Deciphering the Functions of Ets2, Pten and P53 in Stromal Fibroblasts in Multiple

Home , DUSP10, DUSP2, ETS2, GPR77, PTEN (gene), PTPN18

Deciphering the Functions of Ets2, Pten and p53 in Stromal Fibroblasts in Multiple

Breast Cancer Models

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Julie Wallace

Graduate Program in Molecular, Cellular and Developmental Biology

The Ohio State University

2013

Dissertation Committee:

Michael C. Ostrowski, PhD, Advisor

Gustavo Leone, PhD

Denis Guttridge, PhD

Dawn Chandler, PhD

Julie Wallace

2013

Abstract

Breast cancer is the second most common cancer in American women, and is also the second leading cause of cancer death in women. It is estimated that nearly a quarter of a million new cases of invasive breast cancer will be diagnosed in women in the

United States this year, and approximately 40,000 of these women will die from breast cancer. Although death rates have been on the decline for the past decade, there is still much we need to learn about this disease to improve prevention, detection and treatment strategies. The majority of early studies have focused on the malignant tumor cells themselves, and much has been learned concerning mutations, amplifications and other genetic and epigenetic alterations of these cells. However more recent work has acknowledged the strong influence of tumor stroma on the initiation, progression and recurrence of cancer. Under normal conditions this stroma has been shown to have protective effects against tumorigenesis, however the transformation of tumor cells manipulates this surrounding environment to actually promote malignancy. Fibroblasts in particular make up a significant portion of this stroma, and have been shown to impact various aspects of tumor cell biology. Although the contributions of these stromal fibroblasts to tumor progression have been well studied, the role of specific signaling pathways important for these tumor associated functions remain elusive.

In the current studies, we examine the fibroblast specific roles of the protooncogene Ets2 as well as the tumor suppressor genes Pten and p53 in tumorigenesis using conditional knockout mouse models. Importantly, the functions of these genes are assessed in multiple breast cancer models to account for the heterogeneity observed in breast cancer. Additionally, gene expression analysis is used to decipher potential mechanisms by which these genes function in the stroma.

Deletion of Ets2 in fibroblasts in both PyMT and ErbB2 driven tumorigenesis slowed the progression of these tumors without significantly effecting the development of the normal mammary gland. Gene expression profiling revealed Ets2 controls a tumor specific transcription program in fibroblasts that promotes angiogenesis in both breast cancer models. Interestingly, Ets2 seems to target a portion of the same genes in both models, thereby implicating it as a “master regulator” in these tumor associated cells.

Additionally, loss of fibroblast Ets2 impacts gene expression in surrounding endothelial cells. Both the PyMT and ErbB2 derived Ets2 dependent signatures were represented in the stroma of human breast cancer tissue, and could also predict patient outcome in independent whole tumor data.

Using a similar strategy, we also examined the function of Pten in the stromal fibroblast compartment. Interestingly, loss of this tumor suppressor gene promoted

ErbB2 driven tumorigenesis. Gene expression profiling determined that loss of Pten promotes inflammation and ECM remodeling, which is manifested by increased macrophage proliferation and deposition of collagen. The transcription factor Ets2 was shown to be a downstream target of Pten signaling in fibroblasts, which was at least in

iii part mediated by regulation of miR-320. To our surprise, Pten loss in stromal fibroblasts did not impact Ras mediated tumorigenesis. To determine if this collaboration was specific to the Pten tumor suppressor, we also conditionally deleted p53 in mammary fibroblasts in the context of ErbB2 and Ras epithelial drivers. Conversely to what we observed upon Pten deletion, loss of p53 did not affect ErbB2 driven tumorigenesis, but significantly impacted Ras mediated transformation. Through gene expression arrays, we determined crosstalk between these epithelial cells and fibroblasts induces changes in a specific collaborative fashion. Loss of Pten or p53 promoted the hyperproliferation of surrounding epithelial cells, however deletion of these genes alone was not sufficient to cause tumor development.

In summary, we have shown Ets2 to be an important transcription factor driving angiogenesis from the tumor associated fibroblast compartment in both PyMT and ErbB2 breast cancer models. Alternatively, Pten was shown to have strong tumor suppressor function in the stroma in ErbB2 mediated tumorigenesis, however its function was dispensible during Ras induced tumorigenesis. The opposite was observed in the context of fibroblast p53 function which was important in Ras driven tumorigenesis but had modest effects in the context of ErbB2. Understanding this complex communication between tumor cells and the microenvironment is a critical step as we move forward in the battle against cancer.

Dedication

This dissertation is dedicated to my grandma, a breast cancer survivor and my motivation to make a difference for people with this disease. Also to my parents for believing in me

and their unending love and support. And finally to my husband for keeping me going

when I wanted to give up.

Acknowledgments

I owe a debt of gratitude to my advisor Dr. Michael C. Ostrowski for his support and encouragement thoughout my time here. I would also like to thanks my committee members Dr. Gustavo Lenoe, Dr. Denis Guttridge and Dr. Dawn Chandler for their helpful insights and for giving me an opportunity to prove my research abilities.

All other members of my lab, current and past, as well as members of the Leone lab have been invaluable resources for stimulating conversation. You are friends as well as co-workers.

I am grateful for the help of our biostatisticians Soledad Fernandez and Lianbo

Yu. I am also thankful to the histology core, in particular Lisa Maysoon Rawahneh for timely processing and encouragement. And extra thanks to our bioinformatics expert

Thierry Pecot for helping with data analysis and heatmap generation.

Vita

26 August 1982 ...... Born – Cincinnati, Ohio

May 2004 ...... B.S. Zoology, Miami University

September 2006 – December 2006 ...... Teaching Assistant, The Ohio State University

September 2005 – December 2010 ...... Research Assistant, The Ohio State University

January 2011 – December 2012 ...... Graduate Fellow, The Ohio State University

January 2012 – Present ...... Research Assistant, The Ohio State University

Publications

1. Bronisz A, Godlewski J, Wallace JA, Merchant AS, Nowicki MO, Mathsyaraja H, Srinivasan R, Trimboli AJ, Martin CK, Li F, Yu L, Fernandez SA, Cory S, Hallett M, Park M, Piper MG, Marsh CB, Yee LD, Jimenez RE, Nuovo G, Lawler SE, Chiocca EA, Leone G, Ostrowski MC. (2011) Reprogramming of the tumor microenvironment by stromal Pten-regulated miR-320. Nat Cell Biology 14, 159-67.

2. Wallace JA, Li F, Leone G, Ostrowski MC. (2011) Pten in the breast tumor microenvironment: modeling tumor-stroma coevolution. Cancer Res 71, 1203-1207.

3. Li F, Wallace JA, Ostrowski MC. (2010) ETS transcription factors in the tumor microenvironment. The Open Cancer Journal 3, 49-54.

4. Kim Y, Wallace JA, Li F, Ostrowski MC, Friedman A. (2010) Transformed epithelial cells and fibroblasts/myofibroblasts interaction in breast tumor: a mathematical model and experiments. J Math Biol. 61, 401-421.

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5. Trimboli AJ, Cantemir-Stone CZ, Li F, Wallace JA, Merchant A, Creasap N, Thompson JC, Caserta E, Wang H, Chong JL, Naidu S, Wei G, Sharma SM, Stephens JA, Fernandez SA, Gurcan MN, Weinstein MB, Barsky SH, Yee L, Rosol TJ, Stromberg PC, Robinson ML, Pepin F, Hallett M, Park M, Ostrowski MC, Leone G. (2009). Pten in stromal fibroblasts suppresses mammary epithelial tumors. Nature 461, 1084-1091.

6. Chen CL, Hsieh FC, Lieblein JC, Brown J, Chan C, Wallace JA, Cheng G, Hall BM, Lin J. (2007) Stat3 activation in human endometrial and cervical cancers. Br J Cancer 96(4), 591-599.

Fields of Study

Major Field: Molecular, Cellular and Developmental Biology

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Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgments...... vi

Vita ...... vii

Table of Contents ...... ix

List of Tables ...... xix

List of Figures ...... xxi

Chapter 1: Introduction ...... 1

1.1 Mammary Gland Biology ...... 1

1.1.1 Human Breast Development ...... 2

1.1.2 Mouse Mammary Gland Development ...... 4

1.1.3 Stromal Contribution to Breast Development...... 8

1.2 Malignancies of the Breast ...... 9

1.2.1 Origin of Breast Cancer Cells ...... 9

1.2.1.1 Clonal Evolution Theory...... 9

1.2.1.2 Cancer Stem Cell Hypothesis ...... 11

1.2.2 Heterogeneity of Breast Cancer ...... 14

1.2.2.1 Intertumor Heterogeneity ...... 16

1.2.2.2 Intratumor Heterogeneity ...... 19

1.2.2.3 Heterogeneity of Tumor Stroma ...... 21

1.2.3 Targeted Therapies for Treatment of Breast Cancer ...... 23

1.2.4 Modeling Breast Cancer in Mice ...... 26

1.2.4.1 MMTV-PyMT...... 29

1.2.4.2 MMTV-ErbB2 ...... 31

1.2.4.3 Tet-o-KrasG12D and MMTV-rtTA ...... 32

1.2.4.4 Breast Tumor Cell Lines and Injection Models ...... 34

1.3 Breast Tumor Microenvironment ...... 34

1.3.1 Tumor Associated Fibroblasts ...... 35

1.3.1.1 Fibroblasts Contribute to Epithelial Tumor Growth ...... 36

1.3.1.2 Important Signaling Pathways in Fibroblasts ...... 37

1.3.1.3 microRNAs in Tumor Associated Fibroblasts ...... 40

1.3.2 Fibroblast Effects on Other Cellular Heterogeneity of the Microenvironment ...... 41

1.3.2.1 Fibroblasts Important in “Angiogenic Switch”...... 41

1.3.2.2 Fibroblasts and Inflammation ...... 42

1.3.2.3 Fibroblasts and the Extracellular Matrix...... 43

1.4 Ets2 as a Member of the Ets Transcription Factor Family ...... 44

1.4.1 Misregulation of Ets Factors in Cancer ...... 45

1.4.2 Biological Functions of Ets2 ...... 47

1.4.3 Ets Factors in Tumor Stroma ...... 48

1.5 Pten and p53 as Tumor Suppressors ...... 49

1.5.1 Pten and Cancer ...... 50

1.5.2 Tumor Suppressor Function of PTEN ...... 50

1.5.2.1 Molecular Mechanism of Tumor Suppression ...... 50

1.5.2.2 In Vivo Analysis of Pten Function ...... 52

1.5.3 p53 Mutations and Cancer ...... 54

1.5.4 Tumor Suppressor Function of p53 ...... 54

1.5.5 In Vivo Analysis of p53 Functions ...... 56

1.6 Hypothesis...... 58

Chapter 2: Materials and Methods ...... 59

2.1 Mouse Colony Maintenance ...... 59

2.1.1 Animal Care ...... 59

2.1.2 Transgenic Mouse Lines ...... 59

2.1.3 Mouse Genotyping ...... 60

2.1.3.1 Tail DNA Preparation ...... 60

2.1.3.2 Genotyping Primers and PCR Conditions ...... 60

2.1.4 Animal Procedures ...... 61

2.1.4.1 Mammary Gland Dissection and Tissue Harvesting ...... 61

2.1.4.2 Mammary Tissue Transplantation ...... 62

2.1.4.3 Mammary Fat Pad Injection ...... 62

2.1.4.4 Matrigel Plug Injections ...... 63

2.1.4.5 Xenograft Assays ...... 63

2.1.4.6 Intraperitoneal Injection ...... 63

2.1.4.7 Doxycycline Administration in Food...... 64

2.2 Tissue Culture ...... 64

2.2.1 Primary Fibroblast Isolation ...... 64

2.2.2 Primary Epithelial Cell Isolation ...... 65

2.2.3 Establishment of Wild Type, Pten Null and Pten/Ets2 Double Knockout (DKO)

Mouse Mammary Fibroblast Cell Lines ...... 66

2.3 Flow Cytometry/FACS ...... 66

2.3.1 Isolation of YFP+ Cells from Transgenic Mice ...... 66

2.3.2 Isolation of Endothelial Cells and Macrophages using CD31 and F4/80 Antibodies

...... 67

2.3.3 In vivo BrdU Flow Assay ...... 68

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2.4 RNA Analysis and Microarrays ...... 69

2.4.1 RNA Extraction ...... 69

2.4.2 cDNA Preparation ...... 69

2.4.3 Quantitative Real Time PCR ...... 70

2.4.4 Microarray Analysis...... 71

2.4.5 microRNA Profiling...... 71

2.5 Western Blotting ...... 71

2.6 Chromatin Immuoprecipitation ...... 72

2.7 Histology/Immunostaining ...... 73

2.7.1 Whole Mount Staining of Mammary Gland ...... 73

2.7.2 Immunohistochemistry (IHC) Staining Protocol ...... 73

2.7.3 Immunoflourescence (IF) Staining Protocol ...... 74

2.8 Bioinformatic Analysis ...... 75

2.8.1 Gene Set Enrichment Analysis (GSEA) ...... 75

2.8.2 Generating Human Stroma Heatmaps Using Gene Expression Data from Mouse

Models...... 75

2.8.2.1 PyMT Derived Ets2 Fibroblast Gene List ...... 75

2.8.2.2 PyMT Derived Ets2 Dependent Endothelial Cell Gene List ...... 76

2.8.2.3 Pten Null Fibroblast Gene List ...... 76

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2.8.3 Kaplan-Meier Survival Analysis...... 76

2.9 Statistical Tests ...... 77

2.9.1 Ets2 Tumor Study and Immunostaining ...... 77

2.9.2 Pten/p53 Tumor Study and Immunostaining ...... 78

Chapter 3: The Role of Ets2 in Fibroblasts During Mammary Tumor Progression ...... 79

3.1 Introduction ...... 79

3.1.1 Cre Mediated Deletion of Ets2 Using FspCre ...... 81

3.2 Results ...... 84

3.2.1 Normal Development of Mammary Glands Lacking Fibroblast Ets2 ...... 84

3.2.2 Ets2 Deletion in Fibroblasts Slows Progression of PyMT Driven Mammary Tumors

...... 86

3.2.3 Regulation of PyMT Tumor Specific Gene Expression by Ets2 ...... 90

3.2.4 Ets2 Directly Binds and Induces Expression of MMP9 ...... 100

3.2.5 Ets2 in Fibroblasts Promotes Angiogenesis Through VEGFR2 Signaling in

Endothelial Cells ...... 103

3.2.6 Gene Expression Changes in Endothelial Cells Induced by Ets2 Signaling in

Fibroblasts ...... 108

3.2.7 PyMT Derived Ets2 Fibroblast Gene Signature is Represented in Human Breast

Cancer Stroma ...... 112

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3.2.8 Ets2 Fibroblast and Endothelial Gene Signatures Predict Patient Outcome in Whole

Tumor Datasets ...... 115

3.2.9 Fibroblast Ets2 Function Critical in ErbB2 Induced Tumorigenesis ...... 117

3.2.10 ErbB2 Drives Similar Tumor Specific Ets2 Dependent Gene Signature in

Fibroblasts ...... 121

3.2.11 Ets2 in ErbB2 Associated Fibroblasts Promotes Tumor Angiogenesis ...... 130

3.2.12 ErbB2 Driven Ets2 Dependent Signature Represented in Human Stromal Data and

Predicts Patient Outcome ...... 132

3.3 Discussion ...... 135

Chapter 4: The Role of Pten in Fibroblasts During Mammary Tumor Initiation and

Progression ...... 137

4.1 Introduction ...... 137

4.2 Results ...... 138

4.2.1 Efficient and Specific Deletion of Pten by FspCre ...... 138

4.2.2 Deletion of Pten in Fibroblasts Accelerates ErbB2 Driven Tumorigenesis ...... 142

4.2.3 Pten Deletion Drives Pro-Inflammatory Gene Expression Signature in Fibroblasts

...... 146

4.2.3.1 Gene Expression in Cultured Fibroblasts ...... 146

4.2.3.2 Collagen1A-YFP Isolated Pten Null Fibroblasts ...... 154

4.2.3.3 Consistency of Pten Gene Expression Signatures Across Multiple Experiments

...... 164

4.2.4 Increased Macrophage Recruitment and Collagen Deposition in FspCre;PtenloxP/loxP

Mammary Glands...... 166

4.2.5 Activation of Ets2 in Pten Null Mammary Fibroblasts Drives Expression of Mmp9 and Ccl3 ...... 168

4.2.6 Regulation of miR320 by fibroblast Pten Effects Tumor Cell Proliferation and

Angiogenesis ...... 170

4.2.7 Pten Deletion in Fibroblasts Does Not Effect Ras Driven Tumorigenesis ...... 176

4.2.8 ErbB2 Expression in Epithelial Cells, but not Ras Expression, Drives Gene

Expression Program Resembling Pten Signature in Surrounding Fibroblasts ...... 179

4.2.9 Fibroblast Pten loss reprograms similar gene expression changes in surrounding endothelial cells and macrophages...... 201

4.2.10 Fibroblast Pten Absence Promotes Proliferation of Surrounding Epithelial Cells,

Endothelial Cells and Macrophages ...... 225

4.2.11 Pten Signatures Represented in Human Breast Cancer Stroma and Predict Patient

Outcome ...... 229

4.3 Discussion ...... 232

Chapter 5: Stromal p53 Tumor Suppressor Function ...... 234

5.1 Introduction ...... 234

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5.2 Specific and Efficient Deletion of p53 in Stromal Fibroblasts ...... 234

5.3 Results ...... 237

5.3.1 Normal Development of FspCre;p53loxP/loxP Mammary Glands ...... 237

5.3.2 p53 Loss in Fibroblasts Promotes Kras, but not ErbB2, Mediated Tumorigenesis239

5.3.1.1 Transplant Model ...... 239

5.3.1.2 Genetic Study ...... 243

5.3.2 Gene Expression of p53 Null Fibroblasts ...... 246

5.3.2.1 Gene Expression in Cultured Fibroblasts ...... 246

5.3.2.2 Gene Expression in ColYFP Sorted Fibroblasts ...... 247

5.3.3 Loss of Fibroblast p53 Does Not Induce Inflammation or ECM Remodeling in the

Mammary Gland ...... 276

5.3.4 p53 Loss in Fibroblasts Promotes Epithelial Cell Proliferation...... 278

5.3.5 Human Analysis ...... 280

5.4 Discussion ...... 282

Chapter 6: Conclusions and Future Directions ...... 283

6.1 Conclusions ...... 283

6.1.1 Ets2 in Fibroblasts as a Master Regulator of Angiogenesis in Breast Cancer ...... 283

6.1.2 Collaboration of Pten and p53 with Oncogenic Signaling in Epithelial Cells ...... 284

6.2 Future Directions ...... 285

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6.2.1 Global Ets2 Transcriptional Regulation in Pten Null Fibroblasts ...... 285

6.2.2 Exon Level Analysis ...... 286

6.2.3 Fibroblast Pten or p53 Induced Genetic and/or Epigenetic Changes in Epithelial

Cells ...... 286

6.2.4 Mechanisms of Cellular Crosstalk ...... 288

6.2.5 Further Analysis in Human Samples ...... 289

Bibliography ...... 290

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List of Tables

Table 3.1 Genes Regulated by Ets2 in Normal Fibroblasts ...... 92

Table 3.2 Genes Regulated by Ets2 in PyMT Tumor Associated Fibroblasts

...... 93

Table 3.3 Genes Regulated in Endothelial Cells as a Consequence of Ets2 Signaling in

PyMT Tumor Associated Fibroblasts ...... 109

Table 3.4 Genes Regulated by Ets2 in Normal 16-week Mammary Gland Fibroblasts

...... 123

Table 3.5 Genes Regulated by Ets2 in 16-week ErbB2 Tumor Associated Mammary

Gland Fibroblasts ...... 124

Table 4.1 Genes Regulated by Pten in 8-week Cultured Mammary Fibroblasts

...... 149

Table 4.2 Genes Regulated by Pten in 8-week Col1aYFP Sorted Mammary Fibroblasts

...... 159

Table 4.3 Genes Misregulated by Pten in Cultured 8-week Mammary Gland Fibroblasts

...... 187

Table 4.4 Genes Regulated in Epithelial Cells by Fibroblast Pten in 8-week Mammary

Glands ...... 191

Table 4.5 Genes Regulated in Epithelial Cells by Epithelial ErbB2 Expression in 8-week

Mammary Glands...... 197

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Table 4.6 Genes Regulated in Fibroblasts by Epithelial ErbB2 Expression in 8-week

Mammary Glands...... 199

Table 4.7 Genes Regulated in Endothelial Cells by Fibroblast Pten in 8-week Mammary

Glands ...... 207

Table 4.8 Genes Regulated in Macrophages by Fibroblast Pten in 8-week Mammary

Glands ...... 214

Table 5.1 Genes Misregulated by p53 in Cultured 8-week Mammary Gland Fibroblasts

...... 250

Table 5.2 Genes Regulated in Epithelial Cells by Fibroblast p53 in 8-week Mammary

Glands ...... 262

Table 5.3 Genes Regulated by p53 in 8-week Col1aYFP Sorted Mammary Fibroblasts

...... 265

List of Figures

Figure 1.1 Whole Mount Mouse Mammary Glands at Different Stages ...... 7

Figure 1.2 Modeling Breast Cancer Heterogeneity ...... 15

Figure 1.3 Inducible Gene Expression Using Tet-off/Tet-on System ...... 28

Figure 1.4 Stages of Tumor Development in PyMT Mouse Model ...... 30

Figure 3.1 Efficient and Specific Deletion of Ets2 in Stromal Fibroblasts ...... 83

Figure 3.2 Normal Development of Mammary Glands Lacking Ets2 in Fibroblasts

...... 85

Figure 3.3 Ets2 in Fibroblasts Promotes PyMT Driven Mammary Tumor Growth ..89

Figure 3.4 Ets2 Driven Transcription Program...... 97

Figure 3.5 Gene Set Enrichment Analysis (GSEA) of Tumor Specific Ets2 Regulated

Genes...... 99

Figure 3.6 Direct Regulation of Mmp by Ets2 in Stromal Fibroblasts ...... 102

Figure 3.7 Ets2 in Fibroblasts Promotes PyMT Associated Tumor Angiogenesis ....105

Figure 3.8 Ets2 in Fibrobasts Activates VEGF Signaling in Endothelial Cells...... 107

Figure 3.9 Endothelial Cell Gene Expression Changes Induced by Ets2 Fibroblast Ets2

Deletion ...... 111

Figure 3.10 Ets2 Fibroblast and Endothelial Cell Signatures in Human Breast Cancer

Stroma ...... 114

Figure 3.11 Ets2 Fibroblast and Endothelial Cell Signatures Predict Patient Outcome

xxi

...... 116

Figure 3.12 Ets2 in Fibroblasts Drives ErbB2 Mediated Tumorigenesis ...... 120

Figure 3.13 Ets2 Specific Transcription Program in ErbB2 Associated Fibroblasts .127

3.14 Ets2 Promotes Similar Biological Proccesses in ErbB2 Associated Fibroblasts

...... 129

3.15 Ets2 Promotes ErbB2 Associated Angiogenesis ...... 131

3.16 ErbB2 Induced Ets2 Dependent Signature Represented in Human Tumor Stroma and

Predicts Patient Outcome ...... 134

Figure 4.1 Efficient and Specific Deletion of Pten in Mammary Stromal Fibroblasts

...... 141

Figure 4.2 Pten Inhibits ErbB2 Tumorigenesis from the Stromal Fibroblast Compartment .

...... 145

Figure 4.3 Loss of Fibrolast Pten Drives Inflammation and ECM Remodeling ...... 148

Figure 4.4 Expression of ColYFP in Cultured Mammary Fibroblasts ...... 156

Figure 4.5 ColYFP Expression Does Not Overlap with Expression of Cdh1, CD31 or

F4/80 ...... 158

Figure 4.6 Consistent Gene Expression Trends in Pten Null Fibroblasts Across

Experiments ...... 165

Figure 4.7 Pten Deletion in Fibroblasts Promotes Macrophage Infiltration and ECM

Remodeling ...... 167

Figure 4.8 Increased Ets2 Expression and Transcriptional Activity in FspCre;PtenloxP/loxP

Fibroblasts ...... 169

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Figure 4.9 Regulation of miR-320 by Pten in Fibroblasts ...... 175

Figure 4.10 Pten Loss in Stromal Fibroblasts Does Not Promote Ras Driven

Tumorigenesis ...... 178

Figure 4.11 Epithelial Expression of ErbB2 Induces Pten Like Signature in 8-Week

Mammary Fibroblasts ...... 183

Figure 4.12 Epithelial Expression of ErbB2 Induces Pten Like Signature in 12-Week

Mammary Fibroblasts ...... 184

Figure 4.13 ErbB2 Induced Pten Gene Signature is Oncogene Specific ...... 185

Figure 4.14 Pten Fibroblast Loss Induces ErbB Like Signature in Epithelial Cells..186

Figure 4.15 FACS Plot Shows Distinct CD31+ and F4/80+ Populations ...... 203

Figure 4.16 Fibroblast Pten Regulated Changes in Endothelial Cells ...... 204

Figure 4.17 Fibroblast Pten Regulated Changes in Macrophages ...... 206

Figure 4.18 Increased Proliferation of Epithelial Cells, Endothelial Cells and

Macrophages from FspCre;PtenloxP/loxP Mammary Glands ...... 227

Figure 4.19 Increased Ki67 Staining in Epithelial Cells and Endothelial Cells as

Consequence of Fibroblast Pten Loss ...... 228

Figure 4.20 Pten Associated Signature Represented in Human Breast Cancer Stroma

...... 230

Figure 4.21 Pten Associated Signatures Predict Patient Outcomes ...... 231

Figure 5.1 Efficient and Specific Deletion of p53 ...... 236

Figure 5.2 Normal Development of Fibroblast p53 Null Mammary Gland ...... 238

Figure 5.3 Stromal p53 Loss Does Not Impact ErbB2 Tumorigenesis ...... 241

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Figure 5.4 Fibroblast p53 Loss Drives Ras Mediated Tumorigenesis ...... 242

Figure 5.5 Confirmation of Transplant Study Using Purely Genetic Model ...... 245

Figure 5.6 Epithelial Ras Expression Drives p53 Like Signature in Fibroblasts...... 248

Figure 5.7 Fibroblast p53 Deletion Drives Ras Like Response in Epithelial Cells ...249

Figure 5.8 Fibroblast p53 Deletion Does Not Induce Macrophage Recruitment or ECM

Remodeling ...... 277

Figure 5.9 Increased Ki67+ Epithelial Cells in Mammary Glands from FspCre;p53loxP/loxP

Mice ...... 279

Figure 5.10 p53 Signatures Represented in Human Breast Cancer Stroma ...... 281

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Chapter 1: Introduction

Currently, women face a 1 in 8 chance of developing breast cancer at some point in their lifetime. Perhaps more frightening is the fact that breast cancer will be responsible for the woman’s death in about 1 out of 36 of these women afflicted with the disease

(American Cancer Society). Although mortality rates have been declining since 1990, most likely due to advancements in screening and improved treatment options, breast cancer is still the most second deadly cancer in women. Therefore, understanding the exact mechanisms behind the initiation and progression of breast cancer has become necessary for the development of effective therapeutics.

Although much research focuses on genetic and epigenetic modifications in tumor epithelial cells, the function of these cells alone is not the sole determinant of tumor progression. Rather, a complex microenvironment exists that can influence the behavior of tumor cells.

1.1 Mammary Gland Biology

In order to understand the atypical cellular changes that lead to the formation of breast cancer, it is first important to examine development of the breast under normal conditions. This process begins during embryonic life, and continues in different stages throughout most of the lifespan of both humans and mice.

1.1.1 Human Breast Development

Although limitations in available samples have made the study of breast development in humans extremely challenging, several early studies have provided us with significant insight into the initiation and maturation of this reproductive organ. Development of breast tissue in humans begins in the early embryo, and is divided into ten different stages. The first stage of this development is the formation of a 2-4 cell layer milk streak or mammary band, which is a thickening in the ectoderm tissue (Gusterson and Stein).

This band continues to thicken in the thoracic region in subsequent weeks to form the 4-6 cell wide mammary crest, while the rest of the milk streak starts involution. Later, this crest forms a nodule that sinks into the mesenchyme which begins the development of the nipple. The parenchyma of the mammary gland is derived from a single epithelial ectodermal bud (Russo and Russo, 2004), known as the primary bud. Outgrowth of epithelial cells occurs later in embryonic development, resulting in primitive ducts ending in short ductules in the newborn breast. These ductules are lined with 1-2 layers of epithelial cells surrounded by a myoepithelial cell layer (Russo and Russo, 2004).

Mammary gland development is relatively dormant during childhood, doing little more than growing at a proportionate rate with other tissues. At puberty, changes in hormone levels cause the growth and division of mammary ducts and the formation of terminal end buds. Simultaneously, terminal duct lobular units (TDLUs) are formed, which are the main functional components of the adult mammary gland. TDLUs are composed of clusters of short ductal sprouts known as ductules. Lobules are classified as

2 type 1, 2 or 3 based on the number of ductules present, with type 3 being the most well developed with approximately 80 ductules (Russo and Russo, 2004).

During pregnancy, ductules undergo proliferation to form dense and complex structures known as acini, and are classified as type 4 lobules. The epithelial cells comprising the acini increase in number due to proliferation and also are larger in size.

This morphogenesis of the mammary gland is completed by the end of the first half of pregnancy, and subsequent changes involve the formation of milk producing and secreting structures. Following weaning, postlactational regression occurs whereby involutional changes reduce the amount and activity of secretory epithelial cells.

The final stage in adult mammary gland development occurs after menopause and is driven by changes in hormone levels. During this time, there is a decrease in the number of type 2 and 3 lobules, and a corresponding increase in type 1 lobules (Russo and Russo, 2004). Additionally, fat replaces much of the interlobular connective tissue

(Howard and Gusterson, 2000).

Several abnormalities during breast development have been observed, including amastia (lack of breast tissue), polymastia (extra breast tissue) and polythelia (extra nipples) (Merlob, 2003). These phenotypes can occur independently, but have also been observed in pleiotropic syndromes arising from genetic abnormalities. Mutations in the

T-box gene TBX-3 cause ulnar mammary syndrome, in which breast development can be effected (Bamshad et al., 1997). Limb mammary syndrome (LMS) has been identified as another pleiotropic genetic disorder that can cause hypoplasia or aplasia of the breast and nipple (van Bokhoven et al., 1999). The critical region associated with this disorder has

3 been mapped to chromosome 3q27. Other abnormalities during breast development can occur postnatally, however these are usually sporadic conditions or due to hormonal imbalances (Gusterson and Stein).

1.1.2 Mouse Mammary Gland Development

Due to the challenges of studying development in the human breast, rodent models have become a particularly useful tool for deciphering the molecular mechanisms involved in this process. Although a number of morphological differences have been observed between human and mouse mammary glands, their basic biology and histology is astonishingly similar. Embryonic mammary gland development begins around day 10-

11 when the mammary streak appears as an enlargement of the ectoderm (Richert et al.,

2000). Over the next week, a rudimentary mammary sprout is formed which invades underlying fat pad precursor tissue, ultimately forming a basic ductal structure at the time of birth.

The postnatal mammary gland undergoes a period of inactivity lasting approximately 3 weeks after which hormonal cues initiate continued growth of the tissue.

During puberty, the pre-existing ductal structure undergoes a period of rapid growth in which the ducts lengthen and form multiple branches to create secondary and tertiary ducts that eventually extend to fill the mammary fat pad. The primary duct contains a large lumen lined by a layer of luminal epithelial cells, which are identified using keratins

8, 11, 14, 20 and 22 as markers (Asch and Asch, 1985). Surrounding the luminal epithelial cells is a layer of myoepithelial cells, identified by their positive staining for smooth muscle actin (Radice et al., 1997). These contractile cells function in milk

4 secretion during lactation, and also secrete components of the basement membrane throughout development (Richardson, 1949). Separating the epithelial cells from the surrounding stroma is the basement membrane, an organized network of proteins acting to provide structure to the ducts and also to influence epithelial cell shape, polarity, growth and responsiveness to hormones (Streuli and Bissell, 1990). Outside of this basement membrane is a diverse stromal network which will be discussed in more detail in the next section. During this period of growth, the terminal end bud (TEB) is the primary epithelial structure and is the site where ductal elongation and branching occurs to ultimately form alveolar buds (Figure 2.1A).

After puberty, the TEBs reach the limits of the fad pad and regress for a brief period until they are again initiated to develop in response to the cyclic secretion of hormones during estrous cycles. Mammary differentiation peaks during pregnancy, which ellicits substantial proliferation of ductal branches and formation of alveolar buds

(Figure 2.1B). These buds gradually cleave and differentiate into individual alveoli that end up as milk secreting lobules during lactation, and eventually fill the majority of the fat pad (Figure 2.1C) (Richert et al., 2000). Several other morphological changes take place in the gland during pregnancy as well, including an interrupted myoepithelial cell layer allowing direct contact of luminal epithelial cells with the basement membrane, and a significant decrease in the amount of stroma (Barcellos-Hoff et al., 1989). Throughout the process of lactation, adipocytes present in the fat pad become metabolized and the alveoli completely occupy this reproductive organ (Neville, 1999).

After weaning, the mammary gland undergoes involution, a process of cell death and remodeling initiated by milk stasis (Figure 2.1D) (Quarrie et al., 1996). Apoptosis of the secretory epithelial cells occurs, and eventually the alveoli collapse into clusters of epithelial cells. During this time, the adipocyte population in the fat pad is replenished, and dense stroma can again be seen around the ducts. At the end of the involution process, the organization of the mammary gland is very similar to the prepregnant tissue, only slightly more differentiated. The mouse mammary gland undergoes these processes of differentiation and involution with each pregnancy.

Figure 1.1 Whole Mount Mouse Mammary Glands at Different Stages

A. Mammary gland from virgin mouse highlighting ductal differentiation and terminal end bud formation. B. Alveolar and lobular transformation of ductal epithelial cells during pregnancy. C. Increasing proliferation creates dense formations throughout lactation. D. Collapse of the lobulo-alveolar structures during involution.

1.1.3 Stromal Contribution to Breast Development

Although mammary epithelial cells drive the formation of mammary ducts through proliferation and invasion of the fat pad, the stroma plays a critical supporting role in directing the ultimate path of these ducts. Included in this complex stroma are adipocytes, fibroblasts, blood vessels, inflammatory cells and extracellular matrix

(ECM), each of which has a specific function during different phases of mammary gland development. Using genetic models, multiple signaling pathways have been shown to be critical in the development of the mammary gland, and most of these are involved in the crosstalk between the mammary epithelial cells and their surrounding stroma. An excellent example of this is patched-1 (Ptc-1), the receptor for hedgehog (Hh) ligand which is exclusively expressed in the mammary epithelium. Loss of a single copy of Ptc-

1 causes hyperplasia and dysplasia of mammary ducts, as well as the formation of an abnormally dense layer of fibroblastic stroma (Lewis et al., 1999). However Gli2, a downstream target of Hh signaling, was shown to be exclusively expressed in the stroma, and is essential in the stromal compartment for normal mammary gland development

(Lewis et al., 2001). Additionally, the receptor for parathyroid hormone-related protein

(PTHrP), involved in mammary cell fate and branching, is also expressed in stromal cells, again implicating direct epithelial to stromal signaling (Dunbar et al., 1998; Revskoi et al., 1975; Wysolmerski et al., 1998). A final prime example of this intercellular reciprocal signaling involves insulin-like growth factor 1 (IGF1), which is required for mammary gland development and mediates the function of growth hormone (GH) and its receptor (GHR) (Kleinberg et al., 2000). GHR expression in mammary epithelial cells is

8 not required development of the gland, thus implicating a stromal specific role for this protein (Gallego et al., 2001). Interestingly, GH induces the expression of IGF1 through

GHR in the stroma, and IGF1 in turn acts on its receptor (IGF1R) which is required in mammary epithelial cells for proper ductal development in the mammary gland (Bonnette and Hadsell, 2001; Walden et al., 1998).

1.2 Malignancies of the Breast

Breast is a very dynamic tissue, undergoing a multitude of changes throughout the lifespan of a woman, specifically during puberty, pregnancy and menopause. In particular, the epithelial cells of the breast proliferate and regress in response to changes in hormone levels associated with these phases of life.

1.2.1 Origin of Breast Cancer Cells

Mammary epithelial cells are the main functional cells within breast tissue, and not surprisingly they also are the cells that constitute the majority of breast tumors. Although these particular cells have been extensively studied, there is still an ongoing debate concerning the exact cell responsible for the tumor initiating transformation event.

Currently, two different theories are under debate, both of which can potentially explain the cell of origin in breast cancer, and can also account for progression and recurrence of this disease.

1.2.1.1 Clonal Evolution Theory

This theory states that over time cancer cells acquire multiple combinations of mutations within a particular tumor, and that genetic drift and natural selection are responsible for the survival of the most aggressive cells which then drive tumor 9 progression. Therefore, a tumor is derived from a single cell that has undergone changes to promote its growth independent of signaling. As these tumor cells proliferate, certain cells will acquire additional mutations that could potentially give them a growth advantage over other cells in the tumor. Eventually, individual subpopulations will expand or contract based on their acquired abilities for survival. The end result of this continuing process of cell proliferation and cell death is a heterogeneous tumor with cells capable of invasion and metastasis and/or resistance to therapy.

Generally observed characteristics of cancer provide a basis for this theory, with one important piece of evidence being the instability of cancer cell genomes, which is known to enable the uncontrolled growth of cancer cells (Hanahan and Weinberg, 2000).

Strong support for clonal evolution also lies in the fact that various drug resistant clones are detected after therapeutic intervention, such as treatment with the tyrosine kinase inhibitor imatinib (Swords et al., 2005). In this situation, certain subclones may be killed off by treatment, however ones that have acquired the ability to survive and proliferate in the absence of tyrosine kinase activity will repopulate the tumor. Finally, mutational analysis of cells from primary, metastatic and recurrent tumors verifies clonal evolution as a viable hypothesis to explain the initiation and progression of cancer. Genetic alterations found in the primary tumor of a particular patient are also typically found in cells at the metastatic site as well, however some are unique to each as well (Takahashi et al., 2007).

Evidence to support this theory of breast cancer development came from work by

Fujii et al. in which allelic losses identified in less invasive in situ cancers were for the

10 most part conserved in synchronous infiltrating tumors, indicating that these infiltrating tumors were clonally derived from the in situ lesions (Fujii et al., 1996). However distinct allelic losses were also observed in different cells from the same in situ lesions.

Furthermore, analysis of primary breast carcinomas and metastatic lesions by genomic hybridization and fluorescence in situ hybridization (FISH) found the majority of metastases to have a high degree of clonal relatedness to the corresponding primary tumor, however in some cases the genetic composition was almost completely different

(Kuukasjarvi et al., 1997). Finally, multiple analyses of breast tumors have shown high degrees of intratumor clonal divergence, with multiple chromosomal aberrations being observed in different cell populations of microdissected tumor samples (Aubele et al.,

1999; Torres et al., 2007).

1.2.1.2 Cancer Stem Cell Hypothesis

In order to understand the role of stem cells in cancer, it is important to first understand the function of these cells in the mammary gland under normal conditions.

Due to the extensive remodeling and differentiation that occurs in the mammary gland, particularly during menstrual cycles and pregnancy, it has been proposed that normal human adult mammary epithelial stem cells exist. Being defined as a stem cell, these cells have the capacity for self renewal and also the ability to give rise to multiple differentiated cell types. In order to prove the existence of these cells, experiments have been performed in which limiting dilutions of primary mammary epithelial cells were transplanted into the cleared fat pads of recipient mice (Kordon and Smith, 1998; Smith,

1996). In the majority of cases, complete ductal systems were formed that were able to

11 respond to pregnancy as shown by alveolar proliferation. This evidence strongly supports the presence of mammary epithelial stem cells that can give rise to the multiple cell subtypes required for ductal formation. Further studies using retrovirus infected cells and cells isolated based on cell surface marker expression were able to show that a single cell is able to regenerate the entire mammary gland (Shackleton et al., 2006; Stingl et al.,

2006).

The cancer stem cell hypothesis maintains that a particular subset of tumor cells exhibiting stem cell like properties are responsible for the initiation, progression and recurrence of a tumor. These “cancer stem cells” are thought to be derived from normal stem or progenitor cells of an organ, and it is believed that this population remains as a small fraction of cells in a tumor throughout its existence. Also, according to this hypothesis, metastatic disease is caused by the spread of these cancer stem cells, and their resistance to therapy results in recurrence (Diehn et al., 2009; Li et al., 2008).

Several lines of evidence support this hypothesis, including the fact that tissues known to contain normal stem cells also develop cancer, including blood, brain, lung and prostate (Kim et al., 2005; Lam and Reiter, 2006; Osawa et al., 1996; Reynolds and

Weiss, 1996). Because these cells are known to have a long life span, they are likely at an increased risk for developing the multiple mutations required to become malignant

(Miller et al., 2005). Along this line, the cancer stem cell theory is supported by the fact that the risk of breast cancer increases with the age at which a woman’s first full term pregnancy occurs, after which fewer numbers of normal stem cells would be present to undergo transformation due to the differentiation of cells associated with pregnancy

(Cairns, 1975; Russo et al., 2005). More specific evidence to support this theory came with the identification of a tumorigenic cancer cell population. These cells were discovered using studies in which human breast cancer cells were grown in immunocompromised mice, and populations of cells that were able to form new tumors were distinguished by CD44+CD24- cell surface marker expression (Al-Hajj et al., 2003).

These tumor cells exhibited certain properties of cancer stem cells, including the ability of low numbers of CD44+CD24- cells to give rise to new tumors with diverse cell types.

Further characterization of this CD44+CD24- population compared to more differentiated

CD44-CD24+ cells showed enriched expression of genes involved in cell motility, angiogenesis and cell invasion, as well as activation of TGFβ, Wnt and Hedgehog signaling, all of which indicate more stem-like properties (Shipitsin et al., 2007).

However, there are also caveats associated with research methods used for studies of cancer stem cells. To start, only around 10% of cells are recovered from a solid tumor after digestion and sorting, therefore a significant population of cells is missed, which could include the cancer stem cells (Hill, 2006). Additionally, injecting human tumor cells into immunodeficient mice most likely does not reflect the behavior of these cells in their native environment due to the fact that the surrounding microenvironment is removed, which has been shown to play a major role in solid tumor progression. Finally, injecting a specifically isolated population of cells does not accurately recapitulate the heterogeneity represented in the whole tumor and the interactions among these various populations of tumor cells.

Although both the clonal evolution and cancer stem cell models agree on the origin of breast cancer from a single cell that has acquired multiple mutations, they differ in their explanations of tumor heterogeneity. Furthermore, the cancer stem cell hypothesis limits the cell of origin to a pre-existing normal stem cell of the mammary gland, whereas in clonal evolution any normal epithelial cell has the potential to be transformed. Similarly, cancer stem cells alone are postulated to be responsible for tumor progression and drug resistance, whereas any tumor cell has the ability to drive tumorigenesis and can be selected for after therapy. Due to the complexity of breast cancer, it is likely that aspects of both the clonal evolution and cancer stem cell models are correct.

1.2.2 Heterogeneity of Breast Cancer

Previous discussions on the clonal evolution and cancer stem cell theories in the previous section highlight the diversity that has been observed not only between individual tumors, but also among cancer cells within the same malignancy. This intertumor and intratumor heterogeneity also influences the surrounding stroma, which in turn contributes to tumorigenesis. Importantly, these differences among tumors and between tumor cells can strongly influence the therapeutic response to a particular kind of treatment. Figure 2.2 depicts a theoretical model that explains the intratumor and intertumor heterogeneity observed in breast cancer.

Figure 1.2 Modeling Breast Cancer Heterogeneity

Intratumor heterogeneity can be explained by the presence of tumor cells containing diverse genetic/epigenetic modifications, which may give the phenotype of basal like or luminal like breast cancer cells. Intertumor heterogeneity is similarly explained by the incidence of these diverse cells with in a particular tumor. Adopted from (Polyak, 2011).

1.2.2.1 Intertumor Heterogeneity

Although most people might consider breast cancer to be one disease, the multiple distinct subtypes that represent this cancer in fact make it many diseases, each with its own specific characteristics which in turn determine therapeutic strategies and ultimately overall outcome. As detailed in the previous section, the majority of this heterogeneity is explained by clonal evolution and cancer stem cells. Using pathological classification, breast cancer can be divided into upwards of ten different categories. The most common of these accounting for around 75% of cases is invasive ductal carcinoma, followed by invasive lobular carcinoma which is observed in around 10% of cases (Li et al., 2005).

The remaining cases can be categorized as medullary, neuroendocrine, tubular, apocrine, metaplastic, mucinous, inflammatory, comedo, adenoid cyctic and micropapillary types

(Li et al., 2005; Weigelt et al.).

In addition to histopathological classification, breast cancer is one of the few tumor types that can be classified based on gene expression profiling and hierarchical clustering, which divides patients into at least three major subtypes: luminal, human epidermal growth factor receptor 2 + (HER2+) and basal like (Perou et al., 2000; Sorlie et al., 2001). Luminal tumors are positive for estrogen and progesterone receptors (ER and

PR), and can therefore be treated using hormonal therapeutic strategies. This group can be further subdivided into luminal type A and luminal type B which are differentiated based on the expression of genes regulated by the ER signaling pathway. Luminal type A tumors have the highest expression of the ERalpha gene, as well as high expression of

GATA binding protein 3, X-box binding protein 1, trefoil factor 3, hepatocyte nuclear

16 factor 3alpha and estrogen regulated LIV-1 (Sorlie et al., 2001). Also based on gene expression, this subtype was shown to have the best clinical outcome with respect to overall survival and relapse free survival. In contrast to the luminal A subtype, luminal type B tumors have low to moderate expression of luminal specific genes, including the previously mentioned ER regulated genes. Luminal A and luminal B subtypes can also be divided based on histological grand and proliferation index, where luminal A tumors have a low histological grade and proliferation index and conversely luminal B tumors have a high histological grade and proliferation index (Brenton et al., 2005). Importantly, this luminal B subgroup is thought to represent a clinically distinct group associated with worse disease course, particularly when looking at relapse free survival.

Work by Perou and colleagues supports the division of estrogen receptor negative tumors into three groups: HER2+, basal and normal like (Perou et al., 2000).

Additionally, a more recently defined group known as the claudin low subtype was identified (Herschkowitz et al., 2007). HER2+ breast tumors are defined by the overexpression or amplification of this tyrosine kinase receptor, which occurs in approximately 15-30% of cases. Not surprisingly, many of the other genes identified as being unique to the HER2 subtype were located in the same region as the Her2 gene on chromosome 17 and were also amplified at the genomic DNA level (Perou et al., 2000).

This designation of a HER2 tumor as based on RNA profiling is distinct from those identified as HER2+ by immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), as not all HER2+ tumors display the gene expression pattern identified through array based assays. Additionally, some tumors that are positive for ER

17 also express HER2 as determined by IHC or FISH, however these tumors will still fall within the luminal subtype (Brenton et al., 2005).

The basal subtype represents between 2%-18% of breast cancers, and is a part of a larger subgroup known as triple negative breast cancers (TNBCs) which do not express estrogen or progesterone receptors and are not amplified for HER2 (Brenton et al., 2005).

In addition to their triple negative status, basal like tumors must also express markers indicative of basal epithelial cells or normal breast myoepithelial cells including cytokeratins 5, 6 and 17 as well as proliferation associated genes (Perou et al., 2000).

Interestingly, analysis by Sorlie et al. revealed mutations in the tumor suppressor gene

BRCA1 to be a predisposing factor for the basal tumor subtype (Sorlie et al., 2003). Due to the diversity within the TNBC subgroup, Lehmann et al. aimed to further sub-classify tumors in this group through examination of gene expression data from 587 TNBC cases

(Lehmann et al., 2011). This study found that within TNBCs, 6 unique subtypes could be identified, including 2 basal like, an immunomodulatory, a mesenchymal stem like and a luminal androgen receptor group.

Finally, the normal like subtype resembles normal epithelial tissue. An independent study in which a normal like breast group was also identified determined contamination of normal tissue was most likely the cause for this group (Hu et al., 2006).

In 2012, a more thorough characterization of human breast tumors was achieved by examining genomic DNA copy numbers, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays

(Network, 2012). This in depth analysis not only confirmed previous findings concerning

18 the various subtypes in breast cancer, but also revealed novel aberrations that could potentially be used for targeted therapies. .

1.2.2.2 Intratumor Heterogeneity

Beyond the diversity observed between tumors, a surprising amount of heterogeneity also exists between individual cancer cells within one tumor of a patient.

This heterogeneity is typically manifested in genetic and epigenetic alterations, and differences among distinct cancer cells may confer advantages or disadvantages for these cells in different aspects of tumorigenesis, including potential for angiogenesis, invasion and metastasis (Marusyk and Polyak, 2010). Early studies examining chromosome banding in short term cultured breast cancer cells revealed 2-8 clonal aberrations to be present in a significant portion of cases (Pandis et al., 1995). Along the same lines, studies examining clonal heterogeneity using cytogenetic analysis found 2-6 cytogenetically unrelated clones to be detected in individual breast carcinoma samples

(Teixeira et al., 1995). This intratumor diversity was found to not be limited to invasive carcinomas either, as multiple histologic grades, biomarker phenotypes and inherent subtypes were shown to exist within the same ductal carcinoma in situ (DCIS) (Allred et al., 2008). Interestingly, there was a high correlation between mutations in the tumor suppressor gene p53 and observed diversity in DCIS cases. Recently, microRNA profiles have also been examined within primary breast cancers and lymph node metastasis to determine whether heterogeneity also exists in the expression of these small regulatory

RNAs (Raychaudhuri et al., 2012). Comparison of multiple samples from various tumor zones revealed considerable intratumor heterogeneity of miR-10b, miR-210, miR-31 and

19 miR-335. However, the amount of heterogeneity observed was consistent between central, intermediate and peripheral tumor zones, indicating a somewhat uniform variability throughout the tumor.

Much of the heterogeneity observed in breast cancer is thought to be derived from either clonal evolution or cancer stem cells as was previously discussed. To address this further, Shipitsin et al. performed molecular gene profiling on CD24+ and CD44+ cell populations, which have been implicated as breast cancer stem cells, from normal and breast cancer tissues (Shipitsin et al., 2007). Importantly, CD44+ cells isolated from normal breast tissue and breast tumors were shown to have more similar expression patterns than CD24+ and CD44+ cells isolated from the same tissue. Although CD24+ and CD44+ cells were clonally related, CD24+ cells were shown to have unique genetic aberrations that were additional to those shared with CD44+ cells. Additionally tumors that were ER+ were shown to contain CD44+ cells that were negative for ER, although

CD24+ cells were shown to express ER. More recently, IHC analysis of breast cancer samples using antibodies known to be associated with differentiated luminal and stem cell like characteristics revealed diverse staining patterns among the various tumor subtypes and histologic stages examined (Park et al., 2010).

With advances in DNA sequencing technologies, more information concerning heterogeneity in cancer is coming to light due to sequencing of single tumors and even individual tumor cells. Using these methods to examine the cancer cell genomes of both human breast and colorectal cancers led to the discovery of a large number of previously uncharacterized cancer candidate genes, which emphasizes the reality of personal cancer

20 genomics (Sjoblom et al., 2006; Wood et al., 2007). A similar study investigating somatic mutations in a lobular breast cancer metastasis and primary tumor revealed significant heterogeneity in the primary tumor of this relatively low-intermediate histological grade tumor, as well as the significant mutational evolution that transpires with disease progression (Shah et al., 2009). In addition to mutational analyses, sequencing strategies examining somatic rearrangements in breast cancer genomes have also uncovered diverse and complex patterns of rearrangements within individual tumor samples (Stephens et al., 2009). Perhaps the most groundbreaking and informative study to elucidate tumor cell heterogeneity at the single cell level and the evolution of tumors was achieved through flow sorting of individual nuclei, followed by whole genome amplification and next generation sequencing (Navin et al., 2011). This type of analysis gives us the ability to trace the ever changing progression of tumor cells, and eventually could lead to targeted therapeutic approaches based at a single cell level.

1.2.2.3 Heterogeneity of Tumor Stroma

Although the tumor stroma is thought to be more genetically stable than tumor cells, evidence from gene expression studies indicate that there is still a considerable amount of variability within this diverse environment. By laser capturing tumor associated stroma and normal stroma and performing gene expression analysis, Finak et al. were able to identify genes with varied expression in tumor tissue and cluster samples based on this (Finak et al., 2008). Interestingly, this analysis did not divide samples based on the previously described breast cancer subtypes (Perou et al., 2000; Sorlie et al.,

2001; van 't Veer et al., 2002). Instead, three clusters were generated that were associated

21 with recurrence rate and relapse free survival. The poor outcome group as represented by cluster two was shown to be independent of ER, HER2 and lymph node status in addition and age, grade and tumor size (Finak et al., 2008). Similarly, the good outcome group represented in cluster one was independent of these same variables, as well as of several different therapeutic interventions, including radiotherapy, chemotherapy and hormonal therapy. Not surprisingly, examination of biological processes associated with each cluster revealed distinct underlying biologies that are associated with their corresponding outcomes. More specifically, genes with increased expression in the poor outcome cluster were associated with angiogenic, hypoxic and macrophage associated responses, whereas genes expressed predominantly in the good outcome cluster associated with T helper type I immune responses. Further analysis of this data allowed for the construction of a 26 gene stroma derived prognostic predictor (SDPP) list which was able to predict outcome in several whole tumor data sets, thereby indicating that stromal signatures can be detected in data collected from whole tumor tissues.

In an independent study, analysis of extratumoral tissues from invasive breast cancer or ductal carcinoma in situ samples identified two distinct subtypes which were designated as either active or inactive (Roman-Perez et al., 2012). Interestingly, there was a strong association between the active extratumoral subtype and overall survival in

ER+ and endocrine treated patients, thereby implicating a potential prognostic value of these subtypes. However, no significant association was found between the active and inactive subtype with any standard prognostic clinoicopathological parameters, including breast cancer subtype, ER status and tumor size or grade.

In addition to examining the tumor associated stroma as a whole, which is comprised of multiple diverse cell types, studies have also specifically looked at gene expression changes in cancer associated fibroblasts (CAFs) from breast cancer samples.

Studies comparing six CAF samples with matched normal fibroblast controls identified

31 genes to be significantly differentially expressed in CAFs when compared to NFs

(Bauer et al., 2010). Somewhat surprisingly, examination of the overall heterogeneity of gene expression in CAFs and NFs revealed more variable gene expression in NF samples.

Although these studies focusing on differences between tumor fibroblasts and normal fibroblasts are important in determining tumor associated changes, they do not take into account differences among the fibroblasts isolated from various breast cancer subtypes, mainly due to the small sample sizes used. In a recent study, gene expression was analyzed in CAFs isolated from the three main subtypes of breast cancer: HER2+, ER+ and TNBC (Tchou et al., 2012). In contrast to what was observed with stromal gene expression profiling, these fibroblasts displayed subtype specific differences. CAFs derived from ER+ and TNBC tumors were shown to actually be more similar than those derived from HER+ tumors using principal component analysis (PCA), and pair wise comparisons revealed 1,800 genes to be differentially expressed between HER2+ and either ER+ or TNBC samples, whereas only 118 genes were significantly different between CAFs isolated from ER+ and TNBC samples.

1.2.3 Targeted Therapies for Treatment of Breast Cancer

Due to the extreme heterogeneity of breast cancers as described in section 1.2.2, it is not surprising that treatment strategies can be drastically different based on particular

23 characteristics of an individual tumor. In more recent years, the design of clinical trials has shifted from large scale to more individualized including only patients with a molecularly defined tumor subtype. Perhaps one of the best examples of targeted therapies is in patients with luminal (or hormone receptor positive) tumors in which estrogen focused therapies have been shown to be successful. Tamoxifen is one of the most well known endocrine agents for the treatment of breast cancer, and functions as an antagonist of the estrogen receptor, thereby blocking the activation of ER+ tumors.

Additionally, tamoxifen was also shown to be effective at decreasing the development of breast cancer in high risk women (Fisher et al., 1998). Other drugs targeting estrogen are also currently is use, including aromatase inhibitors as well as ER degrading reagents

(Gibson et al., 2009; Robertson et al., 2009).

HER2+ tumors are also commonly treated using targeted therapy, with trastuzumab in combination with chemotherapy being the most routinely offered treatment option in patients with this HER2 expressing disease (Piccart-Gebhart et al.,

2005). Trastuzumab is a monoclonal antibody that binds the extracellular domain of the

HER2 receptor thereby preventing further downstream activation of its intracellular tyrosine kinase (Albanell et al., 1996). In a similar manner, other antibodies and tyrosine kinase inhibitors have been used (Badache and Hynes, 2004; Baselga and Swain, 2009).

Unfortunately, many HER2+ tumors have been shown to acquire resistance to targeted treatment strategies, potentially through loss of expression of HER2 as a result of therapeutic intervention, subsequent activation of mutations downstream from the target and activation of alternative pathways promoting cellular proliferation (Berns et al.,

2007; Mittendorf et al., 2009; Scaltriti et al., 2011). Due to these mechanisms of resistance, other innovative approaches have been developed to target HER2, some of which have been found to be successful in treating these resistant tumors. One strategy in particular that has had considerable success is the covalent binding of trastuzumab to the maytansinoid DM1 via thioether linkage (T-DM1). This antibody-drug conjugate binds to HER2 on the surface of tumor cells, and after being taken up by the cell, DM1 is released and binds to tubulin to inhibit cell proliferation. In addition to having a high clinical benefit rate among patients with pretreated advanced HER2+ disease that had become resistant to trastuzumab, early studies examining T-DM1 as a first line treatment of metastatic HER+ breast cancer have shown similar efficacy compared to trastuzumab plus docetaxel (Burris et al., 2011; Krop et al., 2010; Perez, 2010).

Although there are no targeted therapies for TNBC in general, a large portion of breast cancers in patients with germline BRCA1/2 mutations have a triple negative phenotype and in particular cluster with the basal like group (Collins et al., 2009). BRCA genes are involved in homologous recombination mechanisms used to repair double stranded breaks in DNA, however the defects in this repair caused by mutations in BRCA are not enough to cause cell death. However, upon treatment with inhibitors of PARP proteins, which are involved in base excision of damaged DNA, this dual inhibition of

DNA repair pathways causes the death of BRCA mutated tumor cells (Bryant et al.,

2005).

1.2.4 Modeling Breast Cancer in Mice

To study the roles of particular genes and pathways in the initiation and progression of breast cancer, multiple mouse models have been established, including tumor cell injection model systems, xenograft models and genetically engineered mouse models. In general, these genetic models overexpress oncogenes specifically in the epithelial cells of the mammary gland, eventually causing the formation of tumors at this primary site. For this specific expression in epithelial cells, the mouse mammary tumor virus long terminal repeat (MMTV-LTR) and whey acidic protein (WAP) promoters are typically used.

These promoters are active in the developing virgin mammary gland and may be expressed as early as in the embryonic mammary bud (Wagner et al., 2001). Although these promoters selectively target the mammary gland, their specificity is limited, as other tissue including the lungs, kidneys, salivary glands, seminal vesicles, T-cells, testes, prostate and brain have been shown to have expression of these promoters (Choi et al.,

1987; Henrard and Ross, 1988; Wen et al., 1995).

MMTV and WAP promoters have been used to drive expression of a variety of oncogenes, some of which are discussed in more detail in the following sections.

Although these transgenic approaches are constructive tools for evaluating how oncogeneic signaling contributes to breast tumorigenesis, there are several limitations to these models that must be kept in mind when interpreting experimental data. Non- specific expression of the transgenic oncogenes could cause systemic effects in the mice, which could indirectly impact tumor growth in the mammary gland. Additionally, the level of oncogene expressed through activation of these promoters may not be consistent

26 with the level of expression of these genes in human breast cancer cells. The cells targeted by these promoters may also differ from the cells of origin in human breast cancer.

In addition to overexpression of oncogenes, the MMTV and WAP promoters can also be utilized for specific deletion of tumor suppressor genes using Cre/loxP technology.

Advances in genetic engineering have also allowed for more sophisticated modeling techniques in which gene expression can be controlled in a temporal manner in addition to the already spatial expression in mammary epithelial cells. In these inducible systems, expression of the tetracycline (Tet) transactivator (tTA) or reverse tetracycline transactivator (rtTA) is driven by a tissue specific promoter, for example MMTV-tTa or

MMTV-rtTA. In conjunction with this transgene, tetracycline operator sequences, also known as a tetracycline response element (TRE), are placed upstream of the gene whose expression will be either turned on or turned off. This system also requires the presence of tetracycline or its derivatives, including doxycycline, which bind to tTA or rtTA to repress or induce expression of genes downstream of the TRE, respectively. Figure 2.3 summarizes this inducible tet-off and tet-on system. More detailed descriptions of several models utilized for our genetic studies are discussed below.

Figure 1.3 Inducible Gene Expression Using Tet-off/Tet-on System

A promoter driving expression of tTA will turn off expression of gene of interest in the presence of tetracycline (tet-off). Conversely, a promoter driving expression of rtTA will turn on expression of gene of interest in the presence of tetracycline (tet-on). Adapted from http://mammary.nih.gov/tools/molecular/Hennighausen007/index.html. .

1.2.4.1 MMTV-PyMT

This model utilizes MMTV-LTR to control the expression of the polyoma virus middle T oncoprotein (PyMT) (Guy et al., 1992a). Previous work showed that infection of newborn mice with polyoma virus caused the formation of mammary adenocarcinomas, and the transforming activity of middle T antigen was dependent on its association with a number of proteins, including multiple tyrosine kinases and phosphatidylinositol 3’ kinase (PI3K) (Bolen et al., 1984; Dawe et al., 1987; Whitman et al., 1985). Accordingly, mammary tumors occur in MMTV-PyMT mice by 4 weeks of age, typically starting as a single lesion near the nipple and later developing into multiple small lesions throughout more distal ducts. (Lin et al., 2003)

More in depth analysis of this model identified four distinct stages of tumor progression: hyperplasia, adenoma/MIN, early carcinoma and late carcinoma (Figure 2.4)

(Lin et al., 2003). Importantly, these stages have morphological similarities to classifications made in human breast tumors, ranging from in situ lesions to invasive carcinomas. Additionally, a decrease in ER and PR expression is observed in this model over the course of tumor progression, which correlates with the loss of expression of these receptors as being associated with poor outcome in humans. Similarly, ErbB2 expression is increased as PyMT tumors progress to the malignant stage, and this receptor has also been shown to be overexpressed in patients with poor prognosis (Menard et al.,

2000). Due to the short latency and aggressive behavior of these tumors, it is not surprising that a large percentage of PyMT mice also develop multifocal lesions in the lung with a high penetrance.

Figure 1.4 Stages of Tumor Development in PyMT Mouse Model

MMTV-PyMT expression in the mouse mimics distinct stages of tumor progression in humans, including hyperplasia, adenoma, early carcinoma and late carcinoma. Adopted from (Lin et al., 2003).

1.2.4.2 MMTV-ErbB2

ERBB2 (or NEU) is a member of the epidermal growth factor receptor (EGFR) family and functions as a receptor tyrosine kinase (RTK). All members in this family contain an extracellular ligand binding domain, a single membrane spanning region and a cytoplasmic tyrosine kinase domain (Hynes and Lane, 2005). Once activated by ligand binding and homo- or heterodimerization, these receptors further activate downstream signaling pathways, including the mitogen activated protein kinase (MAPK) and the phosphatidylinositol 3 kinase (PI3K)-AKT pathway (Yarden and Sliwkowski, 2001).

Interestingly, the extracellular region of ERBB2 is unique compared to other members of the EGFR family in that it has a fixed conformation that mimics the ligand activated state, which may explain why ERBB2 is the preferred binding partner for other members of this family (Garrett et al., 2003). ErbB2 is known to be overexpressed in approximately one third of all cases of breast cancer, and patients with elevated ErbB2 expression have a high relapse rate and overall poor clinical prognosis (King et al., 1985;

Slamon et al., 1987; Yokota et al., 1986).

Multiple transgenic lines have been generated in which the activated rat neu/ErbB2 oncogene is under the control of the MMTV promoter. However, in humans, the ErbB2 gene is typically not mutated, but rather undergoes amplification and overexpression which is associated with aggressive tumor behavior, decreased time to relapse and overall poorer prognosis (Berger et al., 1988; Slamon et al., 1987). To model this, transgenic mice were generated that overexpress the normal unactivated form of

ErbB2 (Guy et al., 1992b). This model has a relatively long latency period, with focal

31 mammary tumors beginning to appear after 4 months. Somewhat unexpectedly, a high percentage of mice with MMTV-ErbB2 tumors developed metastasis to the lung.

Interestingly, examination of ErbB2 at RNA and protein levels in tumors and “normal” adjacent epithelial revealed comparable levels of transgene (Guy et al., 1992b).

However, with the use of in vitro kinase assays, it was shown that ErbB2 induced tumors had higher tyrosine kinase activity compared to normal mammary epithelium, which explains the transforming potential of these ErbB2 expressing cells. Both the focal nature and relatively long latency period of these tumors indicate that additional genetic events may be necessary for the neoplastic transformation of the mammary epithelial cells.

Further work by Siegel et al. determined that in frame deletions in the extracellular domain of ErbB2 proximal to the transmembrane domain were in fact responsible for activation of Erbb2 through tyrosine phosphorylation (Siegel et al., 1994).

1.2.4.3 Tet-o-KrasG12D and MMTV-rtTA

Although mutations in Ras are not common in breast cancer (<5%), the activation of this pathway has been shown to promote breast cancer (Clark and Der, 1995). Upon activation of diverse cell surface receptors, including receptor tyrosine kinases, Ras becomes transiently activated to turn on multiple downstream effectors that regulate cell proliferation, survival and differentiation. The most well characterized downstream effectors of Ras are the Raf serine/threonine kinases, which in turn activate the mitogen activated protein kinase (MAPK) signaling cascade (Chong et al., 2003). Additionally,

PI3K has also been shown to be a downstream effector of Ras (Cantley, 2002).

Tet-o-KrasG12D mice were initially generated to study the role of KRas in lung tumorigenesis (Fisher et al., 2001). The ability to control this mutant form of KRas by rtTa and doxycycline was achieved by construction of a 1.8 kb piece of DNA encoding seven direct repeats of the Tet-operator sequence (Tet-o) followed by the murine

KrasG12D cDNA and the mouse protamine gene intron and polyadenylyation site (Fisher et al., 2001; Holland and Varmus, 1998). MMTV-rtTA transgenic mice were created for the purpose of studying the effect of development on breast cancer susceptibility, requiring precisely regulated and mammary specific control of transgene expression at distinct stages of mammary gland development (Gunther et al., 2002). In this case, the

MMTV-LTR is used to drive expression of the reverse tetracycline-dependent transactivator rtTA. Using reporter lines, it was shown that transgene expression could be rapidly induced and deinduced, was mammary specific and expression levels could be titrated over wide range (Gunther et al., 2002). Additionally, the expression pattern of this transgene was extremely homogenous throughout the mammary epithelium, including during puberty, pregnancy, lactation and involution.

When present in the same mouse, Tet-o-KrasG12D and MMTV-rtTA allow for inducible expression of mutated Kras specifically in mammary epithelial cells. The combination of these transgenes was first used in experiments testing the cooperative action of Myc and Kras in tumor maintenance and recurrence (Podsypanina et al., 2008).

Upon receiving doxycycline food after weaning, Tet-o-KrasG12D;MMTV-rtTA mice developed hyperplasia in the mammary gland as early as one week post induction, which progressed to hyperplastic alveolar nodules and eventually palpable mammary tumors

33 with a median latency of 22 weeks (Podsypanina et al., 2008). As expected, no tumors were observed in Tet-o-KrasG12D;MMTV-rtTA mice that were not fed doxycycline.

1.2.4.4 Breast Tumor Cell Lines and Injection Models

Although there are many advantages to using genetically engineered mouse models to study in vivo tumorigenesis, various breast cancer cell lines have been established from both humans and mice to more closely examine how proliferation, migration and apoptosis become deregulated during cancer progression. Along with these cell lines, multiple xenograft and other injection models have also been developed to test whether in vitro characteristics of these cells persist in an in vivo environment.

Multiple distinct mouse cell lines have been established from various tumor models, including Met1, F305-2 and DB7 cells isolated from MMTV-PyMT derived mammary tumors, MVT1 cells derived from MMTV-c-myc and MMTV-VEGF bigenic mice and

NT2.5 cells derived from an MMTV-ErbB2 tumor (Borowsky et al., 2005; Dakappagari et al., 2005; Pei et al., 2004). Each of these individual cell lines has their own unique properties, including growth rate and ability to metastasize.

1.3 Breast Tumor Microenvironment

Early support for the idea that stromal cells were critical for sustaining cancer came from tissue culture experiments in which epithelium from submandibular glands transformed by the polyoma virus could not grow when cultured alone (Dawe, 1972).

This tissue would only grow with the addition of embryonic salivary gland mesenchyme.

Further studies in mammary glands revealed the ability of irradiated mammary stroma to drive a higher incidence of tumor formation in the presence of transplanted functionally 34 normal COMMA-D mammary epithelial cells (Barcellos-Hoff and Ravani, 2000). This data indicates that the effects of radiation on the stroma are sufficient to induce neoplastic progression of these epithelial cells, which otherwise were shown to be non-tumorigenic at early passages (Danielson et al., 1984).

The breast tumor microenvironment is composed of many of the same cell types that are present in normal breast tissue, including fibroblasts, endothelial cells and immune cells including macrophages. This complex microenvironment composed of both cellular and acelluar components has become recognized as a major regulator of carcinogenesis

(Hanahan and Weinberg, 2011). This microenvironment serves as a support system to the tumor, providing growth signals as well as access to oxygen and nutrients to allow the evolution of a tumor from in situ to invasive and metastatic disease. Thorough characterization of the individual components of this microenvironment in breast cancer revealed dramatic changes in gene expression in all cell types, including a significant portion of genes encoding secreted proteins and receptors (Allinen et al., 2004). The chemokines CXCL12 and CXCL14 were identified as potential molecules involved in the crosstalk between the epithelial and stromal compartments. Deciphering the exact signaling pathways involved in the intercellular networks is key for creating new and more effective therapeutics.

1.3.1 Tumor Associated Fibroblasts

Fibroblasts are cells of mesenchymal origin and have an elongated, spindle-like shape that is typically used to identify them. Normal fibroblasts are important for multiple functions, including deposition of ECM, regulation of epithelial differentiation

35 and inflammation and also response to wounding (Parsonage et al., 2005; Tomasek et al.,

2002). Several expression markers have been characterized to identify these cells, including α-smooth muscle actin, vimentin, desmin, fibroblast specific protein 1 (Fsp1) and fibroblast activation protein (Fap) (Sugimoto et al., 2006). Interestingly, the environment in which a fibroblast resides significantly influences its behavior.

Examination of genome wide expression patterns of 50 human fibroblast cultures derived from 16 different tissue sites revealed these populations to be as divergent as those observed among distinct lineages of white blood cells (Chang et al., 2002).

Although fibroblasts have a tumor suppressive function under normal circumstances, they acquire an activated phenotype in the context of oncogenic signaling from neighboring epithelial cells. Fibroblasts compose the majority of tumor stroma in multiple types of cancer, including breast and pancreatic cancer (Kalluri and Zeisberg,

2006; Ostman and Augsten, 2009). These cells have been shown to promote angiogenesis, inflammation and matrix remodeling during adult and embryonic stages of development, processes which are also critical during tumor progression.

1.3.1.1 Fibroblasts Contribute to Epithelial Tumor Growth

Fibroblasts are the most abundant population of cells present in the tumor microenvironment, and therefore have been postulated to play a critical role during tumor initiation and progression. Experiments carried out by Olumi et al. demonstrated the ability of tumor associated fibroblasts (TAFs) isolated from prostate cancer tissue to promote the growth of initiated prostate epithelial cells (TAg-HPE) when grafted as tissue recombinants, whereas normal fibroblasts (NF) had no effect on the growth of the

36 initiated epithelial cells (Olumi et al., 1999). Histological analysis revealed TAF/TAg-

HPE tissue recombinants to resemble poorly differentianted adenocarcinomas, however

NF/TAg-HPE tissue recombinants were benign. Interestingly, combining CAFs with normal human prostate epithelial cells (NHPE) did not increase their growth, however did give them an abnormal histological appearance in which the epithelial cells formed ductal structures with a stratified squamous lining (Olumi et al., 1999). This pivotal study was one of the first to reveal tumor associated fibroblasts as having an active role in the tumorigenic process rather than just playing the role of supportive and responsive cells.

Further xenograft studies mixing TAFs from invasive human breast cancers or NFs with MCF-7-ras human breast cancer cells showed the ability of TAFs to promote the formation of larger tumors, which was accompanied by an increase in tumor cell proliferation (Orimo et al., 2005). These studies also revealed an increased ability of

CAFs to stimulate tumor angiogenesis, mainly by SDF-1 induced recruitment of endothelial progenitor cells (EPC).

1.3.1.2 Important Signaling Pathways in Fibroblasts

Previous work has shown several well known signaling pathways to play a critical role in stromal fibroblasts in the context of tumorigenesis. Pioneering work done in the laboratory of Harold L. Moses demonstrated the key tumor suppressive role of TGF-β signaling in epithelial-mesenchymal interactions (Bhowmick et al., 2004). Conditional deletion of the TGF-β type II receptor (TGFβRII) in fibroblasts led to intraepithelial neoplasia in prostate tissue in 100% of male mice examined, and not surprisingly, the

37 expression of the cell proliferation marker Ki67 was increased 6-fold in the prostate epithelial compartment. Interestingly, an abundance of stromal fibroblasts was observed in mice lacking fibroblast TGFβRII, and a significant increase in Ki67 expression was also observed in these cells. In addition to this hyperproliferative environment observed in the prostate, invasive squamous cell carcinoma of the forestomach was found in

TGFβRII knockout mice which was again accompanied by an abundance of stromal fibroblasts. These results implicate the importance of TGFβ ligand and receptor signaling in the maintenance of tissue homeostasis.

Further work by this group also examined the consequence of conditionally deleting TGFβRII in mammary fibroblasts (Cheng et al., 2005). In this context, disruption of TGFβRII caused severe defects in mammary gland development including reduced ductal elongation and branching that was associated with small terminal end buds. Analysis of ductal epithelial cells revealed an increase in Ki67 positive ductal epithelium, however a significant decrease was observed in the terminal end buds. This observation combined with a corresponding increase in apoptosis of ductal epithelial cells suggested an increase in the turnover of these cells that is most likely due to altered paracrine signaling as a result of disruption of TGFβ signaling in the stroma. Moreover,

PyMT derived tumor cells implanted with TGFβ knockout fibroblasts led to the formation of tumors with an approximate two fold increase in mass compared to those derived from co-injections with control fibroblasts (Cheng et al., 2005).

More recently, fibroblasts isolated from neoplastic skin of K14-HPV16 (HPV) mice were shown to induce a pro-inflammatory gene signature when compared to control

38 fibroblasts (Erez et al., 2010). This signature was also shown to be present in CAFs isolated from mouse models of breast cancer and pancreatic ductal adenocarcinomas

(PDAC), as well as in fibroblasts sorted from human squamous cell carcinoma and

PDAC samples. Nuclear localization of the NF-κB subunit p65 (relA) was shown to be increased in the stroma of HPV skin sections compared to controls indicating a role for this transcription factor in driving the inflammatory signature, since many of the genes shown to be upregulated in HPF CAFs were targets of NF-κB. Knockdown of NF-κB in

CAFs significantly slowed down tumor growth in a coinjection model, thereby highlighting the dependency on NF-κB signaling in fibroblasts to promote tumorigenesis.

Several studies have also examined the role of the well known tumor suppressor p53 in the stroma of prostate tumors. Experiments in a spontaneous prostate cancer model

(TgAPT121) revealed the more rapid development of prostate tumors in mice with a p53 heterozygous background and the tumors exhibited an extensive hypercellular mesenchyme and strong stromal response (Hill et al., 2005). Further analysis revealed that the abnormal proliferation observed in the stroma was associated with the loss of the remaining p53 allele in fibroblasts, however this allele remained intact in the prostate epithelial cells. Similarly, p53 null human non-small cell lung cancer cells were shown to attenuate the induction of p53 in mouse embryonic fibroblasts in coculture experiments

(Bar et al., 2009). In yet another independent study, it was shown that ablation of p53 specifically in fibroblasts not only promoted the growth of PC3 initiated prostate cancer cells into tumors, but also promoted the metastatic spread of these cells (Addadi et al.,

2010). The effect of p53 loss on tumor cell growth was found to be mediated at least in

39 part through increased expression of the chemokine SDF-1/CXCL12 in p53 null fibroblasts.

1.3.1.3 microRNAs in Tumor Associated Fibroblasts

microRNAs (miRs) are small, non-coding RNAs that regulate gene expression through complementary binding to the 3’ untranslated regions (UTRs) of target messenger RNAs (mRNAs). The function of microRNAs has been studied extensively in cancer, as many miRs have been shown to act as tumor suppressors or oncogenes. More specifically, miRs have been shown to regulate cell proliferation, apoptosis, invasion/metastasis and angiogenesis (Hurst et al., 2009; Khew-Goodall and Goodall,

2010; Shenouda and Alahari, 2009). Although not well studied, there is some evidence indicating an important role for microRNAs in tumor associated fibroblasts. Profiling of miRs in fibroblasts established from normal human endometrium or endometrial cancer tissue revealed upwards of 10 microRNAs to be differentially expressed in CAF samples as compared to NF samples (Aprelikova et al., 2010). Specifically, miR-31 was shown to be consistently downregulated in CAFs, and overexpression of this miR in fibroblasts was able to reduce the migration and invasion of endometrial tumor cells in vitro.

Further analysis of downstream targets revealed the homeobox gene SATB2 to be a downstream target of miR-31, and the function of this gene was found to be reciprocal to that of miR-31 as overexpression of SATB2 increased tumor cell migration and invasion.

A similar study in prostate cancer tissues revealed miR-15 and miR-16 to be downregulated in the stroma, particularly in areas that were close in vicinity to the neoplastic tissue (Musumeci et al., 2011). Manipulation of CAFs in vitro to re-express

40 miR-15 and miR-16 led to the reduction of tumor cell migration and proliferation, thereby implicating a role for these miRs in the crosstalk between the tumor and associated stroma. Additionally, co-injection of tumor cells with miR-15/16 transduced fibroblasts decreased tumor growth in vivo compared to tumor cells injected with control empty vector fibroblasts.

1.3.2 Fibroblast Effects on Other Cellular Heterogeneity of the Microenvironment

1.3.2.1 Fibroblasts Important in “Angiogenic Switch”

Although genetic and epigenetic changes in tumor cells are considered necessary in the multistep process of tumorigenesis, the induction of tumor vasculature is also required, which has been termed the “angiogenic switch” (Hanahan and Weinberg, 2000).

Angiogenesis can occur both under normal physiological conditions, including embryonic development, and also under pathological conditions, including tumor growth

(Folkman, 1971; Karamysheva, 2008). In a manner similar to normal tissues, tumors also require a sufficient oxygen supply, as well as an adequate source of nutrients and an efficient waste removal strategy (Papetti and Herman, 2002). Early observations of implanted tumors in the eye of guinea pigs revealed a lack of vascularization as a potential mechanism that was impeding tumor growth (Greene, 1941). Further pioneering work by Judah Folkman revealed that implanted tumors could not grow beyond millimeter diameters without the recruitment of new capillary blood vessels

(Folkman, 1971). In support of this, microscopic in situ tumors have been found in various human organs at high percentages, however the majority of these never expand to become malignant (Black and Welch, 1993). One potential mechanism by which these

41 lesions remain dormant is the capacity of our bodies to inhibit an angiogenic response

(Folkman and Kalluri, 2004).

A balancing act between pro-angiogenic and anti-angiogenic molecules is inherent to the concept of an angiogenic switch. Fibroblasts and other components of the tumor stroma have been shown to contribute to this angiogenic balance. Fibroblasts can secret soluble angiogeneic growth factors, including VEGF, TGFβ and platelet derived growth factor (PDGF) (Antoniades et al., 1991; Fukumura et al., 1998; Paunescu et al.,

2011).

As stated in section 1.3.1.1, fibroblasts have been shown to influence the growth of tumor cells, but also affect other aspects of tumorigenesis, including angiogenesis.

Early studies using cultured fibroblasts from Li-Fraumeni patients found that the angiogenic switch corresponded with loss of heterozygosity (LOH) of the p53 allele, which resulted in reduced expression of the anti-angiogeneic gene thrombospondin-1

(Tsp-1) (Dameron et al., 1994). Around the same time, an independent study demonstrated increased VEGF mRNA and protein levels upon induction of hypoxia in fibroblasts, thereby indicating the possibility of paracrine effects on endothelium within hypoxic tumor regions (Hlatky et al., 1994). Using a gastric cancer mouse model, Guo et al. demonstrated the ability of gastric tumor cells to stimulate expression of VEGF in surrounding fibroblasts (Guo et al., 2008).

1.3.2.2 Fibroblasts and Inflammation

The relationship between inflammation and cancer was established over 20 years ago by Harold F. Dvorak when he described tumors as “wounds that do not heal”

(Dvorak, 1986). Since then, much work has focused on the interplay between cancer cells and the host immune response. Interestingly, epidemiological studies have estimated that almost 15% of cancer incidence worldwide can be associated with some type of infection, including human papilloma virus or hepatitis B and C virus infection which leads to cervical and hepatocellular carcinoma, respectively (Kuper et al., 2000).

Similarly, colorectal and gastric cancer are strongly associated with underlying inflammatory responses (Aggarwal et al., 2006; Balkwill and Mantovani, 2001).

Macrophages in particular have been shown to play an important role in tumor progression and metastasis. This is evidenced by the fact that high focal macrophage infiltration in primary breast turmors is associated with a decreased relapse free and overall survival (Bingle et al., 2002). Pioneering experiments by Lin et al. revealed that depletion of macrophages by use of a null variant of colony stimulating factor 1 (Csf1) significantly slowed the rate of PyMT driven tumor progression, and also decreased the metastatic ability of these cells (Lin et al., 2001). Furthermore, re-expression of Csf1 was accompanied by an increase in tumor associated macrophage (TAM) abundance, which in turn accelerated tumor progression and restored the metastatic potential of tumor cells back to wild type levels. Macrophages are a diverse population of cells, and various subpopulations of TAMs have been identified which are thought to be involved in invasion, angiogenesis, immunosuppression and metastasis (Lewis and Pollard, 2006).

1.3.2.3 Fibroblasts and the Extracellular Matrix

Fibroblasts are known to secret multiple types of collagen and laminin, which contributes to the formation of basement membranes (Chang et al., 2002). Fibroblasts

43 also produce multiple proteases that are responsible for ECM degradation, including matrix metalloproteases (MMPs), which emphasizes their role in regulating ECM homeostasis by controlling ECM turnover (Chang et al., 2002; Simian et al., 2001).

1.4 Ets2 as a Member of the Ets Transcription Factor Family

Ets factors have been implicated in cancer since their discovery over 25 years ago as part of the transforming fusion protein of the avian retrovirus E26. Ets factors are defined by a conserved DNA binding domain (DBD), or ETS domain, which recognizes and binds to a core DNA binding motif GGAA/T. Structural studies on the Ets factor

Fli1 identified the Ets domain as a variant of the winged helix turn helix motif (Liang et al., 1994). Further studies showed this domain to contain three α-helices and four β- sheets, with the major protein-DNA contact points being located in the third α-helix, in the wing between β-strands 3 and 4, and in a loop between α-helices 2 and 3. To date, 28 members of this family have been identified in humans and 26 in mice.

These transcription factors function in multiple biological processes that are important during development and that also play an important role in tumorigenesis, including differentiation, proliferation and apoptosis. Additionally, a subset of Ets factors also contain a second conserved domain known as the pointed (PNT) domain

(Klambt, 1993). This domain has been shown to be important for protein-protein interaction including homo-oligomerization and heterodimerization, and also for transcriptional repression (Baker et al., 2001; Fenrick et al., 1999; Lacronique et al.,

1997). Specifically, previous work from our lab has shown that phosphorylation of Ets1 and Ets2 within this pointed domain (T38 and T72, respectively) is mediated by the Ras

44 signaling pathway and helps to distinguish these factors from other family members

(Fowles et al., 1998; Patton et al., 1998; Yang et al., 1996). Additionally, specificity of

Ets factor function has been shown to be regulated through interactions with other proteins, including other transcription factors, co-activators and co-repressors.

Modulation of Ets factors by these additional regulatory proteins controls their context dependent transcriptional regulation and drives target gene specificity (Li et al., 2000).

1.4.1 Misregulation of Ets Factors in Cancer

Mulitple Ets factors have been shown to be deregulated in a variety of cancer types. Early studies identified chromosomal translocations as a major source of this misregulation in both leukemias and also in solid tumors). One common translocation type in leukemias fuses the tyrosine kinase domain of one gene to a portion (HLH domain or pointed domain) of the Ets factor. The Ets factor TEL has been shown to form fusions of this type with multiple genes at different loci, including PDGFRbeta, NTRK3,

JAK2, ABL and AML1 (Carroll et al., 1996; Eguchi et al., 1999; Golub et al., 1995;

Golub et al., 1996; Peeters et al., 1997). In most cases, these fusion proteins are constitutively phosphorylated, and actually target the same downstream effector molecule or share a similar mechanism to induce transformation. Another well known translocation found in leukemia is the TLS(FUS)/ERG fusion in which the RNA binding domain of TLS (FUS) is replaced with the DNA binding domain of ERG (Ichikawa et al.,

1994).

Gene rearrangements involving Ets factors are not limited to leukemias, as several

Ets factors are also translocated in a variety of solid tumors. Perhaps the most well

45 known of these fusions occur in Ewing’s sarcoma (EWS), the second most common bone tumor in children, and related primitive neuroectodermal tumors (PNET). The most frequent rearrangement found in these tumors is between the EWS gene and the Ets factor FLI1 (Delattre et al., 1992). This translocation places the DNA binding domain of

FLI1 under the ectopic EWS promoter, thereby creating an aberrant transcription factor, which leads to the misregulation of FLI1 targets. In addition to FLI1, the ETS transcription factors ERG, ETV1, E1AF and FEV have also been shown to be fused to identical EWS nucleotide sequences (Jeon et al., 1995; Peter et al., 1997; Sorensen et al.,

1994; Urano et al., 1996).

More recently, translocations involving ETS transcription factors have also been found in other solid tumors, most well know of which are the TMPRSS2/ERG and

TMPRSS2/ETV1 fusions found in prostate cancer (Tomlins et al., 2005). Since

TMPRSS2 is known to have androgen responsive elements present in its promoter, the fusion of the 5’ untranslated region of TMPRSS2 to either ERG or ETV1 causes the overexpression of these ETS family members. Additionally, ETV4 and ETV5 were also found to be fused to TMPRSS2, thus identifying additional molecular subtypes of prostate cancer involving gene fusions (Helgeson et al., 2008; Tomlins et al., 2006). The

TMPRSS2/ERG fusion is the most common and is found in around 40% of human prostate cancers (Tomlins et al., 2005). Studies examining the downstream effects of this hybrid gene revealed co-expression of HDAC1 and increased expression of c-MYC, which caused the repression of prostate epithelial differentiation (Iljin et al., 2006; Sun et al., 2008).

A significant portion of genes in the Ets family have been shown to be overexpressed in a multitude of cancers, including both leukemias and solid tumors, therefore indicating a tumor promoting function of these proteins. Overexpression of

Ets1 has been shown to be responsible for the development of various solid tumors, including breast, lung, prostate and colon (Seth and Watson, 2005). The highly related

Ets2 has been shown to be overexpressed in several cancers, including prostate cancer and hepatocellular carcinoma (Ito et al., 2002; Liu et al., 1997). Additionally, Ets2 protein was shown to be significantly higher in breast carcinomas when compared with fibroadenomas and normal breast tissue (Buggy et al., 2006).

1.4.2 Biological Functions of Ets2

Ets2 has been shown to act as both a transcriptional activator as well as repressor.

The phosporylation of Ets2 at T72 is necessary for strong transcriptional activity of this transcription facor, and this has been shown to mainly be important for protein-protein interactions with other transcriptional co-avtivators. Convesely, Ets2 is also able to repress transcription.

Early studies examining Ets2 function in vivo revealed targeted deletion of the

DNA binding domain of this transcription factor to be embryonic lethal by around embryonic day E8.5, thus hightlighting its important functions during development

(Yamamoto et al., 1998). In particular, Ets2 deficient embryos lack amnion and chorion membranes, and also have a small ectoplacental cone region. Interestingly, rescue experiments in which functional extraembryonic tissues were provided suggested Ets2 function to be important for placental functions. To determine whether Ets1 and Ets2 had

47 overlapping, redundant roles during embryonic and/or adult development, our lab combined homozygous mutant allels for these two genes. This resulted in embryonic leathality due to defects in blood vessel branching, and using further genetic models this defect was found to be autonomous to endothelial cells (Wei et al., 2009).

1.4.3 Ets Factors in Tumor Stroma

Due to the known ability of Ets factors to regulate processes involved in tumorigenesis, including angiogenesis and activation of immune cells, as well as their misregulation in cancer, it seemed logical that these transcription factors might be playing not only a cell autonomous role, but also may have important functions in cells of the surrounding tumor stroma. Pioneering studies led by Robert Oshima’s lab investigated the impact of Ets2 on mammary tumorigenesis by MMTV-PyMT mice with

Ets2 heterozyoges. Interestingly, the tumors from the heterozygous offspring were smaller at all times of observation, and the average weight of Ets2 heterozygous tumors was found to be less than one half of that of Ets2 wild type tumors (Neznanov et al.,

1999). This group later examined the consequences of loss of activation of Ets2 by creating a single condon mutant at the critical Thr-72 phosphorylation site (Ets2A72).

Although homozygous mutant mice were found to be viable and develop normally, combining this allele with a deletion mutant resulted in embryonic lethalitiy thus demonstrating that Ets2A72 is a hypomorphic allele (Man et al., 2003). Interestingly, mammary tumor growth was restricted in homozygous Ets2A72 females. Further study using tumor transplantation revealed tumor size to be correlated directly with stromal

Ets2 activity, and this was accompanied by lower expression of the matrix

48 metalloproteinase Mmp9 in macrophages. In yet another follow up study, Tynan et al. used a conditional Ets2 floxed allele to definitely prove that deletion of Ets2 in mammary epithelial cells had no effect on tumor appearance or growth (Tynan et al., 2005).

Recently, work from our group has examined the macrophage specific function of

Ets2 during mammary tumor initiation and progression (Zabuawala et al., 2010). Using

LysCre to deplete macrophages of Ets2 did not effect primary tumor growth in a MMTV-

PyMT model, however loss of this transcription factor significantly decreased the number and size of pulmonary metastatic lesions. By introducing c-fms-YFP into these mice, macrophage populations were isolated using fluorescence activated cell sorting (FACS) and profiled for global gene expression changes. Interestingly, this analysis revealed many Ets2 target genes to be upregulated, thereby indicating Ets2 to be acting as a transcriptional repressor in this particular context. Closer analysis of these upregulated genes revealed their function to be in the negative regulation of angiogenesis, which could in part explain the differences in tumor cell seeding and growth in the lung.

1.5 Pten and p53 as Tumor Suppressors

Phosphatase and tensin homolog on chromosome 10 (Pten) and p53 are two of the most common tumor suppressors mutated in cancer. In spite of this, concomitant mutations in both Pten and p53 are not common (Kurose et al., 2002). The cellular locations and functions of these two proteins are actually quite different. Whereas the major tumor suppressor function of PTEN is attributed to its role in the cytoplasm, p53 action is known to be mainly nuclear. However more recent studies have uncovered a

49 potential link between these genes. Specifically, Pten mediated activation of Akt is important in the modulation of MDM2 dependent p53 degradation (Zhou et al., 2001).

1.5.1 Pten and Cancer

PTEN, also known as mutated in multiple advanced cancer 1 (MMAC1), was first discovered to be mutated in a variety of cancers over 15 years ago (Li et al., 1997; Steck et al., 1997). Additionally, germline PTEN mutations are linked to patients with PTEN hamartoma tumor syndrome (PHTS), which includes Cowden syndrome, Bannayan-

Riley-Ruvalcaba syndrome and Proteus syndrome (Hobert and Eng, 2009).

Predisposition to malignancy has been documented in patients with Cowden’s disease, including the development of breast, thyroid and skin cancer (Liaw et al., 1997). As of

2011, approximately 1,904 mutations in PTEN in 30 tumor types had been annotated

(Hollander et al., 2011). Although less than 5% of sporadic breast tumors contain mutations in the Pten gene, almost 40% of these tumors have loss of PTEN immunoreactivity (Perez-Tenorio et al., 2007).

1.5.2 Tumor Suppressor Function of PTEN

1.5.2.1 Molecular Mechanism of Tumor Suppression

The PTEN gene contains 9 exons and makes a protein containing two major functional domains: the phosphatase domain and the C2 or lipid membrane binding domain. Additionally, PTEN also contains a PIP2 binding motif, two PEST homology regions and a consensus PDZ binding site. Although the amino terminal phosphatase domain is primarily responsible for the functional activity of PTEN, mutations in the C- terminal C2 domain are found in almost half of the cases of tumorigenic mutations 50

(Waite and Eng, 2002). Further investigation of this C2 domain showed that it was critical for protein stability and productive orientation of the catalytic site (Georgescu et al., 2000).

Perhaps the most well known function of PTEN is the dephosphorylation of the lipid signaling second messenger phosphatidylinositol-3,4,5-trisphosphate (PIP3)

(Maehama and Dixon, 1998). PIP3 is generated by phosphorylation of PIP2 by PI3K on the plasma membrane. PI3K functions as a heterodimer and consists of a p85 regulatory subunit and a p110 catalytic subunit, both of which have multiple isoforms. PI3Ks are typically activated by receptor tyrosine kinase (RTK) signaling, however can also be activated through G protein coupled receptors (Kurosu et al., 1997). In the case of activation by RTKs, Src homology 2 (SH2) domains of the p85 subunit bind to phosphorylated tyrosine residues on the activated RTKs or adaptor molecules, which acts to recruit PI3K to the plasma membrane to find its substrate (Songyang et al., 1993).

Once PIP3 accumulates on the cell membrane, downstream signaling becomes activated.

The most well known downstream effectors of PI3K are Akt and phosphoinositide dependent protein kinase 1 (PDK1). After phosphorylation at residues T308 or S473,

Akt becomes activated an in turn phosphorylates other downstream proteins including glycogen synthase kinase 3α and 3β (GSK3α and GSK3β), MDM2 and BCL2-associated agonist of cell death (BAD), which ultimately results in cancer cell survival and growth

(Shaw and Cantley, 2006). Therefore, by hydrolyzing the 3’ phosphate on PIP3 to generate PIP2, PTEN directly antagonizes PI3K function. Importantly, PTEN is the

51 singularly known lipid phosphatase with this ability to repeal PI3K activation, as no other compensatory family members of PTEN have been identified.

In addition to the critical lipid phosphatase activity of PTEN, the protein phosphatase acivity of this tumor suppressor has been shown to be important for its functions in cell cycle arrest and inhibition of cell invasion in vitro (Davidson et al.,

2010). Research focusing on the nuclear function of PTEN has gained interest in recent years, with experiments showing the binding of PTEN to the centromere protein C1 to be required for centrosome stability (Shen et al., 2007). At the same time, Shen et al. also uncovered spontaneous DNA double strand breaks in Pten null cells, and provided evidence that PTEN acts on chromatin to regulate the expression of Rad51, which is a known DNA repair protein. Furthermore, nuclear PTEN was shown to interact with the anaphase promoting complex (APC) to promote its association with CDH1, and in doing so enhanced the tumor suppressive activity of the APC-CDH1 complex (Song et al.,

2011).

1.5.2.2 In Vivo Analysis of Pten Function

In addition to the biochemical analyses used to determine PTEN function at a molecular level, genetic manipulation of Pten in mice has helped us to gain a more complete understanding of the role of PTEN in vivo. A pivotal study from Di Cristofano et al. defined the necessity of PTEN for embroyinc development, as no homozygous Pten mutant mice lacking exons 4-5 were found to be viable. Further analysis using timed matings found that Pten-/- embryos died around embryonic day E7.5 (Di Cristofano et al.,

1998). An independent study confirmed this result, and found PTEN mutant embryos to

52 have severe defects in anterior and posterior development, with specific disruptions in cephalic and caudal region patterning (Suzuki et al., 1998). Although no gross phenotypical differences were observed in heterozygous Pten mutant mice, histo- pathological examination revealed hyperplasic changes in the gastrointestinal tract, skin, prostate and testes (Di Cristofano et al., 1998). Furthermore, malignant tumors also developed in these mice, including teratomas, thyroid papillary adenocarcomas, borderline hepatocellular carcinoma and leukemias (Di Cristofano et al., 1998; Suzuki et al., 1998).

Further studies using Cre/loxP technology to delete Pten in a cell specific manner revealed the importance of this tumor suppressor in tumor cells. Using MMTV-Cre to delete Pten specifically in mammary epithelial cells led to the development of mammary tumors early in life, including benign fibroadenomas as well as adenocarcinomas (Li et al., 2002). Interestingly, transplantation of Pten null epithelial into wild type stroma revealed similar phenotypes, thereby indicating the essential and cell autonomous role of

Pten in mammary epithelial cells. Similarly, when epithelial specific Pten null mice were crossed with an ErbB2 knockin mouse model of breast cancer, mammary tumor onset was accelerated and an increase in the occurrence of lung metastases was observed

(Dourdin et al., 2008). Work by the same group using a similar model in which the expression of ErbB2 and Cre recombinase are coupled in the same mammary epithelial cells under the MMTV promoter again revealed a dramatic acceleration of multifocal and highly metastatic mammary tumors, which were accompanied by an increase in angiogenesis (Schade et al., 2009).

1.5.3 p53 Mutations and Cancer

A variety of mutations have been identified in the not only the p53 tumor suppressor gene itself but also in genes regulating the activity of p53 and in downstream targets of p53. In fact, more than 50% of human cancers have mutations in p53 and disruption of the p53 pathway is typically observed in tumors that retain a wild type allele of p53 (Soussi, 2007). Additionally, germ line mutations of p53 are associated with a rare autosomal dominant disorder known as Li-Fraumeni syndrome, in which affected individuals have increased susceptibility to multiple cancers, including breast carcinomas, bone and soft tissue sarcomas, brain tumors and hematological neoplasms

(Varley, 2003). In particular, the DNA binding domain of p53 is considered a “hot spot” for mutation, as nearly 80% of mutations in p53 occur in this region (Hainaut and

Hollstein, 2000).

1.5.4 Tumor Suppressor Function of p53

The p53 transcription factor protein can be divided into four domains based on both structural and functional characteristics. The initial 42 amino acids at the

N-terminus contain a transcriptional activation domain that interacts with transcriptional machinery to positively regulate gene expression. There is also a190 amino acid sequence specific DNA binding domain that folds into antiparallel β sheets that are a scaffold for two α helical loops that interact directly with DNA.

In response to cellular stress signals, including hypoxia and DNA damage, p53 accumulates in the nucleus where it activates the transcription of genes involved in cell cycle arrest and apoptosis (Oren, 1999). However under normal conditions, p53 is

54 typically present at low levels. This is due to interactions with the E3 ubiquitin ligase

MDM2, which targets p53 for degradation by the proteasome (Haupt et al., 1997;

Kubbutat et al., 1997). MDM2 is also able to inhibit p53 mediated transactivation of target genes, and can induce the nuclear export of p53, thereby preventing this transcription factor from accessing DNA (Momand et al., 1992; Tao and Levine, 1999).

Interestingly, MDM2 is transcriptionally activated by p53, which serves as a feedback loop to regulate p53 (Barak et al., 1993). p53 is primarily stablilzed through disruption of its interaction with MDM2, which is typically achieved through post-translational modifications, including phosphorylation by various kinases including Chk1 and

Chk2(Appella and Anderson, 2001). Following stabilization and sequence specific DNA binding, p53 interacts with other transcription factors to either activate or repress the expression of its target genes. p53 has been shown to to interact with the histone acetyltransferase CBP/p300 as well as with various histone deacetylases to both induce and inhibit transcription, respectively (Iyer et al., 2004; Murphy, 2003).

p53 becomes activated in response to a varity of stimuli, which can trigger diverse cellular responses to elicit cell cycle arrest, cellular senescence, differentiation,

DNA repair, apoptosis and inhibition of angiogenesis. In particular, p53 plays an important role during the G1/S cell cycle checkpoint, which functions to prevent replication of cells with DNA damage. More specifically, p53 induces expression of the cyclin dependent kinase inhibitor p21, which localizes to the nucleus and prevents the further transactivation of genes involved in DNA replication (Giono and Manfredi,

2006). A role for p53 has also been established during the S-phase checkpoint, whereby

55 upon treatment of cells with inhibitors of DNA replicaion, p53 null cells continued to enter mitosis (Taylor et al., 1999). Finally, p53 has been shown to induce G2/M arrest through disruption of the cyclin B1/Cdc2 complex. In particular, p53 transcriptionally activates 14-3-3σ which prevents the proper nuclear localization of this complex after

DNA damage (Chan et al., 1999).

In addition to cell cycle aresst, p53 has also been shown to be important in cellular senescence. Interestingly, the expression of oncogenes, including Ras, can induce cellular senescence, however upon inactivation of the p53 tumor suppressor, this arrest is prevented (Serrano et al., 1997). Additionally, multiple chemotherapeutic agens known to cause DNA damage thereby activating p53 also induce senescence, however this can again be disabled by disruption of p53 resulting in tumor progression (Schmitt et al., 2002).

Perhaps the most well studied aspect of p53 tumor suppressor function is its ability to induce apoptosis, which was initially discovered in mouse thymocytes that underwent irradiation (Clarke et al., 1993; Lowe et al., 1993). Further studies showed this apoptotic response induced by p53 was also activated in response to signaling from oncogenes as well as by other DNA damaging agents (Lowe and Ruley, 1993).

1.5.5 In Vivo Analysis of p53 Functions

Several groups have generated p53 knockout mice with similar results, in that these mice develop multiple tumor types and have a decreased survival rate (Donehower et al., 1992; Jacks et al., 1994). On average, mice lacking p53 die by around 6 months of age, and approximately 75% of mice develop lymphomas and 35% develop sarcomas,

56 which are the most common tumor types observed in these mice. Additionally, mammary and lung adenocarcinomas, medullobastoma, glioblastoma, testicular tumors and hepatomas are also rarely seen. Interestingly, heterozygous mice also have an increased cancer risk, however the tumor spectrum observed in these mice is quite different from that observed in mice with homozygous deletion in that a higher percentage of these mice actually develop sarcomas. Further analysis examining the p53 status of tumor DNA revealed loss of heterozygosity (LOH) in 9/12 tumors that were tested, which indicates that p53 function is abolished during the development of most tumors in these heterozygous animals (Jacks et al., 1994). Furthermore, placing tumor prone mouse strains typically expressing oncogenes or lacking other tumor suppressors on a p53 null background significantly emphasized the critical function of p53 in suppressing tumorigenesis (Attardi and Jacks, 1999).

To more closely recapitulate defects in p53 functions in human cancer, knockin mice have been generated in which mouse analogues of human p53 tumor mutations are expressed under the endogenous p53 promoter. Knockins producing an altered DNA binding domain confirmation (R172H) and defective direct DNA binding abilities

(R270H) resulted in a similar lifespan as the heterozygous mice, however tumor spectra were considerably altered, mainly by more frequent presentation of carcinomas (Lang et al., 2004; Olive et al., 2004). Interestingly, mice containing one mutant knockin allele developed aggressive and invasive malignancies that eventually metastasized, a phenomenon that was not observed in either p53 null or heterozygous mice. Further experiments revealed these mutants to have gain of function properties to promote the

57 development of carcinomas. Additionally, nuclear accumulation of mutant p53 was observed in tumorls but not in surrounding normal tissue, which implicated stabilization of this protein in cancer cells. Crossing homozygous R172H mutant mice with Mdm2 null mice did not change the over tumor spectrum of the mutant mice, but dd significantly decrease their survival which was accompanied by increased incidence of metastasis

(Terzian et al., 2008).

1.6 Hypothesis

Although much work has focused on the functions of important signaling pathways in tumor cells, the roles of specific genes in individual compartments of the tumor microenvironment have not been examined. Here, we aim to specifically determine the functions of Ets2, Pten and p53 in stromal fibroblasts in the breast tumor microenvironment. Due to previous data implicating Ets2 to function from the stromal compartment in mammary gland tumorigenesis, we hypothesize that deletion of Ets2 in fibroblasts will inhibit the inititation and/or progression of PyMT and ErbB2 driven tumorigenesis. Conversely, we expect loss of either tumor suppressor Pten or p53 to drive tumorigeneis from the fibroblast compartment.

Chapter 2: Materials and Methods

2.1 Mouse Colony Maintenance

2.1.1 Animal Care

All animals were housed according to federal and University Laboratory Animal

Resources standards at The Ohio State University.

2.1.2 Transgenic Mouse Lines

All mice used in these studies were backcrossed at least 10 generations into the

FVB/N background. Ets2db mice were a kind gift from Dr. Robert Oshima and contain a

Neo cassette replacing the DNA binding domain (Yamamoto et al., 1998). Ets2loxP conditional mice were generated with loxP sites flanking exons 3-5 which contain the

Ets2 pointed domain (Wei et al., 2009). This domain is important for protein-protein interactions and signal transduction. PtenloxP conditional mice were generated with loxP sites flanking exons 4 and 5, which contains the lipid phosphatase domain (Trimboli et al., 2009). FspCre mice were generated by cloning 3.1 kb of the Fsp1 gene promoter upstream of the Cre recombinase open reading frame (Trimboli et al., 2008). RosaloxP mice were purchased from Jackson Laboratories and this conditional mouse reporter has been previously described (Soriano, 1999). Teto-KrasG12D mice were a kind gift from H.

Varmus (Fisher et al., 2001). MMTV-rtTA mice were a kind gift from L. Chodosh

(Gunther et al., 2002). p53loxP mice were a kind gift from A. Burns (Jonkers et al., 2001). 59

Col1a-YFP mice were a kind gift from Dr. David Rowe (University of Connecticut

Health Center) (Kalajzic et al., 2002). MMTV-PyMT and MMTV-ErbB2 mice were purchased from Jackson Laboratories.

2.1.3 Mouse Genotyping

2.1.3.1 Tail DNA Preparation

Two methods were used to isolate genomic DNA for polymerase chain reactions

(PCRs). For crude extracts, a 0.2 – 0.5 cm piece of mouse tail was digested overnight at

55°C in PCR buffer with detergents (50mM KCl, 10mM Tris-HCl (pH 8.3), 0.1 mg/mL gelatin, 0.45% NP40, 0.45% Tween 20, 1.0 mg/mL Proteinase K). The following day samples were boiled for 12 minutes to inactivate the Proteinase K, and then stored at 4°C or -20°C until they were used for PCR.

To obtain purified DNA, a 0.2 – 0.5 cm piece of mouse tail was digested in overnight at 55°C in tail lysis buffer (100mM Tris-HCl pH 8.5, 5mM EDTA pH 8, 0.2%

SDS, 200mM NaCl, 0.1 mg/mL Proteinase K). Following centrifugation to move any bone and fur, the DNA was precipitated with the addition of an equal volume of isopropanol. The DNA was pelleted by centrifugation, and then washed once with 70% ethanol. The dried DNA pellet was dissolve d in 500 µL low EDTA TE buffer (0.1 mM

EDTA and 10 mM Tris-HCl, pH 8.0) for one hour at 55°C. After vortexing, samples were stored at 4°C or -20°C until they were used for PCR.

2.1.3.2 Genotyping Primers and PCR Conditions

Following extraction using the previously mentioned protocols, 2 µL of DNA was used as a template for each PCR reaction (20-80 ng/µL). A typical reaction mix 60 contained water, 10X PCR buffer (New England Biolabs), 200 nM dNTPs, 200 nM of forward and reverse primers and 0.2 µL Taq polymerase (New England Biolabs or homemade). Following PCR reactions, products were separated using electrophoresis on

1% agarose gels and visualized using ethidium bromide.

2.1.4 Animal Procedures

2.1.4.1 Mammary Gland Dissection and Tissue Harvesting

Female mice were euthanized using CO2, followed by cervical dislocation as a secondary method of euthanasia. A small cut was made through the skin on the mid- ventral surface of the mouse, taking caution not to cut through the peritoneal wall. This initial cut was expanded in a straight line up towards the chin of the mouse, and also down each rear leg of the animal, making sure not to cut the connecting mammary gland tissue. The mammary gland tissue located on the inner surface of the skin was exposed by pulling the skin away from the body of the animal and pinning. Using forceps and scissors, tissue was gently removed and stored properly until further analysis. For cell culture, mammary gland tissue was placed into sterile Eppendorf tubes on ice. For paraffin embedded tissue, mammary glands were placed in cassettes and left to sit in zinc containing or regular formalin for 24-48 hours, after which tissue was placed in 70% ethanol until further processing. For frozen tissue, mammary glands were placed in partially frozen optimal cutting temperature (OCT) solution to create a flat surface, and were then covered with OCT and completely frozen on a metal block placed in dry ice or liquid nitrogen.

2.1.4.2 Mammary Tissue Transplantation

Mammary tissue was transplanted as previously described (Cases et al., 2004).

Prior to surgery, recipient mice were anesthetized using isofluorane (Abbott

Laboratories) and a roughly 25mm x 25mm area was shaved along the scapular region.

On the day of surgery, two 5-10mm incisions were made on the left and right scapular regions of recipient mice under aseptic conditions. Immediately following this, approximately 5mm x 5mm pieces of inguinal (#4 and #9) and groin (#5 and #10) mammary glands that were removed from 8-9 week old donor female mice were placed subcutaneously into the scapular region of the wild type FVB/N recipient female mice

(Taconic). The incisions were closed using 9mm wound clips. Following the surgery, mice were monitored twice weekly until tumor onset. Experimental and control mice were euthanized based on one of the following criteria: specific time following transplantation, tumor size reached 2cm or health problem such as exterior ulceration at site of tumor required early removal.

2.1.4.3 Mammary Fat Pad Injection

Tumor cells used for injection were trypsinized from a 10cm dish at approximately 80-90% confluence, washed with PBS and counted using a hematocytometer. Cells were resuspended in 50µl PBS and kept cold on ice until injection. Mice receiving injections were typically 8-9 weeks old and of syngeneic background with the tumor cell lines used. After mice were anesthesized with isoflourane, inguinal (#4 and #9) mammary glands were sterilized and the nipples were exposed using a Q-tip with 70% ethanol. Cells were injected just beneath the nipple with

62 a 27G X ½” insulin syringe with needle (Terumo). Following injections, mice were palpated twice weekly to determine tumor initiation.

2.1.4.4 Matrigel Plug Injections

Wild type or Pten null mouse mammary fibroblasts (MMFs) and DB7 mouse tumor cells were cultured as described in Section 2.2. Approximately 1.5 x 106 DB7 cells were mixed with 500,000 wild type or Pten null MMFs with the indicated treatments and suspended in 400 µl of ice cold growth factor reduced matrigel (BD Bioscience). This mixture was injected subcutaneously into the flanks of 8-9 week old FVB/N mice

(Jackson Labs) anesthesized with Isofluorane. Additionally, DB7 and MMFs were injected alone as controls. The matrigel plugs were harvested 5 days post injection, and either placed in zinc containing formalin or frozen in OCT for further analysis.

2.1.4.5 Xenograft Assays

Female NcR nude mice were purchased from Taconic. For cell co-injections, 1.5 x 106 DB7 cells and 500,000 wild type or Pten null MMFs with the indicated treatments were suspended in a 200µL mixture of serum free DMEM and ice cold growth factor reduced matrigel (BD Bioscience) at a 1:1 ratio and injected subcutaneously. Each nude mouse was injected at two sites, one each for control and experimental admixtures. For tumor formation assays, mice were harvested 4 weeks post injection.

2.1.4.6 Intraperitoneal Injection

BrdU solution (10mg/mL) was injected using a 27G X ½” insulin syringe with needle (Terumo) into the intraperitoneal cavity of mice with indicated genotypes. For the injections, mice were held vertically, and the needle was inserted at around a 30° angle 63 just under the skin on the lower right side of the mouse. The needle was aspirated to make sure there was no penetration of the intestines, bladder or a blood vessel, and if there was no aspirate the BrdU solution was injected.

2.1.4.7 Doxycycline Administration in Food

Syngeneic mice used for the transplant study were started on 1g/kg doxycycline food immediately following transplantation. Mice used in the genetic non-transplant model were started on 20mg/kg doxycycline (dox) at 8 weeks of age. Fresh food was administered once/week and food levels were checked regularly to ensure the mice did not run out. Dox food was stored at room temperature in the dark due to light sensitivity.

2.2 Tissue Culture

2.2.1 Primary Fibroblast Isolation

Primary mammary fibroblasts were isolated using a previously described protocol with minor modifications (Soule and McGrath, 1986). Dissected mammary glands from

8-9 week old female mice were minced in an Eppendorf tube using microdissection scissors and digested with collagenase solution (20% mammary epithelial growth media,

0.15% Collagenase I, .2% NaHCO3, .75% Media199, 160 U/ml Hyaluonidase, 1 µg/ml hydrocortisone and 10 µg/ml insulin with penicillin and streptomycin) overnight in a 5%

CO2 incubator at 37°C. The following day tissue was passed through a 1 ml pipette tip approximately 30 times, until large tissue chunks were broken up and solution had cloudy appearance. The collagenase was neutralized with DMEM containing 10% FBS then removed by centrifugation and aspiration. Cells were resuspended in fresh complete media and subjected to 12 minutes of gravity separation. Supernatants were collected 64 and subjected to four more consecutive rounds of gravity separation for 10 minutes each.

After the final sedimentation, cells remaining in the supernatant were plated with 10%

FBS DMEM in a 5% CO2 incubator at 37°C. Primary fibroblast cultures were washed with PBS the following day to remove debris and any dead cells, and fresh media was added.

2.2.2 Primary Epithelial Cell Isolation

Primary epithelial cells were isolated using a previously described protocol with minor modifications (Soule and McGrath, 1986). Dissected mammary tissue was digested using one of two different methods. For overnight digestion, mammary tissue was minced and digested with collagenase solution (20% mammary epithelial growth media, 0.15% Collagenase I, .2% NaHCO3, .75% Media199, 160 U/ml Hyaluonidase, 1

µg/ml hydrocortisone and 10 µg/ml insulin with penicillin and streptomycin (P/S)) in a

5% CO2 incubator at 37°C. The following day tissue was passed through a 1ml pipette tip approximately 30 times, until large tissue chunks were broken up and solution had cloudy appearance. The collagenase was neutralized with DMEM containing 10% FBS then removed by centrifugation and aspiration. Cells were resuspended in fresh complete media and subjected to 12 minutes of gravity separation. Supernatants were removed and the sedimented cells were resuspended in complete media to a total volume of 10 mls and subjected to two more consecutive rounds of gravity separation for 10 minutes each.

Cells remaining in the sediment were plated with 10% FBS DMEM in a 5% CO2 incubator at 37°C. Primary epithelial cultures were washed with PBS the following day

65 to remove debris and any dead cells, and MEGM containing bovine pancreatic extract

(BPE) and P/S was added.

A shorter digestion method was also utilized in cases where multiple cell types were needed from mammary tissue. In this method, tissue was minced and digested with collagenase solution (20mg collagenase type 2 (Worthington), 480 units DNase I (Roche) and 1mM MgCl2) for a total of 35 minutes with shaking at 37°C, with intermittent mixing with a 1 ml pipette every 10-15 minutes. After neutralization with 10% FBS DMEM, the isolation follows the previously described protocol except for the initial gravity separation time, which is 5 minutes in this case.

2.2.3 Establishment of Wild Type, Pten Null and Pten/Ets2 Double Knockout (DKO)

Mouse Mammary Fibroblast Cell Lines

Primary fibroblasts isolated from were isolated following the protocol described in 2.2.1. At the second passage, 300,000 cells were plated in 60mm dishes and were replated at this same concentration every 3 days. Cells were cultured with 10% FBS

DMEM with 100µg/mL penicillin-stretomycin throughout this process of immortalization.

2.3 Flow Cytometry/FACS

2.3.1 Isolation of YFP+ Cells from Transgenic Mice

To prepare tissues for flow cytometry analysis or fluorescent activated cell sorting

(FACS), mammary glands were isolated from mice and minced into 1-2mm fragments using microdissection scissors. Tissue was placed in a round bottom tube (BD Falcon) with pre-warmed tissue culture (TC) phosphate buffer solution (PBS) containing 5mg/mL 66 collagenase (Worthington Biochemical Corporation), 120U/mL DNase I (Roche) and

1mM MgCl2. Typically 5mL of this collagnase solution was used for all the mammary glands of one mouse. Samples were placed in a shaking 37°C incubator for 10-15 minute intervals with occasional pipetting (with 1mL pipette) until a single cell suspension was obtained (usually around 30-40 minutes total incubation time). To terminate the collagenase activity, 8mLs complete media was added to the cell suspension, after which cells were pelleted by centrifugation (1200rpm/5 minutes) and collagenase containing media was removed. Cells were resuspended in 10mLs TC PBS and filtered through a

40µm cell strainer (BD Falcon). Again cells were pelleted and excess PBS was removed.

Cells were then resuspended in 500µL-1mL flow solution (1mM EDTA pH 8.0, 1mM

MgCl2, 60U/mL DNase I (Roche) and 3% heat inactivated FBS) and taken for analysis.

Cells used for flow cytometry analysis were typically discarded after use. Cells isolated using FACS were spun down (1200rpm/5 minutes) and excess flow buffer was removed using a 1mL pipette and typically placed in 1mL Trizol reagent for future RNA isolation.

2.3.2 Isolation of Endothelial Cells and Macrophages using CD31 and F4/80

Antibodies

An identical procedure to that in section 2.3.1 was used for isolation of cells not tagged with an endogenous fluorescent marker, however the cells were counted after filtering. Following centrifugation (1200rpm/5 minutes) and removal of excess TC PBS, cells were resuspended in a volume that created a concentration of 1 x 106 cells/100µL flow solution. Cells were then placed in an Eppendorf tube and 1µL of fluorescently labeled antibody was added per 1 x 106 cells. Samples were placed on a rotor in the cold

67 room for approximately 30 minutes and then cells were collected by centrifugation

(2500rpm/5 minutes). Again cells were then resuspended in 500µL-1mL flow solution and taken for analysis.

2.3.3 In vivo BrdU Flow Assay

This assay was carried out with instructions and reagents from the APC BrdU

Flow Kit (BD Pharminogen). Briefly, mice were injected with 100ug/g mouse body weight (approximately 200 µl) BrdU solution (BD Pharminogen) 3 hours prior to being euthanized. Mammary gland tissue was harvested and digested as in section 2.3.2, and 1 x 106 cells were placed in 50µL staining buffer with appropriate fluorescently labeled antibodies (CD31, F4/80 or Cdh1). Cells were washed and centrifugated (200-300 x g/5 minutes), and then resuspended in BD Cytofix/Cytoperm Buffer. After incubation for

15-30 minutes, cells were again washed and centrifugated (200-300 x g/5 minutes) and left in staining buffer O/N at 4°C. The following day cells were pelleted and then resuspended with BD Cytoperm Plus Buffer and BD Cytofix/Cytoperm Buffer with subsequent washes in between each of these incubations. Cells were then resuspended with 100µL of diluted DNase (300µg/mL in TC PBS) and incubated for 1 hour at 37°C.

Following another wash, cells were resuspended with 50µL of BD Perm/Wash buffer containing diluted APC-conjugated BrdU antibody. After a final wash step, cells were resuspended with 1mL staining buffer and taken for analysis.

2.4 RNA Analysis and Microarrays

2.4.1 RNA Extraction

RNA was harvested using Trizol reagent according to manufacturer’s instructions

(Invitrogen). For RNA isolation from cells collected by FACS, 1 µl of glycogen (Roche) was added to the aqueous phase containing RNA before precipitation with isopropanol.

After washing with 70% ethanol, RNA pellets were dissolved in 15-50 µl nuclease free water (Ambion) by pipetting followed by incubation at 55°C for 10 minutes. Potential

DNA contamination was excluded by using either RNAeasy columns (Qiagen) or Turbo-

DNAse free kit (Ambion) according to manufacturer’s instructions. RNA quantity and quality were assessed using Nanodrop RNA 6000 Nano-assays or 2100-Bioanalyzer.

2.4.2 cDNA Preparation

Two methods were utilized for synthesis of cDNA from total RNA. Using M-

MLV Reverse Transcriptase (Ambion), 2 µg of total RNA was mixed with 200 ng of random hexamers (Invitrogen) and a final concentration of 500nM dNTPs. Nuclease free water was added to make a total volume of 14 µl. Samples were denatured at 70°C for 5 minutes and then placed on ice, briefly spun and then placed back on ice for at least 1 minute. Remaining components were added to the mixture to reach a total volume of 20

µL, including 2 µl 10X First Strand Synthesis Buffer (Ambion, 500 mM Tris pH8.3, 750 mM KCl, 50 mM DTT and 30 mM MgCl2), 1 µl RNase OUT recombinant RNase inhibitor (Invitrogen) and 1 µl M-MLV Reverse Transcriptase. After gentle mixing and a quick spin, samples were incubated in a thermocycler at 42-44°C for 1 hour followed by incubation at 92°C for 10 minutes to inactivate the M-MLV Reverse Transcriptase. After

69 the RT reaction, samples were diluted to a total volume of 200 µl using nuclease free water, making an approximate final concentration of 10 ng/µl.

Using qScript™ cDNA Supermix (Quanta Biosciences), 1 µg or less of total RNA was mixed with 5X qScript cDNA supermix (containing optimized concentrations of

MgCl2, dNTPs, recombinant Rnase inhibitor protein, qScript reverse transcriptase, random primers, oligo9dT) primer and stabilizers) and brought to a final volume of 20 µl with nuclease free water. After gentle mixing and brief centrifugation, samples were incubated in a thermocycler for 5 minutes at 25°C, 30 minutes at 42°C and 5 minutes at

85°C. Following the RT reaction, samples were diluted with nuclease free water to make an approximate final concentration of 10 ng/µl. Typically 5 µl (50ng) of cDNA was used for each quantitative real time PCR (qRT-PCR) reaction.

2.4.3 Quantitative Real Time PCR

Quantitative gene expression was performed using 50 ng complementary DNA per reaction. Taqman Roche Universal Probe Library system probe and primers (Roche) were used following the manufacturer’s instructions. Reactions were performed on the I- cycler iQ5 (Bio-rad) or StepOne Plus (Applied Biosystems) Real Time machines. Primers used for qRT-PCR were designed using the web based ProbeFinder software provided by

Roche. The reference gene used for all qRT-PCR assays was Rpl4, and all reactions were analyzed by melt curve analysis and agarose gels to confirm the specificity of the reaction.

2.4.4 Microarray Analysis

RNA was hybridized to Affymetrix GeneChip Mouse genome 430 2.0 or

GeneChip Mouse Exon 1.0 ST platforms at the Ohio State University Comprehensive

Cancer Center Microarray Shared Resource (OSUCCC MASR). For analysis of sorted samples, RNA amplification was required to obtain a larger quantity of sample to be used for hybridization to gene chips. Data was analyzed using WEDGE++ (Auer et al., 2007) or robust multichip average (RMA) (Irizarry et al., 2003) methods for normalization and determination of differentially expressed genes. Heatmaps were created using specialized R packages.

2.4.5 microRNA Profiling

microRNA profiling was carried out as previously described (Hunter et al., 2008).

Looped primers were used to profile 420 mature human miRs by real time PCR using an

Applied Biosystems 7900HT real time PCR instrument equipped with a 384 well reaction plate. Approximately 500 ng of total RNA was converted to cDNA by priming with a mixture of looped primers (Mega Plex kit, Applied Biosystems) using previously determined reverse transcription conditions (Garzon et al., 2006). Appropriate internal control miRs were used, including small nuclear (sn)RNA U6 as well as 18S and 5S ribosomal RNA (rRNA).

2.5 Western Blotting

One to two million primary fibroblasts were lysed with radio immunoprecipitation assay (RIPA) buffer(50 mM pH7.4 Tris-HCl, 150 mM NaCl, 1 mM EDTA, 1% NP-40,

1% sodium deoxycholate and 0.1% SDS) containing protease and phosphatase inhibitors 71

(Amersham and Roche). Primary antibodies for PTEN (Cell Signaling) and ETS2

(M.C.O. laboratory) were incubated overnight at a dilution of 1:1,000. Washed membranes were blotted with either horseradish peroxidase conjugated anti-mouse IgG or anti-rabbit IgG antibodies and developed with enhanced chemiluminescent substrate

(Thermo Scientific). Membranes were re-blotted with α-tubulin antibody (Sigma) to determine even loading.

2.6 Chromatin Immuoprecipitation

ChIP assays were performed as described previously (Hu et al., 2007). Primary fibroblasts were plated at a density of 2 x 106 cells on a 10cm tissue culture dish one day prior to harvest. The following day, cells were washed and fresh 10% FBS DMEM was added, and two hours later cells were cross linked with 1% formaldehyde and soluble chromatin was prepared with sonication to an average DNA length of 200-1,000 base pairs. Sheared soluble chromatin was pre-cleared with transfer RNA blocked Protein G

Agarose, and 10% of the pre-cleared chromatin was set aside as input control.

Immunoprecipitation was performed with 5µg of Ets2, p-Pol II or rabbit IgG overnight at

4°C. Immune complexes were pulled down with Protein G-Agarose, washed and eluted with elution buffer (0.1M NaHCO3, 1% SDS) and cross links removed by incubating with 200 mM NaCl containing 50 µg ml-1 of RNase A (Sigma) at 65°C overnight. DNA was purified with the Qiagen PCR Purification Kit after proteinase K treatment according to manufacturer’s instructions. Samples were analyzed by quantitative RT-PCR as described in section 2.4.3.

2.7 Histology/Immunostaining

2.7.1 Whole Mount Staining of Mammary Gland

Inguinal mammary glands were isolated from 6 and 9 week old or 14 days pregnant female mice and mounted on glass slides using a Q-tip to spread the tissue.

Tissue was fixed using Carnoy’s fixative (60% ethanol, 30% chloroform and 10% acetic acid) at 4°C overnight, and then stained with carmine alum stain (Sigma) for 12 hours.

After thorough rinsing in water, mammary glands were dehydrated with alcohol and preserved in xylene.

2.7.2 Immunohistochemistry (IHC) Staining Protocol

Paraffin embedded tissue sections were heated at 65°C for 20 minutes to melt the wax and then deparaffinized with two 5 minute incubations with xylene, followed by washes with 100% ethanol, one wash with 95% ethanol and one wash with 70% ethanol followed by hydration with distilled water. A diamond pen was used to outline tissue to ensure the tissue remained covered at all stages of the staining procedure. Antigen retrieval was done using 1X antigen retrieval buffer (DAKO) in a steamer for 30 minutes

-1 hour. To remove endogenous peroxidase activity, sections were incubated with 0.3%

H2O2 in PBS for 10 minutes at room temperature. Following a protein block (Dako), samples were incubated with primary antibodies diluted in antibody diluent (Dako) overnight at 4°C. The following day, slides were incubated with appropriate biotinylated secondary antibodies at a dilution of 1:200 in Dako antibody diluent at room temperature

(RT) for 30 minutes. At this same time, ABC reagent was prepared and allowed to sit at

RT for 30 minutes. Following secondary antibody incubation, slides were incubated with

ABC reagent for another 30 minutes at RT. Subsequently, DAB was added to slides and they were watched under the microscope until a signal was observed, after which the reaction was stopped by placing the slides in water. Slides were counterstained with

Meyer’s hemotoxylene, quickly washed in ammonium water and then dehydrated and coverslipped.

In addition to these steps, BrdU staining required several extra steps following deparaffinization as well as a different antigen retrieval process. After deparaffinization, slides were treated with proteinase K (20µg/mL) for 30 minutes at RT, and antigen retrieval was achieved through incubation with 2N HCl for 45 minutes at RT. Slides were also incubated with 0.1M sodium borate at pH8.5 for 10 minutes at RT prior to hydrogen peroxide treatment.

2.7.3 Immunoflourescence (IF) Staining Protocol

For staining paraffin sections, the same protocol was followed as for IHC through the primary antibody incubation, with the exception of endogenous peroxidase blocking.

Appropriate fluorescently conjugated secondary antibodies were added at a dilution of

1:250 in antibody diluent and incubated at RT for 1 hour. Slides were mounted with media containing DAPI. Frozen sections were first thawed, then fixed with either ice cold acetone or 4% paraformaldehyde (PFA), after which they were treated as the paraffin sections for further staining.

2.8 Bioinformatic Analysis

2.8.1 Gene Set Enrichment Analysis (GSEA)

GSEA v2.0 was used to determine the enrichment of functional categories in our gene expression data (Subramanian et al., 2005). For this analysis, all genes that met our filtering criteria were input for each sample. Gene sets were obtained from indicated categories in ToppGene Suite (Chen et al., 2009). Statistical significance was determined using 1,000 random permutations of each gene set to obtain a nominal P value.

2.8.2 Generating Human Stroma Heatmaps Using Gene Expression Data from

Mouse Models

2.8.2.1 PyMT Derived Ets2 Fibroblast Gene List

Of the 107 genes regulated by Ets2 in PyMT derived tumor fibroblasts, 88 had human orthologs indentified using Ensembl and MGI databases. When queried against the McGill Cancer Center’s Breast Stroma Microarray data (GSE9014 and GSE4823), 85 genes were also represented on the Agilent custom array used by the McGill group. To achieve the best resolution on the heatmap, a variance cutoff of >0.5 was applied across the multiple human samples. This cutoff ensures we only indentified genes with highly variable expression across all samples. The 38 PyMT derived Ets2 dependent genes which met this cutoff criterion were subsequently used to generate a heatmap for the human stroma dataset (52 normal stroma and 49 tumor stroma samples). The separation of normal stroma and tumor stroma was determined using the samples’ rank sums over each signature and the significance of this separation was determined using a one sided

Wilcoxon test. The raw p-value was adjusted using 10,000 permutations.

2.8.2.2 PyMT Derived Ets2 Dependent Endothelial Cell Gene List

Similar analysis as that in section 2.8.2.1 was used for the endothelial cell data, whereby 22 or our 33 genes had human orthologs and were represented in the McGill breast stroma data. After a variance cutoff of >0.5 was applied, 9 genes made the cutoff for further analysis.

2.8.2.3 Pten Null Fibroblast Gene List

Of the 150 unique mouse genes found to be differentially expressed between control and Pten null fibroblasts, 137 had human orthologs identified using Ensembl and

MGI databases. When queried against the McGill Cancer Cent’s Breast Stroma

Microarray data (GSE9014 and GSE4823), 129 of these genes were also represented on the Agilent custom array used by the McGill group. After a variance cutoff of >0.5 was applied across the multiple human samples, a 70 gene subset was derived and used to generate a heatmap for the human stroma dataset (52 normal stroma and 49 tumor stroma samples). The significance of the separation of normal stroma and tumor stroma was determined using the same method as in section 2.8.2.1.

2.8.3 Kaplan-Meier Survival Analysis

Our gene signatures derived from section 2.8.2 using the >0.5 variance cutoff criteria were used to query gene expression data from three independent breast cancer microarrays: Rosetta (NKI dataset), Stockholm (GSE1456) and the McGill University

(GSE9014) datasets (Pawitan et al., 2005; van de Vijver, 2005). A Cox proportional hazards model associated to a L2 norm constraint (Goeman, 2010) was applied to estimate the hazard parameters associated to each gene from the human orthologs 76 corresponding to our gene signatures. A prognostic index for each patient was defined as the sum of the gene expression levels pondered by the estimated hazard parameters. A

10-fold cross validation is used to predict the outcome for each patient: the patients are randomly divided into 10 groups; 9 groups are chosen to form the training set while the last group represents the test set. The hazard parameters are estimated from the training set and the prognostic index for each patient in the test set is computed. This step is repeated 10 times to consider each group of patients as the test set once. The 10-fold cross validation is performed 10 times in order to be independent of the random partitioning. A prognostic index threshold is then defined as the 50 percentile of all prognostic indexes. All the patients that have a prognostic index superior (respectively inferior) to this threshold are considered as high risk (respectively low risk) patients. A log-rank test is finally applied to evaluate the statistical significance between the two groups.

2.9 Statistical Tests

2.9.1 Ets2 Tumor Study and Immunostaining

Analysis of variance (ANOVA) model assuming unequal variance with Holm’s methods was used to study the differences among groups in the PyMT tumor study. A non-parametric Mann-Whitney test was used to compare lesion sizes in ErbB2 tumors. A

Chi square test was used to compare the number of ErbB2 lesions larger than 1mm2. A

Welch’s t-test assuming unequal variance was used to calculate p-values in all other immunostaining quantifications.

2.9.2 Pten/p53 Tumor Study and Immunostaining

For the tumor development study and all histopathological comparisons a Fisher’s exact test was used. For tumor burden data a Wilcoxon Rank sum test was used. A student’s t-test was used for the majority of comparisons of IF and IHC staining.

Chapter 3: The Role of Ets2 in Fibroblasts During Mammary Tumor Progression

3.1 Introduction

The progression of mammary tumors from ductal carcinoma in situ (DCIS) to invasive carcinoma involves not only the transformation of mammary epithelial cells, but also the hijacking of normal stromal tissues which also become altered to promote tumor development. Gain or loss of function mutations in oncogenes and tumor suppressors in breast cancer cells are well established. In fact, genetic tests are available to check for mutations in the BRCA1 and BRCA2 tumor suppressor genes, which if present can raise the lifetime risk of developing breast or ovarian cancer from 12% to 60%.

Additionally, targeted therapeutic strategies are currently based on characteristics of only the tumor cells. While this is important, studies have showed the ability of the surrounding tumor stroma to contribute to tumor cell resistance (Meads et al., 2009).

Specifically it was shown that DNA damage induced expression of WNT16B in B cells signals in a paracrine manner to activate the canonical Wnt program in tumor cells (Sun et al., 2012). It was further shown that this expression of WNT16B in the tumor microenvironment of the prostate could attenuate the effects of chemotherapy in vivo, thereby promoting tumor progression. Additionally, in vitro experiments have shown that adhesion of melanoma cells to fibroblast monolayers could significantly reduce the

79 cytotoxic effects of cisplatin (Flach et al., 2011). Therefore, dual targeted therapy directed at both tumor cells and stroma may have more success.

However, one limitation with this strategy is the identification of specific molecules and signaling pathways in the stroma which may promote tumor initiation and progression. Although several genetic studies using mouse models have identified key molecules in fibroblasts that influence tumor growth, typically the role of these genes is only examined in the context of one tumor model. Due to the heterogenous state of cancer, it will become necessary to determine whether global regulators exist in the stroma or whether there is tumor based specificity involved.

Ets2 is a protooncogene known to be overexpressed in a variety of human cancers including breast and prostate (Buggy et al., 2006; Hsu et al., 2004). As a critical downstream effector of Ras/Raf/MAPK signaling, Ets2 regulates the expression of multiple growth factors, adhesion molecules, extracellular proteases and apoptotic genes, all of which could have potentially important functions in the tumor microenvironment.

Consistent with this, Ets2 was shown to support tumor growth from the stroma, however the cell in which this function was important was undefined (Man et al., 2003; Tynan et al., 2005). Work from our group has shown Ets2 functions to promote tumor metastasis from the macrophage compartment (Zabuawala et al., 2010).

Using a genetic model, we found Ets2 to promote tumor progression from the stromal compartment in both PyMT and ErbB2 driven tumorigenesis. Additionally, Ets2 controlled a tumor specific transcription program in both models that promoted angiogenesis. Consequently, gene expression was mis-regulated in endothelial cells

80 surrounding these Ets2 null tumor associated fibroblasts. Finally, our Ets2 dependent gene signatures from fibroblasts and endothelial cells could predict patient outcome in whole tumor datasets.

3.1.1 Cre Mediated Deletion of Ets2 Using FspCre

We utilized the well characterized Cre/loxP system to efficiently and specifically delete Ets2 in fibroblasts. This technology involves two 34bp DNA sequences known as the loxP sites which flank a particular portion of a gene that is to be deleted. In the presence of the protein Cre recombinase, these two loxP sites are recombined and the fragment of gene between these sites is removed. Initial experiments indicated that deletion of both Ets2 alleles in homozygous Ets2loxP/loxP mice was inefficient with several

Cre-driver lines. Therefore, to facilitate the complete ablation of Ets2 in fibroblasts, we introduced a conventional knockout allele targeting the Ets2 DNA binding domain,

Ets2db, along with our one Ets2loxP conditional allele, such that heterozygous mice with one mutant allele (db) and one conditional allele (loxP) were obtained (Yamamoto et al.,

1998). This strategy was used to increase the efficiency of deletion of our Ets2loxP allele since only this single allele needed to be removed.

To confirm the efficient and specific deletion of Ets2 in our breast cancer model, we first performed genotyping PCR on genomic DNA isolated from primary fibroblasts cultured from PyMT;Ets2db/loxP and PyMT;FspCre;Ets2db/loxP mammary glands (Figure

3.1A). Additionally Western blot analysis was performed on protein isolated from cells from these same genetic groups to show the efficient loss of ETS2 protein in fibroblasts expressing FspCre (Figure 3.1B). The specificity of FspCre was confirmed by

81 introducing the Rosa26LoxP reporter gene into a subset of the experimental mice (Soriano,

1999). X-gal staining of mammary tumor sections confirmed Cre expression to be limited to the stromal compartment (Figure 3.1C).

Figure 3.1 Efficient and Specific Deletion of Ets2 in Stromal Fibroblasts

A. PCR genotyping for conditional Ets2 allele on DNA isolated from cultured fibroblasts of indicated genotypes (Fu Li). B. Western blot for ETS2 protein in cultured fibroblasts

(Fu Li). C. X-gal staining on mammary tumor tissue harvested from 10 week old mice

(Fu Li).

3.2 Results

3.2.1 Normal Development of Mammary Glands Lacking Fibroblast Ets2

Prior to introducing the PyMT oncogene, we wanted to make sure that conditional deletion of Ets2 in fibroblasts did not disrupt normal development of the mammary gland.

To examine this, we performed whole mount staining on mammary glands isolated from

Ets2db/loxP and FspCre;Ets2db/loxP female mice. Mammary glands stained from developing and 14 day pregnant mice revealed no detectable differences in ductal branching or proliferation as a result of Ets2 deletion in fibroblasts (Figure). Furthermore,

FspCre;Ets2db/loxP females were also able to lactate and feed pups, indicating there was no disruption to the normal function of the mammary gland.

A Ets2db/loxP Fsp-cre;Ets2db/loxP

L.N. L.N. Development B

L.N.

L.N. Pregnant 14d Pregnant

Figure 3.2 Normal Development of Mammary Glands Lacking Ets2 in Fibroblasts

A. Whole mount carmine staining of inguinal mammary glands from 6 week old

Ets2db/loxP and Fsp-Cre;Ets2db/loxP mice. L.N., lymph node. Scale bar, 2mm. B. Whole mount carmine staining of inguinal mammary glands from 14 days pregnant Ets2db/loxP and Fsp-Cre;Ets2db/loxP mice. L.N., lymph node. Scale bar, 2mm.

3.2.2 Ets2 Deletion in Fibroblasts Slows Progression of PyMT Driven Mammary

Tumors

To determine the role of fibroblast Ets2 in mammary tumor initiation, control

PyMT;Ets2db/loxP and experimental PyMT;FspCre;Ets2db/loxP mice were palpated bi- weekly to determine the average time for these mammary glands to develop palpable tumors. Next we examined tumor progression by harvesting mammary glands from

PyMT;Ets2db/loxP and PyMT;FspCre;Ets2db/loxP mice 30 days after tumor initiation and measuring the tumor burden in mice. This analysis revealed a more than 1.5-fold decrease in the average tumor weight from PyMT;FspCre;Ets2db/loxP mice compared to

PyMT;Ets2db/loxP mice (Figure 3.3A). This delay in progression in

PyMT;FspCre;Ets2db/loxP tumors could be visualized by H&E staining, whereby in control mammary glands the entire tissue was taken over by tumor however mammary glands from our experimental mice still had areas where the tumor had not invaded and areas of the mammary fat pad remained intact (Figure 3.3B). Similarly, whole mount analysis of 6 week PyMT;Ets2db/loxP mammary glands showed extensive neoplastic progression, whereas tumors in PyMT;FspCre;Ets2db/loxP mice remained restricted to the nipple area, with little or no invasion into and beyond the mammary lymph node (Figure

3.3C).

Ki67 staining revealed an approximate 2.5-fold reduction in epithelial cell proliferation in tumors from mice lacking Ets2 in fibroblasts (Figure 3.3D). Additional characterization of PyMT;Ets2db/loxP and PyMT;FspCre;Ets2db/loxP mammary glands was performed using antibodies against the macrophage marker F4/80 and the cell death

86 marker cleaved caspase 3. However this analysis failed to reveal any differences in macrophage recruitment or apoptosis between genetic groups, therefore indicating that stromal Ets2 impacts tumor growth mainly through modulation of epithelial cell proliferation.

Figure 3.3 Ets2 in Fibroblasts Promotes PMT Driven Mammary Tumor Growth

A. Dissected tumor weight in grams isolated from 14 week old PyMT;Ets2db/loxP (n=21,

12.36±3.8), PyMT;Fsp-Cre;Ets2db/loxP (n=17, 7.83±2.7), and PyMT; MMTV-

Cre;Ets2db/loxP (n=15, 11.3±4.7) mice (**P<0.01, *P<0.05, adjusted P-values were obtained from an ANOVA model assuming unequal variance with Holm’s methods) (Fu

Li and Carmen Cantemir-Stone). B. Representative histological sections from mammary glands of 10 week old PyMT;Ets2db/loxP and PyMT;Fsp-Cre;Ets2db/loxP mice. Scale bar,

50µm (Fu Li and Carmen Cantemir-Stone). C. Whole mount carmine staining of inguinal mammary glands from 6 week old PyMT;Ets2db/loxP and PyMT;Fsp-

Cre;Ets2db/loxP mice. L.N., lymph node. Scale bar, 2mm (Julie Wallace and Carmen

Cantemir-Stone). D. Left: representative IHC staining for Ki67 in mammary glands of

10 week old PyMT;Ets2db/loxP and PyMT;Fsp-Cre;Ets2db/loxP mice. Scale bar, 50μm.

Right: graph represents percentage of Ki67 positive epithelial cells (n=3, bars represent means ± SD, **P<0.01, unpaired Welch’s t-test assuming unequal variance) (Carmin

Cantemir-Stone).

A ** 25

20 (gram) 15

Weight 10

Tumor 5

0 PyMT; PyMT;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=21 N=17 B PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loP

C PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loP

LN LN 6 week 6

** 80 D PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loP 60

Ki67 20 %Ki67+Cells

0 PyMT; PyMT;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=3 N=3

Figure 3.3

3.2.3 Regulation of PyMT Tumor Specific Gene Expression by Ets2

To understand the mechanisms underlying the delay in tumor progression observed in the absence of Ets2 in fibroblasts, we performed global gene expression profiling on RNA isolated from primary mammary fibroblasts using the Affymetrix

Mouse Genome 430A 2.0 GeneChip platform. This analysis was done at the Ohio State

Comprehensive Cancer Center (OSUCCC) Microarray Shared resource. We were interested in early changes to these fibroblasts, and therefore harvested mice that were

10-weeks old. In order to determine the changes that were tumor specific, we profiled fibroblasts from the following genetic groups: Ets2db/loxP, FspCre;Ets2db/loxP, MMTV-

PyMT;Ets2db/loxP and MMTV-PyMT;FspCre;Ets2db/loxP. WEDGE++ analysis was used for data normalization and to determine significant changes in gene expression among our genetic groups of interest (Auer et al., 2007). To identify genes regulated by Ets2 in a tumor specific manner, we compared gene expression changes between non-tumor or normal fibroblasts (NF) and tumor associated fibroblasts (TAFs). Comparison of normal fibroblasts with or without Ets2 yielded only 22 differentially expressed genes (Table

3.1). However, in the context of epithelial expression of PyMT, Ets2 was shown to regulate 107 genes (Table 3.2 and Figure 3.4A). qRT-PCR verified >85% of these tumor specific changes in at least two independent sets of fibroblasts tested (Figure 3.4B).

Comparison of differentially expressed genes in normal vs. tumor fibroblasts revealed only 5 genes commonly regulated by Ets2, which therefore indicates the function of Ets2 in fibroblasts to be driven by oncogenic signaling from neighboring epithelium (Figure

3.4C).

To gain a better understanding of the biological processes being effected by Ets2 deletion in tumor associated fibroblasts, we utilized gene set enrichment analysis

(GSEA)(Subramanian et al., 2005). For this analysis, we queried our filtered gene expression data against gene sets representing processes known to be involved in tumorigenesis. The processes of ECM remodeling, cell migration and cell growth were all found to be significantly enriched (p<0.05) in control PyMT;Ets2db/loxP fibroblasts, indicating that Ets2 is important for these functions (Figure 3.5A). Additionally, angiogenesis was also enriched in these control tumor fibroblasts, however the p-value associated with this correlation did not reach significance (p=.193). Interestingly, the process of response to drug was found to be significantly enriched in fibroblasts from

PyMT;FspCre;Ets2db/loxP mice, indicating that stromal Ets2 function may be important in the efficacy of treatment or in resistance to drugs (Figure 3.5B). Our gene set containing genes involved in inflammatory response was also found to be enriched in Ets2 null tumor fibroblasts, however the p-value associated with this correlation did not reach significance (p=0.068).

Table 3.1 Genes Regulated by Ets2 in normal 8-week mammary fibroblast.

Probeset NLP Ets2+- Ets2-- GENE Log Fold N N Change 1449832_at 5.44 3.316 5.407 1700091H14Rik 2.091 1416200_at 7.13 7.332 9.767 9230117N10Rik 2.435 1427974_s_at 4.60 4.051 6.844 Cacna1d 2.793 1417795_at 4.60 3.982 6.909 Chl1 2.927 1455435_s_at 4.60 6.056 8.090 Chdh 2.034 1437458_x_at 6.28 5.818 8.395 Clu 2.577 1422592_at 5.44 6.199 3.742 Ctnnd2 -2.457 1450839_at 4.60 6.286 8.338 D0H4S114 2.052 1419332_at 7.13 6.062 8.797 Egfl6 2.735 1424007_at 8.82 9.857 12.086 Gdf10 2.229 1421973_at 4.60 4.157 6.282 Gfra1 2.125 1423171_at 7.97 6.338 8.866 Gpr88 2.528 1427300_at 7.13 9.024 11.511 Lhx8 2.487 1423607_at 8.82 9.261 11.622 Lum 2.361 1424234_s_at 4.60 4.603 2.554 Meox2 -2.049 1425521_at 7.13 7.711 4.244 Paip1 -3.467 1418759_at 4.60 3.798 6.354 Ptpn20 2.556 1421856_at 4.60 3.141 7.397 S100a3 4.256 1450826_a_at 7.97 7.676 9.698 Saa3 2.022 1448301_s_at 8.82 5.549 7.763 Serpinb1a 2.214 1417979_at 7.13 8.460 4.252 Tnmd -4.208 1456319_at 4.60 4.621 7.518 X83313 2.897

NLP: Negative log P value. Expression level is represented in log2. Fold change is log2.

Table 3.2 Genes regulated by Ets2 in PyMT tumor associated fibroblasts.

Probeset NLP Ets+-T Ets--T GENE Log Fold Change 1449902_at 4.60 5.058 2.917 1110058A15Rik -2.141 1451382_at 4.60 5.682 8.101 1810008K03Rik 2.419 1420548_a_at 4.60 6.133 3.395 2310008H09Rik -2.738 1427202_at 4.60 5.505 7.511 4833442J19Rik 2.006 1428333_at 4.60 6.598 4.554 6530401D17Rik -2.044 1418979_at 8.82 3.064 6.651 9030611N15Rik 3.587 1420796_at 4.60 5.771 2.841 Ahrr -2.930 1422415_at 4.60 7.304 3.238 Ang2 -4.066 1417130_s_at 4.60 5.883 8.054 Angptl4 2.171 1417732_at 6.28 3.706 5.758 Anxa8 2.052 1419435_at 7.13 5.261 7.464 Aox1 2.203 1439036_a_at 4.60 5.891 3.777 Atp1b1 -2.114 1454838_s_at 6.28 6.788 4.533 AW548124 -2.255 1424652_at 4.60 6.352 3.659 BC014699 -2.693 1434366_x_at 4.60 6.656 4.615 C1qb -2.041 1423954_at 8.82 4.778 7.268 C3 2.490 1424713_at 5.54 4.607 6.686 Calml4 2.079 1418126_at 4.60 5.784 3.763 Ccl5 -2.021 1448698_at 5.44 7.677 10.097 Ccnd1 2.420 1415868_at 4.60 6.814 4.237 Cct4 -2.577 1419703_at 6.28 7.129 4.512 Col5a3 -2.617 1455269_a_at 4.60 5.576 3.430 Coro1a -2.146 1422592_at 5.44 6.986 4.553 Ctnnd2 -2.433 1448710_at 6.28 9.845 5.430 Cxcr4 -4.415 1422812_at 4.60 6.180 3.430 Cxcr6 -2.750 1418507_s_at 7.97 9.074 7.046 D130043N08Rik -2.028 1424625_a_at 5.44 4.821 2.392 Dennd1a -2.429 1438789_s_at 4.60 6.654 4.121 Dpysl3 -2.533 1425295_at 4.60 7.552 5.432 Ear11 -2.120 1424306_at 4.60 6.931 4.293 Elovl4 -2.638 1420964_at 4.60 6.900 4.799 Enc1 -2.101 1419131_at 4.60 3.298 5.426 F13b 2.128 1450779_at 4.60 6.179 3.607 Fabp7 -2.572 1418497_at 4.60 5.389 3.049 Fgf13 -2.340 1438953_at 4.60 8.631 10.706 Figf 2.075 1422977_at 5.44 5.528 3.442 Gp1bb -2.086 1420344_x_at 7.97 9.081 4.644 Gzmd -4.437 (continued)

Table 3.2 (continued)

1450171_x_at 8.82 10.486 7.261 Gzme -3.225 1449455_at 4.60 8.378 5.619 Hck -2.759 1425398_at 4.60 5.209 8.510 Hist1h2bc 3.301 1453573_at 4.60 5.653 7.839 Hist1h3d 2.186 1425874_at 8.82 8.900 6.600 Hoxc13 -2.300 1450783_at 4.60 5.228 7.337 Ifit1 2.109 1449025_at 4.60 4.047 6.787 Ifit3 2.740 1454159_a_at 7.13 6.031 8.923 Igfbp2 2.892 1450091_at 4.60 3.321 5.340 Ighmbp2 2.019 1423608_at 4.60 7.297 4.169 Itm2a -3.128 1418156_at 7.97 8.548 10.775 Kcne4 2.227 1450185_a_at 4.60 4.495 6.574 Kcnj15 2.079 1427679_at 7.13 6.760 4.582 Lats1 -2.178 1415983_at 4.60 8.037 4.878 Lcp1 -3.159 1437477_at 5.44 5.222 3.201 Lrrfip1 -2.021 1431569_a_at 4.60 8.368 4.929 Lypd1 -3.439 1449965_at 7.13 8.637 4.490 Mcpt8 -4.147 1424481_s_at 4.60 6.608 3.397 MGC38735 -3.211 1418377_a_at 4.60 6.619 4.369 MGI:1353606 -2.250 1448416_at 8.82 11.993 9.060 Mgp -2.933 1420450_at 7.13 8.947 5.556 Mmp10 -3.391 1417256_at 4.60 9.058 4.732 Mmp13 -4.326 1418945_at 8.82 9.540 6.874 Mmp3 -2.666 1416298_at 4.60 7.744 3.603 Mmp9 -4.141 1422557_s_at 8.82 10.284 8.215 Mt1 -2.069 1417155_at 7.97 6.549 4.090 Mycn -2.459 1419391_at 4.60 2.841 5.329 Myog 2.488 1450976_at 4.60 3.152 6.114 Ndrg1 2.962 1419663_at 4.60 8.021 10.364 Ogn 2.343 1425521_at 7.13 7.862 4.253 Paip1 -3.609 1449298_a_at 5.44 3.596 7.049 Pde1a 3.453 1421916_at 6.28 4.272 6.834 Pdgfra 2.562 1427038_at 5.44 7.026 2.797 Penk1 -4.229 1421403_at 4.60 6.331 3.969 Pi15 -2.362 1427327_at 8.82 8.634 6.461 Pilra -2.173 1419280_at 5.44 5.583 3.504 Pip5k2a -2.079 1448961_at 6.28 4.363 6.642 Plscr2 2.279 1450905_at 4.60 7.728 3.379 Plxnc1 -4.349 1428494_a_at 4.60 7.306 5.255 Polr2i -2.051 (continued)

Table 3.2 (continued)

1433691_at 6.28 4.050 7.110 Ppp1r3c 3.060 1427414_at 4.60 5.441 3.256 Prkar2a -2.185 1432331_a_at 5.44 6.984 4.668 Prrx2 -2.316 1448899_s_at 4.60 6.739 4.342 Rad51ap1 -2.397 1450107_a_at 5.44 8.156 5.803 Renbp -2.353 1424143_a_at 6.28 7.948 5.869 Ris2 -2.079 1455893_at 7.97 3.941 8.618 Rspo2 4.677 1421856_at 4.60 3.188 7.896 S100a3 4.708 1434342_at 4.60 2.869 5.277 S100b 2.408 1450826_a_at 7.97 8.169 5.679 Saa3 -2.490 1423010_at 6.28 6.630 3.735 Sacs -2.895 1448658_at 4.60 6.223 4.163 Sart1 -2.060 1427020_at 7.97 6.793 4.367 Scara3 -2.426 1419100_at 8.82 5.676 7.891 Serpina3n 2.215 1416318_at 8.82 5.119 8.263 Serpinb1a 3.144 1419082_at 4.60 8.875 3.864 Serpinb2 -5.011 1422804_at 8.82 6.615 9.836 Serpinb6b 3.221 1452031_at 7.97 5.150 7.208 Slc1a3 2.058 1422648_at 5.44 5.092 7.120 Slc7a2 2.028 1418425_at 4.60 6.937 3.559 Sp7 -3.378 1424415_s_at 6.28 7.940 4.261 Spon1 -3.679 1456355_s_at 5.44 6.700 4.154 Srr1 -2.546 1417725_a_at 4.60 6.977 3.521 Sssca1 -3.456 1420894_at 4.60 6.629 3.756 Tgfbr1 -2.873 1416198_at 4.60 5.493 3.036 Th1l -2.457 1417109_at 4.60 5.704 8.109 Tinagl 2.405 1450731_s_at 5.44 10.485 8.320 Tnfrsf21 -2.165 1456225_x_at 4.60 4.904 7.419 Trib3 2.515 1450004_at 4.60 8.700 3.607 Tslp -5.093 1434243_s_at 4.60 7.533 5.053 Tomm70a -2.480 1437281_x_at 4.60 6.330 4.138 Xab2 -2.192

NLP: Negative log P value. Expression level is represented in log2. Fold change is log2.

Figure 3.4 Ets2 Driven Transcription Program

A. Heatmap representing expression levels of 107 upregulated and downregulated genes in PyMT;Ets2db/loxP vs. PyMT;Fsp-Cre;Ets2db/loxP fibroblasts in all indicated genotypes harvested from 9 week old mice (n=1, Log fold change>2, Negative Log P value

(NLP)>4.5) (Carmen Cantemir-Stone and Thierry Pecot). B. Quantitative RT-PCR analysis of indicated genes in independent primary fibroblasts of indicated genotypes.

Gene expression is normalized to Rpl4 expression, and graphed as fold difference between genotypes (values are means between duplicates of one representative sample ±

SD) (Julie Wallace). C. Venn diagram depicting the number of similarly regulated genes in normal fibroblasts (Ets2db/loxP vs. Fsp-Cre;Ets2db/loxP, grey circle) and tumor fibroblasts

(PyMT;Ets2db/loxP vs. PyMT;Fsp-Cre;Ets2db/loxP, white circle) harvested from 9 week old mice (Julie Wallace).

A B PyMT - - + + Ets2 +/- -/- +/- -/- 3 Tslp 6 Lcp1 40 MMP9 2.5 5 30 2 4 1.5 3 20 1 2 10 0.5 1 0 0 0

30 MMP13 5 Serpinb1a 15 Serpinb6b 25 4 20 10 3 15 2 10 5 5 1 0 0 0

30 MCPT8 4 Cxcr4 PyMT;Ets2db/loxP 25 PyMT;FspCre;Ets2db/loxP 3 20 15 2 10 1 5 0 0 C

18 4 103

Normal fibro Tumor fibro

1.5 0 1.5

Figure 3.4

Figure 3.5 Gene Set Enrichment Analysis (GSEA) of Tumor Specific Ets2 Regulated

Genes

A. GSEA plots depicting ECM remodeling, angiogenesis, cell growth and cell migration to be enriched in PyMT;Ets2db/loxP fibroblasts as compared to PyMT;Fsp-Cre;Ets2db/loxP fibroblasts. NES: normalized enrichment score (Julie Wallace). B. GSEA plots depicting response to drug and inflammatory response to be enriched in PyMT;Fsp-

Cre;Ets2db/loxP fibroblasts as compared to PyMT;Ets2db/loxP fibroblasts. NES: normalized enrichment score (Julie Wallace).

A NES=1.52 NES=1.13 P-value=0.01 P-value=0.193

NES=1.42 NES=1.34 P-value=0.007 P-value=0.01

B NES= -1.27 NES= -1.26 P-value=0.04 P-value=0.068

Figure 3.5

3.2.4 Ets2 Directly Binds and Induces Expression of MMP9

Since Ets2 is a transcription factor known to directly bind DNA promoter regions and induce expression of downstream targets, we wanted to determine whether any of these genes we found misregulated in our microarray experiments were direct Ets2 targets. Previous work from our lab had identified matrix metallopeptidase 9 (Mmp9) as a potential target of Ets2 in endothelial cells during embryonic angiogenesis (Wei et al.,

2009). Furthermore, Mmp9 is known to be important in remodeling of the ECM and also for release of growth factors within this matrix (Bergers et al., 2000). Using qRT-PCR, we were also able to show Mmp9 to be consistently downregulated multiple

PyMT;FspCre;Ets2db/loxP fibroblast samples as compared to controls (see Figure 3.4B).

Examination of the proximal promoter region of the Mmp9 gene revealed there to be a conserved Ets2 binding domain (TTCC) approximately 325 base pairs upstream from the transcription start site. Additionally, a conserved activator protein 1 (AP-1) binding domain was adjacent to this Ets2 site, which has been shown to serve as a positive regulator of transcription (Verde et al., 2007). Therefore to test whether Ets2 was directly binding to this promoter to induce transcription, we performed a chromatin immunoprecipitation assay using antibodies for both Ets2 and phosphorylated RNA polymerase II (p-Pol II). As expected, we saw increased binding of the Ets2 and p-Pol II on the Mmp9 promoter in PyMT;Ets2db/loxP fibroblasts as compared to

PyMT;FspCre;Ets2db/loxP cells (Figure 3.6A). As a control, we also used and IgG antibody to determine the amount of non-specific pull down. There was no significant

100 difference in the binding of IgG to the Mmp9 promoter, therefore re-enforcing the specific binding of Ets2 and p-Pol II.

In addition to this in vitro based assay, we also wanted to examine the effect of

Ets2 deletion on Mmp9 protein activity in vivo. To do this, we used a gelatinase in situ zymography assay in which DQ-gelatin releases a fluorescent signal when cleaved by

Mmp9 or Mmp2 (which did not change upon Ets2 deletion in fibroblasts)(Mook et al.,

2003). Examination of frozen tissue sections revealed a significant decrease in Mmp9 activity upon fibroblast specific deletion of Ets2 (Figure 3.6B). This Mmp9 activity was shown to be most abundant around the tumor edge, and was particularly acute within collagen rich stromal locations, which was determined by staining consecutive sections with trichrome (Figure 3.6B). Therefore, these result show direct transcriptional regulation of Mmp9 by Ets2 in fibroblasts, which also effects the activity of Mmp9 protein within the ECM.

101

IgG PyMT;Ets2db/loxP PyMT;Fsp-cre;Ets2db/loxP A Ets2 B p-Pol-II 10 *

** Gelatinase 8

6 MMP9 Pulldown 4

Average 2

0 PyMT; PyMT;Fsp- Trichrome Ets2db/loxP Cre;Ets2db/loxP n=2 n=2

Figure 3.6 Direct Regulation of MMP9 by Ets2 in Stromal Fibroblasts

A. Chromatin immunoprecipitation (ChIP) analysis of ETS2 and phospho-RNA

Polymerase II recruitment to the Mmp9 promoter in PyMT;Ets2db/loxPand PyMT;Fsp-

Cre;Ets2db/loxP fibroblasts (n=2, *P<0.05, ** P<0.01, bar values represent the mean and error bars represent SD.; for statistical analysis the Student’s t-test was used). B.

Consecutive sections stained for Mmp9 gelatinase activity (top) and trichrome (bottom) and counterstained with DAPI from PyMT;Ets2db/loxP and PyMT;Fsp-cre;Ets2db/loxP mammary tumors (Julie Wallace).

102

3.2.5 Ets2 in Fibroblasts Promotes Angiogenesis Through VEGFR2 Signaling in

Endothelial Cells

The importance of fibroblasts in the process of tumor angiogenesis has been well established. Specifically, fibroblasts have been shown to be the main source of VEGF and FGF2 which are two of the major growth factors involved in angiogenesis (Folkman et al., 1988; Fukumura et al., 1998). Additionally, proteins derived from fibroblasts have been shown to be required for endothelial cell lumen formation (Newman et al., 2011).

Since the process of angiogenesis was enriched in fibroblasts isolated from

PyMT;Ets2db/loxP mice, we were interested in determining whether stromal Ets2 was important for the formation of blood vessels in tumors in vivo.

The first approach we used to examine this was to visualize tumor vasculature in vivo by intra-cardiac injection of FITC-labeled lectin (Inai et al., 2004). Assessment of tissue sections using fluorescent microscopy revealed a marked decrease in functional blood vessels in tumors from PyMT;FspCre;Ets2db/loxP mice, thus implicating an important role for fibroblast Ets2 in this tumor associated angiogenesis (Figure3.7A).

Since blood vessels are formed mainly by endothelial cells, we confirmed this result by examining the amount of CD31 positive endothelial cells present in mammary tumors from PyMT;Ets2db/loxP and PyMT;FspCre;Ets2db/loxP mice. As expected, we again saw a significant decrease in the percentage of CD31 positive area in PyMT;FspCre;Ets2db/loxP tumors (Figure 3.7B).

Although this data indicates the importance of fibroblast Ets2 in tumor blood vessel formation, we were interested in determining a mechanism by which this signaling

103 was affecting endothelial cells. As mentioned previously, fibroblasts are a major source of VEGF in the tumor microenvironment, and as we showed also produce active Mmp9

(Fukumura et al., 1998). Mmp9 has been shown to mediate the release of matrix bound

VEGF-A to its active isoforms, including VEGF164 (Lee et al., 2005). Visualiztion of this growth factor revealed significantly decreased levels of VEGF164 in stroma deleted Ets2 tumors (Figure 3.8A). Several receptors for the VEGF164 ligand are located on the surface of endothelial cells, more specifically VEGFR1 and VEGFR2 (also known as Flt1 and Flk1, respectively), which upon phosphorylation become activated to induce proliferation and migration of endothelial cells. In particular, VEGFR2 is one of the most potent mediators of VEGF induced endothelial signaling and angiogenesis (Millauer et al., 1993). Activation of this particular signaling molecule can be assessed by using an antibody that recognizes the phospho-activated form of this VEGF receptor, VEGFRY1173

(Sakurai et al., 2005). Therefore by co-staining with CD31 and VEGFRY1173, we could identify this activated receptor on endothelial cells. This analysis revealed a 4-fold decrease in the number of CD31/VEGFR2Y1173 double positive cells in

PyMT;FspCre;Ets2db/loxP tumors (Figure 3.8B).

104

** A PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loxP 30

20 + Area +

Lectin Lectin

% % 0 PyMT; PyMT;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=3 N=3 PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loxP B 20 ** 15

5 %CD31+ %CD31+ Area CD31/DAPI 0 PyMT; PyMT;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=4 N=4

Figure 3.7 Ets2 in Fibroblasts Promotes PyMT Associated Tumor Angiogenesis

A. Left: in vivo tumor vasculature visualized by intracardiac injection of FITC lectin

(green). Scale bar, 200μm. Right: graph represents percent lectin positive area quantified using ImageJ (n=3, bars represent means ± SD, ** P<0.01, Welch’s t-test assuming unequal variance) (Carmen Cantemir-Stone). B. Left: immunofluorescence staining for

CD31 (red) in mammary gland tumors from 10 week old PyMT;Ets2db/loxP and

PyMT;Fsp-Cre;Ets2db/loxP mice. Scale bar, 200μm. Slides were counterstained with DAPI

(blue). Right: graph represents percent CD31 positive area quantified using Fiji (n=3, bars represent means ± SD, ** P<0.01, Welch’s t-test assuming unequal variance) (Julie

Wallace).

105

Figure 3.8 Ets2 in Fibroblasts Activates VEGF Signaling In Endothelial Cells

A. Visualization (left) and quantification (right) of VEGF164 localization (green) in

PyMT;Ets2db/loxP and PyMT;Fsp-cre;Ets2db/loxP mammary tumors. Slides were counterstained with DAPI (blue). Scale bar, 50µm (n=4, bars represent means ± SD,

**P<0.01, students t-test) (Julie Wallace. B. Tumor vascular endothelial cells visualized by IF double staining with CD31 (green) and p-VEGFR2(Tyr1173) (red) (left).

Quantification represents percentage of CD31 cells also positive for p-VEGFR2. Slides were counterstained with DAPI (blue). Scale bar, 50µm (n=3, bars represent means ±

SD, **P<0.01, students t-test) (Julie Wallace).

106

50- A PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loxP 40-

30- /DAPI

20-

164 Average Average Intensity

164 10- VEGF

0- VEGF PyMT; PyMT;Fsp-Cre; PyMT;Ets2db/loxP PyMT;Fsp-Cre;Ets2db/loxP B Ets2db/loxP Ets2db/loxP

N=4 N=4 CD31 **

100-

(%) 80- Y1173 60-

activation 40-

VEGFR2

- p

VEGFR2 VEGFR2 20-

0- PyMT; PyMT;Fsp-Cre; Ets2db/loxP Ets2db/loxP

N=3 N=3 Merge

Figure 3.8

107

3.2.6 Gene Expression Changes in Endothelial Cells Induced by Ets2 Signaling in

Fibroblasts

Due to the changes we observed in tumor associated angiogenesis as a result of

Ets2 deletion in fibroblasts, we were interested in exploring further the molecular changes in surrounding endothelial cells and how these may be effecting new blood vessel formation. To do this, endothelial cells were sorted from 9-week PyMT;Ets2db/loxP and PyMT;FspCre;Ets2db/loxP mammary glands using a FITC-tagged CD31 antibody.

RNA from three samples/genetic group were profiled for global gene expression at the

OSUCCC Microarray Shared Resource using the Affymetrix Mouse Genome 430 2.0

GeneChip platform. Robust Multichip Average method (RMA) was used for data normalization and to define significant changes between our genotypes (Irizarry et al.,

2003). Our comparison of endothelial cells isolated from PyMT;Ets2db/loxP and

PyMT;FspCre;Ets2db/loxP mammary glands yielded 33 genes to be misregulated by at least

2 fold and with a p-value≤0.05 (Table 3.3, Figure 3.9A).

Again we utilized GSEA to determine the biological processes that were being effected in these endothelial cells. Interestingly, ECM remodeling was again one of the top categories that was shown to be enriched in endothelial cells from control

PyMT;Ets2db/loxP mammary glands (Figure 3.9B). Additionally, genes involved in cell adhesion and cell chemotaxis were also over-represented in endothelial cells from mammary glands with fibroblast Ets2.

108

Table 3.3 Genes regulated in endothelial cells as a consequence of Ets2 signaling in

PyMT tumor associated fibroblasts

Probeset p-value Average Average Gene Log Fold Ets2+-T Ets2--T Symbol Change 1442358_at 0.02 6.83 5.76 AA409587 -1.0662 1443639_at 0.04 4.64 5.84 Apcdd1 1.206 1435761_at 0.03 7.57 6.45 BC100530 -1.116 1427434_at 0.03 6.64 5.62 Birc1f -1.0252 1437726_x_at 0.02 12.04 10.75 C1qb -1.2953 1420249_s_at 0.00 9.63 8.48 Ccl6 -1.154 1419684_at 0.02 10.45 9.28 Ccl8 -1.1642 1427168_a_at 0.01 7.76 6.60 Col14a1 -1.1584 1449218_at 0.02 8.33 7.21 Cox8b -1.1168 1435578_s_at 0.00 3.71 5.48 Dab1 1.7726 1435940_at 0.05 6.44 5.39 Dclk1 -1.0483 1459725_s_at 0.02 10.25 8.70 Dcpp1 -1.5478 1435943_at 0.02 7.18 6.05 Dpep1 -1.1327 1450779_at 0.02 4.51 5.64 Fabp7 1.1377 1418243_at 0.01 7.77 6.52 Fcna -1.2505 1456601_x_at 0.00 7.79 6.51 Fxyd2 -1.2723 1439793_at 0.01 6.14 4.99 Gja3 -1.1414 1422224_at 0.01 4.89 5.92 Gm9880 1.0315 1425763_x_at 0.05 10.35 8.52 Igh -1.8272 1439239_at 0.03 6.93 5.72 Lin7b -1.2124 1453678_at 0.02 6.54 5.35 Mbd1 -1.1932 1460462_at 0.00 6.05 7.71 Med18 1.6565 1450194_a_at 0.00 6.80 5.80 Myb -1.0057 1436188_a_at 0.00 6.49 5.24 Ndrg4 -1.2457 1419663_at 0.03 7.51 6.48 Ogn -1.021 1428751_at 0.04 4.50 5.51 Pacrg 1.0099 1420798_s_at 0.01 6.10 4.51 Pcdha1 -1.5971 1443954_at 0.01 6.37 7.58 Rad18 1.2077 1456822_at 0.02 6.31 5.28 Rad23b -1.0315 1438306_at 0.02 5.45 4.32 Rnf180 -1.1357 1458813_at 0.01 7.15 5.83 Scn5a -1.3204 1457275_at 0.02 7.28 8.46 Synm 1.1782 1455299_at 0.00 6.49 4.72 Vgll3 -1.7784

109

Figure 3.9 Endothelial Cell Gene Expression Changes Induced by Fibroblast Ets2

Deletion

A. Heatmap depicting the genes significantly differentially regulated in endothelial cells isolated from PyMT;Ets2db/loxP vs. PyMT;Fsp-Cre;Ets2db/loxP mammary glands harvested at 9 weeks (n=3, fold change>2, P-value≤0.05) (Julie Wallace). B. GSEA plots depicting ECM remodeling, cell adhesion and cell chemotaxis to be enriched in endothelial cells isolated from PyMT;Ets2db/loxP mammary glands as compared to

PyMT;Fsp-Cre;Ets2db/loxP mammary glands. NES: normalized enrichment score (Julie

Wallace).

110

A PyMT + + B Ets2 +/- -/-

NES=1.34 P-value=0.02

NES=1.31 P-value=0.008

NES=1.30 P-value=0.048

1.5 0 1.5

Figure 3.9

111

3.2.7 PyMT Derived Ets2 Fibroblast Gene Signature is Represented in Human

Breast Cancer Stroma

To determine whether our PyMT driven Ets2 dependent fibroblast gene expression signature was relevant to human breast cancer, we queried our expression data against signatures derived from laser captured human breast tumor stroma and normal stroma (Finak et al., 2008). This analysis identified 85 human orthologs from the 107

PyMT derived Ets2 regulated mouse genes in tumor fibroblasts, of which 38 had highly variable expression across all human stromal samples (variance cutoff >0.5). The heat map generated from the human stroma dataset with this 38 gene list demonstrated that our Ets2 signature could distinguish human breast tumor stroma from normal stroma with the exception of only 3 patients (Figure 3.10A). Similar results were observed using

Principal Component Analysis (PCA).

In contrast to these results obtained using our fibroblast gene signature, the endothelial cell gene signature we obtained was not able to distinguish human breast tumor stroma from normal stroma with significance (Figure 3.10B). One potential explanation for this could be that after human orthologues were identified and variance cutoff >0.5 was applied, only 9 of our original 33 genes were left.

112

Figure 3.10 Ets2 Fibroblast and Endothelial Cell Signatures in Human Breast

Cancer Stroma

A. Heat map displaying the expression of the human orthologues of the PyMT derived

38-gene Ets2 signature in normal- and tumor-stroma from human breast cancer patients.

16 genes were upregulated (denoted by red bar on the y-axis) and 22 genes were downregulated (denoted by the blue bar on the y-axis) in Ets2-null tumor fibroblasts. Red and blue regions inside the heat map indicate relative gene expression levels (red, increased and blue, decreased) between the normal and tumor stroma. (P-value=0.00085, two-sided Wilcoxon rank sum test, based on 10,000 permutations) (Julie Wallace and

Thierry Pecot). B. Heat map displaying the expression of the human orthologues of the

PyMT derived fibroblast Ets2 dependent endothelial cell 9 gene signature in normal- and tumor-stroma from human breast cancer patients. Red and blue regions inside the heat map indicate relative gene expression levels (red, increased and blue, decreased) between the normal and tumor stroma. (P-value=0.1014, one-sided Wilcoxon rank sum test, based on 10,000 permutations) (Julie Wallace and Thierry Pecot).

113

Normal Stroma Tumor Stroma B

Tumor Stroma Normal Stroma

Figure 3.10

114

3.2.8 Ets2 Fibroblast and Endothelial Gene Signatures Predict Patient Outcome in

Whole Tumor Datasets

In addition to determining whether our gene signatures had the ability to distinguish normal breast stroma from tumor stroma, we were also interested in determining the predictive value of these signatures in determining outcome derived from various breast cancer datasets. Initially, we used outcome data derived from a subset of patients used to generate the heatmap in section 3.2.7. Interestingly, our 38 gene Ets2 expression signature correlated with patient outcome as measured by tumor relapse in this

McGill dataset (Figure 3.11A). Examination of two independent and well-annotated whole tumor breast cancer datasets, the NKI and Stockholm datasets, revealed 29 and 36 genes of our original 38 to be represented on these platforms, respectively (Gonda et al.,

2010; van de Vijver, 2005). Importantly, the Ets2 fibroblast signature correlated with poor patient outcome in both of these datasets (Figure 3.11A).

Similar analysis also revealed our 9 gene endothelial signature to also predict patient outcome in the McGill stromal data set (Figure 3.11B). Additionally, this signature was also successful in predicting outcome in both the NKI and Stockholm whole tumor studies, in which 7 genes were represented in each (Figure 3.11B).

115

A PyMT Ets2 Fibro Signature McGill Stockholm NKI 1.0 1.0 0.10

0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4

Survival Function Survival Survival Function Survival Survival Function Survival 0.2 0.2 High risk High risk 0.2 Low risk Low risk 0.0 0.0 0.0 0 20 40 60 0 2 4 6 8 0 20 40 60 Time Time Time B PyMT Ets2 Endo Signature McGill Stockholm NKI 1.0 1.0 0.10

0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4

Survival Function Survival Survival Function Survival

0.2 Function Survival 0.2 High risk 0.2 High risk High risk Low risk Low risk Low risk 0.0 0.0 0.0 0 20 40 60 0 2 4 6 8 0 20 40 60 Time Time Time

Figure 3.11 Ets2 Fibroblast and Endothelial Cell Signatures Predict Patient

Outcome

A. Kaplan Meier Curves based on expression of the 29 PyMT driven Ets2 dependent genes present in McGill, Stockholm and NKI data sets (P>0.0001) (Julie Wallace and

Thierry Pecot). B. Kaplan Meier curves based on expression of the 7 endothelial cell genes dependent on Ets2 fibroblast signaling present in McGill, Stockholm and NKI data sets (P>0.0001) (Julie Wallace and Thierry Pecot).

116

3.2.9 Fibroblast Ets2 Function Critical in ErbB2 Induced Tumorigenesis

In addition to studying the function of fibroblast Ets2 in our PyMT driven model, we were also interested in determining whether this transcription factor played a similar role in a different breast cancer model. One reason for this is that although the PyMT model represents distinct stages of tumor progression that are similar to classifications made in human breast tumors, the actual driving oncogene is not relevant in human cancer. Therefore we decided to examine Ets2 function in fibroblasts in the context of

ErbB2 driven tumors. This oncogene has been shown to be amplified/overexpressed in

20-30% of human breast cancers and is correlated with a high relapse rate and poor clinical prognosis (King et al., 1985; Slamon et al., 1987; Yokota et al., 1986). Another reason for using this secondary model of breast cancer was to determine whether fibroblast Ets2 has different functions in the context of different oncogenic signals.

To determine if fibroblast Ets2 function was oncogene dependent, we combined the MMTV-ErbB2 oncogene with Ets2db/loxP and FspCre;Ets2db/loxP alleles. Mice harboring the MMTV-ErbB2 oncogene were harvested at 16 weeks of age, a time just before palpable tumors could be detected. Using histological sections to calculate the number and sizes of carcinoma lesions, we observed nearly half as many overall lesions in ErbB2;Fsp-Cre;Ets2db/loxP mice as compared to ErbB2;Ets2db/loxP controls, and these lesions were also significantly smaller (Figure 3.12A, left panel). Closer examination of the data revealed a 3-fold decrease in the number of lesions >1mm2 in size in mammary glands from ErbB2;Fsp-Cre;Ets2db/loxP mice (Figure 3.12A, right panel). As illustrated by higher magnification H&E staining, deletion of Ets2 in tumor fibroblasts significantly

117 reduced the expansion of carcinoma lesions into the surrounding fat pad tissue (Figure

3.12B). As in the PyMT model, Ki-67 staining revealed a 2-fold decrease in proliferation of epithelial cells surrounded by Ets2 null fibroblasts, implicating a similar mechanism of controlling tumor growth in this model as well (Figure 3.12C).

118

Figure 3.12 Ets2 in Fibroblasts Drives ErbB2 Mediated Tumorigenesis

A. Left: graph represents lesion sizes in mm2 from 16 week old ErbB2;Ets2db/loxP (n=5,

2.28±4.16) and ErbB2;Fsp-Cre;Ets2db/loxP (n=5, 1.83±7.96) mice (** p<0.01, Non- parametric Mann Whitney test). Right: graph represents number of lesions larger than one mm2 in mammary glands of 16 week old ErbB2;Ets2db/loxP (n=5) and ErbB2;Fsp-

Cre;Ets2db/loxP (n=5) mice (**P<0.01, Chi Square test) (Subhasree Balakrishnan). B.

Representative histological sections from mammary glands of 16 week old

ErbB2;Ets2db/loxP and ErbB2;Fsp-Cre;Ets2db/loxP mice. Scale bar, 50µm (Subhasree

Balakrishnan). C. Left: representative IHC staining for Ki67 in mammary glands from

16 week old ErbB2;Ets2db/loxP and ErbB2;Fsp-Cre;Ets2db/loxP mice. Scale bar, 50μm.

Right: graph represents percentage of Ki67 positive epithelial cells (n=3, bars represent means ± SD, *P<0.01, Welch’s t-test assuming unequal variance) (Subhasree

Balakrishnan).

119

A **

60 2

) 2 50 50 ** 40 40 30 30 20 20

10 10

Lesion Size (mm Size Lesion # of Lesions # >1 mm 0 0 ErbB2; ErbB2;Fsp-Cre; ErbB2; ErbB2;Fsp-Cre; Ets2db/loxP Ets2db/loxP Ets2db/loxP Ets2db/loxP N=5 N=5 N=5 N=5

ErbB2; Ets2db/loxP ErbB2;Fsp-Cre; Ets2db/loxP B

** C ErbB2; Ets2db/loxP ErbB2;Fsp-Cre; Ets2db/loxP 60 40

Ki67 20 %Ki67+Cells

0 ErbB2; ErbB2;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=3 N=3

Figure 3.12

120

3.2.10 ErbB2 Drives Similar Tumor Specific Ets2 Dependent Gene Signature in

Fibroblasts

Gene expression profiling was performed on primary mammary fibroblasts isolated from 16 week old Ets2db/loxP, Fsp-Cre;Ets2db/loxP, ErbB2;Ets2db/loxP and

ErbB2;Fsp-Cre;Ets2db/loxP mice for two main reasons: first, to help us elucidate the molecular mechanisms by which fibroblast Ets2 promotes tumorigenesis in this model, and secondly to determine if this transcription factor regulates the same target genes in fibroblasts regardless of the driving oncogene. Our analysis using these samples yielded a similar tumor specific Ets2 dependent gene expression pattern, wherein only 15 genes were identified as differentially expressed in non-tumor Ets2 null fibroblasts at 16-weeks compared to controls, whereas 69 genes changed when Ets2 was inactivated in ErbB2 associated fibroblasts (fold change >4, Tables 3.4, 3.5 and Figure 3.13A). Again confirming the specific role of Ets2 in the context of tumor, only 1 gene was similarly mis-regulated in these two comparisons (Figure 3.13C). Again, qRT-PCR was used to confirm the microarray data, which validated >85% of the expression changes in at least two independent sets of RNA from tumor fibroblasts (Figure 3.13B). To our surprise,

Mmp9 was not shown to be downregulated in Ets2 null fibroblasts in the microarray data.

However, using qRT-PCR we were able to show the expression of Mmp9 to be down in

ErbB2;Fsp-Cre;Ets2db/loxP fibroblasts as compared to controls (Figure 3.13B).

Again utilizing GSEA, we found angiogenesis, ECM remodeling, cell migration and cell growth-related gene sets to be enriched (p-value <0.05) in ErbB2 associated fibroblasts compared to Ets2 null ErbB2 fibroblasts (Figure 3.14A). In addition to these

121 categories, immune response, cell adhesion, wound healing, cell division and mitosis were also significantly enriched in control ErbB2;Ets2db/loxP fibroblasts. This result indicates that Ets2 functions to control many of the same biological processes in TAFs, seemingly regardless of the driving oncogene in epithelial cells. However, the enrichment of additional categories in ErbB2 associated fibroblasts indicates that Ets2 may also have some functions that are oncogene specific.

Closer examination of the differentially expressed Ets2 dependent genes in our

PyMT and ErbB2 models revealed only 9 genes to be either upregulated or downregulated in the same manner. However, this analysis was done only looking at genes with a fold change of 4 or greater, which excludes a lot of the genes used for our

GSEA analysis. Next, we wanted to determine whether the same genes were driving the enrichment of the overlapping biological processes we observed using GSEA. To do this, we found the number of overlapping genes present in the leading edge subset for each gene ontology (GO) category, which are the main genes driving the enrichment score. Interestingly, between 15-20% of genes present in the leading edge subset of GO categories enriched in our PyMT;Ets2db/loxP sample were also present in the leading edge subset of the same category enriched in our ErbB2;Ets2db/loxP sample (Figure 3.14B).

Again, this indicates that although Ets2 may have unique targets that are specific to a particular oncogene, there is still a significant portion that are regulated similarly independent of the oncogene present.

122

Table 3.4 Genes regulated by Ets2 in normal 16-week mammary gland fibroblasts

Probeset Ets2+- Ets2--N GENE Log Fold N Change 1452244_at 8.316 6.095 6330406I15 -2.222 1441054_at 4.194 7.528 Apol8 3.334 1439036_a_at 5.264 7.391 Atp1b1 2.127 1448182_a_at 5.614 8.058 Cd24a 2.443 1437689_x_at 6.477 8.763 Clu /// LO 2.285 1416579_a_at 5.381 7.833 Epcam 2.452 1423935_x_at 5.543 8.268 Krt14 2.725 1448169_at 5.212 8.781 Krt18 3.569 1423691_x_at 5.920 9.397 Krt8 3.477 1457040_at 7.896 5.877 Lgi2 -2.019 1423413_at 7.337 5.257 Ndrg1 -2.080 1426851_a_at 8.195 5.727 Nov -2.469 1427760_s_at 8.296 12.398 Prl2c2 /// 4.102 1421856_at 6.380 9.834 S100a3 3.454 1448201_at 8.378 6.328 Sfrp2 -2.050

123

Table 3.5 Genes regulated by Ets2 in 16-week ErbB2 tumor associated mammary gland fibroblasts

Probeset Ets2+- Ets2--T GENE Log Fold Change T 1444105_at 8.891 6.703 Acta2 -2.189 1415927_at 8.790 5.997 Actc1 /// -2.793 1416871_at 10.108 7.837 Adam8 -2.271 1422789_at 4.634 6.788 Aldh1a2 2.154 1444176_at 7.523 5.201 Atp6v0d2 -2.322 1451620_at 7.855 5.720 C1ql3 -2.135 1442082_at 8.509 5.898 C3ar1 -2.610 1420249_s_at 9.297 6.454 Ccl6 -2.842 1448182_a_at 7.610 4.816 Cd24a -2.794 1423166_at 10.441 6.692 Cd36 -3.750 1449164_at 10.560 8.214 Cd68 -2.347 1420804_s_at 9.257 6.773 Clec4d -2.484 1425951_a_at 7.235 4.437 Clec4n -2.798 1420699_at 8.831 6.367 Clec7a -2.464 1437689_x_at 9.460 5.013 Clu /// LO -4.447 1455660_at 7.119 4.826 Csf2rb -2.293 1448591_at 11.493 8.995 Ctss -2.498 1448823_at 10.494 7.687 Cxcl12 -2.807 1420512_at 4.902 7.231 Dkk2 2.329 1416579_a_at 8.480 5.625 Epcam -2.855 1450779_at 8.197 6.153 Fabp7 -2.044 1460555_at 7.195 5.178 Fam65b -2.017 1418497_at 8.686 6.184 Fgf13 -2.502 1434458_at 9.834 7.743 Fst -2.092 1436530_at 11.177 8.881 Gm11428 -2.296 1420394_s_at 10.982 8.870 Gp49a /// -2.112 1448303_at 10.950 8.913 Gpnmb -2.037 1448194_a_at 8.133 5.684 H19 -2.449 1418645_at 8.792 6.641 Hal -2.151 1435176_a_at 8.326 5.898 Id2 -2.427 1450678_at 9.305 7.163 Itgb2 -2.142 1423935_x_at 8.260 4.927 Krt14 -3.332 1448169_at 8.523 5.007 Krt18 -3.517 (continued) 124

Table 3.5 (continued)

1423952_a_at 8.517 6.283 Krt7 -2.234 1423691_x_at 8.884 5.158 Krt8 -3.726 1436905_x_at 9.285 7.243 Laptm5 -2.042 1415983_at 9.005 6.963 Lcp1 -2.042 1449153_at 11.614 7.869 Mmp12 -3.745 1417256_at 8.436 5.788 Mmp13 -2.647 1418945_at 12.262 8.292 Mmp3 -3.970 1419598_at 7.320 5.186 Ms4a6d -2.133 1448061_at 8.978 6.546 Msr1 -2.432 1452651_a_at 8.231 5.428 Myl1 -2.803 1448371_at 8.335 5.304 Mylpf -3.031 1419391_at 7.313 5.182 Myog -2.130 1426852_x_at 7.299 10.244 Nov 2.945 1450791_at 8.516 5.961 Nppb -2.555 1439794_at 7.937 5.436 Ntn4 -2.501 1448995_at 8.939 6.268 Pf4 -2.671 1430700_a_at 8.600 5.880 Pla2g7 -2.720 1448749_at 8.240 6.151 Plek -2.089 1449824_at 7.805 4.871 Prg4 -2.934 1427760_s_at 9.371 6.409 Prl2c2 /// -2.962 1428538_s_at 11.150 8.619 Rarres2 -2.532 1415905_at 7.656 5.605 Reg1 -2.051 1417466_at 7.845 5.289 Rgs5 -2.556 1421856_at 5.205 9.678 S100a3 4.473 1450826_a_at 10.230 7.995 Saa3 -2.235 1415823_at 9.518 11.636 Scd2 2.118 1448377_at 11.186 8.613 Slpi -2.572 1440311_at 7.293 4.839 Sorbs1 -2.454 1416114_at 7.940 5.684 Sparcl1 -2.256 1438968_x_at 7.750 5.362 Spint2 -2.388 1417455_at 11.957 9.842 Tgfb3 -2.115 1424967_x_at 6.752 4.175 Tnnt2 -2.577 1426175_a_at 8.335 6.275 Tpsab1 -2.060 1450004_at 9.868 4.220 Tslp -5.647 1450792_at 10.267 8.043 Tyrobp -2.225 1419417_at 8.700 6.632 Vegfc -2.068

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Figure 3.13 Ets2 Specific Transcription Program in ErbB2 Associated Fibroblasts

A. Heatmap representing expression levels of 69 upregulated and downregulated genes in

ErbB2;Ets2db/loxP vs. ErbB2;Fsp-Cre;Ets2db/loxP fibroblasts in all indicated genotypes harvested from 16 week old mice (n=1, Log fold change>2) (Subhasree Balakrishnan and

Thierry Pecot). B. Quantitative RT-PCR analysis of indicated genes in independent primary fibroblasts of indicated genotypes. Gene expression is normalized to Rpl4 expression, and graphed as fold difference between genotypes (values are means between duplicates of one representative sample ± SD) (Subhasree Balakrishnan). C. Venn diagram depicting number of similarly regulated genes in normal fibroblasts (Ets2db/loxP vs. Fsp-Cre;Ets2db/loxP, grey circle) and tumor fibroblasts (ErbB2;Ets2db/loxP vs.

ErbB2;Fsp-Cre;Ets2db/loxP, white circle) harvested from 16 week old mice (Julie Wallace and Subhasree Balakrishnan).

126

A B ErbB - - + + Ets2 +/- -/- +/- -/- 25 Tslp 6 Lcp1 50 Mmp3 20 5 40 4 15 30 3 10 20 2 5 1 10 0 0 0

40 Saa3 10 Mmp9 4 Mmp13

30 8 3 6 20 2 4 10 2 1 0 0 0

5 S100a3 4 ErbB2;Ets2db/loxP ErbB2;FspCre;Ets2db/loxP 3 2 1 0 C

14 1 68

Normal fibro Tumor fibro

1.5 0 1.5

Figure 3.13

127

Figure 3.14 Ets2 Promotes Similar Biological Processes in ErbB2 Associated

Fibroblasts

A. GSEA plots depicting ECM remodeling, angiogenesis, cell growth and cell migration to be enriched in ErbB2;Ets2db/loxP fibroblasts as compared to ErbB2;Fsp-Cre;Ets2db/loxP fibroblasts. NES: normalized enrichment score (Julie Wallace). B. Venn diagrams depicting the number of common genes in the leading edge subset of indicated GO categories enriched in PyMT;Ets2db/loxP fibroblasts (gray circles) and ErbB2;Ets2db/loxP fibroblasts (white circles) (Julie Wallace).

128

A B ECM Remodeling

NES=1.89 P-value<0.001 10 15 37

PyMT fibro ErbB2 fibro

Angiogenesis

NES=2.06 P-value<0.001 16 21 49

PyMT fibro ErbB2 fibro

Cell Growth NES=1.45 P-value=0.002 18 14 42

PyMT fibro ErbB2 fibro

Cell Migration NES=1.76 P-value<0.001

38 37 100

PyMT fibro ErbB2 fibro

Figure 3.14

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3.2.11 Ets2 in ErbB2 Associated Fibroblasts Promotes Tumor Angiogenesis

To determine whether the angiogenesis promoting functions of Ets2 in fibroblasts were retained in ErbB2 induced tumorigenesis, we once again visualized the vasculature in ErbB2;Ets2db/loxP and ErbB2;Fsp-Cre;Ets2db/loxP tumors by lectin injections and CD31 staining. Intrafemoral injection of lectin once again revealed a significant decrease in the vascular network in stromal Ets2 deleted tumors compared to controls (Figure 3.15A).

Striking differences in staining were especially observed around the edge of tumors, where tumor blood vessels were infiltrating the malignant lesions. Consistently, we also observed a decrease in CD31 positivity in ErbB2;Fsp-Cre;Ets2db/loxP tumors (Figure

3.15B).

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ErbB2;Ets2db/loxP ErbB2;Fsp-Cre;Ets2db/loxP ** A 15

10 + Area +

Lectin Lectin

% % 0 ErbB2; ErbB2;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=3 N=3 ** B ErbB2;Ets2db/loxP ErbB2;Fsp-Cre;Ets2db/loxP 20

CD31/DAPI % CD31+ CD31+ % Area 0 ErbB2; ErbB2;Fsp-Cre; Ets2db/loxP Ets2db/loxP N=3 N=3

Figure 3.15 Ets2 Promotes ErbB2 Associated Angiogenesis

A. Left: in vivo tumor vasculature visualized by intracardiac injection of DyLight 594 lectin (green). Scale bar, 200μm. Right: graph represents percent lectin positive area quantified using Fiji (n=3, bars represent means ± SD, ** P<0.01, Welch’s t-test assuming unequal variance) (Subhasree Balakrishnan and Raleigh Kladney). B. Left: immunofluorescence staining for CD31 (red) in mammary gland tumors from 16 week old ErbB2;Ets2db/loxP and ErbB2;Fsp-Cre;Ets2db/loxP mice. Scale bar, 200μm. Slides were counterstained with DAPI (blue). Right: graph represents percent CD31 positive area quantified using Fiji (n=3, bars represent means ± SD, ** P<0.01, Welch’s t-test assuming unequal variance) (Subhasree Balakrishnan).

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3.2.12 ErbB2 Driven Ets2 Dependent Signature Represented in Human Stromal

Data and Predicts Patient Outcome

To determine if our Ets2 signature from ErbB associated fibroblasts was also represented in human breast cancer, we once again queired the expression of these genes in human breast cancer stroma. From this 69 gene tumor specific list, 57 human orthologs were identified and 36 of these genes were variably expressed across all human stomal samples (variance cutoff >0.05). This 36 gene signature was found to be enriched in human breast tumor stroma with only 2 patients mis-categorizing (Figure 3.16A).

Again, PCA analysis confirmed this result. The expression of these 36 genes was significantly correlated with patient outcomes in the McGill stromal data set, as well as those from the NKI and Stockholm whole tumor datasets, where 30 and 33 genes were represented, respectively (Figure 3.16B).

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Figure 3.16 ErbB2 Induced Ets2 Dependent Signature Represented in Human

Tumor Stroma and Predicts Patient Outcome

A. Heat map displaying the expression of the human orthologues of the ErbB2 derived

36-gene Ets2 signature in normal- and tumor-stroma from human breast cancer patients.

3 genes were upregulated (denoted by red bar on the y-axis) and 33 genes were downregulated (denoted by the blue bar on the y-axis) in Ets2-null tumor fibroblasts. Red and blue regions inside the heat map indicate relative gene expression levels (red, increased and blue, decreased) between the normal and tumor stroma. (P-value=0.00004, two-sided Wilcoxon rank sum test, based on 10,000 permutations) (Julie Wallace and

Thierry Pecot). B. Kaplan Meier curves based on expression of the 30 ErbB2 driven Ets2 dependent genes present in the McGill, Stockholm and NKI tumor data sets (P>0.0001)

(Julie Wallace and Thierry Pecot).

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Normal Stroma Tumor Stroma

B ErbB2 Ets2 Fibro Signature McGill Stockholm NKI 1.0 1.0 0.10

0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4

Survival Function Survival Survival Function Survival 0.2 Function Survival 0.2 High risk 0.2 High risk High risk Low risk Low risk Low risk 0.0 0.0 0.0 0 20 40 60 0 2 4 6 8 0 20 40 60 Time Time Time

Figure 3.16

134

3.3 Discussion

Although multiple studies have shown the importance of tumor fibroblasts to epithelial tumor progression, few studies have actually identified specific signaling pathways within these fibroblasts that are critical for these functions. Because these stromal cells are thought to remain more genetically stable than epithelial tumor cells, fibroblasts remain an attractive target for therapeutic intervention. Through these genetic studies, we provide several advances in the study of tumor associated fibroblasts.

First, we show that Ets2 promotes tumor growth from the stromal compartment, at least in part through modulation of an oncogenic gene expression program. Other work from our lab not presented in this dissertation provides evidence that this role of Ets2 in fibroblasts is specific, as its function in tumor epithelial cells was shown to be dispensible. Additionally, another study from our group examining the role of Ets2 in tumor associated macrophages (TAMs) revealed distinct genes to be regulated by this transcription factor in this different cell type (Zabuawala et al., 2010). Interestingly, Ets2 also promotes angiogenesis from this macrophage compartment, however this effect is mediated by repression of anti-angiogenic genes rather than by activation of pro- angiogenic genes as we have shown here. This hightlighs the cell type specific mechanisms of Ets2 function within individual compartments of the tumor microenvironment.

Secondly, we also show these effects of fibroblast Ets2 to be tumor specific, thereby sparing changes in the normal development and function of the mammary gland.

Therefore targeting this transcription factor in fibroblasts would likely have few side

135 effects. Along the same lines, we deteremined Ets2 to be a “master regulator” of angiogenesis from the fibroblast compartment. Therefore, targeting Ets2 in fibroblasts may be a more wide range therapeutic option. This is especially relevant due to the lack of success in most current anti-angiogenic clinical trials typically targeting single molecules such as VEGF. Recent studies examining tumors that were refractory to anti-

VEGF therapy contained fibroblasts that secreted platelet derived growth factor C

(PDGF-C), which was able to induce angiogenesis (Crawford et al., 2009). Therefore therapeutic strategies targeting multiple angiogenic molecules in fibroblasts might have more success.

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Chapter 4: The Role of Pten in Fibroblasts During Mammary Tumor Initiation and

Progression

4.1 Introduction

Pten is one of the most commonly mutated genes in cancer, and importantly immunoreactivity of PTEN protein has been shown to be lost in approximately 40% of breast cancers (Perez-Tenorio et al., 2007). Germline mutations in Pten are also associated with Cowden’s disease, in which female patients have an estimated 25-50% risk of developing breast cancer in their lifetime (Gustafson et al., 2007) Additionally, mutations in Pten have been found in the stroma of human breast tumors, although the methods involved in this data remain controversial (Kurose et al., 2002). In the previous chapter, we determined Ets2 to play a unique role in stromal fibroblasts to promote tumorigenesis, however loss of this transcription factor in tumor epithelial cells has been shown to have no impact on tumorigenesis, thereby indicating cell type specific functions of Ets2. Therefore, it may also be likely for Pten to have unique functions in stromal fibroblasts as well.

Again using a genetic mouse model, we demonstrated loss of Pten in stromal fibroblasts to promote ErbB2 driven tumorigenesis. Gene expression profiling of fibroblasts isolated from PtenloxP/loxP and FspCre;PtenloxP/loxP mice revealed inflammation and ECM remodeling to be the key biological processes effected by Pten in these cells.

137

Interestingly, our previously studied transcription factor was upregulated at both mRNA and protein levels in these cells, and was show bind to the promoter and regulate expression of several genes that were upregulated upon Pten loss. Furthermore, we demonstrate the paracine abilitys of fibroblast Pten to alter global gene expression in surrounding micronevirnoment compartments. Interestingly, gene expression signatures from fibroblasts, epithelial cells, endothelial cell and macrophages from PtenloxP/loxP and

FspCre;PtenloxP/loxP mammary glands were represented in human breast tumor stroma and/or were important in predicting outcome in independent whole tumor datasets.

4.2 Results

4.2.1 Efficient and Specific Deletion of Pten by FspCre

To determine the function of Pten in mammary stromal fibroblasts, we conditionally deleted this gene by breeding FspCre mice with PtenloxP/loxP mice. As an initial step, we wanted to examine how efficient our FspCre was at recombining these two floxed alleles of Pten. Cells were isolated from mammary glands of 8 week old control PtenloxP/loxP and experimental FspCre;PtenloxP/loxP mice and used for further analysis. PCR using genomic DNA isolated from purified fibroblast cultures showed efficient recombination of both PtenloxP alleles in the presence of FspCre (Figure4.1A).

Isolation of RNA from PtenloxP/loxP and FspCre;PtenloxP/loxP fibroblasts also revealed a decrease in Pten mRNA in FspCre;PtenloxP/loxP cells (Figure 4.1B). Furthermore,

Western blot analysis showed a dramatic decrease in the level of PTEN protein present in

FspCre;PtenloxP/loxP fibroblasts compared to control PtenloxP/loxP fibroblasts (Figure 4.1C).

IHC analysis of PTEN protein on tissue sections from 8 week old PtenloxP/loxP and

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FspCre;PtenloxP/loxP mice revealed a specific loss of PTEN in the stroma surrounding mammary ducts (Figure 4.1D). This analysis of Pten at the DNA, RNA and protein level consistently proves efficient deletion.

Although we had shown our FspCre transgene to be fibroblast specific in the context of Ets2, we also wanted to show this same specificity was maintained after breeding with our PtenloxP/loxP mice. To ensure Cre expression was restricted to fibroblasts and not other cells present in the stroma, we isolated fibroblasts we well as epithelial cells, endothelial cells and macrophages from mammary glands of PtenloxP/loxP and FspCre;PtenloxP/loxP mice that also had a Rosa-lacZ reporter allele. Using qRT-PCR, we were able to show the expression of lacZ to be nearly 200-fold higher in

FspCre;PtenloxP/loxP fibroblasts when compared to control PtenloxP/loxP cells (Figure 4.1E).

Additionally, lacZ expression in epithelial cells, endothelial cells and macrophages did not change significantly when comparing between cells isolated from PtenloxP/loxP and

FspCre;PtenloxP/loxP mice (Figure 4.1E).

139

Figure 4.1 Efficient and Specific Deletion of Pten in Mammary Stromal Fibroblasts

A. Pten deletion in the Fsp-cre;PtenloxP/loxP mice by PCR-based assays. DNA extracted purified primary mammary stromal fibroblasts with the indicated genotypes were used as templates for PCR-based measurement of Pten deletion (Fu Li). B. qRT-PCR analysis of

Pten expression in mammary stromal fibroblasts of indicated genotypes (n=3) (Julie

Wallace). C. Representative Western blot analysis of mammary fibroblast lysates derived from 8 week-old PtenloxP/loxP mice with (+) without (-) Fsp-cre (Fu Li). D. Paraffin sections from 8 week-old female mammary glands stained with a Pten-specific antibody; lu, lumen; epi, epithelial compartment; str, stromal compartment; red dotted line indicates the border between the two compartments (Chris Thompson). E. Macrophage (F4/80) and endothelial cells (CD31) were isolated by flow cytometry, using the indicated cell marker, and epithelial and fibroblasts cells were isolated by cell culture from Fsp- cre;Ptenf/f;Rosa26loxP mice. RNA was purified from each cell type and the expression of the conditional lacZ reporter gene was quantified by qRT-PCR (Julie Wallace).

140

A B 100 80

fibroblast gene loxP/loxP H2O 60 + + : Fsp-cre 40

loxP expression null Relative 20 wt 0 - + : Fsp-cre + + : PtenloxP/loxP

PtenloxP/loxP Fsp-cre;PtenloxP/loxP C - + : Fsp-cre D Pten epi str lu

Pten epi Tubulin lu str

E 5000-

500

4000-

400

3000- 300

200- 200

100-

100 Relative gene Relative gene expression

0 - -F4/80++ -CD31++ -Epithel+ -Fibro+ : Fsp-cre + + + + + + + + : PtenloxP/loxP F4/80+ CD31+ Epi. Fibro.

Figure 4.1

141

4.2.2 Deletion of Pten in Fibroblasts Accelerates ErbB2 Driven Tumorigenesis

Due to confounding effects of Pten deletion in fibroblasts in other tissues throughout the body, FspCre;PtenloxP/loxP mice typically do not survive beyond 4 months of age. This fact combined with the long tumor latency period of the ErbB2 model made studying tumor initiation and progression in these mice very difficult (Guy et al., 1992b).

Therefore we employed a mammary gland transplantation model to circumvent these issues (Cases et al., 2004). Mammary glands from 8 week old ErbB2;PtenloxP/loxP and

ErbB2;FspCre;PtenloxP/loxP mice were harvested and subsequently transplanted in the dorsal area of syngeneic FVB/N recipient mice. The transplants were monitored weekly and harvested between 16 to 26 weeks post transplantation. At the 16 week time point, approximately 17% of ErbB2;PtenloxP/loxP mice had developed tumors, however 63% of

ErbB2;FspCre;PtenloxP/loxP mice had developed tumors at this same time point (Figure

4.2A) (Trimboli et al., 2009). Fibroblast Pten deletion alone was not sufficient to induce tumor formation, as no FspCre;PtenloxP/loxP mice developed tumors by 16 weeks (Figure

4.2A,C). Additionally, tumor burden was also significantly increased in

ErbB2;FspCre;PtenloxP/loxP mice as compared to control ErbB2;PtenloxP/loxP mice harvested 26 weeks post transplantation (Figure4.2B).

In additon to this gross analysis of the tumors, we also looked at the tissue histo- pathologically. For this analysis, mammary gland sections from the transplanted tissue were examined under the microscope by a pathologist, and each transplant was categorized as containing normal mammary ducts, mammary intraepithelial neoplasia

(MIN) or carcinoma. Tissue was classified according to the most advanced lesion that

142 was observed (ie mammary tissue containing MIN and carcinoma was classified as carcinoma). Upon expression of ErbB2 alone, approximately 14% of tissues developed carcinoma, whereas 25% and 61% were classified as MIN and normal, respectively

(Figure 4.2D). However, the incidence of carcinoma rose to 63% in tissue with Pten null fibroblasts, whereas diagnoses of MIN remained at 25% and normal fell to 12 % (Figure

4.2D).

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Figure 4.2 Pten Inhibits ErbB2 Tumorigenesis from the Stromal Fibroblast

Compartment

A. Tumor development by 16 weeks in mammary glands with the indicated genotypes.

Tumorigenicity was determined by palpation or histological presentation of adenoma/carcinoma at each implantation site and statistically analyzed using Fisher’s

Exact test, *P≤0.05 (Anthony Trimboli). B. Total tumor burden at 26 weeks post- transplantation in mammary glands with the indicated genotypes. Differences were tested using the non-parametric Wilcoxon Rank Sum test. *P≤0.05 (Anthony Trimboli).

C. Gross examination of tumors collected at 26 weeks post-transplantation (Anthony

Triboli). D. Graphical representation of distribution of histological grading in transplant tissue from indicated genopypes. MIN, mammary intraepithelial neoplasia. Differences in histo-pathological grading were analyzed using Fisher’s exact test, **P<0.01 (Anthony

Trimboli and Shan Naidu).

144

A B * 10010 0 - 1010 - n=8 n=9 90 * 8080 - 88 - 70 0% 17% 63% 6060 - 66 - 4.71 +/- 2.86 50 4040 - 44 -

30 n=5

n=16 n=12 Tumorload(g) 2020 - 22 - 0.73 +/- 1.02 10 00 - 00 - % transplantstumors %with - + + :ErbB2 0.5 +1 1.5 +2 :ErbB22.5 + - + :Fsp-cre - + :Fsp-cre + + + :PtenloxP/loxP + + :PtenloxP/loxP

C ErbB2 ErbB2;Fsp-cre;PtenloxP/loxP

** D 100 80 Carcinoma MIN 60 Normal

40 % Transplants% 20

0 PtenloxP/loxP MMTV- MMTV-ErbB2; n = 16 ErbB2 FspCre; n = 28 PtenloxP/loxP n = 30

Figure 4.2

145

4.2.3 Pten Deletion Drives Pro-Inflammatory Gene Expression Signature in

Fibroblasts

4.2.3.1 Gene Expression in Cultured Fibroblasts

To determine a potential mechanism by which loss of Pten in stromal fibroblasts was contributing to tumor initiation and formation, we isolated mammary fibroblasts from PtenloxP/loxP and FspCre;PtenloxP/loxP mice and performed global gene expression analysis using the Affymetrix Mouse Genome 430A 2.0 GeneChip platform. This analysis was done at the OSUCCC Microarray Shared Resource (MASR). We were interested in changes in these fibroblasts that were Pten specific and preceded tumor development, and therefore looked at cells isolated from 8 week old of PtenloxP/loxP and

FspCre;PtenloxP/loxP mice. Fibroblasts from 3 independent animals of each genotype were used for analysis. RMA method was used for data normalization (Irizarry et al.,

2003). In total, 129 genes were found to be upregulated and 21 genes were downregulated in response to Pten deletion in fibroblasts (Figure 4.3A, B, Table 4.1, fold change >4 and P<0.001) (Trimboli et al., 2009). Functional annotation of these Pten target genes using the Database for Annotation, Visualization and Integrated Discovery

(DAVID) indicated ECM remodeling, wound healing and chronic inflammation as the major biological processes being effected (Dennis et al., 2003; Huang da et al., 2009). A subset of the genes known to be involved in these processes were confirmed using qRT-

PCR (Figure 4.3C).

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Figure 4.3 Loss of Fibrolast Pten Drives Inflammation and ECM Remodeling

A. Heat-map of upregulated genes in Pten-deleted stromal fibroblasts (>4-fold and p<0.01) (Julie Wallace and Carmen Cantemir-Stone). B. Heat-map of downregulated genes in Pten-deleted stromal fibroblasts (>4-fold and p<0.01) (Julie Wallace and

Carmen Cantemir-Stone). C. Quantitative RT-PCR analysis of gene expression of representative genes involved in inflammation and wound healing. Gene expression was analyzed in independent experiments and the average between duplicates of a representative sample are shown ± SD (Julie Wallace).

147

Fsp-cre; Fsp-cre; A loxP/loxP B PtenloxP/loxP Pten PtenloxP/loxP PtenloxP/loxP Fold 1 2 3 1 2 3 Fold 1 2 3 1 2 3 78.0 69.0

4.0 1.0 5.0

Log 2 Downregulation

C Ccl3 Ccl4 Ccl12 Il-1b 4040- 2020- 4020- 10040-

2020- 1010- 2010- 5020-

00- 00- 00- 00- wt P T E N -/- wt P T E N -/- wt P T E N -/- wt P T E N -/- Mmp9 Mmp12 Igfbp5 Cybb 4040- 2020- 2020- 4040-

2020- 1010- 1010- 2020-

00- 00- 00- 00- wt P T E N -/- wt FmodP T E N -/- Prelpwt P T E N -/- Igfbp2wt P T E N -/- Gas6 40- 20- 20- 40- Relative gene expression gene Relative 20 20 40

20- 1010- 1010- 2020- 4.0 6 1.0 Log 2 Upregulation 0- 00- 00- 00- Fsp-cre:- + -wt P+ T E N -/- -wt P T+ E N -/- -wt P T+ E N -/-

Figure 4.3

148

Table 4.1 Genes regulated by Pten in 8-week cultured mammary fibroblasts

Gene Symbol Average Fold Change

--- 11.4 --- 8.5 --- 6 1100001H23Rik 5.8 2310026E23Rik 7.1 Adam8 8 Afp 7.6 AI662270 4.8 AI851790 6.2 Apold1 4.5 Arg1 20.9 Atoh8 -7.9 BC013712 4.8 BC032204 7 Bcl2a1a /// Bcl2a1b /// Bcl2a1d 7.8 C3ar1 7.2 C5ar1 5.3 Ccl12 35.5 Ccl3 77.5 Ccl4 12 Ccl6 17.2 Ccr1 4.8 Cd14 9.8 Cd300lf 9.7 Cd36 8.3 Cd48 8.6 Cd52 4.2 Cd53 4.3 Cd68 6.7 Cd72 6.8 Cd84 4.1 (continued)

149

Table 4.1 (continued)

Cd93 4.1 Cfp 6.4 Clec4a2 5 Clec4d 10.7 Clec4e 33.2 Clec4n 13 Csf2rb2 5.7 Ctsc 8.1 Ctss 8.5 Cxcl2 6.2 Cxcl4 10.6 Cybb 11.6 Dock10 /// LOC630691 4.6 Ebi3 4 Edil3 4.4 Efs -6.7 Emr1 7.4 Erbb3 8 F13a1 15.8 Fcer1g 18.3 Fcgr2b 11.1 Fcgr3 5.6 Fgf5 6.6 Fmod -22.3 Fndc1 -6.2 Fpr1 15.9 Fpr-rs2 19.2 Gas6 -4.9 Gatm 6.8 Gm1960 /// Cxcl3 4.2 Gp49a /// Lilrb4 11.8 Gpr109a 4.3 Hal /// LOC638196 8.9 (continued)

150

Table 4.1 (continued)

Hcls1 7.8 Hdgfrp3 /// Tm6sf1 5.8 Hemt1 5.7 Hk3 5.6 Id4 -4.7 Igf1 -4.7 Igfbp2 -69.3 Igfbp5 13.2 Igsf4a 6.2 Igsf4c 5.3 Igsf6 6 Ikzf1 5 Il1b 45 Il2rg 7.2 Irf8 4.7 Irg1 51.6 Itgb2 9.1 Itgb8 6.6 Kctd12 4.4 Krt19 5 Krt23 7.3 L1cam 5.3 Laptm5 11.4 Lcp1 14.2 Lcp2 7.6 Lgi1 4.2 LOC668101 4.9 Lonrf3 4.2 Lpxn 5.6 Ly9 5.1 Lyzs 14.1 Lzp-s 9.3 Marco 18 (continued)

151

Table 4.1 (continued)

Mfap4 -4.4 Mgp -4.3 Mmp12 15.7 Moxd1 7 Mpeg1 /// LOC671359 13.4 Mrc1 4.5 Ms4a6b 6.5 Ms4a6c 4.7 Ms4a6d 7.9 Ms4a7 4.5 Msr1 13.3 Ncf1 7.7 Ncf2 4.1 Ncf4 5.1 Nckap1l 5 Npy 4.7 Osm 6 Pcsk6 -4.4 Pde8b -4.6 Pdgfb 4 Phb 7.9 Pik3ap1 10 Pilrb1 5.1 Pla2g7 4.4 Pld4 6.9 Plek 11.3 Plp1 4.3 Prelp -19.1 Prg1 10.4 Ptpn6 4.8 Ptprc 8.6 Ptprz1 -5 Rac2 12.6 (continued)

152

Table 4.1 (continued)

Rapgef5 7.6 Rasgef1b 4 Rassf4 7.3 Rerg -9.7 Rnf128 4.1 S100a3 13.6 Selpl 5.5 Sema4f 10.3 Sfrp1 -5.8 Sh3bgrl2 4.1 Slc13a3 4.7 Slc28a2 /// LOC381417 6.3 Smoc1 -4.6 Sned1 -8.4 Syt13 -4.3 Tlr13 4.6 Tlr7 4.3 Tnf 6.4 Tyrobp 13.3 Vav3 4.3 Was 5.5 Wisp2 -5.3

153

4.2.3.2 Collagen1A-YFP Isolated Pten Null Fibroblasts

Although culturing mammary gland fibroblasts allowed us to expand our population of cells in vitro, thereby giving us ample amounts of material for microarrays and other assays, we could not rule out the possibility that culture conditions might cause some sort of selection or other artificial changes to occur in these cells. Therefore we were in need of a tool by which we could directly pull these cells out from digested mammary glands. Since fibroblasts are known to express collagen 1 (Tomasek et al.,

2002), we took advantage of a transgenic mouse model developed in David Rowe’s lab in which a 3.6 kb DNA fragment of the rat collagen type1a1 promoter was linked with yellow fluorescent protein (YFP) (Kalajzic et al., 2002). The expression of this transgene was studied extensively in the context of bone, however since we were interested in using it as an identifier of mammary gland fibroblasts, it was necessary to thoroughly characterize Col1a1-YFP expression in this tissue and the specificity of its expression in fibroblasts. To do this, we initially cultured mammary fibroblasts from mammary glands of Col1a1-YFP mice and checked for YFP expression. As we expected, approximately

95% of our cultured fibroblasts were positive for YFP expression, whereas cells from a non-transgenic animal displayed no expression of YFP (Figure 4.4 A, B). Additionally, we examined YFP expression in frozen tissue sections from Col1a1-YFP mice. This analysis revealed a “halo” of YFP signal coming from the area directly surrounding epithelial ducts, which is the expected location of fibroblasts in the mammary gland

(Figure 4.4C). Although this data definitively proves that mammary fibroblasts express

YFP, it does not rule out the possibility that other cells of the mammary gland may also

154 be positive for YFP expression. To test this, we performed a flow cytometry assay in which we examined the overlap of our YFP+ cell population with cells that were positive for Cdh1, CD31 and F4/80, representing epithelial cells, endothelial cells and macrophages, respectively. This analysis showed a <1% overlap of our YFP+ cell population with any of the previously mentioned markers, therefore indicating that expression of YFP was specific to mammary fibroblasts (Figur 4.5 A-C).

Once the specificity of YFP expression was confirmed, we introduced our

Col1a1-YFP transgene into PtenloxP/loxP and FspCre;PtenloxP/loxP mice to directly sort

YFP+ cells from the mammary glands of these animals and do global gene expression profiling. In general, this YFP+ fibroblast population was found to be around 5%-8% of the total gated cells we were examining using fluorescence activated cell sorting (FACS).

Again this analysis was done at the OSUCCC MASR using the Affymetrix Mouse

Genome 430A 2.0 GeneChip platform. Following RMA normalization, differentially expressed genes between wild type and Pten null fibroblasts were determined

Approximately 92 genes were found to be significantly upregulated in

FspCre;PtenloxP/loxP fibroblasts, whereas 78 genes were significantly downregulated

(Table 4.2, fold change ≥2, p-value ≤0.05). Gene ontology analysis using ToppGene

Suite indicated immune response, apoptosis, angiogenesis and response to wounding to be some of the biological processes represented in these differentially regulated genes, which is consistent with our previous functional annotation from in vitro experiments

(Chen et al., 2007).

155

A Negative Control Negative Control

B Col1aYFP Col1aYFP

C Col1aYFP

Figure 4.4 Expression of ColYFP in Cultured Mammary Fibroblasts

A. Phase contrast (left) and immunofluorescent (right) microscopic images from control mammary fibroblasts (Julie Wallace). B. Phase contrast (left) and immunofluorescent

(right) microscopic images from transgenic Col1aYFP mammary fibroblasts (Julie

Wallace). C. YFP visualization using florescence microscopy on frozen mammary tissue from Col1aYFP transgenic mouse (Julie Wallace).

156

Figure 4.5 ColYFP Expression Does Not Overlap with Expression of Cdh1, CD31 or

F4/80

A. Flow cytometry plots of ColYFP+ cells (x-axis) and Cdh1+ cells (y-axis) and corresponding quantification. Red number indicates percentage of overlapping cells

(Julie Wallace). B. Flow cytometry plots of ColYFP+ cells (x-axis) and CD31+ cells (y- axis) and corresponding quantification. Red number indicates percentage of overlapping cells (Julie Wallace). C. Flow cytometry plots of ColYFP+ cells (x-axis) and F4/80+ cells (y-axis) and corresponding quantification. Red number indicates percentage of overlapping cells (Julie Wallace).

157

A 7.29% .61% 88.6% 3.5%

11.81% .70% 84.12% 3.37%

8.85% .72% 87.15% 3.28%

Figure 4.5

158

Table 4.2 Genes regulated by Pten in 8-week Col1aYFP sorted mammary fibroblasts

Gene Average Wild Average Average Fold p-value Symbol Type Pten Change 2010205A11 8.4685 7.0601 -2.6544 0.03167 2900002J02 4.5798 5.8174 2.3579 0.02944 2900062L11 5.2035 6.3658 2.23829 0.00656 5730469M10 6.3707 7.6905 2.49632 0.00051 A130038J17 5.6972 4.4905 -2.3081 0.00142 A130082M07 6.6938 5.5381 -2.2279 0.00084 A130082M07 6.7621 5.5709 -2.2834 0.00025 A130082M07 5.794 3.6079 -4.5507 7.24E-06 Abcb1a 5.2601 4.0716 -2.2792 0.04946 Acsl1 5.8932 7.7806 3.69968 0.00829 Acsl1 7.8182 9.6902 3.6604 0.0054 Acsl1 6.6979 8.5185 3.53204 0.00608 Acss2 6.236 7.2366 2.00083 0.00444 Adipoq 4.2996 9.753 43.8164 3.63E-06 Aif1l 7.6979 6.4407 -2.3903 0.00766 Aldh1a3 7.5518 6.5263 -2.0357 0.0025 Amph 4.4809 5.5261 2.06365 0.04291 Angpt4 5.894 6.9444 2.0711 0.01655 Apol7c /// 5.2528 3.9857 -2.4068 0.0011 Aqp1 10.1011 9.0716 -2.0413 0.00181 Bcl2a1a // 5.8197 4.7219 -2.1403 0.00027 Bmp2 7.9538 6.7729 -2.267 0.00505 Bub1b 4.8099 6.3717 2.95222 0.03473 Bub1b 5.4979 6.6403 2.20748 0.04257 C1qb 5.8931 7.1357 2.36608 0.00733 C1qb 5.8346 7.0291 2.2885 0.01169 C1qb 6.5607 7.6158 2.07786 0.00278 C1qtnf9 6.4743 5.1147 -2.5661 0.02689 C77370 4.2071 5.4228 2.32253 0.03954 Capn6 6.2966 7.3801 2.11917 0.04857 Car3 8.7591 12.0941 10.091 0.00022 (continued)

159

Table 4.2 (continued)

Car3 6.894 10.0146 8.6975 0.00023 Car8 8.5696 7.35 -2.3288 5.46E-05 Casq2 4.9993 6.5396 2.90855 2.83E-06 Ccl5 7.0197 4.9406 -4.2257 8.67E-07 Ccr7 6.4492 3.5631 -7.3927 2.29E-06 Cd3g 4.5831 3.3673 -2.3227 0.00285 Cd52 6.9348 5.6097 -2.5055 0.00016 Cd69 4.307 2.5271 -3.434 0.00075 Cdkn2c 7.7496 8.921 2.2523 0.00017 Cdo1 6.2173 8.3845 4.49151 0.00196 Ces3 5.8528 7.2217 2.58274 0.02638 Cfd 5.0635 11.08 64.7407 2.51E-07 Cidea 4.3951 6.0268 3.09878 0.03121 Cmbl 5.9238 7.156 2.34925 0.0128 Col23a1 5.7725 6.9088 2.19801 0.00165 Col23a1 6.3935 7.4925 2.14206 0.00362 Coro1a 6.7658 5.4821 -2.4346 0.00029 Crabp1 8.746 10.3275 2.99281 0.01371 Csf1r 7.4693 8.5322 2.08927 0.00661 Csprs /// 5.9771 7.2969 2.49614 0.03944 Csprs /// 4.8845 6.012 2.18465 0.02573 Ctgf 8.9495 10.0969 2.21514 0.00339 Cthrc1 10.0103 11.046 2.05011 0.03948 CU041261.1 4.4963 12.3698 234.525 3.99E-07 Cyp2e1 4.4432 9.3135 29.2487 1.26E-05 Cytip 7.524 6.4008 -2.1783 0.02198 Cytip 7.4013 6.0089 -2.6252 0.00914 Dgat2 6.4973 8.0612 2.95652 8.52E-05 Dgat2 6.4087 7.431 2.03115 0.00051 Dhdh 5.1601 6.4287 2.40928 0.0033 Dmkn 7.7684 5.7903 -3.94 0.00027 Egr3 9.9613 8.586 -2.5942 0.00116 Enpp3 8.7659 7.7243 -2.0585 0.00744 Enpp3 8.3541 7.22 -2.1948 0.00421 Enpp3 8.9977 7.8311 -2.2448 0.00902 (continued) 160

Table 4.2 (continued)

Entpd1 7.6038 6.577 -2.0374 0.0063 Entpd1 8.0981 7.0034 -2.1357 0.00131 Entpd1 8.5349 7.1818 -2.5546 0.00806 Ephx2 6.3436 7.6723 2.51176 0.00287 Epsti1 4.9705 3.743 -2.3414 0.00446 Epyc 7.5113 11.41 14.9151 5.66E-08 Eraf 6.0516 7.7332 3.20761 0.00202 Ets1 8.5527 7.5289 -2.0333 0.00728 Ets1 8.4896 7.1229 -2.579 0.00142 F13a1 6.3048 7.7974 2.81415 5.46E-05 Fasn 8.8176 10.2722 2.74081 0.00043 Fcgr2b 8.9082 9.932 2.03327 0.01012 Fuca2 6.5852 7.6114 2.03665 0.0004 Gimap4 6.7731 5.5421 -2.3475 0.0233 Gjb6 5.0607 6.4104 2.54877 0.04538 Glycam1 8.1913 4.9287 -9.5971 0.00439 Gm106 7.0494 8.5219 2.77502 0.01084 Gm106 6.8143 8.1395 2.50568 0.01088 Gm10883 // 8.6941 7.5028 -2.2837 0.02958 Gm6273 /// 6.3672 5.3476 -2.0274 0.00099 Gm6273 /// 6.3379 4.6104 -3.3115 0.00029 Gsta4 7.6131 8.9871 2.59188 4.52E-06 Gt(ROSA)26 6.6986 5.5449 -2.2248 0.00371 Hist1h1c 9.0927 10.098 2.00736 0.01592 Hist1h2bc 8.0349 9.6379 3.03774 2.53E-06 Hist1h2bc 8.2644 9.713 2.72943 2.57E-06 Hmgcs2 8.5947 9.7156 2.17483 0.02907 Hpgd 8.4424 9.8078 2.57648 0.02509 Hpgd 5.1325 6.3317 2.29612 0.00474 Hsd11b2 7.0701 8.243 2.2548 0.01072 Ifitm1 9.3596 8.028 -2.5166 0.00852 Igh-6 5.9745 4.5053 -2.7687 0.00027 Ighv14-2 5.3985 4.2509 -2.2155 0.00164 Il2rg 6.51 5.0713 -2.7108 0.00177 Il7r 4.9003 3.7386 -2.2372 3.28E-05 (continued) 161

Table 4.2 (continued)

Itih2 5.9807 7.2203 2.36133 0.03054 Kera 8.1464 11.1559 8.05285 0.00103 Klhl29 5.8076 4.6527 -2.2267 0.00642 Krt25 2.8601 7.4082 23.3945 7.87E-09 Krtdap 6.31 4.3161 -3.9834 7.39E-05 Lck 5.695 4.5004 -2.289 0.0007 Ldlr 9.2836 8.2385 -2.0635 0.01337 Lipa 6.4214 7.5615 2.20396 0.00272 Lipa 8.8525 7.157 -3.2389 0.00188 Lipe 6.331 7.3653 2.04812 0.01127 LOC665506 6.469 5.2549 -2.32 0.00057 LOC677317 7.5906 9.2669 3.19607 0.0008 LOC677317 6.6156 7.9789 2.57291 0.00088 Ltb 5.5368 4.4337 -2.1482 0.0003 Meg3 9.9373 11.1985 2.39678 0.00524 Meg3 9.9196 11.1298 2.31386 0.01038 Meg3 8.9534 10.0588 2.15159 0.00603 Meg3 9.7941 10.8857 2.1311 0.0086 Meg3 9.2751 10.3502 2.10672 0.0254 Mgl1 5.4106 6.6658 2.38717 0.00169 Mrap 4.8987 6.2934 2.62934 0.00019 Mrc1 6.0645 7.1673 2.14771 0.00704 Mup1 /// M 2.7294 9.1279 84.3607 6.00E-07 Mup1 /// M 6.0605 8.3432 4.86588 0.00065 Mustn1 7.6424 6.5522 -2.129 0.00597 Nkd2 7.0445 5.9148 -2.1881 0.00252 Nkd2 7.726 6.4843 -2.3649 0.00186 Nr4a2 7.366 6.3042 -2.0875 0.01397 Nr4a2 8.4254 7.3045 -2.1748 0.01234 Nr4a3 9.484 8.2711 -2.3182 0.00366 Nrk 5.7847 7.9424 4.46203 0.00015 Nxnl2 4.8254 5.9182 2.13302 0.03478 Olfm2 5.5603 4.4577 -2.1474 0.04531 P2ry10 5.4414 3.3632 -4.2228 9.90E-05 Pcx 6.2198 8.081 3.6331 0.00147 (continued) 162

Table 4.2 (continued)

Pcx 6.2071 7.7981 3.01258 0.00167 Pcx 5.2274 6.5415 2.48647 0.01218 Prss35 4.2948 6.918 6.16115 0.00035 Ptprc 7.1781 5.707 -2.7721 5.57E-05 Rasgrf1 6.5478 5.5462 -2.0022 0.02168 Retn 5.8763 8.4828 6.09024 0.00014 Rgnef 6.1493 5.0209 -2.1863 0.0127 Rgs1 7.1796 6.0808 -2.1418 0.00032 Rian 6.476 9.1672 6.4585 0.00665 Rian 7.269 8.9694 3.24991 0.00689 S100b 6.0482 7.1981 2.21899 0.00016 Satb1 6.7673 5.6474 -2.1733 0.00213 Sbsn 9.1661 8.1353 -2.0432 0.00263 Sbsn 6.5705 5.479 -2.1308 0.00114 Sbsn 9.2805 8.1256 -2.2267 0.0017 Scd1 10.2877 11.9565 3.17928 0.00084 Scd1 7.7662 9.1806 2.66549 0.0018 Sell 5.6145 3.9888 -3.0859 3.21E-06 Sema3e 5.6578 4.512 -2.2127 0.01358 Sept4 7.1834 6.0478 -2.1971 0.00073 Sept4 6.5637 5.2239 -2.5312 0.00133 Serpine1 11.0985 10.0894 -2.0127 0.02052 Sik1 6.6655 5.2949 -2.5858 0.00171 Slitrk1 6.1348 8.0352 3.73317 0.00071 Smpd3 6.8457 5.3305 -2.8586 0.01626 Smpd3 7.7973 6.1537 -3.1242 0.01898 Smpd3 8.4466 6.5648 -3.6853 0.00517 Sorcs2 5.2519 6.3176 2.09319 0.00122 Stk17b 6.1514 4.9863 -2.2425 0.00025 Thrsp 5.9277 9.1256 9.17622 0.00019 Thrsp 6.0461 8.3638 4.98571 0.0002 Tnc 8.1295 6.6391 -2.8097 0.02024 Tnmd 6.9912 9.7375 6.70994 3.48E-05 Zbtb16 6.8222 7.864 2.05879 0.0227

163

4.2.3.3 Consistency of Pten Gene Expression Signatures Across Multiple

Experiments

In addition to the microarray experiments performed on Pten null fibroblasts as described in 4.2.3.1 and 4.2.3.2, additional experiments were done comparing PtenloxP/loxP and FspCre;PtenloxP/loxP mammary fibroblasts again in independent mice at 8 weeks

(described in section 4.2.8) and also at a 12 week time point (also described in 4.2.8). As a way to visualize consistent trends in gene expression between these four independent experiments, we first determined misregulated genes between control and

FspCre;PtenloxP/loxP fibroblasts from all the individual experiments (fold change ≥1.5, p- value ≤0.05). Next, we generated a heatmap showing expression of all these genes across all experiments (Figure 4.6). This analysis shows consistent upregulation and downregulation of target genes across all experiments. However, samples isolated using

ColYFP look more different than those that were cultured, thus representing the different biologies these cells may have.

164

8-Weeks (1) 8-Weeks (2) 12-Weeks 8-Weeks (3-ColYFP)

Figure 4.6 Consistent Gene Expression Trends in Pten Null Fibroblasts Across

Experiments

Heatmap representation of differentially expressed genes from 4 individual experiments.

The genes represented on the heatmap were differentially expressed by at least 1.5 fold with a p-value ≤0.05 between PtenloxP/loxP and FspCre;PtenloxP/loxP in any of the individual experiments (Julie Wallace and Thierry Pecot).

165

4.2.4 Increased Macrophage Recruitment and Collagen Deposition in

FspCre;PtenloxP/loxP Mammary Glands

Based on the strong inflammatory gene expression signature we observed in Pten null fibroblasts, we examined FspCre;PtenloxP/loxP mammary glands for the infiltration of various immune cells. Although no differences were observed in the number of B and T cells present in this tissue, IHC staining revealed a striking increase in the amount of

F4/80+ macrophages (Figure 4.7A). Initially we assumed this increase was due to solely to the recruitment and infiltration of macrophages through signaling from fibroblasts, however recently it has been shown that resident tissue macrophages actually have the ability to proliferate and this will be addressed in secion 4.2.10 (Jenkins et al., 2011).

Due to the ECM remodeling capabilities of Pten null fibroblasts, we also visualized collagen deposition in mammary glands of these mice by trichrome staining.

A striking increase was observed in the amount of collagen present in FspCre;PtenloxP/loxP mammary glands, which was accompanied by an expansion of the extracellular area surrounding ducts with Pten null fibroblasts (Figure 4.7B).

166

A PtenloxP/loxP Fsp-cre;PtenloxP/loxP F4/80

B PtenloxP/loxP Fsp-cre;PtenloxP/loxP Trichrome

Figure 4.7 Pten Deletion in Fibroblasts Promotes Macrophage Infiltration and ECM

Remodeling

A. Mammary gland paraffin sections on indicated genotypes stained with the macrophage-specific marker F4/80 (Chris Thompson). B. Mammary gland paraffin sections of indicated genotypes stained with Masson’s Trichrome (Chris Thompson and

Julie Wallace).

167

4.2.5 Activation of Ets2 in Pten Null Mammary Fibroblasts Drives Expression of

Mmp9 and Ccl3

Previous work has shown insulin stimulated phosphorylation and subsequent activation of ETS2 to be blocked by PTEN through inhibition of ERK family members in a manner independent of PI3K (Weng et al., 2002). Closer examination of misregulated genes in Pten null fibroblasts revealed the transcription factor Ets2 to be upregulated in the absence of Pten. Using qRT-PCR, we were able to show an approximate 3 fold increase in Ets2 expression at the RNA level using independent samples (Figure 4.8A).

Additionally, Western blot analysis revealed an increase in ETS2 protein levels in Pten null fibroblasts (Figure 4.8B).

Although the binding of Ets2 to the MMP9 promoter was examined in Chapter 3 in the context of epithelial PyMT expression, we were also interested in whether the transcriptional activity of Ets2 changed in the absence of Pten in mammary fibroblasts.

Since several Ets2 target genes were found to be upregulated in the absence of Pten, including Mmp9 which we already validated as a bona fide Ets2 target, we performed

ChIP assays on PtenloxP/loxP and FspCre;PtenloxP/loxP fibroblasts. Promoter analysis also revealed a conserved Ets binding site proximal to the transcriptional start site of Ccl3. As expected, we found enhanced binding of Ets2 protein to the Mmp9 and Ccl3 promoters in

FspCre;PtenloxP/loxP fibroblasts (Figure 4.8C).

168

A B

4- p<0.001 PtenloxP/loxP

3- - - + + : Fsp-cre expression Ets2

2- Ets2 Ets2

1- Tubulin

n=3 n=3 Relative 0- - + :Fsp-cre; + + :PtenloxP/loxP C ChIP: a-Ets2 Mmp9 Ccl3

2- p<0.001 1.21.2 - p=0.037

1 1.5- 0.9- 0.8

1- 0.60.6 -

n=3

n=3 n=3 n=3

n=3 n=3 n=3 n=3 0.4 0.5- 0.3-

0.2

Relative to InputRelativeto Relative to InputRelativeto

0- 00 - Ig Ets2 Ig Ets2 Ig wt Ets2 IgPTEN -/- Ets2 - + - + :Fsp-cre; + + + + loxP/loxP :Pten

Figure 4.8 Increased Ets2 Expression and Transcriptional Activity in

FspCre;PtenloxP/loxP Fibroblasts

A. qRT-PCR analysis of Ets2 expression in PtenloxP/loxP and Fsp-cre;PtenloxP/loxP stromal fibroblasts. Bar values represent the mean ± SD.; for statistical analysis the Student t-test was used (Julie Wallace). B. Western blot analysis of whole-cell lysates derived from fibroblasts with the indicated genotypes (Fu Li). C. ChIP assays using Ets2-specific or

IgG control antibodies and stromal fibroblasts. Bar values represent the mean ±.SD; for statistical analysis the Student t-test was used (Julie Wallace).

169

4.2.6 Regulation of miR320 by fibroblast Pten Effects Tumor Cell Proliferation and

Angiogenesis

In contrast to the extensive studies examining microRNA expression and function in tumor cells, very little has been done to uncover the importance of these small RNAs in tumor associated stroma. More recently, TAFs isolated from human endometrial and prostate cancers showed the downregulation of miR-31 and miRs-15/16, respectively

(Aprelikova et al., 2010; Musumeci et al., 2011). Beyond cell autonomous functions of miRs in stromal cells, these studies also implicate their potential importance in crosstalk with neighboring epithelial cells and other stromal cells of the microenvironment.

To explore the possibility of global miR expression changes as being a contributing factor to Pten mediated tumor suppression in fibroblasts, we compared microRNA levels in PtenloxP/loxP and FspCre;PtenloxP/loxP fibroblasts using a quantitative real time PCR platform (Hunter et al., 2008). Of the 400 miRs profiled, 10 miRs conserved between mouse and human genomes were found to be significantly downregulated in Pten null fibroblasts (Figure 4.9A, fold change ≥2, p-value ≤0.05)

(Bronisz et al., 2011). In particular, miR-320 was an attractive target for further study because not much is known regarding its function in spite of the fact that several reports have shown its downregulation in human cancer, including breast cancer (Ichimi et al.,

2009; Mattie et al., 2006; Schepeler et al., 2008; Yan et al., 2008; Zhang et al., 2006).

Next we aimed determine whether this downregulation of miR-320 in Pten null fibroblasts contributed to the loss of the tumor suppressive phenotype we observed in these cells. To test this, xenograft assays were used in which miR-320 was ectopically

170 expressed in Pten null fibroblasts and the ability of these cells to support epithelial tumor growth was evaluated. For these experiments, DB7 epithelial tumor cells were used which express a variant of the polyoma virus middle T antigen gene (Borowsky et al.,

2005). Co-injection of these cells with FspCre;PtenloxP/loxP mammary fibroblasts led to 4- fold increase in tumor growth when compared to co-injections with PtenloxP/loxP fibroblasts, which is as expected based on our genetic model (Bronisz et al., 2011;

Trimboli et al., 2009). However, restoration of miR-320 expression in

FspCre;PtenloxP/loxP fibroblasts caused a significant decrease in tumor growth, however this reduction did not quite reach levels seen in control xenografts (Figure 4.9B).

As these co-injection experiments had the potential for confounding effects of infiltrating host stromal cells, we also utilized a matrigel plug assay in which the consequences of fibroblast miR-320 or anti-miR320 expression on DB7 tumor cells could be evaluated at an earlier time point in tumor development. For these experiments, Pten null fibroblasts or Pten null fibroblasts re-expressing miR-320 were mixed with DB7 cells in matrigel, injected into the flanks of syngeneic animals and harvested 5 days later.

As we expected, tumor cell proliferation as measured by incorporation of 5- bromodeoxyuridine (BrdU) was decreased by approximately 30% in fibroblasts re- expressing miR-320 (Figure 4.9C). New blood vessel formation as measured by CD31 staining was also significantly decreased by the re-expression of miR-320 (Figure 4.9D).

Conversely, we compared tumor cell proliferation and blood vessel formation in matrigel plugs containing either wild type fibroblasts or wild type fibroblasts knocked down for endogenous miR-320. In these experiments, tumor cell proliferation increased by

171 approximately 25% and the formation of new blood vessels was markedly increased upon anti-miR-320 knock down (Figure 4.9C, D).

172

Figure 4.9 Regulation of miR-320 by Pten in Fibroblasts

A. Expression of selected microRNAs was validated by qRT-PCR in three sets of primary Pten+/+ or Pten-/- MMFs. Relative MiR expression level (normalized to 18S ribosomal RNA) is shown as mean ±SD (Julie Wallace and Aga Bronisz). B.

Representative images of tumors before and after removal from mice injected with DB7 cells admixed with Pten+/+ (n=7) or Pten-/- (n=16) MMFs (left panel), and DB7 cells admixed with Pten-/- MMFs or Pten-/- cells re-expressing miR-320 (n=9) (right panel).

Tumor weights were quantified after four weeks, and the data are expressed as mean

±SD. All three groups were tested by ANOVA and subsequent adjusted pairwise comparisons were performed. These comparisons were significant: Pten+/+ vs. Pten-/-

MMFs; p = 5.58E -06. Pten+/+ vs. Pten-/- miR-320 MMFs; p = 0.022215. Pten-/- vs. Pten-/- miR-320 MMFs; p = 0.007976 (Aga Bronisz). C. Epithelial tumor cell proliferation measured by BrdU incorporation in xenografts composed of DB7 cells admixed with

Pten-/- or Pten+/+ MMFs transfected with either negative control (NC), miR-320 (320, upper right panel) or anti-miR-320 (a320, lower right panel); n=4 for each group. Scale bars: 25m. Data are expressed as mean ±SD *p < 0.05 (Julie Wallace and Aga Bronisz).

D. Angiogenesis determined using CD31 staining to detect blood vessels in nascent tumors composed of DB7 cells and the same Pten-/- or Pten+/+ MMFs transfected with negative control (NC), miR-320 (320, upper panels) or anti-miR-320 (a320, lower panels), as above. The epithelial/fibroblast mixtures were grown for five days subcutaneously in mice. Representative micrographs showing merged images are shown

173

(CD-31 - green, DAPI - blue). Scale bars: 50m. Data are expressed as mean ±SD *p <

0.05 (Julie Wallace and Aga Bronisz).

174

A B

Figure 4.9

175

4.2.7 Pten Deletion in Fibroblasts Does Not Effect Ras Driven Tumorigenesis

Due to the heterogeneity observed in breast cancer, we were eager to investigate how Pten tumor suppressor function in fibroblasts would impact tumor growth in the context of various driving oncogenes in tumor cells. Therefore we also examined the effect of fibroblast specific deletion of Pten in the context of inducible Tet-o-Ras driven tumorigenesis. In this model, Ras expression is controlled in a spatial manner by MMTV driven expression of rtTA, as well as in a temporal manner upon administration of doxycycline food. Again due to the confounding effects of Pten deletion in fibroblasts, a transplant model system was utilized. Tissue transplantation was performed as described in section 4.2.2, however syngeneic recipient mice were started on a 1g/kg doxycycline diet following transplantation. Mice were monitored for 26 weeks, after which transplant tissue was harvested and examined histo-pathologically as described in 4.2.2.

Deletion of Pten alone did not result in the formation of carcinoma in any samples examined, however 12.5% of mammary tissue was classified as having MIN, while the remaining 87.5% of tissue was considered normal (Figure 4.10B). Upon epithelial expression of Ras alone, carcinoma and MIN were observed in approximately 15% and

18% of examined samples, respectively, while 67% of samples were still considered normal. Fibroblast specific deletion of Pten in addition to Ras expression led to a slight increase in the amount of carcinoma observed (22%), however this difference was not statistically significant (Figure 4.10A, B). Accordingly, around 14% of examined samples were classified as MIN and 64% as normal (Figure 4.10B). This data

176 demonstrates that the changes induced by Pten loss in fibroblasts only have a consequence in the context of particular oncogenic signaling.

177

A MMTV-rtTA;Tet-o-Ras ErbB2;FspCre;PtenloxP/loxP

B 120 100 Carcinoma 80 MIN Normal 60

40 % Transplants% 20 0 PtenloxP/loxP MMTV- MMTV-rtTA;Tet-o-Ras; n = 16 rtTA;Tet-o- FspCre;PtenloxP/loxP Ras n = 8 n = 27

Figure 4.10 Pten Loss in Stromal Fibroblasts Does Not Promote Ras Driven

Tumorigenesis

A. Gross examination of tumors harvested at 26-weeks post transplantation (Anthony

Trimboli). B. Graphical representation of distribution of histological grading in transplant tissue from indicated genopypes. MIN, mammary intraepithelial neoplasia.

Differences in histo-pathological grading were analyzed using Fisher’s exact test, P>0.05

(Anthony Trimboli, Shan Naidu and Julie Wallace).

178

4.2.8 ErbB2 Expression in Epithelial Cells, but not Ras Expression, Drives Gene

Expression Program Resembling Pten Signature in Surrounding Fibroblasts

In an attempt to understand the potential mechanism by which stromal Pten signaling collaborates with ErbB2 signaling in the epithelium, we isolated epithelial cells and fibroblasts from mammary glands of 9 week old PtenloxP/loxP, FspCre;PtenloxP/lox P,

ErbB2;PtenloxP/loxP and ErbB2;FspCre;PtenloxP/loxP mice and performed global gene expression profiling. In addition to using cell morphology to identify our purified populations of fibroblasts and epithelial cells, we also checked each for the expression of the fibroblast marker Fsp1 (S100a4) and the epithelial cell marker Cdh1 (E-cadherin).

As we expected, our wild type and Pten null fibroblasts both had high expression of

Fsp1, whereas our epithelial samples had negligible levels. Conversely, high expression levels of Cdh1 were observed in our epithelial samples from PtenloxP/loxP and

FspCre;PtenloxP/lox P mammary glands whereas fibroblasts had very low expression.

Again, this analysis was performed at the OSUCCC MASR. However, instead of using a standard 3’ biased gene expression platform, we opted for the Affymetrix Mouse Exon

1.0 ST Array platform. This particular array contains approximately four probes per exon and roughly 40 probes per gene. The main advantage of this array is the ability to examine exon level expression, thereby detecting alternative splicing and variations in isoform expression. However, due to limitations in bioinformatic analysis, we simply averaged the expression values from all the probes of a particular gene to determine an overall expression level. Normalization was performed using the standard RMA method we had used for all previous microarrays.

179

Consistent with our previous microarray data, 60 genes were upregulated and 74 genes downregulated in Pten null fibroblasts compared to controls (Table 4.3, fold change >2), and using ToppGene Suite for gene ontology analysis, we determined these genes to be involved in immune response and cell migration (Chen et al., 2007).

Interestingly, we observed 152 upregulated genes and 36 downregulated genes in epithelial cells isolated from mammary glands with Pten null fibroblasts (Table 4.4, fold change ≥2), indicating strong paracrine regulation of epithelial cells by

FspCre;PtenloxP/loxP fibroblasts. Again using ToppGene, we identified biological processes significantly represented in our differentially expressed genes in epithelial cells, which included ECM organization, cell adhesion, response to drug and cell proliferation.

Next we wanted to determine the changes elicited in epithelial cells and fibroblasts as a result of epithelial ErbB2 expression alone. For comparisons in epithelial cells, 58 genes were found to be upregulated upon expression of ErbB2 and only 7 genes were downregulated, and in fibroblasts, 18 genes were upregulated and 19 genes were downregulated (Tables 4.5 and 4.6, fold change ≥2). Due to the lesser amount of changes in these comparisons, gene ontology analysis did not reveal any significant processes to be represented. Finally, to determine potential synergistic functions of epithelial ErbB2 expression and fibroblast Pten loss, we also isolated cells from

ErbB2;FspCre;PtenloxP/loxP mice and examined gene expression. In general, a similar number of genes were mis-regulated in these fibroblasts and epithelial cells as with Pten deletion alone, and similar gene ontology categories were represented.

180

Examination of heatmaps decpicting gene expression changes (fold change ≥1.5 and p-value ≤0.05) in all 3 of these genotypes (FspCre;PtenloxP/loxP,

ErbB2;PtenloxP/loxP and ErbB2;FspCre;PtenloxP/loxP) compared to control PtenloxP/loxP cells revealed our ErbB2 induced fibroblast signature to look like our Pten null signature in 2 out of the 4 samples we used for analysis (Figure 4.11). This data therefore led us to hypothesize that epithelial ErbB2 expression may downregulate Pten expression in surrounding fibroblasts. This theory also has the potential to explain our tumor study data, in that since Pten was already missing in fibroblasts, the process of co-opting the microenvironment by the tumor epithelial cells was accelerated. The differences between these biological replicates may indicate some kind of switch in these fibroblasts that happens around this 9 week timepoint.

Similar analysis of fibrobroblasts from these same genetic groups was also performed at a later 12 week time point. Once again, using a heatmap to plot all the differentially expressed genes we saw an even more consistent Pten signature represented in fibroblasts from ErbB2 mammary glands (Figure 4.12). Although we only had 2 biological replicates/genotype in this experiment, we see a much more consistent trend at this later time, indicating that longer exposure of these fibroblasts to ErbB2 signaling drives stronger downregulation of Pten. Perhaps even more interesting was the fact that this reprogramming of fibroblasts was oncogene dependent, as expression of Ras in epithelial cells did not induce a Pten like signature in fibroblasts (Figure 4.13).

To determine the effect of Pten deletion on epithelial cells, we took a similar approach with generation of heatmaps containing differentially expressed genes from all

181 comparisons. In a similar trend, Pten loss in fibroblasts induces changes in epithelial cells that resemble ErbB2 expression (Figure 4.14).

182

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

-3 +3

Figure 4.11 Epithelial Expression of ErbB2 Induces Pten Like Signature in 8-Week

Mammary Fibroblasts

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Thierry Pecot).

183

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

-4 +4

Figure 4.12 Epithelial Expression of ErbB2 Induces Pten Like Signature in 12-Week

Mammary Fibroblasts

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Thierry Pecot).

184

FspCre; ErbB2; MMTV-rtTA; PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Tet-o-Ras

-3 +3

Figure 4.13 ErbB2 Induced Pten Gene Signature is Oncogene Specific

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Thierry Pecot).

185

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

-3 +3

Figure 4.14 Pten Fibroblast Loss Induces ErbB Like Signature in Epithelial Cells

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Theirry Pecot).

186

Table 4.3 Genes misregulated by Pten in cultured 8-week mammary gland fibroblasts

Gene Symbol Average Average Pten Fold Change Wild Type 4732429D16Rik 5.864813 4.850431 -2.02004 Abi3bp 7.234906 6.217533 -2.02423 Acpp 4.22754 6.51047 4.866655 Acta1 7.696617 9.154733 2.747492 Akr1c18 3.425882 4.940947 2.858118 Aldh1a1 7.22029 8.966454 3.354655 Aldh1a3 6.574526 5.296278 -2.42544 Aldh1a7 4.204491 5.913763 3.269959 Ampd3 8.1739 9.255805 2.116829 Apoe 9.604509 7.400951 -4.60614 Arl11 5.837049 4.464218 -2.58978 B830045N13Rik 4.606308 5.970634 2.574559 Bhlhb3 7.454185 6.210191 -2.36853 C1qa 8.313615 6.800897 -2.85347 C1qb 8.53147 6.820198 -3.27449 C1qc 7.848839 6.419082 -2.69401 C1qtnf3 6.943522 5.615397 -2.51076 C4b 7.43963 6.269092 -2.25096 Ccdc85a 5.425997 6.719677 2.451525 Cck 6.947108 8.70226 3.37562 Ccl8 7.391865 8.658438 2.405893 Cd55 6.039562 7.178309 2.201896 Cd74 5.295282 4.269987 -2.03538 Cd80 6.00178 7.598345 3.024224 Cd93 7.791054 6.535059 -2.38832 Cdh3 7.103299 5.789447 -2.48604 Cgnl1 7.445104 8.480574 2.049782 Clec4d 6.262897 5.094859 -2.24706 Clstn2 4.381126 5.769795 2.618371 Cmklr1 8.107638 7.022247 -2.12195 Col15a1 9.35113 8.116946 -2.35248 Coro1a 6.682646 5.551314 -2.19061 Crabp1 7.806135 10.48822 6.417843 (continued) 187

Table 4.3 (continued)

Ctsc 8.363811 7.287293 -2.10894 Ctss 8.918579 7.842467 -2.10835 Cx3cl1 7.684238 6.346846 -2.52694 Cxcl1 6.462712 7.756053 2.450949 Cxcr4 6.454353 5.44935 -2.00695 Cybb 6.873105 5.56487 -2.47638 Cygb 9.411163 7.632398 -3.43132 Cyp4a12b 5.023618 6.837771 3.516532 Dhrs9 4.972957 5.980075 2.009892 Dio2 4.946025 6.645767 3.248429 Dio3 6.421568 5.231884 -2.28103 Dpt 7.123586 8.186715 2.089458 Ednrb 6.382135 5.320051 -2.08795 Eln 6.384313 4.900936 -2.79602 Eltd1 6.047647 5.041936 -2.00793 Emcn 7.621254 6.576648 -2.0628 Emr1 6.273845 5.25356 -2.02832 Ereg 6.293176 7.904872 3.056108 Fap 5.634007 4.511103 -2.17785 Fcer1g 8.477765 7.236833 -2.36351 Fmo2 6.566809 7.993822 2.688893 Fpr1 5.02898 4.009699 -2.02691 Frzb 4.468819 5.965241 2.821422 Fxyd1 7.271397 5.798895 -2.77503 Gimap4 6.226506 4.927368 -2.46082 Gjb2 6.797182 5.366481 -2.69578 Gpr126 4.458103 5.479098 2.029318 Grb14 7.325719 8.965902 3.117054 Gsto2 5.01144 6.065605 2.076517 H19 6.319768 5.211333 -2.15612 H2-T10 9.712561 11.24277 2.888268 Heyl 6.031317 4.846477 -2.27338 Hp 6.071177 7.284303 2.318396 Htr2a 7.911541 9.083767 2.253591 Igfbp2 9.202602 7.810001 -2.62552 Igfbp3 9.07857 7.819797 -2.39292 (continued)

188

Table 4.3 (continued)

Igfbp4 9.166772 8.011039 -2.22798 Igfbp5 8.073565 10.24565 4.506742 Il18rap 3.513102 4.647446 2.195187 Il1rl1 7.738692 9.224662 2.801054 Il2rg 5.960459 4.944743 -2.02191 Islr 9.412689 8.311921 -2.14469 Itgam 7.209065 5.80302 -2.6501 Itih5 4.505014 5.577059 2.102412 Itm2a 8.270568 7.234817 -2.05018 Jag1 8.528625 7.403364 -2.18141 Klf15 5.658669 4.375845 -2.43315 Krt19 6.349343 7.648031 2.460051 Lama1 7.759652 6.04774 -3.27595 Lbp 7.435488 6.061062 -2.59265 Lcn2 4.547985 6.612758 4.183683 Lgr5 5.159765 4.033468 -2.18298 Lilrb4 10.19883 9.037804 -2.23616 Lrrc17 7.332996 6.252788 -2.11434 Ltbp1 6.631887 7.79197 2.234704 Lum 6.937404 8.007982 2.100274 Lyz1 6.252613 4.919067 -2.52021 Lyz2 10.03732 8.906082 -2.19047 Masp1 7.604136 9.098048 2.816517 Mmp10 4.967902 7.420926 5.475626 Mmp11 9.886526 8.814269 -2.10272 Mmp8 4.579726 5.638488 2.083143 Mmp9 5.780849 7.112637 2.517143 Mrc1 6.775416 5.639931 -2.19692 Ms4a6c 6.225135 4.919534 -2.47187 Msr1 6.785767 5.748663 -2.0521 Ncf4 5.692779 4.59014 -2.14747 Nfyc 4.498942 3.340169 -2.23268 Nov 9.322499 6.835165 -5.60741 Npnt 6.318743 7.339377 2.028811 Nrn1 8.182321 9.636466 2.739942 Pcsk5 8.942996 7.902847 -2.05644 (continued)

189

Table 4.3 (continued)

Pcx 7.199008 8.486324 2.440736 Penk1 5.174822 6.4084 2.351493 Phlda1 6.728897 7.750309 2.029904 Pi15 8.190822 9.269064 2.111462 Pld4 7.399427 6.31567 -2.11955 Podn 8.290745 6.731064 -2.94789 Ptpn6 6.079186 5.015413 -2.09039 Ptprv 5.764697 7.047524 2.433153 Ror1 7.51213 6.336771 -2.25849 S100a3 4.12117 8.351422 18.76864 S100a4 8.955591 10.07502 2.172607 Scn7a 6.454766 4.65264 -3.48734 Serpinb2 4.461722 6.757013 4.908529 Serpinb9b 7.122034 8.438364 2.490317 Sfrp2 8.446222 7.102495 -2.53806 Slfn5 8.078945 7.00432 -2.10617 Slpi 6.238473 7.251251 2.017794 Sned1 8.659215 7.308872 -2.54973 Sparcl1 7.425512 6.177364 -2.37536 Spna1 5.318413 6.366021 2.067099 Sprr1a 5.28807 7.201808 3.767841 Srgap3 6.574926 7.662749 2.125531 Stab1 7.358769 6.320814 -2.05331 Steap4 5.107527 6.760143 3.144033 Tcfec 5.837921 6.984222 2.213456 Tek 6.022157 4.880791 -2.2059 Tnfaip3 6.421208 7.482582 2.086918 Tnfrsf9 4.570699 5.647209 2.108929 Tnip3 4.931688 3.865263 -2.09424

190

Table 4.4 Genes regulated in epithelial cells by fibroblast Pten in 8-week mammary glands

Gene Symbol Average Wild Average Pten Fold Change Type 1110018M03Rik 7.744569 8.98224 2.358175 1110032E23Rik 8.121815 9.379644 2.391357 2310007B03Rik 7.341459 5.396228 -3.85099 2810021B07Rik 7.044087 5.932737 -2.16048 3632451O06Rik 3.352084 4.352244 2.000222 5033414K04Rik 4.740991 5.949282 2.310637 5430407P10Rik 7.817203 6.746108 -2.10103 5830467P10Rik 10.17227 9.150905 -2.02984 6330406I15Rik 6.993311 8.346809 2.555308 9930023K05Rik 6.510006 5.283808 -2.3395 Adam23 5.523904 7.099424 2.98043 Adam23 5.132788 6.142871 2.014028 Adamts12 7.28513 8.568336 2.433792 Adamts2 7.801899 9.14506 2.537066 Adh1 5.180887 6.317614 2.198815 Adm 7.24228 8.779342 2.90203 Ak3l1 6.605286 7.722327 2.169017 Aldh1a1 3.866069 7.865798 15.99699 Aldh1a2 4.409541 5.424774 2.02123 Aldh1a3 9.140569 7.972149 -2.24765 Aldh1a7 3.143316 4.683175 2.90766 Aldh3b2 7.486815 6.406119 -2.11506 Aoc3 5.944201 7.204374 2.395246 Arhgap20 4.35634 5.400539 2.062221 Aspn 5.491119 7.532639 4.11679 Atp1a3 6.192149 7.552057 2.566689 AU018091 6.950287 5.914005 -2.05094 Avil 4.991167 6.038378 2.066532 Avpr1a 6.564146 7.602256 2.053535 AY036118 5.613858 8.721259 8.618291 Bbox1 5.269442 4.245169 -2.03393 BC065085 7.109999 5.923991 -2.27522 Bmper 7.28115 8.577172 2.455508 (continued) 191

Table 4.4 (continued)

Bnip3 7.498805 8.657932 2.233223 C030014K22Rik 5.515015 6.711273 2.291445 Car6 4.687836 5.93235 2.369387 Casp1 5.734573 6.939165 2.30472 Ccdc80 9.660599 11.56961 3.755502 Cd248 8.231822 9.262149 2.042487 Cd36 5.187429 6.667651 2.789916 Cd53 7.386185 8.422564 2.051074 Cd68 7.272968 8.359295 2.123328 Cd74 5.421934 4.143639 -2.42552 Cd80 4.238863 6.319778 4.230752 Cdc42ep2 6.429574 7.562092 2.19241 Cdh6 7.654194 8.736379 2.11724 Cfd 4.854232 6.88481 4.085683 Cntfr 7.500394 6.437523 -2.08909 Cntn2 7.539889 6.445911 -2.13462 Col11a1 6.5966 7.715691 2.1721 Col12a1 9.869596 10.91587 2.065185 Col1a2 11.45943 12.55209 2.132669 Col3a1 9.77193 11.01815 2.372194 Col4a1 9.706813 10.73924 2.045463 Col4a2 9.277221 10.52017 2.366814 Col5a1 8.469941 9.638978 2.248616 Col5a2 9.851779 11.18767 2.524305 Col6a1 7.930848 9.508964 2.985796 Col6a2 7.780529 9.119004 2.52884 Colec12 7.436777 8.501243 2.091396 Cpxm1 5.657149 6.940205 2.433539 Csf3 6.297229 5.282313 -2.02078 Csn3 4.106162 5.494275 2.617363 Ctla2a 8.330943 9.346795 2.022096 Ctsk 8.30308 9.68027 2.597619 Cyp4b1 5.784266 6.824386 2.056398 Ddit3 6.796347 7.883905 2.125141 Dpep1 5.387679 6.797222 2.65653 Dpt 5.259359 6.896115 3.109659 (continued)

192

Table 4.4 (continued)

Dsg3 7.400136 6.332108 -2.09656 Dub2a 4.761911 5.891661 2.188209 Eda2r 5.447872 6.811237 2.572846 Efemp1 3.711736 5.384507 3.188264 Egln3 5.429642 6.535778 2.152683 Emilin2 6.722892 8.017277 2.452724 Eno2 6.360352 7.800509 2.713505 Ephx1 7.986791 9.065001 2.111414 F13a1 4.845639 6.931327 4.244775 Fabp4 3.608384 4.799283 2.28295 Figf 4.937668 6.308039 2.58537 Fmo1 4.572521 5.645428 2.103668 Fmo2 5.095668 7.606624 5.699978 Frzb 3.024435 4.275651 2.380419 Gabrp 8.070551 6.495674 -2.9791 Gas7 6.404012 7.649083 2.370303 Gdf6 5.334738 6.807744 2.775998 Gjb2 9.070722 7.695422 -2.59422 Gjb4 8.625197 7.40392 -2.33153 Gldn 4.006311 5.084473 2.111343 Gpd1 5.304867 6.609736 2.470612 Gpx3 7.077112 8.881816 3.493575 Gramd1b 5.543372 6.622742 2.113114 Gria3 4.8374 6.432317 3.020771 H2-M1 6.202684 5.184211 -2.02577 Has2 10.31317 9.201581 -2.16084 Havcr2 5.062402 6.10352 2.057822 Hist1h2ab 5.168419 6.855856 3.220839 Hist1h2bc 4.993673 6.163548 2.249921 Hpse 4.624005 5.8369 2.318024 Hs6st2 6.998553 8.154461 2.228244 Hsd11b1 4.688158 6.542772 3.61655 Hspb7 4.568394 5.658024 2.128195 Htr2b 5.744907 6.846662 2.146156 Ifit1 3.173327 5.343113 4.499566 Igfbp6 7.348296 9.244417 3.72211 (continued)

193

Table 4.4 (continued)

Il11 9.856392 8.289665 -2.96232 Il23a 7.163715 5.961109 -2.30155 Itgb6 8.505128 7.230425 -2.41949 Itgbl1 4.739913 6.70877 3.91458 Kcnab1 4.165479 5.429704 2.401981 Kif1a 6.954082 8.072322 2.170821 Krt5 11.67137 10.59185 -2.11333 Krt6a 5.82715 4.66481 -2.2382 Krt7 10.76127 9.738194 -2.03224 Lama2 5.98213 7.753622 3.414069 Lcp1 9.175957 7.725413 -2.73311 LOC665778 4.905519 6.255887 2.549772 Loxl2 9.150791 10.37386 2.334431 Loxl3 8.583735 9.906954 2.502237 Lpar1 7.131095 8.531045 2.638924 Lpl 7.282213 9.138537 3.620839 Lrg1 5.283332 6.793162 2.847766 Lum 5.694975 7.220719 2.879352 Maob 4.664588 5.96561 2.464033 Meg3 7.171922 8.465894 2.452022 Mgll 7.794016 8.853384 2.084018 Mmp12 8.306464 7.099212 -2.30897 Mmp19 6.854885 8.749892 3.719238 Mmp3 7.412384 8.777124 2.575299 Mrap 4.580127 5.787303 2.308852 Mrc1 4.419077 6.157304 3.336247 Mrc2 8.295087 9.676463 2.605167 Mrgprf 5.568773 6.661511 2.132783 Ms4a4d 4.461945 5.943047 2.791619 Mum1l1 5.893729 7.004088 2.158994 Ndn 7.295077 8.371645 2.109013 Nid1 9.072584 10.28701 2.32048 Nppb 5.957436 4.845718 -2.16103 Nxph4 5.166713 6.326289 2.233918 OTTMUSG00000000971 6.125214 7.49051 2.576292 P2rx2 6.42198 5.165605 -2.38895 (continued)

194

Table 4.4 (continued)

P4ha2 9.185456 10.26251 2.10972 P4ha3 5.996916 7.22228 2.338144 Pard6b 8.742026 7.597773 -2.21032 Pcgf1 6.095364 7.197187 2.146256 Pcolce 9.221683 10.57656 2.557753 Pde7b 5.2555 6.303088 2.067072 Pdgfb 10.49436 9.486076 -2.01152 Pdgfra 6.608925 8.270934 3.164569 Pkm2 4.186391 5.349264 2.239028 Pla1a 8.223593 9.845879 3.078625 Plac8 4.890011 6.061155 2.251902 Plek 5.320629 6.51161 2.28308 Ppp1r3b 4.678746 6.083006 2.646819 Pygl 6.07876 7.568975 2.809308 Rarres2 4.720721 6.577431 3.621808 Rbp4 5.10156 6.321095 2.328717 Rgs4 5.846086 6.993657 2.215405 Rhov 5.552302 4.290436 -2.39806 Rpp25 4.823274 6.122821 2.461516 S100a4 4.986138 7.302243 4.97986 Saa3 3.50268 5.886888 5.220573 Scn7a 4.049007 5.181958 2.193068 Scrn1 5.086962 6.608493 2.870956 Sema3a 4.316992 5.648456 2.516579 Sepp1 5.256584 6.597087 2.532396 Serpina3n 7.885795 10.1604 4.838641 Serpinb9b 5.99041 7.338028 2.544917 Serping1 9.633045 10.84545 2.317236 Sfn 12.25679 10.9791 -2.42451 Sgcd 4.054754 5.127143 2.102912 Sod3 5.722265 6.867794 2.212271 Spon1 7.407925 8.469296 2.086914 Spp1 10.31706 11.35428 2.052278 Srgn 7.565432 8.976602 2.659528 Svep1 6.039922 7.832649 3.464692 Tfpi2 9.8588 11.03965 2.267107 (continued)

195

Table 4.4 (continued)

Tgfbr3 6.172271 7.55255 2.603188 Thbs2 11.01357 12.29446 2.429873 Tmem45a 7.534095 9.269524 3.329786 Tnf 7.435178 6.338431 -2.13872 Tnfrsf26 6.991002 8.285803 2.453432 Tpsab1 4.573688 5.790837 2.324869 Tsc22d3 5.054265 6.126415 2.102565 Txnip 7.809299 8.846287 2.05194 Vsnl1 5.376257 4.246674 -2.18795 Wbscr17 5.115254 6.282321 2.245548 Zcchc5 3.791422 5.424525 3.101795

196

Table 4.5 Genes regulated in epithelial cells by epithelial ErbB2 expression in 8- week mammary glands

Gene Symbol Average Wild Average ErbB2 Fold Change Type 2210407C18Rik 4.87853 6.531173 3.144092 2310007B03Rik 7.341459 5.847521 -2.81657 Avil 4.991167 6.025321 2.047913 AY036118 5.613858 8.211547 6.053164 BC006965 5.372071 6.573473 2.299632 Bmp2 5.426117 6.46518 2.054892 Car6 4.687836 6.502757 3.518402 Casp1 5.734573 6.960936 2.339763 Ccl20 5.108077 6.271093 2.239251 Cd80 4.238863 5.47846 2.361326 Cdh6 7.654194 8.661219 2.009762 Cfb 5.80266 7.02259 2.329354 Ch25h 6.667625 7.971793 2.469412 Chac1 7.950669 6.467663 -2.79531 Col14a1 5.606212 6.695849 2.128206 Cox6c 9.01441 10.08287 2.09719 Cth 6.21886 7.241062 2.031016 Ctsk 8.30308 9.318096 2.020925 Cxcl11 5.067303 6.44105 2.591427 Cyp1b1 8.967669 10.04218 2.106012 Dub2a 4.761911 5.873789 2.161268 Eda2r 5.447872 6.571378 2.178757 Gpr50 8.617651 6.86932 -3.3597 Gria3 4.8374 5.972446 2.196256 Gstp2 5.89232 7.120668 2.342985 Gzme 3.431264 4.632686 2.299663 Hist1h2ab 5.168419 6.898885 3.318349 Hist1h2bc 4.993673 6.535975 2.912588 Hp 8.395016 9.672497 2.424155 Id2 7.247943 8.424579 2.260491 Ifit1 3.173327 5.06033 3.698659 Igfbp5 9.111995 10.32479 2.317857 Igfbp6 7.348296 8.407686 2.08405 (continued) 197

Table 4.5 (continued)

Ins1 6.165936 5.146684 -2.02687 Itgbl1 4.739913 5.836978 2.13919 Lbp 7.936056 9.090765 2.226394 LOC100042767 4.558573 6.17818 3.072914 LOC665778 4.905519 6.60688 3.252077 Mcoln3 4.543287 5.972866 2.69368 Mgp 7.327538 8.460667 2.193338 Mmp13 7.138383 8.505498 2.579543 Mobkl1b 5.432833 7.292351 3.628866 Mrto4 4.81971 5.951852 2.19184 Mt2 11.16206 12.21763 2.078536 Mum1l1 5.893729 7.522965 3.093491 Olfr1505 5.922697 4.450637 -2.77418 OTTMUSG00000000971 6.125214 7.464771 2.530736 Plscr2 4.805388 5.913254 2.155267 Ppbp 5.903709 4.59331 -2.4801 Prss22 6.62856 5.503335 -2.18135 Ptx3 7.595414 8.659409 2.090713 RP23-24J10.5 7.242633 8.34453 2.146368 Rps27l 5.542974 7.015797 2.775646 Saa3 3.50268 4.748528 2.37158 Serpinb9b 5.99041 7.091998 2.145907 Spcs1 7.185141 8.219064 2.047584 Sprr1a 9.666387 10.88401 2.325631 Steap4 6.51415 7.565319 2.072209 Supt4h1 4.708953 5.923687 2.320979 Tc2n 6.405254 7.703885 2.459953 Tnfsf15 5.449475 6.466423 2.023633 Tnip3 7.428147 8.66622 2.358833 Txnip 7.809299 8.932511 2.178314 Xdh 7.322117 8.373341 2.072287 Zfp60 4.721152 5.750845 2.04159

198

Table 4.6 Genes regulated in fibroblasts by epithelial ErbB2 expression in 8-week mammary glands

Gene Symbol Average Wild Average ErbB2 Fold Change Type 1810023F06Rik 5.633751 6.637786 2.005602 Akr1c18 3.425882 4.58207 2.228678 Aldh1a1 7.22029 8.310769 2.129447 Arg1 5.042811 3.913616 -2.18737 C1qa 8.313615 7.108313 -2.30586 C1qb 8.53147 7.486923 -2.06272 Cables2 3.978691 5.111182 2.19237 Ccl11 10.64972 9.359288 -2.44602 Ccl3 8.619494 7.484162 -2.19669 Clec4n 6.810221 5.45254 -2.56273 Cpxm1 9.435479 8.239392 -2.29117 Crabp1 7.806135 6.717232 -2.12712 Cyp4a12b 5.023618 6.597726 2.977513 Dll4 6.422556 5.356556 -2.09362 Ereg 6.293176 7.296886 2.00515 Gjb2 6.797182 8.193678 2.632614 Gsta4 7.577601 9.264825 3.220364 Gzmd 3.809942 5.09417 2.435518 Hist1h2ab 6.068256 7.146217 2.11105 Igfbp3 9.07857 7.461303 -3.06793 Il11 6.989889 8.187744 2.293984 Krt19 6.349343 7.506277 2.22983 Lcn2 4.547985 3.521273 -2.03738 Mcpt8 4.706393 5.848067 2.206369 Mfap4 9.286331 8.122655 -2.24027 Myo7a 7.873089 6.792758 -2.11452 Myom1 5.735202 6.811084 2.108011 Nfyc 4.498942 2.51638 -3.95194 Nov 9.322499 8.135521 -2.27675 Npnt 6.318743 7.564257 2.371031 Nqo1 6.006575 7.107778 2.145336 Nt5e 6.020922 4.686839 -2.52115 Pcsk5 8.942996 7.475315 -2.76577 (continued) 199

Table 4.6 (continued)

Saa3 9.501676 8.233775 -2.40811 Serpinb2 4.461722 6.235749 3.420072 Srgn 7.869186 6.848204 -2.0293 Tfrc 8.113158 9.467312 2.556472

200

4.2.9 Fibroblast Pten loss reprograms similar gene expression changes in surrounding endothelial cells and macrophages

The extensive characterization of Pten null fibroblasts at both mRNA and microRNA levels, along with the overwhelming phenotypes observed in

FspCre;PtenloxP/loxP mammary glands, led us to consider the idea of global gene expression reprogramming in the microenvironment due to disrupted Pten function in fibroblasts. Additionally, potential gene expression changes in neighboring endothelial cells and macrophages could uncover novel mechanisms by which these cells contribute to tumorigenesis as a result of defective signaling in fibroblasts. These cells were isolated from the same mammary glands from which we isolated the fibroblasts and epithelial cells from in section 4.2.8. Whereas fibroblasts and epithelial cells were cultured before RNA harvesting, endothelial cells and macrophages were directly sorted from mammary gland tissue using fluorescently conjugated antibodies recognizing CD31

(Pecam) and F4/80, respectively, followed by fluorescence activated cell sorting (FACS).

To isolate these cells at the same time from the same tissue, we used two different fluorophores to mark CD31+ and F4/80+ cells. Examination of FACS plots clearly showed two distinct populations of cells labeled with these markers, with <1% of cells being double positive thereby giving us confidence in the purity of our isolated cells

(Figure 4.15A, B). Furthermore, qRT-PCR analysis of the endothelial cell marker Cdh5

(VE-cadherin) and the macrophage specific markers Emr1 (F4/80) and Csf1r revealed the specificity of our antibodies for these cell types.

201

Comparison of endothelial cell gene expression from from PtenloxP/loxP and

FspCre;PtenloxP/lox P mammary glands revealed 94 genes to be upregulated and 136 genes to be downregulated (Table 4.7, fold change ≥2). ToppGene analysis showed cell division/cell cycle as well as a multitude of metabolic processes to be significantly represented in these misregulated genes. Following a similar trend, analysis of macrophage gene expression uncovered 79 upregulated and 299 downregulated genes in cells isolated from FspCre;PtenloxP/lox P mammary glands (Table 4.8). Interestingly, cell division/cell cycle was also one of the most significantly represented GO categories within this signature, however unique processes were also represented including immune response, wound healing and cell migration. We also examined changes in these cell compartments in the context of ErbB2 expression with and without Pten deletion.

Examination of heatmaps from this analysis reveals Pten specific changes that are not recapitculated or enhanced upon the presence of ErbB2 (Figure 4.16, 17). Interesting there does seem to be a similar trend in expression of the same genes in these endothelial cells and macrophages as mentioned previously, which might indicate that Pten signaling in fibroblasts illicts similar changes in these surrounding stromal cells.

202

A B

Figure 4.15 FACS Plot Shows Distinct CD31+ and F4/80+ Populations

A. FACS plot showing CD31+ (green) and F4/80+ (red) populations of cells isolated. B.

Analysis showing specific peaks for FITC (CD31+) and PE (F4/80+) cell populations

(Julie Wallace).

203

Endothelial Cells

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

Macrophages

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

Figure 4.16 Fibroblast Pten Regulated Changes in Endothelial Cells

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Thierry Pecot).

204

Figure 4.17 Fibroblast Pten Regulated Changes in Macrophages

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace and Thierry Pecot).

205

Macrophages

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

Endothelial Cells

FspCre; ErbB2; ErbB2;Fsp- PtenloxP/loxP PtenloxP/loxP PtenloxP/loxP Cre;PtenloxP/loxP

Figure 4.17 206

Table 4.7 Genes regulated in endothelial cells by fibroblast Pten in 8-week mammary glands

Gene Symbol Average Wild Average Fold Change Type Pten --- 5.626433 4.533879 -2.13251 1110005A03Rik 7.796328 8.942653 2.213492 2810417H13Rik 5.590999 4.432478 -2.23228 2900062L11Rik 6.114531 8.019195 3.744215 3632451O06Rik 5.429838 4.403858 -2.03634 5730469M10Rik 6.760176 8.197387 2.707967 9030425E11Rik 5.640261 6.817939 2.262124 9030611O19Rik 3.257082 4.508523 2.380791 Aacs 6.434653 7.639642 2.305355 Aadacl1 7.045074 6.040418 -2.00646 Abi3bp 5.803201 7.041782 2.359662 Acaca 6.945497 8.921282 3.933423 Acot4 3.187045 4.505522 2.494027 Acp5 6.194256 7.406195 2.316487 Acsl1 8.122524 10.0719 3.862087 Acss2 5.052867 6.8202 3.404239 Adamtsl1 4.594164 5.969973 2.595135 Adhfe1 5.697755 7.413393 3.284418 Agpat2 7.474428 9.311888 3.573801 Agpat9 5.272929 7.42623 4.448445 Aldh1a1 5.31688 6.675259 2.563969 Aldh6a1 7.858377 8.925372 2.095065 Anln 5.014615 3.577252 -2.70825 Apln 6.537644 5.397979 -2.2033 Apod 7.674457 8.815719 2.205739 Arg1 3.928594 5.310967 2.606968 Arhgap11a 5.607366 4.21237 -2.62988 Arhgef6 7.47486 6.385024 -2.1285 Arpp19 8.118647 7.117088 -2.00216 Aspn 9.351196 8.127658 -2.33519 B4galt6 5.413907 4.334942 -2.11252 BC028528 7.162639 5.970596 -2.28476 Bet1 6.071131 4.973034 -2.14072 (continued) 207

Table 4.7 (continued)

Bmp2 7.808295 5.237617 -5.94089 Bnip3 6.917886 8.362759 2.722389 Bub1 4.551075 2.891023 -3.16028 Bub1b 6.003132 4.285151 -3.28976 C1qa 5.701432 7.112852 2.659988 C1qc 5.978374 7.155096 2.260625 C330027C09Rik 5.23371 3.953898 -2.42807 C3ar1 4.280947 5.302919 2.030693 C79407 4.210788 2.997003 -2.31945 Cap2 5.950247 4.709348 -2.36346 Cat 8.580565 9.795986 2.322085 Ccdc23 5.582883 4.527689 -2.078 Ccdc25 7.181078 6.178918 -2.003 Ccdc88a 6.81714 5.789178 -2.03914 Ccl20 6.554264 4.098574 -5.48576 Ccl5 7.064197 5.283382 -3.4362 Ccna2 6.884733 5.154164 -3.31859 Ccnb2 6.635163 5.438638 -2.29187 Cd109 6.380931 4.752566 -3.09162 Cd24a 8.607663 7.555175 -2.0741 Cd55 8.640047 6.386301 -4.76919 Cd74 9.605613 8.304886 -2.46353 Cdh2 7.633447 6.588425 -2.0634 Cdo1 7.2262 9.078462 3.610659 Cenpe 4.401112 3.282714 -2.17106 Cep55 4.617524 3.235862 -2.60568 Cfb 8.852575 7.579201 -2.41726 Cfd 7.169061 11.52602 20.4915 Chek2 8.127592 6.304724 -3.53784 Chordc1 7.309898 6.052521 -2.39061 Chpt1 6.468299 7.843199 2.593499 Cldn6 2.875471 3.928956 2.075537 Clu 11.24969 9.764836 -2.79889 Cnn2 9.060978 7.866486 -2.28864 Coro1a 7.623614 6.267756 -2.55949 Crabp1 5.225782 6.945583 3.29391 (continued)

208

Table 4.7 (continued)

Csnk1g3 8.142176 6.9428 -2.2964 Ctdspl2 8.496432 6.634576 -3.63475 Cybb 8.175164 6.842277 -2.51906 Cyp1b1 8.761659 6.572322 -4.56096 Cyp2f2 4.598505 6.788303 4.562415 Cyp7b1 5.780795 4.76176 -2.02656 D0H4S114 9.982478 8.855414 -2.18414 D12Ertd553e 7.754957 6.743715 -2.01565 D930046H04Rik 6.083587 5.069077 -2.02022 Dbf4 6.234945 5.186549 -2.06823 Dgat2 6.940335 9.151504 4.630501 Dio2 3.639915 5.139554 2.827718 Dmrt2 4.851365 5.873969 2.031583 Dsg2 6.19003 4.793651 -2.6324 Dtx1 5.86553 7.031577 2.243959 Dub2a 3.801526 5.464816 3.16738 Ect2 5.639305 4.537382 -2.14641 Ehhadh 5.389204 6.812851 2.682628 Eif3e 8.547164 6.992626 -2.9374 Elovl6 5.792237 7.590545 3.478122 Ephx2 6.47818 7.804766 2.508084 Esm1 7.297188 6.154881 -2.20734 F13a1 6.247532 8.057843 3.507178 F630043A04Rik 5.333431 3.997332 -2.52468 Fabp5 7.633623 6.390631 -2.36689 Fasn 8.189705 11.26249 8.413958 Foxf1a 6.151468 5.014624 -2.199 Fxyd6 7.048234 8.373791 2.506296 Gdf6 3.847482 4.876132 2.040114 Glipr2 7.632445 6.489368 -2.20852 Gpam 7.140165 8.970451 3.556074 Gpd1 5.928258 8.370978 5.436656 Gpr109a 4.401175 6.508711 4.309546 Gpt2 6.026894 7.160308 2.193772 Gpx3 8.141583 9.867638 3.30822 Gria3 5.829753 4.508535 -2.49877 (continued)

209

Table 4.7 (continued)

H2-Ab1 7.505041 6.3952 -2.15822 Hells 5.578835 4.094479 -2.79792 Hist1h1b 7.796061 5.6573 -4.40384 Hist1h2ab 4.976727 3.618219 -2.5642 Hist1h2bb 3.491481 2.448459 -2.06054 Hist2h3c1 5.79521 4.512553 -2.43287 Hist4h4 6.211193 4.95646 -2.38623 Hk2 5.08808 6.920396 3.561081 Hp 5.592825 8.076513 5.593254 Hsd11b1 4.045517 5.509184 2.758084 Htr2b 4.734031 6.906783 4.508825 Hvcn1 5.605927 4.591014 -2.02078 Il2rg 9.841322 8.696899 -2.21058 Il6 8.410237 7.372007 -2.05371 Itga2 6.583717 5.497869 -2.12262 Kcnj2 7.020425 5.830923 -2.28074 Kcnk2 4.486354 5.487708 2.001878 Kcnk5 5.562237 6.630667 2.097149 Kif11 5.319744 4.151157 -2.24791 Kif15 4.520036 3.188179 -2.51727 Kif20a 5.619507 4.426578 -2.28616 Kif20b 4.851921 3.781616 -2.09988 Kif23 5.904102 4.753822 -2.21957 Kif2c 5.212725 3.704652 -2.8443 Kifc1 4.706408 5.74717 2.057314 Klf15 4.48358 6.058415 2.979012 Lcn2 9.05933 10.21114 2.221925 Lmbrd1 8.229818 6.890286 -2.53069 LOC100048405 6.376522 4.714577 -3.16443 Lsm3 7.950569 6.841112 -2.15764 Ltbp2 7.039379 5.294129 -3.35253 Ly6d 6.350936 7.655133 2.469462 Mad2l1 6.77017 5.601356 -2.24827 Meox1 8.233665 7.23137 -2.00318 Mfap4 5.958734 7.069735 2.159954 Mki67 6.109163 4.121329 -3.96641 (continued)

210

Table 4.7 (continued)

Mmp3 5.101909 6.803103 3.251698 Mmp9 6.40708 4.400735 -4.01763 Mobkl1b 6.032463 4.974834 -2.08151 Morf4l1 8.021947 7.001364 -2.02874 Mpz 6.650215 8.014303 2.574135 Mpzl2 4.957972 6.763902 3.496544 Mrap 4.444721 7.107188 6.331145 Mrpl40 6.376515 4.94663 -2.69425 Ms4a6c 6.537355 5.532078 -2.00733 Msr1 5.010777 3.909648 -2.14523 Naalad2 6.28164 5.187567 -2.13476 Ndc80 5.277052 4.260623 -2.02291 Ndg2 6.584927 8.039163 2.740114 Neto2 5.20942 3.377442 -3.56025 Nfib 9.222514 7.92288 -2.46167 Nnmt 4.323149 5.614891 2.448234 Nsg1 6.136562 7.272642 2.197831 Ntn1 6.42623 7.688537 2.398791 Nuf2 4.830186 3.811458 -2.02613 Pbk 5.111642 3.953024 -2.23244 Pcx 6.227863 9.298983 8.404257 Pde3b 5.713066 7.679209 3.90722 Pi15 4.883017 3.84733 -2.05009 Plin 5.899872 8.163263 4.801187 Prc1 6.155039 4.689684 -2.76132 Pscdbp 6.870316 5.293214 -2.9837 Psme2 7.338165 6.200019 -2.20098 Ptprd 4.022165 5.225278 2.302359 Pxmp2 5.342798 6.498807 2.2284 Pygl 6.177329 7.949889 3.416595 Racgap1 6.747924 5.737905 -2.01394 Rbm7 7.082869 5.840735 -2.36548 Rbp4 5.406235 6.807149 2.640688 Rmnd1 6.270438 4.966932 -2.46828 Rnf2 8.099096 6.971285 -2.18527 Rnf5 5.297631 3.775515 -2.87212 (continued)

211

Table 4.7 (continued)

Robo1 6.247693 5.048749 -2.29572 RP23-24J10.5 7.750032 5.868359 -3.68502 Rpl38 4.020053 5.069933 2.070357 Rps27l 3.852378 2.557932 -2.45283 Rps8 6.937872 5.846026 -2.13147 S100a3 4.104081 6.090084 3.961377 S3-12 6.508293 7.51413 2.008109 Saa3 2.462572 4.283476 3.533025 Sap18 7.104263 5.866221 -2.35878 Satb1 7.25323 5.618442 -3.10542 Scd1 9.908446 11.62235 3.280461 Scrn1 4.00273 5.052051 2.069556 Serpina1e 5.511337 3.991279 -2.86803 Serpina3g 7.279844 4.874157 -5.29888 Sfrp1 4.751513 5.987408 2.355273 Skp1a 9.314257 8.228059 -2.12314 Slc25a1 7.821197 9.147183 2.507041 Slc27a1 6.876741 9.738363 7.26832 Slc28a3 6.024 4.86921 -2.22652 Slc43a1 5.444211 6.807096 2.57199 Slc45a3 6.387938 7.659577 2.414357 Slc4a4 5.429129 6.487187 2.082126 Slc7a8 4.857407 5.891106 2.047265 Smarce1 8.423919 6.950418 -2.77695 Smc2 6.539773 5.44899 -2.1299 Smc4 7.424374 6.353498 -2.10071 Smoc1 5.413488 6.53058 2.169092 Smtnl2 4.77599 5.902781 2.183723 Snapc5 6.544674 5.462643 -2.11702 Spcs1 6.771202 5.573372 -2.29394 Stk17b 9.130204 7.731405 -2.63682 Taar6 5.049312 6.194453 2.211676 Taar7b 4.276394 5.81646 2.908079 Tank 6.585991 5.566936 -2.02659 Tatdn1 5.431027 4.301901 -2.18726 Thap2 5.152488 4.146146 -2.00881 (continued)

212

Table 4.7 (continued)

Thbs4 4.375287 5.452191 2.109505 Tk1 4.55077 3.473771 -2.10964 Tnc 7.985577 6.330914 -3.1485 Top2a 6.604844 4.467069 -4.40083 Tpx2 6.159875 4.79031 -2.58393 Trim12 6.899966 5.707372 -2.28563 Trim59 6.549629 5.173597 -2.59553 Tspan1 3.416935 4.426146 2.01281 Tst 5.318225 7.279022 3.89277 Tubb2a 6.229444 4.978018 -2.38077 Uba3 8.375202 7.366016 -2.01278 Uhrf1 6.835023 5.415127 -2.67566 Usp1 4.497948 3.199735 -2.45924 Usp48 6.502119 5.474737 -2.03832 Vbp1 7.331089 5.627085 -3.25804 Vcam1 7.413149 6.251366 -2.23734 Zwilch 4.981232 3.868288 -2.16287

213

Table 4.8 Genes regulated in macrophages by fibroblast Pten in 8-week mammary glands

Gene Symbol Average Wild Average Fold Change Type Pten 6-Sep 6.752006 5.10962 -3.12182 1810015C04Rik 7.308749 6.218622 -2.12893 2010002N04Rik 8.569997 6.913371 -3.15278 2310045A20Rik 3.961548 5.18076 2.328197 2610018G03Rik 4.926473 3.255009 -3.18538 2610528K11Rik 6.358523 4.970363 -2.61744 2810417H13Rik 6.530516 4.285157 -4.74155 2900062L11Rik 4.696718 7.219432 5.746621 3000004C01Rik 5.235131 4.204103 -2.04348 3110048E14Rik 7.125643 8.284996 2.233573 4632434I11Rik 5.750908 4.509941 -2.36357 4732429D16Rik 8.64479 9.78792 2.208597 6430706D22Rik 6.136352 4.857246 -2.42688 9030025P20Rik 9.077202 10.20896 2.191257 A530088I07Rik 7.437604 6.409675 -2.03909 Abcc3 9.133138 7.863075 -2.41172 Abcd4 7.359195 8.364753 2.007719 Abcg1 9.738478 8.050599 -3.22183 Abi3 7.409631 5.222767 -4.55315 Abtb2 5.737605 4.630501 -2.15413 Acaca 6.362024 7.471612 2.157841 Acot1 6.618838 5.069737 -2.92635 Acp5 9.941891 8.690458 -2.38078 Acsl1 7.784062 8.974332 2.281954 Acta2 11.02379 9.755174 -2.4093 Adam23 7.3774 5.478047 -3.73046 Adam23 7.981327 5.430107 -5.8613 Adcy6 6.875304 5.733021 -2.2073 Adhfe1 4.812999 6.288404 2.780618 Adrb1 4.539973 5.60785 2.096347 Afap1l1 6.572495 7.651681 2.112843 Aff3 7.217702 5.460701 -3.37995 Agpat9 4.983568 6.197646 2.319926 (continued) 214

Table 4.8 (continued)

Agt 4.234839 5.40767 2.254536 Akap2 7.13069 5.912293 -2.32688 Akt3 8.141283 6.173575 -3.91146 Aldh1a1 6.124262 8.14748 4.064896 Ankrd57 7.115597 5.92786 -2.27795 Ap1s3 5.294828 4.083318 -2.3158 Apln 3.24344 4.384265 2.205071 Apod 7.627652 9.314982 3.220602 Apol9b 6.23688 4.984075 -2.38304 Arhgap22 6.829921 5.778474 -2.07261 Arhgap27 5.131421 3.406228 -3.30624 Arl4c 7.973572 6.775206 -2.2948 Arl5c 7.762792 5.672003 -4.25981 Asf1b 7.52555 6.373459 -2.22236 Aspm 5.05722 3.686801 -2.58545 Atp1a3 7.518879 6.359089 -2.23425 AW544981 5.082561 3.842531 -2.36203 Axl 10.52936 8.261531 -4.81596 B130040O20Rik 5.712401 6.862234 2.218883 Bach2 6.72989 4.584792 -4.42322 BC003266 5.788546 4.738923 -2.06999 BC004044 6.777166 4.475839 -4.92911 BC006779 7.521664 5.94712 -2.97841 Bcl11a 6.230077 4.591387 -3.11383 Bhlhb2 8.721694 7.428392 -2.45088 Brca1 5.215964 4.099558 -2.16806 Bri3bp 7.521748 6.471369 -2.07107 Brip1 5.680367 4.37527 -2.471 Bub1 5.516487 3.775846 -3.34184 Bub1b 6.18058 4.564751 -3.06488 C030003D03Rik 4.964869 3.786794 -2.26275 C330027C09Rik 6.258174 5.242256 -2.02219 C79407 5.445039 4.312831 -2.19194 Cacnb3 8.203898 5.337754 -7.29114 Cadm1 7.531769 5.48962 -4.11859 Car2 5.237652 4.133281 -2.15005 (continued)

215

Table 4.8 (continued)

Car5b 6.6063 8.298937 3.23247 Casc5 5.244474 3.984829 -2.39437 Ccdc88c 6.181678 5.127613 -2.07637 Ccl17 4.97098 3.831087 -2.20365 Ccl5 9.3676 5.725431 -12.4854 Ccna2 7.509535 5.698187 -3.5097 Ccnb1 4.796862 2.898089 -3.72896 Ccnb2 7.089439 6.030654 -2.08318 Ccnd1 8.308555 7.096251 -2.31707 Cd274 8.564925 7.301044 -2.40141 Cd36 11.87348 10.57911 -2.4527 Cd38 8.77665 5.702205 -8.42365 Cd40 7.184264 6.038508 -2.21262 Cd44 8.922209 7.758385 -2.24051 Cd55 7.226016 9.130263 3.743134 Cd74 13.02788 11.74232 -2.43776 Cd93 10.17587 6.91828 -9.5638 Cdc2a 6.126871 4.815367 -2.482 Cdca2 5.455513 4.317755 -2.20039 Cdca3 5.557467 4.374937 -2.26974 Cdh3 5.11629 3.995923 -2.17402 Cdo1 5.723104 7.95139 4.685772 Cenpa 7.386261 6.297166 -2.1274 Cenpe 5.069935 3.781215 -2.44311 Cenpi 4.915241 3.878787 -2.05118 Cep55 5.173215 3.908277 -2.40317 Cfd 4.949282 9.336042 20.91927 Chpt1 6.379954 7.47607 2.137784 Ckap2l 4.907446 3.585785 -2.49954 Clec4n 8.880048 9.898496 2.025739 Clec7a 8.845115 6.653656 -4.56767 Clu 8.408099 7.060847 -2.54427 Cnn1 6.050318 4.878911 -2.25231 Crabp1 6.108665 7.620188 2.851109 Ctsw 4.674406 3.563867 -2.15926 Cxcl11 3.831358 2.591006 -2.36256 (continued)

216

Table 4.8 (continued)

Cxcl16 10.35613 8.968829 -2.61588 Cyp2f2 4.194207 5.482025 2.441585 D2Ertd750e 6.186202 5.175165 -2.01536 D930030O05Rik 5.723417 4.632467 -2.13014 Dctd 5.105023 4.045724 -2.08392 Diap3 6.152289 5.096187 -2.07931 Dlg7 5.462191 4.201384 -2.3963 Dmpk 8.859885 7.720118 -2.20345 Dna2 6.720351 5.254942 -2.76142 Dnm1 8.83269 10.06474 2.349006 Dok3 7.699915 8.881891 2.268873 Dsn1 5.977323 4.919765 -2.0814 Dtl 5.564337 4.353093 -2.31537 Dtx4 8.303873 7.300308 -2.00495 Dusp10 7.011533 5.746805 -2.40282 Dusp14 5.263834 4.140839 -2.17799 Dusp2 8.501688 6.53468 -3.90956 E2f8 5.16587 3.871912 -2.452 Ect2 6.373983 5.055789 -2.49354 Efnb2 6.197099 4.817547 -2.60188 Ehf 6.449974 5.044496 -2.64905 Ell2 7.544966 6.157949 -2.61537 Elovl6 5.627981 6.811299 2.270986 Elovl7 3.713452 4.99938 2.438389 Eno2 6.18057 4.377869 -3.48873 Eno3 7.630223 6.607889 -2.0312 Enpp4 6.636007 5.272844 -2.57249 Epb4.1l3 7.09724 5.167441 -3.81002 Ephx2 4.362942 6.097115 3.326888 Ercc6l 4.198838 3.181427 -2.02428 Esco2 4.265156 3.120034 -2.21165 Ets1 8.45883 6.179166 -4.85565 Etv5 7.563392 6.422862 -2.20462 F630043A04Rik 5.718958 4.410552 -2.47668 Fasn 7.135716 8.939451 3.49123 Fgf10 4.200892 5.715727 2.857663 (continued)

217

Table 4.8 (continued)

Fkbp5 8.364636 9.377544 2.017976 Fmnl2 7.629143 6.059392 -2.96853 Fmnl3 7.327947 6.229928 -2.1406 Foxm1 6.240882 5.173472 -2.09567 Fscn1 9.540382 6.335752 -9.21912 Fzd6 5.291187 7.066302 3.422655 Galnt12 5.387353 4.339747 -2.0671 Galnt3 6.898708 5.434557 -2.75901 Ggta1 6.939115 5.408717 -2.88865 Ghr 5.19233 6.360903 2.247893 Gimap4 7.605839 5.454207 -4.4433 Gimap6 7.257341 5.572562 -3.21491 Gimap9 6.068177 4.724862 -2.53734 Gipc2 3.822134 5.49018 3.177839 Glipr1 7.419355 5.52686 -3.71277 Glis3 5.392894 4.371511 -2.02986 Gpam 6.325018 7.649187 2.503887 Gpd1 5.382967 6.890258 2.842758 Gpr157 7.098184 5.437391 -3.1619 Gpr35 7.597856 6.443693 -2.22555 Gpr56 7.441548 9.040705 3.029662 H2-Ab1 11.25232 9.616295 -3.10808 H2-DMb2 9.31767 7.347686 -3.91764 H2-Eb1 8.90444 6.754501 -4.43809 Havcr2 7.059279 4.873975 -4.54823 Hells 6.194668 4.748138 -2.72552 Hexb 10.5275 9.226929 -2.46326 Hist1h1b 8.385929 6.245583 -4.40868 Hist1h2ab 5.027387 2.929933 -4.27953 Hist1h2bb 2.537012 1.529863 -2.00993 Hist1h2bc 4.735361 6.791603 4.159018 Hist1h4i 5.266822 6.805275 2.904829 Hist2h3c1 4.132443 5.18384 2.072536 Hp 4.814041 6.739693 3.799087 Hyal1 6.977102 7.981945 2.006726 Ifi205 6.337519 7.637867 2.462884 (continued)

218

Table 4.8 (continued)

Igf1r 7.953578 6.454336 -2.82694 Il17ra 8.34332 7.264869 -2.11177 Il1rl2 5.873201 4.722339 -2.22046 Il1rn 8.553599 7.382824 -2.25133 Il27ra 6.166237 5.012796 -2.22444 Iqgap3 6.334728 4.745852 -3.00815 Itga6 8.247855 7.105634 -2.2072 Itgav 8.356824 6.937349 -2.67488 Itgb3 6.10524 4.882052 -2.33462 Itgb5 9.333427 7.895214 -2.70985 Itgbl1 4.811111 6.074943 2.401327 Itpr3 6.907459 5.61027 -2.4575 Kank2 7.550761 8.557854 2.009857 Kcnn4 7.156747 5.489871 -3.17526 Kif11 6.332419 4.52151 -3.50863 Kif15 5.368558 3.900876 -2.76577 Kif20a 6.033922 4.706576 -2.50941 Kif21b 7.21805 6.169952 -2.0678 Kif2c 5.511355 3.962912 -2.92501 Kif4 5.080287 3.671985 -2.65425 Krt14 5.687647 4.181107 -2.84128 Krt17 6.183387 4.865609 -2.49282 Krt5 7.267497 5.911308 -2.56008 Lad1 5.648643 4.565447 -2.11872 Lass4 6.459756 5.035973 -2.68288 Lef1 6.127965 4.152773 -3.9318 Leprel1 9.026411 7.805473 -2.33098 Lgals3 9.474608 8.437529 -2.05207 Lpcat2 8.625668 5.839168 -6.89954 Lphn3 5.339676 4.289383 -2.07095 Lrg1 6.535156 7.545139 2.013889 Lrrc15 4.700053 3.388674 -2.48179 Lrrk2 6.755078 4.794818 -3.89132 Lsp1 10.497 9.354009 -2.20838 Ly75 6.44369 3.977919 -5.52422 Lyz1 11.01739 9.713707 -2.46858 (continued)

219

Table 4.8 (continued)

Mad2l1 7.613737 6.494826 -2.17183 Malt1 9.029308 7.69043 -2.52954 Maml3 5.786397 4.733383 -2.07486 Mapkapk3 7.377221 6.228889 -2.21657 Mastl 4.222159 2.861746 -2.56759 Mbnl3 5.222189 3.856628 -2.57676 Mboat1 6.88167 8.165361 2.43461 Mcm10 6.164435 4.783779 -2.60387 Mcm2 7.425613 6.338382 -2.12466 Mcm5 8.078318 7.070595 -2.01073 Mcm6 8.003797 6.259506 -3.3503 Mcoln3 4.488795 3.200187 -2.44292 Melk 5.639485 4.302998 -2.52536 Mfge8 9.139878 7.853398 -2.43932 Mical1 7.346622 5.864815 -2.79298 Mical3 6.297046 5.018681 -2.42564 Mki67 7.389503 5.592103 -3.47593 Mmp12 8.591523 5.94552 -6.25931 Mmp13 5.851057 4.460362 -2.62205 Mmp14 8.365495 7.203982 -2.23692 Mmp3 5.657694 6.882858 2.33782 Mmp9 10.53983 7.255384 -9.7435 Mpi 8.968937 10.09395 2.181031 Mpzl2 4.79136 5.826266 2.04898 Mrap 4.037023 5.41382 2.596912 Mreg 7.521961 3.883224 -12.4557 Mthfd2 6.312348 4.865745 -2.72565 Myc 9.054114 7.834656 -2.32859 Myh11 8.518466 7.255935 -2.39916 Ncapd2 6.849723 5.607102 -2.36628 Ncapg2 6.449917 5.40465 -2.06375 Ndc80 5.675903 4.538302 -2.20015 Neil3 5.446378 4.138846 -2.47518 Nek2 5.690774 4.435107 -2.38777 Niban 9.130802 8.051108 -2.11359 Npnt 6.063348 4.658262 -2.64833 (continued)

220

Table 4.8 (continued)

Nr4a3 9.298841 8.263814 -2.04915 Nrm 7.929709 6.914505 -2.02119 Nup210 6.973498 4.686486 -4.88044 Nupr1 6.882594 8.017115 2.195456 Nusap1 5.235799 4.009892 -2.33902 Ogn 5.077409 6.251097 2.255877 Olfm1 7.354364 6.054227 -2.46252 Olfml3 8.028102 6.856577 -2.2525 Olr1 5.560439 3.928396 -3.09952 Oxtr 5.851126 4.826018 -2.03511 P2ry1 6.300544 5.225857 -2.10626 P4ha2 4.824265 5.905345 2.115619 Parvg 8.497689 6.398427 -4.2849 Pbk 6.292734 5.017473 -2.42043 Pcx 5.072603 7.350968 4.85128 Pdlim3 6.895141 5.75364 -2.2061 Penk1 6.49848 7.944938 2.725381 Pilra 8.539126 6.675712 -3.63868 Pir 4.214502 5.27448 2.0849 Pkmyt1 6.525667 5.285066 -2.36297 Plaur 8.887556 6.663447 -4.67222 Plekhg5 9.176236 10.27714 2.144886 Plin 5.100248 6.372184 2.414854 Plk1 6.328027 5.171063 -2.22988 Plp1 5.521903 6.553222 2.043892 Plxdc1 6.075994 4.407451 -3.17893 Plxdc2 8.707907 6.828367 -3.67958 Plxna4 6.303871 4.935741 -2.58136 Pmepa1 9.352518 7.796208 -2.94101 Pole 5.695371 4.331191 -2.5743 Prc1 6.799612 4.894369 -3.74572 Prdm1 7.742098 6.714628 -2.03845 Prr11 5.79774 4.427323 -2.58545 Psat1 4.921454 3.669742 -2.38124 Pscdbp 9.804046 7.71232 -4.26258 Pstpip1 6.634175 5.169536 -2.75994 (continued)

221

Table 4.8 (continued)

Ptk2b 8.648656 7.483231 -2.24299 Ptpn13 5.083217 6.095341 2.016878 Ptprcap 6.348968 5.20994 -2.20233 Rab27b 2.955237 4.045972 2.129826 Rab30 5.328893 4.166838 -2.23776 Racgap1 7.723069 6.340655 -2.60704 Rad51 4.876313 3.69576 -2.26664 Rad54l 5.45831 4.351322 -2.15395 Ramp3 5.493789 4.367632 -2.18277 Rasgrp3 8.383981 7.147959 -2.35548 Rcbtb2 7.501436 6.226219 -2.42035 Rfc5 7.431542 6.192494 -2.36043 Rogdi 7.949036 6.423562 -2.87881 Rrad 7.33984 6.302229 -2.05283 Rtn1 6.220902 4.551041 -3.18184 Runx3 7.331517 5.431632 -3.73183 S100a3 3.36461 5.089432 3.305395 S100a4 7.053748 5.944999 -2.15659 Samd5 3.75409 5.023162 2.410064 Satb1 8.365453 5.101871 -9.60364 Sccpdh 5.815479 6.860684 2.06366 Scd1 8.784256 10.24036 2.743669 Sema4d 8.615959 6.280832 -5.04595 Sema6a 8.014858 9.238704 2.335686 Sema7a 6.153657 5.056842 -2.13882 Serpina1e 3.208427 6.042976 7.1332 Serpina3g 7.923599 5.737366 -4.55115 Serpinb5 4.013089 2.974451 -2.05429 Serpinb6b 6.790477 4.55533 -4.70811 Serpinb9 6.761036 5.426506 -2.52193 Serpine1 8.517263 7.287883 -2.34466 Sh3bgrl2 6.223534 7.721743 2.824918 Shcbp1 3.988025 2.987387 -2.00089 Siglecg 5.906798 4.428068 -2.78703 Skil 9.69285 8.582597 -2.15884 Slc10a6 7.215511 8.295267 2.113679 (continued)

222

Table 4.8 (continued)

Slc16a12 4.438028 6.255822 3.525417 Slc28a3 5.27373 4.232696 -2.0577 Slc2a1 7.614829 6.586955 -2.03902 Slc2a6 7.132531 5.82276 -2.47902 Slc38a1 8.794334 7.561867 -2.34968 Slc40a1 10.77894 9.577215 -2.30015 Slc43a1 4.54422 5.555708 2.01599 Slc44a2 7.820525 6.363663 -2.7451 Slc4a4 3.763816 5.139845 2.59553 Slc4a8 6.376217 4.593796 -3.44003 Slc7a1 7.147189 5.851588 -2.45479 Slc7a11 6.639095 4.646667 -3.97906 Slc7a5 7.765822 6.761259 -2.00634 Slc7a6 6.745252 5.536977 -2.31061 Smad7 7.282751 5.532873 -3.3633 Socs2 7.845561 6.669894 -2.25897 Sorl1 7.527974 5.097006 -5.39255 Spag5 5.646805 4.424859 -2.33261 Spsb1 5.811546 4.372774 -2.7109 Stac2 6.127264 4.758292 -2.58286 Stbd1 4.637295 3.342309 -2.45375 Stc2 4.993979 6.093644 2.143049 Stil 4.740764 3.360247 -2.60362 Taar7b 4.669104 3.373445 -2.45489 Tagln 8.828849 7.798608 -2.04237 Tbc1d8 8.23003 7.128755 -2.14544 Tcf7 6.534637 5.165556 -2.58306 Tgfbr1 9.329691 7.57639 -3.37129 Thbs1 8.940578 7.940015 -2.00078 Thbs4 4.925149 6.244146 2.494927 Tk1 5.23208 3.70853 -2.87498 Tmc8 6.000502 4.908418 -2.13182 Tmem119 6.415347 5.055038 -2.5674 Tmem176a 9.587372 7.97537 -3.05676 Tmem56 5.001117 6.11055 2.157608 Tnf 9.967653 8.945145 -2.03145 (continued)

223

Table 4.8 (continued)

Tnfaip2 8.686646 7.667278 -2.02703 Tnfrsf9 4.943773 3.929674 -2.01964 Tnfsf9 7.202556 5.305751 -3.72387 Tnip3 8.964507 6.742503 -4.66541 Top2a 7.687951 5.62351 -4.18272 Tpsab1 4.152738 6.375651 4.668352 Tpx2 6.155318 4.701698 -2.73895 Traf1 7.697806 5.804193 -3.71565 Trem2 8.528844 7.2788 -2.37849 Trim29 6.16349 5.138575 -2.03484 Trim59 6.910655 5.846235 -2.09133 Trim7 5.460333 4.376734 -2.11932 Tspan13 9.156486 7.36346 -3.46541 Tspan33 6.072953 4.708338 -2.57508 Tubb2a 7.228717 6.025384 -2.30271 Uhrf1 7.594585 5.578345 -4.04528 Vps37b 8.604315 7.50825 -2.13771 Xylt1 6.792765 5.695681 -2.13922 Zdhhc15 4.712839 3.514223 -2.29519 Zfp608 6.84529 5.817194 -2.03933 Zwilch 5.612527 4.507045 -2.15171

224

4.2.10 Fibroblast Pten Absence Promotes Proliferation of Surrounding Epithelial

Cells, Endothelial Cells and Macrophages

Gene ontology analysis indicated that loss of Pten in fibroblasts induced hyperproliferation of neighboring epithelial cells as well as other surrounding stromal cells.

More specifically, changes in genes involved in cell proliferation were observed in epithelial cells from FspCre;PtenloxP/loxP mammary glands, whereas genes involved in cell division/cell cycle were shown to be misregulated in endothelial cells and macrophages of these mammary glands. Closer examination of the specific genes involved in these processes revealed unique genes to be misregulated in epithelial cells, whereas many similar cell division/cell cycle genes were misregulated in endothelial cells and macrophages. These differences in misregulation may indicate the different biologies of these cells. Common genes involved in cell division/cell cycle in endothelial cells and macrophages include both cyclin A2 and cyclin B2, as well as several kinesin superfamily genes, such as Kif11, Kif20a and Kif2c.

To determine whether Pten loss in fibroblasts led to increased proliferation of surrounding epithelial cells, endothelial cells and macrophages, we performed an in vivo

BrdU assay in which cells were stained with both cell type specific markers as well as with an antibody against BrdU. Overall, this assay revealed a higher proportion of all these cell types in FspCre;PtenloxP/loxP mammary glands when compared to controls

(Figure 4.18A). This assay revealed an increased percentage of Cdh1+BrdU+,

CD31+BrdU+ and F4/80+BrdU+ double positive cells in mammary glands from

FspCre;PtenloxP/loxP mice when compared to controls, thus indicating an increased

225 proliferation rate of these cells (Figure 4.18B). To confirm this hyperproliferation in epithelial cells, we also did double staining on mammary gland tissue sections for the epithelial cell marker K8 and the proliferation marker Ki67. This analysis revealed a 4- fold increase in proliferating epithelial cells surrounded by Pten null fibroblasts compared to controls (Figure 4.19A). Additonally, endothelial cells isolated from wild type and Pten null mammary glands were isolated and stained for BrdU in vitro.

Consistent with our flow cytometry data, this analysis revealed an approximate 30% increase in BrdU incorporation in endothelial cells from FspCre;PtenloxP/loxP mammary glands (Figure 4.19B).

226

A 30.00

25.00

20.00

15.00

10.00 % Positive% Cells 5.00

0.00 CD31 F4/80 Cdh1 + BrdU + + + Ptenfl/fl 6.53 5.39 3.33 3.03 FspCre/Ptenfl/fl 13.10 23.63 13.77 13.41

B 12.00

10.00

8.00

6.00

4.00 % Positive % Cells 2.00

0.00 CD31 F4/80 Cdh1 BrdU + BrdU + BrdU + Ptenfl/fl 1.01 0.78 0.77 FspCre/Ptenfl/fl 7.33 6.47 2.20

Figure 4.18 Increased Proliferation of Epithelial Cells, Endothelial Cells and

Macrophages from FspCre;PtenloxP/loxP Mammary Glands

A. Flow cytometry analysis of total populations of CD31+, F4/80+, Cdh1+ and BrdU+ cells in mammary glands of indicated genotypes, n=3. B. Flow cytometry analysis of

CD31-BrdU, F4/80-BrdU and Cdh1-BrdU double positive cells in mammary glands of indicated genotypes, n=3 (Julie Wallace).

227

A PtenloxP/loxP Fsp-cre;PtenloxP/loxP ** 40

Ki67/K8+ Cells Ki67/K8+ 10 % % 0 PtenloxP/loxP FspCre; n = 3 PtenloxP/loxP n = 3 ** B PtenloxP/loxP Fsp-cre;PtenloxP/loxP 30 25 20 15

Ki67+ Cells Ki67+ 10

% % 5 0 PtenloxP/loxP FspCre; loxP/loxP n = 3 Pten n = 3

Figure 4.19 Increased Ki67 Staining in Epithelial Cells and Endothelial Cells as

Consequence of Fibroblast Pten Loss

A. IF staining on tissue sections of indicated genotypes for K8 (red) and Ki67 (green), slides counterstained with DAPI. Quantification on left is % of double positive cells, student’s t-test **P<0.05 (Julie Wallace). B. In vitro staining for BrdU (green) in cultured mammary gland endothelial cells from indicated genetic groups, counterstained with DAPI (blue) to count cells. Quantification on left is % of double positive cells, student’s t-test **P<0.05 (Julie Wallace).

228

4.2.11 Pten Signatures Represented in Human Breast Cancer Stroma and Predict

Patient Outcome

To determine whether the changes we observed in the tumor microenvironment as a consequence of fibroblast Pten deletion were relevant in human breast cancer, we queried our gene expression data against that of stromal samples acquired from normal or tumor breast tissue. We previously reported the ability of our fibroblast Pten gene signature to separate normal stroma from tumor stroma, and here we show the ability of our endothelial cell and macrophage signatures to make this same distinction (Figure

4.20). Furthermore, our fibroblast, endothelial cell and macrophage gene signatures were also able to retrospectively predict outcome in a subset of these patients. Next we aimed to determine if these individual stromal signatures as well as our epithelial signature could also significantly predict patient outcomes in several well known whole tumor breast cancer data sets (Figure 4.21). To our surprise, all of the individual signatures were able to predict outcome in 3 out of the 6 data sets we looked at (NKI, Wang and

Stockholm). Additionally, combining all the genes from the signatures predicted outcome with the highest significance (Figure 4.21).

229

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

value value under the Wilcoxon rank sum test -

sided p

One

Neu vsWt Neu vsWt Neu

Neu Neu vsWt Neu vsWt Neu Neu vsWt Neu vsWt

Pten vsWt Pten vsWt Pten Pten vsWt Pten vsWt Pten

- - - -

Pten Pten Pten Pten Fibroblasts Epithelial Cells Endothelial Macrophages Cells

p-value <0.05 & fc > 1.5 p-value <0.05, fc > 1.5 & 0.5 variance cut-off

Figure 4.20 Pten Associated Signature Represented in Human Breast Cancer

Stroma

X-axis indicates gene signatures from indicated cell compartments and genoptyes. Y- axis represents p-value associated with the ability of indicated gene signatures to differentiate normal breast stroma from tumor stroma. Colored dots represent different criteria used for generating gene signatures (Julie Wallace and Thierry Pecot).

230

0.9 0.8 0.7

rank test rank 0.6 - 0.5 0.4 0.3 0.2 0.1

value under the log the under value

Neu vs Wt vs Neu Wt vs Neu Wt vs Neu Wt vs Neu Wt vs Neu

Neu vs Wt vs Neu Wt vs Neu Wt vs Neu Wt vs Neu Neu vs Wt vs Neu

Pten vs Wt vs Pten Wt vs Pten Wt vs Pten Pten vs Wt vs Pten Wt vs Pten

- - - - -

Pten Pten Pten Pten Pten Mc Gill NKI Wang Stockholm Stockholm relapse death Endothelial cells Epithelial cells Fibroblasts Macrophages Combination

Figure 4.21 Pten Associated Signatures Predict Patient Outcomes

X-axis indicates gene signatures used for survival prediction in various stromal and whole tumor datasets. Y-axis represents p-value associated with the ability of indicated gene signatures to separate patients based on outcome as measured by recurrence or death. Colored circles represent individual cell types from which gene signatures were derived (Julie Wallace and Thierry Pecot).

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4.3 Discussion

The importance of Pten tumor suppressor function in tumor cells has been well established, however the stromal influence of Pten on tumorigenesis has remained unknown. Although controversial, mutations in PTEN have been reported in stroma of human breast tumors (Kurose et al., 2002). As the age of personalized medicine is on the horizon, determining the consequences of loss of important tumor suppressors in the context of both malignant cells and the associated stroma is of paramount importance.

To this effect, we have begun to examine the expression of PTEN specifically in the stroma of breast tumor tissue microarrays (TMAs). By looking at multiple TMAs and getting a large sample size inclusive of multiple subtypes and stages of disease, we hope to be able to eventually correlate stromal PTEN status with other clinical parameters, such as ER/PR/HER2 status, time to recurrence and immune cell infiltration.

Using conditional mouse genetics, we were able to specifically and efficiently delete Pten in stromal fibroblasts and in turn determine the effects of this loss in ErbB2 driven tumors. A dramatic increase in the number and size of tumors formed was observed, and this was accompanied by increased macrophage infiltration and ECM remodeling. Additionally, we identified two key downstream effectors in our Pten null fibroblasts, as the Ets2 transcription factor was activated to drive expression of Mmp9 and Ccl3, and miR-320 was downregulated which was in turn shown to directly target

Ets2 as well. Using global gene expression profiling, we were able to show consistent gene expression changes in Pten null fibroblasts as compared to controls. We also were able to utilize and characterize the expression of Col1a-YFP in mammary stromal

232 fibroblasts. Additionally gene expression experiments revealed the complete reprogrammaing of gene expression in epithelial cells, endothelial cells and macrophages surrounded by FspCre;PtenloxP/loxP fibroblasts. Examination of misregulated genes between cell compartments exposed a trend whereby expression of ErbB2 in epithelial cells promoted a Pten like signature in surrounding fibroblasts, and conversely, loss of

Pten in fibroblasts promoted an ErbB2 like signature in epithelial cells. Currently, we are working to design some type of correlation test to determine whether these trends in gene expression are significant.

Interestingly, gene ontology analysis of Pten null fibrblasts indicated cell proliferation and cell division to be effected in the surrounding cells of the microenvironment. Using both in vivo and in vitro assays we were able to confirm that this was in fact the case. This is particularly interesting due to the fact that deletion of

Pten alone in mammary gland fibroblasts does not promote tumorigenesis. Therefore there must be some inhibitory mechanism within these various cell compartments to keep them in check. Perhaps most importantly, we show the ability of most of our gene expression signatures to discriminate normal human breast stroma from tumor stroma, and also predict outcome in independent whole tumor datasets. Interestingly, using a combination of all gene signatures from individual cell types gave us the most robust result, indicating that all these cell types have critical functions in promoting tumorigenesis.

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Chapter 5: Stromal p53 Tumor Suppressor Function

5.1 Introduction

Thus far we have demonstrated the role of Pten in stromal fibroblasts during

ErbB2 driven tumorigenesis. Interestingly, loss of this tumor suppressor had no significant effect on Ras mediated transformation. This suggests a potential collaboration between specific oncogenes in epithelial cells and tumor suppressor genes in stromal fibroblasts. Additionally, previous studies have determined an important role for p53 in stromal fibroblasts in a prostate cancer model (Addadi et al., 2010). In addition to PTEN,

P53 was also shown to be mutated in the stroma of human breast tumors (Kurose et al.,

2002). To determine if the tumor suppressor gene p53 similarly collaborated from the stroma in mammary gland tumors, we examined the effect of its deletion on both ErbB2 and Ras mediated tumorigenesis. Global gene expression profiling was also examined in multiple cell types of the microenvironment surrounding p53 null fibroblasts to determine paracrine effects of p53 loss. Again the ability of our p53 gene signatures were also used to separate nomal stroma from tumor stroma in breast cancer patients.

5.2 Specific and Efficient Deletion of p53 in Stromal Fibroblasts

Once again, to prove the specific and efficient deletion we performed Western blot analysis on purified mammary fibroblasts from p53loxP/loxP and FspCre;p53loxP/loxP mice. This analysis showed efficient deletion of p53 protein in these cells. Additionally, 234 we isolated mammary epithelial cells from these same animals and checked for p53 protein levels. As expected, there was no change in p53 levels in these cells, thereby confirming the deletion to be restricted to fibroblasts (Figure 5.1A). We also performed

IHC staining for P53 protein in mammary tissue sections. However, we observed very low expression of P53 in the normal stroma, therefore it was difficult to confirm FspCre mediated deletion in the stroma (Figure 5.1B). However, clear staining in epithelial cells again confirms no loss of p53 in this cell compartment.

235

A Fsp-cre; loxP/loxP loxP/loxP B Fsp-cre; p53 p53 p53loxP/loxP p53loxP/loxP p53 p53

Tubublin Tublin

C p53loxP/loxP Fsp-cre;p53loxP/loxP p53

Figure 5.1 Efficient and Specific Deletion of p53

A. Western blot for p53 on lysates from fibroblast cultures of indicated genotypes (Julie

Wallace and Jinghai Wu). B. Western blot for p53 on lysates from epithelial cultures of indicated genotypes (Julie Wallace and Jinghai Wu). C. IHC staing for p53 on mammary gland secions of indicated genotypes (Chris Thompson).

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5.3 Results

5.3.1 Normal Development of FspCre;p53loxP/loxP Mammary Glands

To determine if loss of fibroblast p53 played a role in mammary gland development, we performed whole mount staining to visualize ductal brancing on mammary glands from 8 weed old p53loxP/loxP and FspCre;p53loxP/loxP mice (Figure 5.2).

Examination by eye revealed no significant differences in the structure or patterning of ducts from control and experimental mammary glands.

237

p53loxP/loxP Fsp-cre;p53loxP/loxP

Figure 5.2 Normal Development of Fibroblast p53 Null Mammary Gland

Whole mount staining of 8 week mammary glands of indicated genotypes (Julie

Wallace).

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5.3.2 p53 Loss in Fibroblasts Promotes Kras, but not ErbB2, Mediated

Tumorigenesis

5.3.1.1 Transplant Model

Although FspCre;p53loxP/loxP mice do not display any appreciable phenotype and have a normal lifespan, we initially decided to use our transplant model to not only abrogate any confounding effects of p53 deletion elsewhere in the mouse but also to keep the model system consistent beween these studies and our Pten experiments. Conversely to what was previously observed in context of stromal Pten deletion, loss of p53 in fibroblasts did not have a significant effect on ErbB2 driven tumorigenesis at the 26 week time point we examined (Figure5.3A, B). However, it should be noted that the incidence of carcinoma in transplant tissues did increase from approximately 14% in controls to

40% in ErbB2;FspCre;p53loxP/loxP tissues, however this difference was not found to be significant. Tissues diagnosed as MIN decreased from 25% to 13%, as did occurrences of normal mammary ducts which went from 61% of the cases to 47% in

ErbB2;FspCre;p53loxP/loxP transplants (Figure 5.3B). Since these transplants were only harvested at one time point, it is possible that p53 may have important tumor suppressor functions in the stroma at either an earlier or later stage of tumor initiation or development.

To determine whether fibroblast loss of p53 had an effect on Ras mediated tumorigenesis, we utilized the same transplant strategy we used to study ErbB2 driven effects with the exception of recipient mice starting on a 1g/kg doxycycline diet following transplantation of the tissue. Approximately 26 weeks later the tissue was

239 harvested and graded histo-pathologically. Interestingly, transplants containing carcinoma increased from approximately 15% in controls to almost 88% in MMTV- rtTA;Tet-o-Ras;FspCre;p53loxP/loxP tissue (Figure 5.4A, B). Although there were no cases of MIN in these transplants, the perctage of diagnosed normal tissue accordingly decreased from almost 70% in controls to 12% in tissue lacking fibroblast p53 (Figure

5.4B).

240

MMTV-ErbB2; A ErbB2 FspCre;p53loxP/loxP

B 120 100 Carcinoma 80 MIN Normal 60

40 % Transplants% 20 0 p53loxP/loxP MMTV- MMTV-ErbB2; n = 34 ErbB2 FspCre; n = 28 p53loxP/loxP n = 16

Figure 5.3 Fibroblast p53 Loss Does Not Impact ErbB2 Tumorigenesis

A. Gross examination of tumors harvested at 26-weeks post transplantation (Anthony

Trimboli). B. Graphical representation of distribution of histological grading in transplant tissue from indicated genopypes. MIN, mammary intraepithelial neoplasia.

Differences in histo-pathological grading were analyzed using Fisher’s exact test, P>0.05

(Anthony Trimboli, Shan Naidu and Julie Wallace).

241

A MMTV-rtTA;Tet-o- MMTV-rtTA;Tet-o-Ras Ras;FspCre;p53loxP/loxP

B 120 ** 100

80 Carcinoma 60 MIN Normal

% Transplants% 40 20 0 p53loxP/loxP MMTV- MMTV-rtTA;Tet-o- n = 16 rtTa;Tet-o- Ras;FspCre; Ras p53loxP/loxP n = 28 n = 30

Figure 5.4 Fibroblast p53 Loss Drives Ras Mediated Tumorigenesis

A. Gross examination of tumors harvested at 26-weeks post transplantation (Anthony

Trimboli). B. Graphical representation of distribution of histological grading in transplant tissue from indicated genopypes. MIN, mammary intraepithelial neoplasia.

Differences in histo-pathological grading were analyzed using Fisher’s exact test,

**P<0.01 (Anthony Trimboli, Shan Naidu and Julie Wallace).

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5.3.1.2 Genetic Study

To confirm the specific collaboration between p53 and Kras, we replicated our tumor study using a non-transplant model whereby mammary gland tissue was directly collected from MMTV-rtTa;Tet-o-KrasG12D;p53loxP/loxP and MMTV-rtTa;Tet-o-

KasG12D;FspCre;p53loxP/loxP mice after 16-18 weeks of a doxycycline diet (20mg/kg) and again subjected to pathological analysis. As a negative control, the same analysis was also done in the context of ErbB2 at 4-5 months of age. Moreover, to rule out the effect of fibroblast p53 deletion alone as an initiator of tumorigenesis, animals lacking any epithelial oncogenic driver were also harvested and assessed for histological abnormalities. The results of this more direct analysis definitively confirm the results of the transplant study, whereby significantly more carcinoma was diagnosed in mammary glands from MMTV-rtTA;Tet-o-KrasG12D;FspCre;p53loxP/loxP mice than in controls, however the MMTV-ErbB2 mice had an equal incidence of carcinoma regardless of p53 status in fibroblasts (Figure 5.5B, D). Additionally, MMTV-rtTa;Tet-o-

KrasG12D;FspCre;p53loxP/loxP mice had significantly more palpable tumors by 16-18 weeks compared to controls . Isolation of primary mammary fibroblasts and epithelial cells confirmed the FspCre deletion of p53 to be specific to fibroblasts both in the presence and absence of oncogene (Figure 5.5A, C). Representative H&E stained mammary glands from MMTV-rtTa;Tet-o-KasG12D;p53loxP/loxP and MMTV-rtTa;Tet-o-

KasG12D;FspCre;p53loxP/loxP mice show the presence of carcinoma in our experimental mice (Figure 5.5E).

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Figure 5.5 Confirmation of Transplant Study Using Purely Genetic Model

A, C. Graphical representation of the number of mice with palable vs. non-palpable tumors in indicated genotypes (Julie Wallace and Jinghai Wu). B, D. Graphical representation of distribution of histological grading in mammary gland tissue from indicated genopypes. MIN, mammary intraepithelial neoplasia. No p-value represented on the graph indicates no significant difference (Julie Wallace and Jinghai Wu). E.

Representative hisotolgical section from mammary glands of indicated genotypes (Julie

Wallace and Jinghai Wu).

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A B 25 100 n = 9 n = 8 20 80

15 Palpable 60 Carcinoma Non- MIN

10 palpable 40 Normal # of Mice of # 5 20

n = 15 n = 15 0 HistologyGrade % 0 MMTV- MMTV- MMTV- MMTV- ErbB2; ErbB2; ErbB2; ErbB2; p53loxP/loxP FspCre; p53loxP/loxP FspCre; n = 14 p53loxP/loxP n = 14 p53loxP/loxP n = 23 n = 23

* p = 0.042 C D 100 15 n = 2 * p = 0.018 n = 8 80 Carcinoma 10 Palpable 60 MIN Non- Normal palpable 40

# of Mice of # 5

20 % HistologyGrade %

n = 12 n = 5 0 0 MMTV-rtTA; MMTV-rtTA; MMTV-rtTA; MMTV-rtTA; Tet-o-Ras; Tet-o-Ras; Tet-o-Ras; Tet-o-Ras; p53loxP/loxP FspCre; loxP/loxP p53 FspCre; n = 14 p53loxP/loxP n = 14 p53loxP/loxP n = 13 n = 13 E MMTV-rtTA;Tet-o-Ras; MMTV-rtTA;Tet-o-Ras;

p53loxP/loxP Fsp-cre; p53loxP/loxP H&E

Figure 5.5

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5.3.2 Gene Expression of p53 Null Fibroblasts

5.3.2.1 Gene Expression in Cultured Fibroblasts

To understand the potential mechanism of p53 funtion in stromal fibroblasts in both the normal mammary gland and in the context of epithelial Ras expression, we isolated fibroblasts from p53loxP/loxP, FspCre;p53loxP/loxP, MMTV-rtTa;Tet-o-

KasG12D;p53loxP/loxP and MMTV-rtTa;Tet-o-KasG12D;FspCre;p53loxP/loxP mammary glands and performed gene profiling. To make sure the effects of doxycycline did not effect our results, all mice were started on 20mg/kg food at 8 weeks of age and were harvested 8 weeks later (16 weeks total age). Analysis of fibroblast samples revealed the misregulation of 398 genes as a result of p53 deletion, however only 62 genes changed in response to Ras signaling (Table 5.1, fold change >1.5). Similarly, 73 genes were misregulated in epithelial cells as a result of fibroblast p53 loss (Table 5.2, fold change

>1.5).

To determine if Ras and p53 signaling behaves in a manner similar to that observed with ErbB2 and Pten, we once again generated heatmaps displaying differentially expressed genes from all of our genetic groups. As we expected, Ras expression in epithelial cells drove a signature resembling that in p53 null fibroblasts

(Figure 5.6). Conversely, p53 deletion in fibroblasts also reprogrammed epithelial cells to look like Ras expressing cells, however this effect is not as strong as that observed for

Pten and ErbB2 (Figure 5.7). Additionally gene expression arrays were performed on endothelial cells and macrophages from these fibroblast p53 deficient mammary glands.

246

Initial analysis reveals viturally no changes in endothelial cells, however over 100 genes were misregulted in macrophages by more than 2-fold.

5.3.2.2 Gene Expression in ColYFP Sorted Fibroblasts

Examination of gene expression in p53 null ColYFP samples was also examined, and this analysis revealed 364 upregulated genes and 27 downregulated genes (Table 5.3, fold change >2 and p-value <0.05). Comparison of this data with data from our cultured cells revelaed similar trends in the misregulation of some genes, however there were also genes that were misregulated in the opposite direction between these two experiments.

Further analysis in ongoing to examine these changes more closely and determine which changes are represented in vivo.

247

MMTV-rtTA;Tet-o- MMTV-rtTA;Tet-o- FspCre; Ras; Ras;FspCre p53loxP/loxP p53loxP/loxP p53loxP/loxP p53loxP/loxP

-3 +3

Figure 5.6 Epithelial Ras Expression Drives p53 Like Signature in Fibroblasts

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace).

248

MMTV- MMTV-rtTA;Tet- FspCre; rtTA;Tet-o-Ras; o-Ras;FspCre p53loxP/loxP p53loxP/loxP p53loxP/loxP p53loxP/loxP

-3 +3

Figure 5.7 Fibroblast p53 Deletion Drives Ras Like Response in Epithelial Cells

Heatmap depicting differentially expressed genes between indicated genotypes with fold change ≥1.5 and p-value ≤0.05 (Julie Wallace).

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Table 5.1 Genes misregulated by p53 in cultured 8-week mammary gland fibroblasts

Gene Symbol Average Wild Average p53 Fold Change Type 1E+08 10.83624 10.14588 -1.61368 1110032A03Rik 9.320776 8.687611 -1.55096 1700007K13Rik 6.848059 5.863727 -1.9784 1700025K23Rik 7.1016 6.261421 -1.79027 1700094D03Rik 6.866946 6.276692 -1.50551 2010109K11Rik 8.884404 9.524011 1.557905 2210023G05Rik 6.822728 6.204161 -1.53535 2310001H12Rik 6.559981 5.820093 -1.67005 2610008E11Rik 8.607329 7.641482 -1.95321 2610204K14Rik 6.643748 6.039893 -1.51977 3222402P14Rik 6.4095 5.255284 -2.22563 4732429D16Rik 9.160233 9.97049 1.753524 4930506M07Rik 6.599121 7.2758 1.598456 4933411K20Rik 8.321203 7.721523 -1.51538 5930434B04Rik 6.292458 5.004406 -2.44198 8430426H19Rik 7.115477 6.106647 -2.01228 9030617O03Rik 8.564549 7.852501 -1.63813 9230105E10Rik 7.441032 6.604247 -1.78607 9530009G21Rik 6.973606 6.238449 -1.66458 9530019H20Rik 8.392213 7.320204 -2.10236 A330021E22Rik 6.403331 5.740858 -1.58279 A530064D06Rik 6.232282 6.853038 1.53768 A730020M07Rik 9.544824 8.919889 -1.54214 A830023I12Rik 6.514706 5.857765 -1.57674 A930001N09Rik 8.08715 7.249767 -1.78681 AB041803 8.666246 8.018351 -1.56688 Abat 8.149304 7.561375 -1.50309 Abi3bp 9.791066 9.132419 -1.5786 Acvr2a 9.694585 8.858301 -1.78545 Adam8 9.619749 10.39415 1.710479 Adamts5 11.48431 10.83469 -1.56876 Adh1 9.38361 8.714296 -1.59032 AF251705 5.909065 6.691438 1.719958 Agbl3 6.647532 5.95178 -1.61973 (continued)

250

Table 5.1 (continued)

Agt 8.556624 7.968874 -1.5029 Aim1 5.901388 6.724011 1.768619 Akr1c14 6.669553 5.592955 -2.10906 Aldh1a1 9.045577 8.435034 -1.52683 Aldh1a7 6.534537 5.920566 -1.53047 Aldh1l1 8.555294 7.90339 -1.57124 Aldh6a1 8.181697 7.521548 -1.58024 Alox5 7.659351 8.265232 1.521908 Alox5ap 9.7109 10.52076 1.753041 Angpt4 8.361132 7.735929 -1.54243 Ankrd1 11.02053 11.78666 1.700705 Anpep 10.03611 10.63552 1.515101 Arc 8.661178 9.251193 1.505262 Arhgap20 7.968596 7.201499 -1.70184 Arl4c 7.737711 8.350834 1.529567 Armcx1 8.756597 8.121414 -1.55314 Arrb2 9.051716 9.718645 1.587689 Aspn 10.44105 9.757004 -1.60664 Atl1 6.992502 6.40426 -1.50341 Atp6v0d2 7.950317 8.60483 1.574084 BC004728 5.838865 6.551775 1.639107 BC062127 7.243091 6.557697 -1.60814 Blnk 7.505795 8.112774 1.523067 Btf3l4 7.356836 6.737753 -1.5359 Btk 6.009364 6.638229 1.546348 C030003D03Rik 8.650366 9.503435 1.80634 C1qa 9.289433 10.21832 1.903802 C1qb 10.19153 11.00132 1.75296 C1qc 9.098333 9.978301 1.840335 C1qtnf3 9.564119 8.581891 -1.97551 C3ar1 10.46953 11.25345 1.7218 C630028N24Rik 8.218553 7.618473 -1.5158 Cacna2d1 8.739203 8.146042 -1.50855 Cadm1 8.081288 8.822003 1.671004 Ccdc122 7.510807 6.857948 -1.57228 Ccl6 9.465242 10.30333 1.787682 (continued)

251

Table 5.1 (continued)

Ccl8 9.804713 9.177842 -1.54421 Ccng2 8.272633 7.597795 -1.59642 Ccrn4l 9.42773 10.30693 1.839358 Cd14 9.397646 10.11646 1.645832 Cd300a 6.972079 7.688934 1.643595 Cd300lb 7.998834 9.059683 2.08616 Cd300lf 5.708596 6.31602 1.523536 Cd33 8.427339 9.287694 1.815485 Cd36 8.731866 9.673886 1.921216 Cd48 6.492301 7.206487 1.640557 Cd55 7.644777 6.742382 -1.86917 Cd68 10.70284 11.49295 1.729212 Cd83 6.13169 6.753451 1.538753 Cd84 8.011565 8.654727 1.561749 Cdk10 6.900533 6.020394 -1.84055 Cdo1 8.552633 7.669129 -1.84485 Ch25h 9.695935 10.33073 1.552715 Chac1 9.325111 8.721308 -1.51972 Cish 8.314133 9.218798 1.87211 Clca1 7.01433 6.416138 -1.51382 Clca2 6.894013 6.183854 -1.63598 Clec12a 7.533131 8.37015 1.786356 Clec2d 9.709815 8.969789 -1.67021 Clec4a3 5.898679 6.620781 1.649584 Clec4d 7.153532 7.873001 1.646576 Clec4e 7.259792 8.265069 2.007329 Clec4n 7.045913 7.691327 1.564189 Clec5a 7.805057 8.906291 2.145382 Coro1a 8.067198 8.83374 1.701186 Coro2a 7.286972 7.885987 1.514682 Crct1 9.472056 10.45476 1.976166 Csf1r 9.494967 10.2635 1.703532 Csf2rb 7.596629 8.229575 1.550729 Csf2rb2 6.934136 7.986633 2.074116 Cspg4 8.807154 9.42985 1.539751 Csrnp1 9.121068 9.828364 1.632741 (continued)

252

Table 5.1 (continued)

Cstad 7.328552 6.740477 -1.50324 Ctsc 9.503453 10.26151 1.691208 Ctss 9.610175 10.59662 1.981291 Cxcl10 7.379581 8.199124 1.764847 Cxcr4 7.724001 8.375706 1.571024 Cybb 8.641965 9.458895 1.761653 Cyp1b1 10.31231 9.406259 -1.87391 Cyp2j6 8.294378 7.658693 -1.55367 Cyp2r1 7.455492 6.864494 -1.50629 Cysltr1 6.892829 7.520357 1.544916 Cyth4 8.484554 9.107988 1.540537 Dact1 8.789291 8.108862 -1.60262 Ddit3 9.106603 8.131285 -1.96607 Ddit4l 7.483973 6.297093 -2.2766 Dennd4a 10.27294 9.495442 -1.71415 Dkk2 8.6142 7.93378 -1.60261 Dub2a 7.609433 6.91111 -1.62262 Dusp10 9.49765 10.39205 1.85884 Dusp4 10.54092 11.422 1.841754 Dusp6 9.90991 10.51718 1.523368 Dzip1 8.994987 8.203712 -1.7306 Ear11 6.213336 5.532102 -1.60351 Eda2r 9.174941 7.793619 -2.60507 Ednrb 9.366747 8.688957 -1.59969 EG240327 5.772092 6.365185 1.508478 EG245174 6.775456 6.003132 -1.70802 EG631624 6.445964 5.805389 -1.55895 Egr3 8.601875 9.732491 2.189522 Emr1 8.128896 8.801256 1.593678 Eno2 9.102009 8.313618 -1.72715 Epcam 6.293126 6.920184 1.544412 Epha3 8.929504 8.094906 -1.78336 Ephx1 10.99291 10.04641 -1.92719 Epsti1 6.264759 6.907956 1.561787 Ereg 9.678704 10.95859 2.428202 Evi2b 8.477913 9.063828 1.50099 (continued)

253

Table 5.1 (continued)

Exoc4 9.855579 9.25495 -1.51638 F13a1 6.873923 7.788897 1.885535 F8 6.474284 5.802504 -1.59304 Fam13c 7.835992 7.194414 -1.56003 Fam164a 9.470379 8.735365 -1.66441 Fam26e 9.382795 8.671109 -1.63772 Fbxl20 9.216309 8.412149 -1.74613 Fbxo30 10.65524 11.28675 1.549184 Fcer1g 10.98211 11.58168 1.515262 Fcgr2b 8.742842 9.702116 1.944332 Fcgr3 8.279037 8.898212 1.535996 Fgf10 10.00445 9.316117 -1.61142 Fmnl1 7.645636 8.410335 1.699015 Folr2 9.710424 10.30342 1.50838 Foxc2 8.869854 9.898564 2.0402 Foxs1 8.605251 9.329307 1.65182 Frem1 8.116854 7.466191 -1.56989 Frzb 7.130913 6.466748 -1.58465 Fyb 7.209634 7.859651 1.569186 Gadd45a 7.797574 7.063254 -1.66361 Galnt6 7.099238 7.718666 1.536266 Gas6 11.7141 11.05919 -1.57451 Gata2 7.685487 8.28749 1.517822 Gatm 8.834799 9.493843 1.579035 Glt8d4 9.149083 8.416125 -1.66204 Gnpnat1 9.525892 8.622296 -1.87072 Gpr119 6.733402 7.537252 1.745753 Gpr126 6.497712 7.113513 1.532408 Gpr183 7.569111 8.204709 1.553581 Gpr65 6.994655 7.728302 1.662838 Gpr77 7.31946 8.001941 1.604897 Gprc5c 7.452957 8.061943 1.525187 Grb14 8.805288 8.073466 -1.66074 Gria3 9.833331 8.937584 -1.86057 Gsta4 10.33073 9.559139 -1.70715 Gstm7 7.322336 6.578217 -1.67495 (continued)

254

Table 5.1 (continued)

Gstp2 8.445959 7.820544 -1.54265 Gstz1 8.307842 7.692987 -1.5314 Gzme 7.346597 6.464019 -1.84367 Hal 7.956285 9.044028 2.125412 Hbegf 9.796092 11.2171 2.677721 Hbp1 9.487857 8.816161 -1.59294 Hc 6.92884 6.288878 -1.55829 Hcls1 8.100034 8.685077 1.500083 Hist1h2bc 8.15279 6.655268 -2.82357 Hivep3 8.044704 8.796766 1.684198 Htr1b 9.311024 10.20992 1.864639 Id1 8.8456 9.561886 1.642947 Id2 11.04381 11.85422 1.753707 Ifi44 6.850518 7.824182 1.963822 Ifit1 7.742688 8.696496 1.936979 Igfbp5 9.377649 8.59333 -1.72228 Igsf6 7.906189 8.789833 1.84503 Ikzf1 6.754559 7.340836 1.501367 Il11 9.224836 10.53708 2.483279 Il7r 7.40708 8.606147 2.295912 Inpp5d 8.095491 8.80599 1.636369 Irf5 8.485748 9.125777 1.558361 Irf7 7.299845 7.967769 1.588785 Irs1 8.968716 8.225206 -1.67424 Itgam 8.428766 9.252066 1.76945 Itgb2 8.305979 9.051981 1.677138 Itgbl1 8.132891 7.26227 -1.82845 Kcne4 8.303113 7.166603 -2.19849 Kcnn4 8.312439 9.012562 1.624644 Kctd11 9.328438 10.00942 1.60323 Klf10 8.659771 9.612127 1.93503 Klf12 7.85563 7.230064 -1.54282 Klf15 7.135934 6.451745 -1.6068 Klhl24 9.1409 8.221932 -1.89076 Laptm5 10.73712 11.35667 1.536393 Lcp1 9.988004 10.78232 1.734249 (continued)

255

Table 5.1 (continued)

Lcp2 7.97702 8.598723 1.538691 Lfng 9.044724 9.652668 1.524085 Lgr5 8.282579 7.302493 -1.97258 Lif 8.585958 9.181795 1.511349 Lpin1 7.767536 7.070478 -1.6212 Lpxn 7.702987 8.435258 1.661253 Ly86 8.603321 9.503931 1.866855 Ly9 6.873849 7.66695 1.732795 Lyrm5 7.5868 6.947209 -1.55789 Mafb 8.680389 9.430328 1.681721 Mamdc2 8.504028 7.891906 -1.52851 Mapk1ip1 8.4912 7.87422 -1.53366 Mef2d 7.384261 6.392977 -1.98795 Meis2 9.125371 8.502789 -1.53963 Meox2 8.347725 7.417187 -1.90599 Mmp11 10.68844 9.925886 -1.69649 Mmp12 10.41728 11.39214 1.965457 Mmp13 8.768866 8.115272 -1.57308 Mmp3 8.234537 9.199767 1.952375 Mobkl1b 7.629418 6.978182 -1.57051 Mpp5 6.747124 5.930848 -1.76085 Mrc1 8.371626 9.001347 1.547266 Ms4a6c 8.781095 9.647138 1.822656 Ms4a6d 8.267422 8.992946 1.653501 Ms4a7 7.317567 7.937437 1.536736 Msr1 10.04852 10.89265 1.795179 Myct1 6.900199 6.187232 -1.63917 Myo1f 7.517718 8.216564 1.623205 Myo6 8.104381 7.488083 -1.53294 Myst4 7.980026 7.329574 -1.56966 Naalad2 7.39097 6.718816 -1.59345 Ncf1 8.036915 8.671223 1.552193 Nckap1l 8.710067 9.468087 1.691169 Ncoa4 6.544815 5.720468 -1.77073 Nes 8.39174 9.09572 1.628993 Nlrp3 6.874477 7.494582 1.536987 (continued)

256

Table 5.1 (continued)

Nov 9.907612 9.188458 -1.64622 Nox4 8.548496 7.667059 -1.84221 Npr3 10.30768 9.720761 -1.50204 Npy 7.866798 8.672613 1.748133 Nr4a1 10.84868 11.51154 1.583215 Nubpl 7.282643 6.599533 -1.6056 Oas1a 6.47123 7.180806 1.635323 Oas2 6.660008 7.263405 1.51929 Oasl1 7.428344 8.317755 1.85242 Oasl2 7.883712 8.777946 1.858622 Ogn 10.34588 9.730894 -1.53155 Olfr1393 5.60289 6.512447 1.878468 Olfr303 5.815433 6.50348 1.611101 Olfr434 5.682529 6.466029 1.721302 Olfr576 5.611284 6.520342 1.877819 Olfr609 5.721249 6.309742 1.503676 Olfr707 6.537719 7.186642 1.567997 Olfr729 6.179849 6.868833 1.612148 Olfr976 5.695222 6.544084 1.801079 Olr1 9.091661 8.407004 -1.60732 OTTMUSG000000009 10.92745 11.65953 1.661032 OTTMUSG000000167 5.819991 6.454949 1.552892 P2ry13 6.446349 7.088495 1.560649 P2ry6 8.819629 9.581101 1.695219 Pcdhb18 6.826504 6.186712 -1.55811 Pcmtd2 8.419026 7.803506 -1.53211 Pcsk5 9.321302 8.696382 -1.54213 Pdcd4 9.661367 8.630141 -2.04376 Pde7b 8.533259 7.927535 -1.52174 Pdk4 8.296398 7.477355 -1.76424 Perp 7.415591 6.737963 -1.59951 Pf4 7.806876 8.491308 1.607069 Phlda1 9.943732 10.69088 1.678472 Pik3ap1 7.903069 8.550675 1.566567 Pik3cg 7.544719 8.289771 1.676034 Pik3r3 8.116044 7.397994 -1.64496 (continued)

257

Table 5.1 (continued)

Pisd-ps1 8.789872 8.1522 -1.55582 Pld4 9.883699 10.80217 1.890111 Plek 9.798643 10.49869 1.624554 Plxdc1 7.573508 8.257908 1.607033 Polk 9.177848 8.509778 -1.58895 Pou3f4 6.975211 6.120464 -1.80844 Prelp 11.4425 10.85064 -1.50719 Prkcb 7.506166 8.20107 1.618776 Prkcb 5.943518 6.671349 1.656147 Ptgds2 7.834711 8.604562 1.705094 Ptgs2 11.95432 12.87513 1.893181 Ptprc 7.48339 8.276551 1.732867 R74862 6.834319 6.127841 -1.63182 Rac2 9.32498 10.06441 1.66952 Rasgef1b 6.834701 7.481757 1.565969 Rbm12b 6.937783 5.998422 -1.91768 Reg1 6.894038 7.866474 1.962151 Rerg 8.479064 7.873831 -1.52122 Rfc5 9.862703 10.54253 1.601942 Rgs16 10.79672 11.65474 1.812548 Rgs18 7.04807 7.818377 1.705633 Rnf113a2 6.974739 6.343061 -1.54937 Rnf144b 7.342133 8.058158 1.64265 Rnf146 7.332542 6.504072 -1.7758 Rpa3 7.087348 6.113321 -1.96432 Rpl4 6.151202 5.412338 -1.66886 Rps27l 10.21749 9.398691 -1.76394 Rsad2 7.245915 8.083954 1.787619 Rsl1 7.695696 6.237313 -2.748 Rtp4 7.069561 7.66154 1.507313 Runx1t1 9.590828 8.740259 -1.80321 Rxfp4 6.010016 6.731424 1.64879 S100a3 6.453936 8.746719 4.900004 Saa3 7.209626 8.579485 2.584454 Scg2 7.102062 6.017381 -2.12091 Scn7a 8.228724 7.631999 -1.51228 (continued)

258

Table 5.1 (continued)

Sdc4 10.59822 11.2267 1.54593 Selplg 7.582646 8.328687 1.677184 Sema3a 7.907474 7.288253 -1.53605 Sema7a 10.08688 10.9808 1.858228 Senp7 7.805839 7.210878 -1.51043 Senp8 8.579399 7.959901 -1.53634 Serpinb2 7.756797 9.635676 3.677891 Serpinb9b 9.965716 9.211476 -1.68674 Sertad4 8.282203 7.334111 -1.92932 Sesn2 9.353698 8.429373 -1.8978 Setd8 9.059502 8.222829 -1.78593 Sfpi1 8.923564 9.855354 1.907641 Siglec1 8.128861 8.874284 1.676465 Sik1 9.288419 9.987608 1.623592 Slc10a6 7.364084 6.679394 -1.60736 Slc11a1 8.234377 8.97586 1.671893 Slc15a3 8.104997 8.736025 1.548669 Slc16a4 7.309421 6.329151 -1.97283 Slc1a3 8.033994 7.287687 -1.67749 Slc30a1 8.904492 9.574267 1.590825 Slc37a2 7.755551 8.382742 1.544555 Slc7a8 9.173329 9.903654 1.659013 Slfn3 6.622078 7.208161 1.501165 Smad7 10.24158 10.87965 1.556252 Snai1 10.68936 11.31726 1.545314 Socs2 11.53517 12.26663 1.660313 Socs3 6.254086 5.634039 -1.53693 Sod3 10.80058 10.21072 -1.5051 Spcs1 9.677407 9.087132 -1.50553 Srgn 10.2575 11.02038 1.696868 Srr 8.801653 8.152298 -1.56847 Strbp 7.220737 6.552454 -1.58918 Taar7b 8.210146 9.549255 2.529951 Tbx15 10.74587 10.0486 -1.62144 Tceb2 8.259166 7.635962 -1.54029 Tek 6.950219 6.32141 -1.54629 (continued)

259

Table 5.1 (continued)

Tgfbi 9.551951 10.23033 1.600343 Thap2 7.466468 6.874379 -1.50743 Tlr13 9.094122 9.940616 1.798126 Tlr7 9.121321 9.916794 1.735646 Tlr8 6.925488 7.642731 1.644037 Tm6sf1 9.02003 9.811697 1.731074 Tmeff2 8.391036 7.721641 -1.59041 Tmem140 8.544872 7.80725 -1.66743 Tmem195 8.467658 7.837246 -1.54801 Tmem53 8.110945 7.454003 -1.57674 Tnf 7.501895 8.100347 1.514092 Trem2 8.813894 9.666888 1.806246 Trib1 8.561737 9.163726 1.517808 Trib3 8.77795 8.154233 -1.54084 Trim30 8.139233 8.891696 1.684666 Trp53 11.04892 10.29412 -1.6874 Trp53inp1 8.916175 7.425164 -2.81086 Ttc12 8.890224 8.253234 -1.55508 Ttc30b 7.704792 6.974264 -1.65925 Ttc5 5.748018 6.361366 1.529805 Tyrobp 9.165299 9.816175 1.570121 Uba52 10.77844 10.19031 -1.5033 Ubc 8.799023 7.803059 -1.99441 V1rj2 6.094147 6.782048 1.610937 Vav1 7.035502 7.724917 1.61263 Vnn1 6.906416 6.187229 -1.64625 Wnt16 8.146044 7.475771 -1.59137 Ypel2 8.583557 7.709873 -1.83234 Zfp119 6.279463 5.463528 -1.76044 Zfp37 8.487623 7.857133 -1.54809 Zfp449 8.414544 7.701224 -1.63957 Zfp51 8.272828 7.63402 -1.55704 Zfp521 9.304121 8.63317 -1.59212 Zfp677 7.245597 6.632148 -1.52991 Zfp759 6.796815 6.12962 -1.58798 Zfp810 7.461322 6.603028 -1.81289 (continued)

260

Table 5.1 (continued)

Zmat1 6.375563 5.696521 -1.60108 Zmat3 9.336611 8.701681 -1.55286 Zmym6 7.998342 7.334639 -1.58414 Zmynd8 7.747626 7.043814 -1.6288

261

Table 5.2 Genes regulated in epithelial cells by fibroblast p53 in 8-week mammary glands

Gene Symbol Average Wild Average p53 Fold Change Type 2810417H13Rik 8.82230025 8.16313125 -1.57917275 Apod 9.2428575 8.58151075 -1.58155831 Armcx5 6.45284575 7.04260025 1.504990625 B4galnt2 6.3985645 7.14956775 1.682962756 BC003266 8.4313035 9.06629675 1.552930484 C030016D13Rik 6.376126 7.1282635 1.684286425 C1qa 8.322299 7.67582475 -1.56533804 C1qb 9.3034945 8.70255025 -1.51670893 Ccdc80 7.77806575 7.07876625 -1.62371621 Ccl3 8.3908605 7.6921415 -1.623063 Ccl5 7.3497325 8.54478025 2.289524098 Cd74 7.8809795 7.230822 -1.56933951 Clec7a 7.19662525 6.5245845 -1.5933252 Cmpk2 7.5990935 8.497316 1.863768277 Col6a1 8.2564875 7.66474425 -1.50706668 Cpa3 6.56070725 5.764303 -1.73676704 Csf2 7.6852185 6.80527175 -1.84030737 Cxcl10 7.7299385 8.4574425 1.655771969 Cxcl13 8.294079 6.315801 -3.94022496 Cxcl15 7.409992 6.599461 -1.75385685 Cybb 7.2061315 6.60597575 -1.51588021 Dcn 9.7716445 9.00798375 -1.69779321 Ddx60 6.09206875 7.0038215 1.881329764 Dhx58 7.009183 7.74204975 1.661938216 Dmrta1 5.09166475 6.0257415 1.910667516 Efhd1 9.60035125 10.22991975 1.547102197 EG240327 6.3019205 6.91399325 1.528453593 Eml5 6.09562975 6.77743475 1.604145496 Enpp2 7.0328445 6.4142265 -1.53540367 Fmo2 7.39197275 6.75985575 -1.54983755 Gbp2 8.21688775 8.825827 1.52513743 Gbp3 6.68385025 7.342423 1.57852023 Hrh2 5.5398275 6.129503 1.504908216 (continued) 262

Table 5.2 (continued)

Ifi203 6.032777 6.639771 1.523082405 Ifi44 9.1802485 10.291971 2.161035093 Ifit1 8.00595875 9.662375 3.15232493 Ifit2 7.1779495 7.8087265 1.548398698 Igtp 7.5480475 8.496777 1.930172116 Il6 5.6375285 6.541097 1.870687402 Irf7 7.85279775 8.5106725 1.5777567 Irgm1 8.433213 9.04309675 1.52613623 Lilrb4 9.21305325 8.48301725 -1.65868048 Lpar4 6.8975985 7.52133575 1.540861563 Lyz2 9.12688075 8.51460075 -1.52867318 Mmp3 6.95034875 6.2110065 -1.66941455 Mrpl48 6.5141295 5.85519025 -1.57892129 Ms4a6c 8.14288075 7.48148825 -1.58160847 Mx1 4.630197 5.96102425 2.51546872 Mx2 5.7757675 6.85836925 2.117851964 Nxf7 7.46424375 8.153976 1.612984137 Oas1a 7.07955625 7.75665175 1.598917498 Oas1b 6.69125225 7.5980885 1.874929371 Oasl1 8.1784135 9.28839975 2.158435901 Oasl2 8.91548925 9.6486925 1.662325899 Olfr102 6.1817235 5.494578 -1.61009465 OTTMUSG000000009 9.5661335 8.862407 -1.62870633 Ppbp 9.92804525 9.21306875 -1.64145648 Rps7 7.2408205 6.61599925 -1.54201976 Rsad2 8.2943535 9.81194075 2.863118237 Rtn4rl1 6.03326225 6.67388325 1.559000078 Rtp4 8.29613325 9.17974725 1.844991283 Scn7a 6.632518 5.8597645 -1.70852754 Serpina3n 10.40128775 9.46810275 -1.90948687 Serpinb2 7.88995375 7.054764 -1.78409168 Slc16a3 9.3155675 8.648633 -1.58769578 Sp100 7.292227 7.9245235 1.550030389 Sprr1a 10.9248125 10.26695275 -1.5777403 Ssxb2 6.43039975 5.696226 -1.66344452 Tph1 9.02706175 8.25440175 -1.70841681 (continued)

263

Table 5.2 (continued)

Trim30 7.15196625 7.814155 1.582481627 Ube2l6 8.49390175 9.0926485 1.514400455 Vamp5 7.9020075 7.2941435 -1.52400116 Zbp1 7.99742775 8.6865465 1.612298368

264

Table 5.3 Genes regulated by p53 in 8-week Col1aYFP sorted mammary fibroblasts

Gene Symbol Average Average p53 Average Fold p-value Wild Type Change 1E+08 6.9432 8.4322 2.806943 0.000534 1E+08 6.4765 7.9316 2.741756 0.000854 1110059G02 4.9221 5.935 2.017963 0.000156 1190003J15 6.4817 7.7154 2.35153 0.010623 1190003J15 6.3396 7.5515 2.316425 0.018904 1500015O10 6.76 9.0266 4.812211 1.41E-05 1500015O10 6.8985 8.8065 3.752885 6.53E-05 1600029D21 7.7766 10.1829 5.30113 0.029293 1600029D21 8.5226 10.789 4.811211 0.030786 2010300C02 5.1892 6.3658 2.260434 0.000254 2900054C01 4.4735 5.7228 2.37726 0.000101 4632434I11 4.6482 5.9025 2.385348 9.18E-05 8430427H17 5.085 6.4443 2.565607 0.000921 Ablim3 4.6765 5.7251 2.068522 0.022133 Acot1 /// 7.3469 8.5906 2.368051 0.004665 Acot1 /// 7.1961 8.2769 2.115209 0.001886 Acsl1 7.5044 9.1154 3.054635 0.000854 Acsl1 6.4197 8.0148 3.021155 0.000482 Acsl1 5.6007 7.0953 2.81786 0.001734 Acss1 6.2608 7.7999 2.906333 0.000453 Acta2 10.9056 12.2926 2.615524 0.000485 Actg2 6.1425 7.2383 2.137168 0.046932 Afap1l1 5.5031 6.5298 2.0375 0.021284 AI117581 5.7117 6.7875 2.107891 0.016641 Aim1 4.9961 6.4126 2.669371 0.002071 Aldoc 7.1592 9.0315 3.661158 0.018891 Ankrd56 5.5635 6.9675 2.646343 0.003782 Ano1 9.028 10.5235 2.819619 0.024775 Ano1 7.213 8.4329 2.329467 0.024081 Aplnr 8.717 10.0559 2.529584 0.025013 Aplnr 6.6021 7.704 2.146372 0.03215 Apoc1 6.5424 7.9374 2.629886 5.40E-05 Atp1b1 7.0526 8.4399 2.615887 0.001824 Atp6v1c2 5.4861 6.8977 2.660136 0.013652 (continued)

265

Table 5.3 (continued)

BC004728 6.09 7.1449 2.077574 0.006401 Bcl2l11 7.9802 9.383 2.644143 0.010478 Bcl2l11 5.0472 6.293 2.371336 0.010359 Bcl2l11 6.0199 7.134 2.164749 0.04255 Bcl2l11 7.074 8.1066 2.045708 0.013821 Bcl2l15 4.4167 6.5988 4.538136 6.89E-05 Bdh1 4.849 6.2965 2.72735 4.16E-05 Bex1 5.1902 6.4413 2.380228 0.02733 Blnk 4.533 5.7315 2.295009 0.000193 Bub1b 5.4438 6.4594 2.021744 0.044059 Cadm1 5.3127 6.4605 2.215604 0.002535 Cadm1 5.4546 6.5008 2.065083 0.00271 Car2 6.703 8.4182 3.283422 0.000963 Car4 4.8443 7.4747 6.191977 0.030378 Car4 4.8649 6.948 4.237167 0.03846 Car6 3.647 5.9008 4.769044 0.046748 Cav2 8.8277 9.8414 2.019083 0.01648 Cbr2 6.7632 8.8058 4.119873 0.018019 Cd24a 9.5177 11.0443 2.881061 0.000714 Cd24a 9.1213 10.5932 2.77387 0.001687 Cd24a 8.2646 9.5199 2.387168 0.001557 Cd93 7.0716 8.3321 2.395788 0.025061 Cd93 7.9796 9.1939 2.320282 0.028628 Cdc42ep3 6.2551 7.2965 2.058224 0.016755 Cdcp1 6.7808 8.0908 2.479415 0.000235 Cdh1 7.5655 8.9945 2.6926 0.000915 Cdh5 4.8369 5.9897 2.22345 0.037341 Cdh5 5.824 6.9151 2.130364 0.042353 Ceacam1 6.8731 8.76 3.698397 0.00633 Ceacam1 6.0549 7.8331 3.430217 0.004364 Ceacam1 4.4469 6.1293 3.209614 0.001928 Ceacam1 5.5702 7.166 3.02283 0.00468 Ceacam1 6.8153 8.3259 2.849285 0.005209 Ceacam1 5.6031 7.0966 2.815712 0.001788 Ceacam1 // 7.7332 9.4423 3.269341 0.004891 Ceacam1 // 7.0698 8.7733 3.256676 0.006319 (continued)

266

Table 5.3 (continued)

Cel 5.6946 7.7235 4.080653 0.012078 Chchd10 6.5732 8.206 3.101143 0.004734 Chchd10 6.613 8.0103 2.634082 0.003525 Chd7 5.4434 6.5844 2.205338 0.003568 Chdh 4.938 6.7598 3.53522 0.001759 Chka 6.8819 8.3646 2.794713 0.001799 Chka 6.0674 7.3865 2.495104 0.002252 Chka 6.6452 7.7666 2.17558 0.007855 Cidea 4.1496 5.5463 2.633169 0.023558 Cited4 4.7208 6.4464 3.306947 0.000201 Clca1 6.4253 7.6713 2.371829 0.011098 Clca1 6.3384 7.3833 2.063223 0.015641 Clca1 /// 7.7876 9.4041 3.066302 0.023282 Clca2 7.1421 8.7996 3.154694 0.041572 Cldn3 6.8771 8.366 2.806749 0.000643 Cldn3 7.7739 9.2426 2.767724 0.000737 Cldn3 7.9536 9.4085 2.741566 0.001822 Cldn3 7.771 9.1264 2.558503 0.001516 Cldn4 6.7134 8.5269 3.514696 0.027556 Cldn5 7.2244 8.6197 2.630432 0.004795 Cldn7 6.8906 8.3445 2.739476 0.006534 Cldn8 5.358 7.1643 3.497442 0.01166 Clu /// LO 9.0207 10.2067 2.275368 0.043364 Clu /// LO 9.3156 10.4934 2.262315 0.036022 Clu /// LO 9.063 10.2011 2.20091 0.040113 Clu /// LO 9.4494 10.5805 2.190257 0.046552 Cnn1 6.8152 8.3675 2.932843 0.000228 Cntn1 9.1698 7.9273 -2.36608 0.030673 Cntn1 9.1566 7.9049 -2.38122 0.044437 Cobll1 6.6322 7.8874 2.387002 0.006857 Col17a1 4.8693 6.4035 2.896077 0.000944 Col8a1 6.7275 7.9621 2.353161 0.037903 Col9a1 6.726 9.342 6.130055 0.00107 Col9a1 6.7663 8.0681 2.465363 0.002862 Col9a3 5.3106 6.6842 2.591163 0.00196 Copg2as2 4.8017 5.8611 2.084065 0.028548 (continued)

267

Table 5.3 (continued)

Crabp2 6.2879 7.6705 2.607378 1.33E-05 Csn1s1 10.7689 13.043 4.836958 0.000609 Csn1s2a 11.0763 13.0034 3.802636 0.009851 Csn1s2a 11.4314 13.1716 3.340815 0.010751 Csn2 10.7141 12.9463 4.698499 0.000944 Csn2 11.6192 13.0072 2.617156 0.000405 Ctgf 8.7572 9.8073 2.070673 0.011259 Ctss 7.6161 8.6249 2.012237 0.000204 Cxadr 5.7379 7.2631 2.878266 0.009707 Cxadr 5.1843 6.5257 2.533971 0.016129 Cyp2f2 5.6852 6.8058 2.174525 0.001742 Cytip 7.2644 8.4628 2.29485 0.000777 Cytip 7.1391 8.2066 2.095798 0.010095 D330050I23 5.5462 6.7947 2.375943 0.000371 Dbp 5.9393 7.177 2.358223 0.00559 Dennd2d 5.0542 6.3057 2.380723 0.000106 Dsg2 7.2587 8.9536 3.237544 0.003498 Dsg2 5.5723 7.1726 3.032064 0.001143 Dsg2 6.8283 8.3677 2.906736 0.002612 Dsg2 5.7363 6.8673 2.189953 0.000953 Dst 4.1453 5.6364 2.811032 0.002204 Dusp4 5.9138 7.1028 2.279947 0.005172 Echdc3 6.1177 7.1534 2.050108 0.010833 Efcab4a 6.0411 7.6438 3.037112 0.00556 Efna1 5.6486 7.281 3.100283 0.000661 Efna1 6.5416 7.9605 2.673816 0.002612 Egln3 5.7135 7.7201 4.018062 0.000123 Egln3 5.4395 6.9042 2.760061 0.001864 Ehf 5.2726 7.0479 3.423092 1.58E-05 Ehf 6.381 8.0613 3.204946 0.000159 Ehf 5.8642 7.5074 3.123362 0.000473 Elf5 8.1669 10.4087 4.729868 0.001609 Elf5 4.9125 6.9252 4.035367 0.000994 Elovl7 5.7044 6.7306 2.036653 0.008047 Eltd1 6.5431 7.826 2.433276 0.044906 Eltd1 6.2924 7.5045 2.316746 0.047183 (continued)

268

Table 5.3 (continued)

Emcn 7.5508 8.7526 2.300265 0.027079 Enpep 11.5592 10.5417 -2.02441 0.010986 Epb4.1l4b 7.2753 8.6061 2.515421 0.002591 Epcam 8.9146 10.4078 2.815127 0.033715 Eraf 6.3689 5.3493 -2.0275 0.045898 Erbb3 6.6985 7.9986 2.46246 0.015927 Ereg 6.8273 4.5606 -4.81254 0.002259 Erg 4.882 6.0623 2.266239 0.026397 Esam 7.4106 8.4672 2.080024 0.022955 Esm1 5.9375 7.49 2.933047 0.026369 Esrp1 6.5974 8.4495 3.610253 5.12E-05 Esrp2 5.556 6.7304 2.25699 0.000194 Expi 10.021 11.8664 3.593526 1.40E-05 F11r 8.2255 9.295 2.098706 0.00432 Fabp3 4.4985 6.6055 4.307946 0.024671 Fam110a 5.4428 7.2274 3.445229 0.003281 Fam134b 7.2675 8.7741 2.841396 6.91E-05 Fam134b 5.2813 6.4866 2.305852 0.000456 Far2 6.5997 5.1012 -2.82549 0.011822 Fbxo32 6.3615 8.0333 3.186119 0.000223 Fbxo32 5.7297 6.9518 2.33286 2.64E-05 Fgf10 7.3038 6.0211 -2.43294 0.038259 Fhod3 5.9294 7.3925 2.757001 9.41E-06 Fmod 7.8373 8.9973 2.234574 0.002477 Folr1 6.0086 8.1506 4.413735 0.007733 Fxyd3 8.4867 10.1088 3.078441 0.015616 Fzd6 6.4235 7.572 2.216987 0.017 Galnt3 5.857 7.0445 2.277577 0.007567 Gata3 6.1284 7.5617 2.700637 0.019054 Gfra2 7.3521 6.2679 -2.1202 0.031341 Gfra2 9.0657 7.8373 -2.34291 0.032121 Gimap4 6.5307 7.6071 2.108767 0.025848 Gjb2 6.7513 7.8654 2.164599 0.023486 Gm5279 /// 5.7909 7.1771 2.613893 0.008622 Gm5480 6.6149 8.3171 3.253968 0.005499 Gm9706 /// 8.762 7.2597 -2.83294 0.032403 (continued)

269

Table 5.3 (continued)

Gpihbp1 6.8313 8.2173 2.613349 0.008547 Gpr56 5.5556 6.7481 2.285643 0.02954 Gpr56 6.2752 7.4051 2.188436 0.010633 Grb14 4.6815 5.8426 2.236124 0.021882 Grhl1 /// 5.6072 6.8543 2.373638 2.08E-05 Grhl2 5.4213 6.9207 2.827251 0.00015 Grhl2 6.1719 7.4811 2.478041 0.00298 Gria3 8.0744 7.01 -2.0913 0.035824 Hectd2 8.0024 6.9969 -2.00764 0.002947 Hhipl2 5.5784 7.1192 2.909558 0.008231 Hook1 4.9962 6.5136 2.862747 0.000485 Hpca 6.4621 4.7474 -3.28228 8.30E-05 Hspb1 8.7791 9.8152 2.050534 0.017557 Ica1 4.2358 5.559 2.502032 0.009968 Ifit1 7.693 6.0216 -3.18524 0.026944 Ifit3 8.7627 7.1155 -3.13225 0.012223 Il17b 6.7784 8.7854 4.019455 4.64E-05 Inadl 4.7577 5.9279 2.250273 0.010318 Irf6 5.2402 7.0085 3.406759 6.44E-05 Irx2 /// L 6.9415 8.2311 2.444433 0.000725 Irx3 7.9067 9.0839 2.261375 0.001106 Itga6 7.6493 9.421 3.414561 0.000424 Itga6 7.625 9.2643 3.115146 0.000489 Kank4 6.2914 7.3764 2.121375 0.046581 Kcnk1 6.5511 8.1081 2.94221 0.00122 Kcnk1 5.2316 6.6685 2.707385 0.001717 Kcnn4 5.1062 6.8087 3.254419 2.55E-05 Kcnn4 7.3245 8.7377 2.663272 9.84E-07 Kctd1 5.1894 6.3085 2.172114 0.002247 Kit 6.4612 8.076 3.062691 0.014219 Kit 5.5937 7.0473 2.738717 0.008817 Klc3 5.7887 6.9492 2.235349 0.002818 Krt14 6.4001 8.2683 3.650768 0.00061 Krt14 7.4889 9.3186 3.554385 0.000649 Krt17 5.137 6.4254 2.44257 0.009456 Krt18 9.0829 10.8953 3.512261 0.025958 (continued)

270

Table 5.3 (continued)

Krt19 8.8687 10.1706 2.465534 0.025455 Krt5 6.9095 8.1117 2.300903 0.004959 Krt8 8.8092 10.6896 3.681516 0.005622 Krt8 8.7238 10.5567 3.562525 0.00761 Krt8 9.3906 11.1878 3.475691 0.009283 Lama1 5.3965 7.3485 3.869105 6.49E-05 Lama3 5.6754 7.3739 3.245633 0.002641 Lamb3 5.9327 7.4708 2.904118 0.000223 Lcp1 6.5753 7.8481 2.416133 4.35E-05 Lcp1 6.1096 7.2156 2.15248 0.000155 Ldb3 4.6803 6.1989 2.865129 0.005628 Lmo7 5.6075 6.6171 2.013492 0.044152 LOC641050 6.1205 7.9782 3.624043 0.029112 Mansc1 7.0853 8.1251 2.055943 0.044337 Map3k1 4.983 6.1205 2.199995 0.025627 Marveld2 4.6022 6.0523 2.732459 0.012838 Marveld3 5.1752 6.4629 2.441385 0.036478 Marveld3 4.7788 5.8992 2.174223 0.00318 Mbnl3 4.4245 5.6473 2.334154 0.007158 Mfsd6 7.849 8.9228 2.104825 0.001794 Mia1 4.9888 6.4313 2.717726 0.001943 Mmp19 8.823 7.8184 -2.00625 0.004871 Mmp19 9.1881 8.051 -2.19923 0.001145 Mpzl2 5.936 7.1034 2.246065 0.0344 Mtss1 6.6911 7.7777 2.123729 0.00329 Mtss1 7.3957 8.4815 2.122552 0.003279 Muc1 5.2017 6.5316 2.513852 0.018534 Muc15 8.9229 11.113 4.563687 2.99E-06 Myct1 5.3859 6.4535 2.095944 0.041609 Myh11 8.2149 9.6792 2.759296 0.002575 Mylk 8.3339 9.606 2.415296 7.71E-05 Mylk 8.7845 10.0021 2.325595 5.35E-05 Nav2 4.6877 5.791 2.148605 0.004746 Nipal1 7.4554 6.3818 -2.10453 0.000472 Nnat 4.313 5.7966 2.796263 0.000967 Npy2r 6.2203 4.3479 -3.66141 0.017913 (continued)

271

Table 5.3 (continued)

Ntf5 4.8965 5.9891 2.13258 0.002645 Oas1a 8.8945 7.7079 -2.27616 0.03834 Obfc2a 4.3326 5.8102 2.784851 0.049898 Obfc2a 5.5026 6.6289 2.182982 0.037808 Ocln 5.0963 6.4172 2.498219 0.029095 Odz2 5.2398 6.8541 3.061418 0.000923 Ogfrl1 6.4323 7.8726 2.713773 0.00022 Ogfrl1 7.73 8.9612 2.347459 0.0003 Oxtr 7.1413 8.5526 2.659767 0.002473 P4ha3 6.8634 5.7641 -2.14251 0.015071 Pard6b 4.204 5.5292 2.505676 0.00178 Pawr 6.5883 8.0637 2.7808 0.001524 Pcdh21 4.169 6.1538 3.958078 0.048645 Pcx 6.0488 7.2847 2.355282 0.006289 Pcx 6.0955 7.1319 2.051103 0.0097 Pde4b 7.2755 8.8099 2.89688 0.013554 Pde4b 7.5929 9.0779 2.798978 0.007238 Pde4b 7.8928 9.2743 2.605572 0.033597 Pde9a 5.0762 6.2415 2.242798 0.00577 Pdgfb 5.6759 6.9392 2.400442 0.007455 Pdlim3 7.2508 9.1339 3.688412 0.000344 Pdlim3 4.8389 6.1461 2.474608 0.010906 Pecam1 7.7601 8.8316 2.101617 0.03817 Per2 4.5529 6.1499 3.024926 0.01607 Perp 6.826 8.2735 2.727539 0.000413 Pigr 3.9542 7.1857 9.39244 0.003308 Pik3ap1 4.9469 6.0006 2.075847 0.007707 Pkp2 5.1256 6.39 2.402273 0.025797 Pkp4 7.4525 8.4909 2.053948 0.015068 Plb1 6.1102 8.0838 3.927469 0.000306 Plekhb1 7.5413 8.8098 2.408943 0.002587 Ppap2c 7.0052 8.1997 2.288655 0.000975 Ptpn18 5.8761 7.0697 2.287386 0.005983 Pttg1 6.652 7.9608 2.477354 0.002038 Rab17 4.4811 5.8302 2.547532 0.012975 Rapgef5 6.2167 7.6257 2.655346 0.008917 (continued)

272

Table 5.3 (continued)

Rapgef5 6.3874 7.4692 2.116675 0.0082 Rasd1 6.5252 7.6661 2.205185 0.026173 Rasgrf1 6.7923 5.756 -2.0511 0.001917 Rasgrp3 5.0423 6.1551 2.1625 0.020443 Rassf6 4.439 5.7091 2.411616 0.048344 Rbm47 4.1047 5.7325 3.0902 0.001918 Rbp7 7.1752 8.3845 2.312254 0.009638 Reln 6.8811 8.176 2.4536 0.03181 Rgs5 8.2369 9.257 2.027919 0.04043 Rgs5 9.0156 10.0295 2.019363 0.037835 Rgs5 7.1711 8.1715 2.000555 0.047071 Rhov 4.8406 6.2577 2.670667 0.014735 Rhpn2 5.8803 7.5041 3.081857 0.01238 Ripk4 5.64 7.4526 3.512748 0.002132 Rnf128 5.4367 7.3539 3.776893 1.65E-05 Rnf128 4.8794 6.604 3.304885 8.15E-05 Rnf144b 5.3139 6.5643 2.379239 0.001648 Robo4 5.7176 6.7408 2.032422 0.03339 Rps6ka6 4.4985 5.5101 2.016146 0.006319 Scarb1 7.1036 8.2605 2.229778 0.033114 Scarb1 7.1696 8.2385 2.097833 0.029142 Scg3 10.5435 9.4643 -2.11286 0.017491 Sdr39u1 6.5833 5.5355 -2.06737 0.02659 Serpinb5 5.863 7.0742 2.315141 0.011954 Serpinb5 4.6038 5.6771 2.104241 0.031789 She 5.7366 6.7866 2.07053 0.009893 Shroom2 5.7449 6.8832 2.201215 0.002844 Shroom3 6.0677 7.599 2.890462 0.00484 Sidt1 4.6193 6.0404 2.677896 0.000327 Slc15a2 5.5032 6.7645 2.397116 0.00119 Slc28a3 4.9593 6.8832 3.794474 0.02487 Slc28a3 6.3659 8.2614 3.720509 0.04171 Slc2a1 8.1199 9.2189 2.142062 0.004391 Slc2a1 7.1968 8.2781 2.115942 0.005165 Slc44a3 4.5868 5.7419 2.226998 0.009447 Slc5a1 3.3726 5.3312 3.887116 0.000135 (continued)

273

Table 5.3 (continued)

Slc5a1 4.9041 6.1217 2.325434 0.000601 Slc5a3 8.2911 9.3043 2.018383 0.010199 Slc9a3r1 7.4411 8.6683 2.341122 0.001182 Slc9a3r1 7.4494 8.583 2.194055 0.00035 Sorl1 4.8183 6.0641 2.3715 0.004201 Sorl1 6.6723 7.8395 2.245754 0.003495 Sox10 7.3254 8.4989 2.255582 7.51E-05 Sox17 5.969 7.4753 2.840608 0.02572 Sox17 5.6419 6.7672 2.181469 0.038161 Sox18 6.322 7.5655 2.367722 0.015432 Sox7 6.661 7.9561 2.45394 0.017035 Sox9 5.6068 6.9535 2.543297 0.008782 Sox9 5.9667 7.2475 2.429737 0.003341 Spint2 7.9398 9.33 2.620968 0.010969 Spint2 8.5117 9.7423 2.346646 0.004731 Spns2 6.5979 7.9451 2.544179 0.025242 St14 6.3768 7.6528 2.421666 0.009575 St6gal1 7.1783 8.2102 2.044715 0.000425 St8sia4 5.5033 6.7741 2.413121 0.010751 Stap2 5.9789 7.4995 2.869103 0.002327 Stap2 6.3817 7.7551 2.590804 0.004837 Synm 5.5922 6.8833 2.447315 0.026017 Tac1 9.739 8.5426 -2.29167 9.00E-05 Tagln 9.8486 10.8757 2.037924 0.005246 Tcfap2b 4.8873 6.1678 2.429232 0.005601 Tcfap2b 5.3165 6.4816 2.242488 0.004054 Tcfap2c 6.9692 8.6774 3.267529 1.04E-05 Tcfap2c 5.901 7.5055 3.040903 1.02E-05 Tcfap2c 5.481 6.9039 2.681425 1.32E-05 Tcfcp2l1 4.7496 6.6252 3.669542 0.034762 Tes 7.1728 8.3519 2.264355 0.001991 Thrsp 5.4182 8.0427 6.166706 0.00075 Thrsp 5.7756 7.458 3.209614 0.000822 Timd2 4.7108 6.319 3.048924 9.17E-05 Timd2 4.4728 5.6266 2.224992 0.002884 Tinagl1 7.0533 8.0672 2.019363 0.031885 (continued)

274

Table 5.3 (continued)

Tjp2 6.9559 7.9617 2.008057 0.006082 Tjp3 5.6352 7.0373 2.64286 2.10E-05 Tmem180 4.2194 5.7007 2.792002 0.001159 Tmem182 5.1543 6.2717 2.169707 0.029465 Tmem30b 6.8209 8.3162 2.819228 0.046823 Tmprss13 4.6852 6.0585 2.590625 0.008742 Tnk2 8.0553 9.0808 2.035665 0.004008 Tns4 5.0946 6.2234 2.186768 0.00023 Tom1l1 4.8489 5.8525 2.004997 0.001446 Tpd52 7.1678 8.31 2.207173 0.004266 Tpd52 6.9474 7.9509 2.004858 0.008769 Trim29 6.17 7.2865 2.168203 0.004376 Trim59 5.7661 6.9206 2.225917 0.006078 Trim9 7.3346 5.7827 -2.93203 0.018233 Trim9 5.5935 3.6798 -3.76748 0.025521 Trp53inp1 8.7679 9.8587 2.129921 0.049714 Trp63 6.0411 7.3957 2.557085 0.000473 Trps1 6.5959 8.2002 3.040482 0.009806 Trps1 8.019 9.5011 2.793551 0.011521 Trps1 7.2433 8.7181 2.779258 0.015966 Trps1 6.9445 8.19 2.371007 0.007812 Tspan13 7.1635 8.3318 2.247467 0.017427 Tspan13 7.8797 9.0322 2.222988 0.036355 Tspan2 5.9995 7.0966 2.139242 0.000325 Ttpa 6.0158 7.6062 3.011328 0.002281 Tuft1 5.0107 6.3589 2.545943 0.001806 Ushbp1 5.4541 6.4804 2.036794 0.00355 Vdr 6.0874 7.263 2.258868 0.001637 Vdr 5.3733 6.3781 2.006804 0.000543 W91776 5.5287 6.874 2.54083 0.021044 Wap 5.1684 7.8365 6.355475 0.022637 Wfdc3 4.9391 6.1068 2.246533 0.015831 Wif1 5.9601 7.6353 3.193636 0.002349

275

5.3.3 Loss of Fibroblast p53 Does Not Induce Inflammation or ECM Remodeling in the Mammary Gland

To examine whether loss of p53 in fibroblasts also mediated any of the effects we observed in Pten null mammary glands, we checked for infiltration of macrophages as well as collagen deposition in these mammary gland by trichrome staining. Since these mice develop normally and do not have any phenotypic aberrations, it was not surprising to find that neither macrophage recruitment or ECM remodeling were effected in

FspCre;p53loxP/loxP mammary glands (Figure 5.8). Although this analysis does not reveal any obvious phenotypic changes in the mammary gland as a result of p53 deficiency in fibroblasts, further analysis of these same genetic groups in the context of epithelial Ras expression need to be examined.

276

p53loxP/loxP FspCre;p53loxP/loxP F4/80 F4/80

B p53loxP/loxP FspCre;p53loxP/loxP Trichrome

Figure 5.8 Fibroblast p53 Deletion Does Not Induce Macrophage Recruitment or

ECM Remodeling

A. F4/80 staining on paraffin mammary gland sections of indicated genotypes (Chris

Thompson). B. Trichrome staining on paraffin mammary gland sections of indicated genotypes (Julie Wallace).

277

5.3.4 p53 Loss in Fibroblasts Promotes Epithelial Cell Proliferation

To determine whether loss of p53 tumor suppressor function led to increased epithelial cell proliferation, we performed double staining for the epithelial cell marker

K8 and cell proliferation marker Ki67 in mammary glands from p53loxP/loxP and

FspCre;p53loxP/loxP mice. Similarly to what we observed with Pten null fibroblasts, an increase in proliferation epithelial cells was observed in mammary glands lacking fibroblast p53 (Figure 5.9). However, although the percentage of double positive cells was higher in FspCre;p53loxP/loxP mammary glands compared to p53loxP/loxP, this number was still lower than what we observed in FspCre;PtenloxP/loxP mammary glands, thereby indicating these p53 null fibroblasts to promote proliferation of epithelial cells to a lesser extent. Again, it should be mentioned that loss of p53 alone in fibroblasts does not lead to the formation of mammary tumors.

278

p53loxP/loxP Fsp-cre;p53loxP/loxP ** 30

10 % Ki67/K8+ Cells Ki67/K8+ %

0 p53loxP/loxP FspCre; n = 3 p53loxP/loxP n = 3

Figure 5.9 Increased Ki67+ Epithelial Cells in Mammary Gland from

FspCre;p53loxP/loxP Mice

IF staining on tissue sections of indicated genotypes for K8 (red) and Ki67 (green), slides counterstained with DAPI. Quantification on left is % of double positive cells, student’s t-test **P<0.05 (Julie Wallace).

279

5.3.5 Human Analysis

To determine whether the changes we observed in the tumor microenvironment as a consequence of fibroblast p53 deletion were relevant in human breast cancer, we queried our gene expression data against that of stromal samples acquired from normal or tumor breast tissue. Figure 5.10 summarizes this analysis. Whereas our Pten signatures from endothelial cells and macrophages were strongly represented in human stroma, there is no significant representation of these same cell types from our p53 signatures. This may partially be explained by a lack of changes in endothelial cells from

FspCre;p53loxP/loxP mammary glands. However, genes misregulted in our p53 null fibroblasts are significantly associated with tumor associated stroma of human breast cancer patients. A similar analysis was also performed using our gene expression signature derived from ColYFP sorted fibroblasts, which was also found to significantly segregate normal stroma from tumor stroma.

280

0.6

0.5

0.4

0.3

0.2

value value under the Wilcoxon rank sum test 0.1 -

sided sided p

One

p53 vsp53Wt vsp53Wt vsp53Wt p53 vsp53Wt

p53 vsWt p53 vsWt p53 vsWt p53 vsWt

Ras vsWt Ras vsWt Ras vsWt Ras Ras vsWt Ras

- - - -

Ras Ras Ras Ras Fibroblasts Epithelial Cells Endothelial Cells Macrophages

p-value <0.05 & fc > 1.5 p-value <0.05, fc > 1.5 & 0.5 variance cut-off

Figure 5.10 p53 Signatures Represented in Human Breast Cancer Stroma

281

5.4 Discussion

Similar to the coordinated signaling we observed between Pten in fibroblasts and

ErbB2 epithelial cells, we observe collaboration between stromal p53 and Ras mediated tumorigenesis. However, although p53 deletion in fibroblasts did not promote growth of

ErbB2 initiated tumors to a significant degree, there did seem to be a modest effect that perhaps deserves further investigation. Additionally, the point at which we harvested the mice in the non-transplant study was perhaps too late to observe the effects mediated by stromal p53. Therefore it might be important to examine the mammary glands of MMTV- rtTA;Tet-o-Ras;p53loxP/loxP and MMTV-rtTA;Tet-o-Ras;FspCre;p53loxP/loxP mice at an earlier time point, and just use histo-pathological grading to determine any differences.

Interestingly, our gene expression studies revealed distinct differences in differentially expressed genes in our p53 null fibroblasts compared to our Pten null fibroblasts. This is interesting because although several studies have shown these tumor suppressors to be interconnected, we show their regulation of unique transcriptional programs in stomal fibroblasts. In a manner similar to what we have started with PTEN, we would like to examine stromal P53 status in human breast TMAs, which may give us insights into subtype specific correlations of P53 stomal loss.

282

Chapter 6: Conclusions and Future Directions

6.1 Conclusions

6.1.1 Ets2 in Fibroblasts as a Master Regulator of Angiogenesis in Breast Cancer

Preliminary work identified Ets2 as an important mediator of mammary gland tumorigenesis specifically from the stromal compartment (Man et al., 2003).

Additionally, work from our group has deteremined macrophage Ets2 function to be important in the metastatic spread of mammary gland tumors (Zabuawala et al., 2010).

Here we identify an independent and perhaps complimentary fuction of Ets2 in tumor associated fibroblasts to promote angiogenesis in both PyMT and ErbB2 driven tumors.

Specifically, fibroblast Ets2 was shown to increase function blood vessel formation in both models. This increase in angiogenesis was attributed to increased expression of

Mmp9 which was directly regulated by Ets2 and which promoted the release of matrix bound VEGF ligand. Additionally, activated VEGFR2 signaling was observed in endothelial cells from tumors containing fibroblast Ets2. Changes in gene expression most likely downstream from this signaling were also observed, however further study is necessary to determine how these changes promote blood vessel formation. Importantly, both tumor specific and Ets2 dependent gene expression signatures we uncovered using our PyMT and ErbB2 models were represented in the stroma in human breast cancer samples and could actually distinguish this tumor associated stroma from normal stroma.

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These signatures also had the power to predict patient outcome as measured by recurrence in several independent whole tumor data sets.

6.1.2 Collaboration of Pten and p53 with Oncogenic Signaling in Epithelial Cells

In spite of numerous studies indicating direct functional cross talk between Pten and p53, we demonstrate unique functions of these proteins in the context of stromal fibroblast mediated tumor suppression. Loss of Pten alone in fibroblasts promotes remodeling of the extracellular matrix primarily through increased deposition of collagen, and also incites a profound immune response whereby macrophage recruitment and/or proliferation is increased in FspCre;PtenloxP/loxP mammary glands. Conversely, loss of p53 does not incite any phenotypic changes in the mammary gland, which develop normally and look identical to wild type mammary glands that have been H&E stained.

These observations may at least in part be responsible for the differences we observe in

ErbB2 and Ras mediated tumorigenesis. Whereas fibroblast Pten loss strongly promoted

ErbB2 driven tumors, no significant effects were observed upon Ras activation in epithelial cells. One potential explanation for this could be that the changes elicited by

Pten null fibroblasts in the mammary gland do not relay any particular growth advantage to Ras expressing cells, whereas they do promote ErbB2 mediated transformation.

Conversely, p53 loss in fibroblasts did not significantly effect ErbB2 induced tumorigenesis, once again indicating some preceeding changes in the microenvironment are important for ErbB2 transformation. However, Ras mediated tumorigenesis was greatly increased in the absence of p53, thereby implicating some specific crosstalk between these stromal and epithelial cells. One further critical point is the specific and

284 independent gene expression signatures resulting from fibroblasts lacking either Pten or p53. Closer examination of these signatures along with signatures obtained from surrounding cell compartments in the mammary gland will hopefully give us a stronger mechanistic model by which these tumor suppressors and oncogenes collaborate.

6.2 Future Directions

6.2.1 Global Ets2 Transcriptional Regulation in Pten Null Fibroblasts

Determining differences in global transcriptional regulation in tumor cells is critical for understanding all the mechanisms underlying altered gene expression in these cells. We showed the transcription factor Ets2 to be activated in the absence of Pten in mammary stromal fibroblasts, and to specifically bind to the proximal promoter regions of both Mmp9 and Ccl3 (Trimboli et al., 2009). To understand global rearrangements of this transcription factor in Pten null fibroblasts, ChIP sequencing could be performed in control PtenloxP/loxP and FspCre;PtenloxP/loxP mammary gland fibroblasts using an Ets2 specific antibody. Information regarding genome wide regulation of Ets2 targets in Pten null fibroblasts could potentially uncover novel genes regulated by this transcription factor under these particular conditions. We have also generated double knockout cells lacking both Pten and Ets2 that could also be used in ChIP sequencing experiments.

These cells would serve as a proper negative control for experiments using our Ets2 antibody. In addition to examining the global binding locations of Ets2, it would also be interesting to determine changes in histone modifications as a consequence of Pten loss in fibroblasts. Additionally, if we also examine these same marks in our double knockout cells, we may be able to uncover changes that may compensate for Ets2 loss as well.

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6.2.2 Exon Level Analysis

As mentioned previously, the Affymetrix Exon 1.0 ST array contains approximately 40 probes for each of the genes examined on this array, with an average of around 4 probes per exon. Eventually, we would like to more closely examine the expression patterns of these exon specific probes to determine if fibroblast Pten loss may lead to the alternative splicing of targets. Previous work has shown the alternative splicing of fibronectin in SV-40 transformed human fibroblasts as compared to controls

(Carnemolla et al., 1989). Additionally, alternative splicing of VEGFA was shown to result in an alternatively spliced form VEGF165b that was shown to actually be an inhibitor of angiogenesis (Woolard et al., 2004). Therefore it is possible that some of the changes we are observing within the surrounding microenvironment that we have initially attributed to changes in gene expression may actually be to changes in isoforms perhaps involved in paracrine signaling.

6.2.3 Fibroblast Pten or p53 Induced Genetic and/or Epigenetic Changes in

Epithelial Cells

We demonstrated the ability of Pten deletion in stromal fibroblasts to elicit gene expression changes in surrounding epithelial cells (section 4.2.7). Although others have shown changes in fibroblasts induced by tumor cells to be permanent, it is not clear if the reverse is true. Therefore, it is important to discover whether these changes in epithelial cells dependent on continued signaling with fibroblasts, or whether these changes are maintained independently of contact with fibroblasts. Taking an experimental approach, we have begun to examine this concept by isolating and injecting epithelial cells from

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ErbB2;PtenloxP/loxP and ErbB2;FspCre;PtenloxP/loxP mice into the fat pads of FVB/N recipient mice. By removing these ErbB2 cells from their contact with either wild type or

Pten null fibroblasts and placing them in a similar environment, we will be able to elucidate whether the ErbB2;FspCre;PtenloxP/loxP epithelial cells have a Pten associated

“memory”. Additionally this same experimental design could be utilized for epithelial cells isolated from MMTV-rtTA;Tet-o-Ras;p53loxP/loxP and MMTV-rtTA;Tet-o-

Ras;FspCre;p53loxP/loxP mice. Our preliminary studies show an increase in tumor formation and tumor size of injected ErbB2;FspCre;PtenloxP/loxP epithelial cells when compared to controls, however the purity of our epithelial cell populations may be one possible explanation of this result. Flow cytometry analysis of purified epithelial cells from a Col1aYFP mouse revealed approximately 3% of these cells to be YFP positive.

Ongoing studies are underway to try and determine whether this small contamination of fibroblasts can have a major impact on tumor development as well as to standardize a more clean method for epithelial cell isolation.

In addition to these epigenetic changes that may be occurring in epithelial cells, there may also be other genetic hits induced in these cells by Pten or p53 loss in fibroblasts through paracrine mechanisms. It was shown that somatic mutations in the transgene are necessary for epithelial cells tumorigenesis in the ErbB2 model (Siegel et al., 1994). Therefore if deletion of Pten in fibroblasts promotes these genetic changes in the surrounding epithelium this could potentially explain our tumor study data as well in that these induced genetic changes caused increased tumor initiation and growth. To test this, we could go back to our transplant tissues and isolate tumor epithelia using laser

287 capture followed by DNA isolation and sequencing. If we find an increased incidence of mutations in epithelial cells from ErbB2;FspCre;PtenloxP/loxP mammary glands, we can conclude that stomal Pten does in fact modulate genomic stablility in neighboring epithelial cells in a paracrine fashion.

6.2.4 Mechanisms of Cellular Crosstalk

Although the potential genetic effects discussed in the previous secion could at least in part explain the gene expression changes we observed in epithelial cells from fibroblast Pten null mammary glands, other potential explanations exist which may also explain the changes in global gene expression in endothelial cells and macrophages surrounding Pten null fibroblasts. One obvious mechanism is the differential secretion of growth factors and other soluable signaling molecules. To address this, we have isolated conditioned media from both Pten and p53 null fibroblasts and subjected this to proteomic analysis. In a manner similar to our gene expression data, our initial results indicate quite unique secretomes from each of these cell types with few overlapping proteins present in the conditioned media.

In addition to this, recent studies have shown the ability of neoplastic cells to transfer microRNAs to surrounding endothelial cells via exosomes which was shown to promote cell migration and tube formation of these cells (Umezu et al., 2012). Since we already know Pten status in fibroblasts effects miR expression, there is a possibility that these small RNAs may be shuttled to other cell compartments within the micronenvironment and induce tumor promoting changes in these cells.

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6.2.5 Further Analysis in Human Samples

Although we have shown our Ets2, Pten and p53 gene signatures to be represented in human breast cancer stroma, this analysis has only been done on one particular data set which may not represent the extreme heterogeneity observed among breast tumors. Therefore, publicly available data from independent data sets must be used for further analysis. Initially, we must compare our stromal fibroblast derived data against epithelial derived Pten dependent signatures to determine whether the differentially expressed genes we uncovered are unique in the stroma. Next, comparison of our data as well as published epithelial cell data against whole tumor data sets will tell us how well our stromal derived signatures mimic what is seen in human tumors.

Additionally, with more data becoming available every week, we will be able ask more specific questions in relation to our signatures. Currently, we are working with our collaborators at McGill University to query our gene expression data against that derived from the stroma of triple negative breast cancer patieints. Similarly, these same collaborators are also collecting tumor stroma and matched normal stroma from a cohort of HER2+ breast cancer samples which will also be available for comparisons.

Furthermore, studies specifically profiling fibroblasts have also recently been published and will allow us to more precisely correlate our data with human tumor associated fibroblast data. With the advent of personalized medicine in the near future, it is critical for us to understand the functions of critical tumor suppressors and oncogenes in each cell compartment of the tumor and how these functions may be contributing to the overall growth and spread of disease.

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