Duke University Dissertation Template

Molecular Mechanisms Governing the Untoward Tumor Growth Promoting

Effects of Selective Estrogen Receptor Modulators

Kimberly Cocce

Department of Pharmacology and Cancer Biology Duke University

Date:______Approved:

______Sally Kornbluth, Supervisor

______Donald McDonnell

______Susan Murphy

______Neil Spector

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Pharmacology and Cancer Biology in the Graduate School of Duke University

2017

ABSTRACT

Molecular Mechanisms Governing the Untoward Tumor Growth Promoting Effects

of Selective Estrogen Receptor Modulators

Kimberly Cocce

Department of Pharmacology and Cancer Biology Duke University

Date:______Approved:

______Sally Kornbluth, Supervisor

______Donald McDonnell

______Susan Murphy

______Neil Spector

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Pharmacology and Cancer Biology in the Graduate School of Duke University

2017

Abstract

Luminal breast cancer is among the most prevalent diseases in women in the

United States and worldwide. Historically, luminal cancers have been effectively managed with treatments that alter the availability of estrogens or their interaction with the estrogen receptor (ER). Among these, the selective estrogen receptor modulator

(SERM) tamoxifen has been widely used. Being a SERM, tamoxifen can function as either an agonist or an antagonist of ER, depending on the cellular context. Within luminal breast cancers, tamoxifen initially behaves as an antagonist. Tamoxifen also has the added benefit of being an exceptionally well tolerated drug, demonstrating a minimal side effect profile.

While current first line therapeutics demonstrate considerable efficacy as treatments for luminal tumors, experimental and clinical evidence indicates that resistance to these therapies is seemingly inevitable for most patients. Mechanisms of resistance involve processes which are both dependent on and independent of the action of ER. Current drug development has focused on improving the ability of pharmacological agents to directly inhibit ER activity. As such, it stands to reason that the identification of therapeutics that may be paired with ER targeted therapies, or which are effective in the setting of resistance, remains critical.

To address the need for new approaches to treat endocrine therapy resistant disease, we have utilized both in vivo and in vitro derived models of aggressive luminal breast cancer to define potential mechanisms leading to diminished and altered drug response. Integrative analysis of the genome, epigenome and transcriptome of one of these models, TamR, has indicated that in the face of substantial selective pressure, luminal breast cancer cells develop epigenetic and genetic alterations that result in the reprogramming of the transcription factor landscape. Specifically, we have observed that luminal breast cancer cells exhibit a unique genome-wide binding profile of lineage determining transcription factor, FoxA1, and its collaborating transcription factor,

GRHL2, the function of which defines a transcriptional program associated with decreased response to tamoxifen therapy.

The alteration of various cell intrinsic factors downstream of this reprogramming ultimately influences how these cells respond to and interact with the surrounding microenvironment, the result of which further impacts drug response. We demonstrate that one factor regulated by GRHL2, the Ly6/PLAUR containing protein 3 (LYPD3) is increased in abundance in the setting of endocrine resistance. Additionally, we further demonstrate that the presence of extracellular mitogenic factors, including fibroblast growth factors (FGF) and anterior grade protein 2 (AGR2), alters the transcriptional program of breast cancer cells as well as the activity of cancer associated fibroblasts, respectively.

We further extended our investigation to define aspects of the tumor microenvironment impacting treatment outcomes in luminal breast cancer using mouse models of breast cancer. Previously we observed that the presence of the endogenous

SERM, 27-hydroxycholesterol, significantly accelerated the onset and growth of luminal breast cancers. Herein we demonstrate that 27HC derived from macrophages may be responsible, at least in part, for altering the growth of luminal tumors.

The results of these studies provide a comprehensive analysis of alternative pathways that are active in the setting of luminal breast cancer that ultimately alter treatment response. The critical assessment of these pathways has resulted in the indication of potential drug targets in this context, as well as in preclinical testing of alternative treatment approaches. Continued investigation in this vein may provide rational drug combinations for the treatment of advanced luminal breast cancer patients.

Dedication

To my friends, family, mentors and lab mates, for their unending support and encouragement.

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Table of Contents

Abstract ...... iv

List of Tables ...... xiii

List of Figures ...... xiv

Abbreviations ...... xvii

Acknowledgements ...... xxi

1. Background ...... 1

1.1 Thesis Research ...... 1

1.2 Luminal Breast Cancer ...... 1

1.2.1 General information ...... 1

1.2.2 The role of estrogen and ER in luminal breast cancer ...... 3

1.3 Treatment of luminal breast cancers: approaches and shortcomings ...... 11

1.3.1 Treatment approaches of ER positive breast cancer ...... 11

1.3.2 Cell intrinsic mechanisms of resistance ...... 15

1.3.3 Cell extrinsic mechanisms of resistance ...... 19

1.3.4 Next generation SERDs based combination strategies – the new frontier for treatment of luminal cancer ...... 26

2. FoxA1/GRHL2 as mediators of Tamoxifen resistance ...... 28

2.1 Introduction ...... 28

2.1.1 The pharmacologic properties of tamoxifen and insights into resistance ...... 28

2.1.2 Cistrome: master regulator of cell identity ...... 32

2.2 Results ...... 40

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2.2.1 FoxA1 as a critical determinant of chromatin structure and nucleosome occupancy in TamR ...... 40

2.2.2 TamR acquires distinct subtypes of FoxA1 binding events ...... 49

2.2.3 GRHL2 as FoxA1 collaborating factor at activated enhancers ...... 55

2.2.4 Additional FoxA1 co-factors present in TamR model...... 59

2.2.4 GRHL2 expression is increased in the TamR model ...... 66

2.2.5 GRHL2 as prognostic factor in luminal breast tumor samples ...... 69

2.2.6 GRHL2 maintains lineage identity and regulates involved in cell cycle, proliferation, invasion and metastasis...... 72

2.2.7 EGFR, potential activator of GRHL2, is constitutively active in TamR ...... 79

2.3 Discussion ...... 82

2.3.1 GRHL2 as lineage determining transcription factors in luminal breast cancer 82

2.3.2 Aberrant GRHL2 activity as mediator of tamoxifen resistance ...... 86

3. LYPD3 as GRHL2 regulated drug target ...... 90

3.1 Introduction ...... 90

3.2 Results ...... 94

3.2.1 LYPD3 protein is increased in TamR cells ...... 94

3.2.3 GRHL2 as a direct regulator of LYPD3 expression ...... 95

3.2.2 LYPD3 expression is increased in TamR tumors ...... 97

3.2.3 Inhibition of LYPD3 disrupts TamR cell growth in 2D and tumor growth in vivo ...... 98

3.2.5 LYPD3 expression is unlikely to be a direct ER target gene...... 101

3.2.4 LYPD3 expression in other resistance models ...... 105

3.3 Discussion ...... 106

3.3.1 LYPD3 as a driver of collateral pathway in tamoxifen resistance ...... 106

3.3.2 LYPD3 as a biomarker and drug target for treatment of endocrine therapy sensitive and resistant breast cancer ...... 108

4. Cancer cell-tumor microenvironment interaction as mediator of tamoxifen resistance ...... 111

4.1 Introduction ...... 111

4.1.1 Growth promoting cytokines and chemokines present in tumor microenvironment ...... 111

4.1.2 Tamoxifen agonist activity is influenced by the tumor microenvironment ... 112

4.2 Results ...... 113

4.2.1 Fibroblast growth factor as mediator of tamoxifen agonist activity ...... 113

4.2.4 AGR2 stimulates the proliferation of breast cancer associated fibroblasts ..... 116

4.2.5 In vivo targeting of AGR2 inhibits TamR tumor growth ...... 118

4.2.6 Absence of SMAD4 expression as mediator of tamoxifen resistance ...... 119

4.2.7 Lack of SMAD4 in tamoxifen sensitive MCF7 cells leads to expression of subset of genes enriched in TamR ...... 123

4.2.8 Standard MCF7 cells are sensitive to TGF induced growth inhibition, while MCF7-WS8 and TamR cells are not ...... 125

4.3 Discussion ...... 126

5. The influence 27-hydroxycholesterol on mammary gland development, tumor onset and progression ...... 132

5.1 Introduction ...... 132

5.1.1 27-hydroxycholesterol as endogenous SERM ...... 133

5.1.2 Cellular sources of 27-hydroxycholesterol ...... 133

5.2 Results ...... 136

5.2.1 High cholesterol diet increases circulating 27HC in adolescent, wild type mice but does not augment breast epithelial development ...... 136

5.2.2 Macrophage specific knockdown of Cyp27A1 delays tumor onset, but does not influence growth ...... 139

5.2.3 Deletion of Cyp27A1 within macrophages results in decreased intratumoral 27HC, but not circulating 27HC ...... 144

5.3 Discussion ...... 146

6. Conclusions and future directions ...... 152

7. Materials and Methods...... 158

7.1 Chemicals and ligands ...... 158

7.2 Antibodies ...... 158

7.3 Cell culture ...... 159

7.4 RNA isolation and qPCR ...... 159

7.5 Western blotting ...... 161

7.6 Gene overexpression ...... 162

7.7 Chromatin immunoprecipitation ...... 162

7.8 ChIP-Seq library generation and sequencing ...... 166

7.9 siRNA experiments ...... 166

7.10 Proliferation assays ...... 167

7.11 Tissue microarray ...... 168

7.12 Preparation of mammary gland whole mounts ...... 169

7.13 Analysis of mammary gland whole mounts ...... 170

7.14 Isolation of bone marrow derived macrophages ...... 170

7.15 Plasma preparation for oxysterol measurements ...... 171

7.16 Animal breeding, care and use ...... 171

7.17 Xenograft experiments ...... 172

7.18 Statistical Methods ...... 172

7.19 RNA Sequencing ...... 172

References ...... 173

Biography ...... 203

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List of Tables

Table 1.1 Endocrine Therapy for the Treatment of Luminal Breast Cancer ...... 11

Table 2: Complete list of significant FoxA1 interactors identified by Mass Spec ...... 59

Table 3. Complete list of Kinases identified to be increased in TamR relative to MCF7- WS8 cell lines (left) and tumors (right) ...... 80

Table 4: Relative expression of candidate cell-cell communication factors in TamR compared to MCF7-WS8 tumors ...... 114

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List of Figures

Figure 1: PAM50 subtypes by race and ethnicity...... 3

Figure 2: Schematic of ER structure and homology ...... 4

Figure 3: Model of ER action ...... 6

Figure 4: ER interaction with co-regulators...... 8

Figure 5: Simplified model of E2/ER driven tumor growth and progression ...... 10

Figure 6. Components of the Tumor Microenvironment ...... 20

Figure 7: Pro-tumor Functions of Tumor Associated Macrophages ...... 23

Figure 8: Cancer Cell Extrinsic Factors Which Contribute to Treatment Response ...... 24

Figure 9: Overview of chromatin architecture and histone code ...... 34

Figure 10. Cell type-specific enhancer selection and activation ...... 37

Figure 11. The landscape of poised and active enhancers...... 38

Figure 12: DNAse-Seq of MCF7-WS8 and TamR cell lines...... 42

Figure 13: FoxA1 ChIPseq in MCF7-WS8 and TamR...... 44

Figure 14: Gene expression analysis based on FoxA1 binding profile ...... 46

Figure 15: Tumor Growth of MCF7 iRPF vs FoxA1 Overexpression in the Presence of Tamoxifen ...... 48

Figure 16: Genome wide distribution of DHS Sites ...... 50

Figure 17: FoxA1 binding sites with increased binding in TamR subdivided based on histone code...... 52

Figure 18: Gene expression analysis based on FoxA1 associated histone code ...... 54

Figure 19: De novo motif analysis associated with Cluster 1 + 2 FoxA1 binding events.. 56

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Figure 20: Positional enrichment of motifs around FoxA1 subdivided by clusters ...... 58

Figure 21: Subset of FoxA1 binding events which are increased in TamR are also bound by GRHL2 and associated with histone activation marks ...... 62

Figure 22: Gene expression as compared to FoxA1 binding events subdivided by association with GRHL2 – Part 1 ...... 63

Figure 23: Gene expression as compared to FoxA1 binding events subdivided by association with GRHL2 – Part 2 ...... 65

Figure 24: GRHL2 mRNA expression across cell lines ...... 67

Figure 25: GRHL2 protein expression in MCF7-WS8 and TamR cell lines...... 69

Figure 26: GRHL2 expression in patient tumors correlated with time to recurrence...... 71

Figure 27: Differential enrichment analysis of gene expression in TamR siGRHL2 vs siCtrl ...... 73

Figure 28: Candidate GRHL2 target genes ...... 78

Figure 29: LYPD3 expression in cell line models ...... 94

Figure 30: GRHL2 ChIP-Seq at LYPD3 locus ...... 96

Figure 31: LYPD3 expression in TamR is dependent on GRHL2 ...... 97

Figure 32: LYPD3 expression is increased in TamR and LTED tumor models relative to MCF7-WS8 tumors...... 98

Figure 33: TamR cell proliferation is dependent on LYPD3 in vitro ...... 99

Figure 34: LYPD3 antibody treatment suppresses TamR tumor growth ...... 100

Figure 35: LYPD3 is unlikely a direct ER target gene ...... 102

Figure 36: ER ChIP qPCR at FoxA1/GRHL2 binding sites ...... 104

Figure 37: Extracellular FGF treatment results in increased FoxA1 chromatin binding and expression of AGR2 ...... 115

Figure 38: rAGR2 induced proliferation primary breast cancer associated fibroblasts .. 117

Figure 39: Inhibition of extracellular AGR2 suppresses TamR tumor growth ...... 119

Figure 40: TamR cells lack SMAD4 expression ...... 121

Figure 41: Low SMAD4 expression predicts poor response to tamoxifen ...... 122

Figure 42: Decreased SMAD4 in MCF7 cells partially recapitulates TamR gene expression pattern ...... 124

Figure 43: TGF dose response in MCF7, MCF7-WS8 and TamR cell lines ...... 125

Figure 44: Biosynthetic pathways for key regulatory oxysterols ...... 134

Figure 45: High Cholesterol results in increased 27HC, but decreased breast epithelial development ...... 138

Figure 46: Generation of Cyp27A1fl/fl LysMCre...... 141

Figure 47: Confirmation of Cyp27A1fl/fl LysMCre expression ...... 142

Figure 48: Depletion of macrophage derived 27HC results in delayed tumor onset, but does not affect tumor progression, or total tumor volume ...... 143

Figure 49: Oxysterol and cholesterol measurements in tissue and plasma from in Cyp27A1fl/fl LysMCre+ (LysMCre) and LysMCre – (WT) animals ...... 145

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Abbreviations

25HC 25-hydroxycholesterol

27HC 27-hydroxycholesterol

4OHT 4-hydroxytamoxifen

SMA Alpha smooth muscle actin

AI Aromatase inhibitor

AR Androgen receptor

ATP Adenosine triphosphate

CBP/p300 Cyclic adenosine monophosphate response element binding (CREB) protein cDNA Complementary deoxyribonucleic acid

CFS Charcoal-stripped bovine serum

CHD Chromodomain helicase binding protein

ChIP Chromatin immunoprecipitation

DBD DNA binding domain

DNA Deoxyribonucleic acid

E2 Estradiol

EGFR Epidermal growth factor receptor

ER Estrogen receptor

ERE Estrogen response element

xvii

ERK Extracellular signal-regulated kinase

FAP Fibroblast activating protein

FAK Focal adhesion kinase

FBS Fetal bovine serum

Fox Forkhead Box

GRHL2 Grainyhead Like 2

H&E Hematoxylin and eosin

ICI ICI 182,780, fulvestrant

INO80 Inositol requiring protein 80

IL Interleukin iRFP Near infrared-fluorescent protein

JMJD6 Jumonji domain-containing 6

KDM2B Lysine demethylase 2B

MAPK Mitogen activated protein kinase

MMTV Mouse mammary tumor virus

NCoR Nuclear Co-repressor mRNA Messenger ribonucleic acid

NFE2L2 Nuclear factor erythroid like 2

NR Nuclear receptor

OVX Ovariectomy, ovariectomized

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PAM50 Prediction analysis of Microarray 50

PCR Polymerase chain reaction

Pol II RNA polymerase II

PTM Post translational modification

TFF1 Trefoil factor 1 qRT-PCR Quantitative real time polymerase chain reaction

SDS-PAGE Sodium dodecyl sulfate polyacrylamide gel electrophoresis

SERD Selective estrogen receptor degrader

SERM Selective estrogen receptor modulator siRNA Small interfering ribonucleic acid

SALL4 Sal-like protein 4

SMAD2,3,4 Mothers against decapentaplegic homolog 2,3,4

SMRT Silencing mediator of retinoic acid and thyroid hormone receptors

SOX2 Sex determining region Y – box 2

SRC-1 Steroid receptor co-activator-1

TAM Tamoxifen

TCF3 Transcription factor 3

TGF Transforming growth factor 

TNF Tumor necrosis factor 

WISP1 WNT1 inducible signaling pathway protein 1

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Acknowledgements

I am incredibly grateful for the extensive support that I have received both inside and outside of lab throughout my PhD training. Sally Kornbluth and Donald McDonnell both have been exceptional mentors. Their enthusiasm, positivity, and unwavering support always provided a beacon of hope, even during the most frustrating experiences of graduate school. Sally has served as an exceptional role model as a successful female scientist who has taken on and excelled at a number of academic leadership positions.

Donald has guided me to understand the complexities of drug development, from target validation to preclinical study management as well as provided an enriching collaborative environment, full of researchers with diverse interests and areas of expertise. Susan Murphy has always provided a listening ear and has shown genuine interest in the development of my project, as well as me as a person, over the course of my graduate career. Neil Spector has always provided the clinical insight as well as served as a role model as a successful, and caring physician scientist.

To the Kornbluth and McDonnell Labs, thank you. I am humbled and grateful to have had the opportunity to work with some of the smartest, and hardest working people I have ever met. Thank you for your patience and mentorship. I am especially indebted to Jeff Jasper and Logan Everett, who have both served as remarkable teachers and collaborators in my final dissertation work.

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To Tom Westerling and “Mabel,” thank you for instilling in me my critical eye of biomedical research.

To my friends and family, thank you for your support and encouragement throughout the years. I certainly could not have done this without you.

Graduate school has been the most challenging, yet rewarding experience in my career to date. Thank you all for making this possible!

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1. Background 1.1 Thesis Research

The overarching objective of my dissertation is to define the diverse mechanisms by which breast cancer cells exhibit de novo or acquired resistance to currently available treatments. A central theme is the discovery of a mechanism by which the on-target and off-target effects of selective estrogen receptor modulators (SERMs) lead to the paradoxical untoward effects of increased tumor growth. The work herein describes the integration of diverse technical approaches to understand this complex clinical phenomenon as observed in luminal breast cancer.

1.2 Luminal Breast Cancer

1.2.1 General information

Breast cancer is a common and unfortunately very deadly disease. According to the most recent annual report from the American Cancer Society, it is estimated 255,180 new cases of invasive breast cancer will occur in 2017, with 99% of those cases occurring in women (2017). It is also predicted that 40,610 women will succumb to breast cancer in the United States, making it second only to lung and bronchus cancer as a cause of cancer death in women (2017). Early detection strategies and treatment have contributed to a significant decrease in the number of cancer deaths; however, the standard lifetime risk of a diagnosis of breast cancer (1 in every 8 individuals) continues to remain high

(2017).

Breast cancers, like many solid tumors, represent a group of genotypically and phenotypically diverse cancer types, unified as one entity primarily due to their anatomical location. Reflecting this diversity, several systems for subcategorization of breast cancers have been developed. These systems have traditionally been based on histology; however, in recent years, the focus has shifted to transcriptomic approaches.

One of the most widely reported of the transcriptomic approaches is the Prediction analysis of Microarray 50 (PAM50) system of classification (Perou, Sorlie et al. 2000).

Based on complementary deoxyribonucleic acid (cDNA) microarrays of samples derived from patients with breast cancer, the PAM50 tool subdivides breast cancers into five general categories based on gene expression patterns. These categories include luminal

A, luminal B, Her2-enriched, basal-like, and normal-like (Perou, Sorlie et al. 2000)

(Sorlie, Perou et al. 2001). Among these subtypes, the luminal A and luminal B subtypes occur with the highest frequency, independent of race or ethnicity (Sweeney, Bernard et al. 2014) (Figure 1). A common feature of both luminal A, luminal B, and a subset of normal-like breast cancers, is the expression of the estrogen receptor (ER).

Figure 1: PAM50 subtypes by race and ethnicity.

Distribution of breast cancer intrinsic subtypes from PAM50 assay in population based cohort by race and ethnicity. Reprinted with permission from Cancer Epidemiology, Biomarkers & Prevention, (2014), 23/5, 714-24, Sweeney et al. “Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics” with permission from AACR.

1.2.2 The role of estrogen and ER in luminal breast cancer

The physiologically relevant estrogens include 17-estradiol (E2), estrone and estriol. E2, the most potent of these estrogens, is primarily synthesized in the ovary via the metabolism of cholesterol. Testosterone is the immediate precursor of estradiol, the conversion of which is catalyzed by the aromatase enzyme. In the liver and other peripheral tissues, E2 can be converted to estrone and estriol and is frequently conjugated by esterification to sulfates.

Estrogen action occurs primarily via its interaction with its cognate nuclear estrogen receptors (ERs). There exist two genetically distinct subtypes of ER, ER and

ER, encoded by the ESR1 and ESR2 genes, respectively. These two receptors, while highly homologous, demonstrate both shared and unique activities and expression patterns. Structurally they are very similar, sharing an N terminal A/B domain, C terminal zinc finger binding domain (DBD), a hinge region (D domain), and the E domain, which contains the ligand binding domain (LBD). The conserved DBD and LBD enable the receptors to bind the same ligands and regulate nearly identical DNA response elements (Kuiper, Carlsson et al. 1997, Hall, Couse et al. 2001) (Figure 2).

A/B C D E F

ERa A/B C D E F

ERb 18% 97% 24% 58% 12%

Figure 2: Schematic of ER structure and homology

Schematic illustration of ER modular structure. Percentages indicate homology between ER and ER.

The transcriptional activity of ER is facilitated by sequences that comprise specific domains called activation function (AFs). ER contains two (AF-1 and AF-2) while ER only has one (AF-2). Beyond differences in AFs, the specificity of ER and

ER action is largely attributable to their tissue distribution, with ER being expressed widely and ER expressed in only a subset of tissues, including uterus, blood

monocytes, colonic and pulmonary epithelial cells (Couse, Lindzey et al. 1997). The precise biological role of ER remains unclear and its role in the pathobiology of breast cancer and its utility as a therapeutic target in this disease remains controversial. While it has been suggested that ER is expressed at low levels in some breast cancers, ER is regarded as the most important in breast tissue (Palmieri, Cheng et al. 2002).

ER can be activated both in a ligand-dependent or a ligand-independent manner. Ligand-dependent activation is initiated by binding of estrogen to ER, which leads to its dissociation from heat shock proteins. Once occupied by ligand, ER undergoes a conformational change and dimerizes, allowing it to bind to DNA directly at its estrogen response element (ERE) or indirectly via the interaction with other DNA- bound transcription factors, including AP1, Sp1, or NFB. Ligand-independent activation can occur via a variety of cellular pathways, including the mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K), and cyclic adenosine monophosphate (cAMP) – dependent protein kinase A and protein kinase C pathways

(Beekman, Allan et al. 1993, Smith, Conneely et al. 1993) (Figure 3). These kinases can activate ER-transcriptional activity by phosphorylating the receptor directly or through phosphorylation of receptor associated proteins.

Figure 3: Model of ER action

Classically, ER is activated by the action of estradiol (E2), bound intracellularly, leading to the dimerization and nuclear transport of ER. Once nuclear, ER can bind directly to an estrogen response element (ERE) and interact with cofactors (C) on DNA, or it can associate indirectly with DNA via binding to other transcription factors (TF). ER action can also be influenced by the activation of intracellular kinases, which can phosphorylate (P) ER or its interacting cofactors. Finally, E2 activation of membrane bound ER can occur (though controversial) which may lead to the phosphorylation of additional TFs. Image adapted with permission from (Khan and Ansar Ahmed 2015).

Regardless of whether ER is activated in a ligand-dependent or independent manner, a key feature defining the function of ER is its interaction with co-regulators.

Previously it was believed that ER directly impacted the activity of the transcriptional machinery. However, this simplified model has been modified, indicating that the major

function of ER is to serve as a central assembly point for the recruitment of additional proteins with diverse enzymatic activities (McDonnell 2005). These proteins can function as “co-activators”, including steroid receptor coactivator-1 (SRC-1) and CREP binding protein (CBP), leading to the induction of transcription, or function as “co-repressors”, such as silencing mediator of retinoic acid and thyroid hormone receptors (SMRT) and nuclear co-represssor (N-CoR), effectively shutting down transcription (Smith, Nawaz et al. 1997, Varlakhanova, Snyder et al. 2010). The orientation of helix 12 within the AF2 domain of ER is largely influenced by ligand binding of the receptor and has been shown to be a key determinant of co-regulator interaction (Shiau, Barstad et al. 1998, Hu and Lazar 1999) (Varlakhanova, Snyder et al. 2010). Additional determinants of ER/co- regulator interaction include 1) relative co-regulator expression levels, which can differ based on the tissue context, and 2) post-translational modifications of the ER and/or its co-regulators, which can alter the binding affinity/specificity of the proteins for one another or for DNA (McDonnell and Wardell 2010) (Figure 4).

Figure 4: ER interaction with co-regulators

Model depicting interaction of ER with co-regulators. Antagonists induce an ER conformation which stabilizes interactions with co-repressors (CoR), while agonists induce an ER conformation which stabilizes interactions with co-activators (CoA). Selective estrogen receptor modulators can stabilize the inactive or the active state, or can allow the receptor to adopt an intermediate conformation leading to interaction with canonical CoRs, CoAs, or novel coregulators. Adapted from Clinical Cancer Research, (2005), 11/2 pt 2, 871s-7s, McDonnell, D.P “The molecular pharmacology of estrogen receptor modulators: implications for the treatment of breast cancer,” with permission from AACR.

Subsequent to DNA binding and the recruitment of the transcriptional machinery, ER and its co-regulators define a transcriptional network that regulates a variety of physiologic as well as pathologic processes. Estrogens, and ER, are required for the development and maturation of the female reproductive tissues and regulate bone growth, including linear bone growth at puberty, and closure of the epiphyses

(Cutler 1997). In the mature adult, estrogens regulate the menstrual cycle and exert a cardioprotective activity by influencing both plasma triglycerides and serum cholesterol, leading to increases and decreases of these components, respectively (Fahraeus 1988).

Estrogens have also been shown to decrease total serum proteins while also increasing levels of various coagulation factors (Tchaikovski and Rosing 2010). Finally, estrogens have diverse roles in the central nervous system, including improving “working memory” (defined as “memory for information that is relevant to a single behavioral episodes”) while also impairing “reference memory” (defined as “memory for information consistent across trials”) (Daniel 2006).

In the context of breast cancer, estrogen and ER have been shown to be master regulators of oncogenic progression. Estrogens have been indicated to be involved in the initial stages of transformation; however, this precise role remains controversial. The predominant model of estrogen/ER action in breast cancer as proposed by Bonnie Deroo and Kenneth Korach, as well as Joe and Irma Russo, among others, indicates that upon binding estrogen, ER activity results in the expression of a transcriptional program that leads to enhanced mammary epithelial cell growth – this may be at the level of normal epithelium or in an already otherwise transformed cell. By robustly increasing cell number, there is a significant increase in risk for replication errors. Replications errors can lead to alterations in the regulation of normal protective processes, including DNA repair and apoptosis. This increase in replication errors ultimately provides the cellular

heterogeneity which can facilitate the selection of subpopulation(s)of cells which can then lead to the development of clinically detectable cancer (Deroo and Korach 2006).

Additionally, it has been demonstrated that the metabolism of estrogen via the cytochrome P450 family of enzymes results in the production of metabolites that may increase DNA mutagenesis (Russo and Russo 2006, Yager and Davidson 2006). These mechanisms are not mutually exclusive; as such, it is has been proposed that it is likely that a combination of these two effects: enhanced cell division and DNA damage, contribute to the development of ER positive breast cancer (Russo and Russo 2006)

(Figure 5).

Error-prone Expansion of Additional replication Highly transformed, Replication transformed cells errors and expansion of heterogeneous population Figure 5: Simplified model of E2/ER drivencells I ntumor the prese ngrowthce of and progression toxic metabolites Estradiol (E2-blue circles) stimulates the proliferation of breast epithelial cells at early stages of transformation. Excessive proliferation and expansion leads to error-prone replication (red burst), resulting in the alteration of the transformed cell. This process continues whereby the transformed cells further proliferate and expand, experiencing further replication errors. These events continue in the presence of E2 and its toxic metabolites (green circles), ultimately resulting the establishment of a highly transformed, heterogeneous tumor.

1.3 Treatment of luminal breast cancers: approaches and shortcomings

1.3.1 Treatment approaches of ER positive breast cancer

Given the well-established role of ER and estrogens as mediators of breast cancer growth and progression, pharmacologically disrupting estrogen-ER signaling has been of substantial clinical interest. Using compounds originally formulated as fertility agents including ethamoxytriphetol (MER-25) and clomiphene, it was first observed as early as the 1950s that treating breast tumors with an anti-estrogen decreased tumor growth

(Kistner and Smith 1960). While eliciting an anti-tumor response, these compounds induced significant toxicity, emphasizing the need for further drug development

(Herbst, Griffiths et al. 1964).

Generally, therapies targeting estrogen and ER have been designed around three principles: targeting the synthesis of estrogen, altering the conformation of ER to achieve favorable cofactor interactions, or destabilizing the ER to induce its proteasomal degradation, as shown in Table 1.1.

Table 1.1 Endocrine Therapy for the Treatment of Luminal Breast Cancer

Class Examples Mechanism of Action Common Side effects Aromatase Exemestane Inhibition of aromatase Hot flashes, fatigue, Inhibitors Anastrazole enzyme involved final insomnia, depression, (AIs) Letrozole step of production of anxiety, vaginal estrogen from androgenic bleeding, arthralgia, precursors bone pone, gastrointestinal disturbances

Selective Tamoxifen Direct binding of estrogen Edema, hot flashes, Estrogen Raloxifene receptor leading to nausea, vomiting, Receptor conformational change vaginal bleeding and Modulators and modified cofactor discharge, (SERMs) interactions thromboembolic events Selective Fulvestrant Direct binding of estrogen Hot flashes, nausea, Estrogen (ICI 182, 780) receptor leading to vomiting Receptor conformational change Degraders and decreased protein (SERDs) stability

The final step in the production of estrogens from androgenic precursors occurs primarily within the ovaries by the cytochrome p450 CYP19 enzyme (aromatase). This enzyme can also be expressed in adipose tissue, and thus peripheral aromatization of androgens can also occur, an observation of significant importance in breast cancer and particularly in the post-menopausal setting. Therapies designed to disrupt the activity of this enzyme include exemestane, a steroidal irreversible inhibitor, and nonsteroidal inhibitors anastrazole and letrozole. Common side effects of steroidal aromatase inhibitors (AI) include hot flashes, fatigue, central nervous system disturbances including insomnia, depression and anxiety, while treatment with nonsteroidal AIs is commonly associated with vaginal bleeding, musculoskeletal syndrome (significant arthralgias and bone pain) and gastrointestinal disturbances in addition to hot flashes and fatigue (Pagani, Regan et al. 2014) (Francis, Regan et al. 2015).

Selective estrogen receptor modulators (SERMs) comprise a class of compounds which bind to the estrogen receptor and alter its conformation in a manner distinct from 12

that observed with canonical agonists. Depending on the conformation that is induced and the effects that this has on the recruitment of functionally distinct coactivators available in a specific cell, the binding of the SERM can lead to either activation or repression of the ER transcriptional program in a tissue selective manner (Shiau, Barstad et al. 1998). Tamoxifen and raloxifene are among the most commonly used SERMs.

Tamoxifen has been shown to function as an antagonist in the breast and in the brain, but is an agonist in the bone and the uterus (a more complete discussion of which follows in subsequent chapters). In contrast, raloxifene is an agonist in the bone and in the cardiovascular system and has no effect on the uterus or breast (Delmas, Bjarnason et al. 1997). Overall, SERMs are very well tolerated treatments. Common side effects include edema, hot flashes, nausea, vaginal discharge and in rare cases, thromboembolic events (Early Breast Cancer Trialists' Collaborative, Davies et al. 2011, Pagani, Regan et al. 2014, Francis, Regan et al. 2015).

The final class include selective estrogen receptor degraders, SERDs. These compounds, including fulvestrant (ICI), bind competitively to ER, also leading to a unique conformational change in the receptor. This conformation is distinct from that induced by E2 or SERMs, resulting in a unique pattern of ubiquitination and decreased receptor stability (Allan, Leng et al. 1992). By facilitating receptor degradation, SERDs effectively reduce the number of functional targets available for endogenous estrogens

to bind and activate (Osborne, Wakeling et al. 2004). Common side effects of SERDs include hot flashes, nausea, and vomiting (Al-Mubarak, Sacher et al. 2013).

The development of endocrine therapy for the treatment of breast cancer represents one of the first, if not the first, examples of biologically informed targeted therapy for the treatment of solid tumors. This is in contrast to the more broadly used, non-specific chemotherapies, including alkylating agents (e.g. cyclophosphamide), antimetabolites (e.g. fluorouracil), natural DNA damaging agents (e.g. doxorubicin) or mictrotubule modifiers (e.g. taxanes), among others. In recent years, the development of targeted therapies has primarily focused on the identification of inhibitors to specific receptor tyrosine kinases (TKIs) or other downstream targetable enzymes. Clinical experience with endocrine therapies for the treatment of breast cancer as well as the use of TKIs for the treatment of various solid tumors have revealed that resistance to targeted therapies, especially when used as a monotherapy, is for the most part inevitable. By studying drug pharmacology in model systems and leveraging clinical data on their performance in patients, several common mechanisms of resistance have emerged. These include mechanisms restricted to the cancer cell itself (cell intrinsic) as well as those which involve perturbations of factors external to the cancer cell, including the tumor microenvironment (cell extrinsic).

1.3.2 Cell intrinsic mechanisms of resistance

Cell intrinsic mechanisms of resistance to targeted chemotherapy can be subdivided into three general categories, as indicated in Table 1.2. This categorization scheme is based off that originally proposed by Sequist et al. and graphically modeled by Chang et al., describing mechanisms of resistance to epithelial growth factor receptor

(EGFR) targeted therapies (Sequist, Waltman et al. 2011, Chang, Choi et al. 2016).

Table 1.2 Cell Intrinsic Mechanisms of Targeted Chemotherapy Resistance

Drug Target Alterations Enhanced Survival Phenotypic changes Mechanisms 1) Altered expression 1) Activation of 1) Epithelial to 2) Mutation alternative survival mesenchymal 3) Altered conformation pathways transition 2) Inhibition of cell death 2) De-differentiation to 3) Synthesis of drug stem-like state efflux pumps

Some of the most commonly reported mechanisms of both de novo and acquired drug resistance relates to alterations of the drug target itself. The mRNA and/or protein expression of the drug target may be amplified or decreased. In the setting of ER, it is rare for significant changes in expression of ER itself to occur following treatment with endocrine therapy. In mouse xenograft tumors, it has been observed that loss of ER was not required to achieve resistance to tamoxifen or ICI; similarly, clinical studies indicate loss of ER expression in less than 25% of tumors at time of recurrence despite continuous treatment with tamoxifen (Brunner, Boulay et al. 1993, Kuukasjarvi, Kononen et al.

1996). In the rare cases where ER expression is decreased, this does result in a significant decrease in responsiveness to hormonal therapy (Kuukasjarvi, Kononen et al. 1996,

Bachleitner-Hofmann, Pichler-Gebhard et al. 2002).

Mutations of the drug target can also limit drug responsiveness. ESR1 mutations occur in 1 to 3 percent of primary breast tumors and up to 21 percent in metastatic breast cancers (Takeshita, Yamamoto et al. 2015) (Jeselsohn, Buchwalter et al. 2015). ESR1 mutations have been described in the LBD and within phosphorylation sites downstream of various tyrosine and serine kinases, among others. The tyrosine 537

(Y537) residue, which resides within the LBD, is considered a “mutational hotspot”

(Toy, Shen et al. 2013). Phenotypically, the presence of a Y537 mutation results in enhanced transcription and proliferation, and decreased drug responsiveness (Toy, Shen et al. 2013). While these results suggest a causal role of ESR1 mutations in driving resistance, this topic still remains controversial in the field, indicating the need of further work to define and characterize common mutations.

Altered target conformation has also been described as a common mechanism of resistance. This is especially relevant for ER, as the conformation of the receptor has been shown to be a critical determinant of co-factor interaction, the result of which can lead to activation or repression of the ER specific transcriptional program. The altered conformation can be a result of mutations. Mutations in Y537, and sites in close proximity to Y537 as indicated above, result in stabilization of the agonist conformation,

ultimately leading to constitutive activation of the receptor (Toy, Shen et al. 2013).

Alternative splicing of ER mRNA can result in variants that lack one or several exons. As yet it is not clear whether the alternatively spliced transcripts encode functional proteins and/or if they contribute to cancer pathobiology. However, it has been indicated that some of these variants may retain the ability to bind to DNA in the absence of estrogen, leading to ligand independent gene activation in in vitro models (Gallacchi,

Schoumacher et al. 1998).

Beyond alterations in the drug target itself, cancer cells have evolved various mechanisms to overcome the growth inhibiting effects of targeted agents. These mechanisms include the activation of alternative survival pathways, the inhibition of cell death processes, and the activation of multidrug resistance mechanisms, particularly the synthesis of drug efflux pumps. Pathways that are commonly upregulated in cancer cells in response to targeted therapies that facilitate decreased anti-tumor response include the RAS-MAPK and AKT-PI3K signaling cascades (Martz, Ottina et al. 2014). In breast cancer cells, these pathways can become activated via amplification of receptor tyrosine kinases, mutation or amplification of kinases further downstream in the pathway or via the release of growth factors, which can stimulate cancer cell growth in an autocrine fashion. Inhibition of cell death pathways is often a consequence of increased expression of anti-apoptotic proteins, including BCL-2 or MCL-1 and survivin, or the decreased activity of effectors of apoptosis, including caspases (Moriai, Tsuji et al.

2009, Merino, Lok et al. 2016). Multidrug resistance occurs via the increased expression of several drug transporters, including P-glycoprotein also known as the multidrug resistance protein 1 (MDR1) and the breast cancer resistance protein (BCRP) (Kuo 2007).

The expression of these proteins has been shown to lead to the effective export of the majority of commonly used chemotherapeutics and endocrine therapies, and has been shown to occur frequently in breast cancer.

Phenotypic alterations of cancer cells can also alter the tumor response to a given targeted therapy. Epithelial to mesenchymal transition (EMT) involves the biochemical shift from an epithelial cell, characterized by tight interactions with a basement membrane, to mesenchymal cell, which demonstrates features of increased invasiveness, migratory capacity, and resistance to cell death (Kalluri and Weinberg 2009). The phenotyping changes associated with EMT are accompanied by altered expression of cell surface and cytoskeletal proteins, which have been correlated with decreased drug responsiveness (Fischer, Durrans et al. 2015). Similarly, transition and/or enrichment of cancer cells expressing stem-like properties may occur following drug treatment, leading to the later expansion of tumor cells, clinically detected as disease recurrence

(Dean, Fojo et al. 2005). Characteristics of cancer stem-like cells include the ability to self- renew and undergo pluripotency differentiation (Dean, Fojo et al. 2005). It is proposed that these features allow the cancer cells to readily adapt to cellular stress, thereby limiting drug responses.

1.3.3 Cell extrinsic mechanisms of resistance

While early research investigating oncogenesis and tumor response to drug treatments purely focused on cell autonomous mechanisms, it has now become apparent that factors external to the cancer cells themselves can significantly alter the response to cancer therapy, including hormone and anti-estrogen based therapy. Generally, cell extrinsic factors which influence therapy response can be divided into two categories based on whether 1) those factors which reside within the tumor itself, or 2) exist external to the tumor, but still influence tumor biology.

Factors that influence treatment response and exist within a tumor, but exclude the cancer cells, can broadly be referred to as the tumor microenvironment. Important components of the tumor microenvironment include fibroblasts, extracellular matrix components, blood vessels, and immune cells, among others (Pattabiraman and

Weinberg 2014) (Figure 6).

Figure 6. Components of the Tumor Microenvironment

The tumor microenvironment consists of a variety of cell types including adipocytes and cancer associated fibroblasts, immune cells such as macrophages and lymphocytes, blood vessels and extracellular matrix. CSC, Cancer stem cells. ECM, extracellular matrix. MSC, mesenchymal stem cell. Adapted by permission from Macmillan Publishers Ltd: Nature Review Drug Discovery (Pattabiraman and Weinberg 2014), copyright (2014).

Cancer associated fibroblasts (CAFs) are typically among the most abundant cell types within a tumor and comprise a heterogeneous group of cells. The definitive cell type of origin of CAFs is uncertain, with reports indicating CAF development from bone marrow derived mesenchymal cells, (Direkze, Hodivala-Dilke et al. 2004), differentiation of endothelial cells, or even a result of cancer cells themselves that have undergone EMT

(Petersen, Nielsen et al. 2003). Once differentiated, CAFs can be distinguished from normal fibroblasts based on the positive expression of various markers, including alpha

smooth muscle actin (SMA), fibroblasts activation protein (FAP), desmin and S100A proteins (Kalluri 2016). In contrast to resting or quiescent fibroblast, CAFs have a distinct “secretome” that supports tumor growth (De Boeck, Hendrix et al. 2013).

Activated CAFs secrete various growth factors, cytokines, and chemokines, including fibroblast growth factor (FGFs), transforming growth factor beta (TGF), platelet derived growth factor (PDCF), vascular endothelial growth factor A (VEGFA), hepatocyte growth factor (HGF), tumor necrosis factor alpha (TNF), stromal-derived factor-1 (SDF1), interferon gamma (IFN), interleukin 6, 8, an 17A (IL-6,8, 17a) and C-X-

C chemokine ligand 17 (CXCL17)(Kalluri 2016). The collection of these factors leads to the establishment of a pro-growth environment, effectively decreasing any inhibitory effects of a provided therapy.

In addition to the secretome, CAFs can facilitate both the synthesis and deposition of extracellular matrix (ECM) components, as well as its degradation. The

ECM consists of a variety of collagens, fibronectins, laminins, and proteoglycans, and provides structural integrity to a tumor as well as providing a “signaling platform” for cancer cells (Hazlehurst, Damiano et al. 2000, Mori, Shimizu et al. 2004, Park, Zhang et al. 2008). The interaction between ECM and cancer cells is facilitated by cell surface integrins. Integrins constitute a large family of transmembrane glycoproteins, which function as adhesion receptors, translating mechanical and chemical signals from the

ECM to the cells that it supports. Upon binding specific components of the ECM,

integrins can interact with receptor tyrosine kinases to activate canonical survival pathways including RAS-MAPK, PI3K-AKT via focal adhesion kinase (FAK) dependent and independent mechanisms (Hazlehurst, Damiano et al. 2000, Mori, Shimizu et al.

2004, Park, Zhang et al. 2008). ECM mediated activation of these pathways supports tumor growth, both by decreasing the efficacy of a targeted agent on a given cell and by providing an opportune environment for the outgrowth of drug resistant populations.

Among the various immune cell types present within the tumor microenvironment, deciphering the role of tumor associated macrophages has been of particular interest over the past decade due to their abundance and enigmatic biology.

The collection of macrophages within tumors have diverse origin and function, demonstrating both tumor promoting and tumor inhibiting roles. Tumor associated macrophages (TAMs) have long been thought to arise from the bone marrow or from infiltrating blood monocytes (Mantovani, Bottazzi et al. 1992, Qian, Li et al. 2011) ; however, accumulating evidence indicates, although small in number, there exist tissue resident macrophages which mature from precursors established during fetal and embryonic development (Ginhoux, Schultze et al. 2016). “Classically” polarized macrophages secrete high levels of “pro-inflammatory” cytokines including IFN, and

TNF, leading to anti-tumor T-cells response. TAMs, however, tend to align more closely with “alternatively activated” macrophages secreting higher levels of anti- inflammatory cytokines such as IL-17 and IL-6, scavenging receptors, angiogenic factors

and proteases. Consequently, the presence of abundant TAMs leads to enhanced immune evasion, angiogenesis, matrix degradation and remodeling, and tumor invasion and metastasis. The collection of these processes leads to enhanced tumor survival and proliferation effectively decreasing response to anti-tumor therapy (Sica 2014) (Figure 7).

Figure 7: Pro-tumor Functions of Tumor Associated Macrophages

TAMs promote tumor growth through various mechanisms. These include the secretion of growth factors and cytokines, the promotion of tumor resistance to chemotherapy, the secretion of angiogenic factors, the degradation and remodeling of tissue matrix, the suppression of anti-tumor immune responses and the enzymatic activity promoting tumor spread. Reprinted from “Macrophages: Biology and Role in the Pathology of Diseases,” Chapter 20: Tumor Associated Macrophages, 2014, Page 425, Sica, A. Straus, L. and Allavena, A. with permission of Springer.

Indeed, the detection of elevated numbers of cells expressing macrophage associated markers has been found to be associated with poor outcome in many solid tumors, 23

including breast cancers (Komohara, Jinushi et al. 2014). While there exist clear clinical associations between the presence of intratumoral macrophages, drug response and outcome, the understanding of the diverse roles that macrophages play in the tumor microenvironment is in its infancy, warranting further investigation.

Beyond the cancer cell extrinsic elements that influence drug response from directly within the tumor milieu, additional corporal factors can strongly alter the way in which cancer cells responds to a targeted therapy (Kroemer, Senovilla et al. 2015)

(Figure 8).

Figure 8: Cancer Cell Extrinsic Factors Which Contribute to Treatment Response

An overview of cancer cell extrinsic factors which may alter the response to a given cancer therapy, including obesity, hormonal factors, and treatment history in addition to various immune factors previously discussed. Reprinted with permission from(Kroemer, Senovilla et al. 2015) Macmillan Publishers Ltd: Nature Medicine. Copyright 2015.

History of cigarette smoke use, and in particular nicotine consumption, is closely correlated with chemoresistance (An, Kiang et al. 2012). Alterations in renal and liver function, which may result from previous exposure to chemotherapy or other chronic disease processes or differential expression of cytochrome p450 enzymes, may alter drug metabolism, thereby limiting its efficacy (Saladores, Precht et al. 2013). Hormonal status has also been shown to significantly influence drug response, with both sex and menopausal status significantly correlated with differential drug response (Surakasula,

Nagarjunapu et al. 2014). Finally, metabolic syndrome, obesity, and associated hypercholesterolemia have also been shown to be significant risk factors for breast cancer incidence, and to correlate to poor drug response (Lashinger, Rossi et al. 2014).

The links between these chronic conditions and disease states with decreased responsiveness to chemotherapy are likely multifactorial and are at best correlative, highlighting the need for further basic and preclinical studies to distinguish the relative contribution of individual features.

1.3.4 Next generation SERDs based combination strategies – the new frontier for treatment of luminal cancer

Given that a majority of breast cancers express ER and that ER expression is maintained in recurrent tumors, the development of treatment approaches to target ER represents a significant medical need. While the SERD, ICI, demonstrated encouraging pre-clinical and clinical response by decreasing ER positive breast tumor growth, the utility and clinical impact of this drug has been limited largely by the intramuscular route of administration required and its inherently poor pharmaceutically properties

(Valachis, Mauri et al. 2010, Al-Mubarak, Sacher et al. 2013). As such, modern approaches to specifically target ER and estrogen signaling in breast cancer have focused on developing a more effective SERD with enhanced bioavailability. In line with these efforts, two promising and orally bioavailable SERDs include GDC-0810 and AZD9496.

Both these drugs demonstrate robust anti-tumor activity in treatment naïve and endocrine therapy resistance models (De Savi, Bradbury et al. 2015, McDonnell, Wardell et al. 2015). Dose-limiting toxicities observed in the clinic may ultimately limit the clinical impact of these SERDs; however, additional SERDs are in pre-clinical development and entering clinical trials, and the anticipated therapeutic impact of a well-tolerated SERD provides sufficient impetus for continued development of this drug class.

Based on clinical experience with targeted therapies, including antiestrogens and

TKIs, it is to be expected that when used as monotherapy, resistance is inevitable.

Following this logic, there remains a need to define alternative therapeutic approaches that may enhance the efficacy and durability of response to SERD treatment and/or otherwise enhance the response to therapies already clinically available. With the expectation that SERDs themselves decrease the direct activity of ER through degradation of the receptor, the most viable candidates for new drug discovery involve targeting pathways known to be active in luminal cancer but which are not dependent or even related to ER or ER-signaling. Given the diverse mechanisms of chemoresistance previously indicated, it is likely that these pathways are activated downstream of alterations of both cell intrinsic as well as extrinsic factors, the survey of which may indicate the most viable druggable targets. Consequently, a central goal of the following studies was to use the knowledge of established cell intrinsic and cell extrinsic collateral pathways to define novel combination treatment approaches, the result of which, we hypothesize, could result in superior treatment outcomes in patients with ER positive breast cancer.

2. FoxA1/GRHL2 as mediators of Tamoxifen resistance 2.1 Introduction

This chapter outlines our findings with respect to FoxA1 and GRHL2 as potential mediators of tamoxifen resistance using the TamR model, and represents data acquired and analyzed in collaboration between myself, Jeff Jasper, a former graduate student in the McDonnell Laboratory, and Logan Everett, a post-doctoral fellow in the

Mackay/Anholt labs at North Carolina State University.

2.1.1 The pharmacologic properties of tamoxifen and insights into resistance

Tamoxifen therapy has been a mainstay of treatment of breast cancer for the better part of 40 years, touted to have been the “gold standard” of hormone treatment from the late 1980s to 2000 (Jordan 2008). Unexpected based on this clinical success is the history of its development as a pharmaceutical agent, the chronicling of which provides key insights into its unique biology. Here the history is summarized as originally described by Craig Jordan (Jordan 2008).

Tamoxifen was first identified by a team at Imperial Chemical Industries (ICI)

Ltd (now AstraZeneca) in the 1960s via a project aimed to develop post coital contraceptives. In assaying tamoxifen in animal models, it was discovered that it displayed evidence of estrogenic activities in the mouse vagina but functioned as an antiestrogen in rat models (Harper and Walpole 1966, Harper and Walpole 1967).

Seeking to determine the effects in humans, the first clinical testing was carried out in 28

England soon after. These studies indicated that tamoxifen demonstrated modest effects at reducing cancer burden, with the added benefit of a favorable side effect profile (Cole,

Jones et al. 1971). Despite these promising results, however, further development of tamoxifen as a standard of care for the treatment of breast cancer was stalled until later in the 1970s. At this time, Craig Jordan was provided the opportunity to further develop the initial compound. Key findings made in this era that were central to the development of tamoxifen for cancer therapy include: 1) tamoxifen could inhibit the growth of dimethylbenzanthracene (DMBA) induced rat mammary tumors (confirming the antagonist-like activity within tumors) (Nicholson, Golder et al. 1976), 2) tamoxifen and its active metabolite, 4-hydroxytamoxifen (4OHT), could competitively displace E2 binding on ER (indicating a direct, on target effect of tamoxifen on ER) (Jordan, Collins et al. 1977), 3) DMBA induced breast cancer growth could be prevented with just two doses of tamoxifen, (suggesting the long durability of response to tamoxifen) (Jordan

1976). These promising pre-clinical studies quickly led to further trials of tamoxifen in patients. The initial studies indicated efficacy for the treatment of patients with metastatic disease, as compared to high dose estrogen (Ingle, Ahmann et al. 1981). These and other early studies were limited to one year of therapy, largely based on the prevailing hypothesis of the time that short term endocrine therapy would be superior to longer treatment times (Ludwig Breast Cancer Study 1984, Rose, Thorpe et al. 1985).

Overtime, however, it was recognized that longer term treatment could result in more

profound tumor response. Unfortunately, long term therapy, while increasing the efficacy initially, also resulted in the first clinical observations of resistance, a phenomenon which has continued to be observed in recent years (Fisher, Dignam et al.

2001).

Resistance to tamoxifen is a phenomenon of high clinical and academic interest.

Indeed, while the vast majority of patients with luminal breast treated with tamoxifen will respond initially, over 30% patients will develop a recurrence with tamoxifen treatment in the adjuvant setting (Early Breast Cancer Trialists' Collaborative 2005). It has been observed clinically and in mouse models upon the onset of resistance, tumor growth is not only no longer inhibited by tamoxifen, but instead may be stimulated to grow in the presence of continued treatment (Canney, Griffiths et al. 1987, Gottardis and

Jordan 1988). This unique selective estrogen receptor modulating, or SERM, activity whereby tamoxifen is estrogenic or anti-estrogenic depending on the cellular context, forms the foundation of proposed mechanisms of tamoxifen resistance.

Given that tamoxifen has growth promoting effects in the uterus, it has been hypothesized that breast tumors which become stimulated by tamoxifen undergo a phenotypic switch, whereby they acquire the ability to recognize tamoxifen in the same way it is recognized in the uterus (McDonnell 2005). At molecular level, it has been proposed that this can be explained by the ER-cofactor interactions. ER has been shown to bind >100 proteins. Tamoxifen binds ER directly and alters its confirmation thereby

altering, and effectively limiting, the cofactors through which it can interact. The cofactor interaction profile can also vary depending on the expression of these cofactors across different tissues. In line with this model, overexpression of SRC-1, a co-regulator of ER was shown to be sufficient to induce the tamoxifen agonist activity (Smith, Nawaz et al. 1997). HoxB13 and RTA-1 are two additional co-regulators shown to be upregulated in the setting of tamoxifen resistance (Norris, Fan et al. 2002, Goetz, Suman et al. 2006). Currently the knowledge of altered co-regulator expression in the setting of treatment resistance is limited and has of yet failed to determine alternative treatment strategies in the setting of resistance (Ma, Hilsenbeck et al. 2006). As such, it remains to be determined what additional factors may regulate the development of this phenotype.

With the advent of whole genome profiling approaches, various forkhead box

(Fox) family members have been defined as critical co-regulators for ER function.

Among these, FoxA1 has been shown to play a central role in the normal development of the breast and has been found to be dysregulated, leading to the development of cancer (Jozwik and Carroll 2012). It is therefore not surprising that differential binding patterns of FoxA1 have been described in various sensitive and resistant cancers models as well as in patient tumors representing both good and bad outcomes (Carroll, Liu et al.

2005, Eeckhoute, Keeton et al. 2007, Lupien, Eeckhoute et al. 2008, Hurtado, Holmes et al. 2011, Ross-Innes, Stark et al. 2012). Specifically, FoxA1 has been found to acquire

additional binding sites in tamoxifen resistant breast tumors, the mechanisms underlying this acquired activity, however, have not been fully elucidated.

Despite the abundance of studies investigating the function of FoxA1 and other forkhead family members, the understanding of the regulatory features which determine the temporal and spatial specificity of DNA binding of these elements in a given cellular state remains limited. In recent years, it has been appreciated that FoxA1, and other Fox family members, are key regulator of cell lineage specific cistromes

(Golson and Kaestner 2016). As such, further defining the activity of FoxA1 in tamoxifen resistance requires a more complete understanding of the cistrome where it acts.

2.1.2 Cistrome: master regulator of cell identity

Coined by Myles Brown, Yaron Turpaz, Shirley Liu, and others, the “cistrome” defines the composite of a “set of cis-acting targets of a trans-acting factor on a genome- wide scale” (Liu, Ortiz et al. 2011). In essence, the cistrome represents the convergence of a network of transcription factor regulatory sites as well as histone modifications which serve as an indicator of a cell’s developmental history as well as regulatory features which govern cellular identity (Heinz, Benner et al. 2010). As such, interrogation of the cistrome can provide profound insight into the complex regulatory processes at play in a given cellular state, in a relatively unbiased manner.

Chromatin is a highly dynamic structure, incorporating the cellular DNA and its regulatory elements in three-dimensional space. Within the confines of the nucleus,

DNA is wound 1.65 times around a core octamer of basic histone proteins which comprise the nucleosome (Kornberg 1974). Each histone protein (H3, H4, H2A, H2B) consists of 3 alpha helices separated by 2 loops referred to as the “histone fold,” which facilitates the assembly of the histone octamer (Luger, Mader et al. 1997). Linker histones lack the histone fold and are not part of the nucleosome and instead function to stabilize the compaction of chromatin into its higher order structure (Happel, Schulze et al. 2005).

The degree to which regions of chromatin are compacted can be correlated to its activation status. Chromatin which is tightly wound by nucleosomes is typically thought to be transcriptionally silent. In contrast, open chromatin regions indicate areas where transcriptional regulation is likely to occur, as they are accessible by regulatory proteins to bind and exert specific function. Open areas of chromatin correlate with cis- regulatory features located close to a transcriptional start site including promoters and

“proximal promoter elements” as well as features at greater distances, such as

“enhancers, tethering elements, silencers, and insulators” (Spitz and Furlong 2012).

Factors shown to influence chromatin architecture and nucleosome dynamics include chromatin and histone modifiers and transcription factors (the focus of these studies), as well as DNA methylation status, RNA polymerases and its cofactors, and the general transcription machinery (Tsompana and Buck 2014). Adenosine-triphosphate

(ATP) dependent, chromatin-remodeling complexes can be divided into four families:

SWI/SNF, ISWI, chromodomain-helicase DNA-binding protein (CHD) and INO80

complexes (Ho and Crabtree 2010). The differentiating factor among these families is the sequence of the ATPase subunit, which represent the product of approximately 30 distinct genes; however, all members share the ability to use energy from ATP hydrolysis to rearrange histones and to reposition or remove nucleosomes (Chen and

Dent 2014). Closely related to the action of chromatin modifiers are the post- translational modifications (PTMs) which exist on histones (Figure 9).

Figure 9: Overview of chromatin architecture and histone code

Schematic depicting DNA wound around histone octamer in compact heterochromatic, or relaxed euchromatic state. Features which influence chromatin state highlighted included DNA methylation, histone modifying enzymes, and chromatin remodeling complex. Histone code denoted as methylation and acetylation modifications on lysine (K) and arginine (R) residues. Reprinted with permission from (Keating 2014).

Epigenomic studies have revealed that the PTMs of histones surrounding specific genomic loci can serve as the “functional dialogue,” signifying regulatory status of these regions (Klose, Kallin et al. 2006). Enzymes shown to modify histones include histone acetyltransferases and histone deacetylases, lysine and arginine methyltransferases and demethylases, ubiquitlyation enzymes and deubiquitylases

(Chen and Dent 2014). The effect of specific histone PTMs lead on transcriptional regulation is influenced by: the histone modified, the target amino acid residue, and the location of the histone relative to functional genomic elements (coding vs. non-coding regions) (Chen and Dent 2014). Generally, acetylation of lysine residues is associated with remodeling chromatin leading to transcriptional activation; in contrast, methylation is associated with both activation and repression (Sadakierska-Chudy and Filip 2015).

The degree to which a residue is modified provides further specificity, with H3 lysine 4 trimethylation largely accumulating near promoter regions, leading to RNApol II recruitment, while H3 lysine 4 di (H3K4me2) and monomethylation (H3K4me1) is largely associated with cis-regulatory regions far distal to the transcriptional start site

(TSS), consistent with enhancer elements (Sadakierska-Chudy and Filip 2015).

Different classes of transcription factors bind to distinct types and states of cis regulatory elements. Transcription factors can be broadly classified as “lineage determining,” “collaborating,” or “signal dependent” (Heinz, Romanoski et al. 2015)

Classic lineage determining transcription factors (LDTFs) include SPI-1 protoncogene

(PU.1), and CAAT/enhancer-binding proteins (C/EBPs), present in both macrophages and B cells. LDTFs facilitate the expression of genes which are cell type specific. They are also characterized by the ability to facilitate the transition of chromatin from a closed to open state, interacting with other LDTFs facilitating the binding of other transcription factors and are often considered synonymous with “pioneer factors” (Zaret, Caravaca et al. 2010, Sahu, Laakso et al. 2011, Tan, Lin et al. 2011) In contrast to LDTFs, signal dependent transcription factors (SDTFs), like NFB and STAT proteins, typically activate a similar set of genes in different cell types, but can also activate some cell specific genes in a response to particular stimuli. Collaborating transcription factors

(CTFs) are those factors, by exclusion, which help facilitate the interaction between

LDTF and SDTFs, leading to functional transcriptional regulation (Heinz, Romanoski et al. 2015).

The binding of these different classes of transcription factors is proposed to occur in a hierarchical fashion, as represented by two distinct models, as indicated in (Figure

10)(Heinz, Romanoski et al. 2015).

Figure 10. Cell type-specific enhancer selection and activation

Selection and function of cell type specific enhancers a) Interactions between lineage determining transcription factors (LDTF) and collaborating transcription factors (CFs) position enhancer in primed and poised state, until fully activated by signal dependent transcription factors (SDF). B) LDTFs and SDTFs bind enhancers simultaneously. Reprinted with permission from Macmillan Publishers Ltd: Nature Review Molecular Cell Biology (Heinz, Romanoski et al. 2015) copyright (2015).

The first model involves a transition from a completely 1) inactive state, to a 2) primed or poised state, to a 3) final active state. Within this model, a LTDF binds to DNA and scans for its motifs, actively displacing nucleosomes; this scanning process is disrupted via the interaction with CTF. The association of an LTDF with a CTF, stabilizes nucleosome displacement, and allows for the recruitment of histone methyltransferases and acetyltransferases, leading to low levels of acetylation and methylation of surrounding histones. In the presence of a particular intracellular signal, SDTF binds

alongside LTDFs and CTFs facilitating further nucleosome displacement, further histone modifications, and ultimately complete activation. The second model involves the direct transition from an 1) inactive state to 2) an active enhancer. In this model chromatin is completely devoid of transcription factor binding until an appropriate signal is present.

Once a specific signal is available, the SDTF directs both CTF and LDTFs to a specific binding site, enabling concurrent binding of all factors involved. The major difference between these two models is the presence of an intermediate poised state, as indicated in

(Figure 11) (Heinz, Romanoski et al. 2015).

Figure 11. The landscape of poised and active enhancers.

The structural features characteristic of a) poised or b) active enhancers. Poised enhancers have a narrower nucleosome-free region, low H3K4Me2, and low or absent H3K27Ac on flanking histones. Active enhancers have wider nucleosome-free region, H3K4Me2, and high H3K27ac on flanking histones. LDTF, lineage determining transcription factor; CTF, collaborating transcription factor; SDTF, signal dependent transcription factor. NRC, nucleosome remodeling complex; HDAC, histone deacetylase; HDM, histone demethylase; MED, mediatory complex. Reprinted with permission from Macmillan Publishers Ltd: Nature Review Molecular Cell Biology (Heinz, Romanoski et al. 2015) copyright (2015).

Within this paradigm, FoxA1 is recognized as a LDTF in the setting of breast cancer, and in tamoxifen resistance. FoxA1 expression is a central defining feature of the

PAM50 subcategory of luminal cancer and has been shown to regulate the DNA binding and transcriptional activity of ER (Sorlie, Perou et al. 2001, Carroll, Liu et al. 2005). ER represents a SDTF. Additional SDTF shown to interact with FoxA1 include the androgen receptor, AP1 and SMAD transcription factors (Taube, Allton et al. 2010, Fu, Jeselsohn et al. 2016). The knowledge of CTF associated with FoxA1 is limited to Ap2 and GATA family members, which in and of themselves, may be considered LDTFs (Tan, Lin et al.

2011, Theodorou, Stark et al. 2013). Thus, there remains a significant knowledge gap defining mechanisms of FoxA1 mediated, cell specific transcription. As such a central focus of this work was to define such factors, specifically in the context of tamoxifen resistance.

2.2 Results

2.2.1 FoxA1 as a critical determinant of chromatin structure and nucleosome occupancy in TamR

DNAse Hypersensitivity analysis coupled with high throughput sequencing is among the most unbiased techniques to survey genome-wide regulatory elements in a given cellular state and as such is preferable over other widely used genome-wide techniques (e.g. Chromatin immunoprecipitation coupled with high throughout sequencing (ChIP-Seq) for the identification of novel factors. DNAse-Seq functions under the assumption that areas of the genome that are undergoing active transcriptional regulation, including promoters, enhancers, silencers, and insulators are unoccupied by nucleosomes. As such, these areas are sensitive for cleavage by the

DNase I enzyme, and can be isolated, enriched, and sequenced (Song and Crawford

2010).

Previously, a model of tamoxifen resistant breast cancer was derived by serially passaging an aggressive subline of ER positive MCF7 breast cancer cells (MCF7-WS8) as a xenograft in the presence of tamoxifen (Connor, Norris et al. 2001). Overtime, this subline 1) no longer requires estradiol for growth and 2) the growth of these tumors is enhanced in the presence of tamoxifen. This model reflects the phenotype originally observed by Gottardis et al in the 1980s (Gottardis and Jordan 1988). The parental,

MCF7-WS8 cells (tamoxifen sensitive), and the cell line derived from this tamoxifen resistant model, TamR, were used to perform DNAse-Seq. Of note, these data were

previously published by Jeff Jasper (Jasper 2014 ) and have been further adapted for this publication with permission. Analysis of the DNase hypersensitivity pattern revealed

>200,000 DNAse hypersensitivity sites (DHSs) genome wide in both models. The majority of DHSs (192,136) are shared between MCF7-WS8 and TamR cells. This is consistent with the concept that nucleosome positioning patterns are cell-lineage specific, reflecting the maintenance of luminal breast cancer phenotype in TamR. The remaining DHSs demonstrate marked differences between the TamR and MCF7-WS8 models: 9,232 sites show increased hypersensitivity and 4,556 sites show decreased sensitivity in the resistant model as compared to the parental. Among these different subsets, we chose to focus further analysis on the sites that demonstrated increased hypersensitivity in TamR, as these areas most likely indicate a gain-of-function present in the resistant model and therefore, likely represent a targetable axis (Figure 12).

Top Known Motifs at TAMR- DNAse Up sites MCF7-WS8 TAMR MCF7-WS8 TAMR (9,232 Sites) TAMR- DNAse Up P-value Best Match/Details PWM

1e-566 Jun-AP1(bZIP)/K562-cJun- ChIP-Seq/Homer

1e-491 GRHL2(CP2)/HBE-GRHL2- ChIP-Seq (GSE46194)/Homer

1e-296 AP2gamma(AP2)/MCF7- TFAP2c-ChIP-Seq/Homer

1e-185 ELF5(ETS)/T47D-ELF5- DNAse Same ChIP- Seq(GSE30507)/Homer

1e-132 Fox:Ebox(Forkhead,bHLH) /Panc1-Foxa2-ChIP- Seq(GSE47459)/Homer

1e-97 ERE(NR),IR3/MCF7-ERa- ChIP- Seq(Unpublished)/Homer

1e-75 FOXA1(Forkhead)/MCF7- FOXA1-ChIPSeq/Homer TAMR- DNAse Down

Figure 12: DNAse-Seq of MCF7-WS8 and TamR cell lines.

Heatmap of DNAse signal in a 4kb window of: (left) all DHSs identified in MCF7-WS8 and TamR, subdivided based on whether they are increased in TamR (DNAse UP), decreased in TamR (DNAse Down) or the same between cell lines (DNAase Same) and (right): indicates zoomed in view of only those DHSs increased in TamR relative to MCF-WS8. Modified with permission from (Jasper 2014 ).

Motif enrichment analysis at the DHSs with gained hypersensitivity in TamR indicated that transcription factors most likely to be active within these genomic loci

include transcription factors which bind bZip motifs, AP2 factors and FoxA family members (Jasper 2014 ). Comparison of these DNAse data with publically available

ChIP-Seq and ChIP-exo data sets for breast cancer cell lines (GSE25710, GSE48930,

GSE40129) and ENCODE factors (GSE32465, GSE31477) indicates the regions of DNAse signal enhanced in the resistant cells (DNASE_UP) relative to sensitive cells clusters most strongly with FoxA1 ChIP-exo and ChIP-Seq (Jasper 2014 ). Together, these data indicated that a Forkhead family member, specifically FoxA1, was the most likely candidate factor responsible for mediating the altered chromatin structure observed in

TamR.

To confirm these observations, we performed FoxA1 ChIP-Seq in both MCF7-

WS8 and TamR cell lines to identify bona fide robust FoxA1 binding events in our system. These data indicate nearly 50,000 high confidence FoxA1 binding sites in TamR, with 25,099 indicating novel or enhanced FoxA1 binding relative to MCF7-WS8, 10,939 unchanged, and 11,319 showing decreased binding. The question remained whether

FoxA1 could be implicated as a true driver of the nucleosome changes as observed in the

DNAse-Seq. To accomplish this, the distribution of DNAse-Seq peaks were ordered based on the relative genome-wide FoxA1 binding event profile (Figure 13).

43 Figure 1D Differential FoxA1 binding events strongly reflect differential DNAse hypersensitivity pattern, implicating its active role is establishing nucleosome landscape, and corresponds to global gene expression changes

FOXA1 ChIP-seq DNase-seq MCF7-WS8 TAMR MCF7-WS8 TAMR Gene Expr Change (log2)

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5

FOXA1 P Increased FOXA1 Increased

FOXr A1

E Sap me

FOXA1 W DecreasedN

. 3 Gene Groups Up in TAMR Down in TAMR

FOXA1 Same k

e FOXA1 Decreased R

0 FOFOX.UPXA1 FFOOX.SAMEXA1 FFOOX.DOXAWN1 Increased PSeaka Groupme Decreased

Figure 13: FoxA1 ChIPseq in MCF7-WS8 and TamR.

Heatmaps showing signal in a 4kb window of (left) FoxA1 binding events as determined by ChIP Seq in MCF7-WS8 relative to TamR, and (right) DNAseq as in Figure 12, now ordered based on FoxA1 binding profiles. Subgroup naming is determined based on FoxA1 binding profile in TamR relative to MCF7-WS8: sites where FoxA1 binding is significantly increased (FOXA1 increased), where there is no statistically significant difference (FOXA1 same) and sites where FoxA1 binding is decreased (FOXA1 decreased).

By ordering the DNAse-Seq data based on the pattern of FoxA1 binding events observed in TamR relative to MCF7-WS8, it becomes apparent that there exists a strong correlation between FoxA1 binding and nucleosome displacement. Sites where FoxA1 44

binding is significantly increased in TamR relative to MCF7-WS8 (FOXA1 increased),

DNase signal on average is increased. Sites where FoxA1 binding is not significantly different (FOXA1 same), DHS intensity is not significantly different. Sites were FoxA1 binding is significantly decreased in TamR relative to MCF-WS8 (FOXA1 decreased),

DNase signal on average is decreased. While only correlative, these data suggest FoxA1 is a true determinant and potential driver of the changes in nucleosome positioning originally observed in TamR.

The question remains whether FoxA1 alone is sufficient to mediate these changes. To determine the degree to which FoxA1 chromatin binding events were associated with changes in gene expression in our model, we compared the differential pattern of FoxA1 binding events with the RNASeq data from the TamR and WS8 cells.

These data indicate that genes with a TSS located within 10kb of FoxA1 binding sites that have increased binding in TamR (FOXA1 increased), on average are more likely to be increased in expression in the TamR cell line relative to the MCF7-WS8 cell line.

Genes within 10kb of FoxA1 binding events that do not significantly differ between

TamR and MCF7-WS8 (FOXA1 same) on average do not have expression changes, while genes within 10kb of FoxA1 binding sites which are decreased in TamR (FOXA1 decreased) are more likely to have reduced expression (Figure 14).

45 Figure 1D Differential FoxA1 binding events strongly reflect differential DNAse hypersensitivity pattern, implicating its active role is establishing nucleosome landscape, and corresponds to global gene expression changes

FOXA1 ChIP-seq DNase-seq MCF7-WS8 TAMR MCF7-WS8 TAMR Gene Expr Change (log2)

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5

FOXA1 P Increased FOXA1 Increased

FOXr A1

E Sap me

FOXA1 W DecreasedN

. 3 Gene Groups Up in TAMR Down in TAMR

FOXA1 Same k

e FOXA1 Decreased R

0 FOFOX.UPXA1 FFOOX.SAMEXA1 FFOOX.DOXAWN1 Increased PSeaka Groupme Decreased

Figure 14: Gene expression analysis based on FoxA1 binding profile

FoxA1 binding events were categorized based on whether the peak call determined by ChIP Seq was either significantly increased (FOXA1 increased), unchanged (FOXA1 same) or significantly decreased (FOXA1 decreased) in TamR relative to MCF7- WS8 cells. FoxA1 binding events within these categories were compared with gene expression in TamR and MCF7-WS8 cells as determined by RNASeq. Genes with a transcription start site within +/- 10kb of any FoxA1 binding event were then assigned to at most one set of peaks, with the left-most group having highest assignment priority. Analysis is based on (top) Log2 fold change in gene expression or (bottom) relative enrichment of significantly differentially expressed genes relative to a control set of genes with similar average expression level but minimal fold-change. Red bars denote genes significantly up-regulated in TamR vs MCF7-WS8. Blue bars denote genes significantly down-regulated in TamR vs MCF7-WS8

It was recently shown that overexpression of FoxA1 alone in tamoxifen sensitive

MCF7 cells induces the expression of a gene signature that is associated with oncogenic activation and endocrine resistance, in vitro (Fu, Jeselsohn et al. 2016). Building on these studies, we tested in vivo whether FoxA1 overexpression was sufficient to decrease time to resistance to tamoxifen treatment. To this end, we ectopically overexpressed FoxA1, or control near infrared fluorescent protein (iRFP), in the tamoxifen sensitive MCF7-WS8 cells, and performed a xenograft study, tracking tumor growth in the presence of vehicle or continuous tamoxifen treatment. The first arm of this study was carried out for 16 weeks, at which time a subset of the tamoxifen tumors (chosen at random) were passaged, in replicates, to a new set of animals, consistent with the experimental approach by which the original TamR tumors were derived. Tamoxifen treatment was continued and tumors were monitored for onset of resistance (Figure 15).

47 020816 Interim Analysis: Marco iRPF vs FoxA1

MCF7WS8 iRFP vs. FoxA1 Vehicle Treatment 0.5

)

( 0.4 MCF7WS8 - FoxA1 veh

l MCF7WS8 - iRFP veh

o 0.3

m 0.2

a 0.1

v a 0.0 0 5 10 15 20 Weeks post treatment

MCF7WS8 iRFP vs FoxA1 Tamoxifen Treatment

0.5

)

( 0.4

e MCF7WS8 - FoxA1 tamoxifen

l MCF7WS8 - iRFP tamoxifen

o 0.3

m 0.2

t Supplement al Figure 2. FoxA1 overexpression (may) not be sufficient

a 0.1

to reduce tima e to resistance, in vivo 0.0 0 5 10 15 20 Weeks post treatment

iRFP vs FOXA1

) 3

m 0.225

c MCF7WS8-FOXA1 (

0.200

e 0.175 MCF7WS8-iRFP

m u

l 0.150 o

v 0.125

o 0.100

m 0.075

e 0.050 g

a 0.025 *

r * p < 0.05

e 0.000 v

a 0 10 20 30 40 50 60 70 days of growth

Figure 15: Tumor Growth of MCF7 iRPF vs FoxA1 Overexpression in the PresenceP oofst -Tamoxifenpassage

MCF7-WS8NOTE :cells This ex pwereeriment isstably complica teinfectedd by the fact twithhat we h lentivirusad to passage the containing tumors about 4.5 miRPFonths in orto t hFoxA1e study. The most complete data set “pre-passsaged” ends at 16 weeks treatment (n> or = 5 for all groups at this point) construct. A) The cells were injected into the mammary fat pad of female OVX nu/nu mice and grown for an average of 16 weeks in the presence of vehicle (top) or tamoxifen (bottom). B) After 16 weeks, 5 tumors from the tamoxifen treated groups (iRFP and FoxA1) were randomly selected and diced, and a portion of tumor was transplanted into new OVX nu/nu mice (n = 4 recipient mice per tumor). Animals were maintained on tamoxifen therapy. Average tumor volume -/+ SEM is plotted and significance was determined by the two-way ANOVA followed by Bonferroni’s multiple comparison test. Final n = 20.

This study suggests that overexpression of FoxA1 alone was not sufficient to alter the response to tamoxifen treatment. In fact, surprisingly, it appeared that FoxA1 overexpression delayed the time to resistance.

Collectively, these data from our model suggest that luminal breast cancer cells may acquire nucleosome rearrangement as they transition from tamoxifen sensitive to tamoxifen resistant; this restructuring may be mediated in part by FoxA1 and is functionally associated with changes in gene expression. FoxA1 alone, however, may not be sufficient to mediate this change and instead likely requires additional collaborating factors to manifest the true resistance phenotype.

2.2.2 TamR acquires distinct subtypes of FoxA1 binding events

Nucleosome occupancy and transcription factor binding is a highly dynamic process. Furthermore, transcription factors can demonstrate diverse activating and repressing roles, depending on the specific context. To further understand the mechanisms by which FoxA1 has acquired novel activity in the setting of tamoxifen resistance, we chose to characterize the histone architecture surrounding novel cis- regulatory elements bound by FoxA1. As previously indicated, the activity of various regulatory elements can be inferred by the post- translational modifications on histones that surround these elements. Assessing the distribution of the DHS sites, we observed that the majority of DHSs observed occur at intronic and intergenic areas of the genome, suggesting that the nucleosome restructuring that occurs as tumors become resistant

takes place at enhancer elements (data were adapted with permission from (Jasper 2014

)). This is true for the majority of sites identified genome-wide in both models as well as Figure 1C - Distribution of DHS sites highlights greatest amount of for sites determinedc toha beng die fferentialis occurr betwing aeent e nTamRhanc eandr e lMCF7emen-tWS8s (Figure 16).

All DHS Sites (508K)

Differential DHS Sites (7.3K)

Figure 16: Genome wide distribution of DHS Sites

Pie charts representing the distribution of DHS sites in each genomic feature for (top) all DHS sites and (bottom) DHS sites differential between TamR and MCF7-WS8. Adapted with permission from (Jasper 2014 ).

Consistent with the finding that the majority of DHS occur at intronic and intergenic areas, FoxA1 is known to bind predominantly at enhancers. Enhancers are typically defined by both mono and di-methylated H3K4 marks (Serandour, Avner et al.

2011). H3K27ac, a mark commonly associated with the activity of CBP/p300, is indicative of an active transcriptional element and can differentiate between activate (present) and inactive (absent) enhancers (Yan and Boyd 2006). Defining specific subclasses of enhancers was accomplished by performing H3K4Me2 and H3K27Ac ChIP-Seq and 50

comparing these data with the FoxA1 ChIP-Seq. The comparison across these data sets reveals that the MCF7-WS8 and TamR have diverse enhancer pattern differences.

Among the genomic loci defined to have increased FoxA1 binding in TamR, approximately 53% of those sites demonstrate an activated enhancer signature (Cluster 1

+ Cluster 2). Cluster 1 indicates areas of the genome with statistically significant increases in both H3K4Me2 and H3K27Ac. In contrast, Cluster 2 is defined by the presence of H3K4Me2 which is not significantly different between MCF7-WS8 and

TamR and H3K27Ac is significantly increased in TamR relative to MCF7-WS8. Cluster 3 demonstrates increased binding of FoxA1, but no significant change in H3K4Me2 or

H3K27Ac (Figure 17).

51 Figure 2A. Overlay of Histone Modifications at enriched FoxA1 binding si tes highlights most active FoxA1 regulated enhancer elements

MCF7-WS8 ChIP- Seq TAMR ChIP- Seq

FoxA1 H3K27Ac H3K4Me2 FoxA1 H3K27Ac H3K4Me2

Cluster 1: Increase in both H3K27Ac + H3K4Me2

Cluster 2: Increase in H3K27Ac H3K4Me2 unchanged

Cluster 3: No significant change in either mark

Figure 17: FoxA1 binding sites with increased binding in TamR subdivided based on histone code.

ChIPSeq profile of FoxA1 (green) binding event or H3K27Ac (red), and H3K4Me2 (purple) on surrounding histones in MCF7-Ws8 and TamR. Comparison of all increased FoxA1 binding events in TamR across histone mark signature (H3K27Ac and H3K4Me2) revealed 3 patterns, those sites where FoxA1 binding is increased with both histone marks increase (Cluster 1), those where only H3K27Ac is increased (Cluster 2), and those that do not have significant change in either mark (Cluster 3). Heatmaps are centered over a 4kb window of called peaks.

To determine the degree to which the histone modification landscape is functional, we compared the different types of binding profiles to the mRNA expression of genes associated with these binding events. Using the same cluster classification

scheme, we compared FoxA1 binding sites which were increased in TamR to genes within 10kb of these regulatory elements. These data indicate those sites most associated with a histone signature suggesting relatively increased enhancer activity, on average are associated with genes which are increased in expression in TamR relative to MCF7-

WS8 (Cluster 1). This effect is diminished when only increases in H3K27Ac are observed

(Cluster 2). Those sites lacking any significant mark of change in activation status are most closely associated with genes that do not change in expression (Cluster 3). These data are further supported by the observation that the relative enrichment of significantly upregulated genes is highest in Cluster 1 relative to a control list of genes, as well as relative to the other clusters (Figure 18).

53 Figure 2B. Overlay of Histone Modifications at enriched FoxA1 binding sites highlights most active FoxA1 regulated enhancer elements

TAMR ChIP- Seq Gene Expr Change (log2)

FoxA1 H3K27Ac H3K4Me2 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0

Cluster 1: Increase in both

H3K27Ac + H3K4Me2 o

Cluster 1 h

Cluster 2 n

Cluster 2: u Increase in H3K27Ac p H3K4Me2 unchanged

Cluster 3 e

Gene Groups Up in TAMR 4 * Down in TAMR

n Cluster 3: e

No significant change in t

e 2 *

either mark h

0 CluBothster 1 CAcOnlyluster 2 CluNonester 3 Peak Group

Figure 18: Gene expression analysis based on FoxA1 associated histone code

FoxA1 events binding in TamR were subdivided based on whether they were associated with increase in H3K27Ac and H3K4Me2 histone marks (Cluster 1), increase in H3K27Ac only (Cluster 2) and no significant change in either mark (Cluster 3) in TamR relative to MCF7-WS8. FoxA1 binding events within these categories were compared with gene expression in TamR and MCF7-WS8 cells as determined by RNASeq. Genes with a transcription start site within +/- 10kb of any FoxA1 binding event were then assigned to at most one set of peaks, with the left-most group having highest assignment priority. Analysis is based on (top) Log2 fold change in gene expression or (bottom) relative enrichment of significantly differentially expressed genes relative to a control set of genes with similar average expression level but minimal fold-

change. Red bars denote genes significantly up-regulated in TamR vs MCF7-WS8. Blue bars denote genes significantly down-regulated in TamR vs MCF7-WS8. * denotes significant difference from 1, with p value cut-off of 0.01 using Fisher’s exact test.

Collectively, these data indicate a subset of FoxA1 binding events most indicative of gain-of-activation function in TamR as compared to MCF7-WS8.

2.2.3 GRHL2 as FoxA1 collaborating factor at activated enhancers

Seeking to determine which additional factors beyond FoxA1 may be involved in establishing gained enhancer competency in tamoxifen resistance we performed motif enrichment analysis specifically on activated, FoxA1 dependent enhancers, focusing specifically on both Cluster 1 and Cluster 2. This analysis indicates that the most enriched transcription factor motifs at these sites, secondary to FoxA1 itself, include

FoxEbox, GRHL2, AP1 and AP2 family members (Figure 19).

55 Figure 2B Enhancer elements bound by FoxA1 and most highly activated are enriched specifically for GRHL2

Top Known Motifs Cluster 1 + 2 (12820 of Sites)

P-value Best Match/Details PWM

1e-453 FoxA1(Forkhead)/MCF7- FOXA1-ChIPSeq (GSE26831)/Homer

1e-511 Fox:Ebox(Forkhead,bHLH)/P anc1-Foxa2-ChIP- Seq(GSE47459)/Homer

1e-390 GRHL2(CP2)/HBE-GRHL2- ChIP-Seq(GSE46194)/Homer

1e-247 AP-2gamma(AP2)/MCF7- TFAP2C- ChIPSeq(GSE33912)/Homer

1e-201 Jun-AP1(bZIP)/K562-cJun- ChIP-Seq/Homer

Figure 19: De novo motif analysis associated with Cluster 1 + 2 FoxA1 binding events

Top known motifs identified at regulatory elements which demonstrate increased FoxA1 binding in TamR and have increased H3K27Ac and H3K4me2 (Cluster 1) or increased H3K27Ac and no significant change in H3K4Me2 (Cluster 2) relative to MCF7-WS8.

To determine which factors may be specifically associated with the activity status of the

specific regulatory elements, we considered the distribution of the top 3 motifs (besides

FoxA1 family members) across the different Clusters as indicated previously: AP1, AP2,

and GRHL2. More specifically, we examined the location individual motifs of in a

window of 500bp around a FoxA1 binding event. We then compiled the average

intensity of that distribution within each cluster of binding events and then compared

the average distribution across Clusters. AP2 and bZip/AP1 motifs are equally

distributed across all clusters. Furthermore, the centrality of the distribution of the

motifs is weak, suggesting that they are unlikely, on average to be interacting with

FoxA1 directly. In contrast, the motif for GRHL2 is highly centralized around FoxA1

binding events, and the relative frequency of the motif strongly correlates with

H3K27Ac status as it relates to the different clusters (Cluster 1 > Cluster 2 > Cluster 3).

This correlation suggests that the presence of GRHL2 may play an active role in facilitating H3K27Ac, and thereby facilitating gene expression. This observation is further supported by examining the relative percentage of FoxA1 binding events association with GRHL2 motif across clusters: 68%, 57%, and 25% of FoxA1 binding events are associated with a GRHL2 motif in Cluster 1, Cluster 2 and Cluster 3, respectively (Figure 20).

Figure 20: Positional enrichment of motifs around FoxA1 subdivided by clusters

(A-C) Motif enrichment analysis using HOMER was performed on all sites which have significant increase in FoxA1 binding in TamR relative to MCF7-WS8 cells as determined by ChIP-Seq. All analysis is done on +/-500bp of sequence around FoxA1 peak call center. The top 3 distinct motifs (besides Fox) are presented and scanned against 3 different groups of sites: those which have significant increase in H3K27Ac and H3K4Me2 (Cluster 1, red), those which have significant increase only in H3K27Ac (Cluster 2, orange) and those in which the differential peak calls do not reach significance (Cluster 3, grey) in TamR as compared to MCF7-WS8 cells. (D) Bar graph indicated percentage of sites within each cluster of FoxA1 binding events which are associated with a GRHL2 motif (white, GRHL2 motif present) (dark gray, GRHL2 motif absent).

2.2.4 Additional FoxA1 co-factors present in TamR model

We previously performed Mass Spectrometry analysis of endogenous FoxA1 in

TamR cells. This was accomplished by precipitating FoxA1 from cell lysates, as previously described (Jasper 2014 ). From this experiment, a total of 102 unique high confidence FoxA1 interacting proteins, in TamR cells, were identified (Table 2).

Table 2: Complete list of significant FoxA1 interactors identified by Mass Spec

ACIN1 DHX30 H1FX KRT16 PLEC SLC1A5

AKAP13 DIAPH3 HDAC1 MDH2 PLS3 SLC7A5

ARCN1 DNAJA2 HIST1H2BB MLH1 PTBP3 SPECC1

ATP1A1 DNM2 HMGB2 MRPS31 PTPLAD1 SPTBN2

AT2A2 ECH1 HNRNPAB MSH6 RBM28 SRSF7

ATP5A1 EEF1G HNRNPF MYBBP1A RFC2 SVIL

C14orf166 EIF43 HNRNPH3 MYO5B RFC5 TARDBP

CAPZA2 EPPK1 HNRNPR MYO5C RGS20 TFAP2C

COPG1 ETF1 HRNR NELFB RPL3 TUBB4A

CPSF2 EWSR1 HSD17B4 NOP56 RPL5 TXLNA

CTNNB1 FANI HSPA5 NOP58 RRP12 UBTF

DAP3 FBLL1 HSPA9 NUMA1 RSL1D1 UNC45A

DDX1 FEN1 ILF3 PDCD11 RUVBL2 VDAC1

DDX18 FLOT2 IPO4 PFKP SAFB YES1

DECR1 GNB1 KARS PHB SF3B3

DEK GRHL2 KHDRBS1 PICALM SF3B4

DHX15 GTF3C3 KHSRP PKP3 SIN3A

From this list, potentially relevant interactors include H1x, Dek, Sin3a, and

GRHL2. H1x is a variant linker histone. Linker histones have been shown to play central role in stabilizing the compaction of histones; as such, the ability of FoxA1 to specifically bind H1x may be indicative of its role in destabilizing compact chromatin structure

(Happel, Schulze et al. 2005). Dek is an oncogene found to be overexpressed or amplified in various cancer types (Kavanaugh, Wise-Draper et al. 2011). It has been reported to be involved in DNA replication and mRNA splicing. Further it has been shown to bind directly to condensed chromatin leading to nucleosome disassembly, and can directly regulate lysine acetylation leading to activation or repression of gene expression, depending on the cell context (Campillos, Garcia et al. 2003, Hu, Illges et al.

2005). Sin3a, another potentially relevant FoxA1 interactor, is a scaffolding protein, and can serve as a docking site for various enzymes including HDAC1/2. As such, it has been shown to function as a transcriptional repressor, specifically in the context of ER positive breast cancer (Ellison-Zelski and Alarid 2010). Finally, the observation that

GRHL2 co-immunoprecipitates with FoxA1 supports the previous analysis, whereby we identified that FoxA1 binding events were highly associated with GRHL2 motifs. These data indicate that not only do FoxA1 and GRHL2 likely bind DNA in close proximity to one another, but they also exist within a multiprotein complex.

To identify true GRHL2 binding events, we performed GRHL2 ChIP-Seq in

MCF7-WS8 and TamR. GRHL2 ChIP-Seq identified 20,283 and 35,406 high confidence

sites in MCF7-WS8 and TamR respectively. To determine the degree to which GRHL2 influences FoxA1 binding patterns and activity status of cis-regulatory elements, we ordered all FoxA1 binding profiles based on their relationship to GRHL2 binding events and then compared those correlations to the histone mark changes. Among the FoxA1 binding events that are significantly increased in TamR, those which show the greatest relative increase in GRHL2 binding are also associated with the greatest relative increase in H3K4Me2 and H3K27Ac as compared to MCF7-WS8. FoxA1 sites that are increased in

TamR, but show no statistical difference in GRHL2 binding in TamR relative to MCF-

WS8, on average demonstrate a diminished relative increase in H3K4Me2 or H3K27Ac; however, peaks within these regions are still highly modified, indicating they are likely areas active transcriptional regulation. Finally, among the FoxA1 sites that are increased in TamR but lack significant GRHL2 binding, on average have lower levels of H3K4Me2 and H3K27Ac relative to the other groups (Figure 21).

61 Figure 2D – GRHL2 binding events correlate with changes in gene expression

FOXA1 GRHL2 H3K4me2 H3K27Ac Gene Expr Change (log2)

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 MCF7-WS8 TAMR MCF7-WS8 TAMR MCF7-WS8 TAMR MCF7-WS8 TAMR

GRHL2 L

increasedP

GRHL22

M samp e

. GRHL2G

decreased2

. 3 Gene Groups Up in TAMR Down in TAMR

0 GGRHL2.UPRHL2 GGRHL2.SMRHL2 GNO.GRHL2RHL2 Peak Group increased Same Decreased

Figure 21: Subset of FoxA1 binding events which are increased in TamR are also bound by GRHL2 and associated with histone activation marks

ChIP Seq profile of FoxA1 (green) or GRHL2 (orange) binding events, or H3K4Me2 (purple) or H2K7Ac (red) on surrounding histones in MCF7-WS8 and TamR. Only FoxA1 binding events which are increased in TamR are shown. These events were then ordered by GRHL2 binding profile and compared to the distribution of H3K4Me2 and H3K27Ac signals. Heatmaps are centered over a 4kb window of called peaks.

In line with these data, the gene expression analysis comparing these binding events with the RNASeq expression data of TamR and MCF7-WS8 cells indicates that among genomic sites where FoxA1 binding is increased in TamR relative to MCF7-WS8 and are associated with the greatest increase in GRHL2 binding in TamR relative to MCF7-WS8

are associated, on average, with greatest increase gene expression; in contrast, the FoxA1

binding sites that are increased in TamR relative to MCF7-WS8 but on average are Figure 2D – GRHL2 binding events coassociatedrrel awithte GRHL2 wit bindingh ch thata nis notge statisticallys in different with MCF7-WS8 or gene expression associated with sites with no enrichment for GRHL2 binding events in TamR or MCF7- WS8, are on average associated with minimal gene expression change (Figure 22).

FOXA1 GRHL2 H3K4me2 H3K27Ac Gene Expr Change (log2) TAMR MCF7-WS8 TAMR MCF7-WS8 TAMR MCF7-WS8 TAMR −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5

GRHL2L

increased P

GRHG L2

u sameM

. GRHL2G

nullL

. 3 Gene Groups Up in TAMR Down in TAMR

0 GGRHL2.UPRHL2 GGRHL2.SMRHL2 GNO.GRHL2RHL2 increased PSeaka Groupme null

Figure 22: Gene expression as compared to FoxA1 binding events subdivided by association with GRHL2 – Part 1

FoxA1 binding events were categorized based on whether the peak call determined by ChIP Seq was associated with a GRHL2 binding event that was significantly increased (GRHL2 increased), unchanged (GRHL2 same) or not detected (GRHL2 null) in TamR relative to MCF7- WS8. FoxA1 binding events within these categories were compared with gene expression in TamR and MCF7-WS8 cells as determined by RNASeq. Genes with a transcription start site within +/- 10kb of any FoxA1 binding event were then assigned to at most one set of peaks, with the left-most group having highest assignment priority. Analysis is based on (top) Log2 fold change in gene expression or (bottom) relative enrichment of significantly differentially expressed genes relative to a control set of genes with similar average expression level but minimal fold- change. Red bars denote genes significantly up-regulated in TamR vs MCF7-WS8. Blue bars denote genes significantly down-regulated in TamR vs MCF7-WS8.

To further confirm the functional role of GRHL2 at these different subsets of FoxA1 binding sites in TamR, we performed siRNA mediated knockdown of GRHL2 with 2 unique siRNA sequences followed by global transcriptome assessment via RNASeq in

TamR cells. These data were then compared across subsets of all FoxA1 binding events which are increased in TamR cells relative MCF7-WS8 cells as before. The intersection of these data indicate that FoxA1 binding sites associated with the most “activated”

GRHL2 binding events- where FoxA1 is increased and GRHL2 is increased – are on average associated with genes whose expression is decreased in the presence of GRHL2 siRNA. Thus, these data suggest that genes which are regulated by enhanced GRHL2 binding events are those which are also most sensitive to its absence (Figure 23).

Together, these data further support the observation that GRHL2 likely facilitates transcriptional activation at these sites.

Figure 23: Gene expression as compared to FoxA1 binding events subdivided by association with GRHL2 – Part 2

then assigned to at most one set of peaks, with the left-most group having highest assignment priority. Analysis is based on (top) Log2 fold change in gene expression or (bottom) relative enrichment of significantly differentially expressed genes relative to a control set of genes with similar average expression level but minimal fold- change. Red bars denote genes significantly up-regulated in TamR vs MCF7-WS8. Blue bars denote genes significantly down-regulated in TamR vs MCF7-WS8. * denotes significant difference from 1, with p value cut-off of 0.01 using Fisher’s exact test.

2.2.4 GRHL2 expression is increased in the TamR model

It has recently been shown that high expression of FoxA1 mRNA can be associated with poor relapse-free survival (RFS) in patients with ER+ tumors receiving tamoxifen therapy, but not in patients without endocrine therapy (Fu, Jeselsohn et al.

2016); this observation, however, was limited to the highest quartile of FoxA1 expression levels. In our cell line models of tamoxifen resistance, we observe no differences in

FoxA1 mRNA or protein level in the conditions in which our genomic experiments have been carried out, in charcoal stripped serum (to mimic low estrogen levels in postmenopausal state). Similarly, GRHL2 mRNA levels show only a minor increase in expression in the tamoxifen resistant relative to the tamoxifen sensitive models based on

RNA-Seq. This relative change is true for the two coding transcripts that have been described, indicated as variant 1 (ENSEMBL transcript ID ENST00000251808.7, 5239bp in length, encoding a 624aa protein) and variant 2 (ENSEMBL transcript ID

ENST00000395927.1, 2140bp in length, encoding a 609aa protein). Variant 3 (ENSEMBL transcript ENST00000521085.1) is not significantly differentially expressed, but is only

236bp in length and has unknown function. Across experimental replicates, the relative 66

expression of these variants fluctuates on the RNA level. On average there appears to be a trend towards slight increases in mRNA in TamR, however this does not reach statistical significance (Figure 24). As such, we predicted that it is unlikely that the mRNA expression would serve as a strong prognostic indicator when considering patient outcomes.

Figure 24: GRHL2 mRNA expression across cell lines

(Top) RNASeq data indicating expression of different GRHL2 variants across the two cell line models. Cells were treated with vehicle (V) or 4OHT (T) for 24hours following 48hrs of growth in phenol red free, charcoal stripped serum. (Bottom) Confirmation of RNASeq data using qRT-PCR indicating mRNA expression of FoxA1 and GRHL2 in MCF7WS8 and TamR cell lines. Data was normalized to 36B4.

Literature reports investigating the ability of GRHL2 expression to predict outcome are limited and demonstrate mixed results. Indeed, in one study assessment of a dataset of

2898 patients, indicated that elevated GRHL2 mRNA expression, divided by median expression, did predict for decreased interval of relapse free survival. Furthermore, a group of 1354 patients scored based on GRHL2 mRNA expression were assessed for distance metastasis free survival (DMFS), split by above and below the median, which showed high expression of GRHL2 was correlated with decreased DMSF interval

(Xiang, Deng et al. 2012). However, using our own dataset of 4700 annotated breast tumors, we were unable to reproduce the inverse relationship between GRHL2 mRNA expression and time to relapse. Similarly, when using our data set to examine all- comers, GRHL2 mRNA expression did not predict any differences in DMFS, which was also true of luminal subtypes. The only subtype that did show any relationship with

GRHL2 expression was the Her2 subtype, where high levels of GRHL2 did predict decreased DMFS time.

In contrast to the mRNA levels observed in our system, there is a significant increase in GRHL2 protein level in TamR relative to MCF7-WS8 cells. This increase appears to be independent of hormone treatment (Figure 25).

Figure 25: GRHL2 protein expression in MCF7-WS8 and TamR cell lines

MCF7-WS8 and TamR cells were grown for 48hrs in charcoal stripped serum and treated with 10nM E2, 100nM 4OHT, or 100nM ICI as indicated for 24 hrs. Whole cell extracts were analyzed via Western Blot using the indicated antibodies.

This observation led to the hypothesis that GRHL2 protein expression may serve as a stronger, more reproducible predictor of treatment response or outcome.

2.2.5 GRHL2 as prognostic factor in luminal breast tumor samples

Previously, it has been indicated that GRHL2 expression correlates positively with estrogen and progesterone receptor expression (Werner, Frey et al. 2013). GRHL2 expression was also shown to inversely correlate with grade and stage, when considering all-comers. Of significance a key missing feature in this and other previous analyses was an assessment of the potential relationship between GRHL2 protein expression, long-term outcome and treatment response. To investigate the degree to which GRHL2 protein expression may function as indicator of treatment response, we extended these observations to a panel of breast cancers within a Tissue Microarray with

matched clinical outcomes data and correlated with time to recurrence. Interestingly,

GRHL2 protein expression did not significantly correlate with T or N stage in our sample set. When considering all-comers, there was a strong trend towards a shorter time to recurrence observed with increasing GRHL2 expression; however, this was not significant (p = 0.08). Importantly, when analysis was limited only to those patients with

ER positive disease, those patients most likely to have received tamoxifen therapy in the adjuvant setting, there was a strong and significant trend, implicating elevated protein levels of GRHL2 and decreased time to recurrence (p < 0.03) (Figure 26).

Figure 26: GRHL2 expression in patient tumors correlated with time to recurrence.

(top) Representative patient breast tumors stained for GRHL2, (bottom) Kaplan Meier- estimator of time to recurrence (months) of tumors derived from patients with ER positive disease, stratified based on GRHL2 protein expression (1 = low, 3 = high). Statistical significance to determine differences between groups based on scoring was determined using Log-Rank, Wilcoxon, Tarone, Peto, Modified Peto and Fleming tests, all with p<0.03. n = 47

2.2.6 GRHL2 maintains lineage identity and regulates involved in cell cycle, proliferation, invasion and metastasis

We have indicated that GRHL2 may facilitate transcriptional activation in collaboration with FoxA1. We have shown that increased abundance of GRHL2 as observed in TamR cell lines may explain, at least in part, why this novel biology occurs.

The question remains as to what further defines GRHL2 activity that may explain its enrichment specifically at activated enhancers and the influence it may have on processes downstream in TamR.

To this end, we performed a global assessment of pathways and processes that are changed in the absence of GRHL2 expression. RNASeq analysis was performed on

TamR samples treated with control siRNA compared to two independent siRNA sequences to GRHL2 (as indicated previously in section 2.2.3). Subsets of genes which were changed in expression following siRNA to GRHL2, either increased or decreased, were then assessed using Enrichr, an enrichment analysis tool containing over 180,000 annotated gene sets from 102 gene set libraries (Kuleshov, Jones et al. 2016). These data indicate a number of striking patterns likely relevant to GRHL2 biology (Figure 27).

72 Figure 4A –siRNA to GRHL2 GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

Figure 27: Differential enrichment analysis of gene expression in TamR siGRHL2 vs siCtrl

Differential gene calls from RNASeq data of TamR siGRHL2 (A, C, or both) in comparison to siCtrl were analyzed using Enrichr tool to determine gene sets enriched in each subset of genes: genes which were significantly20 increased (sig_genes_up) or 1.5

i decreased (sig_genes_down) in siGRHL2 inn comparison to siCtrl.** Data sets included

s o 15

s s MCF7-WS8 e 1.0

s CHEA 2016, ENCODE and ChEA Consensus TFs from ChIP-X, and MCF7 Perturbations r

e sictrl

p r TamR

x p 10 ** * ** * ** * ** E siGRHL2 A

x from GEO up, comparisons are graphed separately.

v d siGRHL2 C

i l 0.5

5 a

* e

R First, the genes which decrease following siRNA0 knockdown of GRHL2 (sig_genes_dn) 0.0 D 0 4 2 2 K D 0 4 K C P 2 2 K M U A K C P A M M M U A C A M M are enriched for genes that are associated with forkhead binding events; genes which C increase following siRNA knockdown of GRHL2 (sig_genes_up), are not enriched within this same gene set. This supports our finding that FoxA1/GRHL2 collaborate at

sites associated with active gene expression. Second, both sets of genes – those which are increased following knockdown of GRHL2 and those which are decreased following knockdown of GRHL2 - are enriched for genes associated with ER binding events, suggesting that GRHL2 can play a role in both activating and repressing ER activity.

This observation is consistent with the recent data from Jozwick et al indicating that in treatment naïve MCF7 cells, GRHL2 can interact with FoxA1 at a subset of ER regulated sites (Jozwik, Chernukhin et al. 2016). Third, there is a striking inverse correlation with lysine demethylase 2B (KDM2B) binding events, whereby genes the expression of which are increased in the absence of GRHL2 demonstrate a significant enrichment for genes which are associated with KDM2B binding events as determined by ChIPSeq from 5 different leukemia cell lines. KDM2B has been shown to demethylate lysine 4 and lysine

36 of histone H3. More specifically, it preferentially demethylates trimethylated H3K4 and dimethylated H3K36. It has also been shown to have some activity on dimethyl

H3K4, but weak or no activity on mono-or trimethylated H3K36. H3K4Me2 and

H3K36me1 are associated with active transcription. As such, it follows that genes associated with a KDM2B binding event would be actively expressed. The activity of

KDM2B, as with other chromatin modifiers, has been shown to be required to maintain cell lineage identity (Andricovich, Kai et al. 2016). Indeed, in the context of leukemia, the expression of KDM2B is required to maintain lymphoid leukemia differentiation state and “restrain” RAS-driven myeloid transformation. Furthermore, high KDM2B

expression is associated with a progenitor-like state (Andricovich, Kai et al. 2016). Taken together, these data suggest that the presence of GRHL2 suppresses the expression of genes which are associated with leukemic, progenitor-like state. This is in line with

GRHL2 playing a key role in maintaining the luminal cell identity in breast cancer and suggests that this may be in part due to its ability to direct KDM2B binding and activity at chromatin. Along similar lines, genes that are repressed by GRHL2 are strongly enriched with genes which are altered in expression in the absence of Jumonji domain- containing 6 (JMJD6), another chromatin modifier. Increased expression of JMJD6 has been shown in a number of cancer types, including breast, lung, pancreatic, colon and esophageal carcinoma (Zhang, Ni et al. 2013, Wang, He et al. 2014, Poulard, Rambaud et al. 2015). JMJD6 is an arginine demethylase and a lysine hydroxylase, with specificity for R2 and R3 on H3 and H4, and multiple lysl residues on H3, H4, H2A, and H2B

(Chang, Chen et al. 2007, Webby, Wolf et al. 2009, Unoki, Masuda et al. 2013). JMJD6 has also been indicated to demethylate ER; however, the biological consequence of this event is unknown (Poulard, Rambaud et al. 2014). As such the observation that GRHL2 represses genes which are also altered by JMJD6 could mean either that GRHL2 has the ability to alter JMJD6 methylation of histones, thereby regulating its ability to alter the chromatin state and gene expression, or that the function of GRHL2 and JMJD6 converge on the ability of JMJD6 to alter ER methylation status. These explanations are conjecture at best, however, as the functional consequence of JMJD6 activity has, as of

yet, been poorly described. Finally, the CHEA data set also reveals several transcription factors, the binding of which has been shown to correlate with genes which are also activated (sig_genes_dn) but not correlated with genes those that are repressed

(sig_genes_up) by GRHL2 in TamR. These include transcription factor 3 (TCF3) and Sal- like protein 4 (SALL4) the expression of which are associated with early stage breast cancer, sex determining region Y-box 2 (SOX2) and nuclear factor, erythroid 2 like 2

(NFE2L2), both of which have been strongly linked to tamoxifen resistance, and the androgen receptor (AR), the activity of which is closely associated with FoxA1 in the setting of prostate cancer (Kim, Yang et al. 2008, Kobayashi, Kuribayshi et al. 2011,

Slyper, Shahar et al. 2012, Arif, Hussain et al. 2015, Jeter, Liu et al. 2016). As such, it is possible that the GRHL2 collaborates with any number of these factors to lead to increased gene expression in TamR.

Beyond the ability to regulate transcription and chromatin dynamics, the question remains regarding the phenotypic processes which are regulated by GRHL2. siRNA mediated knockdown of GRHL2 in TamR cells resulted in decreased expression of a number of genes which are increased in expression in TamR and involved in pathways shown to be enriched in the tamoxifen resistant state. Among these include

MUC20, MAPK4, and CAM2D. MUC20 has been shown to regulate focal adhesion, invasion and metastasis, and integrin activation (Chen, Shyu et al. 2016). While not much is known about MAPK4, its closest related atypical MAPK, MAPK3, has been

implicated in regulating cell motility and invasiveness (Long, Foulds et al. 2012). Finally,

CAMK2D has been shown to play an important role in migration (Pfleiderer, Lu et al.

2004) as well as proliferation, cell cycle, invasion and metastasis (Wang, Zhao et al.

2015). Taken together, our data implicate an important role of GRHL2 in collaborating with FoxA1 to mediate the expression of a gene signature defined by regulation of focal adhesion, migration, and likely ECM-matrix interaction. Further characterization of the consequence of loss of GRHL2 expression in TamR cells is of high priority, and the subject of current ongoing investigation (Figure 28).

77 Figure 4A –siRNA to GRHL2 GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

20 1.5

o i

n ** s

o 15

i s s MCF7-WS8

e 1.0

s r

e sictrl p

r TamR x p 10 ** * ** * ** * **

E siGRHL2 A

d siGRHL2 C i

l 0.5

t o

5 a

l F

* e R

0 0.0 D 0 4 2 2 K D 0 4 K C P 2 2 K M U A K C P A M M M U A C A M M C

Figure 4A –siRNA to GRHL2 GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

GRHL2 MCF7-WS8 Input GRHL2 TAMR Input RefSeq Gene

Figure 28: Candidate GRHL2 target genes

Validation of select genes predicted to be increased in expression in TamR relative to 20 1.5

MCF7n -WS8 (top, left) and dependent on GRHL2 for expression, as indicated by siRNA

i n **

s o 15

s s MCF7-WSGRHL28 vs siCtrl (top, right). RNA expression was assessed by qPCR, normalized to e 1.0

e sictrl

p r TamR

x p 10 ** 36B4 (Bottom)* IGV** screenshots* ** * of** GRHL2 binding event around indicated gene E siGRHL2 A

v d siGRHL2 C

i l 0.5

5 a

* e

0 0.0 D 0 4 78 2 2 K D 0 4 K C P 2 2 K M U A K C P A M M M U A C A M M C

(CAMK2D, MUC20, MAPK4). Significance determined by two-tailed, unpaired t test. * indicates p < 0.05, ** indicates p < 0.01.

2.2.7 EGFR, potential activator of GRHL2, is constitutively active in TamR

Previously mentioned lines of investigation have indicated two potential mechanisms of enhanced GRHL2 activity observed in TamR: increased protein abundance as well as several potential candidate interacting partners, including chromatin modifiers as well as various transcription factors which have previously associated with breast cancer development and tamoxifen resistance. A remaining potential mechanism by which GRHL2 may be activated in TamR is via a post- translational modification.

It has been previously shown that Grainy head, the Drosphila homolog of

GRHL2, is activated via phosphorylation by ERK to regulate a wound healing transcriptional program (Kim and McGinnis 2011). Consequently, we hypothesized that

GRHL2 may be activated downstream of a kinase specifically active in TamR. To investigate potential pathways upstream of GRHL2 activation in TamR, we performed a

Kinobead precipitation experiment (KiP) in both MCF7-WS8 and TamR cell lines and tumors in collaboration with Doug Chan from Matthew Ellis’s lab at Baylor College of

Medicine. KiP involves the use of sepharose beads coupled to kinase inhibitors which function as ATP-mimetics that bind to the kinase active sites, similar to that which has been previously described (Duncan, Whittle et al. 2012). We performed two independent 79

replicates of complete kinome analysis. Across replicates the kinases identified to be at least 3 times more represented in TamR relative to WS8 are defined as increased in activity in TamR. We then performed the same analysis in TamR and WS8 xenograft tumors (Table 3).

Table 3. Complete list of Kinases identified to be increased in TamR relative to MCF7-WS8 cell lines (left) and tumors (right)

Cell Lines Tumors

CDK13 PTK2B LCK PKN1

PRKAA2 CAMK1D FGFR3 EGFR

PRKD3 DYRK1B FGFR4 LIMK2

TNIK SYK DAPK2 MAPK6

MST1R LIMK2 RPS6KB1 PTK2B

MARK4 FER PRKCD IKBKE

EGFR IKBKE

PRKCD CAMK2D

EPHB3 MINK1

The kinases that are represented within both analyses, and thus demonstrate enhanced activity which is conserved from cell line to tumor, include LIMK2, IKBKE, PRKCD, and

EGFR. LIMK2 and IKBKE are implicated in actin cytoskeleton rearrangement (Rice,

Hansen et al. 2012) as well as viral response(Masatani, Ozawa et al. 2016), cytokine 80

signaling (Barbie, Alexe et al. 2014), and RAS dependent transformation (Rajurkar, Dang et al. 2017), respectively. PRKCD has been reported to play many diverse roles in cancer, with its function ranging from increasing endocrine treatment efficiency, tumor suppression, or tumor promotion, depending on the context (reviewed (Urtreger,

Kazanietz et al. 2012). The only candidate within this list to have been previously implicated in tamoxifen resistance is EGFR (Massarweh, Osborne et al. 2008, Chong,

Subramanian et al. 2011). EGFR is involved upstream of a diverse array of cellular processes leading to enhanced tumor growth and decreased drug response, including cell proliferation, angiogenesis, inhibition of apoptosis, as well as migration, adhesion, and invasion (reviewed (Yewale, Baradia et al. 2013). Interestingly, EGFR has also been described to interact directly with integrins at the cell surface, leading to activation of various pathways including FAK and AKT signaling (Bill, Knudsen et al. 2004, Tai, Chu et al. 2015).

This kinome analysis was coupled with assessment of potential kinase recognition motifs within GRHL2. Among the potential kinases shown to be activated in

TamR, GRHL2 contains 2 consensus motifs (X[E/D]pYX) for EGFR at S53 and Y221. In line with these data, GRHL2 has been identified as a phosphorylated substrate at S53 via

LC/MS-MS, albeit on unrelated tissue (Hornbeck 2015). Taken together, these data suggest that the activation of GRHL2 observed in TamR may be in part a consequence of phosphorylation event downstream of constitutively active EGFR.

2.3 Discussion

2.3.1 GRHL2 as lineage determining transcription factors in luminal breast cancer

Forkhead family members, including FoxA1, have previously been described to be critical regulators of cell fate and identity. This function was ascribed to their central role in determining the transcriptional landscape in which these factors are expressed.

Contemporary with the initial observation of this study, it had also been indicated that

FoxA1 is a key determinant of tamoxifen resistance. Specifically, it had been shown that

FoxA1 binding profiles were altered in cell models of tamoxifen resistance relative to tamoxifen sensitive models. A key question which remained unanswered from these studies is how FoxA1 attains this altered activity in the setting of resistance.

FoxA1 has also been shown to be associated with both activation and repression of gene expression. Given the abundance of FoxA1 binding sites and the known diverse regulatory roles that FoxA1 has been shown to play, we hypothesized that no one mechanism of activation would likely be able to explain all the changes in FoxA1 transcriptional activity throughout the genome. Consequently, the objective was to focus on those binding events which were increased in TamR, as a gain of function. This decision was made for two reasons 1) experimentally, much more is known about the mechanisms by which gene expression becomes activated as opposed to repressed, and

2) from a potential therapeutic perspective, it is simpler pharmacologically to inactivate a target that has become active, rather than reverse a repressed state.

The central objective of this study then became to determine the regulatory elements which define how and why FoxA1 becomes activated at a specific genomic location. At the foundation of this matter is the understanding of the kinetics of activation of different types of enhancer elements, that exist in different states.

Transitions in activation states of the cistrome are highly dynamic. Furthermore there exist abundant states that a potential cis-regulatory element can exist. Our system is limited by the fact that our two models represent polarized ends of a divergent phenotype, captured at one moment in time. As such, it is difficult to truly appreciate the stepwise manner in which an enhancer becomes activated as it transitions from sensitive to resistant. It has been shown, however, that PTM of histones can correlate with activity at a specific enhancer element.

Using the “histone code” to subset our analysis we identified the status of cis- regulatory elements in the parental MCF7-WS8 and TamR models, which is suggestive of at least two different types of transitions to an activated state. The first is the transition from a completely latent unmarked chromatin (no significant FoxA1,

H3K4Me2, or H3K27Ac peaks) in MCF7-WS8 to a fully activated state (significant

FoxA1, H3K4Me2, and H3K27Ac peaks) in TamR. This pattern suggests a mechanism of enhancer activation consistent with classic view of FoxA1 function (Serandour, Avner et al. 2011), whereby FoxA1 encounters compact, silent chromatin, binds nucleosomes and scans for its binding motif; once the motif is identified, binding is stabilized and FoxA1

then is able to recruit and serve as a docking site for chromatin modifiers – histone acetyltransferases and methyltransferases, which modify the surrounding histones. The second type of difference, or transition, observed in MCF7-WS8 to TamR involves the transition of a primed or poised state in MCF7-WS8 (low nucleosome occupancy, absent or low levels of H3K27Ac and low levels of FoxA1 binding), which transition to fully active in TamR (high FoxA1 binding, high H3K4me2 and high H3K27Ac signal). Based on the model introduced by Sven Heinz and Chris Glass, this transition is more indicative of a “signal dependent” change, whereby the enhancer is in a “primed state” waiting for the correct extracellular signal to become fully active (Heinz, Romanoski et al. 2015). Once that signal is provided, the primed enhancer exists in a state where it can more readily recruit additional transcription factors as well histone and chromatin modifier. This results in the increased transcription factor binding and H3K27Ac which is observed. There exists a third set the of gained FoxA1 binding sites, which did not show evidence of significant increases in either of the histone marks. These binding events could be the result of nonspecific sampling of the genome or active repression, the cause of which is worthy of further investigation in the future.

Given the context of these diverse enhancer pattern changes, we hypothesized that there exists another factor that may provide specificity or facilitate the activation of transcription at a subset FoxA1 at enhancers. To this end we examined the sites where we identified enhanced FoxA1 binding and an active “histone signature” (increased

H3K27Ac + H3KMe2 in TamR cells relative to MCF7-WS8 cells) for specific factors. Of the potential factors enriched around FoxA1 binding events, motif enrichment revealed that the only factor whose motif distribution pattern reflected that of the activation status of the various enhancer subtypes was that of GRHL2. We followed these studies to confirm that GRHL2 binding events exist at FoxA1 activated enhancers, as predicated based on the motifs. Of note, the observation that GRHL2 and FoxA1 can collaborate in luminal breast cancer cells is consistent with the recent report published by Jozwick et al. indicating that GRHL2 and FoxA1 share binding events in tamoxifen sensitive MCF7 cells (Jozwik, Chernukhin et al. 2016).

GRHL2 has been implicated as a lineage determining transcription factor in several model systems to date. GRHL2 has been shown to be a key determinant of keratinocyte and uroepithelial differentiation, lung epithelial morphogenesis and previously suggested to be a lineage determining factor in breast epithelium

(Mehrazarin, Chen et al. 2015, Fishwick, Higgins et al. 2017) (Gao, Vockley et al. 2013)

(Xiang, Deng et al. 2012). Indeed, GRHL2 expression has recently been reported to be a key determinant of epithelial identity in the 4T1 mouse model of mammary carcinoma and human breast cancer cell line models (Xiang, Deng et al. 2012) (Cieply, Riley et al.

2012). Both studies indicate that the expression of GRHL2 suppresses EMT in breast cancer cells and that GRHL2 directly or indirectly regulates a broad range of epithelial genes. It has also been observed that GRHL2 expression is required for growth and

survival of standard MCF7 cells (personal communication, Myles Brown). As such, not only does GRHL2 supports transcriptional activation at FoxA1 bound sites, but it also required to maintain luminal breast cell identity.

2.3.2 Aberrant GRHL2 activity as mediator of tamoxifen resistance

Given that we have shown that GRHL2 supports transcriptional activation at

FoxA1 binding events, the question remains as to how GRHL2 binding is regulated. We demonstrated that GRHL2, while not differing much at the transcript level, does demonstrate different levels of protein, and is in fact enriched in the TamR model relative to MCF7-WS8. This observation is corroborated by clinical data indicating enhanced GRHL2 expression is associated with decreased time to recurrence, specifically in patients within known ER positive disease. We predict, however, that this is only a partial explanation for why there is altered activity of GRHL2 in TamR.

The FoxA1 gained binding events that we observe in TamR can be subcategorized in three different ways based GRHL2 binding patterns in TamR relative to MCF7-WS8: those where FoxA1 is significantly increased and GRHL2 is significantly increased, those where FoxA1 is significantly increased and GRHL2 is not significantly different and those where FoxA1 is significantly increased but GRHL2 is not significantly detected. At sites where both FoxA1 and GRHL2 are low in MCF7-WS8 but both are increased in TamR, it is possible that the two factors (FoxA1 and GRHL2) respond to a similar cue and result in collaborative binding. At sites where FoxA1

increased in TamR relative to MCF7-WS8 and GRHL2 is present, but is not significantly different in the degree of binding in the two models may suggest a potential activating pioneer-like role of GRHL2, whereby GRHL2 pre-marks a site and stabilizes nucleosomes, facilitating increased FoxA1 binding in the setting of resistance. Indeed, there are a subset of DHS sites that are bound by GRHL2 and not by FoxA1. The presence of these types of sites suggest that GRHL2 could be the initiating transcription factor. Indeed, Grh, the Drosphila homolog of GRHL2 has recently been suggested to have intrinsic nucleosome binding and displacement ability (Nevil, Bondra et al. 2017).

As such, GRHL2 may have the same ability as FoxA1 to physically displace nucleosomes.

It is possible that the constitutive activation of an intracellular kinase contributes to the activation of one or both FoxA1 and GRHL2 binding events observed in TamR.

Previously it has been demonstrated that treating MCF7 cells with either extracellular

EGF or a cytokine cocktail consisting of EGF, IGF, TNF, and IL6 can effectively

“reprogram” FoxA1 such that it binds to new enhancer elements (Lupien, Meyer et al.

2010, Ross-Innes, Stark et al. 2012). These enhancer elements are enriched for sites identified in patients with poor outcome. As such, it is possible that the constitutive activation of EGFR as observed in TamR may contribute to the TF reprogramming that we observe. Of relevance, GRHL2 has multiple recognition motifs for EGFR, and been shown to be a phospho-substrate of EGFR via LC/MS-MS analysis. Along these lines, it

would be of interest to know the extent to which EGF treatment of WS8 cells results in a transcriptional profile reminiscent of that observed in TamR and whether this is dependent on the expression of GRHL2. It would also be relevant to determine if the expression of those same genes could be altered by EGFR inhibitor treatment in TamR.

While only correlative, these data would suggest that constitutive EGFR expression does in fact alter the GRHL2 activity. These same treatments – EGF treatment of MCF7-WS8 and EGFR inhibitor treatment of TamR – could then be used to help focus the assessment of potential phosphorylation sites by LC/MS-MS enrichment. If confirmed, these sites could be mutated and downstream assays could be performed to determine the functional consequence of this phosphorylation site, including the assessment of chromatin binding and the influence on gene expression.

In summary, by comparing chromatin accessibility, transcription factor binding profiles, and histone activation marks as indicators of the regulatory state of the cistrome and epigenome in tamoxifen sensitive and resistant models, we have identified a distinct regulatory network specific to resistance that is governed by FoxA1 in collaboration with GRHL2. We have shown that while GRHL2 is increased in protein abundance, it also collaborates with novel TFs in our model of tamoxifen resistance and indicated that a portion of the reprogramming observed, may be in fact a result of constitutive activation of intracellular pathways, including EGFR. We have indicated that GRHL2 regulates a number of genes involved in processes consistent with features of aggressive

cancer including enhanced proliferation, invasion and metastasis; however, the true phenotype of GRHL2 loss in TamR remains to be determined. Further identification of both the cause and consequence of GRHL2 activation are detailed in the subsequent chapters.

3. LYPD3 as GRHL2 regulated drug target 3.1 Introduction

Transcription factors, such as FoxA1 and GRHL2 are exceptionally difficult to target directly and specifically with pharmacological interventions. Additionally, the diverse regulatory functions of GRHL2 as well as FoxA1 make them unfavorable for drug development. Indeed, it is possible and likely, that inhibiting the activity of these factors directly for an extended period of time would result in loss of a luminal cell identity and favor the outgrowth of a more resistant, and difficult to treat mesenchymal or stem-like population. In line with this concern, it has been shown previously that stable knockdown of GRHL2 in mouse models of mammary carcinoma resulted in overt

EMT (Xiang, Deng et al. 2012). As such, we chose to investigate potential downstream targets of GRHL2/FoxA1 as means to sensitize tumors to tamoxifen therapy.

Tamoxifen and other SERMs were previously known to elicit ER agonist like activity; however, the direct gene targets that elicited that activity were not known. To meet this need, the McDonnell laboratory performed a comprehensive analysis of gene expression in MCF7 cells treated with E2 alone or in combination with a series of mechanistically distinct SERMs and SERDs, with the objective of identifying genes whose expression was induced by E2 and SERMs, but inhibited by SERD treatment

(Wardell, Kazmin et al. 2012). Approximately 25 genes meet these criteria. This list was then ranked against a data set of mRNA expression profiles of 4700 annotated breast

tumors (Wright, Wardell et al. 2014). Among the top ranked genes was anterior grade homolog 2 (AGR2). AGR2 was of interest given its biological context as an important driver of breast, bladder, and pancreatic cancers, among others (Fletcher, Patel et al.

2003, Innes, Liu et al. 2006, Arumugam, Deng et al. 2015, Ho, Quek et al. 2016).

Following this line of investigation, it was observed that luminal breast cancer cells derived from the TamR model acquire constitutive expression of AGR2 (Wright,

Wardell et al. 2014). In tamoxifen sensitive MCF7 cells, the expression of AGR2 is regulated by both ER and FoxA1. In TamR, however, the expression of AGR2 was shown to have lost its dependence on ER, yet maintain its dependence on FoxA1.

Further, it was observed that overexpression of AGR2 significantly increased the growth of MCF7-cell derived tumors in mice and reduced the antitumor efficacy of tamoxifen

(Wright, Wardell et al. 2014). AGR2 has been shown to have both intracellular and extracellular function (to be discussed in later chapters) (Wright, Wardell et al. 2014,

Fessart, Domblides et al. 2016); however, little had been known about its signaling properties, especially related to its autocrine function. In 2015, a major breakthrough in the understanding of AGR2 function came with the discovery of its putative receptor:

LYPD3 (Arumugam, Deng et al. 2015).

LYPD3, also known as C4.4a, is a glycophospholipid (GPI)-anchored cell surface protein. It belongs to the Ly6 family of receptors including CD59 and urokinase plasminogen activator receptor (uPAR), proteins involved in the inhibition of the

complement system and the regulation of the proteolysis via binding of the extracellular protease urokinase-type activator (uPa), respectively (Chen, Ding et al. 2017) (Smith and

Marshall 2010). The function of LYPD3 was first described as a metastasis associated cell surface protein in rat pancreatic tumor cells (Rosel, Claas et al. 1998). In normal tissues, the expression of LYPD3 mRNA is limited to the placenta, skin, esophagus, and leukocytes (Wurfel, Seiter et al. 2001). In keratinocytes, LYPD3 expression significantly increases during wound healing and has been shown to be involved in cell-cell as well as cell matrix adhesion (Rosel, Claas et al. 1998). LYPD3 has since been shown to be overexpressed in several human malignancies (Seiter, Stassar et al. 2001, Smith, Kennedy et al. 2001, Hansen, Skov et al. 2007). LYPD3 protein expression has been detected specifically at the plasma membrane of the invasive front of colorectal cancers and has been found within exosomes secreted from highly metastatic cell types (Konishi,

Yamamoto et al. 2010, Ngora, Galli et al. 2012).

Little is known about the mechanisms that regulate LYPD3 expression or the pathways downstream of this protein upon binding AGR2. Previously it has been shown that LYPD3 is highly N and O-linked glycosylated, the consequence of which has been suggested to influence the subcellular distribution of the protein (Konishi,

Yamamoto et al. 2010). In MCF10a models of breast cancer progression, transformation with HRAS is sufficient to induce robust increase in expression of LYPD3, the elevated expression of which is maintained as cells further progress to a highly metastatic state

(Yen, Haste et al. 2014). LYPD3 expression has been shown to be negatively regulated by the tumor suppressor liver kinase B1 (LKB1). More specifically, LKB1 expression decreases as esophageal cells progress from normal epithelial cells to aggressive esophageal carcinoma and there is a reciprocal increase in LYPD3 protein expression.

Interestingly, loss of LKB1 in this model is associated with increased cell migration and invasion, an effect which is reversed by LKB1 re-expression, and correlated with decrease in LYPD3 expression (Gu, Lin et al. 2012).

LYPD3, like other GPI-linked plasma membrane proteins, lacks an intracellular domain to autonomously mediate downstream signaling pathways. However, it has been shown to interact with a number of relevant signaling molecules and cell surface proteins. Several reports indicate direct association between LYPD3 and alpha 6 beta 4 integrin (Ngora, Galli et al. 2012, Arumugam, Deng et al. 2015) as well as several laminins including 1 and 5 (Paret, Bourouba et al. 2005), and 111 and 113 (Ngora, Galli et al. 2012). To date, pathways implicated downstream of LYPD3 include FAK and MAPK-

ERK signaling. FAK activation as indicated by phosphorylation is specifically inhibited in the presence of LYPD3 knockdown, while phosphorylation of ERK is decreased in the presence of monoclonal antibodies derived to antagonize the activity LYDP3 (Thuma,

Ngora et al. 2013, Arumugam, Deng et al. 2015) .

3.2 Results

3.2.1 LYPD3 protein is increased in TamR cells

The McDonnell laboratory had previously observed that TamR cell lines and tumors express increased levels of AGR2 mRNA and protein relative to tamoxifen sensitive MCF7 cells. Considering the discovery of LYPD3 as the putative receptor for

AGR2, the first objective was to determine the relative LYPD3 expression in both the tamoxifen sensitive and resistant models. As shown in Figure 29, the mRNA level of

LYPD3 is increased in TamR relative to standard MCF7 cells. MCF7-WS8 cells, the subline of standard MCF7 cells which were selected by Gottardis et al. for their ability to grow as xenografts (Gottardis and Jordan 1988), express nearly equal levels of LYPD3 mRNA on average. At the protein level, TamR cells express significantly more LYPD3 as compared to both MCF7-WS8 and standard MCF7 cells.

LYPD3 mRNA 4 100

n 75 LYPD3

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Figure 29: LYPD3 expression in cell line models LYPD3 mRNA 100 LYPD3 1.5 75 LYPD3

LYPD3 mRNA (left) and protein expression in standard MCF7, MC7-WS8 and TamR cell

o i

s 1.0 100 lines. RNAs expression was assessed by qPCR, normalized to 36B4. Cells were grown for

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Of note, LYPD3 is 346 amino acids is length, and as such is predicted to be approximately 38kDa in size. As such the protein species detected by this antibody is likely a glycosylated form of the protein. Indeed, others have shown that the distribution of the banding pattern of LYPD3 is sensitive to N- and O-glycosidase treatment (Hansen,

Gardsvoll et al. 2004).

3.2.3 GRHL2 as a direct regulator of LYPD3 expression

Little is known about how the direct regulatory mechanisms governing LYPD3 expression. Given that we recently discovered the aberrant activity of GRHL2 as a lineage determining factor in luminal breast cancer cells and have indicated that it may central mediator of transition from tamoxifen sensitive to tamoxifen resistant state, we hypothesized that LYPD3 expression may be regulated by GRHL2. To this end, we first examined the chromatin binding pattern of GRHL2 around the LYPD3 genomic locus using ChIP-Seq. Indeed, we observed several robust binding events of GRHL2 around the LYPD3 locus, as well as a robust binding site within the LYPD3 promoter (Figure

30).

GRHL2 MCF7-WS8 Input

GRHL2 TAMR Input

RefSeq Gene

Figure 30: GRHL2 ChIP-Seq at LYPD3 locus

Integrative Genomics Viewer (IGV) window of GRHL2 binding events in MCF7-WS8 and TamR cell lines. One track shown of a representative sample of each cell lines from 3 biological replicates for each cell line.

Transcription factor binding on chromatin is suggestive of a regulatory event, however, does not confirm function. Consequently, to further define the role of GRHL2 in the regulation of LYPD3 expression, we tested the influence of 3 different siRNA sequences to GRHL2 in TamR cells. Efficient knockdown of GRHL2 mRNA was verified by quantitative real-time PCR and western blot. We then assessed the expression of LYPD3.

In comparison to siCtrl sequence, knockdown of GRHL2 resulted in significant decreases in mRNA and protein levels (Figure 31).

96 LYPD3 mRNA 4 100

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siGRHL2 A siGRHL2 B siGRHL2 C

One way ANOVA Dunnett's test siRNA vs siCtrl Figure 31: LYPD3 expression in TamR is dependent on GRHL2

(Left) mRNA and (right) protein expression of LYPD3 in TamR cells following 72hrs treatment with siRNA control or 3 siRNA to GRHL2. Significance was determined by one way ANOVA with Dunnet’s Test. p < 0.05 as indicated by ***.

Taken together, these data indicate LYPD3 is likely a direct transcriptional target of

GRHL2 in luminal breast cancer cells.

3.2.2 LYPD3 expression is increased in TamR tumors

Previously, we described the observation that TamR cells demonstrate enhanced activation of pathways that define invasive cancers. We also observed that the cistrome of TamR cells is enriched for FoxA1/GRHL2 binding events at activated enhancer elements. Finally, we have shown that LYPD3 is regulated by GRHL2 and is elevated in

TamR cells. These data led to the hypothesis that therapeutically targeting LYPD3 could enhance the efficacy of tamoxifen treatment. To this end, we first confirmed that our in vitro observation of increased LYPD3 protein levels in TamR is observed in intact

tumors. Indeed, TamR tumors demonstrate elevated expression of LYPD3 both at the mRNA and, more dramatically, at the protein level (Figure 32)

Figure 32: LYPD3 expression is increased in TamR and LTED tumor models relative to MCF7-WS8 tumors

(Top left) LYPD3 mRNA is increased in TamR tumors (treated with tamoxifen) relative to MCF7-WS8 tumors (treated with E2), n= 3; (Top right) and in LTED tumors relative to representative MCF7-WS8 and TamR from left panel, each bar represents an individual tumor (Bottom) LYPD3 protein is increased in TamR (treated with tamoxifen) and LTED tumors (no treatment) relative to MCF7-WS8 tumors (treated with E2). RNA expression was assessed by qPCR, normalized to 36B4. Protein expression was assessed by western blot, and assessed using the indicated antibodies.

3.2.3 Inhibition of LYPD3 disrupts TamR cell growth in 2D and tumor growth in vivo

LYPD3 has previously been shown to be an important regulator of cell proliferation and migration in models of pancreatic cancer (Arumugam, Deng et al.

2015). Based on the observation that TamR cell lines and tumors demonstrate increased

LYPD3 expression and the previous observation that AGR2 is required for the growth of tamoxifen resistant cell lines in culture (Wright, Wardell et al. 2014), we hypothesized that LYPD3 expression would also be required for TamR growth. As such, we tested the influence of knockdown of LYPD3 on TamR cell proliferation in vitro using the 3 unique siRNA sequences to LYPD3 relative to siCtrl. Indeed, knockdown of LYPD3 with 3 siRNA sequences resulted in significant decrease in TamR cell proliferation (Figure 33).

Figure 33: TamR cell proliferation is dependent on LYPD3 in vitro

TamR were cells transfected with siCtrl or 3 unique siRNA sequences targeting LYPD3 and monitored for cell growth for 9 days total. Data is representative of two independent biological replicates.

The ultimate objective of these studies was to define alternative treatment regimens for endocrine therapy resistant breast cancer, an effect which cannot be achieved with transient siRNA. As such, we sought to identify a mechanism to target

LYPD3 in vivo. Along these lines, monoclonal antibodies to LYPD3 have recently been 99

developed for this purpose. Using various models of pancreatic cancer, Arumugam et al observed that these antibodies specifically bind to LYPD3 in vitro and result in decreased growth of primary tumors in locally restricted and invasive models. Furthermore, these antibodies significantly improved the tumor response to gemcitabine and decreased the occurrence of metastasis (Arumugam, Deng et al. 2015). Based on these observations, we hypothesized that targeting LYPD3 with the same monoclonal antibodies would reduce the growth of tamoxifen resistant tumors. Indeed, treatment of animals bearing TamR xenograft tumors resulted in a significant decrease in tumor growth compared to IgG control. (Figure 34)

Figure 34: LYPD3 antibody treatment suppresses TamR tumor growth

TamR tumors were grown in tamoxifen treated mice that received further treatment with anti-LYPD3 or IgG control antibodies (15mg/kg, 2X weekly) and were monitored overtime. Average tumor volume -/+ SEM is plotted (n = 8-9 per group) and significance was determined by two way ANOVA followed by Bonferroni’s multiple comparison test. p < 0.05 as indicated by *.

LYPD3 expression in normal tissues is limited to squamous epithelial cells present predominantly in the skin and esophagus. LYPD3 knockout mice are viable, and demonstrate no developmental or reproductive deficits; however, slight delays in 100

wound healing were observed in adult mice (Kriegbaum, Jacobsen et al. 2016).

Consistent with these observations, we observed no untoward side effects when treating

TamR tumors with anti-LYPD3 antibody. It remains to be seen however, if other interventions, such as wound healing responses, would be delayed in the presence of antibody treatment.

3.2.5 LYPD3 expression is unlikely to be a direct ER target gene

Previously it had been observed that LYPD3 protein expression could be induced in MCF10a cells following HRAS transformation (Pilat, Christman et al. 1996). Since

MCF10a cells express little to no ER, we reasoned that LYPD3 expression was unlikely to be directly regulated by ER. To test this hypothesis, we examined the expression of

LYPD3 in MCF7-WS8 and TamR tumors treated with the SERD fulvestrant (ICI) in vivo.

While ICI treatment effectively decreased the expression of a canonical ER target gene,

KRT13, it did not result in significant decrease in LYPD3 mRNA. Similarly, preliminary data indicate that TamR cell lines treated with ICI in vitro also demonstrate a significant decrease KRT13 mRNA, without affecting LYPD3 expression. To test this effect more directly, we also examined the effect of siRNA knockdown of ER. Consistent with the data observed with ICI treatment, preliminary data indicates that decreasing ER expression using siRNA does not result in significant decrease in LYPD3 expression, despite effectively decreasing KRT13 expression (Figure 35).

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TamR Xenograft Tumors TamR Cell Lines

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Figure 35: LYPD3 is unlikely a direct ER target gene

(Top left) TamR tumors or (Top right) cell lines were treated with vehicle or ICI and assessed for mRNA expression of KRT13 and LYPD3. Cells were grown for 48hrs in charcoal stripped serum and treated with 100nM ICI as indicated for 24 hrs (Bottom) TamR cell lines were transfected with siCtrl or 3 unique sequences of siRNA to ER for 24 or 48hrs as indicated, and assessed for mRNA expression of ER, KRT13 and LYPD3. Significance determined using unpaired, two tailed t-test, p < 0.01 where indicated by *. NOTE: this is the first biological replicate for the cell line data (top left and bottom).

To further validate these findings, we investigated whether ER binds chromatin at putative regulatory elements adjacent to LYPD3 gene locus. To this end, we examined

ER binding at several sites throughout the genome in MCF7-WS8 and TamR cells. In this preliminary experiment, while we observed ER binding at classic ER dependent regulatory elements at TFF1 and XBP1, we did not to observe any binding of ER at the putative enhancer element downstream of LYPD3. Of note, this is true of the other

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FoxA1/GRHL2 regulatory elements tested. Taken together, these data indicate that the increased expression of LYPD3 we observe in TamR tumors is not likely to be regulated directly by ER (Figure 36).

103 Supplemental Figure 4. FoxA1/GRHL2 binding sites lack ER binding

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Figure 36: ER ChIP qPCR at FoxA1/GRHL2NOTE: puta tbindingive LYPD3 sites enha ncer = FOXA1/GRHL2 binding site 4

ChIP qPCR of ER in MCF7-WS8 and TamR cells following 45min veh, 10nM E2 or 100nM 4OHT treatment. FoxA1/GRHL2 site 4 is the putative LYPD3 enhancer binding site. NOTE: this is the first biological replicate

3.2.4 LYPD3 expression in other resistance models

In light of these dramatic findings in TamR, a key remaining question was whether this mechanism is exclusive to one resistance model. Aromatase inhibitors are currently considered first line therapy of choice for the treatment of post-menopausal patients with breast cancer. Recently, Dr. Suzanne Wardell generated an in vivo model of aromatase inhibitor resistance by selecting for a population of MCF7-WS8 xenograft tumors which can grow in the complete absence of estrogen, referred to as long term estrogen deprivation (LTED). We assessed mRNA and protein expression in multiple independent LTED tumors, and compared them to standard MCF7-WS8 and TamR tumors. These data, as shown above in Figure 32, indicate that across several samples, the mRNA of LYDP3 is elevated in LTED tumors relative to standard MCF7-WS8 tumors grown in the presence of E2. Even more dramatic than the change in mRNA expression is the relative increase in LYPD3 protein levels in LTED tumors relative to standard MCF7-WS8 tumors. Consistent with this finding, we also observed that an independently derived in vivo model of tamoxifen resistance, “TamI”, also demonstrates increased LYPD3 mRNA relative to tamoxifen sensitive MCF7-WS8 tumors based on

RNASeq data. Of note, this difference is observed whether tamoxifen pellet is present or not.

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3.3 Discussion

3.3.1 LYPD3 as a driver of collateral pathway in tamoxifen resistance

A central objective of this project was to define a collateral pathway, the targeting of which could serve to increase the durability of response to tamoxifen. Given the unique pharmacology of tamoxifen and other SERMs, in that they function as antagonists or agonists in a manner that is influenced by cell and tissue context, it has been hypothesized that mechanisms that reinforce the agonist activity may decrease the antitumor effects of tamoxifen treatment. In line with this hypothesis, AGR2 is a gene whose expression is increased with estradiol treatment and also induced by tamoxifen.

Previously, we have observed that at baseline, TamR cells demonstrate higher levels of

AGR2 expression relative to MCF7 cells (Wright, Wardell et al. 2014). This finding was corroborated by examining protein expression of AGR2 expression in additional in vitro derived endocrine resistant models (personal communication, Rachel Schiff) .

Furthermore, high AGR2 expression has been shown to be a predictor of poor prognosis and decreased response to endocrine therapy in patients with luminal breast cancer

(Wright, Wardell et al. 2014). Until recently, little has been known, however, related to how AGR2 influences tumor biology. Of interest to these studies, Craig Logsdon and

Vijaya Ramachandran recently identified the GPI linked surface glycoprotein, LYPD3, as the putative receptor for AGR2 (Arumugam, Deng et al. 2015).

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Throughout the course of our studies, we observed that breast tumors that are resistant to tamoxifen have elevated levels of LYPD3. We also made the surprising observation that the expression of LYPD3 is dependent on the activity of GRHL2 and that LYPD3 is likely a direct target of the GRHL2. Previously it was shown that suppression of GRHL2 expression in the mouse mammary tumor model, 4T1, was shown to lead to decreased cell-cell attachment and EMT. Indeed, this study further demonstrated that GRHL2 regulates genes involved in “cell adhesion, tight junctions, desmosomes, gap junction, cytokeratins, integrins, Wnt ligand, and epithelial specific splicing factors” (Xiang, Deng et al. 2012). LYPD3 has been shown to play a direct role in mediating cell-matrix as well as cell-cell attachment. It has also been shown to be specifically expressed on the leading edge of invasive tumors, and to be expressed in exosomes released from metastatic cells (Ngora, Galli et al. 2012). As such, it stands to reason that a potential explanation for EMT following GRHL2 knockdown in 4T1 cells is a consequence of decreased expression of LYPD3.

Herein we have demonstrated that a reduction of LYPD3 results in significant decreases in TamR cell proliferation. Preliminary data indicates that LYPD3 is also required for the migration of TamR cells. Of relevance, we have previously observed that TamR cells are metastatic in vivo lung colonization assays via tail vein injection. As such, studies further defining the role of LYPD3 in migration and invasion warrant further investigation and are currently ongoing.

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3.3.2 LYPD3 as a biomarker and drug target for treatment of endocrine therapy sensitive and resistant breast cancer

LYPD3 is not likely to be a direct target of ER. Indeed, treatment of TamR cells with siRNA to ER does not result in a change in LYPD3 expression. We also observe no evidence of ER binding at a putative enhancer downstream of LYPD3. Of note, ER binding in and around the LYPD3 genomic locus was only assessed at one potential site.

Given the nature of enhancer regulation, it is not possible to know for certain whether there is any interaction with ER without information that takes into account the three dimensional structure of chromatin. Nevertheless, the conclusion that LYPD3 is not directly regulated by ER is supported by the ICI treatment studies. Indeed, ICI treatment, which results in downregulation of canonical ER target genes via its ability to effectively degrade the receptor, does not result in significant changes in LYPD3 expression in cell lines or tumors. This is of clinical relevance as it indicates that even with most efficacious inhibitor of ER currently available, LYPD3 expression is not impacted. Consequently, it remains possible that the continued expression of LYPD3 can serve to reinforce collateral pathways, the activation of which leads to cancer cell survival and ultimately tumor recurrence.

Herein we have demonstrated that anti-LYPD3 therapies can effectively decrease the growth of tamoxifen resistant tumors in the presence of tamoxifen. Given that

LYPD3 expression is maintained in the presence of anti-estrogen therapy and is increased in the models of tamoxifen and aromatase resistance, it stands to reason that

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the co-targeting of LYPD3 along with other anti-estrogens would be efficacious.

Importantly, while LYPD3 expression is upregulated in the presence of resistance, it is also expressed in the treatment naïve tumors. Of further significance, the role of LYPD3 as the putative receptor for the secreted protein AGR2, a known predictor of tamoxifen failure, further implicates that targeting of LYPD3 in the treatment naïve setting may delay/prevent the emergence of resistance. Thus, the possibility remains that initiating tamoxifen therapy in the presence of anti-LYPD3 therapy, or cycling anti-LYPD3 shortly after starting tamoxifen treatment may serve to increase the length of time that patients respond to tamoxifen. A treatment regimen such as this would be particularly favorable as one of the primary benefits of tamoxifen over other anti-estrogen cancer therapies is the limited side effect profile. This is especially true when compared with aromatase inhibitors, which are commonly associated with negative side effects, including vaginal dryness, dyspareunia, musculoskeletal pain and osteoporosis (Pagani, Regan et al. 2014,

Francis, Regan et al. 2015). What is more, the side effects of aromatase inhibitors are exacerbated in premenopausal patients as concomitant ovarian suppression is required

(Pagani, Regan et al. 2014, Francis, Regan et al. 2015). Although AGR2 signaling has not of yet been associated with resistance to aromatase inhibitors, our findings that LYPD3 expression is increased in breast cancer models of estrogen deprivation suggests that a similar treatment paradigm may also influence the duration of response to aromatase inhibitors as well. Key to our full understanding of the AGR2/LYPD3 axis is the

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signaling downstream of this interaction and as such, this avenue constitutes the foundation of future studies in setting of tamoxifen and endocrine therapy resistance.

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4. Cancer cell-tumor microenvironment interaction as mediator of tamoxifen resistance 4.1 Introduction

Most studies that have explored the mechanisms by which cells become resistant to targeted therapies have primarily focused primarily on defining cell intrinsic processes that impact drug pharmacology. By focusing exclusively on cell intrinsic events, these studies failed to take account of the diverse and abundant changes which occur among the additional cell types which constitute a tumor. Indeed, beyond the cancer epithelial cell, various cell types exist within a tumor including cancer associated fibroblasts (CAFs), endothelial cells, and several cells representing key elements of both innate and adaptive immunity – including macrophages, natural killer (NK) cells, dendritic cells, and T and B lymphocytes. In addition to these cell types, the extracellular matrix, a web of collagen and fibrin, not only provides the structural support for tumors but also has distinct signaling properties.

4.1.1 Growth promoting cytokines and chemokines present in tumor microenvironment

The interaction between tumors and the surrounding elements of the microenvironment have been shown to alter tumor growth. What is more, the presence of these elements also significantly alters the response of a tumor to specific chemotherapy. One key mechanism by which components within the tumor microenvironment can influence a cancer cell’s response to chemotherapy is via the

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release of specific cytokines or growth factors. Cytokines which have been shown to alter the response to chemotherapy include IL-6, TNF, and IFN, among others.

Similarly, growth factors secreted from various infiltrating cell types, the release of which support enhanced tumor growth include vascular endothelial growth factor

(VEGF), fibroblast growth factor (FGF), epithelial growth factor (EGF), and transforming growth factor  (TGF).

4.1.2 Tamoxifen agonist activity is influenced by the tumor microenvironment

As previously described, the generation of our tamoxifen resistant model involved the serial passaging of MCF7-WS8 under the selective pressure of tamoxifen.

This experiment was carried out until a subset of the original MCF7-WS8 cells emerged

(selected for), which no longer required E2 for growth and were stimulated by tamoxifen. Cell lines were subsequently sub-cultured from these tumors. Interestingly, while many features of the TamR cells are maintained in cell culture relative to the tumors from which they were derived including gene and nuclear receptor expression, the ability of tamoxifen to stimulate cell growth is not observed in 2D culture. This observation led to the hypothesis that the agonist activity of tamoxifen observed in the setting tamoxifen resistance is dependent on the presence of the tumor microenvironment. Along these lines, preliminary data indicates that the ability of TamR tumors to be stimulated to grow in the presence of tamoxifen is dependent on the genetic background of mice used for the xenograft experiment, an observation made by 112

my colleague, Dr. Suzanne Wardell. Indeed, the growth of TamR tumors is stimulated by tamoxifen in nu/nu mice. In contrast, when tumors were injected in NOD/SCID mice, tumors maintained sensitivity to tamoxifen (Wardell). These data suggest that the microenvironment which differs based on the host in these experiments may be critical for TamR tumor growth and progression in the presence of tamoxifen.

4.2 Results

4.2.1 Fibroblast growth factor as mediator of tamoxifen agonist activity

To determine the degree to which the tumor microenvironment contributes to the TamR phenotype, we took advantage of the fact that we had previously performed

RNASeq on xenograft tumors derived from nu/nu mice, the result of a collaborative effort between Tricia Wright, Suzanne Wardell, Jeff Jasper and collaborators at Novartis.

The set of tumors which were sequenced consisted of MCF7-WS8 tumors treated with

E2 in the presence or absence of tamoxifen, as well as TamR tumors grown in the presence of tamoxifen or vehicle. The transcripts from these tumors were separated based on whether they were derived from mouse or human origins. In so doing, it was possible to differentiate transcripts derived from the cancer epithelial cells from the microenvironment, as those with human sequence would be derived from the cancer epithelial cells while those from mouse represent transcripts from other infiltrating cell types derived from the host. We then assessed differential expression of genes, comparing TamR vs MCF7-WS8, within the different component parts – mouse and

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human. After rank ordering the genes based on the degree to which they were differential, we used a candidate approach to select genes that encode for a known product that may be relevant to communication with the tumor microenvironment, the top mouse transcripts with known cell-cell communication properties with LogFc > 2.0

(Table 4).

Table 4: Relative expression of candidate cell-cell communication factors in TamR compared to MCF7-WS8 tumors

Gene Symbol Log2FC

Fgf9 +3.53

Saa3 +3.00

Fgf18 +2.10

Ccl6 +2.90

Sfrp2 +2.39

Ccl8 +2.0

Among these include 2 fibroblast growth factor (FGF) family members – Fgf9 and Fgf18. FGFs comprise a family of secreted ligands, which can act in autocrine or paracrine fashion. FGFs are also known to signal to receptor tyrosine kinases or intracellular non-signaling proteins and have been shown to regulate diverse biological processes, including embryonic development, tissue maintenance and repair, and metabolism (Jing, Wang et al. 2016).

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Coupled with this analysis, FGFR3 was identified to be upregulated in activity, according to the KiP results previously discussed (Chapter 2). Taken together these data suggested the possibility that increased FGF signaling may contribute to the sustained growth of TamR tumors, in vivo.

Among the diverse potential roles of FGFR/FGF signaling, we sought to determine how FGF might directly influence the regulatory process we previously determined to be central to TamR cell phenotype. More specifically, we sought to determine how signaling via FGF might influence FoxA1 biology. To this end we assessed the influence of FGF ligand treatment on both FoxA1 chromatin binding and gene expression (Figure 37).

Figure 37: Extracellular FGF treatment results in increased FoxA1 chromatin binding and expression of AGR2

Treatment of MCF7-WS8 cells with extracellular bFGF (20ng/ml) results in increased FoxA1 chromatin binding as indicated by ChIP qPCR (left) and increased AGR2 mRNA expression (right). Data are representative of two independent biological replicates.

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Based on these results it is evident that FGF treatment stimulates the increased binding of FoxA1 to chromatin at locus previously suggested to regulate AGR2 expression

(Wright, Wardell et al. 2014). Concurrent with the increased binding of FoxA1 observed with FGF treatment, the same treatment results in increases in AGR2 gene expression.

Taken together these data support the idea that in vivo, treatment of TamR tumors with tamoxifen results in the increase of an extracellular ligand, FGF, the binding of which on breast cancer cells is sufficient to result in FoxA1 chromatin binding around a putative

AGR2 enhancer, leading to increased gene expression

4.2.4 AGR2 stimulates the proliferation of breast cancer associated fibroblasts

Previously it has been shown that AGR2 has both cell intrinsic as well as extrinsic functions. What is more, AGR2 can act in both autocrine and paracrine fashion

(Fessart, Domblides et al. 2016). Within the cancer cell, AGR2 functions as a protein disulfide isomerase, assisting in the proper folding of proteins within the endoplasmic reticulum (Higa, Mulot et al. 2011). Upon release from the cancer cell, AGR2 can stimulate the growth of cancer cells from which it is secreted as well as influence the biology of surrounding cell types and has been shown to be present within the serum and urine in cancer patients (Shi, Gao et al. 2014). Indeed, using a model of gastric carcinoma, Tsuji et al demonstrated that gastric carcinoma cells on their own are unable to invade a 3D matrix (Tsuji, Satoyoshi et al. 2015). Similarly, normal fibroblasts are also

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unable to invade the matrix when cultured alone. However, when gastric carcinoma cells which express high levels of AGR2 were co-cultured with fibroblasts both cell types were able to invade the surrounding matrix. Importantly, this interaction was AGR2 dependent, depleting the cancer cells of AGR2 using micro RNA reverses this effect. In this context, the question remained whether this phenomenon was a feature that was specific to gastric carcinoma cells, or whether it could apply to breast cancer as well.

To address this question we established a culture system of fibroblasts isolated from primary human breast tumors. We then assessed the effect of treating the fibroblasts with a recombinant AGR2. As observed in Figure 38, treatment of primary human cancer associated fibroblasts results in significant increase in proliferation.

BrCa Fibroblast Proliferation

80000 **

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Figure 38: rAGR2 induced proliferationP = 0.00 4primary5 breast cancer associated fibroblastsUnpair ed t-test 5 days growth Primary breast cancer associated fibroblast wereDup lgrownicates in the absence or presence of 1ug/ml of rAGR2. Results indicate representative results from two independent biological replicates. Significance was determined using unpaired, two tailed t-test. p < 0.01 as indicated by **.

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Together with the previous data implicating FGF signaling as activator of AGR2 expression, the observation that AGR2 influences fibroblast biology further supports the notion that AGR2 could be at the center of a dynamic cancer cell-fibroblast cross-talk which occurs in the presence of tamoxifen in vivo.

4.2.5 In vivo targeting of AGR2 inhibits TamR tumor growth

If the AGR2 axis is a central mediator tamoxifen resistance phenotype, it follows that inhibiting the AGR2/fibroblast mediated crosstalk would disrupt tumor growth.

Previously, we observed in cell culture models that inhibiting the expression of AGR2 at the transcript level results in significant decreases in cell proliferation in 2D culture

(Wright, Wardell et al. 2014). The interpretation of these results is confounded by the fact that AGR2 has cell intrinsic as well as cell extrinsic functions. As such, targeting

AGR2 expression would not serve as the best approach to test the question as to whether the presence of this ligand influences cancer cell – fibroblast crosstalk. In contrast, the use of a blocking monoclonal antibody affords the opportunity to assess the extent to which extracellular AGR2 alone contributes to tumor growth. Thus, we tested the ability of anti-AGR2 monoclonal antibody to alter the growth of TamR tumors in vivo (Figure

39).

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Figure 39: Inhibition of extracellular AGR2 suppresses TamR tumor growth

TamR tumors were grown in tamoxifen treated mice that received further treatment with anti-AGR2 or IgG control antibodies (15mg/kg, 2X weekly) and were monitored overtime. Average tumor volume -/+ SEM is plotted (n = 8-9 per group) and significance was determined by two way ANOVA followed by Bonferroni’s multiple comparison test. P < 0.05

Indeed, these data indicate that the targeting of extracellular AGR2 disrupts tumor growth. It remains to be determined if disrupting autocrine or paracrine AGR2 signaling contributes to the anti-tumor effect of the antibody treatment. Furthermore, the pathways which are altered following AGR2 inactivation are still unknown. As such, deciphering these effects is the subject of ongoing study.

4.2.6 Absence of SMAD4 expression as mediator of tamoxifen resistance

In addition to FGF signaling, another abundant micro-environmental factor within the mammary fat pad and secreted from activated tumor associated fibroblasts is

TGF. In the context of mammary development and tumor growth, TGF has been shown to play a dichotomous role. In the setting of normal epithelial cells as well as cells

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in the early stages of transformation, TGF has been shown to be growth inhibitory. In contrast, as cancer cells become more aggressive, TGF signaling switches to a pro- tumor stimulus. The mechanisms by which this switch occurs remain largely unknown.

However, overexpression of TGF ligands commonly occurs in advanced tumors and is associated with a poor prognosis, indicative of its tumor growth inducing effects of late- stage cancer (Tang, Vu et al. 2003).

TGF signaling is initiated by the binding of its cognate ligands to a type II TGF

Receptor (TGFBRII) leading to phosphorylation of the type I TGF receptor (TGFBRI).

Subsequently, TGFBRI phosphorylates receptor-regulated SMADs (RSMADs: SMAD1,

2, 3, 5 or 8) which proceed to bind the common partner SMAD (coSMAD: SMAD4). The interaction between RSMADs and coSMADs facilitates binding to DNA and subsequent activation or repression of transcription (Massague 1998, Massague 2012). Interestingly, when analyzing genome wide binding events, we observed that TamR cells lack a significant portion of chromosome 18q. Within this region of 18q is the gene encoding

SMAD4. This observation was confirmed as in Figure 40, indicating that TamR cells do indeed lack the expression of SMAD4.

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kDa SMAD4 mRNA SMAD4 1.20 75

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o 0.40 F 500pM TGFb - - + + - - + + - - + + 0.20 ETOH + - + - + - + - + - + - 0.00 MCF7-WS8 TamR 100nM 4OHT - + - + - + - + - + - +

Figure 40: TamR cells lack SMAD4 expression

MCF7-WS8 and TamR cells were assessed for SMAD4 mRNA (average threshold cycle for MCF7-WS8 values = 23), normalized to 36B4, following growth in charcoal stripped serum for 72hrs.

Loss of heterozygosity of 18q and/or SMAD4 deletion are highly frequent oncogenic alterations observed in colorectal, pancreatic, and head and neck cancers (Levy and Hill

2006). While such a deletion is not commonly reported in breast cancer, alterations in

TGF signaling are common (Barcellos-Hoff and Akhurst 2009). Indeed, SMAD4 expression could be altered as a compensatory survival mechanism is the presence of a

TGF rich microenvironment via transcriptional repression or mutation. As such, we tested the hypothesis that decreased SMAD4 expression at the mRNA level may be predictive of a poor response to tamoxifen. By examining mRNA expression of SMAD4 separated based on quartiles, decreased expression of SMAD4 is strongly predictive of a poor response to tamoxifen long term (Figure 41).

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Figure 41: Low SMAD4 expression predicts poor response to tamoxifen

Gene analytics tool developed by Jeff Jasper was used to assess treatment outcomes for luminal B breast cancer patients based on SMAD4 (Affimetrix probe 202526_at) expression (Jasper 2013). Luminal B patients subdivided in quartiles based on SMAD4 expression and compared across tamoxifen treatment groups. The plot indicates the interval of recurrence free survival (RFS) in months. Patients with low SMAD4 expression demonstrated no difference in recurrence frees survival (RFS) when treated with tamoxifen relative to untreated patients, whereas patients with high SMAD4 expression exhibit significantly increased recurrence free interval relative to untreated patients.

Thus, while a loss of 18q is not a common event in breast cancer, the decreased expression clearly predicts for a poor prognosis.

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4.2.7 Lack of SMAD4 in tamoxifen sensitive MCF7 cells leads to expression of subset of genes enriched in TamR

It remained to be determined to what degree decreased expression of SMAD4 reflects the differences that we observe between parental and TamR cells. Given the diverse changes that likely must occur to generate the stable TamR phenotype, we anticipated that only a fraction of gene changes that occur in TamR may be explained by

SMAD4 loss. To test this hypothesis, we transfected standard MCF7 cells with 2 different siRNA to SMAD4 versus control. In line with our hypothesis, we observed that decreased SMAD4 expression results is changes in gene expression in only a minor of genes in the pattern that is reminiscent of TamR. Among these include LYPD3, and

MUC20. Others were not changed to reflect the state of TamR, including AGR2 and

MAPK4, which were decreased following knockdown of SMAD4 (Figure 42).

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1.5 AGR2 mRNA 3 LYPD3 mRNA

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Figure 42: Decreased SMAD4 in MCF7 cells partially recapitulates TamR gene expression pattern

Standard MCF7 cells were transfected with control (siLuc) or two unique sequences of siRNA to SMAD4 and then assessed for the expression of genes known to be increased in expression in TamR following 72hrs of knockdown using qPCR.

When seeking to follow up these studies with the selection of stable knockdown of SMAD4 we were not able to recapitulate the same gene expression pattern.

Consequently, it remains to be determined whether this is a result of degree of knockdown that was observed, or the differential effects of the stress of transient knockdown.

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4.2.8 Standard MCF7 cells are sensitive to TGF induced growth inhibition, while MCF7-WS8 and TamR cells are not

SMAD4 is a critical mediator of TGF signaling; consequently, loss of SMAD4 has previously been suggested to be central to the phenotypic switch from TGF growth inhibitory to growth stimulatory effects (Ding, Wu et al. 2011, Chen, Hsiao et al.

2014). As such, we reasoned that a lack of SMAD4 may result in resistance to TGF induced growth inhibition and thereby explain in part the phenotype observed in TamR in vivo. To test this hypothesis, we monitored growth of standard MCF7, MCF7-WS8 and

TamR cells in the presence of TGF (Figure 43).

MCF7 MCF7-WS8 TamR

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Figure 43: TGF dose response in MCF7, MCF7-WS8 and TamR cell lines

Cell lines were grown in charcoal stripped serum for 5 days in the presence of the indicated concentrations of recombinant TGF. Data are representative of two independent biological replicates.

In MCF7 cells, treatment with a dose response of TGF results in a dramatic decrease in cell proliferation, highlighting the growth inhibitory effects of TGF. In contrast, treating

TamR cells with the same concentrations of TGF results in no changes in cell growth, indicating robust resistance to TGF induced cell death. Importantly the question

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remained whether lack of SMAD4 was sufficient to explain the differential responses.

Along these lines, we also tested the proliferation of MCF7-WS8 cells. These cells, while retaining the expression of SMAD4, are also resistant to TGF mediated cell growth inhibition. Thus while SMAD4 loss may contribute to decreased responsiveness of TamR cells to TGF is not sufficient to explain the differences observed across the cell lines.

Indeed, the resistance TGF induced cell death is likely multifactorial and requires further examination.

4.3 Discussion

The mechanism by which MCF7-WS8 cells develop resistance to tamoxifen treatment is predicted to be a multifactorial process. This process likely involves changes within the cancer cells themselves along with changes within invading cell types. This process is also highly dynamic, as the cell intrinsic changes on both sides of the interaction results in altered cell-cell communication. It is hypothesized that this cascade

– whereby cells are altered intrinsically, respond to the surrounding environment, and are altered again - ultimately provides a selective pressure whereby cells which are best adapted for growth in these specific conditions will dominate.

In this context, it is likely that considering only cell intrinsic mechanisms of resistance to tamoxifen has severely limited our understanding of the processes which occur to develop resistance. Along these lines, we have previously observed that tamoxifen treatment results in the increase of expression of a number of ER target genes. 126

Among these include AGR2 (Wright, Wardell et al. 2014). AGR2 has been shown to be secreted and facilitate cancer cell interactions with fibroblasts, the result of which improves the growth and invasive behavior of both cell types (Tsuji, Satoyoshi et al.

2015). While this was shown originally in a model of gastric carcinoma, we have observed a similar result whereby extracellular AGR2 induces the proliferation of breast

CAFs. Once activated, CAFs have been shown to secrete a number of growth factors, including VEGFs, EGFs, FGFs (Kalluri 2016). Furthermore, their growth and expansion is largely supported by TGF. Please note, while the idea that AGR2 could mediate a cancer-cell mediated cross-talk with CAFs in breast cancer via the release of various growth factors, including FGFs was original at the outset, this idea has since been published and presented by Li et al. (Li, Wu et al. 2015), and Guo et al (Guo 2016) from the laboratory of Dawai Li.

Various growth factors, and growth factor cocktails have been shown to transform the FoxA1 cistrome. Furthermore, IGF-1 treatment has been shown to result in increased AGR2 expression (Li, Wu et al. 2015). In breast cancer, this induction was shown to be independent of E2 but dependent on ER (Wright, Wardell et al. 2014). As such, it is not surprising that FGF treatment could result in increased FoxA1 chromatin binding and AGR2 expression.

Considering a mechanism of AGR2 mediated tamoxifen resistance, TGF signaling is relevant for several reasons. Using a model of pancreatic carcinoma,

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previously it has been reported that AGR2 expression can be suppressed by TGF stimulation and this effect is dependent on expression of SMAD4 (Norris, Gore et al.

2013). The observed downstream effect of elevated AGR2 expression is an increase in

MUC1, a transcriptional target we also observe is upregulated in our TamR model.

Based on the pro-growth phenotype observed in the absence of SMAD4, this group and others have suggested that a lack of SMAD4 may be a key mediator of the molecular switch whereby the presence of TGF does not inhibit tumor growth and instead leads to enhancer cancer cell proliferation and survival. Indeed, lack of SMAD4 expression is one of most frequent genetic alterations observed in pancreatic and gastric cancers, tumor environments shown to be rich in TGF. In line with these data, we observe a strong relationship between decreased SMAD4 and decreased responsiveness to tamoxifen treatment when assessing patient outcome. In our model, we observe that a lack of SMAD4 results in the induction of a subset of genes that are associated with the tamoxifen resistant state. We did not observe, however, an induction of AGR2 in basal conditions. It follows that a secondary event may be required to fully activate AGR2 expression, such as the activation of RAS signaling which commonly occurs in GI cancers. We did detect that standard MCF7 cells are remarkably sensitive to TGF induced cell death, whereas TamR cells are not. This difference, however, cannot simply be explained by the loss of SMAD4 detected in TamR cells, as the subline of MCF7-WS8 cells, which retains SMAD4 expression is also resistant to TGF induced growth

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inhibition. It is likely then, that MCF7-WS8 cells have acquired another mechanism by which to decrease sensitivity to TGF. Of note, MCF7-WS8 cells were originally selected from the heterogeneous pool of standard MCF7 cells because of their ability to grow in vivo. Given that the mammary fat pad is rich with TGF, it is likely that the resistance to

TGF observed in cell culture is a reflection of this selection.

Collectively, while not complete, we have defined key nodes of a complex cancer cell-fibroblast crosstalk that contributes to tamoxifen agonist activity observed in vivo, mediated by AGR2. Stepwise, we propose a mechanism whereby:

1) Tamoxifen treatment results in the increased expression of AGR2 from the

cancer cell

2) AGR2 serves to act in an autocrine fashion to support cancer cell growth as

well as activate surrounding fibroblasts.

3) This interaction results in the accumulation of pro-growth tumor milieu rich in

secreted AGR2 and various fibroblast secreted growth factors, including

FGF.

4) The presence of these extracellular factors enables the selection of a population

of cancer cells that are most sensitive to the combination of AGR2 and

FGF environmental cues, including the increased expression of the

putative AGR2 receptor (LYPD3, as discussed in previous chapters) as

well as FGFRs.

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5) FGFs serve to activate FoxA1 mediated expression of AGR2, which

serves to reinforce the autocrine and paracrine effects of AGR2.

6) The selective process is further accelerated by the presence of abundant

TGF within the mammary fat pad, which favors the outgrowth of a cell

type which is most resistant to the suppressive effects of TGF, as has

previously been observed with SMAD4 loss.

Consistent with this model, we have observed that inactivation of AGR2 with a neutralizing monoclonal antibody following tamoxifen treatment in vivo is sufficient to inhibit the pro-growth effects of tamoxifen. The success of this treatment is compatible with the “stromal-normalization hypothesis” which states “inhibition and reversal of stromal changes that occur during tumor formation would slow tumor progression and would aid in therapeutic intervention” (Jain 2013).

Importantly, little is known about the signaling properties of AGR2. Indeed, a key missing node in this process is how AGR2 binding to its putative receptor on the cancer cells or in the presence of the microenvironment directly influences cell growth and invasion. Along these line, extracellular AGR2 has been shown to result in increased expression of EGFR and to interact with EGFR (Dong, Wodziak et al. 2015) (Fessart,

Domblides et al. 2016). Interestingly, EGFR is both increased in expression and activity in our TamR model. These results are correlative at this point, and further work is

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necessary to determine whether the AGR2 mediated pro-growth effects which we observe in TamR are indeed facilitated by EGFR signaling, or another intracellular process.

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5. The influence 27-hydroxycholesterol on mammary gland development, tumor onset and progression

This chapter represents a collaborative study with Dr. Sunghee Park a post- doctoral associate in the McDonnell Lab, and with Drs. Will Thompson and Laura

Dubois in the Duke Proteomics Facility.

5.1 Introduction

Obesity has been implicated as a significant risk factor for diagnosis with a variety of solid tumors as well as an increased risk of cancer related mortality. Multiple factors likely contribute to the increased risk and poor outcome associated with this altered metabolic state, including increased circulation of pro-inflammatory adipokines and cytokines, hyperglycemia, dyslipidemia, hypercholesterolemia and even changes in the “gut microbiome” (Boyd and McGuire 1990, Schwabe and Jobin 2013, Park, Morley et al. 2014). Among these, hypercholesterolemia has been shown to be an independent risk factor for breast cancer (Boyd and McGuire 1990, Ferraroni, Gerber et al. 1993,

Kitahara, Berrington de Gonzalez et al. 2011). Indeed, data from the Canadian National

Cancer Surveillance System demonstrated that the top quartile of cholesterol consumers have a 50% increase in breast cancer risk following menopause (Hu, La Vecchia et al.

2012). Using mouse models of mammary tumor growth, the McDonnell laboratory has previously shown that hypercholesterolemia promotes breast cancer cell growth and metastasis. Furthermore, it was also demonstrated that the active cholesterol metabolite

27 hydroxycholesterol is a key mediator in this process (Nelson, Wardell et al. 2013). 132

5.1.1 27-hydroxycholesterol as endogenous SERM

Previous work from the McDonnell lab and others has demonstrated that 27HC behaves as an endogenous SERM: 27HC has been shown to “1) bind competitively with

E2 to ER, 2) induce a unique conformation of ER and 3) display tissue specific effects, with different biological consequences observed in the bone and breast” (DuSell, Nelson et al. 2010) (Nelson, Wardell et al. 2013). Within bone, 27HC decreases bone density, which is the opposite effect that is elicited by E2. Conversely, in the setting of breast cancer, 27HC has been shown to have a pro-proliferative effect. In line with these observations, the gene signature activated by 27HC treatment of MCF7 cells is similar to that induced by E2 treatment (Nelson, Wardell et al. 2013), and treatment of tumor bearing animals with 27HC significantly increased breast tumor growth (Nelson,

Wardell et al. 2013). Furthermore, it was also demonstrated that increased 27HC production resulted in the activation of a second receptor, the Liver-X-Receptor, and dramatically increased metastasis.

5.1.2 Cellular sources of 27-hydroxycholesterol

Oxysterols are a product of oxidation of cholesterol or its byproducts. Oxysterols have been shown to be produced through two primary mechanisms: the synthetic pathway leading to bile acid production or reverse cholesterol transport (Olkkonen VM

2012). Within the liver, cholesterol is metabolized by various cytochrome p450 enzymes, as indicated in Figure 44.

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Biosynthetic pathways for key regulatory oxysterols.

Akira Honda et al. J. Lipid Res. 2009;50:350-357 Figure 44: Biosynthetic pathways for key regulatory oxysterols

©2009 by American SocietBiosyntheticy for Biochemistry pathwaysand Molecular forBiolo keygy regulatory oxysterols. Hydroxycholesterols are synthesized from cholesterol, whereas 24S,25-epoxycholesterol is derived from a shunt in the cholesterol biosynthetic pathway. CH25H, cholesterol 25-hydroxylase; 4β-OH, 4β- hydroxycholesterol; 7α-OH, 7α-hydroxycholesterol; 22R-OH, 22R-hydroxycholesterol; 24S-OH, 24S-hydroxycholesterol; 25-OH, 25-hydroxycholesterol; and 27-OH, 27- hydroxycholesterol. This figure was originally published and reprinted with permission from the Journal of Lipid Research. Akira Honda et al. J. Lipid Res. 2009; 50:350-357. © American Society for Biochemistry and Molecular Biology.

Among the various oxysterols, 27HC is the most abundant detected in serum of normal,

healthy subjects, followed by 24(S), 7, and 4-hydroxycholesterol (Honda, Yamashita et 134

al. 2009). Cyp27A1 and Cyp7B1 are both involved in the initial steps of bile acid synthesis. Cholesterol is converted to 27HC through the action of Cyp27A1. 27HC can then be further metabolized by 1) Cyp7B1 to produce 7,27-dihydroxycholesterol or 2) by Cyp27A1 to produce cholestonic acid (Wojcicka, Jamroz-Wisniewska et al. 2007).

Cyp27A1 is also expressed and functional in non-hepatic tissue, including macrophages, endothelial cells, and fibroblasts (Babiker, Andersson et al. 1997). Lung alveolar macrophages are reported to most abundantly synthesize 27HC from cholesterol precursors, followed by monocyte-derived macrophages, endothelial cells and fibroblasts (Direkze, Hodivala-Dilke et al. 2004). In a mechanism referred to as “reverse cholesterol transport,” these non-hepatic cells uptake and metabolize cholesterol in the periphery, and secrete the metabolite 27HC, which is subsequently taken up by the liver, and converted to bile acids (Holmberg-Betsholtz, Lund et al. 1993). Finally, it has been suggested that oxysterols can be modified via a non-enzymatic process involving

“cholesterol autoxidation”; however, the data supporting this process are less clear

(reviewed (Schroepfer 2000)).

Prior to work from the McDonnell lab, the primary biological activities ascribed to 27HC and other oxysterols involved a role for these metabolites in inflammation and atherosclerosis. Indeed, elevated levels of 27HC and 25HC are associated with hypercholesterolemia and have been found to be incorporated in the plaques of patients with atherosclerosis (Umetani, Domoto et al. 2007, Umetani, Ghosh et al. 2014). Despite

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our dramatic findings implicating 27HC as a pro-growth signaling molecule in breast tumor tissue, the potential effects of 27HC on the breast epithelium (e.g. before the onset of advanced luminal cancer) and also the source of 27HC within the breast that influences tumor growth remained to be determined.

5.2 Results

5.2.1 High cholesterol diet increases circulating 27HC in adolescent, wild type mice but does not augment breast epithelial development

There exists a strong correlation between early onset of thelarche (development of breast tissue) and obesity. What is more, there is a significant inverse correlation between age at thelarche and breast cancer risk. In a large epidemiologic study of over

100,000 subjects, women with a reported age of thelarche before or at 10 years of age had a 20% higher risk of breast cancer than those who reported age of thelarche at older ages

(Bodicoat, Schoemaker et al. 2014). While dramatic in and of themselves, the link between these two observations is largely unknown.

Puberty is a time of significant hormonal changes and, as such, it is likely that exposures that interfere with or modulate hormone action during this period may influence breast cancer risk in adult life. It is also likely that some of the breast cancer risk associated with early thelarche can be attributed to premature initiation of breast epithelial cell proliferation. This would (a) lengthen the period of vulnerability to carcinogenic insults and (b) increase the number of cells at risk for mutation.

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Given the ability of 27HC to stimulate breast tumor growth directly through ER, we hypothesized that the high levels of 27HC present during obesity may have a transforming capability in the normal breast epithelium. To address this hypothesis, we performed a preliminary experiment designed to determine whether mice fed a high cholesterol diet from weaning (3 weeks of age) to average onset of puberty (10 weeks of age) demonstrated altered circulating 27HC levels and/or breast epithelial development.

27HC levels were determined using a novel LC/MS-MS approach developed by Will

Thompson and Laura Dubois in the Duke Proteomics facility. Mammary epithelial development was assessed by quantifying the total length of epithelial branching as measured as the distance from the initiating epithelial bud to the end of the longest duct, relative to the total length of the fat pad, and the number of branch points on whole mounts of the inguinal mammary glands. Of note, there were no differences observed in total mammary fat pad length, indicated the differences are specific to the branching epithelium (Figure 45).

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Figure 45: High Cholesterol results in increased 27HC, but decreased breast epithelial development

Wild Type C57BL/6 mice were fed a high cholesterol diet ad libitum, beginning at 3 weeks of age. Animals were sacrificed at 10 weeks of age, at which time plasma and mammary glands were collected. (Top) Plasma was assessed for 27HC levels via LC/MS- MS. (Bottom) Inguinal mammary glands were harvested, and prepared for whole mount using carmine alum dye. Images were acquired using Zeiss laser capture microdissection microscope and assessed using ImageJ to measure percent of total fat pad length with branching (measured as total distance between initiating epithelial bud to the end of the longest epithelial branch relative, relative to the total fat pad length), and total number of branching points within the fat pad. Total fat pad length is indicated as a control to represent that the effects were specific to the mammary epithelium. n = 6 per group.

Based on these results, it is evident that feeding animals a high cholesterol diet for 7 weeks is sufficient to induce a significant increase in circulating 27HC levels. However, contrary to our hypothesis, we observed a decrease in both surrogate measures of epithelial development in the breast: ductal length and total number of branch points

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were decreased in mice consuming a high cholesterol diet. In light of these results, it is important to note that the high cholesterol diet induced significant hepatomegaly and icterus in the animals. As such, it is possible and likely that diet induced hypercholesterolemia caused abundant systemic effects beyond elevating 27HC, which potentially confound the interpretation of the results.

5.2.2 Macrophage specific knockdown of Cyp27A1 delays tumor onset, but does not influence growth

While conclusions related to the role of 27HC in breast epithelial transformation were unclear, we previously observed that 27HC decreases the time to tumor onset, and increases tumor growth rate, and potentiates metastasis of transformed breast cancer cells (Nelson, Wardell et al. 2013). Indeed, mice lacking the expression of Cyp7B1, the enzyme responsible for metabolizing 27HC to 7, 27dihydroxycholesterol (thereby leading to increased levels of 27HC), demonstrate increased incidence and more rapid progression of tumor growth relative to their wild type counterparts. In contrast, mice in which Cyp27A1 was deleted in all tissues, thereby obviating 27HC production, demonstrated decreased tumor incidence and delayed tumor latency (Nelson, Wardell et al. 2013).

As indicated previously, Cyp27A1 is most highly expressed in macrophages and the liver. It was also noted in clinical breast cancer specimens that expression of

Cyp27A1 within cancer cells was positively correlated with increased grade of the lesion

(Nelson, Wardell et al. 2013). Thus, it remained important to identify the primary 139

source(s) of 27HC in the breast tissue/tumors and establish the temporal relationships between exposure to 27HC and the initiation, growth, and metastasis of breast tumors.

To address this question, we have generated a mouse model in which the Cyp27A1 gene can be selectively modulated using the Cre-Lox approach (Sauer 1998). In brief, exon 2 of the Cyp27A1 gene was flanked by Locus X-over P1 (LoxP) sites in intron 1 and 2. A flippase recognition (Frt) target site flanked neomycin resistant gene cassette was then introduced into intron 2. These mice were then crossed with mice expressing Rosa- flippase (Flp), resulting in the excision of the neomycin cassette to generate Cyp27A1 flox mouse. After crossing mice to generate homozygous Cyp27A1fl/fl mice, the introduction of tissue specific expression of the Cre enzyme (through breeding) allows for cell-lineage specific control of Cyp27A1 deletion. As increased macrophage infiltration in human breast tumors tracks with poor prognosis, we hypothesized that macrophage-produced 27HC is a significant driver of mammary tumor progression.

Murine lysozyme M gene is expressed in multiple cell types within the myeloid lineage, including monocytes, macrophages, and granulocytes (Clausen, Burkhardt et al. 1999).

As such, mice expressing a Cre driven by the Lysozyme M promoter have previously been used to investigate the function of specific genes in macrophages, and other myeloid cells in the setting of atherosclerosis and inflammation (Goren, Allmann et al.

2009, Cuda, Agrawal et al. 2012). Using this approach, we bred animals to generate a

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knockout of Cyp27A1 in the myeloid lineage by crossing Cyp27A1fl/fl mice with mice expressing the LysM-Cre (Figure 46).

A A Targeting vector NEO TK

loxP site CYP27A1 locus frt site 1 2 9

HOMOLOGOUS RECOMBINATION

CYP27A1flox-frt-neo NEO Targeted allele

ROSA-FLP

CYP27A1flox Conditional allele P1 P2 P3 P4 LysM-Cre

CYP27A1null Null allele P1 P4

Macrophage Liver Lung Brain BB 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Primers

1000bp P1/P2 500bp 1000bp 500bp P3/P4

1000bp P1/P4 500bp 1: CYP27A1f/f;LysMCre 2: CYP27A1f/f;LysMCre 3: CYP27A1f/f 4: CYP27A1f/f

Figure 46: Generation of Cyp27A1fl/fl LysMCre

(A) Diagram of the conditional Cyp27A1 targeting scheme, indicating the location of primers (P1, P2, P3, P4) used for genotyping analysis. (B) Genotype PCR of analysis of genomic DNA isolated from indicated tissues using indicated primer sets. A 950bp PCR product was expected from the deleted allele but not the wild-type allele. This figure was reproduced with permission granted by Dr. Sunghee Park.

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A 1.50

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B 1 2 3 4 50 kDa - Cyp27A1 50 kDa - β-actin 37 kDa - 1- Cyp27A1fl/fl 2- Cyp27A1fl/fl 3- Cyp27A1fl/fl; LysMCre 4- Cyp27A1fl/fl; LysMCre

Figure 47: Confirmation of Cyp27A1fl/fl LysMCre expression

Cyp27A1 (A) mRNA and (B) protein in bone marrow derived macrophages of Cyp27A1fl/fl (1,2) and Cyp27A1fl/fl LysMCre (3,4) mice. mRNA was assessed by qPCR, normalized to mouse primer of 36B4. Protein was assessed by western blot of whole cell lysates, immunoblotting with indicated antibodies.

These Cyp27A1fl/fl/LysMCre mice were then crossed with mice expressing the polyoma middle T transgene under the control of the mammary tumor virus (MMTV-

PYMT), a widely used murine model of spontaneous primary mammary incidence and growth as well as metastases (Guy, Cardiff et al. 1992). Tumor onset and progression were then evaluated in control mice (Cyp27A1fl/fl PYMT+, LysMCre -) (WT) and mice

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lacking expression of Cyp27A1 in the myeloid lineage (Cyp27A1fl/fl PYMT+, LysMCre+)

as primary outcome (Figure 48). 3

A. 100 B. m WT m WT 0 100

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m ( onset, but does not affect tumor progression,p = 0.0772 or total tumor volume

e 10

o v

fl/fl fl/fl Female Cyp27A1 ,PYMT+ LysMCre+r (LysMcre) and Cyp27A1 PYMT LysMCre –

o 5

u t

(WT) mice were monitored for (A) time to palpable first spontaneous tumor, n= 20 and

t o

51 respectively, and (B) tumor progressionT 0 of predominant tumor, from time to palpable 3 to >500mm , n = 18 and 47 respectively. SignificanceWT determined using Log Rank (Mantel -Cox) test. LysMCre

Statistics were calculated using Prism 6 software. The resultsRe fromflec ttheses tim studiese to f iindicaterst pal pthatab thele t absenceumor. Oofr Cyp27A1 time expression within from predominant tumor (tumor which reached the myeloid lineage results in a minor, but significant increase in tumor latency (as endpoint of 2000mm3) onset to 500mm3. measuredS uinr daysviva lfrom Cur dateves: of L obirthg R aton timek (M toa nfirstte lpalpable-Cox) Te tumor)st . No significant Total tumor volume: Unpaired two tailed T test differences were observed in the rate of tumor growth in LysMCre+ mice as compared to

WT (as measured from time in days which the predominant tumor was palpable to reaching 500mm3).

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5.2.3 Deletion of Cyp27A1 within macrophages results in decreased intratumoral 27HC, but not circulating 27HC

As previously indicated, we successfully developed an assay to detect oxysterol and cholesterol levels in collaboration with Dr. Will Thompson in the Proteomics and

Metabolomics Core Facility. We previously observed differences in 27HC levels in analyzed plasma, but it remained to be seen whether we could detect oxysterol and cholesterol levels within tissues. As such, we used this assay to measure circulating

(plasma) and intratumoral levels of various oxysterols and cholesterol from

Cyp27A1fl/fl,PYMT mice with and without the expression of LysMCre (Figure 49).

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Tumor 27HC Plasma 27HC

** p = 0.0073 0.15 1.5 NS

)

g ) 0.10

/ 1.0

(

2 0.05 C 0.5

0.00 0.0 T re T e W C r M W C s M y s y L L

Tumor 22(R)HC Plasma 22(R)HC

NS 0.6 0.045 NS

)

g 0.040

)

o 0.4

(

0.035

(

)

R 0.030

( ) 0.2

(

2 0.025

0.0 0.020

T re T re W C W C M M s s y y L L

Tumor 25HC Plasma 25HC

** p = 0.036 NS 5 0.20

) 4 g 0.15

)

M o 3

(

p 0.10

(

2 H

5 0.05 2 1

0 0.00

T re T re W C W C M M s s y y L L

Tumor 24(R/S)HC Plasma 24(R/S)HC

** p = 0.0096 * p = 0.0132 1.5 0.15

)

1.0 u

( 0.10

(

)

S 0.5 ( 0.05

(

2 0.0 0.00

T re T re W C W C M M s s y y L L

Plasma cholesterol Tumor cholesterol NS NS 55 5

)

50 )

m 4

45 (

n 3

(

40 e

e 2

s 35

C 1

h 30

C 25 0

T re T re W C W C M M s s y y L L

Figure 49: Oxysterol and cholesterol measurements in tissue and plasma from in Cyp27A1fl/fl LysMCre+ (LysMCre) and LysMCre – (WT) animals

Tumor tissue (left column) or plasma (right column) were extracted with methyl tert- butyl ether (MTBE) containing stable isotype labeled internal standards (IS) for 22R-, 24(R/S)-, 25-, and 27-HC, as well as cholesterol, derivatized with dimethylaminophenylisocyanate (DMAPI), and the oxysterols are separated based on retention time and MS/MS fragmentation measured using a dedicated LC-MS/MS. n = 10 animals per group. Significance was determined using unpaired, two tailed T test.

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The results of this analysis indicate that the knockout of Cyp27A1 in the myeloid lineage in mice expressing PYMT to drive spontaneous tumor formation results in a decrease of intratumoral 27HC, while circulating 27HC is unchanged. Intratumoral oxysterols

25HC, 24(R/S)HC are also significantly decreased in LysMCre+ expressing animals relative to the LysMCre -/- control while 22HC and total cholesterol levels are unchanged. Plasma levels of oxysterols and cholesterol are also the unchanged between

LysMCre+ and LysMCre -/- control with the exception of 24(R/S)HC, which is increased in the LysMCre+ mice as compared to the LysMCre -/- control.

5.3 Discussion

Previously we observed that 27HC significantly increased the onset and progression of mammary tumors using mouse models. As such, we hypothesized that

27HC may influence earlier stages of mammary oncogenic transformation. Indeed, based on the clinical observations that obesity is associated with early onset thelarche, and decreased age of thelarche is associated with a significant increase in breast cancer risk, we reasoned that elevation of 27HC levels as a result of hypercholesterolemia may impact the development of the breast epithelium. To address this hypothesis, we exposed prepubertal wild type animals to a high cholesterol diet and assessed markers of epithelial development, including mammary epithelial branching and elongation.

While treating animals with a high cholesterol diet for 7 weeks resulted in increased plasma 27HC, we did not observe an increase in mammary epithelial development. In

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contrast, animals fed the high cholesterol diet demonstrated decreased epithelial branching and elongation. Of note, it was observed that these animals exhibited hepatomegaly and signs of icterus following high cholesterol diet exposure, likely reflecting additional systemic effects resulting from the altered metabolic state.

Consequently, additional experiments investigating the direct effects of 27HC on epithelial cells within the mammary gland, designed to exclude the confounders of systemic effects of high cholesterol diet, would be needed to conclusively determine whether 27HC induces premature thelarche.

Separate from the potential differential effects of 27HC on non-transformed tissue, the question remained as to the cellular source of 27HC promoting the growth and progression of transformed cells. Previously we observed that a lack of 27HC resulted in delayed tumor onset and decreased progression when Cyp27A1 was deleted in all tissues, using a constitutive global knockout approach. Several cell types including macrophages, hepatocytes and cancer cells have been shown to express Cyp27A1

(Nelson, Wardell et al. 2013). Importantly, determining the source of 27HC has significant implications from a treatment perspective, as therapies designed to target

Cyp27A1 in different tissues would likely require different pharmacologic properties

(e.g. post-hepatic exposure or route of administration).

Given that the presence of tumor infiltrating macrophages has been associated with tumor progression and worse treatment outcomes, and that macrophages have

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been shown to be abundant producers of 27HC, we reasoned that macrophages were a likely primary source of 27HC within the tumor. Testing this hypothesis, we observed that eliminating Cyp27A1 within the macrophages decreased tumor onset, yet did not significantly alter tumor progression. There are potential technical and biological reasons to explain why we did not observe the same phenotype in mice specifically lacking Cyp27A1 within macrophages as compared to the global knockout mouse.

Two features differing between the experiments using the Cyp27A1fl/fl animals and the global knockout include the genetic background and the use of the Cre-lox approach. To address this issue, we consulted Dr. Franceso DeMayo, an expert in mouse genetics, who recommended the use of Cre driven by the Zona Pellucida 3 (ZP3) gene promoter would be the best approach to derive a mouse with a global knockout using the Cre-lox system. As such, a pilot study to determine the effect of the Zp3 Cre mediated decrease of Cyp27A1 expression on mammary tumor growth and progression has recently been completed. Indeed, initial results indicate the Zp3 Cre expression results in significant decrease in circulating 27HC; analysis of the results of the tumor study is currently underway. It is also possible that the differences we observed between

LysMCre expressing animals and global knockout animals could be an indication of the temporal and spatial differences of 27HC produced by different cell types. The correlation between the isolated change of increased tumor latency and decreased intratumoral 27HC in LysMCre+ as comparted WT animals indicates a potential

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connection between intratumoral 27HC and the initiation of tumor growth. Similarly, these data also suggest that circulating 27HC may play a more important role in driving tumor progression. The major remaining source of 27HC is the liver, with a potential minor contribution from cancer cells. As such, we have recently crossed Cyp27A1fl/fl mice to mice expressing a Cre driven by the albumin promoter (AlbCre) can result in the hepatocyte specific deletion of a floxed gene (Postic, Shiota et al. 1999). The breeding paradigm has resulted in a mixed strain background in the experimental mice.

Importantly, it has been shown that a tumor phenotype can differ based on the genetic background of the mouse; consequently, the ideal experiment would be to repeat these results on a pure strain background to ensure the broader applicability of these findings.

However, comparison of the tumor phenotype in these tissue selective mouse models collectively will enable an appreciation of the importance of macrophage (LysMCre), and liver (AlbCre) produced 27HC, controlling for the genetic background and Cre-Lox approach (Zp3Cre), in breast cancer pathogenesis.

The degree to which macrophage expression of Cyp27A1, and thereby production of 27HC, influences metastatic burden remains to be determined.

Preliminary data assessing the PYMT mRNA expression within the lungs of the experimental mice (Cyp27A1fl/fl PYMT+, LysMCre + and Cyp27A1fl/fl PYMT+, LysMCre -

), a surrogate measure of metastatic burden previously used (Nelson, Wardell et al.

2013), indicates no significant difference between groups. However, for this assessment,

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RNA is isolated from a very small fraction of the total lung tissue and may not be representative of the entire lung burden. Gross genotype-blinded scoring of formaldehyde fixed lungs (scoring tumor burden within lungs on a subjective 0-3 scale) does show a decrease in metastatic burden in LysMCre expressing mice relative to control mice. These initial studies need to be expanded to assess this result more quantitatively. Increasing the number of blinded readers to score the lungs and serial sectioning of the lungs, followed by quantitation of relative size and number of metastases are two approaches that can be used.

Finally, it is also possible that another challenge or stimulus is required to elicit a significant difference in tumor onset and growth that is dependent on Cyp27A1 and

27HC expression in macrophages. Previously, Dr. Erik Nelson observed that growth of

EO771 cells injected into the mammary fat pad of mice lacking Cyp27A1 expression in all tissues was significantly decreased relative to the growth of these tumors in wild type

C57BL6 animals (unpublished data). Seeking to expand upon these observations, Dr.

Wen Liu observed that this effect was present even in the state of circulating estrogens, as the mice in the confirmatory experiment were not ovariecotmized (unpublished data).

Importantly, previous studies using the PYMT model of spontaneous tumor growth were performed in ovariectomized mice. As such, it remains possible that although the tumor growth effect of the syngeneic EO771 tumors appears to be independent of estrogens, depletion of circulating estrogens from a young age may be required to

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observe differences in tumor growth in the setting of spontaneous tumor model.

Furthermore, for this study, mice were fed only a normal, control diet, as the absence or presence of high cholesterol diet did not significantly influence the differences in spontaneous tumor growth observed in mice lacking Cyp27A1 expression in all tissues relative to control (Nelson, Wardell et al. 2013). However, it remains to be determined whether a cholesterol diet challenge may have a more significant impact on the ability of macrophages to influence tumor growth.

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6. Conclusions and future directions

A variety of cellular factors determine the response of a tumor to a given targeted agent. Prior to the past decade, most studies have explored the manner in which genetic differences can contribute to drug response, with a focus on expression of drug targets as well as factors involved in drug transport and drug metabolism (Adams and Workman 1995, Gottesman 2002, Gatti and Zunino 2005, Wilson, Longley et al. 2006,

Engelman, Zejnullahu et al. 2007, Ullah 2008). More recently, with the wide spread use of high-throughput whole genome approaches, it has been recognized that epigenetics, and more broadly the epigenome, serves as a significant determinant of the response to an altered cellular state, including that which is induced by a drug treatment (Brown,

Curry et al. 2014, Easwaran, Tsai et al. 2014). Historically, generation of models of resistance to targeted therapies have provided key insights into mechanism of drug action as well as predictive mechanisms of decreased drug response observed clinically

(Lackner, Wilson et al. 2012, Rosa, Monteleone et al. 2014). Surveying the transcriptional landscapes of these models is particularly informative with regard to those gene expression programs that are at the center of a variety of diverse cellular processes, and as such play an essential role in coordinating a cell’s response to a given therapy.

The activity of the transcriptional machinery is guided by a unique family of

DNA binding proteins broadly recognized as “lineage determining transcription factors” (LDTF), the biology of which can provide key insights into regulatory pathways

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that delineate cellular identity and cellular responses to external cues. LTDF have been shown to have the unique ability to interact with DNA while it remains wound among nucleosomes (Cirillo, Lin et al. 2002). Mechanistically, it has been proposed that these unique factors scan chromatin for elements containing specific DNA motifs; once identified, they are able to stably bind to chromatin and displace nucleosome leading to a rearranged, unpackaged chromatin state. While binding DNA directly, these factors can also facilitate the binding of other regulatory proteins, both cofactors and chromatin modifiers, to chromatin (Carroll, Liu et al. 2005, Laganiere, Deblois et al. 2005, Belikov,

Astrand et al. 2009, Wang, Li et al. 2009).

The specificity LDTF binding events is provided, at least in part, by collaborating transcription factors (CTFs). As a result of changes in LDTF and CTF binding events, changes in gene expression can lead to the establishment of collateral pathways, among other effects, which can alter the response of a cancer cell to a given therapy. Collateral pathways most often involve changes in cell cycle signaling molecules or growth factor receptor pathways, resulting in alternative proliferation and survival stimuli and global resistance phenotypes (Engelman, Zejnullahu et al. 2007, Ullah 2008, Lackner, Wilson et al. 2012). It follows, then, that identification of these collateral pathways can provide key targets to enhance drug efficacy.

The activation of collateral pathways can be a downstream consequence of a multitude of both cell intrinsic as well as cell extrinsic events. Indeed, we believe that the

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TamR model represents the culmination of diverse processes enabling chemoresistance.

By comparing chromatin accessibility, transcription factor binding profiles, and histone activation marks as indicators of the regulatory state of the epigenome in the TamR model, we have identified a distinct regulatory network specific to TamR that is governed by the well-known lineage determining transcription factor FoxA1 and a heretofore understudied transcription factor, GRHL2. We have shown that GRHL2 plays both a lineage determining and a collaborating role in the context of a model of aggressive luminal breast cancer and have effectively demonstrated in patient derived samples that this transcription factor is truly a poor prognostic indictor. Looking more broadly at the GRHL2 transcriptome, we have identified a downstream functional target, LYPD3, and have demonstrated for the first time the utility of therapeutic targeting LYPD3 for the treatment of advanced breast cancer. Further, our studies provide evidence that the expression of LYPD3 is likely independent of the ER action, making it a relevant target to be further explored with the intent of defining combination treatment strategies with SERD therapies, an avenue currently under investigation.

During our studies of the TamR model, it has also become apparent that the selection processes within the tumor microenvironment significantly influenced the development of tamoxifen resistance, with both dynamic autocrine and paracrine signaling facilitated by presence of the surrounding cells and interaction with the

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extracellular matrix playing significant roles. Indeed, we provide early evidence to indicate that secreted factors from aggressive cancer cells, such as AGR2, cross-talk with infiltrating stromal cell types, including fibroblasts, which results in the genesis of a pro- growth tumor milieu. This milieu supports the growth and expansion of the cancer cells, an effect that we believe may be facilitated by tamoxifen. The end result of these processes is a signaling cascade that results in the outgrowth of tumors, which experimentally and clinically are observed as treatment resistant disease.

Key remaining questions that have developed from this work involve further characterization of GRHL2 biology in the context of aggressive luminal cancers. Indeed, we have demonstrated that GRHL2 regulates the expression of genes associated with metastasis and invasion, processes that this transcription factor has previously been shown to regulate in other model systems. It remains to be seen, however, whether alteration of GRHL2 function in the setting of aggressive luminal breast cancer can alter the response to drug treatment. Alternatively, it is also unknown whether GRHL2 can play an active role in altering the cistrome to enable the expression of a gene signature indicative of an aggressive tumor phenotype, or whether the altered behavior we observe is a passive process, whereby some other feature of aggressive luminal cells allows for more transcription factor binding. Additionally, the signaling properties of gene products downstream of GRHL2, namely LYPD3 and AGR2, still remain to be defined. Both LYPD3 and AGR2 have been shown to interact with cell surface receptors,

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although the downstream functional consequence of these events is unknown. Further examination of the mechanism(s) by which the AGR2 and LYPD3-signaling pathways interact is also required to determine whether LYPD3 is truly a “receptor” for AGR2. All of these features together present questions of both academic and clinical interest, as deciphering the signaling downstream of this interaction will not only facilitate a further understanding of the molecular biology regulating these proteins, but also provide more information to further streamline the efficacy of AGR2/LYPD3 targeted therapies.

In addition to our xenograft models, and the subsequent models derived from them, we have also used murine models of spontaneous breast cancer to further our understanding of the signaling properties of tumor microenvironment components and their influence on tumor outcomes. Indeed, it was previously demonstrated that the endogenous SERM 27HC has the undesirable effect of increasing the onset and progression of luminal breast cancers. Along these lines, we have shown that macrophages contribute, at least in part, to these observed differences. Macrophages express Cyp27A1 and have been described as abundant producers of 27HC. The fact the we observe that mice that lack Cyp27A1 expression in the macrophages exhibit slightly delayed time to tumor onset likely indicates that 27HC derived from macrophages may be important for tumor establishment. Alternatively, it is also possible that Cyp27A1 expression may be required for proper macrophage development or homing. As such,

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the differences we observe may be a result of both decreased abundance of 27HC within the tumor, but also altered macrophage function; the distinction between these hypotheses may provide important insights into the function and targetability of

Cyp27A1 for the treatment of breast tumors.

Ultimately, the use of the TamR and murine PYMT models as tools to study aggressive luminal cancers has provided comprehensive insight into the molecular mechanisms that define the untoward tumor growth promoting effects of SERMs.

Importantly, these studies have directed us to features of both of significant biologic and clinical importance, the targeting of which may provide efficacious alternative therapies for the treatment of luminal breast cancer, particularly of the most aggressive progressed form of disease.

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7. Materials and Methods 7.1 Chemicals and ligands

ER ligands obtained from Sigma (St. Louis, MO) include: 17-Estradiol [50-28-2]

(E8875), Fulvestrant (ICI) [129453-61-8] (I4409), and 4-hydroxytamoxifen [68047-06-3]

(H7904). Recombinant Basic Fibroblast Growth Factor (1001-18B) was obtained from

Peprotech. Recombinant TGF (240-B-002) was obtained from R&D Biosystems.

Recombinant AGR2 was obtained from GenWay Biotech (GWB-P1775). Carmine (C-

1022) was obtained from Sigma. PCR and qPCR reagents were purchased from Bio-Rad

(Hercules, CA), Qiagen (Valencia, CA), Integrated DNA Technologies (Coralville, IA), and Sigma.

7.2 Antibodies

The following antibodies were purchased from Santa Cruz Biotechnology: ER

(HC-20) (sc-543), -Tubulin (E-19, R) (sc-12462-R), Abcam: FOXA1 (ab23738), Cyp27A1

[EPR7529] (126785), Sigma: H3K4Me2 (07-030), GRHL2 (HPA004820), and  actin (AC15)

(a5441), R&D Biosystems: Human C4.4a/LYPD3 (AF5428), Diagenode: H3K27Ac

(C15410196). Normal Rabbit IgG was purchased from Santa Cruz (sc-2027).

AGR2 and LYPD3 monoclonal antibodies as produced and validated previously

(Arumugam, Deng et al. 2015) for treatment of xenograft tumors were provided by

Craig Logsdon at MD Anderson. Mouse IgG Isotype control (0107-01) was obtained from Southern Biotech.

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7.3 Cell culture

MCF7, MCF7-WS8 and TAMR cell lines were maintained in Dulbecco’s Modified

Eagle Medium: Nutrient Mixture F-12 (DMEM/F12). TamR cells were also kept under constant selection with 100nM 4-OHT. All cell lines were supplemented with 8% fetal bovine serum (FBS) or charcoal stripped FBS (CFS) (Hyclone Laboratories, Logan, UT),

0.1 mM non-essential amino acids (NEAA) and 1 mM sodium pyruvate (NaPyr).

Primary breast cancer associated fibroblasts were obtained from de-identified samples from the Duke Biospecimen Repository and Processing Center (BRPC). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) + 10% Fetal Bovine Serum and 0.5mM NaPyr and Penicillin/streptomycin (50,000U) from Gibco/ThermoFisher

(15140122).

7.4 RNA isolation and qPCR

Cells were seeded in 12-well plates in phenol red-free media containing 8% CFS for 48 hours and treated with ligands as indicated. After the indicated time period, cells were harvested and total RNA was isolated using the AurumTM Total RNA Mini Kit

(Bio-Rad, Hercules, CA). Five-hundred nanograms to 1 microgram of purified RNA was reverse transcribed using the iScript cDNA synthesis kit (Bio-Rad). The Bio-Rad CFX384

Touch Realtime PCR Detection System was used to amplify and quantitate levels of target gene cDNA. Reactions for qPCR were performed with 1.5 µL of 1:10 diluted cDNA, 1.02 µM specific primers, and iQ SYBRGreen supermix (Bio-Rad). Data are

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normalized to RPLP0 (36B4) housekeeping gene and presented as fold expression relative to controls. Unless otherwise indicated, experiments were performed at least three independent times with coincident results.

Primer sequences used for qPCR, include:

LYPD3: (forward): 5’ TCTCTCTCTCTCTCTCTCTTGCT 3’ (reverse) 5’ TTACATAAGCAAGACATGACTATCTTC 3’

AGR2: (forward)5’ TGAGTGCCCACACAGTCAAG 3’ (reverse) 5’ TCAACAAACATAATCCTGGGGAC 3’

FoxA1: (forward) 5’ AAGGCATACGAACAGGCACTG 3’ (reverse) 5’ TACACACCTTGGTAGTACGCC

GRHL2: (#2) (forward): 5’ GTGATGAGGACAGTGCTGCT 3’ (reverse): 5’ TTGGCTGTCACTTGCTTTGC 3’

MUC20 (forward): 5’ CAAGATCACAACCTCAGCGA 3’ (reverse): 5’ ACCTCCATTTTCACCTGCAC 3’

CAMK2D (forward): 5’ CCAGGTTCACGGACGAGTAT 3’ (reverse): 5’ GCTTCAAAAGACGGCAGATT 3’

MAPK4 (forward): 5’ ACCTCGTGCTCAAGATTGGG 3’ (reverse): 5’ TGGGGAACGGTACCACTTTG 3’

SMAD4 (forward): 5’ CCAGGATCAGTAGGTGGAAT 3’ (reverse): 5’ GTCTAAAGGTTGTGGGTCTG 3’

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36B4 (human) (forward): 5’ GGACATGTTGCTGGCCAATAA 3’ (reverse): 5’ GGGCCCGAGACCAGTGTT 3’

Cyp27A1 #3 (Targeting Exon 2) (forward): 5’ TGGACAACCTCCTTTGGGAC 3’ (reverse): 5’ GCTCTCCTTGTGCGATGAAG 3’

Cyp27A1 genotyping P1- CAG GGT GCT GTG GGT GGA GAC TA P2- AAA CAA GCG GCC TTA CAA CAA GG P3- AGT GTC ATT TTC GCT CAG TTG GG P4- CCA GCT GTA AAC GGA GGA CAG TA

36B4 (mouse) (forward): 5’ AAGCGCGTCCTGGCATTGTCT 3’ (reverse): 5’CCGCAGGGGCAGCAGTGGT 3’

7.5 Western blotting

Cells were seeded in 6-well plates in phenol red-free DMEM containing 8% CFS,

0.1 mM NEAA and 1 mM NaPyr for 48 h and treated as indicated. Following treatment for the indicated time periods, cells were harvested in ice-cold PBS and lysed in RIPA

Buffer [50 mM Tris-HCl pH 8.0, 200 mM NaCl, 1.5 mM MgCl2, 1% Triton X-100, 1 mM

EDTA, 10% glycerol] and 1X protease inhibitor mixture (EMD Chemicals, Inc, San

Diego, CA)] while rotating at 4°C for 30 min. Whole-cell extract was resolved by SDS-

PAGE, transferred to a PVDF membrane (Bio-Rad) and probed with the appropriate antibodies

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7.6 Gene overexpression

pLenti CMV puro iRFP (control) sequence and pLenti CMV puro FoxA1 plasmids were generated by subcloning iRFP and FoxA1 cDNA into pENTR1a

(Invitrogen) and recombining into pLenti CMV puro DEST (invitrogen). Plasmids were transfected (Fugene, Roche Applied Science) with the vsvg, gag-pol and rev packaging vectors into 293FT cells. The viral supernatants were filtered and supplemented with

8ug/mL polybrene before infection MCF7-WS8 cells. Cells were then selected with

2.5ug/ml puromycin yielding the MCF7-WS8 iRFP and MCF7-WS8 FoxA1 cell lines.

7.7 Chromatin immunoprecipitation

Cells were seeded in a 15cm dishes with appropriate media described above.

Cells were grown to 90% confluence in phenol red-free media supplemented with 8%

CFS, 0.1 mM NEAA and 1 mM NaPyr for 3 days. Following treatment with the appropriate ligand for the indicated time periods, cells were subjected to ChIP analysis.

Each plate of cells was cross-linked with 1% formaldehyde PBS solution for a maximum

10 minutes and quenched with 125mM glycine solution containing 5mg/ml bovine serum albumin (BSA) for 5 minutes. Cells were then rinsed once and harvested with ice cold PBS, pelleted at 8000rpm for 30 seconds at room temperature and snap frozen for storage at -80 degrees. All solutions were supplemented with 10mM sodium butyrate and protease inhibitors. For FoxA1, H3K27Ac, H3K4Me2 and GRHL2 ChIP: cell pellets were thawed on ice, and then resuspended in Lysis Buffer containing 1% SDS, 0.25mM

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EDTA, 2.5mM Tris-HCl, pH 8.0, protease inhibitor cocktail. Cell lysates were sonicated using the Covaris E210 (FoxA1 H3K4Me2, and H3K27Ac) or E220 (GRHL2) instrument according to the manufacturer’s instructions. Sheared chromatin was diluted using

Dilution Buffer (20mM Tris [pH 8.0], 150mM NaCl, 2mM EDTA, 1% Triton X-100).

FoxA1, H3K4me2, and H3K27Ac was performed by incubating sheared, diluted chromatin with 3ug of Foxa1 antibody or 2ug of histone mark antibodies. For ChIP qPCR, normal rabbit IgG (sc-2027) was used as control. Antibodies were allowed to bind overnight in a deep 96 well plate at 4C and then captured on protein A magnetic beads (Invitrogen, DynaI). After 45min of incubation with the beads the immunoprecipitates were washed on a 96 well microplate in 150ul volumes a total of 6 times: 4 times in RIPA buffer containing 500 mM LiCl (50 mM HEPES, 1 mM EDTA,

0.7% Na deoxycholate, 1% NP-40, 0.5 M LiCl), and twice with TE buffer (20mM Tris pH

8.0, 2mM EDTA). GRHL2 ChIP was performed as above using 5ug of GRHL2 antibody with the following modification to the 6 ChIP washes: twice with Buffer A (50mM

HEPES, 500mM NaCl, 1mM EDTA, 0.1% SDS, 1% Triton X, 0.1% NaDeoxycholate), twice with Buffer B (20mM Tris pH 8.0, 1mM EDTA, 0.5% NP40, 0.5% Na Deoxycholate,

0.25M LiCl), twice with TE Buffer. Following washes, precipitates were re-suspended in a Reverse Crosslinking Buffer containing 100 mM NaHCO3 and 1% (w/v) SDS.

Crosslink reversal was done at 65C°C for 6 hr and ChIP DNA were isolated using DNA purification beads (MagBio).

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ER ChIP was performed using a nuclear extraction protocol. Cell pellets were collected as above and allowed to thaw on ice. Pellets were then resuspended in Lysis

Buffer 1 (50mM HEPES [pH 7.5], 140mM NaCl, 1mM EDTA, 10% glycerol, 0.5% NP-40,

0.25% Triton) and incubated at 4C with rotation. Cells were pelleted by centrifugation at 2000 rpm for 4 min at 4C. Supernatant was removed and cell pellet was resuspended in Lysis Buffer 2 (10mM Tris, pH [8.0], 200mM NaCl, 1mM EDTA, 0.5mM EGTA). Cells were pelleted by centrifugation at 2000 rpm for 4 min at 4C. Supernatant was removed and cell pellet wash resuspended in Lysis Buffer 3 (0.1% NaDeoxycholate, 0.5% N- laurylsarcosine, 1mM EDTA, 0.5mM EGTA, 10mM Tris pH [8.0], 200mM NaCl). Covaris

S220 was used for shearing according to manufacturer’s instructions. Once shearing was complete, lysates were treated with 3.5% final volume triton-X 100 and debris was pelleted by centrifugation at 13000rpm for 10 min at 4C. Sheared chromatin was diluted in Dilution Buffer (20mM Tris [pH 8.0], 150mM NaCl, 2mM EDTA, 1% Triton X-100) and incubated with 5ug ER (HC-20) antibody. Antibodies were incubated overnight at 4C with rotation and captured on protein A magnetic beads (Invitrogen, DynaI). The same 6

ChIP washes as used for GRHL2 ChIP as indicated above were then performed.

Precipitates were extracted twice with Elution buffer (50mM [Tris pH 8], 1mM EDTA,

1% SDS) heated to 65C for 10 min each. Eluates were pooled, treated with 240mM NaCl and heated at 65C for at least 6 hr, followed by 4.25mM EDTA and 20ug proteinase K at

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42C for 1 hour. Extracted DNA was then cleaned using Qiagen PCR purification Kit according to kit instructions.

For ChIP-qPCR, qPCR analysis was performed in 384-well format with 4ul reactions and data was normalized to the input for the immunoprecipitation.

Primers used for ChIP qPCR, include:

FoxA1/GRHL2 Site 1 (“Keratin Site #1”) Forward: 5’ AGTGGGAAGGCATGGTTATGA 3’ Reverse: 5’ AGCTCATGAAGTTCTCAGCCT 3’

FoxA1/GRHL2 Site 2 (“AGR2/3 #1”) Forward: 5’ TCTGATGTGGTCCCATGAGG 3’ Reverse: 5’ TCTGATGTTTCTTGGTTCTTGCT 3’

FoxA1/GRHL2 Site 3 (“GALNT12 Site 1 #1”) Forward: 5’ TGCACTGTTTCCCTTAGAGGT 3’ Reverse: 5’ TGGTTTTAAATCCAAGATGACCAGG 3’

FoxA1 GRHL2 Site 4 (“LYPD3 Site 1 #3”) Forward 5’ TCTCTCTCTCTCTTGCTGTCTCT 3’ Reverse 5’ AACGAAGGGCTTGTTTAATTTTAATT 3’

Jeff Jasper “AGR2-3” Forward 5’ TCTTCTGTTTGCAAGTGTTTTCCA 3’ Reverse 5’ TCTTCTCTGTTTGCTTTCTAGGC 3’

Jeff Jasper “TFF1-1” Forward 5’AGGTGTTTCCTAGACATGGTCA 3’ Reverse 5’ GCCAAGATGACCTCACCACA 3’

Jeff Jasper “XBP-1” Forward 5’ GGTCACAGGCTGCCAAGTAT 3’ Reverse 5’ AGGTCTGTGTGCTCAGCAAA 3’

Negative Ctrl #1 (Chr 22) Forward 5’ GCTGACACCTTGATTTCAGCC 3’

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Reverse 5’ TGTAGGTCAGAGGTCCAGCA 3’

Negative Ctrl #2 (TW 643 – 644) Forward 5’ CCTGGGGTATGTGTGGGTATC 3’ Reverse 5’ GGTTTAGGCTTGGTCTTCTCC 3’

7.8 ChIP-Seq library generation and sequencing

ChIP library construction was done by an automated protocol using the Kapa

HTP library preparation kit (KR0426 Kapa Biosystems). All automation was performed using the Sciclone NGS Workstation (P/N SG3-31020-0300, PerkinElmer, Waltham, MA).

Independent biological samples in triplicate were sequenced using Illumina NS500

Single-End 75bp (SE75).

7.9 siRNA experiments

For experiments involving transient transfection of small interfering RNA

(siRNA), validated siRNA or siRNA control were used as indicated and listed below.

Cells were plated in phenol red-free DMEM containing 8% CFS, 0.1 mM NEAA and 1 mM NaPyr in the presence of 60 nM siRNA or siRNA control using Lipofectamine RNAi

MAX as the transfection agent according to the manufacturer's recommendations. Cells were harvested for qPCR analysis after 72 hrs or allowed to grow for 9 days to assay proliferation, as detailed below. siRNA sequences used, include: siCtrl No. 1 (AM4611) (Ambion/ThermoFisher) siGRHL2 A (Ambion/ThermoFisher): Sense – GGAGACUGACGAUGUGUUtt 166

Antisense – GAACACAUCGUCAGUCUCCtt siGRHL2 B (Ambion/ThermoFisher): Sense – GGUCACGCUCAUGGAAAUCtt Antisense - GAUUUCCAUGAGCGUGACCtt siGRHL2 C (Ambion/ThermoFisher) Sense – GGGAGAUUGAUAUAUGUACtt Antisense – GUACAUAUAUCAAUCUCCCtt siSMAD4 #1 (10620318) (Invitrogen) SMAD4HSS106255 Sequence: 5’ GCCCUAUUGUUACUGUUG AUGGAUA 3’ siSMAD4 #3 (10620318) (Invitrogen) SMAD4HSS180973 Targeting Sequence: 5’AUUACUUGGUGGAUGCUGGAUGGUU 3’ siCtrl (1027310) (Qiagen) Targeting Sequence: 5’ AATTCTCCGAACGTGTCACGT 3’ siLYPD3 A (SI03082072) (Qiagen) Targeting Sequence - 5’ CCGGCAGGTAATGAGAGTGCA3’ siLYPD3 B (SI03084291) (Qiagen) Targeting Sequence – CGCCAGCGATCATGTCTACAA siLYPD3 C (SI00105707) (Qiagen): Targeting Sequence - CACCAGGACCGCAGCAATTCA

7.10 Proliferation assays

2500 cells per well of TamR cells were reverse transfected with siRNA as indicated above at the time of plating into 96 well plate. Plates were collected 2, 5, 7 and

9 days after transfection as indicated. 2500 cells per well of primary breast cancer associated fibroblasts were plated into 96 well plate. On collection day, media was decanted and plates were frozen at -80C. Plates were thawed completely at room 167

temperature after which 100ul of H2O was added to each well and incubated at 37C for

1 hr. Plates were refrozen, thawed at room temperature, and DNA content was detected using a Fluoreporter assay (Invitrogen) per manufacturer’s instructions.

7.11 Tissue microarray

Tissue microarray was obtained from and prepared by the laboratory of Dr.

Jeffrey Marks. All samples were de-identified prior to receipt of materials.

Immunohistochemistry (IHC) was performed on the prepared microarray slides using

Biocare Medical Supply IHC staining kit, including Background terminator (BT967L), 4+

Biotinylated Universal Goat Link (GU600H), 4+ Streptavidin HRP Label (HP604H), and

Da Vinci Green Diluent (PD900L). Slides heated to 60C for 1 hour to melt paraffin, and then were deparaffinized and hydrated in a graded series of ethanol. Heat retrieval was performed using a sodium citrate buffer at a pH of 6 in a pressure cooker for 25 min and subsequently allowed to cool in sodium citrate to room temperature. Suppression of endogenous peroxidase was achieved using 3% hydrogen peroxide for 15 min.

Background terminator was applied for 10 min. Primary antibody staining was performed using GRHL2 (HPA0004820) at 0.05ug/ul concentration, diluted in Da Vinci

Green antibody dilution solution with 1% goat serum, at 4°C overnight. Tissue sections were washed with 3 times with tris buffered saline with 0.1% polysorbate 20 (TBST), incubated with 4+ Biotinylated Universal Goat link at room temperature for 10 min, and the washed 3 more times with TBST. Immunoreactivity was detected using the Dako

168

liquid DAB+ substrate chromogen system, as follows: incubation with 4+ Streptavidin

HRP for 10 minutes, rinse 3 times with TBST, and incubation with DAB chromogen for 5 min. After washing, hematoxylin staining and Blue Nuclei staining were performed, followed by further washing, and stepwise dehydration with ethanol washes. Final steps include mounting and coverslip application.

Degree of GRHL2 staining was scored by Dr. Allison Hall in the Duke

Department of Pathology, who was provided only the de-identified patient ID number and sample grid. Hemotoxylin & Eosin staining was used to confirm presence of carcinoma within the samples. Samples were then scored on a 0 (absent) to 3 (high) scale to reflect degree of staining.

Statistical analysis was performed by Laurel Jiang and Dr. Terry Hyslop, the

Director of Duke Cancer Institute Department of Biostatistics. Assessment of GRHL2 protein as determined by Dr. Allison Hall, was merged with de-identified clinical variables, including pathologic T and N stage, ER, PR and time to recurrence.

Associations of GRHL2 with T stage, N stage, ER and PR status was completed with chi- square tests. Association with time to recurrence was completed with Kaplan-Meier estimator and the logrank test.

7.12 Preparation of mammary gland whole mounts

Inguinal mammary fat pad was harvested, flattened between glass slides, and fixed overnight in 4% paraformaldehyde. The tissue was rinsed twice in PBS and stained

169

in carmine alum solution overnight at room temperature. The tissue was then washed in an ethanol dilution series: 1 hour in 70% ethanol, 1 hour in 95% ethanol, 1 hour in 100% ethanol. Following washes, tissue was cleared in xylene overnight and subsequently mounted on glass slides.

7.13 Analysis of mammary gland whole mounts

Images of whole mounts of carmine alum stained mammary fat pads were acquired using the Zeiss laser capture and microdissection scope available through the

Duke Microscopy core. Composite images, including the whole mammary fat pad, were used for quantitation. Length of mammary fat pad and total length of branching was measured using the ruler tool in image J. Total length indicates the maximum length of the fat pad. Total length of branching was measured from the initiating bud to the end of the longest branch. Total branches terminal branches (end of the mammary duct) were tallied manually using ImageJ.

7.14 Isolation of bone marrow derived macrophages

Bone marrow was harvested by tibia and femurs from euthanized mice, as indicated. After manual dissection of muscle tissue, bones were placed in phosphate buffered saline (PBS) solution containing solution, 5% fetal bovine serum and EDTA. In a tissue culture hood, bones were ground with mortar and pestle in PBS solution.

Ground material was transferred to a 50 ml conical and pass through a 100uM filter, flushed, and passed through a 40uM filter and flushed again. Collected material was

170

pelleted by centrifugation at 2000rpm and supernatant was removed. The remaining pellet was resuspended in ammonium-chloride-potassium (ACK) Lysis buffer (Thermo

Fisher, A1049201) for 2 min, washed in PBS, pelleted again and resuspended in differentiation media collected from L929 cells. Cells were plated in 6 well plate, and differentiated to macrophages in the presence of L929 media until collection at 7 days.

7.15 Plasma preparation for oxysterol measurements

Following CO2 induced euthanasia, whole blood was collected from the mouse via cardiac puncture and immediately transferred to a EDTA-treated blood collection tube (BD Mictrotainer tubes with K2E (K2EDTA), Catalog number 365974). Cells and platelets were removed from plasma via centrifugation for 15 minutes at 2000 x g using a refrigerated centrifuge at 4C (Eppendorf centrifuge 5415 C with 18 place fixed angle rotor for 1.5- 2.0ml tubes). The resulting supernatant was designated plasma. Following centrifugation, the plasma transferred to a clean polypropylene tube, snap frozen in liquid nitrogen, and stored at -80C.

7.16 Animal breeding, care and use

All studies involving animals were approved by the Institutional Animal Care and Use Committee at Duke University. Animals were housed in a temperature controlled room with a daily 12 hour light / 12 hour dark cycle and free access to food and chow. For high cholesterol diet studies, C57BL/6 mice were placed on a high

171

cholesterol diet (2% cholesterol, 0.5% sodium cholate) or control diet (containing 0.5% sodium cholate), ad libitum.

7.17 Xenograft experiments

MCF7 and TamR xenografts were established in ovariectomized athymic nude mice as previously described (Nelson, Wardell et al. 2013).

7.18 Statistical Methods

qPCR and transfection data are represented as mean +/- standard error of the mean. Tumor growth data was analyzed by either 2-way ANOVA (non-repeated measures) followed by a Bonferroni’s T-test, or by Kaplan-Meier survival analysis.

Prism (Graphpad) was used for graphing and statistical tests unless otherwise indicated within the figure legends.

7.19 RNA Sequencing

Samples for RNASeq analysis for TamR cell lines treated with control siRNA or two independent siRNA sequences to GRHL2 were prepared as above. Independent biological triplicates were assessed using stranded mRNAseq on Illumina Hi-Seq with

50bp Paired End Rapid Run.

172

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Biography

Kimberly Cocce is the middle daughter of Al and Cheryl Cocce, born in

Attleboro Massachusetts on August 19, 1987. She left New England in 2005 to attend

Duke University for undergraduate studies. She received her Bachelor’s Degree in

Biology with a Minor in Chemistry in May 2009. It was during those formative years that she decided she wanted to become a physician scientist. She chose to stay at Duke following completion of her undergraduate degree to pursue a medical degree, entering

Duke University School of Medicine in Fall of 2009. During her first years of medical school, it became apparent to her that in order to be the most effective clinician, further study of pharmacology and drug development was warranted, which lead her to pursue her graduate studies in the Department of Pharmacology. During her time as a graduate student, she has co-authored two papers: Matsuura K, Huang NC, Cocce K, Zhang L,

Kornbluth S. (2017) Downregulation of the Proapoptotic Protein MOAP-1 by the UBR5

Ubiquitin Ligase and Its Role in Ovarian Cancer Resistance to Cisplatin. Oncogene. 36

(12):1697–1706 and Stice J, Wardell S, Norris J, YIlanes A, Alley H, Haney V, White H,

Safi R, Winter P, Cocce K, Kishton R, Lawrence S, Strum J, McDonnell DP. (2017)

CDK4/6 Therapeutic Intervention and Viable Alternative to Taxanes in CPRC. Mol

Cancer Res. At the time of defending her thesis, she is in the process of finalizing her dissertation work for publication. She has also contributed substantially to the

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advancement of the work of other members of the labs in which she has worked, a result of which she is proud given the incredible collaborative nature of modern science. As a graduate student, she was awarded a Ruth Kirchstein NRSA F30 fellowship, which supported the completion of her studies.

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