Tumor-Associated Fibroblasts and their Matrix (The Tumor Microenvironment 4)

Tumor-Associated Fibroblasts and their Matrix

The Tumor Microenvironment Series Editor: Isaac P. Witz

For further volumes: http://www.springer.com/series/7529

Margareta M. Mueller • Norbert E. Fusenig Editors

Tumor-Associated Fibroblasts and their Matrix

1  3

Editors Prof. Dr. Margareta M. Mueller Research Group Tumor and Microenvironment German Cancer Research Center, Heidelberg and HFU (Hoschschule Furtwangen University) campus Villingen-Schwenningen Villingen-Schwenningen Germany [email protected]

Prof. Dr. Norbert E. Fusenig Former Division Head at the German Research Cancer Center, Heidelberg and Emeritus of the University of Heidelberg Heidelberg Germany [email protected]; [email protected]

ISBN 978-94-007-0658-3     e-ISBN 978-94-007-0659-0 DOI 10.1007/978-94-007-0659-0 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2011925928 © Springer Science+Business Media B.V. 2011 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

During the last century cancer research was mainly focussed on the tumor cells alone which could be easily propagated in cell culture. During this time many important findings were obtained clearly demonstrating that cancer is a genetic disease, controlled by the activation and/or inactivation of critical control genes. However during the last two decades it has become increasingly clear that genetic alterations alone are not the sole driving force behind tumor development but that tumor growth and progression are rather intimately controlled by the microenvironment. One could almost speak of are “rediscovery” of the tumor as a highly complex tissue composed of carcinoma cells and surrounding stroma. Studies in different areas of biology including tumour biology have demonstrated that tissue structure, function and dysfunction are highly intertwined with the microenvironment and that during the development of cancer tissue biology and host physiology are subverted to drive malignant progression. It is now clear that the context is crucial and that the status of the cellular microenvironment plays a significant role in determining whether cells within a tissue retain their normal architecture or undergo tumor progression. The tumor stroma or microenvironment is made up of multiple non-malignant cell populations, including fibroblasts, adipocytes, endothelial and inflammatory cells that are embedded in a tumour specific extracellular matrix (ECM). Nowadays, there is a huge interest in tumor stroma research, and in understanding the contributions of the different stromal cell types to tumor growth and progression. One of the key components of the tumor microenvironment in carcinomas are activated fibroblasts termed cancer associated fibroblasts (CAFs). In the meantime our knowledge of CAFs has changed from being viewed as a passive bystander to becoming an important co-mediator of cancer progression. In response to cancer growth, host stromal fibroblasts undergo a dramatic morphologic and biochemical transition to form “reactive stroma” in a desmoplastic reaction much like the granulation tissues found at the site of wound healing. While the malignant cells activate fibroblasts in the tumor stroma by various stimuli, including growth factors and cytokines, cancer associated fibroblasts secrete growth factors and build a permissive soil in which the cancer cells thrive. CAFs are responsible for the elaboration of most of the connective tissue and ECM components v

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as well as, proteolytic enzymes and their inhibitors. The composition and structure of the ECM in the tumor microenvironment is essential for promoting tumor development and metastasis. The constituents of the ECM include collagens, laminins, fibronectin and several proteoglycans. They provide mechanical support for cells, facilitate cell communication and serve as substrates for cell migration. Changes in the composition or architecture of the extracellular matrix within tumors can alter integrin expression and function and promote metastatic progression, angiogenesis and lymphangiogenesis. In this unique textbook world leading experts of the area of tumor microenvironment review the most recent knowledge of the still growing complexity of the tumor microenvironment focussing on tumor associated stromal cells and the most important extracellular matrix components and summarize the role of these players in tumor progression. Moreover, novel therapeutic targets are discussed that have been discovered in the tumor microenvironment and are increasingly used in experimental and clinical tumor therapy. The message from their contributions is clear: the tumor microenvironment and its components are important and essential players in tumor progression and interesting targets for novel therapeutic strategies. However there are still many white areas on the map and we are just beginning to understand the complex interplay between tumor and stromal cells. We express our deepest gratitude to all our colleagues who have made this book the first comprehensive antology covering all major aspects of the role of the tumor microenvironment and its extracellular matrix components. Heidelberg

Margareta M. Mueller and Norbert E. Fusenig

Contents

Part I  The Tumor Microenvironment 1 Critical Roles of Stromal Fibroblasts in the Cancer Microenvironments ����������������������������������������������������������������������    3 Leland W. K. Chung Part II  Stromal Cell Diversity 2 Functional Diversity of Fibroblasts ���������������������������������������������������������   23 H. Peter Rodemann and Hans-Oliver Rennekampff 3 The Role of the Myofibroblast in Fibrosis and Cancer Progression ����   37 Boris Hinz, Ian A. Darby, Giulio Gabbiani and Alexis Desmoulière 4 The Role of Myofibroblasts in Communicating Tumor Ecosystems ����   75 Olivier De Wever, Astrid De Boeck, Pieter Demetter, Marc Mareel and Marc Bracke 5 Tumor Vessel Associated-Pericytes ����������������������������������������������������������   91 Arne Bartol, Anna M. Laib and Hellmut G. Augustin 6 The Role of Cancer-Associated Adipocytes (CAA) in the Dynamic Interaction Between the Tumor and the Host ������������������������   111 Marie-Christine Rio Part III  The Tumor ECM 7 Hyaluronan: A Key Microenvironmental Mediator of Tumor-Stromal Cell Interactions ��������������������������������������������������������   127 Naoki Itano 8  Function of Tenascins in the Tumor Stroma �������������������������������������������   145 Florence Brellier and Ruth Chiquet-Ehrismann vii

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9   Fibulins and Their Role in the ECM ���������������������������������������������������   159 Helen C. M. Cooney and William M. Gallagher 10 Tumor Fibroblast-Associated Metalloproteases ���������������������������������   175 Julie Lecomte, Anne Masset, Dylan R. Edwards and Agnès Noël Part IV  Tumor Modulating-Fibroblast Interactions 11 Multiple Fibroblast Phenotypes in Cancer Patients: Heterogeneity in Expression of Migration Stimulating Factor ���������   197 Ana M. Schor and Seth L. Schor 12 TGF-β Signaling in Fibroblasts Regulates Tumor Initiation and Progression in Adjacent Epithelia ������������������������������������������������   223 Brian R. Bierie and Harold L. Moses 13 The SDF-1-Rich Tumour Microenvironment Provides a Niche for Carcinoma Cells ����������������������������������������������������������������   245 Masayuki Shimoda, Kieran Mellody and Akira Orimo 14 Role of PDGF in Tumor-Stroma Interactions ������������������������������������   257 Carina Hellberg and Carl-Henrik Heldin 15 Radiation-Induced Microenvironments and Their Role in Carcinogenesis ����������������������������������������������������������������������������������   267 Mary Helen Barcellos-Hoff and David H. Nguyen Part V  Tumor-Modulating ECM Interactions 16 The Extracellular Matrix as a Multivalent Signaling Scaffold that Orchestrates Tissue Organization and Function ���������   285 Jamie L. Inman, Joni D. Mott and Mina J. Bissell 17 SPARC and the Tumor Microenvironment ����������������������������������������   301 Stacey L. Thomas and Sandra A. Rempel 18 Integrin-Extracellular Matrix Interactions ����������������������������������������   347 Christie J. Avraamides and Judith A. Varner 19 The Multifaceted Role of Cancer Associated Fibroblasts in Tumor Progression ����������������������������������������������������������������������������   361 Hans Petter Eikesdal and Raghu Kalluri

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Part VI  Therapeutic Application/Targeting 20 Cancer Associated Fibroblasts as Therapeutic Targets ���������������������   383 Christian Rupp, Helmut Dolznig, Christian Haslinger, Norbert Schweifer and Pilar Garin-Chesa 21 Targeting Tumor Associated Fibroblasts and Chemotherapy ����������   403 Debbie Liao and Ralph A. Reisfeld 22 Antibody-Based Targeting of Tumor Vasculature and Stroma ���������   419 Katharina Frey and Dario Neri Index ���������������������������������������������������������������������������������������������������������������   451

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Contributors

Hellmut G. Augustin  Vascular Oncology and Metastasis, German Cancer Research Center (DKFZ-ZMBH Alliance), 69120 Heidelberg, Germany Medical Faculty Mannheim,Vascular Biology and Tumor Angiogenesis, Heidelberg University, 68167 Mannheim, Germany Christie J. Avraamides  Moores UCSD Cancer Center, University of California, San Diego, 3855 Health Sciences Drive #0819, La Jolla, CA 92093-0819, USA e-mail: [email protected] Mary Helen Barcellos-Hoff  Departments of Radiation Oncology and Cell Biology, NYU Langone Medical Center, 566 First Avenue, 10016 New York, USA e-mail: [email protected] Arne Bartol  Vascular Oncology and Metastasis, German Cancer Research Center (DKFZ-ZMBH Alliance), 69120 Heidelberg, Germany Medical Faculty Mannheim, Vascular Biology and Tumor Angiogenesis, Heidelberg University, 68167 Mannheim, Germany e-mail: [email protected] Brian R. Bierie  Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, 691 Preston Research Building 2220 Pierce Ave., Nashville, TN 37232-6838, USA e-mail: [email protected] Mina J. Bissell  Life Science Division, Lawrence Berkeley National Laboratory, One Cyclotron Rd, Berkeley, CA 94720, USA e-mail: [email protected] Astrid De Boeck  Laboratory of Experimental Cancer Research, Department of Radiotherapy and Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium Marc Bracke  Laboratory of Experimental Cancer Research, Department of Radiotherapy and Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium

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Florence Brellier Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerstrasse 66, 4058 Basel, Switzerland Ruth Chiquet-Ehrismann Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerstrasse 66, 4058 Basel, Switzerland e-mail: [email protected] Leland W. K. Chung Uro-Oncology Research Program, Departments of Medicine and Surgery, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center and the University of California, Los Angeles, CA 90048, USA e-mail: [email protected] Helen C. M. Cooney 73 Nutley Lane, Donnybrook, Dublin 4, Ireland e-mail: [email protected] Ian A. Darby Cancer and Tissue Repair Laboratory, School of Medical Sciences, RMIT University, Bundoora, VIC 3083, Australia Pieter Demetter Department of Pathology, Erasme University Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium Alexis Desmoulière Faculty of Pharmacy, Cellular Homeostasy and Pathologies (EA 3842) and Department of Physiology, IFR 145, University of Limoges, 87025 Limoges, France Helmut Dolznig Institute of Pathology, Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria Institute of Medical Genetics, Centre of Pathobiology and Genetics, Medical University of Vienna, Waehringer Strasse 10, 1090 Vienna, Austria e-mail: [email protected] Dylan R. Edwards School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK Hans Petter Eikesdal Department of Oncology, Haukeland University Hospital, 5021 Bergen, Norway Katharina Frey Department of Chemistry and Applied Biosciences, ETH Zurich, Wolfgang-Pauli-Str. 10, 8093 Zurich, Switzerland e-mail: [email protected] Giulio Gabbiani Department of Pathology and Immunology, CMU, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland e-mail: [email protected] William M. Gallagher UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin 4, Ireland e-mail: [email protected] Pilar Garin-Chesa Institute of Pathology, Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria

Contributors

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Boehringer Ingelheim RCV GmbH & Co KG, Dr. Boehringer-Gasse 5–11, 1130 Vienna, Austria Christian Haslinger  Boehringer Ingelheim RCV GmbH & Co KG, Dr. BoehringerGasse 5–11, 1130 Vienna, Austria Carl-Henrik Heldin  Ludwig Institute for Cancer Research, Uppsala University, Box 595, SE 751 24 Uppsala, Sweden e-mail: [email protected] Carina Hellberg  Ludwig Institute for Cancer Research, Uppsala University, Box 595, SE 751 24 Uppsala, Sweden e-mail: [email protected] Boris Hinz  Faculty of Dentistry, Laboratory of Tissue Repair and Regeneration, Matrix Dynamics Group, University of Toronto, Toronto, ON M5S 3E2, Canada Jamie Inman  Life Science Division, Lawrence Berkeley National Laboratory, One Cyclotron Rd, Berkeley, CA 94720, USA Naoki Itano  Department of Molecular Oncology, Division of Molecular and Cellular Biology, Institute on Aging and Adaptation, Shinshu University Graduate School of Medicine, Matsumoto, Nagano 390-8621, Japan e-mail: [email protected] Raghu Kalluri  Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center & Harvard Medical School, 330 Brookline Ave, E/CLS Room #11-090, Center for Life Sciences, 02115 Boston, MA, USA Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA e-mail: [email protected] Anna M. Laib  Vascular Oncology and Metastasis, German Cancer Research Center (DKFZ-ZMBH Alliance), 69120 Heidelberg, Germany Julie Lecomte  Laboratory of Tumor and Development Biology, Groupe Interdisciplinaire de Génoprotéomique Appliqué-Cancer (GIGA-Cancer), University of Liège, 4000 Liège, Belgium Debbie Liao  Department of Immunology and Microbial Sciences, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA Marc Mareel  Laboratory of Experimental Cancer Research, Department of Radiotherapy and Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium Anne Masset  Laboratory of Tumor and Development Biology, Groupe Interdisciplinaire de Génoprotéomique Appliqué-Cancer (GIGA-Cancer), University of Liège, 4000 Liège, Belgium

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Kieran Mellody  CR-UK Stromal-Tumour Interaction Group, Paterson Institute for Cancer Research, The University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK Harold L. Moses  Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, 691 Preston Research Building 2220 Pierce Ave., Nashville, TN 372326838, USA e-mail: [email protected] Joni D. Mott  Life Science Division, Lawrence Berkeley National Laboratory, One Cyclotron Rd, Berkeley, CA 94720, USA Dario Neri  Department of Chemistry and Applied Biosciences, ETH Zurich, Wolfgang-Pauli-Str. 10, 8093 Zurich, Switzerland e-mail: [email protected] Agnès Noël  Laboratory of Tumor and Development Biology, Groupe Interdisciplinaire de Génoprotéomique Appliqué-Cancer (GIGA-Cancer), University of Liège, 4000 Liège, Belgium e-mail: [email protected] Akira Orimo  CR-UK Stromal-Tumour Interaction Group, Paterson Institute for Cancer Research, The University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK e-mail: [email protected] Ralph A. Reisfeld  Department of Immunology and Microbial Sciences, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA e-mail: [email protected] Sandra A. Rempel  Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Hospital, Detroit, MI 48202, USA e-mail: [email protected] Marie-Christine Rio  Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS UMR 7104, INSERM U964, UDS, BP, 67404 Illkirch Cedex, 10142 C.U. de Strasbourg, France e-mail: [email protected] H. Peter Rodemann  Division of Radiobiology & Molecular Environmental Research, Department of Radiation Oncology, Eberhard Karls University Tübingen, Röntgenweg 11, 72076 Tübingen, Germany e-mail: [email protected] Hans-Oliver Rennekampff  Hospital for Plastic, Hand and Reconstructive Surgery, Medical School Hannover, Hannover, Germany Christian Rupp  Institute of Pathology, Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria

Contributors

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Ana M. Schor  Unit of Cell and Molecular Biology, The Dental School, University of Dundee, Dundee DD1 4HR, UK e-mail: [email protected] Seth L. Schor  Unit of Cell and Molecular Biology, The Dental School, University of Dundee, Dundee DD1 4HR, UK e-mail: [email protected] Norbert Schweifer  Boehringer Ingelheim RCV GmbH & Co KG, Dr. BoehringerGasse 5–11, 1130 Vienna, Austria Masayuki Shimoda  Department of Pathology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan CR-UK Stromal-Tumour Interaction Group, Paterson Institute for Cancer Research, The University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK Stacey L. Thomas  Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Hospital, Detroit, MI 48202, USA e-mail: [email protected] Judith A. Varner  Moores UCSD Cancer Center, University of California, San Diego, 3855 Health Sciences Drive #0819, La Jolla, CA 92093-0819, USA Department of Medicine, University of California, San Diego, La Jolla, CA, 920930819, USA e-mail: [email protected] Olivier De Wever  Laboratory of Experimental Cancer Research, Department of Radiotherapy and Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium e-mail: [email protected]



Part I

The Tumor Microenvironment

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Chapter 1

Critical Roles of Stromal Fibroblasts in the Cancer Microenvironments Leland W. K. Chung

1.1 Introduction A mounting body of evidence suggests that the ability of cancer cells to interact reciprocally with the host microenvironment contributes to cancer growth, progression and resistance to therapeutic interventions. Interactions with stromal fibroblasts at the primary site and marrow stromal cells at metastatic sites could create a favorable microenvironment supporting cancer growth, survival, evasion of immune surveillance and resistance to therapy (Mueller and Fusenig 2004; Chantrain et al. 2008; Karnoub et al. 2007; Ronnov-Jessen and Bissell 2009; Chung et al. 2006). Changes in cancer microenvironments could also add selection pressures favorable to cancer cell evolution, increasing cancer cell heterogeneity and reciprocally causing the co-evolution of adjacent stromal fibroblasts, resulting in the development of organ- and stage-specific stromal fibroblasts capable of programming and reprogramming the fate of cancer cells (Hill et al. 2005; Sung et al. 2008; Franco et al. 2010). Over the past several years, active research has broadened our awareness of the plasticity of cancer-associated stroma, which undergo both morphologic and functional transitions supporting the pathogenesis of cancer cells (Sung et al. 2008). The dynamic presence of bone marrow-derived mesenchymal stem cells in localized and metastatic cancers contributes further to the diversity and heterogeneity of cancer-associated stroma and ultimately determines the site of cancer metastasis, while stromal fibroblasts have multiple functional roles modulating cancer growth either positively or negatively (Martin et al. 2010; Molloy et al. 2009; Rhodes et al. 2009; Zhao et al. 2009a). Figure 1.1 depicts how soluble factors, insoluble extracellular matrix proteins, and reactive oxygen species secreted by cancer cells and cells in cancer microenvironments can guide and maintain the growth and differentiation of local and distant cancers and their interactions with host microenvironments L. W. K. Chung () Uro-Oncology Research Program, Departments of Medicine and Surgery, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center and the University of California, Los Angeles, CA 90048, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_1, © Springer Science+Business Media B.V. 2011

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Bone MET

Bone stroma

MSC Secondary metastasis

Hematogenous metastasis Blood vessel Cancer cells

BF Brain

MSC Activation

Macrophages Platelets Activated stroma T and B cells Dendritic cells

ROS GFs

Normal

Lymphatic metastasis

Transformation EMT

ECM NAF

Secondary metastasis

NAF

Benign

ROS GFs

CAF

Transformed

LF

ROS GFs CAF

Malignant

LNF Lymph node

Lung

Prostate

Fig. 1.1   Tumor-microenvironments interactions contribute to prostate cancer skeletal and soft tissue metastases. At the primary site, reciprocal cellular interactions between prostate cancer cells and local/migrating stromal fibroblasts, mediated by soluble and insoluble factors and ROS, promote the transition of normal/benign stromal fibroblasts to cancer-associated stromal fibroblasts which activate and transform prostate epithelial cells to gain increased growth and survival potential. Subsequently, through epithelial-to-mesenchymal transition (EMT), prostate cancer cells gain futher growth, survival, migratory, invasive and metastatic potential allowing them to metastasize to the skeleton via hematogeneous routes. Prostate cancer cells also frequently metastasize to lymph node and then reach the skeleton through lymphatic metastatic routes. Metastatic prostate cancer cells, after reaching the bone, adhere to and exit from marrow endothelium via transendothelial migration, undergo mesenchymal-to-epithelial transition (MET) and colonize in the bone by increased expression of cell–cell adhesive/junction proteins such as E-cadherin. The metastatic cascade is aided by the active participation of both resident fibroblasts, such as lymph node fibroblast (LNF), lung fibroblast (LF), and brain fibroblast (BF), and migrating stromal fibroblasts such as cells derived from mesenchymal stem cells (MHC) at primary and metastatic sites. The multipotent MHC can differentiate into inductive cell populations in the tumor microenvironment, including reactive stroma, macrophage, platelet, dendritic cell and T- and B-cells, in the primary and at metastatic sites, to ‘mark’ the site prior to the occurrence of secondary metastases

(Karnoub et al. 2007; Chung et al. 2006; Desgrosellier and Cheresh 2010; Ishikawa et al. 2008; Jung et al. 2009; Kaplan et al. 2005; Svineng et al. 2008). The therapeutic targeting of cancer cells, though necessary, is insufficient by itself to control the growth of localized and disseminated cancers. This has led to the acceptance of co-targeting both the cancer and its microenvironments, including stromal fibroblasts, the vascular endothelial network, immune cells and host humoral substances (Chung et al. 2005; Cress and Mohla 2004; Tu and Lin 2008; Vessella and Corey

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2006; Pollard 2009). The unique cell signaling networks linking the behavior of cancer cells (i.e. cell proliferation, resistance to apoptosis, cell migration, invasion and metastasis) with the activation of key downstream signaling pathways can be exploited as new and promising druggable targets (Chung et al. 2006; Anastasiadis et al. 2003; Kang and Altieri 2009; Tasseff et al. 2010). In this review, we use prostate cancer as a model to dissect the critical determinants that regulate growth, differentiation and progression of prostate cancer to the skeleton, the lethal human prostate cancer phenotype. Looking forward, the study of cancer-associated stromal fibroblasts is rapidly evolving and could take center stage in revealing the secrets of cancer cell evolution. Recent exciting discoveries include the reprogramming adult normal stromal fibroblasts to form induced pluripotent stem (iPS) cells capable of orchestrating the development and differentiation of an entire embryo (Takahashi and Yamanaka 2006; Yu et al. 2009; Okita et al. 2007; Park et al. 2008; Zhao et al. 2009b). These findings raise new questions about the pathways leading to stromal fibroblast heterogeneity and the potential of in situ reprogramming of adult stromal fibroblasts to undergo iPS transition in an organ-specific environment. iPS cells could serve as progenitor cells for a subsequent generation of derivative cells comprising the entire stromal microenvironments, raising the possibility of developing stroma-based cancer therapy in the future.

1.2 The Roles of Stromal Fibroblasts in the Context of Tumor Microenvironment 1.2.1  Local Microenvironment Cancer growth and evolution is intimately controlled by its microenvironment, and cancer cells also contribute reciprocally to the active process of remodeling their microenvironment (Ingber 2008; Rhee et al. 2001; Pathak et al. 1997). Cancer cells secrete soluble factors such as EGF, IGFs, PDGF, VEGF, HGF, FGFs, and TGFβs which collectively stimulate pleiotropic signaling in converging multi-signaling pathways that induce activated stromal fibroblasts or myofibroblasts, with potent growth-promoting effect on cancer cells. Local microenvironments are heterogeneous and composed of resident vascular endothelial cells, smooth muscle cells, basal/stem cells and, to a lesser extent, cells from neural and neuroendocrine lineages that could interact with cancer cells in a reciprocal manner through secreted soluble factors and insoluble extracellular matrices. In addition to resident stromal fibroblasts, migrating mesenchymal stem cells from bone marrow, with either hematopoietic or mesenchymal lineage, also contribute to stromal heterogeneity. These cells, macrophages, platelets, dendritic cells, T- and B-cells, and activated stroma, are likely to support local cancer growth, progression and distant metastasis through complex cancer–stroma, cancer–immune and cancer–stem cell interactions

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(Josson et  al. 2010; Singh et  al. 2009; Jaganathan et  al. 2007; McKeithen et  al. 2010). The local heterogeneity of stromal fibroblasts could be determined by their anatomical location. Our laboratory recently showed that human prostate stromal fibroblasts derived from the peripheral zone of the prostate gland are more inductive than stromal fibroblasts derived from the transitional or central zones, and these results are consistent with the observation that progressive prostate cancer is derived predominantly from the peripheral rather than transitional or central zones (Thalmann et al. 2009).

1.2.2  Distant Microenvironments Because of the propensity of prostate cancer to metastasize to bone, which is considered lethal, much effort has focused on defining the bone microenvironment and the mechanisms of bone turnover, including enhanced bone resorption, that contribute to the ability of cancer cells to colonize bone. Several key cell types in the bone microenvironment are of particular importance. Among these are the osteoblasts (OBs), bone-forming cells derived from bone marrow mesenchymal stem cells (MSC). Upon interaction with soluble factors such as bone morphogenic proteins, TGFβs, EGF, FGFs, or PDGF, MSC can potentially differentiate into OBs, chondrocytes, or adipocytes. In addition, there are osteoclasts (OCs), bone resorbing cells derived from monocytes of hematopoietic mesenchymal cell lineage. OCs express receptor activator of NF-kappaB (RANK), a receptor responding to RANK ligand (RANKL), secreted by osteoblasts or prostate cancer cells, promoting maturation of OCs, inducing the fusion of monocytes to form activated multinucleated OCs. These matured multinucleated OCs contribute to bone resorption or bone turnover resulting in the release of soluble growth factors, nutrients, calcium ions, and extracellular matrices (Araujo and Logothetis 2009; Buckle et al. 2010; Mizutani et al. 2009). The actions of these soluble and matrix factors alter cancer cell adhesion, proliferation and survival and also the responsiveness of the host microenvironments toward factors secreted by both cancer cells and cells in cancer microenvironments. Collectively, the factors present in the cancer milieu could determine how cancer cellinduced osteoblastic or osteolytic lesions ultimately support cancer cell colonization in bone. In addition to the local action of cancer microenvironments, the factors secreted by cancer microenvironments could conceivably govern the propensity of secondary cancer metastases to organs such as the lung, liver, brain, and kidney.

1.3 The Plasticity of the Stromal Microenvironment 1.3.1  Reactive Stroma In response to cancer growth, host stromal fibroblasts undergo a dramatic morphologic and/or biochemical transition to form “reactive stroma” in a desmoplas-

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tic reaction much like the granulation tissues found at the site of wound healing (Tuxhorn et al. 2001, 2002; Malins et al. 2006). Although the desmoplastic reaction associated with human prostate cancer is less apparent than in human breast cancer and melanoma, the transition of stromal fibroblasts to myofibroblasts at the gene expression level is quite apparent. Upon transition to reactive stromal cells, they express more abundant and diverse classes of extracellular matrix proteins with altered expression of genes associated with myofibroblasts, such as α- and γ-smooth muscle actin, fibronectin, actin bundle, paladin, Thy1, and TGF-β1 (Sung et  al. 2008; Untergasser et al. 2005; Dakhova et al. 2009). A number of soluble growth factors, when added directly to cultured stromal fibroblasts, have been shown to induce myofibroblast transition (Olaso et al. 2003; Cushing et al. 2008; Kennard et al. 2008; Kikuta et al. 2006). Direct interaction between reactive stroma and cancer cells has been observed to promote cancer growth. This is consistent with clinical observations where the detection of reactive stroma in the prostate cancer microenvironments, for example, predicts PSA recurrence and the clinical outcome in prostate cancer patients (Dakhova et al. 2009; Tothill et al. 2008; Yanagisawa et al. 2007). The engagement between cancer cells and reactive stroma could promote tissue reorganization involving the participation of the vascular network and migrating MSC and host stromal fibroblasts to lead to increased cancer growth (Zhao et al. 2009a; Santamaria-Martinez et al. 2009). The prevalence of myofibroblasts in the cancer environment has been shown in many different forms of cancer including colon, liver, lung, prostate, ovary, pancreas, and breast (Tuxhorn et al. 2001; Friedman et al. 1984; Garin-Chesa et al. 1990; Radisky and Przybylo 2008; Yao et al. 2009). The myofibroblastic appearance often precedes the onset of cancer invasion.

1.3.2  Plasticity of EMT and MET Dynamic bi-directional epithelial-to-mesenchyme (EMT) and mesenchymal-toepithelial (MET) transitions have been observed in embryonic development and in cancer progression (Chung et al. 2006; Prindull 2005; Birchmeier et al. 1996; Wells et  al. 2008; Hugo et  al. 2007). These transitions are commonly associated with a predictive switch of cancer behaviors by the affected cancer epithelium, which assumes increased migratory, invasive and metastatic potential, as assessed by changes in cell morphology and gene expression profiles. This is the rationale for designing novel EMT/MET-based targeting strategies (Sabbah et al. 2008; Moen et al. 2009; Ponzo et al. 2009). In the context of cancer microenvironments, these transitions offer a possible new explanation of the origins of the inductive stromal fibroblasts and the responding cancer epithelial cells, since both cancer epithelial cells and stromal fibroblasts can be derived from either resident or migrating pluripotent stem cells or from a selective population of transforming cancer epithelial or reactive stromal cells through interactions with specific cell types or factors in the cancer microenvironment (Santamaria-Martinez et al. 2009; Leber and Efferth 2009). A small side population of pancreatic cancer stem cells was recently reported to be particularly sensitive to EMT induction by TGF-β (Kabashima et al. 2009).

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Likewise, circulating cancer cells were shown to be sensitive to growth factor induction to undergo EMT and MET (Vessella et al. personal communication).

1.3.3  Mesenchymal Stem Cells (MSC) MSC are a class of multipotent stem cells capable of differentiating into osteoblasts, chondrocytes, and adipocytes. They can be derived from bone marrow stromal cells and also from adult linage stem cells with self-renewal capability. These cells were found to be present in cancer microenvironments, with the potential of promoting cancer growth and progression (Roorda et al. 2009), exerting immunosuppression by interfering with dendritic and T-cell functions (Spaeth et al. 2008) and ‘marking’ the sites where cancer cells subsequently metastasize (Jung et al. 2009; Kaplan et al. 2005). These functions are generally accomplished by the ability of MSC to secrete specific factors which, via circulatory network and paracrine interaction, confer migratory, invasive and metastatic potential to cancer cells at the primary site of cancer growth. They also interact at the site of metastasis, for instance by increasing bone turnover to create a favorable microenvironment supporting the dissemination of cancer cells (Kaplan et al. 2007).

1.4 The Mediators and Cell Signaling Network Governing the Plasticity of Stromal Fibroblasts Soluble and insoluble mediators secreted by cancer cells and cells in cancer microenvironments are responsible for supporting the growth and progression of cancer by interacting with selective receptors that transmit signals orchestrating a switch in cancer cell morphology and function compatible with the survival of cancer cells. In this section, specific examples defining prostate cancer interactions with soluble and matrix proteins will be used to illustrate the importance of understanding the cell signaling network to identify relevant therapeutic targets for clinical translation and develop new drugs.

1.4.1  Soluble Growth Factors The general model depicted in Fig. 1.1 shows cancer cell-secreted soluble factors promoting the activation of both cancer-associated resident stromal fibroblasts and migrating MSC and/or their derivative stromal fibroblasts. This triggers additional remodeling of tumor microenvironments, reciprocally affecting the genotype and phenotype of both cancer cells and stromal fibroblasts in the cancer microenvironment via soluble factors including growth factors, cytokines, chemokines, and

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reactive oxygen species (ROS) released in the tumor microenvironment by resident and migrating host mesenchymal or stromal cells. These factors often work in concert to induce a primarily stromal reaction manifested by activated stromal fibroblasts or myofibroblasts producing either higher levels and/or more effective combinations of soluble growth factors capable of inducing cancer growth, invasion and metastasis directly, and also of promoting the reorganization of the vascular network favoring the dissemination of cancer cells to distant organs. In other words, soluble factors produced by cancer and host stroma can be disseminated to distant sites where they become responsible for remodeling the premetastatic niche and facilitating subsequent cancer cell dissemination (Kaplan et al. 2007). In some cases, soluble factors can act at long distance at metastatic sites to modulate tumor microenvironments by providing higher concentrations or more complementary growth factors via increased bone turnover, creating a less hostile environment for the growth of cancer cells via immune suppression, or promoting osteomimicry within cancer cells and creating a metastatic bone ‘niche’ favoring overall cancer cell growth and survival at metastatic sites (Chung et al. 2006; Cooper et al. 2003). Soluble factors secreted by cancer and activated stromal cell components within the tumor microenvironment can also modulate other cell signaling networks mediated by integrin-extracellular matrix interactions (Sangaletti et al. 2008; Chiarugi and Giannoni 2005) and androgen receptor signaling pathways (Huang et al. 2006; Olapade-Olaopa et al. 1999), activating the cell signaling network to upregulate integrins and/or androgen receptor expression (Liegibel et al. 2002; Bonaccorsi et al. 2006).

1.4.2  Extracellular Matrices (ECMs) Cancer cell proliferation and survival within the tumor microenvironment depends on the ability to adhere and attach to ECMs (Desgrosellier and Cheresh 2010). Through ECM-integrin interactions, cancer cells can also gain increased invasive, migratory and metastatic potential, mediated by the activation of converging cell signaling networks downstream that confer growth and survival advantages to cancer cells (Pontier and Muller 2009). ECM-integrin interactions, known to affect embryonic development (Armant 2005), also play a directive role in determining the gene expression profiles of cancer cells, the ability of cancer cells to degrade their surrounding ECMs by matrix metalloproteinases (MMPs), to extravasate into metastatic microenvironments by adhesion to organ-specific endothelial cells (Kargozaran et al. 2007), and the status of differentiation of cancer cells.

1.4.3  ROS and Oxidative Stress High levels of ROS and oxidative stress can be created in cancer microenvironments by the continued growth and expansion of solid tumors which deplete oxygen

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supplies and build up metabolic waste at the site of tumor growth. Cancer cells with downregulated manganese superoxide dismutase (MnSOD) show increased levels of superoxide, which induce hypoxia inducing factor 1 (HIF-1α) and VEGF, causing increased angiogenesis and tumor growth (Kaewpila et al. 2008; Wang et al. 2005). An accumulation of hydrogen peroxide can also be induced by placing cancer cells under hypoxic conditions and in cells with defective catalase (Azad et al. 2009). Hydrogen peroxide is a potential mediator contributing to the co-evolution of the genotype and phenotype of both prostate cancer and bone stroma when they are in contact under 3-D co-culture conditions (Sung et al. 2006, 2008). Hypoxic conditions can also affect the transdifferentiation of cancer cells such as EMT and MET, where hypoxia induces EMT (Klymkowsky and Savagner 2009; Jiang et al. 2007) and hyperbaric oxygen treatment causes MET (Moen et al. 2009) with respective corresponding changes of either increased or decreased cancer growth and invasion in animals.

1.5 Overall Significance and Clinical Translation of Integrated Approaches to Cancer–Stromal Fibroblast Interaction Our laboratory has established a 3-dimensional (3-D) co-culture system to investigate how the information derived from cancer–stromal fibroblast interaction can be applied in the clinic and to understand the molecular pathways that determine the behaviors of cancer cells. This approach is based on our prior work showing that cancer cell phenotype and genotype can be irreversibly “programmed” when cancer cells are grown together with prostate or bone stromal cells under 3-D conditions as prostate organoids (Sung et al. 2008; Rhee et al. 2001) or in mice as tumor xenografts (Sung et al. 2008). We found several important features of these types of cellular interactions. (1) The irreversible “programming” of the phenotype and genotype of cancer cells by stromal fibroblasts is bi-directional. We observed that human stromal fibroblasts co-cultured with human prostate cancer cells under 3-D conditions can program the genotype (assessed by cytogenetics and genome-wide scan (Sung et al. 2008) and phenotype (measured by gene expression and ability to grow tumors with metastatic potential in mice (Sung et al. 2008). Remarkably, normal stromal fibroblasts from mouse, benign/normal human prostate stromal fibroblasts, and the MG-63 osteosarcoma cell line have also been observed to undergo irreversible and non-random genotypic and phenotypic changes when co-cultured with prostate cancer cells under 3-D conditions (Sung et al. 2008; Rhee et al. 2001). (2) Gene expression analyses revealed that stromal fibroblasts, after physical contact with prostate cancer cells, had increased levels of brain-derived neurotropic factor (BDNF), chemokines, CCL5 and CXCL5, versican, tenascin, connective tissue growth factor, stromal cell derived factor-1 (SDF-1/CXCL12), and HIF-1α (Sung et al. 2008). We have validated the overexpression of these biomarkers identified by our cell culture model in clinical tissue and serum samples collected from

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prostate cancer patients with confirmed bone metastasis (Sung et al. 2008). These studies highlight the bidirectional interactions and the co-evolution of tumor-stroma in prostate cancer progression.

1.6 Co-targeting Tumor and Stroma as an Effective Therapeutic Strategy for the Treatment of Cancer and Cancer Metastasis Cancer-host microenvironment communication in the primary and at distant metastatic sites, mediated by soluble factors and insoluble matrices, supports the growth and survival of cancer cells. This provides a sound rationale for co-targeting both the tumor and the host microenvironment to achieve better tumor growth control and improve overall patient survival. Cancer development is complex, involving multiple interactions between different cell types and pleiotropic signaling mechanisms leading to progression. Reciprocal interaction between prostate cancer cells and resident and migrating cells in the tumor microenvironments mediated by cell signaling networks should be considered viable targets. Prostate cancer frequently metastasizes to bone and <50% of the patients with hormonal refractory bone metastases survive more than 5 years. Our laboratory has addressed the critical issues of prostate cancer bone metastasis from both the biological and therapeutic perspectives (Chung et al. 2006; Josson et al. 2010; Chung 1993, 1995). We investigated human prostate cancer cell interaction with human osteoclasts, osteoblasts and marrow stromal cells under 3-D co-culture conditions to mimic tumor growth in vivo (Sung et al. 2008). These studies allowed us to conclude that prostate cancer survives in a tumor microenvironment by the activation of specific cell signaling networks with neighboring host cells. During this process, the cancer cells and cells in the cancer microenvironment “co-evolve” in part through their response to growth factors, extra-cellular matrices and ROS (Sung et al. 2008; Rhee et al. 2001; Thalmann et al. 1994). Cancer cells acquire several mimetic abilities, such as osteomimicry, vasculomimicry, neuromimicry and stem cell mimicry, and undergo a transition from epithelium to mesenchyme with definitive behavioral modifications (Huang et al. 2006; Zhau et al. 2008). To develop an effective targeting strategy for prostate cancer bone metastases, it is critical to consider these interactions and devise the most effective way of targeting not only tumor cells, but also cells in the tumor microenvironment (Hsieh et al. 2004; Kubo et al. 2003). Table 1.1 summarizes a number of ongoing and completed clinical trials proving the concept that the tumor-associated stroma compartment is a new and exciting target awaiting the development of novel therapeutics. Our laboratory first conducted a clinical trial co-targeting prostate cancer and bone with an adenoviral-based therapy using a therapeutic toxic gene driven by an osteocalcin promoter shared in common by cancer and bone cells (Hsieh et al. 2004; Kubo et al. 2003). A number of successful avenues have been opened, including co-targeting the interaction of prostate cancer and endothelium via VEGF-mediated signaling with an antibody (e.g.

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Table 1.1   Soluble growth factors and cells in the bone become attractive targets for the development of novel biological-based therapeutics for the management of prostate cancer local growth and distant metastases to bone and soft tissues Therapeutic targets Drugs References (growth factors, cells) Human epidermal growth factor (hEGF) Vascular endothelial growth factor (VEGF)

Transforming growth factor-beta (TGF-β) Insulin growth factor-1 receptor (IGF1R)

Trastuzumab (Herceptin®), Lapatinib (Tykerb®), Gefitinib (Iressa®), Erlotinib (Tarceva®), Cetuximab (Erbitux®), Panitumumab (Vectibix®) Bevacizumab (Avastin®), Ranibizumab (Lucentis), Lapatinib (Tykerb), Sunitinib (Sutent), Sorafenib (Nexavar), Axitinib, Pazopanib AP12009 (Trabedersen), antisense oligonucleotide against TGF-β2 (Phase III), GC1008 anti-TGF-β monoclonal antibody (Phase I) CP-751,871 (monoclonal antibody) Phase I, AP-12 (monoclonal antibody) Preclinical, 19D12 (monoclonal antibody) Preclinical, EM164 (monoclonal antibody) Preclinical, hC7C10 (monoclonal antibody) Preclinical SU-101 kinase inhibitor, Phase III, Gleevec

Platelet derived growth factor (PDGFR) Endothelin receptor Atrasentan 177 Radiolabelled anti­ Lu-labelled J591, 90 bodies—Prostate Y-labelled J591 specific membrane antigen (PSMA) AGS-PSCA (Phase I) Radiolabelled anti­ bodies—Prostate stem cell antigen (PSCA) Integrins Monoclonal antibodies targeting the extracellular domain of the heterodimer: Vitaxin Synthetic peptides containing an RGD sequence: Cilengitide; KGaA Peptidomimetics of RGD sequence: S247 Integrins-αv family CNTO 095 Osteoclasts Bisphosphonates: Non-N-containing bisphosphonates:Etidronate (Didronel), Clodronate (Bonefos, Loron) Tiludronate (Skelid)N-containing bisphosphonates: Pamidronate (APD, Aredia), Neridronate, Olpadronate, Alendronate (Fosamax), Ibandronate (Boniva), Risedronate (Actonel), Zoledronate (Zometa, Aclasta)

http://www.cancer.gov/ cancertopics/factsheet/ Therapy/targeted http://www.cancer.gov/ cancertopics/factsheet/ Therapy/targeted, http://en.wikipedia.org/ wiki/Vascular_ endothelial_growth_ factor Garber (2009)

Garber (2005)

Gibbs (2000) Lalich et al. (2007) Bander et al. (2005); Milowsky et al. (2004) David et al. (2006)

Stupp et al. (2007)

Trikha et al. (2004) http://en.wikipedia.org/ wiki/Bisphosphonate

1  Critical Roles of Stromal Fibroblasts in the Cancer Microenvironments Table 1.1  (continued) Therapeutic targets Drugs (growth factors, cells) Osteoclasts

Bone-seeking radiopharmaceuticals: Radium-223 (Alpharadin®), strontium-89, samarium-153 Osteoclasts (RANKL) Denosumab (Prolia)

13

References Nilsson et al. (2007); Tu et al. (2005) http://en.wikipedia.org/ wiki/Denosumab

bevacizumab) or small molecules to inhibit receptor tyrosine kinases (e.g. sunitinib); co-targeting interactions between prostate cancer and a number of soluble growth factors secreted by cancer, stromal fibroblasts and/or inflammatory cells using either therapeutic antibodies (Chung et al. 2005; Wu et al. 2005) or small molecule tyrosine kinase inhibitors; and co-targeting the prostate cancer/osteoblast interface with the endothelin receptor antagonists Zibotentan or Atrasentan (Kopetz et al. 2002). Cotargeting extracellular matrix-prostate cancer interactions with integrin antagonists against αvβ3 (e.g. Vitaxin) or αv (e.g. CNTO95) has been tested. Bone-directed cotargeting with a RANKL antibody, denosumab, or osteoclast antagonists, bisphosphonates, has helped to reduce bone pain and skeletal-related events (SRE) (Keller 2002; Saad et al. 2009). Co-targeting patients with confirmed bone metastases with radiopharmaceutics, 89Sr, 153Sm (β-emitters), or 188Re (γ-emitter) radionuclides, plus chemotherapy (e.g. docetaxel, mitoxantrone, estramustine and etoposide), long-term androgen suppression and/or external beam radiation has also improved quality of life and prostate cancer patient survival (Sartor 2009; Tu et al. 2005). A common drawback in these trials is the severe marrow toxicity encountered by patients treated with the co-targeting agents. So far, this has compromised the prospect of repeated administration of these highly effective agents to prostate cancer patients for potential “cure”. Recently, an exciting therapeutic development using bone-seeking 223Ra, an α-emitter, for the treatment of prostate cancer bone metastases has been explored (Nilsson et al. 2005, 2007). 223Ra has the advantage of emitting high linear energy transfer (LET) radiation with a short track length in tissues for up to just a few mm. In a randomized, multicentre placebo-controlled phase II trial (Nilsson et al. 2005, 2007), 223Ra was found to be well-tolerated, sparing myelotoxicity while reducing serum bone-alkaline phosphatase concentration, a marker indicative of prostate cancer growth in bone in patients with bone metastases. 223Ra prevented SRE and improved overall survival in patients with hormonerefractory prostate cancer. 223Ra is currently in Phase III trials in the US and Europe in patients with metastatic prostate cancer bone metastases.

1.7 The Frontier of Future Stromal Fibroblast Research The plasticity of stromal fibroblasts and their ability to induce cancer cell growth, migration, invasion and metastasis, to promote cancer cell survival, and to alter cancer cell sensitivity toward chemotherapy and radiation therapy raises the fol-

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lowing questions, which can be considered as some of the future frontiers of stromal fibroblastic research. (1) What are the regulatory mechanisms determining the plasticity and differentiation status of stromal fibroblasts? The intriguing biology of iPS cells taught us the lesson that a normal adult stromal fibroblast can be reprogrammed by the introduction of a cassette of transcription factors, Oct4, Sox2. Klf4 and c-Myc, to become pluripotent stem cells capable of forming cells and organs of diverse lineages including inductive stromal fibroblasts and MSCs. The critical question that needs to be addressed is whether the engineered transcription factor protein(s) produced within tumor microenvironments can play the reprogramming roles of an adult stromal fibroblast and explain the heterogeneity of stromal fibroblasts. This speculation has now been supported by a stunning laboratory demonstration where recombinant transcription factor proteins, when added to cultured adult stromal fibroblasts, reprogrammed these cells to express markers indicative of a stem cells phenotype (Cho et al. 2010; Tang et al. 2010; Rhee et al. 2011). This suggests the possibility that proteins secreted by cancer cells and cells in the tumor microenvironments could reprogram adult cells and that this could be the molecular basis of the reactivation of embryonic growth potential of the stroma, proposed more than three decades ago by McNeil as a contributing factor to benign hyperplastic growth of the prostate gland (BPH) commonly found in the aging male (McNeal 1978). (2) What are the critical soluble factors and ECMs produced by cells in tumor microenvironments that can dictate the growth, survival and metastasis of malignant prostate cancer cells? Published data suggest that a host of factors, including classical soluble growth and survival factors, ROS, chemokines and cytokines, ECMs and their fragments, can modulate cancer–cancer and cancer–stroma interactions. It is becoming increasingly important to reclassify these factors and their combinations based on their molecular actions with special emphasis on factors that confer lethal phenotypes to cancer cells. Developing better tools to predict the clinical outcome of prostate cancer will support the concept and its implementation in personalized and predictive oncology for improved diagnosis, prognosis and treatment of patients with prostate cancer. (3) What are the most effective means of co-targeting tumor and stroma to prevent cancer cells from developing therapeutic resistance? There are an increasing number of clinical trials based on the concept of co-targeting cancer and cancer-associated microenvironments. An improved fundamental understanding of how tumor–stroma interacts, and how the genotype and phenotype of cancer cells may be “co-evoluted” will help us developing better and more effective co-targeting strategies for the management of lethal prostate cancer. Acknowledgements  The author wishes to thank Dr. Sajni Josson for her help in the construction of the figure and the table. The editorial assistance from Gary Mawyer is also appreciated. This work was supported in part by the following grants: R01 CA122602 and P01 CA098912 from National Caner Institute and W81 XWH-01–1-0172 from Department of Defense, United States Army.

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Pontier SM, Muller WJ (2009) Integrins in mammary-stem-cell biology and breast-cancer progression—a role in cancer stem cells? J Cell Sci 122(Pt 2):207–214 Ponzo MG, Lesurf R, Petkiewicz S et al (2009) Met induces mammary tumors with diverse histologies and is associated with poor outcome and human basal breast cancer. Proc Natl Acad Sci U S A 106(31):12903–12908 Prindull G (2005) Hypothesis: cell plasticity, linking embryonal stem cells to adult stem cell reservoirs and metastatic cancer cells? Exp Hematol 33(7):738–746 Radisky DC, Przybylo JA (2008) Matrix metalloproteinase-induced fibrosis and malignancy in breast and lung. Proc Am Thorac Soc 5(3):316–322 Rhee HW, Zhau HE, Pathak S et al (2001) Permanent phenotypic and genotypic changes of prostate cancer cells cultured in a three-dimensional rotating-wall vessel. In Vitro Cell Dev Biol Anim 37(3):127–140 Rhee YH, Ko JY, Chang MY et  al (2011) Protein-based human iPS cells efficiently generate functional dopamine neurons and can treat a rat model of Parkinson disease. J Clin Invest 121(6):2326–2335 Rhodes LV, Muir SE, Elliott S et al (2009) Adult human mesenchymal stem cells enhance breast tumorigenesis and promote hormone independence. Breast Cancer Res Treat 113:293–299 Ronnov-Jessen L, Bissell MJ (2009) Breast cancer by proxy: can the microenvironment be both the cause and consequence? Trends Mol Med 15(1):5–13 Roorda BD, ter Elst A, Kamps WA, de Bont ES (2009) Bone marrow-derived cells and tumor growth: contribution of bone marrow-derived cells to tumor micro-environments with special focus on mesenchymal stem cells. Crit Rev Oncol Hematol 69(3):187–198 Saad F, Abrahamsson PA, Miller K (2009) Preserving bone health in patients with hormone-sensitive prostate cancer: the role of bisphosphonates. BJU Int 104(11):1573–1579 Sabbah M, Emami S, Redeuilh G et  al (2008) Molecular signature and therapeutic perspective of the epithelial-to-mesenchymal transitions in epithelial cancers. Drug Resist Updat 11(4– 5):123–151 Sangaletti S, Di Carlo E, Gariboldi S et al (2008) Macrophage-derived SPARC bridges tumor cellextracellular matrix interactions toward metastasis. Cancer Res 68(21):9050–9059 Santamaria-Martinez A, Barquinero J, Barbosa-Desongles A et al (2009) Identification of multipotent mesenchymal stromal cells in the reactive stroma of a prostate cancer xenograft by side population analysis. Exp Cell Res 315(17):3004–3013 Sartor O (2009) Radiopharmaceutical and chemotherapy combinations in metastatic castrate-resistant prostate cancer: a new beginning? J Clin Oncol 27(15):2417–2418 Singh S, Singh R, Singh UP et al (2009) Clinical and biological significance of CXCR5 expressed by prostate cancer specimens and cell lines. Int J Cancer 125(10):2288–2295 Spaeth E, Klopp A, Dembinski J, Andreeff M, Marini F (2008) Inflammation and tumor microenvironments: defining the migratory itinerary of mesenchymal stem cells. Gene Ther 15(10):730–738 Stupp R et al (2007) Integrin inhibitors reaching the clinic. J Clin Oncol 25:1637–1638 Sung SY, Kubo H, Shigemura K et al (2006) Oxidative stress induces ADAM9 protein expression in human prostate cancer cells. Cancer Res 66(19):9519–9526 Sung SY, Hsieh CL, Law A et  al (2008) Coevolution of prostate cancer and bone stroma in three-dimensional coculture: implications for cancer growth and metastasis. Cancer Res 68(23):9996–10003 Svineng G, Ravuri C, Rikardsen O, Huseby NE, Winberg JO (2008) The role of reactive oxygen species in integrin and matrix metalloproteinase expression and function. Connect Tissue Res 49(3):197–202 Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126(4):663–676 Tang Y, Lin CJ, Tian XC (2010) Functionality and transduction condition evaluation of recombinant klf4 for reprogramming iPS cells. Cell Reprogram 13:99–112

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Tasseff R, Nayak S, Salim S, Kaushik P, Rizvi N, Varner JD (2010) Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems. PLoS One 5(1):e8864 Thalmann GN, Anezinis PE, Chang SM et al (1994) Androgen-independent cancer progression and bone metastasis in the LNCaP model of human prostate cancer. Cancer Res 54(10):2577–2581 Thalmann GN, Rhee H, Sikes RA et  al (2009) Human prostate fibroblasts induce growth and confer castration resistance and metastatic potential in LNCaP cells. Eur Urol 58:162–172. Tothill RW, Tinker AV, George J et al (2008) Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 14(16):5198–5208 Trikha M et al (2004) CNTO 95, a fully human monoclonal antibody that inhibits alphav integrins, has antitumor and antiangiogenic activity in vivo. Int J Cancer 110(3):326–335 Tu SM, Lin SH (2008) Current trials using bone-targeting agents in prostate cancer. Cancer J 14(1):35–39 Tu SM, Kim J, Pagliaro LC et al (2005) Therapy tolerance in selected patients with androgen-independent prostate cancer following strontium-89 combined with chemotherapy. J Clin Oncol 23(31):7904–7910 Tuxhorn JA, Ayala GE, Rowley DR (2001) Reactive stroma in prostate cancer progression. J Urol 166(6):2472–2483 Tuxhorn JA, Ayala GE, Smith MJ, Smith VC, Dang TD, Rowley DR (2002) Reactive stroma in human prostate cancer: induction of myofibroblast phenotype and extracellular matrix remodeling. Clin Cancer Res 8(9):2912–2923 Untergasser G, Gander R, Lilg C, Lepperdinger G, Plas E, Berger P (2005) Profiling molecular targets of TGF-beta1 in prostate fibroblast-to-myofibroblast transdifferentiation. Mech Ageing Dev 126(1):59–69 Vessella RL, Corey E (2006) Targeting factors involved in bone remodeling as treatment strategies in prostate cancer bone metastasis. Clin Cancer Res 12(20 Pt 2):6285s–6290s Wang M, Kirk JS, Venkataraman S et al (2005) Manganese superoxide dismutase suppresses hypoxic induction of hypoxia-inducible factor-1alpha and vascular endothelial growth factor. Oncogene 24(55):8154–8166 Wells A, Yates C, Shepard CR (2008) E-cadherin as an indicator of mesenchymal to epithelial reverting transitions during the metastatic seeding of disseminated carcinomas. Clin Exp Metastasis 25(6):621–628 Wu JD, Odman A, Higgins LM et al (2005) In vivo effects of the human type I insulin-like growth factor receptor antibody A12 on androgen-dependent and androgen-independent xenograft human prostate tumors. Clin Cancer Res 11(8):3065–3074 Yanagisawa N, Li R, Rowley D et al (2007) Stromogenic prostatic carcinoma pattern (carcinomas with reactive stromal grade 3) in needle biopsies predicts biochemical recurrence-free survival in patients after radical prostatectomy. Hum Pathol 38(11):1611–1620 Yao Q, Qu X, Yang Q, Wei M, Kong B (2009) CLIC4 mediates TGF-beta1-induced fibroblast-tomyofibroblast transdifferentiation in ovarian cancer. Oncol Rep 22(3):541–548 Yu J, Hu K, Smuga-Otto K et al (2009) Human induced pluripotent stem cells free of vector and transgene sequences. Science 324(5928):797–801 Zhao HF, Chen J, Xu ZS, Zhang KQ (2009a) Distribution and differentiation of mesenchymal stem cells in tumor tissue. Chin Med J (Engl) 122(6):712–715 Zhao XY, Li W, Lv Z et al (2009b) iPS cells produce viable mice through tetraploid complementation. Nature 461(7260):86–90 Zhau HE, Odero-Marah V, Lue HW et al (2008) Epithelial to mesenchymal transition (EMT) in human prostate cancer: lessons learned from ARCaP model. Clin Exp Metastasis 25(6):601–610

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Part II

Stromal Cell Diversity

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Chapter 2

Functional Diversity of Fibroblasts H. Peter Rodemann and Hans-Oliver Rennekampff

2.1 Introduction Solid tumours are multi-cellular tissues comprised of tumour cells and stromal cells, including fibroblasts, endothelial cells and inflammatory cells. When a cancer cell metastasizes, it first will be exposed to cancer associated fibroblasts in the immediate tumour microenvironment and subsequently to normal fibroblasts as it traverses the underlying connective tissue on its way to the bloodstream. So far, the interactions of tumour cells with stromal fibroblasts influence tumour biology by mechanisms that are not yet fully understood. It is known that cells of the tumour parenchyma and stroma are in extensive crosstalk, and the composition of the stroma and the nature of tumour stromal interactions change over time with tumour progression (Beacham and Cukierman 2005; Proia et al. 2005). The tumour-stroma crosstalk markedly influences not only tumour growth by modifying and controlling angiogenesis, suppressing or subverting immune responses of the host, but also by modulating extracellular matrix composition, and secreting factors which in turn stimulate cells to further alter cell physiology as well as the cellular and acellular composition of the tumour microenvironment (Stuelten et  al. 2010; Olumi et  al. 1999; Liotta and Kohn 2001). Fibroblasts as ubiquitous stromal cells are accessory cells which influence other neighbouring cell types through secretion of cytokines as well as growth and differentiation factor factors (Baglole et al. 2006; Micke and Ostman 2005). While in the early phase fibroblasts can have tumour suppressing activity the phenotype of fibroblasts can alter to a tumour promoting state as carcinogenesis progresses (Proia et al. 2005). This phenotypic switch does occur in two stages in which a reversible “primed” fibroblasts type is generated first followed the manifestation of an irreversible phenotype of fibroblasts which present tumour-promoting properties H. P. Rodemann () Division of Radiobiology & Molecular Environmental Research, Department of Radiation Oncology, Eberhard Karls University Tübingen, Röntgenweg 11, 72076 Tübingen, Germany e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_2, © Springer Science+Business Media B.V. 2011

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(Beacham and Cukierman 2005). These so-called cancer associated fibroblasts (CAF) have properties distinct from normal fibroblasts and actively promote tumorigenesis as it has been described for example for prostate and breast cancer (Olumo et al. 1999; Orimo et al. 2005). The topic of cancer associated fibroblasts, however, is discussed in separate chapters of this book. In the following various aspects of the functional diversity of normal not cancer associated fibroblast will be discussed as the capacity of fibroblasts to produce and organize the extracellular matrix and to communicate with other cell systems makes them a central component of tissue biology.

2.2 Fibroblast Phenotypes Fibroblasts from different anatomical regions display characteristic phenotypes. Early work on the heterogeneity of fibroblasts performed by Castor and co-workers (Castor et al. 1962) demonstrated metabolic differences between mesothelial fibroblasts, fibroblasts of the skin, articular tissues and periosteum. Studies by Chang et al. (2002) and Rinn et al. (2006, 2008) characterized the gene expression profile of fibroblasts derived from different anatomical regions of the body with cDNA microarray technology. These authors were able to show a site specific gene expression pattern with a striking division into: anterior-posterior, proximal-distal and dermal-non dermal. In addition they demonstrated diversity in topographic expression for genes involved in extracellular matrix synthesis, growth and differentiation, cell migration as well as genes involved in genetic syndromes. Work by Chipev and Simon (2002) indicated that fibroblasts from different body sites differ in size, with palmar fibroblasts being smaller than non-glabrous derived fibroblasts. In addition growth kinetics and TGF (Transforming Growth Factor) -β1 Receptor II expression as well as the ability to contract collagen lattices were found to differ with palmoplantar skin derived fibroblasts having lower receptor levels and an increased mitotic rate. The authors speculated that this regional diversity may in part account for localized susceptibility to disease manifestation like scarring and keloid formation. Recent work on oral mucosal fibroblasts has demonstrated the differences in the capacity of these fibroblasts to reorganize collagen lattices by an increased matrix metalloproteinase (MMP)-2 expression as compared to skin derived fibroblasts (Stephens et al. 2001). Furthermore, it was shown that oral mucosa derived fibroblasts proliferated more rapidly and had a higher capacity for cell doublings. A functional correlation was linked to the fact that cultured oral fibroblasts secret more hepatocyte growth factor (HGF) and keratinocyte growth factor (KGF) than skin fibroblasts (Gron et al. 2002). It was suggested that both effects may contribute to a ‘fetal’ wound healing phenotype of oral fibroblasts. Beside the differences in fibroblasts from different anatomical sites fibroblasts separated from a single tissue are similarly not composed of a homogeneous population but rather consist of subsets of different fibroblasts. Sorrell and Caplan (2004) have reviewed differences between papillary fibroblasts, which reside in the

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superficial dermis and reticular fibroblasts which are located in the deeper dermis, highlighting the difference in matrix molecule production from these two distinct cell populations. Versican is produced at low levels by papillary fibroblasts while reticular fibroblasts produce high levels of this molecule. In contrast, decorin is produced in high levels by papillary fibroblast but only in small amounts by reticular fibroblasts. Collagen type I and collagen type III production and the subsequent ratio of these two dermal collagens were not found to differ between these populations of fibroblasts. Studies from the laboratory of the author also point toward the existence of different morphological and biochemical subsets of fibroblasts within the same tissue. Two major populations of fibroblasts have been detected in the dermis, lung and kidney termed mitotically active, i.e. replicative progenitor fibroblasts (MF) and irreversible postmitotic fibrocytes (PMF). These two subsets have subsequently been classified by cytomorphological and biochemical characteristics (Bayreuther et al. 1988; Rodemann 1989; Rodemann and Mueller 1990; Rodemann and Bamberg 1995; Lara et al. 1996; Rodemann et al. 1996; Burger et al. 1998; Herskind et al. 1998; Hakenjos et al. 2000). Based on in vitro and ex vivo-in vitro experiments it was shown that fibroblasts of human, rat and mouse skin but also lung as well as renal fibroblasts differentiate in vivo and in vitro along a lineage of replicative progenitor fibroblast cell types and non replicative, postmitotic functional fibrocytes of man, rat and mouse (Mollenhauer and Bayreuther 1986; Bayreuther et al. 1988; Rodemann et al. 1991). According to their cytomorphology (Fig. 2.1), replicative potential and ability to synthesize specific cytokines and growth factors, like TGF-β and Keratinocyte Growth Factor (KGF), MF-progenitor fibroblasts can be further classified into the cell types MF I, MF II, and MF III (Rodemann 1989; Herskind et al. 1998; Burger et al. 1998; Hakenjos et al. 2000). Subcloning experiments revealed that these MF-cell types differentiate along the lineage MF I, MF II,

Parameter

MFl

MFll

MFlll

PMF

Cell division potential

+++

++

+

–

SA-ß-Gal expression

–

(+)

+

+++

Total collagen synthesis

(+)

+

+

+++

KGF-production

(+)

+

++

+++

+

+

+++

+++

TGFß1-production

Fig. 2.1   Differentiation lineage, marker and collagen expression, as well as growth factor production profile of progenitor fibroblasts and postmitotic fibrocytes. The differentiation lineage of progenitor fibroblasts (MF) and postmitotic fibrocytes (PMF) along the sequence MFI-MFIIMFIII-PMF cell types was analysed on the basis of cell biological and biochemical parameters. Quantitative and qualitative differences could be demonstrated for the parameter cell division potential, SA-β-galactosidase (SA-β-Gal) expression, total collagen synthesis, KGF and TGFβ1 production. A relative indicator system (− negative; (+) very low; + low; ++ moderate; +++ high) is chosen for cell type specific comparison of these parameters

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MF III, before differentiation into irreversible postmitotic fibrocytes (PMF) occurs (Bayreuther et al. 1988, 1989; Rodemann et al. 1996). MF I-type progenitor fibroblasts have the highest replicative capacity. MFI progenitor fibroblasts of human origin can go through about cell divisions before differentiating into MF II type cells. This cell type can divide approximately 15–20 times before differentiating into cell type MF III which has the potential for only 5–8 further division cycles before differentiating spontaneously into irreversible postmitotic fibrocytes (Rodemann et al. 1989). Based on its biochemical activity the latter cell type reflects the functional cell type of the fibroblast-fibrocyte cell system. Per cell it produces about 5–8 times more total collagen as compared to progenitor fibroblasts and provides the correct ratio of interstitial collagens type I, III, and V needed for homeostasis and maintenance of interstitial tissues (Rodemann et al. 1989). The cellbiological and biochemical parameters of progenitor fibroblasts and postmitotic fibrocytes are summarized Fig. 2.1. As reported earlier for human skin (Bayreuther et  al. 1988; Rodemann 1993; Herskind et al. 1998) as well as rat lung (Hakenjos et al. 2000) a constant ratio in the in vivo proportion of progenitor fibroblasts and functional postmitotic fibrocytes of 2:1 exists and seems to be independent of the age of the donor. Thus it can be assumed that biochemical homeostasis of the dermis as well as of interstitial tissues in various organs depends on a correct ratio of progenitor fibroblasts and functional postmitotic fibrocytes. Disturbance of this ratio by exogenous or endogenous factors may lead to an altered cellular homeostasis in the affected tissue and can result in pathologic tissue remodelling, similar to that observed in renal, lung and skin fibrosis (Rodemann and Bamberg 1995; Rodemann et al. 1996; Burger et al. 1998; Herskind et al. 1998; Rodemann and Mueller 1990; Rodemann et al. 1991; Hakenjos et al. 2000; von Pfeil et al. 2002). Under in vitro conditions, progenitor fibroblasts and functional postmitotic fibrocytes can be classified according to the specific expression of the senescence activated-β-Galactosidase activity (SA-β-Gal), which is also known as pH6-activity of β-galactosidase. Postmitotic fibrocytes show a strong expression of SA-β-Gal activity whereas MF-progenitor fibroblasts do not (Hakenjos et  al. 2000). The expression of this enzyme activity has been used in attempts to identify cellular ageing processes in fibroblasts cultures (Dimri et al. 1995). The expression of pH6β-galactosidase activity could only be demonstrated in fibroblasts which have undergone replicative senescence, i.e. in irreversible postmitotic cells (Dimri et  al. 1995). These fibroblast populations are predominantly composed by the functional and irreversible postmitotic fibrocytes (Herskind et al. 1998; Hakenjos et al. 2000) (for summary see Fig. 2.1). Decreased proliferative potential of fibroblasts has also been noticed in a stress-induced senescence-like phenotype. Replicative senescence can be induced via pathways that accelerate shortening of the telomere, while premature stress-induced senescence can be caused by DNA damage via telomere-independent mechanisms e.g. oxidative stress or UVB exposure (Von Zglinicki et al. 1995; Pascal et al. 2005; Debacq-Chainiaux et al. 2005). The analysis of purified subpopulations of human MF-progenitor fibroblasts derived from skin and obtained

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through subcloning procedures indicated that MFI, MFII and MFIII progenitor fibroblasts show increased telomere-shortening along the differentiation sequence MF1-MFII-MFIII (Herskind et al. unpublished results). As demonstrated for the rabbit kidney two biochemically diverse subpopulations of fibroblasts have been characterized in the cortex and papilla (Rodemann et al. 1991; Knecht et al. 1991). Papillary fibroblasts presented an about 50% longer mitotic lifespan than cortical fibroblasts before both subtypes differentiate into postmitotic fibrocytes. In addition to differences in protein expression, these two renal fibroblast subtypes respond differentially to growth factor stimulation as well as to stimuli from co-cultured epithelial cells. While no differences in growth response of these two subtypes could be observed after mitogenic stimulation with either epidermal growth factor (EGF) or insulin-like growth factor-1 (IGF-1) a significant differential response could be demonstrated for platelet derived growth factor (PDGF). PDGF exerted a potent mitogenic stimulus for papillary fibroblast but was only weakly mitogenic for cortical fibroblasts (Knecht et al. 1991). Additional patterns of fibroblasts heterogeneity have been identified through the study of wounding and wound healing. Wound healing proceeds in three interrelated phases (reviewed in Clark et  al. 1993): inflammation phase, proliferation phase, and maturation phase. Although fibroblasts have long been recognized as key cells in those process, it is only recently that we have begun to understand the origin and phenotypic differences of fibroblasts as related to the specific phases of wound healing. The initial inflammatory phase consists of an orderly extravasation of leucocytes like PMN and monocytes/macrophages into the wound. Bucala et al. (1994) have shown that a population of fibroblasts which arise from blood-borne circulating progenitor cells migrate into the wound site and may account for as many as 10% of the cells that infiltrate sites of acute tissue injury. These cells were also termed fibrocytes and express markers for cells of both hematopoietic and mesenychmal origin. These fibrocytes have to be clearly distinguished from the fibrocytes described by author and which resemble postmitotic fibroblasts. In addition it is unclear whether Bucala’s fibrocyte of hematopoetic origin enters the fibroblast lineage described above. The collagen+/vimentin+/CD 34+/CD 13+/CD45+ positive fibrocyte cells may contribute to normal wound repair by antigen presentation but may also participate in pathologic fibrotic responses (Chesney et al. 1997). Tredget and his group (Yang et al. 2005) identified this cell type in hypertrophic scarring of the skin. During the proliferative phase of wound healing granulation tissue and fibrovascular ingrowth occurs. Rossio-Pasquier et  al. (1999) have demonstrated that in full thickness dermal loss, fibroblasts migrating into the wound originate from the subcutaneous wound bed rather than from the non injured surrounding dermis. Van den Bogaerdt et  al. (2002) have shown that mitotic and contractile activity differs significantly between these dermal and fat derived fibroblasts. Middelkoop (2005) suggested that these differences may have a great impact on the use of either acellular dermal substitutes which repopulate from the subcutaneous fat or dermalderived fibroblast-seeded matrices.

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In the final phase of wound healing, a specialized population of differentiated fibroblasts occur (Gabbiani et  al. 1971, 2003). It has been demonstrated that fibroblasts under mechanical tension and under the influence of Platelet Derived Growth Factor (PDGF) express stress fibers. This cell type is termed proto-myofibroblast. Additional tension, TGF-β and a splice variant of fibronectin promote further differentiation into the myofibroblasts. These contractile myofibroblasts express alpha smooth muscle actin and are involved in the production of extracellular matrix and tissue contraction. Germain et al. (1994) provided evidence that human wound fibroblasts represent a functionally different population than fibroblasts isolated from unwounded dermis. In their analysis they concluded that 30–40% of wound-derived fibroblasts were myofibroblasts whereas only 1% of normal fibroblasts were alpha smooth muscle actin positive. Early studies from Bell et al. (1979) demonstrated that fibroblasts of high population doubling which have left the cell cycle can carry out lattice contraction at least as effectively as cycling cells from early passages, leading the authors to conclude that the contraction of wounds is not due to myofibroblasts. In vivo data indicated that the number of myofibroblasts in the wound is dependent upon the amount of transplanted deeper dermal portion (Rudolph et  al. 1991). Unfortunately it remains unclear whether this effect is due to the matrix or the resident cell population. Regardless of whether myofibroblasts are the true motile force for wound contraction, there is general agreement that wound contraction is an active cellular phenomenon depending on the activity of viable fibroblasts. Biological control of these cells either by the matrix or by surrounding cells may minimize scar contraction (Bell 1995; Bell et al. 1981). Interestingly, and of great importance for the understanding of the biology of chronic wounds, is the fact that a different fibroblast phenotype has been identified in diabetic ulcers when compared to normal undiseased skin as well as healthy skin from non-diabetic volunteers (Loots et  al. 1999). Fibroblasts from non-insulin-dependent diabetic ulcers exhibited a lower proliferation capacity and more flattened morphologic appearance (Loots et al. 1999). It is tempting to speculate that this fibroblast population resembles postmitotic fibroblasts. Whether these phenotypic differences in fibroblasts are the underlying cause for nonhealing diabetic ulcers or a secondary phenomenon reflecting an altered growth factor profile in this type of wound was not examined. Studies by Morocutti et al. (1996, 1997) revealed that dermal fibroblasts from insulin dependent diabetic patient with progressive fibrosis exhibit a remarkably decreased potential for proliferation as well as increased collagen production correlating with an increased proportion of postmitotic fibrocytes. Interestingly, these altered fibroblasts were not derived from lesional skin but from otherwise healthy skin of these patients. Hasan et al. (1997) also presented evidence that fibroblasts from chronic venous ulcer wounds are phenotypically altered, as a slower proliferation rate of wound derived fibroblasts was noticed. In addition a decreased expression of type 2 TGFβ receptors of ulcer fibroblast was accompanied by a failure to phosphorylate Smad 2, Smad 3, and p42/44 mitogen activating protein kinase (MAPK).

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2.3 Functional Effects of Fibroblast Subpopulations Besides the well established matrix building effect of fibroblasts, there is experimental evidence for a fibroblast-derived mesenchymal–epithelial crosstalk regulating epithelial growth factors (Clark 2003). For example reepithelialisation of cutaneous wounds as a concert of proliferation and migration of keratinocytes is strongly dependent on mesenchymal cell- derived factors. Fibroblasts are able to secrete IL-6, IL-8, HGF, and KGF all of which are known to stimulate keratinocyte proliferation and migration. Fusenig and his group (Fusenig 1994; Maas-Szabowski et  al. 1996) have demonstrated that growth-arrested X-ray-irradiated fibroblasts and proliferating fibroblasts profoundly differ in their profiles of IL-1 alpha stimulated cytokines and growth factor secretion. IL-1 alpha stimulated postmitotic fibroblasts depicted lower transcriptional levels (mRNA) of KGF, IL-1 alpha, IL-8, while HGF mRNA was dramatically increased. However, on the protein level IL-1 alpha-stimulated irradiated fibroblasts presented lower HGF levels than proliferating dermal fibroblasts. In previous work by others (Limat et al. 1989; Waelti et al. 1992) the growth promoting effect of postmitotic fibroblasts on keratinocytes was clearly demonstrated in vitro. As recently published (Nolte et  al. 2008), non-stimulated subpopulations of dermal derived MF-progenitor fibroblasts or postmitotic fibrocytes constitutively secrete significantly different amounts of KGF. When compared to progenitor fibroblasts MF II the amount of KGF produced per cell increases significantly in MF III type progenitor cells by a factor of about 2.2 (see also Fig. 2.1). Postmitotic fibrocytes, however, produce more than 3-times more KGF than MFII-type progenitors and app. 1.5-times more than MFIII type progenitor fibroblasts. These observations indicate a specific role of the subtypes of progenitor fibroblasts and postmitotic fibrocytes in the mesenchymal–epithelial interaction and specifically in the homeostasis of the epithelial cell system in skin. As previously described, TGF-β, another important growth factor is secreted by fibroblasts and counteracts the mitotic effect of KGF on keratinocytes. Among the three isoforms of TGF-β, TGF-β1 is the most prominent regulator. TGF-β1 was shown to down regulate epithelial growth and induce differentiation and apoptosis in keratinocytes (Mansbridge and Hanawalt 1988). Hakenjos et al. (2000) and von Pfeil et al. (2002) have previously demonstrated that TGF-β1 is predominantly produced by MF III type progenitor fibroblasts as well as postmitotic fibrocytes (see also Fig. 2.1). This cytokine plays an important role in the autocrine regulation of the differentiation process of progenitor fibroblasts to functional fibrocytes through the induction of cell cycle inhibitor proteins like p21 and p27 mediating permanent cell cycle arrest in G0 (Lee and Bae 2002). The importance of fibroblast/fibrocyte interaction with tissue specific epithelia is also demonstrated by the use of fibroblasts to construct skin substitutes. Fibroblasts seeded into a collagen-GAG matrix were shown to promote rapid epithelial outgrowth on the collagen-GAG matrix as compared to a non seeded collagen-GAG (Boyce et al. 1988, 1995, 2000; Boyce 2001). In contrast, the feeder layer system used to culture keratinocytes utilizes postmitotic fibrocytes, originally derived from

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the irradiated transformed mouse 3T3 cell line for co-culture (Rheinwald and Green 1975). There is strong evidence from radiobiological research that irradiation does not necessarily kill the feeder cells, but rather induces terminal differentiation with irreversible growth arrest (Rodemann and Bamberg 1995), yet largely preserves physiologic function in producing growth factors and extracellular matrix proteins (Rodemann and Bamberg 1995; Lara et al. 1996; Hakenjos et al. 2000; von Pfeil et  al. 2002). So far however, the functional influence of the secreted cocktail of growth factors and cytokines by the various subpopulations of fibroblasts on keratinocyte proliferation remains unclear. Despite the possible qualitative and quantitative differences between the different subpopulations it is well described that a direct cell-cell contact is crucial to promote keratinocyte growth because fibroblast conditioned medium cannot substitute for feeder cells (Briggaman and Wheeler 1968; Krejci et al. 1991). In addition to growth promoting experiments, a novel line of research is focussing on site specific interaction of fibroblast subtypes on development and tissue homeostasis. In an elegant set of experiments (Yamaguchi et al. 2005) it was shown that fibroblasts derived from soles and palms are able to induce keratin 9 mRNA in cultured non-palmoplantar keratinocytes. Non-palmoplantar keratinocytes cultured alone or in co-culture with non-palmoplantar fibroblasts failed to express keratin 9, indicating the extrinsic regulation by signals from site specific fibroblasts. In a similar set of experiment site specific regulation of melanocytes and pigmentation was also demonstrated (Yamaguchi et al. 2005). In the context of functional epitheliazation there is evidence that fibroblasts play a major role in basement membrane formation either alone or in conjunction with overlying keratinocytes (Contard et al. 1993; Cooper et al. 1993; Sahuc et al. 1996). These studies have demonstrated that keratinocytes alone were either unable to or limited in the production of laminin 1, collagen IV and laminin 5. In contrast, human fibroblasts, alone or in combination with overlying keratinocytes showed significant production of laminin 1 and collagen IV, and laminin 5. Subsequently, fibroblasts seeded into a collagen-GAG matrix were shown to promote basement membrane formation and epidermal homeostasis as compared to a non seeded collagen-GAG. Total collagen production by fibroblasts is dependent on the subpopulation of fibroblasts with postmitotic fibroblasts producing 5 to 8 times more collagen type I, III or V as compared to progenitor fibroblasts (Rodemann et al. 1989). Moreover, cultured postmitotic fibrocytes in comparison to progenitor fibroblasts produce the in vivo-like proportion of interstitial collagens me, III and V indicating again the important role of this cell type for tissue and ECM homestasis.

2.4 Functional Effects of Extracellular Matrix on Fibroblasts While it is well accepted that fibroblasts produce and modulate extracellular matrix there is strong evidence that extracellular matrix material as well as the specific lattice shape regulate cell shape and function of fibroblasts and may act as a form of

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switch between proliferative and differentiate states. Eckes et al. (1993) examined fibroblast behaviour in a three-dimensional contracted collagen matrix and compared them to fibroblast monolayers. They found that culture of dermal fibroblasts seeded in collagen gel leads to a time-dependent depression of pro alpha 1(I) collagen mRNA levels. It was not however investigated whether under these conditions fibroblasts changed in morphology or in their receptor expression. There is evidence that activation of the integrin alpha1 beta1 leads to down-regulation of collagen synthesis (Langholz et al. 1995). Studies by Nakagawa et al. (1989) on skin fibroblasts demonstrated that collagen embedding influences the cellular response and subsequent growth factor release of some but not all growth factors, e.g. IL-1β. Contracting lattices placed under tension lead to synthesis of collagen and regulated the expression of cytokines like TGF- beta 1 differently from relaxed collagen lattices embedding (Eckes et al. 2000; Kessler et al. 2001). Unfortunately phenotypic changes of these embedded fibroblasts were not investigated. Different matrix materials were also shown to have a significant influence on fibroblast function. Normal human cultured fibroblasts undergo programmed cell death in a three- dimensional contractile collagen gel while fibrin gels did not exert this effect (Fluck et al. 1998). In addition fibroblasts did contract the fibrin gel but did not proliferate. In anchored collagen gels fibroblasts showed a doubling time of 2 days and no apoptotic cell death. Despite all this evidence only limited in vivo studies have been undertaken to objectively evaluate scarring as a function of different dermal matrices, which is of crucial interest for dermal engineering. Mansbridge and his group (Kern et al. 2001, 2002) analysed fibroblasts behaviour in monolayer collagen cultures and scaffold based three-dimensional cultures with special attention to the expression of molecules associated with immune system activation. In monolayers of dermal fibroblasts expression of HLA class I but not of HLR-DR was found. Induction of HLA-DR and an increase in HLA -class I expression was observed after interferon-gamma stimulation. In contrast fibroblasts cultured in three-dimensional scaffolds consisted of a fraction of fibroblasts which were non-responsive to gamma interferon. After isolation and subsequent monolayer culture this effect was reversible. Interestingly, collagen-gel-based three-dimensional fibroblast cultures did not exert a similar unresponsiveness and it was concluded that the interaction of fibroblasts with naturally deposited extracellular matrix other than collagen was responsible for this effect. Kern et al. (2002) examined the expression of CD40 on fibroblasts as the interaction of fibroblasts and T-lymphocytes through the CD40 receptor was suggested to contribute to fibrogenesis. Human dermal fibroblasts express CD40 in a manner that is inversely related to proliferation and it was concluded that CD40 expression is greater in the stationary phase than in the log phase. In three-dimensional scaffold based fibroblast cultures responsiveness to interferon-gamma with subsequent CD40 expression was significantly reduced providing a possible etiology for the lack of rejection of allergenic three-dimensional scaffold based fibroblast cultures. This effect appears to be significantly dependent upon the scaffold utilized as Navsaria and his group (Griffiths et al. 2004) and Lamme et al. (2002) have shown that allergenic fibroblasts seeded in a collagen matrix do not persist but rather induce a fibrotic response.

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2.5 Concluding Remarks As reviewed in all investigated tissue and organ systems fibroblasts represent a functional diverse population of cells. Phenotypic differences are manifested in a variety of ways: extracellular matrix production and organization as well as the secretion of and response to growth factors. The functional diversity of the different fibroblast subtypes have to be considered when aspects of tissue homeostasis, especially with respect to stromal or mesenchymal-epithelial interactions and crosstalk are discussed. For example as stated by Sorrel and Caplan (2004) for dermal fibroblasts the term “fibroblast” is an oversimplification. All investigations into phenotypic characteristics of stromal fibroblasts indicated that these cells represent a dynamic and diverse population of functional cell types. A specific nomenclature should reflect this fact and greater care should be taken when defining the population of fibroblasts used in experimental studies. The terminology fibrocyte should be reserved for the postmitotic subtype of fibroblasts as the term ‘cyte’ implicates a non-proliferative cell population.

References Baglole CJ, Ray DM, Bernstein SH, Feldon SE, Smith TJ (2006) More than structural cells, fibroblasts create and orchestrate the tumor microenvironment. Immunol Invest 35:297–325 Bayreuther K, Rodemann HP, Francz PI, Maier K (1988) Differentiation of fibroblast stem cells. J Cell Sci Suppl 10:115–130 Bayreuther K, Rodemann HP, Hommel R, Dittmann K, Albiez M, Francz PI (1989) Human skin fibroblasts in vitro differentiate along a terminal cell lineage. Proc Natl Acad Sci U S A 85:5112–5116 Beacham DA, Cukierman E (2005) Stromagenesis: the changing face of fibroblastic microenvironments during tumor progression. Semin Cancer Biol 15:329–341 Bell E (1995) Strategy of the selection of scaffolds for tissue engineering. Tissue Eng 1:163–179 Bell E, Ivarsson B, Merrill C (1979) Production of a tissue-like structure by contraction of collagen lattices by human fibroblasts of different proliferative potential in vitro. Proc Natl Acad Sci U S A 76:1274–1278 Bell E, Ehrlich HP, Sher S, Merrill C, Sarber R, Hull B, Nakatsuji T, Church D, Buttle DJ (1981) Development and use of a living skin equivalent. Plast Reconstr Surg 67:386–392 Boyce ST (2001) Design principles for the composition and performance of cultured skin substitutes. Burns 27:523–533 Boyce ST, Christianson D, Hansbrough JF (1988) Structure of a collagen-GAG skin substitute optimized for cultured human epidermal keratinocytes. J Biomed Mater Res 22:939–957 Boyce ST, Goretsky MJ, Greenhalgh DG, Kagan RJ, Rieman MT, Warden GD (1995) Comparative assessment of cultured skin substitutes and native skin autograft for the treatment of full thickness burns. Ann Surg 222:743–752 Boyce ST, Supp AT, Wickett RR, Hoath SB, Warden GD (2000) Assessment with the dermal torquemeter of skin pliability after treatment of burns with cultured skin substitutes. J Burn Care Rehabil 21:55–63 Briggaman RA, Wheeler CE (1968) Epidermal-dermal interactions in adult human skin: role of dermis in epidermal maintenance. J Invest Dermatol 51:454–465 Bucala R, Spiegel LA, Chesney J, Hogan M, Cerami A (1994) Circulating fibrocytes define a new leukocyte subpopulations that mediates tissue repair. Mol Med 1:71–81

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Burger A, Löffler H, Bamberg M, Rodemann HP (1998) Molecular and cellular basis of radiation fibrosis. Int J Radiat Biol 73:401–408 Castor CW, Prince RK, Dorstewitz EL (1962) Characteristics of human “fibroblasts” cultivated in vitro from different anatomical sites. Lab Invest 11:703–713 Chang HY, Chi JT, Dudoit S, Bondre C, Van de Rijn M, Botstein D, Brown PO (2002) Diversity, topographic differentiation, and positional memory in human fibroblasts. Proc Natl Acad Sci 99:12877–12882 Chesney J, Bacher M, Bender A, Bucala R (1997) The peripheral blood fibrocyte is a potent antigen-presenting cell capable of priming naive T cells in situ. Proc Natl Acad Sci U S A 94:6307–6312 Chipev CC, Simon M (2002) Phenotypic differences between dermal fibroblasts from different body sites determine their responses to tension and TGFbeta1. BMC Dermatol 2:13 Clark RAF (2003) Epithelial-mesenchymal networks in wounds: a hierarchal view. Commentary. J Invest Dermatol 120:9–11 Clark RAF, Fitzpatrick TB, Eisen AZ, Wolff K, Freedberg IM, Austen KF (eds) (1993) Mechanisms of cutaneous wound repair; dermatology in general medicine. McGraw Hill, New York, pp 473–486 Contard P, Bartel RL, Jacobs L, Perlish JS, MacDonald ED, Handler L, Cone D, Fleischmajer R (1993) Culturing keratinocytes and fibroblasts in a three-dimensional mesh results in epidermal differentiation and formation of a basal lamina-anchoring zone. J Invest Dermatol 100:35–59 Cooper ML, Andree C, Hansbrough JF, Zapata-Sirvent RL, Spielvogel Rl (1993) Direct comparison of a cultured composite skin substitute containing human keratinocytes and fibroblasts to an epidermal sheet graft containing human keratinocytes on athymic mice. J Invest Dermatol 101:811–819 Debacq-Chainliaux F, Borlon C, Pascal T, Royer V, Eliaers F, Ninane N, Carrard G, Friguet B, de Longueville F, Boffe S, Remacle J, Toussaint O (2005) Repeated exposure of human skin fibroblasts to UVB at subcytotoxic level triggers premature senescence through the TGF-beta1 signaling pathway. J Cell Sci 118:743–758 Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelly C, Medrano EE, Linskens M, Rubel JJ, Pereira-Smith O, Peacocke M, Campisi J (1995) A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci U S A 92:9363–9367 Eckes B, Mauch C, Hüppe G, Krieg T (1993) Downregulation of collagen synthesis in fibroblasts within three-dimensional collagen lattices involves transcriptional and posttranscriptional mechanisms. FEBS 216:129–133 Eckes B, Kessler D, Aumailley M, Krieg T (2000) Interactions of fibroblasts with the extracellular matrix: implications for the understanding of fibrosis. Springer Semin Immunopathol 21:415–429 Fluck J, Querfeld C, Cremer A, Niland S, Krieg T, Sollberg S (1998) Normal human primary fibroblasts undergo apoptosis in three-dimensional contractile collagen gels. J Invest Dermatol 110:153–157 Fusenig NE (1994) Epithelia-mesenchymal interactions regulate keratinocyte growth and differentiation in vitro. In: Leigh I, Lane B, Watt F (eds) The keratinocyte handbook. Cambridge University Press, Cambridge, pp 71–94 Gabbiani G (2003) The myofibroblast in wound healing and fibrocontractive diseases. J Pathol 200:500–503 Gabbiani G, Ryan GB, Majno G (1971) Presence of modified fibroblasts in granulation tissue and their possible role in wound contraction. Experientia 27:549–550 Germain L, Jean A, Auger F, Garrel DR (1994) Human wound healing fibroblasts have greater contractile properties than dermal fibroblasts. J Surg Res 57:268–273 Griffiths M, Ojeh N, Livingstone R, Price R, Navsaria H (2004). Survival of Apligraf in acute human wounds. Tissue Eng 10:1180–1195 Gron B, Stoltze K, Andersson A, Dabelsteen E (2002) Oral fibroblasts produce more HGF and KGF than skin fibroblasts in response to co-culture with keratinocytes. APMIS 110:892– 898

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Hakenjos L, Bamberg M, Rodemann HP (2000) TGF-β1-mediated alterations of rat lung fibroblast differentiation resulting in the radiation-induced fibrotic response. Int J Radiat Biol 76:503–509 Hasan A, Murata H, Falabella A, Ochoa S, Zhou L, Badiavas E, Falanga V (1997) Dermal fibroblasts from venous ulcers are unresponsive to the action of transforming growth factor-beta1. J Dermatol Sci 16:59–66 Herskind C, Bentzen SM, Overgaard J, Bamberg M, Rodemann HP (1998) Differentiation state of skin fibroblast cultures versus risk of subcutaneous fibrosis after radiotherapy. Radiother Oncol 47:263–269 Kern A, Liu K, Mansbridge J (2001) Modification of fibroblast gamma-interferon responses by extracellular matrix. J Invest Dermatol 117:112–118 Kern A, Liu K, Mansbridge J (2002) Modulation of interferon-gamma response by dermal fibroblast extracellular matrix. Ann N Y Acad Sci 961:364–367 Kessler D, Dethlefsen S, Haase I, Plomann M, Hirche F, Krieg T, Eckes B (2001) Fibroblasts in mechanically stressed collagen lattices assume a “synthetic” phenotype. J Biol Chem 276:36575–36585 Knecht A, Fine LG, Kleinman KS, Rodemann HP, Müller GA, Woo DD, Norman JT (1991) Fibroblasts of rabbit kidney in culture. II. Paracrine stimulation of papillary fibroblasts by PDGF. Am J Physiol 261:F292–F299 Krejci NC, Cuono CB, Langdon RC, McGuire J (1991) In vitro reconstitution of skin: fibroblasts facilitate keratinocyte growth and differentiation on acellular reticular dermis. J Invest Dermatol 97:843–848 Lamme EN, van Leeuwen RTJ, Brandsma K, van Marle J, Middelkoop E (2000) Higher number of autologous fibroblasts in an artificial dermal substitute improve tissue regeneration and modulate scar-tissue formation. J Pathol 190:595–603 Lamme EN, van Leeuwen RT, Mekkes JR, Middelkoop E (2002) Allogeneic fibroblasts in dermal substitutes induce inflammation and scar formation. Wound Repair Regen 10:152–160 Langholz O, Rockel D, Mauch C, Kozlowska E, Bank I, Krieg T, Eckes B (1995) Collagen and collagenase expression in three-dimensional collagen lattices are differentially regulated by alpha 1 beta 1 and alpha 2 beta 1 integrins. J Cell Biol 131:1903–1915 Lara PC, Russell NS, Smolders IJ, Bartelink H, Begg AC, Coco-Martin JM (1996) Radiationinduced differentiation of human skin fibroblasts: relationship with cell survival and collagen production. Int J Radiat Biol 70:683–692 Lee KY, Bae SC (2002) TGF-beta-dependent cell growth arrest and apoptosis. J Biochem Mol Biol 35:47–53 Limat A, Hunziker T, Boillat C, Bayreuther K, Noser F (1989) Postmitotic human dermal dermal fibroblasts efficiently support the growth of human follicular keratinocytes. J Invest Dermatol 92:758–762 Liotta LA, Kohn EC (2001) The microenvironment of the tumor-host interface. Nature 411:375– 379 Loots MA, Lamme EN, Mekkes JR, Bos JD, Middelkoop E (1999) Cultured fibroblasts from chronic diabetic wounds on the lower extremity (non-insulin-dependent diabetes mellitus) show disturbed proliferation. Arch Dermatol Res 291:93–99 Maas-Szabowski N, Fusenig NE (1996) Interleukin-1-induced growth factor expression in postmitotic and resting fibroblasts. J Invest Dermatol 107:849–855 Mansbridge JN, Hanawalt PC (1988) Role of transforming growth factor beta in the maturation of human epidermal keratinocytes. J Invest Dermatol 90:336–341 Micke P, Ostman A (2005) Exploring the tumor environment: cancer associated fibroblasts as targets in cancer therapy. Expert Opin Ther Targets 9:1217–1233 Middelkoop E (2005) Fibroblast phenotypes and their relevance for wound healing. Int J Low Extrem Wounds 4:9–11 Mollenhauer J, Bayreuther K (1986) Donor-age-related changes in the morphology, growth potential, and collagen biosynthesis in rat fibroblast subpopulations in vitro. Differentiation 32:165–172

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Morocutti A, Earle KA, Sethi M, Piras G, Pal K, Richards D, Rodemann HP, Viberti GC (1996) Premature senescence of skin fibroblasts from insulin-dependent diabetic patients with kidney disease. Kidney Int 50:250–256 Morocutti A, Earle KA, Rodemann HP, Viberti GC (1997) Premature cell ageing and evolution of diabetic nephropathy. Diabetologia 40:244–246 Nakagawa S, Pawelek P, Grinnell F (1989) Long-term culture of fibroblasts in contracted collagen gels: effects on cell growth and biosynthetic activity. J Invest Dermatol 93:792–798 Nolte S, Xu W, Rennekampff H-O, Rodemann HP (2008) Diversity of fibroblasts—a review on implications for skin tissue engineering. Cell Tiss Org 187:165–176 Olumi AF, Grossfeld GD, Hayward SW, Carroll PR, Tisty TD (1999) Carcinoma-associated fibroblasts direct tumor progression of initiated human prostate epithelium. Cancer Res 59:5002– 5011 Orimo A, Gupta PB, Segroi DC, Renzana-Seisdedos F, Delaunay T (2005) Stromal fibroblasts present in invasive human breast carcinomas promote growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 121:335–348 Pascal T, Debacq-Chainliaux F, Chrétien A, Bastin C, Dabée AF, Bertholet V, Remacle J, Toussaint O (2005) Comparison of replicative senescence and stress-induced premature senescence combining differential display and low-density DNA arrays. FEBS Lett 579:3651–3659 Proia DA, Kuperwasser C (2005) Stroma: tumor agonist or antagonist. Cell Cycle 4:1022–1025 Rheinwald JG, Green H (1975) Feeder layer system: serial cultivation of strains of human epidermal keratinocytes. Cell 6:331–343 Rinn JL, Bondre C, Gladstone HB, Brown PO, Chang HY (2006) Anatomic demarcation by positional variation in fibroblast gene expression programs. PloS Genet 2:1084–1096 Rinn JL, Wang JK, Liu H, Montgomery K, van de Rijn M, Chang HY (2008) A systems biology approach to anatomic diversity of skin. J Invest Dermat 128:776–782 Rodemann HP (1989) Differential degradation of intracellular proteins in human skin fibroblasts of mitotic and mitomycin C(MMC)-induced postmitotic differentiation states. Differentiation 42:37–43 Rodemann HP (1993) Differential gene expression, protein synthesis and degradation in ageing fibroblasts. In: Bernd A, Bereiter-Hahn J, Hevert F Holzmann H (eds) Cell culture models for dermatological research. Springer, Berlin, pp 272–277 Rodemann HP, Bamberg M (1995) Cellular basis of radiation-induced fibrosis. Radiother Oncol 35:83–90 Rodemann HP, Mueller GA (1990) Abnormal growth, clonal proliferation and 35S-methionine polypeptide pattern of fibroblasts derived from kidneys with interstitial fibrosis. Proc Soc Exp Biol Med 195:57–63 Rodemann HP, Bayreuther K, Francz PI, Dittmann K, Albiez M (1989) Selective enrichment and biochemical characterisation of seven fibroblast cell types of human skin fibroblast populations in vitro. Exp Cell Res 180:84–93 Rodemann HP, Müller GA, Knecht A, Norman JT, Fine LG (1991) Fibroblasts of rabbit kidney in culture: I. characterization and identification of cell-specific markers. Am J Physiol 261:283– 291 Rodemann HP, Binder A, Burger A, Löffler H, Bamberg M (1996) The underlying cellular mechanisms of fibrosis. Kidney Int 49:32–36 Rossio-Pasquier P, Casanova D, Jomard A, Dermarchez M (1999) Wound healing of human skin transplanted onto the nude mouse after a superficial excisional injury: human dermal reconstruction is achieved in several steps by two different fibroblast subpopulations. Arch Dermatol Res 291:591–599 Rudolph R, Vande J, Berg G, Pierce F (1991) Changing concept in myofibroblast function and control. In: Janssen H, Rooman JIS (eds) Wound healing. Wrightson Biomedical Publishing Ltd., Petersfield, pp 103–115

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Sahuc F, Nakazawa K, Berthod F, Collombel C, Damour O (1996) Mesenchymal-epithelial interactions regulate gene expression of type VII collagen and kalinin in keratinocytes and dermalepidermal junction formation in a skin equivalent model. Wound Rep Regen 4:93–102 Sorrell, JM, Caplan AI (2004) Fibroblast heterogeneity: more than skin deep. J Cell Sci 117:667– 675 Stephens P, Davies KJ, Occleston N, Pleass RD, Kon C, Daniels J, Khaw PT, Thomas DW (2001) Skin and oral fibroblasts exhibit phenotypic differences in extracellular matrix organization and matrix metalloproteinase activity. Br J Dermatol 144:229–237 Stuelten CH, Busch JI, Tang B, Flanders KC, Oshima A, Sutton E, Karpova TS, Roberts AB, Wakefield LM, Niederhuber JE (2010) Transient tumor-fibroblast interactions increase tumor cell malignancy by TGF-β mediated mechanism in a mouse xenograft model of brest cancer. PlosOne 5:e9832 Van Den Bogaerdt AJ, van Zuijlen PPM, van Galen M, Lamme EN, Middelkoop E (2002) The suitability of cells from different tissues to be used in tissue engineered skin substitutes. Arch Dermatol Res 294:135–142 von Pfeil A, Hakenjos L, Herskind C, Dittmann K, Weller M, Rodemann HP (2002) Irradiated homozygous TGF-1 knockout fibroblasts show enhanced clonogenic survival as compared with TGF-1 wild-type fibroblasts. Int J Radiat Biol 78:331–339 Von Zglinicki T, Saretzki G, Docke W, Lotze C (1995) Mild hyperoxia shortens telomeres and inhibits proliferation of fibroblasts: a model for senescence? Exp Cell Res 220:186–193 Waelti ER, Inaebnit SP, Rast HP, Hunziker T, Limat A, Braathen LR, Wiesmann U (1992) Coculture of human keratinocytes on post-mitotic human dermal fibroblast feeder cells: production of large amounts of interleukin-6. J Invest Dermatol 98:805–808 Yamaguchi Y, Hearing VJ, Itami S, Yoshikawa K, Katayama I (2005) Mesenchymal-epithelial interactions in the skin: aiming for site specific tissue regeneration. J Dermatol Sci 40:1–9 Yang L, Scott PG, Dodd C, Medina A, Jiao H, Shankowsky HA, Ghahary A, Tredget EE (2005) Identification of fibrocytes in postburn hypertophic scars. Wound Repair Regen 13:398–404

Chapter 3

The Role of the Myofibroblast in Fibrosis and Cancer Progression Boris Hinz, Ian A. Darby, Giulio Gabbiani and Alexis Desmoulière

3.1 Introduction The discovery of the myofibroblast (Gabbiani et al. 1971; Tomasek et al. 2002) has opened a new perspective in the understanding of phenomena such as connective tissue remodeling and epithelial-mesenchymal interactions that play crucial roles in normal and pathological processes including organ shaping during development, tension production in pulmonary alveoli, wound contraction, tissue deformation during fibrotic diseases and, more recently, epithelial tumor invasion or metastasis formation. The myofibroblast has been shown to: (1) produce mechanical force, thanks to the neo-expression of α-smooth muscle actin (α-SMA), the actin isoform typical of vascular smooth muscle cells (SMCs) (Hinz et  al. 2002) and the formation of specialized junctional complexes with the extracellular matrix (ECM) (Dugina et al. 2001; Goffin et al. 2006) (Fig. 3.1), and (2) synthesize collagen type I and III (Tomasek et al. 2002); all these changes take place under the stimulation of local mechanical forces and of transforming growth factor β1 (TGFβ1), produced by infiltrated macrophages or local cells, in conjunction with the ectodomain A (ED-A) sequence of cellular fibronectin (FN) (Tomasek et al. 2002). Thus, the myofibroblast appears as a major player in connective tissue rearrangement. More recently it has been shown that, at least in certain situations, e.g. epithelial tumor invasion, myofibroblasts can influence directly or indirectly the behavior of epithelial cells (De Wever et  al. 2008; Desmouliere et  al. 2004). This activity opens a new perspective for the understanding of tumor progression and is in line with the concept of myofibroblast heterogeneity that has been proposed on the basis of its multiple origins, according to the physiological or pathological process as well as to the organ involved (Hinz et al. 2007). While it is accepted that most myofibroblasts derive from local fibroblastic cells (although it remains open as to whether all fibroblasts can modulate into myofibroblasts), numerous works have G. Gabbiani () Department of Pathology and Immunology, CMU, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_3, © Springer Science+Business Media B.V. 2011

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Fig. 3.1   Cytoskeletal features of a myofibroblast. Cultured bone marrow-derived mesenchymal stem cells were treated for 5 days with transforming growth factor β1 and stained by means of immunofluorescence for α-smooth muscle actin ( blue), F-actin ( green) and focal adhesion protein vinculin ( red)

demonstrated alternative origins, e.g. from perisinusoidal cells in the liver, arterial SMCs in the atheromatous plaque, epithelial or endothelial cells, through epithelial (endothelial)-mesenchymal transition (EMT), in organs such the lungs and the kidney, and, finally, circulating bone marrow (BM)-derived cells that have been called fibrocytes (Table 3.1). The purpose of this article is to review the main biological features as well as the mechanisms of force production by the myofibroblast and to discuss the role of this cell in the development of fibrotic changes and in epithelial cancer progression.

3.2 The Myofibroblast—An Overview 3.2.1  Benefits and Consequences of Myofibroblast Appearance Myofibroblasts are generated from different precursors to populate and to repair injured tissues by secreting and organizing ECM in a contractile process (Desmouliere et al. 2005; Hinz et al. 2007; Tomasek et al. 2002). Although, strictly spoken, tumors are not injured tissues, their local environment closely resembles that of wounds and activates fibroblastic cells in many similar ways (Dvorak 1986). When organs and tissues are damaged the inherent repair mechanisms of our body have to fulfill two urgent tasks: (1) establishing tissue homeostasis, fighting inflammation and discarding debris all of which is carried out by immune and inflammatory cells and (2) rapidly providing mechanical tissue coherence by forming a scar

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Table 3.1   Where the myofibroblast comes from—an overview on possible myofibroblast precursors. The main myofibroblast progenitor after injury of different tissues appears to be the locally residing fibroblast. In the liver, myofibroblasts are additionally recruited from hepatic stellate cells following an activation process. In tumours and in fibrotic liver, lung, and kidney, differentiated myofibroblasts can arise from epithelial- and endothelial-to-mesenchymal transition (EMT), respectively. During atheromatous plaque formation, de-differentiating SMC (i.e. that lose late SMC markers) from the media are considered to be the major source of myofibroblastic cells. The relative contribution of bone marrow-derived circulating fibrocytes to the formation of differentiated myofibroblasts in different fibrotic lesions is unclear at present; it is conceivable that fibrocyte transdifferentiation terminates at the proto-myofibroblast stage. Finally, MSC have been shown to acquire the differentiated myofibroblast phenotype in vitro and in vivo Myofibroblast precursor

Condition

References

Astrocyte

Glial scar

Chondrocyte Endothelial cell (via EndMT)

Cartilage repair Heart fibrosis

Silver and Miller 2004; Moreels et al. 2008 Wang et al. 2000 Zeisberg et al. 2007b

Kidney fibrosis Stroma reaction to epithelial tumors

Epithelial cell (via EMT)

Fibroblast

Kidney fibrosis Liver fibrosis Lung fibrosis Tumor development Fibrotic reactions to body implants Kidney fibrosis Liver fibrosis Skin wound healing Stroma reaction to epithelial tumors

Fibrocyte

Asthma Atherosclerosis Cardiomyopathy, post-myocardial infarct Kidney fibrosis Liver fibrosis Lung fibrosis Skin wound healing Tumor development Vascular remodeling

Kizu et al. 2009; Zeisberg et al. 2008; Li et al. 2009) Zeisberg et al. 2007a; Potenta et al. 2008 Iwano et al. 2002; Zeisberg and Kalluri 2008 Zeisberg et al. 2007c Kim et al. 2006; Chilosi et al. 2003 Kalluri and Zeisberg 2006; Thiery 2002; Radisky et al. 2007) Ariyan et al. 1978; Suska et al. 2008 Qi et al. 2006; Desmouliere et al. 2003 Ramadori and Saile 2004; Li et al. 2007 Darby et al. 1990; Gabbiani et al. 1971 De Wever and Mareel 2002; Desmouliere et al. 2004) Schmidt et al. 2003 Fujita et al. 2007; Sata et al. 2002 Haudek et al. 2006; van Amerongen et al. 2008; Mollmann et al. 2006 Wada et al. 2007; Sakai et al. 2006 Forbes et al. 2004; Kisseleva et al. 2006 Phillips et al. 2004; Andersson-Sjoland et al. 2008; Ishii et al. 2005 Ishii et al. 2005; Mori et al. 2005; Direkze et al. 2003; Fathke et al. 2004 Ishii et al. 2005; Direkze et al. 2004; Ishii et al. 2003 Frid et al. 2006; Varcoe et al. 2006

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Table 3.1  (continued) Myofibroblast Condition precursor Hepatic stellate cell Pericyte Osteoblast Mesenchymal stem cell

Smooth muscle cell

Liver fibrosis Systemic sclerosis, dermal scarring Vascular repair Bone repair Chronic renal disease Gut repair Liver fibrosis Lung injury Skin wound healing Tumor development Atheromatous plaque evolution

References Guyot et al. 2006; Bataller and Brenner 2005; Friedman 2004b Sundberg et al. 1996; Rajkumar et al. 2006 Rajkumar et al. 2006 Kinner et al. 2002a Ninichuk et al. 2006 Brittan et al. 2005; Brittan et al. 2002 di Bonzo et al. 2008 Yan et al. 2007 Ye et al. 2006; Yamaguchi et al. 2005; Sasaki et al. 2008 Ishii et al. 2003 Hao et al. 2006; Bochaton-Piallat and Gabbiani 2006

which is the task of fibroblasts and myofibroblasts (Baudino et  al. 2006; Brown et al. 2005; Desmouliere et al. 2005; Gurtner et al. 2008; Werner and Grose 2003). Under physiological conditions the contractile and secretory activities of myofibroblasts are terminated when the tissue is sufficiently remodeled and repaired. Under most circumstances ‘sufficient repair’ signifies that the damaged tissue regains mechanical coherence but does not necessarily means the restoration of functionality since myofibroblasts are specialized to repair but not to regenerate. At the end of the repair process, myofibroblasts disappear by massive apoptosis, leaving the mature scar behind (Desmouliere et al. 1995). The control of myofibroblast apoptosis is still unclear though it seems likely that this could be regulated by a number of factors including local levels of growth factors such as TGFβ1 and endothelin-1 (Kulasekaran et al. 2009) as well as mechanical forces (see below). In contrast, persisting myofibroblast activity results in tissue deformation by contracture. This is the case for the formation of hypertrophic scars such as those developing after burns (Atiyeh et  al. 2005), in scleroderma (Strehlow and Korn 1998; Varga and Abraham 2007) and in the palmar fibromatosis of Dupuytren’s disease (Tomasek et al. 1999). Myofibroblast-generated contractures further characterize the fibrosis that affects vital organs including liver (Gressner and Weiskirchen 2006; Iredale 2007), heart (Baudino et al. 2006; Brown et al. 2005), lung (Chiappara et al. 2001; Phan 2002; Thannickal et al. 2004) and kidney (Lan 2003). Cells with a myofibroblastic phenotype further contribute to the development of the atheromatous plaque after blood vessel injury (Bochaton-Piallat and Gabbiani 2006). A number of different biomaterials have been shown to induce myofibroblasts which for example create tissue constrictions around solid body implants (Anderson et al. 2008; Comut et al. 2000; Li et al. 2007a; Suska et al. 2008) or contract silicone breast implants (Coleman et al. 1993; Siggelkow et al. 2003). As discussed in detail further below, myofibroblasts are instrumental in the stroma reaction to epithelial tumors and promote

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cancer progression by creating a stimulating microenvironment for the transformed cells (Bhowmick and Moses 2005; De Wever and Mareel 2003; Desmouliere et al. 2004). Before discussing the role of myofibroblasts in fibrosis and tumor biology, we will give an overview on the typical features of these intriguing cells.

3.2.2  What Is a Myofibroblast? In the early 1970s, Gabbiani and coworkers have identified specialized fibroblasts as the active component in dermal wound contraction, which were then named myofibroblasts to account for their ultrastructural similarity to SMCs (Gabbiani et al. 1971). The original myofibroblast definition was based on the co-existence of fibroblastic morphological features including a developed endoplasmic reticulum and SMC features such as actin filament bundles and contractile activity. Specific molecular markers were only defined subsequently. Other morphological features of the myofibroblast include high ECM synthesizing activity, development of cellto-cell and cell-to-matrix adhesions (fibronexus), and secretion of growth factors (for reviews see Eyden 2008; Hinz 2007). Myofibroblasts can be of very heterogeneous origins as discussed further below; however, their development follows a well-established sequence of events. The progress of myofibroblast differentiation can be separated into two phases, each of which is characterized by specific cytoskeletal features (Fig. 3.2). First, myofibroblast precursor cells acquire de novo contractile bundles. These in vivo stress fibers generate sufficient forces to pull cells forward to populate tissue spaces in a migration process and to pre-remodel the ECM (Hinz et al. 2001b). To discriminate such activated and mildly contractile cells from quiescent fibroblastic cells which are devoid of any contractile features, we have previously proposed the term ‘proto-myofibroblast’ (Tomasek et al. 2002) (Fig. 3.2). In the presence of mechanical stress and TGFβ1, proto-myofibroblasts can further evolve into differentiated myofibroblasts, hallmarked by the neo-expression of α-SMA and its incorporation into pre-existing stress fibers (Clement et al. 2005; Goffin et al. 2006; Tomasek et al. 2002) (Fig. 3.2). It is this stress fiber incorporation of α-SMA that renders myofibroblasts highly contractile (Hinz et al. 2001a). In standard cell culture, during which fibroblasts inevitably form stress fibers, the term ‘myofibroblast’ generally describes only the α-SMA expressing cells. De novo expression of α-SMA is also the most widely used criterion to identify tissue myofibroblasts and to diagnose myofibroblast-related diseases. On the basis of α-SMA expression, myofibroblasts are classified as a predominant subpopulation of cancer-associated fibroblasts (CAF) in the tumor stroma (Orimo and Weinberg 2007; Sugimoto et al. 2006). In addition, differentiated myofibroblasts in vivo and in vitro can de novo express the cell-cell contact protein OB-cadherin that has not yet been reported on the surface of α-SMA-negative fibroblasts (Hinz et al. 2004). OB-cadherin-positive stroma myofibroblasts were shown to accumulate around epithelial tumors (Shibata et al. 1996; Tomita et al. 2000). Although α-SMA expression will discriminate differentiated myofibroblasts from normal fibroblasts,

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Fig. 3.2   The mechanical feedback loop in myofibroblast development. Fibroblasts in intact tissue are stress-shielded by a functional extracellular matrix (ECM); they do not develop contractile features and cell-matrix adhesions. After injury, inflammatory signals activate fibroblasts to spread into the provisional wound matrix. Local cell remodeling activity leads to gradual increase in global matrix stiffness that counteracts cell traction forces. The resulting formation of small focal adhesions (FAs) and stress fibers that contain only cytoplasmic actins characterize the proto-myofibroblast. Transforming growth factor β1 and the ectodomain A (ED-A) of cellular fibronectin (FN) stimulate proto-myofibroblasts to express α-smooth muscle actin (α-SMA), which at first remains cytoplasmic and is not incorporated into stress fibers. Continuing ECM fiber alignment creates larger surfaces for adhesion formation; larger adhesions permit development of stronger stress fibers and generation of higher contractile forces. When intracellular tension reaches a critical level it allows incorporation of α-SMA into pre-existing stress fibers. The force generated by α-SMA-containing stress fiber is significantly higher compared to cytoplasmic actin stress fibers leading to further FA enlagement and ECM contraction, thereby establishing a mechanical feedback loop. Myofibroblasts may exit this cycle when the original structure of the ECM is reconstituted and again takes over the mechanical load; stress-released myofibroblasts eventually undergo apoptosis. (Reprinted with permission from Hinz 2007)

this marker obviously fails to make a distinction with vascular SMCs that populate the tumor stroma. In normal adult tissue, SMCs express a number of late differentiation markers that are usually not in the repertoire of the myofibroblast, including smooth muscle myosin heavy chain (Benzonana et al. 1988), h-caldesmon (Eyden 2007), and smoothelin (van der Loop et al. 1996). The muscle intermediate filament

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protein desmin is an often reliable and widely used exclusion criterion but can be expressed in myofibroblasts in some particular conditions (Hinz et al. 2001b; Skalli et al. 1986). Moreover, SMCs in normal vessel tissue not only express desmin but also (or sometimes only) vimentin; both intermediate filament proteins are therefore not reliable markers for one or other cell type (Frank and Warren 1981; Gabbiani et al. 1981). Equally problematic is the differentiation between myofibroblasts and pericytes, which can express vimentin and desmin, are α-SMA positive and smooth muscle myosin negative (Armulik et al. 2005; Eyden 2007; Hughes 2008). However, pericytes are characterized by their close interaction with endothelial cells and lack of contractile features in normal tissues (Eyden 2007). Discriminating myofibroblasts from SMCs and pericytes is a daunting task in conditions of smooth muscle injury, angiogenesis, and in cell culture, in particular if the provenance of the cells is unclear. For example, remodeling of injured arteries is thought to be predominantly driven by SMCs from the media de-differentiating into myofibroblasts but the contribution from adventitial fibroblasts to the myofibroblast population has also been suggested. In both conditions, SMCs lose their late differentiation markers desmin, smooth muscle myosin and smoothelin and acquire a myofibroblastic and synthetic phenotype (Benzonana et al. 1988; BochatonPiallat and Gabbiani 2006; Christen et al. 2001; Hao et al. 2006; Larson et al. 1984; Sartore et al. 2001; Zalewski et al. 2002). On the other hand, some of the late SMC markers can be induced at least in cultured fibroblasts by treatment with TGFβ1 (Chambers et al. 2003; Malmstrom et al. 2004). Hence, considering the expression profile of cytoskeletal proteins, the myofibroblast appears to exist in a continuous differentiation spectrum between fibroblasts and SMCs.

3.2.3  On the Origin of Myofibroblast Species Closely related to the problem of how to identify the myofibroblast is the question of where these cells come from—often with the aim to prevent their formation and pathological accumulation. Indeed, it emerges that myofibroblasts, similar to fibroblasts, are a tremendously heterogeneous population of cells. Thus it may be more appropriate to consider the myofibroblast as a phenotype rather than a cell type. This view is supported by the fact that myofibroblasts can be recruited from a variety of precursor cells depending on the injured tissue and the microenvironment (Hinz et al. 2007) (Fig. 3.3). In many organs, locally residing fibroblasts are considered to be the major source of α-SMA-positive myofibroblasts in response to injury and during fibrosis development, such as in skin (Gabbiani 2003; Hinz 2007), in fibrotic reactions to body implants (Ariyan et al. 1978; Suska et al. 2008), in liver (Li et al. 2007b; Ramadori and Saile 2004), kidney (Desmouliere et al. 2003; Qi et al. 2006), and in the stroma reaction to epithelial tumors (De Wever and Mareel 2002; Desmouliere et al. 2004). EMT is another mechanism of myofibroblast generation from epithelial and endothelial precursors during tumor development (Kalluri and Zeisberg 2006; Thiery 2002; Zeisberg et  al. 2008), as well as in kidney fibrosis (Iwano et al. 2002; Kizu et al. 2009) and possibly lung fibrosis (Chilosi et al. 2003;

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Fig. 3.3   Myofibroblast origins. The main myofibroblast progenitor after injury of different tissues appears to be the locally residing fibroblast (see Fig. 3.1). In the liver, myofibroblasts are additionally recruited from hepatic stellate cells that follow an activation process. In tumors and in fibrotic liver, lung, and kidney, differentiated myofibroblasts can arise from epithelial- and endothelial-to-mesenchymal transition (EMT), respectively. During atheromatous plaque formation, de-differentiating smooth muscle cells (i.e. that lose late smooth muscle cell markers) from the media are considered to be the major source of myofibroblastic cells. The relative contribution of bone marrow-derived circulating fibrocytes to the formation of differentiated myofibroblasts in different fibrotic lesions is unclear at present; it is conceivable that fibrocyte transdifferentiation also terminates at the proto-myofibroblast stage. Finally, mesenchymal stem cells have been shown to acquire the differentiated myofibroblast phenotype in vitro and in vivo. See also Table 3.1. (Modified from Hinz 2009a)

Kim et al. 2006). EMT has been demonstrated to contribute to fibrosis of the heart (Zeisberg et al. 2007b) and liver (Zeisberg et al. 2007c). In fibrotic liver, hepatic stellate cell (HSCs) are another important source of myofibroblasts (Bataller and Brenner 2005; Friedman 2004b; Guyot et al. 2006), whereas de-differentiation of SMCs contributes to the generation of myofibroblasts in the atheromatous plaque (Bochaton-Piallat and Gabbiani 2006; Hao et al. 2006). In systemic sclerosis, vessel repair and dermal scarring, pericytes have been suggested to acquire contractile myofibroblast features (Rajkumar et al. 2006; Sundberg et al. 1996). Although the repair processes of injured brain exhibit many specific features compared with other organs, astrocytes seem to develop a myofibroblastic phenotype in the glial scar (Moreels et al. 2008; Silver and Miller 2004).

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Other myofibroblast sources that have attracted broad interest recently are BM derived, blood-circulating cells, which are locally recruited to inflamed and remodeled tissue. Two cell types crystallize as being prominent blood-borne precursors for the myofibroblast: fibrocytes and mesenchymal stem cells (MSCs); both are not always clearly discriminated. Fibrocytes are characterized by the co-expression of the fibroblast markers collagen types I and II and FN, of monocyte markers CD13 and CD11b, and hematopoietic progenitor markers CD34, CD45, and CD105 (Bucala et al. 1994). More exhaustive lists of fibrocyte features have recently been reviewed (Abe et al. 2001; Bellini and Mattoli 2007; Mattoli et al. 2009; Metz 2003). In culture, TGFβ1-induced and spontaneous up-regulation of α-SMA in fibrocytes correlates with down-regulation of CD34 and CD45 (Schmidt et al. 2003). Consistent with this, in an animal model of wound healing, CD34 and CD45 disappear within hours of fibrocyte recruitment/maturation in the inflamed tissue. This loss of the fibrocyte markers makes it difficult to determine in vivo whether fibrocytes actually differentiate into myofibroblasts or whether they simply localize to sites of myofibroblast accumulation (Mori et al. 2005; Quan et al. 2006). To answer the question of fibrocyte-to-myofibroblast differentiation, a number of studies used irradiated wild-type mice, engrafted with BM obtained from GFP-expressing transgenic mice or from sex-mismatched animals. BM-derived cells were associated in varying proportions with myofibroblast-containing lesions in the context of tumor development (Direkze et  al. 2004; Ishii et  al. 2005) and in animals subjected to fibrotic stimuli in different organs (Andersson-Sjoland et  al. 2008; Forbes et  al. 2004; Haudek et  al. 2006; Ishii et  al. 2005; Kisseleva et  al. 2006; Moeller et  al. 2009; Phillips et al. 2004; Sakai et al. 2006; Schmidt et al. 2003; Wada et al. 2007), during vascular remodeling (Frid et al. 2006; Fujita et al. 2007; Varcoe et al. 2006), and in dermal wound healing (Direkze et al. 2003; Fathke et al. 2004; Mori et al. 2005). Other studies seem to exclude that BM-derived fibrocytes contribute to myofibroblast formation in liver (Kisseleva et al. 2006) and lung fibrosis (Hashimoto et al. 2004); the definitive answer to the question of fibrocyte-to-myofibroblast differentiation remains open. Another population of BM-derived cells, possibly giving rise to tissue myofibroblasts are circulating MSCs (Roufosse et al. 2004). MSCs in culture are generally identified by the conjunct expression of specific cell surface proteins such as CD105, CD90, CD44, CD73, CD166, CD29, CD106 and Stro-1 (Minguell et al. 2001; Pittenger et al. 1999; Simmons and Torok-Storb 1991). MSCs are different from fibrocytes by being negative for the monocyte surface proteins CD13 and CD11b and the hematopoietic markers CD34 and CD45 (He et al. 2007; Pittenger et al. 1999). MSCs can differentiate into a variety of cell types that are of potential use for regenerative medicine and tissue engineering (Caplan 2007). Even listing only the envisaged applications of MSCs would by far exceed the scope of this chapter, and we concentrate here on their involvement in tumor biology. Systemically transplanted MSCs target to the stroma environment of epithelial tumors (Hall et al. 2007; Hung et al. 2005; Menon et al. 2007; Studeny et al. 2004). This specific homing together with the immuno-inactivity of MSCs is possibly useful for the tumor-specific delivery of anti-cancer drugs, cytokines and viruses (Komarova et al.

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2006; Studeny et al. 2002) and to suppress tumor development as such (Khakoo et al. 2006). However, the tumor environment seems to activate myofibroblast differentiation of MSCs similar to CAFs (Mishra et  al. 2009). BM-MSCs, cultured in tumor-conditioned medium differentiate into α-SMA-positive myofibroblasts (Mishra et al. 2008). Recent studies evaluating the interaction between MSCs and epithelial tumor cells in vivo and in vitro indicate that acquisition of the myofibroblast phenotype by MSCs can reduce the success of an envisaged MSC therapy and may even amplify the disease. MSCs that have become activated by mildly invasive human breast carcinoma cells in culture enhance the metastatic potential of the cancer cells when injected subcutaneously; this effect is mediated in a paracrine feedback loop (Karnoub et  al. 2007). BM-MSCs can also spontaneously express α-SMA and up-regulate their contractile activity in standard culture and in response to pro-fibrotic TGFβ1 (Bonanno et al. 1994; Cai et al. 2001; Kinner et al. 2002b; Peled et al. 1991). Proteomic profiling of BM-MSCs revealed a typical myofibroblast differentiation program after treatment with TGFβ1 (Wang et al. 2004). It has to be noted that α-SMA-positive MSCs in culture are not necessarily myofibroblasts but may represent intermediate stages of differentiation into SMCs or pericytes (Bianco et al. 2001; Charbord et al. 1990). It remains to be shown whether MSCto-myofibroblast differentiation is rather a risk or whether it can be beneficial in the context of the tumor environment and for regenerative medicine.

3.3 Formation and Control of the Myofibroblast From this amazingly heterogeneous selection of possible precursor cells it is conceivable that myofibroblasts are a family of different cell types that share common molecular features and the contractile phenotype. To therapeutically counteract organ dysfunction caused by myofibroblasts it is helpful to understand the molecular pathways that commonly regulate their evolution from different precursor cells.

3.3.1  Mechanical Stress Regulates Myofibroblast Development The physiological role of myofibroblast activity is to restore the mechanical integrity of an injured tissue by secreting and organizing new ECM, a process that is precisely controlled by mechanical signals, such as ECM stiffness (Hinz 2009b). What is a ‘stiff’ ECM and how does ECM stiffness develop during tissue repair and remodeling? The provisional ECM laid down after acute tissue injury, e.g. the fibrin clot of dermal wounds, is estimated to be very compliant with a Young’s modulus of 10 to 1000 Pa. Under comparable conditions in vitro, such as growth on very soft two-dimensional polyacrylamide gels and in three-dimensional soft collagen gels, development of stress fibers by fibroblasts is suppressed. Fibroblasts without stress fibers form only very small and immature adhesions with the ECM

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that are called focal complexes or nascent adhesions (Tamariz and Grinnell 2002; Yeung et al. 2005). The proto-myofibroblast phenotype is only produced on stiffer culture substrates exhibiting an elastic modulus of at least 3000 Pa; these cells form α-SMA-negative stress fibers that terminate in mature focal adhesions (Janmey et al. 2009; Yeung et al. 2005). A stiffness of ~18000 Pa has been measured in 7-day-old rat wound granulation tissue which is mainly populated by proto-myofibroblasts. Even stiffer culture substrates with a Young’s modulus of ~20000  Pa and higher are required to permit further myofibroblast differentiation. Expression of α-SMA in stress fibers on stiff substrates is associated with the formation of particularly large focal adhesions (Goffin et al. 2006; Wells 2005). Corresponding to the threshold stiffness for myofibroblast differentiation in vitro, fibrotic tissues and contracting wound granulation tissue were shown to exhibit a stiffness of 25000–50000 Pa (Hinz 2009c). The high contractile activity of myofibroblasts is instrumental in gradually increasing ECM stiffness and stresses, which in turn enhances their differentiation state and level of contraction. This mechanical feed forward loop is considered as one of the most important elements in the development of chronic contractures (Hinz 2009b; Tomasek et al. 2002). The level of stress and/or stress changes are further discussed as important signals for myofibroblast survival. Mechanically stressing dermal wounds in mice causes hypertrophic scarring, presumably by decreasing the rate of apoptotic myofibroblasts (Aarabi et al. 2007). On the other hand, increased apoptotic figures have been reported for fibroblasts in stress-released collagen gels (Grinnell et  al. 1999) and experimentally relaxed wounds (Carlson et  al. 2003). Stress release also leads to a reduction of α-SMA and myofibroblast contractile features in vivo and in vitro (Goffin et al. 2006; Hinz et al. 2001a; Hinz et al. 2001b). The mechanical responsiveness of myofibroblasts and their role in stiffening the stroma ECM have important implications for tumor-stroma interaction and tumor progression. It is a well known fact that the cancer-associated stroma is stiffer than the surrounding normal soft connective tissue (Andersen et al. 2009; Beacham and Cukierman 2005; Butcher et al. 2009; Kumar and Weaver 2009; Paszek et al. 2005). This fact is clinically exploited to detect tumors by palpation and/or elastography techniques (Garra 2007; Glaser et al. 2006; Khaled et al. 2004; Khaled et al. 2006). It has been suggested that small changes in tissue stiffness arising during the inflammatory response precede and drive tissue contractures and fibrotic reactions; this has been demonstrated for the liver (Georges et al. 2007). One possible source of this ‘pre-stiffening’ in the tumor environment may be interstitial fluid pressure (Heldin et al. 2004). Using three-dimensional fibroblast collagen culture it has been shown that even very low interstitial fluid flow can lead to ECM and fibroblast alignment and subsequent myofibroblast differentiation (Ng et al. 2005). Enhanced tissue stiffness is not only a diagnostic tool for tumor detection but also appears to increase the risk and invasiveness of tumors. For breast cancers, a clear correlation exists between the degree of mammographic densities (compact and stiff tissue) and the risk of cancer formation (Boyd et al. 2007; Wolfe 1976). Recent studies demonstrated that substrate stiffness directly governs the behavior and differentiation of murine mammary epithelial cells. On culture substrates that mimic the softness

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of normal mammary gland tissue (150 kPa), these cells form polarized acini surrounded by an intact basement membrane. This structure resembling the in vivo structure of the normal breast ducts is increasingly disturbed with increasing substrate stiffness. On substrates with a stiffness of 5000 Pa, reproducing the mechanical conditions of the tumor stroma, lumen formation is inhibited and no surrounding basement membrane is established. As a consequence of the higher substrate stiffness, epithelial cells lose cell–cell adherens junctions and cell polarity to attain a mesenchymal phenotype with distinct stress fibers and increased migratory activity (Paszek et al. 2005). Very recent studies added to these findings that physiologically soft ECM suppresses the mitogenic activity of epithelial (and mesenchymal) cells; stiffer substrates induce mitogenesis (Klein et al. 2009; Winer et al. 2009). Hence, the stiff ECM generated by the fibroblast/myofibroblast remodeling activities not only impacts the stromal compartment but drives epithelial cells into an invasive and proliferative phenotype, at least in culture. What are the mechanisms and intracellular signaling pathways through which tension controls myofibroblast differentiation? Firstly, stress appears to directly modulate α-SMA gene transcription. Application of force at sites of integrin adhesions, formed with collagen-coated magnetite beads up-regulates the promoter activity of α-SMA in cardiac fibroblasts and osteoblasts. This effect involves binding of serum response factor to the CArGB box in the α-SMA promoter (Wang et al. 2003; Wang et  al. 2002). More recently, it has been shown that stress-regulated activity of the α-SMA promoter further requires Rho/Rho kinase activity and involves translocation of the myocardin-related transcription factor MRTF A/MAL to the nucleus (Zhao et al. 2007). It is noteworthy that mechanical stress alone is generally not sufficient to induce myofibroblast differentiation in the absence of active TGFβ1 (Hinz et al. 2001a, b). Interestingly, mechanical stress and TGFβ1 also collaborate to up-regulate expression of collagen, another hallmark of the myofibroblast (Lindahl et al. 2002). Secondly, the localization of cytosolic α-SMA to stress fibers is directly controlled by the level of stress. Culture experiments revealed that transferring differentiated lung myofibroblasts from stiff plastic culture dishes (Young’s modulus of MPa) to soft silicone substrates (≤16000 kPa) results in the selective dislocation of α-SMA from persisting β-cytoplasmic actin stress fibers within hours (Fig. 3.2). A similar effect is achieved by relaxing differentiated myofibroblasts with Rho kinase inhibitors on stiff culture substrates (Goffin et al. 2006). This function of α-SMA as a mechano-sensitive/responsive protein is still mysterious but suggests a mechanosensitive element within the contractile bundles that specifically binds to α-SMA. The nature of this hypothetical binding partner is unclear; its existence however is supported by the fact that cytoplasmic delivery of the α-SMA specific N-terminal sequence Ac-EEED as a ‘competitive’ peptide selectively removes α-SMA from stress fibers (Chaponnier et  al. 1995; Clement et al. 2005; Hinz et al. 2002). Proteins like zyxin, myosin and α-actinin have been demonstrated to exhibit a similar dependence of their location in stress fibers on mechanical load (Colombelli et al. 2009; Hervy et al. 2006; Peterson et al. 2004). Moreover, stress fibers under tension have been shown to reveal cryptic cysteine residues detected by in vivo labeling (Johnson et al. 2007).

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3.3.2  M  echano-Chemical Control of Myofibroblasts—A Spotlight on TGFβ1 TGFβ1 is the most potent myofibroblast-inducing factor and one of the strongest pro-fibrotic cytokines presently known. TGFβ1 exerts its pro-fibrotic activities by mediating the inflammatory response, causing excessive ECM production, decreasing synthesis of matrix metalloproteinases (MMPs), increasing the synthesis of tissue inhibitors of MMPs (TIMPs), and finally by inducing myofibroblast differentiation (Desmouliere et al. 1993; Grainger 2007; Hinz 2007; Leask and Abraham 2004; Ruiz-Ortega et al. 2007; Taipale et al. 1998). TGFβ1 normally acts to inhibit proliferation of epithelial cells, but it may stimulate proliferation of fibroblasts of dermal and renal origins (Giannouli and Kletsas 2006; Strutz et al. 2001). The fact that cancer cells become insensitive to the growth-arresting action of TGFβ1, together with its pro-angiogenic effects and its potential to induce EMT of cancer cells, also allocates TGFβ1 a central role in tumor development (Bierie and Moses 2006; Pardali and Moustakas 2007; Siegel and Massague 2003). TGFβ1 also has beneficial functions in assuring homeostasis of adult tissues by controlling proliferation of epithelial cells, endothelial cells, immune cells and fibroblasts (Feng and Derynck 2005; ten Dijke and Arthur 2007; Wakefield and Stuelten 2007). Hence, global inhibition of TGFβ1 is considered problematic as an anti-fibrotic or antitumorigenic strategy with many uncontrollable side-effects (Akhurst and Derynck 2001; Varga and Pasche 2009). On the other hand, the complex and diverse mechanisms leading to the activation of latent TGFβ1 potentially provide cell-specific means for TGFβ1 inhibition. Myofibroblasts themselves secrete biologically latent TGFβ1 in complex with the latency associated peptide (LAP). LAP and TGFβ1 form the large latent complex together with the latent TGFβ1 binding protein-1 (LTBP-1). LTBP-1 binds to proteins in the ECM and there provides a reservoir of latent TGFβ (Annes et al. 2004; Jenkins 2008; Wipff and Hinz 2008). The different mechanisms of how cells can dissociate and thus activate TGFβ1 from the latent complex are subject of comprehensive recent reviews (Jenkins 2008; Sheppard 2005; Wipff and Hinz 2008). Here, we will briefly discuss how myofibroblast mechanics play a role in latent TGFβ1 activation. The LAP portion of the ECM-bound latent TGFβ1 provides binding sites for a variety of myofibroblast integrins, including αvβ5, αvβ3, αvβ8, and α8β1 which intracellularly connect to the contractile cytoskeleton (Sheppard 2005; Wipff and Hinz 2008). Stress applied to integrin αvβ5, either by stretching the ECM or by inducing cell contraction renders the active TGFβ1 available for its cell-membrane-bound receptor, possibly by a conformational change in the latent complex (Wipff et al. 2007) (Fig. 3.4). A similar mechanism appears to be in place for epithelial cells to activate TGFβ1 via αvβ6 integrin (Jenkins et al. 2006). In fact, αvβ6 was the first integrin shown to mediate latent TGFβ1 activation in a process that requires polymerized actin (Munger et al. 1999). Importantly, mechanical activation of TGFβ1 by myofibroblasts is not operational in the context of a compliant ECM with Young’s modulus of ≤5000  kPa (Wipff et  al. 2007).

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Fig. 3.4   A schematic model of latent transforming growth factor β1 (TGFβ1) activation by cell traction. Integrin binding to a specific RGD site in the latency associated peptide (LAP) portion transmits intracellular force to the large latent complex. In the context of a soft extracellular matrix (ECM) (unloaded springs) cell pulling will simply drag the latent complex. When bound to a remodeled stiff ECM (loaded springs) integrin-mediated force exertion can trigger a conformation change in the LAP and make TGFβ1 available for its receptor. (Reprinted with permission from Hinz 2009b)

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This is explained by a model in which the cell pulling force transmitted to LAP is counteracted by anchoring the latent complex via the LTBP-1 to a mechanically resistant ECM (Hinz 2009c; Tenney and Discher 2009; Wells and Discher 2008; Wipff and Hinz 2008) (Fig. 3.4). Consistently, deletion of the ECM-binding sequence from LTBP-1 abolishes latent TGFβ1 activation via integrins as shown for αvβ6 integrin (Annes et al. 2004). It is noteworthy that the threshold ECM stiffness for latent TGFβ1 activation is lower than the minimal stiffness required for expression and incorporation of α-SMA into stress fibers. Hence, mechanical activation of TGFβ1 can provide a first control point in the progression of tissue remodeling by translating the level of ECM stiffness (organization) into a profibrotic signal.

3.3.3  More Factors That Stimulate Myofibroblast Differentiation A detailed discussion of the intracellular signaling molecules, cytokines, and ECM proteins that have been reported to modulate myofibroblast differentiation and α-SMA expression will exceed the scope of this chapter and the reader is referred to recent reviews (Hinz 2007; Horowitz and Thannickal 2006; Schurch et al. 2007; Wynn 2007, 2008). Most factors that induce myofibroblast differentiation without involving paracrine effects from other cell types act in synergy with TGFβ1 signaling (Desmouliere et al. 1993; Ronnov-Jessen and Petersen 1993). These include connective tissue growth factor (Shi-Wen et al. 2008), interleukin (IL)-6 (Gallucci et al. 2006), Fizz1 (found in inflammatory zone) (Liu et al. 2004), galectin-3 (Henderson et al. 2006), osteopontin (Lenga et al. 2008; Mori et al. 2008), endothelin-1, (Jain et  al. 2007; Shi-Wen et  al. 2007), angiotensin II (Rosenkranz 2004; Uhal et al. 2007), thrombin (Bogatkevich et al. 2003), possibly semaphorin 7A (Kang et al. 2007), nerve growth factor (Micera et al. 2001) and cleavage of the urokinase receptor (Bernstein et al. 2007). Myofibroblast differentiation is further promoted by adhesion to specific ECM proteins including collagen type VI (Naugle et  al. 2006), tenascin-C (De Wever et al. 2004; Tamaoki et al. 2005) and most importantly cellular ED-A FN (Serini et al. 1998). Another important stimulating factor for myofibroblast differentiation appears to be the production of reactive oxygen species by NADPH oxidases (NOX) in fibroblastic cells (Shen et al. 2006). NOX are transmembrane proteins that regulate intracellular redox signaling by reducing extracellular molecular oxygen to superoxide, generating downstream reactive oxygen species (Bedard and Krause 2007; Griffith et al. 2009). The predominant NOX isoform in fibroblasts is NOX4, which has been shown to mediate TGFβ1induced conversion of cardiac fibroblasts into myofibroblasts (Cucoranu et  al. 2005). NOX4 is further involved in the development of lung fibrosis and ablation of NOX4 in lung fibroblasts suppresses myofibroblast differentiation (Hecker et al. 2009).

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3.3.4  Factors That Inhibit Myofibroblast Differentiation The discovery of factors that suppress myofibroblast development is mainly driven by the search for new therapies to treat organ fibrosis (Brown et al. 2005; Friedman 2004a; Gharaee-Kermani et al. 2007; Horowitz and Thannickal 2006; Scotton and Chambers 2007; Trojanowska and Varga 2007). Growth factors that antagonize myofibroblast development in culture and in animal models have been described, including IL-1 (Kanangat et al. 2006; Shephard et al. 2004), tumor necrosis factor-α (Goldberg et al. 2007; Saika et al. 2006), TGFβ3 (Shah et al. 1995) and interferon-γ (Desmouliere et al. 1992). At present however, none of the countless factors that are implied in normal and pathological wound healing has been successfully targeted to significantly improve the tissue repair process in clinical applications. Another possibility to suppress development of the myofibroblast phenotype is to interfere with the mechanical feedback loop of high contractile activity and ECM stiffening that induces and maintains the fibrogenic cell character (Wipff et al. 2007) (Fig. 3.2). It is difficult to imagine how one could reduce the stiffness of a scar tissue in a controlled manner. However, interfering with the mechanisms through which myofibroblasts perceive extracellular stress is another option. It has been shown that the level of substrate rigidity determines the size and molecular composition of cell-ECM focal adhesions that perceive and communicate extracellular mechanical signals to the cytoskeleton, leading to specific gene expression (Bershadsky et al. 2003; Ingber 2003). By artificially reducing the adhesion area available for cell attachment using microcontact printing on rigid surfaces it is possible to ‘simulate’ a soft environment for myofibroblasts. As a consequence, these cells lose α-SMA expression in stress fibers and contractile capacity similar to culture on soft substrates (Goffin et al. 2006). Finally, the level of myofibroblast intracellular tension can be specifically and directly reduced by targeting α-SMA positive stress fibers using a competitive peptide strategy. Inhibitition of myofibroblast contraction by long term administration of the α-SMA fusion peptide results in decreased collagen production and finally disappearance of the myofibroblast (Clement et  al. 2005; Hinz et al. 2002).

3.4 The Myofibroblast in the Stroma Reaction to Tumors 3.4.1  Activities of the Myofibroblast in the Tumor Environment The tumor stroma is a connective tissue composed of cells and ECM, containing vessels and nerves. The stroma plays equivalent roles in the host reaction or immunological defense against the tumor and in enhancing tumor progression. Morphologically, the tumor stroma gloves tumor strands, follows tumor progression over time and frequently replaces the surrounding normal connective tissue. The tumor stroma contains permanent cells, notably fibroblasts and myofibroblasts,

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and transient visitors, such as the leukocytes involved in the immune response. Fibroblasts and myofibroblasts, also called CAF or tumor-associated fibroblasts, represent very important components of the tumor stroma. Recently, studies have demonstrated a prognostic significance for these cells which could also represent new therapeutic targets (Albini and Sporn 2007; Gonda et al. 2010; Hawinkels et al. 2009; Ostman and Augsten 2009; Pietras et al. 2003). Different subpopulations of CAFs participate in forming the tumor stroma. Markers such as fibroblast-activated protein, fibroblast-specific protein-1 (FSP1/S100A4), neural-glial antigen-2, and platelet-derived growth factor (PDGF)-β receptor have been used to characterize these distinct subsets (Orimo and Weinberg 2007; Sugimoto et al. 2006). Among those, the α-SMA expressing myofibroblast appears to be predominantly involved in chronic tumor–stroma reactions. A great number of myofibroblasts is present in the stroma of many types of malignant tumors and are frequently localized at the front of invasion (De Wever and Mareel 2003). Here they are involved in connective tissue remodeling by producing ECM components. Moreover, they synthesize not only proteases to degrade ECM or basement membrane such as MMPs, but also their inhibitor TIMPs. Depending on the tumor type, the tumor stroma can be inconspicuous, as shown in the main forms of malignant melanoma or hepatocellular carcinoma, or desmoplastic as in scirrhous breast carcinoma. The composition of the tumor stroma strongly resembles the granulation tissue of healing wounds. Thus, it has been suggested that the host stroma reaction promoted by the tumor cells represents a ‘never healing wound response’ (Dvorak 1986). However, tumor growth induces abnormal development of the stroma reaction leading to excessive scarring (Schafer and Werner 2008). Indeed, the stroma reaction presents features rather similar to those of skin hypertrophic scars observed after acute and deep injury such as severe burning. It is important to emphasize that the presence of fibrotic lesions, containing myofibroblasts significantly increases the risk of cancer in many tissues, including lung (Daniels and Jett 2005), liver (Bissell 2001), and breast (Radisky and Przybylo 2008). Women with dense (fibrotic) breast tissue which is linked to a substantial increase in stromal collagen deposition, have a four- to six-fold increased risk of developing breast cancer (Boyd et al. 1998).

3.4.2  Myofibroblast–Tumor Crosstalk: A Fatal Affair It is generally accepted that myofibroblasts are recruited from normal connective tissue fibroblasts that migrate into the stroma and acquire contractile function. However, similarly to the situations found in pathological repair, BM-derived MSCs can be involved (Direkze et al. 2004; Ishii et al. 2003). Furthermore, the contribution of EMT from epithelial and endothelial cells has been discussed for tumor fibroblasts and myofibroblasts (Potenta et al. 2008; Radisky et al. 2007; Zeisberg et al. 2007a). All these possible precursors may represent alternative sources of myofibroblasts that are activated according to the specific situation. In particular when the size of

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the lesion increases, recruitment of local fibroblasts may be insufficient to ‘comply with the request’. This has been demonstrated in pathological repair such as liver cirrhosis (Guyot et al. 2006). What are the signals that these precursor cells perceive to become activated? Recruitment and activity of myofibroblasts directly depend on the inflammatory response. Since Dolberg and coworkers have demonstrated that tumors preferentially grow at sites of wounds in retrovirus infected chickens (Dolberg et al. 1985) numerous studies supported that most chronic diseases, including cancer, are caused by a deregulated inflammatory response (Aggarwal and Gehlot 2009). Interestingly, it has been shown that the transcriptome of the aging prostate stroma is characterized by the up-regulation of several genes that encode secreted inflammatory mediators. The levels of these mediators are sufficient to promote low-level increases in the proliferative rate of both epithelial and stroma fibroblast cell types observed in benign prostatic hypertrophy (Begley et al. 2008). The authors suggested that the same processes promote the proliferation of malignant prostate epithelial cells in the development and progression of invasive prostate cancer. Stroma-tumor interactions depend on multiple factors, including direct cell-cell contacts and paracrine signals (De Wever et al. 2008) (Fig. 3.5). Interactions between tumor cells and myofibroblasts, leukocytes or endothelial cells, participate in the remodeling of the microenvironment and contribute to tumor progression through growth, local spreading and metastasis. It is accepted that the crosstalk between carcinoma epithelial cells and adjacent stroma cell populations leads to the production of a tumor microenvironment that further promotes tumor progression. In a mouse model of prostate carcinoma, Hill and coworkers have shown that tumor cells induce up-regulation of p53 through a paracrine mechanism in stroma

Fig. 3.5   Schematic illustration of the heterologous cellular interactions during tumor development and progression

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fibroblasts and myofibroblasts, which results in decreased fibroblastic proliferation (Hill et  al. 2005). This process exerts a selective pressure that promotes the expansion of a highly proliferative subpopulation of fibroblastic cells, lacking p53 and contributing to tumor progression. Subsequently, co-evolution of the stroma compartment with selection of genetically altered cells can occur as a result of oncogenic stress in the epithelium. Again, this study illustrates the interdependent signaling which controls the stroma-epithelial regulation in cancer. As mentioned above, this type of observation underlines the importance for developing cancer therapies that target the stroma compartment as a means to prevent acceleration or possibly even suppress tumorigenesis (Hill et al. 2005). Interestingly, certain primary tumor cells can release soluble factors which mobilize specific populations of non-malignant hematopoietic cells which colonize distant organ tissue, establishing a ‘pre-metastatic niche’ (Kaplan et al. 2005; Noel et al. 2008). Subsequently, fibroblasts are co-recruited along with hematopoietic cells to establish the supportive tumor stroma (Kaplan et  al. 2006). Fibroblasts and myofibroblasts participate in the niche architecture by developing a microenvironment which favors tumor cell engraftment. In addition, the existence of cancer stem cells is currently discussed. Cancer stem cells are defined as undifferentiated cells with the ability to self-renew, differentiate to multiple lineages and initiate tumors that mimic the parent tumor (Gupta et al. 2009). The cooperation between these cancer stem cells and the connective tissue stem cells could constitute a specific niche, able to develop tumors if specific signals are delivered. Cytokines derived from tumor cells are also believed to modulate the stroma cells to induce expression of growth and angiogenic factors, which in turn provide the tumor with the necessary microenvironment for expansion and invasion. Among these cytokines, numerous studies underline the role of TGFβ1 (involved in myofibroblast generation), platelet-derived growth factor (involved in desmoplasia), vascular endothelial growth factor (involved in angiogenesis and desmoplasia), and hepatocyte growth factor (involved in tumor cell motility). Studies provide evidence for TGFβ1 as a supporting agent in tumor progression through the induction of a perpetual wound healing process in the tumor microenvironment. For example, normal pre-senescent fibroblasts and prostate stroma cells that are subcutaneously co-transplanted with prostate carcinoma cells into nude mice reduce tumor latency and accelerate tumor growth. Even when their TGFβ1 signaling is blocked, the fibroblasts and stroma cells continue to stimulate tumor initiation but no longer support tumor growth as opposed to control cells (Verona et al. 2007). Moreover, gene microarray and quantitative reverse transcription-PCR analyses showed that TGFβ1 up-regulates a selection of genes in stroma cells that are involved in tissue remodeling and wound healing (Verona et al. 2007). In addition to cytokines, MMPs are central regulators in the complex tumor ecosystem due to the high diversity of these enzymes concerning both their substrates and functions. In the majority of cancers, membrane-associated and extracellular MMPs are produced by host cells including inflammatory cells, endothelial cells, fibroblasts, and myofibroblasts (Noel et al. 2008). For example, ADAMTS-1, a disintegrin and MMP with thrombospondin motifs is expressed in a variety of carcinomas

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(Rocks et al. 2008a). Its production leads to increased secretion of TGFβ1 and IL1β, leading to the recruitment of fibroblasts and differentiation into myofibroblasts (Rocks et al. 2008b). Furthermore, the presence of specific stromal proteins such as the oncofetal FN (Nielsen et al. 2008), is a characteristic of the invasive phenotype. It has been shown by in situ hybridization that granulation tissue myofibroblasts show a FN splicing pattern consisting ED-A and ED-B, similar to that found in the embryo (Ffrench-Constant et al. 1989). As mentioned above, ED-A FN is crucial for myofibroblastic phenotype induction by TGFβ1 (Serini et al. 1998). Taken together, these data reinforce the central roles played by TGFβ1 and ECM protein interactions in stroma development and tumor growth. Tumor cell-myofibroblast interactions share common features with the epithelial-mesenchyme interactions seen in morphogenesis; the mechanisms of tumor survival and progression sometimes use processes also observed during normal embryonic development. In the next part, we will illustrate these interactions between myofibroblasts and tumors on the example of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA).

3.4.3  The Myofibroblast of HCC Compared with CCA HCC may have numerous etiologies, notably chronic hepatitis B and C viral infection or chronic alcohol abuse. In rare situations, HCC can develop in non-cirrhotic liver (Fig. 3.6a) but in most cases arises in livers showing cirrhosis (Desmouliere et al. 2004; Farazi and DePinho 2006) (Fig. 3.6b). Independently of the cause, cirrhosis is in itself a precancerous condition (Berasain et al. 2009). Depending on the degree of differentiation, the tumor is composed of large anastomosing plates and acini of tumor hepatocytes surrounded by capillaries. Except in rare forms of scirrhous or fibrolamellar HCC, the tumor stroma is scanty (Fig. 3.6). Sometimes the tumor is encapsulated but most often the tumor stroma is mixed with the fibrous stroma of the surrounding cirrhosis. At low microscopic magnification, the tumor stroma seems to be organized like the connective tissue surrounding the normal liver parenchyma. At higher magnification and on the ultrastructural level, important differences and modifications become evident. The vessels surrounding the tumor plates are not sinusoids but capillaries with a continuous basement membrane. They contain fewer Kupffer cells compared with normal liver (Liu et al. 2003) and instead exhibit numerous mast cells (Terada and Matsunaga 2000). The space between endothelial cells and the tumor hepatocytes does not contain HSCs but cells with myofibroblastic features like α-SMA (Fig. 3.6d). The ECM is remodeled with more deposition of collagen (Faouzi et al. 1999a), fibrillin-1 and de novo expression of elastin (Dubuisson et al. 2001). In the tumor stroma of the liver, myofibroblasts can be derived locally from the surrounding cirrhosis or from HSCs and/or portal fibroblasts. However, as discussed above, different possible sources have been recently discussed, notably BM-derived MSCs. Myofibroblasts are involved in the deposition (Dubuisson et  al. 2001; Faouzi et  al. 1999a; Le Bail et  al. 1999) and

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Fig. 3.6   Hepatocellular carcinoma features. Typical gross appearance of hepatocellular carcinomas undergoing on a non-cirrhotic, or b cirrhotic livers. In both types of hepatocellular carcinomas, a scanty stroma reaction is observed. In b, the non-tumor liver presents typical cirrhotic regenerative nodules. c Inside the tumor, collagneneous extracellular matrix (Masson’s trichrome histochemistry) is very modest in the stroma. d As detected by immunohistochemistry, few α-smooth muscle actin expressing myofibroblasts are localized between the tumor plates and the capillaries

in the remodeling of the ECM, synthesizing proteinases, like urokinase or MMPs (Dubuisson et  al. 2000; Monvoisin et  al. 2002; Monvoisin et  al. 1999). Interactions between malignant hepatocytes and stroma cells are complex. Tumor cells can recruit liver myofibroblasts locally or by migration from the transdifferentiated stroma cells. Numerous ways have been studied. Some growth factors or cytokines are involved in activation and recruitment of stroma cells, such as TGFβ1 or PDGF (Faouzi et al. 1999b). Recently, it was suggested that the TGFβ1/PDGF axis is crucial during hepatic tumor-stroma crosstalk, regulating both tumor growth and cancer progression (van Zijl et al. 2009). During early stages of hepatocarcinogenesis, TGFβ1 released from non-parenchymal compartments, such as immune cells, causes the transdifferentiation of HSCs and portal fibroblasts to myofibroblasts, which then secrete TGFβ1 and PDGF. TGFβ1 induces EMT of neoplastic hepatocytes in cooperation with the mitogen-activated protein kinase signaling, and at the same time stimulates autocrine production of PDGF, which maintains EMT. The resulting malignant hepatocytes produce both TGFβ1 and PDGF, which in turn stimulate the microenvironment to further generate and attract hepatic myofibroblasts.

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Stroma myofibroblasts can also enhance tumor invasiveness, notably by expression of hepatocyte growth factor (Neaud et al. 1997; Neaud et al. 2000) or by secretion of MMPs (Yamamoto et al. 1997). In addition, expression of monocyte chemoattractant protein-1 (MCP-1 or CCL-2) by tumor myofibroblasts can attract more monocytes into the tumor leading to increased fibroblast activation and may also play a role in activating migration of tumor cells (Dagouassat et al. 2009). Recently, as potential mechanism of the tumorigenic effects of HSCs, Ammann and coworkers identified activation of NFκB and extracellular-regulated kinase in HCC cells, two signaling cascades that play a crucial role in HCC progression (Amann et al. 2009). Because good animal models are lacking, the pathogenesis of the stroma reaction in human HCC remains unclear. In most models that have been proposed to study HCC development, a stroma reaction is practically absent. Recently, using a model associating diethylnitrosamine exposure and N-nitrosomorpholine treatment, Taras and coworkers have observed that HCC cells were surrounded by a stroma reaction resembling that observed in humans. Consequently, this model has been used to test different drugs able to interfere with lung metastatic processes of HCC (Taras et al. 2006; Taras et al. 2007). To model CCA in animal models, thioacetamide has been used, accurately mimicking the multi-stage progression of human CCA (Jan et al. 2004; Yeh et al. 2004). CCA is a cancer of the biliary epithelium and is less common than HCC. Worldwide, CCA is the second most common primary hepatic malignancy. The tumor is composed of tubular adenocarcinoma set in an abundant fibrous stroma (Fig. 3.7a). Three main forms are recognized in accordance with the location of the tumor in the liver and in the extrahepatic bile tree: intrahepatic or peripheral CCA, hilar adenocarcinoma (Klatskin tumor) and carcinoma of the extrahepatic bile ducts. Several recent epidemiological studies have shown that the incidence and mortality rates of intrahepatic bile-duct carcinoma are increasing (Khan et al. 2008). Unlike HCC, the stroma of CCA is abundant, sclerous with sometimes calcification, can be extensive and submerges the scanty epithelial component (Fig. 3.7b). The number of myofibroblasts is elevated in the intra-tumor stroma and correlates with the degree of tumor fibrosis (Terada et al. 1996a). Surrounding the tumor, local HSCs and/or portal fibroblasts acquire the expression of α-SMA and seem to form a continuity with the intra-tumor myofibroblasts (Terada et  al. 1996a) (Fig.  3.7c). These are involved in deposition of ECM and in remodeling (Okamura et al. 2005). Stroma cells express MMP-1, MMP-2, MMP-3, MMP-9, and TIMP-1 and TIMP-2 (Terada et al. 1996b). This expression of MMPs and TIMPs is stronger in CCA with severe invasion (Terada et al. 1996b). The question arises why the stroma reaction is scanty in HCC and abundant in CCA. It is conceivable that the cells contributing to the myofibroblastic population and which participate in stroma reaction are different. Precursor cells of the myofibroblast in the liver, HSCs and portal fibroblasts, are differently distributed in the hepatic lobule (Guyot et al. 2006). HSCs resemble pericytes and are located along the sinusoids, in the Disse space between the endothelium and the hepatocytes. In contrast, portal fibroblasts are embedded in the portal tract connective tissue around portal structures such as vessels and biliary structures. Differences

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Fig. 3.7   Intrahepatic cholangiocarcinoma features. a This type of tumor shows important hyaline changes. b The tumor is composed of a typical tubule proliferation in an abundant fibrous stroma (Masson’s trichrome histochemistry). c By immunohistochemistry, the tumor stroma contains numerous α-smooth muscle actin-expressing myofibroblasts

have been reported between these two fibrogenic cell populations, concerning the mechanisms leading to myofibroblastic differentiation, activation and ‘deactivation’ (Guyot et  al. 2006). For example, rat portal fibroblasts and HSCs differ in CD95-mediated apoptosis and response to tumor necrosis factor-α (Saile et al. 2002). Insulin-like growth factor-1 induces DNA synthesis and apoptosis in rat HSCs but DNA synthesis and proliferation in rat portal fibroblasts (Saile et al. 2004). Fibrotic liver remodeling was studied in culture of precision-cut liver slices derived from fibrotic liver: it was shown that after carbon tetrachloride treatment, a proportion of myofibroblasts derived from HSCs seems to dedifferentiate while in bile duct ligation model, myofibroblasts derived from portal fibroblasts disappear by apoptosis (Guyot et al. 2007). It is now widely accepted that the different types of lesions leading to liver fibrosis, e.g. lesions caused by alcohol abuse and viral hepatitis, involve specific fibrogenic cell subpopulations (Desmouliere 2007).

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Concomitantly, the stroma reaction surrounding different liver cancers also involve specific fibrogenic subpopulations. We suggest that HSC-derived myofibroblasts and portal fibroblast-derived myofibroblasts are involved in the stroma reactions encountered in HCC and CCA, respectively. The biological and biochemical characterization of these cells is essential if we are to understand the interactions between tumor cells and stroma myofibroblasts. For example, as mentioned above, HSCs and portal fibroblasts differ in their proliferative and apoptotic capacity, at least in vitro. All this information is required for the development of treatments specifically and efficiently targeting the cells responsible for the development of the stroma reaction. In conclusion, liver fibrosis and cirrhosis generate activated myofibroblasts that are crucial for HCC development, underlining the fact that the presence of fibrotic lesions may increase the risk of cancer. These myofibroblasts provoke malignant hepatocytes to undergo EMT at the tumor edge and thus render them capable to invade and metastasize. It is clear that stroma myofibroblasts, derived from HSCs or from portal fibroblasts in the liver, promote tumor progression. Stroma–myofibroblast interactions represent an interesting tumor differentiation-independent target for therapy of cancers, particularly for HCC and CCA which are highly aggressive cancers.

3.5 Conclusions and Perspectives It is now well established that the myofibroblast plays an important role in developmental processes, several types of response to injury and fibrotic phenomena. As discussed above, the participation of this cell in tumoral phenomena has become more and more evident in more recent years, implying that epithelial-stroma interactions are crucial in cancer biology. This represents a switch in the present paradigm, which considers epithelial cells exclusively responsible for cancer evolution. Many aspects of cancer-stroma interactions however, remain open and should be thoroughly investigated: 1. The participation of mechanical forces in wound healing and fibrosis has been demonstrated. It will be important to clarify the role of force development in tumor-stroma interactions and in cancer cell biology. 2. It appears clear that cancer stroma contains a heterogeneous fibroblast, and possibly myofibroblast, population. The characterization of the specific features of these cells should enhance our understanding of their biological behavior. 3. The interactions between epithelial and fibroblastic cells also take place also through soluble factors produced by both cell categories. Advances in the identification of such factors should be instrumental for the elucidation of these phenomena. Progress along these and other lines should not only advance our knowledge of tumor biology, but also allow new and fruitful approaches to therapeutic strategies.

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Acknowledgements  The work of BH is supported by grants from the Swiss National Science Foundation (#3100A0-113733/1 and #3200–067254), from the GEBERT RÜF STIFTUNG, and from the Connaught Funding Program. The work of AD was supported in part by a grant from the University of Limoges (Contrat Renforcé Recherche). The work of IAD was supported in part by a grant from the Australian Academy of Science and a visiting fellowship at the University of Limoges. We are very grateful to Paulette Bioulac-Sage, and Sébastien Lepreux (Service d’Anatomie Pathologique, Hôpital Pellegrin, CHU Bordeaux, France) for the illustrations of pathological cases.

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Chapter 4

The Role of Myofibroblasts in Communicating Tumor Ecosystems Olivier De Wever, Astrid De Boeck, Pieter Demetter, Marc Mareel and Marc Bracke

4.1 Introduction To explore the transition from non-invasive to invasive forms of cancer we consider cancer as a systemic disease with a tumor as an ecosystem operating inside a living organism. Central in tumor ecosystems is the idea that cancer cells and tumorassociated host cells are continuously communicating and non-redundant changes in a single element inevitably alters the organization of the whole system (De Wever and Mareel 2003; Mareel et al. 2009). The present review focuses on the prominent role of myofibroblasts as host cells in the tumor ecosystem. When analyzing this, we will consider a global communication between local and distant ecosystems situated inside the human body, namely the primary tumor, the bone marrow, the circulation, the sites of lymph node and of distant metastases and the nervous system. When oncogenic alterations change the epithelium of a tissue during cancer development, the stroma inevitably changes also. The corresponding robust myofibroblast reaction activates an invasive growth program in cancer cells making a tumor an alien entity invading the body (De Wever et al. 2008; De Wever and Mareel, 2003; Mareel et al. 2009). This invasive growth program implicates a number of cellular activities, namely: homotypic cell-cell adhesion; heterotypic cell-cell and cell-matrix adhesion; proteolysis; migration; ectopic survival and growth (Mareel et  al. 2009). New experimental models have facilitated the direct observation of the multifaceted invasion process. Novel insights into tumor ecosystems raise the question whether myofibroblasts have a critical impact on various therapeutic strategies, and should be considered as effective targets for therapy, next to or as an alternative for the cancer cells. We must take into consideration that next to myofibroblasts, tumor ecosystems contain a plethora of cells that work in concert such as mesenchymal progenitor cells, tumor associated macrophages, mast cells, neutrophils, endothelial cells, and bacteria (Joyce and Pollard 2009; Possemiers et al. 2009; Wels et al. 2008b). O. De Wever () Laboratory of Experimental Cancer Research, Department of Radiotherapy and Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_4, © Springer Science+Business Media B.V. 2011

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4.2 Definition and Origin of Myofibroblasts Identity and expression profiles of tumor-associated fibroblasts are only partially known, and it is likely that several currently unrecognized subtypes exist (Chang et al. 2002; De Wever et al. 2008). Most studies of tumor stroma consider it as a relatively uniform entity, and the contribution of inter-patient fibroblast variability to the biological and clinical heterogeneity of tumors is only beginning to be recognized and understood (Allinen et al. 2004; Beck et al. 2008). We believe that there exist distinct types of fibroblasts with distinct reaction patterns that affect invasive tumor growth in different ways. The spectrum of phenotypic entities ranges from the non-contractile fibroblast to the contractile myofibroblast with a number of intermediate phenotypes having been described (reviewed in Eyden 2005). One type that recently attracted a lot of attention is the myofibroblast. Myofibroblasts are large spindle-shaped cells defined by indented nuclei, α-smooth muscle actin containing stress fibers and well-developed cell-matrix interactions (fibronexus). Unfortunately, there is no myofibroblast-specific immunocytochemical marker and characterisation is based on a combination of positive markers such as α-SMA, γ-SMA, desmin, vimentin, prolyl-4 hydroxylase and negative markers such as cytokeratin, CD31, CD34 and smoothelin (De Wever et  al. 2008). Besides these cytoplasmic and membrane markers, stromal myofibroblasts are characterised by an elevated production of ECM components, ECM remodelling enzymes, growth factors, cytokines and chemokines creating a growth promoting environment (De Wever et al. 2008; Tuxhorn et al. 2002). The expression of α-SMA is considered to be the main biochemical marker of myofibroblastic differentiation in controlled in vitro systems. Understanding the origin and molecular events for the generation of tumorassociated myofibroblasts is still a matter of debate. Tumor-associated myofibroblasts are thought to arise from several mobilised cell types (Wels et  al. 2008a) including migratory neighbours such as tissue-resident mesenchymal stem cells (MSCs) (Muehlberg et al. 2009) or tissue resident fibroblasts (Rønnov-Jessen et al. 1995), and distant invaders such as bone marrow-derived MSCs (Direkze et al. 2004; Ishii et al. 2003) and fibrocytes (Direkze et al. 2004; Ishii et al. 2003; Mori et al. 2005). These findings demonstrate a high degree of plasticity and a diversity of origins of myofibroblast precursor populations. This diversity may also explain the heterogeneous phenotypic characterization of myofibroblast populations observed within tumors.

4.3 Experimental Ecosystems of Invasion: Implication of Myofibroblasts To advance the discovery and development of effective anti-invasive therapies, it will be necessary to develop theoretical and experimental (cell culture and mouse) models that not only recapitulate the tumor ecosystem but also provide detailed mechanistic information of how target activity and therapeutic response influence

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tumor invasion. Dynamic cell imaging, in association with multiparametric image analysis applied to physiologically relevant models, can help achieve these goals. Covering image analysis, be it only a minor part of, is beyond the scope of the present review (Carragher 2009; Lang et al. 2006). We will elaborate on various theoretical and experimental invasion models that take into account the dynamic communication between cancer cells and their environment, more exactly the tumor-associated myofibroblast. Today, there are compelling signs of renewed interest in mathematical modeling of cancer progression and a new discipline emerged: integrative mathematical oncology (Anderson and Quaranta 2008). Several groups demonstrated that invasion is a predictable process governed by biophysical laws (Anderson and Quaranta 2008; Anderson et al. 2006; Bearer et al. 2009; Frieboes et al. 2006). Multiscale mathematical modeling builds a quantitative representation of a tumor slice, on a two-dimensional graphical grid (lattice). Each cell is accounted for in the lattice and its behaviour (proliferation, migration and death) is tracked on the basis of mathematical functions and this generates a computer simulation of tumor evolution. In essence, the shape and size of the tumor emerges from the properties of individual cells as well as from inputs from parameters from the ecosystem such as oxygen concentration, nutrients availability, and ECM gradients. All known forms of collective cell migration (chains, strands and detached clusters) observed in experimental models and histopathology are consistently reproduced by the mathematical model. For example, a homogenous ECM distribution predicts a large tumor with smooth margins containing a dead-cell inner core and a thin rim of proliferating cells. In contrast a heterogenous ECM predicts a tumor with a dead inner core with a thin rim of proliferating cells displaying a striking, branched fingered morphology of the margins. Thus, multiscale mathematical modeling predicts that the environment determines the invasive phenotype of a tumor (Anderson et al. 2006). This is rather distinct from the conventional view that the environment is a supporting infrastructure for cancer cells (Mueller and Fusenig 2004). Furthermore, mathematical modeling can provide a rigorous, more precise approach for quantifying correlations between tumor parameters, prognosis, and treatment outcomes. A constant dialogue between mathematicians and cancer biologists will assist to frame cell culture experiments in the context of quantitative models. In our view of a tumor as a communicating ecosystem composed of both cancer cells and myofibroblasts we will provide an overview of experimental invasion models that take into account the role of myofibroblasts. Most models make use of purified matrix proteins such as acid-extracted, native collagen type I or mixtures of matrix proteins such as Matrigel. In scope of this review, we consider mainly models that use native collagen type I because tissue invasion requires cancer cells to negotiate a stromal environment dominated by cross-linked networks of collagen type I (Sabeh et al. 2009). Several groups studied invasion of cancer cells seeded on top of myofibroblast-enriched native collagen type I gels. Invasion was scored after 24 h as single cell invasion quantified by phase-contrast microscopy (De Wever et al. 2004). Myofibroblast-stimulated invasion of HCT-8 colon cancer cells into collagen type I

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Fig. 4.1   Phase contrast pictures of single HCT-8/E11 colon cancer cells seeded on collagen type I gel and cultured for 24 h in culture medium (control) or culture medium supplemented with conditioned medium derived from myofibroblasts (CMmyo). Arrows indicate invasive extensions. Scale bar = 20 µm

is characterized by a change from a round, nonmigratory morphotype to an elongated, migratory morphotype (Fig. 4.1). H&E stained paraffin sections from 14 day cocultures confirmed that myofibroblasts stimulated the invasion of colon cancer cells and squamous cell carcinoma cells (Daly et al. 2008; De Wever et al. 2004; Nystrom et al. 2005). Nystrom et al. (2005) further optimized quantification of these organotypic gels by a computer-assisted digital image analysis system that assesses invasion objectively and provides a numerical invasion index taking into account the depth and pattern of invasion. The use of these organotypic gels is not limited in vitro but can also be transplanted into the flank of nude mice (Borchers et al. 1997). A possible disadvantage of single-cell invasion assays is that it lacks structural architecture. A multicellular spheroid system is more adapted to study invasion mechanisms because it takes into account homotypic cell-cell contacts and progressive deprivation in oxygen (hypoxia), nutrients, growth factors as well as limitations in the penetration and action of drugs. Spheroids, prepared from epithelial non-invasive HCT-8/E11 colon cancer cells and placed inside native collagen type I gels, grow but remain ball shaped when followed over at least 96 hours. When these spheroids are co-cultured with primary colon tumor-derived myofibroblasts the spheroids display a less spherical morphology with a rough surface. This roughness reflects an invasive morphology and can be easily evaluated by calculating the factor shape (or inverse roundness, perimeter2/4πarea) (De Wever et al. 2010; Denys et al. 2008). Stromal myofibroblasts exert a dramatic selective force on patterning of cancer cell spheroids, which grow as an invasive mass with collective cell migration leading to fingering margins and individual migration leading to fragmentation of single and/or clusters of cells. This collective multicellular migration pattern is reminiscent of the proteolytic-dependent track clearance by a spindle shaped leader cell followed by multiple cells observed in vivo (Wolf et al. 2007). The round, amoeboid-type of migration observed with the fragmented single cells suggests a non-proteolytic reorganization of the collagen meshwork during invasion

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(Wolf et al. 2003). Consistent with this observation, primary tumor explants (thus including cancer cells and stromal myofibroblasts) cultured in a randomly organized, native collagen type I matrix realigned the collagen fibers, allowing individual cancer cells to migrate out along radially aligned fibers (Provenzano et al. 2006). An alternative approach is to directly confront breast cancer cell spheroids with tumor-derived fibroblast spheroids (Kunz-Schughart et al. 2001). Short-term and long-term confronted spheroids were histologically analysed and revealed cancer cell infiltration into the fibroblast spheroid. Furthermore, α-SMA immunohistochemistry showed accumulation of myofibroblasts around the infiltrating cancer cell nests. Using a bi-transgenic tumor model with increased stromal collagen in mouse mammary tissue, Provenzano et al. (2008) showed that collagen density and stromal collagen fiber reorganization (the so called tumor-associated collagen signature) facilitates local invasion.

4.4 Local and Distant Clinical Ecosystems of Invasion: Implication of Myofibroblasts 4.4.1  Primary Tumor The ecosystem of the primary tumor, instead of the cancer cells only, may dictate the invasive phenotype and therefore metastatic disease. Indeed, tumors with abundant α-SMA+ stromal myofibroblasts are associated with shorter disease-free survival rate for stage II and II colorectal cancer (Tsujino et  al. 2007). Although it is increasingly evident that cancer is influenced by signals emanating from the tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. Neuroendocrine prostate tumors that arise in CR2-Tag transgenic mice and evolve through a series of stages closely mimicking those observed in human prostate cancer have been previously described (Garabedian et al. 1998). The mice are born with a normal prostate and develop prostate intraepithelial neoplasia (PIN) by 8 weeks, which progress to invasive carcinoma by 16–20 weeks. The invasive stage is accompanied by a robust, myofibroblast stromal reaction rendering the model attractive for addressing host tissue responses to invasive tumor growth. Bacac et al. (2006) performed cDNA microarray analysis of laser-microdissected stromal cells derived from PIN and invasive cancer. Human orthologs of differentially expressed genes identified in the myofibroblast reaction of invasive tumors were observed to be associated with shorter survival and recurrence-free periods in human breast and prostate cancer. Dvorak (Dvorak 1986) found the striking resemblance between many of the signaling processes involved in tumor progression and those that occur during wound healing. Its in vitro proxy, serum response of cultured fibroblasts is examined in exquisite detail by Chang et al. (2004) and Iyer et al. (1999). A “wound-response” signature was established from 50 fibroblast cultures whose expression changed after exposure to 10% serum

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and after removal of cell cycle-associated genes. Patients whose tumors expressed this “wound-response” signature have a markedly reduced overall survival and distant metastasis-free survival compared to tumors that did not express this signature (Chang et al. 2005). Interestingly, Finak et al. (2008) used laser capture microdissection to compare gene expression profiles of tumor stroma from primary breast tumors and derived prognostic signatures strongly associated with clinical outcome. The ecosystem, and especially myofibroblasts, may have a critical impact on chemotherapy response of the patient (De Wever et  al. 2008). To better understand the relationship between tumor-host interactions and the efficacy of chemotherapy, Farmer et al. (2009) have reported that a reactive stromal gene expression signature present in estrogen receptor-negative breast tumors predicts resistance to preoperative chemotherapy with 5-fluorouracil, epirubicin and cyclophosphamide.

4.4.2  The Bone Marrow The bone marrow can be considered as a representative site of distant metastases or as a distant source and depot of hematopoietic and mesenchymal precursor cells. In the present review we consider the bone not as a site for distant metastasis although it is not known whether the “source and depot” function changes when cancer cells are present. The cancer cell secretome induces changes in the primary tumor ecosystem, but also directs significant changes in the bone marrow ecosystem. An intricate vascular network and a dense mesenchymal-derived stroma cell scaffold exist within the bone marrow. The stromal matrix includes many essential growth factors, cytokines, chemokines and ECM components that regulate hematopoietic and mesenchymal stem cell proliferation and differentiation. More specifically, the bone marrow releases the hematopoietic and mesenchymal progenitor cells that prepare the niche for metastasis. McAllister et al. (2008) found that human breast cancer cells instigate the growth of otherwise-indolent cancer cells. The first, termed here an ‘‘instigator or inducer,’’ is the experimentally transformed human mammary epithelial BPLER cell line, which yields vigorously growing tumor xenografts that histopathologically resemble invasive ductal adenocarcinomas commonly encountered in breast cancer patients. These xenografts contain abundant stroma, indicating that they are capable of recruiting murine stromal cells. The second, termed here a ‘‘responder,’’ is the experimentally transformed human mammary epithelial cell line, HMLER hygro-H-rasV12 (HMLER-HR). Only 25% of the mice injected with these indolent cells form observable tumors when examined 9 weeks after implantation. In an initial experiment, instigating BPLER cancer cells were injected subcutaneously into the right flanks of nude mice, while indolent responding HMLER-HR cells were injected into the contralateral flanks of these mice. BPLER xenograft systemically instigate invasive growth of HMLERHR xenograft by incorporation of bone marrow-derived cells into the stroma of the distant, once indolent tumors. These bone marrow-derived cells are functionally activated prior to their mobilization. Secretion of osteopontin by instigating tumors

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is necessary for bone marrow-derived cell activation and the subsequent outgrowth of the distant otherwise-indolent tumors.

4.4.3  The Circulation Myofibroblasts can be recruited from circulating precursors such as MSCs and CD14+ monocytes. These monocytes are kept in an undifferentiated state by serum factors, but can be stimulated by soluble factors such as interleukin-4 and -13 (Shao et al. 2008) and by direct contact with T-lymphocytes (Abe et al. 2001). These factors drive the monocytes into the CD34+ fibrocyte stage, and subsequent activation of the chemokine receptors (CXCR4 and CCR7) of these fibrocytes leads to rapid extravasation and tumor infiltration (Bellini and Mattoli 2007). Once infiltrated, CD34+ fibrocytes may differentiate into α-SMA+ myofibroblasts which are abundant in breast invasive ductal carcinoma (Barth et al. 2002). Experiments in vitro have shown that differentiation from CD14+ monocytes to CD34+ fibrocytes can also occur spontaneously, but is delayed by a serum protein coined serum amyloid P (SAP) (Pilling et al. 2003). Like C-reactive protein, this molecule is secreted by the liver and belongs to the pentraxin family. The inhibitory effect of SAP on CD14+ monocytes is presumed to result from an interaction with the Fcγ receptors on the monocyte plasma membrane. It shows indeed high affinity for RI (CD64) and RII (CD32), but low affinity for RIII (CD16). The shared binding sites for SAP and IgG cause competition for Fcγ receptor binding and inhibition of immune-complex mediated phagocytosis by SAP (Lu et al. 2008). Yet, phagocytosis of immune complexes as such is known to inhibit differentiation of CD14+ monocytes as well (Pilling et al. 2006). Although serum concentrations of SAP appear to be fairly constant, in systemic sclerosis, a pathological condition characterized by high myofibroblast recruitment, low serum levels were found (Pilling et al. 2003, 2007; Postlethwaite et al. 2004; Tennent et  al. 2007). Interestingly, serum concentrations can be downregulated pharmacologically. Due to its palindromic structure, the drug R-1-[6-[R-2-carboxypyrrolidin-1-yl]-6-oxo-hexanoyl] pyrrolidine-2-carboxylic acid (CPHPC) can sequester two SAP molecules stoichimetrically for elimination from the circulation (Pepys et al. 2002). Further research to investigate whether SAP modulates cancer progression would be interesting.

4.4.4  Lymph Node Metastasis Ample experimental and clinical evidence supports the invasion-promoter role of myofibroblasts in primary cancers, whereas for lymph node metastasis data is scarce. Stromal reactions in metastatic lymph nodes, possibly containing myofibroblasts, have been described as: reactive and fibrotic tissue with enhanced deposition

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of vitronectin and fibronectin (Reilly and Nash 1988); desmoplasia (Tanaka et al. 2002); nodal fibrosis (Newman et al. 2003); hyaline stroma (Thariat et al. 2008). Immunohistochemical characterisation of myofibroblasts was reported in only one of these publications, concerning a metastatic lymph node from a patient with an adenocarcinoma of the uterine cervix who received preoperative chemotherapy (Tanaka et al. 2002). The glandular cancer cells were situated in the marginal and cortical sinus; they were surrounded by concentric fibrous connective tissue with spindle cells that were recognized by an antibody against α-SMA, but not against calponin, desmin or CD34. In primary tumors from colorectal cancer patients, who received neoadjuvant radiochemotherapy or not, we invariably found bundles of α-SMA-positive cells, associated with the cancer cells (De Wever et al. 2008). Such association was found also in metastatic lymph nodes. Comparison of consecutive 3-µm thick sections stained for CD34, a marker of hematopoietic precursor cells, and for α-SMA respectively shows vessel distribution and myofibroblasts tightly adhering to colorectal cancer cell islands in one-to-three concentric layers (Fig. 4.2).

Fig. 4.2   Immunohistochemical staining of lymph nodes from colorectal cancers. Paraffin sections were made from lymph nodes derived from resection specimens of colorectal cancers. Samples from two patients were stained for α-SMA (smooth muscle actin) ( panel a and c) and CD34 (blood vessel marker) ( panel b and d). Arrows show 2–3 layers of myofibroblasts surrounding a smaller cancer cell nest ( panel a) and infiltrating larger cancer cell nests ( panel c). Arrowheads show absence of blood vessels at a consecutive section as indicated by CD34 staining. Scale bar = 100 µm. (Our unpublished results)

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Possible sources of myofibroblasts in metastatic lymph nodes comprise: resident cells present in the reticulin network (Tanaka et  al. 2002); bone marrow-derived cells as observed in the pre-metastatic niche of regional lymph nodes from cancer patients (Kaplan et al. 2005). Fibrosis in metastatic lymph nodes is a factor of worse prognosis in cancer of the oral cavity (Lehn and Rapoport 1994), and in invasive ductal cancer of the breast (Hasebe et al. 1998). In single, presumably early, lymph node metastases from gastric cancer a strong correlation was found between extracapsular invasion and fibrotic foci, both signing a worse prognosis (Okamoto et al. 2008). In our colorectal cancer material, invasion of the lymph node capsule was accompagnied by thick bundles of myofibroblasts. These observations suggest that stromal cells assist cancer cells invading through the nodal capsule, possibly enhancing metastasis.

4.4.5  Distant Metastasis In distant metastasis, like in lymph node metastases from human epithelial cancers analysis of tumor-associated myofibroblasts is scarce. In the scenario put forward by Vidal-Vanacloche (Vidal-Vanaclocha 2008) for the establishment of the premetastatic niche in the liver, tumor-infiltrating stromal cells appear in the last phase, after formation of an avascular metastasis. Endothelial cell migration towards avascular metastasis occurs only at high density of myofibroblasts. The latter cells come from three sources: hepatic stellate cells transiting to myofibroblasts in response to paracrine factors from cancer cells, from tumor-activated hepatic sinusoidal endothelial cells and from Kuppfer cells. Several studies of hepatic metastasis of B16 melanoma have shown that factors secreted by melanoma cells appear to activate hepatic stellate cells to a myofibroblast-like state associated with SMA expression and cytoskeletal changes (Olaso et al. 1997). A study by Schürch et al. (1981) found presence of myofibroblasts at the invasion front of the primary colon cancer site and in the liver metastasis. Lung and brain metastases from ASPL (alveolar softpart sarcoma, characterized by a 17; X, chromosomal translocation resulting in an ASLP-TFE3 fusion gene) are composed of small islands of cancer cells surrounded by fine strands of stroma (Genin et al. 2008). The nuclei of the cancer cells are clearly TFE-positive, whereas the stromal cells are α-SMA-positive. This distinguishes the cancer cells from the myofibroblasts, at least in the lung metastasis, though in the brain metastasis the distinction through these markers is less clear. In another scenario, considering the lung as the site of distant metastasis, enhanced expression of fibronectin, an ECM glycoprotein, and an increase in PDGFR-expressing cells were localized to premetastatic sites early in tumor progression, prior to the arrival of bone marrow-derived hematopoietic progenitor cells (Kaplan et al. 2005). Furthermore, lysyl oxidase (LOX) accumulates at premetastatic niches in target organs where it modifies the ECM by crosslinking collagen fibrils to make it more receptive for CD11b+ myeloid cell infiltration (Erler et al. 2009). Thus, the cancer

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cell-secretome prepares a premetastatic niche and distant myofibroblast recruitment to support the ectopic survival and growth of incoming cancer cells.

4.4.6  Nervous System Myofibroblasts may also be implicated in pain, for many patients the first sign of cancer. Local cancer cell infiltration and growth can cause nociceptor (a primary afferent sensory neuron) stimulation by mechanical injury or compression. Various factors implicated in invasive cancer growth can also sensitize or directly excite nociceptors without compression. These factors include prostaglandins, endothelins, IL-1 and -6, EGF, TGF-beta, and PDGF. Chronic cancer pain can cause significant alterations in the distant central nervous system. This is believed to underlie the phenomenon of central sensitization—an increased responsiveness of spinal-cord neurons that are involved in the transmission of pain. Confocal imaging of glial fibrillary acidic protein (GFAP) expression in the spinal cord showed an increase in the number of astrocytes on the right side of the spinal cord, which received sensory innervation from the tumor-bearing bone. The left side of the spinal cord, which was not transmitting painful stimuli to the brain, had fewer astrocytes (Mantyh et al. 2002).

4.5 Therapeutic Targeting of Ecosystems: Turning the Tide of the Vicious Cancer Progression Cycle? Understanding the role of myofibroblast in local and distant tumor ecosystems may open the door for new strategies which target the management of myofibroblasts and therefore new possibilities for pharmaceutical design. An intriguing question is: can cancer be reversed by engineering the tumor ecosystem? Recent insights make clear that inhibition of metastasis requires targeting of both the metastasizing cell and its supportive ecosystem (Wels et al. 2008b). Recognition that the preparatory changes in the premetastatic environment occur very early in tumorigenesis suggests that anti-metastatic agents must be used together with the primary therapy. Furthermore, therapy that focuses on the dynamic nature of tumors and their supportive cells, including myofibroblasts, may be critical to preventing and reversing tumor progression. We will quote one example that recently attracted a lot of attention. Halofuginone, which at present is being evaluated in clinical trials (de Jonge et al. 2006), inhibits the fibroblast-to-myofibroblast transition in the tumor microenvironment (Sheffer et al. 2007) by inhibiting Smad3 phosphorylation downstream of TGFβ signaling (McGaha et al. 2002; Yee et al. 2006). Halofuginone may synergize with low doses of chemotherapy in achieving a significant antitumoral effect, avoiding the need of a high dose of chemotherapy and its toxicity without impairing treatment efficacy (Sheffer et al. 2007).

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4.6 Conclusions and Perspectives New evidence is emerging on how supporting host cells composing the tumor ecosystem promote invasive growth and metastasis. The underlying mechanisms of how a cancer cell alters its local and distant ecosystem are also receiving more attention. Dissecting these individual pieces will provide valuable insights and direct future investigations. The ecology of a metastatic tumor includes genetically altered cancer cells and their heterotypic interactions with host cells and their extracellular matrix network. The importance of the myofibroblast in the tumor ecosystem is more and more recognized since the development of appropriate methods to experimentally determine its value. Focus on the dynamic exchange of cells from local and distant environments, to the invasive front of the tumor, to the premetastatic and metastatic niches can provide novel strategies for successfully targeting these processes. Acknowledgements  This work was supported by Fund for Scientific Research-Flanders (Brussels, Belgium), O. De Wever is a post-doctoral researcher and A. De Boeck is a doctoral researcher supported by Fund for Scientific Research-Flanders.

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Chapter 5

Tumor Vessel Associated-Pericytes Arne Bartol, Anna M. Laib and Hellmut G. Augustin

5.1 Introduction Tumor growth and metastasis are critically dependent on the growth of blood vessels (angiogenesis). The identification of the master inductor of the angiogenic cascade VEGF (vascular endothelial growth factor) in 1989 has within 15 years led to the development and clinical implementation of VEGF neutralizing and VEGF receptor activation blocking drugs. These drugs establish a novel paradigm in antitumor drug development since they are the first clinically effective and approved anti-stroma tumor drugs. Thus, despite the limited efficacy of the first generation anti-angiogenic therapies, VEGF/VEGFR blocking compounds have within few years emerged among the highest revenues yielding anti-cancer drugs. VEGF receptors are almost exclusively expressed by endothelial cells (EC) which are consequently the primary target of VEGF. It is for this reason that the field of angiogenesis research has developed in the nineteen eighties and nineteen nineties as an endothelial cell-centric discipline. Clearly, the endothelial cell is at the heart of the angiogenic cascade. Yet, this reductionist, EC-centered view largely ignores that new capillary networks form in close interaction with surrounding cells and that the recruitment of peri-endothelial mural cells (pericytes, smooth muscle cells) constitutes a critical step during vascular morphogenesis. This has changed in the last decade when the study of vascular maturation processes received more attention and mechanistic experiments on molecules of the TGFß family, PDGF molecules and the angiopoietins yielded insights into the crosstalk between EC and peri-endothelial mural cells. Today, the mechanistic and molecular study of pericyte structure and function is a rapidly growing area of ongoing biomedical research and it is increasingly recognized that pericyte-mediate vessel maturation A. Bartol () Vascular Oncology and Metastasis, German Cancer Research Center (DKFZ-ZMBH Alliance), 69120 Heidelberg, Germany e-mail: [email protected] Medical Faculty Mannheim, Vascular Biology and Tumor Angiogenesis, Heidelberg University, 68167 Mannheim, Germany M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_5, © Springer Science+Business Media B.V. 2011

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Fig. 5.1   Overview of pericyte physiology. The capillary lumen is lined by a single layer of endothelial cells. Separated by the basement membrane, capillary EC are covered by pericytes that stretch out with long dendrite-like processes to physically contact EC. The image summarizes the most established markers of pericytes, some of the key regulators of pericyte function, the established physiological functions of pericytes and the roles of pericytes in pathology. MSC mesenchymal stem cells, AMD age-related macular degeneration

is a critical determinant of the therapeutic window of anti-angiogenic therapies (Jain 2005). Historically, pericytes have first been described by Charles Rouget in 1873 and the term pericyte was introduced by Zimmerman in 1923 (Rouget 1873; Zimmermann 1923). Pericytes are the mural cells of capillaries and postcapillary venules. They share the basement membrane with EC and they contribute to basement membrane synthesis. Pericytes display dendrite-like cell extensions which envelop capillaries and enable them to establish cell-cell contacts with multiple EC. The anatomical vicinity between pericytes and EC enables pericytes to sense signals in the environment and to integrate them to orchestrate EC signaling (Gerhardt and Betsholtz 2003). Consequently, pericytes control endothelial functions, including proliferation, differentiation and permeability and they are key mediators of microvascular homeostasis. The most established markers of pericytes, some of the key regulators of pericyte function, the established physiological functions of pericytes and the roles of pericytes in pathology are summarized in Fig. 5.1.

5.2 Heterogeneity of Pericytes 5.2.1  Lineage and Origin of Pericytes During embryogenesis, vessel development occurs by the de novo formation of blood vessels from mesodermal precursor cells that are specified to form angio-

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Fig. 5.2   a Wild type mouse retina vessel (P6) stained with BS-I lectin is covered by pericytes. Staining for α-SMA ( red) and NG2 ( green) identifies pericytes that are positive for both markers ( white arrows). Scale bar 10  µm. b Electron microscopic analysis of a representative tracheal blood vessel that is covered by pericytes. One pericyte is attached to the endothelium of the blood vessel. Scale bar, 12,7  µm. P pericyte, EC endothelial cell, E erythrocyte, N nucleus. (Images kindly provided by Dr. Junhao Hu, Heidelberg)

blasts and further differentiate into EC (vasculogenesis). Pericytes arise during vasculogenic formation of a primordial vascular network from mesodermal progenitor cells. Vessels of the CNS and the cardiac tract vessels recruit pericyte precursors from the neural crest (Creazzo et al. 1998; Ema and Rossant 2003). Both, EC and pericytes may even originate from the same precursor cell during vasculogenesis. Mesodermal VEGFR-2-positive angioblasts appear to give rise to EC following VEGF stimulation or to pericytes following PDGF-B (platelet-derived growth factor-ß) stimulation, respectively (Carmeliet and Luttun 2001; Yamashita et al. 2000). The origin of pericytes during adult angiogenesis remains unclear, but it is believed that pericytes differentiate from tissue- or bone-marrow stem cell derivates. The contribution of bone-marrow derived pluripotent stem cells during physiological or pathological angiogenesis has been discussed controversially. On one hand, bonemarrow derived pluripotent stem cells are recruited to the periendothelial space near blood vessels (Rajantie et al. 2004). In turn, endothelial-to-mesenchymal transition (EndMT) has been reported to give rise to the differentiation of perivascular cells (Armstrong and Bischoff 2004; Zeisberg et al. 2008). During EndMT, EC lose cellcell junctions and endothelial markers (e.g. CD31) and transdifferentiate to acquire a highly motile and invasive phenotype. Simultaneously, these cells express mesenchymal markers such as fibroblast-specific protein 1 (FSP1) and alpha-smoothmuscle actin (α-SMA) (Zeisberg et al. 2007). Pericytes share common characteristics with mesenchymal stem cells. Remarkably, pericytes are able to differentiate into osteocytes, chondrocytes and adipocytes (Crisan et al. 2008), thereby supporting the hypothesis that the vasculature and more

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precisely pericytes themselves constitute a perivascular stem cell niche providing a source for mesenchymal stem cells (da Silva Meirelles et al. 2008).

5.2.2  Molecular Diversity of Pericytes The molecular repertoire of pericytes is incompletely characterized. Yet, a number of reliable markers of pericytes have been established in recent years (Fig. 5.2): (1) Alpha-smooth-muscle actin (α-SMA) and desmin are contractile microfilaments and intermediate filaments, respectively; (2) Regulator of G-protein signaling 5 (Rgs5) belongs to the RGS family and inhibits signaling from G-protein-coupled receptors by increasing intrinsic GTPase activity of activated Gα proteins (Hollinger and Hepler 2002). Tumors grown in Rgs5-deficient mice, for example, show a significant reduction in tumor hypoxia and vessel leakiness due to an increase of mature pericytes in the tumor vasculature (Hamzah et  al. 2008). However, Rgs5 also marks multiple other cell types in tumors and the reliable detection of Rgs5 is limited due to the lack of specific antibodies; (3) Platelet-derived growth factor receptor beta (PDGFRß), a tyrosine-kinase receptor, is one of the best characterized markers expressed by pericytes. PDGFRß signaling plays a key role in the recruitment of pericytes to newly formed blood vessels (Fig. 5.3); (4) Neuron-glial 2 (NG2) is a heparan sulfate transmembrane proteoglycan which binds growth factors (e.g. FGF2) and potentiates growth factor signaling (Goretzki et al. 1999). NG2 can interact with the integrin signaling pathway and contributes to critical processes such as cell survival, motility and proliferation (Fig. 5.4) (Stallcup and Huang 2008); (5) the transmembrane cell surface receptor Endosialin is expressed by pericytes during embryogenesis and tumorigenesis but is absent in pericytes of mature vasculature (Bagley et al. 2008; Christian et al. 2008); (6) the pericyte cell surface expressed enzymes aminopeptidase A and N (Kunz et al. 1994) are involved in the metabolic pathway of angiotensin II and III and control arterial blood pressure (Fournie-Zaluski et al. 2004). There is some degree of pericyte marker selectivity, but most pericyte markers are also expressed by other cell types including fibroblasts, astrocytes and tumor cells. The expression of α-SMA is highly dynamic and differs between different vascular beds, developmental stages or species (Gerhardt and Betsholtz 2003). α-SMA is not only expressed by skin pericytes but is also strongly upregulated during tumor angiogenesis (Abramsson et al. 2002; Morikawa et al. 2002). Comparing different species, α-SMA is, for example, detectable in chicken brain pericytes but absent in mouse brain pericytes (Gerhardt et al. 2000; Hellstrom et al. 1999). The heterogeneity of pericyte marker and phenotyptic behavior is also reflected in the observation that the ratio of pericytes to EC varies highly between species, different tissues or different vessel types. The ratio of pericytes to EC is increased in the retina compared to skeletal muscle (Shepro and Morel 1993) and it varies between quiescent vessels and tumor associated vessels (Wakui et  al. 1997).

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Fig. 5.3   Schematic overview of several key factors regulating a) the recruitment of pericytes and b) migration of pericytes along newly formed blood vessels. a) Heparin binding EGF-like growth factor (HB-EGF) and hepatocyte growth factor (HGF) expressed by endothelial cells (EC) are potential inducers for the recruitment of pericytes. Sphingosine-1-phosphate (S1P) released from erythrocytes signals through SP1R1 on endothelial cells thereby strengthening EC-pericyte cell–cell contacts. b) During angiogenesis, formation of new blood vessels is mainly driven by VEGF stimulation. The angiogenic sprout guided by a tip cell, which is not covered by pericytes, forms filopodias and abundantly expresses VEGFR2 which sensitizes EC to VEGF secreted by the surrounding tissue. Tip cell release PDGF-B which in turn binds to HSPG within the extracellular matrix (ECM). A spatial and temporal PDGF-B gradient along EC following the tip cell, the so called stalk cells, promotes pericytes proliferation and migration towards the leading edge. PDGFB signaling is mediated by PDGFRß expressed by pericytes. BM basement membrane

5.3 Signaling Pathways in Pericytes 5.3.1  TGF-ß—The Role of a Multifunctional Cytokine Transforming growth factor ß (TGF-ß) is a multifunctional cytokine that is expressed by various cell types including EC and pericytes. Genetic deletion of key components of the TGF-ß signaling pathway in mice results in cardiovascular defects and embryonic lethality (Li et  al. 1999; Oshima et  al. 1996). Co-culture experiments

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Fig. 5.4   Signaling pathways involved in EC and pericyte crosstalk. Ang-1/2, PDGF-B, S1P and TGF-ß signaling are illustrated. In addition, NG2 can bind to α3β1 integrin complexes on the EC surface resulting in enhanced β1 integrin signaling (Fukushi et  al. 2004). Black arrows imply links to major signal transduction pathways and cellular functions. Negative feedback signaling is indicated in red

with EC and pericytes revealed that pericytes or EC alone secrete TGF-ß as a latent inactive form. Latent TGF-ß is activated by plasminogen activator following the establishment of EC-pericyte cell–cell contacts (Wakui et  al. 1997). TGF-ß activates two distinct signaling pathways in EC through the ALK1/Smad1/5 or ALK5/ Smad2/3 pathways, which induce EC proliferation or differentiation, respectively (Fig. 5.4) (Goumans et al. 2002). Endoglin, a co-receptor for TGF-ß, is expressed by proliferating EC and promotes ALK1 signaling driving the TGF-ß response towards proliferation (Lebrin et al. 2004). Depending on the concentrations of TGF-ß and the level of ALK1 expression, TGF-ß acts either pro- or anti-angiogenic (Goumans et al. 2002). TGF-ß is a multifunctional cytokine and thereby induces the differentiation of pericytes from precursor mesenchymal cells (Chambers et al. 2003; Ding et al. 2004). Pericytes that are stimulated with TGF-ß express α-SMA whereas pericytes in the absence of TGF-ß express NG2 and desmin (Song et al. 2005).

5.3.2  P  DGFB-PDGFRß—The Recruitment and Expansion Pathway The PDGF family consists of four ligands (PDGF-A, PDGF-B, PDGF-C and PDGF-D) and two members of receptor tyrosine kinases (RTK), PDGFRα and

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PDGFRß. Studies in mice lacking PDGF-B or PDGFR-ß demonstrated that loss of either ligand or receptor causes embryonic death due to vascular defects (Leveen et al. 1994; Soriano 1994). PDGF-B/PDGFRß signaling elicits the recruitment of pericytes to the leading edge of newly formed blood vessels. During angiogenesis, EC of the tip cell region of sprouting vessels express PDGF-B. Expression of PDGF-B is induced by several factors including hypoxia, thrombin and different growth factors (Heldin and Westermark 1999). One key property of PDGF-B is a retention motif, composed of a stretch of positively charged amino acid residues at the C-terminus (Ostman et al. 1991). This motif allows binding of PDGF-B to heparan sulphate proteoglycans (HSPG) in the extracellular matrix which in turn establishes a gradient of PDGF-B presentation around angiogenic sprouts (Fig. 5.3). PDGF-B is temporally and spatially differentially expressed. Abundant PDGF-B expression is found in the tip cell of sprouting vessels in response to VEGF-A, whereas expression decreases in the adjacent EC, the so called stalk cells. This temporal and spatial PDGF-B gradient induces the coverage of stalk cells with newly recruited pericytes. Pericytes express PDGFRß and sense PDGF-B which results in the recruitment to newly formed blood vessels in a paracrine manner. PDGFRß expression is induced by factors including FGF2, TGF-ß or TNFα (Heldin and Westermark 1999). Binding of PDGF-B to PDGFRß triggers downstream signaling pathways including the Ras-MAPK pathway, the PI3K pathway, the FAK and PLCγ signaling cascades. Activation of these downstream targets increases actin reorganization, cell motility, proliferation and inhibit apoptosis in pericytes (Fig. 5.4) (Hu et al. 1995; Kundra et al. 1994; Seger and Krebs 1995). Interestingly, a positive feedback loop between VEGF-C/VEGFR3 and PDGF-B/PDGFRß regulates lymphangiogenesis and lymphatic maturation (Onimaru et al. 2009). In contrast, the forkhead transcription factor gene Foxc2 is thought to maintain the pericyte-free lymphatic capillary network by suppressing PDGF-B expression (Petrova et al. 2004). Additionally, VEGF acts as an inhibitor of blood vessel maturation by negatively regulating the pericyte coverage of nascent blood vessels. This is supposed to be mediated in pericytes by dimerization of VEGFR-2 and PDGFRß in response to VEGF stimulation resulting in suppression of PDGFRß signaling (Greenberg et al. 2008).

5.3.3  Angiopoietin-Tie System—Maintenance of Pericytes The angiopoietin-Tie system is an endothelial cell specific signaling pathway. There are four angiopoietin ligands: Angiopoietin-1 (Ang-1), Angiopoietin-2 (Ang-2), Angiopoietin-3 (Ang-3) and Angiopoietin-4 (Ang-4). The best characterized ligands are Ang-1 and Ang-2. Both ligands bind Tie2, a receptor tyrosine kinase which is expressed by EC. Ang-1 is expressed and secreted by perivascular cells and activates Tie2 in a paracrine manner (Davis et al. 1996; Sato et al. 1995). The activation of Tie2 results in vessel assembly and maturation by promoting survival signals in EC and regulating the recruitment of pericytes. In contrast, Ang-2 is expressed by EC themselves and acts context dependently as an autocrine antagonist of Ang-1/ Tie2 signaling (Fig. 5.4), thereby mediating vessel destabilization. In the presence of

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growth factors (e.g. VEGF), Ang-2 promotes angiogenesis, whereas in the absence of growth factors, Ang-2 leads to the regression of newly formed blood vessels. Analyses of Ang-1- or Tie2-deficient mice revealed an abnormal vascular structure and a reduction of pericyte coverage (Dumont et al. 1994; Sato et al. 1995; Suri et al. 1996). Therefore, increasing evidence suggests that the angiopoietin-Tie system is not only involved in the control of endothelial cell homeostasis but also in the crosstalk between EC and pericytes. Elevated Ang-2 levels resulted in a diabetic rat model in the loss of pericytes from microvessels. Similar effects have been observed following the injection of recombinant Ang-2 directly into the retina (Hammes et al. 2004). The molecular mechanisms explaining these findings are not fully understood. Experiments in a pulmonary hypertension animal models suggested a link between Ang-1 and Serotonin in the context of smooth muscle cell proliferation around small vessels (Sullivan et al. 2003). The two best characterized candidates in angiopoietin-dependent pericyte recruitment are heparin binding EGF-like growth factor (HB-EGF) and hepatocyte growth factors (HGF). Both factors are upregulated in EC after Ang-1 stimulation and induce migration of smooth-muscle cells (SMC) (Iivanainen et al. 2003; Kobayashi et al. 2006). However, HB-EGF-deficient mice only display cardiovascular defects characterized by enlarged hearts and hypertrophic cardiomyocytes (Iwamoto et al. 2003; Jackson et al. 2003). Furthermore, antibodies against PDGFRß inhibit the recruitment of pericytes to newly formed vessels in the mouse retina (Uemura et al. 2002). The absence of pericytes resulted in retinal edema and hemorrage which is rescued by the injection of recombinant Ang-1, demonstrating that Ang-1 is required to prevent retinal defects (Uemura et al. 2002). Pericytes are an important source of Ang-1 (Sundberg et al. 2002; Suri et al. 1996) and loss of pericytes consequently acts negatively on Ang-1 signaling. Simultaneously, the loss of pericytes and reduced Ang-1 signaling may be responsible for the antagonistic function of Ang-2 on vessel maturation.

5.3.4  S  1P, Notch, Ephrin—Increasing the Complexity   of Endothelial Cell-Pericyte Crosstalk Sphingosine-1-phosphate (S1P) is a lipid mediator processed from membrane phospholipids. S1P binds to several G-protein coupled receptors (S1P1to5) and plays a role in cell migration, proliferation and survival (Allende and Proia 2002). Although S1P1 is a widely expressed receptor in vascular and non-vascular cells, EC specific deletion of s1p1 recapitulates vascular defects including abnormal pericyte coverage of vessels which has been observed in s1p1-deficient mice (Allende et al. 2004; Liu et  al. 2000). The observed s1p1-deficient phenotype may explain why S1P1 signaling acts upstream of Rac leading to relocation of N-cadherin and thereby enhancing cell-cell contacts between EC and pericytes (Fig. 5.4) (Paik et al. 2004). Notch signaling affects several vascular processes, including arterio-venous differentiation and regulation of vessel branching (Lawson et  al. 2001; Sainson

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et al. 2005). CADASIL (OMIM no. 125310) is a stroke-like syndrome caused by mutations in the human Notch3 gene resulting in aberrant vascular smooth muscle cell maturation (Domenga et al. 2004; Kalimo et al. 2002). Notch signaling is linked to PDGF signaling because PDGFRß is a Notch target gene. Activation of Notch leads to an increase in PDGFRß expression, Dysregulation of Notch perturbs differentiation and migration of smooth-muscle cells (Jin et al. 2008). The family of erythropoietin-producing hepatoma receptor tyrosine kinases (EphB) and their cell-surface anchored ephrin ligands act in the regulation of arterio-venous blood vessel identity (Adams 2003). Ephrin B2 and EphB are expressed by EC and pericytes. The ephrin signaling cascade is activated upon cell-cell contacts of pericytes with EC exerting forward and reverse signaling. Therefore ephrinB2 plays a pivotal role of endothelial-pericyte assembly during postnatal vascular remodelling (Salvucci et al. 2009).

5.4 Physiological Functions of Pericytes 5.4.1  Microvessel Permeability In general, the presence of attached pericytes to the endothelium is a hallmark of a quiescent microvasculature which prevents vascular leakage and reduces the permeability of EC. Pericytes are in close contact with EC forming the vessel wall and mediating vascular permeability by reducing the abluminal surface area of endothelial cells (Murphy and Wagner 1994). Permeability is dependent on intra- and intercellular processes, in which pericyte contractility alters endothelial cell-cell junctions and modifies the capacity of intercellular transport (Buchanan and Wagner 1990; Cuevas et  al. 1984). Pericytes in the brain are part of the blood-brain barrier (BBB), protecting the brain against toxic compounds of the circulation. Brain pericytes express the enzyme aminopeptidase N which is responsible for the degradation of various neuropeptides. Neuropeptides with vasoactive properties are involved in altering vascular permeability (Kunz et al. 1995). Moreover, pericytes are capable of increasing permeability by production and release of VEGF (Kim et al. 1998; Lonigro et al. 1996).

5.4.2  Regulation of Angiogenesis The angiogenic cascade is tightly controlled by distinct signaling pathways and the interaction between EC and pericytes. One hallmark of vessel maturation is the attachment of pericytes around newly formed blood vessels (Jain 2003; Paik et al. 2004). Induction of angiogenesis by VEGF switch the quiescent endothelium to an

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activated state accompanied by distinct changes of pericyte morphology and behavior, e.g. shortening of membrane extensions, increase of cytoplasmic volume and induction of pericyte proliferation (Diaz-Flores et al. 1992). Critical steps during angiogenesis are regulated by both EC and pericytes including (1) enzymatic degradation and remodeling of the basement membrane (Davis and Senger 2005); (2) regulation of EC migration, proliferation and branching (Carmeliet 2003; Orlidge and D’Amore 1987; Sato and Rifkin 1989); (3) secretion of VEGF and Ang-1 by pericytes promotes EC survival (Benjamin et al. 1998; Reinmuth et al. 2001); (4) pericyte invasion into surrounding tissues (Ozerdem and Stallcup 2003).

5.4.3  P  ericytes During Immunological and Regenerative Processes Pericytes play a major role in maintaining the blood-brain barrier (BBB). Brain pericytes are able to adopt macrophage-like functions (including the expression of macrophage specific markers) such as the uptake of small molecules by pinocytosis. This observation suggests that brain pericytes share physiological properties with macrophages and provide an immunological defence mechanism (Thomas 1999). As previously discussed, pericytes can give rise to different cell types including chondrocytes and adipocytes suggesting that pericytes are a source of mesenchymal stem cells (Crisan et al. 2008). Human dermal skin pericytes have the capacity to promote epithelial cell proliferation by modifying the ECM microenvironment (Paquet-Fifield et al. 2009). These findings implicate that pericytes play an important role in tissue regeneration.

5.5 Pericytes in the Tumor Vasculature The tumor vascular network is formed by angiogenesis. New vessels grow from pre-existing ones by sprouting, bridging and intussusception of EC followed by incomplete maturation (Hanahan and Folkman 1996). The tumor vasculature integrates heterogeneous precursor cells (Furuya and Yonemitsu 2008). Several progenitor subpopulations have been identified in tumor neovessels. Bone marrow-derived endothelial progenitor cells (EPCs), tissue-derived EPCs (Bruno et al. 2006; Zengin et al. 2006) and hematopoietic stem cells (HSCs) (Hattori et al. 2002; Lyden et al. 2001) may transdifferentiate into tumor-associated endothelial cells (TEC). In addition, mesenchymal stem cells (MSCs) and immature myelomonocytic cells expressing endothelial markers potentially have TEC commitment. It has been shown that bone marrow-derived circulating cells (RBCCs) are recruited to sites of ongoing neovascularization (Grunewald et al. 2006). Furthermore, Tie2-expressing mesenchymal progenitors are thought to be able to differentiate into tumor pericytes (De Palma et al. 2005).

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5.5.1  The Abnormalities of the Tumor Vasculature The tumor vasculature differs from normal blood vessels in several biological and structural characteristics. Whereas a physiologically growing neovasculature matures rapidly and coordinately to establish blood flow, the tumor vasculature is highly abnormal, irregularly shaped, tortuous, and largely lacks the normal hierarchical structure of arterioles, capillaries and venules (Baluk et al. 2005). Intratumoral pericytes associated with tumor vessels are loosely attached to the EC layer resulting in gaps between the two cells types. The basement membrane may be incomplete. Pericytes have a sporadic distribution in tumors compared to corresponding normal tissues (Gee et al. 2003; Morikawa et al. 2002). There is hitherto no unique marker available to identify pericytes in pathological conditions such as cancer. The degree of pericyte coverage in vessels of different tumor types ranges from high (Schlingemann et al. 1990, 1991; Wesseling et al. 1995) to little or no coverage (Benjamin et al. 1999; Eberhard et al. 2000; Johnson and Bruce 1997). To some extent, this may be explained by the differential expression of pericyte markers in different tumors. Morikawa et  al. (2002) were the first to show that pericytes in tumors are ultrastructurally different from pericytes under normal, physiological conditions. They uniformly express α-SMA and desmin in capillary sized tumor vessels in contrast to pericytes in capillaries of normal tissues. These authors reported distinctly different morphologies of pericytes in three different tumor models, suggesting a high heterogeneity of pericyte morphology and coverage in different tumor types and different tumor stages (Morikawa et al. 2002). Stromal cells that are positive for αSMA and desmin but not associated with blood vessels are abundant in many tumor types. These cells are usually defined as myofibroblasts and it has been shown that co-culture of fibroblasts with tumor cells induces the transdifferentiation from fibroblasts into myofibroblasts (Powell et al. 1999). The stromal (mesenchymal) origin of pericytes is well documented (Nehls and Drenckhahn 1993) and myofibroblasts are thought to be able to differentiate into pericyte-like cells that have been proposed to guide endothelial sprouts within tumors (Brown et al. 2001; Morikawa et al. 2002). For decades, only the expression of α-SMA or desmin have been tested and absent immunoreactivity was interpreted to reflect the absence of pericytes (Benjamin et al. 1999; Eberhard et al. 2000; Lach et al. 1999; Rangdaeng and Truong 1991).

5.5.2  Pericyte Recruitment to the Tumor Vasculature Under physiological conditions (e.g. embryonic development), pericytes are recruited around vascular EC and subsequently modulated by four different pathways: (1) PDGFB/PDGFRß, (2) S1P/endothelial differentiation gene-1 (EDG-1), (3) Ang-1/ Tie2 and (4) TGF-ß1/activin-like kinase receptor (ALK5) (Jain 2003). Several lines of evidence suggest that aberrant pericyte behavior may be due to imbalanced endothelial-cell/pericyte signaling circuits and/or a limited pool of recruitable pericytes (Abramsson et al. 2002) as well as a lack of proper maturation (Folkman 1985; Gee

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et al. 2003). Pericyte abnormalities in tumors have been shown to be associated with alterations in PDGF signaling (Abramsson et al. 2002, 2003; Furuhashi et al. 2004). PDGF and PDGF receptor gain- or loss-of-function studies have demonstrated a important roles for PDGF-B/PDGFRß signaling in pericyte recruitment to tumor vessels (Abramsson et al. 2003; Betsholtz 2004). Pericyte recruitment is dependent on PDGF-B expression by tumor endothelial cells (TEC) and PDGF-B acts in a paracrine manner on tumor pericytes expressing PDGFRß (Abramsson et al. 2002; Bergers et al. 2003). Ang-1 and Ang-2 regulate vascular maturation and integrity during development. The elevated expression of Ang-2 in tumors has long been recognized. Yet, whereas VEGF is highly expressed by tumor cells in most tumors, Ang-2 is predominately expressed by tumor endothelial cells (Stratmann et al. 1998; Zhang et al. 2003). Tumors grown in Ang-2-deficient mice show an altered pattern of mural cell recruitment and different maturation of tumor blood vessels (Nasarre et al. 2009).

5.5.3  Therapy Tumor blood vessels are the target of anti-angiogenic tumor therapy. The use of different angiogenesis inhibitors (Jain 2005; Kashiwagi et al. 2008; Sennino et al. 2007) has demonstrated that anti-angiogenic therapy not only induces vessel regression or retardation of vessel growth. Instead it also induces normalization of tumor blood vessels—a concept that reflect the transition of an irregular formed tumor vasculature to a more physiological vasculature to improve the availability of drugs and oxygen to disease areas, and thus improve e.g. combination therapy (Baluk et al. 2005; Inai et al. 2004; Jain 2005). Pericyte coverage of intratumoral blood vessels reduces VEGF-A dependency and pericyte recruitment to tumor vessels is mediated by PDGFB/PDGFRß signaling. Consequently, the combined inhibition of VEGF signaling in the endothelium and PDGFRβ signaling in pericytes is a promising combinatorial approach for antiangiogenic tumor therapy, targeting both EC and associated pericytes (Bergers et al. 2003; Saharinen and Alitalo 2003). Indeed, Bergers et al. have provided evidence for the improved efficacy of dual VEGFR plus PDGFR RTK inhibition compared to single treatment. These authors demonstrated that this combination treatment is effective in early- and late-stage tumors (Bergers et al. 2003; Saharinen and Alitalo 2003). The therapeutic effects are due to several cooperative mechanisms affecting the tumor cells directely as well as the EC compartment and the EC–pericyte interaction by paracrine effects. The signaling between different cell compartments in tumors plays a critical role in anti-cancer therapy using radiation. Radiation therapy induces the expression of VEGF in tumor cells, VEGFR-2 in the tumor EC, PDGF in EC and PDGFR in fibroblasts (Abdollahi et al. 2003; Li et al. 2006). Radiotherapy combined with VEGF and PDGF inhibition exerted the most prominent anti-angiogenic and anti-tumorigenic effect in prostate carcinoma and glioblastoma models (Timke et al. 2008).

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The angiopoietin ligands (Ang-1 and Ang-2) and their receptor Tie2 regulate vascular maturation and integrity during vascular development suggesting that their blockade might affect tumor growth and vessel normalization. However, due to the limited availability of selective angiopoietin inhibitors, their effects have not been studied extensively. Selectively blocking of Ang-2 by antibodies and peptibodies (peptide-Fc fusion proteins) had potent anti-angiogenic and anti-tumor effects in several tumor xenograft models (Oliner et al. 2004; Sarraf-Yazdi et al. 2008). Inhibition of Ang-1 expression by antisense RNA in a stably transfected tumor cell line similarly resulted in reduced tumor growth and angiogenesis (Shim et  al. 2001). Selective inhibitors (peptibodies) against Ang-1 and Ang-2 led to more potent antiangiogenic effects than the use of each inhibitor alone. Tumor vessel normalization as seen after Ang-2 inhibition was not observed (Falcon et al. 2009). Drugs targeting epidermal growth factor receptor (EGFR), such as the small molecular weight tyrosine kinase inhibitors gefitinib and erlotinib, as well as the anti-EGFR antibodies cetuximab and panitumumab have established anti-tumor effects and have been approved for clinical use (Lynch et al. 2004; Paez et al. 2004). In addition to the direct effect of EGFR blockade on tumor cells, it has been hypothesized that EGFR inhibition also acts anti-angiogenic. Gefitinib or cetuximab have been reported to inhibit neovascularization in several tumor xenograft models; however, it is not known whether this anti-angiogenic effect (1) is due to indirect suppression of growth factor production by tumor cells, (2) resulting from effects on non-endothelial cells within the tumor (SMCs/pericytes), or (3) has direct effects on TEC (Ciardiello et al. 2001; Morelli et al. 2006; Perrotte et al. 1999; Petit et al. 1997). In addition, relatively little is known about the consequences of EGFR targeting on tumor angiogenesis or vasculogenesis. The effect of gefitinib on tumor neovascularization has only recently been studied using a syngeneic tumor model in mice (Iivanainen et al. 2009). This study provides evidence that gefitinib acts on tumor angiogenesis via two different mechanisms. First, CD31-positive vessels were less covered by NG2-positive peri-endothelial cells and second, less BM-derived progenitor cells were recruited to the perivascular space of tumor vessels. These results suggest that the primary vascular target of gefitinib in vivo are SMCs/pericytes rather than EC. It further suggests that gefitinib treatment results in a relative reduction in the number of mature and fully functional tumor vessels (Iivanainen et al. 2009). Targeting EGFR in tumor therapy might be promising due to its selectivity for tumor angiogenesis.

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Chapter 6

The Role of Cancer-Associated Adipocytes (CAA) in the Dynamic Interaction Between the Tumor and the Host Marie-Christine Rio

6.1 Introduction It is becoming more and more evident that the tumor environment actively participates in cancer progression (Werb et al. 1996; Mueller and Fusenig 2002; DeClerck et al. 2004; Jodele et al. 2006). Adipocytes are present in multiple tissues. Some tissues have adipocyte-rich environments such as the mammary gland and bone marrow. Recent evidence suggest that adipocytes play a role in the progression of tumors. It has been shown that obesity leads to increased cancer risk and poorer patient outcome in several types of cancers (Hausman et al. 2001; Vainio et al. 2002; Calle et al. 2003; Calle and Thun 2004; Wright et al. 2007; Fair and Montgomery 2009). The reported link between adipocytes and cancer is multiple, including systemic and/or local effects. However, the study of the local function of adipocytes in tumors has just began to unfold. The nature of interplay between adipocytes and cancer cells remains at present largely unknown. Very little clear data implicate paracrine function between adipocytes and cancer cells. The present review will examine recent literature addressing the bidirectional impact of adipocytes and cancer cells, and the molecular basis that underlies the pejorative effect of adipocytes on the behavior of cancer cells. Indeed, the key determinant for cancer-associated adipocyte (CAA) effects on tumorigenesis may involve the cancer-induced molecular expression and secretory profile changes of the adipocytes in the local tumor microenvironment. The systemic effects of CAAs will also be discussed.

M.-C. Rio () Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS UMR 7104, INSERM U964, UDS, BP, 67404 Illkirch Cedex, 10142 C.U. de Strasbourg, France e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_6, © Springer Science+Business Media B.V. 2011

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6.2 Evidence That CAAs Favor Tumor Progression 6.2.1  In Vitro Experiments There is some in vitro evidence that environmental cues from CAAs affect tumor cell survival, proliferation, differentiation and migration/metastasis. For example, fat cells clearly increase the invasive function of squamous cell carcinoma of the larynx (Yamada et al. 1999). More recently, the relative function of pre-adipocytes and mature adipocytes on various cancer cell lines have been studied by several laboratories, but results remain controversial concerning their efficiency. Mature adipocytes, but not pre-adipocytes, promote the growth of breast cancer cells in collagen gel matrix culture through cancer-stromal cell interactions (Manabe et al. 2003). However, both pre-adipocytes and mature adipocytes have been shown to promote the proliferation of colon cancer cells. Interestingly, the proliferative effects of mature adipocytes do not occur for colon cancer cells from leptin-deficient ob/ob mice, indicating that leptin is, at least partially, responsible for this adipocyte function (Amemori et al. 2007). Moreover, pre-adipocytes show similar effects in wild type mice and ob/ob mice, showing that other factors are also involved (Amemori et al. 2007). Similarly, it was shown that adipocytes favor the proliferation of PC3 prostate cancer cells (Tokuda et al. 2003). Finally, adipocyte-derived secreted factors have been shown to promote breast cancer cell proliferation and invasion (Iyengar et al. 2003). Another study profiling gene expression of breast cancer cells treated with adipocyte culture medium showed induction of immune system- and wound healing-related genes (Kim et al. 2008).

6.2.2  Mouse Models Several years ago, it was shown that an adipocyte-rich environment facilitates SPI murine mammary carcinoma cell growth after subcutaneous injection in mice. In this case, the adipose tissue exerts an estrogen-dependent positive regulatory effect on SPI (Elliott et al. 1992). The impact of the mammary fat pad in tumor development has also been studied (reviewed by (Edwards 2000)). Adipose stromal cells stimulate the migration and invasion of estrogen receptor (ER)-negative breast cancer cells in vitro and tumor invasion in a co-transplant xenograft mouse model. This function is dependent on adipocyte-secreted IL6 (Walter et al. 2009). However, it should be noted that both adipose stromal cells and the mammary fat pad do not include only pre-adipocytes and adipocytes, but also some other cells, most notably endothelial cells, that may interfere. An in vivo coinjection system using 3T3L1 adipocytes and SUM159PT cancer cells recapitulating host-tumor interactions in primary breast tumors have demonstrated that adipocytes favor tumorigenesis (Iyengar et al. 2003).

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Fig. 6.1   CAAs at the invasive front of breast carcinomas. Hematoxylin-eosin histological examination of the invasive front of a human breast primary tumor show CAAs (lipids in white color) exhibiting reduced size at the interface of invading cancer cells. Immunostaining shows that CAAs express MMP11 ( black staining). Magnification 100×

6.2.3  Clinical Data It is obvious that adipocyte-cancer cell heterotypic cross-talk/interaction occurs in invasive carcinomas when both cell types are physically close. Histological data of human carcinomas show that dynamic desmoplastic events continuously occur at the tumor invasive front located at the periphery of primary tumors (Fig. 6.1). Numerous recent clinical studies have evaluated the prognosis value of local adipose tissue invasion by cancer cells at the tumor margin. The majority of data report a positive correlation with poor patient outcome. This is the case for breast (Kimijima et al. 2000; Yamaguchi et al. 2008), prostate (Sung et al. 2006), pancreas (Bandyopadhyay et al. 2009), kidney (Thomas et al. 2003; Cho et al. 2008; Thompson et al. 2007; Bedke et al. 2009) and colon (Puppa et al. 2007). Proteomic analysis of fattissue samples from breast tumors show alterations in the expression of proteins involved in energy metabolism and adipocyte function (Celis et al. 2005). Altogether, these data indicate that adipocyte-cancer cell cross-talk/interaction is a trivial event during the dynamic tumor progression process, and that CAAs play a pejorative role by favoring a more aggressive tumor phenotype.

6.3 CAA Morphology/Structure Major CAA changes are observed in association with cancer cell invasion of adipose tissue (Fig. 6.1). Thus, CAAs at the invasive front are smaller than those observed at a distance. This size reduction implies lipolysis and therefore modification of lipid droplets (also called lipid bodies, fat bodies or adiposomes). Accordingly, the induction of lipolysis in 3T3-L1 adipocytes causes dramatic fission of lipid droplets

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(Brasaemle et al. 2004). Lipid droplets are specialized structures that store neutral lipids (triacylglycerols and sterol esters). They recently emerged as highly dynamic organelles that play crucial roles outside of cellular energy homeostasis and lipid metabolism (Walther and Farese 2009). Indeed, several proteomic analyses have shown that, in addition, lipid droplets contain numerous proteins and might serve as a depot for proteins, to store them for future use or to sequester them until they can be degraded (Cermelli et al. 2006; Welte 2007). Moreover, the key structural lipid droplet proteins vary according to size, with Tip47 and S3–12 localized to the smallest droplets, adipophilin to intermediate droplets, and perilipin to the largest droplets (Wolins et al. 2006). Thus, given the adipocyte-cancer cell interaction, we might hypothesize that in CAAs, lipid droplets undergo fission, leading to protein alteration and/or release, that may possibly play paracrine function(s) on cancer cells.

6.4 CAA and Steroid Hormones CAAs might promote aggressive hormone-dependent cancers (breast, prostate) by increasing hormone synthesis. Indeed, it is established that breast adipose tissue is an important site of estrogen production in post-menopausal women, and elevated estrogen biosynthesis from intratumoral adipose tissue has been demonstrated (Kamat et al. 2002). This is dependent on aromatase, a key enzyme that is involved in peripheral estrogen aromatisation in the adipose tissue. This function has been previously extensively reviewed (O’Neill et  al. 1988; Bulun et  al. 2005; Suzuki et al. 2005; Fischer-Posovszky et al. 2007; Macedo et al. 2009). Some breast cancer cells themselves possess aromatase activity (Sasano et al. 2006). Moreover, several findings indicate that mature human adipocytes possess ERs, and that adipose tissue might be itself an estrogen-responsive tissue (Cooke and Naaz 2004).

6.5 CAA-Derived Growth Factors, Cytokines, Adipokines Cancer cell invasion in vivo involves multiple cell–cell and cell–matrix contacts as well as soluble factors. Adipocytes might locally provide growth support to cancer cells via the secretion of substances that may offer growth/invasion benefits. Indeed, aside from their energy-storing function, adipocytes are also active endocrine cells that produce and secrete various adipokines, growth factors and cytokines (Maeda et al. 1997). The production and secretion patterns of adipose tissue-specific proteins in normal and obesity conditions have been extensivelly studied in human and mouse (Fain et al. 2004; Rabe et al. 2008). A few studies have reported the modification of this production in peritumoral CAAs. These alterations concern several types of proteins, notably pro-inflammatory cytokines (IL6, TNFa), growth factors (IGF1, IGF-BPs), angiogenic factors (VEGF), transcriptional factors (PPARg) and adipokines (leptin and adiponectin; Vona-Davis and Rose 2007). Interestingly, most of these factors have numerous functions, and are interconnected since they often

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regulate each other directly or indirectly. Thus, TNFa is a pleiotropic factor that regulates the synthesis of aromatase expression in the adipose tissue, thus stimulating estrogen production. It also regulates the synthesis of IL6 which also induce aromatase expression. Finally, TNFa induces leptin expression. Similarly, there is increasing evidence that adiponectin and leptin, secreted by peritumoral adipose tissues in several cancers, are important (Rose et  al. 2004; Miyoshi et  al. 2006; Ishikawa et al. 2007; Schaffler et al. 2007). Leptin appears to be a positive factor for tumor development and aggressiveness, while adiponectin protects against cancers. Like TNFa, leptin exerts various functions. In vitro, it can induce cancer cell growth and mediate angiogenesis through VEGF induction. Leptin not only stimulates aromatase expression but also induces the ER pathway independently of estradiol. Furthermore, microarray studies of leptin-regulated gene expression in MCF7 human breast cancer cells suggest that leptin favors mammary tumor growth through multiple mechanisms, including cell cycle and apoptosis (Perera et al. 2008). However, it should be noted that CAA-specific molecular patterns are very difficult to study in vivo since cancer cells themselves aberrantly express and secrete adipocyte-specific proteins. Indeed, it has been shown that high intratumoral levels of leptin mRNA expression in ER-positive breast cancers are specifically involved in the growth stimulation of tumors through an autocrine mechanism (Miyoshi et al. 2006). Collectively, these data show that the potential effects of CAAs on tumor growth and progression might include cell proliferation, differentiation and apoptosis, as well as tumor angiogenesis.

6.6 CAA Microenvironment The adipocyte is a unique cell since it is encapsulated by two membranes, a plasma membrane and a basement membrane. Differentiation is associated with an increase in the secretion of basement membrane components such as laminin, proteoglycans, and type IV and VI collagens. Once cells are committed pre-adipocytes, the original ECM is remodeled, leading to mature lipid-filled adipocytes through proteinase action (Bernlohr et al. 1984; Nakajima et al. 1998; Lilla et al. 2002). Thus, adipogenesis is characterized by the conversion of a fibronectin-rich matrix to a basement membrane (Gregoire 2001; Selvarajan et al. 2001). There is some evidence of modifications of CAA ECM and ECM-related proteins.

6.6.1  MMP-11 Matrix metalloproteinase 11 (MMP-11) (also named stromelysin-3), is ectopically expressed in almost all invasive human carcinomas, and in a majority of their associated metastases (Basset et al. 1990, 1993). High MMP-11 levels are associated with poor patient clinical outcome and MMP-11 is therefore a factor of bad prognosis (Basset et al. 1997; Rio 2005). A MMP-11 tumor promoting effect has been

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demonstrated in several in vivo mouse tumorigenesis models (Masson et al. 1998; Noel et al. 2000; Andarawewa et al. 2003; Deng et al. 2005), and it has been shown that MMP-11 lowers cancer cell death through apoptosis and necrosis (Boulay et al. 2001; Andarawewa et al. 2005; Wu et al. 2001). MMP-11 is not expressed by the cancer cells themselves, but by stromal cells (Basset et al. 1997). More recently, it has been shown that invasive cancer cells induce the expression of MMP-11 in neighboring CAAs (Andarawewa et al. 2005) (Fig. 6.1). The ECM adaptation via the desmoplastic response (Mueller and Fusenig 2004; Tlsty 2001) is associated with a modification in the nature, and relative ratio, of the stromal cells; notably adipocytes disappear and fibroblast-like cells accumulate. It has been proposed that peritumoral fibroblasts might derive, at least partially, from adipocytes and/or preadipocytes through a trans-differentiation process (Bouloumie et al. 2001; Hennighausen and Robinson 2001; Meng et al. 2001). MMP-11, which in vitro negatively regulates adipogenesis by limiting pre-adipocyte differentiation and/or reversing mature adipocyte differentiation, participates in this process (Andarawewa et  al. 2005; Lijnen et al. 2002). Thus, MMP-11 and CAAs participate in a highly complex vicious tumor progression cycle orchestrated by the invasive cancer cells to support tumor progression (Motrescu and Rio 2008).

6.6.2  Collagen VI The extracellular matrix of adipose tissues has received limited attention to date, despite evidence showing that it is a functionally relevant constituent of adipose tissue physiology. Type VI collagen, a soluble ECM, is a specific member of the collagen family exhibiting predominant expression in the adipose tissue. Type VI collagen was shown to be up-regulated in adipocytes during tumorigenesis and to be critical for tumor progression (Iyengar et al. 2003). Indeed, CAA-derived type VI collagen, notably a C-terminal fragment of the alpha3 chain, affects mammary tumor progression in vivo, (Iyengar et al. 2005). Interestingly, it has recently been shown that MMP-11 cleaves the native alpha 3 chain of collagen VI. This MMP-11 collagenolytic activity is functional during fat tissue ontogenesis as well as during cancer invasive steps (Motrescu et al. 2008). These results indicate that CAA-related ECM and associated enzymes play a role during invasive steps in carcinomas, favoring local tumor progression.

6.7 CAA Systemic Effects We have seen above the local effects of CAAs on primary tumors. However, several clinical studies report altered levels of adipocyte-related factors in normal weight patients with invasive tumors. This suggests that local CAA protein expression and secretion might affect the whole individual, and possibly metastatic processes.

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Accordingly, TNFa and IL6 that are expressed by CAAs have been shown to act both locally and in a systemic way (Maccio et  al. 2009). Moreover, circulating levels of leptin increase in both gynecological and breast cancer patients whereas adiponectin levels decreased (Tessitore et al. 2004; Miyoshi et al. 2006; Vona-Davis and Rose 2007). Similar leptin and adiponectin data were reported for prostate cancer (Mistry et al. 2007). Moreover, it has been shown that leptin stimulates the production of sexual hormones. All of these studies show the difficulties in defining the relative importance of local and systemic mechanisms in influencing a range of cancer processes.

6.8 Lessons from CAAs for Elucidating the Pejorative Impact of Obesity on Cancer The data on CAAs call into question the possible similarity between blood modifications observed in cancer patients and those observed in obese people. Plasma analyses of obese people devoid of cancer show alterations in the levels of several factors that are modified in CAAs. Indeed, the cellular homeostasis and the secretory profile of hypertrophied adipocytes from obese people is altered and increasingly dysregulated compared with normal adipocytes (Bluher et  al. 2002; Guilherme et al. 2008). Thus, obesity is associated with an altered cytokine profile, characterized by a reduction in the release of anti-inflammatory cytokines and an increase in the secretion of proinflammatory cytokines. Consequently, obesity is considered an important source of chronic inflammation (Guzik et al. 2006; Hoene and Weigert 2008). Moreover, in obesity, the adipose tissue is infiltrated by an increased number of TNFa- and IL6-secreting macrophages which may be responsible for the increase of estrogen synthesis (Weisberg et al. 2003). An adipose tissue mass increases the circulating concentration of insulin (Pollak 2008). In addition, hyperinsulinemia promotes the synthesis and biological activity of insulin-like growth factor (IGF1) which regulates cell proliferation (Ezzat et al. 2008; Pollak 2008). Insulin also affects the synthesis and the bioavailability of the male and female sex steroids, including androgens, progesterone and estrogens (Fischer-Posovszky et  al. 2007). Leptin is also activated by obesity-related stimuli in adipocytes while adiponectin is decreased (Tanko et al. 2004; Calle and Thun 2004; Rabe et al. 2008). Finally, expanding adipose tissues require local active angiogenesis and neovascularisation (Hausman and Richardson 2004; Guzik et al. 2006). These data all show that part of the local and systemic molecular alterations occurring in adipose tissue of obese people are similar to those occurring in CAAs of invasive tumors, even if the mechanisms regulating their expression is sometimes different. Thus, in normal weight people who develop tumors, factors that favor tumor progression are provided by CAAs, and the stimuli to convert tumor resident adipocytes into CAAs come from invasive cancer cells. It might be hypothesized that obesity provides a “constitutively activated” local and systemic permissive environment for cancer cells.

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6.9 Concluding Remarks and Future Perspectives Collectively, all of these studies strongly support the concept that CAAs favor invasive cancer cell life in connective tissues and therefore tumor progression. CAAs function at both local and systemic levels. The CAA studies provide a molecular basis for a causal link between obesity and cancer. The main conclusions from epidemiological studies is that obesity influences both cancer risk, tumor behavior and clinical outcome (Abu-Abid et al. 2002; Schaffler et al. 2007; Fair and Montgomery 2009). These results raise two salient questions. First, might obesity-related modifications inhibit and/or reverse cancer cell or microtumor dormancy (Demicheli 2001)? Indeed, this would explain the higher risk of cancer development correlated with obesity, since it is difficult to imagine that adipocytes might lead to cell transformation per se. Second, in addition to the fact that obesity makes a hospitable environment for cancer cells and CAAs exacerbate the local reaction, it might be asked if adipocytes are involved locally in metastasis development? Indeed, the microenvironment of preferential organs for secondary tumors (ie: bone, brain, skin) often contains adipocytes (Siclari et al. 2006). This would contribute to explain the worse prognostic values associated with obesity that have been reported. Studies on the metastatic process in normal weight and obese patients should be done to answer this question. Actually, there is great interest in understanding all the mechanisms by which obesity contributes to the carcinogenic process, since obesity is continuously increasing (Kaur and Zhang 2005; Vainio et al. 2002). Improving knowledge of CAA functions might help to explain the relationship between increased BMI, and higher cancer risk and poor clinical outcome. In this context, modulating the plasticity of adipocytes and elucidating the extracellular and intracellular signaling pathways are major challenges for the future research in the field. Molecules linked to adipocyte biology might offer new therapeutic opportunities for treating cancer. Indeed, while each type of cancer is genetically and phenotypically heterogeneous, adipocytes are cells common to all cancers. Now that the adipocyte is no longer the stepchild of cancer biology, many new insights can be expected from its biochemical functions and biological roles in the coming years. Acknowledgements  We thank Susan Chan for helpful discussion. This work was supported by funds from the Institut National de la Santé et de la Recherche Médicale, the Centre National de la Recherche Scientifique, the Hôpital Universitaire de Strasbourg, the Association pour la Recherche sur le Cancer, the Ligue Nationale Française Contre le Cancer and the Comités du Haut-Rhin et du Bas-Rhin, the Fondation de France, the European Commission (FP6 LSHC-CT-2003–503297; Cancer Degradome project), the Institut National du Cancer, Fond National pour la Santé ACI 2004 Cancéropôle Grand-Est, and the “Ruban Rose” prize.

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Yamada S, Toda S, Shin T, Sugihara H (1999) Effects of stromal fibroblasts and fat cells and an environmental factor air exposure on invasion of laryngeal carcinoma (HEp-2) cells in a collagen gel invasion assay system. Arch Otolaryngol Head Neck Surg 125:424–431 Yamaguchi J, Ohtani H, Nakamura K, Shimokawa I, Kanematsu T (2008) Prognostic impact of marginal adipose tissue invasion in ductal carcinoma of the breast. Am J Clin Pathol 130:382– 388

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Part III

The Tumor ECM

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Chapter 7

Hyaluronan: A Key Microenvironmental Mediator of Tumor-Stromal Cell Interactions Naoki Itano

7.1 Tumor Microenvironment and Tumor-Associated Fibroblasts The tumor microenvironment surrounding tumor cells is composed of not only cellular compartments, such as fibroblasts, lymphocytes, macrophages, and endothelial cells, but also of bioactive substances, including growth factors and extracellular matrix (ECM) (Bissell and Radisky 2001). The tumor microenvironment varies both qualitatively and structurally among cancers, and is known to be remodeled in a stepwise fashion during tumor progression. In breast cancer, microenvironmental changes are characterized by formation of stroma containing abundant fibrous components in the intercellular space during the progression from mammary gland hyperplasia to adenoma formation and then to breast cancer (Wiseman and Werb 2002; Petersen et al. 2003). Dynamic changes in tumor stroma include the active recruitment of stromal and inflammatory cells, ECM remodeling, and increased growth factor production (Bhowmick and Moses 2005; Mantovani et  al. 2006). Such remodeling of the tumor microenvironment is known to influence the cancer cells themselves and alter their characteristics (Mueller and Fusenig 2004; Bhowmick et al. 2004). In other words, an interactive loop between cancer cells and the tumor microenvironment is considered to lead to the enhancement of malignant alterations. It thus becomes apparent that a molecular understanding of the tumor microenvironment may lead to the development of new molecular-targeted therapies to block this signaling loop (Joyce 2005). Fibroblasts that are recruited into cancer masses, called tumor-associated fibroblasts (TAFs), are the main cellular components of the stroma of many solid cancers (Kalluri and Zeisberg 2006). TAFs supply a variety of cytokines, growth factors, tissue remodeling enzymes, and ECM components, all of which modulate tumorN. Itano () Department of Molecular Oncology, Division of Molecular and Cellular Biology, Institute on Aging and Adaptation, Shinshu University Graduate School of Medicine, Matsumoto, Nagano 390-8621, Japan e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_7, © Springer Science+Business Media B.V. 2011

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stroma interactions (Kalluri and Zeisberg 2006; Desmouliere et  al. 2004). Clinicopathological studies of various human cancers have reported that an increased amount of stroma and TAF proliferative activity were correlated with cancer malignancy and prognosis (Hasebe et al. 2000; Yazhou et al. 2004). In one report, TAFproduced growth factors, such as HGF, directly and indirectly promoted the proliferation and survival of tumor cells (Matsumoto and Nakamura 2006). TAFs are also known to vigorously produce ECM components, including collagen, proteoglycans, hyaluronan, and matrix metalloproteinase (MMP), thereby involving themselves in the remodeling of the ECM and the generation of a tumor microenvironment favorable for tumor cell proliferation and invasion.

7.2 Hyaluronan Accumulation in Cancers 7.2.1  Hyaluronan Production and Cancer Prognosis Hyaluronan is the main component of the tumor microenvironment and has thus become an increasingly important target for cancer therapy. While hyaluronan is essential for the construction of normal tissue architecture, it is also known as a polysaccharide closely related to cancer (Toole 2004). The malignant transformation of cells frequently impairs regulation of hyaluronan synthesis, which leads to excessive hyaluronan production (Hamerman et al. 1965; Hopwood and Dorfman 1977). The association between a rise in hyaluronan and cancer dates back to the 1950s, when a study first reported increased hyaluronan production in a mesothelioma (Blix 1951; Truedsson 1951). Since then, hyaluronan has been accepted as a significant prognostic factor in many types of cancers, and high levels of this molecule are correlated with a poor prognosis, including overall survival (OS) and disease-free survival (DFS) (Hopwood and Dorfman 1978; Delpech et al. 1990). In several studies, the proportion of hyaluronan-positive cells and intensity of hyaluronan staining in breast cancer tissues were closely correlated with a poor survival rate and prognosis (Auvinen et al. 2000). In colorectal cancers, the 5-year survival rate of patients with a large fraction of hyaluronan-positive cancer cells was reported to be only 20% (Ropponen et al. 1998). Poorly differentiated or high-grade tumors generally have more stromal hyaluronan than well-differentiated tumors; multivariate analyses have confirmed the rate of positive staining for hyaluronan in tumor stroma to be an independent poor prognostic factor. In ovarian cancers, the hyaluronan level in tumor stroma was correlated with the degree of cancer cell undifferentiation and cancer stage, and the 5-year outlook of the disease deteriorated with increasing levels for both OS and DFS (Anttila et al. 2000). The intensity of stromal hyaluronan signaling was also associated with the poor prognosis of breast cancer patients (Auvinen et al. 2000). Thus, hyaluronan, and especially stromal hyaluronan, is an important factor determining the malignant characteristics of cancer that is expected to be more frequently used for the differential diagnosis of cancers.

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7.2.2  B  iological Significance of Hyaluronan Accumulation   in Cancer Tissues As described above, hyaluronan accumulation is associated with degree of clinical malignancy, and, therefore, has been speculated to be closely involved in cancer progression. Qualitative and quantitative changes in hyaluronan ECM are often observed during carcinogenesis and malignant progression. The biological significance of cancer cell-induced ECM remodeling is considered to lie in the formation of an environment appropriate for cancer growth, invasion, and metastasis. Indeed, cell surface hyaluronan-positive mouse melanoma cells were reported to show a significantly higher metastatic frequency than their cell surface hyaluronan-negative counterparts (Zhang et al. 1995). It appears that hyaluronan promotes changes in cell adhesiveness and motility, leading to the detachment of cancer cells from the primary lesion, invasion of the surrounding tissue, and metastasis to distant organs. In addition to this microenvironmental remodeling by the cancer cells themselves, it has been suggested that further alterations are caused by stromal fibroblasts and inflammatory cells, which are recruited into cancer tissues and contribute to the acquisition of malignant characteristics, such as increased cancer cell motility, invasiveness, and tumor angiogenesis.

7.3 Factors Determining the Properties and Functions of Hyaluronan As shown in Fig. 7.1, hyaluronan is a simple polysaccharide molecule composed of repeating disaccharide units of alternating N-acetylglucosamine and glucuronic acid. Despite its relatively simple chemical composition, hyaluronan mediates many important biological functions, including signaling during embryonic morphogenesis, cellular regeneration, and wound healing (Laurent and Fraser 1992). Hyaluronan’s diverse functions are multilaterally regulated by factors like concentration, polysaccharide chain length, and the association state of binding molecules (Itano 2008). High molecular-weight hyaluronan associates with binding molecules to form the ECM that accumulates around cells. This type of hyaluronan ECM is believed to maintain tissue structure and serve as a scaffold for cell adhesion and migration. Alternatively, oligosaccharides resulting from the catabolic activity of hyaluronidase diffuse through the tissue and bind to hyaluronan receptors on adjacent cells, thereby acting as intracellular signals (Stern et al. 2006). In order to comprehensively understand the functions of hyaluronan with its diverse modes of occurrence and association with cancer, it is necessary to first investigate the synthesis and degradation of hyaluronan along with its interaction states with binding molecules. Additionally, several regulatory factors that determine the molecular state of hyaluronan have been identified to date, which have permitted a more detailed understanding of the properties and functions of hyaluronan.

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N. Itano Hyaluronan O

O C

HO

O

CH2OH O

HO O

OH

HO

O NH O C O C O CH3

OH

CH3 O C NH O

O HO

CH2OH

O

Elongating hyaluronan

→ 4GlcA β1→3 GlcNAc β1→ 4 GlcA β1→3 GlcNAc β1→ O

HO

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CH2OH O HO O OH

O O C

NH

Hyaluronidase OH CH3 O C NH

CH3 OH HO

a

O C O

HAS

O

O HO

O CH2OH

b

UDP-GlcA UDP

UDP-GlcNAc UDP

Fig. 7.1   Diagrams illustrating the proposed reaction mechanisms of hyaluronan degradation (a) and synthesis (b). Hyaluronan is composed of repeating disaccharide units of N-acetylglucosamine (GlcNAc) β(1→4)-glucuronic acid (GlcA) β(1→3). Hyaluronidase cleaves the N-acetylglucosamine β(1→4) glycoside bonds in hyaluronan. HAS transfers both UDP-GlcNAc and UDP-GlcA substrates to elongating hyaluronan and may secrete the product through a pore made by the enzyme itself along with lipid components of the plasma membrane

7.3.1  Hyaluronan Biosynthesis Due to the peculiarities of the hyaluronan biosynthetic pathway and its mode of sugar chain elongation, the complete mechanism of hyaluronan synthesis remains elusive despite nearly 70 years passing since it was first recognized. However, the identification of hyaluronan synthase (HAS), a glyctabosyltransferase involved in hyaluronan biosynthesis, has led to the elucidation of several molecular mechanisms regulating this process (Weigel et al. 1997). Information on glycosyltransferase reactions has also been steadily accumulating since the advent of biochemical enzyme analysis (Weigel and DeAngelis 2007). Hyaluronan synthesis is catalyzed by one of three isoforms of the HAS enzyme: HAS1, HAS2, and HAS3. These isoforms differ from one another in the length of sugar chains synthesized and ECMforming ability (Itano et al. 1999). Thus, the biological significance of HAS isoforms in animal cells is presumably to synthesize functionally different hyaluronan molecules and modulate their diverse functions. Indeed, expression of each HAS gene is regulated in a different manner by various growth factors and cytokines, resulting in temporally and spatially different expression patterns (Sugiyama et al. 1998; Pienimaki et al. 2001).

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7.3.2  Hyaluronan Catabolism The dynamic turnover of hyaluronan is tightly balanced by its synthesis and degradation to maintain a specific concentration and chain length in tissues. Hyaluronan catabolism is predominantly regulated by several hyaluronidases, which are classified as endo-β-N-acetylglucosaminidases according to their hydrolytic mechanisms (Fig. 7.1). Six structurally homologous hyaluronidase genes (HYAL-1, -2, -3, -4, -P1, and PH-20) have been identified so far in mammals (Coska et al. 2001). Among them, the main tissue hyaluronidases HYAL-1 and -2 regulate hyaluronan degradation in a sequential series of catabolic reactions. The hyaluronidase enzymes differ in optimal pH; HYAL-1, -2, and -3 are acidic hyaluronidases with an optimal pH range of 6.0 or lower, and testicular hyaluronidase PH-20, with a pH optimum of around 7.0, acts as a neutral hyaluronidase. Hyaluronidases also have a different catalytic specificity. For example, whereas HYAL-2 degrades high molecularweight hyaluronan into fragments of up to 20 kDa, HYAL-1 breaks down hyaluronan into fragments of up to 800 Da. Extracellular hyaluronan is known to bind to cell surface receptors, such as CD44, and be internalized into cells by endocytosis. During this process, hyaluronan undergoes one catabolic step by HYAL-2 on cell surfaces or in endosomes. Next, the moderately degraded fragments of hyaluronan undergo further degradation by HYAL-1 in lysosomes (Harada and Takahashi 2007). Hyaluronan fragmentation may also occur through the action of free radicals (Myint et al. 1987). Since free radicals are actively generated under pathological conditions, free radical-mediated hyaluronan degradation may occur in conjunction with hyaluronidase-mediated hyaluronan degradation in subjects with cancer or chronic inflammation.

7.3.3  Hyaluronan-Binding Molecules and Signal Transduction The structure and function of the hyaluronan ECM are complexly regulated through association and combination with specific hyaluronan-binding proteins, such as chondroitin sulfate proteoglycans, link proteins, TSG-6, and inter-α-trypsin inhibitor heavy chain (also called SHAP) (Day and Prestwich 2002). For example, hyaluronan modified by SHAP strengthens the adhesiveness of lymphocytes to hyaluronan ECM via cell surface CD44, thereby promoting their recruitment into inflammatory sites (Zhuo et  al. 2006). The complex signals of various hyaluronan sugar chains are transmitted into cells through hyaluronan receptors, such as CD44 and RHAMM, and dynamically regulate the signal transduction, metabolic reactions, and gene expression required for cell proliferation and migration (Turley et al. 2002). Since CD44 interacts with Src kinase in glycosphingolipid-rich rafts, hyaluronan may activate the Src in rafts to regulate this process (Lee et al. 2008). It is also known that hyaluronan upregulates the Ras-MAP kinase and PI3 kinase-Akt pathways through CD44 to promote cell proliferation and motility.

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7.4 Mode of Occurrence and Functions of Hyaluronan in Cancer Tissues The rates of hyaluronan synthesis and degradation are much higher in cancers than in normal tissues, which produce an increase in hyaluronan ECM and excess low molecular-weight oligosaccharides. Thus, cancer cells are exposed to an extremely unusual environment where the functions of hyaluronan ECM and shorter oligosaccharides occur simultaneously. High molecular-weight hyaluronan exhibits spacefilling and water-retention properties and functions to modulate the pericellular ECM microenvironment Hyaluronan species of different sugar-chain lengths are known to have different biological effects on tumor activity (Table  7.1). For instance, whereas high molecular-weight structural hyaluronan inhibits angiogenesis, shorter oligosaccharides promote vascular endothelial cell proliferation and angiogenesis (Feinberg and Beebe 1983; West et  al. 1985; Slevin et  al. 1998). Recent studies have also reported that hyaluronan oligosaccharides act on monocytes and macrophages to differentiate them into M2 macrophages (Kuang et al. 2007). These macrophages produce Th2 cytokines and several angiogenic factors, which are believed to enhance tumorigenicity through tumor immunosuppression and promotion Table 7.1   Size-dependent functions of hyaluronan Hyaluronan length Biological functions related to cancer (mers or kDa) aggressiveness 4–6-mers Activation of NF-κB and upregulation of MMP expression 4–16-mers Induction of cytokines and chemokines Activation and maturation of DCs Suppression of PI3-kinase/Akt cell survival signals and induction of apoptosis 6–32-mers Stimulation of angiogenesis Proliferation, activation and differentiation of ECs Growth inhibition of B16F10 melanoma Suppression of PI3-kinase/Akt cell survival signals, inhibition of anchorageindependent growth and induction of apoptosis 6–36-mers Induction of CD44 cleavage and promotion of tumor cell motility 10 kDa Stimulation of fibroblast migration 40–800 kDa Induction of proinflammatory cytokines and chemokines in Mφ Induction of iNOS and COX-2 in Mφ Activation of innate immunity

Reference Fieber et al. (2004); Voelcker et al. (2008) McKee et al. (1996); Taylor et al. (2004); Termeer et al. (2002); Alaniz et al. (2006) West et al. (1985); Sattar et al. (1994); Montesano et al. (1996); Deed et al. (1997); Lokeshwar and Selzer (2000); Slevin et al. (1998); Takahashi et al. (2005); Zeng et al. (1998); Ghatak et al. (2002) Sugahara et al. (2003) Tolg et al. (2006) Noble et al. (1996); HodgeDufour et al. (1997); BeckSchimmer et al. (1998); Horton et al. (1998); McKee et al. (1997); Sun et al. (2001); Scheibner et al. (2006)

MMP Matrix metalloprotease, DC Dendritic cell, EC Endothelial cell, Mφ Macrophage

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of angiogenesis. Thus, in advanced cancer, abnormal synthesis and degradation of hyaluronan may facilitate the malignant transformation and survival of cancer cells through tumor-led microenvironmental remodeling characterized by the accumulation of hyaluronan ECM and excessive low molecular-weight hyaluronan production.

7.4.1  Increased Hyaluronan Synthesis in Cancer Cells Increased hyaluronan synthesis in cancer cells is considered to be the main cause of abnormal hyaluronan ECM accumulation. Indeed, infection of cells with an oncogenic virus causes not only abnormal cell proliferation, but also a marked increase in hyaluronan synthesis (Hamerman et  al. 1965; Hopwood and Dorfman 1977). It had long been unclear what mechanism induced excess hyaluronan production associated with malignant cell transformation until we clarified a part of the mechanism by demonstrating that HAS activity and transcription rose after transforming the fibroblast cell line with v-ras or v-src oncogenes (Itano et al. 2004). Similarly, studies using several cancer cell lines reported that HAS expression was increased in cancer cells whose ability to produce hyaluronan was enhanced (Simpson et al. 2001; Bullard et al. 2003). Furthermore, amplification of the genomic region that includes the HAS2 gene has also been found with high frequency in prostate cancer (Tsuchiya et al. 2002). Although not directly proven, HAS gene amplification may be directly linked to increased hyaluronan synthesis in cancer cells. These results suggest that abnormal hyaluronan accumulation in cancer tissue is the result of transformed cells acquiring a high hyaluronan-producing ability associated with increased HAS expression. Interestingly, some cancer cells, such as human lung and breast cancer cells, produce large amounts of hyaluronan when transplanted subcutaneously or intramuscularly despite exhibiting low levels of hyaluronan production in cultures (Knudson et al. 1989). It has also been reported that co-culture of these cancer cells with fibroblasts results in increased hyaluronan production. Thus, the interaction of cancer cells with TAFs and intratumoral stromal cells seems to be important for the accumulation of this molecule, and growth factors derived from stromal cells likely stimulate hyaluronan production in cancer cells. Indeed, proliferation signals induced by several growth factors such as EGF, TGF-β, PDGF, IGF-1, and FGF enhance hyaluronan synthesis (Heldin et  al. 1989; Suzuki et  al. 1995; Postlethwaite et al. 1989; Jacobson et al. 2000). Increased HAS expression is known to be closely linked to cancer cell proliferation, tumor formation, and malignant alteration (Yabushita et  al. 2004, 2005; Kanomata et al. 2005). Pathological analysis of clinical samples has demonstrated that the increased expression of HAS1 among the three HAS isoforms in human colorectal cancer tissues is correlated with lymph node metastasis (Yamada et al. 2004). Another study reported that when increased hyaluronan production was induced by excess HAS2 expression in human HT-1080 sarcoma cells, anchoringdependent cell proliferation and subcutaneous tumor formation were promoted in

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mice (Kosaki et al. 1999). Similar results were obtained when prostate cancer cells were forced to express HAS3 (Liu et al. 2001). In the latter case, the promotion of tumor blood vessel formation was noted as well, suggesting that hyaluronan induced angiogenesis. Analysis of HAS gene expression in prostate cancer cells with different metastatic potentials showed that the expression of HAS2 and 3 genes was increased in cells with a high metastatic potential (Simpson et al. 2002a, b). In this experiment, they found that when low-metastatic cells were transfected with the HAS2 gene to promote hyaluronan production and ECM formation, the adhesion of cancer cells to bone marrow endothelial cells was increased. Conversely, when the expression of HAS2 or HAS3 in high-metastatic cells were decreased by antisense suppression, cancel cell adhesion to endothelial cells was inhibited. Although such a close correlation between HAS gene expression and tumor formation and metastasis has been suggested, exceptions have also been observed. Enegd et al. reported that the forced expression of HAS2 in glioma cells resulted in the suppression of tumor formation, and postulated that a balance between hyaluronan degradation by hyaluronidase and synthesis by HAS was responsible for tumor formation (Enegd et  al. 2002). We also investigated the correlation between HAS2 expression and tumor formation using ras-transformed fibroblasts, and were able to determine the optimal level of HAS2 expression for tumor development (Itano et al. 2004). These observations therefore suggest that hyaluronan promotes tumor progression at a specific concentration range and sugar-chain length, but inhibits tumor formation at other concentrations or sugar-chain lengths, which is to say that the malignant characteristics of tumor cells are regulated by the fine balance between hyaluronan synthesis and degradation under the control of several enzymes.

7.4.2  E  ffects of Hyaluronan Oligosaccharides on Tumor Formation Since the time of its cloning, the expression of the hyaluronidase gene has been examined in detail in numerous cancer tissues and cell lines (Lokeshwar et al. 2001; Franzmann et al. 2003; Christopoulos et al. 2006). Pathological analysis of clinical samples has shown that HYAL-1 expression is a good prognostic indicator of prostate and bladder cancers (Lokeshwar et al. 2001), while the analysis of breast cancer cell lines has revealed that highly invasive cancer cells express high levels of HYAL-2 (Udabage et al. 2005). The association between hyaluronidase and tumor formation has been directly confirmed by the fact that HYAL-2 overexpression in mouse astrocytoma promotes the formation of tumors (Novak et al. 1999). On the other hand, similarly to HAS, hyaluronidase exhibited a completely opposite effect in some cases; a human breast cancer transplanted into immunodeficient (SCID) mice markedly regressed after intravenous administration of testicular hyaluronidase (Shuster et al. 2002), and HYAL-1 overexpression in a colorectal cancer model inhibited tumor formation (Lokeshwar et al. 2005). These conflicting results support the notion of a delicate balance between hyaluronan degradation and synthesis in tumor development.

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7.4.3  Metabolism and Function of Stromal Hyaluronan In addition to hyaluronan-synthesizing cancer cells, TAFs and other stromal cells are considered to contribute significantly to the intratumoral accumulation of hyaluronan. In cancer tissues, fibroblasts are activated by exposure to various growth factors and cytokines, resulting in a marked increase in hyaluronan synthesis. As TGF-β can up-regulate HAS gene expression, it may be responsible for the stimulation of hyaluronan synthesis in fibroblasts. Although tumor stromal cells are considered to be non-transformed cells, fibroblasts adjacent to cancer cells exhibit a loss of heterozygosity (LOH) with high frequency (Kurose et al. 2002), suggesting that they carry genetic changes. Therefore, not only active hyaluronan production by normal fibroblasts, but also abnormal hyaluronan synthesis by genetically altered fibroblasts, may help explain the excessive accumulation of hyaluronan in tumor stroma. In prostate cancer, decreased hyaluronidase activity along with decreased hyaluronan degradation in fibroblasts has also been shown to be a cause of hyaluronan accumulation in tumor stroma (Lokeshwar et al. 2001). Considerable information has been accumulated on the function of stromal hyaluronan in cancer progression. Recently, Calabro et  al. investigated the synthesis of hyaluronan in bone marrow mesenchymal progenitor cells (bmMPCs) using bone marrow from myeloma patients, and reported that bmPMCs produced hyaluronan levels about six times higher than those from healthy donors (Calabro et al. 2002). Therefore, bone marrow stromal ECM that is rich in hyaluronan may be important for the expression of malignant characteristics in myeloma cells. Since the functions of hyaluronan are regulated in a complex manner by its molecular weight and associated molecules, hyaluronan species produced by cancer cells and TAFs may have specific individual properties and functions that differentially contribute to cancer progression. However, it remains to be fully clarified whether or not hyaluronan species exert different actions due to the lack of an appropriate experimental system.

7.5 Stromal Formation, Angiogenesis, and Lymphangiogenesis 7.5.1  I nduction of the Tumor Stroma by Hyaluronan Overproduction The proceeding discussion focuses on the complex functions of tumoral and stromal hyaluronan in cancer progression based on findings revealed by analysis of genetically modified mice. In recent years, our laboratory has successfully generated a transgenic mouse model overproducing hyaluronan in breast cancer cells to analyze the actions of tumoral hyaluronan, and we have found that the rate of tumor development and proliferation increases rapidly in hyaluronan-overproducing mice compared with a control group (Koyama et  al. 2007). Histological examination revealed breast cancer formation characterized by the induction of tumor stroma

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and marked recruitment of TAFs into tumor tissues. Very interestingly, numerous blood and lymphatic vessels were observed in or adjacent to tumor stroma (Koyama et al. 2007, 2008), which probably occurred in the context of tumoral hyaluronaninduced tumor stroma formation. We have thus far stated that the main cellular component of the tumor stroma, TAFs, promote tumor formation and progression as a modulator of the tumor microenvironment, and that hyaluronan is important in the recruitment of TAFs into tumor tissue. At the time of writing, the mechanism by which tumor-derived hyaluronan recruits TAFs to enhance tumor stroma formation remains unclear. However, two mechanisms are conceivable: (1) The extracellular accumulation of hyaluronan with its excellent water-retaining and space-filling properties is favorable for the migration of TAFs, and (2) Hyaluronan interacts with cell surface receptors, such as CD44 and RHAMM, to activate intracellular signals and promote stromal cell motility. TAFs are considered to originate both from fibroblasts migrating from surrounding tissue and from hyaluronan-recruited mesenchymal stem cells (MSCs) (Zhu et al. 2006). In addition, hyaluronan overproduction is known to promote epithelial-mesenchymal transition (EMT) (Zoltan-Jones et al. 2003), suggesting that the hyaluronan-dependent EMT of cancer cells also functions as a mechanism of TAF supply.

7.5.2  T  he Role of a Hyaluronan-Rich Tumor Microenvironment in Tumor Angiogenesis HAS overexpression in cancer cells has been reported to induce angiogenesis in xenografted tumors, and the inhibition of hyaluronan synthesis was found to markedly reduce the density of tumor blood vessels (Liu et al. 2001; Simpson et al. 2002b). Our findings with animal models have confirmed that hyaluronan overproduction markedly promotes tumor angiogenesis (Koyama et al. 2007), but excessive hyaluronan accumulation was seen to inhibit tumor angiogenesis in prostate cancer in another study (Bharadwaj et  al. 2007). These conflicting results indicate that the capacity for hyaluronan to promote or inhibit tumor angiogenesis may depend on its state. For many years, low molecular-weight hyaluronan was known to promote vascular endothelial cell proliferation and induce angiogenesis in vivo (Feinberg and Beebe 1983; West et al. 1985; Slevin et al. 1998). However, only shorter hyaluronan oligosaccharides promote tumor angiogenesis, whereas high molecularweight hyaluronan has the opposite effect (Table 7.1). In cancer tissues, the angiogenic inhibitor high molecular-weight hyaluronan and the angiogenic promoter low molecular-weight hyaluronan coexist, suggesting the importance of their balance in angiogenic processes. Interestingly, our studies have shown that, similarly to low molecular-weight hyaluronan, high molecular-weight hyaluronan may actually promote angiogenesis when associated with versican, a chondroitin sulfate proteoglycan, to form a complex (Koyama et al. 2007). In an in vivo experimental model for angiogenesis

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using Matrigel plug assays, the hyaluronan-versican complex is found to actively recruits fibroblasts and vascular endothelial cells into subcutaneously transplanted Matrigels. Since angiogenic processes are regulated by aberrant production of angiogenic factors, it was speculated that the hyaluronan-versican complex indirectly promotes angiogenesis through fibroblast-produced angiogenic factors. Indeed, in the above-described experiments with hyaluronan-overproducing breast cancer, numerous microvessels were present adjacent to tumor stroma containing abundant hyaluronan and versican. Analysis of angiogenic factor expression further revealed increases in the expression of stroma-derived factors, such as b-FGF and SDF-1 chemokines, in hyaluronan-producing tumors. The promotion of angiogenesis by stromal cell-derived factors was also clearly illustrated by another experiment using a mouse embryonic fibroblast (MEF) cell line established from vascular endothelial growth factor (VEGF)-deficient mice. Dong et al. demonstrated that fewer blood vessels were formed when cancer cells were transplanted subcutaneously in the co-presence of VEGF-deficient MEFs compared with wild-type cells (Dong et al. 2004). Furthermore, Orimo et al. reported that TAFs established from human breast cancers recruited vascular endothelial progenitor cells and promoted tumor angiogenesis in xenografted tumors through production of SDF-1 (Orimo et al. 2005). Along with our results, these studies provide compelling evidence that stromal cellderived angiogenic factors are crucial for tumor angiogenesis. Since monocytes and macrophages are the main sources of angiogenic factors in cancer tissue, the involvement of inflammatory cells may be important in promoting angiogenesis in hyaluronan-positive tumors (Lewis et al. 2006). De la Motte et al. showed that hyaluronan and its associated molecules formed cable-like ECM structures that were involved in the adhesion, and presumably recruitment, of monocytes through interactions with CD44 (de la Motte et al. 1999). Furthermore, Kuang et al. found that hyaluronan fragments differentiate monocytes into tumorassociated macrophages (TAMs), which are reportedly M2-type macrophages that actively produce angiogenic factors (Kuang et al. 2007). Thus, the hyaluronan-rich tumor microenvironment recruits effector cells, such as TAFs and TAMs, into tumor tissue, then promotes angiogenesis by inducing production of angiogenic factors (Fig. 7.2).

7.5.3  H  yaluronan-Dependent Promotion of Tumor Iymphangiogenesis Lymphangiogenesis has recently gained attention for its potential involvement in cancer dissemination and metastasis (Alitalo et al. 2005). Since lymphatic vessels in the early stages of carcinogenesis are localized in the peritumoral stroma of several human solid cancers (Leu et al. 2000; Padera et al. 2002; Williams et al. 2003), lymphangiogenesis is likely governed by complex interactions between tumor cells and stroma-based mediators. In a mouse model of hyaluronan overproduction, we have shown that lymphangiogenesis occurs in the context of tumor stroma

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3

Tumor cells HMW & LMW HA

Lymphatic vessels

2

Blood vessels

TAMs

EPCs

ECs

TAFs

Fig. 7.2   Hyaluronan mediates host-tumor cell interactions in the tumor microenvironment. During tumor progression, high-molecular-weight (HMW) hyaluronan forms part of the tumor microenvironment by linking its binding partners into macromolecular aggregates. The hyaluronan-rich tumor microenvironment accelerates the recruitment of TAFs and TAMs. The complex cellular interactions between tumor cells and stromal cells accelerate tumor angiogenesis and lymphangiogenesis by stimulating infiltration of endothelial cells (ECs) and recruitment of bone marrowderived endothelial progenitor cells (EPCs). Low-molecular-weight (LMW) hyaluronan generated by hyaluronan fragmentation simultaneously accelerates tumor angiogenesis

(Koyama et  al. 2008). TAFs were found to give rise to highly lymphatic tumors when transplanted subcutaneously with cancer cells established from breast cancer tissue, indicating that TAFs are important cellular components for tumor lymphangiogenesis. Thus, a hyaluronan-rich tumor microenvironment also seems to promote lymphangiogenesis through the recruitment of TAFs. Recent studies have revealed that stromal cells in cancer tissue promote lymphangiogenesis by producing lymphangiogenic factors. We have shown that TAFs produce lymphangiogenic factors, such as VEGF-C and -D (Koyama et al. 2008), suggesting that the increased production of such stroma-derived factors may result in increased lymphangiogenesis. Furthermore, Schoppmann et al. reported that monocytes recruited into tumor stroma differentiate into TAMs following exposure to active substances to actively produce VEGF-C and -D (Schoppmann et al. 2002). Since hyaluronan promotes the differentiation of monocytes into TAMs, it may be involved in a mechanism that promotes lymphangiogenesis in tumors. Lastly, it is possible that, in addition to the action of humoral factors, the direct interactions between hyaluronan-rich ECM and lymphatic endothelial cell-surface

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receptors contribute to lymphangiogenesis. LYVE-1, which contains a hyaluronanbinding domain, is expressed on the surface of lymphatic endothelial cells and was initially speculated to play an important role in lymphangiogenesis as a hyaluronan receptor on lymphatic endothelial cells (Jackson et al. 2001). However, an analysis of LYVE-1-deficient mice showed no gross abnormalities in lymphatic formation, and these mice did not differ from wild-type mice in their level of pathological lymphangiogenesis (Gale et  al. 2007). In the future, it should be investigated in more detail whether hyaluronan-rich ECM promotes lymphangiogenesis through reaction mechanisms with or without mediation by other receptors.

7.6 Conclusions and Future Prospects In light of recent discoveries, the formation and restructuring of the “hyaluronanrich tumor microenvironment” seems to be the main cause of cancer development and progression. The analysis of cancer cells and genetically-altered mouse models is steadily advancing, and accumulating lines of evidence are suggesting that hyaluronan overproduction in cancer cells and tumor stroma is a key event in the acquisition of malignant characteristics. Cancer tissue is composed of cancer cells and recruited host stromal cells, such as TAFs, and the microenvironment created by the interactions of these cells is closely associated with tumor proliferation and angiogenesis. In addition, the hyaluronan-rich tumor microenvironment appears to promote angiogenesis and lymphangiogenesis through tumor-stromal cell interaction, thereby synergistically accelerating tumor progression. It is our hope that further elucidation of the detailed mechanisms by which hyaluronan promotes cancer progression will lead to the development of new therapeutic strategies. Acknowledgements  I am grateful for Dr Shun’ichiro Taniguchi (Shinshu University Graduate School of Medicine) for his support. This work was supported in part by grants-in-aid for scientific research from the Japan Society for the Promotion of Science.

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Chapter 8

Function of Tenascins in the Tumor Stroma Florence Brellier and Ruth Chiquet-Ehrismann

8.1 Introduction In 1983, Bourdon et  al. (1983) first described glioma-mesenchymal extracellular matrix (GMEM) antigen. Shortly thereafter it became clear that GMEM antigen, now known as tenascin-C, was expressed not only in brain tumors but also in rat mammary tumors (Chiquet-Ehrismann et al. 1986) and in human breast cancer (Mackie et al. 1987). Work that followed demonstrated that tenascin-C was, in fact, overexpressed in almost all solid tumors (for review see Orend and Chiquet-Ehrismann 2006). In addition to tenascin-C there are three other proteins that belong to the tenascin family: tenascin-R, tenascin-X and tenascin-W (for review see Chiquet-Ehrismann 2004; Tucker et al. 2006). The four tenascins fall into two groups that can be distinguished from each other by their patterns of expression. The first group includes tenascin-R and tenascin-X, which have stabile, restricted expression patterns and are present both in developing and in adult tissues. In contrast, tenascin-C and tenascin-W, making up the second group, are mostly absent in the adult organism, are regulated by the tissue microenvironment, and are highly induced in cancer stroma (Tucker and Chiquet-Ehrismann 2009). Therefore, this chapter will summarize the evidence for an important role of these two tenascins, tenascin-C and tenascin-W, in tumor stroma as well as their role in cancer progression.

8.2 Structure, Regulation and Interactions of Tenascins All tenascins are large oligomeric extracellular matrix glycoproteins built from four different building blocks. This is depicted in Fig.  8.1 for human tenascin-C and tenascin-W. The building blocks include an N-terminal oligomerization domain consisting of 3–4 heptad repeats for trimerization flanked by disulphide bridges R. Chiquet-Ehrismann () Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerstrasse 66, 4058 Basel, Switzerland e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_8, © Springer Science+Business Media B.V. 2011

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c Oligomerization domain EGF-like repeat Fibronectin type 3 domain Fibrinogen-related domain

Fig. 8.1   Domain structure of tenascins. a Each tenascin subunit is built up from several different domains. In the case of human tenascin-C there are 8 constant fibronectin type 3 domains and 9 additional ones (shown below the bracket) that can be included or excluded in the mature protein in many different combinations. Tenascin-W exists as a single version of the protein. b Tenascin-C as well as tenascin-W assemble as hexameric proteins by oligomerization including covalent disulfide crosslinks in the central oligomerization domain. c Legend for the building blocks used to create the protein models

covalently crosslinking the three subunits. Furthermore, an N-terminal extension includes additional cysteines that lead to dimerization of the trimers as shown in Fig. 8.1b. This N-terminal region is followed by a series of epidermal growth factorlike repeats (EGF), fibronectin type 3 domains (FN3) and a C-terminal fibrinogenrelated domain (FReD). The number of EGF-repeats ranges from 3 in tenascin-W to 14 in tenascin-C. The number of FN3 domains also differs, with 9 FN3 domains in tenascin-W to 8 constant FN3 domains in tenascin-C where an additional 9 FN3 domains can be included based on alternative splicing. Thus, there exist many different splice variants of tenascin-C that can have different functions (see below) while tenascin-W exists as a single version of the protein.

8.2.1  Tenascin-C The chromosomal location of the tenascin-C gene is 9q33.1 (Degen and ChiquetEhrismann 2008a). Interestingly, this locus has recently been implicated in pro-

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gression of childhood ependymomas (Puget et al. 2009). Using a candidate gene strategy the authors found overexpression of two potential oncogenes, namely tenascin-C and Notch1. This is interesting, since it is known that Notch signaling induces tenascin-C expression (Sivasankaran et al. 2009). There exist many other signaling pathways that induce tenascin-C expression. These signaling pathways are initiated by the action of transforming growth factor-beta, fibroblast growth factors, platelet-derived growth factors, tumor necrosis factor-alpha or even by mechanical strain (for a recent review see Tucker and Chiquet-Ehrismann 2009). Once tenascin-C is expressed it can interact with cell surface receptors and other extracellular matrix proteins triggering cellular responses such as cell proliferation, de-adhesion and migration. One of the most important binding partners of tenascin-C that greatly influences cell physiology is fibronectin (Chiquet-Ehrismann et al. 1991). Mixed substates of fibronectin and tenascin-C can induce collagenase expression in fibroblasts (Tremble et al. 1994) as well as inhibit cell spreading by suppression of Rho activation (Wenk et  al. 2000) through blocking access of the transmembrane proteoglycan syndecan-4 to fibronectin (Huang et al. 2001; Midwood et al. 2004). Other ligands of tenascin-C are the proteoglycans neurocan and phosphacan, the latter being the extracellular domain of protein-tyrosine phosphatase-zeta/beta (Milev et  al. 1994). Furthermore, several integrins have been implicated in binding to tenascinC (Ramos et  al. 1997; Sriramarao et  al. 1993; Yokosaki et  al. 1998; Yokoyama et  al. 2000). Finally, toll-like receptor-4 (TLR4) has recently been shown to be activated by tenascin-C, inducing a signaling cascade that results in the secretion of inflammary cytokines. The FReD domain of tenascin-C was shown to be responsible for this activity (Midwood et al. 2009). In this study it was found that the presence of tenascin-C is a crucial factor mediating persistent inflammation and tissue destruction in a mouse model of arthritic joint disease. Since tumors can be seen as over-healing wounds with chronic inflammation (Schafer and Werner 2008) we speculate that stromal tenascin-C could contribute to this type of derailment in cancer.

8.2.2  Tenascin-W The gene encoding human tenascin-W is located on chromosome 1q25.1 in a tailto-tail configuration next to the tenascin-R gene (Degen and Chiquet-Ehrismann 2008b). Tenascin-W is a tightly regulated protein with even more restricted expression than tenascin-C. Its major site of expression is during bone development (Scherberich et al. 2004). Tenascin-W expression can be regulated by various factors such as bone morphogenic protein (BMP) 2 and tumor necrosis factor-alpha (Scherberich et  al. 2005). It is similarly deposited in the extracellular matrix as tenascin-C, but its interaction partners remain to be identified.

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8.3 Localization of Tenascins in Tumors 8.3.1  Tenascin-C In adult healthy tissues, tenascin-C expression is low and is mainly restricted to tendons, ligaments, and some smooth muscles. In contrast, tumors arising in many different epithelial organs such as breast, colon, prostate, kidney, pancreas, lung, skin, bladder, uterus and ovaries often express high levels of tenascin-C (for a recent review see Orend and Chiquet-Ehrismann 2006). In most epithelial cancers, the cellular source of tenascin-C is not the tumor cells themselves, but rather tumorassociated fibroblasts residing in the tumor stroma. Immunohistochemical analyses of these tumors usually reveal a fibrous network of tenascin-C enclosing unstained tumor nests. Examples of this in breast and colon carcinomas are shown in Fig. 8.2. At the invasive front, where tumor cells are not encapsulated anymore but leave the nests to invade the neighboring normal tissue, tenascin-C is often highly expressed suggesting a link between tenascin-C and tumor aggressiveness (Jahkola et al. 1996). Consistent with this observation, a majority of the studies report a correlation between expression of tenascin-C and bad prognosis (summarized in Orend and Chiquet-Ehrismann 2006). Other solid tumors such as brain tumors or melanomas show a different pattern of tenascin-C expression. In these cases, the tumor cells themselves often secrete tenascin-C. An example of this expression in a glioblastoma is illustrated in Fig. 8.2. In glioblastoma, stromal tenascin-C expression is correlated with shorter patient survival (Leins et al. 2003). In many brain tumors, tenascin-C has also been detected around blood vessels (Zagzag et al. 1995). Our recent study shows that tenascin-C staining encloses both von Willebrand factor and desmin expressing cells, demonstrating that tenascin-C is found not only around the endothelial cells but also encapsulates the next cell layer composed of pericytes (Martina et al. 2010). This suggests that endothelial cells as well as pericytes could be sources of tenascin-C expression. Perivascular staining of tenascin-C was found to correlate with a shorter disease-free time in astrocytoma patients suggesting that tenascin-C may serve as prognostic marker for an earlier tumor recurrence (Herold-Mende et al. 2002). We mentioned above the existence of distinct tenascin-C isoforms. It is interesting to note that larger isoforms are often tumor-specific. For example, in high grade astrocytomas large tenascin-C variants containing the FN3-C domain are abundant around vascular structures and proliferating cells (Carnemolla et al. 1999). Also, FN3-B domain containing isoforms are associated with invasion fronts in ductal breast cancer (Tsunoda et al. 2003) and those with FN3-A1 domain are found in the majority of lymphomas (Schliemann et al. 2009). Part of the reason for the distinct isoform expression pattern between healthy and tumor tissues could be based on the fact that the extracellular pH influences the splicing of tenascin-C mRNA (Borsi et al. 1996). Thus large tenascin-C isoforms are expected to be enriched in tumors known to represent an acidic tissue. Increased acid production due to altered glucose metabolism

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Fig. 8.2   Tenascin-C and tenascin-W expression in tumors. Immunostaining (in brown) reveals stromal deposition of tenascin-C and tenascin-W in breast and colon carcinomas. In glioblastoma tenascin-C is detected throughout the tumor tissue including strong staining of the blood vessels, whereas tenascin-W is found in blood vessels only

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leads to an H+ flow along into adjacent normal tissue and promotes acid-mediated tumor invasion (Gatenby et al. 2006). We speculate that this phenomenon is potentiated by expression of large tenascin-C variants. The presence of large tenascin-C variants in tumors can have additional functional consequences since this will also influence the susceptibility of tenascin-C to proteases (Ambort et al. 2010; Siri et al. 1995).

8.3.2  Tenascin-W Our knowledge of tenascin-W expression in tumors is much more restricted. Analyses of mammary tumors, first in mouse (Scherberich et al. 2005), and then in human (Degen et al. 2007) revealed overexpression of tenascin-W in tumor stroma. Interestingly, the latter study showed that low-grade breast tumors are enriched in tenascin-W compared to high-grade ones, but that normal tissues are devoid of tenascin-W. We also reported that tenascin-W is overexpressed in colon carcinoma, but not in healthy colon tissues (Degen et al. 2008). More recently, brain tumors have been analyzed for tenascin-W expression. Interestingly, all glioma subtypes tested, i.e. oligodendroglioma, astrocytoma and glioblastoma, are enriched in tenascin-W compared to healthy control brain tissue where tenascin-W is not detectable (Martina et al. 2010). The cellular source of tenascin-W differs in different types of tumors. In breast and colon carcinomas, tenascin-W is exclusively stromal and often shows a pattern very similar to tenascin-C (Fig. 8.2). In contrast, tenascinW is restricted to blood vessels in gliomas, where it was shown to colocalize with von Willebrand factor (Martina et al. 2010). According to our immunohistochemical analysis of various types of brain tumors, tenascin-W, in contrast to tenascin-C, is not produced by the tumor cells themselves. Overexpression of tenascin-W in tumors is particularly interesting since tenascin-W is not expressed in many of the normal tissues where it is upregulated after tumorigenesis. This is in contrast to tenascin-C, which is expressed, albeit at lower levels than in the embryo, in healthy adult colon and brain. These observations suggest that tenascin-W could be a more specific tumor biomarker at least for colon and brain tumors. In order to increase the list of tumors rich in tenascin-W it will be important to extend this analysis to other types of tumors. Identification of a novel biomarker would be particularly useful if it could be detected in the circulation. Several studies have shown elevated levels of tenascin-C in serum from patients with various tumors compared to those collected in healthy individuals (Burchardt et al. 2003; Pauli et al. 2002; Riedl et al. 1995). However, high tenascin-C levels may not only be a sign for the presence of a tumor, but also a sign of infections and inflammatory diseases (Schenk et al. 1995). Since tenascinW has yet to be associated with pathologies other than cancer, it may be a superior tumor marker than tenascin-C. Analysis of tenascin-W serum levels in patients with colon and breast tumors showed indeed elevated levels compared to healthy individuals, with a fold induction even higher than for tenascin-C (Degen et  al. 2008). However, as for tenascin-C, values scattered over a very wide range and a

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sizeable number of cancer patients showed similar values as control individuals. More extensive studies need to be performed to strengthen the biomarker potential of tenascin-W.

8.4 Function of Tenascins in Tumors Pro-tumorigenic molecules can have an impact on tumor development at many levels. They may act directly on tumor cells by modulating their proliferation, adhesion properties and migration. In addition, they may influence endothelial cells to increase neovascularization of the developing tumor to supply it with oxygen and nutrients. Finally, they may act on the metastasis process, for instance by promoting the accessibility and intravasation of tumor cells into blood or lymphatic vessels or by providing a “welcoming” site, the metastatic niche, for the growth of a secondary tumor. The possible roles for tenascins in these events are discussed below.

8.4.1  Tenascin-C 8.4.1.1 Impact on Cultured Tumor Cells One of the main characteristics of tenascin-C is its “anti-adhesion” or “inhibition of spreading” capacity. Indeed, tumor cells which spread and adhere nicely in vitro on fibronectin substrates lose their attachment in the presence of tenascin-C (Chiquet-Ehrismann et al. 1988). This is an important aspect to mention since adhesion and migration are tightly linked processes and an intermediate state of adhesion is required for cells to be able to move (for a review see Murphy-Ullrich 2001). The state of adhesion provided by a tenascin-C rich matrix promotes cell migration (Deryugina and Bourdon 1996; Nishio et al. 2005) as well as cell invasion (De Wever et al. 2004). These activities seem to be more pronounced in large tenascin-C variants containing FN3-B and -D as shown in breast cancer models of cell migration and invasion in response to co-cultured fibroblasts transfected with constructs encoding different tenascin-C variants (Hancox et  al. 2009). Furthermore, cell proliferation is also known to be affected by the adhesion state of the cells and tumor cell proliferation has been shown to be stimulated by tenascin-C (Chiquet-Ehrismann et al. 1986; Huang et al. 2001). 8.4.1.2 Impact on Angiogenesis The first evidence that tenascin-C affects the behavior of endothelial cells was established in 1995 when Canfield and Schor showed that tenascin-C promotes the transition from the resting state to the sprouting state, and that this effect could be reversed by addition of anti-tenascin C blocking antibodies (Canfield and Schor

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1995). More specifically, tenascin-C induces mitogenesis, loss of focal adhesions and migration of endothelial cells (Chung et al. 1996). The region of the protein mediating this effect could be its FReD, which was shown to decrease the adhesion of endothelial cells thereby shifting the cells towards an angiogenic phenotype, at least when induced by basic fibroblast growth factor (Schenk et al. 1999). Others have indirectly strengthened these studies by reporting a correlation between tenascinC immunostaining and vascular proliferation and angiogenesis, especially in brain tumors (Kim et al. 2000; Zagzag et al. 1995), but also in breast cancer (Tokes et al. 1999) and in B-cell non-Hodgkin’s lymphomas (Vacca et al. 1996). Furthermore, serum levels of tenascin-C have been reported to correlate with microvessel density of non-small cell lung cancers (Ishiwata et al. 2005).

8.4.1.3 Impact on Metastasis Two independant studies aiming at the identification of genes associated with high metastatic potential in the context of vascular endothelial growth factor (VEGF) (Calvo et  al. 2008) and microRNA regulation (Tavazoie et  al. 2008) have highlighted the key role of tenascin-C in breast cancer metastasis. The latter study reported that miR-335 inhibits metastasis by regulating a set of genes, among which the transcription factor SOX4 and tenascin-C were further analyzed. Although, according to the analysis of a large cohort of tumors, collective expression of a large set of genes is associated with the risk of lung metastasis, knock-down of tenascin-C expression alone in breast tumor cells decreased their propensity to metastasize in a mouse xenograft model. In parallel, a second team listed tenascin-C among the transcripts associated with increased metastatic potential in mammary tumors of myc/VEGF transgenic mice (Calvo et al. 2008). After confirmation of such an association in a cohort of human patients, they could also show that downregulation of tenascin-C decreased the rate of experimental metastasis formation. However, the authors acknowledge that the contribution of tenascinC to the process of metastasis may depend on the initial oncogenic alterations since in other contexts such as PyMT induced mammary cancer, lack of tenascin-C did not reduce metastasis (Qiu et al. 2004). The human relevance of these experimental tumor models of metastasis are supported by the correlations that have been found between tenascin-C expression and the survival of cancer patients (Brunner et al. 2004; Ishiwata et al. 2005; Kaarteenaho-Wiik et al. 2003; Leins et al. 2003).

8.4.2  Tenascin-W 8.4.2.1 Impact on Cultured Tumor Cells Similarly to tenascin-C, tenascin-W does not sustain breast cancer cell adhesion in vitro when used as a single substrate. However, in contrast to tenascin-C,

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tenascin-W neither inhibits spreading of cells on fibronectin (Degen et al. 2007) nor promotes their proliferation (Scherberich et  al. 2005). Tenascin-W can, however, stimulate cell migration and mammary cancer cells of mouse (Scherberich et  al. 2005) or human (Degen et al. 2007) origin showed increased transfilter migration in the presence of tenascin-W. 8.4.2.2 Impact on Angiogenesis How tenascin-W impacts the behavior of endothelial cells was recently investigated in our lab following the observation that tenascin-W is localized around gliomaassociated blood vessels (Martina et  al. 2010). We observed that tenascin-W induces morphologic changes in human umbilical vein endothelial cells by shifting a fraction of the cells from a regular, ‘cobblestone-like’ shape to an elongated, bipolar shape. Furthermore, tenascin-W stimulated migration of endothelial cells and induced sprout formation in spheroid assays, establishing tenascin-W as a novel pro-angiogenic factor. Considering that tenascin-W expression is induced by BMPs, and that several BMP members promote angiogenesis in tumors (Langenfeld and Langenfeld 2004; Ren et al. 2007; Rothhammer et al. 2007), tenascin-W could be a mediator of BMP action in tumors. 8.4.2.3 Impact on Metastasis Up to now, no report about a potential effect of tenascin-W on metastasis has been published. However, since metastasis can involve dissemination of cancer cells through the circulation the pro-angiogenic effect of tenascin-W may indirectly contribute to the metastatic process by promoting vessel formation.

8.5 Conclusion The main features of tenascin-C and tenascin-W expression in different types of cancers are summarized in Fig. 8.3. Tenascin-C and tenascin-W are important extracellular matrix proteins that are highly expressed in the tissue microenvironment of the majority of cancers. In carcinomas these tenascins are, with few exceptions, expressed by the cancer-associated fibroblasts of the tumor stroma. Often tenascin-C is preferentially localized at the invasive front of a primary tumor and seems to promote local invasion and metastasis. In other cancers such as melanoma or glioblastoma, the cancer cells themselves are secreting tenascin-C. In contrast, tenascin-W is not expressed by brain cancer cells, but it is present in brain cancer blood vessels and it seems to be implicated in angiogenesis of oligoendrogliomas as well as highly invasive glioblastomas. In contrast to oligodendrogliomas, glioblastomas are rich in tenascin-C throughout the tumor and again tenascin-C has been associated with

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Fig. 8.3   Summary of the expression and function of tenascins in cancer. In most carcinomas tenascin-C ( blue color) as well as tenascin-W ( green color) are produced by cancer-associated stromal fibroblasts and tenascin-C was reported to be enriched at sites of local invasion and to promote metastasis. In oligodendroglioma both tenascins are highly expressed in blood vessels where the tenascin-W is often surrounded by tenascin-C staining. The same vessel staining is present in glioblastoma but tenascin-C is also present throughout the tumor tissue and seems to contribute to the invasive behavior of this type of cancer

local invasion of this aggressive tumor type. Based on these features of tenascin-C and tenascin-W in the majority of all cancers it will be important to find ways of interfering with tenascin-C and tenascin-W expression and/or function in tumor tissue. Additionally, the prominent presence of tenascin-C and -W in tumors could be used for targeted immunotherapy as addressed in a later chapter of this book.

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Jahkola T, Toivonen T, von Smitten K, Blomqvist C, Virtanen I (1996) Expression of tenascin in invasion border of early breast cancer correlates with higher risk of distant metastasis. Int J Cancer 69:445–447 Kaarteenaho-Wiik R, Soini Y, Pollanen R, Paakko P, Kinnula VL (2003) Over-expression of tenascin-C in malignant pleural mesothelioma. Histopathology 42:280–291 Kim CH, Bak KH, Kim YS, Kim JM, Ko Y, Oh SJ, Kim KM, Hong EK (2000) Expression of tenascin-C in astrocytic tumors: its relevance to proliferation and angiogenesis. Surg Neurol 54:235–240 Langenfeld EM, Langenfeld J (2004) Bone morphogenetic protein-2 stimulates angiogenesis in developing tumors. Mol Cancer Res 2:141–149 Leins A, Riva P, Lindstedt R, Davidoff MD, Mehraein P, Weis S (2003) Expression of tenascinC in various human brain tumors and its relevance for survival in patients with astrocytoma. Cancer 98:2430–2439 Mackie EJ, Chiquet-Ehrismann R, Pearson CA, Inaguma Y, Taya K, Kawarada Y, Sakakura T (1987) Tenascin is a stromal marker for epithelial malignancy in the mammary gland. Proc Natl Acad Sci U S A 84:4621–4625 Martina, E, Degen M, Rüegg C, Merlo A, Lino MM, Chiquet-Ehrismann R, Brellier F (2010) Tenascin-W is a specific marker of glioma-associated blood vessels and stimulates angiogenesis in vitro. Faseb J 24:778–787 Midwood KS, Valenick LV, Hsia HC, Schwarzbauer JE (2004) Coregulation of fibronectin signaling and matrix contraction by tenascin-C and syndecan-4. Mol Biol Cell 15:5670–5677. Midwood K, Sacre S, Piccinini AM, Inglis J, Trebaul A, Chan E, Drexler S, Sofat N, Kashiwagi M, Orend G, Brennan F, Foxwell B (2009) Tenascin-C is an endogenous activator of Toll-like receptor 4 that is essential for maintaining inflammation in arthritic joint disease. Nat Med 15:774–780 Milev P, Friedlander DR, Sakurai T, Karthikeyan L, Flad M, Margolis RK, Grumet M, Margolis RU (1994) Interactions of the chondroitin sulfate proteoglycan phosphacan, the extracellular domain of a receptor-type protein tyrosine phosphatase, with neurons, glia, and neural cell adhesion molecules. J Cell Biol 127:1703–1715 Murphy-Ullrich JE (2001) The de-adhesive activity of matricellular proteins: is intermediate cell adhesion an adaptive state? J Clin Invest 107:785–790 Nishio T, Kawaguchi S, Yamamoto M, Iseda T, Kawasaki T, Hase T (2005) Tenascin-C regulates proliferation and migration of cultured astrocytes in a scratch wound assay. Neuroscience 132:87–102 Orend G, Chiquet-Ehrismann R (2006) Tenascin-C induced signaling in cancer. Cancer Lett 244:143–163 Pauli C, Stieber P, Schmitt UM, Andratschke M, Hoffmann K, Wollenberg B (2002) The significance of Tenascin-C serum level as tumor marker in squamous cell carcinoma of the head and neck. Anticancer Res 22:3093–3097 Puget S, Grill J, Valent A, Bieche I, Dantas-Barbosa C, Kauffmann A, Dessen P, Lacroix L, Geoerger B, Job B, Dirven C, Varlet P, Peyre M, Dirks PB, Sainte-Rose C, Vassal G (2009) Candidate genes on chromosome 9q33–34 involved in the progression of childhood ependymomas. J Clin Oncol 27:1884–1892 Qiu TH, Chandramouli GV, Hunter KW, Alkharouf NW, Green JE, Liu ET (2004) Global expression profiling identifies signatures of tumor virulence in MMTV-PyMT-transgenic mice: correlation to human disease. Cancer Res 64:5973–5981 Ramos DM, Chen BL, Boylen K, Stern M, Kramer RH, Sheppard D, Nishimura SL, Greenspan D, Zardi L, Pytela R (1997) Stromal fibroblasts influence oral squamous-cell carcinoma cell interactions with tenascin-C. Int J Cancer 72:369–376 Ren R, Charles PC, Zhang C, Wu Y, Wang H, Patterson C (2007) Gene expression profiles identify a role for cyclooxygenase 2-dependent prostanoid generation in BMP6-induced angiogenic responses. Blood 109:2847–2853

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Riedl S, Bodenmuller H, Hinz U, Holle R, Moller P, Schlag P, Herfarth C, Faissner A (1995) Significance of tenascin serum level as tumor marker in primary colorectal carcinoma. Int J Cancer 64:65–69 Rothhammer T, Bataille F, Spruss T, Eissner G, Bosserhoff AK (2007) Functional implication of BMP4 expression on angiogenesis in malignant melanoma. Oncogene 26:4158–4170 Schafer M, Werner S (2008) Cancer as an overhealing wound: an old hypothesis revisited. Nat Rev Mol Cell Biol 9:628–638 Schenk S, Chiquet-Ehrismann R, Battegay EJ (1999) The fibrinogen globe of tenascin-C promotes basic fibroblast growth factor-induced endothelial cell elongation. Mol Biol Cell 10:2933–2943 Schenk S, Muser J, Vollmer G, Chiquet-Ehrismann R (1995) Tenascin-C in serum: a questionable tumor marker. Int J Cancer 61:443–449 Scherberich A, Tucker RP, Degen M, Brown-Luedi M, Andres AC, Chiquet-Ehrismann R (2005) Tenascin-W is found in malignant mammary tumors, promotes alpha8 integrin-dependent motility and requires p38MAPK activity for BMP-2 and TNF-alpha induced expression in vitro. Oncogene 24:1525–1532 Scherberich A, Tucker RP, Samandari E, Brown-Luedi M, Martin M, Chiquet-Ehrismann R (2004) Murine tenascin-W: a novel mammalian tenascin expressed in kidney and at sites of bone and smooth muscle development. J Cell Sci 117:571–581 Schliemann C, Wiedmer A, Pedretti M, Szczepanowski M, Klapper W, Neri D (2009) Three clinical-stage tumor targeting antibodies reveal differential expression of oncofetal fibronectin and tenascin-C isoforms in human lymphoma. Leuk Res 33:1718–1722 Siri A, Knauper V, Veirana N, Caocci F, Murphy G, Zardi L (1995) Different susceptibility of small and large human tenascin-C isoforms to degradation by matrix metalloproteinases. J Biol Chem 270:8650–8654 Sivasankaran B, Degen M, Ghaffari A, Hegi ME, Hamou MF, Ionescu MC, Zweifel C, Tolnay M, Wasner M, Mergenthaler S, Miserez AR, Kiss R, Lino MM, Merlo A, Chiquet-Ehrismann R, Boulay JL (2009) Tenascin-C is a novel RBPJkappa-induced target gene for Notch signaling in gliomas. Cancer Res 69:458–465 Sriramarao P, Mendler M, Bourdon MA (1993) Endothelial cell attachment and spreading on human tenascin is mediated by alpha 2 beta 1 and alpha v beta 3 integrins. J Cell Sci 105:1001–1012 Tavazoie SF, Alarcon C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massague J (2008) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451:147–152 Tokes AM, Hortovanyi E, Kulka J, Jackel M, Kerenyi T, Kadar A (1999) Tenascin expression and angiogenesis in breast cancers. Pathol Res Pract 195:821–828 Tremble P, Chiquet-Ehrismann R, Werb Z (1994) The extracellular matrix ligands fibronectin and tenascin collaborate in regulating collagenase gene expression in fibroblasts. Mol Biol Cell 5:439–453 Tsunoda T, Inada H, Kalembeyi I, Imanaka-Yoshida K, Sakakibara M, Okada R, Katsuta K, Sakakura T, Majima Y, Yoshida T (2003) Involvement of large tenascin-C splice variants in breast cancer progression. Am J Pathol 162:1857–1867 Tucker RP, Chiquet-Ehrismann R (2009) The regulation of tenascin expression by tissue microenvironments. Biochim Biophys Acta 1793:888–892 Tucker RP, Drabikowski K, Hess JF, Ferralli J, Chiquet-Ehrismann R, Adams JC (2006) Phylogenetic analysis of the tenascin gene family: evidence of origin early in the chordate lineage. BMC Evol Biol 6:60 Vacca A, Ribatti D, Fanelli M, Costantino F, Nico B, Di Stefano R, Serio G, Dammacco F (1996) Expression of tenascin is related to histologic malignancy and angiogenesis in b-cell nonHodgkin’s lymphomas. Leuk Lymphoma 22:473–481 Wenk MB, Midwood KS, Schwarzbauer JE (2000) Tenascin-C suppresses Rho activation. J Cell Biol 150:913–920 Yokosaki Y, Matsuura N, Higashiyama S, Murakami I, Obara M, Yamakido M, Shigeto N, Chen J, Sheppard D (1998) Identification of the ligand binding site for the integrin alpha9 beta1 in the third fibronectin type III repeat of tenascin-C. J Biol Chem 273:11423–11428

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Yokoyama K, Erickson HP, Ikeda Y, Takada Y (2000) Identification of amino acid sequences in fibrinogen {gamma}-chain and tenascin C C-terminal domains critical for binding to integrin {alpha}v{beta}3. J Biol Chem 275(22):16891–16898 Zagzag D, Friedlander DR, Miller DC, Dosik J, Cangiarella J, Kostianovsky M, Cohen H, Grumet M, Greco MA (1995) Tenascin expression in astrocytomas correlates with angiogenesis. Cancer Res 55:907–914

Chapter 9

Fibulins and Their Role in the ECM Helen C. M. Cooney and William M. Gallagher

9.1 Physiochemical Structure and Composition The fibulins are a group of glycoproteins that function predominantly though not exclusively in the extracellular matrix (ECM). In common with the fibrillins and hemicentins, which are also ECM protein families, their basic characteristic is an FC (fibulin-type) unit at the protein C-terminal preceded by an array of adjoining cbEGF (Ca++ binding epidermal growth factor-like) modules of varying number. They may have up to four protein domains (N, I, II, III) and can be subdivided into two main subgroups (1 and 2) (Timpl et  al. 2003). Subgroup 1, which contains fibulin-1 and fibulin-2, has the largest molecules (Table 9.1). Fibulin-1 can have up to three protein domains but fibulin-2 is unique in also containing a large N domain preceding domain I at the N terminus of the protein. Subgroup 2 contains fibulin-3, fibulin-4 and fibulin-5 and these have three domains, one of which is very small. These complete molecules are much smaller than either of those in subgroup 1. The new fibulins, fibulin-6 and fibulin-7 are only being characterized presently. Fibulin-1 has approximately half as many amino acid residues as fibulin-2, though the degree of glycosylation can also affect their molecular size. Fibulin-1 has three main protein domains and a signal peptide at the N terminal end which is necessary for secretion of the protein from the cell following processing in the Golgi apparatus. Fibulin-1 has four genetic variants due to alternative splicing. These are fibulin 1A, 1B, 1C and 1D and have important functional significance. As shown in Fig.  9.1, the fibulin-1 molecule is globular at both the N terminal and C terminal ends and rod shaped in the central region (i.e. it is dumb-bell shaped). Domain III at the C-terminal end is the typical fibulin module and is present to a variable degree in the fibulin-1 genetic variants being absent in fibulin 1A and very prominent in fibulin 1C and fibulin 1D. Fibulin-1 has an N-terminal end H. C. M. Cooney () 73 Nutley Lane, Donnybrook, Dublin 4, Ireland e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_9, © Springer Science+Business Media B.V. 2011

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Table 9.1   Characteristics of subgroup 1 of the fibulin family Subgroup 1 Variants of Fibulin 1

Fibulin 2

Fibulin 1A

Fibulin 1B

Fibulin 1C

Fibulin 1D

No of Amino acid residues (Timpl et al. 2003)

566

601

683

703

Domains

I,II

I,II,III

I,II,III

I,II,III

1204

N,I,II&III

(short)

THIOL GROUPS

Size (kDa) (Vega et al. 2009) DOMAIN 1 DOMAIN 2

100

––––––––

1–3 Anaphylatoxin-like modules (multiple disulphide bonds) EGF-like modules – usual 3 disulphide bonds

195

6 SH groups – 2 anaphylotoxin modules 5 SH groups – one odd (other module) – dimerization 6 SH groups in all EGF-like modules.

Glycosylation Glycosylation(glucosamine) is mainly in High galactose residues in Nb the rod-like domain II and not in the Domain I

Structure/ Conformation

Dumb bell-like structure

Form under Physiological Conditions

MONOMERS (or loose aggregates)

Length

30 nm

Dumb bell portion with rodlike N terminal monomer

DIMERS Antiparallel association of monomers

70 nm monomer 80 nm dimer

composed of domain I and made up of three anaphylatoxin modules. These modules are common to a number of extracellular proteins and their structure and function has been studied extensively when they first came to attention as part of the complement system.

9  Fibulins and Their Role in the ECM I

Fib 1

161 II

Legend

III

1 2 3 4 5 6 7 8 9 10 11

Fibulin module

N

Fib 2

Na Nb

1 2 3 4 5 6 7 8 9 10 11

I

II

Fib 3

1 2 3 4 5

Fib 4

1 2 3 4 5

Fib 5

1 2 3 4 5

Fib 6

1 2 3 4 5 6 7 8

Fib 7

S 1 2 3

cbEGF-like modules

III Na Nb

S

N domain

Sushi domain

Fibulin module with three anaphylatoxin modules Modified cbEGF-like modules Von Willebrand factor domain C2 Nidogen Domain TSP-1 Repeats Immunoglobulin C-2 Domain

Fig. 9.1   Structural and modular make-up of members of the fibulin family

Anaphylatoxin C3a is derived from complement component C3. By X-ray crystallography the molecule has dimensions 42 × 22 × 16 Å and is drumstick shaped. It has two connected helical segments (Tyr 15 → Met 27 + Gly 46 → Ser 71) and 3 disulphide bonds. Cys 22–49, Cys 23–56 and Cys 36–57 (Huber et al. 1980). Hugli studied a number of closely related anaphylatoxins (Hugli et al. 1986). They suggest that there is a consensus conformation for each and the binding site, which is at the C-terminal end. It contains essential residues LGLAR folded irregularly as a pseudo β turn which is stabilized by the adjacent helical segments. Domain II is the central rod-like portion of the fibulin-1 molecule and comprises a line-up of nine calcium binding epidermal growth factor-like (cbEGF-like) modules. Most modules are calcium binding and they have a consensus sequence which binds Ca++ and can possibly be used for ligation to other proteins. EGF was originally characterized by Cohen and others (Savage et al. 1972; Cohen and Elliott 1989) as a heat stable protein and lacking the three amino acids, phenylalanine, alanine and lysine. It has 53 amino acids with 6 thiol groups forming 3 disulphide bonds. Its biological activity manifests itself as precocious eruption of teeth and eye-lid separation in newborn mice and rats. Full activity remains if the

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six C-terminal amino acids are clipped off. The cbEGF-like modules in fibulins are about 40 amino acids long and have 3 disulphide bonds and are also present in many other proteins of the ECM. Both the fibulins and fibrillins share elements of domain III and both contain the FC (fibulin-type) module at their C-terminal end. This module is approximately 120 amino acid residues long. An alignment of the 120 residues shows an invariant cysteine in two positions at the N-terminal end and also a similar invariant proline (Giltay et al. 1999). In the central portion there is a consensus sequence (XXGNXXXXF). 23% of this protein is shared by at least 6 proteins. It is globular in its conformation (Fig. 9.1). Fibulin-2 is the largest of the fibulin molecules being 190–200 kDa molecular weight approximately double that of fibulin-1 95 kDa (Sasaki et al. 1997). Referring to Fig. 9.1 it can be seen that fibulin-2 has the same three domains I, II and III as fibulin-1 and also a further domain unique to fibulin-2 of approximately 400 amino acid residues, at the N terminal end and called the N domain (Kobayashi et al. 2007). It has a rod-like structure. The first 150 amino acids, which is called the Na region is rich in cysteine residues. The second region of approximately 250 amino acids is free of cysteine but is particularly rich in galactosamine. Sufficient Thr and Ser with their –OH side groups are available in region Nb to carry the galactosamine residues found. Under physiological conditions fibulin-2 exists as a homodimer (Sasaki et  al. 1997). The following evidence would support this statement; 1. Oligomerization was shown to occur through disulphide bonds and this was demonstrated readily on SDS gel electrophoresis as a new band appeared under reducing conditions. 2. Studies on the individual domains showed that domain I was the site of bonding between the molecules. 3. Examination of the sequences of the three anaphylatoxin modules showed that one of the cysteine molecules was absent in the middle module (Module 1b). This was cysteine 1 and only 5 cysteines were found in this module thus leaving an odd cysteine (Cys 574) available for interaction with other molecules. 4. Mutagenesis of the recombinant domain I and replacing 574 Cys by Ser gave rise to no disulphide linked dimers. 5. Domain II interacts with N domain as shown by surface plasmon resonance assay. The binding is facilitated by Ca++ and no binding is found in the presence of the Ca++ chelating agent, EDTA. 6. The homodimers are 80 nm in length whereas the monomers are almost as long at 70 nm (Table 9.1). Thus the monomers come together in a fashion that overlaps each other. Due to the fact that the N domain interacts with domain II and both domain I modules of each monomer must be aligned, each monomer must overlap the other in an antiparallel fashion, i.e. Head-to-toe (Fig. 9.2). The single disulphide bond holds both monomers in the dimer form by a single covalent bond but loose aggregates bonded by weaker bonds will form even under reducing conditions where the disulphide bond is broken.

9  Fibulins and Their Role in the ECM Fig. 9.2   Anti parallel association of fibulin-2 monomers

163

N

I

II

III Disulphide Bridge

Cys 574 Cys 574

III

II

I

N

Fibulins 3, 4 and 5 are much smaller than fibulin 1 and 2 of subgroup 1A though there are many similarities in their modular make-up (Fig. 9.1). Domain 3, the fibulin motif is a similar domain and common to all fibulins and fibrillins. Domain 2 has cbEGF-like repeats but the number is smaller than in fibulin 1 and 2 and is normally five. The main difference from subgroup 1 is in domain 1; instead of the three anaphylatoxin modules there is a modified cbEGF-like module (Table 9.1). It has 6 cysteine residues and disulphide bonds exist between 1 and 4, 2 and 5 and 3 and 6 (Giltay et al. 1999). Between cysteine 4 and 5 there is a polypeptide loop of 88, 28 and 44 amino acids long in fibulin 3, 4 and 5 respectively. The sequence identities between these fibulins are high at ∼50% and electron microscopy showed them as a short rod-like structure with a globular end. These exist as monomers under physiological conditions (Kobayashi et al. 2007) and are 53, 46.8 and 47.7 kDa for fibulin 3, 4 and 5 respectively. The molecular size varies and is considerably larger than the number of amino acids would predict, due to differences in O-glycosylation at threonine and serine residues and N-glycosylation at the two possible sites in these fibulins. They are about 20  nm long (Table  9.2). These three fibulins obviously show greater similarity between each other than with the fibulins of subgroup 1 though all the fibulins have many of the same basic modular components and similar functions. Fibulin 6 has a structure similar to the other fibulins in that it has the C-terminal fibulin domain preceded by a number cbEGF-like repeats, possibly up to ten. It contains a single nidogen module, a row of TSP-1 (Thrombosponin-1) modules, a row of approximately nine immunoglobin-type modules at the N-terminus and at it’s N-terminus it has a highly conserved Von Willebrand A domain (de Vega et al. 2007). Fibulin 6 distribution has been studied in human tissues (expressed from salivary gland epithelial cells) (Sisto et al. 2009). Fibulin 7 is similar structurally to the other fibulins in that it contains the same C-terminal domain III as all fibulins and fibrillins, namely the fibulin motifs (de Vega et  al. 2007). It also contains a line-up of cb-EGF-like modules but has the smallest number, just three. However, the N-terminal domain is totally unlike that of the other fibulins in having a Sushi domain also found in other proteins of the blood coagulation and complement systems. The Sushi domain has approximately 60 amino acids, contains two disulphide bonds and other conserved residues necessary for protein–protein interactions. Fibulin 7, overall have 440 amino acid residues, 3 domains and a signal peptide (Fig. 9.1). It is a secreted protein and interacts with extracellular molecules in teeth and other tissue such as hair and cartilage.

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Table 9.2   Characteristics of subgroup 2 of the fibulin family Subgroup 2

Fibulin 3

Fibulin 4

Fibulin 5

No of Amino acid residues (1)

466

416

367

Domains

I,II,III

I,II,III

I,II,III

80 (mouse) 63

61

66 (mouse) 64 (human)

Size (kDa) (Vega et al. 2009)

THIOL Groups

Glycosylation sites (Thr/Ser residues)

EGF MODULES - 6 Cys. Residues (Different positions in modules)

5

Structure/ Conformation

0

1 (Human) 3 (mouse)

Fib 4 has smaller glob. domains than 3 + 5. Short rods 20 nm long.

Form under Physiological Conditions

Length

ONLY MONOMERS NOT OLIGOMERS

20 nm

~20 nm

9.2 The Physiological Place and Role of Fibulins in the ECM The ECM (Extracellular matrix) has many major players in terms of protein molecules. These are mainly glycoproteins and also proteoglycans. They have many different functions. Collagen forms fibrils which give great mechanical strength to tendons and bones whereas elastic fibrils are responsible for the recoil component

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165

of tissues such as lung and the major arteries. Laminin, nidogen, proteoglycan and Collagen IV are very important in the make-up of the delicate basement membrance which compartmentalizes epithelial and endothelial cells, and thereby influences cell proliferation, adhesion, migration, differentiation and polarization. Some of these proteins may act as ligands for signalling cellular receptors and thereby modulate cellular functions. Integrins are not totally belonging to the ECM but are transmembrane cell adhesion glycoproteins. They interact via their intracellular domains with the actin cytoskeleton within cells and extracellularly with ligands such as the extracellular matrix glycoproteins, complement and other cells. Integrins bind their ligands more weakly than hormone receptors and their strength is due to their presence in large numbers. This fact allows the ligands to move and cells can carry out their functions. They have two noncovalently attached subunits (α and β). Integrin binding to ligands depend on Ca++ and Mg++ ions interacting with the cation binding domains of the extracellular portion of the integrins. Eight integrins made up of different α and β subunits bind fibronectin and five bind laminin. They act as signal transducers to promote cell growth, survival and proliferation. Many proteins exist as complex multimolecular structures, either by self aggregation or forming aggregates of different protein and proteoglycan molecules. Individual families of proteins while having similar domains have many types and several isoforms within each type. Collagen, laminin, integrins (transmembrane proteins) fibrillins and fibronectins have a large number of different types of molecules. Fibulin 1 was first isolated when it was found as a ligand for the short cytoplasmic tail of the β1 receptor in an affinity chromatography column (Argraves et al. 1989). Shortly thereafter it was found to contain a signal peptide and a structure characteristic of ECM proteins. Fibroblast cultures showed it was produced as extracellular fibrils. It could be isolated from mouse tumour basement membrane and was shown to be incorporated in the basement membrane by immunostaining (Timpl et al. 2003). Strong interactions of fibulin 1 with the laminins and nidogens of the basement membrane secured it a place in the basement membrane (Pan et al. 1993). ECM proteins may affect cell mobility positively (e.g. FN Fibronectin, thrombospondin-1 and collagen) or negatively (e.g. tenascin, osteonectin/SPARC aggrecan and versican). The role of fibulin 1 in cell mobility, adhesion and spreading has been studied in vitro (Twal et  al. 2001). The close relationship between fibulin-1 and fibronectin prompted the investigation of whether an interaction between fibulin-1 and fibronectin might regulate the cellular function. They firstly outruled that fibulin-1 on it’s own had a role in cellular mobility when studied in vitro on a number of cell lines namely [MDA MB231 breast carcinoma cells, A375 melanoma cells, gingival fibroblasts, BAECs or CEM lymphoblastic leukaemia cells, Molt-4 lymphoblastic leukaemia cells, HUVECs, WI-38 lung fibroblasts and HT1080 fibrosarcoma cells]. It was clearly shown that fibulin-1 has inhibitory effects on the attachment of nearly all the cell lines studied to FN coated surfaces. Fibulin-1 also decreased cell spreading similarly. Some cells that were transfected to overexpress fibulin-1 supported the previous finding in that they showed decreased spreading on FN surfaces compared with vector—transfected

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cells (i.e. lacking the fibulin gene) which spread as would normally be expected of them. FN stimulated cell mobility was affected by fibulin-1 and inhibited migration of a large number of the cell lines studied. However a number of cell lines mobility was not affected by fibulin-1 and thus the inhibition of this process is cell specific. Mesenchymal cells invasiveness into a collagen I gel was inhibited by fibulin-1 and this required both FN and fibulin-1 to create the signal for the process. These workers examined the possibility that the inhibitory effect of fibulin-1 on FN stimulation of cells could involve integrins as the binding site for fibulin-1 on FN is close to the Arg-Gly-Asp (RGD) site within FN that binds the integrin α5 β1 and another site which binds the integrin α4. The latter integrin α4 was shown not to be involved in fibulin-1 FN inhibition of cell movement. This was demonstrated using α4 deficient CHO cells (Chinese Hamster Ovary). When these deficient cells were compared with CHO cells transfected with α4 and β1 integrin subunits, there was found to be no difference in their fibulin-1 FN cell migration inhibition. The mechanism of fibulin-1 inhibition of the cell mobility promoting activity of FN is unexplained but it is suggested that a conformational change in FN, induced by fibulin-1, may reveal a cryptic anti-adhesive site. Fibulin-1 does not suppress the mobility promoting activity of Collagen I. However (Qing et al. 1997) using fibrosarcoma cells that expressed fibulin-1 showed that these exhibited a greatly reduced ability to migrate through a reconstituted basement membrane compared with vector transfected control cells. Given that basement membrane contains little if any FN it is suggested that fibulin-1 may be able to interact with some other proteins in the basement membrane. These include collagen IV, laminin and nidogen. Fibulin-1 may behave similarly interacting with these proteins as it does with FN. Fibulin-1 and fibulin-2 have distinct yet overlapping molecular interactions and expression patterns (Kobayashi et al. 2007). Both are found in the basement membrane, elastic fibres and other connective tissue structures in the early stages of embryonic development. They bind FN, proteoglycans tropoelastin and other basement membrane proteins. These workers found that the binding of tropoelastin by fibulins was as follows; Fibulin 2, Fibulin 5, > Fibulin 4, Fibulin 1 > Fibulin 3

Fibulin 4 though not fibulins 3 and 5 interact with collagen IV collagen XV derived endostatin and nidogen-2. The second subgroup of fibulins does not interact with FN and most basement membrane proteins. Dieter P Reinhardt and coworkers (Reinhardt et al. 1996) demonstrated in vitro colocalization and high affinity binding between fibulin 2 and fibrillin 1. Fibrillins (1, 2 and 3) are similar glycoproteins to the fibulins. The Fibrillins are the major proteins of microfibrils which are found close to the basement membrane in the ECM. Hubmacher et al. (2008) demonstrated in vitro how fibrillin 1 was able to self assemble. They showed that the C-terminal half of fibrillin 1 assembles into multimeric globular proteins stabilized by disulphide bonds. Then these globules join head to tail and appear like beads-on-a-string. The fibrillin-1 functionally has 43 cbEGF domains interspersed with TGFβ binding protein

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domains and hybrid domains. The N-C self interaction site of fibrillin is on cbEGF 41–43 and the binding site for interaction with fibulin-2 was previously narrowed down to the N terminus of fibrillin-1 at the amino acid positions range 45–450 (Reinhardt et al. 1996). Fibulin 2 was present in perichondrium, elastic intima of blood vessels and kidney glomerulus though not in tendon or lung alveoli. Localization of the fibulin-2 was present at the interface between elastin cores and microfibrils. Kobayashi et al. (2007) has demonstrated that fibulin-5 in present in a very similar distribution in relation to the microfibrils. Fibulin-4 is found on the other hand in the microfibril surrounding the elastin core. They concluded from their work using immunoelectron microscopy and recombinant techniques that in contrast to fibulins 1 and 2 which primarily bind to elastic fibers and basement membrane, members of the second subgroup (3, 4 and 5) primarily bind to elastic fiber components. Ablation of genes showed that fibulin 4 and 5 play an essential role in the assembly of elastic fibres during development. Figure 9.3 has been compiled from (Albig and Schiemann 2004) to depict the effect of fibulin-5 on angiogenic sprouting by endothelial cells and the interactions TGFβ tes ula ion im ess t S pr Ex

Down-regulation +

C

KS

–

BL

O

–

Thrombospondin

+

FIBULIN 5

VEGF

TSP I Expression

– Antagonize

–

– +

E

ND

OTHEIA

gen

ic S

pro

L

gio

L

An

LC

E l elia tion oth fera Endll proli Ce

l lia n the io do nvas n E ll i Ce

utin

g

Fig. 9.3   Factors controlling fibulin 5 activity and its own effect on cell activities

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of other mediators on the effects of fibulins 5. Fibulin 5 shown centrally antagonises the endothelial cell invasion and proliferation. These endothelial cells in turn cause down regulation of fibulin 5 expression. TGFβ stimulates expression of Fibulin 5 there by decreasing angiogenesis. VEGF, a pro-angiogenic factor directly promotes vascularization and indirectly by blocking the effect of TGFβ on fibulin-5. Fibulin 5 modulates the effect of the VEGF signal and also increases TSP I expression which antagonises endothelial cell from invasion and proliferation. VEGF and TSP I have a reciprocal relationship (i.e. where VEGF is low TSP I is high and visa versa). Fibulin 6 functions in the origination of specific epithelial cell junctions (Sisto et al. 2009) and it is located in the pericellular ECM of epithelial cells (Xu et al. 2007). Detachment of cells from their ECM leads to apoptosis known in this context as ANOIKIS (Sisto et al. 2009). Loss of appropriate attachment of epithelial cells to ECM in the salivary gland may give rise to Sjogren’s syndrome. Fibulin 7 is a glycoprotein secreted by 3 pre-odontoblasts and odontoblasts in developing teeth. It is also found in hair, cartilage and placenta. It is a cell adhesion molecule (de Vega et al. 2009), likely by an intregin receptor and a heparin sulphate cell surface receptor. It binds dental mesenchyme cells and odontoblasts but not dental epithelial cells (forerunner of enamel matrix secreting ameloblast cells). Its role appears to be in the development and maintenance of odontoblasts and dentin formation.

9.3 Role of Fibulins in Cancer The fibulins have a number of possible roles in promotion and inhibition of the neoplastic transformation of cells. They may facilitate or inhibit subsequent tissue changes such as angiogenesis and the breakdown of tissue restraints when tumour cells metastasize. Evidence for the involvement of fibulin-1 in tumourigenesis comes from a number of sources. 1. In vitro work showed that fibulin-1 supresses fibronectin regulated cell adhesion and mobility (Twal et al. 2001) and delays invasion and tumour progression in fibrosarcoma cells (Qing et al. 1997). 2. Fibulin-1 expression is elevated in breast and ovarian cancer (Roger et al. 1998; Greene et al. 2003). 3. The first source suggested that fibulin-1 suppressed carcinogenesis whereas the second source pointed to fibulin-1 being oncogenic. Gallagher et al. (2005) suggested the dicotomy could be explained by alternative splice variant production with fibulin 1-C being pro-oncogenic and fibulin 1-D being tumour suppressive. Estradiol has been shown to regulate fibulin-1 expression in ovarian cancers (Moll et al. 2002). Moreover further work has shown that fibulin-1 expression is regulated at transcriptional level and post-transcriptional level. The half life (t1/2) of mRNA translating to fibulin 1-D is decreased while the

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mRNA for fibulin 1-C is not reduced. This results in larger amounts of fibulin-1C being available under these conditions as opposed to the less stable fibulin 1-D (i.e. the ratio of fibulin 1 C/fibulin 1 D is increased). Gallagher et al. (2005) also suggested that due to the close association of fibulin-1 with basement membranes and connective tissue matrix fibres, fibulin-1 might be degraded by proteinases and modulate its ability to regulate tumour invasion through the barrier of the basement membrane. Wlazlinski et al. (2007) found that fibulins as well as laminins were differentially expressed in prostate cancers partly brought about by differences in DNA methylation. Fibulin 1, 4 and 5 were down regulated in prostate cancers relative to controls. Fibulin-1 down regulation did not fit with that found in breast and ovarian cancers and the ratio of splice-variants fibulin 1C to fibulin 1D remained constant. However, fibulin-4 and especially fibulin 5 down regulation in prostate cancer agreed well with that found for other cancers. Xie et al. (2008) found using HT1080 tumour cells that Fibulin 1 and fibulin 5 both inhibited tumour angiogenesis. Fibulin-1 also inhibited tumour growth by induction of apoptosis of tumour cells. Furthermore they used the lysozomal enzyme Cathepsin D to partially degrade fibulin-1. A protein fragment was produced that contained the antiangiogenic activity of fibulin-1. This fragment had a similar molecular size to fibulin-5 and a possible cleavage site for Cathepsin D on fibulin-1 aligned with the N-terminal position of fibulin-5 sequence. Both fibulin-1 and fibulin-5 were derived from the basement membrane. Fibulin-4 has been shown by (Gallagher et al. 2001) to be upregulated in a large number of human colon carcinoma biopsies. They suggest this is due to enhanced translation or increased stability of the mRNA for fibulin-4. Gallagher et al. (1999) showed that fibulin-4 interacts with the tumour suppressor protein p53 and this interaction inhibits tumourigenesis. Independently p53 also inhibits tumourigenesis. The interesting features of p53 are that the wild-type protein causes decreased vascularisation, likely through repression at transcription level of factors such as thrombospondin I and VEGF (Dameron et al. 1994; Mukhopadhyay et al. 1995). A mutant of p53 on the other hand does the exact opposite and increases VEGF with resulting increased angiogenesis and thereby is an oncogene, (Kieser et al. 1994). The p53 mutants interestingly can behave as wild type at 32°C and revert to mutant type as the temperature is increased probably due to a conformation change in the protein. To implicate the interaction of mutant p53 with fibulin-4 as the oncogene stimulus one has to explain how they can interact if fibulin-4 is extracellular and p53 is intracellular. This may be explained by 1. accepting that fibulin-4 on occasion does not have an N-terminal signal peptide, thus making it likely that on these occasions fibulin-4 is not a secreted protein, or 2. accepting that the evidence points to mutant p53 and fibulin-4 meeting in the lumen of the endoplasmic reticulum. Either of these explanations would make it reasonable to suggest that fibulin-4 has mutant p53 oncogenic abilities. As discussed previously there is a large body of evidence that points to fibulin-5 having an anti tumourigenic effect. This is due to its effect on endothelial cells and

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particularly to its negative effect on angiogenesis. The decreased ability to form new blood vessels is thought to decrease a tumours ability to grow and metastasize. This is true of human tumours such as kidney, breast, ovary and colon (Schiemann et al. 2002). Along with in vitro inhibition of angiogenesis it has also been shown that fibulin-5 is down regulated when there is active endothelial cell tubulogenesis. In vivo studies (Spencer et al. 2005) have shown that fibulin-5 deficient mice showed increased cell proliferation, motility and invasion in response to a mutagenic stimulus. This could be reversed by overexpressing fibulin-5. However in the case of HT1080 fibrosarcoma cells fibulin-5 increases cell proliferation and invasion, which is at odds with its effects on the other cell types and tumours. There is evidence for an overall tendency for fibulin-5 to suppress malignancy as many primary and metastatic tumours are accompanied by a down regulation of fibulin-5 (Gallagher et al. 2005).

9.4 Fibulins in Other Disease States As previously mentioned fibulin-5 plays a key role in the development and in maintaining the integrity of elastic tissue. Mutations which cause dysfunctional elastic tissues cause loose skin, joint laxity, defective large arteries and heart valves and emphysematous lungs due to the loss of elastic recoil. Three forms of cutis laxa are known (Loeys et al. 2002) two of which are autosomal recessive and one is autosomal dominant, with loose skin and systemic manifestations of varying severity. One such disease is thought to be due to a homozygous missense mutation in the fibulin-5 gene causing autosomal recessive cutis laxa and pulmonary emyphysema. Loeys and coworkers described a very rare case of cutis laxa type 1 (Loeys et al. 2002). At one month old this child had loose skin of the face and neck with a “droopy” facial appearance which gave the illusion of the eyes being displaced downward (sunset phenomenon). Furthermore this child had supra-aortic valve stenosis with thickened aortic valve and was shown to have pulmonary emphysema at 6 months with recurrent lower respiratory tract infections. Histology of skin sections showed poor elastin fiber development compared to controls. These workers investigated a fibulin-5 gene mutation as the causative agent in this hereditary disease. A mutation (T-C) was found at position 998 of the fibulin-5 gene giving rise to a serine-to-proline substitution at 227 in the fourth Ca++ binding EGF-like domain of fibulin-5. This segregated with the disease phenotype in the pedigree of the child that was studied. The further evidence for cause and effect between the mutation and the disease was in four parts. This evidence supports the belief that serine 227 is necessary for the correct function of the fibulin-5 protein. 1. The T998C mutation was absent in 100 chosen controls. 2. The serine is located at position in the secondary and tertiary structure of the protein that makes it very important for maintenance of protein conformation.

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It is the position of the fourth cbEGF-like domain between cysteines 3 and 4 which remains constant (highly conserved) between different species and in other human fibulins such as fibulin-3. 3. In a similar ECM protein, i.e. fibrillin-1, 31 of its 43 cbEGF-like domains have a serine residue in a corresponding place. 4. The mutation is considered to have important structural and thus functional consequences in other proteins with cbEGF-like modules in that the folding of the recombinantly expressed domain is altered in vitro (Wu et al. 1995). Marfan’s syndrome is a well known disease of elastic tissue and is regarded as a fibrillinopathy. It is caused by an analogous mutation in fibrillin-1, serine to proline in a position between the third and the fourth cysteine in a cbEGF-like domain. Such a change in an analogous position in another cbEGF-like module of fibrillin-1 has been shown to increase susceptibility to proteases and alter the packing of nearby protein modules. Proper folding ensures less proteolysis of the protein (Downing et al. 1996). Hu et al. (2006) provided a further insight into the mechanism by which mutation of fibulin-5 caused impaired elastic fiber development and recessive cutis laxa. Fibulin-5 mutation S227P and another mutation caused reduced synthesis and secretion of fibulin-5. Mutant fibulin-5 had impaired association with elastic fibers due to reduced binding to tropoelastin and fibrilliln-1 microfibrils. Cells that express fibulin-5 are subject to increased apoptosis. Interactions between elastic fibers and fibrillin-1 microfibrils are disrupted by missense mutant fibulin-5 and globular elastin deposits are seen instead of the mature continuous elastic fiber core. Synpolydactyly, a dominantly inherited congenital disorder of the hands and feet, where the metatarsals and metacarpals are fused, is due to a translocation in the last intron of the fibulin-1 gene. The alternative splicing that brings about fibulin 1-D results in its loss following the translocation. This insufficiency probably gives rise to digit malformation because fibulin 1-D is required for cell immigration and apoptosis. Another defect in fibulin 1-D expression is associated with the giant platelet syndromes that result in macrothrombocytopenia and also deafness, renal disease and other anomalies. Interestingly, a protein associated with fibulin-1 namely fibrillin-2 can be subjected to mutation, and this gives rise to the same congenital anomaly of synpolydactyly as a mutation of fibulin-1 itself (Chaudhry et al. 2001). A study by (Stone et al. 1999) showed that a mutation in fibulin-3, in the last cbEGF module, namely Arg 345 Trp, was associated with two autosomal dominant macular degenerative diseases. In support of this mutation being causative, (Chu and Tsuda 2004) argued that recombinantly produced mutant (Arg 345 Trp) fibulin-3 was shown to be misfolded and secreted inefficiently (Marmorstein et al. 2002). The two diseases are known as Malattia Leventineses and Doyne honeycomb retinal dystrophy and they map close to the fibulin-3 locus, Chap. 2 p16–p21. However several families had these diseases without the fibulin-3 mutation (Tarttelin et al. 2001) suggesting molecular genetic heterogeneity. Fibulin-2 being predominantly expressed in aorta and blood vessels has a large number of polymorphisms which may genetically modify this protein to

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be involved in common vascular diseases but this is improved (Chu and Tsuda 2004). Not unlike fibulin-1 and some of the other fibulins a missense mutation in the fibulin-4 protein gives rise to the skin condition cutis laxa. It also gives abnormalities in other tissues which have a high content of elastic tissue necessary for proper function. These are mainly the lungs and the large arteries. Hucthagowder et  al. (2006) studied an autosomal recessive syndrome due to a mutation in the fibulin-4 gene. They found the missense mutation G169A that caused a collection of connective tissue disorders not unlike those due to mutations in the other fibulins. Additionally, other glycoproteins such as fibrillins and laminins also give rise to similar disorders when they undergo a mutation. Fibulin 6 dysfunction has thus far been associated with a least two diseases. 1. Blindness due to age-related macular degeneration. This is partly due to a gln5375arg transition in fibulin 6 (Schultz et al. 2003). 2. Sjogren’s Syndrome which is an autoimmune disease of the salivary and lacrimal glands (decreased salve and tear production) has been shown to be associated with a reduction or dysfunction in fibulin 6 (Sisto et al. 2009). Fibulin 7 dysfunction has not yet been shown to be related to a specific disease state.

References Albig AR, Schiemann WP (2004) Fibulin-5 antagonizes vascular endothelial growth factor (VEGF) signaling and angiogenic sprouting by endothelial cells. DNA Cell Biol 23(6):367–379 Argraves WS, Dickerson K et al (1989) Fibulin, a novel protein that interacts with the fibronectin receptor beta subunit cytoplasmic domain. Cell 58(4):623–629 Chaudhry SS, Gazzard J et al (2001) Mutation of the gene encoding fibrillin-2 results in syndactyly in mice. Hum Mol Genet 10(8):835–843 Chu ML, Tsuda T (2004) Fibulins in development and heritable disease. Birth Defects Res C Embryo Today 72(1):25–36 Cohen S, Elliott GA (1989) The stimulation of epidermal keratinization by a protein isolated from the submaxillary gland of the mouse. 1962. J Invest Dermatol 92(Suppl 4):157S; discussion 158S–159S Dameron KM, Volpert OV et al (1994) The p53 tumor suppressor gene inhibits angiogenesis by stimulating the production of thrombospondin. Cold Spring Harb Symp Quant Biol 59:483– 489 de Vega S, Iwamoto T et al (2007) TM14 is a new member of the fibulin family (fibulin-7) that interacts with extracellular matrix molecules and is active for cell binding. J Biol Chem 282(42):30878–88 de Vega S, Iwamoto T et al (2009) Fibulins: multiple roles in matrix structures and tissue functions. Cell Mol Life Sci 66(11–12):1890–1902 Downing AK, Knott V et  al (1996) Solution structure of a pair of calcium-binding epidermal growth factor-like domains: implications for the Marfan syndrome and other genetic disorders. Cell 85(4):597–605 Gallagher WM, Argentini M et al (1999) MBP1: a novel mutant p53-specific protein partner with oncogenic properties. Oncogene 18(24):3608–3616

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Gallagher WM, Greene LM et al (2001) Human fibulin-4: analysis of its biosynthetic processing and mRNA expression in normal and tumour tissues. FEBS Lett 489(1):59–66 Gallagher WM, Currid CA et  al (2005) Fibulins and cancer: friend or foe? Trends Mol Med 11(7):336–340 Giltay R, Timpl R et al (1999) Sequence, recombinant expression and tissue localization of two novel extracellular matrix proteins, fibulin-3 and fibulin-4. Matrix Biol 18(5):469–480 Greene LM, Twal WO et al (2003) Elevated expression and altered processing of fibulin-1 protein in human breast cancer. Br J Cancer 88(6):871–878 Hu Q, Loeys BL et al (2006) Fibulin-5 mutations: mechanisms of impaired elastic fiber formation in recessive cutis laxa. Hum Mol Genet 15(23):3379–3386 Huber R, Scholze H et al (1980) Crystal structure analysis and molecular model of human C3a anaphylatoxin. Hoppe Seylers Z Physiol Chem 361(9):1389–1399 Hubmacher D, El-Hallous EI et al (2008) Biogenesis of extracellular microfibrils: multimerization of the fibrillin-1 C terminus into bead-like structures enables self-assembly. Proc Natl Acad Sci U S A 105(18):6548–6553 Hucthagowder V, Sausgruber N et al (2006) Fibulin-4: a novel gene for an autosomal recessive cutis laxa syndrome. Am J Hum Genet 78(6):1075–1080 Hugli TE et al (1986) Biochemistry and biology of anaphylatoxins. Complement 3(3):111–127 Kieser A, Weich HA et al (1994) Mutant p53 potentiates protein kinase C induction of vascular endothelial growth factor expression. Oncogene 9(3):963–969 Kobayashi N, Kostka G et al (2007) A comparative analysis of the fibulin protein family. Biochemical characterization, binding interactions, and tissue localization. J Biol Chem 282(16):11805– 11816 Loeys B, Van Maldergem L et  al (2002) Homozygosity for a missense mutation in fibulin-5 (FBLN5) results in a severe form of cutis laxa. Hum Mol Genet 11(18):2113–2118 Marmorstein LY, Munier FL et  al (2002) Aberrant accumulation of EFEMP1 underlies drusen formation in Malattia Leventinese and age-related macular degeneration. Proc Natl Acad Sci U S A 99(20):13067–13072 Moll F, Katsaros D et al (2002) Estrogen induction and overexpression of fibulin-1C mRNA in ovarian cancer cells. Oncogene 21(7):1097–1107 Mukhopadhyay D, Tsiokas L et al (1995) Wild-type p53 and v-Src exert opposing influences on human vascular endothelial growth factor gene expression. Cancer Res 55(24):6161–6165 Pan TC, Kluge M et al (1993) Sequence of extracellular mouse protein BM-90/fibulin and its calcium-dependent binding to other basement-membrane ligands. Eur J Biochem 215(3):733–740 Qing J, Maher VM et al (1997) Suppression of anchorage-independent growth and matrigel invasion and delayed tumor formation by elevated expression of fibulin-1D in human fibrosarcoma-derived cell lines. Oncogene 15(18):2159–2168 Reinhardt DP, Sasaki T et al (1996) Fibrillin-1 and fibulin-2 interact and are colocalized in some tissues. J Biol Chem 271(32):19489–19496 Roger P, Pujol P et al (1998) Increased immunostaining of fibulin-1, an estrogen-regulated protein in the stroma of human ovarian epithelial tumors. Am J Pathol 153(5):1579–1588 Sasaki T, Mann K et al (1997) Dimer model for the microfibrillar protein fibulin-2 and identification of the connecting disulfide bridge. EMBO J 16(11):3035–3043 Savage CR Jr, Inagami T et al (1972) The primary structure of epidermal growth factor. J Biol Chem 247(23):7612–7621 Schiemann WP, Blobe GC et al (2002) Context-specific effects of fibulin-5 (DANCE/EVEC) on cell proliferation, motility, and invasion. Fibulin-5 is induced by transforming growth factorbeta and affects protein kinase cascades. J Biol Chem 277(30):27367–27377 Schultz DW, Klein ML et al (2003) Analysis of the ARMD1 locus: evidence that a mutation in HEMICENTIN-1 is associated with age-related macular degeneration in a large family. Hum Mol Genet 12(24):3315–3323 Sisto M, D’Amore M et al (2009) Fibulin-6 expression and anoikis in human salivary gland epithelial cells: implications in Sjogren’s syndrome. Int Immunol 21(3):303–311

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Chapter 10

Tumor Fibroblast-Associated Metalloproteases Julie Lecomte, Anne Masset, Dylan R. Edwards and Agnès Noël

10.1 Introduction Tumorigenesis and cancer progression rely on the acquisition by tumor cells of novel capacities through the accrual of mutations in genes critical for, at the very least, cell proliferation and survival (Vogelstein and Kinzler 2004). However, tumors are not isolated entities but rather depend on, interact with and react to the adjacent microenvironment. A tumor mass is not only composed of malignant cells but also includes several other cell types (fibroblastic cells, blood and lymphatic endothelial cells, inflammatory cells) which infiltrate the tumor and lead to the elaboration of a permissive stroma. In contrast to the initial view, genetic alterations accumulate not only in tumoral cells but also in stromal cells during cancer progression. The importance of the tumor microenvironment in cancer progression is now recognized (Joyce and Pollard 2009), but the critical molecular changes occurring in the tumor stroma accompanying and affecting cancer evolution remain largely unknown. New technological advances have been helpful recently in exploration of the stromal compartment. Indeed, microarray analysis of breast tumor stroma samples has given rise to some new stroma-derived prognostic predictor genes that can be used to identify sample clusters distinct from previously identified breast tumor subtypes (Beck et al. 2008; Finak et al. 2008). The stromal signature, alone or in combination with other molecular prognostic predictors, promises to improve molecular classification and outcome prediction in cancer, specifically by aiding the identification of patients who may benefit from aggressive therapies, or stratifying cancer subjects for clinical trials (van’t Veer et al. 2002; Santos et al. 2009). Desmoplasia, the stromal reaction associated with most carcinomas, is characterized by the local deposition of abundant extracellular matrix (ECM). Changes in the ECM may be of either a degradative or a productive nature varying as a function of time or localization. It is now recognized that the nature of the connective/stromal A. Noël () Laboratory of Tumor and Development Biology, Groupe Interdisciplinaire de Génoprotéomique Appliqué-Cancer (GIGA-Cancer), University of Liège, 4000 Liège, Belgium e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_10, © Springer Science+Business Media B.V. 2011

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tissue is crucial, and that proteases that are involved in this tissue remodeling contribute to malignancy in early and late stages of primary and secondary tumor development (Mueller and Fusenig 2004). The cancer proteome relies not only on important changes in protein expression, but also on proteolytic post-translational modifications of the elaborated proteins, thus perturbing signaling pathways in both tumor and reactive stromal cells (Doucet et al. 2008; Overall et al. 2004). In this regard, the cancer degradome—the full repertoire of proteases implicated in cancerassociated tissue remodeling—is particularly important. The degradome includes five different families based on the nature of the chemical group responsible for the catalytic activity: serine proteases (plasmin/plasminogen activator), cysteine proteases (B-cathepsin, L-cathepsin), aspartic proteases (D-cathepsin), threonine proteases (proteasome components) and metalloproteinases (MMPs, ADAMs, ADAMTS) (Egeblad and Werb 2002; Lopez-Otin and Overall 2002; Overall et al. 2004). Some of these proteases exist as membrane-anchored forms on the cell surface or as soluble forms excreted into the extracellular surroundings. The relevance of the degradome as a molecular determinant of cancer progression has been recently underlined in studies based on gene expression signatures. Microarray analyses led to the classification of tumor sub-types according to their expression profiles associated with tumour metastasis or adverse outcome in several cancer types (van’t Veer et al. 2002; Santos et al. 2009; van de Vijver et al. 2002). Unsurprisingly, degradome genes are represented in most of these predictors, and the utility of several more proteases and related genes as biomarkers has been established by several profiling studies (Casey et al. 2009; Ma et al. 2009). In this review, we focus on metalloproteinases (MMPs and ADAMTSs) specifically expressed by tumor-associated fibroblasts which are important components of the tumor-host interplay (Kalluri and Zeisberg 2006). Stromal changes at the invasion front include the appearance of myofibroblasts, activated cells sharing characteristics with fibroblasts and smooth muscle cells. The transdifferentiation of fibroblasts into myofibroblasts is modulated by cancer cell-derived cytokines. These “activated” fibroblasts also called peritumoral or reactive fibroblasts, cancer-associated fibroblasts (CAF) and myofibroblasts (De Wever et al. 2008) are described in different chapters of the current issue. Despite being the predominant cell type within the tumor stroma, fibroblasts have received relatively little attention with regards to inflammationassociated tumorigenesis. The main complexity for studying fibroblast populations is the heterogeneity of fibroblastic cells within the tumor stroma (Sugimoto et  al. 2006). Indeed, many fibroblast markers have been characterized but none is specific to all fibroblastic cell subsets (Kalluri and Zeisberg 2006). In addition, their cellular origins appear to be multiple, further increasing the complexity of the tumor stroma (Fig. 10.1). Resident fibroblasts surrounding cancer cells obviously contribute to the tumor stroma. However, another possible source of cancer-associated fibroblasts (CAFs) is their derivation from pericytes or vascular smooth muscle cells around vessels (Desmouliere et al. 2004). In the last few years, bone marrow-derived cells have been identified as cells that can be recruited into the tumor stroma and differentiate into myofibroblasts. It has also been suggested that CAFs are derived from malignant epithelial cells, or normal epithelial cells, undergoing epithelial–mesenchymal

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CAF-associated MMPs and ADAM/TS

Protease function in tumor progression

Bone marrow

Tumor cell MSC

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Cleavage of cell surface protein (E-cadherin, integrin, …)

SMC Pericytes

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MMP1 MMP2 MMP3 MMP9 MMP11 MMP13 MMP14 (MT1-MMP) ADAMTS 12

Release and/or generation of bioactive molecules

2 Degradation of ECM

3

inactive

active

active

inactive

EMT Others?

Cleavage of chemokines, cytokines or growth factors

Fig. 10.1   Multiple functions of stromal proteases produced by fibroblast activated cells ( CAF ). CAF can differentiate from a wide variety of cells such as resident fibroblasts ( local connective tissue), mesenchymal stem cells issued from bone marrow, vascular smooth muscle cells, pericytes, epithelial cells performing epithelial-to-mesenchymal transition. These activated fibroblasts constitute an important source of MMPs and likely ADAMTSs. These enzymes are endowed with multiple functions contributing thereby to cancer progression. These proteases can cleave cell surface proteins, thus controling tumor cell adhesion. By degrading extracellular matrix, they can not only favor cell migration, but also release growth factors from the matrix and generate bioactive molecules from fragments of matrix components. In addition, proteases can participate in a complex network of molecular messages by activating or inactivating chemokines, cytokines and growth factors

transition (Kalluri and Zeisberg 2006; Ostman and Augsten 2009). Endothelial cells were also recently identified as a candidate source of CAFs (Zeisberg et al. 2007). The present review aims at describing several MMPs and ADAMTSs produced by fibroblastic cells that have acquired a modified phenotype within the tumour stroma.

10.2 Metalloproteinases: MMPs and ADAMTSs MMPs are a family of 23 human zinc-binding endopeptidases that can degrade virtually all ECM components, and release and activate/inactivate a growing number of modulators of cell functions (Cauwe et al. 2007; Egeblad and Werb 2002; Overall and Dean 2006). MMPs are multidomain proteins characterized by at least

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three conserved regions: (1) a zinc-binding motif (HEXXHXXGXXH) required for proteolytic activity, (2) a propeptide cysteine site (PRCGXPD) whose cysteine residue interacts with the zinc ion in the zymogen form and (3) a ‘methionine turn’ (XXMXP) which likely maintains the zinc-binding site integrity (Egeblad and Werb 2002). Most of the MMPs are secreted as soluble enzymes but six of them are membrane-type MMPs (MT-MMPs) which associate with the cell membrane by either a COOH-terminal transmembrane domain (MT1-, MT2-, MT3-, MT5-MMPs) or a glycosylphosphatidylinositol (GPI) anchor (MT4-, MT6-MMPs) (Zucker et al. 2003). Proteolytic activity is inhibited by tissue inhibitors known as tissue inhibitors of metalloproteinase (TIMPs). ADAMTSs consist of 19 human MMP-related enzymes characterized by the presence of a disintegrin-like domain and at least one thrombospondin type I repeat (TSP-1) (Porter et al. 2005; Colige et al. 2005; Rocks et al. 2008; Roy et al. 2006). The thrombospondin motifs at the carboxy terminus of these secreted enzymes, together in some cases with an internal spacer region, facilitate their localization in the ECM in close proximity to their cognate substrates. Their multi-domain structure endows these proteins with various functions including the control of cell properties such as cell proliferation, apoptosis, adhesion and migration (Rocks et  al. 2008). In contrast to their universal MMP-inhibitory properties, TIMPs appear to be more selective in inhibiting ADAMTSs. It is worth noting that TIMP3 which is a potent inhibitor of some ADAMs and ADAMTSs is expressed by stromal cells as assessed by laser-capture microdissection and in situ hybridization (Shukla et al. 2008).

10.3 Metalloproteinases as Key Molecular Determinants of Tumor-Associated Fibroblasts MMPs were initially claimed to be important in late stages of tumor progression by degrading connective tissue stroma and basement membrane (Egeblad and Werb 2002). However, due to the rapid development of innovative biochemical techniques (Greenlee et al. 2006; Lopez-Otin and Overall 2002; Overall et al. 2004) and the expanding use of transgenic mice (Cauwe et al. 2007; Page-McCaw et al. 2007), it became obvious that the action of MMPs is not restricted to the massive destruction of physiological matrix barriers (Overall and Dean 2006). MMPs are viewed as key regulators of the multiple cellular functions which dictate malignant growth. Although some MMPs are produced by tumor cells (e.g. MMP7, MT4-MMP) (Chabottaux et al. 2006; Lynch et al. 2007), most MMPs are rather produced by host cells and therefore might be considered as molecular determinants of the ‘seed and soil’ concept proposed by Paget in 1889 (Fidler 2003). Fibroblasts and myofibroblasts are principal producers of several types of MMPs, which highlight their crucial role in ECM remodeling (Kalluri and Zeisberg 2006). One prevailing view is that tumor-associated stroma is activated by the malignant epithelial cells to foster tumor growth—for example, by secreting growth factors, increasing angiogenesis, and

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facilitating cell migration, ultimately resulting in metastasis to remote organ sites (Liotta and Kohn 2001; Mueller and Fusenig 2004). The importance of fibroblastic cells was initially demonstrated through in vivo experiments in which fibroblasts co-injected with tumor cells were shown to promote primary cancer growth (Noel et al. 1993, 1996; Picard et al. 1986). In these systems, the contribution of MMPs is demonstrated by the inhibition of this tumor promoting effect of fibroblasts by using synthetic MMP inhibitors (Maquoi et al. 2004). More recently, mesenchymal stem cells co-injected with tumor cells have been shown to promote breast metastasis (Karnoub et al. 2007). Cancer cells might stimulate fibroblasts to synthesize MMPs in a paracrine manner through the secretion of interleukins, interferons, growth factors and EMMPRIN (Heppner et al. 1996; Noel et al. 1994; Sternlicht and Werb 2001; Zigrino et al. 2005). Fibroblasts constitute therefore an important source of MMPs including mainly MMP1 (Ala-aho and Kahari 2005), MMP2 (Bisson et al. 2003), MMP3 (Sternlicht et  al. 1999), MMP9, MMP11 (Basset et  al. 1990; Rio 2005), MMP13 (Ala-aho and Kahari 2005) and MT1-MMP (MMP14) (Sounni and Noel 2005; Zucker et al. 2003). Of interest are the recent findings of several genes differentially expressed in invasive stroma compared to in situ stroma by combining laser capture microdissection and gene expression microarray (Ma et al. 2009). Among these genes, MMP11, MMP2 and MMP14 showed significant increases in invasive stroma. Other studies demonstrated that MMP13 is expressed by fibroblasts surrounding epithelial tumors and is associated with invasive and metastatic tumors (Pendas et al. 2000). Due to space constraints, we have decided to focus our interest on stromal MMPs identified by the recent microarray analyses performed on human samples: MMP2, MMP11, MMP13 and MMP14. In addition, we will consider members of the ADAMTS family that also appear to be involved in the stromal response to tumors, as is the case for ADAMTS1 and ADAMTS12.

10.3.1  Secreted Stromal MMPs MMP11 or Stromelysin-3 (ST3) is mainly expressed by cells of mesenchymal origin in association with remodeling processes occurring during embryogenesis (Lefebvre et al. 1995; Maquoi et al. 1997) and tissue development (Lefebvre et al. 1992). MMP11 is involved in epithelial homeostasis (Rio et al. 1996) but also in various non-cancerous pathological conditions such as repair processes (Okada et al. 1997; Wolf et al. 1992), human atherosclerosis (Schonbeck et al. 1999) and rheumatoid arthritis (Konttinen et al. 1999; Lubberts et al. 1999). In cancer, MMP11 is associated with tumor invasion and poor prognosis. Its expression is restricted to the stromal fibroblasts adjacent to cancer cells (Basset et al. 1990; Rio 2005), thus suggesting that MMP11 production by stromal cells in tumor tissues could correspond to an individual response by host stromal cells to stimulatory messages derived from cancer cells (Heppner et  al. 1996). Indeed, breast cancer cell lines such as MCF7, MDA-MB-231 and ZR75 were found to induce MMP11 expression by human invasion front fibroblasts (Ahmad et al. 1997).

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Clinical data indicate that MMP11 promotes aggressive behavior in tumors of various origins. Increased MMP11 gene expression is correlated with the local invasiveness of cancer cells in head and neck squamous cell carcinoma (Muller et al. 1993). A high level of MMP11 is a strong independent prognostic indicator of shorter disease-free survival in breast cancers (Ahmad et al. 1998; Chenard et al. 1996) and a marker of aggressive clinical outcome in oesophageal cancer (Porte et  al. 1998; Yamashita et al. 2004). Therefore, MMP11 expression is a useful diagnostic that is of clinical value and this enzyme has therefore been proposed as a potential target for new treatment strategies (Anderson et al. 1995). Recently, MMP11 has been identified as a novel broadly expressed tumor-associated antigen and a candidate target for cancer immunotherapy (Peruzzi et al. 2009). In experimental models, MMP11 appears as a stromal factor that promotes the primary implantation of cancer cells in a tissue environment initially not permissive for tumor growth (Noel et al. 1996; Rio 2005). In Mmp11-deficient mice, the number of apoptotic cancer cells is increased in primary tumors indicating that host MMP11 helps cancer cells in escaping apoptosis (Boulay et  al. 2001). The contribution of fibroblast-derived MMP11 is supported by the lack of tumor promoting effect of fibroblasts generated from Mmp11-deficient mice as compared to their wild type counterpart (Masson et al. 1998). Surprisingly, no tumor phenotype was observed in transgenic mice ectopically expressing MMP11 in the mammary gland epithelial cells (WAP-MMP11), in fibroblasts (vimentin-MMP11), or ubiquitously in all cells (CMV-MMP11) (Noel et al. 1996; Rio et al. 1996; Rio 2005). The role of MMP11 in oncology is complex and unclear. In MMTV-ras transgenic mice, Mmp11-deficiency is associated with more metastases for a similar number and size of primary invasive tumors, indicating that cancer cells evolving in Mmp11-deficient stroma have increased potential for hematogenous dissemination (Andarawewa et al. 2003). Thus MMP11 appears to repress metastatic dissemination while it enhances primary tumor development. These paradoxical functions require further investigation and support the emerging roles of MMPs in tumour repression (Lopez-Otin and Matrisian 2007). An interesting insight into the potential role of MMP11 in the pathogenesis of primary mammary tumors is provided by the recent observation that MMP11 promotes differentiation of adipocytes into fibroblasts at the invasion front, contributing to desmoplasia and creating a more permissive environment for tumor growth (Andarawewa et al. 2003). MMP13 (collagenase-3) originally identified in human breast cancer tissue (Freije et al. 1994) is secreted from cells as an inactive zymogen that can be activated by the MMP2/MMP14/TIMP2 complex (Knauper et al. 1996) or by plasmin (Cowell et al. 1998). The overexpression of MMP13 in several types of malignancy (Balbin et al. 1999; Bartsch et al. 2003; Bostrom et al. 2000; Corte et al. 2005; Curran et al. 2004; Dunne et al. 2003; Leeman et al. 2002; Luukkaa et al. 2006; Pendas et al. 2000) is associated with shorter overall survival of the patients (Curran et al. 2004; Leeman et al. 2002; Luukkaa et al. 2006). It has been described as a potential new tumor marker for breast cancer diagnosis as it is overexpressed in breast cancer tissues compared to normal adjacent tissues (Chang et al. 2009). MMP13 expres-

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sion was also correlated with metastasis formation (Ellsworth et al. 2009; Lee et al. 2009). Consistently, functional studies have demonstrated that MMP13 increases the invasive capacities of malignant cells (Airola et al. 1999; Balduyck et al. 2000; Culhaci et al. 2004; Daja et al. 2003; Johansson et al. 1999). MMP13 acts in the extracellular environment as a potent collagenase capable of degrading a variety of collagen types. While several studies concluded that MMP13 is synthesized predominantly by tumor cells (Balduyck et al. 2000), others claimed that MMP13 is expressed in a subpopulation of stromal myofibroblasts (Kleiner and Stetler-Stevenson 1999; Nielsen et al. 2001, 2007) in invasive breast carcinoma. Interestingly, the presence of microinvasion in ductal carcinoma in situ (DCIS) is associated with focal expression of MMP13 mRNA in stromal fibroblasts (Nielsen et al. 2001, 2007). Direct comparison of the MMP13 mRNA expression pattern with that of the MMP2, MMP11 and MMP14 mRNAs indicates that MMP13 is unique in this respect since the other MMPs are also present in DCIS in the absence of invasion (Nielsen et al. 2001). These observations raise the question as to whether MMP13 is a rate-limiting proteinase that mediates the initial steps in breast cancer invasion. The recent generation of Mmp13-deficient mice will be helpful in unravelling the function of host MMP13 during cancer progression (Inada et al. 2004; Nielsen et al. 2008). However, in the aggressive mouse mammary tumor virus-polyoma middle T-antigen (MMTV-PyMT) model of breast cancer, the absence of MMP13 did not influence tumor growth, vascularization, or metastasis to the lungs, suggesting that MMP13’s role in breast cancer may depend on the nature of the genetic lesions driving malignancy (Nielsen et al. 2008).

10.3.2  Membrane-Associated MMPs According to the structure of their C-terminal extension, MT-MMPs can be classified into two sub-groups: (1) type I transmembrane proteins including MT1-, MT2-, MT3- and MT5-MMP characterized by a long hydrophobic sequence followed by a short cytoplasmic tail and (2) glycosylphosphatidylinositol (GPI)-type MTMMPs (MT4- and MT6-MMP) containing a short hydrophobic signal anchoring to GPI (Zhao et al. 2008). The GPI-anchored MT4-MMP is exclusively expressed by breast tumor cells and contributes to tumor angiogenesis and metastatic dissemination (Chabottaux et al. 2006, 2009). In contrast, the cellular distribution of MT1-MMP is much more complex and depends on the cancer type considered. It was initially reported to be expressed in lung carcinoma cells and in the adjacent fibroblasts (Sato et al. 1994). Such patterns of MT1-MMP expression and MMP2 activation were also observed in many types of tumors, such as lung (Nawrocki et al. 1997; Polette et al. 1996; Sato et al. 1994; Tokuraku et al. 1995), gastric (Bando et al. 1998; Mori et al. 1997; Nomura et al. 1995), colon (Ohtani et al. 1996), breast (Ishigaki et al. 1999; Polette et al. 1996; Ueno et al. 1997), bladder (Kanayama et al. 1998), head and neck (Yoshizaki et al. 1997), thyroid (Nakamura et al. 1999), ovar-

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ian (Afzal et al. 1998; Fishman et al. 1996) and cervical carcinomas (Gilles et al. 1996), and brain tumors (Forsyth et al. 1999; Nakada et al. 1999; Yamamoto et al. 1996). Transcripts were also detected in both tumor cells and surrounding stroma cells (Afzal et al. 1998; Heppner et al. 1996; Ohtani et al. 1996; Polette et al. 1996). It is particularly noteworthy that co-cultivation of fibroblasts with tumor cells induced the up-regulation of MMP2 and MT1-MMP expression by fibroblasts (Noel et al. 1994; Polette et al. 1997). MT1-MMP has been first described as a specific activator of pro-MMP2, with TIMP2 acting as an adaptor molecule which mediates pro-MMP2 binding to MT1MMP (Strongin et al. 1995). MMP2 is frequently co-expressed with MT1-MMP in mesenchymal cells (Apte et al. 1997; Kinoh et al. 1996). In breast cancer, MT1MMP has been demonstrated to be confined to αSMA positive myofibroblasts in close contact to tumor cells whereas MMP2 is produced by different fibroblastic populations (Bisson et  al. 2003) (Fig.  10.2). Carcinoma cells (Stetler-Stevenson et al. 1993; Tryggvason et al. 1993) in tissue and cancer cell lines (Sato et al. 1992) rarely express MMP2, even though they can use MMP2 that is derived from the surrounding fibroblasts by binding and activating it using MT1-MMP on the cell surface. Pro-MMP13 also can be activated by MT1-MMP in a cell-mediated manner (Knauper et al. 1996). This MT1-MMP/MMP2/MMP13 system could be important for tumor cells to invade the basement membrane by degrading type IV collagen and subsequently the stroma by degrading interstitial collagens. Activated MT1-MMP is a potent membrane proteinase with the ability to cleave type I collagen more efficiently than type II or III collagens (Sounni and Noel 2005). Consistently, MT1-Mmp knockout mice are characterized by a defect in cartilage remodeling and bone development (Holmbeck et al. 1999, 2003). MT1-MMP is currently recognized as a key regulator of connective tissue remodeling (Sabeh et al. 2009). Besides its capacity to activate pro-MMP2 and pro-MMP13, there is clear evidence that MT1-MMP and MMP2 are involved at different stages of tumor progression from initial tumor development, growth and angiogenesis to invasion, metastasis and growth at secondary sites (Deryugina et al. 2002; Sounni et al. 2002, 2004). MT1-MMP overexpression strongly promotes cellular invasion in vitro and experimental metastasis. The key role of fibroblast-derived MT1-MMP has been evidenced in co-transplantation of tumor cells and fibroblasts. In contrast to MT1MMP expressing fibroblasts, MT1-Mmp-null fibroblasts were unable to support in vivo growth of tumor cells (Zhang et  al. 2006). However, co-operation between tumor-derived MT1-MMP and stromal MMP-2 in vivo has also been clearly shown using Mmp2-knockout mice and tumorigenic cell lines engineered to over-express MT1-MMP (Taniwaki et  al. 2007). MT1-MMP is a multifunctional protein that can also cleave several soluble, cell surface and pericellular proteins, regulating cell behavior in tumor metastasis and angiogenesis (Sounni and Noel 2005). These regulation mechanisms include (1) the alteration of cell–cell interactions, cell–matrix interactions, (2) the release, activation or inactivation of autocrine, or paracrine signaling molecules, (3) the shedding or activation of surface receptors and (4) the activation of intracellular signaling pathways.

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MT1-MMP

T

MMP2

T

T

a

b

c

d

e

f

Fig. 10.2   Stromal localisation of MT1-MMP and MMP2 in human breast carcinomas. Immunostaining for αSMA (a) and in situ hybridization of MT1-MMP (b) and MMP2 (c) were performed on serial sections of human breast cancer. MT1-Mmp mRNA positive cells (b) are at the proximity of tumor cells, whereas Mmp2 mRNAs (c) and αSMA (a) are more widely distributed. Binary image of αSMA distribution in blue (d) was superimposed to that of MT1-MMP ( in green) (e) or MMP2 ( in green) (f). Colocalization appears in red in (e, f). T, tumor cell islet. Different sub-population of fibroblastic cells express MT1-MMP and MMP2

10.3.3  ADAMTSs The cancer-related functions of the AdamTS gene family are much less well understood than those of the MMPs. Indeed our knowledge of the basic enzymatic activities of these intriguing molecules is still in its infancy. One subgroup (ADAMTS1, 4, 5, 8, 9, 15 and 20) have been termed aggrecanases or hyalectanases based on their ability to cleave matrix proteoglycans; functions are also recognized for the pro-collagen N-proteinases (ADAMTS2, 3 and 14) constitute another group and

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the von Willebrand Factor-cleaving proteinase (ADAMTS13) (Porter et al. 2005; Rocks et al. 2006). However the activities of the other 8 ADAMTSs are unknown. What is clear is that expression of this gene family is altered dramatically during tumourigenesis and progression (Porter et al. 2004; Rocks et al. 2006). Also, several ADAMTS genes are primarily expressed by stromal fibroblasts in non-neoplastic tissues suggesting important roles in homeostasis (Porter et  al. 2004). A further complexity is highlighted by ADAMTS1, which shows both pro- and anti-tumorigenic/metastatic activities depending on whether the enzyme is in full-length or cleaved forms (Liu et al. 2006).

10.4 Stromal MMPs and ADAMTS as Anti-Tumor Regulators Recently, the generation of animal models involving gain or loss of function of specific matrix metalloproteinases (MMPs) has led to the surprising discovery of tumour-suppressive function for some proteases (Lopez-Otin and Matrisian 2007; Lopez-Otin and Bond 2008). These host protective proteases are not produced by tumor cells themselves, but mainly by tumour infiltrating cells including inflammatory cells (MMP8) (Balbin et al. 2003; Gutierrez-Fernandez et al. 2007) and fibroblastic cells (MMP19) (Jost et al. 2006). An increasing body of evidence indicates that members of the ADAMTS family exhibit tumor inhibitory activities. Among them, ADAMTS1 and ADAMTS8 display anti-angiogenic properties (de Fraipont et  al. 2001; Iruela-Arispe et al. 2003; Lee et al. 2006; Vazquez et al. 1999). In the case of ADAMTS1, anti-angiogenesis may involve multiple activities, including sequestration of vascular endothelial growth factor (Luque et al. 2003) and cleavage/activation of thrombospondins-1 and -2 (Lee et al. 2006). AdamTS1, AdamTS9, AdamTS15 and AdamTS18 genes have been found epigenetically silenced in several carcinomas (Jin et al. 2007; Lind et al. 2006; Lo et al. 2007; Viloria et al. 2009). An intriguing opposite regulation of ADAMTS12 expression in colon carcinomas has been recently described (Moncada-Pazos et al. 2009). While the gene is epigenetically inactivated in tumor cells, its expression is transcriptionally induced in surrounding fibroblastic cells. Further studies are required to clarify the putative protective role of this and other ADAMTS family members produced in the stromal compartment. Altogether, these recent findings have broken the dogma of proteases as simple positive regulators of cancer progression and emphasize the urgent need for identifying individual proteases as host protective partners or tumor-promoting agents.

10.5 Conclusion and Perspectives The key conceptual advance in the metalloproteinase field in the past decade has been the recognition that these enzymes are much more than matrix-degrading “scissors” that clear paths through tissue stroma for invading cancer cells. Some

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metalloproteinases do indeed carry out this function, but many have a more significant role as regulators of the pericellular signaling environment, and this latter capacity is arguably their chief relevance in cancer biology. MMPs and ADAMTSs produced by CAFs and other stromal cells will thus shape the tumor microenvironment. These genes are proving to have utility as diagnostic, prognostic or predictive cancer biomarkers. However our knowledge of the precise roles of particular proteases in different types of cancers, and at different disease stages, is still very incomplete. There is thus a need for continuation of functional studies on the cancer degradome. Moreover, the use of mass spectrometry technologies for identification of critical protease substrates is helping to unlock biological mechanisms, which in turn will lead to new strategies for cancer therapy. Acknowledgments  This work was supported by grants from Ministerio European projects (FP7 HEALTH-F2-2008-201279 “MICROENVIMET”), the Fondation contre le Cancer, the D.G.T.R.E. from the « Région Wallonne », the Interuniversity Attraction Poles Programme—Belgian Science Policy (Belgium). DE is grateful to support from the Breast Cancer Campaign, Cancer ResearchUK and the Big C Appeal.

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Part IV

Tumor Modulating-Fibroblast Interactions

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Chapter 11

Multiple Fibroblast Phenotypes in Cancer Patients: Heterogeneity in Expression of Migration Stimulating Factor Ana M. Schor and Seth L. Schor

11.1 Introduction Advances in molecular biology during the past three decades lent support to a genetic model of carcinogenesis according to which the progressive accumulation of mutations in relevant oncogenes and tumour suppressor genes was deemed necessary and sufficient to result in cancer development. The contemporaneous recognition that carcinogenesis is a multi-step process resulted in the now widely held view that cancer development commences with the inception of an initiating genetic lesion affecting the expression of a cancer-critical gene (Knudson 2001). This genetic change is postulated to confer a relative growth advantage to the progeny of the initiated cell. Disease progression results from the subsequent random accumulation of complementary genetic lesions within the initiated clonal population and the selection of derivative sub-clones displaying ever increasing growth advantage (Nowell 1976). Successive iterations of this clonal selection process result in the emergence of progressively dysfunctional populations of pre-neoplastic and neoplastic cells, culminating in the appearance of an overt malignancy. A corollary of this genetic model is that cancer inception and progression are essentially deterministic processes inexorably driven by the progressive accumulation of genomic lesions. Although undoubtedly of major importance, this strictly genetic view is not sufficient in itself to account for all existing data. For example, numerous studies have documented the local and systemic presence in cancer patients of stromal cells displaying morphological, biochemical and behavioural features customarily used to define neoplastic transformation in vitro. These cells were, however, commonly reported to be non-tumorigenic when assayed in vivo and therefore classified as “partially-transformed”. Several genetic mechanisms were invoked to account for the systemic presence of aberrant stromal cells at sites distant from the tumour (usually skin), including the inheritance of a predisposing germ-line mutation in patients A. M. Schor () Unit of Cell and Molecular Biology, The Dental School, University of Dundee, Dundee DD1 4HR, UK e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_11, © Springer Science+Business Media B.V. 2011

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with familial cancer syndromes. Seen within the confines of the prevailing genetic and epithelio-centric model of cancer pathogenesis, these aberrant stromal cells were generally not considered to make a direct contribution to cancer development. Instead, their presence was interpreted as a convenient biomarker of an inherited (or incurred) genetic lesion which only contributed to carcinogenesis when concomitantly expressed by the cancer progenitor cell population. Tumours are, however, a complex community of different cell types, including (in carcinomas) malignant epithelial cells, fibroblasts, myofibroblasts, endothelial cells, pericytes and various types of immune/inflammatory cells. Reciprocal interactions between these diverse cell populations are mediated by the inter-dependent signalling of cell-produced soluble factors and matrix macromolecules, as well as by direct cell-cell contact. Recognition of this inter-dependency prompted earlier workers to postulate that the behaviour of tumour cells may result from their interactions with functionally aberrant tumour-associated stromal cells (Tarin 1972; Cunha et al. 1985; Bissell and Barcellos-Hoff 1987; Schor and Schor 1987; Schor et al. 1987; Skobe and Fusenig 1998). Although not initially meeting with universal approbation, this “contextual” model has recently gained more widespread support (Broxterman and Georgopapadakou 2007; Kiaris et al. 2008; Tsellou and Kiaris 2008; Pietras et al. 2008; Guturu et al. 2009; Anton and Glod 2009; Xu et al. 2009). This chapter will focus on the evidence demonstrating the presence of functionally aberrant fibroblasts in cancer patients and their possible contribution to disease pathogenesis. Our own work has indicated that fibroblasts obtained from a majority of cancer patients resemble fetal cells in terms of their persistent production of a migration stimulating factor (MSF) which is not made by their normal adult counterparts. The multiple bioactivities and target cell populations of MSF suggest several means whereby its inappropriate expression may promote cancer progression. We now specifically propose to review evidence concerning (1) the existence of local and systemic fibroblast heterogeneity and its implications for tumour progression, (2) the molecular characterisation and functionality of MSF, and (3) the potential utility of MSF as a target for developing novel clinical intervention strategies. Our intention is to incorporate these data into a broadened conceptual framework of cancer pathogenesis explicitly recognising the joint contribution of genetic and epigenetic causality.

11.2 Local and Systemic Fibroblast Heterogeneity in Cancer Patients: Implications for Tumour Progression 11.2.1  T  he Presence of Functionally Aberrant Fibroblasts in Cancer Patients There is a substantial literature documenting the presence of functionally aberrant fibroblasts in cancer patients. Pathologists have commonly described aberrantly

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appearing fibroblasts in tumour stroma as “fetal-like”, “plump”, “reactive” or “activated” in an effort to distinguish them from their “resting” or “quiescent” counterparts (“fibrocytes”) in healthy adult tissues (McNeal 1984). In addition to this “local” manifestation of stromal abnormality, aberrantly appearing and behaving fibroblasts have also been detected “systemically” at distant, uninvolved, skin in patients with either apparently sporadic forms of the disease or clearly defined hereditary cancer syndromes. With respect to the former patient group, tumour-associated stromal fibroblasts in patients with sporadic forms of colorectal, breast, ovarian, bladder and lung cancers have been reported to stain with antibodies specific for a cell surface glycoprotein (F19) previously shown to be expressed by sarcomas and proliferating cultured fibroblasts, but not by normal resting cells (Garin-Chesa et al. 1990). These workers further commented on the co-distribution of F19 reactive fibroblasts with fetal isoforms of the matrix molecule tenascin. Fibroblasts displaying aberrant phenotypic characteristics have also frequently been detected at distant uninvolved sites in patients with hereditary cancer syndromes. In this regard, Kopelovich and colleagues noted that skin fibroblasts obtained from patients with hereditary adenomatosis of the colon and rectum (ACR) or neurofibromatosis exhibit a number of aberrant phenotypic characteristics in vitro, including disorganisation of the actin microfilament array, reduced cell substratum adhesion, anchorage-independent growth, elevated sensitivity to overt transformation by oncogenic agents and the expression of a transformation-associated antigen (Frankel et al. 1986; Higgins and Kopelovich 1991; Kopelovich 1987, 1988; Kopelovich and Bias 1980; Kopelovich et al. 1980). Independent studies have corroborated and extended these findings by noting that skin fibroblasts from patients with ACR and other hereditary cancer syndromes (such as aniridia, dysplastic nevus syndrome, von Hippel-Lindau syndrome and Li-Fraumini syndrome) display enhanced sensitivity to transformation by carcinogens and a reduced efficiency of DNA repair (Rasheed and Gardner 1981; Rhim et al. 1981; Abrahams et al. 1998). As all of these familial cancer syndromes result from a germ line mutation, it is reasonable to assume that the annotated fibroblast abnormalities result from the systemic expression of the affected gene. Seen in this context, the aberrant fibroblasts have commonly been classified as “partially-transformed” or “initiated” cells and considered to be a convenient biomarker of an inherited predisposition to develop an overt malignancy by the relevant target cell population. Similarly aberrant skin fibroblasts have been obtained from patients with apparently sporadic cancers. Thielmann et al. (1987) reported that dermal fibroblasts obtained from patients with squamous cell carcinoma (SCC) and (to a lesser extent) basal cell carcinoma exhibited impaired repair DNA synthesis. Similarly, skin fibroblasts obtained from patients with oral SCC (Danes et al. 1990) or sporadic colon cancer (Svendsen et al. 1989) exhibited an increased tendency to develop hyperdiploidy when cultured in vitro. Danes et al. (1990) further noted a correlation between the precise anatomical location of SCC within the oral cavity and the expression of this skin fibroblast abnormality. Explicit use of the term “fetal-like” was made by McNeal (1984) in a comprehensive early study of the pathogenesis of benign prostatic hyperplasia and its

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subsequent malignant transformation in which he noted the early appearance of a “fetal-like” stroma and postulated that its interaction with the emerging population of aberrant epithelial cells was a key factor in disease progression. Tumour-associated fibroblasts have also been reported to resemble fetal cells in terms of their expression of a fetal-specific cell surface antigen (Bartal et al. 1986) and production of stromelysin-3 (Basset et al. 1990). In this regard, it should be noted that fetal cells also form colonies in semi-solid medium (Nakano and Ts’O 1981). Nicolo et al. (1990) have reported that the oncofetal ED-B isoform of fibronectin is preferentially associated with the stroma of several types of malignant neoplasms. We also employed the term fetal-like, noting that (1) tumour-associated fibroblasts obtained from approximately 50% of resected breast carcinomas displayed an elevated migratory phenotype in vitro, similar to that of fetal fibroblasts (Durning et al. 1984; Schor et al. 1994), (2) systemic fibroblasts from approximately 50% of uninvolved skin biopsies obtained from patients with a range of sporadic cancers (including breast and other common carcinomas, soft tissue sarcomas and melanomas) also displayed fetal-like phenotypic characteristics (Schor et al. 1985a, b, 1988b) and (3) systemic fibroblasts obtained from uninvolved skin from 100% of patients with hereditary breast cancer (Schor et al. 1986), and approximately 60–70% of their unaffected first degree relatives displayed these same functional aberrations (Schor et al. 1986; Haggie et al. 1987). Other workers also explicitly noted that skin fibroblasts obtained from sporadic breast cancer patients displayed a number of fetal-like characteristics in vitro, including anchorage-independent growth, colony formation on epithelial monolayers, extended lifespan and continued DNA synthesis at saturation cell density (Azzarone and Macieira-Coelho 1987; Azzarone et al. 1984, 1988; Wynford-Thomas et al. 1986; Schor et al. 1988b). This shift in nomenclature from “partially transformed” to “fetal-like” is more than just a question of semantics (Schor et al. 1987; Schor and Schor 1997). The designation “partially transformed” carries with it the implication that the observed behaviour results from acquisition of a genetic lesion. In contrast, the term “fetallike” implies that the particular phenotypic attributes which define this state (e.g. continued production of MSF) are inherently physiological and reversible, although their expression may be inappropriate in the adult. Taken together, the detection of local and systemic functionally aberrant, fetallike, fibroblasts in cancer patients raises two important questions, namely: • What mechanisms lead to their presence in cancer patients?, and • What, if any, contribution do these fibroblasts make to cancer pathogenesis?

11.2.2  P  ossible Mechanisms Responsible for the Presence   of Aberrant Fibroblasts in Cancer Patients Which factors may lead to the postulated inappropriate expression of fetal phenotypic characteristics by fibroblasts in cancer patients? Our hypothesis is based

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on the fact that fibroblasts (in spite of their similar morphology) are actually a highly diverse cell population, exhibiting a significant degree of inter-site, intra-site and developmental heterogeneity (Schor and Schor 1987; Bayreuther et al. 1988; Sappino et al. 1990; Mukaida et al. 1991; Schor et al. 1996). On the basis of this heterogeneity, we initially proposed a “clonal modulation” model to account for the presence of MSF-secreting fibroblasts in cancer patients(Schor and Schor 1987; Schor et al. 1987). In this model, we suggested that (1) distinct subpopulations of fetal-like fibroblasts pre-exist in the healthy adult, (2) the fetal-like characteristics of these cells do not result from an acquired mutation, but reflect epigenetically regulated changes in gene expression analogous to those which occur during embryonic development and (3) their relative numbers may be increased, both locally and systemically, as a consequence of clonal expansion and/or epigenetic induction in response to both internal and environmental cues. In this regard, studies by Dabbous et al. (1987) revealed the existence of significant clonal heterogeneity in the production of proteases by tumour-associated fibroblasts in response to inductive signals from co-cultured carcinoma cells. Published data confirm the existence of inter- and intra-site heterogeneity in MSF expression by fibroblast subpopulations in fetal skin (Schor et al. 1985a), as well as in the healthy adult (Irwin et al. 1994). In the latter study, fibroblasts obtained from 100% (12/12) gingival biopsies examined produced detectable amounts of MSF, whereas none (0/9) of paired forearm skin fibroblasts obtained from the same individuals did so. Interestingly, wound healing in the oral mucosa is clinically distinguished from dermal healing in terms of both its rapidity and lack of scar formation (McCallion and Ferguson 1996). It is possible that the persistence of MSF-producing fibroblasts in the oral mucosa contributes to this regenerative and characteristically “fetal-like” mode of wound healing. This study further revealed the existence of intra-site heterogeneity in the oral mucosa. This involved separation of gingival lamina propria (connective tissue) from its overlying epithelium by exposure to trypsin and the subsequent microdissection of the lamina propria to allow the selective culture of fibroblasts derived from the tips of the papillae and deeper reticular tissue. Only fibroblasts derived from the papillae produced MSF. Prolonged subculture of papillary fibroblasts resulted in their cessation in MSF production and their adoption of a reticular fibroblast phenotype. Staining of gingival tissue with anti-MSF antibody confirmed the preferential localisation of MSF in the papillae; interestingly, this particular pattern of MSF distribution is identical to that previously described for tenascin (Sloan et al. 1990), another molecule preferentially produced by fetal fibroblasts (Chiquet-Ehrismann et al. 1986). Intra-site heterogeneity with respect to the distribution of MSF-secreting fibroblasts has also been observed in the normal breast (Schor et al. 1994). Intra- and interlobular fibroblasts were isolated by controlled enzymatic digestion and differential sedimentation from normal breast tissue obtained from patients undergoing reduction mammoplasty. Results indicated that 91% (10/11) of the interlobular fibroblasts displayed a fetal-like migratory phenotype, compared to 0% (0/10) of the intralobular cells. Significant differences were also observed with respect to the production of MSF by these cells, with 100% (11/11) of the interlobular lines and none (0/10) of the intralobular lines secreting this factor. These observations

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indicate the presence of a clearly defined intra-site heterogeneity with respect to MSF production by fibroblasts in the normal breast of non-cancer patients.

11.2.3  C  ontribution of Aberrant Fibroblasts to Cancer Pathogenesis The vast majority of early studies documenting the presence of aberrant fibroblasts in cancer patients have avoided ascribing any carcinogenic relevance to them. Indeed, as mentioned above, in those few communications in which this question was explicitly addressed, it was assumed that such fibroblasts resulted from the inheritance of a germ line mutation which only contributed to cancer pathogenesis when also expressed by the relevant epithelial cell population. According to this “innocent bystander” model, aberrant fibroblasts were considered to be convenient phenotypic markers of a putative genetic lesion and not an intrinsic driver of disease progression. This passive interpretation has gradually been replaced by a more proactive perspective explicitly recognising the important role played by interactions between different cell populations in determining their respective phenotypes (Broxterman and Georgopapadakou 2007; Kiaris et  al. 2008; Tsellou and Kiaris 2008; Pietras et al. 2008; Guturu et al. 2009; Anton and Glod 2009). Such interactions have long been recognised to regulate epithelial growth, migration and differentiation during fetal development (Cunha et al. 1985; Gurdon 1988). Shekhar et al. (2001) presented data supporting the role played by aberrant stromal cells in the manifestation of malignant phenotypic characteristics by associated breast carcinoma cells. This conclusion is consistent with a large number of studies indicating that interactions between tumour cells and fibroblasts enhance tumour growth (Camps et al. 1990) and metastasis in vivo (Picard et al. 1986; Tanaka et al. 1988; Gärtner et al. 1992) as well as invasion and various other neoplastic characteristics in vitro (Picard et al. 1986; Tanaka et al. 1988; Matsumoto et al. 1989; Gärtner et al. 1992; Atula et al. 1997). The expression of growth factors and proteases has also been reported to be modulated by interactions between tumour and stromal cells (Wong and Wang 2000; Dong et al. 2001; Sung and Chung 2002; Koshida et al. 2006; Maeda et al. 2006; Sugimoto et al. 2005; He et al. 2007). Perhaps the most dramatic manifestation of such tissue level interactions relate to observations that an appropriate host environment is capable of suppressing carcinogenesis resulting from the expression of oncogenes ordinarily capable of inducing rapid tumour development (Mintz and Illmensee 1975; Stoker et al. 1990). These effects of fibroblast subpopulations on epithelial cells are complex and vary both as a function of developmental stage (fetal vs adult), disease progression (early vs late stages) and fibroblast site of origin (Cornil et al. 1991; Fabra et al. 1992; Lu et al. 1992). In addition to noting a stage-dependency of tumour cell interaction with their associated stromal fibroblasts, Tsellou and Kiaris (2008) further demonstrated that there appears to be a parallel temporal evolution towards manifestation of more neoplastic behavioural features in both cell populations.

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Interactions between epithelial cells and fibroblasts are mediated by the interdependent signalling of soluble regulatory molecules (such as cytokines) and insoluble constituents of the extracellular matrix (ECM). The effects of cytokines and matrix macromolecules are mutually interdependent in the sense that (1) the specific response of cells to cytokines is modulated by the nature of the surrounding matrix, and (2) the deposition of this matrix is modulated by the action of cytokines (Schor 1994; Schor and Schor 1987; Nathan and Sporn 1991). Interactions between tumour cells and fibroblasts have been reported to affect the production of both cytokines and matrix macromolecules in a bidirectional fashion. These interactions involve positive feedback loops which may result in an expansion in cell number and/or amplification of signal molecule synthesis. For example, Cullen et al. (1991) reported that mammary carcinoma cells synthesize PDGF which stimulates fibroblast proliferation and synthesis of IGF I and II; interestingly, the fibroblast-produced IGFs in turn stimulate mammary carcinoma cell proliferation and synthesis of PDGF. Recent studies have documented a reciprocal effect of such local cell-cell interactions on gene expression between breast epithelial cells (non-malignant and malignant) and different populations of fibroblasts (Rozenchan et al. 2009), including alterations in the expression of TGF-β regulated genes. The effects of such tumour-associated stromal cells may be mediated by several mechanisms, including cell-cell contact and the concerted signalling of cell-produced matrix macromolecules and soluble factors. The possible role of aberrant systemic (distant) fibroblasts remains more difficult to conceptualise. At one end of the spectrum is the notion that such fibroblasts make no direct contribution to cancer pathogenesis, but provide a biomarker of exposure to factors (endogenous and/or environmental) which induced similar changes to cells (stromal and epithelial) at the site of tumour development. At the other extreme, it is also possible that such systemic cells do indeed precede and make a direct contribution to cancer pathogenesis, possibly by generating bioactive soluble factors (such as MSF) or by providing a receptive microenvironment (e.g. hyluronan-rich) to support the seeding of metastatic tumour cells at distant sites. Further studies are required to discriminate between these possibilities.

11.2.4  Section Summary Fibroblasts obtained from cancer patients commonly display a number of phenotypic characteristics which distinguish them from their healthy adult counterparts. Significantly, such fibroblasts have been obtained from both the tumour-associated stroma (i.e. locally), as well as from distant, apparently uninvolved, sites (i.e. systemically). We suggest that (1) such fibroblasts may actively contribute to cancer pathogenesis by perturbing normative interactions with neighbouring epithelial cells, and (2) have arisen by changes in gene expression mediated by epigenetic mechanisms resulting from exposure to endogenous or environmental “epigenotoxins” and/or the clonal expansion of a pre-existing subpopulation. Considerably more remains to be learned about the extent of fibroblast functional heterogeneity

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in normal and diseased tissues, the mechanisms responsible for their divergence and their consequences for disease progression.

11.3 MSF: A Novel Bioactive Oncofetal Protein 11.3.1  Molecular Characterisation We initially reported that fibroblasts derived from cancer patients (both tumourassociated and those obtained from distant uninvolved sites) differ from their healthy adult counterparts in terms of their persistent expression when cultured in vitro of a putative migration stimulating factor (MSF) (Schor et al. 1988a). MSF was later shown to be a truncated isoform of fibronectin (Schor et al. 2003). Fibronectin is a modular glycoprotein consisting of the following functional domains, so named on the basis of their binding affinities to other matrix molecules and cell surface integrins (Fig. 11.1): Hep-1/Fib-1 (N-terminal low affinity binding to heparin and type III

1

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intron 12 derived unique decamer

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Fig. 11.1   MSF is a genetically truncated isoform of fibronectin. Fibronectin is a modular glycoprotein consisting of the following “functional domains” defined on the basis of their characteristic binding affinities for other matrix macromolecules and cell surface receptors: Hep-1/ Fib-1 (N-terminal binding to heparin and fibrin), Gel-BD (binding to gelatin/collagen), Cell-BD (RGD-mediated binding to cell surface integrins), Hep-2 (high affinity binding to heparin) and Fib-2 (C-terminal binding to fibrin). Each of these functional domains is composed of several “homology modules”, respectively designated as types I, II and III. Alternative splicing at EDA, EDB and IIICS generates approximately 20 “full-length” fibronectin isoforms (molecular masses in the region of 250–280 kDa). MSF+aa is a truncated (70 kDa) isoform of fibronectin identical to its N-terminus, up to and including the amino acid sequence coded by exon III-1a. MSF message is transcribed from the fibronectin gene by a variation of standard alternative splicing involving read-through of intron 12 (separating exons III-1a and III-b), followed by intra-intronic cleavage. MSF+aa protein terminates in an MSF-unique 10 amino acid sequence (coded by the first 30 bp of intron 12) which is not present in any full-length fibronectin. Arrows indicate the location of the four IGD motifs in modules I-3, I-5, I-7 and I-9. MSF-aa is identical to MSF+aa, with the exception of a 15 amino acid deletion in module II-1

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fibrin), Gel-BD (binding to gelatin/collagen), Cell-BD (RGD-mediated binding to integrins), Hep-2 (high affinity heparin binding) and Fib-2 (C-terminal fibrin binding site). Each functional domain is composed of a different tandem array of three possible homology modules, respectively designated type I, II and III. Approximately 20 “full-length” fibronectin isoforms are produced by alternative splicing involving the inclusion or exclusion of two particular type III modules (EDA and EDB), and a more complex splicing repertoire at the IIICS region (Hynes 1990). All such isoforms consist of two peptide chains (250–280 kDa each) covalently linked by disulfide bonds at their respective C-termini. In the fibronectin gene, all type I and II homology modules are coded by correspondingly named single exons; in contrast, all type III modules are coded by two consecutive exons (designated “a” and “b”), with the exception of the individual exons coding for the “extra domain” type III modules (EDA and EDB) found in those isoforms displaying an oncofetal expression profile. MSF is a genetically truncated isoform (70 kDa) of fibronectin. It is identical to the N-terminus of the fibronectin monomer, up to and including the amino acid sequence coded by exon III-1a, followed by an MSF-specific intron-coded C-terminal decamer (Fig. 11.1) (Schor and Schor 2001; Schor et al. 2003). Murine MSF, truncated at the same exon as human MSF, has also been cloned (unpublished). Other truncated isoforms of fibronectin have been described in Zebrafish embryos (Zhao et al. 2001) goldfish and rainbow trout, as well as mouse and human liver, prostate, ovary, brain and spleen (Liu et al. 2003). Two isoforms of human MSF have been cloned by our group. Both contain the same MSF-specific intron-coded C-terminal decamer and, as will be discussed below, both isoforms also contain the same bioactive IGD amino acid motifs and consequently display the same spectrum of equipotent effects on target cells. The two isoforms differ solely in terms of a 45 bp deletion in exon II-1 and are consequently referred to as MSF+aa and MSF-aa to indicate the retention or deletion of a 15 amino acid sequence in module II-1 (Fig. 11.1). The global term MSF will be employed to denote both isoforms (i.e. total MSF). Based on this information, we have generated the following MSF-specific reagents: (1) a pan-MSF identification antibody raised against the shared MSF-specific C-terminal decamer, (2) a function-neutralising antibody effectively abrogating the bioactivities of both MSF isoforms, (3) an identification antibody specific for MSF-aa raised against the neoantigen generated by its 15 amino acid deletion, (4) riboprobes for MSF message, and (5) a murine-specific pan-MSF identification polyclonal antibody raised against its distinct intron-coded C-terminus. The identification antibodies and riboprobes have been optimised and validated for use in immunohistochemistry (IHC) and in situ hybridisation (ISH) with archival tissue blocks. The identification antibodies have also been validated for biochemical analyses (ELISA, western and dot blots). MSF+aa message is generated by a two-stage processing mechanism. This initially entails generating an MSF-specific primary transcript from the fibronectin gene by read-through of intron 12 (separating exons III-1a and III-1b), followed by intra-intronic cleavage to produce a 5.9 kb MSF pre-message (Kay et al. 2005). The pre-message remains sequestered within the nucleus, where it is rapidly degraded as a consequence of the presence of an AU rich instability element (Bakheet et al. 2001) in its 3′-UTR (Schor et  al. 2003; Kay et  al. 2005). Under standard tissue

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culture conditions, this pre-message is produced by cells that express MSF protein (such as fetal fibroblasts), as well as by cells which do not (such as fibroblast from healthy adult tissues). This unexpected occurrence may reflect the induction of a “partially activated” (“wounded”) phenotype in vitro. In cells which do express MSF protein, the intron-derived 3′ UTR of the pre-message is cleaved a second time to produce a 2.1 kb mature MSF message. This has a significantly shorter (195 bp) intron-derived 3′ sequence containing a 30 bp in-frame coding sequence (immediately contiguous with exon III-1a), followed by a 165 bp 3′-UTR containing several in-frame stop codons and a cleavage/polyadenylation signal. The mature message is rapidly exported to the cytoplasm for translation. The generation of MSF-aa message presumably occurs by a similar mechanism, with the addition of the splicing out of a 45 bp sequence in exon II-1.

11.3.2  M  SF: Oncofetal Expression Profile and the Contextual Control of its Expression by Epigenetic Mechanisms MSF is an oncofetal protein initially shown to be constitutively expressed in vitro by fibroblasts explanted from human fetal skin, but not from the majority of healthy adult skin (Fig. 11.2). MSF-expressing (“fetal-like”) fibroblasts were also derived from excised tumour tissue (Schor et al. 1994), as well as distant uninvolved skin in cancer patients (Schor et al. 1988a, b; Haggie et al. 1987). Subsequent ex-vivo studies confirmed and extended these initial findings by demonstrating that MSF message and protein are both expressed by at least three cell populations (epithelial,

MSF producer MSF non-producer

percentage

100 80 60 40 20 0 fetal

adult

patient (local)

patient (systemic)

Fig. 11.2   MSF expression by fibroblasts in vitro. Skin fibroblasts between passage 8–15 were assessed for their expression of MSF. Cells were derived from fetal skin (n = 37), healthy adult skin (n = 52), breast tumours (local, n = 55) and matching skin from these patients (systemic, n = 55). Serum-free conditioned media obtained from confluent cultures (72 h incubation) were assayed for the presence of MSF by assessing (1) their ability to stimulate the migration into 3D collagen gels of a target line of adult, non-MSF producing, adult skin fibroblasts (as described in Schor et al. 1988a), (2) abrogation of motogenic activity by an MSF-function neutralising antibody and, in some cases (3) removal of motogenic activity by affinity to anti-MSF identification antibody

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fibroblasts and endothelial) in fetal skin, but not by the majority of these cells in healthy adult tissue (Schor et  al. 2003). These studies additionally indicated that MSF was also expressed by tumour and tumour-associated stromal cells in patients with cancer, as well as by skin cells in biopsies obtained from distal uninvolved sites (Schor et al. 2003; unpublished data). Recent findings further suggest that the MSF+aa isoform of MSF may be the only one systemically expressed by skin cells in cancer patients, whereas the MSF-aa appears to be the main isoform produced by tumour and tumour-asociated stromal cells (Fig.  11.3). More recent observations by ourselves and others confirm that MSF is expressed by tumour cells and tumour-associated stromal cells in a majority of cancers examined to date, including carcinomas (breast, colorectal, oral, oesophageal, skin), melanoma and glioma (Hu et al. 2009; unpublished observations).

Fig. 11.3   Expression of total MSF and MSF-aa by skin and tumour tissue. Serial sections of normal skin from a breast cancer patient (a, b) oral carcinoma (c, d) and breast carcinoma (e, f) were stained with antibodies that recognise total MSF (both isoforms) (a, c, e) or only MSF-aa (b, d, e). The two tumours stained positively with both antibodies, whereas staining in the skin was positive for total MSF, but negative for MSF-aa. These data suggest that skin from the breast cancer patient only expresses MSF+aa, whereas both tumours contain predominantly MSF-aa

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MSF grade

number of specimens

0/1 2/3

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0 NB

NB-T Tissues

T

Fig. 11.4   Expression of MSF in breast tissues. Paraffin-embedded archival specimens were stained with MSF-specific identification antibody raised against the MSF-unique C-terminal decamer (Schor et al. 2003). MSF staining was graded as negative (0), weak (1), moderate (2), or strong (3). Results were obtained by consensus of 2–3 independent observers. The tissues examined were: NB (n = 19): normal breast from reduction mammoplasties; NB-T (n = 19): histologically normal breast from breast tumour patients; and T (n = 23): breast carcinomas. MSF expression was significantly increased in a step-wise manner from NB to NB-T (Fisher’s exact test, p = 0.0031) and from NB-T to T (p = 0.0258)

In vitro studies initially indicated that intralobular fibroblasts isolated from histologically normal breast adjacent to a carcinoma expressed MSF, whereas such fibroblasts isolated from normal breast tissue from patients undergoing reduction mammoplasty did not (Schor et al. 1994). This finding has recently been confirmed in a semi-quatitative immuno-histochemical study using archival tissues (Perrier et al. unpublished). In this study, histologically normal breast tissue adjacent to a carcinoma displayed a significantly elevated level of MSF expression compared to normal breast obtained from reduction mammoplasty controls. MSF expression was significantly higher in carcinomas than in either of the normal breast tissues (Fig. 11.4). These findings have implications both for our understanding of the concept of field cancerisation, as well as for patient management (e.g. identification of “disease-free” margins in resected tumour tissues). In addition to being produced by a variety of cell populations in cancer patients (i.e. tumour cells, tumour-associated stromal cells and distal uninvolved host cells), bioactive MSF has also been detected in the serum of breast cancer patients (Picardo et al. 1991; unpublished data). These data indicate that MSF bioactivity (as detected by MSF-specific stimulation of fibroblast migration) is present in 90% of cancer patients compared to only 10% of healthy age-matched controls (Fig. 11.5), a value consistent with the incidence of MSF-secreting skin fibroblasts originally detected in these individuals (Fig. 11.2). Neutrophil gelatinase-associated lipocalin (NGAL) is a potent inhibitor of MSF+aa, but does not affect manifestation of MSFaa activity (Jones et al. 2007; unpublished data). NGAL is present in the serum of both healthy individuals and cancer patients, suggesting that the MSF-aa isoform is

11  Multiple Fibroblast Phenotypes in Cancer Patients negative for MSF activity positive for MSF activity

100 percentage of samples

Fig. 11.5   MSF bioactivity in serum. The presence of detectable levels of MSF bioactivity in the serum of breast cancer patients (n = 30) and age- and sex-matched healthy controls (n = 30) was ascertained as described in Picardo et al. (1991). Data are presented as the percentage of positive (detectable) and negative (not detectable) samples in the two subject groups

209

80 60 40 20 0 control

patients GROUPS

present in the latter. This conclusion has been confirmed in studies that additionally indicate that MSF+aa is in fact present in control sera, but is not able to manifest its motogenic bioactivity as a consequence of its association with an MSF inhibitor. The initial study by Picardo et al. (1991) further indicated that the bioactive MSF detected in patient sera is not a measure of tumour burden, as it persisted for many years after apparently successful resection of the presenting tumour. The cellular origin of MSF in cancer patient sera is not known. The programmed control of gene expression during development has long been recognised to be regulated by epigenetic mechanisms. In contrast to their genetic (e.g. mutational) counterparts, epigenetic mechanisms are mediated by reversible, although heritable (persistent), changes in DNA and/or histone methylation. We have recently demonstrated that MSF expression may be switched on and off in such a reversible and persistent fashion. A transient (2 h) exposure of normal adult skin fibroblasts (non-producers of MSF) growing on a “wounded” substratum in vitro (such as denatured type I collagen or fibrin) induces the expression of MSF protein. This “activated” phenotype is persistent for the entire subsequent lifespan of the treated cells cultured in the absence of TGF-β and irrespective of the nature of the substratum employed. MSF expression may, however, be switched off again at any time by a second transient exposure to TGF-β1, this time when the cells are growing on a “healthy” matrix of native type I collagen (Fig. 11.6). It is important to note that this “on-off” switch is repeatable and strictly requires the concerted action of TGF-β1 and the appropriate matrix: i.e. exposure to TGF-β1 of previously activated cells growing on a wounded matrix will not switch off MSF expression. These observations may shed light on the apparent stage-dependence of TGF-β influence on tumour progression: namely, TGF-β acts as a suppressor of early stage disease and as a promoter of late stage disease (Bierie and Moses 2006). We suggest that these and related perplexing observations might reflect the switching-off of MSF expression by activated fibroblasts associated with early stage disease (in which matrix degradation is minimal) and the converse switching-on of MSF in late

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persistent MSF production healthy matrix MSF-np

MSF-p

persistent non-producer wounded matrix

i. TGF-β iii. 5-azaC

Fig. 11.6   Control of MSF expression. Quiescent adult skin fibroblasts do not produced MSF (MSF-np). They are, however, induced to do so by (i) a transient (2 h) exposure to TGF-β1 when cultured on a “wound” matrix, such as fibrin or denatured type I collagen. This switch-on is persistent and the resultant “activated” MSF-producing cells (MSF-p) continue to express MSF for the entire duration of their in vitro lifespan when cultured under standard tissue culture conditions in the absence of TGF-β and irrespective of the nature of their substratum. MSF expression may be persistently switched-off again by (ii) a subsequent transient exposure of activated cells to TGFβ1, this time when they are grown on a “healthy tissue” matrix (such as native type I collagen). This switch-on and -off is completely reversible and may be repeated for the entire duration of their in vitro life span. MSF-np cells may be similarly activated by a transient exposure to (iii) 5-azacytidine (5-azaC). The persistent MSF expression so induced is again switched off by a subsequent transient exposure of resultant MSF-p cells to TGF-β1 when adherent to a native collagen substratum

stage disease (in which matrix degradation is more extensive). This matrix-modulation of TGF-β functionality is consistent with the extensive literature documenting the functional interactions of cytokines and matrix (Nathan and Sporn 1991; Schor 1994; Pardali and ten Dijk 2009; Padua and Massague 2009). TGF-β1 also up-regulates the expression of full-length oncofetal (EDA and EDB) fibronectins (Hynes 1990). The possible involvement of epigenetic mechanisms in the switching-on and -off of MSF expression has initially been suggested by observations that a transient exposure of normal adult fibroblasts to 5-azacytidine (5-azaC), a pharmacological agent inducing changes in gene expression by CpG island demethylation, results in a persistent switch-on of MSF expression. Again, this induced expression is reversible and may be switched off by a subsequent exposure of the activated cells to TGF-β1 when grown on a native collagen substratum (Fig. 11.6). Cytotoxic/carcinogenic agents have been found to induce MSF expression by fibroblasts and carcinoma cells. For example, Yoshino et al. (2007) reported that exposure of a bronchioloalveolar carcinoma cell line to the tobacco carcinogen benzo[a]pyrene results in the induction of MSF expression. These findings raise

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the possibility that environmental exposure to genotoxins and/or epigenotoxins may induce the local and/or systemic inappropriate persistent switch-on of MSF expression which may then contribute to the development of an overt malignancy. We have also observed that transfection of adult skin fibroblasts with dominantnegative p53 results in the persistent switch-on of MSF-aa expression (unpublished data). Hill et al. (2005) reported that inactivation of tumour suppressor gene pRb in a human prostate carcinoma cell line resulted in the enrichment of mouse fibroblasts harbouring silenced p53 in a xenograft model of cancer progression, presumably as a consequence of selective pressure exerted by malignant prostate cells. These findings suggest that interactions between tumour and stromal cells may lead to the selection of a pre-existing inactivated p53 clonal population of stromal fibroblasts. Finally, the potential clinical relevance of the above findings is highlighted by observations that constitutive MSF expression by fetal and cancer patient fibroblasts may also be persistently switched-off by exposure of cells growing on a native collagen substratum to TGF-β1. These findings suggest that it may be possible to develop novel therapeutic strategies based on the modulation of MSF expression in a clinically desirable fashion by pharmacologic intervention.

11.3.3  M  SF: Spectrum of Bioactivities and Their Contextual Control MSF exhibits a number of potent bioactivities. As its name implies, the first to be demonstrated was its stimulation of fibroblast migration into 3D gels of native type I collagen fibres (Schor et al. 1988a), a process apparently mediated by its stimulation of hyaluronan biosynthesis (Schor et al. 1989). These effects on target adult skin fibroblasts displayed a bell-shaped dose response and were exceptionally potent, with half-maximal activity elicited at femtomolar concentrations, i.e. 1 pg/ml (Fig. 11.7) (Schor et al. 2003). Subsequent studies revealed its potent motogenic effect on various other target cell types, including normal and tumour epithelial cells, melanoma cells, endothelial cells, and pericytes (Houard et al. 2005; Hu et al. 2009; migration HA synthesis

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Fig. 11.7   MSF stimulation of cell migration and hyluronan (HA) synthesis by adult skin fibroblasts. The effects of MSF on migration and HA synthesis by confluent adult skin fibroblasts growing on a 3D native type I collagen matrix were determined as previously described (Ellis et al. 1992). Data are expressed relative to control cultures incubated in the absence of MSF

A. M. Schor and S. L. Schor 50

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Fig. 11.8   The effect of MSF on the migration of breast carcinoma cell lines. The effect of various concentrations of MSF on the migration of three breast cancer lines was tested in the transmembrane assay using membranes coated with native type I collagen. The MSF-7 line displayed a bellshaped dose-response, whereas the two MDA lines displayed a plateau level of stimulation within the concentration range tested

Schor et al. 2005). Certain tumour cell lines exhibit a bell-shaped motogenic dose response to MSF, whereas others display a plateau level in the tested concentration range (Fig. 11.8). MSF induces endothelial cell activation in vitro, as manifest by the formation of a sprouting (angiogenic) phenotype (Schor et  al. 2001; unpublished data). It also induces angiogenesis in vivo when implanted subcutaneously in rats, mice and pigs; as well as when evaluated in the chick embryo yolk sac assay (Fig. 11.9). In the latter, different quantities of MSF were incorporated within dried

angiogenic response (%)

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Fig. 11.9   The Induction of angiogenesis in the chick yolk sac assay by MSF. Different concentrations of MSF were incorporated into methyl cellulose gels (MCGs) and these were then dried and applied to the chick embryo yolk sac membrane. The induction of a radial disposition of vessels 24 h later was scored as a positive angiogenic response. Results are expressed as the percentage of positive angiogenic responses elicited by various concentrations. A significant response was induced over a broad concentration range of 0.5–500  ng/MCG. PMA (300  ng/MCG) was used as positive control. Upper and lower horizontal bars indicate values achieved by the positive and negative controls (excipient only)

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methyl cellulose gels (MCG) which were then applied to the yolk sac membrane. The induction of a subjacent radial array of blood vessels 24 h later was taken as a positive angiogenic response. MSF elicited a significant (bell-shaped) angiogenic response over a broad concentration range of 0.5–500 ng/MCG. Homogeneous monolayer cultures of “resting” endothelial cells (mimicking the endothelium lining mature blood vessels in vivo) maintain their phenotype under basal tissue culture conditions, i.e. in the absence of an exogenous angiogenic factor (Schor et al. 2001). Addition of such a factor (including FGF-2, MSF or VEGF) results in the rapid induction of a network of elongated cells displaying a “sprouting” phenotype, thereby forming a mixed culture of both resting and sprouting cells (Schor et  al. 1983, 2001). We have demonstrated that sprouting endothelial cells produce bioactive MSF. Significantly, exposure of such mixed cultures to an MSF function-neutralising antibody rapidly results in the apoptotic death of the sprouting cell subpopulation without affecting the viability of the co-cultured resting cells (Fig. 11.10). As discussed below, this apparent dependence of sprouting endothelial cell survival on the maintenance of MSF functionality may provide a novel therapeutic strategy based on the selectively induction of tumour-induced angiogenic vessel involution. The IGD tripeptide motif ( isoleucine-glycine-aspartate) is a highly conserved feature of fibronectin type I modules to which no biological functionality was initially ascribed (Hynes 1990). MSF+aa and MSF-aa both contain four such IGD motifs (Fig.  11.1). In vitro mutagenesis studies indicated that the stimulation of fibroblast migration by MSF is mediated by the two IGD motifs present in type I modules I-7 and I-9 (Schor et al. 2003), whereas all four IGD motifs affect endothelial cell migration (unpublished data). Critically, small IGD-containing synthetic peptides mimic all MSF bioactivities, including the stimulation of cell migration, endothelial cell activation (Schor et al. 1999, 2003) and angiogenesis. It is important to note that none of these bioactivities are manifest by any full-length fibronectin isoform, apparently as a consequence of steric hindrance of their constituent IGD motifs (Millard et al. 2007; Vakonakis et al. 2009). Houard et al. (2005) have identified a second motif (HEEGH) in MSF module I-8 which mediates the

Fig. 11.10   MSF is required for the survival of sprouting, but not resting, endothelial cells. Endothelial cells were cultured on type I collagen-coated dishes and maintained under low serum basal conditions when they reached confluence. a Monolayer of resting confluent endothelial cells in control cultures. b The induction of a sprouting cell network by angiogenic stimulus (such as bovine fetal serum or VEGF). c Selective death of sprouting cells following 1–2 day incubation with MSF function-neutralising antibody

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Fig. 11.11   Matrix modulation of the motogenic response of target cells to MSF. The effects of the substratum (native or denatured type I collagen) on the motogenic response of human skin fibroblasts (HSF), bovine retinal endothelial cells (BREC) and bovine retinal pericytes (BRP) to 10 pg/ml MSF were evaluated in the transmembrane (Boyden chamber) migration assay. The number of cells migrated in the presence of MSF is expressed relative to baseline (control) migration. BRP and HSF only responded to MSF when attached to a native collagen substratum. In contrast, the migration of BREC was stimulated by MSF irrespective of substratum

motogenic response of a breast cancer cell line (MCF7) and is additionally required for manifestation of proteinase activity. Our recent data indicate that the motogenic response of certain target cells may be mediated by both IGD and HEEGH, whereas other cells only respond to the IGD motif. The functional responses of target cells to MSF (and its IGD synthetic peptide mimetic) are also modulated by contextual parameters. For example, the stimulation of fibroblast migration is manifest by cells adherent to a native, but not denatured, type I collagen substratum (Schor et al. 1999, 2003). Native collagen may accordingly be considered to provide a “permissive” substratum, whereas denatured collagen is “non-permissive”. The motogenic response of pericytes, but not endothelial cells, exhibits a similar matrix-dependence (Fig. 11.11). The stimulation of fibroblast migration by MSF is also abrogated by a variety of soluble factors, including TGF-β and NGAL, an endogenous MSF inhibitor produced by normal “resting” keratinocytes (Ellis et al. 1992; Jones et al. 2007). Interestingly, a distinct MSF inhibitor is produced by resting endothelial cells (unpublished data).

11.3.4  Section Summary Distinctive features of MSF biology are summarised in Fig.  11.12 in which it is noted that (1) MSF may be expressed by several cell types, including epithelial (normal and tumour), fibroblasts and endothelial cells, (2) MSF is a pleiotropic effector, capable of eliciting multiple responses from a variety of target cell types, and (3) the expression of MSF and its precise effect on target cells are modulated by a hierarchy of contextual control networks involving the inter-dependent signalling

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migration

MSF

migration matrix remodelling migration angiogenesis

endothelium expression of and response to MSF are both regulated by complex interplay of stimulatory and inhibitory factors (soluble and matrix)

Fig. 11.12   Contextual control of MSF expression and effects on potential target cells. MSF may be produced by epithelial cells (normal and tumour), fibroblasts and endothelial cells. These same cell populations are also potential targets of MSF, capable of responding in terms of the stimulation of cell migration, matrix remodelling and angiogenesis. Control of MSF expression and manifestation of its potential bioactivities are both regulated by a complex hierarchy of inter-dependent “contextual” actions of ECM and soluble factors

of soluble factors and the ECM. In this complex framework, MSF may function in both an autocrine and paracrine fashion. The inter-dependent modulatory effects of the ECM and soluble factors on both MSF expression and target cell response are consistent with the now well recognised dynamic and reciprocal contextual control of cell behaviour by multiple tissue-level cues (Bissell and Barcellos-Hoff 1987; Nathan and Sporn 1991; Schor 1994; Schor and Schor 1997; Xu et al. 2009). An important corollary of this understanding is that the temporal and spatial expression of MSF by its various potential producer cells, as well as the precise response (or lack of it) by its potential target cells, is not invariant, but may change in response to alterations to the tissue microenvironment during disease progression.

11.4 Epilogue: Clinical Consequences and Novel Intervention Strategies 11.4.1  Postulated Role of MSF in Cancer Progression Tumour progression is an indolent process in which many decades may elapse between inception of the initiating genetic lesion and the emergence of a clinically recognizable malignancy. Precise information regarding what proportion of initiated cells proceed to develop into an overt malignancy is not available, although published data suggest that this figure may be quite low. For example, Nielsen et  al. (1987) noted the presence of microfoci of carcinoma-in-situ in the breasts

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of apparently healthy women who died in road traffic accidents; surprisingly, this study revealed that greater than 40% of women over the age of 40 had such histologically discernable lesions, although clearly only a relatively small proportion of these would presumable have developed into an overt malignancy during the lifespan of the individual. These observations suggest that factors which alter the kinetics and severity of disease progression may play an important, and perhaps decisive, role in determining the probability of developing a clinically detectable malignancy. It is in this postulated role of an “accelerator” of cancer progression that we view the potential contribution of MSF-secreting fibroblasts to disease pathogenesis. According to this proposal, we suggest that MSF produced by “fetal-like” fibroblasts (as well as other activated cell types) may contribute to the creation of a milieu (“soil”) which is conducive to the clonal expansion of the evolving population of (pre-)neoplastic cells (“seed”). These seed-soil interactions are likely to involve both permissive and inductive mechanisms, and continue to be influenced by the stochastic accumulation of genetic/epigenetic lesions and environmental agents. The diverse bioactivities of MSF are consistent with its postulated influence on cancer progression. In this regard, we draw specific attention to (1) its capacity to stimulate the migration/invasion of tumour and non-tumour cells, (2) its effects on matrix remodelling, including stimulation of HA synthesis, (3) its induction of angiogenesis, and (4) its possible role as a specific survival factor for angiogenic endothelial cells. The postulated deleterious effect of MSF on disease outcome is supported by our recent data indicating that high MSF expression by tumour and tumour-associated stromal cells is associated with poor survival in patients with breast cancer (Fig. 11.13). A similar inverse association has been observed in patients with oral squamous cell carcinoma (unpublished data). Independent evidence below median above median

Percent survival

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Fig. 11.13   Prognostic value of MSF expression in the stroma and of breast cancer patients. Archival sections of breast tumours collected at presentation (n = 75) were stained with an antibody that recognises MSF-aa. MSF staining in the tumour stroma was assessed by image analysis. Overall patient survival was analysed according to the percentage of stromal area stained (divided by the median). Results show Kaplan-Mayer survival curves. High MSF expression was significantly associated with poor survival (log rank test, p = 0.02)

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supporting a role for MSF in cancer pathogenesis has recently been published by Hu et al. (2009). Using an unbiased proteomic screen, these authors identified MSF as a critical angiogenic factor driving oesophageal cancer progression.

11.4.2  Implications for the Development of Novel Therapies Translational research targeting tumour-stromal interactions are now beginning to be evaluated and promise to yield clinically relevant results. Nakajima et  al. (2004) reported that co-culture with fibroblasts reduced the sensitivity of a scirrhous gastric cancer cell line to 5-fluorouracil (a commonly used chemotherapeutic agent) and, more significantly, this protective effect of the stromal cells could be reversed by a pharmacologic agent (tranilast). With particular reference to MSF, our findings suggest that it may be possible to improve disease outcome by developing novel strategies variously designed to abrogate manifestation of its diverse functionality (such as by MSF-specific neutralising antibodies or small molecule inhibitors) and/or switch-off its inappropriate expression (such as by siRNA or epigenetic manipulation) (Aharinejad et al. 2009). The possible ability of such interventions to result in the specific regression of tumour-induced angiogenic blood vessels is a potentially exciting option. Other clinical applications of MSF may include population screening for individuals at elevated risk of developing cancer (by measuring its bioactivity in serum). Finally, quantitative assessment of MSF expression, either in serum and/or by ex-vivo immunohistological examination of the presenting tumour, may contribute to compiling a molecular profile of the individual cancer, thereby assisting in patient stratification and prognostic assessment. Acknowledgements  Work from our laboratory presented in this review has been funded by Cancer Research UK, Breast Cancer Campaign, Scottish Enterprise Proof of Concept Programme, Engineering and Physical Sciences Research Council and Biotechnology and Biological Sciences Research Council. We thank the many collaborators that have contributed to this work by providing specimens and experimental data.

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Schor SL (1994) Cytokine control of cell motility: modulation and mediation by the extracellular matrix. Prog Growth Factor Res 5:223–248 Schor SL, Schor AM (1987) Clonal heterogeneity in fibroblasts: implications for the control of epithelial-mesenchymal interactions. BioEssays 7:200–204 Schor SL, Schor AM (1997) Stromal acceleration of tumour progression: role of fetal-like fibroblast subpopulations. Pathol Update 4:75–95 Schor SL, Schor AM (2001) Tumour-stroma interactions—phenotypic and genetic alterations in mammary stroma: implications for tumour progression. Breast Cancer Res 3:373–379 Schor SL, Schor AM, Rushton G, Smith L (1985a) Adult, fetal and transformed fibroblasts display different migratory phenotypes on collagen gels: evidence for an isoformic transition during fetal development. J Cell Sci 73:221–234 Schor SL, Schor AM, Durning P, Rushton G (1985b) Skin fibroblasts obtained from cancer patients display a fetal-like migratory behaviour on collagen gels. J Cell Sci 73:235–244 Schor SL, Haggie J, Durning P, Howell A, Sellwood RAS, Crowther D (1986) The occurrence of a foetal fibroblast phenotype in familial breast cancer. Int J Cancer 37:831–836 Schor SL, Schor AM, Howell A, Crowther D (1987) Hypothesis: persistent expression of fetal phenotypic characteristics by fibroblasts is associated with an increased susceptibility to neoplastic disease. Exp Cell Biol 55:11–17 Schor SL, Schor AM, Grey AM, Rushton G (1988a) Fetal and cancer patient fibroblasts produce an autocrine migration-stimulating factor not made by normal adult cells. J Cell Sci 90:391–399 Schor SL, Schor AM, Rushton G (1988b) Fibroblasts from cancer patients display a mixture of both fetal and adult-like phenotypic characteristics. J Cell Sci 90:401–407 Schor SL, Schor AM, Grey AM, Chen J, Rushton G, Grant ME, Ellis I (1989) Mechanism of action of the migration stimulating factor produced by fetal and cancer-patient fibroblasts: effect on hyaluronic acid synthesis. In Vitro Cell Dev Biol 25:737–746 Schor SL, Ellis I, Banyard J, Dolman C, Seneviratne K, Gilbert AD, Chisholm DM (1996) Fetallike phenotypic characteristics of gingival fibroblasts: potential relevance to wound healing. Oral Dis 2:155–166 Schor SL, Ellis I, Banyard J, Schor AM (1999) Motogenic activity of the IGD amino acid motif. J Cell Sci 112:3879–3888 Schor SL, Ellis IR, Jones SJ, Baillie R, Seneviratne K, Clausen J, Motegi K, Vojtesek B, Kankova K, Furrie E, Sales MJ, Schor AM, Kay R (2003) Migration-stimulating factor: a genetically truncated onco-fetal fibronectin isoform expressed by carcinoma and tumor-associated stromal cells. Cancer Res 63:8827–8836 Schor SL, Schor AM, Keatch RP, Belch JFF (2005) Role of matrix macromolecules in the aetiology and treatment of chronic ulcers. In: Lee BY (ed) The wound management manual. McGraw Hill, New York, pp 109–121 Shekhar MP, Werdell J, Santner SJ, Pauley RJ, Tait L (2001) Breast stroma plays a dominant regulatory role in breast epithelial growth and differentiation: implications for tumor development and progression. Cancer Res 61:1320–1326 Skobe M, Fusenig NE (1998) Tumorigenic conversion of immortal human keratinocytes through stromal cell activation. Proc Natl Acad Sci U S A 95:1050–1055 Sloan P, Schor SL, Lopes V (1990) Immunohistochemical study of the heterogeneity of tenascin distribution within the oral mucosa of the mouse. Arch Oral Biol 35:67–70 Stoker AW, Hatier C, Bissell MJ (1990) The embryonic environment strongly attenuates v-src oncogenesis in mesenchymal and epithelial tissues, but not in endothelia. J Cell Biol 111:217– 228 Sugimoto T, Takiguchi Y, Kurosu K, Kasahara Y, Tanabe N, Tatsumi K, Hiroshima K, Minamihisamatsu M, Miyamoto T, Kuriyama T (2005) Growth factor-mediated interaction between tumor cells and stromal fibroblasts in an experimental model of human small-cell lung cancer. Oncol Rep 14:823–830 Sung SY, Chung LW (2002) Prostate tumor-stroma interaction: molecular mechanisms and opportunities for therapeutic targeting. Differentiation 70:506–521

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Svendsen LB, Thorup J, Larsen JK, Norgard T, Willumsen H, Hansen OH (1989) Association between tumor DNA aneuploidy and in vitro tetraploidy of skin fibroblasts in patients with colorectal neoplasms. Scand J Gastroenterol 24:755–760 Tanaka H, Mori Y, Ishii H, Akedo H (1988) Enhancement of metastatic capacity of fibroblasttumor cell interaction in mice. Cancer Res 48:1456–1459 Tarin D (1972) Tissue interactions in carcinogenesis. Academic, London Thielmann HW, Edler L, Burkhardt MR, Jung EG (1987) DNA repair synthesis in fibroblast strains from patients with actinic keratosis, squamous cell carcinoma, basal cell carcinoma, or malignant melanoma after treatment with ultraviolet light, N-acetoxy-2-acetyl-aminofluorene, methyl methanesulfonate and N-methyl-N-nitrosurea. J Cancer Res Clin Oncol 113:171–186 Tsellou E, Kiaris H (2008) Fibroblast independency in tumors: implications in cancer therapy. Future Oncol 4:427–432 Vakonakis I, Staunton D, Ellis IR, Starkies P, Flanagan A, Schor AM, Schor SL, Campbell ID (2009) Motogenic sites in human fibronectin are masked by long range interactions. J Biol Chem 284:15668–15675 Wong YC, Wang YZ (2000) Growth factors and epithelial-stromal interactions in prostate cancer development. Int Rev Cytol 199:65–116 Wynford-Thomas D, Smith P, Williams ED (1986) Prolongation of fibroblast lifespan associated with epithelial rat tumor development. Cancer Res 46:3125–3127 Xu R, Boudreau A, Bissell MJ (2009) Tissue architecture and function: dynamic reciprocity via extra- and intra-cellular matrices. Cancer Metastasis Rev 28:167–176 Yoshino I, Kometani T, Shoji F, Osoegawa A, Ohba T, Kouso H, Takenaka T, Yohena T, Maehara Y (2007) Induction of epithelial-mesenchymal transition-related genes by benzo[a]pyrene in lung cancer cells. Cancer 110:369–374 Zhao Q, Liu X, Collodi P (2001) Identification and characterization of a novel fibronectin in Zebrafish. Exp Cell Res 268:211–221

Chapter 12

TGF-β Signaling in Fibroblasts Regulates Tumor Initiation and Progression in Adjacent Epithelia Brian R. Bierie and Harold L. Moses

12.1 Introduction TGF-β is an important regulator of carcinoma initiation, progression and metastasis. Over the past three decades, much of the research related to TGF-β signaling has been directed toward cell autonomous effects of stimulation. However, it is now known that TGF-β signaling regulates intrinsic cell autonomous signal transduction in addition to cross-talk between adjacent cell populations. The latter effect of TGF-β signaling in the tumor microenvironment has been elevated in priority with regard to investigating paracrine cross-talk that may targeted to manage human carcinoma recurrence and improve overall survival. At present several regulatory mechanisms have been identified in association with stromal fibroblast responses to TGF-β that can regulate adjacent epithelial tumor initiation, progression and metastasis. TGF-β can suppress the production of tumor promoting paracrine signals including HGF, Mst-1, TGF-α, WNT-2, WNT3A and WNT5A. When TGF-β signaling was lost in fibroblasts, which has been shown to occur during carcinoma progression, these paracrine ligands may be increased and thereby contribute to adjacent carcinoma progression. Conversely, TGF-β can cause a fibroblast to myofibroblast transition that has also been associated with adjacent carcinoma progression. Further, TGF-β production by fibroblasts has been shown to increase the sensitivity of carcinoma cells to signals such as SDF-1 that is abundantly expressed by carcinoma associated fibroblasts. At present, the literature suggests that the TGF-β response by fibroblasts and fibroblast production of TGF-β ligands can suppress or promote adjacent carcinoma progression depending upon the context of stimulation.

B. R. Bierie () Whitehead Institute for Biomedical Research, Nine Cambridge Center, Rm 309, Cambridge, MA 02142, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_12, © Springer Science+Business Media B.V. 2011

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12.2 TGF-β Signaling and Regulation in Cancer The transforming growth factor beta (TGF-β) ligands TGF-β1, TGF-β2 and TGF-β3 are potent regulators of cell behavior during carcinoma initiation, progression and metastasis (Feng and Derynck 2005; Derynck and Zhang 2003; Massague 2008; Moustakas and Heldin 2007; Lee et al. 2007; Yamashita et al. 2008). Carcinomas, which arise from epithelial cell populations, have been the predominant focus for much of the literature related to TGF-β signaling in this context but it is now evident that stromal-epithelial interactions regulated by TGF-β also significantly contribute to carcinoma progression. TGF-β ligands when expressed are secreted into the extra-cellular matrix where they remain as inactive complexes until subsequent activation (Stover et  al. 2007). The ligands may be released from inactive complexes through interactions involving αvβ6 integrin, calpain, cathepsin D, chymase, elastase, endoglycosidase F, kallikrein, matrix metalloproteinase 9 (MMP-9), neuraminidase, plasmin and thrombospondin-1 (TSP1) (Abe et al. 1998; Munger et al. 1999; Lyons et al. 1988; Akita et al. 2002; Taipale et al. 1995; Miyazono and Heldin 1989; Yu and Stamenkovic 2000; Schultz-Cherry and Hinshaw 1996; SchultzCherry and Murphy-Ullrich 1993). In addition, ionizing radiation and reactive oxygen free radicals are known to activate latent TGF-β complexes (Barcellos-Hoff and Dix 1996; Jobling et al. 2006). Once activated, TGF-β1 and TGF-β3 are able to efficiently bind the Type II TGF-β receptor (TβRII) which results in recruitment and transactivation of the Type I TGF-β receptor (TβRI) (Derynck and Zhang 2003; Shi and Massague 2003). Alternatively, TGF-β2 requires the Type III TGF-β receptor (betaglycan) to efficiently bind TβRII and transactivate TβRI. Most of the signaling associated with TGF-β stimulation occurs via a glycine and serine rich region of the TβRI termed the GS domain (Derynck and Zhang 2003). The ligand bound receptor complex can initiate downstream SMAD dependent and SMAD independent pathways (Derynck and Zhang 2003; Shi and Massague 2003) (Fig. 12.1). The SMADs are transcription factors, and TGF-β has been shown to potently stimulate the phosphorylation of SMAD2 and SMAD3 that are often referred to as R-SMADs (Shi and Massague 2003). Once activated the R-SMADs change confirmation enabling them to homo- or hetero-dimerize with SMAD4 to mediate downstream signaling via co-activation or co-repression of transcription. The activated SMAD hetero- and homodimers form complexes in the cytoplasm and subsequently shuttle into the nucleus. The shuttling is mediated by intrinsic nuclear localization signals. SMAD4 nuclear localization has been associated with importin-alpha binding and SMAD3 is able to associate with importin-beta to mediate nuclear import (Brown et  al. 2007; Massague et al. 2005). In addition, both SMAD2/3 can associate with CAN/ NUP214 and NUP153 nuclear pore proteins to mediate nuclear import (Brown et al. 2007; Massague et al. 2005). Smad3 and Smad4 are capable of bind DNA once they have entered the nucleus while SMAD2 is not able to directly bind DNA due to a 30 amino acid insertion in the MH1 domain (Brown et al. 2007). The SMAD complexes are thought to remain associated while actively regulating transcription. Once the Smads are inactivated and no longer part of a regulatory transcription complex

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-β TGF-β

TβRII

TβRI

SMAD Signaling SMAD2, SMAD3, SMAD1, SMAD5

SMAD7, STRAP, YAP65, SMURF1/2, GADD34, PP1

Degradation Competitive Inhibition De-phosphorylation

Association with SMAD4 Nuclear Import

Co-regulation of Transcription

SKI SKIL

SMAD-Independent Signaling ShcA, RHO, RAC/CDC42, RAS, TRAF6, TAK1, PI3K, PAR6, MAP3K1, DAXX, PP2A

Fig. 12.1   TGF-β ligands when expressed are secreted into the extra-cellular matrix where they remain as inactive complexes until subsequent activation through interactions involving αvβ6 integrin, calpain, cathepsin D, chymase, elastase, endoglycosidase F, kallikrein, matrix metalloproteinase 9 (MMP-9), neuraminidase, plasmin and thrombospondin-1. Ionizing radiation and reactive oxygen free radicals are known to activate latent TGF-β complexes. Once activated, TGF-β can bind the Type II TGF-β receptor (TβRII) which results in recruitment and transactivation of the Type I TGF-β receptor (TβRI). The ligand bound receptor complex can initiate downstream SMAD dependent and SMAD independent pathways. The SMADs are transcription factors that mediate downstream signaling via co-activation or co-repression of transcription. Non-canonical SMAD dependent signaling is known to include activation of ShcA, RHO, RAC/CDC42, RAS, TRAF6, TAK1, PI3K, PAR6, MAP3K1, DAXX and PP2A signaling. Negative regulators of TGF-β signaling are known to include SMAD7, STRAP, YAP65, SMURF1/2, GADD34, PP1, SKI and SKIL. Together, the balance between SMAD dependent and independent signaling determines the impact of TGF-β stimulation in vitro and in vivo

they exit the nucleus as monomers (Brown et al. 2007). SMAD4 has a nuclear export signal (NES) that permits association with CRM1 to facilitate export (Brown et  al. 2007). Alternatively, SMAD2 and SMAD3 are exported through a CRM1 independent mechanism. The inactive SMAD2 and SMAD3 monomers bind CAN/ NUP214 and NUP153 that are thought to mediate their export (Brown et al. 2007). Although SMAD2 and SMAD3 signaling has been studied in great detail, emerging evidence suggests that in some contexts SMAD1 and SMAD5 (also R-SMADs) can be activated by TGF-β in epithelial cells and fibroblasts (Daly et al. 2008; Liu et al. 2009). SMAD1 and SMAD5 have been traditionally considered bone morphogenic

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protein (BMP) dependent SMADs, however recent evidence supports direct activation of these SMAD family members in response to TGF-β stimulation (Daly et al. 2008; Liu et al. 2009). Further, the activation of SMAD1 and SMAD5 which also complex with SMAD4 to regulate transcription, may be clinically relevant as they have been functionally linked to the regulation of anchorage independent growth in response to TGF-β stimulation (Daly et al. 2008; Liu et al. 2009). Canonical SMAD activation is a significant component of TGF-β signaling, however the SMADs also have important non-canonical roles that significantly regulate cell behavior and global gene expression as illustrated by the non-canonical SMAD dependent regulation of micro-RNA processing (Davis et al. 2008). In addition to canonical and non-canonical SMAD dependent signaling, the SMAD independent pathways are also important for the regulation of tumor initiation, progression and metastasis. The SMAD independent pathways are known to include ShcA, RHO, RAC/ CDC42, RAS, TRAF6, TAK1, PI3K, PAR6, MAP3K1, DAXX and PP2A (Feng and Derynck 2005; Derynck and Zhang 2003; Massague 2008; Moustakas and Heldin 2007; Lee et al. 2007; Yamashita et al. 2008). Together, the balance between SMAD dependent and independent pathways determines the impact of TGF-β stimulation in vitro and in vivo (Fig. 12.1). In human cancer the role for TGF-β has been well characterized with regard to carcinoma initiation, progression and metastasis. TGFB1 overexpression has been demonstrated in human carcinomas including those that occur in the breast, colon, esophagus, stomach, lung, pancreas and prostate tissue (Levy and Hill 2006). However, within the carcinoma cells TGF-β signaling is often attenuated or completely abrogated during tumor progression as a result of TGFBR1, TGFBR2, SMAD2 and SMAD4 loss, mutation or attenuation of expression (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a). TGFBR2 mutations are frequently observed in micro-satellite instable (MSI+) carcinomas (Grady and Markowitz 2002; Grady et  al. 1999; Markowitz et  al. 1995). In MSI+ carcinoma cells, mis-match repair defects are common and as a result a 10 bp poly-adenine repeat region (Poly(A)10 tract) from the coding sequence of TGFBR2 are frequently observed. MSI associated Poly(A)10 tract mutations in TGFBR2 have been identified in biliary, colon, gastric, glioma, lung and pancreatic cancers (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a). The Poly(A)10 tract mutations often lead to frameshift mis-sense mutations or an early termination that prevents translation of a functional TβRII protein. In addition to Poly(A)10 tract mutations in MSI+ carcinomas, intragenic mutation, downregulation and loss of TGFBR2 expression has been observed in bladder, breast, colon, esophageal, lung, ovarian, pancreatic and prostate cancers (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a). Loss of expression, downregulation and mutation has been observed in TGFBR1 from biliary, bladder, breast, gastric, liver, ovarian, pancreatic and prostate cancers (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a). The SMADs are central mediators of TGF-β signaling and they are also subject to mis-regulation in human cancer. SMAD4 mutation, deletion and loss of expression is known to be associated with biliary, bladder, breast, cervical, colon esophageal, intestine, liver, lung, ovarian and pancreatic cancers (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a).

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SMAD2 mutation and deletion has been observed in cervical, colon, liver and lung cancer (Massague 2008; Levy and Hill 2006; Bierie and Moses 2006a). In contrast with the other central TGF-β signaling components, SMAD3 is often maintained in human carcinomas suggesting that it may have a non-canonical role in the carcinoma cell that favors tumor progression (Levy and Hill 2006; Bierie and Moses 2006a; Sjoblom et al. 2006). Importantly, genetic mutation and loss of heterozygosity are not the only mechanisms known to regulate central components of the TGF-β signaling pathway during human carcinoma progression. Epigenetic alterations have been shown to silence the expression of TSP1, TGFB2, TGFBR1, TGFBR2 and SMAD4 (Kang et al. 1999; Kim et al. 2000; Shipitsin et al. 2007; Rojas et al. 2008; Aitchison et al. 2008; Hinshelwood et al. 2007). Alternatively, attenuation of TGF-β signaling can involve the expression of an inhibitory SMAD family member, SMAD7, that is known to be amplified in some human cancers (Levy and Hill 2006). SMAD7, when expressed can bind TβRI and inhibit downstream signaling (Nakao et al. 1997; Hayashi et al. 1997). SMAD7 bound to TβRI can inhibit activation of SMAD2 and SMAD3 due to competitive inhibition of the common active site (Inoue and Imamura 2008). SMAD7 also has the ability associate with GADD34 (growth arrest and DNA damage protein 34) to enable de-phosphorylation of TβRI by PP1 (protein phosphatase 1) (Shi et  al. 2004). SMAD7 can associate with the SMAD ubiquitin regulatory factor (SMURF) E3 ubiquitin ligase proteins, SMURF1 and SMURF2 to promote ubiquitylation of the activated receptor complex and thereby target it for proteasomal degradation (Kavsak et al. 2000; Ebisawa et al. 2001). Importantly, SMAD7 may be enhanced by expression of other cofactors such as YAP65 and STRAP proteins that bind promote SMAD7 association with the receptor complex (Datta and Moses 2000; Ferrigno et al. 2002). In addition to SMAD7 expression, the SKI and SKIL (Ski-like; SnoN) proto-oncogenes that are known to be upregulated during human carcinoma progression are transcriptional co-factors that are known to attenuate SMAD activity (Levy and Hill 2006; Stroschein et al. 1999; Luo et al. 1999) (Fig. 12.1). TGF-β production within the tumor microenvironment is thought to have both positive and negative influences on carcinoma cell behavior. TGF-β is able to inhibit epithelial cell cycle progression or enhance proliferation depending on the context of stimulation. In general, non-transformed epithelial cells will respond to TGF-β with growth inhibition associated with activation of the canonical SMAD dependent signaling pathways. TGF-β is able to achieve the growth inhibition primarily through suppression of c-myc and upregulation of cell cycle dependent kinase inhibitors (CDKIs) such as p15INK4B, p16INK4a, p19ARF, p21CIP1 and p57 (Massague 2008; Vijayachandra et al. 2009). Importantly, p15INK4b expression allows the release of inactive p27KIP1 from CyclinD/CDK4 complexes to promote p27KIP1 dependent inhibition of CyclinE/CDK2 and CyclinA/CDK2 complexes (Massague 2008). Together, the changes in CDKI activity can contribute to p107 (Rb) hypophosphorylation thereby preventing G1/S phase cell cycle progression. However in some carcinoma cells, c-myc may be amplified or the expression of p15INK4b, p16INK4A, p19ARF, p21CIP1 and p107 (Rb) proteins can be attenuated or completely

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abrogated—as a result the growth inhibitory effect of TGF-β signaling may circumvented (Massague 2008; Vijayachandra et al. 2009; Chin et al. 1998). Importantly, in some carcinoma cells that circumvent SMAD dependent growth inhibition, TGF-β stimulation can promote proliferation through the non-canonical signaling pathways. In addition to the regulation of carcinoma cell proliferation, TGF-β can promote epithelial cell apoptosis and is known to promote an epithelial to mesenchymal transition that can enhance carcinoma cell motility and invasion. The importance of TGF-β signaling in the epithelium has been well documented; however, it is also known that TGF-β signaling can have an impact on many other cell types such as immune mediators and endothelium within the tumor microenvironment (Bierie and Moses 2006a, b). However, for many years the role for TGF-β signaling in fibroblast cell populations was largely unknown with regard to the impact on adjacent carcinoma initiation, progression and metastasis. It is now clear that the TGF-β response within fibroblasts can both initiate and promote adjacent carcinoma progression (Bhowmick et al. 2004; Cheng et al. 2005, 2007, 2008; Joseph et al. 1999). Emerging evidence suggests that TGF-β signaling within the stromal fibroblast population is altered in association with human carcinoma progression. In breast cancer, stromal LOH occurs frequently in human ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Importantly, LOH in the stromal population is distinct from the LOH observed in adjacent carcinoma epithelium (Moinfar et al. 2000). The distinct LOH profiles for carcinoma epithelium and adjacent stroma associated with DCIS and IDC tissues suggested that selection pressures in a tumor microenvironment can result in amplification of distinct mutant cell populations during tumor progression. Although the TβRII locus in this study was not directly analyzed, a microsatellite marker (D3S2432) that maps to the 3p22–24.2 region (in close proximity to TβRII at the chromosomal 3p22 locus) was shown to have no LOH in the carcinoma epithelium or stromal cells associated with human DCIS or IDC lesions in vivo (Moinfar et al. 2000). In addition to proximal LOH analyses, it has been shown, in a study using 72 breast cancer and 20 normal human tissues, that mutation and loss of coding sequences in the TGFBR2 gene associated with human breast cancer is rare (Barlow et al. 2003). However, in aggressive lymph node positive (LN+), estrogen and progesterone receptor negative (ER- and PR-) tumor stroma, it was shown that the stromal TβRII protein localization was predominantly cytoplasmic suggesting that downstream signaling was attenuated in vivo (Barlow et al. 2003). Notably, loss of TβRII protein was demonstrated in correlation with advanced stages of human prostate cancer progression (Li et al. 2008). In this study it was shown that out of 33 benign prostate lesions 84% scored positive for detectable stromal TGFBR2 by immunohistochemical staining, whereas in 107 cases of Gleason 6–10 prostate cancer only 31% scored positive for TβRII staining (Li et al. 2008). In colon cancer, a similar observation was made using 310 human tissues (Bacman et al. 2007). In this study, decreased TβRI and TβRII protein was detected in tumor stroma in association with increased lymph node metastasis and shorter survival. Importantly, stromal TβRII abundance was an independent prognostic factor for cancer related survival; loss of detectable TβRII in the tumor stroma correlated with poor survival (Bacman et al. 2007). Interestingly, in human head

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and neck and colon cancer a somatic mutation, TGFBR1 * 6A, has been detected in the tumor stroma (Bian et al. 2007). This hypomorphic allelic variant is less effective at transmitting downstream pathway activation when compared with the wild type allele. It was shown that carcinoma stroma, even at a distance of 2 cm from the primary tumor edge, had evidence of the somatic mutation with the highest allelic ratios occurring near the tumor margin (Bian et al. 2007). Although the data related to TGF-β signaling in human tumor stroma is limited at present, these initial studies suggest that loss of the TGF-β response in human cancer stroma does correlate with and may contribute to tumor progression in several primary tumor organ sites.

12.3 Loss of the TGF-β Response in Fibroblasts Can Result in Adjacent Carcinoma Initiation, Progression and Metastasis Carcinoma initiation, progression and metastasis are processes often thought to be driven primarily by aberrant signaling within the carcinoma cells and supported by the surrounding stromal host cells. However, the host stromal cells are potent regulators of adjacent carcinoma initiation, progression and metastasis. Paracrine signals that are derived from host stromal cells significantly regulate adjacent epithelial cell populations. The first clear evidence that stromal fibroblasts had the ability to regulate adjacent epithelial carcinoma initiation, progression and metastasis was derived in mouse models engineered to attenuate and completely ablate TGF-β responses in fibroblasts. The first model that suggested stromal TGF-β responses may have an impact on adjacent epithelial cells involved expression of a dominant negative type II TGF-β receptor (dnTbRII) under control of the metallothionein promoter (Joseph et al. 1999). This promoter was expressed primarily in stromal cell populations and when active effectively attenuated TGF-β signaling. Initial observations in the mammary tissue suggested that loss of stromal TGF-β responses could result in adjacent epithelial cell proliferation. This result was found to correlate with increased HGF production by the fibroblasts that had an attenuated ability to respond to TGF-β (Joseph et al. 1999). The results were similar to those obtained from transgenic mice designed to express HGF from a mammary specific promoter (Gallego et al. 2003). Importantly, in subsequent studies that supported and extended results obtained via attenuation of TGF-β signaling in fibroblasts, it was shown that complete ablation of TGF-β signaling in stromal fibroblasts could initiate and promote adjacent carcinoma progression (Bhowmick et  al. 2004; Cheng et  al. 2005). Mice with a conditional deletion of exon 2 from Tgfbr2 in stromal fibroblasts (Tgfbr2FSPKO) developed intraepithelial neoplasia in the prostate (PIN) and invasive squamous cell carcinoma of the forestomach with 100% penetrance. The mice died around 8 weeks of age, most likely due to the extensive gastrointestinal disruption associated with the squamous cell carcinomas of the forestomach that extended into the fundus. The carcinomas in this mouse model were associated with elevated levels of HGF production and Met activation. It was also shown in this mouse model that modification

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of TGF-β signaling in stromal fibroblasts can lead to increased proliferation and increased apoptosis of adjacent mammary epithelium (Cheng et al. 2005). In addition HGF, Mst-1 and TGF-α expression was upregulated in Tgfbr2FSPKO fibroblasts when compared to the Tgfbr2fl/fl controls when analyzed using sub-renal tissue recombination involving the MMTV-PyVmT and 4T1 mammary carcinoma cell lines. The increased ligand expression in these experiments correlated with increased Met, Ron, ErbB1 and ErbB2 activation in the carcinoma cells that had been grafted with Tgfbr2FSPKO fibroblasts relative to the controls. In vitro, blocking HGF, Mst-1 and TGF-α signaling in the conditioned medium from Tgfbr2FSPKO fibroblasts resulted in a reduction of carcinoma cell proliferation (Cheng et al. 2005). This study complemented and extended previous results wherein expression of dnTβRII was targeted to mammary stromal fibroblasts using the metallothionein promoter (Joseph et al. 1999). Together, these results suggested that stromal fibroblasts significantly contribute to the regulation of adjacent epithelium and set precedence for investigation of molecular mechanisms related to TGF-β mediated stromal-epithelial interactions during tumor initiation, progression and metastasis.

12.4 Exploring the Paracrine Signals that Regulate TGF-β Associated Stromal-Epithelial Cross-Talk in the Tumor Microenvironment It is now clear that TGF-β regulates fibroblast mediated suppression of adjacent epithelial tumor initiation, progression and metastasis. Early work using mammary fibroblasts suggested that the aberrant regulation of several secreted proteins including HGF, Mst-1 and TGF-α could explain the tumor promoting ability of fibroblasts that lacked an ability to mount a TGF-β response. The paracrine signals derived from TGF-β signaling deficient fibroblasts in a primary tumor microenvironment were able to promote lung, liver and spleen metastasis of adjacent carcinoma cells (Cheng et al. 2007, 2008). In addition, it has now been shown using TGF-β signaling deficient prostate fibroblasts that Wnt signaling can contribute to enhanced carcinoma progression in adjacent epithelial cell populations and mediate their response to systemic hormones. Further, TGF-β has been shown to enhance the epithelial cell response to paracrine cross-talk within the tumor microenvironment. It has been known for many years that HGF expression could be suppressed by TGF-β in fibroblasts (Joseph et al. 1999; Gohda et al. 1992). However, the role of this signaling axis was not known with regard to the impact on carcinoma initiation or progression. It is now clear that TGF-β dependent fibroblast derived HGF plays a significant role in the regulation of adjacent carcinoma cells (Cheng et al. 2007). HGF was initially identified as a ligand that could enhance epithelial cell proliferation, dissociation and motility (Stoker et al. 1987; Naldini et al. 1991; Nakamura et  al. 1986; Matsumoto et  al. 1991). It is now known that HGF can promote many processes during tumor progression including carcinoma cell transformation, proliferation, migration, invasion, adhesion and resistance to apoptosis. HGF

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expression is ubiquitous in vivo, however the level of expression and activation of this ligand is significantly enhanced in reactive stroma of tumor tissues (Aguirre Ghiso et al. 1999; Parr et al. 2004). The cognate receptor for the HGF ligand is MET, a proto-oncogene abundantly expressed in many carcinoma cell subtypes (Comoglio et al. 2008). When HGF binds MET, it may activate a number of downstream signaling pathways known to include PI3K/AKT, GRB2-SOS-RAS-RAF-MEKErk, STAT3, SRC and RAC1. (Comoglio et al. 2008; Ponzetto et al. 1994). Together, these signaling pathways contribute to tumor initiation and progression (Fig. 12.2). In cancer, paracrine HGF signaling has been clearly documented (Comoglio et  al. 2008). The current literature also suggests that the receptor is upregulated in epithelium associated with hepatocarcinomas, gastrinomas and carcinomas in the colon, pancreas, stomach, prostate, ovary and breast (Boccaccio and Comoglio 2006). Importantly, elevated levels of MET expression may enhance the carcinoma cell sensitivity to HGF production in the adjacent stroma. This implicates the Epithelium

TGF-β Responsive Fibroblasts HGF, TGF-α, Mst-1, Wnt2, Wnt3A, Wnt5A

-β TGF-β

TGF-β Signaling Deficient Fibroblasts

Impact on adjacent epithelium

X

Increased Met, ErbB1, ErbB2, Ron phosphorylation Increased β-catenin stabilization Adjacent epithelial hyperplasia and carcinoma initiation Epithelium

Increased carcinoma cell proliferation, invasion and metastasis

Fig. 12.2   In fibroblasts, the abrogation of TGF-β signaling can promote adjacent epithelial hyperplasia followed by carcinoma initiation, progression and metastasis. Increased carcinoma cell proliferation, invasion and metastasis observed in the carcinoma cells is known to depend on paracrine signals derived from the TGF-β signaling deficient fibroblast cell population. At present, these paracrine signals are known to include increased secretion of HGF, Mst-1, TGF-α, Wnt2, Wnt3A and Wnt5A ligands. The expression of HGF, Mst-1 and TGF-α by TGF-β signaling deficient fibroblasts can result in adjacent epithelial MET, RON, ErbB1 and ErbB2 phosphorylation that contribute to adjacent carcinoma initiation, progression and metastasis. In addition, increased Wnt2, Wnt3A and Wnt5A expression by TGF-β signaling deficient fibroblasts has been shown to have an impact on adjacent carcinoma progression. Notably, the Wnt3A expression by TGF-β signaling deficient fibroblasts in the prostate been shown to result in carcinoma cell β-catenin stabilization and enhanced androgen independent carcinoma progression

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TGF-β regulated stromal-epithelial HGF-MET axis as an important factor that can regulate tumor progression. The Mst-1 receptor, Ron, is structurally related to Met and is known to enhance carcinoma cell scattering, motility and invasion. Due to the upregulation of Mst-1 in TβRII deficient fibroblasts it was thought that it may also play a role in the observed regulation of tumor progression. However, it was shown that when TGF-β signaling was attenuated in fibroblasts, HGF was responsible for a majority of the previously observed Ron activation in adjacent epithelium (Cheng et al. 2008). The mechanism of Ron transactivation in this model system remains unknown and was not altered in the presence of Met inhibition suggesting an indirect interaction between HGF and Ron (Cheng et al. 2008). Importantly, it was shown that Met had the ability to increase Stat3 and p44/42 activation in mammary carcinoma cells whereas Ron only contributed to Stat3 activation (Cheng et al. 2008). In recent studies that have extended the initial observations, it has now been shown that Stat3 and p44/42 activation were responsible for a significant proportion of the HGF associated carcinoma progression in the TGF-β regulated stromalepithelial recombination model system (Cheng et al. 2007, 2008). In prostate cancer, androgen ablation therapy is often used as a method to manage disease progression. Importantly, the androgen response has been shown to involve stromal-epithelial cross-talk. Specifically, it has been shown that the stromal response to androgen can regulate adjacent epithelium. Notably, abrogation of the fibroblast associated TGF-β response can significantly attenuate the ability to initiate cell death in adjacent epithelium during systemic androgen ablation therapy (Li et al. 2008). Further, it was shown that the enhanced cell survival in prostate epithelium juxtaposed with TGF-β signaling deficient stroma was dependent upon elevated Wnt signaling (Li et al. 2008). Wnt signaling has now emerged as an important paracrine mechanism for stromal-epithelial cross-talk in the tumor microenvironment. In the case of androgen receptor (AR) signaling it is known that canonical Wnt signaling through the frizzled receptor leads to inactivation of GSK3β (Mulholland et al. 2006). The inactivation of GSK3β permits accumulation of β-catenin that is known to work with the androgen receptor to promote androgen independent prostate cancer progression (Mulholland et al. 2006). Importantly, it has been shown that loss of TGF-β signaling in fibroblasts was able to enhance the basal fibroblast derived Wnt2, Wnt3A and Wnt5A expression (Li et al. 2008). The functional impact of enhanced Wnt expression was confirmed in vivo using fibroblast-carcinoma cell recombination in the presence or absence of SFRP expression. SFRP, a secreted factor known to inhibit Wnt3A and Wnt5A activity, restored androgen ablation sensitivity to carcinoma cells in the recombined grafts. Importantly, ablation of the TGF-β response in prostate epithelium had no effect on the epithelial response to androgen ablation (Placencio et al. 2008). It was subsequently shown that systemic attenuation of Wnt3A alone was sufficient to reduce the tumor progression of the androgen sensitive LNCaP prostate carcinoma cell line that had been co-grafted with TGF-β signaling deficient fibroblasts (Li et al. 2008). The enhanced expression of Wnt3A in the TGF-β signaling deficient fibroblasts correlated with increased binding of Stat3 in the Wnt3A promoter (Wojcik et al. 2006). Although it is not known how TGF-β inhibits Stat3 signaling, the data does recapitulate results in previous literature (Cheng et al. 2008; Bright and Sriram 1998; Walia et al. 2003).

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Additional mechanisms will likely be revealed as this line of research continues and it is clear that the stromal-epithelial signaling regulated by TGF-β can significantly alter the outcome of disease progression. However, the regulation of stromal-epithelial interactions by TGF-β is not limited to the impact upon the fibroblasts within the tumor microenvironment. TGF-β also regulates the carcinoma cell response to stromal derived signaling. This has been clearly shown in the case of SDF-1 and its cognate receptor CXCR4 (Ao et al. 2007). In this model system carcinoma associated fibroblasts (CAFs) were recombined with BPH-1 carcinoma cells under the renal capsule. The BPH-1 cells are initiated but non-malignant human prostate carcinoma cells. In the presence of CAFs, however it was shown that the BPH-1 tumor progression is significantly enhanced. It was found that TGF-β derived from the carcinoma associated fibroblast was responsible for the tumor promotion. Interestingly, in this context TGF-β stimulated an increase in CXCR4 expression by the carcinoma cells (Fig.  12.3). The TGF-β dependent increase in carcinoma cell CXCR4 expression thereby enhanced the carcinoma cell response

CAF

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Fig. 12.3   In prostate carcinoma cells it has been shown that TGF-β can upregulate CXCR4 expression. The increased CXCR4 expression has been shown to increase the carcinoma cell sensitivity to SDF-1. TGF-β and SDF-1 are both abundantly expressed by carcinoma associated fibroblasts (CAF). Increased CXCR4 expression was shown to increase AKT activation in prostate carcinoma cells. AKT activation has been linked to increased carcinoma cell proliferation and in some contexts an epithelial to mesenchymal transition that can enhance carcinoma cell migration and invasion

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to SDF-1 (Ao et al. 2007). CAFs are an abundant source of SDF-1 and stimulation of CXCR4 resulted in activation of AKT, and importantly, AKT signaling has been shown to enhance prostate carcinoma cell proliferation and promote context-dependent EMT (Ao et al. 2006, 2007). Further, CXCR4 signaling via SDF-1 production from CAFs has been associated with increased proliferation of human breast carcinoma cells in vitro and in vivo (Orimo et al. 2005).

12.5 TGF-β Can Promote a Fibroblast to Myofibroblast Transition It has been known for more than two decades that TGF-β can promote a fibroblast to myofibroblast conversion (Desmouliere et al. 1993; Ronnov-Jessen and Petersen 1993). This process has been characterized in a number of systems that include both tumor associated fibroblasts and those derived from disease-free tissue. Myofibroblasts are fibroblast-like cells that co-express vimentin, fibroblast activation protein (FAP) and alpha-smooth muscle actin (α-SMA) (Orimo and Weinberg 2007). In early studies it was shown that mammary fibroblasts exposed to TGF-β could respond with increased α-SMA expression (Ronnov-Jessen and Petersen 1993; Sieuwerts et al. 1998). The α-SMA marker is often used as a surrogate for fibroblast activation or a myofibroblast phenotype and TGF-β signaling has a direct positive regulatory role in its expression (Desmouliere et al. 1993; Ronnov-Jessen and Petersen 1993; Hautmann et al. 1997). The process of fibroblast to myofibroblast transdifferentiation associated with TGF-β stimulation has been studied in great detail and a number of genes have been identified at different stages of the process (Chambers et al. 2003). Importantly, myofibroblast cell populations are often found at the leading invasive edge of human carcinomas and it is thought that paracrine signals derived from this cell population can significantly promote disease progression (Sieuwerts et al. 1998; Lewis et al. 2004; De Wever et al. 2004a) (Fig. 12.4). Notably, in squamous cell carcinoma (SCC) it has been shown that HGF signaling is a central mediator of myofibroblast derived paracrine signaling that promotes tumor progression upon TGF-β dependent fibroblast to myofibroblast conversion (Lewis et al. 2004). In this study it was shown that SCC cells can produce enough TGF-β to induce myofibroblast conversion of adjacent fibroblast cell populations. As a result, the myofibroblasts produced more HGF than their fibroblast precursors and the HGF produced by myofibroblasts was shown to significantly enhance SCC invasion (Lewis et al. 2004). This double paracrine mechanism was one of the first to demonstrate a significant interaction directly associating carcinoma cell TGF-β production to myofibroblast production and induction of pro-invasive HGF expression. This is quite interesting in light of the role for TGF-β signaling in suppression of fibroblast HGF expression. At present it is thought that in fibroblasts, TGF-β can suppress HGF expression however once a fibroblast is converted to a myofibroblast the HGF expression is upregulated by virtue of being a new cell type. It is worth noting that some, but not all fibroblasts are able to become myofibroblasts

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TGF -β TGF-β

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Fig. 12.4   In some fibroblast cell populations, TGF-β can promote a fibroblast to myofibroblast transition (FMT) that contributes to adjacent carcinoma progression. However, the FMT process can be balanced by expression of other paracrine factors, such as PGE2, known to be abundantly expressed within the tumor microenvironment. As a result, much like many other processes regulated by TGF-β, FMT is dependent on the cell type and context of stimulation. In addition, TGF-β can promote myofibroblast cell survival through upregulation of Bcl-2. The increased Bcl-2 expression can prevent cell death by apoptotic stimuli (AS) that would otherwise occur in the absence of TGF-β signaling. Further, TGF-β has been shown to suppress iNOS expression to promote myofibroblast cell survival. iNOS expression in response to IL-1b can promote apoptosis and the suppression of iNOS gene expression is another way in which TGF-β can promote myofibroblast accumulation within a tumor microenvironment. Importantly, relative to fibroblasts, myofibroblast cell populations abundantly express the extracellular matrix glycoprotein tenascinC (TNC) and HGF that together synergize to promote carcinoma cell invasion

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and therefore the mesenchymal lineage must be considered when interpreting the observed TGF-β response. Notable, the fibroblast to myofibroblast transition can be attenuated by other paracrine factors often present within a tumor microenvironment such as PGE2 (Thomas et al. 2007; Kolodsick et al. 2003). Interestingly, within the myofibroblast cell population TGF-β has been shown to upregulate extracellular matrix glycoprotein tenascin-C (TNC) that can synergize with HGF to promote carcinoma cell invasion (De Wever et al. 2004a) (Fig. 12.4). It was shown that TNC expression could suppress RhoA activity that was permissive for upregulation of HGF dependent pro-migratory Rac activation in the carcinoma cells (De Wever et al. 2004a). Further, carcinoma cell production of TGF-β was able to promote the invasive ability of myofibroblasts through JNK dependent upregulation of N-cadherin at the tips of myofibroblast filopodia (De Wever et al. 2004b). Interestingly, the induction of a myofibroblast phenotype by TGF-β does not seem to be the only role for this protein. It has been shown that TGF-β signaling can promote survival of myofibroblasts in circumstances that would otherwise promote myofibroblast cell death. Interleukin 1 beta (IL-1b) has been shown to induce cell death in fibroblast and myofibroblast cell populations (Zhang and Phan 1999). Cell death induced by IL-1b is known to involve induction of iNOS, and importantly TGF-β is able to abrogate IL-1b induced iNOS production. In addition, IL-1b has been shown to reduce anti-apoptotic Bcl-2 abundance while TGF-β stimulation was able to prevent loss of Bcl-2 in myofibroblasts. Together, abrogation of iNOS production and maintenance of Bcl-2 by TGF-β in this context can promote myofibroblast cell survival (Zhang and Phan 1999).

12.6 Differential Response to TGF-β in Unique Fibroblast Subpopulations It has recently been shown that TGF-β signaling in fibroblasts can contribute to suppression of tumor promoting stromal-epithelial interactions, however it is currently unknown whether this is a general mode of regulation or dependent on the unique molecular identity of specific fibroblast subpopulations in vivo. It is clear that there are differences between fibroblast cell populations with respect to regulation by TGF-β, however it is not clear which factors regulate the differential responses to TGF-β stimulation. It is likely that the distinct molecular profile, and microenvironment associated with an individual fibroblast population (e.g., prostate stromal fibroblast versus mammary stromal fibroblast or embryonic versus adult derived fibroblasts), determines the response to TGF-β stimulation in vivo. The concept of unique signaling in alternate fibroblast cell populations, has been previously addressed through global mRNA expression analyses, that indicated distinct molecular profiles could be used to identify the tissue from which individual fibroblast cell populations were derived (Chang et al. 2002). The alternate gene expression signatures observed in fibroblasts derived from different areas of the body has been termed positional memory. The effect of positional memory on TGF-β signaling,

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may partially explain why carcinomas were observed specifically in the prostate and forestomach of mice expressing a fibroblast specific ablation of exon 2 from Tgfbr2 (Bhowmick et al. 2004). The results obtained by mRNA expression profiling that were used to define positional memory, suggested that much like epithelial or myeloid cells, there may be distinct fibroblast subpopulations present within each tissue type or state of tumor progression. In support of this concept, a recent study used vimentin, type I collagen, FSP (S100A4), α-SMA, PDGFRβ and NG2 as markers to examine fibroblast heterogeneity within mammary and pancreatic carcinomas (Sugimoto et al. 2006). The analyses indicated that several distinct fibroblast sub-populations could be identified and quantified within the tumor microenvironment. Interesting the FSP+ cell population was a minor fraction of the total fibroblast compartment (this is the cell population targeted in the previously mentioned Tgfbr2FSPKO model) (Bhowmick et al. 2004). Together these results also suggest that individual sub-populations of fibroblasts and myofibroblasts may play similar or alternate roles that together contribute to the regulation of tumor progression. A recent study, that demonstrated this type of differential response to TGF-β stimulation, contrasted fibroblasts derived from fetal and adult tissues. In fetal fibroblasts, stimulation with TGF-β resulted in growth inhibition, while in adult fibroblasts stimulation resulted in enhanced proliferation (Giannouli and Kletsas 2005). This study clearly demonstrated that individual subpopulations of fibroblasts initiate unique molecular programs in response to TGF-β stimulation. Specifically, in fetal fibroblasts TGF-β was able to activate protein kinase A (PKA) and upregulate cyclin-dependent kinase inhibitors p21CIP1 and p15INK4B. Alternatively, in adult fibroblasts TGF-β was shown to promote proliferation through activation of MEK/ ERK signaling (Giannouli and Kletsas 2005). Together these results indicate that the functional impact of TGF-β stimulation is ultimately dependent on the distinct lineage and molecular profile of the fibroblast cells present within a polyclonal stromal tumor microenvironment at the time of stimulation.

12.7 Summary At present, several roles for TGF-β signaling in the regulation of tumor associated fibroblasts have been identified. However this field of study is relatively new and much more work will be necessary to elucidate the true scope of this stromalepithelial signaling axis during carcinoma initiation and progression. The current data suggests that several significant paracrine signals derived from the fibroblast cell population are regulated by TGF-β signaling. These paracrine signals are known to include HGF, Mst-1, TGF-α, Wnt2, Wnt3A and Wnt5A. HGF is perhaps the best characterized ligand in this context and a large number of systemic inhibitors for HGF signaling are in pre-clinical stages of development or clinical trials for cancer therapy (Comoglio et al. 2008). Consequently, it has been shown that the carcinoma cell specific HGF response can promote early tumor cell engraftment and growth (Martin et al. 2003; Corso et al. 2008). In addition, when HGF signaling

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was attenuated after tumors were established, a significant reduction in tumor volume was observed. Notably, colonization and growth of pulmonary metastases were also shown to depend upon the response to HGF signaling specifically within metastatic carcinoma cells (Corso et al. 2008). According to these studies and many others in the current literature, it is clear that a significant impact can be ascribed to carcinoma cell specific HGF/MET signaling during primary and metastatic tumor progression in vivo. HGF has also been shown to promote carcinoma cell survival through activation of AKT and ERK signaling pathways (Xiao et al. 2001; Zeng et al. 2002), and may directly promote invasion through E1AF dependent stimulation of matrix metalloproteinase MMP1, MMP3 and MMP9 expression (Hanzawa et al. 2000). Importantly, once a carcinoma cell leaves the primary tumor tissue and arrives at a distant organ site, the ability to adhere may promote subsequent colonization. HGF signaling has been shown to increase the binding of MDA-MB-231 cells to laminin-1, laminin-5, fibronectin and vitronectin substrates through upregulation of β1, β3, β4 and β5 integrins (Trusolino et  al. 2000). The mechanism of HGF dependent binding to these specific matrix substrates was shown to be PI3K dependent (Trusolino et al. 2000). In support of the mechanistic studies in vitro, functional regulation of metastasis by MET signaling has been linked to activation of both GRB2 and PI3K pathways in vivo (Bardelli et al. 1999). It is clear from these studies and many subsequent reports that fibroblast derived HGF is able to function as a mitogen, motogen, morphogen, inhibitor of apoptosis, stimulatory regulator of matrix degradation and pro-angiogenic factor that can support tumor initiation and progression in adjacent epithelial cell populations. As a result, the stimulation of integrin mediated adhesion by HGF may aid in homing of mammary carcinoma cells to tissues such as the lung that express laminin-5, fibronectin and vitronectin. Although the regulation of stromal HGF signaling mediated by TGF-β has been reported to involve Stat3 and p44/42 signaling in adjacent epithelium, it is likely that a number of the other downstream HGF dependent signaling components also contribute to the observed stromal-epithelial interaction carcinoma initiation, progression and metastasis. It is likely that we have yet to identify critical links between the stromal-epithelial axes regulated by TGF-β during carcinoma progression. In prostate cancer for example, it has been shown that Wnt3A was responsible for conferring resistance to androgen ablation therapy in the presence of TGF-β signaling deficient fibroblasts (Li et al. 2008). It was also shown that TGF-β could upregulate CXCR4 expression in prostate carcinoma cells that increased their sensitivity to SDF-1 (Ao et al. 2007). SDF-1 is abundantly expressed by carcinoma associated fibroblasts and the increased sensitivity to this ligand resulted in enhanced AKT activation in adjacent prostate carcinoma cells (Ao et al. 2007). Importantly, previous work that was not related to TGF-β signaling clearly demonstrated that Wnt3A could work together with AKT signaling in prostate carcinoma cells to promote androgen receptor independent carcinoma progression (Mulholland et al. 2006). These studies together suggest that TGF-β signaling within fibroblasts and TGF-β ligand production by fibroblasts can together regulate androgen independent prostate carcinoma progression. This type of molecular interaction is simple to address, however due this field

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being in early stages of development the potential impact of such interactions have yet to be resolved. The complexity associated with stromal-epithelial interactions mediated by the TGF-β response within tumor derived fibroblasts is not limited to the scope of the molecular interactions within the tumor microenvironment. Our current view of the global complexity within the tumor microenvironment leaves many issues unresolved including the generalization of results attained in the FSP+ fibroblast cell population. It is likely that as we learn how to identify additional fibroblast subtypes and lineages additional analyses will be necessary to determine the impact of TGF-β signaling in each unique subpopulation. Although this issue also remains unresolved, it is clear that the TGF-β pathway can be attenuated within human carcinoma fibroblasts. In addition, it is clear that the loss of TGF-β signaling in fibroblasts can initiate and promote adjacent carcinoma progression through increased HGF, Wnt-2, Wnt-3A, Wnt-5A production. Further, CAF derived TGF-β production can enhance the sensitivity of carcinoma cells to paracrine signaling as demonstrated in the case of SDF-1. Alternatively, we also know that some TGF-β responsive fibroblasts exhibit a myofibroblast phenotype in response to TGF-β stimulation that can promote tumor progression through increased MMP, HGF and TNC production within a local tumor microenvironment. Together, the results presented within the current literature suggest that TGF-β signaling in stromal fibroblasts and TGF-β production by carcinoma associated fibroblasts can suppress or promote adjacent carcinoma initiation and progression depending upon the context of stimulation.

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Chapter 13

The SDF-1-Rich Tumour Microenvironment Provides a Niche for Carcinoma Cells Masayuki Shimoda, Kieran Mellody and Akira Orimo

13.1 Introduction SDF-1, a member of the chemokine superfamily, was initially cloned from a bone marrow-derived stromal cell line (Tashiro et al. 1993), and identified as a pre-B-cell growth-stimulating factor involved in regulating B cell lymphopoiesis (Nagasawa et al. 1994). SDF-1 is secreted in abundance by mesenchymal cells present within sites of damaged tissues and the tumour stroma. This chemokine plays an essential role in promoting tissue repair and tumourigenesis. The main cognate receptor for SDF-1 is known as CXCR4 which belongs to the CXC chemokine receptor subfamily. This protein serves as an essential co-receptor of CD4, which facilitates the binding and entry of human immunodeficiency virus-1 (HIV-1) into T cells (Bleul et al. 1996; Oberlin et al. 1996). The SDF-1-CXCR4 signalling pathway is involved in embryonic development, tissue homeostasis, tissue inflammation, wound healing and tumourigenesis (Burger and Kipps 2006; Kryczek et al. 2007; Ratajczak et al. 2006). For a long time CXCR4 was considered the only available receptor for SDF1. However, recent studies have identified an additional receptor for this ligand known as CXCR7 (Balabanian et al. 2005; Burns et al. 2006). Studies show that CXCR4 and CXCR7 signalling both play a role in tumour progression (Balkwill 2004a; Burns et al. 2006), although little is known about the precise downstream signalling pathway that mediates their oncogenic functions. This chapter focuses on host stroma-derived SDF-1 signalling in cancer and its role in generating a tumourpromoting niche.

A. Orimo () CR-UK Stromal-Tumour Interaction Group, Paterson Institute for Cancer Research, The University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_13, © Springer Science+Business Media B.V. 2011

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13.2 Physiological Role of SDF-1-CXCR4 Signalling Studies in transgenic and knockout mice have demonstrated the importance of the SDF-1-CXCR4 signalling pathway during embryonic development. Deletion of the gene encoding for either SDF-1 or CXCR4 results in an embryonic lethal phenotype due to multiple organ defects (Nagasawa et al. 1996; Tachibana et al. 1998; Zou et  al. 1998). The shared phenotypes seen in the knockout mouse for either protein strongly suggest that CXCR4 is the main receptor for SDF-1. SDF1-CXCR4 signalling is especially important for the development of the stem cell niche, supporting and regulating the homing, retention, survival and quiescence of hematopoietic stem cells (HSCs) in the bone marrow (Heissig et al. 2002; Spiegel et al. 2008; Sugiyama et al. 2006). SDF-1 is produced by resident endothelial cells, CXCL12-abundant reticular (CAR) cells and osteoblasts. High levels of SDF-1 recruit CXCR4-expressing mesenchymal cell progenitors and various myeloid cells into the sites of wounded tissues in order to facilitate the repair and regeneration of damaged tissues (Spiegel et al. 2008).

13.3 Expression and Regulation of SDF-1 and CXCR4 in Cancer SDF-1-CXCR4 signalling has been implicated in over twenty different types of epithelial, mesenchymal and haematopoietic carcinomas (Balkwill 2004b). Within the tumour microenvironment, SDF-1 is produced by several different cell types that include endothelial cells, stromal fibroblasts, epithelial cells and carcinoma cells (Allinen et al. 2004; Orimo et al. 2005; Tait et al. 2007; Zou et al. 2001). CXCR4 is most commonly expressed on carcinoma cells but is rarely detected in normal tissue. Importantly, CXCR4 expression is progressively increased by carcinoma cells during tumour progression and it is actively regulated by crosstalk with several oncogenic signalling pathways (Fig. 13.1). As mentioned above, SDF-1 production is dramatically up-regulated at sites of tissue damage as well as in the tumour stroma. However, little is known about the molecular mechanisms by which the tissue damage response induces SDF-1 production.

13.4 Essential Roles for SDF-1-CXCR4 Signalling in Carcinoma Cells in Promoting Tumourigenesis In a tumour xenograft model, carcinoma cells expressing CXCR4 promote tumour growth, stimulate neoangiogenesis and increase spontaneous metastatic dissemination (Balkwill 2004a). Furthermore, these cells are able to resist anoikis and to colonise distant tissues and organs to form both micro- and macro-metastases

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Fig. 13.1   Regulation and the roles of SDF-1-CXCR4 and SDF-1-CXCR7 signalling in carcinoma cells in promoting tumourigenesis. In tumour tissues, mesenchymal cells such as endothelial cells and fibroblasts are a main source of SDF-1. The latter binds to CXCR4 to activate signalling in the carcinoma cells. The expression of CXCR4 is up-regulated in hypoxia through activation of the hypoxia-inducible factor-1 (HIF-1) signalling pathway (Staller et al. 2003) and cytokines including TGF-β (Bertran et al. 2009) and VEGF (Bachelder et al. 2002). The expression of CXCR4 is stabilised by HER2 through prevention of the ligand-induced CXCR4 degradation (Li et al. 2004). Induction of CXCR4 signalling in turn stimulates transactivation of HER2 through Src kinase activation (Cabioglu et  al. 2005). SDF-1-CXCR4 axis stimulates the distinct downstream signal pathways including AKT, MAPK, RhoA, Rac1 (Bartolome et al. 2004) and integrins (Hartmann et al. 2005), and elevates expression levels of proinvasive factors, such as MMPs, VEGF, IL-6 and IL-8 (Wang et al. 2005). Activation of CXCR4 signalling confers cells with increased proliferation, survival, migration, invasion and adhesion propensities. SDF-1-CXCR4 signalling may also be involved in the induction of an epithelial to mesenchymal transition (EMT) and the self-renewal properties of cancer stem cells (CSCs). Furthermore, CXCR7, an additional receptor for SDF-1, might form heterodimers with CXCR4 to activate the signalling pathway and facilitate tumourigenesis

(Zeelenberg et al. 2003). Concordantly, subpopulations of human breast carcinoma cells that demonstrate an increased metastatic propensity into bone or lung, also display increased levels of CXCR4 expression compared to their parental cells (Helbig et al. 2003; Kang et al. 2003). The increased capacity of CXCR4-expressing cancer cells to promote tumourigenesis is further highlighted by a study showing that oral squamous cell carcinoma cells, when exposed to recombinant SDF-1 protein, display a mesenchymal phenotype (Onoue et  al. 2006). These cells undergo an epithelial to mesenchymal transition (EMT) that confers upon carcinoma cells the invasive and migratory phenotypes required for cancer progression. Conversely, different molecular or biochemical approaches to inhibit CXCR4 signalling within carcinoma cells substantially attenuates their tumourigenic and metastatic capabilities in vivo (Muller et al. 2001; Zeelenberg et al. 2003). Some studies have also shown that CXCR4 expression provides cancer cells with their self-renewing properties. Tentative CD133+CXCR4+ cancer stem cell (CSC) populations isolated from human pancreatic carcinoma cells, when orthotopically injected into mice, demonstrate a greater ability to form liver metastases

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compared to the control CD133+CXCR4− cell populations (Hermann et al. 2007). Furthermore, a putative breast carcinoma stem cell population, capable of forming mammospheres within an in vitro assay, displays a 10-fold increase in their expression of CXCR4 (Krohn et al. 2009). SDF-1 significantly influences the biological behaviour of carcinoma cells in a CXCR4-dependant fashion. Cancer cells cultured in the presence of SDF-1 increase their migratory and invasive propensities through the induction of actin polymerisation (Muller et al. 2001), and the production of pro-invasive factors such as matrix metalloproteinases (MMPs), VEGF, IL-6 and IL-8 (Bartolome et al. 2004; Wang et  al. 2005) (Fig.  13.1). Collectively, these findings indicate an essential role for SDF-1-CXCR4 signalling in tumour progression.

13.5 Tumour-Promoting Role of SDF-1-CXCR7 Signalling CXCR7/RDC1 has been identified as an important gene which facilitates cell transformation induced by Kaposi sarcoma-associated herpes virus (Raggo et al. 2005). CXCR7 expression on carcinoma cells progressively increases during human prostate carcinoma development in patients (Wang et  al. 2008). Cancer cells overexpressing CXCR7 also show accelerated tumour growth kinetics, stimulated neoangiogenesis and increased formation of metastases in vivo (Burns et al. 2006; Miao et  al. 2007; Wang et  al. 2008). Conversely, the inhibition or down-regulation of CXCR7 significantly reduces the growth rate of carcinoma cells. Moreover, in human prostate cancer cells, induction of CXCR7 signalling elevates IL-8 and VEGF expression and increases AKT signalling (Wang et al. 2008). These same molecules are also up-regulated by activation of the SDF-1-CXCR4 signalling pathway, suggesting a shared downstream pathway for both receptors. However, it is unclear whether CXCR7 can contribute to the following downstream signalling either by regulating CXCR4 activity through heterodimer formation or by signalling on its own (Fig.13.1). Further research is required in order to shed light on the precise downstream targets involved in SDF-1-CXCR7 signalling.

13.6 Host-Derived SDF-1 Signalling Promotes Tumour Growth, Invasion and Metastasis Recent accumulated evidence demonstrates that tumourigenesis is dependant upon contextual signals released from the apposing tumour-associated stroma that supports and sustains carcinoma cell growth throughout tumour progression (Bhowmick et al. 2004; Bissell and Radisky 2001; Coussens and Werb 2002; Kalluri and Zeisberg 2006; Mueller and Fusenig 2004; Orimo and Weinberg 2006; Polyak et al. 2009). Tumour-associated stroma includes numbers of fibroblasts and myofibroblasts. The latter is typically positive for α-smooth muscle actin (α-SMA) and these

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fibroblasts are very frequently detected in the stroma of various types of human carcinomas including the breast (Fig. 13.2). The presence of large numbers of myofibroblasts is often associated with high-grade malignancies and poor prognoses in the cancer patients (Cardone et al. 1997; Kellermann et al. 2008; Maeshima et al. 2002). Importantly, unlike fibroblasts found within the non-cancerous stroma, myofibroblasts present within the tumour stroma actively produce SDF-1 (Orimo et al. 2005; Tait et al. 2007) (Fig. 13.2).

Fig. 13.2   Stromal myofibroblasts produce SDF-1 protein in human breast tumour. Paraffin sections of human invasive breast cancer tissue (a, b, d, e, and f) or non-cancerous tissue (c) were immunostained with anti-α-SMA (a) or anti-SDF-1 (b and c) antibodies. Note that SDF-1-positive fibroblast-like cells ( arrows in b) as well as α-SMA-positive myofibroblasts ( arrows in a) are present in the tumour-associated stroma. In contrast, in non-cancerous tissue SDF-1 protein is only detected in epithelial cells ( arrows in c) and not in any fibroblast-like cells ( arrowheads in c). Furthermore, the adjacent tumour section is double-stained with both anti-α-SMA (d) and antiSDF-1 (e) antibodies. Many α-SMA-positive myofibroblasts show positive staining for SDF-1 in carcinoma (f). (From Orimo et al. 2005)

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The tumour-promoting ability of CAFs, which are stromal myofibroblast-rich cell populations extracted from human carcinoma, has been previously investigated using tumour xenograft models (Hu et al. 2009; Hwang et al. 2008; Olumi et al. 1999; Orimo et al. 2005; Yang et al. 2006). Tumours that develop in the presence of CAFs show a more increased growth rate compared to those that develop in the presence of control fibroblasts isolated from non-cancerous tissues. Considerable numbers of CAFs or control fibroblasts stay alongside the carcinoma cells within advanced tumour xenografts (Orimo et al. 2005; Yang et al. 2005). CAF-secreted paracrine factors are therefore likely responsible for the observed increase in carcinoma growth during tumour development. Importantly, CAFs secrete SDF-1 that stimulates carcinoma cell proliferation, and promotes neoangiogenesis by recruiting circulating endothelial progenitor cells (EPCs) into the tumour site (Orimo et al. 2005) (Fig. 13.3). Other studies have also shown that CAFs secrete high levels of TGF-β1 (Ao et al. 2007; Rosenthal et al.

Fig. 13.3   Human breast tumours developing in the presence of CAFs are highly angiogenic. a Human breast MCF-7-ras carcinoma cells were co-injected with either CAFs or the control counterpart fibroblasts extracted from the non-cancerous tissue from the same individual, subcutaneously into immunodeficient mice. Sections prepared from the tumour xenografts were stained with Masson’s trichrome (A, B) and an anti-CD31 antibody (C, D). An extensive vascular formation is observed in tumours containing CAFs, whilst capillaries in tumours containing the control fibroblasts are less developed. b MCF-7-ras breast tumours derived from xenografts implanted with either CAFs or counterpart fibroblasts were dissociated into a single cell suspension at 60–63 days after injection. Cells were stained with antibodies specific to Sca1 and CD31 protein, both of which are makers of endothelial progenitor cells (EPCs). A far higher proportion (4.2-fold) of Sca1+CD31+ EPCs is detected in tumours admixed with CAFs compared to tumours containing the control counterpart fibroblasts. *p < 0.05. (From Orimo et al. 2005)

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2004; San Francisco et  al. 2004), which further facilitates tumourigenesis by increasing CXCR4 expression in human prostate preneoplastic cells. TGF-β-primed myofibroblasts provide more increased invasive propensity to carcinoma cells cultured in a collagen gel (Daly et al. 2008; De Wever et al. 2004). Proinvasive cytokines SDF-1 and HGF secreted from the myofibroblasts indeed mediate the increased invasiveness in oral squamous cell carcinoma and colorectal carcinoma cells. Collectively, stroma-derived SDF-1-CXCR4 paracrine signalling is not only important in promoting tumourigenesis but also enhances the invasive and metastatic properties of carcinoma cells. Breast cancer patients often show relapse years to decades after the initial diagnosis. Understanding the molecular mechanism(s) which underlie latent metastatic breast cancer is essential for the future development of therapies. A recent study finds that activation of Src signalling is associated with late-onset bone metastasis in breast cancers, and the Src-activated disseminated carcinoma cells have a propensity for long-term survival within the bone marrow (Zhang et al. 2009). This signalling can further facilitate activation of CXCR4 signalling induced by host bone marrow-derived SDF-1 and thus activation of the downstream prosurvival AKT signalling in carcinoma cells. This suggests that SDF-1 produced by the host microenvironment not only promotes carcinoma cell proliferation and neoangiogenesis within the primary tumour as described earlier, but also facilitates colonisation of disseminated metastatic carcinoma cells within the bone marrow (Fig. 13.4).

Fig. 13.4   Schematic representation of the tumour-promoting effects of SDF-1 rich in the tumour microenvironment. CAF-derived SDF-1 via CXCR4 enhances tumour growth, not only by stimulating angiogenesis via the recruitment of circulating EPCs into the tumour mass, but also by direct paracrine stimulation of tumour cell proliferation. SDF-1-activated CXCR4 signalling also endows carcinoma cells with greater survival, invasive and metastatic properties. SDF-1-activated CXCR7 signalling also promotes tumourigenesis in a CXCR4-dependent and/or -independent manner. Furthermore, host-derived SDF-1 facilitates survival of disseminated tumour cells in sites of metastasis within the bone marrow

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13.7 Perspective The SDF-1-CXCR4 signalling pathway is critical in allowing carcinoma cells to progress to high-grade malignancies. Stromal cells are major sources of SDF-1 within the tumour microenvironment. The importance of this chemokine acting upon CXCR4-expressing carcinoma cells is reflected in the range of different cellular processes ongoing within the primary tumour, the pre-metastatic niche and disseminated metastatic sites. CXCR4 expression is elevated within the CSC-enriched population and may contribute to maintaining the CSC niche. Recent evidence suggests that cells can switch between a CSC and non-CSC state (Gupta et al. 2009), and that induction of EMT in neoplastic cells can give rise to a population of CSCs that increases the incidence of tumour formation (Mani et al. 2008). However, it has yet to be established if SDF-1-CXCR4 signalling can affect the CSC-differentiation hierarchy by inducing EMT. Pre-metastatic niches are rich in SDF-1 secreted by host cells. This cytokine recruits CXCR4-expressing hematopoietic progenitor cells and carcinoma cells into sites of metastasis and facilitates the formation of micro- and macro-metastasis within these tissues (Psaila and Lyden 2009). In the bone marrow, the host-derived SDF-1 can activate CXCR4 signalling and prime cancer cells, increasing their ability to survive and enhancing their capacity to colonise. It is likely that CAFs are also a major source of SDF-1 within such metastatic sites. The clinical use of CXCR4 antagonists, in combination with conventional chemotherapy, may be an attractive therapeutic approach in the future for targeting the effects of SDF-1-CXCR4 signalling within these niches.

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Chapter 14

Role of PDGF in Tumor-Stroma Interactions Carina Hellberg and Carl-Henrik Heldin

Abbreviations EGF IFP LMW-PTP PI-3 kinase PLC PDGF PKC PTP TC-PTP TGFβ VEGF

epidermal growth factor interstitial fluid pressure low molecular weight protein tyrosine phosphatase phosphatidylinositol-3 kinase phospholipase C platelet derived growth factor protein kinase C protein tyrosine phosphatase T-cell protein tyrosine phosphatase transforming growth factor β vascular endothelial growth factor

14.1 PDGF Signal Transduction Platelet-derived growth factor (PDGF) isoforms are major mitogens for a number of cell types, including mesenchymal cells such as fibroblasts and smooth muscle cells (Andrae et al. 2008), whereas overactivity of PDGF signaling has been linked to the development of atherosclerosis, fibrotic diseases and malignancies (Östman and Heldin 2001).

14.1.1  The PDGF Family The PDGF family consists of four polypeptide chains, the classical PDGF A- and B-chains and the more recently described PDGF C- and D- chains (Fig.  14.1) C. Hellberg () Ludwig Institute for Cancer Research, Uppsala University, Box 595, SE 751 24 Uppsala, Sweden e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_14, © Springer Science+Business Media B.V. 2011

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CC

DD

AB

DE

BB

DD

EE

Fig. 14.1   The PDGF family of growth factors. The PDGF family consists of four polypeptide chains; PDGF-A, -B, -C and -D. The PDGF chains form disulfide-bonded homodimers and in addition, the A and B chains heterodimerize to form PDGF-AB. The PDGF isoforms exert their biological effects by binding two structurally similar tyrosine kinase receptors, the PDGF α- and β-receptor, leading to receptor homo- or heterodimerization. The dominating receptor interactions by the different PDGF isoforms are indicated by the arrows

(Fredriksson et  al. 2004). The PDGF chains form disulfide-bonded homodimers and, in addition, the A and B chains heterodimerize to form PDGF-AB. The PDGF isoforms exert their biological effects binding to class III tyrosine kinase receptors that are activated by ligand-induced dimerization Heldin et al. (1998). PDGF A-, Band C-chains bind the PDGF α-receptor, whereas the B- and D-chains bind PDGF β-receptors. Thus, PDGF αα receptor homodimer are formed after stimulation by PDGF-AA, -AB, -BB or -CC, αβ heterodimeric receptors after stimulation by PDGF-AB or -BB, and ββ receptor dimers after stimulation by PDGF-BB or -DD. The folding of the intracellular juxtamembrane domain (Irusta et  al. 2002), the activation loop, and the C-terminal tail of the PDGF β-receptor (Chiara et al. 2004) contribute to keeping the monomeric receptors inactive. These restraints are released upon ligand-induced receptor dimerization and autophosphorylation, which induces a conformational change in the intracellular domains that activates the intrinsic tyrosine kinase activity. Additional autophosphorylation on tyrosine residues of the PDGF α- and β-receptors (Heldin et  al. 1998) creates docking sites for SH2 domain-containing signaling molecules, including adaptor proteins such as Shc and Grb2, and enzymes such as the Src family of tyrosine kinases, phosphatidylinositol (PI) 3-kinase, phospholipase Cγ (PLCγ) and the tyrosine phosphatase SHP-2 (Heldin et al. 1998). The specificity in the interaction with different SH2 domains is determined by the amino acid residues C-terminal of the phosphorylated tyrosine residue, thus providing selectivity in the signaling pathways induced by receptor activation. The cellular response to PDGF is determined by which subset of SH2 domain-containing proteins that associates to the PDGFR complexes.

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14.1.2  Modulation and Termination of Receptor Signaling The duration of PDGF receptor signaling is controlled by receptor dephosphorylation and degradation (Östman and Böhmer 2001). Regulation of the PDGF β-receptor signaling by protein tyrosine phosphatases (PTPs) has only been partially elucidated. In addition to SHP-2 (Kazlauskas et  al. 1993; Lechleider et al. 1993), several cytosolic tyrosine phosphatases including the low molecular weight (LMW) tyrosine phosphatase (Chiarugi et al. 1995), the T-cell PTP (TCPTP) (Markova et  al. 2003; Persson et  al. 2004) and PTP1B interact with and dephosphorylate the receptor (Haj et al. 2003). In addition, two receptor PTPs, DEP-1 (Sörby and Östman 1996) and CD45 (Mooney et al. 1992), regulate PDGF receptor phosphorylation. Different phosphatases display selectivity for different tyrosine residues in the receptors, thus providing a mechanism whereby PTPs can modulate receptor signaling. Thus, TC-PTP preferentially dephosphorylates the PLCγ binding site (Persson et al. 2004), and DEP-1 have higher affinity toward SH2 binding sites than the autoregulatory site in the activation loop of the PDGF β-receptor (Persson et al. 2002). Site-selective dephosphorylation of the PDGF β-receptor has also been demonstrated by SHP-2 (Klinghoffer and Kazlauskas 1995) and LMW-PTP (Chiarugi et al. 1995). These findings suggest that phosphatases modulate PDGF receptor signaling by selectively dephosphorylating specific tyrosine residues in the PDGF β-receptor. Thus, the expression and activation of tyrosine phosphatases fine-tune and modulate growth factor-induced signals. Following ligand binding, the PDGFRs are internalized by clathrin-mediated endocytosis and transported through early and late endosomes, before being degraded in the lysosomes (Mosesson et  al. 2008; Zwang and Yarden 2009). Internalized receptor tyrosine kinases have been shown to associate with many of their downstream effectors, which provide both spatial and temporal regulation of the assembled signaling complexes (Katz et al. 2007; Wang et al. 2004). Thus, the regulation of the subcellular localization of the receptors and their substrates provides an important step in the regulation of the cellular response to growth factors. Some receptor tyrosine kinases, e.g. the epidermal growth factor (EGF) receptor (Sorkin and Goh 2009), have been shown to recycle back to the cell surface, whereas the PDGF receptors normally display very little recycling. However, activation of protein kinase C-α (PKCα) was recently demonstrated to induce recycling of the PDGF β-receptor in fibroblasts (Hellberg et al. 2009). The induction of receptor recycling coincided with a decreased degradation rate. Since PKC is activated downstream of several different receptor types, including several tyrosine kinase receptors as well as G protein coupled receptors, this finding suggests that activation of PKC constitutes a point of crosstalk between different receptor types, whereby duration of the PDGF β-receptor signaling is regulated. In line with this notion, activation of PKC by the G-protein coupled LPA receptor was found to induce PDGF β-receptor recycling, accompanied by a potentiation of PDGF β-receptor phosphorylation and chemotaxis (Hellberg et al. 2009).

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14.1.3  Physiological Role of PDGF-Induced Signaling During the embryonal development, PDGF isoforms promotes the development of mesenchymal cells of different organs, such as mesangial cells of the kidney, alveolar smooth muscle cells of the lung, smooth muscle cells and pericytes of blood vessels, and glial cells of the central nervous system (Andrae et al. 2008). In adults, PDGF has an important function as a chemotactic and mitogenic factor for fibroblasts and other cells during wound healing (Werner and Grose 2003), and as a regulator of the interstitial fluid pressure of tissues (Rodt et al. 1996). Several signaling pathways participate in the regulation of the mitogenic response, including the Ras-MAPK pathway, Src family kinases, PI 3-kinase, PLCγ and SHP-2 (Tallquist and Kazlauskas 2004). The PDGF receptors also interact directly with and phosphorylate transcription factors of the Stat family, although their involvement in PDGF-induced proliferation remains to be clarified. PDGF also acts as a chemotactic factor for fibroblasts and smooth muscle cells, as well as for neutrophils and macrophages (Heldin and Westermark 1999). Ligation of the PDGF receptors induces rapid cytoskeletal rearrangements leading to cell migration. Activation of the PI 3-kinase pathway, and the subsequent activation of members of the Rho family of small GTPases, is critical for the rearrangement of the cytoskeleton and cell migration Heldin et al. 1998). Activation of PLCγ participate in the regulation of the rate of chemotaxis (Tallquist and Kazlauskas 2004).

14.2 Pathophysiological Role of PDGF in Tumors 14.2.1  PDGF Expression in Tumors Most sarcoma and glioblastoma tumors express PDGF receptors, whereas most epithelial tumors do not (Ostman 2004). However, in all solid tumors, PDGF receptors are expressed on different non-malignant cell types in the tumor, including stromal fibroblasts and cells of blood vessels (Pietras et al. 2003). Since PDGF isoforms often are produced by tumor cells, as well as certain non-malignant cells in tumors, PDGF has paracrine rather than autocrine effects in most tumors (Heldin and Westermark 1999; Ostman and Heldin 2007). PDGF ligand expression has been reported to initiate invasion and proliferation of fibroblasts into the stroma (Micke and Ostman 2004). In addition, PDGF expression initiates vascular maturation by increasing the number of pericytes on the tumor vessels, which has been correlated to increased tumor growth rate, presumably by improving vessel perfusion and thereby tumor blood flow (Jain 2005). Various cells of the tumor stroma are also sources of PDGF (Fig. 14.2). Thus, PDGF expressed by tumor macrophages initiate the infiltration of fibroblasts into the tumor (Ostman and Heldin 2007).

14  Role of PDGF in Tumor-Stroma Interactions Tumor cell

Macrophage

261

Fibroblast

PDGF Pericyte

Vessel

Fig. 14.2   Expression of PDGF and PDGF receptors in tumors. PDGF is synthesized by several cell types in the tumor, including the tumor cells, macrophages and endothelial cells. PDGF receptors are expressed by tumor cells, tumor associated fibroblasts and pericytes. In fibroblasts, PDGF receptor activation results in activation of αvβ3 integrins which mediates contraction of collagen fibers in the extracellular matrix

Macrophages also produce transforming growth factor β (TGFβ), which promotes fibroblast differentiation into myofibroblasts (Micke and Ostman 2004), suggesting a dual role for macrophages in the formation of tumor-associated fibroblasts. Since expression of PDGF-DD has been found to increase macrophage recruitment to skin (Uutela et al. 2009), PDGF could also be part of an amplification mechanism whereby macrophages are recruited to tumors and participate in the recruitment of both fibroblasts and macrophages to the tumor stroma (Fig. 14.2). Endothelial cells also express PDGF, which is of crucial importance for the recruitment of pericytes to the vessels (Betsholtz 2004), as further discussed below. Although solid tumors often express PDGF, a relatively small number of tumor types are dependent on PDGF receptor signaling for autocrine growth (Ostman and Heldin 2007). An example of genetic aberrations leading to autocrine PDGF stimulation is a translocation creating a fusion between the PDGF-B and the collagen 1A1 genes, which is associated with dermatofibrosarcoma protuberens. The collagen-PDGF-B fusion protein is processed to a molecule similar to native PDGF-BB, which binds to PDGF receptors on the producing fibroblasts and drives their proliferation. Other chromosomal translocations that affect PDGF receptor signaling generate fusion proteins where the transmembrane and intracellular domains of the PDGF α- or β-receptor are fused to proteins that can oligomerize. Such aberrations have been identified in chronic myelomonocytic leukemia (Jones and Cross 2004) and hypereosinophilic syndrome (Cools et  al. 2003). The ability of the fusion partner to dimerize or multimerize allows for kinase activation, and although these fusion proteins commonly reside in the cytosol rather than at the plasma membrane, they provide proliferative signals. Finally, amplification of the PDGF α-receptor occurs in glioblastomas (Fleming et al. 1992; Kumabe et al. 1992), making the cells oversensitive for PDGF stimulation, and activating point mutations of the PDGF α-receptor has been identified in gastrointestinal stroma tumors (Heinrich et al. 2003).

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14.2.2  P  DGF Regulation of Pericyte Recruitment   to Tumor Vessels Pericytes are mural cells surrounding capillaries. During embryogenesis, the formation of pericytes, and thereby the stabilization of the vasculature, is critically dependent on PDGF-BB produced by endothelial cells which activates PDGF β-receptors on pericytes (Betsholtz 2004). In tumors, overexpression of PDGF isoforms has been shown to increase the recruitment of pericytes to tumor vessels, thereby promoting the growth rate of tumors (Furuhashi et al. 2004; Ostman and Heldin 2007). The abnormal morphology and leakiness of tumor vessels has been suggested to be caused by increased endothelial cell proliferation due to excess VEGF produced by the tumor. The poor function of these vessels is at least partly due to poor pericyte recruitment (Jain 2003). The pericytes that are found on tumor vessels often display an abnormal phenotype and express abnormal markers (Baluk et al. 2005). In normal tissue, pericytes are lining the capillaries beneath the basement membrane, forming close contact with the endothelium. It is believed that this contact exerts anti-proliferative effects on the endothelial cells (Jain 2005). Despite their growth inhibitory effects, pericytes appear to produce trophic factors that are important for endothelial cell survival, since the presence of pericytes on tumor vessels protects endothelial cells from anti-angiogenic therapies targeting VEGF receptor signaling (Bergers and Song 2005). This notion was strengthened by the finding that combination therapy where VEGF receptor inhibition was combined with targeting of PDGF receptors on pericytes improved the therapeutic response in mouse models (Bergers et al. 2003; Hasumi et al. 2007).

14.2.3  PDGF Regulation of Tumor Interstitial Fluid Pressure In normal tissue, water and nutrients are transported from the capillaries through convection, a process aided by a net outward pressure from the capillaries (Jain 1987a, b). Solid tumors frequently display increased interstitial fluid pressure (IFP), which correlates to a decreased tissue convection, resulting in decreased cellular uptake of nutrients and oxygen as well as drugs. Several studies have shown that elevated tumor IFP correlates with poor prognosis (Heldin et al. 2004). The IFP is regulated by several mechanisms, including the hydrostatic pressures and the colloid osmotic pressures in the vessels as well as in the surrounding tissue (Aukland and Reed 1993). As discussed above, tumor vessels are abnormal in morphology and often poorly perfused, resulting in low capillary blood pressure (Fukumura and Jain 2007). Also, the tumor vessels are typically leaky for macromolecules, resulting in increased tissue colloid osmotic pressure. These effects, together with the fact that most solid tumors do not have proper lymphatic drainage (Karpanen and Alitalo 2008), contribute to the increased IFP of most solid tumors. In addition, tumor fibroblasts contribute to the increased IFP by secret-

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ing abnormal extracellular matrix, which are often rich in thick collagen bundles. Signaling through the PDGF β-receptor not only participates in the generation and remodeling of the abnormal extracellular matrix, but also aggravates the elevated IFP by promoting tumor fibroblast-mediated contraction of the collagen matrix, thereby mechanically increasing the pressure (Heldin et al. 2004). In skin, fibroblasts maintain the IFP by binding collagen through β1 integrins, and inhibition of this interaction results in tissue edema (Liden et al. 2006). Following pathological edema formation, as a result of lowering the IFP of skin, activation of fibroblast PDGF β-receptors leads to αvβ3 integrin-mediated tissue contraction that normalizes the pressure (Liden et al. 2006). In line with these findings, PDGF inhibitors have been found to decrease tumor IFP, which has been found to be associated with increased drug uptake and increased tumor response to cytotoxic drugs (Heldin et al. 2004).

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Mooney RA, Freund GG, Way BA, Bordwell KL (1992) Expression of a transmembrane phosphotyrosine phosphatase inhibits cellular response to platelet-derived growth factor and insulinlike growth factor-1. J Biol Chem 267:23443–23446 Mosesson Y, Mills GB, Yarden Y (2008) Derailed endocytosis: an emerging feature of cancer. Nat Rev Cancer 8:835–850 Ostman A (2004) PDGF receptors-mediators of autocrine tumor growth and regulators of tumor vasculature and stroma. Cytokine Growth Factor Rev 15:275–286 Östman A, Böhmer FD (2001) Regulation of receptor tyrosine kinase signaling by protein tyrosine phosphatases. Trends Cell Biol 11:258–266 Östman A, Heldin CH (2001) Involvement of platelet-derived growth factor in disease: development of specific antagonists. Adv Cancer Res 80:1–38 Ostman A, Heldin CH (2007) PDGF receptors as targets in tumor treatment. Adv Cancer Res 97:247–274 Persson C, Engström U, Mowbray SL, Östman A (2002) Primary sequence determinants responsible for site-selective dephosphorylation of the PDGF beta-receptor by the receptor-like protein tyrosine phosphatase DEP-1. FEBS Lett 517:27–31 Persson C, Savenhed C, Bourdeau A, Tremblay ML, Markova B, Bohmer FD, Haj FG, Neel BG, Elson A, Heldin CH, Ronnstrand L, Ostman A, Hellberg C (2004) Site-selective regulation of platelet-derived growth factor beta receptor tyrosine phosphorylation by T-cell protein tyrosine phosphatase. Mol Cell Biol 24:2190–2201 Pietras K, Sjoblom T, Rubin K, Heldin CH, Ostman A (2003) PDGF receptors as cancer drug targets. Cancer Cell 3:439–443 Rodt SA, Ahlen K, Berg A, Rubin K, Reed RK (1996) A novel physiological function for plateletderived growth factor-BB in rat dermis. J Physiol 495:193–200 Sorkin A, Goh LK (2009) Endocytosis and intracellular trafficking of ErbBs. Exp Cell Res 315:683–696 Sörby M, Östman A (1996) Protein-tyrosine phosphatase-mediated decrease of epidermal growth factor and platelet-derived growth factor receptor tyrosine phosphorylation in high cell density cultures. J Biol Chem 271:10963–10966 Tallquist M, Kazlauskas A (2004) PDGF signaling in cells and mice. Cytokine Growth Factor Rev 15:205–213 Uutela M, Wizrenius M, Paavonen K, Rajantie I, He Y, Karpanen T, Lohela M, Wiig H, Salven P, Pajusola K, Eriksson U, Alitalo K (2009) PDGF-D induces macrophage recruitment, increased interstitial pressure, and blood vessel maturation during angiogenesis. Blood 104:3198–3204 Wang Y, Pennock SD, Chen X, Kazlauskas A, Wang Z (2004) Platelet-derived growth factor receptor-mediated signal transduction from endosomes. J Biol Chem 279:8038–8046 Werner S, Grose R (2003) Regulation of wound healing by growth factors and cytokines. Physiol Rev 83:835–870 Zwang Y, Yarden Y (2009) Systems biology of growth factor-induced receptor endocytosis. Traffic 10:349–363

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Chapter 15

Radiation-Induced Microenvironments and Their Role in Carcinogenesis Mary Helen Barcellos-Hoff and David H. Nguyen

15.1 Introduction Overt cancer in humans is extremely common; one of three Americans will be diagnosed during their lives. Moreover, based on autopsy, cancer is even more frequent at the tissue level in many organs. At age 50, 1 of 4,000 people will be diagnosed with thyroid cancer but 99% of autopsy specimens contain frank malignancies (Tulinius 1991). Breast cancer is much more prevalent at the tissue level than is clinically evident in that of extensive histopathologic review found malignancy in 20% of the breasts from more than 100 autopsies of women ranging from 20 to 54 who died from non-cancer causes (Nielsen et al. 1984; Nielsen 1989). Similarly, although many more Western men compared to Japanese men develop clinical prostate cancer by age 60, carcinomas are equally prevalent in autopsy specimens in both populations (Stemmermann et al. 1992). These observations suggest that random genetic changes occur sufficiently frequently to produce malignant cells with proliferative potential that do not progress at the tissue level. It has been proposed that what these nascent cancers lack is the ability to recruit normal cells into the neoplastic process (reviewed in (Folkman et al. 2000)). A corollary is that the emergence of clinical cancer requires the loss of these crucial cell interactions that suppress cancer. This is a key concept that underlies thinking of cancer as a disease of tissues rather than cells. Although a normal stroma may maintain tumor dormancy, interactions between stroma and tumor cells are dynamic and reciprocal, and may be altered by many factors. At some point during cancer progression to invasive disease, stromal-epithelial communication is fundamentally altered from suppressing to promoting (Fig. 15.1). External agents (Barcellos-Hoff 1993; Rosenkrans and Penny 1985, 1987), or physiological processes like wounding, aging per se or age-related changes in systemic hormones can cause changes in the stroma and microenvironment that in turn create a permissive context for M. H. Barcellos-Hoff () D. H. Nguyen Departments of Radiation Oncology and Cell Biology, NYU Langone Medical Center, 566 First Avenue, 10016 New York, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_15, © Springer Science+Business Media B.V. 2011

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268 Fig. 15.1   Schematic of stromal effect on carcinogenesis. Stroma can be either a positive ( green) or negative ( red) regulator of neoplastic progression, depending on its composition

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cancer. If normal, quiescent stroma enforces tumor cell dormancy, and perturbation of the stroma is a prerequisite for tumor progression, then a detailed understanding of stromal-epithelial interaction is likely to lead to novel therapeutic approaches for cancer. Ionizing radiation is a known carcinogen of humans and experimental models (reviewed in (Preston et al. 2003)). Excess cancers were observed in the Japanese atomic-bomb survivors at ionizing radiation (IR) doses of 10–400 cGy, which are 40–1600 times the average yearly background levels in the United States. In the Japanese populations exposed as a consequence of the atomic bomb, the excess risks vary significantly with gender, attained age, and age at exposure for all solid cancers as a group or for specific tissues (Preston et al. 2007). It has been estimated that if radiation exposure occurs at age 30, the solid cancer rates at age 70 is increased by about 35% per Gy for men and 58% per Gy for women (Preston et al. 2007). Predicting cancer risk in populations exposed to doses lower than ~10 cGy is limited by statistical considerations. To predict cancer risk in the dose region below which epidemiological data are robust, radiation risk models extrapolate the known data using an assumption of linearity. The prevailing paradigm of cancer risk following radiation focuses on the probability of DNA damage that can lead to mutations in susceptible cells (NAS/NRC 2006). An alternative hypothesis is that cancer emerges from irradiated tissues as a result of complex, but ultimately predictable, interactions between mutagenesis from DNA damage and radiation effects on the microenvironment and cell interactions (Barcellos-Hoff 2007). Just as DNA damage elicits a dramatic transition in signaling within a cell, each irradiated tissue has its own set of signals and composition, distinct from those of either unirradiated tissue or other irradiated tissues. Biological responses to radiation damage quickly evolve and amplify, mostly in a non-linear manner, and can alter daughter cell fates such as differentiation and senescence (Herskind and Rodemann 2000; Rave-Frank et al. 2001; Park et al. 2003; Tsai et al. 2005), induce long-range signals that affect non-irradiated cells (Kadhim et al. 1992, 1994, 1995; Clutton et al. 1996), or generate a state of chronic genomic instability (GIN) (Kadhim et al. 1992, 1994, 1995; Clutton et al. 1996; Limoli et al. 1997). The sum of these events, occurring in different organs and highly modulated by genotype, culminates in disease. Hence, it is quite feasible that radiation associated cancer is a result of oncogenic mutations from DNA damage that occur in the context of the irradiated microenvi-

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ronment (Barcellos-Hoff 2005, 2007). Carcinogen-induced microenvironments are not necessarily mutagenic or mitogenic, per se. Rather, changes in the microenvironment can promote neoplastic behavior by disrupting normal cell functions regulated through cell-cell contact, cell-matrix interactions and growth factor signaling. Thus, if a carcinogen, like ionizing radiation, induces a microenvironment that overrides inhibitory cell interactions, then it can promote the malignant phenotype in a way that is functionally equivalent to the acquisition of additional mutations in the initiated cell. Alternatively, the microenvironment elicited by carcinogen exposure could create novel selective pressures that would affect the features of a developing tumor. Disruption of solid tissue interactions is a previously unrecognized activity of carcinogens, and a novel avenue through which to explore new strategies for intervening in the neoplastic process.

15.2 Carcinogenesis in Context The pioneering studies by Mintz and Pierce in the 1970s independently demonstrated that highly malignant cells could be suppressed by normal tissue interactions (Mintz and Illmensee 1975; Pierce et  al. 1978). The evidence is mounting that cancer results from a systemic failure in which many cells other than those with oncogenic genomes determine the frequency of clinical cancer. Some argue that disruption of the cell interactions and tissue architecture can even be a primary driver of carcinogenesis (Rubin 1985; Barcellos-Hoff 1998a; Sonnenschein and Soto 2000; Bissell and Radisky 2001; Wiseman and Werb 2002). Recent experiments using experimental models in which oncogene activation or tumor suppressor loss is engineered provide compelling evidence that microenvironment composition determines whether cancer ensues (Kuperwasser et al. 2004; Bhowmick et al. 2001; Maffini et al. 2004; de Visser et al. 2006). Dvorak proposed that cancer is analogous to a wound that never heals (Dvorak 1986), an idea that implicates the importance of tissue remodeling and inflammation, both of which involve the functions of tissues. It has become increasingly evident that tissue structure, function and dysfunction are highly intertwined with the microenvironment during the development of cancer (reviewed in) and that tissue biology and host physiology are subverted to drive malignant progression (Terzaghi-Howe 1990; Ethier and Cundiff 1987; Ethier et al. 1987). Recent examples that have identified specific signals and cells that contribute to carcinogenesis are discussed in the following section. These experimental models provide strong mechanistic support for dominant control by the microenvironment, even in highly efficient carcinogenesis driven by strong oncogenic programs. A human mammary model developed by Weinberg underscores both the requirement for the appropriate microenvironment that allows epithelial cells to perform in a tissue-appropriate manner and the critical role of abnormal stroma in cancer promotion (Kuperwasser et  al. 2004). The model employs the mouse mammary gland as the host for human fibroblasts, which, when irradiated in vitro, take up

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permanent residence in the cleared fat pad. This humanized stroma supports the growth and morphogenesis of subsequently transplanted human mammary epithelial organoids. Proper ductal morphogenesis depends on the admixture of primary normal breast fibroblasts to these organoids prior to engraftment into humanized fat pads. Although specimens from most individuals gave rise to apparently normal ductal outgrowths, one specimen gave rise to hyperplastic growth, suggesting the presence of neoplastically initiated, but dormant, cells. When that preparation was transplanted in a murine stroma humanized with stromal cells engineered to over express either HGF or TGFβ, the organoids developed into growths closely resembling human comedo-type and basal-type invasive carcinomas, respectively. The authors concluded that an altered stromal environment can promote human breast cancer formation through abnormal epithelial cells that are present, but dormant, in the normal human breast. These examples provide specific mechanisms at play in carcinogenesis driven by experimentally induced oncogenes. Predicting radiation carcinogenesis in humans is much more challenging given the random nature of initiation by mutation, the genetic variation between individuals, and the susceptibility of a particular tissue.

15.3 Stromal Contribution to Radiation Carcinogenesis The effect of cell interactions controlling the frequency of initiation is also evident in cell culture, in which initiated cells are identified by morphological and behavioral benchmarks of neoplastic behavior. Terzaghi-Howe demonstrated that the expression of dysplasia in vivo and neoplastic transformation in cultures of irradiated tracheal epithelial cells is inversely correlated with the number of cells seeded (Terzaghi and Little 1976; Terzaghi and Nettesheim 1979; Terzaghi-Howe 1986, 1989), and identified transforming growth factor β (TGFβ) as a key mediator (Terzaghi-Howe 1990). Likewise, when cells dissociated from irradiated mouse mammary glands are transplanted to unirradiated mice 24 h after radiation exposure, the frequency and persistence of dysplastic foci in culture is increased over that expressed in intact tissues (Ethier and Cundiff 1987; Ethier et al. 1987). Bauer and colleagues showed that the frequency of radiation, chemicals and virally mediated transformation of cultured human and rodent fibroblasts is actively suppressed by non-transformed cells (reviewed in (Bauer 1996)). In a process called intercellular induction of apoptosis, non-transformed cells induce the selective ablation of transformed cells via apoptosis triggered in part by cytokines and reactive oxygen species (ROS) produced by non-transformed neighboring cells (Engelmann et al. 2000). If this control system acts in vivo as efficiently as it does in vitro, tumor formation should require the establishment of resistance mechanisms directed against intercellular induction of apoptosis. Indeed cells from established tumors fail to be influenced by normal cells in culture (Engelmann et al. 2000). A study by Kaplan and colleagues dating back more than 50 years demonstrates that radiation carcinogenesis is complex. These studies used C57BL mice, which

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are very susceptible to thymic lymphomas after radiation exposure. Young mice underwent thymectomy, and 2–7 days later received the first of four consecutive doses of 168 cGy, spaced apart by 8 day intervals. Several hours after the last irradiation, a single thymus from a non-irradiated mouse was transplanted subcutaneously under the right chest or upper abdomen of each of the previously thymectomized, irradiated hosts. Tumors were then tracked by palpation for 15 months. Surprisingly, thymic lymphoma incidence and latency (39% and 214 days, respectively) arising from the grafts, matched that observed in irradiated, intact mice. Furthermore, the tumors were histologically identical to those found in the intact mice, and exhibited a similar pattern of metastasis (Kaplan et al. 1956a). This study showed that radiation-induced thymic lymphomas can occur even when the grafted thymus was never exposed to radiation, suggesting a systemic effect of radiation in the host. This systemic mechanism of tumor induction was elucidated in their second study, which showed that shielding a thigh of the host during irradiation, or promptly injecting fresh bone marrow into the host shortly after the last irradiation, could neutralize the tumor-inducing effect of IR. Using a similar experimental approach as in the first study, but varying the time of implantation after the last irradiation, the authors showed that the tumor promoting effect of IR through the host persisted for up to 8 days, yielding tumor incidences that were not significantly different from implantations performed 1–3 h post-irradiation (Kaplan et al. 1956b). In a third study, Kaplan and colleagues examined the physiological status of the unirradiated thymic graft after it was transplanted into a previously thymectomized and irradiated host. Massive necrosis was observed at 24 h after implantation, with only a few surviving cells under the capsular membrane. These regions of survival, however, would eventually be repopulated within the course of the next 14 d into a graft with a regenerated cortex. At this time point, grafts increased in total size and even formed new lobes, though not always two nor complete lobes. Comparing graft regeneration in thymectomized, irradiated or unirradiated hosts revealed that prior radiation exposure impaired regeneration. Consistent with the finding that bone marrow injection neutralized tumor induction through the irradiated host, thigh-shielded mice exhibited an identical degree of graft regeneration as observed in unirradiated mice, while unshielded mice had significantly impaired regeneration (Carnes et al. 1956). The authors thus concluded that a systemic bone marrow factor in the host was necessary for proper regeneration of unirradiated thymic grafts, and that radiation compromised this factor in the host, thus creating a mechanism for tumor induction. The final publication provided conclusive evidence that the tumors that arose in the unirradiated thymic grafts were indeed composed of donor cells and not invading host cells that had received radiation (Kaplan et  al. 1956a). The susceptible C57BL strain of mice was crossed with the C3H strain, which is resistant to radiation-induced lymphomas, to generate an F1 hybrid. Using the same experimental approach of transplantation into previously irradiated hosts, the authors revealed that though host irradiation could induce lymphomas, the genetic background of the graft donor heavily determined tumor incidence. Hosts bearing grafts from the susceptible C57BL or F1 hybrid strains had more tumors than those bearing grafts

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from C3H donors, thus indicating that susceptibility to radiation-induced lymphomas was a property that was inherent to the thymus, even though the mechanism of induction can occur through the host. Lastly, to prove that tumors induced through host exposure, but arising in the graft were truly cells from the unirradiated implant, tumor fragments were excised from grafts that were either C57BL or F1 hybrid, and then implanted, subcutaneously or intraperitoneally, back into hosts from each of the three genetic backgrounds. The tumor fragments from C57BL grafts only grew in the C57BL and F1 host, not in the C3H host; and fragments from hybrid grafts grew only in hybrid hosts. Thus, the rejection of tumor fragments when they were placed into hosts of a different background shows that the tumor cells were derived from the graft and not the host (Kaplan et al. 1956a). This series of papers highlight the host as an effective target of radiation in the induction of thymic lymphomas in grafts that were never irradiated. Similarly, a study of skin carcinogenesis done by Billingham and colleagues used the carcinogen methylcholanthrene to determine which compartment was the site of carcinogenic action in mouse skin. Skin grafts of various thicknesses (including or excluding hair follicles) from carcinogen-treated sites were transplanted to untreated sites in the same animal. Such an approach revealed that the underlying dermis layer conferred equivalent tumorigenic potential, even if the overlying epidermis was untreated. Tumors occurred when untreated grafts were transplanted into treated dermis, but not when treated grafts were placed into untreated dermis (Billingham et al. 1951). If the host microenvironment created by radiation can promote neoplastic progression in unirradiated epithelial cells, then events occurring “outside of the box” do significantly increase cancer risk. This adverse “bystander effect” of irradiated cells on unirradiated cells is due to extracellular signaling from the microenvironment that supports progression. Greenberger showed that irradiated stromal cells function as biologic tumor promoters in leukemia. Stroma released reactive oxygen species, and produced altered adhesion molecules and growth factors that could block apoptosis and induce DNA strand breaks in closely associated self-renewing stem cells (Greenberger et al. 1996a). Long-term bone marrow cultures were used in which irradiated bone marrow stroma actively contributes to leukemogenesis via growth factors, reactive oxygen and altered adhesion molecules that regulate the expansion of hematopoietic stem cells. The bone marrow stromal cell alterations of CBA/B mice irradiated with 200  cGy persisted 6 months after explant of the cells to culture (Greenberger et al. 1996b). Irradiated bone marrow stromal cell line D2XRII expresses persistently altered fibronectin splicing, increased expression of several transcriptional splice variants of macrophage-colony-stimulating factor, and increased TGFβ (Greenberger et al. 1996c). Morgan and colleagues showed that an immortal myogenic cell line formed tumors far more rapidly in irradiated compared to non-irradiated host muscle. The accelerated tumor phenotype was a direct effect of irradiation on the stroma, not systemic effects, because tumors did not form in distant muscle sites (Morgan et al. 2002). Interestingly, when transplanted to normal mice, these tumors formed large amounts of muscle. Likewise, Ohuchida and colleagues demonstrated that irradiated pancreatic fibroblasts mixed with pancreatic carcinoma cells formed more

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aggressive and invasive cancer than when the pancreatic cancer cells were mixed with non-irradiated pancreatic fibroblasts (Ohuchida et al. 2004). They further demonstrated that an antagonist of hepatocyte growth factor completely blocked the increased invasiveness of pancreatic cancer cells induced by co-culture with irradiated fibroblasts. Similar observations have also been made in the case of chemical carcinogenesis. Hodges and colleagues showed that co-culture of carcinogen-treated stroma with normal bladder epithelium produces neoplastic changes in epithelial morphology (Chang and Terzaghi-Howe 1998). Zarbl and colleagues found that Hras gene mutations in mammary tumors from rats treated with N-nitroso-N-methylurea (NMU) arose from cells with pre-existing Hras mutations that had occurred during early development (Zarbl et al. 1985; Cha et al. 1994). Thus, although NMU is clearly mutagenic in its own right, exposure apparently led to the expansion and neoplastic progression of pre-existing populations containing Hras mutations. A study from by Soto and colleagues showed that the stroma is a target of the NMU in the rat mammary gland (Maffini et al. 2004). Exposing the rat mammary stroma to NMU promoted tumorigenesis of the mammary epithelial cells that were not treated with the chemical carcinogen. In contrast, Medina and colleagues performed a similar experiment using 7,12-dimethylbenzanthrcene-treated mice and found that the treated mouse mammary stroma did not alter tumorigenesis by untreated preneoplastic mouse mammary outgrowth lines (Medina and Kittrell 2005).

15.4 Radiation Carcinogenesis in the Mammary Chimera Model In early studies to determine how radiation affected the mammary gland, we discovered evidence that TGFβ activation accompanies rapid and global remodeling of the stromal extracellular matrix (Terzaghi-Howe 1989). Consistent with increased biological activity, administration of TGFβ neutralizing antibodies block subsequent events in the irradiated mammary gland (Chang and Terzaghi-Howe 1998). We showed that the latent TGFβ is a sensor of reactive oxygen species (ROS), which are generated by IR action, since solution sources of ROS efficiently activate recombinant latent TGFβ (90). Notably, TGFβ activation persists preferentially in the fibrous and adipose stromal compartment for days to weeks following radiation exposure, and mediates several other signaling cascades (Fig. 15.2). Collagen remodeling is a prominent feature as revealed by induction of collagen type III in the adipose stroma and peri-epithelial stroma and collagen type I remodeling in the peri-epithelial stroma. Consistent with the immunoreactivity, production of new collagen fibrils, as quantified using photo-refringement of sirus red staining, is increased in the irradiated mouse mammary gland, which is indicative of disorganized collagen fibrils (Fig. 15.3). Surprisingly, tenascin, which is usually absent from adult mammary gland (91), was rapidly induced in the peri-epithelial stroma (Kadhim et al. 1994). Changes in fibroblasts and other cell types leading

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Plasminogen

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Fig. 15.2   Schematic of radiation-induced TGFβ activation and subsequent alterations in the mammary microenvironment. Ionizing radiation rapidly induces activation of latent TGFβ, probably via the production of reactive oxygen species. TGFβ in turn mediates remodeling of the extracellular matrix (ECM), including collagen type I and III and tenascin. Both plasminogen activator inhibitor (PAI-1) and fibroblast growth factor -2 (FGF2) are TGFβ dependent in irradiated tissue

to accumulation of stromal matrix proteins precede clinical fibrosis in heavily irradiated tissue. The irradiated microenvironment exhibits features of an ‘activated’ stroma, capable of further evolution that could modify the behavior and function of resident epithelial cells. This rapid remodeling of the mammary microenvironment led us to hypothesize that the irradiated stroma augmented breast cancer potential (Barcellos-Hoff 1993, 1998a, b). To test this hypothesis, we created a radiation chimera by transplanting unirradiated, preneoplastic mammary cells into the mammary glands of irradiated hosts (Barcellos-Hoff and Ravani 2000). The undeveloped mammary epithelium was surgically removed at puberty, the animal was irradiated, and some time later, non-irradiated mammary epithelial cells were transplanted into the irradiated host. These studies used COMMA-1D mammary epithelial cells, which undergo mammary morphogenesis when transplanted into a 3-wk old mammary gland. They are non-tumorigenic if injected into the cleared fat pads of 3-wk old mice, subcutaneously in immature and adult mice, or into nude mice. Although clonal in origin, COMMA-1D cells harbor two mutant Trp53 alleles that may confer neoplastic potential (Jerry et al. 1994). When transplanted into mice irradiated 1–14 d earlier with 4 Gy, outgrowths rapidly developed tumors, whose incidence peaked at 100% on day 3. The frequency was twice that of sham-irradiated mice even at 14 d post-irradiation. Furthermore, tumors from irradiated animals were nearly five times larger than the few tumors that arose in sham-irradiated hosts, indicating that tumor biology, as well as frequency, was affected. These data support the idea that

15  Radiation-Induced Microenvironments and Their Role in Carcinogenesis Collagen Fibrils 20

% Collagen

Fig. 15.3   ECM remodeling is induced by radiation. Active remodeling of the stromal ECM is evidenced by newly formed collagen fibrils detected by as bi-refringence by sirus red dye and polarized light. Compared to unirradiated tissue ( top) collagen fibrils are increased in the mammary stroma after whole body irradiation (5 Gy, bottom panel). Image analysis shows that this effect persists at least seven days (graph)

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high dose radiation promotes carcinogenesis by inducing a hospitable tissue environment. The effect of the irradiated microenvironment on neoplastic progression persisted for several weeks and appears to be independent of systemic radiation effects (as tested by hemi-body irradiation). This finding supports the hypothesis that non-mutagenic effects of radiation can contribute significantly to radiation carcinogenesis in vivo. If key signals that promote carcinogenesis in irradiated tissues are identified, then the irradiated microenvironment can be a therapeutic target to mitigate the long-term consequences of inadvertent radiation exposures. TGFβ is widely implicated in radiation responses. Terzaghi-Howe showed that TGFβ produced by the differentiated normal epithelial cells inhibited the growth and phenotype of radiation-transformed cells (Terzaghi-Howe 1986). Bauer described three distinct, but competing, roles for TGFβ during transformation (reviewed in (Häufel et  al. 1999)). TGFβ actually helps maintain the transformed state of mesenchymal cells, enables non-transformed neighbors to recognize transformed cells, and triggers an apoptosis-inducing signal. Bauer and colleagues recently showed that the latter two processes are enhanced following very low radiation doses (Portess et  al. 2007). TGFβ is implicated in tumor processes that affect angiogenesis (Ueki et al. 1992), reactive stroma (Iozzo and Cohen 1994; Mahara et al. 1994), and immunosuppression (Li et al. 1993; Hojo et al. 1999). We postulated a positive net role of the microenvironment via TGFβ activity induced by radiation in vivo and in vitro (Barcellos-Hoff and Brooks 2001). To test this prediction, we used mice and primary cultures to determine the effects of TGFβ on radiation-induced molecular events and cell fate decisions (Ewan et  al. 2002; Kirshner et al. 2006). Radiation-induced apoptosis is significantly reduced in Tgfβ1 heterozygote embryonic liver, skin, and adult mammary gland while Tgfβ1 null embryos fail to undergo either apoptosis or inhibition of the cell cycle in response to 5 Gy (Ewan et al. 2002). Either chronic TGFβ depletion by gene knockout or transient depletion by TGFβ neutralizing antibody, reduced phosphorylation of p53 serine 18 in the irradiated mammary gland (Ewan et al. 2002) because TGFβ is an essential regulator of the intrinsic response to DNA damage in epithelial cells (Kirshner et al. 2006). Irradiated primary epithelial cultures from Tgfβ1 null murine epithelial cells or nonmalignant human mammary epithelial cell lines in which TGFβ ligand or signaling was blocked exhibited 70% reduction of ATM kinase activation, failed to auto-phosphorylate, and neither growth arrested nor underwent apoptosis in response to radiation (Kirshner et al. 2006). Together, these data implicate TGFβ in the genotoxic stress program of epithelial tissues. Inability of the cell to properly repair DNA damage caused by radiation or other DNA damaging agents can lead to genomic instability and increased cancer frequency and progression (reviewed in (Khanna and Jackson 2001; Kastan and Bartek 2004). Notably, epithelial cells deficient for TGFβ show genomic instability (Glick et al. 1996), increased tumor progression (Glick et al. 1993), and are haploid insufficient for carcinogenesis (Tang et  al. 1998). Radiation-induced genomic instability occurs in clonally expanded, finite life span, normal human mammary epithelial cells (HMEC) as measured by aberrant karyotypes and supernumery centrosomes (Sudo et al. 2008). As expected from its role in the DNA damage response, TGFβ inhibi-

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tion also increases genomic instability in irradiated and control HMEC (Maxwell et  al. 2008). However, TGFβ treatment to genomically unstable HMEC actually reduces GIN, as was originally shown by Glick using PALA resistance in primary keratinocytes (Glick et al. 1996). Our studies in HMEC revealed that TGFβ selectively deleted genomically unstable cells via p53-dependent apoptosis, resulting in an overall increase in population stability. Hence, endogenous TGFβ suppresses radiation-induced and spontaneous genomic instability, and attenuation of TGFβ signaling permits survival of genomically unstable cells. The interaction between intrinsic radiation damage response and the extrinsic control via microenvironment determines the prevalence of unstable human cells (Maxwell et al. 2008) and transformed rodent cells (Terzaghi-Howe 1989; Portess et al. 2007). However when irradiated cells are cultured with additional TGFβ, as would be provided from the stroma, the cumulative effects appear to be detrimental. The progeny of irradiated HMEC embedded in reconstituted basement membrane undergo disrupted alveolar morphogenesis if exposed to TGFβ (Park et al. 2003). The underlying mechanism of disrupted morphogenesis by irradiated cells is TGFβ mediated epithelial to mesenchymal transition (EMT). EMT is the product of the intersection of the intrinsic response to IR, in this case activation of the MAP-K pathway, and chronic TGFβ signaling from the microenvironment (Andarawewa et al. 2007). Although radiation-induced TGFβ was demonstrable by media transfer, endogenous radiation-induced TGFβ was insufficient to drive EMT, which underscores the sources and duration of TGFβ activity in stroma as an important determinant of effect. Thus, while endogenous TGFβ primarily eliminates radiation-induced genomically unstable cells via apoptosis, exogenous chronic exposure promotes phenotypic instability. Is this a novel radiation response exhibited only in culture? Interestingly, urinary bladder carcinogenesis in humans exposed to longterm low-dose radiation exhibit significant increases of TGFβ1 and altered localization of E-cadherin/β-catenin complexes (Romanenko et al. 2006). Likewise Arteaga and colleagues showed that IR-induced TGFβ promotes metastatic breast cancer in vivo (Biswas et al. 2007). TGFβ promotion of carcinogenesis is often ascribed to its ability to drive phenotypic switching (Zavadil and Bottinger 2005; Han et al. 2005). Overexpression of constitutively active TGFβ can induce EMT during tumor progression in vivo (Portella et al. 1998) and the overexpression of TGFβ has been associated with poor prognosis of many human cancers (Bierie and Moses 2006). These data suggest that radiation-induced TGFβ in the tissue microenvironment may indeed be more important than its potential as an epithelial tumor suppressor. A specific test of this hypothesis is underway using the radiation chimera model.

15.5 Summary It is clear from the topics covered in this volume that the carcinoma associated stroma is a critical player in tumor growth, and the normal stroma is actually a barrier to malignancy. An important question arises: how does stroma convert from a gatekeeper to an active player in tumorigenesis? Our studies using radiation suggest

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that stroma may be activated by even a single exposure, putting the stroma into play much earlier than generally thought, and perhaps accelerating the carcinogenic process. Understanding the specific signals and mechanisms by which this occurs could provide new avenues to prevent cancer progression from developing into clinical disease. Acknowledgements  The authors wish to acknowledge funding from NASA Specialized Center for Research in Radiation Health Effects, the Low Dose Radiation Program of the Office of Biological and Environmental Research, United States Department of Energy DE AC03 76SF00098, and the Bay Area Breast Cancer and the Environment Research Center grant number U01 ES012801 from the National Institute of Environmental Health Sciences and the National Cancer Institute of the National Institutes of Health.

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Part V

Tumor-Modulating ECM Interactions

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Chapter 16

The Extracellular Matrix as a Multivalent Signaling Scaffold that Orchestrates Tissue Organization and Function Jamie L. Inman, Joni D. Mott and Mina J. Bissell

16.1 Introduction Tissues are composed of multiple cell types with distinct functions, arranged in a highly ordered fashion in a three dimensional and geometrical context. Tissue-specific functions are often lost or drastically altered when normal cells are explanted into culture conditions suggesting that the lowest functional unit in higher organisms is not a cell but a cell associated with a particular microenvironment (Bissell 1981; Bissell and Hall 1987). The observation that cells lose their tissue specific function when removed from their native environment suggests cells within a tissue are modulated by signals from their surrounding microenvironment, which includes other epithelial and stromal cells, as well as proteins in the extracellular matrix (ECM) and soluble factors, in order to maintain function (Bissell et al. 1982). The concept that microenvironment indeed is dominant over the genome within the same organism is not new and indeed should be intuitive. How else can 10 trillion cells with the same genetic information in all cells make the multitudes of tissues and organs with such differing forms and functions? However, we have needed to ask how the microenvironment and the genome collaborate to achieve this, what are the essential components of the microenvironment for any given tissue and what are the signaling pathways involved? It is also becoming increasingly clear that understanding how the microenvironment controls cellular function within a tissue has important clinical implications. In some human cancers, cells adjacent to the tumor appear to be normal but harbor some of the same genetic defects as the tumor cells (Deng et al. 1996; Forsti et al. 2001). One interpretation of this observation is that the microenvironment around these cells can restrain the genetically defective cells from progressing to a tumor or that more mutations are still necessary to allow these cells join the tumor mass. In three-dimensional culture models, we and others have shown that restoration of correct level of microenviornmental signaling can M. J. Bissell () Life Science Division, Lawrence Berkeley National Laboratory, One Cyclotron Rd, Berkeley, CA 94720, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_16, © Springer Science+Business Media B.V. 2011

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‘revert’ the malignant phenotype of breast tumor cells despite the complete retention of the malignant genome (Weaver et al. 1997; Wang et al. 2002; Muschler et al. 2002; Howlett et  al. 1994; Rizki et  al. 2008). Yet in other studies, the persistent presence of an activated matrix metalloproteinase (MMP), either expressed within the cells or added from the outside of the cells, can induce genetic instability and tumor progression, suggesting that disruption of the microenvironment via ECM remodeling enzymes leads to loss of tissue homeostasis and ultimately mammary tumor progression (Sternlicht et al. 1999; Radisky et al. 2005; Sympson et al. 1994; Lochter et al. 1997). The challenge for cell and tumor biologists is to decipher the regulatory signaling components underlying the ‘dynamic reciprocity’ between epithelial cells, tissue structure and the microenvironment and to understand how the microenvironment functions to orchestrate tissue homeostasis. The goal of this chapter is to underscore the importance of the ECM molecules and tissue architecture not only as scaffolds but also as crucial signaling entities for gene expression. As we have emphasized often, understanding the normal context of a tissue provides a solid foundation for studying and interpreting the pathological state.

16.2 The Microenvironment Influences Cellular Behavior Studies from a number of laboratories have provided important insight into the influence of the microenvironment on the developing glandular epithelium. Since most tumors arise from the epithelial component of tissues, investigating the mechanisms that determine the cell fate in developing tissues is pertinent to understanding the failure of a tissue to restrain tumor progression. As early as the 1960s and 1970s, chimeric models were constructed using stroma from one type of tissue cocultured with the epithelial cells from a different tissue. For example, when mammary epithelium was recombined with mammary mesenchyme, a typical mammary ductal pattern developed as would be expected. However, when mammary epithelium was recombined with salivary mesenchyme, the resulting epithelial structures resembled those of salivary gland and not the mammary gland, indicating that the mesenchyme provides instructive signals for pattern formation to the epithelium (Sakakura et  al. 1976). Even skin epithelium can be induced to form a structure that resembles a mammary ductal pattern when transplanted into mammary mesenchyme. Moreover, the skin epithelium transplanted into the mammary mesenchyme can be induced to express milk proteins such as casein and α-lactalbumin, again indicating that the morphology and functional differentiation of the transplanted epithelium is influenced by the surrounding mesenchyme (Parmar and Cunha 2004; Cunha et al. 1995). Other studies have provided dramatic evidence that even for malignant cells the microenvironment can regulate the chromatin structure and cellular differentiation. Long before Dolly (Wilmut et al. 1997), Mintz and Illmensee demonstrated that the embryonic microenvironment had the capacity to turn teratocarcinoma cells into

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apparently ‘normal’ tissues of healthy mice (Mintz and Illmensee 1975). Similar studies were conducted more recently using nuclear transplantation of melanoma cells (Hochedlinger et  al. 2004). In other studies, Bissell and colleagues showed that the embryonic microenvironment inhibited the tumorigenic properties of cells transformed by Rous sarcoma virus (RSV) (Dolberg and Bissell 1984). Although the avian embryonic tissue was refractory to transformation, when these cells were dissociated and placed in a culture dish, they became mass transformed overnight. Even in the adults, wounding or injury was necessary to complement the oncogene (Dolberg et al. 1985; Howlett et al. 1987; Martins-Green and Bissell 1990; Sieweke et al. 1990). Together, these studies suggest that developmental cues defining the cellular form and function within a tissue are derived from the surrounding microenvironment and the intact tissue architecture and are rarely cell intrinsic.

16.3 The Mammary Gland as a Model Organ for Investigation of ECM Regulation of Tissue-Specificity The mammary gland, a modified sweat gland, makes an excellent model to study microenvironmental influences on epithelial cells. The dynamic nature of the mammary gland is highly dependent on mesenchymal-epithelial interactions as well as cell-ECM interactions during development and throughout the life of the mammal. Although it is known that mouse and human mammary glands have some important structural differences, mouse models are used extensively to probe mechanisms of development and tumor progression. The mouse is a tractable model organism with well-developed tools and protocols that allow for deep and informative studies; thus, it is no surprise that much of our knowledge of mammary gland development, function and tumor progression has emerged from studies in the mouse. At birth female mice have a small branched epithelial rudiment that has already invaded but not extended into the fat pad. Continued ductal growth and expansion is mediated by complex stromal-epithelial interactions that guide the correct development of the gland at puberty when female hormones are released. In response to these hormones, the epithelial tree extends to fill the fat pad. In the resting nulliparious mammary gland, epithelial cells turn over cyclically every 4 days via proliferation and apoptosis driven by the estrus cycle (Fata et al. 2001). During pregnancy, the epithelial tree expands to completely fill the fat pad and achieves full differentiation upon lactation. An intriguing observation is that during normal development in the glands of virgin mammals and subsequent cycles of expansion during pregnancy, the epithelium forming the ductal tree does not violate the limit of the fat pad suggesting that there are inhibitory signals providing cues that define the boundary of the organ. The human breast is composed of alveoli clusters that are separated from fat tissue whereas the mouse mammary gland is an evenly spaced tree that is in close contact with the fat pad (Fig. 16.1). The mechanical, chemical, and developmental processes regulated by the stroma likely increase in complexity in the human breast compared to the mouse mammary gland. Both mouse and human mammary glands

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Mouse Mammary Gland

Fat Tissue

Interstitial stroma Periductal stroma Acinus Lobular stroma

Duct

Fig. 16.1   Comparison of the human breast and the mouse mammary gland. The human breast is contained in lobular stroma separated from fat tissue by interstitial stroma. The cross section of a duct and acinar structures are shown surrounded by lobular stroma. The epithelium of the mouse mammary gland is surrounded by periductal stroma embedded directly in fat tissue. (Figure adapted from Ronnov-Jenson et al. (1996) with permission)

are comprised of two types of epithelial cells, the luminal epithelial cells, which produce casein and other milk proteins, and the myoepithelial cells, which are a hybrid of smooth muscle cells and epithelial cells and function to contract and cause secretion of milk during lactation (Fig. 16.2a) (Adriance et al. 2005). Another important function of the myoepithelial cells is to act as ‘tumor suppressors’ for mammary epithelial cells (Sternlicht et al. 1997; Gudjonsson et al. 2002; Hu et al. 2008). The mechanism by which this occurs is not completely understood currently, but it is most likely through the mechanisms that allow tissue polarity. Myoepithelial cells have been shown to produce many ECM proteins and proteinase inhibitors. Indeed one of the most critical ECM proteins for polarity and functional differentiation is laminin-111, the α1 chain of which is only made by myoepithelial cells. The importance of laminin-111 is discussed in more detail below. A number of physiologically relevant organotypic models have been developed to study mammary gland morphogenesis and tumorigenesis in culture (Emerman and Pitelka 1977; Barcellos-Hoff et  al. 1989; Petersen et  al. 1992). The culture model where cells are placed in a three-dimensional (3D) laminin-rich extracellular matrix (lrECM) results in the formation of 3D mammary epithelial structures within an environment with similar elastic modulus (stiffness) as the mammary gland in vivo (Paszek et  al. 2005). Such culture models allow for a systematic approach to dissect and unravel the intricate cellular-ECM signaling that directs cell (and tissue) fate and function. Both primary mouse mammary epithelial cells and nonmalignant human breast epithelial cells form organized, growth arrested acinar-like

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Cross section of a TDLU (an acinus)

a

b Basement membrane Luminal epithelial cell Myoepithelial cell Cultured mammary epithelial cell lrECM

Fig. 16.2   In vivo acinus compared to in culture acinus. a The TDLU (Terminal Ductal Lubular Unit), an acinus, of the human breast is composed of two epithelial cell types: the luminal epithelial cells ( yellow) that make milk during lactation and the myoepithelial cells ( red) that function as contractile and tumor-suppressor cells. The epithelial compartment is encapsulated in basement membrane ( blue). b Mammary epithelial cell culture models frequently feature only one type of epithelial cell, a “cultured mammary epithelial cell.” In 3D lrECM a “cultured mammary epithelial cell” forms an acinus-like structures that growth arrest and establish apical and basal polarity similar to the TDLU of the breast

structures with correct apical-basal polarity when cultured in this 3D lrECM model (Fig. 16.2b). Even more dramatic is the finding that primary murine mammary cells can actually be induced to synthesize milk proteins such as β-casein de novo and secrete it apically into the lumen of the acinar structures formed in the in 3D cultures (Barcellos-Hoff et al. 1989; Streuli et al. 1991). Thus, unlike epithelial cells that have been plated on tissue culture plastic (referred as 2D cell culture), the cues provided by the lrECM simulate those received by the mammary epithelial cells in vivo and they allow the cells to form truly functional structures. The 3D lrECM culture system is a powerful tool for identification of mechanisms defining microenvironmental regulation of tissue homeostasis. In fact, culturing in 3D lrECM distinguishes non-malignant human breast cells from their malignant

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counterparts (Petersen et  al. 1992). Non-malignant human breast epithelial cells grown in a defined medium form growth arrested acinar-like structures with correct apical-basal polarity when cultured in 3D lrECM (Fig. 16.2b) (Weaver et al. 1997). Between days 5–7 of a 10 day culture, the cells begin to organize into colonies and withdraw from the cell cycle. Interestingly, withdrawal from the cell cycle occurs even in the presence of exogenous epidermal growth factor, suggesting that transition from a proliferating group of cells to a quiescent polarized colony occurs through the coordinated integration of cell-cell and cell-ECM signaling and that the formation of the acinar structure is not solely dependent simply on a down-modulation of cellular proliferation. Unlike non-malignant breast cells, human breast cancer cells form disorganized, non-polar colonies that do not growth arrest. However, restoring correct signaling cues allows the tumor cells to regain the ability to form growth arrested colonies with correct basal polarity that phenotypically resemble the non-malignant colonies, in a process that has been referred to as “phenotypic reversion” (Weaver et al. 1997; Wang et al. 1998). Reversion does not alter the genetic make-up of the cells, because re-plating of reverted colonies from the 3D lrECM in 2D culture and subsequent cultivation in 3D lrECM in the absence of a reverting agent does not result in the formation of growth arrested colonies. Rather the re-plated tumor cells form disorganized, non-polar colonies that continue to proliferate just as observed prior to treatment with a reverting agent (Weaver et  al. 1997). Finally, reversion is not simply down modulation of proliferation because tissue polarity and proliferation are separable events (Liu et al. 2004; Aranda et al. 2006; Lott et al. 2009). Together, these observations suggest that tumor cells, which clearly harbor many genetic defects and mutations, can be “retrained” by correcting deranged signaling in 3D to develop into a structure that behaves in a non-malignant manner. Conversely, non-malignant mammary cells that normally form quiescent acinar structures in 3D lrECM can be made to exhibit some of the same characteristics as malignant cells by overexpression of the same signaling molecules such as EGFR (Weaver et al. 1997), or by modulation of other factors that affect acinar morphogenesis (Lott et al. 2009; Furuta et al. 2005; Zhan et al. 2008; Pearson and Hunter 2009). Phenotypic reversion of breast cancer cells has been accomplished using a number of inhibitors targeting EGFR, MAP Kinase, and phosphoinositide 3-kinase (PI3K) in addition to β1 integrin inhibitory antibodies (Wang et al. 1998). Reversion need not occur only by down modulation of pathways: it could be accomplished also by restoration of correct cell-ECM interactions through introduction of lost cell surface molecules such as dystroglycan (Muschler et al. 2002; Weir et al. 2006), CEACAM (Kirshner et al. 2004) or syndecan (Burbach et al. 2004). In addition to providing an excellent model for investigating critical cellular components required for correct cell-ECM interactions, these culture models allow us to study events that would be difficult to study in vivo. Bioengineers have developed many tools to alter matrix stiffness and composition that are applicable to modeling the stiffer microenvironments found in tumors (Paszek et  al. 2005; Levental et  al. 2009). The wealth of tools developed for culture studies promise increasingly complex and faithful simulations of the actual mammary epithelial microenvironment (Nelson and Bissell 2005). On the other hand manipulation of

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mouse mammary glands in vivo to construct a “humanized” gland has allowed for growth of primary human breast tumors in the mouse mammary gland, a feat not easily achieved previously (Kuperwasser et  al. 2004; Wu et  al. 2009). The ability to simulate the complex microenvironment(s) found in the breast allows for investigation into the mechanisms that free initiated cells from the constraint of the microenvironmental signals that enforce normal behavior. Finally, the ability to make complex micropatterns and use these to test thousands of molecules at once in defining important factors that guide normal or malignant behavior, invasion or stem cell fate provide new “systems”-based models of analysis and hold great promise to identify the components that are important for normal tissue homeostasis and its disruption in tumors (Nelson and Bissell 2005; LaBarge et al. 2009; Nelson et al. 2006).

16.4 The Physical Nature of the Extracellular Matrix Influences Cellular Behavior The ECM is composed of a number of macromolecular proteins including collagens, laminins, fibronectin, heparan sulfate proteoglycans, and nidogen, which coalesce into intricate and specialized networks. During the early analysis of the ECM proteins, it was thought that these large insoluble protein networks were simply molecular scaffolds providing physical support for the maintenance of tissue form. Soon after, they were also considered as a reservoir for soluble signaling factors. We proposed early on that these molecules could signal via membrane receptors to the cytoskeleton and provide information to the nucleus for chromatin reorganization and changes in gene expression (Bissell et al. 1982). The nucleus in turn would signal back to the outside in a dynamic and reciprocal process. It is now well accepted that interaction of ECM molecules with cell surface receptors, such as integrins and dystroglycans, provides an ‘outside-in signaling’ to epithelial cells that governs function and differentiation via ‘inside out’ signaling (Legate et al. 2009). Furthermore, both biochemical and physical forces are involved in the dynamic reciprocity leading to integration of form and function. Early work using primary mammary epithelial cells showed that the cells plated on plastic or fixed collagen lost the ability to produce β-casein. However, if the collagen gel was detached or “floated” in the culture dish, the cells could again be induced to make β-casein (Emerman and Pitelka 1977; Emerman et al. 1977; Streuli and Bissell 1990). This simple experiment demonstrated that the physical nature of the substratum influenced gene transcription. Collagen and 3D lamininrich cultures have been used also as models for understanding the mechanisms used by mammary cells to invade the fat pad and to branch (Ewald et al. 2008; Fata et al. 2007; Simian et al. 2001). The physical nature of the ECM is an interesting topic for the cell biologist, but it also has significant clinical implications. The tissue near breast tumors has been reported to be “stiffer” compared to non-tumorigenic tissues (Paszek et al. 2005).

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Furthermore, several studies have suggested that women with high density breast tissue are at a greater risk for developing tumors than those who have low density breast tissue (Boyd et al. 2007, and references therein). Interestingly, fibronectin is nearly absent in the soft and pliable tissue of normal adult mammary tissues (Koukoulis et al. 1993). However increased fibronectin and β1 integrin (a cellular receptor for fibronectin) are correlated with poor survival of breast cancer patients (Yao et al. 2007) suggesting that perturbations and changes occurring in the ECM, which affect the cellular interaction with ECM components, reflect disease outcome. Dense breast tissue is known to provide a higher risk in developing breast cancer (Martin and Boyd 2008), and recent work has begun to reveal the biochemical and mechanical underpinnings of this correlation. Counteracting forces of tension and compression are balanced in tissues leading to mechanical stabilization. Dense breast tissue likely puts breast epithelium under larger compression and tension forces, a balance that is expected to be fundamentally different (in terms of dynamic reciprocity) from that seen in soft breast tissue. The additional observation of increased density at the site of a primary mammary tumor suggests a local shift in the balance of forces that likely contributes to mechanical destabilization and tumor progression. Wozniak et al. (2003) suggested that mammary epithelial cells sense the stiffness of their surroundings both in terms of morphogenesis and differentiation, a concept we showed to be the case in single cell studies (Alcaraz et al. 2008). Studies from the Weaver laboratory and others (Gehler et  al. 2009) have shown that increased ECM stiffness surrounding mammary epithelial cells is linked to intracellular cytoskeletal tension through integrins constituting an integrated mechanoregulatory circuit. A significant increase in ECM stiffness leads to disruption of the circuit, increased growth and decreased tissue organization (Paszek et al. 2005). During breast tumor progression one mechanism of increasing ECM stiffness is by collagen crosslinking. Studies on lysyl oxidase (LOX)-mediated collagen crosslinking during tumor progression show that increased ECM stiffness in a mouse mammary gland model of tumorigenesis significantly contributes to mammary tumor progression. By blocking LOX in this model, tumor progression was inhibited (Levental et al. 2009). A mechanoregulatory mechanism through integrin clustering and signaling was identified. Together, these studies demonstrate the importance of both mechanical and biochemical signals in maintaining the differentiated state.

16.5 The Basement Membrane Serves as a Multivalent Signaling Scaffold The basement membrane (BM) separates epithelial and endothelial cells from surrounding connective tissue and functions as a selective barrier and organizer of organs. Whereas the different BMs throughout the body often appear structurally similar by electron microscopy, their compositions are tissue-specific, suggesting that in different tissues particular BM components may be important for guiding tissue organization and function. Specificity is achieved by variation in protein subtypes and distinct spatial arrangements (LeBleu et  al. 2007). Many steps in pro-

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duction of BM proteins, transcription, splicing, post-translational modifications, epimerization, oxidation, and cross-linking, allow for heterogeneity (thus allowing specificity). The mammary gland is no exception (Villadsen et al. 2007), leading to the hypothesis that stem cell niches have their own distinct BM composition. The BM is comprised of two distinct layers, the basal lamina and the reticular lamina. The reticular lamina has been described as the sub-basement membrane zone as it connects the mesenchyme and the basal lamina. In the mammary gland the lamina reticularis is composed of many proteins including type IV, type V, type VI and type VII collagen, fibronectin, chrondroitin sulphate, laminin and heparan sulfate proteoglycans (Ferguson et al. 1992). With the exception of type VI and VII collagens, the relative amount of ECM proteins within the BM of the mammary gland appears to change during the menstrual cycle (Ferguson et al. 1992). This could be due to the expansion and contraction of the gland that occurs during the menstrual cycle or be part of the overall physiological regulation of the normal gland. The basal lamina is secreted by epithelial cells and forms a thin, 40–50 nm, sheet of proteins basolateral to the epithelia. The basal lamina is comprised of collagen IV, laminins, entactin, and heparan sulfate proteoglycans among other protein components. The basal lamina is uniquely positioned to function as an organizer and serve as a multivalent signaling scaffold capable of functioning on a tissue-wide level. Multivalency is defined as the cooperative engagement of several linked substrates by a species with more than one discrete interaction surface (Mammen et al. 1998; Ruthenburg et  al. 2007). Multivalency allows a dramatic enhancement of binding affinity, specificity and dynamics. Higher binding affinity is achieved by the additive or clustering effects of many weak interactions. Higher specificity is often the result of spatial arrangements of modifications dictating specificity. Biochemical studies support a multivalent model, but conclusive studies must be done in a tissue-specific manner as the interactions and dominant adhesion molecules appear to be different in different tissues (Weir et al. 2006; McKee et al. 2009) and likely at different stages in development (of a tissue or an organism).

16.6 Laminin-111 Provides the Signaling Interface of the Multivalent Scaffold to Guide Tissue Organization and Function in the Mammary Gland In the mammary gland, laminins are the essential component of the BM providing functional signaling to epithelial cells. From a structural point of view, the laminins have evolved exquisitely to participate as a multivalent signaling scaffold within the BM. The importance of laminin within the BM is supported by the observation that knockout or deletion of many of the laminin chains is lethal. There are 11 known laminin chains (5 α chains, 3 β chains, and 3 γ chains) that assemble into 16 distinct trimers identified in mouse and human. Three chains assemble to form a cruciform heterotrimeric protein structure composed of one α, one β, and one γ chain. The current nomenclature reflects the identity of the individual α, β, and γ chains (Aumailley et al. 2005). For example, laminin-111 is composed of laminin α1, β1, and γ1.

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During the genesis of a BM, laminin-111 must polymerize, i.e. assemble into a scaffold, prior to incorporation of other components of the basement membrane such as the collagen IV network. Recently, the mechanisms of laminin polymerization and domain requirements were defined (McKee et al. 2007, 2009). Laminin-111 is over 800 kD in size and its’ expansive reach allows it to interact with several cell surfacelinked molecules including integrin heterodimers, dystroglycan, syndecan-1, and sulfatides (Fig. 16.3) (Miner and Yurchenco 2004). Recent work has demonstrated

Fig. 16.3   Diagram of the laminin-111 heterotrimer and some of its interacting partners. The N-terminal LN domains of each of the chains of laminin-111 allow laminin-111 to polymerize through heterotypic LN-LN-LN interactions (α1-γ1-β1). The LG modules at the C-terminus of the α1chain interact with cell surface molecules such as α6β1 and α6β4 integrins, dystroglycan (αDG and βDG), heparin binding-site anchor (HBSA), sulfatides and syndecans. In addition to its role in polymerization, the LN domain of the α1 chain binds α1β1 and α2β1 integrins as well as heparin. Nidogen (Nd)/entactin binds the arm of the γ1 chain as indicated and anchors the laminin polymer to the type IV collagen network. Overall, integrins mediate a substantial portion of the ‘outside-in signaling’ via anchoring the laminin polymer to the intercellular cytoskeletal networks. This figure depicts one way α6β1integirn can connect the laminin matrix to the actin cytoskeleton via α- and β- parvins bound to integrin linked kinase (ILK) and other adaptor protiens. (Figure reproduced from (Li et al. 2003) with permission)

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alpha and beta dystroglycan Integrin heterodimer Laminin heterotrimer nidogen/entactin Type IV Collagen network perlecan

Fig. 16.4   Assembly of basement membrane proteins in the mammary gland. Cell surface receptors integrins ( blue) and dystroglycan ( yellow) bind the LG modules of the α1chain of laminin-111 and anchor it to the surface allowing polymerization of laminin-111 ( left surface). This results in spatial organization of the receptors at the surface of the tissue. As the BM assembles this spatial organization is reinforced ( right surface). Type IV collagen ( red) forms a network that is physically linked to the laminin polymer by nidogen ( black). Perlecan ( orange) also interacts with laminin and type IV collagen as well as other BM components. The iterative organization presumably organizes the intracellular cytoskeletal network of the tissue as a whole

a novel inside-out pathway for the assembly of cell surface laminin via one of the laminin receptors, dystroglycan. PAR-1b was shown to co-localize with the dystroglycan complex in epithelial cells and play an important functional role in cellular polarity by directing dystroglycan to the basal surface of the cell (Masuda-Hirata et  al. 2009) where it then participates in the assembly of laminin-111 at the cell surface (Fig. 16.4) (Weir et al. 2006). Work from several laboratories has shown that laminin-111 is one of the most important components within the BM for establishment of basal polarity in epithelial cells (Gudjonsson et al. 2002; Weir et al. 2006; O’Brien et al. 2001; Yu et al. 2005; Xu et al. 2009; San Miguel et al. 2008; Schuger et al. 1998), although collagen IV has been shown recently to be also important for helping establish apical polarity of mammary acini (Plachot et al. 2009). Work from the Bissell laboratory has demonstrated that laminin-111 is required for induction of mammary specific gene expression (Gudjonsson et al. 2002; Barcellos-Hoff et al. 1989; Xu et al. 2009; Myers et al. 1998). Much of the work on laminin-dependent cellular polarity and function has been performed in cell culture systems, yet the same observations hold true in vivo

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(Sympson et al. 1994). The myoepithelial cells which were suggested to function as natural tumor suppressors in the human breast (Gudjonsson et al. 2002; Sternlicht and Barsky 1997) appear to be crucial for the formation of polarity since they synthesize the α1 chain of laminin-111. Furthermore, the loss of myoepithelial function has been associated with breast cancer. The mechanism underlying how myoepithelial cells may function as tumor suppressors is only now being uncovered. Even in circumstances where myoepithelial cells retain their differentiated state, if the cells have lost the capacity to synthesize laminin-111, they lose their tumor suppressor capacity (Gudjonsson et al. 2002; Slade et al. 1999). Thus, normal myoepithelial cells maintain correct polarity and function of luminal epithelial cells through their ability to synthesize a BM-containing laminin-111. In summary, the BM is the specific structural and functional regulator of epithelial tissues through the ability to present a multivalent scaffold in which epithelial cells can interact and gain functional cues. Laminin-111 represents the conductor of this orchestration of tissue form and function.

16.7 Perturbation of BM Disrupts Tissue Homeostasis The integrity of the BM is essential for correct signaling to epithelial cells. As discussed above, laminin is the component of the BM that is most critical for mammary epithelium. Cytokines and proteinases embedded within the BM or secreted by stromal fibroblasts are needed for normal tissue remodeling of both the ECM in general and the BM in particular during growth, development or wound healing. However, synthesis and/or activation of these cytokines and proteinases under inappropriate circumstances can lead to scenarios that mimic developmental or wounding programs. Improper resolution or persistence of these programs allows for the establishment of a microenvironment permissive for tumor progression and disease. Degradation of ECM proteins, such as laminins and collagens have the potential to physically change the integrity of the ECM and disrupt the multivalent scaffolding needed to provide signals to epithelial cells for maintenance of tissue homeostasis. Furthermore, MMPs can release and/or activate growth factors and cytokines embedded in the ECM and reveal cryptic peptides from ECM components, which can influence cellular behavior (Mott and Werb 2004). MMPs have been shown to cleave cell surface molecules such as E-cadherin (Lochter et al. 1997; Noe et al. 2001), CD44 (Nakamura et al. 2004) and other cell surface molecules such as dystroglycan (Yamada et al. 2001), which can disrupt important microenvironmental interactions required for maintaining normal tissue integrity.

16.8 Concluding Remarks Many studies have shown that context is crucial and that the status of the cellular microenvironment plays a significant role in whether cells within a tissue retain their normal architecture or undergo tumor progression. BM components in par-

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ticular laminin-111, are essential players in the role of context in the mammary gland. Understanding how reciprocal signaling between the cells and their microenvironments are regulated is crucial to understanding many organ-specific diseases including cancer.

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Chapter 17

SPARC and the Tumor Microenvironment Stacey L. Thomas and Sandra A. Rempel

Abbreviations AML BAE bFGF CAM assay cDNA CHS CN CRAd EC ECM EGF-like EMT F FAK FN HA HSP27 IL-6 ILK ip iv L LN LPA LPS

acute myeloid leukemia bovine aortic endothelial basic fibroblast growth factor chorioallantoic membrane assay complementary DNA cutaneous contact hypersensitivity collagen conditionally replicative oncolytic adenovirus extracellular extracellular matrix epidermal growth factor-like epithelial-mesenchymal transition fibroblasts focal adhesion kinase fibronectin hyaluronic acid heat shock protein 27 interleukin-6 integrin-linked kinase intraperitoneal intravenous leukocytes laminin lysophosphatidic acid lipopolysaccharide

S. A. Rempel () Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Hospital, Detroit, MI 48202, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_17, © Springer Science+Business Media B.V. 2011

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M MCP-1 mets MLL gene MMP(s) Mo mRNA MT1-MMP MVD N NC NF-kappaB P p38 MAPK PDGF PGE2 PMN(s) [R] sc shRNA siRNA SPARC TGF-beta THR TIMP TRAMP U uPA uPAR VN VEGF wt

S. L. Thomas and S. A. Rempel

methylated monocyte chemoattractant protein metastases mixed lineage leukemia gene matrix metalloproteinase(s) macrophages messenger RNA membrane type 1- matrix metalloproteinase microvascular density neutrophils no change nuclear factor kappaB pericytes p38 mitogen activated protein kinase platelet-derived growth factor prostaglandin E2 polymorphonuclear leukocyte(s) VEGF receptor subcutaneous short hairpin RNA short interfering RNA secreted protein acidic and rich in cysteine transforming growth factor-beta T/tAg, hTERT, H-rasV12G tissue inhibitor of metalloproteinase transgenic adenocarcinoma of mouse prostate unmethylated urokinase plasminogen activator uPA receptor vitronectin vascular endothelial growth factor wild-type

17.1 Introduction Secreted Protein Acidic and Rich in Cysteine (SPARC) (Sage et  al. 1984), is a 303 amino acid glycoprotein, approximately 35  kDa, also known as osteonectin (Termine et al. 1981) and BM-40 (Mann et al. 1987). It is a matricellular protein that is secreted into the extracellular matrix (ECM) where it functions, in part, to regulate levels of cell adhesion and cell migration, as well as to regulate cell proliferation, survival, and angiogenesis (Bornstein and Sage 2002; Brekken and Sage 2001). These complex functions are important for normal development and for physiological processes such as tissue remodeling during wound healing (Bornstein

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a

SPARC Acidic 1-52

Follistatin 53-137 KHGK

N

EF hand 1

EF hand 2

C

Kazal family domain

EGF-like domain

b

Extracellular Ca++-binding 138-286

KGHK

Effects of SPARC on Tumor Cells Breast cancer Melanoma Prostate cancer Glioma Lewis lung carcinoma Pancreatic cancer Ovarian cancer AML

c Breast cancer T-cell lymphoma Prostate cancer Lewis lung carcinoma Colon cancer Pancreatic cancer Ovarian cancer Squamous cell carcinoma

Proliferation – – – – – –/ NC – –∗

Apoptosis

Adhesion – +

Migration

–

+/– ∗∗

+

+

–

+

Invasion – + – + –

Effects of Stromal SPARC on Tumor Cells Tumor Growth Proliferation + – – – – NC –∗ NC – – – +

Apoptosis Invasion/Mets + – – NC – – NC – + –

ECM + + + + + +

Angiogenesis Immune Cells + –L – +

+ Mo

+ –

+ N & Mo – Mo

Fig. 17.1   SPARC effects on tumor cells. a SPARC protein and its three domains are illustrated. b Effect of forced or exogenous SPARC on tumor cells. *observed only in cell lines with rearranged MLL. **ECM-dependent. c Effects of stromal SPARC on tumor cells. *decreased number of tumors, not size. L leukocytes, Mo macrophages, N neutrophils. (–) decreases, (+) increases, (+/–) biphasic effects, NC no change, blank space not done

and Sage 2002; Brekken and Sage 2001), and are likely regulated by the different domains within the protein. The protein is composed of a signaling peptide that is removed during processing for secretion into the ECM. The resultant mature protein has three domains, including the N-terminus acidic domain, followed by the follistatin-like domain, and the C-terminus EC (extracellular)-Ca++-binding domain (Fig.  17.1). The N- and Ctermini of the follistatin-like domain have a sub-domain with sequence homology to the epidermal growth factor (EGF)-like domain of Factor IX and a sub-domain similar to the Kazal family of serine proteases, respectively (Fig. 17.1). Although secreted into the ECM, SPARC has also been reported to be in the nucleus, and may function there (Hecht and Sage 2006; Huynh et  al. 2000; Yan et al. 2005). Furthermore, it is possible that the disparate functions of SPARC may be executed not only as a whole protein, but also by its proteolytic cleavage and the resultant biologically active fragments. Studies using proteolytic fragments or synthesized peptides have begun to unravel the role of each domain (Fig. 17.2). For the most part, however, its function as an exogenously added or secreted whole protein has been most intensely investigated. In the ECM, it functions as a counter-adhesive protein, in part, through the manipulation of integrin-ECM interactions (Barker et al. 2005), which in turn can influence growth factor-induced signaling cascades. Its function, therefore, is influenced

304

S. L. Thomas and S. A. Rempel SPARC

a Acidic 1-52

Follistatin 53-137 KHGK KGHK

Extracellular Ca++-binding 138-286 EF hand 1 EF hand 2

N

C Z1 50-85 Z2 149-198 Z3 220-239

FS-E 55-76 FS-K 84-106 EC-N 146-166

Effect of SPARC on Endothelial Cells

b

in vitro Proliferation Whole Z1 Z2 Z3 FS-E FS-K EC-N

Apoptosis

– +/–

in vivo

Adhesion

Migration

–

– NC

– –

Angiogenesis +/–

NC NC

+ + +/– –∗

NC NC –

NC

NC

Weak

Proposed Mechanisms of Action

c Tumor cells

Endothelial cells Paracrine-induced angiogenesis

VEGF VEGFR

No SPARC

VEGF VEGFR VEGF

VEGF

Autocrine-induced proliferation

SPARC inhibition of VEGF infduced tumor growth and angiogenesis

SPARC inhibition of VEGF-VEGFR interaction

SPARC Receptor

VEGF SPARC

SPARC Cleavage Stimulates Angiogenesis

VEGFR

SPARC VEGF SPARC

SPARC

Fig. 17.2   SPARC effects on endothelial cells and angiogenesis in vitro and in vivo. a SPARC protein and its three domains are illustrated. Fragments Z1, Z2, and Z3 (Sage et al. 2003) and FS-E, FS-K, and EC-N (Chlenski et al. 2004) are indicated. b Effects of SPARC on endothelial cells in vitro and in vivo using the chick chorioallantoic membrane assay (Z1, Z2, Z3) or xenograft in nude mice (FS-E, FS-K, EC-N). (*) at high concentration, (−) decreases, (+) increases, (+/−) biphasic effects, NC no change, blank space not done. c Proposed mechanisms of SPARC in angiogenesis. For SPARC-negative tumor cells: VEGF expressed and secreted by the cancer cells binds to its receptor on endothelial cells to induce proliferation. For SPARC-positive cancer cells: SPARC expressed and secreted by the tumor cells binds to a cancer cell surface receptor inhibiting VEGF expression and thereby suppresses endothelial cell proliferation. SPARC secreted binds VEGF and inhibits VEGF/VEGF receptor binding and thereby prevents endothelial cell proliferation. Secreted SPARC is cleaved and angiogenic peptides stimulate angiogenesis

17  SPARC and the Tumor Microenvironment

305

by the integrin expression profile of the cells, the ECM present in the microenvironment, and the growth factor-growth factor receptor status. As a consequence, its role might differ between tissues or even from location to location within a tissue, depending on the microenvironment. That SPARC might function differently in different cell types or in different microenvironments is an important consideration when it comes to deciphering its role in cancer. Two major categories have been delineated: cancers in which the tumor cells are SPARC-positive and cancers in which the tumor cells are SPARC-negative (Table  17.1). Although some reports are conflicting, it is generally accepted that SPARC is overexpressed and considered to be an oncogene in breast cancer, melanoma, prostate cancer, gastric carcinoma, meningioma, glioma, and head and neck cancer. In contrast, it is underexpressed and considered to be a tumor suppressor in renal, esophageal, lung, hepatocellular, uterine, colorectal, pancreatic and ovarian cancers, acute myelogenous leukemia (AML) and neuroblastoma. As a result, it is considered a therapeutic target for tumor cells overexpressing SPARC, but a therapeutic agent for tumor cells underexpressing SPARC. However, such categorizations are likely too simple. Interestingly, in esophageal cancer, tumor cells have been reported to be SPARC-positive (Yamashita et al. 2003) and SPARC-negative (Xue et  al. 2006). However, these studies used different SPARC antibodies, one that recognizes the N-terminus (Yamashita et al. 2003) and one that recognizes the C-terminus (Xue et al. 2006) of SPARC. As discussed above, SPARC can be proteolytically cleaved, and therefore, while the two studies may appear to be conflicting, they may actually be detecting differences in whole or cleavage products. As a result, esophageal cancer may need to be recategorized as a SPARC-positive cancer. This also indicates that some caution is needed in categorizing the tumors based on the profiles of one antibody. For example, immunostaining of ovarian cancer tissues with two different anti-SPARC antibodies showed distinctive SPARC immunostaining patterns (Yiu et al. 2001). That SPARC expression is decreased in many cancer types has been addressed by examining the regulation of SPARC expression in these cancers. The SPARC promoter has been found to be hypermethylated in pancreatic cancer cell lines and xenograft tumors (Sato et al. 2003), primary colorectal cancer specimens and cell lines (Cheetham et  al. 2008), ovarian cancer cell lines (Socha et  al. 2009), lung adenocarcinomas and cell lines (Suzuki et al. 2005), and multiple myeloma patient samples and cell lines (Heller et al. 2008). In many of these cancer types, treatment of cell lines with a demethylating agent restored SPARC expression (Cheetham et  al. 2008; Sato et  al. 2003; Socha et  al. 2009; Suzuki et  al. 2005). These are compelling data because the methylation status has been confirmed in the patients’ samples of some of the cancers as indicated, but again, caution is warranted as promoter methylation was found in mixed lineage leukemia (MLL) cell lines having no SPARC expression, but was not observed, and therefore was not the cause of the loss of SPARC in patient samples (DiMartino et al. 2006). While much characterization of SPARC expression initially focused on the tumor cells themselves, cDNA microarray analyses (Table 17.1) indicate that SPARC is overexpressed in at least a subset of all cancer types examined to date. Further-

Table 17.1   SPARC expression and localization in human tumors and tumor microenvironment, as assessed by cDNA and tissue arrays, and immunohistochemistry, and correlation with survival Cancer type References Normal tissue Tumor tissue Tumor-associated Immunohistochemical or expression expression stromal and endo- cDNA and tissue microarray correlation with grade thelial cells or survival SPARC-positive tumor cells Node-positive > Breast cancer (Barth et al. 2005; Beck et al. 2008b; High in mammary Increased SPARC/poor Upregulated in: node negative; survival Stromal myoepithelial cells, Bergamaschi et al. 2008a; Jones b grades 1 and 3 fibroblasts, low level in interet al. 2004; Porter et al. 2003a; High SPARC/better > grade 2; ducECM and intra-lobular Sarrio et al. 2008b; Watkins et al. survival tal > lobular stromal fibroblasts, Increased with EMT-asso2005) present in arteriole aTumor cells are ciated genes and capillary endonegative thelial cells b Melanoma (Alonso et al. 2007b; Ledda et al. High SPARC correlates Capillary endothelium Highly upregulated Upregulated in: Fibroblasts 1997a; Wessel et al. 2008a) with metastases and in malignant Endothelial cells EMT genes melanoma a - Negative endo, stroma a Upregulated in: Prostate cancer (Lapointe et al. 2004a; Thomas et al. Luminal and basal Primary tumor High SPARC correlates Fibromuscular cells 2000) epithelial cells, with increased grade stromal cells low-moderate; fibromuscular and metastasis Mets highly stromal cells positive Gastric carcinoma (Junnila et al. 2009; Takeno et al. Tumor cells are Negative-weak Increased SPARC/ 2008; Wang et al. 2004) positive decreased survival a Meningioma (Bozkurt et al. 2009; Rempel et al. Absent in normal Tumor cells are Upregulated in: Increased SPARC/ 1999a; Schittenhelm et al. 2006b; cerebral cortex positive Reactive decreased survival a astrocytes Zeltner et al. 2007) Increased SPARC/ increased recurrence b No association with grade

306 S. L. Thomas and S. A. Rempel

(Che et al. 2006; Luo et al. 2004; Wong et al. 2009; Xue et al. 2006b; Yamashita et al. 2003a)

(Koukourakis et al. 2003; Sosa et al. 2007)

Esophageal cancer

Lung cancer

Upregulated in all grades; Heterogeneous expression in higher grades Positive

Tumor tissue expression

Negative in clear cell renal carcinoma Positive in sarcomatoid renal carcinomas a Negative Positive b Negative tumor cells (positive in a few cases) Majority negative Weak SPARC in to weak bronchial epithelial cells, negative for alveolar cells, positive in alveolar Mo,+ chrondrocytes

Negative

(Chin et al. 2005; Kato et al. 2005a)

Head and Neck cancer

SPARC-negative tumor cells Renal Cell carcinoma (Amatschek et al. 2004; Gieseg et al. 2002; Sakai et al. 2001)

Absent in normal cerebral cortex

(Pen et al. 2007; Rempel et al. 1998; Rich et al. 2005)

Normal tissue expression

Glioma

Table 17.1  (continued) Cancer type References

Upregulated in: Stromal cells Endothelial cells

Upregulated in: Stromal cells

Variably positive: Clear cell stroma Highly positive: Sarcomatoid stroma Increased SPARC/ decreased survival Increased SPARC/ increased tumor mets Increased SPARC/ decreased survival

Increased SPARC/ decreased survival (stage II tongue cancer patients)

Increased SPARC, doublecortex, and Semaphorin 3B/ decreased survival

Upregulated in: Endothelial cells Pericytes Reactive astrocytes Upregulated in: Stroma a

Immunohistochemical or cDNA and tissue microarray correlation with grade or survival

Tumor-associated stromal and endothelial cells

17  SPARC and the Tumor Microenvironment 307

(Brown et al. 1999b; Mok et al. Positive- Thecal cell layer, 1996a; Paley et al. 2000; Yiu et al. granulosa cells, 2001a) Negative- stroma Positivea/Negativebsurface epithelial cells

Ovarian cancer

Ductal epithelial cells

(Brune et al. 2008; Hong et al. 2008; Infante et al. 2007; Sato et al. 2003)

Pancreatic cancer

Epithelial cells

(Chan et al. 2008; Wiese et al. 2007; Yang et al. 2007)

Strong cleaved 24 kD cleaved protein in hepatocytes

Normal tissue expression

Colon carcinoma

Hepatocellular cancer (Lau et al. 2006; Le Bail et al. 1999)

Table 17.1  (continued) Cancer type References

24 kD Upregulated in: Stromal fibrous area and sinusoidal regions Upregulated in: Stroma cells

Negative

27.7% negative, 56.5% weak, 15.8% positive 66%- positive 69% -negative stoma, 34% 31% -positive -negative Positive in low and intermediate grade intraductal papillary mucinous neoplasms Negative in high grade Negative in infiltrating tumor cells Low-Mostly absent Upregulated in: Stromal cells, matrix Endothelial cells

Tumor-associated stromal and endothelial cells

Tumor tissue expression

Decreased with increasing grade

Survival: Tumor-/stroma- > tumor+/stroma- > tumor-/stroma+> tumor +/stroma+

Increased SPARC/ Increased survival

Immunohistochemical or cDNA and tissue microarray correlation with grade or survival Stromal/sinusoidal staining increases with increasing grade

308 S. L. Thomas and S. A. Rempel

(Yamanaka et al. 2001)

(Dalla-Torre et al. 2006)

Bladder cancer

Osteosarcoma

Negative

Normal tissue expression

Identifies the references related to the indicated observations

(Rodriguez-Jimenez et al. 2007) (Chlenski et al. 2002)

Uterine cancer Neuroblastoma

a,b

(DiMartino et al. 2006)

AML

Table 17.1  (continued) Cancer type References

Decreased in cell lines with MLL rearrangements Negative Negative to minimal in undifferentiated tumorsNegative in differentiating tumors

Tumor tissue expression

Increased SPARC/ decreased survival

Upregulated in: Stromal cells

Increases with grade; Increased SPARC/ decreased survival High SPARC in almost all tumors; Increased SPARC/ decreased event-free and relapse-free survival

Negative in undif- Decreased SPARC/ Increased grade ferentiated tumors Positive in differentiating tumors

Immunohistochemical or cDNA and tissue microarray correlation with grade or survival

Tumor-associated stromal and endothelial cells

17  SPARC and the Tumor Microenvironment 309

310

S. L. Thomas and S. A. Rempel

more, immunohistochemical analyses of SPARC in tumor tissue sections and in xenograft specimens from animal studies, especially in Sparc-null and Sparc-wt mice, indicate that SPARC is expressed in non-tumor cells, which can also influence tumor progression. Correlations of SPARC expression patterns in stroma and/ or tumor with patient survival suggest that the outcome may be different based on where SPARC is expressed. For example, pancreatic cancer patient survival is greatest when tumors are negative for SPARC in both tumor and stromal compartments and the poorest when tumors are positive for SPARC in both compartments (Infante et al. 2007) (Table 17.1). Indeed, even when tumor cells are negative, the positive expression in stroma correlates with poorer survival (Infante et al. 2007). Therefore, the role of SPARC in tumor progression must be expanded to include its functions in both stromal and tumor compartments, and a determination of how these functions influence tumor progression is essential. The treatment approach will need to be tailored by the particular expression pattern in both the stroma and tumor (not just by the oncogene or tumor suppressor role in the tumor cells), and this further emphasizes the need for individualized tumor assessment to ascertain the best treatment strategy. In summary, the array and immunohistochemical data along with the correlative survival analyses suggest that SPARC functionally contributes to tumor progression. In this chapter we will review the in vivo and in vitro data that assess the expression of tumor and stromal SPARC and the influences of that expression on tumor progression.

17.2 Role of SPARC in the Tumor Microenvironment Determining the role of SPARC in the tumor microenvironment is complicated by the fact that SPARC is produced by multiple cell types and has multifaceted effects that regulate several processes involved in tumor formation and progression. SPARC can be produced and secreted by cancer cells, stromal cells, and immune cells resulting in autocrine and paracrine effects on the tumor microenvironment. The ability of SPARC to cause deadhesion of cells from the ECM results in tumor and stromal cell migration and dissemination. SPARC can also inhibit proliferation in cancer cells, fibroblasts, and endothelial cells by direct effects on the cells or indirectly by binding to various growth factors and inhibiting the ability of the growth factors to activate tyrosine kinase receptors. In addition, SPARC can regulate ECM deposition, assembly, and remodeling by regulating collagen processing and altering the secretion of matrix proteins and matrix degrading proteases from cancer cells and stromal cells. SPARC can also regulate the tumor microenvironment by suppressing immune cell infiltration into the tumor and the ability of the immune system to eliminate tumor cells. The evidence for each of these functions of SPARC in the regulation of the tumor microenvironment will be discussed in detail in the following sections.

17  SPARC and the Tumor Microenvironment

311

17.2.1  Effects on Cancer Cells There are many studies that have examined the role of SPARC in various types of cancers. The following sections will discuss the roles for SPARC in regulating cancer cell proliferation, cell cycle progression, survival and apoptosis, adhesion and migration, invasion and metastasis, ECM production, and signal transduction. 17.2.1.1 Tumor Growth in Animal Models In vivo xenograft models have been used to assess the effects of forced or suppressed SPARC expression on human cancer cell growth in immune compromised rodents (Table 17.2). For example, SPARC expression was forced in the U87 glioma cell line and clones secreting various amounts of SPARC were selected and implanted intracranially in nude rats. All SPARC-expressing clones had significantly reduced tumor volumes at day 7 compared to the parental glioma cell line (Schultz et al. 2002). When the high SPARC-secreting clone and the parental control cells were allowed to grow until the animals showed signs of neurological deficit, the animals with the SPARC-expressing tumors lived longer, 20 days compared to 9 days for the parental cell line (Schultz et  al. 2002). In contrast, SPARC promoted tumor growth when a genetically engineered glioma cell line was injected subcutaneously (Rich et al. 2003). It is not known whether the difference in the proliferation effects reflects differences in the microenvironment (subcutaneous [sc] versus the brain) or the differences in cell lines used; one is derived from a glioma patient (U87) and the other from a human astrocyte genetically altered to express the T/tAg, hTERT, and H-rasV12G (THR) genes (Table 17.2). When the SKOV3 ovarian cancer cell line or the HEP3B hepatocellular carcinoma cell line with forced SPARC expression were injected subcutaneously in nude mice, SPARC significantly reduced tumor growth (Lau et al. 2006; Mok et al. 1996) (Table 17.2). In contrast, a group that suppressed SPARC in melanoma cell lines found the opposite effect. SPARC antisense strongly reduced tumor growth (Ledda et  al. 1997b; Prada et  al. 2007), with a complete inhibition of tumor formation resulting from tumor cell rejection in one study (Ledda et al. 1997b) (Table 17.2). These melanoma studies are interesting because the data suggest that the loss of tumor-expressing SPARC altered the microenvironment structurally in such a way as to permit neutrophil access, resulting in tumor regression. Therefore, in addition to having effects on tumor growth, SPARC might play a role in suppressing the immune system (see Sect. 17.2.4). As a consequence, several in vivo studies have examined the role of tumor and stromal SPARC on tumor growth by comparing cancer cells grown in immune competent Sparc-null versus Sparc-wt mice (Table 17.3). The results of these studies depend on the type of cancer cell used, with ovarian, prostate, pancreatic, lung, and T cell lymphoma showing increased cancer growth and progression in Sparc-null animals, whereas intestinal, mammary, and skin cancer cells have decreased growth and progression in Sparc-null animals.

U87 (Kunigal et al. 2006) SNB19 (Kunigal et al. 2006) U251MG (Seno et al. 2009) Ovarian SKOV3 (Mok et al. 1996) Hepatocellular HEP3B (Lau et al. 2006) Melanoma IIB-MEL-LES (Ledda et al. 1997b) IIB-LES-IAN (Ledda et al. 1997b) IIB-MEL-J (Prada et al. 2007)

Glioma U87 (Schultz et al. 2002) U87 (Yunker et al. 2008) THR (Rich et al. 2003) THR (Rich et al. 2003)

Forced

Suppressed sc

Suppressed sc

Forced

Positive

Positive

Positive

sc

sc

sc

?

Positive

Forced

Forced

Very low

Brain sc

Negative

Forced Forced

Very low Very low

Brain

Very low

Forced

Very low

Brain

Dorsal skin fold Forced Dorsal skin fold Suppressed Brain

Forced

Very low

Table 17.2   Animal models Nude/SCID rodents SPARC Status Xenograft Cancer type/ EndogManipulated site enous Cell lines Invasion

No effect

Suppressed

Suppressed

Decreased

Decreased

Increased

Decreased

Increased Increased mets

Decreased Increased (decreased)

Tumor Growth (proliferation)

Increased CNI

Increased CNI

ECM

Increased

Decreased Decreased

Increased

Increased

Increased

Decreased Decreased

Angiogen- MVD esis (VEGF)

survival

cells

Increased N Increased N Increased No effect F

Increased

Animal

Stromal

312 S. L. Thomas and S. A. Rempel

Suppressed sc

Forced

Positive

Negative

Intracardiac

Suppressed sc

Positive

sc

Forced

Positive

Decreased

Decreased

No effect

Tumor Growth (proliferation)

Decreased mets

Invasion Increased CNI

ECM Increased

Angiogen- MVD esis (VEGF)

survival

cells

Increased No effect F Increased Increased N Increased Increased N

Animal

Stromal

THR [T/t-Ag, hTERT, H-rasV12G], ?- unknown, sc subcutaneous injection, mets metastases, CN collagen ECM extracellular matrix, MVD microvascular density, VEGF vascular endothelial growth factor, N neutrophils, F fibroblasts

A375N (Prada et al. 2007) IIB-MEL-J (Prada et al. 2007) A375N (Prada et al. 2007) Breast cancer MDA-231 (Koblinski et al. 2005)

Table 17.2  (continued) Nude/SCID rodents SPARC Status Xenograft Cancer type/ EndogManipulated site enous Cell lines

17  SPARC and the Tumor Microenvironment 313

?

Positive

Null vs WT

Positive

Positive

Null vs WT

Null vs WT

Null vs WT

Null vs WT

Null vs WT

ID8-VEGF (Said and Socha 2007c) OSEID8 (Phelps et al. 2009) Prostate cancer TRAMP x Sparc-null/ WT (Said et al. 2009) Pancreatic PAN02 (Puolakkainen et al. 2004) PAN02 (Arnold et al. 2008)

?

ID-8 (Said and Null vs Socha 2007c) WT

Increased

Increased

Increased

Pancreas

sc Increased

Increased

Spontane- Increased ous

ip

ip, sc

ip, sc

Table 17.3   Xenograft and spontaneous animal models SPARC-null/ SPARC status Xenograft Tumor SPARC-wt site growth Cancer type/Cell Host Tumor Delivery lines cells Ovarian Null vs ? ip Increased ID8 (Said and WT Motamed 2005)

No change Decreased

Increaseda

Increased/ increased mets

Increased mets, MMP-2 & -9

Decreased Decreased

Decreased

Increased (increased)

Increased

Decreased P, increased Mo Decreased N

Decreased

Decreased

Decreased

Increased

Increased Mo

Decreased

(Increased) [increased]

MVD (VEGF) Host stromal Survival [R] cells

Decreased CNI, III

Decreased CNI

Decreased Decreased Increased CNl I, IIl nodular dissemination Increased MMP-2,-9; decreased TIMP1, 2

ECM

Increased

Invasion/ Metastasis

Apoptosis

Proliferation

aT1 (Sangaletti et al. 2008)

Null vs WT

Low

Table 17.3  (continued) SPARC-null/ SPARC status SPARC-wt Cancer type/Cell Host Tumor lines cells Positive PAN02 ±MMP9 Null vs WT (Arnold et al. 2008) Lewis lung Null vs Positive LLC (Brekken et al. 2003) WT T-cell lymphoma EL4 (Brekken Null vs Negative et al. 2003) WT Colon carcinoma Null vs Null vs APC Min/+ x WT WT Sparc-null/ WT (Sansom et al. 2007) Breast Positive N1G, N2C, N3D Null vs WT (Sangaletti et al. 2003) Positive leukocyte (San- Null vs WT galetti et al. 2003) Null vs Low 4T1 (Sangaletti WT et al. 2008) Mets-no change

Decreased CNI fibers and maturity

Mammary No change fat

iv

Restored ECM

iv Decreased

Decreased CN IV

Decreased CN

Decreased Increased L (decreased)

Decreased area Decreased [decreased] Mo

No change

Decreased

Decreased CN, LNI

MVD (VEGF) Host stromal Survival [R] cells

ECM

Mammary Decreased fat

Increased enterocyte migration

Increased

Increased

sc/iv

Spontane- Decreased #, ous not size

No change No change Increased

Increased

Invasion/ Metastasis

sc/iv

Apoptosis

Further No change No change Increased/ increased inhibited mets

Proliferation

Pancreas

Xenograft Tumor site growth Delivery

Null vs WT

UVDecreased induced

Xenograft Tumor site growth Delivery

Proliferation

Apoptosis

Invasion/ Metastasis

ECM

MVD (VEGF) Host stromal Survival [R] cells

WT wildtype, ?- unknown, sc subcutaneous injection, iv intravenous injection, ip intraperitoneal injection, # number, a increased cyclins A, D1 and decreased p21, 27, MMP matrix metalloproteinase, mets metastases, CN collagen, LN laminin, ECM extracellular matrix, MVD microvascular density, VEGF vascular endothelial growth factor, [R] VEGF-receptor(s), Mo macrophages, P pericytes, N neutrophils, L leukocytes

Squamous cell hairless x Sparc- Null vs WT null/WT (Aycock et al. 2004)

Table 17.3  (continued) SPARC-null/ SPARC status SPARC-wt Cancer type/Cell Host Tumor lines cells

17  SPARC and the Tumor Microenvironment

317

Three different studies found that the intraperitoneal implantation of ovarian cancer cell lines in Sparc-null mice resulted in increased tumor volume and reduced animal survival compared to cancer cells grown in Sparc-wt mice (Phelps et al. 2009; Said and Motamed 2005; Said and Socha 2007c) (Table 17.3). When a murine pancreatic adenocarcinoma cell line was injected either subcutaneously or into the pancreas, the resulting tumors grew larger in Sparc-null compared to Sparc-wt animals (Arnold et  al. 2008; Puolakkainen et  al. 2004). Another study used the transgenic adenocarcinoma of mouse prostate (TRAMP) model of primary prostate carcinogenesis interbred with Sparc-null and Sparc-wt mice. In this model, Sparc-null mice had accelerated cancer development and progression. A SPARCexpressing cell line derived from this model grew significantly larger tumors when subcutaneously implanted into Sparc-null mice and 100% of mice developed tumors compared to 60% in Sparc-wt mice (Said et al. 2009). In addition, two other types of cancer cells, Lewis lung carcinoma and T-cell lymphoma, grew larger and more rapidly when subcutaneously implanted into Sparc-null compared to Sparc-wt mice (Brekken et al. 2003). On the other hand, several studies found that absence of SPARC in the stroma hindered tumor development and progression (Table 17.3). In one study, a mouse mammary carcinoma cell line was injected into the mammary fat pads in Sparc-null and Sparc-wt mice. Sparc-null mice had smaller tumors with undefined lobules and reduced tumor outgrowth (Sangaletti et al. 2003). Another study used a model of spontaneous intestinal tumorigenesis in which Sparc-null mice had significantly fewer adenomas in both the small and large intestines (Sansom et al. 2007). A third report of reduced tumor formation in Sparc-null mice used UV irradiation to induce squamous cell carcinoma in hairless mice crossed with Sparc-null and Sparcwt mice. All 20 Sparc-wt mice developed papillomas and some developed larger squamous cell carcinomas; whereas only six Sparc-null mice developed a small number of papillomas resulting in a highly significant reduction in tumor formation in Sparc-null animals (Aycock et al. 2004). These data suggest that stromal SPARC suppresses tumor growth in the ovary, prostate, pancreas and lung, whereas it is advantageous for tumor growth in the colon, breast, and skin (Fig. 17.1). These combined in vivo studies indicate that SPARC positively or negatively contributes to many aspects of tumor progression, and in vitro and in vivo studies have been utilized to further characterize its role in regulating tumor cell proliferation, cell cycle progression, survival, adhesion, migration, invasion, and ECM deposition, as follows. 17.2.1.2 Cell Proliferation Overwhelmingly, in vitro studies provide evidence that SPARC reduces proliferation in cancer cells, including a Lewis lung carcinoma cell line (Brekken et  al. 2003), pancreatic cancer cell lines (Arnold et al. 2008; Sato et al. 2003), a mouse ovarian cancer cell line (Said and Motamed 2005; Said and Socha 2007c), human ovarian cancer cell lines (Phelps et al. 2009; Mok et al. 1996; Yiu et al. 2001), and

318

S. L. Thomas and S. A. Rempel

AML cell lines with MLL gene rearrangements (DiMartino et al. 2006). This reduction occurs whether the SPARC expression was forced or SPARC was given exogenously (Table 17.4). Further investigation using ovarian cancer cell lines show that exogenous SPARC was also able to inhibit alpha v integrin and beta 1 integrin stimulated proliferation of human ovarian cancer cell lines and a mouse ovarian cancer cell line on different ECMs (Said and Motamed 2005), and decrease LPA and IL-6 stimulated proliferation of a mouse ovarian cancer cell line (Said and Socha 2007c). Interestingly, these cell lines correspond to the human tumor specimens that have low SPARC in the tumor cells but high SPARC in the stroma (Table 17.1), and for which in vivo studies suggest that stromal (exogenous) SPARC suppresses tumor growth (Table 17.2). Outcomes in proliferation are more varied (Table 17.5) when examining the effects of forced SPARC versus suppressed SPARC on cell lines from tumors that normally have high SPARC expression (Table 17.1). Generally, forced SPARC expression was associated with reduced proliferation in a breast cancer cell line (Koblinski et al. 2005), melanoma cell lines (Prada et al. 2007), prostate cancer cell lines (Said et al. 2009), and glioma cell lines (Golembieski et al. 2008; Rempel et al. 2001; Vadlamuri et al. 2003). However, the suppression was not always observed in cells grown as monolayers. In one report using a glioma cell line, SPARC expression significantly delayed growth during the log phase growth of the cells but did not inhibit overall cell proliferation (Golembieski et al. 2001). When these same glioma cells were grown in vivo, there was a reduced MIB-1 proliferation index in SPARCexpressing tumors with a greater reduction in proliferation at the invading tumor edge compared to the tumor core (Schultz et al. 2002) (Table 17.2). SPARC overexpression in melanoma cells (Prada et al. 2007) and breast cancer cells (Koblinski et al. 2005) inhibited proliferation in spheroids but not in monolayer. Although the results of adding SPARC to cells already expressing SPARC appears to produce more variable results regarding proliferation, the reduction of SPARC with antisense in some of these same cell lines produced more consistent results. Inhibition of SPARC increased melanoma proliferation as spheroids (Prada et al. 2007), increased prostate cancer cell proliferation in monolayer (Said et al. 2009), and increased brain tumor cell proliferation under stress conditions in vitro (Shi et al. 2007). Interestingly, a study found that, while exogenous SPARC did decrease normal endothelial cell proliferation, and had biphasic effects on fibroblast proliferation, the melanoma, glioma and colorectal cell lines having SPARC expression were relatively unaffected by exogenous SPARC (Haber et al. 2008). This suggests that human tumors positive for SPARC (Table 17.1) may be less affected by SPARC expression in the stroma. Indeed, it has been reported that SPARC expressed by melanoma tumor cells and not the stromal cells is important for tumor growth (Prada et al. 2007). However, other studies did not detect a difference in proliferation with SPARCexpression in cancer cells. A pancreatic cancer cell line that was treated with exogenous SPARC did not show a significant reduction in proliferation rate (Puolakkainen et al. 2004) (Table 17.4). In addition, it was reported that the forced expression

Table 17.4   In vitro effects of SPARC on proliferation, cell cycle, apoptosis or attachment, adhesion, migration and invasion of SPARC-negative tumor cells Tumor type Cell lines used EndogMe Manipulated Proliferation (cell cycle) Attachment/adhesion/migration/invasion enous SPARC [apoptosis] SPARC Status Forced Exogenous Lewis lung Brekken et al. LLC Positive X Decreased (2003) Pancreatic Positive Decreased Migration increased by MMP-9 Arnold et al. (2008) PAN02 (mouse) ± MMP9 (transwell) Puolakkainen et al. PAN02 (mouse) Positive X No change [increased with (2004) Staurosporine] Sato et al. (2003) AsPC1 Negative M X Decreased BxPC3 Negative M Panc1 Slight H X Decreased U Fibroblasts High CFPAC1 Negative Ovarian Mok et al. (1996) SKOV3 Negative M X Decreased Yiu et al. (2001) MESO High U (mesothelial) HOSE (epithelial) High U X Decreased [none] SKOV3 Negative M X Decreased more [increased] OVCA420 Negative OVCA429 Low OVCA433 Negative X Decreased more DOV13 Interme- U X Decreased more diate

17  SPARC and the Tumor Microenvironment 319

Said et al. (2007a)

Said et al. (2007a)

Said and Socha (2007c)

Said and Motamed (2005)

M

Negative

Negative

?

NIH:OVCAR3

IGROV1

M

U

? Positive

LP9 (mesothelial) Meso 301 (mesothelial) SKOV3

X

X

X

X X

X

M

Negative

NIH:OVCAR3

X

X

M

Negative

X

X

X

X

? Positive

M

X

ID8-VEGF Meso 301 (mesothelial) SKOV3

Negative

NIH:OVCAR3

M

?

Negative

SKOV

X

Inhibits integrin-induced (Pl, FN, CN I) Inhibits integrin-induced (Pl, FN, CN I) Inhibits integrin-induced Pl, FN, CN I)

Decreased; inhibits LPA and IL-6 induced Decreased; inhibits LPA and IL-6 induced

Inhibits VEGF-induced

inhibits

Decreased [increased]

Manipulated Proliferation (cell cycle) SPARC [apoptosis] Status Forced Exogenous

Me

ID8

?

Endogenous SPARC

ID8

Table 17.4  (continued) Tumor type Cell lines used

Decreased integrin-mediated attachment to FN, VN, CN I Decreased integrin-mediated attachment to FN, VN, CN I Decreased integrin-mediated attachment to FN, VN, CN I

decreased chemotaxis and invasion; decreased ERK 1/2 and pAKT decreased chemotaxis and invasion; decreased ERK 1/2 and pAKT

Decreased MMP-2 & MMP-9

Decreased adhesion/invasion FN, CN I, CN IV, LN, VN, HA (transwell) Decreased adhesion/invasion FN, CN I, CN IV, LN, VN, HA (transwell) Decreased adhesion/invasion FN, CN I, CN IV, LN, VN, HA (transwell)

Attachment/adhesion/migration/invasion

320 S. L. Thomas and S. A. Rempel

M

Negative

Negative

NIH:OVCAR3

X X

U M M M

Positive Low Negative

Negative

Negative

X

X

Positive

Kasumi-1 WT MLL ME-1 WT MLL KG1a WT MLL ML-2 rearranged MLL MV411 rearranged MLL THP-1 rearranged MLL

X

X

X

Positive Negative Positive Positive

X X X X

X

X

X

OSEID8 (mouse) SKOV3 ES2 HCC60 M

M

U

Positive

Decreased

No change (no change) Decreased (increase in G1)

No change

Decreased No change Decreased No change

Decreased MCP-I induced

Decreased MCP-I induced

Manipulated Proliferation (cell cycle) SPARC [apoptosis] Status Forced Exogenous

Me

Meso 301 (mesothelial) SKOV3

Endogenous SPARC

Suppressed MCP1-induced invasion (transwell); reduced MMP-9 and uPA activity Suppressed MCP1-induced invasion (transwell); reduced MMP-9 and uPA activity

Attachment/adhesion/migration/invasion

AML acute myelogenous leukemia, MLL mixed-lineage leukemia gene, ?- unknown, U unmethylated, M methylated, LPA lysophosphatidic acid, IL-6 interleukin-6, MCP-1 monocyte chemoattractant protein, VEGF vascular endothelial growth factor, CN collagen, LN laminin, VN vitronectin, HA hyaluronic acid, MMP matrix metalloproteinase, uPA urokinase plasminogen activator, ME methylation

AML DiMartino et al. (2006)

Phelps et al. (2009)

Said et al. (2008)

Table 17.4  (continued) Tumor type Cell lines used

17  SPARC and the Tumor Microenvironment 321

Melanoma/colon/glioma Haber et al. (2008)

Prada et al. (2007)

Melanoma Ledda et al. (1997b)

Breast cancer Koblinski et al. (2005)

X X X X X X X X

Positive Negative Positive Negative Positive Negative Negative Low

X X

HMEC-1 (endothelial) MEC(endothelial) HUVEC (endothelial) WI38 (fibroblasts) HLF1 (fibroblasts) LoVo (colorectal) Hct116 (colorectal) U87 (glioma)

X X

X

X

Positive Positive Positive Positive

Positive

IIB-MEL-IAN

X

A375N IIB-MEL-J A375N J IIB-MEL-J

Positive Positive

Negative

IIB-MEL-J IIB-MEL-LES

MDA-231-GFP

No change

Biphasic Biphasic No change No change

Decreased Decreased

Decreased

Decreased in spheroids Decreased in spheroids Increased in spheroids Increased in spheroids

No change

Decreased attachment, invasion (Matrigel) and migration (transwell) decreased MMP-9 Decreased attachment and invasion (Matrigel)

Decreased in spheroids Decreased adhesion; decreased inva[no change] sion (Matrigel); decreased MMP-2

Table 17.5   In vitro effects of SPARC on proliferation, cell cycle, apoptosis or attachment, adhesion, migration and invasion of SPARC-positive tumor cells Tumor type Cell lines used Endogenous Manipulated SPARC Proliferation (cell Attachment/adhesion/migration/invasion SPARC Forced SupExog- cycle) [apoptosis] pressed enous

322 S. L. Thomas and S. A. Rempel

Prostate Said et al. (2009)

Uveal Melanoma Maloney et al. (2009)

Table 17.5  (continued) Tumor type

Low Negative Positive

DU145

PC3

LNCaP

X

X

X

X

Positive Positive

X X

X

Positive Positive Positive

X

Positive

X

X

Positive

SP6.5, MKTBR, OCM-1

X

Positive

IIB-MEL-J (melanoma) IIB-MEL-LES (melanoma) IIB-MEL-LES (melanoma) BLAST IIB-MEL-LES (melanoma) shRNA A375 (melanoma) MEL-888 (melanoma) SB-2 (melanoma)

Attachment/adhesion/migration/invasion

Decreased (decreased Decreased invasion (Matrigel) cyclin A, DI increased p21, p27) Decreased (decreased Decreased invasion (Matrigel) cyclin A, DI increased p21, p27) Decreased (decreased Decreased invasion (Matrigel) cyclin A, DI increased p21, p27)

Decreased

No change

No change No change

No change (inhibits re-entry)

No change

No change

Manipulated SPARC Proliferation (cell Forced SupExog- cycle) [apoptosis] pressed enous

Endogenous SPARC

Cell lines used

17  SPARC and the Tumor Microenvironment 323

Low Negative Positive Positive Low Negative Positive Positive Low

Low Low Low

DU145 PC3 LNCaP TRAMP1, -2.-3 DU145 PC3 LNCaP TRAMP1, -2.-3

U87

U87

U87

U87

U87

SNB19 THR

Glioma Vadlamuri et al. (2003)

Golembieski et al. (2001)

Golembieski et al. (2008)

McClung et al. (2007)

Kunigal et al. (2006)

Rich et al. (2003)

Low

Low

Endogenous SPARC

Cell lines used

Table 17.5  (continued) Tumor type

X X

X

X

X

X

X X X X X X X

No change No change (no change)

No change

Decreased

Decreased (variable depending on ECM) Delayed

Increased Increased Increased Increased Decreased Decreased Decreased No change

Manipulated SPARC Proliferation (cell Forced SupExog- cycle) [apoptosis] pressed enous

Increased invasion (spheroids) and adhesion & migration LN,CN.HA; biphasic TN Increased invasion (Matrigel) and migration (wound assay, transwell); pP38/pHSP27 Increased MT1-MMP, MMP2, cleaved galectin-3 Increased invasion (Matrigel) increased MMP-9, uPA/uPAR & Rho activation Increased invasion (Matrigel) Increased (Matrigel); increased MMP-3

Variable depending on ECM

Increased invasion (Matrigel) Increased invasion (Matrigel) Increased invasion (Matrigel) Increased invasion (Matrigel)

Attachment/adhesion/migration/invasion

324 S. L. Thomas and S. A. Rempel

Positive Positive

U251MG

U373MG

X

X

X X X X

X

X

X

X

X

X

X

[Decreased under stress; increased pAKT] [Decreased under stress; increased pAKT] [Decreased under stress; increased pAKT] [Increased; decreased FAK & ILK activity] [Increased; decreased FAK & ILK activity]

Manipulated SPARC Proliferation (cell Forced SupExog- cycle) [apoptosis] pressed enous

Decreased invasion (Matrigel), decreased migration (transwell) Decreased invasion (Matrigel), decreased migration (transwell)

Decreased invasion (Matrigel)

Decreased invasion (Matrigel)

Increased (Matrigel) Increased (Matrigel) Increased (Matrigel)

Attachment/adhesion/migration/invasion

[THR T/t-Ag, hTERT, H-rasV12G], CN collagen, LN laminin, TN tenascin, HA hyaluronic acid, ECM extracellular matrix, MMP matrix metalloproteinase, uPA urokinase plasminogen activator, uPAR uPA receptor, HSP27 heat shock protein 27

Seno et al. (2009)

Positive

U373MG

Positive

D54MG Positive

Positive

THR

Shi et al. (2004)

D54MG

Low Positive Positive Low

U87 U251MG D54MG U87

Shi et al. (2007)

Endogenous SPARC

Cell lines used

Table 17.5  (continued) Tumor type

17  SPARC and the Tumor Microenvironment 325

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of SPARC in glioma cell lines (Kunigal et al. 2006) (Table 17.5) did not result in changes in proliferation. A review of the methods used suggests that some of the variability of SPARC effects on proliferation may result from the different assays used, different time points analyzed, and the use or not of different ECM substrates. 17.2.1.3 Cell Cycle Progression Whereas studies of normal cells indicate that SPARC delays the onset of S phase (Funk and Sage 1991), reports of the effect of SPARC on cell cycle progression in cancer cell lines is less consistent. It was found that exogenous SPARC inhibited the progression from G1 phase to S phase in an AML cell line with an MLL gene rearrangement, but had no effect on an acute myeloid leukemia cell line with a wild-type MLL gene (DiMartino et al. 2006) (Table 17.4). A different study used SPARC purified from human melanoma cells to assess the effect on cell cycle in five melanoma cell lines, two colon cancer cell lines, and one glioma cell line. In all the cell lines tested, exogenous SPARC did not alter the onset of S phase (Haber et al. 2008). The same group reported that the use of shRNA or antisense to reduce the endogenous level of SPARC in a melanoma cell line resulted in an inhibition of cell cycle reentry (Haber et al. 2008) (Table 17.5). As discussed above, these cells appear to be refractory to exogenous SPARC. Another group reported a biphasic effect of SPARC on cell cycle that depended on the amount of forced SPARC expression in glioma cells (Table 17.5). SPARCexpressing clones with high and intermediate levels of SPARC had a higher percentage of cells in G0/G1 than parental control cells, whereas the SPARC-expressing clone with the lowest amount of SPARC had a lower percentage of cells in G0/G1 and a higher percentage of cells in G2/M than parental control cells (Golembieski et al. 2001), suggesting that the level of secreted SPARC may dictate how far a cell progresses through the cell cycle. The same group later reported that the regulation of cell cycle progression by SPARC was also dependent on the type of ECM that the glioma cells were grown on. SPARC-expressing clones with high and intermediate levels of SPARC had fewer cells in G0/G1 when grown on vitronectin, fibronectin, hyaluronic acid, or collagen (Vadlamuri et  al. 2003). These results indicate that the amount of SPARC expressed endogenously by the cells, the substrate they are grown on, and the presence of genetic mutations that regulate cell cycle may determine whether or not SPARC has an effect on cell cycle progression. 17.2.1.4 Cell Survival/Apoptosis Since SPARC was found to reduce proliferation in various types of cancer cells, some investigators looked further to determine if decreased cell numbers resulted from a reduction in cell survival, as indicated by an increase in apoptosis. In a pancreatic cancer cell line, SPARC alone did not induce apoptosis in vitro, but when

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327

SPARC was combined with staurosporine the cells had increased active caspase-3, indicating that SPARC may prime the cell for apoptosis (Puolakkainen et al. 2004) (Table  17.4). The same pancreatic cancer cell line was grown subcutaneously in Sparc-null and Sparc-wt mice. A loss of stromal SPARC resulted in fewer tumor cells positive for active caspase-3, suggesting that stromal SPARC is supportive of apoptosis (Prada et al. 2007). Further support for this role was observed when exogenous SPARC treatment resulted in significantly increased apoptosis in an ovarian cancer cell line (Said and Motamed 2005; Yiu et al. 2001) (Table 17.4). In contrast, glioma cells with forced expression of SPARC were found to have a survival advantage under the stress of serum withdrawal. The increase in cell number was not due to increased proliferation. Rather, analysis revealed that SPARC significantly decreased apoptosis in three glioma cell lines, and sensitivity to apoptosis could be restored with a PI3K inhibitor or an Akt inhibitor, indicating that the Akt pathway is involved in the increased survival induced by SPARC (Shi et al. 2004) (Table 17.5). The same group later reported that reducing SPARC expression in glioma cell lines with siRNA resulted in increased apoptosis under conditions of serum withdrawal (Shi et al. 2007) (Table 17.5). Other reports indicate that SPARC does not have an effect on apoptosis in cancer cells. One study found that exogenous SPARC did not induce apoptosis in acute myeloid leukemia cell lines with MLL gene rearrangements even though SPARC reduced the growth of these same cell lines (DiMartino et al. 2006) (Table 17.4). Another study reported that tumors formed by Lewis lung carcinoma cells grown in Sparc-null mice did not show a difference in apoptosis compared to tumors in Sparc-wt mice (Brekken et al. 2003) (Table 17.4). Similarly, a breast cancer cell line with forced expression of SPARC had no increase in apoptosis even though colony size and formation were reduced when the cells were grown in Matrigel (Koblinski et al. 2005) (Table 17.5). Combined, the reports suggest that SPARC plays a role in survival under stressful conditions, such as with serum withdrawal or drug treatment (discussed further in Sect. 17.3), and that for some cancers, stromal SPARC may influence tumor cell survival. 17.2.1.5 Cell Adhesion/Migration Since SPARC was shown to have a counter-adhesive function in normal cells (Motamed and Sage 1998) many investigators have examined the ability of SPARC to alter adhesion and migration in cancer cells. For three ovarian cancer cell lines, exogenous SPARC produced a significant inhibition of adhesion to several ECM proteins including fibronectin, collagen I, collagen IV, vitronectin, hyaluronic acid, and laminin (Said and Motamed 2005) (Table 17.4). A later study by the same group found that SPARC significantly reduced the basal adhesion of three ovarian cancer cell lines to fibronectin and vitronectin. Agonists for integrins alpha v and beta 1 increased the adhesion of these cell lines to ECM proteins and mesothelial cells, and SPARC was able to inhibit the integrin-mediated adhesion (Said et al. 2007b)

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(Table 17.4). In contrast, another group found that when melanoma cell lines express antisense for SPARC there is a strong reduction in the ability of the cells to adhere to Matrigel (Ledda et al. 1997b) (Table 17.5). These different results may be due to the differences in substrate used for these studies. For example, a study using glioma cells demonstrated that the effects of SPARC on attachment are ECM protein-specific. Glioma cells with forced expression of SPARC had increased attachment to laminin, collagen, and hyaluronic acid, a biphasic attachment to tenascin, and no change in attachment to vitronectin and fibronectin (Golembieski et al. 2001) (Table 17.5). The changes in cancer cell adhesion induced by SPARC may result in changes in the ability of the cells to migrate. A study using a glioma cell line with forced expression of SPARC found that SPARC expression increased migration in three different assays (Golembieski et  al. 2008; Rempel 2001) (Table  17.5). However, when the migration of glioma clones that express different amounts of SPARC were compared, there was a biphasic effect on migration such that the clone that secreted the most SPARC migrated the farthest, the clone with intermediate SPARC migrated the least, and the clone with the lowest SPARC migrated the most like the parental control cells (Rempel et  al. 2001). Therefore, other conditions being equal, the amount of expressed and secreted SPARC appears to influence the extent of migration as well. Corroborating data demonstrate that anti-sense suppression of SPARC in glioma cells resulted in decreased migration (Seno et al. 2009) (Table 17.5). Similarly, antisense suppression of SPARC reduced the migration of a melanoma cell line (Ledda et al. 1997b) (Table 17.5). These observations emphasize that the effects of SPARC on tumor cell attachment, adhesion, and migration are influenced by the integrin expression profile of the cells and the ECM in the immediate microenvironment. 17.2.1.6 Invasion/Metastasis The effects of SPARC on invasion appear to depend on the type of cancer being studied with the literature providing evidence that SPARC increases invasiveness of glioma and melanoma and decreases invasiveness of breast, prostate, and ovarian cancers (Fig. 17.1). A study with melanoma cells found that antisense for SPARC reduced the ability of the cells to invade Matrigel by 70–80% in two cell lines, and the third cell line, which had the greatest suppression of SPARC, was completely noninvasive (Ledda et al. 1997b) (Table 17.5). Similarly, four in vitro studies provide evidence that SPARC increases the invasive potential of glioma cells. Glioma cells with forced expression of SPARC were found to have an increased ability to invade rat fetal brain aggregates (Golembieski et al. 1999) (Table 17.5), and Matrigel (Golembieski et al. 2008; Kunigal et al. 2006; Rich et al. 2003) (Table 17.5). Another study found that when two glioma cell lines that naturally express SPARC were treated with SPARC siRNA, invasion through Matrigel was inhibited by approximately 50% (Shi et al. 2007) (Table 17.5). In addition, when SPARC-expressing glioma cell lines and the parental control cell line were xenografted intracranially

17  SPARC and the Tumor Microenvironment

329

in nude rats, control cells gave rise to a well-circumscribed tumor mass whereas SPARC-expressing cells produced invasive tumors with invasion into the adjacent brain and along the corpus callosum. SPARC expression was detected in invading single cells and distant tumor masses (Schultz et al. 2002) (Table 17.5). The opposite approach demonstrated that SPARC suppression decreased glioma invasion (Seno et al. 2009) in vitro and in vivo (Tables 17.2, 17.5, respectively) In contrast, studies of other cancer types provide evidence that SPARC can also suppress invasion. Forced endogenous expression of SPARC was found to significantly inhibit invasion through Matrigel in a breast cancer cell line (Koblinski et  al. 2005) (Table  17.5) and several prostate cancer cell lines (Said et  al. 2009) (Table 17.5). In agreement with the latter study, five prostate cancer cell lines were treated with siRNA resulting in a significant increase in invasion in vitro (Said et al. 2009) (Table 17.5). Furthermore, several studies with ovarian cancer cell lines reported that SPARC suppresses invasion. Forced SPARC expression in several cell lines significantly inhibited the ability of the cells to invade various ECM proteins (Said and Motamed 2005; Said and Socha 2007c) (Table 17.4). The balance of SPARC expression between the environment versus the tumor cells may play an important role in regulating the invasive phenotype. SPARC expression in ovarian cancer cell lines was able to inhibit invasion induced by macrophages or macrophage chemoattractant protein-1 (MCP-1) (Said et al. 2008) (Table 17.4), and Sparc-null ascitic fluid increased cell invasiveness compared to Sparc-wt ascitic fluid (Said and Socha 2007c). Further evidence that exogenous or stromal SPARC can suppress invasion comes from an in vivo study in which pancreatic cancer cells grown in SPARC-null mice were more invasive with greater involvement of local organs compared to tumors in Sparc-wt mice (Arnold et al. 2008) (Table 17.3). Although different tumor types displayed different invasive phenotypes depending on SPARC expression, there was good correlation between the effects of SPARC on migration and invasion. When measured, SPARC-induced migration correlated with SPARC-induced invasion and vice versa, SPARC-suppressed migration correlated with SPARC-suppressed invasion (Table 17.5). The effects of SPARC on metastasis also differ depending not only on the cancer type, but also on the SPARC status in the microenvironment, and on where the cancer cells are injected. It was reported that intracardiac injection of a breast cancer cell line that was forced to express a high level of SPARC resulted in a significant reduction in the overall number of metastases and a decrease in the number of metastases to all organs analyzed (Koblinski et al. 2005) (Table 17.2). Another group used two different models to assess the metastasis of a breast cancer cell line in Sparc-null and Sparc-wt mice. If the cells were injected into the mammary fat pad, then metastases to the lungs, liver, lymph nodes, and brain were significantly reduced in Sparc-null animals (Sangaletti et al. 2008). However, if the cells were injected into the tail vein, then numerous metastases were seen in the lungs of both Sparc-null and Sparc-wt mice suggesting that stromal SPARC is involved in the ability of the cells to leave the primary tumor but not to seed in a different organ (Sangaletti et al. 2008).

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A group using the TRAMP model of primary prostate carcinogenesis crossed with Sparc-null versus Sparc-wt mice found that a higher percentage of Sparc-null animals had distant metastases in the lung, liver, and lymph nodes (Said et al. 2009) (Table 17.3), suggesting that stromal SPARC is suppressive to metastatic invasion. In contrast, when glioma cell lines with forced expression of SPARC, which are not metastatic when implanted in the brain, were exposed to a different microenvironment by subcutaneous injection into the flank of mice, 50% of mice with SPARCexpressing tumors developed intrathoracic and/or intraperitoneal metastases compared to only 1 out of 40 mice with control or parental tumors (Rich et al. 2003) (Table 17.2). These studies indicate that the ability of SPARC to induce a metastatic phenotype in cancer cells may depend on the initial microenvironment in which the tumor develops. 17.2.1.7 ECM Production and Deposition The proliferation and migration potential of cancer cells expressing or exposed to SPARC may be influenced by alterations in the ECM that are present in the tumor microenvironment. Sparc-null fibroblasts were found to have an increased amount of processed collagen I but less collagen I incorporated into the ECM (Rentz et al. 2007). In addition to fibroblasts, cancer cells can also produce ECM components and regulate ECM composition in the tumor environment. There are many studies that report alterations in ECM when cancer cells are grown in Sparc-null compared to Sparc-wt mice. By far the major finding in these studies is that the amount of collagen fibers is reduced and the collagen is less mature in the tumor microenvironment in Sparc-null animals (Arnold et al. 2008; Brekken et al. 2003; Puolakkainen et al. 2004; Said et al. 2009; Said and Motamed 2005; Sangaletti et al. 2008; Sangaletti et al. 2003) (Table 17.3). When specific types of collagen were examined, collagens I, III, and IV were found to be reduced in Sparc-null tumors (Said et al. 2009; Said and Motamed 2005; Sangaletti et al. 2003). Two other ECM components, laminin and fibronectin, were also found to have reduced expression in Sparc-null tumors. One study in which a breast cancer cell line was injected into the mammary fat pad in Sparc-null and Sparc-wt mice found that fibronectin was reduced in Sparc-null tumors in addition to a reduction in collagen (Sangaletti et al. 2008). Another study reported reduced staining for laminin 1 in the outer region of Lewis lung carcinoma tumors which coincided with the region of the tumor where differences in SPARC expression were seen in Sparc-null mice (Brekken et al. 2003). Further evidence that SPARC regulates collagen abundance in the ECM comes from studies in which SPARC is overexpressed in cancer cells. Glioma cells with forced expression of SPARC produced tumors with increased collagen I staining and more mature collagen fibers compared to tumors from parental cells (Yunker et al. 2008) (Table 17.2). SPARC hyperexpression in melanoma cells led to a twofold increase in collagen deposition at the tumor-host tissue interface (Prada et al. 2007) (Table 17.2).

17  SPARC and the Tumor Microenvironment

331

Additional studies analyzed the in vitro collagen expression by tumor cell lines. Exogenous SPARC reduced collagen I synthesis by an osteosarcoma cell line, whereas the production of laminin was downregulated by exogenous SPARC in several different cancer cell lines (Kamihagi et al. 1994). Antisense-mediated reduction of SPARC expression in a melanoma cell line resulted in increased collagen I expression and decreased collagen V expression as detected by proteomic analysis of CM (Sosa et al. 2007). These studies provide evidence that SPARC plays a key role in ECM production and composition during tumor development. Such changes can significantly affect the ability of immune cells to infiltrate the tumor microenvironment, which in turn can have profound effects on tumor growth and progression (see Sect. 17.2.4). 17.2.1.8 Signal Transduction Based on the above review, it is clear that SPARC can positively or negatively regulate many aspects of tumor growth and progression, and this has many consequences regarding its utility as a therapeutic agent or target (See Sect. 17.3). For example, in tumor cells where SPARC inhibits proliferation but promotes tumor migration and invasion, targeting SPARC may very well provide the desired reduction in invasion, but it may also promote an undesired increase in proliferation. An understanding of how SPARC regulates these different aspects of tumor progression may permit strategies to selectively inhibit one SPARC-regulated pathway, but retain its regulation of others. As reviewed below, many insights have been made in understanding SPARC-induced signal transduction in governing cell proliferation, cell cycle progression, apoptosis, cell attachment, migration and invasion. SPARC has been shown to regulate signaling pathways that are involved in cell proliferation. In a model of spontaneous prostate cancer progression, neoplastic tissue in Sparc-null mice showed a significant increase in cyclins A1 and D1 and a decrease in p21 and p27, which likely contributes to the increased proliferation index in these tissues (Said et  al. 2009) (Table  17.3). In the inverse experiment, forced expression of SPARC in prostate cancer cells resulted in decreased cyclins A1 and D1 and increased p21 and p27 (Said et al. 2009) (Table 17.5). Thus, tumorand stromal-derived SPARC suppressed cell-cycle progression. The ability of SPARC to enhance cancer cell survival was found to involve the Akt signaling pathway. In glioma cell lines, exogenous SPARC induced Akt phosphorylation and kinase activity and PI3K inhibitors blocked the induction of Akt by SPARC (Shi et al. 2004) (Table 17.5). In addition, a PI3K inhibitor or an Akt inhibitor restored the sensitivity of SPARC-expressing glioma cells to apoptosis upon serum withdrawal (Shi et al. 2004). The forced endogenous expression of SPARC in glioma cells or exogenous SPARC-treated glioma cells had increased FAK phosphorylation and ILK activation (Shi et al. 2007). SPARC-mediated Akt activation, cell survival, and invasion were found to be dependent on FAK and ILK activation (Shi et al. 2007) (Table 17.5).

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Since SPARC was found to regulate invasion in cancer cells and regulate the processing and deposition of collagen and other ECM proteins, several studies have analyzed the role of SPARC in modulating the expression of matrix degrading proteases. The literature demonstrates that SPARC is associated with an increase in matrix metalloproteinases (MMPs) and urokinase plasminogen activator (uPA) and its receptor (uPAR) expression in cancer types in which SPARC increases invasion and, conversely, is associated with a decrease in MMP and uPA expression in other cancers in which SPARC suppresses invasion and metastasis. Studies with glioma cell lines that are forced to express SPARC have found that the expression and activity of MMP-9 (Kunigal et al. 2006; Rich et al. 2003), MMP-2 (McClung et  al. 2007), uPA/uPAR (Kunigal et  al. 2006), as well as the expression of MMP-3 (Rich et al. 2003) and MT1-MMP (McClung et al. 2007) are increased with SPARC (Table  17.5). Glioma cells that express SPARC were also found to have increased cleavage of galectin-3 in conditioned media, which is an indictor of protease activity as galectin-3 can be cleaved by MMP-2, MMP-9, and MT1-MMP (McClung et al. 2007). An inhibitor of MMP-3 reduced the ability of SPARC-expressing glioma cells to invade Matrigel (Rich et  al. 2003). Another group reported that in a melanoma cell line, as SPARC expression is reduced with antisense, MMP-2 mRNA levels and activity are reduced accordingly (Ledda et al. 1997b) (Table 17.5). However, SPARC was shown to decrease MMP expression and activity in ovarian cancer cells, which have reduced invasion with SPARC. In ovarian cancer cells, forced SPARC expression reduced MMP-9 and uPA activity in two different cell lines (Said et al. 2008) (Table 17.4) and treatment of cells with exogenous SPARC reduced MMP-2 and MMP-9 mRNA (Said and Socha 2007c) (Table  17.4). The inhibitory effect of SPARC on invasion was reinforced using SPARC-null mice, in which loss of stromal SPARC correlated with increased MMP expression. For example, ovarian tumors in Sparc-null mice had increased MMP-2 and MMP-9 mRNA and decreased MMP inhibitors TIMP-1 and TIMP-2 (Said and Socha 2007c) (Table 17.3), and Sparc-null ascitic fluid had enhanced MMP-2 and MMP-9 proteolytic activity (Said and Motamed 2005; Said and Socha 2007c). Furthermore, a model of spontaneous prostate adenocarcinoma also demonstrated increased MMP2 and MMP-9 activity in Sparc-null mice (Said et al. 2009) (Table 17.3). SPARC can also regulate the ability of cancer cells to adhere to and migrate on ECM components by modulating integrin activation. Preincubation of ovarian cancer cell lines with SPARC inhibited alpha v and beta 1 integrin-induced adhesion to ECM proteins and mesothelial cells (Said and Najwer 2007b) and proliferation (Said and Najwer 2007b; Said and Socha 2007c) (Table 17.4). In glioma cell lines that have forced expression of SPARC and an increased migration and invasion potential, SPARC was found to activate RhoA but not Rac1 or Cdc42 (Kunigal et al. 2006) (Table 17.5). Another study using glioma cell lines with forced SPARC found that SPARC increases activation of the p38MAPK/HSP27 pathway and SPARC-mediated increases in migration and Matrigel invasion could be inhibited with siRNA to HSP27 (Golembieski et al. 2008) (Table 17.5).

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It has been suggested that, prior to acquiring a migratory and invasive phenotype, tumor cells need to adopt a process similar to the developmentally regulated epithelial-mesenchymal transition (EMT), where epithelial cells lose their polarity and develop a mesenchymal phenotype. This hypothesis remains controversial (Tsuji and Ibaragi 2009) however it is interesting to note that SPARC expression downregulates epithelial markers of EMT such as E-cadherin, and increases the induction of the mesenchymal genes such as FAK and snail in melanoma (Craene et al. 2005; Said and Najwer 2007b; Smit and Gardiner 2007). In addition, SPARC upregulation was associated with other EMT-related markers by expression array in basal-like breast cancer (Sarrio et al. 2008). In summary, these studies demonstrate an increased understanding of SPARC signaling in cancer cells. As discussed in the next sections, SPARC can also regulate cellular function and signaling in endothelial cells, fibroblasts, and immune cells.

17.2.2  Effects on Endothelial Cells and Tumor Vascularity Several in vitro studies have shown that SPARC has an inhibitory effect on endothelial cell proliferation, migration and ECM production (Fig. 17.2). SPARC can suppress endothelial cell proliferation by binding to VEGF, and thereby blocking the VEGF-VEGFR1-induced phosphorylation of ERK (Kupprion et al. 1998). SPARC can also indirectly inhibit bFGF-induced proliferation and DNA synthesis of endothelial cells (Motamed and Blake 2003). Another in vitro study demonstrated that SPARC can inhibit bFGF-stimulated human umbilical vein endothelial cell migration (Chlenski et al. 2006). One study tested five different preparations of human SPARC (four recombinant and one derived from melanoma cells) on bovine aortic endothelial (BAE) cells and found that all five types of SPARC inhibited proliferation, adhesion, and migration (Haber et al. 2008). In addition, a study found that the addition of SPARC to endothelial cell cultures greatly reduced mRNA for fibronectin and thrombospondin-1, and increased mRNA for type 1-plasminogen activator inhibitor (Lane et al. 1992). Modulation of these ECM components by SPARC could change the ECM in a way that is suppressive of endothelial cell proliferation and migration and reduces angiogenesis. Fragments of SPARC protein have been assessed to determine which domains are important in these functions. MMP-3-induced cleavage fragments of SPARC were examined and found to regulate BAE cell proliferation and migration (Sage et al. 2003) (Fig. 17.2). It was found that two fragments (Z2 and Z3) had inhibitory effects on proliferation, whereas one fragment (Z1-containing the Cu2+ binding sequence KHGK) produced a biphasic effect, with stimulation of proliferation at low concentrations and inhibition at higher concentrations. SPARC fragments Z2 and Z3 stimulated endothelial cell migration, whereas fragment Z1 had no effect on migration. Therefore, while the effects of full-length SPARC on endothelial cell proliferation and migration are inhibitory, peptides of SPARC that are cleaved by matrix metalloproteinases in the tumor microenvironment may have stimulatory

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effects on these processes. In support of this, a stimulatory effect was observed with the Z1 KHGK-containing fragment using the in vivo chick chorioallantoic membrane (CAM) assay. Whereas the Z2 and Z3 fragments had no effect, the Z1 fragment showed biphasic effects on angiogenesis, with low concentrations increasing vessel density and higher concentrations decreasing vessel density (Sage et al. 2003) (Fig. 17.2). Another study used a neuroblastoma xenograft model and administered SPARC continuously by osmotic pump after tumors were established, which resulted in a decreased vessel density in SPARC-treated tumors (Chlenski et al. 2002). The angiosuppressive effect of SPARC was contained in the FS-E peptide encompassing the EGF-like module of the follistatin domain (Chlenski et al. 2004) (Fig. 17.2). This peptide could inhibit neovascularization induced by bFGF or neuroblastoma cells in the Matrigel plug angiogenesis assay. Such fragment studies are thus beginning to define the regions of SPARC necessary for its different effects on endothelial cells. In vivo studies show that SPARC can be either pro- or anti-angiogenic depending on the cancer model system that is used. Several studies in which SPARCexpressing or -transfected cancer cells were implanted into nude rodents reported that SPARC expression leads to a significant reduction in tumor vascularity. Glioma cells with forced expression of SPARC had reduced VEGF165 transcript, and glioma xenograft tumors that expressed SPARC had reduced VEGF staining and decreased vascularity compared to control tumors (Yunker et al. 2008) (Table 17.2). Furthermore, subcutaneous injection of a hepatocellular carcinoma cell line (Lau et al. 2006) (Table 17.2) or a transformed human embryonic kidney cell line into mice (Chlenski et  al. 2006) having forced SPARC expression resulted in tumors with decreased vascularity. The suppressive effects of SPARC on vascularity were also inferred using Sparcnull mice. Ovarian cancer cells grown in a Sparc-null environment had significantly upregulated mRNA and increased immunostaining for VEGF and the VEGF receptors VEGFR2 and neuropilin-1 in tumor nodules, as well as higher VEGF protein levels in ascitic fluid (Said and Motamed 2005) (Table  17.3). Further study correlated the loss of SPARC with increased mean vascular density (Said and Socha 2007c). Similarly, VEGF protein expression was increased in spontaneous prostate adenocarcinomas in Sparc-null mice, which correlated with increased vessel density (Said et al. 2009) (Table 17.3). In contrast, studies of other tumors grown in Sparc-null mice suggest that SPARC produced by the stroma can also stimulate angiogenesis. Two reports demonstrated a decrease in the number of vessels supplying the tumors in Sparc-null mice. One study used a pancreatic adenocarcinoma cell line injected into the pancreas of Sparcnull and Sparc-null mice (Arnold et al. 2008) (Table 17.3) and the other study used a SPARC-producing mammary carcinoma cell line derived from transgenic mice and injected into the mammary fat pad of Sparc-null and Sparc-wt mice (Sangaletti et al. 2003) (Table 17.3). In addition, when Lewis lung carcinoma cells were implanted in Sparc-null mice, tumors had reduced mRNA for all three VEGF receptors with no change in VEGF expression. Although the number of vessels was not reduced, the tumors had decreased vascular area (Brekken et al. 2003) (Table 17.3).

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The literature therefore indicates that SPARC secreted from cancer cells or present in the stroma can have differing effects dependent upon the cancer type examined, possibly due to cleavage of SPARC to produce active fragments that can either stimulate or suppress angiogenesis. However, the majority of in vivo studies indicate that SPARC can attenuate vascularity or angiogenesis by suppressing VEGF expression, by binding to VEGF growth factor to inhibit VEGF-VEGFR interactions, and/or by reorganizing the ECM in such a way as to diminish vascular formation.

17.2.3  Effects on Fibroblasts and Other Stromal Cells Evidence that SPARC plays a role in fibroblasts comes from reports that SPARC expression is altered during fibroblast pathology. There are several reports of reduced SPARC expression in fibroblasts that have been transformed with either a virus or an oncogene (Colombo et al. 1991; Kraemer et al. 1999; Vial and Castellazzi 2000). One study analyzed fibrosarcoma development after the injection of viral oncogene transformed chick embryo fibroblasts into the wing web of newborn chicks. Treatment with exogenous SPARC or reexpression of SPARC in transformed fibroblasts strongly reduced the number and size of tumors (Vial and Castellazzi 2000). In support of these studies, an in vivo model showed that fibrosarcomas induced by subcutaneous injection of a chemical carcinogen have between 32–89% less SPARC mRNA than normal fibroblasts (Colombo et  al. 1991). These results indicate that SPARC expression represses tumorigenesis in fibroblasts. In contrast, when SPARC is expressed by peritumoral fibroblasts in pancreatic adenocarcinoma, stromal SPARC is associated with a significantly worse prognosis (Infante et al. 2007). Therefore, SPARC produced by fibroblasts may have different effects on cancers cells than on the fibroblasts themselves. Studies of Sparc-null fibroblasts compared to normal fibroblasts have shown changes in phenotype including altered activation, proliferation, migration, collagen processing, and metalloproteinase expression. Skin fibroblasts, aortic smooth muscle cells, and mesangial cells from Sparc-null mice exhibited accelerated proliferation in growth media. The addition of exogenous SPARC to fibroblasts and mesangial cells reduced the proliferation rates of both Sparc-null and Sparc-wt cells with a greater reduction in proliferation for Sparc-null cells (Bradshaw et al. 1999). SPARC may reduce the proliferation of fibroblasts in part by forming a complex with PDGFAB and PDGF-BB and inhibiting the binding of PDGF to fibroblasts (Raines et al. 1992). SPARC has also been shown to enhance the migration of bFGF-stimulated fibroblasts (Chlenski et al. 2007) which may explain the increased numbers of fibroblasts in SPARC-expressing tumor xenografts (Chlenski et al. 2007; Prada et al. 2007). In addition, SPARC was shown to inhibit the activation of fibroblasts with TGF-beta as assessed by smooth muscle actin expression (Chlenski et al. 2007). Inhibition of fibroblast activation may alter the processing and deposition of collagen by fibroblasts. Sparc-null fibroblasts were found to have an increased amount

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of processed collagen I associated with cell layers and a reduced amount of collagen incorporated into an insoluble matrix suggesting that SPARC regulates the conversion of procollagen I to collagen I and reduces the incorporation of collagen I into the ECM (Rentz et al. 2007). Furthermore, the addition of exogenous SPARC to rabbit synovial fibroblasts induced the expression of the metalloproteinases collagenase, stromelysin, and the 92-kD gelatinase; and peptides from domains III and IV of SPARC could also induce collagenase expression. Further investigation revealed that SPARC-depleted conditioned media was sufficient to induce collagenase expression, indicating that the effect may be indirect through a secreted protein that is regulated by SPARC (Tremble et  al. 1993). These studies provide evidence that SPARC is expressed by fibroblasts and is an important regulator of fibroblast activity. Future research should assess the contribution of fibroblast-derived SPARC to tumor formation and progression. One study found that SPARCexpressing fibroblasts had no effect on the in vitro or in vivo growth of melanoma cell lines; however, SPARC hyperexpression by melanoma cells also did not affect in vivo tumor growth in this model (Prada et al. 2007).

17.2.4  Effects on Immune Cells Studies of the immune system in Sparc-null mice reveal alterations in immune cell numbers and function. Sparc-null mice have larger spleens characterized by follicular lymphoid hyperplasia with prominent germinal centers, attenuated marginal zones, increased white pulp, and reduced marginal zone B cells (Rempel et al. 2007). A blood panel analysis showed that Sparc-null mice have no change in the percentage of monocytes, a lower percentage of neutrophils, and a higher percentage of lymphocytes; however, the total white blood cell counts were significantly lower in Sparc-null animals. Analysis of Sparc-null bone marrow demonstrated an increase in CD3+ T cells and a decrease in CD19+ B cells. Importantly, Sparc-null mice were unable to mount an immune response as evaluated by footpad swelling in response to LPS injection (Rempel et al. 2007). Another study tested the immune response in Sparc-null mice using a cutaneous contact hypersensitivity (CHS) model. This study found that after immune system challenge, sensitized skin in Sparc-null mice had increased edema and increased numbers of neutrophils and macrophages. The enhanced CHS response was due to faster migration and increased numbers of dendritic cells in draining lymph nodes, which resulted in the accelerated priming of T cells in Sparc-null mice (Sangaletti et al. 2005). This study suggests that, as with other cell types, SPARC regulates the proliferation and migration of immune cells. Additional evidence that SPARC regulates immune cell function comes from studies of tumors grown in Sparc-null mice or tumors expressing SPARC antisense. Two studies in which melanoma cell lines expressing high amounts of SPARC were transduced with SPARC antisense and then grown in vivo demonstrated that reduction of SPARC in the cancer cells resulted in a strong recruitment of polymorpho-

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nuclear leukocytes (PMNs) and inhibition of tumor growth resulting in complete cancer cell rejection in some animals (Alvarez et al. 2005; Prada et al. 2007). The use of neutralizing antibodies to deplete PMN abrogated the inhibitory effect of SPARC reduction on tumor growth (Prada et al. 2007). It was also found that human PMN have an increased capacity to adhere to and exert a cytotoxic effect on melanoma cells in which SPARC is knocked down with antisense (Alvarez et al. 2005). Similarly, a study in which a mouse mammary carcinoma cell line was grown in Sparc-null and Sparc-wt mice found that Sparc-null mice and mice receiving bone marrow from a Sparc-null donor had a significantly larger leukocyte population with a greater number of all types of leukocytes than Sparc-wt animals. In addition, Sparc-wt animals had leukocytes located only in the perilobular stroma, whereas Sparc-null mice had a strong infiltration of leukocytes throughout the tumor parenchyma and associated with necrotic areas (Sangaletti et al. 2003) (Table 17.3). A similar change in the distribution of macrophages was found when pancreatic cancer cells were grown subcutaneously. Tumors in Sparc-null mice had macrophages uniformly distributed throughout the tumor, whereas tumors in Sparc-wt mice had macrophages only at the tumor margin (Puolakkainen et al. 2004) (Table 17.3). Another study using a model of peritoneal ovarian carcinomatosis found that tumor tissue and ascites in Sparc-null mice had increased macrophage infiltration compared to Sparc-wt mice and that Sparc-null ascitic fluid had significantly higher levels of MCP-1 and increased expression of the proinflammatory mediators IL-6 and 8-isoprostane (Said and Socha 2007c). Further in vitro assessment of the chemotactic effect of ovarian cancer cells on macrophages demonstrated that overexpression of SPARC in cancer cell lines significantly attenuated MCP-1 production and the migration of macrophages toward cancer cells (Said et al. 2008). Both macrophages and cancer cells can produce proinflammatory mediators, and exogenous SPARC or forced endogenous SPARC was shown to reduce PGE2 and 8-iosprostane production by ovarian cancer cells or co-cultures of cancer cells with macrophages and/or mesothelial cells (Said et al. 2008). SPARC also significantly suppressed the expression of IL-6 in co-cultures of ovarian cancer cells and macrophages (Said et al. 2008). Co-culture of ovarian cancer cell lines with macrophages or PGE2 treatment resulted in NF-kappaB activation, which was significantly attenuated when the ovarian cancer cells were forced to express SPARC (Said et al. 2008). Just as SPARC can attenuate the migration of macrophages, macrophages can attenuate the effects of SPARC. Alternatively activated macrophages that express stabilin-1 can efficiently internalize SPARC and target it to the endocytic pathway for lysosomal degradation. SPARC was found to bind to the extracellular EGF-like domain of stabilin-1 (Kzhyshkowska et al. 2006). Furthermore, the regulation of leukocyte transendothelial migration by SPARC has also been investigated. It was found that the expression of SPARC by leukocytes is required for transendothelial migration because leukocytes derived from Sparc-null mice have a significantly reduced capacity to migrate through endothelial cell monolayers. Leukocytes may need SPARC to induce intercellular gaps and compromise barrier function in endothelial cells (Kelly et al. 2007).

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These studies bring up the question of whether the increased immune cell infiltration of the tumor tissue when SPARC is absent is a result of alterations in ECM deposition that reduce the interference to immune cell migration or changes in the migration of the immune cells themselves because the cells are not expressing SPARC. Further studies are necessary to determine the molecular mechanisms involved in the effects of SPARC on immune cell function.

17.2.5  Summary Overall, the research into the role of SPARC in the tumor microenvironment indicates that SPARC plays a key role in regulating tumor growth and invasion, angiogenesis, ECM production and composition, and immune cell infiltration during tumor development and progression. The conflicting data regarding the roles of SPARC in various types of cancer shed light on the importance of the microenvironment in which the tumor develops. The interaction of cancer cells with stromal and immune cells is regulated by the expression of specific integrins, ECM components, matrix-degrading proteases, and growth factors within the tumor environment. As described above, SPARC can inhibit the activity of certain integrins and growth factors and increase the production of ECM constituents by cancer and stromal cells. SPARC regulates the production of matrix-degrading proteases, which can not only remodel the ECM but also cleave SPARC into fragments that can have opposing biological activity compared to full-length SPARC. SPARC can also alter the sensitivity of cancer cells to the cytotoxic effects of the immune system. It is important to consider whether the in vitro and the in vivo data reflect what is observed in the human tissues and the survival patterns of the patients. Interestingly, the studies using the Sparc-null and Sparc-wt mice suggest that stromal SPARC is suppressive to tumor growth, invasion and metastasis for the majority of tumor types (Fig. 17.1). An expectation might be then that patients having tumors with high stromal SPARC should have better survival outcome. However this is not the case (Table 17.1). In general, high SPARC expression correlates with poor patient survival. However, the emerging data suggests that stromal SPARC may suppress the infiltration of macrophages and inhibit the infiltration of leukocytes, which would reduce the ability of the immune system to fight cancer cells. These latter observations would support the immunohistochemical and clinical correlative data, such that high SPARC expression would correlate with poor patient survival. More research is required to understand the relationship between the SPARC-positive tumors and the SPARC-negative tumors and their interactions with SPARC-positive and SPARC-negative stroma. Regardless of the differences in the effects of SPARC in various cancers, the literature provides solid evidence that the regulation of SPARC has the potential to control cancer and therapeutic strategies should be devised.

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17.3 Potential for Utilizing or Targeting SPARC to Control Cancer In certain cancer types, such as breast cancer, head and neck cancer, glioma and melanoma, SPARC may provide a therapeutic target to inhibit cancer cell invasion. However in other cancers, like neuroblastoma and ovarian, lung, and prostate cancers, the reexpression of SPARC in the tumor may provide a treatment for cancer. Several groups are attempting to utilize SPARC in the development of new cancer therapies based on both strategies. For tumors having high expression of SPARC, one approach is to directly target SPARC using the immune system. For example, one group used a SPARC peptide to immunize mice against SPARC. When a breast cancer cell line was inoculated subcutaneously, 50% of immunized mice had complete tumor rejection and the other 50% had significantly smaller tumors compared to non-immunized mice (Ikuta et al. 2009). Because SPARC has a high binding affinity to albumin, it was proposed that SPARC secreted by tumors could bind to albumin-bound drug and accumulate the drug within the tumor (Desai et al. 2009). Albumin-bound paclitaxel is in clinical trials for the treatment of head and neck squamous cell carcinoma, and the data indicate that the response to treatment was significantly higher for patients with SPARC-positive cancer compared to SPARC-negative cancer patients (Desai et al. 2009). Targeting SPARC can also be done indirectly. The anti-cancer agent Ukrain was found to reduce SPARC protein expression in glioblastoma cells (Gagliano et al. 2006). Furthermore, the fact that cancer cells and stromal cells can express high amounts of SPARC provides a target for directing viral gene expression to the tumor microenvironment, by exploiting the use of the SPARC gene promoter sequence. One group used the SPARC gene promoter to specifically target suicidal gene expression in SPARC-expressing melanoma cancer and stromal cells. Melanoma cells expressing the suicidal thymidine kinase gene under the control of a SPARC promoter were implanted subcutaneously and then treated with ganciclovir 10 days later. Tumor regression was seen in all mice and in most cases no visible tumor was present (Lopez et al. 2006). Similar results were obtained when a mix of tumor cells with and without the suicidal gene was implanted or when endothelial cells expressing the suicidal gene were mixed with melanoma cells (Lopez et al. 2006). These results indicated the efficacy of the approach and that targeting stromal cells was as effective as targeting the tumor cells. This model was advanced by the development of a conditionally replicative oncolytic adenovirus (CRAd) based on the SPARC promoter. This virus was used to assess the cross-talk between the tumor and stromal cells. The SPARC-based CRAd had a potent anti-tumor effect on melanoma xenografts with complete tumor disappearance in some mice; however a delay in tumor growth with no cure was seen when melanoma cells were mixed with fibroblasts or endothelial cells, indicating that targeting the tumor cells alone was insufficient.

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The CRAd was also effective in SPARC-negative prostate cancer cells; however, the presence of stromal cells enhanced viral activity, possibly due to the induction of cell cycle in the cancer cells (Lopez et al. 2009). These experiments emphasize the need to study both the tumor cells and stromal cells to define therapeutic strategies. For tumors having a methylated SPARC promoter, an approach has been to reactivate SPARC expression. Demethylating agents such as 5-azacytidine and decitabine have already been used in clinical trials for myelodysplastic syndrome, and results indicate that 5-azacytidine prolongs the overall survival in these patients (Gurion et al. 2009). Indeed, SPARC is one of the genes upregulated in multiple myeloma cells treated with demethylating agents (Heller et  al. 2008). Since the suppression of SPARC correlates with poor overall survival of multiple myeloma patients, its reactivation is considered to be a therapeutic approach (Heller et  al. 2008). Although these drugs have not been successful for glioma treatment, their use is also being investigated for the reactivation of SPARC in other solid tumors. For example, a demethylating agent could restore SPARC expression in prostate cancer cell lines (Sato et al. 2003). Other studies show that aberrant methylation of SPARC leads to chemotherapy resistance in colorectal cancer (Tai et al. 2005), and direct administration of SPARC was shown to enhance apoptosis and potentiate chemotherapy sensitivity (Tang and Tai 2007). The same effects were accomplished using a demethylating drug; 5-aza-2′-deocycytidine treatment increased SPARC expression and improved response to therapy (Cheetham et al. 2008). In addition, the nonsteroidal anti-inflammatory drug NS398 reactivated SPARC expression in lung cancer cells by promoter demethylation, and this treatment was found to decrease the invasiveness of lung cancer cells in vitro (Pan et al. 2008). Finally, the effects of SPARC were enhanced with vitamin D, which was found to act synergistically with SPARC to increase the susceptibility of therapy-resistant colorectal cancer cells to chemotherapy (Taghizadeh et al. 2007). For tumors having suppressed SPARC in the absence of a methylated promoter, such as neuroblastoma (Yang et al. 2007), an approach has been to administer SPARC directly. For example, SPARC delivered by osmotic pump resulted in significantly reduced neuroblastoma tumor volume and vascularity in vivo (Chlenski et al. 2002). This direct approach can also work for tumors having methylated SPARC. These preclinical studies provide promising data that research efforts into the function of SPARC will be translated into the use of SPARC for the targeting or treatment of various types of cancer.

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Dalla-Torre CA, Yoshimoto M et al (2006) Effects of THBS3, SPARC and SPP1 expression on biological behavior and survival in patients with osteosarcoma. BMC Cancer 6:237 De Craene B, van Roy F et al (2005) Unraveling signalling cascades for the Snail family of transcription factors. Cell Signal 17(5):535–547 Desai N, Trieu V et al (2009) SPAR+C Expression correlates with tumor response to albuminbound paclitaxel in head and neck cancer patients. Transl Oncol 2(2):59–64 DiMartino JF, Lacayo NJ et al (2006) Low or absent SPARC expression in acute myeloid leukemia with MLL rearrangements is associated with sensitivity to growth inhibition by exogenous SPARC protein. Leukemia 20(3):426–432 Funk SE, Sage EH (1991) The Ca2(+)-binding glycoprotein SPARC modulates cell cycle progression in bovine aortic endothelial cells. Proc Natl Acad Sci U S A 88(7):2648–2652 Gagliano N, Moscheni C et al (2006) Effect of Ukrain on matrix metalloproteinase-2 and Secreted Protein Acidic and Rich in Cysteine (SPARC) expression in human glioblastoma cells. Anticancer Drugs 17(2):189–194 Gieseg MA, Cody T et al (2002) Expression profiling of human renal carcinomas with functional taxonomic analysis. BMC Bioinforma 3:26 Golembieski WA, Ge S et  al (1999) Increased SPARC expression promotes U87 glioblastoma invasion in vitro. Int J Dev Neurosci 17(5–6):463–472 Golembieski WA, Thomas SL et al (2008) HSP27 mediates SPARC-induced changes in glioma morphology, migration, and invasion. Glia 56(10):1061–1075 Gurion R, Vidal L et al (2009) 5-azacitidine prolongs overall survival in patients with myelodysplastic syndrome – systematic review and meta-analysis. Haematologica 2009 Sep 22 [Epub ahead of print] Haber CL, Gottifredi V et al (2008) SPARC modulates the proliferation of stromal but not melanoma cells unless endogenous SPARC expression is downregulated. Int J Cancer 122(7):1465–1475 Hecht JT, Sage EH (2006) Retention of the matricellular protein SPARC in the endoplasmic reticulum of chondrocytes from patients with pseudoachondroplasia. J Histochem Cytochem 54(3):269–274 Heller G, Schmidt WM et  al (2008) Genome-wide transcriptional response to 5-aza-2′deoxycytidine and trichostatin a in multiple myeloma cells. Cancer Res 68(1):44–54 Hong SM, Kelly D et al (2008) Multiple genes are hypermethylated in intraductal papillary mucinous neoplasms of the pancreas. Mod Pathol 21(12):1499–1507 Huynh MH, Hong H et al (2000) Association of SPARC (osteonectin, BM-40) with extracellular and intracellular components of the ciliated surface ectoderm of Xenopus embryos. Cell Motil Cytoskeleton 47(2):154–162 Ikuta Y, Hayashida Y et al (2009) Identification of the H2-Kd-restricted cytotoxic T lymphocyte epitopes of a tumor-associated antigen, SPARC, which can stimulate antitumor immunity without causing autoimmune disease in mice. Cancer Sci 100(1):132–137 Infante JR, Matsubayashi H et  al (2007) Peritumoral fibroblast SPARC expression and patient outcome with resectable pancreatic adenocarcinoma. J Clin Oncol 25(3):319–325 Jones C, Mackay A et al (2004) Expression profiling of purified normal human luminal and myoepithelial breast cells: identification of novel prognostic markers for breast cancer. Cancer Res 64(9):3037–3045 Junnila S, Kokkola A et al (2009) Gene expression analysis identifies over-expression of CXCL1, SPARC, SPP1, and SULF1 in gastric cancer. Genes Chromosomes Cancer 2009 Sep 24. [Epub ahead of print] Kamihagi K, Katayama M et  al (1994) Osteonectin/SPARC regulates cellular secretion rates of fibronectin and laminin extracellular matrix proteins. Biochem Biophys Res Commun 200(1):423–428 Kato Y, Nagashima Y et al (2005) Expression of SPARC in tongue carcinoma of stage II is associated with poor prognosis: an immunohistochemical study of 86 cases. Int J Mol Med 16(2):263–268 Kelly KA, Allport JR et al (2007) SPARC is a VCAM-1 counter-ligand that mediates leukocyte transmigration. J Leukoc Biol 81(3):748–756

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Koblinski JE, Kaplan-Singer BR et al (2005) Endogenous osteonectin/SPARC/BM-40 expression inhibits MDA-MB-231 breast cancer cell metastasis. Cancer Res 65(16):7370–7377 Koukourakis MI, Giatromanolaki A et al (2003) Enhanced expression of SPARC/osteonectin in the tumor-associated stroma of non-small cell lung cancer is correlated with markers of hypoxia/ acidity and with poor prognosis of patients. Cancer Res 63(17):5376–5380 Kraemer M, Tournaire R et al (1999) Rat embryo fibroblasts transformed by c-Jun display highly metastatic and angiogenic activities in vivo and deregulate gene expression of both angiogenic and antiangiogenic factors. Cell Growth Differ 10(3):193–200 Kunigal S, Gondi CS et al (2006) SPARC-induced migration of glioblastoma cell lines via uPAuPAR signaling and activation of small GTPase RhoA. Int J Oncol 29(6):1349–1357 Kupprion C, Motamed K et  al (1998) SPARC (BM-40, osteonectin) inhibits the mitogenic effect of vascular endothelial growth factor on microvascular endothelial cells. J Biol Chem 273(45):29635–29640 Kzhyshkowska J, Workman G et al (2006) Novel function of alternatively activated macrophages: stabilin-1-mediated clearance of SPARC. J Immunol 176(10):5825–5832 Lane TF, Iruela-Arispe ML et al (1992) Regulation of gene expression by SPARC during angiogenesis in vitro. Changes in fibronectin, thrombospondin-1, and plasminogen activator inhibitor-1. J Biol Chem 267(23):16736–16745 Lapointe J, Li C et al (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci U S A 101(3):811–816 Lau CP, Poon RT et al (2006) SPARC and Hevin expression correlate with tumour angiogenesis in hepatocellular carcinoma. J Pathol 210(4):459–468 Le Bail B, Faouzi S et al (1999) Osteonectin/SPARC is overexpressed in human hepatocellular carcinoma. J Pathol 189(1):46–52 Ledda MF, Bravo AI et al (1997a) The expression of the secreted protein acidic and rich in cysteine (SPARC) is associated with the neoplastic progression of human melanoma. J Invest Dermatol 108(2):210–214 Ledda MF, Adris S et al (1997b) Suppression of SPARC expression by antisense RNA abrogates the tumorigenicity of human melanoma cells. Nat Med 3(2):171–176 Lopez MV, Blanco P et al (2006) Expression of a suicidal gene under control of the human secreted protein acidic and rich in cysteine (SPARC) promoter in tumor or stromal cells led to the inhibition of tumor cell growth. Mol Cancer Ther 5(10):2503–2511 Lopez MV, Viale DL et al (2009) Tumor associated stromal cells play a critical role on the outcome of the oncolytic efficacy of conditionally replicative adenoviruses. PLoS One 4(4):e5119 Luo A, Kong J et  al (2004) Discovery of Ca2+-relevant and differentiation-associated genes downregulated in esophageal squamous cell carcinoma using cDNA microarray. Oncogene 23(6):1291–1299 Maloney SC, Marshall JC et  al (2009) SPARC is expressed in human uveal melanoma and its abrogation reduces tumor cell proliferation. Anticancer Res 29(8):3059–3064 Mann K, Deutzmann R et al (1987) Solubilization of protein BM-40 from a basement membrane tumor with chelating agents and evidence for its identity with osteonectin and SPARC. FEBS Lett 218(1):167–172 McClung HM, Thomas SL et al (2007) SPARC upregulates MT1-MMP expression, MMP-2 activation, and the secretion and cleavage of galectin-3 in U87MG glioma cells. Neurosci Lett 419(2):172–177 Mok SC, Chan WY et al (1996) SPARC, an extracellular matrix protein with tumor-suppressing activity in human ovarian epithelial cells. Oncogene 12(9):1895–1901 Motamed K, Blake DJ (2003) Fibroblast growth factor receptor-1 mediates the inhibition of endothelial cell proliferation and the promotion of skeletal myoblast differentiation by SPARC: a role for protein kinase A. J Cell Biochem 90(2):408–423 Motamed K, Sage EH (1998) SPARC inhibits endothelial cell adhesion but not proliferation through a tyrosine phosphorylation-dependent pathway. J Cell Biochem 70(4):543–552 Paley PJ, Goff BA et al (2000) Alterations in SPARC and VEGF immunoreactivity in epithelial ovarian cancer. Gynecol Oncol 78(3 Pt 1):336–341

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Pan MR, Chang HC et  al (2008) The nonsteroidal anti-inflammatory drug NS398 reactivates SPARC expression via promoter demethylation to attenuate invasiveness of lung cancer cells. Exp Biol Med (Maywood) 233(4):456–462 Pen A, Moreno MJ et al (2007) Molecular markers of extracellular matrix remodeling in glioblastoma vessels: microarray study of laser-captured glioblastoma vessels. Glia 55(6):559–572 Porter D, Lahti-Domenici J et  al (2003) Molecular markers in ductal carcinoma in situ of the breast. Mol Cancer Res 1(5):362–375 Prada F, Benedetti LG et  al (2007) SPARC endogenous level, rather than fibroblast-produced SPARC or stroma reorganization induced by SPARC, is responsible for melanoma cell growth. J Invest Dermatol 127(11):2618–2628 Puolakkainen PA, Brekken RA et al (2004) Enhanced growth of pancreatic tumors in SPARC-null mice is associated with decreased deposition of extracellular matrix and reduced tumor cell apoptosis. Mol Cancer Res 2(4):215–224 Raines EW, Lane TF et al (1992) The extracellular glycoprotein SPARC interacts with plateletderived growth factor (PDGF)-AB and -BB and inhibits the binding of PDGF to its receptors. Proc Natl Acad Sci U S A 89(4):1281–1285 Rempel SA, Ge S et al (1999) SPARC: a potential diagnostic marker of invasive meningiomas. Clin Cancer Res 5(2):237–241 Rempel SA, Golembieski WA et al (1998) SPARC: a signal of astrocytic neoplastic transformation and reactive response in human primary and xenograft gliomas. J Neuropathol Exp Neurol 57(12):1112–1121 Rempel SA, Golembieski WA et al (2001) SPARC modulates cell growth, attachment and migration of U87 glioma cells on brain extracellular matrix proteins. J Neurooncol 53(2):149–160 Rempel SA, Hawley RC et al (2007) Splenic and immune alterations of the Sparc-null mouse accompany a lack of immune response. Genes Immun 8(3):262–274 Rentz TJ, Poobalarahi F et al (2007) SPARC regulates processing of procollagen I and collagen fibrillogenesis in dermal fibroblasts. J Biol Chem 282(30):22062–22071 Rich JN, Shi Q et al (2003) Bone-related genes expressed in advanced malignancies induce invasion and metastasis in a genetically defined human cancer model. J Biol Chem 278(18):15951– 15957 Rich JN, Hans C et al (2005) Gene expression profiling and genetic markers in glioblastoma survival. Cancer Res 65(10):4051–4058 Rodriguez-Jimenez FJ, Caldes T et al (2007) Overexpression of SPARC protein contrasts with its transcriptional silencing by aberrant hypermethylation of SPARC CpG-rich region in endometrial carcinoma. Oncol Rep 17(6):1301–1307 Sage H, Johnson C et al (1984) Characterization of a novel serum albumin-binding glycoprotein secreted by endothelial cells in culture. J Biol Chem 259(6):3993–4007 Sage EH, Reed M et al (2003) Cleavage of the matricellular protein SPARC by matrix metalloproteinase 3 produces polypeptides that influence angiogenesis. J Biol Chem 278(39):37849– 37857 Said N, Motamed K (2005) Absence of host-secreted protein acidic and rich in cysteine (SPARC) augments peritoneal ovarian carcinomatosis. Am J Pathol 167(6):1739–1752 Said N, Najwer I et  al (2007a) Secreted protein acidic and rich in cysteine (SPARC) inhibits integrin-mediated adhesion and growth factor-dependent survival signaling in ovarian cancer. Am J Pathol 170(3):1054–1063 Said NA, Najwer I et al (2007b) SPARC inhibits LPA-mediated mesothelial-ovarian cancer cell crosstalk. Neoplasia 9(1):23–35 Said N, Socha MJ et al (2007c) Normalization of the ovarian cancer microenvironment by SPARC. Mol Cancer Res 5(10):1015–1030 Said NA, Elmarakby AA et al (2008). SPARC ameliorates ovarian cancer-associated inflammation. Neoplasia 10(10):1092–1104 Said N, Frierson HF Jr et al (2009) The role of SPARC in the TRAMP model of prostate carcinogenesis and progression. Oncogene 28(39):3487–3498 Sakai N, Baba M et al (2001) SPARC expression in primary human renal cell carcinoma: upregulation of SPARC in sarcomatoid renal carcinoma. Hum Pathol 32(10):1064–1070

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Sangaletti S, Gioiosa L et  al (2005) Accelerated dendritic-cell migration and T-cell priming in SPARC-deficient mice. J Cell Sci 118(Pt 16):3685–3694 Sangaletti S, Stoppacciaro A et  al (2003) Leukocyte, rather than tumor-produced SPARC, determines stroma and collagen type IV deposition in mammary carcinoma. J Exp Med 198(10):1475–1485 Sangaletti S, Di Carlo E et al (2008) Macrophage-derived SPARC bridges tumor cell-extracellular matrix interactions toward metastasis. Cancer Res 68(21):9050–9059 Sansom OJ, Mansergh FC et al (2007) Deficiency of SPARC suppresses intestinal tumorigenesis in APCMin/+mice. Gut 56(10):1410–1414 Sarrio D, Rodriguez-Pinilla SM et al (2008) Epithelial-mesenchymal transition in breast cancer relates to the basal-like phenotype. Cancer Res 68(4):989–997 Sato N, Fukushima N et al (2003) SPARC/osteonectin is a frequent target for aberrant methylation in pancreatic adenocarcinoma and a mediator of tumor-stromal interactions. Oncogene 22(32):5021–5030 Schittenhelm J, Mittelbronn M et al (2006) Patterns of SPARC expression and basement membrane intactness at the tumour-brain border of invasive meningiomas. Neuropathol Appl Neurobiol 32(5):525–531 Schultz C, Lemke N et al (2002) Secreted protein acidic and rich in cysteine promotes glioma invasion and delays tumor growth in vivo. Cancer Res 62(21):6270–6277 Seno T, Harada H et al (2009) Downregulation of SPARC expression inhibits cell migration and invasion in malignant gliomas. Int J Oncol 34(3):707–715 Shi Q, Bao S et  al (2004) Secreted protein acidic, rich in cysteine (SPARC), mediates cellular survival of gliomas through AKT activation. J Biol Chem 279(50):52200–52209 Shi Q, Bao S et al (2007) Targeting SPARC expression decreases glioma cellular survival and invasion associated with reduced activities of FAK and ILK kinases. Oncogene 26(28):4084–4094 Smit DJ, Gardiner BB (2007) Osteonectin downregulates E-cadherin, induces osteopontin and focal adhesion kinase activity stimulating an invasive melanoma phenotype. Int J Cancer 121(12):2653–2660 Socha M, Said N et al (2009) Aberrant promoter methylation of SPARC in ovarian cancer. Neoplasia 11(2):126–135 Sosa MS, Girotti MR et al (2007) Proteomic analysis identified N-cadherin, clusterin, and HSP27 as mediators of SPARC (secreted protein, acidic and rich in cysteines) activity in melanoma cells. Proteomics 7(22):4123–4134 Suzuki M, Hao C et al (2005) Aberrant methylation of SPARC in human lung cancers. Br J Cancer 92(5):942–948 Taghizadeh F, Tang MJ et al (2007) Synergism between vitamin D and secreted protein acidic and rich in cysteine-induced apoptosis and growth inhibition results in increased susceptibility of therapy-resistant colorectal cancer cells to chemotherapy. Mol Cancer Ther 6(1):309–317 Tai IT, Dai M et al (2005) Genome-wide expression analysis of therapy-resistant tumors reveals SPARC as a novel target for cancer therapy. J Clin Invest 115(6):1492–1502 Takeno A, Takemasa I et al (2008) Integrative approach for differentially overexpressed genes in gastric cancer by combining large-scale gene expression profiling and network analysis. Br J Cancer 99(8):1307–1315 Tang MJ, Tai IT (2007) A novel interaction between procaspase 8 and SPARC enhances apoptosis and potentiates chemotherapy sensitivity in colorectal cancers. J Biol Chem 282(47):34457– 34467 Termine JD, Kleinman HK et al (1981) Osteonectin, a bone-specific protein linking mineral to collagen. Cell 26(1 Pt 1):99–105 Thomas R, True LD et  al (2000) Differential expression of osteonectin/SPARC during human prostate cancer progression. Clin Cancer Res 6(3):1140–1149 Tremble PM, Lane TF et  al (1993) SPARC, a secreted protein associated with morphogenesis and tissue remodeling, induces expression of metalloproteinases in fibroblasts through a novel extracellular matrix-dependent pathway. J Cell Biol 121(6):1433–1444 Tsuji T, Ibaragi S (2009) Epithelial-mesenchymal transition and cell cooperativity in metastasis. Cancer Res 69(18):7135–7139

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Vadlamuri SV, Media J et al (2003) SPARC affects glioma cell growth differently when grown on brain ECM proteins in vitro under standard versus reduced-serum stress conditions. Neuro Oncol 5(4):244–254 Vial E, Castellazzi M (2000) Down-regulation of the extracellular matrix protein SPARC in vSrcand vJun-transformed chick embryo fibroblasts contributes to tumor formation in vivo. Oncogene 19(14):1772–1782 Wang CS, Lin KH et al (2004) Overexpression of SPARC gene in human gastric carcinoma and its clinic-pathologic significance. Br J Cancer 91(11):1924–1930 Watkins G, Douglas-Jones A et al (2005) Increased levels of SPARC (osteonectin) in human breast cancer tissues and its association with clinical outcomes. Prostaglandins Leukot Essent Fatty Acids 72(4):267–272 Wessel C, Westhoff CC et al (2008) CD34(+) fibrocytes in melanocytic nevi and malignant melanomas of the skin. Virchows Arch 453(5):485–489 Wiese AH, Auer J et al (2007) Identification of gene signatures for invasive colorectal tumor cells. Cancer Detect Prev 31(4):282–295 Wong FH, Huang CY et al (2009) Combination of microarray profiling and protein-protein interaction databases delineates the minimal discriminators as a metastasis network for esophageal squamous cell carcinoma. Int J Oncol 34(1):117–128 Xue LY, Hu N et  al (2006) Tissue microarray analysis reveals a tight correlation between protein expression pattern and progression of esophageal squamous cell carcinoma. BMC Cancer 6:296 Yamanaka M, Kanda K et al (2001) Analysis of the gene expression of SPARC and its prognostic value for bladder cancer. J Urol 166(6):2495–2499 Yamashita K, Upadhay S et  al (2003) Clinical significance of secreted protein acidic and rich in cystein in esophageal carcinoma and its relation to carcinoma progression. Cancer 97(10):2412–2419 Yan Q, Weaver M et al (2005) Matricellular protein SPARC is translocated to the nuclei of immortalized murine lens epithelial cells. J Cell Physiol 203(1):286–294 Yang E, Kang HJ et al (2007) Frequent inactivation of SPARC by promoter hypermethylation in colon cancers. Int J Cancer 121(3):567–575 Yiu GK, Chan WY et al (2001) SPARC (secreted protein acidic and rich in cysteine) induces apoptosis in ovarian cancer cells. Am J Pathol 159(2):609–622 Yunker CK, Golembieski W et  al (2008) SPARC-induced increase in glioma matrix and decrease in vascularity are associated with reduced VEGF expression and secretion. Int J Cancer 122(12):2735–2743 Zeltner L, Schittenhelm J et  al (2007) The astrocytic response towards invasive meningiomas. Neuropathol Appl Neurobiol 33(2):163–168

Chapter 18

Integrin-Extracellular Matrix Interactions Christie J. Avraamides and Judith A. Varner

18.1 Introduction The tumor microenvironment is comprised of extracellular matrix molecules, tumor cells, endothelial cells, immune cells and fibroblasts. Fibroblasts synthesize extracellular matrix, and secrete growth factors and matrix metalloproteinases, which contribute to tumor growth and metastasis. Extracellular matrix proteins include collagens, laminins, fibronectin and proteoglycans (reviewed in Marastoni et  al. 2008). These proteins provide mechanical support for cells, facilitate cell communication and serve as substrates for cell migration (reviewed in Kalluri 2003). Extracellular matrix (ECM) remodeling promotes embryonic development, wound healing and tumor progression. The ECM plays a key role in tumor growth and metastasis by promoting the growth of neoplastic cells, new blood vessels (angiogenesis) and new lymphatic vessels (lymphangiogenesis). The ECM may also promote the arrest of the tumor cells in capillary beds of distant organs. Intracellular signals triggered by the interaction of the ECM with integrin receptors occur in macromolecular structures called focal adhesions (Marastoni et al. 2008). In this review, we examine the contribution of integrins to tumor cell-ECM interactions. Angiogenesis, the development of new blood vessels from pre-existing vessels, is important for tumor growth and metastasis (Carmeliet 2005). Fibroblasts, tumor cells and tumor-associated macrophages secrete growth factors, such as vascular endothelial growth factor (VEGF-A) that promote angiogenesis (Adams and Alitalo 2007; Lin and Pollard 2007; Schmid and Varner 2007). Integrins regulate tumor angiogenesis by enabling endothelial cell migration and survival (reviewed in Avraamides et al. 2008). In addition, lymphangiogenesis, the growth of new lymphatic vessels, promotes tumor metastasis (Adams and Alitalo 2007; Dadras et al. 2005; Hirakawa et al. 2005; Roma et al. 2006). Tumor cells secrete factors that stimJ. A. Varner () Moores UCSD Cancer Center, University of California, San Diego, 3855 Health Sciences Drive #0819, La Jolla, CA 92093-0819, USA e-mail: [email protected] Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0819, USA M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_18, © Springer Science+Business Media B.V. 2011

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ulate tumor and lymph node lymphangiogenesis, leading to increased lymph node metastasis (Dadras et  al. 2005; Hirakawa et  al. 2005; Roma et  al. 2006). Recent studies indicate that integrins modulate tumor lymphangiogenesis (reviewed in Avraamides et al. 2008). As integrins regulate tumor growth and metastasis, tumor angiogenesis and tumor lymphangiogenesis by interacting with ECM proteins, integrin and the ECM are considered excellent targets for potential clinical applications.

18.2 Fibroblasts in Tumor Progression Fibroblasts affect the tumor microenvironment by synthesizing ECM, secreting proteases, and secreting growth factors (Kalluri 2003; Nyberg et al. 2008). Normal fibroblasts are embedded within the ECM of normal connective tissue, which consists of primarily of type I collagen and fibronectin. Fibroblasts interact with their surroundings via integrins. Within tumors, fibroblasts acquire an activated phenotype. Activated fibroblasts exhibit increased proliferation and enhanced secretion of ECM proteins, such as type I collagen and cellular fibronectin, which contains alternatively spliced CS-1 and EDA domains. These activated fibroblasts are called carcinoma-associated fibroblasts (CAFs) or tumor associated fibroblasts (TAFs). Tumor cell secreted growth factors such as transforming growth factor-β (TGF-β) appear to activate fibroblasts in the tumor microenvironment (Kalluri and Zeisberg 2006). Once activated, TAFs may promote tumor growth. For example, TAFs in invasive human breast carcinomas promote the growth of mammary cells and enhance tumor angiogenesis by expressing VEGF-A (Kalluri and Zeisberg 2006). Fibroblasts also remodel the ECM within tumors, producing matrix-metalloproteinases that degrade collagen fibers and thereby promote changes in integrin expression and fibroblast motility (Perentes et al. 2009). Fibroblasts may promote collective tumor cell metastasis by digesting ECM in advance of migrating clusters of tumor cells (Gaggioli et al. 2007; Friedl and Gilmour 2009).

18.3 Fibronectin Fibronectin is encoded by a single gene on human chromosome 2 (Ayad et  al. 1994), and alternative splicing generates multiple isoforms. Fibronectin is a large molecular weight extracellular glycoprotein present at low concentration in basement membranes, high concentrations in remodeling extracellular matrices and at 300  µg/ml in plasma (Mosher 1984). This ECM protein is a dimer composed of two subunits (220–250 kDa) joined by a disulfide bond near the carboxy terminus. Two forms of fibronectin exist, an insoluble form of disulfide bonded oligomers and fibrils and a soluble dimeric form. Each fibronectin monomer has twelve type I,

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Structure of Fibronectin EDB Domain EDA Domain 1 2

Type I domain

3

4

5

6

7

8

9 10 11

RGD

12 13 14

15

IIICS

Type II domain Type III domain

Fig. 18.1   Cellular fibronectin structure. Fibronectin is comprised of two chains, which are disulfide bonded at the carboxy terminus. Cellular fibronectin isoforms contain 12 type I, 2 type II and 15 type III domains. Fibronectin isoforms arise from alternative splicing, which can include or exclude EDA, EDB and IIICS domains. Plasma fibronectin lacks the EDA and EDB domain, while cellular fibronectin isoforms can contain variable proportions of these domains. One monomer of plasma fibronectin contains a IIICS variable region, while the other does not. However, IIICS domains are expressed cellular fibronectin secreted by endothelial cells. In humans, five variants of the IIICS region generate up to 20 fibronectin isoforms. The RGD-binding integrins, such as α5β1 and αvβ3, bind the Arginine-Glycine-Aspartic Acid ( RGD) moiety located in an extended loop in the tenth type III repeat, while the integrin α4β1 bind an Glutamic Acid-Isoleucine-Leucine-Aspartic Acid-Valine (EILDV) peptide located in the alternatively spliced IIICS domain

two type II and fifteen to seventeen type III protein domains (Fig. 18.1). Alternative type III domains also form the EDA (extra domain A) and EDB (extra domain B), which are alternatively spliced domains of cellular fibronectin which are absent in plasma fibronectin. The type III V (CS) domain is present in both subunits of cellular fibronectin but only in one subunit of plasma fibronectin (Magnusson and Mosher 1998). This alternatively spliced domain contains binding sites for integrins α4β1, α4β7 and α9β1.

18.4 Collagen The collagens form a family of structurally related, fibrous glycoproteins with high glycine and proline content. Collagens have high tensile strength and are mainly produced by fibroblasts. The main types of collagen found in connective tissue are type I, II, III, V and XI; type I is the most common isoforms in connective tissue (Shoulders et al. 2009). These isoforms are called fibrillar collagens, because they assemble into cable-like fibrils, which in turn assemble into thick fibers. Not all collagens form fibers. For example, type IV collagen, the most abundant constituent of the basement membrane is nonfibrillar and these collagen molecules are organized into a flattened network. Collagen molecules are trimers consisting of three polypeptide chains called α chains that wrap around each other to form a triple helix. Collagens are characterized by a unique primary sequence in which every third amino acid is a glycine (Gly-X-Y motif). The X and Y position amino acids of the

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motif are usually hydroxylysine or hydroxyproline (Shoulders et al. 2009). Prolines stabilize the helical conformation of the α chain, while glycine is regularly spaced at every third residue throughout the central region of the α chain. This structure allows tight packing of three α-helical chains to form a collagen superhelix.

18.5 Extracellular Matrix Proteins and Cancer Overexpression of provisional ECM molecules that can promote cell survival enhances tumor growth and progression (Marastoni et  al. 2008). Disruption of the normal tissue ECM composition or architecture usually precedes tumor formation and can trigger genomic changes in tumor cells (Ghajar and Bissell 2008). While quiescent endothelial cells express fibronectin poorly, endothelial cells express this ECM protein during embryonic development and tumor progression. Since fibronectin is highly upregulated in transformed tissues, it has been called an oncofetal antigen. Increased fibronectin expression has been demonstrated in many tumor types, including nonsmall cell lung carcinoma (NSCLC) (Marastoni et al. 2008). In small cell lung carcinomas (SCLC), fibronectin expression is also elevated; at least 25% of tumor cells express fibronectin (Ritzenthaler et al. 2008). The EDA isoform of fibronectin is normally expressed during embryogenesis, while in adult tissue it is present only in areas of wound healing or tumor growth. The alternatively spliced EDA fibronectin is present in 47% of breast carcinoma cells and in 69% of adjacent stromal cells (Ritzenthaler et  al. 2008). Transforming growth factor β (TGFβ), a growth factor expressed by many tumor cells, promotes the inclusion of the EDA domain into oncofetal fibronectin (Muro et al. 2008). Endothelial cells in tumors also secrete fibronectin that contains EDA, EDB and IIICS domains. Integrins α4β1, α5β1, and α9β1, all of which are expressed by vascular endothelial cells in tumors (Avraamides et al. 2008), facilitate cell adhesion and spreading by binding to fibronectin (Liao et  al. 2002; Manabe et  al. 1997). Tumor and endothelial cells secrete fibronectin containing the EDB domain, an isoform that is absent in plasma and tissues of healthy adults (Kaspar et al. 2006). However, fibronectin containing the EDB domain are present around neovascular structures, on invasive ductal breast carcinoma and in brain tumors (Kaspar et al. 2006). Most studies indicate that increased fibronectin is important for tumor angiogenesis and tumor invasion. However, loss of fibronectin can also result in loss of contact inhibition and tumor metastasis, for example, in head and neck cancers (Beier et  al. 2007). Fibronectin loss is associated with the expression of human papilloma virus early protein −2, a transcription factor that suppresses fibronectin expression (Beier et al. 2007). The ECM collagen also has a role in tumor pathogenesis. For example, type IV collagen found normally in the basement membrane, is associated with tumor fibrosis and accumulates in the tumor during tumor development (Kalluri 2003). Collagen VII is necessary for tumorigenesis of Ras-transformed keratinocytes in a model of squamous cell carcinoma (Ghajar and Bissell 2008). Breast tumors exhibit

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tissue stiffness due deposition of large amounts of collagen by activated fibroblasts (Ghajar and Bissell 2008). This matrix stiffness acts via β1 integrin clustering and sustained activation of Rho to disrupt mammary epithelial cell differentiation. The importance of integrin β1 in tumor progression is highlighted by the fact that treatment with integrin β1 antibody reduces tumor growth and genetic deletion of integrin β1 in murine breast cancer models reduces the number of tumors (Ghajar and Bissell 2008). The ECM is thus important in promoting tumor growth and metastasis and it communicates with the surrounding cells via integrins.

18.6 The Integrin Family of ECM Receptors Integrins are receptors for extracellular matrix proteins and immunoglobulin superfamily molecules (Hynes 2002). These adhesion proteins are divalent cationdependent heterodimeric membrane glycoproteins comprised of non-covalently associated α and β subunits that promote cell attachment on the extracellular matrix. Eighteen α and eight β subunits associate to form twenty-four integrin heterodimers. Each integrin subunit consists of an extracellular domain, a single transmembrane region, and a short (approximately thirty to forty amino acids) cytoplasmic region (Hynes 2002). Growth factor or chemokine receptor signaling can alter integrin conformation (“inside-out” signaling) and regulate integrin activity. In an inactive integrin, the cytoplasmic regions of the α and β subunits are closely associated with one another. The N-termini of the α and β chains form a globular head region that is bent towards the plasma membrane (Arnaout et  al. 2005; Beglova et  al. 2002; Lu et al. 2001; Vinogradova et al. 2002). Growth factors promote association of talin with the beta chain tail, thereby breaking a salt bridge between the alpha and beta tails and allowing conformational shifts to occur. Unbending and elongation of the dimer and separation of the cytoplasmic domains mark integrin activation (Arnaout et al. 2005; Beglova et al. 2002; Lu et al. 2001; Vinogradova et al. 2002), allowing interaction of α and β cytoplasmic domains with intracellular proteins. Once activated, integrins bind ligands, cluster, and initiate signaling cascades. On endothelial cells, the expression of integrins α1β1, α2β1, α4β1, α5β1 and αvβ3 can be induced by growth factors or chemokines (Brooks et al. 1994a; Friedlander et al. 1995). Receptor-mediated signaling can also directly activate integrins. In carcinoma cells, integrin αvβ5 is constitutively expressed in inactive form, but it is activated by insulin-like growth factor mediated signal transduction (Brooks et al. 1997). In monocytes and other leukocytes, α4 and β2 integrins are generally inactive until chemokines, hormones, or other growth factors stimulate the cells (Grabovsky et al. 2000). Thus, integrins roles in cancer may be controlled either by intracellular signaling (inside-out signaling) or by expression. In tumors, growth factors may continuously stimulate integrin expression and activity, thereby promoting tumor growth and invasion.

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18.7 The Beta 1 Integrin Subfamily and Cancer The β1 integrin subfamily contains 11 members: αvβ1, α1β1, α2β1, α3β1, α4β1, α5β1, α6β1, α7β1, α8β1, α9β1, α10β1, and α11β1. β1 integrins activate many signaling pathways, which promote cell proliferation, migration, invasion and survival (Boudreau and Varner 2004; Vogel et al. 1990; Yao et al. 2007). Fibronectin receptors include α3β1 (Takada et al. 1988), α4β1 (Elices et al. 1990), α5β1 (Pytela et al. 1985), α8β1 (Schnapp et al. 1995), α9β1 (Weinacker et al. 1995) and αvβ1 (Vogel et al. 1990) (Table 18.1). Collagen receptors include α2β1, α1β1, α3β1, α10β1 and α11β (Mercurio 2002) (Table  18.1) Integrins α4β1, α5β1 and α9β1 play roles in tumor angiogenesis (Garmy-Susini et al. 2005; Kim et al. 2000; Staniszewska et al. 2007; Vlahakis et al. 2007), while integrins α3β1 (Morini et al. 2000; Tang et al. 2008), α4β1 (Gosslar et al. 1996; Matsuura et al. 1996; Okada et al. 1999) and α5β1 (Caswell et al. 2007) also play roles in tumor invasion and metastasis. Increased β1 integrin expression levels are found in a variety of human tumors (Yao et al. 2007). High integrin β1 levels correlate with poor prognosis in cancers of the lung, pancreas and cutaneous melanoma. In addition, high β1 integrin expression levels are associated with poorer overall survival rates in patients with early stage invasive breast cancer (Yao et al. 2007). In small cell lung carcinoma (SCLC), ECM surrounding primary and metastatic tumor cells may induce integrin β1 mediated cell survival, suppressing chemotherapy-induced apoptosis. Therefore, inactivation of integrin β1 mediated survival-signaling may provide therapeutic benefit in the treatment of SCLC (Marastoni et al. 2008). β1 integrins are also important in controlling the initial proliferation of micrometastatic cancer cells disseminated in Table 18.1   Collagen and fibronectin-binding integrins

Integrins

Ligands

α1β1 α2β1 α3β1 α4β1 α5β1 α8β1 α9β1 α10β1 α11β1 αvβ1 αvβ3

Collagen, laminin Collagen, laminin Fibronectin, collagen, laminin, epiligrin, entactin Fibronectin, VCAM-1 Fibronectin, fibrinogen, L1-CAM Fibronectin Tenascin, collagen, laminin Collagen Collagen Fibronectin, vitronectin Fibronectin, vitronectin, von Willebrand factor, thrombospondin, Del-1 Fibronectin Fibronectin, fibrinogen, von Willebrand factor Fibronectin, VCAM-1

αvβ6 aIIbβ3 α4β7

A number of integrin β1 family members, which are expressed by most cells, bind fibronectin and collagen. Several αv family members also bind fibronectin. The platelet integrin aIIbβ3 and the leukocyte integrin α4β7 also bind fibronectin

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the lungs. While intravenously injected, nonmetastatic cancer cells ceased to proliferate after extravasating into the parenchyma of the lungs, metastatic cells continued to proliferate (Shibue and Weinberg 2009). While non-metastatic cells were unable to trigger adhesion-related FAK activation, metastatic cells activated FAK, enabling their tumor cell proliferation within the lung parenchyma in vivo. Activation of FAK depended on expression of integrin β1, and proliferation of cancer cells was diminished by knocking down the expression of either FAK or integrin β1. These results demonstrated the critical role that integrin β1-FAK signaling can play in promoting proliferation of micrometastases (Shibue and Weinberg 2009). Expression of integrin α5β1 by lung cancer cells has also been associated with a worse prognosis in lung carcinoma patients; as this integrin is not found in normal lung tissue, it may serve as therapeutic target (Ritzenthaler et al. 2008). Recent studies show that integrin α5β1 activation can be triggered by fibronectin deposited in tumors. Integrin α5β1 mediated signaling reduces cell apoptosis in a COX-2 dependent manner and results in inhibition of cyclin-dependent kinase inhibitor p21 gene expression (Marastoni et al. 2008). Recent studies showed that an activating β1 integrin mutation increases the conversion of benign papillomas to malignant squamous cell carcinomas (Ferreira et al. 2009). T188I beta1 integrin is a heterozygous mutation identified in poorly differentiated squamous cell carcinoma (SCC) that activates extracellular matrix adhesion and inhibits keratinocyte differentiation in vitro. Overexpression of this mutant in the basal layer of mouse epidermis promoted the conversion of papillomas to SCCs after chemical carcinogenesis. T188T beta 1 papillomas showed increased Erk activity and reduced differentiation. These observations showed that a genetic integrin β1 variant affects tumor progression (Ferreira et al. 2009). Other integrins, including α2, α6 and β4 subunits may promote colorectal cancer cell extravasation. Using in vivo models and intravital video microscopy, function blocking antibodies directed against these integrin subunits significantly reduced colon carcinoma cell extravasation and migration (Robertson et al. 2009).

18.8 The αv Subfamily The αv integrin subunit can combine with several different beta subunits (β1, β3, β5, β6 and β8). Integrin αvβ3 is a receptor for RGD-containing proteins such as vitronectin, fibronectin, fibrinogen and osteopontin and was the first of the alpha v integrins to be characterized (reviewed in Stupack 2005). It was also the first integrin to be found to regulate angiogenesis (Brooks et al. 1994a). Aggressive tumors, including melanoma, carcinomas of the prostate, breast, cervix and pancreas express integrin αvβ3. In cervical carcinoma, expression of αvβ3 correlates with disease progression and shorter survival. In pancreatic ductal adenocarcima, 58% of human tumors express integrin αvβ3 and expression of this integrin is associated with increased lymph node metastasis (Desgrosellier et al. 2009). Another αv integrin, αvβ6, has been shown to promote invasion and metastasis in oral carcinoma (Ramos

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et al. 2002). Increased integrin αvβ6 expression has been reported in carcinomas of the lung, breast, pancreas, stomach, colon, ovary and salivary gland. Integrin αvβ6 promotes tumor cells to invade and metastasize through an αvβ6-extracellular signal-related kinase (ERK) direct signaling pathway (Peng et al. 2009). Ligated integrins lead to anchorage-dependent survival and proliferation of tumor cells. However, recently it has been demonstrated that unligated integrins can influence the malignant properties of tumor cells by activating apoptotic pathways leading to integrin-mediated death (IMD). Mechanisms that tumor cells develop to escape IMD, contributes to their metastatic behavior. Integrin αvβ3 expressed in carcinoma cells enhanced anchorage-independent tumor growth in vitro and increased lymph node metastases in vivo. These effects required recruitment of c-Src to the β3 integrin cytoplasmic tail, leading to c-Src activation, Crk-associated substrate (CAS) phosphorylation and tumor cell survival that was independent of cell adhesion or focal adhesion kinase (FAK) activation. Therefore αvβ3 contributes to tumor progression by enhancing anchorage-independent growth and by activating c-src independent of tumor cell adhesion (Desgrosellier et al. 2009).

18.9 Integrins in Angiogenesis and Lymphangiogenesis Tumor growth and metastasis are dependent on the growth of blood vessels into or proximal to the tumor mass. Integrins promote angiogenesis. β1 integrins play key roles in angiogenesis as animals with an endothelial specific deletion of the β1 integrin die during embryogenesis by E10.5 with severe vascular defects (Tanjore et al. 2007). One beta 1 integrin that promotes angiogenesis is integrin α5β1. Integrin α5β1 is poorly expressed on quiescent endothelium but is upregulated during tumor angiogenesis in both mice and humans (Kim et  al. 2000). Antagonists of integrin α5β1 inhibit tumor (Kim et al. 2000), corneal (Muether et al. 2007) and choroidal (Umeda et al. 2006) angiogenesis and suppress tumor growth. Integrin α4β1 expression is induced on neovessels in murine and human tumors in response to VEGF, bFGF, IL-8 and TNFalpha. Integrin α4β1 promotes the survival of both endothelial cells and pericytes during angiogenesis. (Garmy-Susini et  al. 2005). Another β1 integrin with a role in angiogenesis is integrin α9β1 (Staniszewska et al. 2007; Vlahakis et al. 2007). The direct binding of VEGF-A by integrin α9β1 promotes VEGF-A stimulated angiogenesis and blocking antibodies to α9β1 suppress VEGF-A induced angiogenesis (Vlahakis et al. 2007). Integrin αvβ3 is expressed on tumor blood vessels, but is not expressed on vessels in normal human tissues. Growth factors such as bFGF and TNF-α stimulate its expression on endothelial cells and its expression is important in survival and migration during angiogenesis. Integrin αvβ3 plays a key role in endothelial cell survival and migration during angiogenesis (Brooks et al. 1994a, b). Antagonists of either αvβ3 or αvβ5 inhibit angiogenesis and tumor growth in a variety of animal models of cancer and block choroidal angiogenesis in animal models of ocular disease (Brooks et al. 1995; Friedlander et al. 1995, 1996; Fu et al. 2007).

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In contrast, embryonic deletion of alpha v integrin in mice suggested αv integrins are not absolutely required for embryonic vascular development (Bader et al. 1998). Most mice lacking αv integrins die in utero at E9.5, although 20% die shortly after birth. These 20% of mice have severely abnormal vessels in the brain and intestines although other organs appear normal. In contrast to these preceding findings, studies of animals with mutations in the integrin β3 cytoplasmic tail indicate that integrin β3 is required for angiogenesis (Mahabeleshwar et  al. 2006). The point mutations Y747F and Y759F in the integrin β3 cytoplasmic tail impair β3 integrin signal transduction and cell migration. Knockin mice bearing these mutations survived embryogenesis but exhibited reduced growth factor and tumor induced angiogenesis in adult mice. These studies confirm that integrin αvβ3 plays an important role in angiogenesis. Lymphatic vessels drain fluid and cells from tissues and are lined by loosely associated endothelial cells without a covering of mural cells. These vessels promote trafficking of immune cells and tumor cells. Importantly, lymph nodes are the first organs in which metastases are detected. Recent identification of lymphatic markers, including Prox-1, LYVE-1 and podoplanin made it possible to study mechanisms regulating lymphangiogenesis. The growth factors VEGF-C and VEGF-D promote lymphangiogenesis by activating the lymphatic endothelial cell receptor VEGFR-3. Integrins also regulate lymphangiogenesis. Integrins that regulate lymphangiogenesis include α9β1 and α4β1. Quiescent lymphatic endothelial cells express integrin α9β1. Integrin α9β1 null mice die 6 to 12 days after birth due to chylothoraces, an accumulation of lymph in the pleural cavity, suggesting a role for α9β1 in developmental lymphangiogenesis (Huang et al. 2000; Vlahakis et al. 2005). Integrin α4β1 is highly expressed on tumor lymphatic endothelium and antagonists of this integrin can block lymphangiogenesis and tumor metastasis.

18.10 Clinical Applications Tumor cells and proliferating endothelial cells express alternatively spliced cellular fibronectin isoforms. The EDA domain of FN can be used as a marker of tumor angiogenesis as it accumulates around blood vessels of certain tumors (Villa et al. 2008). F8, B7 and D5 are human monoclonal antibodies that have been developed against the EDA domain. The F8, B7 and D5 antibodies do not stain normal tissue but selectively stain neovascular structures on freshly isolated frozen tumor sections (Villa et al. 2008). L19 is a human antibody that is specific for the EBD domain that has been tested in preclinical animal models (Kaspar et al. 2006). L19 targets lung, colorectal or brain cancer and is able to distinguish between quiescent and growing lesions. Fusion proteins and chemical derivatives based on L19 have also been produced. Fusion proteins with IL2 (L19-IL2) have anticancer activity in orthotopic murine models of hepatocellular carcinoma and pancreatic cancer (Kaspar et  al. 2006).

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Peptide and antibody antagonists of αvβ3 block tumor angiogenesis and growth (Brooks et  al. 1994b). Vitaxin/MEDI-522 is a humanized version of the antiintegrin αvβ3 monoclonal antibody LM609, which blocks tumor angiogenesis by inducing apoptosis in newly formed endothelial cells (Gutheil et al. 2000; McNeel et al. 2005). When Vitaxin was tested on patients with metastatic cancer who had failed other treatments, the disease stabilized without any toxicity. A Phase II study on metastatic melanoma showed that 53% patients treated with Vitaxin survived greater than one year versus 27% of patients receiving standard therapy (Hersey et al. 2005). A humanized anti-α5β1 antibody, M200 (volociximab), developed by Protein Design Labs and now by Biogen-Idec Pharmaceuticals, has shown low toxicity in Phase I studies and was evaluated in Phase II trials for metastatic melanoma, renal cell carcinoma and non-small cell lung cancer (Figlin et al. 2006; Kuwada 2007). Antibodies against integrin α5β1 decrease angiogenesis in vitro and in vivo (Bhaskar et al. 2008). Additionally, Volociximab inhibits endothelial cell growth in vitro. A mouse antibody to against α5β1, antibody 339.1, inhibits murine EC migration and tube formation and promotes cell death (Bhaskar et  al. 2007). In xenograft models it inhibited the growth of tumors 60% and this correlates with a decrease in vessel density and slows tumor growth in vivo (Bhaskar et al. 2007).

18.11 Conclusions The extracellular matrix and its receptors play crucial roles in tumor development and metastasis. Tumors alter the surrounding extracellular matrices and integrin expression levels. Targeting of integrins may provide therapeutic benefit, as integrins are expressed on tumor cells as well as endothelial cells, and suppressing their functions can decrease angiogenesis and tumor metastasis, thereby preventing tumor growth and metastasis.

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Chapter 19

The Multifaceted Role of Cancer Associated Fibroblasts in Tumor Progression Hans Petter Eikesdal and Raghu Kalluri

Abbreviations αSMA α-Smooth muscle actin bFGF Basic fibroblast growth factor BMH Bone marrow-derived hematopoietic precursor cells BMM Bone marrow-derived mesenchymal precursor cells CAF Cancer associated fibroblast CK5 Cytokeratin 5 DAB 3,3′-diaminobenzidine ECM Extracellular matrix EMT Epithelial-to-mesenchymal transition EndMT Endothelial-to-mesenchymal transition ER Estrogen receptor FAP Fibroblast activation protein FSP1 Fibroblast specific protein-1 HGF Hepatocyte growth factor IGF Insulin-like growth factor KGF Keratinocyte growth factor MMP Matrix metalloproteinase MMPI Matrix metalloproteinase inhibitor NG2 NG2 chondroitin sulfate proteoglycan R. Kalluri () Division of Matrix Biology, Department of Medicine, Beth Israel Deaconess Medical Center & Harvard Medical School, 330 Brookline Ave, E/CLS Room #11-090, Center for Life Sciences, 02115 Boston, MA, USA e-mail: [email protected] Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA H. P. Eikesdal Department of Oncology, Haukeland University Hospital, 5021 Bergen, Norway M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_19, © Springer Science+Business Media B.V. 2011

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PDGF Platelet derived growth factor SCC Squamous cell carcinoma TGFβ Transforming growth factor-β.

19.1 Introduction The non-random appearance of metastases from breast cancer was described by Stephen Paget already in 1889 (Paget 1889). His “seed-and-soil” hypothesis states that the tumor cells need a certain soil to grow. Today, our knowledge of the “seed” has increased immensely, but we are still almost as far away as Dr. Paget from understanding why metastases appear where they do (Mueller and Fusenig 2004). In this chapter we will discuss what is currently known about fibroblasts and their role in establishing a proper “soil” for tumor cells to grow in. A malignant epithelial tumor can consist of up to 90% stroma (Elenbaas and Weinberg 2001). In Hodgkin lymphoma the case is even more extreme, with the malignant Hodgkin and Reed-Sternberg cells accounting only 0.1–3%, and the activated tumor stroma making the rest (Aldinucci et al. 2004). The tumor stroma is made up of multiple non-malignant cell populations, including fibroblasts, adipocytes, endothelial and inflammatory cells (Mueller and Fusenig 2004). Additionally the tumor stroma contains the extracellular matrix, the scaffold upon which the cells attach. There is a huge interest in tumor stroma research today, and our knowledge of the cancer associated fibroblast (CAF) has changed from being viewed as a passive bystander to becoming an important comediator of cancer progression. As we will discuss below fibroblasts are actually a family of many different cell types, they can arise from different cell populations and they might even initiate epithelial cancers.

19.2 Fibroblast Subtypes The exact definition of a fibroblast is difficult to make. Historically, fibroblasts have been cathegorized based on their morphology—spindle shaped cells scattered in the tissue stroma (Strutz et al. 1995). Recently, an increasing number of molecular markers have been identified—enabling immunolabeling of fibroblasts (De Wever et al. 2008). CAFs were originally defined as α-smooth muscle actin (αSMA) positive cells (Elenbaas and Weinberg 2001). However, experiments carried out in our laboratory and elsewhere demonstrate that tumors contain many subpopulations of fibroblasts, with different immunoreactivity and phenotypic characteristics (Fries et al. 1994; Ostman and Augsten 2009; Sugimoto et al. 2006). Apart from αSMA, subpopulations of tumor fibroblasts show immunoreactivity for NG2 chondroitin sulfate proteoglycan (NG2), platelet derived growth factor receptor-β (PDGFRβ) or fibroblast specific protein-1 (FSP1) (Fig. 19.1a). These fibroblast subtypes have

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Fig. 19.1   a FSP1+ fibroblasts in a squamous cell carcinoma of the anus. Immunolabeling with an FSP1 antibody (3, 3′-diaminobenzidine (DAB), brown). Arrow points to a FSP1+ fibroblast. * indicates an FSP1+ fibroblast “misplaced” in an island of epithelial cancer cells. E: epithelial cancer cells. S: stroma. Scale bar: 20 µm. b Fluorescence image of a section of testis from a FSP1-GFP transgenic mouse, indicating that FSP1 ( green) and αSMA ( red) positive fibroblasts are to a large extent separate cell populations. Arrow points to a FSP1+ and αSMA− fibroblast in the stroma (S). No immunolabeling used for FSP-GFP, whereas TRITC-conjugated immunolabeling was used for aSMA. Blue: DAPI nuclear staining. Scale bars: 100 µm. c Squamous cell carcinoma (SCC) of the anus. Before: CK5 immunolabeling (DAB, brown) of the epithelial cancer cells in the tumor as they looked before in vitro culture. After: CK5 immunolabeling after reimplanting from in vitro culture a comixture of CAFs and epithelial cancer cells from the anal SCC. Scale bars: 100 µm. d Highly proliferative anal SCC fibroblasts in vitro. Immunolabeling with Ki67 antibody ( green). Scale bars: 100 µm. e Crosstalk between epithelial cancer cells and CAFs. The cancer cells secrete growth factors X to stimulate the fibroblasts, whereas the fibroblasts secrete growth factors Y to stimulate the cancer cells. f Estrogen receptors (ER) are highly expressed both on anal SCC cancer cells and fibroblasts. Arrow points to a fibroblast. E: epithelial cancer cells. S: stroma. Immunolabeling with phosphorylated ERα antibody (DAB, brown). Scale bar: 100 µm

different localization, as for instance αSMA, PDGFRβ and NG2 typically label pericytes, whereas fibroblasts scattered in the stroma are αSMA or FSP1 positive (Sugimoto et al. 2006). Another important marker specific for CAFs and pericytes in tumors is fibroblast activation protein-α (FAP), which is stroma-specific in 90% of epithelial cancers tested (Haslam and Woodward 2003; Santos et al. 2009). Immunolabeling experiments are prone to unspecific staining, and to assess further the distribution of αSMA and FSP1 fibroblast subtypes, we used a transgenic mouse, where the FSP1 promoter is coupled to green fluorescent protein (FSP1-GFP) whereas αSMA was visualized by immunolabeling with a TRITC

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fluorochrome1. Accordingly any cell that is FSP1 positive will be green and any cell that is αSMA positive will be red. In this experiment we found that αSMA and FSP1 positive fibroblasts are indeed two separate cell populations (Fig. 19.1b). We are currently in the process of examining the role of these fibroblast subtypes for primary tumor growth and metastasis. There has been a lot of discussion about the specificity of FSP1 as a fibroblast marker. Originally identified as a cytoplasmic protein in mesenchymal cells, present after embryonic day 8.5, FSP1 was described as a selective marker of fibroblasts or epithelial cells undergoing epithelial-to-mesenchymal transitition (EMT) during kidney fibrosis (Strutz et al. 1995). Later studies have shown that FSP1, also known as S100A4, is also expressed in metastatic tumor cells and in very early granulocytic lineages from the bone marrow—but not in normal epithelial cells (Bhowmick et al. 2004a; Inoue et al. 2005). The mts1 gene, encoding S100A4, was found upregulated in normal cells with high invasiveness like macrophages, neutrophils and Tlymphocytes (Grigorian et al. 1994). However, testing a set of different macrophage antibodies it was found that apparently FSP1 positive macrophages were rather a case of unspecific staining of fibroblasts using macrophage antibodies (Inoue et al. 2005). In a recent paper by Bhowmick et al. they find that FSP1 is expressed in dendritic cells, using FSP1 genetic tracing (Boomershine et  al. 2009). They did not find FSP1 expressed in macrophages, B- and T-lymphocytes. The specificity of their analysis is hampered by the use of antibody staining for dendritic cells—a method inherently prone to unspecificity. What can probably be extracted from the above discussion about FSP1 is the fact that it is a good marker for stroma cells and epithelial cells that have or are in the process of transitioning to mesenchymal cells during EMT. Accordingly, in our hands we do not find FSP1 expressed in epithelial cells in any organ of the body.

19.3 How do Fibroblasts Differ in Malignant and Normal Tissues? Cancer cells alter the environment they sit in to generate a reactive stroma, and this includes the activation of fibroblasts mediated by growth factors such as transforming growth factor-β (TGFβ) and platelet derived growth factor (PDGF) (Arendt et  al. 2009; Mueller and Fusenig 2004). CAFs differ from fibroblasts in normal tissues in at least two ways; they have a very different gene expression profile, and they have a profound stimulatory effect on the epithelial cancer cells (Haviv et al. 2009). There are several articles demonstrating a shift in gene expression from fibroblasts to CAFs (Allinen et al. 2004; Fiegl et al. 2006; Hanson et al. 2006; Hu et al. 2005; Ricci et al. 2005). Accordingly, stromal cells within an epithelial cancer have been found to exhibit a particular gene expression profile or signature (Farmer et al. 1 

The FSP1-GFP mice were a kind gift from E.G. Neilson.

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2009; Finak et al. 2008). The CAF genome is generally hypomethylated, reflecting the fact that there are extensive epigenetic changes in tumor fibroblasts with a multitude of genes overexpressed (Alphonso and Alahari 2009; Arendt et al. 2009). Also, not only do the stromal cells in breast cancer differ in gene expression from the normal breast—but there is also a difference between the intratumoral and the juxtatumoral area (Iacobuzio-Donahue et al. 2002). The tumor promoting effect of CAFs on epithelial cells is well established (Elenbaas and Weinberg 2001; Orimo et al. 2005). In an elegant study from 1999, Olumi et al. demonstrated that CAFs could make a non-tumorigenic human prostatic epithelial cell line tumorigenic, whereas normal tissue fibroblasts were not able to elicit tumorigenesis (Olumi et  al. 1999). A potential flaw in their set-up was the fibroblast isolation method where tissue adjacent to prostate cancer was taken out, and purified based on filtration and centrifugation. This method, along with karyotyping, strongly suggests that they had a pure CAF population, but there still could be a minor contamination of prostate carcinoma cells. We established separate primary cultures of fibroblasts and epithelial cancer cells from a spontaneous squamous cell carcinoma arising in the anus of a mouse. After expanding the fibroblasts in vitro we injected them subcutaneously in Nu/Nu mice and observed that no tumors appeared. We then injected the same Nu/Nu mice with the epithelial tumor cells without any tumors appearing. Thereafter fibroblasts from a normal anus was coinjected with epithelial tumor cells, and again no tumors developed. Finally, tumor fibroblasts and epithelial tumor cells were coinjected and seven out of seven mice developed squamous cell carcinomas with the same characteristics as the original tumor (Fig. 19.1c). The purity of the fibroblast and tumor cell primary cultures was only established by immunolabelling in this experiment, but again—the data strongly suggests there is a profound stimulatory effect of CAFs on carcinoma cells. Interestingly, normal tissue fibroblasts are also modified by physiologic processes, such as pregnancy. The stroma of multiparous rats were shown experimentally to inhibit tumor growth when breast cancer cells where injected, whereas tumors developed much faster when grown in virgin rats (Maffini et al. 2005). This suggests that inhibitory stromal factors in the breast of multiparous animals might explain why pregnancy protects from breast cancer (Kelsey et al. 1993). Also, the correctly activated fibroblasts or CAFs seems necessary to permit tumor progression. This points to the profound importance of the crosstalk between CAFs and epithelial cancer cells discussed below.

19.4 Where do Fibroblasts Come from? Fibroblasts in tumors originate from various cell compartments. Apart from resident fibroblasts, they can arise through recruitment of mesenchymal stem cells from the bone marrow and through transdifferentiation of epithelial and endothelial cells (Arendt et al. 2009; Shimoda et al. 2010).

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Resident fibroblasts are traditionally believed to be a major source of new fibroblasts during for instance scar formation and other fibrotic processes, but the contribution of resident fibroblasts in malignant tumors is less clear (Ostman and Augsten 2009; Shimoda et al. 2010). The extensive proliferative capacity of CAFs grown in vitro suggests that a large number of them derive from local expansion within the tumor stroma (Fig. 19.1d). In a study of liver metastases from patients with colorectal cancer, it was found that fibroblasts within the metastatic tumor tissue had a very high resemblence to the resident fibroblasts in the liver (Mueller et al. 2007). However, the lack of genetic tracing of the fibroblasts and their similarity assessment based on immunolabeling does not rule out a significant contribution from other sources. Epithelial-to-mesenchymal transition (EMT) is a process in which epithelial cells change phenotype to become mesenchymal (Polyak and Weinberg 2009). In this way mesenchymal cells such as fibroblasts, chondrocytes and muscle cells are generated (Kalluri and Neilson 2003). During the EMT process epithelial cells loose their polarity and cell–cell adhesions, and instead acquire increased motility, invasiveness and anchorage-independence—typical fibroblast features (Polyak and Weinberg 2009). Classically, epithelial cells loose E-cadherin expression and acquire mesenchymal features such as vimentin and α-smooth muscle actin when transitioning (Thiery and Sleeman 2006; Tse and Kalluri 2007). EMT is a highly regulated process, where certain signaling pathways, such as the TGFβ pathway, are switched on while others are silenced (Polyak and Weinberg 2009; Tse and Kalluri 2007). EMT is increasingly appreciated as an important process of fibroblast generation, in diseases such as cancer, kidney fibrosis and liver fibrosis (Kalluri and Neilson 2003; Thiery and Sleeman 2006; Zeisberg et al. 2007). Additionally, the EMT process makes epithelial cancer cells less anchorage-dependent and thus facilitates invasion and metastasis. Recently, endothelial-to-mesenchymal transition (EndMT), a process similar to EMT, was discovered as a source of fibroblasts in disease processes such as cardiac fibrosis and desmoplasia within malignant tumors (Zeisberg et al. 2007a, b). In this process, local endothelial cells transition to acquire mesenchymal features. EndMT, like EMT, was originally described as a developmental process during embryogenesis, but as it turns out, it is a major contributor of fibroblasts in adult tissues as well (Zeisberg et al. 2007b). Another source of CAFs are the recruitment of mesenchymal stem cells from the bone marrow (Ostman and Augsten 2009). Similar to bone marrow-derived hematopoietic precursor cells (BMH), bone marrow-derived mesenchymal precursor cells (BMM cells) are recruited to the primary tumor and to sites of metastasis (Hung et al. 2005; Klopp et al. 2007; Mishra et al. 2008; Studeny et al. 2004). These BMM cells increase the growth and metastatic potential of cancer cells, as demonstrated both for breast cancer and pancreatic cancer in mice (Hwang et al. 2008; Karnoub et al. 2007). A subpopulation of fibroblasts found in the cancer stroma are bone marrow-derived (Direkze et al. 2004, 2006; Ishii et al. 2003; LaRue et al. 2006; Mori et al. 2005; Studeny et al. 2004), and they

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can amount to as much as 25–40% of the myofibroblast population (Direkze et al. 2004; Ishii et al. 2003). Fibroblasts involved in wound healing and cancer stroma development seem to arise in part from BMM cells (Klopp et al. 2007; Mishra et al. 2008; Mori et al. 2005; Studeny et al. 2004), although BMH cells may also be fibroblast precursors (LaRue et al. 2006). When mesenchymal stem cells are isolated from the bone marrow and labelled ex vivo, they localize to the tumor stroma as CAFs after being injected i.v. (Hung et al. 2005; Mishra et al. 2008; Studeny et al. 2004). What remains to be assessed is the relevance of the different CAF sources, i.e. what happens if any one of these recruitment pathways are blocked?

19.5 Crosstalk Between Fibroblasts and Cancer Cells Crosstalk between the fibroblasts and epithelial cancer cells is important for tumor progression, and a large number of growth factors are known to be secreted by these two cell compartments to stimulate each other (Fig. 19.1e). For an extensive description of which growth factors that are involved in this crosstalk, several detailed reviews are available (Bhowmick et al. 2004b; De Wever et al. 2008; Elenbaas and Weinberg 2001; Kalluri and Zeisberg 2006; Matsumoto and Nakamura 2006; Mueller and Fusenig 2004; Zvaifler 2006). Cancer cells produce growth factors such as PDGF, TGFβ, basic fibroblast growth factor (bFGF) to activate the tumor-associated fibroblasts (Mueller and Fusenig 2004). In return, the fibroblasts secrete growth factors, such as hepatocyte growth factor (HGF), keratinocyte growth factor (KGF) and insulin-like growth factor 1 and 2 (IGF-1 and -2), to stimulate the epithelial cancer cells (Bhowmick et al. 2004b; Nakamura et al. 1997). Additionally, cancer cells stimulate the fibroblasts to produce matrix metalloproteinases (MMPs), which binds to the cancer cells and is used for degradation and invasion through the ECM (Pavlaki and Zucker 2003). Also, the crosstalk involves other cell populations, like the tumor endothelium and inflammatory cells (Mueller and Fusenig 2004). Besides secreting growth factors to stimulate the cancer cells, CAFs can quench the inhospitable microenvironment generated by hypoxia and low pH by removing acidic metabolites (Alphonso and Alahari 2009). In a recent interesting article, Pavlides et al. describe what they call a “reverse Warburg effect” (Pavlides et al. 2009). They hypothesize that tumor cells can induce aerobic glycolysis in CAFs causing them to produce lactate and pyruvate which the tumor cells utilize as an energy source. In mice that are caveolin-1 (cav-1) deficient they show that fibroblasts are constitutively activated, like CAFs, with upregulated TGFβ signaling and tumor promoting capabilities. Proteomic analysis demonstrated that these cav-1 null fibroblasts have several glycolytic enzymes upregulated which are key regulators of the Warburg effect (Pavlides et al. 2009).

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19.6 Fibroblasts and Interaction with the Extracellular Matrix The extracellular matrix (ECM) consists of long, filamentous proteins making up a mesh on which cells attach, migrate and communicate with each other (Kalluri 2003). Fibroblasts are situated, along with other stroma cells, within this scaffold, and they are the principal source of ECM proteins (Elenbaas and Weinberg 2001; Kalluri and Zeisberg 2006). In support of the crucial role of fibroblasts in producing the tumor ECM, one finds that malignancies that has little fibrotic tissue, like poorly differentiated small cell lung cancer, have very few CAFs as well (Elenbaas and Weinberg 2001). The constituents of the ECM are, besides fibrillar proteins like collagens and elastin, proteins such as fibronectin and laminin and various glycosaminoglycans (Strutz et al. 1995). Additionally, various growth factors are stored in the ECM, and these factors are liberated during matrix breakdown by cancer cells to stimulate angiogenesis and cancer progression (Kalluri 2003). The ECM differs between the organs of the body, and empirically the chance of tumor cells proliferating to develop a solid mass increases when tumor cells are injected orthotopically instead of ectopically, indicating that the soil (including the local mixture of ECM and stromal cells) matters for tumor growth (Elenbaas and Weinberg 2001). Basement membranes are a special type of ECM, with a dense, sheet-like structure consisting of type IV collagen and laminin, typically separating epithelial cells or endothelial cells from the stroma (Kalluri 2003). In the tumor stroma basement membranes are partly degraded, liberating both pro- and antiangiogenic proteins (Kalluri 2003; Nyberg et  al. 2005). When tumor cells are coinjected with basement membrane proteins, like in Matrigel, it increases tumor take and metastasis frequency profoundly (Elenbaas and Weinberg 2001). Fibroblasts are an important source of ECM-degrading proteases such as MMPs, which highlights their crucial role in regulating ECM turnover (Marx 2008). It was originally thought that cancer cells were the major source of MMPs based on immunolabeling data, but in situ hybridization studies have later demonstrated that MMPs are synthetized by fibroblasts and immune cells in the tumor stroma (Pavlaki and Zucker 2003). However, the cancer cells stimulate the stroma cells to produce MMPs and make docking proteins that will facilitate adherence of MMPs to the cancer cell cytoplasmic membrane (Pavlaki and Zucker 2003). The MMP family consist of different enzymes, many of which are upregulated in malignant tumors (Pavlaki and Zucker 2003). Their role in breakdown of the ECM to facilitate cancer invasion and angiogenesis led to large scale clinical trials to test MMP inhibitors (MMPIs) as anti-cancer drugs. However, these trials failed, and many reasons have been proposed to explain this (Eikesdal et al. 2002; Eikesdal and Kalluri 2009; Marx 2008; Pavlaki and Zucker 2003). It seems that MMPs are most important in the early stages of tumor progression, whereas the clinical MMPI trials were undertaken in patients with very advanced disease. A crucial point in the MMPI trials were also the opposing roles that MMPIs can have. At one end

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they may inhibit invasion through the ECM, but at the same time they may inhibit the liberation of antiangiogenic proteins—thereby promoting angiogensis and thus tumor progression (Mueller and Fusenig 2004; Pavlaki and Zucker 2003). Furthermore, different MMPs have different roles in ECM turnover, and many of the tested MMPIs were broad-spectrum inhibitors blocking a whole series of the enzymes (Pavlaki and Zucker 2003). Fibroblasts attach to the ECM via integrins (Alphonso and Alahari 2009; Chiquet et al. 2003; Desgrosellier and Cheresh 2010; Galbraith and Sheetz 1998). The cytoskeleton is reorganized and integrins and focal adhesion contacts between the cell and the ECM dynamically change location as the fibroblast migrates (Galbraith and Sheetz 1998). The integrin receptors sense stretch and thereby activate, through focal adhesion complexes, Rho-ROCK and MAPK signaling and upregulate tenascin C (Chiquet et  al. 2003). This tenascin C upregulation can be blocked by β1-integrin antibodies (Chiquet et al. 2003). Fibroblasts also express the Nischarin protein which binds to α5β1-integrin to facilitate cell migration on fibronectin (Alphonso and Alahari 2009). Integrin α11 was found to be upregulated in CAFs in non-small cell lung cancer, and overexpression of α11β1 on tumor fibroblasts increased IGF-1 secretion which stimulated tumor growth (Desgrosellier and Cheresh 2010). Interestingly, antibody-based therapy, directed at β1-integrin is currently being tested clinically (Desgrosellier and Cheresh 2010). The fibrotic response commonly seen in epithelial cancers is called desmoplasia and is found as a dense, amorphous tissue with few cells in it. The most intense desmoplastic response is typically seen at the advancing tumor periphery (Elenbaas and Weinberg 2001). The desmoplastic tumor tissue differs from fibrous tissue elsewhere by increased amounts of fibrillar collagens (in particular type I collagen), fibronectin, proteoglycans, and glycosaminoglycans (Elenbaas and Weinberg 2001). The hardness of tumors, probably due to the desmoplastic tissue response, is the focus of research as a potential marker of malignant as opposed to benign neoplasms—using elastography (Bilgen et al. 2003; Giovannini et al. 2009; Itoh et al. 2006; Pallwein et al. 2008). Furthermore, the increased desmoplastic reaction seen in certain epithelial cancers has been proposed to correlate with increased risk of recurrence and metastasis (see Sect. 19.8 below).

19.7 Fibroblast Mutations in Epithelial Malignancies The current understanding of epithelial carcinogenesis is dominated by the notion that cancer develops due to sequential accumulation of epithelial mutations (Hanahan and Weinberg 2000; Maffini et al. 2004; Vogelstein and Kinzler 1993). However, there is an ongoing debate whether fibroblasts and other stromal cells in epithelial malignancies can acquire mutations and whether this matters (Campbell et al. 2009; Eng et al. 2009). There are multiple studies, both in human and animal tumors, indicating that fibroblast mutations exist (Hardwick et al. 2008; Hill et al. 2005; Kurose et al. 2001, 2002; Moinfar et al. 2000; Patocs et al. 2007; Weber et al.

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2006, 2007). Contrary to this, others find that CAF mutations are very rare, or nonexistant (Campbell et al. 2009; Crawford et al. 2009; Hu et al. 2005; Qiu et al. 2008; Shimoda et al. 2010; Walter et al. 2008). The potential flaws in analyzing fibroblast mutations in human cancer material are many. In most studies tumor stroma is isolated from tissue sections using laser microdissection, followed by PCR and genomic analysis. With this method the DNA yield is frequently low and when this is compensated for by an extensive number of PCR-cycles, the risk of false positive “mutations” increases dramatically (Campbell et al. 2009). This is further exaggerated by fragmented DNA found in formalin-fixed, paraffin sections from archival material. Thus, there are strict recommendations regarding DNA quality and concentration when using this method (Campbell et al. 2009). However, even if the DNA yield is good, there is no exact anatomical border between carcinoma cells and stroma cells inside a tumor (Fig. 19.1a). Therefore, the commonly used method of laser microdissection of stromal areas is prone to include carcinoma cells as well. Alternatively, tumor tissue removed from the patient can be degraded enzymatically and sorted by flow cytometry based on fibroblast specific antigens. The problem with this method is unspecific staining, which can occur with all antibodies. Also, antigens like FSP1 are localized inside the cytoplasm (Skalli et al. 1989; Strutz et al. 1995), requiring permeabilization to immunolabel them—which makes unspecific staining an even larger problem. Apart from these issues, another one is even more challenging: EMT is potentially a frequent phenomenon in epithelial cancers. So fibroblasts harboring mutations could simply be carcinoma cells that have undergone EMT—in which case the same mutations would be found in stroma and epithelial cancer cells. However, the presence of different mutations in these two compartments indicates that stromal mutations occurs independently and not only through EMT of cancer cells (Hill et  al. 2005; Kurose et  al. 2002; Moinfar et al. 2000; Shiraishi et al. 2006). The issue of fibroblast marker specificity can be controlled for more thoroughly in animal models by genetic tracing. I.e. a traceable genetic change is inflicted once a cell expresses a fibroblast marker. We investigated this by crossing mice expressing Cre recombinase under the FSP1 promoter (FSP1-Cre mice) with R26R-EYFP reporter mice, which have the enhanced yellow fluorescent protein (EYFP) coupled to the constitutively active ROSA promoter2. In these mice any cell expressing FSP1 will express Cre recombinase, and delete the STOP codons that inhibit the transcription of the ROSA promoter—thus causing continous expression of EYFP in any cell that has been or is expressing FSP1 ever after. In our hands this genetic tracing showed that FSP1 was specifically expressed only in stromal cells. In another interesting experiment, Maffini et al. gave rats with cleared mammary fat pads the N-nitrosomethylurea (NMU) carcinogen or sham treatment, before injecting mammary epithelial cells exposed to NMU or vehicle into the breast (Maffini et al. 2004). Breast carcinomas developed only if the stroma had been exposed FSP1-Cre and R26R-EYFP reporter mice were kindly provided by E.G. Neilson and B. G. Neel respectively. 2 

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to NMU, whereas NMU-treated mammary epithelial cells caused no tumorigenesis if the stroma had not been exposed to the carcinogen previously. A potential pitfall in this experimental set-up is whether the mammary fat pads were completely cleared of mammary duct epithelium before starting the NMU exposure. However, the investigators assured complete mammary gland removal by whole mount staining of the excised fat pads. The experiment suggests that fibroblast mutations are required for epithelial cancer growth. Clearly if mutations are present in fibroblasts, there are many ways in which this would change our understanding of epithelial cancers. One question is whether fibroblast mutations occur before mutations in the epithelial cell layer. A few papers indicate that certain mutations are found exclusively in the stroma, and not in the epithelial cancer cells (Hill et al. 2005; Jacoby et al. 1997; Kurose et al. 2002; Moinfar et al. 2000). Such independent fibroblast mutations suggests they can have an initiating role in carcinoma development. Furthermore, fibroblast mutations mean that therapy targeted at this stromal cell compartment could face the same inherent problems of acquired drug resistance as other malignant cells, due to genetic instability. Interestingly though, investigators found that the occurrence of p53 mutations in fibroblasts sensitize tumors to chemotherapy—not the opposite (Lafkas et al. 2008). Finally, it seems that the stimulatory role of CAFs to make non-tumorigenic epithelial cells tumorigenic persists even if the CAFs are removed—suggesting that CAFs have a mutagenic effect on the epithelial cells (Mueller et al. 2001; Mueller and Fusenig 2004; Tlsty 2001) From this assumption one can speculate that perhaps fibroblasts normally inhibit epithelial cell layers to avoid mutagenesis. Therefore, if fibroblasts acquire mutations that reduce their control function, the epithelium would hyperproliferate and undergo neoplastic changes as well.

19.8 The Prognostic Relevance of Fibrosis The increased desmoplastic reaction seen in certain epithelial cancers has been proposed to correlate with increased risk of recurrence and metastasis. This has been suggested by clinical studies of desmoplastic breast cancer, non-small cell lung cancer and scirrhous gastric cancer (linitis plastica) (An et al. 2008; Cardone et al. 1997; Chen et al. 2002; Maeshima et al. 2002). There is however a debate as to the prognostic relevance of desmoplasia. When cases of scirrhous gastric cancer are matched with non-scirrhous cases with the same tumor stage, the difference in survival disappears—indicating that the prognostic factor is not the fibrosis content per se, but rather the tumor stage (Park et al. 2009). Additionally, increased fibrosis in other cancers, such as desmoplastic malignant melanoma does not seem to infer a worse prognosis (Livestro et al. 2005). It is known that high breast density, detected by mammography, gives a 4–6 fold increased risk of breast cancer (Boyd et al. 2007). It is not known why this is so, but increased collagen I content and upregulated IGF-1 signaling have been proposed

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as tumor stimulatory factors (Arendt et al. 2009; Yang et al. 2010). There are only a few publications where breast density has been investigated in detail, on a molecular level—so hopefully future research will address this issue further (Arendt et al. 2009). In animal xenograft studies desmoplasia is a rare phenomenon, and in those cases where desmoplastic cancers have been observed, cancer growth was slower than in less desmoplastic tumors (Shao et al. 2000). Fibroblasts may inhibit tumor growth by secreting lysyl oxidase which crosslinks collagen and elastin to protect ECM from protease degradation (Elenbaas and Weinberg 2001). On the other hand, fibrotic tissue may also protect the tumor cells from immune cells attacking them, and facilitate tumor growth by physically shielding the malignant tissue (Elenbaas and Weinberg 2001). In an experimental tumor model, preventing desmoplasia therapeutically by blocking collagen synthesis led to increased tumor growth and metastasis (Elenbaas and Weinberg 2001). Integrin receptors are commonly upregulated in epithelial cancers, and the stimulatory effect of increased fibrillar collagens within desmoplastic tumors seems to be mediated via collagen-integrin interaction (Desgrosellier and Cheresh 2010; Sethi et al. 1999) Apart from promoting cell growth and survival, adherence of colorectal cancer cells to collagen I conferred resistance to 5-FU chemotherapy. Interestingly this resistance could be counteracted by therapy directed at the αvβ3 and αvβ5 integrins (Conti et al. 2008). Using laser microdissection, Finak et al. assessed stromal gene expression in 53 patients with breast cancer and was able to derive a stromal gene expression profile strongly associated with clinical outcome (Finak et al. 2008). The stroma of poor outcome patients showed upregulation of genes related to angiogenesis, hypoxia and macrophage recruitment and downregulation of for instance negative regulators of Wnt signaling. Genes upregulated in the good outcome group were typically related to eliciting an immune response of T-lymphocytes and natural killer (NK) cells in the stroma (Finak et al. 2008). The stroma gene expression signature also came out as an independent prognostic variable in multivariate analysis, alongside known prognostic factors such as estrogen receptor status, HER2 status and lymph node involvement. In another study it was also established that a particular stromal signature was linked to a worse disease outcome in breast, lung and gastric cancer patients (Chang et  al. 2004). Furthermore, a recent study showed that a stromarelated gene signature was predictive of resistance to neoadjuvant chemotherapy in advanced breast cancer (Farmer et al. 2009). However, this last analysis was undertaken on whole tumor material, i.e. not a selective analysis of tumor stromal cells. After quantifying αSMA positive CAFs in 192 human colorectal carcinomas, it was established that an increased number of fibroblasts in the tumor was independently related to a shortened disease-free survival, and in multivariate analysis, the number of αSMA fibroblasts came up as an independent prognostic factor (Tsujino et al. 2007). Similarily, increased expression of fibroblast activated protein (FAP) by immunolabeling was associated with a higher risk of disease recurrence postoperatively in colon and pancreatic cancer patients (Cohen et al. 2008; Henry et al. 2007). Also, the expression of secreted protein acidic and rich in cysteine (SPARC)

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by peritumoral fibroblasts in pancreatic carcinoma is associated with a worse prognosis, when analyzed by multivariate analysis (Infante et al. 2007). The expression of SPARC by the tumor cells was of no importance.

19.9 Therapy Directed at Cancer Associated Fibroblasts Due to the stimulatory role of fibroblasts in malignant neoplasms, there is a natural interest in designing therapy directed at the CAF cell population. Such therapy is not yet implemented clinically, but based on the success of targeting tumor endothelial cells, another stroma component, the potential of such treatment is definitively there. Strategies tested so far focus on two different ways of targeting CAFs, one being the direct inhibition of fibroblast function and the other to inhibit paracrine signaling between fibroblasts and other cell populations in the tumor (Arendt et al. 2009). Therapy directed at the tumor fibroblasts is made feasible either by specific fibroblast markers or by overexpression of common antigens on CAFs which make them especially sensitive to treatment. FAP is a membrane-bound serine protease which is overexpressed on and relatively specific for CAFs (Lebeau et al. 2009). FAP-specific protoxins can be synthesized which are selectively activated in the tumor stroma to kill the fibroblasts, as well as nearby epithelial tumor cells due to a bystander effect (Lebeau et al. 2009). Antibodies to FAP, conjugated with an anti-mitotic toxin, also demonstrated therapeutic potential in vivo (Ostermann et al. 2008). Genetic inactivation or pharmacologic inhibition of FAP by different small molecular compounds was also shown to cause tumor growth inhibition (Santos et al. 2009). Estrogen receptors (ER) are classically known to be expressed by subgroups of breast cancer cells, but additionally these receptors are expressed in the tumor stroma (Fig. 19.1f). Moreover, stromal ER expression seems more important than epithelial ER expression for estrogen-induced epithelial cell proliferation (Haslam and Woodward 2003). Certain types of antihormonal therapy, such as tamoxifen and fulvestrant, function as antagonists of ER and are used clinically for breast cancer treatment. However, the high expression of ER on stromal cells suggests that the therapeutic response of such drugs in part could be due to CAF inhibition (Arendt et al. 2009). Aromatase inhibitors, another group of antihormonal therapy in breast cancer therapy, inhibit aromatase function in the tumor stroma and stromal cells elsewhere in the body. Interestingly ER positive cells in the tumor stroma are stimulated by estrogen even when the breast cancer cells are ER negative (Arendt et al. 2009; Gupta et al. 2007). However, the clinical importance of this is doubtful as antihormonal therapy has been shown repeatedly to have no effect in ER negative breast cancer. The other way of targeting tumor fibroblast function is by interference with paracrine growth factor signaling. As mentioned above there is a large number of signaling substances that are secreted between various cell population in the tumor

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stroma and between the cancer cells and the stroma. PDGF is secreted by epithelial cells and epithelial cancers to stimulate CAFs and pericytes through activation of PDGFR (Pietras et al. 2008). The crucial role of PDGFR signaling in fibroblasts is demonstrated by the increased fibrotic stroma response seen when cancer cells are transfected to overexpress PDGF (Arendt et al. 2009). Imatinib mesylate, a multiple tyrosine kinase inhibitor halts proliferation and modulates cytokine expression in human tumor fibroblasts from colorectal metastases (Shibue and Weinberg 2009). Imatinib was found experimentally to halt tumor growth in part through PDGFR signal inhibition on CAFs, and in part through angiogenesis inhibition (Pietras et al. 2008). There is currently a lot of interest in developing drugs directed at HGF and its receptor c-Met (Eder et al. 2009; Peruzzi and Bottaro 2006; Porter 2010). HGF is a growth factor primarily secreted by fibroblasts to activate c-Met on epithelial cells (Birchmeier et al. 2003; Christensen et al. 2003; Eder et al. 2009; Haslam and Woodward 2003). The c-Met receptor is commonly hyperactivated on epithelial cancer cells, either because of paracrine or autocrine stimulation, or because of acquiring activating mutations (Birchmeier et al. 2003). This activation makes the tumor cells proliferate and become more invasive and motile, as part of an induced EMT programme (Birchmeier et al. 2003). Most of the c-Met directed drugs currently undergoing clinical trials are receptor tyrosine kinase inhibitors (TKIs), but antibody based strategies are also being assessed (Eder et al. 2009). Additionally NK4 and geldanamycin antagonists of HGF and c-Met respectively are being tested therapeutically (Birchmeier et  al. 2003). The advantage of antibodies are their selectivity, the long half life, and that they elicit an immune response against the targeted cell. However, the small molecule TKIs can usually be administered orally, have better tissue penetration and a lower overall production cost (Eder et al. 2009). Several c-Met TKIs are now in phase II/III randomized trials, showing promising response rates clinically (Eder et  al. 2009; Porter 2010). Although >150 cancer cell lines expressed c-Met and showed sensitivity to c-Met TKIs in vitro, it seemed that overexpression or amplification of the receptor was required for an in vivo effect (Christensen et al. 2007). Only approximately 5% of the cell lines tested exhibited such increased c-Met expression (Christensen et.al. 2007). Also, many of current compounds are broad-spectrum TKIs, antagonizing not only c-Met but a series of other receptor tyrosine kinases as well, which makes it difficult to interpret the isolated role of c-Met inhibition (Eder et al. 2009). The extensive tumor necrosis induced by some of the c-Met inhibitors points for instance to additional antiangiogenic effects (Christensen et al. 2003; Zou et al. 2007). The tumor fibroblasts can also be targeted indirectly by drugs directed at the ECM and ECM turnover. Tenascin C is upregulated in the ECM of many malignant tumors, and is a potential target for radioimmunotherapy, where tenascin antibodies are conjugated with radioisotopes (De Santis et al. 2006). Such therapy has shown promising results in glioma patients, and is also being developed further for epithelial malignancies (De Santis et al. 2006; Goetz et al. 2003). Apart from killing

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the tumor cells, such targeted irradiation will have the potential to inflict collateral damage on tumor stromal cells. As we described above MMPIs have already been tested in clinical trials, and effort is still made to develop these drugs further despite negative results in the past. These compounds target the MMPs produced by CAFs. Yet another potential way of targeting the tumor stroma is by genetically engineering mesenchymal cells from the bone marrow to produce for instance interferon β—which is then delivered by transfected cells travelling via the blood stream to the tumor stroma (Marx 2008). Therapy affecting CAFs can also occur as side effects of treatment directed at other cell populations. Drugs such as sunitinib and sorafenib are marketed as angiogenesis inhibitors, but they also inhibit receptors expressed on fibroblast subpopulations, such as PDGFRα and PDGFRβ (Oudard et al. 2007). Drugs are being developed clinically which target integrin receptors, and the survival of fibroblasts as well as many other cell populations within a tumor could be reduced if integrinECM interactions are disrupted (Desgrosellier and Cheresh 2010; Sethi et al. 1999). Various integrin-directed therapies are currently being tested in cancer patients (Desgrosellier and Cheresh 2010). Finally, chemotherapy utilizes the increased proliferation rate in cancer cells to achieve selective cytotoxicity in the tumor. CAFs also proliferate extensively, and could therefore be targeted by chemotherapy and this may contribute to the tumor regression observed clinically (Fig. 19.1d).

19.10 Conclusion Fibroblasts play a crucial role in cancer progression. They provide the scaffold on which the epithelial tumor cells sit, and they produce growth factors which promote tumor cell proliferation. Our increasing knowledge of the crosstalk between fibroblasts and tumor cells points to a potential for fibroblast-directed therapy in the future, as a new way of treating epithelial malignancies. The current controversy of fibroblast mutations should be investigated further, as the occurrence of such genetic alterations within the non-malignant cell populations would dramatically alter our understanding of the carcinogenesis process. Additionally, the origin of fibroblasts occuring in primary tumors and metastases should be investigated further to better understand how tumor fibroblasts are recruited. Today, tumor fibroblasts have become key players in the generation of the tumor microenvironment, and we are starting to understand how the “soil” affects the “seed” during tumor progression. Acknowledgements  This work was primarily supported by National Institutes of Health Grant DK62987 and partially by National Institutes of Health Grants DK55001, DK61688, AA13913, and CA12550 and funds from the Department of Medicine for the Division of Matrix Biology at Beth Israel Deaconness Medical Center. This work was also supported by the Champalimaud Foundation. HPE was supported by grants from the University of Bergen and the Eckbo Legacy, Norway.

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Part VI

Therapeutic Application/Targeting

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Chapter 20

Cancer Associated Fibroblasts as Therapeutic Targets Christian Rupp, Helmut Dolznig, Christian Haslinger, Norbert Schweifer and Pilar Garin-Chesa

20.1 Introduction The tumor stroma is an essential, intrinsic part of epithelial cancers and plays a primary role during carcinogenesis. Extensive clinical evidence and a variety of experimental mouse models have demonstrated the active role of the tumor stroma in promoting tumor growth. The advances in our understanding of the molecular basis for cancer initiation and progression provide the basis for the design of novel targeted agents that selectively address deregulated pathways in malignant cells. Drugs that target the stromal component of tumors may represent a further important approach to the overall control of cancer. The discovery and development of molecularly targeted drugs requires translational research, which include the identification of new molecular targets, target validation and the development of appropriate models to test the new drugs with regard to their mechanism of action, safety and efficacy before translating these findings into the clinic. Here we will address the challenges for drug development of new therapeutic agents directed towards the tumor stroma, in particular those targeting the cancer associated fibroblasts (CAFs), the limitations in the available experimental models and the complexity of the model systems in which the new targets can be studied in detail.

P. Garin-Chesa () Boehringer Ingelheim RCV GmbH & Co KG Dr. Boehringer-Gasse 5-11, 1130 Vienna, Austria [email protected] Institute of Pathology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria

M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_20, © Springer Science+Business Media B.V. 2011

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20.2 Histological and Molecular Heterogeneity of Human Cancers Carcinomas which comprise the majority of human cancers are composed of malignant epithelial cells as well as mesenchyme-derived stromal cells, such as fibroblasts, myofibroblasts, endothelial cells, pericytes, smooth muscle and hematopoietic cells embedded in a matrix of extracellular proteins (ECM). Different histological subtypes of carcinomas exist and the extent and composition of the stroma varies among tumors. Certain tumor types such as those occurring in the pancreas, breast, and colon (Fig. 20.1) are characterized by the presence of a prominent stromal reaction (desmoplasia). The fibroblasts in those tumors express markers of activated fibroblasts such as fibroblast activation protein alpha (FAPα) (Garin-Chesa et al. 1990) and alpha-smooth muscle actin (α-SMA) (Desmouliere et al. 2004) that differ from their expression in resting fibroblasts of the adjacent normal tissues (Fig. 20.1a), indicating the phenotypic differences between normal and tumor fibroblasts. Different subsets of CAFs have been observed to occur in different tumor types (Huber et al. 2006; Koperek et al. 2007) suggesting that the activation programs of CAFs in cancer may be linked to the tissue of origin and might indicate functional differences of CAFs in tumor invasion and metastasis (Sugimoto et al. 2006). Several clinicopathologic studies have shown that the characteristics of the tumor stroma correlate with prognostic factors and patient survival (Koperek et al. 2007; Hasebe et al. 2001; Kunz-Schughart and Knuechel 2002).

Fig. 20.1   a Cancer associated fibroblasts are the main cellular stromal component of carcinomas and display molecular heterogeneity. Carcinomas arising in the pancreas, breast and colon display prominent stroma reaction (desmoplasia) separating the clusters of tumor cells. The fibroblasts in those tumors express markers of activated fibroblasts such as fibroblast activation protein (FAPα) and alpha-smooth muscle actin (α-SMA). FAPα is selectively expressed in the tumor stroma and is absent in normal tissues (see normal colon vs. colon cancer). In contrast, expression of α-SMA can be seen in subsets of fibroblasts surrounding the crypts and in the muscularis mucosa of the normal colon as well as in activated tumor fibroblasts. FAPα and α-SMA expression ( brown) visualized by the ABC immunoperoxidase method with hematoxylin counterstaining. b Subcutaneous xenograft models in immunodeficient mice. Like in the majority of xenograft models, subcutaneous injection of Colo205 cells, a human colorectal cancer cell line, induces tumors with a highly atypical morphology, characterized by clusters of tumor cells, with very little tumor stroma, and absence of glandular differentiation (for comparison see the human colon cancer sample above). Certain human tumor cells such as FaDu cells, derived from a head and neck squamous cell carcinoma, are able to induce a more prominent stroma reaction, with FAPα positive activated stromal fibroblasts and histotypic features resembling the human counterpart. Therefore, a careful selection of the in vivo models is required to determine the efficacy of drugs targeting the activated tumor fibroblasts. Hemaoxylin-eosin and immunohistochemical staining for FAPα ( brown; bottom panel)

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20.3 Cancer Associated Fibroblasts (CAFs): Molecular Characterization Fibroblasts represent the major cellular component of the stroma of epithelial cancers. Several in vitro and in vivo studies have demonstrated that the growth, differentiation and invasive behaviour of malignant epithelial cells are influenced by the surrounding stroma (Tlsty and Hein 2001; Bhowmick et al. 2004; Joyce 2005; Mueller and Fusenig 2004). Normal fibroblasts have been reported to prevent the progression of transformed epithelial cells (Hayashi and Cunha 1991), in contrast, the presence of activated tumor stromal fibroblasts was shown to enhance malignant epithelial transformation in several cancer models (Nakamura et al. 1997; Olumi et al. 1999; Orimo et al. 2005). Cancer associated fibroblasts (CAFs) differ considerably from normal resting fibroblasts, and display distinct molecular signatures which can be linked to clinical outcome (Finak et al. 2008). Recent studies have identified new functional roles for CAFs and the existence of different CAFs subsets in human cancers by gene expression analysis (Chang et al. 2002; Iacobuzio-Donahue et al. 2002).

20.4 In Vitro and In Vivo Models for Tumor Stroma Interaction Understanding the molecular mechanisms that control the heterotypic interactions between malignant cells and the surrounding stroma may help to develop new targeted therapies. However these studies have been hampered by the challenges in studying multi-cellular interactions in experimental models (Fig. 20.2). The in vitro study of freshly dissociated cancer cells or established tumor cell lines and fibroblasts in two dimensional (2D) cultures has provided important insights into basic tumor cell biology and has enabled the identification of common genetic alterations in cancer cells that can be targeted therapeutically (Cornil et al. 1991; Yashiro et al. 2005; Elenbaas et al. 2001; Jones et al. 2008). However, such in vitro approaches have proven somewhat limited in studying stromal targets. Only a limited number of stromal-derived cells are available in culture and phenotypic changes can be induced under culture conditions (Orimo et al. 2005). In addition, many physiological aspects of tumors such as cell–cell and cell–matrix interactions are lost under conventional 2D culture conditions. Cells grown in three-dimensional (3D) scaffolds or as 3D multicellular spheroids recapitulate the architecture of tissues and tumors in vivo to a higher extent. They offer new opportunities to analyze the activation of differentiation programs and the pathways involved in cell migration and invasion when cells are grown in a heterotypic and physiologically relevant context (Kunz-Schughart and Knuechel 2002; Schmeichel and Bissell 2003). Organotypic 3D co-culture models have been

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Fig. 20.2   Carcinomas are heterogeneous mixtures of malignant cells and stromal cells, such as fibroblasts, blood vessels and immune cells embedded in a matrix of extracellular proteins (ECM) and grow as three dimensional structures. An example for this heterogeneity is shown in a histologic picture from a human non-small cell lung cancer ( left panel). Tumor cell clusters ( blue) are separated by strands of activated fibroblasts which are PDGFR-β positive ( brown) and blood vessels with CD31 positive endothelial cells ( dark blue). In conventional cell culture models, cells are grown as homogeneous cultures on plastic surfaces. Under these culture conditions many features of the in vivo growth, such as tissue architecture, cell–cell contact, heterotypic cellular interactions and signalling networks are lost ( middle panel). Using 3D cultures in the presence of ECM components, the heterotypic interactions of tumor cells and stromal cells can be studied in detail. A phase contrast picture of this model shows a culture of tumor cells grown as a multicellular spheroid, co-cultured with fibroblast embedded in a collagen I gel, recapitulating the in vivo heterogeneity

used to study the functional interplay between genetically altered epithelial cells and fibroblasts (Okawa et al. 2007; Sadlonova et al. 2005) and to study fibroblastled invasion in models of skin, breast, pancreatic and brain cancers (Gaggioli et al. 2007; Froeling et al. 2009). A novel experimental set-up has been developed (Dolznig et al. manuscript in preparation) combining multi-cellular spheroids, 3D collagen gel cultures and cocultures of human epithelial cancer cells with normal human fibroblasts or CAFs in one assay (Fig. 20.2). Using this model system the feasibility to study the tumor– stroma interactions phenotypically and at the molecular level was demonstrated. Gene expression profiles from these 3D co-cultures have been obtained and ongoing studies are exploring the applicability of the model to study the role of these new stromal targets in tumor invasion using knock-in/knock-down experiments of selected genes in the tumor cells or in the fibroblast population.

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Xenograft tumor models are commonly used to analyze new mechanisms of action and to validate the efficacy of novel drugs in preclinical studies. The majority of these in vivo assays are performed in immunodeficient mice following the inoculation of established tissue culture cell lines into ectopic sites. From a histopathological view these tumor models show a highly atypical morphology with very little stroma or histotypic features resembling the human cancer counterparts (Fig.  20.1b). A more authentic histological appearance is observed in carcinoma models derived by direct transplantation of surgical specimens, purified cell suspensions freshly obtained from surgical specimens or in orthotopically implanted tumors (Rubio-Viqueira et al. 2006; Shu et al. 2008; O’Brien et al. 2007; Ostermann et al. 2008). Nevertheless, most of the preclinical validation studies are carried out using the ectopic (mostly subcutaneous) in vivo models, that are relatively easy to set up and that can be generated in large numbers of similar-sized tumors for randomization as pre-requisite to assess the effects of drugs. However, in many cases, the results obtained from xenograft models do not translate well in subsequent clinical studies (Ostermann et al. 2008). Genetically engineered mouse models (GEM) are promising alternatives (Frese and Tuveson 2007; Gopinathan and Tuveson 2008). These models, generated through the introduction of genetic mutations associated with specific human malignancies closely recapitulate the human disease at the pathophysiological and molecular level. To date, models have been developed for many common tumor types (e.g. lung, breast, prostate, colon and pancreatic cancer). Evidence for the usefulness of GEM has been demonstrated in preclinical studies evaluating targeted therapies in models of lung and breast cancer (Bhowmick et al. 2004; Politi et al. 2006; Beppu et al. 2008; Rottenberg and Jonkers 2008; Perera et al. 2009; Santos et al. 2009). These studies suggest that GEM can more accurately predict the therapeutic responses to those observed in the clinic.

20.5 Therapeutic Opportunities Different molecular targets have been shown to distinguish the cancer associated fibroblasts (CAFs) and different strategies to target these molecules are under evaluation. Here we will focus on potential drug candidates with special attention to those in more advanced clinical development.

20.5.1  Fibroblast Activation Protein Alpha Fibroblast activation protein alpha (FAPα) is an integral cell surface protein selectively expressed by activated stromal fibroblasts of several types of human epithelial cancers. In normal tissues, FAPα expression is highly restricted to developing organs, healing wounds, and tissue remodeling. Epithelial tumor cells and most normal adult human tissues lack FAPα expression (Garin-Chesa et al. 1990; Rettig

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et al. 1993; Niedermeyer et al. 1997; Niedermeyer et al. 2001; Huber et al. 2003; Dolznig et al. 2005). FAPα is a serine protease capable of degrading type I collagen which places FAPα into the group of enzymes involved in tumor tissue remodeling (Scanlan et al. 1994; Park et al. 1999; Niedermeyer et al. 1998). FAPα activity can be detected in tumor samples and shows a good correlation with FAPα expression detected by immunohistochemistry (Huber et al. 2003; Park et al. 1999). Based on the selective expression of FAPα in the reactive stroma of many epithelial cancers, the lack of expression in normal adult tissues, and its protease activity, FAPα is an ideally suited stroma target to be exploited in the clinic. Two different approaches have been used to target FAPα in tumors. The first was to employ FAPα-specific monoclonal antibodies. Initial studies with radiolabeled murine and humanized antibodies against human FAPα have shown highly specific tumor targeting properties (Welt et al. 1994; Scott et al 2003), however no clinical efficacy could be demonstrated using the unlabeled humanized antibody in a study in metastatic colorectal cancer (Hofheinz et al. 2003), probably due to the lack of effector-function properties of the naked antibody. More recently, a novel antibody-maytansinoid conjugate (FAP5-DM1), targeting a shared epitope of human, mouse and cynomolgus monkey fibroblast activation protein alpha, has been developed. Using this conjugate in stroma-rich histotypic cancer xenograft models we were able to induce long-lasting inhibition of tumor growth and complete regressions in models of lung, pancreas and head and neck cancers, with no evidence of toxicity (Ostermann et  al. 2008). The second approach has been to target the enzymatic activity of FAPα with small molecule inhibitors. Using the peptidase inhibitor PT-100 (talabostat) a reduction in tumor growth rate was shown in a variety of tumor models in mice (Cheng et al. 2005; Adams et al. 2004). This particular compound, however, inhibits multiple intracellular and extracellular dipeptidyl peptidases (e.g. FAPα, DPPIV, DPP7), so that the anti-tumor effect could not be directly attributed to FAPα inhibition. More recently, using FAPα-null mice and a more selective inhibitor (PT-630), the endogenous role of FAPα in tumorigenesis and the control of tumor growth mediated by pharmacologic inhibition of FAPα enzymatic activity has been reported (Santos et al. 2009; Pure 2009). Deletion of FAPα resulted in a marked reduction of tumor growth in a LSL-K-rasG12D genetic mouse model of lung cancer and in a syngeneic colon cancer model, suggesting a tumor promoting activity of endogenous FAPα. Treatment with PT-630 of tumorbearing wild type animals resulted in a marked inhibition of tumor growth in both models, supporting further clinical studies.

20.5.2  Matrix Metalloproteinases (MMPs) Cancer associated fibroblasts are a major source of MMPs in tumors, including MMPs 1,2,3,9,11,13 and MT1-MMP (Bisson et  al. 2003; Sternlicht et  al. 1999). Fibroblast derived MMPs have been extensively investigated in xenograft models, demonstrating the important role for these proteases in promoting tumor growth, metastasis, and angiogenesis (Egeblad and Werb 2002). Based on the results of

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the preclinical studies MMPs were seen as attractive anticancer targets and several inhibitors have been developed and tested in the clinic in a variety of cancer types (Coussens et al. 2002). These trials however had failed to demonstrate a survival benefit despite the promising activity shown in pre-clinical models (King et al. 2003). Possible explanations include differences in the biology of the MMPs between mice and humans, lack of anti-immune response in the xenograft models used pre-clinically, dose-limiting toxicities at least in part due to off-target effects, a narrow therapeutic window for some of the inhibitors and perhaps the challenging fact that MMPs can have tumor-promoting as well as tumor-suppressor activities. Thus a better understanding of the functional complexity of this family of proteases and the use of second generation inhibitors with improved selectivity profile may provide better therapeutic outcomes (Konstantinopoulos et al. 2008).

20.5.3  Endosialin/TEM1 Endosialin is a highly sialylated, C-type lectin-like surface receptor structurally related to thrombomodulin and complement receptor C1qRp (Rettig et  al. 1992; Christian et al. 2001). First identified with a monoclonal antibody, mAb FB5, endosialin was discovered independently through a SAGE screen (serial analysis of gene expression) of human cancer endothelial cells, leading to the alternative designation of tumor endothelial marker 1 (TEM1) (St Croix et al. 2000). Endosialin/TEM1 is expressed to varying degrees by tumor endothelial cells, pericytes and stromal fibroblasts (MacFadyen et al. 2005; Brady et al. 2004; Rupp et al. 2006a). Endosialin expression has not been detected in capillary endothelium in most normal tissues. The physiological role of endosialin is unknown. Endosialin/Tem1 knock-out mice are fertile and develop normally, however, when human HCT116 colon carcinoma cells were implanted orthotopically onto the serosal surface of the large intestine of nude KO mice, both tumor take and growth rate were reduced (Nanda et al. 2006). Recent evidence suggests that endosialin/TEM1 might interact with extracellular matrix components, including collagen type I, IV and fibronectin in promoting cell adhesion and migration processes during tumor invasion and metastasis (Tomkowicz et al. 2007). A humanized Endosialin/TEM1 blocking antibody (MORAb-004) is currently in clinical studies and might provide a therapeutic benefit in a broad range of tumors, based on its ability to target the endothelial cells as well as the peri-vascular stromal component of the tumors.

20.5.4  PDGF/PDGFR Pathway Platelet-derived growth factors (PDGFs) play important roles during embryonic development and wound healing (Betsholtz 2004) and expression of their tyrosine kinase receptors (PDGFRs) in the tumor stroma is a common feature of human

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cancers (Ostman and Heldin 2007). During tumorigenesis, PDGFR can drive tumor growth directly by autocrine stimulation of receptor-expressing tumor cells or in a paracrine manner by acting on the tumor stroma fibroblasts and pericytes (Pietras et al. 2008). The importance of the paracrine signaling network for the recruitment of cancer associated fibroblasts and pericytes has been shown in a number of studies (Skobe and Fusenig 1998; Anderberg et al. 2009). Pericytes provide both survival signals and structural support to endothelial cells contributing to a mature, functional vasculature and thus to tumor growth (Carmeliet 2003). Multiple tyrosine kinase inhibitors with anti-PDGFR activity, such as imatinib, sorafenib, and sunitinib, have been approved and are presently under further clinical development (Levitzki 2004; Steeghs et al. 2007). The most commonly used, imatinib, is a bcr-abl inhibitor with additional PDGFR and c-kit kinase inhibitory activity (Carroll et  al. 1997). In experimental cancer models, imatinib has been shown to inhibit PDGFR activity on fibroblasts and pericytes and to significantly decrease the stromal reaction which was accompanied by a reduction in tumor cell proliferation and pericyte coverage of tumor vessels (Pietras et al. 2008; Kitadai et al. 2006). Furthermore, inhibition of PDGFRs increases the uptake and therefore the antitumor effect of conventional chemotherapeutics like paclitaxel by lowering tumor interstitial pressure (Pietras et  al. 2002). Other multi-kinase inhibitors, such as BIBF1120, a triple angiokinase inhibitor of the VEGFR, PDGFR and FGFR families, has been shown to decrease the pericyte coverage of tumor blood vessels in experimental cancer models (Hilberg et al. 2008) which together with the reduction in tumor microvessel density contributed to the pronounced anti-tumor effects of the inhibitor. BIBF1120 is in clinical development for several tumor indications. Collectively, these results indicate that inhibition of PDGF receptor signaling might provide a complementary approach to conventional treatments. To date, it is still unknown to what extent selective blockage of stromal PDGF signalling contributes to the observed anti-tumor effects of these multi-kinase inhibitors.

20.5.5  Transforming Growth Factor β (TGF-β) Pathway Transforming growth factor β (TFG-β) is recognized for its dual and opposing functions, a tumor-suppressor activity in the pre-malignant state and a tumor promoter activity during malignant progression (Bierie and Moses 2006; Massague 2008). This dual role has made the design and development of drugs targeting this signaling pathway in cancer particularly complex. In tumors, activation of TGF-β is linked to the activity of several oncogenic pathways linked to the induction of epithelial mesenchymal transition (EMT) that enhances tumor cell invasion (Oft et al. 1996). TGF-β can have an additional role in tumor growth that is mediated through its activity on the tumor stroma, facilitating tumor tissue remodeling and neoangiogenesis. Studies with fibroblast specific TGF-β type II receptor knock-out models provided evidence for the tumor suppressor role of TGF-β in fibroblasts,

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by blocking the production of tumor cell growth-promoting paracrine factors such as hepatocyte growth factor (HGF) (Bhowmick et  al. 2004; Cheng et  al. 2007). On the other hand, it was demonstrated that TGF-β stimulates myofibroblast differentiation and that blocking of TGF-β signaling in stromal fibroblast leads to a significant reduction of tumor-growth in a co-transplantation xenograft model (Verona et al. 2007), suggesting that pro- or anti-tumoral effects of TGF-β signaling may very much depend on individual tumor models. Considering the direct effect of TGF-β on tumor cells and its indirect effect on the tumor stroma, TGF-β signaling appeared as an attractive therapeutic concept. Several approaches to inhibit the TGF-β pathway have been investigated in preclinical models and clinical studies. Neutralizing antibodies that inhibit the ligand-receptor interaction, antisense oligonucleotides and small molecule inhibitors of the TGF-β receptor kinase complex have been developed and are at different stages of clinical development (Lahn et al. 2005; Jones et al. 2009). It is expected that this class of agents will be active in a broad range of tumors but due to the complex roles of this growth factor receptor family in tumorigenesis a careful selection of patients will be required to address their therapeutic benefits in patients.

20.5.6  Hedgehog Pathway The Hedgehog (Hh) family of proteins have been shown to control cell growth, survival and fate during embryonic development and when mutated or misregulated to contribute to tumorigenesis. Aberrant activation of the Hh pathway by mutations are causally associated with basal cell carcinoma of the skin, medulloblastoma and rhabdomyosarcoma (Varjosalo and Taipale 2008). Furthermore, components of the Hh pathway have been described to play a role in the growth of a variety of epithelial cancer types, including small cell lung cancer, pancreatic and prostate cancer even in the absence of mutations (Watkins et al. 2003; Thayer et al. 2003; Karhadkar et al. 2004). Recent studies in experimental cancer models support a model in which Hh acts in a paracrine manner on stromal cells. Hh increases tumor growth by stimulating the expression of extracellular matrix proteins and factors like IGF or Wnt in the stroma and thereby promoting stromal desmoplasia (Yauch et al. 2008). The most commonly used Hh antagonists are the plant alkaloid cyclopamine and its derivatives (Taipale et al. 2000). The anti-tumor effect of the semisynthetic cyclopamine-derivative IPI-926 was investigated in a mouse model of pancreatic ductal adenocarcinoma refractory to gemcitabine (a drug commonly used in the clinic). Mice treated with IPI-926 alone or in combination with gemcitabine were depleted of desmoplastic stroma reaction in the tumors and displayed increased intratumoral vascular density. These changes correlated with a more effective delivery of the co-administrated gemcitabine, resulting in enhanced efficacy of the drug (Olive et al. 2009). This study has identified a potential novel mechanism for anti-stroma therapy.

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20.6 Identification of Novel Therapeutic Targets by Gene Expression Profiling The recent technological advances for high-throughput DNA and RNA detection have shown that specific germline and somatic mutations, loss of heterozygosity, and DNA amplifications occur during cancer progression. Oncogenome signatures of human tumors have been shown to correlate with metastatic behaviour and clinical outcome in different cancer types (Alizadeh et al. 2000; Perou et al. 2000; Ramaswamy et al. 2003). However, the specific contribution of malignant epithelial cells and stromal cells to these genetic signatures is in most cases unclear, since most of the studies have used bulk tumor samples. Our approach to establish the molecular differences between CAFs and normal resting fibroblasts has been to generate gene expression signatures from microdissected cancer and corresponding normal tissues. We focussed on colorectal cancer and developed a protocol for laser capture microdissection guided by antibodies against FAP to separate epithelial cells from activated stromal fibroblasts (Rupp et al. 2006b). We performed whole genome Affymetrix GeneChip® analysis and obtained transcriptional signatures from tumor cells and activated tumor stroma that were compared with the expression profiles from microdissected normal colonic epithelium and normal fibroblasts, obtained from the same patients (Fig. 20.3, Rupp et al manuscript in preparation). Bioinformatic analysis comparing the tumor stroma vs. the normal stroma signatures identified a number of selectively up-regulated genes. Well characterized tumor stroma markers such as FAPα, MMP-2, PDGFR-β and FGFR1 among others appeared specifically up-regulated in the stroma compartment (Fig.  20.3) and served as a validation parameter for our screen. To further analyze the functional significance of these gene signatures in the context of tumorigenesis we performed a similar genetic screen in our above described 3D co-culture model of tumor cell spheroids and fibroblasts (normal and cancer-derived) grown in collagen gels. We established transcriptional profiles from the different cellular components grown in collagen gels in mono-cultures and compared the gene expression responses induced upon co-cultivation (Dolznig et al. manuscript in preparation). We observed a remarkable concordance between the gene sets obtained in our ex vivo study (colorectal cancer study from human samples) and this in vitro co-culture system. Examples of commonly regulated genes included COL11A1 and MMP3 (Fig. 20.3), well characterized markers of activated fibroblasts. Gene-Set Enrichment Analysis (GSEA) (Mootha et al. 2003) using the gene-set collections from the Molecular Signature Database (Broad Institute) (Subramanian et  al. 2005) and Pathway analysis (Ingenuity®) revealed datasets and gene-networks that were significantly enriched in both screens. Gene-sets involved in extracellular matrix deposition, angiogenesis, wound healing and EMT were significantly up-regulated in both studies. Interestingly, many of the genes identified in our study have been reported in studies performed in vitro including the “wound response signature” of fibroblasts in response to serum stimulation (Chang et al. 2004), a hypoxia-associated response (Chi et al. 2006) as well as the signatures obtained from co-cultures of cancer cells

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and fibroblasts cell lines of different origins (Sato et al. 2004; Gallagher et al. 2005). Using independent datasets from human cancers, it was shown that the “woundresponse signature” was strongly predictive of metastasis and progression in breast, lung and gastric cancers and was an independent predictor of outcome in a followup study in breast cancer (Chang et al. 2004). Other in vivo signatures have been described (West et al. 2005) comparing the expression patterns of good versus poor outcome in fibroblastic tumors. A subsequent comparison of these signatures with a breast cancer data set suggested that distinct patterns of stroma reaction defined two groups of breast cancers with significant differences in overall survival, indicating that the stromal response varies significantly in different subtypes of carcinomas and may be clinically relevant. Expression signatures from different tumor compartments have also been established using serial analysis of gene expression on antibody-sorted stromal components in breast cancers (Allinen et al. 2004) or laser capture microdissection in breast cancer and basal cell carcinoma of the skin (Casey et al. 2009; Micke et al. 2007). Using a set of genes expressed by the microdissected tumor stroma, a stroma-derived prognostic predictor signature (SDPP) was developed and shown to separate primary breast cancers into three distinct groups associated with different clinical outcomes (Finak et al. 2008). In another study, a stromal signature was shown to predict the response of estrogen-receptor negative breast tumors to chemotherapy (Farmer et al. 2009). The authors used a novel bioinformatics method that decomposes gene expression signals from a mixture of tumor and stromal cells into multiple independent signatures. They obtained a 50-gene stromal signature including FAPα, MMP2, MMP14, PDGFR-β which predicted resistance to chemotherapy. Fig. 20.3   Identification of novel tumor stroma markers by expression profiling analysis. a Antibody-guided laser capture microdissection allows the separation of epithelial cells from the activated stromal compartment in colon cancer samples. Activated tumor stromal fibroblasts were visualized by immunohistochemical staining with an antibody to FAPα. In the figure, the borders between epithelial and stromal structures are indicated by red dotted lines. Normal fibroblasts were isolated from normal colonic tissue after hematoxylin staining and morphological examination. After RNA isolation, whole genome Affymetrix GeneChip analysis was performed. Bioinformatic evaluation identified novel tumor stroma targets by comparing the tumor stroma vs. the normal stroma signatures. b Well characterized tumor stroma markers such as FAPα, MMP2, PDGFRß and FGFR1 were significantly up-regulated in the tumor stroma compartment. The expression levels are indicated by whisker box plots, the bold centre-line indicates the median; the box represents the interquartile range (IQR). Whiskers extend to 1.5 times the IQR. TC, tumor cells; NS, normal stroma; TS, tumor stroma. c Comparison of the transcriptional profiles obtained in our ex vivo screen in colorectal cancer samples with those obtained in an in vitro screen with a colon cancer cell line (LS174T) cultured in the presence colon-derived human CAFs in a 3D coculture assay. Gene-Set Enrichment Analysis (GSEA) revealed gene sets involved in extracellular matrix deposition, angiogenesis and wound healing significantly upregulated in both studies.Two representative examples, Collagen 11A1 (COL11A1) and matrix metalloprotease 3 (MMP3) are shown. TC, tumor cells; NS, normal stroma; TS, tumor stroma; blue whisker box blots indicate the expression levels after 3.5 days of LS174T/CAF co-cultivation (TC/CoCult); yellow box blots show the levels of expression of individually cultured LS174T cells and CAFs mixed together after cultivation (TC/CAF Mix). Whisker box plot as in b

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Taken together, these studies demonstrated that tumors express a variety of functionally different genes in their tumor stroma, representing different activation stages or different subtypes of CAFs that may be relevant for the invasiveness or clinical behavior of the tumors. The gene expression signatures derived from this type of analysis appear to have clinical significance in different cancer types and have provided new genetic markers in the tumor stroma that may serve as targets for novel therapeutic approaches.

20.7 Conclusions The rapid progress of research in molecular cancer biology has contributed to a better understanding of the role of the tumor stroma during tumor growth and metastasis formation and has led to the identification of selected tumor stroma markers that serve as targets for novel therapies. A number of monoclonal antibodies, small-molecule inhibitors and anti-sense approaches have been developed and investigated in pre-clinical models, some of these molecules have entered clinical development. Future approaches to stroma-targeted therapy will have to be based on further refinement of our understanding of the molecular mechanisms that control the tumor–stroma interaction, improved preclinical models that adequately reproduce the complexity of the tumor tissue, and a biomarker-based selection of patients most likely to benefit from the novel therapies.

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Chapter 21

Targeting Tumor Associated Fibroblasts and Chemotherapy Debbie Liao and Ralph A. Reisfeld

21.1 Introduction Effective treatment of solid tumors by chemotherapy depends on several key factors including: (1) systemic delivery of the chemotherapeutic drug to the tumor, (2) effective distribution of active drug in sufficient quantities to kill tumor cells, and (3) sensitivity of tumor cells to the chemotherapeutic drug (Fig. 21.1). Many tumor-cell-intrinsic mechanisms that contribute to chemoresistance have been identified such as: expression of drug efflux transporters (multi-drug resistanceassociated proteins) and detoxifying enzymes (glutathione S-transferase) by tumor cells, as well as defects in apoptosis regulatory proteins in tumor cells (Tannock 2001). However, non-transformed cells that reside in the tumor microenvironment can also contribute to chemoresistance of tumors. In particular, cancer associated fibroblasts (CAFs) are key mediators of tumor growth and can contribute to chemoresistance (Ostman and Augsten 2009). CAFs express and secrete many cytokines, growth factors, and extracellular matrix proteins that enhance survival of tumor cells, promote tumor angiogenesis and alter the composition of the extracellular matrix (ECM) in the tumor microenvironment (TME); all factors that can influence the delivery, uptake and activity of chemotherapeutic drugs to be favorable for tumor growth (Ostman and Augsten 2009). In this chapter, we will review the ways in which CAFs can influence the efficacy of chemotherapy by discussing its effects on drug delivery, activity, and drug sensitivity of tumor cells. Lastly, we will summarize the current approaches being developed by our laboratory and others for the therapeutic targeting of CAFs to improve cancer chemotherapy.

R. A. Reisfeld () Department of Immunology and Microbial Sciences, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_21, © Springer Science+Business Media B.V. 2011

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D. Liao and R. A. Reisfeld Tumor cells Drug hypoxia C A

IFP

B

D

E ECM

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Fig. 21.1   Drug distribution in solid tumors. ( A) Systemic delivery of chemotherapeutic drugs to the tumor involves transport through the blood vessels of the circulatory system. ( B) Once drugs have reached the tumor, they must transport across the microvascular wall into the tumor interstitium. Dispersion of drugs across the microvascular wall and through the tumor can be impeded by elevated interstitial fluid pressure ( IFP). ( C) In areas that are distal to blood vessels, local hypoxia can result in acidosis, which can decrease the cellular uptake and pharmacological activity of certain drugs. ( D) Proteins of the tumor ECM, including collagens and proteoglycans, are a source of physical resistance and can directly impede drug penetration through the interstitial space. ( E) ECM components, including collagen, can also directly bind chemotherapeutic drugs, thus acting as a sink to prevent further drug distribution

21.2 Cancer Associated Fibroblasts and Effects on Drug Delivery 21.2.1  Vascular Physiology Systemic delivery of a chemotherapeutic drug involves transport through blood vessels of the circulatory system (Jain 1989). The efficiency of this processes is governed by vascular morphology and the blood flow rate (Jain 2001a). Normal blood vessels are highly organized and strictly regulate the movement of molecules across the vascular wall (Jang et al. 2003). The formation of new blood vessels, or angiogenesis, occurs normally in adults during situations such as wound healing, where a new vascular bed is required to oxygenate an area of regenerating tissue, and during the ovarian cycle (Papetti and Herman 2002). In these cases, angiogenesis occurs

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by a well-orchestrated and tightly regulated process that results in the formation of new functional vessels (Bergers and Benjamin 2003). In contrast, tumor-associated angiogenesis occurs in a rapid and disorganized fashion, and leads to the formation of tortuous vessels with excessive branching and arteriolar-venous shunts (Jain 2001b). These abnormal vessels are characterized by an absence of pericytes, vessel leakiness, and often exhibit irregular blood flow (Brown and Giaccia 1998; Less et al. 1991). This, in turn, leads to formation of necrotic and avascular regions, in addition to stabilized regions with microcirculation within the same tumor (Jain 2001a). This chaotic formation of blood vessels can result in regional differences in perfusion rates and heterogeneous spatial distribution of therapeutic agents (Jain 2001a; Endrich et al. 1979). For example, blood flow rates in necrotic and semi-necrotic regions are relatively poor while such rates in stabilized areas can be extremely variable (Jain 2001a). Since the systemic delivery of chemotherapeutic drugs depends on a functional circulatory system, dysfunctional vessels produced by tumor-associated angiogenesis can significantly impede the delivery of anti-cancer drugs to tumor cells and thus reduce their anti-tumor effects. CAFs have been shown to promote and mediate tumor angiogenesis and thus can negatively affect drug delivery in this way. CAFs can directly stimulate tumor angiogenesis by production of growth factors, including vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) (Mueller and Fusenig 2004). Interestingly, normalization of tumor vasculature by blocking VEGF signaling has been shown to improve drug penetration in mouse xenograft models (Jain 2001b; Tong et al. 2004; Winkler et al. 2004). CAFs also secrete cytokines such as stromal-derived factor-1 (SDF-1), which promotes the recruitment of CXCR4 expressing endothelial cells that participate in angiogensis (Chometon and Jendrossek 2009). Additionally, using CAFs extracted from human breast carcinomas, Orimo et  al. showed that co-implantation of CAFs with breast carcinoma cells significantly enhanced tumor growth in a xenograft model, compared to carcinoma cells implanted with normal fibroblasts (Orimo et  al. 2005). In this model, enhanced tumor growth resulted from increased recruitment of endothelial progenitor cells and increased angiogenesis in response to SDF-1 secreted by CAFs (Orimo et al. 2005). These studies demonstrate that CAFs mediate tumor angiogenesis and thus can negatively affect the efficiency of systemic drug delivery.

21.2.2  Tumor Interstitial Fluid Pressure Once the chemotherapeutic drug has traversed the circulatory system and reached the tumor, drug transport out of blood vessels into the interstitial space relies on transcapillary pressure gradients (Jain 1987a). These pressure gradients are, in turn, determined by the hydrostatic and colloid osmotic pressures between the capillaries and interstitial space (Heldin et al. 2004). After exiting the vasculature, penetration of drugs into the tumor mass through extracellular space depends on concentration gradients and convection (Jain 2001a). In general, small molecules, such as oxygen and

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glucose, are transported by diffusion whereas larger molecules, such as soluble proteins and chemotherapeutic drugs, are transported by convection (Heldin et al. 2004). Typically in normal tissues, the overall transcapillary pressure gradient is slightly negative in the interstitium and results in a net outward transcapillary flow (Heldin et  al. 2004). However, in solid tumors the hydrostatic and osmotic pressures of the interstitium are often increased, resulting in elevated interstitial fluid pressure (IFP) (Jain 1987a, b; Milosevic et al. 1998; Stohrer et al. 2000). The net effect of increased IFP is decreased diffusion and convection of systemically delivered drug compounds out of the circulation into the tumor. Additionally, after exiting the circulation, further penetration of drugs into the tumor mass by convection is dependent on the interstitial fluid velocity (Jain 1987b), which can also be reduced by IFP thus limiting the amount of drug delivered to distal tumor cells. Clinically, increased tumor IFP is commonly associated with worsened disease prognosis. For example, in patients with metastatic melanoma or non-Hodgkin’s lymphoma, the IFP of metastatic tumor nodules was shown to increase with disease progression (Curti et al. 1993). Additionally, a study on patients with cervical cancer showed that high IFP was a predictor of disease recurrence following radiotherapy, and was associated with increased mortality due to disease progression (Milosevic et al. 2001). CAFs can contribute to increased tumor IFP in several ways. First, CAFs promote tumor angiogenesis, as described previously, that leads to formation of dysfunctional and leaky vessels. These dysfunctional vessels allow increased outflow of macromolecules and proteins from the circulation thus causing an overall rise in the colloid osmotic pressure of the tumor interstitium (Curti et al. 1993). This increase in IFP due to leaky blood vessels can also be magnified by dysfunctional tumor lymphatics, which impedes the drainage of fluid and proteins from the interstitial space (Minchinton and Tannock 2006). Second, it has been proposed that fibroblasts can actively modulate IFP by directly regulating the amount of tension applied to the the ECM (Reed et al. 2001). This model stipulates that fibroblasts exert tension on the collagen microfibrillar network through collagen-binding integrins that, in turn, restrains the intrinsic swelling pressure of hyaluronan and proteoglycans in the ECM thus increasing IFP (Heldin et al. 2004; Meyer 1983). This kind of fibroblast contraction has been shown to occur in vitro in response to platelet-derived growth factor (PDGF) and transforming growth factor-β (TGF-β) (Clark et al. 1989; Montesano and Orci 1988). In this way, it is possible that CAFs can actively regulate IFP by controlling the amount of tension applied to the tumor ECM.

21.2.3  Extracellular Matrix Drug penetration into solid tumors can be directly impeded by components of the tumor ECM. Since CAFs produce a majority of the constituents that make up the ECM, they can significantly influence the rate of drug transport through the

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interstitial space (Kalluri and Zeisberg 2006). Components of the ECM include fibrous proteins (collagen and elastin) and polysaccharides (proteoglycans and hyaluronans) that link together to form a permeable mesh (Jang et al. 2003). Thus, by acting as a source of physical resistance, the ECM can directly influence the rate of macromolecular trafficking (Jang et al. 2003). For example, presence of glycosaminoglycan (GAG) can increase the resistance of water and solute transport through tissue (Jang et  al. 2003). Additionally, collagen can also inhibit the transport of macromolecules as shown by xenograft studies in mice, where tumors with a welldefined collagen network exhibited increased rigidity and were more resistant to penetration of high molecular-weight drugs when compared to tumors with poorly organized and loose collagen networks (Netti et al. 2000). Intriguingly, in tumors with a well-defined collagen network, drug delivery could be improved by treatment with collagenase (Netti et al. 2000). Drugs can also bind directly to components of the ECM, thus preventing further penetration into more distal regions of the tumor (Berk et al. 1997). Studies using three-dimensional spheroids have shown that binding to ECM can directly affect drug penetration and distribution. For example, cisplatin and 5-fluorouracil, which do not bind readily to cellular macromolecules, diffused readily into spheroids whereas drugs which readily bind cellular macromolecules, such as doxorubicin and paclitaxel, were concentrated at the periphery of spheroids (Erlanson et al. 1992; Nederman and Carlsson 1984; Nicholson et al. 1997). In this way, the ECM can act as a sink and prevent further drug penetration into the tumor.

21.3 Cancer Associated Fibroblasts and Effects on Drug Activity and Sensitivity Effective chemotherapy is critically dependent on the delivery of bioactive drugs to tumor cells, which in turn depends on delivery and diffusion times. Therefore, conditions that delay drug delivery and diffusion, including compromised bloodflow and increased IFP, can significantly hinder the delivery of bioactive drugs. Since CAFs have significant influence on both tumor bloodflow and IFP, as described previously, they can significantly influence the delivery of bioactive drugs. However, there are other mechanisms by which CAFs can affect drug activity and sensitivity of tumor cells; these mechanisms will be discussed in this section and are summarized in Fig. 21.2.

21.3.1  Hypoxia Hypoxia, or low oxygen levels, is a hallmark of solid tumors (Hanahan and Weinberg 2000). During tumor growth, hypoxia occurs when rapidly proliferating tumor cells outgrow their blood supply, resulting in increased diffusion distances between

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Fig. 21.2   How cancer associated fibroblasts affect chemotherapy. CAFs express and secrete many growth factors and ECM proteins that result in chemoresistance by promoting tumor angiogenesis, altering the composition of the tumor ECM, and enhancing the survival of tumor cells. Increased deposition of ECM proteins (collagen type 1 and fibronectin) and secretion of pro-angiogenesis proteins ( VEGF, PDGF and SDF-1) can increase interstitial fluid pressure ( IFP), which inhibits drug transport and penetration into the tumor. ECM proteins can also directly impede drug penetration or cause cell adhesion-mediated drug resistance ( CAM-DR). Growth factors produced by CAFs, such as TGFb and HGF, cause tumor growth and expansion that can result in areas of hypoxia, due to increased distances from blood vessels. Hypoxia, in turn, can increase chemoresistance of tumor cells by increasing the expression of transcription factor HIF-1 which, in turn, increases the expression of multidrug resistance ( MDR) and anti-apoptotic genes, as well as inducing cell cycle arrest

blood vessels (Liao and Johnson 2007). CAFs can mediate the development of tumor hypoxia by producing growth factors that promote tumor cell proliferation (e.g. TGFβ and HGF) as well as tumor angiogenesis (e.g. VEGF and PDGF) (Kalluri and Zeisberg 2006). Hypoxia can influence the efficacy of chemotherapy because the activity of many chemotherapeutic drugs is affected by the oxygenation status of the microenvironment and is therefore influenced by hypoxia. For example, the cytotoxic effects of cyclophosphamide and doxorubicin were shown to be oxygen dependent, both in vitro and in vivo, and decreased with hypoxia (Harrison and Blackwell 2004).

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Additionally, hypoxia often results in acidosis, or low extracellular pH, due to increased rates of anaerobic respiration that generate lactic acid, while intracellular pH remains unchanged (Tredan et al. 2007). This results in significant extracellularintracellular pH gradients that can affect the uptake of certain chemotherapeutic drugs (Gerweck and Seetharaman 1996). For example, lowered extracellular pH can decrease the cellular uptake and activity of drugs that are weak bases such as mitoxantrone, doxorubicin, and bleomycin (Vukovic and Tannock 1997). An acidic extracellular pH can also inhibit cellular uptake of drugs, such as methotrexate, across the cell membrane due to inhibition of active transmembrane transporters (Cowan and Tannock 2001). Hypoxia can also decrease the availability of free radicals, which reduces the DNA damaging effects of drugs such as etoposide, bleomycin, and anthracyclins (Harrison and Blackwell 2004). In addition to affecting the chemical activity of drugs, hypoxia can also directly influence the sensitivity of tumor cells to cytotoxic drugs. Because most chemotherapeutic drugs preferentially kill cells that are actively dividing, conditions in the tumor microenvironment that decrease cell cycling can affect the sensitivity of tumor cells to chemotherapeutic agents. For example, hypoxia often causes cells to undergo G0/G1 cell cycle arrest, thus limiting the cytotoxicity of drugs that specifically target rapidly proliferating cells such as mitoxantrone, paclitaxel and topotecan (Vukovic and Tannock 1997; Au and WaJ 2005). Hypoxia can also have marked effects on the gene expression of tumor cells and induce the expression of genes that confer resistance to chemotherapeutic drugs. In response to hypoxia, cells alter the expression of many oxygen-regulated genes that encode protein products involved in increasing oxygen delivery and activating alternate metabolic pathways that do not require oxygen (Semenza 2004). A key mediator of the cellular response to hypoxia is the transcription factor hypoxia inducible factor (HIF)-1. Stabilization of the HIF-1α subunit under low oxygen tensions results in transcription of many oxygen dependent genes that mediate diverse biological functions including angiogenesis, glycolytic metabolism, and cell survival (Semenza 2004). For example, under hypoxic conditions the pro-apoptotic genes Bid and Bad are down regulated in a HIF-1 dependent and independent manner, respectively (Erler et al. 2004). In contrast, hypoxia results in upregulation of anti-apoptotic proteins such as Bcl-2, Bcl-XL and IAP family members (Park et al. 2002). This hypoxia-induced alteration in the balance of anti- and pro- apoptotic genes protects tumor cells from drug-induced apoptosis. Hypoxia can also induce increased expression of multidrug resistance (MDR) genes. Classical MDR genes encode for ATP-dependent pumps belonging to a family of ATP-binding cassette (ABC) transporters that either exclude or extrude anticancer drugs from cells (Gottesman et al. 2002; Gottesman et al. 1996). Drugs that have been shown to be effluxed by ABC transporters include: actinomycin-D (an RNA transcription inhibitor), paclitaxel (a microtubule-stabilizing drug), vinca alkaloids (vinblastine and vincristine), anthracyclines (doxorubicin and daunorubicin) and taxanes (Gottesman et al. 2002; Leonessa and Clarke 2003). In response to hypoxia, HIF-1 was shown to upregulate the expression of the MDR1 gene product P-glycoprotein (Pgp) in human prostate cancer cells (Wartenberg et al. 2003). This

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hypoxic upregulation of Pgp expression resulted in increased resistance to doxorubicin in cultures of multicellular spheroids (Comerford et al. 2002). MDR1 expression has also been demonstrated in human leukemias, oesophegeal carcinoma, non-small-cell lung cancers, and breast carcinomas (Leonessa and Clarke 2003; Nooter et al. 1995). Similarly, hypoxia induces expression of breast cancer resistance protein (BCRP), also known as ATP-binding cassette, subfamily G, member 2 (ABCG2), in a HIF-1 dependent manner (Krishnamurthy et al. 2004). Like MDR1, BCRP encodes an ATP-dependent transporter of xenobiotic drugs (Doyle et al. 1998). BCRP has been shown to transport indolocarbazole topoisomerase I inhibitors (NB-506 and J-107088) and anthracyclins (mitoxantrone, daunorubicin, and doxorubicin), thus inducing drug resistance in human cancer cell lines (Doyle et al. 1998; Komatani et al. 2001; Miyake et al. 1999). In summary, CAFs mediate the development of tumor hypoxia by production of growth factors that promote both tumor cell proliferation and tumor angiogenesis. As a consequence of hypoxia, the uptake and activity of chemotherapeutic drugs is decreased. Additionally, the sensitivity of tumor cells to cytotoxic effects of chemotherapeutic drugs is also decreased by hypoxia through induction of cell cycle arrest and changes in gene expression that favor survival of tumor cells.

21.3.2  CAM-DR: Cell Adhesion-Mediated Drug Resistance Drug resistance acquired by tumor cell adhesion to extracellular matrix (ECM) is classified as cell adhesion-mediated drug resistance (CAM-DR). CAM-DR involves signaling through integrins, which are cell-surface molecules, consisting of a family of 18-α and eight-β subunits, that heterodimerize to form transmembrane receptors which bind to ECM proteins like collagen, fibronectin (FN) and laminin (Hehlgans et al. 2007). Ligand binding to the extracellular domain of integrins potentiates “outside-in” signaling and results in diverse intracellular responses that can affect drug resistance (Shain and Dalton 2001). CAFs are the main producers of ECM proteins in the TME, including FN and collagen type 1, and therefore mediate CAM-DR (Kalluri and Zeisberg 2006). Cell adhesion-mediated therapy resistance was first demonstrated in 1972 using monolayer and spheroid cultures, which were shown to be sensitive and resistant to radiation therapy, respectively (Hazlehurst and Dalton 2001). Since that time, CAM-DR to varying chemotherapeutic drugs has been demonstrated in many different cancers. For example, the human myeloma cell line 8226/S, that is normally sensitive to doxorubicin, was shown to become drug resistant when plated in direct contact with immobilized fibronectin (FN) (Damiano et al. 1999). Conversely, sensitivity of these cells to doxorubicin was restored by removal of FN (Damiano et al. 1999). Similarly, adhesion of U937 human histiocytic lymphoma cells and LiM6 colon cancer cells to ECM induced resistance in these cells to mitoxantrone and etoposide therapy, respectively (Hazlehurst et  al. 2006; Kouniavsky et  al. 2002). Also, adhesion to collagen VI conferred resistance to cisplatin in human ovarian

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cancer cells (Sherman-Baust et al. 2003). Additionally, adhesion to collagen type 1 caused resistance to paclitaxel in human colon, lung, and breast carcinoma cells (Ohbayashi et  al. 2008). Futhermore, in a study of 1286 primary breast cancer specimens, high mRNA expression of FN was shown to be associated with shorter distant metastasis-free survival after adjuvant tamoxifen treatment of ER-positive lymph node-positive patients (Helleman et al. 2008). However, the mechanism by which adhesion to ECM proteins confers CAMDR differs depending on cell type. For example, in the human prostate cancer cell line PC3, β1 integrin-dependent adhesion to extracellular FN protected these cells from tumor necrosis factor-α (TNF-α) induced apoptosis through upregulation of survivin, a member of the inhibitor of apoptosis (IAP) family (Fornaro et al. 2003). In contrast, adhesion of myeloma cells to FN induced G1 cell cycle arrest and conferred drug resistance to etoposide through increased p27Kip1 protein levels (Hazlehurst et  al. 2000). Further, adhesion of human histiocytic lymphoma cells to FN induced resistance to mitoxantrone though it diminished topoisomerase II levels and activity (Hazlehurst et al. 2006). In contrast, resistance to etoposide in human cervical carcinoma cells, grown as spheroids, correlated with a redistribution of topoisomerase IIα from the nucleus to the cytoplasm (Oloumi et al. 2000). Finally, CAM-DR to doxorubicin, cyclophosphamide and etoposide in human small cell lung cancer was caused by β1 integrin-stimulated tyrosine kinase activation leading to inhibition of caspase activation and apoptosis (Sethi et al. 1999). These examples illustrate the diverse mechanisms of how CAM-DR occurs in tumor cells. However, despite this diversity, the ultimate result of adhesion to ECM components in these studies is resistance to chemotherapy.

21.4 Improving Chemotherapy by Therapeutic Targeting of Cancer Associated Fibroblasts As evidenced above, CAFs are key mediators of therapeutic drug resistance in tumors and thus are attractive targets for improving chemotherapy. In general, three main avenues of attack are conceivable: (1) blocking recruitment and expansion of CAFs, (2) disruption of CAF-associated pro-tumorigenic signals involved with chemoresistance, and (3) abolishing CAF-associated interactions entirely by directly eliminating CAFs themselves in the TME. In this section, we will review the current strategies being developed that are aimed at targeting CAFs to improve chemotherapy.

21.4.1  Modulators of IFP Many strategies to improve chemotherapy have focused on therapeutically reducing tumor IFP in order to improve drug delivery and penetration in solid tumors.

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As described previously, fibroblast contraction in response to PDGF was proposed as a mechanism by which CAFs can directly control IFP. PDGF was first demonstrated to control IFP in the skin using a rat model of anaphylaxis and was subsequently shown to control the IFP in tumors using experimental rat colonic carcinomas (Pietras et al. 2001; Rodt et al. 1996). In the latter study, treatment of rats with STI571, a selective PDGF receptor kinase inhibitor, decreased tumor IFP and increased transcapillary transport in tumors grown subcutaneously (Pietras et  al. 2001). Additionally, STI571 treatment caused increased uptake of the drug Taxol and resulted in enhanced anti-tumor effects (Pietras et al. 2002). Similar to STI571, prostaglandin (PG) E1 has also been shown to reduce intradermal IFP in vivo (Berg et al. 1998). Additionally, in experimental carcinomas, administration of PGE1 was shown to transiently lower tumor IFP thus facilitating the uptake of 5-fluorouracil (Salnikov et al. 2003). Protease-mediated digestion of ECM proteins produced by CAFs has also been tested as a strategy for reducing tumor IFP. In this context, systemic or intratumoral administration of collagenase, which digests collagen, reduced IFP of human osteosarcoma xenografts and was shown to improve the uptake of monoclonal antibodies (Eikenes et  al. 2004). Additionally, treatment of penetration-resistant tumors, containing an extended collagen network, with collagenase improved interstitial diffusion rates in human colon adenocarcinoma xenografts (Netti et al. 2000). Similarly, it was shown that spread of the oncolytic herpes simplex virus vector MGH2 within human melanoma xenografts was inhibited by fibrillar collagen, and could be improved by co-injection with collagenase (McKee et al. 2006).

21.4.2  A DNA-Vaccine Targeting Cancer Associated Fibroblasts In our own laboratory, efforts to improve drug delivery in solid tumors have focused on the targeted elimination of CAFs within the TME. To achieve this, we have designed a DNA vaccine against fibroblast activation protein (FAP), which specifically targets CAFs for elimination by the host immune system. FAP is a type II transmembrane protein that functions as a serine protease and is specifically overexpressed on 90% of CAFs in colon, breast, and lung carcinomas (Scanlan et al. 1994). We have shown that oral vaccination of mice with doubly attenuated RE88 S. typhimurium transduced with full-length cDNA encoding murine FAP (pFAP), elicits a specific host cellular immune response against FAP-expressing CAFs (Loeffler et al. 2006). As a result, CAFs are effectively eliminated in the TME by cytotoxic T lymphocytes through a MHC Class 1 antigen response (Loeffler et al. 2006). We have shown that elimination of CAFs by vaccination with pFAP reduced collagen type 1 expression in the stroma of murine breast and colon carcinomas grown in immune competent mice (Loeffler et al. 2006) (Fig. 21.3). This reduction of collagen type 1 in the tumor stroma resulted in a significant increase in uptake of systemically administered doxorubicin by primary murine breast tumors (Loeffler et al. 2006) (Fig. 21.3). Additionally, the increased uptake of doxorubicin also

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Fig. 21.3   A DNA vaccine targeting cancer associated fibroblasts improves drug uptake in experimental tumors. Expression of FAP, collagen type 1 and intratumoral uptake of doxorubicin. a Vaccination with pFAP markedly reduced the expression of both FAP and collagen type 1 by the tumor interstitium. b Analysis of protein expression by western blotting showed a decrease in collagen type 1 expression by tumors from mice vaccinated with pFap. c Following pFap vaccination, the reduction in collagen type 1 correlated with a significnat increase in doxorubicin uptake by the primary tumor (P < 0.001). (Adapted from Loeffler et al. 2006)

correlated with reductions in tumor IFP in mice vaccinated with pFAP (unpublished results). This vaccine-mediated elimination of CAFs enhanced the anti-tumor effects of doxorubicin and delayed primary tumor growth of 4T1 murine breast carcinomas while significantly increasing life-span of mice with experimental colon carcinoma metastasis (Loeffler et al. 2006) (Fig. 21.4). In summary, vaccination with pFAP effectively reduced CAFs in the TME and significantly improved the anti-tumor effects of doxorubicin chemotherapy by increasing drug uptake. As a result of enhanced drug uptake, improved suppression of primary tumor growth as well as increased survival of animals with metastatic disease was observed. However, whether our pFAP vaccine also has effects on other mechanisms of CAF-mediated drug resistance, such as CAM-DR or hypoxia, remains to be determined.

21.5 Concluding Remarks CAFs are widely recognized as key mediators of tumor growth. However, more recently, the tumor promoting activities of CAFs have also been shown to negatively impact the delivery and efficacy of chemotherapy in solid tumors. CAFs can alter the characteristics of the tumor microenvironment by diverse mechanisms ranging

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Fig. 21.4   Eliminating cancer associated fibroblasts in the tumor microenvironment improves the anti-tumor effects of doxorubicin chemotherapy. Vaccination of mice with pFap in a prophylactic and therapeutic setting markedly suppressed primary tumor growth and increased the life-span of animals with experimental tumors and metastasis, respectively. a Prophylactic setting. BALB/c mice were vaccinated with pFAP or empty vector and then challenged by injection of 3 × 105 4T1 murine breast carcinoma cells into the mammary fat pad. Following tumor cell challenge, mice were treated with doxorubicin or phosphate buffered saline ( PBS) by intravenous (i.v.) injection as indicated. Tumor volume was calculated by measuring tumor dimensions with calipers, where volume = length/2 × width2. (n = 8, mean ± SEM). b Therapeutic setting. Mice were challenged with 105 D2F2 murine breast carcinoma cells by i.v. injection to produce experimental metastasis. Five days after i.v. injection of tumor cells, mice were vaccinated with pFAP or empty vector and one day after each immunization, mice were treated i.v. with doxorubicin or PBS as indicated (*statistically significant compared with vector group, P < 0.0001, **statistically significant compared with vector, vector/dox, and pFap groups, P < 0.0001). (Adapted from Loeffler et al. 2006)

from deposition of collagen, and other components of the ECM, to perturbation of the tumor vasculature and increased IFP. These alterations, in turn, result in reduced delivery and penetration of drugs into the tumor as well as inhibition of drug uptake and decreased sensitivity of tumor cells to chemotherapeutic agents. Pre-clinical studies targeting CAF-mediated signaling, their secreted products or CAFs themselves have shown promising results for potential improvement of chemotherapy for treatment of solid tumors.

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Chapter 22

Antibody-Based Targeting of Tumor Vasculature and Stroma Katharina Frey and Dario Neri

Abbreviations CDR EDA EDB EGF GM-CSF IFN IgG IL-2, -12, -15 PDT PET RIT SAGE scFv SIP SPECT TEM TNF

Complementarity determining region Extra-domain A of fibronectin Extra-domain B of fibronectin Epidermal growth factor Granulocyte-macrophage colony-stimulating factor Interferon Immunoglobulin G Interleukin-2, -12, -15 Photodynamic therapy Positron emission tomography Radioimmunotherapy Serial analysis of gene expression Single-chain Fragment variable Small immunoprotein Single photon emission computed tomography Tumor endothelial marker Tumor necrosis factor

K. Frey () · D. Neri Department of Chemistry and Applied Biosciences, ETH Zurich, Wolfgang-Pauli-Str. 10, 8093 Zurich, Switzerland e-mail: [email protected] D. Neri e-mail: [email protected] M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0_22, © Springer Science+Business Media B.V. 2011

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22.1 Vascular Tumor Targeting: Concepts and Definitions Most conventional cytotoxic drugs currently used for the treatment of cancer often suffer from a lack of selectivity, leading to a poor therapeutic index and considerable toxicities to healthy tissues. In fact, the majority of intravenously administered pharmaceuticals preferentially accumulate in most normal organs rather than in the tumor (Bosslet et al. 1998). One avenue towards the development of more selective anti-cancer drugs consists in the targeted delivery of bioactive compounds (drugs, radionuclides, cytokines, photosensitizers, procoagulant factors, etc.) to the tumor site by means of binding molecules (e.g. human antibodies) specific to a tumorassociated marker. In this context, the targeted delivery of therapeutic agents to newly-formed blood vessels (“vascular targeting”) in the tumor is particularly attractive for two reasons: the formation of new blood vessels is a rare event in the adult except for angiogenic events during wound healing and the female reproductive cycle, and tumor progression and metastasis require the formation of new vessels. In addition, structures of the neovasculature are attractive targets due to their inherent accessibility to systemically administered therapeutic agents. Using such a targeting approach will ultimately result in a local accumulation of the drug at the tumor environment while sparing the patient’s healthy organs. In this chapter, we will restrict the definition of vascular tumor targeting to pharmacodelivery applications based on ligands such as antibodies capable of selective recognition of markers that are expressed at sites of tumor neoangiogenesis. It is important to consider that this therapeutic area is conceptually and practically different to the inhibition of angiogenesis where the growth of new blood vessels is prevented by the inhibition of a target molecule in a signaling pathway (e.g. inhibition of tyrosine kinases VEGFR2, PDGFR, FLT-3 and c-KIT by sunitinib) (Faivre et al. 2007) or by the blockade of soluble growth factors such as the vascular endothelial growth factor VEGF-A by means of the monoclonal antibody bevacizumab (Ferrara 2004). Similarly, vascular tumor targeting is also distinct from the use of vascular disrupting agents such as combretastatins which depolymerize microtubules of the tumor endothelial cell, leading to a shutdown in blood flow and disruption of tumor vasculature resulting in extensive tumor cell necrosis (Tozer et al. 2005b; Chaplin et al. 2006; Kanthou and Tozer 2009). According to our definition of vascular tumor targeting, a molecule on the vasculature or subendothelial matrix is used as an accessible target for the delivery of bioactive effector molecules to tumor blood vessels that already exist. The basis for vascular targeting is provided by the existing anatomical and physiological differences in the endothelium and stroma of tumors versus normal organs. The tumor vasculature is marked by a high rate of endothelial cell proliferation, abnormalities in the vascular basement membrane and an increased vascular permeability. In addition, the vasculature is strikingly disorganized and tortuous, the vessels are thin-walled and irregular in diameter (Konerding et al. 2001; McDonald and Choyke 2003). Intratumoral blood flow is often sluggish and at times might be stationary or even experience a reversal in the direction of flow (Tozer et al. 2005a). All these characteristics give rise to a profoundly hypoxic, acidic and nutrient

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starved tumor microenvironment (Brown and Bicknell 2001). In this environment, the endothelial cell is remarkably different to the normally quiescent endothelium and responds transcriptionally to all these stimuli, resulting in the generation of a variety of new proteins (cell adhesion proteins, growth factor receptors, proteases, morphogenic molecules) either on the cell surface or in the surrounding extracellular matrix. Once there is an adequate discrimination in the abundance or accessibility of a potential marker between tumor vessels and quiescent vascular structures, this marker can be used as a candidate target for vascular targeting approaches. Structures of the subendothelial extracellular matrix and of the vasculature may be superior targets compared to markers on tumor cells, due to a lower degree of genomic instability which causes antigen heterogeneity on tumor cells and in different tumor microenvironments. Tumor cells may downregulate the expression of the tumor-associated antigen targeted by the biopharmaceutical agent and therefore bear a higher risk to escape therapy due to resistance. Furthermore, due to the fact that any kind of tumors larger than 1–2 mm3 are not sufficiently supplied with nutrients and oxygen by simple diffusion, neoangiogenesis is an essential requirement for further tumor growth in all different types of cancer. In principle, vascular targeting strategies should be applicable to a wide range of different tumor entities. Already in the early 1970s, it was postulated by Folkman that the growth of new blood vessels supplying the tumor with nutrients and oxygen is essential for tumor development (Folkman 1972, 1990). The selective destruction or occlusion of tumor blood vessels (e.g. by the antibody-mediated delivery of toxins or procoagulant factors) interrupts the blood supply to neoplastic tissues, leading to a cascade of tumor cell death (Denekamp 1990; Burrows and Thorpe 1993; Huang et al. 1997). In the last years, other vascular targeting strategies have been developed that still rely on the selective localization of antibody derivatives to the tumor vasculature but exert a more indirect anti-cancer activity without direct damage of the tumor cell or the destruction of the blood vessel (Neri and Bicknell 2005; Schliemann and Neri 2007). For instance, antibody-cytokine fusion proteins (“immunocytokines”) which accumulate at the subendothelial extracellular matrix at tumor sites, have, dependent on the cytokine, immune modulatory function, promote proliferation and activation, as well as recruit immune cells to the tumor site. Natural killer (NK) cells that are activated by immunocytokines infiltrate the tumor mass and directly kill tumor cells by apoptosis due to the release of perforin and granzymes (e.g. Carnemolla et al. 2002; Halin et al. 2002). Alternatively, radiolabeled antibodies or antibody-photosensitizer conjugates can be used to deliver diffusible toxic moieties, such as electrons or reactive oxygen species to the subendothelial neoplastic mass (Birchler et al. 1999; Berndorff et al. 2005; Fabbrini et al. 2006; Tijink et al. 2006). Dependent on the type of radioactive emitter which is coupled to an antibody, radiolabeled antibodies can not only be used for therapy but also for imaging applications (Boswell and Brechbiel 2007). Several types of biopharmaceutical agents targeting the extracellular matrix or vasculature of tumors have already begun clinical testing, as we will see in the following chapters (Carnemolla et al. 2002; Brack et al. 2006; Freimark et al. 2007; Palumbo et al. 2007; Marlind et al. 2008; Reardon et al. 2008; Rodon et al. 2008; Zalutsky et al. 2008; Sauer et al. 2009; Tijink et al. 2009).

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22.2 Methodologies for the Discovery of Novel Vascular and Subendothelial Tumor Targets Ligand-based drug delivery strategies fundamentally rely on the identification of high-quality markers of angiogenesis, which are expressed at sites of tumor neoangiogenesis and which show a clear distinct expression pattern from blood vessels and structures in healthy tissues. Furthermore, target accessibility from the bloodstream is of particular importance for ligand-based pharmacodelivery applications. Historically, the first markers of angiogenesis were discovered by serendipity or by extensive immunohistochemical profiling of certain monoclonal antibodies. In fact, after the discovery of an alternatively-spliced variant of fibronectin (extradomain B, EDB) in tumor fibroblasts that was found to be absent in normal plasma fibronectin using limited proteolysis experiments (Zardi et al. 1987), it was observed that an antibody specific to EDB-containing fibronectin (L19) preferentially stains blood vessels in tumors rather than in normal tissues (except for the endometrium in the proliferative phase, some vessels in the ovary and placenta) (Carnemolla et al. 1989, 1996; Castellani et al. 1994, 2002; Neri et al. 1997; Birchler et al. 2003; Driemel et al. 2007; Sauer et al. 2009; Schliemann et al. 2009a). Other concepts for the identification of tumor-associated vascular markers were based on the screen of primary endothelial cells in in vitro cultures. There, endothelial cells were exposed to culture conditions mimicking characteristic normal versus tumor tissue conditions such as hypoxia vs. normoxia, proliferation vs. quiescence or exposure to tumor-cell-conditioned media (e.g. Clarke and West 1991). Another popular approach for the discovery of new markers has been the immunization of rodents with tumor-derived complex antigen mixtures. This has led to the discovery of the prominent markers endoglin (CD105), endosialin and prostate-specific membrane antigen, upregulated at sites of tumor angiogenesis (Buhring et  al. 1991; Rettig et al. 1992; Nanus et al. 2003). With the development of antibody phage libraries in the 1990s, screening methods became more powerful. Antibody phage libraries are collections of bacteriophages displaying a repertoire of billions of different antibody variants on the phage surface (McCafferty et  al. 1990). The use of antibody phage libraries allows the large-scale screening of a specimen with the advantage that the phenotype (i.e. an antibody with a defined binding specificity, bound to a target of interest) is physically linked to the genotype (i.e. the antibody sequence as part of the phage). Antibody phage libraries have been panned directly on endothelial cells in vitro (Mutuberria et al. 2004), on tissue sections followed by laser capture microdissection and phage recovery (Ruan et al. 2006) or directly in vivo. For in vivo phage display, unselected phage libraries are injected intravenously into mice, tissues of interest are excised, bound phages are characterized and the targets further validated. With this method, peptides displayed on phages have been identified that home to tissue- and tumor-specific endothelial markers such as aminopeptidase N (Essler and Ruoslahti 2002), αvβ3 and αvβ5 integrins (Arap et al. 1998) and proteoglycan NG2 (Burg et al. 1999). However, these efforts led to the identification of many

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new endothelial markers but failed to identify differentially expressed proteins at the molecular level. Real advances have been made with the development of transcriptomic technologies that allow full genome analysis, in particular serial analysis of gene expression (SAGE) in combination with bioinformatics, and more recently with proteomic methodologies. The comparative search for tumor endothelial cell markers based on differences in expression patterns has initially been tackled by subtractive cDNA analysis methodologies, an approach in which cDNAs of endothelial cells of lung metastases and healthy lung tissue were isolated and compared for differences in expression (Wyder et  al. 2000). Subsequently, the transcriptome of tumor-derived endothelial cells has been investigated using serial analysis of gene expression (SAGE) (St Croix et al. 2000) or microarray technologies (Zhang et al. 1999; Ho et al. 2003; Ghilardi et al. 2008) after enrichment or isolation of endothelial cells by e.g. laser capture microdissection (Roy et al. 2007). SAGE is a method for comprehensive analysis of gene expression patterns and is based on the serial sequencing of short sequence tags that are unique to identify a certain transcript. Microarray techniques rely on the hybridization of labeled tissue extracts (cDNA, RNA) to unique oligonucleotides as probes on a chip. The investigation of the transcriptome of tumorderived endothelial cells by SAGE technology revealed 46 transcripts which were specifically upregulated in the tumor endothelium. This has led to the identification of several novel tumor endothelial markers (TEMs) by St. Croix and colleagues. For instance, TEM1, TEM5 and TEM8 showed strong tumor endothelial expression but were nearly undetectable in blood vessels of healthy tissue (Carson-Walter et  al. 2001). Interestingly, after the discovery of TEM1 by SAGE, TEM1 turned out to be identical with endosialin (Christian et al. 2001). In a recent SAGE experiment by Seaman et al. (2007), also differences in expression pattern of 13 potential vascular targets could be detected comparing endothelial cells derived from tumors (pathological angiogenesis) and from normal regenerating tissue (physiological angiogenic events). Comparative transcriptomic approaches provide precise information about the quality and quantity of mRNA transcripts which are expressed in the investigated tissues of interest. Nevertheless, validation of a potential target candidate is of particular importance, as surface-accessibility and abundance of an endothelium-associated protein is not supposed to be equally correlated with protein expression levels. As more and more transcriptomes of solid tumors and associated endothelial cells have become available, investigators have put effort in the comparison of these datasets by the screen of databases using bioinformatic procedures for the discovery of novel tumor-associated endothelial markers. One such approach applied a subtractive algorithm to the sequence tag expression data that is available in public databases to identify novel tumor vascular marker genes (Huminiecki and Bicknell 2000). These genes were then screened for expression by in situ hybridization, leading to the identification of magic roundabout 4 (ROBO4) and an endothelial-specific protein disulfide isomerase (EndoPDI) as tumor endothelial markers (Huminiecki et al. 2002; Sullivan et al. 2003). In addition, another marker called endothelial cell specific chemotaxis regulator (ECSCR, also ECSM2), discovered in this bioinfor-

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matic study (Armstrong et al. 2008), has recently become of interest as it has been shown to be the first completely endothelial specific factor to mediate endothelial migration (Armstrong et al. 2008; Ma et al. 2009). Similarly, the combined analysis of gene expression data from well-defined cell culture models and from diagnostic samples of human diseased tissues by Gerritsen et al. (2002) revealed stanniocalcin-1 (STC-1) as putative tumor vascular marker. One of the most obvious and straightforward approaches to identify new vascular or extracellular matrix targets is definitely the search for accessible markers in vivo. Loss of normal morphology as well as differences in protein expression levels of isolated endothelium are drawbacks in in vitro analyses or transcriptomic studies. As a consequence, several proteomic technologies have recently focused on the in vivo perfusion of tumor-bearing animals under native and (patho)physiologically relevant conditions. Such a method involves the in vivo labeling of vascular structures in healthy and diseased tissues, followed by their isolation and comparative proteomic analysis of proteins. The group of Jan Schnitzer invented a method for in vivo perfusion of tumor-bearing rodents with colloidal silica beads in order to achieve an in vivo coating and physical stripping of membrane proteins from the surface of endothelial cells. Luminal membrane proteins were enriched by subcellular fractionation and subsequently submitted to comparative proteomic analysis revealing certain antigens that were overexpressed in tumor endothelial cells (Oh et al. 2004). The most prominent target protein found in this study was annexin A1 which was further validated not only by immunohistochemical staining but also by in vivo biodistribution and scintigraphic imaging with radiolabeled antibodies. In addition, annexin A1 was targeted in a radioimmunotherapy experiment with an 125 I-radiolabeled monoclonal antibody showing therapeutic benefit in rats. However, the relevance of this finding is unclear as the Auger electron emitter radionuclide iodine-125 would not be expected to promote a therapeutic activity at the low dose used in this study. Additional preclinical therapeutic data have not been reported and the product has not progressed to clinical trials. Until recently, most research efforts in the discovery of vascular tumor targets have focused on endothelial cells. However, due to leaky tumor vasculature and abnormalities in the endothelial basement membrane, also the extracellular matrix offers highly specific tumor markers which are available for therapeutic agents. New methodologies have been developed that offer the advantage to identify novel tumor markers in the extracellular matrix besides vascular markers. In vivo biotinylation is a technology for the in vivo chemical labeling of vascular and subendothelial proteins based on the terminal perfusion of tumor-bearing animals with reactive ester derivatives of biotin (Rybak et al. 2005; Roesli et al. 2006). The use of a small chemical molecule such as biotin instead of bulky particles allows the biotinylation of proteins of the blood vessel as well as the subendothelial compartment, which are readily accessible via the bloodstream. The purification of biotinylated proteins from tumor and healthy organ lysates on a streptavidin column followed by comparative proteomic analyses based on LC-MS/MS methodologies permits the identification of hundreds of accessible tumor-associated proteins. Indeed, in vivo biotinylation and subsequent comparative proteomic analysis is able to reveal both

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quantitative and qualitative differences of recovered biotinylated antigens between the tumor and normal organs. This technique has been extended to the ex vivo perfusion of surgical resected human organs with kidney or colon cancer (Castronovo et al. 2006; Conrotto et al. 2008), to the discovery of markers of metastasis in comparison to non-invasive cancer (Rybak et al. 2007; Roesli et al. 2009) as well as to the study of lymphangiogenesis (Roesli et al. 2008). For instance, the most prominent markers of the extracellular matrix which have been found and validated in these studies include the extra-domain A (EDA) of fibronectin, versican, periostin, annexin A4, melanoma-associated antigen MG-50 and GW112.

22.3 Validated Tumor Targets of the Subendothelial Extracellular Matrix In the following chapter, we will describe a selection of tumor markers of the extracellular matrix whose suitability for in vivo ligand-based targeting applications has been validated (by imaging studies, quantitative biodistribution analysis, by ex vivo immunofluorescence analysis after intravenous administration of specific ligands, or by staining of human tissue samples) and which are relevant for therapeutic applications. Even though markers of the subendothelial matrix are less accessible than luminal endothelial markers, they have the advantage that they are usually expressed at much higher levels and abundance.

22.3.1  EDA and EDB of Fibronectin Fibronectin is a high molecular weight adhesive glycoprotein present in the extracellular matrix of tissues and in soluble form in the plasma. During tissue remodeling processes like in tumor formation but also during wound healing and the female reproductive cycle, certain extra-domains get inserted in the oncofetal fibronectin molecule by alternative splicing (Fig. 22.1a). The extra-domain A (EDA), the extradomain B (EDB) and the IIICS domain are usually absent in normal adult tissues, but are abundantly expressed with a prominent stromal and/or perivascular staining pattern in many aggressive tumors (Neri and Bicknell 2005) (Fig.  22.2). Under pathological conditions, tumor cells, fibroblasts, pericytes and endothelial cells may contribute to the synthesis of fibronectin slicing isoforms. Both EDA and EDB exons show a high degree of homology between vertebrates from man to frog, but have only 29% amino acid sequence identity within a species. The functional roles of EDA and EDB are still unclear, however, the high level of conservation between species and their presence during embryogenesis strongly suggest a conserved role in development. Knock-out studies in mice have shown that single deletions of EDA or EDB do not manifest in a significant phenotype, but the simultaneous deletion of both the EDA and EDB exon in the fibronectin gene leads to embryonic

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Fig. 22.1   Model of the domain structures of a oncofetal fibronectin and b tenascin-C, validated tumor markers of the extracellular matrix. Due to alternative splicing in tissue remodeling processes, extra-domains ( white domains) get inserted into the molecules which are absent in normal tissues and can be used for targeting approaches

Fig. 22.2   Immunohistochemical analysis of a human lung adenocarcinoma, b human lung metastasis of renal cell carcinoma, and c U87 human glioblastoma xenograft ( s.c.) for the expression of extra-domain B ( left panels), extra-domain A ( middle panels) of fibronectin ( Fn) and extradomain A1 of tenascin-C ( Tn) ( right panels). Sections were stained using the antibodies SIP( L19), SIP( F8) and SIP( F16), respectively. Staining of representative specimens illustrating the expression of Fn-EDB, Fn-EDA and Tn-C, which is associated with the tumor vasculature and/or the tumor stroma. Scale bar, 100 µm

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lethality at E10.5 characterized by severe cardiovascular defects (reviewed in White et al. 2008). As the EDB and EDA sequence is identical in mouse, rat, rabbit, monkey, dog and man, the generation of anti-EDB/EDA antibodies by hybridoma technology has not been possible so far. This limitation has been overcome with the development of human antibody phage libraries which allowed the isolation of specific EDA and EDB antibodies. Nowadays, the conservation of the EDA and EDB domain between mouse and man facilitates animal experiments for the development of antibody-based pharmacodelivery applications. Using antibody phage libraries, the high affinity human monoclonal antibodies L19 and F8, specific to EDB (Pini et  al. 1998) and EDA (Villa et  al. 2008), respectively, have been isolated. The ability of the monoclonal antibody L19 to stain tumor neo-vascular structures (Castellani et  al. 2002; Birchler et  al. 2003; Driemel et al. 2007), to target EDB in vivo in animal models of cancer (Tarli et al. 1999; Viti et al. 1999; Borsi et al. 2002, 2003; Berndorff et al. 2005; Tijink et al. 2006; Schliemann et al. 2009a) and also in patients with different cancer entities (Santimaria et al. 2003; Birchler et al. 2007; Sauer et al. 2009) has been intensively investigated. The initial characterization of EDA as tumor-associated antigen for certain cancer pathologies (Borsi et al. 1998) has recently been complemented by the discovery that EDA is an excellent neovascular marker of metastasis (Rybak et al. 2007). In addition, EDA has also been shown to serve as tumor-associated marker in human Hodgkin and non-Hodgkin lymphomas, investigated by immunohistochemical analysis using the human monoclonal antibody F8 (Schliemann et al. 2009b). Regarding pharmacodelivery applications for therapy, the human monoclonal antibodies L19 and F8, specific to the splicing isoforms EDB and EDA of fibronectin, have been modified with several bioactive moieties, including cytokines, procoagulant factors, therapeutic radionuclides, enzymes and photosensitizers as described below.

22.3.2  Extra-Domains of Tenascin-C The tenascins are a highly conserved family of large oligomeric glycoproteins found in many vertebrates and include the members tenascin-C, -R, -X, -W. Tenascin-C is a large extracellular adhesion-modulating glycoprotein which is abundant in many tissues, especially in tumor tissues. Originally, tenascin-C was discovered as a glioma mesenchymal extracellular matrix antigen (Bourdon et al. 1983). Further work revealed that the intensity of tenascin-C staining correlated with tumor grade and poor prognosis (Korshunov et al. 2000; Herold-Mende et al. 2002) and was also linked to angiogenesis (Jallo et al. 1997). Tenascin-C may contain extradomains, which are inserted in the central part of the protein by alternative splicing processes of the primary transcript. This leads to the generation of tenascin-C “large” isoforms, containing one of nine extra-domains A1 to D (Fig. 22.1b). Large isoforms of tenascin-C are usually absent in normal adult tissues but abundantly

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expressed in various tumor types (e.g. breast cancer, Borsi et al. 1992; Adams et al. 2002) serving as a good quality marker for vascular targeting. More recently, it has been observed that among all domains, the extra-domain C of tenascin-C displays the most restricted expression pattern, as it is extremely abundant in certain tumor types such as lung cancer or high-grade astrocytomas (grade III and glioblastoma) (Carnemolla et al. 1999; Silacci et al. 2006), with a prominent perivascular staining pattern. In the latter study, the tumor targeting property of a high-affinity human antibody G11 specific to the domain C of tenascin-C has been confirmed in a rat glioma model using immunohistochemical analysis and in vivo biodistribution studies (Silacci et al. 2006). The monoclonal antibody ST2146 (Tenatumomab) specific to an EGF-like epitope of tenascin-C was tested in a pre-targeted antibody guided radioimmunotherapy approach using sequential administration of biotinylated antibody, avidin and 90Y-labeled biotin in a Phase I/II study of glioblastoma (De Santis et al. 2003; Palumbo et al. 2007). Similarly, also the human monoclonal antibody F16 against domain A1 of tenascin-C (Brack et al. 2006) and the chimeric antibody 81C6 which is specific to the domain D have extensively been investigated in biodistribution studies and their derivatives are now in clinical trials. A recent comparative investigation of the immunohistochemical performance of F16 in comparison with the clinical-stage anti-fibronectin antibodies F8 and L19 revealed that F16 exhibits the highest potential for targeted therapies of human lymphomas (Schliemann et  al. 2009b) and thoracic cancer (Pedretti et  al. 2009) (Fig. 22.2).

22.3.3  Other Targets of the Extracellular Matrix The grade of expression of the proteoglycan versican, expressed by some tumor cells but mainly by activated stromal fibroblasts, has been correlated with the outcome of different cancers. Versican has been associated with tumor invasion and metastasis as it is involved in the processes of cell adhesion, proliferation, migration and angiogenesis (Ricciardelli et al. 2010). Four different splicing isoforms of versican have been described (V0–V3) with distinct biological functions, but the isoforms V0 and V1 have been shown to be the predominant ones in cancer tissues (Ricciardelli et al. 2002; Nikitovic et al. 2006; Arslan et al. 2007). Several studies have confirmed versican as a suitable tumor extracellular marker, either by staining of human cancer specimen (Kodama et al. 2007; Gambichler et al. 2008; Stylianou et al. 2008; Kischel et al. 2010) or by the ex vivo perfusion of surgical excised kidney cancer or tumor-bearing rodents (Castronovo et al. 2006, 2007). In addition, the recent finding that versican strongly promotes metastasis (Kim et al. 2009) suggests versican as target for antibody-based pharmacodelivery strategies. The cell adhesion protein periostin has been shown to be expressed in the periosteum and during cardiac development. Recent clinical development has also revealed an involvement of periostin in the development of different tumors such as breast, lung, colon, pancreatic and ovarian cancers (reviewed in Kudo et al. 2007;

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Ruan et  al. 2009). At present, five splicing isoforms of periostin are described which are differentially expressed in cancer (Kim et al. 2008a). Also, interaction of periostin with other prominent tumor markers of the extracellular matrix such as fibronectin or tenascin-C could be revealed (Takayama et al. 2006). High periostin expression correlates with aggressiveness, tumor progression, angiogenesis and poor outcome of various cancers types (Puppin et al. 2008; Soltermann et al. 2008; Takanami et al. 2008). In addition, periostin expression is upregulated by hypoxia, a common feature in tumor progression (Ouyang et al. 2009). While periostin serves as a prognostic biomarker for different tumor entities due to its secretion into the serum (Ben et al. 2009; Hong et al. 2009; Paulitschke et al. 2009), recent work also demonstrated that antibodies against periostin were able to accumulate in liver metastases of colorectal carcinoma (Borgia et al. 2010) promoting periostin for tumor targeting purposes.

22.4 Antibody-Based Pharmacodelivery for Cancer Therapy Monoclonal antibodies and antibody fragments are the best investigated binding molecules for pharmacodelivery applications in cancer therapy. In fact, antibodies are at present the only class of binding molecules that can be raised rapidly and with excellent specificity and well-defined affinity against virtually any target protein of pharmaceutical interest. For pharmacodelivery applications, also other ligands besides antibodies have been developed, which will not be discussed in this chapter. These ligands include peptides (Scott and Smith 1990; Temming et al. 2005), aptamers (Hicke et al. 2006), small globular non-antibody scaffolds (Binz et  al. 2005; Grabulovski et  al. 2007; Skerra 2007) and synthetic organic binding molecules (Scheuermann et al. 2006). Monoclonal antibodies of rodent origin were first generated in 1975 by hybridoma technology (Kohler and Milstein 1975). A decade later, progress has been made to reduce the percentage of the murine portion of the antibody, at first by grafting complete variable domains of a murine antibody with known specificity on a human antibody framework leading to a chimeric antibody that contains 25% sequences of mouse origin (Boulianne et al. 1984; Morrison et al. 1984). In 1986, the group of Greg Winter pioneered the generation of humanized antibodies by CDR grafting, a technique in which only the complementarity determining regions (CDRs) of murine antibodies were grafted onto a human antibody scaffold (Jones et  al. 1986). Such a humanized antibody contains only 5% sequences of mouse origin and dramatically reduces immunogenicity in the human host. The biggest breakthrough in the generation and isolation of human monoclonal antibodies has definitely been the introduction of antibody phage display technology which allows large-scale screening of fully human antibody variants on a target protein of interest (Winter et al. 1994; Viti et al. 2000). Furthermore, this technology facilitates the development of high-affinity antibodies in low or subnanomolar range by site-specific randomization of the CDR loops of a parental antibody with moderate

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affinity (affinity maturation) (Pini et al. 1997). In 1999, ribosome display has been proposed as a fully in vitro system for the generation and affinity maturation of human monoclonal antibodies (Schaffitzel et al. 1999). For in vivo antibody-based pharmacodelivery applications only antigens located at the cell surface or in the extracellular space can be considered, since proteins do not generally cross the cell membrane. Antibody internalization into target cells may nonetheless be considered for certain cell membrane located antigens which rapidly recycle. Depending on the therapeutic strategy antibody internalization can be of favor (e.g. release of drugs of antibody-drug conjugates in the lysosome), but also disadvantageous, as it is true for antibody-cytokine fusion proteins which mediate the recruitment of immune cells. By the end of 2009, twenty-two monoclonal antibodies were approved by the FDA for therapeutic use, among them nine for the treatment of cancer. While monoclonal antibodies in the IgG format represent the most common used antibody format type for therapeutic applications (Carter 2006), smaller antibody derivatives are more and more considered as vehicle for pharmacodelivery applications (Neri and Bicknell 2005; Schrama et al. 2006; Schliemann and Neri 2007). At present, nearly all marketed antibody pharmaceuticals are in the IgG format and the majority contains a human Fc region of the IgG1 isotype. Conventional IgG’s typically exert a therapeutic activity either by modulating the biological function of their target antigen (e.g. neutralization or blockade of a functional epitope on the target molecule) or by exhibiting biological functions mediated by the Fc portion of the antibody molecule (Murphy et  al. 2008). Despite the activation of the complement machinery, essentially the engagement of immune cells appears to be the main avenue for achieving a selective tumor cell killing in vivo (Nimmerjahn and Ravetch 2005; Ferrara et al. 2006). Tumor cell lysis is mediated by natural killer (NK) cells after their activation upon binding of the therapeutic antibody to Fcγ receptors (e.g. CD16) and the subsequent release of lytic granules, a process called antibody dependent cellular cytotoxicity (ADCC). The use of whole antibodies still remains an important area in pharmaceutical development, and advances in the potentiation of Fc-mediated effector functions by mutagenesis (Shields et al. 2001) or by glycoengineering (Umana et al. 1999) led to the improvement of their therapeutic performance. Currently, a humanized IgG1 (TRC093/MT293, Micromet and TRACON) is investigated in Phase I and II clinical trials in advanced solid cancer. TRC093 is specific to a cryptic epitope on cleaved collagen which gets exposed after collagen IV cleavage by matrix metalloproteinases (MMPs) during tumor growth and neovascularization (Pernasetti et al. 2006; Freimark et al. 2007). The number of different antibody formats available today results in a large difference in the pharmacokinetic behavior between the formats (Fig.  22.3). At the two extremes, full IgG’s with a molecular weight of about 150 kDa display a long residence time in the blood stream and are cleared via the hepatobiliary route (Borsi et al. 2002), whereas small single-chain Fv (scFv) fragments of about 25 kDa are rapidly eliminated from the circulation via the kidneys, with a clearance of >90% injected dose from the blood within one hour (Pietersz et al. 1998; Tarli et al. 1999).

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Fig. 22.3   Schematic representation of antibody formats, including IgG, scFv, diabody, Fab and the small immunoprotein ( SIP/minibody)

The rapid clearance of a scFv turns it into a suitable agent for imaging applications in nuclear medicine. Antibody derivatives of intermediate size of about 80 kDa such as the small immunoprotein (SIP or mini-antibody), which consists of two scFv fragments homodimerized by means of a constant immunoglobulin domain, reveal intermediate clearance profiles between those of IgG’s and scFv’s. While the large molecular weight of a full antibody is advantageous regarding the pharmacokinetic profile, IgG’s are hindered in the extravasation into the tissue. Indeed, smaller antibody derivatives like SIP, Fab and scFv can more easily penetrate the vascular endothelium and enter the subendothelial compartment (Dennis et al. 2007). For this reason, small antibody variants are the most suitable derivatives for antibody-based pharmacodelivery applications which, in particular, target the extracellular matrix at sites of disease. In addition, smaller antibody fragments are preferred because of easier expression (thus facilitating functionalization strategies based on genetic fusion), rapid blood clearance and the lack of the Fc portion, which avoids the undesired binding of the functionalized antibody constructs to cells bearing Fc receptors. Nevertheless, some therapeutic strategies such as antibody-drug conjugates may benefit from the long circulation time of IgG’s (Senter 2009), whereas SIP’s may be the best format for vascular targeted radioimmunotherapy, providing a rational compromise between molecular stability, clearance rate and tumor accumulation (Borsi et al. 2002; Berndorff et al. 2005). In the next paragraphs, we will focus on the most prominent and advanced strategies for antibody-based pharmacodelivery applications including cytokines, radionuclides, drugs and photosensitizers. For some antibody derivatives (e.g. radiolabeled antibodies), the therapeutic action is displayed immediately after intravenous administration. In this case, the anticancer selectivity directly results from the pharmacokinetic comparison of the areas under the curve for neoplastic lesions and normal organs. By contrast, certain therapeutic strategies (e.g. antibody directed enzyme prodrug therapy) present a delayed mode of action at time points, when the antibody derivative is still present in the tumor but has cleared from the circulation and normal organs. These therapeutic strategies appear the most promising for the selective killing of tumor cells, while sparing normal tissues.

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22.4.1  Cytokines Many recombinant proinflammatory cytokines such as tumor necrosis factor alpha (TNFα), interleukin-2 (IL-2) and interferons (IFN) display potent anticancer activities, but their preclinical and clinical application is often hampered by severe toxicities and strong side effects. Cytokines have often been administered at high, but suboptimal dose to achieve a therapeutic effect, however, further dose escalation to the most effective dose is prohibited by devastating toxicities to the patient. In fact, the high therapeutic potential of proinflammatory cytokines in cancer therapy is evidenced by the observation that direct injection into solid tumors can be curative (Mattijssen et al. 1992). Such kind of cytokines are solely applied in certain tumors that can not be treated in a different way: for instance, metastatic kidney cancer or metastatic melanoma have been treated with recombinant IL-2 (Atkins et al. 1999; Yang et al. 2003), or IFN-alpha (Quesada 1989; Kirkwood et al. 2000), indicating rare but spectacular responses for IL-2 and a role for IFN-alpha in adjuvant therapy. Similarly, the proinflammatory cytokine TNFα, which causes a severe anaphylactic shock when injected systemically, is of practical medical use when high-dose TNFα is locally administered in isolated limb perfusions in combination with cytotoxic chemotherapy with manageable systemic side effects and yet unsurpassed clinical responses (Eggermont et al. 2003; Grunhagen et al. 2006; Lejeune et al. 2006). Interleukin-12 (IL-12), another proinflammatory cytokine, never found its way into routine clinical practice due to extraordinary high toxicities and several death cases in a Phase II clinical trial (Cohen 1995; Colombo and Trinchieri 2002). However, since the majority of advanced tumor stages are neither localized nor accessible on the skin, the approach of antibody-based delivery of proinflammatory cytokines to the tumor environment would allow to improve the therapeutic index, to apply optimal and localized higher drug doses and to expand the areas of application of these potent anticancer agents. Several immunocytokines have been generated which target the extracellular matrix. The L19 antibody, targeting the extra-domain B of fibronectin, possibly represents the most studied molecule in this research field. Fusion proteins of L19 in the scFv format and either IL-2 or TNFα exhibited excellent tumor uptake and potent antitumor activity in animal models of different solid tumors (Carnemolla et  al. 2002; Borsi et  al. 2003; Wagner et  al. 2008) and disseminated lymphoma (Schliemann et al. 2009a). IL-2 has also recently been conjugated to F16, an antibody specific to the extra-domain C of tenascin-C, and strongly enhanced the potency of chemotherapeutics in breast cancer animal models (Marlind et al. 2008). Currently, these immunocytokines are in clinical trials in Italy and Germany. L19IL2 is investigated in Phase II clinical trials as monotherapy in patients with renal cell carcinoma and in combination with dacarbazine in patients with metastatic melanoma. Furthermore, L19-IL2 is used in combination with gemcitabine in Phase Ib studies for the treatment of patients with pancreas cancer. L19-TNFα is being investigated as monotherapy in a Phase I clinical trial in patients with different types of malignancies, and in a Phase II trial in combination with melphalan in isolated limb perfusion procedures for the treatment of patients with in-transit melanoma metastases. The anti-tenascin fusion protein F16-IL2 is being investigated in Phase

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Ib studies in combination either with doxorubicin in ovarian and breast cancer or with paclitaxel in breast and lung cancer. The fusion of L19 to the proinflammatory cytokine IL-12 led to promising therapeutic results in animal models, especially in combination with L19-TNFα, despite only modest performance in biodistribution studies (Halin et al. 2002, 2003; Gafner et  al. 2006). The antibody L19 was also fused to a variant of murine IFNγ and showed strong antitumor performance, which could be further enhanced when combined with other immunocytokines and doxorubicin (Ebbinghaus et al. 2005). In analogy, the coupling of human IL-15 and murine GM-CSF to L19 demonstrated antitumor activity in syngeneic immunocompetent mouse models of subcutaneous and metastatic tumors (Kaspar et al. 2007). Nevertheless, one potential drawback in the clinical use of certain immunocytokines such as e.g. IFNγ or GM-CSF fusion proteins lies in the abundance of their cognate receptors. Immunocytokines with IFNγ or GM-CSF are trapped by cells, which express the cytokine receptor on the cell surface, as evidenced in biodistribution studies in receptor-knock-out mice (Ebbinghaus et al. 2005) and after saturation of receptors injecting molar excess of the fusion protein (Kaspar et al. 2007). Competition between antibody-mediated tumor targeting and the capture of the immunocytokine by non-neoplastic cells (e.g. in the blood) diminishes the tumor targeting performance of an immunocytokine, especially when administered and distributed via the blood stream. This competing effect is most prominent for cytokines which feature abundant and high-affinity receptors (e.g. IFNγ and GM-CSF), but undetectable for potent proinflammatory cytokines, effective at low doses (e.g. IL-2, TNFα and IL-12). It remains to clarify if a therapeutic strategy of receptor saturation prior to immunocytokine administration is reasonable in humans, especially as the main goal of cytokine targeting aims at a reduction in cytokine dose and systemic toxicities.

22.4.2  Radionuclides The success of 90Y-ibritumomab tiuxetan (Zevalin, IDEC Pharmaceuticals and Bayer Schering) and 131I-tositumomab (Bexxar, GlaxoSmithKline), two murine antiCD20 antibodies labeled with β−-emitting radionuclides, in radioimmunotherapy (RIT) for non-Hodgkin’s lymphoma has renewed enthusiasm in RIT as an avenue for the selective eradication of disseminated diseases (Koppe et al. 2005; Sharkey and Goldenberg 2008). Both radiolabeled antibodies are more efficacious at inducing remissions than the unlabeled anti-CD20 antibody rituximab (Davis et al. 2004) with excellent clinical results (20–40% complete response rates and 60–80% overall response rates) (Sharkey et al. 2005). So far, RIT of patients with solid tumors has been less successful than in patients with lymphoma. This is mainly due to a lower radiosensitivity of solid tumors and reduced antibody targeting efficiency caused by limited vascularization of bulky tumors, elevated interstitial pressure and heterogeneous uptake of the radiolabeled antibody (Jain 1990).

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Dependent on the therapeutic application in either solid tumors or lymphoma, the choice of the radionuclide is crucial and needs to be adapted. An alpha particle (e.g. 213 Bi, 225Ac, 211At) has a very short path length of <100 µm but a very high energy deposit. This offers the possibility of selective tumor cell killing with less damage to the surrounding normal tissue and a higher radiobiological effectiveness. Alpha emitters are therefore most suitable for RIT in hematologic and micrometastatic diseases. Some preclinical studies have favored alpha particles over beta emitters in RIT (Behr et al. 1999; Aurlien et al. 2002; Singh Jaggi et al. 2007). Another option for RIT in lymphoma is the use of β-emitters, as applied by the only approved radiolabeled antibodies Zevalin and Bexxar. Beta-emitting radionuclides (e.g. 90Y, 131 67 I, Cu, 177Lu) are the most often employed in clinical trials. While Yttrium-90 is a pure β−-emitter of high energy, the β−-emitter Iodine-131, Copper-67 and Lutetium-177, also emitting γ-rays, are of much lower energy and of shorter distance than 90Y. With regard to high beta-energy and longer mean range, beta emitter offer the advantage to kill both the target cell as well as the neighboring cell, which is of particular importance in treating bulky or poorly vascularized solid tumors (reviewed in Boswell and Brechbiel 2007). The low energy beta particles 131I, 67Cu and 177Lu are favored for adjuvant therapy or for small solid tumors while the high energy β emitter 90Y is suited for patients with clearly detectable lesions because of its higher penetration capability. In addition to therapeutic applications, radiolabeled antibodies are also applied for tumor imaging. In general, the same antibody used in therapy is suitable for imaging and only the radionuclide needs to be exchanged. For imaging, low energy β+-emitter (e.g. 66Ga) are chosen for positron emission tomography (PET) imaging while 111In and 99mTc, two γ- and Auger electron emitter, are a popular choice in single photon emission computed tomography (SPECT) imaging. Lower β+ and γ energies are associated with higher PET and SPECT image quality, respectively, with PET operating at tenfold higher sensitivity than SPECT. Apart from the radionuclide, a further critical issue for RIT is the selection of the antibody format. The size of the antibody has an impact on the circulation time, extravasation capability to the tumor, tumor penetration and clearance. For solid tumors, long circulating radiolabeled IgG’s are of disadvantage for RIT as radiation is deposited in normal healthy tissues for a long time. Generally, RIT is limited by the absorbed dose to radiosensitive organs (bone marrow, lung, liver and kidney), representing the bone marrow as the first dose-limiting organ. In contrast to IgG’s, small radiolabeled antibody variants such as scFv are rapidly cleared from the blood and normal tissues, offering the possibility of earlier imaging and a reduction of radiation-absorbed dose to normal tissue for therapy. Additionally, scFv antibodies penetrate into the tissue more efficiently, leading to a higher therapeutic index and an increased homogeneity of radiation dose deposition within the tumor. Biodistribution studies in tumor-bearing mice using different formats of L19 (homodimer scFv, SIP and IgG) have demonstrated that the SIP format offers the best compromise between molecular stability, clearance rate and tumor accumulation (Borsi et al. 2002). This result has been confirmed in therapy experiments in mice, identifying 131I-labeled SIP as the best suited radiolabeled antibody format in comparison

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with 131I-labeled dimeric scFv or IgG. In fact, in the F9 teratocarcinoma mouse model, 131I-labeled SIP(L19) led to significant tumor growth retardation with a favorable toxicity profile (Berndorff et al. 2005). SIP(L19), labeled with iodine-131, is currently being evaluated in Phase I/II clinical trials in radioimmunotherapy of patients with solid tumors and lymphoma with encouraging results (Sauer et  al. 2009). Similarly, the anti-tenascin antibody 131I-SIP(F16) has been investigated Phase II clinical trials for the radioimmunotherapy of patients with solid and hematologic malignancies. F16 and L19 have also been conjugated with iodine-124 for PET imaging (Tijink et al. 2009).The murine anti-tenascin monoclonal antibody 81C6, labeled with 131I or with 211At, has been investigated in several radioimmunotherapy clinical trials for targeting to a surgically-created resection cavity in patients with glioma (Reardon et al. 2006, 2008; Zalutsky et al. 2008). Another strategy to minimize exposure time of normal tissue to radiation represents pretargeting approaches. In pretargeted RIT, the administration of the nonradioactive antitumor antibody is separated in time from the injection of the radioactive molecule which binds to the antibody. The tumor is targeted by the antibody, which is allowed to clear from the circulation and normal organs. After injection of the small radioactive molecule, which binds with high affinity to the pretargeted antibody, unbound radioactive molecules are cleared from organs, where no antibody is present, thereby reducing radiation-induced toxicity of normal organs (Boerman et  al. 2003; Goldenberg et  al. 2006). Thus, such an approach provides increased tumor-to-organ ratios and the delivery of a higher therapeutic dose. Pretargeting approaches have been investigated in clinical trials in non-Hodgkin’s lymphoma (Weiden et  al. 2000; Forero et  al. 2004), in glioma (Paganelli et  al. 2006) or in anaplastic large cell lymphoma (De Santis et al. 2006; Palumbo et al. 2007). In the latter case study, the biotinylated anti-tenascin antibody tenatumomab (ST2146) was administered at first, followed by the sequential administration of avidin and radioactive 90Y-biotinDOTA, resulting in complete responses of the disease without significant side effects (Palumbo et al. 2007). In addition, effort has been made to further enhance and optimize the uptake and efficacy of radiolabeled antibodies for the treatment of solid tumors involving biological modifiers (e.g. blood flow, vascular permeability, stromal barriers, target cell binding) (reviewed in Jain et al. 2007). However, the practical implementation of RIT in the clinical routine remains problematic due to cost issues, needs of radioprotection, highly specialized teams, availability of the nuclide and procedural and logistic hurdles. Furthermore, there are considerable concerns about long-term side effects of antibody-based radiopharmaceuticals, particularly if they are cleared via the renal route (Adams et al. 2004).

22.4.3  Drugs Antibody-drug conjugates represent another important class of therapeutic agents to circumvent damage to normal tissue and dose-limiting toxicities of potent anticancer cytotoxic drugs. The only clinically approved antibody-drug conjugate

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today is gemtuzumab ozogamicin (Mylotarg, Wyeth Pharmaceuticals), a humanized CD33-specific IgG4 antibody conjugated to a calicheamicin derivative and associated with overall response rates of ~30% and relapse-free survival time of 6.8 months (Sievers and Linenberger 2001; Linenberger 2005; Stasi 2008). This agent was approved in May 2000 in the USA for the treatment of relapsed acute myeloid leukemia (AML), however, the approval of Mylotarg was refused by the European Medicines Agency in 2007 and the refusal of the granting was confirmed in 2008 as the risk-benefit balance was unfavorable (http://www.emea.europa.eu/humandocs/ Humans/EPAR/mylotarg/mylotargR.htm). In general, the three components of antibody-drug conjugates, that is the antibody, the linker, and the cytotoxic drug, have been extensively studied and optimized. Although antibody-drug conjugates are simple in principle, the design is challenging as antibody-drug conjugates can decompose before reaching the target, as the conjugation process can disturb the binding performance of the antibody, as the linkers might have inappropriate stability characteristics and as the drugs may not be released in an active state or at efficient quantities for therapeutic efficacy. As discussed before, a selection of a high quality antibody is crucial. This includes an antibody of high affinity and selectivity to the tumor target of high abundance with low background signals, a humanized or human antibody and the choice of the format dependent on the preferred pharmacokinetic profile and therapeutic application (SIP, IgG for localized or hematologic diseases, respectively). With respect to the cytotoxic drug, the drug should have a high cytotoxic potency, a suitable functional group for the coupling to the antibody, and a reasonable solubility and stability in aqueous solutions. The linker between the antibody and the drug features a pivotal function: it needs to be designed in a way that it ensures stability during circulation in the blood but allows the rapid release of the cytotoxic drug in its fully active form inside the tumor cell. In addition, it must retain stability during storage and prevent steric hindrance of the drug on antibody targeting (Chari 2008). Furthermore, control over stoichiometry, site-specific coupling to lysines on the antibody and the chemistry of the coupling process contribute to the production of homogeneous antibody-drug conjugates. There are two major mechanisms by which the delivered drug can enter the tumor cells once the antibody has reached its recognized antigen expressed in the target tissue: first, the drug can be conjugated to an antibody using a cleavable linker which can be cleaved by enzymes at the tumor site and exert its action either extracellularly or intracellularly after diffusion or transport into the cell (Suzawa et al. 2000). Second, the drug can be coupled to an antibody which binds to a cell surface receptor and is internalized by receptor-mediated endocytosis of the whole immunoconjugate. In the latter case, the extent of internalization and endocytosis depends on the nature of the receptor. With the use of either acid-labile or enzymatic-labile linkers, the drug is released from the antibody in the lysosome at acidic pH (pH ~5, e.g. hydrazone or disulfide linker) or by lysosomal enzymes such as peptidases or esterases (Dubowchik and Walker 1999). While acid-labile linkers are relatively stable at neutral pH in the blood, they undergo hydrolysis in the lysosome. In contrast to acid-labile or disulfide linkers, peptide linkers offer greater

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systemic serum stability but are rapidly hydrolyzed by proteases that are mainly localized intracellularly and inactive in the serum due to protease inhibitors. After cleavage, the released drug is able to diffuse out of the cell and kill neighboring cells by a bystander effect (Xie et al. 2004) which is important in vivo as not every single tumor cell will internalize the immunoconjugate. Different classes of cytotoxic drugs for antibody-drug conjugates have been evaluated in clinical trials, including doxorubicin, calicheamicin derivatives, maytansinoid derivatives and auristatins (reviewed in Wu and Senter 2005; Carter and Senter 2008; Chari 2008; Senter 2009). Currently, most efforts in the field are being made by biotech companies such as Seattle Genetics, Genentech, ImmunoGen and Medarex, and some constructs are investigated in clinical trials (e.g. Phase I clinical trials for MDX1203 in advanced renal cell cancer and non-Hodgkin’s lymphoma, Phase III clinical trials for SGN-35 in hematologic malignancies) (Kim et al. 2008b; Lewis Phillips et al. 2008; Oflazoglu et al. 2008a, b; Rodon et al. 2008; Gerber et al. 2009a; Polson et al. 2009). While most developments have been focused on antibody-drug conjugates specific to tumor associated antigens on the tumor cell itself, there has been a recent interest in the use of antibody-drug conjugates to target tumor endothelial cells (Gerber et  al. 2009b) and in the targeting of the tumor extracellular matrix (Ostermann et al. 2008).

22.4.4  Photosensitizers Photodynamic therapy (PDT) is a treatment for cancer and non-cancerous lesions involving light and a sensitizing drug, a so-called photosensitizer. As PDT relies on the excitation of light, it is a promising approach for the treatment of superficially localized tumors accessible to external irradiation such as non-melanoma skin cancer, prostate, bladder, colon and head and neck cancers (Dolmans et al. 2003). Exposure of laser light of an appropriate wavelength excites the photosensitizer, which then mediates the conversion of oxygen into reactive oxygen species (ROS). This finally leads to the death of tumor cells by necrosis and apoptosis as well as to the destruction of the vasculature leading to tumor infarction. Excitation of a photosensitizer with light in the red or near-infrared wavelength is of favor, as light of longer wavelength penetrates deeper in the tissue. Although some level of selectivity may evolve from the photosensitizer’s pharmacokinetic profile and the localized illumination of the target area with light of the appropriate wavelength, PDT is limited by a lack of selectivity, resulting in considerable (photo)toxicities in surrounding non-diseased tissues. The conjugation of photosensitizers to antibodies mediates their targeted delivery to the tumor site where they are activated in situ by exposure to light (photoimmunotherapy). The conjugation step is of particular importance as the conjugation of the photosensitizer to lysines or thiol groups on the antibody surface should not reduce the binding capacity of the antibody and the photosensitizer is often poorly soluble in aqueous conditions. In addition, a too close proximity of two photosensitizers leads to a quenching effect reducing the energy available which is needed for

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ROS generation and tumor damage. Most work in the field of photoimmunotherapy has been done with antibodies targeting EGFR or HER2 with encouraging antitumor responses in animal models (reviewed in van Dongen et al. 2004; e.g. Bhatti et al. 2008) while small antibody formats such as scFv have been shown a better coupling efficiency of up to eight molecules photosensitizer per scFv (Milgrom 2008). ScFv’s have also been shown to be superior to whole antibodies regarding tumor penetration, blood clearance and tumor-to-nontumor rates (Batra et al. 2002). In the field of antibody-based targeting of the extracellular matrix, the photosensitizer Tin (IV) chlorin e6 (bis(triethanolamine)Sn(IV) chlorin e6) was coupled to scFv(L19) targeting the alternatively spliced EDB domain of oncofetal fibronectin. Subsequent irradiation with red light mediated complete and selective occlusion of ocular neovasculature and promoted apoptosis of the corresponding endothelial cells (Birchler et al. 1999). The treatment with mice bearing different subcutaneous tumors with the same photosensitizer linked to SIP(L19) led to occlusion of tumor blood vessels and a substantial tumor growth retardation (Fabbrini et al. 2006).

22.4.5  Other Effector Molecules In analogy to antibody-drug conjugates, antibody directed enzyme prodrug therapy (ADEPT) is a pretargeting strategy that leads to the generation of an active drug at the tumor site. This is a two-step approach in which an antibody-enzyme conjugate is administered that localizes to tumor-associated antigen in the tumor mass and clears from the circulation over time. At that time point, a nontoxic prodrug is injected which is converted into the active drug by the targeted enzyme at the tumor site. Enzymes that have been coupled to antibodies included the bacterial enzymes β-lactamase, cytosine deaminase, carboxypeptidase G2 and human β-glucuronidase, abzymes (catalytic antibodies), carboxypeptidase A1 (Senter and Springer 2001; Sharma et al. 2005). Success in this highly promising strategy will strongly depend on the availability of efficient non-immunogenic human enzymes and high-quality prodrugs. With respect to the tumor stroma, the antibody L19 specific to the EDB domain of fibronectin in the SIP format has been conjugated to a human prolyl endopeptidase releasing a melphalan prodrug (Heinis et al. 2004). The antibody L19 in the scFv format has furthermore been conjugated to pegylated liposomes containing the drug 5-FdU-NOAC. Targeted release of 5-FdUNOAC at the tumor site led to a substantial tumor growth reduction compared to untargeted drug liposomes (Marty et al. 2002). Similarly, immunoliposomes specific to the fibroblast activation protein FAP of the extracellular matrix have been developed (Kontermann 2006; Baum et al. 2007). Another approach to occlude the blood vessels in tumors represents the fusion of a procoagulant factor to an antibody targeting the tumor vessel or stroma. The selective delivery of coagulation cascade initiating factors such as the truncated tissue factor (tTF) has initially been demonstrated by Ran et al. (1998) and Huang et al. (1997). In these reports the antitumoral performance of tTF was investigated, after targeting to the tumor vascular endothelium by means of antibodies to VCAM-1 or

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to an artificially introduced antigen on vascular endothelial cells, respectively. The therapeutic outcome of the so-called “coaguligands” was very promising, resulting in the retardation of tumor growth and even total collapse of the tumor. Likewise, the fusion protein of tTF to scFv(L19) accumulated selectively around the tumor blood vessels in vivo and mediated the selective and complete infarction of different types of solid tumors in mice (Nilsson et al. 2001). Complete tumor eradications were observed in at least 30% of mice without apparent side effects or evidence of systemic activation of the coagulation cascade (Huang et al. 1997; Nilsson et al. 2001).

22.5 Concluding Remarks The tumor stroma and tumor vasculature represent attractive targets for antibody-based pharmacodelivery applications in cancer therapy and imaging. High abundance, accessibility via the bloodstream and genomic stability favor the extracellular matrix and the vasculature as tumor marker over tumor cells. The identification and validation of new promising markers of the tumor microenvironment still represents a major aim for future developments. Antibodies of high affinity are now available which have demonstrated a remarkable ability to selectively localize to the tumor microenvironment. Used as carrier molecules, they offer a unique opportunity to deliver high concentrations of therapeutic agents to the tumor site. Antibody conjugates, especially antibody-cytokine fusions and radiolabeled antibodies, have been proven to be highly efficacious in various preclinical animal models of cancer. The results of on-going and future clinical trials will shed light on the real therapeutic potential of vascular and stromal targeting antibodies and their derivatives. The recent guidelines for Phase 0 clinical trials (Kummar et al. 2007) appear to be ideally suited for a preliminary immuno-PET characterization of novel antibody therapeutics (Tijink et al. 2009), thus revealing their tumor targeting potential and facilitating their transition from the bench to the clinic.

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Index

A activated tumor fibroblasts, 384 angiogenesis, 91, 93–95, 97–100, 102, 103, 151–153, 212, 213, 216, 420, 422, 423, 427–429 B biomarkers, 10 C Cancer, 383, 384, 386–389, 391, 393, 396, 419, 421, 425, 427–430, 432, 437, 439 cancer associated fibroblast, 361, 403, 408, 413, 414 cancer cells, 310, 311, 317, 318, 326, 327, 329–340 cancer progression, 129, 135, 139 carcinogenesis, 268–270, 272, 273, 276, 277 cell migration, 213 cell-matrix adhesions, 42 chemotherapy, 403, 407, 408, 411, 413, 414 Collagen VI, 115, 116 contraction, 37, 41, 42, 47, 49, 52 CXCR4, 245–248, 251, 252 CXCR7, 245, 247, 248, 251 D desmoplasia, 175, 180, 366, 369, 371, 372, 379 E endothelial cell, 306, 310, 333, 334, 337, 339, 347, 354–356 epithelial-stromal crosstalk, 365 extracellular matrix, 127, 236, 347, 351, 353, 356, 421, 424–429, 431, 432, 437–439

F fibroblast, 176–182, 184, 223, 225, 228–230, 232–234, 236–239, 261, 263, 306, 310, 313, 319, 322, 330, 333, 335, 339, 348 fibroblast mutations, 369–371, 375 fibronectin, 347–350, 352, 353, 355 fibrosis, 39, 40, 43, 45, 51, 52, 58–60 Fibulins G Glycoproteins H hyaluronan, 127–133, 135–138 I immune cells, 310, 331, 333, 336, 338 integrin, 347–349, 351–356 interstitial fluid pressure, 260, 262, 404, 406, 408 Interstitial Fluid Pressure, 262 invasion, 75–79, 81, 83 Ionizing radiation, 268, 269 M mammary gland, 269, 273, 274, 276, 286–288, 291–293, 295, 297 Matrix metalloproteinase 11, 115 Mesenchymal Stem Cells, 3–6 metastasis, 80, 81, 83–85, 151–153 microenvironment, 84, 127, 145, 153 MSF, 198, 200–217 myofibroblasts, 248, 249, 251 O obesity, 111, 114, 117, 118

M. M. Mueller, N. E. Fusenig (eds.), Tumor-Associated Fibroblasts and their Matrix, The Tumor Microenvironment 4, DOI 10.1007/978-94-007-0659-0, © Springer Science+Business Media B.V. 2011

451

Index

452 P paracrine, 223, 229–232, 234, 236, 237, 239 PDGF, 257–263 Pericyte, 91–95, 97–103, 262 prognosis, 371 prostate cancer, 4–8, 10–14, 169 proteases, 176, 177, 184, 185 S SDF-1, 245–252 signalling SPARC, 302, 303, 305–312, 314, 317–319, 322, 326–340 stroma, 75, 76, 79, 80, 82, 83, 127, 128, 135–137, 139 Stroma reaction, 39 stromal cells, 306, 310, 318, 338, 339 stromal-epithelial interactions, 268 T targeted therapy, 396 targeting, 403, 411, 413, 414

tenascins, 145, 148, 151, 153 TGF-beta tissue architecture, 286, 287 tissue remodeling, 37, 51, 53, 55 tumor, 91, 102, 103, 145, 147, 148, 150–154 Tumor-Associated Fibroblasts, 403 tumor growth, 178, 180, 181 tumor-microenvironment, 403, 409, 413, 414 Tumor reversion tumor-stroma, 11, 14, 260, 261, 383, 384, 390–393, 395, 396 tumour progression, 198, 209, 215 Tumour-associated fibroblasts, 200, 201 V vascular targeting, 420, 421, 428 W wound healing, 39, 40, 45, 52, 55, 60