ELUCIDATING THE ROLE OF E2F2 LOSS IN MEDIATING HUMAN BREAST CANCER METASTASIS. Inez Yuwanita A DISSERTATION

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1 ELUCIDATING THE ROLE OF E2F2 LOSS IN MEDIATING HUMAN BREAST CANCER METASTASIS By Inez Yuwanita A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Microbiology and Molecular Genetics Doctor of Philosophy 2015

2 ABSTRACT ELUCIDATING THE ROLE OF E2F2 LOSS IN MEDIATING HUMAN BREAST CANCER METASTASIS By Inez Yuwanita In human breast cancer metastasis, it is not the primary tumor that is the cause of mortality but tumor metastasis to distant sites. Therefore, it is important to elucidate the biological mechanism that underlies the metastatic processes. Previous work noted that, when MMTV-Myc mouse model was crossed with various activator E2Fs mutants, loss of various E2Fs transcription factors affected tumor latency and incidence. Specifically, loss of E2F1 increased incidence and decreased latency whereas loss of E2F2 or E2F3 decreased incidence and increased latency in the MMTV-Myc mice crossed with E2F1, E2F2, or E2F3 mutant background. Surprisingly, the percentage of mice with pulmonary metastases also increased when MMTV-Myc mice were crossed with E2F2 -/- mice. Subsequently, these observations in mice were tested bioinformatically to examine whether they translate to human breast cancer. In addition, bioinformatic predictions were also generated to identify the genes that were involved in mediating metastasis after E2F2 loss. To examine the effects of E2F2 loss in human breast cancer, the cell line MDA-MB-231 was used. Reduced level of E2F2 expression in the MDA-MB-231 cell line resulted in increased migration in vitro and increased lung colonization in vivo. To elucidate the mechanism by which E2F2 mediates metastasis, genes that were differentially regulated between MMTV-Myc tumors, MMTV-Myc E2F2 -/- tumors and lung metastasis lesions were examined. These genes were stratified based on their correlation with human distant metastasis survival, Cox-hazard ratio, or presence of putative E2F binding site. Subsequently, PTPRD was identified as a primary putative target gene. To further explore the role of PTPRD in E2F2-mediated breast cancer metastasis, knockdown of PTPRD was performed on MDA-MB-231. Decreased level of PTPRD was found to decrease migration in

3 vitro and decreased metastatic lesion in vivo. Examination of protein-protein interaction network map showed that PTPRD may be related to E2F2 through BCAR1 or Myc and STAT3. Taken together, these findings demonstrated the role for E2F2 in human breast cancer metastasis.

4 This work is dedicated to my family: Mum, Dad, Jen, and Larry. Especially dedicated to the little one whose patience and playfulness got me through life s hardest spots, Elsa. Her brand of Corgi insanity brings joy and sanity to an otherwise insane mind. To my friends, past and present. Thank you for being understanding and patient as I go through life being absolutely self-absorbed and focused on nothing but me. To God. To the Universe. To Science. Thank you. iv

5 ACKNOWLEDGEMENTS I would like to thank my mentor, Dr. Eran R. Andrechek, for his initial ideas and continuous support and guidance for the research. I would also like to thank the following committee members for their continuous support and critical advice on the research project: Dr. Titus Brown, Dr. Susan Conrad, Dr. Michele Fluck, and Dr. Vilma Yuzbasiyan-Gurkan. I would like to thank Dr. Sandra O Reille who provided invaluable training and guidance for xenograft implant and helped perform xenograft implantation into the nude mice. In addition, I would like to thank Amy Porter and Kathy Joseph (MSU Histopathology laboratory) who provided invaluable assistance in understanding histology and histology techniques. I would like to thank current and former members of the laboratories of Dr. Susan Conrad (Dr. Limin Wang), Dr. Kathleen Gallo (Dr. Jian Chen, Eva Miller, and Chotirat Rattanasinchai) and Dr. Sandra Haslam (Dr. Mark Aupperlee) for their help with protocols pertaining to tissue cultures, bioassays, and image capture. I would like to thank student volunteers in the Andrechek laboratory: Lauran Carney, Kaitlyn Klabunde, Michael Monterey, and Jacqueline Saunders for their contribution to the project. I would also like to thank members of the Andrechek laboratory, in particular Danielle Barnes M.S., for her assistance in examining the hazard ratio of candidate genes, and Jordan Honeysett, for his assistance in examining the correlation between candidate genes and human distant metastasis free survival as well as daily laboratory functions. I would like to thank Dr. Ernest Thomas Lawson, Ruth Lawson, and Jen Lawson for discussions and critical reading of the document as well as their support throughout my academic career. Finally, I would like to thank my family for their unwavering support and patience as I pursue my research: Dr. Yunita Sadeli, Ir. Gunawan Tatang Komara MM., and Elsa. v

6 PREFACE A portion of Chapters 1-6 had been published as: Yuwanita, I., D. Barnes, S. O Reilly, M. Monterey, and E. Andrechek Increased metastasis with loss of E2F2 in Myc-driven tumor. Oncotarget. (Online publication: October 15, 2015). vi

7 TABLE OF CONTENTS LIST OF TABLES...x LIST OF FIGURES... xi KEY TO ABBREVIATIONS... xiii Chapter 1: Introduction Breast cancer Metastatic breast cancer Heterogeneity Origins of tumor heterogeneity Dissecting the heterogeneity of breast cancer by bioinformatics Background Unraveling breast tumor heterogeneity using bioinformatics tools Intrinsic classification of breast cancer Pathway signature Utilization of pathway signatures: using gene expression datasets to understand biological mechanism underlying breast cancer development and progression Databases TCGA METABRIC ENCODE Linking bioinformatics analyses and their clinical implications: potential for personalized therapy Mammaprint Oncotype DX Prosigna Breast Cancer Prognostic Gene Signature Assay Mouse models of metastasis c-myc oncogene Historical overview of c-myc oncogene Structure and functions of the c-myc oncogene Roles for c-myc oncogene in cancer development and metastasis E2F transcription factors A brief history of the E2F transcription factors E2F transcription factor functions E2F transcription factors and cancer...18 Chapter 2: Materials and Methods Animal work Animal husbandry and tumor observation Tumor transplantation Retroorbital injection Gene analysis...22 vii

8 2.2.1 Pathway prediction Identification of E2F2 target genes Cell culture, shrna transfection, western blotting, and migration assays Cell culture Generation of shrna stable knockdown cell lines Western blotting E2F2 western blotting PTPRD western blotting Migration assays Wound-healing assay Transwell migration assay qrt-pcr RNA from human cell lines RNA from mouse tumors and lung metastases Data analysis...28 Chapter 3: Using bioinformatics to dissect breast cancer metastasis Introduction Utilization of pathway signatures to understand biological mechanisms underlying human breast cancer development and progression Gene expression alterations associated with lung metastasis Identification of candidate genes that are involved in E2F2-mediated metastatic pathway Discussion Kallikrein Myosin Heavy Chain Protein Tyrosine Phosphatase Receptor type D Troponin C type Validation...54 Chapter 4: MMTV-Myc mouse model of breast cancer and metastasis Introduction E2F2 induces metastasis in Myc-driven tumors Background effect Discussion Identification of genes that were involved in E2F2 mediated metastasis...72 Chapter 5: Understanding human breast cancer metastasis: Application of results of obtained from mouse models and bioinformatic predictions Introduction E2F2 and human breast cancer metastasis Identification of candidate genes that are involved in E2F2-mediated metastatic pathway PTPRD PTPRD and human breast cancer metastasis Connecting E2F2 and PTPRD...81 viii

9 5.5 Discussion Connecting E2F2 and PTPRD through BCAR Connecting E2F2 and PTPRD through Myc and STAT3 signaling axis...87 Chapter 6: Conclusions and Future Directions Conclusions Future directions...93 APPENDIX...94 BIBLIOGRAPHY ix

10 LIST OF TABLES Table 1. Common histological types of ductal carcinoma and their prognosis...4 Table 2. Candidate genes obtained from pipeline to identify the mechanism by which E2F2 loss mediated metastasis...49 Table 3. Predicted background-tumor interactions...70 Table A3.3. Gene expressions that correlated with human distant metastasis free survival Table A3.4. Gene expressions with significant Cox-hazard ratio Table A3.5-A. Genes with putative E2F binding site (Transfac and SwissRegulon) Table A3.5-B. Genes with putative E2F binding site (Xu et al. 2006) Table A3.6. Transfac analysis of genes that were associated with DMFS in Clusters A and B 119 x

11 LIST OF FIGURES Figure 1 - The effects of various activator E2Fs deletion on tumor latency and growth rate20 Figure 2 - E2F pathway probabilities in human breast cancer samples...31 Figure 3 - E2F2 pathway activation was predicted to be low in lung metastasis samples...35 Figure 4 - Cross-reference of bioinformatics analysis of the roles of activator E2F transcription factors with existing online database for survival analysis...36 Figure 5 - Gene expression associated with lung metastasis...39 Figure 6 - Specific human breast cancer cluster co-clusters with MMTV-Myc lung metastasis samples...42 Figure 7 - Pipeline to identify factors that are involved in E2F2-loss mediated metastasis..45 Figure 8 - Pathway probabilities for human breast cancer cell lines...55 Figure 9 - E2F2 loss increased the percentage of metastasis of MMTV-Myc mouse model 59 Figure 10 - E2F2 loss increased the percentage of metastasis of MMTV-Myc WT21 mouse model...60 Figure 11 - Histology of lung metastases...61 Figure 12 Figure 13 - Background effect on tumor onset, growth rate, and metastases of transplanted MMTV-Myc WT21 tumors and MMTV-Myc WT21 E2F2 -/- tumors PTPRD is differentially expressed between MMTV-Myc tumors, metastatic MMTV-Myc E2F2 -/- tumors and lung metastasis...73 Figure 14 - E2F2 knockdown increased migration in vitro and lung colonization in vivo...77 Figure 15 - PTPRD knockdown decreased migration in vitro and lung colonization in vivo.82 Figure 16 - Regulatory network connected E2F2 and PTPRD through BCAR1 or through Myc and STAT Figure 17 - Possible interactions between PTPRD, Myc, and E2F2 leading to metastasis...92 Figure A1.1 - Analysis of the effects of Myc gene alterations and expression levels on overall survival, relapse and metastasis of breast cancer...95 xi

12 Figure A2.1 - Principle component assay of gene expression samples before and after batch normalization...96 Figure A2.2 - Inconsistency of PTPRD detection...97 Figure A2.3 - Selection of PTPRD primers...98 Figure A3.1 - Low E2F2 probabilities association with decreased time to distant metastasis is unique to cluster B...99 Figure A3.2 - Correlation between high or low E2F2 probabilities with distant metastasis in all clusters Figure A3.6 - A. Gene correlated with DMFS is unique for Clusters A and B Figure A3.7 - TCGA and K-M Plotter analysis for KLK Figure A3.8 - TCGA and K-M Plotter analysis for MYH Figure A3.9 - TCGA and K-M Plotter analysis for PTPRD Figure A TCGA and K-M Plotter analysis for TNNC Figure A4.1 - Patterns of Myc, E2F2, and PTPRD expression in all clusters Figure A5.1 - Confirmation of E2F2 knockdown results in MDA-MB-231 cell line Figure A5.2 - Confirmation of the effect of E2F2 knockdown on migration in MCF7 cell line127 Figure A5.3 - Confirmation of PTPRD knockdown results in MDA-MB-231 cell line Figure A5.4 - Confirmation of the effect of PTPRD knockdown on migration in MC7 cell line130 xii

13 KEY TO ABBREVIATIONS ALL AML = Acute lymphoid leukemia = Acute myeloid leukemia BCAR1 = Breast Cancer Antiestrogen Resistance 1 BRCA1 Cdc25A = Breast cancer 1, early onset = Cell division cycle 25A CDH1 = Cadherin1, type 1 CDK cdna ChIP = Cyclin-dependent kinase = complementary DNA = Chromatin immunoprecipitation CHN2 = Chimaerin 2 CK5/6 = Cytokeratin 5/6 COX2 DMFS DNA E2F = Cyclooxigenase-2 = Distant metastasis free survival = Deoxyribonucleic acid = E2 factor E2F1 = E2 factor 1 E2F2 = E2 factor 2 E2F3 = E2 factor 3 E2F4 = E2 factor 4 E2F5 = E2 factor 5 E2F6 = E2 factor 6 xiii

14 E2F7 = E2 factor 7 E2F8 = E2 factor 8 EDTA EGFR EMT ENCODE ER = Ethylenediaminetetraacetic acid = Epidermal growth factor receptor = Epithelial-mesenchymal transition = Encyclopedia of DNA element = Estrogen receptor ERBB2 = Erb-B2 receptor tyrosine kinase 2 ERK/MAPK = Extracellular signal-regulated kinases/mitogen-activated protein kinases FBW7 = F-box and WD repeat domain containing 7 GAPDH GATHER GEO = Glyceraldehyde-3-phosphate dehydrogenase = Gene annotation tool to help explain relationships = Gene expression omnibus GRB2 = Growth factor receptor-bound protein 2 GSE = GEO series HER2 = Human epidermal growth factor receptor 2 HMEC HR IDC = Human mammary epithelial cell = Hazard ratio = Invasive Ductal Carcinoma Ink4A = Inhibitor of cyclin-dependent kinase 4 ITIH4 = Inter-α-trypsin inhibitor heavy chain family, member 4 KLK1 = Kallikrein 1 LAR = Leukocyte Antigen Related xiv

15 MAS5 = Microarray suite 5.0 MbI MbII MC29 = Myc box I = Myc box II = Avian myelocytomatosis virus MCAF/MCP1 = Monocyte chemotactic protein 1 MCF7 = Michigan cancer foundation MDA-MB-231 = MD Anderson metastatic breast METABRIC mirna MMP = Molecular taxonomy of breast cancer international consortium = micro RNA = Matrix Metalloprotease MMP16 = Matrix metalloprotease 16 MMTV mrna MT MYC = Mouse Mammary Tumor Virus = messenger RNA = Mammotropin = Myelocytomatosis MYH2 = Myosin Heavy Chain 2 OS p16 PAM50 PBS PCR = Overall survival = Cyclin-dependent kinase inhibitor p16 = Prediction analysis of microarray = Phosphate buffered saline = Polymerase chain reaction PLP/DM20 = Proteolipid protein 1 PMSF = Phenylmethylsulfonyl fluoride xv

16 prb P-TEFb = Phosphorylated retinoblastoma = Positive transcription elongation factor PTPN22 = Protein tyrosine phosphatase non-receptor type 22 PTPRD PTPRF PVDF PyMT qrt-pcr RAS Rb RFS RMA RNA RPM SCF sh S-phase ST6GALNAC5 = Protein Tyrosine Phosphatase Receptor type Delta = Protein Tyrosine Phosphatase Receptor type F = Polyvinylidene fluoride = Polyoma middle-t = Quantitative reverse transcription polymerase chain reaction = Rat sarcoma = Retinoblastoma = Relapse free survival = Robust multi-array average = Ribonucleic acid = Rotation per minute = complex of SKP1, CUL1, and F-box protein = small hairpin = Synthesis-phase = ST6 (α-n-acetyl-neuraminyl-2,3-β-galactosyl-1,3)-nacetylgalactosaminide α-2,6-sialyltransferase 5 STAT3 = Signal Transducer and Activator of Transcription 3 TCGA = The cancer genome atlas TNNC2 = Troponin C type 2 TP53/p53 = Tumor protein p53 TRP1/TYRP1 = Tyrosinase-related protein 1 xvi

17 TRRAP UACC Ub UCSC VEGF = Transformation/transcription domain-associated protein = University of Arizona Cancer Center = Ubiquitin = University of California, Santa Cruz = Vascular Endothelial Growth Factor WAF1 = Wild type activating fragment 1 WAP = Whey acidic protein WT21 = Wild type 21 xvii

18 Chapter 1 Introduction 1.1 Breast cancer Breast cancer is the most commonly diagnosed cancer in women in the U.S. The National Cancer Institute (NCI) estimated 60,290 cases of in situ disease, 231,840 new cases of invasive disease and 40,290 deaths in The Susan G. Komen foundation reported that although the incidence of breast cancer declined in the early 2000s, the incidence of breast cancer remained stable since Improved breast cancer treatment and early detection results in the identification of more cases at earliest stage when chances of survival are highest which lead to decreased breast cancer mortality since The American Cancer Society recorded that although incidence of localized breast cancer remained relatively stable, the incidence rates of regional-stage disease and distant-stage disease increased by 1.6% and 1.8% per year, respectively. Because 79% of newly diagnosed cases will be invasive breast cancer, which in advanced stages may lead to metastatic breast cancer, this is especially of concern because the five-year relative survival of distant-stage disease is 24%, significantly lower than 5-year relative survival of 99% for localized disease or 84% for regional disease Metastatic breast cancer The Metastatic Breast Cancer Network reported that metastatic breast cancer comprised 6-10% of newly diagnoses cancer with about 20-30% propensity of existing cancer to become metastasis with median survival of 3 years. Often times, it is not the primary tumor but the 1

19 metastasis to distant sites that is the cause of patient mortality from breast cancer. Hence studies aimed at elucidating the signaling pathways that underlie the metastatic processes are warranted. Human breast cancer metastasis is a complex multistep process that involves detachment of tumor cells from the original site, intravasation into the blood vessel, extravasation out of the blood vessel, and, finally, colonization of distant organs such as the bone, brain, lung, and liver (1 3). Since the term metastasis was coined in 1829, scientists have studied the characteristics of tumor cells and the mechanisms of metastasizing tumor cells (4). One of the complicating factors in studying the processes that underlie biological processes of human breast cancer, including metastasis, is the heterogeneity of the disease. Indeed, differences in estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2) receptors expression were found between primary tumors and matched lesions (5). This is further complicated by differences in tumor microenvironment (6) and dormancy (7,8) Heterogeneity Complication in treating breast cancer is mostly attributed to the heterogeneity of the disease. To date, breast tumors have been classified primarily based on their histopathological profile in an effort to diagnose and/or treat the disease (5,9). Heterogeneity in breast cancer can be found in tumor and stroma as well as metastatic lesions, all of which pose challenges in understanding tumor progression and metastasis (10) Origins of tumor heterogeneity Breast cancer may arise from three distinct breast tissues: ductal, lobular, and, although rarely, connective tissue (11). The most common ductal and lobular breast carcinomas are associated with different gene mutations. Ductal carcinomas were found to be associated with 2

20 BRCA1 and TP53 mutations whereas CDH1 were associated exclusively with lobular carcinoma (12). Further complications arise from the heterogeneity of these tumors since differences in tumor architecture accounted for their invasiveness, overall prognosis, and metastatic capabilities. Table 1 lists histological types of ductal carcinoma with their corresponding tumor architectures, prognosis, and metastatic capabilities to illustrate the heterogeneity of breast cancer. For example, the lobular carcinoma, which accounted for 10% of breast carcinoma were found to have generally good prognosis, however, this type of breast carcinoma were also found to be highly metastatic (13). This suggested that histological profiling of human breast cancer alone is not adequate to characterize breast carcinoma. Based on their gene expression, molecular subtypes differentiated human breast cancer into: luminal A/B, basal, Her2, normal breast-like, and claudin-low (14 17). These molecular breast cancer subtypes possess distinct characteristics and are associated with different tumor histology. For instance, basal-like subtype that is associated with poor clinical outcome is associated with a rare form of breast cancer, the metaplastic breast cancer (18). Both metaplastic and basal-like tumors showed similar histologic profile whereby they are ER-, HER2-, EGFR+, and CK5/6+ (19) Conversely, luminal subtype is associated with mucinous or tubular histological type, of which both types were known to be slow-growing tumors with good prognosis (18). These findings illustrate the complexity of breast cancer. 3

21 Table 1. Common histological types of ductal carcinoma and their prognosis Histological type Description Prognosis Ductal carcinoma in situ Invasive ductal carcinoma (IDC) Subtypes of IDC Comedo ductal carcinoma Inflammatory ductal carcinoma Medullary ductal carcinoma Mucinous ductal carcinoma Non-invasive. Earliest form of breast cancer. Characterized by the presence of abnormal cells inside ducts. Cancer cells had broken through the confines of the luminal epithelial cells of the ducts and has now infiltrated the breast Cancer cell filled ducts with central necrosis. Breast appears red and inflamed due to cancer cells blocking lymph node of the skin Tumor is soft and fleshy, resembles medulla of the brain. Common in women with BRCA1 mutation Tumor is composed of cell surrounded by large amount of mucus Stage 0 breast cancer Highest survival rate Divided into three grades (I- III) whereby higher grade increases chances of invasive cancer and recurrence. Prognosis is dependent on stages Staging criteria: size of tumors, number of affected lymph nodes, and presence of metastases Poor. High grade cancers that often recur within 5 years Poor Good prognosis Rare metastasis Generally favorable prognosis Slow growing tumors Rare metastasis Papillary ductal carcinoma Tubular ductal carcinoma Tumor cells grow in finger-like projections Tumor is made up of tubules. Cells appear similar to normal, healthy cells Generally good prognosis Tumors present as low grade tumors Very good prognosis Slow growing tumor Tumors are not aggressive 4

22 1.2 Dissecting the heterogeneity of breast cancer by bioinformatics Background One of the advantages of using DNA microarray hybridization technique is that it allows for measurement of thousands of genes simultaneously (20). In cancer biology, early experiments utilizing this technology was aimed towards analyzing the differences in gene expression associated by tumor suppression in human melanoma cell line, UACC-903, upon the introduction of normal human chromosome 6 (21). This experiment found several gene candidates that might be of importance in melanoma tumorigenicity such as the human brown locus protein (TRP1), myelin PLP/DM20, monocyte chemotactic protein 1 (MCAF/MCP1), and mediator of p53 tumor suppression (WAF1) (21). In 1998, the introduction of hierarchical clustering allowed further analysis of gene expression datasets. Hierarchical clustering organizes genes based on similarities of expression patterns as demonstrated in the budding yeast, Saccharomyces cerevisiae (22). Clustering has since then been employed to dissect the genetic basis of cancer, particularly focusing on tumor classification. The utility of gene expression datasets allow for further classification of tumors as demonstrated by Golub et. al in 1999 (23). The authors sought to distinguish acute leukemia of lymphoid precursors (ALL) from acute leukemia of myeloid precursors (AML). By sorting 6817 genes, they found that 1100 genes were correlated with AML-ALL class distinction. These genes were further filtered until 50 genes most closely correlated with AML-ALL distinction remained. The predictive capabilities of these 50 genes were validated using leave-one out cross-validation and known leukemia samples. Thus, a signature to distinguish AML from ALL was generated. Both methods implied their possible use in cancer therapy as predictive and diagnostic tools and as a guide to cancer therapy. The utility of hierarchical clustering and genomic 5

23 signatures in dissecting the heretogeneity of human breast cancer and elucidating the biological bases of breast tumor processes including their potential in breast cancer therapy will be discussed further subsequently Unraveling breast tumor heterogeneity using bioinformatic tools The complexity of human breast cancer is evident from the genetic pathways that are involved in tumor development and progression. The genetic basis of the biology of breast cancer include both somatic genetic aberration and epigenetic events (24). Thus, breast cancer is not a single disease, but a heterogeneous disease. The availability of genomic analysis tools provided the ability to begin to disentangle the complex heterogeneity of the disease, including intratumor heterogeneity (25). As previously mentioned, gene expression data can be analyzed by hierarchical clustering (22). The resulting groups of genes,termed clusters,were represented as tree branches in which the lengths of the branch represented the extent of similarities between objects (genes) (22). The clustering of gene expression data from mammary epithelial cells in vitro (HMEC) and a limited sample of human breast tumor were able to separate genes that were differentially expressed by cultured HMEC, breast tumor, tumor associated stromal cells and macrophages (26). This indicates the capability of gene expression analysis not only to identify genetic properties intrinsic to the tumor samples but also tumor stromal properties. This finding enabled the distinction between intrinsic genetic aberration and stromal effects Intrinsic classification of breast cancer Further analyses of an expanded set of human breast tumor samples led to the identification of subtypes of human breast tumors: basal-like, ERBB2+, normal breast-like, luminal/er+ (14). Of these subtypes, luminal breast tumors were further classified into luminal 6

24 type A and luminal type B breast tumors (16) and an additional subtype, claudin low, was added and characterized (17,27). These subtypes were validated using independent datasets using selected intrinsic gene list consisting of 534 genes that showed the most variation among tumors from different patients. Due to differences in platform where gene expression data were obtained, a total of 461/534 genes were used to validate the presence of the subtypes in van tveer et. al cohort and 242/534 genes were used to validate the presence of the subtypes in West et. al cohort (15). The presence of breast tumor subtypes in two independent datasets suggested that these subtypes represent biologically distinct disease entities. Furthermore, by including samples from BRCA1 carriers in the analysis, the authors found the association between BRCA1 mutation and basal tumor subtype (15). In addition, further characterization of these subclasses revealed the correlation between these subtypes to their clinical implications, namely that the basal and ERBB2+ breast tumors were associated with shorter survival (28). In addition to the validation described above, the presence of breast cancer subtypes was also validated across platforms. Using newer and more advanced arrays, the intrinsic gene list was regenerated into 1300 genes. A total of 306/1300 genes were present in all platforms and was used to cluster test datasets. Interestingly, all five clusters (basal-like, luminal A and B, ERBB2+, and normal-like breast tumors) were found in the test dataset (29). Currently the list of 1300 genes was expanded to include an additional 600 genes and included genes that identified the claudin-low cluster (30,31). This conclusion supported the notion that breast tumor subtypes represent distinct disease entities Pathway signature Although the classification of breast tumor samples allowed examination of potential oncogenes, it did not identify higher order structure inherent within the gene expression data. 7

25 Thus, another method was devised to predict the clinical status of human breast cancer samples. West et. al identified a set of 100 genes that were able to predict the status of estrogen receptor (ER) in human breast cancer (32). This set of genes was designated as ER signature. A number of signatures exist for major oncogenic pathways such as RAS, MYC, and P53. Using these signatures, the probability of a certain signaling pathway being activated in a breast tumor could be calculated (33). When these signatures were used to predict oncogenic pathways in human breast cancer subtypes, the authors discovered a relationship between the intrinsic subtypes and classification based on pathway probabilities. For instance, basal-like subgroups exhibited low ER and PR pathway probabilities and high Ras and Myc probabilities whereas inversed relationships were found in luminal tumor subtypes (34). Thus, pathway probabilities classification aided in dissecting the heterogeneity of breast tumors present in the subtypes (34). These findings demonstrated that both intrinsic subtyping and pathway predictions can be used to dissect the heterogeneity within human breast cancer Utilization of pathway signatures: using gene expression datasets to understand biological mechanism underlying human breast cancer development and progression As previously mentioned, pathway signatures can be used to predict major oncogenic pathways that may occur in breast tumor samples. However, embedded in gene lists defining pathway signatures were also genes particularly associated with the pathways. For example, genes for proteins that synergized with ER were included within 100 genes that define ER pathway activation (32). This indicates the potential for the utilization of genomic signatures to elucidate the biological mechanisms underlying the biology of breast cancer, including metastasis. 8

26 Metastasis is a process that involves several steps: local invasion, intravasation, survival in the circulation, extravasation and, eventually, colonization at distant sites. Primary breast tumors had been found to metastasize to distant sites such as bone, brain, liver, and lung (1,35 38). Often times it is the metastasis to distant sites rather than the primary tumor that is the cause of death. Hence, one of the important focuses in breast cancer studies is to elucidate the mechanism underlying metastasis processes. Signatures predicting the propensity for metastatic events, particularly in lymph-node negative patients have been developed in addition to site-specific metastatic signatures (35,36,39,40). By employing in vitro and in vivo isolation techniques and gene expression analysis, specific gene signatures exist that identifies genes that were responsible for breast cancer metastasis to the bone (35), lung (41), and brain (37). Interestingly, analysis of these genes showed shared mediators between brain and lung metastases not found in bone metastasis. COX2 and EGFR ligands, and α2, 6-sialyltransferase (ST6GALNAC5) were genes that was uniquely involved in mediating breast cancer metastasis to the brain (37). On gene level, stratification of human metastatic clinical annotation using gene expression of a particular gene infers the direct correlation between expression of a gene and its effects on metastasis. Thus, human breast cancer metastatic pathways can be studied and genes that are involved in the metastatic pathway can be identified Databases Genomic investigations into cancer-related pathways resulted a massive amount of data. These data were organized into databases that serve as comparative studies or validations. Some of the major databases are discussed below. 9

27 TCGA The Cancer Genome Atlas project (TCGA) assembled data from 91 cancer studies. Specifically for breast cancer, TCGA contains 6 studies for a total of 2,314 tumor samples (42,43). The TCGA database is accessible through cbioportal ( cbioportal allows examination of copy number alterations, mutations, mrna expressions and protein phosphorylation events. Perhaps the most useful role of cbioportal is in visualization of gene-gene interactions; cbioportal allows input of multiple genes and examination of how queried genes are connected in a network METABRIC METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) consisted of 997 primary tumors and validation set of 995 tumors (44). Analysis of copy number variants, single nucleotide polymorphisms and acquired copy number aberrations of the METABRIC data revealed 10 novel subgroups (44) which enhanced the current molecular classification of breast cancer. Unlike cbioportal that allows queries for individual genes, the METABRIC data is available for independent analysis through ENCODE The Encyclopedia of DNA Elements (ENCODE) allows for examination into DNA regulatory mechanisms such as examination of transcription factor binding site(s). The ENCODE database mapped regions of transcription, transcription factor association, chromatin structure and histone modifications (45). The study is accessible both through UCSC genome browser ( and through the ENCODE portal for ENCODE results after 2013 ( 10

28 1.2.5 Linking bioinformatics analyses and their clinical implications: potential for personalized therapy The focus of breast cancer research has been to develop diagnostic tools and to treat the disease. Therefore, clinical application of clustering is concentrated on classifying breast tumor samples into distinct groups with clinical implications. As previously mentioned, intrinsic gene list divided breast tumors into six subtypes: basal-like, luminal A, luminal B, normal-like, ERBB2+, and claudin-low (14 17,27). Further characterization of these subclasses revealed the correlation between these subtypes and their clinical implications, namely that the basal-like and ERBB2+ breast tumors were associated with shorter survival (16). Also, the classification of human breast tumors into these subtypes led to the process of establishing a 50-gene signature, PAM50, which was designed to standardize the classification of human breast tumors. PAM50 signature not only predicts the subtype of human breast tumors and risk factors associated with the subtypes, but also gives the prediction for neoadjuvant chemotherapy efficacy (46). The power of diagnostic also lies within existing signatures for various oncogenic pathways. Signatures that represent oncogenic pathways have been generated by integrating various methods in studying breast cancer biology: in vitro cell line models and in vivo mouse models. As previously mentioned, these signatures can be applied to breast cancer gene expression datasets to calculate the probability of a certain oncogenic pathway being activated. For instance, the application of E2F1-3 signatures to a panel of human breast tumor gene expression datasets led to the discovery of potential E2F2 role in human breast cancer processes whereby low probability of E2F2 pathway activation was associated with increased relapse free survival (47). Thus, this diagnostic feature enables personally tailored chemotherapy for breast cancer patients while taking into consideration the heterogeneity of breast cancer (48). 11

29 Mammaprint In 2002, Van tveer et. al (49) identified a group of 70 genes, now called the MammaPrint, that could predict the clinical outcome of breast cancer. These 70 genes are able to distinguish patients with distant metastases within 5 years, irrespective of lymph node metastasis status (49). The MammaPrint genes were validated and found to be a stronger predictor for metastasis risk compared to the traditional diagnostic tools based on clinical and histological criterias (39). In addition, the MammaPrint recapitulates the 6 hallmarks of cancers as defined by Hannahan and Weinberg (50) Oncotype DX In addition to the MammaPrint, another commercially available diagnostic tool based on gene expression exists. The Oncotype DX was established based on 21 recurrence genes and was capable of diagnosing the risk of breast cancer recurrence in early-stage breast cancer and associated chemotherapy benefits (51) Prosigna Breast Cancer Prognostic Gene Signature Assay The Prosigna diagnostic for breast cancer is based on the PAM50 gene expression signature. The assay calculates risk of occurrence, risk category, as well as classification of breast tumors into molecular subtypes: Luminal A/B, ERBB2+, or Basal-like tumors (52). From the above iterations, it is clear that gene expression analyses of breast tumors provide a promising tool for the diagnosis and treatment of human breast tumors in addition to standard clinical methodologies. 12

30 1.3 Mouse models of metastasis Mouse models are an indispensable tool in cancer research. Since the first demonstration by Stewart et al in 1984 that the mouse mammary gland can be targeted to examine the expression of a particular oncogene, numerous mouse models have been developed to study human breast cancer (53). Indeed, twenty-five years after the MMTV-Myc mouse was engineered, the model was found to recapitulate the heterogeneity that was found in human tumors whereby it produced 6 distinct histology subtypes (54). Current utilization of gene expression analyses tools revealed heterogeneity in additional mouse models, including intratumor heterogeneity (55,56). It is then obvious why the mouse model also serves to examine metastasis in vivo. Metastasis occurs in different stages, therefore, various techniques are employed to examine metastasis in vivo, in the context of mouse models (57). Mouse models of metastatic processes such as MMTV-Neu and MMTV-PyMT allow examination of in vivo metastasis progression because the two models are prone to spontaneous metastasis to the lungs (58) whereas xenografting human breast cancer cell line into immunocompromised mice allow for examination into the later stage of metastasis (57). Generally, the MMTV-Myc mouse model is known as poorly metastatic model (59). Interestingly, however, this mouse model was found to capture the heterogeneity found in human breast tumors (54,55). Investigation utilizing this mouse mammary tumor model found that the MMTV-Myc model displayed heterogeneity in breast tumor that was analogous to the heterogeneity found in the human breast tumor (54,60). This heterogeneity can be further dissected based on the patterns of E2F pathways activation (47). This finding showed that there were clear roles for activator E2F transcription factors in mediating breast cancer tumor 13

31 development and progression and that the roles of E2F transcription factors were subject to tumor heterogeneity. 1.4 c-myc oncogene Historical overview of Myc oncogene Myc identification stemmed from the study of avian oncogenic retroviruses by Peyton Rous in 1911 (61). Myc, named after the condition that it caused in chicken, myelomatosis, was initially identified as v-myc in the late 1970s though isolation of transforming sequence of the avian virus MC29 (62). This finding led to the discovery of the cellular homolog in uninfected vertebra cells through an assay examining whether MC29 cdna would bind to DNA from other species (63). In the same year, a separate experiment demonstrated the same finding whereby specific sequences that were related to the transforming ability of replication defective-leukemia virus were present in the avian and mammalian DNAs (64). Three years after, the c-myc oncogene was characterized (65). In the same year, Myc was found to be activated through translocation in Burkitt s Lymphoma (66,67). The 1980s saw the first Myc boom which resulted in new insights into how oncogene interactions and the importance of apoptosis as control for tumor development (61). Another Myc boom in 2006, ushered by the advent of genomic studies identified Myc as one of the four genes that could reprogram cells into their pluripotent stem cell state (61,68) Structure and functions of the c-myc oncogene The general architecture of Myc consists of transcriptional activation domain, central portion, nuclear localization domain and DNA binding domain (61). Alignment of Myc family 14

32 members revealed five conserved regions called Myc boxes of which the first and second boxes (MbI and MbII respectively) being well characterized (61). The transcriptional activation domain of Myc was found to be important in Myc s transforming activity in vitro since perturbation to this domain resulted in partial cytoplasmic localization (69). Equally as important was the DNA binding domain of Myc which allowed dimerization with Max and together the heterodimer complex bound DNA to mediate Myc functions (68). Indeed the interaction between Myc and Max was absolutely required for Myc to function as oncogene (70). Interestingly, Max also functions as an antagonist to Myc activity (70,71). Both MbI and MbII were found to play a role in Myc oncogenicity. Mb1 facilitated contact with a cyclin-cdk complex, p-tefb, that phosphorylated RNA polymerase II and stimulated transcription and elongation (72,73). MbI was also found to promote the stability of Myc protein by facilitating phosphorylation events that created a binding site for SCF Fbw7 Ubligase (74,75). On the other hand, MbII domain of Myc is essential for Myc to function as transcriptional activator or repressor (61). MbII interacted with TRAPP and the interaction resulted in recruitment of histone acetyltransferase complex to acetylate Histone H4. Acetylation of histone H4 then allowed chromatin structure to open and transcription to occur (76 78). Myc can activate or repress genes depending on cell types and Myc has been reported to interact with tens of thousands of genes (79,80). Research showed that Myc was involved in cell cycle and differentiation and apoptosis (71). Myc can bind target genes acting as transcription factor or it could act through activation of micrornas, mir-17-92, to either activate or repress its targets (68). 15

33 1.4.3 Roles for c-myc oncogene in cancer development and metastasis The Myc protein is a potent transcription factor protein, regulating the majority of genes whose functions are proliferation, differentiation, and apoptosis (61,81,82). c-myc oncogene was found to be expressed in rapidly proliferating cells (80) and was shown to activate Erk/MAP kinase pathway (83). Indeed two well-characterized Myc boxes, MbI and MbII, played a role in Myc oncogenic capabilities (69,84,85). In human breast cancer, the role of Myc was ambiguous. TCGA query for Myc in a cohort of breast cancer showed that Myc amplification was found in 17% of the cases. However, survival plot from the TCGA did not show that Myc aberration correlated with survival whereas K-M plotter showed that Myc overexpression is associated with decreased time to relapse and distant metastasis (Figure A1.1). Indeed, some studies suggested that the overexpression of Myc coupled with BRCA1 loss led to the development of basal-type breast cancer (86). Furthermore, overexpression of Myc was found in triple negative breast tumor and that it is associated with poor prognosis (87). Myc was also found to cause metastasis by repressing the mirna let-7 or through activation of mir-9 (88,89). Conversely, inactivation of Myc inhibited distant metastasis (90). However, recently overexpression of Myc in human breast cancer cell lines, MDA-MB-231 and BT549, was shown to reduce the metastatic capabilities of those cell lines in vitro and in vivo, by silencing αv and β3 integrin subunits (91). In the mouse model of breast cancer, Myc alone was not sufficient to drive metastasis (59). Taken together these findings suggested that Myc requires additional cofactor to mediate metastasis. 16

34 1.5 E2F transcription factors A brief history of the E2F transcription factors E2F studies stemmed from the study of the adenovirus promoter (92). E2F was discovered as the factor that stimulates the transcription of the viral E2 promoter (93). Four years after the discovery of the E2Fs, they were found to be interacting with prb family of proteins (94). Currently, 8 members of the E2F family have been identified with E2F1-3 classically thought of as activator E2Fs and E2F4-8 as repressors (95) E2F transcription factors functions E2F transcription factors are a group of transcription factors that are critical in cell cycle progression (96,97). Members of this family of transcription factors are generally divided into activators (E2F1-3) and repressors (E2F4-8) (98,99). Numerous studies of the activator E2Fs showed that activator E2Fs were critical for cell proliferation, differentiation, and apoptosis by acting through the Rb/E2F pathway ( ). The E2Fs transcription factors were also found to be regulated by Myc. This is achieved through the transcriptional activation of Cyclin D and Cdc25A gene which led to phosphorylation of Rb protein and activation of E2Fs (108). In addition, Myc could also regulate E2F through activation of mir-17 cluster, specifically mir-17-5p and mir-20a, which resulted in the attenuation of E2F1 (109). Different cellular processes were shown to require distinct E2F. For instance, while E2F1-3 were notably known as critical for cellular proliferation, E2F1 alone was involved in apoptosis (110). The specificity of the E2F was determined by the sequence of adjacent site that also bound cooperating transcription factor (111). Furthermore, the Myc-E2F interactions were shown to produce different results depending on which E2F was involved (112). Study showed that loss of E2F2 or E2F3 resulted in impaired Myc ability to induce S-phase, but the loss of 17

35 E2F1 resulted in reduced Myc-mediated apoptosis (112). Despite the specificity of the members of E2F family, the most interesting property of this family of transcription factor is that their overlapping functions resulted in compensatory mechanism (113) E2F transcription factors and cancer In cancer, activator E2Fs were implicated in tumor development and progression (99). In Eµ-myc model of lymphoma, loss of E2F2 was observed to accelerate the tumor onset suggesting a tumor suppressive role for E2F2 (108). However, in prb-deficient mice, E2F3 was reported to work as both tumor promoting and tumor suppressing in that the loss of E2F3 was observed to suppress the development of pituitary tumor while simultaneously promoting the development of highly metastatic medullary thyroid carcinoma (114). In breast cancer, E2F1 expression was shown to be reduced in primary and metastatic breast carcinoma (115,116). In addition, deletion of E2F2 (chromosome 1p32-pter) was also observed in human breast cancer (117). Furthermore, perturbation of individual E2Fs in the MMTV-PyMT mouse model was shown to affect latency, histology, vasculature, and, importantly, the metastatic capability of these tumors (118). Specifically, the loss of E2F1 accelerated tumor onset whereas the loss of E2F3 delayed tumor onset and E2F1 and E2F2 loss was shown to vastly reduce metastasis. An examination of MMTV-Neu mouse models also showed that loss of E2Fs impacted latency and metastasis. Specifically, the loss of any E2Fs accelerated tumor onset and the loss of E2F1 and E2F2 reduced metastasis (119). Interestingly, however, in the MMTV-Myc mouse model, the loss of E2F1 accelerated tumor onset whereas the loss of E2F2 and E2F3 delayed tumor onset (47). Taken together, the results of these studies showed that there were clear roles for activator E2F transcription factors in mediating breast cancer metastasis specifically in the context of c-myc overexpression (Figure 18

36 1). Based on initial findings in the MMTV-Myc mouse model, this document focuses on elucidating the role of E2F2 transcription factors in mediating breast cancer metastasis. 19

37 Genotype Percent Tumor Free A B Time (days) MMTV-Myc -/- MMTV-Myc E2F1 -/- MMTV-Myc E2F2 * MMTV-Myc MMTV-Myc E2F1 -/- MMTV-Myc E2F2 -/- MMTV-Myc E2F3 +/- +/- MMTV-Myc E2F3 * Time from palpation to tumor end point (days) Figure 1 The effects of various activator E2Fs deletion on tumor latency and growth rate MMTV-Myc mice were interbred with various activator E2Fs mutant mice. Loss of E2F1 (red line) was found to decrease tumor latency compared to MMTV-Myc (blue line) (A; p = ). Loss of E2F2 (green line) and E2F3 (orange line) increased tumor latency of MMTV-Myc mice (p< for both). E2F1 loss also accelerated tumor growth (B; p = ) whereas E2F3 delayed tumor growth rate (p = ). E2F2 did not affect tumor growth rate. 20

38 Chapter 2 Materials and Methods 2.1 Animal work Animal husbandry and tumor observation Animal use and husbandry was in accordance to institution and federal guidelines. MMTV-Myc (WT21) (54) and E2F2 -/- (120) were used. Progeny were genotyped to obtain MMTV-Myc WT21 E2F2 -/-. Subsequently, females were kept in pregnancy and lactation cycles. Mice were palpated once a week for tumor onset. Tumors were measured once a week until they reach 20 mm in the largest dimension. Mice were euthanized prior to tumor harvest. Primary tumors were harvested for flash frozen samples, fixed in 10% formalin for histological analysis, or frozen in Dulbecco s Modified Essential Medium (Sigma-Aldrich, St. Louis, MO) supplemented with 3.5 g/l D-glucose, 20% Fetal Bovine Serum (FBS), 2.0 mm L-glutamine, and 10% dimethyl sulfoxide (DMSO; Sigma-Aldrich) for frozen viable transplantable tumors. In addition, lungs were harvested and fixed in 10% formalin for histological analysis to examine the presence of metastasis. Tumor volume was calculated using the formula: Tumor volume = length x width x height x Tumor transplantation Frozen viable tumors were harvested from MMTV-Myc WT21 mouse and MMTV-Myc WT21 E2F2 -/- mouse and passaged for 3 passages to create tumor lines that were non-metastatic (MMTV-Myc WT21) and metastatic (MMTV-Myc WT21 E2F2 -/- ). For transplantation experiment, frozen viable tumor fragments from the most recent passage were utilized. Tumor fragments were thawed, washed briefly in 37 o C sterile PBS, and kept in cell culture freezing 21

39 medium without DMSO for the duration of the transplant. Tumors were transplanted into 20 mice for the following genotypes: MMTV-Myc WT21, MMTV-Myc WT21 E2F2 -/-, E2F2 -/-, and wild-type. Tumor growth was monitored as previously described. Upon end point, tumors and lungs were harvested as described above Retroorbital injection Nude mice were generously donated by the laboratory of Dr. Justin McCormick (Michigan State University, East Lansing, MI). Colonization assay was performed by retroorbital injection of nude mice with parental MDA-MB-231 cell line, shscramble control transfected MDA-MB-231 cells, and she2f2 or shptprd transfected cell lines. Cells were trypsizined and then centrifuged at 5000 RPM for 5 minutes before being resuspended in sterile PBS. Cells were injected at concentration of 5x10 5 cells in total volume of 50 µl as previously described (121). Mice were kept for 30 days and observed for signs of labored breathing weekly. At end point, mice were euthanized and lungs were harvested for hematoxylin and eosin staining. Metastases were quantified by counting the number of metastatic lesions per lung. 2.2 Gene analysis Pathway prediction Publicly available GEO datasets: GSE11121, GSE14020, GSE2034, GSE2603, GSE3494, GSE4922 (Singapore cohort), GSE6532, GSE7390 and gene expression dataset from human breast cancer cell lines E-TABM-157 were obtained as well as their corresponding clinical annotation. The samples were pooled and normalized for batch effects (Figure A2.1). To identify samples with Myc and E2F2 activation, Myc and E2F2 signatures were applied as previously described to predict the pathway activation of Myc and E2F2 (32,34,48). 22

40 Binary regression conditions for E2F2 signature applications were as follows: 500 genes, 3 metagenes, no quantile normalization (RMA format gene expression dataset) Identification of E2F2 target genes Mouse gene expression dataset GSE24594 and gene expression from 6 lung metastasis samples (GSE71815) were obtained. The combined dataset was filtered to include genes with standard deviation >0.8. Unsupervised hierarchical clustering was performed in these samples by Cluster 3.0. Clusters were identified based on the majority of the histology subtypes that was present in a cluster. To identify samples with human relevance, gene expression from previously compiled human breast tumor datasets were clustered with mouse tumor dataset. Fold change differences between MMTV-Myc, MMTV-Myc x E2F2 -/- and lung metastasis samples were analyzed (fold change cut off = 1.5). 451 genes homologous to human genes were identified. These genes were then examined for their correlation with human time to distant metastasis (122,123) and Coxhazard ratio by Univariate Cox regression analysis, which allowed genes to be ranked by effect size and did not require the normal assumption of proportional hazards to eliminate bias and maintain stability (124). To determine whether candidate genes were in/direct targets, genes were submitted to GATHER (125), SwissRegulon (126) and compared with published E2F ChIP data (127). 2.3 Cell culture, shrna transfection, western blotting, and migration assays Cell culture The cell lines MCF7 and MDA-MB-231 were chosen based on the probabilities of Myc and E2F2 pathway activation. Cells were cultured in Dulbecco s Modified Essential Medium 23

41 (Sigma-Aldrich) supplemented with 3.5 g/l D-glucose, 10% Fetal Bovine Serum (FBS) and 2.0 mm L-glutamine. Cells were incubated at 37 o C incubator supplemented with 5% CO Generation of shrna stable knockdown cell lines shrna constructs targeting human E2F2 and PTPRD were purchased from OriGENE (Rockville, MD). Plasmids were amplified for transfection according to the manufacturer s protocol. For transfection, cells were seeded at the density of 1 3 x 10 5 in 6-well plates in order to achieve 70% - 80% confluency overnight. Cells were transfected using ExtremeGENE HP transfection reagent (ROCHE, Indianapolis, IN) according to manufacturer s protocol. Briefly, shrna plasmid was mixed with OptiMEM serum free medium prior to the addition of transfection reagent at a ratio of 1:1 µg DNA: µl transfection reagent. The mixture was then incubated at room temperature for 20 minutes before transfection. Cells were selected using 2 µg/ml puromycin 48 hours after transfection. Both populations and colonies were tested using western blot to determine the knockdown efficiency Western blotting Whole cell protein lysates were obtained by using protein extraction buffer containing: 50mM Tris, 150 mm NaCl, 10 mm NaF, 2 mm EDTA, and 1% NP40 supplemented by 1 mm Na 3 VO 4, mm PMSF, 10 µg/ml aprotinin and 10 µg/ml leupeptin as protease inhibitors. Protein concentrations were determined by using Bradford assay (Biorad, Hercules, CA; dilution 1:5). Briefly, protein samples were diluted 1:5 in a total volume of 30 µl of sample. Samples were then mixed with prepared Bradford reagent. Absorption value at 595 nm was obtained using NanoDrop 2000c. 24

42 E2F2 western blotting 25 µg of protein samples were separated by electrophoresis using 15% Bis/acrylamide gel under reducing condition. The samples were then transferred onto PVDF membrane for 1 hour at 4 o C at 120 V. To examine the presence of target protein, the membrane was blocked with 3% milk in TBS for 1 hour prior to incubation with 1:300 primary antibody: rabbit anti-e2f2 (clone E-19 Santa Cruz, Dallas, TX) or rabbit anti-grb2 (Cell Signaling Technology, Danvers, MA) diluted in blocking solution overnight at 4 o C. Membrane was washed with 0.05%Tween20- TBS solution prior to incubation with 1:1000 goat anti-rabbit secondary antibody diluted in 0.01 M NaHPO 4, 0.25 M NaCl, and 15 mg/ml bovine serum albumin (Thermo Scientific, Rockford, IL). The membrane was then incubated in luminol/coumaric acid solution and hydrogen peroxide in 15 ml 0.01 mm Tris ph 8.6 before being exposed to film PTPRD western blotting Similar method was performed to examine PTPRD protein levels with modifications as follows: µg of protein samples were separated by electrophoresis using 10% Bis/Acrylamide gel under reducing condition. Electrophoresis was performed for 2-3 hours at 80 V prior to blotting at 4 o C overnight at 90 ma. Primary antibody rabbit anti-ptprd was used at dilution 1:100 (Abcam, Cambridge, MA) and primary antibody rabbit anti- β-actin was used as internal control (Cell Signaling Technology, Danvers, MA, dilution 1:500). Due to the inconsistency in the PTPRD primary antibody, an optimization experiment is highly recommended to determine the amount of total protein to be loaded for sufficient detection of PTPRD expression levels (Figure A2.2). In addition, fresh sample must be prepared prior to gel electrophoresis to avoid degradation of PTPRD protein which may result in false positives. 25

43 2.3.4 Migration assays Wound-healing assay To perform wound-healing assay, cells were seeded at the density of 1 3 x 10 5 /well in 6 well plates overnight. Confluent monolayer of cells was then scratched with TipOne µl pipette tips. Wells were then washed with sterile PBS and growth medium supplemented with 2 µg/ml mitomycin was added. 4 regions of the scratch were observed and pictures were taken every 12 hours. Percent area reduction was expressed by pixels and counted by Adobe Photoshop CS Transwell migration assay Cells were seeded at the density of 1 x 10 6 in 100 mm plates prior to transwell migration assay. 24 hours after seeding, cells were serum starved for 24 hours. After serum starvation, cells were then trypsinized and 1 x 10 4 cells (MDA-MB-231) or 1 x 10 5 cells (MCF7) in 400 µl/well were seeded into the inserts (cat#3422, Corning, Tewksbury, MA). Cell growth medium was used as chemoattractant and cells were allowed to migrate for 6 hours (MDA-MB-231) or 24 hours (MCF7). Cells were fixed in 3% paraformaldehyde for 15 minutes and washed briefly in PBS prior to staining in 1% crystal violet for 10 minutes. Cells were then washed and 5 images/well were taken at 100X. Number of cells that migrated across the membrane was counted per image with ImageJ using cell counter plugin and averaged (number of migrated cells/well). 26

44 2.4 qrt-pcr RNA from human cell lines RNA samples were isolated using RNeasy Plus mini-kit (Qiagen, Valencia, CA) according to the manufacturer s protocol. Briefly, 7x10 6 cells were harvested by trypsinization and centrifuged at 5000 RPM for 5 minutes. Cells were then washed twice with sterile PBS before lysis. RNA concentration was quantified using 2 µl samples with NanoDrop 2000c. The primers for PTPRD were designed using Primer3 ( Briefly, full genomic sequence of PTPRD (all transcript variants) with both exons and introns were downladed from UCSC genome browser ( Exons were identified and primers were designed by inputting two exons to obtain primers that span exon-exon junction. Primers were tested and optimized for MDA-MB-231 cell lines. The PTPRD primer sequences were 5 -TCACCAAGCTGCGTGAAATG-3 (forward) and 5 -CAGCCATGGGATC TACAACAAA-3 (reverse) (IDT, Coralville, IA). The following GAPDH primers were used as internal loading control: 5 -GGAGTTGCTGTTGAAGTCGC-3 (forward) and 5 - TCATGACCACAGTGGATGCC-3 (reverse). qrt-pcr reaction was performed on 20 ng of total RNA (MDA-MB-231) using QuantiTECT Sybr green PCR kit (Qiagen) according to the manufacturer s protocol in 25 µl total reaction volume RNA from mouse tumors and mouse lung metastases RNA samples from mg of flash frozen tumors were isolated by using PureLink mini RNA extraction kit (Invitrogen, Carlsbad, CA) according to manufacturer s protocol. Total RNA was quantified using NanoDrop as previously described. 27

45 PTPRD primers were designed by Primer3 as previously described. A total of 9 primers were tested. Primers for PTPRD was shown to be inefficient, thus, the examination of PTPRD mrna levels was performed using PTPRD as internal reference samples instead of GAPDH. Optimization experiments to determine total RNA to be loaded to achieve similar PTPRD levels as reference sample was determined (Figure A2.3). Based on the optimization experiments, 1 ng total RNA (lung metastases samples and MMTV-Myc WT21 E2F2 -/- metastatic transplant tumors), 2 ng total RNA (MMTV-Myc tumors), and 5 ng total RNA (MMTV-Myc WT21 non-metastatic transplant tumors) were assayed by using QuantiTECT Sybr green PCR kit (Qiagen) as previously described. 2.5 Data analysis Statistical analysis was performed using GraphPad Prism 5 and GraphPad Quickcalcs ( t-test was performed on the quantification of tumor metastases, wound-healing assay percent area reduction, and transwell migration assays. To examine the difference in latency and distant metastasis free survival, Kaplan-Meier survival plot was employed. To examine the percentage of mice with metastasis, Fisher s exact test with 2x2 contingency table was used (GraphPad Quickcalcs). 28

46 Chapter 3 Using bioinformatics to dissect breast cancer metastasis 3.1 Introduction Breast cancer is a heterogeneous disease. Indeed recent reviews stated that each aspect of the hallmarks of cancer previously defined by Hanahan and Weinberg is a potential axis of heterogeneity (128). Complications then arise because the heterogeneity of breast cancer affects the progression of the disease, treatment of the cancer, and subsequently the propensity of recurrence after treatment (9, ). Recent developments in bioinformatics allow further examination of breast cancer heterogeneity that enhances existing classifications of breast cancer based on their histological types, grades, and stages. Indeed, currently breast cancer could be classified molecularly as Luminal A/B, Basal, ERBB2+, Normal-like, and Claudin-low (14 17). Furthermore, bioinformatic tools allow examinations into breast cancer metastasis. For example, the MammaPrint test, which was derived from a 70-gene predictor (39,49), assessed the aggressiveness of breast cancer and calculated the propensity for metastasis. To date, efforts were made to identify such predictors for breast cancer metastasis to the lung, bone, and brain (35,37,41). Further development of bioinformatic analysis tools allows observations at pathway level. Gene expression signatures enables examination of pathway activations for 22 major oncogenic pathways including Myc (32 34,132). Indeed Gatza et al. in 2010 demonstrated that pathway signature was able to cluster samples in conjunction with intrinsic subtypes of breast tumor (34). Taken together, bioinformatics provide valuable insights as discovery tools 29

47 simultaneously over numerous samples. Here I utilized bioinformatic tools to generate testable predictions in order to examine the role for E2F in mediating human breast cancer metastasis. 3.2 Utilization of pathway signatures to understand biological mechanisms underlying human breast cancer development and progression Initial findings in the MMTV-Myc mouse model implicated E2F2 in the metastatic process. To demonstrate the utility of genomic signatures in understanding the pathway by which activator E2Fs regulate breast cancer processes in human breast cancer, existing genomic signatures were applied to a combined datasets of 1500 samples pooled from 9 publicly available gene expression datasets to predict the probability of oncogenic pathway activations for activator E2Fs. The resulting probabilities of pathway activation were then used to stratify clinical annotations for breast cancer relapse and distant metastasis. Stratification of relapse free survival clinical annotation (N = 799; quartile split) by E2F1 and E2F2 probability values showed that high probability of E2F1 and E2F2 pathway activations were correlated to decreased time to relapse free survival (Figure 2B and 2D; p = and p = , respectively), Whereas E2F3 did not seem to have any correlation with relapse free survival (Figure 2F; p = ). Using the same approach to examine metastasis (N = 794), E2F2 was found to be the only activator E2F that was correlated with distant metastasis free survival, specifically low probability of E2F2 pathway activation was correlated with increased time to distant metastasis (Figure 2E; p = ). Neither E2F1 nor E2F3 was found to be correlated with distant metastasis (Figure 2C and 2G; p = and p = , respectively). 30

48 A Figure 2 E2F pathway probabilities in human breast cancer samples Pathway signatures were applied to human breast cancer gene expression datasets (A). Clinical annotations concerning relapse and distant metastasis were stratified based on the probability of E2F pathway activations. Comparison between lower and upper quartiles of the probability values revealed that high probability of E2F1 and E2F2 pathway activation was correlated to decreased time to relapse (B and D; p = and p = , respectively; N = 799). Interestingly, only high probability of E2F2 pathway activation was correlated to decreased time to distant metastasis (E; p = ; N = 794). 31

49 RFS (%) DMFS (%) RFS (%) DMFS (%) RFS (%) DMFS (%) Figure 2 (cont d) B E2F1 C E2F Low probability 20 High probability p = Time (Days) 40 Low probability 20 High probability p = Time (Days) D 100 E2F2 E 100 E2F Low probability 20 High probability p = Time (Days) 40 Low probability 20 High probability p = Time (Days) F 100 E2F3 G 100 E2F Low probability 20 High probability p = Time (Days) 40 Low probability 20 High probability p = Time (Days) 32

50 Interestingly, the probabilities of E2F2 pathway activation in samples that were taken from human primary breast tumors (N = 1262), human primary metastatic breast tumors (N = 130), and human lung metastases samples (N = 15) differed. High probability of E2F2 was found to be associated with primary metastatic breast tumor relative to primary breast tumor and lung metastasis (Figure 3; p < and p = ). This finding suggested that E2F2 pathway might be activated in different steps of the metastatic process. To verify the previous bioinformatics analysis, correlations between activator E2F transcription factors were compared to correlation data obtained from KMplotter database (122). Using auto select best cutoff and JetSet best probe set options for existing relapse free survival database (N = 3557) or distant metastasis free survival (N = 1610), the correlations between various activator E2Fs and either relapse free survival or distant metastasis free survival were calculated. Here, only high expressions of E2F1 and E2F3 were correlated to decreased time to relapse (Figure 4A and 4E, p < for both). Interestingly, all activator E2Fs were found to be correlated with human distant metastasis survival (Figure 4B, 4D, and 4F; p < , p = 0.012, and p = for E2F1, E2F2, and E2F3 respectively). The one obvious difference between pathway analyses and that of the K-M plotter database was that the pathway analyses utilized the application of E2F signatures to a panel of human breast cancer (a group of genes). The result of signature application was the probabilities of E2F pathway activations, whereas K- M plotter database employed single probe approach which is based on gene expression (probe level expression) instead of signature to examine the correlation between activator E2Fs and metastasis. 33

51 Perhaps the most important conclusion that can be derived from this comparative analysis is that although there is a discrepancy between the two methods as to which activator E2Fs expression correlates with either relapse or distant metastasis free survival, clearly there is a role for activator E2Fs in human breast cancer progression and metastasis. Importantly, E2F2 seemed to be the predominant activator E2F that regulated human breast cancer metastasis. This result agreed with the observation found in the MMTV-Myc mice whereby the percentage of mice with lung metastases increased after E2F2 loss. 34

52 Probability Probability Probability A B C E2F E2F2 * * E2F Primary breast tumor Primary-metastatic tumor Samples Lung metastases 0.00 Primary breast tumor Primary-metastatic tumor Samples Lung metastases 0.00 Primary breast tumor Primary-metastatic tumor Samples Lung metastases Figure 3 E2F2 pathway activation was predicted to be low in lung metastasis samples The probability of E2F2 pathway activation increased in primary metastatic breast tumor (N = 130) compared to breast tumors (B; p<0.0001; N = 1262) and decreased in lung metastases (N = 15) samples compared to primary metastatic breast tumor (B; p = ). No significant difference was found between the probability of E2F2 pathway activation in primary breast tumors and lung metastases (B; p = ). E2F1 and E2F3 probabilities remained similar between primary breast tumors, primary metastatic breast tumors, and lung metastases samples.. 35

53 A E2F1 (204947_at) - RFS B E2F1 (204947_at) - DMFS C E2F2 (207042_at) - RFS D E2F2 (207042_at) - DMFS Figure 4 Cross-reference of bioinformatics analysis of the roles of activator E2F transcription factors with existing online database for survival analysis Existing database with relapse free survival (N = 3557) revealed the correlation between high expression of E2F1 and E2F3, but not E2F2, with decreased time to relapse (A and E; p < ). Interestingly, high expressions of all activator E2Fs were found to be correlated to decreased time to distant metastasis free survival (N = 1610) (B, D, and F). 36

54 Figure 4 (cont d) E E2F3 (203693_s_at) - RFS F E2F3 (203693_s_at) - DMFS 37

55 Taken together these data suggested that there was a role for activator E2F transcription factors in mediating human breast cancer progression. In particular, there was a role for E2F2 in mediating breast cancer metastasis as was observed from the MMTV-Myc mouse model. Therefore, an examination into the mechanisms by which E2F2 loss mediated breast cancer metastasis is warranted. 3.3 Gene expression alterations associated with lung metastasis To begin to examine the biological processes that were involved in the findings observed in the MMTV-Myc mice crossed with E2F1 -/-, E2F2 -/-, or E2F3 +/- mice, 20 tumor samples from each genotype and a limited set of lung metastasis samples were analyzed by microarray for their gene expressions. Unsupervised hierarchical clustering divided these samples based on their histological type, rather than by their genotypes with lung metastasis samples being clustered together. This suggested that E2F ablation in MMTV-Myc mice did not affect the heterogeneity of the MMTV-Myc tumors and that the gene expression of lung metastasis samples were unique (Figure 5A). Sub-clusters of genes that were up-regulated uniquely in each cluster were identified and examined for their predicted transcription factor binding sites. Interestingly, there was an enrichment of genes with predicted E2F binding sites in the cluster of genes that were upregulated in lung metastasis (gene cluster D; Figure 5B). Taken together these analyses showed that there was a role for activator E2F transcription factors in mediating tumor metastasis to the lung and that activator E2Fs regulation of lung metastasis involved a unique subset of genes independent of histology (phenotype). 38

56 A Figure 5 Gene expression associated with lung metastases Gene expression analysis of MMTV-Myc tumors as well as tumors from MMTV-Myc mice crossed with E2F1 -/-, E2F2 -/- and E2F3 +/- mice and a limited sample of lung metastasis (black bars). Unsupervised hierarchical clustering clustered the samples based on their histological type rather than their genotype with lung metastasis samples being clustered together (A). Examination of genes that are relatively upregulated in each cluster for their predicted transcription factor binding sites showed that in cluster D (genes that were upregulated in lung metastases samples) there was number of genes that were highly predicted to be bound by E2F (B). 39

57 Bayes Factor Figure 5 (cont d) B Cluster A Cluster B Cluster C Cluster D Cluster E 5 0 AP1 E2F GABP GATA IRF MYCMAX Transcription Factor NFKB NRF2 SMAD4 40

58 3.4 Identification of candidate genes that are involved in E2F2-mediated metastatic pathway Computational predictions for the roles of E2F2 in mediating human breast cancer metastasis revealed that high probability of E2F2 pathway activation were correlated to decreased time to distant metastasis. However, in the MMTV-Myc mouse model, it was the loss of E2F2 that resulted in increased percentage of mice with lung metastasis. These two seemingly disparate observations can be attributed to the heterogeneity of breast tumor. Computational predictions were made across a panel of 1500 samples of human primary breast tumor, primary metastatic breast tumor, a set of samples from distant metastatic sites, and human breast cancer cell lines. Recent finding demonstrated that the heterogeneity of MMTV-Myc tumors reflected the heterogeneity of human breast cancer on both pathways and gene expression levels (55). Tumor samples obtained from the MMTV-Myc mouse models with various activator E2Fs knockout were clustered into three different major clusters based on their histology with lung metastasis samples clustered separately (Figure 6A): papillary, microacinar, and EMT. The three clusters bore relevance with the human tumors: luminal A/B and claudin-low (55). To examine the relevance of MMTV-Myc lung metastasis and human breast cancer, co-clustering of human breast cancer dataset and mouse tumor dataset was performed. Co-clustering of gene expression datasets from MMTV-Myc mice and human breast cancer resulted in the identification of 8 clusters. Particularly of note is the cluster in which all lung metastases samples were clustered with samples taken from human tumors (Figure 6A; Cluster B). Samples with low probabilities of E2F2 pathway activity in this cluster and a neighboring cluster, Cluster A, were identified by calculating z values to identify quartiles. 41

59 A Figure 6 Specific human breast cancer cluster co-clusters with MMTV-Myc lung metastasis samples Human (black bars) and mouse gene (orange bars) expression datasets were clustered to examine the relevance between gene expression patterns from mouse tumors and human tumors. Unsupervised hierarchical clustering revealed a cluster of human breast tumors that clustered together with all lung metastases samples (A; cluster B). Low probability of E2F2 pathway activation in cluster B (N = 24/99) correlated with decreased time to distant metastasis compared to low probability of pathway activation in a neighboring cluster A (B; p<0.0001; N = 24/124). 42

60 DMFS (%) Figure 6 (cont d) B Cluster A 20 Cluster B p = Time (days). 43

61 Indeed, low probabilities of E2F2 in Cluster B correlated to decreased time to distant metastasis compared to low probabilities in a neighboring cluster, Cluster A, in which low probabilities of E2F2 pathway activation correlated to increased time to distant metastasis (Figure 6B; p = ). These findings showed that the role for E2F2 in mediating human metastasis was not a global effect for human breast cancer; rather it was specific to a subgroup of human tumors. Examinations of other resulting clusters showed that in addition to cluster A, clusters 5 and 6 also exhibited similar observation whereby low E2F2 pathway probabilities were associated with increased time to distant metastasis (Figure A3.1). Independent comparisons between low and high E2F1-3 pathway probabilities within a cluster did not yield any significant association between E2F1-3 with distant metastasis with the exception of cluster A whereby low E2F2 was associated with increased time to distant metastasis (Figure A3.2). To identify genes that were involved in E2F2 loss-mediated human breast cancer metastasis, fold change was examined for genes that were differentially regulated in MMTV- Myc, MMTV-Myc E2F2 -/- tumors and lung metastases samples (Figure 7A). This fold change analyses resulted in a total of 451 genes (fold change cut-off >1.5). These genes were further pared down by examining their correlations with human breast cancer distant metastasis survival (quartile split high-low gene expression to stratify DMFS; 61 genes; Table A3.3), Cox s hazard ratio values (28 genes; Table A3.4) and the existence of E2F binding motif in their promoters (21 genes; Table A3.5-A and Table A3.5-B). These analyses resulted in the identification of seven candidate genes: CHN2, ITIH4, KLK1, MMP16, MYH2, PTPRD, and TNNC2 (Figure 7B-J). 44

62 A B C MMTV-Myc MMTV-Myc E2F2 -/- Lung metastases Homologous putative targets (451 genes) DMFS (61 genes) Hazard ratio (28 genes) E2F binding (21 genes) Validation in vitro and in vivo Figure 7 Pipeline to identify factors that are involved in E2F2-loss mediated metastasis Genes that were differentially expressed by MMTV-Myc, MMTV-Myc x E2F2 -/- and lung metastasis samples with fold change cut-off = 1.5 were examined. 451 (789 probes) candidate genes were homologous to human genes (A and B). Those genes were further examined in context of cluster B (N = 99) and ranked based on their correlation with human distant metastasis free survival, Cox-hazard ratio, and the existence of predicted E2F binding motifs (C). Elevated gene expressions of CHN2, MMP16, and PTPRD were found to be correlated with decreased time to distant metastasis free survival (D, p = ; H, p = ; J, p = , respectively) whereas low expression of ITIH4 (F ; p = 0.004), KLK1 (E; p = ), MYH2 (G; p = ); and TNNC2 (I; p = ) were correlated to decreased time to distant metastasis free. High PTPRD expression was found to be correlated with decreased time to distant metastasis in basal breast cancer subtype (K; p = , HR = 2.3 ( )). 45

63 DMFS (%) DMFS (%) DMFS (%) DMFS (%) DMFS (%) DMFS (%) Figure 7 (cont d) D 100 CHN2 E 100 KLK Low Expression High Expression Time (days) p = Low Expression High Expression Time (days) p = F ITIH4 G MYH H 20 Low Expression High Expression Time (days) p = MMP16 I 20 Low Expression High Expression Time (days) p = TNNC Low Expression High Expression Time (days) p = Low Expression High Expression Time (days) p =

64 DMFS (%) DMFS (%) Figure 7 (cont d) J PTPRD K Basal breast cancer subtype Low Expression High Expression Time (days) p = Low PTPRD High PTPRD Time (months) p = (K-M plotter) HR = 2.3 ( ) 47

65 Regulations of four of these genes: KLK1, MYH2, PTPRD, and TNNC2, agreed with the fold change regulations found in mouse samples (Table 2). Cox s hazard ratio showed that of all four genes, PTPRD had high overall effects on metastasis. In human breast cancer, low expression of PTPRD was correlated with increased time to distant metastasis free survival (Figure 7J; p = ). In addition, K-M plotter query showed that high expression of PTPRD correlated with decreased time to distant metastasis in the basal subtype of breast cancer (Figure 7K; p = ). Taken together, these analyses showed that E2F2 loss might play a role in mediating human breast cancer metastasis by acting through PTPRD. 48

66 Table 2. Candidate genes obtained from the pipeline to identify the mechanism by which E2F2 loss mediated metastasis Gene symbol CHN2 ITIH4 KLK1 MMP16 MYH2 TNNC2 Regulation in mouse Downregulated in lung metastasis Upregulated in lung metastasis Fold change (vs. MMTV- Myc) Human gene expression correlated with DMFS Gene name 2.47 High Chimerin Low Inter-alpha (globulin) inhibitor H4 Downregulated in MMTV-Myc 3.15 Low Kallikrein 1 x E2F2 -/- Downregulated in lung metastasis Downregulated in lung metastasis Downregulated in lung metastasis 2.21 High 4.65 Low 3.79 Low Matrix metalloprotease 16 Myosin heavy chain 2 Troponin C type 2 Function Role in proliferation and migration of smooth muscle cell. Cleaves plasma kallikrein into two smaller forms Releases vasoactive peptide Involves in the breakdown of extracellular matrix Skeletal muscle contraction Regulation of striated muscle contraction PTPRD Upregulated in lung metastasis 2.83 High Protein tyrosine phospatase receptor type D Regulation of cellular processes 49

67 3.5 Discussion Breast cancer is a heterogeneous disease. As such, the role of E2F2 in the metastasis of the human breast cancer cannot simply be defined without taking into account the heterogeneity of breast tumors. Indeed, when human distant metastasis survival annotation was stratified by probabilities of E2F2 pathway activation, E2F2 seemed to play a role in mediating metastasis rather than the metastasis suppressor role that was observed in the MMTV-Myc mice. However, co-clustering of MMTV-Myc tumor samples with human breast tumor samples revealed a distinct cluster that clustered together with MMTV-Myc lung metastases samples (Cluster B). Indeed an examination of the role of E2F2 within that cluster and subsequent comparison with its adjacent cluster (Cluster A) showed that the observation that E2F2 loss led to increased metastasis was only applicable to a subgroup of human breast tumor. Furthermore, identification of genes which expressions were significantly correlated with human distant metastasis free survival in Cluster B revealed the presence of putative E2F targets in Cluster B that was not found in Cluster A (Figure A3.6 and Table A3.6). Suggesting that in Cluster B E2F2 expression played a role in impeding metastasis possibly through regulating the expressions of the candidate genes discussed below: Kallikrein 1 KLK1 is a member of the serine protease enzyme family (133). Generally, the Kallikreins are divided into plasma Kallikreins and tissue Kallikreins (134). KLK1 was the first member to be discovered in 1985 with KLK2 and KLK3 which followed in the late 1980s. For 10 years, it was thought that the Kallikreins had only 3 members until the discovery of additional 11 Kallikreins within the period of (135). 50

68 Kallikreins are present in various different tissues and cell types. They are also implicated in regulating a wide array of normal physiologic processes due to their serine protease activity. Specifically, the role of KLK1 involved the release of lysyl-bradykinin which in turn mediated processes such as regulation of blood pressure, smooth muscle contraction, and vascular cell growth (135). In cancer, KLK1 was present in colon, breast, lung, and many other cancers. KLK1 through its kinin action was thought of as mediator for vascularity, mitogenicity, metastasis and angiogenesis (135). Owing to the research progress of the Kallikreins, they are now considered an attractive target for cancer therapy (136). Based on the TCGA data, KLK1 was amplified in 3% of human breast cancer and TCGA survival analysis, although not significant, showed increased survival for patients with altered of KLK1 gene. In addition, query into the K-M plotter and stratification of human DMFS with KLK1 expression values showed that low expression of the KLK1 gene was associated with decreased time to distant metastasis similar to the findings that KLK1 was downregulated in MMTV-Myc E2F2 -/- tumor samples compared to MMTV-Myc tumor samples (Figure A3.7). Taken together these showed that KLK1 was a suitable potential target gene Myosin Heavy Chain 2 The gene MYH2 codes for myosin heavy chain that functions in skeletal muscle contraction. However, research showed that Myosin II was also involved in the contractility of carcinoma cells (137). Indeed, MYH2 was shown to be important for the migration of glioma cells across pores that were smaller than the size of their nucleus which suggested a mechanism of how glioma invaded the brain (137). Non-muscle myosin II was also shown to affect scar 51

69 remodeling (138). In the breast cancer, MYH2 was shown to co-localize with S100A4 protein which then allowed for motility and potential metastasis (139). TCGA query revealed that MYH2 was altered in 1% of breast cancer (deep deletion and missense mutations) and that alterations of the MYH2 gene were associated with decreased time of survival. Interestingly, both K-M plotter query and stratification of human breast tumor by MYH2 expression revealed that low expression of MYH2 was associated with decreased time to distant metastasis (Figure A3.8). These findings also agreed with the finding in MMTV-Myc mouse whereby MYH2 was downregulated in lung metastasis compared to tumors. It is possible that MYH2 was activated only for migration and its expression reduced when disseminated tumor cells arrived and colonized distant organ, similar to what was found in scar remodeling. Taken together these findings suggested that MYH2 is a promising gene candidate in examining the metastatic process Protein Tyrosine Phosphatase Receptor type D PTPRD is a member of the Protein Tyrosine Phosphatases (PTPs) that is involved in various biological processes in cancer (140). Specifically, PTPRD has been shown as tumor suppressor in glioma (141,142), liver, lung, head and neck, colorectal and melanoma (143). Perhaps the most well-defined role of the PTPRD pathway was established through studies in glioblastoma whereby PTPRD was shown to be deleted through array comparative genomic hybridization and copy number analysis (142). Further exploration into the mechanistic function of PTPRD in glioblastoma showed that the loss of PTPRD led to the accumulation of phospho- STAT3 and, thus, constitutive activation of STAT3 in p16 Ink4A-/- mouse model (144). In breast cancer, PTPRD was discovered through The Cancer Genome Atlas project to be a novel gene 52

70 that was frequently mutated in addition to PTPN22, suggesting the emerging roles of protein tyrosine phosphatases in breast cancer associated biological processes (145). TCGA analysis showed that PTPRD was altered in 3% of human breast cancer with missense mutations being the most commonly found mutation. However, alterations of PTPRD did not seem to affect survival. K-M plotter query showed a trend that that high expression of PTPRD was associated with decreased time to distant metastasis which agreed to the DMFS analysis that was previously done for this research (Figure A3.9). This finding also agreed with the observations in the mouse whereby PTPRD was upregulated in the lung metastases compared to tumors from either MMTV-Myc or MMTV-Myc E2F2 -/-. The seemingly contradictive finding of TCGA may be due to the fact that survival analyses of TCGA took into account all alterations of PTPRD including missense mutation which accounted for 69% of alteration cases. The role of PTPRD in mediating breast cancer metastasis will be discussed further in the subsequent chapter Troponin C type 2 Little is currently known about the role of TNNC2 in cancer. A literature search yielded no result in the subject. However, TNNC2 protein was fairly well characterized in that studies showed the structure and role of TNNC2 in muscle contraction (146). A query through the TCGA database showed that TNNC2 was altered in 4% of human breast cancer with amplification being the majority of alteration cases. However, survival analysis from TCGA did not show any relevance between TNNC2 and patient survival. K-M plotter analysis showed that high expression of TNNC2 was associated with decreased time to distant metastasis (Figure A3.10). Both bioinformatics analysis in this research and observation in the mouse showed that low expressions of TNNC2 were associated with decreased time to distant metastasis and expression of TNNC2 was downregulated in the lung metastases samples. 53

71 Taken together these findings suggests that further investigation is needed to define the function of TNNC2 in cancer. 3.6 Validation The value of gene signature to predict the probability of pathway activation allowed for selection of relevant models to test the resulting predictions (55). Selection of human cancer cells that was relevant to test the prediction that E2F2 played a role in mediating human breast cancer metastasis was achieved by applying pathway signatures to 51 human breast cancer cell line gene expression dataset E-TABM-157 (147). MDA-MB-231 and MCF7 were selected as models due to their relatively high probabilities of Myc and E2F2 pathway activations which paralleled the biological condition found in the MMTV-Myc mouse model (Figure 8). Further analysis of the effects of E2F2-loss in human cell lines and the role of PTPRD in mediating breast cancer metastasis are discussed in chapter 5. 54

72 Figure 8 Pathway probabilities for human breast cancer cell lines Pathway signatures were applied to a panel of human breast cancer cell lines. Red boxes showed cell lines that were chosen as models to examine the effects of E2F2 loss in human breast cancer metastasis as an extension of findings in MMTV-Myc mouse model. 55

73 Chapter 4 MMTV-Myc mouse model of breast cancer and metastasis 4.1 Introduction The c-myc protooncogene was found to be the central gene in transcription factor networks for cellular functions such as proliferation and apoptosis (81). Deregulation of c-myc was found in multiple cancers including Burkitt s lymphoma, colon carcinoma, glioblastoma, and breast carcinoma (148). c-myc is capable of transforming several types of cell line and initiating tumor formation in transgenic mouse model (148,149). Interestingly, however, MMTV-Myc mouse model is poorly metastatic (59). Further evidence showed that the relationship between Myc and human breast cancer metastasis is ambiguous. For instance, inactivation of Myc was found to suppress metastasis (90), but in the human breast cancer cell line MDA-MB-231 elevated Myc expression inhibited metastasis (91). These findings suggest that Myc requires additional co-factors to mediate metastasis and that the role for Myc oncogene in mediating breast cancer metastasis is dependent on the signaling context. c-myc also interacts with the Rb/E2F pathway (150). Myc interactions with E2Fs are achieved through either activation of Cyclin D1 or interaction with E2F promoters that are important in loading the transcription factors into the promoter (151,152). The interaction between Myc and E2F had been shown to play a role in Myc-induced lymphomagenesis in Eµmyc mice (108,153). In addition to findings in mouse lymphoma model, E2F ablation was also found to affect latency and tumor growth rate in the MMTV-Myc mouse model (47), specifically, ablation of E2F1 decreased latency and accelerated tumor growth rate whereas ablation of E2F2 and E2F3 increased latency and decreased incidence with E2F3 ablation alone 56

74 decreased tumor growth rate. These data suggest a role for E2F transcription factors in the development and progression of MMTV-Myc tumors. 4.2 E2F2 loss induces metastasis in Myc-driven tumors Previous research showed that MMTV-Myc tumors were clustered based on patterns of E2F pathway activation (47). Genetic examination of the roles for E2F transcription factors in MMTV-Myc (149) tumor development demonstrated that when MMTV-Myc mice were interbred with E2F2 -/- mice, the loss of E2F2 increased time to tumor onset by an average of 160 days (Figure 9A). Interestingly, despite increased latency, the loss of E2F2 also increased the percentage of mice with pulmonary metastasis by 53% (Figure 9B). Other studies noted that E2F2 loss in Wap-Myc mice decreased time to tumor onset (154). Therefore, another MMTV-Myc mouse strain, WT21, was examined. In this strain, when E2F2 was ablated, 50% of MMTV-Myc WT21 E2F2 -/- mice developed tumor 147 days earlier than MMTV-Myc WT21 mice (Figure 10A). This difference in latency could be explained by differences in integration site which led to variations in Myc expression levels. Furthermore, because both strains were hormone dependent, timing of Myc expression could also explain the latency differences between the strains (155). Irrespective of differences in latency in MMTV-Myc or MMTV-Myc WT21, similar trend for metastatic frequency was observed. In the MMTV-Myc WT21, the percentage of mouse with lung metastasis was increased by 35% when E2F2 was lost (Figure 10C). This observation confirmed that metastasis increase due to E2F2 loss was not a strain specific effect. Examination of histology taken from MMTV-Myc, MMTV-Myc WT21, MMTV-Myc E2F2 -/-, and MMTV-Myc WT21 E2F2 -/- lung metastases revealed well-differentiated structures 57

75 within the metastatic lesions. Interestingly, the presence of these structures in these metastatic lesions appeared to be strain dependent: the well-differentiated structure found within the MMTV-Myc E2F2 -/- metastatic lesion is similar to that of the structures found within the MMTV-Myc metastatic lesions, yet differed from differentiated structures found within the MMTV-Myc WT21 or MMTV-Myc WT21 E2F2 -/- (Figure 11). 58

76 Percent of tumor bearing mice with pulmonary metastasis Time (days) A * 100 B 0 Genotype * MMTV-Myc (Leder) MMTV-Myc (Leder) MMTV-Myc E2F2 -/- -/- MMTV-Myc E2F1 Genotype MMTV-Myc E2F2 -/- +/- MMTV-Myc E2F3 Figure 9 E2F2 loss increased the percentage of metastasis of MMTV-Myc mouse model Loss of E2F2 in the MMTV-Myc mice increased time to tumor onset (A; p = ). MMTV- Myc mice are poorly metastatic with only 13.34% of the tumors resulting in metastasis to the lung (B; N=2/13). Metastatic incidence is increased to 66.67% when E2F2 is lost (N=6/9; p = ). 59

77 Percent of tumor bearing mice with pulmonary metastasis Percent Tumor Free Genotype A MMTV-Myc WT21 MMTV-Myc E2F2 -/- B MMTV-Myc (WT21) /- MMTV-Myc E2F Time (days) Time from palpation to tumor end point (days) C MMTV-Myc (WT21) Percent Metastasis MMTV-Myc (WT21) MMTV-Myc E2F2 -/- Genotype Figure 10 E2F2 loss increased the percentage of metastasis of MMTV-Myc WT21 mouse model When MMTV-Myc WT21 mice were crossed with E2F2 -/- mice, loss of E2F2 decreased time to tumor onset (A; p = ). However, loss of E2F2 did not affect tumor growth rate (B; p = ). A similar trend was observed whereby metastatic incidence was increased after E2F2 lost (C; N= 3/20 for MMTV-Myc WT21 and N = 5/10 for MMTV-Myc WT21 E2F2 -/- ; p = ). 60

78 Figure 11 Histology of lung metastases Low magnification (A, C, E, G) and high magnification (B, D, F, H) pictures of lung metastases taken from MMTV-Myc, MMTV-Myc E2F2 -/-, MMTV-Myc WT21, and MMTV-Myc WT21 E2F2 -/- mice. High magnification revealed well-differentiated structures within the lung metastases from MMTV-Myc (B) and MMTV-Myc E2F2 -/- (D). Differentiated structure did not appear in the lung metastases of MMTV-Myc WT21 (F) or MMTV-Myc WT21 E2F2 -/-. 61

79 4.3 Background effect Cancer cells are in constant contact with their surroundings, termed microenvironment. Together, cancer cells and their microenvironment secrete a multitude of chemokines that regulates tumor growth and differentiation as well as provide support for the metastatic niche ( ). Previous research showed the involvement of activator E2F transcription factor in mediating metastasis by affecting tumor stroma, specifically by regulating matrix metalloprotease and extravasation out of the blood vessels (159,160). Furthermore, initial computational analyses of human breast tumor samples showed that there was an increased probability of E2F2 pathway activation in samples taken from primary metastatic breast tumors compared to samples taken from primary breast tumors and lung metastasis, which suggested stromal involvement in E2F2-mediated breast cancer metastasis. To examine whether the findings that the loss of E2F2 increased metastasis in MMTV- Myc mouse model by altering the microenvironment of the tumor, frozen viable tumors from MMTV-Myc WT21 (54) and MMTV-Myc E2F2 -/- were transplanted into the mammary gland of four backgrounds: wild type, E2F2 -/-, MMTV-Myc WT21, and MMTV-Myc E2F2 -/-. There were few interesting observations from this experiment. First, the tumor onset of MMTV-Myc WT21 tumors were significantly delayed when transplanted into any of the four backgrounds compared to tumors from the MMTV-Myc WT21 E2F2 -/- (Figure 12A, 12C). However, this result reflected the tumor onset of the parental donors. Secondly, MMTV-Myc WT21 E2F2 -/- tumors grew faster when transplanted into the E2F2 -/- background compared to MMTV-Myc WT21 background (Figure 12D; p = 0.006) or MMTV-Myc WT21 E2F2 -/- background (Figure 12D; p <0.0001). The tumors also grew faster in the wild type background compared to the MMTV-Myc WT21 E2F2 -/- background (Figure 12D; p = ). However, although there was a trend that the 62

80 MMTV-Myc WT21 E2F2 -/- tumors grew faster in the wild type background compared to the MMTV-Myc WT21, the rate of tumor growth in either of those backgrounds were not statistically significant (Figure 12D; p = ). Interestingly, the percentage of mice with metastasis was significantly increased by 35% when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc E2F2 -/- background compared to the original tumor (Figure 12E; p = ). In addition, compared to the MMTV-Myc WT21 tumors that were transplanted into MMTV-Myc WT21 host, there was an increase of metastasis by 22% (Figure 12E) when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc WT21 E2F2 -/- host. Although tumors taken from MMTV-Myc E2F2 -/- grew rapidly, the percentages of mice with metastases were not found to be significantly different than spontaneous tumors when transplanted to any of the four backgrounds (Figure 12F). 63

81 A B Time from palpation to tumor end point (days) Figure 12 Background effect on tumor onset, growth rate, and metastases of transplanted MMTV-Myc WT21 tumors and MMTV-Myc WT21 E2F2 -/- tumors In general, transplantation of MMTV-Myc WT21 tumors to either MMTV-Myc WT21, MMTV- Myc WT21 E2F2 -/-, E2F2 -/-, or wild type showed increased latency (A). Transplantation of MMTV-Myc WT21 E2F2 -/- tumors, however, did not exhibit any increased latency (C). No change in growth rate was observed when MMTV-Myc WT21 tumors were transplanted into any of the four backgrounds (B). Interestingly MMTV-Myc WT21 E2F2 -/- tumor grew faster when transplanted into the E2F2 -/- or wild type background compared to transplantation into the MMTV-Myc WT21 or MMTV-Myc WT21 E2F2 -/- (D; p = 0.01, with the exception of MMTV- Myc WT21 vs. wild type p = ). No significant change in metastases was observed with MMTV-Myc E2F2 -/- tumor transplants into any of the four backgrounds (F). Metastatic rate of MMTV-Myc WT21 tumors increased when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc E2F2 -/- background compared to the metastatic rate of spontaneous MMTV- Myc WT21 (E, p = ). 64

82 Figure 12 (cont d) C D * * * Time from palpation to tumor end point (days) E F * 65

83 4.4 Discussion Although Myc is a potent oncogene, Myc tumors were found to be poorly metastatic (59). One of the reasons why Myc tumors were poorly metastatic was that the Myc transcription factor directly silenced the activity of α v and β 3 integrin (91). However, despite findings that Myc was poorly metastatic, Myc remained a potent transcription factors that regulated poor-prognosis genes (161). Despite the ambiguous role for Myc in metastatic progression, it is clear that Myc require additional mutations to either promote or inhibit metastasis. Similarly, the role of E2F2 in mediating metastasis was also ambiguous. In the MMTV- Neu and the MMTV-PyMT mouse models E2F2 loss decreased percentage of mice with lung metastases (118,119). However, in these models, E2F2 affected growth and latency differently. In the MMTV-Neu mouse model, E2F2 loss caused delayed onset with no observed effects on tumor growth whereas in the MMTV-PyMT mouse model loss of E2F2 did not affect tumor onset. In c-myc initiated mouse model of breast tumor, E2F2 loss affected growth and latency differently depending on the strain and promoter. In the MMTV-Myc mice, loss of E2F2 increased latency without affecting growth rate (47), however, in both MMTV-Myc WT21 and Wap-Myc mice E2F2 loss decreased latency (154). This difference can be attributed to promoter differences, background, differences in transgene integration and expression as well as developmental timing of transgene activity (162). Interestingly, in either MMTV-Myc or MMTV-Myc WT21 stain, loss of E2F2 increased the percentage of mouse with lung metastasis. Tumor transplantation in mice has been shown to lead to clonal selection. Using the melanoma B16 cell line, researchers showed that passages of these cells in vivo selected for clones with enhanced abilities to form brain tumors (163). In mammary tumors, in vivo selection 66

84 was observed in the mammotropin dependent cells MT-W9. The original tumors were observed to be mammotropin dependent which then gave rise to the MT-W9B tumor that could grow in rats as autonomous tumor irrespective of their hormonal status (164). These examples explained the general increase of metastasis when MMTV-Myc WT21 or MMTV-Myc WT21 E2F2 -/- tumors were transplanted into their respective parental background. However, the increase in metastasis when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc E2F2 -/- tumor was likely due to the effects of E2F2 loss in the background of the host. The difference in metastatic percentage was not statistically significant due to the size of the samples. The rate of metastasis of 28% when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc WT21 recipient and of 50% when MMTV-Myc WT21 tumors were transplanted into the MMTV-Myc WT21 E2F2 -/- background would be significant if the sample size was to be expanded to include 50 mice (projected p=0.0397). Myc was implicated in many cellular functions including growth and proliferation (165). However E2F2 was previously demonstrated to inhibit Myc function in cellular growth and proliferation by altering the expression of genes such as Cyclin E (166). This may explain the differential growth when MMTV-Myc WT21 E2F2 -/- tumors were transplanted into E2F2 -/- or wild type backgrounds. When MMTV-Myc WT21 E2F2 -/- tumors were transplanted into the E2F2 -/- background, for example, E2F2 was absent from both tumor and the background. As such, Myc activity was uninhibited in both tumor and background, which resulted in increased growth rate. However, this did not quite explain how growth of MMTV-Myc WT21 E2F2 -/- tumors was not accelerated in MMTV-Myc WT21 but accelerated in the wild-type background. In both background, E2F2 should be expressed normally. 67

85 Constitutive expression of Myc was shown to induce apoptosis (167). This finding might be the reason for increased tumor latency and long growth rate when MMTV-Myc WT21 tumors were transplanted into any of the four backgrounds. It might also explain why MMTV-Myc WT21 E2F2 -/- tumors grew faster in wild-type background but not in the MMTV-Myc WT21 background. In the wild type background there was no constitutive expression of Myc in the background. As such, perhaps constitutive Myc expression by tumor transplant alone was not sufficient to activate Myc apoptotic pathway, although the presence of normal level of E2F2 in the background was sufficient to slightly reduce tumor growth rate. Indeed, the longest growth rate was observed when MMTV-Myc WT21 E2F2 -/- tumors were transplanted into MMTV-Myc WT21 E2F2 -/- background. In both MMTV-Myc WT21 and MMTV-Myc WT21 E2F2 -/- backgrounds, Myc was constitutively expressed in both background and tumor. Perhaps Myc control of proliferation or apoptosis is dependent on Myc expression level and that intrinsic constitutive expression of Myc or loss of E2F2 is sufficient to affect tumor proliferation. Table 3 summarized possible effects of different expressions of Myc and E2F2 in tumor and background. Alternatively, the differences between the growth rates of MMTV-Myc WT21 or MMTV-Myc WT21 E2F2 -/- tumors in different background could be attributed to intratumor heterogeneity (168,169). In HER2+ breast tumors, for instance, studies showed intratumor heterogeneity for HER2 in 11%-18% of the samples (170). Since only 2.0 mm tumor piece were taken from donor to be transplanted to the mammary gland of the recipient, it is possible that each 2.0 mm tumor piece contains cells with different growth and survival rates which affects the onset of tumor and growth rate. 68

86 Taken together, these findings led to the conclusion that the loss of E2F2 in the MMTV- Myc mouse model is sufficient to increase metastasis frequency. This suggests that loss of E2F2 is the likely event that c-myc needs to drive metastasis of the mammary tumor. 69

87 Table 3. Predicted background-tumor interactions Donor Recipient Myc level Donor E2F2 level Myc level Recipient MMTV-Myc WT21 MMTV-Myc WT E2F2 level MMTV-Myc WT21 E2F2 -/ E2F2 -/ Predicted effects 1. E2F2 level may inhibit Myc effects on proliferation; 2. Constitutive expression of Myc in both tumor and background may activate Myc apoptotic pathway; 3. Possible treadmilling effect between Myc proliferation and apoptotic pathways 1. E2F2 from donor may inhibit Myc action on apoptosis; 2. Myc overexpression by both tumor and background may induce proliferation and apoptosis 1. Overexpression of Myc by donor alone is not sufficient to induce apoptosis but is sufficient to induce proliferation; 2. Normal Myc expression in the background is not sufficient to induce apoptosis Legend: ++ = Constitutively active 0 = Unchanged -- = Deficient Wild type Overexpression of Myc by donor alone is not sufficient to induce apoptosis but is sufficient to induce proliferation 70

88 Table 3. (cont d) Donor MMTV-Myc WT21 E2F2-/- Recipient Myc level Donor E2F2 level Myc level Recipient E2F2 level MMTV-Myc WT Predicted effects 1. Constitutive expression of Myc by both tumor and background may induce both proliferation and apoptosis; 2. E2F2 expression by background may dampen the effects of constitutive Myc expression MMTV-Myc WT21 E2F2-/ Constitutive expression of Myc by both tumor and background may proliferation and apoptotic pathway; 2. Loss of E2F2 may promote Myc activity which may lead to treadmilling between cell proliferation and apoptosis E2F2-/ Wild type Overexpression of Myc in the tumor is sufficient to induce proliferation but normal level of Myc in the background did not allow activation of apoptotic pathway; 2. Lack of E2F2 in the background promotes Myc activity 1. Overexpression of Myc in the tumor is sufficient to induce proliferation ; 2. The presence of E2F2 in the background may slightly decrease Myc activity 71

89 4.5 Identification of genes that were involved in E2F2 mediated metastasis Previous bioinformatics analyses of genes that were differentially regulated between MMTV-Myc and MMTV-Myc E2F2 -/- tumors and lung metastases from the mice as well as human gene expression datasets implicated PTPRD as one of the genes that may be involved in E2F2 mediated metastasis. Fold change differences taken from gene expression analysis showed that PTPRD was upregulated in lung metastasis compared to MMTV-Myc tumor (2.83 fold) or MMTV-Myc E2F2 -/- (2.70 fold). This fold change was confirmed by qrt-pcr that showed that in the lung metastasis, PTPRD transcript was expressed at least 2-fold higher compared to transcripts obtained from tumors harvested from MMTV-Myc (Figure 13A). To further confirm that PTPRD was involved in metastasis, tumor samples from MMTV- Myc WT21 transplants that did not result in metastasis were compare with tumor samples from MMTV-Myc WT21 E2F2 -/- transplants that resulted in metastasis. PTPRD transcripts were found to be expressed by at least 5-fold higher levels in tumor samples taken from MMTV-Myc WT21 E2F2 -/- metastatic transplant tumors (Figure 13B). Furthermore, analysis of PTPRD gene expression in Cluster B showed that PTPRD gene expression was upregulated in mouse samples compared to other clusters (Figure A4.1). Taken together, these data strongly implicated PTPRD as candidate gene that was involved in Myc-driven metastasis after E2F2 loss. 72

90 A B Figure 13 PTPRD is differentially expressed between MMTV-Myc tumors, metastatic MMTV-Myc E2F2 -/- tumors and lung metastatic Confirmation of gene expression analyses results that PTPRD was upregulated in the lung metastasis compared to tumor samples taken from MMTV-Myc or MMTV-Myc E2F2 -/- tumors. PTPRD was expressed two-fold in lung metastases samples compared to samples taken from MMTV-Myc tumors (A). Non-metastatic MMTV-Myc tumor expressed PTPRD five-fold less than metastatic MMTV-Myc E2F2 -/- tumors (B; D = donor, R = recipient). 73

91 Chapter 5 Understanding human breast cancer metastasis: Application of results obtained from mouse models and bioinformatic predictions 5.1 Introduction Human breast cancer metastasis is a complex multistep pathway that involves detachment of tumor cells from primary site, intravasation into the blood vessel, extravasation out of the blood vessel and eventually colonization of distant sites such as the brain, bone, lung, or liver (1,2). Metastasis is often time the cause of mortality rather than the primary tumor (171). Therefore, it is important to examine the metastatic pathway to identify risk factors and/or methods to treat metastatic breast cancer. One of the complicating factors in studying the signaling pathways that underlie biological processes of human breast cancer metastasis is the heterogeneity of the disease. This is evident from the variety of genetic pathways that are involved in tumor development and progression. The genetic bases of the biology of breast cancer include both somatic genetic aberrations and epigenetic events (24). To elucidate breast tumor heterogeneity at a signaling level, genomic signatures (32 34,48,172) were utilized to predict the probabilities of activation of critical pathways in a set of human breast cancer samples. Results from this prediction implicated E2F2 as a factor that played a role in human breast cancer metastasis. Furthermore, application of pathway signatures to a series of breast cancer cell line allowed for selection of suitable cell line to test this prediction in vitro and in vivo. 74

92 In addition to bioinformatic tools, mouse models exist to simplify the study of human breast cancer development and progression. One such model is the MMTV-Myc model. Investigation utilizing this mouse mammary tumor model had found that the MMTV-Myc model displayed heterogeneity in breast tumor that is analogous to the heterogeneity found in the human breast tumor (54). Further examination into the gene expression of tumors harvested from the MMTV-Myc mouse model found correlation between the lung metastasis and enrichment of activator E2F transcription factors targets while genetic testing of the effects of E2F2 loss in MMTV-Myc mice showed that the loss of E2F2 resulted in increased percentage of mice bearing lung metastases, further implied that E2F2 had a role in mediating breast tumor metastasis. By employing these methods, important signaling pathways and critical genes that are involved in mediating breast tumor metastasis in a subgroup of human breast cancer could be identified, specifically for the role of E2F2 transcription factor that differs from its canonical role as a regulator of cell cycle. 5.2 E2F2 and human breast cancer metastasis To extend the findings found in mouse, suitable human breast cancer cell lines should be used. In order to identify cell lines that were suitable for in vitro and in vivo testing, signatures were applied to a set of 51 human breast cancer cell line gene expression dataset: E-TABM-157. MDA-MB-231 and MCF7 were selected as models due to their relatively high probabilities of MYC and E2F2 pathway activation, which paralleled the biological condition found in the MMTV-Myc mouse model. To examine the effects of E2F2 loss in human breast cancer, stable knockdown clones were generated by transfecting plasmids carrying shrna against E2F2. The extent of the 75

93 knockdown was assayed by western blotting. Clones with E2F2 knockdown were tested for their ability to migrate in vitro by transwell migration assay and their ability to colonize the lungs of immunocompromised mice. The knockdown of E2F2 resulted in increased cell migration of MDA-MB-231 and MCF7 cell lines through the transwell insert membrane (Figure 14B-D; p<0.0001). These results were confirmed in a separate clone and cell line (Figure A5.1 and A5.2). MDA-MB-231 was found to be more efficient to inject into an in vivo model system compared to MCF7. Thus, MDA-MB-231 parental, shscramble and she2f2 cells were retroorbitally injected into immunocompromised mice. The result of colonization assay showed that there was an increase in the number of metastatic lesion when mice were injected with she2f2 cells compared to parental (untransfected) or scramble controls (Figure 14E-G; p=0.0184). Taken together these results in vitro and in vivo showed that knockdown of E2F2 increased the metastatic capability of MDA-MB-231 cell line, similar to the findings in the MMTV-Myc mouse model. 76

94 A B C D Figure 14 E2F2 knockdown increased migration in vitro and lung colonization in vivo. E2F2 knockdown in human breast cancer was achieved by transfection of MDA-MB-231 cells with she2f2. Efficacy of E2F2 knockdown was assayed by western blotting (A) with Con=untransfected MDA-MB-231, Scr.=MDA-MB-231 transfected with shscramble, C3= she2f2 construct #3, C4= she2f2 construct #4. Migration of MDA-MB-231 control cells (B) and with E2F2 knockdown (C) in transwell migration assays revealed that the percentage of cells that migrated across the membrane increased when the level of E2F2 was decreased (D; p<0.0001). In colonization assays with and without the knockdown, lesions were found in the lungs of mice injected with MDA-MB-231 (E) and greatly increased with transfection of she2f2 (F). Quantification of the numbers of metastatic lesions revealed an increased number of metastatic lesions in mice injected with MDA-MB-231 transfected with she2f2 (G; p=0.0184). 77

95 Figure 14 (cont d) E F G 78

96 5.3 Identification of candidate genes that are involved in E2F2-mediated metastatic pathway Due to the heterogeneity of the disease, it is imperative to examine whether findings in mouse model of breast tumor recapitulate the disease in human context. This can be achieved by co-clustering of human breast cancer dataset and mouse tumor dataset. Indeed co-clustering of tumors harvested from MMTV-Myc and MMTV-Myc crossed with various activator E2Fs knockouts and a limited set of lung metastases resulted in the identification of a cluster in which all lung metastases samples from the mice were clustered together with samples taken from human tumors (Cluster B). Low probabilities of E2F2 pathway activation in Cluster B correlated to decreased time to distant metastasis compared to low probabilities of E2F2 pathway activation in a neighboring cluster, Cluster A, which was correlated to increased time to distant metastasis. These findings showed that E2F2 role in mediating human metastasis was not a global effect for human breast cancer, rather it was an effect that was specific to a subgroup of human tumors. Fold change was examined for genes that were differentially regulated in MMTV-Myc, MMTV-Myc E2F2 -/- tumors and lung metastases samples which resulted in a total of 451 genes (fold change cut-off >1.5). These genes were stratified based on their correlation with human breast cancer distant metastasis survival (61 genes), Cox s hazard ratio (28 genes) and the existence of E2F binding motif in their promoters (21 genes). These analyses resulted in the identification of seven candidate genes: CHN2, ITIH4, KLK1, MMP16, MYH2, PTPRD, and TNNC2. Four of these candidate genes: KLK1, MYH2, PTPRD, and TNNC2, showed agreement between the fold change regulations found in mouse samples and their expressions as correlated with human DMFS, which made them potential candidate genes to be further explored. Finally, Cox s hazard ratio showed that of all four genes, PTPRD had high overall 79

97 effects on metastasis. In human breast cancer, low expression of PTPRD was correlated with increased time to distant metastasis free survival. Furthermore, through K-M plotter query, PTPRD overexpression was shown to be correlated to decreased time to DMFS in the basal breast cancer. Taken together, these analyses showed that E2F2 might play a role in mediating human breast cancer metastasis by acting through PTPRD. 5.4 PTPRD As previously mentioned, PTPRD is a member of the Protein Tyrosine Phosphatases (PTPs) that is involved in various biological processes in cancer (140). The role of PTPRD as tumor suppressor was best demonstrated in glioma (141,142) in addition to liver, lung, head and neck, colorectal and melanoma (143). Mechanistic function of PTPRD in glioblastoma demonstrated that the loss of PTPRD led to the accumulation of phospho-stat3 and, thus, constitutive activation of STAT3 in p16 Ink4A-/- mouse model (144). In 2012, TCGA discovered PTPRD to be a novel gene that frequently mutated in addition to PTPN22, suggesting the emerging roles of protein tyrosine phosphatases in breast cancer associated biological processes (145). Initial findings in breast cancer showed PTPRD to be hypermethylated in late-stage breast cancer (173). Examination of 9p24 gain/amplification region in basal-like primary breast tumor, brain metastasis, and xenograft samples showed that PTPRD gene was located at or adjacent to the centromeric boundaries (174). In breast cancer xenograft studies, PTPRD was found to be altered in 41.4% cases (175). 80

98 5.4.1 PTPRD and human breast cancer metastasis Assessment of how PTPRD regulated metastasis was achieved by generating stable knockdown by transfection of shrna against PTPRD in MDA-MB-231 and MCF7. The effect of PTPRD knockdown in vitro was assayed by transwell migration assay. Transwell migration assay revealed that PTPRD knockdown resulted in significant decrease of the percentage of cells that migrated through the transwell insert membrane (Figure 15B-D; p<0.0001). This result was further confirmed in separate clones and with wound healing assay for both MDA-MB-231 and MCF7 (Figure A5.3 and A5.4) In vivo, the knockdown of PTPRD resulted in fewer metastatic lesion in nude mice (Figure 15E-G; p=0.0009). These results indicated that PTPRD knockdown significantly decreased the metastatic capability of MDA-MB-231, similar to the findings in the MMTV-Myc mouse model where PTPRD was found to be upregulated by 2.83 and 2.70 in lung metastasis compared to MMTV-Myc and MMTV-Myc E2F2 -/- tumors respectively Connecting E2F2 and PTPRD PTPRD was not shown to be directly under control of E2F2 as predicted by Transfac or SwissRegulon databases. However, query into the TCGA database for PTPRD and E2F2 gene symbols showed that PTPRD was indirectly connected to E2F2 through BCAR1. Expansion of this query to include the interaction between PTPRD and STAT3 included the interactions between STAT3 and Myc (Figure 16). Taken together the network analysis showed that PTPRD was not a direct target for E2F2. 81

99 A B C D Figure 15 PTPRD knockdown decreased migration in vitro and lung colonization in vivo. PTPRD knockdown in human breast cancer was achieved by transfection of MDA-MB-231 cells with shptprd. Efficacy of PTPRD knockdown was assayed by qrt-pcr (A; p=0.01). Migration of MDA-MB-231 control cells (B) and with PTPRD knockdown (C) in transwell migration assays revealed that the percentage of cells that migrated across the membrane decreased when the level of PTPRD was decreased (D; p<0.0001). In colonization assays with and without the knockdown, lesions were found in the lungs of mice injected with MDA-MB- 231 (E) and decreased with injection of MDA-MB-231 transfected with shptprd (F). Quantification of the numbers of metastatic lesions revealed a decreased number of lesion in mice injected with MDA-MB-231 transfected with shptprd (G; p=0.0009). 82

100 Figure 15 (cont d) E F G 83

101 A Figure 16 Regulatory network connected E2F2 and PTPRD through BCAR1 or through Myc and STAT3 TCGA analysis of protein-protein interaction network found that E2F2 and PTPRD were connected through BCAR1 (A). To consider the interaction between PTPRD and STAT3, protein-protein interaction map was expanded to examine the interactions between E2F2, PTPRD, and STAT3. Expanded protein-protein interaction map showed that E2F2 and PTPRD might be connected through MYC and STAT3 axis (B). 84

102 Figure 16 (cont d) B 85

103 5.5 Discussion Experiments showed that when E2F2 level was reduced in human breast cancer cell lines, MDA-MB-231 and MCF7, in vitro migration and in vivo lung colonization was increased, mimicking the results found in MMTV-Myc mouse model and bioinformatics predictions which showed the potential role for E2F2 in the metastasis of MMTV-Myc driven tumors. Further analyses into gene expression differences between samples taken from MMTV- Myc tumors with or without E2F2 knockout and lung metastases as well as human gene expression datasets resulted in the identification of PTPRD as a candidate gene that could be involved in E2F2-loss mediated metastasis. Reduced PTPRD expression resulted in decreased migration in vitro and decreased lung colonization in vivo which suggested that PTPRD increased metastasis by promoting migration and colonization. Indeed in mice PTPRD was shown to be upregulated in the lung metastases samples compared to MMTV-Myc or MMTV- Myc E2F2 -/-. This fold change analyses was confirmed by qrt-pcr that showed that PTPRD mrna was expressed 2-fold higher in the MMTV-Myc tumor. Further evidence showed that metastatic MMTV-Myc WT21 E2F2 -/- tumors expressed PTPRD mrna by 5-fold higher than non-metastatic MMTV-Myc WT21 tumors. These findings suggested that high expression of PTPRD was acting to promote metastasis. In glioblastoma and many other cancers, however, PTPRD was shown to be a tumor suppressor gene that acted through modulation of STAT3 activation (141,142,144). Interestingly, in glioblastoma, where PTPRD was found to be a tumor suppressor, loss of E2F2 inhibited the tumorigenicity of glioblastoma cells which was contrary to findings in the MMTV-Myc mouse model whereby E2F2 loss increased metastasis (176). These data suggest additional role for PTPRD in mediating breast cancer metastasis in conjunction with the loss of E2F2 and that 86

104 perhaps the role of both E2F2 and PTPRD were specific to cancer type and subtype. Indeed, a query into the K-M plotter showed that in basal breast cancer subtype, high expression of PTPRD was correlated with decreased time to distant metastasis Connecting E2F2 and PTPRD through BCAR1 Examination of the network between E2F2 and PTPRD through protein-protein interaction database in Cbioportal (42,43) showed that E2F2 was connected with PTPRD indirectly through BCAR1. In yeast two-hybrid assay using BCAR1 protein as bait, E2F2 was shown to be one of the interacting proteins (177). However, the nature of how E2F2 regulates BCAR1 activity is currently unknown. The BCAR1 protein, also known as the p130cas protein, itself was shown to be involved in regulating migration, plasticity of breast cancer cells, and metastasis ( ). BCAR1 expression however could be destabilized by LAR protein (PTPRF) overexpression (181). Interestingly, the first catalytic domain of LAR protein binds the second catalytic domain of PTPRD (182), albeit weakly. Given the interaction between PTPRD, PTPRF, and BCAR1, it is possible that loss of E2F2 increased PTPRD expression which in turn prevented the activity of PTPRF and stabilize BCAR1 protein Connecting E2F2 and PTPRD through Myc and STAT3 signaling axis In addition, given the interaction between PTPRD and STAT3, the indirect connection between PTPRD and E2F2 can be expanded to include Myc. Indeed, a putative Myc binding site exists in the PTPRD promoter, which suggests that Myc may be able to regulate the expression of PTPRD directly which may lead to overexpression of PTPRD. Taken together these findings suggested that E2F2 loss regulated breast cancer metastasis in a subpopulation of human tumors possibly through interactions between PTPRD and BCAR1, as well as MYC and STAT3. 87

105 Chapter 6 Conclusions and Future Directions 6.1 Conclusions Amplification of c-myc was often associated with poor prognosis and distant metastasis (183). However, problems arise when studying c-myc associated breast cancer in mouse model because the MMTV-Myc mouse model itself is poorly metastatic and c-myc requires additional co-factor to mediate metastasis (59). Previous studies have shown that one such co-factor for c- Myc is the transcription factor E2Fs (112,150,184,185). Indeed in c-myc initiated mouse models of breast tumor, activator E2Fs were shown to affect latency. Specifically in the MMTV-Myc mouse model, E2F1 was shown to decrease tumor latency and accelerate tumor growth whereas E2F2 and E2F3 were shown to increase tumor latency and E2F3 specifically delayed tumor growth (47). Other studies have identified the roles of E2F transcription factor in c-myc mouse model of breast tumors. In Wap-Myc mouse model and MMTV-Myc WT21, E2F2 was shown to decrease tumor latency (154). In other mouse model of breast tumor, E2Fs were shown to also play a role in tumor growth and latency. The loss of E2Fs in MMTV-Neu tumors delayed tumor onset and E2F1 loss alone accelerated tumor growth (119). In the MMTV-PyMT mouse model, the loss of E2F1 has been shown to accelerate tumor onset whereas E2F3 loss delayed tumor onset (118). These data suggest that the roles of E2F transcription factors in tumor initiation and cancer progression are dependent on the initiating oncogene. This research identified an additional role for E2F transcription factor, specifically E2F2, in mediating progression of c-myc mediated breast tumor. These studies showed that the loss of 88

106 E2F2 in MMTV-Myc mouse tumor model increased metastasis by 53% and that lung metastasis gene expression is very distinct from primary tumor as evident by the clustering of lung metastases samples. Furthermore, examination into a cluster of genes that were upregulated in lung metastases samples revealed that there was an enrichment of E2F-bound genes. As an extension of the mouse tumor model, knockdown of E2F2 in human breast cancer cell line MDA-MB-231 increased migration in vitro and lung colonization in vivo. Interestingly, although E2F1 and E2F2 were previously shown to act to promote metastasis in the MMTV-Neu model (119) and MMTV-PyMT model (118), in MMTV-Myc mouse model E2F2 alone acted as metastasis suppressors. This suggested that there was specificity in conjunction with initiating oncogene with regards to the role of activator E2Fs in mediating metastasis. Given that there is compensatory mechanism in place with regards to the activator E2Fs (113), it is highly possible that the loss of E2F2 in the MMTV-Myc mouse model increased metastasis by increasing the expression of E2F1 and thus promotes metastasis by upregulating genes that are involved in invasion (159,160). To begin to elucidate the mechanism by which E2F2 mediated breast cancer metastasis, fold change analysis coupled with examination of correlation between gene expressions of candidate genes and examination of Cox hazard ratio implicated PTPRD as a candidate gene that may act in regulating E2F2-loss mediated human breast cancer metastasis. Knockdown of PTPRD in MDA-MB-231 resulted in decreased migration in vitro and decreased colonization in vivo, suggesting that PTPRD activation was required for migration and colonization. This was consistent with findings in the MMTV-Myc mouse model where PTPRD was found to be upregulated by 2.83-fold and 2.70-fold in lung metastasis compared to MMTV-Myc and 89

107 MMTV-Myc E2F2-/- tumors respectively. These observations in mice were further validated with the finding that PTPRD was also upregulated by 5-fold higher in metastatic MMTV-Myc WT21 E2F2 -/- tumors compared to MMTV-Myc non-metastatic tumors. In human breast cancer, low expression of PTPRD was correlated with increased time to distant metastasis free survival and overexpression of PTPRD was correlated to decreased time to distant metastasis in the basal subtype of breast tumors. PTPRD is involved in various biological processes in cancer and its role as tumor suppressor is well-defined in glioblastoma ( ). In glioblastoma, PTPRD was shown to suppress tumor development by acting though STAT3. Specifically, the loss of PTPRD led to the accumulation of phospho-stat3 and, thus, constitutive activation of STAT3 (144). PTPRD was also discovered through The Cancer Genome Atlas project to be a novel gene that was frequently mutated in addition to PTPN22, suggesting the emerging roles of protein tyrosine phosphatases in breast cancer associated biological processes (145). Query of protein-protein network interaction between PTPRD and E2F2 using the TCGA database showed that PTPRD was an indirect target of E2F2. The link between E2F2 and PTPRD was achieved through linking PTPRD-PTPRF-BCAR1-E2F2. E2F2 was shown to directly bind to BCAR1 (177). Although the mechanism by which E2F2 regulates BCAR1 expression is currently poorly defined, E2F2 loss may lead to BCAR1 activation and, thus, cancer progression and metastasis. Since the second catalytic domain of PTPRD binds to the first catalytic domain of PTPRF (182), it may be possible that the binding of the two domains resulted in reduced the activity of PTPRF. As PTPRF functions to destabilize BCAR1 protein and, thus, reduce its activity, 90

108 inhibition of PTPRF activity may led to increased activity of BCAR1 that may led to increased proliferation, migration and, eventually, metastasis ( ). In consideration that PTPRD acts through STAT3 and is an indirect target of E2F transcription factor, to explore the pathway by which E2F2 and PTPRD was connected in human breast cancer, cbioportal was queried to place E2F2, PTPRD, and STAT3 in context. Interestingly, the connection between E2F2 and PTPRD was indirectly achieved by the nodes connecting STAT3 and MYC. Indeed, perturbation of STAT3 levels has been shown to alter the expression of the oncogene c-myc ( ). Myc pathway was linked to the Rb/E2F pathway (165,189) and past study showed the interplay between Myc and E2F2 pathway whereby E2F2 was shown to suppress cellular proliferation and c-myc induced tumorigenesis (166). Given the presence of c-myc binding site on the PTPRD promoter ( it is plausible that the loss of E2F2 allows increased expression of PTPRD mediated by c-myc which leads to increased lung metastasis. This is a role for PTPRD and E2F2 unique to a subpopulation of breast cancer. Taken together, these findings showed that E2F2 mediates human breast cancer metastasis through the interplay between PTPRD, PTPRF, BCAR1, as well as Myc and STAT3 signaling axis in MMTV-Myc mouse model and that further explorations and validations of those interactions is warranted (Figure 17). 91

109 Figure 17 - Possible interactions between PTPRD, Myc, and E2F2 leading to metastasis E2F2 loss may lead to compensatory mechanism by other members of E2F activator transcription factors which eventually led to increased metastatic promoting genes (e.g. MMP, VEGF) (yellow box). At the same time, loss of E2F2 may lead to increased PTPRD levels due to increased Myc activity (green box) or loss of E2F2 may lead to increased expression of BCAR1 (blue box). Increased PTPRD expression may lead to inhibition of LAR/PTPRF activity which resulted in the stabilization of BCAR1 (blue and black boxes). Increased BCAR1 is correlated to increased metastasis. 92

110 6.2 Future directions As previously discussed Myc is involved in various cancers including breast cancer, even though the extent of Myc involvement in tumor progression and metastasis is subject for debate (190). However, several studies showed that Myc itself is poorly metastatic (59). In this study, loss of E2F2 in the context of Myc amplification resulted in increased percentage of metastasis. Since Myc and/or its downstream targets have been an attractive target for cancer therapy (191), it is imperative that the development of Myc inhibitors directed at signaling molecules downstream of Myc signaling includes consideration into their effects on the E2F pathway, specifically E2F2. Consider the connection between E2F2 and BCAR1. Little is currently known of how E2F2 modulation affects BCAR1 levels. However, through yeast two-hybrid system, the two proteins were shown to be interacting (177). BCAR1 has a role in breast cancer that confers resistance to estrogen (192,193). As such, a treatment that includes down regulation of E2F2 may result in recurrence of tumors that are estrogen resistant. Additionally, E2F2 was shown to regulate Myc activity (166). Therefore, loss of E2F2 may result in the increase of Myc oncogenic activity. As Myc was shown to bind PTPRD, it is possible that the increase of Myc activity resulted in overexpression of PTPRD. This overexpression could lead to the downregulation of PTPRF (LAR) which in turn stabilized BCAR1 (181,182), effectively converting the resulting recurring tumor into estrogen resistant tumor. Indeed, high expression of PTPRD is correlated to decreased time to distant metastasis in the basal breast tumors. 93

111 APPENDIX 94

112 A B C D Figure A1.1 - Analysis of the effects of Myc gene alterations and expression levels on overall survival, relapse, and metastasis of breast cancer TCGA analyses showed that alterations to the Myc gene did not affect breast cancer overall survival (A). Although K-M plotter query showed agreement with TCGA data that Myc levels did not correlate with overall survival of breast cancer patients (B), the query showed that Myc overexpression was correlated to decreased time to relapse and distant metastasis (C-D). 95

113 A B Figure A2.1 - Principle component assay of gene expression samples before and after batch normalization. Prior to normalization gene expression samples appeared to be separated in batches of study (A). Batch normalization brought the range of values so that the samples are within similar range of values (B). 96

114 A PTPRD P Scr. P Scr. B PTPRD β-actin β-actin Figure A2.2 - Inconsistency of PTPRD detection PTPRD was earlier optimized at 1:100 primary antibody dilution with 25 µg total protein per well (A; 0.75 mm spacer). Subsequently a new batch of antibody was obtained and subsequent optimization required 75 µg of total proten loaded per well (B; 1.5 mm spacer) in order to reproduce similar results. Note the bleeding of β-actin loading control in B. 97

115 A B Data Ct Log Mass C Figure A2.3 - Selection of PTPRD primers A total of 9 primers were tested. Primers that span exons 21 and 22 junction were ultimately chosen due to the presence of only one peak (C). Primer working range is between ng of total RNA (A), The efficiency of primer did not fall between acceptable range of slope = -3.1 (- 3.6) (B; slope = ). 98

116 DMFS (%) DMFS (%) DMFS (%) DMFS (%) A Cluster 1 Cluster B B Cluster 2 Cluster B C p-value= Time (days) Cluster 3 Cluster B D p-value= Time (days) Cluster 4 Cluster B p-value= Time (days) p-value= Time (days) Figure A3.1 - Low E2F2 probabilities association with decreased time to distant metastasis is unique to cluster B Low E2F2 probabilities in every cluster were compared to low E2F2 probabilities in cluster B. With the exception of Cluster 4 (D), in every cluster low E2F2 probabilities are associated with increased time to distant metastasis. 99

117 DMFS (%) DMFS (%) Figure A3.1 (cont d) E Cluster 5 Cluster B F Cluster 6 Cluster B p-value= Time (days) 20 p-value= Time (days) 100

118 DMFS (%) DMFS (%) DMFS (%) DMFS (%) DMFS (%) DMFS (%) A B C Low E2F2 20 High E2F2 p-value = Time (days) D Low E2F2 20 High E2F2 p-value = Time (days) Low E2F2 High E2F2 20 p-value = Time (days) Low E2F2 20 High E2F2 p-value = Time (days) E F Low E2F2 High E2F2 20 p-value = Time (days) Low E2F2 High E2F2 20 p-value = Time (days) Figure A3.2 - Correlation between high or low E2F2 probabilities with distant metastasis in all clusters Comparison between high E2F2 probabilities and low E2F2 probabilities showed that in clusters 1-6 (A-F) E2F2 did not affect with distant metastasis free survival. In Cluster A, low E2F2 probabilities were correlated to increased time to distant metastasis (G) whereas in Cluster B, although not significant, low E2F2 probabilities seemed to correlate with decreased time to distant metastasis (H). 101

119 DMFS (%) DMFS (%) Figure A3.2 (cont d) G H Low E2F2 20 High E2F2 p-value = Time (days) Low E2F2 20 High E2F2 p-value = Time (days) 102

120 Table A3.3. Gene expressions that correlated with human distant metastasis free survival Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title ABLIM1 low down down up actin-binding LIM protein 1 ACAA1 low up up down acetyl-coenzyme A acyltransferase 1A /// acetyl- Coenzyme A acyltransferase 1B ADRB2 low down down up adrenergic receptor, beta 2 AEBP1 high up down down AE binding protein 1 ALDH1A1 low down down up aldehyde dehydrogenase family 1, subfamily A1 AOX1 low down down up aldehyde oxidase 1 BLNK low down up up B-cell linker BMP4 low down down up bone morphogenetic protein 4 103

121 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title CD163 high up down down CD163 antigen cig5 high down up up radical S-adenosyl methionine domain containing 2 CITED2 low down down up Cbp/p300- interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 2 CMKOR1 high down down up chemokine (C-X- C motif) receptor 7 COL18A1 high up down down collagen, type XVIII, alpha 1 CRABP2 high down up up cellular retinoic acid binding protein II CXCL12 low up down down chemokine (C-X- C motif) ligand 12 CXCL13 low down up up chemokine (C-X- C motif) ligand

122 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title DCN low up up down decorin DKFZP58 6A0522 low down down up DSCR6 high down up up DUSP6 low down down down EMU1 high up down down EPHX2 low up down down FIGF low down down up FLT1 high down down up HIG1 domain family, member 1C /// methyltransferase like 7A2 ripply3 homolog (zebrafish) dual specificity phosphatase 6 EMI domain containing 1 epoxide hydrolase 2, cytoplasmic c-fos induced growth factor FMS-like tyrosine kinase 1 FMOD low up down down fibromodulin FN1 high down down down fibronectin 1 105

123 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title G1P2 high down up up GATA2 high down up up GBP2 low down up up HSD11B1 low down up up ICAM1 low down up up ICAM2 low down down up ID1 low down down up IFI44 high down up up predicted gene, /// predicted gene, /// ISG15 ubiquitin-like modifier /// hypothetical protein LOC GATA binding protein 2 guanylate binding protein 2 hydroxysteroid 11-beta dehydrogenase 1 intercellular adhesion molecule 1 intercellular adhesion molecule 2 inhibitor of DNA binding 1 interferon-induced protein

124 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title IFIT1 high down up up interferon-induced protein with tetratricopeptide repeats 1 IFIT4 high down up up interferon-induced protein with tetratricopeptide repeats 3 IGJ low down up up immunoglobulin joining chain INHBA high up down down inhibin beta-a LRIG1 low up down down leucine-rich repeats and immunoglobulinlike domains 1 MAPT low down up up microtubuleassociated protein tau MFAP4 low down up up microfibrillarassociated protein 4 MIA low up down down melanoma inhibitory activity 1 107

125 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title MMP3 high up 1.18 down down matrix metallopeptidase 3 MX1 high down up up myxovirus (influenza virus) resistance 2 MX2 high down up up myxovirus (influenza virus) resistance 1 NDRG2 low up down down N-myc downstream regulated gene 2 OAS1 high down up up 2'-5' oligoadenylate synthetase 1A OAS2 high down up up 2'-5' oligoadenylate synthetase 2 OAS3 high down up up 2'-5' oligoadenylate synthetase 3 OASL high down up up 2'-5' oligoadenylate synthetase-like 1 PAX6 high up up down paired box gene 6 108

126 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title PLSCR1 high down up up phospholipid scramblase 1 PLVAP high down down up plasmalemma vesicle associated protein PON1 low down down up paraoxonase 1 PTGER3 low up up down prostaglandin E receptor 3 (subtype EP3) S100A8 high down down up S100 calcium binding protein A8 (calgranulin A) SFTPB low down up up surfactant associated protein B SFTPC high down down up surfactant associated protein C SLC28A3 low down up up solute carrier family 28 (sodium-coupled nucleoside transporter), member 3 SP100 high down up up nuclear antigen Sp

127 Table A3.3. (cont d) Gene Expression with decreased DMFS K-M plotter p-val. Fold change ([2KO] Vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] vs [Myc]) Gene Title THY1 high up down down thymus cell antigen 1, theta 110

128 Table A3.4. Gene expressions with significant Cox-hazard ratio Gene Hazard Ratio Fold change ([2KO] vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] Vs [Myc]) Gene Title ACE down down up angiotensin I converting enzyme (peptidyl-dipeptidase A) 2 BMP up down down bone morphogenetic protein 1 CHIA down up up chitinase, acidic CHN up down down chimerin (chimaerin) 2 ELN down down up elastin ENPEP down up up glutamyl aminopeptidase HP down up up haptoglobin IDUA up down down ITIH down down up KLK down down down MMP up down down predicted gene, MUC down up up mucin 4 inter alpha-trypsin inhibitor, heavy chain 4 kallikrein relatedpeptidase 6 matrix metallopeptidase

129 Table A3.4. (cont d) Gene Hazard Ratio Fold change ([2KO] vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] Vs [Myc]) Gene Title MYH up down down NAALAD up down down myosin, heavy polypeptide 1, skeletal muscle, adult N-acetylated alphalinked acidic dipeptidase 2 NOV up up down nephroblastoma overexpressed gene NRP down down Up neuropilin 1 PCDH down up Up protocadherin 21 PRX down up Up periaxin PTGIS down up Up PTPRD down up Up RASGRF down up Up prostaglandin I2 (prostacyclin) synthase protein tyrosine phosphatase, receptor type, D RAS protein-specific guanine nucleotidereleasing factor 1 SCEL down down Up sciellin SCIN up down down scinderin SNCA down up Up synuclein, alpha 112

130 Table A3.4. (cont d) Gene Hazard Ratio Fold change ([2KO] vs [LM]) Regulation ([2KO] Vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] Vs [Myc]) Gene Title TACSTD down down Up tumor-associated calcium signal transducer 2 TNNC up down down troponin C2, fast TTID up down down myotilin 113

131 Table A3.5-A. Genes with putative E2F binding site (Transfac and SwissRegulon) Gene Sequence Fold change ([2KO] vs [LM]) Regulation ([2KO] vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] Vs [Myc]) Transfac DB BF N/A down up Up Y N CDH2 N/A up down down Y N Swiss Regulon DB COL18A1 N/A up down down Y N COL5A1 N/A up down down Y N COL9A2 N/A up down down Y N CPD N/A up down down Y N DKFZP58 6A0522 N/A down down Up Y N FGFR1 N/A up down down Y N FLT1 N/A down down Up Y N FN1 GCGGGA AA down down down Y Y ICAM1 N/A down up Up Y N MMP23B N/A up down down Y N 114

132 Table A3.5-A. (cont d) Gene Sequence Fold change ([2KO] vs [LM]) Regulation ([2KO] vs [LM]) Fold change ([2KO] Vs [Myc]) Regulation ([2KO] Vs [Myc]) Fold change ([LM] Vs [Myc]) Regulation ([LM] Vs [Myc]) Transfac DB Swiss Regulon DB MX1 N/A down up Up Y N OAS2 N/A down up Up Y N OAS3 N/A down up Up Y N PAX6 GCGGGA AA up up down Y Y PLSCR1 N/A down up Up Y N PRELP N/A down up Up Y N SP110 N/A up up Up Y N TACR3 GCGCCA AA up down down Y Y TGFB2 N/A down up Up Y N 115

133 Table A3.5-B. Genes with putative E2F binding site (Xu et al. 2006) Gene E2F1 E2F2 E2F3 E2F4 E2F6 Gene Title BF N N N N N complement factor B CDH2 N N N N N cadherin 2 /// similar to N-cadherin COL18A1 Y N N N N collagen, type XVIII, alpha 1 COL5A1 N N N N N collagen, type V, alpha 1 COL9A2 N N N N N collagen, type IX, alpha 2 CPD N N N Y Y carboxypeptidase D DKFZP586A0522 N N N Y N HIG1 domain family, member 1C /// methyltransferase like 7A2 FGFR1 N N N N N fibroblast growth factor receptor 1 FLT1 N/A N/A N/A N/A N/A FMS-like tyrosine kinase 1 FN1 N N N N N fibronectin 1 ICAM1 Y N Y N N intercellular adhesion molecule 1 MMP23B N/A N/A N/A N/A N/A matrix metallopeptidase

134 Table A3.5-B. (cont d) Gene E2F1 E2F2 E2F3 E2F4 E2F6 Gene Title MX1 N N N N N myxovirus (influenza virus) resistance 2 OAS2 N N N N N 2'-5' oligoadenylate synthetase 2 OAS3 Y Y Y N N 2'-5' oligoadenylate synthetase 3 PAX6 N/A N/A N/A N/A N/A paired box gene 6 PLSCR1 N Y N Y N phospholipid scramblase 1 PRELP N N Y N N proline arginine-rich end leucine-rich repeat SP110 N/A N/A N/A N/A N/A RIKEN cdna C130026I21 gene /// similar to C130026I21Rik protein TACR3 N N N N N tachykinin receptor 3 TGFB2 N N N N N transforming growth factor, beta 2 117

135 ALL CLUSTER A CLUSTER B Figure A3.6 - Genes correlated with DMFS is unique for Clusters A and B Representative Venn diagram showed that genes that were correlated to distant metastasis differed between overall samples (ALL), Cluster A and Cluster B. 118

136 Table A3.6. Transfac analysis of genes that were associated with DMFS in Clusters A and B Cluster A # Annotation Total genes Bayes factor 1 V$E12_Q V$MYC_Q V$MYCMAX_B: c-myc:max binding sites Cluster B 1 V$KROX_Q V$NRF2_01: nuclear respiratory factor V$PTF1BETA_Q V$E2F_Q3_ V$IRF_Q

137 A B Figure A3.7 - TCGA and K-M Plotter analysis of Kallikrein1 A query into the TCGA database (A) and K-M plotter (B) showed that while alterations of KLK1 gene does not affect survival, low expression of KLK1 tends to correlate with decreased time to distant metastasis. 120

138 A B Figure A3.8 - TCGA and K-M Plotter analysis of Myosin Heavy Chain 2 A query into the TCGA database (A) and K-M plotter (B) showed that alterations of MYH2 gene affects survival and low expression of MYH2 tends to correlate with decreased time to distant metastasis. 121

139 A B Figure A3.9 - TCGA and K-M Plotter analysis of PTPRD A query into the TCGA database (A) and K-M plotter (B) showed that alterations of PTPRD gene did not affect survival and high expression of PTPRD tends to correlate with decreased time to distant metastasis. 122

140 A B Figure A TCGA and K-M Plotter analysis of TNNC2 A query into the TCGA database (A) and K-M plotter (B) showed that alterations of TNNC2 gene did not affect survival and high expression of TNNC2 tends to correlate with decreased time to distant metastasis. 123

141 A B C D Figure A4.1 - Patterns of Myc, E2F2, and PTPRD expression in all clusters PTPRD was expressed differently in samples taken from mouse in Cluster B with the exception of one probe (F, H, J; p = , , and , respectively). No appreciable difference in Myc and E2F2 expression pattern in either mouse or human samples for any cluster (A-D, E, G, I). 124

142 Figure A4.1 (cont d) E F G H I J 125

143 A B C D Figure A5.1 - Confirmation of E2F2 knockdown results in MDA-MB-231 cell line An additional clone for E2F2 knockdown in MDA-MB-231 was assayed for migration in vitro to ensure that the observed effects of E2F2 knockdown were not a clonal artifact. While E2F2 knockdown in the MDA-MB-231 cell line did not affect cell growth (A), reduced E2F2 level affected migration in vitro (B-D; p <0.05). 126

144 A B C D G E F Figure A5.2 - Confirmation of the effect of E2F2 knockdown on migration in MCF7 cell line To ensure that the effects of E2F2 knockdown was not an artefact of cell line, MCF7 cell line was used to confirm the findings (A; Con = MCF7 parental, Scr. = MCF7 transfected with shscramble, S2 = MCF7 clone S2, S6 = MCF7 clone S6). Similarly, knockdown of E2F2 in the MCF7 cell line did not affect cell growth rate (B). Examination of a different stable clone of the knockdown confirmed that reduced level of E2F2 expression increased migration in a transwell migration assay (C-G). 127

145 A B C D F E Figure A5.3 - Confirmation of PTPRD knockdown results in MDA-MB-231 cell line To ensure that the effects of PTPRD knockdown was not merely a clonal effect, a separate clone was examined and the levels of the knockdown was assayed by western blotting (A; Con = MDA-MB-231 parental, Scr. = MDA-MB-231 transfected with shscramble, R7 = MDA-MB-231 clone R7, CR5 = MDA-MB-231 clone CR5). PTPRD knockdown did not affect cell growth (B). As expected, knockdown of PTPRD decreased migration in a transwell migration assay (C-F; p <0.01). In addition, scratch assay also showed decreased migration as expressed by decreased percentage of scratch area reduction (G, H). 128

146 Figure A5.3 (cont d) G H 129

147 A B C D G E F Figure A5.4 - Confirmation of the effect of PTPRD knockdown on migration in MCF7 cell line Confirmation of the effects of PTPRD knockdown in a different cell line, MCF7. Two clones were assayed for the levels of the knockdown (A; Con = MCF7 parental, Scr. = MCF7 transfected with shscramble, 1E = MCF7 clone 1E, 1G = MCF7 clone 1G). In the MCF7 cell line, knockdown of PTPRD did not affect growth rate (B). However, reduced levels of PTPRD showed decreased migration in a transwell migration assay (C-F; p<0.05). Reduced PTPRD levels on MCF7 migration was confirmed by scratch assay (H, I). 130

148 Figure A5.4 (cont d) H I 131