemicals . . . . . . . . . . . . . . . . . . . 315 Marion M. Chan and Dunne Fong

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

Contributors

Bharat B. Aggarwal Department of Experimental Therapeutics, Unit 0143, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Preetha Anand Department of Experimental Therapeutics, Unit 0143, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Stacey J. Baker Fels Institute for Cancer Research and Molecular Biology, Temple University School of Medicine, 3307 North Broad Street, Philadelphia, PA 19140, USA Elisa Barbarotto Department of Experimental Therapeutics–Unit 36, The University of Texas M. D. Anderson Cancer Center, 1515 Holocombe Blvd., Houston, TX 77030, USA Susan E. Bates National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA Oliver B¨ogler Department of Neurosurgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA George A. Calin Department of Experimental Therapeutics–Unit 36, The University of Texas M. D. Anderson Cancer Center, 1515 Holocombe Blvd., Houston, TX 77030, USA Marion M. Chan Department of Microbiology and Immunology, Temple University School of Medicine, 3400 North Broad Street, Philadelphia, PA 19140, USA Natalie Charnley Academic Department of Radiation Oncology, Christie Hospital, Withington, Manchester M20 4BX, UK Dominic Fan Department of Cancer Biology, Cancer Metastasis Research Center, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Isaiah J. Fidler Department of Cancer Biology, Cancer Metastasis Research Center, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA

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Jansina Y. Fok Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd. – Unit 362, Houston, TX 77030, USA Dunne Fong Department of Cell Biology and Neuroscience, Rutgers University, 604 Allison Road, Piscataway, NJ 08854, USA Jason I. Herschkowit Department of Cell Biology – Rosen, Baylor College of Medicine, Houston, TX 77030, USA Katherine A. Hoadley Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA P.S. Hodkinson Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK Peng Huang Department of Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA Sun-Jin Kim Department of Cancer Biology, Cancer Metastasis Research Center, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Seiji Kondo Department of Neurosurgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Yasuko Kondo Department of Neurosurgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Ajaikumar B. Kunnumakkara Department of Experimental Therapeutics, Unit 0143, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Robert L. Langley Department of Cancer Biology, Cancer Metastasis Research Center, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Kapil Mehta Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd. – Unit 362, Houston, TX 77030, USA Bulent Ozpolat Department of Experimental Therapeutics, Unit 422, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA Orsolya Polg´ar National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA Pat Price Academic Department of Radiation Oncology, Christie Hospital, Withington, Manchester M20 4BX, UK

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E. Premkumar Reddy Fels Institute for Cancer Research and Molecular Biology, Temple University School of Medicine, 3307 North Broad Street, Philadelphia, PA 19140, USA Robert W. Robey National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA Tariq Sethi Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK Zahid H. Siddik Department of Experimental Therapeutics – Unit 353, The University of Texas M. D. Anderson Cancer Center, 1515 Holocombe Blvd., Houston, TX 77030, USA Gergely Szak´acs Institute of Enzymology, Hungarian Academy of Sciences, H-1113 Budapest, Hungary Dunyaporn Trachootham Department of Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA Kenneth K. To Wah National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA Melissa A. Troester Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC, USA Catharine West Academic Department of Radiation Oncology, Christie Hospital, Withington, Manchester M20 4BX, UK Tomohisa Yokoyama Department of Neurosurgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Wan Zhang Department of Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Chapter 1

Multidrug Resistance Mediated by MDR-ABC Transporters Gergely Szak´acs, Kenneth Kin Wah To, Orsolya Polg´ar, Robert W. Robey, and Susan E. Bates

Abstract Cancer cells are constantly being selected for survival and proliferation. During this process, tumor cells often co-opt basic physiological mechanisms to protect themselves from toxic chemotherapy. Of these mechanisms, the one that is most commonly encountered in laboratory studies is the increased cellular efflux of a broad class of cytotoxic drugs that is mediated by a family of energy-dependent transporters, known as ATP-binding cassette (ABC) proteins. Although 48 human ABC transporters have been described, only a handful are likely to be involved in drug resistance. These include P-glycoprotein (Pgp), the multidrug resistance associated protein (MRP), and the breast cancer resistance protein (BCRP), also termed ABCG2. In this chapter, we will provide an overview of the general structural and mechanistic features of the human ABC transporters thought to be involved in multidrug resistance in cancer cells. These transporters can no longer be understood only as candidate drug targets for overcoming multidrug resistance. Rather, these transporters are involved in multiple normal tissues in protection from drugs and xenobiotics. These include the blood brain barrier, oral bioavailability of drugs and xenobiotics through expression of transporters in the GI tract, drug disposition, and fetal protection through expression at the maternal–fetal barrier. We describe emerging information related to their potential role in drug and xenobiotic resistance and argue that studies of this drug resistance mechanism should continue in the laboratory and in the clinic. Keywords ABC transporter · P-glycoprotein · ABCG2 · Drug resistance · Drug transporters

S.E. Bates (B) Medical Oncology Branch, Bldg 10 Rm 12N226, 9000 Rockville Pike, Bethesda, MD 20892, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 1, 

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G. Szak´acs et al.

Introduction Cancer cells are constantly being selected for survival and proliferation. During this process, tumor cells often co-opt basic physiological mechanisms to protect themselves from toxic chemotherapy. Drug resistance can be the result of a variety of cellular and pharmacological mechanisms that are detailed in the chapters of this volume. Of these mechanisms, the one that is most commonly encountered in the laboratory is the increased efflux of a broad class of cytotoxic drugs that is mediated by a family of energy-dependent transporters, known as ATP-binding cassette (ABC) transporters. ABC transporters translocate a wide range of structurally unrelated compounds across biological membranes, including most of the routinely used agents of the current chemotherapy regimens. While several ABC transporters, particularly in the ABCC family, have been shown to transport certain drug substrates, three in particular have been studied for their role in drug disposition and in multidrug resistance. The three major multidrug resistance (MDR) proteins include members of the ABCB (ABCB1/MDR1/P-glycoprotein), the ABCC (ABCC1/MRP1), and the ABCG (ABCG2/MXR/BCRP) subfamilies. In this chapter, we will provide an overview of the general structural and mechanistic features of the human MDR-ABC transporters. We will also describe emerging information related to their potential role in drug and xenobiotic resistance and argue that studies of this drug resistance mechanism should continue in the laboratory and in the clinic.

Mechanistic Aspects of ABC Transporters ABC transporters are present in all living organisms from bacteria to humans and are of great importance to various cellular functions. Forty-eight ABC transporters are identified in the human genome, grouped into seven subfamilies (ABCA– ABCG) based on phylogenetic analysis (Dean et al. 2001). Several human ABC transporter genes have been associated with inheritable diseases, including cystic fibrosis (ABCC7), Dubin–Johnson syndrome (ABCC2), adrenoleukodystrophy (ABCD1), and sitosterolemia (ABCG5/ABCG8) (Borst and Elferink 2002). ABC transporters are large membrane-bound proteins built of two nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs). The NBDs contain the conserved Walker A and B motifs, found in all ATP-binding proteins, as well as the C signature motif that distinguishes the superfamily from other ATPases. A subset of these ABC proteins has been shown to be drug transporters. The drug translocation pathway is formed by the transmembrane domains, which typically contain 12 (2×6) membrane-spanning alpha helices (TMs). This structural organization is either encoded in a single gene (a full transporter) in the case of P-glycoprotein (ABCB1) or MRP (ABCC1) or is achieved through the homo-, or heterodimerization of two half-transporters, containing a single TMD and NBD, in the case of the breast cancer resistance protein (ABCG2). Unfortunately, crystallization has, so far, not been accomplished for these transporters to a resolution sufficient for proper insight into how the pumps might actually

1 ABC Transporters in Drug Resistance

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work. Crystallization of membrane-embedded proteins remains a very difficult task. Currently, crystal structures of approximately 160 transmembrane proteins ˚ while the number of available are available to a resolution greater than 4 A, structures for soluble proteins is in the thousands. There are seven ABC transporter structures solved, all found in bacteria (Locher et al. 2002, Dawson and Locher 2006, Hvorup et al. 2007, Hollenstein et al. 2007, Pinkett et al. 2007, Ward et al. 2007, Oldham et al. 2007). Of these, Sav1866 from Staphylococcus aureus likely resembles the most the three major human multidrug resistance transporters. Indeed, all three have already been modeled using the Sav1866 structure as a template and tested to some extent using data derived from mutagenesis (O’Mara and Tieleman 2007, DeGorter et al. 2008, Hazai and Bikadi 2008). As noted above, Pgp, MRP1, and ABCG2 have several common structural features; on the other hand, they differ significantly in some aspects. Pgp and MRP1 are so-called full transporters, composed of a tandem repeat of transmembrane and nucleotide-binding domains (TMD–NBD–TMD–NBD). ABCG2 has only one NBD and a single TMD, and it requires dimerization in order to be functional. There is compelling evidence supporting homodimerization of ABCG2 (Ozvegy et al. 2001, Henriksen et al. 2005, Kage et al. 2002). In addition to being a half-transporter, the domain organization of ABCG2 is reversed when compared to Pgp, MRP1, or Sav1866, with an N-terminal NBD and a C-terminal TMD. The importance of this reverse orientation, if any, has yet to be determined, nevertheless, it makes it even harder to predict the structure of ABCG2 in the absence of a crystal structure. In addition to a Pgp-like core, MRP1 has an extra transmembrane domain with five membrane-spanning alpha helices, termed TMD0 connected to TMD1 via an intracellular loop (L0 ), present in several other ABCC subfamily members. TMD0 could not be modeled based on Sav1866 which has only 12 TMs. Interestingly, deletion of TMD0 did not prevent the transport function and proper membrane localization of MRP1 (Bakos et al. 2000). However, certain mutations introduced to TMD0 had structural and functional consequences (Yang et al. 2002). There are no such striking differences between the ATP-binding sites of the three transporters, although the two sites demonstrate less symmetry in MRP1 than in Pgp. Upon binding and hydrolysis of ATP, conformational changes are presumed to occur that are transmitted to the TMDs, ultimately resulting in the translocation of the substrate from one side of the membrane to the other. Based on the observations from multiple bacterial NBD structures, the two ATP-binding sites are thought to form a “sandwich dimer”, in which the Walker A and B motifs from one subunit bind ATP together with the C signature sequence of the other subunit (Hollenstein et al. 2007)

MDR-ABC Transporters Based on a great deal of clinical and experimental work, it has been established that the MDR-ABC proteins recognize and transport a very wide range of drug substrates. This is relevant to their presumed normal role in human physiology in

4

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regulating the GI absorption and normal tissue exposure of xenobiotics. Although recognized substrates are mostly hydrophobic compounds, MDR pumps can also extrude amphipathic anions and cations. As discussed in detail below, ABCB1 preferentially extrudes large hydrophobic molecules, while ABCC1 and ABCG2 can transport both hydrophobic drugs and large anionic compounds, e.g., drug conjugates. This “promiscuous” character and the additional overlapping substrate recognition by the three major classes of the MDR-ABC transporters provide an amazing network of drug resistance capacity in cancer cells (Sarkadi et al. 2006). These transporters were initially described in multidrug-resistant cancer cell lines (Juliano and Ling 1976, Cole et al. 1992, Doyle et al. 1998, Allikmets et al. 1998, Miyake et al. 1999); therefore, the first substrates identified were chemotherapeutic agents. Later, a wide variety of transported compounds unrelated to cancer therapy have been recognized, leading to an emerging role of these pumps in protection against xenobiotics and most recently in pharmacokinetics and pharmacogenomics. Although Pgp, the first of the three transporters, was identified more than 30 years ago, we have yet a long way to go before fully understanding the exact nature of their mechanism of action, and the role these pumps play in normal physiology and clinical multidrug resistance.

Transported Substrates The list of compounds transported by Pgp, MRP1, and ABCG2 is rapidly expanding and demonstrates significant overlaps. Many of the substrates are structurally dissimilar, while certain trends for favored compounds for each transporter can be established (Table 1.1) Pgp, similarly to ABCG2, preferentially transports large, hydrophobic, positively charged molecules. Its substrates include a wide variety of clinically relevant compounds, including anticancer drugs, human immunodeficiency virus (HIV)-protease inhibitors, immunosuppressive agents, antiarrhythmics, antihistamines, H-2 receptor blockers, antiemetics, steroids, cardiac glycosides, antibiotics, antihelmintics, antidepressants, antiepileptics, and analgesics. Among the chemotherapeutic agents transported by Pgp are anthracyclines (daunorubicin, doxorubicin, epirubicin), anthracenes (bisantrene, mitoxantrone), vinca alkaloids (vinblastine, vincristine, vinorelbine, vindesine), camptothecins (topotecan), taxanes (paclitaxel, docetaxel), and epipodophyllotoxins (etoposide, teniposide). Steroid hormones, phospholipids, and cytokines are among the potential physiological substrates of Pgp. Besides effluxing mostly neutral hydrophobic compounds, MRP1 is capable of transporting organic anions, such as molecules conjugated to glutathione (GSH), glucuronate, and sulfate. Among these conjugates are the compounds considered as endogenous substrates of the protein, e.g., GSH conjugates of leukotriene C4, leukotriene D4, leukotriene E4, prostaglandin A2, glucoronide conjugates of estradiol, and sulfate conjugates of estrone and dehydroepiandrosterone. Interestingly, GSH itself is a substrate for the pump. MRP1 also transports drugs currently used in the clinic, such as HIV-protease inhibitors,

1 ABC Transporters in Drug Resistance

5

ABCG2

ABCC1

ABCB1

Table 1.1 Clinically relevant substrates of the MDR-ABC transporters (Szakacs et al. 2006)

Vinblastine Vincristine Daunorubicin Anthracyclines Doxorubicin Epirubicin Epipodophyllotoxins Etoposide Teniposide Docetaxel Taxanes Paclitaxel Gleevec Kinase inhibitors Flavopiridol Irinotecan (CPT-11) Campthotecins SN-38 Topotecan 6-Mercaptopurine Thiopurines 6-Thioguanine 5-FU Bisantrene Other Cisplatin Arsenite Colchicine Estramustine Methotrexate Mitoxantrone Saquinivir PMEA Actinomycin-D AZT Digoxin Vinca alkaloids

antibiotics, antiandrogens, and chemotherapeutic agents. The latter group includes anthracyclines (doxorubicin, daunorubicin), vinca alkaloids (vinblastine, vincristine), epipodophyllotoxins (etoposide, teniposide), camptothecins (topotecan, irinotecan), and methotrexate (Bakos and Homolya 2007). The manner in which MRP1 handles its substrates is rather complex. Transport of certain hydrophobic compounds, such as vincristine, was shown to be stimulated by GSH. In addition, vincristine transport was suggested to be dependent on physiological concentrations of GSH (Loe et al. 1998). These observations have led to the assumption that the two molecules are cotransported, but the question is complicated by the fact that efflux of some compounds, such as daunorubicin, is stimulated by GSH, while they themselves have no effect on GSH transport (Renes et al. 1999). In contrast, verapamil, which is not a substrate of MRP1, stimulates GSH transport. Cotransport could be envisioned with

6

G. Szak´acs et al.

two separate substrate-binding sites or with a single larger site that can accommodate the two molecules at the same time. The existence of an allosteric site that modulates the affinity of the substrate site is also possible. A third possibility is that some compounds alter the membrane environment which in turn has an effect on the affinity for certain substrates (Borst et al. 2006). Of the three multidrug resistance transporters, ABCG2 is the most recently described, nevertheless, it also has a long list of established substrates comparable to that of Pgp and MRP1. ABCG2, as mentioned above, primarily transports large, hydrophobic, positively charged molecules, among which are flavonoids, porphyrins, antibiotics, HMG-CoA reductase inhibitors, antivirals, H-2 receptor blockers, antihypertensive agents, vitamins, and folic acid. A substantial number of chemotherapeutic agents are also effluxed by ABCG2, including mitoxantrone, flavopiridol, campthotecins (e.g., topotecan, irinotecan, SN-38), indolocarbazoles, antifolates, tyrosine kinase inhibitors (e.g., imatinib, gefitinib, erlotonib, nelotinib). Besides the anticancer and other clinically important drugs, it is worth mentioning that several fluorescent compounds are transported by Pgp, MRP1, and ABCG2, such as Hoechst 33342 (Pgp, ABCG2), rhodamine 123 (Pgp), calcein (MRP1), calcein-AM (Pgp). These molecules proved to be excellent tools in studying these transporters in the laboratory.

Gene Regulation P-glycoprotein is constitutively expressed in many normal tissues including the adrenal gland, liver, kidney, intestinal mucosa, muscle, and the endothelial cells of the blood–brain barrier and blood–testes barrier (Cordon-Cardo et al. 1990). The human MDR1 gene is located on chromosome 7, band p21–21.1, and it extends over more than 100 kb containing 28 exons. The transcriptional regulation of MDR1 has been extensively studied. The MDR1 gene, as is true for all of the human multidrug transporters examined to date, has a “TATA-less” promoter. Activation of MDR1 occurs rapidly in response to a variety of cellular stresses and stimuli ranging from DNA injury to serum starvation, linking Pgp to a general anti-apoptotic mechanism. Interestingly, signals from these stimuli were shown to converge on a common region of the MDR1 promoter referred to as the “MDR1 enhanceosome” (Jin and Scotto 1998). This region contains an inverted CCAAT and a GC-rich element, which serve as binding sites for the trimeric transcription factor NF-Y and the Sp family of GC-binding transcription factors. Upon DNA binding, these transcription factors recruit histone-acetyl transferase PCAF to the MDR1 promoter, resulting in regional histone modification followed by further chromatin remodeling and subsequent gene activation (Hu et al. 2000). In addition, the MDR1 promoter has been reported as susceptible to mulitple other regulatory mechanisms: the orphan nuclear receptor (SXR/PXR), steroid receptor, and the xenobiotic receptor may directly bind and activate the promoter (Cerveny et al. 2007, Song et al. 2004, Synold et al. 2001). Similarly, HIF-1α activates the MDR1 promoter through a

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consensus-binding sequence (5’-GCGTG-3’) (Comerford et al. 2002). GC-binding protein Sp1 may be involved in the hypoxic response. Mutant p53 proteins have been shown to transactivate the MDR1 promoter (Sampath et al. 2001). It has been shown that random chromosomal rearrangements could trap MDR1 in close proximity to a constitutively active heterologous promoter, resulting in aberrant activation of the MDR1 gene in drug-resistant cell lines and in a subset of drug refractory acute lymphocytic leukemias (Huff et al. 2006). MDR1 expression can also be regulated at the epigenetic level. Methylation of the MDR1 promoter has been shown to silence basal MDR1 expression in cell line models and clinical samples (El-Osta et al. 2002, Harikrishnan et al. 2005). It has been suggested that the upregulation of MDR1 expression in cancer cells following chemotherapy treatment may in some settings be attributable to a loss of MDR1 promoter methylation (Nakayama et al. 1998, Tada et al. 2000). In related studies, drug-induced upregulation of MDR1 was associated with increase in histone H3 acteylation and induction of methylated H3K4 within discrete regions of the MDR1 locus (Baker et al. 2005). More recently, the posttranscriptional regulation of MDR1 has been reported. In a drug-resistant variant of K562 cells, the presence of an additional 5’ untranslated fragment in the MDR1 mRNA was shown to greatly improve the translational efficiency of the mRNA, leading to overexpression of Pgp (GomezMartinez et al. 2007). Another study demonstrated that two microRNAs miR-27a and miR-451 are involved in the activation of MDR1 expression and the multidrugresistance phenotype (Zhu et al. 2008). MicroRNAs are known to have the ability to reduce protein expression by causing translational inhibition or degradation of the target mRNA. The effects of the microRNAs appear to be indirect and are likely mediated through inhibiting expression of some transcriptional factors involved in suppressing MDR1 activation. The human MRP1 gene maps to chromosome 16p13.11–p13.12 and comprises 31 exons, spanning 200 kb. MRP1 was found to be amplified in various anti-tumor drug-resistant cell lines (Cole et al. 1992, Paul et al. 1996), but its increased transcription can also occur without gene amplification. The basal promoter activity was localized to a GC-rich region of the MRP1 gene between nucleotides –91 to +103. The binding of Sp1 to some putative binding sites in this GC-rich region was shown to be important for optimal MRP1 transcriptional activity (Zhu and Center 1994, 1996). Although the MRP1 promoter does not contain a p53 consensus sequence, it has been shown that wild type p53 may repress MRP1 by reducing the binding of Sp1 (Wang and Beck 1998). A putative AP1 site at the MRP1 promoter that can be bound by a complex containing c-jun and junD is also important for MRP1 transcription (Kurz et al. 2001). Decreased expression of MRP1 has been reported in cells transfected with mutant c-jun (Cripe et al. 2002). ABCG2 is constitutively expressed in many normal tissues, including liver, placenta, brain, hematopoietic stem cells, and putatively other types of stem cells. The human ABCG2 gene is located on chromosome 4q21–4q22, and it extends over 66 kb, containing 16 exons and 15 introns. The molecular mechanisms regulating ABCG2 expression are not well understood. The ABCG2 promoter lacks a consensus TATA box found on many protein-encoding genes and instead contains numerous

8

G. Szak´acs et al.

Sp1, AP1, AP2 sites and a CCAAT box, presumably important for initiation of transcription. A basal ABCG2 promoter has been identified at about 312 bp directly upstream from the transcriptional start site (Bailey-Dell et al. 2001). ABCG2 has a putative CpG island located within its promoter. To date, most studies examining the regulation of ABCG2 under physiological conditions are focused at the transcriptional level. Two functional cis elements in the ABCG2 promoter, namely the estrogen and hypoxia (Krishnamurthy et al. 2004) response elements, and a peroxisome proliferator-activated receptor γ (PPARγ ) response element, upstream of the ABCG2 gene (Szatmari et al. 2006), have been identified and validated by electrophoretic mobility shift analysis. Ebert and colleagues proposed that aryl hydrocarbon receptor (AhR) upregulates ABCG2 expression, but the responsive element has not been identified (Ebert et al. 2005). Conflicting data have been reported for the effect of the sex hormones estrogen, progesterone, and testosterone on ABCG2 expression (Imai et al. 2005, Yasuda et al. 2005). In particular, the effect of estradiol on the ABCG2 expression appears cell dependent and controversial (Imai et al. 2005). ABCG2 expression is upregulated in the mammary gland during lactation (Jonker et al. 2005). Overexpression of the ABCG2 gene is frequently observed in cancer cell lines selected with a number of chemotherapeutic drugs (Doyle et al. 1998, Maliepaard et al. 1999, Miyake et al. 1999, Robey et al. 2001, Volk et al. 2002). Gene amplification and chromosome translocation have been shown to play a role for the increased expression of ABCG2 (Knutsen et al. 2000). The use of alternative 5’ promoters at the ABCG2 gene due to differential expression of splice variants at the 5’-untranslated region (5’UTR) of ABCG2 mRNA in drug-selected cells may offer another novel mechanism of ABCG2 upregulation (Nakanishi et al. 2006), a finding similar to observations in MDR-1 where rearrangement of the 5’ region of MDR-1 resulted in capture of that gene by another promoter (Huff et al. 2005). ABCG2 has been shown to be under control of a number of epigenetic mechanisms. DNA methylation can repress ABCG2 expression in human renal carcinoma (To et al. 2006) and multiple myleoma cell lines (Turner et al. 2006). Recently, we reported that overexpression of ABCG2 in resistant cells was correlated with increased binding of a set of permissive histone modification marks, RNA polymerase II, and a chromatin remodeling factor Brg-1, but decreased association of a repressive histone mark, class I HDACs and Sp1 with the proximal ABCG2 promoter (To et al. 2008). Enhanced mRNA stability and protein translation are also involved in the overexpression of ABCG2 in drug-resistant cancer cells. ABCG2 mRNA is less stable in a number of sensitive parental cancer cell lines than in their drug-selected and ABCG2-overexpressed resistant counterparts (To et al. 2008). Interestingly, the 3’-untranslated region (3’UTR) of the ABCG2 mRNA was found to be longer in the parental cells than in the resistant cells. We identified a microRNA binding site (hsa-miR-519c), in the portion of 3’UTR missing from ABCG2 mRNA in the resistant cells. In resistant S1M180 cells, hsa-miR519c cannot bind to ABCG2 mRNA because of the shorter 3’UTR and thus mRNA

1 ABC Transporters in Drug Resistance

9

degradation and/or repression on protein translation are relieved, contributing to overexpression of ABCG2 (To et al. 2008) .

Significance of MDR-ABC Transporters in Cancer Effective treatment of metastatic cancer requires the use of toxic chemotherapy. In most cases, multiple drugs are used, since resistance to single agents occurs almost universally. In vitro, the three MDR-ABC transporters efflux a broad range of chemotherapeutics used clinically for first- and second-line treatment of cancer. In that setting, inhibitors can sensitize drug-resistant cell lines to known substrates (see Table 1.2), yet despite the wealth of information collected in the laboratories, translation of this knowledge from the bench to the bedside has proved to be unexpectedly difficult. The clinical relevance of Pgp is proven by correlative studies evaluating the effect of Pgp expression on patient survival. It remains to be seen whether clinical trials designed to evaluate transporter inhibition will translate into improved patient survival.

1st generation

2nd generation

3rd generation

Other

Amiodarone Cyclosporin A Quinidine Quinine Verapamil Nifedipine Dexniguldipine PSC833 VX-710 (Biricodar) GF120918 (Elacridar) LY475776 LY335979 (Zosuquidar) XR-9576 (Tariquidar) V-104 R101933 (Laniquidar) Disulfiram CBT-1 Flavonoids FTC Progesteron MK571 Pluronic L61 Campothecin

ABCG2

ABCC1

ABCB1

Table 1.2 Inhibitors of MDR-ABC transporters (Adapted from, Szakacs et al. 2006)

10

G. Szak´acs et al.

Association of MDR-ABC Transporters with Treatment Failure Anticancer drugs fail to kill cancer cells for a variety of reasons. To discern the contribution of individual factors, systemic studies using large cohorts of patients must be performed. To prove the association of a biomarker with poor prognosis, the studies must be evaluated in a strictly standardized fashion. Unfortunately, the scientific community working on MDR-ABC transporters has failed to define and embrace universally accepted guidelines for clinical validation. To date, differences in assay protocols result in confusing data that are often impossible to compare across institutions. As a result, the role of ABC transporters in clinical anticancer resistance has been difficult to assess. Evidence linking Pgp expression with poor clinical outcome is conclusive for breast cancer, sarcoma, and certain types of leukemia, suggesting that Pgp contributes to treatment failure of these malignancies (Szakacs et al. 2006). Importantly, over one-third of leukemic samples are found positive for Pgp expression. Pgp expression in patients with acute myelogenous leukemia (AML) has consistently been associated with reduced chemotherapy response rates and poor survival, and it was found to be an independent prognostic variable for induction failure in adult AML (Pallis and Russell 2004, van der Holt et al. 2005). The prognostic significance of the other two MDR-ABC transporters in clinical MDR is still unknown. MRP1 is not a significant factor in drug resistance in AML (Leith et al. 1999), and its prognostic implication in chronic lymphocytic and promyelocytic leukemia, non-small cell lung cancer (NSCLC), and breast cancer remains controversial (Michieli et al. 2000, Linenberger 2005, Filipits et al. 2005). Even less is known clinically about ABCG2, as most of the studies have been performed on drug-selected cell lines. According to the analysis of blast cells from acute leukemia patients, higher ABCG2 expression is associated with poor response to chemotherapy (Benderra et al. 2004, Steinbach et al. 2002, Suvannasankha et al. 2004, Suvannasankha, Minderman et al. 2004, Uggla et al. 2005). Like adult stem cells, cancer stem cells express high levels of ABC transporters, including Pgp and ABCG2. According to the cancer stem cell model, this population of drug-resistant pluripotent cells defies treatment and serves as an unrestricted reservoir for drugresistant tumor relapse (Dean et al. 2005).

Overcoming MDR with Inhibitors The activity of MDR-ABC transporters can be readily inhibited by a plethora of compounds (see Table 1.2). Due to the promiscuity of the transporters, it has been relatively easy to find high affinity substrates that block transport. In vitro, sensitization of resistant cells is achievable through the prevention of the efflux of cytotoxic drugs (Dano 1973), suggesting that inhibitors would prove effective in a therapeutic setting (Tsuruo et al. 1981). Emerging data from knockout mice studies (loss of mdr1a and mdr1b does not result in an obvious phenotype) were also

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interpreted to suggest that pharmacological modulation of human Pgp is a feasible strategy to treat multidrug-resistant cancer. Over the years, the pharmaceutical industry has invested considerably into clinical trials. The first phase I and II clinical trials tested the clinical potential of “first-generation” Pgp inhibitors, including verapamil, quinine, and cyclosporine A, which were already FDA approved for other medical purposes. In general, these compounds were ineffective or toxic at the doses required to attenuate Pgp function. Accordingly, most of the trials conducted with first-generation inhibitors failed to show improvement of the outcome, and toxic side effects were common (see Table 1.3) (Daenen et al. 2004). Thus, the ultimate goal of the trials was not met, and it became evident that inhibition of ABC transporters had far-reaching pharmacokinetic consequences influencing drug clearance and metabolism. Despite these failures, the few positive outcomes suggested that successful Pgp modulation is feasible (Wattel et al. 1999). The hope of conquering MDR through the inhibition of ABC transporter-mediated drug efflux encouraged further development of second-generation inhibitors. Much interest was focused on cyclosporine A (CsA), which proved to be beneficial in a randomized phase III clinical trial conducted with poor-risk acute myeloid leukemia patients treated with cytarabine and daunorubicin (List et al. 2001). Second-generation inhibitors were synthesized around first-generation pharmacophores. The primary aim was to increase the affinity to Pgp while reducing dose-limiting toxicities. PSC-833 (Valspodar), a second-generation cyclosporine D analog lacking immunosuppressive effects, has been tested most frequently in clinical trials (see Table 1.3). While PSC833 is a high-affinity Pgp inhibitor, devoid of immunosuppressive effects, its use was associated with unwanted pharmacokinetic interactions. As a result of the inhibition of “physiological Pgp” in pharmacological barriers, PSC-833 decreased the systemic clearance of anticancer drugs, thus increasing exposure to normal and malignant cells. Pgp inhibitors, with a drug-binding site similar to that of cytochrome 3A (CYP3A), also interfered with CYP3A-mediated intestinal or liver metabolism, further reducing drug clearance. Taken together, these factors increased the severity and/or incidence of adverse effects associated with the anticancer therapy. To accommodate expected elevations in systemic drug exposure, the chemotherapy regimens were modified empirically. As a result, some patients were undertreated, while others were overdosed. Related to these problems, a phase III trial using PSC833 in previously untreated AML patients over age 60 was closed early due to excessive mortality during induction in the experimental arm (Baer et al. 2002) (see Table 1.3). A subsequent dose-escalation trial of 410 AML patients under age 60 revealed an overall survival advantage in an unplanned subset of patients younger than 45 years old when given PSC833 with variable doses of daunorubicin, etoposide, and fixed doses of cytarabine (Kolitz et al. 2004). That apparent benefit has not been duplicated, nor is it likely to be, as development of PSC-833 has been discontinued. Similarly, active development of another second-generation inhibitor showing initial promise (VX-710, Biricodar) has been curtailed (Goldman 2003). Third-generation inhibitors are products of combinatorial chemistry programs designed specifically for the development of compounds showing high transporter

3rd generation

2nd generation

1st generation

Poor-risk acute leukemia De novo AML

NSCLC SCLC Myeloma Breast SCLC Relapsed and refractory AML Poor-risk AML, RAEB-t Myeloma AML AML Untreated AML

Untreated AML

Ovarian Refractory AML, high-risk MDS NSCLC AML AML, MDS

Quinine

Verapamil Verapamil Verapamil Verapamil Megestrol acetate Cyclosporine

PSC-833

PSC-833 PSC-833

PSC-833 PSC-833 LY335979

Cyclosporine PSC-833 PSC-833 PSC-833

Cyclosporine

Quinine

Breast High-risk MDS

Cancer type

Quinidine Quinine

Modulator

Carboplatin, paclitaxel IL-2 Daunarubicin, Ara-C

VAD Daunorubicin, Ara-C, etoposide Mitoxantrone, etoposide, Ara-C Daunorubicin, etoposide, cytarabine Daunorubicin, etoposide, cytarabine Carboplatin, paclitaxel Mitoxantrone, etoposide, cytarabine

Daunorubicin, cytarabine

Vindesine, Ifosfamide CAVE VAD Vindesine, 5FU CAV/EP ADE (cytarabine, daunorubicin, etoposide)

Idarubicine, cytarabine, mitoxantrone

Mitoxantrone, Ara-C

Epirubicin Mitoxantrone, cytarabine

Anticancer drugs

Sonneveld et al. (2001) van der Holt et al. (2005) Sonneveld et al. (2001) Baer et al. (2002) Kolitz et al. (2004) Joly (2002) Greenberg et al. (2004)

Novartis Cancer.gov Cancer.gov

No O.S. advantage age > 45, survival benefit < age 45 No benefit No benefit Term. early 2◦ toxicity Results pending Results pending

List et al. (2001)

Millward et al. (1993) Milroy (1993) Dalton et al. (1995) Belpomme et al. (2000) Wood et al. (1998) Liu Yin et al. (2001)

Solary et al. (2003)

Wishart et al. (1994) Wattel et al. (1998) and Wattel et al. (1999) Solary et al. (1996)

References

No benefit No benefit No benefit Term. early 2◦ toxicity

Improved O.S. in CsA group

Significant improvement of the CR rate in Pgp-positive patients. No O.S. advantage Improved O.S. No benefit No benefit Improved O.S & R.R. No benefit No benefit

No benefit

No benefit Improved O.S. in Pgp + patients

Outcome

Table 1.3 Characteristics and results of complete and ongoing phase III clinical trials with ABC transporter inhibitors (Adapted from, Szakacs et al. 2006)

12 G. Szak´acs et al.

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affinity and low PK interaction. Clinical trials conducted with Pgp inhibitors indicated that the “ultimate inhibitor” was to be efficient, devoid of unrelated pharmacological effects such as pharmacokinetic interactions with the concomitantly administered drugs or CYP interaction. Indeed, inhibition of CYP3A, responsible for many adverse PK effects with previous generation inhibitors, has generally been avoided with the third-generation of inhibitors, including laniquidar (R101933), OC144-093 (ONT-093), zosuquidar (LY335979), elacridar (GF-120918), CBT-1, and tariquidar (XR9576) (Rumpold et al. 2005). Tariquidar (XR9576) has the added benefit of extended Pgp inhibition, as a single intravenous dose inhibited efflux of rhodamine from CD56+ cells (biomarker lymphoid cells that express Pgp) for at least 48 hours (Stewart et al. 2000). In 2002, phase III clinical trials began using tariquidar as an adjunctive treatment in combination with first-line chemotherapy for patients with non-small-cell lung cancer (NSCLC). Despite the promising characteristics mentioned above, the studies were stopped early because of toxicities associated with the cytotoxic drugs. This study also illustrates a defect in experimental design, since there is no strong evidence to suggest that NSCLC expresses Pgp to a significant extent. Also, the combination chemotherapy was administered at a dose higher than the maximum tolerated dose in combination trials (Fox and Bates 2007). Following the review of the aborted trials, the National Cancer Institute has commenced further exploratory phase I/II and phase III studies with tariquidar. Zosuquidar (LY335979) has recently been evaluated in patients with AML. Preliminary analysis indicates that zosuquidar may be safely given without chemotherapy dose reductions; trial endpoints have not yet been analyzed (Szakacs et al. 2006).

Emerging Role of MDR-ABC Transporters in Resistance Against Targeted Agents The era of novel drug targets was supposed to leave chemotherapy behind, and with it, multidrug resistance. It was thought that the targeted agents, and especially those with vascular targets, would be so specific that resistance would not develop. While the introduction of targeted agents has had a marked impact on outcomes in patients, the disappointing fact is that most of these therapies improve progression-free survival but induce no cures. As a highly effective therapy for chronic myelogenous leukemia (CML), imatinib confers a complete hematologic response in 95% of patients and a complete cytogenetic response in 74% of patients – but, by 5 years, 20% of patients will have experienced disease progression (Moen et al. 2007). Several mechanisms mediating imatinib resistance have been identified (such as the amplification of the bcr-abl gene, the overexpression of the Bcr-Abl protein, and compensatory activation of the Src kinases); the most frequent mechanism seems to be the appearance of point mutations in the kinase domain of Abl. However, mutations are found in only 45% of patients with refractory CML, and only 26% in early chronic phase (Apperley 2007). Furthermore, when these mutations have been studied in vitro for their ability to confer resistance to imatinib, many were found

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to confer only limited resistance (Apperley 2007). For example, while the most commonly reported mutation, T315I, confers a 23-fold resistance (as compared to the wild type), the third most commonly reported mutation confers only 2-fold resistance in biological assays. Furthermore, increasing the imatinib dose results in major cytogenetic response and complete hematologic response in 27% and 55% of patients, respectively (Kantarjian et al. 2007). These observations, together with data showing that though levels of imatinib are correlated with depth of response, are consistent with the notion that exposure to imatinib is important (Picard et al. 2007). These results, together with laboratory studies showing unequivocally that imatinib is a substrate for transport by both Pgp and ABCG2 (Rumpold et al. 2005, Brendel et al. 2007), suggest a hypothesis wherein the drug transporters, Pgp and ABCG2, could be important in drug resistance in CML. Oral absorption occurs because imatinib inhibits the transporters at the high local concentrations present in the GI tract. At the lower concentrations surrounding the tumor, however, a drug transporter could reduce intracellular concentrations just enough to allow a weak mutation to confer drug resistance. Unfortunately, systematic studies evaluating MDR1 expression in CML have not been performed.

Conclusion The exclusive focus on mutations in imatinib resistance reflects a recent bias that drug transporters are not important in clinical oncology. Rather, the story should show that drug resistance is complex, and that a single mechanism is unlikely to be exclusive, even in targeted therapy. A large number of studies have identified Pgp as a negative prognostic factor, particularly in acute myelogenous leukemia; such studies have not been done in CML. For any cancer, studies with ABCG2 have only just been initiated. The question is not whether ABC transporters can confer drug resistance, but rather in what disease and whether that drug resistance can be blocked in the clinic. The strategy of Pgp inhibition failed primarily because of the inadequate trial designs and the lack of proper target validation. An analysis of the literature clearly demonstrated serious flaws in the phase III clinical trial designs (Szakacs et al. 2006). Numerous studies were carried out with inferior agents, and few studies were actually been performed with the more potent, nontoxic agents that we now have available. Results from improved phase III trials using third-generation inhibitors will be pivotal in determining whether inhibition of Pgp, or other ABC transporters, can result in improved patient survival. The improved clinical trials should be based on a standardized system used to determine whether a tumor expresses the ABC transporter of interest. Using a proper, quantitative laboratory diagnostic method, an adequate transporter expression and/or function should be measured as a criterion for trial enrollment (the beneficial effect of transporter inhibition will likely be confined to patients “positive” for the transporter target). An improvement of therapy outcome is expected only if the chemotherapeutic regimens involve transported substrates. Additionally, chemotherapy–inhibitor

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combinations should be used at concentrations previously proven safe and effective in phase I/II trials, taking into account potential PK interactions with either the parental drug compound or its metabolites. Drug pharmacokinetics and early signs of hepatic, neurological, or bone marrow toxicity should be monitored closely. To ensure abrogation of the MDR phenotype, surrogate assays should be performed to assess the effect of the inhibitor in each patient. This may either be done ex vivo, by using flow cytometry to measure Pgp function in CD56+ cells taken from patients treated with inhibitors, or in vivo using 99mTc-sestamibi (Agrawal et al. 2003) or other imaging modalities to directly image accumulation of Pgp substrates within tumors. With an ever-expanding list of drugs in the anticancer armamentarium, there is more reason than ever to develop methods of detecting Pgp-mediated or transportermediated drug resistance. Sensitive imaging modalities are needed to teach us the scope of the problem. In 2008, roughly 40% of the patients diagnosed with cancer in the United States are destined to die of their disease. Virtually, all of these deaths represent a failure of systemic therapy. We no longer think of resistance as solely due to Pgp or any other drug transporter as we may have in the 1980s; nor should its presence be ignored. We know that every other physiologic cell survival mechanism can be conscripted to cause drug resistance in cancer – such as DNA repair or antiapoptotic mechanisms – and there is no reason to think that drug transporters would not as well be exploited by cancer cells. Acknowledgments Support from OTKA (PF60435), NIH (R01TW007250), and EU-FP6 (046560, 041547) is gratefully acknowledged. Gergely Szak´acs is the recipient of a J´anos Bolyai Scholarship and a Special Fellow Award from the Leukemia and Lymphoma Society.

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Chapter 2

Metastasis and Drug Resistance Dominic Fan, Sun-Jin Kim, Robert L. Langley, and Isaiah J. Fidler

Abstract Multidrug resistance (MDR) phenotype emerging from chemotherapy is a major problem in managing patients with metastatic cancers. The discovery that a cardiovascular drug, verapamil, can bind to P-glycoprotein and reverse MDR initiated serious research efforts in MDR-reversal by various compounds and modes of pharmacological modifiers. Those include major calcium channel blockers such as bepridil, diltiazem, felodipine, isradipine, nicardipine, nifedipine and nimodipine, verapamil and analogs; calmodulin antagonists; antibiotics and analogs; indole alkaloids; cyclosporins and analogs; hormones and antihormones; pharmaceutical emulsifying surfactants; liposomal encapsulation; etc. The majority of the studies targeted one of the MDR mechanisms, P-glycoprotein. These studies have been successful under in vitro and limited in vivo animal conditions; the correlations for clinical trails are still lacking. Therefore, an effective MDR-reversing chemotherapy is not available. It is the purpose of this chapter to review the past and current experimental reversal of MDR and, in particular, the importance in targeting drug resistance in relevant cancer metastasis models. Keywords Metastasis · MDR · Apoptosis · Animal models

Introduction Despite improvements in diagnosis, surgical techniques, patient care, and adjuvant therapies, most deaths from cancer are due to metastases that are resistant to conventional therapies (Fidler 1990). The major obstacle to effective treatment is tumor cell biologic heterogeneity. Moreover, the metastases can be located in different organs, and the specific organ environment can influence the biologic behavior of metastatic D. Fan (B) Department of Cancer Biology, Cancer Metastasis Research Center, Unit 854, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA e-mail: [email protected]

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cells, including their response to systemic therapy. Only a better understanding of the molecular mechanisms that regulate the process of metastasis and the interactions between the metastatic cells with the organ microenvironment can provide a foundation for the design of more effective therapy.

The Pathogenesis of Metastasis The process of metastasis is highly selective and consists of a series of sequential, interrelated steps. To produce clinically relevant lesions, metastatic cells must complete all steps of this process. After the initial transformation and growth of cells, vascularization must occur if a tumor mass is to exceed 1 mm in diameter. The synthesis and secretion of several proangiogenic factors by tumor and host cells and the absence of antiangiogenic factors play a key role in establishing a capillary network from the surrounding host tissues. Next, local invasion of the host stroma occurs as a consequence of the enhanced expression of a series of enzymes (e.g., collagenase). Once tumor cells penetrate lymphatic or vascular channels, they may grow at the invasion site or detach and be transported within the circulatory system. The tumor emboli must survive immune and nonimmune defenses and the turbulence of the circulation, then arrest in the capillary bed of receptive organs, extravasate into the organ parenchyma, proliferate, and establish a micrometastasis. Growth of these microscopic lesions requires development of a vascular supply and evasion of host defense cells. When the metastases grow, they can shed tumor cells into the circulation to produce metastasis of metastases (Fidler 1990). The outcome of the metastatic process depends on multiple and complex interactions of metastatic cells with host homeostatic mechanisms (Fidler 1997). More than a century ago, Stephen Paget researched the mechanisms that regulate organ-specific metastasis, i.e., pattern of metastasis by different cancers, and questioned whether the organ distribution of metastases produced by different human neoplasms was due to chance and analyzed more than 700 autopsy records of women with breast cancer. His research documented a nonrandom pattern of visceral (and bone) metastasis. This finding suggested to Paget that the process was not due to chance but, rather, that certain tumor cells (the “seed”) had a specific affinity for the milieu of certain organs (the “soil”). Metastases resulted only when the seed and soil were compatible (Paget 1889). A current definition of the “seed and soil” hypothesis consists of three principles. First, neoplasms are biologically heterogeneous and contain subpopulations of cells with different angiogenic, invasive, and metastatic properties (Fidler 2003; Langley and Fidler 2007). Second, the process of metastasis is selective for cells that succeed in invasion, embolization, survival in the circulation, arrest in a distant capillary bed, and extravasation into and multiplication within the organ parenchyma. Although some of the steps in this process contain stochastic elements, as a whole, metastasis favors the survival and growth of a few subpopulations of cells that preexist within the parent neoplasm (Fidler and Kripke 1977; Talmadge et al. 1982).

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Thus, metastases can have a clonal origin, and different metastases can originate from the proliferation of different single cells (Fidler and Talmadge 1986; Hu et al. 1987; Talmadge et al. 1982). Third, and perhaps the most important principle for the design of new cancer therapies, is that the outcome of metastasis depends on multiple interactions (“cross-talk”) of metastatic cells with homeostatic mechanisms, which the tumor cells can usurp (Fidler 1995). Therapy of metastasis, therefore, can be targeted not only against tumor cells but also against the homeostatic factors that promote tumor cell growth, survival, angiogenesis, invasion, and metastasis.

Multidrug Resistance One of the most depressing and predictable facts of cancer management is the development of the multidrug resistance (MDR) phenotype in patients treated chronically with certain natural chemotherapeutic drugs. This clinical phenomenon accounts for the unsatisfactory low incidence of response rate for the majority of solid tumors to chemotherapy – one of the few conventional treatments for metastatic diseases in past few decades. Since the heterogeneous nature of tumor and cancer metastasis was conceptualized (Paget 1889; Fidler 1973; Fidler and Kripke 1977; Fidler 1978; Poste and Fidler 1979; Fidler and Poste 1985; Fidler 2001), it is clear that MDR is a resultant clinical outcome manifested by successful cancer cells endowed with multiple mechanisms for survival. Like other vital traits of metastatic cancer cells, MDR should be conceived as a phenotype marked by a collection of independent or collateral modifications, overexpressions, and/or amplifications of endogenous molecules that interplay with distinct normal cellular pathways (Fig. 2.1) that include

MDR P-glycoprotein PKC Expression ABC Transporters Tubulin Mutation Episomes Amplification Topoisomerase II Mutation Altered Cellular Calcium Levels Enhanced Sodium Pump Activity Altered Reduction-Oxidation Pathways Formation of Double Minute Chromosomes Overexpression of Cytoplasmic 22 kDa Sorcin Fig. 2.1 MDR-associated mechanisms

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P-glycoprotein (Juliano and Ling 1976; Kartner et al. 1983), protein kinase-C (PKC) overexpression (Fan et al. 1992a, b; Aftab et al. 1994), ABC transporters (Adachi et al. 2007), tubulin mutation (Inaba et al. 1987), episomes amplification (Ruiz et al. 1989), formation of double minute chromosomes (Von Hoff et al. 1990), altered cellular calcium levels (Nair et al. 1986), topoisomerase II mutation and altered reduction–oxidation (Deffie et al. 1988), and the overexpression of cytoplasmic 22kDa sorcin (Hamada et al. 1988). Those cancer cells employing single and especially unique oncogenic mechanism, presumably exist, would likely be eliminated by the host defense mechanisms during the progression of cancer or by conventional cancer therapy and would be without further clinical manifestation. Therefore, initial treatment with chemotherapeutic drugs and subsequent reversal of MDR should be combined as a standard protocol for effective chemotherapy at the onset of cancer management, rather than resolving to salvage therapies – when the patients return with compromised performance status and growth of refractory tumors. Unfortunately, an effective MDR-reversing chemotherapy is not available. Since the inception of an organized drug-screening program (DeVita et al. 1979), research efforts have been largely compound-oriented (sensitive drug-screens) (Frei 1982; Venditti 1981) rather than disease-oriented (tumor panels) (Alley et al. 1988), metastasis-oriented (orthotopic animal models) (Wilmanns et al. 1992; Singh et al. 1994; Killion et al. 1999; Langley and Fidler 2007) or MDR-oriented (relevant resistant drug-screens) (Mickisch et al. 1991a, b, Dong et al. 1994). In the following sections, we review the past and current experimental reversal of MDR and, in particular, the importance in targeting drug resistance in cancer metastasis.

Reversal of Experimental MDR The majority of the MDR-reversing studies were in vitro assays that cannot address the complexity of physiology and pathology in cancer patients, and in particular metastasis of the cancer. It is physiology and pathology that modulate the progression of cancer and metastasis and the pharmacokinetics (what the host and cells do to the drugs) and pharmacodynamics (what the drugs do to the host and cells) (Ford and Hait 1990). Decisively, the validity of an in vitro assay is governed by its ability to derive an acceptable level of sensitivity (the prediction of true positives) and specificity (the prediction of true negatives) (Fan et al. 1985).

Calcium Channel Blockers Verapamil and Other Clinically Approved Agents As the elements of time and costs stacking up against the development of new anticancer drugs, the initial observation of Tsuruo et al. (1981) of a reversal by verapamil (a coronary vasodilator) on an MDR phenotype in P388 leukemia cells (Tsuruo et al. 1981, 1982) inscribed the beginning of an explosive search for

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MDR-reversing anticancer therapeutics. Several major calcium channel blockers approved for clinical use in the United States were candidates for such a search: bepridil (a pyrrolidylamine), diltiazem (a benzothiazepine), felodipine, isradipine, nicardipine, nifedipine and nimodipine (dihydropyridines), and verapamil (a benzeneacetonitrile). Although one of the major biological effects of verapamil is the blockage of the slow-channel-mediated calcium entry into cardiac cells (Kohlhardt et al. 1972) and drug-resistant cancer cells (Bucana et al. 1990), its MDR-reversing mechanism is not clearly understood and may be quite apart from its physiologic action, in which a trial depolarization plays an important role and is related to the fast-channel effect for sodium influx (Rougier et al. 1969). Verapamil-mediated enhancement of intracellular accumulation of MDR-linked anticancer drugs is universally observed and attributed to an effect on P-glycoprotein-mediated efflux in a variety of cancer cell lines (Tsuruo et al. 1982; Inaba et al. 1979; Harker et al. 1986). Extensive efforts were made to identifying and translating the unique MDRreversing properties of verapamil and other calcium channel blockers into clinical terms (Slater et al. 1982; Tsuruo et al. 1983a, b; Fojo et al. 1985; Fine et al. 1987; Ford et al. 1990; Fan et al. 1994a, b). Unfortunately, the adverse hematodynamic effects of verapamil have limited its clinical potential for routine use in adjunct chemotherapy (Ozols et al. 1987). In addition to verapamil, many clinically approved calcium channel blocker were shown to affect intracellular accumulation of MDR-linked anticancer drugs and to reverse MDR phenotype in vitro (Tsuruo et al. 1983a, b; Schuurhuis et al. 1987; Hollt et al. 1992; Fan et al. 1994a, b). However, with the exception of the bepridil studies, most preclinical studies employed concentrations of calcium channel blockers much higher (to achieve experimental MDR-reversing activity) than the peak plasma levels derived from patients whose performance status was less compromised than those of cancer patients entering clinical trials with advanced disease. Therefore, it was not surprising that difficulties were encountered in clinical trials using verapamil (Ozols et al. 1987) and with other calcium channel blockers for reversing drug resistance to standard chemotherapeutics in advanced cancer patients. At a micromolar dose range of verapamil, the cytotoxic effects to normal cells are remarkably severe (Lampidis et al. 1986). Furthermore, the high-dose requirement for reversal of MDR in vitro suggested additional effects (mechanisms of action) other than a simple physiologic blockage of the calcium channels (Huet and Robert 1988). Several groups have shown that verapamil can bind to P-glycoprotein and compete for binding sites for MDR-related agents to P-glycoprotein (Cornwell et al. 1987; Safa et al. 1987; Akiyama et al. 1988; Beck et al. 1988). It was shown that in the process of reversing an MDR phenotype, verapamil also stimulated marked ultrastructural changes (an MDR-associated twofold increase in the number of intramembrane particles) of drug-resistant P388 cells (Garcia-Segura et al. 1992). Moreover, under certain experimental conditions, treatments of the drug-resistant human colon LS 180 with verapamil, nifedipine, nicardipine, or diltiazem could increase mdr-1 mRNA expression and induce cell differentiation (Herzog et al. 1993). If one examines the bulk of in vitro literature and the clinical pharmacokinetic information, one finds in general a lack of consideration for controlled

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pharmacokinetic parameters (e.g., plasma elimination half-life of chemosensitizers and of standard anticancer drugs) to simulate relevant pharmacodynamic effects in vitro. As nonphysiologic as in vitro assays are, chemical–cell interactions do follow the law of concentration and time; this kind of pharmacologic consideration may help in reducing the frequency and costs in deriving false-positive experimental new drugs and MDR-reversing agents. Therefore, while it may be feasible to seek enhancement for the efficacy of standard anticancer drugs by verapamil and other calcium channel blockers, the selectivity, scheduling, and dose intensity of the chemosensitizers must be taken into consideration inasmuch as these parameters may influence MDR, tumor spread, and clinical outcome of therapy.

Verapamil Derivatives and Other Experimental Calcium Channel Blockers The disappointment of the initial clinical trials with verapamil (Ozols et al. 1987) stimulated an intense effort to develop chemosensitizers that were less cytotoxic to normal cells: one of the critical parameters in systemic cancer therapeutics (Lampidis et al. 1986; Fan et al. 1988). A number of structural analogs to verapamil (e.g., devapamil, emopamil, gallopamil, D528, D595, D792) have been implicated in the reversal of MDR in vitro (Pirker et al. 1990) with marginal toxicity in animal models (Nawrath and Raschack 1987; Pirker et al. 1989). The less calcium antagonistic and less cardiotoxic R-enantiomer of verapamil (versus that of clinically approved racemic verapamil) could reverse an MDR phenotype in vitro (Mickisch et al. 1990a, b). R-enantiomer of verapamil decreased the expression of P-glycoprotein, resistance to tamoxifen, and experimental pulmonary metastases of the R3230AC rat mammary adenocarcinoma in vivo (Kellen et al. 1991). Therefore, the potential for clinical enhancement of standard chemotherapeutic drugs mediated by verapamil and its derivatives remains on the horizon (Chatterjee et al. 1990; Mickisch et al. 1991a; Hollt et al. 1992; Kroemer et al. 1992; Teodori et al. 2005; Shen et al. 2008).

Calmodulin Antagonists Calmodulin is an intracellular calcium-binding protein that plays critical roles in a wide range of cellular activities (Ramakrishnan et al. 1989). Although the lack of down-regulation of calmodulin was found to produce higher levels of this protein in transformed cells (Jaffr´ezou and Laurent 1993), such difference was not found between the drug-sensitive and MDR P388 leukemia cells (Nair et al. 1986). However, its calcium-sequestrating regulatory roles prompted investigation on the MDRreversing effects of the potent calmodulin antagonist trifluoperazine (Tsuruo et al. 1982, 1983b; Klohs et al. 1986), and many antipsychotic phenothiazines marketed in the United States for clinical use and investigational compounds were found to reverse experimental MDR (Ganapathi et al. 1984; Ford et al. 1989; Ford et al. 1990; Fan et al. 1994b; Zhu et al. 2005).

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Antibiotics and Analogs New drug development is time consuming and costly, hindering the availability of effective anticancer drug for the treatment of metastatic cancer. Similar to the circumvention of clinical side effects of anticancer drugs (Tsuruo et al. 1983a, b), one approach to overcome drug resistance of cancer cells would be the development of derivatives amongst clinically proven chemotherapeutic compounds. Several antibiotics such as the third-generation broad-spectrum cephalosporins (cefoperazone and ceftriaxone) (Gosland et al. 1989), protein synthesis inhibitor antibacterial erythromycin (Hofsli and Nissen-Meyer 1989), veterinary antimicrobial monensin (Ling et al. 1995), a variety of vinca alkaloid derivatives (Ruiz et al. 1989; Nasioulas et al. 1990) were found to reverse experimental MDR phenotypes. Of particular interest is the MDR-reversing effect of an anticancer benzylisoquinoline plant alkaloid thaliblastine that binds to P-glycoprotein and reverses doxorubicin resistance of the P388 MDR cells (Chen et al. 1993). Its low toxicity (Todorov 1988) and structural similarity to other compounds that have a photoaffinity for P-glycoprotein (Beck and Qian 1992) make it a potential chemosensitizer with a promising prospect (Pajeva et al. 2004).

Indole Alkaloids Mdr-like genes exist across the entire phylogenetic spectrum. The reversal of MDR phenotypes in mammalian cells by calcium channel antagonists has functional analogies with the effects of agents circumventing chloroquine resistance in parasite protozoa (Bitonti et al. 1988), in which the drug-resistant phenotype was conferred by a protein coded by a gene closely related to mammalian mdr1 (Wilson et al. 1989). The indole-containing antimalarial quinine and structurally related compounds such as its anti-arrhythmic stereoisomer sodium channel blocker quinidine have been found to produce an MDR-reversing activity (Tsuruo et al. 1984; Lehnert et al. 1991; Sato et al. 1991). Many of those compounds are neurohumoral antagonists that include reserpine (antihypertensive and antipsychotic) and yohimbine (α-adrenergic blocker chemically similar to reserpine) (Fan et al. 1994a). Although it is clear that the functionality of the human mdr1 gene product is distinct from the malarial counterpart (Ginsburg and Krugliak 1992), the reversal of drug resistance in both systems by similar compounds implies the possibility of similar responses for action (Vezmar and Georges 2000).

Cyclosporins and Analogs Cyclosporin A, the complex hydrophobic fungal cyclic undecapeptide, is commonly used as an immunosuppressant for organ transplantation. At concentrations achievable clinically, it is considered one of the most effective MDR-reversing agents.

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The original reports of Slater et al. (1986a, b) initiated extensive studies on MDRreversal, mediated by cyclosporins and related compounds (Twentyman et al. 1987; Chao et al. 1990; Dorr and Liddil 1991; Spoelstra et al. 1991; Loor et al. 1992; Arceci et al. 1992). Although cyclosporins have high affinity for P-glycoprotein (Goldberg et al. 1988; Foxwell et al. 1989), the reversal effects of cyclosporins did not correlate consistently with either drug accumulation (Slater et al. 1986a, b; Chambers et al. 1989; Hait et al. 1989) or direct interaction with P-glycoprotein (Hait et al. 1989). Nevertheless, cyclosporins and related compounds continue to produce reversal of experimental MDR phenotypes (Shen et al. 2008). Its derivative SDZ PSC-833 (Boesch et al. 1991; Ludwig et al. 2006; Shen et al. 2008) and the semi-synthetic cyclic peptolide derivative SDZ 280–446 (Loor et al. 1992; Lehne et al. 2000) showed MDR-reversing effects superior even to those of cyclosporin A, which was about one order of magnitude more active than other known chemosensitizers such as verapamil.

Hormones and Antihormones The induction, in pregnant murine uterus, of high levels of mdr1 mRNA, mediated by estrogen and progesterone (Arceci et al. 1988), and the cross-resistance of MDR breast carcinoma cells to antiestrogens with concomitant loss of estrogen receptors (Vickers et al. 1988) and progesterone receptors (Kacinski et al. 1989) initiated extensive studies on the role of steroid hormones in MDR-reversal (Berman et al. 1991; Hu et al. 1991; Fleming et al. 1992; Stuart et al. 1992; Mutoh et al. 2006). Although the effects of antiestrogens tamoxifen, toremifene, and 4-hydroxy tamoxifen may be influenced by serum protein binding (Wurz et al. 1993; Chatterjee and Harris 1990), the reversal effects by the most active hormone progesterone have been shown to interact directly with P-glycoprotein (Yang et al. 1989; Naito et al. 1989; Safa et al. 1990). Subsequently, it was demonstrated that progesterone distinguishes two mdr gene products (Yang et al. 1990) and specifically regulates the activity of the mdr1b promoter via the A form of the progesterone receptor (Piekarz et al. 1993). However, results from clinical trials with vinblastine and high-dose megestrol acetate were unremarkable (Matin et al. 2002).

Pharmaceutical Emulsifying Surfactants Woodcock et al. (1990) found that Cremophor EL, a relatively inert formula of polyethoxylated castor oil commonly used as pharmaceutical emulsifier (e.g., for preparations of water-insoluble compounds such as cyclosporins and taxol), could reverse experimental MDR at attainable clinical concentrations. This reversal effect has been since confirmed by using various MDR cells and pharmaceutical surfactants such as Solutol HS15 (Coon et al. 1991), Triton X-100, and Thesit (Friche et al. 1990; Spoelstra et al. 1991; Woodcock et al. 1992). There was also evidence

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that nontoxic amounts of Cremophor EL and Tween 80 could effectively compete for P-glycoprotein binding with photoaffinity azidopine (Friche et al. 1990). The reformulation of conventional anticancer agents to include sufficient but nontoxic concentrations of these surfactants may enhance their clinical efficacy and overcome MDR.

Liposomal Encapsulation A major limitation to the use of anticancer drugs is their nonspecific clinical toxicities that impair the therapeutic efficacy of these agents. Liposomes are biodegradable, nonimmunogenic, and relatively nontoxic, and they can be safely used to modify pharmacokinetic properties such as distribution, circulatory transit time, and drug metabolism, to target drugs and biologicals to most of the major organs in animals and humans (Lopez-Berestein et al. 1984; Fogler et al. 1985), to avert systemic clinical toxicities (Forssen and Tokes 1981), and to improve therapeutic efficacy of antimicrobials (Lopez-Berestein et al. 1985), anticancer drugs (Huang et al. 1992; Ahmad et al. 1993), immunomodulators (Fidler 1988), and growth factors (Schackert et al. 1989; Fan et al. 1989). Other studies have shown that liposomes composed of various phospholipids can enhance the cytotoxicities of MDR-linked drugs such as doxorubicin, vinblastine, vincristine, and annamycin (Fan et al. 1990; Seid et al. 1991; Rahman et al. 1992; Thierry et al. 1993). Although the mode of action for MDR-reversal by liposomes is not clearly understood, the experimental reversal of drug resistance by liposomes containing specific phospholipids has been attributed to perturbation of the plasma membranes (Fan et al. 1990), to increasing drug incorporation and intracellular redistribution (Thierry et al. 1993), or to direct interaction with P-glycoprotein (Thierry et al. 1993). The practical utility of liposome encapsulation in cancer treatment is obvious, but its mechanism remains to be defined (Zalipsky et al. 2007).

Other Molecules The studies using calcium channel blockers and calmodulin antagonists to overcome MDR phenotypes continued. Dexniguldipine (B-859-35), the (–) isomer of antihypertensive niguldipine, was found better than verapamil in reversing MDR (Hofmann et al. 1991; Reymann et al. 1993; Dietel et al. 1996; He and Liu 2002). Various less toxic derivatives of verapamil (e.g., Ro11-2933) (Abderrabi et al. 1996), dihydropyridine (e.g., S16324, S16317) (Saponara et al. 2007), benzothiazepines (MDL 201,307) (Newman et al. 1996), and isoquinolinesulfonamides (e.g., W-77, CKA-1083) (Maeda et al. 1993), have been found potential agents for overcoming MDR. In the past years, many innovative molecules have also been investigated for their ability to circumvent MDR. Those findings included the studies of employing MRK16 anti-P-glycoprotein antibody to reverse bone marrow drug resistance

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of MDR transgenic mice (Mickisch et al. 1992a, b). Another anti-P-glycoprotein antibody, UIC2, was also effective in reversing experimental MDR (Mechetner and Roninson 1992). Other important compounds capable of reversing MDR include those of the adenyl cyclase inhibitor forskolin (Wadler and Wiernik 1988; Morris et al. 1991; Yin et al. 2000), potassium-sparing diuretic amilorides (Epand et al. 1991; Miraglia et al. 2005), α- or β-adrenoceptor antagonists amiodarone (Lehnert et al. 1996) and SKB 105854 (Fan et al. 1994b), antidepressant trazodone (Fan et al. 1994a), antipsychotic benzquinamide (Mazzanti et al. 1992), triazine S 9788 (Dhainaut et al. 1992; Moins et al. 2000), and the hydrophobic platelet anticoagulant dipyridamole (Verstuyft et al. 2003) and its derivative BIBW22BS (Schr¨oder et al. 1996). The antihistaminic terfenadine (Seldane) restored the sensitivity of MDR cells to doxorubicin (Hait et al. 1993). FB642 is a systemic benzimidazole fungicide with antitumor activity against a broad spectrum of tumors and drug-resistant and MDR cell lines (Hammond et al. 2001). Furthermore, it was found that introduction of an MDR1-targeted small interfering RNA duplex into drug-resistant cancer cells markedly inhibited the expression of MDR1 mRNA and P-gp and restored sensitivity to multidrug-resistant cancer cells (Wu et al. 2003).

Clinical Reversal of MDR Extensive clinical trials have been conducted in the past decade. As the first of such reversing agents entering clinical trials, calcium channel blocker verapamil was met with mixed outcomes that were discouraging in some studies (no response) (Rougier et al. 1969; Benson et al. 1985; Saltz et al. 1994) but were exceptionally promising in others (>50–70% response rate) (Cairo et al. 1989; Holmes et al. 1989; Figueredo et al. 1990; Miller et al. 1991; Salmon et al. 1991). Lymphoma was consistently more responsive to the chemosensitizing effects of verapamil (Holmes et al. 1989; Miller et al. 1991; Chabner et al. 1994). Although the number of patients was small, the combination trial of chloroquine with conventional chemotherapy and radiotherapy was clinically effective in improving midterm survival for glioblastoma multiforme (Sotelo et al. 2006). Unfortunately, the number of clinical studies of phenothiazines such as the calmodulin-inhibitor trifluoperazine (Miller et al. 1988; Murren et al. 1996) and antiemetic prochlorperazine (Sridhar et al. 1993; Raschko et al. 2000) was small, and the outcome was marginal. Likewise, trials of doxorubicin derivative 4 -iodo-4 -deoxydoxorubicin (Sessa et al. 1992), antiestrogen tamoxifen (Stuart et al. 1992; Trump et al. 1992; Millward et al. 1992), and calcium channel blocker nifedipine (Philip et al. 1992) were not highly remarkable. However, isolated trials of the benzothiazepine calcium channel blocker diltiazem and antimalarial quinine showed enhancement of leukemia responses to cytarabine, mitoxantrone, and vincristine (Bessho et al. 1985; Solary et al. 1992). Although cyclosporin A may not be effective in enhancing the efficacy of epidoxorubicin in colon cancer patients (Verweij et al. 1992), a Southwest Oncology Group study showed responses in poor-risk acute myeloid leukemia patients

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receiving sequential treatments with cytarabine and daunomycin with concurrent infusion of cyclosporin A (List et al. 1992). Although clinical correlations are wanting (Holzmayer et al. 1992; Hait and Yang 2005), the use of biochemical modulation of clinical MDR remains a viable approach to improve treatments of cancer with conventional chemotherapy (Fojo and Bates 2003).

Overview of Experimental MDR-Reversal Many compounds, whose primary mechanism of action is blocking calcium channels, have been found to have MDR-reversing activities. It is not clear why chemicals that affect the flux of calcium in cells can elevate the accumulation of MDR-associated natural chemotherapeutic drugs. Many MDR-reversing agents are cationic amphiphiles (contains both hydrophobic and polar regions) that are highly lysosomotropic (Jaffr´ezou et al. 1991; Akiyama et al. 1984; Ramakrishnan et al. 1989) and could modulate intracellular turnover and trafficking of specific phospholipids that may affect the MDR phenotype (Jaffr´ezou et al. 1992; Jaffr´ezou and Laurent 1993). These implications may be unorthodox because of the seeming departure from the functionality of P-glycoprotein. It must be noted that proteins are rigid in nature and their functionality (mechanisms of action) and efficiency (kinetics) are often conformationally regulated by phospholipid matrix. This was shown by restoration of calcium accumulation across lipid bilayers by reconstitutive sarcoplasmic reticulum phospholipid-mediated, calcium- and magnesium-activated ATPase activity (Racker 1972) and by alterations in lipid fluidity that modulates P-glycoprotein-mediated drug transport in rat liver canalicular membrane vesicles (Sinicrope et al. 1992). Amplification of mdr1 and overexpression of its messenger underline that typical MDR phenotypes are probably by way of induction via plasma membrane with specific chemotherapeutic drugs. Unlike proteins, phospholipid compositions are remarkably different in transformed and tumor cells (Bergelson et al. 1970; Hatten et al. 1977), and the lipolytic activity of neoplasms is accentuated (Elwood and Morris 1968; Franson Patriaria and Elsbach 1974). The negative headgroup of the commonly internal phosphatidylserine (externalized in undifferentiated and neoplastic cells) has high affinity for calcium via coordination-chelation bonds (Paphadjopoulous 1968) that could initiate a localized drop in pH and a lateral phase separation of phosphatidylserine in the plasma membrane (Ohnishi and Ito 1973; Tr¨auble and Eibl 1974). Acidic pH can produce a reversible nonbilayer inverted micelle type in membranes containing phosphatidylserine (Hope and Cullis 1980). In addition to the regulatory effects of calcium and pH, phosphatidylserine was found deacylated by a unique membrane-associated phospholipase-A in SV40-transformed 3T3 fibroblasts and in human gastric carcinoma cells (Fan and Voelz 1977, 1980, 1984) to generate short-lived but fusogenic lysophosphatidylserine (Stein Y and Stein O 1966; Ahkong et al. 1973). Therefore, the unique properties of plasma membrane lipids such as phosphatidylserine allow them to participate in biological phenomena by

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means of a transient rearrangement of the membrane structure similar to that seen in MDR P388 leukemic cells (Garcia-Segura et al. 1992), and it regulates the efficiency (conformational changes and kinetic enhancements) of the efflux pump. It has been demonstrated that MDR phenotype of the human KB-V1 cell line (Ambudkar et al. 1992) and expression of the human mdr1 in Sf9 insect cells can generate a high-capacity drug-stimulated membrane ATPase (Sarkadi et al. 1992), and alterations in lipid fluidity can modulate P-glycoprotein-mediated drug transport in rat liver canalicular membrane vesicles (Sinicrope et al. 1992) and partially in the purified MDR CHRC5 Chinese hamster ovary cell plasma membrane P-glycoprotein (Doige et al. 1993). Therefore, the ATP-dependent calcium channel P-glycoprotein in cardiac and tumor cells could be retarded by blocking agents via a “nonspecific” perturbation (evidenced by the manifold variety of effectors) of the plasma membrane phospholipids to conformationally hinder ATPase activity (energy supply) and consequentially the functions of calcium channel and P-glycoprotein.

Metastasis and Drug Resistance The various modes of MDR-reversal put into effect that cancer is biologically heterogeneous and metastatic cells are the champions of survival. The process of tumor metastasis is highly selective and consists of a series of sequential and unified steps (Fidler 1990). Despite improvements in diagnosis, surgical techniques, patient care, and adjuvant therapies, most deaths from cancer are due to metastases that are resistant to conventional therapies. The major obstacle to effective treatment is the biologic heterogeneity of tumor cells. Moreover, metastases can be located in lymph nodes and different organs, and the specific organ microenvironment influences the biologic behavior of metastatic cells, including their response to systemic therapy (Fidler 2002). One of the factors is the development of drug resistance phenotype in metastatic cancer cells (Dutour et al. 2007; La Porta 2007). While cancer metastases are of clonal origin (Talmadge et al. 1982), variant clones with diverse phenotypes can form and rapidly result in the generation of significant cellular diversity within individual metastases (Fidler and Hart 1982). The outcome of the metastatic process depends on multiple and complex interactions of the metastatic cells with the host homeostatic mechanisms (Liotta et al. 1991; Fidler 1997). We determined whether the expression level of metastasis-related genes is regulated by specific organ microenvironments. Highly metastatic clones of human prostate cancer were implanted into the prostate (orthotopic site) and subcutis (ectopic site). Tumors were harvested and processed for in situ hybridization (ISH) analysis. Spontaneous metastases in the lymph nodes were also evaluated. Tumors growing in the prostate exhibited higher levels of epidermal growth factor-receptor (EGF-R), basic fibroblast growth factor (bFGF), interleukin (IL)-8, type IV collagenase, and the multidrug resistance (mdr-1) gene than those growing in the subcutis (Greene et al. 1997).

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The orthotopic implantation of human cancer cells was mandatory for analysis of metastasis-related genes. Specifically, highly metastatic cells expressed higher mRNA levels of type IV collagenase (which affects invasion), bFGF and IL-8 (which affect angiogenesis), and mdr-1 compared with cells of low metastatic potential. No difference in EGF-R expression (which affects growth) was found between the cells, but the expression of E-cadherin (which affects cell cohesion) was decreased in the metastatic cells. Vascular endothelial growth factor/vascular permeability factor (VEGF/VPF), which affects tumor angiogenesis, has also been found to be overexpressed in prostate cancer in comparison with normal epithelium or benign prostatic hyperplasia. We found that VEGF/VPF levels correlated with microvessel density and metastatic potential of human prostate cancer cells growing in the prostate of nude mice (Balbay et al. 1999). Furthermore, there is an intratumoral heterogeneity of expression of tyrosine kinase growth receptors found in human colon cancer surgical specimens and in orthotopic tumors (Kuwai et al. 2008). Collectively, these data suggest that the expression level of metastasisregulating genes by metastatic cells can be induced by factors in the organ microenvironment and can influence the drug resistant phenotype of metastases. Since the expression of various cytokines, growth factors, and their receptors on metastatic tumor cells and their microenvironment (e.g., endothelial cells) can interact to provide survival advantage and mediate MDR phenotype, combining chemotherapy and targeting the receptors of specific tyrosine protein kinases may be effective in treating metastatic diseases. Our group has been successful in such combination therapies against several metastatic cancers in orthotopic animal models. These included inhibiting the EGFR signaling by PKI166 in human renal cell carcinoma growing orthotopically in nude mice (Kedar et al. 2002); targeting the expression of platelet-derived growth factor receptor (PDGF-R) by STI571 (Kim et al. 2006); targeting EGF-R by PKI166 and VEGF-R by AEE788 with irinotecan in orthotopic colon carcinoma (Kitadai et al. 2006; Sasaki et al. 2007); simultaneously inhibiting EGF-R/VEGF-R by AEE788 and PDGF-R by STI571 with gemcitabine against human pancreatic carcinoma (Yokoi et al. 2005); targeting tumor cells and tumor-associated endothelial cells in human prostate cancer cells growing in the bone of nude mice by inhibiting EGF-R using PKI166 (Kim et al. 2003); inhibiting EGF-R by PKI166 and PDGF-R by STI571 with taxol (Kim et al. 2004); or STI571 with zoledronate and paclitaxel (Kim et al. 2005).

Tumor Angiogenesis The survival and growth of cells is dependent on an adequate supply of oxygen and nutrients and on the removal of toxic molecules. Oxygen can diffuse from capillaries for only 150–200 μm. When distances of cells from a blood supply exceed this, cell death follows (Gimbrone et al. 1974; Folkman and Klagsbrun 1987; Kerbel and Folkman 2002; Fidler and Ellis 2004). Thus, the expansion of tumor masses beyond 1 mm in diameter depends on neovascularization, i.e., angiogenesis

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(Folkman 1986). The formation of new vasculature consists of multiple, interdependent steps. It begins with local degradation of the basement membrane surrounding capillaries, followed by invasion of the surrounding stroma and migration of endothelial cells in the direction of the angiogenic stimulus (Fidler et al. 2005). Proliferation of endothelial cells occurs at the leading edge of the migrating column, and the endothelial cells begin to organize into three-dimensional structures to form new capillary tubes (Auerbach and Auerbach 1994). Differences in cellular composition, vascular permeability, blood vessel stability, and growth regulation distinguish vessels in neoplasms from those in normal tissue (Fidler and Ellis 1994). The onset of angiogenesis involves a change in the local equilibrium between proangiogenic and antiangiogenic molecules (Fidler 2001; Kerbel and Folkman 2002). Some of the common proangiogenic factors include bFGF, which induces proliferation in a variety of cells and has also been shown to stimulate endothelial cells to migrate, to increase production of proteases, and to undergo morphogenesis (Folkman and Klagsbrun 1987). Likewise, VEGF/VPF has been shown to induce the proliferation of endothelial cells, to increase vascular permeability, and to induce production of urokinase plasminogen activator by endothelial cells (Dvorak 1986; Dvorak et al. 1995). Additional proangiogenic factors include IL-8, a cytokine produced by a variety of tissues and blood cells (Singh et al. 1994; Yoneda et al. 1998), platelet-derived endothelial cell growth factor, which has been shown to stimulate endothelial cell DNA synthesis and to induce production of FGF (Kim et al. 2005, 2006), hepatocyte growth factor (HGF), or scatter factor, that increases endothelial cell migration, invasion, and the production of proteases (Bussolino et al. 1992), and platelet-derived growth factor (PDGF) (Risau et al. 1992). Moreover, the structure and architecture of tumor vasculature can dramatically differ from those found in normal organs (Ebhard et al. 2000; Nor and Pulverini 1999; Nels et al. 1992). Indeed, blood vessels in tumors are different than those found in wound healing, and inflamed tissues. The blood flow through tumors can be tortuous and is characterized by regions of necrosis, rapid cell division, and presence of infiltrate cells. Receptors for VEGF (KDR in humans, Flt-1 in mice) are expressed specifically by tumor endothelium as well as the angiopoietin tyrosine kinase receptor, Tie-2 (reviewed in Liu et al. 2000). In addition, receptors for PDGF and EGF are found on tumor endothelial cells (Uehara et al. 2003; Suhardja and Hoffman 2003). The endothelium is fragile, and upregulation of survival factors (such as Bcl-2 and survivin) by molecules found in abundance within the tumor microenvironment such as VEGF and bFGF helps to prevent apoptosis of new endothelium (Wang et al. 2002; Karsan et al. 1997; Gerber et al. 1998). There is increased leakiness to macromolecules (perhaps due to the presence of VEGF) (Jain 1987; Dvorak 1990), and vessels often lose distinct features of arteriole, capillary, and venule formation. Modern techniques, such as phage-display targeting, have defined “vascular addresses” that may be distinct for different organs as well as tumors in those organs and perhaps offer attractive targets for antivasculature therapy (Pasqualini et al. 2002). Angiogenic heterogeneity exists within a single tumor (zonal or intralesional) between different metastases even in a single organ, and different neoplasms of the

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same histologic type are also documented (Kumar et al. 1998; Yu et al. 2001). For example, the expression of proangiogenic molecules (and, therefore, blood vessel density) in murine or human tumors growing at orthotopic sites in athymic mice is zonal, i.e., demonstrates intralesional heterogeneity. Small tumors (3–4 mm in diameters) expressed more bFGF and IL-8 than large tumors (>10 mm in diameters), whereas more VEGF is expressed in large tumors. Immunostaining showed a heterogeneous distribution of these angiogenic factors within the tumor; expression of bFGF and Il-8 was highest on the periphery of a large tumor, where cell division was maximal. VEGF expression was higher in the center of the tumor (Kumar et al. 1998). Similarly, heterogeneous dependence on angiogenesis was reported for cell subpopulations isolated from human melanoma xenografts having differential expression of hypoxia-inducing factor-1 (Yu et al. 2001). Heterogeneity of blood vessel distribution in surgical specimens of human cancers is well documented (Weidner et al. 1992). Benign neoplasms are sparely vascularized and tend to grow slowly in contrast to highly vascularized and rapidly growing malignant tumors (Weidner et al. 1992). The distribution of vessels in a tumor, however, is not uniform, and Weidner et al. cautioned that to predict the aggressive nature of human cancers, one must determine the mean vessel density (MVD) in the “areas of most intense neovascularization”, i.e., tumors exhibit intralesional and zonal heterogeneity for MVD (Weidner et al. 1992; Jain 1987, 2008). Similarly, the expression of proangiogenic molecules in surgical specimens of human colon carcinoma was determined by in situ hybridization technique. Matrix metalloproteinase-9 and bFGF were overexpressed at the periphery of the tumor where cells were rapidly dividing, whereas VEGF expression was higher in the center of the lesions (Kitadai et al. 1995). The extent of angiogenic heterogeneity in malignant neoplasms is also regulated by the organ microenvironment. For example, human renal carcinoma cells implanted into the kidney of athymic mice produced a high incidence of lung metastasis, whereas those implanted subcutaneously did not (Singh et al. 1994). Histopathologic examination of the tissues revealed that the tumors grown in the subcutis of nude mice had few blood vessels, as compared to tumors in the kidney. The subcutaneous tumors also had a significantly lower level of mRNA transcripts for bFGF than tumor in the kidney, and the expression of the naturally occurring angiogenic inhibitor, IFN-β (which downregulates bFGF) was high in epithelial cells and fibroblasts surrounding the subcutaneous tumors. This was not detected in or around tumors grown in the kidney (Singh et al. 1995). The production of IL-8 by melanoma cells is regulated by complex interactions with skin keratinocytes (Herlyn 1990). IL-8 expression can be increased by co-culture of melanoma cells with skin keratinocytes, and this expression is inhibited by coincubation of melanoma cells with hepatocytes from the liver (Gutman et al. 1995). The organ microenvironment also influences the expression of VEGF. Human gastric cancer cells implanted into the stomach were highly vascularized and expressed high levels of VEGF, as compared to implantation into an ectopic (subcutaneous) site, such as the skin. In addition, metastasis only occurred from the tumor implanted in the stomach (Takahashi et al. 1996).

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The molecular cross-talk that occurs with tumor cells and endothelium within the tumor microenvironment results in sufficient recruitment of a vascular supply that has physiological properties that allow migration and eventual escape of subpopulations of tumor cells able to complete a cascade of events necessary for metastasis.

Antivascular Therapy of MDR Prostate Carcinoma Cancer of the prostate is the most common cancer affecting men in North America and is the second leading cause of cancer-related deaths. Mortality from prostate cancer usually results from the metastasis of hormone-refractory cancer cells. Reports examining the pattern of metastasis in advanced prostate cancer indicate that dissemination to bone and lymph nodes occurs in over 80% of the cases (Garnick and Fair 1996). The pathophysiology of prostate cancer bone metastases is complex and involves the interaction of tumor cells with osteoclasts, osteoblasts, endothelial cells and an assortment of regulatory proteins (e.g., steroid hormones, cytokines, and growth factors). To study the factors that are critical for growth of prostate cancer cells in the bone, we established a murine model of hormone-refractory prostate cancer bone metastasis. To generate prostate cancer growth in the bone, we performed a percutaneous intraosseal injection on nude mice by inserting a 27-gauge needle into the tibia immediately proximal to the tuberositas tibia (Uehara et al. 2003). After penetrating the cortical bone, we deposited 20 μl of tumor cell suspension (2 × 105 androgen-independent PC3-MM2 cells) in the bone cortex with the use of a calibrated, push button-controlled dispensing device. Five weeks later, we resected the tumor-bearing leg and performed an extensive immunohistochemical survey of the bone lesions in an effort to identify potential factors that may be involved in the regulation of prostate tumor cell growth. A preliminary immunohistochemical evaluation revealed robust tumor cell expression of bFGF, VEGF, IL-8, PDGF BB, and its receptor PDGFR-Rβ. Expression of these proteins was most pronounced in tumors that were growing adjacent to the bone. In contrast, in those tumors that had lyzed the bone and extended their growth to include the surrounding muscle, we detected only minimal levels of the angiogenic proteins, suggesting that factors within the bone environment were influencing the phenotype of the tumor cells. A more comprehensive examination of distribution pattern of PDGFRβ revealed that PDGFR-β was present on both prostate tumor cells and on tumor-associated endothelium and that, moreover, this receptor tyrosine kinase was activated. Phosphorylated PDGFR-β was not found in either the contralateral nontumor leg or in tumor cells growing away from the bone, i.e., in the muscle. These findings indicate that the PDGF BB produced by tumor cells was acting in an autocrine manner to stimulate tumor cells and in a paracrine fashion to convey information to tumorassociated endothelial cells. The expression pattern of activated PDGFR-β in the bone metastases suggested that it might be a target for therapy in that inhibition of

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this signaling cascade could potentially affect both the malignant cell population and the tumor blood supply. To test this hypothesis, we treated mice with experimental bone metastasis using the tyrosine kinase inhibitor of PDGFR-β, STI571 (imatinib mesylate, Gleevec). In mice treated with STI571 or the combination of STI571 plus paclitaxel, we found induction of significant apoptosis of endothelial cells and tumor cells that resulted in inhibition of tumor growth, a significant decrease of lymphatic metastases, and a significant decrease of bone lysis (Uehara et al. 2003). These experiments demonstrated that tumor-associated endothelial cells express phosphorylated PDGFR when adjacent tumor cells express PDGF, and that inhibition of this activation with a PDGFR tyrosine kinase inhibitor, particularly in combination with chemotherapy, can produce significant therapy. To determine the molecular mechanism for the antiangiogenic effects observed on the tumor-associated endothelial cells in vivo, we established cultures of murine bone microvascular endothelial cells and examined their response to stimulation with PDGF BB ligand and to blockade of PDGFR signaling with STI571 (Langley et al. 2004). Cultured bone endothelial cells expressed PDGFR-β, and PDGF BB-induced phosphorylation on these cells could be inhibited by STI571 in a dosedependent manner. Stimulation of the bone endothelial cells with PDGF BB resulted in activation of Akt and ERK1/2, and this signaling cascade could be completely abrogated by STI571. In addition, we found that bone endothelial cells respond to PDGF BB by increasing their cell division and upregulating the anti-apoptotic protein Bcl-2. We then examined the response of bone endothelial cells to treatment with STI571 and taxol. Treatment of bone endothelial cells with only a single agent produced little effect. However, the combined treatment of STI571 and taxol resulted in a significant increase in the number of cells expressing activated caspase3 and a concomitant decline in Bcl-2. Consistent with these results, we found that when bone endothelial cells were confronted with both STI571 and low levels of taxol, there was a threefold increase in their cytotoxicity. Collectively, these data suggest that a primary target for the STI571 and paclitaxel therapy may be the tumor-associated blood vessels. To test this hypothesis, we established a multidrug resistant prostate cell line by chronically exposing PC3MM2 cells to increasing concentrations of taxol (Kim et al. 2006). The resulting cell line, PC3-MM2-MDR, is 70 times more resistant to paclitaxel in vitro, and the growth of the cells is not affected by treatment with paclitaxel or the combination of paclitaxel and STI571. When the PC3-MM2-MDR cells were implanted into the bone microenvironment, they displayed the same angiogenic profile as the parental cell line. Endothelial cells in normal tissues rarely divide, whereas 2–3% of the endothelial cells in prostate cancer divide daily (Augustin et al. 2002; Eberhard et al. 2000). These dividing endothelial cells should be sensitive to anticycling drugs such as paclitaxel. Nevertheless, in the present experiment, paclitaxel did not decrease the MVD appreciably, likely because of the fact that stimulation of endothelial cells with PDGF leads to resistance to paclitaxel, and that blockade of PDGF-R phosphorylation with imatinib reverses the resistance to paclitaxel (Langley et al. 2003). As stated above, the first wave of apoptosis in bone tumors from mice treated with imatinib and paclitaxel for only 2 weeks occurred in tumor-associated endothelial cells,

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followed by apoptosis of tumor cells and ultimately tumor necrosis. By the fourth week of treatment with imatinib and paclitaxel or imatinib alone, concurrent apoptosis of tumor cells and tumor-associated endothelial cells was observed. Without paclitaxel, imatinib may produce therapeutic effects by the blockade of PDGF-R, which serves as a survival factor (Langley et al. 2003). Thus, the imatinib-induced blockade of PDGF-R combined with paclitaxel appears to target the tumor-associated endothelial cells. Whether this approach can be useful for other types of tumors is unknown. The heterogeneity of angiogenesis in human tumors and the findings that endothelial cells of different organs are phenotypically distinct (Langley and Fidler 2007) indicate that further investigations to understand the interaction of different types of tumor cells and endothelial cells in different organs are necessary for the development of optimal regimens of targeted antivascular therapies. For this reason, we performed another series of experiments using the multidrug resistant PC-3MM2-MDR cells growing in the prostate of nude mice and treated the mice with paclitaxel and the tyrosine kinase inhibitor, AEE788, that targets phosphorylation of EGF-R/VEGF-R (Busby et al. 2006). The significant inhibition of local tumor growth and lymph node metastases again demonstrated that tumor-associated endothelial cells, rather than the tumor cells, were the primary target of the chemotherapy. Those studies provide a better understanding of the molecular mechanisms that regulate the process of metastasis and of the complex interactions between the metastatic cells and the organ microenvironment (Kim et al. in press).

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Chapter 3

The Role of Autophagy and Apoptosis in the Drug Resistance of Cancer Tomohisa Yokoyama, Yasuko Kondo, Oliver B¨ogler, and Seiji Kondo

Abstract Drug resistance is a major obstacle that limits the effectiveness of cancer therapy. Many cancer cells acquire resistance mechanisms that allow them to survive apoptosis, contributing to the initiation and progression of cancers. Therefore, novel therapeutic strategies are needed to address the emerging problem of drug resistance. Recent studies have suggested that the induction of autophagy could be a useful therapeutic approach to overcome drug resistance of cancers to some therapeutic agents, particularly those which typically induce an apoptotic response. However, the functional relationship between apoptosis and autophagy is extremely complex, and the molecular machinery is still obscure. Therefore, understanding the regulation of apoptotic and autophagic signaling pathways could provide important information for the development of novel cancer therapies in drug resistance. Keywords Autophagy · Apoptosis · Drug resistance · Cancer

Introduction Programmed cell death (PCD) is any form of cell death, mediated by an intracellular program, and it has been recognized since the 1960s (Kerr et al. 1972); tremendous amount of information has been accumulated regarding the molecular events of apoptotic signaling pathways (Schweichel and Merker 1973). In 1990, Clarke described that PCD is most commonly associated with apoptosis (type I PCD), but it can also occur through other mechanisms, including autophagy (type II PCD) and non-lysosomal vesiculate cell death (type III PCD) (Clarke 1990). Type I PCD is characterized by cell shrinkage, chromatin condensation, nucleosomal DNA degradation, and fragmentation. Activation of caspases gives rise to these characteristic S. Kondo (B) Department of Neurosurgery, BSRB1004, The University of Texas M. D. Anderson Cancer Center, 1515, Holcombe Boulevard, Houston, TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 3, 

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morphological features of apoptosis (Gozuacik and Kimchi 2004, Bursch et al. 2000a, Bursch 2001). Type II PCD is characterized by the degradation of the Golgi apparatus, polyribosomes and endoplasmic reticulum before nuclear destruction, and the formation of numerous autophagic vacuoles. Type III PCD is defined as non-lysosomal vesiculate degradation. When cell death involves autophagy, it is designated as type II PCD, or autophagic cell death in contrast to apoptosis, which is referred to as type I (Bursch et al. 2000b). Drug resistance is a major obstacle in cancer treatment, and the acquisition of resistance to apoptosis is considered to be an important component. Recent studies have suggested that autophagy could be a useful therapeutic approach for the drug resistance of cancer, to some therapeutic agents (Shao et al. 2004, Carew et al. 2007, Amaravadi et al. 2007). This chapter covers the accumulating data about the role of autophagy and discusses some potential strategies for overcoming drug resistance in cancer.

Role of Autophagy in the Drug Resistance of Cancer The problem of drug resistance is complex as numerous factors affect drug sensitivity, such as accelerated drug efflux, DNA repair, drug activation and inactivation, alterations in drug target, DNA methylation, processing of drug-induced damage, and evasion of apoptosis (Wilson et al. 2006). Here we will focus on resistance mechanisms related to the cell’s ability to avoid entering PCD in the face of a drug’s impact. We believe that understanding how anticancer therapies induce cancer cell death will pave the way for the design of new approaches capable of overcoming this type of drug resistance.

Role of Autophagy in Cancer Treatment As cancer cells are frequently resistant to apoptosis, many therapeutic strategies involve sensitizing tumor cells to apoptosis-inducing treatments. However, the induction of autophagy in apoptosis-defective or apoptosis-resistant cancer cells might also be an effective therapeutic approach (Shao et al. 2004, Carew et al. 2007, Amaravadi et al. 2007). Indeed, cultured malignant glioma cells are typically resistant to apoptosis induced by such antitumor therapies as chemotherapy, γ-radiation therapy, and some adjuvant therapies (B¨ogler and Weller 2002). However, temozolomide (TMZ), a DNA-alkylating agent, rapamycin, an inhibitor of the mammalian target of rapamycin (mTOR), and γ-irradiation induce autophagy, but not apoptosis, in malignant glioma cells (Table 3.1) (Kanzawa et al. 2004, Takeuchi et al. 2005, Yao et al. 2003, Ito et al. 2005). This resistance is largely due to the fact that most malignant glioma cells express the BCL2 gene, which renders many tumor cells resistant to cancer therapies (Kondo et al. 1995). Another investigation showed that histone deacetylase inhibitors sodium butyrate and suberoylanilide hydroxamic

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Table 3.1 Therapies that induce autophagy in cancer cells Treatment

Proposed target

Cancer type

References

Temozolomide Rapamycin

DNA mTOR

γ-irradiation

DNA

Sodium butyrate and SAHA

HDAC

Kanzawa et al. (2004) Takeuchi et al. (2005) and Gills et al. (2007) Yao et al. (2003), Ito et al. (2005) and Paglin et al. (2001) Shao et al. 2004

Tamoxifen

Oestrogen recepter

Arsenic trioxide

Multiple targets (for example, mitochondria) Multiple targets (for example, estogen receptor and mitochondria) Unknown

Malignant glioma Malignant glioma, lung cancer Malignant glioma, breast cancer, prostate cancer Cervical cancer that overexpresses BCL-XL , leukemia Breast cancer, lymphoma Malignant glioma, leukemia Ovarian cancer, lung cancer

Opipari et al. (2004) and Ohshiro et al. (2007)

Colon cancer

Ellington et al. (2005)

Cervical cancer, leukemia Malignant glioma, brain tumor stem cell

Maiuri et al. (2007) and Kessel et al. (2006) Ito et al. (2006), Jiang et al. (2007) and Alonso et al. (2008)

Resveratrol

Soybean B-group triterpenoid saponins ABT737 HA14-1 Oncolytic adenovirus

Beclin 1 and Bcl-2/Bcl-XL hTERT

Bursch et al. (1996) and Amaravadi et al. (2007) Kanzawa et al. (2003) and Kanzawa et al. (2005)

HDAC, histone deacetylase; mTOR, mammalian target of rapamycin; SAHA, suberoylanilide hydroxamic acid; hTERT, human telomerase reverse transcriptase.

acid (SAHA) induced autophagy in parental HeLa cervical cancer cells that overexpress the anti-apoptotic protein BCL-XL ; however, they induce apoptosis in parental HeLa cells (Shao et al. 2004). Thus, if the activity of anti-apoptotic proteins (BCL2 and BCL-XL ) is excessive, as it is in some cancers, the consequent suppression of autophagy could allow damaged cells to complete a cancerous transformation (Marx 2006). Other anticancer treatments also induce autophagy or autophagic cell death. Tamoxifen, which targets the estrogen receptor, induces autophagic cell death in breast cancer cells and lymphoma, and this has been shown to occur through downregulation of protein kinase B/AKT (Bursch et al. 1996, Scarlatti et al. 2004). The natural product arsenic trioxide (As2 O3 ) induces autophagy in malignant glioma cells, and the natural product resveratrol – a phytochemical found in grapes and red wine – has anti-neoplastic activities and induces autophagy in ovarian and lung cancer cells (Kanzawa et al. 2003, 2005, Opipari et al. 2004, Ohshiro et al. 2007). Another natural product, the soybean B-group triterpenoid saponins, causes autophagy in

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colon cancer cells (Ellington et al. 2005). Pharmacological BCL2homology-3 (BH3) mimetic such as ABT737 and HA14-1 induces autophagy in HeLa cervical cancer and leukemia cells (Maiuri et al. 2007, Kessel and Reiners 2007). Telomeraseselective oncolytic adenovirus also induces autophagy in malignant glioma and brain tumor stem cells (Ito et al. 2006, Jiang et al. 2007, Alonso et al. 2008).

Role of Autophagy in Tumorigenesis To appreciate how alterations in autophagic pathways impinge on cancer therapy, it is necessary for us to first outline the role of autophagy in tumorigenesis. Autophagy is a dynamic multistep process in which intracellular membrane structures sequester proteins and organelles to degrade and turn over these materials (Kondo et al. 2005). At the beginning of autophagy, portions of the cytoplasm, as well as intracellular organelles, are sequestered in double-membrane-bound structures that are known as autophagosome (Fig. 3.1). These autophagosomes then fuse with lysosomes to form autolysosomes, and the sequestered contents are degraded by lysosomal hydrolases and are recycled. Thus, autophagy has cellular homeostasis function and can eliminate superfluous, damaged, or aged cells or organelles (Shintani and Klionsky 2004). To date, 30 Autophagy-related (ATG) genes, which are involved in various subtypes of macroautophagy, have been identified in yeast model systems (Xie and Klionsky 2007). BECN1 (the mammalian orthologue of ATG6), the first identified mammalian autophagy gene product, is a haplo-insufficient tumor suppressor that was originally isolated as a BCL2-interacting gene protein (Liang et al. 1999, Liang et al. 1998). Introduction of BECN1 into MCF7 breast cancer cells induced autophagy and inhibited tumorigenicity. Levels of BECN1 were significantly decreased in 18 of 32 breast cancer samples, compared with normal epithelial cells from the breast (Liang et al. 2006). These findings indicate that decreased expression of BECN1 might contribute to the development or progression of breast cancer. The allelic deletion of chromosome 17q21, where BECN1 is located, is common not only in breast tumors but also in ovarian and prostate tumors, suggesting that the deletion of BECN1 is involved in multiple types of cancer. Recently, other suppressors such as a novel coiled-coil ultraviolet irradiation resistance-associated tumor suppressor gene (UVRAG) and Bif-1 were also identified (Liang et al. 2006, 2007, Takahashi et al. 2007). UVRAG interacts with BECN1, leading to activation of autophagy and therefore inhibition of tumorigenesis. Bif-1 interacts with BECN1 through UVRAG and activates the BECN1/class III phosphatidylinositol 3-kinase (PI3K) complex, which regulates autophagosome formation and forms a complex that localizes in the trans-Golgi-network (TGN) (Kondo et al. 2005, Linder and Shoshan 2005). Thus, UVRAG and Bif-1 may play a role in cell growth control and/or tumor suppression (Levine and Kroemer 2008, Mizushima et al. 2008). In addition, damage-regulated modulator of autophagy (DRAM) is a new regulator of autophagy involved in the induction of autophagy by p53 in response to genotoxic stress (Crighton et al. 2006). That is, DRAM alone causes minimal cell

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LC3

LC3

Membrane isolation (Phagophore)

3-MA ATG6/BECN1 siRNA ATG5 siRNA ATG10 siRNA ATG12 siRNA

Autophagosome

Autolysosome

Degradation

Bafilomycin A1 Vinblastine Nocodazole CQ Monensin LAMP2 siRNA

Fig. 3.1 The cellular process of autophagy. Autophagy begins with the isolation of double membrane (phagophore) inside an intact cell. The membrane structures elongate and mature, and microtubule-associated protein 1 light chain 3 (LC3) is recruited to the membrane. The elongated double membranes form autophagosomes, which sequester cytoplasmic proteins and organelles such as mitochondria. The formation of the pre-autophagosomal structure can be inhibited by the class III phosphatidylinositol 3-phosphate kinase (PI3K) inhibitor 3-methyladenine (3-MA) and short interfering RNAs (siRNA) targeted against ATG6 (BECN1), ATG5, ATG10, and ATG12. Sequestration requires ATP and is regulated mainly by class III PI3K (Petiot et al. 2000). The autophagosomes mature with acidification by H+ -ATPase and fuse with lysosomes to become autolysosomes [80]. Microtubules are important mediators of this fusion process. This process is inhibited by the H+ -ATPase inhibitor bafilomycin A1 , by microtubule inhibitors such as vinblastine and nocodazole (Reunanen, Marttinen and Hirsim¨aki 1988, Punnonen and Reunanen 1990, Kihara et al. 2001, Saeki et al. 2000), or by lysosomotropic agents such as chloroquine (CQ), monensin, and siRNA targeted against lysosome-associated membrane protein 2 (LAMP2). Eventually, the sequestered contents are degraded by lysosomal hydrolases for recycling

death; however, activation of p53 induces autophagy in DRAM-dependent manner. Knockdown of DRAM inhibits the accumulation of autophagosomes induced by p53 and also reduces the induction of apoptosis.

Mechanism Regulating Autophagy and Apoptosis Signaling pathways are extremely complex, with a number of feed-forward and feed-back loops and crosstalk with many other signaling networks; furthermore, signaling pathways that regulate autophagy, proliferation, and apoptosis are frequently

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altered in cancer cells. Here we will briefly touch upon some of the most important pathways that regulate PCD.

PI3K-AKT-mTOR Signaling Pathway The PI3K-AKT-mamalian target of mTOR pathway, which is activated in many types of cancer, is important in autophagy (Gozuacik and Kimchi 2004, Shintani and Klionsky 2007, Blume-Jensen and Hunter 2001, Vivanco and Sawyers 2002, Ogier-Denis and Codogno 2003). AKT is a serine–threonine kinase, located downstream of class I PI3K, that activates the kinase mTOR, leading to suppression of autophagy (Fig. 3.2) (Schmelzle and Hall 2000, Gingras et al. 2001). A role of this

Growth factor receptor Plasma membrane

PI3K Class III

LY294002 Wortmannin

BECN1

PI3K Class I

BCL2

AKT

ER stress p53

UCN-01

RAS

PTEN

BNIP3 3-MA RAF1

DRAM

p53 PD98059

DAPK

ERK1/2

DRP1

mTOR

MEK1/2

p70S6K

Rapamycin CCI-779 RAD-001

Autophagy

Fig. 3.2 The molecular regulation of autophagy. In the presence of growth factors, growth factor receptor signaling activates class I phosphatidylinositol 3-phosphate kinase (PI3K) at the plasma membrane to keep cells from undergoing autophagy (Kondo et al. 2005). PI3K activates the downstream target AKT, leading to activation of mammalian target of rapamycin (mTOR), which results in inhibition of autophagy. p70S60 kinase (p70S6K) might be a good candidate for the control of autophagy, downstream of mTOR. Overexpression of phosphatase and tensin homologue (PTEN) gene, by an inducible promoter, antagonizes class I PI3K and induces autophagy (Arico et al. 2001). RAS has dual effect on autophagy – when it activates class I PI3K (Furuta et al. 2004), autophagy is inhibited, but when it selectively activates the RAF-1-mitogen-activation protein kinase (MEK)-extracellular signal-regulated kinase (ERK) cascade, autophagy is stimulated (Ogier-Denis et al. 2000). Activation of p53 inhibits mTOR activity and regulates its downstream targets, including autophagy (Feng et al. 2005, 2007). Further, activation of p53 positively regulates in DNA-damaged cells in a DRAM-dependent manner (Crighton et al. 2006). A complex of class III PI3K and BECN1 induces autophagy at the trans-Golgi network acts to induce autophagy (Kihara et al. 2001). This pathway is inhibited by 3-methyladenine (3-MA). Downregulation of BCL2, or upregulation of BCL2-adenovirus E1B 19-kD-interacting protein 3 (BNIP3) at the mitochondria, also induces autophagy, interacting that BCL2 protects against autophagy (Saeki et al. 2000). Autophagy is also induced by the cell death-associated protein kinase (DAPK), the deathassociated related protein kinase 1 (DRP1), and endoplasmic reticulum (ER) stress (Ogata et al. 2006, Inbal et al. 2002)

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signaling pathway in the negative regulation of autophagy has been observed in nonmammalian cells (Levine and Klionsky 2004, Wang and Klionsky 2003). Downregulation of this pathway has been implicated in tumorigenesis and resistance to therapy in breast, prostate, pancreatic, ovarian, and stomach cancers (Nicholson and Anderson 2002). Class I and class IIII PI3K regulate autophagy differently – the class I PI3K-AKT-mTOR signal, which is activated in cancer cells through the growth factor receptor, inhibits autophagy. By contrast, class III PI3K promotes the sequestration of cytoplasmic material that occurs during autophagy (Petiot et al. 2000). Oncogenic forms of RAS are also implicated in the negative control of autophagy, through activation of class I PI3K (Furuta et al. 2004). The phosphatase and tensin homologue (PTEN) is tumor-suppressor gene that also regulates autophagy. PTEN dephosphorylates PIP3 and thereby antagonizes the function of class I PI3K, suppressing AKT activity and allowing autophagy to be initiated (Arico et al. 2001). Several studies have demonstrated a high frequency of PTEN mutations or deletions in a variety of human cancers, such as malignant gliomas and prostate, breast and endometrial cancers (Steck et al. 1997). When PTEN is deleted, mutated, or otherwise inactivated, activation of PI3K/AKT/mTOR pathway leads to tumorigenesis, and in addition to promoting proliferation suppresses autophagy (Cully et al. 2006). This antagonism of PCD may be how activation of the PI3K/AKT/mTOR pathways confers resistance to many type of cancer therapy, and so is poor prognostic factor for many types of cancers.

BECN1 BECN1 (ATG6), the first identified tumor suppressor protein that functions in the lysosomal degradation pathway of autophagy, was isolated as a BCL2-interacting protein (Liang et al. 1999, Liang et al. 1998). When mutant forms of BECN1 that cannot bind to BCL2 are expressed, this resulted in excess levels of autophagy in the absence of exposure to nutrient deprivation or autophagy specific stimuli (Pattingre et al. 2005). These excess levels of autophagy are associated with cell death that is caspase-independent and inhibited by small interfering RNA (siRNA) directed against a downstream autophagy execution gene, ATG5. Thus, BCL2 blocks autophagic cell death by binding to BECN1. In addition, BECN1 binds to a class III PI3K complex, which regulates autophagosome formation. Consistent with the role of the BECN1-classIII PI3K complex in autophagy induction, 3-methyaldenine (3-MA), an inhibitor of PI3K, inhibits autophagy (Shintani and Klionsky 2004).

BCL2 and Adenovirus E1B 19kDa Interaction Protein 3 (BNIP3) BNIP3 is a cell-death-inducing factor that is member of the BCL2homology 3 (BH3)-only subfamily of the BCL2 family protein. It is expressed on the mitochondrial membrane, thereby inhibiting the anti-apoptotic BCL2-family members (BCL2 and BCL-XL ) or activating the pro-apoptotic BCL2-family members (BAX and BAK). Overexpression of BNIP3 induced caspase-independent non-apoptotic

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or necrosis-like cell death in MCF7 and HeLa cells (Vande et al. 2000). BNIP3 was upregulated in several malignant glioma cell lines that were undergoing autophagy as a result of treatment with As2 O3 and ceramide (Kanzawa et al. 2003, Daido et al. 2004). Murine embryonic fibroblasts (MEFs) and bone marrow-derived cells from mice deficient in the BCL2 family proteins BAX and BAK are resistant to apoptosis but undergo autophagy after exposure to chemotherapeutic agent etoposide or growth factor withdrawal (Shimizu et al. 2004, Lum et al. 2005). Another study also reported that BAX and BAK double knockout MEFs were more radiosensitive than the wild-type MEFs, and that irradiation of cells resulted in an increase in pro-autophagic proteins such as the ATG12-ATG5 complex, and BECN1, which resulted in up-regulation of autophagy (Kim et al. 2006). These observations indicate that the pro-apoptotic protein (BAX and BAK) not only control apoptosis, but also regulate the autophagic pathway. Thus, mitochondria-associated cell death protein BNIP3 plays a central role in both apoptotic and autophagic cell death.

p53 Tumor Suppressor Pathway The transcription factor p53, which is mutated in about half of all human cancers, coordinates DNA repair, cell cycle arrest, apoptosis, and senescence to preserve genomic stability and prevent tumor formation (Levine 1997, Vogelstein et al. 2000). Activation of p53 inhibits mTOR activity and regulates its downstream targets, including autophagy, via AMP-activated protein kinase (AMPK) and tumor suppressors tuberous sclerosis-1 (TSC1) and TSC2 (Feng et al. 2005, 2007). In addition, another study reported that p53 positively regulates autophagy in DNAdamaged cells in a DRAM-dependent manner . Thus, p53 can be an inducer of autophagy and induce cell death.

ER Stress The endoplasmic reticulum (ER) has a role in cellular homeostasis regulation, particularly in unfolded protein response (UPR), which plays a major role in cancer and many other diseases (Kim et al. 2006, Moenner et al. 2007). When UPR is activated, the expression of chaperones such as glucose-regulated protein 78 (GRP78)/immunoglobulin binding protein (BiP) is induced, resulting in up-regulation of the cellular folding capacity. Several anticancer agents generate survival responses through activation of the UPR. When the ER system is overloaded with misfolded proteins, cells will undergo cell death, typically by apoptosis. However, hypoxia and glucose starvation, which are common features of solid tumors, are involved in tumor progression and drug resistance (Tsuruo et al. 2003). For example, GRP-inducing conditions caused significant resistance to VP-16 and adriamycin through a decreased expression of DNA topoisomerase II in human ovarian, breast cancer cells (Yun et al. 1995). Thus, glucose-related stress response

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activates the UPR, and subsequent downstream signaling events can undermine the therapeutic efficacy. Some papers recently connected ER stress to the induction of autophagy (Ogata et al. 2006, Yorimitsu et al. 2006). Under conditions of ER stress, the pre-autophagosomal structure is assembled, and transport of autophagosomes to the vacuole is stimulated in an ATG protein-dependent manner. However, our understanding of ER stress-induce autophagy is at a very early stage, and further studies are needed.

Other Pathways Other molecules have been shown to regulate autophagy in cancer cells, such as death-associated protein kinase (DAPK), death-associated related protein kinase 1 (DAP1) as well as the mitogen-activated kinases (Inbal et al. 2002, Ogier-Denis et al. 2000). The DAPK and DRP1 proteins are Ca2+ -calmodulin-regulated serinethreonine kinases that are identified as a positive mediator of apoptosis in response to various stimuli (Inbal et al. 1997). A recent investigation indicated that DAPK and DAP1 induce autophagy in MCF7 breast and HeLa cervical cancer cells (Inbal et al. 2002). The mitogen-activated protein kinases are a family of serine-threonine kinases that are involved in regulating a wide range of cellular responses to growth factor receptor signaling (Su et al. 1996). This includes promoting autophagy: the extracellular signal-regulated kinases 1 (ERK1) and ERK2, when stimulated by RAS-RAF1-mitogen-activated protein kinase (MEK) signaling pathway, have been shown to induce autophagy in HT-29 colon cancer cells (Ogier-Denis et al. 2000).

Targeting Autophagy in Drug Resistance of Cancer Although autophagy is designated type II PCD, the role of autophagy in cell death has been a controversial issue (Kondo et al. 2005, Levine and Yuan 2005, Maiuri et al. 2007). In fact, inhibitors of autophagy can produce different outcomes – cell survival or cell death – that highlight the need to elucidate the mechanisms of autophagy, and its inhibition, in detail. It is necessary for clinical oncologists and cancer researchers to determine which cancer cell types most commonly undergo autophagy in response to therapy, and whether increased autophagy is a sign of drug responsiveness or resistance.

Crosstalk Between Autophagy and Apoptosis Autophagy can be inhibited at several levels, and inhibiting autophagy might be an effective therapeutic approach for overcoming drug resistance in cancer (Table 3.2) (Shao et al. 2004, Carew et al. 2007, Amaravadi et al. 2007). The class III PI3K inhibitor 3-MA inhibits pre-autophagosome formation and prevents the death of

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T. Yokoyama et al. Table 3.2 Inhibitors of autophagy

Compound

Modification of cellular component

Cancer type

References

Breast cancer, prostate cancer, colon cancer, malignant glioma, cervical cancer

Bursch et al. (2000a), Amaravadi et al (2007), Kanzawa et al. (2004), Bursch et al. (1996), Petiot et al. (2000), Boya et al. (2005) and Yamamoto et al. (1998) Boya et al. (2005)

Inhibition of autophagosome formation 3-MA

Class III P13K inhibitor. Inhibits the formation of preautophagosomal structure.

siRNA against Blocks translation of these proteins. ATG6/BECN1, ATG5,ATG10, ATG12

Cervical cancer

Inhibition of lysosomal proton pump Bafilomycin A1

H+ -ATPase inhibitor. Blocks the fusion of the autophagosome and lysosome.

Breast cancer, prostate cancer, colon cancer, malignant glioma, cervical cancer

CQ

A lysosomotropic agent. Block the fusion of the autophagosome and lysosome.

Leukemia, cervical cancer

Monensin

Protein exchange for potassium or sodium. Blocks the fusion of the autophagosome and lysosome. Blocks translation of the lysosome protein.

Leukemia, cervical cancer

siRNA against LAMP2

Cervical cancer

Kanzawa et al. (2003, 2004), Komata et al. (2004), Boya et al. (2005), Yamamoto et al. (1998) and Paglin et al. (2001) Carew et al. (2007), Amaravadi et al. (2007), Boya et al. (2005), Yamamoto et al. (1998) and Gonzalez-polo et al. (2005) Boya et al. (2005)

Gonzalez-polo et al. (2005)

3-MA, 3-methyladenine; CQ, chloroquine; P13K, phosphatidylinositol 3-phosphate kinase; siRNA, small interfering RNA.

breast cancer cells treated with tamoxifen (Bursch et al. 2000a, b, Carew et al. 2007, Kanzawa et al. 2004, Bursch et al. 1996, Petiot et al. 2000, Boya et al. 2005, Paglin et al. 2001). On the other hands, bafilomycin A1 , chloroquine (CQ), and monensin and siRNA targeted against lysosome-associated membrane protein2 (LAMP2) inhibit autophagy by preventing the fusion of autophagosomes and lysosomes (Boya et al. 2005, Paglin et al. 2001, Yamamoto et al. 1998, Gonz´alez-Polo et al. 2005). Several earlier studies have demonstrated that the use of autophagy inhibitors, such as 3-MA, CQ, bafilomycin A1 , and siRNA targeted against autophagy-associated genes, such as BECN1, and ATG5, and LAMP2 induced apoptosis in various cancer

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cells. However, these agents have different effects. 3-MA inhibits γ-irradiationinduced autophagy in breast, prostate and colon cancer, as well as inhibiting TMZinduced autophagy in malignant glioma cells (Kanzawa et al. 2004, Paglin et al. 2001). When autophagy was inhibited by 3-MA, these cancer cells did not undergo apoptosis. In contrast,, when bafilomycin A1 was used to inhibit γ-irradiationinduced autophagy in breast, prostate and colon cancer cells, cells still underwent apoptosis, and the antitumor effect of γ-irradiation was increased (Paglin et al. 2001). These results indicate that autophagy could be defense mechanism of cancer cells against γ-irradiation-induced apoptosis. A similar effect of bafilomycin A1 was observed in As2 O3 - and hyperthermia-induced autophagy (Kanzawa et al. 2003, Komata et al. 2004). So, if autophagy protects cells from drug-induced apoptosis (‘protective autophagy’), the use of agents that disrupt the autophagic pathway might strengthen the antitumor efficacy of cytotoxic agents. More recent study reported that either CQ or 3-MA synergistically enhanced the antitumor effects of SAHA in chronic myelogenous leukemia (CML) cell lines and primary CML cells expressing wild-type and imatinib mesylate- (an inhibitor of the BCR-ABL tyrosine kinase) resistant mutant forms of BCR-ABL (Carew et al. 2007). Another study also reported that inhibition of autophagy with either CQ or short hairpin RNA (shRNA) targeted against ATG5 enhanced the antitumor effects in Myc-driven model of lymphoma, which is resistant to apoptosis due to a lack of nuclear p53 (Amaravadi et al. 2007). These findings support the use of agents that disrupt the autophagy pathway for drug resistance of cancer. Especially, CQ has been used safely in patients with malaria prophylaxis and with rheumatoid arthritis (O’Neill et al. 1998, Kremer 2001), therefore, the time is ripe for a large-scale randomized trial for drug resistance of cancer. In addition, when apoptosis was inhibited by defects in the function of the proapoptotic proteins BAX and BAK, autophagic cell death was triggered by DNAdamaging agents such as etoposide (a topoisomerase-2 inhibitor) in MEFs and bone marrow-derived immortalized cells (Shimizu et al. 2004, Lum et al. 2005). Therefore, another possible approach is to inhibit apoptotic pathways, using some apoptosis inhibitors. For example, when apoptosis was inhibited in mouse fibroblasts by a caspase-8 inhibitor, autophagic cell death, depend on ATG7 and BECN1 activity, was induced, and autophagy inhibitor decreased the amount of cell death (Yu et al. 2004). However, if cancer cells already acquired apoptosis-resistance, this approach would not be expected to have efficacy. Collectively, these findings raise the possibility of the existence of unknown feedback or crosstalk network between autophagy and apoptosis, and also suggest an attractive strategy for drug resistance of cancer; when tumor cells induce protective autophagy, inhibition of autophagy could sensitize tumor cells to the treatment by activating apoptosis (Fig. 3.3).

Combination Treatment for Drug Resistance The PI3K/AKT/mTOR pathway is an attractive therapeutic target in cancer, because it regulates tumorigenesis, tumor metastasis, and drug resistance as well as autophagy (Nicholson and Anderson 2002, West et al. 2002). Together with the RAS/

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a. Cancer cells that undergo apoptosis after treatment Anticancer therapy (Apoptosis inducer)

Cell death Apoptosis

b. Cancer cells that undergo apoptosis after treatment Anticancer therapy (Apoptosis inducer)

Cell death by autophagic cell death Apoptosis inhibitor (for example, caspase-8 inhibitor)

c. Cancer cells that undergo autophagic cell death after treatment Anticancer therapy (Autophagy inducer)

Cell death Autophagy

d. Cancer cells that undergo protective autophagy after treatment Anticancer therapy (Autophagy inducer)

Cell death by apoptosis Autophagic inhibitor (for example, bafilomycin A1)

Fig. 3.3 Potential strategies for drug resistance of cancer. (a) Cancer cells that are capable of undergoing apoptosis in response to anticancer therapies might be treated with apoptosis inducers to promote apoptosis-induced cell death. (b) Cancer cells that undergo apoptosis might be treated with apoptosis inhibitors, such as caspase-8 inhibitor, to promote autophagy-induced cell death. (c) Cancer cells that are capable of undergoing autophagy in response to anticancer therapies might be treated with autophagy inducers, such as rapamycin, to promote autophagy-induced cell death. (d) Cancer cells that undergo autophagy, to protect themselves from the effects of anticancer therapies, might be treated with autophagy inhibitors, such as bafilomycin A1 or short interfering RNAs specific for the autophagy-related genes, to induce apoptosis. However, it should be noted that inhibition of apoptosis or autophagy does not always induce cell death by each other

RAF-1/MEK1/2/ERK pathway, which is regulated by amino acid levels, it is well known to regulate autophagy in response to starvation in mammalian cells (Codogno and Meijer 2005, Meijer and Codogno 2004). In our study, curcumin, which is natural compound, inhibited the former pathway and activated the latter pathway thereby inducing autophagy (Aoki et al. 2007). Another study also showed that triterpenoid B-group soyasaponins induce autophagy by inhibiting AKT signaling and enhancing ERK activity, in accordance with our findings (Ellington et al. 2006). Thus, AKT inhibition and ERK activation are probably the common mechanism of autophagy induced by anticancer agents. Interestingly, inhibition of ERK1/2 pathway using PD98059, a MEK1 inhibitor, inhibited autophagy and induced apoptosis, thus resulting in the enhanced cytotoxicity of curcumin. Consequently, these two autophagic pathways have opposite effects on curcumin’s cytotoxicity. That is, autophagy that is inhibited by the AKT pathway confers cell death, whereas the autophagy that is stimulated by the ERK1/2 pathway confers cell

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survival. Therefore, modulating autophagy using pathway-specific inhibitors may be helpful in promoting tumor regression as well as another autophagy inhibitors. Several preclinical and early clinical studies indicate that the PI3K/AKT/mTOR pathway inhibitors might be effective in inhibiting the growth of a broad range of tumors (Chan 2004, Lopiccolo et al. 2007). Furthermore, these pathway inhibitors have been studied in combination with standard chemotherapies. For example, rapamycin, an inhibitor of mTOR, induces apoptosis in multiple myeloma, lymphoma, and rhabdomyosarcoma cells (Clemens et al. 1998, Hosoi et al. 1999, Pene et al. 2002) and combined with cisplatin, it sensitizes some tumor cells to apoptosis induction (Shi et al. 1995). PI3K kinase inhibitors such as LY294002 and wortmannin have also been shown to enhance apoptosis of several cell lines when used in combination with paclitaxel, cisplatin, gemcitabine, or 5-fluorouracil (Hu et al. 2002, Asselin et al. 2001, Ng et al. 2000, Wang et al. 2002). In addition, LY294002 and UCN-01, an AKT inhibitor, synergistically sensitize rapamycin-sensitive and rapamycin-resistant malignant glioma cells to rapamycin by promoting autophagy, but not apoptosis (Takeuchi et al. 2005). Thus, these pathway inhibitors might be an effective approach to drug resistance of cancer, when combined with proximal pathway inhibitors. However, mTOR, which is a more distal pathway component, can control whether a cell undergoes apoptosis or autophagy, which may depend on the cell type, as in some cells the inhibition of mTOR stimulates autophagy rather than apoptosis (Takeuchi et al. 2005, Clemens et al. 1998, Hosoi et al. 1999, Pene et al. 2002, Castedo et al. 2002). Thus, it is still unclear what determines why cells enter apoptosis or autophagy. Recently, several agents such as AKT inhibitors (perifosine, phosphatidylinositol ether lipid analogues; PIAs, and triciribine), rapamycin analogues (CCI-779 and RAD-001), and dual inhibitor of PI3Kα and mTOR (PI-103) have been designed for development as anticancer agents (Lopiccolo et al. 2007). Only a few clinical trials combining PI3K/AKT/mTOR pathway inhibitors with various type of chemotherapy have been reported, however, and showed no objective responses (Lopiccolo et al. 2007). A potential explanation of these negative results is that off-target effects are involved. If the PI3K/AKT/mTOR pathway is not activated in tumor cells, these inhibitors would not be expected to have efficacy; however, they might be a valid approach to treat drug resistance of cancer in patients selected on the basis of data on which pathways are mediating this resistance. Overall, the crosstalk between apoptosis and autophagy is complex, and the two processes share common inducers. Therefore, the cross-inhibitory interactions between apoptosis and autophagy may cause polarization between the two processes. In this strategy, conventional cancer therapies that can induce autophagy might be combined to augment the antitumor effect of autophagy inducers. This idea is supported by recent investigations indicating that mTOR inhibitors have increased efficacy when combined with γ-irradiation therapy, chemotherapy, and anti-angiogenesis therapy (Eshleman et al. 2002, Mondesire et al. 2004, Stephan et al. 2004). Recently our studies also indicated that autophagy-inducing agents (rapamycin and temozolomide) not only enhance oncolytic adenovirus-induced autophagy but also synergize with oncolytic adenovirus to induce cell death of

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malignant glioma cells (Yokoyama et al. 2008). This may be a common feature of pathway inhibition, whereby rapamycin, temozolomide, and oncolytic adenovirus inhibits the PI3K/AKT/mTOR pathway, and the antitumor effect of oncolytic adenovirus would be augmented by combinatorial therapy with other autophagyinducing agents. Thus, combination of autophagy-inducing agents may give us novel therapeutic ideas for overcoming the drug resistance of cancer. To date, several methods has been developed to prove the presence of autophagy in normal and cancer cells, such as electron microscopy, acridine orange, and green-fluorescent protein (GFP)-tagged-rat microtubule-associated protein 1 light chain 3 (LC3) expression vector (Klionsky et al. 2008). However, these assays are not applicable to tissue samples. Recently, we developed an antibody against the isoform B of human LC3 (LC3B), which is useful tool for monitoring the induction of autophagy in various types of clinical specimens, including surgically resected cancer cells (Aoki et al. 2008). Future trials could prospectively assess induction of autophagy in clinical tissue samples.

Conclusion Autophagy has been associated with both cell survival and cell death, however, the role of autophagy in PCD is still poorly understood. A huge increase in the number of research publications on autophagy and cancer therapy suggests that it plays an important role in drug resistance of cancer to some therapeutic agents, which typically induce apoptosis. If autophagy only has a defensive role in cancer treatment, then inhibitors such as CQ or siRNAs specific for autophagy-regulatory genes might provide a clear therapeutic benefit. Additionally, understanding the relationship between autophagic and apoptotic pathways may provide novel therapeutic approaches to treating drug resistant cancers.

References Alonso MM, Jiang H, Yokoyama T et al. (2008) Delta-24-RGD in combination with RAD001 induces enhanced anti-glioma effect via autophagic cell death. Mol Ther 16: 487–493. Amaravadi RK, Yu D, Lum JJ et al. (2007) Autophagy inhibition enhances therapy-induced apoptosis in a Myc-induced model of lymphoma. J Clin Invest 117: 326–336. Aoki H, Takada Y, Kondo S et al. (2007) Evidence that curcumin suppresses the growth of malignant gliomas in vitro and in vivo through induction of autophagy: role of Akt and extracellular signal-regulated kinase signaling pathways. Mol Pharmacol 72: 29–39. Aoki H, Kondo Y, Aldape K et al. (2008) Monitoring Autophagy in Glioblastoma with Antibody against Isoform B of Human Microtubule-Associated Protein 1 Light Chain 3. Autophagy 4: 467–475. Arico S, Petiot A, Bauvy C et al. (2001) The tumor suppressor PTEN positively regulates macroautophagy by inhibiting the phosphatidylinositol 3-kinase/protein kinase B pathway. J Biol Chem 276: 35243–33246. Asselin E, Mills GB, Tsang BK (2001) XIAP regulates Akt activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian epithelial cancer cells. Cancer Res 61: 1862–1868.

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Chapter 4

Mechanisms of Resistance to Targeted Tyrosine Kinase Inhibitors Stacey J. Baker and E. Premkumar Reddy

Abstract The recent surge in the development of targeted kinase inhibitors R as therapies for cancer was spurred from the success of imatinib (Gleevec, STI571) for the treatment of Philadelphia chromosome-positive chronic myelogenous leukemia. This drug, for the first time, showed that a small molecule could be designed to inhibit an oncogenic tyrosine kinase (BCR-ABL) that was responsible for inducing malignant transformation of a particular cell type. Targeted inhibitors against the epidermal growth factor receptor (EGFR), gefitinib and erlotinib, soon followed for the treatment of certain forms of lung cancer. While these drugs can effectively treat disease without many of the unwanted side effects associated with more traditional chemotherapeutic agents, it has become clear that some patients become resistant to these agents due to the development of mutations in the kinase domains of their target proteins. This review discusses the development of these inhibitors as well as the mechanisms associated with resistance to them. Secondgeneration kinase inhibitors aimed at overriding resistance to these therapies are also discussed. Keywords Kinase inhibitor · Targeted therapy · Resistance

Introduction It has been recognized for almost a century that cancer is a multistep process, which takes decades to develop. The facts that cancer cells grow at a much faster rate than normal cells and do not obey rules of ordered growth immediately suggested that cancer is an aberration associated with cell growth. This observation, combined with the fact that cancer is a multistep process, suggested the involvement of multiple E.P. Reddy (B) Fels Institute for Cancer Research and Molecular Biology, Temple University School of Medicine, 3307 North Broad Street, Philadelphia, PA 19140, USA e-mail: [email protected]

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genetic events that are associated with the growth deregulation seen in cancer cells. Research during the past few decades has shown that these genetic events include the activation of a group of genes termed “oncogenes” and inactivation of another group of genes termed “growth suppressor genes.” A fine balance between the activities of these two groups of gene products seems to dictate normal cell growth, and disruption of this balance appears to provide a cell with a growth advantage that ultimately results in a neoplastic state.

Discovery of Viral and Cellular Oncogenes Oncogenes were initially discovered through the study of transforming retroviruses that produced tumors in animals. An important breakthrough in cancer research came from the discovery that oncogenes of acute transforming viruses are in fact derived from normal cellular DNA and that this genetic information is transduced by acute transforming viruses via genetic recombination. In the past two decades, several of the acute transforming viruses isolated from different animal species have been characterized and have been found to encode growth factors, their receptors, and other signaling molecules that transduce proliferation and survival signals transmitted by growth factor receptors. Studies within the last few decades have also shown that tumor cells often gain a growth advantage through amplification or mutation of various oncogenes, the end result of which appears to be a profound enhancement in the amplitude of the growth and survival signals. Some examples of these changes include amplification of the epidermal growth factor (EGF; also known as ErbB1) and ErbB2 receptors in lung and breast tumors, mutation of the ras oncogene in a wide variety of human tumors and chromosomal translocations such as the Philadelphia chromosome, which results in the activation of the ABL tyrosine kinase. With an understanding of these molecular changes that accompany cell transformation, cancer drug discovery has undergone a dramatic change in the past few years. While most of the emphasis in the past has been placed on developing drugs that induce cell death based on mechanisms that do not discriminate between normal and tumor cells, recent strategies have emphasized targeting specific mechanisms that have gone awry in tumor cells. The elucidation of signaling pathways that are deregulated in tumor cells as well as the identification of mutations in both oncogenes and growth suppressor genes in these cells has suggested multiple targets and revealed approaches for the development of new classes of drugs including antibodies to receptors and small molecule inhibitors to mutant kinases. The most R (imatinib, STI57; Novartis), successful of these types of agents, by far, is Gleevec and it is because of the tremendous success that this drug has had in the clinic that additional kinase inhibitors have been and are being developed. To date, there are numerous kinase inhibitors undergoing clinical trials, and many more are in various pre-clinical stages of development. Nearly all of these compounds are ATP mimetics and are therefore, for the most part, inhibitory to more than one kinase.

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Because the past 5 years have seen a large amount of research performed in the area of targeted kinase inhibitors, it is regrettably not possible to review all of the agents that are currently being developed. We have therefore limited this article to the discussion of a few rationally designed therapies that have been approved for use in the clinic by the US Food and Drug Administration. All of these agents exemplify the dramatic success of targeted therapies as well as highlight the limitations of targeted kinase inhibitors.

BCR-ABL Targeted Therapy The Philadelphia Chromosome The Philadelphia chromosome (Ph) was discovered in 1960 by Nowell and Hungerford, who analyzed samples derived from seven patients suffering from what was known at that time as chronic granulocytic leukemia. Each patient harbored a similar “minute chromosome,” and none showed any other chromosomal abnormality (Nowell and Hungerford 1960). We now know that this abnormal Ph chromosome results from a reciprocal translocation between chromosomes 9 at band q34 and 22 at band q11. More importantly, this translocation fuses the breakpoint cluster region (Bcr) gene with the Abl gene and creates the BCR-ABL oncogene (Heisterkamp et al. 1985), whose expression is responsible for greater than 90% of chronic myelogenous leukemias (CML) (reviewed in Shah and Sawyers 2003).

BCR-ABL Signaling in CML Oncogenic forms of ABL play a key role in the pathogenesis of CML as these proteins induce growth factor independence (Hariharan et al. 1988, Rovera et al. 1987) and resistance to apoptosis (Bedi et al. 1994, McGahon et al. 1994). These phenotypes are associated with enhanced expression of effector proteins such as Ras, PI-3 kinase, Bcl-2 and the signal transducer and activator of transcription (STAT) family of transcription factors (reviewed in Steelman et al. 2004). Constitutive STAT activation has been documented in lymphoma and leukemic cells, thereby implicating them in leukemogenesis. STATs 1, 3, 5a, and 5b are constitutively activated in BCR-ABL+ hematopoietic cell lines and CML cells, although the majority of BCRABL induced STAT activation is due to STAT-5. Activation of STAT-5 by BCRABL proceeds by a mechanism that is distinct to other effectors and plays a role in mediating the antiapoptotic and cell cycle effects that are imposed by BCR-ABL (Nieborowska-Skorska et al. 1999).

Imatinib Until recently, CML was treated with a variety of chemo- and biotherapeutic drugs (reviewed in Hehlmann 2003). Because the BCR-ABL protein is active in the

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majority of CML cases, and because our understanding of the mechanisms by which this protein functions has increased dramatically, it has been possible to synthesize small molecules that inhibit BCR-ABL kinase activity in leukemic cells without R (STI571, imatinib mesyadversely affecting the normal cell population. Gleevec late; Novartis) (Fig. 4.1) is a small molecule that binds to the kinase domain of BCR-ABL when the protein is in its closed, inactive conformation (Druker et al. 1996). In this conformation, the catalytic central domain is blocked by the regulatory activation (A) loop, and mutations within this loop have been shown to prevent the kinase from adopting an inactive conformation (reviewed in Apperley 2007). As with most kinase inhibitors that are ATP mimetics, imatinib inhibits several tyrosine kinases, including but not limited to platelet-derived growth factor receptor (PDGFR) a and b, c-Kit, Lck, FGFR-1, VEGFR-1, 2, 3, and c-raf (reviewed in Deininger et al. 2005). A recent study has also shown that the drug inhibits the NQO2 oxidoreductase (Rix et al. 2007), which is not a kinase. Imatinib, however, is most active against c-ABL and, more so, its oncogenic forms. BCR-ABL+ cells that are exposed to this drug do not proliferate and have been shown to undergo apoptosis, while normal, IL-3-dependent cells remain virtually unaffected (Druker et al. 1996, Deininger et al. 1997). In the clinic, the Phase I trials aimed at assessing the safety of imatinib were remarkably successful. In those CML patients who were previously treated with interferon (IFN)-α but failed to respond, almost all of the patients who were treated with 300 mg imatinib or greater achieved complete hematological responses. Furthermore, complete cytogenetic responses were observed in 13% of these patients. In Phase II studies evaluating the efficacy of imatinib as a single agent, the results mirrored those obtained in Phase I trials, whereby a significant number of patients in various stages of disease showed cytogenic responses. Phase III trials and those assessing the effectiveness of imatinib in combination with other cytotoxic agents and/or those aimed at improving the efficacy of imatinib monotherapy were equally successful, whereby imatinib proved to be the most effective treatment for various stages of leukemic disease. As a result, imatinib is now considered as a first-line therapy for the majority of CML cases due to its high efficacy level and relatively mild side effects (reviewed in Deininger et al. 2005).

Imatinib Resistance in CML In spite of the fact that the majority of patients receiving imatinib respond to treatment at both the hematological and cytogenetic levels, relapse occurs in a subset of patients with chronic disease, and this number jumps significantly to nearly 100% in those patients whose disease is in the advanced stages (reviewed in Shah and Sawyers 2003). Several studies have attempted to address the mechanism(s) by which CML cells acquire imatinib resistance.

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Imatinib

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Fig. 4.1 Chemical structure of imatinib, dasatinib, nilotinib, gefitinib, and erlotinib

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Nonmutation-Dependent Mechanisms The use of in vitro-generated resistant subclones has revealed that elevated levels of BCR-ABL are sometimes expressed as a result of genomic amplification of the BCR-ABL locus (le Coutre et al. 2000, Mahon et al. 2000, Weisberg and Griffin 2000). Amplification of the locus and/or multiple copies of the Philadelphia chromosome have also been reported in patients, although the percentage of such cases that represent primary imatinib resistance is small (Gorre et al. 2001, Hochhaus et al. 2002). Similar studies also revealed that imatinib resistance is also mediated by increased levels of the Pgp multidrug resistance protein (MDR1; ABCB1) (Mahon et. al. 2000). However, analysis of patient-derived samples does not support such a mechanism in a clinical setting, and even overexpression of MDR1 in in vitro model systems does not confer imatinib resistance (Ferrao et al. 2003). Other studies have examined signaling pathways and proteins that are activated downstream of BCR-ABL. The Src family kinase Lyn is overexpressed in an imatinib-resistant variant of the K562 cell line (Donato et al. 2003), and in advanced stages of CML, it is overexpressed and constitutively phosphorylated independently of imatinib treatment and/or BCR-ABL expression. In the chronic phase of the disease, however, imatinib can actually inhibit phosphorylation of the protein, suggesting that Lyn may play different roles depending on the stage of the disease (Donato et al. 2003, 2004, Wu et al. 2008). Another member of the Src family, Hck, associates with BCR-ABL (Warmuth et al. 1997), and a kinase-defective Hck mutant has been shown to abrogate cytokineindependent growth of BCR-ABL transformed cells (Lionberger et al. 2000). In some blast-crisis samples derived from patients treated with imatinib, levels of phosphorylated Hck expression are increased (Donato et al. 2003), suggesting that like Lyn, Hck may mediate sensitivity to imatinib in a BCR-ABL independent mechanism. In addition to Src family kinases, Janus kinase 2 (JAK2) has also been shown to play a role in imatinib (and second-generation ABL kinase inhibitor) resistance. Inhibition of JAK2 expression in cells ectopically expressing both wildtype and imatinib-resistant forms of BCR-ABL results in their apoptosis (Samanta et al. 2006). Subsequent studies have also revealed that STAT5 is activated in a BCR-ABL independent mechanism in response to GM-CSF in LAMA cell clones and that inhibition of JAK2 using a targeted JAK-2 inhibitor (AG490) antagonized this activation (Wang et al. 2007). Interestingly, GM-CSF mRNA and protein levels were increased in a percentage of imatinib-resistant patients, and these elevated levels were independent of BCR-ABL mutation status (Wang et al. 2007). Other BCR-ABL independent mechanisms that mediate imatinib resistance include (but are not limited to) the activation of the Akt/PI-3 kinase/mtor pathway (Burchert et al. 2005), activation of RUNX transcription factors (Miething et al. 2007), activation of MAP kinase activity (Chu et al. 2004), and inactivation/mutation of p53 (Wendel et al. 2006; reviewed in Burchert 2007).

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Mutation of BCR-ABL In spite of these studies, the majority of reports indicate that the mechanism that accounts for the majority of imatinib-resistant leukemias, in vivo, is mutation of the BCR-ABL gene itself. Mutation within the kinase domain is most common. The first study that analyzed the frequency of these mutations was published in 2001 by Gorre et al. This study examined cells derived from 11 patients in the advanced stages of CML who were being treated with imatinib and had initially responded well to treatment but had relapsed. Although genomic amplification was detected in three of the samples, a specific mutation within the BCR-ABL kinase domain was present in six cases. Specifically, this mutation resulted in the substitution of a threonine residue with isoleucine at amino acid position 315 (T315I). This substitution, based on the crystal structure of the Abl kinase domain complexed with an imatinib derivative, is thought to decrease the binding affinity of imatinib to the BCR-ABL protein whereby the isoleucine substitution prevents hydrogen bond formation between BCR-ABL and imatinib (Schindler et al. 2000). This hypothesis is supported by the fact that cell lines that ectopically express this mutant protein are markedly insensitive to imatinib (Gorre et al. 2001). Since that initial report, more than 50 mutations in the kinase domain of the BCR-ABL gene have been identified in imatinib-resistant leukemic cells. The list of mutations has been extensively summarized and reviewed in 2007 by Apperley (2007). Although the exact mechanism(s) that drive the mutation process is at present unclear, it is widely believed that many (if not all) result from the “selection pressure” exerted by exposure to imatinib. However, recent studies indicate that they may also arise due to genetic instability induced by BCR-ABL (Jiang et al. 2007a, b) and inhibition of the mismatch (MMR) and nucleotide excision repair (NER) processes (Canitrot et al. 2003, Sliwinski et al. 2008, Stoklosa et al. 2008). The imatinib-resistant mutations that arise in BCR-ABL can be classified as those that potentially interfere with the ability of imatinib to interact directly with the BCR-ABL kinase domain and those that are predicted to destroy or hinder the ability of the BCR-ABL kinase domain to adopt a conformation that is required for imatinib binding (reviewed in Shah and Sawyers 2003, Sawyers 2004). The number of mutations that occur with the highest frequency tend to be clustered within the P-loop (ATP-binding site), the imatinib-binding site, the catalytic domain, and the activation loop. In patients, it is clear that certain mutations are present in particular phases of CML. However, it is at present unclear whether some or any of the mutations are causative of disease or whether they merely correlate with certain phases (reviewed in Apperley 2007).

Second-Generation BCR-ABL Inhibitors Aimed at Overcoming Imatinib Resistance Because of the frequency of mutations, efforts have been focused on the identification of novel inhibitors that are active against imatinib-resistant mutants of

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BCR-ABL. Several approaches have been reported to overcome this resistance in at least some CML cases. MEK1, RAF-1, PI-3 kinase, farnesyltransferase, CDK, proteasome, and other inhibitors have been shown to have growth inhibitory effects on certain imatinib-resistant leukemias (reviewed in Deininger et al. 2005). However, the most successful alternatives to imatinib to date are second-generation inhibitors of BCR-ABL that inhibit the protein at lower concentrations than imatinib. Some agents, such as dasatinib, are unrelated to imatinib and are also inhibitory to other tyrosine kinases, making them active against a variety of imatinib-resistant tumors. Nilotinib, on the other hand, was developed as a rationally designed alternative to imatinib that exploited imatinib’s affinity and selectivity for the Abl kinase domain.

Dasatinib Originally termed BMS-354825 (Lombardo et al. 2004, Shah et al. 2004), dasatinib R Bristol-Myers Squibb) (Fig. 4.1) is an orally bioavailable inhibitor of (Sprycel; BCR-ABL and was the first drug to be approved for the treatment of imatinibresistant Ph+ leukemias. Although structurally unrelated to imatinib, dasatinib also inhibits BCR-ABL (albeit at lower concentrations than imatinib), c-Kit, and PDGFR. It is also inhibitory to other kinases such as the Src family tyrosine kinases, the Tec kinases Btk and Tec, and the ephrin A (EphA) receptor tyrosine kinase (Lombardo et al. 2004, Nam et al. 2005, Schittenhelm et al. 2006, Hantschel et al. 2007). Dasatinib does not share structural conformation requirements for BCR-ABL with imatinib and therefore binds to both active and inactive conformations of the protein (Tokarski et al. 2006). As a result, the drug has been successful in inhibiting nearly all imatinib-resistant forms of the protein. In in vitro assays, dasatinib was inhibitory to all imatinib-resistant mutations tested, with the exception of T315I (Shah et al. 2004, O’Hare et al. 2005a). In the clinic, the drug is remarkably and equally effective in patients harboring imatinib-resistant mutations with the exception of T315I, where no effect was observed (reviewed in Ramirez and DiPersio 2008).

Dasatinib Resistance In the clinic, dasatinib has been successful in treating patients who were intolerant to imatinib or whose disease was resistant to imatinib. In clinical trials, the drug has shown the greatest efficacy in patients in the chronic phase of CML. Nonetheless, cases of dasatinib resistance have begun to emerge. Specifically, sequencing of BCR-ABL+ clones isolated from patients receiving dasatinib after failing or showing intolerance to imatinib revealed a predominance of mutations at the T315 and F317 residues. In addition to the T315I mutation, which was consistent with earlier in vitro assays, a second mutation at this residue, T315A, has also been detected in one patient (Soverini et al. 2007, Khorashad et al. 2008). Interestingly, this same

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mutation was isolated during a mutagenesis screen aimed at isolating dasatinibresistant forms of BCR-ABL (Burgess et al. 2005). At the F317 residue, four different mutations have been identified (F317I/L/V/C), although the F317L mutation has been documented in the greatest percentage of cases. These results suggest that these two residues may be key in the ability of dasatinib to inhibit BCR-ABL (Soverini et al. 2006, 2007). It is interesting to note that the F317L was tested as part of the panel of imatinib-resistant mutants in in vitro cell-based assays as well as in animal tumor model systems (Shah et al. 2004, O’Hare et al. 2005a). While it has been noted that higher amounts of dasatinib were required to inhibit kinase activity and cellular proliferation, the levels were not such that they substantially exceeded those for other imatinib-resistant mutations that do not arise in dasatinib sensitive patients. Additional studies have identified mutations at several additional residues (Shah et al. 2007, Soverini et al. 2007, reviewed in Lee et al. 2008), and compound mutations have also been observed in patients undergoing sequential imatinib and dasatinib therapies. However, of these mutations, the V299L is of interest as it occurs at a residue that directly binds to dasatinib. This mutation, however, is sensitive to both imatinib and nilotinib (Shah et al. 2007), further underscoring the differences between these drugs.

Nilotinib R AMN107; Novartis) (Weisberg et al. 2005) (Fig. 4.1) is Nilotinib (Tasigna, structurally related to imatinib and has a greater affinity (approximately 20-fold) for wild-type BCR-ABL (reviewed in O’Hare et al. 2005b). Because of the structural similarities between the two drugs, in addition to inhibiting BCR-ABL, nilotinib is also inhibitory to c-Kit, PDGFR, and the TEL-PDGFRα and FIP1L1-PDGFRα fusion proteins (Stover et al. 2005, reviewed in Lee et al. 2008). Also similar to imatinib, nilotinib binds to BCR-ABL when it is in an inactive conformation; however, its design allows it to bind more tightly, thereby enhancing its inhibitory activity (Manley et al. 2005). As a result, most of the imatinib-resistant forms of BCR-ABL tested in in vitro cell-based assays are also 20-fold more sensitive to this drug, although studies using certain imatinib-sensitive cell lines have shown that the increased potency may be as high as 60-fold (Weisberg et al. 2005, Golemovic et al. 2005). Nilotinib does, however, when compared to wild-type BCR-ABL, show a reduced ability to inhibit P-loop mutations, and this is correlated with a response to the drug in the clinic (Kantarjian et al. 2006). The only exception is the T315I mutation, which is completely insensitive to nilotinib in vitro and in vivo as well as in the clinic (Weisberg et al. 2005; reviewed in Deininger 2008).

Overcoming T315I Resistance The T315 residue is commonly referred to as the “gatekeeper” due to the fact that the T315I mutation confers complete resistance to imatinib, dasatinib, and nilotinib.

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Developing inhibitors that have the ability to overcome such resistance is a challenging and ever-growing area of research. It has become clear that the use of ATP mimetics that have the ability to target the kinase domain drives the emergence of such mutations, and hence, compounds that inhibit the T315I conformation directly are invaluable. Use of x-ray crystallography in conjunction with structure-guided optimization has led to the isolation of SGX393, a compound that inhibits both T315I and T315A. It does not, however, inhibit certain other P-loop mutations. Fortunately, some of these mutations are inhibited by either nilotinib and/or dasatinib, suggesting that this agent, should it be used in a clinical setting, could be used in combination therapy (O’Hare et al. 2008). Other drugs with the ability to inhibit T315I directly that are undergoing clinical trials are inhibitors of the Aurora kinases and include MK-0457, XL228, PHA-739358, and KW-2449 (reviewed in Quint´as-Cardama et al. 2007, Quint´as-Cardama and Cortes 2008). In addition to T315I inhibitors, it may also be possible to target signaling pathways that are either downstream or parallel to BCR-ABL. There are numerous compounds at various stages of development that could possibly fill such a need, including (but not limited to) Src family kinase inhibitors, inhibitors of HSP90, inducers of apoptosis, activators of protein phosphatases and histone deacetylase (HDAC) inhibitors (reviewed in Quint´as-Cardama et al. 2007, Quint´as-Cardama and Cortes 2008).

EGFR-Targeted Therapy EGFR and Cancer The significance of the epidermal growth factor receptor (EGFR) in cancer was suggested years before it was shown that the protein was altered or aberrantly expressed in tumors. During the early 1980s, studies of the avian erythroblastosis virus led to the identification of viral, transforming versions of the EGFR (Anderson et al. 1980, Vennstrom and Bishop 1982, Yamamoto et al. 1983), and subsequent studies showed that introduction of the EGFR gene into NIH-3T3 cells results in ligand-dependent transformation (Di Fiore et al. 1987, Velu et al. 1987, Hudziak et al. 1987). In certain types of tumors, such as gliomas, the EGFR locus is amplified, resulting in high levels of the receptor. Other tumor types express mutant, constitutively active forms of the protein (reviewed in Pao et al. 2004a). Activation of the EGFR stimulates proliferation, metastasis, angiogenesis, and other phenotypes that are beneficial for tumor growth. EGFR is expressed in a variety of cell types and tumors, including (but not limited to) those of the bladder, lung, head and neck, breast, colon, and pancreas. In certain types of malignancies, such as head and neck cancers, the level of EGFR expression is of prognostic value where elevated levels are correlated with decreased survival, whereas in others, this link is not as well defined (reviewed in Pao et al. 2004a).

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Gefitinib and Erlotinib: EGFR-Targeted Therapies R AstraZeneca) (Fig. 4.1) belongs to a class of comGefitinib (ZD1839, Iressa; pounds termed anilinoquinazolinones and is an inhibitor of EGFR enzymatic activity in the low nanomolar range (Ward et al. 1994). Subsequent studies also revealed that gefitinib effectively inhibited the proliferation of a variety of established tumor cell lines (Sirotnak 2003), although this did not necessarily correlate with EGFR expression. Gefitinib, as well as most targeted kinase inhibitors developed to date, acts by reversibly competing with adenosine triphosphate (ATP) for binding to the EGFR, thereby inhibiting its autophosphorylation and activity (Wakeling et al. 2002). Cells that have been exposed to this drug undergo cell cycle arrest and apoptosis (Magne et al. 2002, Di Gennaro et al. 2003) and express lower levels of pro-angiogenic proteins such as VEGF (Ciardiello et al. 2001, Hirata et al. 2002). EGF has been shown to regulate the expression of VEGF, IL-8 and other angiogenic factors. It is therefore not surprising that gefitinib has the ability to regulate tumor growth by controlling the neovasculature (reviewed in Ono and Kuwano 2006).

Response to Gefitinib Treatment In the United States, Gefitinib was initially approved for the treatment of nonsmall lung cancer (NSCLC) in patients who had failed or were not able to receive standard chemotherapeutic regimens. However, as it was later shown that the drug was only modestly effective as a single agent and in combination therapy, the US FDA restricted administration of the drug to only those patients who were currently enrolled in clinical trials or who had benefitted from its prior use. As a result, gefitinib was removed from the US drug market, although it continues to be used in several other countries (reviewed in Dutta and Maity 2007, Gridelli et al. 2007). In general, non-smokers, women, and Asians have an overall more favorable response to EGFR kinase inhibition (reviewed in Fukui and Mitsudomi 2008). However, overall response to gefitinib has been shown to vary considerably in these and other patient cohorts, thereby prompting research to identify biomarkers that could account for this variability and predict sensitivity. Sequence analysis of the EGFR in primary NSCLC tumors derived from patients in Japan and the United States (Lynch et al. 2004, Paez et al. 2004) identified mutations within the tyrosine kinase domain that correlated with sensitivity to gefitinib. For the most part, the abnormalities are in-frame deletions in amino acids 747–750 (del746–750) or a point mutation at position 858 (L858R) (Lynch et al. 2004, Paez et al. 2004, Pao et al. 2004b). These studies also showed that such mutations, which result in increased levels of ligandinduced receptor autophosphorylation, are more common in non-smokers, women, Japanese patients, and those with the adenocarcinoma form of the disease. Since these initial studies, other mutations in the forms of in-frame deletions and insertions, point mutations, and duplications within exons 19–22 of the EGFR gene that

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confer some level of response to gefitinib have been identified. However, the “classical” L858R (exon 21) and the del746–750 (exon 19) mutations still account for roughly 90% of the alterations in this region (reviewed in Irmer et al. 2007). Overall, NSCLC patients treated with gefitinib (or erlotinib) who harbor the del746–750 mutation have a longer median survival when compared to those with the L858R mutation (Riely et al. 2006, J¨anne and Johnson 2006). In addition, immunohistochemical staining of tumor specimens for proteins that are downstream of the EGFR revealed that patients with tumors that stained positive for phosphorylated Akt (pAkt) responded better to gefitinib treatment than those which stained negative; however, there was no such correlation for phosphorylated MAP kinase (Cappuzzo et al. 2004). Similar results were obtained using human NSCLC cell lines, although these studies also revealed an association between p-Akt and ERK1/2 phosphorylation in gefitinib-resistant cells (Ono et al. 2004, Han et al. 2005).

Erlotinib R OSI-774, Genentech) (Fig. 4.1) belongs to the class of quinaErlotinib (Tarceva, zolinones and is a selective but reversible inhibitor of EGFR enzymatic activity in the low nanomolar range (Pollack et al. 1999). It also inhibits the constitutively active EGFRvIII variant that contains an in-frame deletion in exons 2–7 of the EGFR gene (Lal et al. 2002). Erlotinib is currently approved for the treatment of advanced or metastatic resistant NSCLC patients and for use in combination therapy with gemcitabine in treating advanced, unresectable, or metastatic pancreatic cancer (reviewed in Bareschino et al. 2007, Gridelli et al. 2007). In in vitro assays, while the degree to which wild-type EGFR and the delL474-S752 mutant was inhibited by both gefitinib and erlotinib was similar, the L858R exhibited an approximate 10-fold increased sensitivity to both drugs. It is therefore not surprising that in the clinic, patients whose tumors harbor mutant forms of the EGFR respond to treatment with erlotinib, which is similar to what has been observed with gefitinib (Pao et al. 2004b, reviewed in Gridelli et al. 2007).

Gefitinib and Erlotinib Resistance Secondary EGFR Mutations In spite of the success that these drugs have had in treating NSCLCs with EGFRactivating mutations, resistance emerges in a percentage of patients who have initially responded to treatment. Biochemical analysis and structural modeling have resulted in the identification of a second point mutation at amino acid position T790 (T790M) in a patient who had initially responded to gefitinib treatment but who had relapsed (Kobayashi et al. 2005). At that time, resistance was also predicted to occur in patients treated with erlotinib (Kobayashi et al. 2005), and as expected, cases of erlotinib-resistant disease that harbor this mutation have emerged in the

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clinic (Pao et al. 2005a). The same mutation has also been identified in a family with several cases of inherited NSCLC and is thought to act in cis with a second somatic T790M mutation (Bell et al. 2005). In cis cooperation has also been observed with both the L858R and del746–750 EGFR mutations as is evidenced by increased transformation potential and ligand-independent signaling (Godin-Heymann et al. 2007). Termed as EGFR’s “gatekeeper,” T790 is analogous to T315 of BCR-ABL as it is necessary to maintain proper conformation of the ATP-binding cleft and because of its ability to confer resistance to gefitinib and erlotinib. However, T790M does differ from T315I in that it does not have increased enzymatic activity in vitro (Pao et al. 2005a). At a basic mechanistic level, T790M increases the affinity of the EGFR kinase domain for ATP (Yun et al. 2008), and this single mutation accounts for approximately 50% of all resistance to both gefitinib and erlotinib (Kosaka et al. 2006, Balak et al. 2006). In addition to T790M, other mutations that arise in TKIresistant tumors inhibition have been isolated in rare instances (Balak et al. 2006). Non-EGFR Mutation-Dependent Mechanisms In addition to mutation of the EGFR, mutations in RAS family genes are present in approximately 15–30% of lung adenocarcinomas in patients with a history of cigarette smoking. Most of these mutations are located in exons 12 and 13 of the K-ras gene and are associated with resistance to gefitinib and erlotinib (Pao et al. 2005b). Activation of parallel signaling pathways or those that are downstream of the EGFR can also result in resistance to gefitinib and erlotinib. MET locus amplification was originally identified in a gefitinib-resistant cell line, and the resultant increased levels of MET protein signal through Akt via ErbB3. Subsequent analysis revealed that the same pathway was activated in the majority of gefitinib- and erlotinib-resistant tumors with MET amplification and that this amplification occurs in the presence or absence of the T790M mutation (Engelman et al. 2007, Bean et al. 2007). Other proteins/pathways whose activation is associated with EGFR kinase inhibition include, but are not limited, to PI-3 kinase/Akt (Engelman et al. 2006), the insulin-like growth factor receptor (IGF-1R) (Morgillo et al. 2006, Guix et al. 2008), s-Src (in conjunction with c-Met) (Mueller et al. 2008), and MAPK (Morgillo et al. 2006).

Overcoming T790M Resistance Overcoming resistance to the T790M mutation is of critical importance in the area of EGFR kinase inhibition. This mutation is a “generic” mutation, and biochemical assays and x-ray crystallization predict that it will confer resistance or at least reduce the inhibitory properties of most if not all ATP-competitive inhibitors (Yun et al. 2008). Both erlotinib and gefitinib are reversible inhibitors of the EGFR, and T790M reduces the affinity of the EGFR for both. Second-generation irreversible EGFR

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inhibitors, such as HKI-272 (Rabindran et al. 2004, Kwak et al. 2005), have shown efficacy in mouse models and are predicted to overcome this resistance through their ability to form covalent bonds with the EGFR protein (Li et al. 2007, Yun et al. 2008). While these results are encouraging, resistance to HKI-272 has recently been shown in in vitro cell culture systems (Godin-Heymann et al. 2008); it is predicted that other irreversible EGFR inhibitors will be required to overcome the effects of T790M.

Concluding Remarks When we look back at the advances made in cancer research in the past two decades, we cannot but be amazed at the monumental progress that has been achieved in our understanding of how normal cells grow and how events that disrupt this process result in a cancerous state. This fundamental understanding of the molecular biology of cancer has made it possible for us to develop new therapies that will make cancer a much less dreaded disease. While most of the emphasis in the past has been placed on developing drugs that induce cell death based on mechanisms that do not discriminate between normal and tumor cells, recent strategies have emphasized targeting specific mechanisms that have gone awry in tumor cells. A glimpse into the future of these new therapies is shown through the development of imatinib, a specific inhibitor of the BCR-ABL kinase, which has revolutionized the treatment of CML. The remarkable success of this drug can be attributed to several factors: the critical role of BCR-ABL in the pathogenesis of CML, exceptional target specificity of imatinib, and a very high therapeutic index shown by this compound. Imatinib’s success in the clinic has spawned the development of other successful targeted therapies for CML (dasatinib and nilotinib), as well as EGFRtargeted inhibitors (erlotinib and gefitinib) for the treatment of NSCLC. While it is clear that we are witnessing a change in the paradigm of cancer therapy, resistance to these inhibitors presents a challenge to researchers and physicians alike. The emergence of gatekeeper and other mutations in the ATP-binding domains of tyrosine kinases highlights the difficulty in generating inhibitors that can effectively overcome resistant forms of the disease and reminds us that treating TKI-resistant tumors will almost certainly involve inhibition of multiple signaling pathways. Acknowledgments

We are thankful to M.V. Ramana Reddy for preparing Fig. 4.1.

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Chapter 5

Targeting Transglutaminase-2 to Overcome Chemoresistance in Cancer Cells Kapil Mehta and Jansina Y. Fok

Abstract The ability of tumor cells to develop resistance to chemotherapy and radiation therapy and to metastasize to distant organs represents a major roadblock in the successful treatment of cancer. In fact, more than 90% of cancer deaths can be attributed to the failure of drugs to kill tumor cells. One feature common among drug-resistant and metastatic tumor cells is an increased resistance to apoptosis— the ability of cancer cells not only to grow and survive successfully in the stressful environments of foreign tissues (metastasis) but also to display resistance to genotoxic therapies. Therefore, to improve cancer therapy, tumor-encoded genes, whose increased expression in cancer cells contributes to the development of resistance to drug-induced damage (apoptosis), need to be defined. One such gene is TGM2 (NM-004613), whose expression is upregulated in many drug-resistant and metastatic tumors and tumor cell lines. The product of the TGM2 gene, tissue type transglutaminase-2 (TG2), is structurally and functionally a complex protein that is implicated in such diverse processes as apoptosis, wound healing, cell migration, cell attachment, cell growth, angiogenesis, and matrix assembly. In this chapter, we will provide recent evidence linking aberrant expression of TG2 with the development of chemoresistance and a metastatic phenotype in cancer cells. In addition, we will discuss some specific antiapoptotic pathways that are regulated by TG2 and how inhibition or downregulation of this protein can enhance the sensitivity of cancer cells to conventional chemotherapy. Keywords Metastasis · Drug resistance · Focal adhesion kinase · Integrins · NF-κB · AKT · PTEN · Autophagy · Apoptosis

Supported in part by a grant from the Susan G. Komen for Cure. K. Mehta (B) Department of Experimental Therapeutics, Unit 362, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA e-mail: [email protected]

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Introduction Six decades after the first anticancer drug was developed, chemotherapy still remains the major therapeutic tool for treating unresectable or disseminated tumors. Although chemotherapy has a significant impact on the overall survival of patients with certain malignancies, the majority of human cancers exhibit resistance to chemotherapy, either at presentation or acquired after the initial response to therapy. This suggests that resistance is either an intrinsic property of certain tumor cells or that it can evolve under the pressure of drug treatment. In general, tumor cells exhibit resistance not only to one particular drug or to a group of structurally and functionally similar drugs but also to unrelated drugs. This type of resistance is referred to as multidrug resistance (Gottesman et al. 2002). Various mechanisms have been implicated in the development of chemoresistance, including exclusion of a drug from cells by overexpression of P-glycoprotein (P-gp) or other members of the multidrug resistance-related proteins (MRP), altered expression of drug targets, detoxification of drugs due to increased glutathione S-transferase (GST), increased metabolism of the drug to inactive molecules, enhanced ability of tumor cells to repair the damage, and failure of the tumor cells to respond appropriately to the drug-induced damage (that is to undergo apoptosis). Although it is generally accepted that the majority of cancers arise from a single precursor cell, it is na¨ıve to think that a tumor is a collection of genetically identical cells. Genetic instability and the accumulation of mutations are important hallmarks of cancer cells. Thus, dividing cancer cells are able to acquire genetic and epigenetic changes that will favor their malignant phenotype as well as drug resistance (Richardson and Kaye 2005). In addition to acquired genetic and epigenetic changes, host factors such as microtumor environment, tumor vascularity, drug bioavailability, metabolism, elimination, and dose-dependent toxicity are some of the limitations that play a role in the final outcome of conventional chemotherapy. For example, the tumor vascularity may affect the transit time of drugs within tumors and the interaction of tumor cells with the surrounding interstitial cells (Green et al. 1999). Also, vascularity may dictate the extent to which high-molecular-weight monoclonal antibodies and immunotoxins are able to penetrate tumors (Pluen et al. 2001). The realization that most anticancer drugs kill tumor cells by activating apoptotic pathways raised an interesting notion that deregulation of apoptotic pathways may be a key determinant in the development of chemoresistance by tumor cells (Mashima and Tsuruo 2005). Indeed, numerous alterations that confer resistance to apoptosis have been associated with highly malignant phenotype. For example, advanced cancers often exhibit activation of prosurvival signal transduction pathways such as those mediated by Ras, PI3K/Akt, or NF-κB; inactivation of apoptotic pathways (e.g., due to mutation or silencing of p53, pRb, Bax, Bad, Apaf-1, or caspase-8 genes); or overexpression of prosurvival proteins such as Bcl2, IAP, and FLIP (Fesik 2005). Therefore, many recent approaches to improve cancer therapy have been based on combinations of cytotoxic agents with new molecularly targeted agents. The rationale for such combination cancer therapies

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is based on the assumption that agents affecting molecular signaling pathways involved in apoptosis or other cell death mechanisms (Chen et al. 2006), migration and invasion (Sierra 2005), angiogenesis (Broxterman and Georgopapadakou 2005, Morabito et al. 2006), tumor cell signal transduction (Bloom and Kloog 2005, Adjei 2006), and protein turnover (Richardson et al. 2003, Demarchi and Brancolini 2005, Nawrocki et al. 2005, Horton et al. 2006) will lead to synergistic tumor cell death. Experts in the field have summarized their views on various mechanisms that are known to confer drug resistance as well as approaches to overcome this problem elsewhere in this monograph. Though all of these approaches seem logical and promising, the understanding of cellular drug resistance behavior does not necessarily translate to understanding completely the resistance observed in tumors clinically, because tumors consist of a mixed population of cells that may not exhibit very high levels of drug resistance. Therefore, a considerable interest still exists to define novel resistance mechanisms as potential targets for novel compounds with greater effectiveness and for optimal drug combinations. This review will focus on recent information on the significance of increased TG2 expression in the development of drug resistance and the therapeutic potential of this protein to overcome chemoresistance in cancer cells.

TG2—The Protein with a Split Personality TG2 is a structurally and mechanistically complex protein with both intracellular and extracellular functions (Lorand and Graham 2003, Griffin et al. 2002, Mehta 2005). TG2 can catalyze protein cross-linking reactions during apoptosis, act as a G-protein in adrenoreceptor signaling, act as protein disulfide isomerase in maintaining the mitochondrial integrity, or be secreted outside the cell for stabilization of the extracellular matrix (ECM). TG2-mediated cross-linking of ECM plays a role in the deposition and stabilization of the ECM, which in turn promotes cell attachment and spreading (Verderio et al. 2005). TG2 can translocate to the cell membrane where it binds tightly to both the cell-surface integrins and fibronectin and consequently promotes cell attachment, migration, and invasion (Akimov et al. 2000, Akimov and Belkin 2001, Herman et al. 2006). Another enigmatic feature of TG2 is that it can function as both a proapoptotic (Rodolfo et al. 2004) and an antiapoptotic (Boehm et al. 2002, Milakovic et al. 2004, Yamaguchi and Wang 2006) (Fig. 5.1) protein. Under normal physiological conditions, TG2 is enzymatically inactive in the cytosolic compartment (where GTP’s concentration is approximately 100 μM [IC50 (GTP) = 9 μM]) (Lai et al. 1998), and the free calcium level is approximately 100 nM (Vmax [Ca2+ =2 mM]) (Datta et al. 2006). This cytosolic-inactive fraction of TG2 may fulfill another function by serving as a scaffold protein and associating with certain signaling proteins such as integrins, focal adhesion kinase (FAK), fibronectin, and PTEN (Fig. 5.2). Such protein interaction with TG2 can modulate the function and stability of the interacting protein. For example, the association of TG2 with FAK and IκBα proteins

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Normal cell Stress chemical infection Radiation hypoxia

Nrf2(?)

ROS–

TG2 DNA Damage

Low Ca2+ High GTP

Ca2+

Tumor cell

ΔTG2

Repair Macrophages Lymphocytes Endothelial cells

Apoptosis

chemoresistance and metastasis

Integrins NF-κB FAK PTEN

Fig. 5.1 TG2 serves as a prosurvival and proapoptotic signaling protein in normal and cancerous cells. TG2 is an early response gene that is upregulated in response to inflammatory or other stresses. It is likely that transcription factors, such as Nrf-2 or NF-κB, that get activated in response to inflammatory and reactive oxygen species (ROS- ) are responsible for the upregulation of the TGM2 gene. Under normal circumstances, the low cytosolic calcium (nM) and high GTP concentrations do not permit the activation of TG2 to its protein cross-linking configuration. However, this catalytically inactive TG2 can perform other functions; the signaling pathways can be activated by associating with integrins and other such signaling molecules. The TG2-mediated activation of these pathways can confer resistance to apoptosis. When ROS- -inflicted cell damage has been repaired, the cells return to ground level, including shutting down of TGM2. Nevertheless, if the stress or cellular damage is too severe, the calcium probably can escape inside the cell due to leaky membranes, leading to the activation of TG2 and massive cross-linking of cellular proteins and apoptosis. In contrast, once the TG2 expression is turned on in tumor cells (probably in response to ROS- , hypoxia, chemotherapy, or radiation), there is no mechanism to turn it off. We have shown previously that in chemoresistant tumor cells, the activation of TG2, by increasing cytosolic calcium, is an extremely lethal event

has been implicated in constitutive activation of FAK/Akt and NF-κB, respectively (Mhaouty-Kodja 2004, Kim et al. 2006, Mann et al. 2006, Verma et al. 2006) and confers protection against stress-induced cell damage (ROS- , hormones, TNF, drugs) (Fig. 5.1). Under extreme stress or trauma conditions, Ca2+ homeostasis may be perturbed due to leaky membranes, resulting in the activation of cytosolic enzymes and cross-linking of intracellular proteins as observed during apoptosis (Fesus and Piacentini 2002) or necrosis (Nicholas et al. 2003). Interestingly, despite the wide range of functions in which TG2 participates, the TGM2 gene in mice showed no phenotype, and the mice were anatomically, developmentally, and reproductively normal (De Laurenzi and Melino 2001, Nanda et al. 2001).

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GRP56 Protein kinase A (Xu et al. 2006) (Lewis et al. 2005) C-Jun (Ahn et al. 2008) IGFBP-3 HIF-1β (Mishra et al. 2004) (Filiano et al. 2008)

Fig. 5.2 TG2 interacts with a large number of proteins and modulates their function and stability. The association of TG2 with various cellular proteins has been documented both biochemically and immunochemically. Such interaction with TG2, in general, has been shown to modulate the function, activity, or stability of the partner protein. For example, association of TG2 with IκBα could prevent its binding to NFκB, resulting in its constitutive activation. In contrast, association of TG2 with tumor-suppressor protein, PTEN, prevents its phosphorylation and promotes ubiquitination and proteasomal degradation. Association with cell surface integrins increases their stable interaction with the ECM proteins

TG2, Drug Resistance, and Metastasis Besides resistance to chemotherapy and radiation therapy, dissemination and metastasis of tumor cells in distant organs represent a significant clinical problem. Although studies of drug resistance and metastasis have generally proceeded along separate research pathways, there are several reasons to believe that the two phenotypes may have some common features. Evidence exits supporting the idea that expression of certain oncogenes or altered expression of suppressor genes can promote not only tumor cell growth and aggressiveness but also the relative expression of drug resistance (Fesik 2005). For example, advanced cancers, which might be expected to harbor a number of genetic alterations such as overexpression of bcl-2 and inactivation of p53, should also express an “apoptosis-resistant” phenotype. Not only would this property endow the tumor cells with an increased ability to grow and survive in foreign tissue sites but it would also allow the cells to express a drugresistant phenotype. Indeed, tumor cells selected for resistance to drugs in vitro are more malignant in vivo. Conversely, metastatic tumors generally exhibit higher resistance to drug and radiation therapies than their primary counterparts. In view of this information, it is tempting to speculate that overexpression of TG2 represents one such link between drug-resistant and metastatic tumor cells. Thus, a number of cancer cell lines, including breast carcinoma, malignant melanoma, pancreatic ductal adenocarcinoma, glioma, lung adenocarcinoma, ovarian carcinoma, prostate carcinoma, and colorectal carcinoma—when selected for resistance to drugs—expressed elevated levels of TG2 (Verma and Mehta 2007a, Mehta in

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press). Similarly, metastatic tumors from patients with breast cancer (Mehta et al. 2004), malignant melanoma (Mehta et al. 2006, Fok et al. 2006), and ovarian carcinoma (Hwang et al. 2008) showed a more significant increase in TG2 than their primary counterparts. Importantly, downregulation or inhibition of TG2 in various cancer cell types is associated with their increased sensitivity to chemotherapeuticinduced cell death and inhibition of invasion (Herman et al. 2006, Kim et al. 2006, Hwang et al. 2008, Yuan et al. in press). In line with these observations, Satpathy et al. (2007) observed that increased expression of TG2 in ovarian cancer cells enhanced their adhesion to fibronectin and promoted directional cell migration. Accordingly, ovarian cancer cells with knockdown TG2 showed diminished tumor dissemination on the peritoneal surface and in mesentery in an intraperitoneal ovarian xenograft mouse model. Furthermore, in patients with ovarian cancer, overexpression of TG2 in tumor samples was associated with significantly worse overall patient survival in both univariate and multivariate analyses (Hwang et al. 2008). Similarly, overexpression of TG2 in pancreatic tumor samples was strongly associated with nodal metastasis and lymphovascular invasion (Verma et al. 2006). These results imply that expression of TG2 in cancer cells is aberrant and involved in promoting drug resistance and metastatic phenotype. It is generally believed that mechanisms responsible for promoting growth, survival, and invasion of cancer cells also operate in normal cells. The major difference is that in normal cells, these pathways are under tight control; once the stimulus or event that triggered these pathways dies down, cells revert to a quiescent state. In contrast, as a result of genetic or epigenetic changes, cancer cells generally become independent of the need for such stimuli to activate their growth and survival (Hanahan and Weinberg 2000). In this context, the significance of increased TG2 expression in normal cells is worth mentioning. Several years ago, we observed that macrophages collected from inflammatory sites accumulated large amounts of TG2 protein (Khera and Mehta 1989). In view of our current understanding on TG2 overexpression in highly malignant cancer cells, induction of TG2 in inflammatory macrophages may be related to their ability to migrate to the site of inflammation and the protection from cytotoxic mediators that they produce in response to infectious agents. Indeed, many recent reports have documented a direct role for TG2 in promoting migratory functions of normal cells. Expression of TG2 in T lymphocytes has been shown to play a role in their transmigration across the endothelial cells (Mohan et al. 2003). TGF β-induced expression of TG2 in retinal pigment epithelial cells has been linked to their increased migration on fibronectin-coated matrices (Priglinger et al. 2004). Retinoic acid-induced TG2 expression in neuroblastoma SH-SY5 cells augments their migration and invasion functions (Joshi et al. 2006). Similarly, ectopic expression of TG2 in mesenchymal stem cells (MSCs) promotes their adhesion on fibronectin and significantly increases phosphorylation of focal adhesion-related kinases FAK, Src, and PI3K (Song et al. 2007). Moreover, TG2-transfected MSCs can effectively restore the cardiac functions of infarcted myocardium (Song et al. 2007). In an attempt to identify metastasis-associated proteins by proteomic analysis, Jiang et al. (2003) observed that TG2 was 1 of the 11 proteins that were selectively

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amplified in metastatic human lung carcinoma. Similarly, comprehensive analysis of more than 30,000 genes by three different techniques revealed that TG2 was one of the most differentially expressed genes in pancreatic tumors (IacobuzioDonahue et al. 2003). Treatment of cancer cells with epidermal growth factor (EGF) induced the expression of TG2 and protected cells from doxorubicin-induced apoptosis (Antonyak et al. 2004). These observations imply that expression of TG2 promotes cell resistance to chemotherapeutic drugs and invasion. On the other hand, there have also been reports suggesting that in certain types of cancer, downregulation of TG2 expression is related to the onset of chemoresistance and to an altered ability of tumor cells to metastasize (Birckbichler et al. 2000, Jones et al. 2006). Given the known functions of TG2, reduced levels or activity of TG2 can confer resistance to apoptosis and less stable ECM, resulting in attenuated cell adhesion and increased migration, which facilitates the initial step in metastasis. Xu and Hyne (2007) recently identified GPR56 as a protein downregulated in metastatic melanoma cells and showed that TG2 was a binding partner for GPR56, suggesting that TG2 can act as a tumor-suppressor protein through its interaction with GPR56. These conflicting results of TG2 expression in cancer cells suggest that the relevance of TG2 to cancer biology may depend on the type, location, and possibly the stage of cancer. Therefore, a precise understanding of TG2 functions in the context of cancer stage and type is important for the implementation of TG2-based interventions to disrupt malignant invasion, growth, and survival.

TG2-Mediated Cell Signaling Integrins are one important class of molecules; TG2 can bind to them and modulate their activity (Fig. 5.2). Integrins are a family of cell-surface proteins that serve as receptors for the ECM proteins (fibronectin, vitronectin, laminin, collagen), and they influence several aspects of cancer cell behavior, including motility, invasion, growth, and survival in response to binding to the ECM ligands. In general, integrins are low-affinity receptors (106 –109 L/mol) and bind to their ligands only when they exceed certain critical numbers in the form of focal contacts or hemidesmosomes. In response to certain stimuli (growth factors, cytokines), integrins cluster together, and their combined weak affinities give rise to a spot on the cell surface (focal contact) that now has enough avidity to support stable interaction of cells with the ECM. Thus, under normal circumstances, the interaction of integrins with their ECM ligands is tightly regulated to ensure controlled growth, survival, and migration of cells. Interestingly, certain cellular proteins can selectively bind to integrins and promote their affinity for ECM. The first such interaction was identified between β3 integrin and integrin-associated protein (IAP), also referred to as CD47 (Brown and Frazier 2001). The signaling cues transmitted by a specific integrin inside the cells can be modulated as a result of such an interaction between the integrin and the protein. For example, the cytoplasmic protein, nischarin, was recently shown to promote the cell motility of fibroblasts as a result of its interaction with the

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cytoplasmic domain of integrin α5β1 (Alahari et al. 2000). In view of these observations, the finding that TG2 can associate in a complex with β1 integrin (Akimov and Belkin 2001, Fok et al. 2006, Herman et al. 2006, Mangala et al. 2007) may have important implications in terms of TG2-mediated cell signaling to promote proliferation, survival, and migratory functions in cancer cells. Depending on the cell type, 20% to 30% of β integrin on the cell surface can exist in a complex with TG2 (Akimov and Belkin 2001). The association of TG2 with integrins is known to occur primarily at extracellular domains of integrins and promotes their interaction with the ECM ligands, such as fibronectin, collagen, and vitronectin (Akimov and Belkin 2001). Importantly, the interaction between TG2 and integrin is independent of the cross-linking activity of TG2 and results in increased cell adhesion, migration, and activation of the downstream survival signaling pathways (Akimov and Belkin 2001, Herman et al. 2006). More recently, syndecan-4 (S4) has been shown to interact with TG2-bound integrins, raising the possibility that TG2 contributes to cell adhesion via binding to S4. Indeed, S4-null fibroblasts showed a lower level of FAK (Y397) during the initial phase of cell adhesion to fibronectin than wild-type cells did. A similar defect was observed in early FAK (pY397) activation in TG2null mouse embryonic fibroblasts when compared with the wild-type fibroblasts; the early FAK activation was accompanied by hyperactivation of RhoA and decreased cell migration (Verderio et al. in press). TG2 is also known to regulate FAK activity. FAK is a 125-kDa nonreceptor protein tyrosine kinase that plays a significant role in cell survival, migration, and invasion (Sieg et al. 2000, Hsia et al. 2003). FAK-induced signaling pathways are initiated at sites of integrin-mediated cell adhesions—the so-called focal adhesions—and carry out protein–protein interaction adaptor functions at sites of cell attachment to the ECM. FAK is activated by integrin clustering and transmits adhesion and growth factor-dependent signals into the cell interior to promote cell growth, cell survival, and invasive functions (Schlaepfer et al. 1999, McLean et al. 2005). Overexpression and increased activity of FAK has been observed frequently during the advanced stages of a wide variety of human cancers. For example, increased levels of FAK were reported in 17 of 20 invasive tumors and in all 15 metastatic tumors of different origins but were reported in none of the six normal tissue samples (Owens et al. 1995). Given the important role of FAK in many processes involved in tumorigenesis and metastasis, FAK can be a potential target in the development of anticancer drugs. FAK itself can be regulated by a range of mechanisms, including tyrosine phosphorylation, serine or threonine phosphorylation, and protein–protein interactions. For example, association of TG2 with β1 integrin has been shown to promote the activation of FAK in response to fibronectin binding (Akimov and Belkin 2001, Herman et al. 2006). Our recent results suggested a direct role of TG2 in the activation of FAK and the activation of its downstream PI3K/Akt pathway in cancer cells (Verma et al. 2006). This activation of FAK is mediated by direct interaction between TG2 and FAK. Although the precise mechanism by which TG2 affects autophosphorylation of FAK is not yet understood, the observation that TG2 physically associates with FAK suggests that such interaction may induce a conformational change in FAK protein and lead to its autophosphorylation.

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Once phosphorylated, FAK can recruit Src kinase that phosphorylates other tyrosine sites on FAK (Y407 , Y576 , Y577 , Y861 , and Y925 ), leading to the recruitment and activation of several downstream signaling proteins such as RAS/ERK, PI3K/Akt, and Crk/Dock180/Rac, which can impact the growth, survival, and invasion of cancer cells (McLean et al. 2005). Adding further complexity to the role of TG2 in PI3K/Akt pathway regulation, we recently observed a novel function of TG2 in regulating PTEN expression. TG2 inversely regulates the expression of PTEN (Verma et al. 2008a). PTEN is known for its tumor-suppressive functions such as inhibition of cell growth, invasion, migration, and focal adhesions (Tamura et al. 1998), primarily by dephosphorylation and inactivation of signaling proteins Akt and FAK. Gene deletion and mutation of PTEN are observed frequently in various human cancers. These genetic changes, however, are not necessarily the only way to inactivate PTEN during tumorigenesis; some subtle changes induced by posttranslational modifications can also affect the stability and degradation of PTEN, which can profoundly affect tumorigenesis. Indeed, a recent report demonstrated that the oncogenic protein NEDD4-1 acts like E3 ubiquitin ligase for PTEN and promotes its degradation via ubiquitin-proteasomal pathway (Wang et al. 2007). Very little is known about the regulation and stability of PTEN, except that phosphorylation can affect its stability in certain cell types (Vazquez et al. 2001, Okahara et al. 2004). Published evidence suggests that phosphorylation of PTEN by casein kinase 2 (CK2) is important for the stability of PTEN protein in the face of proteasome-mediated degradation. In a separate study, phosphorylation of the PTEN tail was shown to negatively regulate PTEN stability (Vazquez et al. 2001). Phosphorylation of PTEN by CK2 prevents its interaction with MAGI-2 and its consequent recruitment into a high-molecularweight complex (PTEN-associated complex). In line with these reports, our results revealed that TG2 expression could affect the stability of PTEN protein by preventing its phosphorylation at ser380 (Torres and Pulido 2001, Vazquez et al. 2001) and promoting ubiquitination-mediated proteasomal degradation. Thus, TG2 can promote the activation of the FAK/PI3K/Akt pathway in two complementary ways: one by direct phosphorylation of FAK and second by inhibiting the phosphatase activity of PTEN, resulting in constitutively activating the PI3K/Akt pathway (Fig. 5.3).

TG2-Mediated NF-κB Activation The transcription factor NF-κB plays an important role in regulating genes that are involved in controlling cell growth, apoptosis, and metastatic functions (Baeuerle and Baltimore 1996, Bours et al. 2000, Karin et al. 2002). Constitutive activation of NF-κB has been observed in various cancers, and NF-κB’s role has been implicated in drug resistance and metastasis (Bours et al. 1994, Bargou et al. 1997, Mori et al. 1999, Pahl 1999, Mukhopadhyay et al. 2001). TG2 expression contributes to constitutive activation of NF-κB in a classical IKK-independent manner (Verma and Mehta 2007a). The expression of TG2 in various cancer cell types is associated

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Drug resistance Fig. 5.3 TG2-mediated signaling promotes cell survival and invasion. Association of TG2 with integrins (e.g., β1, β3, β4, β5) can increase their avidity for extracellular matrix proteins, such as fibronectin (FN), and result in the activation of downstream cell survival FAK/PI3K/Akt signaling pathways. TG2 can also affect FAK phosphorylation directly by associating with FAK or indirectly by promoting PTEN degradation via the ubiquitin-proteasomal pathway. Either collectively or individually, activation of these signaling molecules in the PI3K/Akt axis leads to the phosphorylation of various downstream substrates, activation of MDM2 and NF-κB, and the inhibition of FKHR and BAD proteins, resulting in increased cell survival and chemoresistance. On the other hand, TG2—by cross-linking IκBα (inhibitor of kappa B alpha)—can induce dissociation of the NF-κB/IκBα complex, resulting in constitutive activation NF-κB in a nonclassical IKK-independent manner

with constitutive activation of NF-κB. Cancer cells with high basal levels of TG2 or forced expression of TG2 in low TG2-expressing cancer cells are associated with high NF-κB activity. The NF-κB activation in these cells has been significantly inhibited by TG2 enzyme inhibitors but not by dominant-negative IκBα (Mann et al. 2006). Conversely, a knockdown of endogenous TG2 expression by siRNA resulted in strong inhibition of NF-κB. Interestingly, in situ activation of the TG2 transamidating activity by calcium ionophore A23187 treatment caused strong activation of NF-κB, while inhibition of the activity by enzyme-specific inhibitors such as 5-(biotinamido) pentylamine (BPA) and monodansylcadaverine (MDC) attenuated NF-κB activation. Thus, TG2 expression and its cross-linking function are critical for NF-κB activation. Indeed, IkBα has been shown to be a good substrate for TG2-catalyzed cross-linking reaction (Lee et al. 2004). Thus, in vitro incubation of IkBα protein with purified TG2 and calcium could effectively catalyze the crosslinking of IkBα into high-molecular-weight polymers. Importantly, the polymeric

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form of IκBα had much lower binding affinity for the p65:p50 complex, suggesting that TG2-mediated posttranslational modification of IkBα may hamper its ability to associate with the p65:p50 complex and consequently result in constitutive activation of NF-κB. In a recent report, Kim et al. (2006) observed that inhibition of TG2 activity attenuated NF-κB activation and reversed the sensitivity of drug-resistant breast cancer cells to doxorubicin. Based on these observations, these authors concluded that TG2 expression and its cross-linking function are critical for NF-κB activation and for conferring drug resistance phenotype to cancer cells. We observed a similar reversal in drug resistance of MCF-7/DOX cells in response to downregulation of endogenous TG2 by siRNA (Herman et al. 2006). Alternatively, TG2 can directly associate with the p65:p50 complex in the cytoplasm and with p65 in the nucleus. The significance of this association is still not clear. We speculate that such an association could mitigate the binding of IκBα to the NF-κB complex in the cytoplasm, leading to its constitutive activation. Under certain conditions, TG2 can also serve as a kinase, and its serine–threonine kinase activity can phosphorylate histones and p53 (Mishra et al. 2006). Because p65 undergoes phosphorylation by various kinases at the Ser536 site (Bohuslav et al. 2004, Douillette et al. 2006), it is likely that p65 serves as a substrate for TG2 kinase activity. In addition, since TG2 is a bulky protein (80 kDa), it could cause stearic hindrance or conformational changes when complexed with p65 and permit preferential binding of the NF-κB (p50:p65) complex only to high-affinity or selective promoters resulting in differential transcriptional regulation and expression of target proteins that are involved in drug resistance and metastasis. All these possibilities warrant further investigation to harness NF-κB as a target for cancer therapy.

TG2 Promotes Cell Survival and Chemoresistance While delivery of a drug to its target is critical to its therapeutic effect, the ability of a drug to successfully induce cancer cell death generally requires transduction of intrinsic signaling pathways to initiate the cellular program that causes the cancer cell to die. Therefore, novel therapeutic strategies that promote apoptosis and block pathways or proteins that inhibit apoptosis could significantly enhance cancer sensitivity to conventional therapies. In this regard, TG2 overexpression could offer a promising therapeutic target for enhancing the sensitivity of cancer cells to drugs. As discussed above, overexpresssion of TG2 in various cancer cell types is associated with constitutive activation of antiapoptotic signaling pathways (FAK, NF-κB, PI3K, etc). Indeed, Ai et al. (2008) recently concluded that epigenetic silencing of the TGM2 gene in breast cancer cells could serve as a marker for chemotherapeutic drug sensitivity. Moreover, downregulation of TG2 by siRNA, antisense, or ribozyme results in the reversal of chemoresistance, while ectopic expression conferred resistance to drugs in breast, melanoma, glioblastoma, and ovarian cancer cells (Herman et al. 2006, Kim et al. 2006, Verma and Mehta 2007a, Hwang et al. 2008, Yuan et al. in press).

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In addition to apoptosis, TG2 expression can protect cancer cells from another type of cell death called autophagy. Autophagy is type II programmed cell death that plays a crucial role in various physiologic processes such as development, cell homeostasis, elimination of unwanted cells, and carcinogenesis. Cancer cells exhibit defective autophagic cell death compared with their normal counterparts (Edinger and Thompson 2003), which may contribute to chemoresistance. Autophagic cell death is caspase independent and is characterized by autophagosome formation that surrounds various cellular organelles such as Golgi complexes, polyribosomes, and endoplasmic reticulum (Kondo et al. 2005). Subsequently, autophagosomes merge with lysosomes and digest the organelles, leading to cell death (Kroemer and Jaattela 2005). A recent observation made by our research group showed that inhibition of TG2 expression by siRNA resulted in induction of autophagy in pancreatic cancer cells and eventual cell death (Akar et al. 2007). Notably, inhibition of PKCδ activity by rottlerin or its downregulation by PKCδ siRNA was associated with a parallel decrease in TG2 expression and induction of autophagy in pancreatic cancer cells. These observations suggest that PKCδ is an upstream regulator of TG2 expression, and therefore targeting PKCδ, TG2, or both may represent an effective approach to treat patients with pancreatic cancer.

Therapeutic Significance of TG2 Based on the information discussed in the preceding sections, we speculated that inhibition of TG2 could be a promising therapeutic target for reversing chemoresistance in cancer cells. Initially, we determined the significance of TG2 expression on growth and survival of pancreatic ductal adenocarcinoma (PDAC) in vivo by downregulating TG2 expression. TG2 knockdown PDAC cells were tested for their ability to grow in xenografts in a nude mouse model (Verma et al. 2008a). The MDAPanc28 subclone (Panc28/cl.10), which expressed 70% to 80% lower TG2 than the vector alone or parental MDA-Panc28 cells, was selected for stable transfection with TG2-shRNA. Xenografts were grown subcutaneously in athymic nude mice using MDA-Panc28/cl.10 or Panc-28/empty vector (control) cells. Panc28/cl.10 xenografts showed significantly more retarded growth than Panc28/empty vector controls (Verma et al. 2008a). The tumor growth was followed for 5 weeks, and mice were killed during the sixth week. The Panc28/cl.10 cells consistently produced xenografts with 60% to 80% lower tumor volumes than the empty vectorPanc28 xenografts (P = 0.017). The tumors were analyzed for TG2 expression to ascertain that TG2 expression remained suppressed in vivo in Panc28/cl.10 tumors. Indeed, TG2 expression in Panc28/cl.10 xenografts was significantly lower than in control tumors. Next we determined the therapeutic potential of elevated TG2 expression for treatment of orthotopically grown pancreatic and ovarian tumors. Using small (≤100 nm) neutrally charged liposomes (dioleoyl-phosphatidylcholine, DOPC) as the delivery system, we found that TG2 siRNA could effectively downregulate TG2

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expression in orthotopically growing tumors. Thus, inhibition of TG2 by siRNADOPC retarded growth and rendered PDAC tumors sensitive to gemcitabine. Importantly, the number of metastatic foci that was recovered from mice treated with DOPC-TG2 siRNA alone or in combination with gemcitabine was more dramatically reduced (>80%) than was that for mice treated with DOPC control siRNA or gemcitabine alone (Verma et al. 2008b). Similarly, treatment of mice with TG2 siRNA-DOPC alone or in combination with docetaxel demonstrated significant antitumor activity against orthotopically growing chemotherapy-sensitive (HeyA8) and resistant (HeyA8-MDR) ovarian tumors (Hwang et al. 2008). To determine potential mechanisms underlying the antitumor effect of TG2 siRNA-DOPC, we examined its effect on several biological endpoints, including cell proliferation (Ki-67), angiogenesis (CD31), and apoptosis (TUNEL). More than 90% reduction in Ki-67 expression was evident (P < 0.001) in tumors obtained from mice given TG2 siRNA-DOPC alone or in combination with chemotherapy. Tumors obtained from mice treated with chemotherapy alone or with control siRNA-DOPC consistently showed high levels of Ki-67 staining. We also evaluated the blood vessel density in tumors obtained from mice treated with control siRNA, chemotherapy alone, TG2 siRNA, or a combination of chemotherapy plus TG2 siRNA. The results obtained revealed a significant decrease in the mean blood vessel density in tumors recovered from mice that received TG2 siRNA-DOPC or chemotherapy alone (Verma et al. 2008b, Hwang et al. 2008). Densitometric analysis showed a significant difference in the mean microvascular density (P < 0.0013) in the mice that received TG2 siRNA-DOPC plus chemotherapy. Finally, we evaluated apoptosis in orthotopic tumors using TUNEL staining. In ovarian tumors, minimal cell apoptosis was apparent in empty liposome, control siRNA-DOPC, or control siRNA-DOPC with docetaxel treatment groups. However, treatment with TG2 siRNA-DOPC alone and TG2 siRNA-DOPC plus docetaxel resulted in a significant increase in apoptosis (P = 0.027 and P = 0.001, respectively). Interestingly, the increase in apoptosis in the TG2 siRNA-DOPC plus docetaxel group was greater than in the TG2 siRNADOPC alone group (P = 0.004). In summary, targeted therapy with TG2 siRNA in combination with chemotherapy significantly reduces tumor growth and metastasis in chemotherapy-sensitive and chemotherapy-resistant models. Given the clinical relationship between TG2 expression and poor prognosis of patients with cancer, our findings raise the possibility that TG2 silencing combined with chemotherapy could be a promising therapeutic option for treatment of these lethal cancers. In this regard, the use of siRNA to silence TG2 expression holds great promise for the development of therapeutics directed against drug-resistant and metastatic cancer cells. The use of siRNA as a method of gene silencing has rapidly become a powerful tool for determining protein function and gene discovery (Stenvang and Kauppinen 2008). The sequence specificity of siRNA makes it an attractive tool for cancer therapy. Moreover, siRNA-based therapeutics have the potential to inhibit molecular targets that are not treatable with conventional small molecules, antibodies, or other biological agents. However, a major challenge in using siRNA-based drugs in humans is the effective delivery to the target tissue. Naked siRNA is unstable in plasma. Chemically modified siRNA are thought to be relatively stable when

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administered systemically in mice but they must be administered in large doses (30–50 mg/kg). Such large doses of siRNA may not be practical for therapeutic applications in humans because of the costs and safety issues (Morrissey et al. 2005). However, encapsulation of siRNA in lipid-based nanoparticles of ≤100 nm in diameter provide effective dosing in mice and monkeys at far lower amounts (0.2– 2 mg/kg). For example, the use of DOPC nanoliposomes for delivering TG2 siRNA to orthotopically growing pancreatic (Verma et al. 2008b) and ovarian (Hwang et al. 2008) tumors in mice was effective in silencing the target gene and inhibiting tumor growth. Similarly, two recent reports further documented the effective delivery of siRNA for silencing the oncoprotein EphA2 (Landen et al. 2005) and FAK (Halder et al. 2006) using DOPC nanoliposomes. More recently, Gray et al. (2008) reported that DOPC nanolipsomes were effective in delivering the siRNA to silence neurophilin proteins, which then inhibits metastasis of colorectal cancer in mice. The same group observed that this delivery system was equally effective in silencing the expression of interleukin-8 in orthotopically growing ovarian tumors (Merritt et al. 2008). From these studies, it is apparent that the effectiveness of DOPC liposomes for delivering TG2 siRNA can be rapidly translated into the clinical setting for treatment of chemoresistant cancers.

Conclusions The observations of elevated TG2 expression in drug-resistant cancer cells have raised great interest in terms of understanding its contributions to the development of drug resistance. TG2 expression results in a constitutive activation of FAK/PI3K/AKT and NF-κB cell-survival signaling pathways as outlined in Fig. 5.3. Either collectively or individually, these pathways confer resistance to chemotherapeutic drugs by preventing apoptotic or autophagic death. Increased threshold of cancer cells to undergo apoptosis enables them not only to display resistance against many anticancer drugs but also to proliferate and survive successfully in stressful environments of foreign tissues (metastasis). Therefore, TG2 may represent an attractive target to investigate for overcoming drug resistance in cancer cells.

References Adjei, A. A. 2006. Novel combinations based on epidermal growth factor receptor inhibition, Clin. Cancer Res. 12:4446–4450. Ahn, J.-S., Kim, M.-K., Hahn, H.-H., Park, J.-H., Park, K.-H., Cho, B.-R., Park, S. B., Kim, D.-J. 2008. Tissue transglutaminase-induced down regulation of matrix metalloprotease-9. Biochem. Biophys. Res Commun. 376:743–747. Akar, U., Ozpolat, B., Mehta, K., Fok, J. Y., Kondo Y., and Berestein, G. L. 2007. Tissue transglutaminase inhibits autophagy in pancreatic cancer cells. Mol. Cancer Res. 5:241–249. Akimov, S. S., Krylov, D., Fleischman, L. F., and Belkin, A. M. 2000. Tissue transglutaminase is an integrin-binding adhesion coreceptor for fibronectin. J. Cell. Biol. 148:825–838. Akimov, S. S., and Belkin, A. M. 2001. Cell surface tissue transglutaminase is involved in adhesion and migration of monocytic cells on fibronectin. Blood 98:1567–1576.

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Chapter 6

Extracellular Matrix-Mediated Drug Resistance P.S. Hodkinson and Tariq Sethi

Abstract The prognosis from cancer remains poor largely due to the resistance of tumour cells to drug therapy. Single cell studies of drug-resistant clones have revealed important mechanisms of acquired chemoresistance but have failed to take into account the contribution of host factors. The tumour microenvironment plays a crucial role in cancer growth and spread. Extracellular matrix (ECM) proteins have been identified within the tumour stroma and have been shown to promote tumour growth in vivo. Initial work by our group and others clearly demonstrated that ECM could protect cancer cells from the pro-apoptotic effects of several diverse chemotherapy agents in vitro. Subsequent research has elucidated the mechanisms by which ECM mediates these effects. This chapter will review these pathways in detail giving particular mention to the role of ECM–integrin interactions. The challenge for the future will be in applying these advances to develop effective therapeutic strategies for drug resistance. Keywords Extracellular matrix · Drug resistance and cancer Abbreviations ECM, extracellular matrix; PI3-K, phosphoinositide-3-OH kinase; SCLC, small cell lung cancer; GMCSF, granulocyte macrophage colony stimulating factor; GSK-3β, glycogen synthase-3β; CDK, cyclin-dependent kinase; ILK, integrin-linked kinase; FAK, focal adhesion kinase; PDK, PI3-K-dependent kinase; IgG, immunoglobulin G

T. Sethi (B) Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 6, 

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Introduction The discovery of cytotoxic drug therapy as an effective treatment modality for cancer more than five decades ago heralded a new era for the management of malignant disease (Goodman et al. 1984). Subsequently, chemotherapy has become the mainstay of treatment for many patients with metastatic solid organ tumours and haematological malignancies (Joensuu 2008). In addition, cytotoxic drug therapy has replaced surgery as the primary treatment option for some solid malignancies (e.g. small cell lung cancer (SCLC)), and adjuvant chemotherapy has been shown to provide additional survival benefit following the surgical removal of locally advanced cancer (Osterlind 2001, Molina et al. 2006) . In haematological malignancies, combination chemotherapeutic regimes can frequently produce sustained objective tumour remission, resulting in long-term survival (Savarese et al. 1997). Unfortunately, this situation is not reflected in solid organ cancers. Here, although chemotherapy can induce significant initial tumour responses, residual or recurrent disease resistant to further drug treatment results in cancer progression and ultimately death (Tannock 2001). It is estimated that intrinsic or acquired resistance of cancer cells to cytotoxic drug therapy accounts for 90% of treatment failure. This is highlighted in SCLC, where an initial objective response rate (ORR) of up to 95% can be achieved in limited disease with a combination of etoposide and a platinum-based agent (Bunn and Carney 1997). Unfortunately, the response duration is short (1–2 years) and trials of second-line chemotherapy have been disappointing (ORR 20%) resulting in poor 5-year survival (less than 5%) (Lally et al. 2007). It is clear that understanding the mechanisms of drug resistance is of paramount importance. Multiple factors can affect the sensitivity of cancer cells to cytotoxic drugs. Early studies have investigated ‘genetic’ drug resistance by exposing cancer cells to cytotoxic agents and studying single surviving cells (unicellular model). This field of research has revealed several important drug resistance mechanisms including drug-efflux pumps (e.g. P-glycoprotein pump), drug inactivation systems (e.g. glutathione-S-transferase) and altered drug targets (e.g. topoisomerase II mutations) (Kartner et al. 1983, Cole et al. 1992, Goto et al. 2001, de Jong et al. 1990, Lage et al. 2000). Although these mechanisms operate in vitro to protect cancer cells from cytotoxic drugs and may contribute to acquired chemoresistance in recurrent tumours following first-line cytotoxic therapy, their role in initial drug resistance is unclear (Desoize and Jardillier 2000). The failure of unicellular models to account for drug resistance has prompted investigation of other mechanisms. Unicellular models do not take into account host–tumour interactions. The importance of the host in the development of drug resistance has been indicated from studies of mouse mammary tumours, in which drug resistance induced in vivo was not apparent in cell culture in vitro (Teicher et al. 1990). Furthermore, the recurrence of some solid organ malignancies (e.g. SCLC) at the primary tumour site following first-line chemotherapy has suggested the presence of local factors that promote cancer cell survival (Rintoul and Sethi 2001). It is now widely accepted that tumours exist not in isolation, but within a specialised microenvironment that has properties

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similar to a chronic wounding reaction, containing cytokines and growth factors, which may affect cytotoxic drug responses (Mueller 2006). Examples include IL-6 and granulocyte macrophage colony stimulating factor (GMCSF), inhibiting the cytotoxicity of vincristine, adriamycin and cyclophosphamide (Lotem and Sachs 1992). Host–tumour interactions can also occur between stromal cells (leukocytes and fibroblasts) and with extracellular matrix (ECM) proteins. Data collected over the past 15 years strongly suggests that cancer cell–ECM interactions may be important in cytotoxic drug resistance. This chapter will review the evidence for the role of ECM in drug resistance and consider the cellular mechanisms involved.

ECM Proteins Promote Drug Resistance The ECM is produced by stromal cells (e.g. fibroblasts) and secreted to form an interstitial matrix or basement membrane, which provides structural support for cells and regulates cell–cell interactions. ECM consists of a network of carbohydrates (glycosaminoglycans) and proteins existing as either proteoglycans or nonproteoglycans (Iozzo 1998). Collagen, fibronectin, tenascin and laminin comprise a significant portion of non-proteoglycan matrix components (Iozzo 1998). Immunohistochemical studies of sections from human cancers have demonstrated the presence of these non-proteoglycan matrix proteins within the tumour microenvironment. Our group investigated the matrix composition of SCLC primary tumours (n = 23) (Sethi et al. 1999). We observed high levels of fibronectin, collagen IV and tenascin staining in all samples, which was localized to the stroma infiltrated by tumour cells but not surrounding normal lung tissue. In addition, fibronectin and laminin staining was visible within tumour cells (∼25% of tumours), suggesting that SCLC cells may produce some components of the local ECM. Several other groups have demonstrated the presence of ECM proteins within both the primary and metastatic tumour microenvironment of breast, ovarian, head and neck and gastrointestinal tract cancers (Bergamaschi et al. 2008, Chin et al. 2005, Aishima et al. 2003, Grigioni et al. 1991, Sherman-Baust et al. 2003). Furthermore, these studies have demonstrated that the presence or expression profile of ECM components correlates with poorer patient prognosis, suggesting an important role for ECM in cancer pathogenesis. Cellular adhesion is essential for untransformed epithelial cells to survive in vitro and if detached cells undergo apoptosis, a process known as anoikis (Frisch and Francis 1994). In contrast, cancer cells display a key feature of tumourigenicity, anchorage-independent growth (Shin et al. 1975). This ability to escape anoikis is an important feature that allows malignant cells to survive and metastasise. Therefore, it would seem unlikely that tumour cells would derive important survival signals from interaction with ECM components. However, data collected in the early 1990s suggested that despite this, ECM proteins could promote malignant transformation and cancer cell growth. In 1990, Fridman et al. demonstrated that reconstituted basement membrane proteins (in the form of matrigel) could enhance SCLC

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cell line growth in vivo (Fridman et al. 1990). Subsequently, matrigel has been shown to enhance the in vivo and in vitro growth of several diverse tumour cell lines (Vukicevic et al. 1992, Passaniti et al. 1992, Albini et al. 1992). Furthermore, Fridman et al. (1992) demonstrated that subcutaneous injection of athymic mice with untransformed NIH-3T3 cells mixed with matrigel resulted in the formation of invasive tumours, but without matrigel the cells were non-tumorigenic (Fridman et al. 1992). These initial data, suggesting an important role for ECM proteins in the tumour microenvironment, have prompted experiments investigating the effect of cancer cell–ECM interaction on cytotoxic drug sensitivity. In addition to demonstrating enhanced tumour growth with matrigel, Fridman et al. (1990) demonstrated that SCLC cells could adhere to laminin in vitro, protecting the cells from cytotoxic drug-induced growth inhibition (Fridman et al. 1990). Many chemotherapeutic agents exert their cytotoxic effects by inducing apoptosis (Hannun 1997). Developing Fridman’s initial observation, our group investigated the effects of ECM proteins on apoptosis induced in SCLC cells by cytotoxic drugs used clinically to treat this aggressive cancer (Sethi et al. 1999). We employed an in vitro model of drug resistance in which SCLC cells were allowed to adhere to purified ECM proteins (laminin and fibronectin) and then treated with chemotherapeutic agents including etoposide, adriamycin and cyclophosphamide. This allowed the effects of individual ECM proteins to be examined without other contaminating factors (e.g. growth factors) that may be present in matrigel. Crucially, we found that adhesion to laminin or fibronectin strongly protected SCLC cells from apoptosis induced by chemotherapeutic agents, with diverse mechanisms of action (Sethi et al. 1999). Subsequently, other groups have demonstrated that ECM proteins can protect a variety of cancer cell lines from cytotoxic drug therapy in vitro. Chronic myeloid leukaemia and myeloma cell lines have been shown to bind to fibronectin and consequently display increased resistance to diverse genotoxic drugs (Hazlehurst et al. 2000, Damiano et al. 2001). Breast cancer, fibrosarcoma and glioma cell lines have all been shown to be more resistant to the apoptotic effects of cytotoxic drugs in the presence of ECM proteins or basement membrane extracts (Aoudjit and Vuori 2001, Pogany et al. 2001). Furthermore, data from a clinical study has suggested that the presence of ECM proteins in the tumour microenvironment prior to treatment can predict response to chemotherapy (Jayne et al. 2002). However, the role of ECM as a mechanism of drug resistance in vivo remains to be defined definitively. Despite these limitations, the current in vitro data suggests that ECM proteins promote drug resistance, and that this may represent an important mechanism by which cancer cells can survive an initial cytotoxic insult allowing tumour relapse.

Mechanisms of ECM-Mediated Drug Resistance It is clear from our data, and that of other groups, that ECM proteins protect cancer cells from the effects of mechanistically diverse cytotoxic drugs. This has led to the proposal of several mechanisms to explain ECM-mediated drug resistance. These

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include altered drug penetration, reduced DNA damage, effects on cell proliferation and interaction with cell-surface integrins. This section will review the evidence for each of these potential pathways.

Altered Drug Penetration It has been hypothesised that ECM surrounding cancer cells may form a physical barrier to chemotherapeutic drug entry thus promoting drug resistance. In 2002, Tannock et al. demonstrated that penetration of chemotherapeutic agents through a 200-μM thick multi-cellular layer of mouse mammary tumour or human bladder cancer cells containing ECM proteins was reduced in comparison to a control Teflon membrane (Tannock et al. 2002). They concluded that drug entry into areas of tumour without blood vessels could be impaired, explaining why some cancer cells escape initial cytotoxic therapy. In support of this theory, Netti et al. showed inhibition of macromolecule (IgG) diffusion through tumour tissue by an extensive collagen network, which could be circumvented by collagenase treatment (Netti et al. 2000). Furthermore, pre-clinical and preliminary clinical trials using hyaluronidase, an enzyme that lowers the viscosity of hyaluronic acid and aids tissue permeability, have demonstrated an additive effect to cytotoxic drug therapy (Beckenlehner et al. 1992, Spruss et al. 1995, Smith et al. 1997, Baumgartner et al. 1998). Unfortunately, these effects have not been reproduced in larger randomised controlled trials (Baumgartner et al. 1998). In addition, ECM-mediated drug resistance can be demonstrated in monolayer cell culture systems in vitro, where the matrix is unlikely to prevent drug penetration. In support of this, we have clearly demonstrated that adhesion of SCLC cells to fibronectin does not prevent etoposide from inhibiting its intracellular target, topoisomerase (Sethi et al. 1999). Therefore, it seems unlikely that reduced drug entry into cancer cells is solely responsible for ECM-mediated drug resistance.

Reduced DNA Damage Many cytotoxic agents cause damage to cellular DNA promoting cell cycle arrest and apoptosis (Helleday et al. 2008). It has been proposed that ECM modulates DNA damage or repair thus conferring drug resistance. This was initially suggested from in vitro experiments in untransformed lung endothelial cells, which demonstrated that adhesion to collagen and laminin caused a reduction in bleomycininduced DNA damage (Hoyt et al. 1997). The role of this mechanism in cancer was supported by experiments using topoisomerase II inhibitors in haemopoietic cancer cell lines. In 2001, Hazlehurst et al. demonstrated that mitoxantrone- and etoposideinduced DNA double-strand breaks in U937 cells were significantly reduced by adhesion to fibronectin, and this correlated with altered nuclear distribution of topoisomerase II beta (Hazlehurst et al. 2001). The cellular pathways involved in

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ECM-mediated protection from DNA damage have not yet been elucidated. In addition, this mechanism of drug resistance may not be widely applicable. For example, we have demonstrated in SCLC cells that adhesion to ECM proteins (collagen IV, laminin and fibronectin) protects against etoposide-induced apoptosis but does not alter topoisomerase inhibition or DNA damage (Sethi et al. 1999).

Effects on Cell Proliferation It is generally accepted that the development of cancer requires escape from the normal controls of cellular proliferation (Rozengurt 1999). DNA damage induced by cytotoxic drugs promotes cell cycle arrest thus preventing cancer cell proliferation (Schwartz and Shah 2005). Adhesion of untransformed epithelial cells to ECM typically results in cycle progression and increased cellular proliferation (Lin and Bissell 1993). Therefore, it has been proposed that ECM may override chemotherapy-induced inhibition of cellular proliferation, promoting drug resistance. This is supported by our data demonstrating that adhesion of SCLC cell lines to fibronectin prevents G2/M cell cycle arrest induced by etoposide treatment (Hodkinson et al. 2006). It is generally accepted that cancer cells can promote cellular proliferation by secreting growth factors that act on the same tumour cells (autocrine growth loops) (Rozengurt 1999). We have demonstrated that SCLC cells in vivo stain intracellularly for ECM proteins (Sethi et al. 1999). Other groups have also demonstrated ECM expression by cancer cells (Gladson et al. 1995, van Riet et al. 1994). This data suggests the possibility of an autocrine growth loop involving cancer cell production of ECM proteins, which promote cell cycle progression and drug resistance. However, this may not be a generally applicable mechanism, as research in haemopoietic cancer cell lines has indicated that adhesion to ECM may inhibit cellular proliferation possibly allowing greater time for DNA repair (Lundell et al. 1996, Hazlehurst et al. 2000).

Interaction with Cell-Surface Integrins Adhesion to ECM components, such as collagen and fibronectin, is mediated by a family of cell-surface proteins called integrins. Structurally, integrins are composed of α and β subunits that are non-covalently linked as heterodimers (Humphries 2000). Each subunit is a type I transmembrane protein that consists of a large extracellular domain, linked by a single-pass transmembrane segment to a smaller intracellular domain or tail (Hynes 2002). Eighteen α and eight β subunits have been identified. Extensive research has demonstrated that integrins are central to many of the cellular processes important in cancer, including survival, proliferation and migration (Moschos et al. 2007). A wide range of integrin heterodimers have been demonstrated to play a role in many types of cancer, including haematopoietic, breast, lung and colon (Mizejewski 1999). Clinical studies have shown that increased integrin expression independently correlates with poorer patient survival

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(Oshita et al. 2002, Hazelbag et al. 2007, Yao et al. 2007). Furthermore, in vitro research has indicated that several diverse cancer cell lines can adhere to ECM proteins via specific surface integrins (Mizejewski 1999). These studies strongly suggest that integrins are important in the pathogenesis of cancer, prompting the investigation of ECM–integrin interactions in drug resistance. In 1999, we published important data demonstrating for the first time that SCLC cells could bind to ECM proteins in vitro via cell-surface integrins and that this interaction provided protection from chemotherapy-induced apoptosis (Sethi et al. 1999). We found that SCLC cell lines derived from human patients expressed predominantly α2β1, α3β1, α6β1 and αvβ1 integrins. These integrins have been shown to mediate cell binding to collagen, fibronectin and laminin (Hynes 2002). We demonstrated that adhesion to these ECM proteins conferred resistance to apoptosis induced by etoposide, cis-platinum and doxorubicin and that this effect was completely abrogated by a function-blocking β1 integrin antibody (4B4) (Sethi et al. 1999). Subsequently, we have confirmed that a β1 integrin-activating antibody (TS2/16) can also reproduce the chemoprotective effect of ECM, strongly implicating β1 integrins in ECM-mediated drug resistance (Hodkinson et al. 2006). Further to this research, other groups have demonstrated that this phenomenon is applicable to other cancer cells. Damiano et al. showed that adhesion of K562 chronic myeloid leukaemia cells to fibronectin protected against chemotherapy-induced apoptosis, a mechanism mediated via α5β1 integrins (Damiano et al. 2001). Aoudjit and Vuori (2001) demonstrated that β1 integrin ligation by ECM proteins significantly protects breast cancer cell lines from apoptosis induced by paclitaxel and vincristine (Aoudjit and Vuori 2001). In addition, β1 integrins have been shown to confer resistance to drug-induced apoptosis in hepatoma and lymphoma cell lines (Zhang et al. 2002, Hazlehurst et al. 2001). Drug resistance has also been observed with other integrin subunits. In 1999, Uhm et al. showed that vitronectin, through αvβ3 and αvβ5 integrins, could protect glioma cells from apoptosis induced by the inhibition of topoisomerase (Uhm et al. 1999). Data collected over the last decade has indicated that ECM–integrin interactions can modulate the effects of cytotoxic drug therapy on apoptotic pathways. It has been shown that integrin ligation protects breast cancer cell lines from paclitaxel-induced apoptosis through reduction in mitochondrial cytochrome c release, a key intermediate in apoptosis (Aoudjit and Vuori 2001). Indirect evidence has also implicated the Bax/Bcl-2 system in integrin-mediated drug resistance. A study in B-lymphoma cells demonstrated that α4β1 integrin ligation by vascular cellular adhesion molecule-1 (VCAM-1) promoted Bcl-X(L) gene transcription and prevented etoposide from disrupting Bax-Bcl-X(L) binding (Taylor et al. 2000). Furthermore, vitronectin-induced chemoresistance in glioma cell lines has been shown to be associated with an increase in expression of Bcl-2 and Bcl-X(L) proteins (Uhm et al. 1999). Despite the clear role of integrins in cell cycle control and proliferation, data regarding the role of cell cycle effectors in ECM-mediated drug resistance is sparse. DNA damage by cytotoxic drugs frequently results in cell cycle arrest and escape from this is a crucial mechanism by which cancer cells may resist drug therapy and continue to proliferate with DNA damage. The key regulators of the cell cycle

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have been defined and include cyclins A, B, D and E and their associated cyclindependent kinases (CDK) (Boonstra 2003, Woo and Poon 2003). Cell arrest can occur at two main checkpoint, G1/S and G2/M, mediated by factors including p53, p21 and p27 (Schwartz and Shah 2005, Vidal and Koff 2000). In 2000, Hazelhurst demonstrated that ligation of β1 integrins by fibronectin on myeloma cells promoted drug resistance and that this was associated with increased p27 levels and cell cycle arrest at the G1 checkpoint (Hazlehurst et al. 2000). Subsequently, we have demonstrated a contradictory mechanism in SCLC cells, showing that ECM–β1 integrin interaction prevents etoposide-induced G2/M cell cycle arrest (Hodkinson et al. 2006). This discrepancy suggests cell context dependent differences in the response to integrin ligation. The collected data clearly demonstrates an important role for ECM components in the initial resistance to a diverse array of chemotherapeutic drugs. Although it is possible that ECM may alter drug effects or provide a physical barrier to drug penetration, it is clear that the interaction of ECM with integrins represents the most widely applicable mechanism of cancer cell resistance to cytotoxic injury. An obvious extrapolation of this conclusion is that disruption of ECM–integrin interaction could prove to be a novel therapeutic strategy to circumvent drug resistance. It has been proposed that integrin-blocking antibodies or peptides could be used to prevent cancer cell–ECM binding. In support of this hypothesis, data from 3-dimensional breast cancer cell line cultures has indicated that β1 integrin inhibition promotes cancer cell loss without affecting normal cells (Park et al. 2006). Specific studies addressing the efficacy of β1 integrin blockade as a strategy for preventing drug resistance in vivo are lacking. However, β1 integrin-blocking antibodies inhibit tumour growth in vivo without toxicity to the animals (Park et al. 2006). Although this initial data is encouraging, global integrin blockade is likely to have complex effects on the host. This can be appreciated by considering cells commonly found in the tumour microenvironment. Fibroblasts, leukocytes and endothelial cells all utilise integrins for essential cellular functions, e.g. leukocyte migration and activation, fibroblast viability and matrix assembly or endothelial cell survival and angiogenesis (Sixt et al. 2006, Tian et al. 2002, Mettouchi and Meneguzzi 2006, Luscinskas and Lawler 1994). Therefore, integrin blockade could prevent an effective immune response or alter tumour blood vessel formation, facilitating cancer growth rather than promoting effective drug therapy. It is clear that prior to the development of novel therapeutic strategies, we require a better understanding of the integrin signalling mechanisms activated by ECM that promote drug resistance in cancer cells.

Integrin Signalling Pathways and Drug Resistance Integrins do not simply function as cell adhesion molecules. Extensive research has demonstrated that matrix binding to the extracellular domain of integrin subunits promotes integrin clustering, leading to the recruitment of multiple intracellular

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proteins to localised cell membrane areas called focal adhesions (Zaidel-Bar et al. 2004). These macromolecular complexes form an essential mechanical link between integrins and the cytoskeleton. Many adaptor proteins are involved and their role in regulation of integrin function is beyond the scope of this chapter. Geiger et al. have published an excellent review of this topic (Geiger et al. 2001). In addition to adaptor molecules, several proteins with intrinsic kinase activity are recruited to focal adhesion complexes, including focal adhesion kinase (FAK) and integrin-linked kinase (ILK) (Wozniak et al. 2004, Legate et al. 2006). Subsequently, ECM–integrin interaction is translated into intracellular effects involving other signalling molecules (e.g. Akt, glycogen-synthase-3β (GSK-3β), phosphoinositide-3-kinase (PI3-K) and Src) (Schaller 2001, Klinghoffer et al. 1999, Khwaja et al. 1997). The contribution of these signalling pathways to ECM-mediated drug resistance has been the focus of considerable research over the past 5 years and will be reviewed in this section.

The Role of FAK as a Mediator of Drug Resistance Following its description over 15 years ago in v-Src transformed chick embryo cells, the 125-kDa non-receptor tyrosine kinase FAK has been shown to regulate downstream signalling by integrins and growth factor receptors (Schaller et al. 1992, Hanks et al. 2003, Sieg et al. 2000). Structurally, FAK possess several important regions: an N-terminal FERM domain which mediates interaction with growth factor receptors; a central kinase domain; two proline-rich regions that act as binding sites for Src homology 3 (SH3) domains in other proteins (e.g. p130Cas); and a C-terminal focal adhesion targeting domain (FAT) that is necessary for localisation of FAK to focal adhesion complexes and also facilitates binding of integrinassociated proteins (e.g. talin, paxillin) (Dunty et al. 2004, Hildebrand et al. 1993, Schaller et al. 1994, Polte and Hanks 1995). Integrin or growth factor receptor interaction results in FAK autophosphorylation at tyrosine 397, which promotes the binding and activation of Src kinase, to form the FAK–Src complex (Schaller et al. 1994, Calalb et al. 1995). Subsequently, FAK is further phosphorylated promoting the binding and/or phosphorylation of several important focal adhesion molecules (e.g. the p85 subunit of PI3-K, p130Cas and Grb2) (Xia et al. 2004, Polte and Hanks 1995, Schlaepfer et al. 1994). As a result, crucial cell signalling pathways are activated including Akt, JNK and Ras/ERK that promote cell survival, proliferation and motility. Thus FAK would appear to be a key regulator of many processes important to cancer pathogenesis and in particular integrin-mediated drug resistance. Extensive research, both in vitro and in vivo, has suggested a central role for FAK signalling in cancer. Gene expression analysis has demonstrated increased FAK expression in human tumours (Bhattacharjee et al. 2001, Yeoh et al. 2002). Furthermore, increased FAK phosphorylation has been shown in some transformed cells (Datta et al. 2001). Work investigating the development of skin tumours following chemical carcinogen exposure has demonstrated an important role for FAK in tumorigenesis, although FAK overexpression has not been shown to promote

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malignant transformation in vitro (McLean et al. 2001). FAK is critical for normal cell survival, and overexpression of FAK in epithelial cells has been shown to be sufficient to prevent anoikis (Frisch et al. 1996). Furthermore, expression of the C-terminal portion of FAK (FRNK), which inhibits FAK signalling, has been shown to suppress tumour cell proliferation in vitro and suppress tumour growth in vivo, suggesting a central role for FAK in cancer cell proliferation (van Nimwegen et al. 2005). Interestingly, stable FRNK expression in v-Src transformed NIH-3T3 fibroblasts reduced tumour cell metastasis but not growth in nude mice (Hauck et al. 2002). This data clearly implicates FAK in cancer cell survival, growth and spread. Several recent studies have also addressed the influence of FAK on cytotoxic drug sensitivity. In 2005, Halder et al. demonstrated that docetaxel induced cleavage of FAK in drug-sensitive but not drug-resistant ovarian cancer cell lines, indicating that FAK degradation may play a role in docetaxel resistance (Halder et al. 2005). To confirm this, they used siRNA and demonstrated increased docetaxel sensitivity in cells with reduced FAK expression. Following this, van Nimwegen et al. (2006) demonstrated that expression of FRNK sensitised a rat mammary adenocarcinoma cell line (MTLn3) to doxorubicin-induced cell death (van Nimwegen et al. 2006). In addition, Duxbury et al. (2003) demonstrated that reduction in FAK expression using siRNA promoted gemcitabine sensitivity in a nude mouse xenograft model of human pancreatic adenocarinoma (Duxbury et al. 2003). These studies have also suggested that FAK-mediated drug resistance relies in part on PI3-K/Akt signalling. However, the exact mechanisms by which FAK regulates drug resistance remain to be elucidated.

The Role of ILK in Drug Resistance ILK, which was first described in 1996, is a non-receptor-bound serine/threonine kinase that has been shown to directly interact with the cytoplasmic tail of β1 integrins (Hannigan et al. 1996). This 59-kDa protein has three distinct regions: four N-terminal ankyrin repeats that are required for localisation to focal adhesion complexes and binding adaptor proteins (e.g. particularly interesting new cysteine histidine rich protein (PINCH)); a central pleckstrin homology (PH) domain; and a C-terminal domain that is also required for localisation to focal adhesions and mediates interactions with β integrin subunits and cytoplasmic adaptor proteins (e.g. paxillin and actopaxin) providing connection to the actin cytoskeleton. In addition to its structural interactions, ILK possesses a C-terminal kinase catalytic domain (Tu et al., 1999 Delcommenne et al. 1998, Nikolopoulos and Turner 2001, Nikolopoulos and Turner 2002). The serine/threonine kinase activity of ILK is stimulated by ECM–integrin interaction in a PI3-K-dependent manner, resulting in phosphorylation of Akt and GSK-3β (Lynch et al. 1999, Troussard et al. 1999). Furthermore, through interaction with PINCH and its interacting protein Nck-2, ILK provides a connection between integrins and growth factor receptor signalling (Tu et al. 1999).

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These important downstream signalling pathways regulate cell survival, proliferation and motility suggesting that ILK may be important in cancer. Knockout experiments have demonstrated that ILK is essential for embryonic viability (Sakai et al. 2003). Furthermore, constitutive activation or overexpression of ILK has been shown in vitro to protect mammalian cells from anoikis, effects that are mediated by activation of Akt, suggesting a central role for ILK in cell survival (Attwell et al. 2000). ILK has also been shown to promote cell invasion in vitro (Troussard et al. 2000). Analysis of human tumours, including melanoma and prostate cancer, has demonstrated an increased expression of ILK, which correlates with cancer stage and progression (Dai et al. 2003, Graff et al. 2001). Importantly, specific expression of ILK in mammary epithelial cells has been shown to result in mammary tumour development in transgenic mice, and inhibition of ILK using a small molecule inhibitor has been shown to suppress thyroid cancer cell growth in vivo (White et al. 2001, Younes et al. 2005). Despite the clear role of ILK signalling in cell survival and oncogenesis, the role in drug resistance has received relatively little attention. In 2005, Duxbury et al. demonstrated that human pancreatic adenocarcinoma cells were sensitised to the pro-apoptotic effects of gemcitabine by suppression of ILK expression using siRNA and protected by overexpression of ILK (Duxbury et al. 2005). Furthermore, they showed that constitutively activated Akt could promote gemcitabine resistance in the presence of ILK knockdown. In contrast to this data, Cordes (2004) demonstrated that overexpression of ILK could sensitise cells to the cytotoxic effects of radiation, a finding also supported in A549 lung cancer cells (Cordes 2004). It is proposed that this discrepancy relates to differences in the cell death pathways activated by different cytotoxic agents. Further experimentation is required to determine the exact role of ILK in drug resistance and which key signalling events are responsible for the effects.

The Role of PI3-K Signalling in Integrin-Mediated Drug Resistance Cross-linking of integrins by ECM results in activation of PI3-K, both in a FAK-dependent and FAK-independent manner (Xia et al. 2004, Velling et al. 2004). Subsequently, through PI3-K-dependent kinases, PDK1 and PDK2, downstream signalling molecules Akt and GSK-3β are phosphorylated (Fresno Vara et al. 2004). Akt has been shown to promote cell survival by phosphorylating proteins involved in apoptosis including Bad and pro-caspase 9 (Cardone et al. 1998). Furthermore, activated Akt has been demonstrated to reduce expression levels or activity of cell cycle inhibitors p21 and p27 (Sun et al. 1999, Zhou et al. 2001). GSK-3β promotes degradation of cyclin D1, which is crucial for cell cycle progression (Diehl et al. 1998). Akt phosphorylates and inactivates GSK-3β thus stabilising cyclin D1 promoting cell proliferation. Thus integrin-mediated activation of PI3-kinase may represent an important mechanism by which ECM proteins protect cancer cells from chemotherapy.

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We have recently demonstrated that the PI3-K pathway is a key regulator of integrin-mediated drug resistance in SCLC cells (Hodkinson et al. 2006). Initially we observed that adhesion of human SCLC cell lines to ECM proteins or ligation of β1 integrins on SCLC cells with activating antibodies (TS2/16) increased PI3K activity resulting in phosphorylation of downstream Akt and GSK-3β. Subsequently, we investigated the role of PI3-K in ECM-mediated chemoprotection using two separate strategies: pharmacological inhibition of PI3-K with LY294002 and genetic manipulation of PI3-K signalling by overexpression of constitutively active or dominant negative Akt. Crucially we found that inhibition of PI3-K signalling prevented β1 integrin-mediated resistance to etoposide in SCLC cells. Furthermore, we demonstrated that adhesion to ECM via β1 integrins phosphorylated Bad protein and prevented etoposide activation of the caspase cascade in a PI3-K-dependent manner. Thus we have defined an intracellular pathway mechanism by which ECM can promote drug resistance. The importance of this pathway is supported by research in other types of cancer. Auodjit and Vuori (2001) demonstrated that ECM-mediated drug resistance in breast cancer correlated with an increase in Akt and that inhibition of PI3-K reversed the chemoresistance (Aoudjit and Vuori 2001). In 2006, Cordes et al. demonstrated that integrin/PI3-K/Akt signalling was central to glioma cell survival in the context of radiation-induced cytotoxic injury (Cordes et al. 2006). Estrugo et al. (2007) showed that HL60 human leukaemia cells were protected from chemotherapyinduced procaspase-8-mediated apoptosis by a β1 integrins/PI3K-dependent pathway (Estrugo et al. 2007). It has been suggested by some authors that this data should be interpreted with caution as alteration of PI3-K signals may lower integrin affinity for ECM. This has not been reported, and we have found that mutants of the small GTPase H-Ras, which preferentially activate PI3-K, do not suppress integrins (Lad et al. 2006). In addition to determining the effect of PI3-K inhibition on integrin-mediated resistance to apoptosis, we have also investigated the effect on etoposide-induced cell cycle arrest (Hodkinson et al. 2006). We have shown that etoposide (and ionising radiation) promoted G2/M cell cycle arrest in SCLC cells and that adhesion to ECM via β1 integrins prevented this, driving cell cycle progression. This correlated with reduced expression of p21 and p27, reduced phosphorylation of CDK1 and maintained expression of cyclins D, E, A and B and phosphorylation of CDK2. These effects were abrogated by chemical and genetic inhibition of PI3-K signalling. Furthermore, we observed that SCLC cells displayed similar degrees of DNA damage and repair whether adhered to ECM or not. This suggests that ECM via β1 integrin/PI3-K signalling can prevent etoposide-induced cell cycle arrest and subsequent apoptosis, allowing the cells to survive with persistent DNA damage, which could promote the development of acquired drug resistance mechanisms (e.g. altered drug targets). Therapeutic strategies designed to selectively inhibit PI3-K may therefore enhance the efficacy of traditional drug regimes improving anticancer treatment. The data presented here clearly indicates a central role for downstream integrin signalling pathways in drug resistance. It is also apparent that considerable overlap

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exists between the pathways activated by ECM–integrin interaction and that several molecules recruited to the focal adhesion complex allow binding to growth factor receptors introducing a further layer of complexity. In addition, some of these mechanisms are cell context dependent demonstrating that no single pathway will be applicable to all cancer cells and many of these cascades are active in normal cells. Further work is needed to define the mechanisms activated in cancer cells in vivo and to determine the best strategies for inhibition to limit toxicity to normal cells.

ECM-Mediated Drug Resistance in the Context of Metastatic Cancer The mechanisms described here by which adhesion to ECM proteins within the tumour microenvironment protect cancer cells from drug-induced apoptosis do not conform to our concept of cancer as a highly invasive and metastatic disease. Furthermore, malignant tumours frequently show high expression levels of matrix metalloproteinases (MMP). These proteolytic enzymes facilitate tumour invasion and metastasis by degrading ECM (Bonomi 2002). Inhibitors of MMP have also been shown to reduce tumour growth and metastasis in animal models of cancer, including breast, colon and pancreatic cancer, suggesting a central role for matrix degradation in cancer pathogenesis (Lozonschi et al. 1999, Naglich et al. 2001, Lein et al. 2002). Thus, it would seem unlikely that cancer cells would use their surrounding ECM as a pro-survival signal if they actively produce enzymes to degrade it. SCLC is highly metastatic, and MMP expression has been demonstrated in SCLC tumour sections from patients (Michael et al. 1999, Ylisirnio et al. 2000). On the basis of this, a phase III trial of an MMP inhibitor, Marimastat, in SCLC was conducted. Unfortunately, Marimastat treatment demonstrated no survival advantage over placebo (Shepherd et al. 2002). The reasons for the poor efficacy of MMP inhibitors are complex but one suggestion is that the majority of trials were conducted in patients with widespread metastatic disease and thus MMP inhibitor treatment to this group of patients could not be expected to alter this process. Interestingly, data supporting the role of ECM in drug resistance suggests that MMP inhibition may actually enhance matrix stability, promoting cancer cell survival and growth, a situation deleterious to patient survival. The affinity of integrin for ligand binding is a dynamic process regulated by intracellular signals. This allows cellular adhesion to ECM to vary in strength, facilitating changes in cell behaviour e.g. migration or mitosis. Thus, it seems likely that cancer cells would also modulate their integrin affinity, utilising ECM-derived survival signals when strongly adherent and being more motile and invasive when less adherent. In this later setting, the cancer cells, unlike untransformed cells would survive anoikis due to intracellular pro-survival signals and autocrine growth loops. In the context of chemotherapy, a small number of cancer cells with high integrin affinity and thus strongly adherent would survive cell cycle arrest and apoptosis despite

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DNA damage. The result would be recurrence of tumour at the original site of disease, as is often seen in SCLC, with the potential development of acquired multidrug resistance. Implicit in this concept is that new therapies designed to disrupt ECM–integrin survival mechanisms should be appropriately timed with chemotherapy. Furthermore, modulators of integrin affinity may prove to be more important targets for anti-cancer therapy.

Conclusions and Future Directions Cancer continues to be a significant health burden, in part due to initial and subsequently acquired drug resistance. This chapter has outlined several important areas of research that have demonstrated a key role for ECM in cancer cell drug resistance. Furthermore, mechanisms underpinning this have been described with particular reference to the role of integrin signalling. It seems most likely that cancer cells have a dynamic relationship with the surrounding ECM, allowing escape from chemotherapy, and also metastasis. At a simple level, interruption of ECM–integrin interaction could increase the sensitivity of many cancers to initial chemotherapy, improving patient survival. However, non-specific integrin inhibition is likely to have widespread and possibly toxic effects on the host’s normal cells. This could prevent an adequate anti-tumour response or may promote cancer cell growth. The future challenges faced by researchers in this field are not easy to surmount. Crucial to the successful development of real therapeutic strategies is the description of an in vivo model that will allow study and manipulation of integrin signalling in cancer development and treatment. Furthermore, we need to understand the complex interactions of the downstream signalling events initiated by ECM binding to integrins and the signals that modulate integrin affinity. The ultimate hurdle will be to develop a rationale therapy that has minimal host side effects and will reduce cancer drug resistance without promoting undesirable cancer cell behaviour (e.g. metastatic invasion). Realistically, no single approach will be applicable to all cancers. It is predictable that definition of the molecular signature of a patient’s tumour cells will allow rational application of several targeted therapies to compliment traditional chemotherapy and thus improve patient survival.

References Aishima, S., Taguchi, K., Terashi, T., Matsuura, S., Shimada, M. & Tsuneyoshi, M. (2003) Tenascin expression at the invasive front is associated with poor prognosis in intrahepatic cholangiocarcinoma. Mod Pathol, 16,1019–27. Albini, A., Melchiori, A., Garofalo, A., Noonan, D. M., Basolo, F., Taraboletti, G., Chader, G. J. & Gavazzi, R. (1992) Matrigel promotes retinoblastoma cell growth in vitro and in vivo. Int J Cancer, 52,234–40. Aoudjit, F. & Vuori, K. (2001) Integrin signaling inhibits paclitaxel-induced apoptosis in breast cancer cells. Oncogene, 20,4995–5004.

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Vukicevic, S., Somogyi, L., Martinovic, I., Zic, R., Kleinman, H. K. & Marusic, M. (1992) Reconstituted basement membrane (Matrigel) promotes the survival and influences the growth of murine tumors. Int J Cancer, 50,791–5. White, D. E., Cardiff, R. D., Dedhar, S. & Muller, W. J. (2001) Mammary epithelial-specific expression of the integrin-linked kinase (ILK) results in the induction of mammary gland hyperplasias and tumors in transgenic mice. Oncogene, 20,7064–72. Woo, R. A. & Poon, R. Y. (2003) Cyclin-dependent kinases and S phase control in mammalian cells. Cell Cycle, 2,316–24. Wozniak, M. A., Modzelewska, K., Kwong, L. & Keely, P. J. (2004) Focal adhesion regulation of cell behavior. Biochim Biophys Acta, 1692,103–19. Xia, H., Nho, R. S., Kahm, J., Kleidon, J. & Henke, C. A. (2004) Focal adhesion kinase is upstream of phosphatidylinositol 3-kinase/Akt in regulating fibroblast survival in response to contraction of type I collagen matrices via a beta 1 integrin viability signaling pathway. J Biol Chem, 279,33024–34. Yao, E. S., Zhang, H., Chen, Y. Y., Lee, B., Chew, K., Moore, D. & Park, C. (2007) Increased beta1 integrin is associated with decreased survival in invasive breast cancer. Cancer Res, 67,659–64. 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., Patel, A., Cheng, C., Campana, D., Wilkins, D., Zhou, X., Li, J., Liu, H., Pui, C. H., Evans, W. E., Naeve, C., Wong, L. & Downing, J. R. (2002) Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell, 1,133–43. Ylisirnio, S., Hoyhtya, M. & Turpeenniemi-Hujanen, T. (2000) Serum matrix metalloproteinases -2, -9 and tissue inhibitors of metalloproteinases -1, -2 in lung cancer–TIMP-1 as a prognostic marker. Anticancer Res, 20,1311–6. Younes, M. N., Kim, S., Yigitbasi, O. G., Mandal, M., Jasser, S. A., Dakak Yazici, Y., Schiff, B. A., EL-Naggar, A., Bekele, B. N., Mills, G. B. & Myers, J. N. (2005) Integrin-linked kinase is a potential therapeutic target for anaplastic thyroid cancer. Mol Cancer Ther, 4,1146–56. Zaidel-Bar, R., Cohen, M., Addadi, L. & Geiger, B. (2004) Hierarchical assembly of cell-matrix adhesion complexes. Biochem Soc Trans, 32,416–20. Zhang, H., Ozaki, I., Mizuta, T., Matsuhashi, S., Yoshimura, T., Hisatomi, A., Tadano, J., Sakai, T. & Yamamoto, K. (2002) Beta 1-integrin protects hepatoma cells from chemotherapy induced apoptosis via a mitogen-activated protein kinase dependent pathway. Cancer, 95, 896–906. Zhou, B. P., Liao, Y., Xia, W., Spohn, B., Lee, M. H. & Hung, M. C. (2001) Cytoplasmic localization of p21Cip1/WAF1 by Akt-induced phosphorylation in HER-2/neu-overexpressing cells. Nat Cell Biol, 3,245–52.

Chapter 7

Oxidative Stress and Drug Resistance in Cancer Dunyaporn Trachootham, Wan Zhang, and Peng Huang

Abstract Increased generation of reactive oxygen species (ROS) is observed in many types of cancer cells. Besides the well-recognized effect of ROS in causing mutations and promoting cancer cell growth, recent evidence further suggests the involvement of oxidative stress in anticancer drug resistance. Consistent with the tumor-promoting effect of ROS, many in vitro studies have reported tumorsuppressing properties of ROS-scavenging enzymes. However, enhancement of those enzymes in tumor cells in vivo has been implicated in chemoresistance and seems to be associated with poor prognosis. In this chapter, we summarized the relevant observations in the field and discuss evidences that may explain these seemingly paradoxical findings. Malignant transformation is often associated with a moderate increase in cellular ROS content as a result of evelvated ROS production and/or decreased ROS-scavenging capacity. Because the increase in ROS stress may induce oxidative damage of cellular components leading to cell death, cancer cells that are able to survive the intrinsic stress and develop tumor must be equipped with sufficient adaptive mechanisms to tolerate the ROS stress. The adaptation processes involve activation of certain redox-sensitive transcription factors, which consequently lead to increased expression of the downstream genes encoding various ROS-scavenging enzymes, and redox-sensitive survival machineries. These adaptation mechanisms lead to increase in cell survival capacity in response to stress and alteration in drug metabolism and transport, which together confer drug resistance. Therefore, strategies to modulate cellular adaptation to oxidative stress may be used as an effective approach to overcome drug resistance in cancer cells under intrinsic stress. Keywords Reactive oxygen species · Redox homeostasis · Redox adaptation · Oxidative stress · Antioxidant · Glutathione · Cell survival · Cancer · Redoxsensitive transcription factors · Drug resistance · Apoptosis · Redox-based therapeutic strategies P. Huang (B) Department of Molecular Pathology, The University of Texas M.D. Anderson Cancer Center, Box 0951, 1515 Holcombe Boulevard, Houston TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 7, 

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Introduction Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are highly reactive molecules found in living organisms and their environment. ROS includes free radical such as superoxide (O2 ·− ) and hydroxyl radical (OH· ) and non-radical species such as hydrogen peroxide (H2 O2 ). RNS includes nitric oxide (NO) and peroxynitrite (ONOO– ). Proper levels of ROS/RNS are essential in maintaining cellular homeostasis. Under physiological conditions, cells control proper ROS levels through redox balance between their generation and elimination by ROS-scavenging systems such as superoxide dismutase, glutathione, catalase, and thioredoxin. An increase of ROS production or a decrease of ROS-scavenging capacity due to exogenous stimuli or endogenous metabolic alterations can disrupt the redox homeostasis, leading to an overall increase of intracellular ROS levels or oxidative stress. Emerging evidences suggest that many types of cancer cells exhibit increased levels of ROS/RNS, compared to the normal cells. While the involvement of oxidative stress in initiation and progression of malignancies have long been recognized, recent studies further suggest that cellular adaptation to ROS stress may also confer both constitutive and acquired resistance to multiple anticancer agents (Pelicano et al. 2004; Pervaiz and Clement 2004; Sullivan and Graham 2008). For example, the BCR/ABL tyrosine kinase induces production of ROS in hematopoietic cells, which in turn increases its tyrosine kinase activity through inhibition of phosphatase (Sattler et al. 2000). Leukemia cells with upregulation of Bcr-Abl were found constitutively resistant to multiple chemotherapeutic agents (McGahon et al. 1994). A recent study showed that ROS production induced by paclitaxel treatment could stimulate the expression of angiogenic factor VEGF, which may then induce acquired resistance to anticancer agents (Kim et al. 2008). Furthermore, the resistance to paclitaxel, doxorubicin, or platinum-based drugs was shown to be correlated with increased antioxidant capacity (Ramanathan et al. 2005; Hoshida et al. 2007). Although the exact mechanisms of how redox alterations contribute to drug resistance still remain to be further elucidated, several studies have suggested that it is possible to use ROS-modulating compounds, given as single agent or in drug combination, to successfully overcome drug resistance and sensitize cancer cells to conventional anticancer therapeutics (Dasmahapatra et al. 2006; Cai et al. 2007; Efferth et al. 2007; Gallego et al. 2008; Sullivan and Graham 2008). These observations not only support the important role of oxidative stress as a mechanism to induce drug resistance but also provide a paradigm for developing new treatment strategies for eliminating cancer cells resistant to standard therapeutic regimens. In this chapter, we summarize the role of intrinsic oxidative stress in the development of anticancer drug resistance, based on the relevant observations reported in the literature. It is worth noting that redox regulation and the consequences of redox imbalance are rather complicated and are dependent on specific cellular context. For instance, while ROS stress may promote cancer development and antioxidant enzymes may have tumor-suppressing effect in normal cells, the increase of antioxidant mechanisms in tumor cells in vivo has been implicated in chemoresistance and was associated with poor prognosis (Kinnula and Crapo 2004). It is possible

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that at the initial phase of tumorigenesis, increased ROS production and downregulation of ROS-scavenging system may serve as the mechanisms of the intrinsic ROS stress, which promote malignant transformation. Reduction of ROS levels at this stage would likely to have tumor-suppressing effect. On the other hand, ROS is a double-edge sword that could either promote cell proliferation or kill the cells depending on the levels and duration of ROS stress. Therefore, cancer cells that survive the intrinsic stress and develop tumor must be equipped with an adaptive mechanism to counteract this ROS stress. For example, the increase of ROS may stimulate redox-sensitive transcription factors such as NF-KB, which consequently increases the expression of certain ROS-scavenging enzymes and other survival molecules, leading to drug resistance (Fig. 7.1). As such, it is possible then to target these adaptation mechanisms in cancer cells as a new strategy to overcome drug resistance. In the following sections, we review the observations and mechanisms of redox alterations in cancer cells, the adaptive responses to oxidative stress, the contribution of

Proteosome inhibitors siRNA

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Fig. 7.1 Schematic illustration of oxidative stress and redox adaptation in cancer cells and their roles in drug resistance and therapeutics. Due to the intrinsic active metabolism and extrinsic stimuli in the microenvironment, cancer cells accumulate high levels of reactive oxygen species (ROS). The cancer cells that are capable of activating sufficient adaptation mechanisms can survive the intrinsic/extrinsic oxidative stress. The adaptation mechanisms include upregulation of redoxsensitive transcription factors, elevation of ROS-scavenging capacity, and alterations of redoxsensitive death/survival factors. These mechanisms together lead to improved cell survival and altered drug metabolism, leading to drug resistance. Based on the critical role of adaptation to oxidative stress in the development of drug resistance, interference of these adaptive processes in cancer cells can be an effective strategy to overcome drug resistance. Specific examples of strategies targeting each mechanism of adaptation are indicated with compound names in italic

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the adaptation to drug resistance, and redox-based therapeutic strategies to eliminate drug-resistant cancer cells.

Redox Alterations in Cancer The Findings It has been known for some time that compared to normal cells, many malignant cells have higher levels of ROS in culture and in vivo (Szatrowski and Nathan 1991; Kawanishi et al. 2006). Although in some cases the observed ROS increase could be due to sample handling, analytical artifacts, or cell culture conditions (Swartz and Gutierrez 1977; Halliwell 2007), many evidences strongly support the increase of ROS. For example, leukemia cells freshly isolated from blood samples from patients with chronic lymphocytic leukemia or hairy cell leukemia patients showed increased ROS production compared to normal lymphocytes (Zhou et al. 2003; Kamiguti et al. 2005). In solid tumors, many studies have shown increased levels of oxidative damage products such as oxidized DNA base (8OHdG) and lipid peroxidation products in clinical tumor specimens, plasma, and cancer cell lines (Sanchez et al. 2006; Patel et al. 2007; Tsao et al. 2007; Kumar et al. 2008). Since ROS level is the outcome of the redox equilibrium between its generation and elimination, the increased ROS in cancer cells indicates the imbalance in redox homeostasis or oxidative stress.

Role of Oxidative Stress in Tumorigenesis The intrinsic oxidative stress in cancer cells has been shown to correlate with the aggressiveness of tumors and poor survival of the cancer patients (Patel et al. 2007; Kumar et al. 2008). The ROS stress is thought to play an important role not only in cancer cell proliferation (Hu et al. 2005), transformation (Behrend et al. 2003), and genetic instability (Radisky et al. 2005), but also in evasion of cell death. Several underlying mechanisms have been suggested. For instance, increased ROS stress in cancer cells may activate survival pathways including mitogenic signaling (Irani et al. 1997), disrupt the cell death signaling (Pervaiz 2006), and/or evade senescence (Chen et al. 2005). Since a high level of oxidative stress can kill cells, the cells that are equipped with flexible machineries in response to oxidative stress may have a higher possibility to adapt themselves to survive under the ROS stress. For that purpose, cancer cells likely utilize genomic instability leading to aberrant amplification of gene expression to dynamically respond to oxidative stress. Recent evidence also suggests a direct role of oxidative stress in promoting epithelial-mesenchymal transition (EMT) and metastasis through regulating transcription of redox-sensitive genes including E-cadherin, integrin, and MMP (Radisky et al. 2005; Wu 2006). Prolonged cell survival together with increased proliferation, metastasis, and angiogenesis is known to be required for development of cancer (Hanahan and Weinberg

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2000). Taken together, the increased ROS stress in cancer cells evidently plays a pivotal role in almost every process required for initiation and progression of cancer.

Mechanisms of the Intrinsic ROS Stress in Cancer Cells While the exact reason responsible for the intrinsic ROS stress in cancer cells remains unclear, several mechanisms are believed to play important roles in causing oxidative stress during cancer development. Emerging evidence suggests that ROS production is induced after the expression of genes associated with tumor transformation such as Ras, Bcr-Abl, and c-Myc (Behrend et al. 2003). Constitutively active Ras, a guanine nucleotide triphosphatase (GTPase), either by overexpression or by mutation is common in a variety of human cancers (Schubbert et al. 2007). It has been shown that Ras expression promotes ROS production. In the H-Rasv12 transformed NIH3T3 fibroblasts cells, a large amount of superoxide was generated through a pathway involving flavoprotein and Rac1, which activate NADPH oxidase-mediated ROS generation (Irani et al. 1997). Further study by conditional deletion of Rac1 confirmed that Rac1 function is required for Ras-mediated tumorigenesis and loss of Rac1 caused a substantial reduction in cell proliferation (Kissil et al. 2007). Interestingly, besides the direct activation of ROS-production machinery, a recent study showed that Ras oncogenic signal also induced repression of antioxidant gene SESN1 (Kopnin et al. 2007), resulting in a shift of redox state toward increased ROS level. In addition to Ras, activation of c-Myc, a helix-loophelix leucine zipper transcription factor was shown to induce a significant increase in H2 O2 and oxidative DNA damage (Felsher and Bishop 1999; Vafa et al. 2002). Such ROS stress was reversible by antioxidant NAC (Vafa et al. 2002) and vitamin C (KC et al. 2006). Abnormal upregulation of c-Myc proto-oncogene can lead to aberrant activation of its downstream pathways and deregulate chromatin state. These subsequently cause genomic instability; thus, activated c-Myc has been implicated in a wide spectrum of human cancers (Vita and Henriksson 2006). Increased intracellular levels of ROS and decreased protein tyrosine phosphatases (PTPases) are also observed in cells transformed by Bcr-Abl, an oncogenic tyrosine kinase, when compared with quiescent, non-transformed hematopoietic cells (Sattler et al. 2000). Furthermore, treatment of Bcr-Abl-expressing cells with reducing agents such as NAC or PDTC led to a decrease in ROS level and protein tyrosine phosphorylation, likely through activation of PTPases. This suggests a positive feedback regulation between ROS generation and Bcr-Abl activation (Sattler et al. 2000). Furthermore, Bcr-Ablinduced ROS stress may induce DNA double-strand breaks. Unfaithful repair of the damaged DNA may result in mutations and genetic instability, and thus promotes the progression of chronic myelogenous leukemia (Nowicki et al. 2004). Besides oncogenic transformation, mitochondria DNA mutation was also shown to correlate with increased ROS level observed in certain types of cancer cells (Indo et al. 2007; Ishikawa et al. 2008). Mitochondrial DNA (mtDNA) encodes several

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protein components of electron transport chain, and mutations of mtDNA likely cause dysfunction of electron transfer leading to leakage of electrons and generation of superoxide, the initial reactive molecules that can sequentially be converted to other types of ROS/RNS. Interestingly, chemotherapeutic agents with DNAdamaging property may cause mtDNA mutations in primary leukemia cells, which are associated with increased ROS generation (Carew et al. 2003). Not only ROS production seems to be increased in cancer cells but also the levels of ROS-scavenging enzymes such as superoxide dismutase (SOD), glutathione, and peroxiredoxin (Prx) were shown to be significantly altered in malignant cells (Oberley and Oberley 1997) and in primary cancer tissues (Saydam et al. 1997; Hu et al. 2005; Murawaki et al. 2008). These suggest that redox homeostasis is impaired in cancer cells. Most evidences linking a deficit in antioxidant capacity to cancer development mainly came from animal studies where antioxidant molecules were either knocked out or over-expressed, and tumor incidences were then compared to the wild-type animals. For example, more than 30% of SOD1–/– mice developed liver tumors and more than 70% of the mice developed tumor nodules (Elchuri et al. 2005). Further study showed that the mutation frequency of the SOD1-deficient mice was significantly increased and the mutation types were mainly GC to AT transversions and GC to AT transitions, consistent with mutations induced by oxidative stress (Busuttil et al. 2005). Transgenic mice overexpressing glutathione peroxidase (GPX-1) or coexpression of GPX-1 and SOD1 were found to have increased incidence of tumorigenesis in a DMBA/TPA two-stage skin carcinogenesis model (Lu et al. 1997). These observations suggest that a precise redox homeostasis is essential and that overexpression of GPX-1 might disturb redox balance and contribute to cancer development. Interestingly, Prx1 knock out mice revealed higher level of ROS and predisposition to cancer. Prx1 seems to abrogate c-Myc-mediated transformation through interaction with the transcriptional regulatory domain (Mu et al. 2002), suggesting its potential role as a tumor suppressor. A number of studies suggest that p53 plays an important role in controlling cell fate through regulation of cellular ROS level (Tomko et al. 2006; Bragado et al. 2007; Ding et al. 2007). Under normal physiological conditions, p53 protein has a short half-life and is maintained at a low level by MDM2-mediated inactivation and ubiquitin/proteasome degradation (Oliner et al. 1992; Maki et al. 1996; Haupt et al. 1997; Kubbutat et al. 1997). Upon certain stimuli such as oxidative stress and DNA damage, p53 is stabilized by posttranslational modifications and translocates to the nucleus, where it serves as a major transcription factor. While a low level of stress induces p53 to upregulate the expression of genes encoding ROS-scavenging enzymes, high level of stress induces p53 to upregulate genes encoding pro-oxidant such as PIG3, p66Shc , and proline oxidase (Giorgio et al. 2005; Rivera and Maxwell 2005; Sablina et al. 2005). Interestingly, when cells are under severe stress, activated p53 can also directly interact with ARE-containing promoters and suppress Nrf2-dependent transcription of antioxidant response genes such as x-CT, NQO1, and GST-α1, resulting in elevation of ROS and apoptosis (Faraonio et al. 2006). Furthermore, intact p53 plays a critical role in sensing and removes oxidative DNA damage, thus, preventing oxidative gene mutation and genetic instability (Achanta

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and Huang 2004; Zurer et al. 2004). Cells and mice with defective p53 exhibited increased ROS stress, high mutagenesis, and increased tumor growth rate, which can be delayed by antioxidant NAC. Reactivation of p53 in p53-deficient tumors can cause a complete tumor regression (Attardi and Donehower 2005). Furthermore, recent work demonstrated that s-Arf/s-p53 mice, which have increased levels of Arf and p53, show decreased ROS and reduced oxidative DNA damage (Matheu et al. 2007). These mice seemed resistant to H-RasV12 and E1A oncogenic transformation (Matheu et al. 2007). The incidences of both sporadic cancer and carcinogeninduced cancer (fibrosarcomas and papillomas) were significantly decreased in the s-Arf/s-p53 mice (Matheu et al. 2007). Owing to its function as a redox regulator and genomic guardian, a loss of functional p53 due to deletion or mutation would lead to increased oxidative stress and genomic instability and likely contributes to the development of many types of human cancer (Achanta et al. 2005; Horn and Vousden 2007). It is worth noting that besides the internal stimuli such as oncogenic stress, DNA damage, and mitochondrial dysfunction, extrinsic factors such as inflammatory cytokines, lack of nutrient, and hypoxic environment could also affect intracellular redox homeostasis (Azad et al. 2008). For example, tumor-associated macrophages have been shown to deliver a sublethal oxidative stress to murine mammary tumor cells possibly through secretion of tumor necrosis factor-α (Fulton and Chong 1992; Kundu et al. 1995). Likewise, glucose deprivation was shown to rapidly induce cellular oxidative stress in the MCF-7 breast carcinoma cell line possibly through attenuation of ROS decomposition associated with the depletion of intracellular pyruvate (Spitz et al. 2000). While the intrinsic mechanisms of increased oxidative stress in cancer cells have been studied extensively in recent years, the extrinsic mechanisms which are account for the interaction between the cancer cells and the microenvironment are at the beginning stage of exploration. Thus, more research effort in this important area is apparently required. Although a moderate increase of ROS may promote proliferation and acquired mutations, high level of ROS could also induce cell death. Therefore, only the cancer cells capable of counteracting the damaging effect of ROS stress can survive and eventually proliferate to develop tumor. In order to escape the damaging effect of ROS stress, those cells must acquire the adaptive mechanisms to counteract the intrinsic ROS stress and provide survival advantage (Irmak et al. 2003).

Adaptive Response to Oxidative Stress It has been shown that pre-exposure of normal epithelial cells to low levels of exogenous oxidants confer cellular resistance to subsequent oxidative stress at higher levels (Choi et al. 1997), indicating that cells are able to adapt in response to oxidative stress. In the case of cancer cells, the increased levels of intracellular ROS generation are likely to stimulate the cellular protective mechanisms to cope with oxidative stress. ROS may affect protein function in different ways. Different proteins can be

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either activated or inhibited by redox modifications. Thus, whether the consequence of oxidative stress will lead to cell survival or death is likely dependent on the integration of the redox-sensitive cell death or survival signals. The intrinsic oxidative stress acts as a selection pressure to enrich the population of cancer cells capable of stress adaptation. Thus, the surviving cells likely have activation of survival factors, suppression of cell death machineries, and/or elevation of antioxidant capacity. Although in certain cases apoptosis resistance can also be the result of nongenetic stress adaptation (Vogel et al. 2006), genomic instability seems to be an important mechanism that provides cancer cells the flexible capacity to overexpress those factors for the adaptation purpose (Kim et al. 2006a). Dynamic alterations of numerous genes through genetic or epigenetic mechanisms can accumulate during carcinogenesis without markedly changing phenotype. This genomic instability allows silent adaptation until it is sufficient to provide survival advantage in the tumor microenvironment (Schneider and Kulesz-Martin 2004). The increase of ROS in cancer cells itself could cause genomic instability via genetic and epigenetic mechanisms (Limoli et al. 2003; Chiera et al. 2008; Franco et al. 2008). These will result in abnormal upregulation or downregulation of many genes. Owing to the function of p53 as a genomic guardian to sense and repair damaged DNA, its loss is thought to be a major factor contributing to genomic instability. This is consistent with the finding that p53-deficient cells were more resistant to apoptosis induced by ROS-inducing cytotoxic agents (Achanta et al. 2005). The genetically unstable cells may be more prone to aberrant upregulation of certain redox-sensitive transcription factors that promote cell survival. Altered expression of NF-κB, Nrf2, AP-1, and HIF-1 was found to be critical for the adaptation process. Activation of these factors results in alterations of expression of many molecules involved in the regulation of redox balance and cell fate. The possible mechanisms of adaptation to oxidative stress in cancer cells are illustrated in Fig. 7.1 and discussed in the following sections.

Activation of Redox-Sensitive Transcription Factors A number of transcription factors such as NF-κB, AP-1, and HIF-1α contain redox-sensitive cysteine residues at their DNA-binding sites (Haddad 2002). Thiol oxidation of these proteins may affect their DNA-binding activities due to possible protein conformational changes (Turpaev 2002). In addition to the protective effect of reduced glutathione (GSH) on oxidative modifications of nuclear DNA, GSH may also play a critical role in maintaining reducing environment for proper function of the transcriptional factors to ensure optimal gene transactivation (Green et al. 2006). To activate gene transcription, not only transcription factors themselves should be in proper form but also DNA strains need to be uncoiled to allow accessibility of transcription factors to the promoter regions of the target genes. And histone acetylation, catalized by transcriptional co-activators such as CBP/p300, is required for that process (Ogryzko et al. 1996). Interestingly, enzyme histone deacetylase

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(HDAC), which reverses the histone acetylation, was recently found to be redox sensitive (Rahman et al. 2004). Thus, in addition to direct oxidative modifications of the transcription factors, alteration in ROS/RNS level may regulate gene expression through modulation of chromatin remodeling. The detailed mechanisms by which oxidative stress regulates the redox-sensitive factors have been reviewed recently (Trachootham et al. 2008a). NF-κB Nuclear factor kappa B (NF-κB) is a redox-sensitive transcription factor, which is involved in the regulation of immunity, inflammation, development, cell proliferation, and survival. In mammals, the NF-κB family consists of NF-κB1 (p50/p105), NF-κB2 (p52/p100), RelA (p65), c-Rel, and RelB. All members are characterized by the presence of the Rel homology domain (RHD), which mediates DNA binding, dimerization between the family members and the association of NF-κB dimers with the inhibitor kappa B (IκB) (Hayden and Ghosh 2004). NF-κB-mediated gene expression can be stimulated by oxidative stress and is thought to play a role in coordinating the adaptation to such stress. Therefore, the elevated level of ROS within tumor microenvironment is an important stressor that drives the upregulation of NFκB-mediated pro-survival pathways (Sullivan and Graham 2008). This is consistent with the observation that NF-κB activity is constitutively elevated in many types of human cancers (Bours et al. 1994; Smirnov et al. 2001). In response to oxidative stress, activation of NF-κB leads to elevated expression of antioxidants such as Mn-SOD and ferritin heavy chain (FHC), inhibitors of apoptosis such as Bcl-2 family members including Bcl-xL and A1/Bfl-1, the inactive homologue of caspase-8 (FLIPL ), caspase inhibitors such as IAPs, TNF receptor-associated factor TRAF1, and Gadd45 (inhibitor of JNK-mediated cell death) (Karin and Lin 2002). Furthermore, recent work also suggests that in phagocytes NF-κB activation induced by TNF-α can directly control the amount of cellular ROS/RNS production through modulating the expression and activity of ROS-producing enzyme NADPH oxidase (Gauss et al. 2007). Since several cytokines including TNF-α were found to be released in tumor microenvironment (Inoue et al. 2007), it is reasonable to speculate that these cytokines may affect cellular redox status of cancer cells through NF-κB activation. Nrf2 NF-E2-related factor 2 (Nrf2) is a member of p45 NF-E2-related protein family that includes p45 NF-E2, Nrf1, Nrf2, and Nrf3 (Kobayashi and Yamamoto 2005; Kobayashi and Yamamoto 2006). The proteins in this family require heterodimeric formation with small Maf proteins for DNA binding (Motohashi et al. 2004). Under normal condition, Nrf2 localizes in the cytoplasm where it interacts with the actinbinding protein, Kelch-like ECH-associating protein 1 (Keap1) (Itoh et al. 1999). Keap1 functions as an adaptor of Cul3-based E3 ubiquitin ligase and targets Nrf2 for rapid degradation by the ubiquitin–proteasome system (Kobayashi et al. 2004).

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Oxidative stress and electrophiles are major activators of Nrf2 pathway. Dissociation of Nrf2 from Keap1 is a key step in activating Nrf2. The free Nrf2 then translocates to nucleus, heteromerizes with Maf(s), and binds to a cis-acting element in DNA known as antioxidant responsive element (ARE) or electrophile responsive element (EpRE) within the regulatory regions of many genes. Nrf2 activation promotes cell survival under oxidative stress through multiple mechanisms. One major function is the transactivation of many antioxidant proteins including heme oxygenase-1, ubiquitin/PKC-interacting protein A170, peroxiredoxin 1, the heavy and light chain of ferritin, catalase, glutathione peroxidase, superoxide dismutase, and thioredoxin (Ishii et al. 2000). These proteins are directly or indirectly involved in scavenging free radicals and thus reduce the toxic effect of ROS. Furthermore, Nrf2 regulates the synthesis of glutathione by controlling both the basal and the inducible expression of genes encoding glutamylcysteine synthetase (Bea et al. 2003). Since glutathione is not only the most abundant scavenger of ROS but also the key controller of redox status of proteins affecting cell survival and cell death, the regulatory effect of Nrf2 on glutathione synthesis plays an important role in cell survival. In addition, Nrf2 was shown to modulate the elimination of pro-oxidative electrophilic compounds through regulating expression of phase II detoxification enzyme such as glutathione-s-transferase (GST) and transporters such as multidrug resistance-associated protein 1/ATP-binding cassette transporter C. The direct roles of Nrf2 on cell survival and cell death pathways are also evident. Nrf2 has been identified as an inhibitor of Fas-induced apoptosis (Kotlo et al. 2003; Morito et al. 2003). In the absence of Nrf2, death-receptor-induced apoptosis was found to be enhanced. The cell death could be suppressed by supplementation of glutathione, suggesting that anti-apoptotic effect of Nrf2 was through elevating intracellular glutathione levels (Kotlo et al. 2003; Morito et al. 2003). Accumulation of unfolded polypeptides following oxidative stress could also trigger apoptosis. In response to unfolded protein stress, Nrf2 is a direct substrate of phosphorylation by PERK and acts as an effector of PERK-dependent cell survival (Cullinan et al. 2003). PERK is an ER transmembrane protein kinase that phosphorylates the subunit of translation initiation factor 2 (eIF2a) in response to ER stress. Phosphorylation of eIF2a reduces the global translation, allowing cells sufficient time to correct the impaired protein folding (Wek and Cavener 2007). Induction of 26S proteosome and heat shock proteins by Nrf2 facilitates the repair or elimination of the damaged proteins and thus protects cells from apoptosis (Kwak et al. 2003). c-Jun c-jun is a member of AP-1 family which consists of several groups of basic leucine zipper domain (bZIP) proteins including Jun (c-Jun, JunB, JunD), Fos (c-Fos, FosB, Fra-1, and Fra2), Maf (c-Maf, MafB, MafA, MafG/F/K, and Nrl), and ATF (ATF2, LRF1/ATF3, B-ATF, JDP1, JDP2) sub-families (Angel and Karin 1991). AP-1 proteins form hetero-dimers and bind to the target DNA sequence. The mitogenactivated protein kinase (MAPK) plays a major role in controlling activation of AP1 proteins through phosphorylation. c-Jun is regulated mainly by JNK and ERK in

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various cell types. The mechanism by which c-Jun mediates cell survival or death seems to depend on the balance between the pro-apoptotic and the anti-apoptotic target gene transcriptions. FasL, Bim, and Bcl3 are target genes of c-Jun. Induction of FasL and Bim may promote apoptosis, whereas upregulation of BCL3 by c-jun may potentiate its anti-apoptotic function (Shaulian and Karin 2002). This induction of target gene transcription may be cell type and stimulus dependent. Furthermore, cell fate may be determined by the integration of signal from c-jun and other factors such as p53 and p21. c-Jun was shown to regulate the decision of p53-mediated cell cycle arrest or apoptosis. High level of c-Jun, which repressed p53-mediated p21 induction, was shown to prevent UV-induced growth arrest and shift most of p53 activity toward the induction of apoptosis (Shaulian et al. 2000). Interestingly, a recent report showed that Jun proteins (c-Jun, JunD, and JunB) upregulate AREmediated expression of antioxidant genes, such as thioredoxin, by associating with Nrf2 and Nrf1 and binding with ARE (Venugopal and Jaiswal 1998). This function may be important in adaptive response to survive under oxidative stress. HIF-1 Hypoxia-inducible factor 1(HIF-1) is generally known as an important transcription factor regulating cell survival under hypoxic stress (Brahimi-Horn and Pouyssegur 2007). However, HIF-1 can also be activated by non-hypoxic stimuli such as thrombin and CoCl2 under normoxia (21% oxygen) (Pouyssegur and Mechta-Grigoriou 2006). HIF-1 is composed of alpha and beta subunits (HIF-1α and HIF-1β). Active HIF requires heterodimeric formation of the two subunits, which then translocate to nucleus, bind to a hypoxia-response element (HRE), and associate with coactivators such as CBP/p300. The binding results in activation or suppression of around 70 genes involved in metabolism, angiogenesis, invasion/metastasis, and cell survival/death (Brahimi-Horn and Pouyssegur 2007). HIF-1 actively contributes to adaptive responses to promote cell survival under hypoxia, through transcriptional regulation of angiogenic factors and glycolytic enzymes (Fulda and Debatin 2007). In tumor cells, HIF-1 plays a major role in metabolic switch that shunts glucose metabolites from mitochondria respiration to anaerobic glycolysis (the Warburg effect) (Lu et al. 2002). HIF activation not only promotes glycolysis but also attenuates mitochondrial respiration. The former occurs through the upregulation of genes encoding glucose transporter (GLUT), hexokinases, aldolase A, and lactate dehydrogenase A (LDH-A), the enzymes that convert pyruvate to lactate. Attenuation of mitochondrial respiration by HIF-1 is through the induction of pyruvate dehydrogenase kinase 1 (PDK1) that inhibits pyruvate dehydrogenase, the enzyme that converts pyruvate into acetyl-CoA. Inhibition of pyruvate dehydrogenase prevents the entry of pyruvate to the TCA cycle and shunts pyruvate toward lactate formation through LDH (Kim et al. 2006b; BrahimiHorn and Pouyssegur 2007). Owing to the role of mitochondrial as a major source of ROS production, it has been proposed that the increased glycolysis mediated by HIF facilitates cell survival through maintaining ATP production and preventing deleterious effect of ROS generated from mitochondrial respiration (Kim et al. 2006b).

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Recent study suggests that the increase in lactate production as a result of metabolic adaptation may play an important role in tumorigenesis (Fantin et al. 2006).

Alterations in Regulators of Redox Homeostasis Since the effect of oxidative stress on cell fate can be either pro-death or prosurvival depending on the intensity and duration of the ROS stress, the adaptation to survive under oxidative stress seems to be largely influenced by the capacity of ROS-scavenging systems that control of ROS level in the cells. In a study using a genetically defined human ovarian cancer H-Rasv12 model, it was shown that the Ras-transformed cells, which had increased O2 – and H2 O2 levels, were shown to have an upregulation of multiple antioxidants compared to their nontumorigenic parental cells (Young et al. 2004). Importantly, this study also suggest that the enhanced antioxidant capability serves as a key mechanism to evade apoptosis induced by ROS stress, as evidenced by the resistance to H2 O2 -induced cell death observed in the Ras-transformed cells (Young et al. 2004). Consistently, recent work demonstrated that the Ras-transformed cells were more sensitive to depletion of glutathione, leading to massive ROS accumulation and cell death (Trachootham et al. 2006), suggesting a critical role of antioxidant for cell survival. Thus, it is conceivable that maintaining redox homeostasis in a high dynamic state (active ROS-scavenging to counteract increased ROS generation) may be an adaptation mechanism used by cancer cells to survive under Ras oncogenic stress. Likewise, studies using inducible c-Myc in melanoma cells showed that c-myc transcriptionally control expression of glutathione synthesis enzyme. Also, apoptosis induced by downregulation of c-myc was associated with cellular depletion of reduced glutathione (Benassi et al. 2006). This indicates that the survival effect of c-myc may be regulated by redox homeostasis and the upregulation of the glutathione antioxidant synthesis may represent an important adaptive mechanism to survive under c-myc-induced oxidative stress. Besides ROS-scavenging system as a redox regulator, recent studies show that cancer cells can control the amount of ROS production through suppression of mitochondria-mediated ROS production by uncoupling protein-2 (UCP2). For example, hepatoma HepG2 cells were found to have increased expression of UCP2 (Brand and Esteves 2005). UCP2 can limit oxidative damage in these cells in response to oxidative stress resulting in improved cell function and resistance to apoptosis (Collins et al. 2005). Recently, an interesting hypothesis was proposed that the cell fate in response to “pro-oxidant” milieu in cancer cells may depend on the ratio of intracellular superoxide to hydrogen peroxide (Pervaiz and Clement 2007). The authors suggest that a dominant increase in superoxide might support the adaptation for cell survival and promote oncogenesis, whereas high levels of hydrogen peroxide might facilitate cell death signaling and prevent carcinogenesis. This suggests that the control of species-specific production of ROS might also play a role in dictating the adaptation process. Obviously, further studies in this area are required.

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Alterations in the Function of Proteins Involving in Cell Survival The effect of oxidative stress on the outcome of cell fate is dependent not only on the levels of ROS but also on the states of stress–response signals and pro-/antiapoptotic factors, which are cell-type specific. Many of those factors are target genes of redox-sensitive transcription factors, and thus their expression levels are controlled through redox-sensitive transcriptional regulation (Sen and Packer 1996). Furthermore, certain stress-response signals and pro- or anti-apoptotic factors are redox sensitive, and their activities can be directly regulated by redox system. Oxidative stress could act as stimuli to activate stress-response-signaling pathways such as SAPK and PI3K/Akt or directly alter the function of proteins at the cell death execution level through multiple mechanisms (Trachootham et al. 2008a). Direct oxidative modification seems to be a major mechanism for redox regulation of protein functions (England and Cotter 2005). Such oxidative modifications of proteins are often mediated by OH· and NO· , with the sulfur-containing amino acids such as methionine and cysteine as preferred targets. Mild oxidative stress can induce modifications of Cys such as reversible glutathionylation (Ghezzi 2005), disulfide formation (O Brian and Chu 2005), and s-nitrosylation (Sun et al. 2006), which are known to have regulatory roles in the function of many proteins. For example, oxidative inactivation of PTEN through intramolecular disulfide bond or S-nitrosylation of the active Cys is known as an important mechanism of PI3K/Akt activation by oxidative stress (Lee et al. 1998; Yu et al. 2005). The reduction of oxidized PTEN in cells appears to be mediated predominantly by the Thioredoxin (Trx) system (Lee et al. 2002). Interestingly, redox modification of PTEN was recently shown to be a mechanism to promote survival of cancer cells with mitochondrial dysfunction (Pelicano et al. 2006). Normally, active PTEN is maintained in a reduced state by NADPH/Trx system. A defect in mitochondrial respiration, which causes an increase in NADH and a decrease in NADPH, can lead to oxidative inactivation of PTEN and activation of PI3K/Akt survival pathway (Pelicano et al. 2006). These findings provide an explanation of how metabolic and redox alterations in cancer cells may gain a survival advantage and withstand therapeutic agents. At the cell death execution level, the release of cytochrome c which is considered as a hallmark of mitochondrial-mediated apoptosis (Gogvadze et al. 2006), can be redox regulated. First, oxidation of the cardiolipin of the mitochondrial membrane may cause the dissociation of cytochrome c from the inner mitochondrial membrane leading to its release. Once cytochrome c is released to cytosol, it can induce apoptosis only if it is in an oxidized form. Under physiologic conditions, the presence of high levels of cytoplasmic-reduced glutathione keeps the released cytochrome c in an inactive (reduced) state. As such, glutathione seems to function as a fail-safe mechanism if cytochrome c is released from mitochondria. However, if the redox status of the cell is disturbed due to the presence of hydrogen peroxide and/or depletion of GSH, the cellular redox status will be shifted toward oxidized state and the released cytochrome c will be in an active form, which can trigger caspase activation and apoptosis (Hancock et al. 2001). Besides cytochrome c, caspases are also redox sensitive. Several lines of evidences suggested that reducing environment is required

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for proper function of the Cys-containing active sites of caspases. Oxidative modifications of caspases such as direct oxidation, glutathionylation, and S-nitrosylation were shown to attenuate the proteolytic activities and inhibit apoptosis (Hampton and Orrenius 1997; Kim et al. 1997; Hannes Hentze 1999; Hentze et al. 1999). Furthermore, glutathionylation of pro-caspase 3 was recently shown to render resistance to proteolytic cleavage, thereby preventing caspase activation and inhibiting apoptosis (Pan and Berk 2007). Therefore, the increased glutathione observed in many types of cancer cells may prevent the pro-death function of cytochrome c and caspases, resulting in defect in cell death and drug resistance.

Mechanisms of Drug Resistance as the Consequence of Redox Adaptation Cancer cells may acquire drug resistance as a result of selection pressure dictated by unfavorable microenvironments. Recent studies suggest that adaptation to survive under oxidative stress is one of critical events in the resistance to apoptosis (Pervaiz and Clement 2004; Tiligada 2006; Sullivan and Graham 2008). The alterations in the regulator of redox homeostasis and cell survival machineries could result in increased capacity to tolerate high levels of ROS, decreased apoptotic execution, and elevated DNA repair capability. These all contribute to enhancement of cell survival. Furthermore, the alterations in ROS-scavenging systems such as glutathione also have significant effect on the metabolism of chemotherapeutic drugs. Augmentation in cellular protective mechanisms together with abnormal drug transport can all contribute to the drug-resistant phenotype in cancer cells (Fig. 7.1).

Enhanced Cell Survival As mentioned above, activation of redox-sensitive transcription factors plays an important role in the adaptation to survive under oxidative stress and confers drug resistance. This is consistent with the observation that NF-κB, Nrf-2, and HIF-1 are shown to be constitutively activated in the drug-resistant cancer cells (Zhong et al. 1999; Talks et al. 2000; Stacy et al. 2006; Vermeulen et al. 2006; Kim et al. 2007). As a result of their activation, transcription of the respective downstream target genes is increased, leading to promotion of cell survival by increasing the capacity to maintain redox homeostasis, inhibiting apoptotic execution, and enhancing DNA repair.

Increased Capacity to Maintain Redox Homeostasis Many anticancer agents currently used in cancer treatment have been shown to cause increased cellular ROS generation (Pelicano et al. 2004). These therapeutic agents include arsenic trioxide (Meister 1988; Pelicano et al. 2003), anthracyclines

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(Serrano et al. 1999; Tsang et al. 2003), bleomycin (Hug et al. 1997), bortezomib (Ling et al. 2003), cisplatin (Miyajima et al. 1997), N-(4-hydroxyphenyl) retinamide (Suzuki et al. 1999; Asumendi et al. 2002; Tosetti et al. 2003), and emodin (Yi et al. 2004). In cancer cells which produce high levels of ROS and under increased oxidative stress, overproduction of ROS by those drugs not only causes cellular injury but also exhausts the capacity of antioxidant defense (Kong and Lillehei 1998; Kong et al. 2000). Therefore, only the cells with enhanced ROS-scavenging capacity may sustain the deleterious effect of those chemotherapeutic agents and exhibit resistant phenotype. Previous study showed that H2 O2 -resistant fibroblasts were also resistant to clonogenic inactivation by doxorubicin (Spitz et al. 1993). This is consistent with the observation that drug-resistant cancer cells acquired high capacity to scavenge cellular ROS. For example, acute myeloid leukemia cells (AML) resistant to doxorubicin were found to have better capacity to remove hydrogen peroxide than the parental cells. Microarray analysis showed that the doxorubicin-resistant AML cells have a significant increase in expression of antioxidant molecules such as glutathione transferase GST-Pi, peroxiredoxin-2, thioredoxin-2, and glutaredoxin (Oh et al. 2004). Likewise, paraquat (PQ)-resistant AML cells were also found to have increased activities of both superoxide dismutases SOD1 and SOD2 (Choi et al. 2000). In MCF-7 cells, increased expression of the manganese-containing SOD (Mn-SOD or SOD2) is associated with increased resistance to the oxidative stress caused by doxorubicin (Pani et al. 2000; Sgambato et al. 2003). Stable transfection of the antioxidant GSTP1 into HepG2 led to a decrease in doxorubicin sensitivity by threefold (Harbottle et al. 2001). Significantly higher levels of glutathione are seen in the doxorubicin-resistant mouse leukemia cells compared with their doxorubicin-sensitive parental counterparts (Furusawa et al. 2001). N-acetyl-Lcysteine and reduced GSH antioxidants abolished H2 O2 -induced cell death. Therefore, it is believed that the reduced effect of oxidative stress toward the resistant cells may be related to an increase in intracellular GSH level (Furusawa et al. 2001). Glutathione, the most abundant small peptide and powerful scavenger of ROS in the cells, has long been recognized to play a critical role in mechanism of drug resistance (Estrela et al. 2006). High levels of reduced glutathione (GSH) were found in many multidrug-resistant tumors including lung cancer, breast cancer, colon cancer, melanoma, and leukemia (Estrela et al. 2006). This is consistent with the recent finding that stable overexpression of Nrf2, a major transcription factor that promotes the synthesis of glutathione, resulted in enhanced resistance of cancer cells to chemotherapeutic agents including cisplatin, doxorubicin, and etoposide (Wang et al. 2008a). A study in OCI/AML-2 leukemic blasts following treatment with cytosine arabinoside, etoposide, and gamma-irradiation showed an increase in mitochondrially generated ROS. Upregulation of glutathione and thioredoxin in response to drug-induced ROS stress might represent a cellular adaptation that promotes cell survival and acquired drug resistance (Pham and Hedley 2001). Furthermore, recent evidences suggest a critical role of gamma-glutamyltransferase (GGT), a key enzyme of GSH metabolism, in cancer drug resistance (Pompella et al. 2007). For example, overexpression of GGT was found to play a critical role in resistance to

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cisplatin in Hela cells (Daubeuf et al. 2002). The mechanisms that GSH and GSHdependent reactions contribute to drug resistance include (1) defense against oxidative stress produced by ROS generating drugs, (2) drug inactivation and increase in drug export, (3) increased repair and tolerance of DNA damage, and (4) apoptosis inhibition (Estrela et al. 2006). Besides the glutathione system, thioredoxin-1 (Trx-1) expression has also been shown to increase in a variety of human malignancies and it is associated with decreased patients survival (Kakolyris et al. 2001; Han et al. 2002; Kim et al. 2003; Lincoln et al. 2003; Raffel et al. 2003). Trx-1 plays a crucial role in regulating cellular redox homeostasis and affecting apoptosis. It is directly involved in thioldisulfide exchange reactions to activate a number of transcription factors including NF-κB, Nrf2, p53, and HIF1a (Welsh et al. 2002). In addition, Trx-1 can bind to and inhibit pro-apoptotic proteins such as Ask-1 (Saitoh et al. 1998) and PTEN (Meuillet et al. 2004). Trx has been shown to contribute to drug resistance in cancers. For example, a high expression of Trx-1 is associated with resistance to cisplatin in pancreatic cancer (Arnold et al. 2004), to docetaxel in primary breast cancer (Kim et al. 2005), and to a number of anticancer agents in lung cancer patients (Rosell et al. 2006). Although the glutathione and thioredoxin systems play important roles in resistant to multiple anticancer agents, they may not always be reliable indicators for intrinsic resistance to some other anticancer drugs. A recent study of 19 standard anticancer drugs in 14 human cancer cell lines showed that the enzyme activities of glutathione reductase, glutathione s-transferase, and thioredoxin reductase were only correlated with the cytotoxicity of certain antitumor agents (Bracht et al. 2007). Thus, other factors such as expression of ATP-binding cassette transporter or p53 status should be considered together in this context. Besides enhanced antioxidant capacity, regulation of ROS production was also found as a mechanism used by drug-resistant cancer cells to limit oxidative stress. Interestingly, a recent work showed that upregulation of UCP2, which is present in drug-resistant lines of various cancer cells (Harper et al. 2002), serves as a critical adaptive response to oxidative stress. Overexpression of UCP2 in HCT116 human colon cancer cells decreases ROS generation and attenuates apoptosis after exposure to chemotherapeutic agents in vitro and in vivo (Derdak et al. 2008).

Moderate Increase of Oxidative Stress Inhibits Apoptotic Execution The ability of oxidative stress to promote cell proliferation and malignant transformation is widely recognized (Irani et al. 1997; Suh et al. 1999; Behrend et al. 2003). Interestingly, recent evidence suggests that a mild increase in intracellular ROS can inhibit apoptotic signaling in tumor cells, irrespective of the trigger (Pervaiz et al. 1999; Pervaiz et al. 2001; Tohyama et al. 2004). This may explain the resistance of tumor cells to chemotherapy due to suppression of drug-induced apoptosis (Pervaiz and Clement 2004). One interesting observation was the finding that an increase

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in intracellular O2 – concentration in the absence of cytotoxic production of H2 O2 resulted in inhibition rather than activation of the apoptotic execution pathway (Pervaiz et al. 1999). This work raises a possibility that O2 – inhibits apoptosis while high concentrations of H2 O2 induce cell death. Thus, the overall outcome on cell fate could be dictated by the balance between O2 – and H2 O2 concentrations (Clement and Pervaiz 2001). This idea was supported by the findings that overexpression of Cu/Zn SOD, an enzyme converting O2 – to H2 O2 , in a human melanoma cell line (M14) induced a significant decrease in intracellular level of O2 – with a concomitant increase in cell sensitivity to anticancer drugs such as etoposide, daunorubicine, and pMC540. Conversely, an increase in intracellular O2 – due to repression of Cu/Zn SOD protein inhibited apoptosis induced by the same anticancer drugs (Pervaiz et al. 1999). Similar results have been reported using other apoptotic stimuli such as CD95 and Sindbis virus (Clement and Stamenkovic 1996; Lin et al. 1999). These observations led to a hypothesis that elevated intracellular O2 – may serve as a survival signal and that strategies to low O2 – might favor cell death execution (Pervaiz and Clement 2004). Mechanistic studies suggest that O2 – control cell fate by directly regulating the critical cell death machinery. A decrease in cellular O2 – seems to facilitate activation of caspase-3 activity, whereas an increase of O2 – may suppress this apoptotic enzyme. This phenomenon was further supported by the findings that caspase activity was only evident after pharmacological inhibition of O2 – production by the NADPH oxidase in neutrophils treated with a potent neutrophil activator phorbol 12-myristate-13-acetate (PMA) (Pervaiz et al. 1999). Likewise, inhibition of the NADPH oxidase in constitutive as well as CD95/Fas-triggered apoptosis resulted in increased rather than suppressed level of caspase activity, suggesting that O2 – may prevent caspases from activation in these cells. Consistently, it has been shown that oxidation of the caspases proteolytic site blocks their enzymatic activity (Nicholson et al. 1995; Hampton et al. 1998). In contrast to the cell death inhibitory effect of O2 – , high micromolar concentrations of H2 O2 was known to induce apoptotic or necrotic cell death depending on the concentration (Hampton and Orrenius 1997; Clement et al. 1998). Apoptosis induced by H2 O2 is triggered by caspases 3 and 9 activation likely through release of mitochondrial apoptogenic factors (Yamakawa et al. 2000; Pervaiz and Clement 2004). However, it is unclear if O2 – and H2 O2 could have different effects on the caspases proteolytic site and how such differential effect can be brought about. The decrease in O2 – and an increase in H2 O2 may cause a shift of cellular pH to a significantly acidic level, a phenomenon observed during receptor- or drug-induced apoptosis (Matsuyama et al. 2000; Clement et al. 2003). Taken all together, certain alterations in redox state and the composition of the specific species of ROS may modulate caspase activity and attenuate apoptosis. Owing to the pro-oxidative milieu in cancer cells, it is possible that the malignant cells may develop survival and drug resistance by adaptive inactivation of their caspases. Besides the direct inhibition of apoptotic execution, cancer cells may adapt themselves to survive under oxidative stress by activation of pro-survival factors. For instance, overexpression of Bcl-2 was known to induce resistance to ROS-mediated

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apoptosis by suppression of lipid peroxidation and induction of glutathione synthesis (Ellerby et al. 1996). Furthermore, in doxorubicin-resistant K562, MCF-7, and SKOV-3 cells, a similarity was found between the change in ROS levels and the alterations in bcl-2 gene expression. Also, elevation of the anti-apoptotic BclxL gene expression was also observed in these cells (Kalinina et al. 2006). Interestingly, a recent study in CEM cells showed that creating a slightly pro-oxidant environment is a key mechanism to inhibit cell death induced by CD95 ligation or drug exposure in Bcl-2 overexpressing cells (Clement et al. 2003). The pro-oxidant activity of Bcl-2 appeared mediated by O2 – . This was evidenced by the enhanced sensitivity to receptor- or drug-induced apoptosis in the Bcl-2 overexpressing cells after the suppression of intracellular O2 – by an inhibitor of O2 – -producing enzyme NADPH oxidase DPI or the dominant negative form of Rac1 (Clement et al. 2003).

Increased Repair and Tolerance of DNA Damage DNA is a well-known target of anticancer drugs. A typical example is alkylating agents, which have been used in the treatment of malignancies such as leukemia, lymphomas and breast cancer, and other solid tumors. These drugs exert cytotoxic effect through the formation of interstrand DNA cross-links (Hansson et al. 1987). However, cells with enhanced ability to repair DNA damage may become resistant to the DNA alkylators (Bedford et al. 1988). Accumulating evidences indicate that the reduced form of glutathione (GSH) can facilitate DNA repair which contributes to the positive correlation between cellular glutathione and acquired resistance to the DNA-cross-linking agent cisplatin in ovarian cancer cells (Chen et al. 1995). The inhibition of DNA repair following depletion of glutathione further supports the protective effect of glutathione on DNA (Lai et al. 1989; Lertratanangkoon et al. 1997). Besides the direct effect on DNA repair, GSH also can conjugate with the drug precursors prior to their binding with DNA, resulting in the formation of less toxic lesions which are easily repaired (Ali-Osman 1989; Sharma et al. 1994).

Altered Drug Metabolism Certain redox-modulating molecules may affect cellular sensitivity to anticancer agents by altering drug metabolism and transport. For instance, besides its major function as a ROS scavenger, GSH protects cells via inactivation and elimination of cytotoxic agents. The increase in cellular glutathione following the adaptation to oxidative stress confers drug resistance not only through increased cell survival but also through the effect of glutathione on drug removal or export from the cells. GSH-drug conjugation is an important mechanism of GSH-mediated drug resistance, especially for drugs with an electrophilic nature including nitrogen mustards, melphalan, cyclophosphamide, and BCNU (Tew 1994). GSH-drug adducts may form spontaneously or may be catalyzed by glutathione S-transferases (GST).

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These conjugates are often more hydrophilic and less reactive, and thus less likely to damage cellular component such as nucleic acids and proteins. The rate of GSTcatalyzed conjugation is significantly greater than that of spontaneous conjugation (Schroder et al. 1996). Thus, GST may play an important role in GSH-mediated drug resistance. Glutathione S-transferases (GSTs) are a family of phase II detoxification enzymes that react with compounds directly or after being activated by phase I enzymes such as the cytochrome p450 system (Li et al. 2007). Among the six isoforms that have been characterized, GST-pi has been found highly expressed in many tumors such as ovarian cancer, non-small cell lung cancer, breast cancer, colon cancer, pancreatic cancer, and lymphomas (Salinas and Wong 1999; Townsend and Tew 2003; Balendiran et al. 2004; Turella et al. 2005), as well as in a wide range of drug-resistant cell lines (Tew 1994; Tew 2007). The detail mechanisms of GST and its role in drug resistance are discussed in a separate chapter of this book.

Therapeutic Strategy to Overcome Drug Resistance The development of drug resistance in tumor cells is a major clinical challenge. It is clear that the adaptive response to oxidative stress in cancer cells is strongly associated with chemotherapy resistance. Thus, strategies to abrogate these adaptation mechanisms may help to overcome the resistance to cancer chemotherapeutics, and understanding the basic mechanisms of oxidative stress-mediated drug resistance may help to develop more effective targeting of the adaptation pathways or factors and improve therapeutic outcomes. Figure 7.1 illustrates several strategies that may be used to overcome drug resistance associated with redox alterations.

Targeting Transcription Factors that Regulate Survival Pathways A major adaptive mechanism in response to oxidative stress in cancer cells is by activation of transcription factors that regulate the expression of molecules involving in redox homeostasis and cell survival. Consequently, the activation of the downstream target genes results in increased repair of DNA damage, evasion of apoptosis, and/or alterations in drug metabolism, which together lead to emerging of drug resistance phenotype. Since the activation of the transcription factors serves as a key event during the adaptation process, inhibition of these transcription factors may thus abrogate drug resistant and sensitize the cells to apoptotic induction. For instance, several approaches that target various stages of NF-κB activation are now being explored and tested. These strategies include inhibition of IKK (Holmes-McNary and Baldwin 2000; Estrov et al. 2003; Jeon et al. 2003; Mathas et al. 2003), suppression of proteasome (Wilk and Figueiredo-Pereira 1993; Craiu et al. 1997), IkBa phosphorylation and stabilization (Pahl et al. 1996; Pierce et al. 1997; Gehrt et al. 1998), and interference with NF-κB nuclear translocation (Katsman et al. 2007) and DNA binding (Shishodia and Aggarwal 2004; Siedle et al. 2004; Xia et al. 2004). Preclinical

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studies have shown that these NF-κB-modulating agents, either as single compound or in combination with conventional drug, can reverse drug resistance in various tumors (Ariga et al. 2002; Katsman et al. 2007). Furthermore, recent data from clinical trials of bortezomid, a proteasome inhibitor that suppresses the degradation of IκB and the consequent activation of NF-κB, showed a promising result in treatment of multiple myeloma (Lightcap et al. 2000; Adams 2002). It should be pointed out that the therapeutic activity of bortezomid may be due to multiple mechanisms, not just due to inhibition of NF-κB. It is also worth noting that under certain conditions NF-κB may have pro-apoptotic function in certain cell types (Kasibhatla et al. 1998; Kasibhatla et al. 1999). Further studies are obviously required to define the effects of NF-κB inhibitors on the efficacy of different chemotherapeutic agents in different tumors. Besides interfering the NF-κB pathway, several compounds that impact the HIF1α pathway such as PX-478 (Welsh et al. 2004) and Q93 (Weng et al. 2008) have been synthesized and shown to decrease the protein level of HIF-1α, leading to induction of apoptosis. Furthermore, combination of liposomal antisense oligonucleotiedes (ASOs) against HIF-1α and doxorubicin showed that suppressing cellular defense by targeting HIF1α might be an effective way to enhance the cytotoxicity of anticancer drugs, especially in the hypoxic environment of solid tumors (Wang and Minko 2004). Recently, this novel therapy has been tested in animal model based on nonviral nanoscale-based delivery of ASO targeting HIF-1α and showed an enhanced efficacy in drug-resistant tumor (Wang et al. 2008c). Owing to its function as a major transcriptional regulator of many ROSscavenging enzymes, Nrf-2 plays a critical role in cellular adaptation under oxidative stress and thus may serve as an attractive target for overcoming drug resistance in cancer. For example, Nrf-2 deficient mice were shown to be more susceptible to toxicities of drug-induced oxidative stress (Kensler et al. 2007). Likewise, downregulation of the Nrf2-dependent response by overexpression of its negative regulator Keap1 or small interfering RNA (siRNA) against Nrf-2 can sensitize drugresistant cancer cells to cisplatin, doxorubicin, and etoposide (Cho et al. 2006; Wang et al. 2008a). Furthermore, the importance of Nrf-2 in determining drug response in lung carcinoma, breast adenocarcinoma, and neuroblastoma has also been reported (Wang et al. 2008b). Although development of safe and effective small molecule inhibitors of the Keap1-Nrf2-ARE pathway remains to be an area of future research, it is likely that inhibition of Nrf-2 is a promising strategy to overcome drug resistance in cancer cells that utilize Nrf-2 pathway during adaptation to oxidative stress.

Targeting the Regulators of Redox Homeostasis As discussed in the previous sections, the ROS-scavenging systems such as glutathione and thioredoxin play a major role in the adaptation to oxidative stress and contribute to drug resistance. Elevated levels of these ROS scavengers not only increase the cellular capacity to maintain redox homeostasis but also inhibit

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apoptotic execution, increased DNA repair, and alter drug transport. Thus, targeting these systems may serve as a powerful mechanism to suppress stress adaptation and overcome drug resistance in cancer cells. Among the intracellular antioxidant systems, glutathione is the abundant molecules and plays an essential role in maintaining redox homeostasis. Since increased levels of glutathione and glutathionedependent enzymes are correlated with drug resistance, several strategies have been proposed to inhibit the glutathione function. This includes direct conjugation and depletion of glutathione, inhibition of glutathione synthesis, and inactivation of glutathione-dependent enzymes. Recent studies demonstrated that depletion of glutathione could be used as an effective strategy to disrupt the cellular adaptation, shift the redox balance toward increased oxidative stress and potentially eliminate drug-resistant cancer cells. For example, the natural compound β-phenylethyl isothiocyanate (PEITC), which can directly conjugate with reduced glutathione and promote its export leading to a rapid depletion of glutathione (Zhang et al. 2008), has been shown to effectively kill fludarabine-resistant primary chronic lymphocytic leukemia (CLL) and Gleevecresistant chronic myeloid cells (CML) cells (Trachootham et al. 2008b; Zhang et al. 2008). It was also shown that a subpopulation of LNCaP prostate cancer cells resistant to DNA-damaging agents was equally sensitive to PEITC as the parental LNCaP cells (Chen et al. 2002). Furthermore, combination between fludarabine and PEITC also shows a favorable outcome in drug-resistant CLL cells (Trachootham et al. 2008b). The biological basis for using PEITC to modulate cellular redox state and overcome drug resistance is illustrated in Fig. 7.2. In response to oxidative stress, cancer cells upregulate the synthesis of glutathione, which confer resistance to multiple drugs. However, these cancer cells become more dependent on the glutathione to maintain redox balance due to active ROS generation. As such, depletion of glutathione would be an effective strategy to abrogate the cancer cell survival mechanism and overcome drug resistance. Indeed, PEITC was shown to effectively kill cancer cells with intrinsic ROS stress by disabling the glutathione system through depletion of GSH and inhibition of GPX and by oxidative inactivation of survival factors such as NF-κB and Ras (Trachootham et al. 2006). In addition, PEITC causes further increase of oxidative stress which can trigger cell death through direct membrane damage or c-Jun kinase-mediated caspase activation (Chen et al. 2002; Xu et al. 2005; Trachootham et al. 2006). Furthermore, the ROS stress induced by PEITC also accelerates cell death process by attenuation of anti-apoptotic factors. For example, study using primary CLL cells showed that PEITC-induced degradation of anti-apoptotic protein MCL1 prior to the onset of cell death. Since resistance to fludarabine was known to correlate with increased levels of MCL1, the abrogation of MCL1 through ROS-mediated mechanism likely facilitates the elimination of fludarabine-resistant cells. Interestingly, recent work also showed that PEITC can disrupt mitochondrial redox homeostasis, as indicated by a specific oxidation of peroxiredoxin 3 associated with cell death (Brown et al. 2008). Due to its low toxicity to normal cells (Trachootham et al. 2006) and its high effectiveness against drug-resistant cancer cells, PEITC is a promising compound for further clinical evaluation.

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Cancer Cells ROS Drug Resistance

Adaptation

Apoptotic/Necrotic Cell Death

Survival signals NF-κB, MCL-1, etc. Cell Survival

Redox Adaptation

PEITC

Redox regulation GSH Dependency

ROS ROS

Fig. 7.2 Example of overcoming drug resistance by modulating cellular redox status using PEITC. Cancer cells that adapted to oxidative stress have acquired survival advantage and drugresistant phenotypes due to increased survival signals such as NF-κB and MCL-1 and elevated ROS-scavenging capacity such as GSH. However, the intrinsic active ROS generation renders the drug-resistant cells highly dependent on the survival signals and ROS-scavenging system such as GSH to maintain redox balance and survival. As such, exposure of these cancer cells to compounds such as PEITC, which disrupts glutathione system and abrogates survival signals, will abolish the redox adaptation mechanisms leading to a severe accumulation of ROS, which can then induce cell death either by c-Jun Kinase-induced apoptosis or by direct damage to membranes and other cellular molecules

Depletion of glutathione can also be achieved by targeting its synthesis. A clinically relevant compound is buthionine sulfoximine (BSO), which is an inhibitor of glutamyl–cysteine synthetase (γ-GCS), the rate-limiting GSH synthesis enzyme (Griffith and Meister 1979). Numerous in vitro studies showed that BSO can sensitize drug-resistant tumor cells to cell death induced by cisplatin, As2 O3 , doxorubicin, and melphalan (Fruehauf and Meyskens 2007) and may also reverse the acquired resistance (Barranco et al. 1990; Teicher et al. 1991). Phase I clinical studies with BSO have shown that this compound can be safely given to patients with a 40% maximal GSH depletion in peripheral mononuclear cells (Bailey et al. 1994; Bates et al. 1994). What remain to be determined are whether this extent of glutathione depletion could enhance tumor cell sensitivity and whether this glutathione depletion might increase toxicity in normal cells. Further clinical studies are required to answer these important questions. Cellular GSH biosynthesis may also be modulated by the availability of cysteine/cystine, a component of GSH precursor whose intracellular concentration is very low compared to glutamate and glycine (Kaplowitz et al. 1985). There are two mechanisms to acquire cysteine for GSH synthesis. One is through cystine uptake by Xc– cystine/glutamate antiporter (Lo et al. 2008) which is crucial for certain tumors incapable of cysteine synthesis. Due to the function of cystine antiporter

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in regulating intracellular GSH levels, alteration in the transporter may affect drug sensitivity. Thus, inhibition of the transporter might be able to overcome drug resistance in malignant cells whose glutathione synthesis relies on the transporter (Gout et al. 2003; Lo et al. 2008). Another mechanism in obtaining cysteine substrate is the recycle of cysteine from glutathione metabolism through the γ-glutamyl cycle catalyzed by γ-glutamyl transpeptidase (GGT). High expression of GGT was shown to correlate with resistance to cisplatin, L-phenylalanine mustard, and 5-fluorouracil in ovarian cancer (Godwin et al. 1992) and leukemia (Ahmad et al. 1987). Therefore, modulation of GGT activity may have potential therapeutic implications. Glutathione S-transferase (GST) and glutathione conjugate export pump (GS-X pump) in cancer cells have been shown to confer resistance to a number of anticancer drugs, suggesting the potential therapeutic value of modulating GST and GS-X activities. A variety of GST inhibitors are shown to modulate drug resistance by sensitizing tumor cells to anticancer drugs. Ethacrynic acid (EA) is a wellcharacterized inhibitor and has been evaluated in clinical trial (O Dwyer et al. 1991). By binding to both GST and GSH, EA inhibits GST activity and also deplete GSH. Preclinical and clinical data suggest that this compound is effective in reversing certain drug resistance (Townsend and Tew 2003). Although overexpression of GST in cancer cells can render drug resistance, this may also provide an opportunity to selectively target cancer cells. An interesting strategy has been developed to make use of GST enzyme to convert pro-drug to active form in cancer cells (Tew 2007). TLK286 is an example of such novel pro-drug that is converted to active compound by GST-pi and has been shown to be effective in drug-resistant cancer cells. The therapeutic potential of GS-X pump inhibitors has also been explored. For example, GS-Pt and ONO-1078 are reported to be capable of inhibiting GS-X pump and reverse cisplatin resistance in cancer cell lines that express multidrug-resistance protein (Nagayama et al. 1998; Nakano et al. 1998). Of note, GSH, GST, and GS-X pump are major players within the cellular detoxification system, but each of them plays different role. In order to choose a proper inhibitor, it is necessary to carefully assess their individual role in the detoxification of a certain drug in a specific type of cancer. Thioredoxin is another important factor contributing to drug resistance in many cancer cells (Powis and Kirkpatrick 2007). Thus, inhibitors of the Trx system may have the potential to reverse drug resistance. The expression of thioredoxin isoforms Trx1 and Trx2 has been studied in doxorubicin-sensitive KOV-3 and doxorubicinresistant SKVLB human ovarian carcinoma cells. The development of doxorubicin resistance was accompanied by a significant increase in the expression of TRX1 and a less pronounced increase in TRX2 gene expression (Kalinina et al. 2007). Likewise, high thioredoxin expression in pre-chemotherapy tumor samples is associated with resistance to docetaxel in breast cancer patients (Kim et al. 2005). Much attention has been focused on Trx-1 and thioredoxin reductase 1 (TR1), aiming at the rational design and development of specific inhibitors targeting Trx-1 and TR1. PX-12 (1-methylpropyl 2-imidazolyl disulfide), for example, is developed as a Trx-1 inhibitor, which seems to disrupt the interaction between Trx-1 and Ask-1 thus inducing apoptosis. PX-12 is shown to enhance the growth inhibitory

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actions of CDDP in pancreatic cancer (Arnold et al. 2004) and has entered clinical trial (Ramanathan et al. 2007). Inhibition of thioredoxin reductase by its specific inhibitor auranofin was shown to induce apoptosis in cisplatin-resistant human ovarian cancer cells (Marzano et al. 2007). Mechanistic studies suggest that auranofin specifically inhibits thioredoxin reductase, leading to an increase of cellular hydrogen peroxide and a more oxidized condition that alters the mitochondrial functions (Rigobello et al. 2005). The expression of antioxidant enzymes, such as SOD, GPX, HO-1, NQO1, and catalase, is altered in cancer cells under oxidative stress and plays essential roles in protecting cells against ROS stress. Inhibition of these enzymes is expected to compromise the cellular ability to cope with ROS stress and sensitize cancer cells to antitumor drugs. For instance, superoxide dismutase (SOD) is an important determinant of malignant cell resistance to chemotherapy (Kobayashi et al. 1997). Suppression of SOD has been found to augment the sensitivity to doxorubicin in colon cancer cell line (Kuninaka et al. 2000). Inhibition of SOD by 2-methoxyestradiol (2-ME) is also effective in killing leukemic cells with low toxicity to normal lymphocytes (Huang et al. 2000). The cytotoxicity of 2-ME in chronic lymphocytic leukemia cells (CLL) is significantly correlated with intracellular ROS levels (Zhou et al. 2003). Interestingly, 2-ME is an inducer of p53 but its killing effect in leukemic cells is not dependent on p53 status (Huang et al. 2000). In addition, 2-ME is also found to increase susceptibility of cancer cells to conventional anticancer agents (Halliwell 2000; Golab et al. 2003). Besides ROS-scavenging enzymes, regulator of ROS production such as mitochondrial uncoupling protein 2 (UCP 2) can also be a potential target to modulate drug sensitivity. Importantly, it has been proposed that increased expression of UCP2 may provide a marker of chemoresistance in p53-mutant cell lines (Derdak et al. 2008). Since p53 mutation is a major player in the mechanism of drug resistance in a variety of cancer (Soussi 2003), specific inhibitors to UCP2 could be a potential tool to eradicate drug-resistant cells and warrants significant research effort.

Targeting Redox-Sensitive Factors Involving Cell Survival and Apoptosis Targeting redox-sensitive transcription factors and modulating ROS-scavenging systems will affect the entire cellular redox homeostasis and may be used as strategies to impact the whole cellular adaptation process and affect drug sensitivity. However, in certain cancer cells, where the expression of pro-survival molecules or inactivation of cell death machinery is predominant; directly targeting cell death/survival machineries might be a preferable therapeutic approach. Strategies aiming at restoring apoptosis induction in tumor cells have been proposed. These include activation of pro-apoptotic mediators such as death receptors, p53, and second mitochondria-derived activator of caspases SMAC/DIABLO, as well as inhibition of anti-apoptotic factors such as IAPs (inhibitor of apoptosis proteins) and

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BCL-2 (B-cell chronic lymphoid leukemia/lymphoma) proteins. These strategies have been recently reviewed in detail (Fischer et al. 2007). An activator of caspases smac/Diablo and its mimetic were shown to sensitize breast cancer cells to apoptosis induced by paclitaxel, doxorubicin, etoposide, tamoxifen, and TRAIL (Fandy et al. 2008). Interestingly, combination of the low-molecular weight Smac mimetic LBW242 with other anticancer agents, including tumor necrosis factor-related apoptosisinducing ligand (TRAIL), the proteasome inhibitors bortezomib and NPI-0052, and the conventional anticancer agent melphalan, exhibited additive or synergistic anticancer activity (Chauhan et al. 2007). Furthermore, inhibition of pro-survival factors BCL-2 family proteins was found to be a promising strategy to enhance apoptosis. For example, combining BCL-2 inhibitor ABT-737 with conditionally expressed pro-apoptotic BAK enhanced apoptosis in drug-resistant non-small cell lung cancer cells (Wesarg et al. 2007). Antisense Bcl-2 and antisense Mn-SOD together with glutamine-enriched diet facilitate elimination of highly resistant B16 melanoma cells by tumor necrosis factor-alpha and chemotherapy (Benlloch et al. 2006). Induction of further oxidative stress by pro-oxidant compound such as nitroxide Tempol can also inactivate BCL2 protein and overcome the anthracycline resistance in breast cancer MCF-7 cells (Gariboldi et al. 2006).

Concluding Remarks Drug resistance remains a major obstacle in cancer therapy. Emerging evidences suggest that the adaptation to intrinsic oxidative stress in cancer cells provides survival advantage and plays an important role in the mechanism of resistance to many standard chemotherapeutic agents. Therefore, targeting the adaptive response by manipulating redox status or modulating redox-sensitive factors may have broad applications in overcoming drug resistance. Owing to the redox difference between normal and cancer cells, therapeutic strategies based on redox modulation are likely to be effective with high therapeutic selectivity. Novel agents, including certain natural compounds have been identified to be effective redox-modulating agents with promising anticancer activity. Development of these novel agents and investigation of their interaction with the redox-signaling pathways in cancer cells are important research areas with significant therapeutic implications in cancer treatment.

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Chapter 8

Nuclear Factor-κB and Chemoresistance: How Intertwined Are They? Ajaikumar B. Kunnumakkara, Preetha Anand, and Bharat B. Aggarwal

Abstract The nuclear factor-kappaB (NF-κB) was first discovered in the nucleus of B cells where it binds to the enhancer of the kappa chain of immunoglobulin. This transcription factor has now been shown to be present in all cell types in the body. Under normal conditions, activated NF-κB is found only in immune cells, but constitutively active form is found only in tumor cells. In tumor cells, NF-κB regulates the expression of different genes involved in tumor cell survival, proliferation, invasion, angiogenesis, and metastases. Interestingly, most chemotherapeutic drugs used today also activate NF-κB and induce resistance to chemotherapeutic agents. How NF-κB mediates chemoresistance is becoming quite apparent by its close relationship with MDR1, COX-2, survivin, Bcl-2/bcl-xL, IAP-1, IAP-2, XIAP, cFLIP, adhesion molecules, AKT/PI3K, mTOR signaling, PTK, 5-LOX, EGFR, IL-6, HER-2, PKC/PKA, cyclin D1, C-myc, integrins, TGase, and STAT3. This chapter describes various mechanisms by which NF-κB mediates chemoresistance in tumor cells. Keywords Nuclear factor-κB · Multidrug resistance · Apoptosis inhibitor proteins · Cell adhesion · Cell cycle · Chemoresistance · Chemosensitization Abbreviations 5-Lox, 5-lipoxygenase; IL-6, interleukin-6; COX-2, cyclooxygenase-2; MDR1, multidrug resistance-1; IAP, inhibitor of apoptosis protein; PTK, protein tyrosine kinase; mTOR, mammalian target of rapamycin; AM, adhesion molecules; cFLIP, cellular FLICE-like inhibitory protein; PKC, protein kinase C; PKA, protein kinase A; STAT3, signal transducers and activators of transcription factors; PI3K/Akt, Akt/phosphatidylinositol-3-kinase; TGase, transglutaminase

B.B. Aggarwal (B) Cytokine Research Laboratory, Department of Experimental Therapeutics, Unit 0143, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 8, 

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Introduction Cancer chemotherapy has been designed to exploit a host of different intracellular and extracellular pathways in order to exert their toxic effects on tumor cells. Chemotherapeutic agents are able to activate certain native cellular apoptotic pathways to bring about their desired effect. These apoptotic-triggering mechanisms of cell death are in addition to, and in conjunction with, an oncologic chemotherapeutic drug’s ability to exert direct cellular toxic mechanisms to selective intracellular macromolecules. However, despite the observed success of some oncologic chemotherapeutic regimens, cellular adaptations – innate or acquired – have enabled tumor cells to evade many, if not all, of the chemotherapeutic drugs used in our therapeutic armamentarium. One such cellular chemoresistance factor that has been actively investigated is the transcription factor nuclear factor-kappa B (NF-κB). The ability of activated NF-κB to induce gene expression depends on the cell type and the type of NF-κB inducer. For example, in cell types that are sensitive to TNF and chemotherapy-induced apoptosis, NF-κB is inactivated by caspases and the induction of NF-κB-dependent cell survival signals is markedly reduced. In contrast, activation of NF-κB by growth factors or IL-1 can cause an increase in anti-apoptotic gene expression and subsequent resistance to TNF and chemotherapy. Inhibition of NF-κB activation by IκB overexpression can convert TNF- and chemotherapy-resistant cells to a sensitive phenotype (Beg and Baltimore 1996). The present chapter summarizes the role of NF-κB in cancer and chemoresistance.

What Is NF-κB? Nuclear factor-κB (NF-κB) is a nuclear transcription factor that was first identified by Baltimore and coworkers in 1986 (Aggarwal 2004). It was so named because it was found in the nucleus bound to an enhancer element of the immunoglobulin kappa light chain gene in B cells (Aggarwal 2004; Shishodia and Aggarwal 2004). It was originally considered to be a B-cell-specific transcription factor but was later shown to be expressed ubiquitously in the cytoplasm of every cell type, from Drosophila to man. The Rel/NF-κB transcription factor family is composed of several structurally related proteins that exist in organisms ranging from insects to humans. These transcription factors in the vertebrates include five cellular proteins: c-Rel, RelA (also called p65), RelB, p50/p105 (also called NF-κB1), and p52/p100 (also called NF-κB2). These proteins can form homodimers or heterodimers that give diverse combinations of dimeric complexes, which in turn bind to DNA target sites known as κB sites, where they directly regulate gene expression. A commonly known NF-κB consists of a p50/RelA heterodimer. The different Rel/NF-κB proteins show a distinct ability to form dimers, distinct preferences for different κB

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sites, and distinct abilities to bind to inhibitory subunits known as IκBs (Ahn and Aggarwal 2005). Thus, different Rel/NF-κB complexes can be induced in different cell types and, by means of distinct signals, interact in distinct ways with other transcription factors and regulatory proteins to regulate the expression of distinct gene sets. IκBs bind to NF-κB dimers and sterically block their nuclear localization sequences, thereby causing their cytoplasmic retention. Most agents that activate NF-κB mediate the phosphorylation-induced degradation of IκB. Upon receiving a signal, IκB is phosphorylated at two conserved serine residues (S32 and S36) in its N-terminal regulatory domain. Several well-characterized IκB kinase (IKK) complexes consist of IKKα and β serving as kinases and IKK functioning as a regulatory subunit. Once phosphorylated and while still bound to NF-κB, IκBs almost immediately undergo a second posttranslational modification called polyubiquitination. The major ubiquitin acceptor sites in human IκB are lysines 21 and 22. Protein ubiquitination occurs through E1 ubiquitin-activating enzyme, E2 ubiquitin-conjugating enzyme, and E3 ubiquitin protein ligases. After ubiquitination, IκBs are degraded to 26S proteasomes, leading to the release of NF-κB dimers that translocate into the nucleus (Hacker and Karin 2006). Activation of NF-κB is a tightly regulated event. In normal cells, NF-κB becomes activated only after the appropriate stimulation and then upregulates the transcription of its target genes. NF-κB is activated by many divergent stimuli, including proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), epidermal growth factor (EGF), T- and B-cell mitogens, bacteria and lipopolysaccharides (LPS), viruses, viral proteins, double-stranded RNA, and physical and chemical stresses (Aggarwal et al. 2006b). Cellular stresses such as ionizing radiation and chemotherapeutic agents also activate NF-κB (Sethi and Aggarwal 2006). One of the first genes that NF-κB activates is IκB itself, which transports activated NF-κB from the nucleus to the cytoplasm. NF-κB activation is therefore an inducible but transient event in normal cells. In tumor cells, different types of molecular alterations may result in impaired regulation of NF-κB activation. In such cases, NF-κB loses its transient nature of activation and becomes constitutively activated. This leads to deregulated expression of NF-κB-controlled genes.

NF-κB and Cancer NF-κB has been implicated in the development of cancer because of its critical roles in cell survival, cell adhesion, inflammation, differentiation, and cell growth (Shishodia and Aggarwal 2002). This transcription factor is constitutively active in most tumor cell lines, whether derived from hematopoietic tumors or solid tumors (Table 8.1). It is rarely found to be constitutively active in normal cells except for proliferating T cells, B cells, thymocytes, and astrocytes. Constitutively active NFκB has been identified not only in human cancer cell lines but also in tumor tissues

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Tumors Cell lines: Acute lymphoblastic leukemia Anaplastic large-cell lymphoma Breast cancer

Burkitt’s lymphoma Colorectal cancer Diffuse large B-cell lymphoma Fibrosarcoma Head and neck cancer Hodgkin’s lymphoma Mammary carcinoma Mantle cell lymphoma Melanoma Multiple myeloma Lung cancer Ovarian cancer Pancreatic cancer Squamous cell carcinoma Thyroid cancer Vulva Patient samples: Acute myelogenous leukemia Breast cancer Colorectal cancer Esophageal carcinoma Gastric cancer Laryngeal squamous cell carcinoma Liver cancer Lung cancer Multiple myeloma Pancreatic cancer Squamous cell carcinoma of oral cavity

References Kordes et al. 2000 Mathas et al. 2005 Nakshatri et al. 1997; Sovak et al. 1997; Patel et al. 2000; Bhat-Nakshatri et al. 2002; Mann et al. 2006 Rath 2005 Cusack et al. 2000; Lind et al. 2001; Voboril and Weberova-Voborilova 2006 Davis et al. 2001 Higgins et al. 1993 Ondrey et al. 1999; Tamatani et al. 2001; Jackson-Bernitsas et al. 2007 Bargou et al. 1997 Cogswell et al. 2000; Biswas et al. 2001 Shishodia et al. 2005; Pham et al. 2003 Shattuck- Brandt et al. 1997; Devalaraja et al. 1999; Yang and Richmond 2001; Huang et al. 2000a Feinman et al. 2004 Baby et al. 2007 Huang et al. 2000b Wang et al. 1999 Tamatani et al. 2001; Budunova et al. 1999 Visconti et al. 1997; Ludwig et al. 2001 Seppanen et al. 2000 Guzman et al. 2001; Birkenkamp et al. 2004; Bueso-Ramos et al. 2004; Braun et al. 2006 Buchholz et al. 2005; Van Laere et al. 2005 Yu et al. 2003 Abdel-Latif et al. 2004; Izzo et al. 2006 Sasaki et al. 2001; Wang et al. 2004; Lee et al. 2005 Du et al. 2003 Tai et al. 2000; Qiao et al. 2006 Shinohara et al. 2001; Tang et al. 2006 Bharti et al. 2003 Fujioka et al. 2003 Nakayama et al. 2001

derived from patients with multiple myeloma (Feinman et al. 1999), acute myelogenous leukemia (Griffin 2001), acute lymphocytic leukemia (Kordes et al. 2000), chronic myelogenous leukemia (Baron et al. 2002), prostate cancer (Palayoor et al. 1999), and breast cancer (Nakshatri et al. 1997). The role of NF-κB in different steps of tumorigenesis is discussed below.

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NF-κB Regulates the Expression of Cell Proliferative Gene Products Numerous lines of evidence have shown that NF-κB regulates the expression of genes required for cell proliferation. These include growth factors such as TNF, IL1β, and interleukin-6 (IL-6) (Ahn and Aggarwal 2005). TNF has been shown to be a growth factor for different cancers including glioblastoma (Aggarwal et al. 1996, Mukhopadhyay et al. 2002) and cutaneous T-cell lymphoma (Giri and Aggarwal 1998). IL-1β has been shown to be a growth factor for acute myelogenous leukemia (Estrov et al. 1998), and IL-6 is a growth factor for multiple myeloma (Bharti et al. 2003) and head and neck squamous cell carcinoma (Kato et al. 2000). Moreover, certain cell cycle regulatory proteins (e.g., cyclin D1, which is required for transition of cells from G1 to S phase) are also regulated by NF-κB (Mukhopadhyay et al. 2002). In some cells, PGE2 has been shown to induce proliferation of tumor cells. The synthesis of cyclooxygenase-2 (COX-2), which controls PGE2 production, is also regulated by NF-κB activation (Yamamoto et al. 1995). It has also been shown that growth factors such as EGF and platelet-derived growth factor (PDGF) induce proliferation of tumor cells through activation of NF-κB (Habib et al. 2001; Romashkova and Makarov 1999). Studies from our laboratory have recently shown that the EGF receptor activates NF-κB through tyrosine phosphorylation of IκB at residue 42 in lung cancer cells (Sethi et al. 2007). NF-κB signaling has also been shown to promote both cell survival and neurite process formation in nerve growth factor-stimulated PC12 cells (Foehr et al. 2000).

NF-κB Regulates the Expression of Anti-apoptotic Gene Products The research over the past two decades found that several gene products that inhibit apoptosis are regulated by NF-κB, including IAP-1, IAP-2, XIAP, cFLIP, TRAF1, TRAF2, Bcl-2, Bcl-xL, and survivin (Ahn and Aggarwal 2005). Bcl-xL suppresses the release of cytochrome C from the mitochondria, IAPs inhibit caspase-3 and caspase-9, and FLIP inhibits caspase-8. NF-κB has been linked to anti-apoptotic function in tumors such as T-cell lymphoma, melanoma, pancreatic cancer, bladder cancer, and breast cancer and in tumor-related cell types such as B cells, T cells, granulocytes, macrophages, neuronal cells, smooth muscle cells, and osteoclasts (Aggarwal 2004).

NF-κB Regulates the Expression of Invasive Gene Products Several proteases that influence tumor invasiveness, including the matrix metalloproteinases and the serine protease urokinase-type plasminogen activator (uPA), are reportedly regulated by NF-κB (Bond et al. 1998; Novak et al. 1991). Matrix metalloproteinases (MMPs) promote growth of cancer cells through the interaction of extracellular matrix (ECM) molecules and integrins, cleaving insulin-like

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growth factors and shedding transmembrane precursors of growth factors, including transforming growth factor-β (TGF-β). MMPs promote angiogenesis by increasing the bioavailability of proangiogenic growth factors. MMPs also regulate invasion and migration by degrading structural ECM components, in particular, by cleaving laminin-5. uPA is another critical protease involved in tumor invasion and metastasis. Novak et al. reported that transcriptional activation of the uPA gene by phorbol 12-myristate 13-acetate (PMA), IL-1β, and TNF requires the induction of NF-κB activity and the decay of its short-lived repressor protein, IκB. The uPA promoter contains an NF-κB-binding site that directly mediates the induction of uPA expression by RelA. Recent studies have further shown that constitutively active phosphatidylinositol-3-kinase (PI3K) controls cell motility by regulating the expression of uPA through the activation of NF-κB (Novak et al. 1991). Thus, one potential way to block the invasion of tumors is to target NF-κB and thus its activation of genes involved in cancer progression.

NF-κB Regulates the Expression of Genes Involved in Angiogenesis It is now well recognized that angiogenesis is critical for tumor progression and that this process is dependent on chemokines (e.g., MCP-1, IL-8) and growth factors [e.g., vascular endothelial growth factor (VEGF)] produced by neutrophils and other inflammatory cells (Aggarwal et al. 2006c). The production of these angiogenic factors has been shown to be regulated by NF-κB activation (Aggarwal et al. 2006d). NF-κB has been shown to mediate the upregulation of IL-8 and VEGF expression in bombesin-stimulated PC-3 cells. The studies showed that NF-κB expression was associated with VEGF expression and microvessel density in human colorectal cancer (Yu et al. 2003). In another study, Pollet et al. (2003) demonstrated that LPS directly stimulated endothelial sprouting via TRAF6 in vitro and that inhibition of NF-κB activity downstream from TRAF6 was sufficient to inhibit LPS-induced endothelial sprouting. Also, inhibition of NF-κB activity blocked basic fibroblast growth factor (bFGF)-induced angiogenesis. These studies further establish the role of NF-κB activation in angiogenesis.

NF-κB Regulates the Expression of Genes Involved in Tumor Cell Metastasis The metastasis of cancer requires the migration of cancerous cells both into and out of the vessel walls that transport them to other parts of the body. The ability to penetrate through vessel walls is mediated by specific molecules that are expressed on the endothelial cells of the blood vessels in response to a number of signals from inflammatory cells, tumor cells, and others. Among those special molecules are ICAM-1, ELAM-1, and VCAM-1, all of which have been shown to be expressed in response

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to NF-κB activation (van de Stolpe et al. 1994). Helbig et al. have demonstrated that NF-κB can regulate the motility of breast cancer cells by directly upregulating the expression of CXCR4 (Helbig et al. 2003). These results implicate NF-κB in the migration and organ-specific homing of metastatic breast cancer cells. These studies demonstrate the importance of suppressing NF-κB activation in reducing the metastasis of cancer cells to other sites.

Chemotherapeutic Agents Activate NF-κB and Induce Drug Resistance The primary goal of chemotherapy is to cause cancer cell death. However, a side effect of many commonly used chemotherapeutic drugs is the activation of NF-κB, a potent inducer of anti-apoptotic genes, which may blunt the therapeutic efficacy of these compounds. Studies have shown that a series of topoisomerase poisons (actinomycin D, camptothecin, daunomycin, and etoposide) activate NF-κB (NFKB1/RelA dimer) in ACH-2 and CEM cells (Piret and Piette 1996). The studies performed by Das and White (1997) showed that paclitaxel, vinblastine, vincristine, daunomycin, and doxorubicin each caused activation of NF-κB in the human lung adenocarcinoma cell line A549 by increased protein kinase C activity. Another study showed that both cisplatin and doxorubicin activated nuclear ERK2 and NF-κB of SiHa cells. This study also showed that the MEK–ERK signaling pathway plays a role in the chemosensitivity of SiHa cells and that suppression of this pathway increases cisplatin resistance partly via an increase of NF-κB activation (Yeh et al. 2002). The studies performed by Andriollo et al. (2003) showed that doxorubicin activated NF-κB only in ADR-sensitive human lung carcinoma GLC(4) cells in a time- and dose-dependent manner by stimulating IκBα degradation. Uetsuka et al. showed that 5FU, most commonly used as a drug for stomach and colon cancer, induces NF-κB in NUGC3/5FU/L cells, and inhibition of inducible NF-κB activation by using a NF-κB decoy could induce apoptosis and reduce chemoresistance against 5FU (Uetsuka et al. 2003). The studies performed by Dolcet et al. (2006) showed that proteasome inhibitors, including bortezomib, induce cell death in endometrial carcinoma cells and primary explants, but at the same time they activate NF-κB instead of blocking its transcriptional potential. Another study showed that the chemotherapeutic agents gemcitabine and paclitaxel activated NF-κB in the pancreatic cancer cell line MIA PaCa-2 and stimulated BCL-2 gene promoter activity and that the stimulation of BCL-2 promoter function was directly regulated by NF-κB. The interesting feature of this study was that the inhibition of Akt in this cell line inhibited NF-κB activity and enhanced the efficacy of chemotherapy. Another study showed that celecoxib induced NF-κB activation in non-small cell lung cancer cell lines, with increased expression of NF-κB-dependent genes, such as bcl-2, bcl-xL, and survivin, leading to apoptosis resistance (Gradilone et al. 2007). However, other studies showed that NF-κB activation is needed for the apoptosis induced by some chemotherapeutic agents. The studies from our laboratory showed that

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Drug

Cell lines

References

5FU Actinomycin D Camptothecin Daunomycin Etoposide Cisplatin

NUGC3/5FU/L ACH-2, CEM (T cells) ACH-2, CEM (T cells) ACH-2, CEM (T cells) ACH-2, CEM (T cells) ACH-2, CEM (T cells) SiHa cells ACH-2, CEM (T cells) IK cells (endometrial carcinoma Mia PaCa-2 RT4V6, KU-7 Mia PaCa-2, A549 MDA-MB-435 Murine macrophages A549 A549 A549 A549 HeLa cells HeLa cells GLC(4) KBM-5, Jurkat SK-MES-1, SK-LU-1, COLO 699 N

Uetsuka et al. 2003 Piret et al. 1996 Piret et al. 1996 Piret et al. 1996 Piret et al. 1996 Piret et al. 1996 Yeh et al. 2002 Piret et al. 1996 Dolcet et al. 2006 Fahy et al. 2004 Kamat et al. 2007 Fahy et al. 2004 Das and White 1997 Aggarwal et al. 2005 Hwang and Ding 1995 Das and White 1997 Das and White 1997 Das and White 1997 Das and White 1997 Bottero et al. 2001 Bottero et al. 2001 Andriollo et al. 2003 Ashikawa et al. 2004 Gradilone et al. 2007

Mitomycin C Bortezomib Gemcitabine Paclitaxel

Taxol Vinblastine Vincristine Daunomycin Doxorubicin SN38 Doxorubicin

Celecoxib

A549, human lung carcinoma; SiHa, human ovarian cancer cells; GLC(4), human small cell lung carcinoma; SK-MES-1, SK-LU-1, COLO 699 N, non-small cell lung cancer cell lines.

activation of NF-κB in KBM-5 and Jurkat cells is essential for the cytotoxic effects of doxorubicin and its analogues (Ashikawa et al. 2004). Another study showed that doxorubicin treatment activates NF-κB signaling and produces NF-κB complexes that are competent for NF-κB binding in vitro. Surprisingly, these NF-κB complexes suppress, rather than activate, constitutive and cytokine-induced NF-κB-dependent transcription (Ho et al. 2005). Table 8.2 summarizes the chemotherapeutic agents that activate NF-κB.

NF-κB-Mediated Drug Resistance Mechanisms Research over the past two decades has found that NF-κB regulates genes, transcription factors, and growth factors that induce drug resistance in cancer cells. Figure 8.1 shows the different drug resistance molecules that are regulated by NF-κB.

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IL-6

PTK STAT3

Integrins

AKT/PI3K

TGase

PKC/PKA

5-LOX COX2

AM

NF-κB

mTOR

MDR1

PTK

EGFR

HER2

Survivin C-myc

cFLIP IAP

Cyclin D1

Fig. 8.1 Role of NF-κB pathway in chemoresistance

Multidrug Resistance (MDR) Gene P-Glycoprotein (P-gp) encoded by the MDR gene is one of the main factors in multidrug resistance in cancer cells (Ledoux et al. 2003). Research found that NFκB activity and the NF-κB p65 subunit level were constitutively higher in MDR cells than in drug-sensitive parental cells (Um et al. 2001). Induction of MDR1 was mediated by the DNA sequence located at –6092, which contains an NF-κB binding site (Kuo et al. 2002). The studies showed that the hepatocarcinogen 2acetylaminofluorene (2-AAF) efficiently activates rat mdr1b expression in cultured cells and in Fisher 344 rats and that activation of rat mdr1b in cultured cells by 2AAF involves a cis-activating element containing a NF-κB binding site located –167 to –158 of the rat mdr1b promoter. Several other studies showed that NF-κB is the major player of the activation of the MDR1 gene and that the inhibition of NF-κB inhibits MDR1 and sensitizes tumor cells to chemotherapy. The studies also showed that the insulin-induced mdr1b expression is mediated by transcription factor NF-κB via the Raf-1 kinase signaling pathway. Um et al. (2001) identified a faster running NF-κB DNA-binding complex as Ku, a DNA damage sensor and a key double strand break repair protein, and Ku was positively correlated with the NF-κB activity in MDR cells and Ku- or both subunits of NF-κB-transfected cells. Flynn et al. (2003) showed that NF-κB can block the paclitaxel-induced apoptotic signaling pathways in the prostate cancer cell line DU145, possibly by increasing the expression of antiapoptotic and MDR1 gene products, leading to the development of chemoresistance in these cells. More recent studies showed that mutated IκBα plasmid transfection can markedly reverse the multidrug resistance of hilar cholangiocarcinoma cells, and interruption of NF-κB activity may become a new target in gene therapy for hilar cholangiocarcinogenesic carcinoma. Song et al. (2005) showed that cepharanthine hydrochloride inhibited NF-κB activation and reversed drug resistance in mice bearing EAC/ADR cell homografts by the inhibition of MDR1.

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COX-2 The cyclooxygenase (COX) isoenzymes catalyze the rate-limiting step in the conversion of arachidonic acid into prostaglandins and is regulated by NF-κB (Yamamoto et al. 1998). Several lines of evidence showed that the COX-2 isoenzyme is overexpressed in several cancers, including pancreatic cancer, colon cancer, breast cancer, prostate cancer, bladder cancer, and lung cancer and has been associated with increased invasiveness, tumor progression, proliferation, angiogenesis, and poor prognosis. Recent evidence suggests that COX-2 has a central role in the chemoresistance of established disease. In drug-resistant cell lines that overexpress MDR1/P-gp170, COX-2 expression was also significantly upregulated. The studies of Das et al. (2006) showed that discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase activated by various types of collagens, induces COX-2 and promotes chemoresistance through NF-κB pathway activation. The studies by Zatelli et al. (2007) showed that COX-2 inhibitors can prevent or reduce the development of the chemoresistance phenotype in breast cancer cells by inhibiting P-gp expression and function. In 2008, Singh et al. (2008) showed that COX-2 expression in MCF-7 breast cancer cells induced genomic instability, Bcl-2 expression, and doxorubicin resistance and made the cells significantly more tumorigenic. Recent studies showed that COX-2 overexpression inhibits death receptor 5 (DR5) expression and confers resistance to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-induced apoptosis in the human colon cancer cell line HCT 15 and that COX-2 inhibition sensitizes human colon carcinoma cells to TRAIL-induced apoptosis through clustering of DR5 and concentrating death-inducing signaling complex components into cholesterol-rich and ceramide-rich domains known as caveolae. More recent studies showed that COX-2 uses PGE2 to stimulate the activities of protein kinases A and C to induce selectively tamoxifen and 4-HPR resistance in ERalpha-positive breast cancer cells. Recent studies also showed that mitochondrial COX-2 in cancer cells confers resistance to apoptosis by reducing the proapoptotic arachidonic acid. Selective and non-selective COX-2 inhibitors have been shown to induce apoptosis in cancer cell lines as well as potentiate the growth inhibitory effects of chemotherapeutic agents, including gemcitabine, and inhibit the growth of human pancreatic cancer cell lines.

Survivin Survivin is a structurally unique member of the inhibitor of apoptosis protein (IAP) family that acts as a suppressor of apoptosis and plays a central role in cell division. Expression of survivin has also been detected in a variety of benign and preneoplastic lesions, is associated with clinical tumor progression (Zaffaroni and Daidone 2002), and is regulated by NF-κB (Angileri et al. 2008).The growing body of evidence suggests that survivin expression is associated with drug resistance in cancer cells. Overexpression of survivin correlates with paclitaxel resistance in human

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ovarian cancer, staurosporine resistance in the human neuroblastoma cell line SKN-MC, cycloheximide (CHX) resistance in genetically modified cells, betulinic acid resistance in human melanoma cells, cisplatin resistance in uveal melanoma cells, and doxorubicin and cisplatin resistance in thyroid cancer cells. Studies have also shown that epidermal growth factor receptor and hypoxia-inducible factor-1alpha signal pathways increase resistance to docetaxel-induced apoptosis by upregulating survivin gene expression. Research has also suggested that calcitonin induces apoptosis resistance in prostate cancer cell lines against cytotoxic drugs via the Akt/survivin pathway. Moreover, the downregulation of survivin induces chemosensitivity in different cancers. The inhibition of survivin expression appears to enhance the therapeutic effects of flutamide and enhances sensitivity of human prostate cancer cells to paclitaxel, overcomes imatinib resistance in CML cells, and cytotoxicity of cisplatin and doxorubicin to thyroid cancer cells, human glioma and neuroblastoma cells to TRAIL-induced apoptosis. The studies also showed that survivin antisense RNA induces apoptosis and sensitizes the ovarian cancer cell line SKOV3 to docetaxel, sensitizes human hepatoma cells to TRAIL-induced apoptosis, and inhibits tumor cell growth and angiogenesis in breast and cervical cancers (Li et al. 2006). More recent studies showed that gambogic acid, a potent inhibitor of survivin, reverses docetaxel resistance in gastric cancer cells (Wang et al. 2008).

Bcl-2/Bcl-xL The anti-apoptotic proteins Bcl-2 and Bcl-xL, which display sequence homology in all BH1–BH4 domains, promote cell survival and are regulated by NFκB (Aggarwal 2004). In human malignancies, increased expression of Bcl-2 and Bcl-xL proteins commonly occurs and is associated with disease maintenance and progression, resistance to chemotherapy, and poor clinical outcome. Antisense oligonucleotides targeting Bcl-xL and Bcl-2 have been shown to facilitate apoptosis in various tumor types. Antisense oligonucleotide 4625 targets a high homology region shared between the Bcl-2 and Bcl-xL mRNAs and simultaneously inhibits the expression of both oncoproteins. Enforced overexpression of Bcl-2 or Bcl-xL has been shown to increase the resistance of NSCLC cells to cisplatin and gemcitabine, and a decrease in Bcl-2 and Bcl-xL expression in pancreatic and breast carcinoma cells correlates with increased sensitivity to gemcitabine. Bcl-2-specific antisense oligonucleotides have been shown to improve the effect of doxorubicin on breast carcinoma, of paclitaxel on prostate carcinoma, and of cyclophosphamide on lymphoma cells in preclinical models. Most interestingly, adjuvant administration of both bcl-2 and bcl-xL monospecific antisense oligonucleotides strongly improved the performance of paclitaxel therapy in the Shionogi prostate tumor model. The strong synergistic interactions between oligonucleotide 4625 and the anti-cancer drugs are in agreement with the common knowledge that doxorubicin, paclitaxel, and 4-HC induce apoptosis via the mitochondrial pathway, which is controlled by

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bcl-2 and bcl-xL. In support of this, overexpression of bcl-2 or bcl-xL was shown to inhibit doxorubicin-induced apoptosis in BJAB and Jurkat T cells (Fulda et al. 2001).

Inhibitor of Apoptosis Proteins The IAPs are a family of anti-apoptotic proteins overexpressed in different cancers. The human IAPs XIAP, cIAP1, and cIAP2 have been reported to bind and potently inhibit caspase-3 and caspase-7 with Kis (Tamm et al. 2000); they also inhibit apoptosis and have been found to be regulated by NF-κB (Stehlik et al. 1998, Erl et al. 1999). Therefore, the inhibition of NF-κB might be a useful strategy to block their upregulation and increase chemosensitivity to chemotherapeutic agents. The overexpression of IAPs has been observed in different cancers and found to be responsible for the chemoresistance in different cancers, including doxorubicin resistance in multidrug-resistant HL-60 cells; doxorubicin resistance in bladder cancer cell lines; IB-MECA resistance in HL-60R cells; cisplatin resistance in prostate cancer LNCaP sublines; and paclitaxel, doxorubicin, CDDP, and 5-fluorouracil resistance in pancreatic cancer cell lines. Moreover, studies have suggested that inhibition of IAP expression induces cisplatin chemosensitivity in human ovarian surface epithelial (hOSE) cancer and C2-ceramide sensitivity in human glioma cells. The studies also showed that antisense downregulation of IAPs induces apoptosis in cisplatinresistant cells and resistant melanoma cells to Apo2L/TRAIL-induced apoptosis. Recent work found that actinomycin D-induced downregulation of XIAP (Signal I) and Apo2L/TRAIL-induced release of cytochrome c (Signal II) led to the reversal of resistance to Apo2L/TRAIL-mediated apoptosis in prostate tumor cells (Ng et al. 2002).

cFLIP The anti-apoptotic protein cellular FLICE (Fas-associated death domain-like IL1beta-converting enzyme) inhibitory protein (cFLIP) protects cells from CD95 (APO1/Fas)-induced apoptosis in vitro and was found to be overexpressed in different human cancers including melanoma, colonic adenocarcinoma, endometrial adenocarcinoma, and non-Hodgkin’s lymphoma and correlates with tumor progression and patient outcome. The studies showed that cFLIP expression is regulated by NF-κB (Kreuz et al. 2001). A high level of cFLIP correlates with resistance to death receptor-induced apoptosis in bladder carcinoma cells and survival of human hepatocellular carcinoma. Studies showed that exogenous expression of MRITalpha1/cFLIP(L) isoform protects against cell death induced by a diverse group of chemotherapeutic drugs, including doxorubicin, etoposide, cytosine arabinoside, daunorubicin, chlorambucil, and cisplatin. It has been shown that acquired TRAIL resistance in human breast cancer cells is caused by the sustained cFLIP(L), and

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this resistance prevents TRAIL-induced apoptosis of hepatocellular carcinoma cells by inhibiting the lysosomal pathway of apoptosis. Kang et al. showed that albuminbound LPA and S1P prevent TRAIL-induced apoptosis by upregulation of cFLIP expression through the activation of PI3K/Akt pathway (Kang et al. 2004). Inhibition of FLIP expression was sufficient to sensitize tumor cells for TRAIL-induced apoptosis. The combination of TRAIL and FLIP-targeting siRNA could therefore be a useful strategy to attack cancer cells, which are resistant to TRAIL alone. Moreover, a significant reduction of cFLIP alone is sufficient to sensitize human RCCs, melanoma, and human ovarian cancer cells to TRAIL apoptosis.

Cell Adhesion Molecules Cell adhesion molecules (CAMs) of the immunoglobulin supergene family may play important roles in tumorigenesis and the development of metastatic disease. These molecules, which include CD11/CD18, CD54 (ICAM-1), ELAM, VCAM, and CD58 (LFA-3), are overexpressed in different cancers, including renal cell carcinoma, B-chronic lymphocytic leukemia, esophageal carcinoma, breast cancer, non-Hodgkin’s lymphoma, colon cancer, non-small cell lung cancer, and head and neck cancer. Research has found that the expression of these molecules is regulated by NF-κB (Collins et al. 1995). Several tumors, including Wilms tumor and neuroblastoma, have been found to express a developmentally regulated form of NCAM, which inhibits a variety of cell–cell interactions. Studies have shown that the high lysability by LAK cells of colon carcinoma cells resistant to doxorubicin is associated with a high expression of ICAM-1, LFA-3, and NCA. The studies performed by Schmidmaier et al. showed that ICAM-1, VLA-4, and VCAM expression was higher in pre-treated patients than in chemo-naive patients, and the expression levels increased with the number of chemotherapy regimens. Primarily multidrug-resistant patients had significantly higher expression levels of VLA-4 and ICAM-1 than did responders (Schmidmaier et al. 2006).

PI3K/Akt The PI3K/Akt pathway regulates fundamental cellular functions such as cell growth, survival, and movement (Datta et al. 1999, Katso et al. 2001). Inappropriate activation of the PI3K/Akt pathway has been associated with the development of cancer. Alterations of PI3K have been demonstrated in human malignancies, including carcinomas of the ovary, colon, breast, and lung. Studies also have shown that Akt activates NF-κB (Kane et al. 1999) and that NF-κB induces several genes that are required for cancer cell proliferation, survival, invasion, and metastasis (Aggarwal 2004). Recent studies indicated that expression of XIAP and HER-2/neu rendered tumor cells resistant to TNF or chemotherapeutic agents by activation of the PI3K/Akt pathway. A report further demonstrated that ovarian cancer cells either

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overexpressing constitutively active Akt/AKT1 or containing AKT2 gene amplification were far more resistant to paclitaxel than were cancer cells expressing low AKT levels. Recent studies showed that cisplatin-sensitive ovarian cancer cells (A2780s and OV2008) transfected with constitutively active AKT2 became resistant to cisplatin, whereas overexpression of dominant-negative AKT2 rendered cisplatinresistant ovarian cancer cells (A2780cp and C13) susceptible to cisplatin-induced apoptosis. Treatment of human ovarian cancer cell lines with cisplatin increased Akt activity without measurable cytotoxicity, and small molecule inhibitors and genetic approaches were used to verify that the induction of Akt activity by cisplatin was responsible for the observed chemotherapeutic resistance. It was further demonstrated that genetic alteration of the Akt downstream substrate Bad could sensitize the cells to chemotherapy-induced cell death. In a separate set of studies, the addition of paclitaxel to human ovarian carcinoma cells increased Akt activity and was correlated with low levels of cell death (Mabuchi et al. 2002). These studies used small molecule inhibitors as well as genetic alterations of both Akt and the downstream Akt substrate Bad to corroborate their results of paclitaxel-induced Akt activation and increased chemotherapeutic resistance. Moreover, the studies also indicated that AKT activation and LOH of PTEN play an important role in conferring a broad-spectrum chemoresistance in gastric cancer patients.

mTOR Pathway The serine/threonine kinase mammalian target of rapamycin (mTOR) is a highly conserved protein kinase that integrates signals from nutrients and growth factors to regulate cell growth and cell cycle progression (Fingar and Blenis 2004). The mTOR signaling pathway has been implicated in multiple anti-cancer drug resistance mechanisms, and it is known to activate NF-κB (Dan et al. 2008). Many mutations in cancer such as those in EGFR, Ras, PI3K, and AKT confer survival signals, and therefore the anti-apoptotic effects of mTOR and p70S6K1 (serine/threonine kinase downstream of mTOR) signaling are logical potential mechanisms of drug resistance. mTOR signaling also induces resistance to rapamycin. First, mutations of mTOR or FKBP12 prevent rapamycin from binding to mTOR, conferring rapamycin resistance. Second, mutations or defects of mTOR-regulated proteins, including S6K1, 4E-BP1, PP2A-related phosphatases, and p27 (Kip1), also render rapamycin insensitivity. In addition, the status of ATM, p53, PTEN/Akt, and 14-3-3 is also associated with rapamycin sensitivity. mTOR activation has also been associated with vincristine resistance, resistance to TRAIL in human glioblastoma, resistance to imatinib mesylate or chemotherapy in lung cancer cells, cytarabine resistance in acute myeloid leukemia cells, and resistance to EGFR inhibitors. Co-treatment with the mTOR inhibitor rapamycin in vitro, or with the ester of rapamycin CCI-779 in vivo, inhibited mTOR activity and restored sensitivity to tamoxifen, suggesting that Akt-induced tamoxifen resistance is mediated in part by

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signaling through the mTOR pathway. CCI-779 inhibits mTOR and overcomes cisplatin resistance in lung cancer.

Protein Tyrosine Kinases Protein tyrosine kinases (PTKs) are a family of proteins that are involved in the transformation of normal cells to malignant cells and which are known to activate NF-κB (Aggarwal 2004). The constitutive activation of the Abelson (Abl) protein tyrosine kinase (PTK) is a causative event in chronic myeloid leukemia, where intense chemotherapy currently fails to eradicate the leukemic clone. This protein kinase abrogates the response of multipotent hemopoietic cells to the growth inhibitor macrophage inflammatory protein-1alpha. It is also reported that BCRABL oncoprotein is a constitutively active protein tyrosine kinase (PTK) that alters cell signaling and is responsible for the changes that characterize the malignant cells of CML, and the increased tyrosine kinase activity of BCR-ABL is a requirement for transformation and is therefore a legitimate target for pharmacological inhibition. Another protein tyrosine kinase, v-src, has been reported to induce cisplatin resistance by increasing the repair of cisplatin–DNA interstrand cross-links in human gallbladder adenocarcinoma cells. It has also been shown that C-terminal actinbinding domain in BCR/ABL mediated survival and drug resistance in cancer cells. The studies also showed that targeting PIM kinases impairs survival of hematopoietic cells transformed by kinase inhibitor-sensitive and kinase inhibitor-resistant forms of Fms like tyrosine kinase 3 and BCR/ABL. Moreover, the inhibition of these PTKs restrains the chemoresistance and induces sensitivity of tumor cells to chemotherapeutic agents. For example, the protein kinase inhibitors staurosporine and genistein stimulate 1-(beta-D-arabinofuranosyl)cytosine (AraC)-induced apoptosis in the multidrug-resistant human promyelocytic leukemia cell line HL-60, and the protein tyrosine kinase inhibitor SU5614 inhibits VEGF-induced endothelial cell sprouting and induces growth arrest and apoptosis by inhibition of c-kit in AML cells (Spiekermann et al. 2003).

5-LOX The arachidonic acid-metabolizing enzyme, 5-lipoxygenase (5-LOX), is overexpressed in a variety of cancers (including lung cancer, pancreatic cancer, testicular cancer, ovarian cancer, esophageal cancer, oral cancer, and colorectal cancer) and has been implicated in tumor development (Schroeder et al. 2007). 5-LOX is regulated by NF-κB (Bonizzi et al. 1999). This enzyme was found to be expressed in human pancreatic cancers but not in pancreatic ducts in normal tissue. Studies have shown that 5-LOX inhibitors attenuate the growth of human pancreatic cancer xenografts and induce apoptosis through the mitochondrial pathway. These inhibitors also induce carbonic anhydrase expression in human pancreatic cancer

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cells. Moreover, lipoxygenase inhibitors (MK-886) induce apoptosis in gastric cancer through upregulation of p27kip1 and bax. More recent studies have shown that lipoxygenase inhibitors prevent urological cancer cell growth and bladder cancer cell growth.

EGFR The epidermal growth factor receptor (EGFR) is a 170-kDa transmembrane glycoprotein, implicated in cell proliferation and angiogenesis; it affects components associated with cell–cell adhesion in the integrin pathway and promotes metastasis by stimulating tumor cell motility. EGFR is frequently expressed in a variety of epithelial tumors, including non-small cell lung cancer (NSCLC, 40–80%), colorectal cancer (CRC, 72–82%), head and neck cancer (95–100%), breast cancer (14–91%), and renal cell cancer (50–90%), and activates NF-κB (Sethi et al. 2007). Studies have shown that cross-talk between epidermal growth factor receptor and hypoxia-inducible factor-1alpha signal pathways increases resistance to apoptosis by upregulating survivin gene expression and that the mutant epidermal growth factor receptor (EGFRvIII) contributes to head and neck cancer growth and resistance to EGFR targeting. Changes in epidermal growth factor receptor expression and response to ligand associated with acquired tamoxifen resistance or estrogen independence in the ZR-75-1 human breast cancer cell line, cisplatin (CDDP) resistance in U87MG cells, tamoxifen resistance in human breast cancer, gefitinib or erlotinib resistance in lung adenocarcinomas, gefitinib resistance in bladder cancer cells, and resistance to topoisomerase II toxins in human squamous carcinoma A431 cells are observed. Studies have shown the anti-proliferative effects of tyrosine kinase inhibitors such as genistein, lavendustin A, and 2,5-dihydromethylcinnimate toward tamoxifen-sensitive and tamoxifen-resistant human breast cancer cell lines in relation to the expression of epidermal growth factor receptors (EGFR) and the inhibition of EGFR tyrosine kinase. More recent studies also showed that the T790M mutation in EGFR kinase causes resistance to gefitinib and erlotinib by increasing the affinity for ATP, and the T790M ”gatekeeper” mutation in EGFR mediates resistance to low concentrations of an irreversible EGFR inhibitor HKI-272 (GodinHeymann et al. 2008).

IL-6 Interleukin (IL)-6-mediated signaling pathways have been implicated in tumor progression and chemoresistance in epithelial (e.g., cholangiocarcinoma) and hematopoietic (e.g., multiple myeloma) human tumors, and IL-6 activates NF-κB. Autocrine interleukin-6 (IL-6) production contributes to chemotherapy resistance in prostate cancer, cholangiocarcinoma, endometrial cancer, lung cancer, and breast cancer, and it increases the paclitaxel resistance of U-2OS human osteosarcoma

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cells. It has also been reported that IL-6 contributes to Mcl-1 upregulation and TRAIL resistance via an Akt-signaling pathway in cholangiocarcinoma cells. Moreover, recent studies have shown that PS-1145, an IKK inhibitor, blocks the protective effect of IL-6 against dexamethasone-induced apoptosis and that NF-κB inhibition by PS-1145 enhances docetaxel anti-tumor activity. The studies also showed that IL-6-producing MCCs showed minimal spontaneous and dexamethasone-induced apoptosis, whereas a regular amplitude of apoptosis occurred in the IL-6(–) MCCs. Autocrine IL-6 production and deregulated apoptosis may induce expansion of selective IL-6(+) myeloma cells resistant to spontaneous and drug-induced cell death. Moreover, a study also found that the IL-6 receptor antagonist SANT-7 overcomes bone marrow stromal cell-mediated drug resistance of multiple myeloma cells (H¨onemann et al. 2001).

HER-2 HER-2 (also called ErbB2 or Neu) tyrosine kinase plays a critical role in the control of diverse cellular functions involved in differentiation, proliferation, migration, and cell survival via multiple signal transduction pathways, and HER-2 activates NF-κB (Pianetti et al. 2001). Trastuzumab (Herceptin) is most effective in patients who are HER-2 positive by fluorescent in situ hybridization analysis. However, even in patients with HER-2-amplified tumors, the objective response rate to single-agent trastuzumab as first-line therapy for metastatic breast cancer is only 34%. Furthermore, many patients who have an initial response acquire resistance within a year. Thus, many ongoing studies are focused on the mechanisms of intrinsic and acquired trastuzumab resistance. In vitro, IGF-IR/HER-2 heterodimerization contributes to trastuzumab resistance. HER-2 kinase domain mutation results in constitutive phosphorylation and activation of HER-2 and EGFR and resistance to EGFR tyrosine kinase inhibitors. Moreover, studies also found that HER-2/PI3K/Akt activation leads to a multidrug resistance to paclitaxel, doxorubicin, 5-fluorouracil, etoposide, and camptothecin in human breast adenocarcinoma cells and chemoradioresistance in esophageal squamous cell carcinoma. Studies also showed that the HER tyrosine kinase inhibitor CI1033 enhances the cytotoxicity of 7-ethyl-10hydroxycamptothecin and topotecan by inhibiting breast cancer resistance proteinmediated drug efflux, and antisense inhibition of the HER-2/neu (c-erbB-2) gene increased sensitivity to cisplatin in gastric cancer and head and neck cancer; HER2 amplification impedes the anti-proliferative effects of hormone therapy in estrogen receptor-positive primary breast cancer, and inhibition of HER-2 abrogates anti-estrogen resistance in human breast cancer. More recent studies showed that pharmacological blockade of fatty acid synthase (FASN) reverses acquired autoresistance to trastuzumab by transcriptionally inhibiting “HER-2 super-expression” occurring in high-dose trastuzumab-conditioned SKBR3/Tzb100 breast cancer cells.

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PKC/PKA The signal transduction pathway of cAMP, mediated by the cAMP-dependent protein kinase (PKA), is involved in the regulation of metabolism, cell growth and differentiation, and gene expression, and it activates NF-κB (Aggarwal 2004). It has been reported that protein kinase A/C isozymes are overexpressed in multidrugresistant ovarian cancer cells, and these isozymes are thought to play a role in tumor progression and drug resistance in colon carcinomas, anti-estrogen resistance in mammary tumor cells, doxorubicin resistance in patients with superficial bladder carcinoma, tamoxifen resistance in T47D human breast cancer tumors, and resistance of Hodgkin’s lymphoma cell lines to doxorubicin and CPT-induced apoptosis. Studies have also reported that protein kinase C interacts with P-glycoprotein in human multidrug-resistant carcinoma cells. The studies showed that 8-chloro-cyclic AMP reverses resistance to doxorubicin in doxorubicin-resistant HL-60 leukemia cells is associated with reduction of type I cyclic AMP-dependent protein kinase and cyclic AMP response element-binding protein DNA-binding activities and that 8-Cl-cAMP, a site-selective analogue of cAMP, decreased mdr1 expression in multidrug-resistant human breast cancer cells MCF-7TH . Moreover, inhibition of PKC induced apoptosis in glioblastoma cells and reversed multidrug resistance in KB tumor cells (Merritt et al. 1999).

Cyclin D1 The sequential transcriptional activation of cyclins, the regulatory subunits of cell cycle-specific kinases, is thought to regulate progress through the cell cycle, and is regulated by NF-κB (Aggarwal 2004). Cyclins are therefore potential oncogenes, and cyclin D1 overexpression and/or amplification at its genomic locus, 11q13, is a common feature of several human cancers, including breast cancer, head and neck cancer, non-small cell lung cancer, and mantle cell lymphomas. Studies suggest that high cyclin D1 expression is related to cis-diamminedichloroplatinum (CDDP) resistance in cancer cell lines, increased dihydrofolate reductase (DHFR) expression and resistance to methotrexate in human sarcoma cells, tamoxifen resistance in breast cancer cells, anti-estrogen resistance in breast cancer cells, resistance to progestin therapy in breast cancer, cisplatin resistance, resistance to endocrine therapy, and resistance to human A431 squamous carcinoma. Recent studies showed that tamoxifen stimulates the growth of cyclin D1-overexpressing breast cancer cells by promoting the activation of signal transducer and activator of transcription 3. Research has also shown that inhibition of cyclin D1, in addition to suppressing the growth of pancreatic cancer cells, enhances their responsiveness to multiple chemotherapeutic agents such as the fluoropyrimidines 5-fluorouracil and 5-fluoro2 -deoxyuridine and to mitoxantrone (Kornmann et al. 1999).

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c-Myc MYC, a pleiotropic transcription factor that has been linked to a diverse range of cellular functions (such as cell cycle regulation, proliferation, growth, differentiation, and metabolism) is regulated by NF-κB. Not surprisingly, aberrant MYC signaling has been observed in human cancers, and MYC has been shown to promote cell transformation and tumor progression (Junttila and Westermarck 2008). Studies found that constitutive expression of ectopic c-Myc delays glucocorticoid-evoked apoptosis of human leukemic CEM-C7 cells, confers resistance to anti-estrogen in human breast cancer cells, and confers doxorubicin resistance on breast cancer cells. Moreover, studies found that c-myc antisense oligodeoxynucleotides sensitize human metastatic melanoma cells to cisplatin and sensitize human colon cancer cell (LoVo) to vinblastine. It is also reported that ligand activation of peroxisome proliferator-activated receptor gamma induces apoptosis of leukemia cells by downregulating the c-myc gene expression via blockade of the Tcf-4 activity. Loss of MYC confers resistance to doxorubicin-induced apoptosis by preventing the activation of multiple serine protease- and caspase-mediated pathways. While some other studies showed that enforced expression of c-myc in SW480 and SW620 lines sensitizes cells to cisplatin-induced apoptosis, the downregulation of c-myc in SW480DDP and SW620DDP increases cisplatin resistance (Funato et al. 2001). Moreover, studies also found that myc overexpression sensitizes colon cancer cells to camptothecin-induced apoptosis (Arango et al. 2003, Albihn et al. 2007).

Integrin Integrin-mediated adhesion influences cell survival and may prevent programmed cell death. Integrins are overexpressed in a variety of cancers and are regulated by NF-κB (Aggarwal 2004). It has been shown that drug-sensitive 8226 human myeloma cells, demonstrated to express both VLA-4 (alpha4beta1) and VLA-5 (alpha5beta1) integrin fibronectin (FN) receptors, are relatively resistant to the apoptotic effects of doxorubicin and melphalan when pre-adhered to FN, compared with cells grown in suspension. Moreover, another study showed that upregulation of alpha2beta1 integrin cell surface expression protects A431 cells from epidermal growth factor-induced apoptosis; adhesion to fibronectin via beta1 integrins regulates p27kip1 levels and contributes to cell adhesion-mediated drug resistance (CAM-DR) in myeloma cells; beta1 integrin signaling pathway induces drug resistance in breast cancer cells (Aoudjit and Vuori 2001), induces in vitro resistance of B-chronic lymphocytic leukemia cells to fludarabine, chemoresistance in small cell lung cancer (SCLC) cells, beta1 integrin-mediated adhesion increases Bim protein degradation and contributes to drug resistance in leukemia cells, high expressions of integrin alpha5 and beta5 in ovarian epithelial carcinoma are risk factors for drug resistance and poor disease prognosis, beta2 integrins mediate chemoresistance in cycloheximide-induced U937 apoptosis, alpha5 and beta3 integrins mediate drug

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resistance in human laryngeal carcinoma cells, and fibronectin (FN) adhesion by means of beta1 integrins appears to protect U937 cells from initial drug-induced DNA damage by reducing topo II activity (Hazlehurst et al. 2001). Moreover, beta1 integrin protects the hepatoma cells HepG2, Huh7, and HLE from apoptosis induced by PD98059 (an ERK inhibitor), SB203580 (a p38MAP kinase inhibitor), wortmannin (a phosphatidylinositol-3-kinase inhibitor), and herbimycin A (a tyrosine kinase inhibitor) via a mitogen-activated protein kinase-dependent pathway. It has also been shown that increased expression of integrin beta1 is a poor prognostic factor for patients with small cell lung cancer and that these molecules are drug targets for overcoming innate drug resistance. Studies have shown that the loss of integrin functions overcomes chemoresistance and induces apoptosis in cancer cells. It has been also reported that beta1 integrin inhibitory antibody induces apoptosis of breast cancer cells, inhibits growth, and distinguishes malignant from normal phenotype in three-dimensional cultures and in vivo (Park et al. 2006) and that the loss of the beta4 integrin subunit reduces the tumorigenicity of MCF-7 mammary cells and causes apoptosis upon hormone deprivation (Bon et al. 2006).

TG-2 Tissue type II transglutaminase (TGase) is a member of the TGase family that catalyzes Ca2+ -dependent covalent cross-linking of several amines to the gammacarboxamide group of protein-bound glutamine residues. This enzyme has been linked with the constitutive activation of NF-κB (Mann et al. 2006). It is the most diverse and ubiquitous member of the calcium-dependent transglutaminase family of enzymes and is overexpressed in many human cancer types, blocks apoptosis, and promotes drug resistance and metastatic phenotypes. The degree of therapeutic efficacy or toxicity of drugs may be related to their ability to serve as a substrate for TGase and their covalent linkage to glutamine residues of regulatory proteins through its catalytic action (Mehta 1994). Research has found that doxorubicinresistant human breast carcinoma MCF-7ADR cells exhibited 40- to 60-fold higher TGase activity than control drug-sensitive MCF-7WT cells (Mehta 1994), and the stepwise induction of resistance to doxorubicin and vincristine in PC-14 lung cancer cells was accompanied by a gradual increase of TGase 2 expression (Han and Park 1999). Recent studies showed that TG2 expression contributes to the development of chemoresistance in malignant melanoma cells (Fok et al. 2006) and in pancreatic ductal adenocarcinoma (Verma et al. 2006). The MDA-MB-231 breast cancer cells express high basal levels of TG2. Two clones derived from this cell line, MDA231/cl.9 and MDA231/cl.16, showed a 10- to 15-fold difference in TG2 level. TG2-deficient MDA231/cl.9 cells exhibited higher sensitivity to doxorubicin and were less invasive than the TG2-sufficient MDA231/cl.16 cells. The MCF10A-derived sublines had increased TG2 expression as they advanced from noninvasive to an invasive phenotype. Importantly, the metastatic lymph node tumors from patients with breast cancer showed significant higher levels of TG2 expression

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compared with the primary tumors from the same patients (Mehta 2004). It has also been reported that TG2 inhibition promotes cell death and chemosensitivity in glioblastomas (Yuan et al. 2005) and reverses drug resistance in breast cancer cells through NF-κB inactivation (Kim et al. 2006).

STAT3 Signal transducer and activator of transcription 3 (STAT3) plays an important role in the regulation of cell proliferation and is regulated by NF-κB (Nadiminty et al. 2006). STAT3 is overexpressed in a variety of cancers, including breast cancer, brain cancer, lung cancer, myeloma, and lymphoma (Aggarwal et al. 2006a, Bharti et al. 2004). It has been reported that STAT3 signaling maintains overexpression of heat shock proteins 90alpha and beta in multiple myeloma cells, which critically contribute to tumor cell survival. Studies showed that constitutive activation of STAT3 signaling confers resistance to apoptosis in human U266 myeloma cells, chemotherapy resistance in breast cancer cells, and cisplatin resistance in human non-small cell lung cancer cells. It has also been shown that tamoxifen stimulates the growth of cyclin D1-overexpressing breast cancer cells by promoting the activation of signal transducer and activator of transcription 3. However, other studies showed that the expression of phospho-STAT3 protein and constitutive activation of STAT3 between two human stomach adenocarcinoma cell lines were different. Compared with the parental cell line SGC7901, the STAT3-DNA-binding activity and the expressive intensity of phospho-STAT3 protein were lower in the drug-resistant (5FU) cell line SGC7901/R. The inhibition of the activation of this transcription factor overcomes resistance and induces apoptosis in a wide variety of cancer cells. Studies have found that interruption of STAT3 signaling can reverse resistance to paclitaxel, increase doxorubicin sensitivity in a human metastatic breast cancer cell line, and enhance chemosensitivity in hepatocellular carcinoma. More recent studies showed that NCX-4016, a nitro derivative of aspirin, inhibits STAT3 signaling and modulates Bcl-2 proteins in cisplatin-resistant human ovarian cancer cells and xenografts (Selvendiran et al. 2008).

NF-κB Inhibitors and Chemosensitization From the above discussion, it is clear that NF-κB-regulated gene products play a major role in resistance of tumor cells to different chemotherapeutic agents. Therefore, the inhibitors of NF-κB activation are likely to sensitize tumor cells to chemotherapeutic agents. Numerous cytokines, antibodies, and small molecules have been identified that can suppress the activation of this transcription factor (NFκB website ref). The work from our laboratory has shown that curcumin is a potent inhibitor of NF-κB (Singh and Aggarwal 1995; Aggarwal et al. 2006d) and can downregulate the expression of MDR1 (Anuchapreeda et al. 2002), COX-2 (Zhang

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et al. 1999), survivin (Tomita et al. 2006), bcl-2, bcl-xL (Mukhopadhyay et al. 2001), IAP-1, IAP-2, XIAP (Aggarwal et al. 2005), cFLIP (Mackenzie 2008), adhesion molecules (Kumar et al. 1998), Akt/PI3K (Squires et al. 2003), mTOR (Beevers et al. 2006), PTK (Bian et al. 2001), 5-Lox (Hong et al. 2004), EGFR (Chen and Xu 2005), IL-6 (Gukovsky et al. 2003), HER-2 (Hong et al. 1999), PKC/PKA (Liu et al. 1993), cyclin D1 (Moragoda et al. 2001), c-myc (Kakar and Roy 1994), integrin (Ray et al. 2003), TGase (Mehta et al. 1997), and STAT3 (Bharti et al. 2003). Additionally, curcumin has been shown to sensitize various types of tumors to chemotherapeutic agents, including multiple myeloma (Bharti et al. 2003), ovarian cancer (Lin et al. 2007), lung cancer (Aggarwal et al. 2005), pancreatic cancer (Kunnumakkara et al. 2007), bladder cancer (Kamat et al. 2007), breast cancer (Aggarwal et al. 2005), and colon cancer (Li et al. 2007). Studies by others have shown that genistein enhanced the effects of cisplatin (Banerjee et al. 2007) and gemcitabine (Banerjee et al. 2005) to inhibit pancreatic cancer growth in nude mice. Another study showed that lupeol, a potent NF-κB inhibitor, enhanced the effect of cisplatin in inhibiting head and neck cancer growth and nodal metastases in mice (Lee et al. 2008). Studies are progressing along these lines as NF-κB is a target for chemosensitization of different cancers.

Conclusion NF-κB is overexpressed in almost all cancers, and the overexpression of NFκB induces different genes that are required for proliferation, survival, invasion, metastasis, angiogenesis, and multidrug resistance. The activation of NF-κB by chemotherapeutic agents is another reason for developing drug resistance during chemotherapy. Studies have shown that NF-κB may exert resistance in two ways, innate and acquired. The research over the past decade showed the different mechanisms of NF-κB-mediated drug resistance and its inhibition by different agents. Further studies are needed to elucidate the exact mechanism of NF-κB-mediated drug resistance. Acknowledgment We would like to thank Michael Worley for carefully editing this manuscript.

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van de Stolpe, A., Caldenhoven, E., Stade, B.G., Koenderman, L., Raaijmakers, J.A., Johnson, J.P., and van der Saag, P.T. 1994. 12-O-tetradecanoyl-phorbol-13-acetate- and tumor necrosis factor alpha-mediated induction of intercellular adhesion molecule-1 is inhibited by dexamethasone: functional analysis of the human intercellular adhesion molecular-1 promoter. J. Biol. Chem. 269:6185–6192. Van Laere, S., Van der Auwera, I., Van den Eynden, G.G., Fox, S.B., Bianch, I.F., Harris, A.L., van Dam, P., Van Marck, E.A., Vermeulen, P.B., and Dirix, L.Y. 2005. Distinct molecular signature of inflammatory breast cancer by cDNA microarray analysis. Breast Cancer Res. Treat. 3: 237–246. Verma, A., Wang, H., Manavathi, B., Fok, J.Y., Mann, A.P., Kumar, R., and Mehta, K. 2006. Increased expression of tissue transglutaminase in pancreatic ductal adenocarcinoma and its implications in drug resistance and metastasis. Cancer Res. 21:10525–10533. Visconti, R., Cerutti, J., Battista, S., Fedele, M., Trapasso, F., Zeki, K., Miano, M.P., de Nigris, F., Casalino, L., Curcio, F., Santoro, M., and Fusco, A. 1997. Expression of the neoplastic phenotype by human thyroid carcinoma cell lines requires NFkappaB p65 protein expression. Oncogene. 16:1987–1994. Voboril, R., and Weberova-Voborilova, J. 2006. Constitutive NF-kappaB activity in colorectal cancer cells: impact on radiation-induced NF-kappaB activity, radiosensitivity, and apoptosis. Neoplasma 6:518–523. Wang, T., Wei, J., Qian, X., Ding, Y., Yu, L., and Liu, B. 2008. Gambogic acid, a potent inhibitor of survivin, reverses docetaxel resistance in gastric cancer cells. Cancer Lett. 2:214–22. Wang, W., Abbruzzese, J.L., Evans, D.B., Larry, L, Cleary. K.R., and Chiao, P.J. 1999. The nuclear factor-kappa B RelA transcription factor is constitutively activated in human pancreatic adenocarcinoma cells. Clin. Cancer Res. 1:119–127. Wang, W., Luo, H.S., and Yu, B.P. 2004. Expression of NF-kappaB and human telomerase reverse transcriptase in gastric cancer and precancerous lesions. World J. Gastroenterol. 2:177–181. Yamamoto, K., Arakawa, T., Ueda, N., and Yamamoto, S. 1995. Transcriptional roles of nuclear factor kappa B and nuclear factor-interleukin-6 in the tumor necrosis factor alpha-dependent induction of cyclooxygenase-2 in MC3T3-E1 cells. J. Biol. Chem. 270:31315–31320. Yamamoto, S., Yamamoto, K., Kurobe, H., Yamashita, R., Yamaguchi, H., and Ueda, N. 1998. Transcriptional regulation of fatty acid cyclooxygenases-1 and -2. Int. J. Tissue React. 20: 17–22. Yang J., and Richmond A. 2001. Constitutive IkappaB kinase activity correlates with nuclear factor-kappaB activation in human melanoma cells. Cancer Res. 12:4901–4909. Yeh, P.Y., Chuang, S.E., Yeh, K.H., Song, Y.C., Ea, C.K., and Cheng, A.L. 2002. Increase of the resistance of human cervical carcinoma cells to cisplatin by inhibition of the MEK to ERK signaling pathway partly via enhancement of anticancer drug-induced NF kappa B activation. Biochem. Pharmacol. 63:1423–1430. Yu, H.G., Yu, L.L., Yang, Y., Luo, H.S., Yu, J.P., Meier, J.J., Schrader, H., Bastian, A., Schmidt, W.E., and Schmitz, F. 2003. Increased expression of RelA/nuclear factor-kappa B protein correlates with colorectal tumorigenesis. Oncology. 65:37–45. Yuan, L., Choi, K., Khosla, C., Zheng, X., Higashikubo, R., Chicoine, M.R., and Rich, K.M. 2005. Tissue transglutaminase 2 inhibition promotes cell death and chemosensitivity in glioblastomas. Mol. Cancer Ther. 4:1293–1302. Zaffaroni, N., and Daidone, M.G. 2002. Survivin expression and resistance to anticancer treatments: perspectives for new therapeutic interventions. Drug Resist. Updat. 5:65–72. Zatelli, M.C., Luchin, A., Tagliati, F., Leoni, S., Piccin, D., Bondanelli, M., Rossi, R., and Degli Uberti, E.C. 2007. Cyclooxygenase-2 inhibitors prevent the development of chemoresistance phenotype in a breast cancer cell line by inhibiting glycoprotein p-170 expression. Endocr. Relat. Cancer. 14:1029–1038. Zhang, F., Altorki, N.K., Mestre, J.R., Subbaramaiah, K., and Dannenberg, A.J. 1999. Curcumin inhibits cyclooxygenase-2 transcription in bile acid- and phorbol ester-treated human gastrointestinal epithelial cells. Carcinogenesis. 20:445–451.

Chapter 9

Drug Resistance and the Tumor Suppressor p53: The Paradox of Wild-Type Genotype in Chemorefractory Cancers Zahid H. Siddik

Abstract The tumor suppressor p53 has a central role in drug-induced cell death. Its mutation in about 50% of all cancers is a source of drug resistance from the loss in apoptotic signaling, but loss of apoptosis is also found in many resistant tumors that retain wild-type p53 and this represents a paradox. Indeed, resistance seems to be substantially greater with wild-type p53 present as compared to mutant p53. From the perspective of response and survival in cancer patients, responses are observed independent of p53 gene status, but the 5-year survival rate is significantly greater when wild-type p53 is present. This indicates that an effort to increase the response rate in cancers having the wild-type p53 should translate into an increase in the survival rate. To accomplish this goal, an understanding of the mechanisms contributing to resistance in wild-type p53 cancers becomes important. These mechanisms are multifactorial and include loss in DNA damage recognition, alterations in post-translational modification of p53, failure to activate p53 due to target gene silencing, and failure to transrepress antiapoptotic genes. Keywords p53 gene status · DNA damage recognition · Post-translational modification · Transactivation · Transrepression · Apoptosis

Introduction Chemotherapy is a seminal modality in the clinical management of human cancers. The fact that it works is an attestation that such cancers are inherently more sensitive to the antitumor agent than are normal cells. The genetic cause of this greater tumor sensitivity is not known, particularly since tumor cells may differentially express several hundred genes in comparison to normal cells (Zhang et al. 1997, Ismail et al. 2000). Unfortunately, with continued treatment, sensitive tumors Z.H. Siddik (B) Department of Experimental Therapeutics, Box 353, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 9, 

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acquire resistance not only to the primary antitumor agents but also to diverse unrelated cancer drugs. This is well exemplified with ovarian cancers, which are normally treated with cisplatin-based regimens that induce a high 70% initial clinical response rate, but the therapy eventually fails and results in a low 5-year survival rate of around 20% (Ozols 1991). A similar situation is also observed in small cell lung cancer, where the survival rate can be as low as 5% (Giaccone 2000). Understanding how resistance arises is an important step toward defining therapeutic strategies for circumventing such resistance, but the task poses a significant challenge, primarily because the mechanism of resistance is multifactorial, and the several associated mechanisms are not consistent from tumor to tumor, even within the same tissue type. This challenge can best be appreciated by considering that gene expression profiles from sensitive and resistant tumor systems demonstrate that the number of altered genes in resistant cells is large and, in the case of cisplatin resistance, can reach 100–200 as a conservative estimate (Helleman et al. 2006, Stewart et al. 2006). From this number, a single gene (such as Src, E2F3, or Stat1 (Dressman et al. 2007, Roberts et al. 2005)) or a signature composed of a small number of genes in various combinations dependent on the tissue type or the phenotype (Dressman et al. 2007, Helleman et al. 2006) may be proposed as the best predictor for response or resistance, but the utility of such information in the clinical setting must await prospective analysis. However, one gene that alone wields much promise as a target in cancer research is the tumor suppressor p53, primarily because it is the most commonly mutated gene in cancer (Hollstein et al. 1991, Soussi 2000). A majority of the mutations (∼90%) are missense mutations consisting of single amino acid substitutions (Vousden and Lu 2002), which can impact responses to antitumor agents. In this chapter, we will explore how loss of p53 function contributes to chemoresistance, particularly the resistance in many tumors that retain the wild-type p53 gene status.

Role of p53 in the Response of Sensitive Tumor Cells to Antitumor Agents Antitumor agents, such as cisplatin and cyclophosphamide, kill tumor cells by interacting with DNA as the critical target. The DNA adducts formed are detected by damage recognition proteins and, as a result, signals are transduced through a complex network that cause tumor cells to take appropriate actions. These actions induce both cell survival and cell death signals, with the net balance between the two determining the final outcome. Thus, if DNA damage is low, the signals activate checkpoint response to stop the cell cycle at the G1/S and/or G2/M transition points and allow DNA repair to proceed. If DNA damage is excessive and repair is incomplete, the arrested cells take further action by activating programmed cell death (or apoptosis). This effectively prevents DNA replication or mitosis in the presence of damaged chromosomes. Interestingly, cell cycle arrest, DNA repair, and pro-apoptotic processes following DNA damage are orchestrated by p53 (Helton and Chen 2007,

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el Deiry 2003), and it is possible that they are linked through cross-talk and/or in time to ultimately determine cell fate. The p53 protein is a transcriptional factor that is stabilized and activated through post-translational modifications following DNA damage (Fig. 9.1). Depending on the modifications, it binds to DNA in a sequence-specific manner to transactivate target genes. Of the many important genes transactivated by p53, three are widely known, but two of these are perhaps the more critical in antitumor response. One is p21, which is essential for inhibiting cyclin-dependent kinases (Cdk) Cdk4/cyclin D and Cdk2/cyclin E, in order to arrest the cell cycle in G1 phase (Iliakis et al. 2003, Samuel et al. 2002). Although p21 can also inhibit Cdk2/cyclin A in S-phase and Cdc2/cyclin A and Cdc2/cyclin B in G2-phase, its requirement is not as vital since other mechanisms exist to inhibit S- and G2-phase Cdk’s (Pietenpol and Stewart 2002). The second critical gene target of p53 is Bax, which in its pro-apoptotic dimeric form is reported to be an essential component for mitochondrial release of cytochrome c and caspase activation, in order to complete the process of apoptosis (Fig. 9.1). Although p21 may be viewed as a pro-survival gene (since it induces cell cycle arrest to allow DNA repair) and Bax as pro-death, the specific roles of these genes may be more complex as regards drug response since evidence exists that supports a correlation between G1 arrest and drug sensitivity (Vekris et al. 2004) and, contrary to established dogma, not all tumors overexpressing Bax demonstrate proapoptotic activity (Burger et al. 1998, Knudson et al. 2001), but instead may demonstrate increased proliferation (Knudson et al. 2001). Indeed, reports indicate that Bax activation is regulated and may require additional p53-dependent gene target products to induce Bax-dependent apoptosis following exposure of tumor cells to antitu-

Drug

DNA damage

Damage recognition

Checkpoint activation Mdm2

Bcl-2 p53 Bax Bax

p21Waf1

Survivin

Cyt c

Caspases

Apoptosis

CDK/cyclin inhibition

?

Cell cycle arrest

DNA repair Cell survival

Fig. 9.1 Signal transduction pathways involved in p53-dependent apoptosis. The p53 induced and activated by DNA damage transactivates genes involved in cell cycle arrest and apoptosis. There is some indication that checkpoint response mediated by p21 may also be involved in apoptotic activation, but how this occurs is unclear. Apoptosis in a p53-dependent manner is also facilitated through suppression of antiapoptotic genes Bcl-2 and survivin

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mor agents (McCurrach et al. 1997, Kim et al. 2006). Although puma and noxa may be considered as potential candidates in this respect (Villunger et al. 2003), it is well possible that this gene product is p21 since the knockdown of either Bax or p21 in a prostate tumor model protected cells from p53-dependent apoptosis (Hastak et al. 2005). This involvement of p21 in the p53-dependent apoptotic process is supported by a growing number of literature reports and is consistent with its recently acquired recognition as a tumor suppressor (Martin-Caballero et al. 2001, Poole et al. 2004, Barboza et al. 2006). However, there is also a suggestion that this p21-dependent apoptosis following ectopic p21 expression may not involve Bax and could well be distinct from apoptosis mediated by p53 (Wu et al. 2002). This suggestion would then be in line with the concept that cell cycle arrest by p21 and apoptosis by Bax are independent processes (Attardi et al. 1996). On the other hand, it is worth noting that the role of p21 in both processes and linked intimately through cross-talk has not been stringently tested as yet. The third transactivated gene of interest is hMdm2, which regulates p53 function by providing feedback inhibition of p53. In contrast to its critical transactivation function, p53 can also transrepress a number of genes. Survivin and Bcl2 are two of the genes involved in suppressing apoptosis (Fig. 9.1), and their downregulation by p53 contributes to its pro-apoptotic activity (el Deiry 2003). Similarly, repression of Chk1 by p53 is also consistent with apoptosis associated with several agents, including daunorubicin, PALA, and actinomycin D (Gottifredi et al. 2001). It is noteworthy that for this transrepressive function of p53, the consensus p53-binding element is not required, but in the case of survivin and Chk1, the presence of p21 appears to be an absolute prerequisite (Lohr et al. 2003, Tang et al. 2004, Shats et al. 2004, Gottifredi et al. 2001). It is likely that p53 and p21 function as a complex in the transrepression process, which provides p21 a direct link to drug-induced apoptosis in a p53-dependent manner. It is appropriate to indicate that p21 will also promote antitumor activity through its role in G1 arrest, and that this is an independent process comes from reports that engineered mutant p21 proteins that have lost CDK-binding activity still retain the ability to suppress transcription (Delavaine and La Thangue 1999).

Concept of Drug Resistance Caused by p53 Mutation In sensitive cells, lower concentrations of the antitumor agent will activate proapoptotic pathways to induce cell death (Fig. 9.1). Since DNA damage signals for apoptosis go through p53, mutation in p53 will attenuate its apoptotic function and induce drug resistance. Many of the mutations are in the DNA-binding region of p53, with the three major hotspots located at amino acid positions 175, 248, and 273 (Toledo and Wahl 2006). In such resistant cells, the apoptotic pathway is not totally inactivated but becomes desensitized to DNA damage, and this increases the threshold required for DNA damage to activate apoptosis. Based on this understanding, the concept has emerged that the ability of tumor cells to tolerate increased levels of DNA damage is a general mechanism of drug resistance (Johnson et al. 1997).

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However, resistant tumor cells will die following exposure to a high drug concentration, and the magnitude of the increase in this concentration to exceed a higher threshold of DNA damage and kill tumor cells is a quantitative measure of drug resistance. Since the use of higher concentrations is not an issue in tissue culture studies, the concept of drug resistance in the laboratory can be difficult to grasp, and this perpetuates the use of supra-toxic concentrations to achieve a desired effect. In the clinical situation, however, such high concentrations cannot be accomplished without significant life-threatening risks to the patients. Thus, the clinical consequence of drug resistance is substantial. It is essential to recognize that for apoptotic activity, p53 must retain transactivation functions (Bargonetti and Manfredi 2002), although transcription-independent mechanisms cannot be totally ignored (Schuler and Green 2005). DNA damage by antitumor agents induces post-translational modification that modifies the conformation of p53, which is essential to stabilize the protein and enable it to interact with DNA in a sequence specific manner. Mutation in p53 distorts this conformation and prevents p53 from binding to DNA. In fact, all of the hotspots of mutation are located within the DNA-binding region of p53, and such mutations prevent its ability to bind and transactivate and, thereby, inhibit apoptosis. Although this simple model can explain how resistance arises following homozygous mutations, it doesn’t explain why the heterozygous mutation should also lead to a loss in p53 functions since the wild-type p53 in these situations should be able to bind DNA and transactivate target genes. This is explained by the knowledge that p53 functions as a tetramer, and mutation in one allele results in the high probability of non-functional heteromeric complexes being formed that have at least one mutant p53 molecule (Roemer 1999, Willis et al. 2004). For loss of function, this mutant p53 must be dominant negative as not all mutations negate p53 functions (Kester et al. 2003, Blagosklonny et al. 2001).

Mutations in p53 Modulate Drug Response and Specificity The quantitative impact of p53 mutations on drug response is readily appreciated from studies conducted at the NCI using a tumor panel composed of 18 wildtype p53 and 39 mutant p53 cell lines from 9 tissue types that were exposed to 123 antitumor agents, comprising a number of drug classes, including DNA crosslinking agents, antimetabolites, antimitotic agents, and topoisomerase I/II inhibitors (O Connor et al. 1997). In general, these extensive analyses demonstrated that cell lines in the mutant-p53 group tended to be more resistant to cytotoxic drugs as compared to those in the wild-type p53 group. The extent of resistance for each specific agent varied, and for cisplatin, 5-fluorouracil, and bleomycin, the median resistance was respectively 3-, 6-, and 10-fold greater in the mutant group (O Connor et al. 1997). Interestingly, antimitotic agents were an exception and indicated that not all classes of antitumor drugs are dependent on wild-type p53 for their cytotoxic activity. This may explain why within the mutant-p53 group, the response of each cell line varied from exquisite sensitivity to one agent to exquisite resistance to

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another. Other factors may also modulate drug response, including cell type/context and the specific mutation in p53. The influence of specific mutation dictating drug response is exemplified by a study in H1299 lung adenocarcinoma cells, where ectopic expression of mutant p53His273 had limited effect in inducing resistance to etoposide, whereas this specific mutation caused much greater resistance to cisplatin (Blandino et al. 1999). Moreover, not every dominant-negative mutation will necessarily induce resistance, and this has been amply demonstrated in malignant glioma cell lines following ectopic expression of the p53V143A mutant, which did not alter sensitivity to ionizing radiation and a variety of antitumor agents, including BCNU, topotecan, taxol, and cisplatin (Bartussek et al. 1999). It is reasonable to consider, therefore, that additional factors may be required for dominant-negative mutants to impart cellular resistance. Such limited understandings in spite of the wealth of data available in the literature have stifled opportunities to rationally treat cancers with p53 mutations. It appears from recent studies that cancer therapy, under the complexities imposed by mutation of p53 that accompany numerous other resistancepromoting molecular changes, must include restoration of p53 function, since this step alone has shown to be sufficient to reverse therapeutic resistance (Kastan 2007).

Gene Status of p53 and Clinical Drug Resistance Given the important role of wild-type p53 in apoptosis and sensitive drug response, and since mutation of p53 in human cancers is common (Hollstein et al. 1991, Soussi 2000), the substantial research focus on mutant p53 as a cause of drug resistance seems appropriate. This is consistent with the dogma that tumor types associated with a high frequency of p53 mutation are chemoresistant and those retaining wildtype p53 are chemosensitive. Such an understanding also extrapolates to long-term survivors, with the greatest 5-year survival rate (>90%) observed in seminomatous germ cell tumors, which harbor predominantly wild-type p53, and a relatively lower rate (10–20%) is noted in ovarian, head and neck, and metastatic bladder cancers, which demonstrate a 40–60% frequency of p53 mutation (Bradford et al. 2003, Reles et al. 2001, Sarkis et al. 1995, Mayer et al. 2003). Thus, mutation in p53 is considered a major barrier in the clinical management of cancer. Further assessment of the data with tumor types expressing 40–60% mutation of p53 also leads to a logical but important conclusion that the low survival rate of 10–20% in these cancers cannot be exclusively attributable to chemoresistant mutant-p53 tumors; that is, patients with wild-type p53 tumors are also succumbing to the disease. Moreover, examination of clinical reports provides evidence that positive antitumor responses within a specific cancer tissue type is not restricted to the wild-type p53 group, and this is exemplified by published data, as summarized in Table 9.1 for the selected disease types (Schmidt et al. 2003, King et al. 2000, Mayer et al. 2003, Lavarino et al. 2000, Righetti et al. 1996, Kandioler-Eckersberger et al. 2000). Among mutant p53 cancers, a lack of response of advanced breast cancers to taxol or the 5-FU/epirubicin/cyclophosphamide combination in two

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Table 9.1 Response of tumors to chemotherapy based on the gene status of p53 Tumor response Cancer 1

Advanced breast Advanced breast2 Advanced breast2 NSCLC3 Germ cell4 Advanced ovarian5 Advanced ovarian6

Treatment

mu p53

wt p53

Taxol Taxol FEC Taxol Pt Taxol/Pt High dose Pt

0% (0/11) 83% (5/6) 0% (0/7) 75% (6/8) N/A 86% (25/29) 37% (7/19)

45% (10/22) 38% (10/26) 64% (18/28) 47% (8/17) ∼90% 47% (9/19) 46% (6/13)

NSCLC, non-small cell lung cancer; Pt, platinum; mu, mutant; wt, wild-type; N/A, not applicable (the p53 mutation rate in germ cell tumors is negligible); FEC, 5-FU/epirubicin/cyclophosphamide. 1 Schmidt et al., 2003; 2 Kandioler-Eckersberger, 2000; 3 King et al., 2000; 4 Mayer et al., 2003; 5 Lavarino et al., 2000; 6 Righetti et al., 1996

of the three studies is consistent with expectations, but it is notable that 37–86% responses were observed in non-small cell lung, ovarian and breast cancers with the mutant p53 genotype. This and the response in breast cancer in the table are contrary to what may be expected for cancers with mutant p53, but the data clearly represent the reality of treatment outcome in the clinical setting. That is, mutant p53 cancers do indeed respond to selected chemotherapeutic agents that may in part be due to a p53-independent mechanism of drug action. Among wild-type p53 cancers, responses to chemotherapy are not generally universal. In fact, apart from the highly sensitive germ cell tumors, which demonstrate ∼90% response to platinum therapy, the other tissue types show only a 38–64% response rate (Table 9.1). This indicates that about a half of these wild-type p53 cancers do not respond to therapy and represent a drug resistant phenotype. Thus, in non-small cell lung cancer, ovarian cancer, or one of the three breast cancer studies using therapy based on taxol and/or platinum, mutant p53 bestows a therapeutic advantage over wild-type p53. Unfortunately, the greater response rate in the mutant category does not necessarily provide an advantage in configuring the 5-year survival rate. This is exemplified with ovarian cancer, where the approximate 20% overall long-term survivors is likely the net result of 40% survivors from the wild-type p53 group and 5% from the mutant p53 group (van der Zee et al. 1995). A greater survival rate in conjunction with wild-type p53 has also been reported in other tumor types, such as breast (Geisler et al. 2001, Berns et al. 2000).

Relative Resistance Based on p53 Gene Status and the Role of the Antitumor Agent Although clinical data indicate that resistance is seen in both mutant and wild-type p53 groups, the relative resistance between the two genotypes of p53 is not possible to ascertain from such information. For this, tissue culture data has been more

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useful. The reported NCI analysis demonstrates that the cell line in the wild-type p53 group that was least sensitive to bleomycin, 5-fluorouracil, or cisplatin was, on the other hand, more responsive than some of the cell lines harboring mutant p53 status (O Connor et al. 1997). This indicates that, in general, resistant tumor cells with mutant p53 will have greater resistance than those with wild-type p53. In contrast to the NCI data from a panel that includes nine different tissue types, results from a specific tissue type may not be in general agreement. For instance, resistance of prostate tumor cells to ionizing radiation was actually higher when wild-type p53 was present (Scott et al. 2003). Similarly, in a small panel of cisplatin-resistant ovarian tumor models, the IC50 (drug concentration inhibiting growth by 50%) for cisplatin in the group of resistant wild-type p53 models was greater than in those models with mutant p53, and this finding was independent of drug-exposure schedule (Fig. 9.2A and 9.2B) (Hagopian et al. 1999). Thus, the data in ovarian models clearly indicate that resistance of cells is greater in the presence of wild-type p53 than mutant p53. If this is the case, then one may predict that in such resistant models that harbor wild-type p53, loss of p53 function may actually reduce resistance and give the appearance of an increase in drug sensitivity. This needs to be tested vigorously, but in several apoptotic-defective models, such as MCF-7 breast and HCT-116 colorectal systems, disruption of wild-type p53 function by transfecting the human papillomavirus (HPV) type-16 E6 gene or a dominant-negative mutant p53 gene sensitized tumor cells to cisplatin and etoposide (Fan et al. 1997, Fan et al. 1995, Levesque and Eastman 2007). However, this was a selective drugdependent effect since it had no effect on the cytotoxicity of other agents, such as ionizing radiation, taxol, or vincristine, and in the case of 5-fluorouracil, resistance was observed. Chemotherapy works because normal cells are relatively resistant to antitumor agents, and this resistance in normal cells was also reduced by disrupting p53 function with the HPV-16/E6 oncogene, as has been demonstrated by increased sensitivity of normal human foreskin fibroblasts to cisplatin and carboplatin by a factor of 6- to 9-fold and to taxol by 8- to 12-fold (Hawkins et al. 1996). As indicated above, cytotoxic response of tumor cells following disruption of wild-type p53 function is dependent on the antitumor agent. This is further illustrated with the results reported with breast tumor models MCF-7 and ZR-75-1 that have wild-type p53. With 5-fluorouracil, ZR-75-1 cells demonstrated 10-fold greater resistance than sensitive MCF-7 cells, whereas with doxorubicin the converse was true, with ZR-75-1 cells demonstrating a 2-fold greater sensitivity (Troester et al. 2004). Similarly, ovarian tumor models that harbor wild-type p53 and express resistance to cisplatin were sensitive to another platinum drug 1R,2Rdiaminocyclohexane-diacetato-dicholoro-PtIV (DAP), independent of the exposure protocol (Fig. 9.2C and 9.2D) (Hagopian et al. 1999). In the case of this differential response to cisplatin and DAP, it is believed that resistance from a failure of cisplatin to activate the p53/p21 pathway is reversed by DAP, which activates different DNA damage response signals to restore the defective pathway (Mujoo et al. 2003). Such reversal of cisplatin resistance by activating independent pathways has also been demonstrated with ionizing radiation (Siddik et al. 1998). One may speculate that since the basal gene expression profile in every tumor, whether sensitive

wild-type p53

mu or null p53

OVCA-432

Hey

IC50 of the Platinum Drug

0

25

50

75

100

125

0

25

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wild-type p53

D. DAP - 2h exposure

mu or null p53

mu or null p53

Fig. 9.2 Cytotoxicity of platinum-based agents against cisplatin-resistant ovarian tumor cells. The panel of resistant cancer cell lines is grouped in either the wild-type p53 or the mutant (mu)/null p53 category. The data represent the IC50 concentration of cisplatin or the analog DAP that induces 50% growth inhibition following either a continuous or a 2-h acute drug-exposure protocol. Data from Hagopian et al. 1999

0

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OCC-1

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SK-OV-3

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OVCA-429

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or resistant, is different, it is likely that assimilation of genes in this unique profile with drug-specific gene changes associated with the mode of action of the antitumor agent may induce a sensitive response in one tumor and a relatively resistant response in another. In many cases, it is difficult to explain the specific response since signal transduction pathways affected by a majority of agents are ill defined. Consider that in the case of the microtubule-stabilizing agent taxol, tumor cells must go through mitosis to induce cell death, and if these cells arrest prior to mitosis, as has been demonstrated in combination with cisplatin, the antitumor effect of taxol is antagonized and a resistant phenotype is observed (Zaffaroni et al. 1998, Jekunen et al. 1994). Thus, taxol supposedly should be more effective in the presence of mutant p53, which should facilitate rapid entry into mitosis, but as the clinical data in Table 9.1 shows the response to taxol is not necessarily greater in mutant p53 cancers, and this suggests that other factors must influence the final decision of tumor cells to live or die.

Resistance Mechanisms in Tumor Cells Harboring Wild-Type p53 Since wild-type p53 is pro-apoptotic and this genotype correlates with a greater survival rate, it is a paradox that wild-type p53 should be associated with highly resistant cancers. What is becoming increasingly obvious is that the function of wild-type p53 can become suppressed and lead to a resistant phenotype. It becomes readily apparent that restoration of this function will be important in any therapeutic strategy where the goal is to increase the 5-year survival rate. In this regard, it is fortuitous that wild-type p53 cancers demonstrating acquired or intrinsic resistance to one agent are not cross-resistant to all the others. However, for a more rational approach to therapy, it is important to appreciate how, besides mutation or gene deletion, wild-type p53 function may generally be lost in the resistant disease. Since wild-type p53 is subject to stabilization/activation by post-translational modification following DNA damage, its function may be impacted by upstream events, through negative regulation, or through changes in response of critical genes subject to transactivation/transrepression (Fig. 9.3). Factors that are not directly associated with p53 function are excluded from the discussion here.

Downregulation of DNA Damage Recognition Proteins DNA damage by antitumor agents causes unwinding and bending of DNA, and this distortion in DNA is recognized by specialized proteins, which propagate damage signals. A variety of recognition proteins may be involved, and the number depends on the antitumor agent and the degree of DNA distortion that is induced. For cisplatin, which forms several types of DNA interstrand and intrastrand cross-links, at least 20 different recognition proteins participate (Chaney and Vaisman 1999,

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Drug

p21

DNA damage Damage recognition

hMLH1 hMSH2 ? HMGB1

Upstream posttranslational p53 modification

ATM Chk2 HIPK2

Transrepression by p53 Activation of p53

E6 E1B hMdm2 hMdm4 14-3-3σ p14ARF

Apoptosis Transactivation by p53

Promoter hypermethylation

Fig. 9.3 Mechanisms inhibiting wild-type p53 function. The DNA damage signal is transduced through p53, which becomes stabilized and activated through post-translational modification, and then it transactivates pro-apoptotic and/or transrepresses antiapoptotic genes to induce apoptosis. The transduction of signal is inhibited when specific gene products are either increased (upward arrow in box) or decreased (downward arrow or by an increase in promoter hypermethylation). How changes in HMGB1 expression impact resistance is unclear, and this may depend on cell context and the ability to recognize DNA damage induced by the antitumor agent

Chaney et al. 2005), and it is not surprising, therefore, that each agent induces a large number of signal transduction events. Two of these recognition proteins are hMSH2 and hMLH1, which are components of heteromeric complexes of the mismatch repair (MMR) system and induced by p53 (Helton and Chen 2007). With the induced MMR proteins in turn augmenting the activity of p53 following DNA damage (Luo et al. 2004), this places p53 and the MMR proteins as dependent on one another to generate and propagate the apoptotic signal. One proposal for the mechanism of drug-induced cell death via MMR is that the protein complex senses the DNA damage, becomes entangled in futile cycles to repair the damage, and a failure to repair activates apoptotic pathways (Vaisman et al. 1998). Therefore, it is not surprising that loss of MMR through gene deletion, mutation, or silencing is strongly associated with drug resistance (Fink et al. 1998, Lage and Dietel 1999, Stojic et al. 2004). Moreover, loss of both MMR and p53 functions increases this resistance to a variety of agents, including cisplatin, CCNU, taxol, etoposide, and gemcitabine (Aquilina et al. 2000, Lin et al. 2001). A third example of a recognition protein of interest is HMGB1, which interacts with p53 at the DNA damage site and facilitates the activation of the tumor suppressor (Fojta et al. 2003, Imamura et al. 2001, Jayaraman et al. 1998). This is consistent with the report that overexpression of HMGB1 sensitizes the antitumor activity of cisplatin and bleomycin (He et al. 2000, Baldassarre et al. 2005). An alternative explanation for this sensitization has also been proposed that involves the role of HMGB1 in suppressing the expression of BRCA1 gene and inhibiting DNA repair (Baldassarre et al. 2005). In contrast to the sensitization effect, HMGB1

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has been reported to inhibit apoptosis activated by UV, TRAIL, and Bax and to be overexpressed in tumor cells resistant to cisplatin and in several other human cancers, including primary breast carcinoma (Nagatani et al. 2001, Brezniceanu et al. 2003). These aspects of HMGB1 are not easily explained in the light of the role of HMGB1 in regulating p53, and it leads to the conclusion that the function of this chromatin-associated nucleoprotein may depend on cell context. However, one may speculate that since HMGB1 preferentially recognizes DNA damage induced by specific agents and not by others (Chaney et al. 2005, Chaney and Vaisman 1999), the effect of increased or decreased expression of HMGB1 on p53-mediated cell death will depend on the antitumor agent being examined.

Downregulation of Upstream Pathways Stabilization and functional activation of p53 depends on post-translational modifications, which include phosphorylation, dephosphorylation, acetylation, deacetylation, methylation, glycosylation, and ribosylation (Horn and Vousden 2007). There are multiple sites for each specific type of modification, as exemplified by 23 individual sites on p53 that are subject to phosphorylation or dephosphorylation (Toledo and Wahl 2006). It is important to appreciate that these modifications require participation of many enzymes, with each enzyme having the ability to modify more than one site, and each site capable of being modified by more than one enzyme. Thus, serine-15 can be phosphorylated by at least eight kinases, and Chk2 kinase can phosphorylate seven sites (Toledo and Wahl 2006). However, not all potential sites become modified within the p53 molecule, but are targeted in a concerted manner by upstream pathways that are activated selectively by the antitumor agent. This is well demonstrated with the serine-392 site of p53, which is phosphorylated by the ubiquitous protein kinase CK2, and although this kinase is constitutively active, phosphorylation only occurs after DNA damage with agents such as UV or cisplatin, but not with others such as ionizing radiation or the novel platinum analog DAP (Shieh et al. 2000, Mujoo et al. 2003). This suggests that specific, as yet undefined, pathways activated by UV and cisplatin must interact with CK2 for this kinase to phosphorylate serine-392. In this way, each DNA-damaging agent produces a unique signature of individual modifications at multiple p53 sites, which is necessary to activate the tumor suppressor (Kapoor et al. 2000). This signature dictates the final structural conformation of p53 and, thereby, its sequence-specific binding to DNA to transactivate the selected genes. If an upstream pathway is downregulated, either the stabilization of p53 or its transactivation potential will be impacted. In this section, mechanisms will be discussed in general terms and supported by examples using key players contributing to or implicated in drug resistance. ATM/Chk2 and ATM/HIPK2 Pathways One upstream pathway known to become dysfunctional in cells involves ATM, which is a member of the phosphoinositol 3-kinase-like kinase (PIKK) family and

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acts as a sensor of DNA damage and propagates the damage signal. In this respect, ATM not only phosphorylates p53 directly at several sites, including serine-6, -9, -15, -20, -46 and threonine-18 (Banin et al. 1998, Canman et al. 1998, Herzog et al. 1998, Zhang et al. 2002) but also induces indirect post-translational modification through activating a number of downstream targets, including Chk2 and HIPK2, that add to the intricate role of ATM in regulating p53 (Lavin and Gueven 2006). To add to the complexity, other pathways may also activate these targets, as has been reported for ATM-independent activation of Chk2 by cisplatin, etoposide, and adriamycin (Damia et al. 2001, Theard et al. 2001). The ATM gene is mutated in ataxia telengectasia and in several cancers, including the 20–40% mutation rate in B-cell chronic lymphocytic leukemia (CLL) cases. The loss of ATM activity prevents stabilization of p53, its transactivation function following DNA damage, and its apoptotic function, with resultant therapeutic resistance to several antitumor agents, including fludarabine (Kojima et al. 2006). Depending on cell context and the mode of action of the antitumor agent, loss of ATM may also increase sensitivity to DNA-damaging agents (Burdak-Rothkamm et al. 2008), and this is analogous to drug sensitization observed following disruption of wild-type p53 function in selected cell lines (Fan et al. 1997, Fan et al. 1995, Levesque and Eastman 2007). The role of upstream kinases in activating p53 function is clearly demonstrated by ATM-targets Chk2 and HIPK2. Chk2 is considered a tumor suppressor, and this function is mediated in part through its ability to phosphorylate p53 at several residues, including serine-314, serine-366, and serine-377 (Toledo and Wahl 2006). This is consistent with reports that loss of Chk2 prevents p53-dependent apoptosis normally induced by DNA-damaging agents (Jack et al. 2002, Antoni et al. 2007). In this regard, loss of Chk2 by gene silencing through promoter hypermethylation has been demonstrated in chemoresistant ovarian and non-small cell lung tumor model systems and in clinical biopsies (Zhang et al. 2005a, Zhang et al. 2004). It is likely that the failure of cisplatin to induce p53 in resistant cells is due to loss of Chk2, and that p53 induction is restored with ionizing radiation, presumably through activation of ATM (Siddik et al. 1998). Loss of the second ATM target HIPK2 also causes drug resistance, and indeed low expression of this target is associated with poor survival of colon cancer patients (Puca et al. 2008). This colon study demonstrated that phosphorylation at serine-46 by HIPK2 is crucial for proper folding of wild-type p53 and its apoptotic function, and that downregulation of the kinase results in misfolding of p53 into a mutant conformation that cannot be functionally activated in response to antitumor drug treatment. ATR/Chk1 Pathway Another member of the PIKK family of DNA damage sensors having an important role in post-translational modification of p53 is ATR, which has substantial overlap with ATM in downstream signaling (Matsuoka et al. 2007). However, there are some clear distinctions since several agents, such as cisplatin or UV light, are not dependent on the ATM pathway, but rely on ATR to induce post-translational

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modifications of p53 and induce cytotoxicity (Zhang et al. 1996, Harris and Levine 2005). Like ATM, ATR phosphorylates p53 directly at serine-15 and serine-37, but also indirectly at other sites by activating Chk1. Since the ATR/Chk1 pathway is important for apoptosis with some agents, one may anticipate that their downregulation may lead to drug resistance. Interestingly, this has not been easy to observe in knockouts since ATR or Chk1 deficiency is embryonically lethal (Brown and Baltimore 2000, Zhou and Sausville 2003). However, use of a dominantnegative mutant indicates that loss of ATR function is tolerated in adult systems and leads to increased sensitivity to DNA-damaging agents, presumably because the loss of DNA damage-sensing function of ATR and the resultant lack of S and G2/M checkpoint responses increase cellular sensitivity (Burdak-Rothkamm et al. 2008, Wright et al. 1998, Cliby et al. 1998). In consonant with this, inhibition of Chk1 by UCN-01 also abrogates these checkpoint responses and induces cell death (Zhou and Sausville 2003), and it is notable that p53-dependent apoptosis by the antitumor drugs daunorubicin and camptothecin is mediated by concurrent suppression of Chk1 activity (Gottifredi et al. 2001, Zhang et al. 2005b). These reports serve to indicate that the ATR/Chk1 pathway activates p53, which then suppresses the upstream pathway by downregulating Chk1 to facilitate cell death. Therefore, direct genetic loss of this pathway has the potential to obviate the need for post-translational modification/activation of p53 and induce cell death independent of p53.

Failure to Turn “on” the p53 Activation Switch It is believed that post-translational modifications fine-tune p53 for transactivating specific genes, but the mechanism that turns “on” p53 following DNA damage requires the regulatory role of other proteins. In this regard, several tumors harboring wild-type p53 are resistant to therapy as a result of its uncontrolled proteosomal degradation by viral oncogenes such as HPV-16/E6 and adenovirus E1B proteins that prevent p53 activation (Toledo and Wahl 2006). Physiologically, the “on” switch for p53 is provided by hMdm2, deletion of which is embryonically lethal and requires the deletion of p53 also to prevent this p53-dependent lethality. Specifically, p53 protein levels are kept at low levels by hMdm2, which is an E3 ubiquitin ligase that binds to and targets p53 for proteosomal degradation. Post-translational modifications of p53, particularly at serine-15, threonine-18, and serine-20 residues, and phosphorylation of hMdm2 at serine-395 by upstream pathways such as ATM inhibit the binding and stabilize p53 (Toledo and Wahl 2006, Lavin and Gueven 2006). Subsequent transactivation of hMDM2 by p53 re-establishes this binding and restores p53 degradation that prevents the tumor suppressor from sustained function. Uncontrolled hMdm2 expression has been demonstrated in 7–10% of all cancers, with hepatocellular carcinomas and Hodgkin disease demonstrating 40–70% amplification of hMdm2 that is likely associated with inhibition of wild-type p53 function and resistance to chemotherapy (Toledo

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and Wahl 2006). With this understanding, efforts to identify peptide and non-peptide inhibitors of the p53-hMdm2 interaction have been underway and have led to the discovery of several candidates, including the pharmacologic small molecule Nutlin-3a that has clinical potential, even against cancers that may have upregulation of hMdm2 due to ATM mutation (Toledo and Wahl 2006, Chene 2003, Kojima et al. 2006). It is important to note that several proteins modulate the function of hMdm2 as a p53 switch, and they can influence activation of p53. One protein of interest is hMdm4, which stabilizes hMdm2 by preventing its self-ubiquitination and proteosomal degradation. In this way, an increase in hMdm4 levels by overexpression or amplification of the gene that is observed in 10–20% of cancers increases the p53-inhibiotry function of hMdm2 and induces drug resistance (Toledo and Wahl 2006, Lavin and Gueven 2006). Like hMdm2, 14-3-3σ is also a transactivation target of p53 and has a positive feedback effect on p53 either directly or by inhibiting hMdm2. Expression of 14-3-3σ is downregulated in several cancers, including prostate, breast, lung and ovarian cancers, and its role in contributing to the drugresistant phenotype is indisputable (Lee and Lozano 2006). A third example of a protein that modulates hMdm2 function is p14ARF , which inhibits hMdm2 to activate p53, and its contribution to resistance is appreciated by the knowledge that p14ARF is silenced by promoter hypermethylation in several cancers, including a subset of chemoresistant colorectal cancers that harbor wild-type p53 (Shen et al. 2003).

Downregulation of Promoter Response to p53 in Target Genes Transactivation of critical genes by p53 is essential for apoptosis, and two genes of interest in correlating the failure of transactivation with resistance are Bax and p21. Loss of Bax expression is associated with loss of p53-dependent apoptosis, and decreased Bax expression has been correlated with drug resistance and a shorter patient survival in a number of human cancers, including those of breast, colorectal, and pancreatic origin (Sturm et al. 1999, Krajewski et al. 1995, Friess et al. 1998). Downregulation of Bax expression could be ascribed to mutation in the coding region, as has been demonstrated in about 50% of colorectal and gastric cancers (Miquel et al. 2005), or to mutation in p53, but clinical results indicate that reduced Bax expression also occurs in tumor cells harboring both the wild-type p53 and Bax genes (Sturm et al. 2000). In the case of p21, mice lacking this gene go on to develop tumors (Martin-Caballero et al. 2001), and this is consistent with recent reports recognizing p21 as a tumor suppressor (Gartel and Radhakrishnan 2005, Liu and Lozano 2005). Indeed, clinical cancers expressing p21 are associated with both a superior therapeutic outcome and an improved patient survival rate (Shoji et al. 2002, Rose et al. 2003). A number of reports have provided definitive data to support the pro-apoptotic activity of p21, particularly when combined with antitumor agents, including cisplatin and 5-FU (Gartel and Tyner 2002, Liu

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et al. 2003). This includes the demonstration that ectopic p21 can enhance cisplatin cytotoxicity in ovarian tumor and hepatoma cell lines having defective p53 (Lincet et al. 2000, Wu et al. 2002, Qin and Ng 2001). As further support, loss of p21 expression following treatment with antitumor agents is observed in several resistant tumor models harboring wild-type p53 (Delmastro et al. 1997, Perego et al. 2003, Siddik et al. 1998). It is pertinent to ask how Bax or p21 expression may be downregulated in resistance. Normally, activation of p53 allows it to bind to response elements on the promoter of target genes and induce transcription in cooperation with other transcription factors. One of these factors is Sp1, which is essential for p53-dependent induction of p21 and Bax (Lagger et al. 2003, Thornborrow and Manfredi 2001). The p21 promoter harbors six conserved “GC box” binding sites for Sp1, whereas the Bax promoter appears to have only one such site. Examination of the p21 promoter demonstrates that these sites have 5’-CpG islands, which become hypermethylated and induce epigenetic silencing of the p21 gene (Allan et al. 2000, Gartel and Radhakrishnan 2005, Zhu et al. 2003). Interestingly, p21-promoter hypermethylation was associated with a low survival rate of 6–8% in patients treated for acute lumphoblastic leukemia (ALL) compared to about 60% when the promoter was hypomethylated and fully functional (Roman-Gomez et al. 2002). Circumvention of resistance in these situations has been observed following chronic treatment with 5-aza-2-deoxycytidine, which inhibits the enzyme DNA (cytosine-5)methyltransferase, reverses the hypermethylation phenotype, and restores sensitivity (Allan et al. 2000). Downregulation of promoter response also impacts the transrepressive function of wild-type p53, which normally reduces the expression of several genes, including antiapoptotic Bcl-2 and survivin genes, to reduce the threshold of DNA damage for inducing apoptosis. For transrepression, p53 requires the cooperation of p21 induced by DNA-damaging agents in a p53-dependent manner (Lohr et al. 2003). Loss of p21 expression due to gene silencing will likely attenuate gene repressive functions and contribute to the resistant phenotype.

Conclusion The limited body of work reviewed in this chapter demonstrates that drug resistance in tumor cells is associated with not only the mutant p53 but also the wild-type p53. In fact, the resistance appears to be greater when wild-type p53 is present in resistant cells. Major mechanisms that impede the function of wild-type p53 include events that downregulate (1) recognition or sensing of DNA damage, (2) posttranslational modification of p53, (3) activation of p53, (4) transactivation function due to promoter inactivity of the target gene, and (5) transrepression function from loss of p21 transactivation. These understandings need to develop further so that rational therapies may be developed to circumvent resistance in tumors that retain wild-type p53. Acknowledgments

Supported by NIH Grant RO1 CA127263.

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Chapter 10

Resistance to Differentiation Therapy Bulent Ozpolat

Abstract Incorporation of all-trans-retinoic acid (ATRA) into the treatment of acute promyelocytic leukemia (APL), a type of acute myeloid leukemia (AML), revolutionized the therapy of cancer and introduced the concept of differentiation therapy in the last decade. ATRA, a physiological metabolite of vitamin A (retinol), induces complete clinical remissions (CR) initially in about 90% of patients with APL. In contrast to the cytotoxic chemotherapeutics, ATRA can selectively induce terminal differentiation of promyelocytic leukemia cells into normal granulocytes without causing bone marrow hypoplasia or exacerbation of the frequently occurring fatal hemorrhagic syndromes in patients with APL. Unfortunately, ATRAinduced remissions are transient and most APL patients become quickly resistant to the therapy and relapse, thus limiting the use of ATRA as a single agent. Based on in vitro, in vivo, and clinical observations, several mechanisms including induction of accelerated metabolism of ATRA, decreased bioavailability and plasma drug levels, point mutations in the ligand-binding domain of PML–RARα fusion protein and other molecular events have been proposed to explain resistance to differentiation therapy by ATRA. Although different compounds, such as phorbol ester (TPA), vitamin D3 , interferons, and dimethyl sulfoxide can induce differentiation in vitro, ATRA represents the first successful use of differentiation therapy in the clinic; therefore, here we will focus on mechanisms that regulate ATRA-induced myeloid cell differentiation and the molecular mechanisms causing resistance to ATRA and possible clinical approaches to overcome resistance to differentiation. Keywords Differentiation therapy · Acute promyelocytic leukemia · All-transretinoic acid · Therapy · Resistance · Retinoid metabolism

B. Ozpolat (B) Department of Experimental Therapeutics, Unit 422 University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 10, 

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Introduction Undifferentiated phenotype is a common feature of cancer cells and often it is associated with progressive disease and bad prognosis. Failure to terminally differentiate into mature blood cells or differentiation arrest at early steps of maturation is a major feature of acute myeloid leukemias (AML). Current standard chemotherapy cures only 30% of the AML patients and about 70% of AML patients die to disease in 5 years, suggesting that alternative treatment strategies are required to cure these patients and increase patient survival. Differentiation therapy is based on the concept that immature leukemia progenitor cells can be forced to differentiate into a more mature or terminally differentiated phenotype by using differentiation-inducing agents. Differentiation therapy holds promise as an alternative or complementary to standard chemotherapy. This type of treatment has the advantage of being potentially less toxic than standard chemotherapy. Treatment of APL with retinoic acid is the first model of differentiation therapy, and it has proven extremely successful in inducing clinical remission in most patients. Thus, ATRA-induced differentiation of promyelocytic cells provides an excellent in vitro model for studying myeloid cell differentiation. Although development of quick resistance to the differentiation therapy is commonly observed, when combined with chemotherapy this therapy can dramatically increase patient survival by enhancing efficacy of chemotherapy.

Acute Promelocytic Leukemia and Differentiation Therapy Acute promyelocytic leukemia (APL), M3 type of acute myelocytic leukemia (AML) based on FAB classification, is uniquely sensitive to undergo terminal differentiation by differentiation-inducing agents, such as retinoids (i.e., all-trans-retinoic acid (ATRA), 9-cis-retinoic acid), phorbol ester, vitamin D, and dimethylsulfoxide (DMSO). Therefore, APL represents an excellent model to study differentiation of normal and myeloid leukemia cells. APL, which represents 10%–15% of all AML, is characterized by chromosomal translocations fusing retinoic acid receptor alpha (RARα) gene on chromosome 17 and one of four different genes, including promyelocytic leukemia (PML), promyelocytic zinc finger (PLZF), nucleophosmin (NPM), nuclear matrix associated (NuMA), or signal transducer and activator of transcription 5b (Stat5b) gene (Lin et al. 1999). The most common form of translocations is t(15,17)(q22,q21) encoding PML–RARα (Fig. 10.1) and t(11,17)(q23,q21) encoding PLZF–RARα fusion receptor proteins, found in 99% and >1% of APL patients, respectively (Slack and Gallagher 1999). The translocations are usually reciprocal chromosomal translocations, leading to creation of reciprocal hybrid receptor proteins (X-RARα and RARα-X). APL expressing PML–RARα, NPM–RARα, or NuMA–RARα are responsive to ATRA-induced differentiation effects with the exception of PLZF– RARα-type APL which is resistant to ATRA (Pandolfi 1996).

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Fig. 10.1 Oncogenic PML–RARα receptor proteins expressed in APL due to chromosomal translocation t(15,17). Chromosomal translocations involve retinoic acid receptor alpha (RARα) gene on chromosome 17 and either promyelocytic leukemia (PML) gene. Breakpoints may vary in PML gene; however, it is always located at same point in RARα gene

Retinoids and All-trans-Retinoic Acid Retinoids are a family of molecules that are structurally related to retinol (vitamin A) and known to play a critical role in many physiological functions such as cell proliferation, differentiation, apoptosis, homeostasis, reproduction, and fetal development (Lotan 1980). Retinol is absorbed from the diet in the form of retinyl-esters or β-carotene and stored in the liver as retinyl palmitate. All-trans-retinoic acid (ATRA, tretinoin), 9-cis-retinoic acid (9-cis-RA), 13cis-retinoic acid (13-cis-RA, isotretinoin), and retinal are physiologic or synthetic derivatives of retinol (Lotan 1988). Even though only small percent of retinol and β-carotene are converted to ATRA and 9-cis-RA, they are ∼100- to ∼1000-fold more potent than other natural retinoids. Retinol, ATRA, and 13-cis-RA are found in the human plasma at levels ∼2 μM, ∼8 nM, and ∼5 nM, respectively, and can induce differentiation of promyelocytic leukemia cells.

The Biologic Effects of Retinoids Are Modulated Through Nuclear Receptors Retinoid receptors belong to a superfamily of ligand-inducible transcription factors including steroid, vitamin D, thyroid hormone, peroxisome proliferator-activated

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receptor, and orphan receptors with unknown functions (Chambon 1996). Two classes of nuclear retinoic acid receptors (RAR) and retinoid X receptor (RXR), each consisting of three isotypes (α, β, and γ ) encoded by separate genes and their isoforms (e.g., α1, α2, β1–β4, γ 1, or γ 2), have been identified and discussed in great detail in recent reviews (Pemrick et al. 1994). RARs and RXRs contain different domains A through F with diverse functions (Fig. 10.2A). A and B domains located at the amino terminal of each particular receptor contain isoform-specific, ligand-independent transactivation functions, AF-1. These receptors bind to retinoic acid response elements (RARE) through conserved DNA-binding domain (C domain) containing zinc finger motifs. Ligands (retinoids) bind to a ligand-binding domain (LBD) or E domain at the C-terminus of the receptors that contain sequences involved in dimerization of the receptors, ligand-dependent transactivation (AF-2), and translocation to the nucleus. The functions for F and D domains have not been clearly defined.

Retinoid nuclear receptors

(a)

DNA binding domain

A

B

C

Receptors

Fig. 10.2 A. retinoid nuclear receptors in normal cells. ATRA and its isomers (9-cis-RA and 13-cis-RA) bind ligand-binding domain for transactivation of the target genes. B. Receptor fusion proteins due to the translocation t(15,17). t(15,17) leads to expression of three different PML–RAR alpha isoforms

(b)

Ligand binding domain

D

E Ligands

RARα RARβ RARγ

ATRA 9-cis-RA 13-cis-RA (Low affinity)

RXR α RXR β RXR γ

9-cis-RA 13-cis-RA (Low affinity)

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The complex diversity and pleiotropic effects in retinoid-signaling pathway is provided not only due to existence of multiple isoforms of RA receptors but also as a result of combinations of RAR–RXR heterodimers or homodimers and presence of different ligands (Chambon 1996). The RARs can be transcriptionally activated by binding to either ATRA or 9-cis-RA; however, RXRs can be activated only by 9-cis-RA but not by 13-cis-RA or ATRA. 13-cis-RA, a stereoisomer of ATRA, shows a lower affinity for RARs and RXRs (11.2). Upon ligand binding, activated nuclear receptors that bind to RAREs found in the upstream sequences (promoters) of RA responsive genes lead to transcription of the target genes. RARα plays a major role in ATRA-induced differentiation in HL-60 myeloid cells (Collins et al. 1990). However, RXRα mediates induction of apoptosis in the same cell line by ATRA or 9-cis-RA (Mehta et al. 1996). ATRA treatment of APL cells induces expression of RARα mRNA suggesting that ATRA can also modulate its own receptor, RARα, in addition to differentiation-related genes (Melnick and Licht 1999). The availability of the retinoid ligands to its cognate receptors can be determined by the level of presence of certain non-receptor proteins, such as cytoplasmic retinoic acid-binding proteins and heat shock proteins (Boylan and Gudas 1991). Moreover, isoforms of PML–RAR may alter the retinoid signaling with or without ligand binding (Fig. 10.2B).

Pathogenesis of Acute Promyelocytic Leukemia PML–RARα fusion receptor protein is expressed at high levels in APL blasts and interferes with the physiologic functions of PML and RARα proteins, exerting a dominant negative effect (Rousselot et al. 1994). Expression of the PML–RARα fusion receptor protein blocks differentiation of myeloid precursor cells at promyelocytic stage, leading to accumulation of immature hematopoietic cells in bone marrow (Grignani 1993, Brown et al. 1997). It was also shown that overexpression of dominant negative or wild-type RARα causes a differential block at the promyelocytic stage (Tsai et al. 1992). Recently, transgenic mice expressing PML–RARα had a block at the promyelocytic stage of myeloid maturation in blast cells implicating important role of PML–RARα abnormal receptor protein in leukomogenesis (He et al. 1998). PML is involved in the regulation of proliferation and apoptosis (Wang et al. 1998a). Cells lacking PML are resistant to apoptosis by gamma irradiation, grow faster, and have longer survival time, while cells overexpressing PML undergo apoptosis by the same stimulus. It was shown that PML is located in the nucleus of normal cells in punctuate nuclear structures (PODs) or nuclear bodies associated with nuclear matrix; however, in PML–RARα positive APL cells, localization and the normal pattern of nuclear bodies are disrupted (Dyck et al. 1995, Hodges et al. 1998). Overall data suggest that disruption of PML function has been proposed to contribute to APL pathogenesis (Kogan and Bishop 1999).

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Molecular Basis of ATRA Therapy in APL ATRA induces differentiation of immature leukemic blasts into terminally differentiated granulocytic cells that is associated with clinical remissions (Kogan and Bishop 1999). ATRA-induced differentiation of APL blasts requires expression of PML–RARα receptor protein. PML–RARα can heterodimerize with RXR or form homodimers and subsequently binds to retinoic acid response element (RARE), located in the promoters of the ATRA-responsive target genes. ATRA can bind to PML–RARα with an affinity comparable to RARα. In the absence of ligand, RAR– RXR in normal blasts and PML–RARα–RXR heterodimers in APL cells recruit nuclear co-repressor proteins, N-CoR or SMRT, and Sin3A or Sin3B which in turn form complex with histone deacetylase enzymes (HDAC1 or HDAC2), resulting in transcriptional repression or silencing (Grignani et al. 1998) (Fig. 10.3A and B). The transcriptional suppression occurs because deacylation of histone protein creates conformational changes, limiting access, and binding of transcription factors and RNA polymerase to related genes (Fig. 10.3A). At physiologic concentrations of ATRA (10–9 –10–8 M), the nuclear co-repressors protein and HDAC complex are

A. Physiological levels of ATRA inhibits Nuclear repressor complex activity in nomal blasts mSin3A

ATRA

mSin3A SMRT/ N-CoR

RXR

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SMRT/ N-CoR

P/CAF

HDAC

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CPB/p300

HDAC

SRC-1 Ac

X

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Ac Ac

Ac

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B. Pharmacological levels of ATRA dissociates Nuclear Repressor Complex from RAR in APL with PML-RARα ATRA

mSin3A mSin3A SMRT/ N-CoR

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P/CAF

HDAC

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HDAC

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SRC-1 RXR

RARα

Ac

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Fig. 10.3 Molecular mechanisms causing transcriptional repression and differentiation block in APL. Nuclear co-repressor proteins, N-CoR or SMRT, and Sin3A or Sin3B which form complex with histone deacetylase enzymes (HDAC1 or HDAC2), resulting in transcriptional repression or silencing. HDAC activity causes deacylation of histone protein creates conformational changes, which in turn prevent transcription of target genes. Ac, acetylated histones

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dissociated from RARα in normal blasts, which in turn results in recruitment of co-activators with histone acetyltransferase (HAT) activity, such as steroid receptor coactivator-1 (SRC-1), PCAF, p300/CBP, ACTR, TIF2, or P/CIP. Acetylation of lysine residues in the N-terminal of histones by HAT activity results in transactivation of responsive genes leading to differentiation. However, the physiologic concentration of ATRA does not cause dissociation of nuclear co-repressors protein and histone deacetylase complex from the PML–RARα fusion receptors in APL blasts, leading to differentiation block (Fig. 10.3B). The co-repressor complex is dissociated from PML–RARα at only pharmacological concentrations (10–7 –10–6 M) of ATRA, resulting in removal of transcriptional repression and transcription of genes related to differentiation (Heinzel et al. 1997). In addition to release of transcriptional repression, the other possible mechanisms involved in ATRA effectiveness in myeloid cell differentiation include expression of different class of genes including induction of expression of p21WAF1/Cip1 cyclinedependent kinase inhibitor, upregulation of C/EBP-γ , β, and ε, interferon regulatory factor-1 (IRF-1), and regulation of the localization of PML oncogenic domains (PODs). In APL cells isolated from patients, ATRA upregulates expression of RARα at mRNA and protein levels (Agadir et al. 1995a) whereas it causes the degradation of PML–RARα (Raelson et al. 1996). Therefore, the ratio of RAR/RXR to PML–RARα would be higher that helps overcoming the dominant negative effects of PML–RARα protein.

Resistance to Differentiation Therapy ATRA therapy (45 mg/m2 /day) induces complete remission (CR) in 72%–95% of APL patients through induction of differentiation of immature promyelocytic blast cells into mature granulocytes, which subsequently undergo apoptosis (LoCoco 1998, Fenaux et al. 2001). The success of ATRA in the induction of complete remission in APL patients represents the first differentiation therapy in cancer and constitutes now a frontline treatment in combination with chemotherapy (LoCoco 1998). Unfortunately, resistance to ATRA treatment was encountered in the early clinical trials (Castaigne et al. 1990, Delva et al. 1993). Later clinical studies demonstrated that ATRA as a single agent cannot maintain remission and almost all APL patients routinely relapse within 3 months to 1 year (Muindi et al. 1992). The resistance is acquired rapidly in most cases within 1–3 months of ATRA (Delva et al. 1993). Therefore, now ATRA-induced CR is combined with chemotherapy (i.e., anthracyclins) (Tallman 1998, Warrell 1997). Pharmacokinetic studies showed that the chronic oral administration of ATRA results in progressive decline in plasma drug concentrations which associate with early relapses and resistance to ATRA in APL patients (Muindi et al. 1992, Adamson 1996). Plasma levels of ATRA which usually start to decline as early as 1 week from the initiation of ATRA therapy probably result in decreased intracellular ATRA levels below effective pharmacological concentration (Lefebvre et al. 1991).

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The higher ATRA plasma concentration correlates with lower number of peripheral blast count in APL patients. The reduction in plasma levels after administration of ATRA has been observed in other species such as monkeys and mice. However, this phenomenon is not seen with ATRA isomers such as 9-cis-RA and 13-cis-RA, suggesting that ATRA uptake and metabolism are different than its isomers (Lefebvre et al. 1991). Recently, it was shown that higher intracellular concentration of ATRA correlates with ATRA-induced differentiation of APL cells, indicating the importance of keeping ATRA at levels that support differentiation (Agadir et al. 1995b). Relapsing patients were shown to be resistant to higher doses of ATRA; doubling the initial ATRA dose failed to induce CR and to maintain stable plasma ATRA concentrations (Muindi et al. 1992). In addition, APL cells isolated from patients at the time of relapse were sensitive to ATRA (10–6 M) in vitro. However, the response to ATRA was found to be decreased in vitro sensitivity in half of the cases in terms of induction of differentiation (Delva et al. 1993). Interestingly, it was observed that acquired resistance to ATRA may be reversible after discontinuation of the ATRA therapy and patients may gain sensitivity to ATRA in usually 6 months to 24 months, suggesting that ATRA resistance is reversible. After in vitro and clinical experiences with ATRA over a decade, the following mechanisms involved in the development of the drug resistance have been proposed (Fig. 10.4). These include (1) induction of accelerated metabolism of ATRA;

Fig. 10.4 Metabolic pathways leading to inactivation of ATRA. P450-mediated metabolism is the major pathway for inactivation of ATRA

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(2) increased expression of cellular retinoic acid-binding proteins (CRABPs); (3) constitutive degradation of PML–RARα; (4) point mutations in the ligand-binding domain of RARα of PML–RARα; (5) P-glycoprotein expression; (6) transcriptional repression by histone deacetylase activity; (7) isoforms of PML–RARα; (8) persistent telomerase activity; (9) expression of type II transglutaminase; and (10) topoisomerase II activity.

Accelerated ATRA Metabolism The major pathway for ATRA inactivation is the oxidative metabolism by microsomal cytochrome P450 isoenzyme system that is initiated by the 4-hydroxylation of ATRA to form 4-hydroxy-RA and 4-oxo-RA (Fig. 10.5) (Roberts et al. 1979). Chronic oral administration of ATRA results in autoinduction of ATRA metabolism by cytochrome P450-dependent enzymes leading to progressive reduction in plasma ATRA concentrations that may be the most important mechanism for development of resistance to therapy. The decrease in peak plasma levels of ATRA is associated with urinary excretion of 4-oxo-ATRA which is found to be increased about 10-fold during the continuous ATRA treatment suggesting that decreased plasma levels of ATRA may not be due to impairment in gastrointestinal uptake of the drug (Muindi 1992). In vitro and in vivo studies with cytochrome P450 inhibitors (ketoconazole and liarozole) which suppress ATRA metabolism resulted in increased plasma levels

Fig. 10.5 Possible mechanisms involved in the development of ATRA resistance. Selective P450 inhibitors and liposomal ATRA may circumvent metabolic pathways and mechanisms involved in accelerated elimination of ATRA

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and delayed ATRA plasma clearance in animals and humans, thus further supporting this hypothesis (Miller et al. 1994). Recently, a novel human P450 enzyme (CYP26) with specific RA 4-hydroxilase activity was cloned from zebrafish, mouse, and human. CYP26, which is rapidly inducible by ATRA, is expressed in tissues, including liver, kidney, lung, placenta, skin, and intestinal cells (White et al. 1997, Ozpolat 2000, 2002 and 2005). ATRAinduced expression of CYP26 was also shown in some human tumors such as hepatocellular carcinoma cell line, non-small cell lung carcinoma, breast cancer cells, as well as myeloblastic and promyelocytic leukemia cells (Sonneveld et al. 1998). The expression of full-length human cDNA for CYP26 in transfected cells closely correlated with the accumulation of 4-hydroxy-RA and 4-oxo-RA, the major metabolic products of ATRA (White et al. 1997). CYP26 metabolizes ATRA into 4-hydroxy-ATRA, 4-oxo-ATRA, 18-hydroxy-ATRA, and polar metabolites in F9 cells. CYP26 was shown to be highly specific for the hydroxylation of ATRA but not for the hydroxylation of 13-cis-RA or 9-cis-RA. Several studies demonstrated that the expression of CYP26 is regulated by RARs and RXRs suggesting a feedback loop mechanism for the regulation of ATRA levels. We found that pharmacological doses of ATRA induce acute expression of CYP26 mRNA in myeloid (HL-60) and promyelocytic leukemia (NB4) cells. Its expression in these cells is regulated solely by RARα-type receptor, indicating the existence of substrate-mediated control of ATRA metabolism (Ozpolat 2001). The induction of CYP26 expression in response to ATRA treatment is reversible and dependent on the continuous presence of ATRA; the expression returned to baseline after withdrawal of the ATRA. These studies suggested that ATRA-induced CYP26 expression might be responsible for accelerated metabolism of ATRA leading to decreased sensitivity and acquired resistance to ATRA in APL patients. Intracellular levels of ATRA are strictly controlled through regulation of synthesis, metabolism, and probably uptake. CYP26 is highly inducible and specific for hydroxylation of ATRA; thus it might be the most important in P450 enzyme system for the regulation of plasma and intracellular levels of ATRA. It has been shown that CYP1A1, CYP2C8, CYP2C9, and CYP3A4 in microsomes of human liver cells were able to hydroxylate ATRA, but none of these enzymes at protein and mRNA levels were inducible by ATRA and have low specificity for ATRA. It is likely that the metabolic fate of ATRA after continuous administration is determined by the induction of CYP26 in leukemia and other metabolically active tissues such as liver, intestine, and skin. Following ATRA treatment increased CYP26 activity in promyelocytic leukemia cells may reduce intracellular ATRA concentrations below the level that does not support differentiation and thus leading to ATRA resistance.

Increased Cellular RA-Binding Proteins (CRABPs) In the cytoplasm, ATRA is bound by cellular retinoic acid-binding proteins I and II (CRABPs I and II). CRABPs, which are conserved in vertebrates, are high-affinity proteins for ATRA. CRABP-I is expressed almost in all types of cells, whereas

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CRABP-II is expressed mainly in the skin. The difference in tissue expression patterns suggests that CRABP-I and CRABP-II have distinct functions in ATRAmediated responses. Early studies linked CRABPs with metabolism and regulation of cytoplasmic levels of ATRA. Therefore, the other possible explanation for progressive decline in the plasma levels of ATRA after continuous therapy with the drug may be the induction of CRABPs. It has been proposed that high levels of CRABP may sequester intracellular ATRA, resulting in decreased drug levels in plasma as well as in normal bone marrow and APL cells (Delva et al. 1993). It was shown that the rate of ATRA metabolism in F9 teratocarcinoma but not in transfected CV-1 and COS-7 cells correlates with the expression levels of CRABP-I, suggesting that CRABP-I regulates the metabolism of ATRA depending on cell type. Boylan and Gudas also showed that increased CRABP-I expression resulted in decreased sensitivity of F9 cells to ATRA-induced differentiation suggesting that this molecule functions as a regulator of intracellular ATRA levels by delivering ATRA to microsomes, facilitating catabolism, and/or sequestering ATRA (Fig. 10.4). CRABP molecules have been shown to be present not only in cytoplasm but also in the nucleus, suggesting that CRABPs may function to deliver ATRA to the nuclear retinoid receptors. It is also possible that CRABPs may be involved in transcriptional activation or inhibition of retinoic acid receptors. It was shown that expression of CRABP-II, but not CRABP-I, significantly induced RAR-mediated transcriptional activation of a reporter gene, indicating that CRABP-II indeed may be involved in transcriptional activity of ATRA. Recent studies in breast cancer and APL cell lines showed that CRABP-II associates with RARα and RXRα complex in ligand-independent manner. CRABP-II may function as a transcriptional regulator of ATRA signaling by binding RARE on the target genes as part of receptor complex. Increasing levels of CRABP-II were shown in normal and leukemia cells of APL patients undergoing ATRA treatment (Delva et al. 1993). They found that CRABP-II reached to maximum levels after 3 months of continuous ATRA treatment and its levels decreased within a month after ATRA withdrawal. In relapsing patients, high levels of CRABP-II were detected in APL cells but not before ATRA therapy, suggesting that in a hypermetabolic state excess CRABP might bind ATRA and prevent drug transport to the nucleus. CRABP might also act as a transporter to the microsomes in endoplasmic reticulum (ER) where ATRA is metabolized. However, no difference between CRABP-II levels in pretreatment and at the time of relapse in APL patients was found. Constitutive expression of CRABP-II implicates that it may not be related to ATRA resistance in APL patients. Interestingly, CRABP-I/II knockout mice did not show significant phenotype difference and signs of toxicity, indicating that these proteins may not play important role in regulation of ATRA metabolism and signaling. 9-cis-RA and 13-cis-RA have stable plasma concentrations after continuous administration. This might be due to their lower affinity for CRABP compared with ATRA, which has progressive reduction of plasma levels with continuous treatment. The other possibility might be that these isomers do not induce specific p450 enzymes as ATRA does.

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Mutations at the Ligand-Binding Domain of RARα Leukemic blasts isolated from some ATRA-resistant APL patients are less sensitive or completely resistant to ATRA and 9-cis-RA-mediated differentiations in vitro, suggesting that ATRA resistance mechanisms may involve selection of ATRAresistant clones. Point mutations in the LBD (E-domain) of RARα in HL-60 cells and LBD of RARα of PML–RARα fusion protein in NB4 cells can be induced by prolonged culture in the presence of ATRA; thus these point mutations lead to ATRA resistance (Robertson et al. 1992, Shao et al. 1997). Shao et al. (1997) identified a point mutation located at the amino acid 398, L398P (leucine replaced by proline), in LBD of PML–RARα in an ATRA-resistant NB4 clone (NB4-R4). The mutant receptor does not bind ATRA, but was able to bind RXRα and RARE, expressing dominant negative activity (Fig. 10.4). They also found that pharmacological doses of ATRA could not dissociate the co-repressor SMRT from mutant PML–RARα, preventing expression of ATRA-responsive target genes. Recently, point mutations leading to amino acid substitution in E-domain (ligandbinding domain) of RARα of PML–RARα fusion receptor protein were also detected in APL cells isolated from relapsing patients (Ding et al. 1998). The mutations were absent before ATRA treatment. Imauzimi et al. (1998) reported acquisition of missense mutations of G815A or A889 in sequence of RARα cDNA, leading to amino acid replacement of R272Q (arginine to glutamine) and M297L (methionine to leucine) in RARα of PML–RARα. The mutations found in APLs isolated from two patients at the time of relapse, exhibiting ATRA resistance, were localized to middle region of the E-domain. However, mutations detected in ATRA-resistant HL-60 and NB4 subclones are located at the carboxyl terminal of the E-domain. Furthermore, site-directed mutagenesis at A272 of RARα has been shown to inhibit binding of ATRA to RARα. Recently, Marasca et al. (1999) also reported that although no mutation could be detected before the onset of ATRA treatment, point mutations in LBD of PML–RARα in two relapsed patients were observed, confirming previous findings (Marasca et al. 1999). Ding et al. (1998) found mutations in PML–RARα in APL blasts of 3 of 12 patients following ATRA treatment. The mutations were located at codon 290 (L290V), 394 (R394W), and 413 (M413T). These mutations interfere with ATRA-binding activity and result in dominant negative function leading to resistant state and providing growth advantage of APL blasts carrying the mutation. Currently, what percent of the ATRA-resistant patients having these mutations is not known. Therefore, studies with larger number of patients are required to clarify the clinical importance of these mutations.

Constitutive Degradation of PML–RARα Expression of PML–RARα has been linked to initial ATRA sensitivity (Grignani et al. 1993). NB4 cells expressing dominant negative PML–RARα are resistant to ATRA (Shao et al. 1997). Expression of the PML–RARα protein in U937

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cells enhanced the sensitivity to retinoic acid-induced differentiation. These results suggested the biological function for PML–RARα to transactivate differentiationrelated genes that are critical for therapeutic response of ATRA in APL. ATRA therapy was shown to induce degradation of PML–RARα through the action of proteosome, likely by caspases 3-like activity in APL cells isolated from patients and NB4 as well as U937 myeloid precursor cells expressing PML–RARα. Fanelli et al. (1999) demonstrated that ATRA-resistant NB4 subline, which was selected under the selective pressure of ATRA, expresses normal levels of PML– RARα mRNA, but does not express PML–RARα protein. They were able to restore ATRA sensitivity partially in the ATRA-resistant NB4 cells by proteosome inhibitors by blocking the degradation of the fusion receptor protein. Similarly, expression of PML–RARα by retrovirus-mediated transduction resulted in restoration of ATRA sensitivity in ATRA-resistant NB4 cells protein. These results suggested that alterations in proteosome pathway resulting in constitutive degradation of PML–RARα protein may lead to ATRA resistance since previous data showed that expression of PML–RARα is critical for ATRA sensitivity in APL cells. Downregulation of PML–RARα by ATRA probably results in reorganization of the PML nuclear bodies. Nervi et al. (1998) found that prevention of ATRA-induced degradation of fusion protein by a member of caspase 3 family did not abolish the ATRAinduced differentiation, suggesting that PML–RARα is involved in ATRA sensitivity of APL cells. Interestingly, short isoform of PML–RARα (bcr3-PML–RARα), which is found in about 35% of APL patients, does not contain the caspase cleavage site (Asp522, α-helix, located in PML part) and is not degraded after ATRA treatment (Gallagher et al. 1997). However, these APL patients with the short isoform respond to ATRA indicating degradation of PML–RARα may not be essential for ATRA-induced differentiation. It is not known if ATRA treatment results in degradation of other ATRA-sensitive variants of APL with NPM–RARα or NuMA–RARα. Whether ATRA-induced degradation of PML–RARα is a cause or result of therapy needs to be clarified.

P-Glycoprotein Expression P-glycoprotein (P-gp) is a membrane protein functioning as an ATP-dependent drug efflux pump that decreases intracellular accumulation of various lipophilic compounds (Fig. 10.4) (Gerlach et al. 1986). P-gp is the product of multidrug resistance1 (MDR1) gene that confers drug resistance to a variety of agents. P-gp is overexpressed in a variety of human tumor cells, leading to resistance to chemotherapy. Therefore, it is possible that increased expression of P-gp results in resistance of APL cells to ATRA by decreasing intracellular ATRA concentrations. It has been shown that expression of P-gp is low in newly diagnosed APL patients, but higher in APL cells isolated from two relapsed ATRA-resistant patients (Kizaki et al. 1996). It was also reported that expression of P-gp in HL-60 was lower when compared to ATRA-resistant HL-60 cells. Moreover, treatment of HL-60 cells with P-gp

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antagonist (Verapamil) in the presence of ATRA partially restored ATRA resistance in resistant HL-60 and APL cells, implying that P-gp may play a role in ATRA resistance. More importantly, the direct evidence indicating that P-gp is responsible, in part, for acquisition of ATRA resistance in APL cells came from the experiment using ribozymes, which are able to target MDR1 RNA by a catalytic activity. HL60-resistant cells stably transfected with 196 MDR1 ribozyme showed inhibition in the expression of MDR1 and were able to undergo differentiation and growth inhibition in a dose-dependent manner. However, Takeshita et al. recently reported that they did not find any difference in the intracellular levels of ATRA between NB4 cells and a MDR1 transfected a NB4 subline or ATRA-resistant NB4 cells (Takeshita et al. 2000). They found similar results with APL cells isolated from patients relapsed after ATRA therapy, suggesting that P-gp may not be involved in the development of ATRA resistance. P-gp expression was also found significantly lower in APL than in other AML cells. This may be an important mechanism providing biological basis for sensitivity of APL cells to chemotherapy and ATRA when compared to the AMLs.

Histone Deacetylase (HDAC) Activity APL cells expressing PLZF–RARα receptor fusion protein are resistant to ATRAinduced differentiation (He et al. 1998). Recent findings revealed that RA-signaling pathway is constitutively repressed by HDAC activity at physiologic levels of ATRA in PLZF–RARα-type APL blasts, leading to transcriptional repression/silencing and differentiation block (Grignani et al. 1998). RARα part of PML–RARα fusion protein has one binding site for nuclear co-repressor proteins and HDAC complex that is removed by binding of ATRA to PML–RARα/RXR dimer; thus pharmacological concentrations of ATRA induce differentiation of PML–RARα positive APL blasts in vitro and in vivo. However, the same effect is not observed in PLZF–RARα positive APL cells, since PLZF–RARα protein has two nuclear co-repressors proteinbinding sites (Heinzel et al. 1997). In order to transactivate responsive genes leading to cell differentiation, the removal of both of the co-repressor complexes from the PLZF–RARα is required. Even though ATRA is able to dissociate nuclear corepressor proteins and HDAC complex from RARα of PML–RARα protein, the second co-repressor proteins and HDAC complex cannot be removed. Therefore, while ATRA induces differentiation of PML–RARα positive APL blasts at pharmacological concentrations, PLZF–RARα expressing blasts are resistant to ATRA-induced differentiation unless a HDAC inhibitor such as trichostatin A is used (Grignani 1998). The presence of HDAC inhibitors and ATRA induces significant differentiation in most resistant APL cells with PLZF–RARα.

The Role of PML/RARα Isoforms in Resistance Variable breakpoints on PML gene on chromosome 15 result in expression of distinct PML–RARα isoforms (Figs. 10.1 and 11.2B). Short (S) isoform (bcr3 ) is created by a breakpoint in intron 3, while long (L) isoform (bcr1) results from a

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breakpoint located in intron 6 of PML gene, found in 35% and 60% of adult APL patients with t(15,17), respectively. The rest of the patients have the other isoform called variable (V), having breakpoint located in exon 6 of PML (Slack et al. 2000). The S isoforms of PML–RARα associate with high white blood cell (WBC) count, M3v-type morphology, CD34 and CD2 expression, and secondary cytogenetic abnormalities. Although no significant correlation between type of PML–RARα isoform and ATRA-induced clinical response was found in most studies, in some studies patients expressing S-type PML–RARα had shorter remission time and poor prognosis with ATRA therapy (Vahdat et al. 1994). In vitro treatment of APL blasts from patients with S and L types by ATRA was shown to induce differentiation in these blasts to a similar degree (Gallagher et al. 1995). However, when compared to L isoform, the S isoform has lower binding affinity for ATRA but higher affinity and specificity for 9-cis-RA (Slack and Gallagher 1999). Gallagher et al. (1995) showed that APL cells from patients with V (Bcr2) isotype have decreased in vitro response to ATRA. A recently completed clinical study with liposomal ATRA at our institution reported that CR rates were 50% (4 out of 8) of the patients with S isoform while about 86% (6 out of 7 patients) of the patients with L isoform, suggesting that S isoform might be playing a role in resistance to ATRA (Estey et al. 1999). Overall, based on data available, it is hard to find clear correlation with the type of PML– RARα isoform and outcome of ATRA therapy.

Telemerase Activity It has been reported that there is a link between decreased telomerase activity and terminal differentiation of some tumor cells including NB4 cells (Reichman et al. 1997). Nason-Burchenal et al. (1997) showed that ATRA-resistant NB4 cells did not have repression in the activity of telomerase after ATRA treatment compared to ATRA-sensitive NB4 cells. However, when ATRA-sensitive and ATRA-resistant NB4 cells were treated by phorbol 12-myristate 13-acetate (PMA) and vitamin D3, all cells were induced to differentiate into monocytic cells and telomerase activity markedly declined, suggesting that persistent telomerase activity may be linked to ATRA resistance. This effect might be due to a defective signaling in ATRAresistant cells, resulting in a block in decreasing telomerase activity.

Tissue Transglutaminase Expression Transglutaminase II (Tgase-II) is a calcium-dependent enzyme that catalyzes amine incorporation and a cross-linking of proteins. Intracellular Tgase-II is induced when human promyelocytic leukemia cells (NB4) and fresh leukemia cells isolated from APL patients were treated with retinoic acid. It was reported that ATRA induces Tgase-II mRNA in NB4 cells but not in ATRA-resistant NB4 cells or in patient APL cells lacking the t(15,17). This induction correlated with ATRA-induced growth arrest and granulocytic differentiation. ATRA did not induce growth arrest and

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differentiation and type II Tgase activity in an ATRA-resistant subclone of the NB4 cell line, or in leukemic cells derived from two patients morphologically defined as APL but lacking the t(15,17). ATRA-induced expression of Tgase-II in U937 cells transfected with PML–RARα but not in untransfected U937 cells, indicating that the expression of Tgase may be mediated by PML–RARα (Benedetti et al. 1996). ATRA-induced expression of Tgase-II in HL-60 cells is mediated by RXRα. Induction and expression of Tgase-II in HL-60 and other cell types are associated with apoptosis (Mehta et al. 1996). It is also suggested that Tgase-II expression may be related to induction of differentiation, since its expression is an early event in response to ATRA treatment. Therefore, loss of Tgase-II induction in resistant cells may be an important factor resulting in resistance to ATRA therapy.

Topoisomerase II Activity Topoisomerase II beta negatively modulates RARalpha transcriptional activity and those increased levels of and association with TopoIIbeta cause resistance to RA in APL cell lines (McNamara 2008). They showed that knockdown of TopoIIbeta could overcome resistance by permitting RA-induced differentiation and increased RA gene expression. Overexpression of TopoIIbeta in clones from an RA-sensitive cell line caused resistance by a reduction in RA-induced expression of target genes and differentiation. Using chromatin immunoprecipitation (CHIP) assays they also demonstrated that TopoIIbeta is bound to an RA response element and that inhibition of TopoIIbeta causes hyperacetylation of histone 3 at lysine 9 and activation of transcription, suggesting a novel mechanism of resistance. However, this mechanism needs to be validated in samples from ATRA-resistant patient in terms of frequency and significance.

Potential Treatment Strategies to Overcome ATRA Resistance in APL Liposomal ATRA New treatment modalities are being investigated to overcome ATRA resistance and further to improve the outcome of disease. To circumvent accelerated metabolism of ATRA, liposome incorporated-ATRA, inhibitors of cytochrome p450 enzyme system, such as ketoconazole and liarozole, and lower or intermittent doses of ATRA administration have been tested (Benedetti et al. 1996). Liposomal ATRA was developed to provide an intravenous (IV) formulation to generate potential pharmacological advantages over the oral formulation (Fig. 10.6). An IV administration of liposomal ATRA was shown to be superior to oral ATRA (non-liposomal) in terms of maintaining higher plasma levels in animal models and in humans (Estey et al. 1996, 1999, Mehta et al. 1994, Parthasarathy and Mehta

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Fig. 10.6 Possible strategies to prevent accelerated metabolism of ATRA. Selective P450 inhibitors and liposomal ATRA may circumvent metabolic pathways and mechanisms involved in accelerated elimination of ATRA

1998, Lopez-Berestein et al. 1994 ). IV administration of liposomal ATRA to rats over a prolonged period (7 weeks) did not cause a decrease in the levels of ATRA in plasma over time (Mehta et al. 1994). In contrast, chronic oral administration of ATRA (non-liposomal) in rats resulted in decreased drug plasma concentrations after the same period of time. In the same study, liver microsomes isolated from animals that were repeatedly treated with oral ATRA showed a significant increase in metabolism of drug in vitro. However, microsomes isolated from animals that received IV liposomal ATRA the same number of times with the same doses showed that metabolism of the drug was not altered. Similarly, when F9 teratocarcinoma cells were treated with both liposomal and free ATRA, liposomal ATRA was metabolized at slower rate than non-liposomal ATRA (Parthasarathy and Mehta 1998). These results demonstrated that encapsulation of ATRA in liposomes and IV administration generates better pharmacokinetic profile than oral ATRA by circumventing hepatic metabolism of ATRA. In addition to bypassing the hepatic clearance, liposomal ATRA was shown to distribute in skin to less extent, which may contribute in maintaining steady and higher ATRA concentrations in the plasma (Mehta et al. 1994). Evaluation of liposomal ATRA in phase I trial in patients with refractory hematological malignancies showed that in contrast to the decline in plasma AUC (area

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under the concentration–time curve) of ATRA seen 3–4 days of initiation of oral ATRA, there were no difference between the AUC on day 1 and day 15 following liposomal ATRA treatment (Estey et al. 1996). In the same study, liposomal ATRA was shown to be safe and toxicity profiles were similar to oral ATRA, although liposomal ATRA produced much higher AUC. IV administration of liposomal ATRA (90 mg/m2 ) monotherapy was shown to be effective in newly diagnosed APL patients, inducing PCR-negative molecular CRs in a high proportion of patients (Estey et al. 1999, Douer et al. 2001). These studies supported the hypothesis that IV liposomal administration may improve activity of ATRA by altering its pharmacological profile and remain elevated following extended treatment, providing a basis for long-term therapy in APL.

Arsenic Trioxide (As2 O3 ) Arsenic compounds, which have been used more than 500 years in traditional Chinese medicine, have been shown to be highly effective in the treatment of APL. Arsenic alone induces CRs about 90% of APL patients with t(15,17) (Shen et al. 1997). More importantly, arsenic induces CRs not only in de novo APL patients but also in patients with relapses after ATRA/chemotherapy who have become resistant to these drugs. Recently, arsenic trioxide was approved by FDA for APL patients who relapsed or failed to respond to standard therapy. Although arsenic is extremely effective especially in ATRA-resistant APL patients, its moderate toxic effects need to be further investigated. In vitro and in vivo studies showed that arsenic triggers apoptosis at high concentrations (0.5–2.0 μM) and induces differentiation at low concentrations (0.1– 0.5 μM) in APL cells (Niu et al. 1999). No cross-resistance has been observed between ATRA and arsenic. Arsenic induces degradation of PML–RARα and endogenous PML and enhances acetylation of histones (Niu et al. 1999). Arsenicinduced apoptosis might be mediated by downregulation of Bcl-2 and upregulation of death-associated protein (DAP5/p86) that leads to activation of caspase 1 and 3 (Ozpolat et al. 2008). Arsenic has been effective in t(11,17)-type APL expressing PLZF–RARα in a mouse model. Studies suggested that mechanism of effect of As2 O3 on promyelocytic leukemia is different from that of ATRA. As2 O3 shows antitumoral activity in APL cells that do not harbor t(15,17), variety of hematologic cancer cell lines including chronic myeloid leukemia (CML) (that are resistant to other agents), multiple myeloma, lymphoma, chronic lymphocytic leukemia (CLL), acute lymphoblastic leukemia (ALL), and megakaryocytic leukemia. A recent trial using intravenous arsenic in patients with relapsed or refractory APL showed that 70% of patients achieved molecular remission and most of them stayed disease free CR in a 16-month follow-up. Although toxicity and serious side effects of arsenic were reported in the same study, these effects were not permanent and did not cause interruption to therapy. Another study reported that CR rates induced by arsenic were 90% in APL patients who relapsed after ATRA-based

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therapy. More important, recent clinical studies suggested that combination therapy (ATRA and As2 O3 ) was more effective at prolonging survival than either drug alone, suggesting that combination of ATRA and As2 O3 acts synergistically.

Histone Deacetylase Inhibitors Induction of differentiation of ATRA-resistant APL with PLZF–RARα, using a combination of pharmacological dose of ATRA and HDAC inhibitors, (TSA or sodium phenylbutyrate) opened a new avenue in the treatment of not only APL but also AML1-ETO AML (Grignani et al. 1998). Although butyrate was the first identified HDAC inhibitor, it is not specific for HDAC. Trichostatin A and trapoxin are more specific and potent HDAC inhibitors (Yoshida et al. 1990). The major problem regarding the use of these non-specific HDAC inhibitors might be side effects because of changing chromatin structure in cells other than leukemia.

Others Am-80, a synthetic retinoid, has been successful against relapsed APL patients previously treated with ATRA, inducing CR in about 60% of patients (Tobita 1997). However, in addition 4-HPR, 1,25-dihydroxyvitamin D3 and K2 in combination with ATRA have been shown to be effective in ATRA-resistant APL cell lines inducing differentiation. Recently, 3-hydroxymethylglutaryl coenzyme A (HMGCoA) reductase inhibitors (statins) were shown to have anti-leukemic activity against leukemia cells. Simvastatin was found to be the most active statin in the family and induced cytotoxic potency against HL-60 cells. Combination of retinoic acid and tumor necrosis factor can overcome the maturation block in a variety of retinoic acid-resistant acute promyelocytic leukemia cells, suggesting that combination with retinoic acid can enhance the potency of other drug or induce additional pathways that cannot be triggered in resistant cells by ATRA alone. In collaboration with Dr. Michael Danilenko, we demonstrated that combination with rosemary extract or its active compound carnosic acid can enhance ATRA-induced differentiation effects in NB4 and HL60 and resistant APL cells (Dr. Ozpolat, unpublished findings).

Conclusion Although the use of ATRA has improved greatly the treatment of APL, rapid development of ATRA resistance limits its use as a single agent. Therefore understanding the mechanisms involved in acquired ATRA resistance and designing new therapeutic strategies would significantly improve the rate and long-term maintenance of CR in APL patients. Combination of ATRA with chemotherapy is currently the mainstay therapy in APL. In conclusion, designing drugs with favorable plasma

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pharmocokinetics and without exhibiting resistance and side effects will be the main goal of future studies for developing successful therapeutic strategies. New strategies based on our understanding of the fate of ATRA in patients with APL will facilitate the development of non-toxic and effective therapeutic modalities.

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Chapter 11

MicroRNAs and Drug Resistance Elisa Barbarotto and George A. Calin

Abstract MicroRNA alterations are involved in the initiation, progression, and metastases of human cancer. The main molecular alterations are represented by variations in gene expression, usually mild and with consequences for a vast number of target protein coding genes. MicroRNA expression profiling of human tumors has identified signatures associated with diagnosis, staging, progression, prognosis, and response to treatment. Recent studies proved that miRNAs are involved in resistance to chemotherapy and therefore understanding their involvement could be of benefit for cancer patients’ treatment. This chapter’s focus is on the main studies proving the new link between microRNAs and drug resistance. Keywords microRNAs ·Drug resistance

Introduction The first known microRNA (miRNA), lin-4, was discovered in 1993 in Caenorhabditis elegans (Lee, Feinbaum, and Ambros 1993). This discovery changed the molecular biology landscape because the gene product of lin-4 did not code for a protein but for a noncoding RNA (ncRNA). This RNA regulates several critical genes involved in the timing and progression of the nematode life cycle and larval development at the post-transcriptional level (Wightman, Ha, and Ruvkun 1993). Lin-4 acts by downregulating the level of lin-14 protein, creating a temporary decrease in this protein (starting in the initial larval stage) without any shift in lin-14 mRNA levels. The binding of lin-4 to the three prime untranslated region (3 UTR) of lin-14 induces specific translational repression of lin-14 messenger RNA (mRNA), making lin-14 the first-known target of miRNA. G.A. Calin (B) Department of Experimental Therapeutics – Unit 36, The University of Texas M. D. Anderson Cancer Center, 1515 Holocombe Blvd, Houston, TX 77030, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 11, 

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Modulating the development of several organisms, including mammals, miRNAs are a large family of approximately 22-nucleotide (nt) noncoding RNAs; these tiny molecules are highly conserved in sequences of distantly related organisms. For example, the complete mature sequence of let-7, isolated for the first time in C. elegans, has been evolutionarily conserved from worms to humans (Johnson et al. 2005). In humans, the let-7 miRNA family may function as tumor suppressor by targeting the oncogene Ras, and there is some evidence that reduced expression of let-7 family members is common in non-small cell lung cancer (Kumar et al. 2008). Over 6000 members of a new class of miRNAs (Ambros 2003; Bartel 2004) have been identified in the last 6 years in vertebrates, flies, worms, plants, and even viruses (Griffiths-Jones et al. 2006). In humans, the microRNoma (defined as the full complement of miRNA present in a genome) contains more than 700 miRNAs that have been cloned experimentally or in silico, and the total number is expected to reach over 1000. The miRNAs in the human genome are located in all chromosomes except the Y. Bioinformatic analysis indicates that the human genome may encode over 1000 miRNAs, which likely regulate at least one-third of all human transcripts. Many algorithms (http://cbcsrv.watson.ibm.com/rna22 downloads.html; http://www.microrna.org/; http://www.targetscan.org/) have been developed for predicting miRNA interactions. The algorithms show that each miRNA could potentially regulate a large number of mRNA targets (Sood et al. 2006); it seems reasonable to conclude that most, if not all, mRNAs are post-transcriptionally regulated by miRNAs.

Biogenesis About 60% of miRNA genes are located in intergenic regions, while 40% reside within introns of protein coding genes or other transcriptional units (Lagos-Quintana et al. 2003; Baskerville and Bartel 2005). A large number of the intergenic miRNAs are found in clusters. Recent findings support the idea that clusters of proximal miRNAs are typically expressed as polycistronic, coregulated units and that intronic miRNAs are generally coexpressed with their host genes. Since they have similar expression profiles, these clustered miRNAs are likely expressed from a common promoter (Baskerville and Bartel 2005). However, there are exceptions, suggesting the possibility of independent promoters for some intronic miRNAs (Aboobaker et al. 2005). Microarray profiling of miRNA expression is a useful strategy for examining the global expression profiles of this abundant class of small RNAs. Biogenesis of miRNA is driven by a complex network of nuclear and cytoplasmatic proteins/enzymes, with the first steps of miRNA maturation occuring in the cellular nucleus, while the final steps take place in the cytoplasm, as shown in Fig. 11.1. Transcription of miRNA is known to be orchestrated by RNA polymerase II (pol II)–which is also the enzyme involved in the transcription of protein-coding

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miRNA gene

Cytoplasm

Nucleus

Pri-miRNA

Drosha

Pre-miRNA

Exportin-5

DICER

miRNA duplex Helicase

Partial homology: TRANSLATIONAL REPRESSION

Mature miRNA mRNA target RISC

Perfect homology: mRNA CLEAVAGE RISC

Fig. 11.1 miRNA biogenesis. In normal tissues, the normal rates of cellular growth, proliferation, differentiation, and cell death are a consequence of correct miRNA transcription, processing, and binding to complementary sequences on the target mRNA. This results in the repression of targetgene expression through a block in protein translation or altered mRNA stability

genes– but recent findings suggest that a subset of miRNA is transcribed by pol III (Borchert, Lanier, and Davidson 2006). As for the mRNAs, also the miRNAs are subject to post-transcriptional modifications by pol II, which adds a 5 7-methylguanosine cap and 3 poly A. This first step generates the primary transcript, known as pri-miRNA. Pri-miRNA ranges from hundreds to thousands of nt in length, with one or more double-stranded regions. Pri-miRNAs are processed in the nucleus into pre-miRNAs, a shorter, imperfect stem-loop structure from 60 to 110 nt in length. In animals, this step is driven by a complex of proteins (also known as the “microprocessor complex”) formed by the RNase III endonuclease Drosha (Lee et al. 2003) and its cofactor, the double-strand RNA-binding protein Pasha. In mammals, this complex is better known as the DiGeorge syndrome critical region gene 8 (DGCR8) (Gregory et al. 2004). Pasha contains an RNA-binding domain that binds the pri-miRNA intermediate required for proper processing, which is essential for Drosha activity. Drosha cleavage defines one end of the mature miRNA and leaves its characteristic 3 overhang of two nt with the 5’- phosphate group on the intermediate pre-miRNA (Lee et al. 2003; Diederichs and Haber 2007).

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In mammals, pre-miRNA is transported from the nucleus to the cytoplasm by the nuclear export factor Ran/GTP/exportin-5 (Lund et al. 2004). Once in the cytoplasm, the pre-miRNA is trimmed to its mature structure by the activity of another endonuclease, Dicer (RNase III enzyme), which cuts off the stem-loop structure, leaving the imperfect double-stranded structure approximately 21 to 23 nt in length. The duplex is probably unwound by an as-yet-unidentified helicase, and only one strand is selected for integration into the RNA-induced silencing complex (RISC). Generally, the strand with the 5’ terminus located at the thermodynamically less stable end of the duplex is selected to function as the mature miRNA; this strand is known as the guide strand (antisense with reference to the target mRNA sequence; Bartel 2004), while the other strand, known as the antiguide strand or passenger strand (sense strand with reference to the target mRNA or miRNA∗), is degraded as an RISC complex substrate (Preall et al. 2006). The most important components of the RISC are proteins of the Argonaute family (AGO), of which there are four known in mammals, AGO1 to AGO4. It has been reported that the passenger strand is degraded by the cleavage activity of AGO2 (Kim, Lee, and Carthew 2007; Diederichs and Haber 2007).

Cellular Function At present, the precise mechanisms by which miRNAs recognize their target site on mRNAs is not completely known (Karginov et al. 2007), but their main function seems to be related to gene regulation (Calin et al. 2008; Calin and Croce 2007). How the mature miRNA causes gene silencing depends on the degree of complementary miRNA on the mRNA target sequence. Tuschl and colleagues (1999) showed that if there is perfect complementarity between the miRNA and its target, miRNA/AGO2 will cleave the mRNA target from the miRNA 5’ end between bases 10 and 11. As reported first by Lee, Feinbaum, and Ambros (1993) and, more recently, Bagga and colleagues (2005), when there is imperfect complementarity (as happens in the majority of cases), it is usually in the central part, which causes the destabilization and translational repression of the mRNA target. The sequence motif recognized by the miRNA is located in the 3’-UTR of the transcript, generally located between the protein-coding region and the poly A tail of the mRNA target but also targeting on 5-UTR and coding sequence were proved. Most bioinformatic algorithms have revealed that a single miRNA might bind a large number (about 200) of target genes, which can be diverse in their function. Furthermore, recent findings suggest that miRNAs can also regulate the expression of other noncoding RNAs (Calin et al. 2007).

MiRNA and Cancer It is known that miRNAs can act at multiple levels, as they can induce chromatin modifications, RNA cleavage, and regulate mRNA translation of genes involved in

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cell growth, proliferation, and apoptotic pathways. Cloning technologies and bioinformatics predictions suggest that miRNAs may regulate up to 20% to 25% of genes in mammals (Doran and Strauss 2007). The list of proposed miRNA functions includes controlling the fate of hematopoietic B-cell lineages (miR-181), B-cell survival (miR-15a and miR-16-1), cell proliferation (miR-125b and let-7), brain patterning (miR-430), pancreatic cell insulin secretion (miR-375), and adipocyte development (miR-143). Despite years of research, the molecular basis for the majority of familial cancers is unknown. Cancer arises when mutations, deletions, or epigenetic alterations occur in initiating gene(s) known as tumor-suppressor genes and oncogenes. In keeping with the role of miRNAs as major regulators of growth and proliferation, loss or amplification of miRNA genes has been reported in a variety of cancers, and altered patterns of miRNA expression may affect cell cycle and survival (Calin and Croce 2007). The involvement of miRNAs in cancer is much wider than initially thought; there is now ample evidence implicating these tiny noncoding molecules in tumorigenesis, as they are frequently located close to fragile sites as well as in genomic regions associated with cancer (Calin et al. 2004). Alterations in miRNA may cause a predisposition to cancer; for instance, germline mutations (the role of which is not completely clear) have been reported in the pri-miR-16-1/15a precursor genes in patients with familial chronic lymphocytic leukemia (CLL) or breast cancer in first-degree relatives (Calin et al. 2005). Furthermore, germline single nucleotide polymorphisms (SNP) were identified in two recognition sites in c-KIT oncogenes for miR-221, miR-222, and miR-146, all of which are strongly overexpressed in thyroid cancers (He et al. 2005b). Therefore, as the thermodynamics of RNA–RNA binding plays essential roles in miRNA interaction with the target mRNA, sequence variations influencing this interaction may predispose to cancer.

MiRNAs as Oncogenes and Tumor Suppressors Alterations in cancer cells seem to be represented by aberrant gene expression, characterized by abnormal levels of expression for mature and/or precursor miRNA sequences. The classic models of tumorigenesis postulated alterations in proteincoding oncogenes and tumor suppressor genes; miRNAs can also function as oncogenes (onco-miRNAs) (Fig. 11.2a) as in the case of miR-155 (Volinia et al. 2006), the miR-17-92 cluster (He et al. 2005a), and miR-21 (Chan, Krichevsky, and Kosik 2005; Volinia et al. 2006), and/or as tumor suppressors (TS-miRNAs) (Fig. 11.2b) as in the case of miR-15a and miR-16-1 (Cimmino et al. 2005) and the let-7 family (Johnson et al. 2005). Onco-miRNAs promote cell proliferation and cell survival, while TS-miRNAs reduce cell proliferation and survival. Since miRNAs have several potential targets that may be the mRNAs of both oncogenes and tumor suppressors, the actual function of a particular miRNA as either TS-miRNA or oncomiRNA may depend on the cellular context (Table 11.1).

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a

miRNA downregulation

Cytoplasm

miRNA gene Nucleus ? Pri-miRNA

?

?

Pre-miRNA

miRNA duplex ?

Mature miRNA

? AAAAA

MCpppG

mRNA AAAA

AAAA

mRNA AAAA

mRNA TRANSLATION Oncoprotein

Effect Tumor formation

b

miRNA overexpression

Cytoplasm

miRNA gene Nucleus ? Pri-miRNA

?

Pre-miRNA

?

miRNA duplex

?

Mature miRNA ?

inhibition inhibition

TRANSLATION

Effect Tumor formation

Fig. 11.2 (continued)

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Relatively minor variations in the levels of expression of miRNAs or mutations that moderately affect the conformation of miRNA::mRNA pairing could have important consequences for the cell because of the large number of targets available to each miRNA. The downregulation of the suppressor miR-15a/miR-16-1 induces overexpression of BCL2 and possibly other genes that may be important for tumorigenesis (Cimmino et al. 2005), while the overexpression of oncogenic miR-17-92 abets c-MYC in stimulating lymphomagenesis (He et al. 2005a). Alterations in miRNA may initiate or contribute to tumorigenesis when occurring in somatic cells, while representing cancer predisposing events if present in the germline. A paradigm for this model is human B-cell CLL, in which miR-15a and miR-16-1 are located in the most frequently deleted genomic region, are downregulated in the majority of cases, harbor mutations in familial cases, and induce apoptosis in a leukemia model by targeting the antiapoptotic BCL2 gene.

MiRNA and Drug Resistance The genetic basis of resistance and sensitivity to anticancer drugs is complex, involving multiple processes such as DNA repair, apoptosis, and drug delivery and metabolism. Genomic and proteomic studies have made big steps in understanding these processes. To this day, the targets and modulators of cancer drug therapy have been DNA, mRNA, and proteins. Therefore, mutations, copy number changes, epigenetic variables at the DNA level, and expression changes at the mRNA and protein levels have been widely studied to probe mechanisms of pharmacologic response. Expression profiling has been most extensive at the mRNA level, but levels of mRNA are often not proportional to those of the encoded proteins. That lack of proportionality could have a number of causes, among them the regulatory influences of miRNAs. The pharmacogenomics of miRNA (defined as the study of how miRNA variations and polymorphisms in their target genes determine drug behavior in order to improve the efficiency of drugs; Bertino, Banerjee, and Mishra 2007) is a novel and promising field of research holding new possibilities for medical therapy. Such research has strong clinical implications because miRNAs represent very attractive drug targets 

Fig. 11.2 (continued) miRNA as tumor suppressor (a) and as oncogene (b). (a) The reduction or deletion of a miRNA, because of defects at any stage of miRNA biogenesis (indicated by question marks), that functions as a tumor suppressor leads to tumor formation because of the overexpression of the miRNA-target oncoprotein (gray squares). The overall outcome might involve increased proliferation, invasiveness or angiogenesis, decreased levels of apoptosis, or undifferentiated or de-differentiated tissue. (b) The amplification or overexpression of an miRNA that has an oncogenic role would also result in tumor formation consequent to the reduction in the expression of a miRNA-target tumor-suppressor gene (dashed squares). Increased levels of mature miRNA might occur because of amplification of the miRNA gene, a constitutively active promoter, increased efficiency in miRNA processing or increased stability of the miRNA (indicated by question marks)

17q23.2

21q21.3

miR-17-92 cluster

miR-21

miR-155

Overexpression Overexpression

Downregulation Downregulation

Overexpression

Overexpression

Amplification/ overexpression

Downregulation Downregulation

HOXD10 RAS, PRDM1

BCL-2

Target

TCL1 ERBB2 Onco-miRNA KIT Onco-miRNA KIT

TS-miRNAs TS-miRNAs

Onco-miRNA AGTR1

Onco-miRNA PTEN, BCL-2

Onco-miRNA TSP1, CTGF

TS-miRNAs TS-miRNAs

Deletion, mutation TS-miRNAs (rare)

Mechanism(s) of Suggested alteration in cancer activity

He et al. 2005b He et al. 2005b

Pekarsky et al. 2006 Doran and Strauss 2007

Chan, Krichevsky and Kosik 2005;Volinia et al. 2006 Volinia et al. 2006

He et al. 2005a

Volinia et al. 2006 Johnson et al. 2005

Cimmino et al. 2005

Ref.

Abbreviation: APL, acute promyelocytic leukemia; B-CLL, B-chronic lymphocytic leukemia; AML, acute myeloid leukemia; CML, chronic myelogenous leukemia, DLBCL, diffuse large B-cell lymphoma; TS-miRNAs, tumor suppressors-miRNAs.

Thyroid Thyroid

13q31.3

miR-10b Let-7 family

9q33.3 11q24.1 11q21.1 miR-221/222 cluster Xp11.3 miR-146 10q24.32

2q31.1 Multiple loci

miR-15a/cluster 16-1

APL, B-CLL, Pituitary adenoma AML, Breast AML, APL, Lung, Colon ALL, CML, Lymphoma, Colon DLBCL, Glioblastoma, Breast Hodgkin, DLBCL, Breast Aggressive CLL Breast cancer

miR-181b miR-125b

13q14.3

miRNAs

Malignancy

Chromosome location

Table 11.1 miRNAs as oncogenes and tumor suppressors

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for many different reasons: they are expressed differently in malignant versus normal cells (Volinia et al. 2006), they can act as oncogenes and/or tumor suppressors (Chan, Krichevsky, and Kosik 2005; Volinia et al. 2006; Cimmino et al. 2005; He et al. 2005a; Johnson et al. 2005), and they can regulate the expression of several important cellular proteins (Calin et al. 2008; Calin and Croce 2007; He et al. 2005a). The role of miRNA in drug resistance and drug sensitivity has only recently been reported (Fig. 11.3). Mishra and colleagues (2007) showed that a polymorphism in an miRNA-binding site could lead to drug resistance and sensitivity. The term miRSNP/polymorphism was coined and defined as a novel class of SNPs/polymorphisms that interfere with the function of miRNA. Mishra and colleagues (2007) further reported that an miR-24 mRNA-binding site, SNP 829C→T in dihydrofolate reductase (DHFR) 3 UTR, led to loss of miR-24 function and resulted in DHFR overexpression and methotrexate (MTX) resistance (Bertino, Banerjee, and Mishra 2007; Mishra et al. 2007). They also proposed that miRSNPs located in an miRNA or at or near the miRNA-binding

Cytoplasm

miRNA gene

Nucleus

Pri-miRNA Exportin-5

Drosha

PrePre-miRNA Mature miRNA

miRSNP or miR-polymorphism Interfere with miRNA function. LOSS of miRNA FUNCTION

miRNA binds to 3’UTR of its mRNA target RISC

MCpppG

DICER

Downregulation of drug-target protein EFFECT

DRUG SENSITIVE

AAAA

MCpppG

AAAA

Overexpression of drug-target protein EFFECT

DRUG RESISTANT

Fig. 11.3 miR-Polymorphisms affecting drug response. The microRNA–RISC complex binding to 3 UTR of the drug target gene regulates its expression, resulting in lowering the drug target levels in the cell. Weak or no binding between microRNA–RISC complex and the 3 UTR of the drug target gene could be a consequence of an miRSNPs/miR-polymorphisms that may interfere with miRNA function. This results in high drug target gene levels in the cells that express the polymorphism leading to drug resistance

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site in 3’ UTRs of many important drug target genes may affect drug response in patients and possibly lead to drug resistance or sensitivity. By overexpressing miR-24 mimics and inhibiting endogenous miR-24, they demonstrated that SNP-829C→T acts as a loss-of-function mutation and results in the enhanced expression of DHFR mRNA and protein levels in mutant-expressing cells by affecting the binding of miR24 to 3’ UTR of DHFR. These findings suggest that the miRSNP causes a loss of miRNA function, that result in the overexpression of the target gene, which confer drug resistance (Bertino, Banerjee, and Mishra 2007; Mishra et al. 2007). One way to accelerate the advance toward molecularly based cancer therapy is to integrate various types of molecular information on the same set of cancer samples in order to develop a comprehensive molecular portrait of the cells. The National Cancer Institute (NCI) has screened more than 100,000 chemical compounds and natural product extracts for anticancer activity since 1990 (Boyd and Paull 1995) on a panel of 60 different human cancer cell lines (the NCI-60). Included in this “integromic” model (Weinstein 2004, 2006) are nine different categories of cancer cells: leukemias, melanoma, breast, ovarian, colon, lung, prostate, renal, and central nervous system. The NCI-60 provides an unparalleled public resource for integrated chemogenomic studies aimed at elucidating molecular targets, (http://discover.nci.nih.gov and http://dtp.nci.nih.gov) identifying biomarkers for personalised therapy, and understanding mechanisms of chemosensitivity, and chemoresistance, and miRNAs could provide a critical link in such understanding. They could be helpful in explaining discrepancies between mRNA and protein levels which complicate the use of mRNA profiles in studying chemoresistance (Huang et al. 2004). Blower and colleagues (2007) have shown through cell line clustering, based on miRNA expression patterns, that cell groupings are generally consistent with tissue type and mRNA expression. They suggested that newfound miRNA microarray data (Sarkans et al. 2005) will make it possible for researchers to use the entire array of integrated NCI-60 molecular databases to study the role of miRNA in cellular response to chemotherapy. Blower’s group also sees the potential of miRNA profiling, when combined with gene expression and other biological data in multivariate analyses, to provide critical information for understanding cancer chemosensitivity and chemoresistance (Blower et al. 2007). In a recent report, Blower and colleagues (2008) studied the pharmacologic roles of three miRNAs (let-7i, miR-16, and miR-21), previously implicated in cancer biology, in order to test the hypothesis that miRNAs modulate sensitivity and resistance to anticancer drugs. They challenged three NCI-60 human cancer cell lines: A549 (non-small cell lung), SNB-19 (central nervous system; glioma), and OVCAR-3 (ovarian) with 14 structurally different chemotherapeutic agents that have various putative mechanisms of action to assess the role of miRNA in drug resistance and sensitivity. They showed that changing the cellular levels of let-7i, miR-16, and miR-21 affected the potencies of a number of anticancer agents by up to 4-fold. The effect was most prominent with miR-21, with 10 of 28 cell-compound pairs showing significant shifts in growth inhibitory activity upon treatment. Moreover, they showed that changing miR-21 levels changed potencies in opposite directions

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depending on the compound class–indicating that different mechanisms determine toxic and protective effects. Ovarian cancer is the fifth leading cause of cancer death in women, the leading cause of death from gynecologic malignancy, and the second most commonly diagnosed gynecologic malignancy. According to the American Cancer Society, there is no true test for ovarian cancer. Yang and colleagues (2008) showed that several miRNAs are altered in human ovarian cancer, such as miR-214, miR-199a∗, miR-200a, miR-100, miR-125b, and the let-7 cluster. Interestingly, they showed that miR-214 negatively regulates PTEN by binding to its 3’-UTR, leading to inhibition of PTEN translation and activation of the Akt pathway. Consequently, miR-214 induces cell survival and cisplatin resistance, which were overridden by either small-molecule Akt inhibitors or the expression of PTEN cDNA lacking 3’-UTR. It has been well documented that constitutive activation of Akt contributes to chemoresistance in different types of tumors, including ovarian carcinoma (Testa and Bellacosa 2001); miR-214 inhibits PTEN translation leading to activation of the Akt pathway. These findings indicate that miR-214 plays an important role in cisplatin resistance by targeting the PTEN/Akt pathway. This study provided direct evidence that miRNA is of critical importance in the chemoresistance of human ovarian cancer and might help lead to a specific test for it. Stomach cancer is the fourth most common cancer worldwide; the American Cancer Society estimates that around 21,260 Americans were diagnosed in 2007. It is a disease with a high death rate (700,000 per year), making it the second most common cause of cancer death worldwide after lung cancer. Chemotherapy remains the primary treatment in many cases; however, therapies often fail because of multidrug resistance (MDR). In a recent report, Xia and colleagues (2008) studied the expression profiles of several miRNAs in an MDR cell line, SCG7901/VCR (cell line derived from human gastric adenocarcinoma cell line SCG7901 by stepwise selection using vincristine (VCR) as an inducing reagent). Their evidence showed that miRNAs may be involved in the development of MDR in gastric cancer cells. Through regulating BCL2 expression, miR-15b and miR-16 could modulate the sensitivity of gastric cancer cells to some anticancer drugs, at least in part.

Conclusion There is no doubt that miRNAs are involved in the regulation of tumorigenic pathways and in tumor development and progression. The extraordinary progress in understanding the roles of miRNA in cancer in the recent past has augmented expectations that miRNAs will prove to be valid molecular targets with feasibility for practical clinical use. A newly discovered mechanism of drug resistance is that mediated by miRNA, and it could be argued that the molecular basis for such resistance will be found to be similar to the mechanisms already identified for other types of drug resistance such as mutations, deletions, translocations, and amplifications. This should reinforce the thesis that, while diverse mechanisms of drug resistance

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exist, all have a similar molecular basis that involves some level of recurrent genetic or epigenetic changes differing only in their manifestations. Additionally, the abundance of a particular miRNA polymorphism associated with drug resistance in a specific gene population would allow the development of specific treatments. The increasing knowledge of miRNA functions will be useful in evaluating pharmacological tumor response and improving cancer drug discovery. Acknowledgments Elisa Barbarotto was supported by a postdoctoral research fellowship from the American–Italian Cancer Foundation. George A. Calin was supported in part as a University of Texas System Regents Research Scholar and a Fellow of The University of Texas MD Anderson Cancer Center Research Trust and in part by the Chronic Lymphocytic Leukemia Global Research Foundation. We apologize to the many colleagues whose work was not cited because of space limitations. We thank Maude E.Veech, ELS, Department of Scientific Publications, The University of Texas MD Anderson Cancer Center, for expert editorial assistance.

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Chapter 12

Molecular Signatures of Drug Resistance Melissa A. Troester, Jason I. Herschkowitz, and Katherine A. Hoadley

Abstract Genomic methods are helping to overcome limitations of individual markers in predicting response to chemotherapy. Molecular signatures of cancer heterogeneity and drug resistance are being developed that use data from both observational and experimental settings. Genomic signatures that represent specific pathways and biological processes are being integrated from diverse data types, including cell line models, genetically engineered mouse models, and patient studies of drug resistance. This chapter highlights recent advances and future directions in genomics of drug resistance, with emphasis on integrating insights from different study settings. Keywords Microarrays · Animal models · Molecular signatures · Stem cells · Microenvironment · Cancer heterogeneity

Introduction Gene expression microarrays allow the expression patterns of thousands of genes to be simultaneously evaluated, resulting in improved classification of the biological and clinical heterogeneity of cancers. This has led to advances in our understanding of cancer and also to new opportunities to understand variability in chemotherapy response. In fact, several genomic tests for a variety of cancers are now available for clinical use. These genomic tests promise improved predictive accuracy by combining multiple markers. Historically, some individual pathologic and immunohistochemical markers have shown independent predictive value in breast cancer, such as estrogen receptor status (Harris et al., 2007) or proliferation index (Colozza et al., 2005), which appears to predict sensitivity to cytotoxic regimens, and HER2 M.A. Troester (B) Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC, USA e-mail: [email protected]

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overexpression, which predicts response to anthracycline regimens (Menard et al., 2001; Moliterni et al., 2003). However, few markers have sufficient evidence to support their clinical use in selecting chemotherapy regimens (Harris et al., 2007). Thus, genomic methods are helping to overcome the predictive limitations of individual markers by identifying molecular signatures of cancer heterogeneity.

Microarrays in the Heterogeneity of Cancer Microarrays were first used to study the heterogeneity of cancers by Perou et al. (Perou et al., 2000). In that study, messenger RNA (mRNA) was extracted from a set of 65 surgical specimens of human breast tumors from 42 different individuals. The mRNA was reverse-transcribed, labeled with fluorescent dye, and hybridized to a glass slide containing 8,000 cDNA spots. The arrays were then scanned for the amount of fluorescently labeled cDNA bound to each oligonucleotide spot, and the fluorescence measurements served as a quantitative score correlated with gene expression. Sets of co-expressed genes were identified that distinguished the intrinsic biological characteristics of different tumors. The intrinsic subtypes identified by Perou and colleagues (Perou et al., 2000; Sørlie et al., 2001) correlated with recognized markers of clinical heterogeneity [i.e., estrogen receptor (ER) status and HER2 overexpression], but were able to further subdivide ER-positive tumors into two groups, luminal A and luminal B, and were able to subdivide the ER-negative tumors into normal-like, HER2-enriched, and basal-like breast cancers. The basal-like subtype, sometimes referred to as “triple-negative” because most basal-likes do not express ER, progesterone receptor, or HER2, was previously unrecognized as a distinct subtype. This subtype shows high proliferation rates, poor survival times, and high expression of cytokeratins indicative of basal epithelium, including cytokeratins 5/6. The intrinsic subtypes identified by these molecular signatures have been confirmed in larger, subsequent analyses (Hu et al., 2006; Sørlie et al., 2001; Sorlie et al., 2003) and in population-based investigations (Carey et al., 2006). Based on these studies identifying new “intrinsic subtypes” of breast cancer, a new conceptualization of breast cancer as many diseases rather than as a single disease has emerged. In addition, variation in pathologic complete response rates (pCR) among the different subtypes has been characterized (Carey et al., 2007). Thus, the subtypes inform prognosis and may also be predictive of response to therapy. Since these original studies, the intrinsic heterogeneity in many cancers such as head and neck (Chung et al., 2004), lung (Hayes et al., 2006), colorectal (Birkenkamp-Demtroder et al., 2002; Croner et al., 2005; Ghadimi et al., 2005), and prostate (Singh et al., 2002) has been characterized by microarray and the use of gene expression profiles in cancer research has become well established.

Observational vs. Experimental Studies in Drug Resistance These original studies of breast cancer heterogeneity involved genomic observations with patient samples. However, much of the data on drug response and much of our

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understanding of drug resistance mechanisms have historically come from experimental studies using cell line models. Using genomics, experimental and observational data are being integrated in informative ways. Figure 12.1 illustrates how multiple data types are being integrated to facilitate our understanding of drug resistance mechanisms. Data from cell lines can be used to elucidate important molecular pathways (Fig. 12.1A) and these insights can be combined with mouse data about organism-level responses to cancer and cancer therapy (Fig. 12.1B), and ultimately with human responses recorded in observational and experimental studies (Fig. 12.1C). These studies and data can be integrated into a coherent framework by identifying pathways that are altered in drug-resistant phenotypes. The study types illustrated in Fig. 12.1 can be either observational or experimental, and delineating these two study types is important to appropriate interpretation of study findings. Table 12.1 provides examples of experimental and observational studies in each setting (mouse, cell line, human patients). Each of the examples is an investigation aimed at examining drug resistance and drug resistance pathways and each utilizes genomic data. However, these examples illustrate that in settings traditionally thought of as experimental, such as cell line studies, investigations can be designed as observational studies. Investigation of copy number alterations associated with chemotherapy response in a panel of nonisogenic cell lines (Neve et al., 2006) is an observational study because the effect of interest (copy number alteration) is not assigned by the investigator but is being assessed in relation to the outcome (chemotherapy response). These examples also illustrate that while some important comparisons in drug resistance can be studied with experimental approaches [e.g., gene expression

Fig. 12.1 Model systems used to study drug resistance. Different model systems used to study resistance include (A) cell line studies, (B) studies using mouse models, and (C) studies with human patients. A pathway-based approach is often used to integrate findings in each setting. Pathways identified in model systems (A and B) can be studied in patients (C) or processes known to be important in patients (C) can be used to conduct experiments in model systems (A and B). Pathways and processes that are conserved across species may be particularly important in cancer biology

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Table 12.1 Examples of experimental and observational studies utilizing genomic data or genomic insights Experimental

Observational

Human cell lines

Knockdown of p53 with siRNA to create isogenic cell line pairs and identify p53-dependent signals in each cell line (Troester et al. 2006)

Mouse models

Use of genetically engineered mouse models to study effects of chemotherapeutics (Rottenberg et al., 2007)

Human patients

Phase III trial to compare six cycles of carboplatin with six cycles of the docetaxel, in patients with triple-negative breast cancer as described in Kilburn (2008)

Use of a collection of breast cancer cell lines with different characteristics and representing different breast cancer subtypes to identify molecular features that predict response to therapy (Neve et al. 2006) Study of a panel of mouse models to identify mouse models with expression features most similar to human tumors (Herschkowitz et al. 2007) Expression profiling study of human breast cancers to identify subtypes of breast cancer (Perou et al. 2000, Sørlie et al. 2001, Sorlie et al. 2003)

response in a drug-treated vs. untreated cell line (Troester et al., 2004b)], there are many important conditions in cancer that are not experimentally manipulable but that are nonetheless critical to our understanding of the disease and/or drug resistance. If it is not possible to interrogate specific mechanisms or pathways by isolating and randomizing the condition of interest (e.g., node status or tumor size), observational studies can provide critically important insights. Important challenges in observational microarray studies have been reviewed previously (Potter, 2003). Recognition of distinctions between observational and experimental studies is important and can help in integrating results across studies.

Microarray Analysis: Supervised, Unsupervised, Significance, and Prediction While study design and experimental systems vary considerably, there are certain terms that can be applied to most microarray analyses. Uniform use of this terminology is essential to integrating different data types and to assessing reproducibility of findings across studies. The terms unsupervised and supervised distinguish the two main classes of microarray analyses. Unsupervised analysis involves examining gene expression without assigning classes a priori. It is a common exploratory step to look at overall gene expression patterns and neither requires nor uses prior knowledge of the biology of the samples. Hierarchical clustering is commonly used to conduct these unsupervised analyses, with a commonly used implementation of

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cluster analysis developed by Eisen and colleagues (Eisen et al., 1998). Samples that have shared biology will have similar gene expression and will be grouped together on a dendrogram in hierarchical clustering. For example, in comparing gene expression responses to two different chemotherapeutics in four different cell types, unsupervised methods grouped samples according to cell line and cell type, but did not segregate according to more subtle differences in responses to different chemotherapeutics (Troester et al., 2004b). To identify more subtle responses or to specifically identify differences between investigator-identified classes, supervised approaches are more appropriate. Supervised analysis uses information on class membership provided by the investigator to identify significantly associated genes. Class is referred to as a supervising parameter. For example, in the experiment described above (Troester et al., 2004b), classes could be defined according to treatment: chemotherapy or sham. Other examples of supervising parameters include clinical response, demographic parameters, or genetic variants in cell lines or mouse models. Just as with unsupervised clustering, lists of genes obtained using these methods can then be used to perform hierarchical clustering. In both supervised and unsupervised clustering, the sample groupings are dependent on the gene list used for clustering. Thus, pre-selection of a gene list associated with class membership is referred to as a supervised cluster. One of the more commonly used tools for supervised analysis is Significance Analysis of Microarrays (SAM) (Tusher et al., 2001). SAM can use several different kinds of supervising parameters: 1-class, 2-class, multiclass, time course data, paired and unpaired samples, as well as censored survival data. Based on a modified t-test or non-parametric Wilcoxon test, genes are identified that are associated with the supervising parameter. To deal with multiple testing, permutation is used to compute expected gene expression under the null, and observed gene expression is compared against this distribution. Thus, SAM estimates a false discovery rate (FDR) and investigators commonly constrain the FDR to 10%, 5%, or 1% and identify lists of genes at these levels. Recent improvements to SAM have also allowed investigators to examine the association between a supervising parameter and a gene set (Efron and Tibshirani, 2007). In this type of analysis, the functional unit is considered to be a group of genes rather than individual genes. Gene sets with similar function may be defined a priori, and the investigator can query whether a biological process is altered based on a change across a set of genes. This type of “gene set” analysis is an important new direction in handling genomic data (Subramanian et al., 2005). Significance analysis methods such as SAM can help generate lists of genes that significantly differ between experimental groups. However, it is often desirable to develop a classifier that sorts samples with unknown class into categories on the basis of gene expression. The goal is also to identify which genes contribute most to the classification, and often to identify the smallest possible predictive gene list. An example of a predictive method for gene expression data is Prediction Analysis of Microarrays (PAM) (Tibshirani et al., 2002), though many other methods are available [reviewed in (Granda, 2003)]. PAM (Tibshirani et al., 2002) uses shrunken centroids in class prediction to develop signatures that distinguish user-defined classes.

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A critical feature of most predictive algorithms, including PAM, is the use of tenfold cross-validation. Here, the samples are divided into ten groups of approximately equal size and in iterative rounds, nine-tenths of the data are used to develop a sample classifier, or predictor. The remaining tenth is used as a test set and the accuracy of class prediction is recorded. This can be done with each tenth in rotation to estimate the accuracy of a predictor of a given size. Once the optimum size of the gene list and the genes themselves are selected, a separate training data set can be used to validate the classifier. Therefore, using cross-validation the minimum list that robustly identifies the classes in each data subset is identifiable. Prediction analysis complements significance testing because it allows identification of the smallest gene set that is predictive and because it builds in the repeated sampling of the same data set so that only the most stable patterns are recognized. The majority of genomic studies discussed in this chapter utilize some form of supervised analysis. The methods vary widely and continue to evolve, but these analytical tools have enabled important advancements in our understanding of the mechanisms of drug resistance. A common understanding and use of the terminology of microarray analysis are fundamental to discussion and integration of genomic research.

Cancer Cell Lines and Signatures of Drug Resistance Cell lines have historically played a prominent role in the identification of drug resistance mechanisms. While cell lines differ in important ways from tumor tissue, the tumors themselves are often inaccessible for experimentation, so cell lines represent convenient experimental models. Genomic profiling has been used to correlate chemosensitivity patterns with baseline gene expression in a large number of untreated cell lines. Several studies have identified gene expression that correlates with resistance using the NCI60. The NCI60 is a panel of 60 cancer cell lines derived from a variety of tissues and organs and extensively studied by the National Cancer Institute’s Developmental Therapeutics Program (DTP). The DTP measured sensitivity of these 60 cell lines to common chemotherapeutics (http://dtp.nci.nih.gov). Scherf et al. (2000) used unsupervised analyses to link the baseline gene expression and drug sensitivity. Based on the sensitivity patterns of 118 drugs over the 60 cell lines, five large clusters were identified that corresponded closely to mechanisms of action: DNA and DNA/RNA metabolites, tubulin inhibitors, DNA-damaging agents, topoisomerase 1 inhibitors, and topoisomerase 2 inhibitors (Scherf et al., 2000). These authors then identified gene–drug pairs where specific expression levels were found to be associated with drug sensitivity (e.g., 5-FU sensitivity was negatively correlated with dihydropyrimidine dehydrogenase (DPYD), the rate-limiting enzyme in endogenous uracil and thymidine catabolism and in exogenous 5-FU catabolism). Such drug–gene pairs were interpreted as proof of principle that baseline gene expression signatures can indict important pathways in chemoresistance.

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In a complementary supervised approach, Staunton et al. (2001) used a subset of NCI60 lines to develop a gene expression-based predictor of chemosensitivity. Using the sensitivity and resistance data for 232 chemical compounds and gene expression levels of 6,817 genes, they recapitulated the observation of Scherf et al. (2000) that drugs with similar mechanisms of action clustered together based on their growth inhibitory profiles. They then performed supervised analysis to search for genes that correlated with the clusters of drugs. For example, they identified genes that were significantly positively and negatively correlated with at least two of four anti-metabolite drugs (5-FU, carmofur, tegafur, and doxifluridine). Some of the gene expression patterns were shared by multiple mechanistic classes of drugs, but many were also unique to specific mechanisms. Finally, these authors demonstrated that gene expression-based classifiers of sensitivity or resistance gave improved prediction of sensitivity or resistance relative to what would be expected by chance. Both of these observational microarray studies using NCI60 data used genomewide expression profiling without selecting the specific genes to be studied in advance. This is the most common approach in microarray studies, particularly with the commercial availability of whole genome arrays that represent the vast majority of genes in the human genome. However, it is also possible to use the same signature-based approach with a more focused gene set. An example of this approach is afforded by the studies of Szakacs et al. (Szakacs et al., 2004), who focused on 48 known human ABC transporters and used real-time RT-PCR to obtain quantitative measures of gene expression. Many of the microarray data analysis methods have thus lent themselves to more focused, hypothesis drive investigations of particular pathways. Each of these studies has the advantage of being able to efficiently study many different chemotherapeutics at the same time because only untreated profiles are utilized. The expression responses to treatment were not identified. Useful information about mechanisms of drug resistance can also be obtained by studying molecular profiles in response to treatment. For example, experiments using a panel of breast cancer cell lines treated with two different chemotherapeutics, doxorubicin and 5-fluorouracil, showed that toxicant- and or mechanism-specific signature was identifiable (Troester et al., 2004a), but the predominant signature was that of general stress (Troester et al., 2004b). However, the stress responses of the individual cell lines varied considerably. Thus in addition to characterizing how baseline characteristics affect chemosensitivity, improved understanding of how baseline characteristics of individual cell lines predict stress responses may help to provide mechanistic insights into drug response. For example, cell line models of luminal breast tumors have different signaling responses to chemotherapy treatment than cell line models of basal-like breast tumors (Troester et al., 2004b). To address the importance of tumor heterogeneity and the challenge of choosing a representative cell line, Neve et al. have assembled a collection of breast cancer cell lines for studying functionally distinct breast cancer subtypes (Neve et al., 2006). These authors demonstrate that cell lines display heterogeneity in copy number and gene expression similar to that observed in primary tumors. The cell lines can also be classified based on expression profiles into tumor subtypes such as luminal and

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basal-like and show heterogeneous responses to target therapies. Thus, this collection of cell lines sets up a system for observational studies of therapeutic response that are grounded in the genomic profiles of the cell lines.

Linking Cell Line Data with Human Observational Data These in vitro, cell line studies help identify the genes and pathways involved in drug resistance. The translation of these findings from cell lines to patient recommendations requires a means of linking the observations with patient and in vivo phenotypes. One such link is the study of the pathways that mediate the responses (Fig. 12.1). A number of studies have focused on developing gene expression signatures for pathways with known roles in drug resistance. The tumor suppressor p53 has been widely studied as it is one of the most frequently mutated genes in cancer (Greenblatt et al., 1994; Hainaut et al., 1997; Levine, 1997). Mutations in p53 change the responsiveness of cells to chemotherapy, sensitizing them to taxanes and increasing resistance to vinca alkyloids (Zhang et al., 1998). Both cell and animal models have demonstrated that p53 mutation increases resistance to some cytotoxic regimens (Lowe et al., 1994; Lowe et al., 1993). It has also recently been reported that p53-mutant cancers are more sensitive to dose-dense epirubicin–cyclophosphamide regimens (Bertheau et al., 2007). Based on the observation that p53 alters response to chemotherapy drugs and based on reports that p53 mutation status adversely impacts prognosis (Langerod et al., 2007; Pharoah et al., 1999), there has been interest in identifying transcriptional targets of p53. Gene expression signatures associated with p53 mutation status in tumors have been identified by comparing gene expression of p53 mutant and p53 wildtype tumors (Miller et al., 2005; Troester et al., 2006). However, because p53 status is associated with other factors such as grade and breast cancer subtype, some of the p53-associated gene expression changes may not be directly due to p53 status. These comparisons of tumors are observational studies and are therefore factors that are associated with p53 status and also with gene expression will represent confounders. To address this challenge, these observational data sets were combined with experimental data from cell lines (Fig. 12.2). Selective knockdown of p53 protein (using short interfering RNAs) in a panel of breast cancer cell lines allowed identification of downstream transcriptional targets of p53 (Troester et al., 2006). Using this experimental data on downstream targets, in combination with the observational data from patients with differential mutation status, the list of p53associated genes was refined to identify 52 p53-downstream, p53-associated genes. This gene set allowed classes of p53-mutant-like and p53-wildtype-like tumors to be defined based on gene expression and was prognostic across multiple independent data sets. Another important example of pathway-based analysis focuses on targeted therapy with antiestrogens. Almost all hormone antagonist-responsive cancers are hormone receptor positive, expressing either ER or progesterone receptor or both.

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Fig. 12.2 Integration of observational and experimental data. The integration of observational and experimental data from different settings can help to identify pathway signatures. For example, using observational microarray data from breast tumors of patients with known p53 mutation status, a p53-associated gene list can be identified. Using isogenic breast cancer cell line pairs, with and without p53 siRNA to knockdown expression, p53-downstream genes can be identified. The results of these supervised microarray analyses can be compared to identify a refined signature of p53 status that is prognostic in independent data sets (Troester et al. 2006)

However, many hormone receptor-positive cancers do not respond to tamoxifen and the mechanisms for resistance are not known (Bardou et al., 2003; Group, 2005; Osborne et al., 1980). Several investigators have defined signatures predictive of tamoxifen response using genomic methods (Dai et al., 2005; Jansen et al., 2005; Ma et al., 2004; van ’t Veer et al., 2002). More recently, Oh et al. developed an alternative signature indicative of ER pathway activity, again by combining experimental and observational data (Oh et al., 2006). The goal was to identify a signature for predicting the estrogen responsiveness of luminal tumors. First, an estrogen responses signature was identified using MCF-7 cells. Second, up-regulated genes from this signature were used to identify two different groups of luminal tumors with different prognosis. One group showed more classic estrogen responses, expression of GATA-3 regulated genes, and was more highly differentiated. The other group was more poorly differentiated and showed a profile that was associated with grade. Third, the signature was used to predict survival time among women treated with tamoxifen and it was demonstrated that this signature provided prognostic value beyond what grade alone provided. Thus, a signature grounded in the mechanism of estrogen response had predictive value in defining two distinct groups of estrogenresponsive tumors. Both p53 and ER are well-established breast cancer markers that have been studied with genomic methods. However, some recent work has also endeavored to explore pathways that are not as well established and that may represent novel

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targets for therapy. Epidermal growth factor receptor (EGFR) inhibitors are used to treat lung cancer and are currently being investigated for use in breast cancer. In lung cancer, mutations in the intracellular kinase binding domain of EGFR dramatically sensitized patients to respond to EGFR inhibitors (Lynch et al., 2004; Paez et al., 2004; Pao et al., 2004). These findings in the lung were not replicated in breast, where mutations in the kinase domain of EGFR have not been found. However, several trials investigated EGFR inhibitors in the treatment of breast cancer. Single agent gefitinib showed modest effects for breast cancers overall (Baselga et al., 2005), though some studies did suggest a possible benefit for ER+ tumors (Ciardiello et al., 2006; Polychronis et al., 2005). Because EGFR has been shown by RNA and protein expression to be highly expressed in approximately half of basal-like patients (Nielsen et al., 2004), the lack of response to EGFR inhibitors may have resulted from application in unselected patient populations. Basal-like tumors represent between 15% and 25% of all breast cancers (Sorlie et al., 2003); therefore, an unselected patient population may contain a very small percentage of patients with high expression of the drug target. To test this hypothesis, cell line models representing luminal and basal-like breast cancers were tested for sensitivity and gene expression responses to two different EGFR inhibitors: gefitinib and cetuximab (Hoadley et al., 2007). Consistent with the expectation based on EGFR expression, the basal-like models were more sensitive to the inhibitors. EGFR-associated gene expression profiles (that were induced in treated cell lines when inhibitors were withdrawn) were then used to cluster human tumors, resulting in three EGFR-associated gene expression clusters. These clusters were prognostic in two independent breast cancer data sets. The EGFR signatures improved on using EGFR expression alone in prediction because while EGFR expression may be elevated in 50% of basal-like breast cancers, highly expressed EGFR-downstream genes, such as RAS and CRYAB, could confer EGFR-independent activation and EGFR inhibitor resistance. Others have also used cell line models to demonstrate the contribution of EGFR-downstream effectors such as the RAS-MAPK pathway or the PI3K/AKT pathway to EGFR inhibitor resistance (Bianco et al., 2003; Lev et al., 2004; Normanno et al., 2008; Normanno et al., 2006). By effectively bypassing the requirement for EGFR for activation, many tumors are resistant to the EGFR inhibitors. Taken together, these studies have demonstrated that a pathway-based genomic approach can help to elucidate novel mechanisms of drug resistance in well-studied pathways. These three case studies illustrate a pathway-focused approach that integrates experimental data and observational patient data and can provide new mechanistic information. Among these three pathway-derived signatures, only the ER response signature was tested for predictive value. However, Bild et al. have applied pathway signatures in prediction. They used recombinant adenoviruses to express various common oncogenes (Myc, Ras, E2F3, Src, and beta-catenin) in primary human mammary epithelial cells (Bild et al. Nature 2006). When evaluated on large human cancer data sets, predictors derived from these pathways showed associations with clinical outcome. Moreover, signatures of pathway deregulation in breast cancer cell lines were shown to predict the cell line sensitivity to therapeutic agents that

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targeted the pathway components. Specifically, Ras pathway activity was able to predict response to a farnesyltransferase inhibitor and a farnesylthiosalicyclic acid (FTS) and a Src pathway signature was able to predict cell line response to a Src inhibitor. While predictive value was demonstrated using cell line sensitivity, extension of this pathway approach to predict tumor sensitivity is promising. Each of the pathway signatures discussed above used gene expression (RNA) to predict response. However, future work will integrate multiple data types, including DNA copy number, methylation, microRNA expression, protein expression, and phosphorylation patterns. Chin et al. highlight ways in which pathway-specific investigations benefit from integration of multiple data types in their investigation which identified FGFR1, IKBKB, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1 as novel pathways that had high-level amplifications in breast cancer and which are considered druggable (Chin et al., 2006). The authors used gene expression and copy number together with patient clinical data to identify these genes. Past approaches that rely exclusively on expression data to identify important pathways are only able to identify processes that are regulated at the transcriptional level. Many important biologic processes are regulated at the DNA level and by posttranscriptional methods. Tumor genomic DNA copy number alterations (Chin et al., 2006; Pollack et al., 2002) and microRNA expression (Blenkiron et al., 2007; Volinia et al., 2006) have already been shown to be important determinant of outcomes, and integrative approaches are likely to add further value. Analytic methods for incorporating diverse and complex data types into a cohesive analytic framework will be needed.

Animal Models and Signatures of Drug Resistance A great deal has been learned by integrating cell line experiments with observational data from patients. However, an experimental alternative to cell line studies is the use of animal models. Mouse tumor models have been in use for nearly a century, starting with tumor-prone inbred strains and carcinogen-induced models. Researchers have made substantial advances over the last three decades by genetically engineering mouse models to study human cancer with increasing complexity, using tissue-specific expression of oncogenes or dominant-negative tumor suppressor genes, or knock-out and knock-in mice with constitutive or spatiotemporally restricted targeted mutations in oncogenes or tumor suppressor genes (Jonkers and Berns, 2002; Van Dyke and Jacks, 2002). As a result, drug resistance can be studied in mouse models using a wide range of methods including xenografts, human tumors or cell lines propagated in immunodeficient mice, and genetically engineered mouse models (GEMs) or carcinogen-induced animal tumor models. Mouse models, in combination with genomic methods, are useful in drug studies for target validation, assessment of response of the tumor, determining pharmacodynamic markers of drug action, modeling intrinsic and acquired resistance, and understanding toxicity (Frese and Tuveson, 2007; Sharpless and Depinho, 2006).

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An important recent finding using mouse models to study breast cancer heterogeneity demonstrated that these models have substantial biologic variability, just as human cell lines and tumors do. Using gene expression profiling, GEM models of breast cancer can be grouped according to subclasses with features of human subtypes (Herschkowitz et al., 2007). For example, BRCA1-deficient mouse models appear to produce tumors that have expression features similar to human basal-like cancers. Thus, where different cancer cell lines represent distinct breast cancer subtypes (Neve et al., 2006), it is also possible to identify mouse models that are better representatives of different breast cancer subtypes. Having identified mouse counterparts to breast cancer subtypes, current studies are aimed at determining whether these models have similar responses to therapy as their human counterparts. Rottenberg and colleagues have studied in vivo responses of conditional BRCA1- and p53-deficient mouse mammary tumors to treatment with doxorubicin, docetaxel, or cisplatin (Rottenberg et al., 2007). The response of individual tumors was variable, but eventually tumors became resistant to doxorubicin or docetaxel. The authors discovered that up-regulation of the multidrug resistance transporters, Abcb1a and Abcb1b, appears to be responsible for the doxorubicin resistance. The tumors also responded to cisplatin and do not become resistant, which is noteworthy given that platinum drugs are being considered for treatment of human BRCA1-deficient tumors (Rottenberg et al., 2007). The cisplatin sensitivity also corresponds to findings by Shafee et al., who studied another conditional BRCA1- and p53-deficient mammary tumor model and also found that these tumors responded to cisplatin treatment (Shafee et al., 2008). In contrast to the Rottenberg model (Rottenberg et al., 2007), it took 2–3 months for this mouse model to experience tumor relapse following treatment, and these tumors had become refractory to successive rounds of treatment. The results of these studies illustrate the promise of using mouse models for the study of therapy response in human cancer. In the future, integrating in vivo genomic response data from mouse and human is an approach which may help to identify strong signals associated with drug resistance. Conserved modules or pathways that are present in both species will help dissect the most relevant resistance mechanisms.

Linking Mouse Models with Human Cancer Biology Just as cell lines can be used to examine specific pathways, mouse models can help to identify those features that are conserved about a given signaling mechanism. Among the advantages of using mouse models to study drug resistance are the ability to manipulate their genes and genomes, their short generation times, and small size. They physiologically and molecularly resemble humans more than many other model organisms, and like the human, the mouse genome is also now fully sequenced. Thus, mouse models provide an in vivo model system for the evaluation of different therapies and prevention strategies. While xenograft models have had

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limited success in predicting drug efficacy, early results with genetically engineered mice (GEM) have been promising (Sharpless and Depinho, 2006). For target-specific therapies, response to treatment can be studied using GEM models with the ability to reversibly control target gene expression (Jonkers and Berns, 2002; Weinstein and Joe, 2006). Inhibition of the relevant drug target in tumors can be mimicked by taking mice on or off doxycycline or tamoxifen, thereby removing the expression of an oncogene or restoring a tumor suppressor gene. Using this approach, it has been demonstrated that oncogenes that are crucial for the initial development of a tumor may also be needed for tumor maintenance in a phenomenon known as oncogene addiction (Weinstein and Joe, 2006). Thus, if HER2 is required for tumorigenesis, targeting of HER2 should be an effective therapy against these tumors. There is evidence for this theory in the use of HER2-targeted antibodies to treat breast cancer (Vogel et al., 2002). However, as stated by Weinstein (Weinstein, 2002), “by unraveling the molecular circuitry that maintains the biologic properties of cancer cells, we will be better able to predict selective molecular targets for cancer therapy.” Mouse studies have been critical in furthering our understanding of oncogene addiction and helping to illustrate why targeted therapies fail. For example, in one mouse model, doxycycline-induced c-Myc expression was targeted to the mammary epithelium (using MMTV promoter-driven expression of the reverse Tet transactivator), resulting in the development of mammary adenocarcinomas (D’Cruz et al., 2001). Many tumors regressed when doxycycline was removed to reduce c-Myc expression, while others plateaued or grew independent of c-Myc expression. The authors showed that a significant subset of the cMyc-independent tumors harbored somatic K-Ras mutations. Of the tumors that regressed, about half of them later relapsed, several without depending on high transgenic or endogenous c-Myc expression (Boxer et al. 2004). Thus, to overcome c-MYC dependence, additional mutations were required, highlighting a complex mechanism of tumorigenesis. This adaptation of tumors to overcome oncogene addiction has also been observed in another GEM model, where doxycycline, bitransgenic MMTVrtTA/TetO-NeuNT mice develop multiple invasive mammary carcinomas and metastases that regress following transgene deinduction (Moody et al., 2002). Thus, Neu-induced tumorigenesis is reversible. However, most animals that had fully regressed neu-induced tumors developed recurrent tumors that progressed to neuindependence. Expression of the transcriptional repressor, Snail, was increased after tumor recurrence in this model and was also shown to be predictive of outcome in human breast tumor data. While these models, and the specific pathways altered during carcinogenesis, may not be entirely conserved across species, these models enable the identification of in vivo mechanisms for escaping target-specific therapies. Murine models do have differences from humans, for example in mammary gland physiology and estrous cycle. There are also indications that there is a difference in the capacity of primary cells to be transformed between the two species (Rangarajan et al., 2004; Rangarajan and Weinberg, 2003). Some genes altered in human cancer may not be a target in

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mouse carcinogenesis. However, those elements that are conserved may help us to pinpoint important processes in cancer.

Human Studies and Signatures of Drug Resistance While cell lines and animal models can provide us with genomic data that advance our understanding of drug resistance mechanisms, translation of these findings to patient responses and clinical outcomes is the ultimate goal. The original studies identifying breast cancer subtypes showed that genomic subtypes had prognostic value (provided value in predicting outcome regardless of therapy) (Perou et al., 2000; Sørlie et al., 2001; Sorlie et al., 2003). However, these subtypes are also showing value in prediction, or identifying patients who will respond to specific therapies. By subdividing patients that express certain markers that predict chemotherapy response, such as ER+ and HER2 amplification (Colleoni et al., 2004; Zhang et al., 2003), into more homogeneous groups corresponding to intrinsic breast cancer subtypes, we can further advance our understanding of patient responses (Carey et al., 2007; Rouzier et al., 2005). In two recent studies, pathologic complete response (pCR) was significantly more frequent in the basal-like (HER2–/ER–) and the HER2+ subtypes, while the luminal subtype had less than 10% pCR. These results seem paradoxical given the subtype-specific survival patterns, where patients with luminal A breast cancer have the best survival and basal-like and HER2+ tumors have poor survival. However, Carey et al. demonstrated that despite higher rates of pCR in ER-negative and HER2+ patients, when these patients do recur they are much more likely to die of disease (Carey et al., 2007). Given that not all basal-like and HER2+ tumors experience pCR, Rouzier et al. (Rouzier et al., 2005) attempted to identify genes that could distinguish basal-like and HER2+ patients with and without pCR. Sixty-one genes were significantly associated with pCR among basallike tumors, but none of these genes were significantly associated with pCR among HER2+ tumors. This analysis provides some in vivo suggestion, consistent with the suggestions from cell line studies (Neve et al., 2006; Troester et al., 2004b), that different cancer subtypes have different mechanisms for sensitivity. Other pharmacogenomic predictors have also been developed to identify patients who are likely to experience pCR. Patients with high recurrence scores using the Oncotype Dx assay (performed on paraffin-embedded core biopsy tissue) have high rates of pCR to paclitaxel/FAC (fluorouracil–doxorubicin–cyclophosphamide) chemotherapy (Gianni et al., 2005) compared to patients with low recurrence scores. Hess et al. have developed a 30 gene predictor that has good predictive accuracy in identifying patients with pCR (Hess et al., 2006). Each of these studies has tended to identify ER-negative, high-grade, and highly proliferative tumors as those likely to respond to therapy. Thus, despite differences in the membership of the respective gene lists, these studies are capturing similar biology. These studies have also demonstrated that the best predictive accuracy can be obtained by combining genomic information with standard clinical and pathological data (Hess et al., 2006).

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In addition, these studies have elucidated biological processes, such as immune response (Gianni et al., 2005), that warrant further investigation as to their role in pCR.

Future Directions in Molecular Signatures of Drug Resistance Combining different model systems and experimental designs, along with different types of genomic data, can contribute important mechanistic insights. These same methods are being applied to address several important biological and analytical problems that have been emerging as important problems in the science of drug resistance.

Stem Cells It has recently emerged that tumors can contain a minor subpopulation of cells with distinct properties of somatic stem cells (Pardal et al., 2003). Like normal stem cells, these cells have the ability to self-renew and to regenerate all, or at least a subset of, the cell types within the tumor mass. These cells have been referred to as cancer stem cells or tumor-initiating cells (Pardal et al., 2003). This is an old hypothesis, but the field has recently exploded, initially with the discovery that the cell capable of initiating human acute myeloid leukemia (AML), the leukemia-initiating cell, possessed the potential for self-renewal and differentiation expected of a leukemia stem cell (Bonnet and Dick, 1997). This was shown using cell surface markers and transplantation into non-obese diabetic mice with severe combined immunodeficiency disease (NOD/SCID mice). Recently, using similar approaches, tumor-initiating cells have been identified in many solid human tumors including colon, brain, and breast (Al-Hajj et al., 2003; Ricci-Vitiani et al., 2007; Singh et al., 2004). Additionally, it has been shown that these cells may have an intrinsic resistance to therapy (Bao et al., 2006; Li et al., 2008; Liu et al., 2006). Therefore, current treatment strategies may affect the bulk of the tumor cells, but leave tumor-initiating cells behind, serving as a reservoir for disease recurrence. Therapies are needed to specifically target these cells. To follow up on the importance of tumor-initiating cells using genomic tools, Lui et al. recently identified a signature, referred to as the “invasiveness” gene signature, derived by comparing tumor-initiating cells, defined by a CD44+CD24–/low immunophenotype, to normal breast epithelium (Liu et al., 2007). This signature was predictive of outcome in several breast cancer data sets. Because tumor cells were compared to normal cells, the “invasiveness” gene signature is enriched for proliferation genes which may have contributed to the signature’s predictive ability. In another study, a gene expression-based “tumorigenic” signature was obtained by using the CD44 and CD24 cell surface markers (Creighton, in press). These authors isolated CD44+/CD24–/low tumor-initiating cells from breast tumors using

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fluorescence activated cell sorting (FACS) and compared the gene expression profiles of this stem cell-enriched fraction to the remaining fraction (the nonCD44+CD24–/low fractions) (Creighton, in press). The signature was further refined by comparison with an expression profile derived from human tumor mammospheres (Creighton, in press). Mammospheres are cells growing in non-adherent, non-differentiating conditions and are commonly used as an assay for stem cell selfrenewal (Dontu and Wicha, 2005). This signature contained genes indicative of the developmental process of epithelial–mesenchymal transition (EMT). EMT has been implicated in generation of stem cell properties based on experiments with human mammary epithelial cell lines (Mani et al., 2008). Creighton et al. also showed that the refined tumorigenic signature was enriched following both chemotherapy and endocrine therapy. The enriched tumorigenic signature in after-therapy samples suggests that cells with these tumor-initiating or CD44+/CD24–/low features and corresponding gene expression profiles represent a rare subpopulation of tumor cells that can be found across subtypes. However, this tumorigenic signature is also enriched in a rare molecular subtype of breast cancer called Claudin-low (Creighton, in press). In contrast to the invasiveness signature of Liu et al. (2007), the tumorigenic signature of Creighton et al. (in press) is not predictive, but is very important biologically. Because tumor-initiating cells only make up a small percentage of the tumor, this signature may be difficult or impossible to detect in pre-therapy whole tumor expression profiles. If future research can address the challenge of detecting tumor-initiating cells in patient samples, these signatures may represent a biomarker for predicting patient chemosensitivity and guiding treatment decisions.

Microenvironment Prediction of patient chemosensitivity is challenging because the response to drug depends on not only responses that are cell intrinsic and cell autonomous but host metabolic properties and cell–cell interactions. Understanding of how the pleiotropic mechanisms of chemotherapeutics are integrated in a heterotypic environment requires novel experimental approaches. Cocultures of epithelial and stromal cells can elucidate biologically significant interactions. For example, coculture experiments have elucidated mechanisms of paracrine regulation of hormone responsiveness in endometrium that were not observable in monoculture (Arnold et al., 2001; Arnold et al., 2002). Coculture experiments comparing fibroblasts from normal breast (NAF) and cancer-associated fibroblasts (CAF) have demonstrated altered growth inhibitory potential of CAF relative to NAF (Sadlonova et al., 2005). And recently, gene expression studies demonstrated that interaction between breast cancer cells and normal stromal fibroblasts can induce an interferon response (Buess et al., 2007). The biological relevance of the latter gene expression signature was established by demonstrating that interferon signaling is associated with increased risk of tumor progression. Thus, previous literature supports the notion that coculture experiments can provide novel insights into the biology of cancer and

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dysregulated tissue homeostasis that are not possible from monocultures. These novel coculture systems should now be applied to study chemotherapeutic responses.

Pathways or Modules vs. Individual Genes as Functional Units This review illustrates that identification of key signaling pathways helps to integrate data across different study settings. However, this approach is also warranted based on other considerations. In a recent review, Segal et al. pointed out that while approaches such as clustering and identification of gene signatures have been successful in creating new diagnostic tools, these methods have several limitations including ignoring signals in the data because the significant genes have unrecognized biological functions, and inherent noise in individual genes preventing identification of true signals (Segal et al., 2005). These authors note that “to transcend from individual genes to biological processes several recent methods use gene modules as the basic building block for analysis,” and suggest that this module building approach has substantial advantages. Modules are sets of genes that act in concert to carry out a specific biological function. The use of modules allows identification of the signal in the data and provides results that are more biologically interpretable than long gene lists (Segal et al., 2003). Moreover, several authors have demonstrated the improved power of analyzing complex traits using gene modules rather than individual genes or gene lists (Chang et al., 2004; Lamb et al., 2003; Mootha et al., 2003; Rhodes et al., 2004). Phylogenetic conservation is also a strong criterion for identifying functionally relevant co-expression (Pellegrino et al., 2004; Stuart et al., 2003), so by maximizing the data overlap and making comparisons across species and across model systems as depicted in Fig. 12.1, the pathways or “modules” that are most strongly associated with drug resistance can be identified.

Conclusions Major advances in the development of predictive gene expression signatures in breast cancer have occurred in the past decade. A strength of genomic research is that discipline-wide standards have been established for data sharing. Many scientific and biomedical journals require authors to post data in public databases at the time of publication [see Minimum Information about a Microarray Experiment, or MIAME as described in Brazma et al. (2001)]. Thus genomic data are also affording unprecedented opportunities for systematic reviews and meta-analyses. These integrative analyses are essential to advancing the field and moving from anecdotes to principles. Furthermore, by combining data from multiple experimental systems and increasingly, across different data types (from DNA structure and copy number to epigenetics, RNA expression, and RNA processing), important advances are being made in understanding the biology of drug resistance. The field of genomics

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has already altered our biologic understanding of drug resistance and has begun to change clinical practice in cancer treatment.

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Chapter 13

Assessment of Drug Resistance in Anticancer Therapy by Nuclear Imaging Natalie Charnley, Catharine West, and Pat Price

Abstract The ability to assess and monitor resistance to drugs in situ has potential to provide an image-guided means of individualising cancer chemotherapy. Molecular imaging of factors associated with drug resistance could be used to prevent the use of ineffective treatments and eliminate the need to wait for conventional measures showing lack of response. Molecular imaging also has the potential to increase understanding of mechanisms underlying drug resistance in vivo in man, which should aid the development of approaches for increasing the effectiveness of cancer treatment. Resistance may be due to several processes including pharmacokinetics, metabolism, physiology and molecular biology, all of which can be assessed by molecular imaging. Keywords Positron emission tomography · Angiogenesis · Hypoxia · Hormone receptors · Apoptosis · Signal transduction pathways · Proliferation · DNA repair · Integrin

Introduction The ability to assess and monitor resistance to drugs in situ has potential to provide an image-guided means of individualising cancer chemotherapy. Molecular imaging of biochemical and physiological factors associated with drug resistance could be used to prevent the use of ineffective treatments and eliminate the need to wait for conventional measures showing lack of response. Molecular imaging also has the potential to increase understanding of mechanisms underlying drug resistance in vivo in man, which should aid the development of approaches for increasing the effectiveness of cancer treatment (Hoogsteen et al., 2006).

N. Charnley (B) Academic Department of Radiation Oncology, Christie Hospital, Withington, Manchester M20 4BX, UK e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 13, 

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Use of PET Positron emission tomography (PET) can image molecular pathways and physiological processes associated with disease processes. The imaging modality can provide quantitative kinetic data at the sub-picomolar level. PET radiotracers have been developed for a number of disease investigations, including neurology, psychiatry, cardiology and oncology. The commonest tracer used for routine PET scanning is the glucose analogue, 18 F-fluorodeoxyglucose (FDG). The high rate of glucose uptake in tumour compared with normal cells underlies its routine use for cancer diagnosis, staging and restaging. PET is an ideal tool for imaging resistance to drugs. PET can image not only tumour physiological processes but also the functionality of receptors and enzymes. PET is highly specific and non-invasive so that imaging can be easily and rapidly repeated. The imaging modality provides temporal and spatial information so that intra- and inter-tissue variability can be assessed. Clinical development of new agents may take years. Early clinical trials can involve hundreds of patients and may often reveal an ineffective drug. By using PET to demonstrate in vivo activity or resistance in proof of principle studies, this may avoid some of the attrition in these lengthy and expensive studies.

Imaging with PET PET imaging is based on positron emission from short-lived isotopes produced by a cyclotron. A radiotracer or probe is produced by replacing a molecule of interest with a radiolabelled compound, which is injected into a patient. Positrons emitted by nuclear decay from the tracer collide with electrons in the tissues. This reaction produces two annihilation photons, 511 keV gamma rays in opposite directions, at 180◦ . In a scanner, pairs of scintillation ring detectors transmit a coincident signal when both are stimulated simultaneously (Fig. 13.1). Data are corrected for photon attenuation and scatter, and then reconstructed. For PET data analysis, regions of interest are drawn on tissues. The simplest way to analyse radioactivity in these regions is to determine a semiquantitative standardised uptake value (SUV). SUV is the amount of radioactivity at a given time, normalised for body weight and injected activity of isotope. However, to assess complex biological processes such as enzyme activity or receptor function, quantitative data must be derived. Kinetics of radioactivity uptake and elimination are measured in tissue regions of interest within individual time frames. An input function is obtained from continuous or rapid discrete arterial blood sampling or from drawing regions of interest in large vessels. Biological data analysis can, therefore, require lengthy and complex modelling techniques.

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dia

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Fig. 13.1 Positron emission tomography (PET). A labelled probe is injected intravenously into a patient. Positrons emitted by nuclear decay collide with electrons in the tissues. This annihilation reaction produces two photons separated by 180◦ . The photons are detected in a scanner that comprises a large number of scintillation detectors

Use of FDG to Identify Patients Resistant to Chemotherapy The simplest use of PET to assess drug resistance involves FDG to image tumour metabolic response to treatment. Patients who are resistant to a line of chemotherapy can be identified so this may be stopped and replaced by an alternative treatment if available. Neoadjuvant chemotherapy is given prior to definitive surgery or radiotherapy in a number of tumour sites. Resistance to chemotherapy may allow the tumour to progress while delaying definitive treatment. In an imaging study of neoadjuvant chemotherapy in patients with locally advanced breast cancer, PET correctly predicted lack of response in five/six patients. MRI did not correctly identify response in any patient (Chen et al., 2004). In an exploratory study of patients undergoing neoadjuvant chemotherapy for locally advanced adenocarcinoma of the oesophagogastric junction, FDG-PET was performed before chemotherapy and during treatment. Histological responders had a larger decrease in SUV compared to non-responders (Weber et al., 2001). In a subsequent phase II study using PET after 2 weeks of chemotherapy, metabolic non-responders had immediate surgery (Lordick et al., 2007). There was no detriment to survival in comparison with a historical cohort.

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Biological therapies are often cytostatic rather than cytotoxic, so tumour shrinkage may not be apparent soon after treatment. As PET can reflect cell kill, this would be a more useful imaging tool than CT or MRI to demonstrate drug resistance. The tyrosine kinase inhibitor imatinib has been used to successfully treat the rare gastrointestinal stromal tumours (GISTs) with a KIT TK constitutive activation. Following imatinib, GISTs become more cystic rather than shrinking; thus true responders are often mistaken for non-responders. The value of PET has been demonstrated in this scenario – PET/CT accurately diagnosed tumour response in 95% of patients at 1 month and 100% at 3 months, compared to 44 and 60%, respectively for CT (Bauer et al., 2003). The imaging changes seen with FDG-PET following imatinib may be due to KIT signalling being involved directly in glucose uptake (Blasberg and Tjuvajev, 2003).

Imaging Pharmacokinetic Resistance One of the most basic requirements of a drug is that it localises in tumours. Reduced tumour uptake or increased efflux could lead to drug resistance. It is possible to label chemotherapeutic agents with short-lived isotopes to generate a PET probe to evaluate these processes. The development of a labelling method is a considerable task, however, and may take up to 2 years. Probes must then be explored in preclinical models and ultimately validated and safety tested in humans. Complex data modelling is needed to assess uptake of the probe and derive pharmacokinetic parameters. Unfortunately, it is usually impossible to distinguish radiolabelled metabolites from their parent probe, which further complicates data analysis. Several pharmacokinetic parameters can be evaluated in vivo using radiolabelled drugs, such as uptake, retention and area under the curve (AUC). AUC is the curve of drug concentration, or PET probe SUV against time, and reflects tissue exposure to the drug or probe. Several drugs have been labelled with radioactive isotopes and used to demonstrate tumour localisation, for example 5-FU (Kissel et al., 1997), cisplatin (Ginos et al., 1987), BCNU (Mitsuki et al., 1991), the topoisomerase I and II inhibitor XR5000 (DACA) (Propper et al., 2003) and temozolomide (Hutchinson et al., 2003). In a pharmacokinetic study of labelled XR5000 at the maximum tolerated dose (MTD), PET demonstrated localisation of the drug into tumours and showed uptake to be more variable than in normal tissues (Propper et al., 2003). In addition PET showed a lack of saturable uptake in tumours at the drug’s MTD, likely to predict resistance (Propper et al., 2003). In a PET study of the alkylating agent temozolomide, a higher AUC was seen in brain tumours than in normal tissue, supporting further development of this drug (Saleem et al., 2003). Several groups have looked at pharmacokinetic parameters and response or resistance to chemotherapy. The earliest preclinical studies with radiolabelled 5-FU in murine xenografts showed that tumour retention of 5-FU was related to response to the drug (Shani and Wolf, 1977, Visser et al., 1996). The work progressed to clinical studies in patients with metastatic colorectal cancer. It was shown that patients with

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higher levels of 5-FU uptake achieved stable disease following treatment with 5-FU (Moehler et al., 1998) and, in another study, tumour uptake correlated with survival (Moehler et al., 1998). In a study of labelled temozolomide quantitative uptake of the tracer also correlated with patient response (Saleem et al., 2003). Using PET methodology, it has been possible to explore agents which could selectively increase uptake and retention of chemotherapy. N-(phospho-nacetly)-Laspartic acid (PALA) is a compound which biomodulates 5-FU and has demonstrated preclinical efficacy in combination with 5-FU. In vivo, however, PALA reduced tumour blood flow and 5-FU uptake (Harte et al., 1999). Folinic acid (FA), a drug known to increase efficacy of 5-FU, resulted in higher uptake and retention of labelled 5-FU in a study of patients with metastatic colorectal cancer (Kissel et al., 1997). By demonstrating the effect of novel agents on tumour drug exposure, imaging can guide towards development or abandonment of a drug.

Metabolic Resistance Metabolism of a drug can lead to drug resistance. Excessive metabolism can lead to reduced bioavailability of active drug; however, metabolism is sometimes needed for activation of a drug and if deficient could also lead to resistance. Metabolic activation of temozolomide has been demonstrated (Saleem et al., 2003). Temozolomide is a pro-drug that was hypothesised to target tumours and become active in an alkaline pH by ring opening and decarboxylation in the 3–4 position (Baker et al., 1999). Temozolomide was labelled with carbon-11 in two different positions, the 3-N-methyl and 4-carbonyl positions. To confirm the hypothesis the 11 C of the 11 C-carbonyl temozolomide should be converted to 11 CO2 during drug activation. Paired studies with the two different tracers were performed in patients with gliomas. Higher levels of 11 CO2 were seen in plasma and exhaled air using 11 Ccarbonyl temozolomide which confirmed the theory. In addition, the study showed no difference in ring opening in tumours compared to normal tissue, implying that activation does not preferentially occur in tumours (Saleem et al., 2003).

Metabolism of 5-FU and Resistance 5-FU has been used for decades in the treatment of colorectal cancer. It works by competitive inhibition of thymidine synthase (TS), which leads to inhibition of both DNA and RNA syntheses. However, resistance is a problem, and there are numerous mechanisms which bring about this. Activating enzymes including tyrosine kinase (TK) are needed to metabolise 5-FU and these can be reduced in activity. Dihydropyrimidine dehydrogenase (DPD) is the major extracellular degrading enzyme of 5-FU. However, resistance to this is due to the thymidine salvage resistance pathway which transports thymidine intracellularly and phosphorylates it.

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Several PET probes have been developed to elucidate these stages in 5-FU resistance. FLT is phosphorylated in cells by TK, and uptake of FLT correlates with TK activity. Thus FLT uptake may be a surrogate for TK activity in vivo (Rasey et al., 2002). 18 F-FUdR is also an indirect measure of TK (Seitz et al., 2001). Additional probes may detect steps in this pathway – 11 C-dUrD, 11 C-2-deoxyuridine, 18 F-FLT and 18 F-3-deoxy-3-fluorothymidine. The salvage resistance pathway has been assessed in a study of the TS inhibitor, nolatrexed in patients with gastrointestinal tumours, using the PET probe 11 C-thymidine. By inhibiting TS, nolatrexed led to activation of the salvage pathway and thus increased intracellular 11 C-thymidine retention by tumours (Wells et al., 2003). Eniluracil was developed as an inactivator of DPD. A pharmacokinetic study of eniluracil and [18 F] radiolabelled 5-FU in patients with metastatic colorectal cancer was designed to investigate the activity of eniluracil. It was hypothesised that inactivation of DPD would result in a decrease in uptake of [18 F] radiolabelled 5-FU by the liver, the primary site of 5-FU catabolism. In practice, there was indeed a significant decrease in hepatic uptake and exposure to the radiotracer following exposure to eniluracil, and additionally an increased half life of 5-FU in tumours (Saleem et al., 2000). This study suggests further development of eniluracil would be worthwhile to overcome resistance and improve efficacy of 5-FU. In addition, as eniluracil prevents catabolism of 5-FU, it reduces the levels of labelled 5-FU metabolites which confound PET data, and so it has been explored as an agent to improve PET imaging in this setting (Bading et al., 2003). Different resistance pathways will be active in different patients following 5-FU. By using different probes PET has the versatility and sensitivity to assess these and relate them to efficacy.

Imaging Physiological Causes of Resistance Blood Flow Abberations in tumour blood flow can lead to drug resistance in different ways. Adequate flow is important in drug delivery and tumour exposure to chemotherapy. Conversely, high tumour blood flow may identify a more aggressive cancer, as this might reflect high angiogenesis. Monitoring tumour blood flow as a pharmacodynamic response to a drug can be applied to a wide range of different therapies and is much less labour intensive than labelling individual drugs. In the early development of the anticancer agent XR5000, pharmacokinetics of the labelled drug were investigated in a PET study. A positive relationship was seen between tumour blood flow and AUC of XR5000, suggesting that delivery of the drug was a determinant of tumour uptake (Saleem et al., 2001). By identifying aberrant tumour blood flow on imaging, it may be possible to modify the blood flow. In a pharmacodynamic study of labelled 5-FU in patients with metastatic colorectal cancer, carbogen and nicotinamide increased delivery of the 5-FU, but not its retention (Gupta et al., 2006). Vascular disrupting agents

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(VDAs) disrupt tumour blood vessels. PET may be used to assess response to these drugs. In a phase I study of the tubulin-binding anti-vascular agent combretastatin, tumour perfusion was assessed after administration of the drug (Anderson et al., 2003). Tumour perfusion decreased with increasing doses of the VDA. PET imaging of blood flow is a well-established and validated technique (Laking and Price, 2003), which yields quantitative data. For clinical practice, the freely diffusible tracer method of perfusion analysis is used, which measures the rate of tracer uptake. The approach involves injection of a bolus of 15 [O]H2 O, which requires an on-site cyclotron for its generation. Kinetic data are obtained and the arterial input function is derived from blood sampling or a large arterial pool from the PET image. A time–activity curve of changing 15 O activity is constructed. The Kety– Schmidt model based on Kety’s original model (Kety, 1951) is employed to model kinetic data, though this process is highly complex. A typical scan takes 10 min, and extensive blood sampling is required (50 ml per scan). Volume of distribution (Vd) is another important parameter, which is the fractional volume of blood/plasma to account for the activity within a region. A high Vd reflects a large volume of perfused tissue which may be exposed to a drug.

Angiogenesis Angiogenesis partly determines tumour perfusion and so may be a cause of drug resistance. In addition, in recent years there has been interest in the development of antiangiogenic drugs as well as VDAs. Resistance to antiangiogenic drugs can develop due to selection of mutant cells able to survive in a more hypoxic environment (Yu et al., 2002) and redundancy of angiogenic growth factors (Kerbel et al., 2001). Non-invasive molecular imaging can detect molecules involved in angiogenesis which are selectively expressed on tumour vasculature. Probes are often labelled ligands or receptor antagonists. Many of these studies are still in an early phase of preclinical and clinical development. Table 13.1 lists some of the novel probes that have been studied in cancer patients. The integrin alphavbeta3 is expressed selectively on activated tumour vasculature and also has a role in metastasis. A labelled antibody specific for this integrin is in clinical development and has been validated histologically (Posey et al., 2001). Such a probe may be useful in studying resistance to drugs which target the alphavbeta3 receptor such as cilengitide. The TGFb receptor, endoglin, is a proliferationassociated endothelial marker, which has also been validated with histology-labelled anti-endoglin bound specifically to neovasculature in an animal model (Bredow et al., 2000). VEGF expression in primary tumours and metastases in patients with pancreatic cancer has been imaged using 123 I-labelled VEGF (Li et al., 2004). However, a further study showed heterogeneous distribution and clearance of 124 iodine-labelled anti-VEGF antibody between and within patients (Jayson et al., 2002). In the future, these probes may be useful in assessing resistance to drugs which target VEGF, such as Avastin.

n

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47 11

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Reference

Li et al., 2004 Jayson et al., 2002 Kulasegaram et al., 2001 Mortimer et al., 1996

Linden et al., 2006 Haas et al., 2004

Wells et al., 2002

Chen et al., 2007

Mullamitha et al., 2007

Posey et al., 2001 Fukumoto et al., 1999

Del Vecchio et al., 1997 Kurdziel et al., 2007

(99m)Tc-sestamibi [(18)F]fluoropaclitaxel

Monoclonal antibody to anti-alpha(v) integrins (99m)Tc-Vitaxin (99m)Tc-tetrofosmin (99m)Tc-TF

[18F]fluorothymidine

2-[(11)C]thymidine

(123)I-VEGF(165) (124)I-HuMV833 (111)In-DTPA-N-TIMP-2 16alpha-[18F]fluoro-17 beta-estradiol [(18)F]fluoroestradiol (99m)Tc-Annexin-V

Probe

Breast Breast

Various Lung

Various

Intra-abdominal malignancies Glioma

Breast Lymphoma

Pancreas Various Kaposi sarcoma Breast

Tumour

Imaging in a patient with alphavbeta3 positive Tumour 14–18 tumours with high retention had good response to chemoradiotherapy. No retention in 12 tumours with no response Retention higher in tumours with low Pgp levels (p<0.001) Accumulated in tumour responsive to chemotherapy but not in tumour where there was partial response

Majority of tumours were visualised by probe Heterogeneous uptake of tracer within and between patients No accumulation in tumour Sensitivity of 76% and specificity of 100% compared to in vitro assay of oestrogen receptor status 0–15 patients with low tracer uptake responded to hormones Uptake of tracer increased after radiotherapy. Concordant with apoptosis on cytology 2-[11 C]thymidine uptake correlated with tumour proliferation (Ki-67) Patients with a >25% reduction in tumour FLT uptake survived three times as long as non-responders; p<0.003 Localisation in tumours positive for alpha(v) integrins

Comment

Table 13.1 Summary of clinical PET studies undertaken using novel probes

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There have been some developments in imaging matrix metalloproteinases (MP) and FGF expression, which also have a role in angiogenesis. An inhibitor of MP labelled with 111 In was administered safely to patients with Kaposi sarcoma, but there was no tumour uptake (Kulasegaram et al., 2001). Preclinical studies of labelled FGF antibody showed retention of antibody only in tumour which expressed FGF (Kobayashi et al., 1993). There is a need for further development of probes for markers of angiogenesis to image drug resistance specifically to antiangiogenesis agents, but also as a marker of response to anticancer drugs.

Hypoxia Hypoxia adversely affects prognosis (Nordsmark et al., 2005) and is associated with resistance to several chemotherapeutic agents including 5-FU, doxorubicin, bleomycin and cisplatin (Grau and Overgaard, 1992, Liang, 1996, Koch et al., 2003, Sakata et al., 1991, Wang et al., 2007). There is considerable interest in developing methods for identifying hypoxic tumours prior to treatment so that patients could be selected for approaches developed to overcome hypoxia (Kaanders et al., 2002). A number of PET probes are being developed for measuring hypoxia of which 18 FFMISO is the most widely studied. This is a member of the group of nitroimidazoles which include 18 F-FETNIM, 18 F-FETA and 18 F-EF5. The compounds diffuse into a cell and undergo electron reduction in hypoxia to form reactive species that form covalent bonds with macromolecules and become trapped intracellularly. There are studies of hypoxic imaging with 18 F-FMISO in human head and neck cancer, soft tissue sarcoma, breast cancer and glioblastoma multiforme (Rajendran et al., 2004). New nitroimadazoles are being developed, which may have better biokinetics and imaging characteristics than 18 F-FMISO. 18 F-FETNIM and 18 F-FAZA are more hydrophilic than FMISO, hence more rapid plasma clearance and higher tumour to blood ratios. 60 Cu-ATSM is not a nitroimidazole, but imaging is based on reduction of metal proportional to level of hypoxia, and has the advantages of good uptake and rapid washout.

Molecular Resistance Hormone Receptor-Mediated Resistance Endocrine therapies such as Tamoxifen and aromatase inhibitors are used for patients with early and advanced stage breast cancer. To assess hormone responsiveness, tumour biopsies are analysed for expression of hormone receptors. However, in advanced disease there may be practical problems in accessing tissue, sampling may lead to morbidity and there may also be sampling error. Functional imaging of the oestrogen receptor could potentially give information about hormone resistance.

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Dosimetry of 16alpha-18 F-fluoro-17beta-estradiol (FES) has been assessed and found to be clinically acceptable (Mankoff et al., 2001). When compared with ER status on histology, the sensitivity of FES-PET for patients with advanced breast cancer was 76% and specificity was 100% (Mortimer et al., 1996). In a study by Krohn’s group, response to hormone therapy was correlated with quantitative FES uptake. No patients with an initial SUV less than 1.5 responded to hormonal therapy, whereas 34% with SUV higher than 1.5 did respond. The relationship was significant, suggesting PET as a predictive tool for resistance to hormone therapy (Linden et al., 2006). There are a range of hormones which have anti-androgenic effects and are used in the treatment of locally advanced and metastatic prostate cancer. However, there is currently no useful clinical assay to assess tumour androgen receptor status. PET probes such as 7alpha-(fluoromethyl)dihydrotestosterone (7alpha-FM-DHT) with affinity for the androgen receptor are being developed to assess androgen receptor status, though these are at an early stage of development (Parent et al., 2007, Garg et al., 2001).

Apoptosis Some tumour cells which are resistant to apoptosis are resistant to anticancer drugs. Apoptosis has been imaged in clinical studies using the radiolabel (99m)TcAnnexin-V (Collingridge et al., 2003). This probe binds strongly to phosphatidylserine PS specifically expressed on apoptotic cells. This potentially could determine a tumour’s likelihood of undergoing apoptosis in response to treatment. Patients with recurrent follicular lymhoma were imaged with (99m)Tc-Annexin-V PET before and after 2×2 Gy radiotherapy. Annexin uptake increased following radiotherapy, and this corresponded with histology measurements of apoptosis. In addition, tracer uptake on day 4 predicted a complete remission at 1 week (Haas et al., 2004). Unfortunately, (99m)Tc-Annexin-V has a poor biodistribution with poor image contrast (Cauchon et al., 2007). Attempts are being made to discover new tracers. NST-732 is a 18 F-labelled small molecule which is being assessed as an apoptosis tracer. The molecule was taken up selectively by apoptotic cells in a murine model (Aloya et al., 2006). It is also possible to pre-target apoptotic cells with biotinylated Annexin-V and then image indirectly with 64 Cu-labelled streptavidin (SAv) in preclinical models (Cauchon et al., 2007). Another approach involves imaging changes in mitochondrial membrane potential, seen in apoptosis. This has been demonstrated in a preclinical study using the PET probe 18 F-fluorobenzyl triphenylphosphonium (Madar et al., 2007).

p53 Tumour Suppressor Inactivation of p53 is associated with drug resistance in certain tumours (Sturm et al., 2003), and this can be indirectly imaged in vivo using PET. Indirect imaging

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of gene expression involves coupling of a reporter gene to a reporter probe. The level of probe accumulation correlates with reporter gene expression. Herpes simplex virus1-tyrosine kinase (HSV1-tk) is a reporter gene which can be transfected into cells by a vector and then subsequently translated into HSV1-tk. When the radiolabelled reporter probe FIAU enters the cell it is phosphorylated by HSV1-tk and becomes trapped intracellularly. The extent of probe accumulation in the cell reflects HSV1-tk expression. Blasberg’s group has used these principles of indirect reporter imaging using 124 IFIAU PET to demonstrate upregulation of p53 transcriptional activity in response to DNA damage (Doubrovin et al., 2001). A retroviral vector (Cisp53/TKeGFP) was generated by placing the herpes simplex virus type 1 thymidine kinase (tk)-enhanced green fluorescent protein (eGFP) fusion gene (TKeGFP, a dual-reporter gene) under control of a p53-specific response element. PET imaging with 124 FIAU was carried out. The PET images corresponded with upregulation of genes in the p53 signal transduction pathway in response to DNA damage.

Imaging Signal Transduction Pathways Tyrosine kinases (TKs) are ubiquitous in cell signal transduction processes and, in the evolution of cancer, are involved in the processes of proliferation and apoptosis. The tyrosine kinase inhibitor (TKI) imatinib is used in the treatment of chronic myelogenous leukaemia and GIST. Resistance to TKIs is common and occurs because of activation of alternate signalling pathways – for imatinib, resistance is mostly due to mutation and amplification of TKs (Cools et al., 2005). FDG-PETCT imaging has been used to assess resistance to TKIs in patients with GIST, and as described previously compares better than CT alone in assessing response. It has been possible to label the TKIs Iressa (Wang et al., 2006a) and fluorinated 2-arylbenzothiazole (Wang et al., 2006b) as PET probes and thus gives the potential to image TK inhibition directly. However, these probes are early in development and have not yet been introduced preclinically. Targeted antibodies against TK-associated receptors have clinical efficacy such as Cetuximab and Herceptin, which target EGFR and HER2, respectively. Noninvasive quantitative imaging of tumour receptor expression may enable prediction of response or resistance to treatment. Several radiolabelled ligands have been generated against EGFR or the HER2 receptor tyrosine kinase, and these have been validated against histology, but not yet been assessed clinically (Wang et al., 2007) (Cai et al., 2007). The problem with labelling a whole antibody is poor penetration and long half life, leading to a high background signal. The use of antibody fragment probes and a two-step labelling technique has improved this. An 18 F-labelled irreversible inhibitor of EGFR has been generated, but showed little EGFR specific uptake (Abourbeh et al., 2007). Reversible inhibitors are not useful as PET probes due to the speed of their dissociation from their TK-binding

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site on the EGFR receptor (Bonasera et al., 2001). A new generation of inhibitors are required that have reduced clearance and metabolism.

Imaging Proliferation Rapid tumour cell proliferation suggests an aggressive tumour, though conversely due to the high S-phase fraction of tumour cells may lead to drug sensitivity. FDG is a surrogate for cell number and activity. However, other markers are available which more accurately reflect cell proliferation and may be more suitable candidates for assessing response or resistance to drugs. 11 C-Thymidine has been used to assess proliferation and has been validated against the proliferation marker Ki-67 (Wells et al., 2002). The compound is readily metabolised, though the main metabolite, CO2 , can be measured and corrected for (Shields et al., 1996). New analogues have been developed, however, with a more stable glycosidic bond and hence reduced sensitivity to Urd/TdR phosphorylasese, e.g. 18 F-FLT. An advantage of 18 F-FLT over 11 C-thymidine is the longer half life of 18 F, so that an on-site cyclotron is not required. However, the probe also has its limitations. There is high background uptake in liver and bones, and additional signal from metabolites. 18 F-FLT has been used preclinically to image growth inhibition by the TKI mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK) in a mouse melanoma (Solit et al., 2007), and also to image growth inhibition by a ErbB kinase inhibitor (Waldherr et al., 2005). 18 F-FLT uptake has been validated histologically with the tumour proliferation index (proliferating cell nuclear antigen) (Waldherr et al., 2005). In a direct comparison between 18 F-FLT and FDG, when a murine tumour was treated with 5-FU, 18 F-FLT decrease was greater than FDG (Barthel et al., 2003). There have been a small number of studies where 18 F-FLT has been used clinically. In a study of patients with lung cancer, uptake of 18 F-FLT correlated better than FDG with the proliferation index (Ki-67) (Buck et al., 2003). In a recent study of patient with high-grade glioma receiving irinotecan and bevacizumab, reduction in 18 F-FLT uptake predicted response to treatment (Chen et al., 2007).

DNA Repair Repair of DNA in response to chemotherapy may be a cause of drug resistance. In response to the alkylating agent temozolomide, DNA damage is repaired by O6-alkylguanine alkyl transferase (Atase/MGMT). The MGMT DNA repair gene undergoes silencing by promotor methylation. Methylation of the gene is associated with better overall survival in patients with high-grade glioma treated with an alkylating agent (Esteller et al., 2000). Radiolabelled guanine derivatives are being assessed for evaluating MGMT in tumours (Zheng et al., 2003, Calabrese et al.,

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2004). In addition, labelled temozolomide could indirectly assess MGMT activity; high levels of uptake would indicate high MGMT activity. (PARP/ Poly(adenosine5 -diphosphate[ADP]-ribose)polymerase/synthetase PARS) also repair damage following exposure to alkylating agents. The PARP/PARS inhibitor 3,4-dihydro-5-methoxy-1(2H)-isoquinolinone (MIQO) has been labelled with 11 C and may be useful for in vivo assessment of PARP/PARS activity (Miyake et al., 2000).

Integrin Signalling Resistance to chemotherapeutic drugs may be affected by the interaction between integrins and growth factor receptors. This interaction controls cellular functions such as proliferation and cell survival (Zutter, 2007). Most of the published data involves imaging the integrin alphavbeta3 which is expressed selectively on activated tumour vasculature and so has relevance to imaging angiogenesis. Most interest over the past few years has been in generating labelled RGD peptides which binds to integrin alphavbeta3 (Haubner et al., 2004). However, in addition antibodies have been generated against the integrin. In a promising phase II study of an anti-alphavbeta3antibody using gamma camera imaging, retention occurred only in one patient who had alphavbeta3 positive tumour (Posey et al., 2001). A human monoclonal antibody has been developed to the alpha(v) family of integrins which is tolerated and localised to tumour in a phase I study (Mullamitha et al., 2007).

MDR MDR describes a phenotype of tumours which are resistant to a variety of natural chemotherapeutic drugs. Resistance may be innate or acquired, and in many tumours is caused by overexpression of the membrane pump P-glycoprotein (Pgp) which leads to active pumping of drug out of the cell (Tan et al., 2000). Imaging an MDR phenotype may identify people who should be treated with alternative therapy and also those who should receive MDR-inhibiting drugs. Early clinical studies with gamma camera imaging used the radiotracers sestamibi (Del Vecchio et al., 1997) and tetrofosmin (Fukumoto et al., 1999). Quantitative studies with PET have recently been performed with 11 C-daunorubicin (Elsinga et al., 1996), 11 C-verapamil and 11 C-colcinine. In murine models, uptake of 11 Cverapamil was modified by cyclosporin A, an MDR modulator (Hendrikse et al., 1999), and uptake of 11 C-colchicine correlated with murine tumour sensitivity to taxol (Levchenko et al., 2000) – resistance to colchicine correlates with resistance to taxol. Some patients show MDR-mediated resistance to paclitaxel. 18 FFluoropaclitaxel (FPAC) has been generated. Uptake in murine models is inversely proportional to tumour MDR expression. 18 F has a longer half life than

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11

C so allows for imaging of evolving physiological processes. Imaging MDR phenotype has progressed to clinical studies – FPAC was taken up by cells in breast tumour that responded to chemotherapy, but not by disease where there was partial response (Kurdziel et al., 2007).

Conclusions PET is able to visualise in vivo many of the mechanisms involved in resistance to anticancer therapy. PET has excellent sensitivity compared with other imaging modalities and is able to detect molecules at picomolar concentrations. PET-CT combines sensitivity with improved resolution. However, considerable radiopharmaceutical skills are necessary for development of ligands, in addition to an onsite cyclotron. Progress in this expanding field depends on the development of new labelled ligands. Candidate tracers should demonstrate a relatively high level of specific signal bound to the site, but also have kinetics that are amenable to correction for background/non-specific binding and quantitation. Developments in data modelling are also essential for evaluating pharmacokinetic and pharmacodynamic data.

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Chapter 14

Overcoming Drug Resistance by Phytochemicals Marion M. Chan and Dunne Fong

Abstract Cancer drug resistance is a major cause of treatment failure. Various cancer chemopreventive phytochemicals can sensitize chemoresistant and radioresistant cancer cells. From in vitro tissue culture and in vivo xenograft studies with drug-resistant human cancer cell lines, synergistic effects have been shown between cancer therapeutics and molecules isolated from the spice turmeric, the beverages such as green tea and red wine, other sources of dietary fruits, vegetables, and grains, as well as herbal medicine. However, concerns on safety and bio-accessibility must be addressed further, and the proof-of-concept clinical data, to date, are still missing. Keywords Phytochemicals · Synergy · Combination therapy · Cancer prevention · Epigallocatechin gallate · Curcumin · Resveratrol · Cancer stem cells

Introduction Cancer Drug Resistance Cancer cells exhibit hallmark alterations in their physiology, namely (1) selfsufficiency in growth signals, (2) insensitivity to growth inhibitory signals, (3) evasion of programmed cell death, (4) limitless replicative potential, (5) sustained angiogenesis, (6) tissue invasion and metastasis, and (7) avoidance of immune surveillance. These peculiarities of cancer cell metabolism may offer novel targets for cancer treatment (Hanahan and Weinberg 2000; Kroemer and Pouyssegur 2008). In reality, however, cancer treatment presents clinical dilemmas, the main problem being cancer drug resistance (Teicher 2006). Drug resistance is a major factor that M.M. Chan (B) Department of Microbiology and Immunology, Temple University School of Medicine, 3400 North Broad Street, Philadelphia, PA 19140, USA e-mail: [email protected]

K. Mehta, Z.H. Siddik (eds.), Drug Resistance in Cancer Cells, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-89445-4 14, 

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limits the efficacy of chemotherapy. The cancer may be intrinsically resistant to specific drugs, or resistance may develop during the course of treatment. Resistance may be toward a specific drug, but more often it is pleiotropic, a phenomenon known as multidrug resistance. Cross-resistance to a range of chemotherapeutic drugs ultimately leads to treatment failure in over 90% of patients with metastatic cancer. The problem of cancer drug resistance is complex. Numerous factors are involved, namely (1) accelerated drug efflux, (2) drug activation and inactivation, (3) alterations in the drug target, (4) DNA methylation, (5) processing of drug-induced damage, and (6) evasion of apoptosis (Wilson, Longley and Johnston 2006). Besides the intrinsic factors, there are extrinsic factors due to cancer cell microenvironment. Conditions such as the components of extracellular matrix, the presence of tumor-associated host cells such as fibroblasts and immune cells, the hypoxic and acidic state resulting after tumor metabolism, and the proximity to blood vessels for solid tumors will affect the efficacy of chemotherapy (Mart´ınez-Lacaci et al. 2007). To further illustrate cancer drug resistance, chemotherapy of ovarian cancer is used as an example. Ovarian cancer is the most lethal of all gynecologic malignancies in the western world. Because of the lack of symptoms, most patients are already in the advanced stages upon initial diagnosis, and they die of recurrence from the disease that has become resistant to conventional treatment (Chien et al. 2007). Current treatment is surgery followed by chemotherapy. Administration of platinum- and taxane-based chemotherapy is the standard of post-operative care for patients with advanced ovarian cancer. Molecular mechanisms for ovarian cancer drug resistance have been investigated. Resistance to cisplatin and other platinum compounds may occur via (1) increased drug efflux, due to activation of transporters such as multidrug-associated protein (MRP), (2) activation of detoxification system, due to drug inactivation by metallothionein and the thiol glutathione, (3) increased repair of DNA damage, due to activation of nucleotide excision repair (NER), (4) increased tolerance of DNA damage, due to defect in mismatch repair (MMR) with gene silencing by DNA methylation, (5) evasion of apoptosis, due to activation of anti-apoptotic pathways such as B-cell lymphoma-2 (BCL-2), (6) loss of tumor suppressor function, due to mutations in p53, and (7) altered gene expression, such as activation of nuclear factor kappa B (NFkB), phosphoinositide 3-kinase/serine threonine protein kinase Akt (PI3K/Akt, Akt alias protein kinase B or PKB) pathways. Resistance to paclitaxel and other taxanes may occur via (1) increased drug efflux, due to activation of transporters such as P-glycoprotein (MDR1), (2) alteration of drug targets, due to mutations in beta tubulin, (3) evasion of apoptosis, due to activation of anti-apoptotic pathways such as BCL-2, and (4) altered gene expression, such as activation of the NFkB pathway (Chan and Fong 2007a). Summarizing this section, with respect to cancer drug resistance, innovative approaches and novel compounds are urgently anticipated. With this background we address the concept of using phytochemicals to overcome cancer drug resistance.

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Phytochemicals Fruit and vegetable intake has been shown to reduce the risk of cancer in numerous epidemiological studies (Block, Patterson and Subar 1992). Active components are non-nutrient dietary phytochemicals that may modulate gene expression and signal transduction pathways (Kris-Etherton et al. 2002; Manson 2003; Surh 2003). Phytochemicals have been isolated and characterized from fruits such as grapes and apples, vegetables such as broccoli and onion, spices such as turmeric, beverages such as green tea and red wine, as well as many other sources. Collectively, these compounds became known as chemopreventive agents, with chemoprevention referring to the use of agents to inhibit, reverse, or retard tumorigenesis. In this sense chemopreventive phytochemicals are applicable to cancer therapy, since molecular mechanisms may be common to both chemoprevention and cancer therapy (D’Incalci, Steward and Gescher 2005; Sarkar and Li 2006). Multiple molecular targets of dietary phytochemicals have been identified, from pro- and anti-apoptotic proteins, cell cycle proteins, cell adhesion molecules, protein kinases, transcription factors to metastasis and cell growth pathways (Awad and Bradford 2005; Aggarwal and Shishodia 2006a; Choi and Friso 2006). Among the diverse phytochemicals implicated in the inhibition of carcinogenesis, one major group comprises of polyphenols. Multiple laboratory studies have been done. However, concentrations used in cell cultures to demonstrate efficacy tend to be much higher than the ones attainable in vivo. Bioavailability data, as well as the extrapolation of findings from animal studies to human situations, are in need of further and more extensive investigation (Lambert et al. 2005). The polyphenolic phytochemicals are virtually ubiquitous in plant materials and may occur at very high levels. Phenolics in plants are mostly synthesized from phenylalanine via the action of phenylalanine ammonia lyase (PAL). They are very important to plants and have multiple functions. Plant phenolics serve as structural polymer components and are also responsible for ultraviolet light screening. They have antioxidant activity and act as signaling molecules. Colored phenolics become attractants, making flowers attractive to pollinating animals. The most important role of plant phenolics may be in plant defense against pathogens and herbivore predators. With the discovery of health benefits of plant polyphenols, it has been proposed to optimize the phenolic content of the diet so as to obtain favorable consequences for general health of the population (Parr and Bolwell 2000). Phytochemicals including plant polyphenols that show health benefits may act via similar or different mechanisms as those functional in plants. In addition, there may be differential response between normal and cancer cells (McEligot, Yang and Meyskens 2005). Phytochemicals may modulate transcription factors (Andreadi et al. 2006), redox-sensitive transcription factors (Surh et al. 2005), redox signaling, and inflammation (Rahman, Biswas and Kirkham 2006). As an example, nitric oxide (NO), a signaling molecule of importance in inflammation, is modulated by plant polyphenols and other botanical extracts, as shown by us and others (Chan and Fong 1999; Shanmugam et al. 2008). Many phytochemicals have been classified as phytoestrogens, with health-promoting effects (Moutsatsou 2007). With

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these attributes, individual phytochemicals have been touted as neutraceuticals in marketing schemes to the consumer, but it is imperative to separate facts from fiction for these compounds (Esp´ın, Garc´ıa-Conesa and Tom´as-Barber´an, 2007).

Phytochemical–Chemotherapeutic Synergy Epidemiological data convincingly suggest a reduction in cancer risk via the intake of fruits and vegetables. Laboratory investigations have substantiated aspects of health attributes, and individual phytochemicals involved have been characterized. Initially, it is advocated that polyphenolic compounds may be useful as cancer chemopreventives. Subsequently, this is extended to the proposal that the same may be true for cancer chemotherapy. Therefore, it is a small step to proceed further that the compounds may be applicable to sensitize the cancer toward chemotherapy and radiotherapy (Dorai and Aggarwal 2004; Garg, Buchholz and Aggarwal 2005). The last extension is to demonstrate synergism between phytochemicals and cancer chemotherapeutics (HemaIswarya and Doble 2006). Hence, here comes the concept of overcoming drug resistance by phytochemicals. We have recently summarized our work and those of other investigators on the topic of overcoming ovarian cancer drug resistance with phytochemicals and other compounds (Chan and Fong 2007a and 2007b). [The publisher has incorporated the review in two monographs.] In this chapter we shall expand the topic to cancer in general. We shall concentrate on three phytochemicals, namely epigallocatechin3-gallate, curcumin, and resveratrol, although others will also be mentioned. The three are the same ones selected by Francy-Guilford and Pezzuto (2008), who have concluded that, despite decades of exhaustive research, a definitive mechanism of action for carcinogenesis is still not available. Furthermore, we shall discuss potential molecular targets, possible antagonistic instead of synergistic effects, as well as safety and bioavailability.

Epigallocatechin-3-gallate Health Effects Epigallocatechin-3-gallate (EGCG) is a polyphenol from green tea. [See Fig. 14.1 for chemical structure of EGCG.] Tea is made from leaves of the plant Camellia sinensis, originally from China. EGCG is the major green tea polyphenol, accounting for 30–40% dry weight of the water extractable material of green tea. It is converted to dimers and polymers during black tea production. Tea is the most popular beverage consumed in the world, second only to water. Approximately 20% of the world’s tea is consumed as green tea, which is popular in parts of China and Japan, whereas 78% of tea is consumed as black tea, which is the main tea beverage in the United States, Great Britain, and Europe.

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OH

HO

O O

O

OH HO OH HO

OH

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O O OH

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Fig. 14.1 Chemical structure of epigallocatechin gallate (EGCG), curcumin, and resveratrol

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Multiple health benefits have been associated with EGCG and tea consumption, as summarized in monographs (Hara 2001; Zhen, 2002) and recent reviews (Zaveri 2006; Khan et al. 2006; Yang et al. 2006a and 2006b; Yang et al. 2007a; Khan and Mukhtar 2007 and 2008). These include cancer chemoprevention, atherosclerosis, hypercholesterolemia, Alzheimer’s, and other aging-related diseases. The green tea polyphenol EGCG targets multiple signaling pathways, such as the induction of cell cycle arrest and apoptosis, modulation of cell signaling by inhibition of nuclear factor kappa B (NFkB), mitogen-activated protein kinase (MAPK), epidermal growth factor receptor (EGFR), metalloproteinase (MMP), proteasome, cyclooxygenase2 (COX-2), and many others. For example, we have shown EGCG inhibition of inducible nitric oxide synthase (iNOS) both at the gene expression level and at the enzyme activity level (Chan et al. 1997). EGCG is antimicrobial. It may prevent dental caries (green tea extract in toothpaste). EGCG is even active against methicillin-resistant Staphylococcus aureus (MRSA); it enhances activity of the drug carbapenems against the pathogen (Hu et al. 2002). We end this section with a commercial note. In October 2006, FDA approved an extract of green tea as a prescription drug for topical treatment of genital warts. The extract is Polyphenon E (Veregen) ointment (from a German biotech company MediGene and its marketing partner Bradley Pharmaceuticals, Inc.). This is the first prescription botanical (herbal) drug approved by FDA under its current regulatory policy for botanical products as well as FDA’s new Drug Application Process (Blumenthal 2007; Gross et al. 2007). Cancer Drug Resistance Tea and its polyphenol EGCG have been investigated in many studies of cancer chemoprevention, cancer therapy, and, more recently, cancer drug resistance. There are conflicting epidemiological data on tea consumption and cancer. A populationbased study among 40,530 Japanese adults aged 40–79 years shows that green tea consumption is associated with reduced mortality due to all causes and due to cardiovascular disease, but not with reduced mortality due to cancer (Kuriyama et al. 2006). In contrast, another study of 69,710 Chinese women aged 40–70 years shows that regular consumption of green tea reduces the risk of colorectal cancer (Yang et al. 2007b). A third study of 61,057 Swedish women aged 40–76 years shows that consumption of tea (mostly black tea) reduces the risk of ovarian cancer in a dose– response manner, since each additional cup of tea per day was associated with an 18% lower risk (Larsson and Wolk 2005). Tea and its polyphenol EGCG have been investigated in laboratory and clinical trials. For example, EGCG and other compounds from tea inhibit the growth of prostate cancer, ovarian cancer, and other cancer cells in vitro (Ravindranath et al. 2006). Human cancer cells can be propagated in immunodeficient mice as xenografts, and the effect of drugs can be tested. Oral administration of green tea (in drinking water) significantly inhibits the growth of HEY ovarian carcinoma xenografts (Spinella et al. 2006). A prodrug of EGCG, named peracetate-protected EGCG (at 50 mg/kg in subcutaneous injection), shows better results than EGCG

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in MDA-MB-231 breast cancer xenografts (Landis-Piwowar et al. 2007). Human trials, unfortunately, are less promising. For example, a phase II clinical trial of 42 patients with metastatic prostate cancer, using 6 g of green tea extract per day, shows a very modest effect, with only one patient whose prostate-specific antigen (PSA) level decreased but was not sustained beyond 2 months. Adverse effects reported include nausea, emesis, insomnia, fatigue, diarrhea, abdominal pain, and confusion (Jatoi et al. 2003). Whereas green tea extract may have limited activity on human cancer, EGCG sensitization of human cancer cells to therapies has been demonstrated in vitro and in vivo. We have shown that EGCG acts synergistically with the chemotherapeutic compound cisplatin to kill ovarian cancer cells. Even for the cisplatin drugresistant cell line C200, enhanced killing by cisplatin was seen in the presence of 20 μM EGCG in cell culture (Chan et al. 2006). Similarly, enhanced killing by cisplatin or tamoxifen of glioma cells was detected in the presence of 100 μM EGCG (Shervington et al. 2008). EGCG treatment (at 100 μg/ml) of human hepatocellular carcinoma cells sensitizes them to killing by tumor necrosis factor-related apoptosisinducing ligand (TRAIL, at 100 ng/ml) (Nishikawa et al. 2006). Synergistic effects of EGCG and chemotherapeutic drugs have been reported in vivo. With the human oral epidermoid carcinoma doxorubicin-resistant cell line KBA-1, there is reversal of drug resistance by EGCG (40 mg/kg intraperitoneal injection for 9 days) in xenografts (Zhang, Wei and Liu 2004). With the human squamous cell carcinoma of the head and neck (SCCHN) cell line Tu212 xenografts, there is synergistic inhibition of tumor growth by a combination of EGCG (125 mg/kg via oral gavage) and the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib (50 mg/kg) (Zhang et al. 2008). These examples illustrate the finding that the green tea polyphenol EGCG acts synergistically with chemotherapeutic drugs in tumor xenografts in immunodeficient mice, even for drug-resistant human cancer cell lines.

Curcumin Health Effects Curcumin is a diferuloylmethane from the Indian spice turmeric. [See Fig. 14.1 for chemical structure of curcumin.] (Curcumin is also responsible for the yellow color of curry powder.) Turmeric is produced from rhizome of the plant Curcuma longa. In addition to its use as a spice, turmeric has multiple applications in traditional Asian Indian medicine, ranging from treating insect bites to wound healing. Multiple health benefits have been associated with curcumin and turmeric, as summarized in a recent monograph (Aggarwal, Surh and Shishodia 2007) and many reviews (Duvoix et al. 2005; Sharma, Gescher and Steward 2005; Maheshwari et al. 2006; Anand et al. 2007 and 2008; Kunnumakkara, Anand and Aggarwal 2008; Goel, Jhurani and Aggarwal 2008; Goel, Kunnumakkara and Aggarwal 2008; Strimpakos and Sharma 2008). Multiple and diverse bioactivities have been

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attributed to curcumin, including cancer chemopreventive and anti-inflammatory activities. Curcumin acts on multiple cellular targets. These include enzymes such as cyclooxygenase, lipoxygenase, inducible nitric oxide synthase (iNOS); transcription factors such as nuclear factor kappa B (NFkB) and activating protein-1 (AP1); cytokines such as tumor necrosis factor (TNF); and many gene products linked with cell survival, proliferation, invasion, and angiogenesis. For example, we found that curcumin inhibits NFkB and TNF in a human monocytic cell line (MonoMac 6) (Chan 1995), and we have also reported that, in the endotoxin-induced murine sepsis model, curcumin ingestion by gavage induces bioactivity as shown by downregulating iNOS gene expression in the murine liver (Chan et al. 1998). We end this section with a note on curcumin and patentability. In March 1995 USPTO awarded the patent “Use of turmeric in wound healing” (US Patent 5,401,504) to Suman Das and Hari Har Cohly, with the University of Mississippi as assignee. The patent was withdrawn in August 1997, after protest from India’s Council of Scientific and Industrial Research (CSIR), and demonstration of the lack of novelty criterion required for patenting, since turmeric has been used for centuries by the Indian population for wound healing (Jayaraman 1997). Novel curcumin analogues with enhanced potential have been synthesized, including ones exhibiting growth suppressive activity 30 times that of curcumin (Ohori et al. 2006). Furthermore, according to an article titled “Curry compound fights cancer in the clinic,” there were 22 phase I or II cancer-related clinical trials with curcumin in 2007, most of which are still ongoing (Carter 2008). Cancer Drug Resistance Turmeric and its active component, curcumin, have been investigated in many studies of cancer chemoprevention, cancer therapy, and, more recently, cancer drug resistance. For example, it has been shown that a daily dose of 3.6 g curcumin can achieve pharmacologically efficacious levels of the drug in colorectum, as seen by a decrease in an oxidative DNA adduct after curcumin treatment, in 12 patients (aged 47–72) with colorectal cancer (Garcea et al. 2005). Synergistic effects between curcumin and chemotherapeutic drugs have been demonstrated in vitro and in vivo. We have shown that curcumin (15 μM) enhances the cisplatin-mediated killing of the human ovarian cancer cell lines CAOV3 and SKOV3 in cell cultures; the SKOV3 cells are multidrug resistant (Chan et al. 2003; Chan and Fong 2007a). Curcumin (15 μM) enhances tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-induced killing of drug-resistant ovarian cancer cell lines SKOV3 and ES-2 (Wahl et al. 2007). Even by itself, curcumin (50 μM) has been shown to kill cisplatin-resistant human ovarian cancer cells (Weir et al. 2007). Curcumin (20 μM) enhances cytotoxicity of the drug oxaliplatin in the killing of human colorectal cancer cell line HCT116 and their p53 and p21 knockout variants (Howells, Mitra and Manson 2007). A synergistic effect between curcumin (10 μM) and the drugs paclitaxel or 5-fluorouracil (5-FU) has been seen in human prostate cancer cell line PC-3 (Hour et al. 2002). Curcumin (30 μM) increases sensitivity to the drug mitoxantrone in human breast cancer MCF-7 multidrug-resistant (MDR)

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cells (Limtrakul et al. 2007). In addition to human cancer drug resistance, curcumin also acts on murine cancer drug resistance. Curcumin (40 μM) reverses the resistance of multidrug-resistant mouse leukemia L1210 cells to adriamycin (Choi et al. 2008). The effect of curcumin in human cancer xenografts has been observed. Using human pancreatic cancer cell line MIA PaCa-2 in nude mice, curcumin (1 g/kg, oral, daily) potentiates the effect of the drug gemcitabine (Kunnumakkara et al. 2007). Using the multidrug-resistant human ovarian cancer cell line HeyA8-MDR, curcumin (500 mg/kg) reduces the tumor burden by 47%, whereas curcumin plus the drug docetaxel reduces the tumor burden by 58% (the combination shows no statistical significance) (Lin et al. 2007). However, curcumin (2% w/w in the diet) inhibits lung metastasis of the paclitaxel-resistant human breast cancer cell line MDA-MB-435 with paclitaxel treatment and after surgical removal of primary tumor (Aggarwal et al. 2005). For the TRAIL-resistant human prostate cancer cell line LNCaP, curcumin (30 mg/kg, 3 days per week, oral injection) sensitizes their killing by TRAIL after removal of primary tumor (Shankar et al. 2008). These examples illustrate the finding that curcumin will potentiate chemotherapeutic drugs in tumor xenografts in immunodeficient mice, even for drug-resistant human cancer cell lines.

Resveratrol Health Effects Resveratrol is a stilbene from red wine, grapes, peanuts, and pine nuts. [See Fig. 14.1 for chemical structure of resveratrol.] Red wine is made from whole grapes (including grape skin) from the fruits of the grape vine plant Vitis venifera and related species. Resveratrol is present in red wine because the molecule is associated with the skin portions of the fruit to fight against fungal infections of the plant. The molecule resveratrol actually is an example of phytoalexins, which are compounds produced by plants against pathogen attack. Similar to plant fungal pathogens, we have shown that resveratrol is active against human dermal fungal pathogens: resveratrol may be useful for treating athlete’s foot (Chan 2002). Multiple health benefits have been associated with resveratrol and wine consumption, as summarized in several monographs (Cheze, Vercauteren and Verpoorte 2001; Das and Ursini 2002; Aggarwal and Shishodia 2006b) and many reviews (Soleas, Diamandis and Goldberg 1997a and 1997b; Baur and Sinclair 2006; Fulda and Debatin 2006; de la Lastra and Villegas 2007; Opie and Lecour 2007; Athar et al. 2007; Gescher 2008; Harikumar and Aggarwal 2008; Kundu and Surh 2008; Pirola and Fr¨ojd¨o 2008; Saiko et al. 2008). Resveratrol’s recent claim to fame involved its action as activator of sirtuin deacetylases, leading to lifespan extension from organisms as diverse as yeasts, nematodes, fruit flies, and fish. Resveratrol’s prior claim to fame involved a phenomenon known as the “French Paradox.” This term refers to the observation that the French enjoy a relatively low risk of cardiovascular disease despite a diet that is high in saturated fat.

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Resveratrol improves general health in mice and retards their aging parameters (Baur et al. 2006; Barger et al. 2008; Pearson et al. 2008). In this sense it mimics a phenomenon known as caloric restriction, a method that retards aspects of the aging process in mammals. Indeed, this polyphenolic stilbene has multiple health benefits, especially cardioprotection and cancer chemoprevention. Resveratrol has anti-inflammatory activity. It down-regulates the inflammatory response by targeting molecules such as cyclooxygenases and nuclear factor kappa B (NFkB), and this activity may be the basis of its protective effect. With respect to the “French Paradox,” it is believed that resveratrol and alcohol in red wine may be part of the reason. In relation to this we have shown a synergistic effect between resveratrol and alcohol in the inhibition of the enzyme inducible nitric oxide synthase (iNOS) (Chan et al. 2000). In sum, moderate consumption of red wine may be beneficial to health. We end this section with a commercial note. In April 2008, FDA granted Orphan Drug Status to resveratrol, for the treatment of MELAS Syndrome (Israelsen 2008). MELAS stands for mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes, and patients with this disease have mutations in their mitochondrial DNA (Chinnery et al. 1997). The sponsor was Sirtris Pharmaceuticals, a company in Massachusetts with expertise in resveratrol and its analogs, and the resveratrol formulation was SRT501. Orphan drug designation for resveratrol in MELAS Syndrome would provide the company with 7 years of marketing exclusivity. 1 We hasten to add that other companies and investigators are studying resveratrol and its analogues and have found some more active than the stilbene itself (Pan et al. 2008; Saiko et al. 2008). Metabolic engineering of resveratrol is also being actively pursued, with an aim of transferring the resveratrol pathway to appropriate systems (Halls and Yu 2008). Cancer Drug Resistance Resveratrol and its analogues have been investigated in studies of cancer chemoprevention, cancer therapy, and, more recently, cancer drug resistance. In cell cultures, resveratrol kills cancer cells and also sensitizes them to killing by chemotherapeutic compounds. For example, for the human ovarian cancer cell line OVCAR-3, resveratrol (24 μM) shows an additive effect with cisplatin. For the human uterine cancer cell line Ishikawa, resveratrol (15 μM) shows an effect with doxorubicin (Rezk et al. 2006). For the human neuroblastoma cell line SHEP, resveratrol (30 μM) sensitizes its killing by various drugs including doxorubicin, taxol, and methotrexate. Resveratrol as a chemosensitizer has been shown in other human cancer cells, including glioblastoma U373MG, breast carcinoma MCF7, prostate carcinoma LNCaP, pancreatic carcinoma Panc1, as well as leukemia Junkat T and Reh B cells (Fulda and Debatin 2004). In addition, the compound is effective against drug-resistant cancer

1 In

late April 2008, the drug giant GlaxoSmithKline announced the purchase of Sirtris for $720 million in cash.

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cells. Resveratrol (50–400 μM) sensitizes the killing by etoposide in the etoposideresistant human colon cancer cell line HT-29 (Hwang et al. 2007). It kills drugresistant human multiple myeloma cell lines, dexamethasone-resistant MM.1, and doxorubicin-resistant and melphalan-resistant RPMI 8226 (50 μM). For the human multiple myeloma cell line U266, resveratrol (30 μM) sensitizes its killing by the drugs bortezomib and thalidomide (Bhardwaj et al. 2007). Resveratrol (100 μM), in combination with irradiation or arsenite, has been shown to reactivate the apoptotic death pathway in tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)resistant human melanoma cell lines in vitro (Ivanov et al. 2008). Similarly, resveratrol (100 μM) plus TRAIL kills docetaxel-resistant variants of human prostate cancer cell lines DU-145 and PC3 (Sallman et al. 2007). Resveratrol and its analogues have been tested in vivo in experimental animal systems (see review by Saiko et al. 2008). For example, with human breast cancer cell line MDA-MB-231 xenograft to immunodeficient mice, two studies show tumor suppression by resveratrol. Resveratrol was administered differently, either by intraperitoneal injection (25 mg/kg per day for 3 weeks) or by oral gavage (50 mg/kg per day for 5 weeks) (Garvin, Ollinger and Dabrosin 2006; Sareen et al. 2007). Resveratrol (50 mg/kg daily for 5 weeks by oral gavage) decreases tumor size in xenograft of human neuroblastoma cell lines NGP and SK-N-AS (van Ginkel et al. 2007). The resveratrol analogue trimethoxystilbene MR-3 (50 mg/kg three times a week for 23 days) also decreases tumor size in xenograft of human colorectal cell line COLO 205 (Pan et al. 2008). Resveratrol (15 mg/kg abdominal injection daily for 10 days) sensitizes liver cancer to 5-fluorouracil (5-FU) in an animal model using the murine hepatocellular carcinoma cell line H22 (Wu et al. 2004). The replication-deficient adenoviral gene therapy vector that produces tumor necrosis factor (TNF) using the early growth response gene promoter (TNFerade Biologic) has been shown to be induced by resveratrol (20 mg/kg four times) in a murine xenograft using the human pancreatic cancer cell line MiaPaCa 2 (Bickenbach et al. 2008). Although xenografts of drug-resistant cancer cell lines and resveratrol sensitization for chemotherapeutic drugs have not been reported yet, we would predict their feasibility based on cell and animal studies provided above. [For example, use an 81-fold gemcitabine-resistant variant of MiaPaCa 2 instead of the parental MiaPaCa 2 (Nakahira et al. 2007).]

Other Phytochemicals We have presented in vitro and in vivo data supporting the idea that epigallocatechin-3-gallate, curcumin, and resveratrol are useful in combating and overcoming drug resistance in cancer. Arguably, these are contributions to the field of cancer drug resistance from dietary compounds derived from the East (tea in China and turmeric in India) and from the West (red wine from France and other countries). A wide range of other phytochemicals have been characterized, and the ones with potential for activity against cancer drug resistance are briefly presented

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below. We start with individual phytochemicals and end with botanical extracts to be further characterized. In addition, we begin with dietary phytochemicals and end with ones from medicinal herbs. Quercetin Probably the next phytochemical with capacity toward cancer drug resistance is quercetin. This was the fourth compound listed in our previous review on phytochemicals overcoming ovarian cancer drug resistance (Chan and Fong 2007a). Quercetin is a flavonol found in common fruits and vegetables, such as apples and onions. It has antioxidant activity and has been reviewed as safe in vivo, although mutagenic in vitro (Harwood et al. 2007; Boots, Haenen and Bast 2008). We reported that quercetin (20 μM) sensitizes the killing by cisplatin of the drugresistant human ovarian cancer cell line SKOV3 (Chan et al. 2003). Quercetin (25–50 μM) enhances the recombinant tumor necrosis factor-related apoptosisinducing ligand (TRAIL)-induced killing of human leukemia cell lines (HPB-ALL, Jurkat T, K-562 and U-937) (Russo et al. 2007). In a human breast cancer cell line MDA-MB 435 xenograft, quercetin (22 μg/kg, 2 weeks of two treatments via subcutaneous injection) shows tumor apoptosis and growth suppression (Dechsupa et al. 2007). Genistein Genistein is the isoflavone from soybean. A soy-rich diet is implicated in having a role in reducing the incidence of breast and prostate cancer in Asian countries. Genistein has multiple cellular targets including cell cycle products and nuclear factor kappa B (NFkB) (Banerjee et al. 2008). With respect to cancer chemosensitization, genistein (100 μM) acts synergistically with 5-fluorouracil (5-FU) to kill the 5-FU-resistant human colon cancer cell line HT-29 (Hwang, Ha and Park 2005). It (25 μM) sensitizes tamoxifen in the killing of human breast cancer cell line BT-474 (Mai, Blackburn and Zhou 2007). Genistein (30 μM) sensitizes many cancer cell lines to chemotherapeutic drugs, including human prostate cancer cell line PC-3 to cisplatin or docetaxel, human breast cancer cell line MDA-MB-231 to doxorubicin or docetaxel, human pancreatic cancer cells BxPC-3 to cisplatin or docetaxel, and human lung cancer cell line H460 to cisplatin or docetaxel (Li et al. 2005). In human cancer cell xenograft to immunodeficient mice, genistein (1 mg/kg daily for 8 days intraperitoneally) sensitizes two leukemic cells, the acute promyelocytic leukemia cell lines NB4 and HL-60, to cytosine arabinoside (Ara-c) (Shen et al. 2007). Genistein sensitizes human pancreatic cancer cells in murine models. For the human pancreatic cancer cell line COLO-257 L3.6pl, genistein (1 mg/day for 10 days by gavage) sensitizes its killing by gemcitabine or cisplatin (Banerjee et al. 2005 and 2007). For the human pancreatic cancer cell line BxPC-3, genistein (800 μg/kg for 5 days by gavage) also sensitizes the killing by cisplatin (Mohammad et al. 2006). Advanced pancreatic cancer is clinically difficult to treat because of de novo resistance to therapy. These results demonstrate that genistein will sensitize chemotherapeutic drugs in drug-resistant cancers.

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Indole-3-carbinol Indole-3-carbinol (I3C) is a derivative of glucobrassicin, a secondary plant metabolite abundant in cruciferous vegetables. Thus, I3C comes from broccoli and cabbage. Its chemosensitization capacity has been demonstrated in many cancer cells in vitro (see review by Weng et al. 2008). As an example, for the murine B16 melanoma transfected with the human multidrug resistance (MDR)-1 gene and injected intraperitoneally into mice, treatment of I3C (500 mg/kg daily for 9 days added to food pellets) sensitizes cancer cell killing by doxorubicin (Christensen and LeBlanc 1996). I3C may be useful in sensitizing drug-resistant human cancer to chemotherapeutic drugs. Phytochemicals from Medicinal Herbs We introduce here two phytochemicals characterized from medicinal herbs that may be important for sensitization of drug-resistant cancers for chemotherapy. However, additional experimentation is definitely needed to substantiate this claim. The two compounds are voacamine and shikonin. Voacamine is a bisindolic alkaloid characterized from the plant Peschiera fuchsiaefolia and other species. It was found to be active against drug-resistant malaria (to chloroquine-resistant Plasmodium falciparum) in vitro, but its effect as an anti-malarial drug in the field is still in doubt (Orellana 2003). In cell culture experiments the compound enhances multidrugresistant cancer cell killing by chemotherapeutic drugs, including vinblastine killing of multidrug-resistant human cervical carcinoma cell line KB-V1 and doxorubicin killing of multidrug-resistant human osteosarcoma cell line U-2 OS-R (You et al. 1994; Meschini et al. 2007). The second phytochemical is shikonin. Isolated from the medicinal plant Lithospermum erythrorhizon and other species, shikonin is a naphoquinone with profound cytotoxicity against cancer cells and has anticancer and anti-inflammatory activities (Chen et al. 2002). Significantly, shikonin has recently been shown to induce necrosis in the P-glycoprotein-mediated drugresistant human breast cancer cell line MCF-7/Adr, both in cell culture and in xenograft to immunodeficient mice (Han et al. 2007). Summarizing this section, both voacamine and shikonin deserve further investigation with regard to aspects of overcoming cancer drug resistance. Botanical Extracts In the study of natural products, one goes from plant extracts to individual phytochemicals. We conclude this review of phytochemicals effective for cancer drug resistance with botanical extracts. Ginseng is highly valued in traditional Chinese medicine. It is the root of the plant Panax ginseng and related species. The American ginseng is Panax quinquefolius. A hot water extract of the American ginseng has been shown to act in combination with doxorubicin to suppress tumor growth of doxorubicin-resistant human breast cancer cell line MCF-7 xenograft in nude mice (Murphy, King and Smith 2008).

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Whereas ginseng is an herbal medicine, intake of fruits and vegetables has been encouraged for health benefits. These include the berry fruits such as strawberry, blueberry, and cranberry. A cranberry proanthocyanidin fraction has been reported to sensitize the drug-resistant human ovarian cancer cell line SKOV3 to killing by cisplatin, suggesting a synergistic effect between cranberry proanthocyanidins and the chemotherapeutic drug (Seeram 2008). Novel phytochemicals that can overcome cancer drug resistance await isolation and characterization in our daily fruits and vegetables.

Discussion Major phytochemicals that may be used in overcoming cancer drug resistance are reviewed in the previous section. Here we discuss aspects of phytochemicals that must be considered for eventual clinical application. Topics of antagonistic effects, combination therapy, and multiple molecular targets will be briefly touched upon. The most important topics, safety and bioavailability, will be discussed in some detail.

Antagonistic Effects Epigallocatechin-3-gallate (EGCG) from green tea, curcumin from turmeric, and resveratrol from red wine are the three phytochemicals potentially valuable in combating cancer drug resistance. These three will be used as examples for the topic of antagonistic effects. Effects of EGCG and curcumin on keratinocyte have been investigated, because these cells comprise the barrier that protects the body from a range of environmental insults. EGCG and curcumin show opposing effects: EGCG enhances keratinocyte differentiation whereas curcumin inhibits its differentiation; curcumin activates apoptosis whereas EGCG does not (Balasubramanian and Eckert 2007). Thus, phytochemicals can have distinct and even opposing effects. Phytochemicals may modulate chemotherapeutic drug action. In a study of human breast cancer cell line BT-474 xenograft in immunodeficient mice, curcumin inhibits cyclophosphamide-induced tumor regression (Somasundaram et al. 2002). Phytochemicals may exhibit different effects in vitro versus in vivo. In examples of melanoma and prostate cancer, resveratrol inhibits the growth of cancer cells in cell culture but not in animal models. For the human melanoma cell line A375, there is growth inhibition and apoptosis of cells in culture, but no inhibition in xenograft in mice. Two routes of resveratrol administration were used, with resveratrol-containing food pellets and with slow-releasing resveratrol-containing pellets implanted next to the tumor site. The second treatment was designed to bypass rapid resveratrol metabolism by the liver (Niles et al. 2003 and 2006). Similar findings are seen in the human prostate cancer cell line LNCaP. Resveratrol

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inhibits cell growth in both serum- and estradiol-supplemented media. In xenograft, however, resveratrol not only has no effect on tumor growth but also leads to an increase in angiogenesis and inhibition of apoptosis of tumor cells (Wang et al. 2008). In this case, the in vivo dose used was 3–6 mg/day, equivalent to consumption of 500–1000 ml of red wine/day, readily achievable in humans. Summarizing this section, adverse effects of specific phytochemicals must be analyzed with care before any clinical use.

Combination Therapy Despite the potential opposing effects between individual phytochemicals, combination of phytochemicals has been investigated, with interesting results. For example, in the drug-resistant ovarian cancer cell line SKOV3, a synergistic effect in killing is noted between resveratrol and indole-3-carbinol (I3C) (Raj et al. 2008). Remarkable findings with respect to tumor suppression are also seen in vivo. A diet containing a combination of curcumin and phenylethyl isothiocyanate (PEITC), another phytochemical found in cruciferous vegetables (like I3C), in a 26-week treatment, inhibits murine prostate cancer (known as the TRAMP mouse model) (Barve et al. 2008). Two separate combination studies with human breast cancer xenograft also show tumor suppression. A triple combination of EGCG, quercetin, and biochanin A (BCA), an isoflavone from the red clover Trifolium pretense, is effective in the human breast cancer cell line MCF-7 xenograft (intraperitoneal injection daily for 4 weeks) (Moon et al. 2008). In another triple combination of resveratrol, quercetin, and catechin in the human breast cancer cell line GFP-MDA-MB-231 xenograft (oral gavage thrice per week for a total of 118 days), tumor growth inhibition was observed when the three were administered at 5 mg each (Schlachterman et al. 2008). These data indicate potential future combinatorial use of phytochemicals for cancer drug resistance.

Multiple Molecular Targets Drug selectivity describes its ability to affect a particular population in preference to others. In addition to drug selectivity, it is also worthwhile to know drug function at the molecular level. However, cancer, especially solid tumors, can be very complex. Cancer drug resistance adds yet another dimension to the situation. To deal with this dilemma of complexity, there is a concept of multi-component therapeutics, with the notion that there is strength in numbers, by using an optimized combination and formulation of multiple active ingredients (Keith, Borisy and Stockwell 2005). Therefore, for cancer, a promiscuous drug or a “dirty” drug may work better than the ones with individual targets (Mencher and Wang 2005; Fojo 2008).

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Phytochemicals such as EGCG, curcumin, and resveratrol tend to aim at a multitude of molecular targets. It is because of these characteristics that definitive mechanisms of action are not available despite decades of research (Francy-Guilford and Pezzuto 2008). The multi-target nature of phytochemicals may be beneficial in overcoming cancer drug resistance. (This multi-faceted mode of action probably hinders the cancer cell’s ability to develop resistance to the phytochemicals.) Defects in cell death pathways are common in cancer, and there are apoptotic and non-apoptotic pathways of cell death (Okada and Mak 2004). Activation of cell survival pathways may also be important in cancer drug resistance. One recent concept in this regard is to bypass resistance by activating multiple death pathways (Hu and Xuan 2008). As an example, an apoptotic-resistant cancer can be sensitized to induce a non-apoptotic pathway, by using shikonin for necrosis. Many phytochemicals have been shown to induce cancer cell killing (Pan, Ghia and Ho 2008).

Safety Consumer Use of Phytochemicals Consumers in the United States have easy access to dietary supplements, especially after passage of the Dietary Supplement Health and Education Act of 1994 (Rapaka and Coates 2006). The estimated herbal dietary supplement sales figures are $4.59 billion for 2006 and $4.79 billion for 2007, with green tea among the top 25 sellers (Cavaliere, Rea and Blumenthal 2008). How safe are herbal supplements? Let us take St. John’s wort (also among the top 25 sellers) as an example. Because of its supposed activity in mild to moderate depression, cancer patients use this supplement. For cancer patients using the herb together with the drug irinotecan, plasma level of active metabolite of the drug is 42% lower (Mathijssen et al. 2002). Such use of herbal supplements can cause decreased therapeutic activity, or even unacceptable toxicity, depending on specific situations (Tascilar et al. 2006). Concomitant use of conventional and herbal medicines thus leads to clinically relevant herb–drug interactions. The two may interact either pharmacokinetically or pharmacodynamically (Izzo 2005). Clinical risk management of adverse herb–drug interactions has to be undertaken (De Smet 2006).

Phytochemical Safety Molecular mechanisms of herb–drug interaction have been investigated. The most notable involve the ATP-binding cassette drug transporters such as P-glycoprotein (You and Moris 2007) and the drug metabolizing enzymes (known as phase I and phase II enzymes), especially cytochrome P450 3A4 (CYP3A4) (Pal and Mitra 2006; Meijerman, Beijnen and Schellens 2006).

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Food–drug safety and synergy has been reviewed in a recent monograph (Thompson and Ward 2005). The dietary phytochemicals, proposed here as potentially useful for cancer drug resistance, are known to interact with the above discussed pathways. Therefore, to apply them toward cancer drug resistance, safety has to be a top priority. Potential toxicity and safety of these phytochemicals have been investigated (Galati and O’Brien 2004; Kawanishi, Oikawa and Murata 2005). In the presence of metal ions or peroxidases, EGCG, resveratrol, and quercetin have pro-oxidant activity that may lead to DNA damage. Curcumin and genistein also exert pro-oxidant activity after metabolic activation. Curcumin exhibits mitochondrial toxicity by inducing the collapse of mitochondrial membrane potential. Curcumin, quercetin, and genistein also modulate activity of phase II drug metabolizing enzymes. Potential safety issues exist if megadoses of phytochemicals are consumed daily (Galati and O’Brien 2004). With an emphasis on their putative cancer chemopreventive use, safety of a group of phytochemicals, including EGCG, curcumin, quercetin, and I3C, has been reviewed (Verschoyle, Steward and Gescher 2007). For EGCG, daily oral doses for 4 weeks at 800 mg/day in 40 volunteers only cause minor adverse effects. In a 90-day study of polyphenon E (a formulation of green tea extract with 53% EGCG), the oral no effect level (NOEL) values are 90 mg/kg/day for rats and 600 mg/kg/day for dogs. For curcumin, given to cancer patients at 3600 mg/day for 4 months or 800 mg/day for 3 months, only minor adverse effects are seen. For resveratrol, a single oral dose at 5 g in 10 volunteers only causes minor adverse effects (Boocock et al. 2007). More extensive toxicological testing of these phytochemicals is recommended, if they are to be taken as cancer chemopreventive agents on a long-term basis by healthy individuals. While this is true for chemoprevention, their use for cancer drug resistance is expected to involve a shorter time period. However, safety concerns should still be seriously considered.

Bioavailability Bioefficacy Effects of phytochemicals and chemotherapeutic agents on drug-resistant cancer cells have been investigated in cell cultures and in animal models. So as to bridge the gap between in vitro efficacy and clinical use, attention must be paid to the use of physiologically relevant concentrations. Appropriate biomarkers have to be identified in vitro and then verified in vivo. Proteomics and genomics approaches may yield novel biomarkers and even offer novel ways of predicting clinical efficacy (Manson et al. 2007). Bioavailability and bioefficacy of dietary phytochemicals may differ, depending on conditions varying from pure compounds, plant extracts, to mixtures with other food components (EGCG versus green tea extract, genistein versus soymilk or tofu, quercetin versus apple cider or apples) (Manach et al. 2005). For phytochemicals such as plant polyphenols, the effects in vivo, although significant, are more limited

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than those observed in vitro (Williamson and Manach 2005). However, it is possible to design appropriate interactions that affect the bioavailability of phytochemicals in vivo (Scholz and Williamson 2007). For example, the presence of piperine enhances the absorption of EGCG and curcumin (Shoba et al. 1998; Lambert et al. 2004). Extended treatment of human breast cancer cell line MDA-MB-231 cancer cells in culture with low concentrations of dietary phytochemicals (EGCG, curcumin, genistein, and I3C), achievable in vivo, inhibits clonogenic growth and results in apoptosis (Moiseeva et al. 2007). We use oral gavage and starvation (an empty stomach) to enhance curcumin uptake in mice. Using this approach, we have demonstrated in vivo inhibition of the inflammatory inducible nitric oxide synthase (iNOS) in murine liver (a potentially beneficial outcome) and in vivo exacerbation of the murine visceral leishmaniasis, a protozoan parasitic disease (a potentially detrimental outcome) (Chan et al. 1998; Adapala and Chan 2008). Our findings with these effects of curcumin in mice suggest caution with regard to its clinical use: curcumin may have the capacity to reactivate latent infections.

Hormesis The effect of curcumin has been described as an example of xeno-hormesis (Salvioli et al. 2007). The concept of hormesis suggests that the fundamental nature of the dose–response curve is neither linear nor threshold, but rather U-shaped or J-shaped. This shows a low-dose stimulatory response known as hormetic effect, representing overcompensation in response to disruptions in homeostasis (Calabrese and Baldwin 2001; Calabrese 2004). For phytochemicals among an estimated 10,000 secondary products (natural pesticides), it has been proposed that human ancestors evolved a generalized defense mechanism against low levels of phytochemicals to enable their consumption of many different plant species containing variable levels of natural pesticides (carcinogens) without subsequent ill health. Traces of phytochemicals found in fruits and vegetables may potentiate the immune system and help to protect against cancer (Trewavas and Stewart 2003). Phytochemicals show biphasic dose responses on mammalian cells. At high concentrations they can be toxic. Sub-toxic doses may induce adaptive stress response. There is activation of signaling pathways that result in increased expression of genes encoding cytoprotective proteins. It is suggested that hormetic mechanisms of action may underlie many of the health benefits of phytochemicals (Mattson 2008), and health benefits would include their action against cancer drug resistance.

Conclusion and Perspective Cancer drug resistance can be appreciated from the viewpoint of cancer stem cells (CSCs). A tissue stem cell is one that gives rise to two daughter cells with alternate fates (via asymmetric cell division), namely self-renewal or differentiation.

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The CSC hypothesis predicts that long-lived stem cells are more likely to accumulate the initial mutations leading to cancer than their short-lived differentiated progeny (Lobo et al. 2007). CSCs have been identified in a range of hemopoietic and solid tumors. It is believed that conventional chemotherapy kills most cancer cells but leave CSCs behind, and CSCs might be an important mechanism of resistance. CSCs express high levels of ATP-binding cassette transporters. In fact, this characteristic has been utilized to select for the “side population” cells from cancer as CSCs (exclusion of the fluorescent dye Hoechst 33342 via fluorescence-activated cell sorting, FACS) (Dean, Fojo and Bates 2005; Donnenberg and Donnenberg 2005). Phytochemicals such as curcumin are active against multidrug-resistant transporters, so they are expected to be able to overcome drug resistance of CSCs. We propose the hypothesis that phytochemicals act in synergy with chemotherapeutic drugs in overcoming cancer cell drug resistance. Application of specific phytochemicals may allow the use of lower concentrations of drugs with an increased efficacy. However, the pleiotropic nature of phytochemicals makes difficult the identification of the most sensitive target in the resistant cancer cell. Phytochemicals increase the therapeutic effect by blocking one or more targets of the signal transduction pathway, by increasing bioavailability of the chemotherapeutic drug, or by stabilizing the other drug in the system (HemaIswarya and Doble 2006). To use a metaphor, when used in low concentrations, the phytochemical causes multiple wounds without killing the drug-resistant cancer cell. However, the treatment increases its vulnerability to the “magic bullet” represented by the chemotherapeutic drug (Russo 2007). In addition to cancer drug resistance, dietary phytochemicals may be beneficial for cancer treatment in other ways. Diet can influence tumor metastasis and dormancy. In a human breast cancer xenograft model, adjuvant administration of dietary genistein, initiated after surgical resection of the primary tumor, reduces the lung metastasis burden by over 10 times (Vantyghem et al. 2005; Chambers 2009). Before clinical application, phytochemicals and health benefits need experimental substantiation, as an evidence-based approach is favored (Higdon 2007). Research needs funding. Industry avoids phytochemicals with multiple targets because what can be beneficial at one site can cause side effects at another (Carter 2008). Cancer drug resistance is an important topic, and the involvement of phytochemicals urgently needs funding from government agencies and private foundations. Pilot studies and limited trials for phytochemicals in cancer are available, and the evidence supports phase III clinical trials for tea polyphenols (Thomasset et al. 2007). However, a proof-of-concept clinical study of drug-resistant cancer with the phytochemical–chemotherapeutic drug synergistic approach is yet to be performed. We look forward to the days of overcoming cancer drug resistance using phytochemicals in a clinical setting. Acknowledgments We thank Arthur Yeh for reading the manuscript. The writing of this chapter was supported, in part, by an award from the American Institute for Cancer Research to Marion Chan.

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Index

Note: Page numbers followed by “f” represent a figure and page numbers followed by “t” represent a table. A Abdel-Latif, M. M., 180t Abderrabi, M., 29 Aboobaker, A. A., 258 Abourbeh, G., 305 Achanta, G., 142–144 Activating protein-1 (AP1), 7, 8, 322 Acute lymphoblastic leukemia (ALL), 224, 250 Acute myelogenous leukemia (AML), 10, 234, 246, 285 Acute promyelocytic leukemia (APL), 234, 235f, 237–240, 243–248, 264t Adachi, T., 24 Adams, J., 156 Adamson, P. C., 239 Adapala, N. S., 332 Adjei, A. A., 97 Aftab, D. T., 24 Agadir, A., 239, 240 Ahkong, Q. F., 31 Ahmad, I., 29 Ahmad, S., 159 Ahn, J. S., 99 Ahn, K. S., 179, 181 Ai, L., 105 Akar, U., 106 Akimov, S. S., 97, 99, 102 Akiyama, S., 25, 31 AKT inhibitors, 65 Alahari, S. K., 102 Albihn, A., 195 Albini, A., 118 Al-Hajj, M., 285 Ali-Osman, F., 154

Allan, L. A., 224 Alley, M. C., 24 Allikmets, R., 4 Alonso, M. M., 55 Aloya, R., 304 Altered drug metabolism, 154–155 Amaravadi, R. K., 54, 61, 63 Ambros, V., 257, 258, 260 Ambudkar, S. V., 32 Anand, P., 177, 321 Andreadi, C. K., 317 Andriollo, M., 183, 184 Angel, P., 146 Angileri, F. F., 186 Angiogenesis, 33, 182, 301–303 Animal models, drug resistance, 281–282 Anticancer, drug resistance in FDG to identify patients resistant to chemotherapy, use of, 297–299 5-FU and resistance metabolism, 299–300 molecular resistance as, 303–305 apoptosis, 304 hormone receptor-mediated resistance, 303–304 p53 tumour suppressor, 304–305 PET imaging, 296–297 use of, 296 proliferation imaging, 306–308 DNA repair, 306–307 integrin signalling, 307 MDR, 307–308 resistance, physiological causes of, 300–303 angiogenesis, 301–303

343

344 Anticancer, drug resistance in (cont.) blood flow, 300–301 hypoxia, 303 signal transduction pathways, 305–306 Antoni, L., 221 Antonyak, M. A., 101 Anuchapreeda, S., 197 Aoki, H., 64, 66 Aoudjit, F., 118, 121, 126, 195 Apperley, J. F., 13–14 Apoptosis, 209–214, 211f, 304 Aquilina, G., 219 Arango, D., 195 Arceci, R. J., 28 Arico, S., 58f, 59 Ariga, A., 156 Arnold, N. B., 152, 160 Arnold, J. T., 286 Ashikawa, K., 184 Asselin, E., 65 Asumendi, A., 151 Athar, M., 323 ATP-binding cassette (ABC), 2, 146, 152, 330, 333 ATRA therapy in APL mechanisms involved in the development of resistance in, 241f metabolic pathways in, 240f molucular basis of, 238–239 potential treatment strategies to overcome resistance, 248–251 arsenic trioxide (As2 O3 ), 250–251 histone deacetylase inhibitors, 251 liposomal ATRA in, 248–250 to prevent accelerated metabolism, 249f Attardi, L. D., 143, 212 Attwell, S., 125 Auerbach, R., 34 Auerbach, W., 34 Augustin, H. G., 37 Autophagosome, 56 Autophagy, 106 cellular process of, 56f inhibitors of, 62t molecular regulation of, 58f therapies that induce autophagy in cancer cells, 55t tumorigenesis, role in, 56–57 Autophagy/apoptosis, role of in drug resistance of cancer combination treatment, 63–66 crosstalk, 61–63 mechanism regulating

Index BECN1, 59 BNIP3, 59–60 ER Stress, 60–61 other pathways, 61 PI3K-AKT-mTOR signaling pathway, 58–59 p53 tumor suppressor pathway, 60 in cancer treatment, 54–56 in tumorigenesis, 56–57 therapies that induce autophagy in cancer cells, 55t Autophagy-related (ATG) genes, 56 Awad, A. B., 317 Azad, N., 143 B Baby, J., 180 Bading, J. R., 300 Baer, M. R., 11, 12t Baeuerle, P. A., 103 Bagga, S., 260 Bailey, H. H., 158 Bailey-Dell, K. J., 8 Bakos, E., 3, 5 Balak, M. N., 85 Balasubramanian, S., 328 Balbay, D., 33 Baldassarre, G., 219 Baldwin, A. S., Jr., 155, 180 Baldwin, L. A., 332 Balendiran, G. K., 155 Baltimore, D., 103, 178, 222 Banin, S., 221 Bao, S., 285 Barboza, J. A., 221 Bardou, V. J., 279 Bareschino, M. A., 84 Barger, J. L., 324 Bargou, R. C., 103, 180 Barker, P. A. 184 Baron, F., 180 Barranco, S. C., 158 Bartel, D. P., 258, 260 Barthel, H., 306 Bartussek, C., 214 Barve, A., 329 Baselga, J., 280 Baskerville, S., 258 Bast, A., 326 Bates, S., 31, 333 Bates, S. E., 1, 13, 158 Bauer, S., 298 Baur, J. A., 323, 324

Index BCL2 and adenovirus E1B 19kDa interaction protein 3 (BNIP3), 59–60 Bcl-2/bcl-xL, 187–188 BCL2 gene, 54 BCL2homology-3 (BH3), 56, 59 BCR-ABL+ cells, 76 BCR-ABL targeted therapy imatinib, 75–76 resistance in CML, 76, 77f mutation in dasatinib resistance, 80–81 nilotinib, 77f, 81 second-generation BCR-ABL inhibitors aimed at overcoming imatinib resistance, 79–80 T315I resistance, 81–82 nonmutation-dependent mechanisms, 78 Philadelphia chromosome (Ph), 75 signaling in CML, 75 Bea, F., 146 Bean, J., 85 Beckenlehner, K., 119 BECN1, 56, 59 Bedford, P., 154 Bedi, A., 75 Beevers, C. S., 198 Beg, A. A., 178 Behrend, L., 140, 141, 152 Beijnen, J. H., 330 Belkin, A. M., 102 Bellacosa, A., 267 Belpomme, D., 12t Benassi, B., 148 Benedetti, L., 248 Benlloch, M., 161 Benson, A. B., 30 Berenson, J., 180t Bergamaschi, A., 117 Berk, B. C., 150 Berman, E., 28 Berns, A., 215, 283 Bertheau, P., 278 Bertino, J. R., 265, 266 Bhardwaj, A., 325 Bharti, A. C., 180t, 181, 197, 198 Bhat-Nakshatri, P., 180 Bhattacharjee, A., 123 Bian, Z. M., 198 Bianco, R., 280 Bickenbach, K. A., 325 Bild, A., 280 Birckbichler, P. J., 101 Birkenkamp, K. U., 180t

345 Birkenkamp-Demtroder, K., 272 Bishop, J. M., 82, 141, 237, 238 Bissell, M. J., 120 Biswas, D. K., 180t Biswas, S. K., 317 Bitonti, A. J., 27 Blagosklonny, M. V., 213 Blandino, G., 214 Blasberg, R., 298, 305 Blenis, J., 190 Blenkiron, C., 281 Block, G., 317 Bloom, R., 97 Blower, P. E., 266 Blume-Jensen, P., 58 Blumenthal, M., 320, 330 BNIP3, 59–60 Boehm, J. E., 97 Boesch, D., 28 B¨ogler, O., 53, 54 Bohuslav, J., 105 Bolwell, G. P., 317 Bon, G., 196 Bonasera, T. A., 306 Bonavida, B., 188 Bond, M., 181 Bonizzi, G., 191 Bonnet, D. 285 Bonomi, P., 127 Boocock, D. J., 331 Boonstra, J., 122 Boots, A. W., 326 Borchert, G. M., 259 Borisy, A. A., 329 Borst, P., 2, 6 Bottero, V., 184t Bours, V., 103, 145 Boxer, R. B., 283 Boya, P., 62 Boyd, M. R., 266 Boylan, J. F., 237, 243 Bracht, K., 152 Bradford, C. R., 214 Bradford, P. G., 317 Bragado, P., 142 Brahimi-Horn, M. C., 147 Brancolini, C., 97 Brand, M. D., 148 Braun, T., 180 Brazma, A., 287 Bredow, S., 301 Brendel, C., 14 Brezniceanu, M. L., 220

346 Broxterman, H. J., 97 Buchholz, T. A., 180t, 318 Buck, A. K., 306 Budunova, I. V., 180 Bueso-Ramos, C. E., 180 Buess, M., 286 Bunn, P. A., 116 Burchert, A., 78 Burdak-Rothkamm, S., 221, 222 Burger, H., 211 Burgess, M. R., 81 Bursch, W., 54, 55, 62 Busby, J. E., 38 Bussolino, F., 34 Busuttil, R. A., 142 C Cai, W., 305 Cai, Y., 138 Cairo, M. S., 30 Caenorhabditis elegans, 257 Calabrese, C. R., 306 Calabrese, E. J., 332 Calalb, M. B., 123 Calcium channel blockers, MDR verapamil and clinically approved agents, 24–26 verapamil derivatives and experimental, 26 Calin, G. A., 257, 260, 261, 265 Camellia sinensis, 318 Cancer (and microRNAs), 260–261 Cancer-associated fibroblasts (CAF), 286 Cancer chemotherapy, 178 Cancer metastasis, 23, 24 See also Multidrug resistance (MDR) Cancer stem cells (CSC), 285–286, 332–333 Cancer therapy NF-␬B activation, 103–105 Cancer treatment drug resistance in, 54 autophagy in, 54–56 Canitrot, Y., 79 Canman, C. E., 221 Cappuzzo, F., 84 Cardone, M. H., 125 Carey, L. A., 272, 284 Carney, D. N., 116 Carter, A., 322, 333 Carthew, R. W., 260 Castaigne, S., 239 Castedo, M., 65 Cauchon, N., 304 Cavaliere, C., 330

Index Caveolae, 186 Cavener, D. R., 146 CD47, see Integrin-associated protein (IAP) Cell adhesion-mediated drug resistance (CAM-DR), 195 Cell adhesion molecules (CAMs), 122, 189, 319 Center, M., 7 Cerveny, L., 6 cFLIP, 188–189 Chabner, B. A., 30 Chambers, S. K., 28 Chambon, P., 236, 237 Chaney, S. G., 218–220 Chang, H. Y., 287 Chao, N. J., 28 Chatterjee, A. 26, 28 Chauhan, D., 161 Chemotherapy, 209–210 Cheze, C., 323 Chien, J. R., 316 Chiera, F., 144 Chin, D., 117 Chin, K., 281 Chinnery, P. F., 324 Cho, H. Y., 156 Chong, M. J., 143 Chong, Y. C., 143 Christensen, J. G., 327 Chromatin immunoprecipitation (CHIP), 248 Chromosomal translocation receptor fusion proteins due to, 236f receptor proteins expressed in APL due to, 235f Chronic lymphocytic leukemia (CLL), 140, 157, 160, 189, 195, 221, 250, 261 Chronic myelogenous leukemia (CML), 13, 14, 63, 76, 78, 80, 86, 141, 191 BCR-ABL signaling in, 75 imatinib, 75–76 Chu, F., 149 Chu, S., 78 Chung, C. H., 272 Ciardiello, F., 83 Cimmino, A., 263, 264, 265 Clarke, P. G., 53 Claudin-low cancer, 286 Clemens, M. J., 65 Cliby, W. A., 222 Coates, P. M., 330 Cocks, B. G., 181 Codogno, P., 58, 64 Cogswell, P. C., 180

Index Cole, S. P., 4, 7, 116 Colleoni, M., 284 Collingridge, D. R., 304 Collins, S. J., 148, 189 Colozza, M., 271 Comerford, K. M., 7 Cools, J., 305 Coon, J. S., 28 Cordes, N., 125, 126 Cordon-Cardo, C., 6 Cornwell, M. M., 25 Cortes, J., 82 Cotter, T. G., 149 Craiu, A., 155 Crapo, J. D., 138 Crighton, D., 56, 58f Cripe, L. D., 7 Croce, C. M. 260, 265 Croner, R. S., 272 Cullinan, S. B., 146 Cullis, P. R., 31 Cully, M., 59 Cusack, J. C. Jr., 182 c-Myc, 195 Cyclin D1, 125, 181, 194, 197, 198 Cyclin-dependent kinase (CDK), 80, 122, 126, 211–212 Cyclooxygenase-2 (COX-2), 181, 186 Cytotoxic drug therapy, 116 D Dabrosin, C., 325 Dai, D. L., 125 Dai, H., 279 Daido, S., 60 Daidone, M. G., 186 Dalton, W. S., 12t Damage-regulated modulator of autophagy (DRAM), 56, 60 Damia, G., 221 Damiano, J. S., 118, 121 Dan, H. C., 190 Dano, K., 10 Das, D. K., 323 Das, K. C., 183, 184t Das, S., 186 Dasmahapatra, G., 138 Datta, A., 123 Datta, S., 97 Datta, S. R., 189 Daubeuf, S., 152 Davidson, B. L., 259 Davis, R. E., 180t

347 Dawson, R. J., 3 Death-associated protein kinase (DAPK), 61 de la Lastra, C. A., 323 De Laurenzi, V., 98 De Smet, P. A., 330 Dean, M., 2, 10, 333 Debatin, K. M., 147, 323, 324 Dechsupa, S., 326 Deffie, A. M., 24 DeGorter, M. K., 3 Deininger, M., 76, 80 Del Vecchio, S., 302t, 307 Delavaine, L., 212 Delcommenne, M., 124 Delmastro, D. A., 224 Delva, L., 239, 240, 243 Demarchi, F., 97 Depinho, R. A., 281, 283 Derdak, Z., 152, 160 Desoize, B., 116 Devalaraja, M. N., 180t Developmental Therapeutics Program (DTP), 276 DeVita, V. T., 24 Dhainaut, A., 30 Differentiation therapy acute promelocytic leukemia and, 234–235 modulation through nuclear receptors in, 235–237 resistance to drug accelerated ATRA metabolism, 241–242 constitutive degradation of PMLRARα, 244–245 HDAC activity, 246 increased CRABPs, 242–243 mutations at ligand-binding domain of RARα, 244 P-glycoprotein expression, 245–246 PML/RARα isoforms, 246–247 telemerase activity, 247 tissue transglutaminase expression, 247–248 topoisomerase ii activity, 248 retinoids and All-trans-Retinoic acid in, 235 Di Fiore, P. P., 82 Di Gennaro, E., 83 Diamandis, E. P., 323 Dick, J. E., 285 Dickson, K. M., 184 Diederichs, S., 259, 260 Diehl, J. A., 125

348 Dietel, M., 29, 219 DiGeorge syndrome critical region gene 8 (DGCR8), 259 Dihydrofolate reductase (DHFR), 194, 265 Dihydropyrimidine dehydrogenase (DPD), 299–300 Ding, A., 184t Ding, J., 142 Ding, W., 244 Dioleoyl-phosphatidylcholine (DOPC), 106–108 DiPersio, J. F., 80 DNA damage recognition, 218–220 DNA repair, 306–307 Doble, M., 318, 333 Doige, C. A., 32 Dolcet, X., 183, 184t Donato, N. J., 78, 181, 198 Donehower, L. A., 143 Dong, Z., 24 Donnenberg, V. S., 333 Dontu, G., 286 Doran, J., 261, 264t Dorr, R. T., 28 Doubrovin, M., 305 Douer, D., 250 Douillette, A., 105 Doyle, L. A., 4, 8 Dressman, H. K., 210 Drug resistance as autophagy combination treatment for, 63–66 potential strategies for, 64f and microRNAs, 263–267 Druker, B. J., 76 Du, J., 180t Dunty, J. M., 123 Dutour, A., 32 Dutta, P. R., 83 Duvoix, A., 321 Duxbury, M. S., 124, 125 Dvorak, A. M., 34 Dvorak, H. F., 34 Dyck, J. A., 237 E Eastman, A., 216, 221 Eberhard, A., 37 Ebert, B., 8 Eckert, R. L., 328 ECM–integrin interaction, 128 See also ECM-mediated drug resistance ECM-mediated drug resistance

Index in metastatic cancer, 127–128 integrin signalling pathways and, 122–127 FAK as mediator of drug resistance, 123–124 ILK in drug resistance, 124–125 mechanisms of, 118–122 altered drug penetration, 119 cell proliferation, effects on, 120 cell-surface integrins, interaction with, 120–122 reduced DNA damage, 119–120 proteins in, 117–118 ECM proteins, 117–118 Edinger, A. L., 106 Efferth, T., 138 Efron, B., 275 EGFR targeted inhibitor, 86 EGFR-targeted therapy and cancer, 82 erlotinib, 84 gefitinib and erlotinib, 83 non-EGFR mutation-dependent mechanisms, 85 secondary EGFR mutations, 84–85 T790M resistance, 85–86 response to gefitinib treatment, 83–84 Eibl, H., 31 Eisen, M. B., 275 Ekmekcioglu, S., 196 el Deiry, W. S., 211, 212 Elchuri, S., 142 Ellerby, L. M., 154 Ellington, A. A., 55, 64 Ellis, L. M., 34 El-Osta, A., 7 Elsbach, P., 31 Elsinga, P. H., 307 Endoplasmic reticulum (ER) stress, 60–61 Engelman, J. A., 85 England, K., 149 Enhanced green fluorescent protein (eGFP), 305 Epand, R. F., 30 Epidermal growth factor (EGF), 74, 83, 101, 181 Epidermal growth factor receptor (EGFR), 32, 82–84, 192, 280, 305–306, 320, 321 Epigallocatechin-3-gallate (EGCG), 318–321, 319f, 328, 332 Epithelial-mesenchymal transition (EMT), 140, 286 ErbB1, see Epidermal growth factor (EGF) ErbB2, see HER-2

Index Erl, W., 188 Eshleman, J. S., 65 Esp´ın, J. C., 318 Esteller, M., 306 Esteves, T. C., 148 Estey, E. H., 247, 248, 250 Estrela, J. M., 151, 152 Estrov, Z., 155, 181 Estrugo, D., 126 Extracellular matrix (ECM), 97, 101–102, 117–118 -mediated drug resistance in, 127–128 mechanism, 118–122 Extracellular signal-regulated kinases 1 (ERK1), 61 F Fahy, B. N., 184t Fair, W. R., 36 Fan, D., 21, 23, 24, 25, 26, 27, 29, 30 Fandy, T. E., 161 Fanelli, M., 245 Fantin, V. R., 148 Farnesylthiosalicyclic acid (FTS), 281 Ferritin heavy chain (FHC), 145 18F Fluoropaclitaxel (FPAC), 307–308 Focal adhesion, 123 Focal adhesion kinase (FAK), 97, 100, 102–103, 123–124 See also TG2 Fojo, A. T., 25, 31 Fojo, T., 329, 333 Fok, J. Y., 95, 102, 196 G Galati, G., 331 Gallagher, R. E., 234, 245 Gallego, M. A., 138 Ganapathi, R. 26 Garcea, G., 322 Garg, P. K., 304 Gariboldi, M. B., 161 Garnick, M. B., 36 Gartel, A. L., 223, 224 Garvin, S., 325 Gauss, K. A., 145 Gehrt, A., 155 Geiger, B., 123 Geisler, S., 215 Genetically engineered mouse models (GEMs), 281 Georges, E., 27 Georgopapadakou, N. H., 97 Gerber, H. P., 34

349 Gerlach, J. H., 245 Gescher, A. J., 317, 321, 323, 331 Ghadimi, B. M., 272 Ghezzi, P., 149 Ghosh, S., 145 Gianni, L., 284, 285 Gimbrone, M., 33 Gingras, A. C., 58 Ginos, J. Z., 298 Giorgio, M., 142 Giri, D. K., 181 Gladson, C. L. 120 Gleevec, 74, 76 Glutathione S-transferase (GST), 96, 155, 159 Glycogen synthase-3β (GSK-3β), 123–126 Godin-Heymann, N., 85, 85, 192 Godwin, A. K., 159 Goel, A., 321 Gogvadze, V., 149 Golab, J., 160 Goldberg, D. M., 323 Goldberg, H., 28 Goldman, B., 11 Golemovic, M., 81 Gonz´alez-Polo, R. A., 62 Goodman, L. S., 116 Gorre, M. E., 78, 79 Gosland, M. P., 27 Goto, S., 116 Gottesman, M. M., 96 Gottifredi, V., 212, 222 Gout, P. W., 159 Gozuacik, D., 54, 58 Gradilone, A., 183, 184 Graham, C. H., 138 Graham, R. M., 97 Granulocyte macrophage colony stimulating factor (GMCSF), 117 H Ha, I., 257 Ha, J., 326 Haas, R. L., 302t, 304 Haber, D. A., 259, 260 Habib, A. A., 181 Hacker, H., 179 Haddad, J. J., 144 Haematological malignancies, 116 Haenen, G. R., 326 Hagopian, G. S., 216, 217f Hainaut, P., 278 Halder, J., 108, 124 Hall, M. N., 58

350 Halliwell, B., 140, 160 Halls, C., 324 Hamada, H., 24 Hamilton, J. A., 181 Hammond, L. A., 30 Hampton, M. B., 150, 153 Hanahan, D., 100, 140, 317 Hancock, J. T., 149 Hanks, S. K., 123 Hannes Hentze, G., 150 Hannigan, G. E., 124 Hannun, Y. A., 118 Hansson, J., 154 Hantschel, O., 80 Hara, Y., 320 Harbottle, A., 151 Hariharan, I. K., 75 Harikrishnan, K. N., 7 Harker, W. G., 25 Harper, M. E., 152 Harris, A. L., 28 Harris, L., 271, 272 Harris, S. L., 222 Hart, I. R., 32 Harte, R. J., 299 Harwood, M., 326 Hastak, K., 212 Haubner, R., 307 Hauck, C. R., 124 Haupt, Y., 142 Hawkins, D. S., 216 Hayden, M. S., 145 Hayes, D. N., 272 Hazai, E., 3 Hazelbag, S., 121 Hazlehurst, L. A., 118, 119, 120,. 121, 122 He, L., 29, 237, 246, 261, 264, 265 He, Q., 219 Hedley, D. W., 151 Hehlmann, R., 75 Heinzel, T., 239, 246 Heisterkamp, N., 75 Helbig, G., 183 Helleday, T., 119 Helleman, J., 210 Helton, E. S., 210, 219 HemaIswarya, S., 318, 333 Hendrikse, N. H., 307 Henriksen, U., 3 Henriksson, M., 141 Hentze, H., 150 Hepatocyte growth factor (HGF), 34 HER-2, 193

Index Herlyn, M., 35 Herman, J. F., 97, 99f, 100, 102, 105 Herpes simplex virus1-tyrosine kinase (HSV1-tk), 305 Herschkowitz, J. I., 271, 274t, 282 Herzog, C. E., 25 Herzog, K. H., 221 Hess, K. R. 284 Higdon, J., 333 Higgins, K. A., 183 Hildebrand, J. D., 123 Hirata, A., 83 Histone deacetylase enzymes (HDAC1/HDAC2), 238 Histone deacetylase (HDAC) activity, 82, 145, 238, 246 Ho, C. T., 330 Ho, W. C., 184 Hoadley, K. A., 271 Hochhaus, A., 78 Hodges, M., 237 Hodkinson, P. S., 115, 121, 122, 126 Hoffman, H., 34 Hofmann, J., 29 Hofsli, E., 27 Hollenstein, K., 3 Hollstein, M., 210, 214 Hollt, V., 25, 26 Holmes, J., 30 Holmes-McNary, M., 155 Holzmayer, T. A., 31 H¨onemann, D., 193 Hong, J., 198 Hong, R. L., 198 Hoogsteen, I. J., 295 Hope, M. J., 31 Hormone receptors, 304–304 Horn, H. F., 143 Horton, T. M., 97 Hoshida, Y., 138 Hosoi, H., 65 Hour, T. C., 322 Howells, L. M., 322 Hoyt, D. G., 119 Hsia, D. A., 102 Hu, F., 23 Hu, L., 65 Hu, X. F., 28 Hu, Y., 140, 142 Hu, Z., 6, 272, 320 Huang, P., 137, 143 Huang, S. K., 29, 180t Huang, Y., 266

Index Hudziak, R. M., 82 Huet, S., 25 Huff, L. M., 7, 8 Hug, H., 151 Humphries, M. J., 120 Hung, M. C., 198 Hungerford, DA., 75 Hunter, T., 58 Hutchinson, O. C., 298 Hvorup, R. N., 3 Hwang, J. T., 326 Hwang, J. Y., 100 Hwang, P. G., 3 Hwang, S., 184t Hyne, R. O., 101 Hypoxia, 305 Hypoxia-inducible factor 1(HIF-1), 144, 147–148 I Iacobuzio-Donahue, C. A., 101 Iliakis, G., 211 Imai, Y., 8 Imamura, T., 219 Immunoglobulin G (IgG), 119 Inaba, M., 24, 25 Inbal, B., 58f, 61 Increased cellular RA-binding proteins (CRABP), 242–243 Indo, H. P., 141 Inducible nitric oxide synthase (iNOS), 320, 322, 324, 332 Inhibitor of apoptosis protein (IAP), 101, 181, 186, 188 Inoue, J., 145 In situ hybridization (ISH) analysis, 32 Iozzo, R. V., 117 Integrins, 120 Integrin-associated protein (IAP), 101, 145, 160, 181, 186, 188 Integrin-linked kinase (ILK), 123–125 Integrin signalling, 122–123, 307 Interleukin-1β (IL-1β), 179 Interleukin (IL)-6-mediated signaling pathways, 192–193 Irani, K., 140, 141, 152 Irmak, M. B., 143 Irmer, D., 84 Ishii, T., 146 Ishikawa, K., 141 Ismail, R. S., 209 Isoform B of human LC3 (LC3B), 66 Israelsen, L., 324

351 Ito, H., 54, 55 Ito, T., 31 Itoh, K., 145 Ivanov, V. N., 325 Izzo, A. A., 185 Izzo, J. G., 180 J Jaattela, M., 106 Jack, M. T., 221 Jacks, T., 281 Jackson-Bernitsas, D. G., 180 Jaffr´ezou, J. P., 26, 31 Jain, R. K., 34, 35 Jaiswal, A. K., 147 Jansen, M. P., 279 Jardillier, J., 116 Jatoi, A., 321 Jayaraman, K. S., 322 Jayaraman, L., 219 Jayne, D. G., 118 Jekunen, A. P., 218 Jeon, J. H., 99 Jeon, K. I., 155 Jhurani, S., 321 Jiang, D., 100 Jiang, H., 55 Jiang, X., 79 Jin, S., 6 Joe, A. K., 283 Joensuu, H., 116 Johnson, B. E., 84 Johnson, S. M., 258, 261, 264t, 265 Johnson, S. W. 212 Joly, F., 12t Jones, R. A., 101 Jonker, J. W., 8 Jonkers, J., 281, 283 Joshi, S., 100 Junttila, M. R., 195 K Kaanders, J. H., 303 Kacinski, B. M., 28 Kage, K., 3 Kakar, S. S., 198 Kakolyris, S., 152 Kalinina, E. V., 154, 159 Kamat, A. M., 184t, 198 Kamiguti, A. S., 140 Kandioler-Eckersberger, D., 214 Kane, L. P., 189 Kang, Y. C., 189 Kantarjian, H., 14, 81

352 Kanzawa, T., 54, 55, 60, 62, 63 Kaplowitz, N., 158 Kapoor, M., 220 Karginov, F. V., 260 Karin, M., 103, 145, 146, 147, 179 Karsan, A., 34 Kartner, N., 24 Kasibhatla, S., 156 Kastan, M. B., 214 Kato, T., 181 Katsman, A., 155, 156 Katso, R., 189 Kauppinen, S., 107 Kawanishi, S., 140, 331 Kaye, S. B., 96 Kedar, D., 33 Keith, C. T., 329 Kellen, J. A., 26 Kensler, T. W., 156 Kerbel, R. S., 301 Kerbel, R., 33, 34 Kerr, J. F., 53 Kessel, D., 55 Kety, S. S., 301 Khan, N., 320 Khera, V., 100 Khorashad, J. S., 80 Khwaja, A., 123 Kihara, A., 57f, 58f Kilburn, L. S., 274t Killion, J. J., 24 Kim, B. J., 212 Kim, D. S., 98, 100, 105 Kim, K., 260 Kim, K. W., 60 Kim, S. J., 21, 33, 34, 37 Kim, W. H., 150 Kimchi, A., 54, 58 King, M. L., 327 King, T. C., 214 Kinnula, V. L., 138 Kirkham, P. A., 317 Kirkpatrick, D. L., 159 Kissel, J., 298, 299 Kissil, J. L., 141 Kitadai, Y., 35 Kizaki, M., 245 Klagsbrun, M., 33, 34 Klinghoffer, R. A., 123 Klionsky, D. J., 56, 58, 59, 66 Klohs, W. D., 26 Kloog, Y., 97 Knudson, C. M., 211

Index Knutsen, T., 8 Kobayashi, A., 145, 160 Kobayashi, H., 303 Kobayashi, S., 84 Koch, S., 303 Koff, A., 122 Kogan, S. C., 237, 238 Kohlhardt, M., 25 Kojima, K., 221, 223 Kolitz, J. E., 11, 12t Komata, T., 63 Kondo, S., 53, 54, 56, 58f, 61 Kondo, Y., 106 Kong, Q., 151 Kopnin, B. P., 141 Kordes, U., 180, 180t Kornmann, M., 194 Kosaka, T., 85 Kotlo, K. U., 146 Krajewski, S., 223 Kremer, J. M., 63 Kreuz, S., 188 Krichevsky, A. M., 261, 264t, 265 Kripke, M. L., 22, 23 Kris-Etherton, P. M., 317 Krishnamurthy, P., 8 Kroemer, G., 56, 106, 317 Kroemer, H. K., 26 Krugliak, M., 27 Kubbutat, M. H., 142 Kulasegaram, R., 302t, 303 Kulesz-Martin, M., 144 L Lee, J. W., 104 Ligand-binding domain (LBD), 236, 244 5-Lipoxygenase (5-LOX), 191–192, 198 Lithospermum erythrorhizon, 327 5-LOX, 191–192 Locher, K. P., 3 Lysosome-associated membrane protein2 (LAMP2), 62 M McCurrach, M. E., 212 McEligot, A. J., 317 McGahon, A., 75, 138 McLean, G. W., 102, 103, 124 McNamara, K., 248 Magne, N., 83 Maheshwari, R. K., 321 Mahon, F. X., 78 Maity, A., 87 Maiuri, M. C., 55, 61

Index Mak, T. W., 330 Makarov, S. S., 181 Maki, C. G., 142 Mammalian target of rapamycin (mTOR) pathway, 190 Manach, C., 331–332 Manfredi, J. J., 213, 224 Mangala, L. S., 102 Mani, S. A., 286 Mankoff, D. A., 304 Manley, P. W., 81 Mann, A. P., 98, 99f, 104, 180t, 196t Manson, M. M., 317, 322, 331 Marasca, R., 244 Martin-Caballero, J., 212, 223 Martinez, R. V., 7 Mart´ınez-Lacaci, I., 316 Marttinen, M., 56f Marx, J., 55 Marzano, C., 160 Mashima, T., 96 Mathas, S., 155, 180t Matheu, A., 143, 169 Mathijssen, R. H., 330 Matin, K., 28 Matrix metalloproteinases (MMP), 127, 181–182 Matsuoka, J., 221 Matsuoka, S., 221 Matsuyama, S., 153 Mattson, M. P., 332 Maximum tolerated dose (MTD), 13, 298 Mayer, F., 214 Mazzanti, R., 30 MDR gene, 185 MDR proteins, 2, 15 MDR-ABC transporters clinical and experimental aspects gene regulation, 6–9 transported substrates, 4–6 clinically relevant substrates of, 5t mechanistic aspects of, 2–3 significance in cancer, 9, 9t association with treatment failure, 10 emerging role in resistance against targeted agents, 12–14 with inhibitors, 10–12 MDR1 expression, 7 Mechetner, E. B., 30 Mechta-Grigoriou, F., 147 Mehta, K., 95, 97, 99, 100, 103, 105, 196–197, 198, 237, 248, 249 Meijer, A. J., 64

353 Meijerman, I., 330 Meister, A., 150, 158 MEK–ERK signaling pathway, 183 Melino, G., 98 Melnick, A., 237 Menard, S., 272 Mencher, S. K., 329 Meneguzzi, G., 122 Merker, H. J., 53 Merritt, J. E., 108 Merritt, W. M., 194 Meschini, S., 327 Metabolic resistance, 299 Metalloproteinase (MMP), 127, 140, 181–182, 320 Metastasis defined, 22 and drug resistance, 32–33 pathogenesis of, 22–23 Mettouchi, A., 122 Meuillet, E. J., 152 Meyskens, F. L., Jr., 158 Mhaouty-Kodja, S., 98 Michael, M., 127 Mickisch, G. H., 24, 26, 30 Microarrays of cancer, 274 MicroRNAs biogenesis of, 258–260, 259f and cancer, 260–261 cellular function, 260 and drug resistance, 263–267 history of, 257–258 as oncogenes and tumor suppressors, 261–263, 262f, 264f polymorphisms affecting drug response, 265f Miething, C., 78 Milakovic, T., 97 Miller, L. D., 278 Miller, T. P., 30 Miller, V. A., 242 Millward, M. J., 12t, 30 Milroy, R., 12t Minko, T., 156 Miquel, C., 223 Miraglia, E., 30 MiRSNP/polymorphism, 265 Mishra, P. J., 265–266 Mishra, S., 99f, 105 Mitogen-activated protein kinase (MAPK), 146, 320 Mitra, A., 322 Mitra, A. K., 330

354 Mitsudomi, T., 83 Mitsuki, S., 298 Miyajima, A., 151 Miyake, K., 4, 8 Miyake, Y., 307 Mizejewski, G. J., 120, 121 Mizushima, N., 56 Moehler, M., 299 Moen, M. D., 13 Moenner, M., 60 Mohammad, R. M., 326 Mohan, K., 100 Moins, N., 30 Moiseeva, E. P., 332 Molecular signatures of drug resistance animal models and, 281–282 cancer cell lines and, 276–278 heterogeneity of cancer in, 272 human studies and, 284–285 linking cell line data with human observational data and, 278–281 microarray analysis, 274–276 microenvironment, 286–287 model systems used, 273f mouse models with human cancer biology, 282–284 observational vs. experimental studies, 272–274 pathways or modules vs. individual genes as functional units, 287 stem cells in, 285–286 Molina, J. R., 116 Moliterni, A., 272 Mondesire, W. H., 65 Moody, S. E., 283 Moon, Y. J., 329 Mootha, V. K., 287 Morabito, A., 97 Moragoda, L., 198 Morgillo, F., 85 Mori, N., 103 Morito, N., 146 Morris, D. I., 30 Morrissey, D. V., 108 Mortimer, J. E., 302t, 304 Moschos, S. J., 120 Motohashi, H., 145 MRP1 gene, 7 Mu, Z. M., 142 Mueller, K. L., 85 Mueller, M. M., 117 Muindi, J., 239 Muindi, J. R., 239, 240, 241

Index Mujoo, K., 216, 220 Mukhopadhyay, A., 103, 181, 198 Mukhtar, H., 320 Mullamitha, S. A., 302t, 307 Multidrug-associated protein (MRP), 3–7, 96, 316 Multidrug resistance (MDR), 307–308 antivascular therapy of prostate carcinoma, 36–38 associated mechanism, 23–24, 23f clinical reversal of, 30–31 concept, 23–24 defined, 23 experimental reversal, 31–32 metastasis and, 32–33 reversal of experimental, 24 antibiotics and analogs, 27 calcium channel blockers, 24–26 calmodulin antagonists, 26 cyclosporins and analogs, 27–28 hormones and antihormones, 28 indole alkaloids, 27 liposomal encapsulation, 29 molecules, other, 29–30 pharmaceutical emulsifying surfactants, 28–29 tumor angiogenesis, 33–36 Murata, M., 331 Murawaki, Y., 142 Murine embryonic fibroblasts (MEF), 60, 63 Murphy, L. L., 327 Murren, J. R., 30 Mutoh, K., 28 N Nadiminty, N., 197 Nagatani, G., 220 Nagayama, S., 159 Naglich, J. G., 127 Nair, S., 24, 26 Naito, M., 28 Nakahira, S., 325 Nakanishi, T., 8 Nakano, R., 159 Nakayama, M., 7 Nakshatri, H., 180t Nam, S., 80 Nanda, N., 98 Nasioulas, G., 27 Nason-Burchenal, K., 247 Nathan, C. F., 140 National Cancer Institute (NCI), 13, 266 Nawrath, H., 26

Index Nawrocki, S. T., 97 Nels, V., 34 Nervi, C., 245 Netti, P. A., 119 Neve, R. M., 273, 274t, 277, 282, 284 Newman, R. A., 29 NF-E2-related factor 2 (Nrf2), 145–146, 147, 151, 152 Ng, C. P., 188 Ng, I. O., 224 Ng, S. S. W., 65 Nicholas, B., 98 Nicholson, D. W., 153 Nicholson, K. M., 59, 63 Nieborowska-Skorska, M., 75 Nielsen, T. O., 280 Nikolopoulos, S. N., 124 Niles, R. M., 328 Nishikawa, T., 321 Nissen-Meyer, J., 27 Niu, C., 250 Non-small cell lung cancer (NSCLC), 10, 13, 83–86, 192, 215t No oral no effect level (NOEL), 331 Nor, J. E., 34 Nordsmark, M., 303 Normanno, N., 280 Novak, U., 182 Novel resistance mechanisms, 97 Nowell, P. C., 75 Nowicki, M. O., 141 Nuclear Factor-␬B (NF-␬B) in anti-apoptotic gene products in, 181 and cancer, 179–180 in cell proliferative gene products, 181 in chemotherapeutic agents and chemoresistance, 183–184, 184t, 185f discussion, 178–179 in genes involved in angiogenesis, 182 inhibitors and chemosensitization, 197–198 of invasive gene products, 181–182 -mediated drug resistance mechanisms Bcl-2/bcl-xL, 187–188 cell adhesion molecules, 189 cFLIP, 188–189 c-Myc, 195 COX-2, 186 Cyclin D1, 194 EGFR, 192 HER-2, 193 IL-6, 192–193

355 inhibitor of apoptosis proteins, 188 integrin, 195–196 5-LOX, 191–192 MDR gene, 185 mTOR pathway, 190–191 PI3K/Akt, 189–190 PKC/PKA, 194 protein tyrosine kinases, 191 STAT3, 197 survivin, 186–187 TG-2, 196–197 in tumor cell metastasis, 182–183 of tumor tissue types, 180t Nuclear factor kappa B (NFkB), 322 Nuclear retinoic acid receptors (RAR), 236 Nucleotide-binding domains (NBDs), 2 Nucleotide excision repair (NER), 79, 316 O Oberley, L. W., 142 Oberley, T. D., 142 Objective response rate (ORR), 116 O’Brien, P. J., 331 Ogata, M., 58f, 61 Ogier-Denis, E., 58, 58f, 61 Ogryzko, V. V., 144 Oh, D. S., 279 Oh, Y. K., 151 O’Hare, T., 80, 81 Ohnishi, S., 31 Ohori, H., 322 Ohshiro, K., 55 Oikawa, S., 331 Okada, H., 330 Okahara, F., 103 Oldham, E. A., 3 Oliner, J. D., 142 Ollinger, K., 325 O’Mara, M. L., 3 “Oncogenes,” 74 Ondrey, F. G., 180t O’Neill, P. M., 63 Ono, M., 83, 84 Opie, L. H., 323 Opipari, A. W. Jr., 55 Orellana, C., 327 Orrenius, S., 150, 153 Osborne, C. K., 281 Oshita, F., 121 Osterlind, K., 116 Overgaard, J., 303 Owens, L. V., 102 Owens, W., 102

356 Oxidative stress in cancer adaptive response to, 143–144 drug resistance mechanisms as redox adaptation, 139f altered drug metabolism, 154–155 apoptotic execution in, 152–154 enhanced cell survival, 150 increased capacity to maintain redox homeostasis, 150–152 increased repair and tolerance of DNA damage, 154 proteins involving in cell survival in, 149–150 redox alterations in findings, 140 intrinsic ROS stress in cells, 141–143 oxidative stress in tumorigenesis, 140–141 redox homeostasis regulators in, 148 redox-sensitive transcription factors, 144–145 c-Jun, 146–147 HIF-1, 147–148 NF-␬B, 145 Nrf2, 145–146 therapeutic strategy targeting redox-sensitive factors, 160–161 targeting regulators of redox homeostasis, 156–160 targeting transcription factors, 155–156 Ozols, R. F., 25, 26 Ozpolat, B., 233 Ozvegy, C., 3 P P53 activation switch of, 222–223 clinical drug resistance and, 214–215 downregulation of promoter response in in target genes, 223–224 gene status, 215–218 gene status of, 214–215, 215t mutation drug resistance caused by, 212–213 modulate drug response and specificity, 213–214 relative resistance, gene status and the role of antitumor agent, 215–218 role in sensitive tumor cells to antitumor agents, 210–212 Packer, L., 149 Paez, J. G., 83, 280

Index Paget, S., 22, 23 Paglin, S., 62 Pahl, H. L., 103, 155 Pajeva, I., 27 Pal, D., 330 Palayoor, S. T., 180 Pan, M. H., 324, 325, 330 Pan, S., 150 Panax quinquefolius, 327 Pancreatic ductal adenocarcinoma (PDAC), 106 Pandolfi, P. P., 234 Pani, G., 151 Pao, W., 82, 83, 84, 85, 280 Pardal, R., 285 Parent, E. E., 304 Park, C. C., 122 Park, J. M., 196 Park, O. J., 326 Parthasarathy, R., 248, 249 Particularly interesting new cysteine histidine rich protein (PINCH)), 124 Pasqualini, R., 34 Passaniti, A., 118 Patel, B. P., 140 Patel, N. M., 180 Pathway ATM/Chk2 andATM/HIPK2, 220–221 ATR/Chk1, 221–222 Patriaria, P., 31 Patterson, B., 317 Pattingre, S., 59 Paul, S., 7 Paull, K. D., 266 PDGFR-ˆa, 36–37 Pearson, K. J., 324 Pelicano, H., 138, 149, 150 Pellegrino, M., 287 Pemrick, S. M., 236 Pene, F., 65 Peng, X., 99f Perego, P., 224 Pervaiz, S., 138, 140, 148, 152–153 Peschiera fuchsiaefolia, 327 Petiot, A., 56f, 59, 62 Pezzuto, J. M., 318, 330 P-glycoprotein expression, 245–246 P-glycoprotein (P-gp), 6, 185, 245 Pham, L. V., 180t Pham, N. A., 151 Pharmacokinetic resistance, 298–299 Pharoah, P. D., 278 Phenylalanine ammonia lyase (PAL), 317

Index Phenylethyl isothiocyanate (PEITC), 329 Philip, P. A., 30 Phosphatase and tensin homologue (PTEN), 59 Phosphatidylinositol-3-kinase (PI3K), 182 Physiological Pgp, 11 Phytochemical-chemotherapeutic synergy curcumin cancer drug resistance, 322–323 health effects, 321–322 epigallocatechin-3-gallate cancer drug resistance, 320–321 health effects, 318–320 other phytochemicals, 325–328 genistein, 326 indole-3-carbinol, 327 quercetin, 326 Phytochemicals as drug resistance bioavailability, 331–332 bioefficacy, 331–332 hormesis, 332 discussed, 317–318 phytochemical-chemotherapeutic synergy, 318–328 phytochemicals from medicinal herbs, 327 antagonistic effects, 328–329 combination therapy, 329 multiple molecular targets, 329–330 resveratrol cancer drug resistance, 324–325 health effects, 323–324 safety, 330–331 consumer use of phytochemicals, 330 phytochemical safety, 330–331 PI3K/Akt pathway, 189–190 PI3-K-dependent kinase (PDK) Piacentini, M., 98 Pianetti, S., 193 Picard, S., 14 Piekarz, R. L., 28 Pierce, J. W., 155 Pietenpol, J. A., 211 Piette, J., 183 Pinkett, H. W., 3 Piret, B., 183, 184t Pirker, R., 26 Platelet-derived growth factor (PDGF), 33, 34 Pluen, A., 96 Pogany, G., 118 Pollack, J. R., 281 Pollack, V. A., 84 Pollet, I., 182 Polte, T. R., 123 Polychronis, A., 282

357 Pompella, A., 151 Poole, A. J., 212 Poon, R. Y., 122 Posey, J. A., 301, 302t, 307 Positron emission tomography (PET), 296, 297f Poste, G., 23 Post-translational modification, 222 Potter, J. D., 274 p50/p105, see Nuclear factor-␬B (NF-␬B) Pouyssegur, J., 147, 315 Powis, G., 159 Preall, J. B., 260 Prediction Analysis of Microarrays (PAM), 275 Price, P., 295 Price, P. M., 301 Priglinger, S. G., 100 Programmed cell death (PCD), 53 Proliferation Imaging, 306 Proliferator-activated receptor γ (PPARγ ), 8 Promyelocytic leukemia (PML), 234 Promyelocytic zinc finger (PLZF), 234 Propper, D. J., 298 Prostate-specific antigen (PSA), 321 Protein tyrosine kinases (PTK), 191, 198 p53 tumour suppressor, 60, 304–305 Puca, R., 221 Pulido, R., 103 Punnonen, E. L., 56f Q Qian, X. D., 27 Qiao, L., 180 Qin, L. F., 224 Quint´as-Cardama, A., 82 R Rabindran, S. K., 86 Radhakrishnan, S. K., 223, 224 Radisky, D. C., 140 Raelson, J. V., 239 Raffel, J., 152 Rahman, A., 29 Rahman, I., 145, 317 Raj, M. H., 329 Rajendran, J. G., 303 Ramakrishnan, S., 160 Ramanathan, B., 138 Ramirez, P., 80 Rangarajan, A., 283 Rapaka, R. S., 330 Raschack, M., 26 Raschko, J. W., 30 Rasey, J. S., 300

358 Ravindranath, M. H., 320 Ray, S., 198 Rea, P., 330 Reactive nitrogen species (RNS), 138 Reactive oxygen species (ROS), 138–139 Reddy, E. P., 73 Redox alteration intrinsic ROS stress in cancer, 141–143 oxidative stress in tumorigenesis, 140–141 Reichman, T. W., 247 Reiners, J. J., Jr., 55 Reles, A., 214 Rel homology domain (RHD), 145 Renes, J., 5 Retinoid nuclear receptors, 236f Retinoid X receptor (RXR), 236 Reunanen, H., 56t Reymann, A., 29 Rezk, Y. A., 324 Rhodes, D. R., 287 Ricci-Vitiani, L., 285 Richardson, A., 96 Richardson, P. G., 96, 97 Richmond, A., 180t Riely, G. J., 84 Righetti, S. C., 214, 215 Rigobello, M. P., 160 Rintoul, R. C., 116 Risau, W., 34 Rivera, A., 142 S Sablina, A. A., 142 Sachs, L., 117 Sadlonova, A., 286 Saeki, K., 56f, 58f Safa, A. R., 25 Saiko, P., 323, 324 Saitoh, M., 152 Sakai, T., 125 Sakata, K., 303 Saleem, A., 298, 299, 300 Salinas, A. E., 155 Sallman, D. A., 325 Salmon, S. E., 30 Saltz, L., 30 Salvioli, S., 332 Samanta, A. K., 78 Sampath, J., 7 Samuel, J., 211 Samuel, T., 211 Sanchez, M., 140 Saponara, S., 29

Index Sareen, D., 325 Sarkadi, B., 4, 32 Sarkans, U., 266 Sarkar, F. H., 317 Sarkis, A. S., 214 Sasaki, T., 180t Sato, W., 27 Satpathy, M., 100 Sattler, M., 138, 141 Sausville, E. A., 222 Savarese, D. M., 116 Sawyers, C. L., 58, 75, 76, 79 Saydam, N., 142 Schackert, H., 29 Schaller, M. D., 123 Schellens, J. H., 330 Scherf, U., 276, 277 Schindler, T., 79 Schittenhelm, M. M., 80 Schlachterman, A., 329 Schlaepfer, D. D., 102, 123 Schmelzle, T., 58 Schmidmaier, R., 189 Schneider, B. L., 144 Scholz, S., 332 Schroder, C. P., 155 Schroeder, C. P., 191 Schubbert, S., 141 Schuler, M., 213 Schuurhuis, G. J., 25 Schwartz, G. K., 120, 122 Schweichel, J. U., 53 Scott, S. L., 216 Scotto, K. W., 6 “Seed and soil” hypothesis, 22 Seeram, N. P., 328 Segal, E., 287 Seid, C. A., 29 Seitz, U., 300 Selvendiran, K., 197 Semiquantitative standardised uptake value (SUV), 296 Sen, C. K., 149 Serrano, J., 151 Sessa, C., 30 Sethi, G., 179, 192, 198 Sethi, T., 115–118, 119, 120, 121 Sgambato, A., 151 Shafee, N., 282 Shah, M. A., 122 Shah, N. P., 75, 76, 79, 80, 81 Shani, J., 298 Shankar, S., 323

Index Shanmugam, K., 317 Shao, W., 244 Shao, Y., 54, 55, 61 Sharma, M., 154 Sharma, R. A., 321 Sharpless, N. E., 281, 283 Shats, I., 212 Shattuck-Brandt, R. L., 180t Shaulian, E., 147 Shen, F., 26, 28 Shen, J., 326 Shen, L., 223 Shen, Z. X., 250 Shepherd, F. A., 127 Sherman-Baust, C. A., 117 Shervington, A., 321 Shi, Y., 65 Shieh, S. Y., 220 Shields, A. F., 306 Shimizu, S., 60, 63 Shin, S. Y., 117 Shinohara, Y., 180t Shintani, T., 57t, 59 Shishodia, S., 155, 178, 179, 180t, 317, 321, 323 Shoba, G., 332 Shoji, T., 224 Shoshan, M. C., 56 Siddik, Z. H., 209, 216, 221, 224 Siedle, B., 155 Sieg, D. J., 102, 123 Siegel, D. S., 180t Sierra, A., 97 Signal transducer and activator of transcription 3 (STAT3), 197 Signal transduction pathways, 305–306 Significance Analysis of Microarrays (SAM), 275 Sinclair, D. A., 323 Singh, B., 186 Singh, D., 272 Singh, R. K., 24, 34, 35 Singh, S., 197 Sinicrope, F. A., 31, 32 Sirotnak, F. M., 83 Sixt, M., 122 Slack, J. L., 234, 247 Slater, L. M., 25, 28 Sliwinski, T., 79 Small cell lung cancer (SCLC), 116, 195 Smirnov, A. S., 145 Smith, K. A., 327 Smith, K. J., 119

359 Solary, E., 12t Soleas, G. J., 323 Solit, D. B., 306 Somasundaram, S., 328 Song, H., 100 Song, Y. C., 185 Sonneveld, E., 242 Sonneveld, P., 12t Sood, P., 258 Sørlie, T., 272, 274t, 284 Sotelo, J., 30 Soussi, T., 160, 210, 214 Sovak, M. A., 180 Soverini, S., 80, 81 Spiekermann, K., 191 Spinella, F., 320 Spitz, D. R., 143, 151 Spoelstra, E. C., 28 Spohn, W. H., 198 Spruss, T., 119 Squires, M. S., 198 Sridhar, K. S., 30 Stacy, D. R., 150 Stamenkovic, I., 150 Staunton, J. E., 279 Steck, P. A., 59 Stehlik, C., 188 Stein, O., 31 Stein, Y., 31 Steinbach, D., 10 Stem cells, 285–286 Stenvang, J., 107 Stephan, S., 65 Steward, W. P., 317, 321, 331 Stewart, A., 13 Stewart, D., 332 Stewart, J. J., 210 Stewart, Z. A., 211 Stockwell, B. R., 329 Stojic, L., 219 Stoklosa, T., 79 Stover, E. H., 81 Strauss, W. M., 261, 264t Strimpakos, A. S., 321 Stuart, J. M., 287 Stuart, N. S., 28, 30 Sturm, I., 223, 304 Su, B., 61 Subar, A., 317 Subramanian, A., 275 Suh, Y. A., 152 Suhardja, A., 34 Sullivan, R., 138, 145, 150

360 Sun, H., 125 Sun, J., 149 Supervised analysis, 275 Supervised cluster, 275 Supervising parameter, 275 Surh, Y. J., 317, 321, 323 Suvannasankha, A., 10 Suzuki, S., 151 Swartz, H. M., 140 Sweeney, C. J., 180t Synold, T. W., 6 Szakacs, G., 5f, 9f, 10, 12t, 13, 14 Szatmari, I., 8 Szatrowski, T. P., 140 T Tada, Y., 7 Tai, D. I., 180t Takahashi, Y., 35, 56 Takeshita, A., 246 Takeuchi, H., 54, 65 Talks, K. L., 150 Tallman, M. S., 239 Talmadge, J. E., 22, 23, 32 Tamatani, T., 180t Tamm, I., 188 Tamura, M., 103 Tan, B., 307 Tang, X., 212 Tannock, I. F., 116, 119 Targeted therapy BCR-ABL imatinib, 75–76 imatinib resistance in cml, 76, 77f mutation of, 79–82 nonmutation-dependent mechanisms, 78 signaling in CML, 75 EGFRand Cancer, 82 erlotinib, 84 gefitinib and erlotinib, 83 response to gefitinib treatment, 83–84 secondary EGFR Mutations, 84–85 Tascilar, M., 330 Taylor, S. T., 121 Teicher, B. A., 116, 158, 315 Teodori, E., 26 Testa, J. R., 267 Tew, K. D., 154, 155, 159 TG2 cell survival and chemoresistance, 105–106 drug resistance and metastasis and, 99–101

Index expression in drug-resistant expression in cancer cells, 108 -mediated cell signaling, 101–103 -mediated NF-␬B activation, 103–105 protein with split personality, 97–99, 98f therapeutic significance of, 106–108 TGM2 gene, 98 Theard, D., 221 Thierry, A. R., 29 Thomasset, S. C., 333 Thompson, C. B., 106 Thornborrow, E. C., 224 Thymidine synthase (TS), 299 Tian, B., 122 Tibshirani, R., 275 Tiligada, E., 150 Tissue transglutaminase expression, 247–248 Tissue type II transglutaminase (TGase), 196–197 T790M resistance, 85–86 Tjuvajev, J. G., 298 To, K. K., 8, 9 Todorov, D. K., 27 Tohyama, Y., 152 Tokarski, J. S., 80 Tokes, Z. A., 29 Toledo, F., 212, 220–223 Tomita, M., 198 Tomko, R. J., 142 Topoisomerase II activity, 248 Torres, J., 103 Tosetti, F., 151 Townsend, D. M., 155, 159 Trachootham, D., 137, 145, 148, 149, 157 TRAIL-induced apoptosis, 187 Transactivation, 213, 223–224 Transforming growth factor-β (TGF-β), 182 Transglutaminase II (Tgase-II), 247 Trans-Golgi-network (TGN), 56 Transmembrane domains (TMDs), 2 Transrepression, 219f, 224 Trewavas, A., 332 Troester, M. A., 216, 271, 274t, 275, 277, 278, 279, 284 Troussard, A. A., 124, 125 Trump, D. L., 30 Tsai, S., 237 Tsang, W. P., 151 Tsao, S. M., 140 Tsuruo, T., 10, 24–27, 60, 96 Tu, Y., 124 Tumorigenesis, role of autophagy in, 56–57

Index Tumor necrosis factor-α (TNF-α), 179 Turella, P., 155 Turner, C. E., 124 Turner, J. G., 8 Turpaev, K. T., 144 Tuschl, T., 260 Tusher, V. G., 275 Tuveson, D. A., 281 Twentyman, P. R., 28 Tyner, A. L., 223 Type I PCD, 53 Type II PCD, 54 Type III PCD, 54 Tyrosine kinase inhibitor (TKI), 305 U Uehara, H., 34, 36, 37 Uetsuka, H., 183, 184t Uggla, B., 10 Uhm, J. H., 121 Ultraviolet irradiation resistance-associated tumor suppressor gene (UVRAG), 56 Um, J. H., 185 Uncoupling protein-2 (UCP2), 148, 152, 158, 160 Unicellular models, 116–117 Unsupervised analysis, 274 3’-Untranslated region (3’UTR), 8 Urokinase-type plasminogen activator (uPA), 181–182 Ursini, F., 323 V Vafa, O., 141 Vahdat, L., 247 Vaisman, A., 218, 219, 220 van ’t Veer, L. J., 279 van de Stolpe, A., 183 van der Holt, B., 10 van der Zee, A. G., 215 Van Dyke, T., 281 van Ginkel, P. R., 325 Van Laere, S., 180t Vantyghem, S. A., 333 Vascular cellular adhesion molecule-1 (VCAM-1), 121 Vascular disrupting agents (VDA), 302–303 Vascular endothelial growth factor (VEGF), 182 Vascular permeability factor (VPF), 33 Vazquez, F., 103 Vekris, A., 211

361 Velling, T., 125 Velu, T. J., 82 Venditti, J. M., 24 Vennstrom, B., 82 Venugopal, R., 147 Vercauteren, J., 323 Verderio, E. A., 97, 102 Verma, A., 98, 99, 100, 196 Vermeulen, L., 150 Verpoorte, R., 323 Verstuyft, C., 30 Verweij, J., 30 Vezmar, M., 27 Vickers, P. J., 28 Vidal, A., 122 Villegas, I., 323 Villunger, A., 212 Viral/cellular oncogenes, 74–75 Visconti, R., 180t Visser, G. M., 298 Vita, M., 141 Vivanco, I., 58 Voboril, R., 180t Voelz, H., 31 Vogel, A., 144 Vogel, C. L., 283 Vogelstein, B., 60 Volinia, S., 261, 264t, 265, 281 Volk, E. L., 8 Von Hoff, D. D., 24 Vousden, K. H., 143, 210, 220 Vukicevic, S., 118 Vuori, K., 118, 121, 126, 195 W Wadler, S., 30 Wahl, H., 212, 220 Wakeling, A. E., 83 Waldherr, C., 306 Wang, C. W., 59 Wang, D. Z., 180t Wang, E., 65 Wang, H., 97, 305 Wang, J. G., 329 Wang, J. H., 34 Wang, J. Q., 305 Wang, Q., 7 Wang, Y., 78 Wang, Y. Y., 156 Wang, Z. G., 237 Ward, A., 3 Ward, W. H., 83 Warmuth, M., 78

362 Warrell, R. P., 239 Wattel, E., 11, 12t Weber, W. A., 297 Weberova-Voborilova, J., 180t Wei, D., 321 Weidner, N., 35 Weinberg, R. A., 100, 140, 283 Weinstein, I. B., 283 Weinstein, J. N., 266, 283 Weir, N. M., 322 Weisberg, E., 78, 81 Wek, R. C., 146 Weller, M., 54 Wells, P., 300, 302t, 306 Welsh, S. J., 152, 156 Wendel, H. G., 78 Weng, J. R., 327 Weng, Q., 156 Wesarg, E., 161 West, C., 295 West, K. A., 63 White, C. W., 183, 184t White, D. E., 125 White, J. A., 242 Wicha, M. S., 286 Wiernik, P. H., 30 Wightman, B., 257 Wild-type p53 resistance mechanisms in tumor cells DNA damage recognition proteins, downregulation of, 218–220 upstream pathways, downregulation of, 220 Wilk, S., 155 Williamson, G., 332 Willis, A., 213 Wilmanns, C., 214 Wilson, C. M., 27 Wilson, T. R., 54, 316 Wishart, G. C., 12 Wolf, W., 12 Wolk, A., 320 Wong, M. G., 155 Woo, R. A., 122 Woodcock, D. M., 28 Wozniak, M. A., 123 Wright, J. A., 222 Wu, J., 78 Wu, H., 30 Wu, Q., 212, 224 Wu, S. L., 325 Wu, W. S., 140 Wurz, G. T., 28

Index X Xia, H., 123, 125 Xia, L., 267 Xia, Y. F., 155 Xie, Z., 56 Xu, C., 157 Xu, J., 198 Xu, L., 101 Xuan, Y., 330 Y Yamaguchi, H., 97 Yamakawa, H., 153 Yamamoto, A., 62 Yamamoto, K., 181 Yamamoto, M., 145 Yamamoto, S., 186 Yamamoto, T., 82 Yang, C. P., 28 Yang, H., 267 Yang, J., 180t Yang, J. M., 30 Yang, Y., 3 Yao, K. C., 54 Yao, E. S., 121 Yasuda, S., 8 Yeh, P. Y., 183, 184t Yeoh, E. J., 123 Yi, J., 151 Yin, Y., 30 Ylisirnio, S., 127 Yokoi, K., 33 Yokoyama, T., 53, 66 Yoneda, J., 34 Yorimitsu, T., 61 Yoshida, M., 251 You, G., 330 You, M., 327 Younes, M. N., 125 Young, T. W., 148 Yu, C. X., 149 Yu, H. G., 180t Yu, J. L., 35, 301, 324 Yu, L., 63 Yuan, J., 61 Yuan, L., 100, 105, 197 Yun, C. H., 85, 86 Yun, J., 60 Z Zaffaroni, N., 186, 218 Zaidel-Bar, R., 123 Zalipsky, S., 29 Zatelli, M. C., 186

Index Zaveri, N. T., 320 Zhang, C. C., 278 Zhang, F., 197 Zhang, H., 121 Zhang, P., 221 Zhang, W., 137, 157 Zhen, Y., 320 Zheng, Q. H., 306 Zhong, H., 150

363 Zhou, B. P., 125 Zhou, J. R., 326 Zhou, Y., 140, 160, 222 Zhu, H., 7 Zhu, H. J., 26 Zhu, S., 224 Zisman, A., 188 Zurer, I., 143 Zutter, M. M., 307