Exploring Estrogen Receptor Gene Regulatory Mechanism in Breast Cancer

Exploring estrogen receptor gene regulatory mechanism in breast cancer

______

A Dissertation Presented to

the Faculty of the Department of Biology and Biochemistry

University of Houston

______

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

______

Anne Chinenye Katchy

August 2013

Exploring estrogen receptor gene regulatory mechanism in breast cancer

______Anne Chinenye Katchy

APPROVED

______Dr. Cecilia Williams, Chairman

______Dr. Robert Schwartz

______Dr. Paul Webb Methodist Research Institute, Houston, TX

______Dr. Xiaolian Gao

______Dean, College of Natural Science and Mathematics

Acknowledgements

This dissertation was made possible because of the guidance from my advisor, guidance from my committee members, and help and support from family and friends.

I would like to express my deepest gratitude and appreciation to my advisor, Dr. Cecilia

Williams, for believing in me and letting me do my dissertation in her lab, for her patience and excellent guidance and support. I would like to thank you, Cecilia, for making this dissertation possible, and for your time and effort that you have put into preparing me for this moment. I am forever grateful to you. I would also like to thank my committee members: Dr. Robert Schwartz, Dr. Paul Webb, and Dr. Xiaolian Gao. I want to thank you all for your guidance, advice, and time you have given me to make this dissertation possible. I am very grateful to you all.

I would like to thank all my dear friends and colleagues Trang, Philip, Jun, Eylem, Karin,

Kim, Caroline, Ka, Lucy, Fotis, Efi, Sharanya, Stella, Ejike, and colleagues at CNRCS.

Thank you all for the discussions, advice, and support. I appreciate you all.

I would like to thank Ana, Esra, and Bukky. I have never known such good friends. I thank you all for your love and support. I remember when we first started, and look at us now. I love you all.

I would most especially like to thank my family. I would like to thank my parents for their everlasting love and care. I thank you both for giving me the opportunity to study abroad and for believing in my dreams. I thank you both for everything. I would like to say a special thank you to my older sister, Ngozi. You have been an inspiration to me. I thank you for your encouraging words, and for never letting me give up on anything. You

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are my strength and I would always love you. To my brothers, Nnamdi and Kenechukwu,

I would like to say thank you for your love and support, and especially for always being protective of me. To my baby sister, Oby, I thank you for your love and for being you.

The sky is the limit for you, my dear. And finally, I would like to thank my friend, Laura, for being a sister to me. I thank you for your support, love, and care. You are the true definition of what a friend should be. You are my family and I appreciate you.

Exploring estrogen receptor gene regulatory mechanism in breast cancer

______

An Abstract of a Dissertation

Presented to

the Faculty of the Department of Biology and Biochemistry

University of Houston

______

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

______

Anne Chinenye Katchy

August 2013

ABSTRACT

Estrogen has vital roles in development and maintenance of mammary gland and supports the growth of the majority of primary breast cancer. It carries out its function through the estrogen receptors (ER): ERα and ERβ. In this dissertation, we focused on identifying the functional and genome wide effects of both receptors in breast cancer cell lines (T47D and MCF7).

First, we aimed at identifying microRNAs (miRNAs) that are significantly associated with normal or disrupted estrogen signaling in breast cancer cells, and are regulated by ERα and ERβ. Despite the fact that 24h estrogen treatment results in strong changes to expression of about 900 protein-coding transcripts, we found no significant changes in mature miRNA expression levels by 17β-estradiol (E2)-activated ERα in neither T47D nor MCF7 breast cancer cells. On the other hand, when studying the effects of exogenously expressed ERβ, we identified miR-135a, miR-21, miR-200c, and miR-

522, among others, as being regulated. Most of the ERβ effects were ligand-independent, but we observed a significant response to E2 in the MCF7-ERβ cells. Also, the MCF7-

ERβ showed enhanced stem cell abilities in comparison to the MCF7-Control cells as illustrated by increased mammospheres formation during several generations.

Secondly, we aimed at investigating environmental factors and its effect on the risk for breast cancer. We identified the gene expression response for each of these compounds: BPA, genistein (Gen), E2, and soy formula extract (SF) in MCF7 cells, and found that each regulated similar genes in the same manner. Many target genes regulated

by BPA, Gen, and SF, were involved in important biological processes identical to those of estrogen. Investigation of non-ERα mediated regulations for these EDCs suggested that these compounds may regulate gene transcription solely through ERα, in the cells and doses used. Furthermore, we found that these compounds acted together in a sub- additive manner, and tumor clustering with the gene expression profile of these EDC compounds revealed a significantly lower disease free survival.

Altogether, the data presented in this dissertation would aid in increasing the knowledge of early risk factors for breast cancer. Our work provides data for future use of the estrogen receptors in clinical applications by providing new candidates for pharmaceutical drug development, as well as, biomarkers for diagnosis and prognosis.

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Table of Contents

Page Chapter 1 1 Introduction 1 1.1 Breast cancer 2 1.1.1 Origin and causes 3 1.1.2 Molecular characteristics of breast cancer 7 1.1.3 Treatment approaches 12 1.2 MicroRNA 13 1.2.1 miRNA biogenesis and mechanism of action 14 1.2.2 miRNA and breast cancer 17 1.3 Molecular functions of estrogen in breast cancer 20 1.3.1 Estrogen synthesis 20 1.3.2 Estrogen receptors 21 1.3.3 Estrogen receptor and miRNA 25 1.3.4 Influence of endocrine disruptive chemicals 26 Chapter 2 30 Materials and methods 30 2.1 Materials 31 2.2 Cell cultures 32 2.3 RNA preparation and cDNA synthesis for mRNA and miRNA analysis 33 2.4 Gene expression analysis 34 2.4.1 qPCR for mRNA and miRNA expression analysis 34 2.4.2 Microarray experiment for mRNA and miRNA analysis 35 2.5 Bioinformatics 36 Chapter 3 37 Estradiol-activated ERα does not regulate mature miRNAs in T47D breast cancer cells 37 viii

Page 3.1 Abstract 38 3.2 Introduction 39 3.3 Methods 41 3.3.1 Cell culture 41 3.3.2 RNA extraction 42 3.3.3 cDNA synthesis 42 3.3.4 miRNA microarray analysis 42 3.3.5 qPCR analysis of mRNA and miRNA expression 43 3.3.6 Bioinformatics 44 3.4 Results 45 3.4.1 Strong ERα-mediated transcription of protein-coding genes occur at 24h in T47D cells 45 3.4.2 Profiling showed minimal ERα-mediated regulation of miRNAs at 24h 47 3.4.3 qPCR confirms non-significant ERα regulation of miRNAs 48 3.4.4 Previously reported estrogen-induced miRNA regulations could not be reproduced 51 3.4.5 Time-course study of miRNA reveals a slight variation in miRNA expression 55 3.4.6 miRNAs with an ERα chromatin-binding site nearby are not regulated in T47D cells 57 3.4.7 miRNAs can differentiate between non-tumor and tumor cell lines 60 3.5 Discussion 60 3.6 Conclusion 64 3.7 Supplemental figures to Chapter 3 65 Chapter 4 68 Estrogen Receptor β regulates mature miRNAs in breast cancer cells 68 4.1 Abstract 69 4.2 Introduction 70 4.3 Methods 73 ix

Page 4.3.1 Cell culture 73 4.3.2 miRNA microarray analysis 73 4.3.3 qPCR analysis of mRNA and miRNA expression 74 4.3.4 siRNA analysis 74 4.3.5 Mammosphere formation assay 74 4.4 Results 76 4.4.1 Strong ERβ-mediated transcription of protein coding genes occurs at 24h in T47D and MCF7 cells 76 4.4.2 miRNA profiling reveals possible ERβ mediated regulation of miRNAs 78 4.4.3 qPCR confirms ERβ mediated regulation of miRNAs in T47D cells 80 4.4.4 Presence of ERβ may be responsible for miRNA regulation in T47D cells line 82 4.4.5 MCF7 showed a more pronounced ERβ-mediated regulation of miRNA 82 4.4.6 ERβ mediates a ligand-dependent regulation of miRNAs in MCF7 cells 85 4.4.7 ERβ affects mammosphere formation abilities in breast cancer cells 85 4.4.8 miR-135a in breast cancer cells 90 4.5 Discussion 97 4.6 Conclusion 100 Chapter 5 101 Bisphenol A and Phytoestrogens mediate identical and sub-additive effects on ERα-mediated transactivation 101 5.1 Abstract 102 5.2 Introduction 103 5.3 Methods 105 5.3.1 Cell culture 105 5.3.2 RNA preparation and cDNA synthesis 105 5.3.3 qPCR analysis for mRNA expression 106 5.3.4 Microarray experiment and analysis 106

Page 5.3.4 Bioinformatics 107 5.3.5 Survival cluster analysis 107 5.4 Results 108 5.4.1 Comparison of dose-dependent transcriptional activation of endogenous ERα by BPA, genistein, and soy formula extract 108 5.4.2 Sub-additive effects of EDCs on ERα-mediated transcriptional activation 110 5.4.3 Transcriptional regulation by BPA, genistein, and SF are solely through an ERα-mediated pathway 112 5.4.4 Extensive qPCR confirmations demonstrate that BPA, genistein, SF, and E2 all regulate identical genes 113 5.4.5 Gene ontology analysis showed that BPA, genistein, and SF can affect crucial pathways and biological processes 115 5.4.6 E2, BPA, genistein, and SF have similar patterns of response in gene regulation 118 5.4.7 EDCs target gene profile depict good and poor prognosis patient 120 5.5 Discussion 124 5.6 Conclusion 128 Chapter 6 129 Concluding remarks and future aims 130 Bibliography 133

Table of Contents for Figures

Page Figure 1.1 BC subtypes characteristics and their association with the expression pattern of CSC biomarkers 11 Figure 1.2 MicroRNA synthesis 16 Figure 1.3 ERα and ERβ structural comparison 22 Figure 3.1 Expression of ERα-regulated genes at varying time points 46 Figure 3.2 miRNA profiling of E2-activated ERα in T47D cells 48 Figure 3.3 Validations of miRNA microarray 50 Figure 3.4 qPCR analysis of previously reported miRNA regulations 53 Figure 3.5 miRNA regulation in other ERα-positive breast cancer cells 54 Figure 3.6 Time assays of miRNAs suspected to be regulated 56 Figure 3.7 Validation of miRNA with EREs within 200kb of ERα-binding site 59 Figure 3.8 Expression levels of miRNAs in different breast cancer sub-type and non-tumor breast cell lines 61 Figure S3.1 Basal expression levels of miRNAs in T47D breast cell lines 65 Figure S3.2 Basal expression levels of miRNAs in MCF7 breast cell lines 66 Figure S3.3 snRNA U6 expression in different breast cancer sub-type and non-tumor breast cell lines 67 Figure 4.1 Expression of pS2 gene by ERα and ERβ 77 Figure 4.2 Validations of microarray data in T47D-transduced cells 81 Figure 4.3 E2-ER regulation of miRNA in T47D-transduced cells 83 Figure 4.4 Validations of microarray data in MCF7-transduced cells 84 Figure 4.5 E2-ERβ regulation of miRNA in ERβ-transduced MCF7 cells 86 Figure 4.6 The mammosphere formation capacity is significantly enhanced by ERβ expression 89

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Page Figure 4.7 ICI treatment reduced ER-regulated miRNA levels 92 Figure 4.8 Silencing of ERα by siRNA in transduced MCF7 cancer cells 93 Figure 4.9 ERβ is responsible for miRNA regulation in MCF7 transduced cancer cells 94 Figure 5.1 Activation of ERα with varying concentrations of E2, BPA, genistein, and SF 109 Figure 5.2 Combined treatment of BPA and SF effects on ERα activation 111 Figure 5.3 Comparisons of BPA, genistein, SF and E2-induced gene expression profiles via ERα in MCF7 cells 114 Figure 5.4 qPCR confirmations of EDC gene expression profiles in MCF7 cells 116 Figure 5.5 EDCs time assays on known ERα-regulated genes 119 Figure 5.6 The EDCs gene expression profile cluster breast cancer patients with different outcomes 121

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Table of Contents for Tables

Page Table 4.1 Top regulated miRNAs from miRNA profiling of E2-activated ERβ-transduced T47D cells 79 Table 4.2 Pattern of regulation of miRNAs in T47D-ERβ and MCF7-ERβ 87 Table 4.3 Signaling pathways and biological process represented in overlap between miR-135a gene targets and ERβ gene profiles 96 Table 5.1 Gene ontology analysis of EDC-ERα differentially expressed gene lists 117 Table 5.2 Disease outcome for EDCs gene expression profile cluster analysis 122 Table 5.3 Gene ontology studies on genes overlapping between the EDC gene profile and survival cluster genes 123 Appendix A. miRNA profiling in ERβ-transduced T47D cells: Upregulated miRNAs 159 Appendix B. miRNA profiling in ERβ-transduced T47D cells: Downregulated miRNAs 160

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Manuscripts

The following publications and manuscripts have resulted from the dissertation:

 Katchy A, Edvardsson K, Aydogdu E, Williams C. Estradiol-activated estrogen receptor α does not regulate mature microRNAs in T47D breast cancer cells. (2012). Journal of Steroid Biochemistry and Molecular Biology, 128(3-5): 145- 53. Epub 2011.

 Katchy A, Pinto C, Jonsson P, Nguyen-Vu T, Pandelova M, Schramm KW, Gustafsson JA, Bondesson M, Williams C. Co-exposure to Phytoestrogens and Bisphenol A mimic estrogenic effects in a sub-additive manner. In revision.

 Ma R, Fredriksson I, Govindasamy K, Rosin G, Katchy A, Lindström L, Hilliges C, Blomqvist L, Frisell J, Williams C, Bergh J, Hartman J. Breast cancer stem cells express estrogen receptor β and can be targeted by a selective endocrine therapy. In preparation.

 Katchy A, Jonsson P, and Williams C. ERβ regulates mature miRNA in breast cancer cells. In preparation.

The following publications and manuscripts have resulted from collaborations:

 Aydogdu E, Katchy A, Tsouko E, Lin CY, Haldosen LA, Helguero L, Williams C. MicroRNA-regulated gene networks during mammary cell differentiation are associated with breast cancer. (2012). Carcinogenesis, 33(8): 1502-11.

 Thomas C, Rajapaksa G, Nikolos F, Hao R, Katchy A, McCollum C, Bondesson M, Quinlan P, Thompson A, Krishnamurthy S, Esteva FJ, Gustafsson JA. ERβ1 represses basal-like breast cancer epithelial to mesenchymal transition by destabilizing EGFR. (2012). Breast Cancer Research, 14(6): R148.

 Krementsov D.N, Katchy A, Case L.K, Carr F.E, Davis B, Williams C, Teuscher C. Studies in experimental autoimmune encephalomyelitis do not support developmental Bisphenol A exposure as an environmental factor in increasing Multiple Sclerosis risk. (2013). Toxicological Sciences. [Epub ahead of print].

 Jonsson P, Katchy A, and Williams C. Support of a bi-faceted role of estrogen receptor beta in estrogen receptor alpha positive breast cancer cells. Submitted.

Chapter 1

Introduction

1.1 Breast Cancer

Cancer has existed and has been known for over 3000 years, starting with the discovery in ancient Egypt where it was described as the Edwin Smith Papyrus. The word ‘cancer’ came from the father of western medicine, Hippocrates. He initially named the hard tumors “carcinos” for non-ulcer forming tumors, and “carcinoma” for ulcer-forming tumors. These names in Greek referred to a crab. This term was used because the tumors looked like a dark mass with fingerlike projections coming from the mass. The Greek words were later translated in Latin to the word “cancer”. Since this time, scientists have made tremendous efforts to understanding the human body and this disease especially the causes of cancer. Other theories have been postulated in regards to the causes of cancer since the humoral theory, from the infectious disease theory (Lusitani and Tulp, 1600s), to the lymph (Stahl and Hoffman, 1700), and finally to the blastema theory (Johannes

Muller, 1800s). Muller, a German pathologist, proposed that cancer was made up of cells that developed from the budding elements between normal tissues, thus dismissing previous theories. Later, Rudolph Virchow (Muller’s student) showed that all cells

(including cancer cells) were derived from other cells. The blastema theory and the discovery of Deoxyribonucleic Acid (DNA) (Watson and Crick, Mid 1900s) has created the platform from which scientist study the biology of cancer. Today, the causes of cancer is still not clearly defined, although external factors such as environmental chemicals and radiation, and internal factors such as genetic susceptibility, the immune system, and hormones play key roles in the development of this disease. So, what exactly is cancer? Cancer is an abnormal growth of cells caused by damage to DNA which leads 2

to multiple changes in gene expression and protein function that ultimately leads to an imbalance of cell proliferation and cell death. This imbalance leads to the formation of a population of cells that may invade tissues and metastasize to distant sites (other tissues).

There are numerous types of cancer depending on what tissue they affect. The cancer of specific interest for this thesis is breast cancer. Breast cancer (BC) is a malignant tumor that starts in the tissues of the breast. It is the leading cause of cancer-related death in women and a major health concern in the world today.

1.1.1 Origin and causes

There are several factors that could lead to BC. Age and sex are the two main risk factors: the older the woman, the more the risk of having this disease (Reeder and Vogel 2008).

Another factor in females is the absence of childbearing or breastfeeding.

Epidemiological studies showed that the earlier a woman gave birth, the more children she had, and the longer she breastfed, the more she was protected from breast cancer

(Cancer 2002). This may be related to hormone balance, which is an additional risk factor. High hormone levels can increase the chances of developing BC. The presence of estrogen is known to cause initiation, growth, and progression of carcinogenesis (Yager and Davidson 2006). Additional risk factors include obesity and an individual’s diet. Fat tissues produce excess estrogen as well as hormones known as adipokines (leptin and adiponectine) that stimulate or inhibit cell growth/proliferation respectively (Calle and

Kaaks 2004; Kershaw and Flier 2004). Other effects of fat include a direct/indirect effect on tumor growth regulators, inflammation, altered immune response, and oxidative stress. 3

Exposure to environmental chemicals, such as endocrine disrupting chemicals and naturally occurring compounds from diet, and radiation have led to occurrence of BC and other cancer types (Macon and Fenton 2013). Finally, inherited genetic mutations/alterations can lead to increased risk of having BC. These mutations account for about 5-10% of breast cancer cases (Gage, Wattendorf et al. 2012). There are two well-known high frequency (80-90% of cases) and high penetrance mutations: mutations in BRCA1 and BRCA2 genes. Both genes are tumor suppressor genes. BRCA1 is involved in DNA repair, cell cycle checkpoint control, transcriptional regulation, protein ubiquitination, and homologous recombination repairs. BRCA2 has a much less role than

BRCA1, but it is also involved in homologous recombination repairs. Mutation to these genes leads to an uncontrolled cell growth (cancer) due to the inability to regulate cell death (apoptosis) (Narod and Foulkes 2004), increasing the risk of breast cancer by 10-20 folds (Chen and Parmigiani 2007; Stratton and Rahman 2008). Other high penetrance but low frequency mutations include tumor supressors p53, PTEN, and STK11. A p53 mutation leads to what is known as the Li-Fraumeni syndrome. This is a rare hereditary cancer condition characterized by cancer development at a young age (average age of 25) and multiple primary tumors (Lustbader, Williams et al. 1992; Gonzalez, Noltner et al.

2009). p53 mutations account for less than 1% of all BC. A female with this syndrome has a breast cancer risk of 50% by age 45 and of 90% by age 60 (Le Bihan and Bonaiti-

Pellie 1994; Chompret, Brugieres et al. 2000; Birch, Alston et al. 2001; Allain 2008).

Mutations of PTEN, a protein phosphate, lead to the Cowden syndrome (CS), a multiple hamartoma syndrome characterized by multiple abnormal formations of normal tissues around the body and an increased risk of cancers. CS is accountable for less than 1% of 4

familial BC syndromes (Brownstein, Wolf et al. 1978; Starink, van der Veen et al. 1986;

Schweitzer, Hogge et al. 1999). Women with CS have a 67% risk of developing benign breast disease (Fackenthal, Marsh et al. 2001). Lastly, a mutation of STK11, a serine threonine kinase, leads to the Peutz-Jeghers syndrome (PJS). PJS is characterized by gastrointestinal polyposis and mucocutaneous pigmentations (Gage, Wattendorf et al.

2012). These individuals have an increased risk for developing BC disease (Boardman,

Thibodeau et al. 1998; Lim, Olschwang et al. 2004).

Thus, the numerous source of cancer vary, and may explain the variability between and within tumors. Variability between tumors of the same organ (intertumoral heterogeneity) and variability within the same tumor (intratumoral heterogeneity) are still under debate as to their cell of origin and causes. Two models have been postulated to explain this concept: The clonal evolution theory and the cancer stem cell theory

(Visvader and Lindeman 2008; Visvader 2011). The clonal evolution theory explains cancer as being formed through accumulations of genetic changes (mutations) in cells, and any of these genetically altered cells can give rise to cancer and drive tumor progression (Gil, Stembalska et al. 2008; Visvader and Lindeman 2008). Thereby, tumor cells with a growth advantage expand and become the driving clone of tumor progression

(Merlo, Pepper et al. 2006; Polyak 2007). Additional mutations can occur at any level of progression and there are also changes in the stromal microenvironment (Polyak, Haviv et al. 2009). The cancer stem cell model explains that tumors originate from stem cells

(cancer stem cells) or progenitor cells (Costa, Le Blanc et al. 2007). The transformation of a normal stem cell (SC) into a cancer stem cell (CSC) is due to accumulation of genetic modifications and epigenetic changes (Costa, Le Blanc et al. 2007). The CSC 5

model assumes CSCs to have the ability to self-renew, to have multi-differentiative potential, and to resist apoptosis. CSCs have the ability to maintain tumor progression, and their daughter cells will differentiate into different cell types of that tumor

(intratumoral heterogeneity). Thus, the CSC theory explains that the initiation and progression of cancer is driven by CSCs (Visvader 2011).

The study of stem cells provides a platform to understand the origin and progression of BC (and other cancer types). In breast development, mammary stem cells

(MaSC) have been implicated. The mammary gland develops from a small number of cells derived from the ectoderm after birth (Stingl, Eirew et al. 2006). These cells generate a series of branching ducts that end in sac-like lobules in stromal tissue, which is composed of the epithelium. This epithelium consists of the outer myoepithelial cells

(basal layer) and the inner layer of luminal cells (comprising of the ductal and alveolar subtypes) (Shackleton, Vaillant et al. 2006; Stingl, Eirew et al. 2006). The mammary stem cells have been implicated in the expansion of the mammary epithelium (that is, into myoepithelial and luminal cells) during puberty and pregnancy, and the regenerative capacity during each reproductive cycle (Shackleton, Vaillant et al. 2006). The actual identity of the MaSC and its distribution in each tissue is not known (Visvader 2009), but there are several cell surface markers that have been found to be suitable for differentiating stem cell/progenitor cell populations from normal human mammary tissue.

These markers in human are CD44, CD24 (Al-Hajj, Wicha et al. 2003; Clarke, Dick et al.

2006), and aldehyde dehydrogenase 1 (ALDH1) (Ginestier, Hur et al. 2007).

The origin of cancer, causes of cancer, progression of cancer, cancer stem cell identity and distribution, and the hierarchical relationship between stem cells and 6

differentiated cells need to be further studied to provide a better understanding for the origin of this disease.

1.1.2 Molecular characteristics of breast cancer

Breast cancer is a heterogeneous disease. These differences arise from their molecular

(gene expression) profile, cellular morphology, proliferative index, genetic lesions, expression of hormone receptors and growth-factors receptors (specific markers), and their therapeutic response (Visvader 2011). Breast cancer is clinically evaluated by tumor size and morphology, lymph node status, and expression of specific markers ERα, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)

(Wesolowski and Ramaswamy 2011). Molecular evaluations of BC have led to the identification of five molecular subtypes: luminal A, luminal B, HER2+, basal-like, and normal breast-like (Perou, Sorlie et al. 2000; Herschkowitz, Simin et al. 2007; Polyak

2007). These subtypes are conserved across ethnic groups, and each has their distinct tumor progression pathway (Polyak 2007). Both luminal A and luminal B subtypes are characterized by the expression of ERα, PR, and ER response genes. They both share similar gene profile of the luminal epithelium of the breast and express luminal cytokeratins 8 and 18 (Sorlie, Tibshirani et al. 2003). Luminal A tumors encompass about

40% of all BC, are HER2 negative with concurrent low expression of HER2-cluster genes, and have low expression of proliferative cluster of genes including Ki67. About

15% of luminal A tumors habor p53 mutation (Koboldt DC 2012). Patients with luminal

A tumors have the best prognosis, with high survival rates, and low recurrence rates

(Carey, Perou et al. 2006; Voduc, Cheang et al. 2010). Luminal B tumors are distinct from luminal A, in that they have a variable expression of HER2-cluster genes, a higher expression of Ki67, and a relatively lower expression of ERα-related genes. Luminal B tumors’ prevalence is about 20%, and patients with this tumor type have about 30% occurrence of a p53 gene mutation and a poorer prognosis (Carey, Perou et al. 2006;

Lund, Butler et al. 2010; Dawood, Hu et al. 2011). The HER2 subtype tumors express high levels of HER2 and HER2 associated genes (Bertucci, Borie et al. 2004), and about

75% of HER2 tumors contain p53 mutations (Carey, Perou et al. 2006; Kim, Ro et al.

2006). They are ERα and PR negative, usually lymph node-positive, and susceptible to early and frequent recurrence and metastases (Carey, Perou et al. 2006; Kim, Ro et al.

2006; Dawood, Hu et al. 2011; Yang, Chang-Claude et al. 2011). The prevalence of

HER2 tumor is about 10-15. Most basal-like tumor subtype are ERα negative, PR negative, and HER2 negative, and is thus also called triple-negative BC. Basal-like tumors cells share similar features to the cells of the outer/basal cells lining the mammary ducts. These tumors tend to express HER1 and cytokeratin 5 and 6, and most of these tumors contain p53 mutations (Carey, Perou et al. 2006; Turner and Reis-Filho 2006;

Koboldt DC 2012). The prevalence of the basal-like tumor is 15-20%, and these tumors are usually aggressive and have poorer prognosis. Most BRCA1 BC belong to the basal- like/triple-negative subtype (Turner and Reis-Filho 2006; Hartman, Kaldate et al.

2012).The normal breast-like tumor subtypes encompass about 6-10% of all BC, and these tumors are often small and have good prognosis (Carey, Perou et al. 2006). Normal

breast-like tumors are characterized by the expression of adipose tissue genes and the genes of non-epithelial cells (Sorlie, Perou et al. 2001). Recently, a sixth subtype of breast tumors has emerged and is known as the claudin-low BC. This tumor is characterized by low gene expression of tight junction proteins claudin 3, 4, and 7, and low expression of E-cadherin (a cell-cell adhesion glycoprotein) (Herschkowitz, Simin et al. 2007; Prat, Parker et al. 2010). These tumors are mostly triple-negative and have an inconsistent basal gene expression signature (Herschkowitz, Simin et al. 2007). A summary of these BC subtypes can be seen in Figure 1.1.

Other than the clinical use of these molecular subtypes in prognosis and for case- specific treatments, these subtypes of breast tumors have been linked to the cancer stem cell hypothesis. Each molecular subtype is presumed to be composed of cancer cells with a different level of differentiation, with CSC being the most prevalent in the least differentiated molecular subtype (Figure 1.1). A recent study has shown that the basal- like tumor subtype has the highest occurrence of tumor cells with a CD44+/CD24-/low and

ALDH1+ CSC phenotype (Ricardo, Vieira et al. 2011). Another study has shown enrichment of CD44-/CD24+ BC cell population in the luminal tumor subtypes

(Sheridan, Kishimoto et al. 2006; Fillmore and Kuperwasser 2008). CD44+ cells are considered to be more stem cell-like and CD24+ cells are more differentiated (Park, Lee et al. 2010; Schmitt, Ricardo et al. 2012). The prevalence of this CD44+/CD24-/low CSC phenotype in the basal tumors has led to poor/worse prognosis (Honeth, Bendahl et al.

2008; Park, Lee et al. 2010). A few studies have hypothesized that these basal-like tumors have a differentiation block since the majority of the cells do not display a differentiated phenotype (Charafe-Jauffret, Ginestier et al. 2006), and that the 9

transformation of basal-like stem cells to basal-like cancer requires mutation or downregulated BRCA1 expression in ERα-negative stem cells (Liu, Ginestier et al.

2008). Although the CD44 and CD24 status is associated with the BC subtypes, the

ALDH1 is not associated with any particular BC subtype (Ginestier, Hur et al. 2007).

ALDH1 cells are more often found in basal-like tumors and HER2-subtype tumors than in the luminal tumors. The claudin-low tumors have a tumor-initiating cell signature and a CD44+/CD24-/low CSC phenotype (Hennessy, Gonzalez-Angulo et al. 2009). This tumor subtype displays epithelial-to-mesenchymal transition (EMT) features, which includes high levels of ZEB1, ZEB2, Vimentin, Snail1, Snail 2, TWIST1, TWIST2, and low levels of E-cadherin and luminal genes (Hennessy, Gonzalez-Angulo et al. 2009).

These characteristics show a CSC and EMT phenotype and a low level of tumor differentiation.

The study association of CSC and the molecular BC subtypes increase our knowledge on the molecular and morphological heterogeneity of BC and can help with identifying the cell-of-origin of BC tumors.

Figure 1.1: BC subtypes characteristics and their association with the expression pattern of CSC biomarkers. Each subtype is associated with cancer stem cells with different levels of differentiation. Claudin-low has the most CSC phenotype, and the luminal subtypes have the most differentiated phenotype (Schmitt, Ricardo et al. 2012).

1.1.3 Treatment approaches

There are different types of treatments available for BC: lumpectomy and mastectomy surgery, external and internal radiation therapy, chemotherapy, and targeted therapy. Surgery involves the removal of part of all of the tissue area affected by cancer, but surgery cannot remove single metastatic cells and is usually followed by radiotherapy or chemotherapy. Chemotherapy uses drugs taken orally or injected into the vein to kill fast-growing cancer cells. Despite the benefits of chemotherapy and radiation in killing cancer cells, these traditional therapies are capable of harming normal cells as well as inducing gastrointestinal and musculoskeletal problems. Radiation and chemotherapy are still commonly used today. Targeted therapy is designed to identify and attack specific cancer cells without harming any normal cells. This form of treatment is either achieved by using monoclonal antibodies, tyrosine kinase inhibitors, and targets specific BC subtypes like the triple-negative BC (PARP inhibitors) and HER2 subtypes (Trastuzumab antibody) or by blocking estrogen synthesis or its receptor in ERα-positive tumors.

Numerous researches have been done on the mechanism of action of steroid hormone receptors, and this has led to the identification of potential molecular targets in cells

(Abdulkareem and Zurmi 2012). Common hormonal therapy uses tamoxifen or aromatase inhibitor. Tamoxifen treatment is used for both early and metastatic stages of

BC, especially for luminal subtypes that are ERα positive. Tamoxifen is an antagonist for

ERα receptors. Side effects of tamoxifen treatment include bone loss, endometrial cancer, and lowered cognitive abilities. Aromatase inhibitors are used to treat hormone-

dependent BC in post-menopausal women. This inhibitor blocks the production of estrogen, and thereby estrogen driven cancer growth. However, these cancers frequently develop resistance to tamoxifen and aromatase inhibitors.

The targeted and hormonal therapy forms of treatments are still under study to improve their effectiveness. Newer therapies are being developed, such as stem cell transplant which replaces blood-forming cells after normal cell damage from chemotherapy and radiation. Also, new potential targets are being studied among which include microRNA (miRNA).

1.2 MicroRNA

miRNA are small non-coding RNAs, about 19-24 nucleotides (nt) that function in post-transcriptional gene regulation. The initial discovery of miRNA dates back to 1993, when Victor Ambros, Rosalind Lee, and Rhonda Feinbaum studied the lin-4 and lin-14 genes of C. elegans. They discovered that the lin-4 small RNA pairs were complementary to the 3’ untranlated region (UTR) of the lin-14 gene, and repressed lin14 gene expression (Lee, Feinbaum et al. 1993; Wightman, Ha et al. 1993; Bartel 2004). The shorter lin-4 RNA is now recognized as the founding member of small regulatory RNAs called microRNAs (Lee and Ambros 2001). In 2000, miRNAs was detected in other species, including humans, with the discovery of the let-7 gene which shared similar functions with lin-4 and was about 22 nucleotides in length (Reinhart, Slack et al. 2000;

Slack, Basson et al. 2000). These lin-4 and let-7 miRNAs function in distinct stages of development. Other miRNAs with similar features, but different function have been 13

discovered since then. miRNAs are conserved in animals. Currently, the human genome is known to encode over 1,000 miRNAs (Bentwich, Avniel et al. 2005), and these target approximately 60% of the mammalian genes (Friedman, Farh et al. 2009). miRNAs continue to gain interest in research because they play critical roles in numerous biological processes such as apoptosis, cell growth/proliferation, differentiation, and development (Cheng, Fu et al. 2009). miRNAs have been shown to function as tumor suppressors or oncogenes (Calin and Croce 2006; Esquela-Kerscher and Slack 2006), and thus, have become a topic in clinical treatments of various cancer and other diseases.

Knowing the complete mechanism of these miRNAs would enhance understanding and prognosis of diseases.

1.2.1 miRNA biogenesis and mechanism of action

miRNAs are generated from either of the two sources: from their own genes (intergenic or transcribed as independent units), or from introns (transcribed together with the host gene) (Figure 1.2). miRNA genes are transcribed by RNA polymerase II in the nucleus.

The resulting transcript forms one or several double stranded hairpin structure that is poly-A tailed at the 3’end and capped at the 5’end, and this transcript is called the primary miRNA (pri-miRNA). A subset of miRNA is transcribed by RNA polymerase III

(Borchert, Lanier et al. 2006). The pri-miRNA may contain up to six miRNA precursors

(pre-miRNA), with each precursor containing about 70 nt. The pri-miRNA is processed by the microprocessor complex in the nucleus: the enzyme Drosha and the nuclear protein DGCR8 (DiGeorge Syndrome Critical Region 8), into the pre-miRNAs. The pre- 14

miRNA hairpins are exported from the nucleus into the cytoplasm by the nucleocytoplasmic shuttle protein, Exportin5. In the cytoplasm, the 70 nt pre-miRNA is cleaved by the RNase III enzyme Dicer to produce a double stranded mature miRNA

(about 20 nt long). The dicer enzyme has been thought to unwind the strands of the mature miRNA, after which the mature miRNA is incorporated into the RNA-induced silencing complex (RISC). Only one strand of the mature miRNA is incorporated into the

RISC. The Argonautes (Ago) protein in the RISC complex binds to the mature miRNA and orient it for interaction and partial complementation with the 3’UTR of its target mRNA, while the complimentary mature miRNA strand is being degraded (Bartel 2004).

miRNAs regulate gene expression post-transcriptionally by either blocking its translation or degrading the target mRNA. The level of complementarity between the miRNA sequence and the sequence of the 3’UTR of its target mRNA determines through which mechanism the miRNA action would proceed (Lim, Lau et al. 2005). A near complete complementation between the miRNA and the target mRNA sequence would result in the degradation of the mRNA by the Ago2 protein, while an incomplete complementation would lead to silencing by blocking translation. The blocking of translation may be done either at the initiation step or the elongation steps of translation.

The essential aspect of the complementation for both mechanisms is in the seed sequence of the miRNA, usually located at positions 2 to 8 from the 5’ end of the miRNA, which must have a perfect match for gene regulatory activity (Inui, Martello et al. 2010).

Figure 1.2: MicroRNA synthesis. A model showing of miRNA biogenesis. miRNA is transcribed by RNA polymerase II, either from its own gene or from its host gene.

Drosha and Dicer enzymes process the pri-miRNA to mature miRNA, after which the miRNA active strand is incorporated into the RISC complex (Cheng, Fu et al. 2009).

1.2.2 miRNA and Breast Cancer

There is a global decrease in miRNA expressions in human cancer when compared to normal cells. This decrease has revealed that most miRNAs act as tumor suppressors (Lu,

Getz et al. 2005). A study has shown that the loss of miRNA expression (by knockdown of the protein enzymes: Drosha and Dicer) in cancer promoted tumorigenesis (Kumar, Lu et al. 2007). Also, miRNA levels have been shown to be lower in less differentiated tumors compared to the more differentiated tumors, thus implicating miRNA in cell differentiation (Gaur, Jewell et al. 2007; Aydogdu, Katchy et al. 2012).

In the years following the discovery of miRNAs, numerous miRNA profiling have revealed miRNAs that are uniquely expressed in various human cancers. In breast cancer, a few of these have been studied, and have been classified into tumor suppressors and oncogenes. Some of the tumor suppressor miRNAs include miR-17-5p, miR-206, miR-125a and -125b, the miR-200 family, miR-34a, and the let-7 family. Some of the oncomirs include miR-21, miR-155, and miR-373 and -520c family.

miRNAs involved in cell proliferation include tumor suppressors miR-206 and miR-17-5p among others. miR-17-5p is known to repress the translation of the nuclear receptor coactivators NCOA3 (SRC-3/AIB1) mRNA (Hossain, Kuo et al. 2006;

Edvardsson, Nguyen-Vu et al. 2013). NCOA3 enhances the transcriptional activity of

ERα, and repression of NCOA3 leads to decreased estrogen-stimulated proliferation and estrogen-independent breast cancer cell proliferation (Hossain, Kuo et al. 2006). Another study found that miR-17-5p also inhibited proliferation of breast cancer cell by repressing

CyclinD1 translation (Yu, Wang et al. 2008). miR-206 is known to suppress ERα expression and to inhibit the growth of MCF7 breast cancer cells (Adams, Furneaux et al.

2007). These two miRNAs are examples of candidates for novel breast cancer therapy.

Some miRNAs have also been associated with other breast tumor subtypes. miR-

125a and miR-125b are known to be downregulated in HER2-overexpressing breast tumors (Mattie, Benz et al. 2006). Their overexpression suppresses HER2 mRNA and protein levels, which causes a decrease in cell growth, cell motility, and invasiveness

(Scott, Goga et al. 2007). miR-34a is transcriptionally regulated by p53, and is known to be reduced in triple negative and mesenchymal breast tumors compared to normal epithelial and HER2-positive tumors (Kato, Paranjape et al. 2009). The p53 mutations in these tumors may be responsible for the low levels of miR-34a. miR-34a is also known to affect sensitivity to radiation in triple-negative breast tumors. Overexpression of miR-34 lead to a decreased sensitivity to radiation, thus, is protecting the cells from radiation- induced cell death (Kato, Paranjape et al. 2009). These miRNAs may also have therapeutic potential in these breast tumors.

Metastasis of breast cancer is a major concern in breast cancer research and treatment and some miRNAs have been associated with breast tumor metastasis. The oncomir miR-21, is known to be upregulated in breast tumors compared to the normal breast tissues, and suppression of miR-21 leads to a decreased cell growth and apoptosis and increased response to anti-cancer treatment (Si, Zhu et al. 2007; Zhu, Si et al. 2007). miR-21 is also involved in invasion and metastasis of tumors by targeting multiple anti- metastatic genes [reviewed in (O'Day and Lal 2010)]. The family of miR-373/520c has

also been identified to promote cancer cell invasion and migration both in vitro and in vivo (Kato, Paranjape et al. 2009). Identification of these miRNAs has increased understanding the mechanism of metastasis in breast tumors.

To further understand metastasis of breast tumors, some miRNAs have been identified to be involved in EMT, which promotes tumor invasion and migration. EMT is characterized by a loss of cell adhesion, a repression of E-cadherin, and increased cell motility. EMT is activated by transcriptional repressors including the Snail family and

ZFH family (ZEB1 and ZEB2) of transcription factors. The miR-200 family, a tumor suppressor miRNA family, is known to induce the epithelial by suppressing the expression of the EMT inducers ZEB1 and ZEB2 (Gregory, Bert et al. 2008). This miRNA family is also known to reduce stem cell self-renewal genes, reduce tumor growth, increase neural differentiation, and suppress tumorigenicity of breast cancer stem cells [reviewed in (O'Day and Lal 2010)]. Also the let-7 family is involved in EMT. This miRNA is less expressed in human cancers compared to normal tissue, and further reduced in breast tumor-initiating cells, but increases with differentiation (Yu, Yao et al.

2007). There are some oncogenic miRNA that promote EMT. miR-155 is overexpressed in human (malignant) tumors (Iorio, Ferracin et al. 2005). A study in mouse mammary cells has revealed that expression of miR-155 increased the occurrence of TGFβ-induced

EMT, cell invasion and migration by downregulating the RhoA gene that is responsible for cell adhesion, cell motility, and cell junction formation (Kong, Yang et al. 2008).

Additionally, miR-21 has been shown recently to induce EMT and CSC phenotype through the AKT and ERK1/2 pathways by targeting PTEN (Han, Liu et al. 2012).

microRNAs have been identified to play important roles in tumor progression and metastasis. Due to their small size and potential for in vivo delivery, they have become promising targets for therapeutic applications [reviewed in (Ishida and Selaru 2013)]. The complete mechanism and function of miRNAs is still unknown, and research in this area would be important in deciphering its function.

1.3 Molecular functions of estrogen in breast cancer

1.3.1 Estrogen synthesis

Estrogen is a hormone that is important in women during mammary gland development and for the reproductive cycle. Majority of breast cancers are ERα+ and depend on estrogen for growth (Section 1.1.2). This has made estrogen signaling a target for treatment in hormone-receptor positive breast cancer. Estrogen is synthesized in all vertebrate and in some insects, which depicts its evolutionary history (Ryan 1982).

Estrogen can either be natural (steroid hormone) or synthetic (xenoestrogens). There are three steroid hormone estrogens: estrone (E1), estradiol (E2), and estriol (E3). Estradiol

(E2) is the dominant form of estrogen. Estrogen is synthesized in the ovary (by the theca and granulosa cells). The theca interna cell synthesizes androstenedione from cholesterol.

Androstenedione crosses the basal membrane into the surrounding granulosa cells, and is converted to estrone or into testosterone. Both testosterone and estrone can then be converted into estradiol. The conversions of androstenedione into estrone and

testosterone into estradiol are catalyzed by the enzyme aromatase, which is found in the granulosa cells, while conversions of androstenedione into testosterone and estrone into estradiol are catalyzed by 17β-hydroxysteroid dehydrogenase (17β-HSD). Then, estrogen carries out its function by binding to and activating the estrogen receptors.

1.3.2 Estrogen receptors

Estrogen receptors belong to the nuclear receptors super family class I, which is characterized by its binding to steroids and ability to bind as homodimers (Mangelsdorf,

Thummel et al. 1995). There are two types of estrogen receptors: ERα (ESR1) and ERβ

(ESR2). ERα was the first nuclear receptor to be characterized (Green, Walter et al. 1986;

Greene, Gilna et al. 1986), and ten years later ERβ was discovered (Kuiper, Enmark et al.

1996). The estrogen receptors are homologous in their DNA binding domain (Figure 1.3) with nearly 97% similarity, and least similar in their AF-1 domain with a 24% similarity

(Dahlman-Wright, Cavailles et al. 2006). Both receptors are able to form homodimers, as well as, heterodimers which make them unique in the class I nuclear receptor family

(Pettersson, Grandien et al. 1997). The cellular and tissue expression and distribution of these two receptors are different and vary from cell to cell and tissue to tissue.

Figure 1.3: ERα and ERβ structural comparison. ERα has 595 amino acids compared to ERβ that has 530 amino acids. The most conserved component of the ER structure is the DNA-binding domain, and the least conserved is the AF1 domain (Dahlman-Wright,

Cavailles et al. 2006; Leitman, Paruthiyil et al. 2010).

The ERs can exert transcriptional regulatory function by activation with ligand

(estrogen). Estrogen exerts its function by binding to estrogen receptors, which in turn bind to their cognate DNA sequences (EREs), leading to the recruitment of co-activators and chromatin remodeling complexes that destabilize chromatin structure resulting in gene regulation of specific target genes. In addition to its genomic activity, estrogen has

“nongenomic” or “membrane-initiated” effects such as interaction with G-proteins, the p85 subunit of P13K, c-Src, and Cav-1 to initiate P13K/AKT and MAPK signaling cascades (Klinge 2009). Also, both receptors can be activated in a ligand-independent manner, by posttranslational modification including methylation, phosphorylation, acetylation, and SUMOylation of the ERs (Atanaskova, Keshamouni et al. 2002;

Thomas, Sarwar et al. 2008). Due to the differences in their ligand-binding domain

(LBD) and their AF1 domain, activation of the estrogen receptors can lead to different responses from each receptor type. Heterodimers can also lead to different responses and regulation of genes upon activation compared to homodimers (Papoutsi, Zhao et al.

2009). Co-expression of the two receptors has indicated that ERβ can oppose ERα action

(Matthews and Gustafsson 2003; Chang, Frasor et al. 2006; Williams, Edvardsson et al.

2008).

The ERE is a short sequence of DNA which when recognized is able to bind to the estrogen receptor and then regulate transcription. The ERE is cis-regulatory element, and is usually a pair of inverted repeats separated by three nucleotides, that can be bound by a homodimer. Studies have revealed that most EREs are located at both proximal and distal sites of the transcription start site (J.S. Carroll 2006), and that ERα can regulate transcription of a gene through both the proximal and distal binding site (Lin, Vega et al. 23

2007). Also, ERα transcription profiles have been well studied and established in breast cancer cells, with a number of identified ERα regulated genes such as PS2, KCNK5,

SPINK4, BCL2, and MYC (Williams, Edvardsson et al. 2008). ERβ, on the other hand, has less of established transcriptional profiles. This is because endogenous ERβ is expressed at very low levels in breast cancer cell lines, and various methods of overexpression of this receptor type in breast cancer cells is required. ChIP studies of

ERβ binding sites have confirmed a pattern of binding sites similar to that of ERα (Zhao,

Gao et al. 2010), and gene expression profiles have been published (Chang, Frasor et al.

2006; Williams, Edvardsson et al. 2008). There are five ERβ splice variants that exist:

ERβ1 to ERβ5 (Moore, McKee et al. 1998). The expression of these variants has been found to be linked with distinct prognostic outcome for breast cancer patients (Shaaban,

Green et al. 2008). ERβ1 is the most widely studied of these variants, and is the only variant that binds ligand. Reports on ERβ function in breast cancer have revealed that

ERβ can have multiple effects in breast cancer cells [reviewed in (Leygue and Murphy

2013)]. ERβ has been reported to decrease as tumor progresses (Speirs, Skliris et al.

2002), and has been associated with good prognostic markers, as well as, leading to better clinical outcomes (Omoto, Inoue et al. 2001; Nakopoulou, Lazaris et al. 2004; Sugiura,

Toyama et al. 2007). On the other hand, some reports have correlated ERβ expression to the proliferative marker Ki67 (Jensen, Cheng et al. 2001; Honma, Horii et al. 2013) and have associated its expression with higher risk of relapse in breast cancer patients

(Novelli, Milella et al. 2008). Also, ERβ has been shown to be anti-proliferative and proapoptotic in ERα-positive breast cancer cell lines (Lazennec, Bresson et al. 2001; Strom,

Hartman et al. 2004; Williams, Edvardsson et al. 2008). These divergent roles of ERβ 24

reported depend on the circumstance under which ERβ is expressed. This may result from alternative post-translational modifications, ERα status in the breast cancer cells, clonal selection or mutation in the ERβ-transfected constructs. It is still not entirely clear what role of ERβ plays in breast cancer progression; thus, its function still needs to be elucidated. Given the important role estrogen plays in breast cancer progression, understanding the molecular mechanism of both receptors is crucial.

1.3.3 Estrogen receptor and microRNA

MicroRNAs have been associated with various tumor subtypes (Section 1.2) and are also regulators of certain hormone receptor. As described above, miR-206 has been reported to repress ERα in MCF7 cells (Leivonen, Makela et al. 2009). Many studies have focused on miRNA regulation of estrogen receptors, and a few have focused on the effects of estrogen on miRNAs. Also, the majority of the studies conducted on E2 regulation of miRNA have been done on MCF7 breast cancer cells. Of the few studies conducted, it was found that ERα interacts with drosha and suppresses drosha’s activity in MCF7 cells and in the mouse uterine epithelial cells, thereby having a general effect on miRNA maturation (Yamagata, Fujiyama et al. 2009). E2-ERα has been reported to regulate let-7, miR-21, miR-22, miR-221, miR-222, and miR-206 among others [reviewed in (Klinge

2012)]. The regulation between miR-206 and ERα, suggests feedback loop mechanism that is yet to be corroborated. As reviewed by Klinge, there are only 19 studies that have

directly examined E2-ERα regulation in human cell lines, and of these, 14 of them were carried out in MCF7 cells (Klinge 2012). These studies have conflicting results on the regulation of these miRNAs. These differences could possibly arise from the mode of treatment with E2, time of exposure to E2, and the fact that E2-ER regulates miRNAs in a cell/tissue-specific manner. There are few direct studies of ERβ regulation of miRNA

(Grober, Mutarelli et al. 2011; Paris, Ferraro et al. 2012), and one which reports miR-92 as a regulator of ERβ (Al-Nakhle, Burns et al. 2010). More research is required to identify and characterize E2-regulated miRNAs in breast cancer cells, which may provide new insights into the function of estrogen signaling, and generate biomarkers and new therapeutic targets in breast cancer and other estrogen associated diseases.

1.3.4 Influence of endocrine disruptive chemicals

Endocrine disrupting chemicals (EDCs) are a large group of synthetic and naturally occurring agents that are capable of disrupting normal hormonal signaling of both endocrine and reproductive system in humans. Depending on which hormone system they disrupt, the consequences of exposure will vary. Many EDCs have the capability to interfere with estrogen signaling. This is partly an effect of the ligand-binding pocket of the estrogen receptors being more promiscuous than other NR pockets. Examples of estrogen disrupting agents are xenoestrogens such as bisphenol A (BPA) and phytoestrogens such as the isoflavones genistein and daidzein.

BPA is an industrial monomer used in the production of polycarbonates and epoxy resins. It is found in plastics, lining of cans, dental sealant, food wrappings, and 26

infant feeding bottles, where it can leach into food and drinks consumed by humans

(Brotons, Olea-Serrano et al. 1995; Singleton, Feng et al. 2004). Daily adult intake from such food-contact sources is between 0.05-10 µg per kilogram body weight and almost all Americans are exposed to the chemical BPA and have measurable levels in their blood, serum, urine, saliva, and various bodily fluids (Ikezuki, Tsutsumi et al. 2002;

Ouchi and Watanabe 2002; Sasaki, Okuda et al. 2005; Dong, Terasaka et al. 2011). As

BPA has estrogenic activity, and it is well established that a high lifetime estrogen exposure correlates with an increased incidence of breast cancer (Yager and Davidson

2006), there is a concern that BPA exposure has an impact on breast cancer development.

Epidemiologic evidence support that BPA is related to diseases in women (vom Saal and

Hughes 2005), including those of the breast, neuronal, brain development, immune, and ovarian disease. Exposures to BPA have been shown to induce changes in the mammary gland that are time and dose specific in rats (Moral, Wang et al. 2008). Other abnormalities that have been reported to be induced by BPA include ovary cysts, endometrial hyperplasia, and severe uterine lesions (Newbold, Jefferson et al. 2007).

Even though different risk assessment has led to different conclusions regarding the safety of BPA in the general population, it is of general agreement, based on estimations obtained from food consumption and concentration data, that infants have the highest BPA-exposure in the general population (Beronius, Ruden et al. 2010). While infants, and fetuses in utero, are normally shielded from their mothers estrogen; BPA can pass the placenta and expose fetuses, and can be excreted into breast milk and expose nursing babies. Formula-fed babies and young children are exposed through food, water and plastic containers. Thus, today, both fetuses and children are exposed to estrogenic 27

compounds. Animal studies have shown that pre-natal and early exposure alter mammary gland development (Markey, Luque et al. 2001), and pre-pubertal BPA exposure in rats increased susceptibility to mammary carcinogens (Betancourt, Wang et al. 2012).

Moreover, the risk assessments of BPA have not taken into consideration the combination of estrogenic compounds that infants may be exposed to, such as the isoflavones present in soy-based infant formula.

The most abundant phytoestrogens found in soybeans and soy-based food products include the isoflavone genistein. This compound has structural homology to endogenous estradiol (Sirtori, Arnoldi et al. 2005), and can bind to the estrogen receptors

(Satih, Chalabi et al. 2010). Studies have shown that isoflavones can exert agonistic or antagonistic effects on breast cancer cell proliferation (Wu, Yu et al. 2008) and have other biological effects (Satih, Chalabi et al. 2010). Genistein has been shown to both induce (Lucki and Sewer 2011; van Duursen, Nijmeijer et al. 2011) or inhibit the proliferation of estrogen-dependent breast cancer cells in vitro, with higher concentrations of the phytoestrogen (above 20µM) having an anti-proliferative effect on

MCF7 cell growth (Hsieh, Santell et al. 1998). Also, results show that both timing of exposure and endogenous estrogen environment play a critical role in the potential chemopreventive or tumor stimulating effects of genistein or soy products on the development of mammary cancer in vivo (Helferich, Andrade et al. 2008; Betancourt,

Wang et al. 2012).

Although there is general agreement that that the consumption of soy-rich diets can be beneficial to adults, controversy has developed over the adequacy and safety of soy formula for infant use based on these studies. Soy-based infant formulas (SF) account 28

for nearly 25% of the formula market in the United States, a number that represents 15% of all infants in this country, or about 750,000 infants per year (Bhatia and Greer 2008).

Infants fed soy-based formulas are exposed to 6-11 mg isoflavones per kg body weight day, while the intake of phytoestrogens from human milk is calculated to be <0.01 mg/day, negligible when compared to the amounts provided by soy formulas (Setchell,

Zimmer-Nechemias et al. 1998). Measured circulating concentrations of total genistein in 4-month-old infants fed soy-based formulas are 2.53 ± 1.64 µM, and this level is maintained due to frequent feedings and long half-life of the phytoestrogens (Setchell,

Zimmer-Nechemias et al. 1997). The high plasma isoflavone concentrations indicate that the absorption of soy isoflavones from the intestinal tract is efficient in infants consuming soy-based formula (Setchell, Zimmer-Nechemias et al. 1998). Disturbingly, the serum concentration levels of phytoestrogens in soy-based formula fed infants are 13,000 to

22,000 times higher than the levels of estradiol in early life (Winter, Hughes et al. 1976), and even though phytoestrogens are weaker estrogens relative to estradiol, it is possible that these circulating levels are enough to produce significant biological effects in infants

(Setchell, Zimmer-Nechemias et al. 1998). This makes soy-based formulas an important risk factor to consider in assessments of diseases.

Given that these EDCs have estrogenic activity and are able to activate the estrogen receptor, studying these compounds and how they affect transcription would present a platform from which preventive measures and treatment can be established.

Chapter 2

Materials and methods

The descriptions in this section are general materials and methods commonly used in subsequent chapters. Each chapter may have specific methods described within.

2.1 Materials

Cell Cultures: Dulbecco's modiﬁed Eagle's medium (DMEM), F12, Penicillin

Streptomycin, phenol red-free DMEM, and L-Glutamine were obtained from Life

Technologies™ (Grand Island, NY). Fetal Bovine Serum (FBS), dextran-coated charcoal treated FBS (DCC-FBS), insulin, and epidermal growth factor (EGF) were obtained from

Sigma (St. Louis, MO). For mammosphere cultures, Mammocult® Human Medium Kit, hydrocortisone, and heparin were obtained from Stemcell Technologies (BC, Canada).

RNA preparation: miRNeasy and RNeasy kits were used for total RNA with miRNA and total RNA without miRNA extractions respectively. Both kits and TRIzol were obtained from Qiagen (Valencia, CA).

cDNAsynthesis: For mRNA, the SuperScript III First-strand cDNA synthesis kit was used and obtained from Life Technologies. For miRNA, Ncode miRNA First-Strand cDNA synthesis kit (Life Technologies™ Grand Island, NY), High-specificity miRNA 1st strand cDNA synthesis Kit (Agilent Technologies Santa Clara, CA), and Taqman microRNA assays (cDNA) (Life Technologies™ Grand Island, NY).

Real-time PCR: Fast SYBR Green Master Mix (used for both mRNA and miRNA assays) and Taqman miRNA assay reagents were obtained from Life Technologies™

(Grand Island, NY).

Primers were custom designed and obtained from Integrated DNA Technologies

(Coralville, IA).

Compounds: 17β-Estradiol, bisphenol A, genistein, and ICI 181,780 were obtained from

Sigma-Aldrich (St. Louis, MO).

2.2 Cell Cultures

T47D and MCF7 cells were cultured in Dulbecco's modiﬁed Eagle's medium (DMEM) in proportion with F12 [DMEM/F12 (1:1)] and DMEM only, respectively, and each supplemented with 5% FBS and 1% Penicillin Streptomycin (Life Technologies™ Grand

Island, NY). The cells were synchronized by changing the medium to phenol red-free

DMEM/F12 (1:1) medium supplemented with 5% dextran-coated charcoal-treated FBS

(DCC) for 24 hours. The serum was then reduced to 0.5% DCC for 48 hours. The cultured cells were treated with 10nM of E2, vehicle (ethanol), 10nM-1μM ICI, 10nM-

10μM BPA and Genistein, or 0.01%-0.2% Soy formula extract (SF) for 0-72h. MCF10A cells were cultured in DMEM/F12 (1:1)supplemented with 5% FBS, 0.5mg/ml

Hydrocortisone, 20ng/ml EGF, 10g/ml Insulin, 2nM D/L- Glutamine, and 1% Penicillin

Streptomycin. MDA-MB-231 cells were cultured in DMEM/F12 (1:1) supplemented with

10% FBS, and 1% Penicillin Streptomycin. All cells were harvested in TRIzol (Life

Technologies™ Grand Island, NY) for RNA extraction.

2.3 RNA preparation and cDNA synthesis for mRNA and miRNA analysis

RNA Extraction: RNA was extracted and purified using the miRNeasy Mini Kit (for miRNA) and RNeasy Kit (Qiagen, Valencia, CA) with DNase I DNA degradation according to manufacturer’s protocol. RNA concentrations were measured using

Nanodrop 1000 Spectrophotometer (Thermoscientific, Pittsburgh, PA) and RNA integrity was measured using Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA).

cDNA Synthesis: For mRNA analysis: 1μg of the extracted total RNA was used for each cDNA synthesis reaction. First-Strand cDNA was synthesized using SuperScript III and random hexamer primers. A final concentration of 5ng/μl of cDNA was prepared from stock for use in qPCR. For miRNA analysis: 1μg of total RNA was used for each polyA tailing and First-Strand cDNA synthesis of miRNA using the NCode™ miRNA First-

Strand cDNA Synthesis and qRT-PCR Kit (Life Technologies™ Grand Island, NY) according to manufacturer’s protocol. For TaqMan miRNA Assays: Reverse transcription of 10ng of total RNA was performed according to manufacturer’s protocol using the

GeneAmp® PCR System 9700 (Life Technologies™ Grand Island, NY).

2.4 Gene expression Analysis

2.4.1 qPCR for mRNA and miRNA expression analysis

For mRNA expressions: 10ng cDNA was amplified using 1pmol of each of the forward and reverse primers, and SYBR green PCR master mix in a 10μl final reaction volume.

ARHGDIA or 18S RNA were used for normalization of mRNA expression.

For miRNA expressions: 16ng of poly-A tailed cDNA was amplified using 2pmol of each of the specific forward primer and the universal primer, and SYBR green PCR master mix in a 10μl final reaction volume. Expression of miRNA was additionally quantified using TaqMan MicroRNA Assays, according to manufacturer’s protocol (Life

Technologies™ Grand Island, NY). U6 snRNA was used for normalization of miRNA expression for both methods.

All runs were performed using the 7500 Fast Real-Time PCR System (Life

Technologies™ Grand Island, NY). Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. The change in mRNA or miRNA expression was calculated as fold change relative to control.Melting curve analysis was performed for all SYBR Green runs, confirming the amplification of one specific fragment. Student’s t-test was used for statistical analysis, using two-tailed distribution and two-sample unequal variance parameters. Regulations were considered significant when p<0.05.

2.4.2 Microarray experiment and analysis for mRNA and miRNA analysis

For miRNA microarray analysis: 5μg of isolated RNA from cells were sent to LC

Sciences (Houston, TX) where microarray expression profiles were determined using human miRNA microarray dual-color sample array by μParaflo® Microfluidic Biochip

Technology. The miRNA microarray contained 894 mature miRNA, based on Sanger miRBase Release 14.0, and 50 controls unique probes in quadruplets. Hybridizations were performed in duplicates with dye swap procedure. Differentially expressed miRNAs were considered significant when p-value<0.10 according to the company recommendations. Additional expression profiles for 381 miRNAs including U6 snRNA in quadruplets were additionally determined using TaqMan Low Density Array (TLDA)

(Life Technologies™ Grand Island, NY) using TaqMan Human microRNA A cards v2.0 according to protocol using the 7900HT Fast Real-Time PCR system (Life

Technologies™ Grand Island, NY). A starting total RNA amount of 700ng was used, and this assay was performed in duplicate.

For mRNA microarray analysis: Two-color comparative microarray technique covering the fully known transcriptome of 35,000 genes and variants using Operon’s long- oligonucleotide spotted array complemented with 133 oligos specifically synthesized for analysis of nuclear receptors, splice variants and co-regulators were used (Microarray

Inc®, AL, USA). Each comparison was replicated and dye-swapped (Cy5/Cy3).Analysis was performed essentially as described (Williams, Edvardsson et al. 2008). From each sample, 15-20µg of total RNA was used per cDNA synthesis and labeling. Labeled cDNA was hybridized to arrays for 35–40 hours at 42ºC, washed and scanned at 10µm 35

resolution using the Axon GenePix® 4400A microarray scanner (MDS Analytical

Technologies, Sunnyvale, CA, USA). The obtained Tiff images were analyzed using the

GenePix Pro 6.1 software (MDS Analytical Technologies). All data analysis steps were performed in the R environment as previously described (Richter et al., 2006).

Differentially expressed genes were identified using the empirical Bayes moderated t-test within the Limma package (Smyth, 2004), which generated a list of differentially expressed genes with p-values and B scores. Cut-off for differential expression was set to p-value <0.05and a logFC of > |0.3|.

2.5 Bioinformatics

For Gene ontology studies, Gene set enrichment analysis (GSEA) and sub-network enrichment analysis (SNEA)were performed for differentially expressed genes using the

Ariadne Pathway Studios program (Elsevier Inc. MD, USA), p-values <0.05 were considered significant.

For chromatin-binding comparisons, the University of California Santa Cruz (UCSC)

Genome Bioinformatics website, http://genome.ucsc.edu, was used to observe the relative distances between the possible miRNA promoter and ER-binding sites using the coordinates from ERα and ERβ ChIP data. miRNA coordinates were taken from miRBase (http://www.mirbase.org): the miRNA database in hg19 format and converted to usable format using UCSC LiftOver tool.

Chapter 3

Estradiol-activated ERα does not regulate mature

microRNAs in T47D breast cancer cells

All results shown in this section has been published (Katchy, Edvardsson et al. 2012)

3.1 Abstract

Breast cancers are sensitive to hormones such as estrogen, which binds to and activates the ERs leading to significant changes in gene expression. miRNA have emerged as a major player in gene regulation, thus identification of miRNAs associated with normal or disrupted estrogen signaling is critical to enhancing our understanding of the diagnosis and prognosis of breast cancer. We have previously shown that E2-induced activation of

ERα in T47D cells results in significant changes in the expression of protein-coding genes involved in cell cycle, proliferation, and apoptosis. To identify miRNAs regulated by E2-activated ERα (E2-ERα), we analyzed their expression in T47D cells following

E2-activation using both dual-color microarrays and TaqMan low density arrays, and validations were carried out by qPCR. Although estrogen treatment results in altered expression of up to 900 protein-coding transcripts, no significant changes in mature miRNA expression levels could be confirmed. Whereas previous studies aiming to elucidate the role of miRNA in ERα-positive breast cancers cell lines have yielded conflicting results, the work presented here represents a thorough investigation of and significant step forward in our understanding of ERα-mediated miRNA regulation.

3.2 Introduction

Estrogen has a significant impact on the risk, development, maintenance, treatment, and prognosis of breast cancer. The majority of breast tumors are dependent on estrogen for proliferation; hence therapeutic intervention typically consists of small molecule selective estrogen receptor modulators such as tamoxifen and raloxifene (Jordan 2004). Estrogen exerts its function by binding to the ERs (as discussed in section 1.3.2). ERα is essential for estrogen-dependent growth in breast cancer. Many genes associated with the control of cell cycle, proliferation, and apoptosis are regulated by ERα, which acts as the response determinant to endocrine therapy and prognosis in ERα-positive breast cancer

(Yamashita, Yando et al. 2006; N. Kondo 2008).

miRNAs are non-protein coding transcripts which have emerged as major gene regulators (as discussed in section 1.2). They control expression by blocking mRNA translation or targeting mRNA transcripts for degradation, by binding to the 3’- untranslated region (3’ UTR) of the target mRNAs located in the cytoplasm. miRNA encoding genes are transcribed by RNA polymerase II, processed by Drosha into short hairpin RNAs, which are then exported from the nucleus to the cytoplasm, and processed by Dicer to form mature 21-25 nucleotide miRNAs. miRNAs are finally transferred to

Argonaute proteins to form RISC complex (Cuellar and McManus 2005;

Wickramasinghe, Manavalan et al. 2009) (details in section 1.2.1) where they bind to their target mRNAs. ERα-positive breast cancers display a distinct expression profile featuring elevated expression of both let-7 and miR-21 miRNA family members

compared to ERα-negative breast cancers (Mattie, Benz et al. 2006; P. Bhat-Nakshatri

2009). More targeted studies have reported that miR-221/222 can induce tamoxifen resistance through downregulation of ERα (Zhao, Lin et al. 2008). Thus, changes in miRNA expression correlate with diagnostic and prognostic markers used in breast cancer therapy.

Despite these findings, few studies have addressed the direct hormonal regulation of miRNA expression (Klinge 2009), and nearly all have used only the ERα-positive

MCF7 breast cancer cell line as a model. Moreover, these have yielded conflicting results; one study investigating the role of miR-21 in estrogen signaling found it to be downregulated in E2-activated ERα in MCF7 breast cancer cells (Wickramasinghe,

Manavalan et al. 2009), while another showed the same miRNA being upregulated by hormone treatment in the same cell line (P. Bhat-Nakshatri 2009). These differences may be a consequence of differences in duration of estrogen treatment (6h and 4h respectively), assay methods employed (TaqMan and SYBR Green technology, respectively), or variations within the cell line (from culturing cells overtime).

Our group has previously performed a genome-wide study of the transcriptional effects of the estrogen receptors in T47D breast cancer cells (Williams, Edvardsson et al.

2008). We have shown that there is a significant change in transcript levels of a large number of genes following 24h estrogen activation of ERα. Among the transcripts identified, genes involved in processes such as proliferation, apoptosis, cell signaling, development, and ion transport were overrepresented. miRNAs have been reported to be

involved in similar biological processes (Cheng, Fu et al. 2009), and we speculated that these function may be in part through ERα-regulated miRNAs.

In this study, we performed a thorough analysis of mature miRNAs regulated by 24 hour

E2-activated ERα in T47D breast cancer cells. We used both dual-color microarray technique and TaqMan Low Density Arrays (TLDA). Microarray expression profiles were confirmed by qPCR using both SYBR Green and TaqMan chemistry. Comparisons to previous work on regulated miRNAs were performed to validate results. Our results illustrate the inherent ambiguity and complexity of miRNA profiling, and provide valuable insights into the study area of miRNA regulation in these cells.

3.3 Methods

3.3.1 Cell culture

T47D and MCF7 cells were cultured as described in the general materials and methods.

The cultured cells were treated with either 10nM of ER ligand E2, vehicle (ethanol), or

10nM ICI for 0-72 hours. MCF10A and MDA-MB-231 were cultured as described in the general materials and methods. MCF10A, T47D, MCF7, and MDA-MB-231 cells grown in complete FBS serum were used as positive control for miRNA expression levels.

3.3.2 RNA extraction

RNA was extracted and purified using the miRNeasy Mini Kit (Qiagen) with DNase I

DNA degradation according to manufacturer’s protocol. The measurement of RNA quantity and quality is as described in the general materials and methods.

3.3.3 cDNA synthesis

For mRNA analysis: 1μg of the extracted total RNA (from T47D and MCF7 cells) was used for each cDNA synthesis reaction. First-Strand cDNA was synthesized using

SuperScript III and random hexamer primers (Life Technologies™ Grand Island, NY). A final concentration of 5ng/μl of cDNA was prepared from stock for use in qPCR. For miRNA analysis: 1μg of total RNA was used for each polyA tailing and First-Strand cDNA synthesis of miRNA using the NCode™ miRNA First-Strand cDNA Synthesis and qRT-PCR Kit (Life Technologies™ Grand Island, NY) according to manufacturer’s protocol. For TaqMan miRNA Assays: Reverse transcription of 10ng of total RNA was performed according to manufacturer’s protocol using the GeneAmp® PCR System 9700

(Life Technologies™ Grand Island, NY).

3.3.4 miRNA microarray analysis

Five μg of isolated RNA from T47D cells treated with either Vehicle (EtOH) or 10nM E2 for 24 hours were sent to LC Sciences (Houston, TX) where microarray expression profiles were determined using human miRNA microarray dual color sample array by 42

μParaflo® Microfluidic Biochip Technology (Sanger miRBase Release 14.0) (as described in section 2.4.2). TLDA was performed using 700ng total RNA as described in section 2.4.2.

3.3.5 qPCR analysis of mRNA and miRNA expression

For mRNA expressions: 10ng cDNA (from above), 1pmol of each of the forward and reverse primers, and SYBR green PCR master mix in a 10μl final reaction volume.

ARHGDIA and 18S RNA were used for normalization of mRNA expression. For miRNA expressions: 16ng of poly A cDNA (from above), 2pmol of each of the specific forward primer and the universal primer, and SYBR green PCR master mix in a 10μl final reaction volume. Expression of miRNA was additionally quantified using TaqMan

MicroRNA Assays, according to manufacturer’s protocol (Life Technologies™ Grand

Island, NY). U6 snRNA was used for normalization of miRNA expression for each method. snRNA U6 and miRNA basal expressions for each stage of serum starvation for

T47D and MCF7 cell lines, and snRNA U6 basal expression for each cell type are included in supplemental figures (Figures S3.1, S3.2, and S3.3 respectively). All runs were performed using the 7500 Fast Real-Time PCR System (Life Technologies™ Grand

Island, NY). Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. The change in mRNA or miRNA expression was calculated as fold change relative to control (EtOH-treated).Melting curve analysis was performed for all SYBR Green runs, confirming the amplification of one specific fragment. Student’s t-test was used for statistical analysis, using two-tailed distribution 43

and two-sample unequal variance parameters (P<0.001 (***), P<0.01 (**), and P<0.05

(*)).

3.3.6 Bioinformatics

For chromatin-binding comparisons, the University of California Santa Cruz (UCSC)

Genome Bioinformatics website, http://genome.ucsc.edu, was used to observe the relative distances between the possible miRNA promoter and ERα-binding sites, and miRNA promoter and MYC-binding sites using the coordinates from: ERα (J.S. Carroll 2006;

Hua, Kallen et al. 2008; Cicatiello, Mutarelli et al. 2010) and MYC (Hua, Kallen et al.

2008) ChIP data. The following custom tracks were used: Carroll et al, (ERα: 10,599 tracks applied in hg17 format), Huaet al, (ERα: 1,615 tracks applied in hg16 format, and c-MYC: 311 tracks applied in hg16 format), and Cicatielloet al, (ERα: 3,561 tracks applied in hg18 format). miRNA coordinates were taken from miRBase

(http://www.mirbase.org): the miRNA database in hg19 format and converted to the necessary usable format using UCSC LiftOver tool.

3.4 Results

3.4.1 Strong ERα-mediated transcription of protein-coding genes occurs at 24h in

T47D cells

We previously performed a gene expression analysis of 24h E2-ERα in T47D cells and found significant changes in genes associated with proliferation, cell-cycle, and apoptosis

(Williams, Edvardsson et al. 2008). In order to investigate the effect of various exposure times on ERα gene regulation, T47D cells were treated with a control (EtOH), 10nM E2, and 10nM ICI for 0h - 24h. ICI is a high-affinity ER ligand that antagonizes most estrogen-mediated action by ERα inhibition and degradation. The pS2 (TFF1), KCNK5, and SPINK4 genes, which have been shown to be regulated by ERα (Williams,

Edvardsson et al. 2008), were used to confirm ERα transcriptional activation by qPCR.

These are involved in a range of cellular processes: pS2 is similar in function to some growth factors, KCNK5 is a potassium ion channel involved in breast cancer proliferation

(Alvarez-Baron, Jonsson et al. 2011), and SPINK4 is a serine peptidase inhibitor. PS2 has a classical ERE in its promoter sequence, and KCNK5 has a binding site for ERα and is directly regulated through ERα DNA binding (Alvarez-Baron, Jonsson et al. 2011).We expected that 24h treatment would result in a marked response of these genes, and that regulation would occur solely through ERα. As shown in Figure 3.1, significant differences were observed between time points. Whereas the KCNK5 gene had its maximum response at 1h (Alvarez-Baron, Jonsson et al. 2011), all three genes showed a strong and significant response at 24h; at this time point, genes were shown to be 45

upregulated between 10- and 57-fold. ICI induced ERα depletion resulted in inhibition of expression, demonstrating the key role of this receptor in their regulation. Since the research objective was to measure mature miRNAs, a 24h time point was chosen in order to ensure pre-miRNAs processing had taken place, and to ensure maximal transcriptional activation.

Figure 3.1: Expression of ERα-regulated genes at varying time points. qPCR results showing E2-activated expression of KCNK5, pS2, and SPINK4. T47D parental cells were treated with EtOH, 10nM E2, and 10nM ICI for 4h and 24h. Expressions of these genes were normalized to the expression of ARHGDIA (reference gene). Student T-

TEST: P<0.001 (***), P<0.01 (**), and P<0.05 (*). Values are the average of three separate experiments +/- SD.

3.4.2 Profiling showed minimal ERα mediated regulation of miRNAs at 24h

We have shown that at 24h there is a massive transcriptional regulation in these cells by

E2-ERα, especially in processes such as proliferation and apoptosis. miRNAs have been reported to be involved in similar processes (Cheng, Fu et al. 2009), and are transcribed as pre-miRNAs using RNA polymerase II in the same manner as protein coding genes.

ERα-positive tumors have been shown to express miRNAs differentially to ERα-negative tumors. We therefore set out to test the hypothesis that, at 24h, E2-ERα would be able to regulate pre-miRNA transcripts as substantial as the protein coding transcripts. miRNAs are not active until they are exported into the cytoplasm and processed into their mature form. In order to identify mature miRNAs that are regulated by E2-ERα, we compared miRNA expression of control and E2-treated T47D cells using miRNA microarrays. Only

7 miRNAs were indicated to be significantly regulated (Figure 3.2) using a p-value of <

0.1, whereas no regulated miRNA exhibited a p-value of <0.01. The remaining 801 miRNAs (Katchy et al, 2012 Supplemental data) showed statistically insignificant regulation. This result indicated that miRNAs were only minimally regulated by ERα. To eliminate method bias, we performed an additional round of profiling using the TaqMan®

Low Density Array (TLDA). This method indicated that 57 miRNAs were regulated

(Katchy et al, 2012 Supplemental data). Further analysis of these regulated miRNAs would be done using qPCR assays.

Figure 3.2: miRNA profiling of E2-activated ERα in T47D cells. Microarray analysis of 24h E2-ERα in T47D cells from LCSciences showing the seven most significantly regulated miRNA. All have P-value < 0.10. EtOH treated sample was used as a control.

Analysis was done in duplicates.

3.4.3 qPCR confirms non-significant ERα regulation of miRNAs

To validate array results, we performed qPCR on RNA from three separate treatments; the two replicated treatments used for the array and a third, additional treatment. First, we tested the 7 miRNAs identified by microarray using SYBR Green technology, which detects mature miRNA alone. As seen in Figure 3.3A, miR-29c and three more miRNAs showed a slight estrogen-induced upregulation in all three treatments, in accordance with 48

the array results, none of which were found to be statistically significant. The three remaining miRNAs (one shown in Figure 3.3A) showed no change. These results were obtained from an average of three biological sample sets, and were inconsistent with the array data that showed significant changes in expression levels. For example, miR-16-2* and miR-198 showed microarray-detected changes of 1.88 and 2.06-fold respectively.

We also tested miRNAs detected by the TLDA array alone, using the same SYBR green qPCR technology. We observed no significant changes in any of the miRNAs tested

(examples shown in Figure 3.3B). Also of note, these miRNAs were recorded as unchanged using microarray technology. U6 snRNA expression showed no variation between treatments and across the three biological samples (Figure 3.3C). The data collected so far, led to the reasonable conclusion that no miRNAs are significantly regulated by 24h E2-ERα in T47D breast cancer cell lines.

To further confirm the accuracy of the validations and remove any bias, we used TaqMan miRNA technology to validate array results, and to verify that the qPCR results using

SYBR green technology were reproducible, given that TaqMan probes are more specific at detecting changes. As seen in Figure 3.3D, miR-29c showed a 1.7-fold increase in expression, though not statistically significant. The remaining miRNAs tested (including miR-198, miR-16-2* and miR-183) showed no change. From these results, we conclude that miRNAs are not strongly regulated by 24h E2-ERα in T47D cells. The miR-29c may be the only miRNA regulated, but variations between treatments reduced the significance of its expression. We also conclude that both microarray and TLDA technology generate false positives, but of these, the microarray results correlate better with qPCR results.

Figure 3.3: Validations of miRNA microarray. (A) qPCR results to confirm microarray results of the top seven miRNA. Samples (T47D) were poly-A tailed and

SYBR Green technology was used to detect amplification. (Β) qPCR results to confirm regulated miRNA from both microarray and TLDA analysis. Samples (T47D) were poly-

A tailed and SYBR green technology was used to detect amplification. (C) qPCR for snRNA U6 expression for each sample set of treatment (SYBR Green). (D) qPCR results to confirm regulated miRNAs from microarray and TLDA using TaqMan MicroRNA assays. Each miRNA was carried out in triplicate with each sample. All miRNA expressions were normalized to snRNA U6, except for Figure 3.3C, where relative levels of U6 were plotted against input RNA showing the level of variation between samples.

Analysis and relative expressions were determined using comparative threshold cycle

(ΔΔCt) method. All have P>0.10. Values are the average of three separate experiments

+/- SD. 50

3.4.4 Previously reported estrogen-induced miRNA regulations could not be reproduced

In order to ensure comprehensive analysis of potentially regulated miRNAs, we tested a range of those previously reported in the literature, for which ERα-induced regulation data remains largely inconclusive. Previously miR-125b, miR-107/103 (Cicatiello,

Mutarelli et al. 2010), and miR-206 (N. Kondo 2008; Klinge 2009) have been reported to be downregulated in the presence of E2 in MCF7 cells. The miR-200 family has been reported to be upregulated by E2 (Klinge 2009) and miR-21 to be downregulated by ERα in MCF7 cell lines (Klinge 2009; Wickramasinghe, Manavalan et al. 2009) but not in

T47D cell lines (Wickramasinghe, Manavalan et al. 2009). However, miR-21 has also been reported to be strongly upregulated by ERα in both MCF7 and T47D breast cancer cell lines (P. Bhat-Nakshatri 2009). As seen in Figure 3.4A, qPCR (SYBR green) revealed no significant changes in relative expression of these miRNAs, with the exception of a non-significant upregulation of miR-206. miR-206 and miR-21 were further analysed using TaqMan probes (Figure 3.4B). miR-21 expression showed no significant changes, in agreement with our SYBR green qPCR results and

Wickramansingheet al (Wickramasinghe, Manavalan et al. 2009), but not with Bhat-

Nakshatriet al (P. Bhat-Nakshatri 2009). miR-206 was also not significantly regulated, but showed different tendencies depending on the technique employed; SYBR green qPCR showed a non-significant upregulation, while TaqMan chemistry showed a non- significant downregulation of miR-206. Other miRNAs tested, including miR-98 and -

101, showed no changes in expression. Previously reported results pertaining to ERα- 51

regulated miRNAs could not be reproduced. One possible explanation is that most previous studies have utilized MCF7 cell lines, and may show patterns of miRNA regulation distinct from T47D.

In order to evaluate the importance of cell line specific miRNA regulation, we performed the miRNA studies in MCF7 cells. MCF7 cells were treated with control and

E2 for 24h, and the same protein coding genes tested to confirm treatment and response to E2. At 24h E2 treatment, E2-responsive genes showed significant induction (Figure

3.5A). We then performed qPCR analysis of miRNAs reported to be regulated by ERα in

MCF7 using SYBR green technology. As shown in Figure 3.5B, no significant changes were observed in any of the miRNAs tested. However, previously reported downregulated miRNAs miR-21, -107, and -22 (Mattie, Benz et al. 2006; Klinge 2009;

Cicatiello, Mutarelli et al. 2010) showed some indications of downregulation following

E2 treatment, although not significant (p>0.05). Other miRNAs tested but not shown included miR-221, -222, -103, -200a, -125b, -18a, and -92a. To further investigate potential regulation we performed TaqMan miRNA analysis on miR-21 and -206 (Figure

3.5C), which showed no significant changes, directly contradicting previous reports relating to these miRNAs(P. Bhat-Nakshatri 2009; Wickramasinghe, Manavalan et al.

2009). U6 snRNA expression showed no variation between treatments and across the three biological samples (Figure 3.5D). An important consideration was the fact that our data were collected 24h after E2 exposure, whereas the literature largely reports 1, 4, 6, and 12 hour time courses for exposure to E2, with some in between the 24-72 hour range

(Maillot, Lacroix-Triki et al. 2009; Cicatiello, Mutarelli et al. 2010). A time-point assay was therefore conducted to address this variable. 52

Figure 3.4: qPCR analysis of previously reported miRNA regulations. qPCR results to confirm regulated miRNA from the literature. (A) Samples (T47D) were poly-A tailed and SYBR green technology was used to detect amplification. (Β) TaqMan MicroRNA

Assays were used to detect changes in expression. Each miRNA was carried out in triplicate with each triplicate sample. All miRNA expressions were normalized to snRNA

U6. Analysis and relative expressions were determined using comparative threshold cycle

(ΔΔCt) method. All have P>0.10. Values are the average of three separate experiments

+/- SD.

Figure 3.5: miRNA regulation in other ERα-positive breast cancer cells. (A) qPCR results showing E2-activated expression of pS2 and KCNK5. MCF7 cells were treated with EtOH or 10nM E2 for 24h. Expression of these genes was normalized to the expression of ARHGDIA (reference gene). Triplicate samples were used, and student t- test was used to determine significance: P<0.001 (***), P<0.01 (**), and P<0.05 (*). (B) qPCR results to confirm regulated miRNA from the literature. Samples were poly-A tailed and SYBR Green technology was used to detect amplification (All have P>0.10).

(C) qPCR results using TaqMan MicroRNA assay method. (D) qPCR for snRNA U6 expression for each sample set of treatment. Each miRNA was carried out in triplicate with each triplicate sample (All have P>0.10).All miRNA expressions were normalized to snRNA U6, except for 5D where relative levels of U6 were plotted against input RNA showing the level of variation between samples. All analysis and relative expressions 54

were determined using comparative threshold cycle (ΔΔCt) method. Values are the average of three separate experiments +/- SD.

3.4.5 Time-course study of miRNA reveals a slight variation in miRNA expression

Differences in miRNA regulation observed across multiple experiments may be a consequence of variable E2 exposure times. A time point assay on selected miRNAs was therefore carried out in order to verify the potential for miRNA expression changes different time points in the T47D cell line. Duplicate T47D cells treated with EtOH or E2 for 0, 1, 4, 8, 24, and 72 hours were analyzed for miRNA expression changes, in particular miR-29c, -21, and -206. We found that miR-29c showed variation in expression at different time points (Figure 3.6A). Comparisons within time points between treated samples (EtOH vs. E2), showed a statistically insignificant upregulation of miR-29c at 24h E2 treatment (p>0.05), as detected in previous treatments (Figure

3.3A). For miR-21 we noted a small statistically significant increase at 8h E2 treatment only (p<0.05) (Figure 3.6B). At remaining time points, including the previously analyzed

24h (Figure 3.6B), no significant changes were observed. miR-206 showed no significant estrogen regulation at any time point (Figure 3.6C). miR-135a, however, showed a significant upregulation at 72h estrogen treatment (p<0.01) (Figure 3.6D). miR-183 showed no significant estrogen regulation at any time point (Figure 3.6E). Overall the miRNAs varied slightly over the time-courses measured, yet, the T47D cell lines did not exhibit any major estrogen-induced miRNA regulation at any time point.

Figure 3.6: Time assays of miRNAs suspected to be regulated. qPCR results for time point assay on (A) miR-29C, (Β) miR-21,(C) miR-206, (D) miR-135a, and (E) miR-183.

Samples (T47D) were poly-A tailed and SYBR green technology was used to detect amplification. Each miRNA was done in triplicate for each duplicated treatment.

Expression levels were compared relative to the 0h sample. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. All have p<0.955, and student t-test were determined: P<0.001 (***), P<0.01 (**), and P<0.05 (*). Values are the average of two separate experiments +/- SD.

3.4.6 miRNAs with an ERα chromatin-binding site nearby are not regulated in

T47D cells

Estrogen activates ERα through binding to its ligand-binding domain, which allows transcription by the receptors binding to EREs in the chromatin, or by tethering to other transcription factors, and subsequent induction of gene transcription. Potential miRNAs could be directly regulated by ERα, or indirectly via an ERα regulated transcription factor. The proto-oncogene MYC is upregulated by ERα, which in turn regulates its sets of gene targets, such as those involved in breast cancer progression (Dubik and Shiu

1992; Cheng, Jin et al. 2006). Thus, it is plausible that miRNAs with either an ERα or a

MYC binding site nearby might be up or downregulated following estrogen treatment 57

within 24h. Although we had not found any E2-induced miRNA regulations so far, we decided to perform an in-depth analysis of such miRNAs. Several studies have mapped

ER-binding regions using ChIP studies on MCF7 breast cancer cells as a model of hormone-dependent breast cancer (J.S. Carroll 2006; Hua, Kallen et al. 2008; Cicatiello,

Mutarelli et al. 2010), similar ERα-binding sites is functional in other breast epithelial cell lines such as T47D cells (J.S. Carroll 2006). To study miRNAs with a potential ERα- or MYC-binding site nearby, we used the UCSC genome browser to search for ERα- binding sites within 200kb of the miRNA genomic location using the coordinates provided by previous studies on ERα (J.S. Carroll 2006; Hua, Kallen et al. 2008;

Cicatiello, Mutarelli et al. 2010) and c-MYC (Hua, Kallen et al. 2008). Of the miRNAs that had shown altered expression at any time point (miR-21, -206, -183, and -29c), miR-

21 was shown to possess both ERα (two sites, 170 bases to 5kb downstream)- and MYC

(134kb upstream)-binding sites located nearby (not shown). miR-196a, -301a, -652, and -

342-3p were found to have ERα-binding sites within 200kb. We used qPCR to investigate their ERα regulation in both T47D and MCF7 (Figure 3.7A and B), and we observed no change in expression at 24h. Others tested were miR-590-5p and miR-140-

5p, and no changes were observed (data not shown). Our results show that potential regulation of miRNAs through proximal or distal ERα-binding sites do not result in changes of mature miRNA expression.

Figure 3.7: Validation of miRNA with EREs within 200kb of ERα-binding site. qPCR was used to validate miRNAs that have been reported to be regulated by ERα and have ERα-binding sites within 200kb of their genomic sites. Samples (A) T47D and (Β)

MCF7, were poly-A tailed and SYBR Green technology was used to detect amplification.

Each miRNA was done in triplicate with each triplicate sample (All have P>0.10). All miRNA expressions were normalized to snRNA U6. All analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Values are the average of three separate experiments +/- SD.

3.4.7 miRNAs can differentiate between non-tumor and tumor cell lines

Although, T47D cells showed no major estrogen-induced changes in miRNA expression, cell to cell variation is known to exist, and we have previously shown that basal miRNA levels can differ between different breast cancer cell lines (Aydogdu, Katchy et al. 2012).

In order to demonstrate and acknowledge differences in miRNA expression, we analyzed changes in miRNA expression levels in non-tumor and triple-negative breast cancer cells as positive controls (Figure 3.8). miR-200, -183, and -206 showed no variation among the two ERα-positive breast cancer cells (T47D and MCF7), but were reduced by approximately 50% compared to non-tumor cells (MCF10A). miR-21 was greatly reduced in T47D cells and unchanged in MCF7 cells. miR-200b, -135a, and -183 levels were also greatly reduced (levels close to zero) in triple-negative cells (MDA-MB-231) in relation to their expression in MCF10A. Thus, there are significant differences in miRNA expression between different cell lines, although these are not directly regulated by ERα, as shown in this study.

3.5 DISCUSSION

The conflicting results for estrogen-regulated miRNAs, such as miR-21, previously reported as both up-, and downregulated by ERα in MCF7, and found to be regulated in neither MCF7 nor T47D by the current study, may be due to several factors. The short length of mature miRNA, the heterogeneity in their GC content (melting temperature),

and the fact that the target sequence is present in the primary transcript, makes miRNA expression profiling technically challenging (Benes and Castoldi 2010).

Figure 3.8: Expression levels of miRNAs in different breast cancer sub-type and non-tumor breast cell lines. qPCR (SYBR green) results showing selected miRNA expression levels in T47D, MCF7, and MDA-MB-231, relative to their expression in

MCF10A cells. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method.

Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of three separate experiments +/- SD.

Microarray-based methods are the most extensively used approach for miRNA identification and quantification, although methods such as cloning and northern blot are still used (Mattie, Benz et al. 2006). Previous studies have raised concern about the normalization procedure in miRNA microarray analysis. This is an important and critical step in miRNA microarray analysis (Pradervand, Weber et al. 2009), but because of the smaller population of miRNAs compared to mRNAs, the commonly used normalization methods (e.g. whole-genome gene expression microarray) are not appropriate (Y.J. Hua

2008). qPCR has some major advantages over microarrays in that it is fast, more sensitive, has a larger dynamic range and requires lower amount of starting material

(Benes and Castoldi 2010). For miRNAs analysis, however, qPCR is challenging due to the short 22 nucleotide size, which requires additional steps to allow annealing of both forward and reverse primers.

Also, the effectiveness of the qPCR depends on several parameters including

RNA extraction and integrity, cDNA synthesis, primer design, amplicon detection, and data normalization (reference gene). No reference miRNA had been identified to date, and instead small nucleolar U6 (snRNA U6) is commonly used for normalization of miRNA expression. Furthermore, the time of exposure to E2, cell variations, and method of detection are factors that could lead to variables in miRNA research. We also took into consideration the chemistry for detection of qPCR products. SYBR green detects mature miRNA but it cannot discriminate between different amplicons and binds to all double- stranded DNA, including non-specific products such as primer-dimers. We performed a melting point analysis (dissociation curve) for each qPCR reaction, which enabled us to monitor the homogeneity of the qPCR products. To eliminate the possibility of the 62

detection method artifacts, we also performed TaqMan miRNA assays using TaqMan probes as a more specific approach. Primer-dimers and other non-specific amplification products can still be formed, but they would not generate any fluorescent signal. All these differences may account for the variation in results on identifying miRNAs within the same cell type.

A previous study further validated the estrogen exposure time used in our study;

Hua et al. carried out gene expression profiling in a time course study in an effort to map the ERα-binding sites in MCF7 cell line (Hua, Kallen et al. 2008). The highest number of responsive genes was seen at an E2 exposure time of 24 hours. Also, it was reported that a few of these estrogen responsive genes found had ERα-binding sites within 10-50kb of the transcription start site (J.S. Carroll 2006; Hua, Kallen et al. 2008). Genes found to be out of that range may use transcription factors on a different chromosome, may be regulated from distal sites, may not be annotated, or may be independent of ERα-binding

(J.S. Carroll 2006; Hua, Kallen et al. 2008). Hua et al. further analyzed their data by using distances within and greater than 200kb to identify their ERα-binding regions, validating our use of a distance within 200kb for both proximal and distal interactions. It has been reported that E2-ERα interacts with the Drosha complex and reduces primary miRNA processing (Yamagata, Fujiyama et al. 2009), and that E2-ERα also induces

Dicer mRNA (P. Bhat-Nakshatri 2009). However, according to our results, this does not result in changes of mature miRNA levels within 24h of ERα activation. It has also been reported that processing of pre-miRNA to mature miRNA is delayed for at least 12h after

E2 stimulation, leading to an increase in the expression of mature miRNA from 24h to

72h after E2 stimulation (Castellano, Giamas et al. 2009). Recently reported estrogen- 63

induced transcription of pri-miRNA has revealed that regulation of pri-miRNA occurs early (40 mins – 3 hours) and those pri-miRNAs that undergo sustained and significant changes in expression are usually reflected as changes in the processed, mature miRNAs

(Hah, Danko et al. 2011).

Differences in miRNA expression in ERα-positive and ERα-negative tumors have been observed in this and other studies. Although this is likely an effect of ERα expression, such as a higher level of differentiation in ERα expressing breast cancer, we suggest that it is not a direct effect of ERα transcription. We cannot exclude that ERα regulates transcription of pre-miRNA, but if so, this does not result in alterations in mature miRNA expression within 24h in the cells analyzed here. Also, E2-ERα is known to induce MYC expression (Santos, Scott et al. 1988; Wang, Mayer et al. 2011), and c-

MYC, in turn, regulates miRNA expression (O'Donnell, Wentzel et al. 2005; Castellano,

Giamas et al. 2009). This suggests that there may be an indirect relationship between

ERα signaling and miRNA expression.

3.6 CONCLUSION

In conclusion, we have shown that although the T47D cell line demonstrates very strong estrogen-ERα-mediated gene transcription, mature miRNAs are not directly regulated at

24 hours. Understanding the complete mechanism of ERα is important in breast cancers, which would aid in better treatment and diagnosis of this disease. We believe that this study will contribute to narrowing the study area of ERα-regulated miRNA in breast cancer cells, and help to unravel the function of ERα in breast cancer cells. 64

3.7 Supplemental figures to chapter 3

Figure S3.1: Basal expression levels of miRNAs in T47D breast cell lines. qPCR

(SYBR green) results showing: (A) Selected miRNA basal expression levels in T47D cells in 5% Serum, 5% DCC (24 hours) and 0.5% DCC (48 hours), and (B) snRNA U6 expression levels. All miRNA expressions were normalized to snRNA U6. Relative levels of U6 were plotted against input RNA showing the level of variation between samples. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD.

Figure S3.2: Basal expression levels of miRNAs in MCF7 breast cell lines. qPCR

(SYBR green) results showing: (A) Selected miRNA basal expression levels in MCF7 cells in Serum, 5% DCC (24 hours) and 0.5% DCC (48 hours), and (B) snRNA U6 expression levels. All miRNA expressions were normalized to snRNA U6. Relative levels of U6 were plotted against input RNA showing the level of variation between samples. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD.

Figure S3.3: snRNA U6 expression in different breast cancer sub-type and non- tumor breast cell lines. qPCR (SYBR green) results showing snRNA U6 expression levels. Relative levels of U6 were plotted against input RNA showing the level of variation between samples. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Average Ct-values are shown above each bar. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD.

Chapter 4

ERβ regulates mature miRNA in breast cancer

cells

Some of the results shown here will be included in (Ma, Govindasamy et al. In

preparation) and will be included in (Katchy, Jonsson et al. In preparation).

4.1 Abstract

Estrogens play a role in breast cancer progression by regulating genes involved in certain cellular functions through ERα and ERβ). miRNAs are short, non-coding RNAs that regulate genes by either blocking translation or by mRNA degradation. Thus, identifying miRNAs that are associated with breast cancer progression by normal or disrupted estrogen signaling would provide new insights to the prognosis and diagnosis of breast cancer. We have previously shown that no significant regulation of miRNAs occurred within 24h through E2-activated ERα (E2-ERα) in T47D breast cancer cell line. Noting that ERb can heterodimerize with ERα and that both ER receptors’ homo- and heterodimers have different functions, and the fact that less is known about how ERβ influences miRNA regulation, we proceeded to investigate if ERβ regulates miRNAs in this cell line and in a second ERα-positive cell lines, MCF7. To identify miRNAs regulated by ERβ, cells were transduced with ERβ and miRNA expression profiles before and after E2 (10nM) treatment were analyzed using dual-color microarrays and validations were carried out using qPCR. Several miRNAs were found to be regulated upon introduction of ERβ, including miR-22, miR-522, miR-135a, and let7b. Of these, miR-22 is a known ERα suppressor and inhibits estrogen signaling by targeting ERα mRNA. There was a higher regulation of miRNAs upon E2 treatment in MCF7-ERβ cells compared to the T47D-ERβ cells. Moreover, the strong downregulation of the miR-200 family indicated that stem cell abilities may be affected. MCF7-ERβ showed a major enhancement of stem cell maintenance as determined by multi-generational

mammospheres cultures. A significant increase of mammosphere formation ability was noted also in T47D-ERβ cells. A thorough investigation of ER regulation of miRNAs will enhance our understanding of the mechanism of action of the estrogen receptors.

4.2 Introduction

Estrogen plays a vital role in cell growth and differentiation of the mammary gland (Hall,

Couse et al. 2001; Heldring, Pike et al. 2007), and exert its function through the estrogen receptors: ERα and ERβ (Section 1.3). Both receptors are transcription factors that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (Grober, Mutarelli et al. 2011). Both receptors are quite similar in sequence and structure, but differ in their biological effects and gene-expression profiles

(Katzenellenbogen, Montano et al. 2000; Pettersson, Delaunay et al. 2000; Heldring, Pike et al. 2007). They have different expression pattern in breast cancer. ERα is higher and

ERβ is lower in malignant breast cancer cells compared to normal mammary epithelial cells (Speirs, Skliris et al. 2002; Saji, Hirose et al. 2005). Both receptors can be found co- expressed, and can play specific roles and sometimes oppose each other’s action (details in Section 1.3). The role of ERα in breast cancer has been studied extensively, and ERα is still the most important marker of response to hormonal therapy in breast cancer. The role of ERβ in breast cancer is still not clear, and the role of ERβ has been suggested to be bi- faceted (Leygue and Murphy 2013).

An important step to understanding the molecular mechanism of ERβ is deciphering how this receptor regulates miRNA. miRNA expression have been commonly reported to be regulated by ERα in breast cancer (Section 1.3.3), although conflicting data have been presented to support this. We previously showed that E2-ERα in both T47D and MCF7 did not regulate mature miRNA, but showed significant regulation for some miRNAs at extended exposure to E2 (beyond 24h). ERα and ERβ share some functions and can have different functions. So, it is possible that ERβ could regulate miRNA. So far, there is only one report on miRNA regulating ERβ (Al-Nakhle,

Burns et al. 2010). There are no concrete reports on ERβ regulation of miRNA, but there is evidence of interaction between ERβ and miRNA that suggest possible hormonal control of miRNA by ERβ (Grober, Mutarelli et al. 2011). There is also evidence that

ERβ control the synthesis, maturation, and steady-state levels of a significant number of miRNAs in breast cancer cells by interacting with ERα activity or by acting autonomously (Paris, Ferraro et al. 2012). Both of the reported evidence has been observed using MCF7 cells stably transfected with ERβ.

The ERs have been implicated in breast cancer stem cells (CSC). ERα have been shown to be at low levels in stem cells (Simoes, Piva et al. 2010), and the absence of ERα results in a morphological change from an epithelial to mesenchymal phenotype in breast cancer (Gadalla, Alexandraki et al. 2011). Also, estrogen has been shown to inhibit expression of stem cell genes and mammosphere formation (Simoes, Piva et al. 2010).

However, estrogen and progesterone has been shown to enhance stem cell formation

(Asselin-Labat, Vaillant et al. 2010). ERβ has recently been shown to be expressed in

CSC, and induce proliferation in CSC and tumor growth in mice when activated (Ma, 71

Govindasamy et al. Submitted). Thus, deciphering ERβ regulation of miRNA would further explain the molecular mechanism of this receptor in breast cancer and BSC.

miRNAs are important in both stem cells and cancer (Section 1.2.2). miRNA expression profile from breast cancer stem cells reveal that miRNAs play critical roles in maintenance of stem cell properties (Feifei, Mingzhi et al. 2012). Repression of some miRNAs such as the miR-200 family, have been reported as important for maintaining stem cell properties (Feifei, Mingzhi et al. 2012; Guttilla, Phoenix et al. 2012; Yu, Jiao et al. 2012). Repression of the oncomir miR-21 has been shown to decrease EMT and CSC phenotype in breast cancer cells (Han, Liu et al. 2012).

In this study, we investigate whether ERβ regulates miRNAs in ERα-positive breast cancer cells (T47D and MCF7). miRNA microarray was performed to attain miRNA expression profile for ERβ. Regulated miRNA target genes profile was compared to ERβ gene expression profile. Mammosphere formation assays were performed to determine ERβ function in stem cell growth. siRNA studies were done for functional analysis of the ERs. We show that the presence of ERβ in ERα-positive breast cancer have functional implication in regulation of miRNA and for CSC.

4.3 Methods

4.3.1 Cell culture

T47D-Control (Mock), T47D-ERβ, MCF7-Control (Mock), and MCF7-ERβ stable cell lines were derived by lentiviral transductions with CMV-driven, empty vector or FLAG- tagged full-length ERβ cDNA (530 aa), respectively, as described in (Hartman,

Edvardsson et al. 2009; Jonsson, Katchy et al. Manuscript). Clone mixes from two separate transductions were used for experiment. T47D and MCF7 cells were cultured in

Dulbecco's modiﬁed Eagle's medium in proportion with F12 [DMEM/F12 (1:1)] and

DMEM respectively, supplemented with 5% FBS and 1% Penicillin Streptomycin

(Invitrogen, Carlsbad, CA). Transduced cells were selected with 5 μg/ml blasticidin

(Invitrogen). The cultured cells were treated with either 10nM E2, vehicle (ethanol), or

10nM ICI for 24h.

4.3.2 miRNA microarray analysis

5μg of isolated RNA from T47D or MCF7 Control and ERβ cells treated with 10nM E2 for 24h were sent to LC Sciences (Houston, TX) where microarray expression profiles were determined using human miRNA microarray dual color sample array by μParaflo®

Microfluidic Biochip Technology (based on Sanger miRBase Release 14.0). Protocol and analysis are as described in Chapter 3 methods (Section 3.3.4).

4.3.3 qPCR analysis of mRNA and miRNA expression

For mRNA expressions: as described in Chapter 3 methods (Section 3.3.5). ARHGDIA

RNA was used for normalization of mRNA expression.

For miRNA expressions: as described in Chapter 3 methods. Student’s t-test was used for statistical analysis, using two-tailed distribution and two-sample unequal variance parameters (p<0.001 (***), p<0.01 (**), and p<0.05 (*)).

4.3.4 siRNA analysis

MCF7-Control and ERβ cells were transfected with ESR1 siRNA and non-targeting control siRNA (Dharmacon, Thermo Fisher Scientific, Waltham, MA, USA). 35nM total siRNA concentration was used and transfection was carried out according to manufacturer’s protocol (Thermo Fisher Scientific). Transfected cells were incubated for

24h. Transfection was repeated for another 24h before treatment with vehicle (DMSO) or

10nM E2 for 24h.

4.3.5 Mammosphere formation assay

Mammosphere assay was performed essentially as described (Dontu, Abdallah et al.

2003). Cultured and attached cells were trypsinized with trypsin-EDTA (GIBCO®) and triturated for single cells. Cells were seeded at 20,000 cells/mL per well in 6-well

Ultralow Adherence plates (Corning Inc., Corning, NY) in MammoCult™ proliferation

Supplements in proportion with MammoCult™ Basal Medium (1:10) (STEMCELL

Technologies Inc, Canada), 4 μg/mL heparin solution, 0.48 μg/mL hydrocortisone, and

1% penicillin streptomycin, modified from manufacturer’s protocol (STEMCELL

Technologies). With 7 days interval, mammospheres were collected by centrifugation, washed with PBS, dissociated to single cells with tripLE™ Express(GIBCO®) using pasteur pipet, and seeded at 20,000 cells/mL per well in 6-well Ultralow Adherence plates to obtain the next generation of mammospheres. Cell count and viability were performed using trypan blue staining before seeding. For each generation of mammospheres, colonies were counted and their size evaluated after 3 days of incubation using the GelCount™ and its software (Oxford Optronix Ltd., Oxford, United Kingdom).

Student’s t-test was used for statistical testing.

4.4 Results

4.4.1 Strong ERβ-mediated transcription of protein coding genes occur at 24h in

T47D and MCF7 cells

As previously described, 24h E2-activated ERα (E2-ERα) in T47D cells have been found to have significant effects in gene regulation (Williams, Edvardsson et al. 2008; Katchy,

Edvardsson et al. 2012). And since both ER receptors are activated by E2, we expected that ERβ would produce similar effects at 24h. In order to investigate the effect of 24h

E2-ERβ on gene regulation, T47D-Control, T47D-ERβ, MCF7-Mock, and MCF7-ERβ cells were treated with a vehicle and 10nM E2. The pS2 (TFF1) was used to confirm ERα transcriptional activation by qPCR. pS2 is similar in function to some growth factors, and has a classical ERE in its promoter sequence. We observed a strong and significant response at 24h for the E2-treated samples in both the control and ERβ populations of both cell lines (figure 4.1). The E2-treated T47D-Mock samples had an 8-10 fold change increase compared to the control, and that of the T47D-ERβ had a 12-17 fold change increase. The MCF7 samples showed a more pronounce response with the E2-Mock having a 40 fold change increase, and the E2-ERβ with a more elevated expression level

(> 100 folds). The elevated gene response in the ERβ samples of both cell lines suggests possible action from the presence of both ER receptors. Thus, a 24h time point was chosen to ensure maximal transcriptional activation.

Figure 4.1: Expression of pS2 gene by ERα and ERβ. qPCR results showing E2- activated expression of PS2. T47D and MCF7 Control (Mock)/ERβ-transduced cells were treated with vehicle and 10nM E2 for 24h. Expressions of these genes were normalized to the expression of ARHGDIA (reference gene). Student t-test: p<0.001

(***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments

+/- SD.

4.4.2 miRNA profiling reveals possible ERβ mediated regulation of miRNAs

We have shown that there is a massive transcriptional regulation in these cells by E2-ERα and E2-ERβ, and that E2-ERα did not regulate mature miRNA in T47D and MCF7 parental cell lines (Katchy, Edvardsson et al. 2012). Also, it is known that both ER receptors can act differently, and ERα-positive tumors have been shown to express miRNAs differentially compared to ERα-negative tumors. Thus, we hypothesized that, at

24h, ERβ may be able to regulate miRNAs as substantial as it does protein-coding genes.

In order to identify mature miRNAs that are regulated by ERβ, we compared miRNA expression of E2-Control and E2-ERβ T47D and MCF7 cells using miRNA microarrays.

Only one miRNAs, miR-522, was indicated to be significantly regulated (Table 4.1) using a p-value of < 0.1, whereas no regulated miRNA exhibited a p-value of <0.01. The other miRNAs (Appendix A and B) showed statistically insignificant regulation. The microarray analysis suggested that miRNAs were only minimally regulated by the presence of ERβ. However, we observed large fold-change values for about 70 miRNAs

(Appendix A and B), like miR-377 with a fold change value greater than 4 (Table 4.1).

This suggests possible regulation of miRNA by ERβ, and qPCR was needed to validate this findings.

Table 4.1: Top regulated miRNAs from miRNA profiling of E2-activated ERβ- transduced T47D cells. Table shows the one statistically significant regulated miRNA

(p-value in blue), and other regulated miRNA with substantial fold-change values.

4.4.3 qPCR confirms ERβ-mediated regulation of miRNAs in T47D cell

In order to validate array results and confirm the fold-change values observed, we performed qPCR on RNA of the two replicated treatments used for the array (E2-Control vs. E2-ERβ T47D cells). We initially tested the one miRNA (miR-522) identified by microarray using SYBR green technology. We saw that miR-522 was significantly down regulated, thus confirming the array results (Figure 4.2A). Then, we performed qPCR on miRNAs that had considerable fold change values on the array albeit the regulations were not statistically significant, and we were able to identify that some of these miRNAs were significantly regulated using the more exact method of qPCR. For example, miR-22, miR-224, miR-301a, and let-7i were all differentially expressed in T47D-ERβ cells compared to control T47D cells. We also examined miRNA that were reported in literature (miR-21, -200b, and -205) and in our previous study (miR-135a) to be regulated in ERα-positive breast cancer cells, and we found these miRNAs were regulated significantly by ERβ. To investigate if these regulations were dependent on E2 or were ligand-independently regulated, we examined these miRNA in the vehicle-treated cells

(Control-Mock vs. Control-ERβ). We found similar significant regulations of these miRNAs (Figure 4.2B). These results suggest that the presence of ERβ, with or without ligand, may be responsible for the regulation of these miRNA.

Figure 4.2: Validations of microarray data in T47D-transduced cells. qPCR results comparing the control cells to the ERβ cells in (A) presence of E2 (Β) absence of E2.

Samples were poly-A tailed and SYBR green technology was used to detect amplification. Each miRNA amplification was performed in triplicate for each duplicate biological sample. Expression levels were compared relative to the control sample. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD.

4.4.4 Presence of ERβ may be responsible for miRNA regulation in T47D cell line

We know that there were no regulations of mature miRNA by E2-ERα in T47D and

MCF7 parental cell lines, and so, we proposed that we would not observe any regulation of E2-ERα in the control transduced cells (E2-Control vs. Vehicle-Control). In order to confirm this notion, we performed qPCR on these samples and compared treatments within the control-transduced cells (Vehicle vs. E2). We found no significant E2- mediated regulation of miRNAs between these samples, which is in concordance with our previous reports. To analyze whether the ERβ-mediated miRNA regulations were ligand dependent, we analyzed the ERβ samples (Vehicle vs. E2) in ERβ samples (Figure

4.3). This further suggests that the regulations of miRNA that we observe are due to the presence of ERβ in this cell line and this effect may be ligand independent.

4.4.5 MCF7 showed a more pronounced ERβ mediated regulation of miRNA

Studies to investigate ERβ and miRNA interactions have been carried out in MCF7 cell line. In order to ensure complete analysis of potentially regulated miRNAs and to evaluate the importance of cell line-specific miRNA regulation, we performed the miRNA studies in MCF7-Control and MCF7-ERβ cells treated with vehicle or E2 for

24h. We initially compared the E2-treated samples (E2-Control vs. E2-ERβ), which revealed signficiant regulation of miRNAs by ERβ (Figure 4.4A). miR-135a was the greatest regulated miRNA (> 20 fold change) of all miRNAs examined. There was also a

significant downregulation of miR-21, a well-known oncomiR, by ERβ. We did not observe any changes in miR-21 in the T47D-ERβ cells, and the miRNA regulations observed in the MCF7-ERβ cells were more apparent than in the T47D-ERβ cells. We also examined miRNA regulation by ERβ in the vehicle-treated samples of MCF7

(Vehicle-Ctrl vs. Vehicle-ERβ), and we observed similar pattern of ligand-independent miRNA regulation by ERβ, except that the miRNA gene response appeared enhanced by the presence of ligand (Figure 4.4B).

Figure 4.3: E2-ER regulation of miRNA in T47D-transduced cells. qPCR results comparing the vehicle and E2 treatments within the ERβ cells. Samples were poly-A tailed and SYBR green technology was used to detect amplification. Each miRNA amplification was performed in triplicate with each duplicate sample. Expression levels were compared relative to the vehicle. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05

(*). Values are the average of two separate experiments +/- SD. 83

Figure 4.4: Validations of microarray data in MCF7-transduced cells. qPCR results comparing the control cells to the ERβ cells in (A) presence of E2 (Β) absence of E2.

Samples were poly-A tailed and SYBR Green technology was used to detect amplification. Each miRNA was done in triplicate with each duplicate sample.

Expression levels were compared relative to the control sample. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD. 84

4.4.6 ERβ mediates a ligand-dependent regulation of miRNAs in MCF7 cells

Given that we observed increased miRNA regulations in these cell lines in the E2-treated samples, we suspected that the presence of ERβ may cause a ligand response in the

MCF7 cells. So, we performed qPCR and analyzing effect of E2 treatment within the control samples (Vehicle vs. E2) and within the ERβ samples (Vehicle vs. E2) of the

MCF7 cells. We found no significant E2-induced miRNA regulation within the control samples (not shown), which is in concordance with our previous study and confirms that there are no miRNA regulations by E2-ERα at 24h. On the other hand, comparisons within the ERβ samples (Vehicle vs. E2) revealed a significant regulation of miRNA, especially miR-135a (Figure 4.5). Thus, in MCF-ERβ cells, there is a ligand-independent and a possible ligand effect on miRNA regulation.

4.4.7 ERβ affects mammosphere formation abilities in breast cancer cells.

The miR-200 family, miR-221/-222, and miR-205 were significantly downregulated in the MCF7-ERβ cells (Table 4.2), and miR-205 was significantly downregulated in the

T47D-ERβ cells. The downregulation of these miRNAs have been associated with breast cancer stem cell properties such as self-renewal (Feifei, Mingzhi et al. 2012; Guttilla,

Phoenix et al. 2012). They also play important roles in regulating tumor progression and metastasis (Gregory, Bert et al. 2008). The miR-200 family has been reported to inhibit

EMT by regulating E-Cadherin in breast cancer cells (Shimono, Zabala et al. 2009; Sun,

Liao et al. 2010; Aydogdu, Katchy et al. 2012; Thomas, Rajapaksa et al. 2012). Further,

ERβ in colon cancer cells were observed to repress miR-200a/b and thereby enhance

ZEB1 or ZEB2, and decrease E-cadherin (Edvardsson, Nguyen-Vu et al. 2013). Thus, since ERβ downregulated these miRNA, we proposed that ERβ may be involved in maintenance of stem cell properties in these transduced ERα-positive cell lines.

Figure 4.5: E2-ERβ regulation of miRNA in ERβ-transduced MCF7 cells. qPCR results comparing the vehicle and E2 treatments within the ERβ cells. Samples were poly-A tailed and SYBR Green technology was used to detect amplification. Each miRNA was done in triplicate with each duplicate sample. Expression levels were compared relative to the vehicle. All miRNA expressions were normalized to snRNA U6.

Analysis and relative expressions were determined using comparative threshold cycle

(ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD. 86

Table 4.2: Pattern of regulation of miRNAs in T47D-ERβ and MCF7-ERβ. Table presents a summary of miRNA that were significantly regulated in the MCF7 and T47D

ERβ-transduced cell lines.

To study the effects of ERβ in stem cells, we performed a mammosphere culture assay. Mammosphere culture has been utilized to test for adult stem cells and for cancer stem cell abilities (Dontu, Abdallah et al. 2003; Charafe-Jauffret, Ginestier et al. 2009).

We found that the T47D-ERβ mammospheres formed more colonies after 5 generations of culture in comparison to T47D-Control mammospheres (Figure 4.6A). ERβ- transduced cells were able to maintain their mammospheres slightly better than ERα-only cells. Even more stunning, we found that the MCF7-ERβ cultures showed significant and major enhancement in the maintenance of stem cell formation in all generations of the mammosphere culture (Figure 4.6 B). ERβ was more effective in this cell line compared to T47D, aligning with its stronger effects also on miRNA gene response. MCF7 cells expressing ERβ formed more mammospheres in each generation, exhibiting 3-fold more mammospheres in the second and subsequent generations, compared to MCF7 cells not expressing ERβ. These results suggest that ERβ may be responsible for the maintenance of the stem cell self-renewal property in these cell lines. Also, we observed no changes in the size of the colony in neither T47D nor MCF7-transduced cell lines (Figure 4.6C and

4.6D respectively), suggesting ERβ may have no effect on mature progenitor cell proliferation in these cell lines.

Figure 4.6: The mammosphere formation capacity is significantly enhanced by ERβ expression. (A) T47D and (B) MCF7 breast cancer cells, with and without ERβ expression, was assayed for mammosphere formation ability for up to seven generations.

For each generation of mammospheres, colonies were counted and their size evaluated after 3 days of incubation. (C) T47D and (D) MCF7 mammosphere colony size for each generation of culture. This was repeated in two separate experiments. Student’s t-test was used for statistical testing, p<0.05=*, p<0.01=**, p<0.001=***.

4.4.8 miR-135a in breast cancer cells

Other miRNAs regulated by ERβ have been reported to perform vital roles in breast cancer progression. miR-21, which was repressed by ERβ in MCF7 cells, has been shown to promote growth, senescence, and motility in breast cancer cells (O'Day and Lal 2010;

Terao, Fratelli et al. 2011). miR-206, also repressed by ERβ in a ligand-independent manner in MCF7 cells, suppresses ERα expression and inhibits growth in MCF7 breast cancer cells (Adams, Cowee et al. 2009). Most importantly, miR-135a, which was highly responsive to ERβ in both transduced cell lines, has been reported to promote cell migration and invasiveness in breast cancer, especially in breast cancer with an invasive phenotype (Chen, Zhang et al. 2012). In the study conducted by Chen et al, invasive

BT549 cells expressed endogenous miR-135a, and SKBr3 and MDA-MB-231 breast cancer cells increased invasion and migration after miR-135a overexpression, but no effects on proliferation were observed in these breast cancer cells. Most importantly, they showed that miR-135a overexpression rendered no effects on invasion or migration in

MCF7 cells. The effect of ERβ upregulation of miR-135a in ERα-positive breast cancer remains to be elucidated, and is important to understand the molecular mechanism of the

ER receptors.

To study the functions of miR-135a in breast cancer, we first performed a qPCR on our transduced cell lines treated with 10nM ICI to corroborate that miR-135a is regulated by the ER receptors. ICI is a high-affinity ER ligand that antagonizes most estrogen-mediated action by inhibiting and degrading ERα. We found that ICI reverts the

ERβ ligand-independent increase of pS2 gene (Figure 4.7A), and reduces but not eliminates the ligand-independent increase of miR-135a in the ERβ cells and (Figure

4.7B, C). We also note that ICI treatment reduces the basal levels of miR-135a in control

MCF7 cells, indicating that perhaps presence of ERα also affects miR-135a. This is supported by the observation that long-term E2 treatment (48-72h) led to miR-135a upregulation in MCF7 parental cells (expressing ERα only) (Katchy, Edvardsson et al.

2012).

To further investigate if these regulations were by ERβ, we performed siERα transfections on the MCF7 transduced cell line. We silenced ERα in these cell lines, as shown in Figure 4.8, and performed qPCR on miR-135a and miR-21 miRNAs. Upon silencing of ERα, we found that miR-135a was still expressed in the presence of ERβ, and still upregulated by E2 treatment (Figure 4.9A, B). We also observed that miR-21 remained downregulated in the presence of ERβ alone. We found no response within the control samples for either miRNA, and a significant response (p<0.001) by miR-135a to the E2-ERβ sample only (not shown). Thus, ERβ is most likely responsible for the regulation of these miRNAs.

Figure 4.7: ICI treatment reduced ER-regulated miRNA levels. (A) qPCR results showing E2-induced and ICI-reduced response of pS2 in MCF7 transduced cells. Both clones of the MCF7-Ctrl (M1 and M2) and MCF7-ERβ (ERβ1 and ERβ2) cells were treated with vehicle, 10nM E2, or 10nM ICI for 24h. Expression of these genes was normalized to the expression of ARHGDIA (reference gene). (B, C) qPCR results showing ICI depletion of the ER regulation of miRNA in T47D and MCF7 transduced cell lines. Samples were poly-A tailed and SYBR Green technology was used to detect amplification. Each miRNA was done in triplicate with each duplicate sample.

Expression levels were compared relative to the vehicle. All miRNA expressions were 92

normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*). Values are the average of two separate experiments +/- SD.

Figure 4.8: Silencing of ERα by siRNA in transduced MCF7 cancer cells. qPCR results showing ERα mRNA expression in MCF7-Ctrl and MCF7-ERβ cells. These cells were transfected with siControl and siERα and then treated with either vehicle or 10nM

E2 for 24h. Expression of these genes was normalized to the expression of human 36B4

(reference gene). SYBR Green technology was used to detect amplification. Each miRNA was done in triplicate with each duplicate sample. Expression levels were compared relative to the vehicle. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01

(**), and p<0.05 (*). 93

Figure 4.9: ERβ is responsible for miRNA regulation in MCF7 transduced cancer cells. qPCR showing miRNA expression levels between the Control (Mock) and ERβ

MCF7 cells after transfection with (A) Control siRNA (siCtrl) and (B) ERα siRNA

(siERα). Samples were poly-A tailed and SYBR Green technology was used to detect amplification. Each miRNA was done in triplicate with each duplicate sample.

Expression levels were compared relative to the vehicle. All miRNA expressions were normalized to snRNA U6. Analysis and relative expressions were determined using comparative threshold cycle (ΔΔCt) method. Student t-test: p<0.001 (***), p<0.01 (**), and p<0.05 (*).

To investigate if miR-135a is directly regulated by ERβ, we checked for ERβ- binding sites near the promoter of miR-135a by using previously reported ERβ ChIP studies (Zhao, Gao et al. 2010; Grober, Mutarelli et al. 2011) and the UCSC genome browser. We found miR-135a to have ERβ-binding sites 4.0kb, 4.9kb, and 56kb downstream of its promoter and 177kb upstream of its promoter. This indicates that there is a possible direct interaction between this receptor and miR-135a cis-regulatory sequences. To further decipher the function of miR-135a, we did a gene ontology analysis on the expression targets of miR-135a. We first defined the expression targets of miR-135a using Target Scan. Gene ontology on the expression targets of miR-135a (from

TargetScan) revealed that miR-135a may play vital roles in cell cycle, angiogenesis, ion transport, and tissue development (data not shown). As miR-135a is increased by ERβ in these cells, it is expected that its targets will be decreased. We compared ERβ gene profiles of MCF7 and T47D cells with the expression targets of miR-135a, and found about 100 genes that were anti-correlated to the miR-135a expression. Overexpression analysis indicated that specific signaling pathways and biological processes were affected

(Table 3.1). Among other processes and pathways, the process of cell differentiation was significantly represented, and ERBB4, AKT1, and NFKB1 targets were also represented in this list. ERBB4 plays essential roles in development of the mammary gland, gene transcription, cell proliferation, cell differentiation, migration, and apoptosis. AKT1 regulates many processes among which are proliferation, cell growth, and angiogenesis.

NFKB1 is related to processes in inflammation, differentiation, cell growth, apoptosis,

and tumorigenesis. This suggests that miR-135a may be a potential target for breast cancer cells expressing ERβ.

Table 4.3: Signaling pathways and biological process represented in overlap between miR-135a gene targets and ERβ gene profiles. Gene ontology analysis was performed on overlapped genes using the Ariadne pathway studios software. Pathways and processes are shown with their respective p-values. All have p-values less than 0.05.

4.5 Discussion

ERβ is known to be downregulated in breast cancer cells compared to normal cells, but still expressed in the majority of ERα-positive breast cancers. The function and mechanism of ERβ action in these cancers are still not well understood. Certain challenges have made the ERβ mechanism of action difficult to study. One of such difficulties is the lack of cell lines expressing sufficient levels of endogenous ERβ

[reviewed in (Deroo and Buensuceso 2010)]. There are also controversial reports of cell lines, such as MCF7, expressing ERβ (Bianco, Perry et al. 2003; Chen, Ni et al. 2009).

Another challenge to studying mechanism of ERβ action is the lack of highly specific commercially available ERβ antibodies. Nevertheless, different approaches using exogenously expressed ERβ in breast cancer cell lines have been used for study in understanding the mechanism of ERβ action.

Microarray-based methods are the most extensively used approach for miRNA identification and quantification, although there are challenges to this method and the study of miRNA (details in Section 3.5). In our study the microarray was unable to significantly detect all regulated miRNA in the replicates, but the resulting fold-change values led us to believe that there were some miRNAs regulated. Validation with qPCR revealed significant regulations of these miRNA by ERβ, and none by ERα. The difference between the regulation of miRNA by ERα and ERβ may result from the fact that these receptors regulate different genes by binding to distinct regulatory elements

(Leitman, Paruthiyil et al. 2010). Also, these receptors regulate different genes by

recruiting distinct coregulators and chromatin remodeling factors (Leitman, Paruthiyil et al. 2010). Thus, these receptors have considerably different biological effects from each other.

We showed earlier (Chapter 3) that E2-ERα regulated no mature miRNA, but with the presence of ERβ in the same cell line, we found several mature miRNAs being regulated. To support our findings, ERβ has been shown to change the behavior of ERα- positive breast cancer cell in vivo in a mouse xenograft study (Paruthiyil, Parmar et al.

2004; Behrens, Gill et al. 2007). This could be explained by the fact that the ERα homodimers and the ERα/ERβ heterodimers promote target gene activity differently, possibly by ERα/ERβ heterodimers’ co-factor recruitment or by inducing recruitment of co-regulatory complexes different from ERα homodimers (Klinge, Jernigan et al. 2004;

Matthews, Wihlen et al. 2006; Zhao, Matthews et al. 2007; Nassa, Tarallo et al. 2011).

This further explains why we observed a reduction in the miRNA response to ERβ in the siRNA studies where we silenced ERα, and these results show that the heterodimers of these receptors are more active than the homodimers of each receptor. Our analysis of siERα cells however, indicated that it is ERβ in the form of homodimer is responsible for the miRNA regulations, although we noticed effects on miR-135a basal expression by

ERα silencing.

The ERα-positive Luminal A breast cancer cells are poorly invasive or metastatic, leading to a more favorable prognosis in patients (details in Section 1.2). The silencing of

ERα has been shown to lead to a morphological change from epithelial phenotype to a mesenchymal phenotype (Gadalla, Alexandraki et al. 2011). Also, prolonged

mammosphere culture of MCF7 cells was found to induce EMT with a reduced expression of ERα, and an increased miR-221/-222 expression plus a reduced expression of miR-205 and miR-200c (Guttilla, Phoenix et al. 2012). Also, ERβ was recently shown to be induced in mammary stem cells, and enhancing the growth of CSC (Ma,

Govindasamy et al. Submitted). We found miR-200c and miR-205 expressions significantly reduced in the MCF7-ERβ cell lines, which led us to believe that ERβ may play a role in inducing EMT. In mammosphere culture, the number of mammospheres from each generation of culture indicates the level of self-renewal properties of the cells, while the size of the colony indicates the rate of the proliferation of the more mature progenitor cells (Dontu, Abdallah et al. 2003). Our mammosphere culture assay results revealed maintenance of self-renewal properties by ERβ, but we did not detect any change of mature progenitor proliferation since the size of the colony remained the same through generations of the mammosphere culture. It has been reported that estrogen treatment in MCF7 and T47D parental cells mammosphere cultures reduced the self- renewal properties of the cells, but increased proliferation in the cells (Simoes, Piva et al.

2010). So, it is possible that estrogen treatment of our ERβ-transduced cell lines would result in an increased size in colony (proliferation).

Among the other miRNAs regulated by ERβ, miR-135a was highly induced in our

ERβ-transduced cell lines. There may be a direct interaction between ERβ and miR-135a, since miR-135a has very close ERβ binding sites to its promoter. The reports on miR-

135a mentioned earlier (Section 4.4.8), revealed a function of miR-135a in invasion and metastasis of breast tumor, although this was not observed in MCF7 parental cell lines

that express only ERα. The role of ERβ-mediated upregulation of miR-135a in ERα- positive breast cancer remains to be investigated and validated in the clinic.

4.6 Conclusion

We have shown that ERβ regulates miRNAs, which in turn, regulates cellular processes involved in cancer stem cell mechanisms. The identification of miR-135a and its involvement in important cellular processes (possibly through ERβ), provides a new biomarker in cancer research and provides a better understanding of the mechanism of

ERβ. Our results emphasize the interaction and mechanism between the ERs signaling, miRNA function, and CSC.

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Chapter 5

Bisphenol A and phytoestrogens mediate identical

and sub-additive effects on ERα-mediated

transactivation

All results shown here have been included in (Katchy, Pinto et al. In revision)

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5.1 Abstract

Xenoestrogens (e.g. bisphenol A, BPA) and phytoestrogens (isoflavones) are endocrine disrupting chemicals (EDC) that are capable of interfering with hormonal signaling in mammary and reproductive systems. These compounds are similar in structure to estrogen and bind to the estrogen receptors. It is likely that these compounds, either alone or in combination, could affect the same biological processes or have their own subsets of target pathways, and may contribute to the development of disease such as breast cancer.

Here, we extensively identify target genes and signaling pathways for BPA and phytoestrogens. Dose-response curves and combinatory actions for these compounds were measured using qPCR on known ERα-target genes. Gene expression profiling was performed on MCF7 breast cancer cell lines using dual-color comparative array.

Validations of array results were carried out using qPCR. Gene ontology studies were done to identify the mechanism of action of these compounds. Non-ERα mediated targets and the kinetics of each EDC were investigated in detail. We found a sub-additive effect of these compounds when combined with each other. BPA, genistein, and soy formula extract (SF) transcriptional regulation were comparable to that of E2 and mediated through ERα. Many target genes regulated by BPA, genistein and SF were involved in biological processes such as cell cycle and proliferation, similar to that of estrogen. This study emphasizes the implications of exposure to these compounds to both infants and adults and contributes to an increased understanding of the mechanisms of these EDCs’ action.

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5.2 Introduction

The main functional pathway of estrogen is to bind to ERα and ERβ. Upon estrogen binding, the ERs bind to their cognate DNA responsive elements (EREs), and recruit co-factors and chromatin remodeling complexes that increase or decrease target gene transcription. It has been speculated that different conformational changes allow different set of co-factors to form complex with the ERs, thereby differently influencing target gene transcription. In addition to its genomic activity, estrogen has “nongenomic” or “membrane-initiated” effects such as ERα interaction with G-proteins to initiate

MAPK signaling cascades (Chambliss, Simon et al. 2005). Many genes associated with the control of cell cycle, proliferation, and apoptosis are regulated by the ERs. Aberrant

ER expression is present in breast cancer, and the level of expression of ERα is a very important determinant of response to endocrine therapy and prognosis in ER-positive breast cancer. Besides their activation by estrogen, there are other ligands (natural or artificial) that can activate ERs, including many EDCs.

EDCs belong to a large group of synthetic and naturally occurring agents that are capable of disrupting normal hormonal signaling of both endocrine and reproductive system in humans. Some of these EDCs are xenoestrogens such as BPA or phytoestrogens such as the isoflavones genistein and daidzein, all discussed in detail in section 1.3.4. The long-term adverse health effects of exposure to xeno- and phytoestrogens especially in the reproductive system and other hormone-sensitive tissues

(mammary gland) is of concern as to the development of cancer. These disrupting agents may have effects on cancer risks, e.g. breast cancer, and the specific molecular and 103

genomic mechanism of these agents in cancer cells is still largely unknown. Toxicology testing today is largely performed on one compound at a time. However, the effect of a compound needs to be examined in its environmental context; e.g. in combination with other man-made compounds or natural compounds.

There is a concern that different EDCs act in synergy and that the effects seen in humans is a result of the so-called cocktail effect. There is a lack of studies analyzing the specific effects and combined exposure to environmental estrogenic ligands. The goal of this study was to determine in depth the specific effects (molecular and genomic) of different xeno- and phytoestrogen action in breast cancer cells. Using MCF7 breast cancer cells, we investigated to what extent these agents mirror estrogen, which genes are specifically induced by the different agents, their non-ERα mediated effects, and the implications of combinatory effects. An increased knowledge of the molecular actions of xeno- and phytoestrogens alone and in combination will contribute to understanding if these compounds affect the development and prognosis of breast and ovary cancers. This work will ncontribute to determining what preventive measures need to be taken to reduce certain cancer risks.

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5.3 Methods

5.3.1 Cell culture

MCF7 parental cells expressing ERα were cultured in Dulbecco’s Modified Eagle’s

Medium (DMEM) supplemented with 5% FBS and 1% Penicillin Streptomycin (Life

Technologies™ Grand Island, NY). The cells were synchronized by changing the medium to phenol red-free DMEM/F12 (1:1) medium supplemented with 5% Dextran-Coated

Charcoal serum (DCC) for 24h. The serum was then reduced to 0.5% DCC for 48h. The cultured cells was treated with 1nM to 10nM of E2 (Sigma-Aldrich, St. Louis, MO), ethanol or acetone/hexane (1:3)(Ac/Hx) (as control), 10nM to 10µM of BPA, or genistein

(Sigma- Aldrich, St. Louis, MO), 0.01% to 0.2% of SF, or 10nM/1µM ICI 181,780

(Sigma- Aldrich, St. Louis, MO) for 0-48h. All cells were incubated in a 5% CO2 humidified atmosphere at 37°C.

5.3.2 RNA preparation and cDNA synthesis

Treated cells were collected in TRIzol® for RNA extraction. RNA was extracted as described in the general materials and methods section for mRNA analysis. For cDNA synthesis: as described in the general materials and methods (section 2.3) for mRNA analysis.

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5.3.3 Quantitative real-time PCR analysis of mRNA expression

10ng cDNA was amplified using 1pmol of each of the forward and reverse primers, and

SYBR green PCR master mix in a 10μl final reaction volume. ARHGDIA was used for normalization of mRNA expression.

All runs were performed as described in the general materials and methods

(Section 2.4.1) for mRNA analysis.

5.3.4 Microarray experiment and analysis

Two-color comparative microarray technique covering the fully known transcriptome of

35,000 genes and variants using Operon’s long-oligonucleotide spotted array complemented with 133 oligos specifically synthesized for analysis of nuclear receptors, splice variants and co-regulators were used (Microarray Inc®, AL, USA), as described in the general materials and methods (Section 2.4.2). Each comparison was replicated and dye-swapped (Cy5/Cy3) with 100% EtOH or Ac/Hx as controls.

Analysis was performed as described in the general materials and methods

(section 2.4.2). Cut-off for differential expression was set to p-value <0.05 and a logFC of > |0.70|. Microarray data is available on NCBI’s GEO under accessions: GSE45557

(E2) and GSE45721 (BPA, genistein, and SF).

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5.3.5 Bioinformatics

Gene set enrichment analysis (GSEA) and sub-network enrichment analysis (SNEA) were performed for differentially expressed genes using the Ariadne Pathway Studios program (Elsevier Inc. MD, USA), p-values <0.05 were considered significant. Fischer’s exact test was applied to delineate enriched Gene Ontology (GO) functional groups among the differentially expressed genes.

5.3.6 Survival cluster analysis

For analysis of E2 and all EDCs gene signature in breast cancer patient samples, the expression profiles of the differentially expressed genes (genes that were present in at least two of the ligand gene profiles) were used to group 258 clinical, untreated, primary breast cancer samples from a patient cohort from Uppsala, Sweden (Miller, Smeds et al.

2005), by hierarchical clustering (Eisen Cluster and TreeView) of the tumors’ gene expression profiles. Disease-free survivals of the two patient groups were graphed using the Kaplan-Meier plot function in the SigmaPlot software, and associations with disease parameters (ERα status, PR status, lymph node positivity, high grade, recurrence, distant metastasis, death) were calculated using the Fisher’s exact test.

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5.4 Results

5.4.1 Comparison of dose-dependent transcriptional activation of endogenous ERα by BPA, genistein, and soy formula extract

Reports have shown that BPA binds to ERs 1,000 to 10,000 fold less efficiently than E2

(Kuiper, Lemmen et al. 1998). This suggests that BPA most likely would be able to produce significant effects at a concentration 1000-fold higher than E2. We and others have previously found that a 24h exposure to E2 produce significant changes in gene transcription in breast cancer cells (Williams, Edvardsson et al. 2008; Alvarez-Baron,

Jonsson et al. 2011), and have mapped endogenous ER-target genes (Williams,

Edvardsson et al. 2008). We first set out to determine the concentrations at which the

EDCs; BPA, genistein, and an extract of baby soy formula (SF), would activate ERα- mediated transcription in MCF7 with endogenous ERα expression.

We investigated the dose-dependent endogenous transcriptional activation of

BPA, genistein, and SF in MCF7 cells using qPCR (Figure 5.1A and B). We assessed the established E2-ERα target gene pS2, activated through a classical ERE (Williams,

Edvardsson et al. 2008), after treatment with different concentrations of each EDC. We observed that all compounds showed a dose-dependent effect on transcriptional activation. Both genistein and BPA significantly regulated this gene at 1µM concentrations but neither regulated pS2 with the same efficacy as E2 (Figure 5.1A).

BPA showed a lower transcriptional potency and efficacy than the phytoestrogens in

MCF7 cells, peaking at 1µM at approximately 80% of 10nM E2 activation. The soy 108

formula extract showed a comparable endogenous pS2 response to 10nM E2 at 0.05%

(Figure 5.1B). All compounds showed a dose-dependent effect on transcriptional activation, but at 100µM genistein did not induce further increase and BPA reduced its transactivation, which is in concordance with previous reports (Vivacqua, Recchia et al.

2003; Buteau-Lozano, Velasco et al. 2008).

Figure 5.1: Activation of ERα with varying concentrations of E2, BPA, genistein, and SF. qPCR results showing ligand-activated ERα transcriptional activation of PS2 in

MCF7 parental cells (A) treated with Ethanol, 10nM E2, and 0.1nM to 10µM of BPA, and genistein for 24h. (B) treated with Ac/Hx, and 0.001% to 0.2% SF for 24h

Expression of these genes in MCF7 was normalized to the expression of ARHGDIA

(reference gene). Concentrations are shown in Molarity. SYBR green technology was used to detect amplification, analysis and relative expression were determined using comparative threshold cycle (∆∆Ct) method, and values are the average of three separate experiments +SD. Student t-test: *** p<0.001, ** p<0.01, * p<0.05.

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5.4.2 Sub-additive effects of EDCs on ERα-mediated transcriptional activation

As BPA is found in our immediate environment (plastics, cans, food and water) and phytoestrogens are found in soy-based products (food), most humans are subjected to a combined exposure of these compounds. Such combinatory effects of environmental together with nutritional EDCs are poorly studied. In order to analyze combined responses of phytoestrogens and BPA in MCF7 cells, SF at a final concentration of

0.01% was selected, which induced approximately 50% transcriptional activation relative to E2 (Figure 5.1). We used one known upregulated ERα-ERE gene (CCNA2) and one downregulated gene (GABBR2). The combination SF and BPA at lower concentrations

(10nM and 30nM) resulted in significant increased gene response in a sub-additive manner (Figure 5.2A) for CCNA2. GABBR2 was also significantly repressed at those low concentrations of BPA and also at 100nM and 300nM (Figure 5.2B). We observed that for both genes, we started attaining a saturation of the receptors at 1uM concentrations. A combination of these compounds could attain as much saturation as

BPA at relatively high doses and leads to a more pronounced response when compared to the SF alone. Thus, these results show that a combined exposure of SF and BPA activate gene transcription (comparable to E2) to a higher degree than these compounds/extract single handed and this response can vary between genes.

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Figure 5.2: Combined treatment of BPA and SF effects on ERα activation.

Combined effects of SF and BPA on (A) CCNA2 and (B) GABBR2 transcriptional activation of ERα in MCF7 cells. Concentrations are shown in molarity. SYBR green technology was used to detect amplification, analysis, and relative expression were determined using comparative threshold cycle (∆∆Ct) method, and values are the average of three separate experiments +SD. Student t-test: *** p<0.001, ** p<0.01, * p<0.05 compared to respective BPA alone; ### p<0.001, ## p<0.01, # p<0.05 compared to SF alone.

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5.4.3 Transcriptional regulation by BPA, genistein, and SF are solely through an

ERα-mediated pathway

Ligands transcriptionally activate ERα by binding to its ligand-binding domain, thereby inducing a conformational change that mediates its binding to chromatin and co-factors.

It has been suggested that different ligands induce different conformational changes, causing ERα to bind to different enhancer elements and/or different set of cofactors, resulting in ligand-specific gene regulations (Singleton, Feng et al. 2006). BPA has, for example, been reported to induce a different set of genes compared to E2 (Singleton,

Feng et al. 2006). In addition, certain phytoestrogens, such as genistein, have ER- independent activities and would as such regulate their own subset of genes (Maggiolini,

Vivacqua et al. 2004; Lucki and Sewer 2011). We compared genes induced by BPA, genistein, SF, and E2 in MCF7 cells using dual-color microarray gene expression profiling comparing the control (vehicle treated) with ligand-treated samples. This screen revealed a vast number of genes to be regulated above the cut-off (p-value < 0.05 and logFC > |0.70|) (Figure 5.3A). BPA significantly regulated 421 genes, E2 727 genes, genistein 921 genes, and SF 215 genes (Figure 5.3B). The comparison of all EDCs showed that many genes were regulated in common, whereas SF had the least number of differentially expressed genes and genistein had the highest number of differentially expressed genes.

We further determined if the observed regulations were ERα mediated or non-

ERα mediated. We treated the cells with 1μM ICI before treating with each ligand. ICI has a high affinity for ERα and antagonizes most E2-mediated action by inhibition and 112

degradation of ERα. As demonstrated by qPCR in figure 5.3C, ICI completely depleted the expression of PS2 and SPINK4 (two classical E2-ERα target genes) in the presence of all the ligands. We analyzed whether any remaining effects of the ligands remained after

ICI treatments by microarray, comparing the control (EtOH+ICI) with ligand-treated samples (Ligand+ICI). A minimal number of genes were regulated over the cut-off (p- value<0.05) that shared very few genes with the ERα-mediated counterparts (Figure

5.3D). These results suggest that in MCF7 cells, all genes regulated by genistein, BPA and SF are regulated almost exclusively via ERα and that it is unlikely to find non-ERα mediated regulations. These results suggest that BPA, genistein, and SF may act solely through ERα when it comes to transcription regulation in MCF7 cells.

5.4.4 Extensive qPCR confirmations demonstrate that BPA, genistein, SF and E2 all regulate identical genes

The array results indicated that many genes were similarly regulated by all tested ligands, but it also indicated significant ligand-specific gene sets. This correlates to previous observations also using microarray (Singleton, Feng et al. 2006; Satih, Chalabi et al.

2010). However, microarray as a technique is not exact. While useful for genome-wide screening purposes, microarray is optimized to detect genes that are changed, and not define genes that are not changed. Microarray cannot identify regulated genes with complete reliability and frequently misses genes also when identical samples are compared. Therefore, qPCR confirmations are necessary to confirm the array results and explore ligand-specific gene regulations. 113

Figure 5.3: Comparisons of BPA, genistein, SF and E2-induced gene expression profiles via ERα in MCF7 cells. (A) Heat map of differentially expressed genes of E2,

BPA, Genistein, and SF. (B) Overlap between E2, BPA, genistein, and SF-induced differentially expressed genes after 24 h treatment. (C) qPCR results showing depletion of ERα activity after 24h treatment with 1μM EDCs and 1μM ICI treatment. (D) Overlap between ERα and non-ERα differentially expressed genes for each of BPA, genistein, and

SF after 24 h treatment. Expression of these genes was normalized to the expression of

ARHGDIA (reference gene), SYBR green technology was used to detect amplification, analysis and relative expression were determined using comparative threshold cycle

(∆∆Ct) method, and values are the average of three separate experiments +SD. Student t- test: *** p<0.001, ** p<0.01, * p<0.05.

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We investigated genes that according to the array results were commonly regulated (e.g.

SFXN2 and PCNA), regulated by BPA only (e.g. CDCA3F and THRB), by BPA and

Genistein only (e.g. FANCD2, GABBR2, BARD1, and TIPIN), by Genistein only (e.g.

DHRS2, E2F2, CLIP4, RGS6) and by SF only (e.g. SYNE2) (Figure 5.4A and B). We found that all tested genes were similarly regulated by all three ligands.

5.4.5 Gene ontology analysis showed that BPA, genistein, and SF can affect crucial pathways and biological processes

To classify our array gene list and explore if the different ligands induced different biological effects, we used the PathwayStudio® tool to search for biological processes that were significantly affected by the genes from each of the ligands. As seen in Table

5.1, BPA and genistein, in comparison to E2, regulated the expression of genes with significant involvement in nucleosome assembly (which corresponds to chromatin remodeling and gene expression), cell cycle, DNA repair, and MAPK activity/cell signaling. Genistein changed the expression of many more genes than BPA did and these genes clustered in many more processes (not shown) that were independent of both BPA and E2. Such processes included angiogenesis, G-protein coupled receptor (GPCR) signaling, chemotaxis, cell differentiation/proliferation, and ion transport. We did not see any differences in the expression of genes regulated by these EDCs when confirmed with qPCR (Figure 5.4), and interestingly though, we found that most processes were identically affected by all ligands.

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Figure 5.4: qPCR confirmations of EDC gene expression profiles in MCF7 cells. qPCR analysis confirms top genes from ERα array results for (A) E2 (10nM), BPA

(1μM), and genistein (1μM), and (B) SF (0.05%). Expression of these genes was normalized to the expression of ARHGDIA (reference gene), SYBR green technology was used to detect amplification, analysis and relative expression were determined using comparative threshold cycle (∆∆Ct) method, and values are the average of three separate experiments +SD. Student t-test: *** p<0.001, ** p<0.01, * p<0.05.

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Biological process p-value

nucleosome assembly 1.08E-13 cell cycle 2.94E-05 DNA replication 4.56E-04 DNA repair 1.18E-03 telomere maintenance 2.86E-03 cell division 9.85E-03 DNA recombination 1.12E-02 regulation of phosphorylation 2.14E-02 actin cytoskeleton organization 3.04E-02 nucleotide-excision repair 3.26E-02 glycolysis 3.82E-02

Table 5.1: Gene ontology analysis of EDC-ERα differentially expressed gene lists.

Table reveals biological processes that are affected by BPA, genistein, E2, and SF activated ERα. Gene ontology analysis was performed using the Ariadne pathway studios software. Processes are shown with their respective p-values. All have p<0.05.

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5.4.6 E2, BPA, genistein, and SF have similar patterns of response in gene regulation

Although we could not detect ligand-specific gene regulations, we did note that genistein at higher doses (>1uM) could induce a higher maximal transcriptional response than BPA and E2 (Figure 5.1A), also noticed for most genes already at 1uM (Figures 5.3C, 5.4A).

We speculated that there might be differences in the kinetic of transcription that is ligand- specific. Thus, a time point assay was performed on E2 (10nM), BPA (1µM), genistein

(1µM), and SF (0.05%) in MCF7 cells to investigate if these compounds result in different transcriptional kinetics on ERα-activated genes: PS2, GABBR2, PCNA. All compounds were observed to have similar patterns of gene response where a significant upregulation were noted at 8h and maximal response at 24h (Figure 5.5A and B), but not for the repression gene GABBR2, which is maximally repressed at 48h. This indicates that the kinetics of ERα-mediated up and down regulated genes may be different.

However, we note that while BPA is near its maximal regulation at 12h, E2 and genistein continue to more than double the gene induction between 12h and 24h. This indicates that although the different ligands appear to regulate the same genes, they may differently affect the activity of ERα over time. It is possible that different ligands affect ERα’s stability, phosphorylation and/or degradation, thereby genistein may enable an enhanced response over longer time, and this may explain why we could detect more genes regulated by genistein and fewer by BPA in our microarray analysis.

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Figure 5.5: EDCs time assays on known ERα-regulated genes. qPCR results showing varying time points (0-48h) after (A) E2, BPA, and genistein (Β) SF treatment.

Expression of these genes was normalized to the expression of reference gene ARHGDIA and relative to the 0h time point. SYBR green technology was used to detect amplification. Analysis and relative expression were determined using comparative threshold cycle (∆∆Ct) method. Values are the average of two separate experiments +SD.

Student t-test: *** p<0.001, ** p<0.01, * p<0.05.

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5.4.7 EDCs target gene profile depict good and poor prognosis patients

To explore the relationship between the genes identified to be regulated by at least two of these EDCs and poor-prognosis breast cancer, we did a clustering of 258 patient’s primary breast tumors. These genes clustered the patients into two groups, of which one had a significantly lower disease free survival (p=0.002). 215 genes separated these two groups (identified in the orange box in Figure 5.6A) and were upregulated in the poor- prognosis tumors (Figure 5.6 and Table 5.2). Also, a comparison of genes in this clusters with our EDC gene profile revealed that the majority of these genes (194 of the 215 genes) were upregulated by the EDC compounds in MCF7 cells. A gene ontology of this overlap revealed important biological processes such as nucleosome assembly and regulation of gene transcription, and pathways such as the E2F, MYC, and p53 pathways

(Table 5.3). This result suggests that genes regulated by these compounds are linked to poor prognosis in breast cancer patients, and exposure may results in adverse effect in terms of breast cancer risks.

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Figure 5.6: The EDCs gene expression profile cluster breast cancer patients with different outcomes. (A) Tumor samples’ transcriptional profile obtained from 258 primary breast cancer tumors clustered with BPA, genistein, and SF gene profiles. (Β)

Kaplan-Meier plots of disease recurrence for clusters 1 (green) and 2 (red) patients revealed differential outcomes dependent on expression of EDC profile genes.

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Cluster 1 Cluster 2 p-Value (n=122) (n=136)

LN+ 29 55 2.00E-03 LN- 90 75 ER+ 115 103 3.51E-06 ER- 4 32 PgR+ 109 83 1.90E-07 PgR- 13 53 G1+G2 119 79 0 G3 1 54 Recurrence (DFS) 32 59 2.00E-03 Metastasis (DMFS) 21 48 1.00E-03 Death (DSS) 17 38 4.00E-03

Table 5.2: Disease outcome for EDCs gene expression profile cluster analysis. Table reveals EDCs gene signature to be significantly correlated to node positivity, hormone receptor status, high grade tumors, recurrence, metastasis, and death.

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Table 5.3: Gene ontology studies on genes overlapping between the EDC gene profile and survival cluster genes. Table reveals biological processes/pathways that are affected by the overlapped genes. Gene ontology analysis was performed using the

Ariadne pathway studios software. Pathways and processes are shown with their respective p-values. All have p<0.05.

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5.5 Discussion

The high levels of exposure to environmental compounds with endocrine disruption abilities, such as BPA, makes understanding the effect that these compounds have on our system increasingly important. Since estrogenic activity is specifically linked to development of breast cancers, a risk assessment in this respect is critical.

Understanding how exposure of humans to these environmental compounds/factors affects the normal hormonal signaling is critical in understanding the molecular mechanism of these compounds. In doing so, we have done a thorough functional and gene-profiling analysis of these compounds in order to decipher the molecular mechanism of these EDCs.

We profiled different compounds; BPA, genistein, and soy formula extract and compared them to E2 in terms of transcriptional activation of both exogenous ERE- constructs and on genome-wide endogenous gene regulations. We found that phytoestrogens (genistein and SF) have a higher maximum efficacy than E2 on transcriptional activation, although not cell specific. We also found that there is no difference in which genes are activated by any of the ligands in MCF7 cells, and that all effects can be inhibited by ICI treatments, suggesting that their effects are solely through

ERα. Reports have speculated and shown that there exist non-ER mediated pathways that these phytoestrogens affect. Dong et al have shown that the G-protein-coupled receptor

30 (GPR30) is required for BPA activation of Erk1/2 (by phophorylation of Erk1/2) and increase in c-fos mRNA level in both ERα-positive and ERβ-negative tumors at higher

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concentrations (10µM) (Dong, Terasaka et al. 2011). This is a significantly higher concentration that we used for our study. Meanwhile other studies have suggested that

BPA does not have any nongenomic pathways at lower concentrations (Bulayeva and

Watson 2004).

The microarray indicated a large number of ligand-specific gene regulations, similar to what has been reported in other studies (Satih, Chalabi et al. 2010). These studies claiming ligand-specific effects have based this conclusion on microarray studies without performing thorough qPCR confirmations. However, a careful analysis using qPCR showed that this merely reflected the inability of the microarray technique to detect all regulated genes in all replicates. This is similar to recent conclusion for other nuclear receptors, where earlier claims of ligand-specific regulations have been disproven

(Wardell, Kazmin et al. 2012; Yuan, Lin et al. 2012). Also, the fact that the microarray detected many more genes regulated by genistein may be given to their significantly higher effect on some target genes at 24h (Figures 5.4). We speculate that a reason for this may result from these ligands’ effect on ERα stability, e.g. different ligand may, due to their structural shape, affect the degradation pattern of ERα differently. Also, the binding affinity of these compounds varies which may affect the cycling times for activation and degradation of ERα. Furthermore, the presence of ERβ, may affect the induction of gene expression by these compounds, given that the affinity for these compounds to each receptor differs. The presence and function of ERβ in ERα-positive breast cancer cells is still under study, and reports has shown that the effect of

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phytoestrogens in ERα-positive cells depends on the ratio of both receptors in these cells

(Sotoca, Ratman et al. 2008).

We further analyzed the combinatory effects of these EDCs and we found that co- exposure to phytoestrogens and BPA were able to induce sub-additive effects on ERα- mediated transcription compared to these compounds alone. The fact that humans are continually exposed to these compounds both environmentally and nutritionally, may raise concerns as to our health risk. Although the levels of BPA we are exposed to are low, many studies have shown numerous effects of low doses of BPA. At low doses,

BPA has been reported to induce chemoresistance to multiple anti-cancer drugs

(Lapensee, Tuttle et al. 2009), have epigenetic (and heritable) effects (Weng, Hsu et al.

2010; Singh and Li 2012; Wolstenholme, Edwards et al. 2012), and yet have anti- proliferative effects (Morice, Benaitreau et al. 2011). Isoflavones, especially genistein, have been reported to have agonistic effects at lower doses in breast cancer cell, but antagonistic effects at higher doses (Wu, Yu et al. 2008). Epidemiological studies have shown that high levels of soy intake was correlated to reduced breast cancer risk

(Messina and Wood 2008), and in association with all breast tumor subtypes (Zhang, Ho et al. 2010). Despite the positive attributes of this high exposure to breast cancer risk reduction, there is still debate as to the adverse effects the exposure to various doses of these compounds may have on breast and other tissues. To elucidate the possible adverse effects of these compounds, we performed a gene profile analysis of these compounds.

We noted that all compounds analyzed; phytoestrogens and xenoestrogens, regulated genes identical to E2 (Figure 5.3). We also observed significant changes in relative gene

126

expression for each compound at different doses, even at low concentration. Our study suggests that, as long as the compounds in vivo are taken up by the different tissues, their effects through ERα will be identical to that of estrogen.

The implications of the exposure of these compounds to infants and their effects on development are of major concern. As mentioned earlier, reports have shown that infants who are fed soy-based formulas are exposed to 6-11 mg isoflavones per kg body weight per day and the high plasma isoflavone concentrations in infants indicate that the absorption of soy isoflavones from the intestinal tract is efficient in infants consuming soy-based formula (Setchell, Zimmer-Nechemias et al. 1998). This is over 13,000 fold higher levels than infants’ endogenous levels of estrogen. It is likely that these abnormal circulating levels produce significant biological effects in infants. Indeed, we observed that a soy-based infant formula extract was able to fully activate ERα in MCF7 cells with the same efficacy as E2 (Figures 5.1). BPA has been measured repeatedly in human blood with a central measure of distribution in the 1–19.4 nM range (Vandenberg, Hauser et al. 2007). A toxicokinetic model predicted that plasma BPA levels could be approximately 11 times greater in newborns than in adults exposed to the same weight- normalized dose. This ratio is believed to drop to 2 by 3 months of age, although exposure to BPA through food may be greater in this developmental stage than in adults

(Edginton and Ritter 2009). These relevant mechanistic and biological features of xenoestrogens present in soy formula and plasticizers raise major questions about potential estrogen-like effects caused by soy-based formula feeding and BPA exposure in early life. In addition, the possibility of additive biological effects from exposure to

127

various chemicals raises important concerns about the risks involving simultaneous exposure to different dietary nutrients and environmental pollutants in infants as well as in the general population.

Our study was performed in breast cancer cells and we noted that all compounds to some extent: induced transcriptional activation, regulated the same genes, had similar kinetics, and shared effects in similar biological processes. The presence of BPA in the environment has been reported to play a defining role in demonstrating and preserving tumor aggressiveness and poor patient outcome (Dairkee, Seok et al. 2008). Given that the EDCs in our study all acted similarly, we expected that genistein and SF may demonstrate poor patient outcome. We related the genes regulated (by all compounds) to outcome of breast cancer patients and this clearly suggested that there is a significantly lower disease-free survival with exposure to these compounds.

5.6 Conclusion

Identifying environmental factors that contribute to health, disease, and response to treatment would help in identifying and reducing potential risk factors. These EDCs studied here are present in our immediate environment and diet, and are a potential risk, since they disrupt normal hormonal signaling. This study contributes to an increased understanding of the mechanisms of EDC action, and their involvement in diseases such as breast cancer. This study also contributes to a more reliable risk assessment of different EDC compounds.

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Chapter 6

Concluding remarks, future aims, and

bibliography

129

Concluding remarks and future aims.

Breast cancer is the leading cause of death in women, and estrogen plays a vital role in development and progression of breast cancer. The actions of estrogen are carried out through the estrogen receptors: ERα and ERβ. These receptors, although homologous in structure can have different functions and regulate transcriptional activities differently.

Estrogen signaling results from a balance between these two receptors. Among their transcriptional regulatory function, miRNAs are key players for gene expression regulation. miRNAs are being extensively studied in cancer research today because of their role in regulating transcription. Finding miRNAs associated with breast cancer and exploring the estrogen receptors and their gene regulatory mechanism can provide new therapeutic and preventive methods in cancer research and new biomarkers.

In order to explore these receptors and its association with miRNAs, we began with identifying miRNAs that are significantly associated with normal or disrupted estrogen signaling in breast cancer cells, and are regulated by ERα and ERβ. We found no significant changes in mature miRNA expression levels by E2-activated ERα in neither T47D nor MCF7 breast cancer cell lines (Katchy, Edvardsson et al. 2012). There were no direct regulations of mature miRNAs expression even with miRNAs that had

ERα binding sites close to their promoter. We noted that some miRNAs were regulated at longer exposures to E2, such as miR-135a, suggesting possible indirect relationship between ERα signaling and miRNA expression. Although we could not detect significant change in miRNA expression by E2-ERα, we found that there is a significant variation in

130

miRNA expression between different breast cancer cell lines, although not regulated directly by ERα. These findings brought us a step closer to understanding the relation between ERα and miRNAs in these cell lines.

We proceeded to explore ERβ regulation of miRNA in breast cancer. T47D and

MCF7 cells that were control (only ERα) or ERβ-transduced (ERα and ERβ), were used for this study. We identified miR-22, miR-200c, miR-522, miR-21, miR-135a, miR-205, and let-7b among others, as being regulated by ERβ in these cell lines. There were no significant regulations between the Vehicle-treated and the E2-treated samples in the

T47D transduced cells and the MCF7-Control cells, which were in concordance with our previous study (Katchy, Edvardsson et al. 2012). There was a significant response to E2 in MCF7-ERβ cells. The differences in the miRNA level of regulation and the response to E2 shows that miRNAs regulation is to some extent cell-specific, and further explains the controversies that exist in regards to ER regulation of miRNA. These miRNAs identified in our study are involved in several cellular processes, especially in cancer stem cell maintenance. For example, miR-22 is a known ERα suppressor and the miR-

200 family is involved in stem cell development. We found that ERβ downregulated the miR-200 family, whose expression is known to repress EMT in breast cancer cells. We also found that ERβ can enhance stem cell self-renewal properties. This makes ERβ an interesting target in the treatment of breast cancer. Functional analysis, so far, on miR-

135a, have revealed that it is regulated by ERβ, and may be involved in biological processes that affect breast cancer progression.

131

We intend to understand the relationship between miR-135a and ERβ. We would proceed to confirm that ERβ regulates this miRNA by conducting siRNA studies on ERβ.

Also, we would perform miRNA mimic studies on miR-135a in these cell lines for further functional studies of this miRNA. Finally, ChIP studies would be done to confirm the direct relationship between this miRNA and ERβ.

To further explore the estrogenic mechanism, we studied the role of EDCs in breast cancer. These compounds are similar in structure to estrogen and bind to the estrogen receptors (ER). They could affect the same biological processes or have their own subsets of target pathways. We found that at 1µM of both BPA and Gen induce a comparable gene expression response as 10nM E2 after 24h. SF showed comparable response at 0.05% concentration. Also, these compounds in comparison to estrogen had similar gene profiles, similar gene regulatory patterns, and their actions were mediated solely through ERα. Additionally, we defined sub-additive effect of these compounds when combined. Today, we are continually exposed to these compounds both nutritionally and environmentally, and our study has revealed a potential risk factor assessment in breast cancer development and progression.

Altogether, we have increased our understanding of the estrogen receptors and have provided possible new candidates for diagnosis and prognosis of breast cancer disease.

132

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Appendix A: miRNA profiling in ERβ-transduced T47D cells: Upregulated miRNAs.

Log2 miRNA p-value (G2/G1) hsa-miR-377 4.44E-01 4.63 hsa-miR-224 1.61E-01 1.71 hsa-miR-4451 7.86E-01 1.60 hsa-miR-3121-3p 9.60E-01 1.35 hsa-miR-4320 8.68E-01 1.33 hsa-miR-338-5p 5.92E-01 1.20 hsa-miR-302f 6.75E-01 1.13 hsa-miR-10b 6.42E-01 1.11 hsa-miR-1227 5.56E-01 1.02 hsa-miR-4647 8.46E-01 1.00 hsa-miR-10a 3.54E-01 0.98 hsa-miR-486-3p 6.09E-01 0.93 hsa-miR-767-5p 1.57E-01 0.86 hsa-miR-4472 7.86E-01 0.84 hsa-miR-500b 2.32E-01 0.82 hsa-miR-3195 2.34E-01 0.79 hsa-miR-497 2.04E-01 0.76 hsa-miR-371-3p 7.23E-01 0.73 hsa-miR-4802-3p 7.47E-01 0.73 hsa-miR-20b 3.28E-01 0.71 hsa-miR-301a 2.33E-01 0.71 hsa-miR-500a* 2.55E-01 0.69 hsa-miR-1180 2.94E-01 0.68 hsa-miR-17 2.24E-01 0.67 hsa-miR-501-5p 2.33E-01 0.67 hsa-miR-4713-5p 4.14E-01 0.66 hsa-miR-489 5.16E-01 0.66 hsa-miR-187 1.08E-01 0.65 hsa-miR-660 3.11E-01 0.63 hsa-miR-92b* 5.70E-01 0.61 hsa-miR-423-3p 4.96E-01 0.61 hsa-miR-374b 1.75E-01 0.59 hsa-miR-532-3p 3.49E-01 0.59 hsa-miR-106a 3.00E-01 0.58 hsa-miR-421 2.76E-01 0.58 hsa-miR-454 3.41E-01 0.57 hsa-miR-105 1.56E-01 0.56 hsa-miR-502-3p 3.02E-01 0.56 hsa-miR-362-5p 5.58E-01 0.55 hsa-miR-20a 3.47E-01 0.54 hsa-miR-4449 3.59E-01 0.54 hsa-miR-581 8.24E-01 0.54 hsa-miR-2467-3p 5.46E-01 0.52 hsa-miR-3187-3p 4.11E-01 0.50 hsa-miR-103a 6.74E-01 0.50 hsa-miR-374c 2.20E-01 0.49 159

Appendix B: miRNA profiling in ERβ-transduced T47D cells: Downregulated miRNAs.

Log2 miRNA p-value (G2/G1) hsa-miR-3154 6.67E-01 -0.49 hsa-miR-627 6.44E-01 -0.51 hsa-miR-23c 6.89E-01 -0.52 hsa-miR-4303 5.96E-01 -0.52 hsa-miR-629* 3.73E-01 -0.53 hsa-miR-575 7.00E-01 -0.55 hsa-miR-4521 4.50E-01 -0.56 hsa-miR-3131 5.98E-01 -0.58 hsa-miR-939 3.49E-01 -0.59 hsa-miR-933 5.67E-01 -0.59 hsa-miR-4762-3p 2.36E-01 -0.60 hsa-miR-3126-3p 5.29E-01 -0.63 hsa-miR-4302 5.57E-01 -0.64 hsa-miR-1246 3.33E-01 -0.65 hsa-miR-3151 6.46E-01 -0.67 hsa-miR-3646 2.11E-01 -0.68 hsa-miR-197 8.06E-01 -0.70 hsa-miR-1290 6.13E-01 -0.80 hsa-miR-22 2.17E-01 -0.82 hsa-let-7g 3.36E-01 -0.89 hsa-let-7i 2.53E-01 -0.95 hsa-miR-184 3.17E-01 -0.99 hsa-miR-4632 4.57E-01 -1.01 hsa-let-7d* 3.70E-01 -1.02 hsa-miR-98 5.60E-01 -1.07

160